The World Bank World A ~Development kk) 1 n dIndicators 19140 March 1999 4~~~~~~~ Low Income Togo St. Vincent and I Aruba Afghanistan Turkmenistan the Grenadines Australia Albania Uganda Suriname Austria Angola Vietnam Swaziland Bahamas, The Armenia Yemen, Rep. Syrian Arab Republic Belgium Azerbaijan Zambia Thailand Bermuda Bangladesh Zimbabwe Tonga Brunei Benin Tunisia Canada Bhutan Lower middle income Ukraine Cayman Islands Bosnia and Algeria Uzbekistan Channel Islands Herzegovina Belarus Vanuatu Cyprus Burkina Faso Belize West Bank and Gaza Denmark Burundi Bolivia Yugoslavia, FR Faeroe Islands Cambodia Bulgaria (Serbia/Montenegro) Finland Cameroon Cape Verde France Central African Republic China Upper middle Income French Guiana Chad Colombia American Samoa French Polynesia Comoros Costa Rica Antigua and Barbuda Germany Congo. Dem. Rep. Cuba Argentina Greece Congo, Rep. Djibouti Bahrain Greenland C6te d'lvoire Dominica Barbados Guam Eritrea Dominican Republic Botswana Hong Kong, China Ethiopia Ecuador Brazil Iceland Gambia, The Egypt, Arab Rep. Chile Ireland Ghana El Salvador Croatia Israel Guinea Equatorial Guinea Czech Republic Italy Guinea-Bissau Fiji Estonia Japan Haiti Georgia Gabon Korea, Rep. Honduras Guatemala Grenada Kuwait India Guyana Guadeloupe Liechtenstein Kenya Indonesia Hungary Luxembourg Kyrgyz Republic Iran, Islamic Rep. Isle of Man Macao Lao PDR Iraq Lebanon Martinique Lesotho Jamaica Libya Monaco Liberia Jordan Malaysia Netherlands Madagascar Kazakhstan Malta Netherlands Antilles Malawi Kiribati Mauritius New Caledonia Mali Korea, Dem. Rep. Mayotte New Zealand Mauritania Latvia Mexico Northern Mariana Moldova Lithuania Oman . Islands Mongolia Macedonia, FYR Palau Norway Mozambique Maldives Poland Portugal Myanmar Marshall Islands Puerto Rico Qatar Nepal Micronesia, Fed. Sts. Saudi Arabia Reunion Nicaragua Morocco Seychelles Singapore Niger Namibia Slovak Republic Slovenia Nigeria Panama South Africa Spain Pakistan Papua New Guinea St. Kitts and Nevis Sweden Rwanda Paraguay St. Lucia Switzerland Sao Tomes and Principe Peru Trinidad and Tobago United Arab Emirates Senegal Philippines Turkey United Kingdom Sierra Leone Romania Uruguay United States Somalia Russian Federation | Venezuela Virgin Islands (U.S.) Sudan Samoa Tajikistan Solomon Islands | High Income Tanzania Sri Lanka Andorra Tho. weld byincoice L- (9785 o, less' , 4 Lowe m dde $ 786 J.125) C) Classified according tO Uppers ddle 12.126-9f55) ( World BAnk estinhtna of H e; ($9.f56 rorr o- 1997 GNP pnr capita. ND dat- $ -- .. ; , ~~~~- A_;m w'; i , C__D-~, g,,_ .,J II ,,,, P,,- ;,, , _ -t.- . .-.. . ' -1-1 * - - g. ,t _ Ilt yN,,Ih- . L.,.; i -~~ ~ ~ . t _ Des gred, 55 rer ene plDduoed hy a Cemreunocaolers Dove opseor Ieoerporlfeo, ADnseengtsfl DC eeoC Gr.rr 9s Roriheoge. Loorson World ________ ______ Development I Indicators Copyright 1999 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street. N.W., Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing March 1999 This volume is a product of the staff of the Development Data Group of the World Bank's Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank's Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This edition marks the first use of the Robinson projection for maps. replacing the Eckert IV equal-area projection used in earlier editions. The Robinson represents both area and shape rea- sonably well for most of the earth's surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrghted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without ask- ing a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive. Danvers, Massachusetts 01923, U.S.A. Photo credits: Curt Carnemark/World Bank. If you have questions and comments about this product, please contact: Development Data Center The World Bank 1818 H Street, N.W., Room MC2-812, Washington, D.C. 20433, U.S.A. Hotline: (800) 590 1906 or (202) 473 7824; fax (202) 522 1498. Email: info@worldbank.org Website: http://www.worldbank.org or http://www.worldbank.org/data ISBN 0-8213-4374-2 World ______ . Development _OTT I ndicators The World Bank I ~~~-.ti"st00 K -§ @ D0 ¢ XmJS 0000 0 @30 00 X j 00 ~~~~~*ufwh ft0 §W ffmo8 0ld >e~v= pWo-n MWN VOWl @o&) riftn-3 t@ llI@gfRsO Dro wDk 0M a - 1 ;,1 to o D -: .~-i,1 Iz a>bS W&xO_ 30 M "0n - - 0 DO30t W M 3 03 0 0 - X mo toa c- nRg W ftWw 5o pwy &dcO mg W - nW$ MO OwDo nM 2 otm.x Mm CnsW§ En O: ftk.ab 0-nx 0M &w Sn ft §§0 W l mpMg-W W §mpoX aw:O §WM "-W ft mum§ Oft~~~~~~~~~~ ft2af b%m, W§ °n bXan ora *wdhW*S§,addcQf. m O>Do enKeeDo ,m3 W0 ha 0 im a g 4ffi m*l AW 1tUm DsgW-tm Ae 0X 0 ? D5 T Oo it? w D0 0 QO30 oenb w 0Xso b00 0 b fie ua m3 of -Wa oWoft oIV 0 CF eM eW f00 WR a- W~ ~=futfm~~?M e NRouK -~wo pWn&- 6p~pm)K WWI kw w0 0 %T = %W a w & o caw mmubew mWko Tom wm wVL §Xoo dK Dt~W§M t wqa k30 tC Q buf0f A0 DdM 00aD¢W pw" DO f ICt cgqlwv @RnCV Xq mu0 ffW 0o 9 0uf enpt mXm 0 Oaa 30 Wn m-dkCi@ pngM3 " tmC O WqMflN~ 01? §3Dmh VFM 0 knWA WW QaMk§ X nw kmg, t-j 0M th X @bO OmmaAfW 0OFftmUO @ ;Ti4&fjit m R , v~§ac~mpmt .riott f~ &mM~pt tt Suf t /=M D l O0 W @O WV M h S~f UNry-pd v-b Mm4m mW5O d@kkb SMumef u ~ w41 w Wmqf MtwM30iw~ 0 w0 C-0gh t tbmt aWpw Acknowledgments This book and its companion volume, the World Bank Atlas, were prepared by a team led by Eric Swanson. The team consisted of Swami Aiyar, Mehdi Akhlaghi, David Cieslikowski, Richard Fix, Masako Hiraga, Demet Kaya, M. H. Saeed Ordoubadi, Sulekha Patel, Lavan Sujarittanonta, K. M. Vijayalakshmi, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD-ROM development team included Mehdi Akhlaghi, Azita Amjadi, Elizabeth Crayford, Reza Farivari, Angelo Kostopoulos, and William Prince. K. Sarwar Lateef served as adviser to the team and provided substantial inputs. The work was car- ried out under the management of Shaida Badiee. The choice of indicators and textual content was shaped through close consultation with and substantial contributions from staff in the World Bank's four thematic networks-Environmentally and Socially Sustainable Development; Finance, Private Sector, and Infrastructure; Human Development; and Poverty Reduction and Economic Management-and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, we received substantial help, guidance, and data from our external partners. For individual acknowl- edgments of contributions to the book's content, please see the Credits section. For a listing of our key partners, see the Partners section. Bruce Ross-Larson was the principal editor, and Peter Grundy, the art director. The cover and page design and the layout and desktopping were done by Communications Development Incorporated, with Grundy & Northedge of London. Staff from External Affairs oversaw publi- cation and dissemination of the book. vi 1999 World Development Indicators Preface The World Development Indicators, now in its third year, is the World Bank's most general statisti- cal publication. Along with its companions, the World Bank Atlas and the World Development Indicators CD-ROM, it offers a broad view of the record of development and the condition of the world and its people. It also provides a continuing survey of the quality and availability of internationally comparable indicators. The organization of the World Development Indicators reflects a comprehensive development framework that integrates the measures of social progress and the quality of life of people with those of economic development, physical infrastructure, government policy and performance, and the con- dition of the environment. In this year's edition new indicators have been added to the People sec- tion, providing data on wages and eamings; expanded coverage of education, and a full table on the global HIV/AIDS epidemic. The Environment section includes two new tables: one of city-level indica- tors and another that extends last year's measures of genuine savings to 121 economies. The other sections have been revised as needed while preserving the now-familiar order and layout of the book. The World Development Indicators also tracks progress toward the international development goals adopted by the World Bank, the United Nations, and the Development Assistance Committee of the OECD. This year the opening World View section reports on the prospects for developing countries in the aftermath of the financial crisis that swept much of the world. It is too soon to draw conclusions-indeed, we are only beginning to measure the effects. What is clear is that the world cannot afford another lost decade like the one Latin America endured after the debt cri- sis of the 1980s. Such a setback would put all our goals out of reach and leave millions of peo- ple in still greater distress. Like everyone who uses these indicators, we remain concerned with the quality, coverage, and timeliness. As in previous editions, each table is followed by a discussion of the data, short descrip- tions of the indicators, and the sources for each of them. Despite the enormous improvements in communications and information management in the past decade, the gathering of statistical infor- mation remains difficult, costly, and time-consuming. The scope of the World Development Indicators reflects the efforts of many groups and organi- zations to survey, measure, and otherwise compile all the necessary data. We continue to depend on the support and cooperation of our many partners-who make this publication possible. We are grateful to each of the international organizations, statistical offices, nongovernmental organizations, and private firms that have provided their data and contributed to this product. We also appreciate the comments and responses from users-helping us measure how we are doing in continuing to make the World Development Indicators a useful tool. So, please write to us at info@worldbank.org. And for more information on the World Bank's statistical publications, please visit our website at http://www.worldbank.org and select data from the menu. Shaida Badiee Director Development Data Group 1999 World Development Indicators vii Contents I .j - I Front matter Introduction 3 .. .. .... . ... ... I. .... . 1.. .... .. .I. .... .... . .... .... .. . .......... .... .... . . .... .... .. .. .... .... ... .. .... .... . ... . .... . . . .. ... .. .... ... . . ..... .. ... .... .. . . .... .... . . . .... .... .. ... .... ... I. .... .... .. . . . ... .... . . .. .. ...I.... . .... .... ............................................................ Foreword v International development goals 10 Acknowledgments vi 1.1 Size of the economy 12 .................... ....... ... .. ................................... . .. ... .. . ........ . ....... ... ....... ...... .. . . ........... . ............ ..............I....... .....I............... ..........I........ - .. ........ .. .................. ......... ... .... .... ...... ..... .. Preface vii 1.2 Development progress 16 Partners xii 1.3 Gender differences 20 .... . . . ....... ..... .. ......... .. . ........ . ........ ... .. . ... ...... .. ........ . ...... .. . .... .. ........... .. .............. ....I.......... . .. ....................... .........I....... . .. ........... . ................. .............................................. Users guide xxii 1.4 Trends in long-term economic development 24 1.5 Long-term structural change 28 . .. .................... .I.......... ............. - . .................... ... ....... .. ................ - . ......... .... 1.6 Key indicators for other economies 32 ......... .. ...I.......I.. .......... ........... I. ... ......... . .... ............ .........I ............. .. . . ..... ....... .......... ................... Box la Measuring the impact of the crisis on Indonesia's poor 6 ...... .....I... .................... I........... I. ......................... I......... . ... ................ ............. ........... . .... ................ ........... Figures Figures..... ................... . ........................ ......... ... .. ......... la Gini coefficients in transition economies 7 ........... ........... . ...... .............. ... ...I..... . .......................I......... . ....... ...... ...... 1.1 Where money goes farther 15 1.4 Botswana and Maurntius are growing as fast as East Asian economies 27 1.5 Agriculture loses ground 31 ..... .. . .......... ...... .......I..... . ........ .. ..... 11.. ........... ........ ...... .. ........ .... .......I ............. .... .... Text tables .... .. ....... ..... .. ....................I... ...... .............. ........ .I.. ......... . . .... ............... . ....... . ............... la Real GDP growth, 1991-2001 4 lb Progress in social indicators 5 ..... ..I. ................... ............... . ...... ........ .I . ...... ...... ............... ...........I..... .. ............ -.. ....... . lc Estimated poverty in transition economies, 1987-88 and 1993-95 7 ld Performance gaps and distance to goals 8 ............................. ....I...... .... ....... I I......... - . . - ........... . .... .......I....... .. 11.......... . le Regional average growth rates in real consumption needed to halve poverty in 25 years 9 C Wo-/d Vew includes a new table that contasts the status of men and women. and of boys and girls, using key social indicators. In talde 1. surface area-a country's total land area including areas under inland bodies of water-tas replaced land a.ea as thse measu-e of size. Table 1.3 shows indicators than measure progress toward the inter-ational development goals, and the introduCtion looks at how thn financial criss has affeuted the pr-spnhts of attaining thess goals. viii 1999 World Development Indicators XI sioleoipul luewdoIeAea] PIJOM 666T $1J!P 0!I! Aqo. pu-Wtoodw- --j-'UuossIioo04AO I 0 010fp000001 40 SOUSO M j Ud0IO...P Aq . .......! t!P 0iOl0 2 1!i WUJSO 0t p.O -;-P Olfl Lfl! P10GM e4t Sq 1os SnOP 014 ...!q00 - P0 P .....G 00,040 P.. SO000044 p.. 00104.... 20!2d10-p 0T sueild uo!132 121u2wuo11AU2) 12U0112U 40 sn424S etT7E £66 6-066T 'SI14pluno e)woouh-moI u! 81u!pueds qpileLj oilqnd eCT16 is suoilednooo p6jeurwop-Yepua~u!bi !Mo d1do5 ioqel lein51nuopelO e286 T±1 210424442e AI!SeAlApoiq pue ci9ueqo elewil Vc 1£seIqI ;xo 191 uoi;npoid Pl1om ui ninoeonodlno seloAois 61£C £91 s22490 Auew u01oeo u01202 U uoite SO!4uuo0 28212m22 pjoqesnCH T11£ 68 8ej 0; enuluoco s uewlloiue sj1119 61T6 69T wo00q 04 s2fulluoo0 uo0elndodCuL.qIn s,ppom GI.LL.01£ LI s204luno 2wo3u!-eIpp!w pun -mol 99T ~~~u0I12eU28441011;oelje qojdiui sejeuiw.op4 pleoo 6£S ewos u! eIqezis s! uoi4eonpe uo0 8upulads aleAPd 66z 191 weoeins sJ4l2OeagL le Aj2OfjdwGWR5eGAe IUSi2epue . q8os Es e.SP1IPU242 5UIA!l PUe T1T1 . .u!sw 00 ieqoj8 u! Li M018 sseidep 23104 20o4el 21L1I u2 se213u! pid2l seon] £6-Z . WAOAd~ 166d SPUi&&%e iPfi,1d(j e8£ 61' s2I14uno08u!do12A2p u! 40u fnq ..s2Ilunfoo l2!Al4snpu! u! £171 966T u1! esn AL8~aleuenaiwwoo jo qlmojtuo1lendodd6 spp5e udi;ej8iw jeuoI42u124u1 66Z ;uewd H4&isowlejol6 pajtuno6oe saujuno ino1 L's St' uoi4ejndod 5u!9n 2141 16i £171 21E1142212 Uees SUOISSIWe 222144 U! eUl1oep 8£ q4 018 awoou! ol puodsnel 4u op sju;uewioiue iewni pz 991 2212 puel paj?ajoid lo.LEOUOLUWAGI1432 q1489 jo ee;uren8 ou 212 s4ueLwI10Ius 14H q4 JeinilsW91e Ki Ae SiV-1064WtUi1 tl.£ gp suo!82 2aw02 u14 uo!;Blndod . T9T ... s2plun00 20w0ou4-V841 u! 4sa4se; umdla s6Lj pla1Al66idb e£-E £61 96-066T u! 22421 uoiialo2;o104p ls2t48!1 2144 qfl m s2Il4uflo 24j T 6£ 4U2w2)A211o32 ;uepnis u! dn o1j32s s22uno40 m H- q6: SOlJOId 8£ uop23npa sjui8 E2Ao1dwI olsaI82w4jS 26 soxo, 911 402 WUOI!AUa 2141 duunsuea 14£ 911 uo!l2z1ue2n14 0 Sed~Uel12143 IeIUG)WUOJ!AUG e14_ 2£ OTT &4!1210V4 916z 601.dauejjeqoeain;nj pue 22o0432 2s1 :q10422H 9176 17L1 S8U1A22 2u1nu22840 21ns22W 2 p1emoj 91£E 86 14412214 2Ai43npoldeli T- 8991 uoiflIod liv £T1£ 06 , 22 PUn '2231AJ2S 1fnl4!pu2dx2 144122H £16z 1'91 0442s81103 pue2 3444211 61£ 98 uo!leonipa pue 12puE9E ZT 091 IU2WUOI!AU2 u21q1n 147£ 69 s2woo4no uo0923nP3 116Z 99T uo0142z!uejnf 01£E 82 uop423pp ut uoi 2dio I ed 016Z 69T 4413111022l0 sa3lfnoS 6£C V7t s4fndu! uoi23nP3 66 981 SUO9SJW2 P112 43112131442 481211]~-~JGU 8£oCLdwfnsuoo j0 awoou1 40 uo10n!4014!410 8 0171 uo!onliod A24eM 9£C 69g 435A;ofpoid pue naem 9. 96T JE)IeMqSGJJ~12441421 9£C 89 luwodan 96 691T s222e p243e4o2d.2UeA9SSAGAPO!3 l' 8£ 8 MI!A13on wouo5& Aq iuew%516w3 . ' 861 ~4AI4A!opo2d pue Ind4lno jInljnoifO!28 ££E 09 21fonl434 2310410o424 £6 t861 s4ndu14j2n2n4403uV 6£ 1' 293io!L-u2Ap uo!l2Ilnd0d Z 0 .. ... ... .. . ...2.1.. ..2... . . ..2.. .22.. .. 1... .. 1. .. . . ..£. . . . 61'.. ...0.... . - n.d . .. . 811T 121PM 01' Up2pe01sn 2ue j2U044230u2 40 nd0d24 T 9 1 1 . . . . . . . . .. . . . . .I -. . . . . . . . . . . . . . . U IP I...... .. .. . .. .. .. . .. . ..3.. . . P0. - . . .. .. . STT iolem ot, uE)wu!elle jew onpe lo su. .1 s.io~leopul luawdJoeAaso PIJOM 6661 X S-!J-001 OSOl100P 00 .4.P ..10i!001 ~IS*l!.. qq 05z 10 50.JJ0 05 4. 511001 991; si0osepu! olwouooe0nsew i4e> q17 t78T eo~~~~~~9uewio1jad o!wou0oe juqoa8 et, T9Z eISV Ise3 u UIu!OUueu4 WjalV0LlS UO GOUe!I5I ~uIM0J9 V 6117 Isow suo!901j eql u! spuedxe ~uip.jujjVy pue .1ueg pIJoM 91:t7 C92; ql~~i4mo0i9ol AjeIojooese Cs jpap punoooe jue.uno LT.t7 917~~~~~ . ~~seplunoo awoou!-eipp!w Auew u; puai.rno 4062e5. 12~j0.ejo uno0DO5saxel kf4poes IepoS 9117, Lee 5e!V ),SB3 40 ISOW u! auee leo55P5sgi4.iuasJoM ET117 £EZ se!W0U0oa aw0oU!-alppiW pus -MOI ul aAisuadxa aiow 254 Si Nu5wlsGAu! lel!deo inq ... se!wouooa EKUODUi-49411 U! GA!suEGdxa ,Aj5A!jeja 0.1 S90!AJ9S G250 tjpleDHj ZT1 L~ZZ U5WIS5AUI OISBW.PSSOJ~I UI sPue9JI lO'398 OT't' ETC l~~~~~~~~auiewil eq ol ssaooe iseq 6TZ slJodw! amiasS u! aousuiwop ~uI14r4s V 8917 E)l e esv4ilg pueI 5ISV 151nq ...C sildwooSleuod 0l 9TF sliodxe iAi5s jo. ssaooe u! suo[952j UIqdo[OAEp ~?U0We PSG149!tl SMUeJ eDiJGwV u4el TT19 ae.11s 3u!mo.j5 e ~upujews ae sopluoo ul3Udoj0amO V't' 60C.s5uoLJd5la op ssaooe T71Z suo!~?5j ssojoe Le GWS 5LionlwsMolsl0iSodwi 4oainlornils L4 917 ELZ: ~~~~~~~~IUOWPSGAUI 1fl5J!P U9!0GJ0 U! 661 flmo049 9up!jfoBfnuew ui spugjl juo!9eaj AUlp sSUiun0ooueuiwo] C£1' 99Z sasi9po u!suq ~ ~~ '.~~~~~ I p ;q .~~~~~~~~~~~ l. A J .."u... .. .. .. .. .. . - . .. .... .. .. .. .. .. . .. .. .. .. .. .. .. .. . .. .. .. . 6.. .. .... . 299 ~ ,e!~oaE) u! Isow Jn3ooouo9 dnl.oo saop aja4M 59 t7q96 Nap 2WIeu03 9T171 .S~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~ ~~~~sJinja osz punoooe jua!.1no slaAdjooee 2. t' 9t7Z sao!jd pue sioleoipu! A.1e4uon 911t7 XO9 9S6 se.1nl!puadxa juawujaAo93liwIueo 17T't I 96 sw S Dd U! UO!pWflS0 4 le)in GOn 1:1:17 909 SU~~~~~~~~~~~~~~~~~~~~~'0950!uflO91Pu pOus O iT' T76 Zuw SWU pusdd uoodwsuoi 40 41AM02l 011 tITC A ~~~~~J~DJss4i ids6± 9 i .puw P 4 iin. a 1 .. . . . ...6 6. .. . . . .. . . . .. . . . .. . . . . . . . s. . . . .. . . ....d1 I ............... .. I . 9. . . .. . I . . . . .. . . . . . . I. . . . . . . .. . . . . . s.. .. .. .. .. .. 00 A G I .. ... .. .. ... .. .. ... .. .. 9066u!ounuo pus eo jamod. 9T9 906z juWSaAU! 5spue u14wnuoo 40 q.1noniD 917t 826 7supu UisIlesiiluew peum-Aui0I4iSd.V 961 sUiodirn4AlsW 40 GirnonipS V't' 996Z weu spi puew sojn!udaau ejaAo9 j9L 6ZTZ61sj.oedx OAJSo aounjonije L't £ 9 6. . . . . . .. .. . . . . . . .. . . . . . . . . . . . .- 1. .. . . . . . . . I . . . . . . . . u P f o4 I . .. . . .. L.. I. . . . .. . . .. . . .u o . .. . . . .. . . . .. . . . .. . . . .. . . . . . . .. . . . .. . . . .. . . . .. . . 06Z selei a5ueqoxe pue a idOI2G ' o low speoe oa nUnl 9 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . ..). . . . . . . . . . . . . . . . . Introduction 319 Back matter Financial flows, 1990-98 322 Statistical methods 371 6.1 Integration with the global economy 324 Primary data documentation 373 6.2 Direction and growth of merchandise trade 328 Acronyms and abbreviations 381 6.3 OECD trade with low- and middle-income economies 331 Credits 382 6.4 Primary commodity prices 334 Bibliography 384 6.5 Regional trade blocs 336 Index of indicators 391 6.6 Tariff barriers 340 Distributors of Bank publications 400 6.7 Global financial flows 344 Order form 401 6.8 Net financial flows from Development Assistance Committee members 348 6.9 Aid flows from Development Assistance Committee members 350 6.10 Aid dependency 352 6.11 Distribution of net aid by Development Assistance Committee members 356 6.12 Net financial flows from multilateral institutions 360 6.13 Foreign labor and population in OECD countries 364 6.14 Travel and tourism 366 Box 6a The dependence on aid 320 Figures 6.1 Large capital flows relative to GDP reflect extensive integration with global capital markets 327 6.3 Manufactures dominate U.S. and European trade with developing economies 333 6.9 Aid flows fell again in 1997 351 6.10 Countries receiving aid amounting to more than 15 percent of their GNP 355 6.11 Poor economies were not the sole recipients of aid from the top donors in 1997 359 6.12 Nonconcessional lending by the World Bank increased sharply in 1997 363 6.13 OECD countries drew immigrants from around the world in 1997 365 6.14 Countries with tourism receipts accounting for more than 15 percent of exports in 1997 369 Text tables 6.8a Official development assistance from non-DAC donors 349 6 aIe~f Lr,&sproIdes ..peeded co-erge o tariff raes. is table hfG, jealadigrd. satfo moecutisthan last year and mare recent data far s.r.s c...ntries. The.i H..d.t...i.use the becefita and risks ef giobalinati,,. Data ee g-awa of -.rh.odise trade bae bees --de t 1999 World Development Indicators xi Partners Defining, gathering, and disseminating international statistics is a collective effort of many peo-. ple and organizations. The indicators presented in the World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and house- hold surveys to the committees and working parties of the national and international statistical agencies that develope the nomenclature, classifications, and standards that are fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publish- ing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made the World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the success of the World Bank's efforts, and to those of many others, to improve the qual- ity of life of the world's people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For your convenience we have included URLs (Web addresses) for organizations that maintain web- sites. The addresses shown were active on 1 March 1999. Information about the World Bank is also provided. International and government agencies The Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global change data and information analysis center of the U.S. Department of Energy. CDIAC's scope includes poten- tially anything that would be of value to users concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere; the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases; emissions of carbon dioxide to the atmosphere; long-term climate trends; the effects of elevated carbon dioxide on vegetation; and the vulnerability of coastal areas to rising sea level. For information contact CDIAC, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6335, U.S.A.; telephone: (423) 574 0390; fax: (423) 574 2232; email: cdiac@ornl.gov; website: cdiac.esd.ornl.gov. Food and Agriculture Organization The Food and Agriculture Organization (FAO), a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization pro- vides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. Statistical publications of the FAO include the Production Yearbook, Trade Yearbook, and Fertilizer Yearbook. The FAO makes much of its data available on diskette through its Agrostat PC system. FAO publications can be ordered from national sales agents or directly from the FAO xii 1999 World Development Indicators Sales and Marketing Group, Viale delle Terme di Caracalla. 00100 Rome, Italy; email: Publications-sales@fao.org; website: www.fao.org. International Civil Aviation Organisation The International Civil Aviation Organisation (ICAO), a specialized agency of the United Nations, was founded on 7 December 1944. It is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of inter- _______ national civil aviation operations. The ICAO promotes the adoption of safety measures, estab- lishes visual and instrument flight rules for pilots and crews, develops aeronautical charts, coordinates aircraft radio frequencies, and sets uniform regulations for the operation of air ser- vices and customs procedures. To obtain ICAO publications contact iCAO, Document Sales Unit, 999 University Street, Montreal, Quebec H3C 5H7, Canada; telephone: (514) 954 8022; fax: (514) 954 6769; email: sales_unit@icao.org; website: www.icao.int. International Labour Organisation The International Labour Organisation (ILO), a specialized agency of the United Nations, vMf 0 1, seeks the promotion of social justice and internationally recognized human and labor rights. V , lY Founded in 1919, it is the only surviving major creation of the Treaty of Versailles, which brought the League of Nations into being. It became the first specialized agency of the United Nations in 1946. Unique within the United Nations system, the ILO's tripartite structure has workers and employers participating as equal partners with governments in the work of its governing organs. As part of its mandate, the ILO maintains an extensive statistical publi- cation program. Yearbook of Labour Statistics is its most comprehensive collection of labor force data. Publications can be ordered from the International Labour Office, 4 route des Morillons, CH- 1211 Geneva 22, Switzerland, or from sales agents and major booksellers throughout the world and ILO offices in many countries. Fax: (41 22) 799 85 78; email: cabrera@ilo.org; website: www.i lo. org. International Monetary Fund The International Monetary Fund (IMF) was established at a conference in Bretton Woods, New Hampshire, U.S.A., on 1-22 July 1944. (The conference also established the World Bank.) The IMF came into official existence on 27 December 1945, and commenced financial operations on 1 March 1947. It currently has 181 member countries. 'WAK7i'e The statutory purposes of the IMF are to promote international monetary cooperation, facilitate the expansion and balanced growth of international trade, promote exchange rate stability, help establish a multilateral payments system, make the general resources of the IMF temporarily available to its members under adequate safeguards, and shorten the dura- tion and lessen the degree of disequilibrium in the international balances of payments of members. The IMF maintains an extensive program forthe development and compilation of international statistics, and is responsible for collecting and reporting statistics on international financial trans- actions and the balance of payments. In April 1996 it undertook an important initiative aimed at improving the quality of international statistics, establishing the Special Data Dissemination 1999 World Development Indicators xiii Standard to guide members that have or seek access to international capital markets in provid- ing economic and financial data to the public. The IMF's major statistical publications include Intemational Financial Statistics, Balance of Payments Statistics Yearbook, Govemment Finance Statistics Yearbook, and Direction of Trade Statistics Yearbook. For more information on IMF statistical publications contact the International Monetary Fund, Publications Services, Catalog Orders, 700 19th Street, N.W., Washington, D.C. 20431, U.S.A.; telephone: (202) 623 7430; fax: (202) 623 7201; telex: RCA 248331 IMF UR; email: pub- web@imf.org; website: www.imf.org; SDDS bulletin board: dsbb.imf.org. International Telecommunication Union Founded in Paris in 1865 as the International Telegraph Union, the International Telecommunication Union (ITU) took its current name in 1934 and became a specialized agency of the United Nations in 1947. The ITU is an intergovernmental organization within which the public and private sectors coop- erate for the development of telecommunications. The ITU adopts intemational regulations and treaties governing all terrestrial and space uses of the frequency spectrum and the use of the geo- stationary-satellite orbit. It also develops standards for the interconnection of telecommunications systems worldwide. The ITU fosters the development of telecommunications in developing countries by establishing medium;term development policies and strategies in consultation with other partners in the sector and providing specialized technical assistance in management, telecommunications pol icy, human resource management, research and development, technology choice and transfer, net- work installation and maintenance, and investment financing and resource mobilization. The Telecommunications Yearbook is the ITU's main statistical publication. Publications can be ordered from ITU Sales and Marketing Service, Place des Nations, CH-1211 Geneva 20, Switzerland; telephone: (41 22) 730 6141 (English), (41 22) 730 6142 (French), and (41 22) 730 6143 (Spanish); fax: (41 22) 730 5194; email: sales.online@itu.ch; telex: 421 000 uit ch; telegram: ITU GENEVE; website: www.itu.ch. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) was set up in 1948 as OECD the Organisation for European Economic Co-operation (OEEC) to administer Marshall Plan fund- ing in Europe. In 1960, when the Marshall Plan had completed its task, the OEEC's member countries agreed to bring in Canada and the United States to form an organization to coordinate policy among industrial countries. The OECD is the international organization of the industrialized, market economy countries. Representatives of member countries meet at the OECD to exchange information and harmonize policy with a view to maximizing economic growth in member countries and helping nonmember countries develop more rapidly. The OECD has set up a number of specialized committees to fur- ther its aims. One of these is the Development Assistance Committee (DAC), whose members have agreed to coordinate their policies on assistance to developing and transition economies. Also associated with the OECD are several agencies or bodies that have their own governing statutes, including the International Energy Agency (lEA) and the Centre for Co-operation with Economies in Transition. The OECD's main statistical publications include Geographical Distribution of Financial Rows to Developing Countries, National Accounts of OECD Countries, Labour Force Statistics, Revenue xiv 1999 World Development Indicators Statistics of OECD Member Countries, International Direct Investment Statistics Yearbook, Basic Science and Technology Statistics, Industrial Structure Statistics, and Services: Statistics on Intemational Transactions. For information on OECD publications contact OECD, 2, rue Andre-Pascal, 75775 Paris Cedex 16, France; telephone: (33 1) 45 24 82 00; fax: (33 1) 49 10 42 76; email: sales@oecd.org; websites: www.oecd.org and www.oecdwash.org. United Nations The United Nations and its specialized agencies maintain a number of programs for the collec- tion of international statistics, some of which are described elsewhere in this book. At United Nations headquarters the Statistics Division of the Department of Economic and Social Information and Policy Analysis provides a wide range of statistical outputs and services for pro- ducers and users of statistics worldwide. The Statistics Division publishes statistics on international trade, national accounts, demog- raphy and population, gender, industry, energy, environment, human settlements, and disability. Its major statistical publications include the International Trade Statistics Yearbook, Yearbook of National Accounts, and Monthly Bulletin of Statistics, along with general statistics compendiums such as the Statistical Yearbook and World Statistics Pocketbook. For publications contact United Nations Publications, Room DC2 853, 2 UN Plaza, New York, NY 10017, U.S.A.; telephone: (212) 963 8302 or (800) 253 9646 (toll free); fax: (212) 963 3489; email: publications@un.org; website: www.un.org. United Nations Centre for Human Settlements (Habitat), Global Urban Observatory The Urban Indicators Programme (UIP) of UNCHS (Habitat) was established to address the 49 T lA roX7JAbry urgent global need to improve the urban knowledge base by helping countries and cities ^^t-? 1I1 design, collect, and apply policy-oriented indicators related to urban development at the city level. In 1997 the UIP was integrated into the Global Urban Observatory (GUO), the prin- cipal United Nations programme for monitoring urban conditions and trends and for track- ing progress in implementing the goals of the Habitat Agenda. With the Urban Indicators and Best Practices programmes, the GUO is establishing a worldwide information, assessment, and capacity building network to help governments, local authorities, the pri- vate sector, NGOs, and other organizations of civil society. Contact Christine Auclair (guo@unchs.org), Urban Indicators Programme, Global Urban Observatory, UNCHS (Habitat), P.O. Box 30030. Nairobi, Kenya; tel (2542) 623694; fax: (2542) 624266/7; website: www.urbanobservatory.org. United Nations Children's Fund The United Nations Children's Fund (UNICEF), the only organization of the United Nations dedicated exclusively to children, works with other United Nations bodies and with govemments and non- 0 )S governmental organizations to improve children's lives in morethan 140 developingcountriesthrough community-based services in primary health care, basic education, and safe water and sanitation. unicef UNICEF's major publications include The State ofthe World's Children and The Progress of Nations. For information on UNICEF publications contact UNICEF House, 3 United Nations Plaza, New York, N.Y. 10017, U.S.A.; telephone: (212) 326 7000; fax: (212) 888 7465 or 7454; telex: RCA- 239521; email: publications@un.org; website: www.unicef.org. 1999 World Development Indicators xv United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. It was estab- lished as a permanent intergovernmental body in 1964 in Geneva with a view to accelerat- ing economic growth and development, particularly in developing countries. UNCTAD UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical coop- eration. UNCTAD produces a number of publications containing trade and economic statistics, includ- ing the Handbook of International Trade and Development Statistics. For information contact UNCTAD, Palais des Nations, CH-1211 Geneva 10, Switzerland; telephone: (41 22) 907 12 34 or 917 12 34; fax: (41 22) 907 00 57; telex: 42962; email: reference.service@unctad.org; website: www.unctad.org. United Nations Educational, Scientific, and Cultural Organization The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations established in 1945 to promote "collaboration among nations U NESCO through education, science, and culture in order to further universal respect for justice, for the rule of law, and for the human rights and fundamental freedoms . .. for the peoples of the world, without distinction of race, sex, language, or religion UNESCO's principal statistical publications are the Statistical Yearbook, World Education Report (biennial), and Basic Education and Literacy: World Statistical Indicators. For publications contact UNESCO Publishing, Promotion, and Sales Division, 1, rue Miollis F, 75732 Paris Cedex 15, France; fax: (33 1) 45 68 57 41; email: publishing.promotion@unesco.org; website: www.unesco.org. United Nations Environment Programme The mandate of the United Nations Environment Programme (UNEP) is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. UNEP ' 5f_"T~~ publications include Global Environment Outlook and Our Planet (a bimonthly magazine). For infor- mation contact UNEP, RO. Box 30552, Nairobi, Kenya; telephone: (254 2) 62 1234 or 3292; fax: (254 2) 62 3927 or 3692; email: oedinfo@unep.org; website: www.unep.org. United Nations Industrial Development Organization The United Nations Industrial Development Organization (UNIDO) was established in 1966 to act uf\i as the central coordinating body for industrial activities and to promote industrial development UNIDO and cooperation at the global, regional, national, and sectoral levels. In 1985 UNIDO became the sixteenth specialized agency of the United Nations, with a mandate to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. UNIDO's databases and information services include the Industrial Statistics Database (INDSTAT), Commodity Balance Statistics Database (COMBAL), Industrial Development Abstracts (IDA), and the International Referral System on Sources of Information. Among its publications is the Intemational Yearbook of Industrial Statistics. xvi 1999 World Development Indicators For information contact UNIDO Public Information Section. Vienna International Centre, P.O. Box 300, A-1400 Vienna, Austria; telephone: (43 1) 260 26 5031; fax: (43 1) 213 46 5031 or 260 26 6843; email: publications@unido.org; website: www.unido.org. United States Arms Control and Disarmament Agency The mission of the United States Arms Control and Disarmament Agency (ACDA) is to strengthen the national security of the United States by formulating, advocating, negotiating, implementing, and ver- ifying effective arms control, nonproliferation, and disarmament policies, strategies, and agreements. In so doing, ACDA ensures that arms control is fully integrated into the development and conduct of U.S. national security policy. On 18 April 1998 ACDA began integrating with the U.S. Department of State, merging both agencies related arms control and nonproliferation functions. For publications contact ACDA, 320 21st Street, N.W., Washington, D.C. 20451, U.S.A.; (800) 581 2232; fax: (202) 647 6928; website: wrw.acda.gov. The World Bank Group The World Bank Group is made up of five organizations: the International Bank for Reconstruction and Development (IBRD), the International Development Association (IDA), the International Finance Corporation (IFC), the Multilateral Investment Guarantee Agency (MIGA), and the International Centre for Settlement of Investment Disputes (ICSID). Established in 1944 at a conference of world leaders in Bretton Woods, New Hampshire, the World Bank is a lending institution whose aim is to help integrate developing and transition coun- tries with the global economy, and reduce poverty by promoting economic growth. The Bank lends for policy reforms and development projects and provides policy advice, technical assistance, and nonlending services to its 181 member countries. For information about the World Bank visit our website at www.worldbank.org. For more infor- mation about development data contact the Development Data Center, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A.; telephone: (800) 590 1906 or (202) 473 7824; fax: (202) 522 1498; email: info@worldbank.org; website: www.worldbank.org/data. The World Health Organization The constitution of the World Health Organization (WHO) was adopted on 22 July 1946 by the International Health Conference, convened in New York by the Economic and Social Council. The objective of the WHO. a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. The WHO carries out a wide range of functions, including coor- dinating international health work; helping governments strengthen health services; providing tech- nical assistance and emergency aid; working for the prevention and control of disease; promoting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promot- ing and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for bio- logical, pharmaceutical, and similar products; and standardizing diagnostic procedures. The WHO publishes the World Health Statistics Annual and many other technical and statis- tical publications. For publications contact Distribution and Sales, Division of Publishing, Language, and Library Services, World Health Organization Headquarters, CH-1211 Geneva 27, Switzerland; telephone: (41 22) 791 2476 or 2477; fax: (41 22) 791 4857; email: publications@who.ch; website: wrw.who.ch. 1999 World Development Indicators xvii The World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations based in Geneva, Switzerland. The objectives of WIPO are to promote the protection of intellec- tual property throughout the world through cooperation among states and, where appropriate, in collaboration with other international organizations and to ensure administrative cooperation among the intellectual property unions-that is, the "unions" created by the Paris and Berne Conventions and several subtreaties concluded by members of the Paris Union. WIPO is respon- sible for administering various multilateral treaties dealing with the legal and administrative aspects of intellectual property. A substantial part of WIPO's activities and resources is devoted to development cooperation with developing countries. For information contact the World Intellectual Property Organization, 34, chemin des Colombettes, Geneva, Switzerland; mailing address: P.O. Box 18, CH-1211 Geneva 20, Switzerland; telephone: (41 22) 338 9111; fax: (41 22) 733 5428; telex: 412912 ompi ch; email: publications.mail@wipo.int; website: www.wipo.int. World Trade Organization The World Trade Organization (WTO), established on 1 January 1995, is the successor to the General Agreement on Tariffs and Trade (GATT). The WTO provides the legal and institutional ~~' P . -.l;.foundation of the multilateral trading system and embodies the results of the Uruguay Round of trade negotiations, which ended with the Marrakesh Declaration of 15 April 1994. The WTO is mandated to administer and implement multilateral trade agreements, serving as a forum for multi lateral trade negotiations, seeking to resolve trade disputes, overseeing national trade policies, and.cooperating with other international institutions involved in global economic- policymaking. The WTO's Statistics and Information Systems Divisions compile statistics on world trade and maintain the Integrated Database, which contains the basic records of the outcome of the Uruguay Round. The WTO Annual Report includes a statistical appendix. For publications contact World Trade Organization, Publications Services, Centre William Rappard, 154 rue de Lausanne, CH-1211, Geneva, Switzerland; telephone: (41 22) 739 5208 or 5308; fax: (41 22) 739 5458; email: publications@wto.org; website: www.wto.org. Private and nongovernmental organizations Currency Data & Intelligence, Inc. Currency Data & Intelligence, Inc. is a research and publishing firm that produces currency-related products and undertakes research for international agencies and universities worldwide. Its flag- ship product, the World Currency Yearbook, is the most comprehensive source of information on Currency Data & Intelligence,inc. currency. It includes official and unofficial exchange rates and discussions of economic, social, and political issues that affect the value of currencies in world markets. A second publication, the monthly Global Currency Report, covers devaluations and other critical developments in exchange rate restrictions and valuations and provides parallel market exchange rates. For information contact Currency Data & Intelligence, Inc., 328 Flatbush Avenue, Suite 344, Brooklyn, N.Y. 11238, U.S.A.; telephone: (718) 230 7176; fax: (718) 230 1992; email: curncydata @aol.com; website: pacific.commerce.ubc.ca/xr/cdi/. xvill 1999 World Development Indicators Euromoney Publications PLC Euromoney Publications PLC provides a wide range of financial, legal, and general business infor- mation. The monthly Euromoney magazine carries a semiannual rating of country creditworthiness. For information contact Euromoney Publications PLC, Nestor House, Playhouse Yard, London EC4V 5EX, U.K.; telephone: (44 171) 779 8888; fax: (44 171) 779 8656; telex: 2907002; email: hotline@euromoneyplc.com; website: www.euromoney.com. Institutional Investor, Inc. Institutional Investor magazine is published monthly by Institutional Investor, Inc., which develops country credit ratings every six months based on information provided by leading international banks. For information contact Institutional Investor, Inc., 488 Madison Avenue, New York, N.Y. 10022, U.S.A.; telephone: (212) 224 3300; email: info@iimagazine.com; website: www.iimagazine.com. International Road Federation The International Road Federation (IRF) is a not-for-profit, nonpolitical service organization. Its purpose is to encourage better road and transportation systems worldwide and to help apply SL X technology and management practices that will maximize economic and social returns from national road investments. The IRF has led global road infrastructure developments and is the international point of affiliation for about 600 member companies, associations, and governments. The IRF's mission is to promote road development as a key factor in economic and social growth, to provide governments and financial institutions with professional ideas and expertise, to facilitate business exchange among members, to establish links between IRF members and exter- nal institutions and agencies, to support national road federations, and to give information to pro- fessional groups. The IRF publishes World Road Statistics. Contact the Geneva office: 2 chemin de Blandonnet, CH-1214 Vernier, Geneva, Switzerland; telephone: (41 22) 306 0260; fax: (41 22) 306 0270; or the Washington, D.C. office: The Watergate Office Building, 2600 Virginia Avenue, N.W., Suite 208, Washington, D.C. 20037, U.S.A.; telephone: (202) 338 4641; fax: (202) 338 8104; email: info@irfnet.com; website: www.irfnet.org. Moody's Investors Service Moody's Investors Service is a global credit analysis and financial opinion firm. It provides the q Moodys Investors Service international investment community with globally consistent credit ratings on debt and other securities issued by North American state and regional government entities, by corporations worldwide, and by some sovereign issuers. It also publishes extensive financial data in both print and electronic form. Clients of Moody's Investors Service include investment banks, bro- kerage firms, insurance companies, public utilities, research libraries, manufacturers, and gov- ernment agencies and departments. Moody's publishes Sovereign, Subnational and Sovereign-Guaranteed Issuers. For information contact Moody's Investors Service, 99 Church Street, New York, N.Y. 10007, U.S.A.; telephone: (212) 553 1658; website: www.moodys.com. 1999 World Development Indicators xix The PRS Group Political Risk Services is a global leader in political and economic riskforecasting and market analy- sis and has served international companies large and small for about 20 years. The data it con- , r , tributed to this year's World Development Indicators come from the International Country Risk Guide, a monthly publication that monitors and rates political, financial, and economic risk in 140 countries. The guide's data series and commitment to independent and unbiased analysis make it the standard for any organization practicing effective risk management. For information contact The PRS Group, 6320 Fly Road, Suite 102, RO. Box 248, East Syracuse, N.Y. 13057-0248, U.S.A.; telephone: (315) 431 0511; fax: (315) 431 0200; email: custserv@PRSSgroup.com; website: www.prsgroup.com. PricewaterhouseCoopers Drawing on the talents of more than 140,000 people in 152 countries, PricewaterhouseCoopers, formed by the merger of Price Waterhouse and Coopers & Lybrand, provides a full range of busi- ness advisory services to leading global, national, and local companies and public institutions. These services include audit, accounting, and tax advice; management, information technology, and human resource consulting; financial advisory services, including mergers and acquisitions, business recovery, project finance, and litigation support; business process outsourcing services; and legal advice through a global network of affiliated law firms. PricewaterhouseCoopers publishes Corporate Taxes 1998: Worldwide Summaries and Indi- vidual Taxes 1998: Worldwide Summaries. For information contact PricewaterhouseCoopers, 1301 Avenue of the Americas, New York, N.Y. 10019; telephone: (212) 596 7000; fax: (212) 259 5324; website: www.pwcglobal.com. Standard and Poor's Rating Services Standard and Poor's Sovereign Ratings provides issuer and local and foreign currency debt ratings for sovereign governments and for sovereign-supported and supranational issuers worldwide. Standard & Poor's Rating Services monitorsthe credit quality of $1.5 trillion worth of bonds and other financial instruments and offers investors global coverage of debt issuers. Standard & Poor's also has ratings on commercial paper, mutual funds, and the financial con- dition of insurance companies worldwide. For information contact The McGraw-Hill Companies, Inc., Executive Offices, 1221 Avenue of the Americas, New York, N.Y. 10020, U.S.A.; telephone: (212) 512 4105 or (800) 352 3566 (toll free); fax: (212) 512 4105; email: ratings@mcgraw-hill.com; website: www.ratings.standardpoor.com. World Conservation Monitoring Centre The World Conservation Monitoring Centre (WCMC) provides information on the conservation and sustainable use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of organizations and people to increase access to the information needed for wise management of the world's living resources. Committed to the principle of data exchange with other centers and noncommercial users, the WCMC, when- WORLD CONSERVATION MONITORING CENTRE ever possible, places the data it manages in the public domain. For information contact World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 ODL, U.K.; telephone: (44 12) 2327 7314; fax: (44 12) 2327 7136; email: info@wcmc.org.uk; website: www.wcmc.org.uk. xx 1999 World Development Indicators World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides-and helps other institu- tions provide-objective information and practical proposals for policy and institutional change that L079-H-1 will foster environmentally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For information contact World Resources Institute, 10 G Street, N.E., Washington, D.C. 20002, U.S.A.; telephone: (202) 729 7600; fax: (202) 729 7610; telex 64414 WRIWASH; email: lauralee@wri.org; website: www.wri.org. 1999 World Development Indicators xxi Users guide I Principal sections Are signposted by these icons: * 9 5.1 Credit, investment, and expenditure Section 1 World view P dle I--- _~~~~~~~~~~~~~~~~~~~~~~~~~~P09 7001, 2600 loonmeO Credit xtol | . Cont | rOF Section 2 People 3 dO0- .00o | W0 2d Section 3 Environment - 6 i Section 4 Economy Section 5 States and markets eD* j Section 6 Global links I The tables Tables are numbered by section and display the identi- ; . ; .-- fying icons of each section. Countries and economies are listed alphabetically (except for Hong Kong, China, o . which appears after China). Data are shown for 148 6 economies with populations of more than 1 million . i people and for which data are regularly reported by : -:, | the relevant authority, as well as Taiwan, China, in - selected tables. Selected indicators for 62 other - L ' - economies-small economies with populations between 30,000 and 1 million, smaller economies if they are members of the World Bank, and larger economies for which data are not regularly reported- 270 |0099. are shown in table 1.6. The term country, used inter- changeably with economy, does not imply political independence or official recognition by the World Bank, but refers to any territory for which authorities report separate social or economic statistics. When Indicators available, aggregate measures for income and Indicators are shown for the most recent year or period regional groups appear at the end of each table. for which data are available and, in most tables, for an earlier year or period. Time-series data are available in the World Development Indicators CD-ROM. xxii 1999 World Development Indicators Data are shown whenever possible for the individual countries formed from the former Czechoslovakia-the Czech Republic and the Slovak Republic. 5.1 Data are shown for Eritrea whenever possible, but in most cases before 1992 Eritrea is included in the data Pdvd 19109 dll7999Inlxnl90rsdl ts Pd990 st9 XF9994 ooo for Ethiopia. Data for Germany refer to the unified Germany % d 20.s d°tt.'.t,. d desms unless otherwise noted. 12x 1990 1990d 097 1997 1Fd9 1990 1997 10 1997 1980 109X Data for Jordan refer to the East Bank only unless otherwise noted. In 1991 the Union of Soviet Socialist Republics 007 . . . ~ ~ ~~~~. . 00 2,91970 .. 407 14 0.0 440 7710 . 451 381 was dissolved into 15 countries (Armenia, Azerbaijan, 19310 - -- - - - 21 40.7 .033 -- os .- Belarus, Estonia, Georgia, Kazakhstan, Kyrgyz Republic, 3,09 01.0 0070 01 Latvia, Lithuania, Moldova, Russian Federation, 263 286 Tajikistan, Turkmenistan, Ukraine, and Uzbekistan). 26; 172 166 Whenever possible, data are shown for the individual countries. L0070 - 000 . 40.0 01 . 104~~~~~~~0 04 0.0 010Data for the Republic of Yemen refer to that country 7907007 . 900 00 1.0 433 100 3.2900 from 1990 onward; data for previous years refer to aggregated data of the former People's Democratic - - 00 170 3Republic of Yemen and the former Yemen Arab Republic 230 219 unless otherwise noted. 200 Whenever possible, data are shown for the individ- ua countries formed from the former Yugoslavia- 36 . . 216 - Bosnia and Herzegovina, Croatia. the former Yugoslav 00770,0 . 70.4 2 0 17 4 0.0 . 3301 33.4 Republic of Macedonia, Slovenia, and the Federal Republic of Yugoslavia. All references to the Federal Republic of Yugoslavia in the tables are to the Federal Republic of Yugoslavia (Serbia/Montenegro) unless otherwise noted. . . Additional information about the data is provided in ;__ - . . : ' Primary data documentation. That section summarizes national and international efforts to improve basic data 79731 ~76.6 j47 04 4 69117070900 11 . . .. collection and gives information on primary sources, 70150 00~~~~~6 . , '0 16900 . census years, fiscal years, and other background. Statistical methods provides technical information on 03937 1071,0i7 . 6.4 1.4 . 807 some of the general calculations and formulas used throughout the book. Discrepancies in data presented in different editions of the World Development Indicators reflect updates by countries as well as revisions to historical series and Statistics changes in methodology. Thus readers are advised not to compare data series between editions of the World Data are shown for economies as they were constituted in 1997, and historical data are revised to Development Indicators or between different World Bank reflect current political arrangements. Exceptions are publications. Consistent time-series data for 1960-97 noted throughout the tables. are available on the World Development Indicators On 1 July 1997 China resumed its exercise of CD-ROM. Except where noted, growth rates are in real sovereignty over Hong Kong. Data for China do not terms. (See Statistical methods for information on the inclueidata fovr Hong Kong, Cia,a forTaiwa China , methods used to calculate growth rates.) Data for some include data for Hong Kong, China, or Taiwan, China, unless otherwise noted. economic indicators for some economies are presented Data for the Democratic Republic of Congo (Congo, in fiscal years rather than calendar years; see Primary Dem. Rep. in the table listings) refer to the former data documentation. All dollar figures are current U.S. Zaire. For clarity, this edition also uses the formal dollars unless otherwise stated. The methods used for name of the Republic of Congo (Congo, Rep. in the converting national currencies are described in table listings). Statistical methods. 1999 World Development indicators xxiii The World Bank's classification of economies For operational and analytical purposes the World Bank's main criterion for classifying economies is gross national product (GNP) per capita. Every econ- omy is classified as low income, middle income (sub- divided into lower middle and upper middle), or high r,v,stm,nt F | govo,,w,, income. For income classifications see the map on the inside front cover and the list on the front cover oi ,,,e, m,,, flap. Note that classification by income does not G 9 7 S7 O 7 necessarily reflect development status. Because GNP per capita changes over time, the country com- position of income groups may change from one edi- tion of the World Development Indicators to the next. Once the classification is fixed for an edition, all his- torical data presented are based on the same coun- try grouping using the most recent year for which GNP per capita data are available (1997 in this edi- tion). Low-income economies are those with a GNP per capita of $785 or less in 1997. Middle-income economies are those with a GNP per capita of more than $785 but less than $9,656. Lower-middle- income and upper-middle-income economies are sep- arated at a GNP per capita of $3,125. High-income economies are those with a GNP per capita of $9,656 or more. The 11 participating member coun- tries of the European Monetary Union (EMU) are pre- . . - sented as a subgroup under high-income economies. Aggregate measures for income groups The aggregate measures for income groups include 210 --S economies (economies presented in the main tables plus the economies listed in table 1.6) wherever data are available. To maintain consistency in the aggregate mea- *'^ -- sures over time and between tables, missing data are imputed where possible. Most aggregates are totals (designated by a t if the aggregates include gap-filled estimates for missing data; otherwise totals are desig- nated by an s for simple totals), median values (m), or I weighted averages (w). Gap filling of amounts not allo- cated to countries may result in discrepancies between subgroup aggregates and overall totals. See Statistical Footnotes methods for further discussion of aggregation methods. Knownotes Known deviations from standard definitions or breaks Aggregate measures for regions in comparability over time or across countries are The aggregate measures for regions include only low- either footnoted in the tables or noted in About the and middle-income economies (note that these mea- data. When available data are deemed to be too sures include developing economies with populations of weak to provide reliable measures of levels and less than 1 million, includingthose listed in table 1.6). trends or do not adequately adhere to international The country composition of regions is based on the standards, the data are not shown. World Bank's analytical regions and may differ from common geographic usage. For regional classifications see the map on the inside back cover and the list on the back cover flap. See Statistical methods for further discussion of aggregation methods. xxiv 1999 World Development Indicators Notes about data About the data provides a general discussion of inter- | .:%-' national data standards, data collection methods, and - . - 5.1 ^tItt sources of potential errors and inconsistencies. ________________________ ________________________ Readers are urged to read these notes to gain an understanding of the reliability and limitations of the 1Tlelnd .,the nthwge met emittezem eage, ademsscolmy emp b Ite See Aeeut . Pe.et teetetet tem s GernpGosut 5 tteeePt- d-,-nsntd shsbt7 dt ddgpnetenepote) d data presented. For a full discussion of data collection ,snozealpeet agesnaette, ts I ot datat eetdb gesem et S ethosoan to defelionsdrdetaders sheoul css ges -mmethettetete.Theee.etees Iete,at,odoe tttethet,p Ittse,oS e fats Fs,eIdtGetIItII.I. mtechnical documentation provided by the original com- ettegetnte a e used tG rCt gen- ne . eeg"G ftesteGtdtetble, f- mee mtt e egysetey .-t nntstmetttto teqo-s Ltg.btgman getett nb pilers cited in Data sources. ketfatlees-e t e ubteee,detmente tesest TltsemtneGg.ste.eyIndeseoebletautnotlet eatllerpnteem of teseetsetolnaeet g te dts gd Si--g tee astattte p v ttetttbatadep- t m-eb ans In ede1 pr se-op- eieg saccm mofetfsn t m G e etse,elmgstea p,osecedmoe elEettee Aatglet etonttese.teebetege,e yi suwtc eseudetbetle eeeto, tt e tseSumt eteeee.,sefg smelmtt sleopnvete steMsiettme,tntetaflteee.t IgsIstIlitensi ttsasgsm e.sa tIttetess. Ro st fangt,eetesl-eYteceat .edstt,t-tt mptvr- I. -- a n- In me -- I P.,-" - Definitions - ? :~to eeeo 5t aly d estspn1m oe- t Ded Definitions provide short descriptions of the main - G GAs . Mlektte,tdftttetts trefBtetense indicators in each table. glettlt-le thesee lte eetet mtteattled . . t,ed gytte see ef mt entl geee,tmem WIle thes. . - e cetted es.emtI mis ts,,elI tee Iagestesteetoo b - , euesdtees, ate detceg n . ....Sources pseeteemst,es.-esteerGatelL t,gseteete , I-a, Partners are identified in the Data sources section adee d gG_ following each table, and key publications of the 4.dteheSo etmSnpageectteetth>w aysanm mcSmtes , d j b tSaIsct,nmtmDnSncOd taseetattesottSslseseedo bt A I I.4t . . - -- partners drawn on for the table are identified. For a tseesv-dmmetlaGcoutg,neabailaee.eehee t 4 i L . :l , , description of our partners and information on their lesttl oLlatsmeofsY£Get e detteAnseteeee .., . data publications see the Partners section. Ctst,a. ~~~~~~~~~Figures thee dheast mated.. e nM Ia When appropnattablesareaccompanied,byafig- getsebIetteetmc getttnveG mee Is tt.ttimbttled a -.* -"t.n*'.* t* urea highlighting particularltrendsuorrissues. .itsfee.esebb ett Ito glgesdoms,t, 51bt s Y n dt atsd I, .tdlasL -sp di-eeoa 5McSyolat seet IDtatamernttena con, n m b ees ed cau hmsr-es of misig dat in teyas shwn isntaayiaiennfl in~~~~~ dae,a_, 909, mean tha th peo of tie usal 12 . A trllo is , bilin PGsnddmontaths, Erstoraddlresl tw alnaryasnrfrst cro yer a suve - Figuesn i aprpitalic iniatlsae daataccomaniedfo byer org tcomdyowear,or focidscalyear.fe peid ote thntoeseiid $ f means currstent US. omLlfarts un:lesothrienoted . Dat foryeas thaglgtin pareoeticuan threendas fomsus te,tmanuetMtanmehatetM)aspdeelsttedee cpt *mmmn mhdleaecbens moe tha.th aneshwaefototd et, mpateanst eests tthea on bthte of,m dermeseT codaefratis Fetbryhe o oer 0.0md mlesnlauns zderyoorless thanchalfmtheunitshown.Abillionis1,000Dmillion. / in dates, as in 1990/91, means that the period o~~~~~~~~~~~~~~~~~1f tie usual 12 *eeomn Asnar trlio s1,0 ilin months, straddles two calendar years and refers to a crop year, a survey * Figures in italics indicate data that are for years or year, or a fiscal year. periods other than those specified. $ means current U .S. dollars unless otherwise noted. * Data for years that are more than three years from > means more than. the range shown are footnoted. c means less than. The cutof f date for data is 1. February 1999. 1999 World Deveiopmnent indicators xxv LI r . ;siiAus a crisis to protect the poor l'~~q:~; In 1998 the economic engines that powered the East Asian miracle ran out of steam, setting off a chain reaction across the globe. Millions had their incomes plummet. Some lost theirjobs, while others worried about losing theirs. Ethnic conflict and food riots broke out in Indonesia, and students took to the streets, toppling a president who had been in power for 32 years. Farmers protested in Thailand, and workers marched in the Republic of Korea. Beyond these expressions of pain and dissent was a quieter crisis. Children were being pulled from school and put to work. Food was being carefully , 9-,< 5-., - -. rationed in households. Middle-class families were cutting back sharply on con- sumption. !C; e > - This human crisis has been a rude awakening. Developing country growth (excluding the transition economies) had averaged 5.3 percent a year in ;. i k --s-.1991-97, two percentage points higher than in the 1980s. This strong perfor- 4 mance raised expectations that the international development goals set during ,V - ;, +. this decade for 2015 might just be attained. But those expectations must now 4#eC2' >be tempered-by the sudden slowdown of growth in 1998, to be followed almost certainly by more disappointment in 1999. Growth fafters un 1L998 World growth fell sharply in 1998-to 1.9 percent from 3.2 percent in 1997 and an average of 2.3 percent in 1991-97.1 Developing country growth fell by half- to 1.9 percent from 4.8 percent in 1997 and an average of 3.1 percent in 1991-97 (table la). The developing East Asia and Pacific region (excluding '.~ . Sr V. China) saw its average per capita income fall for the first time since 1970. These output contractions turned out far worse than was expected a year ago when the East Asian crisis was gathering momentum.Japan slid from stag- nation into a full-blown recession. The recession in the five East Asian crisis countries was deep and severe. And the consequences of El Ninlo and other A natural disasters-particularly severe for low-income countries in Central America and for Bangladesh, Thailand, and Indonesia-were worse than i=7pi;- anticipated. Japan's poor performance contributed 0.5 percentage point to -. * the decline in world GDP, while East Asia's five crisis countries contributed ; another 0.4 percentage point. Output fell by 13 percent in Indonesia, 8 per- - - J.; cent in Thailand, 7 percent in Korea, and 6.7 percent in Malaysia. Real GDP growth. 1991-2001 1997 forecast Current estimate Current forecasts 1991-97 1997 1993 1998 1999 2000 2001 WI! -r, _ 2 r~ 1 9- 1 - 2J L oSf il.r m:l ,n.mnr,.- ' I J r .I 19 1.S s.1 F FI,,f,,-,1.JIc,f ,.j II J .'1 J 1 ' 9 r -l..,, --. nl'l. - , .J . -I a 4 7 East -t, .* 45 -77 35. 1 ]1 C.- :r, _i.l. .1 5 | | ] C _ l C L sr- rrer, s ri 's.- ,:rr.r.: ir, . ar 2.7 1') -'3 v S 1.1.11- E,' - T,,r,I .:n :rr.c v I - 1 ' 'I -- - - s|lE,^j ;' Je s.- f: J 53 .4 J.S. -.,r- Ervm3.;r.1 :1, r., ... - 1 1 _ , ,--j,,-¢rI'_ *r.r, ::-i,9lrSC.,;.E, - ,E,,.:.0I5iE ,r,IS)'l- *,5*::,. .3,,, rl ru| U' .:Ir,:..,, n . '-S..ur'' .:r -..- . ,. F, ; r ,fl F_., fl, ) . .11. b)l 5i ;F .Uf, The East Asian crisis hit every corner of the developing world, in 10, with progress led by China and Indonesia, home to 92 per- Commodity markets were already in a cyclical decline after the mini- cent of East Asia's poor in 1975. boom of 1994-96, and the East Asian crisis pushed world demand Social indicators-which track progress in life expectancy, down further. East Asia had been responsible for much of the recent health, and schooling-had also improved (table ib) . The aver- growth in global consumption-and with Japan it accounted for a age East Asian now lives to around 70. Only 37 East Asian infants third of consumption. Its collapse in demand-alongwith the lagged of every 1,000 die before their first birthday, less than half the supply response to the 1994-96 miniboom-pulled commodity level in 1970. And access to education (measured by net primary prices dowvn. Hit hard were the Middie East, North Africa, and the school enrollment ratios) is close to universal in most countries. former Soviet Union, reflecting the sharp fall in crude oil prices. Also But even before the crisis, cracks were evident in East Asia's hit hard was Sub-Saharan Africa, which depends on primary corn- success in fighting poverty. Low initial income inequality is often modities for 80 percent of its export earnings (see table 4.5) . cited as a reason for EastAsia's rapid growth, but there is evidence But the most visible dimension of the crisis was the turbu- that it has been rising in recent years. Rising returns to education lence in financial markets-capital outflows, falling currencies, have widened the wage differences between skilled and unskilled failing banks, and investors' loss of confidence in East Asia, Latin workers. And concentrations of modern sector activities have left America, and Russia. Net long-term private capital flows to devel- some areas behind-among them, the interior provinces of oping countries (including Korea) fell sharply from $299 billion China, northeastern Thailand, and the outlying islands of the in 1997 to $227 billion in 1998. Although flows of foreign direct Indonesian archipelago. investment appear to have held up, the collapse in confidence in Such rising inequality reduces growth's contribution to poverty emerging markets is evident in the sharp decline in debt and reduction.3 This effect shows up in distribution-corrected rates of portfolio equity flows-from $136 billion in 1997 to $72 billion growth for per capita consumption in East Asia, which are lower in 1998-and in the large increases in secondary market spreads than the uncorrected growth rates (table 1.2). Rising inequality for developing country debt. This was only partially offset by an can also stir up social tensions, particularlywhere the gains are seen increase in net of ficial flows from $39 billion to $48 billion, to be divided unequally across ethnic lines, as in Indonesia. A second crack was the breakdown of traditional safety nets to deal with job losses, disabilities, and aging. While progress was East Atsia's poor bear the costl eroding the traditional social safety nets of family and kinship, The one region that had shown the way in rapid poverty reduction efforts to replace them with more formal safety nets affected only now faces a sharp reversal. Between 1975 and 1995 poverty in East small parts of the population. Asia fell by two-thirds (based on a headcount index using a $1 a day The cracks have widened with the crisis: poverty line in 1985 purchasing power parity, or PPP, terms) .2 In * Unemployment has risen as the economies have contracted, 1975 about 6 in 10 East Asians lived in poverty-in 1995, about 2 but greater underemployment and falling real wages are likely 4 1999 World Development Indicators WORLD VIEW Progress in social Indicators Life expectancy at birth Infant mortality rate Primary net enrollment ratio years per 1.000 live births .. of relevant age group 1970 1997 1970 1997 1930 1996 Eai 6va .; J - ; 5 I I.fi - A rlsla la 1;: ~ ~ ~ ~~~.2 2. Ž . 10:: Tr,,.,- :f: Tr. 311all, Cs, ,, 7:5 ';,nA .Er.,T* a.) .15. , jr.!toA 7 K n-r r .n I ..:. _A ,rsl- 1 yr i n.jE , ' rr.. - , 1:J J zCjQ -4 4Žr.5 ia-s -: :t. .,~r,,rsr, Ir. JJI 5I I r: Le .e':;np n uzvraeeS :' Ia:- i. us. ;1 - I r1- to be the main expression of the falling demand for labor in effects have been apparent, too, in transition economies, where countrieswith large informal sectors and flexible labor markets. the continuing declines in output have been accompanied by a * With large exchange rate depreciations, the consumer price sharp worsening in income distribution and rising numbers in index has risen sharply in all countries, as has the index for poverty-from 14 million in 1989 to 147 million today. In both food prices. As stability returns, the rate of inflation will fall, regions social indicators held up-but their improvement slowed but the short-term impact has been severe. or stagnated. * With large price changes, relative prices have shifted enor- mously. For example, prices of basic medicines have risen Latin America's debt crisis in the 19SOs sharply, and food prices have risen relative to other prices. The 1980s debt crisis killed growth in Latin America-and with * Social unrest, particularly serious in Indonesia, is also a dan- itjobs, wages, and investment.4 Because income inequality was so ger elsewhere-and with it go threats to political stability. high, the poor were hit especially hard. Brazil, Peru, Venezuela, Early evidence from Indonesia suggests that the initial and much of Central America reported increases in poverty in impacts on poverty and unemployment are more modest than the 1980s. The highly unequal income distribution, believed to had been feared. But the crisis is far from over, and the story have worsened in many Latin American countries, contributed to could change as later effects kick in (box la). these increases.5 The transmission mechanisms were clear: infla- tion, the enemy of the poor, rose sharply, while employment and Poverty and social indicators in times of crisis real wages declined. Faltering growth clearly hurts the poor, but it hurts them more The paradox in the Latin American story is the behavior of when the inequality of income distribution is increasing-and social indicators. Despite a significant decline in social spending, much less, if at all, when it is declining. What, for example, would both health and education indicators improved in the 1980s. Why? be the effect of having national consumption or income fall by * There were lags between reductions in inputs and changes in 10 percent over 1998-2000? With no change in income distribu- outcomes: many outcome indicators were leveling off toward tion, poverty would double in Indonesia. But if income distribu- the end of the decade. tion were to worsen-by, say, a 10-point rise in the Gini * Aggregate data hide serious disparities within countries. coefficient-the incidence of poverty would almost triple. For Middle-income groups were much more likely to protect their Indonesia the large projected decline in aggregate growth would education and health standards. The poorer regions, such as force an additional 6 percent of the population into poverty by North East Brazil, probably were affected much more severely. 2000, assuming income distribution does not worsen. Thailand * The efficiency of spending may have increased as resources would see an additional 8 percent in poverty. for social spending declined. This was true, say, for pharma- The pernicious effects of inequality and faltering growth were ceuticals, whose prices came down. also apparent in Latin America in the 1980s, where high and * Basic social indicators, when fairly high, tend to reach a widening inequality increased the numbers in poverty. These plateau, and they need to be supplemented with more detailed 1999 World Development Indicators 5 The crisis in Europe and Central Asia- more than 100 million new poor Measuring the impact of the crisis on Indonesia's poor In 1989 about 14 million people in the transition economieswere liv- r r re h;eC r, r r rr, r Injne a. r. 3ed ;cr i1 3 i 3, ing under a poverty line of $4 a day (1990 purchasing power parities cer rW l,, |FPP at 1'f cr :*e -.: a; e sl,n;ed i! t~r. rer-r,. unIand exchange rates) 6 By mid-1990 that number was about 147 mil- lt. n,n,:c.r, r;c,r Prxw .e. L'6 ,r, .~ n- ,r,r,.n i l .D-r ,, [rh-re i o%,-trr,ner e e&,inmre.] r(..r .,r 11 ser.;r,r. ar 2_raIl:rl [rur Z rhe :rn., lion, one person in three. The distribution of income in the com- r.,ore. r-C.r,e , 'leicreEll,lW ;i;rml,lr.. Ec.I.3r,nZr,,;,r.u trr.ae onr munist period was relatively egalitarian, primarily because of a .3 :r ;rln~ - ro;e[rC.r,, 3D-,' n;,I,n i' -luOt n,,II,Cr,. relatively flat wage distribution, but also because of the virtual triC- I i'C-'r r.~, i, ,oia Gr v,e e .. .;C- D&Tur. . , .Jurrng ,.e ,:r C F ..ur-, 2 . .l rn Inr,.r, r F,;nn,.l L,r .ur,e, ,:;r.ed absence of income from property and the redistribution of income outc,F 4r C CC,rp-,r.EI,..r n Coldtlrar .S,rr L&,t3g Den; ,trriIi "l through social transfers. Low Gini coefficients, ranging from 23 to ---1l.;re hr.;r re- --rveen ,in;-:ncr'.r,1,s Delbe 26, were fairly common. Today, some eight years later, income distri- 1|9^4E.ir 2 r.3uselr,:.I3sn rE.,rn 0 ro;,n.:e Trn re,I(jI, bution hasworsened sharply, particularly in the former Soviet Union. * z .e:,, e -PerollJr--- d- ,l ,,, r,.l,]:l,r.e- r. . .:.- IC As in Latin America, the social indicators have held up, but ,ua,rier ; iar,2,e ze--:Iar;e C., 5r,,-ar eul n,er.ian e. ,rez r.ell the stress is showing in the declining or stagnating life expectancy ri ei- ;; ; [I. 2 r.Cr:err ir.-. E,iE . - ir. ujP)r up .-Er -Cr,e. nu .-I,1h n r.i- dC-.r'o-s n rxr ,:;Enp,,r.-5p n..r, and sharply worsening adult mortality. Today, for example, the * A , ran)n -GnrDe3r .:. r;. ur. r, are v o, probability that a 1 5-year-old Ukrainian male will survive until his nr,,.re rn E Tr,,rl, ,,r rural 59Cr..i.lri Iviil L) 1. pvr-Cr,I sixtieth birthday is a mere 65 percent, down from 72 percent in * fieC,.:rn ,rncj' ,r;r.er.3r,ed rr.- [1,A tne jJI e ,.n.:n,, ..,,tr, rr,;- Ci ,lrr .I C *J1 c:IIrr.j :rn,r, , .[ j:r ,i, -r.C-r,-oCr:e D . - 1980. The Europe and Central Asia region is the only part of the rnr,~' r E3lII.larI J...a PEe:rn 5liEcuC.i h;, .rouLnI alr.ee I r,C. I, n,.. developing world with rising adult mortality rates. Even Sub- * trne ,rr,Dl,ed ., err. rare: rlr,- ' rn 1J i,r.:er,r r,,2 ,II,r,.a Saharan Africa, with its AIDS epidemic, is seeing a reduction in .r,(I',I.:.nrase r 3i El. ,-,rr,e ..n , ,L.rC rd I., V .CI DerCerIIep mca r rIll,.:. rar-~ , r.; rn as, ,:.- er ,, rne ;, , adult mortality (see table 2.18). r.- -ee nr.--,rn.j r., * ur.e, .;r l,0r ..II -rge nr,d.j.r.i b: IrC- :Cfl Driving these trends in the transition economies is a decline k,re3 Ell SEan t,A 'jfld.er LIrJEF SGlD or-,rSC=l in economic output. Before the collapse of the Soviet Union H.... d ;re (ne-e r&;lI-. ,i, en jr, (,.;-;,e . ,, , growth rates averaged about 2-3 percent. But by 1990 all coun- or,:.S | ;.;rI,rJElr;n, rr-I,;er ,r,:rease i.~od t;,Jr:.s .:JElg)1. r, *r tries were in a decline that continued through 1992. After that Docerr, D.3sC-e or , p; o:..e'r. I,neC-~ ^.1I t~e rii;r.E dr ..ir.. r mI nn,;, the Eastern European economies began to grow, with recovery ne . .Elr* e.... I,:,ri. ll:uI .3r,e9i rr- a . C g r,er r . er . bplen-jau~ 1,rei :rle: r. 3 ooo-jJ rn| nz 3?e ioi ne lern, Incmr,P,e ,rr, slower in many countries of the former Soviet Union. The excep- -.ro: rn r ...r : :.n,,,r >ui :.e. ger.u-. ,re,. Iule n. i .rur.. tions, with eightyears ofcontinuous decline in output: Russia and r1r;. ne. Sn;r,rr rerr., ,:una e i r, e nrn 1eC- Ukraine, which together have some 200 million people. And con- -,ciCele;.{rr. i ur r.u-hI1: n.3. :. it.e Dr,lns T,.TC-:I Cra .rs t em e *en,.J ,I,,Ie tulIv E,sr re rICrz errn,,.el il IF ~- e, c , e:-i n sider this: in Russia every percentage-point decline in GDP throws IC-duCT.,,,r, ,n, *--er. Cr I huru.,a;, or, mrro,,p,r,! r,,non,Er;,Iei 3 ucn, ; hlrrI, 700,000 people into poverty. ,)r .eg.j.>,Ic,:, | 1G ;, r.:,r rI8r,er-.t.II:e j 6 1aGIe- 1,r nle i 1--CIEE. lI:.u e~r, A second big factor is the worsening income distribution. The *rn,r r.ne,r n..I,r rre.].,.I,,.n, I;, .. CrC , e ..,,r.r. .:.1 .,- r e,:r9-:e. Inen.;, Enp. 'er;i r e.OC-CI.3i,or :.r lC.er ,n.C.r-- pattem that seems to have emerged in the transition economies is a pe: r.CeI: .l ir. nr ;, r,or -nic,e,, ic.e- quick, large decline in outputatfirst,with littleworsening in income Er,,.enIe Ir.d; 'pIr .E; ... :r,. E. rcI-.rr [r..,i -i distribution,followedbyaslowerdecine,orevenanincrease,inout- I Ei rg ,,n;oer D : r,.:,.,! er .;r* ;CII,n, nESEr. Cur uC .',r,d r,y i - n.eer, -u.e.e. e en.. ri.:.n rC,eirr, 3r ..ed al,;r,put but a much sharper worsening in income distribution. During the transition the average Gini coefficient has risen from 24 to 33, at 1.5 points a year, much faster than the fairly rapid deterioration information, such as the proportion of children who progress in the United Kingdom and the United States in the 1930s. Russia's from primary to secondary school. and Ukraine's Gini coefficients doubled-to about 48. Only the * Despite the overwhelming importance of social spending, it Central European states avoided this increase (figure la). bears a weak relationship to outcomes and attainment (Filmer The numbers in poverty are large, but the estimates are and Pritchett 1998). highly sensitive to the poverty line used and the quality of data on Hotly debated in Latin America is whether the macroeco- the population's income or expenditures. When household sur- nomic reforms increased poverty and inequality. Much depends vey data are corrected for possible understatements of income by on the counterfactual (Lustig 1995). If fiscal deficits had been using macroeconomic data for household income, the poverty larger, the longer-run impact on growth would have been even estimates for the region fall from 168 million to 147 million (table more severe, with a much larger impact on the poor. Indeed, the Ic). Poverty in the former Soviet Union-which accounts for the reforms helped the region to come out of the crisis during the bulk of the poor (about 100 million live in Russia and Ukraine)- 1990s and to achieve average growth nearly double that of the is also fairly shallow. For all transition economies the poverty 1980s. But policymakers could have avoided unnecessary cuts in gap-how far below the poverty line the average income of the social spending or been more judicious when faced with trade- poor falls-is estimated at 2.8 percent of GDP. Thus poverty could offs between large subsidies to rescue sick financial institutions be significantly reduced if social safety nets worked. But because and employment benefits to help cushion the impact of the cri- of leaks from the safety net system, the real cost of removing sis on the pOOL poverty could be three times the estimated poverty gap. 6 1999 World Development Indicators WORLD VIEW _T, _1.;'' ~~~~~~~~~~For the@ future The calm that descended on financial markets toward the end of Gini coefficients in transition economies 1998 reflected confidence in the industrial countries' willingness to address some of the underlying issues that have led to the tur- -: . . ; , bulence. But the prospects for a quick recovery in developing country growth are limited-for three reasons. The dependence on primary commodity exports is high in many countries. The *,, Clarge current account deficits in many countries are being financed by costly private capital inflows. And many economies continue to -- - *'-,,:*.r'e  -|; .9 @ @ 1 ;r, @; zdepend on markets in East Asia andJapan. .2...1,. Preliminary World Bank forecasts suggest a continuing decline in developing country growth in 1999-to 1.5 percent a year in the base case. This reflects the sluggish recovery expected in East Asia, with growth running at about half the rates in the early 1990s. China, South Asia, and Eastern Europe will remain :.,, - Mr.l,,.: . ..- l~: I f ,u -,'.' J arelatively unaffected by the crisis. Not so the former Soviet Union, the Middle East and North Africa, and Latin America and the Caribbean. If the base case holds, growth is expected to return to the While the numbers in poverty are staggering, these trends 1991-97 rates after 2000 in most developing regions. The excep- could be reversed with growth rates of 4-5 percent and no fur- tions: the four worst-affected East Asian countries. For them ther increases in inequality. But the experience in Poland and recovery will be more protracted because of the vulnerable bal- elsewhere suggests that where growth has resumed, it has merely ance sheets of their firms and banks. kept poverty from increasing. Widening inequality has blocked And things could turn sour again. The outlook for thejapanese progress in reducing poverty. economy is still shaky. Uncertainties continue to loom in the large Unlike the poor in Latin America, those in the transition economies of Latin America, particularly Brazil. And there is always economies do not belong to an underclass. But if present the possibility thatadownturn in equitymarkets in the United States trends persist, they may become an underclass less able to and Europe would push the industrial world into recession. respond to opportunities that come with resumed growth. While it is too early to pronounce on the precise impact of Understanding what is driving inequality and reversing the the global financial crisis on the attainability of the international sharp deterioration could be as high a priority as restoring eco- development goals, the review here suggests that: nomic growth. * The consequences for poverty are severe. Estimated poverty In transitlon economies. 1987-88 and 1993-95 Poverty headcount index I I Total number of poor imillions) Corrected Corrected data data 1987- 8 1993-95 1993-95 1987-S8 1993-9S 1993-95 f:~~~~~~~~~~~~~~~~~~~~~.li ..i ; :1I I ef F.-,-., ;.,., - -r .: r. H e1p2r.h:' " ic' FiJn,!1 W D 29dict2-s See i 2;] i *rf:i : : ruE3. - . J,U,;f erl -F, , 5'. '- 11 - '3e 1 11. .;,- : -. .. i* 1- -- * ,l;:.,,r,:.,.elll .:...r; r.: r,e: ,, -eC j'r: -fl .;, ..r *AflE3lJ. ;.n, Ci 3,r.._ JlE l3CC:.. J[:; r3,:.r. :|Br- rCr..i .e e.e>,,,,-r5F .F.flll:-..K~3'3C. , I..d;I.r.]1,.: .:..Ir';1.; ,,¾I .,.,._ 1999 World Development Indicators 7 GNP Under-five mortality rate Net Gross per primary school female primary capital enrollment ratio school enrollment ratio predicted predicted predicted on the basis on the basis on the basis of GNP ratio of distance of GNP ratio of distance of GNP ratio of distance per capita actual to from per capita actual to from per capita actual to from $ per 1,000 predicted 1997 value % predicted 1996 value % predicted 1996 value 1997 1997 1997 to goalb 1996 1996 to goal 1996 1996 to goal tuouc.- ucro; h_-,o 1 P 19 0.9 93 44J 1.22 46- 41 0'.93 I.)110 1. 2 -,c 11. i4 ' I _ 6' 65 1J Bura 140 14 1 15 1 *. 41 1 02 5 ji.-.:urnr.;3.ae 1-1 0 1741 1.1'5 14 4J J0 6 c'. 4,Al1 1.01J1 Hglier 2(6' . . .. 50IU 0.4C 7 6 -2 I.C.9il 12 T 5n.l3lar., 2 10 l12 1.36 14) 5'C 12 e2 42 111 n2r. -ia 2161 1r2 0.c-4 f.4 20 0 95 52 42 1.17 1 Ijer.i, .2.."' 136': lJ 71 .. CSrad 2.3'0 1S. liEW 121 52 t)S9 54 43 0S0 -I; Erarca :.- 15. C0 ',:, '. 9 7' ) 43 1 0W. GuincaBist 3j 23': 1_9 147 .. Eurf,r. Fss:, 250 15'? l 1 c e'' *?9 1 '''91 11 lla.5Jffl3,:5r 2ee 155 1 .C2 105 '2 11 31 II. 1 11 An;.;s21:0 133 1.-7 119 Mlv 26') 153 154 157 53 052 42 0.02 10' \.eir& e0;er. 270 151 C' '1 31 .. 42 ('64 22' N.it)hfl 190l 14. ',7, 77 . . 41 1 04, C.amoo'a, ?.00 146: 10(1 23 S6 1 74 2 43< 1.03 5 Ts1,1')sr. :.:0 111 0.26 24 . Q4C 1.11 1 LIg.rnt,o1s 33.0 l 3i, JC0r 14 1.04 4 UJaiTit.i. Ti,e '.4I' .. .. 53s 1.10) I'S 440, . a, l.a.Tl 4 12Ž '1 .5 . .14 1.1' 1 109.;N. 1:l,0. 139 ,OC. i2 '-. , 1.16 l'. I44 u!.%4_ s In-la J . 13j4 0 66) se . 44 0 37) C 1_J4 1.1 126 C1 .24 ,3 44 10- Ber'h 36'); 132 1.13 '31llg 1.64 37 -1- ('62 11- 1I.icrg.31 a i-ll' 17.) '35_ 46 :o 12'2 19 45- 1 14 -1 L; PEPI 4'0' . . lA 4 1f2 5 Sli lj;ilargur 416' 127 0 v.45. 13 1._2 -- 43 1.1_ i ,1,ural,ni 491) 121 1 2I - r5 ;, 4* 45 l1.' .3 I.l:lJ:"|5 J-.. 111 "0_U 12 . . 4'5 107l 1 I.~rg,: R*Pep r: 480 . .. '68 1 46' S 4;J 1J. 1 ,l__er.,n ulr51') 163' 021 11 . . 46 1 ('4 .2 Sernegal 540I - 104 1 J .. 3 71 0r1l 42 4r 0 30 Armrenia 560 . . .. ... 7 10r -1 Conrgo., Pro ':.u i 1.1' -.97I .. 46 j 1.00 . LCC7l.|) 6.60 7'j 1.7..:s '71 B71 0 .v 4i 1.0)c -2 C.:.[ LI4 13 .3':.7 454S 2'mr*- a o : e 72'? T ; 14'.'X * : ^_ : -, . 4Ž 101 1 Crdaraf | d740 JA ' 4 _ 1 1_) ."I9 1 '31 Aioa,na 7 i '6i OX62 2?: 66 I 1 . 43 '3.9 2 a. Calculated using the Atlas method (See Statistical methods). b. The goal for under-five mortality was determined to be the lesser of one-third of the 1997 level or 45. For countries where under-five mortality was less than 45 in 1997, the goal was assumed to be 12. c. Refers to mainland Tanzania only. 8 1999 World Development Indicators WORLD VIEW * Cuts in public transfers and services. Cuts in social spending are likely to affect the poor much more than others, as in the Latin Regional average growth rates in real consurnption needed American debt crisis. In Chile, where per capita social spend- to halve poverty in 25 years ing fell 20 percent between 1981 and 1986, the poorest 40 per- Pove.ty lines cent of families were hit hardest-because these families Sl a day 52 a day received some 50 percent of public health and education E,- a <, ,ro, in F ;1, spending and 20 percent of social security payments E.,,:5. . .-r.l.S r ..... I_ L 3U--- ti *.5 Sd .~(Bourguignon and Morrison 1992). 'l. |j.ii- E, .f >r,.r 1:. ., Fr-Hi .-r; 1 * Inflation. Rising prices, by sharply worsening inequality, hurt Ease. -,Vt. h.-., I . the poor the most. Changes in relative prices also hurt the rut.-: ,r.;r,;,-,:,,, : poor when prices of essential commodities such as food or r,c.,.c,, -.ir.- . :- .1 r....... ri.~ 11--. ~ ..1 energy rise. : pr.,:. r .i.. ;r.,,3 ir.., r I, : : .r..,ri,F--.l - -L.: 'o Howhouseholds are affected depends on how theychoose to respond to crises. They may cut back on consumption, increase .:.u,,: v Y.:.11 * E,ITsI iii. l :;;,,.e, their output, change jobs, pull children out of school and send them to work, tap savings, or sell assets. Some of these actions may have long-term consequences by * Inequality may worsen, exacerbating poverty, if governments eroding human capital and jeopardizing future ability to earn. fail to restore macroeconomic stability or if the policies Mothers who are malnourished are more likely to give birth to adopted to do so hurt the poor. underweight babies, who in turn may suffer from poor health or * Social indicators, which respond slowly to shocks, may be learning difficulties. Children deprived of schooling may have thrown off their trend line (though with a lag), reflecting difficulties moving up the skills ladder. Longer-term conse- their correlation with income. While social indicators gener- quences of crises may thus perpetuate poverty and inequalities in ally improve with growth in income, many are performing society. Evidence from East Asia suggests that parents are indeed well below potential (table l d). responding in such ways, hurting their ability to participate in the All developing regions have lost momentum in achieving their recovery. poverty goals. In 1991-97 only the two Asian regions were growing Governments therefore need to adopt policies that mitigate fast enough to reduce poverty by half by 2015 (table le). Current the consequences of such crises and address some of the adverse forecasts for 1998-2001 suggest that only South Asia and Chinawill effects of household responses. This may require: grow fast enough to halve poverty by 2015. The most dramatic loss * Adopting stabilization policies that have the least cost to the in time has been for East Asian countries. Comparing current poor and the vulnerable in the short and long term. growth forecasts with those of a year ago, the average number of * Pursuing structural adjustment policies that enhance long- years it will take to reduce poverty by half has risen by about two term opportunities for the poor. years for Indonesia. In the Philippines, where income growth has * Protecting social expenditures that are critical to the poor. historically been low, the further slowdown will increase the time While there remain large uncertainties relating to the depth it takes to halve poverty by as much as 10 years. and future course of the current financial crisis, our understanding All this assumes that inequality remains constant. But crises usu- of how to protect the poor continues to improve. And governments ally mean rising inequality, unless governments adopt policies to pro- and policymakers can no longer be excused if their policy responses tect the poor and adjust promptly. So, if governments are serious are insensitive to the needs of the poor. Today's challenge for the about reducing poverty, they need to worry about not only restoring international community is to devote all energies to reversing the economies but doing so in a way that does not worsen inequality. impact of the crisis and to generating fresh momentum for policies Crises-and the policy responses designed to deal with them-have and programs that enhance the prospects for attaining the inter- vastly different impacts on different sections of society, impacts often national development goals. differentiated by gender, race, and socioeconomic group.7 The impact of a macroeconomic crisis is transmitted through Notes the sources of household income-wages, salaries, profits from busi- . For more on developing country growth prospects see World Bank 1999a, on which the fol- lowing discussion is based. ness, returns on assets, and government transfers-and through 2. The followingdiscussion draws heavily on World Bank 1998b. See also Ahuja and others 1997. inflation and changes in the relative prices households face: 3. Ravalllon (1997) has found from empirical tests for 23 developing countries that the rate of poverty reduction is directly proportional to a rate of growth of private consumption that has * Labor demand shocks. The reduced demand for labor may mean been corrected for distribution: (1 - G)r, where G is the Gini inde anrid va the rate of growvth increased unemployment (East Asia) or falling real wages (for- of private consumption. 4 The following discussion draws on Edwards 1995 and Lustig 1995. mer Soviet Union). And it may affect skilled and unskilled work- 01 The evidence is sparse. Only 12 countries boast more than one observation point in the ers differently-with skilledworkers more likely to enjoy greater decade, and for these countries the starting period is typically the mid-i980s. So generaliza- job security, and unskilled workers morc likely to move into the tions about the decade as a whole need to be treated with caution. job secrity, nd unsilled wrkers ore liely to ove ino the 6. The following discussion draws heavily on Milanovic 1-998. informal sector and see their wages fall. 7. For a fuller discussion see World Bank forthcoming. 1999 World Development Indicators 9 to reduce infant mortality rates by to reduce poverty by half L7M6U.6 two-thirds by 2015 1993 1 Regional goals for 2015 t 1990 | Regional goals for 2015 U ~~~~~Headcount *Deaths per 50 40 30 20 10 0 (percent)' 100 80 60 40 20 0 1,000 live births 2015 goal, - 2015 goal, - all developing all developing countries countries East Asia and East Asia and 26 - . the Pacific 41 i the Pacific a Europe and 28 > m E~ urope and 3.31 Central Asia 28 Central Asia Lai neiaadLatin America and 23.5 - the Caribbean 41 tin aricaean 41a r * Middle East and . . Middle East and North Africa .. North Africa 43_1 -ty9S'-t33Zhfl, South Asia 87 South Asia 39. 1 Sub-Saharan 100 Sub-Saharan Africa -Africa source Wor d BarS steff es-t-ates a Poople g on lth PPPR1oa Source: World Bank staff estimates. W--h = to achieve universal primary education by 201 __ k _ __ ~~~~~~~~~~~~~Girls' prinnary enrollnieilt as a share of total_ -en 1996 Percent 0 20 40 60 80 100 F ..- I S - _ . _ ~f 2015 goal, all developing countries - d the Pacific D_ _ Europe and _9' ~ ~ Latin America and the Canbbean -1, Middle Eastand d87 North Africa 8 South Asia 80s Sub-Saharan Africa 57b Source: UNESCO. a. Data are for 1994 b. Data are for 1995. 10 1999 World Development Indicators The international community has set ambitious goals for itself in the next century: reducing poverty and closing large gaps in social development. Any setbacks to the world economy make achieving these goals more difficult-and call for renewed commitments by developing countries, the international development agencies, and the advanced countries. FE~~~~~~~~~~~~~~~~~~~~~~~~~~w ensure access to reproductive health for all ..,~~~~~~~~~~~~~~~~~~~~~~I. i'0'. - -~SEE 1997 oi i a ~20 40 60 80 100 -. - ~~~~~~~~~~~~~~~~~~~~~~~2015 goal, all developing countries - 't _~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I C~hile _ _ ~ ~ ~ ~ ~ ~ ~ __ _ _ _ _ _ _ _9 99 L3~~~~~~~~ ~~94 I ~ ~~~~ 76~~~~9 _~~~~~~~~~~~ 1999Wo Dee omen ndiatos99 fi~~~~~~~~~~~~~~ _n imoml ~ _.~~~~~~~~~~~~~~~~~~~~~~~~7 [~I M I e ~ ~~~~~~~~~~~~~~19 ol eeomn niaos1 ci LI. Size of the economy Population Surface Population GNP GNP per PPP GNP' area density capita Average Average annuaI annual Per thousand people growth growth capita millions sq. km per sq. km $ billions Rank % $ Rank % $ billions $ Rank 1997 1996 ±997 1997b 1997 1996-97 1997b 1997 1996-97 1997 1997 1997 Albania 3 29 120 2.5 139 -7.1 760 151 -8.2 7C 2,1700 o 154 Algeria 29 2.382 12 43.9 54 1.7 1,500 122 --0.5 1240 4,250c 106 Angola ....1 ... ........ ....12 194 . .... 3PA3 . ....04 260 ... 1 3 -2.5 .. ....10, 8200 200 Argentina 36 2,780 1 319.3 17 8.1 8,950 57 6.7 360 10,100 64 Armenia 4 30 134 2.1 148 86.6...... 560 159 8.2 .. .. 10 2,540 143 Australia 19 7,741 2 382.7 14 0:6.6 .. 20,650 23 -0-6 362 19,510 24 Austria 8 84 97 225.4 22 0.8 27,920 12 0.7 178 22,010 16 Azerbaijan 8 87 87 3.9 125 3.1 ...... 510 163 ..... 2.5 12. 1.520 ..170 Bangladesh 124 144 935 44.1 53 6.3 360 179 4.6 135 1,090 189 Belarus........- ... 10 ...... 208 50 22.1 63 11.1 2.150 105. 11.4 49 4,820 98 Belgium 10 33 310 272.4 20 2.4 26,730 14 2.1 235 23,090 13 Benin ............ . 6 .113 .....51 . 2.2 142 5~6.6..... 380. 175 ... 2.77 1260 182 Bolivia 8 1,099 . 7.6 97 3.7 970 141 1.4 22 2,810 139 Bosnia and Herzegovina 251 44- Botswana ...... ......2..... 582 ....3 5.1 108 5.5 3,310 88 3.0 11 7,430 78 Brazil 164 8,547 19 784.0 8 3.3 4790 73 1.9~ 1,039 6,350 90 Bulgaria 8 111 76 9.8 85 -6.5 1,170 132 -6.0 32 3,870 115 Burkina Faso 10 274 37 2.6 138 5.7 250 196 3.2 110 1,0000 o 193 Burundi 6 28 245 0.9 174 0:7.7... 140 207..... -1.5 40 6200 207 Cambodia 10 181 58 3.2 133 10 300 187 -1430 ,20 18 Cameroon 14 475 29 8.6 89 4.6 620 158 1.7 25 1,770 159 Canada 30 9,971 3 595.0 ~ 9 . 4.0 19,640 25 2.9 659 21,750 17 Central African Republic ... ..3..... 623 5... 1.1... 171 .....5.6 ......320 185 3.... 6 . 40 1,310 c 178 Chad 7 1,284 6 1.6 163 6.7 230 198 3.5 70 9500 195 Chile 15 757 19 70.5 43 7.2 4,820 72 5.7 179 12,240 . 5 China 1,227 9,597e 130 1,055.4 7 8.5 860 145 7.4 3,770 3,070 135 h.:.,,21. r,r,~ I .~37. 1C~ ':-j, 2.1 158 24,350 9 Colombia 40 1,139 38 87.1 40 3.2 2,180 104 1.2 2630 c 6,570 0 83 qonIgo, Oem. Rep. 47.235 0 5.2 . 107 .. ...57 110 . 209 . -8.6.3c 76 .1 20 COW!.Rep.~~3 342 8 1.8 ... 160 0.. .0...OO 670 156 -2.7 ...... 3 .. .. 1,290..... 180 Costa Rica 3 51 67 9.3 87 3.0 2,680 95 1.7.. ...23 6,510 ......85 MSe dIlvoire 14 322 44 10.2 84 68.8... 710 154..... 4.3 24 .... 1,690 161 Croatia 5 57 85 19.3 70 3.7 4.060 78 3.8 23 4,930 97 Cuba 11 I11 100 .. .. Czech Republic .......... . 10 .79 ..... 133. .54.0 48 1.0 5,240 ..70 1.2 107. 10380 6 Denmark 5 43 124 184.3 25 4.1 34,890 7 3.6 124 23,450 12 Dominican Republic 8 49 165 14.1 78 8.3 1,750 113 6.4 38 4,690 102 Ecuador 12 284 42 18.8 72 6.0 1,570 117 3.9 56 4,700 101 E~~..r.r trur. F.~~~ I C.,1 -: a: 4 .1 ~~ 1,200 129 4.5 186 3,080 134 El Salvador 6 21 280 10.7 82 4.1 1,810 110 1.8 17 2,860 138 Eritreea4 118 36 0.9 176 13.5 230 198. 10.4 .......40 1,0400 192 Estonia 1 45 35 4.9 ill 8.0 3,360 86 8.67 5,090 94 Ethiopia 60 1,104 . 58 6.5 103 57 110 209 3.0 30 . 50 299 Finland 5 338 17 127.4 32 6.2 24,790 20 5.9 101 19,660 23 France 59 552 106 1,541.6 4 3.6 26,300 15 3.2 1,301 22,2i 15 Gabon 1 268 4 4.8 114 5.9 4,120 77 3.3 8 6,560 84 Gambia, The 1 11 115 0.4 194 5.2 340 180 2.1 20 1,4400 c 175 Georgia 5 70 78 4.7.. 115 13:2.2..... 860 145 13.1 11 1,980 156 Germany 82 357 235 2,321.0 3 1.9 28,280 11 1.7 1,737 21,170 19 Ghana 18 239 77 7.0 99 4.3 390 173 1.7 290 1,6100 165 Greece 11 132 81 122.4 33 1.1 11,640 49 0.7 132 12,540 54 Guatemala 11 109 94 16.6 75 4.6 1,580 116 1.8 43 4,060 110 Guinea 7 246 27 3.8 126 43.3 550 160 .... . 1.9 12 1,790 158 Guinea-Bissau 1 36 40 0.3 200 6.8 230 198 4.4 . Haiti 7 28 266 2.9 136 1. 380 175 -1.1 90 1,2600 182 Honduras .... 1........ 6 112 52 ..... 4.4 118 .: . 6.4 . 740 .. . 152 3.5 14 2,260 150 12 1999 World Development Indicators Population Surface Population GNP GNP per PPP GNP' area density capita Average Average annual annual Per thousand people growth growth capita millions sq. km per sq. km $ billions Rank % $ Rank % $ billions $ Rank 1997 1996 1997 19970 1.997 1.996-97 1997b 1997 1996-97 1997 1997 1997 Hungary ~~~10 93 110 45.8 52 4.7 4,510 75 5.1 71 6,970 80 India 962 3,288 318 357.4 15 6.1 370 177 4.3 1,599 1,660 163 Indonesia 200 1,905 109 221.5 23 4.3 1,110 135 2.6 679 3,390 125 Iran, Islamic Rep. 61 1,633 37 108.6 35 3.2 1,780 112 . 1..47 5,690 92 I raq 22 438 49 .. . Ireland 4 70 53 65.1 44 8.2 17,790 28 7.3 64 17,420 31 Israel 6 21 276 94.4 38 1.9 16,180 32 -0.6 103 17,680 30 Itly 58 301 195 1,160.4 6 1.6 20,170 24 1.4 1.156 20,100 22 Jamaica 3 11 234 4.0 123 -2.3 1,550 119 -2.9 9 3,330 129 Japan ~~~~126 378 334 4,812.1 2 1.8 38,160 4 1.5 3,076 24,400 8 Jordan 4 89 49 6.8 100 0.9 1,520 120 -1.8 15 3,350 127 Kazakhstan 16 2,717 6 21.3 64 17 1,350 12 2.4 5 3530 124 Kenya 29 580 49 9.7 86 2.9 .340 180 0.4 33 1,160 184 Korea, Daem. Rep. 23 121 188 7.. . . Korea, Rp. 46 99 461 485.2 11 4.9 10,550 53 3.9 618 13,430 52 Kuwait 2 18 95 h. . Kyrgy Republic 5 199 24 2.2 143 8.6 480 166 7.2 10 2,180 153 Lao PDR 5 237 20 1.9 155 6.5 400 171 3.8 6 1,300 179 Latvia 2 65 40 6.0 105 6.6 2,430 101 7.7 10 3,970 113 Lebanon 4 10 399 13.9 79 . 3.350 87 . 25 6.090 91 Lesotho 2 30 65 1.4 167 4.5 680 155 2.1 50 2.490 ~ 144 Libya 5 1,760 3.. . ... ... Lithuania 4 65 57 8.4 91 4.0 2,260 103 4.1 15 4.140 107 Macedonia, FYR 2 26 78 2.2 145 1.2 1,100 136 0.4 6C 3.1800 132 Madagascar 14 587 24 3.6 128 4.7 250 196 1.5 13 900 197 Malami 10 118 106 2.1 147 5.2 210 202 2.5 7 700 204 Malaysi'a 22 330 64 98.2 37 7.5 4,530 74 4.8 168 7.730 77 Mali 10 1,240 8 2.7 137 6.6 260 193 35.5. 7 720 202 Mauritania 2 1,026 2 1.1 172 5.0 440 168 2.1 4C 1,6500 164 Mauritius 1 2 559 4.4 117 5.2 3,870 80. 3.9 11 9,230 69 Mexico 94 1,958 49 348.6 16 8.2 3,700 81 6.3 765C 8,1100 73 Moldova 4 34 131 2.0 152 -0.3 460 167 0.0 6 1.450 174 Mongolia 3 1.567 2 1.0 173 3.3 390 173 1.5 40 1,490c 171 Morocco 27 447 60 34.4 56 -2.2 1,260 128 -3.9 88 3,210 131 Mozambique 17 802 21. 2.4 '141 13.3 140 207 10.5 120 690C 205 Myanmar 44 677 66 d. Namibia 2 824 2 3.4 131 1.2 2,110 106 -1.3 80 5,1000 93 Nepal. 22 147 152 4.9 112 4.2 220 201 1.7 24 1,090 189 Netherlands 16 41 457 403.1 12 3.4 25,830 18 2.8 332 21,300 18 New Zealand 4 271 14 59.5 47 0.9 15,830 35 -0.4 59 15,780 38 Nicaragua 5 130 37 1.9 156 135.5. 410 170 10.4 90 1,8200 157 Niger 10 1,267 7 2.0 153 35.5 200 206 0.0 80 8300 199 Nigeria... 118 .924 126 33.4 57 5:1.1..... 280 191 27.1 . 102 860 198 Norway 4 324 14 159.0 28 3.4 36,100 5 2.8 107 24,260 10 Oman 2 212 10 .. . ... ... Pakistan 128 796 163 64.6 45 0.0 500 164 -2.4 202 1,580 168 ....... ........ . .Panama 3 76 36 8.4 90 4.3..80 9 .5.9 6,890 . 81 Papua Nem Guinea 5 463 10 4.2 120 -14.0 930 142 -15.9 Paraguay 5 407 12 10.2 83 10.2 2,000 108 7.4 20 3,860 116 Peru 24 1,285 19 63.7 46 7.3 2,610 98 5.4 112 4,580 103 Philippines 74 300 241 88.4 39 5.3 1,200 129 3.0 270 3,670 119 .............. .... . . . ...1......8.......83... 6.7 ......251...... Poland 39 323 127 138.9 30 6.8 350 3 67 21 6,510 85 Portugal 10 92 109 109.5 34 4.5 11,010 52 4.3 141 14,180 47 Puerto Rico 4 9 426 .. . ... I . .-. Romania 23 238 98 31.8 60 -6.7 1,410 124 -6.5 96 4,270 105 Russian Federation 147 17,075 9 394.9 13 0.3 2,680 95 0.6 631 4,280 104 1999 World Development Indicators 13 Population Surface Population GNP GNP per PPP GNP' area density capita Average Average annual annual Per thousand people growth growth capita millions sq. km per sq. km $ billions Rank %6 $ Rank %6 $ billions $ Rank 1997 1.996 1.997 1.997b 1997 1996-97 1997b 1997 1996-97 1997 1997 1997 Rwanda 8 26 273 1.7 162 10.8 ......1210. . .202 -5.6 5...... 650 206 Saudi Arabia 20 2,150 9 14.3.4 29 1.9 7,150 64 -1.4 211~ 10,5400 61 Senegal .............9..... 197 44 4.8 113 5.4 540 161 2.5 15 1,690. 161 Sierra Leone 5 72 65 0.8 179 -186.6 160 206 -20.6 .......2.. ... 410 210 Singapore 3 1 4,991 101.8 36 8.8 32,810 8 6.7 91 29,230 3 Slovak Republic 5... ... 49 ... 11 Il 19.8 66 6:.1 ... 3,680 82 5.3 42 7,860.. 74 Slovenia 2 20 99 19.5 68 3.6 9,840 54 3.8 24 11,880 57 South Afri'ca 41 1221 33 130.2 31 1.3 3210 89 -0.~4 2920 7.1900 79 Spain 39 506 79 569.6 10 3.0 14,490 38 2.8 617 15,690 39 Sri Lanka 19 66 283 14.8 77 7.3 800 148 5.9 46 2,460 146 Sudan 28 2,506 11 7.9 94 6.4 290 189 4.2 380 1,3700 177 Sweden 9 450 21 231.9 21 1.4 26,210 16 1.3 168 19,010 25 Switzerland 7 41 179 305.2 18 2.7 43,060 3 2.5 188 26,580 7 Syrian Arab Republic 15 185 79 16.6 74 3.6 1,120 134 0.9 45 3,000 136 Tajikistan 6 143 42 2.0 149 22.2 330 . ... 183 ....0.5 7 1,100 188 Tanzania 31 945 35 6.6i 101 3.9 210J 202 1.2 19 620 207 Thailand 61 513 117 165.8 26 -1.1 2,740 94 -2.1 393 6,490 87 Togo 4 57 78 1.5 164 4.8 340 180 2.0 60 1,4600 173 Trinidad and Tobago 1 5 253 5.6 106 7.9 4,250 76 7.0 8 6,460 89 Tunisia 9 164 58 19.4 69 10.8 2,110 106 9.2 .......47.. 5,050 95 Turkey 64 775 81 199.3 24 8.6 3,130 91 6.8 412 6,470 88 Turkmenistan 5 488 10 3.0 135 -24.0 640 157 -25.0 7 1.410 176 Uganda.... 20 .241..... 99... 6.6 102 6.0 330 183 3.0 240 1,1600 c 184 Ukraine 51 604 88 52.6 50 -3.2 1,040 139 -2.4 110 2.170 154 United Arab Emirates3 84 29 h. . .9 . . United Kingdom 59 245 243 1,231.3 5 4.0 20.870 22 3.7 1,222 20,710 20 United States 268 9,364 29 7,783.1 1 3.8 29.080 10 2.8 7,783 29,080 4 Uruguay 3 177 19 20.0 65 5.0 6,130 69 4.2 30 9,110 70 Uzbekistan 24 447 56 24.2 61 . 1,020 140 . Venezuela 23 912 25 79.3 41 7.4 3.480 85 5.2 197 8,660 71 Vietnam 77 332 232 24.0 62 5.6 ..... 310 186 3.8 122 1,590 167 W est Bank and Gaza 3 .. .. . ... ......... .-I... .... ..... ............... Yemen, Rep. 16 528 30 4.4 119 2.0 270 192 -0.~5 12 720 202 Yugoslavija, FR (Serb./Mont.) . 11 102 104 ..g. Zambia . ...... ..9 753 12 3.5 129 4.4 ......370 177 1.89 910 196 Zimbabwe 11 391 29 8.2 9 2 2.1 720 153 01.1 26 2,240 151 Low income 2.036 31,244 66 712 4.8 350 2.6 2,842 1,400 Middle income 2,857 70,141 41 5,411 478.8.... 1,890 3.6 12.345 4,320 Lower middle income 2,283 47,035 49 2,803 4.3 1,230 3.2 7,989 3500 Upper middle income 574 23,106 25 2,608 5.4 4,540 3.9 4,356 7,590 Low & middle income 4,893 101,385 49 6,124 4.8 1,250 3.2 15,187 3.100 East Asia & Pacific 1,751 16,284 109 1,700 6.2 970 4.9 5,546 3,170 Latin America & Carib. 494 20,462 24 1,945 5.6 3,940 3.9 3,325 6,730 Middle East & N. Africa 280 11,000 25 578 2.9 2,070 0.8 1,294 4,630 South Asia 1,281 5,140 263 493 5.3 380 3.4 2,033 1,590 Sub-Saharan Africa 612 24,290 25 311 3.0 510 0 ... .......O2 .... 893.. 1.,460 High income 927 32,182 30 24,001 3.0 25,890 2.3 21.253 22.930 Europe EMU 291 2,374 127 6,815 2.6 23402.3 5,678 20,230 a. PPP is purchasing power parity; see Definitions. b. Calculated using the Wodd Bank Atlas method. c. The estimate is based on regression; others are extrapolated from the latest International Compadson Progmamme benchmark estimates. d. Estimated to be emw income ($785 or less). e. Includes Taiwan, China. f. GNP data reter to GDR. g. Estimated to be loser middle income i$786 to $3,1251. h. Estimated to be high income ($9ee5e or more). i. Estimated to he upper middle income i$3,126 to $9,655). j. Data refer to mainland Tanzania only. 14 19l99 World Development Indicators I1.1 Population, land area, and output are basic mea- price indexes calculate real values over time. The PPP * Population is based on the de facto definition of sures of the size of an economy. They also provide a conversion factors used here are derived from the most population, which counts all residents regardless of broad indication of actual and potential resources. recent round of price surveys-covering 118 coun- legal status or citizenship-except for refugees not Therefore, population, land area, and output-as tries-conducted by the International Comparison permanently settled in the country of asylum. who are measured by gross national product (GNP) or gross Programme (ICP). The surveys, completed in 1996, are generally considered part of the population of the domestic product (GDP)-are used throughout the based on a 1993 reference year. Estimates for coun- country of origin. The values shown are midyear esti- World Development Indicators to normalize other tries not included in the survey are derived from sta- mates for 1997. See also table 2.1. * Surface area indicators. tistical models using available data. See About the data is a country's total area including areas under inland Population estimates are generally based on for tables 4.11 and 4.12 for more information on the bodies of water and some coastal waterways. extrapolations from the most recent national census. ICP and the calculation of PPPs. * Population density is midyear population divided See About the data for tables 2.1 and 2.2 for further All economies shown in the World Development by land area in square kilometers. * Gross national discussion of the measurement of population and Indicators are ranked by size, including those that product (GNP) is the sum of value added by all resi- population growth. appear in table 1.6. Ranks are shown only in table 1.1. dent producers plus any taxes (less subsidies) that The surface area of a country or economy includes (The World Bank Atlas includes a table comparing the are not included in the valuation of output plus net inland bodies of water and some coastal waterways. GNP per capita rankings based on the Atlas method receipts of primary income (employee compensation Surface area thus differs from land area, which with those based on the PPP method for all economies and property income) from nonresident sources. excludes bodies of water, and from gross area, which with available data.) No rank is shown for economies Data are in current U.S. dollars converted using the may include offshore territorial waters. Land area is for which numerical estimates of GNP per capita are World Bank Atlas method (see Statistical methods). particularly important for understanding the agricul- not published. Economies with missing data are Growth is calculated from constant price GNP in tural capacity of an economy and the effects of included in the ranking process at their approximate national currency units. * GNP per capita is gross human activity on the environment. (See tables level, so that the relative order of other economies national product divided by midyear population. GNP 3.1-3.4 for other measures of land area, rural popu- remains consistent. In 1997 Luxembourg and percapita in U.S. dollars isconverted usingthe World lation density, land use, and productivity.) Recent Liechtenstein were judged to have the highest values Bank Atlas method. Growth is calculated from con- innovations in satellite mapping techniques and com- of GNP per capita in the world, but exact values were stant price GNP per capita in national currency units. puter databases have resulted in more precise mea- not available at the time of publication. Therefore, the * PPP GNP is gross national product converted to surements of land and water areas. highest rank for GNP per capita shown in the World international dollars using purchasing power parity GNP, the broadest measure of national income, Development Indicators and the Atlas is 3. rates. An international dollar has the same purchas- measures the total domestic and foreign value added ing power over GNP as a U.S. dollar in the United claimed by residents. GNP comprises GDP plus net States. All ranks are calculated for economies report- receipts of primary income from nonresident Where money goes farther ing data. sources. The World Bank uses GNP per capita in U.S. dollars to classify countries for analytical purposes . .. 1 - , ...; . . Data sources and to determine borrowing eligibility. See the Users guidefordefinitions ofthe income groups used in this Population estimates are prepared by World Bank book. Also see About the data for tables 4.1 and 4.2 staff from a variety of sources (see Data sources for for further discussion of the usefulness of national 4 table 2.1). Data on surface and land area are from income as a measure of productivity or welfare. the Food and Agriculture Organization (see Data When calculating GNP in U.S. dollars from GNP sources for table 3.1). GNP and GNP per capita are reported in national currencies, the World Bank fol- * estimated by World Bank staff based on national lows its Atlas conversion method. This involves using *2 accounts data collected by Bank staff during eco- a three-year average of exchange rates to smooth the nomic missions or reported by national statistical effects of transitory exchange rate fluctuations. See , . - offices to other international organizations such as Statisticalmethodsforfurtherdiscussion of the Atlas -- the Organisation for Economic Co-operation and method. Note that growth rates are calculated from - Development. Purchasing power parity conversion data in constant prices and national currency units, factors are estimates by World Bank staff based on not from the Atlas estimates. data collected by the International Comparison Because exchange rates do not always reflect inter- : Programme. national differences in relative prices, this table also shows GNP and GNP per capita estimates converted Usi.ng purcnas-ng power pa,'ces to e%aluaie GNP ouh a higner xaIueoe nontra edgo.(d.and ,erices. into international dollars using purchasing power pan- re ;dll.ng 'n a higner value of GCNP for manV de. ties (PPPs). PPPs provide a standard measure of real e,oping economies. price levels between countries, just as conventional 1999 World Development Indicators 15 4 ~1.2 Development progress Private Net primary enrollment Infant Under-five Maternal Safe consumption ratio' mortality rate mortality rate mortality water per capita ratio average annual % growth Male Female per % Of 1980-97 % of relevant % of relevant per 1,000 100,000 population distribution age group age group live births per 1,000 live births with access uncorrected corrected 1980 ±996 1.980 1996 1970 1997 1.970 1997 199G.-970 1996 Albania .. ~~~~ ~~~~~~~ ~~~ ~~~ ~~~ ~~~~101 . 103 66 2 6 82 .. 40.... 28 .. 76 Algeri'a -1.8 -1.2 91 97 71 91 139 32 192 39 140C Angola -7.8 . . . 178. 125 ... .301 209 1,500d 32 Argentina 52 22 71 24 1000 65 Armenia.. .... 15 . 20 21C Australia 1.7 1.1 102 .. ... 95 102 95 18 5 20 790 99 Azerbaijan .....20... 23 44. Bangladesh 2..1 1..5. 69 48 . 140 75 239 104 8500 84 Belarus -35.2....7.4..12.. 1 22. Belgium 1.6 1.2 97 ~~~ ~~~ ~~~ ~~~ ~ ~~~~ ~~98 98 98 21 6 29 7 10 Benin -0.7 . 80 47 146 88.............. .. 149 .. soon 5 0 72 Bolivia 0.1 0~O.0 . 84. . 74 15.....3.... .. 66. 24 96... . ...... 70 Boania and Herzegovina 59 13 Botswana 5.4 69 79 82 83 95 58.... 139. 88 2500 70 Brazil....-. .. 0.5...0.2....... ... 95 34 135 44 1600 69 Bulgaria -0.6 -0~4.4 ...... 96 93 96 91 27 18 32 24 ..... 200 Burkina Faao 0.3 18 37 .... 11 .24 141 ...... 99.. 278..... 169 9300 Burundi -0.7 23 . 16 ... 138 119 228 200 1,300d 58 Cambodia . .. . .. 161 13 24 17 90 13 Cameroon -1.3 ......... .... .. .. 126 52. 215 78 5500 41 Canada 1.3 0~9 96 96 96 94 19 6 23 8 60d 99 Central African Republic -1.2 73 . 41 .... 139 98 248 ..... 160. 7000 23 Chad 0.0 ~~~ ~~~~~~ ~~~~ ~~~~ ~~~~ ~~~~59 . 33 171 100 252 182 8400 2 Chile 3.8 L17 89 . 87 77 11 96..... 13. 650 91 China 7.7 4.5 101~7 102 69 3 120 3 9 9S 83 .ogKong, China . 5.2 95 88 96 91 195.... . ....... 7-- Colombia 1.2 0~5.5 ...... : ...... 70 24 13 3000 75 Congo, Dem. Rep. ...... ... 45...... 82 60 60 ..... 47.... 131 .92 .. .. 245 .. .. 148 8700 Congo, Rep. -0.2 .. 99 9 101 90 160 145 8900 Costa Rica 0.8 0. 89 .. ...... 90.. . .......... 62 12 77 15 55d 100 C6te dIlvoire -2.3 -1.5 . 63 47 135 87 240 140 8lOe 72 Croatia .. . . 83829 . 1020 6 Cuba . .. 95 10 95 100 39 7 43 9 360 91 Czech Republic . . 92 . 91 2 6 24 8 20 - Denmark 1.7 1.3 96 99 95 99 14 6 19 6 90...... Dominican Republic -0.2 -0. 1 73 79 73 83 98 40 128 4 7 110 7 Ecuador -0.2 -0.1 . 93 . 94 100 33 140 39 1500 55 Egypt, Arab Rep. 2.0 1.3 . 98 . 88 158 51 235 66 1700 84 El Salvador..... 2.9 1.5 ... ........ 78 ......-78 .107 32 160 . 39 3000 53 Eritrea . ..32 29.. 6 . 9 1,00 7 Estonia -2.2 -. 78 0 1 7 1 2 Ethiopia -1.3 29.... 29.. 18 ..... 158. 107 239 175 1,4000 26 Finland 1.4 1.1 .. 8 99 13 4 16 5 110d 98 France 1.7 1.1 100 100 99 100 18 5 24 6 150 100 Gabon -2.3 ...138 87 23 16 500 67 Gambia, The -2.6 . 66 72 34 57 185 78 319 110 1,0500 50 Geoga . ..87 87 . 17 2 9 Germany.. . . 100 100 23 5 26 6 220 Ghana 0.2 01.1 112 66 186 102 7400 65 Greece 1.8 _. 9 0 7 9 3 4d 0 Guatemala 0.1 0.0 66 . 58 .. 107 43 18 5 190 6 Guinea10 05.. . 181 120 34 18 800 5 Gu'inea-Bissau 0 0O 0 0 63 31 .. 185 130 316 220 9100 53 Haiti ... .. .. ....... ....... . ...:......... .. .... .. 141 ... . 71. 221 125 600 e. 39 Honduras -01 00 ~~~ ~~~ ~~~ ~~~ ~~78 89 78 91 110 36 170 48 2200 7 16 1999 World Development Indicators 1.2 Private Net primary enrollment Infant Under-five Maternal Safe consumption ratioa mortality rate mortality rate mortality water per capita ratio average annual % growvth Male Female per % Of 1980-97 % of relevant % of relevant per 1,000 100,000 population distribution age group age group live births per 1,000 line births with access uncorrected corrected 1980 1996 1980 1996 1970 1997 ±.970 ±997 1990-97b 1996 Hungary -04.1 ... -04.1.... .. 94 97 95 96 36 10 39 12 14" India 2.6 1.9 .. . .. . 137 7 1 206 88 440" 85 Indonesia 4.5 3.0 93 99 83 95 118 47 172 60 390" 65 Iran, Islamic Rep. 0.2 .. . 91 . 88 131 ... 32 208 35 1208 90 Iraq.. . 103 81 94 72. 102 112 127 140 3108 7 7 Ireland 2.7 1.8 100 100 100 100 20 5 2 77 10d Israel 3.3 21.1. .. ... 25 7 27 8 7d 99 Italy 2.2 1.5 . 100 .. 100 30 5 33 7 12' Jamaica 2.2 1.3 95 97 43 12 62 14 120" 93 Japan 2.9 . 101 103 101 103 13 4 21 6 18" 96 Jordan -1.2 -0.7 .. 29 . 35 1508 98 Kazakhstan .. . . 24 .. 2953 Kenya 0.9 0.4 92 89 102 74 156 112 6508 45 Korea, Dem. Rep.. . 51 56 7 47d 100 Korea, Rep. 7.0 . 104 92 105 93 46 9 54 11 30" 83 Kuwait 89 54 80 53 48 12 59 13 20" 100 Kyrgyz Republic .. 97 . 93 .. 28 .. 36 32" 81 Lao PDR . 76 . 69 146 98 218 170 6608 51 Latvia . 92 . 87 21 15 27 19 15" Lebanon 50 28 50 32 3008 94 Lesotho -.2.8 -1.2 55 ... 64. 79 76 134 93 190 137 6108 62 Libya.. . 122 24 160 30 2208 95 Lithuania.. . 24 10 30 13 13" Macedonia, FYR . ..96 95 .. 16 .. 17 22" Madagascar -2.4 -0.2 . 60 . 62 153 94 285 158 500" 16 Malawi 0.6 . 48 66 38 71 193 133 330 224 620" 60 Malaysia 3.1 1.6 . 102 . 102 45 11 63 14 34" 89 Mali .... -1.1 ..... 33 ... ..... ... 22 204 118 391 235.. 580". 48 Mauritani'a 0.1 0.1 .. 61 . 53 148. 92..... 250 149 800" 64 Mauritius 5.2 . 80 98 79 98 56 20 86 23 110" 100 Mexico 0.1 0.0.. . 73 31 110 38 110" 95 M o do... a.................... .. ... .. . ... . ... .. .. . .. 20........I.... 24 . 23" 56 Mongolia . . . 79 . 83 102 52. 158 658 5 Morocco 1.6 10.0. 75..... 83 47 65 128 51 187 ... 67 ... 370u 57 Mozambique -2.3 . 39 45 34 34 171 135 281 201 1,100od 24 Myanmar 12 9 7 11 588 60 Namibia -2.1.. ...... ..... ........ 118 . . 65 155 101 220" .60 Nepal 2.1 1.3.. . 166 83 23 117 1,5008 59 Netherlands 1.6 1.1 91 100 94 99 13 5 15 7 128 99 New Zealand 0.9 . 10 10 10 10 177 27 258 9 Nicaragua -2.7 -1.3 70 77 71 79 104 43 168 57 1608 62 Niger -2.6. -1.7. 30 . 19 170 118 320 .. .. 590" .. . 48 Nigeria -4.7 -2.6 139 77. 201 122 1,0008 50 Norway ....1.. .5.. .1.2 98 ... 99 . .. 99 99 13 4 15 6 68 100 Oman :.... .. .. 54 70 31 68 119 18 200 .. 28 .. 88 Pakistan 2.0 1.4 142 95 183 136 3408 62 Panama 1.8 0.8 89 . 89 47 21 71 26 558 84 Papua New Guinea -1.1 -0.5 . . 112 61 130 82 3708 31. Paraguay .1.9 0.8 90 91 88 91 55 23. 76 28 190" 39 Peru -0.5 -0.3 93 91 89 90 108 40 178 52 2808 66 Philippines 0.7 0.4 95 . 92 . 67 35 90 41 210" 83 Poland 0.9 0.6 98 95 98 94 37 i1 36 12 5" Portugal 3.1 .. 97 . 100 . 56 6 62 8 15d 82 Puerto Rico.. . .. . .. . 29 11... 21" 97 Romania 0.3 0.2 . 96 . 95 49 22 . 26 41" 62 Russian Federation .. 93 .. 93 .. 7 . 25 53" 1999 World Development Indicators 17 1.2 Private Net primary enrollment Infant Under-flve Maternal Safe consumption ratio' mortality rate mortality rate mortality water per capita ratio average annual % growth Male Female per % Of 1980-97 % of relevant % of relevant per 1,000 100,000 population distribution age group age group live births per 1,000 live births with accesa uncorrected corrected 1.980 1996 1980 1996 1.970 1.997 1970 1997 1990-97b 1.996 Rwanda -1.3 -0.9 62 . 57 . 142 124 210 209 1,300d Saudi Arabia 60 63 37 60 119 21 185 28 180 93 Senegal -0-8 -0~4 44 64 30 52 135 70 279 110 5100 50 Sierra Leone................. -3.2 ...-1 2. 55 . 39 ...... 197 170 363 286 . 34 Singapore 4.9 100 . 99 . 20 4 276lo 10010 Slovak Republic 259 29 13 80 Slovenia.. .95 . 94 24 5 29 650 98 South Africa -0.3 -0.1. .. . 7 9 48 108 6 5 230 59 Spain 2.2 1.5 102 ..... 105 102 .... 104 28_. 5 34 .. 77 Sri Lanka 2.8 1~9 . .. . .. 53 1 100 19. 30 ....70 Sudan.. . .. . 118 71 177 115 37C 6 Sweden 0.7 0~5.. 96 102.... 97 102 11 4 15 5.. 70 Switzerland 0. ... . . .6 04... .. .O ~ .... .......... 15 5 18660 d 100 Syrian Arab Republic 1.0 . 99 95 80 87 96 31 129 38 1800 88 Tajik..st.. .......... .....30 .....36 . 580... 69..... Tanzania ... I.................. 68 47 65 48 129 85 .....218 136 5300 49 Thailand 5.5 2.9 . .. 73 33 10 3 20d 8 To -0.5... ........ .. .... 91 .98 . ....60 72 . ....134. 86 .... 216 138 6400 5 Trinidad and Tobago -0.6 ...... 89.... 90 .91 ..... 89. 52 .... .12 57 15.. 900 96 Tunisia 1.0 0.6 92 99 72 96 121 30 201 33 1700 90 Turkey 2.3 . 98 . 94 144 40 201 50 1800 Turkmenistan .. . . . . . . 40 50 40 6 Uganda 1.7 1.0 43 . 35 . 109 99 185 162 5509 42 U kraine ... ... ...... . ..... ....... .... ........: .. ... .. . _ 22 14 . .. .. 17 ... 300 55 United Arab Emirates.. . 72 84 75 82 87 8 90 11 260 98 United Kingdom 2.6 1.8 100 100 100 400 19 6 23 790 100 United StatesI1.9 1.1...... 94 95 20 7 26 9 12~ 73 Uruguay 2.4 . 92 93 46 16 57 20 850 89 Uzbekistan . .. . . 24 . 31 240 57 Venezuela -0.8 -.0.4 83 . 85 53 21 61 25 1200 79 Vietnam . ........... 104 29 157 40 1050 47 West Bank and Gaza .. . . 25 28 Yemen, Rep.. .. 64 38 186 96 303 137 1,400d 39 Yugoslavia, FR (Serb./Mont.) . .. . .. 54 1 . 2012 Zambia -3.7 -.2 0.... 81 ..... 76 ..... 73.. 74 106 113 181 189 6500 53 Zimbabwe 0.2 . 96 6 3 10 8 77 Low income .1.1 _1.2 .. . .. . 139 82 214 118 69 Middle income 4.7 2.9 . .. 95 .. 94 82 34.. 130.. 43 79 Lower middle income 5.6 3.4 . 10.. 94 81 36 133 44 78 Upper middle income 0.8 0.2 .. . .. . 83 30 118 38 Low & middle income 3.2 2 2 . .. . .. 107 6 168 84 75 East Asia & Pacific 6.8 4.0 .. 01. 102 79 37 12 47 77 Europe & Central Asia . . 93 .. 92 23 . 0 Latin America & Carib. 0.5 0.2. . ..... 84 32 123 41 75 Middle East & N. Africa 0.7 . 91 83 134 49 200 63 South Asia 2.5 1.8 139 7 29 100 81 Sub-Saharan Africa -2.1 -1. 137 9 2 147 47 High income 2.4 1.2 .. 97 97 22 6 28 7 Europe EMU2.0 1.3 .. 4011 100 25 5 29 6 a. Net enrollmnent ratios exceeding 100 indicate discrepancies between the estimnates of school-age population and reported enrollment data. b. Data are for the most recent year available. c. Official estimate. d. UNICEF-WHO estimate based on statistical modeling. e. Indirect estimate based on a sample survey. f. Based on a survey covering 30 provinces. g. eased on a sample survey. 18 1999 World Development Indicators 1.2 The indicators in this table are intended to measure * Growthofprivateconsumptionpercapitaistheaver- progress toward the development goals for the 21st age annual rate of change in private consumption divided century proposed by the Development Assistance by the midyear population. For the definition of private Committee of the Organisation for Economic Co- consumption see Definitions for table 4.10. operation and Development (OECD) and discussed in * Distribution-corrected growth of private consump- the introduction to this section. The net enrollment tion per capita is 1 minus the Gini index multiplied by ratio, infant and under-five mortality rates, maternal the annual rate of growth in private consumption per mortality ratio, and access to safe water are included capita. * Net primary enrollment ratio is the ratio of the in the set of 28 social and environmental indicators number of children of official school age (as defined by selected for monitoring development progress by the the education system) enrolled in school to the number OECD, the World Bank, and the United Nations in con- of children of official school age in the population. sultation with countries that provide and those that * Infant mortality rate is the number of deaths of receive development assistance. infants under one year of age during the indicated year The growth of private consumption per capita is per1,000livebirthsinthesameyear. * Under-fivemor- included here as an indicator of the effect of eco- tality rate is the probability of a child born in the indi- nomic development on the welfare of individuals. cated yeardyingbefore reachingthe age offive, ifsubject Positive growth rates are generally associated with a to current age-specific mortality rates. The probability is reduction in poverty, but where the distribution of expressed as a rate per 1,000. * Matemal mortality income or consumption is highly unequal. the poor ratio is the number of women who die durng pregnancy may not share in the improvement. The relationship and childbirth, per 100,000 live births. * Access to between the rate of poverty reduction and the distri- safe water is the percentage of the population with rea- bution of income orconsumption, as measured by an sonable access to an adequate amount of safe water index such as the Gini index, is complicated. But (including treated surface water and untreated but Ravallion (1997) has found that the rate of poverty uncontaminated water, such as from springs, sanitary reduction is directly proportional to the "distribution- wells, and protected boreholes). In urban areas the corrected rate of growth" of private consumption per source may be a public fountain or standpipe located not capita. The distribution-corrected rate of growth is more than 200 meters away. In rural areas the definition calculated as (1 - G)r, where G is the Gini index (O = implies that members of the household do not have to perfect equality, 1 = perfect inequality) and r is the spend a disproportionate part of the day fetching water. rate of growth in mean private consumption. In empir- An adequate amount of safe water is that needed to sat- ical tests covering 23 developing countries, Ravallion isfy metabolic, hygienic, and domestic requirements- estimated that factor of proportionality to be 4.4, usually about 20 liters a person a day. The definition of implying a growth elasticity of poverty reduction of safe water has changed over time. between 3.3 for a low Gini index of 0.25 and 1.8 for Data sources a high Gini index of 0.60. Estimates of the share of people living in poverty appear in table 2.7. Discussions of the other indicators can be found The indicators here and throughout the rest of the in About the data for tables 2.10 (net enrollment book have been compiled by World Bank staff from ratio), 2.15 (maternai mortality ratio), 2.18 (infant primary and secondary sources. More information and under-five mortality rates), and 2.14 (access to about the indicators and their sources can be found safe water). in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections. 1999 World Deveiopment Indicators |19 L ~1.3 Gender differences Female Female advantage population Child mortality rate Labor force Adult Net primary Life female- participation illiteracy rate enrollment ratio expectancy at birth male % ef total ratio of female to male female-male difference female-male difference female-male difference difference :1997 1970 1997 1970 1997 1980 1996 1970 1997 1988-98a Albania 486.6 07.7 07.7 . 3. .... 3 6 0 Algeria 49.4 0.3 0.3 27. 25 ..... -20 -72...3.......... Angola 50.6 0.9 0.9 .. .... . .. ... . ........ ..... .... 3 ..3 Argentina 50.9 0.3 0.51. .... 07 7 Armenia 51.4 0.09 ....67 Australia 50.1 0.5 0.8.0 . 1 76 Austria 509.9 0.6 07 ........ . ..107 6 Azerbai ....a . 51.0. . 0.8.I..... 0....8..... . . ...... 8 Belarus 530.0 1.0 1.0 14 1.... I-3 .......8 11. Belg ..Ium ...... ...51.... . 0..... 0.4 07......... . .... ....... 1.0 7 .. .....7... Benin ... '.... . 50.7 .. 0.9 0.9 11 27 . -32 2... ... 4 1 Bolivia 50.. . ....I.. . 3. . 0.5 0.6 25 14 -10..4.... 3 -6 Bosnia and Herzegovina 50.4 0.6 0.6 .4 Brazil 50.5 0.3 0.5 7 0 ..48i1 Bulgaria 51.2 0.8 0.97 1 0-2 57 Burkina Faso 50.6 1.0 0'9 10 19 -7 -13 42 3 Burundi 51.0 1.0 10 24 18 -8..3 313 Cambodia 51:7 1.0 1.13.....3 Cameroon 50:3 0.6 0.6 26 14 3 2 11 Canada 50.4 0.5 0.8 . . -276 Central African Republic 51.4 ... 9 26 -31..541 Chad 50.5 0.7 0.8 .26...... ..-633-7 Chile 50.5 0.3 0.52 0..-2 . .... .6 ..6 ..... -1 China 48.4 0.7 0.8_.. 31 16 .1.1 3I1 Hong Kong, China 49.9 ....... 0.5 0.6 27 8. 1 .. .... ..36.... .. 6 Colombia 50.6 0.3 06 3 0 46 0 Congo, Dem. Rep. . 50:6 ..... 0.8 0.8 .... .. -13 ....33.... Con Rep. ~~51.1 0.7 ........0.8 28 .... 15 -65 5 Costa Rica 49.3 0.2 0.4 10 1 4 5 CMe dIlvoire 49.0 0.5 0.5 1.7 17 . -16 3....... 1-13 Croatia 51.6 ......0.6 0.8 14 3 . ... . . -1 ... ... . 9. Cuba 49.8 0.3 0.6 ..... 0 0 0-1 .... .... 34........ Czech ...... ....... .... . Republic..51.3 0.8.... 0.9.. .....-...I...7. Denmark 50.5 06.6 0.9 .0 0 55.. Dominican Republic ... 49.2 ...... 0.3 .. . . 0'4 ... .....4 1.3 4 4.. ... ...0 Ecuador..49.8.0.2.04.10 . 4 ....I ........... 1..... . I............ 35..I.. ..-3 . Egypt, Arab Rep. 49.1 0.3 04 29 24 . -11 3 36 El Salvador 51.0 0.3 0.5 11 6.1 4. ... 63 Eritrea 50.4 0.9 0.9 . .. -3 3 3-11 Estonia 53.1 1.0 1.0 . .. -29 11 Ethiopia .. ........49.8 .07.7 07.7 14 . ..... 12. -10 32........ Finland 51.3..... 0 .8 ... 09....... ........... ... 1....I. 8...... .... France 51.3 0.6 0.0 8...... ......0.8 8. Gabon 50.7 0.8 0.8 ........ .... ... . . :... . ......3 ...3 Gambia, The 50.6 0.8 ..... .0.8 6. .......14. -32 .. .....-15..3 4.. .... -4 Georgiap23 0.9 0.9. -1 8 Germany 51.1 ... 0.6 ..... . 0.7 . .0 6 ... .6 .. .. .... Ghana . .......... 50.3 1.0 1.0 27 203 4.. -1 Greece 508.8... 0.3 0.6 163 1 04 .... 5 Guatemala 49.6 0.2 0.4 16 15 26 2 Guine ......49.7. .0.9.I.0.9............. .....1I.1..-1.0 Guinea-Bissau 50.8 0.7 0 7 19 31 -32 .2 3 Haiti 50.9 .. .. 0.9 0.87 ..5....... .3 5-1 Honduras 49.6 ..... 0.3 0.461 ..1 .. . ... 2 ... . 4 ......... .5 .. 20 1999 World Development Indicators 'r i soeo!pui IUEwdoleAoo PIJOMA 6666 z- St, T~~~- 6 6 8,0 9,0 ao0s. uw 6L 2o 60( 80o £69 U 6 . .. 6- .. 6~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. to...........9.. 6- 17 6- .. 69. 0 . T0 96t8 seflBdd!d 21- 6 . 0 ~ ~~~~....... ... .- -..... . ..... . . . .. . .PMN nd 9 ... 6 ... 0 . . 968~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .2W......d 96 ..... OS6 t7 *0 ...e6nE)MG u2S11de 9 6 6-~~~....... ....6.. 0.0. ... *9 9 0 0 60 V .......8...86-6 o. 0 .1- rT >1 1:~~0 96o......8. 7' '. ueiez MEN, 9 9~~~~~~~~~~~~~~~~. .90. .... 80 o ... 9-09 ....... ........ ..-..... ....68-2. .£~~~~~- .... C6z -66 t,6t"d.... 2IqIw E- TT- 65 . .Z 6'0 06 .. 6 abiq~wezoLA 6- 8 ST E E 7 , , 00 DOO 6 9 06 O 0- 6 0 909'.... O0!x2t, 21 6 .8. 0. .6- ..8.06 '- E60' 609. !!jeV Z6 17C6. 6S.tds01229 ZT- S £ OT 6Z 8609.'T L'SWe 66 T8 21. L .. 606 6>Tu . seueq! .176T 9- 0z 5z 906L' 06 . 09 2!A327- T T6 21- 90 ' OT O19 dVoe] 6E6 8- . '' i .Iarj; 0 6 . 8T8-' 96.60 . :o 66. .... ........o..... 6...... i-, 6~~~~~ 95, V0 0' .68.8. .... 1 Ofn 2 97 6 6£ . 0 to 't d? w(i'oo~ Zt7 t-ST T6 6to 8to't,eu~ .6 06- 6- .. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..668. ..b...6.i.... 9- T T . ......6- - 9 666 o ~ 86 TuSc~ u 6lqeeij .96 6~~~9 t Lo . 90 Xiguee 6L9 6 . 0 89.0 . VO 70 .AII o86S86T 66T 016 96T 06 6 001 166T 01S 16T'( ui eOuwawp 20U0J241p eie -alew. .........j..... elw ee e eOuIeJip21I-21WI........12142 40...1....12..0.. 0 ...... Z14J~3 o E43de02 uwou 232 02e6111 o13d z; 1312d~i -212.. .. ... ... .iew. d..... ..... .0 0 ... .9.. .. 2T3-2 C 1 'O 867'ddow js uj .. . . . . . . . I . . . . . . . . . . . . . . . .. . . . . 0. .. . . .. . . . . C- VZ c- OT- TT sz )'O V'O V~~~~~~~~~~~~~~~~~~~OS e!ifuopu ........... e .e......B..G..... ... ZE 6 XoIu ET~ L 0 7.t 0 ~1.3 Female Female advantage population Child mortality rate Labor force Adult Net primary Life female- participation illiteracy rate enrollment ratio expectancy at birth mule % of total ratio of female to male female-male difference female-male difference female-male difference difference 1997 ±970 ±1997 1970 1997 ±.980 1.996 ±.970 1997 1988-98, Rwanda .. .... .. 50.6 1.0 1'.0 ... .....23 . .... 15 -5..3 ..... ...2 ..... -14 Saudi Arabia 44.6 0.1 0.2 32...... 19 -24 -3 3 2......... ... Senegal 50.1 0.7 0.7 17 ... 20 .....-14 -11 5 4... -2 Sierra Leone .51.0 0.6 0.6 '. 3.. 3.. Singapore 4 .7 . 0.... 3 . .. . ..0.6 2 79 ..........0 ...... ... ..... 5 5 Slovak Republic 51.2 0.7 .........0.9 . ..... ... .......... ....... 8 ... Slovenia 51.4 0.6 0.9 00 0 7 8 South Africa 51.9 0.5 0.6 42 6 6 Spain . . ... ... .. 51.1 . 0.3 .0.6 72.1 -I 5. 7. ... Sri Lanka 49.1 0.3 0.6 17 6 2 5 -1 Sudan 49.8 .. 0.4 0.4 28 24 3 3 1 Sweden. .....50:6 .......06 0.9 -I55 Switzerland -505 ... ....075 0.7 .. . .. ......66 Syrian Arab Republic 49.4 0.3 0.4 40 30 -1 835 Tajikistan 50.2 0.8 0.8 10 ........1 5 6 Tanzani:a 50.5 1.0 1.0 33 2 0 1 3 2 -7 Thailand .. . .... .. ... 50. 0.9 0.9 . 9 .... 15 4 4 ... .... 6 0 Togo 50.5 0.6 0.7 25 ..... .30 . -26 3 3 15 Trinidad and Tobago 50.1 0.4 0.5 52 2 -15 5 -1 Tunisia 49.5 ........03.3 0.42 6..... 22 ..... .-20. -41 2 0 Turkey 49.4 0.6 0.6 33 19 -4 4 5 2 Turkmenistan 50.5..0.8.0.8.... . ....77 Uganda 50.2 0.9 ......0'9 29. 22 ....2 -1 -10 Ukraine 535 10 0.9 ...8 11 United Arab Emirates 33.6 0.0 0.2 21 -3 3 -2 43 United Kingdom 50.9. ...0.6 0.80 0 65 United States 50.7 0.65 0.8 . ... . ............ . 0.. ....0... - ... .............8...I .... .... ... 6......... Uruguay 51.5 0.4 0.7 -1 -1 1 7 8 Uzbekistan 50.4 .... 0.9 0.9 6 Venezuela 49.7 . .... 0.3 0.5 61 2. ..... 5 6 Vietnam 51.1 0.9 1.0 23 6 . West Bank and Gaza 49.1 ... .. .5 Yemen, Rep. 48.9 0.4 0.4 25 43 . -26 0 1 6 Yugoslavia, FR (Serb./Mont. 50.2 0.6 ..... 0.7 .. ... .. ....4 .... 5 Zambia..... 50.4 0.8 0 8 30 16 -. 8 ... -2 3 .0.-3 Zimbabwe . .... .50.4 . 0.8 0.8 15....7 ..3 3 0 Low income 49.2 .......06.6 .. 0.6 27 ..... 24 02 Middle income 49.6 0.6 0.7 22 . 11..-2 3 5 Lower middle income 49.4 0.7 0.8 25 13 -2 2 4 Upper middle income 50.3 0.4 0.6 10 4 5 7 Low &.middle income ..... 49.4 0.6 0 7 23 16 2. 4 East Asia & Pacific 4890.7 ...08 ...28. 14 ....... . 1 .2 . ... 3 Europe & Central Asia 51.9 0.9 0.9 11 4 -19 Latin America & Carib. 50.4 0.3 0.5 7246 Middle East & N. Africa 49.0 0.3 0.4 27 23 -8 2 3 South Asia 48.5 0.5 0.5 28 27 -1.... ... .. 1. ... ... Sub-Saharan Afri'ca 50.5 0.7 0.7 19 16 3 3 High income 50.7 0~5.5.....1. 0.7 0 6 6 Europe EMU 51.2 0.5 0.7 0 6 a. Data are for the moat recent year available. 22 1999 World Developmnent Indicators S. V 1.3 Differences in the opportunities and resources avail- * Female population is the percentage of the popu- able to men and women exist throughout the world, lation that is female. * Labor force comprises peo- but they are most prevalent in poor developing coun- ple who meet the International Labour Organisation tries. This pattern begins at an early age, with boys definition of the economically active population: all routinely receiving a larger share of education and people who supply labor for the production of goods health spending than girls do, for example. and services during a specified period. It includes Inequalities in the allocation of such resources as both the employed and the unemployed. While education, health care, and nutrition matter because national practices vary in the treatment of such of the strong association of these resources with groups as the armed forces and seasonal or part-time well-being, productivity, and growth. Girls in many workers, in general the labor force includes the armed developing countries are allowed less education by forces, the unemployed, and first-time job-seekers, their families than boys are, and this is reflected in but excludes homemakers and other unpaid care- lower female primary school enrollment and higher givers and workers in the informal sector. * Adult illit- female illiteracy. As a result women have fewer eracy rate is the percentage of adults aged 15 and employment opportunities, especially in the formal above who cannot, with understanding, read and write sector. Women who do work outside the home often a short, simple statement about their everyday life. also bear a disproportionate share of the responsi- * Net primary enrollment ratio is the ratio of the bility for household chores and child-rearing. number of children of official school age (as defined Life expectancy has increased for both men and by the education system) enrolled in school to the women in all regions, but female morbidity and mor- number of children of official school age in the popu- tality rates sometimes exceed male rates, particu- lation. * Life expectancy at birth is the number of larly during early childhood and the reproductive years a newborn would live if prevailing patterns of years. In high-income countries women tend to out- mortality atthe time of its birth were to stay the same live men by six to eight years on average, while in throughout its life. * Child mortality rate is the prob- low-income countries the difference is much ability of dying between the ages of one and five, if narrower-about two to three years. The female dis- subject to current age-specific mortality rates. advantage is best reflected in differences in child mortality rates in some countries. Child mortality Data sources captures the effect of preferences for boys because adequate nutrition and medical interventions are Calculations of gender ratios and differences were particularly important for the age group 1-5. carried out by World Bank staff. For the sources of Because of the natural female biological advantage, the underlying indicators see Data sources for the when female child mortality is on par with or higher tables referred to in About the data. than male child mortality, there is good reason to believe that girls are discriminated against. This table contrasts male and female outcomes for selected social indicators: labor force participa- tion, adult illiteracy, net primary school enrollment, life expectancy at birth, and child mortality. For more information on the underlying indicators see About the data for tables 2.1 (population), 2.3 (labor force), 2.10 (net primary enrollment), 2.11 (illiter- acy), and 2.18 (child mortality and life expectancy at birth). For other gender-related indicators see tables 1.2 (maternal mortality), 2.1 (women per 100 men aged 65 and older), 2.3-2.5 (labor force, employ- ment, and unemployment), 2.11 (educational attain- ment), 2.12 (pupils and teachers), 2.15 (reproductive health), 2.16 (prevalence of anemia and smoking), and 2.18 (adult mortality). 1999 World Development Indicators 23 1.4 Trends in long-term economic development Gross national Population Value added private Gross Exports product consumption domestic of goods fixed and investment services average annual average annual average annual % growth % growth % growth average average average Per Labor annual annual annual Total capita Total force Agriculture Industry Services % growth % growvth % growth 1965-97 ±965--97 ±965-97 1965-97 1965-97 1965-97 1965-97 1965--97 1965-97 1965-97 Albania ... 18 2.2 3.2 -6-5 Algeria 4.0 1... .... 1' .. 2.8 3'2 4.9 2-.9 ........42 5.... . 0.. 2 426 Angola .. . 2.5 2.1 Argentina 1.8 03.3 .... 1.5 .........1.5 ...... 1:2 0.7 27 ..09.5 Armenia 1~7 ... 2.4. Australia 3.2 1.7 1.5 2.1 18 2.2 3.5 3.4 25........ 5.7 Austria 2~9 ... 2'6 0.3....... 0.5 ........0 8 2.0 2.6 3.0 2.9 6.3 Azerbaijan .. . 16 2.0 Bangladesh 3~9.9. ... 1.4 23 2.3 1.9 4.2 .......4.5 ..... 37.7 3.3 7.3 Belarus ...................... 0.6 0.7... Belgium 2.5 2.3 0.2 .......0.5 .........19 2.0 2.1 2.6 1.7 5.1 Benin 3.1 0.1 28.8 2.3 . 4.1 ........4.1 ..... ...2.6 2.5 3 2 Bolivia................:........ .... . .. 2 .3 . ..... . 2.4 ....... .. .... .. . 2 34 .8 5.1 Bosnia and Herzegovina.. -1 2 -0.9 .. Botswana 11.4 7.7 3.2 2.9 3:5 ........11.3 ........1172.2 .. .... . ... .... ................. Brazil 4.4 2.3 2.1 . .... ..2.9 3.5. 4 7 .. 5 0 .... ... 4.5 1... .....I.7 8.5 Bulgaria -0.6 ~~~~ ~ ~~~~ ~~~~ ~~-0.1 0~.0...... -0 1... .....-3.0 -1 0 23....... -10 0...... -4.8 -12.2 Burkina Faso 3.2 0.9 2.2 1J 7 ..... 2 6. 2~4 56........ 3.0 ........5.8 3.4 Burundi 3.4 1.1 22 2 21 2:7 4~1 .........39.9........33.3. -.4.. 2.4 Cambodia.. . 17.7 1.7 Cameroon 4.2 1.4 2.7 2.3 3.3 .........6.7 3...... 5 3.6. 03.3 ..... 6.2 Canada 3.1 1.8 1.3 2.3 3.3 4.1 5.8 Central African Republic 1.1 -12221:5 2..... .2!2.. 0.... 8.... 2.2 1.5 .......1.8 Chad 1:6.6.. -0.8 2.4 2.2 .... 1:5 1.6 2 5. 27 .......: 1.4 Chile . ... ... 3:4.4 .. ... 1.7 1.7 .... 2.4 ... ....4:0 3.1 4.6 . .... ..3:1 .... 4.5 8.2 China 8:5 . ... 6.8. 1.7 2..1 ... .....4.3 .... .. 11.1 9.6 .........77 .7 ... .. 10.911.5 Hong Kong, China 7.6. 5.7. 1.8 ...... 2 6 .. . .. . ... . . .. ...................... 8.07 7 . .7 . . 11.9 Colombia 4.3 2.1 2.2 .......3..3 34 4.4 .... ....4.9 4.1 4.8 5.8 Congo, Demn. Rep. -0.7 -3.7 3.1 2.6 20 -3.0 -22 0.1 -0.5 2.6 Congo, Rep. 4.6 1.7 2.8 ...... .2..6 ...... .28 ... 76 ... . ..4-.4.... ... 38 . .. 6.6 Costa Rica 4.0 1.1 2.7 ..... 3.4 3.2 .........47 .. .. .41 3:2 ........4-.9...... 6.9 C61:e dIlvoire 2.8 -0.9 3.6 3.3 2.2 6.2 292.6 0.0 5.3 C roatia .... . .... ... ... . 0..O 3 .. .....0.3 .... Denmark 2.1 . ..... 1.9 0.3 0.9 2.3 1.9 .........26 1.7 07 ........4.5 Dominican Republic 4.8 2.3 2A.4 ..... 3.3 3.0 5 7 .........5.1 ..... ...4 .3 . . .. .6 .5....... 7.9 Ecuador 4.7 1.9 2.6 .... .3.1 ........3:5 633 4. . . 6... .. . .4.3 .. 3 .2 ...... .7.3 Egypt,Arab Rep. 5.8 .3.4 2.2 2.4 2:8 .......6.7. 8.0 ........5.2 60 5.5 El Salvador 1.4 -0.5 2.1 ... . 2 9 0.7 0.62.1 .. ......1.8 .2.4 .... ....1.1 Eritrea... 2J.7 2.5 . . Estonia . . 04 0 5 .......... Ethiopiab 2.2 -0'5 2.7 2.4 18 -033.8 1~5.5..I... 6.0 0.5 Finland 2.8 2.4 0~4.4...... 0.6 0.2 3 .0. 3.4 . . ....2 81.2 4.8 France 2.7 2.1 0.6 0.7 1.8 09.9. .. .. 2.7 2.9 2.0_ 56 Gabon 3.4 ..... 04.4 26.6 ....... 2.1. -0 32.5 2.2 ...... ..37.7 .-3.3 5.7 Gambia,The .... .... ..4.0 ...... 0.5 3.4 ..... .. 3.2 1:9 4.1 4:2 ........1.5 9 7 ... ..73 4 Georgia -0.3 -10 0.6 0.8... Germany 03 4 Ghana 1.8 -0.9 2~6 2.6 ........12 06 .... 3.1 1.4 ........0.5 -0.9 Greece . -. ...- .371. 2.4 06 9 ......13 3.2 4:0.. 3 .5 ...... 1,5. 76 Guatemala ... .... . 3.4 0.7 .2.6 . ......2.8 . 2!8 ..... ...3.6 ..... .3.5 .........33.3 .2.3 .. ... 2.3 G uinea ... . . .. .....: ........... 2~1 . .......1 8 ... Guinea-Bissau ...2.9 ..... 0 1 2.4 ....... 2.1... 1.5 2!7 ..... ...6 7 1.2 2.8 Haiti 1.1 -0.7 1.9 1.2 0.3 1.4 .........1.6 .... ....18 8.6 3.9 Honduras 3.7 0.5 3.1 32.6 44.4.83 9 27 24 1999 World Development Indicators 1.4~~S Gross national Population Value added Private Gross Exports product consumption domestic of goods fixed and investment services average annual average annual average annual % growth % growth % growth average average average Per Labor annual annual anniual Total capita Total force Agriculture Industry Services % growth % growth % growth ±965-97 1965-97 1965-97 1965-97 1965-97 1965-97 1965-97 196S-97 196S-97 196S-97 Hungary 2.3 2.3 0.0 -0.3. -1:8 .... ..-1:6.6 ..... 0.7 11........ 2.2....... 4.2 India .. ...4 6.6 2'3 ..... 2 .1 ... 2.1 2.8 ...... 5.5 5 6.6 4.1 ....... 5.5 .......6.3 Indonesia 7 0 48 2.0 2.7 3.9 9.1 7.9 7.2 9.2 5.7 Iran, Islamic Rep. 1.5 -1.4 2.8 2.6 4.6 -0.2 1.5 3.6 -1~4 -2'2 I raq -0.3 31. 2.8 Ireland 3.7 2.8 0~8. 09 ........3.0 3.0 8.6 Israel 5.0 .......2.5 2.6 31 .......5.6.3.1 71 Italy ~~~2.8 2.5 0.3 0.6 0 9 21 2 93.4 16 5.5 Jamaica 0.8 -0.4 1.2 2.0 0:9. 0-.0 ...... . 2:0 .. .... .1:6 .......-0~3 1.9 Japan ....... 4.4 3.6 0.8 1.1 . ... -0.1I ..... .4.5 4.7 42.2 ....... 4.7 7.7 Jordan 3.9 -0.4 4.3 ..... .4.4 6.5 5.6 4.1 5.0 ......_5.0 8.4 Kazakhstan .. 9 1.4 . Kenya 4.9 1.3 3 .43.4 3.5 5-.7 . 5.5 ... ....4.3 ........1.4 . ......2.6 Korea, Dem. Rep. . 2.0 2.7 Korea, Rep. 8.2 6.7 1 5 ... ....2.6 ..... 2.0 .......12.3 ... ....8.2 . . ...7:4 .. 12~4.4 ... 16.0 Kuwait 1.1 -3.0 4.2 4.3 9.8 -4.2 63 7 88.5 -30O Kyrgyz Republic 1~ ... 18 2.0 Lao PDR .2 .2.. Latvia 1.9 1.5 03 03 -3.9 -5.7 0:4 Lebanon ... .... .......... .... .... 1.9 . .......2..5 ... .. Lesotho 5.7 3.2 2.3 2.0 -1.2 13.1 5.8 3:6 .. 11.6 5.8 Libya 1.2 . .. .. . .3.6 ...... .3.3 10.3 -12.2 114.4 ...I.. 12.1 ..... .. -1.2 Macedonia, FYR Madagascar 0.7 -1.9 25.6. ...... 2.4 . 1~5 -02 0.3 02 10 -0.6 Malawi 3:7. 0.5 3.0 2..7....... 2.8 3.7 4.0 30O.... -3.4 3.7 Malaysia 6.8 4.1 2.6 3.1 3.7 8.5 7.1 61 104.1........9.7 Mali 2.9 0'.5 2~4 . 21 ....... 3.2 .........3.1. _.... 2.8 255.1 ..... 7.1 Mauritania24 -2 2. 2. 1 1.25 323623 2.8 Mauritius ... .5:1 3.8 ....1.3 .....2.4 .-0.3 7.6 6.5 5.0 4.4 5.7 Mexico 4 .0 15 2.4 .......3.3 2.2 45.5 4~O...0 37.7 .... 3.8........ 8.3 Moldova 0.8 0 ...7...O Morocco.....4.3 ........2.0. .....2..2 .. 2.6 2:7 ...... 4.1 ..... . .5.5 4.0 4.5 5.1 Mozambique 16 -0.1 22 9. -0.6635 Myanmar 3.5 1.9 2.0 3:3. 42 3.42.9 50 3.8 Namibia....... ..3.4 .....07 2.6 .2..2 3.5 08 ........2'4 ........06.6 2.025 Nepal 36 1.0 24 .4 ...... 2.0. 2.3 ...._..8.1 5.5 3.9 6.3 8.7 Netherlands 2 6 1.9 0.7 1.5 4.14 1......13 2.5 2.7 1.35 0 New Zealand 1.7 0 7 11 2.0 3:6 1.....1- ... .19 .......-1.6 2.1 43 Nicaragua -~~ ~ ~~075....... -3.4 3.0 36 -01 1.........2 -2.5 -1.2 0.5 0.4 Niger 05 -25 31 28 00 5.2~~-2. 0.212.2 -7.1 -0. 9 Nigeria 3:0 0.0 2~9. 27. 1.5. 4.1.. 4.8... 2.4 00. . 'O..... .2.5 Norway 3.5 30 0.5 1.3 1.4 3.8 2 52.8 1~9 ........5.3 Oman 9.7 5.1 4.0 3.9 Pakistan 5.7 2.7 2.8 ......3.0 ~ 4. .....0 6..7 6.3 5 1 4.4 64 Panama 3.0 0.7 2.3 2.9. 2.2 2.1.. 22 3.9 28 2.6 Papua New Guinea 2.8 0.5 2.3 ..... 2.1 .......2.7 ....... 5.9 2:6 2.7 1 .7 .. 7.4 Paraguay 5:2.2 ..... 2.22 .83 .0 ...... 4 .4 . 5.4 . ..... 58.8 ...... 5!9 5.4 8.7 Peru 2.0 ~~~~ ~~~~ ~~~~ ~~~-0.4 2.4 2.....9.. 16...... ....... 22 .........2 1....... 20 251 Philippines 3.6 0.9 2.6 2.9 2.4 36 4.03:7 .....4~5 . 65 Poland 1.5 1.0 0.6 0.6 . 1:4 2~2 6 8 Portugal 36 - 3.2 - 0.3 - 1.0 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ..... ........ 34..- 31................ 54.......... Puerto Rico 2.9 . 1.2' 2.1 1.-7 4~3 3 329 0 4.. 4.4 Romania 0.0 -0.2 0.5 0.0 Russian Federation 0.5 07 . 1999 World Development Indicators 25 1. 4 Gross national Population Value added Private Gross Exports product consumption domestic of goods fixed and investment services average anneal average anneal average annual % growth % growth % growth average average average Per Labor annual annual annual Total capita Total force Agfculture Industry Services % growth % growth % growth 1965-97 ±1965-97 1.965-97 1965-97 1965-97 1965-97 1965-97 1965-97 1965-97 1965-97 Rwanda 2.8 0. ..... ..1 2.8 ... .. 2.8 . . . ..2.4 ...... 2.5 4.44 ... .....33.3 6.0 3..3 Saudi Arabia 5.6 0.7 45.5 5.0 ....... 7.4 ...... 3.2 6.9... .... Sene a.2.3 -0.5 2.8 2.6 1.1 ...... .3.7 2.4 . .....24.4 3-.1 1. ......I.5 Sierra Leone .....I....... 0:6 .... -1.4 2.1 1..7 34.4 -1.1 0~3 -1.0 -8.4 -3.7 Singapore 8:3 6.3 1~9 ....... 3. 1. -1.4 8.6 8.3 6.7 9.6 12.2 Slovenia 0 6 08 South Africa 2:4........ 0.1 2.2 2.4 2:2 .........17 3.1 .........3.2 ... . 1-.5 1... ...I.6 Spain 3.0 2.3 0.6 1.0.29 2.7 7.2 Sri Lanka 4:6 .. .. .... 3.0 1-.6 ........2.2 . 27 ...... 4.9 ... . .5.3 .. .....4.1 .... 7.7 4..3 Sudan 2:4 -0.2 2.5 ........2.6 .. 27 3~7.7 ...... 3.5 4.0 ........ . .... -2.1 Sweden -18....... 1.4 0.4 1.0 0.5 1.4 2.4 ........1.4 1.0 4.4 Switzerland 1.7 ........1.2 0.6 1.0......11.8 1.9 3.8 Syrian Arab Republic.. 5.5 .........2.1. 3.2 ........3.3 4...... .4A 84.4 .... 6.3 4.7 0.6 6.3 Tajikistan ...2 7 2 .. 7 ..... : Tanzania .. . 3.1 2. Thailand 7~4 5.1 2.1 .... 2.7 40 ........97 75 .........63 90. ...... 11.3 Togo 2 4 -6 31 2359 1530 1~6 ... 36 .rnddadTobago ...... 3:8 2.6 12.2 ....... 2.0 -2-3 0.1 2 4........3:9.35 Tunisia 5:1 2.7 2.2........ 2.8 3~9 6.1 -50 ........58 4.4 6.9 Tur~key ..... ...... ....4 3.3 .. 2.0 ........ 2.2 2.2 1.3 ..... .5.6 5.0 . Turkmenistan . 2.8 .......3.1 U gand a .. . . . . . . .... 2 9.9 . . .. . . 2 .7 .. .. . . . . .. : . . . . . . .. .. . . . . . . . .. . . . . . . . ... . . U kraine ..0 .3 . . . .. 0 3 . . .. . . .. . . .: .. .. . . . . . .. . . . . . . . .. . . . .. . . United Arab Emirates 3.8 -4 0 9.6 10 7 11.5 1.2 6.4 United n dmKingdom..03 .... ....0 21 .......... ..19.......03......05...... 25 . .. 1.8.. 4.0....... ' United States 2.5 1.5 10 ... ..... 1-6 .......... .......... ...I.... ...... 3 02.3 5.5 Uruguay 1.7 1.2 0 6 1.0 15 1.2 2:3 16.6 1-9 ........5.9 Uzbekistan .. ............. .. 2~6 .......2.8 .. Venezuela ............ 2.0 ...... -0.9 2.9 3.8 2.7 1.6 2.7 . .....I.2.5 19.9 ..... 1. 7 Vietnam 2.2 .2.2 . 2.2. ....2.2 West Bank and Gaza . . . Yemen, Rep. . 3.2 2. 8... Zambia 1.0 .......-2.0 ..... ..30 ... ..2.7 .........1:0~....... 04.1 ....... 2.1 ... .....0 6.6 -59 -0.8 Zimbabwe ............. 3.6 0.5 2:9 . ......2.9 .... 20 .........1.3 4.5 ...... 4:1.1 .... . 2.9 . 6.3 .. . ... . . . . . . . . . . . . . . . I . . . . . .. . . . . . . . . . .. . . . .. . . . . Low Income 3.8 1.4 2.4 2.3 2.5 4.5 4.7 3.5 3.9 4.2 Middle income 4:0 ........2.2 1.7 2..1 ...... ..3.0 4.1 ....... 4 6 3.4 2.1 ........5.8 Lower middle income 4.7 3.0 1.7 2.1 3.4 .....~.5 8. 5.0 ...... ..5.0 ........3.8 4.0 Upper middle income 3:6 .. I....1.5 1.9 2.3 .... 2.5 ........32 4.2 ...... 23. 1.9 6.9 Low & middle Income. ..3:9 ...._ 1.9 2.0 .2.2 .... . 2.9 . ....4 1.1 . 4.6........ 36.6 ..... 24.4 ..... 5.8 East Asia &Pacific.. ... . .73 ... .5.4 1.8 .....2.2 4:0 ... ....9 57.8 .... 67 97...... .... .. 9'0 Europe & Central Asia 0.8 . .. ....0-. 9 . . .. ....... .. ........ ...... ..-~......... Latin America &Carib .. ...... 3:5 1.3 2.1.. 2 8..... ...2.6 3~2. 3~9. 39 1.8. 5.6 Middle East & N. Africa .3.0 0.1 28.8 2.8 4.5 1.2. 42.2 ..... ...4 ... .... .............. South Asia 4.6 2.3 2.2 2.2 2.8 5.5 ...... 5`.6 ........4.2 .. 5 1 ..... . 6 3 Sub-Saharan Africa 2.6 -0.2 ... 2.8 .2 6 1.9 .... 23.3 3 2 ........2.7 -0.3. 2.4 High Income 3.1 2.3 0.8 1.2 . ..... . ... ...... .. 3.3 .. ... 3 .1 5.9 Europe EMU . . 0.4 0..5.5 a. Date refer to GDP. b. Data for GNP and its components prior to 1992 include Eritrea. 26 1999 World Development Indicators ii: ::1.4 I The long-term trends shown in this table provide a * Gross national product is the sum of value added by view of the relative rates of change of key social and all resident producers plus any taxes (less subsidies) economic indicators over the past 32 years. In view- fast as East Asian economies that are not included in the valuation of output plus net ing these growth rates, it may be helpful to keep in receipts of prmary income (employee compensation mind that a quantity growing at 2.3 percent a yearwill iJI:r-;p, r,a._ act- = -P., c and propertyincome)from nonresidentsources. Growth double in 30 years, while a quantity growing at 7 per- - j is calculated from constant price GNP in national cur- cent a year will double in 10 years. But like all aver- .-. rency units. * GNP per capita is gross national product ages, the rates reflect the general tendency and may divided by midyear population. * Average annual disguise considerable year-to-year variation, espe- / growth of total population and labor force is calculated cially for economic indicators. :-': using the exponential endpoint method. * Labor force Average annual growth rates of gross national prod- , - _; comprises all people who meet the International Labour uct, value added, private consumption, gross domestic 0 Organisation's definition of the economically active pop- fixed investment, and exports of goods and services are . . ulation. * Value added is the net output of a sector after calculated from data in 1995 constant prices using the -' Z' adding up all outputs and subtracting intermediate least-squares method. See Statistical methodsfor more inputs. It is calculated without making deductions for information on the calculation of growth rates. -... E.-, depreciation of fabricated assets or depletion and degra- All the indicators shown here appear elsewhere in t dation of natural resources. The industrial origin of value the World Development Indicators. For more informa- .-3 H added is determined by the International Standard tion about them, see About the data for tables 1.1 _ r vl ., Industrial Classification (ISIC) revision 2. * Agriculture (gross national product and GNP per capita), 2.1 (pop- __ - is the value added of ISIC major divisions 1-5. i|r, v,i ,rj. ulation), 2.3 (labor force), 4.2 (value added by indus- . . * Industry is the value added of ISIC divisions 10-15. trial origin), 4.9 (exports of goods and services), and * Services are the value added of lSC divisions 15-37. 4.10 (private consumption and gross domestic fixed BotsHana and Mauritius are the ronl, two countries * Private consumption is the market value of all goods in Sub-Saharan Afilca whose pei capita growth investment), rates approach those of the East Aslan countries. and services, including durable products, purchased or received as income in kind by households and nonprofit institutions. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. * Gross domestic fixed investment consists of outlays or additions to fixed assets of the economy plus net changes in inventory. * Exports of goods and services are the value of all goods and market services provided to the rest of the world. Data sources The indicators here and throughout the rest of the book have been compiled by World Bank staff from primary and secondary sources. More information about the indicators and their sources can be found in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections. 1999 World Development Indicators 27 1.5 Long-term structural change Agriculture Labor force In Urban Trade Central Money value added agriculture population government and quasi revenue money % of total % of total % of GDP labor force population % of GDP % of GDP % of GOP 1970 1997 1970 ±990 1970 1997 ±970 1997 1970 1997 1970 1997 Albania 63 66 5 32 3 49 21 52 Algeria 11 11 47 26 40 57 51 54 33 51 37 A ngola ............ .... ..... 9 .......78 .......75 . ..... 15 32 ......... .... . 133 Argentina 10 7 16 12 78 89 10 20 . 12 21 21 Armenia .. 41 27 18 5 69 . 7 .. . . 8 Australia 6 3 8 6 85 85 29 40 21 25 44 65 Austria ........................I . .. _15 ........8 65 64 60 82 28 37 53 89 Azerbatan 22 ~~~ ~~~ ~~~ ~~~ ~ ~~~35 31 50 56 . 56 .. . ..10 Bangladesh 37 24 81 65 8 19 15 31 . 29 Belarus 14 35 20 44 72123 32 . 12 Belgium 3 1 5 3 94 97 100 132 35 44 44 83 Bolivia 16 55 4 41 6.. 5.. 7. 4 Bosnia and Herzegovin 50 11 27 42 Botswana 28 3 82 46 8 65 71 94 20 49 . 20 Brazil 12 8 45 23 56 80 14 18S 17 27 Bulgaria ..... ... 23 ....... 35 ....... 13 52. .. 69 ........: ....... 117. 32 20 Burkina Faso 35 35 92 92 6 17 23 44 . 8 23 Burundi 71 53 94 92 2 8 22 24 . 14 9 1 Cam bodia ...............:.. ...... 51 ... ._79 ....... 74 12 22 14 72 ............ 11 Cameroon 31 41 85 70 20 46 51 49 . 13 14 13 Canada 5 . 8 3 76 77 43 75 19 . 36 62 Central African Republic 35 .......54 .......89 .... 80 30' .. 40 .......73 .......44.. .. ..... ... .. .15 20 Chad 40 39 92 83 12 23 38 52 8 . 7 12 Chile 7 7 24 19 75 84 29 56 29 23 12 40 China 35 19 78 72 17 32 5 41 .. 5 . 12 Hong Kong, China .. 0 4 1 8 95 181 267 . 0 Colombia 25 11 41 27 57 74 30 33 11 . 18 22 Congo, Dem. Rep .......... . 15. 58....... 75....... 68 30 29 .. 35 46 11 5 84 Congo, Rep. 18 10 66 49 33 60 93 145 22 17 14 Costa Rica 23 15 43 26 40 50 63 93 15 27 19 39 C6te d'lvoire 32 27 76 60 27 45 65 86 .. .. 25 26 Croatia .. 12 50 16. 40 57 . 95 .. 45 . 29 Cuba . . 30 18 60 77 . . . Czech Republic.. . 17 11 52 66 . 121 . 34 . 71 Denmark 6 4 11 6 80 85 60 66 34 39 44 57 Dominican Republic 23 12 48 25 40 63 42 99 18 15 17 25 Ecuador 24 12 51 33 40 60 33 59 .. . 20 30 Egypt ra, R p Ar.......ab...Rep. 2 1 292. ..18 .. 522 5 40....4233..45....330...45.. 3......35 . 4 34 .. . 74 7 El Salvador . 13 57 3 9 4 9 59 1 20 41 Eritrea .. 9 ~~~ ~~~ ~~~ ~~~ ~ ~~~86 80 11 18 . 120 Estonia . 18 4 6 4 166 . 33 26 Ethiopia .. 55 91 869 1.. 4 .... 1 Finland 12 4 20 8 50 64 53 67 26 33 40 51 Gabon 19 7 79 52 25 52 88 106 ..: 15 15 Gambia, The 34 30 87 82 15 30 79 108 16 . 17 26 Georgia .. 32 37 26 47 59 . 35 . 7. Germany .. 1 9 4 80 87 . 47 .. 32 . 66 Ghana 47 36 60 59 29 37 44 62 15 . 18 16 Greece 15 11 42 23 53 60 23 39 22 22 34 45 Guatemala 27 24 62 52 36 40 36 42 9 . 17 22 Guinea 23 92 87 1 1 . 9. 9 Guinea-Bissau 47 54 89 85 15 23 34 61 - 13 Haiti 30 74 68 20 33 31 31 .. . 12 29 Honduras 32 20 65 41 29 45 62 85 12 . 19 32 28 1999 World Development Indicators Agriculture Labor force in Urban Trade Central Money value added agriculture population government and quasi revenue money % of total % of total % of GDP labor force population % of GDP % of GDP % of GDP 1970 1997 1970 1990 1970 1997 1970 1997 1970 1997 1970 1997 H ungary~~~~....6 25 .....15 49 66. 63 91 ... 37 . 39 India 45 25 71 64 20 27 8 27 . 14 22 48 Indonesi'a 45 16 66 55 17 37 28 56 13 17 8 50 Iran, Islamic Rep. 25 44 39 42 60 36 . 24 35 Iraq 47 16 56 75.. . 227 Ireland 6 26 14 52 58 79 138 30 34 54 49 Israel 10 4 84 91 79 76 33 43 47 81 Italy 8 3 19 9 64 67 33 48 . 45 73 56 Jamaica 7 8 33 25 42 55 71 115 .. . 30 46 Japan.6 2 20 7 71 78 20 19 I1I . 69 112 Jordan 12 3 28 ...15 .... 51 .. ..73 .. 125 29. 54 . ....99 Kazakhstan . 12 27 2 2 50 60 . 73 .. . .. 9 Kenya 33 29 86 80 10 30 60 66 17 27 27 43 Korea, Dem. Rep. 55 38 54 62 4:...... . : .. .......... Korea, Rep. 27 6 49 18 41 83 38 77 15 22 29 45 Kuwait 0 0 2 1 78 97 84 93 42 . 36 81 Kyrgyz Republic . 45 36 32.. 37 ..... 39 . .. 84 Lao PDR . 52 81 78 10 22 . 65 15 Latvia.. 7 19 16 62 73 i.l11 32 24 Lebanon .., 12 20 7 59 88 . 64 17 138 Lesotho 35 11 43 40 9 26 65 160 20 38 32 Libya 2. ... .29 .. ....11 .... 45 ... 86 89 20 Lithuania . 13 31 8 50 73 120 26 . 16 Macedonia, FYR . 12 50 22 47 61 96 Madagascar 24 32 84 78 14 28 41 52 14 9 17 19 Malawi 44 36 91 87 6 14 63 59 16 18 14 Malaysia 29 12 54 27 34 55 80 187 20 23 31 97 Mali 66 49 93 86 14 28 33 61 14 22 Mauritania 29 25 84 5 14 54 74 88 9 15 Mauritius 16 9 34 .17 42 41 85 127 .. 2 35 73 Mexico 12 5 44 28 59 74 15 60 10 15 14 25 Moldova. 31 54 33 32 53 - 129 . .. . 19 Mongolia .... . .. .. ~' ... 37 .48 ... ...32. .45 62 .. ..: ..... 114 . 24 . 21 Morocco 20 15 58 45 35 53 39 60 19 28 28 65 Mozambique . 31 86 83 6 36 . 52 .. . 24 Myanmar 38 59 78 73 23 27 14 2 7 24 25 Namibia . 11 64 .... 49. 19 38 ....: ..... I l .. 1 1 . . .. . ...... 41 Nepal 67 41 94 83 4 11 13 64 5 11 11 36 Netherlands . 3 7 5 86 89 89 101 . 46 55 83 New Zealand 12 12 10 81 86 48 57 28 34 20 85 Nicaragua 25 34 50 28 47 63 56 106 12 25 14 3 7 Niger 65 38 93 90 9 19 29 40 5 10 Nigeria 41 33 71 43 2 41 20 75 10 9 12 Norway.. 2 12 6 65 74 74 73 32 42 49 52 Oman 16 . 57 45 11 79 93 3 32 31 Pakistan .... 37 25 .......59.. 52 .25 .......35 22 37 ......... 16 .41 . .... 45 Panama.. 8 42 26 48 56 . 185 . 26 22 65 Papua New Guinea 37 28 86 79 10 17 72 116 . . . 39 Paraguay 32 23 53 39 37 54 31 46 11 17 28 Peru 19 7 48 36 57 72 34 29 1-4 16 18 23 Philippines 30 19 58 46 33 56 43 108 13 19 23 55 Poland.. 6 39 27 52 64 .. 57:...... 39 35 Portugal.. 4 32 18 26 37 50 69 34 76 94 Puerto RICO 314 4 58 74 107 .. Romania 20 49 24 42 57 . 66 . 28 19 Russian Federation ..... . ... .. .....8 19 14 . .... .62 ... 77 43 . .. 19 . 16 1999 World Development Indicators 29 1. 5 Agriculture Labor force In Urban Trade Central Money value added agriculture population government and quasi revenue money % of total % of total % of GDP labor force population % of GDP % of GOP % of GDP 1970 1997 1970 1990 1970 1997 1970 1997 1970 1997 1970 1997 Rwanda 66 37 94 92 3 6 27 30 11 14 Saudi Arabia 4 6 64 19 49 84 89 76 : 13 51 Senegal 24 18 83 77 33 45 56 71 16 14 21 Sierra Leone 30 50 76 67 18 35 48 31 11 13 13 Singapore 2 0 3 0 100 100 232 358 21 24 62 82 Slovenia 5 50 6 32116 38 South Africa 8 5 31 14 48 50 48 54 21 30 60 57 Spain ............... :........3 .... .. 26 .. ... 12 _ 66 ... .... 77 27 50 18 30 6975 Sri Lanka 28 22 55 48 22 23 54 80 20 19 22 30 Sudan 44 77 69 16 33 33 17 17 8 Sweden . 81 83 48 73 29 42 Switzerland ..8 6 4 6 4 6 14 23 10 10 Syrian Arab Republic 20 50 33 43 53 39 70 25 23 35 35 Tajikistan 46 4 37 3228 Tanzania 471 90 84 7 26 580 21 Thailand 26 11 80 64 13 21 34 93 12 18 27 84 Togo 34 42 74 66 13 32 88 69 .......17 22 Trinidad and Tobago 5 2 19 11 63 73 84 106 . 27 27 46 Tunisia 17 13 42 28 45 63 47 90 23 30 32 46 Turkey 40 15 71 53 38 72 10 55 14 18 20 28 Turkmenistan .. 38 37 48 45 .... 7 Uganda 54 44 90 85 8 13 43 33 14 17 11 Ukraine 12 31 20 55 71 . 85 . .. 12 United Arab Emirates - . 9 8 57 85 ... .54 United Kingdom 3 2 3 2 88 89 45 60 37 36 United States 3 2 4 3 74 77 11 25 18 21 63 59 Uruguay 18 8 19 14 82 91 30 45 24 30 21 38 Uzbekistan ...............! ...... 31 .... ...44 .......35 ........37 ....... 42 76 . Venezuela 6 4 26 12 72 86 38 50 17 24 19 18 Vietnam ... ....... ..... .... ...26 .......77 . .. 71 18 20 . .. .... 100 ...22 Yemen, Rep 18 ~~~~~~::70 61 13 35 .. 96 .. 38 . 3 Yugoslavia, FR (Serb./Mont.) . . . 50 30 39 58 . Zambia 11 16 79 75 30 44 90 71 22 19 .25 16 Zimbabwe 17 19 77 68 17 33 .. 79 25 Low income 42 28 74 66 19 28 18 40 .. 14 Middle income 21 11 61 53 33 49 25 50 . 18 Lower middle income 32 15 65 58 28 42 21 56 .. 15 Upper middle income 13 8 41 25 55 74 27 457 LOW & middle income 23 13 66 58 28 40 24 49 .. 17 East Asia & Pacific 35 18 76 69 19 33 20 64 .. 11 Europe & Central Asia .: 12 33 23 52 67 .. 64 .. 25 LatCin America & Carib. 12 8 41 25 57 74 19 31 1 M iddle East & N. Africa ... .......13.. . 14 .50 ... ....35 .......41 ... ....58 .... ..65 ........64 South Asia 43 25 71 63 19 27 12 31 .. 15 Sub-Saharan Africa 21 18 78 68 19 32 48 64 19 High income 5 2 13 6 72 76 .......29 ........41 .......19 29 Europe EMU .. 2 15 7 71 7.. 57 .. 38 a. Data for GOP and its components refer to mainland Tanzania only. 30 1999 World Development Indicators 1.5 X Over a period of 25 years or longer, cumulative ] -. * Agriculture value added is the sum of outputs of processes of change reshape an economy and the the agricultural sector (International Standard social order built on that economy. This table high- AgricuRure loses ground Industrial Classification major divisions 1-5) less the lights some of the notable trends that have been at ; cost of intermediate inputs, measured as a share of work for much of the 20th century: the shift of pro- - gross domestic product (GDP). * Labor force in agri- duction from agriculture to manufacturing and ser- culture is the percentage of the total labor force vices; the reduction of the agricultural labor force and recorded as working in agriculture, hunting. forestry, the growth of urban centers; the expansion of trade; and fishing (ISIC major divisions 1-5). * Urban pop- the increasing size of the central government in most ulation is the share of the total population living in countries-and the reversal of this trend in some; areas defined as urban in each country. * Trade is and the monetization of economies that have the sum of exports and imports of goods and ser- achieved stable macroeconomic management. r es r in . a ervices, measured as a share of GDP. t Central gov- All the indicators shown here appear elsewhere in I ernment revenue includes all revenue to the central the World Development Indicators. For more infor- . - .: government from taxes and nonrepayable receipts mation about them, see tables 2.4 (labor force in *- - (other than grants), measured as a share of GDP. agriculture), 3.10 (urban population), 4.2 (agriculture - * Money and quasi money comprise the sum of cur- value added), 4.13 (central government revenue), - -: ,: rency outside banks, demand deposits other than 4.16 (money and quasi money), and 6.1 (trade). those of the central government, and the time, sav- - A . .- ings, and foreign currency deposits of resident sec- , , .. I 1 ,.. i;;. tors otherthan the central government. This measure of the money supply is commonly called M2. Ine share of agi-culiure %alue added ,r GDP has declined in all regions excepl Inc Middle East and Data sources North Airkca. The indicators here and throughout the rest of the book have been compiled by World Bank staff from primary and secondary sources. More information about the indicators and their sources can be found in the About the data, Definitions, and Data sources entries that accompany each table in subsequent sections. 1999 World Development Indicators 31 1.6 Key indicators for other economies Population Surface Population Gross national product Life Adult Carbon area denisity expectancy Illiteracy dioxide at rate emissions birth Per capita average average PPP % of thousand people annual growth annual growth PPP per capita people 15 thousand thousands aq. km per aq. km $ millions % $ % $ millions $ years and above metric tons 1.997 1996 1997 19970 1996-97 19971 1996-97 1997b 1997b 1997 1.997 1996 Afghanistan 24,965.... 652 .1 37 ..... . .......... c 45 67 1,176 American Samoa 62 02 298 ..d282 Andorra 64 02 1420 Antigua and Barbuda 66 0.4 149 489 1.5 7,380 0.7 573 8,650 ..... 75 322 Aruba 89 0.2 452 *0 1,517 Bahamas, The 289 13.9 28 74 4 1,707 Bahrain 620 0.7 868... 73 14 10,578 Barbados 265 0.4 615 .. d 76 835 Belize 230 23.0 10 614 . 2.4 2,670 -0.7 936 4,080 75 355 Bermuda 63 0.1 1,242 .e 462 Bhutan 737 47.0 15 315 7.0 . .. 430. ..... 4.9 61 260 Brunei 308 5.8 57 .0 76 10 5,071 Cape Verde 401 4.0 97 .. . 436 3.8 1.090 1.9 1,184f 2,950f 68 29 121 CaymnanlIslands 36 0.3e 282 Channel Islands 148 .e 78 Comoros 518 2.2 226 209 0 6 400 -2.4 792f 1,530f 60 45 55 Cyprus.747 9.3 80 0 ..e 78 4 5,379 Djibouti 636 23.2 27 . . . . .50 1........366 Dominica 74 0.8 98 225 2.7 3,040 2.4 297 4,020 7 81 Equatorial Guinea 420 28.1 15 444 129.4 1,060 125.5.. 5 20 143 Faeroe Islands 44 1.4 e. 630 Fi i 815 18.3 44 2,007 -0.3 2,460 -2.0 3,145 3,860 73 8 762 French Guiana 157_. .90.0 .. . .. 920 French Plyneia 224 4.0 60 . 72. 561 .. . . ~ - . . . . . . . . . . . - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I . . . . . . . . .. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Greenland 56 341.7 0 .e .. 509 Grenada 96 0.3 279 300 4 9 3,140 4.3 455 4,760.72 161 Guadeloupe 427 1 7 250 ..d. 77. 1,513 Guam 146 0.6 263e 77 . 4,078 Guyana 848 215.0 4 677 7.1 800 6.7 2,376 2,800 64 2 953 Iceland 271 103.0 3 . . 79. 2,5 Isle of Man 76 0.6d This table shows data for 62 economies-small * Population is based on the de facto definition of economies with populations between 30,000 and I population, which counts all residents regardless of million, smaller economies if they are members of legal status or citizenship. Refugees not permanently the World Bank, and larger economies for which data settled in the country of asylum are generally con- are not regularly reported. Where data on GNP per sidered part of the population of the country of ori- capita are not available, the estimated range is given, gin. The values shown are midyear estimates for 1997. See also table 2.1. * Surface area is a coun- try's total area including areas under inland bodies of water and some coastal waterways. * Population density is midyear population divided by land area in square kilometers. * Gross national product (GNP) is the sum of value added by all resident producers plus any taxes (less subsidies) that are not included in the valuation of output plus net receipts of primary income (employee compensation and property 32 1999 World Development Indicators 1.6 Populaton Surface Population Gross national product Ufa Adult Carbon area density expectancy illilteracy dioxide at rate emissios birth Per capita average average PPP % of thouaand people anneal growth annual growth PPP per capita people 15 thousand thouaands sq. km per aq. km $ millions % $ % $ millions $' years and above metric tone 1997 1996 1997 19971 1.996-97 19971 1996-97 1997b 1997b 1997 1997 1996 Kiribati ..........83 0.. . 7.. j . ... 112 ......76 1... ... 4 ... ..910 . ......-1.1 ....... .. . 60 ................22 Liberia 2,886 111.4 29 .. . ... .. . 47 52 326 Liechtenstein 31 0.2 Luxembourg 422 2.6 e :76 8,281 Macao 448 0.0 21,961 e78 8 1,407 Maldives 256 0.3 831 301 8.5 1,180 6.3 855 3,340 6 74 297 Malta 375 0.3 1,166 3,498 4.8 9,330 4.2 5,018 13,380 79 1,751 Marshall Islands 60 0.2 .. 9 . 1.610 . . . Martinique 393 1.1 367 e. . 79 3 2,023 MayotLe 108 0.3 . d Monaco 32 0.0 Netherlands Antilles 210 1.0 260 . e 75 4 6,430 New Caledonia 202 18.6 11 . .. 73 1,751 N orthern M ariana Islands 54 0 .5 . .. .. . .. .. . I .. .. . . .. . . .. .. . . .. .. .. .. . .. . .. . .. . .. .. . .. .. . .. .. . . .. .. .I. .. .. .. .. .. . .. .. . . .. .. .. . .. .. .. . I. .. Palau 17 0.5 d. ... ....... 245 Qatar 721 11.0 63 .e. 74 2 29,121 Ribunion 678 2.5 267 e 75 14 1,561 Samoa 174 2.8 61 199 -0.5 1,140 -1.7 622 3,570 69 132 S5o Tom6 and Principe 138 .... 1.0 141 40 -7.0 290 -9.4 64.. 77 Seychelles 78 0.5 170 537 3.8 6,910 2.4 71 169 Solomon1 Ilands 403 28.9 14 350 26.6 870 0....0. 9151 2,270O..... 70 ................161 Somalia 8,775 637.7 14 .. . ... 47 15 St. Kitts and Nevis 41 0.4 114 256 7.4 6.260 7.6 317 7,770 70 103 St. Lucia 159 0.6 259 558 1.9 3,510 1.2 800 5,030 70 191 St. Vincent and the Grenadines 112 0.4 286 272 3.0 2.420 2.5 456 4,060 73 125 Suriname 412 163.3 3 544 25.7 1,320 25.7 . 70 2,099 Swaziland 958 17.4 54 1,458 7.1 1,520 3.4 3,536 3,690 60 . .3 341 Tonga 98 0.8 135 177 2.2 1,810 1.7 ... 70 117 Vanuatu 177 12.2 14 238 3.9 1,340 1.5 5721 3,2301 65 6 Virgin Islands (U.S.) 117 0.3 339 e . 77 . 12,912 a. Calculated using the World Bank Atlas method. b. PPP is purchasing power parity. See Definitions. c. Estimated to be low income ($785 or less). d. Estimated to be upper middle income ($3,126 to $9,655). e. Estimated to be high income ($9,656 or more). f. The estimate is based on regression; others are extrapolated from the latest International Comparison Programme benchmark estimates. g. Estimated to be lower middle income ($786 to $3,125). income) from nonresident sources. Data are in cur- patterns of mortality at the time of its birth were to The indicators here and throughout the rest of the rent U.S. dollars converted using the World Bank stay the same throughout its life. * Adult Ililiteracy book have been compiled by World Bank staff from Atlas method (see Statistical methods). GrowthXis rate as the percentage of adults aged 15 and above primary and secondary sources. More information calculated from constant price GNP in national cur- who cannot, with understanding, read and write a about the indicators and their sources can be found rency units. * GNP per capita is gross national prod- short, simple statement about their everyday life. in the About the data, Definitions, and Data sources uct divided by midyear population. GNP per capita in * Carbon dioxide emissions are those stemming entries that accompany each table in subsequent U.S. dollars is converted using the World Bank Atlas from the burning of fossil fuels and the manufacture sections. method. Growth is calculated from constant price of cement. They include carbon dioxide produced dur- GNP per capita in national currency units. * GNP In ing consumption of solid fuels, liquid fuels, gas fuels, PPP terms is gross national product converted to and gas flaring. international dollars using purchasing power parity rates. An international dollar has the same purchas- ing power over GNP as a U.S. dollar in the United States. * Life expectancy at birth is the number of years a newborn infant would live if prevailing 1999 World Development Indicators 33 a *-*\-*-*.*, '¼ A &I' a.-. (It 1- - oln has long been viewed as a key to social and economic develop- ment, but the insights of human capital theory beginning in the 1960s helped to provide a new understanding of education as a form of investment in a par- ticularly durable if intangible asset. Education was seen to be the principal means of increasing the quantity and quality of human capital available in the economy. Studies found social and private returns to investrments in education to be comparable to, or higher than, returns to investments in physical capi- tal. In recent years new theories of growth have emphasized the importance of the accumulation of knowledge in fostering technological change and thus in achieving faster and sustained economic growth. And the experience of developing countries has shown that education helps to reduce poverty and to strengthen the civil institutions that support sound economic and social policies. 1 But many developing countries still face unprecedented social problems. Nearly a billion people will enter the 21st century unable to read or write- ! ! mmuch less operate a computer or work with other complex technologies. They - W W will continue to be poorer and sicker than those who are literate (UNICEF, The State of the World's Children 1999). And their numbers are growing. Education thus remains a priority sector for countries striving to meet these challenges. The target is receding, however, because today's political, economic, and tech- nological developments are having a profound impact on the kinds of educa- tion needed to meet the challenges of the coming century. Countries able to benefit from technological advances and learning opportunities have a distinct advantage over those with limited educational opportunities. New technologies promise high rewards to those who possess more advanced skills, while limit- ing the opportunities of those who do not. - al? yet In 1990 five international agencies-the United Nations Children's Fund (UNICEF), the United Nations Development Programme (UNDP), the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the United Nations Population Fund (UNFPA), and the World * - Bank-sponsored a landmark international meeting inJomtien, Thailand: the World Conference on Education for All. Two goals recommended by the con- ference-universal primary education and gender equality of enrollments in primary and secondary education-were included in the core set of interna- tional development goals proposed in Shaping the 21st Century - (OECD 1996c) and have been adopted by many organizations, High fertility is driving rapid growth in the school-age population including the World Bank. in some regions The Education for All conference recognized the need to prepare young people to lead productive lives in rapidly chang- ing societies. This was reflected in its "expanded vision, " focusing not only on enrollment levels, but also on learning outcomes, :'" I vocational training, and essential skills and knowledge (WCEFA Inter-Agency Commission 1990). But progress sinceJomtien has ' 7, been modest, and many countries will fail to meet its targets. The Pi' cost to countries that fail to keep up may be substantial, and the burden will fall hardest on the young and the poor. - :' , . Universal primary education is the first important step toward I `11 improving education outcomes. Remedial efforts and adult edu- . .;. cation programs can help to correct past deficiencies, but a gen- ' eral improvement in education levels must begin with a basic - ..; - education. Reaching this goal requires improvements along three dimensions: * Access and participation, measured by net primary enroll- , ment. *.. ..I5 * Retention in school, measured by progression to grade 5; * Achievement, measured by the literacy rate of 15- to 24-year- ; t 5 . ,. Sr:E irl. j -.'ie olds. Expanding access Developing countries have made enormous progress in expand- ing access to schooling, enrolling more children than ever before. Between 1960 and 1992 the share of 6- to 11-year-olds enrolled (1998) note that the most important shortfall in primary enroll- rose from less than half to 77 percent, although recently the rate ments is caused by children who complete grade 1 but fail to com- of growth has slowed considerably. In the same period the share plete primary school. This suggests that issues of school quality, of 12- to 17-year-olds in school more than doubled (from 21 per- rather than access to school facilities, may influence the decision cent to 47 percent), and for 18- to 23-year-olds it more than tripled to continue. On the demand side, families may withdraw chil- (from 4 percent to 14 percent) (World Bank 1998c). dren, especially girls, from school for their labor. This is good news, but regional enrollment ratios show large variations (table 2.10). East Asia has achieved universal primary Enhanoing achievemioent enrollment, and Latin America and Eastern Europe are nearing The low primary completion rates mean that in many countries full primary enrollment. But Sub-Saharan Africa experienced no the formal education system is failing to provide basic, functional increases in enrollment ratios between 1980 and 1996. Although literacy for many children. But attendance alone does not guar- reasons for this stagnation have not been established, high fertil- antee adequate achievement. Even in high-income countries with ity rates leading to rapid growth in school-age populations are a full enrollments in basic education programs and low repetition driving force (table 2.15; figure 2a). In many countries the and dropout rates, achievement levels varywidely and can be sur- school-age population is growing faster than enrollments. prisingly low (figure 2b). Although all these countries have net secondary enrollment ratios above 80 percent, functional literacy Keeping students in school rates for 16- to 25-year-olds range from 35 to 80 percent. Com- Despite the overall success of many developing countries in pro- parable data are not yet available for many developing countries, viding access to schooling, only two-thirds of the children who but in those where data are available test scores were lower than start primary school are still there five years later (World Bank the international mean for all countries compared-in some cases 1998c). Again, there are large regional differences. More than by more than one standard deviation (World Bank 1995d). half the countries with data in Latin America have completion rates above 80 percent, while many African countries experi- The other important goal is to ensure gender parity in education. enced declines between 1980 and 1996 (table 2.11). Low completion rates result from high repetition and Several indicators-including enrollment, literacy rates, and dropout rates. The two are closely linked: repetition often leads years of schooling (tables 1.2 and 2.11)-reveal that the level of to dropout. On the supply side, low completion rates may reflect female education in many poor countries is low. They also suggest problems with the quality of education. Filmer and Pritchett that the environmentwithin which education decisions are made 36 1999 World Development Indicators High enrollments are no guarantee of high achievement Some developing countries have far to go to reach universal primary enrollment .,~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~iKl ,n,Ir,r. '31... ,1,. -.2,L.9-i I,i a.~~rllll:- -3 . 1 I. ; ......... . |: ~~~~~~~~~~~-.,.-,X ..*:e. ,] A.~~~~~~~~~~~~~~~~~~ I. E leads to underinvestment in girls' schooling and to a persistent developing countries are not progressing fast enough to achieve gender gap. the goal. Some countries have performed above par. But many Despite an overall increase in the share of girls enrolled in will need to increase primiary enrollment by mnore than 25 per- school, an average six-year-old girl in a low- or middle-incomne centage points (table 2. 10). Developing countries as a group will country can expect to attend school for just 7.7 years, compared need to enroll some 200 million more children in 2015 than are with 9.3 years for a boy. The largest gender disparity in access enrolled today, an increase of more than 40 percent. exists in South Asia, where in 1990 a girl could expect 6 years of The goal of gender parity is equally elusive for most poor schooling, and a boy 8.9 years. The gender gap is now very small countries. Unlike other social indicators, female enrollment does in countries in Eastern and Central Europe and in Latin America, not seem to benefit from faster economic growth (figure 2d). Nor and is closing in all regions except South Asia, where policies to has making primary schooling compulsory and free, as many improve access seem to have benefited boys more than girls countries have done, been enough to improve girls' participa- (World Bank 1995d). tion. New strategies and renewed commitment by governments The gender gap in school enrollment is not just a matter of are required. access. In many countries demand for girls' education is low, reflecting cultural norms and the opportunity cost of their time Strategies spent in school. For a family, the costs and benefits of educating How, then, should countries move forward to reach universal pri- daughters may be quite different from those of educating sons. mary enrollment and ensure equity? Many of the benefits of educating girls in developing countries Access remains a problem for the poor and disadvantaged in are public: female education is linked with better health for any society. World Bank research shows that in many countries in women and their children, and lower fertility levels (Hill and South Asia and Western and Central Africa a large fraction of King 1993). But many of the costs-ranging from opportunity poor children never enroll in school. In Benin, for example, only costs to direct costs-are private. Where the quality of education 26 percent of the poor aged 15-19 have completed grade 1 or is low, parents may conclude that their daughters should not more, compared with 80 percent among the rich (Filmer and attend or remain in school. Literate women, however, are more Pritchett 1998). Improving access-building schools and getting likely than illiterate ones to enroll their daughters in school, and children into them-is vital. Multiple shifts, multigrade class- the regions with the highest illiteracy among women are those rooms, and nontraditional schooling are some of the strategies with the widest gender gap. being implemented. Beyond increasing supply, however, special measures will be prefspetg for achieving targets needed to encourage the enrollment of girls and the poor. The international development goals call for all countries to attain Because poor parents do not always appreciate the value of edu- universal primary education by 2015, and gender paritytin primay cating their children, and many parents place a higher priority on and secondary education by 2005. How likely are developing educating their sons than on schooling their daughters, educat- countries, in the aggregate and individually, to achieve the targets? ing parents can be important in enrolling and keeping poor chil- Reaching the net primary enrollment target of 100 percent dren and girls in school. Subsidies, school meal programs, and will not be easy for most poor countries (figure 2c). Overall, scholarships also influence the enrollment decisions of the poor 1999 World Development Indicators 37 Female enrollments do not respond to income growth Stratelies to improve girls' education .T1 ,f, i.:r.i .I.: learn ir,::1..:.1 A f.-i , r E.. ri., .- Fr ;]o i rori 2c .1 * .:.:- r.rr,e rx lr: r;.. :* *: * *S * * b **. .. . * * Providing incentives for girls' attendance, especially those that *! g... . ................ N N * , , - - --- - -- -s - -*. ~ ~~-~~ ~ ~ *' - a~ red uc e the direct and opportunity costs of their attendance. .- --0* * . . I.,:dar-rlr.. lteridi *:.e.:r,e- I.:,r . :,,c,ser .ir.l,r,- BrI... ........................ :car:.e r,-car' ru.r .... a.: J:' 5 @5 ne.i-c .111 lrA ad e urr.:.r.i ai;d Ic lb:4 - i.,e a;1l ili,r.jter * Bringing schools closer to homes and making them sater. r lulr.le - 0 s.r,od 'ln'. 5aredIIlc B:I:.OIT nrrIug,dri,]. laB ,..c.iriz arid d, rai.e -u.,aro n F,r alt:. do nri .:I , ir.or-Fe aricId n ,B *:; Bera.l f . ::: 11 -: :i * Improving the quality and t elevance of education. . r I - r ro,-,. *:is> r.-' BEM .II. ,mr, 1: :. .u'I.CaS. lB3...lrEr r,riair. a cr..r c. i ior,.,:, . s 3.. r.r: r,,;r,. .icl,nc ri Frl. ,, w, -..~.i E ar.r B. lJB o. ..: --mr,i.nBer,ar r., aore, T;d mrr.uer . . .cur.: uluir orer:r;. ernder tcnr.B-an,on Tr.E r .:.i FT..a:nher r&:r,.,ilirSrl .;r rcnnaiac ['.aCher'. and r lr,e. r, .:d c ,,Fi hauL ai;slr,r * Establishing supportive national policies. ,.: r..:.l cIl l: ,e; rnT iar,- l Br,--.,,:r, B ,,. ,-richn ,.i,-, to n. ; uu.i ajl.. : (Harbison and Hanushek 1992).. Other ways to improve girls' nr,:.-,ri.:.n-..t. r,re rI-: ,irl' e.: ar.:.r, nrB .-rdr.d:l] r-.Fl,:, enrollment and persistence is to address constraints that inhibit * Pursuing sound economic policies and promoting labor market school attendance-by providing incentives, improving the rele- regulations that support equal employment and pay policies. En .r,i; i ol aitean-br ri,.rBl,e I ; n ,i,.Trli e m3l 1r, l a.-r ia . n irer. r,iurai vance of education, and establishing supportive national policies 3,J -i In1 r ii;, ha,n'r, ir,ail.i.>it Tro.eB i. - .: ar, ipr:- e (box 2a). .fl r,,p.Bn c l..,mr-rr arO-re.:c-i ri .-: ,,.l hr .,.:.rr. r The real challenge for schools, however, is to offer education l : e rlcr . of sufficient quality to keep the students once they have enrolled. The main reason many poor children do not complete primary school is not that they fail to start; it is that they drop out. Dropouts account for 60-94 percent of those who fail to complete primary The emphasis must be on how well education systems are school in Latin America and East Asia, and 85-90 percent in meeting their responsibility for developing students' cognitive Indonesia and the Philippines (Filmer and Pritchett 1998). abilites and skills. An orientation toward outcomes means setting Poor quality of education can depress the demandfor schooling, relevant standards and measuring how well schools attain them. even where access exists (World Bank 1998c; Filmer and Pritchett But the informaton systems in place today focus on administra- 1998; Swanson 1988; Harbison and Hanushek 1992). Large class tive record keeping: school numbers, student enrollments, and sizes, lack of materials, indifferent teaching, and deteriorating build- efficiency indicators such as pupil-teacher ratios, rates of repeti- ings are likely to influence a decision by poor parents to discontinue tion and dropout, and model-generated cohort completion rates. school attendance. Labor experts have found that some children Despite an obvious interest in what education achieves, few sys- would rather work than be subject to a school curriculum irrelevant tems in industrial or developing countries systematically collect to their needs (UNICEF, The State of the World s Children 1999). information on the quality of educational achievement. The emphasis on quality and school effectiveness is not new. After Jomtien it became clear that appropriate systems for What is new is the recognition that quality must be of preeminent assessing learning achievement were needed to measure whatwas concern, and that there is little point in expanding access unless being learned as well as how well the education system was work- there is reasonable quality. To improve education outcomes, ing. Countries are developing a variety of tests based on their country policies and strategies must put special emphasis on: national curriculum to monitor student performance (Greaney * Improving access to relevant learning opportunities, which and Kellaghan 1996). International assessments that measure will require quality teaching environments. education outcomes in several countries, and thus provide a com- * Using financial and nonfinancial resources more efficiently. parative framework, also are under way (box 2b). * Strengthening institutional capacity, so that countries are bet- Many developing countries are likely to encounter a range of ter able to set objectives and achieve them in their own way. common problems in conducting national or international assess- ments. These include unavailability of current school-age popula- Monitoring progress tion estimates and enrollment figures, lack of experience in How will countries gauge whether students know what they need administering large-scale assessments, and insufficient funds and to know? If the success of education is to be assessed by what and trained staff to rigorously analyze national or international data. how children learn, better ways must be found to measure edu- The World Bank and other donors are assisting country-specific cation outcomes. and international initiatives in monitoring and evaluation. 38 1999 World Development Indicators How countHei stack up in studenl achievement ir, Trd,,. IrilermnilC,r.51 1`3mn--, : sl, i:Q 9.irie Slt. iTil.I Can eD .,n ,r, 199J- Ciffc.. in-e IlricrnElOr&l *, .3iS r, i.:r e.rln.ir Cii Cl El.g.. SliCrigl Acn,e e,cn.c ,: Ire I-E-l russ ;n sI;ris inTlern-j 5!.) iii. Cl * li :rnie einieri iei *:.:ndu,.,l: i ' Eel - lril, ,n 4 .:.t,r,r,e. , nirrici; 51.7S snd - nE,e ard I :11cc lcd e lers RE r,i-..l:r, i.rr .: 1l e l.cSCtl,Z ale I;Err.n,r,, i.EIrersri.: 3rn 1cenre iCr.. E . IE; E n,El s :r.rCI Brnr,.IC.3 H.-. Cre sre ie i.,ilErer,.-e lr, mei 3l.C n , ; e nlE .:,1 r,e I:..r,,r.1,lIe ,n.r:.r,;e.E *r>ulrre *..i,,riSu.l a ; rr,..:cr,r rij.ecr I - in I r , ;- 1r.-L *.' Ire TIMS SZ TIr,1 Fi. I>iiC E dIinili d _ __n.rr l iir E ICu c. ..rnr,e!. _n_ , CE men, _.-, _nd .jJiE JreC , r r, - Tir.l i I, n . - IS -l -- t SCni.-.nneir icnlEl r,n rliEnEniErIW En Er,..EeS U drjuE E r.nnn.rcw. ,D' Iici~r nncIE! zid . rIAsErE, 3.j .On.: Dee E, n ._ nOlele duES ...n _.rE r lea sc den.,: _rCOSI ;l.¶n. 'r _ _ _ r_ _. _-_i *irE: I'7E End .c.. I Cd r'r' '' : u n r s. -nj Er enre d ' C - S In I Cl DrinCiQal "ll oiC~dE inICIi :iC.:n jr,,ui E.r.rIncaalrEl Tn, Tirlez ;-Is ' :.I I j.. r m ni- ir, El r.)- pr.~r r, *nInc Tl.IMSS rr,rJE . naIl S r. i ar Tr- eicnlri esie Ie.I ES jzI SE . I.- EI,SiC r.rini¶ nial.n .:.n n-Sn., fl-c-Alr 5,5 ' I. T I l .iII -ld e I,-..-I C r.:rilr3E 1,-31 d pd Cr,;CI . in , TIP 1% in.. 9 re,-~cic.e; 91, Sine T1SCid EiclCSliie F,1 Cni End ;],n,sll,e, Innr f,nlt !'Crr Si : l Q( IviS . in .nzl*5r Srl.C : SI r,er.;, ,:.i E! The goals of Education for All will not be achieved easily or soon by many poor countries. But for countries and donors to aim for anything less would be to aim too low, and would fail to set the right expectations, with the right sense of urgency. 1. This paragraph reflects the findings of Schultz 1961: Becker 1964; Psacharopoulos and Tzannatos 1992; Romer 1986: Lucas 1988; Azariadis and Drazen 1990: Barro 1991; and World Bank 1995d. 1999 World Development Indicators 39 - Pir,e4i Muiaaie Poore,r Brazil 1996 Guatemala 1995 India 1992-93 Enroilment profiles of lne poor differ - ------- -- -ur-r _ across countries but fall into dislinctive - ,, _ regional patterns. In much of Latin - America nearly all of the poor enroll in first - ns MF - -~ _ Pwgrade. but then they drop out m ciroves. in other parts of the world including much f l of South Asia and West Africat Ene poor never enroll. Both patterns . ________ 9 _ lead to low attainment. - - -- - --------- Indonesia 1994 Mali 1995-96 Philippines 1993 Male Fee. ; ,~~~~~~~~~~~~~~ . . - - _ . . .. . r ~~~~~~~~~~~~~~~~~~- ,.= .1. . *s I - Male Female Benin 1993 Brazil 1996 Egypt 1995-96 Proportionl Proportioni Proport on 1.0 1-0 O 0 3 0 5 Ism 0 5 ON Grade Grade Grade Kenya 1993 Morocco 1992 Nepal 1996 Proportilon MU. 0_____ 0 _____ ____ __ 0 5 DN 0 5 IsN 0 5 ON Girls are at a big educational Grade Grade Gr-ade disadvantage in some countries. Source: Filmer and Pritchett 1998. 40 1999 World Development Indicators - - S ; - ~~~~~~~ ~~ PEOPLE Patterns of educational attainment vary greatly across countries, and across population groups within countries. For some, basic education is practically universal-for others, attainment is dismal. -~~~~~~~~~~~~~~~~~~~~~-~ r, -;- ; a _ _ _ t~~, I' T= =_-r_ _m R 15<& -x ,'-, L'.= L 2- .~~~~~-i JL _q,' . .--_m---:=r_s :.a -......................................... .~-_4 . ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Z *'-,- 7 ;--sSLn;..ff __l..:Sp=N V wz _ j '.I-,, . ' , : : r - . . '~~~~~~ gOM - . :iE;,,; ........... rtcE s-Sa}z<4Es _S -rz:ff !E- se..>l-- :; -S Z r E - 7 ;~ J r - S s _ ; 1 i z A i s a i - > - ^ F e : r s 3 - > > : 2 F i S ! z i 5 R - Z - .i --,: N t w s Z.~ 4 . i7a N _ ___,_s _a tJ . *oE i-Z4Ees:7xfizw =2 ]-@ s ir= y. ._.7zs5>%= <~--._i ;F 2,,SpR 5,.,s431 m^ __ r'-!X::z}h= ~~~~~~~~~~~ 'I2X-2§S!_a!L rcFtws_lI-" < S~~~~~~~~~~~~~~~~~~~ . l|yrgZ ^> ; _*-<_-~~~~~~~~~~N6 _----r .7- r _.-warSi_soos;a;s -"'~~~~~~~~;'< ____~~~~~~~z- T' - r4 < ks'' .,,>>e * -,-- - ,--zZs.s.x..s-n_EE_ViKrz_-.v., r4 E;slw:- 2.1 Population Total Average annual population Age dependency Population aged Women population growth rate ratio 65 and above aged 65 and above dependents as proportion of working- millions%age population % of total per 100 men 1 980 1997 2015 1980-97 1997-2015 1980 1997 1997 2015 1997 2015 Albania 2.7 ......33.3..... 3.9 1.3 0.9 0.7 0..6. 6.3 8.5 130 123 Algeria 18.7 29.3 41.9........ 2.7 2.0 1.0..... .0.7 3'7 4 6 . 114 117 Angola 7.0 11'.7 19.1 ......3.0 2.7 0.9..... 1.0 ......29 2.6 124 123 Argentina 28.1 35.'7 42.5 1.4 1.0 0.6 0 6 9.5.. 10.7 143 143 Armenia 31 3.8 4.1 1.2 0.5 0.6 05 8.0 11'4 151 150 Australia 14........ . 7.... 1 ...8.5 . . 21.1 1.4 0.7 0 5 0. ... 11.8 .... 15.2 129 120 Austria 7.6 8.1 .......80 0.4 0.0 0.6 0.5 . .. 14.8 18.9 16.9..... 133 Azerbaijan 6.2 7:6 ..I...8.7 1.... .2 .....I ...0.7 0.7 0-6 .... 6.2 77 163 155 Bangladesh 86.7 123.6 160:2 2.1 1.4 .......10 ......0.8 3.3 4.1 81 94 Belarus 9.6 10.3 9.4 0.4 -0.5 0.5 0.5 12.8 13.5 204 180 Belgium 9.8 ......102 2..... 10.1 0.2 -0. 1 ..... 0.5 .... 0.5 16.0 19.0 .....147 137 Benin 3.5 5!8 . ...9:2 3.0 26 1.0 I1.0 2.9 2.7 101 118 Bolivia 5.4 7.8 11.2 2.2 2.0 0.9 0.8 3.9 4.4 123 128 Bosnia and Herzegovina 4.1 2.3 3.1 -33 1.5 0.5. 0.4 8.1 10.7 148 140 Botswana 0.9 1.5 I1.9 3 11.2 1.0 0.8 2.3 1.9 174 147 Brazil 121.7 163.7 200.5 .... ...177.7 ... ..... 1.1 0... .. O.7.. 0.6 4.8 ......66 6 . .. 128 145 Bulgaria 8.9 ......83 ......7,3.3 ...... -04.4 ...... -0.7 0. .... O.5 ..... 0-.5 .... 15.0 18.6 133 148 Burkina Faso 7.0 10 5 16.0 2.4 2.3 1.0 1.0 2.8 2.2 108 146 Burundi 4.1 6.4 9:5 2.6 2.2 0.9 ... 0-.9 ...-.2.7 1.9 155 147 Cambodia 6.5 10.5 143 .......28 1.7 0.7 ..... 0.8 ...... 3'0 .....39 176 164 Cameroon 8.7 13.9 21.7 28 2.50.9 0.9 3.5 3.1 120 117 Canada 24.6 30.3 332 2 ........ 1.2 .....I ..0.5 0.5 .... 0.5 ..... 12.2 .... 16.5 135. 122 Central African Republic 2.3 3.4 4.6 2.3 1.6 0.8 . .. 0.9 3.7 2.7 137 .....142 Chad 4.5 7.2 11.6 2.8 2.7 0.8 1.2 3.1 2.3 87 151 Chile 11.1. 14.6 17.7 1.6 1.1 0.6 0.6 6.8 9'8 142 138 China 981.2 1,227.2 1,390.3 1.3 0.7 0-7 0.5 6.6 8.8 106 104 Hong Kong China 5.0 6.5 7.4 1.5 .... 0.7 0.5 0..4... 9.8 13.4 126 113 Colombia 28.4 40.0 51.4 2.0 1.4 0.8 .....0.65 4.5 5.9 128 141 Congo, Dem. Rep. 27.0 46.7 78:7 3.2 2.9 .... .. 1.0 ......1.0 .....28 2.6 135 .... 126. Congo, ep. 17 2.7 4.3 2.8 2.6 0.9 .....1.0 3.2 2.4. 131... 136 Costa Rica 2.3 3.5 4.4 24.4 1.3 0.7 06 .. 4.9 ....7.4 115 120 C6e d'lvoire 8.2 14:2 .... .1~9.2 .......3.2 1.7 1.0 ... 09 ... 2.7 .... 2.4 94 Ss8 Croatia 4.6 4.8 4.7 0.2 -0.1 0.5 0.5 13.6 18.0 171 157 Cuba 9.7 11.1 11.8 0.8 0.4 07 0 .9-1 13~9 109 .....120 Czech Republic 10.2 10.3 10.0 0.0 -0.2 0... .. 6 0.....O.5 .... 13.3 18.7 161.... 141 Denmark 5~1 5.3 5:4 .......0.2 ...... 0.1 0.5 0.5 14.7 18.0 140 126 Dominican Republic 5.7 8.1 10.4 2.1 1.4 0.8 0.6 .....4.2 6.2 105 120 Ecuador 8.0 11 9 15.7 2.4 1.5 0.9 0.7 4.4 5.7 118 128 :g,c0i Ir3b Peo, 719 03n3 - 4 5.5 120 116 El Salvador 4.6 5.9 8-1 15.5 1.7 1.0 0.7 4.6 5.4 131 133 Eritrea 2.4 3.8 5:.8 2 7 2.4 ... .. 0.9... 2.7. . 2.7 131 121 Estonia 1.5 1.5 1.3 -0.1 -0.6 0..5. 0..5 13.3 .... 17.9 212 203 Ethiopia 37.7 59.8 88.6 . ......2 7 2.2 ..... 1.0 ..... 1.0 .... 2.7 2.0 128 ... . 109 Finland 4.8 5.1 .. 5.3 0.4 0.2 .... _0.5.. 0..5.. . 14.4.. 19.9 167 136 France 53.9 586.6 .... 60.4 0:5 0.2 .... 0.6 ......0.5..... 15.4 18.0 150 141 Gabon 0.7 12 7 3.0 2.0 0...... O.7 ......0-.8..... 5.8 5.0 124... 122 Gambia, The 0.6 1.2 1.8 3.6 2.3 0.8 0.8 2.9 3.6 ..... 124 .... ..113... Georgia 5-1 .. 5:4 5.4 0 4 0.0 0.5 ... 0-.5..... 11.9 14.4 171.... 173. Germany 78.3 821.. ...80.1 0.3 -0.1 0.5 0..5. 15.5 20.5 171 133 Ghana 107.7 . 18:0.0 .... 27.3 3.0 2.3 0..... O.9 ...._0.9 ... . 3.1 3.5 120 119 Greece 9.6 10.5 10.4 0e-.5..... -0. 1 0..6 0.5 16.6 20.3 127.. 131 Guatemala 6.8 10.5 15.8 2.5 2.3 1 0 ....0-. 9..... 3.4 3.4 108 127 Guinea 4.5 6.9 10.1 2.6 2.1 0..9.I....0.9. 2.6 2.7 110.. 105 Guinea-Bissau 0.8 1.1 1.6 2.1 1.8 0.8 0.9 4.0 3.5 125 124 Haiti 5.4 7.5 10.1 2.0 1.6 0..8. 0.8...... 3.6. 3:7.7 ... 125 142 Honduras 3.6 6.0 8.9 3.0 ....... .2.2 1.0 -.0 .9 .. ...3.2.. 3.6.. 117 124 42 1999 World Development Indicators 2.1 Total Average annual population Age dependency Population aged Women population growth rate ratio 65 and above aged 65 and above dependents as proportion of work ng- millions%age population % of total per 100 men 1980 1997 2015 1980-97 1997-2015 1980 1997 1997 2015 1997 2015 Hungary 10.7 10.2 ....9.6 -0.3 -0.3 0.5 0..5 ..... 14.2 17.1 165 167 India 687.3 962.4 1,202.8 ..... .2.0 1.2 0.... .. 7 0. 7...O ...... 4.7 ..... 5.9 ..... 108 .... 107 Indonesi 148.3 2004.4 . 251.3 ..... ..1.8 1.3 0.8 . 06 .... 4.4 6.0 ..... 116 ... ..122 Iran, Islamic Rep. 39.1 60.9 81.2 2.6 1.6 0.9 0.7 4.5 4.8 102 101 Iraq 13.0 21.8 31.9 3.1 2.1 0.9 0.8 3.0 4.1 115. 112 Ireland 3.4 3:7 .. 4.1 ........0.4 0 ... . 7.. 0 7 07 O.5.. 11.3 13.1 134 126 Israel 3.9 5.8 7.5 .... 2.4 .........1.4 0.... .. .7 0 .. .6 .. 9.3 10.2 133 126 Italy 56.4 57.5 54.7 ... 0.1 -0.3. 0 .5 0......5.. .16.6 21.1 145 140 Jamaica 2.1 2.6 3.0 1.1 0.9 0.9 0.6 ......6.4 ..... 7.1 125 133 Japa 116.8 126.1 125.3 0.5 0.0 0.5 0.4 15.5 25.4 141. 128 Jordan 2 ... ... ... .22 .. 4.4 6.8 4.2 2.-4. 1. 1 . 0-8 ...... 2.8 4.1 79... ... 101 Kazakhstan 14.9 15.8 16.6 0.4 0.3 0.6 7.1 8.2 200 173 Kenya 16.6 28.6 40.2 3.2 1.9 1.1 0.9 2.9 2.2 118 110 Korea, Dem. Rep. 17.7 22.9 26.5 1.5 0.8 0.8 0.5 4.8 7.1 197 125 Korea, Rep..... ... 38:.1 .... 460.0 . .. 51.7 ..... ...1.1 0.6 0... .O.6 .... 0.4 6.0 10.8 165. 139 Kuwa'it 1.4 1.8 2.8 1.6 2.3 0.7 0.6 1.8 5.0 88 101 Kyrgyz Republic 3.6 4.6 5.6 1.4 1.1 0.8 0.7 5.8 5.5 173 157 Lao PDR 3.2 48.8 ..7.3 2.4 2.3 0.8 0.9 3.6 3.3 Il1 133 Latvia 25.5 2.5 ....2.1 -0.2 -0:8.8 ... 0.5 0.5 137.7. 17.7 220 198 Lebanon 3.0 4.1 5.2 1.9 1.3 0.8 0.6 5.6 6.0 117 130 Lesotho 1.3 2.0 2.8 2.4 1.8 0.9 0.. 8.... 4.1 4.2 132 .....129 Libya 3.0 5.2 7.7 3.2 2.2 1.0 0.8 2.9 4.9 92 ... 93 Lithuania 3.4 3.7 3.6 0:.5 -0.01 0.5 0.5 12.6 157.7. 194 196 Macedonia, FYR 1.9 2.0 2.2 0.3 0.5 0.6 0.5 9.2 124.4 .120 ...... 1.32 Madagascar 8.9 14.1 22.9 2.7 2.7 0.9.. 0.9 3.0 3.2 124 122 Malawi .6.2 10.3 15.3 ... ....3.0 2.2 1.0 1.0 2.5 2.4 119 106 Malaysia 13.8 21.7 28.9 2.7 1.6 0.8 0.6 3.9 6.0 117 120 Mali 6.6 10.3 17.2 2.6 2.9 1.0 1.0 3.3 2.4 133 144 Mauritania 1.6 2.5 3.8 2.7 2.4 0.9 0.9 3.1 3.3 138 120 Mauritius 1.0 1.1 1.3 1.0 0.9 .... 0 6 0.5 6.0 8.6 134 145 Mexico 67.6 94.3 120.2 2.0 1.3........ 1.0 0.6 4.4 6.3 125 138 Moldova 4.0 4.3 4.2 0.4 -0.1 0 5 0-5 9.5 10.6 167 160 Mongolia 1.7 25.5 3.3 2.5 1.5 . ... ..0.9 ......0.7 . .....3.9 .. .. 4.5. 129 .... 114 Morocco 19.4 27.3 35.8 2.0 1.5 ..... 0.9 0.6 4.3 5.2 114 132 Mozambique 12.1 16.6 23.0 1.9 1.8 0 9 1O 0 . 4.0 3.3 81. 1..35 Myanmar 33.8 43.9 52.3 1.5 1.0 0.8 0.5 4.5 5.3 117 122 Namibia 1.0.. . ... . 1.6 .. ....2.3 2.7 1.9 0.9... 0.8 ..... 3.7 ...... 3.5 .... 121.. 113 Nepal 14.5 22.3 32.2 2.5 2.0 0.8 0.8 3.6 4.0 99 104 Netherlands 14.2 15.6 16.1 0.6 0.2 0.5 0.5 13.4 18.2 146 123 New.Zealand 3.1 3.8 4.1 1.1 0.5 0.6 0.5 ... 11.3..... 14.2 131 127 Nicaragua 2.9 4.7 7.0 2.8 2.2 1 0 0.9 3.0 3.6 126 126 Niger 5.6 9.8 17.2 3.3 3.1 1.0 .. ...10 ... 2.4. 2.2 129 138 Nigeria 71.1 117.9 185.4 3.0 2.5 0.9.. .0.9 2.5...... 2.8 136 127 Norway 4.1 4.4 4.7 0.4 0.3 0.6 0.5 15.7 17.6 137 121 Oman 1.1 2.3 3.5 4.2 2.4 0.9 . ... 0.9 2.6 4.8 105 7 4 Pakistan 82.7 128.5 193.0 2.6 2.3 0.9 0.8 3.1 3.7 97 101 Panama 2.0 2.7 3.4 2.0 1.3 0.8 0.6 5.3 7.5 106 119 Papua New Guinea 3.1 4.5 ..6.3 2.2 1.9 0.8 0j.7. ... 3.0 3.9 105 113 Paragua 3.1 5.1 7 .329 2.0 0.9 .....0 .8...... 3.5 4.2 140 124 Peru 17.3 244.4... 32.4 2.0 1.6 0.8 07 4.4 5.8 119...... 126 Philippines 48.3 73.5 102.8 2.5 1.9 0.8 0.7 3.5 5.1 119. 116 Poland 35.6 38.7 39.7 ........0.5 0.1 0.5 ..... 0.5... .. 11.4 14.5 165 157 Portugal 9.8 9.9 ......9.7 0.1 -0:.1 ...... . 0.6 0.5 16.1 16.6 160 .....155 Puerto Rico 3.2 3.8 4.4 1.00.8D0.7 0. .5 10.0 12.9 132 169 Romania 22.2 22.6 21.4 0.1-030.6 0.5 12.3 14.3 138. 149 Russian.Federation ... 139.0 147.3 139.0 0.3 -0.3 0...... O.5 .....0.5 12.2 13.3 226 .... 192 1999 World Development Indicators 43 2. 1 Total Average annual population Age dependency Population aged Women population growth rate ratio 65 and above aged 65 and above dependents as proportion of working- millions% age population % of total per 100 men 1.980 1997 2015 1980-97 1997-2015 1980 1997 ±.997 2015 1997 2015 Rwanda 5.2 7.9 11.6 2.5 2.1 1.0 0.9 ...... 1.9...... 1.6 139 118 Saudi Arabia 9.4 20.1 35.0 4.5 3.1 0.9 0.8 ......2.8...... 4.0 99 69 Senegal ..5 88 13.6 2.7 24.4~ . .... 0.9 0..... .9 ...... 2.6...... 2.7 123 121 Sierra Leone 3.2 4.7 6.7 2.3 1.9 0.9 09 2.6 2.6 134 136 Singapore 2.3 3 1 3.7 .........1.8 0.9 0.5 0.4 6.5 11.3 ..... 123 118 Slovakt Republic 5.0 5 4 5.5 0.5 0.1 0.6 0 5 10.9 13.8 158 156 Slovenia 1.9 270 ......20.0 0.3-. 0.5 0 4......12.8 1. 8 4 South Africa 27.6 40.6 49.3 .. 2:.3 .... . 1.1 0.8 06 6...... 4.8 4.7 16.2...... 134 Spain 37.4 39.3 37.9 0.3 -0.2 0.6 0.5 15.9 18.6 140 139 SriLanka 14.7 18.6 22:4 .........14 ..........1.0 .. . 0.7 0.5 .......62 9.1 10.2 121 Sudan 18.7 27.7 ......40.6 2 3 2.1 . ..... 0.9 ......0.8 .......3.1 ......3.9 ......12.0 112 Sweden 8.3 8.8 8.9 0.4 0.0 0.6 0.6 17.3 21.1 135 122 Switzerland 6.3 7.1 7.1 0.7 ...........0.0 .........0..5 ......0..5. 14.7. 20.7 148 ...... 131 Syrian Arab Republic 8.7 14:9 .. ...22 3. 3.2 2.2 1.1... ..0..9 ......30 ......3.4 110 128 Tajikistan 4.0 6.0 8.0 2 5 1.6 0.9 0.8 4.3 4.1 143 130 Tanzania 18.6 31:3 ...-45:4 ........3:1 2.1 1.0 0.9 2.5 2.2 123 116 Thailand 46.7 60.6 68.5 15 0.7 0.8 0.. .. .5 .......52 7.4 129 ...... 134 Too 2.6 4.3 6.6 30 2.4 0.9 10 3.0 2.5 124 119 Trinidad and Tobago ...........1.1 1.3 1.5 1.1 0.9 0j.7 .....0.5 .......6.2 ......86 117 120 Tunisia 6.4 9.2 11.7 2.2 1.3 0.8 0.6 5.6 6.2 96 112 Turkey.44.5 637.7 79.-4. ...... 2:1 1.2 0.8 ......05 5.3 7.1 120 124 Turkmenistan 2.9 4.7 6.0M2 1.4 0.8 0.7 4.2 4.3 159 142 Uganda 12.8 20.3 30.7 2.7 2.3 1.0 1.0 2.2 1.4 116 96 Ukraine 50.0 507.7 . .. 44-5 0.1 -0.7 0.........O.5..... 0.5 13.9 14.9 208 181 United Arab Emirates 1.0 2.6 37 5.3 2.0 0.4 0.4 2.0 8.4 52 United Kingdom 56.3 59.0 59.5 03 0.0 0.6 0.5 15.8 18.7 139 126 United States 227.2 267:6.6... 296.9 1.0 0.6 0.5 0..5..... 12.3 14.2 143 131 Uruguay 2.9 3.3 3.6 0.7 0.6 0.6 0.6 12.4 13.1 147 165 Uzbekistan 16......... 0 ..... 237 ..... 30.9 2.3 ........15 ......... 0.9 0.8 4.4 4.5 155 ...... 139 Venezuela 15.1 22.8 29.9 2.4 1 5 0.8 0.7 4.2 6.3 122 123 Vietnam 53.7 76:7.7 .... 96.1 2.1 1 3 0.........O.9 0.. .7 ...... 4.8 4.8 142 150 West Bank and Gaza . 2.6 4.8 .3.5 1.0 3.5 2.8 125 137 Yemn,e Rep 8.5 16:1.1.... 25.5 3.7 2.6 1. 1.1 1 3.1 2.4 91 106 Yugoslavia, FR (Serb./Mont.) 9.8 10.6 10.9 0.5 0.2 0.5 0.5 12.6 14.4 129 128 Zambia...... .. 5.7 ...... 94 ..... 13.4 2.9 ........19 ......... 1.1 0.9 2.2 2.0 97 ...... 109 Zimbabwe 7.0 11.5 14.5 2.9 1.3........ 1.0 0.8 2.8 2.5 117 11 Low income 1,385.6 2,035.6 2,7588 .........2.3 1.7 0.8 0-.8 ...... 3.9 4.4 ill ill Middle income 2,217.3 2,856.9 3,370.4 1.5 0'9 0.7 0.5 6.4 8.0 127 122 Lower middle income 1,792.5 2,282.9 2,663.0 1:4 ....0 9 0..7 0.5 6.6 8.1 125 118 Upper middle income 424.9 573.9 707:4 .........1.8 1.2 0..7 .....0.6 ...... 6.0 7.6 138 138 Low & middle income 3602.9.4,892.5 6,129.2 1.8 1.3 0.8 0.6 5.4 6.4 122 118 East.Asia &.Pacific 1,359.4 1,751.2 2,050.6 1:5 0.9 0... .7.. j.... 0.5 ......6.0 7.8 110. . . 109 Europe & Central Asia......... 425.8.... 47470.0 ... 487:3 0:6 0.2 0,5.6 ... 0O.5..... 105.5 .. . 11.7 ..... 183 ...... 165 Latin.America & Carib. .. 360.3.... 493.9 .....624.3 19 9 ....... 1.3 0.8 ... 0.6 S.1 6.7 128 138 Middle East & N. Africa 174.1 279.6 393.9 2.7 1.9 0.9 0.7 3.9 4.7 109 108 South Asia 902.6 1,281.3 1,651.9 2.1 1.4 0.8 0.7 4.4 5.4 104 106 Sub-Saharan Africa 380.7 612.3 921.3 2.8 2.3 0.9 0.9 2.9 2.7 126 122 High income ..I...... 826.9 927O 0... 972.1 0.7 ........0.3 0.5 ... 0.5 13.6 17.4 144 131 Europe EMU 275.9 290.6 286.8 0.3 -0.1 0.5 0.5 1 .6 19.4 154 137 44 1a99 World Development Indicators 2.1 X=R1- II Knowing the size, growth rate, and age distribution of Population projections are made using the cohort * Total population of an economy includes all resi- a country's population is important for evaluating the component method. This method involves compiling dents regardless of legal status or citizenship- welfare of the country's citizens, assessing the pro- separate projections of fertility, mortality, and net migra- except for refugees not permanently settled in the ductive capacity of its economy, and estimating the tion levels by age and gender, then applying them to the country of asylum, who are generally considered part quantity of goods and services that will be needed to age and genderstructure in the 1995 baseyear. Fertility, of the population of their country of origin. The indi- meet its future needs. Thus governments, businesses, mortality, and net migration levels are projected using cators shown are midyear estimates for 1980 and and anyone interested in analyzing economic perfor- demographic models with current levels and trends as 1997 and projections for 2015. * Average annual mance must have accurate population estimates. inputs. Countries where fertility has been falling are population growth rate is the exponential change for Population estimates are usually based on assumed to have further declines at the rate of the pre- the period indicated. See Statistical methodsfor more national censuses, but the frequency and quality of vious 10 years until fertility reaches the replacement information. * Age dependency ratio is the ratio of these censuses vary by country. Most countries con- level of about two children. In countries where fertility dependents-people younger than 15 and older than duct a complete enumeration no more than once a has remained high, the transition to smaller families is 65-to the working-age population-those aged decade. Precensus and postcensus estimates are assumed to occur atthe average rate of decline of coun- 15-64. * Population aged 65 and above is the per- interpolations or extrapolations based on demo- tries that are currently making this transition. Countries centage of the total population that is 65 or older. graphic models. Errors and undercounting occur even where fertility is below two children per woman are * Women aged 65 and above is the ratio of women in high-income countries; in developing countries assumed to remain at this level for another decade, to men in that age group. such errors may be substantial because of limits on after which fertility rates will gradually return to replace- the transportation, communications, and resources ment level. Similarly, mortality changes are modeled by Data sourmes required to conduct a full census. Moreover, the inter- assumingthatthe rate of change in the previous decade national comparability of population indicators is lim- will continue in the near future. Future mortality in coun- The World Bank's population ited by differences in the concepts, definitions, data tries with high levels of HIV infection is adjusted to reflect estimates are produced by the collection procedures, and estimation methods used the lagged impact of the disease on mortality. Human Development Network by national statistical agencies and other organiza- . and the Development Data tions that collect population data. Group in consultation with the Of the 148 economies listed in the table, 132 The aging population Bank's operational staff and (almost 90 percent) conducted a census between 1988 resident missions. Important and 1998. The currentness of a census, alongwiththe i.3.jj.. . . . - C . 3fl., .. inputs to the World Bank's availability of complementary data from surveys or reg- demographic work come from the following sources: istration systems, is one of many objective ways to judge : census reports and other statistical publications from the quality of demographic data. In some European country statistical offices; demographic and health sur- countries registration systems offer complete informa- veys conducted by national agencies, Macro tion on population in the absence of a census. See I International, and the U.S. Centers for Disease Control Primary data documentation for the most recent census J., and Prevention; United Nations, Department of or survey year and for registration completeness. Economic and Social Affairs, Statistics Division, Current population estimates for developing coun- .. i A I Population and Vital Statistics Report (quarterly); tries that lack recent census-based population data, ' . United Nations, Department of Economic and Social and precensus and postcensus estimates for coun- . Affairs, Population Division, World Population tries with census data, are provided by national sta- . - Prospects: The 1998 Revision; Eurostat, Demographic tistical offices or by the United Nations Population Statistics (various years); South Pacific Commission, Division. The standard estimation method requires Pacific Island Populations Data Sheet 1997; Centro fertility, mortality, and net migration data, which are Li 19 Latinoamericano de Demografia, Boletfn Demogrlfico often collected from sample surveys, some of which (various years); and U.S. Bureau of the Census, may be small or have limited coverage. These esti- - .re -,.- r.mvei International Database. mates are the product of demographic modeling and so are also susceptible to biases and errors due to The elderly population is grov,ng rapid[) in bolh relative and absolute terms as lower lerntitv leads shortcomings of the model as well as the data. to smaller birth cohorts and a:. morralirr In oloer The quality and reliability of official demographic cohorts declines. In most countrles women data are also affected by public trust in the government, numbedring them a m age 60o. the government's commitment to full and accurate enu- meration, the confidentiality and protection against mis- use accorded to census data, and the independence of census agencies from undue political influence. 1999 World Development Indicators 45 2.2 Population dynamics Crude death Crude birth Projected Population Average annual population rate rate population momentum growth rates by 2030 Age Age Age per 1,000 per 1,000 0-14 15-64 65± people people %% 1980 1997 1980 1997 millions 1997 1980-97 1997-2010 1980-97 1997-2010 1980-97 1997-2010 Albania 6 7 29 19 4 14.4...... 0.4 -1.0 1:7.7... 1.5 2.5 2.9 Algeria 12 5..... 42 27 .... 51....... 1.6 1.5 0.9 3:6 2.8 2.3 2:9 Angola.23 19 50 48 26....... 1.5. 3.4 2.5 2:6 .. 3.2 28.8 2:2 Argentina 9 8 24 20 48 1.4 1.0 -0:1 1.5 1.4...... 2.3 1.4 Armenia 6 6 23 12 4 1.2 0.4 -3.1 1.3 .....1.2 ...'3'0 ... ..2.7 Australia 7 7 15 14 22 12 0'3 -0.1 1:5 ......1.0 2.6 1.77 Austria 12 10 12 10 8 1.0 -0'6 -1.6 0.7 0.1 0.2 1.1 Azerbaijan ......... .. 7.........6 ...... 25 18 10 1:4 0.6 -2:2 .....1.5 ......16 2.0 2.3 Banglads 18 10 44 28 189 .......1 .5..... 1.4 -0.8 26.6 2.7 1.8 2.4 Belarus 10 13 16 9 9 1.0 -0.4 -3.2 0.4 0.1 1.4 -0.2 Belgium 12 10 13 11 10 ...... 1: 0.. .. -0.6 -0.9 0:3 0.1 0.9 0.6 Benin 19 .......13..... 49 43 13 1.6 3.3.. 1.8 2.9 3'4 1... . .1 1.4 Bolivia 15 9 39 33 14 1.6 1.8 1.2 2.4 2.7 2.8 2.6 Bosnia and Herzegovina 7 7 19 13 3 1.1 -46 0.8 -29 19 1. 3.3 Botswana ............. 10.. . .........15 ... ...45 .......34 . . . .2 ... . ...1.3 .. ..2 3.3 ... 0.3 .... .3.6 . 2.1 4.0 -0:8 Brazil 9 7 31 21 225 1.4 0.5 -0.2 2.4 ......1.6 2.6 2.6 Bulgaria 11 14 15 9 7 079 ......-1.9 -3.2 -03.3.... -0.4 1.0 0.2 Burkina Faso 20 19 47 45 22 1.4 24 2.0 2.5 2.7 2.5 0:4 Burundi 18 20 46 43 12 -1.4 2.8 1.6 2:6 2.8 1.0 -0.6 Cambodia 27 12 40 34 17 1.4 3.2 0.4 .... 2 5 2.7. 3.1 2.8 Cameroon 16 11 45 39 28 1.5 2.8 2.2 2 8 28 2.7 1.4 Canada 7 7 15 12 34 1.1 04 -. 12 088 18 Central African Republic 19 19 43 37 6 1.3 .... 24.4..... 1.0O. 2:2 2...... 22 1.9 -. Chad 22 17 44 45 16 1.5 3.8 2.0 ......1.7 ......38 1.8 0.9 Chile 7 6 23 20 20 1:4 0.7 -0.5 19 1.5 2.8 2.9 China ..............6.........8.. .18 17 1,486 12 -0.6 -0.8 2.-0 1..1 .... 3.3 2.1 Hong Kong, China5 6 17 10 8 1:1 -. -08 17 1.2 4 1 9 Colombia ............ 7.. ......6...... 31 25 60 1:5 .... .. 0.9 0.0 2 .6 ..... 2.1 3.0 2:3 Congo,. Dem. Rep. 16 ... 15. ... 48 47 112....... 1:5 3.4...... 2.7 3:.1..... 3.2 3.1 274 ..on..go,......ep... 16. 16 45 44 6 15 3.0 2.4 28 2. 2.4 0 Costa Rica 4 4 30 23 5 16 1.6 -0.7 2.9 ... 2.0 4.3 3:4 M8e dIlvoire 17 16 51 37 23 1.4 2.9 1.0 3.4 2.4 3.7 0.5 Croatia . 13 . 10 5 09 -. -12 0.3 -. 1.1 1. Cuba ............ 6.. . ....7. .....14 14 12 1.2 -1.5 -1.5 1:5 ......06 1.9 2.5 Czech Republic 13 11 15 9 10 1.0 -1.6 -4 0 0.0 1.1 Denmark 11 11 11 13 5 1.0 .....-0.7 ......0.0 0.4 0.1 0.3 0.7 Dominican Republic ......... 7 . .......5... ....33 26 ........12 ..... 1.6 0.9 ..... -0.1 2.8 2.1 3.7 3.3 Ecuador 9 ....... 6...... 36.. ..... 25 .... . 19 ...... 1.6 ... ....1.2 ....00 3.1 2.4 2.9 2.7 Egypt, rbRp 13 7 39 25 93 1.5 18 -0.2 2.5 2.5 2.8 2.5 El Salvador 11 ........6.......36 28 .......10 .......176.6 .... 0.3 0.7 2:2 ... ..2.5 .... 37 2.1 Eritrea ..... 1 .12 .. .. . ..... ..... .418 ....... 1.4 .... . .... 2:2 2.7 ..... ........ 2:1 Estonia 12 ...... 13 15 9.. .....1....... 0.9 -0.8 -3.4 0:1 -0.4 0.3 1 .0 Ethiopia 22 .. 20....... 48 46 115 ...... 1.4 2.9 2:0 2.5 2.5 .2.3 -0.1 Finland 9 10 13 12 5 0.9 00 -0.5 ....-0:4 .01...... 1.5 1.4 France 10 .......9 15....... 12 61 1.1 -0.4 -M 0.6 06 0.3 1.1 0.5 Gabon 19 16 33 37 2 1.4 3.8 2.1 2.6 .... 2.2 2.4 1.0 Gambia,The 24 13 48 43 2 1.4 3.5 2.4 3.6 24 3.5 3:6 Georgia 9 7 18 10 5 1.1 -0.4 -2~7 0:4 ......05 2.0 14 Germany~~~12 10 11 10 76 0.9 .-0.6 -1.3 0.5 .....-03.3 0.2 1. Ghana ............. 14 . ..9....... 45 36 34 1:6.6 .. ... 2.9 1.3 3.1 ... 3.2 3.5 .....3.2 Greece 9 10 15 10 10 1.0 . -1.5 -1.1 0.8 -0.1 1.9 1.1 Guatemala 11 7 43 34 20 1:6 .2.3...... 1.1 2.7 3'3.3.... 3.4. 2.1 Guinea 24 17 46 41 13 1.5 2.6 1.2 26.6 .... 3.0 2.6 2 3 Guinea-Bissau 2 6 2 1 44 42 2 1.4 2.6 1.6 1.6 2.3 2.1 1.1 Haiti 1 5 ...... 13. _ ... 3 7 32 12 1.5 ........20.0 .... 0.4 2.0 2.6 0.8 1.4 Honduras ...........10 .. ....5.......43 34 11 1.7 2.5 1.0 3.5 .....3.3 3.9 2:8 4 61999 World Development Indicators Crude death Crude birth Projected Population Average annual population rate rate population momentum growth rates by 2030 Age Age Age per 1.000 per 1,000 0-14 15-64 65+ people people%%% 1980 1.997 1.980 1.997 millions 1997 1980-97 1997-2010 1980-97 1997-2010 1980-97 1997-2010 Hungary 14 14 14 10 9 0.9 -1.6 -1.5 0.0 -0.2 0.0 0.4 India 13 9 34 2 7 1.384 1.4 1.4 -0.3 2.3 2.0 2.9 2.4 Indonesia 12 8 34 24 287 1.4 0.4 0.0 2.5 1.8 3.4 3.2 Iran, Islamic Rep. II.1 ... 6 .44 22 .. 96 1.6 .....I.1.6 -0.8 3.2 .... 2.9 4.3 1.7 Iraq .. . .... 10. 41.... 33 39 .... 1.6 2.4 07.7 3 5.5 3._ 1 .... .3.8 . ....3.7 Ireland 10 9 22 14 4 1.2 -1.3 0.0 1.1 0.9 0.7 1.0 Israel 7 6 24 21 9 1.4 1.5 0.4 2.7 2.1 2. 13 Italy 10 10 11 ..9. .51 ... 09.9 -2.3 -0.9 0.4 -0.4 1.5 1.0 Jamaica 7 6 28 24 4 1.5 -0.2 -0.7 2.0.... 1.5 0.8 1.2 Japan 6 7 14 10 118 0.8 -2.0 -0.8 0. -0.5 3.6 2.7 Jordan ... 4 . .. ...31 8 1.7 3.0 1.6 5.2 3.1 3.6 4.5 Kazakhstan 8 10 24 14 18 1.2 . -1.7 .. 08 1.0 Kenya 13 13 51 37 49 1:.5 2.5 0.8 3.8 2.9 2.2 -0.6 Korea, Dem. Rep. 6 9 21 21 29 12.2..... -0.7 -0.2 25.5 1.2 3.4 2.8 .. Korea, Rap. 6 6 .. 22. 15. 54 . .. 1.2 -1.3 -0.3 1.9 ..... 0.7 3.8 4.1 Kuwait 4 2 37 2 2 3 1.6 0.8 0.5 1.7 3.9 3.5 8.1 Kyrgyz Republic ..... 9 7.... 30 22 7 1:.5.. 1.2 -13 1 5 2.3 1.5 0.6 Lao PDR..... 20 ... . . 14 ... 45 .389 1.5 2.6 1.7 2.2 ... .3.0 4.0 1.6 Latvia 13 13 15 8 2 0:9.9.. -0.6 -4.2 -0.2 . -0.4.... 0.1 .....0.8 Lebanon9 6. 30. 22 6 15.5 0.8 .-0.6 25.5. 2.2 2.2 1.4 Lesotho 15 12 40 35 3 1.5 2.1 1.2 2.6 2.4 2.2 1.8 Libya 12 5 46 29 9 1.7 2.3 1.0 3.8 2.9 4.7 5.1 Lithuania ... 10 ..12 16 10 4 1:.0 -. -2.6 0~.6 0.2 1.1 1.2 Macedonia, FYR 7 8 21 16 2 1.2 -0.8 -0.6 0~.5 0.7 2.0 2.3 Madagascar 16 11 47 42 30 1.6 ..-...2.9 ... 2.1 27 3.3 11 3:0 Malawi 23 23 55 48 20 1.4 2.8 1.7. 3.1_ 2.8 3.6 1.7 Malaysia 6 5 ... 31 .26 34 1.5 2.0 0.4 .... 3.0 2.4 3.1 3.6 Mali 22 16 49 47 24 1.6 2.7 2.7 2.5 3.2 .... 4.1 0.9 Mauritania 19 14 43 41 5 1.5 2.8 1.9 2.7 2.9 3.0 2.6 Mauritiu 6 7 24 17 2 1.3 -0.8 -0.5 1.6 ... 1.4 3S.8 2.3 Mexico 7 5 34 25 141 1.6 0.4 -0.4 3.0 2.1 2.8 3.1 Moldova ...... I.. 10 10 ... 20 11 4-1.1 0.0 -2.6 0.4 0.5 1.6 0.4 Mongolia .... 11.. 7 .... 38 ...23 4 1.5 1.6 -0.9 3.0 2.7. 4 2 .... .2.3 Morocco .. 12 ... 7 38 26 42 1:5.5.. .. 0.6 .... 0.4 3.0 22.2 2.3 2.3 Mozambique 20 20 46 .41 28.... .. 1.5 ...2 .1 0).7.......1.5 ..30.0 .I ..3.3 0.4 Myanmar 14 10 36 27 58 13.3.. -0.1 0.3 24.4 1.3 2.3 2.0 Namibia 14 12 41 36 3 1:.5. 2.5 1.4 2.8 2.5 2.7 1.2 Nepal 17 11 43 34 39 1.5 2.4 1.1 2.6 28 36 2.5 Netherlands 8 9 13 12 16 1.0 -0.6 -0.9 0.8 0.3 15.5. 1.4 New Zealand 9 7 16 15 4 1.2 0.0 -0.1 1.1 0.8 1.9 1.1 Nicaragua 11 5 46 32 9 1.7 2.3 0.6 3.1 3.5 3.8 3.0 Niger... 23. 18 51 52 25 1.5 3.5 3.2 3.1 3.3 3.2 2.5 Nigeria 18 12 50 40 241 1.5 2.8 2.2 3.1 3.0 2.7 3.2 Norway 10 10 12 14 5 0.9 -0....3.1 .. . -.1... . 0.6 0.5 08 .0:2 Oman 10 3 45 30 4 1.8 4.2 0.5 4.3 3.7 4.2 5:4 Pakistan 15 8 47 36 244 1.7 2.3 1.2 2.7 3.2 3.1 .....3.2 Panama 6 5 30 23 4 1:.5 0.7 -0.5 2 7 . 2.0 2.9 2.8 Papua New Guinea 14 10 36 32 8 14.4 1.7 1..4..I 25.5..... 2.4 6.1 3.1 Paraguay .8 5 37 ..31 9 1.6 2.6 0.5 3.1 3.1l 1.3 2.7 Peru 10 6 35 27 38 1.5 1.0 0.5 26.6.... 2.2 3.2 2.8 Philippines 9 6 35 29 123 1.6 1.8 0.8 2.8 2.5 3.9 3.6 Poland 10 10 20 11 40 1.1 -0.2 -18 06 0.5 1.2 1.0 Portugal 10 11 16 11 10 10 -2.2 -0.9 0.3 0.1... 2.6 ....7-03 Puerto RICO 6 8 23 16 5 1.3 -0.4 0.1 1~4 1-0 2.4 1.9 Romania 10 13 18 10 21 1.0 -1.7 -2.2 0 5. 0 0~ ...._1.1 0.6 Russian Federation 11 14 16 9 132 1.0 -0.1 -2.6 0.3 0.2 ......I1.4 -0.2 1999 World Development Indicators 47 2.2 Crude death Crude birth Projected Population Average annual population rate rate population momentum growth rates by 2030 Age Age Age per 1,000 per 1,000 0-14 15-64 65+ people people%% 1980 1997 1980 1997 millions 1997 1980-97 1997-2010 1980-97 1997-2010 1980-97 1997-2010 Rwanda 19 22 51 46 15 1.3 ........1.6... 2.8 2.3 3.2 1.2 0.5 Seudi Arabiae . ....I.......9... ....4. ... 43 35 49 16.6 4.1 3.0 4.9 3.2 4.5 4 7 Senegal 18 13 46 40 18 175.5. .... 2.7 2.1 2.8..... 2.9 ..... 2.2 2.6 Sierra Leone 29 26 49 46 8 13 2.6 1.2 179. 2.6 .1.1 2.0 SlovakRepublic 10 10 9 1 1 -0.7 -1.9 0:8 .....0-6 07........ 10......... Slovenia 10 10 15 9 2 0.9 -1.5 ......-1.9 ......0:7 .....-0.1 0 9 1.9 South Africa 12 8 36 25 56 1.5 .....14 0 .... 0. ..... 27.7 .... 1.7 4.4 0.3 Spain 8 10 15 9 36 1.0 -2.9 -1.1 0:.8 -0..1 ....112..6 . 0.5 Sri Lanka 6 6 28 19 25 1:3....I.. 0.0 -0.2 1:.8 1.4 3.4 2.8 Sudan ............ 17 ...... 12 ...... 45 33 50 175 1.7 1.7 2.8 2.4 .. ...3-.1 .... 3.2 Switzerland -............9 ... 9... 12 117 1.0 -... 0.1 -1.3 0~8.8 ... -0.1 1.0 ~....1.7 Syrian Arab Republic 9 5 46 29 28 17.7...... 2.5 ... 0.7 3.8 3.5..... 2.8 2.9 Tajikistan 8 6 37 23 10 -1.6 2.2 -0.5 2 7 .....30 2.2 1:3.3.. Tanzania 15 16 47 41 57 1.4 28 1 3.2 2.7 34 0.7 Thailand .. .........-87 .. .._28 _.1 7 73 13.3 -0.8 -1 0 2.6 1.2 3.8 2.4 Tog .... ........ ....... 16 . ... 16 ..... 45 ..... 41 ...... 9 . ... 1.4 3 1 1:9 ..... 2.8 .. ...2.9 . .....2.6 . ....0.9 Trinidad and T obag7 . 7 29 ......16 ... ...2 ......1.3 00..... -0:9 .......1:6.6 . . 1.4 1.8 2.1 Tunisia 9 7 35 23 14 1.5 ......0.8 .....-0,2 2.9 2.1 4.5 17 Turkmenistan ...... .......8. ..... 7. , .34 . .. 24 ... .. 7. 1 ... 2.4 -0.9 3.2 2.7 2.8 1:3 Uganda............. 18 ....... 20 ..... 49 48 40 17.4 ..... 2.8 1.9 2.6 . ... 28.8 ..... 1.9 -1.3 Ukraine 11 15 15 9 42 09 -06 -2.9 0:1 -0.4 1.0 -0:3 United Arab Emirates 5 ......3..... 30. ..18. 4 . 12 5.2 0.5 5:1 ... 2.8 ....178 9.8 United Kingdom 12 11 13 12 60 1.0 -0.3 -1.0 0:3 0 2 0.5 0.6 United States 9 .... . .8..... 16 15 313 ......11.1 .. 08.8 .... -0.2 . ....0:9 ......0.9..... 1.5 0.7 Uruguay 10 10 19 18 4 1.2 02 -0.1 07 08 1.7 0.8 Uzbekistan 8 .......6. ..... 34 ...27 .. . ...37 . . ...1.6 .... 2 0.0 -0,3.6 ..... 2:6 .... 27 1.5 .....1.6 Venezuela 6 5 33 25 35 1.6 1..... ...6 -0.2 2.9 2.4 3.9 3.4 Vietnam 8 7 3 6 21 112 175.5 . ... 1.0. -.. ...1.1 ......2.8 2.5 2.2 1.2 West Bank and Gaza. ... ... .41 7 18.832 4.2 ............. 179 Yemen, Rep. 19 13 53 40 33 16.6 3.6 1.4 3:8. 3.8 4.8 0:7 Yugoslavia, FR (Serb./Mont.) 9. 11 18 13 11 1.0 -0.3 -0.5 0.5 0'3 1.9 1O.0.. Zambia ...' ........1115.... .. 19 ..... 50 42 16 1.4 2.5 1.2 3:3 . . 2.7 2.5 0:7 Zimbabwe .... .. 12 ..... .12 ...... 43....... 31 17 14.4 2.2 -0.3 3S5..... .24 3.3 0~1 Low income 15 11 40 32 3,379 1.5 1.9 0.7 2:5 ......2.4 2.7 2.2 Middle income 8 8 24 19 3,723 1.3 0.2 -0.4 2.1 14 2.7 1:9 Lower middle income .. ..... 8 .......8 . ... 23 19 2,916....... 1.3 .......0 1 -0.6 2~.0 . . 1.3 ..... 28 ......1:8 Upper middle income 9 7 29_ .... 21 ..807. 14.4 0.7 ....-01 .....2.3 .. 1.7 .... 2.2 ......2.1 Low & middle income 11 9 30 25 6,669 1.4 1.0 0.1 2.2 1.8 2.7 2.0 East Asie & Pacific . ........ 8...... .7 ..... 22 19 2.243 ...... 13 -........0 2 2 .... -0.5 22.2 1.4 3.3 2.2 Erope & CePntral Asia 10...... 11. . 19 13 498 .....11. .0.2 -. 1.6 1.0 0.65 1.4 0.5 Latin America & Carib. 8... 7....... 31 23 720 1.5 .....-0.8 -0.1 25.5 1.9 2.7 2.5 Middle East & N. Africa 12 7 41 27 48-1 1.6 .......2.0 ......0.5 3.3 2.8 3.3 2.5 South Asia 14 9 37 29 1,937 1.4 1.5 0.0 2:4 2.2. . 2.8 2~5 Sub-Saharan Africa .. ...... .18 .... ..15..... . 47 41 1,192 1.5 2.7 1.9 ......2:9 9 .2.8 2.8 1:6 High income 9 8 i 12 981 1.0 -0,3 -0.6 .....0:9 .....0.3 17.7 1.4 Europe EMU 10 10 13 10 278 1.0 -1.3 -1.0 0.6 -0.1 1.1 1.0 48 1999 World Development Indicators 2.2 The vital rates shown in the table are based on data nomenon, called population momentum, is a facet of * Crude death rate and crude birth rate are the derived from birth and death registration systems, the youthful age structures typical of developing coun- number of deaths and the number of live births occur- censuses, and sample surveys conducted by try populations. It occurs because large cohorts born ring during the year, per 1,000 population estimated national statistical offices, United Nations agencies, in previous years move through the reproductive ages, at midyear. The difference between the crude birth and donor organizations. The estimates for 1997 for generating more births than are offset by deaths in the rate and crude death rate is the rate of natural many countries are based on extrapolations of levels smaller, older cohorts. increase. * Projected population by 2030 is the and trends measured in earlier years. The growth rate of the total population (see table total number of people expected to be alive in 2030, Vital registers are the preferred source of these 2.1) conceals the fact that different age groups may based on a cohort component projection in which data, but in many developing countries systems for reg- grow at very different rates. In many developing coun- assumed future patterns in fertility, mortality, and istering births and deaths do not exist or are incom- tries the population under 15 was earlier growing international migration are applied to the current age plete because of deficiencies in geographic coverage rapidly, but is now starting to shrink. Previously high structure. * Population momentum is the ratio of or coverage of events. Many developing countries carry fertility rates and declining mortality are now reflected the population when zero growth has been achieved out specialized household surveys that estimate vital in rapid growth of the working-age population. to the population in year t (in this case 2000), given rates by asking respondents about births and deaths - the assumption that fertility remains at replacement in the recent past. Estimates derived in this way are level from year t onward. * Average annual popula- subject to sampling errors, as well as errors due to International migration adds to population tion growth rates are calculated using the exponen- inaccurate recall by the respondents. growth in industrial countries ... tial endpoint method (see Statistical methods for The Statistics Division of the United Nations' F: l :".'' r ,- - : -- more information). Department of Economic and Social Affairs monitors 'i I the completeness of vital registration systems. It pro- Data sources vides quarterly reports of the latest birth and death rates, as well as an indication of their completeness, e, The World Bank's population in the Population and Vital Statistics Report. The share - ;_- - 'i|.fl2tie eatimates are produced by of countrieswithatleast90percentcompletevitalreg- . i ' E _ _ the Human Development istration increased from 45 percent in 1988 to 52 per- . ., - Network and the Development cent in 1998. Still, some of the most populous " ' - _, Data Group in consultation developing countries-China, India, Indonesia, Brazil.,. .,* with the Bank's operational Pakistan. Nigeria, Bangladesh-do not have complete - . r staff and resident missions. vital registration systems. Fewer than 25 percent of Important inputs to the World ...but not in developing countriesImotnipusothWrl vital events worldwide are thought to be recorded. Bank's demographic work come from many sources: International migration is the only other factor census reports and other statistical publications from besides birth and death rates that determines a 1 country statistical offices; demographic and health country's population growth. In the industrial world surveys conducted by national sources, Macro about 40 percent of annual population growth in ' International, and the U.S. Centers for Disease Control 1990-95 was due to migration, while in the devel- ;, , - . - and Prevention; United Nations, Department of oping world migration reduced the population growth ,. . : ', Economic and Social Affairs, Statistics Division, rate by about 3 percent. Estimating international ' . ' ,- Population and Vital Statistics Report (quarterly); migration is difficult. At any time many people are United Nations, Department of Economic and Social located outside their home country astourists, work- E.:, E:.n.,,,. '....trr,'r. ..... Affairs, Population Division, World Population ers, or refugees, or for other reasons. Standards Prospects: The 1998 Revision; Eurostat, Demographic relating to the duration and purpose of international Tne siza of a crountry s forelgn-.orn population. Statistics (various years); South Pacific Commission, usuali, expressed as a percentage ol the tot3i moves that qualify as migration vary, and accurate population. is determined b, current anr past Pacific Island Populations Data Sheet 1997; Centro estimates require information on flows in and out of migration fioe and by naturalization policies and Latinoamericano de Demografia, Boletin Demografico countries that is difficult to collect. preferences. Because departures ot migrants are (various years); and U.S. Bureau of the Census, rarelty recorded as %ell as arrivals are. estimates Overthe next several decades the population of low- ot Lhe stock of migrants are ubeul for prolecting International Data Base. and middle-income countries will continue to grow. The the long term elfect of migrallon on population rates ofgrowth will decline, but the absolute increases growth. will be large-and accompanied by substantial shifts in the age structure. Even when fertility reaches the replacement level of abouttwo children per couple, the number of births will remain high-and population growth will not stop for several decades. This phe- 1999 World Development Indicators 49 2.3 Labor force structure Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 millions millions%% of labor force % of age group 1980 1997 1980 1997 2010 1980-97 1997-2010 1980 1997 1980 1997 A lbania .. .......2 . .........2 . . ..... . 1 ... ...2 . . . ...2 1.8 1.5 . ... 39 ..... 41 4 . 1 Algeria ..............9 ....17 59 15 3:9 38 21. 26 ......7 1 Angola4 6 3 5 8 2.6 3.0 47 46 30 27 A r en in 17.... .......... ......... " .......... ........ 22..... .. .....14.. 11.7....... 1 . 9 ..... 28.. 32...... 8 4...... .. Banladesh44 69 41 63 82 2.6 2.1I42. . 42 ......5 29 Belgium 6 74 4 4 03 0.0 34 4 0 0 0 Bolivia3 42 3 42:5 2.7 33.. 38...... 19. 13 Bosnia and Herzegovina 322 1 1 -2.3 2.0 33 38 1 0 Botswana0 10 1 131.1 1.6 ... 50 .....46 26 .... 16 Burkina Faso 3 ...... . . ..5... . 4 .... 5 ....... 7. ... .18 2.0 48 .....47 71. 48 Burundi2 .... .....3... ...2 ..... 3 ...... 5. .. 2 5 .. 2.6 50 49 ... 50 49 Cambodia 46 4 5 72:6 .... 24 55 52 27 ..... 24 Cameroon .... ....I. ....5 .. ...... ...74 6 82:7 27 ...- .. 37 ..... 38 34 24... Central African Republic 12 . . . ... . 39 30 Chile7 9 4 6 8 2.6 2.0 26 33 0 0 China 586 829 540 736 823 1.8 0 9 43 45 ... . 30 10 Hong Kong, China .........3. . 3....5 .. 2.. ....3 4 1 .8 .... 1.0 34 37 . ... 6 .. 0 Colombia ..... ........16 .. . ....25 .... 9 ...... 17 23 3.6 ..... 2.2 26 .. 38 12... 6 Congo, Dem. Rep. 14 ........ .23 ..........12 ...... 20 29 2.9 2.9 45 .... 44 33 ... . 29 C- go Rep.............1.......1... 1 1. ................... 2. 7.... 2.8........ 42I...... 43. 27.. .. 26. . CM e dIlvoire4 7 3 6 73:2 ......21 ........32 . 33 ......28. 20 Croatia-.......... 3 ......... .32 2202 0 .2 .. -0.1 40 44 .. 0 ... ....0 Cuba ,....I....... 6. ' ...... ...8 4 5623.3 .... 0.7 31 39 ...... 0. 0 Dominican Republic 3 52 3 5 30 2.3 25 30 25 15 Egypt, Arab Rep. ........ ..23 .......... 36 . ....... 14 22 32 2.6 2.7 27 29...... 18 10 Eritrea ........ .....I...............I21 ....2 ....... 3 ..... 2.6 2.7 47 47 ... . 44 39.. Finland 3 32 3 2 0.5 -0.4 47 48 0 0 France .... . 34 ...... 38 24 26 27 0.6 0.2 40.. 45 .... ..0 0 Gabon 01 0 1 1 2.4 1.8 45 44 29 17 Georgia .. ..........3 . .... ...43 3 30.3 0.3 49 470 0 Guinea2 42 3 4 2.2 2.2 47 47 41 33 Guinea-Bissau0 1 0 1 1 1.7 1.9 40 40 43 38 Haiti ..........3 ..... ......4 33 4L1 6 1.7 45 ..... 43 33 ..... 24 Honduras2 3 1 2 3 3.4 3.7 25 31 14 8 50 1999 World Development Indicators 2.3 Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-144 millions millions% % of labor force % of age group 1980 ±997 ±.980 1.997 2010 1980-97 1997-20±10 ±980 ±997 1980 1997 Hungary ~7.. . ..... 7 5 5 5 .... -0:4.4 . -0.3 43 45 0 0 Hungary .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..... ... India 394 583 302 423 537 2.0 .......1.8 34 32 21 13 Indonesia 83 127 58 94 124 2.9 2.1 35 40 13 9 Iran, Islamic Rep. 20 35 12 18 28 2.6 3.2 20 25 14 4 Iraq 7 12 4 6 9 3.1 3.0 17 19 1 1 3 Ireland 2 2 1 2 21.0 1.4 28 34 1 0 Israel 2 4 1 2 3 3.1 2.4 34 40 0 0 Italy 36 39 23 25 25 0.7 -0.2 33 38 2 0 Jamaica . ..... .. 1..2 1 12 1.9 1.3 46 460...... 0 Japan ... 79 87 57 68 66.... 1:0.0.. -03.3 38 41 0 0 Jordan 1 3 1 1 2 5.3 . ....3.6 15 23 4 0 Kazakhstan ..10 . 8 8 .. 0.8 48 47 0 0 Kenya 8 15 8 15 20 3.7 2.4 46 46 45 40 Korea, Dem. Rep.. 10112.13. 2.6.0.7. . .4 3 0 Korea,Rep. 24 33 16 23 27 2.3 1.2 39 41 0 0 Kuwait 1I1 0 1 1 1.9 4.2 13 31 0 0 Kyrgyz Republic ... 2 . .... 3 2 2 31.3 2.1 48 . 47..... O. 0 Lao PDR 2 3 . . . . . . 31 26 Latvia 2 2 1 1 1 -0.4 -0.4 51 50 0 0 Lebanon 2 3 1 1 2 3.0 2.5 23 29 5 0 Lesotho 1I1 1 1 1 2.2 2.3 38 37 28 22 Libya 2 3 1 2 2 2.8 2.6 19 22 9 0 Lithuania 2 2 2 2 2 0.4 0... ..1 50 48 ... 0 0 Macedonia, FYR 1 1 1 1 1 0.7 0.7 36 41 1 0 Madagascar 5 7 4 7 10 2.5 3.1 45 45 40 35 Malawi 3 5 3 5 7 2.7 2.2 51 49 45 34 Malaysia 8 13 5 9 12 3.0 2.8 34 37 8 3 Mali 3 5 3 5 7 2.4 2.9 47 46 61 53 Mauritania 1 1 1 1 2 2.5 2.7 45 44 30 23 Mauritius 1 1 0 01 .... 2.1 ... ...1..1 .... 26 32 53 Mexico 35 58 22 38 51 3.1 2.3 27 32 9 6 Moldova 3 3 2 2 2 0.1 0.3 50 49 3 0 Mongolia 1 1 1 1 2 2.9 2.2 46 47 4 2 Morocco 10 17 7 11 15 25.5 2.5. 34 35 21 4 Mozambique........ . 6 ... . ... . ..8. ..... ..7.. . ..9 11 1 5.5 . 2.0 49 48 . 39 . 33 Myanmar. 19 29. 17 .... 23 28 1.8 1.3 44. 43 28.. 24 Namibia 1 1 0 1 1 2.4 2.1 40 41 34 20 Nepal ~~~8 12 7 10 14 2.3 2.5 39 40 56 44 Netherlands 9 11 6 7 7 1.5 -0.1 32 40 0 0 New Zealand 2 2 1 2 2 2.1 0.4 34 44 0 0 Nicaragua 1 2 1 2 3 3.6 3.4 28 35 19 13 Niger 3 5 3 5 7 2.9 3.4 45 44 48 45 Nigeria 36 62 29 47 68 2.8 2.8 36 36 29 25 Norway 3 3 2 2 2 0.9 0.2 41 46 0 0 Oman 1I1 0 1 1 3.6 2.8 6 15 6 0 Pakistan 44 69 29. 48 71 2.9 3.1 23 27. 23 17 Panama 1 2 1 1 1 2.9 2.0 30 34 6 3 Papua New Guinea 2 3 2 2 3 2.2 2.2 42 42 28 18 Paraguay 2 3 1 2 3 2.9 2.7 27 29 15 7 Peru .. . 9 ......... 15 5 9 13 3.0 2.8 24 30 42 Philippines 27 43 19 31 4 3 2.9 2.5 35 37 14 7 Poland 23 26 19 20 20 07.4 0.3 45 46 0 0 Portugal 6 7 5 5 5 0.5 0.0 39 44 8 2 Puerto Rico 2 2 1 1 2 1.9 1.4 32 36 0 0 Romania 14 15 11 11 11 -0.2 0.0 46 44 0 0 Russian Federation 95 10.0 76 .78 79 0.1 ......-0.1 . ..-49 49 0. 0 1999 World Development Indicators 51 2 3 Population aged Labor force 15-64 Average annual Total growth rate Female Children 10-14 millions millions%% of labor force % of age group 1980 1997 1.980 1997 2010 1980-97 1997-20±0 ±980 1997 ±980 1997 Saudi Arabia 511 3 7 10 5.0 3.5 8 14 5 0 Senegal3 53 4 6 2.6 2.5 42 43 43 30 Slova Republic3 42 3 3 0.9 0.4 45 48 0 0 South Africa 16 25 10 16 19 2:4 ....I 1.5 35 38 ..... 1 0 Sri Lanka .. ..... . ....9 .... 12 ......... 5 .8 ... ...10 _ . ....2~2 .......1 6 27 36 ... ...4 2 Sudan.... ... .. .. 10 .. 16 .... .. 7 ...... 11 16 2:6 . 2.8 27 29 ... . 33 ....... 29 Sw itzerland .....I ....... 4 . . . . ..5 ...... 3 ....... 4 .. 4 .........1 4 0 .0 ... .....37 400 . ......0 Tajikistan .... ......2 . .... 3 2 2 32.3 3.0 47 44 .......00 Tanzania 916 9 16 21 3.1 2.2 50 49 43 38 Thailand............. 26 ...41 24 36 41 2:4.4 .... 0.9 47 46...... 25. 15 Turkvey 25 41 19 30 38 2.8 1.8 36 37...... 21 22 Turkmenistan2 3 1 2 3 3.0 2.5 4 7 46 0 0 Ukraine 33 ...... ... 34 27 25 24 -0.3 -0.3 ..... 50 ...... 49 ... 0 .. .....0 United Arab Em irates ...1 ...... ...... 2...1 ... 1....2.........4.9 2.0 5 14 .......0. 0 United States 151 .... . 175 . .....109. 136 ..... 150 ........1 3 0.7 41 ..... 46 ... 0 ........0 Uruguay 22 1 1 2 14 0.9 31 41 4 2 Uzbekistan 9 .............13 6 .. 10 14 ... 2:5 2.6 48 46 0...... O. 0 Venezuela 814 5 9 13 3:4 25 27 ... 34 4....... 1 Vietnam ...... ..... 28.. .... 45 ...... .26 .. 39 49 2:5 1.7 48 ...... 49 .22 .......8 West Bank and Gaza 1. . ... Yemen Rep.4 82 5 7 43 2.9 33 28 26 20 Yugoslavia, F (Serb./Mont. 6 . 7 . 4 5 5 0.6 0.5 39 43 0. 0 Zimbabwe 363 5 7 3.0 ..1.8 44 44...... 37 28 Low Income 758 1.161 612 902 1,192 2.3 2.1 37 37 27 21 Middle income 1,299.18 2 1,044 1,455 1,712 .... 1.9 1.3 40...... 42...... 20 .......8 Lower middle income 1,054 1,490 877 1,206 ~ p 140 ... ... 9 ..1.2 42 43...... 22 .......8 Upper middle income 245 362 167 .... 249 ..... 307 23.3 ..... 1.6 33 36...... 11 9 Low & middle income 2,057 3,014 1,656 2,357 2,904 2.1 1'6.6....... 39 40...... 23 ...... 14 East Asia & Pacific ..... 796 1,156 ....... 703 ..... 990 1.145 20.0 ..... 1.1 43 45...... 27 10 .uoe entral Asia 265 313 207 235 252 0.7 0.5 47 46 .......3 4 Latin America & Carib. 201 ..... .... 306 .130 ..... 209 269 28.8 ..... 1.9 28...... 34...... 13 9 Middle East & N. Africa.......... 91 160.......... 54. 92 136 3.1 3.1 24 ...... 26 14 .......5 South Asia 508 759 392 563 730 2:1 2.0 34...... 33 .....23 . ....16 Sub-Saharan Africa 195... 320 170 268 372 2 7 2.5 42...... 42. .35 ...... 30 High income 529 621 372 449 470 1.1 0.3 38 43 0........0 Europe EMU 178 196 119 134 134 0.7 0.0 37 41 1 0 52 1999 World Development Indicators S.S 2.3 The labor force is the supply of labor in an economy. It a series consistent with these population estimates. * Population aged 15-64 is the number of people who includes people who are currently employed and people This procedure sometimes results in estimates of labor could potentially be economically active. * Total labor who are unemployed but seeking work, as well as first- force size that differ slightly from those published in the force comprises people who meet the ILO definition of the time job-seekers. Not everyone who works is included, ILO's Yearbook of Labour Statistics. economically active population: all people who supply however. Unpaid workers, family workers, and students The population aged 15-64 is often used to provide labor for the production of goods and services during a are among those usually omitted, and in some countries a rough estimate of the potential labor force. But in many specified period. It includes both the employed and the members of the military are not counted. The size of the developing countries children under 15 work full or part unemployed. While national practices vary in the treat- labor force tends to vary during the year as seasonal time. And in some high-income countries many workers ment of such groups as the armed forces and seasonal or workers enter and leave the labor force. postpone retirement past age 65. As a result labor force part-time workers, in general the labor force includes the Data on the labor force are compiled by the participation rates calculated in this way may systemat- armed forces, the unemployed, and first-timejob-seekers, International Labour Organisation (ILO) from census or ically over- or underestimate actual rates. but excludes homemakers and other unpaid caregivers labor force surveys. Despite the ILO's efforts to encour- Estimates of women in the labor force are not com- and workers in the informal sector. * Average annual age the use of international standards, labor force data parable internationally because in many countries large growth rate ofthe laborforce is calculated usingthe expo- are not fully comparable because of differences among numbers of women assist on farms or in other family nential endpoint method (see Statistical methodsfor more countries, and sometimes within countries, in definitions enterprises without pay, and countries differ in the crite- information). * Females as a percentage of the labor and methods of collection, classification, and tabulation. ria used to determine the extent to which such workers force shows the extent to which women are active in the In some countries data on the labor force refer to people are to be counted as part of the labor force. labor force. * Children 10-14 in the labor force is the above a specific age, while in others there is no specific Reliable estimates of child labor are hard to obtain. share of that age group that is active in the labor force. age provision. The reference period of the census or sur- In many countries child labor is officially presumed not vey is another important source of differences: in some to exist, and so is not included in surveys or in official Data sources countries data referto a person's status onthedayofthe data. Underreporting also occurs because data exclude census or survey or during a specific period before the children engaged in agricultural or household activities Population estimates are from inquiry date, while in others the data are recorded with- with their families. Most child workers are in Asia. But the World Bank's population out reference to any period. In developing countries, the share of children working is highest in Africa, where, database. Labor force activity where the household is often the basic unit of production on average, one in three children aged 10-14 is engaged rates are from the ILO database and all members contribute to output, but some at low in some form of economic activity, mostly in agriculture Estimates and Projections ofthe intensity or irregular intervals, the estimated labor force (Fallon and Tzannatos 1998). Available statistics sug- -""*" Economically Active Population, may be significantly smaller than the numbers actually gest that more boys than girls work. But the number of 1950-2010. The ILO publishes working (ILO, Yearbook of Labour Statistics 1997). girls working is often underestimated because surveys ,, ,, estimates of the economically The laborforce estimates in the tablewere calculated exclude those working as unregistered domestic help or active population in its Yearbook of Labour Statistics. byWorld Bankstaffby applyingactivity rates from the ILO doingfull-time household workto enable their parents to database to World Bank population estimates to create work outside the home. Does rapid increase in the labor force depress growth and living standards? --r, v;u a rc.ta^r. 1--':'-'; Rapid expansion orfne labor rorce does not depre.s growth. accoroing to the Woric Bank's World Deseeopment Report 1995: Workers In ani : Integrating Worln. While growth raiss of the - rorlung-age population sa'ied optS slgnalt across ragions betwseen 1980 3nd 1997. GDP growth rares 'tditered w.deit. Nheertheless. the short lerm :roolem of whal to do with *rture labor suppl in countries with slagnating ecorom;es remains. _:___________________ The biggest challenge Is generating sufticlent ___ -m; s -employnent. While industr al countries hase to I1 _ 5 J - ; " lgenerate work for a labor force growsing at about 0.5 percent. de%elop;ng countries will hase to * F :C.rI=.1,-'. o;s ] 1=--1 expand gob opportbrities bt seerat llimes that _- amount just to mairnaiain cuireni errpllojmeni letels. 1999 World Development Indicators 53 2.4 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female labor force labor force labor force labor force labor force labor force 1980 1990-971 1.980 1990-971 1980 199G-971 1980 199G-971 1980 1990-971 1980 1990-971 Albania 54 51 62 60 28 26 17 19 18 24 21 21 Angola 67 65 87 86 13 14 1 2 20 21 11 13 Argentina 17 16 3 3 40 39 18 17 44 46 79 81 Armenia 21 24 21 11 48 47 38 39 31 29 41 51 Australia 7 6 4 3 34 32 15 11 44 58 67 80 Austria 9 6 13 8 51 42 25 17 41 48 62 72 Azerbaijan 28 27 42 36 36 35 20 21 36 38 38 43 Bangladesh 67 53 81 76 5 11 14 8 29 33 5 11 Belarus 29 26 23 13 44 45 33 36 28 30 44 51 Belgium 4 3 2 2 41 34 16 1 1 5 0 54 68 7 Benin 66 62 69 65 10 12 4 4 24 27 27 30 Bolivia 53 48 53 45 21 22 11 10 26 30 36 45 Bosnia and Herzegovina 26 9 38 16 45 54 24 37 30 37 39 48 Botswana 53 39 74 55 18 30 2 9 28 31 24 36 Brazil 4 1 28 2 6 14 28 2 8 14 13 31 43 60 74 Bulgaria . .. 22 13. Burkina Faso 92 91 93 94 3 2 2 2 5 75 5 Burundi 88 86 98 98 4 4 1 1 9 101 1 Cambodia 70 69 80 78 7 8 7 8 23 24 14 14 Cameroon 65 62 87 83 11 12 2 3 24 26 11 14 Canada 6 5 3 2 34 29 14 11 52 57 74 78 Central African Republic 79 74 90 87 5 6 1 0 16 20 9 13 Chad 82 77 95 91 6 7 0 1 12 164 8 Chile 20 18 2 4 25 32 13 13 52 45 79 76 China 71 69 79 76 16 17 12 13 14 14 10 11 Ho.gKong, China 1 1 1 1 47 39 56 33 52 60 43 66 Colombia . 1 . 0 29 .. 18 .. 60 66 Congo, Dem. Rep. 62 . 58 84 81 18 20 4 5 20 23 12 14 Congo,Rep. 42 33 *81 69 20 23 2 4 38 44 17 27 Costa Rica 43 27 5 5 23 24 20 17 34 43 75 69 OSte dIlvoire 60 54 75 72 10 12 5 6 30 34 20 22 Croatia 23 17 28 15 38 38 28 28 39 45 45 57 Cuba 30 24 10 8 32 36 22 21 39 41 68 71 Czech Republic 14 13 12 9 67 54 44 36 19 .33 45 55 Denmark 10 7 3 3 41 37 17 16 44 55 76 81 Dominican Republic 40 31 11 9 26 32 16 23 34 38 73 68 Ecuador 44 39 22 16 22 20 16 16 34 41 63 68 Egypt ,Arab Rep. 43 29 8 32 20 22 10 7 32 39 ...... 56 ......37 El Salvador 56 50 9 7 20 22 18 19 24 29 73 74 Eritrea 79 77 88 85 7 8 2 2 14 16 11 13 Estonia 19 18 12 11 50 48 36 34 31 34 52 55 Ethiopia 90 86 89 86 2 2 2 2 8 11 10 12 Finland 12 10 9 5 42 38 21 14 38 49 64 66 France 9 6 7 5 43 38 22 17 48 56 71 79 Gabon 59 46 74 59 18 21 5 10 24 33 21 32 Gambia,The 78 74 93 92 10 12 2 3 13 15 56 Georgia 31 27 34 24 .. ... 32 38 21 ...... 23 ..... 37 34 45...... 52 Germany 6 4 8 4 54 48 33 24 40 48 59 . .. 72 Ghana 66 64 57 55 12 12 14 14 22 25 29 31 Greece 25 19 39 23 34 32 18 17 40 47 39 51 Guatemala 65 64 17 16 17 16 27 23 19 21 56 61 Guinea 86 83 96 92 2 3 1 1 12 153 7 Guinea-Bissau 81 78 98 96 3 3 0 0 17 192 3 Haiti 79 76 61 57 8 9 8 8 13 15 31 35 Honduras 63 51 40 . 1 19 . 26 20 27 51 64 54 1999 World Development Indicators SO SjoleoiPul luawdO19nAG( PIIOM 666T 99 O 6s .TE . £ LE 81 09 OT ST .LT 6T . UO92ieGpe, Ue!SnfH SS 9S j5 617~.6Z .. ... 9. 09..... .. 179 £17 TS 9S ST 9T~~~~~k dT 9 ~ Z " T 9Z5j£5. St 9t ST Z 9T 68 . 66 . . 9L .e....... Pen0dd 98 08 L~ ~~~9Z 09 ZT t'T 55 017nA7 9 8 T 6T T6 T 96 68 86 EL8 98 aj a ne 69 95 6 .... fl~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~2I2~~~~~~~~~~~. .... 7 . .. 0.... 6T 5 6Z. ... fds . ....O' ST S 6S 8 OT T 0 ST 86 86 t6 . T6.ulsedN St Z1 ST 17 .Z~. 7 96 EC Z£6 Z S 0£v Zs fl!qeZoL ST 0T . iS 69 6T Kt'T £6v . ...6 9S......6t ...... eg 8C 6i7 8C C 9 6 OT 186 ST 6T 9S g 1iePab 9S T OT51 69T19 09 6 07 . 8 .17. . 9n8unelpN 69 t7 V 9 CZ STZ 56 6T 9 6 17 SC 9S ..ne f 9L ZL Zg 8T . . T T 6T Z S 6 96 9 8£ CT PueeI9 ON 6 9 6OS . 6T . . S O T86 8 86 0£ S6 le3edRp1N 96 TS EC 66 9 9 TZ7 S9 69 179 6S Z9 O1fqOw0N 6 z 9T VT T T 9T tT£5 9 L96 617OS 17Tab qezoVe rr r .~~~~....... . ..... 69 £17~~~~~~LE 6 9£ T 17 SS 85 6......9 .. 85, ST 66. ... 66 09 99 9£ 9 96 C L£ OS 86 86 BE St7 EAMON 69 817 61 017 ST 17S 9S 017 £ . tT £C StT.0! 9 £9t 997 gt9 ST ST 6V 8Z tT.Z2 nl!nV 69 6 SC 96 VT 9T 176 6E 69 S 6 67 9 !e!nv 8T LST 9T 96Z 68 68 0S 95 .d8j!W PAiU Z T ES 6 6 ST,t7 C 6T LT ST tL 67 69 £9 e!PUeIe £9 7 6T ZT T T 8 617 81 TT 96 6T TS !ie UAH L6-O6 8 OZL-66 si 6T L6ZO6 0 L6OT6 06T 88-06 086 ~L66t OBOToe~epv 2. .. .I ... . . . . .. . . .. . . ..- . . .. .. ... .. .. . . - . . .. ....i.q.. . .. ..0. ... . -.e .0 . ... .. .. . .. . .. . . . . . .. . . . . . . . .. . . .. . . . eiue9 0% lw4 OS2W04 Z0 % eje sil % 8E EleUe %t Z o A eIeuJ40 % .. . . I e-... .I . . . . .. . . . . . . . I .. .. .. .. .. .. . . .. . . .. .. . . . .. .. Ie ... .. .. . . ..l. .. . .. . .. . . . . . .. . . . Eg !17 S AZits n LuiLt, CT 6 CZ 9Z L!ueql! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . - . . . . 89.c 9 69 Z 6Z 8 9 L 9 A! 2.4~ Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female ft of male % of female labor force labor foroe labor force labor force labor force labor force 1.980 .1990-97a 1980 1990-971 .1980 .1990-971 1980 .j990-97a 1980 i990-97a 1.980 .1990-97V Rwanda 88 86 98 98 5 6 1 1 7 812 Saudi Arabia 45 20 25 12 17 21 5 6 39 59 70 82 Senegal 74 70 90 86 9 10 2 4 17 20 8 11 Sierra Leone 63 60 82 81 20 22 4 4 17 18 14 16 Singapore.2 0 1 0 32 35 39 30 63 62 57 68 Slovak Republic 15 14 13 9 38 36 34 31 48 50 54 60 Slovenia 14 5 17 6 49 52 37 39 38 43 46 54 South Africa 18 16 16 10 45 42 16 15 37 42 68 76 Spain .. 19 11 16 7 42 39 21 14 36 46 56 65 Sri Lanka 41 36 44 32 16 18 12 17 26 37 21 21 Sudan 66 64 88 84 9 10 4 5 24 26 8 11 Sweden 8 5 3 2 45 34 16 11 46 52 79 81 Switzerland 6 421949 7 Syrian Arab Republic 27 22 78 69 35 30 7 6 39 49 15 25 Tajikistan 36 37 54 46 29 28 16 17 35 35 30 37 Tanzania 80 78 92 91 7 8 2 2 13 14 7 7 Thailand .. 55 61 20 19 26 29 Togo 70 66 67 65 12 12 7 7 19 22 26 29 Trinidad and Tobago : 11 4 29 .. 10 47 65 Tunisia 33 23 53 42 30 33 32 32 37 44 16 27 Turke 45 29 88 59 22 27 5 12 33 38 8 19 Turkmenistan 33 34 46 41 32 30 16 14 36 36 38 44 Uganda 84 81 91 88 6 7 2 2 10 12 8 10 Ukraine 26 24 24 16 46 46 33 34 28 31 44 50 United Arab Emirates 5 9 0 0 40 30 7 3 55 61 93 98 United Kingdom 3 3 1 1 44 32 21 13 44 51 72 80 United States 5 4 2 1 35 32 15 13 50 60 74 81 Uruguay 22 21 4 4 31 31 23 21 47 48 74 70 Uzbekistan 35 35 46 36 34 30 19 19 32 35 36 45 Venezuela 19 19 3 2 32 25 19 13 48 48 7 6 75 Vietnam 71 70 75 73 .16 17 10 11 13 13 15 16 West Bank and Gaza . .. . . Yemen Rep. 60 50 98 88 19 22 1 6 21 2917 Yugoslavia, FR (Serb./Mont.) 34 28 47 32 35 38 19 26 31 34 33 41 Zambia 69 68 85 83 13 13 3 3 19 19 13 14 Zimbabwe 63 58 85 81 19 13 4 2 18 29 12 17 Low Income 65 61 81 75 13 15 8 10 22 25 11 14 Middle Income 55 50 59 56 23 23 17 17 22 26 24 27 Lower middle income 59 56 63 61 21 21 16 16 20 22 20 22 Upper middle income 34 27 31 22 32 31 20 17 33 42 49 61 Low & middle Income 59 54 67 62 19 20 14 15 22 25 19 23 East Aaia & Pacific 69 66 75 72 15 16 12 13 16 17 13 14 Europe & Central Asia 26 24 27 22 44 42 31 30 30 34 42 48 LatCin America & Carib. 39 29 21 12 28 28 18 16 32 42 61 70 Middle East & N. Africa 37 27 53 55 25 26 10 11 36 46 29 29 South Asia 63 59 82 75 14 17 10 14 23 25 8 10 Sub-Saharan Africa 66 62 79 75 12 12 4 4 22 26 17 21 High Income 8 5 8 4 .. 34 22 17 .. 53 :: 73 Europe EMU 10 8 11 6 46 40 26 19 41 50 59 69 a. Data are for the most recent year available. 5e 1999 World Development Indicators 2.4 The International Labour Organisation (ILO) classifies reducing labor market flexibility and the economy's * Agriculture includes hunting, forestry, and fishing, economic activity on the basis of the International ability to adapt to change. This segregation is particu- corresponding to major division 1 (ISIC revision 2) or Standard Industrial Classification (ISIC) of All larly harmful for women, who have a much narrower tabulation categories A and B (ISIC revision 3). Economic Activities. Because this classification is range of labor market choices and lower levels of pay * Industryincludesminingandquarrying(includingoil based on where work is performed (industry) rather than men. But it is also detrimental to men when job production), manufacturing, electricity, gas and water, than on the type ofwork performed (occupation), all of losses are concentrated in industries dominated by and construction, corresponding to major divisions an enterprise's employees are classified under the men and job growth is centered in service occupations, 2-5 (ISIC revision 2) or tabulation categories C-F (ISIC same industry, regardless oftheirtrade or occupation. where women often dominate, as has been the recent revision 3). * Services include wholesale and retail The categories should add up to 100 percent. Where experience in many countries. trade and restaurants and hotels; transport, storage, they do not, the differences arise because of people There are several explanations for the rising impor- and communications; financing, insurance, real who are not classifiable by economic activity. tance of service jobs for women. Many service jobs- estate, and business services; and community, social, Data on employment are drawn from labor force sur- such as nursing and social and clerical work-are and personal services, corresponding to major divi- veys, enterprise censuses and surveys, administrative considered "feminine" because of a perceived simi- sions 6-9 (ISIC revision 2) or tabulation categories records of social insurance schemes, and official larity with women's traditional roles. Women often do G-P (ISIC revision 3). national estimates. The concept of employment gener- not receive the training needed to take advantage of ally refers to people above a certain age who worked, or changing employment opportunities. And the greater Data sources who held a job, during a reference period. Employment availability of part-time work in service industries may data include both full-time and part-time workers. There lure more women, although it is not clear whether this Employment data are com- are, however, many differences in how countries define is a cause or an effect (United Nations 1991). piled by the World Bank's and measure employment status, particularly for part- Development Data Group time workers, students, members of the armed forces, using an ILO database corre- and household workers. Countries also take very differ- Nonagricultural labor force working sponding to table 2a in its ent approaches to the treatment of unemployed people. in gender-dominated occupations Yearbook of Labour Statistics. In most countres unemployed people with previous job experience are classified according to their last job. But VWomen in Men in in some countries the unemployed and people seeking fem3le- male- their first job are not classifiable by economic activity. dominated dominated Because of these differences, the size and distribution countrV group occupations occdpations of employment by economic activity may not be fully CEC .' comparable across countries (ILO, Yearbook of Labour '' : e : " - Statistics 1996, p. 64). Mnok t w' r1.-:' 1' The ILO's Yearbook of Labour Statistics reports data by major divisions of the ISIC revision 2 or tabu- lation categories of the ISIC revision 3. In this table the u.,;. ..,.: r, ;_ ., ,, r . reported divisions or categories are aggregated into -r -;. : *.... .. ,. three broad groups: agriculture, industry, and ser- x ' .y-' v'f i - vices. An increasing number of countries report eco- 1! 1 i ' nomic activity according to the ISIC. Where data are -.'" ; ''i supplied according to national classifications, how- Labor torce paitlcioat.on data using detailed occupa- ever, industry definitions and descriptions may differ. tonal classifications snow significant segmentalion In addition, classification into broad groups may by gencer in bah indastrial and de%elopingcountr-es obscure fundamental differences in countries' indus- T?uical male occupatiorns are rnanagers architects. trial pattems. The distribution of economic activity by englneeiu and related workers. production suparye- gender reveals some interesting patterns. Agriculture bofs. and legislatlKe and goFrnment admnistiators accounts for the largest share of female employment Typical temale occuparions are clustered in a narror in much of Africa and Asia. Services account for much range. prirnarily in teaching and medical prolessionrs In muh of fnca nd Asa. Srvice accontformuch and indome stic nork. Osercom.ngihrs segmentatIon of the increase in women's labor force participation in cnd in an imporkn Otep.ngthis genti could be an impontant itep 'n oromoting genderi North Africa, Latin America and the Caribbean, and equalaft anrier -.1ngam.re Ile.Ible andresrosise high-income economies. Worldwide, women are under- labor marset. represented in industry. Segregating one sex in a narrow range of occupa- tions significantly reduces economic efficiency by 1999 World Development Indicators 57 2.5 Unemployment Male Female Total unemployment unemployment unemployment % of % Of % of male labor force female labor force total labor force 1980 1990 1997 1980 1990 1997 I 1980 1990 1997 Algeria 21.7 26.9 ..17.0 24.0 ..19.7 26.4 Angola Argentina 15.4 17.6.313 Armenia 1.6.9.3 Australia 5 2 6.7 8.8 .. .....7:9.9 ........ 7.2 8.3 6.1 6.9.. 8.4.... Austria 3 039. .646 .. 6.4... Azerbaijan 0.1.. 1..1... Bangladesh ......... Belarus013. Belgium 4.5..................7.4..... .....11.4......5 .12.8....... 74.....7.2... 1 12.77. 1 . B enin... . . .. .. . .. . . .. .. . .. . .. Bolvi 6............................9 3.7 .7.8 4.5 ..7.3 4.2 Bosnia and Herzegovina .................... Botswana 19.4 23.9 21:5 Brazil 4~2 ..........3.8 ...........5 7.7 .......44.4 ......... 3.4 8.8 4.33.69 Bulgaria 11511.1 Burkina Faso . . .............I.............. ...... Burundi Cambodia . Cameroon . Canada 8.1 9.2 8.92.8194 Central African Republic ................... Chad Chile 10.6 5.7 4.7 100O.......... 5.7 66 .... 10.4 ...........5.65. China 0.9 .1.2 . 4. 2..........5. 0........ Hong Kong, China 3.9 1.3 2 3 3.4 1.3 2.0 3.8 ...........1.3 ...........2.2 Colombia 7.5 8.1 9.8 -11.5 13.2 15.1 9.1 1.2.. 12. Congo, Dem. Rep........................... Congo, Rep.. .. Costa Rica 5.3 4.2 4.9 .. .....7.8 5....... . 9.. 75 .... 59 ...........4.6 5.7 C6te d'lvoire ................ C roati . . . . . . . . . . . .. . . ... . .. .. . .. . .. . . . . . . ... .. . . . . . . . . . .. . . . . .......... 9 .3 1 5.9 C uba .. .. . . ..- . .... . .. .. .: .. . Czech Republic ... .. .0.3 3.1 Denmark ~ ~ ~~~~~ ~ ~~~~~ ~ ~ ~~.....4...9.... .. 7.6... . .............I. . 8..1. Dominican Republic 12.5 9.5 -.3. 2. .1971. Ecuador 4.3 7.0 .......... .......... 9.1 12.7 . . . Egypt, Arab Rep. 3 9 .. .......5..2 . .........7.6 19.2 17.9 241 528.6 11.3 El Salvador .. 10. 9.5 ..9.8 5.3 12.9 10 .....0.0... Estonia ... . .... .1 .. Ethiopia !........ . Finland 4.7 ...........4.0 ......... 14 .0 . . 4.7 .... 2.8 ..........15.0 ...... 4:7.7 .. ... 3.4 14.7 France 4.3 ...........6.7 .... .....11.0 ... ....9.5 ..........11.7 ..........14..3 .......6.4 ...........8.91 . G abon................. .. Gambia, The G eorgia. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . ..... ..... ... . 0 .2 2 .8 Germany .6.0 9.2 ....88..0.6.....7.21.1........ G hana .............. .............. Greece 3.3 4.3 6.6 ........5 7.7 .... 11 i.7 ..........15..9 .......4 .0 ....... 7.0 10.4 Guatem ala ...................... Guinea-isu. . . .. . Haiti Honduras 8 6 4.4 2.1 6.0.6.2.11.7.3.4.863. 58 1999 World Development Indicators 2.5 Male Female Total unemployment unemployment unemployment % of % Of % of male labor force female labor force total labor force 1980 1990 1997 1980 1990 1997 1980 1990 1997 Hungary..... 0.8 10.5 India.... Indonesia.... Iran, Islamic Rep.. . Iraq Ireland 12.5 10 4 ... .. 13.8 10.3 . .. 12.9 10.8 Israel 4.1 .... 8.4 6.8 6.0 11.3 8.8 .....4.8 ........ 9.6 7.7 Italy 4.8 ..... 7 .3 .95 .5 ... 13:1.1 . ...... 17.1 16.8 ..... 7.6 11..........i .0 12.1 Jamaica 16 3.. 93 39.623.1 .....27.. 3.. .... 15.7 Japan 2.0 2.0 3.4 2.0 2.2 3.4 2.0 2.1 3.2 Jordan Kazakhstan ..05 3 Kenya Korea, Dem. Rep. Korea,Rep. 6.2 .........29 2.8 35 1'8 ......... 23 5.2 2.4 2.7 Kuwait Kyrgyz Republic . 0.0 4.4 Lao PDR Latvia .. ........... .... ..... .. 0.9 70 Lebanon Lesotho Libya Lithuania .. .03 7 Macedonia, FYR. 23.0 38.8 Madagascar : Malaysia . 5.1 2.5 Mali Mauritania M auritius .....I..................7 .8 ... .. ...... . 13..9 . .. .... 9.8 Mexico 1.7 ~~~~~~~~~~~~~~~~2.3 34 3.622 4.5 Moldova ... .0.1 1..6 Mongolia . M orocco ... .... ..........13..9 .... . ....15.8 ...... ...19 6 ..........23 0 ..15.4 .. ..... .17.8 Myanmar . Namibia .. Nepal .. Netherlands 6 3 54 4.5 13.4 10.7 7.0 7.9 7.5 6.2 New Zealand816~6 72. ..67...... .7.8... 6.0 . Nicaragua ...9.. 0. . ......12.6 .. .......15 4 .... .....14 8 ..... .. ..11.1. 13.3 N ig er . . . .. . . . . . . . .. . . . . . . . . . . .. . .. .. . . . .. . I.. . . . . . . Nigeri Norway 1.3 5.6 4!.0 ......2-.3.......... 4.8 4.2 1:7.7 ........ 5.2. 4.5 Oman Pakistan .... 3.0O........ . 3.4. 4.1 ......7-.5 0... .. .....O9 13.7 ... ..36 6 ..... 3.1 5.4 Panama 6.3 12,6 10.7 13.3 22.6 18.1 8 416.2 13.4 Papua New Guinea ::-! Paraguay 3.8 .66 .........7 8 4-8 6.5 8.6 4.1 6.6 8.2 Philippines 3.2 7.1 7.5 7-5 9.8 8.5 4:8 ..........8.17.9 Poland ..... ..6.1 .13.6 Portuga 4.1 3.2 6.4 13-0 6.6 8.2 7.8 4.7 7.1 Puerto Rico19.516.2 144.4 ..... 12..3. 10.7 12.1 17.1 14....I.............11.5. Romania ..... ....3.0 6.3 Russian Federation ....... . OL3.4 1999 World Deveiopment Indicators 59 sJolecIpul juawdoIGASc1 PIJOM 666T 09 e!qweZ .. . . . .. . .. . Z .a . . .. . . . . ... .. . .. . . .. . . . . . .. .. . . . .. . .. .. . .. . . . .. .. . .. . .. . .. . . .. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . -. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . COT 61; . 69 8~~~~~~~~~~~~~~J £6 06 601; ~~~~~~~~~~~~~~~Iaflz'u~aw) .. . . . . . . .. . . . ..'.. . . . . . . ..0. . . . . . . . . . .U.. . . . .. .. . . .. . . . . . .. . . . . .. . . . . . .. . . . . . .. . . . . . . .. . . . . . . . . . . . . .. . . . . . . . .l, 9 8 ... '0 08 6 9~~~~~~~~~~~~~~~~~~~~eeE uemesIo .. .. .. . - 9.. .. . .. .L.. . .0.. . .. . . . .. . . . . .. . I . .. . ... .. . . . .. . . . . .. . . . .. . . . . 6 .. .. .. .. .. . 9. . . . . .. . .. 9.. . .. . . .. . .. . . . .. . .. . .. f6 c & ... ..... d I....I.... ...... .........9.......... . 99 89 59 V...... ... .................... ...... 6 i~6l piU fl ....... ... ... .... ... ... . . ... .... ... .. ... .... ... ... .. .W.... .. ..p ... ... 9 b......7 .... ~ ... .... Ui~aqznf .. .. . . .. . . . . . .. . . . .. . . . . ..0.. . .. . . . . .. . . . . . . .. .. .. .. . . .. .. .. . .. .. .. .. . .. .. l. . e6eij vbifUnze T 6 C O U e1S0.. .. . . . . . .. . . . . .. . . .9... . . . . . .. . . . ... .f.. . .. . . . .. . . . . .. . . . . . .. . . . . . .. . . . . .. . . . . .. . . . . . .. .. .. .. .. .. .. .. .0.. .. .. . .. .. .. .. .... .. .. .. .. ..... .. : . . . . 9 1;... .. . . . .. . . . . .. . .. . . .. . . . . .. . . . . .. .. . . . . . . . .. . . .. . . . .. . .. . . . .. . .. . . . .. . . . . . . .. . . ...L. .. . . . 9 70 ... .. . .. . .I. .. . .. . .. . . .. . . .. .. . . .. . . . .. . .S.. . 1; 66 £91;~~. .......... - .1 1 ...... 616 .... . ... ... . . ....... ...... ............bd5.. ... . .. .. .. .. .. . .. .. .... .. . . .. .. . .. . . m.. .. .. . .. .... .. . .. .. .. .. . . . .. .. .. .. . . .. . . .. . .. .. .. .. .. . .. .. .. .. . 6 £1; 17 e!UGAOIS~~~~~~~~~~~........ .~ .......... ..... ....... .... ... ....... 90........ . II9 66t o~ Vj &:foP ....I......................6: ...... 110£ 1 6;............... .... .... ......... .6. . . ... ............ .....61 ... . . ... e ........... .. ...§ :6 ..... ... . 6 '& ........ ..... i4 Z 6 ... ...... & ..... .. ... ... .. ............. .i -t .... ... ... . . ... .. ... ... ... ... .. ... . . ... ... .. . . ... . . ... .. . . ... ... ... ... ... ... ... ... .. ... . . .. . . ... .. ... ... .. .. ePUeMu-J .... ... .. 066.... .. ..86.. . ....1. .. . ..... ..... ..0 . ..1 . ..... .66 0.86 . . . .. . . . . . .. . . . . .. . . . . . wewAoldweun ~ ~ ~ ~ uawAoIdwaun ;uewAoidweunzl!m ........ ... .... t ~ - ........ .. ~f...... . .. : ....z- . .. ...... . ...0......... - u p m 2.5 The International Labour Organisation (ILO) defines the mation drawnfrom oneormoreofthe above sources. Labor * Unemployment is the share of the labor force that is unemployed as members of the economically active popu- force surveys generally yield the most comprehensive data without work but available for and seeking employment. labon who are without work but available for and seeking because they include groups-particularly people seeking Definitions of laborforce and unemployment differ by coun- work, including people who have lost their jobs and those work forthe firsttime-not covered in other unemployment try (see About the data). who have voluntarily left work. Some unemployment is statistics. These surveys generally use a definition of unavoidable in all economies. At any time some workers unemployment that follows the international recom- Data sources are temporarily unemployed-between jobs as employers mendations more closely than that used by other sources, look for the right workers and workers search for better and generates statistics that are more comparable Unemployment data are from an jobs. Such unemployment, often called frictional unem- internationally. ILO database corresponding to ployment. results from the normal operation of labor mar- By contrast, the quality and completeness of data table 3a in the ILO's Yearbook of kets. Changes in unemployment over time may reflect obtained from employment offices and social insurance Labour Statistcs, the Organ- changes in the demand for and supply of labor, but they programs vary widely. Where employment offices work isation for Economic Co-opera- may also reflect changes in reporting practices. High and closely with social insurance schemes, and registration tion and Development's (OECD) sustained unemployment, however, indicates serious inef- with such offices is a prerequisite for receipt of unem- Employment Outlook (1997b), ficiencies in the allocation of resources. ployment benefits, the two sets of unemployment esti- the UNICED-ICDC TransMONEE The ILO definition of unemployment notwithstanding, mates tend to be comparable. Where registration is database,theWorldBank'sSocialChallengesofTransition reference periods, crteria for seeking work, and the treat- voluntary, and where employment offices function only in database, and country statistical sources. ment of people temporarily laid off and those seeking work more populous areas, employment office statistics do forthefirsttimevaryacrosscountries. In manydeveloping not give a reliable indication of unemployment. The most countries it is especially difficult to measure employment common exclusion from both these sources is discour- and unemployment in agriculture. The timing of a survey, aged workers who have given up their job search because for example, can maximize the seasonal effects of agricul- they believe that no employment opportunities exist or do tural unemployment. And informal sectoremployment is dif- not register as unemployed after their benefits have been ficultto quantify in the absence of regulation for registering exhausted. Thus measured unemployment may be higher and tracking informal activities. in economies that offer more or longer unemployment Data on unemployment are drawn from labor force sam- benefits. ple surveys and general household sample surveys, social Economies for which unemployment data are not con- insurance statistics, employment office statistics, and offi- sistently available or were deemed unreliable have been cial estimates, which are usually based on combined infor- omitted from the table. Countries by type of social security program, selected years Type of program 1940 1949 1958 1967 1977 1987 1997 ;7, 5. 5,; fi . l:o 1"|. 141 J 1I 1Ir,~nic. I :. r'~n' ' 3-i ., J, - Une mployment isseen asa policy concern because irdeprieaw0orkers and theirfamilie sot laborincome-decreasing theircurrentconsumptionardwelfare-anddeprivestfrecconomyotineunemployedworkers'contributionsrooutput Yet uremploymcnt benefits remain amongthe most controversial social policies. and only a minoriTy ol countriesThat have some typ eol socal security program also have an unemployment benefit scheme Empiricale videncegenerally confirms the positive relationship betaseen the generosity of benefits and the unemploymenr rate. resultng primarily trom the eriect of redruced pressure for reemployment on the duran-on of unemployment. 1999 World Development Indicators 61 2.6 Wages and productivity Average hours Minimum wage Agricultural wage Labor cost Value added worked per week per worker per worker In manufacturing innuatu-ring $ per year $ per year $ per year $ per year 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 Albania. Algeria . 44 6,242 .. 11,306 Angola . Argentina 41 40 6,768 7,338 33,694 37,480 Armenia ..... Australia 34 36 . 12,712.11,212.15,124 14,749 26,087 b 27,801 57,857 Austria 32 a 11,949 34,674b 20,956 53,061 Azerbaijan .. . ..14,613 *. 26,778 Bangladesh 48 50. 49 240 556 671 1,820 1,711 Belarus . .: !: Belgium 34 37 7,66:1 15,882 b 6,399 12,805 28,735b 25,579 58,678 Benin . . . Bolivia 47c . 420 4,432 2,343 21,519 26,282 Bosnia and Herzegovina.. Botswana . .650 1,223 3,250 7.791 Brazil . 40 . 688. 10,080 14,134 43,232 61,595 Bulgaria . .. .1,372 .. 1,179 Burkina Faso . . 695 891 . 3,282 .. 15,886 Burundi ....... Cambodia . .. Cameroon...... Canada 32 33 4,974 7,897b 20.429 30,625 17,710 28,346 b 36,903 60,712 Central African Republic Chad : Chile 43 45 1,098 . 6,234 5,822 32,805.32,977 China . . .~~~~~~~~: 349 325 472 434d 3,061 2,885 Hong Kong, China 48 46. .. 4,127 13,539b 7,886 19,533 Colombia . 1,128 ...2,988 2,507 15,096 17,061 Congo, Dm Rep. . ...P R I . . . . . . .. . . . . . . . . I . .. . . . . . . . . . . ..'. . . . . . . . . . . . . . . . . . . ..P . . . . . . . . . . . .. . . . . . . . . . .. . . . . P.. . .. . . . . . . . . . . . .. . . . . . . . . . . Costa Rica . 47 . 1,638 982 1,697 1,788 2,645 7,185 7,184 C6edIore . 1,247 872b 5,136 9,995 d 16,158 Czech Republic . .-.. Denmark .. 37 9,170 19,933~~~~~~~~~~~~~~~~~~~~~~~. . .~~16,169 35,615b 27,919 49,273 Dominican Republic 44 44 . 1,439 2,191 . 8,603 Ecuador . .. 492 5,065 3,738 12,197.9,747 Egypt, Arab Rep. 58 .. 367 2,210 1,863 3,691 5,976 El Salvador .. 790 3,54. 14,423 Eritrea....-7 Ethiopia . 1,596 7.094 Finland 38 a 11,522 31,330 b 25,945 55,037 France 39 39 10,815 22,955~ b .. 16,060 38,900b 26.751 61,019e Gabon... ... Gambia, The....... Georgia.... ... Germany41 40 a a 21,846 d 63,956b,d Ghana .. . .1,470 . 2,306 12,130 Greece .. 41 . . 5,246 . 6,461 15,899 b 14,561 30,429 Guatemala .. 459 2,605 1,802 11,144 9,235 Guinea 40 . ... Guinea-Bissau 48 .. Honduras 44 . . 163. ,4 2,658 7,458 7,2 62 1999 World Development Indicators 2.6 Average hours Minimum wage Agricultural wage Labor cost Value added worked per week per worker per worker in manufacturing in manufacturing $ per year $ per year $ per year $ per year 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 Hungary 35 33 1,132b 1,186 1,766 1.410 2,777 4,307 6,106 India 48 48 408 . 1,035 1,192 2,108 3,118 Indonesia 241 . 898 1,008 3,807 5,139 Iran, Islamic Rep... . 9,737 30,562 17,679 89,787 Iraq....... 4,624 13,288 13,599 34,316 Ireland 41c 4 10,190 25,414 b 26,510 86,036 Israel 36 36 5,861 4,582 7,906 13,541 26,6351; 23,459 35,526 Italy 32.a 15,895 35,138 b Jamaica 39 782 692 . 5,218 . 12,056 11,091 Japan.47 46 3,920 8,327b. 12.306 40,104 b 34,456 92,582 Jordan . .. 4,643 3,125 16,337 11,906 Kazakhstan Kenya 41 39... 508 568 1,040 940 2,340 2,280 Korea, Dem. Rep.. . .. Korea, Rep. 52 48 3,903 b 3,153 15,819b 11,617 40,916 Kuwait ..8,244 0,8.. 30,341 Kyrgyz Republic Lao PDR Latvia ... 366 Lebanon Lesotho 45 . . 1,442 6,047 Libya .. 8,648 .. 21,119 Lithuania Macedonia, FYR Madagascar 40 1,575 . 3,542 Malawi Malaysia .:. a . 2,519 3,429 8,454 12,661 Mali .. 31 459 .. 2,983 .. 10,477 Mauritania .. .. . .. Mauritius.... 1,465 1,973 2,969 4.217 Mexico 34 1,002 843 1,031 908 3,772 6,138 17,448 25.991 Moldova Mongolia . Morocco . 1,672 2,583 3,391 6,328 9,089 Mozambique Myanmar Namibia ... Nepal . . 371 .. 1,523 Netherlands 40 39 9,074 15,170~ b.. 18,891 39,865 b 27,491 56,801 New Zealand 39 39 3,309 9,091b . 10,605 23,767 b 16,835 32,723 Nicaragua 44 ...... Niger .................. 40..... .......... ........... 4,074 .. 22,477 Nigeria . 300 .. 4,812 . . 20,000 Norway 35 35 a 14.935 38,415b 24,905 51,510 Oman .... 3,099 61,422 Pakistan 48 ... 600 ...... 1,264 6,214 Panama .. 4,768 6,351 15.327 17,320 Papua New iuinea 44 ... 4,825 .. 13.563 Parjaguy36 39 ... 1,606 1,210 2,509 d 3,241 . 14,873 Peru 48 :...... . ..... ...... ...... 944 2,988 . 15,962 Philippines 43 . 1,067 380 . 1,240 2,459 5,266 9,339 Poland ... ,584 b 1,754 1,197 1,682 1,257 6,242 7,637 Portuga. 9 40 1,606 4,086b .. 3.115 7,577 b 7,161 17,273 Puerto Rico. .. ........ Romania 40 . 1,669 1.864 1,739d 1,190 . 3,482 Russian Federation :: 1999 World Development Indicatora 63 02.62 6 Average hours Minimum wage Agricultural wage Labor cast Value added worked per week per worker per worker in manufacturing in manufacturing $ per year $ per year $ per year $ par year 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980-84 1990-94 1980484 1.990-94 Rwanda 1,871 9,835 Saudi Arabia 9,814 Senegal 2,8286,1 Sierra Leone 441,2787 Singapore 46 4,856 5,576 21,534b 16,442 40,674 Slovak Republic. Slovenia 9,632 1,3 South Africa 42 a 6,261 8,475 12,705 16,612 Spain 38 35 3,058 5,882 b 8,276 20,585 b 18,936 47,016 Sri Lanka 50 53 a a 198 264 447 717 b 2,057 3,405 Sudan - Sweden 36 37 9.576 27,098.13.038 29,043 b 32,308 56,675 Switzerland 44 42 a . 22,734 59,913 b.. 61,848 Syrian Arab Republic 2,844 4,338 9,607 9,918 Tajikistan Tanzania 1,123 3,339 Thailand 48 1,083 2,305 2,705 11,072 19,946 To.o Trinidlad and Tobago 40c 2,974 14,008 Tunisia 42 1,381 1,525 668 98 3,4 7,111 Turkey 48 54 124 1,015 2,896 3,582 7,958 13,994 32,961 Uganda.43 .... . ..... . ... .. .......... .. ... .... - 2 53I. ..... ....... Ukraine United Arab Emirates !: 6,968 20,344 United Kingdom 42 40 a 11,406 26,045 b 24,716 55,060 United States 35 34 6,006 8,056 b 19,103 32,O13b 47,276 81,353 Uruguay .. 42 1,233 967 1,289 . . 4,128 3,738 13,722 16,028 Venezuela 41 . 1,536 11,188 4,667 37.063 24,867 Vietnam . .. Yugoslavia, FR (Serb./Mont.) .... Zambia . 45 ... 3,183 4,292 11,753. 1,1 Zimbabwe 1. . 065 9 4,097 3,422 925 1194 a. Country has sectoral minimum wage but no minimum wage policy. b. Data refer to 1995-99. c. Data refer to hours worked per week in manufacturing. d. Data refer to wage per worker in manufacturing, a. International Labour Organisation data. 64 1999 World Development Indicators 2.6 Much of the available data on labor markets are col- any social security benefits. International compar- * Average hours worked per week refer to all work- lected through national reporting systems that isons of agricultural wages are subject to greater ers (male and female) in nonagricultural activities or, if depend on plant-level surveys. Even when these data reservations than those of wages in other activities. unavailable, in manufacturing. The data correspond to are compiled and reported by international agencies The nature of the work carried out by different cate- hours actuallyworked, to hours paid for, orto statutory such as the International Labour Organisation or the gories of agricultural workers and the length of the hours of work in a normal workweek. * Minimum United Nations Industrial Development Organization, workday and workweek show considerable variation wage corresponds to the most general regime for differences in definitions, coverage, and units of from one country to another. Seasonal fluctuations nonagricultural activities. When rates vary across sec- accountlimittheircomparabilityacrosscountries.The in agricultural wages are more important in some tors, only the one for manufacturing (or commerce, if indicators in this table are the result of a research pro- countries than in others. And the methods followed manufacturing is not available) is reported. * Agri- jectatthe World Bankthathascompiled resultsfrom in differentcountriesforestimatingthe moneyvalue cultural wage is based on dailywages in agriculture. more than 40 national and international sources in an of payments in kind are not uniform. * Labor cost per worker in manufacturing is obtained effort to provide a set of uniform and representative Labor cost per worker in manufacturing is some- by dividing the total payroll by the number of employ- labor market indicators. Nevertheless, many differ- times used as a measure of international competi- ees, or the number of persons engaged, in manufac- ences in reporting practices persist, some of which tiveness. The indicator reported in the table is the turing establishments. * Value added per worker in are described below. ratio of total compensation to the number of workers manufacturing is obtained by dividing the value added Analyses of labor force participation, employment, in the manufacturing sector. Compensation includes of manufacturing establishments by the number of and underemployment often rely on the number of direct wages, salaries, and other remuneration paid employees, or the number of persons engaged, in hours of work per week. The indicator reported in the directly by the employer plus all employers' contribu- those establishments. table is the time spent at the workplace working, tions to social security programs on behalf of their preparing for work, or waiting for work to be supplied employees. But there are unavoidable differences in Data soufces or for a machine to be fixed. It also includes the time concepts and reference periods and in reporting prac- spent at the workplace when no work is being per- tices. Remuneration for time not worked, bonuses The data in the table are drawn from a forthcoming formed but for which payment is made under a guar- and gratuities, housing allowances, and family World Bank working paper by Martin Rama and anteed work contract or time spent on short periods allowances should be considered part of the com- Raquel Artecona, "A Database of Labor Market of rest. Hours paid for but not spent at the place of pensation costs, along with severance and termina- Indicators across Countries." work, such as paid annual and sick leave, paid holi- tion pay. These indirect labor costs can vary days, paid meal breaks, and time spent in commuting substantially from country to country, depending on between home and workplace, are not included, how- the labor laws and collective bargaining agreements ever. When this information is not available, the table in force. Data on labor costs per worker are from reports the number of hours paid for, comprising the plant-level surveys covering relatively large firms, hours actually worked plus the hours paid for but not usually employing 10 or more workers and mostly in spent in the workplace. Data on hours worked are the formal sector of the economy. Figures are con- influenced by differences in methods of compilation verted into U.S. dollars using the average exchange and coverage as well as by national practices regard- rate for each year. ing the number of days worked and overtime, making International competitiveness also depends on comparisons across countries difficult. productivity. Value added per worker in manufacturing Wages refer to remuneration in cash and in kind is a frequently cited measure of productivity. The indi- paid to employees at regular intervals, but exclude cator reported in the table is the ratio of total value employers' contributions to social security and pen- added in the manufacturing sector to the number of sion schemes as well as other benefits received by employees engaged in that sector. Total value added employees under these schemes. In some countries is estimated as the difference between the value of the national minimum wage represents a "floor," with industrial output and the value of materials and sup- higher minimum wages for particular occupations and plies for production (including cost of all fuel and pur- skills set through collective bargaining. In those coun- chased electricity) and cost of industrial services tries the agreements reached by employers associa- received. Data on value added per worker are from tions and trade unions are extended bythe government plant-level surveys covering relatively large firms, to all firms in a specific sector, or at least to large usually employing 10 workers or more and mostly in firms. In general, changes in the national minimum the formal sector of the economy. Figures are con- wage are associated with parallel changes in the min- verted into U.S. dollars using the average exchange imum wages set through collective bargaining. rate for each year. In many developing countries agricultural workers The data presented in the table are period aver- are hired on a casual or daily basis and do not enjoy ages and refer to workers of both sexes. a999 World Development Indicators 65 2.7 Poverty National poverty line International poverty line Population below the Population below the Population Poeerty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % % % year % % % year % % Albania 1994. . ........ 289... .. 1996 ...196.. .... Algeria 1988 16.6...... 73.3 .. 12.2 1995 30:3.3... 14.7 22.6 1995 .......<2 . 17.6 4.4 Angola. .. ... . ... Argentina 1991 . .. 25.5. . ... Armenia .. .. .. .. .. .. ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............. .... Australia Austria . . . AzerbaUjan 1995 . .. 68.1 . .. Bangladesh 1991-92 460.0.... 23.3. . 427.7.. 1995-96 39.8 143.3... 356.6. Belarus 1995 ..2. . . 1993 <2 . 64 08 Belgium ..... .......... Benin ... ... 1995. .......... 3 .0... .. Bolivia Bosnia and Herzegovina . . .. . Botawana .. . 1985-86 33.0 12.4 61.0 30.4 Brazil ............1990 .....326.6 113.1 17.4 . .. 1995 .....23 6 10.7 43.5 22.4 Bulgaria . . . . .1992 26 08 23.5 6.0 Burktina Faso. . . ... Burundi 1990 .. . 36.2 . . ... Cambodia 1993-94 43:1.1.... 24.8 39.0 1997 40:1.1 .. 21.1 36.1 .. .............. Cameroon .................1984. ._32:4 .....44.4 40.0 .. Canada Central African Republic . .. Chad ...... .. 1995-96 . .. 67 0 0 .... 63.0 ... 64:0 ... ............ .. ...... .. ......... Chile 1992 .. . 216 19 .. . 20.5 19 15.0 4.9 38.5 16.0 China 1994 11.8 <...... 2..... 8.4 1996 79.9..... <2 6.0 1995..... 22..2 6.9 57.8 24.1 .ong Kong, China Colombia ............ 1991 .. 290 0..... 7.8 16.9 1992 31:2.2.... 8.0 17.7 1991...... 7.4 2.3..... 21.7 8.4 C ongo, .. D am .. ep.. .. . . . . . . . . . . . . . . . . . ..... . . . . . . . . . . . . . . . . . .... .. . . . . . . .... ... .... . . . . . . . . . Congo, Dm Rep. Costa Rica . .. 1989 18. 72 438 14 CSte divoiro .. ~~~ ~ ~~~~~~~~ ~~~~~~~~~~~~~~~~~~. . ..198 17.7 4.3 54.8 2. Croatia Cuba Czech Republic .. . .. . 1993 3. 04 5.1 40 Denmark _: ~ Dominican Republic .... .... 1989.... 274.4.... 23.3.... 24.5 1992 298 8.... 10.9 20.6 1989..... 19.9 6.0 47.7 20.2 Ecuador 1994. 470.0 ~...25.0 35.0 . .. 1994..... 30:4 9.1..... 65.8 29.6 Egypt, Arab Rep.. 1990-91 7. 11 5.9 53 El Salvador 1992. 55.7 . ....43.1 48.3 ........ .................. .......... . ....... .. . ...... Eritreea... . .. Estonia 1994 .....14.7 ......6.8 8.9 .. . . 1993 6.0 1.6 32.5 10.0 Finland France Gabon ... . . . Gambia, The 1992 ..64.0. .. ... . G er m a n y .... . . . ..... .. . . . . .. . . .. . . . .. . . .. . . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . . .. . . .I.. . . .. . . . I, . . . Ghana 1992 34.3 26:7 31.4.....31.4 G uatem ala .......................1989... .... ....53.3 28..................5......76.8 .... ..... ..47... ....6.. Guinea . . . 1991 263 1. 502 56 Guinea-Bissau ......I..... 1991 ....60:9 24-.1 . 48:8 ............... .... ... : _ ....... 199.1..... 88.-2 59.5 96.7 76.6 Haiti.. .... ... .. 1987......... 6 .0. ....... 8. 0. ... . Honduras 1992 46.0_ 56.0. 50 06.0 . .. 1992 46.9 2. 7 4. 66 1999 World Development Indicators 2.7~~~~S National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban National Survey $1 a day $1 a day $2 a day $2 a day year % %year % % %5 year % % % % Hungary 1993 ..02.37....199.<2...0.7 2..1 India......... 1992.... 43:5.5 ... 33.7 _ .40.-9 ..... 1994... 367 7 ... 305 5 .. 350.0 1994 470 0... 12.9 87.5 42.9 Indonesia 1987 16.4 20.1 ..174.4 .. .. 1990 .. 143 3.... 16 8 15:.1 .. . 1996 7..... 7:7... 09 9.... 50.4 15.3 Iran, Islamic Rep.. . . Ireland Israel Jamaica 1992 . .. 34.2... 193 43 05 29 75 Jordan 1~~~ ~ ~ ~ ~~~~~~~~~~ ~ ~~~99 .......... ... 1.0..... ......192..50..2.5 6 Kazakhstan 1996 39.0. . . 30.0 .34.6... . .. . ...........1993 <2.. ... 7 . 124.1 2.5 Keny 1992...46.4...29.3...42.0 ............ 1992..50.2.22.2 7.1 4 .4.. JyrgydRpuln 1993 481 2 500. 19932 89 50 53 2. Latvia ~ ~ ~ -..... .. ...... .. 1993............1 .O...... ..... ....... 7..... .. <25 . <.523. Laaksotho 1993 539.9 27. 8 349.6 198638 48. 23. 27.1 43.5 Lihun ia 19 2 4 : .... 93 ... 20.... .......7 . ...1993 2 ..... .5 7 2 . 18.9 44.1 Macedna, FYm.Rep.. Muauitis19.. 06 Mexico pbic .......... 1988.... 4:. ..2 7 10.10 1992 14.9 3. 8 540 0 2159 LaoPDRv 1992...19368... 12 306....24097. Mongoia 1995 33<235 632 Mybanoar Naibya Letherands1 9 2 . ....1 . . New Zealand .. . ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............... Niaraguasa 19 761 39 503. . 1993 438 1. 7.5 97 Paawian 199091 36. 28. 34.0.. . 191 16 26 570 86 Panam a ..................... ... ............... ...... .. 9 9 25 6 1 .6 4.2.-.. 24. . 5 ... Perusi 19894 670 461 53.15 1997 64.72240.4 49.0 8 Poland 1993 .... ............ ........... 2.8.. ................. ..... .. ...... .. 1993..8.4..15. 7. 7... PMtual. P u e rt o R ic o . .. . . .. . .. . .. . . . . .. . I . . .. . . .. .. . . . . . . . ...... . Marimania 19940 79 204 2 57 .. --1992. 17.7. 434 1.2 70.9 24.7 MaritussinFdrto 1994 .. . 0. 93 2 . 1. . ......................................I......9.9.W orld.Developm ent .nd..atora 67 02.72. National poverty line International poverty line Population below the Population below the Population Poverty Population Poverty poverty line poverty line below gap at below gap at Survey Rural Urban National Survey Rural Urban NationalI Survey $1 a day $1 a day $2 a day $2 a day year % % year % % year % % % % Rwanda 1993 .. . 51.2 .. . .1983-85 457 1. 8.7 23 Saudi Arabia.. . .. . Senegal 1991 40.4.. 16.4 33.4.. . 1991-92 5. 25.5 79.6 47.2 Sierra Leone 1989 76.0 . ....53 0 0 . 68.0 ........ ........ . .... .. ........... .... ... .... Singapore Slovak Republic .. .. .. . 1992 12.8 2.2 85.1 27.5~~~~~~~~~~~~~~~~~~~~~~~~~192 2 851 7. Slovenia ... . 1993 <2 .. <2 South Africa.... .. . 1993 23.7 6.6 50.2 22 5 Spain . . Sri Lanka .............1985-86 ....45.5 .....26.8 40.6 1990-91. .38.1 28.4 ....35:3 ......1990. .4:0 0.7 41.2 11.0 Sudan . .. Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania 1991 . 511.. Thailand 1990 80 1992 15:5. 10:2 13.1 1992 ......<2 23.5 5.4 Tog.....................1987-89 ......... 32.3 Trinidad and Tobago 1992 .. 21.0 . .. Tunisia 1985 29:2..... 12.0 19.9 1990 21.6 8.9 14.1 1990...... 3.9 0.9 22.7 6.8 Uganda 1993 . .. 55:0 .............1989-90 693 29.1 92.2 56.6 Ukraine 1995 .. 31.7 .. . 1992 <2 .. <2 U nited .A rab E m irates '.. .. .. .. .. .. . . .I.... . .. .. .. .. . .. . .. . ..., .. ..... . ... .. .. .. .... .... .. .. .. ....... ... .... ...I,.. . U nited . . .g . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . .: . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . ... . . . K ingdom... United States . . Uzbekistan Venezuela 1989 .31.3 . .. 1991 1. 31 322 12.2 Vietnam 1993 57.2 .....25.9 ....50.9 ...... .. .. Weat Bank and Gaza . . .:. .. ..- Yemen,... Rep .........................199 .19. 18..6.19.1........ Yugoslavia, FR (Serb./Mont.) . .. . Zambia :1991 88.0 46.0 68.0 1993. 86.0 1993 84.6 53.8 98.1 73.4 Zimbabwe 1990-91 . 25.5 .. . 1990-91 41.0 14.3 68.2 35.5 as 1999 World Development Indicatora 2.7 International comparisons of poverty data entail both sumption as a welfare indicator is one issue. Incomes * Survey year is the year in which the underlying data conceptual and practical problems. Different countries are generally more difficult to measure accurately, and were collected. * Rural poverty rate is the percentage have different definitions of poverty, and consistent com- consumption accords better with the idea ofthe standard of the rural population living below the national rural parisons between countries can be difficult. Local of livingthan does income, which can vary overtime even poverty line. * Urban poverty rate is the percentage of poverty lines tend to have higher purchasing power in rich if the standard of living does not. But consumption data the urban population deemed poor. * National poverty countries, where more generous standards are used are not always available, and when they are not there is rate is the percentage of the population living below the than in poor countries. little choice but to use income. There are still other prob- national urban poverty line. National estimates are Is it reasonable to treat two people with the same lems. Household survey questionnaires can differ based on population-weighted subgroup estimates from standard of living-in terms of their command over com- widely, for example, in the number of distinct categories household surveys. * Population below $1 a day and modities-differently because one happens to live in a of consumer goods they identify. Survey quality varies, $2 a day are the percentages of the population living on better-off country? Can we hold the real value of the and even similar surveys may not be strictly comparable. less than $1 a day and $2 a day at 1985 international poverty line constant between countries, just as we do Comparisons across countries at different levels of prices, adjusted for purchasing power parity. * Poverty when making comparisons over time? development also pose a potential problem, because of gap is the mean shortfall below the poverty line (count- Poverty measures based on an international poverty differences in the relative importance of consumption of ing the nonpoor as having zero shortfall), expressed as line attempt to do this. The commonly used $1 a day nonmarket goods. The local market value of all con- a percentage of the poverty line. This measure reflects standard, measured in 1985 international prices and sumption in kind (including consumption from own pro- the depth of poverty as well as its incidence. adjusted to local currency using purchasing power pari- duction, particularly important in underdeveloped rural ties, was chosen for the World Bank's World Develop- economies) should be included in the measure of total Data sources ment Report 1990: Poverty because it is typical of the consumption expenditure. Similarly, the imputed profit poverty lines in low-income countries. Purchasing power from production of nonmarket goods should be included Poverty measures are pre- parity (PPP) exchange rates, such as those from the Penn in income. This is not always done, though such omis- . 'i pared by the World Bank's World Tables, are used because they take into account sions were a far bigger problem in surveys before the Development Research Group. the local pricesofgoods and services that are not traded 1980s. Most survey data now include valuations for con- National poverty lines are internationally. But PPP rates were designed not for mak- sumption or income from own production. Nonetheless, ' W based on the Bank's country ing international poverty comparisons but for comparing valuation methods vary-for example, some surveys use g poverty assessments. Inter- aggregates from national accounts. As a result there is the price at the nearest market, while others use the 4 . national poverty lines are no certainty that an international poverty line measures average farmgate selling price. based on nationally represen- the same degree of need or deprivation across countries. The international poverty measures shown here are tative primary household surveys conducted by national Just as there are problems in comparing a poverty based on the most recent PPP estimates from the latest statistical offices or by private agencies under govem- measure for one country with that for another, there can version of the Penn World Tables (PWTL5.6). It should be ment or international agency supervision and obtained also be problems in comparing poverty measures within noted, however, that any revisions in the PPP of a coun- from government statistical offices and World Bank coun- countries. For example, the cost of living is typically try to incorporate better price indexes can produce dra- try departments. higher in urban than in rural areas. (Food staples, for matically different poverty lines in local currency. The World Bank has prepared an annual review of example, tend to be more expensive in urban areas.) So Whenever possible, consumption has been used as poverty work in the Bank since 1993. The most recent the urban monetary poverty line should be higher than the welfare indicator for deciding who is poor. When only is Poverty Reduction and the World Bank: Progress in the rural poverty line. But it is not always clear that the household income is available, average income has been Fiscal 1998. difference between urban and rural poverty lines found adjusted to accord with either a survey-based estimate in practice properly reflects the difference in the cost of of mean consumption (when available) or an estimate living. For some countries the urban poverty line in com- based on consumption data from national accounts. This mon use has a higher real value-meaning that it allows procedure adjusts only the mean, however; nothing can poor people to buy more commodities for consumption- be done to correct for the difference in Lorenz (income than does the rural poverty line. Sometimes the differ- distribution) curves between consumption and income. ence has been so large as to imply that the incidence of Empirical Lorenz curves were weighted by household poverty is greater in urban than in rural areas, even size, so they are based on percentiles of population, not though the reverse is found when adjustments are made households. In all cases the measures of poverty have only for differences in the cost of living. As with interna- been calculated from primary data sources (tabulations tional comparisons, when the real value of the poverty or household data) rather than existing estimates. line varies, it is not clear how meaningful such urban- Estimation from tabulations requires an interpolation rural comparisons are. method; the method chosen was Lorenz curves with flex- The problems of making poverty comparisons do not ible functional forms, which have proved reliable in past end there. Further issues arise in measuring household work. living standards. The choice between ncome and con- i999 World Development Indicators es 2.8 Distribution of income or consumption Survey year Gin! Index Percentage share of Income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20% 20% 20% 10% Albania Algeria 1995a,5 35.3 ...... 2.8. .. 7.0 11.6 16~.1 _... 22:7.7 ..... 42:6.6 ...... 26.8 Angola....... Argentina Australia 1989c0d 33.7 ... ........2.5 ........7.0 12.2 16:6 ......2 3 .3 ......40 9 24.8 Austria 1987c,d 23.1 4.4 10.4 14.8 18.5 2......2 .9 .... 33 3 ........19:3 AzerbaiUan Bangladesh 1992a b 28.3 .... ..... 4~ ...... 9.4...... 13.5 17.2 22.0 37.9 23.7 BelIarus 1 5 ,028.8 3.4 ........8.5 .......13.5 ......17.7 ......23..1 .......37.2 ........22.6 Belgium .19920, 25.0 ............3..7.~..... 9.5 .......14.6 .......18.4 ......23.0 ...... 34 .5 ... 20.. .2 Bolivia 19900,d 42.0 2.3 5.6 9.7 14.5 22.0 48.2 31.7 Bosnia and Herzegovina .. . . Brazil 1995c,d 60:.1 _......... 0.8 2.5 5.7 9.9 17..7 .......642.2 ...... 47.9 Bulgaria 1992c,d 30:8 ... ........3.3 8.3 13.0 17.0 22.3 ...... 393.3 ...... 24.7 Burkina Faso . 1994a,b 48 2 2.2 5....... 5. 8.7 12-.0 .. 18.7 55.0O....... 39.5 Burundi Cambodia Cameroon Canada 1994 d31.5 2.8 7.5 12.9 17.2 23~.0......39 .3.... 23.8 Central African Republic.. . Chad Chile 199400d 56:5 ............14 ........3.5 6.6 10-.9 .......18 .1 ......61.0 ........46.1 China 19950,d 41 5 2:2 .....I..5.5 9.8 -14.9 .......22..3 ... . .47.5 ... ....30.9 H.ng Kong, Chins Colombia 19950,d 57.2 ............1:.0 ...... 3.1 6.8 109.9 ..... 176 6...... 61.5 46.9 Congo, Dem. Rep. Costs Rica 199600d 470 ............1.3 4.0 8.8 ..... 13.7 .......217 .......51.8 34.7 CMe dilvoire 1988a.b 36. 28 6.8 11.2 15.8 22 2 44.1 28.5 Croatia Cuba Czech Republic 1993c,d 266 6............46 ...... 10.5 13.9 16.9 ......21 3 .......37.4 23.5 Denmark 199200d 24.7 3.6 9.6 14.9 18.3 22~7 .......345 ... 20.5 Dominican Republic 1989c.0 50.5 ......1.6 ........42 7.9 12.5 ......19 .7 ..... 55.7 ........39.6 Ecuador 1994a.b 46.6 ...........2 3 5.4 8.9 13.2 1 9 .9......52.6 37.6 Egypt, Arab Rep. 1991a,b 32.0 3.9 .......8.7 12.5 ......16.3 ......21.4 .......41.1 ........26.7 El Salvador 19950,d 49.9 1:2 .... ...3.7....... 8.3 13.1 20.5 54.4 38.3 Eritrea Estonia 1995c,d 35.4 ...... 22 2... 6.2...... 12.0_ .. 17.0 23.1 418.8 ...... 26.2 Ethiopia 1995a,5 40.0 ............30 0....... 7.1 10.9 14 5 .......19 8 47.7 33.7 Finland 990025.6 ............42 10.0 14.2 17 6 ......22 3 ...... 35.8 21...... .6 France 198900d 32.7 2.5 7.2 12.7 17.1 ......22~8.8..... 40.1 24.9 Gabon Gambia, The 1992a,b 47.8 1.5 4.4 9.0 13.5 20.4 ......52:8 ........376 Georgia Germany 1989c,d 28.1 37 7....... 9.0...... 13.5 17.5 22~9.9..... 37.1 22.6 Ghana 1997 a,b 32.7 ............36 ....... 8.4 ...... 12.2 15.8 21.9 ...... 417.7 ...... 26.1 Guatemala 19890,0 59.6 0.6 2.1 5.8 10.5 .......18.6 .......63.0 46.6 Guinea 1994a,5 40.3 2:6.6 ...... 6.4 10.4 148.8 ..... 21-.2 ...... 47:2.2 ...... 32.0 Guinea-Bissau 1991a.b 56.2 ............0:.5 ...... 2.1 6.5 12.0 20-.6 589....... 42:4 Guyana93 , .................. 4 .40 ....2 ... 2.4... 6.3.....10.73107 15.0 ..... 21.2 ......46.9. .... 32.0. 3 . Haiti Honduras 19960,d 53 .......... - ......... .......I . . 7.... ........... 1 .2 .......3.4 7.1 11 7.7 . .................................. 1 .7 ... 580. 4 . 70 1999 World Development Indicators 2.8 Survey year Gini Index Percentage share of income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20%/ 20% 20% 10% Hungary1 93, 27:9 ...........4.1 9.7 13.9 16..9.... 214.4 38:.1 . 24.0 India194b 29.7 47.1..... 9.2 13.0 168.8.... 21.7 39:3.3..... 25.0 Indonesia 1996C,d .............. 365.5 .......... 3.6 8. 0 . ... 11.3 15~1.1 ... 20.8 ..449 9... 30:3 Iran, Islamic Rep. Iraq Ireland 1987C,d 359. 2:5.5.... 6.7 11.6 16.4..... 22.4 42.9 27.4 Israel 1992C,d 355.5 2 8 .... ...6.9 11.4 16.3 22-.9 ..... 42.5 26.9 Italy 1991c,d 31 2 .. ...... 2.9 . .....76.6 . .. 12.9 1-7.3 23-.2... 38.9 . .... 23:7 Jamaica 1991a,b 41.1 2:4.4.... 5.8 10.2 14.9..... 21.6 47.5 31..9 Jordan 1991a,b 43 4 ... 2:4 .. 5:9. 9.8 13.9 203 50 .1. 34.7 Kazakhstan 1993c,d 32 7 3 1 7 5 12.3 16.9 22.9 40.4 24.9 Kenya 1994a.b 44.5 1:8 .......5.0 9.7 14.2. 20~9 50.2 34~9 Korea, Dem. Rep, Kora,Rep. Kyry Republic 1993c,d I ... .....35-.3 .... 2.7 .......6.7... 11.5 16.4 231 1 .... 42.3 26.2. Lao PDR 1992a, . ........... 304.4 42.2 9.6 12.9 16-.3 21.0 402.2 26.4 Latvia 1995c,d 28.5 33 3....... 8.3 13.8 18O 0... 22.9 37.0 .... 22.4 Lebanon Lesotho 1986- 7 a, 56.0 ......... ..0:9. 2.8 ... ...6 5 11.2 ..... 19 4 601 1...... 43.4 Libya Lithuania193d 33.6 3:4.. 8.1 12.3 16.2 21.3 421.1 28.0 Luxembourg 1991c,cl 26.9 4 2 9 5 13.6 17.7 22.4 36.7 22..3 Macedonia, FYR Madagascar 1993a,b 46.0 1.9 5.1 9.4 13.3 20.1 52.1 36.7 Malawi Malaysia 1989c,d 48.4 1.9 4.6 8.3 13.0 20.4 53.7 37.9 Mali 1994~ 50.5 1.8 4.6 8.0 11.9 19.3 56.2 40 4 Mauritani'a 1995a,b 38.9 2.3 ....... 6.2 10.8 15.4 22.0 456.6 29.9 Mauritius Mexico 1995C.d 53.7 1:4.4.... . 3.6 7.2 118.8.... 19..2 5872.2... 42.8 Moldova 1992c,d 34.4 ..... 2.7 . ...6.9 11.9 16.7 23.1 415 5... 25.8 Mongolia195a 332.2. . .. 2.9. 7.3 .... 12.2 16.6 23-.0 .. 40.9 24..5 Morocc'o 1990..91a,b 39 2 ..........2.8 6.6 10.5 15.0 21.7 46.3 30..5 Mozambique Myanmar Namibia Nepal 19-6, 36.7 3.2 7.6 11.5 15.1 21.0 44.8 29.8 Netherlands 9cd 31.5 2.9 8.0 13.0 16.7 22.5 399 9.. . 24 7 New Zealand Nicaragua 1993a,b 50.3 ..16 ......4.2 ... ..80 12~6 ......20.0...... 55.2 39.8 Niger 1995a,b 50.5 0:8.8...... 2.6 ..... 7.1 13.9 23..1 . .. 53.3 35.4 Nigeria 1992-93ab 45.0 ...1.3 ........4.0..... . 8.9 14.4 23.4...... 49.4 31.4 Norway 1991c,d 25.2 4.1 10.0 14.3 17.9 22.4 353.3 21.2 Oman Pakistan 1996a, ... ......... 31 2 ...... -4.1 9.4 . ..13'.0 ... .16.0 20.3 41.2 .... .27.7 Panama 1995c, 57.1 0.7 2.3 6.2 11.3 19.8 60..4 .... 43.8 Papua New Guinea 1996~ 50:9 ..... .....1.7 4.5 79.9 .... 11.9...... 19.2 56.5 40..5 Paraguay.1995c,d 59.1 ..0.7 .......2.3 ... ..5.9 .10..7 .....18..7 ..... 62.4 46.6 Peru196d 462 2........... 1.6. _ 4.4 9.1 14 .1....21.3 51.2 35.4 Philippines 1994a,b 42 9 24.4 . 5.9 9.6 13.9 21.1 496.6... . 33.5 Poland 19a 27.2 4.0 9.3 13.8 17.7 22.6 36.6 22.1 Portugal. Puerto Rico ..... Romania 9 4 ,d.28:2 .... ......3:7 ... ...89 13.6 17-.6 .... 22.6 . I... 373 22.7 1999 World Development Indicators 71 C ~2.8 Survey year Gini index Percentage share of income or consumption Lowest Lowest Second Third Fourth Highest Highest 10% 20% 20% 20%/ 20% 20% 10% Rwanda 1983-~85a,b 28:9 4.2 9.7 13.2 16-.5. 21.6 .....39.1 ....... 24.2 Saudi Arabia Senegal 1991a,b 5381. 3. 7. 121 95 5.9 43 Sierra Leone 1989a, 62~9 0.5 1.1 2.0 9.8 23.7 63.4 43.6 Singapore Slovak Republic 1992C,d 1 . 19 1. 88 2 14 1 Slovenia 1993c 29.2 4.0 9.3 13.3 16~9 .....21:9_....38.6 24.5 South Africa 1993-94a, 59.3 1.1 2.9 5.5 9.2 17.7 64:8 45.9 Spain 1990c,d 32.5 2.8 7.5 12.6 17.0 22.6 .....40 3 .......25.2 Sri Lanka ..I........1 90a,b 30.1 ...38 _.8 .... 8.9 13.1 169 9 ... .. 21 7...... 393 3 ... .. 25.2 Sudan Sweden 1992c,d 25 0... 3.7 ... 9.6 14.5 18 1 .....23-.2 .. 34 .5 .. ....20.1 Switzerland 1982c, 36 1 2.9 74 11.6 15.6 21.9 43.5 28 6 Syrian Arab Republic Tajikistan Tanzania 1993a,b 38.2 2.8 6.8 11.0 15.1 21 6 45.5 ... ....30.1 Thailand 1992a,b 46'.2 2.5 5.6 8.7 13.0 20.0 527.7 37.1 Togo Trinidad and Tobago ... Tunisia 1990 a, 40.2 2.3 5.9 10.4 15.3 22.1 46:3. 30 7 Turke.y Turkmenistan 1993c,d 35.8 2.7 6.7 11.4 163.... 22 8 42:8 26.9 Uganda 199293, 39 2 2.6 6.6 10.9 15.2 21.3 ......46:1 31..... 2.. .. Ukraine 1995c,d 47.3 1.4 4.3 9.0 13.8 208.8.. 52:2 ......36:8 United Arab Emirates United Kingdom ....... 1986c,d 32.6 .....2.4 .7.1 12.8 .. 17.2 23.1 .. 398 8....... 24 7 United States 1994c,d 40.1 15.5...... 4.8 10.5 16-.0 23-.5 ...... 45:2 28.5 Uruguay. Uzbekistan Venezuela 1995c d 46.8 . . .... .1.5 4.3 ... 8.8 ...... 13-.8 ..... 213 3 ..... 51 8 ........3-56 Vietnam 1993a,b 35:7 ..... 3.5 ... ....7.8 11.4 15.4 214.4 440 0....... 29.0 West Bank and Gaza Yemen,Rep 1992a,b 39.5 2 3 6.1 10.9 15.3.. 21.6 ......467_.1 . .. 30.8 Yugoslavia, FR(Serb./Mont.) Zambia 1996a,b 49:8 16.6. 42 .......8.2 128 20.1 548 39.2 Zimbabwe 1990a,b 56 8 18 40 6.3 10.0 17.4 623 49 a. Refers to expenditure shares by percentiles of population. h. Ranked by per capita espenditure. c. Refers to income shares by percentiles of population. d. Ranked by per capita income. 72 1999 World Development Indicators 2.8 Inequality in the distribution of income is reflected in Whenever possible, consumption has been used * Survey year is the year in which the underlying data the percentage share of either income or consump- rather than income. Households have been ranked by were collected. * Gini index measures the extent to tion accruing to segments of the population ranked by consumption or income per capita in forming the per- which the distribution of income (or, in some cases, income or consumption levels. The segments ranked centiles, and the percentiles are of population, not consumption expenditures) among individuals or lowest by personal income receive the smallest share households. The income distribution and Gini indexes households within an economy deviates from a per- of total income. The Gini index provides a convenient for high-income countries are directly calculated from fectly equal distribution. A Lorenz curve plots the summary measure of the degree of inequality. the Luxembourg Income Study database. The estima- cumulative percentages of total income received Data on personal or household income or con- tion method used here is consistent with that which against the cumulative number of recipients, starting sumption come from nationally representative house- is applied to developing countries. with the poorest individual or household. The Gini hold surveys. The data in the table refer to different index measures the area between the Lorenz curve years between 1985 and 1997. Footnotes to the sur- and a hypothetical line of absolute equality, expressed vey year indicate whether the rankings are based on as a percentage of the maximum area under the line. per capita income or consumption. Each distribution Thus a Gini index of zero represents perfect equality, (includingfor high-income economies) is based on per- while an index of 100 implies perfect inequality. centiles of population-rather than of households- * Percentage share of income or consumption is the with households ranked by income or expenditure per share that accrues to subgroups of population indi- person. Where the original data from the household cated by deciles or quintiles. Percentage shares by survey were available, they have been used to directly quintiles may not add up to 100 because of rounding. calculate the income (or consumption) shares by quin- tile. Otherwise, shares have been estimated from the Data sources best available grouped data. The distribution indicators have been adjusted for Data on distribution are compiled bythe World Bank's household size, providing a more consistent measure Development Research Group using primary house- of per capita income or consumption. No adjustment hold survey data obtained from government statisti- has been made for spatial differences in cost of living cal agencies and World Bank country departments. within countries, because the data needed for such Data for high-income economies are from national calculations are generally unavailable. For further sources, supplemented by the Luxembourg Income details on the estimation method for low- and middle- Study database. income economies see Ravallion and Chen (1996). Because the underlying household surveys differ in method and in the type of data collected, the dis- tribution indicators are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for table 2.7). The following sources of noncomparabillty should be noted. First, the surveys can differ in many respects, including whether they use income or con- sumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used in surveys are usually very different. Consumption is usually a much better wel- fare indicator, particularly in developing countries. Second, household units differ in size (number of members) and in extent of income sharing among members. And individuals differ in age and consump- tion needs. Differences between countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. :1999 World Development Indicators 73 22.9I Education inputs Public expenditure Expenditure per student Expenditure Primary Duration on education on teaching pupil- of materials teacher primary ratio education Primnary Secondary Tertiary Primary Secondary % Of % Of % of % Of % of total % of total pupils per GNP GNP per capita GNP per capita GNP per capita for level for level teacher years ±980 1996 1980 1996 1980 1996 1990 1996 1996 1996 1996 1995 Albania 3.1 . 95 . . . .. . 188 Algeria 7.8 5.1 . 26.4 23:9 . .. .... 27 6 Angola7 4:~ :: Argentina 2.7 .... 3.5 ...... 65 8.3 . 29.3 .. 19.8 ..... . ............. ........... 16 7 Armenia 2.0 ......... .. .......... 26.0 ...19 4 Australia 5.5 5 6 . 17.4 44.7 18.8 51.3 30.4 .. . 18 7 Austria 5~4 ....._5 6 15.7 23.1 ..... .... 25.1.... 37.3 37.6 . .12 .........4 Azerbaijan . 3:3 169..... ....... 149..169....... .................. ..49 21 4 Barngladesh1.5 2 9 4.8 . 14.1 . 46.7 . .. ...5 Belarus . 671 ......I....... 47.4 .. 9 19.3 .. 20 4 Belgium 6.0 3.2 17.7 15.1 34:3... 25.2 51.8 32.9 .......0.2 . 12 .........6 Benin 3.2 . 11.8.. . 249.1 . .5 Bolivia 4~4 .. 5.6 13.7 . 15:2 ....... . ...........8. Bosnia and Herzegovina.. . .. . Botswana 6~0 _ .. 10:4.4 ..... 12,5 ......... .. .. ... .......611.7 . . 25 7 Brazil ..... ....... ....I... 3.6.5.58 ....110.. 58........ . 6.. . . . 23.. 8.... Bulgaria 4.5 3.3 17 5 ... 31.9 ......51.3 1...I 0 .... .. .. ............ ........... 17 .........4 Burkina Faso 2.2 1:5.... 23 1 21.2 87.3 .. 2,960.8 ..... 0.8 . 516 Burundi 34 3.1 18.6 222.1 . 1,479.0 . . . 50 6 Cambodia 2. 2 .9 44 ...... ...........44 Cameroon 3.6 2 9 11.0 4 02............................ ...4002....... . 6 Canada 6~9 ......7.0 . . 48.5 51.0 39.1 ...40.5 ..1 6 6 C entral. .......I..African........ . .Republic. .......... ...............22.0... ...................939......5...................... ..........6 . Chad .. 2.4 9.. ......676 Chile 4~6 3.1 9.5 10.1 16.7 10.8 111.2 18.8 0.0 30 8 China 2 .5 2.3 ... .....3.8 6.5 . 15.0 245.4 65.9 ..... . .. .. . ....... .......... 24 .........5 Hong Kong, China 2.4 29.9 ... 6.7 .....7.8 ......8.2 12.5 45.5 ............. 0.3. 24 .........6 Colombia 2.4 44.4 ....... 6.6 10.4 .:.... 11.4 56.0 37.4 .. 25 5 Congo, Dem . Rep. 26 . . ... 748..1 ......... ................... ........ 4.....5 ..... ...6 Congo, Rep.7 .0 6 2 10!.1 .. 19.1 3 8.... .. ..3...9.8.. .. 0.1 ....... 14.4 .......... 70 .........6 C6te dlvoire 72....... ....... 5:0 .......22 7.7 . 16.9 .. .. ... :........... 378.3 2120.1 . 4.1 .........6 Croatia . 5.3 .. . . . .. .194 Cuba 7.2 .. ...!....... .10.4 . . 28.5 . 5.7 1.0 12 6 Czech Republic 5.4 . 16.3 .. 23.7 ..4. 61194 Denmark... ......6.8 ......8 2 ... . 38:4 25.9 11.6 ...36.6 51.5 55.3 .......4.3 . 10 .........6 Dominican Republic ......... 2 2 2.0 3.1 3.3 4.4 ......... 9.8.. 9.8 35 .........8 Ecuador 56 .......3.5 ..... ...56 ....7.6 24.2 .............. 25 .........6 Egypt, Arab Rep. 57 4.8 . 25.9 57.9 . . 0.7 235 El Salvador 3~9 ... ...2:2 12.5 7.0 . 5.5 141S5 .. .... .. ............_...........33 ... .....9 Eritrea . 1:8 .. 9.2 . 9.9 44.............44.5 ...ston ...ia..........73..... :............. 44.8..44 8 .... 384 5 ..........4.5 1 ...17 .... 6. Ethiopia 3 1 4.0 17.7 34.0 . 61.6 1,120.6 962.0 2.5 386 Finland 5~3 7.6 20.7 23.3 28-6 ....37-.4... 49.4 6.2 . .6 France 5.0 6.1 .12:0 . . 15, _.8 . 202 ..26 9 29.3 26.0 .................. ......... .. 19 ....... 5 G ab o n. . . . . . . . .. . . 2 7..2 8............ .... .. ..5 1. 6. .. Gambia,The 3.3 ....60 .....194 ....14.5 . . 286.5 .................. 2.4 ..... .....30 .........6 Georgia 5:2 . 25.0 .. ... 25.5.... 18 .........4 G erm any............... . .....4....8..........30.7... ......373.... ........ ... _.37 317 ... .....4 Ghana 3.1 . 3.7 .. . .. 26 7 . 1.6 ...6 Greece 2 0 3.0 7.0 16.0 9.6 147 301 20.7 2.4 15 6 Guatemala 1.8 17 ....... 4.8 6.1 10.4_........ .. 44.8 311. .. 31. 35 6 Guinea .. 7.8 . .. . 260.2 ...49 6 Guinea-Bissau. 32.1 . 0. . . .. .. 6 Haiti 15 . 5~~~ ~~~~~~~~~~~~~~~~.9.... ....130.0....1.5..... Honduras 3~2 . 3.6 10.7 90. . . .. .. ...... 77.4... 688.8 ...... 3.6 0.3 35 .........6 74 '1999 World Development Indicators 2.9 Public expenditure Expenditure per student Expenditure Primary Duration on education on teaching pupil- Of miaterials teacher primary ratio education Primary Secondary Tertiary Primary Secondary % of % Of % Of % of % of total % of total pupilsaper GNP GNP per capita GNP per capita GNP per capita for level for level teacher yeara 1980 1996 ±980 1996 1980 1996 1980 1996 1996 1996 £996 1995 Hungary 4.7 4.7 14.1 ...20.6 ..... .... 20.3 85.4 47.0. .... . . 11 . ... 4 India 3.0 3.4 10:.5 11.6 ., . 88.5 7418 . 64 5 Indonesia 1~7 ... .1:4 ..........~ . .I..... 6.6 .. 12.9 .....23. Iran, Islamic Rep. 7.5 4.0 16.2 7.6 . 10.1 67.5 43.2 . 1..2 31 5 Iraq 30O.. .... 77.1 . 6.5 .... 87.8 ..... .. 20 6 Ireland 6 3 5.8 11.6 14.4 24.3 22.3.... 60.0 ...36.5 .... 0.3 ... . 23 ..... 6 Israel 7.9 7.2 15.4 -19.6 32.7 7. 35.2 9.9 16 6 Italy 4.7 . 2-1-5 ... .. .... . 21.9 11 5 Jamaica 7~0 _.....7~4 .......138 22.0 ....202.6 17....: ....... 1.7.... .11...6 Japan 5~8 .. 3:6.....- 14:8 19.3 16.6 19.0 21.0 13.9 ..... 4.9 19 .. 6 Jordan 6.6 7.3 .. .. ...... ........... 90.5 60.1 74.7 ... .... 4.6 21 10 Kazakhstan I....... .... .. .4.7 ...........22.0 . .18 4 Kenya 6.8 6.6 15.7 ...17.0 ..... 9287 ......30 Korea, Dem. Rep. 4 Korea, Rep. 3.7 3 7 104.4 18.8 9.2 12.9 15.9 6.0 ..... 2.2 ....0.1 31 6 Kuwait 2 4 .. 5 7 14.8 21.8. 12.4 14.6 37.5 89.5 6.3 . 154 Kyrgyz Republic 5.7 .. 36.4 . 50.5 .....0.6204 Lao.PDR. 2:5 . .......... ........ 60.6 9.4 .. .... 30...5 Latvia 33 . .....6:5 . .... ................... 38.2 19.1 33.8 0..7.. 13 4 Lebanon . 5. 18.5 . 22.3....... ... 5 Lesotho 5.1 7.0 8.6 16.2 72.2 62.0. 9997.7. 397.6 0.4 .... :7 .. 7 Libya 3.4 . 58.2..9 Lithuania 5.6 .... 28.4 . 43.0 0.5.16.4 Macedonia, FYR 5.6.... 22.1 .... ...29.8 ..... .75.6 0.1 19 8 ... - .. .. ... .. .. .. ... .. .. .. ... .. . .. ... .. .. .. .I . .. .. .. I .. .. .. .. .. .. I. .. ... .. .. .. ... . .. . ... .. .. .. . I. .. .. . .3 7. .. .5 Madagascar . 44 1.9 9.2 5.1 36.2 . 402.7 1.1 37 Malawi 3~4 ... 5.5 7.6 9-0.0...... .. 79.9 1,839.9 1,636.0., .... 59.8 Malaysia 6.0 5.2 12.0 11.4 . 148.9 78.8 6.0 14.7 20 6 Mali 3.7 2.2 31.7 15.4 . . 924~4 ..... 2.2 70 6 Mauritania .. 5 1 305 1.703256. .4506 M auritius 5 3 . .....4 3 .3 ... . 15.8 ....9.8 .... ..... ......... 343.6..67 .5 .0: . ..... .. 24...... 6 ... Mexico 4.7 4:9 4.4 11-.9. 17.9 26.4 46.9 ... .. 28 6 Moldova . 9.7 57.9 23 4 Mongolia .... .............6.4 .....46.2. 52.1 .... 31.3 Morocco 6~1 .... ..5.3 .......156 14.3 548 .... 155.4 72.3 0.2 ......1~0.... 28 6 Mozambique 4.4 . . . .........585 Myanmar 1.7 1.2 . 9.0 5 Namibia 1~3 .......9.1 .... ..... ...15.5 ..... ....33.8 100.7 ..... . .. Nepal 1.8 2.8 146...14. 271~9 140.6 7.2 .... ... ..... 5 Netherlands 7 6 ... 5.2 13.9 . . 23.3 ..21.0 734.4 .47.6 ... I..3.1 .6 New Zealand 5~8 ... ...73.3...... 15.1 ..17.9 13.6 23.8 59.8 45.8 ......79.9. 19 6... Nicaragua 3A.4 ... . 3.7 ........8.2 11.4 15.5 .....: ... 25.8 ....... 09.. .. . 38 6 Niger 3.1 26.2 ...... . . 1,539.5 ... ......... ...... . 41 6 NigePria 64 0.9 4.7 . . 5298 ... 376 Norway 6-5 7 5 .......27.2 ....31.1 ....14.9 20.1 38.1 ..48.0 ... 2.0 .....6 Oman 2.1 4.4 13.1 . . 22.1 . .27.4 . 0.9 26 6 Pakistan 20 ....... 3:0 ........8.7 . .. 1. ......... : . 27'4 ... 521.2 .5 Panama 4.9 4.6 12.3 . 11.5 29.8 41-1 1:8 .6 Papua New Guinea .. . .386 Paraguay 1~5 3.9 . 19 . 2090. 21 6 Peru 3.1 2.9 7.2 . 11.4 0.7 24.4 28 6 Philippines 1 7 .... 2 2 .8 . 4.3 ... ...... 138.8 . 35 6 Poland - 52 81 17.1 14 16 47 401. 158 Portugal 3.8 .....55 135 209 ... 20 9 36~2 25.2 ... 0.2 12.6 Puerto Rico . ... . .8 Romania 3~3 .......3.6 ... .... ..20.0 ... ....8.7 316 ... 20 4 Rusaian Federation ..... 3 5 . ....4:1 .............I...20 3 1999 World Development Indicatora 75 O ~2.9 Public expenditure Expenditure per student Expenditure Primary Duration on education on teaching pupil- of materials teacher primary ratio education Primary Secondary Tertiary Primary Secondary % of % Of % of % of % of total % of total pupila per GNP GNP per capita GNP per capita GNP per capita for level for level teacher yeara 1980 1996 1980 1998 1980 1.996 1980 1996 1996 1996 1996 1995 Rwanda...... .. 2-.7 ... 11.1 . . 902.6 .. ....... 3.5 ...... . ......7 Saudi Arabia 4.1 55 ......18.9 36.67. . 109.3 70.6 . .13 .... ..6 Senegal ....... ......... ... 35 ..5 . 24.6 10.6 .. . 442.7 . 4.0 58.6 Sierra Leone 3.5 . . . . . .7 Singapore 2.8 . . ...3 .0 _.... 6.8 ....7.8...... .40:6. 28-2 0.0 22 6 Slovak Republic . 4.9 ... .......... 23.4 .... . 4.1 . 30.5 05....19 4. Slovenia . 58 .. 20.5 ~ ~~~~.......... ............... .24.2.. . 37.6 6 ... 14 8.. .. .. South Africa 7.9 ............ 15.5 . . 237.. . 64.7 4.7 36. .....1. 7... Spain .....................2~3 .....4.9 15 3.3 ..16.8..16 Sri Lanka 2.7 3.4 -14 65.6 84.8 28 5 Sudan 4.8 . 26.9 . 8. . . . 29 8 Sweden 9.0 8.3 ......43.0 27.6 15.8 34.8 35.0 75.7 3.9 1 .... ...... 6... Switzerland 4.8 53 18~8 ....305 28.4 59.6 44.5. . 12 ........6 Syrian Arab Republic 4.6....... 4!2 ........8.0 8.2 15.1 16.4 74.7 ......... 1.9 ...... 8 2.2 ........ 23 ..... 6 Tajikistan .. 2.2 ...... .:....I.. M. . 39.5 0.5 24..... 4 Tanzania .. . .367 Thailand ......3.4 .......4 .1 . .....8.8 16 0 . 60~2 ...30.7 .......10 4.3..6 Tog . ......I .......5~6 .......4.7 ....8.3 7.8.. . 889.9 ..501.4 .. ... 0.2 8.5 51 ..... 6 Trinidad and Tobago 4.0 .3.7 ........9..2 . 201 .. .... .. 59,5......5... 1.0 2.8 25 ...... _7 Tunisia 5.4 ....6~7 ... 11.8 15.3 37.8 194.7 .79.3 2.0 24 6 Turkey 22 . ..2 2.2 . ... 6:4 13.4 94.4 ... 95~.1 .. 51.8 0.1 ....... . 28 ........5 Turkmenistan . . . . .4 Uganda 1~2 ......2 6 4.3 ...1,036.9 . .1.335 7 Ukraine 5.6 ... 72 .....21.2 43-.4 .. 7.3.... 20~.1 .. 22.6 . 20 .... 4 U nited a E iat s .... A rab. .1. . ......mir.....a..ea... ...1....3.....1 8. .,.. ...... ................ ................ .............116 ...... 6. United Kingdom5.6 5 4 1670.0.. 18.8 .... 22.2 .. 20.6 79.9 40.9... 2.9 18 ... 6 United States 6.7 5.4 27.0 18.5 .. 28 48.2 24.7 . 16 6 Uruguay 2.3 33.3 ..... 11.1 9.3 2~ .... 2 .1 . 24 3.....- 5 8 20 ........6 Uzbekistan 8.1... . .. ... 0324 Venezuela 4.4 .. ....5.2 .... 5:7 .....2.2 23.1 4.8 71.2 ..... 0.7 1.7 .. .. ....... ........9 Vietnam 26 5 West Bank and Gaza .. . 1 Yem en, Rep. 6:5 ...... ......... . . .... : .. .. .... 30 9 Yugoslavia, FR (Serb./Mont.)4. . . . . .. Zambia 4.5 2:2 10.6 5.1 . 15.4 605.0 369.0 ... 2.8. 39. 7 Zimbabwe 6.6 8:3 ... 24.3 20.2 38.2 407.1 406.5 ...... 9.8 ........ 39... ...7 Low inco m e 3.4 .. .. .. .. .. .. .. .. .. 56~~~~~.... 6.... M iddle income 4.0 4.9 .... 9.2 ... .. ...... 59 8 .. .. ................. ...... .....24 6 Lower middle income3.9 5.1.. . .. .256 Upper middle income 4.0...... 4.8 8.9 14.7 59.5 50.2 24 ........6 Low & m iddle incom e ... ... . 3..5 . ....4.3 ....... ......... .. .. ............. ... ...... ....... 36. 6... . ....... 6 .. East Asia &.Pacific 2.1 ......2.7 ............. ...49.8 ...... 25 5... Europe & Central Asia ....... 5.4 ........ ....I,...... ... 40.4 . 20 4 Latin America &Carib. 3.8 3:7 ...... 8:9. ...... ............ 58~6 ... 25 6. .... Middle East & N,.MArica .... 50. .. .. 5.2 .. ....... . ..... 81 2 .. 26 6 South Asia 2~.0 .......3.0 10:5 . ... 88.5 .. ...:,.63 .........5 Sub-Saharan Africa 4 .1 .....4.3 13.2 ....7879-.41 6 Europe EMU 5.3 53 13 9..21..5..26..0 37.....43.9..... . 0.3_166 76 1999 World Development Indicators 2.9 Data on education are compiled by the United Nations the number of hours taught. Moreover, the underlying * Public expenditure on education is the percentage Educational, Scientific, and Cultural Organization enrollment levels are subject to a variety of reporting of GNP accounted for by public spending on public edu- (UNESCO) from official responses to surveys and from errors. (See About the data for table 2.10 for further dis- cation plus subsidies to private education at the primary, reports provided by education authorities in each coun- cussion of enrollmentdata.) While the pupil-teacher ratio secondary, and tertiary levels. * Expenditureonteach- try and are used for monitoring, policymaking, and is often used to compare the quality of schooling across ing materials is the public spending on teaching mate- resource allocation. For a variety of reasons education countries, it is often only weakly related to the value rials (textbooks, books, and other scholastic supplies) statistics generally fail to provide a complete and accu- added of schooling systems (Behrman and Rosenzweig as a percentage of total public spending on primary or rate picture of a country's education system. Statistics 1994). secondary education. * Primary pupilteacher ratio is are often out of date by two to three years. The infor- In many countnes the duration of primary education the number of pupils enrolled in primary school divided mation collected focuses more on inputs than on out- changed between 1980 and 1995 (see Definitions for bythe number of primary school teachers (regardless of comes. And coverage, definitions, and data collection table 2.10 for definitions of primary, secondary, and ter- their teaching assignment). * Duration of primary edu- methods vary across countries and over time within tiary education). As a result the relative size of public cation is the minimum number of grades (years) a child countries. Data on education should therefore be inter- spending on education by level and primary pupil- is expected to cover in primary schooling. preted with caution. (For further discussion of the relia- teacher ratios also may have changed. These changes bility of education data see Behrman and Rosenzweig affect the comparability of gross enrollment ratios over Data sources 1994.) time and across countries. The data on education spending refer solely to pub- International data on edu- lic spending-that is, government spending on public *- --'-t ' B-a-cation are compiled by education plus subsidies for private education. The data Private spending on education is sizable UNESCO's Division of Sta- generally exclude foreign aid for education. They also in some low- and middle-income countries tistics in cooperation with may exclude spending by religious schools, which play . - national commissions and a significant role in many developing countries. Data for ' " national statistical services. some countries and for some years refer to spending by l,.-. The data in the table were the ministry of education of the central government only compiled using a UNESCO (excluding education expenditures by other ministries - electronic database corresponding to various and departments, local authorities, and so on). tables in UNESCO's Statistical Yearbook 1998. Many developing countries have sought to supple- ' ment public funds for education. Some countries have J,- '. adopted policies to charge tuition fees in order to recover part of the cost of providing education services - - - I ( I | or to encourage development of private schools. - i Charging fees raises difficult questions relating to equity, efficiency, access, and taxation, however, and some governments have used scholarships, vouchers, , - ,- and other methods of public finance to counter this cnt- c E '.'- i. icism. Data for a few countries include private spending, L' . -. 1 - although national practices vary with respect to whether parents or schools pay for books, uniforms, and other Eniollments in privale scnools are substantial in supplies. Readers seeking greater detail should consult some countries In Latin America. for exanmple. almosi a third of secondarm enrolimnnts ae pivvme. the country- and indicator-specific notes in the source The wou-d trade in ecucation was estimated aT cited below. S26 billion in 1996 ane is projected to inciease The percentage of GNP devoted to education can be dramaticall%. hew miethods of public linance ot ilsvate educairon I scholarships and XouchersI interpreted as reflecting a country's effort in education. ale increasing competit.on oetween public and Often it bears a weak relationship to measures of out- pihsare inst.tutions. 'ndireclliv pro,ing education put of the education system, as reflected in educational qualbtn. and public toprliate transters are epanoming access ior low income students. attainment. The pattern suggests wide variations across countries in the efficiency with which the government's resources are translated into education outcomes. The comparability of pupil-teacher ratios is affected by whether both full- and part-time teachers are included, whether teachers are assigned nonteaching duties, and by differences in class size by grade and in 1999 World Development Indicators 77 2.10 Participation in education Gross enrollment Net enrollment ratio ratloa Prepri'mary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1996 1980 ±996 1980 1996 1980 1996 1980 1996 1980 1996 Albania 39 ............113 .... .107. 67 ......38 ..........5 ~ -....11 102 Algeria 2 95 108 33...... 636 ....... 13 81 ..... 94 ...... 31....... 56 Angola 62 2.10 1 Argentina 54 106 113 56 77 22 42 Armenia 25 8790 12 Australia 78 112 101 71 148b 25 76 102 95 70 92 Austria 80 99 11 93 104 22 48 87 100 8 Bangladesh 7 5 61 18 3 6 Belarus 81 104 98 98 93 39 44 85 Belgium 121 104 103 91 146b 2 6 57 97 98 99 Benin 3 67 78 16 17 1 3 63 Bolivia 42 87 3716 24 79 16 Bosnia and Herzegovina Botswana 91 108 19 65 1 6 76 81 14 45 Brazil 58 98 120 34 45 11 12 80 90 :14 20 Bulgaria 62 98 99 85 77 16 41 96 9 2 7 3 74 Burkina Faso 2 18 40 3 80 1 15 31 Burundi 1 2 6 51 3 71 1 20 Cambodia 5 110 29 2 1 98 Cameroon 11 98 89 18 2 72 4 15 Canada 64 99 102 88 105 57 90 95 93 Central African Republic 6 71 14 :1 1 56 Chad 1 58 10 0146 6 Chile 93 109 101 53 7 512 30 8 58 China 29 113 120 46 70 2 6 102 Hong.Kong, China 85 107 94 64 73 10 28 95 90 61 71 Colombia 35 112 113 39 67 9 19 85 50 Congo, Dem. Rep.. 1 92 72 24 26 1 2 54 1 Congo, Rep. 2 141 114 74 53 5 8 96 Coata Rica 72 105 103 48 47 21 33 89 91 39 43 CMe dIlvoire 3 75 71 19 24 3 5 55 Croatia 39 87 7 219 28 82 6 Cuba 88 106 106 81 81 17 12 95 101 59 Czec Rpblic 91 96 104 114 99 18 23 91 87 Denmark 81 96 102 105 121 28 46 96 99 88 87 Dominican Republic 27 118 103 42 41 10 23 81 22 Ecuador 56 118 127 53 50 35 26 97 Egypt, Arab Rep. 9 73 11 51 516 23 93 68 El Salvador 34 75 93 24 34 1377 21 Eritrea 4 53 20 1 30 1 Estoni'a 68 103 94 127 104 25 42 87 83 Ethiopia 1 37 38 9 ..12 0 1 28 Finland 43 96 99 10 116 32 T1 99 93 France 86 Ill 106 85 111 25 52 100 100 79 94 Gambia, The 28 53 77 11 25 2 50 65 Georgia 28 93 88 109 77 30 40 ,...... 87 71 Germany 91 1021427 45 100 8 Ghana 36 79 41 21 Greece 62 103 94 81 95 17 43 96 90 7 87 Guatemala 33 71 88 19 26 S 8 59 13 Guinea 3 36 48 17 12 5 1 37 Guinea-Bissau 1 68 62 6 473 Haiti 37 1 Honduraa 15 98 111 30 32 8 11 78 90 78 1999 World Development Indicators 2.10 Gross enrollment Net enrollment ratio ratioa Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1.996 1980 1996 1980 1996 1980 1996 11980 1996 1980 1996 Hu gar ..... 112 ....... 96...... 103 ....... 70. 98 . ...... 14 25 95 977.... .. .87 India 5 83 100 30 49 5 7 . Indonesia 20 107 .. 115 29 .48 ..... 4 11 88 9 742 Iraq 7 113 .. 85. 57...... 42 .. ..... 9 11 99 76 47 Ireland 118 100 104 90 116 18 40 100 100 78 86 Israel 70 95 98 73 88 29 44 Italy 96 100 101 7 2 94 2 7 43. 100 Jamaica 83 103 100 67 7 8 96 . 64 Japan 49 101 102 93 103 31 43 101 103 93 98 Jordan ... ..27... Kazakhstan 30 ............. 85.. 98 .......93 .... 87 34 32 . ..... Kenya 36..~......... 115 ..... .85 _..20 ......24 .1 2 91 . Korea, Dem. Rep. .. . .. Korea, Rep. 89 110 94 78 102 15 60 104 92 70 97 Kuwait 55 102 75 80 65 11 27 85 54 54 Kyrgy Republic 8.............116 ......104 ......110 ..... 79 16 12 .95 Lao PDR 8 114 112 21 29 0 3 72 18 Latvia 47 102 96 99 84 24 33 . 90 79 Lebanon 74 Ill Ill 59 81 30 27 . 76- Lesotho . 104 108 18 31 1 2 67 70 13 17 Libya 5 125 111 76 8 20 62 Lithuania 40 .......79 .......98. 114 86 35 31 ............... 80 Macedonia, FYR 24 100 99 61 63 28 18 9 51 Madtaascar . ...4 ............ 130. ..... 92 ........ ....- . 16 . ... 3 2 61. Malawi 60 89 5 17 1 1 43 68 Malaysia 59 93 102 48 61 4 11 102 Mali 3 26 45 8 10 1 1 20 28 Mauritani'a 1 37 79 11 16 1 4 .. 57 Mauritius 83 93 107 50 65 1 7 79 98 Mexico 73 120 115 49 61 14 16 101 51 Moldova 44 83 97 78 81 30 26 Mongolia 25 107 88 92 56 22 17 . 81 53 Morocco 63 83 86 26 39 6 11 62 74 20 Mozambique .. 60 5 70 1 40 . 6 Myanmar . 91 121 22 30 5 6 7 Namibia 9 .. ~~~~131 61. 9 91 36 Nepal ~~~1 86 109 22 383 5 Netherlands 102 100 107 93 137b 29 50 93 99 81 91 New Zealand 77 111 99 83 114 27 59 . 100 81 97 Nicaragua 21 94 103 41 44 13 13 70 78 23 27 Niger 1.................... ....I ....... . . 25 .......29 570 .... ..1. 21 25 46 Nigeria ..109 9 18 3.. 4 Norway 104 100 99 94 116 26 62 98 99 84 96 Oman 4 51 78 12 66.. 6 4 69 10 Pakistan 16 39 74 142 3 Panama 71 107 104 61 68 21 32 89 46 Papua New Guinea 1 59 80 12 142 3 Paraguay 53 106 112 27 44 9 11 89 9138 Peru 38 114 123 59 70 17 31 86 9.1 53 Philippines 11 112 116 64 77 24 35 94 101 45 60 Poland 48 100 96 77 98 18 24 98 95 71 85 Portu ...............al...... 59 ....23...... 128 .... A ~ ..... 37 IO6b 11 38 99 104 78 Puerto RICO . 48 48 . ! Romania 53 104 104 94 78 12 23 . 95 73 RussianFedleration.............. 74............ 102... 107 ..... 96 87 46 41 ':.... 93.... ... 1999 World Development Indicators 79 2.10 Gross enrollment Net enrollment ratio ratioa Preprimary Primary Secondary Tertiary Primary Secondary % of relevant % of relevant % of relevant % of relevant % of relevant % of relevant age group age group age group age group age group age group 1996 1980 1996 1980 1996 1980 ±.996 ±980 1996 ±980 1996 Rwanda 2 63 3 ..0 1 59 Saudi Arabia 8 61 76 30 61 7 16 49 61 21 42 Senegal 2 46 68 11 16 3 3 ..... ...37 .... .58 ....... ........ ... Sierra Leone 2 52 .. 14 1 2 Singapore ........... 19 ..... . .... 108 101 60 67 8 . .... 39 ........ 99 Slovak Republic 76 102 94 18 22 Slovenia 68 98 98 38 92 20 36 95 South Africa 31 90 131. 94 5 19 51 Spain 71 109 109 87 122 23 51 102 105 74 Sri Lanka 60 103 109 55 753 5 Sudan 24 5 0 51 16 212 4 Sweden 73 97 106 88 137b 31 49 102 98 Switzerland Syrian Arab Republic 7 100 101 46 43 17 15 90 91 39 38 Tajikistan ... .... .. ....10 .... ........ . ....95 .7 82 4 20 Tanzania 0 93 66 3 5 0 1 68 48 Thailand 63 99 87 2 9 56 15 21 Togo 3 118 120 3 3 2 7 2 4 85 Trinidad andTobago 12 99 98 70 74 4 8 90 90 Tunisia 11 102 117 27 65 5 14 82 98 23 Turkey 7 96 105 35 56 5 18 96 50 Turkmenistan 37 ..23 20 Uganda : 50 74 5 121 2 Ukraine 61 102 87 94 91 42 42 United Arab Emirates 57 89 94 52 80 3 12 74 83 71 United Kingdom 30 103 115 84 -133b 19 50 100 100 79 92 United States 70 99 102 91 97 56 81 95 90 Uruguay 36 107 109 62 85 17 29 93 Uzbekistan 50 81 78 106 94 29 36 Venezuela 44 93 91 21 40 21 25 82 84 14 22 Vietnam 36 109 114 42 4 7 2 5 95 West Bank and Gaza . . . .. Yemen, Rep. 170 . 34 4 4 52 Yugoslavia, FR (Serb./Mont. 32 69 . 6218 23 Zambia 1 90 89 16 27 2 3 77 75 . 17 Zimbabwe ..85 11 91 7 Low income 14 78 93 25 42 4 5...... Middle income 36 106 114 51 70 11 15 9 Lower middle income 33 108 114 52 69 10 14 . 99 Upper middle income 51 101 I11 47 61 13 19 . 94 -. 44 Low & middle income 28 96 108 41 58 8 11 .. East Asia & Pacific 29 Il1 118 43 69 3 8 .. 101 Europe & Central Asia 53 99 100 87 83 30 32 92................... Latin America & Carib. 56 105 113 42 52 14 19 . 91 33 Middle East & N. Africa 14 87 96 42 64 11 16 . 87 61 South Asia 14 76 100 27 48 5 6.. . Sub-Saharan Africa 12 78 77 15 271 3 .. High income 71 103 103 86 106 34. 58 ............ 97 91 Europe EMU 88 106 104 81 108 25 48 . 101 90 a. Net enrollment ratios enceeding 100 indicate discrepancies between the estimates of school-age population and reported enrollmnent data. b. Includes training for the unemployed. so 1999 World Development Indicators 2.10 School enrollment data are reported to the United In using enrollment data, it is also important to * Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural consider repetition rates, which are quite high in some ment, regardless of age, to the population of the age Organization (UNESCO) by national education authon- developing countries, leading to a substantial number group that officially corresponds to the level of educa- ties. Enrollment ratios are a useful measure of partici- of overage children enrolled in each grade and raising tion shown. Based on the International Standard pation in education, butthey may also have significant the gross enrollment ratio. A common errorthat may Classification of Education (ISCED), * Preprimaryedu- limitations. Enrollment ratios are based on data col- also distort enrollment ratios is the lack of distinction cation refers to the initial stage of organized instruction lected during annual school surveys, which are typically between new entrants and repeaters, which, other designed primarily to introduce very young children to conducted atthe beginning of the school year and there- things equal, leads to underreporting of repeaters and a school-type environment. * Primary education pro- fore do not reflect actual rates of attendance or overestimation of dropouts. Thus gross enrollment vides children with basic reading, writing, and mathe- dropouts during the school year. And school adminis- ratios provide an indication of the capacity of each matics skills along with an elementary understanding trators may report exaggerated enrollments, especially level of the education system, but a high ratio does of such subjects as history, geography, natural science, if there is a financial incentive to do so. Often, the num not necessarily indicate a successful education sys- social science, art, and music. * Secondary education ber of teachers paid by the government is related to the tem. The net enrollment ratio excludes overage stu- completes the provision of basic education that began number of pupils enrolled. Behrman and Rosenzweig dents in an attempt to capture more accurately the at the primary level, and aims at laying the foundations (1994), comparing official school enrollment data for system's coverage and internal efficiency. It does not for lifelong learning and human development, by offer- Malaysia in 1988 with gross school attendance rates solve the problem completely, however, because ing more subject- or skill-oriented instruction using from a household survey, found that the official statis- some children fall outside the official school age sim- more specialized teachers. * Tertiary education, tics systematically overstated enrollment. ply because of late or early entry rather than because whether or not leading to an advanced research quali- Overage or underage enrollments frequently occur, of grade repetition. fication, normally requires, as a minimum condition of particularly when parents prefer, for cultural or eco- admission, the successful completion of education at nomic reasons, to have children start school at other the secondary level. * Net enrollment ratio is the ratio than the official age. Children's age at enrollment may of the number of children of official school age (as also be inaccurately estimated or misstated, espe- defined by the national education system) who are cially in communities where registration of births is enrolled in school to the population of the correspond- not strictly enforced. Parents who want to enroll their ing official school age. underage children in primary school may do so by overstating the age of the children. And in some edu- Data sources cation systems ages for children repeating a grade may be deliberately or inadvertently underreported. c_ The gross enrollment rabos are As an international indicator, the gross primary enroll- a from UNESCO's Statistical ment ratio has been used to indicate broad levels of par- Yearbook 1998, and the net ticipation as well as school capacity. It has an inherent . enrollment ratios are the weakness: the length of primary education differs sig- results of UNESCO's 1998-99 nificantly across countries (see table 2.9). A short dura- enrollment estimates and pro- tion tends to increase the ratio and a long duration to . .... jections. decrease it (partly because of more dropouts among older children). Other problems affecting cross-country comparisons of enrollment data stem from errors in esti- mates of school-age populations. Age-gender structures from censuses or vital registration systems, the primary sources of data on school-age populations, are com- monly subject to underenumeration (especially of young children) aimed at circumventing laws or regulations; errors are also introduced when parents round up chil- dren's ages. While census data are often adjusted for age bias, adjustments are rarely made for inadequate vital registration systems. Compounding these prob- lems, pre- and postcensus estimates of school-age chil- dren are interpolations or projections based on models that may miss important demographic events (see the discussion of demographic data in About the data for table 2.1). 1999 World Development Indicators 81 2.11 Education outcomes Percentage of cohort Youth Illiteracy rate Expected years of schooling reaching grade 5 Male Female Male Female % aged 15-24 % aged 15-24 Males Females 1980 ±996 1980 1996 1980 1997 1980 1997 1980 1995 1980 1995 Albania 81 83 :~ .I, : Algeria 90 94 85 95 26 14 55 32 9 11 6 10 Angola 8 7 Armenia Austri'a 11 14 11 14 Azerbaijan I, 11: I Bangladeh18 26 52 42 74 6 Belgium 14 16 13 15 Benin 59 64 62 57 57 33 84 67 Bolivia 7 3 21 8 9 8 Boania and Herzegovina Botswana 85 87 88 93 32 17 25 9 7 11 8 11 Brazi 15 10 13 7 9 9 Bulgaria 1 0 1 1 11 12 ~ 11 13 Burkina Faso 77 74 74 77 73 58 92 81 2 3 1 2 Burundi 100 96 49 36 72 43 3 5 2 4 Cambodia 49 36 72 43 Cameroon 70 718 7 34 1086 Canada 15 115 18 Central African Republic 63 50 44 27 76 48 ......... ~ I, Chad 62 53 Chile 94 100 9 7 100 3 2 3 1 12 12 China 93 94 4 1 16 4 Hong Kong, China 98 -- 99 3 1 4 0 12 13 12 13 Colombia 36 70 39 76 8 4 7 3 Congo, Rep. 81 40 83 78 12 3 27 5 Costa Rica 77 86 82 89 4 2 3 2 10 - 10 CSt dIlvoire 86 77 79 71 52 33 76 4 Croatia 0 0 1 0 12 12 Cuba 2 0 2 0 11 12 Czech Republic 13 1 Denmark 99 100 -99 99 14 15 14 15 Dominican Republic 18 10 17 9 11 11 Ecuador 84 86 6 3 9 3 .gpt, Arab Rep . .......... . 92 88 36 5 6 41 119 El Salvador 46 76 48 77 19 12 24 14 10 1 Eritrea 73 6754 Estonia 96 97 12 13 Ethiopia - 57 53 59 47 78 51 Finland 100 100 15 16 France 13 15 13 16 Gabon 57 58 56 61 Gambia, The 74 78 71 83 64 39 79 5564 Georgia 10 10 Germany155 Ghana V22 9 48 17 Greece 99 98 1 0 1 0 12 14 12 14 Guatemala 52 47 26 16 43 29 - Guinea 59 41 Haiti 33 34 53 40 57 41 Honduras 29 22 29 21 82 1999 World Development Indicatora 2.11 Percentage of cohort Youth illiteracy rate Expected years of schooling reaching grade 5 Male Female Male Female % aged 15-24 % aged 15-24 Males Females ±e98o 1996 1980 1996 1980 1997 19ao 1997 1980 1995 1980 1995 India 32 23 6 1 44 . Indonesia 7 2 1 5 4 10 10 Iran, Islamic Rep. -.. . 6 5 0. Iraq 77 . 4 .. ... .129 97 Ireland . . . 1 1 4 114 Israel .... . ±0 Italy 99 100 99 100 0 0 0 0 Jam aica 91 . 91 .. 17 1..... .... .......... ... ... ... ...1 .....8 ...... 4 . . . ..... 11 . 11 Japan 100 .. 10 . .. . .. . 13 14 12 14 Kazakhstan . . .. Kenya 60 62 13 5 32 8 Korea, Dem. Rep. . .. .. Korea, Rep. 94 100 94 100 0 0 0 0 12 15 11 14 Kuwait .. . .17 9 23 8 9. .9 Ky .Rgy Rpublic.. . L ao .P D R ........ .. . ... .. .. ... .. . .. .. . . .. .... . .. .8 ... ..6 Latvia..0 0 0 0.. 11 12 Lebanon .. ..7 3 18 8 Lesotho 50 68 29 18 5 2 89 Libya 5 9 10 Lithuania . . 0 0 0 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... .. ...... ........ .~.... ........... Macedonia, FYR . 95 . 95 .. ..10. 10 Madagas.car . 49 .. 33 Malawi 48 .. 40 29 20 60 42 Malaysia 97 .. 97 : _8 3 15 3 Mali 48 87 42 82 66 34 83 49 Mauritania 61 .. 68 53 44 84 62. M auritius 94 .98 .......94 . .99 .... ...11 .. ...8 . .... 16 . . ..7 . ........... .... Mexico . 85 . 86 6 3 9 5 Moldova . ... 0 0 0 0 Mongolia . ..68 Morocco 79 79 78 77 43 26 72 47 Mozambique . 52 39 38 28 79 59 5. 4 4 3 Myanmar . .. . . 11 9 19 1 Namibia . ... 18 11 20 8 Nepal .. .. 48 2 86 62 Netherlands 94 . 98 ..14 16 13 15 New Zealand 94 . 94 .. 14 1 13 17 Nicaragua 40 . 4 7 . 37 33 37 32 8 99 9 Niger 74 72 72 74 82 71 95 88 Nigeria .. . . . 32 13 57 20 Nora 100 100 100 100 .. 13 15 13 15 Oman 96 96 87 96 18 1 64 7 5 9 2 8 Pakistan .. . .. 48 31 7 61 . Panama 74 . 79 .. 5 3 7 4 li . 11. Papua New Guinea.. . . ... Paraguay 59 .. 58 6 3 7 3: 9 9 Peru 78 . 74 5 2 13 6 11 13. 10 12 Philippines 68 . 3 . 10 11 11 11 Poland . . .. . 0 0 0 0 123 12 1 PortIgl. . . 0 2 0.. 14 . 15 Puerto Rico . . 6 3 4 2 Romania . .. 1 1~~~.......... 1 0.. 12 . 11 Russian Federation . . 0 0 0 0 .. .. 1999 World Development Indicators as C ~~2.1 1 Percentage of cohort Youth illiteracy rate Expected years of schooling reaching grade 5 Male Female Male Female % aged 15-24 % aged 15-24 Males Females 1980 1996 1.980 1996 1980 1997 1980 1997 1980 1995 1980 1995 Rwanda 55 59 32 16 51 21 SaudilArabia 82 87 86 92 16 6 40 12 7 9 5 8 Senegal 89 89 82 81 59 43 79 62 Sierra Leone Singapore 100 110 2 1 3 0 11 11 Slova. Rpublic. Slovenia 0 0 0 0 South Africa 72 79 15 10 15 10 13 1 Spain 95 : 94. 1.: 0. 1 0 13 1 12 16 SriLanka 92 83 91 84 6 3 9 4 Sudan 68 71 36 19 66 33 . Sweden 98 98 99 97 . . . 12 14 13 15 Switzerland I. . . . . 4 1 3 1 Syrian Arab Republic 93 93 88 94 13 5 47 25 11 10 8 9 Tajikistan .. 0 00 0 Tanzania 8990 . 1 8 43 5 Trinidad and Tobago 85 87 . 11 10 11 1 Tunisia 89 90 84 92 12 4 41 15 10 . Turkey .. 6 2 58.. 11.9 Turkmenistan . . . Uganda 82 73 .. 27 16 53 3 Ukraine United Arab Emirates..... ..... 100 100 ,....... 26... ... 14 22 7 ..... 8 10 7. .... 10 United Kingdom ... . 13 16 13 1 United States. .. . 14 15 15 16 Uruguay 97 99 2 1 1 0 . Uzbekistan .. :... ... Venezuela 86 92 6 3 6 2 .. 10 .. 11 West Bank and Gaza . Yemen, Rep.. 45 19 89 61 Yugoslavia, FR (Serb./Mont.) . Zambia 88 82 .. 18 10 35 18 8 .. 7 Zimbabwe 82 78 76 79 7 0 14 2 Low Income . .. . .. 34 23 59 41 Middle Income . 7 4 15 6 Upper middle income .. . 9 5 12 6 Low & middle Income I.. 1 12 3 21 East Asia &Pacific . 93 .. 94 5 2 15 4 Latin America & Carib. .. 11 7 11 6 Middle East & N. Africa 88 .. 84 26 14 52 27 South Asia .. . . 36 2 4 4 Sub-Saharan Africa . ... 34 20 55 29 High Income . . . . . Europe EMU . . . . 84 1999 World Development Indicators 2.11 Indicators of students' progress through school, esti- formance not of individual students, but of all or part * Percentage of cohort reaching grade 5 is the mated by the United Nations Educational, Scientific, of the education system. share of children enrolled in primary school who even- and Cultural Organization (UNESCO), provide a mea- The youth illiteracy rate measures the accumu- tually reach grade 5. The estimate is based on the sure of an education system's success in maintain- lated outcomes of primary education over the previ- reconstructed cohort method (see About the data). ing a flow of students from one grade to the next and ous 10 years or so, by indicating the proportion of * Youth illiteracy rate is the percentage of people thus in imparting a particular level of education. people who have passed through the primary educa- aged 15-24 who cannot, with understanding, read Although school attendance is mandatory in most tion system but have not acquired the basic literacy and write a short, simple statement about their countries, at least through the primary level, stu- and numeracy skills, for reasons that may include everyday life. * Expectedyearsofschoolingarethe dents drop out of school for a variety of reasons, inability to access schooling, dropping out before average number of years of formal schooling that a including discouragement over poor performance, reaching grade 5, and failures in achieving basic child is expected to receive, including university edu- the cost of schooling, and the opportunity cost of learning competencies. cation and years spent in repetition. They are the sum time spent in school. In addition, students' progress Although the illiteracy rate is defined as the per- of the underlying age-specific enrollment ratios for pri- to higher grades may be limited by the availability of centage of people who cannot, with understanding, mary, secondary, and tertiary education. teachers, classrooms, and educational materials. read and write a short, simple statement about their Dat sources The rate of progression-sometimes called the everyday life, it is in practice difficultto define and to rate of persistence or survival-is estimated as the measure. To estimate illiteracy using such a defini- proportion of a single-year cohort of students that tion requires census or sample survey measure- The data shown in the table eventually reaches a particular grade of school. It ments under controlled conditions. In practice, many - were compiled by UNESCO measures the holding power and internal efficiency countries estimate the number of illiterate people and are published in of an education system. It illustrates the situation from self-reported data, or by taking people with no UNESCO's World Education regarding the retention of pupils and, conversely, the schooling as illiterate. _ Report 1998 and Statistical magnitude of dropouts. Progression rates approach- Literacy statistics for most countries cover the Yearbook 1998. The data on ing 100 percent indicate a high level of retention and population aged 15 and above, by five-year age youth illiteracy are based on a low level of dropout. groups, but some include younger ages or are con- the results of the 1998 Because tracking data for individual students gen- fined to age ranges that tend to inflate literacy rates. UNESCO literacy estimates and projections. erally are not available, aggregate student flows from As an alternative, UNESCO has proposed a narrower one grade to the next are estimated using data on age range of 15-24, which better captures the abil- enrollment and repetition by grade for two consecu- ity of recent participants in the formal education tive years. This procedure, called the reconstructed system. cohort method (Fredricksen 1993), makes three sim- Going beyond primary education, the indicator plifying assumptions: dropouts never return to expected years of schooling is an estimate of the school; promotion, repetition, and dropout rates total years of schooling that an average child at the remain constant over the entire period in which the school-entry age will receive, given the current pat- cohort is enrolled in school; and the same rates apply terns of enrollments across cycles of education. It to all pupils enrolled in a given grade, regardless of may also be interpreted as an indicator of the total whether they previously repeated a grade. Given education resources, measured in school years, that these assumptions, cross-country comparisons a child will acquire over his or her "lifetime" in school. should be made with caution, because other flows- Because the calculation of this indicator assumes caused by new entrants, reentrants, grade skipping, that the probability of a child's being enrolled in migration, or school transfers during the school school atanyfuture age isequaltothe currentenroll- year-are not considered. ment ratio for that age, changes and trends in future The percentage of the cohort reaching grade 5, enrollment ratios are not accounted for. rather than some other grade, is shown because it is generally agreed that children who reach grade 5 should have acquired the basic literacy and numer- acy skills that would enable them to continue learn- ing. This indicator provides no information on learning outcomes, however, and only indirectly reflects the quality of schooling. Assessing learning outcomes requires setting standards and measuring the attainment of those standards. In general, national assessments are concerned with the per- 1999 World Development Indicators S5 2.12 Gender and education Primary education Secondary education Tertiary education Female teachers Female pupils Female teachers Female pupils Female teachers Female pupils % of total % of total % of total % of total % of total % of total 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 Albania 50 60 47 48 35 51 49 20 31 50 57 Algeria 37 45 42 46 45 39 48 30 Angola 74 7 :33 15 Argntina 92 89 49 49 66 43 50 Armenia 97 5144 52 41 5 Australia 70 76 49 49 45 0 4945 5 Austria 75 83 49 49 49 55 47 15 26 42 48 Azerbaijan 80 48 48 :42 50 Bangladesh 8 37719 11 14 Belarus 48 55 Belgium 59 72 49 48 53 50 51 36 44 49 Benin 23 24 32 36 : 12 19 19 Bolivia 48 47 43 Bosnia and Herzegovina Botswana 72 77 55 50 37 43 55 52 28 35 47 Brazil 85 55430 38 48 53 Bulgaria 72 89 49 48 53 72 48 50 39 41 56 61 Burkina Faso 20 24 37 39 34 3 422 23 Burundi 47 50 39 45 20 37 25 Cambodia 36 42717 1 Cameroon 20 32 45 20 : 35 Canada 66 67 49 48 44 67 49 49 24 34 50 53 Central African Republic 25 37 16 15 8 Chad 8 77 34 4 0 13 Chile 72 9 4 2 52 5143 5 China 37 47 47 25 36 4430 33 Hong9 Kong, China 73 76 46 49 49 50 49 49 16 25 31 4 Colombia 79 77 50 49 42 48 53 20 28 45 52 Cong.,PDm. Rep. 22 42 41103 Congo,Rep. 25 36 48 48 16 9 .. 15 Costa Rica 79 78 49 49 54 59 53 Cdte dIlvoire 15 21 40 43 ...24 Croatia 73 89 49 .. 63 51 34 5 Cuba 75 81 48 48 46 57 52 38 45 48 60 Czech Republic 93 486502 40 7 Denmark 58 49 49 52 49 49 3 49 55 Dominican Republic 71 52 50 . 49 .. 57 .. 32 .. 57 Ecuador 65 67 49 49 38 .. . . Egyt AraRep.4 9 40 45 31 39 45 .. 32 El Salvador 65 4 49 27. 52 3 29 31 50 Eritrea 36 . 45 . 142.. 3.. 13 Estonia 89 . 48 8 .. 52 .. 4 55 3 Ethiopia 22 28 35 36 I. 1 1 3 1 Finland 49 49. .. . 53.483 France 68 79 48 48 55 58 52 49 .. 33 48 55 Gabon 27 39 49 50 24 18 .. 16 13 .. 26 Gambia, The 32 29 35 44 25 17 30 . .. 2 .. 36 Georgia 89 95 . ~~~ ~~~ ~~~ ~~~~~~~~~48 54 71 .. 49 . .. 47 51 Germany 81 .. 49 .. 49 .. 48 . 29 .. 45 Ghana 42 44 .. 21 . ... .. 21 Greece 48 56 48 48 49 55 46 49 32 34 41 48 Guatemala 62 45 46 36 . . . . . Guinea 14 25 33 34 10 .. . . 3 4 19 11 Guinea-Bissau 24 32 . 21 .. 20... . . Haiti 49 4. .... .. 1.. 30 Honduras 74 73 50 50 48 . . 29 29 38 44 86 1999 World Development Indicators 2.12 Primary education Secondary education Tertiary education Female teachers Female pupils Female teachers Female pupils Female teachers Female pupils % of total % of total % of total % of total % of total % of total isso 1996 1980 1996 1980 1996 1980 1996 1980 2.996 1980 1996 ................ ................... ........................ ............................. .................................. ..... .................... - - . .................... ........ ........................ ... .. ............................. !71ynp ry 80 .92 49 48 ........... . ....... ........ 66 50 29 ......... .31.. 50 ....... . .53 .......................................... ...... ................................... I................ ..........I...... .................... ... ... ....... India 26 33 39 43 26 36 .................................... I..................................... I.................................. ............................... ................ ............ .. I., ............... .... . ........................... .......... Indonesia 33 52 46 48 3 745 1 731 35 ..................................................................................... ....................................... ..................... .. .... .. .. .............. ....... ........................ .. ..........I........ . ....... Van, Islamic Rep. 57 55 40 47 30 44 46 19 18 30 36 ........................ .............................I.......... ...........................I............................... - - ...................... - ........................................................... .......... ............ Iraq 48 71 46 45 40 56 32 17 32 ............................ .................................................................................................... .. . .. .. ..................... - I ..................... ........ ............... ... ... . I. . . .. ....... Ireland 74 78 49 49 50 54 52 50 41 51 ....................... .......... ..... ... .... .... ..... ....- Israel 84 49 49 57 65 48 ............................ .............................................................................. ............................ ......I.........I............. ..................... .... ............... .. .... .. ....... I 1 87 94 49 48 58 64 32 43 53 .t  .Y .................................................................... .................................... ....... .. .......................... ... .. ......... ......... . .. ........ ....... . ...... .............. ......... Jamaica 87 90 50 66 53 ................................................. .................................. I................................ ............ I........... ...................... ... ........ .. ..................I.............. ..... ..... ARap 57 62 49 49 26 33 49 49 14 22 33 44 ............................... .................................................................... ............ ............ ..I ....................- . ........ ..I............. ....... ... I..... Jordan 59 61 48 49 43 45 45 47 16 18 46 47 ......................... .............................I.................................. .................................... ............ ..... . .................. .. .. ......I.. .................. ........... ...... Kazahhstan 97 49 55 .... ............................................ .................... - ................................ .. .................... : ... ......................... ... ............... 7...................... .......................... ..I ....... ppya 31 40 4 749 ............_........... ...... ............I......................... ...... ..................... . ........................... ................... I............................. ...... ............................................... Korea, Dem. Rep. .... ........... I...... ................................. ...... ......... :............... - :: ...............:................................... ...... Korea, Rep. 37 61 49 48 26 39 45 48 15 24 26 37 ............................... I...................................... ................................_ ...................... ..... ..... ..................... - , ..................... ... ... . ........... ... .. .. ... .. ............ ........ Kuwait 56 71 48 49 50 54 46 50 23 19 57 62 ............. ...............................I................ ................. ............................ ........................ _ .......... ........... .- ..................... . .......... ............ .. . .. . .......... 88 83 49 67 51 ... ....... 52 ..... .......... ....................... ........................ ........ ........................ ...... ....... .. .. ........ .......... Lao PDR 30 42 45 43 38 39 18 29 31 30 ...... ........................... .. ....................... .. ...... ......... . ..... ... ,.. . ......... . . ............ ....................................... ................... . .... .... .... ..... ... ....... Latvia 95 48 79 51 49 57 60 .......................................................................... .........................I.................................. ..................................................... ......................... ....... - .1 .........I........ . Lebanon 32 -36 49 ...................... .....................- ...................I......... :.................. :: .............. ........................ - I .. ................... ........... 3 1 . .3 3 Lesotho 75 79 59 52 53 60 59 50 54 .......... ............. . ....... .......... . ............ ... .... .. ...................................... ....... ........................ .... ..................... ..... ..... ....... .. iby 47 47 ........:............ 24 ... ........... 40 ................. .. ........ ... :7 ....... ... .......... .. ...... 25 ........................... I........ ....................................................... ....I ..... ........... Lithuania 97 91 48 87 50 47 55 59 .................. .................................... ... .................. ...........................I................... ....... .......I......................... .................... ... ..................... - I.. .... . ......... ....... Macedonia, FYR 54 48 51 48 41 54 .......................................... - ......................... .................................... ......................................... I........ ....................... - 11 ..................... - ............ 11 ........I....... ..I ....... Mad scar 51 ............. 49 .. .......... 49 ...... ... .. .................... .......................... .. 33 ............... 7:.. 29 45 ....... ..................... .............................I............ ... ..... .......I....... ... ....... ....... . Malaw 32 39 41 4 7 30 ................... .. ................ ............... .............................. ...... Malaysia 44 60 49 49 45 60 26 39 ................................................ ......................... .......................... .......................... .. ..... ............... ......... ....................... ...................... ....... I....... ... .....I. ........ Mali 20 23 36 40 11 .................................................. ........ I................ - ....................... ........ .. ..................... ............... ..............I......... .... ..................I .......... . ................. Mauritania 9 20 35 47 517 ............................................................ ........................... ...................... ... ..................... ........ I...................... - I.. ......... ....... .. .. .........I ............ .......... ... Mauritius 43 51 49 49 45 15 31 31 49 ........................ ............................ .......................... ............................ ........................ I ................................. ............... ........................ - . I.................. .. Mexico 49 48 48 33 47 ........................................................... ...................... - ....................... ... ...................... . ..... :: ......... .............I ........... ......... ..... ................... . ..... ....... .. ....... Moldova 96 97 49 73 52 45 55 ............ I......................... .... ................................................ ...................... ... ..................... ...... ............. .......... ........... .... ....... ....... Mongolia 87 90 50 51 59 66 57 40 36 61 69 ......................................................................................................................... 1 1 ........................ I........................ .. .... ........ ...... ........... .. ....I.. .. ................ Moroc o 30 38 37 42 26 32 318 20 41 .......... .................. ............................I..................... ....................... ............ ........... ........................ § ........... ........ _ ................. .. . ......... ............ I........I....... Mozambique 22 23 43 42 22 17 28 39 2102 4 ....................................................... ............. ...... . ...... ............. _ ........ ....... . ................ .. .::. I........... . 2 .. . .. . . ..... ..... ..... .................. ........ .... ..... ....... Myanmar 54 6 7 1 : :: .1.1 73 ..... .... .. . . ..... Namibia 50 61 ............................... ............................................................. ................ .............. , .... .... .... .................................. .... ........ ........... .................... . .. ..... 28 9 16 22 ....................... ................ . ............... ..................... ............... Netherlands 46 65 49 31 48 4 724 40 47 ................................. ......................................................................... _ :: ...... ........... .. .... .................. ............................ .... .. ..... .. ................. .... ... New Z aland 66 82 49 49 57 495184 ....................... .................... .................. ... I.... ........... . . ..... Nicaragua 78 84 51 50 53 36 4 751 ... ........................................ I.............. ................... ............... ................... . . ....... . ....... .. ........... ......... 35 38 21 14 29 35 13 20 .... ..... .... ... 1- 1 .................. ....... ........... ...................... ... ............. Ni ria 34 46 43 44 2 36 21 ... o p ........................................................ ..................... - .... ................................. ........................... I...................... ................- ............... ... . ... ....... . . ..... Norw y 56 49 49 48 55 Oman 34 50 34 48 27 48 24 49746 ....................................... - ....................... .................. I............................... ............ .. ........ ............ ..... ................................... . .................. .......... ........ .... Pakistan 32 33 30 112 17 ........................................................... .................. ...................... ................... ............ . :: ... . ........... ... ......... . ...... ............ .. ..... - .1 ....... Panam 80 48 ::_ 53 . ..... 52 32 36 55 59 ..... ...................... ............. ... ..... .......... Papua New Gui'nea 27 36 42 45 32 39 22 32 ........................................................... ................ ............ ........ ... .................. . ............ ...... . . .. .................... _ .............. .. ...........I.............. ........................ Paraguay 48 48 66 51 55 ........................................................... ............. ................. .. .................... .... ............ ..... I I..................I.. ......... ........ .. ............ ...... ........................ ....... Peru 60 58 48 49 46 39 46 48 35 ......................................................... . .................... ..................... .................... . ............ ...... . ............ ... . ........... . .:7 ........... . ............ ... . Philippines 80 49 ...... ....:........ .... ..... . ......... 53 53 53.. 57 ..................................................... I..... .................... .................. .. .............I..... .....I............... ........... . .......... Poland 48 55 7 ..................I................... - - ................. .............. I......I.............. ............... ... .... ........... ........ ....5 .........4 ...... ....... .. .... .. ...... . ......... . 6 ....... Portugal 48 48 59 148 57 ............. - ........................................... ....................................... ..... ..... ..... ....... ..................... .. ...I ...... . ....... .. Puerto RICO : :! :: :: 59 ........................... I............- .......... .... Roma ia 70 85 ......... 9..................................... ...... ............ .............. ............. 49 ..........4 ..... 63 49 .. ......... 30. 38 43 ....... 53 ..... ............. ........- ......... .. ... ................ ...... 11 .... Russian Federation 98 98 49 56 56 .......... 1999 World Development Indicators 87 C ~2.12 Primary education Secondary education Tertiary education Female teachers Female pupils Female teachers Female pupils Female teachers Female pupils % of total % of total % of total % oftotal % of total % of total 1980 1.996 1980 1996 1980 1996 1980 1996 ±980 1996 1980 1996 Rwanda 38 . 48 16 9 10 Saudi Arabia 39 52 39 48 34 50 38 46 19 30 28 47 S e e a 24 2 6 4 0 4 5 .... .. .. ...I,.... ... .. . . .. .. . ... . .. ... .. . _ 18 ... .. .. . ... Sierra Leone 22 27 Singapore 66 77 48 48 19 31 39 44 Slovak Republic 91 49 70 50 38 50 Slovenia 92 49 51 7 49 18 28 546 South Africa 74 49 64 54 37 48 Spain 67 66 49 48 40 52 50 21 32 . .... 44 53 SriLanka 96.......... .... ....... 48.......48......6248 ......51251 .. 34 .. 3 4.......444. 4 Sudan 31 62 40 45 45 16 8 27 Sw eden ... 72.......I.... . .... .49 .. ....49 ... 4 58.... ....52 .....5 .53 ........53.......55.. 65 Switzerland 69 49 49 ...... .. 46 47 30 38 Syrian Arab Republic 54 65 43 47 22 44 37 46 29 41 Tajikistan : 54 49 33... . Tanzania 37 44 47 49 28 26 44 5 11 21 16 Thailand . 48 Togo 21 14 39 41 12 128 15 15 Trinidad and Tobago 66 74 50 .. ... 49 .... ....... ........ 50 43 ........:... Tunisia 29 49 42 47 29 38 37 9 27 30 45 Turkey 41 44 45 47....... 35 .. 40 39 25....... 33.. ...26....... 38 Turkmenistan Uganda 30 32 43 46 19 19 18 7 19 23 33 Ukraine 97 9 49 . :56 United Arab Emirates 54 70 48 48 46 54 50 8 49 United Kingdom 78 80 49 49 55 50 52 14 30 37 50 United States 8 49 49 ...... 56 49 26 39 51 56 Uruguay 48 49 30 53 Uzbekistan 78 82 . 49 Venezuela 83 75 51 50 53 5 8 28 Vietnam 65 47 2 31 24 West Bank and Gaza :.M : Yemen Rep. 17 28 18 13 Yugoslavia, FR (Serb./Mont.) . 49 35 54 Zambia 40 43 47 48 38 21 30 Zimbabwe 38 44 48 49 -36 44 17 . 36 Low income 28 35 40 43 .. 25 Middle income 52 51 . 48 38... 39 Low~er middle income 50 51 . 47 28 38 45 . . 3 Upper middle income . . . .44 48 ..o .......middle ... incom 42.44. ..46..... East Asia & Pacific 40 48 . 47 25 37 44 . 30 . 33 Europe & Central Asia 84 85 . 48 . ... ... 50 Latin America & Carib. . . .....43 Middle East & N. Africa 46 48 42 45 32 42 . 29 South Asia 24 34 38 43 . . 25 36 .u-S ha a A fr.. .. .. .Ica . . . . . . . . .. . . . . . . . . 30. 3 8 _ . 44.§ .. 4 5 . . . . . . . . .. . . . . . . . . . . . .... . ... .. . .. . . . ... . . . . . ... .. . . . . . . . . Hig .. ncom e ........... .......... :.... ..... . 77 ....49 .49 ... ..... .... ..55 . 49 . 31 ... ...44 .... .51 Europe EMU 72 81 49 48 . 55 . 49 30 46 50 88 1999 World Development Indicators 2.12 Data on female enrollment levels suffer from the women, and the number of female teachers is a * Female teachers as a percentage of total teach- same problems affecting data on general enrollment revealing indicator of employment opportunities for ers includes full-time and part-time teachers. discussed in About the data for table 2.10. But women in the modern sector. In addition, female * Female pupils as a percentage of total pupils female enrollment as a share of total enrollment is a teachers are important role models for girls, particu- includes enrollments in public and private schools. relatively simple indicator raising no serious prob- larly where female education is not encouraged or lems of cross-country comparability. men are forbidden to teach girls. Over the past decade Data sources Because disparities in enrollment are not corre- the share of female primary school teachers has lated with an overall standard of living, such as GNP increased everywhere. But data on teachers may not r The estimates in this table per capita, countries can achieve gender parity in pri- reflect the functions they perform. Schools may were compiled using the mary and secondary schooling if public policies and employteachers in manycapacities outsidethe class- United Nations Educational, education strategies address constraints that inhibit room, and the responsibilities assigned to male and _. Scientific, and Cultural Organ- female attendance. Providing segregated schools female teachers may differ systematically. - ization's electronic database and separate sanitation facilities, recruiting female ,,________on institutions, teachers, and teachers, and reducing the direct and opportunity = - pupils. costs of educating girls are among the strategies that Girls' enrollments continue to lag have worked in some countries (see box 2a). But dis- parities remain, and female enrollment ratios tend to - e " , - 11- be positively correlated with other indicators of devel- opment, such as maternal and child health and total fertility rates (UNRISD 1977). - : Girls' enrollments have caught up with boys' in . . most high-income, Latin America and Caribbean, and I . - Eastern European countries. But they lag behind in : aj . : South Asia and the Middle East. And regional aggre- J ; ° I : gates mask large disparities between countries. In j I , ;i Africa, for example, although Mauritius and South Africa have achieved nearly universal primary enroll- ment, a significant number of countries still have pri- mary enrollment ratios for girls that are less than 50 percent (table 1.2). In low-income and lower-middle- income countries dropout rates at the primary level are higher for girls than for boys, indicating that the gap in actual enrollment in these countries is wider than is reflected by enrollment ratios. One reason for I':.'.' . , . -: ..r,.-. r,j . this is early child-bearing in many of these countries, , which is clearly incompatible with schooling. Many : r.:e i, : girls, especially in South Asia, still remain outside the formal education system. And girls who attend school Erucal on tor All eltorts omei the p3st aecade hae signiicantl, increased enroliments at Ihe primart tend to be directed away from science, mathematics, Ie.el Ne.ertheles9. girls enrollments comlirue and other technical subjects in high demand in the TO lag behind boys in mnio legions A big parT Of labor market. the reason n many deseloping countries '5 The poor qualiTy or edacarlorn inputs ava processes. Limited employment opportunities and lower mar- s%hicn incre3ses the opportunlt cost of girls' lime ket returnsforwomen discourage parents of girls from in school Highdropout ana losk persr.tence highirghl the nted to improse school qi.ality and to d6 ;lfn investing in education. Consequently, female partici- ter,ed to ihai schoo Int t accut he iern ilnter entionh that take Into account Lhe baririrs pation inthe labor marketis limited,with manywomen girls race in attending postprimary school isee concentrated in the informal sector and those in the Dox 2an. modern sector relegated to the low end of the hierar- chy. Ensuring that the market is competitive, making labor laws gender-neutral, and strengthening the machinery that enforces labor laws can improve women's employment prospects. Traditionally. teach- ing has been one of the first professions open to 1999 World Development lndicators 89 -T 2.13 Health expenditure, services, and use Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total PPP per 1,000 per 1,000 % Of % of GDP % of GDP % of GDP $ $ people people population days 1990-97a 1990-~971 1990-97A,b 1990-971 ±990-971 ±980 1990-978 ±9so i99o-97r ±990-971 1990-971 1990497a A lbania 2 :5 ........ -:......... 1..........::.........:....... .. .... 1.4 3.2 . 13 . ......2 Algeria 3:3 .... 13 4:6 ... 210 .... ...85 0.8 . 2.1 ....... . ... Angola 3 .9.... 01. 13 Argentina4 3 5.4 .... 9.7 .... ....931 .......799 2.7. 4.6........I,.... Armenia 3.1 4.7 7.8 156 27 3.5 3.1 8.4 7.86 8 15 a3 Australia 5.8 2. 8.5 1786 1,798 1.8 2.2 . 8.9 17 16 7 Austria 5.7 .....22 .2.. 7.9 1,747 2,012 2.3.... 3.5 11.2 9.3 25 11 6 Azerbaijan 1.1 5.0 62.2 92 ...... 31 .......3,_.4 . 3.9 9.7 9.9 6 18 1 B~angladesh .. ... .... 1.2..... 1.3.... 2.4 24 6 0.1 0.2 0.2 0.3 Belarus 5.2 1 1 6.4 269 134 3.4 4.2 12.5 12.3 26 18 11 Belgium 6.7 0.9 7.6 1,748 1,816 2.5 3.8 9.4 7.6 20 12 8 Benin 1.7 ... . 01 1.5 0.2 Bolivia 3.8 .... 27.7 . 6.155.. 194. ..... 54 ........0.5 0.4 1.4 - ... Bosnia and Herzegovina .... 0.9 3.1 . 15 Botswana 1.8 1.4 3:1 0.1 0.3 2.4 1.6 Brazil 1.9 4.9 ... 6.8 ..... 382 .. 351 0......O8 ... 1.4 ........ 3.0 0. 2 Bulgaria 3.5 1.4 6 7 287 68 2.5 3.5 11.1 10.6 18 :14 5 Burkina Faso 4.7 3 2 ... 55.5 . 49 ...... 17 0.0 0.0 0.3I.......... Burundi 1.0 . .. . . 0.1 0.7 Cambodia 0.7 6.5 7.2 .. .... .... 18 .. .1 ....... ..... 2.1 Cameroon 1.0 0.4 1.. I.4 ... ......24... 7 ........... .. 0.1 ....... ..... 2.6........ Canada 6.3 2.9 9.2 2,112 1,829 1.8 2.2 .. 5.1 ......13 12..............7 Central African Republic, 2.0 .......2 0 0.0 .. .0.0 1.6 0.9 Chad 1.6 0.1 ..... ..2:7. ......33 .. .......6 ......... .... 0.0 . 0.7 ..... Chile 2.3 3.77 9 .........783 .......295 . 1.1 ......3.4 .....3.2 : ~....... China 2.1 1.8 3.8 90 19 0.9 1.6 2.0 2.4 4 15 HogKong, China 2.3 . 2.8 50 . 1,170.1,134 . 0.8 1.3 4.0 .. 2I. Colombia 2.9 4.4 7.4 477 140.. 09 1 1.3 Congo, Demn. Rep.0.2 . .. . . 0.1 1.4 Congo, Rep. 1.8 3.2 6.3 ..... 116....... 77 0.1 0.3 ....... :..... 3.3:........ Coata Rica 6.0 2.2 ......8.5...... .544 ... .215. 0.9 3.3 2.5 ..........1.... M6e d'lvoire 1.4 2.1_ .... -35 57 ... . 25 0.1 ..- .... 08....... Croatia 8.4 1.6 9.9 414 302.. 21 . 5.9 12 Cuba 7.9 . 1.4 ....3.6 . 5 .4 ...... ................ .. Czech Republic6 .4 06 ..... ..7:0. 80.6 .... 360 . 3.0 6.7 ... ...19 .......12 ... 15 Denmark 5.1 2.7 78 1,811 .2,88 . 24 2 ..... . 4.9 207........5 Ecuador 2.0 3.2 5 3 259 78 . . 1.9 1.6: Egypt, Arab Rep. 1:7 ..... 2.1 37.7 103 .......38 ......11.... 1.8 ... 2.0 .....2.1 3 10 5 El Salvador 2.4 3.4 5.9 153 86 0.3 0.7 .. 1.4 Entre 1 1. ..0.9.. .2.0. ...... . ....... 0.0..... ... .....I ....................... Eatonia 5.8. .. 42 3.0 1. 8.1 18 12 .......6 Finland 5:7.7 .. . . 1.7....... 7.4 1,409 1,666 1.9 2.8 15.5 9.3 ...... 26....... 12 .......4 France 7.7 2.1 98 2,086 2,349 2.2 2.8 . 8.9 23 11 6 Gabon 086:: 0 5 05 . . 3.2 Gambia, The 2.0 . . 0.0 .. 06 Georgia 06 . 4.8 3.5 107 .....4.8 5 13 .......2 Germany 8.1 ... 2.4 10.4 2,235.2,677 2.2 3.4 ...... ..... 97 ...... 21 14 6 Ghana 2.9 ... 0 1-.1 ..... 1.7 31 6........ .. 60.1 . 1.5 1........ ... Greece 5.3 1.8 7.1 843 803 2.4 3.9 6.2 5.0 15 8 Guatemala 1.7 1.5 3.2 Ill 41 ... .. 0.3 1 21 2 Guinea 1.2 ... .............. . ..............0.0 0.1 ....... :.. 0.. .6 . Guinea-Bissau 1 1. . . .1 0. 2 1.8 1.5 . Haiti 1.2 .. .....2.3 ......3 6 6 ..... .. 44 ........ 14 ... .... 0 .1 ... 0.1 0.7 0.8 ..... .........1........I,.... Honduras 2.82 8 5.6 .... ....106 .........33 ...... 0.3 0.4 1.3 1~l. .... ..... ........: ....... . ... 90 1999 World Development Indicatora 2.13 I~ Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total PPP per 1,000 per 1,000 % of % of GDP % of GDP % of GDP $ $ people people population dayu i99o-97r 1990-971 1L990-97a, 19so-97r 1990-971 1980 1990-.97a 1980 1990-971 1990497- 1990..97a i99go97r Hungary ..........4.5 .... .. .2.0 .6.5 . .......459 .. .....291 . 2..5. 3.4 .....9.1 9.. 0. .24 .11 . .....10 India 0.7 4.4 ... ...5:6.6 ...... 64 18 0..4 ....04 _ 0:8 0.8 ...-..... Indonesia 0.7 1.2 1.8 54 17 0.1 0.2 0.7 Iran, Islamic Rep. 1.7 2.5 ... 42.2 210 ... ....89 ..... 0.3 0.3 1.5 1.4 ... Iraq ....... 06 . ... Ireland 5.1 1.7 6.7 1,331 1,337 1.3 2.1 9.7 ...5.0......16 7 .... Israel 0.3 0.3 4.1 651 551 2.5 3.8 5.1 6.0 Italy 5.3 2.3 .......7.6 1,589 1,515 1.3 1.....7 6.4 ..... 16. 10 Jamaica 25.5.. 2.4 4:9.9....... 176 ... 76 04 0.5 2.1 ..... Japan 5.7 ........1!7.7..... 7.3 1,670 2,442 1.4 1.8 11.3 16.2 ..... 9......44 16 Jordan 3.7 4.2 ...... 7.9 261....... 117 0 ......8 1.6 1.3 1.6. 1 3 3 Kazakvhstan 2.5 0.8 ......3.3 107........ 42 3.2 3.6 13.2 10.3 15 16 1 Kenya 1.9 1........I0 2.6 28....... 8 01 0.1 1.6 Korea, Dem. Rep.. . . . . 5 . . . Korea, Rep. 2.3 ........1.7 _ 4.0 ... ....522 ..... .397 0.6 1'2~ . . 1~7 ....4.4 6.. 13. 10 Kuwait 3.5 . .. . 1 0.2 4.1 . Kyrgyz Republic 2.929 .....29 3.3 12.0 8.8 21. 15 1 Lao PDR 1.3 ... .....1.3 2.6 .. ...... 31 .. 10 .... .. .... 0.2 2 6.. 2 .6 ..... Latvia 3.5 . ........... 4.1l.. 3.0.. .137.7 .10.3 2143 Lebanon 3.0 7.0 ......10.0.. 87 1.7 .1.9 ~ ........... 3.1 14 4 Lesotho 3.7 . ..........0.1... Libya -.. .. .. .. . ... .. .. .. .. .. .. .. ... ... .. . .. . .. . .. .... .. . ..- 1 .3 .. 1 .1 .. 4.8 4 .2 Lithuania 5.0 -...... ........... .. ... .. 39.9 . 4.0 12. 1 10 6 30 ......14.7 Macedonia, FYR 6.2 0.9 .... ..83.3 171 ...... 23 . 0 1 M adagascar ............. 1.. 4.... . ... .... ...... 01 2.1 09 .. ...5 2 .. . 0I ... Malawi 2.3..0 001 Malaysia 1.4 1.5 2.9 317 140 0 3 ....04 4.. 23 2 0 Mali 2.0 15 .... 27.7 17 .... 8 0.0... 0.1 M auritani'a 1.8 4 1 ... ...5.2 .. ... . .81. 28 ...... . . .. 0.1 , 0:7.7 ... .. ... ............. Mauritius 2.2 ...... 17.7 . .... 4:0.0 .... 304 116 0.5 09 .9 .... 3:1 ....3.1 0.. 0... o4 Mexico 2.8 1.9 4.7 361 200 0.9 1.3 1.2 6 4 2 Moldova 6.2 .. .......3.13.6 ....12.0 12A.1 19... ... 18 Mongolia 4.3 0.4 4.7 82 26 9.9 2.7 11.2 11.5 .. 5 Morocco 1.2 2 4 .......36.6 124........ 49 0.1 0.4 1.2 1.1 3 3 Mozambique 4.6 . 0 . . . Myanmar 0.4 0.1.. . 0.2 0.1 09 0.6..... Nam ibia ...... ......4.1 .... ....3.4 . 6.8 .........315 127 .. ...... 0.3 Netherlands 6.2 ........24.4 8.5 1,784 1,978 2.1 . 2.5 12.5 11.3. 11. 33 .... 5 New Zealand 5.9 ... 17.7... 7.6 1,309 1,348 1.6.... 2.1. .. .7.3 ... .14. 7. Nicaragua 53.3 3.4 ........86.6 174 .......37 0.4 0.6 1.8 .... Niger 1.6 ... .. 0.0.. ............ Nigeria 0.2 .... 0j.7 ... 1.0 .... 11 5 0.1 0.2 0.9 1.7.... Norway 6.2 1.3 7.5 1,844 2,622 1 9 3.1 15.0 13.3 15 10... l. 4 Om an -.. .....2.5 ....... . 7.. ......... ~ . ...... ........ 0~5 . 0.9 . . .1.6 .....2.11 5 . 00... Pakistan 0.8 ......2j 7... .. 3.5 45 ..... 13 0.3 0.5 0.6 0.7 . 3 Panama 4.7 67 2 .7..... 457 199 1.0 1.8 . 2.5.. Papu New.. Guna.... .. 0....1...0 ..1 . 5..... 4.... 0 Paraguay 1.8 ~ .......33.3 ... ..5.1 173 . 86 06 0.3 .. 0:6 ....... .......... Peru 2.2 1.5 3.7 156 80 0.7 1.0 . 1.4 OC 2 Philippines 1.3 1.0 24.4 ....... 67 17 0.1 0.1 1.7 1.1 . .... ...... . ...... Poland ....... 4.8 .....0-.4 ..... 5.2 297... 182 1.8 2.3. 5.6 ....6.2 14 11 ... 6 Portugal.4.9 3 3 8.2 1,114 805 2.0 3.0 . 4.1 11 10 3 Puerto Rico 65.5 . Romania 2:9 .....1~5 ..18 8:8 7..6 8 1 Russian Federation 4.1 0~6.6...... 5.8 262..... 117 4.0 4.1 13.0 11..7 22. 17.. 8 1999 World Development Indicators 91 2.13 Health expenditure Health Physicians Hospital beds Inpatient Average Outpatient expenditure admission length visits per capita rate of stay per capita Public Private Total PPP per 1,000 per 1,000 % of ftof GDP ft of GDP ft of GDP $ $people people popalation daye j990-971 j990.971 199g-971,b 1.990-97a 1990-971 1980 199G-971 1980 1990-971 1.990-97a 1990-97- 1990-971 Rwanda 1.9.. ... . 00 00 1.5 1.7 Saudi Arabia 6:4. 16 8:0 ..... ...221 .......161 0.5 1.3 1:.5 2.5. 11 ...... 11 1 Senegal 1:2 ..... ......01 01...........0.7 Sierra Leone 16 2.0 01 1.2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . Singapore 1i5........ 1 9 .....3.3 829 943 0.9 1.4 4.2 3.6 12 Slovak Republic 6.1 .. 3.0 7.5 20 1-1 12 Slovenia 7.1 .......... ......... ......... .... 2.1 7.0 5.7. 16 11 ... ......... South ria ..... .....Africa....4 3 .......3.69........4.34 .. ....7.9 ......06542....... 258........ .... ..0.....6. Spain 5.8 1.6 7.4 1,146 1,003 2 8 4.1 . 4.0 10 11 Sri Lanka 1.4 0.4 1.9 38 ....... 11 0.1 0.1.. ... 2.9 .....27.7..... Sudan .. 1.9 0~~ ~ ~ ~ ~~~ ~~~~~~ ~~~~~~~~~~~~~~.2 . ........ 4 01....09...1.1. Sweden .... ....... 7.2 .....1:4.4 ...... 8.6 1,699 2,222 2.2 .. 3.1 14.8 6.3 18 8. 3 Switzerland 7.1 3.1 10.2 2,527 3,603 3 2 .. 20. 15 .. 11 Syrian Arab Republic .. .._.4 0.8 1.1 1.1 Tajikistan 2.4 . . 2.4 2.0 10.0 ....8.-8 1 6 15 Tanzania -1.1 . .. 0.0 14.4 ~ 0.9q Thailand .......I ... 2.0 .1.9 . ......3:9.9 ....... 230 . 96 0.1 0.2 15 .... 1.-7.......... Togo1.6 2.2 3.4 59 15 01 0.1 1.5 Trinidad and Tobego 2.1 1.3 ........34.4 ....... 211 ......131. 0.7 0.7 . 3.2 Tunisia 3.0 2.8 5.9 ... .....260 ..... ...99 . 0.3 0.6 2.1 1' J 8 .......8 ........:.. ....... Turkey 2.7 1.1 ...... .3:8.8 .. .... 227 .... . 113 0.6 1.1 2. . .22.... 2.5 66 1 Turkm enistan 1.2 ... ... . .. .......... ... ........ ...... . 2 9.9 . 3.2 10.6 11.5 ...... 17 .... ... 15. Uganda 1.9 2.1 .3:9... ...... 34 .........90.0 " ..... 1.5 0.9 Ukraine 3.9 .. . 37 4.5 ... 12.5 ....99 9...... 20 .......17. 10 United Arab Emirates 20.0 .... 0.5 . . ...2:5 .........390 .......379 1 1 0.8 2 .8 3.1I 11 5 United Kingdom 5.7 10 0....... 6.7 1,386 1,454 16 6 .. 1.5 ..... 93 .....47.7 .. 23...... 10 .......6 United States 6.6 7:5.5 ..... 14.1 3,95 4,093 . 18....25..... : . . 2. ...... ... ... Uruguay 1.9 ..... 6.5 ...... 875.5 .... .. 618....... 440 2.0 3.2 4.5.......... Uzbekistan 3.3 . . . 2.9 3.2 11 5 8.3 19 14 Venezuela 1.0 4.5 .. .....7.5 .........617. 213 0.8 1.6 0.3 2.6 ............. Vietnam 1.1 ........4.1 .......5:2.2 .. ... 63 . ......9 0.2 0.4 3.5 3.87 8 ...... 3 Yemen, Rep. 1.3 ........4 6.6 -59 ... ......19 ........39 0.1 0.1. 0.8 ............. Yugoslavia, FR Ser./Mon... . . ..2. 3 1 Zambia 29.9 0.7 ........33.3 ........ 31. 17 0.... . . 1.... 0.1 3.5 ' ........... Zimbabwe 17 31 4.7 133 41 02 0.1 3.1 0.5 Low Income 1.0 3.2 4.5 52 15 0.3 ... ... 1.1 ... ..... ......9.... ...113 Middle income 2:4 ........2 0 _ 4.4 .... ....183 ........89 . ......1..3 ....1.7 3.9.3.6.7.1495 Lower middle income .......... 2:2 ......17 .......39 ........ 119 38 ....... 1~4. 1-8 3.9 3.67 15 6 Upper middle income 3.0 3 .1 62 .... ....427. 283 ........10.0 .15 ..... ...6 ..... 7. .3 Low &middle income.... .. 1:8 2.5 4.4 133 61 .. 0.9 . .1.2 2.7 2.7 .. 7 ..14 4 East Asia &Pacific 1.8. 1 8 3.6 118 46....... 0.8 14 20 2.1 4 15 ...5 Europe & Central Asia3.9 ...... 10 .55 ........279 .... 144 3.0 3.6 .....104.4 . 10.1 . ....17 .......14. . . 6 Latin America &Carib. 26 ........3.7 63 .........412. . 274 0.8 1.4 .. ........ .2........4 2 Middle East & N. Africa.......... 2:3 ........24 . ......4:7.7 ... ... 176 ..89 .. ... .0 7.7 .. 1.7 ....1.8 58 4 South Asia 0.8 3.8 5 57 16 0 0.4 0.7 0.7... i 3 Sub-Saharen Africa.... 1.7 ... .....1:.5 2.7 .. ......82 .... 34 ....... . 1..2 .... . ...4 High income 6.0 3.6 9.6 2,280 2,485 1.8 2.5 . 5.8 14 15s Europe EMU 6.7 ....... 2.1 8.9 1.810 1,969 2.1.... 3.2 ...... 79 9 ..... 18 ...... 13 .6 a. Data are for the moat recent year available. b. Data may not surn to totals because of rounding. c. Lest then 0.5. 92 1999 World Development Indicators National health accounts track resource inputs to the differences in cases and financing methods, cross- * Public health expenditure consists of recurrent and health sector, including both public and private expen- country variations in average length of stay may result capital spending from government (central and local) ditures. By contrast with industrial countries, few devel- from differences in the role of hospitals. Many develop- budgets, external borrowings and grants (including oping countries have health accounts that are ing countries do not have separate extended care facil- donations from international agencies and nongovern- methodologically consistent with national accounting ities, so hospitals become the source of both long-term mental organizations), and social (or compulsory) approaches. The difficulties in creating national health and acute care. Other factors may also explain the vari- health insurance funds. * Private health expenditure accounts go beyond data collection. Before beginning to ations. Data for some countries may not include all pub- includes direct household (out-of-pocket) spending, pri- establish a national health accounting system, a coun- lic and private hospitals. Admission rates may be vate insurance, charitable donations, and direct ser- try needs to define the boundaries of the health care overstated in some countries if outpatient surgeries are vice payments by private corporations. * Total health system and a taxonomy of health care delivery institu- counted as hospital admissions. And in many countries expenditure is the sum of public and private health tions. The accounting system should be comprehensive outpatient visits, especially emergencyvisits, may result expenditures. It covers the provision of health services and standardized, providing not only accurate book- in double counting if a patient receives treatment in (preventive and curative), family planning activities, keeping, but also critical information on the equity and more than one department. nutrition activities, and emergency aid designated for efficiency of health financing to inform health policy- health but does not include provision of water and san- making and health system reform. itation. * Physicians are defined as graduates of any The absence of consistent national health account- Public health spending in low-income faculty or school of medicine who are working in the ing systems in most developing countries makes cross- countries, 1990-97 country in any medical field (practice, teaching, country comparisons of health spending difficult. research). * Hospital beds include inpatient beds Records of private out-of-pocket expenditures are often Aa, of available in public, private. general, and specialized lacking. And compiling estimates of public health expen- level reQuired hospitals and rehabilitation centers. In most cases Pet capita for minimum ditures is complicated in countries where state or provin- Counap ta helth beds for both acute and chronic care are included. Countrs USS health packaee cial and local governments are involved in health care r.,,,p;oer 6 , * Inpatient admission rate is the percentage of the financingand deliverybecausethe dataon public spend- , .n population admitted to hospitals during a year. ingoften are not aggregated. The data shown in the table E,ic. 'e * Average length of stay is the average duration of are the product of an effort to collect all available infor- H ,, m inpatient hospital admissions. * Outpatient visits per mation on health expenditures from national and local L W" 1: ? capita are the number of visits to health care facilities government budgets, national accounts, household sur- - per capita, including repeat visits. veys, insurance publications, international donors, and - existing tabulations. Data sources Health service indicators (physicians and hospital ..: , - ." .. :. beds per 1,000 people) and health utilization indicators Health expenditure estimates (inpatient admission rates, average length of stay, and Los income counm.es spend less on neallh than come from country sources, outpatient visits) come from a variety of sources (see do middle 3nd high income countries. both In supplemented by information below). Data are lacking for many countries, and for oth- absolute terms ana as a shaie of GDP. In man, lon_ from international agencies income courtries the amount spent per capita Is ers comparability is limited by differences in definitions. much les, than Ihe eslimatd S12 a Vear neeaed and World Bank country and For example, some countries incorrectly include retired to secuie the minimum pre.ent-se ano essential sector studies, including the physicians (because deletions are made only periodi- clinical ser.ices. Human Development Net- cally) or those working outside the health sector in esti- work's SectorStrategy: Health, mates of health personnel. There is no universally Nutrition, and Population. Data were also drawn from accepted definition of hospital beds. Moreover, figures World Bank public expenditure reviews, the International on physicians and hospital beds are indicators of avail- Monetary Fund's government finance data files, and ability, not of quality or use. They do not show how well other studies. Data on private expenditure are largely trained the physicians are or how well equipped the hos- from household surveys and World Bank poverty pitals or medical centers are. And physicians and hos- assessments and sector studies. The Organisation for pital beds tend to be concentrated in urban areas, so Economic Cooperation and Development (OECD) pro- these indicators give only a partial view of health ser- vided data on public and private health expenditures and vices available to the entire population. use of health services for member countries. Data for Average length of stay in hospitals is an indicator of physicians and hospital beds are from the World Health the efficiency of resource use. Longer stays may reflect Organization (WHO), supplemented by country data. a waste of resources if patients are kept in hospitals beyond the time medically required, inflating demand for hospital beds and increasing hospital costs. Aside from 1999 World Development Indicators 93 2.14 Access to health services Safe water Sanitation Tetanus Child Immunization vaccination V0 of %of % Of Measles DPT population population pregnant % of children % of children with access with access women under 12 months under 12 months 1982 1995 1982 1995 j995-97O 1980 1997 1980 1997 Albania 92 76 58 58 90 95 94 99 Angola 28 32 18 15 15 26 78 6 41 Argentina 55 65 69 75 75 58 98 41 85 Armenia .. 2 8 Australia 99 99 99 68 87 33 86 Austria 99 . 25 90 90 90 Azerbaijan 36 36 99 ::95 Bangladesh 40 84 4 35 35 1 97 1 98 Belarus 100 ...98 .97 Belgium 98 . .. 50 64 95 62 Benin 14 72 10 24 24 82 ::78 Bolivia 53 70 36 41 41 13 98 11 82 Bosnia and Herzegovina ..41 41. .. 85 .79 Botswana 77 70 36 55 55 63 79 66 76 Brazil 75 69 24 67 67 56 99 40 79 Bulgaria 85 . I., 98 93 97 94 Burkina Faso 35 .5 ..23 68 2 70 Burundi 23 58 52 48 48 30 50 38 63 Cambodia ..13 . .68 70 Cameroon 36 41 36 40 40 16 43 5 44 Canada 97 99 60 95 95.983 Central African Republic 16 23 19 45 45 12 46 13 53 Chad 31 24 14 21 21 .30 1 24 Chile 86 91 67 81 81 87 92 94 91 China ..83 ..7895896 Hong Kong, China. 74 82 73 88 Colombia 91 75 68 59 59 14 89 15 81 Congo, Dam. Rep. . .............: -. . .. 18 20 18 18 Congo, Rep. . 40 9 9 49 18 42 23 Costa Rica 93 100 95 97 97 60 99 86 91 C6te dIlvoire 20 72 17 51 51 ..68 .7 Croatia 70 63 67 61 61 ..93 ..92 Cuba 61 91 31 92 92 48 99 67 99 Czech Republic 100 . .... 96 . 98 Denmark.. . 100 100 . 84 85 90 Dominican Republic 49 73 66 80 80 29 80 35 80 Ecuador 58 55 57 53 53 24 75 10 76 Egypt, Arab Rep. 90 84 70 70 70 78 92 84 94 El Salvador 51 53 62 77 77 45 97 43 97 Eritrea 7 ......53 ..60 Estonia.... 74 88 84 85 Ethiopia 4 26 ..8 8 4 52 3 63 Finland 95 98 100 100 100 70 98 92 100 France 98 100 ... .. 15 83 79 97 Gabon 50 67 50 76 76 . 57 14 41 Gambia, The 42 50 77 37 37 71 91 80 96 Georgia ... .. .95 ::92 Germany 90 . ..35 75 45 Ghana .. 65 26 32 32~~~~~~~~~~~~~~~~~~~~~~~... ..6. 59.. 7. 60.... Greece 85... 90 72 85 Guatemala 58 67 54 67 67 23 7443 78 Guinea 20 55 12 14 14 56 .: 53 Guinea-Bissau 24 53 25 21 21 519 63 Haiti 38 39 19 26 26 .32 3 34 Honduras 50 77 32 82 82 35 89 31 94 94 1999 World Development Indicators 2.14 Safe water Sanitation Tetanus Child immunization vaccination % of % of % of Measles OPT population population pregnant % of children % of children with access with access women under 12 months under 12 months 1992 ±995 1982 1995 1995-971 ±1980 1.997 1980 1997 Hungary ~~87 9910 99 100 India 54 85 8 16 16 81 31 90 Indonesia 39 65 30 55 55 2 92 1 91 Iran, Islamic Rep. 50 90 60 81 81 39 96 32 100 I.raq 74 77 69 70 70 35 98 1 92 Ireland 9 7 . . .. 10..3 Israel .100 99 . 100 100 69 94 8492 Italy 99 ....5 75 60 Jamaica 96 93 91 74 74 12 88 34 90 Japan 99 96 99 100 100 69 94 60 100 Jordan 89 98 76 98 98 29 95 30 93 Kazakhstan .....97 ..96 Kenya 27 45 44 45 45 32 36 Korea, Dem. Rep. 100 100 100 100 100 29 100 50 100 Korea, Rep. 83 83 100 100 100 4 85 70 80 Kuwait 100 100 100 100 100 48 95 67 96 Kyrgyz Republic ..81 ...98 ..98 Lao PDR . 51 32 32 . 67 . 60 Latvia ... .... ... ................. .. 97 ..75 Lebanon 92 94 59 9 7 97 .894 9 Lesotho 18 62 12 . 49 53 56 57 Libya 90 95 70 86 86 65 92 60 96 Lithuania ....96 .90 Macedonia, FYR ... .98 97 Ma aacar 31 16 . 34 34 . 68 48 73 Malawi 32 60 60 64 64 49 87 58 95 Malaysia 71 89 75 94 94 11 83 58 91 Mali. 48 21 37 37 . 56 52 Mauritani'a 37 64. 32 32 45 20 18 28 Mauritius 99 100 9 7100 100 34 85 8 789 Mexico 82 95 57 76 76 35 97 41 83 Moldova .56 .5009997 Mongolia 100 54 50 ............ .. .. 17 98 76. 92 Mozambique 9 24 10 23 23 32 70 56 61 Myanmar 20 60 20 43 43 8 4 90 Namibia . 60 . 42 42 . 57 . 63 Nepal 11 59 0 23 23 2 85 8 78 Netherlands 100 99 100 100 91 96 96 95 New Zealand 87 90 88-..80114 76 86 Nicaragua 50 62 27 59 59 15 94 15 94 Niger 37 48 9 17 17 19 42 6 28 Nigeria 36 50 57 57 55 69 . 45 Norway.99 100 100 100 80 9 90 92 Oman 15 88 39 85 85 22 98 18 99 Pakistan 38 62 16 39 39 1 74 2 74 Panama 82 84 81 90 90 47 92 47 95 Papua New Guinea : 3-1 25 25 1 40 32 45 Paraguay 23 39 49 32 32 19 61 17 82 Peru 53 66 48 61 61 21 94 14 98 Philippines 65 83 57 77 77 9 83 47 83 Poland 82 ......92 96 9695 Portugal 66 82 . .. 54 99 73 95 Puerto Rico . 97 .. .. .. Romania 77 62. 44 44 83 97 9 Russian Federation ..95 87 1999 World Development Indicators 95 2.14 Safe water Sanitation Tetanus Child immunization vaccination % of % of % of Measles DPT population population pregnant % of children % of children with access with access women under 12 months under 12 months 1982 1995 1.982 1995 1995-971 1980 1997 1980 1997 Rwanda ..94 94 42 66 17 77 Saudi Arabia 91 93 76 86 86 8 92 41 92 Senegal 44 50 65 : 65 Sierra Leone 24 34 13 36 28 13 26 Singapore 100 100 85 100 100 47 89 84 93 Slovak Republic 43 51 51 98 98 Slovenia 98 80 98 98 82 91 South Africa 59 53 53 74 76 74 73 Spain 99 9 7 97 8 90 88 Sri Lanka 37 70 66 75 75 94 46 97 Sudan 40 60 5 22 22 1 92 1 79 Sweden 100 : 56 96 99 99 Switzerland 100 -100 Syrian Arab Republic 71 88 45 71 71 13 93 13 95 Tajikiatan 69 62 62 95 95 Tanzania 52 49 86 86 45 69 59 74 Thailand 66 89 47 96 96 92 49 96 Too 35 55 14 41 41 38 33 .rndd n obago 98 96 100 96 96 88 24 90 Tunisia 72 90 46 80 80 65 92 36 96 Turkey 69 27 76 42 79 Turkmenistan 660 0 0 100 9 Uganda 16 42 13 67 67 22 60 9 58 Ukraine 55 ~~~~~~~~~~~~~~~~~~~~~~~49 49 97 53 96 United Arab Emirates 100 98 75 95 95 34 95 11 92 United Kingdomn 100 100 96 96 52 95 44 95 United States 100 73 98 8 89 96 94 Uruguay .. 83 89 59 61 61 50 80 53 88 Uzbekistan 57 18 18 88 96 Venezuela 84 79 45 72 72 50 68 56 60 Vietnam 4 7 30 60 60 1 96 4 95 Yemen, Rep. 39 19 19 2 51 1 57 Yugoslavia, FR Srb./Mont. 91 94....... Zambia 48 53 4 751 51 69 83 70 Zimbabwe 10 77 5 66 66 56 73 39 78 Low income 47 69 12 29. 74 .25 75 Middle income ..79 93 91 Lower. m iddle incom e . 78 ..... ........... ..... ........... .. .. ..... .... 93 .93 Upper middle income 77 . 50924 82 Low & middle Income . 75 ...52 .83 ..83 East Asia & Pacific . 77 ...36 . 3.9 Europe & Central Asia ........91 8 Latin America & Carib. 73 75 46 68 57 42 93 37 82 Middle East & N. Africa 69 .. 62 5 44 88 42 90 South Asia 50 81 9 20 74 . 81 27 87 Sub-Saharan Africa 47 47 39 S 53 High income 97 .~~~~~~~~~~~~~~~~~~~~~~~.............. .. . .. Europe EMU 95. 75. 69 a. Data are for the most recent year available. 96 1999 World Development Indicators 2144 The indicators in the table are provided to the World shots during the first pregnancy and one booster shot * Percentage of population with access to safe Health Organization (WHO) by member states as part during each subsequent pregnancy, with five doses water is the share of the population with reasonable of their efforts to monitor and evaluate progress in considered adequate for lifetime protection. access to an adequate amount of safe water (includ- implementing national health-for-all strategies. Information on tetanus shots during pregnancy is col- ing treated surface water and untreated but uncon- Because reliable, observation-based statistical data lected through surveys in which pregnant respon- taminated water, such as from springs, sanitary for these indicators do not exist in some developing dents are asked to show antenatal cards on which wells, and protected boreholes). In urban areas the countries, the data are estimated. These estimates tetanus shots have been recorded. Because not all source may be a public fountain or standpipe located may be skewed by a country's desire to show women have antenatal cards, respondents are also not more than 200 meters away. In rural areas the progress or to establish a need for international asked about their receipt of these injections. Poor definition implies that members of the household do assistance. recall may result in a downward bias in estimates of not have to spend a disproportionate part of the day Indicators of access to health services measure the share of births protected. But in settings where fetching water. An adequate amount of safe water is the supply of services but reveal little about benefits receiving injections is common, respondents may that needed to satisfy metabolic, hygienic, and or rate of use. They provide no information on how erroneously report having received tetanus toxoid. domestic requirements-usually about 20 liters a the quality or consumption of services differs among person a day. The definition of safe water has groups within countries, regions, or communities, for changed over time. * Percentage of population with example. And unless the indicators are based on sur- access to sanitation is the share of the population vey statistics, they may not fully reflect the situation with at least adequate excreta disposal facilities that in the country. can effectively prevent human, animal, and insect People's health is influenced by the environment contact with excreta. Suitable facilities range from in which they live. A lack of clean water and basic san- simple but protected pit latrines to flush toilets with itation is the main reason diseases transmitted by sewerage. To be effective, all facilities must be feces are so common in developing countries, correctly constructed and properly maintained. Drinking water contaminated by feces deposited near * Pregnant women receiving tetanus vaccination homes and an inadequate supply of water cause dis- are the percentage of pregnant women who receive eases that account for 10 percent of the total dis- two tetanus toxoid injections during their first preg- ease burden in developing countries (World Bank nancy and one booster shot during each subsequent 1993c). To date, however, efforts to improve the pro- pregnancy. * Child immunization is the rate of vac- vision of water, sanitation, and drainage have been cination coverage of children under one year of age disappointing. At the end of the 1980s-which had for four diseases-measles and diphtheria, pertus- been declared the International Drinking Water sis (or whooping cough). and tetanus (DPT). A child is Supply and Sanitation Decade by a coalition of inter- considered adequately immunized against measles national aid agencies-most people in poor regions after receiving one dose of vaccine, and against DPT still lacked adequate sanitation. after receiving two or three doses of vaccine, depend- Governments in developing countries usually ing on the immunization scheme. finance immunization against measles and diphthe- ria, pertussis (or whooping cough), and tetanus (DPT) Data souces as part of the basic public health package, though they often rely on personnel with limited training to The table was produced provide the vaccines. According to the World Bank's r using information provided to WorldDevelopmentReport1993: Investingin Health, the WHO by countries as part these diseases account for about 10 percent of the , of their responsibility for disease burden amongchildren underfive, compared ' monitoring progress toward with an expected 23 percent at 1970 levels of vacci- "health for all" as well as nation. In many developing countries, however, data -- .. data from the WHO's EPI recording practices make immunization difficult to Information System: Global measure (WHO 1996). Summary, September 1998, and the United Nations Neonatal tetanus is an important cause of infant Children's Fund (UNICEF), The State of the World's mortality in some developing countries. It can be pre- Children 1999. vented through immunization of the mother during pregnancy; the mother transfers immune protection to the baby through the placenta. Recommended doses for full protection are generally two tetanus 1999 World Development Indicators 97 2.15 Reproductive health Total fertility Adolescent Women at risk Contraceptive Births attended Maternal rate fertility of unintended prevalence by skilled mortality rate pregnancy rate heaith staff ratio births % of per 1,000 married % of per births women women women 100,000 per women aged 15-19 aged 15-49 aged 15-49 % of total live births 1980 1997 1997 1990-95, 1990-958 1982 i996-98, 1990.97a Algeria 6.7 ....... ...3.6 ..22 .51 .............. 7 7 4 k Angola 6.9 .... ....6 8 219 . ....34. 17 1,500C Argentina 3.3 . 2.6 ............65 ...97 ....... ...1 0c Arm enia 2.3 1.5 47 ................................. Australia 1.9 1.8 22 ...99 9 Azerbaijan 3.2 2.1 23 ... . 994 Bangladesh 6.1. 3.2 144 16 49 2 8 5c Belarus 2.0 1.2 32 .... ...... .... ...... ...............2 B elgium 1 ....... .7 1.6 ............11 ..... I. .......... .... .................. .. i. ........... on Benin 7 0 5.8 115 21 16............ 34 60 50d Bolivia 5.5 4.4 79 24 45 .. 4687 Bosnia and Herzegovina 2.1 1.6 33 .,................ ......... ............ Botswana 6.1 4.3 78 .. .. ...77 250 c Brazil 3.9 2~3 73 7 77............ 98 ........881 0 Bulgaria 2.0O .......... 1 1 45 ............ .... ........ 99 b Burkina Faso 7.5 66 .......... 147 33 8 ............ 12 41......... 930 B urundi 6 .8 6 .3 55 ...... .... .... .. .......... . .. ................... ........ 24 Cambodia 4.7 4.6 14 ..........31 .......9O Cameroon 6.4 5:3 140 22 16 ............. ........ 58......... 550 C C anada ..................I. 1 7..I.....16..... 24.. ................. ... .. . . Central African Republic 5.8 4.9 133 16 14 .................... 4670 Chad 6.9 6.5 190 9 4 -24 15 ............ Chile 2.8 2..4 48 . 2 9 65b China 2.5 1.9 15 . 85 . 8 Hong Kong, China 2.0 1.3 7 . .89 .......100....... Colombia 3.9 2.8 88 8 72 . 85 ...........C0 Cong,.De.. Rep6.6..4.27.......... 870 Congo, D m Rep. 6.3 6.1 2141 .................. .. . .........50 ...... Costa Rica 3.7 2~~~~~~~~~. . 8......... 85.............93...97 Croatia ......... ..... . . ...1.6.9....... Cuba 2.0 1.5 65 ~~~~~~~~~~~~~~~~~~~~~~~~.......... ...........99 . 36..... Czech, Republic 2.1 1223.69. 10089 Denma Rkc 1.5 18 9.............. . ............ Ecuador....- ......I ............. . 50 30.... 68..57626 M d'lvoire 75 1 3 513 16 48 .............:.........468 0 Croto ia 2.01293 ........... .. ................ .. 100................. 2 Ethu pa 26 6 65 157.................. ., ..4 . .............. 8..... 19400 Finland ..................... 16 1.... . 9.... .................lie Franche pu lc21. 1.793 69 ........... .. ... .. 15n Gabon 45 5.2 166 ~~~~~~~~~~~~~~~~~~~~~~~~~~~.... . : ....... ............ .......80 ............. Germank 1.4 1.41 . ..9. 2 Ghana 6.5 4.9 106~~~~~~~~~~~~~~~........... 33....... 20...... ...... ................... 44..................... Greece ..I................... 2 ..2_.3. .......... 1 173 64 .. ... ion...11 G umine ca n e u6i .1 ..... 5..5.190 25............. 2............................... ...... 31.... 96 ...... ...... Guinea-Bissau............... 5 8. 5.......... . 8..190..... 57 62 64 9100c Hcaiti. ..........3 06 ......... 59 4470...............48.. 18.... 34............. 20.................... Honduras 65.............. ........ 43 115.. ....50.........2200. 98 1999 WR ld. Dev lop en 3Indicators....5 1 .....4 .. .......... .....4 ........1 2.15 1 Total fertility Adolescent Women at risk Contraceptive Births attended Maternal rate fertility of unintended prevalence by skilled mortality rate pregnancy rate health staff ratio births % Of per 1,000 married % of per births women women women 100,000 per woman aged 15-19 aged 15-49 aged 15-49 % of total live births 1980 1.997 1997 1990-98, 1990-95o 1.982 1996-98a 1990-971 Hugry 1.9 1.4 28 ...99 .......96 4 India 5.0 3.3 118 20 41 23 ....... 35 40 Indonesia 4.3 2.8 ...........61 ii. 57 ............27 . 36 ........ 390d Iran, Islamic Rep. 6.7 2.850 ..73 . 74 1200 Iraq 6.4 4.7...40........47 4 54 3100 Ireland 3.2 1.9 15 . 60 . . .. 100 Israel 3.2 2.7 19 . .. 99 70 Italy.1.6 1.2 8 .....120 Jamaica 3.7 2.7 104 .!................. 65 .......... 86 .92 ..... .1200 Japan 1.8 1.4 3.................... .............18 Jordan ~~~~~~~~~~6.8 4.2 42 22 53. 97 150 0' Kazakhstan 2.9 2.0 46 11 -59 9953 Kenya 7.8 4.7 11236 38... . ...45.... ...... 0... Korea, Dam. Rep. 2.8 2.0 2 100 700 Korea, Rep. ~~~~~~~2.6 1:7 ...........4 70 .. .... 95 3Qb Kuwait 5.3 2:9 ............34 ..98 ....... 98 200 W.,r&yz Republic 4 1 2 5 3512 Lao PDR 6.7 5.6 43 . 30 660.0 Latvia 2.0 1.1 32 .. 100 150 Lebanon 4.0 2.5 ............26 89 89 3000 Lesotho 5.5 4.8 86 . 23 50 6100 Libya 7.3 3.8 5 6 45 ............68 ... 94 2200c Lithuani'a 2.0 1.4 35 . 100 130 Macedonia, FYR 2.5 1!9 39 ... 952 Madagascar 6.6.......... 5.8 175 26 19 ....... .. .... .57 500od Malawi 7.6 6.4 154 36 22 .... ..5 2 Malaysia 4.2 ... .. 3.2 25... 88 98 340 Mali 7.1 6.6 181 26 7........... .14 .... 24 8 d Mauritania 6.3 5.5 135. ... ................. .... ....... 40 8000c Mauritius 2.7 1:9.9 .......... 37 75 ........ .... 7........ 97 110 b Mexico 4.7 2.8 70... 75 1100 Moldova 2.4 1.6 29 7 4 3 Mongolia 5.3 2.6 47 ............. . : .... ..99. .650 Morocco 5.4 3.1 5016 50.. 20....... 40 370' Mozambique 6.5 5.3 162 76... ........29 .44 1, 1000C Namibia 5.9 4.9 105 22 29.................. 68 20 Nepal 6.1 4.4 12028 9 1,500c Netherlands 1.6 1.5 4 ..100 -..120 New Zealand 2.0 1.9 52 .... . ....... .. 25c Nicaragua ~~~~~~~~~6.3 39 .......... 135 44 61 ........ 1600 Niger 7.4 7.4 215 19 8..... 26. 15 5900 Nigeria 6.9 5.3 116 22 6...... .... 31 1,0000, Norway 1.7 1.9 16 ............ 100~ ....... 60 Oman 9.9 4.8 65 ...... .91 Pakistan 7.0 5.0 104 32 24 . 18 3400c Panama 3.7 2.6 82 . 80 84 550 Papua New Guinea 5.8 4.3 68 29 26 53 . ...... 3700 Paraguay 5.2 3.8 92 15 51. 61 9 Peru 4.5 3.2 68 12 64............ 30 ....... 56 2800 Philippines 4.8 36.6 .......... 43 26 48 ............. . ...... 53 210d Poland 2.3 1.5 23. .. 98 50 Portugal 2.2 1 .42 4..... ... . ... . . . ..21. .. . 1. .. .. ... .. .. ... .. .. .. .. . .. .. ... .. .. . .... .. .. . .. .. . . ..... .. .. .150. . .. ... .. . . ... .. ... .. Puerto Rico 2.6 1.9 69 .7.......... ....... 78 . 99 210 Romania 2.4 1.3 41 57. 99 410 Russian Federation 1.9 1.3 46 . 34 ..530 1999 World Development Indicators 99 C ~2.15 Total fertility Adolescent Women at risk Contraceptive Births attended Maternal rate fertility of unintended prevalence by skilled mortality rate pregnancy rate health staff ratio births % Of per 1,000 married % of per births women women women 100,000 per women aged 15-19 aged 15-49 aged 15-49 % of total live births 1980 1997 1.997 ±990-95, 1990-98, 1982 1996-981 1990-971 Rwanda 8.3 6.2 56 37 21 20 26 1,300c Saudi Arabia 7.3 5.9 114............ . ................. . ..... 90...g 18b Senegal........... 6.8 ........5.6 119 ............. 33 13 . .. 47. Slo Sierra Leone 6.5 6.1 202 .. ... 25 Singapore 1................I.7 ....... ...1.7 ............10 ...100 .......100 100 Slovakt Republic 2.3 1~4 33 .. .. 10 Slovenia 2.1 1~3 16 .. .. 10 South Africa 4.6 2.8 43. 69 . 82 2300c Spain 2.2 1~18 7C Sri Lanka 3.5 2.221 30859 Sudan 6.5 4.6 54 25 10 23 86 . 370b Sweden 1-7 1:7 10 70M Switzerland 1.5 1.5 5... Syrian Arab Republic 7.4 4.0 44 40 43............ .. .......77...... . ...... ......................0............... Tajikistan 5.6 3.5 29 ..92 581; Tanzania 6.7 5.5 126 24 18 38 530 d Thailand 3.5 1.7 70. 40 7 12000c Togo ................ 6.6 6.1 .... 117 ....32 . 6400 Trinidad and Tobago 3.3 1.9 46 .. 98 900 Tunisia 5.2 2.8 14 60 50 .10 Turke 4.3 25.5.......... 44 11 70 76 1800C Turkmenistan 4.9 3.0 17 .. . . 9 Uganda 7.2 6:6.6......... 196 29 15 ..... 38 55O1 Ukraine 2.0 1.3 34 3. . United Arab Emirates 5.4 3.5 56 . 94 99 260 UntdKingdom 1.9 1.7 28 :.9 . United States 1.8 2.0 60 76 100 120 Uruguay 2.7 2.4 70 . 96 850 Uzbekistan 4.8 3.3 36.. 9824b Venezuela 4.2 3.0 98 ................ 82 ... 97 120b Vietnam 5.0 2.4 28 75107905 West Bank and Gaza ..60 100 . Yemen Rep. 7.9 6.4 105. 21 . 43 1,400 c Yugoslavia, FR (Serb./M ont.) 2.3 ........1.9 .......... .35 ............... .......... 93 2 Zambia 7.0 5.6143 27 26 :!. 4765d Low income 5.6 4.0 118 Middle Income 3.2 2.3 41 Lower middle income 3.1 2.2 35 U pper m iddle incom e .._ . .....3.8..2 5 ............6 . . ............2.5.................... ....--........61................ Low & middle Income.......... 4..1 3.0 75 East Asia & Pacific 3.1 2.1 27 Europe & Central Asia ...........2 5 .. _......1.7 ............38 Latin America & Carib. 4.1 2.7 75 Middle East & N. Africa 6.2 3.6 52 South Asia 5.3 3.5 119 Sub-Saharan Africa 6.6 5.5 134 High Income 1.9 1.7 26 Europe EMU 1.8 1.4 11 a. Data are for mnoat recent year avaiiabie. b. Official estimate. c. UNICEF-WHO estimate based on statistical modeling. d. Indirect estimate based on a sample survey, a. Based on a survey covering 30 provinces. f. Based on a sample sarvey. 100 1999 World Development Indicators 2.15 Reproductive health services include antenatal care, on national sources, derived from official community * Total fertility rate is the number of children that safe childbearing, provision of safe and effective con- and hospital records. Some reflect only births in hos- would be born to a woman if she were to live to the traception, and treatment and prevention of sexually pitals and other medical institutions. In some cases end of her childbearing years and bear children in transmitted diseases. These services will need to smaller private and rural hospitals are excluded, and accordance with current age-specific fertility rates. expand rapidly over the next two decades, when the sometimes even primitive local facilities are * Adolescentfertilityrateisthenumberofbirthsper number of women and men of reproductive age is included. Thus the coverage is not always compre- 1,000 women aged 15-19. * Women at risk of unin- expected to nearly double (Conly and Epp 1997). hensive, and cross-country comparisons should be tended pregnancy are the percentage of fertile, mar- Total and adolescent fertility rates are based on made with extreme caution. ried women of reproductive age who do not want to data on registered live births from vital registration Measurement of maternal mortality is subject to become pregnant and are not using contraception. systems or, in the absence of such systems, from many types of errors. Even in industrial countries with * Contraceptive prevalence rate is the percentage censuses or sample surveys. As long as the surveys vital registration systems, misclassification of mater- of women who are practicing, or whose sexual part- are fairly recent, the estimated rates are generally nal deaths has been found to lead to serious under- ners are practicing, any form of contraception. It is considered reliable measures of fertility in the recent estimation. In developing countries, few of which have usually measured for married women aged 15-49 past. In cases where no empirical information on age- vital registration systems, data on maternal mortality only. * Birthsattendedbyskilledhealthstaffarethe specific fertility rates is available, a model is used to are sometimes from hospitals or other health ser- percentage of deliveries attended by personnel estimate the share of births to adolescents. For vices records, and it is unknown how representative trained to give the necessary supervision, care, and countries without vital registration systems fertility they are. In rural areas few pregnant women have advice to women during pregnancy, labor, and the rates for 1997 are generally based on extrapolations access to health services, while in other settings hos- postpartum period, to conduct deliveries on their own, from trends observed in censuses or surveys from pitals receive a high proportion of women in need of and to care for newborns. * Matemal mortality ratio earlier years. emergency obstetric care. Household surveys such as is the number of women who die during pregnancy and An increasing number of couples in the developing the Demographic and Health Surveys attempt to mea- childbirth, per 100,000 live births. world want to limit or postpone childbearing but are sure maternal mortality by asking respondents about not using effective contraceptive methods. These cou- survivorship of sisters. This sisterhood method is ples face the risk of unintended pregnancy, shown in essentially a variant of a demographic approach to the table as the percentage of married women of estimating adult mortality. Its main disadvantage is Data on reproductive health come from demographic reproductive age who do notwantto become pregnant that the estimates of maternal mortality that it pro- and health surveys, the WHO's Coverage ofMaternity but are not using contraception (Bulatao 1998). duces pertain to the 12 years or so before the survey, Care (1997a), and the WHO and UNICEF's Revised Information on this indicator is collected through sur- making the method unsuitable for monitoring recent 1990 Estimates of Maternal Mortality: A New veys and excludes women not exposed to the risk of changes or observing the impact of interventions. An Approach (1996). pregnancy because of postpartum anovulation, meno- alternative approach collects more detailed birth and pause, or infertility. Common reasons for not using survivorship histories for siblings, as well as the age contraception are lack of knowledge about contra- at death for any who have died. This approach ceptive methods and concerns about their possible requires very large sample sizes and relies on the health side-effects. accuracy of the reported dates of births and deaths. Contraceptive prevalence reflects all methods- The maternal mortality ratios in the table are offi- ineffective traditional methods as well as highly ef- cial estimates from administrative records, survey- fective modern methods. Contraceptive prevalence based estimates using the sisterhood method, or rates are obtained mainly from demographic and derived from a demographic model developed by the health surveys and contraceptive prevalence surveys United Nations Children's Fund (UNICEF) and the (see Primary data documentation for the most recent WHO. The maternal mortality ratio should not be con- survey year). Unmarried women are often excluded fused with a true rate: the ratio includes maternal from such surveys, which may bias the estimates. deaths (mainly from abortion) in the numerator for The share of births attended by health staff is an which no births are included in the denominator. indicator of a health system's ability to provide ade- quate care for pregnant women. Good antenatal and postnatal care improves maternal health and reduces maternal and infant mortality. But data may not reflect such improvements because health infor- mation systems are often weak, maternal deaths are underreported, and rates of maternal mortality are difficult to measure. The data in the table are from the World Health Organization (WHO). They are based 1999 World Development Indicators 101 2.16 Health: risk factors and future challenges Prevalence Low- Prevalence Smoking Tuberculosis Consump- of anemia blrthwelght of child prevalence tion of babies malnutrition Iodized salt Weight for age Height for age % of % of % of Incidence Prevalence pregnant children children Male Female per 100,000 thouaanda % Of women % of births under 5 under 5 % of adulta % of adults people of caaaa householda 1985-95, 1982 1993-961 1992-97- 1992-971 j995-95O 198"5-9 1997 1997 ±992-981 Albania 6 7 18 50 8 28 2 Algeria 42 12 9 13 18 53 10 44 14 92 Angola 29 :::35 238 56 10 Argentina 26 .......... ..... 7 2 ...........5 ..........40 .........23 56 30 90 Armenia ::!.44 2 Australia .. 0 0 29 21 i Austria ..6 6 ...42719 2 Azerbaijan ..6 10 22 ...587 Bangladesh ........53. . ....... ....... 50 56 55 60 15 246 620 ........ 78 Belarus ..6 5 ......65 10 37 Belgium ..6 6 ...31 19 16 2 Benin 41 .29 25 .. . 220 21 79 Bolivia 51 10 10 8 29 50 21 253 27 92 Bosnia and Herzegovina ... .. .. 15 Botswana .. 827... 503 9 27 Brazil 33 ..6 11 40 25 78 194 95 Bulgaria ... 7 ...491746 Burkina Faso 24 21 21 33 29 ... 155 19 23 Burundi 68 1638 ... . 252 16 80 Cambodia .18 38 .. 70 10 539 107 Cameroon 44 . 13 ...... 133 35 86 Canada ..6 ... .31 2 Central African Republic 67 23 ..23 28 .. .. 237 9 65 Chad 37 11 . 39 40 .. . 205 22 55 Chile 13 7 7 1 2 38 25 29 5 China 52 .. 6 16 31 67 13 2,721 8 Ho go o g,C ing........ong..., China....... ................................2..........._3 ....29.... 9 .3.....95.. ....6 . Colombia 24 3 9 8 15 35 19 55 31 92 Congo, Dem. Rep. 7 6 13 .. 34 45 40 25 263 188 90 Congo, Rep. .. 1 62 5. . 2711 Costa Rica 28 . 7 5 6 35 20 18 1 89 C6te d'lvoire .. 14 14 24 24 ... 290 48 Croatia ... 811 .. 64 5 70 Cuba 47 10 8 8 . 49 25 18 2 45 Czech Republic 23 .. 6 1 ..43 31 20 2 Denmark .. 6...5..........537 37 111 Dominican Republic. ...........~ ......... 10 ......... :.........._6 11 66 14 114 14 13 Ecuador 17 .. 13 17 . .. 6 29 Egypt, Arab Rep. 24 7 1 5 26 40 1 36 350 El Salvador 14 9 9 11 23 38 12 74 7 91 Eritrea .. .. ..... 44 38 ..227 15 80 Estonia ... .. .52 24 5 Ethiopia 42 . 16 48 64 .. 5 1 Finland ..4 5 ...27 19 131 France ..5 6 ...40 27 19 11 Gabon 10 . .. 7 Gambia, The 80 35 26 30 . .. 211 4 0 Georgia .... .67 5 Germany ......37 22 15 12 Ghana 17 27 26 .. . 214 67 10 Greece ..6 9 :.. 46 82 29 3 Guatemala 39 10 14 27 50 38 18 85 13 64 Guinea ..18 13 24 .. ..-.. 171 22 Guinea-Bissau 74 13 20 23 ... . 114 37 Haiti_ ......... :....38... 5 ...15 ..... 8 .28 ....... 32 ........................ 385.......38 ..... 36.. 6 ..... 10...1 Honduras 14 9 9 18 40 36 11 96 9 85 102 1999 World Development Indicators 2.16 Prevalence Low- Prevalence Smoking Tuberculosis Consum- of anemia blrthwelght of child prevalence tion of babies malnutrition iodized salt Weight for age Height for age % Of % of % of Incidence Prevalence pregnant children) children Male Female per 100,000 thousands % Of woman % of births under 5 under 5 % of adults % of adults people of cases households 1985-95a 1982 1993-96a 1992-971 1992-971 19815-951 1988-95, 1997 1997 1992-98o Hungary9 9 40 27 47 7 India 88 33 5 2 4 3 187 4,854 7 Indonesia 64 14 11 34 42 53 4 285 1,606 62 Iran, Islamic Rep. . 4 1 61 55 62 9 Ira 18 6 24 12 40 5 160 56 10 Ireland . 4 29 28 21 1 Israel . 7 8 . 45 30 7 0 Italy . 7_ _7.. 38 26 10 5 Jamaica 40 10 10 10 10 43 13 8 0 100 Japan .. 5 6 59 129 48 Jordan ... 2 10 .11I175 Kazakhstan 11 . 9 8 16 . 104 27 53 Key 35 18 16 23 3429 106 10 Korea, Dem. Rep. .. .. . 178 915 Korea, Rep. ..9 4.68 7129 Kuwait 40 7 6 11 11 52 12 81 3 Kyrgyz Republic .. .. 6~~~~~: 11 25... 99 7 Lao PDR.. . 18 40 47 62, 8 167 17 93 Latvia .. 4 . 67 12 82 2 Lebanon ... 19 3 12 ...26 192 Lesotho 7 8 . 6 4 38 1 407 13 73 Libya ..5 5 5 15.. 19 290 Lithuania . 4 4... 52 10 80 5 Macedonia, FYR . 8 ..47 2 100 Madagascar . 15 34 0. . 0 8 7 Malawi 55 22 . 30 48 . . 404 33 58 Malaysia 56 10 8 20 . 41 4 112 30 Mali 58 13 17 40 3029 58 9 Mauritani'a... 9 23 44 . .261 Mauritiu 29... 15 10 47 66 10 Mexico 14 . 8 14 . 38 14 41 6 0 99 Moldova 50 .........73 5 Mongolia 45 . 10 12 26 40 7 205 9 62 Morocco 45 9 4 10_ 24 40 .9 122 28 Mozambique 58 16 20 26 36... 25 66 62 Myanmar 58 16 4 5 5 171 163 14 Namibia 16 . 26 29 .~~~~~~~~~~~~~~~.. ....527.. 12.. 59.... Nea 65 23 47 4 9 13 211 99 93 Netherlands .4 4..36 2911 New Zealand . 5 6 . . 24 22 5 0 Nicaragua 36. 15 12 24. 95 5 98 Niger 41 15 43 40. 148 32 7 Nigeria 55 18 16 39 38 24 7 214 442 98 Norway .. ~~ ~~ ~ ~ ~~~ ~~~ ~ ~ ~~~4 5. 36 36 6 0 Oman 54 . 8 14 16... 13 0 65 Pakistan 37 . 25 38 27 4 181 583 19 Panama .. 8 8 6 10 57 2 92 Papua New Guinea . 13 230. 46 28 250 30 Paraguay 29 7 9 .24 6 73 5 79 Peru 53 9 11 8 26 41 13 265 70 93 Philippines 48 11 30 33 43 8 310 481 15 Poland 16 8 9 . 51 29 44 26 Portugal . 8 5 . 38 15 55 4. Puerto Rico ..9 100 Russian Federation 30 . 3 67 30 106 241 30 1999 World Development Indicators 103 Prevalence Low- Prevalence Smoking Tuberculosis Consump- of anemia birthweight of child prevalence tion of babies malnutrition Iodized salt Weight for age Height for age % of % of % of Incidence Prevalence pregnant children children Male Female per 100.000 thousands % of women % of births under 5 under 5 % of adults % of adults people of cases households 19B5-95a 1982 1993-961 1992-97o 1992-97o 1985-951 19B5-95a 1997 1997 1992-981 Rwanda.. . 17 29 49... 276 17 9 Saudi Arabia . . 53 . 46 14 Senegal 26 .. 22 23 223 33 9 Sierra Leone ............ ......... ............. ................ .... 315 23 .75 Singapore 8 . 1432 3 48 2 Slovak Republic 6 43 2632 Slovenia 8 6 .. 35 23 30 1 South Africa 37 ..- 3 5 7 34 266 4 Spain 1 1. 48 25 61 23 Sri Lanka 39 25 18 38 24 55 1 48 14 47 Sudan 36 17 15 34 34 . 180 112 0 Switzerland 5 . 36 26 11 1 Syrian Arab Republic .. 10 7 13 21 ..75 174 Tajikistan 50 . .. 792 Tanzania 14 31 43307 147 Thailand 57 12 7 .49 4 142 180 50 Togo 48 .. 20 1 34 65 14 353 19 1 Trinidad and Tobago 53 .. 10 ..110 Tunisia .. 7 16 9 23 ..40 6 98 Turkey .. 8 8 10 21 63 24 41 42 18 Turkmenistan .. .. 2 7 1 7450 Uganda 30 ............ 26 38 ..312 94 69 Ukraine . 6 8 ..57 22 1 4 United Arab Emirates 46 8 8 7 ...211 United Kingdom . 6 ..~~~~~~~~7 : 28 26 18 11 United States . .... .... . ...... 7 8 1 ...........2 28 23 ..........7 15 ... Uruguay .20 8 8 4 10 41 27 311 Uzbekistan .. 19 31 40 1 81 290 Venezuela 29 9 9 5 15 29 12 42 11 65 Vietnam 52 25 17 45 47 7 3 4 189 221 65 Weat Bank and Gaza ... .... 26 1 Yemen, Rep . . 19 29 42 ill.11 31 21 Yugoslavia, FR (Serb./Mont.) .. . .2 7 52 31 51 8 70 Zambia 34 . 124 42 37 57 619 Zimbabwe .. 15 14 16 21 36 15 543 74 80~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~162 6 37 Low income 42 5 211 Middle income 55 12 119 Lower middle income 57 10 129 Upper middle income 44 21 81 Low & middle income 50 10 157 East Asia & Pacific 59 6 151 72 Europe & Central Asia 59 26 75 25 Latin America & Carib. 39 20 81 89 Middle East & N. Africa ..6648 South Asia 41 4 193 65 Sub-Saharan Africa .. . 267 61 High income 39 .........22 ..........24 Europe EMU 39 24 22 a. Dats are for the most recent year available. 104 1999 World Development Indicators 2.16 The limitedavailability of dataon health status is a major cates long-term, cumulative effects of inadequacies of * Prevalence of anemia, or iron deficiency, is constraint to assessing the health situation in develop- health, diet, or care. It is often argued that stunting is a defined as hemoglobin levels less than 11 grams ing countries. Surveillance data are lacking for a num- population proxy for multifaceted deprivation. per deciliter among pregnant women. * Low-birth- ber of major public health concerns. Estimates of By the mid-1990s there were an estimated 1.1 bil- weight babies are newborns weighing less than prevalence and incidence are available for some dis- lion smokers worldwide, or one in three of the global 2,500 grams, with the measurement taken within eases but are often unreliable and variable. National adult population. While the prevalence of smoking the first hours of life, before significant postnatal health authorities differ widely in their capacity and will- has been declining in high-income countries in the weight loss has occurred. * Prevalence of child ingness to collect or report information. Even when past three decades, it has been increasing in many malnutrition is the percentage of children under five intentions are good, reporting is based on definitions low- and middle-income countries, and more than 80 whose weight for age and height for age are less that may vary widely across countries or over time. To percent of smokers live in developing countries. In than minus two standard deviations from the compensate for the paucity of data and ensure reason- 1990 tobacco was estimated to cause about 3 mil- median for the international reference population able reliability and international comparability, the World lion deaths worldwide. Tobacco causes heart disease aged 0-59 months. For children up to two years of Health Organization (WHO) prepares estimates in accor- and othervasculardiseases, and cancers of the lung age. height is measured by recumbent length. For dance with epidemiological and statistical standards. and other organs. But the delay between starting to older children, height is measured by stature while Adequate quantities of micronutrients (vitamins smoke and developing a fatal disease may be as standing. The reference population, adopted by the and minerals) are essential for healthy growth and much as 30 years. Thus the health impact in devel- WHO in 1983, is based on children from the United development. Studies indicate that more people are oping countries, where the smoking epidemic is still States, who are assumed to be well nourished. deficient in iron (anemic) than any other micronutrient, at an early stage, is not yet fully visible but is * Smoking prevalence is the percentage of men and most of those suffering are women of reproduc- expected to increase rapidly in the future. Data on and women over 15 who smoke cigarettes. tive age. Anemia during pregnancy can harm both the smoking are obtained through surveys. Because they * Incidence of tuberculosis is the estimated num- mother and the fetus, causing loss of the baby, pre- give a one-time estimate of the prevalence of smok- ber of new tuberculosis cases (pulmonary, smear mature birth, or low birthweight. Estimates of the ing with no information on intensity or duration, they positive, extrapulmonary). * Prevalence of tuber- prevalence of anemia among pregnantwomen are gen- should be interpreted with caution. culosis is the number of people suffering from erally drawn from clinical data, which suffer from two Tuberculosis (TB) is the major cause of death from tuberculosis in 1997. * Consumption of iodized weaknesses: the sample is not random because it is a single infectious agent among adults in developing salt refers to the percentage of households that use based on those who seek care, and private clinics or countries (WHO 1998). In industrial countries TB has edible salt fortified with iodine. hospitals may not be part of the reporting network. reemerged largely as a result of cases among immi- Low birthweight, which is associated with maternal grants. From an economic point of view this epidemic Data sources malnutrition, raises the risk of infant mortality and is about wasted lives and lost productivity. From a stunts growth in infancy and childhood, increasing the health perspective it is about the need to efficiently The data presented here are drawn from a variety of incidence of other forms of retarded development. organize and finance the health sector to serve the sources, including the United Nations' Update on the Estimates of low-birthweight infants are drawn mostly needs of the population. And from a social perspective Nutrition Situation; the WHO's World Health Statistics from hospital records. But many births in developing it is about the need to provide equitable access to Annual, Global Tuberculosis Control Report 1997, and countries take place at home, and these births are sel- appropriate health services because TB is most likely Tobacco or Health: A Global Status Report, 1997; dom recorded. A hospital birth may indicate higher to be contracted by the poor. The estimates on TB inci- UNICEF's The State ofthe World's Children 1999; and income and therefore better nutrition, or itcould indicate dence presented in the table are based on a new the WHO and UNICEF's Low Birth Weight: A Tabulation a higher-risk birth, possibly skewing the data on birth- approach in which reported cases are adjusted using of Available Information (1992). weights downward. Changes in this indicator are more the ratio of case notifications to the estimated share of likely to reflect changes in reporting practices than cases detected by panels of 80 epidemiologists con- actual changes in the rate of low birthweight. The data vened by the WHO. should be treated with caution, and no comparisons Iodine deficiency (IDD) is the single most impor- within or across countries should be attempted. tant cause of preventable mental retardation, and it Estimates of child malnutrition, based on both weight contributes significantly to the risk of stillbirth and for age (underweight) and height for age (stunting), are miscarriage. One indicator of iodine deficiency is goi- from national survey data. The proportion of children ter. Severe cases of IDD include cretinism and underweight is the most common indicator of malnutri- dwarfism; less severe cases in both adults and chil- tion. Being underweight, even mildly, increases the risk dren may lead to a decline of 10-15 intelligence quo- of death and inhibits cognitive development in children. tient (IQ) points. Iodized salt is the best source of Moreover, it perpetuates the problem from one genera- iodine, and a global campaign to iodize edible salt is tion to the next, through malnourished women having significantly reducing the risks (UNICEF, The State of low-birthweight babies. Height for age reflects linear the World's Children 1999). growth achieved pre- and postnatally, and a deficit indi- 1999 World Development Indicators 105 2.17 Confronting AIDS Adult People Adult HIV-± prevalence Stage of Socially Reported condom use with HIV currently in selected populations HIV/AIDS marketed nonregular partner prevalence infected epidemica condom Women sales attending % of urban population High-risk antenatal aged 15-49 Survey group Survey clinics millions Survey Male Female 1997 1997 year % infected' year % infected' 1997 1997 year % Albania 0.01 <100 UK 0.6 Algeria 0.07 UK Angola 2.12 110,000 1992 25 5d,e 1995 0. f9 ........... Argentina 06 120,000 1994-95 ... .67 O ....... 1997 0.8J C . ... ..:... ........................... Armenia 0.01 <100 . .. UK AuYstralia 0.14 11,000 1992 20 N........ ..N Austria 0.18 7,500 1990 13.5k.1 . . C 1992 38m Azerbaijan <0.005 <100 1995 0.Oe N Bangladesh 0.03 21,000 1996 0O5e.. . N 140 Belarus 0.17 9,000 . .. UK Belgium 0.14 7,500 UK Benin 2.06 54,000 1995-96 43 0n 1997 3.7 C 3 Bolivia 0.07 2,600 . ...... UK5 1994 65 33 Bosnia and Herzegovina 0.04.. . UK Botswana 25.10 190,000 1997 43.9e,f' 1997 34.0'1 G 2 Brazil 0.63 580,000 1997 30.0'"1 1995 2.5' f C 35 1996 15 4 Bulgaria....... ... 0.01 . 1993 .. 00 .. . 1993 0.0 N ...... . Burkina Faso 7.17 370,000 1994 58.2fn 1997 9 7 G 10 1993 58 38 Burundi 8.30 260,000 .. . 1997 17 o0 G 1 Cambodia 2.40 130,000 1997 423.4fn 1997 .29C 10 Cameroon ...........4.89 320,000 ...1995 .....18 W. ....... 1996.69 G. ..... 12 ... ...... ... Canada 0.33 44,000 . .. U K Central African Republic 10.77 180,000 1996.... 30:1~ 1996 13.8f . G . 2 1994-95 37 25 Chad ...........2.72 87,000 ... 1995 .... 13 4 . .4 ..... 1995 2.4 C . . ......3 .......... .......:.. ........... Chile 0.20 16,000 1994 0.e, 1994 0.i1 f N 1997 33 18 Chine 0.06 400,000 1997 77.2' P 1997 0.0 C 17 Ho.gKong, Chine 0.08 3,100 .. . . . UK Colombia 0.36 72,000 1994 26.2fi 1994 O.5~ f C 7 Congo, Dem. Rep. . 4.35 950,000 1997 29.0h,nl.q.19.7 4.7.f C 2 Congo,Re.I P7.78 100,000 1997 12.0d,e 1997 5.8 G 0.2 Costa Rica 0.55 10,000 1995 15:1.8' ..... 1992 0.0 C 3 1995 55 42 C6te d'lvoire 10.06 700,000 19 63_0~ 1997G9Oh 16 1994 43 17 Croatia 0.01 . UK Cuba 0.02 1,400 1993 0,e........ 1996 oar N Czec Republic 0.04 2000 1992 1073S .......1995 0.0 s C.1944.. .41.3 Denmark 0.12 3,100 . .. UK . Dominican Republic 1.89 83,000 1994 11.7' P 1995 2.41 C 1997 48m Ecuador 0.28 18,000 1993 36e....6... 1992 0.3 N. ....... Egypt, Arab Rap. 0.03 . 1994 7.6' 1994 0.0 C El Salvador 0 58 18,000 1995-96 6:0.0 1994-95 0 or C 2 Eritree 3.17 . . 1994 25.0h,n 1994 3.0 hr C Ethiopia 9.31 2,600,000 1992 37.5 e,f,h 1997 16.4" 1, G 28 1994 48 47 Finland 0.02 500 1996 0.1k,I 1994 O.O0 N France 0.37 110,000 1993 30.4h,!, 1994 0l4 k C 1993 65 50 Gabon 4.25 23,000 1996-97 16.7e,p .. 1996-97 4.7 C.... ..... ... ....... ........ Gambia, The 2.24 13,000 1993..... 34*7 r,,q ..t . 1997 2.Of ~ C..... ..........:'.... .. . .. Georgia <0.005 <100 .. UK Germany 0.08 35,000 . 19 00~ k UK Ghana 2.38 210,000 1997 75:8ri, 1996 1.7f C. 5... ..... ... .... ...... Greece 0.14 7,500 1995 0.4k' .I. N Guatemala 0.52 27,000 1990-93 5.3" ej. C 1 Guinea 2.09 74,000 1994.... 36,6 n 1996 1.5fh~,q C.........3. 1992 28 15 Gu'inea-Bissau 2.25 12,000 1987 36.7"'Aq 1997 2.0 C 0.7 ......... . :. ..... Haiti 517 190,000 1992 21.4~~~~~~~~~~~~~~~ 1996 6.0 ~ G 8 1994-95 .51 Honduras 1.46 43,000 1989-92 30.0f,h,i 1996 i.0 h C 1 106 1999 World Development Indicators 2.17 Adult People Adult HIV-1 prevalence Stage of Socially Reported condom use with HIV currently in selected populations HIV/AlDS marketed nonregular partner prevalence infected epidemica condom Women sales attending % of urban population High-risk antenatal aged 15-49 Survey group Survey clinicn millionn Survey Male Female ±.997 1.997 year % infected' year % infected' ±.997 ±1997 year %% H ungary 0~~~~.04.... 2,000.. ...... ........ .... . . .... ..... ........ ...... . ........ .. .UK ........ India 0.82 4.100,000 1996 64.5f-l 1998 0.7 fu C 300 Indonesia 0.05 52,000 1997 02v.2 . 1995-96 0.0 N 14 Iran, Islamic Rep. <0.005 . 1995 0.Oe 1995 0.0 PJ N Iraq <0.005.. . ... . U Ireland 0.09 1,700 UK Israel 0.07 ..UK Italy 0.31I 90,000 1993 23.0k,l 1992 0.2k C Jamaica 0.99 14,000 1996 9.2n,P .1996 .... 2.0 C.... Japan 0.01 6,800 1996 0.01 1997 0.0r NL Jordan 0.02 . . 1994 0.f3e 1994 0.0 N.. ..... K azakhst ... . .... ........ 0 .03.... 2,500.... ..... .......I....... ... ..... UK .... Kenya 11.64 1.600,000 1993-95 55.20 1996-97 14.4 G 8 1993 35 13 Korea, Dem. Rep. <0.005 :.. .. . . . UK 2 Korea, Rep. 0.01 3,100 1985-92 0.3 N.... K uw ait.. . 0.12.. .. .... .. ... .. . I .. .. .. . .. .. ... .. .. . .UK... .. . .. .. . . .. .. ... . Kyrgyz Republic <0.005 <100 UK .... Lao PDR 0.04 1,100 1990-93 1.2n 1993 QQP1 N........ Latvia 0.01 <100 . .. . UK 19 69 66 Lebanon 0.09 . 1993 2.2 1,P . .N 1996 69m Lesotho .... 8.35 85,000 1993-94.... 35*6e 1994 12.9 f G... L.. .Ibya .. .... .. .. .. . 0.. . 05.. . . .. . . . .... . ...... ....UK. Lithuania 0.01 <100 1995 00els. .1995 Q* S N Macedonia, FYR 0.01 <100 .................. .. UK...... Madagascar 0.12 8,600 1996..... 0.3" 1996 0.1N.... .3......... Malawi .. .. 14.92 710,000 1994 .86,n.... 1996 21.9f GC6 Malaysia 0.62 68,000 1997 .59.51.0 1996 0.2'1 C 6 Mali 1.67 89,000 1997... 31..8n,q 1997 2.7 l C 4 Mauritania 0.52 6,100 1996 1.7e 1993-94 0.5 q N Mauritius 0.08 . . . 1986 00 UK19 28 Mexico 0.35 180,000 1988-97 30.1' 1991-96 aijr C199 6 Moldova .0.11 2,500 1995 0.1I1l$ 1995 0.OS .. ....N .. Mongolia 0.01 <100 1987-93 0.0n 1987-93 ..... 0.0 N Morocco 0.03 . 1991-97 0.3e 1997 0.2 r N Mozambiqu 14.17 1,200,000 1995 21.0e,f 1996 18.2 CG 10 1997 31 19 Myanmar 1.79 440,000 1997 51.0'.l 1997 1.5 C 2 Namibia 19.94 150,000 1993-94 18.2e-h 1996 17.41 GC 0.02 Netherlands 0.17 14,000 1996 5.1k,l 1996 03 k c New Zealand 0.07 1,300 1991-92 0.2e,f N Nicaragua 0.19 4,100 1997 0.20nN Niger 1.45 65,000 1997 25.On,q 1993 1.3 C Nigeria 4.12 2,300,000 1996 44.2~" 1996 3.4 f C 37 1994 8 Norway 0.06 1,300 1992 0.1e,k . ..N.. 1992 8 5 Oman 0.11 . . . . . UK . Pakistan 0.09 64,000 1995 2.0e,f. 1995 0.2~ f N 103 Panama 0~.. . 61. . ... 9...,000..1994 0.. . 3.. UK..........- . .......... Papua New Guinea 0.19 4,500 1995 1.9e 1995 0.2 N.. 1994 38 12 Paraguay 0.13 3,200 1987-90 8.8' 1992 0.0 C . 1996 .. 79 Peru 0.56 72,000 1997 14.01.P 1996-97 0.5 C Philippines 0.06 24,000 1994 0.1i" N 13 Poland 0.06 12,000 1995 4.81 N Portugal ~~0.69 35,000 1992 5*8e,k,0 c Puerto Rico . 1993 321n... 1992-94 1.31' C.... Romania 0.01 5,000 . ..U K..... Russian Federation.... 0.05 40,000 1995 0.1' .... 1995 0.0 N 1 1999 World Development Indicators 107 C ~2.17 Adult People Adult HIV-± prevalence Stage of Socially Reported condom use with HIV currently In selected populations HIV/AIDS marketed nonregular partner prevalence infected epidemica condom Women sales attending % of urban pnpulation High-rink antenatal aged 15-49 Survey group Survey clinica milliona Survey Male Female 1997 1997 year % infected' year % infectedc 1997 1997 year % Rwanda 12.75 370,000 1996 34.8e,f 1996 17.9f G 2 Saudi Arabia 0.01 ... . UK Senegal 1.77 75,000 1996 8.4fn~ 1996 0.2f C 2 Singapore ... ........ 0.15 3,100 ..1992 .......3 6 ..N ........ .......... ................. Slovak Republic 0:01 <1..... :00 .....1995 0..... 0,es ... 1992 0.0 N ........ ........ Slovenia <0.005 <100 1995 0 QI'S 1995 00a S N. 1996 178 South Africa 12.91 2,900,000 1994 201 . ...... 1997 16.0wG .........3..................... Spain 0.57 120.000 1996 45)0 1995 03 k C 1995 49 33 Sri Lanka 0.07 6,900 1993 0.f 1993 0.0f N 7 Sudan 0.99 . 1989 19.1e,f 1995 3.0 h C. 1995 20 17 Sweden 0.07 3.000 1995 5.3k,l C Switzerland 0.32 12,000 1996 3.3k1lN . 1994 57 37 Syrian Arab Republic.. 0.01 . ..... .....1994 .......1 e. ....... 1993 0.0 r .....N. ..... ................ Tajikistan <0.005. <100. .... UK ..... .. Tanzania 9.42 1,400,000 1993 60.~ 95-96 13.7 G 11 1996 35 17 Thailand 2.23 780,000 1997 37.5f.l 1997 1.9f C Togo ............ 8.52 170,000 . 1992 78:9nq .1996 5.1fG .........4 .................. ......... Trinidad and Tobago 0.94 6,800 1993-95 11.9~ 1995-96 1.0 h,r c T unisia ........ 0.04 ................ .. ..... ... .. ..... .. -U...K... Turkey 0.01 1992 .......0,1e . sN 16 Turkmenistan 0.01 <100 .. ... . UK. Uganda 9.51 930,000 1997..... 65,9np ..... 1997 9.9f,h G. 17 .. 1995 20 9 Ukraine 0.43 110,000 1997 13.1f, 1997 0 Ir c United Arab Emirates 0.18 . .. U K United Kingdom 0.09 25,000 1996 9.8h,I,t 1996 0.3~ k C 1991 23 18 United States 0.76 820,000 ... . UK Uruguay.0.33 5.200 1996 13.01.q C Uzbekistan <0.005 <100 .. w. UK Venezuela 0.69 82,000 1994 25.O0h,i .C 0.1 Vietnam 0.22 88,000 199 18.81.l 1997 0.l1 C 33 1995 3Qm Went Bank and Gaza .. .. .. .. . UK Yeme.,Rep. 001 .. 1992 0*0e,n,P .N Yugoslavia, FR (Serb./Mont.) 0.10 . . ... UK Zambia 19.07 770,000 1992-93 58.0d,a 1995 33.8 P G7 1996 46x 23x Zimbabwe 25.84 1,500,000 1994-95 86.0on 1996 47. f G 2 1994 60 38 a. C in concentrated. G ia generalized. N in nascent. UK in unknown. b. Samples of high-rink populations may not be reprenentative of those populations. c. Not necensar'ily representative of women who do not nune antenatal Services. d. Femaie. a. Patients with senually transmitted diseasen. f. Data are averaged. g. Classification in based on previous yearn' observations that included HIV-2. h. Sample nize unknown. i. Homosexual or biaexual men. j. Women without syphilis. k. UNAIDS and WHO 1998. Median. 1. Injecting drug users. m. Both male and female, a. Sex workers. o. Male. p. Data are the bent available but are not necennarily reliable due to small Samples (<100). q. HIV-1 and/or HIV-2. r. Not specifically urban, a. WHO-EC Collaborating Centre on AIDS, European HIV Prevalence Database. t. UNAIDS, Epidemiological Fact Sheet. a. National AIDS Control Organization, Government of India. v. Military. w. National data. x. Urban sample only. ±05 1999 World Development Indicatorn 2.17 At the end of 1998 the Joint United Nations be representative of those populations. Women attend- * Adult HIV prevalence is the percentage of people Programme on HIV/AIDS (UNAIDS) estimated that ing prenatal clinics are assumed to have a lower risk of aged 15-49 who are infected with HIV. * People 33.4 million people worldwide were infected with the contracting HIV, but because they are a self-selected currently infected include all estimated cases, human immunodeficiency virus (HIV) or suffering from group, their HIV prevalence rates are not necessarily rep- regardless of age. * Adult HIV-4 prevalence in high- AIDS and that 13.9 million people had died of AIDS resentative of other low-risk individuals who do not use risk groups represents the most recent and highest since the beginning of the epidemic. More than 9 of these services. Further, these data may be collected level of infection found in one of the following every 10 people infected with HIV live in developing from a nonrandom sample of clinics. groups: sex workers, injecting drug users, patients countries, and two-thirds of all infections are in Sub- Whenever possible, the entries representthe results with sexually transmitted diseases, men who have Saharan Africa. HIV infection and AIDS mortality dis- of one or more studies with a sample size of at least sex with men, or the military. * Stage of HIV/AIDS proportionately affect prime-aged adults at the peak 100 people. If more than one study was available forthe epidemic is defined as nascent (N) if H IV prevalence of their economic and reproductive lives. Already the same year and covered comparable geographic areas, in high-risk populations is less than 5 percent, con- AIDS epidemic has resulted in huge setbacks in the an unweighted average was taken. Surveys with excep- centrated (C) if HIV prevalence in high-risk popula- quality of life in the most severely affected countries, tionally small sample sizes (less than 100) were used if tions is 5 percent or higher but still less than 5 reducing life expectancy as much as 10-20 years. they provided the only estimate available. Estimates percent among women attending antenatal clinics based on small samples have been noted. and generalized (G) if H IV prevalence among women HIV prevalence and distribution attending antenatal clinics is 5 percent or higher. Because of the long symptom-free perod between the Stages of the epidemic Epidemics in countries with no data on prevalence in time of HIV infection and the development of AIDS-an The stage of the HIV/AIDS epidemic indicates the extent high-risk groups or only data prior to 1992 are clas- average of about 10 years-the number of people actu- to which HIV has spread among those practicing high- sified as unknown (UK) unless data from an earlier ally sick with AIDS at any time is only a small percent- risk behavior and outward to low-risk populations. year with a sample size greater than 100 indicated age of those infected. There are few nationally Countries are classified by stage of the epidemic if HIV very high infection levels in a high-risk group. representative surveys of HIV infection levels. The esti- prevalence has been reported for a high-risk population * Socially marketed condom sales refer to sales mates of HIV prevalence among adults and of the total since 1992. But there are many countries for which the through special programs that sell condoms at low, number of people currently infected are based on plau- available data predate 1992 and many with no infor- heavily subsidized prices and make condoms avail- sible extrapolations from surveys of smaller, nonrepre- mation on high-nsk groups. The table reflects conserv- able through nontraditional outlets. * Reported sentative groups. ative estimates of the spread of HIV; many countries condom use with nonregular partner shows the per- HIV tends to move in a series of small, overlapping may be further along than indicated. China and India centage of men and women who used a condom with epidemics first among people who practice high-rsk have been classified as having concentrated epidemics their last nonregular partner (that is, not a spouse behavior-people with many sexual partners who do not on the basis of a concentrated epidemic in at least one or long-term partner) in the past 12 months or who use condoms and people who inject drugs and share province or state. ever used a condom with a nonregular partner in unsterilized needles and syringes. These individuals are some shorter reference period. most likely to become infected when HIV first enters the Condom sales and condom use population and are also most likely to unknowingly pass Despite important progress in prolonging and improving Data sources HlVto others. HlVtypicallyspreads rapidlyamongthose the quality of AIDS patients' lives, there still is no cure with the riskiest behavior. It then spreads more slowly for HIV/AIDS nor is there an effective preventive vaccine. The data presented here are among people with lower-risk behavior-some of whom For the time being the most effective way of reducing CONFOiNTING drawn from a variety of are monogamous and do not inject drugs-through HIV transmission is through behavioral change-reduc- AIDS sources, including the World unprotected sex with an infected partner. Women who ing the number of sexual partners, increasing condom Bank's Confronting AIDS: become infected can transmit HIV to their children use, and using safe injecting practices. A number of t. Public Priorities in a Global before, during, or after birth. Preventing HIV transmis- countries have adopted social marketing programs that Epidemic (1997b); the U.S. sion among those with the riskiest behavior as early as sell condoms at subsidized prices at nontraditional out- Bureau of the Census's possible can have a large 'multiplier effect" in prevent- lets and popularize their use. In most countnes con- HIV/AIDS Surveillance Data- ing infections in the rest of the population. doms are also distributed free of charge through base (1998); the WHO-EC Collaborating Centre on Estimates of HIV prevalence for 'high-risk' groups government programs and can be purchased from pri- AIDS's European HIV Prevalence Database; UNAIDS and represent the results of a specific study or an average vate sources. WHO's Report on the Global HIV/AIDS Epidemic (June of prevalence rates from several sources or sites in a The effect of increased condom use on the spread of 1998); UNAIDS's Epidemiological Fact Sheets; the gov- country for the most recent year available. The data HIV infection will depend on the extent to which condom ernment of India: DKT International's 1997 Contra- come from surveys measurng HIV-1, except where oth- use increases among people with the largest number of ceptive Social Marketirig Statistics (1998); and results erwise noted. Footnotes indicate the groups to which partners. The data on condom use with nonregular part- of the Demographic and Health Surveys. the data refer. Because of the difficulty of establishing ners come from nationally representative surveys of sex- samples of individuals with certain characteristics, stud- ual behavior as reported to UNAIDS or calculated directly ies based on samples of high-risk populations may not from Demographic and Health Surveys. lass Worid Development Indicators 10 9 2.18 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality at birth rate mortality rate rate rate per 1,000 Male Female Male Female years live births per 1,000 per 1,000 per 1,000 per 1.000 per 1,000 1980 1997 11980 1997 11980 1997 1988-981 ±988.98a ±980 1997 1980 1997 Albania 69 72 47 26 57 40 15 15 140 122 82 65 Algeria 59 70 98 32 139 39 226 160 197 125 Angola 41 46 154 125 261 20959 412 458 355 Argentina 70 73 35 22 38 24 205 165 102 80 Armenia 73 74 26 15 . . . . 158 164 85 81 Australia 74 78 11 5 13 7 . 178 110 85 5 7 Austria 73 7 7 14 5 17 7 197 123 92 61 Azerbaijan 68 71 30 20 . 23 . 262 213 127 101 Banglades 48 58 132 75 211 104 37 47 383 285 388 309 Belarus 71 68 16 12 . . . 5 333 95 115 Belgium 73 77 12 6 15 7 . 173 134 90 62 Benin 48 53 116 88 214 149 89 90 486 362 397 304 Bolivia 52 61 118 66 170 96 53 47 357 269 273 220 Bosnia and Herzegovina 70 - 31 13.. .. 181 170 10893 Botswana 58 47 71 58 94 88 18 16 341 600 278 552 Brazil 63 67 70 34 . 44 89 221 202 161 139 Bulgaria 71 71 20 18 25 24-. 190 213 106 106 Burkina Faso 44 44 121 99. 169 107 110 467 540 362 520 Burundi 47 42 122 119 193 200 101 114 489 550 400 491 Cambodia 39 54 201 103 330 147 ... 473 349 355 303 Cameroon 50 57 94 52 -173 78 64 75 489 390 415 352 Canada 75 79 10 6 13 8 ... 16 106 85 52 Central African Republic 46 45 117 98 160 63 64 540 567 424 476 Chad 42 49 123 100 235 182 106 99 556 448 449 383 Chile 69 75 32 11 35 13 32 218 143 120 73 China 67 70 42 32 65 39 10 11 185 165 148 137 Hong Kong, China 74 79 11 5 .. .. 150 109 87 56 Colombia 66 70 41 24 58 30 77 237 210 162 115 Congo,DemT. Re. 49 51 112 92 210 148 Congo, Rep. 50 48 89 90 125 145 .. 408 464 298 402 Costa Rica 73 77 20 12 29 15 . 159 117 10 70 C6te dIlvoire 49 47 108 87 170 140 71 58 421 510 346 490 Croatia 70 72 21 9 23 10 . 233 176 106 78 Cuba 74 76 20 7 22 9 .. 135 125 94 - 80 Czech Republic 70 74 16 6 19 8 ... 225 181 102 82 Denmark 74 75 8 6 10 6 ... 163 144 102 81 Dominican Republic 64 71 76 40 92 47 13 13 183 150 138 94 Ecuador 63 ... .70 ... 74 .......33._ .0 39 . 129....... 2 7 0 .. .. ~ - . ... .. .. .. . .... . ... .. .. .. .. . -. .... . .. . 1 0 2 29.. . . . . . . . . . . . .. . . . . .. . . 180.. .. . . . .. .. . 176.. . .. . .. . 1 0 4 . ... . Egypt, ArabRe.. 56 66 120 51 175 66 22 28 257 198 204 174 El Salvador 57 69 84 32 120 39 17 20 410 207 178 117 Eritrea 44 51 91 62.. 9 89 78 .. 40 . 403 Estonia 69 70 17 10 25 13 291 284 110 95 Ethiopia 42 43 155 107 213 175. 491 550 401 510 Finland 73 77 8 4 9 5 206 141 74 61 France 74 78 10 5 13 6. 190 -130 85 51 Gabon 48 52 116 87 194 136 . 474 380 387 340 Gambia, The 40 53 159 78 216 . 83 79 584 404 466 339 Georgia 71 73 25 17 21-. 210 197 94 83 Germany 73 77 12 5 16 6 ... 177 133. 90..... 66 Ghana 53 60 94 66 157 102 63 62 400 278 334 226 Greece 74 78 18 7 23 9 ... 134 114 8661 Guatemala 57 64 84 43 . 55 22 24 336 296 266 193 Guinea 40 46 185 120 299 182 122 112 589 399 507 400 Guinea-Bissau 39 44 169 130 290 220 ... 535 469 517 416 Haiti 51 54 123 71 200 125 59 58 348 427 275 332 Honduras 60 69 70 36 103 48 306 193 237 15 110 1999 World Development Indicators 2.18 Life expectancy Infant mortality Under-five Child mortality Adult mortality at birth rate mortality rate rate rate per 1,000 Male Female Male Female years live births per 1,000 per 1,000 per 1,000 per 1.000 per 1,000 1980 1L997 1980 1.997 1980 1997 1988-98a 1988-98, 1980 1997 1980 1997 Hungary 70 71. 23. 10. .. 26 .12 ..... ..270 ...330 ...130 138 India 54 63 115 71 177 88 29 42 261 212 279 202 Indonesia 55 65 90 47 125 60 30 27 368 240 308 188 Iran, Islamic Rep. 60 69 87 32 126 35 221 165 190 162 Iraq 62 58 80 112 95 140 207 198 191 174 Ireland 73 76 11 5 14 7175 135 103 73 Israel 73 77 15 7 19 8138 ill 85 69 Italy 74 78 15 5 17 7163 117 80 54 Jamai'ca 71 75 21 12 39 14 186 141 121 86 Japan 76 80 8 4 11 6129 100 70 46 Jordan 64 71 41 29 48 35 6 6. 160 . 121 Kazakhstan 67 65 33 24 .... 29. 10 5 312 383 140 168 Kenya 55 52 75 74 115 112 33 33 417 425 339 397 Korea, Dem. Rep.. 67 63 32 56 43 74 ... 270 268 156 207 Korea Rp. 67 72 26 9 18 11 270 209 156 97 Kuwait 71 76 27 12 35 13 ... 172 124 116 65 Kyrgyz Republic . .....65 .. 67 ... 43 28.... 2 10 11 296 305 131_... 143 Lao PDR 45 53 127 98 200 . . . 531 378 439 323 Latvia 69 69 20 15 26 19 .. . 281 306 106 100 Lbnon. 65 ...70 48 ... 28 .. .. .32 241 177 181 3 Lesotho 53 56 119 93 168 137 371 320 279 282 Libya 60 70 79 24 30 6 5 276 187 218 135 Lithuania 71 .71 20 10.. I.. 24 .... 13 243 269 92 88 Macedonia, FYR... ....... - . ..72. .54 1..6 69 17 .......164. .107 Madagascar 51 57 119 94 216 158 75 68 353 332 278 295 Malawi 44 43 169 133 265 224 126 114 429 454 349 471 Malaysia 67 72 30 11 42 14 4 4 230 187 149 114 Mali 42 50 184 118 .... 235. 136 138 454 416 362 334 Mauritania 47 53 120 92 175 149 ... 505 344 416 294 Mauritius 66 71 32 20 40 23 .. 277 205 181 97 Mexico 67 72 51 31 74 38 15 17 205 166 121 83 Moldova 66 67 35 2 0 24 ... 289 316 173 177 Mongolia 58 66 82 52 68 320 203 273 170 Morocco 58 67 99 51 152 67 21 19 264 207 207 150 Mozambique 44 45 145 135 223 201 84 82 468 400 361 354 Myanmar 52 60 109 79 134 131 384 263 313 217 Namibia 53 56 90 65 114 101 30 34 427 366 366 341 Nepa 48 57 132 83 180 117 .. . 376 274 395 314 Netherlands 7 6 78 9 5 11 7 . 133 121 74 62 New Zealand 73 77 13 7 16 7 . 177 132 91 68 Nicaragua 59 68 84 43 143 57 277 202 189 126 Niger 42 47 150 118 320 . 212 232 562 450 453 342 Nigeria 46 54 9 9 77 196 122 118 202 535 398 453 335 Norway 76 78 8 4 11 6 . 144 114 71 59 Oman 60 73 41 18 95 28 ... 389 143 326 108 Pakistan 55 62 127 95 161 136 22 37 283 175 291 158 Panama 70 74 32 21 36 26 . 172 137 117 81 Papua New Guinea 51- 58 78 61 1-00 82 28 21 514 348 478 333 Paraguay 67 70 50 23 61 28 10 12 198 185 144 124 Peru 60 69 81 40 126 52 19 20 287 200 229 124 Philippines 61 68 52 35 81 41 28 25 323 200 259 153 Poland 70 73 26 10 . 12 254 238 105 91 Portugal 71 75 24 6 31 8199 154 95 72 Puerto Rico 74 75 19 11 .. . .. . 159 158 78 63 Romani'a 69 69 29 22 36.. 26. 7 5 216 270 119 119 Russian Fedceration 67 ... 67 22 17 . 25 32. 341 370 120 130 1999 World Development Indicators ill C ~2.18 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality at birth rate mortality rate rate rate per 1,000 Male Female Male Female years live birtha per 1.000 per 1,000 per 1,000 per 1,000 per 1.000 1980 1997 1980 1997 1980 1997 1988-98o 1988-98, 1980 1997 1980 1997 Rwanda 46 40 128 124 209 87 73 503 585 409 534 Saudi Arabia 61 71 65 21 85 28 .. 283 171 241 147 Senegal 45 52 117 70 190 110 76 74 586 453 516 381 Sierra Leone 35 37 190 170 336 286.. 540 544 527 483 Singapore 71 76 12 4 13 6 .199 136 115 77 Slovak Republic 70 73 21 9 23 .. ......226 ... 208 105 .... 90 Sloveni'a 70 75 15 5 18 6 250 .173 105 76 South Africa 57 65 67 48 91 65 . 261 160 Spain 76 78 12 5 16 7 .. 144 124 69 56 Sri Lanka 68 73 34 14 48 19 10 9 210 156 152 99 Sudan 48 55 94 71 145 115 62 63 537 373 462 328 Sweden 76 79 7 4 9 5 . .. 142 105 76 54 Switzerland 76 79 9 5 11 6 145 106 70 50 Syrian Arab Republic 62 69 56 31 73 38 . 206 141 Tajikistan 66 68 58 30 36 ... 190 234 129 143 Tanzania 50 48 108 85 176 136 59 52 451 502 370 460 Thailand 64 69 49 33 58 38 11 11 280 219 210 122 Togo 49 49 1-10 86 175 138 75 90 457 477 375 432 Trinidad and Tobago 68 73 35 12 40 15 . 43 234 168 166 - 103 Tunisia 62 70 69 30 100 33 19 19 227 171 224 153 Turke 61 69 109 40 133 50 12 14 . 165 . 120 Turkmenistan 64 66 54 40. 50 263 282 154 159 Uganda 48 42 116 99 180 162 82 72 ....463. 580 395 590 Ukraine 69 67 17 14 . 17 282 350 112 135 United Arab Emirates 68 75 55 8 ~ . 11. 153 127 106 93 United Kingdom 74 77 12 6 14 7 160 . 123 96. 627 United States 74 76 13 7 15 . 194 150 102 80 Uruguay 70 74 37 16 42 20 176 171 91 76 Uzbekistan 67 69 47 .. . 31. . 219 230 116 128 Venezuela 68 73 36 21 42 25 -219 160 123 91 Vietnam 63 68 57 2 9 105 40 . . 262 205 204 150 We..st.Bank.and Gaza . 71 25.. 28 10 7. 170 . 112 Yemen Rep. 49 54 141 96 198 137 41 47 382 340 304 330 Yugoslavia, FR (Serb./Mont.) 70 72 .. 33 . .. 14 ....... ...20 . 7....... 164 180 106 108 Zambia 50 43 90 113 149 189 96 93 482 512 413 524 Zimbabwe 55 52 80 69 108 108 26 26 389 449 321 388 Low income 52 59 116 82 182 118 327 274 312 255 Middle income 65 69 57 34 85 43 230 199 161 137 Lower middle income 65 69 56 36 87 44 231 200 166 142 Upper middle income 65 70 60 30. 38 226..... 193 136 116 Low & middle income 60 65 87 60 138 84 266 227 217 182 East Asia & Pacific 65 69 56 37 83 47 222 183 180 148 Europe & Central Asia 68 69 41 23 . 30 281.. 287.. 115 122 La9tin America & Carib. 65 ...... 70 .....60 32 . 41 225 189 151 116 Middle East & N. Africa 59 67 95 49 137 63 248 190 207 164 South Asia 54 62 119 77 180 100 279 219 292 212 Sub-Saharan Africa 48 51 115 91 189 147 486 428 403 375 High income 74 77 13 6 15 7 174..... 133.. 91 66 Europe EMU 74 77 12 5 16 6 172 128 83 59 a. Data are for the moat recent year available. 112 1999 World Development Indicators 2.18 Age-specific mortality data such as infant and child more diverse and affect men more. Among the causes * Life expectancy at birth is the number of years a mortality rates, along with life expectancy at birth, are are a high prevalence of smoking, a high-fat diet, newborn infant would live if prevailing patterns of mor- probably the best general indicators of a community's excessive alcohol use, and stressful conditions tality at the time of its birth were to stay the same current health status and are often cited as overall related to the economic transition. throughout its life. * Infant mortality rate is the num- measures of a population's welfare or quality of life. ber of infants who die before reaching one year of age, They may be used nationally to identify populations in per 1,000 live births in agiven year. * Under-five mor- need, or internationally to compare levels of socio- Ufe tables: an essential tool for mortality tality rate is the probability that a newborn baby will economic development. Despite variations in the qual- analysis die before reaching age five, if subject to current age- ity of these data, discussed below, there is general specific mortality rates. * Child mortality rate is the MI-.el l, ie I iv: .e e*e -.i vie,'1 - r...-rl.TeI. :,,- agreement that age-specific mortality rates, espe- ; r ,J.. r il-l CiT. probabilityof dying between the ages of one and five, cially child mortality rates, are a key indicator in any e.jrT,l.enl.e .l ITr 1'. 'nlc ITn - ,-:ul.ea if subject to current age-specific mortality rates. health monitoring system. ,r. :rr ., s v, ,; ,ni-r, ¶r.eJI' il*i nI,r, * Adult mortality rate is the probability of dying The main sources of mortality data are vital regis- . ,n.i' .ei'. ie 'sr'e ,rv dee ce- r& between the ages of 15 and 60-that is, the proba- tration systemsanddirectorindirectestimates based .e.-,e:- r, rMI;.r. , .C r r,D I,:'- C1 i .r,rer .:r bility of a 15-year-old dying before reaching age 60, if on sample surveys or censuses. Acompletevital reg- ri c.nl S ifl't I .'ii-lt- % subject to current age-specific mortality rates - r.e..e-er.:, rSTi,r, rile.=.::r.5,.l,er.r ni l'........................... ir, -.C.l.;Il, istration system-that is, a system covering at least - AII ri.e ' I,i',rr.. Ir,e r l,?. In r , between ages 15 and 60. 90 percent of the population-is the best source of 31C re, .r'r'ni3':.r., iC - ie.:',r.r,: 11W ..1i,fl age-specific mortality data. But such systems are - V . ;. :.:.r.. ,- Data sources Ir.- ae li 1r,,t S.C.ff,JIe in' .. -e Cge l, fairly uncommon in developing countries. Thus esti- ; , 3rI.r.,.r, .1 ;r.r..,r s.c -l mates must be obtained from sample surveys or e;.:r f3, rr, .r, :.,r- B':'S I rr.,le.l United Nations Department derived by applying indirect estimation techniques to r.ll:I rIlnI C ; ee Cr*:j, ie*d ITl\ iie - , of Economic and Social in, )IlerAr . 3 l ;-:*5 .eI .'. - nl,l registration, census, or survey data. Survey data are jil;;lOW 1X i ,i r r..:or....rr . - Affairs, Population and Vital subject to recall error; and surveys estimating infant i.iee. '-- , ore r ....'. : re iel r 1'.e'r. '' Statistics Report; demo- deaths require large samples, because households in .r Tmo1*J.-;g_? ,..r..er. i' I.Ihf r', ,'jr'!, ur ii? graphic and health surveys fCr.f'ier .r,.e :nre.'JI.i..,f.,Wiiu .eI...Seiielr.l ie _ .e.S which a birth occurs during a given year cannot ordi- n -e, . | ,r. . - . Ii t': *%- i* ;- l from national sources; and narily be preselected for sampling. Indirect estimates -, , :.-Xr. ;r1 ir ii -r,r m: t.:.n I . fi .e.: ... United Nations Children's rely on estimated actuarial ("life") tables that may be j. ; , ' . .T. I c il er ''. ' ' . - - - Fund (UNICEF), The State of inappropriate for the population concerned (see box). the World's Children 1999. Because life expectancy at birth is constructed using infant mortality data and life tables, similar reliability issues arise for this indicator. Life expectancy at birth and age-specific mortality r ..:n rates for 1997 are generally estimates based on vital registration or the most recent census or survey avail- able (see Primarydata documentation). Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work. Infant and child mortality rates are higher for boys than for girls in countries in which parental gender pref- ' erences are absent. Child mortality better captures the effect of gender discrimination than infant mortality, as ' malnutrition and medical interventions are more important in this age group. When female child mor- n nr i. tality is higher, as in several countries in South Asia, it r i F ..-. F.e.a.-r '.:r;ei,e. is likely that girls have unequal access to resources. , . ' Cr-j reT i 1>* Adult mortality rates have increased in many coun- tries in Sub-Saharan Africa as well as in Eastern Europe and the countries of the former Soviet Union. In Sub-Saharan Africa the cause of the increase is AIDS-related mortality and is affecting both men and women. In Europe and Central Asia the causes are 1999 World Development Indicators 1 13 l -, - ~ ~ ~ ~ ~ ~ ~ ~ * . . . \....~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ wA: - j he natural environment presents familiar opportunities: rich soils, abundant water, natural beauty, minerals, energy, forests, and fish, all sources of income and human welfare. Some, with proper management, are renewable. But oth- ers are exhaustible. And as populations and economies grow, many nations crash into the constraints. Air and water do not provide infinite sinks for the waste products of human activity. Mismanagement of the resource base can lead to the collapse of biological resources and the degradation of both natural and --. - -. - agricultural areas. And while many traditional environmental issues relate to natural areas or to sectors that depend directly on the natural resource base (agriculture, forestry, and fisheries), the rising tide of urbanization presents new challenges for the preservation of environmental quality (box 3a). There is a substantial and growing base of biophysical indicators to inform - --.- decisionmakers, analysts, and the public about the state of the natural envi- - ronment and the range of human activities that place pressures on the envi- ronment (box 3b). There are even rudimentary indicators pertaining to human responses to changing environmental quality. The Organisation for - Economic Co-operation and Development's (OECD) Pressure-State-Response framework for environmental indicators is gradually being fleshed out. But what sorts of indicators can deepen our understanding of the links between the economy and the environment? And what can we say about the sustainability of development? - - .:- .'a mesurof3 sustagna1tuS3RY Many of the key indicators in the Economy section are derived from a consistent I framework, the System of National Accounts, which relates to a specific body of macroeconomic theory. This is a great source of strength for those indicators, because there is considerable understanding of how one economic aggregate relates to another. Many traditional environmental indicators, by contrast, have no similar integrating framework. The Pressure-State-Response framework is at first glance causal: human activities place pressure on the environment, leading to changes in the environmental state that ultimately produce human : i:--< responses. But this causality breaks down as soon as there is aggregation of indi- cators or sufficient complexity in the causes of environmental change. Given the close link between economic activity and environmental change, -~ -.: -there is a strong argument for developing indicators that integrate the economy and the environment more closely. One promising approach is environmental The environmental challenges of urbanization Measuring the environment Ar. irriDorirari 3ddi.iori To, tri-il )care~ Ltcrd -'et,eiomr afui.Jif 'S Soirie r-e.rrirriEnrirI iridicaiors dal, -.'..ir. g2o~.ii zuchi as prosecisi table or cir, indl,:F.I.,;ir or rri-c urban eri.rornmeri deI.1. i Aiiri.:igr aru,rbci~r',tm .iOrc;maucta&dirsain not all1 cC*unI-ir.c airc rc-ireci-iiratr. Ihre probieris hiNgli-rit. cd are~ c ormmo.n a'' ar-i aer ociriuiii.r taol-c 1. 3 ri-and 3 1 3r Still oInrs.. mci-or '- nc-st or ire~ w%c.d :1cri hi- and p-c-Jr *.,nurrri- aITenc r- ir.. iC-.iriin lisa rn-Aer)n e s.see (iIric in arl.>[ MEc Poet1 srniou en..r%-mrim~iair. ir-Oeislos numbrstr orT inrearer,jci spsc.:z . Sat ndiTrore Are nsr,.Tir an. rr.ieenr c.ice. soad.iru -ncs iSueS ~d rien masure ic c r.ecause ir.e i.nis rer,wen itire enicr,men adiC Cic- are~ t-pi- or e s pol,e;i miron~r ,r.-arcu rin-icarci.rs More gcErzral rna,o3.n3.I diata dietMl mreiiA -rurt-ani-zalcrnr arc irn r aCdic 3.10.'- Bur Trinni, rme-ienr iiI-acr; are Y ri- A' iiip ir,.--CLause of Cre. I. n, hoe t air-csi iair rihc vi-rl-i C o-NOPi-. arc grc-t.ng t,,vc,e in Tunt-it, *-:%A.ra0e an-i cc.rcrr,; Cirjout si -c ilaSi, Ai r--?Obie~ lioc ir me -Tries r.-, ;eer hf r orl. arid 5 ni,grer sra-d1nar-j :i or S a ,ran-m i.31 The tonr-i is C. dde-i -nr- aorsae ada-r,e si.ng rn A;,a. nor eianpre. a mnu og maea pei,r as riic w rurLara.nTa T r naIn .ois.ue.e ii rice caa-:e air and %%aler inc urpar i-.b:.p,iar.o)n i. ipe cI. rEa,r, 2.5. prori.: in 20,25. r.rree iris' rc.iiaon -o ro, repc no.n.irunAi Mar, rirciii.mn tri-r 1~9O1 leerl Ar present 57 Asia,n cilia; '33 *:r kremi. ria r- problemsrei-rn.iraidj i)airseii Arcrirnri.. In-liarii re more rior Tar. a rroiiiciri ~ple-i each ir, si-c, pas -it -Tcc aues [ri-i -ci 4 rid- SitJr £nrM Li-iis ciricace loca' i-si-orail re.g.CnaI Ar-i Irir. ai urrban bic.Palaioi-i ri-aS groon 4lioa.ii, .i si- aicrags rare or 4.2 per.:enr a 3:ie-S(L c- en..ronrrienTal brxobIflic. ,Cr re liSi-i,T .1 u rpar in,,, iir.. g5ereased m ri- L:air inrL eric a. irare s- Tr, flai3r. ri-nc.5arsi prescnt5d ricre cove,r riibc.rt.fliem Ort i urPSin poraieicirio 5c.i ioJr 7U percen[ t ini rie i Ai, C.~ 5ri nrie iri-ar. n-aiiOnalICmi-irirAidi-Cn A Si' 6 Sr31ie Isi- u-C ikr s5r -oi'ri Europe '0A3 p-er.:,ni. S,ur.-Sarar.ari AfrCicA A-iih -T,i-i, perTe:ra , li pr0lT-iSiEJ ara.rreei-w.CIr, PnErier p-rediauCI..n an-i uS,~ -rnergy rirn- nieorer in urb-an areas, reanQu.l rural 0 .b-rarscr nd trsoc --i250e-.tri\Cirrif -Ar-On di-id:mric trprar, , .sell .: n0lt rh~ nri.ronnren wi issue snread. urA- rSi-. tunfiC SldCi-eiOr.srPiarni 3 gc.-errin,.ran r.n0icnmcniar Dr.pni aria-I Fciri sb-s urlLin r-.,prddu.-iim Tr riarpT r crsrrrr ri-i er.rrlr(,,T,rmenl PrnarervrsrLn T.%.'. new. Sets :.ii-dari- irdusir-al ac k..l.c- and me cTr.,cidrgcriayn -ciri- n rerir tC. g~erunrls u iCand uir,c isu rc nrIrd,uced. prc-t.rEn-.,-include~ 0ollrsrir.r or ri- air an.-i .Aicriand A.-:cuniulAiiorn TJ ;.ud- Traie Inc s-scs andi-iT nlp-rirsn CoSI of uti-,;i pollution .5 10. numnan i-canr- W-:.rr Bink i-i, iŽ a, snc-~ ti-At Air Cin- .-ater ci.Jiiuicin i.niPiarc .,-i ri-i it J i-Cs-J :,Tia- -.an 2. Au IC iC.t -i in-)uia .soa ci iearnc. ri-ri-Ti-is PIT ase 5 ni mof atIC5.&i5S.:des nio changes in this portfolio provides a means to track development dolla-11rs in fosr diddii:.ti.iir, ano-ui oul i.imarnsaa-e.jniEre -eli-,n cnrn, progress and to give afirt-s indication of its sustainability. riaorcr5Alroan ii lr i.rl Snieaces.shre i-ar?prt.rrr..Sustainability and sustainable development have many possi- wise[r p.-rr4ionir tends Ti i rn55 mc.g -rJus in S)u1n. Scaune ACT. and Cenira' Asia-and air criruon iaz kri-a warirriac ain Cr-iria. Lair, ble definitions. Broadly speaking, sustainable development deals America. ano Eaiier- Europe. with whether current actions limit or reduce the income and wel- !s.nt(ii, are inc numan3 and Trianiziarl cc-:s -:i rpc.rrur..n iraie, iiE., fare opportuninies for futuire generations. This question is gener- send r to ail asbr.T.p.:.rti-~i-a.I -Ti ine pvcir. S-- .si.rci,rrt.n piTilton isiU.5 5ruIrE OnStb r nir AS ll as~ ec-Tinornic. and ally assumed to have three facets: economic, ecological, and social. en..ronnCrr.,ar gr.-.urde. Tacre 31 h,hi-ligi-r man, or si-ese muSaCS vWh Economists attempt to simplify- the question by assuming a social .n.icatrirz on i-,Tornicrv ss n.:rm~ djsrrnuipa n. crr.T.ting. andi a ScsT welfare function that reflects the contributions of each facet to asiar-in Si arie wair and Idoid 'tSvie c-rre,:s:. Seecc -ar c-ri air iJuAi[r; an. mrrpcrrarn dere~rmrnan i ofn-earth iecisA are in ta- 3; 13 human well-being. The question of sustainability then comes downi Forr-i-aa.r1. -e AS fro.ne n'P Ar iar-. &leh:i.-.c .nier,.rnron; i-ate to whether there is an expected decline in welfare along thle future flae easnare rira erlir ;~e.~:rre tltic. aS irT*-i Sai' development path as a result of decisions made in the present. 19;Tar. Fetiri -2-uiaon ai4cT proau:e; -)ii-r ri-fpC-rlIant DeiSr,,fis sucr AS 1655 damarge ro uaii.in,s And eTTestsr6155 rirnS pdiuur,S Air. The link between sustainable development and expanded rccreait-Tnnai inpdriannrr ii ri-4lakes r..cS. Si-i sesar-Trra arid asset accounting rests on the assumption that flows ofincome and rn-S Triprdad,Es r- alaus or a croaner. nrii:.re nfAurA rv.sn.ionulni~n. well-being are ultimately derived from the total stocks of pro- duced, natural, and human assets. Declines in the aggregate value of these stocks must eventually lead to declines in welfare. accounting, which aims to derive "greener" measures of national One measure of such changes in total assets is provided by 'gen- income, savings, and wealth by adding natural resources and pol- uine" saving: the true savings rate of a nation, accounting for the lutants to the assets and liabilities measured in the standard measured economic "surplus" as well as the depletion of natural national accounts. This expansion of the balance sheet to include resources, accumulation of pollutants, and investments in human natural assets and liabilities is the basis for the United Nations capital. The first estimates of genuine savings were published in System of Integrated Environmental and Economic Accounts Expanding the Measure of Wealth (World Bank 1997c), and they are (United Nations 1993a) - The first cross-country estimates of net updated here (table 3.15). savings adjusted for resource depletion and environmental dam- The overall accounting structure is straightforward: genuine age were published by Pearce and Atkinson (1993). savings equal gross domestic savings, minus depreciation of pro- Extending the asset concept further, development may usefully duced assets, minus depletion of minerals and energy, minus net he conceived as a process of portfolio management, where the por-t- depletion of forests, minus pollution damage (not elsewhere folio consists of not only produced assets but also natural resources measured), plus investments in human capital. Note that net for- and human capital. In this view, development outcomes are linked eign savings are included implicitly in gross savings. to the production of capital goods, the exploitation of naftural Although the theoretical link between genuine saving and resources, and the accumulation of human skills. Measuring sustainability is clear, there are important reasons for this sec- 1li0 1999 World Developmernt rIndicators tion to be titled "Toward a measure of sustainability." Each of low or negative genuine savings rates are an issue for many devel- the adjustments required to derive genuine savings from gross oping countries. savings involves the use of estimates that may vary, often sig- Beyond the savings analysis, many components of genuine nificantly, from what would constitute an ideal measure. savings are of considerable interest on their own, particularly the Moreover, because the accounting is not exhaustive (in partic- estimates of resource depletion. Broadly speaking, the lowest ular, soil degradation, local air and water pollution, and fish- rates of genuine saving are recorded for the countries with the ery depletion are excluded for lack of comprehensive data), highest dependence on resource rents relative to GDP, particu- there is an inherent upward bias in the genuine savings mea- larly for countries endowed with minerals and fossil fuels. These sures, and thus in the resources estimated as available for rents are a considerable share of GDP in many countries, sug- future development. gesting that the management of natural resources and resource A brief summary of the limitations of the data and the revenues should become an even more important economic approximations used to calculate genuine savings illustrates management issue in these countries. these concerns: There is also much to be gained from presenting environ- • Depreciation of produced assets should represent the decline mental issues in a framework that ministries of finance and plan- in market value of these assets as a result of both wear and tear ning can relate to. Too often ministries of environment and in use and obsolescence. In practice, most countries assume natural resources design policies in isolation from the broader certain fixed asset lives and model depreciation on this basis. macroeconomic framework-and finance and planning min- Where tax records are the source of depreciation estimates, istries ignore the environment and natural resources because the tax laws may themselves influence how institutions esti- they do not fit into their own indicator framework and budget mate depreciation. Because of technological improvements, lines. Genuine savings can begin to bridge this gap. obsolescence has become a more important element of depre- ciation, and in practice, the shorter economic life of assets that become obsolescent cannot be predicted. * Depletion of exhaustible resource deposits should be simi- larly measured as the change in market value of these deposits as a result of extraction. But markets for resource deposits generally do not exist. This leads to a reliance on esti- mates of resource rents and assumptions about future rents in valuing depletion. Resource rents represent the difference between the market price of a unit of resource and the eco- nomic cost (including returns on capital) of extracting the resource. * Net forest depletion figures are calculated as the estimated levels of unit resource rents times the difference between har- vest rates and net natural growth of forests. Net natural growth estimates by region are based on expert opinion at the World Bank. Excess deforestation is not captured by this measure. * Pollution damage is estimated only for carbon dioxide emis- sions, which means that local air and water pollutants are excluded (largely for lack of comparable local damage esti- mates). The damage estimate for carbon dioxide emissions is itself controversial, given the range of assumptions about the future that go into it. * Investment in human capital is measured simply as current education expenditures (that is, operating as opposed to cap- ital expenditures), which in most cases can be expected to understate the incremental asset value created. Depreciation of human capital, through death and obsolescence of skills, is not estimated. The genuine savings estimates here must thus be heavily qual- ified. But making progress on sustainability indicators is impor- tant enough to present these first steps toward a measure of sustainability. The measures may be imperfect, but it is clear that 1999 World Development Indicators 117 More tr3n 430 million people liR in countries %vinr ".aler stress l Iess than 1.7D) CuDIc nleters oi freshvater a%ailabrle per personi or *.etr scarci7 less than 1,1J00 cub,c mneers per persioni. lacins .cr,on,c ard 'despread ..ater shortrges. The shlre of the ,.oflrds populari.li ezper,encing v.ater st,ess coulir increase -more Lhar fnvefold r, 2050. 1995 2050 Total population: 5.7 billion Total population: 9.4 billion FrSl,.tr Siresv 24% More than one billion of the world's people lack access to safe water: some 270 million in urban areas and close to 900 million in rural areas. Almost all of them live in low- and middle-income economies. j j ; ,1 S .j jj I ili i,,i i Percent Rural t Urban 100 Source: Table3 3.5.5 118[ 1999 world De9elopment edicators The availability and quality of water *, are crucial to economic growth and development-and to the survival of -~ I <) terrestrial and aquatic systems. Global per capita water supplies are ,C, !- ! < ) declining and are now a third lower than they were 25 years ago. Further : ,.. D ) increases in population and economic activity are expected to boost demand for water, exposing many countries to periodic water stress. j -r'- - / ,..~~~~ -. ~ i--Global water withdrawal by sector. 1900-2000 . Z, _ '',qg. 1~~~~~~~N ti---~~~~~t ;3' pi.r- - .<~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. N~~~~~~ C ~~~~~~ Pt~~~~~~~~~~~~ - X,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4, VI %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 3.1 Land use and deforestation Land Rural Land use Forest Average annual area population area deforestation density people Permanent thousand per sq. km Arable land cropland Other thousand sq. km of arable land % of lend area % of lend area % of land area aq. km sq. km % change 1996 1996 1 1980 ±996 1980 1998 1980 ±1996 1995 1.990-95 ±990-95 Albania 27 355 21.4 21.1 4.3 4.6 74.4 74.4 10 ... 0 0.0 Algeria 2,382 166 2 9 3 2.. , 0.3 .......0:2 ......96 8 96.6 19 234 1-.2 Angola.1,247 258 2.3 2.4 0.4 04 97.3 97.2 222 2,370 1 0 Argentina 2,737 16 9.1 9.1 0.8 ......0:8 .....90.1 ~ 90.1 339 894. 0 3 Armenia.... ....... ... 28 .......197 . 21:2 ...... ......3 5.5 ..... .... 78.73 -84 -277 Australia 7,682 6 5.7 6:5 0.0 00 0.. ... 94.2...... 93 9 409 -7 . Austria ............I. 83 .... ..202 18.6 17:2 .... ..1.2 1.1 80.2 81.7 39 . ... 0.. 0.0O~ Azerbaijan .... .....- -87 ..__ .208 . 18.5 ..... . 4.6 ... ....... 76.9. 10 0 .... 0.0 Bangladesh 130 1,161 68:3..-.. 65.3....... 2.0 2.5 29.6 32.4 10 88 0 8 Belarus 207 47 . 30.0 . . 93 7 -688 -1 0 Belgium' 33 23:2.2 ... 22.0 0.4 05 ...... 764 ..... 75.8 7 0 0.....0.. Benin ill 240 12.2 12.9 4.0 4.1 83.8 83.0 46 596 1.2 Bolivia 1,084 147 1.7 1.8 0.2 0.2 98.1 97.9 483 5,814 1.2 Bosnia and Herzegovina 51 .... 264 .. 9 8 2.9 ................. 84.3 270... 0... 00 Botswana 567 161 0.7 6...... 0. 0........0 0.0 ... 99.3 0.0 139 708 0 5 Brazil 8.457 64 4.6 6.3 -1.2 1.4 ....94.2 92.9 5,511 25,544 0 5 Bulgaria 111 62 34.6 38.0 3.2 1.8 62.2 61.8 ..... 32 .........-6 0.0 Burkina Faso 274 252 10.0 12.4 0......O. 1 01 89 8 87.5 43. 320 0.,7 Burundi .............. 26....... 753 ..... 35:8 30.0 10..1.... 129 9.. ... 54.0 56.8 314 0.4 Cambodia 177 217 11.3 21.1 0.4 06 88.3 78.4 98 1,638 1.6 Cameroon 465 124 12.7 128.8 . 2.2 .... 2:3.. 85. 1 84.9 196 1,292 0.6 Canada 9,221 15 4.9 4.9 0.0 0.0 95.0 95.1 2,446 -1,764 -0.1 Central African Republic 623..... 105 3.0 3 1 . ....0.1 0.1 969.9... 96.8 299 1,282 0.4 Chad 1,259 166 2.5 ... .2.6. O...... 0.0 0.0.. .. 97.5 97.4 110. 942 0. .... O8 Chile 749 68 5.4 4.5 0.3 0.4 94.3 94.6 79 292 0.4 China' 9,326 675 10.4 13.3 0.4 1.2 89 3 85.7 1,333 866 0.1 Hong Kong, China 1 5,085 7.0 6.1 1.0 1.0 92.0 92.9 Colombia 1,039....... 549 3.6 1.9 1.4 .......2.4 95.0 95.7 530 2,622 05.5.. Congo, Dem. Rep. 2,267 464 3.0 3.1 0.3 0.4 96.6 96.5 Congo, Rep. ............. 342 796 0.94 0.4 .0 1 0.1 99.6 995 5 .. 195 ...... 416 0..... 2 Costa Rica ........... 51 . ... 602 5.5 5:6.6.... 4.4 48.8~ .. 90.1 89.6 12 414 3.0 C6te dIlvoire 318 268 6 1 .......9.1 ......7.2 13.5 86.6 .... . 78.9 55 308, 0.6 Croatia 56 169 .. 22 .222 . 78 8 0 0. Cuba 110 70 23:9 34.3 6.4 6.8 69 7 58.9 18.. 236 ..... 1..2 Czech Republic .... .... .77 .. ... 115 40.1 3 .. 31 ........ 56.2 26 - . Dominican Republic ... .... . 48.. .... 221 .... 22.1 27.9 7 2. 11.4 70.6 62.4 16.. 264..... 1.6 Ecuador 277 300 5.6 5.7 3.3 5.2 91.1 89.0 i11 1,890 1.6 Egypt, Arab Rep. 995 1,167 2.3 2.8 0~2 ......05.5..... 97.5 96.700.. 0.0 El Salvador 21 498 27 0 30.7 8..0 10.5 65.0 59.9 138 3..3 Eritrea 101 689 .. 4.4 ........ 0 8 ........... 94.9 3 0..... 0.0 Estonia 42 35 .. ....26.7 ..... .. ... 04 4 ... ........... 72.9 20 -196 -1 0 Ethiopia 1,000 434 11.3 . . . 8. 3 2 . Finland 305 76 8.4 8.1... 0.0 200 166 0.1 France 550 80 318.8.. .. 33:2 ....._2 5 2.1 65.7..... 64 7 150 -1,608 -1.1 Gabon 258 169 1.1 1.3 0.6 0:7.7. 98.2 98.2 179 .... .910 0.5 Gambia ,The 10 460 15.9 17.5 . .. 0.0 18 0.9 Georgia 70 288 . 11.1 .. 4.7 ....... ........ 83.8 . ....30..0.. 0.0. ~ Germany ...............-349... .... 92...... 34:4 33.9 1.4 0.7 64 1 65.5 107 ... 0 .... 0.0 Ghana 228 398 8:4.4 ... 123.3... 7.5 7.5. . . 84.2 80.2 90 1,172 1.3 Greece .......... .. 129...... 148 22.5 22:2.2 .... 7.9.... ... 84.4 ..... 69.6 69.1 65 -1,408 -2.3 Guatemala 108 458 11.7 ... 12 6 .....44 5.1 83.9. 82 4 38 824.. 2.0 Guinea 246 796 2.0 2.4 0.9 12.2 97.1 96.7 64 748 1.1l Guinea-Bissau .......... ... 28 ...... 289 9.1 10:7 .. ... 11 1 :41.4 ..... 89.9 87.9 23 104 0..4 Haiti 28 885 198.8.. 20.3 .... 12..5 12.7 67.7 67.0 0 8 3.4 Honduras 112 191 13 9 151 18 3.1 843 81.9 4 1.022 2.3 120 1999 World Development Indlicators 3.1 Land Rural Land use Forest Average annual area population area deforestation density people Permanent thousand per sq. km Arable land cropland Other thousand sq. km of arable land % of land area % of land area % of land area sq. km sq. km % change ±.996 1996 19eo ±.996 ±1980 ±996 ±.980 1996 1995 1990-95 1990-95 Hungary . ... 92 ... .. 74 54.4 521.1 3.3 24.4 422.2.. 46..1 17.... -88 -0. India 2,973 424 54.8 ... 54.7 1.8 2.4 43.4 42.9 650. -72 0.0 Indonesia 1,812 699 .... 99 .9 .... 9.9 4.4 7.2 85.6 83.3 1,098 10,844 1.0 Iran, Ialamic Rep. 1,622 .. 37 .... 8.0 ......109.9 ... . 0.5 1.0 91..5 ... 88.3 15 284 1.7 iraq 437 97 12.0 12.6 0.4 ... 076.6 .... 87.6 86.8 .. 10 0.0 Ireland 6 9 116 16.1 .19.3 .. 0.0 070.0. 83.9 80.9 6 -140... -2..7 Israel 21 149 15.8 17.0 4.3 4.2 80.0 78.9 .. 1 0 0.0 Italy... ..... 294 236 32.2 .. 27.6 ...10-0 ... 971 1 . 57.7 62..1 65. -58. -0.1 Jamaica 11 646 16.6 16.6 5.5 6.1 77.8 79.8 2 158 7.2 Japan.... 377 ... 693 11.4 105.5 1.6 1.0... 87.0 88.3 251 132 0.1 Jordan .. 89 378 3.4 3.6 0.4 1.0 962.2. 95.4 .... 012 2.5 Kazakhstan 2,671 20 . 11.9 ..- .. ... 0.1 ........... 86.9 105 -1,928 -1.9 Korea, Dem. Rep. 120 511 13.4 14.1 2..4 2.5 84.2 .. 83.4 620. 0..0.. s Korea, Rep. 99 462 20.9 17.7 1.4 2.0 77.8 79.4 76 130 .. 02 Kuwait 18 977 0.1 0.3 0.0 0.. 0 0. Kyrgyz Republic .... .......192 .. ...310 . 4.7 27.7 92.6 7 0.... 0.. .0 Latvi a62 40 2: 7 .. 27.3. ....0...5 72.0 29 -250 -0.9 Lebanon 10 264 20.5 18.2 8 9 12.5 70.6 70.0 152 7.8 Lesotho 30 463 9.6 10.5 : 0.0 0 0 0.0 Libya 1,760 40 1.0 170.0 ... 0.2 02.2 988.8 98.8 ... 40 0.0 Lithuania 65 35 . 45.5 0:9 53.4... 20 -112 -0.6 Macedonia, FYR 25 129 . 23.9 1:9 74.0 10 2 0.0 Madeagacar... 1.. 582 .. 391 4.3 44.4 0.9 09.9 94.8 94.7 151 1,300 0. O.8 Malawi 94 540 1. 17.0 0.9 :1. 85.8 81.9 33 546 1.6 Malaysia 329 530 3.0 5.5 11.6 17.6 85.4 76.9 155 4,002 2.4 Mali 1,220 157 1.6 3.8 0.0 00.0 98.3 97.5 116 1,138... 1.0 Mauritani'a 1,025 233 0.2 0.5 0....0.. 0 .0 .......O-.....99.8. 99.6.. 60 0.0 Mauritius ...-2 673 49.3 .. 49.3 34.4 3.0 47.3 47.80 0 0.0 Mexico 1,909 97 12.1 13.2 08.8---- 1.1- --87.1_ 87.0___ 554 5,080 0.9 Moldova 33 116 . 53.8 12.5 33.6 4 0 0.0 Mongolia 1,567 73.. _0.8 0.8 0.0 0.0 99.2 99.2 94 0.. 0.0 Morocco 446 145 16.6 19.7 1.1 1.9 82.3 .. 78.2 38 118 0.3 Mozambique 784 357 3.6 3.8 0.3 0.3 96.1 95.9 169 1.162 0.7 Myanmar....... .. 658.. 336 14.6 14.5 0.7.... 09.9 84.8 .. 84.7 272 3,874 1.4 Namibia 823 122 0.8 1.0 0.0 0.0 99..2 99.1 124 420 0.3 NepaI.. '..... 143. 668. 1 6.0.... 20.4 0.2 07.4 83.8 80.8 48 548 1.1 Netherlands .........34 . ...192 .... 23.3 26.1. .. . 0:9 1 0 75.8 .. ..72.9 3 00.0 New Zealand 268 33 9.3 5.8 3.7 6.4 86.9 88.5 79 -434 -0.6 Nicaragua 121 69 9.5 202.2 1.5 2.4 .. 89.1 78.8 56 1,508 ...... 2.5 Niger 1,267 154 2.8 39.9 0.0 0.0 97.2 96.4 260 0.0 Nigeria 911 225 30.6 33.3 2.8 2.8 66.6 64.1 138 1,214 0.9 Norway . 307 .. ..117 2.7 .... 3.3. ...' ... 0.0.. 81 -180 --0.2 Oman 212 3,086 0.1 . .... 0.1 .......0.1 ..I....02.2 99.8 99.70 .... 0 Pakistan 771 388 25.9 27.3 0.4 0.7 73.7 .72 .1.. 17. .. 550 2.9 Panama 7 4 235 5.8 6.7 1.6 2.1 92.5 91.1 28 836 2.1 Papua New Gui'nea 453 6,140 . 0.0 0.1 09 I1.1 99.0 98.8 369 0.43 Paraguay 397 106 4.1 .......5:.5 . 0.3 0.2 95.6 94.3 115 3,266 2.6 Peru 1,280 .. 186 2.5 2. 0.3 0.4 97.2 96.8 676 2,168 0.3 Philippines 298 621 14.5 17.5 14.8 14.4 70.8 68.6 68 2,624 ..... 3.5 Poland 304 98 48.0 46.3 1.1 1.2 50.9 51.9 87 -120q -0.1 Portuga 92 295 26.5 23.5 7.8 8.2 65.7 67.7 29 -240 -0.9 Puerto Rico 9 4,526 5.6 2.5 5.6 4.4 88.7... 91.3 324 0.9 Romania 230 106 42.7 40. 2.9 2.4 54.4 56.9 62 12.. 0.0 Russian Federation 16,889 27 .. 7.8 .. 01. 922 7650 0.0 1999 World Development Indicators 121 3.1 Land Rural Land use Forest Average annual area Population area deforestation density people Permanent thousand per sq. km Arable land cropland Other thousand aq. km of arable land % of land area % of land area % of land area aq. km aq. km % change 1.996 1996 1980 1996 1980 1996 1980 1996 1995 1990-95 1.990-95 Rwanda 25 746 30.8 34.5 ±-0.3 ......12.2 ......58.9 53.4 34 0.2 Saudi Arabia 2,150 87 0.9 ......177.7 .... 0.0 0.1 .....-9.9 1 98.2 218 0~8 Senegal .......193.. ..212 12.2 1 17 0:0 0.1 87.8 87.7 74 ......496 0.7 Sierra Leone ..............72... 629 6.3 6.8 0.7 0.8 930 0 ..... 92.5 ..... 13 ....... 426 3.0 Singapore1 0 33 1.6 9.8 0.0 86.9 0.0 0 0 0.0 Slovakt Republic 48 147 30.8. 2.7 . 66.5 20 -24 -0.1 Slovenia 20 418 11.5 2.7 ................ 85.8 .... 11 00.0 South Africa 1,221 134 ... 102.2 .12:3 . ....0:7 . .....0.7 ... 89.1 87.3 85 150 ... .. 0.2 Spain 499 60 3.1.1 30.5 9 9 9.8 59.0 59 7 84 0.....0..0 Sri Lanka .... ... ..... 65 1,601 .13.2 ..... 137 7. ... 15 9 15.5 70.8 .... 70.9 18 202 1.1 Sudan 2,376 142 5.2 5:4 ....0.0 0.0 94.8 94.5 416 ...... 3,526 ....... 0.8 Sweden 412 53 7.2 6.8 . 0.0 24424 0.0 Switzerland 40 686 9.9 10.1 .......0.5 0.6 89 6 89.0 11 0 0~0 Syrian Arab Republic . .. 184. .. 153 ...285 5 .. 24.4 ....2 5 3.9 69.1 70.1 ... 2 52 2 2 Tanzania 884 738 2.5 3 5 1 0 ...... 10...... 96.5 95.6 325 3,226 1.0 Thailand ...... ..... 511.... 280 32.3 33.4 3.5 66.6. 64.2 60.0 116 3,294...... 2 .6 Tcgo 54. .... .141 .. 36.8 38.1 6.6 6.6 56.6 55.9 12.... 186 1~4 Trinidad and Tobago 5 481 13.6 14.6 .....9 0 ....9:2.2. 77.4 76.2 226 1.5 Tunisia 155 119 20.5 18.3 9 7 13.1 69.7 69.0 630 0.5 Turkey 770 76 32.9 31.8 4.1 32.2 63.0 63.9 89 0 0.0 Turkmenistan 470 176 3.1 .. 0.... ....1... 969 380 0 Uganda 200 340 20 4 25.3 8.0 ....8.8.. 71.6 65.9 61 592 0.9 Ukraine 579 45 57:3. _1.8_ . .. 40.7 92 -54 -0.1 United Arab Emirates 84 1,108 0.2 ... ...0.4 .... 0.1 0.5 99 7 99.0 1 0 0.....0 .1 United Kingdom 242 104 28.7 .......25:2 ..... 0:3 .... 02 71.1 75.4 24...... -128 -0.... .5 United States 9,159 36 20. 19.1 0.2 0.2 79.2 80.2 2,125 -5,886 -0.3 Uruguay 175 24 8.0 7.2 .... 0.3 03 91'7 925 84 0.0 Uzbekistan 414 301 . 10 9 .........0 9 88.2 91 -2,260 -2.7 Venezuela 882 117 3.2 3 0 0..9..1.0 995.9 95.9 440 5,034 1.1 Vietnam 325 1,102 18.2 16.9 1 9 .....3-8.8... . 79.8 79.2 91 1,352 1. W est Bank and G aza .............: ........:... . .......7 '00 .. .....: ... . ... ... ... Yemen, Rep 528 714 2.6 2:7 .. ...0:2 ......0.2... 97.2 97.1 0 0 0..0 Yugoslavia. FR (Serb./Mont.) 102 122 . 36.4 3.5 . 60.0 18 0 0.0 Zambia 743 99 6.9 7.1 0.0 0.0 93.1l 92.9 314 2,644 0.8 Zimbabwe 387 246 6.4 8.0 .....0.3 ....0:3.3... 93.4 92.6 87 500 0..6 Low income 30,175 487 11.9 123.3. 0.9 11....... 86.6 86.8 4,924 37,622 0.7 Middle income 68.983 573 7.8 9.5 2..... 1.221.2..... 90.9 89.4 21,277 75,666 0.4 Lower middle income 46,158 619 8.6 10.2 12 1.2 90.2 8. 13,499 33,358 0.2 ~~............. .. ... .... .....7. ......... Upper middle income 22,825 166 7.0 . ....8.1 .... 1.1 13.3. 91.7 90.7 ....7.778 42,308 . Low & middle income 99,158 530 9.4 10.3 1.1 1.1 89.3 88.6 26,201 113,288 0.4 East Asia & Pacific 15,869 683 10.0 11.9 1.6 2.6 87.4 85.7 3,756 29,826 0.8 Europe & Central Asia 23,844 115 38.6 .... 12J.1 ..... : ........ 0.4 ..... .. 87.3 8,579 -5,798 -....0.1 Latin America & Carib. 20,064.... 252 5.8 6:....., 1 3 93.1 92.3 9,064 57,766 0 6 Middle East & N. Africa 10,972 501 4.4 52.2 0.4 0:7.7..... 95.1 94.1 89....... 800 0...... O9 South Asia 4,781 ... 523 ...42.5 42.6 1.5 ........19.9 ..... 56'.1 .... 55.5...... 744 0.16O2 Sub-Saharan Africa 23,628 . .. 364 5.4 6.4 .. 0.7 0.9 ..... 93.7 92.8 3,969 29,378 0.7 High income 30.990 215 12.0 11.6 . 6,512 -11,564 -0.2 Europe EMU 2,275 .. . 152 27.7 .. 27.1 .... ..54 ......5.0 63.9 64.8 683 -1,880_ ... -0.3 a. Includes Luxembourg. b. Includes Ta man, Chins. 122 1999 World Development Indicators 3.1 - iL :P The data in the table show that land use patterns are on forest cover as of 1995 and a revised estimate of * Land area is a country's total area, excluding area changing. They also indicate major differences in forest cover in 1990. Forest cover data for developing under inland water bodies, national claims to continen- resource endowments and uses among countries. countries are based on country assessments that were tal shelf, and exclusive economic zones. In most cases True comparability is limited, however, by variations prepared at different times and that, for reporting pur- the definition of inland water bodies includes major in definitions, statistical methods, and the quality of poses, had to be adapted to the standard reference rivers and lakes. (See table 1.1 forthe total area of coun- data collection. For example, countries use different years of 1990 and 1995. This adjustment was made tres.) * Rural population density is the rural popula- definitions of land use. The Food and Agriculture withadeforestationmodeldesignedtocorrelateforest tion divided bythe arable land area. Rural population is Organization (FAO), the primary compiler of these cover change over time with ancillary variables, includ- the difference between the total surface area popula- data, occasionally adjusts its definitions of land use ing population change and density, initial forest cover, tion and the urban population (see Definitionsfortables categories and sometimes revises earlier data. (In and ecological zone of the forest area under consider- 2.1 and 3.10). * Land use is broken into three cafe- 1985, for example, the FAO began to exclude from ation. Although the same model was used to estimate gories. Arable land includes land defined by the FAO as cropland land used for shifting cultivation but cur- forest cover forthe 1990 forest assessment, the inputs land under temporary crops (double-cropped areas are rently lying fallow.) And following FAO practice, World to State of the World's Forests 1997 had more recent counted once), temporary meadows for mowing or for Development Indicators 1999 breaks down the cate- and accurate information on boundaries of ecological pasture, land under market or kitchen gardens, and land gory cropland. used in last year's edition, into arable zones and, in some countries, new national forest cover temporarily fallow. Land abandoned as a result of shift- land and permanent cropland. Because the data assessments. Specifically, for the calculation of forest ing cultivation is excluded. Permanent cropland is land reflect changes in data reporting procedures as well cover for 1995 and the recalculation of the 1990 esti- cultivated with crops that occupy the land for long peri- as actual changes in land use, apparent trends mates, new forest inventory information was used for ods and need not be replanted after each harvest, such should be interpreted with caution. Bolivia. Brazil, Cambodia, Cote d'lvoire. Guinea-Bissau, as cocoa, coffee, and rubber; this category includes Satellite images show land use that differs from Mexico, Papua New Guinea, the Philippines, and Sierra land under flowering shrubs, fruit trees. nut trees, and that given by ground-based measures in both area Leone. The new information on global totals raised esti vines, but excludes land under trees grown for wood or under cultivation and type of land use. Furthermore, mates of forest cover. For industrial countries, the timber. Other land includes forest and woodland as well land use data in countries such as India are based United Nations Economic Commission for Europe and as logged-over areas to be forested in the near future. on reporting systems that were geared to the collec- the FAO use a detailed questionnaire to survey the for- Also included are uncultivated land, grassland not used tion of tax revenue. Because taxes on land are no est cover in each country. for pasture, wetlands, wastelands, and built-up areas- longer a major source of government revenue, the No breakdown of forest cover between natural for- residential, recreational, and industrial lands and areas quality and coverage of land use data (except for crop- est and plantation is shown in the table because of covered by roads and other fabricated infrastructure. land) have declined. Data on forest area may be par- space limitations. (This breakdown is provided by the * Forest area is land under natural or planted stands ticularly unreliable because of differences in FAO only for developing countries.) For this reason of trees, whether productive or not (see Aboutthe data). definitions and irregular surveys. the deforestation data in the table may underesti- * Average annual deforestation refers to the perma- Estimates of forest area are from the FAO's State of mate the rate at which natural forest is disappearing nent conversion of natural forest area to other uses, the World's Forests 1997, which provides information in some countries. including shifting cultivation, permanent agriculture, ranching, settlements, and infrastructure development. Deforested areas do not include areas logged but The countries with the highest deforestation rates in 1990-95 intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative BI j~'.:~' ,'. -;, " '., .;' wel.:. numbers indicate an increase in forest area. r,;r,:,l~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ l ,~ , ir]... 1 ,: ,1. ,. .,. :~~~~~~~~~~~~~~~~~~~~~~~~~~~~~r .: A, Data sources 3~ M 1s -n q 7 ;n t'3 |Data on land area and land use - 4 4 l '- are from the FAO's electronic I :' ; !S imi r. i i [1 :2 2 1Z1 !. . " i files and are published in its I ,; . Production Yearbook. The FAO .-I 1 j 0 u1 2 li 2 0 0 - r3 fi h j,"l: S i t1 | kj: v F 3 ! gathers these data from H ~~~~~~~ ~ ~ ~ ~ ~ ~~national agencies through annual questionnaires and by analyzing the results of national agricultural censuses. Forestry data are from ni, r-, , .....in......, |-, ...,,- ., , , ' I ;''' ' the FAO's State of the World's Forests 1997. : T,cr-: L 1999 World Development Indicators 123 3.2 Agricultural inputs Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumption production Tractors Tractors hundreds of grams per thousand per hundred hectares % of thousand per hectare agriculture! hectares of per capita cropland hectares of arahle land workers arable land 1979-81 1994-96 1979-81 1994-96 1979-81 1.998-97 1979-81 1995-97 1979-81 1.994-96 1979-81. 1994-96 Albania ................ 0.22 ....0.18 53.0 48.4 .. 367 227 1,556 207 15 10.......I. 173 150 Algeria 0 37 0 27...... 3.4 6.9 2,968 2,453 .27 100 27 43.. 68 ....125 Angola 041 0.27 2.2 271.1 705 789 49 33 4 3. 35 ....34 Argentina 0............... 9 89 ..... 0:71 .5:.8 . .6.3 11,099 10,126 46 .. 254 ... 132 ...... 190 ..... 73 112 Armenia .................-0:16 ........ : 43.7 181 . 130 64 ........ :..... 306 Australia 2.97 2.70 3.5 4.8 15,986.15,854 269 376 751 698 . .5 65 Austria 0.20 0.18 0.2 0.3 1,062 829 2,615 1,704 ..... 945. 1,492. . 2,084 2,481 Azerbaijan 0.21 5 617 244 ...... 31 181.... .. Banglaesh0:l.10 00 17.1 39.1 10,823 10,825 459 1,374 0 0 5 6 Belarus 0.60 . 1.9 2,508 .969..13196184 Belgium, 0.1 0.1 426 34 5,323 4,245 1,30 1,7 1,465 Benin 0:39 0.26 0.3 0.5 525 718 12 214 0 0.. 1.. 1. . Bolivia 0.35 .... 0.27...... 6.6 3.7 559 ...... 726 23 41 ......4 4.. 21 ..... 27 Bosnia and Herzegovina 0.21 - 0.3 227 210 228 548 Botswana 0.44 0.23 0.5....... 0:3.3 . 153 178 33 62... 9....... 21 ....... 54 163 Brazil 0:32 ...0:33 .....3.3 4.9 20,612 19,554 915 898 31 ... ... 51...... 139 .142 Bulgaria 0 43 0.49 28.3 18.7 2,110 1,975 2,334 459 66 61 161 64 Burktina Faso 0.39 0.34 0.4 0.7 2,026 *2,998 26 70 0 00 5 Burundi 0:22 ..... 012 0.. ....O 7 17.3 .. 203 206 ...... 11 61 0...._.. ........0. .......12 Cambodia 0.30 0.37 4.9 4.5 1,241 .1,50 45. 30 ......0. 0...... O. 6... .3 Cameroon 0.68 0.45 0.2 0.3 1,021 935 56 53 ....0.........0 1- 1 Canada 1.86 .... 1.52...... 1.3 1.6 19,561 19,330 416 545 824 1,683 144 163 Central African Republic 0.81 0.58 .. 7 194 140 5 6 0 01 1 Chad .............0.70 0.48 0.2 0.4 907 1,523 .... 6 270.... 0.........1 . 1..... Chile 0.36 0.24 29.6 32.6 820 621 321 1.131 43 44 86 119 China 0.10 ...0.10 .... 45.1 37.0 94,647 91,370 1,494 2,732 2 1. ..... 76...... 56 Hong Kong, China 0.00 0.00 43.8 28.6 00 0 0 0 7 Colombia 0:13 .....0705 7.7 23.4 1,361 1,239 812 2,853 8.... 7....... 77..... 118 Congo, Dem. Rep . ..... ... 0.25 ......0.16 0.1 0.1 1,115 2,063 . . .16 ......0 . .....0.. ...3..... 4 Congo, Rep.0.07 0.05 ... . 0.7 0.6 19 26 30 170 2 1.... 55 52 Costa Rica 0:12._ 0.08..... 12-.1..... 23.8 136 70 2,650 3,636 22 23 ......210 .... 246 C6te dIlvoire 024 021 10 1.0 1,008 1,556 261 229 1 1 16 13 Croatia-.........I. .. ......0:25 ...... :. ... 0 2.2 ........ .626 . 1,577 14 31 Cuba ..... ......0.27. ..0:34... 229 9..... 202 2 .... 224 182 2,024 .... 546 78_ 94... 259 207 Czech Republic 0:30 0.7 . 1,618 1,122 148.........:..... 252 Denmark 0.52 0.44 14.5 20.3 1,818 1,2 ,5 1.914 973 1,088 708 62 Dominican Republic... ...... 0.19 .....0.17. .11.7 13.7 149 ...... 140 572 722 .......3........320 18 Ecuador 0.20 0.14 19.4 8.1 419 1,038 471 752 .... 6........7....... 40 56 Egt, Arab Rep. 0.06. 0.05 100.0 100.0 2,007 2,632 2,864 3,750 4 10 158 .... 304 El Salvador 0.12 0.11 14.8 14.2 422 431 1,330 1,261 5........5.. 59 54 Eritree 0.12 5.4 292 781 19 Estonia ....I..0:76 . 305 268 475....440 . Ethiopia . 0.20 _ ........ 1.6 . 7,790 126 -...... 0... 3. Finland 0:54 0.49 . 1,190 1,059 1,868 1,397 721 1,301 ...... 824 911 France -........... ....0.32 ...0.31. 4.6 8.2 9,804 8,780 3,260 2,679 737 1,189...... 836 721 Gabon 0.42 ......0.29 ......0.9 0:.8 6 ... . .18 ..... 20 ......13 .......5 7.. . .. 43 48 Gambia,The 026................... 016. .. -. .... .. 54...... .. 99..132. 49..0 0..... 3. 3.. ....... Georgia40.14 .. ...42 ...... 2..... 346 422 28 21...9 Germany .0:.15... . 0.14..... ..3.7 3.9 7,692 6,753 4,249 2,410.. 624 954 1,340 1,041 Ghana 0.18 0.16 0.2 0:1 902 1,291 104 44 1..1.18 15 Greece 0.30 0.27 24.2 33.8 1,600 1,306.1,92 . ,85 120 .. 26.. 45 80. Guatemala 0.19 0.13 5.0 6.5 716 ...... 629 ..... 726 1,324 3........ 2.. .... 32...... 32 Guinea 0.11 0.09 12.8 ......109 ..... 708 692 ....... 23 47 0 ..... ...0..... . ..3 .. .. .9 Guinea-Bissau 0.32...... 0.27 ......60 5.0 142.... 133...... 24 43 0 0.1... . I HaitiI. ... .I....0.10 0.08 7:9 .... 9:7 .....416 ...... 418 62 89 .... 0 ...... 0 ........ 3 .......4 Honduras 0.44 0.30 4.1 3.6 421 49 16 3857 21 29 124 1999 World Development Indicators 3.2 Arable land Irrigated land Land under FeTtilizer Agricultural machinerY cereal consumption production Tractors Tractors hundreds of grams per thousand per hundred hectares % of thousand per hectare agricultural hectares of per capita cropland hectares of arable land workers arable land 1979-81 1994-96 1979-81 1994-96 1979-81 1995-97 1979-81 1995-97 1979-8± 1994-96 1979-81 1994-96 Hungary 0.47 . ...0.47.. 3.6 4.2 2,878 2,840 2,906 836 59.. 54 111... 68 India 0.24 0.17 22.8 32.0 104,349 100,140 345.. 856 2 5 24 82 Indonesia 0.12 0.09 16.2 15.0 11,825 14,942 645 1,468 01 5 34 Iran, Islamic Rep. 0.....3 ..0.30 35.5 38.0 8,062 8,921 430 600 17 39.... 57 133 Iraq 0.40 0.26 32.1 61.0 2,159 3,090 172 637 23 54 44 ... 67 Ireland 0.33 0.37.. ..... 425 289 5,373 5,514 606 978 1,289 1,265 Israel..... 0.08 0.06 49.3 453 3... 129 86 2,384 2,963 294. 336 ... 809. 731 Italy 0.17 0.14 .... 19.3 24.9 5,082 4,210 2,295 280 30 86 ,1 179 . ............. .... ... ...... ..... ... . .... .... ....-.370 86 1,117.... 1,797.. Jamaica 0.08 0.07 13.6 14.0 4 3 923 1,547 9 11 156 181 Japan.0.04 ...0.03 62.6 62.7 2,24 2,57. 4,687.4,18. 209 593 3,091 539 Jordan... ... 0.14 0.08 11.0 18.2 .158 109 404.. 544. 48 42... 153 206 Kazakhstan . 1.99 . 6.9 . 17,158 . 34... 106 . 51 Kenya 0.23 0.15 0.9 1.5 1,692 1,805 160.. .278 1 1 17 35 Korea, Dem. Rep.0.9 0.8 58.9 73.0 1,625 1,410 4,688 2,418 13 19 27 41 Korea, Rep. ........ 0.05 ... 0.04 59.6 60.7 1,689 1,174 3,920 5,291 1 34 14 563 Kuwait 0.00 0.00 0 0 4,500 2,000 3... 14. 20 0 Kyrgyz Republic ......... 7..... 0.19 76 8. . 596 . 9 . 4. 238 Lao PDR 0.21 0.17 15.4 20.3 751 597 40 58 ... 0.. 08. 11 Latv..I. ... .. . ..... .... .. 0.. . 68. . .. - .... .......I ...... 445.... 655. 284 ... ...316 Lebanon 0.07. 0.05... 28.3 28.4 34 39 1,663 2,507 28. 77. 141 219 Lesotho 0.22 0.16 ... ...... 7 203 178 ... 150. 207 6. 6 47 61 Libya . ... 0.58 0.36 10.7 22.2 538. 466 .. .357 449 101. 275 134. .187... Lithuania .. 0.79 .. . . 1,101 . 410 . 39. 23 Macedonia, FYnR .. 0.31 9.4 . 228 751 . 323 7 MadagCascr0.28 0.19 21.5... 35.0 1,309 1,350 31 48 1.. . . 1 11 13 Malawi 0.20 0.16 1.3 ...1.6 1.155 1,391 246 213 0O.0 10 9 Malaysia 0.07 0.09 6.7 4.5 729 696 4,273 6,375 4 23 77 230 Mali 0.31 0.40 2.9 2.3 1,346 2,538 61 79 01 5 7 Mauritania 0.14 0.20 22.8 10.3 125 253 57 93 1 1 13 7 Mauritius 0.10 0.09 15.0 17.0 0 0 2,547 3,7~4 4.... .....6 33 3 7 Mexico 0.34 0.28 20.3 23.1 9,547 10,923 570 538. 16 20. 54 71 Moldova 0:41 . 41 900 . 652 . 82 281 Mongolia 0.71 0 53 3.0 6.1 559 304 83 19 32 22 82 56 Morocco 0.8 0.33 1... 5.2 13.0 4,414 4,967 273 331 7 10 35 48 Mozambique 0.24 0.18 2.1 3.4 1,077 1,756 109 31 11 20. 19 M . .. a .............. .. ... ...- 1I... I... ........ ...... . 5,133I6,303-111 163... 1. 1. 9.10 Myanmar ~~0.28 0.22 10.4 15.9 513 633 il 1311 Namibia 0.64 .0.52 0.6 0.8 195 332 ... I.... .. ..10 . 11.. 39 40 Nepl071.16 ... 0.14 22...5 30.6 2,251 3,249 98 336 0 0 10 16 Netherlands 0.06 0.06 58.5 61.5 225 198 8,620 5,923 561 646 2,238 2,060 New Zealand 0.80 0.43 5.2 8.9 193 167 1,965 4,247 619 451... 367 488 Nicaragua .....0.39 . .0.55 6.0 3.3 266 383. 392 147 6 7 19.. 11 Niger 0.62 0.54 0.7 1.4 3,872 6,860 10 19 0 00 0 Nigeria 0.39 0.27...... 0.7... 0.7 6.048 18,139 59..... 68 .. 1. 1. 3... 4 Norway 0.20 0 923 "I.... 311 336 3,146 2,138 824 1,251 1,603 1,539 Oman.. . 0...01 0...01.. ... 92.7 98.4 2 3 840 7,083 11 76 ....94 Pakistan 0.24 0~.1 727 8. 0,693 12,285 525 1,115 5 12 50 144 Panama 0.22 0.19 5.0 4.9 166 169 692 720 27 20 122 100 Papua New Guinea 0.01 0.012 2 3,8272 2 3,12 699 203 Paraguay 0.52 0.45 3.40 3.0 304 596. 44. 120 14. 25 45 75 Peru 0.19 .. 0.16.. 32:8.8 . .. 41.8 732... 874 381 453 5... 3.. 37 25 Philippines 0.09 . 0.07. ... 14.0 ... 16.7 6.790 6,608 765 1,131.. 24 ... 22 Poland 0.41 0.37...... 0.7 0.7 7,875 8,730 2,393 1,074 112 ... .277 425. 923 Portupg 0.25 0.22 20.1 21.7 1,099 706 1,113 1,198. . 72 203 .. 351 692 Puerto Ricoa0.02 0.01 39.0 57.8 1... J0.... .. ..... Romania 0.44 . 0.41 21.9 31.4 6,340 6,202 1,448 483... 39 80 150 175 Russian Federation 0 89 . 4.0 .. 52,759 129 . 122. 88 1999 World Development Indicators }125 3.2 Arable land Irrigated land Land under Fertilizer Agricultural machinery cereal consumpytion production Tractors Tractors hundreds of grams per thousand per hundred hectares % of thousand per hectare agricultural hectares of per capita cropland hectares of arabia land workers arable land 1979-81 1.994-96 1979-81 1994-96 1979-81 1995-97 1979-81 1995-97 1979-81. 1994-96 1979-81 1.994-96 Rwanda 0.15 0.13 0.4 03.3 ..... 239 140 3 1 .....0..... 0C. 1. 1 Saudi Arabia ..........-0.20 ......0.19 ....289.9 38.7 388.... 621 228 873 ..... 2.... 11 ... 10. 26 Senegal 0.42 0.27 2.6 3.1 1,216 1,204 104 74 0 02 2 Sierra Leone 0.14 ......0.11 4.1 5.4 ... 434 361..... 58 62 0... 1 . 6... 11 Singapore 0.00 0.00 .. .. 22,333 50,940 3 16 20 65 Slovak Republic .... .. .. 0.28. . 13-.4 ... .. 851 . 660 ..... ....... 100 199 Slo eni ....... ...... . ....... . 0.. .12 ... .. ..... 0.7 ... .. .99 . 3,116 ... .. . ... .. .2,762. 3,800 South Africa 0.45. 0.38 8.4 8.1 6,645 616 84 511 .. 90.. 69 140 87 Spain 0.42 0.39 14.8 17.7 7,391 6,788 1,012 1,285 200 513 335 525 Sri Lanka I..........0.06 0.05 28.3 29.2 864 775.1,789 ..2.324 89 *276..... 356 Sudan ...... 1.... 0.66 .... .0.48 ....14.5 15.0 4,447 8,377 51t 52 2 .... 2... 8. 8 Sweden 0.36 0.32 .. .. 505 118 1,654 1.121 71 931 623 592 Switzerland 0 06 0:06 6.2 5.9 172 195 4,623 3,058 494 616 2,428 2,815 Syrian Arab Republic .... 0:60 ....0.32.. 9.6 20.4 2,642 349 250 761 29. 65 54 ... 178 Tajikistan 0:14 . 80.6 ....353 850 3736 Tanzania 0.12 0.10 3.8 4.6 2,835 3,171 . -43 .11. .4 2 Thailand 0:35. 0:29 16.4.... 23.2 10,625 11,215 177 . 873.. 1. 7.... 11 82 Togo ..... .... 0.76.. 0.50 . ....0.3 0.3 . 416 771 13 73 0.. 0 .... 1. 2 Trinidad and Tobago .......0.06_... 0.06 17.8 18.0 . ..4 4 1,064 1,022 50.... 49 337 353 Tunisia 0.51 0.32 4-9 7 5 1,416 1,223 212 312 .....30 .... 39. 79 121 Turkey 0:57..... 0.40 9.6 15.4 13,499 13,95 529 . 678. 38 57.... 169 318 Turkmenistan ......0:32 87.8 .... 390 919..... ... 83 ... ... :. 347 Uganda .........-0:32._ . 026 ..0.1 01 I 752 1,315 1 6 0 16 9 Ukraine 0:6.5 75 ... .. 12,789 277 92 123 United Arab Emirates 0.01 0:01 ......... .89.9~9..... 0 1 2,250 9,672 6... 4. 106 68 United Kingdom 0.12 .. .0.10 2.0 1.8 3,930 3,346 3.185 3,700 726 871.... 742, . 837 United States 0.83. 0.67. 10.8 12.0 72,630 63,137 1,092 1,134 1,230 1,452. 253 271 Uruguay 0.48 ... 0.39. 54.4.... 10.7 .... 614 628 564 777 171...... 172.... 236 262 Uzbektistan .. 0.20 . 8. . 1,72 1,089 59 . 376 Venezuela 0.19. 0.12 3.6 5.2. 814. 772 711 1,024 50 58 133 182 Vietnam 0 11 0.07 24.1 29.6 5,964 7,568 302 2,593 14.... 38. 172 West Bank and Gaza Yemen, Rep. ..... ....... 0.16 .. 0:09 . 199 31.3 ..... 865 ... 719 .... 93 .... 85 ... ...3. 2 33 41 Yugoslavia, FR (Serb./Monrt) ... 0.35 1 6 437 Zambia............ 0:89.. 0:58 ......0.4 0...9 595 748 145 96 32... 9 11 Zimbabwe 0.36 0.28 3.1 4.6 1,633 2,025 609.54 719 7,074 66 81 Low income 0.25 ....0.19 18.4 23.5 187,031 225,820 .73 1154. 2 ....5.. . . . .. . .. . . .. ... . . I . .. . . . . . 2 0.. . . . . . . . . .. . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . .5 8.. I. . . . . . . . . . . . .. . . . . . . . . . Middle income 0.19. 0.23 " .... 17.2 244,882 336,911.1,542 1,463 7 1 100... 124 Lower middle income. 0.14 0.21 . 20.2 168,621 257,156 1,958..1596. 69 91 Upper middle income. ..0.39 .....0.33 ....8.3 10.0 76,261 79,755 972 .1,117 .43 . 73, 144 210 Low & middle income. 0.22 ...0.21 ..19.7 19.4 431,913 562,3 1,170 1332 5 . 8 62 100 East Asia & Pacific ... 0.12 0.11 7:... . . 139,907 142,975 2,444.3,076 2.... 2...55 61 Europe & Central Asia 0.61 9.7 37,380 129,657 810 67 103 223 172 Latin America & Carib: 0.33 ... 0.28 .... 9.8 11.2 49,979 50,234 .. 786 931 25 34 95 112 Middle East & N. Africa 0.29 0.21 . .23.6 31.1 25,657 28,696 .. 605 992 12 . .. .24. ... 61 118 South Asia 0:23 0:16.. 27 8 37.2 132,128 130,003 918 1,370.. 2. 5.... 26 83 Sub-Saharan Africa ... 036 0.26 3.6 3.8 46,864 81,166 ..41 _ 573.... . . 2. ...23 18 High income .... 0.46 ...0.40 . . 156,274 139,679. . 429 756 383 43. 6 Europe EMU 0.24. 0.22 .. . 33,970 29,611 1,949 2,343 45 12 888. .95 a. Includes Luxembourg. 126 1999 World Development Indicators 3.2 Agricultural activities provide developing countries - * Arable land includes land defined by the FAO as with food and revenue, but they also can degrade land under temporary crops (double-cropped areas natural resources. Poor farming practices can The world is growing more cereal are counted once), temporary meadows for mowing or cause soil erosion and loss of fertility. Efforts to for pasture, land under market or kitchen gardens, increase productivity through the use of chemical ,,,...,,.: - I, e, and land temporarily fallow. Land abandoned as a fertilizers, pesticides, and intensive irrigation meth- i'-" result of shifting cultivation is excluded. * Irrigated ods have environmental costs and health impacts. "' land refers to areas purposely provided with water, Excessive use of chemical fertilizers can alter the :: including land irrigated by controlled flooding. chemistry of soil. Pesticide poisoning is common in Cropland refers to arable land and land used for per- developing countries, And salinization of irrigated -W manent crops (see table 3.1). * Land under cereal land diminishes soil fertility. Thus appropriate use production refers to harvested areas, although some of inputs for agricultural production has far-reaching countries report only sown or cultivated areas. effects. i-, I | * Fertilizer consumption measures the quantity of This table provides indicators of major inputs to ,, L] I plant nutrients used per unit of arable land. Fertilizer agricultural production: land, fertilizers, and agricul- * products cover nitrogenous, potash, and phosphate tural machinery. There is no single correct mix of fertilizers (includingground rockphosphate).The crop inputs; appropriate levels and application rates vary year (July through June) is the time reference for fer- by country and over time, depending on the type of * tilizer consumption. * Agricultural machinery refers crops, the climate and soils, and the production ' to wheel and crawler tractors (excluding garden trac- process used. The data shown here and in table 3.3 tors) in use in agriculture at the end of the calendar are collected by the Food and Agriculture Organi- In Lhe Dast 17 ?ears lhe area under cereal pioduc.. year specified or during the first quarter of the follow- zation (FAO) through annual questionnaires. The FAO tion has grown aboui 20 Dercenl votriwide. Altrioagn ing year. tries to impose standard definitions and reporting the ai3 of prodLrction has declined in high-income methods, but exact consistency across countries and economies. cereai ,ield in Ihese economies har Data sources Increased more I nan 40 perce nm. overtime is not possible. Data on agricultural employ- ment in particular should be used with caution. In - The data in the table are from many countries much agricultural employment is i, electronic files that the FAO informal and unrecorded, including substantial work makes available to the World performed by women and children. i Xj Bank. Data on arable land, Fertilizer consumption measures the quantity of irrigated land, and land under plant nutrients in the form of nitrogen, potassium, cereal production are pub- and phosphorous compounds available for direct lished in the FAO's Produc- application. Consumption is calculated as production tion Yearbook. plus imports minus exports. Traditional nutrients- animal and plant manures-are not included. Because some chemical compounds used for fertil- izers have other industrial applications, the con- sumption data may overstate the quantity available for crops. To smooth annual fluctuations in agricultural activ- ity, the indicators in the table have been averaged over three years. 1999 World Development Indicators 127 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989-91 10 1989-91 = 100 1989-91 = 100 per hectare 1995 $ 1979-81 1995-97 197941I 1995-97 197981. 1995-97 1979481 1995-97 1979-8 1995-97 Albania .... . . . 2,500 2,662 1,19 1,717 Algeria 784.4..... 1191 1.. ... 697 ..... 118.2 52.0 109.2 656 982 1,41 190 Angola.102.0 147 0 919 9.. 130.1 87.8 106.4 526 542.. 117 Argentina 83 9 133 2 94....9 121.9 107.0 106.1 2, 2,2,5 1,9 13833 Armenia . 114:4. 82.3 . 62..4. ....... :....... 1,678 4,477 Australia 779.9 141.2 91.5 126.9 85.5 103.5 1,321 1,957 20,880 29,044 Austria 92 8 93.0 92.3 100.0 94.5 105.4 4,131 5,605 9,761 15,474 Azerbaijan........ 54 5 ........ ........ 55.6 65.0 . : 1,571 . 847 Banglades 80.0 .... 101.7 792.2. 106.0 81.3 132.3 1,938 2,679 181 221 Belarus 101.2.. 58.9 56.0....2,162.......3,461.. Belgiumu 84:9.... 122.3 88.4 114.4 88.8 ... .113.6 4,861 7,566 Benin 54.1 156.0 63.4 129.5 68.9 113.9 698 1,067 302 504 Bolivia ......1 ...... 71.3 ... 137.5 71.0 .... 126.7 75.6 114.8 1,183 1,664 Bosnia and Herzegovina . . 2,622 Botswana 89.. .... . .6. ...... 88.4 .... 87.6 .......~1042.2 .87.5 .. 106.8 203. 290. 619 647 Brazil 75.3.... 1159.9. 695.5 122.2 67.9 126.9 1,496 2,442 2,047 3,931 Bulgaria 1077.7 70.0 -105..3 68.3 ..... 96 .3 ......59.4 3,853 2,687 2,754 4,351 Burktina Faso 59.2 122.2 62.6 1224 4...... 59.9.. 120..2. 575 754 134 ...... 159 Burundi 79.9 94.7 80.5 96.4 88.1 93.4 1,081 1,378 177 139 Cambodia 55.2 126.3 51.1 124.8 ... 32.3 .. 121.2 1,025 1,759 . 407 Cameroon 91.1 119.8 83.2 118.7 61.3 107.9 849 1,313 834 958 Canada 77.6 115.2 799 9...... 112.7.... 88..3 .. 115.0. 2,173 2,712 Central African Republic 103.2 121.4 79.9 122.7...... 49.0..... 123..5..... 529..... 971 396 439 Chad 668.8 123.4 ..... 90.6 .. . 117.5 120.4 108..7 587 627 ..... 155 212 Chile 707 7..... 123.5 71.5 128.7 75.8 136 3 2,124 4,412 ......2,612 5,211 China 67 1 132!3 610 155.8 45.3 202.6 3,027 4,808 162 296 .n g Kong, China 133.6 77.1 9 56.7 188.7 45.3 1,712 Colombia 84.3 101.3 76:0.0.. 110.8 72.7 117.2 2,452 2,734 1.926 2,890 Congo, Dem. Rep . ...........72.2 10....i3~4 ...... 71-.9 ...... 104.9...... 77.3 104.6 807 780 270 285 Congo, Rep. 82.5 113!8 80.~3 ......114:5. 80.0.. 116.9. 825 818 ....... 391 470 Costa Rica 70.6 127.8 73.0 128.4 77.2 120.6 2,498 3,179 3,159 4,627 C6te dIlvoire 73.9 110.7 70.9 ........1192.2 74.7 119.7 867 . 1,100 :1074 1,005 Croatia .....80.2 .... ... ..... 57.7 . 46..2 4,632 . 7,144 Cuba 87 3 58 5 .... 9 00 62.09 66.5 2,458 2,126 Czech Republic .. 86.4 ..... 81.9 . 77.8 .....4,172 Denmark 65:2.2. 928.8 83.2 102.5 95.0 110.5 4,040 6.081 21,321 46,621 Dominican Republic ...... 963 3 .... .. 92:2.2 .... 85. 1... . 109.1...... 68.8 .._. 133.9 3,024 3,933 1,839 2,454 Ecuador 78 2 134 0. 766.6 136.9 73.1 140.5 1,633 1,821 1,206 1,764 Egypt, Arab Rep 75.7 129.4 68.4 1298 60 1222 405 637 71 1,163 -. ...... .. . . ...... I . .. .... .. .. ... .... . .. ... .. .... ... .. . . 129. . . 8.. .. . 67.0.. .. .. .. . ... .. .. . . . .. . 4,05 6,357... 721. . . . .. .. ... . .. .. . El Salvador 120.3 104 0 90.8 109.5 88.9 123.4 1,702 1,949 2,013 1,705 Eritrea 119.3. 102.3 . 91.3 ...... 465 Estonia . 69 2 49.3 43.1 . 1,964 .. ............ 3,342 Ethiopia 91.8 ........ .. ....90.2 89.1 ..... 1,229 .. .... ....:..... ....... Finland 74 9 95 1_ 9278.8...... 92.4 107.1 91.6 2,511 3,412 16,995 28,296 France 87.6 104.8 93.7 103.6 97.8 105.0 4,700 6,809 14,956 34,760 Gabon .............. 76.4..... 107.0 ... 79..2 105.2 86.4 107.5 1,718 1.r754. 1,822 1,809 Gambia, The 79:4.4.. 77.4 82.6 81.6 93.3 108.9 1,284 1,035 325 216 Geoqrgia .. 63.3 74.6 . 80.9 . 1,789 . 1,838 Germany 89.6 106 910 90.9 98.4 84.8 4,166 6,289. 19,930 Ghana 724.4..... 158. 2.... 735 5. 147.7 79.7 102.8 807 1,383 663 533 Greece 868 1026.6 12 9..99 9 7 300 366 884 12,611 Guatemala 89.6 1079.9. 699.9 114.0 76.7 117.6 1,578 1,869 2,110 1,902 Guinea 89.8 127.0 96.5 129.2 116.4 129.5 958 1,251 262 Guinea-Bissau 658.8..... 111.2 69.2 111.3...... 78..4 .... 111,1 .6 .... 711 1,422 221 326 Haiti 1-03.4 842.2 10 5 .5...... 90.5 112.1 113.7 1,009 9235740 Honduras 90.3 111.0 88.2 104.7 81.0 1-12.5 1,170 1,567 697 1,018 128 1999 World Development Indicators 3.3 Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 100 1989-91 = 100 per hectare 1995 $ 1979-81 1995-97 1979-81 1995-97 1979481 1995-97 1979481 199S-97 1979481 1995-97 Hungary 93.4 79.0 .... 91O.0 76.8 94.2 72.3 4,519 4.305 3,389 4,655 India 71.0 116.5 68.4 117.1 62.5 122.8 1,324 2,189....... 253 343 Indonesia ..........66:6.6. ... 120.0 63.5 122.4 50.3 137.1 2,837 3.950 610 745 Iran, Ialamic Rep. 576.6 135.5. .60~9.9.... 136.8 68.2 132.7 1,108 1,841 2,533 3,831 Iraq .....-- .74.2 .104.7 . ... 77~7.7 ..... 92.8 81.8 .56..3 ...... 832 833 Ireland 93.9 109.2 833.3 106.2 83.3 106.0 4,733 6,787 Israel 98.2 109.5 857.7 . .. 114.1 ... 78.7. 114.2 1,840 2,159 ... Italy 106:1.1.. 98.1 101.5 99.7 93.1 102.9 3,548 473 9,994 19,001 Jamaica 98.6 132.9 86.0 117.9 73.9 98.9 1,667 1.267 892 1,294 Japan 108.4 ......94.0. 94.2 98.9 85.1 96.5 5,252 6,074 15,698 28,665 Jordan 521.1 1597.7. 553.3.... 157.3 51.4 162.1 521 1,059 1,447 1,634 Kazakhstan .. 566.6 . ..6 .. 63..8.. 650 .........:......1,477 Kenya 74.8 1098.8 67:7.7.... 102.9 60.1 99.2 1,364 1,634 262 230 Korea, Dem . Rep. 7. ..... ........ . . ...... .. .. ... .......... ...-3,405 2,965 Korea, Rep. 87 6 107O 0. ... 779.9. 119 1 52.6 146.3 4,986 6,333 3,957 10,962 Kuwait 41.4 101.6 98.9 . 139.3 108.8 148.5 3,124 4,988 Kyrgyz Republic .. 87.2 . ...... 123.8 . 92.0 . 2,373 2,917 Lao PDR........... 72.9 ..... 107.9 . ... 71.2 112.4 64.4 . .. 1277 1,402 2,620 526 Latvia . 75... 49... 42.. 1,992 3,125 Lebanon 52.0 114.4 57.8 117.6 87.6 132.1 1,307 2,494 Lesotho ~~~~~~~~~~~ ~~95.1 102.....6. 89.4 104.4..... 87.7 114.2 977 ..976 4931 Libya 77:6.6. ... 88.2 81.7 97.4 68.9 97.1 430 .. 686. Lithuania 91.3 69.8 572,214 2,907 Macedonia, FYR 94. 95.996 2791,528 Madagascar 83.0 . ... 102.7 82:1.1..... 105.3. 89-.6 105.5 1,664 1,992 198 . ... 180 Malawi 84.1 ...... 114O.0 91.2 105.3 .... 79..2 100.0 1,161 1,216 100 122 Malaysia 74.7 109.0 55.4 124.0 41.4 141.2 2,828 2,988 3,279 6,267 Mali 594.4 131.1 79.7 118.7 94.6 116.4 804 909 225 241 Mauritani'a 59.8 1346.6 86.1 103.2 89.4 99.0 384 744. 301 439 Mauritius 92.4 973.3..... 891.1 106.1 65..9...... 137.4 2,536 4,664 3,087 5,400 Mexico 87.9 113.1 84.9 120.6 84..8. 124.1 2,152 2,575 1,482 1,690 Moldova .. .... ....... 75 2. .. 58.3 . 46.3 . 2,887 1.473 Mongolia 44.6 31 9 88:2.2. ... 81.6 93.1 86..6 573 795 727 1,085 Morocco 548.8....... 916.6 55.9 94.9 59.8 100.8 811 990 1,117 1,593 Mozambique 1094.4..... 1264.1..... 992.2 119.5 82.6 96.1 603 765 76 Myannmar...... 89.0 ..1357.7..... 87.8 133.5 86.2 .... 122.9 2.521 2,916 Namibia 806.6... 104.8 107:4.4. ... 118.8 115.9 121..1 377 311....... 876 1,235 Nepa 627.7..... 114.9 651.1I.. . 113.5 75.2 110.1. 1,615 1,943 162 187 Netherlands 79.8 111.0 .... 87:0.0. 106.1 88..6 ..... 102..8 5.696 7,952 21,663 43,836 New Zealand 75.4 ... 129.1 90~8.8. 120.3 95.5 113.4. 4,089 5,361 ............. Nicaragua 122:8.8. 1194.4 117.9 ... 123.7 139..7 ..... 115.1 1,475 1,742 1,334 1,407 Niger 95:1.1..... 120.3 10. 118.4 109.83115.1.440.325 222 190 Nigeria 52:.1...... 135.5 57:7.7. ... 134.2 82.5 127.1 1,269 1,191 370 541 Norway 91.2 93.5 91~8.8. 99.7 94.8 102.9 3,634 3,829 17,044 31,577 Oman 604.4. 110.0 62.5 96.1 61.6 92-.1 982 2,170......... Pakistan 65.6 114.2 66~4.4. ... 130.5 59.5 138.1 1,608 2,036 392 585 Panama 97.1 96.0 85.6 102.5 71.3 113.7 1,524 1,914 2,122 2,463 Papua New Guinea 865.5.. 106.3 861.1 106.8 84..5 ... 105.4 2,087 2,493 717 ~827 Paraguay 58.4 103.9 60.6 .. 116.7 62.1 112.3 1,511 2,241 2,506 3,295 Peru 81.0 1328.8... 78.4 131.5 78.2 124..9 1.944 2,688 1,349 1,619 Philippines 879.9 111:6.6.... 86.4 120.63 74.1 145.7 1,611 2,359 1,348 1,379 Poland 84.6 89.3 87.9 84.8 98.0 79.2 2,345 2,926 1,647 Portugal 845.5... 931.1 719.9...... 99.8 71..8 ....109.9 1,102 2,187557 Puerto Rico 131.2 66.4 99.7 83.7 90.3 89.2..... 8.925 6,774 . Romania 113.7 102.7 112.7 100.5 110.0 94.5 2.854 3,004 3,170 Russian Federation 80.9 69.5 . 62 1 1,366 . 2,540 1999 World Development Indicators 129 3.3 Crop Food Livestock Cereal Agricultural production production production yield productivity index index index Agriculture value added kilograms per worker 1989-91 = 100 1989-91 = 100 1989-91 = 100 per hectare 1995 $ 1979-81 1995-97 1979-81 1995-97 1979-81 1995-97 1979-8± 1995-97 1979481 1996-97 Rwanda 88.9....... 73.9 ......89 .7 ..... 76.9 81.0 85.5 1,134 1,365 ...... 307 201 Saudi Arabia 272.2 99.1 31.0. 90.8 32.8 118.8 820 3,491 2,167 10,507 Senegal 78.5 99.5. 74 5 109.1 643.3 132.6 690 779 341 321 Sierra Leone ........... 80.3 .93.1. 84.5 94.7 84.1 107.4 1,249 1,223 368 404 Singapore 595:0.0...... 56.6..... 154.3 37.9 173.7 38.0 13,937 39,851 Slovak Republic 7:........ 92O 0........ :...... .. 74.4 . 65.1 4,170 3,347 Slovenia . 120.3 .. 1 00. 99.3 4,977 26,006 South Africa 96.5 ........99.9 ..... 928.8 97.5 89.6 91.0 2,117 1.839 2,465 3.355 Spain 83.0 95.6 821.1 99.4 84.1 108.3 1,986 2,613 . 12,022 Sri Lanka 99A.4 110.5_ 98.4 113.0 93.2 133.5 2,462 3.113 648 732 Sweden 92.0 ... 92.2 .....1002.2 ..... 95.1 103.9 1 1 355 471 Switzerland 95.5 94.7 95.8 96.2 98.8 95.9 4,883. 6,606 Syrian Arab Republic. 100:5.5. .. 146.2 94.5 136.7 72.6 105.2 1,156 1,588 ...... . ....... Tajikistan 7......... 57.8 ......... :.. ...... 67.9 . 61.4 1,176 Tanzania 81....... .6... 93..1.. 76.8. ....97.2 69.3 .....110.5 1,063 1,317 15 Thailand 80:.5. 112A.4 .. 799.9.... 107.2 65.4 112.5 1,911 2,385 630 928 Trinidad and Tobago. 119.9 103 0 101 9 10 7 43 1. 3,167 3,703 .....3,067 1,838 Tunisia 686.6....... 994 4...... 67-.6.. ... 108.3 63.7 129.9 828 1,167 1,743 2,750 Turkey..I.......I 766.6. ..108:7 75.8 106:3 80.6 . .... 102.6 1,869 2,081 1,852 1,835 Turkmenistan .. 80.0 .. 108.7 . 119.6 1,959 Uganda.67.6 1 .... 109.9 ..... 70.5 107.7 84.8 112.9 1.555 1,331 326 Ukr in .. ........................... ........ 69.5 .......... .... 69.9 62 3 2,338 2,259 United Arab Emirates ........ . 389.9~.. 204 1 47.0 182.2 418 8 .... 140.2 5,608 7,729 United Kingdom 79 3 103.1 91.6 100.5 98.1 99.2 4,792 6,970 United States 98.9 1137 7...... 94-.7 ... 113.7 89.0 112.9 4,151 5,043 34,727 Uruguay 86.4..... 142 2 .. 86-.8 128.8 85.5 120.0.1,644 3,301 6,822 9,384 Uzbekistan ........86:1 ....... .... ..100.7 ...... 103.1 . . .. 1,957. 2,085 Venezuela 764 4...... 1082 2...... 79..6....... 114.0 84.9 117.4 1,904 3,068. 401 ,3 . .. .. . . .. .. . . . .. . .. .. .. . .. .. . .. .. .. . . .. .I .. . .. .. .I .. .. .. . .. . . 4 ,0 41.. . .. .I . . . .. . . .. . .. .4 ,9 31... . . .. . . . .. . . .. . . . Vietnam 66.6 134.3 64.0 132.7 54.2 135.2 2,049 3,670 226 West Bank and Gaza . . . Yemen, Rep. 823.3.. ... 1118.8...... 759.0 115.5 68.9. 121.5 1,038 982 305 Yugoslavia, FR (Serb./Mont.) . ... Zimbabwe 78.0 105.5 821 1. ..... 948.8...... 84..9 .......97.1 1,359 1,095 307 316 Low income .. .......... 73:3.3 .. .. 119.4 ..... 725.5 ... .. 120:3.3 ... 71.9 132.0 1,347 1,852 ..... . 344 Middle income 74.4 132.7 72.1 141.7 69.4 163.5 2,425 2.889 Lower middle income 71.7 143.1 67.6 160:0.0 . 57.8 201.1 2,610 2,969 Upper middle income 80.8 ..108.9 ..... 80.6...... 108.3 84.3 115.2 2,013 2,631 Low & middle income ..74.1 .......128!5 . . ..722.2 135.1 70. 15.1 1,958 2,473 . 567 East Asia & Pacific........ 69.6 129.9 ....I64.5 142.3 49.6 173.4 2,765 4,248 Europe & Central Asia . 195.2 . 204.9 273.2 . 1,843 . 2,202 Latin America & Carib. .82:1.1..... 115.6...... 80 4 118.9 82.2 118.8 1,840 2,576 Middle East & N. Africa 68.5 ... .1222 ... ....672 2...... 123.2 65.0 .... 120.8 1,173 1,880 South Asia 725.5 1163 3.. ... 703 3.... . 119.2 63.9 127.3 1,410 2,197 265 347 Sub-Saharan Africa ........ ...77.1 ......111.9 .795.5 ...... 108.3 92.6 118.9 1,089 1,050 . . 418 371 High income 92.7 ..... 108.6 92.2 107.9 91.5 106.7 . ............. ..... .. Europe EMU 90.1 - 101.5 916.6...... 100.4 95.0 99.5 3,612 5,180. 18,918 a. Includes Luxembourg. 130 1999 World Development Indicators 3.3 The agrcultural production indexes in the table are pre- put due to transitory movements of nominal exchange * Crop production index shows agricultural produc- pared by the Food and Agriculture Organization (FAO). rates unrelated to the purchasing power of the domes- tion for each year relative to the base period 1989-91. The FAO obtains data from official and semiofficial tic currency. Unlike the International Comparison It includes all crops except fodder crops. Regional and reports of crop yields, area under production, and live- Programme (ICP), the FAO calculates international prices income group aggregates for the FAO's production stock numbers. If data are not available, the FAO makes only for agricultural products. Substantial differences indexes are calculated from the underlying values in estimates. The indexes are calculated using the may arise between the implicit exchange rate derived by international dollars, normalized to the base period Laspeyres formula: production quantities of each com- the ICP and that ofthe FAO. Forfurther discussion ofthe 1989-91. Data in this table are three-year averages. modity are weighted by average international commod- FAO's methods see FAO (1986). (See About the datafor However, missing observations have not been esti- ity prices in the base period and summed for each year. tables 4.11 and 4.12 for a discussion of the ICP.) mated or imputed. * Food production index covers Because the FAO's indexes are based on the concept of Data on cereal yields may be affected by a variety food crops that are considered edible and that contain agriculture as a single enterprise, estimates of the of reporting and timing differences. The FAO allocates nutrients. Coffee and tea are excluded because, amounts retained for seed and feed are subtracted from production data to the calendar year in which the bulk although edible, they have no nutritive value. the production data to avoid double counting. The result- of the harvest took place. But most of a crop har- * Livestockproductionindexincludesmeatandmilk ing aggregate represents production available for any vested near the end of a year will be used in the fol- from all sources, dairy products such as cheese, eggs, use except as seed and feed. The FAO's indexes may lowing year. In general, cereal crops harvested for hay honey, raw silk, wool, and hides and skins. * Cereal differ from other sources because of differences in cov- or harvested green for food, feed, or silage and those yield, measured in kilograms per hectare of harvested erage, weights, concepts, time periods, calculation usedforgrazingareexcluded. Butmilletandsorghum, land, includes wheat, rice, maize, barley, oats, rye, methods, and use of international prices. which are grown as feed for livestock and poultry in millet, sorghum, buckwheat, and mixed grains. Pro- To ease cross-country comparisons, the FAO uses Europe and North America, are used as food in Africa, duction data on cereals refer to crops harvested for international commodity prices to value production. Asia, and countries of the former Soviet Union. dry grain only. Cereal crops harvested for hay or har- These prices, expressed in international dollars (equiva- Agricultural productivity is measured by value added vested green for food, feed, or silage and those used lent in purchasing power to the U.S. dollar), are derived per unit of input. (See About the data for tables 4.1 and for grazing are excluded. * Agricultural productivity using a Geary-Khamis formula applied to agricultural out- 4.2 for further discussion of the calculation of value refers to the ratio of agricultural value added, mea puts (see Inter-Secretariat Working Group on National added in national accounts.) Agricultural value added sured in constant 1995 U.S. dollars, to the number of Accounts 1993, sections 16.93-96). This method includes that from forestry and fishing. Thus interpreta- workers in agriculture. assigns a single price to each commodity so that, for tions of land productivity should be made with caution. example, one metric ton of wheat has the same price To smooth annual fluctuations in agricultural activ- Data sources regardless of where it was produced. The use of inter- ity, the indicators in the table have been averaged national prices eliminates fluctuations in the value of out- over three years. l a Agricultural production indexes are prepared by the FAO and ,;= ; F ';it-t,f4a >> -- 4 published annually in its Cereal yield has grown fastest in high- ... but food production overall has grown Production Yearbook. The FAO income countries ... most in middle-income countries r makes these data and data on cereal yields and agricultural I,l:.;-r, r-:r, ,,- S*-* ZLul -Ir. .ll- 1 4 = ''' - employment available to the World Bank in electronic files -. ....... . that may contain more recent information than the pub- cc,,,,, yrr^5.s- ! 9: l ished versions. l''' ne [ S: H . :i,,-,-. V- ~ ~ ~ ~ ~ ~ ~ ~ ~~ i The Impiohed yield in high *ricome countries is During 1980-97 popdlauion grown.n was siower thar largeli a resail of ;ncreased Lse of agr.ct!tu:al gsowthtnlooopioducLion:pooulationgrew47peircrnI machinerV (see table 3 21. In ioxiencon1e ecrnomies. 30 percenl in mriddle ,ncome economneS. 3nd 12 perceni in hgnrncaome economies. 1999 World Development Indicators 131 S ~3.4 Biodiversity and protected areas Nationally Mammals Birds Higher plants' Reptiles Amphibians protected areas % of thousand total Threatened Threatened Threatened Threatened Threatened sq. km land area Species species Species species Species species Species species Species species 1996b 1996b 1996b JL996b 1996b 1996b 1997 1997 ±996b 1996b Ise 1996b .96 Albania 0:.8 . 2.9 68 2.......230 7 3,031.. .. 79 31 1 13 0 Algeria 58:9.. 2.5 92 15 ...... 192 .......8...... 3,164 141 .. 1 ........ 0 Angola.81.8 6.6 276 17 765 .......13 ......5,185 ... 30 . ... . 0 Argentina 46:6 1.7 320 27 897 ...... 41...... 9,372 .247 220 5..... 145 .......5 Armenia 2.1 7.4 .. 4 .. 5. 31 46 36 0 Australia 563.9 7.3 252 58 649 45 15,638 2,245 748 37 205 25 Austria 23.4 28.3 83 . ... 7 . 213 .......5...... 3,100 ...... 23 14 1_.... 20 .0 Azerbaijan 4:8 5 5 .. 11 . .... . .. 8 .. . ..... ...... 28 523 ........8 ........0 Bangladesh 1 0 0.8 109 18 295 30 5,000 ...... 24 119 13....... 19 .......0 Belarus 8:6 4.1 . 4.......221 ........4 1 8 0 10 0 Belgium 0.8 .................. 58 6 180 3 1.5502 ........8 ........0 ....... 17 0 Benin 7.8 7.1 188 9 307 1. 2,201 4 . 2. 0 Bolivia 156.0 14.4 316 24 1,274 27 17,367 227 208 3 112 0 Bosnia and Herzegovina 0.2 0.4 .. 10 . 2. 64. 01 Botswana 105.0 18.5 164 5 386 7 2,151 7 157 0 38 0 Brazil 355.5 4.2 394 71 1,492 103 56,215 1,358 468 15 502 5 Bulgaria 4.9 4.4 81 13 240 12 3,572 106 33 1 17 0 Burkina Faso 28.6 105 147 6 335 1 1,100 0 ..- 1 0 Burundi 1.4 5.5 107 5 451 6 2,500 1 0 0 Cambodia 28:6 16.2 123 23 307 18 . 5 82 9.......28 .......0 Cameroon 21.0 4.5 297 32 690 14 8,260 89 . ...............3..... ......... 1..... Canada 9210.0 10.0 . . . 193 .......7...... 426 5 3.270 278 41 3 41 1 Central African Republic 51.1 8.2 209 11 537 2 3,602 110 Chad 114.9 9.1 134 14 370 3 1,600 12 .. 1. 0 Chile........... 141.3 18.9 91 16...... 296 18 5.284 .... 329 72 1...... 41 .......3 China 598.1 6.4 394 75 1,100 90 32,200 312 340 15..... 263 1. ..... .ogKong, China 0.4 40.4 24 0 76 14 1,984 9 72 1 23 0 Colombia 93.6 9.0 359 35 1,695 64 51,220 712 584 15 .... 585 0 Congo, Dem. Rep. 101.9 4.5 415 38 929 26 11,007 78 3 0 Congo, Rep. 15.4 4.5 200 10 449 3 6,000_ -3 ...,.......2 0 Costa Rica 7.0 13.7 205 14 600 13 -12,119 ..... 527 214 7.... 162 1 C6te d'lvoire 19.9 6.3 230 16 535 12 3,660 94 . 4. I Croatia 3.7 .......6.6 ... .....10 224 .. .. ...4 .......... .......6 . .. . . 0..1 Cuba 19.1 17 .4 31 9 137 13 6,522 888 102 7 41 0 Czech Republic 12.2 15.8 7 199 6 8.. 0 . 0 Denmark 13-7 32.3 43 3 196 2 1,450 2 5 0 14 0 Dominican Republic 12.2 .._25 .2 20 .......4..... 136 11 5,657 ..... 136 105 10 35 1 Ecuador 119:3.3.... 43..1...... 302 28 1,388 53 19,362 824... 374 ...... 12 ..... 402 .......0 Egypt, Arab Rep. 7.9 0.8 98 15 153 11 2,076 82 83 6 6 0 El Salvador 0.1 0.5 135 2 251 0 2,911 42 73 6 23 0 Erntrea 5.0 5.0 112 6 319 3 03. Estonia 5.1 . ....12-.1 .65 .. .... 4 .......213 . ......2.. . ....2 ........5 ....... 0 . . 11 . . ...0 Ethiopia 55.2 5.5 255 35 626 20 6,603 163 . 1. 0 Finland :18.2 6.0 60 4 248 4 1,102 6 5 0 5 0 France S8.8 10.7 93 13 269 7 4,630 195 32 3 32 2 Gabon 7:2.2 ..... 2.8 190 12. 466 4 6,651_ 91 ................ 3 .. ........0 Gambia. The 0.2 2.0 108 4 280 1 974 1 .. 1 . 0 Georgia 19 .......2.7 . 10 . ...............5........ .: 29 46 7 11 0 Germany 94.2 27.0 76 8 239 5 2,682 14 12 0 20 0 Ghana 11.0 4.8 222 13 529 10 3,725 103 . 4 .. 0 Greece 3 1 2.4 95 13 251 10 4,992 571 51 6 iS 1 Guatemala 18:2.2 ... 16.8 ...... 250 .......8 ...... 458 .. 4..... 8,681 ..... 355 2319 ....... 99 .......0 Guinea 1.................... . 6.. .. 0.7.190 - ....1. 409. 12 .3,000 39........ -.. 3-....... .....1 Guinea-Bissau 0.0 0.0 108 4 243 1 ,oqO . 0 .. 3. 0 Haiti........... .. 071.1 ..... 0.4 ........3 .. 4 75 11 5,242 ..... 100 102 6 ....... 46 .......1 Honduras 11.1 9.9 173 7 422 4 5,680 96 152 7 56 0 132 1999 World Development Indicators CT sioleoIpul juawdoliAGeo PIJOM 666T o 6 9 89 VlT6 SC S69 TS 696 TSE L.9T9 UO!IiGPj ue2U!ssnl 0 ~~6T Z6 9 66 OOt"9 TT 2.176 9T . VS .9 X6L'T. eue 6 6T 6 9V SZZ 6V'6 TT 90T £ ' TOoldoln .LT . 66 696 .....6o 1 i.. L06 ... ST Sg 694 OD 1J Olfd o ST 0 6. . 6 gvi 9 2.66 OT 18 9'6 . 66.PeO 6 699 .06T 099 T66'8 98 965 6tV 99T 61V 9VtT sudd T 9T 6 566 906 9V6z'8T 179 89 917 VVSC L' 917C n5. 0 80OT 66T TG8'L 96: 9..T/0 SE O'7 enSeiBd 6.086TOZ Z66 t9T TS . V179 2.9 VTZ 00.'eun)mO ne 6 . 179T . 966 ZO9T 2T6'6 OT z6L2 LT STZ T6T 6V7T eweuBd 0 . LT .9 ZT VT 096't7 96 9L5 ST 6gT 817 6'LS uelsW1d 6 ...79 OS tOZ'T 9 LOT 6 99 T'9T S't's L 0 T 0 OVT 6 .Z 6~6Z. T TST 9'L 6'96 JA 0 69 2 T9T 86 0666 .. . 06.2Voz t' 0'6 enflSeJD!N T c~* TT 017 TTZ 6596 t717 O6T 9 OT . 96s C*59 PuBZ MON 6 9T 0 2 T TZ6'T T6T 9 9 T' 176 sPu2elAL9ON 0 9 9 05 06 CL6'9 2.6' TT9 86, L9T . SL T'TTldN T Zs8 5 L tLT'S . 69tV TT t79T 67T 6'90T 6!q!fweN 6 9L O 966 Z69 6oo'L Vt 298 TS T96 E' L'T.iweI 0 69 9 65 . Z69'9 V 5 6t' ST 62 T.9flt7abi LuezOIN 0 698T 92.95 TT OTZ ST 0T LO ZEo~o0J0oA 0 ST T 6 9L LLT . 6 89 6'T t'0 eAOPIOVYJ c ~~986. ST L89 669'T TLO'96 95 . 69 . 099ost L'9 . OT2..!a 09 ~~ ~~~TT t766 09L . OT.L t V 6. 7 TV. 0.jn-v 0 5 OO0T'T.EZ 1T 9 LT . 9LT. euj.nV 0 T .T 9T TtL'T 9 265 ST 2ST L'9 59' !IIeAd 0 SS VT 896 06V7 0099T V7S T09 Z67 9 9 't 'V.BsAlI 0 69 6 VT T9 992.8 6 TZ9 2 6T.TT.9O Mlv 6 7t'T LT 696 96o 909'6 86 Z06 917 90T 6'T . Z'T. es~lp 0TST 0 O V 06 5 o6T 99 2I8U2flL9I1 0cLg SS2.9 965T S T 6 TT 92. TO L'T e 0 0 S T69'T 9 89 6 90.OOLioe 0 69 000 9 V9T 9 179 00 . 6Oo.uouej- 0 ST 6 2 59T'7 9 LT6' tV . 8 97T 82 2iA)el 2.5 2. 99 6 2. L.5t 09 ZLT 00 00 d6cd O:1 0 tVT 0 96 99 868'6 6T ZTT 9 6V, 6'9 99 daa 'eajo~1 0 t7T 0 6T t 868'Z 6T 9TT 2.96 T'S ddw( ej 0 . 88 9 LST 0V76 V06 . VS: lt t 699 T'9 Og A .OT T. 2. L T T .6 V9'L Ujqee 0 6 OO6T'Z V . . .lTTL VS' OSEupo OT 69 5 99 LOL 999's EC 096 66: Z5T 59 9'96 ee V, TZ 8 9 VVtL qbs's 2. TT 1 1 7 V6 . 6 00 e3IBwer V7 17S V7 01 TT8 669'9 2. V96 OT 06 52. 'T6. AIeil 0 9 6 2.TS'6 8 08OT ST 66 0'9T T' 9eis 0 0 T096 T 6t7T Z6 9 60 9Opeli 0 9 Z S6T Z.T L2. T8 06O 00Obi z ~~TT . . . .T6oo's V7T 565 06 OV7T T 095 . ddeiwelsi luejl 6 02. 6T TT9 179Z 62.6'6Z 176T 6T2'T SST 9517 9'OT S76T e!sGuOPul 2.6T 9T . 685 996ZT 00'9T.....~ ... ' " 6 92 9T9C 51r 6 .. . 6VT- e! 0 LT. 9T 09.Vi" T so . . 62. ' E9A)~n q966T q966T q966T q966T 166T L66T q966T q966T q966T q966T q966T q966T saopods sa!oad 0OdS soos saloedS sopeds se!3adS selpeds seoiodS se!Oeds sos!Osd SMS puss wl bs Paualsegit4j poualegJl. pSuMSSlJJ.q peUMssJiqj peuae4eeJJ leioi puesnoip 40 % sueiqiqdwuy soIg4dot .9jueld Jeq0IH SPJI9 siewUUWe i AjIuO!eN 3.4 Nationally Mammals Birds Higher plants' Reptiles Amphibians protected areas % Of thousand total Threatened Threatened Threatened Threatened Threatened sq. km land area Species species Species species Speciea species Species species Species species 1996b 1996b 1996b 1996b 1.996b 1996b 1 3997 1997 1996b 1996b 1996b 1996b Rwanda 3.6 14.6 151 9 513 6 2,288 00 0 Saudi Arabia ............... 49.6 2..3 .. .... 77 9 .... 155 11 2,028 ... ...7 ...... 84.2 . 0..... .. . ... Senegal 21 8. 11.3 155 13 384 6 2,086 317 0 Sierra LeoneM0 8 1 147. 9..... 466 12 2,090...... 293......._0 Singapore 0.0 00 45 6 118 9 2,168 29 .. 1 0 Slovak Republic 10.5 21.8 .. 8 209 4 650 0 Slovenia 1.1 5.5 69 10 207 3.. 13 21 0I1 South Africa 65.8 .. 5.4 247 33 596 16 23,420 2,215 299 19 95 .......9 Spain 422.2.... 8.4 82 19..... 278 10 5,050 985 53 6 25 3 Sri Lanka 8.6 13.3 88 14 250 11 3,314 455 144 8 39 0 Sweden 362.2 8.8 60 5 249 4 1,750 13 6........0.....13 0 Syrian Arab Republic 0.0 0.0......63 4..... 204 7 3,000 .......8 3 0 Tajikistan 5.9 4.2 5 9.. 50 38 12 0 Tanzania ......... 1382.2.... 15.6 316 .. .. 33 822 3 10,008 .436..... 284 4 .124 .......0 Thailand 70.7 13.8 265 34 616 45 11,625 385 298 16 107 0 Trinidad and Tobago..... 02.2.. . 3.9 100 1 260 3 2,259 21 70 5 26 0 Tunisia 0.4 0.3 78 11 173 6 2,196 24 . 2 0 Turkey 10.7 1.4 116 15 302 14 8,650 1,876 102 12 18 2 urkme... I..tan 19........... . . 8.4.2 . ......... . 12 ... .... .. :: .... 17 80 22 ..... .0 Uganda 19.1 9.6 338 18 830 10 5,406 15 1491 50 0 Ukraine 9.0 1...... .6......... 15. 263 10.. ..... 52 19 2....... 16 ..... 0 United Arab Emirates 0.0 0.0 25 3 67 40 372 0 United Kingdomn 50.6 20.9 50 4 230 2 1,623 18 8 0 7 0 United States 1,226.7 13.4 428 3 650 50 19,473 4,669 280 28 233 24 Uruguay 0.5 0.3 81 5 237 11 2,278 150 0 Uzbekiatan 8.2 2 ... 7 .. 11 . 1 5 Venezuela 319.8 36.3 305 24 1,181 22 21,073 426 259 14 .....199 0 Vietnam 99.9 3.0 ..... 213 38 535 47 10,500..... 341 180 12.... 80 .......1 West Bank and Gaza .. .. Yemen, Rep. 0:0 00 .O.O.. .. 66 5.... 143 13 . 149 77 2 . ....... 0 Yugoslavia, FR Srb./Mont.) 0.0 0.0 .. 0 0. 0 Low income 1,650.0 5.5 ... Middle income 3,592.5 5.2 Lower middle income 2,301.4 5.0.... . Uppe & middle income 5 2 251,291.1........... .. 5.7...... .... .. ............ . ................ ... ... East Asia & Pacific 1,095.4 ... 6.9............. .... Europe & Central Asia 768.0 3.2 . Latin America & Carib. 1,4563.3. 7.3 ............... Middle East & N. Africa 242.0 2.2 South Asia 213.0 4.5 . High income 3,301.0 10.8 Europe .11....... . .. EMU... 268.4...... 1.7..... . . . a. Flowering plansa only. b. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Center in 1996. 134 1999 World Development Indicators 3.4 Habitat conservation is vital for stemming the decline extraction is allowed. For small countries that may only * Nationally protected areas are totally or partially in biodiversity. Conservation efforts traditionally have have protected areas smaller than 1,000 hectares, this protected areas of at least 1,000 hectares that are focused on protected areas, which have grown sub- size limit in the definition will result in an underestimate designated as national parks, natural monuments, stantially in recent decades. Measures of species rich- of the extent and number of protected areas. nature reserves or wildlife sanctuaries, protected land- ness are one of the most straightforward ways to Threatened species are defined according to the scapes and seascapes, or scientific reserves with lim- indicate the importance of an area for biodiversity. The IUCN's classification categories: endangered (in dan- ited public access. The data do not include sites number of small plants and animals is usually esti ger of extinction and unlikely to survive if causal fac- protected under local or provincial law. Total land area mated by samplingofplots. It is also importantto know tors continue operating), vulnerable (likely to move is used to calculate the percentage of total area pro- which aspects are under the most immediate threat. into the endangered category in the near future if tected (see table 3.1). * Mammals exclude whales This, however, requires a large amount of data and causal factors continue operating), rare (not endan- and porpoises. * Birds are listed for countries time-consuming analysis. For this reason global analy- gered or vulnerable, but at risk), indeterminate included within their breeding or wintering ranges. ses of the status of threatened species have been car- (known to be endangered, vulnerable, or rare but not * Higher plants refer to native vascular plant species. ried out for only a few groups of organisms. Only for enough information is available to say which), out of * Reptiles refer to a class of air-breathing vertebrates birds has the status of all species been assessed. danger (formerly included in one of the above cate- that include lizards, snakes, turtles, alligators, and Although mammals approach birds in this respect, an gories but now considered relatively secure because crocodiles. * Amphibians include frogs, toads, newts, estimated 45 percent of mammal species remain to be appropriate conservation measures are in effect), and salamanders. * Threatened species are the num- assessed. For plants the World Conservation Union's and insufficiently known (suspected but not definitely ber of species classified by the IUCN as endangered, (IUCN) 1997 IUCN Red List of Threatened Plants pro- known to belong to one of the above categories). vulnerable, rare, indeterminate, out of danger, or insuf- vides the first-ever comprehensive listing of threatened Figures on species are not hecessarily comparable ficiently known. species on a global scale, the result of more-than 20 across countries because taxonomic concepts and years' work by botanists from around the world. Nearly coverage vary. And while the number of birds and Data sources 34,000 plant species, 12.5 percent of the total, are mammals is fairly well known, it is difficult to make an threatened with extinction. accurate count of plants. Although the data in the Data on protected areas are The table shows information on protected areas, table should be interpreted with caution, especiallyfor from the WCMC's Protected numbers of certain species, and numbers of those numbers of threatened species (where our knowledge Areas Data Unit. Data on species under threat. The World Conservation is very incomplete), they do identify countries that are . . species are from the WCMC's Monitoring Centre (WCMC) compiles these data from major sources of global biodiversity and show national . ..Biodiversity Data Sourcebook a variety of sources. Because of differences in defi- commitments to habitat protection. . - . (1994) and the IUCN's 1996 nitions and reporting practices, cross-country com- . :, IUCN Red List of Threatened parability is limited. Compounding these problems, , - - Animals and 1997 IUCN Red available data cover different periods. List of Threatened Plants. Nationally protected areas are areas of at least High-income economies have the largest share of protected land area 1,000 hectares that fall into one of five management categories defined by the WCMC: ., ,. : * Scientific reserves and strict nature reserves with 12 limited public access. * National parks of national or international signifi- cance (not materially affected by human activity). . * Natural monuments and natural landscapes with unique aspects. , r i * Managed nature reserves and wildlife sanctuaries. * , ri H r * Protected landscapes and seascapes (which may include cultural landscapes). . b i ri The first three categores, referred to as "totally pro- -: : S . tected," are areas maintained in a natural state and closed to extractive uses. (Designating land as a pro- ; tected area does not necessarily mean that protection is in force, however.) The last two categories, referred to -;,, - ,r as "partially protected," are areas that may be managed for specific uses, such as recreation or tourism, or that But several deteloping economies ranK in the top ±0-Ecuaor. Venezuela. and the Dominican Republic provide optimal conditions for certain species or com- piotect more than 25 percent or the.r land. munities of wildlife, and in which some natural resource 1999 World Development Indicators 135 S ~3.5 Freshwater Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita billion % of total % for % for % for % of population % of population 1997 on. ml' resources' agriculture' industry' domestic' 1982 ±L995 ±982 1995 Albania 16,785" 0.2 d 0.4 76 18 6 100 97 88 70 Algeria 4630 4.5 ........332c ... 60...15. 250 100 .70 ....... Angola 15,782 0.5 0.3 760 ioe 140 80 69 15 15 Argentina 27,861c 27!6d 2.8C 73 18 9 63 71 17 24 Armenia 2,1360 38 47.00 72 15 13 Autralia 18,508 146 6d 43 33 2 85 Austria 11,1870 2.4 2.60 9 58 33100 . 98 Azerbaijan 4,339 0 15.8 479 90 74 224 Bangladesh 19,065 0 22.5 1.00e 96 1 3 24 49 40 Belarus 1,8410 3.0 15.90c 19 49 32 100 100 Belgium 1,2270 9.0 72.20c 4 85 11 100 91 Benin 4,4510 0.2 0.601 670 100 23 e 45 82 9 69 Bolivia 38,625 1.2 0.4 85 5 10 8.1 88 27 43 Bosnia and Herzegovina .. . . .. . Brazil 42,4590 36.5 Q5 c 59 19 22 . 80 52 28 Bulgaria 24,663 0 13.9 ..... 6.801....... 22 76 3 95 67 Burkina Faso 1,671 0.4 2.2 810 O- l9e 50 26 Burundi 559 0.1 2.8 640 0O 360 33 22 Cambodia 47,530e0.5 0.10c 94 1 5 . 20 :: 12 Cameroon 19,231 0.4 0.1 35' 190 460 46 30 30 Canada 95,7850 45.1 1~60 12 70 18 100 . 100 Central African Republic 41,250 0.1 0.0 740 50 2.1 24 20 5 25 Chad 6,011c 0.2 0.401 820 20 160 27 48 30 17 Chile 32,007 68 3.6 89 5 6 97 99 22 47 China 2,282 460.0 ... .... 16~4.4 ... ... 87 7 6................ ..... ... .ong Kong, China V: :: Colombia 26,722 5.3 0.5 . ....43 ...... ...16 ..........41 100...... o ..... 90 ........76 .......32 Congo, Dem. Rep ..... ...... 21.81600:4 0........ 0,0c........ 230 160 61043 895 26 Congo, Rep. 307,2830 0.0 0.00 110i 270 620 42 .7 11 Costa Rica 27,425 1.4 ..... ... 1.4 ....... ..89 .. ........7 ...........4 .... 100.. o .... 100 ........82 ......99 C6te dIlvoire 5,468 . 07096e1, 2e 0... . . 0....... Croatia 12,879 . .. .. 2 7 46 41 Cuba 3,120 8.1d 23.5 89 2 9 . 98 . 72 Czech Republic 5,649 2.7 .........4.7 ....... ..2 .. ....... 57 ..........41 .........100 100 . .. ... Denmark 2,460' c12 9.20 43 27 30 100 99 Dominican Republic 2,467 3.0 14.9......14 9...... 89 . 9 ...... 6... ..5.......72..5 .... .88.. 7 .....24... ... 55... ..5 Ecuador 26,305 5.6 1.8 90 3 7 83 81 33 10 Egypt, Arab Rep. 9660 55.1 94.5 e 860 80 60 93 95 61 74 El Salvador 3,197 1.0" 5.3 ....... 89 4 7 76 .....8247 24 Eritrea 2,332- 0 Estonia 12,0710 3.3 18.80.... 8 8 c.........3 .........2 .. ....3. .5 .........92.......... ...... 5..... Ethiopia 1,841 2.2 2.0 860 30 II0 93 42 Finland 21,985 0 2.2....... .. 1.90 .........3 85 12 98....... 86 ...... France 3,029 0 37.7........ 213.30 .... . 15 .......... 69 ......... 16 ........ 100 100 95 100 Gabon 142,278 0:1 .........0.0 ...... ...60 220 720e 75 34 ....... Gambia, The 6,775' 0.0 0.30 910 20 7e 100 .. 27 Georgia ...... .. ... 8,2910 4.0 . 8.90 42 .......... 37 . ....... 21 ............. Ghana 2,958' 0.3 0.6' 520 13' 350 57 88 40 52 Greece 5.289' 5.0 9~1' 63 29 8. 91 73 Guatemala 11,028 0.7" 0 O6 ........74 17 9........ 89 97 39 48 Guinea 32,661 0.7 0.3 .........87'. ..... . 3' 10' 91 ......55 2 44 Guinea-Bissau 23,745 c 00 0.10 36' 40 60' 21 38 37 57 Haiti 1.468 0 0 0.. ......O.4 68 8......... 24 59 38 32 39 Honduras 9,2590 -15 2.70 91 5 4 51 91 49 66 136 1999 World Development Indicators 3.5 Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita billion % of total % for % for % for % of population % of population 1997 cu. Ml resources' agriculture0 industry' domeStic0 ±982 1995 1982 1995 Hungary 11,8170' .. 6.8 ..... ....5.7 c . ....36 ...55. ... 9. .92 ....... . ...... 81 India 2,167 c 800 18.201 934.... 372 . 47 82 Indonesia 12,625 16.6 0~.7 . 76.... 11 1 3........ 60 87 32. .57 Iran, Islamic Rep. 1,3390 70 0 d .. 85, c.80 . . .. 92 . ....2.... 670 . ..98 33 .82 Iraq 3,4510 428~ d 56.80 92 53 92 92 22 44 Ireland 13,6570 ~ 0.8u 1.6 10 74 116 100 . 92 Israel 3770 1.9 84 ' .10 790 50 160 1 .....100 100 100 .... 95 Italy 2,903 c 56.2 33.701 59 27 14 1100 . 96 Jamaica 3,250 0.30 3.9 86 7 799 . 93 Japan.4,338 90.8 16.6 50 33 1 7 Jordan 1980 .5 51.10 75 322 100 65 Kazakhstan 8,696c 37.9 27.60 79 17 4 Kenya 1,056c 2.1 .........6.801 . .. 760 40 200 61 . 21 Koea Dm.Re. ,97 .14:2.2.... 11.... .. 73 16 111 .....100. ..100. 100 .100 Korea, Rep. 1,438 27.6 ........41.7 46 35 19, ..... 93 . 77 Kuwait....... 11 ... 0:.5 2,700.0 60 ..... 2 37 100 100 100 100 Kyrgyz Republic 2,509 11.0 94.9 95.3. ..... 2 Lao PDR 55,679 .. .. 1.0 0...... . 4. A .... 82 10.8. ......... Latvia 13,793c 07.7 2.10.... 14. 44 42 92 . .... .. Lebanon 9410 1.30 33.10 68 428 95 85 Lesotho 2,597 0.1 1.0 560 220 220 3 64 1-4 60 Libya 115 4.6 766.7 870 20 110....i 92.. 90 75 91 Lithuania 6,5310 4.4 18.20 1 3907 Macedonia, FYR . . .100 98 Madagascar 23,819 16.3 4.8 990 00 . 10 1 . 1.7.. Malawi . ..... .. 1,8140 0.9 .........4.80 860 30 100 60 ... 97 53 .... 52 Malaysia 21,046 940 2.1 47 30. 23 . . 100 . 86 Mal 9,718 c 1.4 1.40 970 10 20 58 56 20 20 Mauritania 4,6320 1.60 ... 14.30 c 922...6 ....... 80 87 116. 41 Mauritius 1,925 0.4'd 16.3 77070 160 100 .. 98 Mexico 3,788 7 d 21.7 ... 86 86 95 .. 50 Moldova 3970 3.7 216.40 23 70 7. .... 98. 18 Mongolia 9.677 0:6.2.2..... 62 27 11 ... 100 . .. 100 100 68 M orocco . .............. 1. 08810~9 36...... . 5 ..... 920 ... ...3 . ....5063 97 ..... 2 . ....20 Mozambique 12,9890 0.6 030 89 20 90 82 . 2 40 Myanmar 24,651 4.0 0.4 90 3 736 78 21 50 Namibia 28,042 0 03.3 05.50 680 . 30....290 ...... Neap.7,616 . 2.7 1.6 95*1 475 611 6 59 Netherlands 5,7670 7.8 8.7 0 34 61 5 100 . 99 New Zealand 532 2.0 100.0 44 10 46 100 . 100 Nicaragua 37,420 0.9 d 0.5 54 21 25 77 93 13 28 Niger 3,3170 0.5 1.50 c 820 20 160 80 70 40 44 Nigeria 2,3750 3.6 1.30 540 150 31 60 80 30 39 Norway 89.0080 2.0 0.50 C 872 20 1100 .100 95 100) Oman 439 1.2 123.2 94 2 570 .. 10 Pakistan 3,2560 556 37.20 c 97 2277 85 22 56 Panama 52,961 1.3 0.9..77 11 12 1100. 99 64 7 Papua New Guinea 177,963 0.1 0.0 4922954 . 10 Paraguay 61,750c 04.4 0.10 . .... 78 .... 715 49. 708. 6 Peru 1,641 61.1 15 .3 72 919 73 91 117 31 Philippines 4,393 29 5d 9.1 61 21 18. 911 81 Poland 1.454 0 12.3 21 .90 11 76. 13 .. 89 73 Portugal 6,9980 7.3 10.50 48 37 15 97 50 Puerto Rico . . . .7. Romania 9,222 c26.0 12.50 c 59 ..... 33 ... 8 91 50 Russian Federation 30,168 0 1170.0. 2.60 23 60 17 .. ..-- 1999 World Development Indicanors 137 3.5 Freshwater Annual freshwater withdrawals Access to safe water resources cubic meters Urban Rural per capita billion % of total % for % for % for % of population % of population ±.997 cu. ml resources' agriculture0 industryb domestic' 1982 1995 1982 1995 Rwanda 798 0.8 12.2 940 20 50 55 79 60 44 Saudi Arabia 120 17.00 709.2 90.19.9 .... 87. ..... Senegal 4,482 1.4 35C 920.3. 5..6 90. 27.............. 44. Sierra Leone 33,698 0:4 0.........O2 .... 890 40 7.....50....58....3 . 21 .. Singapore 19 .0 37 451 45..100. ..va .e..i......5,720 18 5 Slovenia .. ~~~~~~~~~~~~.... . ............ ...... South ..A.rIca ... .... ..1,2310.... .... 13.3 .. .....26.60 ..... 720 110 170 90 3 Spain 2... ..........I .... , 398....I.... ....30.8 .. .....32..60 62 26 12 100 95.... Sri Lanka 2,329.63.146.96..2.76 .8 26............ 65....... Sweden ... .... ....I... 20,34092.9d 14.60 96523009 Tau kisan 126. 875............... .. .... Tanzania 2,8420.......... 1.2.. ...... 1.30... ...20 90... 88 8 40 4 Thailand ~~~~~~~~20,9540 31.9.17.80.90.6.4..... 94.....88.. Tu........rI........y. .. 2,254601.622.1072 113 160' 730 65 l o Turkmenistan 3,9500 22.8 123.90 91 8 1~~~~~~~~~~~~~~~~~~~................... ...... .... .... United Kingdom 1,203 ~~~~ ~~~~~11.8 162.6 327 20710 100 100 100.. Uzbekistan 5..4760....82..2..63.40...84...12..4 Venezuela 57,8210~~~~~~~~~~~. .....3 4 117 438 95 7 Yugoslavia, FR (Serb./Mont.) .. . . .. ~ ~ ~ ~~~~~~~~~~~~~~......... . . ............. Zambia 1~~~~~2,2842 1.1..50.77 ..70 160..70..66 32..37 Low... income.... 6,252. ... ...................I . .92..4 4 69 80939 6 . Mhidadl income..9,522....74..17.9... 8.7.......76 ......... 7... LTrinia Aeiand Coargb 27,386 0.2d 2773511 127 ... 83 47 36 Southi sia 4,8947 31..3 . 70....... ...... 43 .84 Sub.Saharan Africa 8,565 85~~~~~~~~~~~~~. ... ....... 4..10. 63 28 7 High income 9319 40 45~~~~~~~~~~~~~~~~~.. . .... 15 ......... ... 99. 295 9. Europe EMU...... ..... ,4 1 .. 3,568 ... 2 .13725e310 .9 s. efes o ay ourfro ±80 o 997unessothrwse otd. . nles ohewis nted sctoel itdraalshaes reestmaed or198..c.Ttalwaerjesurcs i6lde7icr fow from other coutries. d. Datarefer to estimtes for yearsbefore 1980 Ise Primary datadocumentation) a. Data refe to years othe than 1987 Ise.Prima.y.data..cum.nta.i.n. Tu38meisten world.....Development........ndicators.... 3.5 Data on freshwater resources are based on estimates * Freshwater resources refer to total renewable of runoff into rivers and recharge of groundwater. resources, which include flows of rivers and ground- These estimates are based on different sources and water from rainfall in the country. and river flows from refer to different years, so cross-country comparisons other countries. Freshwater resources per capita are of data on freshwater resources should be made with calculated using the World Bank's population esti- caution. Because they are collected intermittently, the mates (see table 2.1). * Annual freshwater with- data may hide significant variations in total renewable drawals refer to total water withdrawal, not counting water resources from one year to the next. The data evaporation losses from storage basins. Withdrawals also fail to distinguish between seasonal and geo- also include water from desalination plants in coun- graphic variations in water availability within coun- tries where they are a significant source of water with- tries. Data for small countries and countries in arid drawals. Withdrawal data are for single years between and semiarid zones are less reliable than those for 1980 and 1997 unless otherwise indicated. With- larger countries and countries with higher rainfall. drawals can exceed 100 percent of renewable sup- Finally, caution is also needed in comparing data on plies where extraction from nonrenewable aquifers or annual freshwater withdrawals, which are subject to desalination plants is considerable or where there is variations in collection and estimation methods. significant water reuse. Withdrawals for agriculture World Development Indicators 1999 defines fresh- and industry are total withdrawals for irrigation and water resources as including river flows arising out- livestock production and for direct industrial use side the country. The data in this year's edition (including withdrawals for cooling thermoelectric therefore are not comparable with those published in plants). Withdrawals for domestic uses include drink- last year's, which exclude external sources. ing water, municipal use or supply, and use for public While information on access to safe water is services, commercial establishments, and homes. widely used, it is extremely subjective, and such For most countries sectoral withdrawal data are esti- terms as safe and adequate amount may have very mated for 1987. * Access to safe water refers to the different meanings in different countries despite of fi- percentage of people with reasonable access to an cial World Health Organization definitions (see adequate amount of safe water in a dwelling or within Definitions for table 2.14). Even in industrial coun- a convenient distance of their dwelling (see About the tries treated water may not always be safe to drink. data). While access to safe water is equated with connec- tion to a public supply system, this does not take Data sources account of variations in the quality and cost (broadly defined) of the service once connected. Thus cross- - Data are compiled by the country comparisons must be made cautiously. - World Resources Institute Changes over time within countries may result from from various sources and pub- changes in definitions or measurements. lished in World Resources .1998-99 (World Resources Institute and others 1998). - ..; The Departement Hydro- . . . . . . geologie in Orilans. France, compiles data on water resources and withdrawals from published documents, including national, United Nations, and professional literature. The Institute of Geography at the National Academy of Sciences in Moscow also compiles global water data on the basis of published work and, where necessary, estimates water resources and consumption from models that use other data, such as area under irrigation, livestock populations, and precipitation. 1999 World Development Indicators 139 3.6 Water pollution Emissions Industry shares of emissions of organic water poiiutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics, kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % % % % %% :1980 ±9960 1980 1L9960 19961 19960 1.996a 2.9960 1996, 1996a 19960 1.9960 Albania . . 4,654 .... 0.22. .. 29.0 .... 1.7 7.2 53.6 0.6 43.3~... 1:1..1 .... 2.6 Algeria 60,290 103,569 0 19_... 0_25_... 44.6 3~8 ..._40.8 0.4 8.0.... 2.5 .. 0.0 Angola : Argentina 244,711 186,844 0.18 0.2-1 6.3 12.6 8.1 .5.4 0.2 7.4 1.5 9.2 Armenia . 14,833 . 0.22 I........... ......... 0.0 . 64.2 .... .. :...... 35.8 Austria 108,416 79,779 0.16 0.15 13.9 19.3 9.4 35.4 0.3 6.9 4.2 21.3 Azerbaijan . . 45,02 . 0171. 6 2.5.12.0 49. 0 2.18.1 .1.0 ......11.3 Bangladesh 66,713 .. 0.16 . . . . . Belarus I ...... ... Belgium 136,452 ~ 0.16 ...... ....... .. ............. ......... Bolivia 9,343 10,251 0.22 0-23 4.7 13.8 6.5 61.8 0.3 9.0 2.6 2.4 Bosnia and Herzegovina . 3,217 . 0.19 .. 4.4 18.6 .9.7 .44.9 0.1 -16.0 ..4:0 4~5 Botswana 1,307 4,561 0.24 ...0.19 0.0 11.2 2.7 67.6 0.0 13.1. .2.0 6.5 Bulgaria 152,125 93,669 0.13 0.14 13.9 8.7 11.0 38.4. 0.3 16.1 2 .1.... 18.9 Burkina Faso 2,385 . 0.29 . . .. . Burundi 769 0. . ..O 22 . . ...... . .................... .......... C a m b o dia. .. . .. . .. . .. . .. . .. . .. . ... .... . . I . Cameroon 14,569 12,472 0.29 0.24 2.3 5.2 20.1 65.5 0.0 3.2 3.4 0.6 Canada 330,241 292,089 0.18 0.17 9.9 30.0 .8.8 34.2 0.1 ....57.7. .3.9 .... 14..9 Central African Republic 861 0.26 ... . Chad ' ::1:. Chile 44,371 78,917 .0.21.. 0.23 6.4 119.9..... 7.8 60.5 0 1 .......80 2.5 5.4 China 3,377,105 8,6,2 0.14. 0.13 19.0 13.2 . .. 11.8_ 28.9 0.6 16.1 1.1_ 18.4 .ogKong, China . 102,002 61,073 0.11 0.14 1.3 33.6 4.9 .18.2 0.0 33.4 04 16 3 Colombia 96,055 114,136 0.19 0.20 3.5 14.2 10.2 53.2 0:2 ..... 144 1.0 6.4 Congo, Demn. Rep. _ : : :: Congo. Rep. 1,039 0.21 Costa Rica ! : . ! M6 e dIlvoire 15.414 .. 2 3 ......... ....... . . ............ .. ..... ... ........ ........ . ....... .... . .. Croatia 55,250 0.16 5.1 12.8 8.5 46.1 0.2 17.7 3.5 12.3 Cuba 120,703 .. 0.24 .. .. .. . Czech Republic .7 .. . .. . . .. Denmark 65,465 88,461 0.17 0.17 2.1 29:2.. 7.7 45.7 0.2 3.4 29.9 17.6 Dominican Republic 54,935 . 0.38 . .. .. _..: : Ecuador 25,297 28,713 0.23 0.25 2.8 -124 85 65.6 0.1 7.1. 1:6 3,8 Egypt, Arab Rep. 169,146 215,264 0.19 0.20 11.4 6.1 8.0 53.4 0.3 17.3 0:4 6.2 El Salvador 9,390 10,399 0.24 ....0.19 ......3.0 ..... 17.5 11.2 412.2 0 2 24.7 1.0 2 5 Eritrea Estonia Ethiopia 19,496 0~22 .. 2.1 ..... 10.1 3.1 57.2 0.2 25.1 1.7 Finland 92,275 65,585 0.17 0.18 9.5 39.2 7.1 .29..4 .......0 1 ....30 3.5 16.1 France 729,776 585,382 0.14... 0.15 11.6 21.2 108.8 37.7 0.2 6.0 1.8 21..5 Gabon 2,661 1,886 0.15 ... 026 .... 0.0 ...._ 6.0 ......4.9 .....79.7 .....0:.1 1.2 ......6.9 2.4 Gambia, The 549 .. 0.30 .. : .. .. . Georgia : :: ..~:~ Germany .. 811,315 . 0.12 12.7 16.8 15.5 30.6 0.3 4.8 2.2 34.3 Ghana 15,868 14,449 0.20 0.17 9.8 16.9 10.5 39.5 0.2 9.1 12.4 3.3 Greece 65,304 58,533 0.17 0.20 6.1 12.3 8.4 53.3 0.3 14:6 .....1.5 ........7.0 Guatemala 20,856 19,052 0.25 0.28 5.3 8.0 6.2 71,4 0~1 6-9 1. 1 2.0 Guinea Guinea-Bissau Haiti 4,734 0.19.. . . . Honduras 13,067 34,036 0.23.... 0.20 IA1 7.8 3.9 55.5 0~1.1.... 26-8 .....4~O.0.... 1.5 140 1999 World Development Indicators 3.6 Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics. kilograms per day metaln and pulp Chemicala beverages and gleans Textiles Wood Other per day per worker % % % 'A % % % 1980 19961 1980 1996, 1996a ±996, ±996, 19961 1996a 1996a 1996a 1996a Hungar 201,888 138,248 0.15 0.18 10.5 10.0 8.1 51.7 0.2 10.9 1.9 13.5 India 1,457,474 1,694,090 0.21.. 0.19 14.1 7.5 7.6 52.8 0.2 12.2 .... 0.3 10.4 Indonesia 214,010 749,872 0.22 0.18 2.2 7.8 8.1 53.9 02.2 204.4 4.9 5.1 Iraq 32,986 .. 0.19 Ireland 43.544 34,000 0.19 .0.16 1.8 17.2 10.6 52..2 0.2 6 8 1.8 1&88 Israel 39,113 55,029 0.15 0.16 3.7 19.7 9.4 43.9 0.2 12.1 1.8 18.6 Italy 442,712 359,578 0.13 ... 0.13 12.1 16.0 11.8 28.7 0.3 16.1 2.5 25.2 Jamaica 11,123 17,972 0.25. 0.29. 7.2. .. 7.0 4.3 70.4 .0.1 9.7. 12.2 Japan ~~1,456,016 1,479,350 0.14. .0.14.. 8.7 21.8 88 38.9 0.2.... 7.1 . . 1.9 2. Jordan 4,146 15,189 0.17 0.18 4.2 14.6 14.2 52.1 0.6 7.8 3.3 6.4 Kazakhstan... .. . Kenya 26,834 48,354 0.19 0.24 .4.1 11.7 5.6 65.2 0.1 8.7 -19 5'3 Korea, Dem. Rep... .. ..-.. Korea, Rep. 281,900 353,295 0.14 . .0.12 ... 12.2 17.2 11.5 26.4 0~ 3..... 16 5 1.6 28~8 Kuwait 6,921 7,913 0.16 0.14 2.9... 4.5 13.6 49.7 0.4 -18.1 4 1 13~3 Kyrgyz Republic .. 20,700 ...... 0.16.... 13.7. 02.2.. 0.9. 54.8... 0.4 21.0 1: 0. 16.0 Lao PDR . .. Latvia . 26,542 0.17 3.2 10.4 4.9 57.8 0.2 11.6 5.6 12.5 Lebanon 14,586 0.20 . ..... ... ....... Lesotho 993 2,550 0.24 0.16 0.8 2.2 0.9 41.0 0.1 54.7 072 Libya 3,532 0.21.. .. .... .... .... ... Lithuania 48,621 .. 0.15 ... 1.3 8.4... 3.9 55.4 .... 04 19'2 ... 4.2 14.. . 8 Macedonia, FYR . 22,663 0.18 12.0 9.8 6.2 43.6 0.1 21.7 1.9 9.2 Madegascer9,131 . 0.23 .. .. Malawi 12,224 9,055 0.32 0.26 00 O...12.6 5.1 67.7 0.1 11:6 1.7 2.2 Malaysia 77,215 156,515 0.15 0.11 7.0 14.9. 16:0.0 287.7. 0.3 .... 84.4 8~6.6... 32..1 Mali 2,726 0.20 Mauritius 9.224 17,780 0.21 .0.16 1:0 .5.8 2.3 38.9 0.1 50'0 .....0.8 .....2.4 Mexico 130,993 142,921 0.22 .. 0.19 9.9 9.4 13.2 545.5 02.2. 66.6 0.4 11.5 Moldova- 34,234 . 0.29 0.2 4.0 14.4 81.7 0.2 10-.8 _ 1.3 1I.1 Mongolia 9,254 7,939 0.19 0.18 1.8 4.3 0.9 64.2 0.3 24.6 4.9 1.6 Morocco 26,598 84,077 0.15 0.19 0.8 7.9 6.7 54.7 0.3 26.1 -1.0 5.1 Mozambique 12,486 .. I -0.27 .. 3.0. 8.0 ... 4.4 .... 73.7 .. . 0.1 .... 7.7 .1.6 2.8 Myanmar : 3,192 . . 0.11 22.4 7.9 5.8 21.9 0.3 5.1 34.8 3.2 Namib.....I . .... . . ..... 7.. 350. ... . . 0.... 35 00 5.0 .....1.6 90.4 07.1 ..2.0.9 1.6. Nepal 18,692 32,544 0.25 0.14 1.9 .....5.4 4.1 47.4 1 4. 37.8 1.3 1.6 Netherlands 165,416 127,566 0.18 . 0.18 7 5 25.7 11.6 43.1 0....1. 2.5 1.1 16.7 New Zealand 59,012 0.. .... .C 2.1 .... ..:: 'I... ..... .... . .... . Niger 372 0.19 Nigeria 72,082 0~17 ..... ..... . . .... ...... Norway 67,897 49,504 0.19. 0.20 .... ..9.8.... 31.7 5.3 42.2 0.1 1:.7 2.5 13.4 Oman .. 236........... ...... .... 0 15. 2.5 5.9 13.6 551 0.4 1104. 13. Pakistan 75,125 0.17 . .. Panama 8,241 12,963 0.26 0.29. 1.0 .. 10.1 .... 4.9 ....75.2 ....0.1 ..7.0 1:.1 1.2 Papua New Guinea 4,365 0.22 . . . . . Paraguay- Peru -50,367.018..... . . Philippines 182,052 0919 Poland .....1580,869 374,540 0.14 0.16 .14.7 5.4 70.0 .. 49.2 0.3 13i.1. .2.1 16.6 Portugal 105,441 147,077 0.16 0.14 .... .3.2 13.4 4.8 ... 38.7 0.4 292.2. 4.5 11.6 Puerto Rico 24,034 18,202 0.16 0.14 .....0.9 .... 9.7 16.9 41.4 01. ' 21.. 9...1..17.... 16.0 Romania 343,145 333,168 0.12 0.14 17.1 6.7 9.0 34.3 0.3 18.5 4.8 18.8 Russian Federation .. 1,706,742 . 0.14 17.6 6.8 9.0 43.5 0.4 9.7 2.8 20.5 1999 World Development Indicators 141 3.6 Emissions Industry shares of emissions of organic water pollutants of organic water pollutants Stone, kilograms Primary Paper Food and ceramics. kilograms per day metals and pulp Chemicals beverages and glass Textiles Wood Other per day per worker % % %%% 1980 ±996" 1980 1996" 1996" 1996" 1.9961 1996" 1996" 1996" 1996" 1996" Rwanda ' : : :: .:: :: ! Saudi Arabia 18,181. 0.12 . . . . . Senegal 9,865 8,840 0.31 0,32 0.O 5.1 7,4 83.1 0:1.1.. 3.1 0.1 2.1 Sierra Leone 1,612 . 0 24 Singapore 28,558 34,317 0.10 0.09 2.5 27.1 13.4 19.8 0.1 5.9 1.3 59.7 Slovak Republic . 64,293 . 0.14 15.5 13.8 9.6 34.9 0.3 14 0 ......1: 6. 20. 7 Slovenia . 42,059 . 0.16 26 1. 80 2.7 .2 38 44 12 6 South Africa 237,599 240,228 0.17 0.17 11.6 17.0 9,8 40.2 0.2 11:5 .....3.3 ....12~8 Spain 376,253 334,624 0.16 0.16 73.3. 17.8 8.8 46:4 ....0:3 8.5 34.4.... 15.2 S ri Lanka 3 0 ,0 8 6 .. . . ..18... . . . .. 8 ... . . .. . .. .. . . ... . . ... ... . .. . . .... . . . .. . . . . .. . . . .. . . . .. . ... . . ... .. . . . .. . .. S u d a n . .. .. .. .. ..~... ... .. . . . . . . . . . . . . . . . . . . . . ... . . Sweden 130.439 92.068 0.15 ...0.16 11..8 36.4 7.5 28.-3 0 1 1 7 .....30.0..... 22.2 Switzerland 129,957 . 0.17 25.3 23.6 10~3 24.7 0.2 3.4 4:2.... 17.0 Syrian Arab Republic 36,262 21,421 0~19 ...0-22..... 2.9 .... 1.5. .8.4 ...68.3 0.4 17.2 ..... 03 .....2.2 Tajikistan .. :.. .. :: : : :: Tanzania 21,084 . 0.21 Thailand 2-13,271 . 0.22 Togo 963 . 0.27. . .% .. _. . Trinidad and Tobago 7,835 11,963 0.18 .. 028 4.2 10'6 6.4 73.4 0.1 29.9..... 1.4... 2.0 Tunisia 20,294 44,289 0~16 _016 7.2 . 8.1 5,9 41.1 0.4 32.6 1. .... 7 6.2 Turkey 160.173 173,258 0.20 0.17 12.9 7.8 7 2 44:2 .......03_.3... 22.1 0.9 9..5 Turkmenistan . Uganda. Ukraine-..........I ... 551,834 . 0.15 186 6.... 3.8 7.4 51.0 0.5 7.3 1.8 19.3 United Arab Emirates 4,524 . 0 15 . .. . United Kingdom 964,510 695,784 0.15 0.16 11.2 25.6 9.8 35.0 0.1 6 9 .....2 0 .......19.0 United States 2,742,993 2,559,947 0.14 ... 0.15 8.5 32.6 10.1 27:5 .....0.2 .......7.2 ... 27.7..I. 22.2 Uruguay 34,270 31,567 0.21 0....24 1.2 ... 12.3 5.9 65.2 0.2 12.7 .... 0.9 ... 3.4 Uzbekistan . . . .. .... Venezuela 84,797 9814 020 ..0.21 ......1. .. ..1.5 13.8 50.0 0.2 7.1 ...._1.4 5.9 Vietnam . . . West Bank and Gazea. Yemen, Rep. . 7,462 ..... 0.28 ....5.4 .....5.2 12.4 71.6 0.2 5.0 0.2 Yugoslavia, FR (Serb./Mont.) . 123,680 . 015 9 12 7. 4.9 0 16 21 16.9 Zambia 13,605 11,433 0.23 0.22. .. 3.4 ..10.8 7.3 636 0 2 93 2:9 ......4:7 Zimbabwe 32,681 31,598 0.20 ....0.19 14.0 10.7 5. 5 48.5 0.2 13 8 ... .3:7 ........7.1 Low~ income . . . Middle income : : : : : Low~er middle income........ Lw&middle income East Asia & Pacific Latin.America & Carib.. Middle East & N. Africa .. . South Asia . . . . High income . . . Europe EMU. Note: Industry shares may sot sum to 100 percent because data may be from different years. a. Oats rarer to any year from 1993 to i9ee. 142 1999 World Development Indicators 3.6 1 -T =_ Emissions of organic pollutants from industrial activ- * Emissions of organic water pollutants are mea- ities are a major cause of water quality degradation. sured in terms of biochemical oxygen demand, which Water quality and pollution levels are generally mea- China and India accounted for a large refers to the amount of oxygen that bacteria in water share of the emissions of organic water sured in terms of concentration, or load-the rate of pollutants in 1994 .. will consume in breaking down waste. This is a stan- occurrence of a substance in an aqueous solution. dard watertreatment test forthe presence of organic Polluting substances include organic matter, metals, pollutants. Emissions per worker are total emissions minerals, sediment, bacteria, and toxic chemicals. divided by the number of industrial workers. This table focuses on organic water pollution result- * Industry shares of emissions of organic water pol- ing from industrial activities. Because water pollution lutants refer to emissions from manufacturing activ- tends to be sensitive to local conditions, the national- ities as defined by two-digit divisions of the level data in the table may not reflect the quality of . International Standard Industrial Classification (ISIC) water in specific locations. - - revision 2: primary metals (ISIC division 37), paper The data in the table come from an international and pulp (34), chemicals (35), food and beverages study of industrial emissions that may be the first to ' (31), stone, ceramics, and glass (36), textiles (32), include data from developing countries (Hettige, wood (33), and other (38 and 39). Mani, and Wheeler 1998). Unlike estimates from earlier studies based on engineering or economic Data sources models, these estimates are based on actual mea- surements of plant-level water pollution. The focus is Hl, 1, The indicators for 1980-93 are from a 1998 study by on organic water pollution measured in terms of bio- 1-.; ; c J. Hemamala Hettige, Muthukumara Mani, and David chemical oxygen demand (BOD) because the data for * 11 Wheeler, "Industrial Pollution in Economic this indicator are the most plentiful and reliable for Development: Kuznets Revisited" (available on the comparing emissions across countries. BOD mea- World Wide Web at http://www.worldbank.org/NIPR). sures the strength of an organic waste in terms of . . . and have not seen the decline in These indicators have been updated through 1996 by the amount of oxygen consumed in breaking it down. these emissions seen elsewhere the Development Research Group of the World Bank, A sewage overload in natural waters exhausts the with the same methodology used in the initial study. water's dissolved oxygen content. Wastewater treat- ,,, Sectoral employment numbers are from UNIDO's ment, by contrast, reduces BOD. , industry database. Data on water pollution are more readily available le, than other emissions data because most industrial pollution control programs start by regulating emis- L i sions of organic water pollutants. Such data are fairly j I F reliable because sampling techniques for measuring _ Y water pollution are more widely understood and much less expensive than those for air pollution. 7 I i * P In their study Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental pro- tection agencies and sector-level information on out- put and employment from the United Nations Industrial Development Organization (UNIDO). Their 'j. v,.E r, Ce , ...,.- econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time. The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1975-96. The sectoral emissions estimates were then totaled to get daily BOD emissions in kilograms per day for each country and year. 1999 World Development Indicators 143 3.7 Energy production and use Commercial Commercial energy use Comnmercial energy use Net energy energy per capita imports production thousand thousand average average % Of metr'ic tone of metric tuns of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use 1980 1996 ±980 1996 1980-96 1980 1996 1980-96 1980 1996 Albania 3,428 1,079 3,049 1,188 -8.6 1,142 362 -8.0 -12 9 Algeria 67,061 116,207 12,410 24,150 3.7 665 842 1.0 -440 -381 Angola 11,301 40,485 4,538 6,017 1.8 647 532 -1.2 -19 57 Argentina 38,813 74,860 41,868 58,921 21 149 1,673 0.7 7-27 Armenia -1,263 7 ,7 1,790 -3.7 346 474 -4.9 -18 59 Australia 86,096 189,045 70,372 100,612 2.3 ,9 ,9 - .. ......I.... .........3 4,790. .... .......I.......I.....5,494..... .......................9I...........-22..1 ......... .-88....... 2 8 Austria 7,655 7788 23,450 27,187 1.3 3,105 3,373 0.9 67 71 Azerbaijan 14,821 14.387 15,002 11,862 -4.4 2,433 1,570 -5.6 1 -21 Bangladesh 13,224 21.50:1 14,920 23,928 3.1 172 197 0.9 11 10 Belarus 2,566 3,147 2,385 24,566 8.0 .247 2,386 7.5 -8 87 Belgium 7,445 11,881 46,100 56,399 1.7 4,682 5,552 1.4 84 79 Ben in 1,212 1,951 1,63 1,920 . ... 2:.1 ... ... 394 ..... 341...... -1.0 11 - Bolivia 4,289 5,239 . .,35 3,633 2.2 436 479 0.0 -84 4 Bosnia and Herzegovina 626 1,750 . 777 64 Botawana ... Brazil 62,069 1239 108,997 163,374 2.8 896 1,012 1 0 43 31 Bulgaria 7,737 10,348 28,673 22,605 -. 3,235 2,705 -2.0 73 54 Burkina Faso . : Burundi Cambodia_: Cameroon 5,824 10,016 3,687 00 1.7 426 369 -1.1 -58 -100 Canada 207,417 357,279 19,0 23,7 1.6 7,4 7,880 0.3 -7 -51 Central African Republic...... .. Chad Chile 5,664 7,843 9,525 20,456 .....54 4.. ... 855 1,419 3.7 41......... 62 China 608,625 1,100,390 593,109 1,096,800 4.1 604 902 2.6 -3 0 Hong Kong, China 3 4 ,..11,10 .7 1,127 1,3 .4 . 99 4o0. Colombia 18,212 66,739 19,127 31,393 3.1 672 799 1.0 5 -113 Congo, Dem. Rep. 8,697 13,689 8,706 13,799 3.1 322 305 -0.20 1 Congo, Rep. 3,970 11,493 . 845 -1,205 2.2 .........506 .....457 ......-0 6 -370 -854 Costa Rice 767 736 1,527 2,248 3.4 669 657 0.7 50 67 CMe dIlvoire 2,419 4,762 3,662 5,301 2.8 447 382 -0 6 34 10 Croatia .. 3,908 .. 6,765 . . 1,418 . .42 Cuba 3,891 6,709 14,570 15,953 0.4 1,501 1,448 -0.5 7358 . .. . .. . . .. .. . .. .. . .. .. . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . ..7. .. ..n. . . . . . . . . . . . . . I .. . . . . . . . . . . . . . . . . . .. . . I . . . . . . . . . . . . . . Czech Republic 42,697 31,528 46,910 40,404 -1.7. 4,585. 3,917 -1.7 922 Denmark 896 17,549 19,734 22,870 0.9 3,852 . 4,346 0.8 95 23 Dominican Republic .... 1,332 1,462 3,464 5,191 2.2 608 652 0.1 62 72 Ecuador 11,756 21,869 5191 8,548 2.6 652 731 .2 -126 -156 Egp,Aa e. 34,168 5975 15,970 3779 5.1 391 638 2.6 -114 -58 El Salvador 1,915 .. 2,607 2,540 4,058 25 ... 554.... 700 1.0 25 .. ..... 36 Estoni' 3,853 5,621 . . 3,834 ..31 Ethiopia 10,588 15,536 11,157 16,566 2.7 . 26 24 -.5 .. . . . . .. . . . . .. . . I . .. . . . .. . . . . .. . . . . . . . . .. . . . . . .. I . . . .. . . . . .2 96.. .. . . . . .. . . . . ... 84. .... . . . . . . . . .. . . . . .. . . . .. . . . . .. . .- .. . . . . . 6. . . . . Finland 6,912 13,570.25,413.31,482 . 1.5 . 5,316 6,143 1.1 73 57 France 45,603 129,811 190,111 254,196 2.1 3,528 4,355 1.6 76 49 Gabon 9,441 19,706 1,493 1,578 -0.9 2,160 1,403 -3.9 -532 -1,149 Gambia, The .. Georgia 4,706 701 4,474 1,576 -5.4 882 291 -5~8 -5 55 Germany ~~185,628 140,445 360,441 349,552 -0.2 4,603 4,267 -0.5 48 60 Ghana 3,305 .564 4,071 6,657 3.6 379 380 0.4 19 16 .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . . .. . . . .. . . . . . . . . . . . . .. . . . .. . . . . . . . . .. . . .. . . .. . . . . .. . . . .. . . . .. . . . .. . .I5,6 0 4.I . . . . .. . . . I. . . . .. . . . Greece 3,696 8,810 15,960 24,389 3.1 1,655 2,328 2.5 77 64 Guatemnala 2,503 3,997 3,754 5,224 2.6 SSO SIO 0.0 33 23 Guinea Guinea-Bissau... ... Haiti 1,87 1,598 2,099 1,968 -0.9 392 268 -2.8 11 19 Honduras 1,315 1,763 1,877 2,925 2.9 526 503 -0.2 30 40 144 1999 World Development Indicators 3.7 Commercial Commercial energy use Commercial energy use Net energy energy per capita imports production thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalenit % growth energy use ±980 1996 1980 1996 1980-96 1980 1996 1980-96 1980 1996 Hungar 14,886 12,843 28,895 25,470 -1.2 2,699 2,499 -0.8 48 50 India 221,887 390,602 242,024 450,287 .. 40.0 352 476 1.9 813 Indonesia 128,403 219,187 59,561 132,419 5.4 402 672 3.5 -116 -66 Iran, Islamic Rep. 84,001 .220.894 38,918 89,340 6.0 995 1,491 3.2 -116 -147 Iraq 136,643 32,584 12,030 25,027 4.0 925. 1,174 . .8. -1,036 -30 Ireland 1,894 3,470 8,484 11,961 2.3 2,495 3,293 2.0 78 71 Israel 153 573 8,609 16,185 5.1 2,220 2,843 2.6 98 96 Italy 19,644 29,305 138,629 161,140 1.4 2,456. 2,808 1.3 86 82 Jamaica 224 547 2,378 3,718 3.4 1,115 1.465 2.3 9 185 Japan 43,193 102,377 346,491 510,359 2.8 . 2967 4,058 2.4 8 880 Jordan 1 181 1,714 4,4.75176 100 0.6 . 109 Kazakhstan 76,799 62,628 76,799 43,376 -4.4 5,163 2,724 -4.9 0-44 Kenya 7,891 11,245 9,791 13.279 2.2 589 476 -1.1 19 15 Korea, Dem. Rep. 29,136 21,052 31,793 24,002 -1.6 1,799 1,063 -3.1 812 Korea, Rep. 12,162 . 22,752 43,756 162,874 9.3 1,148..3.576 ... 8.1 72 86 Kuwait 94,085 112,600 9,564 13,859 10.0 6,956 8.167 0'.7 -884 -712 Kyrgyz Republic 2,190 1,442 1,717 2,952 5.6 473 645 4.1 -27 51 Lao PDR . .. Latva 261 998 . 4,171 .. 1,674 54 76 Lebanon 178 195 2,483 4,747 3.7 . 827 1,164 1.7 93 . 96 Lesotho . .. . . .. Libya 96,662 77,742 7,173 14,911 4.4 2,357 2,935 1.1 -1.248 -421 Lithuania 534 4,168 11,701 8,953 -3.4 3,428 2,414 -4.0 95 53 Macedonia. FYR Malaysia 16,644 69,559 11,128 41,209 .9:0 .. 809 1,950 6.0 -50 ... -69 Mauritania... Mauritius... Mexico 149,365 213,524 98.904 141.384 2.2 1,464 1,525 0.2 -51 -51 Moldova 35 53 4,601 . 1,064 106 99 Mongolia... .. Morocco 877 868 4,778 8,822 4.2 247 329 2.1 82 90 Mozambiqlue 8,5 ,4 8,386 7,813 -0.4 693 481 -2.0 -2 7 Myanmar 9,513 11,835 9,430 12.767 1.8 279 294 0.3 -1 7 Namibia ... ........ .: Nepal 4,504 6,374 4,663 6,974 .. .. 27 7.. .. 322 .. 320 0.1 .3 9 Netherlands 71,830 73,384 65,000 75,797 1.5 4,594 4,885 O.9 -11 3 New Zealand 5,488 13585 9,251 16,295 3.8 . 2972 4,388 2.9.4 1 Nicaragua 910 1,495 1,562 2,391 2.6 535 525 -. 42 37 Nigeria 148,479 170,453 52,846 82,669 2.9 743 722 -0.1 -181 -106 Norway 55,743 208,145 18,819 23,150 1.7 4,600 5,8 .2 . 196 . 799 Oman 15,175 47,310 1,387 4848 10.0 1,260 2,231 5.4 -994 -876 Pakistan 20,998 41,494 25,479 55,903 5.0 308 446 2.3 18 26 Panama 529 756 1.865 228 1.7 957 853 -0.3 72 67 Papua New Guinea . .. .aauy1605 . .,78 2,094 4,285 4.6 672 865 1.5 23 -56 Peru 14,655 12,354 11.700 13,933 0.8 675 582 -1.2 -25 11 Philippines 10,670 17232 .21,212.37,992 3.7 439 528 1.1 50 55 Poland 121.848 102,363 124,806 108.411 -1.5 3,508 2,807 -2.02 6 Portu~a 1,481 2,432 10,291 19,148 . 4.5 1054 1,928 . 4.5 86 87 Puerto Rico . .. .. .. .. Romania 52,587 31,317 64.694 45,824 -2.8 2,914 2.027 -2.9 19. 32 Russian Federation 749,289 948,631 764,349 615,899 ... -3.3 5,499 4,169.. -3.6 2 -54 1999 World Development Indicators 145 3.7 Comnmercial Commercial energy use Commercial energy use Net energy energy per capita Imports production thousand thousand average average % of metric tons of metric tons of annual kg of oil annual commercial oil equivalent oil equivalent % growth equivalent % growth energy use ±.980 1996 1.980 ±996 ±980-96 1.980 ±996 ±980-96 ±1980 1998 Rwanda... Saudi Arabia 533,071 474,997 35,357 92,243 5.2 3,773 4,753 0.4 -1,408 -415 Sierra Leone... Singapore :........._55 6,054 23,851 10.0 2,653 7,835 8.1 100 .. 100 Slova.kRpublic . 3,416 . 4,818 20,810 .1,49 -. 4,175 3,266 -1.8 8472 South Africa 73,068 127,859 65,355 99,079 20 237 248 04................79 South ...I...I...................Africa...I... .. .... ............ ............ .2,482.......... -04......... -12.-29 Spain 15,644 32,622 68,583 101,41 3.1 183 2,583 2.8 77 68 ... . . . . . . . . . . . I . . . . . . 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ I . . . . I . . . . . . . . . . . . . . I . . . . . . .. . . . Sri Lanka320 4,204 4,493 6,792 2.1 305 371 0.7 29 38 Sudan ~ 7,089 . 9,486. 8,169 10,787 . ..: 37 37 -0.5 13 12 Sweden 16r134 31,804 40,984 52,567 1.3 4,932 5,944 0.9 61 39 Switzerland 7,3 049 2,6 5,622 .. 1.6 . 3,301 3,622 0.8 66 59 Syrian Arab Republic 9,502 33,705 5,348 14,541 5.7 614 1,002 2.4 -78 -132 Tajikistan 1,986 1,343 1,5 ,1 7.741 594 5.1 -20 62 Trinidad and Toago 13,141 13,575 3,873 7,887 4.2 3,580 6,081 3.1 -239 -72 Tunisia 6.966 6,289 3,900 6,676 3.7 611 735 1.4-76 Turkmenistan 8,034 32,623 7,948 12,164 -7.6 2,778 2,646 -10.5 -1 -16 Uganda. Ukraine 109,708 79,042 97,893 153,37 1.2 . 196 302 10-24 .. . .. .. . . . .. .. . .. .. .. . I . .. . .I .. . I. .. .. .. . .. .. .. .. . .. .. .. I. . .. .. .. .. . 1 .... . . .. . .5 6... . .I . . .3 ,0 12... . . . . .. . . .. . . 1 ... . . .. . .. . . . -12.. . .. . .. . . 4 9... . . United Arab Emirates 93,915 148,818 8,576 32,336 7.3 8,222 13,155 1.7 -995 -360 United Kingdom 196,792 268,126 201,299 234,719 . 1.1 3,574 3,992 0.8 2 -14 United States 1,553,260 1,687,300 1,811,650 2,134,960 1.4 793 8,051 0.4 14 21 Uruguay 664 1,021 2,637 2,955 0.9 905 912 0.2 75 65 Uzbekistan 4,615 47,406 4,821 42,406 9.5 302 1,826 7.4 -12 Venezuela 132,934 193,993 35,026 54,962 2.1 2,321 2,463 -0.4 -280 -5 West Bank and Gaza Yemen, Rep. 60 18,181 1,424 2,936 4.7 167 187 0.6 96 -519 Ygsai,R Sr.~/Mont.. . .,8714,420 1,364 2 Zimbabwe 5,688 8,721 6,511 10,442 3.4 929 929 0.4 13 16 Low income 563,859 916,261 493,168 837,277 3.6 390 461 1..2..-14. -9 Middle income 3,552,971 4,833,815 2,635,621 3,652,666 4.6 1,0 1,305 2.7 -35 -32 Lower middle income 2,200,925 3,205,571 1,954,874 2,667,023 5.8 1,103 1,1t90 3.9 -13 -20 Upper middle income 1,352,046 1,628,244 680,747 985,643 2.1 1,643 1,766 0.2 -99 ~ -65 Low&. middle income 4,116,830 5.750,076 3,128,789 4,489,943 4 4 906 973 ......'2.3 -32 ........-28 East Asia & Pacific 832,224 1,521,645 768,319 1,458,927.42572 . 55 2.6 Europe & Central Asia 1,240,595 1,437,762 1,338,028 1,287,193 7.6 3,300 2,732 ....... 3.9 ...........7......_-12 Latin America & Carib. 468,430 751,684 376,913 557,686 2.4 1,062 1,163 0.5 -24 -35 Middle East & N. Africa 989,486 1,096,558 146,618 337,967 . 842 1,246 2.2 -577 -225 South Asia 263,822 464,175 291,579 543,884 4.0 329 441 1.8 10 15 Sub-Saharan Africa32 27 4 825 207,332 304,286.... ... 23 3... 720.. .... 670...... -0.6 ... ......... High Income 2,794,673 3,688,405 3,814,560 4,827,461 1.8 4,625 5,259 1.1 27 ... .. . 24 Europe EMU 363765 444,746 940,145 1.091,718 1:2 3,408 3,768 ...._0.9 ..........61 .........59 146 1999 World Development Indicators 3.7 In developing countries growth in commercial energy L Commercial energy production refers to commer- use is closely related to growth in the modern sec- cial forms of primary energy-petroleum (crude oil, tors-industry, motorized transport, and urban Four countries accounted for almost natural gas liquids, and oil from nonconventional 40 percent of commercial energy use areas-but commercial energy use also reflects cli- in 99fi sources), natural gas. and solid fuels (coal, lignite, matic, geographic, and economic factors (such as the and other derived fuels)-and primary electricity, all relative price of energy). Commercial energy use has - - converted into oil equivalents (see About the data). been growing rapidly in low- and middle-income coun- * Commercial energy use refers to apparent con- tries, but high-income countries still use almost seven , ' sumption, which is equal to indigenous production times as much on a per capita basis. Because com- plus imports and stock changes, minus exports and mercial energy is widely traded, it is necessary to dis- fuels supplied to ships and aircraft engaged in inter- tinguish between its production and its use. Net energy national transportation (see About the data). * Net imports show the extent to which an economy's use 1 energy imports are calculated as energy use less exceeds its domestic production. High-income coun- production, both measured in oil equivalents. A tries are net energy importers; middle-income coun- minus sign indicates that the country is a net tries have been their main suppliers. exporter. Energy data are compiled by the International Energy Agency (IEA) and the United Nations Data sources Statistical Division (UNSD). IEA data for non-OECD countries are based on national energy data that ur-.3 I r_, 4 Data on commercial energy have been adjusted to conform with annual ques- 'r,n ..it: '.' production and use are pri- tionnaires completed by OECD member govern- , marilyfrom lEAelectronic files ments. UNSD data are primarily from responses to .- 4,, 'N ',: that are also published in its questionnaires sent to national governments, sup- 0 -' ,, annual publications, Energy plemented by official national statistical publications -, - - ' ' . .. Statistics and Balances of and by data from intergovernmental organizations. - Non-OECD Countries, Energy When official data are not available, the UNSD pre- The United Stares ases the most commercial *.l2-.n=,i.' r- .: Statistics of OECD Countries. pares estimates based on the professional and com- energy. OEher big consumers are Russia. Japan, and Energy Baiances of OECD Countries, and from the ard Germant. Aithough these four countries mercial literature. This variety of sources affects the comDlned account tot Iess tharn 1I percent of United Nations' Energy Statistics Yearbook. cross-country comparability of data. the nvoIld's population together ttrea consume Commercial energy use refers to the use of almost 40 percent of the orlId's commercial energy. domestic primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from com- bustible renewables and waste, which comprises solid biomass and animal products, gas and liquid from biomass, industrial waste, and municipal waste. Biomass is defined as any plant matter used directly as fuel or converted into fuels or electricity or heat. (The series published in the previous editions of the World Development Indicators did not include energy from combustible renewables and waste.) All forms of commercial energy-primary energy and primary electricity-are converted into oil equivalents. To con- vert nuclear electricity into oil equivalents, a notional thermal efficiency of 33 percent is assumed; for hydroelectric power 100 percent efficiency is assumed. s999 World Development Indicators 147 3.8 Energy efficiency and emissions GDP per unit Traditional Carbon dioxide emissions of energy use fuel use 1995 $ per kg % of total Total Per capita kg per 1995 $ oil equivalent energy use million metric tons metric tons of GDP 1980 1996 1980 1996 1 1980 1996 1980 1996 1980 1996 Albania 0.8 2.2 12.3 .........8..6 4.8 1.9 ........1.8 0.6 2.0 0.7 Algeri~a 2.5 1.8 2.7 2.0 66.2 94.3 3.5 3.3 2.1 2.2 Angola 0.9 4. 59.5 53 510.8 05.. Argentina 5.7 5 0 6.5 4 0 107.5 129.9 3.8 3.7 0.5 0:4 Arm n a5.2 1.7 ....... ....: ......... . : ... ..... ........ 3.7 ... .. 1.0 ..... . 0 1.2 Auatralia ~~3.3 3.7 2.1 ... . ...3 8 202.8 306.6 13.8 16.7 0.9 0.8 Austria 7.1 8.7 1.4 2.8 52.2 59~3 .......6 9...... 74 0.3 0:3 Azerbaijan .. 0.3 ..3. . 4.08 BangIles 1.3 1:7......... 67.8 49.9 7.6 23.0 0.1 0.2 0.40.6 Belarus ... . 0.8 ... _.... .........0:8 61.7 .. .... .... ..I.... 6 0 ...... 3.2 Belgium 4.6 4.9 0.2 0.9 127.2 106.0 12.9 10.4 0.6 0.4 Benin 0.9 1.1 84:9 925.5. 0.5 0.7 0.1 0.1 0.4 0.3 Bolivia 2 3 1.9 19.8 12.8 4.5 10.1 0 8 130.14 Bosnia and Herzegovina .. . . . 9 3.1. 1.-4 Botswana . .. 35.7 .. 10 2.1 1.1 I14 0.7 04 Brazil 4.7 4.4 41.2 27.5 183.4 273.4 1.5 1.7 0.4 0.4 Bulgaria 0.4 0.5 0.....7 08 75.3 55.3 8.5 6.6 6.4 4.7 Burkina Faso . . 914 930. 10 0.1 0.1 0.3 0.4 Burundi .92:7 .....88~8 0.1 012 0 .... 0 0......00 0.2 0 Cambodia . 71.2 75.3 0.3 0.50.0 0.0 0.2 Cameroon 1.7 1.7 69.4 77.3 3.9. .... 35.5....... 0.4 0.3 0.6 0.4 Canada 2.1 2.5 0.6 0.6 420.9 409.4 17.1 13.7 1.0 0.7 Central African Republic . . 90:8 ..... 890 .........0.1 0.2 0.0 0.1 0.1 0.2 Chad.. . 874 920.2 0.1 0.0 0.0 0.2 0:1 Chile 2.8 3.1 14.5 ... 13.3 27.9 48~8 ..... 2.5 3.4 10 .... ... 0.8 China 03.3.... 0:7 .... 8.0O....... 5.6 1,476.8 3,363.5 1.5 2.8 8.9 4.4 *Hong Kong, China 10.0 12.00.9 0.3 16.3 23.1 3.2 3.7 0.3 0:2 Colombia 2 4 2.6 214.4 21:.1 39:8.8 ..... 65.3........ 1.4 ..... 1.7 .. ......0 9 0 8 Congo, Dem. Rep. 1.0 0.5 79.5 83.9 3.5 2.3 0.1 0.1 0.4 0.4 Con. 9,.Rep: .............. 1.5 .......1..9 559 9 . . 61:0 0.45. 0.2 ...... 1.9 0.3 2.2 Costa Rica 3.7 4.0 40.4 12:7.7. ..... 2.5 4.7 1.1 1.4 0.40.5 M6e d'ivoire 2:3 2.0 53:5 ........6772 .........46 .... .. 13.1...... .. 0.6 0.9 0.5 1.2 Croatia . 2.8 .... ....... .... 3.0.17...3.7 0:9 Cuba . . 28.1 19.7 30 1.32 28 Czech Republic . 1.3 0.5. 127.. 23 2.4 Denmark 6.8 8.2 0.3 3.3 62.9 56.6 12.3 1.0.7 0.5 0:3 Dominican Republic 2.2 2.5 28.3 121.1 6.4 12.9 1.1 1.6 0.I.0 Ecuador 2.4 2.1 .........26.5 14.8 13.4 24.5 1.7 2.1 1.1 1.3 Egypt, Arab Rep. 1.8 1.6 5-0 3.3 45.2 97~9. 11 17.7. 1.5 1.6 El Salvador 2.9 2.4 50.3 42.9 2.1 4.0 0.5 ..... 0.7 ........0.3 .... 0.4 Eritrea Estonia . 092:3 .. 16.4 ....... ........... 11.2 . 3.3 Ethiopia 0.4 .... ..0.4_ ~ .... 92~4 90 1 1.8 3.4 .... ....0.0 ......0.1. 0.4 0.5. Finland 3.7 4.1 3.8 51 1.... 54.9 59.2 11.5 11.5 0.6 ....._0.5 France 6.1 6.1 .........1.3 1.0 482.7 361.8 9.0 6.2 0.4 0.2 Gabon 2.4 3.3 33:6.6... .. 5178.8...... 4.9 3.7 7.1 3.3 1.4 0.7 GambiaThe .~~~~~~: 797 81.2 02 0.2 0.2 0.2 0.7 0.6 Georgia 277.7 .... 2.1 ..... 13 3.0 .... 0.5 .0:9... Germany .. 7.0. 07. 861.2 . 105. 0.4 Ghana 1.0 1.0 68.2 79:0 2.4 4-.0 . . ... 0'2...... 0.2 0.6 0.6 Greece 5.7 4.8 28 1,5.5.. 51.7 80.6 5.4 7.7 0.6 ....... 0.7 Guatemala 2.9 2.9 53.1 59.9 4.5 6.8 0.7 0.7 0.4 0.4 Guinea 68.4 69.9 0.9 1.1... I 02 0.2 0:3 GuineWsau ...1....- ..... 7 .1 ... 70.5 0.1 020 2 0.2 1.0 0 9 Haiti 1~5 .......1.4 82.4 80.3 0.8 1. 0.1 0.1 0.2 0.4 Honduras 1 4 1.4 61.2 .... 49.3 ..._....2.1 4.0 0.6 0.7 0.8 1.0 148 1999 World Development Indicators 3.8 GDP per unit Traditional Carbon dioxide emissions of energy use fuel use 1995 $ per kg % of total Total Per capita Kg per 1995 $ oil equivalent energy use million metric tons metric tons of GDP 1980 1996 1980 1996 1980 1.996 1980 1996 1.980 1996 Hungary... .. .1.6 . ....1-.8 2.1 .... 1.8 .... 82.5 59.5 7.7 5.8 1.8 1.3 India0.6 0.8 34.7 23.3 347.3 997.4 0.5 1.1 2.4 2.8 Indonesia .......1.3 16..6 .... 51.6 .. ... 299.9 .. ... 94.6 ... 245~1.1 .. 0.6 1.2 1.3 1.1 Iran, Islamic Rep. ....I.....14 .. .... I 1.6.... 0.8 116.1 266~7.7 . .... 3.0 ... ...44 .4 .... 2.1 2.9 Iraq ...... ... ........ : ...... .0.2.. .... 0 .1 44.0 _ 9 . 3.4 .... 4:3. Ireland...4~.0 ... .5.9 0 . ..... 1. 1 .... 02 2.. 25.2 34.9 7.4 9.6 0.7.... 0.5 Israel 5.1 5.6 0.0 7 21.1 52.3 5.4 9.2 0.5 0.6 Italy .... .... .... .... ..6~.0 .... 6.8 ... 0.7 ... ..... 1.9 371.9 403.2 6.6 7.0 0.5 0.4 JamaicaI13 1.16.2 80... 8.4.. 10.1 4.0 4.0 2.7 ... 2~4 Jardan 22..15 . 0.0. 9.3 10:5 .....0.1 0.........O5 .920.4 1,167.7.... 7.~9 9:3 0.3 0.2 Kazakhstan . 0.5..0. 13.8 109 8:7 .pya ~~~0.6 07 75.4 76.. .1. ...I. 6.2 6 8 . 04.. 0.21.07 Korea, Dem. Rep. .2.7 3. 2. 54371 1. Korea, Rep. 3 1 .....3.0 .I.....57.7 0j.7 125.2 408.1 3.3 9.0 0.9 0.8 Kuwait .. 2.4 ...1.7 0.0........... V,r~~~~~,: P~~~~~ur.t.: 1 2 ?~~~~~~~~~~.1 I2 17 Lao PDR.. . 86.6 85.1 0.2 0.3 0.1 .1...0. Latvia 16.0 1.5 .. 8... 9.3 . 3.7 . 1.4 Lebanon.. . 4.32.66. 1422.1 3.5 Lesotho Libya : 1.7 0.8 26.9 40.6 8.8 8.0 Lithuania 0.8.. 56 13.8 3:7 1:9 Macedonia, FYR.. 69 . 1.7.I.4. . Madagascar 77.1 ... 83.8 16... 1.2 0.2 0105 0 M alawi ~. .... . !..... ...... ....... 89.:1 86. ..... .8-. 7..... 0... . 7... .... 0.1 0.1 0.7 0.5 Malaysia 2.9 2.3 14.4 6 ..... .676 . ..28.0 1... 19.1 .... 2.0 5.6 09........9 1.3 Mali .. 85.2 87.4 0.4 050.1 0.0 0.2 0.2 Muiania . 0.7 . 06 2.9 04 120.8 2.6 Mauitus .44.1 4. 0. 0.6 . 150.3 0.4 Mexico 2.3 21 1....... 4.4 .4.4 251.6 348.1 3.7 3.8 1.1 1.2 Modo..-...a 0... 6...0.5.12.1 2.8 .. 4.2 Mongolia 140.0 36.6.. 6.8 8.9 4.1 3.6 9.2 9.1 Morocco 4.5 4.25.4 4.7 15.9 27.9 0.8 1.0 0.7 0:8 Mozambique 0.2 0.3 726.6 ..86.0 .3.2 1.0 0.3 0 1 2.0 ... 0.5 Myanmar 66.5 69.4 4.8 7.3 0.1 0.2 Namibia Nepal 0.5 0.7 948.8 88.9 0.5 1.6 ... 0.0 0.1 0.3 ... 0.3 Netherlands 4.4 5.4 0.0 0.5 152.6 155.2 10.8 10.0 0.5 0.4 New Zealand 4.7 3.8 0.2 ........... 17.6 29.8 5.6 8.0 0.4 0.5 Nicaragua 1.3 1.0 50.4 45.8 2.0, 2.9........ 0.7 0.6 1.0 ........1 2 Niger . . 78.096 06 110.1 0.1 0.3 0.6 N~igeria 0.4 0.4 63.7 56.6 68.1 83.3 1.0 0.7 3.0 2.8 Norway . 5 1 6.7 ... 0.8 .... 1 1 90.4 67.0 22.1 15.3 0.9... 0.4 Oman 2.8 2 6 . 0.0 59 15.1 5.3 7.0 1.5 1.3 Pakistan L1.0... 1.1. 27.2 20. ... 2 2 ......31.6 ....943.3 04.4.0..1..1. Panama 2.8 3,6 26.4 19.4 3.5 6.7 1.8 2.5 0.7 0.8 Papua New Guinea 64! .1 ... 58 9.9 ........ 1.8 2.4 0.6 0.5 0.6 0.5........ ..... .... ..... ... . ............................I.................... . : Paraguay....I.... 2.8 ... 2.1 66.1 51.5 1.5 3-.7 0.5 0.7 0.3 0.4 Peru 4.1 4.3 187.7 . 22.9 23.6 26.2 ...... 1.4 .... 11 ......... 0.5. 0.4 Philippines 2.7 2.1 35.8 30.5 36.5 63.2 .......0.8 ......0 9 0.6 0.8 Poland 0.9 1.2 0.4 1.1 456.2 356.8 12.8 9.2 4.2 2.8 Portugal......... 6-.8 .56.61.1 0.7 27.1 47.9 2.8 4.8 0.4 0.4 Puert Rico ......... ..... '.... ...... ..... .... 0.0. .. 14.0 .... 15 8.8 4.4 4 2 2 ... Romania 0.6 .. 0.7 1.5 .... .215.5 191.8 119.3 8.6 5.3 5.3 ....._.3:5 Russian Federation 0.5 0.5 1... 1,579.5 . 10.7 . 4.7 1999 Worldl Development Indicators 149 03.83. GDP per unit Traditional Carbon dioxide emnissions of energy use fuel use 1995 $ per kg % of total Total Per capita kg per 1995 $ oil equivalent energy use million metric tons metric tons of GDP 1980 1.996 1980 1996 1980 1996 ±980 1996 1980 1996 Rwanda . 84.8 85.7 0.3 0.5 0.1 0. . . 1....... 0... . 2..... 0.3... Saudi Arabia ........... 3 0 ..... 1 4 0.0 .. ......... 130.7 267.8 14.0 13.8 1.2 2.1 Senegal 1:6 1.8 4876 55.9 2.8....... 3~1.1....... 0.5 0.4 0.9 0.6 Sierra Leone . 63.5 640 04 0.2 ... 01 0.......6..05 Singapore 4.6 3.8 0.0 . ... .... .. 301 1. ... 65 8 13.2 21.6 1.1 0.7 Slovak Republic ... 14.1 ..... ..... 0.5 39.6 . .. 7.4 . 2.1 Slovenia 3.1 . ...0.8 .... . ..... 1.3. .6.5 .. ........... 0.7 South Africa...... ... 1.7 .... 14.4 4 5 ........3.9 ... ...211.3 292 7 7.7 7.3 1.9 2.1 Spain 5.7 5.6 05 0.6 ... ..200 .0 .. ..232~5 ....... 5.3 5.9 0 5..... 0...O4 Sri Lanka 1.5 ....2.0 ... 54.3 48:4....... . 3.-4 7.1 .........02 0.4 0.5 0.5 Sudan 0.5 0.7 76.4 76.4 3.3 3.5 0.2 0.1 0.7 0.4 Sweden ..... .. 4.5 .... 4 5.5 ... 3.9 .... ..2 5.5 ... .. 71.4 54.1 8.6 ....... 6.1 ..........0.4 0.... .2 Switzerland 12:.1... 12.0 1.1 2.1 409.9..... 4472.2. 6.5 .. ... 6.3 0.2 0:1 Syrian Arab Republic 1.7 1.2 0: ~.1 ........ ....... 19.3 44.3 2.2 .... .. 3:1.1 ... 2.1 ........ 2.6 Tajikistan 05. 5.8 . 1.0 . 3:6 Tanzania 0:3 83.7 89:6 1.9 24.........01 0.1 0:5 Thailand 2.3 2 2 48.3 32.7 40.0 205.4 0'9 3.4 0.8 1.2 TPo .... ...... .... ..... .... .. 38.3..73:1 0.6 0.8 0.2 0.2 ..... 0.5 ...... 0.5 Trinidad and Tobago 1.3 0.7 1.8 1.0 16:7 22.2 15.4 17.1 3.4 4.0 Tunisia 2.7 2.9 154.4. 12:9 9 4 16.2 1.5 .. ... 1 8 0.9 0.8 Turkey 2 8 2.8 18.0 3.1 76.3 178.3 1.7 2.8 0.9........ 1.0 Turkmenistan 0.3 .. 34.2 ...... . - 7.4 .8.2 Uganda ..... .. . ...872.2 _...89.2 . .....0.6 1.0 0.. ... 0.1.1 0.2 0 Ukraine 0.5 0.4 397.3 . 7.8 .. 5.1 United.Arab.Emirates ~. 0:0 363 3 .... 81.8 ......34.8 33.3 United Kingdom 4:0 .....4.8 0.0 ..... 1.1 583.8 557.0 10.4 9.5 0.7 0.5 United States -2.7 ... ..3:4 12 ........4.2 4,575.4 5,301.0 20.1 20.0 0.9 0.7 Uruguay 5.8 6.4 20.4 26.7 5.8 56 6........ 2.0 1.7 0.4 0.3 Uzbekistan - 0.5... 90 4.1 . 4:2 Venezuela 1.7 14.4 10.0 ........ 1.-2 .. ... 89.6 ... . 144~5.5 . .... 5.9 6.5 1.5 1.9 Vietnam 0.7..... .. 53.5 ........49.1 16.8 37.6 .. . 0.3 0.5 . 1.7 West Bank and Gaza . .. Yugoslavia, FR (Serb./Mont. . 100 0.... Zambia .... 0.7 0: 6........ 54:6 .... 712.2 ... ... 3.-5 ..... 2..4 0.6 0.3 1.1 .... 0.7 Zimbabwe 0.7 0.7 33:6 27.4 9.6 18~4.4. .... 1.4 1.62.1.... 2:4 Low income 0.8 50.1 38.0 559.4 1,448:.1. 0.4 0. 7 17.7.. ... 2.1 Middle.income .... 1:3.3 . 12.3 7.3 4,247.0 10,068.9 ...... ..22.2 ...... 3 6 ....... 1.7 2.1 Lower middle income. 0.9 12.9 7.2 2,509.0 7,512.7 1.5 34 3.0 3.1 Upper middle income .. 2.4 11.5 7.5 1,738.0 2,556.2 .3 451.1 1.... .. 11 Low & Tniddle income. .. 1 2 18.8 12.0 4,806.4 11,517.01. 2.4 1.7.. 2.1 East Asia & Pacific10 15 8 11.6 1,833.3 4,309.51. 254329 Euoe&Central Asia 0:8 3-5 2. 886.9 3,412.7 . 7.-4. 3:4 Latin America & Carib. ... ... ... 32 2.... 20.3 15.6 848.5 1,209.1 2.4...... 2.5 0.6 0.7 Middle East & N. Africa. 1 6 .. 2.0 1.2 494.6 .. .. 98813.6 .... _3.0 .. ....3 9 ..... 1.5 1.8 South Asia . 0 9 37.5 2. 392.4 1,2.04 091923 Sub-Saharan Africa 0 9 46.6 4. 5. 472.10. 081615 High income 5.0 10 25 8844 1,3. 19 1 . Europe EMU 5.6 6 30.9 1.1 1,504.4 2,329.5 7.6 8 0 0.5 0.3 150 1999 World Development Indicators 3.8 The ratio of real GOP to energy use provides a mea- * GDP per unit of energy use is the U.S. dollar esti- sure of energy efficiency. Differences in this ratio mate of real GDP (at 1995 prices) per kilogram of oil over time and across countries are influenced by Historical tfends show accelerating equivalent of commercial energy use. * Traditional growth in global CO2 emissions ... structural changes in the economy, changes in the fuel use includes estimates of the consumption of energy efficiency of particular sectors of the econ- rr,:,, ,.] ,,,,,, ,..,, . ,,. .;, fuelwood, charcoal, bagasse, and animal and veg- omy, and differences in fuel mixes. etable wastes. Total energy use comprises commer- For traditional fuels, fuelwood and charcoal con- cial energy use (see table 3.7) and traditional fuel sumption estimates are calculated by the Food and use. * Carbon dioxide emissions are those stem- Agriculture Organization (FAO) based on population ming from the burning of fossil fuels and the manu- data and country-specific per capita consumption fig- J facture of cement. They include carbon dioxide ures. Estimates of bagasse consumption are based produced during consumption of solid, liquid, and gas on sugar production data. fuels and gas flaring. Carbon dioxide (CO2) emissions, largely a by-product of energy production and use (see table 3.7), account . Data sources for the largest share of greenhouse gases, which are 1,.:, 1.4 1 1 associated with -global warming. Anthropogenic CO, C . -.- ~. ...~ Undierlying data on commer- emissions result primarily from fossil fuel combustion .I.. r . - - ' , .1 cial energy production and and cement manufacturing. In combustion, different , . use are from International fossil fuels release different amounts of C02 for the Energy Agency (IEA) and same level of energy use. Burning oil releases about United Nations sources. Data 50 percent more C02 than burning natural gas, and t. on CO2 emissions are from burning coal release about twice as much. During - the Carbon Dioxide Infor- cement manufacturng about 0.5 metric ton of CO2 is - - r mation Analysis Center, released for each ton of cement produced. A -A ' Environmental Sciences Division, Oak Ridge National The Carbon Dioxide Information Analysis Center . . . and a rising average temperature at Laboratory, in the U.S. state of Tennessee. Data on (CDIAC), sponsored bythe U.S. Department of Energy, the earth's surface traditional fuel are from the World Resources calculates annual anthropogenic emissions of CO2. Institute's World Resources, the United Nations' These calculations are derived from data on fossil fuel Energy Statistics Yearbook, and FAO electronic files. consumption, based on the World Energy Data Set maintained by the United Nations Statistical Division, 4 and from data on world cement manufacturing, based on the Cement Manufacturing Data Set maintained by I J6 the U.S. Bureau of Mines. Emissions of CO2 are often calculated and reported in terms of their content of elemental carbon. For this table these values were converted tothe actual mass of CO2 by multiplyingthe carbon mass by 3.664 (the ratio of the mass of car- .. i , bon to that of C02). Although the estimates of global CO2 emissions I-,.-, 3; - 1 . are probably within 10 percent of actual emissions r!;: ....l, 3 i, o, :..J r Jl. l .. 9 (as calculated from global average fuel chemistry and - use), country estimates may have larger error Since record-keeping began in 1866. the 13 bounds. Trends estimated from a consistent time waimeet years have occurred since 1979 and the series tend to be more accurate than individual val- four watmest s,nce 1990. ues. Each yearthe CDIAC recalculates the entire time series from 1950 to the present, incorporating its most recent findings and the latest corrections to its database. Estimates do not include fuels supplied to ships and aircraft engaged in international trans- portation because of the difficulty of apportioning these fuels among the countries benefiting from that transport. 1999 World Development Indicators 1 5I 3.9 Sources of electricity Electricity Sources of electricity production Hydropower Coal Oil Gas Nuclear power billion kwh%%%%% 9 1.980 1996 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 Albania 3.7 5.9 79.4 96.6 . 20.6 3.4 Algeria 7.1 . .20.7 3.6 0.7 .... ........ I. 12.2 3.6 84 ..1 . .95.7 Angola 0.7 1.0 88.1 90:0 ....................... 11 .9 - .10.0 M..... .... Argentina 39.7 69.8 38.1 .. .32 9 ....2.5 2.2 .....31.9 5.4 21.0 48.3 59 10.7 Armenia 13.0 6.2 12.0 25:3 .. 548 8..... 2.0 353 3.... 33.2 37.4 Australia 95.2 177.0 13.6 8.7 73:3 78.9 .... 5.4 16 7:3.3.. 9.0 Austria 41.6 53 5 69.1 64.0 7.0 11.5 14.0 3.7 92.2 .. 17.5 Azerbaijan ...........115.0 ...... 17.1 7.3 9.0 ..... .. .. 92:7 0.5. 90.5 Bangladesh ... ... ...2.4..... 11.5 24.8 6:4 . . 26.6 6.5 ~48.6 87 1 Belarus 34.1 23.7 0.1 0.199. 9.999 .29:1 ........ ...... 70.8 Belgium 53.1 75.2 0.5 0.3 294.4 . 24.2 ....34.7 1.7 11:2.2... 14.6 23.6 57..6 Benin 0.0 0.0 . . 100.0 100.0 . Bolivia 1~6_... .32 68.2 630.0 10.3 6. 18.4 29.6 ............ Bosnia and Herzegovina 2.2 .. 64.5 ..... 35 .5 . Botswana. .. .... . Brazil ..... 139A.4 289.8 92.5 91.7 2:0 ....1-.6... 3.8 .. 3.1 0 2 . 0.8 Bulgaria 34.8 '41.5 10.7 ... 470.0.. 49.2 41.9 22.5 3.2 . 7.2.._ 17~7.7.. 43..6 Burkina Faso..... . ...... C a m b o dia . . . . . . . . . . . . . I . . - ........ . . Cameroon 1.5 .... .2.9 93.9 98.9 ...... .............6:.1 .. . 1.1 Canada 373.3 570.6 67.3 62.4 16:0 ....16-.2 37 1.6 2:5 ... .2.9 10.2 16 3 Central African Republic .. .... .. . . Chad Chile 11.8 .. 308 ... 62.5 54.8 14.9 ... 351 19.1 8.4 1.2 1.0.. China 300.6 1,080.0 19.4 17.4 59:3. 75-0 .. ..21.1 6.0 0.2 0.2 ..... ......-1.3 .og Kong, China -12.6 28.4 . . 0 0 98.4 100.0 1.6 Colombia 20.6 44.6 70.0 79A.1... .9.7 6..9 .. ....2 1. . . 0.5 17.6 12.8 C ngo, Dem. Rep. 4.4 6.3 95.5 97.9 .4.5 2.1. Congo, Rep. 0.2 0.4 62.2 98,6- 35.9 .....0.7. .... 1.9 ......0.7 Costa Rica 2.2 ... 4.9 95.2 859 ............I...4.8 13.9 ....... ........ MSe dIlvoire 1.7 3.0 77.6 62.1 ... 22.4 37.9 ...... ... ........ Croatia-.. 11.1. 59.4 ... .. .2.7 ...27.7 . 10.1 Cuba .... ......9.9 .... 132 1.0 0:7.7 .. ... 89.6 92.2. 0.1 ..... -. Czech Republic 52.7 63.8 4.6 3 1 84.8 ~....73..3 9.6 ... 1.1 .... ..1.1 1.2 . 20..1 Denmark 268 53.6 0.1 0.0 818.8.... 74.0O. ... 18.0 10.8 . 10.7 Dominican Republic 3.3 6.8 18.8 271.1 . ,4.3.. 78.8 ... 681 1 ....... ................. Ecuador3.4 94 25.9 68.1 . 74.1 31.9 Egypt, Arab Rep. 18.9 57.6 51.8 18.8 . 277 31 205 41 El Salvador 1.6 3.5 61.9 53.4 . 5.2 26.6 ...... ... ........ Eritrea Estonia 189 ... 9.1 0:0 ..... 96.6.... 100.0 1.1 . 2.2 Ethiopia 0.7 1.3 70.2 87.0 . 27.6 7.8 . . Finland 40.7 69.4 25.1 17:1 42.6 31.8 10.8 1.9 4.2 . ...12.3 17.2 28 1 France 256.9. ..508.1 269 9 . 128 8 .. 27 2.. .6-.1 18.9 1.5 2 7 ......08 .... 23.8 ~.. 78~2 Gabon 0.5 1.0 49.1 76.4 . .. 50.9 12.9 10 7 Gambia, The . ... .. Georgia 14.7 7.2 43.8 85.1 . 56.2 1.0. 13.9 Germany 466.3 550.6 4.1 4:0 62.9 .... 05 ...... 5.7 1.4 14.2 ......8.7 11.9 29.1 Ghana 5.3 6.0 99.2 99.9 . 0.8 0.1.. . Greece 22.7 42.3 15.0 103.3 44:8 69.3 40.1 20.2 0.2 Guinea Guinea-Bissau . . .. . Haiti 0 .306 70.1 43.5 : 26.1 53.3 Honduras0.9 2.8 84.0 99.5160 . 152 1.999 World Development Isdicators 3.9 Electricity Sources of electricity production Hydropower Coal oil Gas Nuclear power billion kwh %% 1980 1.996 ±1980 1996 1980 1996 1980 1996 ±980 1.996 1980 1996 Hungary 23.9 35.1 0.5.... 0.6 . 50.2 .. 128.3 150.0... 12.6. 34.3 18.1. ... 40.4 India 119.3 435..1 39.0 159.9 49.9 73.2 7.7.....2.8 0.8 6.2 2.5 1.9 Indonesia8.4 67.1 16. 13.3 . 25.9 84.0 25.4 . 32.0 Iran, Islamic Rep. 22.4 90.9 25.1 8.1 50.1 37.1 24.8 54.7 Iraq .. .... ..11 4.4 . 29.0O .. 6.1 2.0 9.9 . ... 98'0.0 ... ... ... . .....7:........ Ireland 10.6 18.9 7.9 .. 3S.8 .. 16.4 ... .484.4... 604.4 . 14.2 .15:2 .33.2 ........ Israe 112.5 32.5 . 0.2 17.9 68.9 100.0 30.9 Ita ly 1835.5 239.5 24.7 17.6..... 9.9 ....10.6 ....57.0 48.9 5.0 21.0..... 1.2 Jamaica 1.5 ..6.0 .. 8.3 . ...2A.187.9 93.3.....: ....... .. Japan 572.5 1,003.2 15.4 8.0 9.6 18.2 46.2 21.0 14.2 20.2 14.4 30.1 Jordan 11.1 61 .1 . .. 0.4 ... 100.0 87.6 .... . 120O............... Kazakhstan 61.5 58.7 9.3 ... ..12.5 72.0 90.7 7.3 8.2. Kenya1 5 4 0 71.1 81.6 28.9 8.8 Korea, Dem. Rep. 35.0 35.0 64.3 64.3 35.7 35.77 Korea, Rep. 37.2 223.1 5.3 1.1 6.7 35.4 78.7 18.2 12.1 9.3 33.1 Kuwait 9.4 25.5 . .37.2 21.7 628_ 78.3 Kyrgyz Republic 9.2 13.8 53.1 89.1 6.6 46.9 4.3 Lao .P D R I . .. . . .. . .... . .. . . ..... . . .. . .. . . .. . .. . . . .. . . ...... ... .... .......... . ... . ....I..Lebanon ....2.8 7... .0 . 30.9 11.5 ..... ...69:.1 88.5 .. ... ...... Lesotho . . . Libya 4.8 18.2 .. . 100.0 10. Lithuania 11.7 16.2 40 0 96.0 8-1 . 4~1 85..8 Macedonia, FYR ' Madagascar Malawi Malaysia 10.0 51.4 13.9 10.1 . 6.4 .84.7 12.3 .... 1.3 71.2 Maili Mauritania M a.uritius . ...... Mexico .... .I.67.0 ..162.5 ...25.2 . 19.3 .. 00 .... 10 9 .... 579.9 50.1 15.5. 11.3 ...... .....-48 Moldova 15.4 6.1 2.6 .6.0 . .. .22.2 ...97.4 6.3 ........ 65.5 Mongolia Morocco 52 12.4 28.9 15.7 19:5 44:9 . ~.. 516 .. 39.4 ...... ........ ................ Mozambique .... 1470.0 ... 0.6... 96.8 8.4 12 4 3.2 65.5 . 137.7 Myanmar.1.5 4.3 53.5 ...383.3 2.0 0.1 31.3 159.9..... 13.2 45.7.. Nanilbia Nepal 02.............. .... 1.2 ....82.3 902 17:7 9.8 Netherlands 64.8 85.0 071 137. 3 316 38-4 ...4~6 ......39:8 556 6.5 4.9 New Zealand 22.6 36.2 83.6 71.2 19 30 0.2 0.0 7:5.... 17-8 ...... ........... Nicaragua 1.1 1.9 51.3 22.6. . 45.3 60.6 N i g e r .. . . . .. . . . . . . . . . . . . . . . . . .. . . . .... . . I .. . . . . .. . . .-.. . . . . . . .. . . . . . . . . . .. . . . . .... . . .. . ... Nigeria 7.1 15.0 39.0 36.7 0:4_.... ... 45.1 26.1 15:5. 37 2 Norway.......... 838.8 . 104..5 .99.8 99.2 0.0.. 0:2 0.1 . 0 .3 OmanI... 0- ...O 8 .... 6,.8 .. .. ...... . ... . .. 18 2 .100.0 .... 81..8 . . Pakistan 15.0 57.0 58.2 40.7 02.2 0.8 1.1 30.8 .... 405.5 26.8. 0.0 0.8 Panama 2.0 3-9 49.4 61..7 ... .... .48.4 37.4 -s....... ............ Papua New Guinea.. ... . Paraguay 0..8.. 48.2 ..80.0 ....99j.7 ... 13.4 0. Peru 10.0 17.3 69.8 77.1. 27.4 20,9 1.7 1.3 Philippines 18.0 36.7 19.6 19.3 1.0 13 2 ~...67.9 49.6 49 0.1 Poland 120.9 141.2 1.9 1.4' 94 7.. 96 8 2.9 1.2 01 . Portugal 15.2 34.4 52.7 42.9 2.3 36.6 42.9 17.5 . Puerto Rico ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . . ..... ...... ... .. ..... ..... . ... ..... .. ... .. . .... Romania 67.5 61.4 18.7 25.7 31.4 33.9 9.6 109.9 38.2 27.~2 2.3 Russian Federation 804.9 ....846..3 16.1 18.2 . 18:. 5.. 77.2 9440.4 .. 4 .4 ...6.7 12.9 1999 World Development Indicators 153 3.9 Electricity Sources of electricity production Hydropower Coal Oil Gas Nuclear power billion kwh%%% 1980 1996 ±980 1996 1980 1996 1980 1996 1980 1996 1.980 1996 Rwanda Saudi. Arab ..I.... .....a... 205..... 97.8...I.......... ... ..585 5 .1 ..57 ...415 ...41 .42 5....429... Senegal 06 12 .. ....... ................ ....100:0 88.1 . 11.9 Sierra Leone . Singapore 7:0.0 ... 24.1 .... .. ....... .. .... 100.0 78:7_- 18.6 Slovak Republic 20.0 25.0 11.3 17.2 37:9_ 237 1. 7.-9 4.9 12 93 2 Slovenia 8.0 12.8 42.3 28.8 51.6 32.8 3 9 2.7 2. . . 2 2 3.4 35.7 South Africa 990_.0.. 198.3 1.0 0.7 99.0. 93.2 .. 5 Spain 109.2 _ 173.4 27.1 23.0 30:0 315_ .5 .. 35.2 8.0 2.7 3.9 4.7 32.5 Sri Lanka 17..7 4..5 887 7. 718 . 11.3 28.2 . Sudan 0.8 2.1 70.0 52.1. 30.0 ....47-9.9 ...... ....... Sweden 96.3 139.6 61.1 36.9 02 M30 10.4 5.2 03 27.5 52.5 Switzerland 48.2 55.6 68.1 51.0 01 ..... 0.0 1.0 0.5 0.6 1.2 29.8 45.2 Syrian Arab Republic ..... 4.0 17.0 64.7 40.8 31.9 29.1 3.4 3. Tajikistan 13.6 15.0 93.4 98.8 . 6.6 1.2 Tanzania 0.8 ... 2~.0 ....86.4 .....87:8 ..... ........ 13.6 12.2 Thailand 14.4 87.5 8.8 8.4 9.8 20.0 81.4 29.3 9.9 42.0 Trinidad and Tobago.... 2.0 4.5 . ..2.3 . 96.5 99.3 Tunisia.....2 .9 75 0.8 0.9 I.:................64.5 ...14.2 34:7 85.0 Turkey 23.1 94..9 49.0 42.7 25.8 32.1 25.2 6.9 ..............184 1........ Turkmenistan ... ..6.7 10.1 0.1 00 . . 99.9 . 1 000 .. .......... ... Uganda : !! ! : Ukraine ........ 2360.0 1817.7 .. 5.7 .. ..4.9 289 9.. 88.3 3.8 187 ..... 60 0.... 43.8 United Arab Emirates .. .... 6.3 19.7:....... . .... : 448.8 . _177.1 .... 55.2 82..9........: United Kingdom 284.1 346.3 ..1.4 1.0 7372. .42 4 11.7 4.0 0.7 23.6 130 .... 27.3 United States 2,427.3 3,652.0 11.5 9.6 51.2 .....52.7 10.8 26 6. ... 153.3. . 13..2 .....1110.... 19.6 Uruguay 3.4 6.7 67.9 86.5 . 31.8 12.6 Uzbekistan 339.9 45.4 14.6 144.4 4 0 85.4 . 11.2...170.5 Venezuela 36.9 75.4 39.6 71.4 .14 14 5 2:5 45:9 ....26,.1......:....... ... Vietnam 3.6 16.9 41.8 0.0 39.9 .. 14.0O .... 183.3 .. 8.6 ... ...0.4 77~3.3 ...... ... ... West Bank and Gaza Yem en Rep.... .. .... .... 0 5.. 2..... 4..... .....- ... . .... ..100 0... 100.0......... ... Yugoslavia, FR (Serb./Mont.) 38.1 30 2 6.72437 Zambia 9:5 .......78 98-8 99:5 07 0.5 0.5 0:.0.... ..... .... ... Zimbabwe 4.3 7.9 87.6 29.4 11:.8 . 70.6 ..... .0.6 ......... ...... ..77... Low income 274.4 6716.6 46.9 26.5 22.5 49.1 24.5 6.9 3.5 15.6 2.7 1.7 Middle income 2,5.13.6 4,447.3 20.9 23.8 20.8 .... 37.1 ....50.5 13 1 .......4.3 .....18-.5 3.2 6.8 Lower middle income 1,813.5 3,040.6 17.5 19.7 12.9 39.1 62:6.6... 12.3 ...... 2:6 20.6 4.1 7.8 Upper middle income 700.2 1,406.7 296.6.. 32.7 41.2 32.8 19.1 14.7 8.5 ... 13-.9 ...... 1.0 4.8 Low & middle income 2,788.1 5,118.9 23.4 24.1 ......20.9 ....38.7 .....47.9 12.3 4.2 18.1 3.2 6.2 East.Asia & Pacific 391.6 1,378..8 .23.1. 17.5 49.5 63.0 26.6 ...9j.7..... 0.3..... 8.1 .... 1.0 Europe & Central Asia 1,639.9 1,780~O.0... 13.5 ... 17:3.3. 136 6.. 313.3 I... 65 4 7.5 .......2..2 28-.8 ......5. 1.... 14.8 Latin America & Carib. 361.0 .810.3 59.8 64.1 2.1 4.7 24.2 16.6 11.6 10.4 0.6 2.... 22 Middle.East & N. Africa 104.6 379.8 20.4 7.5 1.0 . .....1.5 ......52.3 49.0 263.3... 42~O.0...... ... South Asia 138.5 509.2 41.5 19.1 43.0.... 62.6 7.4 6:2 .....59.9. 10-.3 .......22.2 ..1:7 Sub-Saharan Africa 1526.6. .260.8 ...304.4. 165.5 -.646.6 73..1 4.3 3.... 33..... ..07 2.3. 4:5 High income 5,439.3 8,502.5 19.4 14.9 39.4 38.3 18.1 7 4 11.2 12.8 11.5 24.7 Europe EMU 1,242.9 1,808.5 17.0 12.8 37.2 28-.2. 22..9 9.1 100 10.6 11.9 37.6 154 1999 World Development Indicators 3.9 Use of energy in general, and access to electricity in . . * Electricity production is measured at the termi- particular, are important in improving people's stan- nals of all alternator sets in a station. In addition to Coal still dominates in global electricity dard of living. But electricity generation also can dam- generation hydropower, coal, oil, gas, and nuclear power gener- age the environment. Whether such damage occurs ation, it covers generation by geothermal, solar, wind, depends largely on how electricity is generated. For and tide and wave energy, as well as that from com- example, burning coal releases twice as much carbon bustible renewables and waste. Production includes dioxide-a major contributor to global warming-as the output of electricity plants that are designed to does burning an equivalent amount of natural gas produce electricity only as well as that of combined (see About the data for table 3.8). Nuclear energy - ; heat and power plants. * Sources of electricity refer does not generate carbon dioxide emissions, but it to the inputs used to generate electricity: hydro- produces other dangerous waste products. The table . power, coal, oil, gas, and nuclear power. Hydropower provides information on electricity production by 1_; - refers to electricity produced by hydroelectric power source. ' : plants, oil refers to crude oil and petroleum products, The International Energy Agency (IEA) compiles gas refers to natural gas but excludes natural gas liq- data on energy inputs used to generate electricity. IEA uids, and nuclear power refers to electricity produced data for non-OECD countries are based on national 1.: . by nuclear power plants. Shares may not sum to 100 energy data that have been adjusted to conform with percent because other sources of generated elec- annual questionnaires completed by OECD member tricity (such as geothermal, solar, and wind) are not governments. In addition, estimates are sometimes . shown. made to complete major aggregates from which key data are missing, and adjustments are made to com- - i . Data sources pensate for differences in definitions. The IEA makes - . these estimates in consultation with national statis- Data on electricity production tical offices, oil companies, electricity utilities, and are from the IEA's electronic national energy experts. - . files and its annual publica- The IEA occasionally revises its time series to tions, Energy Statistics and reflect political changes. Since 1990, for example, it Balances of Non-OECD Coun- has constructed energy statistics for countries of the -=, @ t tries, Energy Statistics of former Soviet Union. In addition, energy statistics for O-ii i DECD Countries, and Energy other countries have undergone continuous changes . Balances of OECD Countries. in coverage or methodology as more detailed energy accounts have become available in recent years. Breaks in series are therefore unavoidable. . - s * H.I, :- r .: :1999 World Development Indicators 155 3410 Urbanization Urban population Population In Population in Access to urban agglomerations the largest city sanitation of more than one million In urban areas % of total % of total % of urban ft of urban millions population population population population 1980 1997 1980 1997 1980 1995 2015 1980 1995 1982 1995 Albania 0.9 1 3 .......34 .......380 ... .. 0 ...... ... . ........ ............. . 97 Algeria 8.1 16.8 43 57 11 13 15 25 24 95 Angola 1.5 3.8 21 32 13 20 29 63 61 27 71 Argentina 23.3 31.6 83 89 35 39 36 43 39 76 80 Armenia 2.0 2.6 66 69 34 35 41 51 50 Australia 12 6. .15.7 86 85 47 58 58 26 23 Austria 4 9 5.2 65 64 27 26 27 42 40 Azerbaijan 3 3 4.3 53 56 26 25 28 48 44 Bangladesh 9.8 24.1 11 19 59 15 33 39 20 41 Belarus 54 .... 7:4.4 ..... 56 ...... 72 14 17........ 20 ...... .. 24 .... 24 Belgium 9.4 9.9 .......95 .. ... 97 12 11 . . ... 11 ..... 13 ....... 11 ~........ . . ... ... Benin 0.... .......O9 . .. 2.3 . .....27 40 0 ..... .0 ............ . ......... 45 60 Bolivia 24 4.8 46 ..... 62 ......... 14 17 19 ......... 30 28 51 ...... 77 Bosnia and Herzegovina 1.5 1 0 36 42 Botswana 01 1.0 15 65 0 0 . 79 91 Brazil 80.5 ....130.1 ......66 .... 80 27 33 .. ..... 34 ........ .. 16 ....... 13 ......... 33 ...... 74 Bulgaria 5.4 5 7 61 69 12 16 20 20 21 Burkina Faso 0.6 1:8 . .......9 170 0 ......... ..........44 ... ... 52 .........38 ........... Burundi 0.2 0.5 4 8 00 . .90 Cambodia 0.8 2.3 12 22 Cameroon 2 .7 65 .......31.. 46 ....... 6 .. ..... 10 14.... 19 22 Canada 18.6 23.3 76 77 29 36 35 16 19 Central African Republic 0.8 1.4 35 400 0.36 Chad 0.8 1.6 19 23 0 0 455. 7 Chile 9.0 12~3.3 ..... 81~ ..... 84 33 36....... 35 41 41..... 79...... 95 China 192.3 390.7 20 328 1I 14 64 .. 68 Colombia 18.2 29.4 64 74 21 27 27 20 22 96 70 Congo, Dem. Rep. 7.8 13.7 29 29 10 . 28 34 8 53 Congo, Rep. 0.7 1.6 41 60 .. .39 67 67 17 Costa Rica 1 0 1.7 43 50 0 0 61 55 100 100 CM e dIlvoire 2~9 .... 6 3 35 .. 45 ...... 15 21 35..... 44 ...... 48 ...... . 13 ...... Croatia 2.3 2.7 50 57 00 2 37 72 71 Cuba 6.6 8.5 68 77 20 20 22 29 27 . 92 Czech Republic 6.5 6.8 64 66 12 12 13 18 18 Denm ark............ 4-.3 ......4 5 5 ... 84 .. 85 ....._ 27 ........ 25 ....... 25 32 30 .. Dominican Republic 2 9 5 1 51 63 25 33 36 50 65 72 89 Ecuador 3.7 72 47 60 14 26 30 30 27 79 70 Egypt, Arab Re. 17 9 .. 27.2 .......44~ .... 45 23 23..... . 2~5 ........ 38 37. 95... 95 El Salvador 1.9 2.7 42 46 . 22 . 39 48 89 89 Eritrea 0.3 0.7 14 18 .. .. 12 Estonia 1.0 1.1 70 74 00 . .~ Ethiopia 4.0 9.7 11 16 34 7 30 28 Finland 29 3.3 60 64 00. 2 3 0 0 0 France 39.5 44.0 73 75 21 21 20 23 22 Gabon 0.2 0.6 34 52 0 0 Gambia, The 0.1 0.4 20 30 00 Georgia 26 3.2 52 59 22 25 . 31 42 42 Germany 64.7 71.3 83 87 38 41 43 109 Ghana 3.4 6.6 31 37 9 10 14 30 27 47 75 Greece 5.6 6.3 58 60 31 35 3854 50 Guatemala ............. 2-.6 . ....4 2 ... .37 40 21 ...29 .... 57 73 91 ... Guinea 0.9 2 1 . .. 19 ..... 31 12 23 38 .... .... 65 ..... .. 81 54 24 Guinea-Bissau 0.1 0 3 17 23 0 0 2 32 Haiti 1.3 25 24 33 13 18 26 55 64 42 43 Honduras 1.2 2 7 35 .....45 00... . ..........33 .......40. 22 91 156 1999 World Development indicators 3.10 Urban population Population in Population in Access to urban agglomerations the largest city sanitation of more than one million in urban areas % of total % of total % of urban % of urban millions population population population population 1980 1997 1980 1997 1980 1995 2015 1980 1995 1982 1995 Hungary 6.1 6.7 57 .... 66... 19 20 21 34.... 31 India 158.8 264.1 23 27 6 10 12 5 625 IndonesiaI...... 32..9 .. . 74.8 22 37 7... .... 13...... 16 18 13 .30 88 Iran, Islamic Rep. 19.4 36.6 .....50 ... .60. 13 22...... 26 26 20 90 86 Iraq 8.5 16.5 66 75 26 22 23 39 28 30 85 Ireland 1.9 2.1 -55 58 0O0 48 44 ..... Israel 3.4 .5.3 ..... 89 ... . 91 37 .... 35 ..... 32 .... . .... 41 39 .... . 100 Italy ..... 37.6 .384 ..... 67 67 26 20 21 14 11 Jamaica 1.0 1.4 47 55 00 -...92 99 Japan89.0 98.9 76 78 34 37 .39 25 28 Jordan 1.3 3.2 60 73 29 28 34 49 39 91 Kazakhstan . 9.6 54 60 6 8 10 . 13 Kernya .2.7 .8.7 .16 ... ...30 . .... . 5.8.13 . ...... ...32 ..... ..23. 75 Korea, Dem. Rep. 10.1 14.2 57 62 10 11 13 18 18 100 100 Korea, Rep. 21.7 38.3 .57 83 37. 52 54 2... 2100 100 Kuwait 1.2 1.8 90 97 60 69. 57 67........ 71. 100 100 Kyrgyz Republic 1.4 ......1:.8 .. 38 39 0 0.... .. ...... 78 ..... Lao PDR 0 ..... 4 . ....1.1 ... . 13 . .. ..-22 .0 0... .O ... . ... Latvia 1.7 1......8... .68 730 0..... 49 50 ...... .. 90 Lebanon 2~2 3.7 74 88 .... .. . 46 ..... 55 .. 52 94..... Lesotho 0.2 0.5 13 26 . ... 0 ... .. ...0 ... ..... ..... 22 76 Libya 2A.1 .. _45.5 ..... 69 86 26 . . ... 31 ...... 31 38 .. .... 40 100 .... 90 Lithuania 2.1 2.7 61 730 0 Macedonia, FYR ... 1.0 1.2 ....-53 61 .0 0 .. ..62 ... Madagascar ........I... 1.6 3.9 .... 18 ..28 0 ..... .O .0 29 25 8... Malawi 0.6 1.5 9 14 0 0.. 88 94 Malaysia 5.8 11.9 4 2 55 76 7 16 11 . 94 Mali 1.2 2.9 19 28 00 40 35 90 61 Mauritani'a 0.4 13.3 27 54 0. 0 ...7 44 M auritius . 0. ..... 4 .... 0.5 ... ...42 . .....41 .. . ....0 . .... 0 . 100 Mexico 44.8 696.6 66 74 .. ...... 27 28 26 31 25 77 93 Moldova .......1.6 ..... 23.3 40 ....53 00 ... . 96 Mongolia 0.9 1.6 52 62 00 ... . 100 Morocco 8.0 14.5 41 53 11 18........ 22 26....... 23 85 97 Mozambique 1.6 6.0 ...13 ......36 6 14 .. 25 47....... 41 51 68 My.nmar......... 8.1 .11.7 24. 277 9.. 14. 27 . ..... 35 ..... 34 56 Namibia 02 .... 061 ... .. 23 . .... 38 .. ... ...0 . .... .0 Nepal 09 . 2~4 .......7...... 11 0 0... . . 5 74 Netherlands 12.5 13.9 88 89 7 14 14 8 8 New Zealand 2.6 . . 3.2 83 860 0.. 30 .... ....31 ....-. .... ........... Nicaragua 1.6 3.0 53. 63 22 27 32 41 41 35 .....88 Niger 0.7 1.9 13 19 00 ...20 - Nigeria 191.1 48~7.7 . .. 27 41 6 11 15 23 23 30 82 Norway 2.9 3.2 71 74 00 . . 10 Oman.... .... 0.3 ..... 18.8 .. 32 79 0.0 ........... 60 Pakistan .......... . .... 232.2 ... 45~4.4 .. .. 28 .... 35 11 19........ 25 22. 23 48 75 PanamaI10 1.5 50 56 00 62 66 99 99 Papua New Guinea 0.4 .... 0.7 13 ......170 0.. 51 Paraguay1.3 2.7 42 5 4 00 52. 43. 66 ..... 20 Peru 11.2 175.5 65 ..... 72 26 32 .. 33 39 ....... 40 67. 78 Philippines 18.1 41.1 37 56 12 13 14 33 24 88 Poland 20.7 24.9 ... 58 64 18 18.. 20.. 16 ... 14 ....... Portugal 2.9 3. 29 713 19 24 46 53 . Puerto Rico 2.1 2.8 67 74 34 30 29 51 48 Romania 10.9 12.8 49 57 9 9 10 18 17 Russian Federation 97.0 112.9 70 77 16 19 20 8 8 1999 World Development Indicators 157 3.10 Urban population Population In Population in Access to urban agglomerations the largest city sanitation of more than one million in urban areas % of total % of total % of urban % of urban millions population population population population 1980 1997 1980 1997 1980 1995 2015 1980 1995 1982 1995 Rwanda 0.2 0.5 5 6 0 0 ... .60 Saudi Arabia 6:2 16.8 66 84 19 21 23 16 17 100 Senegal............. 2:0 ......40 ...... 36 ... ... 45 18 .... 24 30 47 47 . ... 87 68 Sierra Leone 0.8 1.6 24 35 0 0 ....30 Singapore 2.3 3.1 100 100 100 100 100 100 100 85 Slovak Republic 2.6 3.2 52. 60. .0 ... . 0............... Slovenia 0.9 1.0 48 52 0 0 90 10 South Africa 13.3 20.2 48 50 11 20 23 12 11 78 Spain 27.2 ....30.2 . .....73 ..... .77 ..........20 18 18 ... .... 16 .... 14 Sri Lanka 3.2 4.2 22 23 0 0 ... .81 Sudan 3.7 9.2 20 33 6 9 14 31 27 70 79 Sweden 6.9 7.4 83 83 17 17 19 20 21 Switzerland 3.6 4.4 57 62 0 0 20 21 Syrian Arab Republic.. ......... 4.1 ......79 9...... 47 53 28 28 35 34 28, ... .....58 .. ... Tajikistan 1.4 2.0 34 32 0 0 Tanzania 2.7 8.0 15 26 5.........6. 9 30 24 93 ....... :... Thailand 79.9 .... 12:5.5 ..... 17 21 10........ 11....... 15 59 55 50 98 Togo.06 1.4 23 32 0 0 34 76 Trinidad and Tobago 0.7 0.9 63 73 0 0.... 100 97 Tunisia 33 3 ......5:8.8 ..... 52 .. ... 63 17 23 ...... 26 35 31 64 100 Turkey 19.5 45.7 44 72 17 24 25 23 19 Turkm enistan 1.3 .. ....2.1 ......47 ... ... 45 00 ... ..... ..... .. ....- .. ..... Ukraine 30.9 36.1 62 71 14 16 1978 United Arab Emiratea 0.7 2.2 71 85 0 0 ..31 4193 United Kingdom ..... .......50.0 .....52 :7.7 ..... 89 ..... 89 ..........25 .........23 .......22 .. .. 15 ........15 ..... ...... ....... United States 167.6 204.8 74. 77 36 39 40 9 8 Uruguay 2.5 3.0 85 91 42 41 40 49 46 59 56 Uzbekiatan 6.5 9:9.9..... 41 .... . 42 11 10 12 28 ....... 24 Venezuela 12.0 19.7 79 86 16 27 28 21 16 57 74 Vietnam ......... ...10.3 .....15.0 ......19 .......20. . . .56 9 27 . 25 ..... . ... ... Yemen, Rep. 1.7 ... ..5.7 .......20 .......35 0 0 ... . 40 Yugoslavia, FR (Serb./Mont.) 4.5 6.1 46 58 11 13 1S 24 20 Zambia-........... 2.3 ... ..4:1.1 ..... 40 .... . 44 9 iS 23 23 34 56 . ... 66 Zimbabwe 1.6 ......3.8 ......22 .......33 .13 .39 40 100 .. . .. . . .. . .. . . .. . .. . . .. . .. . . .. . . . . . . . .. ... . . . . .. . .. . . Low income 307.7 577.7 22 28 6 9 13 16 19 29 Middle income 824.3 1,389.9 37 49 12 16 18 19 16 .. 77 Lower middle income 559.0 966.2 31 42 10 13 16 16 14 . 75 Upper middle income 265.4 423.7 62 74 22 27 27 24 20 Low & middle income 1,132.1 1,967.7 31. 40 10 13 16 18 17 Easat Asia & Pacific 288.4 578.0 21 33 8 11 14 13 9.. 74 Europe & Central Aaia 240.1 317.7 56 67 14 16 18 15 15 Latin America & Carib. 233.8 366.5 65 74 24 28 28 27 25 60 80 Middle East & N. Africa ...... _83.7~ 161.9 48 S8 ........17 ........21 .......23 31 27 81 South Asia 198.5 345.5 22 27 6 10 13 9 11 27 High income 616:1 .. 708 4 .......75 ...... 76 31 33 35 17 1 . .. 6 ...... ...:.......... Europe EMU 203.7 222.4 74 77 25 25 25 16 15 158 1999 World Development Indicators 3.10 The population of a city or metropolitan area depends * Urban population is the midyear population of on the boundaries chosen. For example, in 1990 areas defined as urban in each country and reported Beijing, China, contained 2.3 million people in 87 The worlds urban population continues to the United Nations (see About the data). to boomtoteUie Nain (seAotteda) square kilometers of 'inner city" and 5.4 million peo- * Population in urban agglomerations of more than ple in 158 square kilometers of "core city." The pop- e. one million is the percentage of a country's popula- ulation of 'inner city and inner suburban districts" "' tion living in metropolitan areas that in 1990 had a was 6.3 million, and that of "inner city, inner and , population of more than one million people. outer suburban districts, and inner and outer coun- ;i * Population in the largest city is the percentage of ties" was 10.8 million. (This table uses the last r a country's urban population living in that country's definition.) largest metropolitan area. * Access to sanitation in Estimates of the world's urban population would ? r. i 4 urban areas is the percentage of the urban popula- change significantly if China, India, and a few other I - tionservedbyconnectionstopublicsewersorhouse- populous nations were to change their definition of | l r , hold systems such as pit privies, pour-flush latrines, urban centers. In fact, according to China's State , . I i septic tanks, communal toilets, and similar facilities. Statistical Bureau, by the end of 1996 urban resi- . - ' -, , - dents accounted for about 43 percent of the coun- Data sources try's population, while in 1994 only 20 percent of the *"" population was considered urban. Besides the con- Data on urban population, lTr.-i-it,,r- ; -- t |l- Ir -,- tinuous migration of people from rural to urban areas, : -.;,. ...i...i ...i ;,.,, ...'' .rilC i population in urban agglom- one of the main reasons for this shift was the rapid l Llrjalmi ior erations, and population in growthin the hundreds of towns reclassified as cities ." r F . ' , I l - ' the largest city come from in recent years. Because the estimates in the table p. the United Nations' World are based on national definitions of what constitutes While orly 3 ol l0 people lhed in urban areas m in 19.0 8 ' Urbanization Prospects: The city or metropolitan area, cross-country compar- 6eef 10 oil by 2030. esuirr.ig in s ubniantial uoMh I-ii ). ; 1996 Revision. Total popula- the derrTano for ser.ica6 and. polemia1i,. gr'eater con'm isons should be made with caution. tlo,andpollution ' - " on figures are World Bank gerstlon and poluluton. To estimate urban populations, the United Nations' estimates. Data on access to sanitation in urban ratios of urban to total population were applied to the areas are from the World Health Organization. World Bank's estimates of total population (see table 2.1). The resulting urban population estimates were used to calculate the population in the largest city as a percentage of the urban population. Urban areas with access to sanitation services are defined as urban populations served by connec- tions to public sewers or household systems such as pit privies, pour-flush latrines, septic tanks, commu- nal toilets, and similar facilities. These definitions and definitions of urban areas vary, however, so com- parisons between countries can be misleading (see About the data for table 2.14). 1999 World Deveiopment Indicators 159 3.11 Urban environment City Urban Urban Average Income House Crowding Work Travel Households with area population house- differential price to trips by time access to services hold average income public to work income income ratio trans- is fifth Average portation Regular Access to quintile floor space Sewerage waste potable to first per person connection collection water sq. km thousands $ quintile sq. m % minutes % % 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1.993 Angola Luanda .. 1,400. 40.713 5 38 Armenia Yerevan 215 1,223 1,407 28.4 39.0 13.0 ..... 98 52....... 93 .... 81 98 Australia Melbourne 1.148 3,023 30,216 12.0 3.6 55 0 16 25 9 9 100 100 Azerbaijan Baku 2,300 977 8.7 13~.0......12:9 80 57 7 9......100....... N .. Bangladesh Dhaka 1,194 7,500 478 6.9 5.0 2.7 .... ....... ...... 44 50 Tangail 32 155 228 6.9 8.0 1.2 15.. . 51 Benin Coto0nou 88 559 2,745 6.0 1.6 5 9 . 60 1. 25 ..... 60 Porto Novo 50 183 1.479 ..... 6:1 ......3.4 ...... . 5 ........ 40 .. ... 1...... 25 ...... 76 Bolivia Santa Cruz de la Sierra 165 742 3,786 7.6 2.6 60 25 22 100 87 La Paz 51 726 3,787 11.7 1.2 . 51 35 58 92 90 El Alto 58 442 1,786 7.2 1.4 . . 25 20 95 86 Cochabamba 68 425 4,035 8.3 2.6. 46 17 47 95 71 Botswana Gaborone . 473 . 7.2 12.5 42 20 33 98 100 Brazil Rio de Janeiro 1,255 5,554 12,087 20.3 2.5 18:9.9..... 67 51 87. 88.... 98 Recife . 1,503 815 28.7 2.2 15:5 70 J 40 38~ ..... 95 ......95 Curitiba 1,352 1,091 16.1 5.7 21.0 72 30 75 95 97 BrasTilea 199 -12,087 203.3.. 3.0 17.3 . 49 . .....74 .......95 89 Bulgaria Sofia 1,294 . 5.8 16.7 75 35 98 95 100 Burkina Faso Oagadougou 170 716 2,622 3-3 8~5.5. .. 12.2 . 22 . 40 75 Bobo-Dioulasso 67 284 2,379 9.1 10.2 12 0 ...... 15 .... ....... 30 81 Burundi Bujumbura 100 278 1.823 17 0 ......1 .9 5 8 30 ...... 29 41 93 Cameroon D u la 144 1,094.. 4 6 10 0 1 453 60 8 Yaound8 . 923 677 3.9 12 . 6503 44 8 Canada Toronto 4,236 49,791 9.5 3.9 41.1 30 23 100 100 100 Central African Rep. Bangui 163 471 . 6.2 112.2 . 451 25 4 Chile Santiag 4,820 8,043 16.6 2.4 14.4 54 36 92 95 98 China Hefei 3,809 2,080 13.8 ...... 11.0 .. ....... 57...... 100 Quingda 2,121 1,165 1.8 . 11.1 .11 . 100 .Shan~~~ ~ ~ ~ ~ ~~gha.. .............9.9.4.58....10 Foshan 32 385 3,354 3.2 16.3.. 10.. 100 Zhangjiagang . 178 8,468. 14.3 . .. 910 Colombia Bogot6 482 5,314 7,120 14.7 ....31 8:8 75 39 99 94 97 Congo, Dem. Rep. Kinshasa 591 4,566.2,241 67.7......... ....... 61 120 3........0 70 COte d'lvoire Abidjan 369 2,462 2,827 7 9 7.2 7.2 49 90.. 45 ......70 62 Bouake . 439 1,820 9~5 56 74 0 3 .. 35 2 Croatia Zagreb. 868 4,354 5.9 11 22.1 52 26 80 100 90 Cuba Havana 2,176 . 2.1 16:0 58 42 85 .... 100 85 .8~~~~~~~~CmauyY 155 296 . 18:7 .......6 30 46 ...... 9.. 71 Cienfuegos 44 131. 1.5 19.2 . 30 70 97 100 Pinar del Rio ......28 129 ...... . ........ 3.7 ......21~0 .........80 ...48 .....100 ......93 Czech Republic .Prague 496 1,214 .. 11.9 26 67 5 4 10 10 Denmark Copenhagen 2,863 1,326. 29,320 14-0 ....3 1. .440 .......27 .... .22 ......100 . ...100 .....100 Djibouti Djibouti.. . 6,856 12. 37 13. 19 215 65 6 Ecuador Guayaquil 178 1,773 5,406 12.1 2.0 15.6 50 45 55. 70 85 Quito 178 1,615 . 2.4 86 093 89 C ~rc- Z.- 1 -Z. 1 4531 : C.1 Lt9 .....13.0O.... 58 60 91 .......65 . . 98 Gharbeye . 383 1,656 . 6.1 3.9 13.3 32 30 91 45 99 Assiout 10 322 1,721 6.7 3.1 1470 .. 29 ... 25 ........30. 25 ......93 El Salvador San Salvador 163 1,343 4.320 12.7 ......27 6.6 80 ......46. . 91 Santa Ana 18 142 2,998 10.6 3.2 .......8 1 57..... 90 ......82 San Miguel . 132 3,420 13.2 4.3 9.7 ...... : 46 ... ...99 56 Estonia Tall in 165 468 3 6 21.3 .. 27 959 100 Ethiopia Addis Ababa ..- 8,044. ..654 7 France ...... ..Paris 2,586 9,319 20,899 14.7 4.3..... 30.0 40 35 98 ..... 100. 100 .~~~~~~~Lyon 1,262 . 90 10 100 Marseilles 351 800 14,640 5.2 ........ 25 ......99 ...... 99 ..... 100 160 1.999 World Development Indicators 3.11 City Urban Urban Average Income House Crowding Work Travel Households with area population house- differential price to trips by time access to services hold average income public to work income income ratio trans- in fifth Average portation Regular Access to quintile floor apace Sewerage waste potable to firat per person connection collection water aq. km thousands $ quintile aq. m % minuteas % % 1993 1993 .1993 1993 1993 1993 1.993 1993 1993 1993 .1993 Straabou rg..... 78 .388 15,942 9.7 ............... 15 98 100...io. 100 Gabon Libreville .. .. 362 5,726 12.. .3. 38 40 10 Gambia, The Banjul . 479 230 8.1 4.8 11.5 60 40 13 35 74 Georgia Tbilisi 204 1,295 ..162.2..... 98 70 100. 52 100 Germany Cologne 405 1,006 .. ... ....... .. 34.0 17.. ..... ..... 99 . 100. 100 Duisburg 233 536 .. . 79 32.1 21 . 100 100 100 ......11.....Leipzig ..1 . 151 .. 481 . 33.0 33 . 95 100 100 Wiesbaden 204 266.. . 37.0 23 100 100 100 Erfurt. 268 .... 213 5~ ... 5.1 29.1 32 95.... 100 100 Ghana Accra 411 1,718 403 . . .0 6.2 . .7 45 12...... 60. 86 Kumasi 758 822 2.9 17.8 5.8 55 20 12 11 57 Tamale 22 193 682 1.9 ....17.4 5.2 45 18 6 5.... 38 Greece......... Athens . . 1,464 3 1 29.0 34 53 95 90 ..... 100 Guatemala Guatemala City . 1,327 2,760 76.7 90 8. 53 40 . 3 6 Guinea Conakry 1,308 6.4 65.5 . 26 55 17..... 50 75 Hungary Budapest 320 5,621 9.2 772 94 6 40 9 0 0 India Hubli-Dharbad . 1,114 7.1 3.6 6.2 37 22 37 89 89 Bombay .. 12,810 1,504 6.7. 3.5 .... 3:5.5 .... 79 33 51..... 90...... 96 Delhi 624 8,957 1,196 11.4 ..70.0 6.9 53 .. 44 .... .40. 77 92 Madras 612 5,651.1,184 . 8.0. 7.0 . 6.2 ..... 42 22 37 90.. ._60 Bangalore . 4,472 .1.,224 6.5 10..8 ......95.5 .. 46 18 35 .... 96. 81 Lucknow 1,804 992 75.5 . 4..6 .. 5.5 1 23 30.. 74 88 Varanasi 104 1.078 928..... 7.8 5.1 4.5 .. 21 22 .. 41 88...... 85 Mysore 701 1,236 6.4 7.5 11.8 13 20 60 60 . ... 90 Bhiwandi ...... ... 26 572 0.3 2.4 . ... 8 15 15 4 86 . yGu.brga 33 1,028 7.6 3.5 6.1 8 114 74 90 Tu.m....... ur......... 194....809...6.1...4.9. 7.4 21 8... . ... 50 .......86 Indonesia Banjarmasi'n.. . 1,474 4.4 40.0 6.4 12... 37..... .. 70 94 Surabaya 1,970 8.1 8.6 11.5 23 2.. 87 99 Jakarta 13,048 2,460 6.6 9.9 15.0 38 82 . 84 93 Bandun1,89 1,625 58 1 13.1 . 29. 2 97 86 Medan 1,810 1,674 4.5 5.5 139.9..... 44 30 19 19.. 94 Semarang 1,076 1,351 6.0 5.4 12.0 14 25 . 69 88 iran, Islamic Rep. Tehran .. 11.0 ......22.6 36.. .. 100........:.... Jordan Amman .. 12,813 13.9.... 6.5 15.4. 14. 31 79 100 100 Kazakhstan Almaty 1,173 .. 7.2 1. 435 883 10 Kenya Mombasa 234 382. 1.9 5.9 31 27 2 40 95 Nairobi 1,598 . 12.5 11.3 68 48. 35 47. 93 Kyrgy'z Republic Bishkek 703 . . . 60 35 65 8 100 Latvia Riga 1.026 .19.4 . ....57 ... ..27 97 85 100 Liberia Monrovia 697 . 24.0 14.0 75 60 1 0 20 Lithuania Vilnius 670.. 5.4 162_.2 . . 49 .......25 ..... 94. 95 100 Madagascar Antananarivo 932 . 18.7 6.4 30 . 17 95 M alawi ....... ... Blantyre 403 .. .... ... ...... 8.3 ..... 8.3 39 44 ...8 . ... 20 ......80 Lilongwe ........220 ... ... 4.2. 6.6 5 31 . ......12 . 80 Mzuzu 115 .. 5.2 ... ..6.5 . ... 10 35 16.. 16 ... 60 Mali Bamako 267 . 3.7 3.2 12 40 2 95 53 Mauritania Nouakchott 72. 576 1,481 8.9 6.4 10.0 45. 50 4 15 .....68 Moldova Chisinau 131 662 1,055 9.7 13.0 15.0 48 25 86 83 100 Mongolia Ulaanbaatar 3,542 . 317 3.2 37.7 9.2 85 29 51 .. 49 Morocco Rabat . 1,345 7,514 8.1 6.8 10.0 . . 95 90 100 Mozambique Maputo 414 4.9 . 12.0 13 . 23 37 73 Namibia Windhoek 69 142 11,618 15.2 6.0 43.0 . 20 75 93 98 Netherlands Amsterdam 202 724 21,687 5.2 3.S 38.3 22 22 100 100 100. New Zealand Auckland 942 25,900 8.1 4.4 40~.0.... .6 98.................. Niger Niamey 224 505 1,369 13.2 7.3 7.7 17 27 25 77 1999 World Development Indicators 161 3.11 city Urban Urban Average Income House Crowding Work Travel Households with area population house- differential price to trips by time access to services hoid average Income public to work Income income ratio tfans- in fifth Average portation Regular Access to quintile floor space Sewerage waste potable to first per person connection collect'ion water sq. km thousands $ quintile sq. m 5 minutes S % % 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 Nigeria ............. n. it . ...... 9......... 623..... :...... 18.5. 12.0 ......53 33 .38 .......95 .~~~~~Lagos 959 5968 . 492 18 2 10.0 5.5 54 85 2 8 75 lbadan 2,937 1,941 415 50:0 6.8 9.0 40 40 40 70 Kano 123 1,510. 340 6.9 3.2 .......2.8 ......56 25 .......38 ......16 Pakistan Lahore . . 5,150 3,298 7.7 16.0 1:2.2..... 16 25 74 ...... 50 ...... 90 Paragua Asunci6n 67 949 5,496 8.8 53 4.7 31 60 10 79 58 Peru Lima . 6,232 1,109. 9.2 25.7 65 35 69 57 87 Trujillo 45 509 .. 3.8 15 2 74~~~~~~~~~~~~~~~~... 30. 71. 48.. 98.... Philippines Manila. 9,286 5,318 8.4 34: 1... . 40 120 .... . 80 .......85. .94 Poland... Warsaw . 2,219 3,021 3-1 ...54 4..... 18.2 34. ...... 91 ..... 97 ..... 100 Romania Bucharest . 2,5.. 6 8 1.9 6 78 90 86 98 Russian Federation Kostroma .. 2,357 5.1 5 78 65 21. .91 90 100 Moscow . 9,269 4,040 7.6 17.0 19.7 85 62 100 100 100 Nizhny Novgorod . 2,459 4.6 6.4 17.1 78 35 95 100 100 Novgorod . 2,865 5.9 7.3 16.3 44 30 96 99 100 .~~~~~~Ryazan 2,348 6.9 8.9 16..2 88 25. 92 ...... 99 ... . 100 Rwanda Kigali 47 275 2,279 11.6 ... 32 . . . 48 Senegal Dakar . . 1,801 3,008 17.0 3O 0 .... 8.1 53 45 25 75 ...... 92 Kaoloack . 187 1,488 20.9 .. 13 27 3..... 56 Ziguinchor . 155 1,150 22.0 . 27 20 2 . 30 Mbour. 101 2,192 15.9 . 20 31 2 79 Sierra Leone Freetown 82 395 370 11.4A 10: 0..... 84 ....... 1 ........I...... .... 53 Slovak Republic Bratislava 2,144 651 3,984 5.1 5.6 22.3 72 34. 96 ..... 100 100 Slovenia Ljubljana 275 316 11,729 6.1.. 1 299 9 10 Maribor 738 185 9,314 6.2 . 41 258 9 10 Sri Lanka Colombo . 2,1J90 436 3 4 ........ 187.7......74 35..60 998 Sudan Khartoum 249 826 ..29 63 43 12 55 Sweden Stockholm 309 1,545 30,840 4.5 46 6 .... 40:0.0..... 37 35 100 ... 100..... 100 Tanzania Arusha ..564 41 50 50 630 16 . 0 Dar es Salaam ...... 564 .. ....4.1 ......50 4:5 48 30 ........6. ... 25 .. ....60 Mwanza 94 . ....... 5.0 4.0 24 3 8 15 74 TogoLomC8 288 802 . .. 3.5 ......12.0 30..30..... 37 Tunisia Tunis. 1,684 4,032 6.0 5.2 ......12.0 ..... ..:.. 45 ....... 73 61 ...... 96 Uganda Kampala 202 840 . 2.3 4.0 45 23 9 20 87 United Arab Emirates Dubai 604 594 26,564 22.8 ... . 18 60 0 100 United Kingdom .. Hertfordshire 1,604* 1,000 28,270 10.9 6.0 34.8 .......7.. 27...... 100 100 100 .~~~~~GI sow . 618 7,329 1.8 4.5. 39 ....... 99 ............... 99 Bedfordshire . 539 32,080 10.9 3.0 34.6 10 . 93 98 98 Cardiff 137 306 2.9 .... 17.5 13 . 100 ......100 ......100 United States New York .. 16.332 39,256 14 8 6.3 . 51 37 99 100 ...... o Des Moines ..31,732 8.6 1.8 .. 16 ...99 100............. o Atlanta ..32,966 22.4 3.1 . . 24 98 . 10 Vietnam Hanoi 47 510 3 4 10.4 5.8 .. .............. !....... 40 ...... 45 ..... 100 Yeme Sans.. .1 .... ................ ....... ..183... .......... 17-.0 40.. .... : 15 12.... . ..... 51 60. ....G Yugoslavia, FR Belgrade 765 1,318 16.0 19.4 . 35 71 86 99 Novi Sad 290 232 3. 21.8 60 21 93 9 00 Nis 150 214 7.4 19.7 61 25 84 87 92 Zambia Lusaka 867 14:0 ......6.5 ... .6:9 65 20 ....... 36 ..... 60 Zimbabwe Harare. 754 5 0 98 8 .0 48 56 93 10 9 162 1999 World Development Indicators 3.11 Despite the importance of cities and urban agglomer- * Urban area refers to the city proper along with the ations as home to almost half the world's people, the suburban fringe and any built-up, thickly settled areas Household sewerage connections remain data on many aspects of urban life are sparse. scarce in many cities lying outside, but adjacent to, the city boundaries. Compiling comparable data has been difficult, and the * Urban population refers to the population of the available indicators have been scattered among inter- I r :1l- urban agglomeration, a contiguous inhabited territory national agencies with different mandates. Even without regard to administrative boundaries. * Average within cities it is difficult to assemble an integrated C household income is the average of the household data set. Urban areas are often spread across many 5,*, income in all five quintiles. Household income is the jurisdictions, with no single agency responsible for col- - total income of all household members from all sources, lecting and reporting data for the entire area. Adding including wages. pensions or benefits, business earn- to the difficulties of data collection are gaps and over- 4ings, rents, and the value of any business or subsis- lap in the data collection and reporting responsibili- tence products consumed (for example, foodstuffs). ties of different administrative units. Creating a N ' * Income differential is the ratio of average household comprehensive, comparable international data set is income in the highest quintile to that in the lowest quin- further complicated by differences in the definition of '5, .,-, " - "- ,, - ' t:ile. * House price to income ratio is the average house an urban area and by uneven data quality. price divided by the average household income. The United Nations Global Plan of Action calls for a Il '1.. 5'.' z;,' * Crowding is measured by floor area per person, the monitoring the changing role of the world's cities and Of Ihe237citiesco,&reabb theUNCHSUrbanindica medianusablelivingspace perperson in square meters. human settlements. The international agency with a lors Pogramnme 200providedinformationonsewiel * Work trips by public transportation are the percent- mandate to assemble information on urban areas is ageconnecOonsin 193n oneof ouoethesecie-. age of trips to work made by bus or minibus, tram. or lessthan 10 percent of hoi-ehold4 had a see rge corn the United Nations Center for Human Settlements necIion.Andirn1oofthesecitiesalmosfSopeicent train. Buses or minibuses refer to road vehicles other (UNCHS, or Habitat). Its Urban Indicators Programme of InehobsehIoldshad nOCOnneCLiOn. than cams taking passengers on a fare-paying basis. is intended to provide data for monitoring and evalu- Other means of transport commonly used in developing ating the performance of urban areas and for devel- countries, such as taxi, ferry, rickshaw, or animal, are oping government policies and strategies. These not included. * Travel time to work is the average time data are collected through questionnaires completed in minutes, for all modes, for a one-way trp to work. Train by city officials from more than a hundred countries. and bus times include average walking and waiting The table shows selected indicators for 150 cities times, and car times include parking and walking to the taken from the UNCHS data set covering 237 cities workplace. * Households with sewerage connection and 43 indicators. A few more indicators are included are the percentage of households with a connection to in the World Development Indicators CD-ROM. sewerage. * Households with regular waste collection The original UNCHS selection of 237 cities has a are the peroentage of households with regularwaste col- bias toward smaller cities rather than reflecting pop- lection including household collection and regular ulation weights or the economic importance of "dumpster" group collection, but not household trans- cities. Furthermore, it is based on demand for par- port of garbage to a local dump. * Households with ticipation in the Urban Indicators Programme. As a access to potable water are the percentage of house- result the database excludes a large number of holds having access to safe or potable dninking water major cities. The table reflects this bias, as well as within 200 meters of the dwelling. Potable water is water the criterion of data availability for the indicators that is free from contamination and safe to drink with- shown in the table. out further treatment. The data should be used with care. Because dif- ferent data collection methods and definitions may Data sources have been used, comparisons can be misleading. And because data are available only for 1993, no con- Data are from the Global Urban Indicators database clusions can be drawn about any improvement or ofthe UNCHS's Urban Indicators Programme. worsening of conditions. 1999 World Development Indicators s63 S ~3.12 Traffic and congestion Motor vehicles Passenger Two-wheelers Road traffic Traffic cars accidents people injured or killed per 1,000 per kilometer per 1,000 per 1,000 million vehicle per 1,000 people of road people people kilometers vehicles 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 Albania 31 .........6 20 2 3,676 5 Algeria 52 14 .......30 .......25 1 Angola 20 31 Argentina 155 154 20 25. ... ....... 127 I1 56,590 Armenia .. 2 I 0_ 2 Australia 502 601 . 12 401 485 24 16 2045 Austria 330 507 23 20 297 469 76 71 35,430 27 13 Azerbaijan 47 ........8 .. 36 0 2,207 8 Belarus 1121 110 54 . 8 Belgium 349 477 28 33 433 22 45,779 59,884 25 15 Benin 8 7 ' 7 . 44 6,575 74 Bolivia 19 48 3 7 29 .9 795 1,730 41 Bosnia and Herzegovina .. 48 .. 5 . 43 . . 25 Botswana 27 44 3 4 9 15 1 . 50 94 Brazil 85 79 7 6 75 . 3..3 4 Bulgaria . 238 . 5 92 28 . 63 65. 4 Burkina Faso . 6 . 5 4 .. 10 Burundi Cambodia . 6 2 5. 44 1,407 . 31 Cameroon 8 12 4 4 . 7 . 112 Canada 548 563 ... 18 417 441 19 10 205,515 . . 14 Central African Repulic 8 0 1 0 . 0 .. 0-1,250 Chad .. 4 ~~~~~~~~~~~~~~~~~.............. . .. ..........2 ....... - I..... Chile 61 110 8 20 45 71 4 2 7,540 . 38 33 China 2 8 2 7 .. 3 . 8 2,032 165,000 12 22 Hong Kong, China . 54 77 234 276 41 55 5 5 4,407 0,336 77 3 Colombia . 37 . 13 11 19 1 2,480 Congo, Dem. Rep.. 31 . 9 .. 17 .. . Congo,Rep. 20 .4 ..4 ... .. 21 Costa Rica 132 . 13 20 85 . 15 4,244 . 2 C6te dIlvoire 24 33 .. 9 .. 21... Croatia 158 . 28 .. . .. . 24 Cuba . 32 . 6 .. 16 . 1 .. 28 Czech Republic . 372 . 30344 . 172,9 . 1 Denmark 322 388 24 28 271 331 . 10 26,300 43,214 10 5 Dominican Republic 36 47 11 30 20 28 12. 18 Ecuador .. 44 . 1 8 40 . 2 .29,17 .. 17 Egypt, Arab Rep.. 30 28 8_ 23 . 7 6,222 .. 16 El Salvador .. 61 36 1 0. 54,731 .. 26 Eritrea .. 2 . I . 2~~~~~~~~~~~~~~~.. ... .. ............ .................... ......... Estonia . 350 . 31 . 293 4 .4 Ethiopia 2 2 1 3 1 1 0 0 2 .. 38 Finland 288 433 18 29 256 379 36 32 26,750 55,696 7 4 France 402 530 27 35 355 442 97 . 298,000 466,200 16 6 Gabon .. 36 .. 5 . 22... Gambia, The .. 15 . 1 8 1. Georgia .. 86 .. 23 . 9 . ,0 Germany 399 529 51 68 297 500 38 30 . 554,394 . 12 G h a n a. .. .. . .. . . . . . . . . . . . . . . . .... . . . . . . 8.. . . . . .. . . . . . . . .. . . . . .4 . . . . . .. . . . . ..... . . .. . . . . .. . . . . . 5. . . . . . . . . . .. . . . . .. . . . . . .. . . . .... . . . . .. . . . . . Greece 134 313 35 28 91 223 12 184 510 23 10 Guatemala 19 15 -. 10 . Guinea .. 5 .. I .. 2 ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~............. .. .:.......... Guinea-Bissau .. 11 . . 6. . . Haiti .. 7 13 4. . Honduras .. 35 .. 13. 164 1999 World Development lndicat,ors 3.12 Motor vehicles Passenger Two-wheelers Road traffic Traffic cars accidents people injured or killed per 1,000 per kilometer per 1.000 per 1,000 million vehicle per 1,000 people of road people people kilometers vehicles 1980 1.997 1980 1997 1980 1997 1980 1997 1980 1997 1.980 1997 Hungary 108 259 13 14 95 226 .. 14.. . 22 10 India 2 7 1 2 4 . 2 . 61 Indonesia 8 22 8 13 . 1.3 59 59 1 Iran, Islamic Rep ~ ~~.. 41 . 15 . 30 .. 43 . . .. 14 Iraq . 51 . 23 36 .. .. 5 Ireland 236 305 9 -12 216 272 8 6 14,917 28,390 11 Israel 123 265 114 100 107 213 .. 1 10,442 33,726 38 31 Italy 334 581 65 108 303 533 114 44 226,569 657,849 12 8 Jamaica.. : 50 7 .. 41 . .. Japan 323 547 34 60 203 373 102 1 389,052 737,771 16 14 Jordan 56 68 25 44 41 50 2 0 623 6,030 63 54 Kazakhstan .. 84 .. 11 62 .... 821910 Kenya8 13 3 6 7 10 1 1.. 11,894 74 Korea, Dem. Rep. Korea, Rep. 14 226 11 123 7 165 6 56 8,728 255,829 212 34 Kuwait 390 408 .. 16 .. 317 .. . 12,189. 7 Kyrgyz Republic .. 32 . 8 32. . . 2 LeoPOR .. 4 . I -. 3~~~~~~~~~~~~~~~~~~~~~~~.. .....49 Latoi PDR24 3.9 . 758. 1 Lebanon . 320 .. 205 .. 299 . 13 ........ Lesotho 10 19 3 8 3 6 0 ... 445 8 Libya .. 230 .. 48 159..0.8 Lithuania.. 265 .. 14. 238 . 5. 198 .. 12 Macedonia, FYR . 151 . 35 .. . . .. 4,247 .. 12 Madagascar.. 6 3 . 5 ..1 .. 11 Malawi 5 6 3 4 2 3 2 .... . 89 Malaysia. 182 . 3 7 52 154 101 200. 14 Mali . 4 .. 3 . 3. .3 22 Mauritania .. 12 4.. 8.... Mauritius 44 84 23 51 27 64 2 7 90 46. 39 Mexico 141 52 60 93 . 3 ! Moldova. 54 19 . 39 .. 25 9-10 .. 15 Mongolia. 28 . 14 . 10 1,736 23 M orocco ........ ..... ...48 . 21. 38 7......... 1 ........ 18 ..... .. ....... ......... 46 Mozambique - I .. 00 . Myanmar . 2 2. I Namibia 852 . 4 7 . 1Z25475 Nepal. Netherlands 343 412 52 322 372 61 5 770,825 144,894 12 3 New Zealand 492 555 17 22 420 451 43 13 16,545 . 11 9 Nicaragua ~~~~~~~:.. 38 8 16 . 5 . 150 29 Niger 6 6 5 5 4 240 63 Nigeria 4 12 3 7 3 7 4 123 Norway 342 493 17 24 302 399 36 49 .. 25,386 8 5 Oman 142 9 97 .. 2 . ..23 Pakistan 2 7 5 4 2 5 3 13 . 14,361 71 8 Panama 104 25 76 . 3 . ..34 Pap .u ......... .New... . Guiea. 26... 6..... ...7.... Paraguay.. 24 4 .. 14 Peru 122 . 40 . 59 . . Philippines 30 . 14 6 10 .4 13 193 .2 Poland 86 262 10 27 67 221 4 44,597 134,876 . 9 Portugal .3 26 ..928 78 283 85,957 31 18 Puerto Rico 282 74 232 .. . Romania 125 18 . 107 . 15 . . 33,9004 Russian Federation . 153 39 . 120 .. . . 10 1999 World Development Indicators Lo5 3.12 Motor vehicles Passenger Two-wheelers Road traffic Traffic cars accidents people injured or killed per 1,000 per kilometer per 1,000 per 1,000 million vehicle per 1,000 people of reed people people kilometers vehicles 1980 1997 1980 1997 1990 1997 1980 1997 .1980 1997 1980 1997 Rwandea2 4 2 2 1 2 1 Saudi Arabia 163 151 26 18 67 90 0. 120,698 13 Senegal 19 14 8 8 10 0 56 86 Sierra Leone 6 .....2 .......: ........4 2 .............. . 29 . ..... ..30 Singapore 168 173 721 122 55 42 14 Slovak Republic 241 74 211 15 651 10 Slovenia 415 5 .. 56..... 385 4 8,037 1 South Africa 133 142 18 16 85 100 7 7 52,939 25 26 Spain 239 472 120 53 202 389 33 34 70,489 411,453 .13 7 Sri Lanka 14 3 8 6 6 28 1,149 75 Sudan 12 28 10 Sweden 370 456 24 19 347 418 2 28 35,0.00 65,410 75 Switzerland 383 508 38 51 356 469 128 103 50,650 14 8 Syrian Arab Republic - ... 29 10 10. 24 Tajikistan . 110 Tanzania 3 5 1 2 2 1 1 104 Thailand 13 104 13 97 9 28 19 171 16,824 99,900 29 13 Tog .. 27 1 15 19 14 386 Trinidad and Tobago 113 . 18 9 Tunisia 38 .. 64. 10 25 20 30 2 4 .. ...... 45 Turkey 23 73 4 12 59 14 14,785 41,015 26 25 Turkmenistan . . . Uganda 1 4 1 .. 2 0 2 479 ::. .. 130 Ukraine 93 . 27 96 55 . 60,168 . 5 United Arab Emirates 103 52 82 United Kingdom 303 415 50 66 268 37-1 24 11 245,900 437,541 19 13 United States . 773 25 32 536 489 30 15 2,418,619 3,831,200 17 U ruguay ....... . ...174... .... .. 1 4 . .. ..... .. 63.... 158.......... .....15 1 .. ...11...... ........ 5.. Uzbekistan . .. ..!~ Venezuela 112 88 27 23 91 68 40 56,900 32 Vietnam 45 West Bank and Gaza7 ..... Yemen, Rep. 34 . 8 8 15 3 1,251 11,477 15 Yugoslavia, FR (Serb./Mont.) 118 188 23 40 . 173 459 1 Zambia' 26 4 17 17 Zimbabwe 32 . 19 29 . 32 54 M iddle income . .... . . ... ...2 2 55 37 ..... .. ....... .. Lower middle income 5 35 22 Upper middle incomne 90 132 70 119 Low & middle income 14 36 . 24 Europe & Central Asia 161 113 Latin America & Carib. 92 62 72 Middle East & N. Africa . 59 . . 40 . . . South Asia 2 6 . . 4 . Sub-Saharan Africa . 2-1 .. 14 High income 321 554 . 415 Europe EMU 345 508 296 454 166 1999 World Development Indicators 3.12 Traffic congestion in urban areas constrains eco- because data are incomplete or difficult to * Motor vehicles include cars, buses, and freight nomic productivity, damages people's health, and compare. vehicles but do not include two-wheelers. Population degrades the quality of their lives. The particulate air The data in the table are compiled by the figures refer to midyear population in the year for pollution-the dust and soot in exhaust-emitted by International Road Federation (IRF) through ques- which data are available. Roads refer to motorways, motor vehicles is proving to be far more damaging to tionnaires sent to national organizations. The IRF highways, main or national roads, or secondary or human health than was once believed. (See table uses a hierarchy of sources to gather as much infor- regional roads. A motorway is a road specially 3.13 for information on suspended particulates and mation as possible. The primary sources are national designed and built for motor traffic, separating the other air pollutants.) road associations. When such an association is lack- traffic flowing in opposite directions. * Passenger In recent years ownership of passenger cars has ing or does not respond, other agencies are con- cars refer to individual four-wheel transport. * Two- increased, and the expansion of economic activity tacted, including road directorates, ministries of wheelers refer to mopeds and motorcycles. * Road has led to the transport by road of more goods and transport or public works, and central statistical traffic is the number of vehicles multiplied by the aver- services over greater distances (see table 5.9). offices. As a result the compiled data are of uneven age distances they travel. * Traffic accidents refer These developments have increased demand for quality. The coverage of each indicator may differ to accident-related injuries reported to the authorities roads and vehicles, adding to urban congestion, air across countries because of differences in defini- and to deaths resulting from accidents that occur pollution, health hazards, traffic accidents, and tions. Comparability also is limited when time-series within 30 days of the accident. injuries. data are reported. Moreover, the data do not capture Congestion, the most visible cost of expanding the quality or age of vehicles or the condition or width Data source,s vehicle ownership, is reflected in the indicators in of roads. Thus comparisons over time and between the table. Other relevant indicators-such as aver- countries should be made with caution. . The data in the table are from age vehicle speed in major cities or the cost of traf- The population numbers used to compute motor the IRF's annual World Road fic congestion, which exact a heavy toll on vehicles per 1,000 are World Bank estimates (see Statistics. economic productivity-are not included here table 2.1). Bicycles outpace cars in world production ,, E T.,T . After surging In ine 1960s and 1970s. car production slowed as a result of oil price increases and en%itronmental awareness In lhe first half of the 1970s. Meanwhile, the demand for bicycles boomed as Incomes rose rapidly In China and as many ciTie, stai red to encourage their use. B) 1980 the world was producing more than twice as many bIc,cles (62 million) as passenger cars. 1999 World Development Indicators 167 3.13 Air pollution City City Total Sulfur Nitrogen population suspended dioxide dioxide particulates In many towns and cities exposure to air pollution is the main environmental threat to human health. micrograms per micrograms per micrograms per Winter smog-made up of soot, dust, and sulfur diox- thousands cubic meter cubic meter cubic meter ide-has long been associated with temporary ±995 1995, 1995, 1995, Ar~gentina C6rdoba City 1,294.......... 97 ..................9 .......... .......... ................... .......I......... ...to.high..levelsigofl soot o ando small s particlescl inin thee air Australia Sydney 3,590 54 28 .~~~ebun ,9 530 also contributes to a wide range of chronic respira- ............ . .. M elbourne........ 3.. .........094 .... .... .........-35........ . .~~eth1220 4519 tory diseases and exacerbates heart disease and Austria Vienna 2,060 47 144other conditions. Particulate pollution, either on its BelgiM-.......... Brussels 1,122 .........78~ ........ 20. .......48 ow r in combination with sulfur dioxide, leads to an Brazil Sao Paulo 16,533 86 4383enrosbre ofilhat,cung tlat .ugraSfa1188 195 39 122 500,000 premature deaths and 4-5 million new B ulgaria . . . .. . . . . .. . . . .. . . . . I . . . . .. . . . . . . . . .. . . . ..Sofia . . . . . . . . . .. . . . .. . . . . Canada Toronto 4,319 36 17 43 cases of chronic bronchitis each year (World Bank Vancouver 1,823 29 14 37 ....... Emissions of sulfur dioxide and nitrogen oxides Chile Santiago 4,891 29 81 la otedpsto fai anadohraii China Shanghai 13,584 246 53 73 Beijing 11,299 377 90 122 compounds over long distances-often more than Tianjin 9,415 306 82 50 1,000 kilometers from their source. Acid deposition Shenyan~g 5,116 374 99.... ...... 73 ....... changes the chemical balance of soils and can lead Cheng8du 4,323 *3~66t h7lahngo7rcemnrasadnurets4i Wuhan 4,247 211 40 43 clt re n lns h ik ewe oetdm Guangzh 4,056 295 57 136 Zibo 3,779 453 198 43 age and acid deposition are complex. Direct exposure Liupanshul 3,615 408 102 . to high levels of sulfur dioxide or acid deposition can Chongquing 3,525 320 340 70 cuedflainaddeak Harbin 3,303 359 23 30 Weeca stepiayfe o oe lns Qui ngdao 3,138 . 190 64 selmls nutilbies n oetchaig Dalian 3,132 185 61 100 Jinan 3,019 472 132 45 the result is usually high levels of urban air pollu- Changchun 2,523 381 21 64 tion-especially particulates and sometimes sulfur Taiyuan 2,5258215........d ioxid e-and, if the sulIfu r conte nt of th e coal i s h igh, .~Pniag 2040 276 75 Zhengzhou 1,999 474 63 95imotnprmrfuloisuebylaswthfec Kunming 194 253 19 33 Guiyang 1792 30424 53 tive dust control, the worst emissions of air pollu- Lanzhou 1,747 732 102 104 tants stem from the combustion of petroleum Anshan 1,648 305 115 88.. products. Nanchang 1,646 279 69 29Daaoaipoltoarbseoneotsfm Urumqi 1,643 515 60 70 ...................... ... ..I........................ ........I........I.........urban.. .monitoring.......sites.....Annualmontom eanste . (m easured mea ur ini Colombia Bogota 6,079 120 Croatia Zage b981 71 31 micrograms per cubic meter) are average concentra- Cuba Havana 2,241.1 5 tions observed at these sites. Coverage is not com- Czech Republic Prag.. ue 1,2255 2 23 prehensive because not all cities have monitoring Denmark Coehae 1326 61 75sses4o xmpe aaaerpotdfrjs Ecuador Guayaquil 1,831 2715 .... ..... ............... ..... ........ .......... ................... ...... cities.. in..Africa....but..forciim ore A rthanut87r ocities 7 inie China.na Quito 1,298 1531 Egpt AabRe. Cairo .. 9.690 . 69 . Pollutant concentrations are sensitive to local condi- Finland Helsinki I1,059 40 435 tions, and even in the same city different monitoring France Paris 9,523 14 14 57 sites may register different concentrations. Thus Germnany FranKfurt 3,606 36 11 ths4aasol5b osdrdolyagnrlmi Berlin 3,317 50 18 26 -............. .. ........ I............................ . .. ..... ........ . . ....cation....of...air....uality....in. eachnofcity,altyandea hcross-countryco ntr Munich 2,238 45 853 Ghana Accra 1,673 137 comparisons should be made with caution. World Greece Athens '3.093~ 178 34 64 Health Organization (WHO) annual mean guidelines Hungar Budapest 2.01 63 3 1 ... for air quality standards are 90 micrograms per cubic -I.Iceland .................... 10-................ 24. 5 42...... meter for total suspended particulates, 50 micro- India Bombay 15,138 240 33 39 .......... ...... ..... ....................... ......I........................ ..gram s ...per ..cubic....m eter....for....sulfurub c dioxide,ufur di ande a 505 Calcutta 11,923 375 49 34grm .... ..... .. ..... ................... .... .........I....................... ......m icrograms.......per..cubic....m eterirogforpernitrogenr or i dioxide.ide 168 1999 World Development Indicators 3.13 City City Total Sulfur Nitrogen _ _ _ _ _ _ _ _ _ _ _ population suspended dioxide dioxide particulates ~~~~~~~~~City population is the number of residents of the city as defined by national authorities and reported micrograms per micrograms per micrograms per to the United Nations. * Total suspended particu- thousands cubic meter cubic meter cubic meter lates refer to smoke, soot, dust, and liquid droplets 1995 1995, 1995, 1995, ........... . ....... ...... .............................. ..... ........... from..com bustion . ..that. are omintithehaair. iParticulatetiulat lev Delhi 9,948 415 24 41 Hyderabad 5,477 152 12 17 ing and the state of a country's technology and Bangalore 4,799 123... pollution controls. * Sulfur dioxide (SO2) is an air Ahmedabad 3,711 299 30 21 pluatpoue hnfsi ul otiigsl Pune 2,955 208fuarbundItcnrbtstacdaiadcn Kanpur 2,227 459 ~~15 14 ........ . .......... .. ...........p. ,2 .......59.... . .......... .. ........damage human health, particularly that of the young Lucknow 2,078 463 26 25 Nagpur 1,851 185 613 . and the elderly. * Nitrogen dioxide (NO2) is a POiSo- Indonesia Jakarta 8,621 271 nous, pungent gas formed when nitric oxide com- Iran, Islamic Rep. Tehran 6,836 248 209 bines with hydrocarbons and sunlight, producing a Ireland Dublin 911 20phtheiarecinThscodinscurn Italy . . Mian . ,517 31 248bohntrladnhrpgncaivie.N2s Rome 2,931 7 : Torino 1,294 151 emitted by bacteria, nitrogenous fertilizers, aerobic Japan Tokyo 26,959 49 18 68 decomposition of organic matter in oceans and soils, Osaka 10,609. 43 19 63 combustionoffuelsandbiomass,andmotorvehicles Yokohama 3,178 100 13 ....._ and industrial activities. Kenya . airobi 1,810 69 Korea Rep. Seoul 11,609 84 446 Taegu 2,432 72 81 62 Malaysia ....... .Kuala Lumpur 1,238 85 ... 24 ... . .... i.... The data in the table are from Mexico Mexico City 16,562 279 74 130 teWOsHatyCte i Netherlands Amsterdam 1,108 40 10 58Mage nt nrran New Zealand Auckland 945 26 320 Norway Oslo 477 15 843t System and the World Philippines Manila 9,286 200 33 . ., EI Resources Institute, which Poland Warsaw 2,219 16 32 1' relies on various national Lodz 1,063 21 43ore a elas mn Portugal Lisbon 1,863 61 852 ohr,teUie ain Romania Bucharest 2,100 82 10 71 Russian Federation Moscow 9,269 . 100 109 . Environment Programme (UNEP) and WHO's Urban Omsk 1,199 100 930 Air Pollution in Megacities of the World, the Singapore Singapore 2,848. 20 30 Organisation for Economic Co-operation and Slovak Republic Bratislava 651 62 21 27Deeomn'(EC)EC EvinetaDta South Africa saptown 2,671 21 72 Durban 1,149 31 Protection Agency's National Air Quality and Spain . Madrid 4,072 . 42 11 25 Emissions Trends Report 1995 and AIRS Executive Sweden Stockholm 1,545 9 529Yerok19,adteKeaStitalerbo Switzerland Zurich 897 31 11 1995 Thailand Bngkok 6,547 223 1-1 23 TurkeyIstabu 7,911 .. 120 . Ukraine Kiev 2,809 100 14 51 United Kingdom London 7,640 25 7 Manchester 2,434 26 49 Birmingham 2.271 945 United States New York 16,332 2679 Chicago 6r844 14 57 Venezuela Caracas 3,007 53 33 57 a. Oata are most recent available for 1990-95. Most are for 1995. 1999 World Developrment Indicators 169 3.14 Government commitment Environmental Country Biodiversity Participation in treaties' strategy environmental assessmenit, or action profile stratogy, or Staftus of national environmeontal paacinplan action plans LawCoped Climate Ozone CFC of the Biological Colee change layer control Sean diversity Ama.rira Gfwega k%frg6ri Albania 1993 1995. ........1994 .i'3 r4r,'r Algeria 1994 1993 1993 1996 1995 15nr.glaavh G'&n3oa MlOzSME.ve. Argentina 1992 . 1994 1990 1990 1996 1995 Br.'a'Bne aa Armenia194193 B'e Australia 1992 1994 1994 1988 1989 1995 1993 &,;V'3 tiaa Fao,man Auatria 1994 1988 199 195 194d Wjr1 Azerbaijan7.....1995 1996Swgr, Hungar) r.9~ Bangladesh 1991 1989 1990 1994 1990 1990194 ~ j., P:,,r .e.ar.s ... ............ ...... -.... .1 8 19 91993 f nxj ~ n c.o. Belgium ......... ... . .......... .. ... 1996... .1989... 1989 . .. . 1997 ~~h 3l Benin 1993 1994 1993 1993 1994 ~ ~ ~ rr~~~ Bolivia 1994 1986 1988 1995 1995 1995 1995 1995 'rr. ,- Boania and Herzegovina 1992 1994 in-srn~ m~~ Botswana 1990 1986 1991 19.94 1992 1992~ 1994 1~996 ~ :~n: ~ " Brazil 1988 1994 1990 1990 1994 1994 Bulgaria 1994.... 1995 1991. 1991 1996 1996 ~ 3 ~ Ltun Burkina Faso 1993 1994 1994 1989 1989 ..1993 1k n Burund....i .. 1994..... 1981... 1989.. ........ ... ....... El i ,nma ozcx uigaro Ca bo i 1997......... .... ........ ...... 1996 .... ... . .1995 ~" , - Cameroon 1989 1989 1995 1989 1989 1994 1995 a. T. 0 Canada 1990 1994 1994 1988 1989 1993 ~ ~ II.. Central African Republic 1995 1993 . 19 Chad 1990 1982 1994 1989 1994 . 1994 Being prepared Chile 1987 1993 1995 190194T-s is , tgv China 1994 1994 1994 1989 1991 1996 1993 15ja.c Hong Kong, China £7,m ,ran RCrx, M.iIaia,a VecL03M Colombia .... .... ..... 1990 1988 ... 1995 .1990.. 1994 .....: .. 1995 iOIl Congo, Darn. Rep . 1986~~I..... . 1990. 195 99.99-195......Gn~ATJe Congo, Dm Rep. 19 90 199 7 1995 1995 1996 Costa, Rica199 197 1992 1994 1991 19915 94 19 C6te d .or 1994I... ..... 1991.. ...1995..- 1993.. 1993 1994. .. :.19956.,.. ~ e :,.n ,r..'r,.,,I.r3 Czech Repbica 19 187 1192 1994 1993 1993 1996 1994 Ecua dloire19934 98 1995 1994 1990 993 0 99 1993 Erotnia 1994 1997 1997 1994 19 ..thi..p.. 1994 .......... ........... ........ ... ... 1991 .1994 1995. 1995 1994 Franchepbi 1990 1994 1988 19893 1996 1994 Germany 19941994 1988 1989 19 1994 Ghmniana 1992li 19854 98 1995 19893 18 1994399 Greece .. . 1994 1989 1989 1995~~~~~~~~~~~~~~~~. ... 1994 Gcuatemal 1994 1984 1988 1996 1988 1990 .. 1995 EguineAabRp 1994 1983 1988 1994 1992 19892 1994 19 EliSalviador 199 4 198 1991 199.9 1 994 19999 Haitoia. 95. 1996 . .4 1 996199 1996 Ehionduas19934 98 ..9 1996 19945 199 199 1995 Fr0an99 World19Development9Indicators 3.14 Environmental Country Biodiversity Participation in treaties' strategy environmental assessment, or action profile strategy, or Climate change and biodiversity plan action plan at the fore Law ',:n1F Climate Ozone CFC of the Biological ,,I 1f change l.. ayer conrtrol Sea' diversity Hungary 1995 . 1994 1988 1989 . 1994r I.......i..... 1993 ...... 1989 ... 1994... 1994I..... 1991 1992. 1995.1994 Indoeia 1992 19894 1993 1994 1992 1992 1994 1994 Iran, Islamic Rep . 1996 1991 1991I . 1996 I1raq . . . 1994 . i, Ireland 1994 1988 1989 .. 1996 ...r.el 1994. .. .... .. . ....1992 1...992 .. .... 1995 Italy 1994 1988 1989 1995 1994 Jamaica 1994 1987 1995 .. 13 194 95 ...... 1994....1988...... 19893 1996 1993 Jordan 1991 1979 1994 1989 1989 1995 1994 ~ ~ .~ nr ,,.rn, Kazakhstan. . ... ., .... 1995 . ........... 1994 Kenya 1994 1989 ... .1992 1994 .1989... 1989 194 1994 Between February 199 5and Septem ber 199S the Korea, Dem. Rep. ..... .........................1995. 1995 ...1995 ... .. 1995 Global Enii~ronmentI FacIifit p,ovided aboun 52 bl-1 Korea, Rep. 1994 1992 192 1996 1995 lion t0 cevieloping and transition economies lo, Kuwait 195 19 99 94oec t san dacti v tleb ta rget ing globa Ibe ne Fn1Ia I Kyrgyz. Republic ..... .. . ........-... . ...... . ... 1996 one or more focus areas: blodhversity. climate I-- ........... ..... ..... . ............. ....... ........:......... .. . chana ge. inl ernaaloo nal watee r anandoozonedde lieetoo. Lao PDR 1995 . 1995 1996 Latvia .. .. .. 1995 1995 1995 ~~~~~~~~~~~~~~~~~~~~~~~~~.... 1996... Lebanon . .. 1995 1993 1993 .1995 1995 Lesotho 1989 1982 . . 1995 1994 1994 . .1995 Libya . .. . 1990 19 Lithuania . 1995 1995 1995 . 1996 Macedonia, FYR. 199 194 1994 Madagascar 1988 . 1991 1996 1997 1997 . 1996 Malawi 1994 1982 . 1994 1991 1991 .. 1994 Malaysia 1991 1979 1988 1994 1989 1989 1997 1994 Mali . 1991 1989 1995 1995 1995 1994 1995 Mauritani'a 1988 1984 . 1994 1994 1994 1996 1996 Mauritius 1990... 1994 1992 1992 1994 1993 Mexico. 1988 1994 1988 1989 1994 1993 Moldova . 1995 1997 1997 . 1996 Mongolia 1995 . 1994 1996 1996 . 1993 Morocco .... .......... 1980.. 1988.... ... 1996 .1996 1996. .. 1995 Mozambique 1994 .. 1995 1994 1994 . 1995 Myanmar 1982 1989 1995 1994 1994 1996 1995 Namibia 1992... 1995 1993 1993 1.994 Nepal 1993 1983 - 1994 1994 1994 . 1994 Netherlands 1994... 1994 188 198 196 94 New Zealand 1994... 1994 1988 1989 1996 1993 Nicaragua 1994 1981 . 1996 1993 1993 . 1996 Niger 1985 1991 1995 . 1993 . 1995 Nigeria 1990 1992 1994 1989 1989 1994 1994 Norway .. ~~~~.... 1994 .. 1994 ..1988.. .1989 ..1996 1993 Oman ..... 191 . 1995 .... . .... 1L994 1995 Pakistan 1994 1994 1991 1994 1993 1993 .. 1994 Panama 1990 1980 . 1995 1989 . 1996 1995 Papua New Guinea 1992 1994 1993 1994 1993 1993 . 1993 Paraguay. 1985 . 1994 1993 1993 1994 1994 Peru . 1988 1988 194 18 9 93 . 19 Philippines 1989 1992 1989 1994. 1991 1991 1994 1994 Poland 1993 . 1991 1994 1990 1990 . 99 Portugal 1995... 1994 1989 18 . 19 Puerto Rico .. .. . R .man.a ... .. ... ....... ... .. . . .. 1994-- 1993 199 1997 1994 Russian Federation 1.994 1995 198 98 9 .. 19 1999 World Development Indicanors 171 3.14 Environmnental country Biodiversity Participation in treaties' strategy environmental assessment, or action profile strategy, or plan action plan Law Climate Ozone CEO of the Biological chankge layer control Sea' diversity Rwanda 1991 1987 199 Saudi Arabia .. 1995 1993 1993 Senegal . 1984 1990 1991 1995 1993 1993 1994 1995 Sierra Leone 1994 1995 1995 1995 Singapore 1993 1988 1995 .. 1989 1989 1994 1996 Slovak Republic ...............................M. 1994 1993... 1993... 1996 1994 Slovenia 1996 .. 1992 19 99 South Africa 1993 .. 199 1990 1994 1996 Spain . . 1994 1988 1989 .. 1994 Sri Lanka 1994 1983 1991 1994 1990 1990 1994 1994 Sudan . 1989 .. 1994 . 19 1994 1996 Sweden ........... ..... .. ......I..... .... . ..... 1994 .1988 ....1989 .1996 1994 Switzerland . . 1994 1988 1989 .. 1995 Syr'ian Arab Republic . 1981 1996 1990 1990 .. 1996 Tajikistan Tanzania 1994 1989 1988 .. 1993 11993 1994 1996 Thailand . 1992 .. 1995 1989 1989 Togo 1991 . 1995 1991 1991 1994 1996 Trinidad and Tobago .1994 1989 1989 1994 1996 Tunisia 1994 1980 1988 1994 1989 1989 1994 1993 Turkey . 1982 ..1991 1991 Turkmenistan . . 1995 1994 199 .. 16 Uganda 1994 1982 1988 1994 1988 1989 1994 1993 Ukraine . ... 19688 189 1995 United Arab Emirates .. 1996 1990 199 United.Kingdom 1995 . ............ 1994 1988 1989 .. 1994 United States 1995 .. 1995 1994 1988 1989 Urugua 1994 1989 1991 194 94 Uzbekistan .1994 19 9319 Venezuela .1995 1988 1989199 Vietnam . 1993 1995 1994 1994 1994 1995 West Bank and Gaza . .. . ..::. Yemen, Rep.. 1990 1992 1996 1996 1996 1994 Yugoslavia, FR (Sr./Mont.) . Zambia 1994 1988 .. 1994 1990 1990 1994 1993 Zimbabwe 1987 .1982 .. 1994 193 193 1994 1995 a. The year the treaty entered into force in that country. b. Convention became effective 16 November 1994. 172 1999 World Development Indicators 3.14 National environmental strategies and participation To address global issues, many governments have * Environmental strategies and action plans pro- in international treaties on environmental issues pro- also signed international treaties and agreements, vide a comprehensive, cross-sectoral analysis of con- vide some evidence of government commitment to launched in the wake of the 1972 United Nations servation and resource management issues to help sound environmental management. But the signing Conference on Human Environment in Stockholm and integrate environmental concerns with the develop- of these treaties does not always imply ratification. the 1992 United Nations Conference on Environment ment process. They include national conservation Nor does it guarantee that governments will comply and Development (the Earth Summit) in Rio de strategies, national environmental action plans, with treaty obligations. Janeiro: national environmental management strategies, and In many countries efforts to halt environmental * The Convention on Climate Change aims to stabi- national sustainable development strategies. The degradation have failed, primarily because govern- lize atmospheric concentrations of greenhouse years shown refer to the year in which a strategy or ments have neglected to make this issue a priority, a gases atlevelsthatwill prevent human activitiesfrom action plan was adopted. * Country environmental reflection of competing claims on scarce resources. interfering dangerously with the global climate. profiles identify how national economic and other To address this problem, many countries are prepar- * The Vienna Convention for the Protection of the activities can stay within the constraints imposed by ing national environmental strategies-some focus- Ozone Layer aims to protect human health and the the need to conserve natural resources. The years ing narrowly on environmental issues, others environment by promoting research on the effects of shown refer to the year in which a profile was com- integrating environmental, economic, and social con- changes in the ozone layer and on alternative sub- pleted. * Biodiversity assessments, strategies, cerns. Among such initiatives are conservation strate- stances (such as substitutes for chlorofluorocar- and action plans include biodiversity profiles (see gies and environmental action plans. Some countries bons) and technologies, monitoring the ozone layer, About the data). * Participation in treaties covers have also prepared country environmental profiles and taking measures to control the activities that pro- five international treaties (see About the data). and biological diversity strategies and profiles. duce adverse effects. Climate change refers to the Convention on Climate National conservation strategies-promoted by the * The Montreal Protocol for CFC Control requires Change (signed in New York in 1992). Ozone layer World Conservation Union (IUCN)-provide a compre- that countries help protect the earth from excessive refers to the Vienna Convention for the Protection of hensive, cross-sectoral analysis of conservation and ultraviolet radiation by cutting chlorofluorocarbon the Ozone Layer (signed in 1985). CFC control refers resource management issues to help integrate envi- consumption by 20 percent over their 1986 level by to the Montreal Protocol forCFC Control (formally, the ronmental concerns with the development process. 1994 and by 50 percent over their 1986 level by Protocol on Substances That Deplete the Ozone Such strategies discuss current and future needs, 1999, with allowances for increases in consumption Layer, signed in 1987). Law of the Sea refers to the institutional capabilities, prevailing technical condi- by developing countries. United Nations Convention on the Law of the Sea tions, and status of natural resources in a country. * The United Nations Convention on the Law of the (signed in Montego Bay, Jamaica, in 1982). National environmental action plans (NEAPs), sup- Sea, which became effective in November 1994, Biological Diversity refers to the Convention on ported by the World Bank and other development establishes a comprehensive legal regime for seas Biological Diversity (signed atthe Earth Summit in Rio agencies, describe a country's main environmental and oceans, establishes rules for environmental de Janeiro in 1992). The years shown referto the year concerns, identify the principal causes of environ- standards and enforcement provisions, and develops in which a treaty entered into force in a country. mental problems, and formulate policies and actions international rules and national legislation to prevent to deal with them (table 3.14a). The NEAP is a con- and control marine pollution. Data sources tinuing process in which governments develop com- * The Convention on Biological Diversity promotes prehensive environmental policies, recommend conservation of biodiversity among nations through Data are from the World specific actions, and outline the investment strate- scientific and technological cooperation, access to Resources Institute, UNEP, gies, legislation, and institutional arrangements financial and genetic resources, and transfer of eco- and UNDP's World Re- required to implement them. logically sound technologies. sources 1994-95; the World Country environmental profiles identify how To help developing countries comply with their Resources Institute, Inter- national economic and otheractivities can staywithin obligations under these agreements, the Global national Institute for Environ- the constraints imposed by the need to conserve nat- Environment Facility (GEF) was created to focus on ment and Development, and ural resources. Some profiles consider issues of global improvement in biodiversity, climate change, I UCN's 1996 World Directory equity, justice, and fairness. Biodiversity profiles- international waters, and ozone layer depletion. The of Country Environmental Studies; the World Bank's prepared by the World Conservation Monitoring UNEP, United Nations Development Programme 1998 Catalog: Operational Documents as ofJuly 31, Centre and the IUCN-provide basic background on (UNDP), and World Bank manage the GEF according 1998; and the World Bank Environment Depart- species diversity, protected areas, major ecosystems to the policies of its governing body of country rep- ment's National Environmental Strategies: Learning and habitat types, and legislative and administrative resentatives. The World Bank is responsible for the from Experience (1996d). support. In an effort to establish a scientific base- GEF Trust Fund and is chair of the GEF. line for measuring progress in biodiversity conserva- tion, the United Nations Environment Programme (UNEP) coordinates global biodiversity assessments. 1999 World Development Indicators 1 73 3.15 Toward a measure of genuine savings Gross Consumpytion Net Education Energy Mineral Net Carbon dioxide Genuine domestic of fixed domestic expenditure depletion depletion forest damage domestic savings capital savings depletion savings % of % Of % Of % of % of % Of % Of % Of % of GDP GDP GDP GDP GOP GOP GOP GOP GOP 1997 1997 1997 1997 1997 1997 1997 1997 1997 Albania ........-132 12:4 -25.6... 2.8 .... ....0.0 01 0.0 0.5 ...... .-23 .5 Algeria 34.5 9.3 25.26.3 24 0.1 0:0 1.1 27.9 Angola 27.3 6.0 21 .22.6 29:7 ..... ..0.0 ..... ....00 04 27 Argntina 18.4 10.5 7:9 2.4 050.00.0 0.2 9.6 Armenia -28.8 0.0 00 0 00 0.0 1.3 Australia 20.7 14.6 6.1 4.7 12 1.5 0.0 0.4 7.6 Austria 23.5 12.9 10:5 4.9 0.1 0.0 0.0 0.1 15.2 Azerbaijan 9.5 14.0 -4 5 0.0 21.8 0'0 0.0 5.1 -31.4 Banglads 14.7 7.2 75.5 2-.1 0.2 .... 0.0 0.0 0.3 9.1 Belarus 21.6 17.2 4.4 4.7 0.0 0.0 0.0 1.77.4 Belgium 22.3 10.1 12.2 4.9 0.0 0.0 0.0 0.2 16.9 Benin 10.8 5.4 5.4 0.0 0.0 0.00.0 0.2 5.2 Bolivia 10.1 8.1 2.0 2.6 0.9 ......... 1.1 0 00:7 ..... 18 Bosnia and Herzegovina......00 0.0 0 0 Botswana 44.7 13.3 31:4.4... 6.9 0.0 0.8 0.0 03. 37.2 Brazil 18.6 7.5 .......-11.1 ... ..... 4.2 0.6 0.7 ..0.0 . ..... 0 2.2 ...... 13.9 Bugaia17.4 10.17. 4.0 0:.5 1.30.276.7 Burundi 2.6 4.4 -1.8 3.0 0:0 0.0 8.5 01-. Cambodia 4.2 5.0 -0.8 0.0 0 0 0 0.0 0 0.1-0. Cameroon 20.6 7.5 13.132.3 7.4 .... 00... 0... .0: ........ 0.3 7 . .. . 7. Canada 21.5 125 9.06.1 1.5 04 0.00.4 12.8 Central African Republic 6.7 5.2 15 5......... 3.8 0.0 0'0 . .... 0.0 0.1 5.1 Chad 1.2 4.6 -3.400 0.0 a 0.0 0.0 0.0 -. Chile 24.5 6.8 17.7 3.2 0.1 6.4 0.0... ~ 0.4 14.1 China 42.7 6.2 36.5 1.9 0.0 0.5 0.6 2.4 34.9 Hong Kong, China . ...... .30.6 .2.0 00 0 0.0 0.1 ........ Colombia 15.8 6.5 9.4 2.8 4.4 0.1 0~ 0... 0... 4A 7.3 CongoRep. 34.8 9.2 25.6 4.3 23.9 0.00.0 045.6 Costa Rica 25.0 2.5 22.5 4.60.0 00 0.0 0326.8 C6te dIlvoire 23.1 7.0 1670.0 ... 5.7 1.5 0'0 .... 0:0 .........07....... 19.6 Crata 3 .2 -5.9 0.0 0 9 0005-7.3 Cuba ... 0.0 0.0 0.0. Czc Republic 28.4 17.2 11.2 5.3 0.4 0.0 0.0 13 ... 14.8 Denmark .. 15.3. 7.7 0.40.0002 Dominican Republic .......22.1 ...... 5.9 16.1 .1.2 0.0 0.5 00 0 .:8 16.0 Ecuador .... 21.2 6.9 142.2 ... 2.7 12.0 .. 0'0 ... .. 0.0 0.7 4.2 Egy Arab Rep 13.0 7.9 5.1 4.8 3:2 0.1 0.0 0.7 5.9 El Salvador 4.5 6.1 -1.6 2.2 0.0 0.0 2.0 0.3 -1.6 Eritrea -17.4 4.1 -21.5 0.0 00 0.0 .... ..... ........ Estonia .....1 .18.4 10-5 7:9 4.3 1:7 ..... 00 0... .0 02.3 8.2 Ethiopia 8.7 2:9000.0003 Finland ....... . .. . 24.6 16.7 ... 7.9 .... 72.2 00.. . 0.0 0.0 0:2 14.8 France 19.7 12:9 6.85.0 00 0.0 0.0 0.1 .. .. 11.6 Gabon 48.3 15.2 33.1 3.1 15.8 0.0 0.0... ...0.4..... 20.0 Gambia, The 3.8 12.3 -8.6 ... . 3.3.. 0.0 0.0 0.0 0.3 -5.6 Georgia -3.7 .. 0.0 00 0 00 0.00.9... ... ... Germany 22.4 13.2 9:2 4.4 01. OI..... 0.0 ..... 0:00.2 13.4 Ghana 9.8 4.3 55 2.4 00 2.5 0.0 0.4 5.0 Greece . 8.5000 0.0 Guatemala 8.1J 6.0 .2.1 2.1 0:6 0.0 20.0 02.2..I... 1.4 Guinea 18.7 6.1 12.6 2.3 0 8 . 0.2-. Haiti -4.5 1.5 -6.0 2.3 0.0 0.0 7.4 0.1 -11.3 Honduras .......... 21.8 6.0 .. 15.8 3.7 0.0 0.2 0.0 0.4 18.8 174 1999 World Development Indicators 3.15 Gross Consumprtion Net Education Energy Mineral Net Carbon dioxide Genuine domestic of fixed domestic expenditure depletion depletion forest damage domestic savinkgs capital savings depletion savings %Of % Of % Of % Of % Of % Of % Of % Of % Of GDP GDP GDP GDP GDP GDP GDP GDP GDP 1997 1997 1997 1.997 1-997 1.997 1997 1997 1997 Hungary 26.9 8.0 18.9 5.2 0. 00 0:7 ... 23.0 India 20.0 10.0 10.0 4.3 2 60.5 2 3 1.6 7.4 Indonesia 30.6 5.0 25.6 0.9 3.8 0.8 0 70.9 20.5 Iran, Islamic Rep. 15.3 .00 0.0 070 Iraq 90 .0 0.0 00( Ireland 33.1 9.2 23.9 5.1 0.0 0.1 0.0 0.3 28.7 Israel ... ................. 8.7 13.8 ...... -571 6.7 070 0.1 0.0 0.3 1.3 Italy 22.3 12.4 9.9 4.2 0.1I0.0 0 00.2 13.9 Jamaica....1.....I 21.6 ....... 6.4 ....... 15.2 4.4 0~0 12.5 0:0 1.1 60. Japan 30.5 15.8 14.6 . ....5.8 0.0 0.0 0- 0 0 1 20.3 Jordan -.........15..59.7 -4.2 3.4 0.0 1.2 0.0 1.1 -3..1 Kazakhstan 13.5 7.4 6.1 0.0 18.5 0.00.55 -17.9 Kenya 11.4 6.7 4.7 5.9 0.0 0.0 8.0 0.4 2.1 Korea, Demn. Rep.. 0.0 Korea, Rep. 34.2 10.0 24.3 3.0 0.0 0.0 0.0 05.5 26.7 Kuwait 25.2 8.6 16.6 4.3 445.5 00 0 0. .......... -23.6 Kyrgyz Republic 13.8 10.2 3.6 4.3 0.6 0.0 0.0 2.3 5.0 Lao.PDR 11.4 5.5 ... 5.8 1.8M...00 ... 1.4 00 O0.1 6.2 Latv a ........ 9.6 11.7 -2.1 6.5 . ...00 ... ..... 0.0 0.0 . .172..... ... 3.1 Lesotho -9:8 .... 83 .... ...-18:1 ... 4.8 00 ..... O0 .0 .. 0.0.... 0-13.3 Libya.... 0:0 0.0 0.0 Lithuania 16.0 7.1 8.9 44 0.00.0 0.0 1.012.3 Macedonia, FYR 3.5 5.7000. 0.0 Madagascar 3.6 4.9 -1.3 2.3 00 0 0 00.0 0.2 .........0.8 Malawi 2.1 6.4 -4.3 3.2 0.0 0.0 5.4 0.2 -6.7 Malaysia 44.4 ....... .9.3 35.1 4.8 ......... 4.1 0.1 2..1 ... 0......O7 .... ... 32.9 Mali 13.6 5.8 7.8 2.8 0.. .. . 0... 00...........0 0 .001 10.5 Mauritania 8.5 8.6 -0.1 4.9 0.0 14.6 0.0I1.7 -11.5 Mauritius 241 1 .. 7.7 16.4 3.1 00.0 .. .... 0.0 0.0 .... ..02.2 . -19.3 Mexico 26:4 .......10.-4 . ..... .16.0O .. . 3.7. 4.8 .........02.2 0.0 0.5 14.2 Moldova 0.3 ..5.0 .. 00.0 0.0) 0.0 2.8 Mongolia 17.5 .........7.6 ..... ....99.9 .. 5.9 0:0 9.6 0.0 .6.2 0.1 Morocco 16.87 7 9.1 4.7 0.0 .........04 4. 0.0 0.5 13.0 Mozambique 13.6 3:6 .. ....10.0 . .. 3.9 0.0 .... 00 0. ... .. 3.7 ..... ..0.2 9.9 Myanmar 2.8 ..... 0.0 0.00.0 Namibia 14.2 13.8 0:4.4..... .. 1.7 0.0 0.6 ... . . 0 0 1.5 Nepal 10.0 3.0 7:0.0 ..... 3.4 0.0 0.0 ... .. 10.3 ....... .0 2. 0.0 Netherlands 26.3 11.7 14.6 ... .....6.0 0.1 0.0 0.0 0.2 20.3 New Zealand 22.5 9.4 13.1 5.0 0.3 0.1 070 0.2 17.4 Nicaragua 2.8 6.3 -3:5 3.9 0.0 0.1 0.0 0.9 .....-0-.6 Niger 3.3 4.5 -12 1.9 0.1 0.0 0.0 0.4 0.2 Nigeria 21.9 2.4 19.5 0.8 30.7 0.0 0.0 1.5 -12.0 Norway ......... 16.4 . 6.7 5.9 0.0 0.0 0.2 Oman . .. 0 0 0.0 0.0 Pakistan 10.4 6.4 4.0 1.91 1I0.0 1.5 0.8 2.5 Panama 32.0 7.2 24.8 5.0 0.0 0.0 0.0 0.4 29.5 Papua New Guinea 33.2 1. 22.2 5.8 6.7 8.2 0.0 0.3 12.8 Paraguay 20:3 ..... 78 8.... .. 12.5 1.5 0.0 0.0 0.0 0.2 13.8 Peru 20.8 4.3 16.6 3.1 0.6 0.8 .... ...0.0 03 18.0 Philippines 14.5 9.0 5.5 3.1 0.0 0.2 1 3 04 6.7 Poland 18.1 8.9 9.3 5.7 0.6 0.3 0.0 1712.5 Portugal 45. 5.0 0 00.1003 Puerto Rico . 6.6 .0 0.0 0.0 Romania 14.5 7.6 ... ......6:9 ... 35 .........33... 0.1 0.0 2.1 4.9 Russian Federation 24.7 19.3 5 3 41 9 3 0.0 0.0 1.8 - .... 1.6 1999 World Development Indicators 175 3.15 Gross Consumption Net Education Energy Mineral Net Carbon dioxide Genuine domestic of fixed domestic expenditure depletion depletion forest damage domestic savings capital savings depletion savings % Of % of %of %Of % of %of %Of %Of %of GDP GDP GDP GDP GOP GOP GOP GOP GOP 1997 1997 1997 1.997 1997 1997 1997 1997 1997 Rwanda-.5 5.6 -13:1 ...... 3.2 0.0 ...... 0.1 0.0 0.2 -10.2 Saudi Arabia 34.6 10.0 24.6 5.8 43:6 ..........00 00 10.0 -14..2 Senegal 13.2 5.3 7!9.9 ........ 4.1 0:0 .........0.4 0....... .000.4 11 ..1 Sierra Leone -8.0 57.7 -13..8 2.5 0.0 36 0.0 02.2.... . -15.1 Singapore 51.2 13.2 38.1 2 20.0 0.0 0 -005 39.8 Slovak< Republic 28.4 15.4 12.9 5.0 070 0.0 001.3 16.6 Slovenia 23.1 16.9 6.2 7.1 0.0......... 0'0 . 0 00.4 12.9 South Africa 17.0 13.8 3 2 6.6 2.1 1.9 0.1 1.4 4.4 SpainI.......... 21.4 11.4 . 10.0 4.8 0..0 0.1 0.0 ..........02.2 ... ... 14.5 Srit Lanka 17.3 5.0 12.3 2..5. 0.0 0.0 0.0- 0........ 2 14.6 Sudan 5.5 ..2.6 0.0 .0.0..0.0..0.2 Sweden 21.3 13.3 8:0 6 6 0.0 0.1 0.0 0.1 14.4 Switzerland . 10.5 5.2 0.0 0.0 0.0 -0.1 Syrian Arab Republic 19.0 3.5 1575 .... .....2.8 ...... 22.5 0.1 0.0I1.6 -5 9 Tajikistan........ .... .. . .... 5.3 . . .... ..... ........ 0.0 0.0 0.0 Tanzania 3.4 2.8 0.6 2.9 0.0 0.0 0.0 0.2 3.2 Thailand 35.7 109 ........ 24:8 2.9 0.2 00 0.0 0:6 ....... 26.9 Togo 9.8 5.1 47 5.3 0.0 2.4 0.0 0.3 7.4 Trinidad and Tobago 29.1 11.2 17.9 4.1 10.8 0.0 0 018 9.4 Tunisia 24.2 8.7 15.4 6.0 2.4 0.7 0:4 05 17.3 Turkey. 19:3 .........6:5 12.8 ..........30 ...... 03. 01 0.0 0.5 14.9 Turkm enistan 7:0 ..... ... . ............ ........ 0 0 0.0M . .......00 :................ Uganda 7.5 5.0 2.5 2.6 0.0 0.0 3.4 0 1 ... 1.6 Ukraine 16.3 18.4 ~~~~ ~ ~~~~~~~~~~~~~~-2.1 4630.0.03.0 -3.4 United Arab Em irates 1 . ' . .. ... 14....... 6 .......: ...... . . ...0. . 0.. 000. ...... 00.... ... ............ .............. United Kingdom 15.1 10.4 4.8 4.50.8 0.0 0.0 0.3 8.2 United States 16.0 10.7 5.3 5.8 0.7 0.0 0.0 0.4 9.9 Uruguay ........ .12.5 7.4 ... .. 5.1 2.6 0.0 0.0 0.4 0.1 7.1 Uzbekistan 18.6 4.4 14.2 7.7 8.2 0.0 0.0 2.4 11.4 Venezuela 26.9 7.1 1........J9.7 .........4.1 22.5 0.7 0.0 1.1 -0.4 Vietnam 21.1 ...5.0 ..... 16 2 1.4 4.0 0.1 3.-4 ..... 0.8 9.2 West Bank and Gaza .. 0.0 0.0 0.0 Yemen Rep 12.8 7.7 5.1 3.9 34.7 0.0 0.0 . -25.7 Yugoslavia, FR (Serb./Mont.) . . 0:0 00 0:0 Zambia 9.8 9.9 -0:1 3.8 0.1 1.3 0.0 0.4 1.9 Zimbabwe 11.9 6.0 5.9 8.2 0.8 9'9 0.4 1.0 2.0 .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . - . . . .I . . . . . . . . . . . ..I.. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . _ . . . . Low income 17.0 8.0 9.1 3.4 4.2 0.6 1.8 1.2 4.8 Middle income 26.2 9.2 17:0.0 ........ 3.5 3.8 ........ 0.5 02 2 . ..... 1.1 15.0 Lower middle income 30.3 9.4 21 0 2.8 3:0 ..........04 0.3 1.6 18.5 Upper middle income........... 22.4 9.1 ....... 13:2 . .4.1 ..... .... 4.5 ..... _.06 0.1 ...... 0.6 11.6 Low & middle income 25.1 9.1 16.1 3.5 38 0.5 0.4 1.1 13.7 East.Asia & Pacific 38:3 .........6.9 31.4 2.1 0~9 0.5 0.7 1.7 29.7 Europe & Central Asia 21.4 13.7 7.9 4.2 4.9 . ........0.1 .......0 0 1656 Latin.America & Carib. 20.5 8.3 12.2 3.6 2 7 ........-0 7 0.0 0.3 12.1 Middle East & N. Africa ..24:.1 ........ 8.8 15.3 .. ... 5.2 19.7 .....0.1 0.0 .0.9 -0.3 South Asia 18.2 9.1 ......9.1 3.8 2 1 04 2.0 1.3 7.1 Sub-Saharan Africa 16.8 9.1 7.8 4.5 5.9 1.......J 4 ..... 0..O.S ....... 0-.9 .........3.4 Highincome 21.4 12.4 9.0 5.3 0 5 00........ O'..0.0 ..........03.3 .... . 13.5 Europe EMU......... 22.1 12.5 ....... 9 5 4.7 0.1 .........0'0 0.0 ......... 0 2 14.0 176 1999 World Deveioprnent Indicators 3.15 Genuine domestic savings are derived from standard timber are probably too high for countries with low- * Gross domestic savings are calculated as the dif- national accounting measures of net domestic sav- grade timber resources, so caution is required in ference between GDP and public and private con- ings by making three types of adjustments. First, cur- viewing some of the net forest depletion estimates, sumption. lttherefore includes the value of both gross rent expenditures on education are added to net especially for Sub-Saharan Africa. In addition, domestic investment and net foreign lending. domestic savings as an approximate value of invest- because the depletion estimates reflect only timber * Consumption of fixed capital represents the ments in human capital (in standard national values, they ignore all the external benefits associ- replacement value of capital used up in the process accounting these expenditures are treated as con- ated with standing forests. of production. * Net domestic savings are equal to sumption). Next, estimates of the depletion of a vari- Pollution damage Is calculated as the marginal gross domestic savings less the value of consumption ety of natural resources are deducted to reflect the social cost associated with a unit of pollution multi- of fixed capital. * Education expenditure is the cur- decline in asset values associated with their extrac- plied by the increase in the stock of pollutant in the rent operating expenditures in education, including tion and harvest. Finally, a deduction is made for receiving medium. For carbon dioxide the unit dam- wages and salaries and excluding capital investments damage from carbon dioxide emissions. age figure represents the present value of damage to in buildings and equipment. * Energy depletion is There are important gaps in the accounting of nat- economic assets and decline in human welfare over equal to the product of unit resource rents and the ural resource depletion and costs of pollution. On the the time the unit of pollution remains in the physical quantities of energy extracted. It covers resource side, key estimates that are missing include atmosphere. crude oil, natural gas, and coal. * Mineral depletion the value of fossil water extracted from aquifers, is equal to the product of unit resource rents and the depletion and degradation of soils, and net depletion physical quantities of minerals extracted. It refers to of fish stocks. The most important pollutants affect- bauxite, copper, iron, lead, nickel, phosphate, tin, ing human health and economic assets are also gold, and silver. * Net forest depletion is calculated excluded, because no internationally comparable as the product of unit resource rents and the excess data are widely available on damage from particulate of roundwood harvest over natural growth. * Carbon emissions, ground-level ozone, or acid rain. dioxide damage is estimated to be $20 per ton of car- Estimates of resource depletion are based on the bon (the unit damage) times the number of tons of car- calculation of unit resource rents. An economic rent bon emitted. * Genuine domestic savings are equal represents an excess return to a given factor of pro- to net domestic savings, plus education expenditure duction. Because natural resources are fixed in and minus energy depletion, mineral depletion, net extent (at least for a given state of technology), forest depletion, and carbon dioxide damage. resource rents will persist over time; in contrast, for produced goods and services competitive forces will ; ', " expand supply until economic profits are driven to zero. For each type of resource and each country, unit Gross domestic savings are derived from the World resource rents are derived by taking the difference Bank's national accounts data files described in the between world prices and the average unit extraction Economy section. Consumption of fixed capital is or harvest costs (including a "normal" return on cap- from the United Nations' National Accounts ital). Unit rents are then multiplied by the physical Statistics: Main Aggregates and Detailed Tables, quantity extracted or harvested in order to arrive at a 1996, extrapolated to 1997. Education expenditure depletion figure. This figure is one of a range of deple- data are from the United Nations' Statistical tion estimates that are possible, depending on the Yearbook, extrapolated to 1997. The wide range of assumptions made about future quantities, prices, data sources and estimation methods used to arrive and costs, and there is reason to believe that it is at at resource depletion estimates are described in a the high end of the range. Some of the largest deple- World Bank working paper, "Estimating National tion estimates in the table should therefore be Wealth" (Kunte and others 1998). The unit damage viewed with caution. figure for carbon dioxide emissions is from A positive depletion figure for forest resources Fankhauser (1995). implies that the harvest rate exceeds the rate of nat- ural growth, and a negative figure that growth exceeds harvest. In principle, there should be an addition to savings in countries where growth exceeds harvest, but there is good reason to believe that most of this net growth is in forested areas that cannot be exploited economically at present. The average world prices used to estimate unit rents on 1999 World Development Indicators 177 C-- - ir iLXii k W hat began in mid-1997 as a currency crisis in Thailand had by mid-1998 become a worldwide financial and economic crisis. Most observers failed to anticipate the breadth and extent of the crisis. A year ago World Bank econo- mists predicted that GDP growth in developing countries would recover to 4 percent in 1998. In fact, it worsened to 1.9 percent. Yet the situation was not one of unrelieved gloom. GDP growth exceeded 5 percent in several Eastern European and African countries, as well as China and India. Most important, growth in the United States and Europe was robust and helped to counterbal- ance the recessionary forces from East Asia. The weakening world economy The financial crises that spread from East Asian countries reduced aggregate GDP by an estimated 8 percent in the five countries most affected-Indonesia, the Republic of Korea, Malaysia, the Philippines, and Thailand. A long-maturing domestic financial crisis contributed to a 2-3 percent fall in output inJapan- further dimming prospects for neighboring countries and greatly magnifying the deflationary shock from Asia to the rest of the world. Weakening world demand fostered steep price declines for oil and other primary commodities, inflicting income losses on commodity exporters in every region. Low oil prices combined with the effects of policy and institutional weaknesses in Russia to pro- voke a run on the ruble, culminating in a default on foreign debt. There followed a sudden, sharply increased aversion to risk in international capital markets. Private capital flows to previously less affected emerging mar- kets in Latin America and other regions dried up. Interest rate spreads widened dramatically. And more currencies came under pressure, leading to a further marked deterioration in the growth prospects for developing countries. In some countries natural disasters caused much more damage than finan- cial contagion or falling commodity prices. El Nifno brought Indonesia its worst drought in 50 years, parching crops and igniting forests. It also affected agri- culture in southern Africa, the Sahel, Latin America, and South and Southeast Asia. Floods in China inflicted $50 billion of damage, while Hurricane Mitch devastated Honduras and Nicaragua. World output growth fell from 3.2 percent in 1997 to 1.9 percent in 1998. The deceleration for developing countries was much steeper, from 4.8 percent to 1.9 percent, and per capita income growth plummeted to almost zero. Brazil, Indonesia, Russia, and 33 other developing countries-account- eventually suffered sharp depreciation when it was floated in ing for 42 percent of the GDP of developing countries-suffered January 1999. The region's high current account deficits will a fall in their per capita incomes. The growth of world trade slid need trimming, however. And total inflows into the region-half from 9-10 percent in 1997 to 4.8 percent in 1998. of which are foreign direct investment-may fall in 1999. Policy measures undertaken toward the end of the year For the long term, the prospects are brighter. GDP growth promised to provide some support for an otherwise weak world could return to 4 percent a year by 2001. Foreign direct invest- economy in 1999. Among them: interest rate cuts in the United ment in the region is shifting away from traditional commodity States and Europe, further fiscal stimulus and financial restruc- sectors to manufacturing and services, where revenues are less turing packages in Japan, IMF-led financial support for Brazil, volatile. The major risks are rising fiscal deficits (especially in and fresh financial support for the East Asian crisis countries. Brazil), high dependence on commodity exports, relatively low domestic savings rates, and high levels of foreign debt. and Central Asia-Russia crashes but rapid transition, countries stand firm Sub-Saharan Africa-Nigeria and South Africa Russia's crisis was the main event in the region. Long-standing pull down the region problems of poor governance and fiscal imbalances were aggra- GDP growth, after running at only 2 percent or so in the 1980s and vated by falling oil prices. East Asia's troubles undercut investor early 1990s, recovered to 3.8 percent a year in 1994-97. And coun- confidence, leading to a run on the ruble in August 1998, a run tries with three essentials-civil peace, macroeconomic stability, that an international support program could not stem. So Russia and good resource allocation-averaged 5.5 percent growth. But opted to devalue, restructure domestic debt, and put a morato- regional GDP growth fell to 2.1 percent in 1998. CFA countries rium on repayment of some foreign debt. Foreign confidence averaged 5 percent growth, and Mauritius and Senegal fared even evaporated, and interest rate spreads across the region widened. better. Slower growth in the region's two most populous coun- Despite all this, many countries in Central and Eastern tries-Nigeria and South Africa-pulled the average down. Europe fared well in 1998, notably Hungary, Poland, Slovenia, So little private finance enters the region that itwas not much and the Baltics. Their resilience is an important success story. As affected by the global financial crisis, South Africa being the main strong, early reformers in the region, they benefited from sound exception. But the sharp fall in commodity prices delivered a macroeconomic and trade policies. They also benefited from the regional terms-of-trade shock equal to 2 percent of GDP. The prospect of integration with the European Union (EU), with the impact was greatest for oil exporters such as Nigeria, Gabon, and Czech Republic, Estonia, Hungary, Poland, and Slovenia short- Angola. Civil war tore apart the Democratic Republic of the listed for accession. Their dependence on Russia as an export Congo, with Uganda, Rwanda, Zimbabwe, and Angola all drawn market was low, and foreign direct investment flowed in to estab- into the conflict. War and civil unrest also disrupted Ethiopia, lish production for export to the European Union. Eritrea, Guinea-Bissau, Lesotho, and Sierra Leone. A second group-slow transition countries-such as Kazakh- Political stability, macroeconomic stability, and good stan, Romania, and Ukraine-fared poorly. They depended on resource allocation have proved a winning combination. Foreign their trade with Russia. Some were also hit by low commodity prices. direct investment, though modest, has helped complete almost Romania, Russia, and Ukraine suffered a decline in GDP in 1998, 3,000 privatizations worth $3 billion in recent years. If civil con- and recovery will not be quick. flict can be contained, the higher growth in recent years should The low-income countries in the region make up a diverse prove sustainable. group with varying policies and outcomes. Albania and Georgia enjoyed GDP growth of around 10 percent, while Tajikistan and Rfiddle East and North Africa-sil-dependent countries Uzbekistan fared poorly. Those depending on remittances from suffer, diversifiers thrive their workers in Russia were hit by the debacle there. In other GDP growth fell to 1.5 percent in 1998, and per capita income countries structural reforms have withered, fiscal deficits were sig- fell 0.5 percent. The region's oil exporters had a 24 percent dete- nificant, and privatization was slow for want of buyers. rioration in their terms of trade. The GDP of sparsely populated oil exporters (Oman, Saudi Arabia) contracted 2.5 percent, while Latin America and the Caribbean-storm clouds c tr. - that of densely populated oil exporters (Algeria, Iran) stagnated. The region grew strongly in 1997 and early 1998, aided by foreign By contrast, growth held up fairly well (3.5-4 percent) in diversi- capital inflows averaging $100 billion a year. But it has been hit fied economies (Egypt, Jordan, Lebanon, Morocco, Syria, hard by the fall in commodity prices in the wake of the East Asian Tunisia). But even they were hurt by a drop in workers' remit- crisis and by the sharp downturn in private capital flows to devel- tances and aid from the Gulf. oping countries after the Russian crisis in August 1998. Regional The global financial crisis reduced access of the diversified GDP growth slid from 5.2 percent in 1997 to an estimated 2.0 per- economies to international capital. Egypt, Lebanon, and cent in 1998, and GDP may decline outright by almost 1 percent Morocco had been able to raise $1.5 billion in long-term financ- in 1999. ing in 1997, but such financing dried up in 1998. Foreign inter- No country in the region suffered an Asian-style currency col- est in equity markets also fell. lapse, but the Brazilian real came under strong pressure, and The region's growth should recover to 3-4 percent a year in 180 1999 World Development Indicators the next decade. Some diversified economies have signed agree- ther to 1.6 percent in 1999, but could recover to more than 5 per- ments with the European Monetary Union (and others are nego- cent in 2001-07. tiating them) under the Euro-Mediterranean Initiative. However, there is a substantial danger: the world economy Providing for a phased lowering of trade and investment barriers could fall into serious recession instead. The risks include a deep- and for EU assistance for institutional modernization and adjust- ening of the Japanese recession, a further loss of confidence halt- ment, the agreements should do much to improve growth. ing capital flows to Latin America, and a sharp fall in American and European stock markets, where price-earnings ratios are high. South Asia-insulated fronm contagion Japan is taking steps to revive demand and restructure its Having opened to the global economy only recently, South Asia troubled financial system, but difficulties in implementation may was insulated from the East Asian meltdown. But its export hamper progress. Domestic demand could contract, and busi- growth and its ability to raise project finance declined-com- ness confidence could fall further. Now strengthening, the yen pounded by G-8 sanctions on India and Pakistan after they tested could weaken again. WithJapan as a major export destination for nuclear devices in March 1998 (sanctions partially lifted in the crisis-hit countries of Asia, a setback to Japan could also be a November). Pakistan came close to default on its foreign debt, setback to them. and its freezing of offshore accounts discouraged fresh inflows. Private flows to emerging markets have fallen sharply since Bangladesh was hit by floods. Russia declared a moratorium on servicing its external debt. The region's GDP growth slowed from 6.9 percent in 1996 to Brazil's troubles have worsened investor confidence. Creditor- 5.0 percent in 1997 and an estimated 5.2 percent in 1998. Even so, debtor workouts have not gathered momentum in Russia and in the region was a stabilizing influence in Asia. Regional GDP several Asian countries. So capital flows to developing countries growth should recover to 5-6 percent a year in the next decade. could continue falling. The region most at risk: Latin America. The main risks are domestic: more political will is needed to accel- Some experts believe that U.S. and European stock markets erate economic reforms. are overvalued and that they could slide by 20-30 percent. If this happens, many investors will feel poorer and reduce their con- East Asia-no quick recovery from a financial typhoon sumption, while business confidence would also fall. Such a burst The downturn in the five most affected East Asian countries was of a stock market bubble could, along with hits to other regions, far deeper than anticipated. Indonesia's GDP decline of 15 per- lead the global economy into recession. That would slow devel- cent was one of the sharpest in history. Industrial output in oping country growth further-tojust 0.7 percent in 1999-and Indonesia and Thailand declined 20 percent or more. The large the terms of trade of commodity exporters would deteriorate deficits in the current accounts of the East Asia-5 turned to sur- even more. pluses, a turnaround of $117 billion. Currency depreciations Is a global recession avoidable? Yes, with good management. helped export volumes grow 15-25 percent, but low export prices Interest rate cuts helped revive slumping stock markets in the meant that dollar earnings stagnated, hurting the ability of com- United States and Europe in late 1998. Reforms to strengthen panies to service their foreign debt. financial systems and supervision-along with quicker debtor- Even so, the contraction in Korea and Thailand slowed by late creditor workouts-could revive confidence in developing 1998. Interest rates declined below their precrisis levels. Equity countries. So could changes in the architecture of the inter- markets rallied. And unexpectedly large trade surpluses helped national financial system to reduce excessive risk-taking and rebuild foreign exchange reserves (Korea hit its target of $41 bil- volatility. lion four months ahead of schedule). Foreign direct investment flows came back in selectively, notably for purchasing equity in the region's banks. Korea and the Philippines are expected to start growing again in 1999, but the outlook for the others is less clear. GDP growth for the East Asia-5 as a group is still projected to be about zero in 1999. China withstood most of the financial storm in 1998. Its GDP growth slowed from 8.8 percent in 1997 to 7.8 percent in 1998, but should recover in 1999. Foreign direct investment declined only slightly, from $44 billion in 1997 to $42 billion in 1998. Speculation about devaluation led to some capital flight, but for- eign exchange reserves remained a healthy $140 billion. And now, global recovery or recession? Gradual economic recovery is possible in 1999, but stagnation seems more likely. The latest estimates suggest that world GDP growth could edge downward from 1.9 percent in 1998 to 1.8 per- cent in 1999. Developing country growth should decelerater fur- 1999 World Development Indicators 181 to XD. !6 ],; ' i Go '-C N ZIs- 0 ._Lm . N C - .7 CNd Hh @ =i r -t r : 2 . :- -Z0 010~~m Et E,- . s, *- - -"'- .ad - 'a '. C d}N .d . . . .. c _ . . _ . N _ di Cd d 0 t a g - X . . 1 ^ 2 i s 0 - = . si C; mm 9~~~~~~~~~1c N dm Cc.~~~~~~~~~~~~~~~~~~~~~~~~~~~~1 Sc / - ------ --- -_ _ . . . .. p = m Z ~ *:- N c-Cd SI~ 71'. :c 'N ) it' N~~N n n - , CN i _ -% I 0 -m -di -~~~~~ 0 .N Lo dic. cm / N a I~~~~~~~~~~~~~~~~~cm-L I~~~~~~~~~~~~~~~~~~I . I ~ ~~~~~~~4 - OD -- ------- L-. --- 0 ______~~~~~~~~~c 0c 0 r ~ ~ ~ ~ ~ ~ ~ 2-sec ____ ______~~~~~~~~~~~~~~~~~~~~~~c After plunging in 1998, developing _country growth will slow further in t - tl ~1999 but then begin to recover. _ _ _ . I,However, there is a substantial _ _ = __ = danger: the world economy could _ _ _ _ _ _still fall into a serious recession. Weak growth in Japan and Europe and a global decline ,r, cor-,liu-a, . X prices hurt the prospects for many developing counir-t........................ Average annual percentage change, except for LIBOR economies (a) economies(a,baWorl)tradrldotrade gro htsj - g ~~~~~~~~~~~9.5 - \k|66e~ 12 5 7 19 21 3 ; 1~ i- \ 2 \0 r -0.9 97 ~ ~ ~ ~ ~ ~ :- 98j. 99 2000 200 Current Forecast LIBOR, 6 months Change i nnppetooleum e~stiae c pe yAr c) pric Inde a. Canada, France,| the United Kingdom, ||; b ,g§ =SL>f,5 anri the United States. \ |-$<' c. London interbank / offered rate.V1 Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross International product and services and services balance reserves months average annual average annual average annual of import % growth % growth 96 growth 96 growth 96 of GDP $ millions coverage 1997 1998 1.997 1998 1997 1998 1997 1998 1997 1998 1998 1998 AlgEria 1~~ ~~~~~~.3 3. I .1 5. 2.0 5.7 14.3 63 5.3.. - 3.3 £138, t3. a Argenirna 5.63 4.; 12.1 7 .3 27.6 1(41 0 2 1.1 - 3.1 -. .Armen,a~ 3 1 6. 9 1 2 53E 2 3 1 Q9 17.3 1 1 6e-l . -17.4 290C 35 Bargladesn 59 156 14.2 13 3 1 '6 1..3 4.') -2.2 -19 1.75(' 2.,. Boiva -4.2 4.7 -'3.5 65 193 (5 54 7 - 8. -91 1. 14 5 . Brazil 3 2 0).5 1.5 -24- 139O -49E 7.5 3.3 -1 -48 4455 ICtO Bulgaria -6 9 415 71 4 -44 J 1I1 9 1')I 230) 44 -0.6 3.05 2 C arne~r&,-n 81 ') 115 47 15 73 27 1.2 -1 3 -24 Cn'se 7 1 4.2 9.9 .0 14 6 9.8 8 . -5 3 -7.2 15.519 7.1 C n.ina E56 758 276 C 4.6. 13 1 15 12 -1. _7 CoIomu'a 3 1 2 5 6 1 7.4 9.6 I 3 l-2 17.0 -59 -63- S. 139i 4 2 Cong-o.)Pep. -19 C, S 108 5 -26.2 -3.4 57 -1 -IiC -113S Cosra Rca. 3 2 E82 4.4 13".1 1 4.4 153 14.5, 1 2.3 -2.7 -9 109 5 24 ( 5Ee .1,,.re 6.6' 5I 13-.4 3 1 54 I 32 3 2 3,.3 C,'3 -4 4 1.179 ) Crozai'a 0.& 30 O 4 2 6.')I -12.5 - 7.6 2 515 3.1i Domirmcan REpuulic h.2 7'' 11.4 105 1.2 290 8 3 C' .. -3 455 0.7 Ecua.dor 3 4 038 36- -16.0 6.-1 2 25.9, 59 -35 7 279 i Egypt. Arao Peo. ta bi 2 1 . 6 . 6 1 -5 2)24 1Ž El Swalooar 4.0 36S 21 .3 11.5 S 1. 19.5, 4.E 39 0, 9 -10C 1 869 5.C Eamlon-a 11.4 50 26,2 11.4 z. 3 11 4 95 -12.3 -10-I4 Gabon 4.1 2.1 3 6 -2 7 150C -73~ -0. 1 -6.8 .. -1 7 Ghana 4.2 4.5 -) 34 141.4 1.4.71 6I3 193 . 12 5 .. 5 Gu-atmalq I43 4 7 10 2 8 5 19.3, 17 1 E.4 7 5 -35 -5 4 1. 369C 332 Hungar% -1.6~ 4.9~ 26 4 18' E 5 1516 16S4 13 6 -2.2 -3 6 S. 125 32 Inaia 5 2 5. 4 6) 34 15.4 .10 -. 2 845 5 nlOr.oorSpa 4.9. -13.0 A. 2.5 14.7 -21 5 1 2' 900( . -2.3 4.5. 2 1.431, Irarn. Islan.i. Rep . 17. -2.5 .. 2.3 . 16 4 .. .. 4.194 jan'a~~~~~~:a ~~-2.4 -. -5 32 14 2.0 11 I 9.2 -91I . 692 2') jordan 1.7 -. 1 5 43 9 -07 4.6 2.7 3 7 -2.9-: -5.1 1.6341 3.1 P.a:IMSrraar 1.7 -1.5 25 2 -0 2 16 1 2 1 -1.1 -5 1 2.1 34 Klen,a 2.1 2.7i -12 0 5.1 24 41-18 1.1.6 9 6 i -29 Larvael 4 0 1 3.5 -12 v. .1 6 4C,. 9 791) Le~banonr4 5 92j 146 42 5. 9 5.5 80 C -2332 -22.6 1 1 s, 14 5 0 continues on page 186 184 19S9 World Development Indicators Nominal exchange rate Real effective Money and Gross Real Interest Short- exchange rate quasi money domestic credit rate term debt' local currency units average annual average annual %of per $ % change 1990 = 100 % growth % growth 91% exports 1998 1997 1998 1997 1998 1997 1998 1997 1998 1997 1L998 1997 Algeria co04 4.1) 3.3 61.9 63 I 19.6 9.8 . . t Argenrrr.s5 1.) O.i' '30 C 23 2 3 2 9' .4 1.2.9 A,mcrne,a 220 13.9 5.3 10-4 S 115.7 29.37 8.3 . 3 1 3 . 6 Ecingiadea 49 h 7.1 6.7 .. .h 1'0 2 12 3 1 2 1 121 3 ECJa-a 5 6 3.5 5 2 99.2 101.9w 16.5 . 2 2.3. 42.4 24 3 29. 3 Brs:I 1.? 74 91 .. . 13.4 3 16.3 11.7 31 3 Bulgaria 1 67'. 1 264.3 -5 7 105.1 124.1; 36 2 1 13.5 135 5 -11.7 -82.3 a1. C 3nier.).,.r, g-.2 2 14 3 -61 6. 4 64? 1c.6 46 1 97 1.2. Crilt 473.3 3. 7. 7 1 3 5-. 130 1 16.3 10.9 14.3 12 4 9.3 14IC' 45.9 Crnr,4 31 -3 . 4 C5 2(7. 1.7 . 3.. 11 Coionrnoa 1307 25 1.35 161 1 154.2 411.9 231.4 12 2' 3.9 12.5 24 7 2336 Cc'npo. P~~. -622 1413 -6 1 . 95a 0 1 143 16,6 .1. 1.7 Co)~ .o Rica 271I 4 ilK 11.1 104.6 104a.1 16 4 . 3 3.6 . ' . 11 3 COlE a wre562 14.13 -6.1 I 6E9.3 73 9) . .1 . . 53 6 Croal-a 63.2 13,6 -)19 9 9.71 100.2 37 7 12.1 13.5 -1229 .. . .0 Do.rn,n,-7sn -peubI,c 1St 2.2 . 121') 117. 24.l 3 1a. 2 216 233 11.7 21.') IC' C' Ec:uador 6.6.230 21.8 34.1I 13S.1 13. 32.8. 3-7.68 46.3 396 1 3.6IF 17 4 33 9 E&.-'r Arato Rep. 3.4 (.C'.'. 10.8 3 13.94 13.6 7. e 2 13 9 El Ssl~~~dor 63 0.0 0.0 . .. ~~~~16.S, 20.1 20 4 9 2 1i~ 103 2. E:~kvn'a 134 15 -. 3.6 8 C' S13 2. . .3 Gancin 563221 14.3 --6 1 533 54. 11 3 6 2 141.4 12.9 .. 141.6 Gnana 29.3 6._4 91I2 455- 7.9.. 39.6 Gijak,rna61 39 3.9 .. .. -4 2 -10 1 1. 7 1.1 7 5 7.5 31.3 Hur.gar..21.' 23 4 .6 1.12 .3 1'i:. . .26 .. 13.6' lnijla ~~~425 93 [41 1 7.7 15.2 1 19 1 2 S9 9 91 lr,.Jcj,C-Q.a 025') 95.1 72.6 .-. 2.2 74 25 7 47 1 .3 S .. 3 7 Iran. Islam,.: R~p 1.750.9] 0.3 -).2 10,1 9. 12 2 23 7 . 13 7 . . . 17.2 JSms'CE 16 4~~ ~~~~~~~~.2 2. . 13.3 IC' 2 42 3 23.6. 22. 5 21.7 17 3 Jordan 0.7 0.0 OC' 7.6. . 17 .. 93 13.3 :ai~~~nnrar, 13.6 ~~~~~3 1 13.9 . .. 2-I 1 -14.3 -2 5 29.0 . . 4.-5 ie r. v a 613 139c -1 2 1. . 166 93 21Ž . 16.7 .. 26.6 Latvia O~0 '3.1 - 16.7 .. 36 . 1. 3. 2,2 Lebanon I 1...) O -1 6 -I 2 .19 6 16S.6 32.6 Ž51. 7 10.9 315.2 0 continues on page 187 1999 World Development Indicators 185 Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross International product and services and services balance reserves months average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 1997 1998 1997 1998 1997 1998 1997 1998 1997 1998 1998 1998 L"n~~~~~~~ sr" ' t.~~~~~~~~~~~~~~' . 14~~~~~~~ .9 4 --- -I . 1.35 l, 39;_ 2.4I C' u ~~~~~1.1 I I4 -'?'6 134 17.7 -Ileu -, 7 4 C Mala ca 7.0 9E, I'97' 10.1 -11-5 1C, iE 4 -6 -3. A524- 22 -2'' 3 '. 13 Dl&. 2.0 2919) I -'1. 8 ..3 Ls.52C 41 ~~ -IC' 1.2 ~~~~-144 1x. -1 X' iC'.2 _''? 1 I-,.,: 256 17 r j,tir-4 '3 -33 1 1 -1.5 1494 -0 2. 31.4 119 Guree - rj-- l. 1:4 ~.s 2. 24 1 .3.1 -12 5 - 36 19.4 17 P.3rt~~ MUc'. 2.'.: . 3. ii.:' -4 7 -9 "",.C 17 F~~~~ru ~~~7.2 Cr 30 CS 11 i'O 4 t 33 64 1.t674 93 -S 1 7.5 -7 14.4 -1' 5 4 -. 11I 10.230'. Polana 5~~c<~~' I-3 7 2,.' 26. 3 I 4. 5 11.2, -.4.2 -37 !K3470 60L Rv'ic.an F.oer3i-,-n 0E -I" 3 -C, 2 lIC -13 1 16 6 26 S CS 1.2' 1.6 '1ic'., -r&D'joi": '33 '-' 17.2 7 241 67 "3' 70c -10' 2.957 2.2 3.:.um Alr.5~~~~~~ 1 7 1.') 7 ~~~~~~.c 4 '7 -i.':' 7 3 16~~~~~~J. C' -1 C: 1 2 172 4.1 Sr Larva 6.4 5.13' ~~~~ ~~ ~~~~116 .' 10 lf vO C' 25 -26 8 .02~' S,nr.r A, ~r, Fe,,l :-'' '' -3 .: 5u . '' ~ 2 -C 9.QSCj10 T 2~~~~~4 3 1. t n 41 -. 7 -4. 2.411 .' Tun, 77 3 6~~~~~I'l 7 1. 21 S I1. 3' -1.4 C,' 21..I. 8 .5?i' I ii ra.n!! -15 . -cs . - 21 17.2 13.6 -2.7 -S. 1.2C'.3 07 LI'rug ua 1 6 11 ? 1. 13 1 '' . -2L. O '' 51 nze T,,54 i . 1. -2. Z 3.1. 325 23 2 ' 1. 21 35 Note: Data for 1998 sre preliminary. Source: World Bank staff estimates. 186 1999 World Development Indicstors Nominal exchange rate Real effective Money and Gross Real interest Short- exchange rate quasi money domestic credit rate term debta local currency units average annual average annual % of per $ % change 1990 = 100 % growth % growth % % exports 1998 1997 1998 1997 1998 1997 1998 1997 1998 1997 1998 1997 Umual^n,i C C' c' 0 'J:' . . C34.1 16.7 2-- 2 19.S -OJ .4 . 3.1 41- - 51 0. .1 5 J1 21 124 . 131 . 4.3 r4lala. Z. 3S R339 -2.4 104- 12.5 17.- u., 232 -2'j .:5 157 ril.31jRlllllf 2JeC' z 12 C 1.4 . .. 164 7 4 S 21 12 3 19.1 I-lef, 9 3- .0 222i . 2J.6 190 -01 35.7 47 . .5 21.7 7 *_ 21' S 119 4 3J S -12.9 26.5 '2.4 19 . . 1.9 Mol.rr.:c9.7 10.4 -4 7 11.1 1166 b.1 o.1 . . .C.. _ 21 I ) '3 r, 17 I 097. 16 C -2' 3 32, P,-,;rir, -1,:C 9., 4.4 4 7 )913 19.9 11.5 9.3 130 . . 21.5 Far,in;a lC' C' 0' 'IC 01!'| . 3.4 .. .. S'O ParTkrJ.Iur rvj,n' 2 1 3c.0. 2e 2 93.6 71.4 7.7 24 4 . 9 e 2 F;,;gIs Cl 124.1 112? . 27.3 17'e 11 Perlv ~~ ~~32 3.') 15 S . 3;0:6 1c2 7.4 2.d ' 7l 19 9 2r.6 71.1'' 3'?lllraljlne- 1')1 52.1 -2.2 125. J l1: 4 2V1 1 31.2 . C-i . 2J') Pc'I d 3 22 -0.4 21t 22 14.u 2. 2 22 4 26 4 24.7 S.9 . 91 Ru:-,6n,.; n,i --. l l - -65 . 1 050 4')J 5.7 e9.3 .. Fjus,;n FeJ.rat,jr D. 7 2 24r6 129.:' e' 2 295 268 2:1 5 I1C 133 142 3.9 5err,h Lr :r,., 1l1124 4l J .7 15.5 1.:2 6- .9 471 .. 1 S . '5 3 SI':.It R&pul:h,c . s 91 I 6 11lS 1:S-2.1 . 3 .8.1 34 4 -('.6 113 . 35.1 .:.:j'r Lirn.: j 4. D .C 2J.4 9 657 17.5 I.'2 . 112 . 21.4 rr, Laris av 51 -i CS .. . 1.'5 8 s4 1.7 14.2 25 b ' - S.r,;r L r.,l, R& I r.I. 11.2 0 : O . -4S.3 .. . .. 25.e Tnr,i,lar 8. e4 5 -22 _ * 16.5 11.4 32 3 2.3 7.8 .. 45 7 T,,n,.ia.i Dn.n T,,.C:. 1.7 -C' - S44 55 2 11 17 6 16.2 2.0 Cl' . 11 2 0 T.,r. ;l I l 1l.' -4 1 1C . 1626 1-5 e 2 1SC, 12 1 . 17J T,rLe1 Ezl7.1.44., 90.6 5_.' 9 ;3 J LUi ri,nn 34 C.5 5O.5 133.6 1 26 J 3'.9 25.3 325 S2.J 27.2 . 53 L'rui'u;. l C . 1-2 1471 1C96 254 27.1' . . 45. 415 39.5 C 17. 5'. 11 9' 1S4 1 1 55 1.- 36.4 5 -I4J.1 1 lemDoa .. 1. 117 C' l02 2:-& Cu . 169 2.. 8 374fbDC 7 . 17f 4CC'l 6 41.2 23.5 .5:1.1 7.. J 9.5 317 Note: Data for 1998 are preliminary and may not cover the entire year. a. More recent data on short-term debt are available on a website maintained by the Bank for International Settlements, the International Monetary Fund, the Organisation for Economic Co- operation and Development, and the World Bank: http://www.oecd.org/dac/debt. Source: International Monetary Fund, Intemational Financial Statistics; World Bank, Debtor Reporting System. 1999 World Development Indicators 187 4.1 Growth of output Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth ±980-90 1990-97 1980-90 1990-97 1980--90 1990-97 1980-90 1990-97 1980-90 1.990-97 Albania 1.5 1.8 1.9 . .......8.1 .........2.1 ........-9.7 ..... . .. . ....-0.4 4.7 Algeria 2.7 0~8 .........4.6 ........2 5 ....23 -2.. . 2. 3.3 -1 70.2 3 . . 6... 3.8 Angola.3.7 -1 0.5 -6.9 6428 -111 -4.217 -5.7 Argentina -0.4 5 4 07 .........16 -13...4.4...-0.8 3.2 ........0.0 6.4 Armenia .. -10.3 . 0.2 . -18.1 . -13.1-1. Australia 3.4 3.6 3.3 1.1 29 ........ 2.5 1... .. 9 2.2 3.7 4.4 Austria 2.2 2.0 1.1 -0.7 1.9 1.3 ........2.7 0.8 2.5 2.2 Azerbaijan .. -15.1. -4.6 .. -8.4: ...........-6.6 Bangladesh 4.3 4.7 2.7 12 49 .. ......7.0 3.1 7.5 .........50 ......_571 Belarus .. ~~~~~~~ ~~~~ ~~-6~1 ......... -59. ~........ .. -7.8....-65 -3.8 Belgium 2.0 1 4 2.0 .........1.6 .........22 .. . .. 0. 7 .........2.7 .........0 6.6 .... 1.8 .... 1.3 Benin 2.9 4 .5 5.5 5.2 3.0 4.2 5.2 . 1.4 4.1 Bolivia -0.2 4..1 . .. . Bosnia and Herzegovina Botswana 10.3........ 4.5......... 2.2 -0.8. 11.1 .........2.4 8.8 3.1 10.9 ........ 7.2 Bulgaria.......... ... 3.4 -3....... .3 -....... .2~1 ..... -3 1 ... .....5.2 -........5..5 4.5 -0... . 6 . Burkina Faso 3.6 3.3 3.1 ........3.6 3.7 2 2 2.0 2.0 4 6. 2.8 Burundi 4.4 ....... -3 .6 .........371.1 ...... -3.0O..... .4.5 ..... .-8:4 5.7 -9.9 5.6 -3 .1 .. Cambodia . 5. . 22. 10. 8.2 7:6 Cameroon ..3.4.......-0.13 2.1...... .21 . . 4.5 .5.... 5.9... :9.... 49... 9.50......-23 25......... : -03.. . Canada 3.3 2.21.2 1.2 3.1 1.83.7 3!0. 3.6 1:8 Central African Republic 1.4 ....1 .2 ........1.6 3.2 1.4 0.3 5.0 -0.6 .........1 0 -1.6 Chad 3.7 4 .6 2.3 54 8.1 0.0 7.0 .7.7 -0.5 Chile 4.2 8 .3 5.9 5.6 3.5 7:1 3.4 6.3 4.4. 9:3 China 10.2 11.6 5.9 4.4 11.1 16.3 ........10.4 15.5 13.7 9 5 Hong Kong, China 6.9 5.3 Colombia ..3.6. .... 4.4 ........ 2.9........ 1.5.5...... 5.0. :0.... 28.... 3551 15 .........331 ..... . 65. : Congo, Dam. Rep 1 ............ .6 ... ....-6 0 2.5 3.1 0.9 -11:7 .........1.6 -13.4 13 ...... -15.2 Costa Rica 3.0 3.8 3.1I 2.9 2.8........ 3.5 .........3.0 3.4 31 4:4 C6te dIlvoire 0.7 3 .0 0.3 2.6 4.4 42 3.0 2.4 -0.3 2.8 Croatia -1.0 . -4 ..4 ..- 2 -8.1 ..-39............................ .......... ... ......... ... Cuba .. .. .. Denmark 2 .3 2 .5 3.1 1.7 2.9 .... ....1.9 ........1.4 ...... 1.3 2.6 1.4 Ecuador 2 0 3.1 4.4 2.8 1.2........ 4.1 .........00 3.2 1.7 2.6 Egypt, Arab Rep. 54 4.0 2.7 1 12. 5.2 4048 .6 3 El Salvador 0.2 ........5.6 -1.... .1.1.2 0.1 5....... 3 -0... . 2. 5. 0.7 6.8 .. . ..ritr . . . . .. . . .. . . .. . . . .. . . .. . . .. . .. . .5.2. .. . . .. . . .. . . . .. . . ....... . . . . . .. - . . .. . . .. . . .. ..... . . . . . . ...t.n I ... . . . . . . .. . . . .. .. . . 2.2. . . -3 8. . . . . . . .. . . . . . .... -.4.. . . .. . . . ... . . .. -8.. .. . 2.. . . .... . . . . . . . .: -1.. .. . 5.. . . . . . . . . . . . .... . .... -.4... . Ethiopia' 2.3 4 .3 0.6 3.0 3.1 4.1 2.7 4.4 1.7 6.9 Finland .......... .. 3.3 ........1.~4 . .......-0.2 0.2 3.3.3 3 ........2 - 3 4 ....214 94 34.......-............... ...49 . ... .........4.1.... . ..............-0...............1..:... France 2.3 1.3 2.0 0.4 1.I 0.1 0.8 0.5 3.0 1.6 Gabon 0 9 3.2 1.2 -2.3..1.5 27 1.8.06.0.1 4.6 Gambia,The 3 .6 2 .2 0.9 0.6 4:7 .........0.1 .........7.8 ........05 .........27 3.8 G e........rg .......a..... 0.. . 4 ...... -16.3............ Germanyb 2.2 1.4 .........1.7 0........O8 1.2 ....... -1 0.0 ....... 2.9 ........ 2:5 Ghana 3.0 4 .2 10.0 . ..... 2.7 3.3.. . ..4:3 .........3.92.7 5J.7 .. ... 5:7 Greece 1.8 1 .6 -0.1 2.0 1.3 -0.5 0.5 -1.1 2.7 1.8 Guatemala 0.8........ 4.1 ..... 1.2 2.7 -0.2 4.2 0.0 2.8 0.9 ........ 4.7 G uinea .. .... 5.0. ............4. ....4.4 .... : ......... 1.2 ..................... 0 80 8 ...............8.1..... . Guinea.Bsa 4.0 34 4 47 5....... 55........ 2!2.2 ....... 2.7........ .. 4.5 3.9 1.4 Haiti -0.2 -2.5 -0:1 49 -1.7 -2.7 -1.7 -6.9 0.5 -0:5 188 1999 Worid Deveiopment Indicators - 4.1 1 ~ l Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-97 1980-90 1990-97 1980-90 1990-97 1980-90 1990-97 1980-90 1990-97 Hungary 1.3. -0.2 1.7 -3.8. 0.2 14-..1 ..... ............ 65.5 .... 2.1 0.3 India 5.8 6.0 3.1 ........2.7 .... ... 7.1 ....... 7.2 ....... 74 8.0 6.7 7.8 Indoneaia 6.1 7.5 3.4 2.8 6.9 9.......979....... 12 6 10.8 7.0 7.2 Iran, Islamic Rep.1.7 4.0 4.5 4.8 ....33 .3 ..... 3.8 4.5 .....46.6 ..... -1.0 6.0 Iraq -6.8 Ireland ........... 3....................2. 0.... ... . Israel 3.5 5.8 ... Italy 2.4 1.1 0.1 1.3 2.0 0.8 2.9 1.2 2.8 1.1 Japan 40.0 1.5 .........1.3 -1.6 4.2 0.7. 4.8 ........0 .5 . .. 3.9 2.1 Kazakhstan -79. -4 4. -272.9 Kenya 4.2 2.1 3.3 .... ...1..0 . ....3.9 . . 1....i9 ...... ...4.9 ........2: 5 ... 4.9 3.7 Korea, Dem. Rep.. Kuwait 1.3 . 14.7 I. .0.. 2.3 . 2.1 Kyrgyz Republic .. -9.5 .. -2.7 .. -15.6 . -9.2 .. -8.7 Lao PDR .. .. 6.7... 3.5 ... 4.5 6.1 11.9 8.9 ....... 12.7 .........33 6.7 Latvia 3.5 -8.5 2 3 -10.8 4.3 .... .-159.9 ..4.4 -15.4 3.2 .... ..-0 2 Lesotho 4.4 7.8 2.2 4.0 71.1 12.1 13.7 9.4 4.6 6:9 Libya -5.7 Lithuania .. -7.1 :-2.8 -12.6 ...... . .................... -1.7 Macedonia, FYR . -0.8 . 1 ~4. -5.6 ... -1.1 Madagascar 1.1 0.9 2.5 1.6 09 1.71 2.1 0.6 0.3 1.0 Malawi 2.5 3.6 2:0 8.6 2:9 ........0:9......... 3.6 5.1 3.6 -0.2 Malay ia 5.3 8.6 3.8 2.0 7.2 10.8 8.9 13.1 4.2 8.8 Mali .. .......2.8 3........ .3 .........3.3 3.4 4.3 .... ..7.0 .. 6.8 5.0. 1.91. Mauritania 1.8 4.2 1 7 ........4.8 .........49 ........3.7 -2.1 1.3 0.4 4.3 Mauritius ... 6.2. 5-.0 2.9 0.4 10.3 ........ 5.5 ........ 11.1 5.5 5.4 6.3 M exico 0... 7 ... ... 2-.2 ....... ..0.8 ... .....1.6 ........ 1.1 ...... 2.5 ... .. .... 1.5 2.9 0.6 2.1 Moldova 3.0 -14.1. -71. -13.0 .. 1. Mongolia 5.4 -0.6 -1.4 7.6 6.7 -5.1 . 5.8 -2.1 Morocco 4.2 . .. .. 1 9 ..... ....6.7 -1.2 3.0 .........2.9 .........4:1 2.5 4.2 _ ..... .2.4 Mozambique -0.1 4.9 2.1 6.1 -8.3 9.1 . 952.2 Myanmar 06 6.3 0.5 5.0 0.5 10.1 -0.2 7.0 0.8 6.4 Namibia 0 9 3.8 1.8 4.0 -1.2 3.6 ....... .3.7 3.9 1.5 ........ 3.7 Nepal......... 4.6........ 5.1 .... 4.0 2.2 8:7.7 ....... 8.1......... 9.3 11.2 3.9 13.2 Netherlandas2.3 24 3.4 3.7 1.6 1........2. 2.3 1.7 2.6 2.3 New Zealand 1.8 3.4 3:8 2.2 1.1 3.7 0. 3.9 1.9 3.5 Nicaragua -2.0 4.1 -5:8 8.7 2.1 -...... 4.8 .......-8.6 -0.8 -2.8 2.0 Niger-01 157 2.2 -1.7 12 -27 5 -.7 105 Nigeria 1.6 2.8 3.3 2.9 -1.1 1.2 07 183.7 3.5 Norway 2.8 4.0 -0.2 4.5 3.3 5 6 02 2.1 2.7 3.1 Oman 8.4 5.9 7.9 .. 10.3 .................. 20~6 5.9 Pakistan.......6-.3 . .... 4~2.2 .... . 4.3 3.7 7.3 .........5.2 7...... .77........ ~ 51.1 6.8 4.7 Panama 0.5 4.8 2.5 2.2 -1.3 7.9 0:4 5.1 0.5 4.5 Papua New Guinea 1.9 5.7 1.8 4.1 1.9 .........8:9 0.1 5.8 2.0 4.3 Paraguay 2.5 3.1 3.6 ........2..9 ..... -0.3 24 2.1 12 2 ....... 3.3 3.5 Peru -0.3 6.2 27 5.7 -0.9 74 -0.3 5:7 -0.7 5.3 Philippines 1.0 3.3 1 0.... 2.0....... -0.9 37 .........0 2 3.1 2.8 3.7 Poland 1.8 4.1 -0.7 ......-1..6 ....... -1.3 4.7 -1.5 3:5 2.8 3.0 Portugal 3.1 ... 2.1 .... . -0~4 ..... ....7......... 0 5 ....... -....0 2 ....... ............. 2 3 Puerto Rico 4.0 . 1.8 3. .5 4.6 Romania 0.5 -03 .. 0.6 . -0:1 . .0.0 Russian Federation .. -7.7 . -7.5 .. -9.3 . -5.4 1999 World Development Indicatora la9 Gross domestic Agriculture Industry Manufacturing Services product average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1980-90 1990-97 1980-90 1990-97 ±980-90 1990-97 .1980-90 1990-9711980-90 1990-97 Rwarnda 2.2 -5~7 .........0:5....... -7.4 2.5 -3:3 .........2.6 2:2 5.5 .. ... -574 Saudi Arabia 0.0 1.7 13.4 0.7 -2:3 .........15 ..... .7:5 2.7 1.3 ......2.0 Senegal 3.1 ..2.5 .........2:8 1.6 4:3 3:3 ..... ..4.6 1.8 2.8 2.5 Sierra Leone .. .... .... 0-.3 -........44.4 ........ 3.1 1.6 17.7 ... .. -7:8. 5.0 -2.8-3 Singapore 6.6 8.5 .........-62.2 . ... 2.1 5.4 8:8 6.6 7:5 .... ..-7.5 8:4 Slovak Republic 2.0 0.6 1.6 -0-4 2.0 .-6.5. 0.8 8.1 Slovenia . 14 . 02 0830. 3 8 South Africa 1.2 1.5 .........29.9 . ..... 2.5 0:0 .... ....0:8 -0. 11:3 2.4 1:8 Spain 3.0 1.6 ... -2.5. -0.4. .. -0.7 . -13.1 Sri Lanka 4.0 53. 272 1.. ....l.5 ........46.65 6.3 8.8 4.7 6.3 Sudan 0.4 7~7 ....... -06.6 16.3 2.5 .......4:7 .. . 3.4 1.0 ..... ..1.7 ...... 30 Switzerland 2.0 0.2 ..:7. Syrian Arab Republic .... ..... .1.5 6.3 -0.6 ..........6.6. 0.1 Tajikistan -16.4 .... . .. Tanzania'. 2.7...37. 1:2 ........... 1.9 .. .. ....I.. .... 1.9... .... Thailand 7.6 7.4 3.9 3.1 98 0 9 . . . Too 1.7 1.9 5.6 4.6 1.1 2.2 1... .7...1.5 -0.3 -0.5 Trinidad end Tobago -0.8 1.2 ........-5:8 -0.3 -5.5. 1.2 -10..1 2.4 - Tunisia 3..3 ........4.3 .........2.8 1.0 3.1 44 ....... 3.7 .. ....5:5 3.5 5.2 Turkey 5.4 4.1 1.3 1.1 7.8 5 0 7 9....... 5:9.9....... 4.4 4.1 Turkmenistan -9.6 Uganda.29 7.4 2.1 3.8 5.0 13.0 3.7 13.9 2.8 8.5 Ukraine . -13 16.4 -21.4....-16.4. .-160.0 ........... ..... -8.6 United Arab Emirates .... ..... -3.5 9 6 -4.2 ... ......7..... .... 3.1. 3.6 United Kingdom 3.2 2.0 United States .............3.0 3.0 2.0 .. ......: ........ 4.3 5:2 1.9 U uguay.0.4 ........ 4.0 ......... 0:0.0 ..... 4..5 ... .... -0:2 0. .. O8 0:4 .... ---0 2.2 ..... . 0.8 5.4 Uzb .. ta -2.4... .. .... ....... ... 1 5. -61 ...-5.. 6.. .......... -.2........ Venezuela 1.1 2.2 3.0 ..... 1.2 1.6 3.8 4:3........ 1.8 0.5 0.7 Vietnam 4.6 8.6 4.3 5.1 . 13 3 . 8.8 West Bank and Gaza Yemen,Rep.. 3.7 4.8. 64. 1.0 . . Yugoslavia. FR (Serb./Mont.) .. Zambia 1.0 1.0 3 6 -5.0 0.8 -5.2 .......4.1 -16.7 -0.4. 9 6 Zimbabwe ............. 36 .... . 1.8 ~ 3.1 3.2 3.2 .......-1.5 ....... 28 -2.0 3.1 3.2 Low income 4.4 3.9 3.0 2.6 4.9 4.6 6.0 5.9 5.1 5.4 Middle Income 2.9 2.8 3 5 1.1 28 8. 3.8 3.7 6.7 3.1 3.3 Lower middle income 4.9 2.3 4.2 0.7 6.0 4.4 7.1 99.9 5.6 3.0 Upper middle income 1.8 3 4 2:4 1.9 1.1 ........3.4......... 1.7 3.4 2.0 3.6 Low& middle income 3.1 ........3.0. ..... 3:4 1.4 3.0 .... .. 3.9 . ......3:9 6.6 3.3 3.5 East Asia &Pacific 75... 9.4. . .. .5..4.7. .. .4. 38..... 84 .. ...13.2 93......... 84 ..13.2. 1 . .....87. 801.287 . Europe &Central Asia -4.3 . -6.3 -55 ... -1:4 Latin America & Carib. 1.6 3.8 2.1 2.7 1.2 ........3 6 ..... 1:2 ........2:8.8 ....... 1.6 3.9 Middle East & N. Africa.......... 2.1 ...... ..2.9 .........5.5 1.7 0.6..... ... 2 32 ... .. 28 2 1 .... ...3.6 South Asia 5.6 5.6 3.2 2.7 6.8..... ... 6.9 .........7O 0..... .. 76 6... ... 6.4 7.0 Su-ub aanAficah...aran.. ...Africa.........1.2 8 ..2.0 ......2.4 . .... 2.. 5 0...... 9...1.0 .....0...8 70.1...1...2.408 .1.2.04 . High Income 3.2 2.2 2.3 0.6 23.20.... 1.9I Europe EMU .. 1.6 . 0.7 ... . 0.3 . a. Data prier to 1992 include Eritrea. b. Data prier to 1990 refer to the Federal Republic of Germany before unification. c. Data cover mainland Tanzania only. 190 1999 World Development Indicators L.1 11 Growth rates are calculated using constant price data ported activity. How consistent and complete such * Gross domestic product at purchasers' prices is in the local currency. Regional and income group estimates will be depends on the skill of the compil- the sum of the gross value added by all resident and growth rates are calculated after converting local cur- ing statisticians and the resources available to them. nonresident producers in the economy plus any taxes rencies to U.S. dollars using the average official and minus any subsidies not included in the value of exchange rate reported by the International Monetary Rebasing national accounts the products. It is calculated without making deduc- Fund for the year shown or, occasionally, an alterna- Countries occasionally rebase their national accounts tions for depreciation offabricated assets or for deple- tive conversion factor determined by the World Bank's by collecting a complete set of observations on the tion and degradation of natural resources. Value added Development Data Group. The growth rates in the value and volume of production in a new base year. is the net output of a sector after adding up all outputs table are annual average compound growth rates. Using these data, they update price indexes to reflect and subtracting intermediate inputs. The industrial ori- Methods of computing growth rates and the alterna- the relative importance of inputs and outputs in total gin of value added is determined by the International tive conversion factor are described in Statistical output, and generate volume indexes to reflect rela- Standard Industrial Classification (ISIC) revision 2. methods. tive price levels. The new base year should represent * Agriculture corresponds to ISIC divisions 1-5 and normal operation ofthe economy-that is, a yearwith- includes forestry and fishing. * Industry comprises Measuring growth out major shocks or distortions. But the choice of value added in mining, manufacturing (also reported An economy's growth is measured by the increase in base year and the timing of economic surveys are also as a separate subgroup), construction, electricity, value added produced by the individuals and enter- determined by administrative convenience, resource water, and gas. * Manufacturing refers to industries prises operating in that economy. Thus measuring real availability, and international agreement. Some devel- belonging to divisions 15-37. * Services correspond growth requires estimates of GDP and its components oping countries have not rebased their national to ISIC divisions 50-99. valued in constant prices. In principle, real value accounts for many years. Using an old base year can added can be estimated by measuring the quantity of be misleading because implicit price and volume Data sources goods produced in a period, valuingthem at an agreed weights become progressively less relevant and set of base year prices, and subtracting the cost of useful. National accounts data for inputs, also in constant prices. This double deflation The World Bank collects constant price national developing countries are col- method, recommended by the United Nations (UN) accounts series in national currencies and the coun- 'e; lected from national statistical System of National Accounts, requires detailed infor- try's original base year. To obtain comparable series I r ' organizations and central mation on the structure of prices of inputs and out- of constant price data, it rescales GDP (and value i ; i :i i banks by visiting and resident puts. In many industries, however, value added is added) by industrial origin (agriculture, industry, and . , World Bank missions. Data for extrapolated from the base year using single volume services) to a common reference year, currently '' jefj-' industrial countries come from indexes of outputs or, more rarely, inputs. In others, 1995. This process gives rise to a discrepancy Organisation for Economic Co- particularly services, real output is imputed from labor between the rescaled GDP and the sum of the operation and Development (OECD) data files. The inputs, such as real wages or the number of employ- rescaled components. Because allocating the dis- World Bank rescales constant price data to a common ees. The real output of governments and other crepancy would give rise to distortions in the growth reference year. The complete national accounts time unpriced services are calculated in the same way. In rate, the discrepancy is left unallocated. series is available on the World Development Indicators the absence of well-defined measures of output, mea- 1999 CD-ROM. For information on the OECD national suring the real growth of services remains difficult. Changes in the System of National Accounts accounts series see the OECD's Main Economic Technical progress can lead to improvements in Most countries use the definitions of the UN System Indicators (monthly). The United Nations publishes production and in the quality of goods. If not properly of National Accounts (SNA), series F, no. 2, version 3, detailed national accounts for member countries in accounted for, either effect can distort measures of referred to as the 1968 SNA. Version 4 of the SNA National Accounts Statistics: Main Aggregates and value added and thus of growth. When inputs are used was completed in 1993. Until new economic surveys Detailed Tables; updates are published in the Monthly to estimate output, as in services, unmeasured tech- can be implemented, most countries will continue to Bulletin of Statistics. nical progress leads to underestimates of the quan- follow the 1968 SNA. A few low-income countries still tity and real value of output. Unmeasured changes in use concepts from older SNA guidelines, including val- the quality of goods produced also lead to under- uations such as factor cost, in describing major eco- estimates of real value. The result can be under- nomic aggregates. estimates of real growth and productivity change and overestimates of inflation. Informal activities pose a particular problem, espe- cially in developing countries, where much economic activity may go unrecorded. Obtaining a complete pic- ture of the economy requires estimating household outputs produced for local sale and for home use, barter exchanges, and illicit or deliberately unre- 1999 World Development Indicators 1 91 4.2 Structure of output Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GDP % of GDP % of GDP 1980 1997 1980 ±997 1980 1997 ±980 1997 ±980 1997 Alania 2,460 34 63 45 ......... 18 ..... ...............21 ........ 19 Algeria 42,345 47,072 10 11 54 49 9........936 39 A!ngola7,662 962 429 Argentina 76,962 325,012 6 7...... 41 33 29 ....... 22 .. ... 52 ....... 61 Armenia 1,628 41 36 25 . 23 Australia 160,109 393,519 5 3 36 ...... 26. 19. 14 .... ..58 ..........71 Austria 78,539 206,232 4 1 36 30 25 20 60 68 Azerbaijan 4,399 22 18 18 .................60 Bangla.esh . 17,430 41,419 34 24 24 27 18 17 .... 42 49 Belarus 22,629 144 742 Belgium 119,938 242,523 2 1... . .34 27 21 18 ........ 64.. ..... 72 Benin 1,405 2,141 35 38 12 14 8 852 48 Bolivia 2,750 7,7 16 33 19 51 Bosnia and Herzegovina Botswana 1,035 5,70.13 45 48 4 543 4 9 Brazil 234,873 820,381 1I 8 44 35 33 23 45 57 Bulgaria 20,040 10,085 14 23 54 26 18 32 50 Burkina Faso 1,709 2,395 33 35 22 27 16 20 45 38 Burundi 920 957 62 53 13 17 710 25 30 Cambodia 3,044 . 51 . :15.6 34 Cameroon 6,741 9,115 29 41 23 21 910 48 38 Canada 266,003 607,744 4 . .38 - 19 .. -58 Central African Republic 797 1,019 40 54 20 18 7 940 28 Chad 1,033 1,603 45 39 915 12 4646 Chile 27,572 77,082 7 73 73121 15 55 6 1 C~h'ina ...... 201,687 901,981 30 19, 49 49 41 3 72 132 Hong Kong, China 28,495 171,401 . . 0 32 15 24 767 84 Colombia 33,385 95,745 19 11 32 20 23 17 49 69 Congo, Dem. Rep. 14,922 6,101 25 . 58 3 71 42 25 Congo, Rep. 1,706 2,298 12 10 47 57 76 42 33 Costa Rica 4,815 9,521 18 15 27 23 19 17 55 62 C6te dIlvoire 10,175 10,251 26 27 . 20 21 13 18 54 51 Croatia 19,081 12 25 20 6 Cuba .1. . .. . . . .. . . . . . . .. . . . .. . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . . .. . . . . . .. . . . . . .. . . . ..: . . .. . . .. . . ! . . . .. . . . .. I . . .. . . .. . ... . . .. . . .. . . .:7. . . Czech Republic 29,042 52,035 7 63 Denmark 67,791 170,037 5 4 29 27 20 19 66 69 Dominican Republic 6,631 15,039 20 12 28 32 15 17 52 55 Ecuador 11,733 19,768 12 12 38 35 18 21 50 53 Egypt, Arab Rep. 22,912 75,605 18 18 37 32 12 25 45 51 El Salvador 3,574 11,264 38 13 22 28 16 21 4060 Eritrea 655 9 .30 16 61 Estonia 4,682 728176 Ethiopia' 5,179 6,381 56 55 12 7 8032 38 Finland 51,306 119,834 10 4 40 34 28 25 51 62 France 664,595 1,392,501 4 2 34 26 24 19 62 72 Gabon 4,279 5,153 7 7 60 55 5 533 37 Gambia, The 241 407 31 30 15 15 6 654 55 ....eo .......giaI........5,244..... : . ...24 .... 32 ...... 36 ........2328 18 . .. ... ...40 .........45 Germany 2,092.320.1 ..24.44 Ghana 4,445 6,884 58 36 12 26 8 930 39 Greece 48,613 122,946 14 11 25 18 16 1061 71 Guatemala 7,879 1-7,772 25 24 22 20 17 14 53 56 Guinea .. 3,888 .23.35 .4..42 Guinea-Bissau 111 266 42 54 19 11 . 9 3 Haiti 1,462 2,1 30.0*. Honduras 2,566 4,491 24 20 24 28 15 16 52 52 192 1999 World Development Indicators 4.2~~~~~~~~~~ Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GDP % of GOP % of GOP 1980 1997 1980 1997 1980 1997 1L980 1997 1980 1.997 Hungary 22,1i86 45,725 19 647 34 25 34 60 India 172,370 381,566 38 25 26 30 18 19 36 45 Indonesia 78,013 214,995 24 16 42 43 13 26 34 41 Iran, Islamnic Rep. 92,664 89,979 18 25 32 34 914 50 40 Iraq 47,562 !::.. ........ Ireland 20,080 75,030 ..6 .62 Israel 21,885 98,081 .. ~ Italy 449,913 1,145,560 6 339 31 28 20 55 66 Jamaica 2,652 4,135 88 38 35 17 16 54 57 Japan 1,059,254 4,190,233 4 242 38 29 24 54 60 Jordan 3,962 7,015 83 28 25 13 13 64 71 Kazakhstan : 22,165 ... . ....12 .. 27 . 61 Kenya 7,265 10,240 33 29 21 16 13 10 47 56 Korea, Dem. Rep.. .. Korea, Rep. 62,803 442,543 15 640 43 28 26 45 51 Kuwait 28,639 30,373 0 075 54 611 25 46 Kyrgyz Republic 1,764 . 45 ..23. 18 33 Lao PDR 1,753 52 . 21 . 16 26 Latvia 5,527 12 751 31 46 21 37 62 Lebanon 14,962 . 12 ..7 27 . 17..6 Lesotho 369 950 24 11 29 42 717 47 47 Libya 35,545 . .. ... ........ 2 . ..... ..... 76 ....2 ..... .... ..... .22 ..... .... Lithuania 9,585 . 13.. ...... ...... .32 21. 55 Macedonia, FYR 2,201 . 12 .27.61 Madagascar 4,042 3,546 30 32 16 14 . :11 54 55 Malawi 1,238 2,519 44 36 23 18 14 14 34 46 Malaysia 24,488 98,473 22 12 38 47 21 34 40 41 Mali 1,686 2,532 48 49 13 17 7 738 34 Mauritania 709 1,097 30 25 26 29 . 10 44 46 Mauritius 1,132 4,398 12 926 33 15 25 62 58 Mexico 223,505 402,963 8 533 26 22 20 59 69 Moldova 1,872 31 35 . 83 M ongolia .............. .......1862 ... 15 37 33 23 .. ... .. 52 .. .... .40 Morocco 18,821 33,514 18 15 31 33 17 18 51 51 Mozambcique 2,857 2,753 37 31 35 24 . 10 27 45 Myanmar 47 59 1 10 10 741 31 Namibia 2.262 3,280 11 11 55 33 914 34 56 Nepal 1,946 4,929 62 41 12 22 4 926 36 Netherlands 171,861 360,278 3 332 2 718 18 64 70 New Zealand 22,395. 64,572 11 3-1 2.5 Nicaragua 2,144 1,971 23 34 31 22 26 16 45 44 Niger 2,508 1,855 43 38 23 18 4 734 44 Nigeria 64,202 39,856 21 33 46 47 8 534 20 Norway 63,419 153,363 4 235 32 15 11 61 66 Oman 5,982 12,102 3..691..2 Pakistan 23,690 61,667 30 25 25 25 16 17 46 50 Panama 3,810 8,244 10 821 18 12 969 73 Papua New Guinea 2,548 4,639 33 28 27 36 10 940 36 Paraguay 4,579 10,180 29 23 27 22 16 14 44 5 Peru 20,651 63,849 10 7 42 36 20 23 48 57 Philippines 32,500 82,157 25 19 39 32 26 22 36 49 Poland 57,068 135,659 ..6 3..55 Portugal 28,729 102,133 ..4 .32. 22 64 Puerto Rico 14,436 42,364 339 .37 . 58 Romania 34,843 20 . 45 ..36 Russian.Federation 446,982 8 37 ... .55 1999 World Development Indicators 193 4.2 Gross domestic Agriculture Industry Manufacturing Services product value added value added value added value added $ millions % of GDP % of GOP % of GOP % of GDP 1980 1997 1980 1.997 1980 1997 1980 1997 1.980 1997 Rwanda 1,163 1,863 50 37 23 26 17 19 27 36 Senegal 2,986 4,542 19 18 15 22 11 15 66 59 Singapore 11,718 96,319 1 038 35 29 24 61 65 Slovak Republic . 19,461 533 62 Slovenia 18,201 539. 29 . 57 South Africa 78.744 129,094 7 550 39 23 24 43 57 Spain . ..... . . .. 213,308.. 532,034...... .... .. . ...I 3 .... ...... . ..................I . 25... ................... Sweden 125,557 227,639 4..34 :. 23 63 Sw itzerland.......... .... ... .... . .... 107,474..255,265 .. .......... ... ... I .. . ..... ... Syrian Arab Republic 13,062 17,899 20 23 56 Tajikistan .. 1,990 Tanzaniab.. .. .. .. . I. .. . . ... .. .. 6.9 2 0 . . . ..... ..... 4 7 ... . .. .21 .... . .. .. . ... . 7 . . .. . .. ... .. . ... 31 Tunisia 8,742 18,937 14 13 31 29 12 19 55 58 Turkey 68,824 189,878 2 615 2 228 14 18 5 1 57 Turkmenistan .. 4.397 . Uganda 1,244 6.582 7 2444 17 4 823 39 U kr in .. ........... .. ...... .. .. 49,677 ...::.... . . ....12. .406 .. .. 6 .... 4 ... .....8 United Arab Emirates 29,625 39,107 1...........77.4 ........22... United Kingdom 537,383 1,286,488 2 2........43 . ..... 31 27........ 21......... 55. .67 United States 2.709,000 7,834,036 3 233 27 22 18 64 71 Uzbekistan .. 25,047 . 31 . 27 .9. 42 Venezuela 69,369 87,480 54 46 41 16 16 49 55 Vietnam .. 24,848 26 31 ~~~~~..... . ....43.... West Bank and Gaza Yemen, Rep. .. 5,656 . 8. 49 .1 34 Yugoslavia, FR (Serb/Mont.) .. .. .. ..~~~~~~~~~~~~~~...:..~....... 11............ ............ Low income 529,916 752,857 36 28 25 28 16 17 39 43 Middle income 2,530,074 5,408,717 16 11 42 37 27 24 4152 Upper middle income 1,179.965 2,708,856 10 843 34 26 21 47 58 Low & middle income 3,063,059 6,160,626 18 13 40 36 25 23 41 51 East Asia & Pacific 444,404 1,528,013 28 18 43 45 31 33 29 37 Europe & Central Asia ..1,139,383 . 12 .34. 54 Latin America & Carib. 782,093 2,088,919 10 840 32 28 21 50 60 Middle East & N. Africa 391,073 498,583 10 14 53 389 1437 48 South Asia 223.083 512,453 37 25 26 29 17 19 38 46 Sub-Saharan Africa 269.559 326,714 18 18 39 34 16 17 43 48 High income 7,990,861 22,848,467 4 237 31 25 21 59 63 Europe EMU . 6,283,899 2 21 58 a. Data prior to 1992 include Eritrea. b. Oats cover mainiand Tanzania only. 194 1999 World Development Indicators 4.2 Output by industrial origin is measured by the value of Industrial output ideally should be measured * Gross domestic product at purchasers' prices is the gross output of producers less the value of interme- through regular censuses and surveys of firms. But in sum of the gross value added by all resident and non- diate goods and services consumed in production. most developing countries such surveys are infre- resident producers in the economy plus indirect taxes This concept is known as value added. A country's quent and quickly go out of date, so many results and minus any subsidies not included in the value of gross domestic product represents the sum of value must be extrapolated. The choice of sampling unit, the products. It is calculated without making deduc- added by all producers in that country. Since 1968 the which may be the enterprise (where responses may tions for depreciation of fabricated assets or for deple- United Nations System of National Accounts (SNA) be based on financial records) or the establishment tion and degradation of natural resources. * Value has called for estimates of value added by industrial (where production units may be recorded separately), added is the net output of a sector after adding up all origin to be valued at either basic prices (excluding net also affects the quality of the data. Moreover, much outputs and subtracting intermediate inputs. The indus- taxes on products) or producer prices (including net industrial production is organized not in firms but in trial origin of value added is determined by the taxes on products paid by the producers, but exclud- unincorporated or owner-operated ventures that are International Standard Industrial Classification (ISIC) ing sales or value added taxes). Some countries, how- not captured by surveys aimed at the formal sector. revision 2. * Agriculture corresponds to ISIC divisions ever, report such data at market prices-the prices at Even in large industries, where regular surveys are 1-5 and includes forestry and fishing. * Industry com- which final sales are made-which may affect esti- more likely, evasion of excise and other taxes lowers prises value added in mining, manufacturing (also mates of the distribution of output. Total GDP as the estimates of value added. Such problems become reported as a separate subgroup), construction, elec- shown in the table and elsewhere in this book is mea- more acute as countries move from state control of tricity,water,andgas.* Manufacturingreferstoindus- sured at market prices. Value added by industry is nor- industry to private enterprise, because new firms tries belonging to divisions 15-37. * Services mally measured at basic prices. When value added is enter business and growing numbers of established correspond to ISIC divisions 50-99. valued at purchasers' prices, this is noted in Primary firms fail to report. In accordance with the SNA, out- data documentation. put should include all such unreported activity as well Dat sources While GDP by industrial origin is generally more reli- as the value of illegal activities and other unrecorded, able than estimates compiled from income or expen- informal, or small-scale operations. Data on these The national accounts indica- diture accounts, different countries use different activities need to be collected using techniques other tors for developing countries definitions, methods, and reporting standards. World than conventional surveys..' are collected from national Bankstaffreviewthequalityofnationalaccountsdata In industries dominated by large organizations and , .*:ljr-.i,{j . statistical organizations and and sometimes make adjustments to increase con- enterprises, such as public utilities, data on output, ' central banks by visiting and sistency with international guidelines. Nevertheless, employment, and wages are usually readily available . i t resident World Bank missions. significant discrepancies remain between interna- and reasonably reliable. But in the service industry .t.ii t Data for industrial countries tional standards and actual practice. Many statistical the many self-employed workers and one-person busi- come from Organisation for offices, especially those in developing countries, face nesses are sometimes difficult to locate, and their Economic Co-operation and Development (OECD) data severe limits on the resources, time, training, and owners have little incentive to respond to surveys, let files (see the OECD's Main Economic Indicators). The budgets required to produce reliable and comprehen- alone report their full earnings. Compounding these complete national accounts time series is available on sive series of national accounts. problems are the many forms of economic activity that the World Development Indicators 1999 CD-ROM. The go unrecorded, including the work that women and United Nations also publishes detailed national Data problems In measuring output children do for little or no pay. For further discussion accounts for member countries in National Accounts Among the difficulties faced by compilers of national of the problems of using national accounts data see Statistics: Main Aggregates and Detailed Tables; accounts is the extent of unreported economic activ- Srinivasan (1994) and Heston (1994). updates are published in the Monthly Bulletin of ity in the informal or secondary economy. In develop- Statistics. ing countries a large share of agricultural output is Dollar conversion either not exchanged (because it is consumed within To produce national accounts aggregates that are the household) or not exchanged for money. internationally comparable, the value of output must Agricultural production often must be estimated indi- be converted to a common currency. The World Bank rectly, using a combination of methods involving esti- conventionally uses the U.S. dollar and applies the mates of inputs, yields, and area under cultivation. This average official exchange rate reported by the approach sometimes leads to crude approximations International Monetary Fund for the year shown. An that can differ over time and across crops for reasons alternative conversion factor is applied if the official other than climatic conditions or farming techniques. exchange rate is judged to diverge by an exceptionally Similarly, agricultural inputs, which cannot easily be large margin from the rate effectively applied to allocated to specific outputs, are frequently "netted domestic transactions in foreign currencies and out" using equally crude and ad hoc approximations. traded products. Shares of output by industrial origin For further discussion of the measurement of agricul- are calculated from data in local currencies and cur- tural production see About the data for table 3.3. rent prices. 1999 World Development Indicators 195 4.3 Structure of manufacturing Value added in Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing and equipment tobacco $ millions % of total % of total % oftotal % of total % of total ±980 1996 1980 1998 1980 1996 1980 1996 1980 1996 1980 1996 Albania ..-: : Algeria 3,257 3,4783 273 1327 1 1.......8 14.. . .4 ........ 0 ... ..110. ...15.3...... 5.54 43 .... 54.5 Angola 257 Argntina 22,685 66,686 19 . 3 . 994 Arm enia 367 ..........._ ..... .. Australia 30,722 56,576 17 7 - 21 7 46 Austria 19,263 45,387 16 19 10 16 25 16 7 8 42 4 Azerbaijan 652 Bgan.aeh3,101 6,783 24 34 16 14 Belarus 6,111 Bellgim. 2,73 48,881 17 18. 2 1 1 0 5 Benin 112 .183 59 .. 14 .. . . 6 .. 21 Bolivia . 1,408 28 34 11 5 4 1 3 3 54 7 Bosnia and Herzegovina .. .. ..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....... . .....I...I........... ... ...... Botswana 43 234 . . 46. 16 .. 38 Brazil 70,984 150,057 1411 . 25 .. 11 40 Bulgaria... 1,944 19 .. 10 12 .. .. 58 Burkina Faso 261 455 59. 19 . 3 .. 1 1 Burundi 63 75 . . ... Cambodia .. 168 ... . .. Cameroon 593 903 56 3-1 9 8 4 1 3 3 29 56 Canada 46,128 ..14 14 7 5 23 30 8 9 48 42 Central African Republic 54 104 49. 22 . 8. 11 10 Chad .. 181 . . . . Chile 5,911 10,736 27 28 9 6 6 4 8 12 51 49 China 81,836 308,941 10 14 18 14 22 25 11 10 38 38 Hong Kong, China 6,392 10,352 5 . 42 . 18 . 2 .. 34 Colombia 7,769 14,796 . 32 12 .:. 7 . 63 C onpgo, D am . R ep. 2,144 ... ........ ............... ..... .................. ....... ...................... Congo Rep.. 128 ...... 164 35 . 16 ...5...44 Costa Rica 895 1,664 46 46 10 7 8 9 7 12 28 26 CSte d'lvoire 1,304 1,866 35 15 . 10 ..40 Croatia . 2,847. ... ... Cuba . 55 . 7. . 1 ..37 Czech.Reublc. Denmark 11,411 29,628 24 5 . 25 . 10 37 Dominican Republic 1,015 2,265 66 . 6 . I . 6 21 Ecuador 2,072 4,123 .. . .. .. Egypt, Arab Rep ~~~~~2,678 15,354 19 . 30 11.. 931 El Salvador 589 2,156 37 47 22 37 4 1 11 5 27 10 Eritrea ..85 . .. .. . Estonia .. 649 ~~~~~........ . . ......... . . Ethiopia 3811 . . :.. I.., .. Finland 13,019 28,129 .. 11 . 3 .. 26.. 8 France 160,811 290,184 13 13 8 12 30 30 8 52 41 42 Gabon 195 262 24 . 4 . 9 4 . 58 Gambia,The 12 22 35 . 2 . .. 3 .. 60 Georgia ~~~~~~~~~~~~~838...... ....... Germany. 556,177 . . .*. Ghana 347 598 37 43 11 5 2 2 5 7 46 43 Greece 6,968 10,719 18.28 23 14 14 12 8 11 37 35 Guatemala 1,312 2,190 39 . 10 5 . 17 28 Guinea-Bissau19 ... . .. Honduras 344 637 51 64 9 7 2 0 5 7 34 49 196 1999 Worid Deveiopment Indicators 4.3~~~~~~~~ Value added in Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing and equipment tobacco $ millions % of total % of total % of total % of total % of total 1980 1996 ±980 1996 1980 1996 1980 1996 1960 1996 1980 1996 Hungary 8,918 11.28 23 11 13i4 India 2,36 62,704 9 .. 21 .. 25 .. 14 .. 30 Indonesia 10,133 58,244 32 23 14 19 13 15 11 9 30 34 Iran, Islamic Rep. 8,567 12,64-1 29. 35 . 12 . 3 21 Ireland . . 29 25 8 2 17 34 14 20 32 18 Israel . 12 20 12 15 26 16 8 6 42 42 Italy 1~~26,012 2.44,901 9 .. 12 . 29 11 39 Jamaica 441 ..681 47.. 6 ...:. . .. 37 Japan 309,747 1,117,435 9 9 7 4 33 39 9 11 43 3 Jordan 447 754 23 30 7 17 1 4 7 2 62 48 Kazakhstan.. . ... Kenya 796 799 34 44 12 9 15 10 9 9 30 29 Korea, Dem. Rep.* Korea, Rep. 17,686 12531.1 8.19 9 17 41 10 8 36 35 Kuwait 1,581 2,913 7 7 5 7 4 7 7 3 76 76 Kyrgyz Republic 202 Lao PDR . 287 : ~ Latvia 937 39-.. 11 . 20 .. 5. 26 Lebanon 2,018.. . ... Lesotho 21 121 73 7 ... 4 .. 16 Libya 682 3 10 . .. . 16 .. 43 Lithuania . 1,581... . Macedonia, FYR ..24 . 22 . 16 .. 10 29 Madagascr. 428 34 45 . 3 6 . 13 Malawi 152 292 58 12 . 4 .. 5 . 20 Malaysia 5,054 34,030 24 8 7 5 20 40 5 9 43 38 Mali 106 180 29 51 .. 8.. . 11 Mauritania .. 117.. Mauritius 147 915 36 30. 6 . 6 .. 23 Mexico 49,445 65,088 . 29 3 22 . 17 28 Moldova 387 Mongolia 25 .. 35 . 1 Morocco 3,167 6,252 25 . 19 8 . 52 Mozambique .. 167 . . Myanmar. Namibia 187 361 Nepal 78 407 ! Netherlands 30,866 70,407 23 24 4 3 2 7 25 14 14 32 35 New Zealand 4,950 9,504 26 22 11 14 17 19 6 5 40 4 Nicaragua 552 316 53 49 8 14 1 . 10 9 28 20 Niger 94 129 30 25 2 16I 28 Nigeria 5,195 1,657 21 13 13 . 13 3 Norway 9,196 17,602 15 23 4 2 27 26 7 8 48 4.1 Oman 39 :: . Pakistan 3,389 9,859 32 22 22 38 9 5 12 11 25 2 Panama 408 704 49 45 10 7 2 2 6 4 28 42 Papua New Guinea 242 457 40 1 16 . 3 41 Paraguay 733 1,230 . . . Peru 4,173 14,158 25 13 . 3 . 10 4 Philippines 8,354 18,908 30 38 13 9 12 9 14 11 31 32 Poland ..12 132831 Portugal. 23,536. .3 15.22 20 16 15 7 2 46 Puerto Rico 5,306 17 . 5 . 13 . 52. 1 Romania. . ... Russian Federation .17 . 5 . 25 . 9 .- 40 1999 World Development Indicators 197 4.3 Value added in Food, Textiles Machinery Chemicals Other manufacturing beverages, and clothing and transport manufacturing and equipment tobacco $ millions % of total %of total % of total % of total %of total 1980 1996 1980 1.996 1980 1996 1980 1996 1.980 1996 1980 1996 Saudi Arabia 7,740 12,737 . Sierra Leone 54 53 51 .:.... 5 ..44.. .... Singapore 3,415 23,520 5 4 5 1 44 59 5 8 41 28 Slovak Republic .. .......13 .... ....... 10 20 .10 47 Slovenia 4,553 . 1 5 ., 11 : 18 13 43 Spain 103,623 16 18 12 7 2 79 1 1 3 Sri Lanka 670 2,015 - Sudan 518 Sweden 26,293 38,821 10 9 3 1 33 34 7 11 47 44 Switzerland1 . 4 29- 57 Syrian Arab Republic...... Tajikistan . ... Tanzaniab 9 3 . 38 630 Thailand 6,960 51,525 55...... 55...... 83 3..........8. 7.. 8.............21..I.... 24... . .... Turkey 9,337 29,415 18 13 15 17 14 19 10 12 42 39 Turkmenistan . Uaa.53.................. 432. . ...... . . . ...... Ukraine 2,632 United Arab Emirates 1,130 ... ... : . .12 ..2 . ...... . .......7..77. United Kingdom 125,830 213,752 13 14 6 5 33 31 10 13 38 37 United States 589,100 1,343,500 11 12 6 5 34 33 10 12 40 38 Uzbekistan .. 1,720 . . . Venezuela 11,111 12,459 19.. . ...7 ... ..... 9 ... .... 8... .... 57.. Vietnam West Bank and Gaza ..... ' Yemen, Rep . ......... ..... .. . 546 Yugoslavia, FR (Serb./Mont.) Low income 64,515 107,331 Middle income 419,041 1,193,688 Lower middle income 214,829 708,160 Upper middle income 486,771 Low & middle income 488,395 1,300,894 East Asia & Pacific 120,724 484,195 Europe & Central Asia Latin America & Carib. 377,773 Middle East & N. Africa 62,650 South Asia 35,172 83,115 Sub-Saharan Africa 33,031 43,094 High Income.1,913,783.4.688,394. Note: Shares may not add to 100 percent dae to rounding asd unallocated data. a. Includes Eritrea. b. Data cover mainland Tanzania only. 198 1999 World Development Indicators 4.3 Data on the distribution of manufacturing value added nomic behavior. The ISIC emphasizes commonalities * Value added in manufacturing is the sum of gross by industry are provided by the United Nations in the production process and is explicitly not intended output less the value of intermediate inputs used in Industrial Development Organization (UNIDO). UNIDO to measure outputs (for which there is a newly devel- production for industries classified in ISIC major divi- obtains data on manufacturing value added from a oped Central Product Classification); nevertheless, the sion 3. * Food, beverages, and tobacco comprise variety of national and international sources, includ- ISICviewsan activityasdefined by "...a process result- ISIC division 31. * Textiles and clothing comprise ing the United Nations Statistical Division, the World ing in a homogeneous set of products" (United Nations ISIC division 32. * Machinery and transport equip- Bank,theOrganisationforEconomicCo-operation and 199Ob [ISIC, series M, no. 4, rev. 3], p. 9). Typically, ment comprise ISIC groups 382-84. * Chemicals Development, and the International Monetary Fund. firms use a multitude of processes to produce a final comprise ISIC groups 351 and 352. * Other manu- To improve comparability over time and across coun- product. For example, an automobile manufacturer facturing includes wood and related products (ISIC tries, UNIDO supplements these data with information engages in forging, welding, and painting as well as division 33), paper and related products (ISIC division from industrial censuses, statistics supplied by advertising, accounting, and many other service activ- 34), petroleum and related products (ISIC groups national and international organizations, unpublished ities. In some cases the processes may be carried out 353-56), basic metals and mineral products (ISIC data that it collects in the field, and estimates by the by different technical units within the larger enterprise, divisions 36 and 37), fabricated metal products and UNIDO Secretariat. Nevertheless, coverage may be but collecting data at such a detailed level is not prac- professional goods (ISIC groups 381 and 385), and less than complete, particularly for the informal sec- tical. Nor would it be useful to record production data other industries (ISIC group 390). When data for tex- tor. To the extent that direct information on inputs and at the very highest level of a large, multiplant, multi- tiles and clothing, machinery and transport equip- outputs is not available, estimates may be used that product firm. The ISIC has therefore adopted as the ment, or chemicals are shown as not available, they mayresult in errors in industrytotals. Moreover, coun- definition of an establishment "an enterprise or part are included in other manufacturing. tries use different reference periods (calendar or fis- of an enterprise which independently engages in one. cal year) and valuation methods (basic, producers', or or predominantly one, kind of economic activity at or Data sources purchasers' prices) to estimate value added. (See from one location ... for which data are available..." also About the data for table 4.2.) (United Nations 199Ob, p. 25). By design, this defini * Data on value added in man- Data on manufacturing value added in U.S. dollars tion matches the reporting unit required for the pro- ufacturing in U.S. dollars are are from the World Bank's national accounts files. duction accounts of the SNA. , * - from the World Bank's These figures may differ from those used by UNI DO to . .f national accounts files. The calculate the shares of value added by industry. Thus . -- - data used to calculate shares estimates of value added in a particular industry Dominant countries drive regional trends of value added by industry are group calculated by applying the shares to total value in manufacturing growth F i>, provided to the World Bank in added will not match those from UNIDO sources. i : . electronic files by UNIDO. The The classification of manufacturing industries in :.:. most recent published source is UNIDO's the table accords with the United Nations Inter- International Yearbook of Industrial Statistics 1997. national Standard Industrial Classification (ISIC) revi- ' The tSIC system is described in the United Nations' sion 2. First published in 1948, the ISIC has its roots International Standard Industrial Class/ficat/on of all in the work of the League of Nations Committee of Economic Activities, Third Revision (1990b). The dis- Statistical Experts. The committee's efforts, inter- ,,,, cussion of the ISIC draws on Jacob Ryten's paper rupted by the second world war, were taken up by the "Fifly Years of ISIC: Historical Origins and Future United Nations Statistical Commission, which at its Perspectives" (1998). first session appointed a committee on industrial clas- sification. The ISIC has been revised at approximately . ; 20-year intervals. The last revision, ISIC revision 3, - - , , was completed in 1989. Revision 2 is still widely used . . for compiling cross-country data, however, and con- r.n.jj' e>* :. cordances matching [SIC categories to national sys- _ jr .,: -.1, ,. - ;. air,: tems of classification and to related systems such as ,,r c ..] e ,,. i the Standard International Trade Classification (SITC) are readily available. Regiona' trends in manufacturing growth reflect trends in ihe dominant countires. Led b~ man In establishing a classification system, compilers Lfacttr:ng groslh in China. East Asia and the Pacific mustdefine boththe types ofactivities to be described has overtaken Laliri America and tie Caribbean. and the organizational units whose activities areto be where growth is faitering ,n Brazil. South Asia is r.sing with India. In Sub-Saharan Atrica. where reported. There are many possibilities, and the there is no dominari manufacturer. gra*Lh has choices made affect how the resulting statistics can beer slow. be interpreted and how useful they are in analyzing eco- 1999 World Development Indicators 199 4.4 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % grow h % growth 1995 =100 1980-90 1.99G-96 1.980-90 ±1990-96 ±L980-90 ±990-96 1980-90 1990-96 1L990 1996 Albania -5..9.. 7.2 . .. Algeria 4.9 -. -69 18 -0.8 0.0 -. -39.4 126 124 Angola 13.8 6.4 -1.7 1010-0 8.63.1 ... .... 3.6 ........108 ........103 Argentina 2.8 1.7 -4.4 18.5 51 3.3 0.2 20.2 90 89 Armenia' ... . 1 Australia' 6.2 0,2 5.5 10 6.6 0 .7 64 .1... .2.... .......... Austriat 6.6 5.7 .10..2 ........1.4 ... . ..8.7 1..... I0........ Azerbaijan Bangladesh 10.0 9.6 6.0 8.4 12.0 10 0 104.4 ... 11.1 ........108 .........99 Belarus Belgium Benin -6.2 -17.2 -6.6 20.8 -2.4 -14.0 -2.5 29 9 ........109. .. 102 Bolivia 2.7 -3.8 -2.2 7.9 1.0 -5~O........02 .8 ... . 9 1 .... . 124 . ......111 Bosnia and Herzegovina Botswanaa .. 17.9 0.6 9A.4 0....... O 3 .... .................... Brazil 5.5 2.2 3.3 3.8 5.8 3.8 49.9... . 4.3 97 ....... 106 Bulgaria' -3.8 -3.3 -12.5 2.6 3.3 Burkina Faso 7.7...... -17.0 4.1 ..... -2.1 13.4 -. .16.0 ..... ..8 .5 ........ 1.4 ..... .. 117. 102 Burundi' . 250 2~2 .......--0. Cambodia ! . Cameroon 10.5 6.0 -1.1 -9.2 9.9 6.7 3.3 -8.1 110 112 Canadaa 6.4 1.0 7:4.4 ...... 0.8 6.8 1.2 .......7.9 0....... .O8 - .. ................. Central African Republic -3.9 14.1 0.9 -0.7 -1.1 19.3 6 0 1.1 88 101 Chad 2.5 4.7 2.0 -1.7 8.8 7~4........ 64 -0.7 98 112 Chile ...68..... 6.2 10.4 7.6 15.0 5:2 . 14.3 ..........8.4 .........96 ... .....76 Chmnatfe' ........ ........ 12 .8 ..... ..1.6 .. .. ..13 5. ..1.0 Colombia 13.6 0.5 -1.7 -1.9 11.3 1.5 2 4-1.1 98 93 Congo, Dem. Rep............. -3.7 -3.8 1 5 -7.5 0.6 -5~.0. ..... 6 3-5.4 125 101 Congo, Rep ~~~~~-0.6 3.2 -61 1.8 -4.0 2 8 -22 2 113 10 Costa Rica 5 8 3.9 6.3 5.4 70 6.6 10.55.6 86 96 M6e d'lvoire 3.1 -1.3 -18 13.0 3.5 0:6 ........24 .........148.. 100....103. Croatia' . 1.32 Cuba -7.0 -21.1 -5.5 -3 9 -3.4 -18.9 -10 -2.8 95 104 Czech Republic .... ... . 32.7 Denmark' 4.1 0.2 3.1 0.4 8.4 1.0 ........6.3 1.2 ........... Dominican Republic 01 2.3 8 2 -2.1 4.3 3.3 7.9 85 88 Ecuador 2.2 6.9 -2.4 1.4 -01 4.1 2.62.2 128 108 Egypt, Arab Rep.-12 ....... 8.7 -6.2 7.1 -2.6 8:5.5 .. . -1793 116 103 El Salvador -5.5 5.7 -0.2 6.3 -4.7 1.8 3.4 36 102 90 Eritrea...... Estonia . .. .. Ethiopia 1.0 6.2 -1.0 7.6 -1.6 8.3 21 . .......9~.1 ......................... Francea 3.6 0.5 3.7 0.4 7.5 1.1 6.510 Gabon 0.7 1.5 -5.8 -2.6 -1.2 0.2 -0'9 2.6 163 127 Gambia, The -2.9 -12.0 4.4 -3.7 -3.3 -11.3 9.2 -1.2 109 101 Georgia Germ any'.' 4:5 ..... .... ... . .. 4.9 ...... 9.2 .........1:.1 ....... 7.1 . .... ...0.7 Ghana 118 ..... 4.0 7.8 14.8 10.9 4.0 ...... 116.6 ... .. 166 ..... 112. 100 Greece' 5.0.6.4 .5.8 08 ........6~6.06........... Guatemala -1. 2.5 2.7 6,4 -0.1 2.2 6.3 69 97 87 Guinea . ..41 1.0. Guinea-BissaUa ... . -4.6 0.0 -5.21.4 . Haiti' -1.2 0.2 -2.9 . ........28 ..................... Honduras 0.7 10.6 -2.9 11.5 3.0 4.6 0.8 10.8 132 97 t Data for Taiwan, China' 11.6 0.3 9.9 0.4 14.8 0.9 12.4 1.1 . 200 1999 World Development Indicators Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1.980-90 1990-96 1980-90 1990-96 19180-90 1990-96 1.980-90 1990-96 1990 1996 Hungarya 3.4 4 0.5 1.3 .. ... 0.1 1.4 0.8 0.1.. Ol: 0.0 .........I. India 8.0 3.6 5.7 2.5 .... 110. ... . 5:2 7.... .. 3 30 92....... 98 Indonesia 7.1 8.0 0.7 3.4 2.0 6j 7.. 4.6 4~3.. 118 104 Iran, Islamic Rep. 1.1 4.8 -79 .0.3 -4.25. -354 1512 Iraq 2.1 -7.3 -12.3 -12.8 -3.2 -9.0 .......-7 7 -11.3 121 ...... . 91 Ireland' .... 9:3.3 ... . 1.4 4.8 IIl ..... 12.7 1.8 . ...70O ... . . 16.6 . ... Israel0 6.9 10 . .... 5.8 0.7 8..3 . .... 11 ....... 5~9 ..... ..1.0 ......... Italy' 4.4 1.0 5.4 0.5 8.7 1-2 6.9 ..... 0:9.9 ..... .... Jamaica 4.5 4.7 4.1 6.4 8.2 0.7 6:07.4 105........ 99 Japana 5.1 0.1 6.6 0.8 8.9 .7. DJ ..... .1 ..........1.2 ........ Jordan ~~6.8 2.5 -6.2 4.1 8.9 ... ... 3.9 .....-2.6 5.0 100 ....... 101 Kazakhstan . . Kenya 3.2_ ...._7.3 5.0 6.0 0.1 8~5. 7. 7.6 105 ~ 98 Korea, Dem. Rep.... ... Korea, Rep.1. 6.3 12.6 5.4 -16.7 _5:1 15.6 58 101 92 Kuwait -1.1 ....135 -10.4 ... 5.8 -5.4 ...18~0 ..... -5.8 5.3 ..... 91.... 109 Kyrgyz Republic .. ............. ....... Lao PDRa 11.2 1.5 .... .7.4 ........2 4.4 ...... ...... ... Latvia .. .. .. Lebanon 1.2 .... 10.2 .... .-8.2 ...... 5.9 4.5 13.5 -38.8 92 2....... 104 ill Lesotho0 ..3.0 1.3 ..... .34.4 0:3.3 ...........7.. Libya 3.1 -6.2 -7.0 3.1 -2.8 -5.1 -2.1 8.8 146 127 Lithuania .. .. Macedonia, FYR Madagascar 2.4 -1.4 -1.4 2.0 0-0 1.2 ........2.7 37...98..1027 Malawi 1.8 0.6 6.2 -0.6 5-0 -1.5 10.7 0.8 125 104 Malaysia-..... .10.8 ....... 5.2 7.0 .5.3 10.6 .6.5 ... 11.3 ....... 6:5 ........102 102 Mali 7.3 4.7 2.9 -112 ....13.4 6.2 .... 72 ... 132 ....I..101 .......102 M auritani 140........7 ....... I.... ... ... 3.8 8 0 . 1.2.. -2... .. ...21 4:0 _..... 111 .. .... . 98 Mauritius 9.8 -2,4 17.9 4.6 20.9 -2.4 23.2 5.9 94 104 Mexico 3.2 7.9 11.3 8.7 1.2 4.0 16.7 8.4 123 100 Moldova ...... Mongoliaa . -0.5 2.7 -3.8 3.4 Morocco 5.4 0.0 5.5 -2.0 .11.2 --0.3 8.7 3.7 131 104 Mozambique -2.8 2.8 3.9 -6.0 1.5 3.3 ........7 .5.. -5.3 ... 102 ...... 103 Myanmar -9.8 16.0 -7.0 18.3 -7.3 14.9 -2.2 20.3 115 97 Namibia Netherlands' 4:5 0.7 4.5 0.8 4.6 1.3 .. 4.4 1..3.......... ....I..... New Zealand0 3.5 ... .... 0 2 .. .....4 3 .. 1.1 .... 6..2 1 0.0 ....... 5.4 .... .... 1!7.7 ......... ... Nicaragu -56.. .....I.... ... - 2 7... ... . -7.7 ..10.5 ..41. -2.. .7.-45 9.3..100 .. 85 Niger.... ...... .... .... -5.1 1.2 -0.1 4 7 0.0 -0.8 3.1 ..64.4 ....... 119 100 Nigeria..........1:8.8 ....... 9.5.... -14.8 41 1 .... -2.6 89 9 -10.3 9.5 160 ... 128 Norway' 4.1 0 6 3.4 0.7 5.3 1.0 6.2 172.2.... Om an ... .................:....... ..... ... .. 2.9 0..... O 8 0j.7 072.2 ..... ... Pakistan 10.0 3 6 1.4 4.4 11.1 6.7 4.4 5.8 88 98 Panama 1.5 12.4 -6.2 6.9 1.3 13.0 -3.7 9.7 ill 103 Papua.New Guinea 6.7 -0.6 .. ..... ..... ..... ..... 4..9 . ......0.7 ... ....1.3 -0.. .. . 8 ........... Paraguay 15.9 -5.5 5.7 -20.7 20.6 -14.6 9.3 -20.1_ 170 100 Peru -3.5 5.5 0.5 7.7 1.1 3.9 .... 15.6 8.0 .... 112 95 Philippines 3.8 5.7 7.2 6.8 8.3 7.0 9.9 7.3 94 99 Poland' .... ... .4.8 .... . 1.1 1.5 ... 0.9.. 1.4 1.5 -3.2 0.5 Portugal0'19 . 15.1 . 15.1 1.4 030.9 Puerto Rico Rom ania8-........ ....!............. . ......... I......... -4 -.0 .. 2:6 ..... -3 8 3!2 1999 World Development Indicators 201 4.4 Export Import Export Import Net barter volume volume value value terms of trade average annual average annual average annual average annual % growth % growth % growth % growth 1995 = 100 1980-90 1990-96 1980-90 1990-96 1980-90 1990-96 1980-90 1990-96 1990 1996 Rwanda...... .....I. 4.3 ... -3.8 -1.8 42.0 2.4 -5.6 .......2.6 42.2 119......... 84 Saudi Arabia 2.8 3.5 -11.4 1.3 -2.0 5.1 -68 6.5 133 116 Se~negal 0.3 6.4 1.2 5.3...... 4.4 8.3 41 ......64.4... 96.. 102 Sierra Leone -1.6 -22.2 -3.2 1.8 2.5 -2.7 0714107 112 Singaporea 12.1 1.3 8.6 1.0 9.9 1.6 8.0 1.5 Slovak Republic Slovenia : South Africaa .... ... 3.3 08...O. ...... -08 .... 0 .8 .... 0.8 .. .. 0 6. -1 31 6.6 ....... .......... Spain 3 0 .. 8.4 ...10.9 1.3 10.6 1.0 ......... Sri Lanka 5.6 9.0 2.8 9.0 6.0 5.8 5.9 8.2..... 93 ..... 93 Sudan -3. 7 -0.6 -6 .6.. . 4.7 1.0 -4.0 -2.8 5.3 ... 124...... 97 Swedena 4.4 0.2 5.0 0.3 8.0 1:6... .6~7 1.4........ Switzerland' 3.7 ... .... .... .4.3 ...... .. .... 9.5 .... .1.0 8.8 . 1. 0 Syrian Arab Republic 11.1 0.3 -132 03 8.8 19 -10.5 3.1 Ill 114 Tajikistan Tanzania ....... ..3.. -4.4 -1.4 -3.4 -0.5 -4~3 21 -2:0........ 107....... 102 Thailand 18.3 8.4 17.3 3.6 22.0 6.5 20.2 4.1 ..... .114 105 Togo8.8 9.3 8.6 8.3 7.0 9.6 123 .......9.9 105 102 Trinidad and Tbago0.2 2.5 -11.5 7.1 -4.3 6.2 1-9.8.108..116 126 Tunisia 110 1.9 4.7 1.1 10.5 3.2 8.3 4.1 ... .... 114...... 104 Turkey' ..... . .... .0-6 1...... ...... . 0 ' 14.0 1.3 9.3 1.9 Turkmenistan :: Uganda -6.2 30.0 -3.4 24.7 -11.3 29.1 .....- 1 5. 26.5 124 106 Ukraine United Arab Emirates 10.6 1.2 1:4 8.0 3.4 3 4 5.9 13:2.2.... .. 131 117 United Kingdom' 4.5 .... 0.8 ..... 6.7 ..... 0.6 5.8 1:0 ... ..8.4 ..1:0.0 ... ..... %......... 7.. United States' 3.6 0.6 7.2 0.9 5.7 0.8 ... 8-.2 ... .....1:1.1 . ....... :....... Uruguay ~~~3.6 -0.5 6-5 6.7 9.61.4 9.793 9 Uzbekistan Venezuela . ... ..... 4.0 .... 0.6 . . -4~9 ... ... 5.7 -09 ... -0,6 ..... .0:0 78.8 . 130 .. 108 Vietnam ' ...... ....I. .... .... ........ .....18.9 .... 2 7.7 . 8.7 ... 3 2.2 Yemen, Rep. Yugoslavia, FR (Serb./Mont.)... ....... ...... ...... Zambia -0.7 14.0 -0.5 4.7 8.2 8.9 3.3 8.3 152 102 Zimbabwea 2~.0... -0.6.. 2.5 13.3 -0.4 . a. Data are from the International Monetary Fund's International Financial Statistics database. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 202 1999 World Development Indlicators 4.4 Data on international trade in goods are recorded in and the World Trade Organization also compile data * Growth rates of export and import volumes are each country's balance of payments and by customs on trade prices and volumes. The growth rates and average annual growth rates calculated for low- and services. While the balance of payments focuses on terms of trade for low- and middle-income economies middle-income economies from UNCTAD's quantum the financial transactions that accompany trade, cus- were calculated from index numbers compiled by index series or from export and import data deflated toms data record the direction of trade and the phys- UNCTAD. Volume measures for high-income eco- by the IMF's trade price deflators. * Growth rates of ical quantities and value of goods entering or leaving nomies were derived by deflating the value of trade export and import values are average annual growth the customs area. Customs data may differ from using deflators from the MF's International Financial rates calculated from UNCTAD's value indexes or from those recorded in the balance of payments because Statistics. Terms of trade were computed from the current values of merchandise exports and imports. of differences in valuation and the time of recording. same indicators. * Netbartertermsoftradearetheratiooftheexport The 1993 United Nations System of National The terms of trade measure the relative prices of price index to the corresponding import price index Accounts and the fifth edition of the International a country's exports and imports. There are a number measured relative to the base year 1995. Monetary Fund's (IMF) Balance of Payments Manual of ways to calculate terms of trade. The most common (1993) have attempted to reconcile the definitions is the net barter, or commodity, terms of trade, con- Data sources and reporting standards for international trade sta- structed as the ratio of the export price index to the tistics, but differences in sources, timing, and import price index. When the net barterterms of trade . The main source of trade data national practices limit comparability. Real growth increase, a country's exports are becoming more - , for developing countries is rates derived from trade volume indexes and terms of valuable or its imports cheaper. UNCTAD's annual Handbook trade based on unit price indexes may therefore differ of International Trade and from those derived from national accounts " Development Statistics. The aggregates. I MF's Intemational Financial Trade in goods, or merchandise trade, includes all Statistics includes data on goods that add to or subtract from an economy's the export and import values material resources. Currency in circulation, titles of and deflators forhigh-income and selected developing ownership, and securities are excluded, but monetary economies. gold is included. Trade data are collected on the basis of a country's customs area, which in most cases is the same as its geographic area. Goods provided as part of foreign aid are included, but goods destined for extraterritorial agencies (such as embassies) are not. Collecting and tabulating trade statistics is diffi- cult. Some developing countries lack the capacity to report timely data. As a result it is necessary to esti- mate their trade from the data reported by their part- ners. (See About the data for table 6.2 for further discussion of the use of partner country reports.) In some cases economic or political concerns may lead national authorities to suppress or misrepresent data on certain trade flows, such as oil, military equip- ment, or the exports of a dominant producer. In other cases reported trade data may be distorted by delib- erate underinvoicing or overinvoicing to effect capital transfers or avoid taxes. And in some regions smug- gling and black market trading result in unreported trade flows. By international agreement customs data are reported to the United Nations Statistical Division, which maintains the Commodity Trade, or COMTRADE, database. The United Nations Conference on Trade and Development (UNCTAD) compiles a variety of international trade statistics, including price and vol- ume indexes, based on the COMTRADE data. The IMF 1999 World Development Indicators 203 4.5 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total 96 of total ±.980 1997 ±980 1997 1980 ±997 ±980 1997 1980 1997 1980 1997 Albania 210 11 14 1965 Algeria 15,624 13,894 1 0 0 0 98 96 0 1 0 3 Angola i,902 4,15 7 9 ..... .......... 0.: ..... .78 ... ........ 0 ......13.I ........ Argntina 8,019 26,263 65 49 6 3 3 12 2 2 23 34 Armenia ........ 256a Australia 21,279 56,228 34 24 1 1 86 11 20 17 1 6 22 29 Austria 17,478 57,684 a 4 4 8 3 2 1 4 , 3 83 88 Azerbaijan 781a Banglads 7 40 3,8870a 12 -10 19 2 0 0 0 0 68 87 Belarus 7,174 a BelgiUMb 63,967 165,725 Benin 49 2890 62 25 .. 4I13 Bolivia 1,036 1,162 8 32 3 10 24 9 62 33 3 16 Bosnia and Herzegovina . Botswana' . Brazil 20,132 52,478 46 31 4 4 2 1 9 9 37 54 Bulgaria 10,372 4,232 .. Burkina Faso 901900 41 48 -- 0 -. 0 - 1:1 Burundi 129 730 70 6 . ..6 - 4 - Cambodia 15 6210 2 7 -.. 0 27 -.. 64 Cameroon 1,321 1,814 48 24 16 25 3 1 36 2 6 4 8 Canada 63,105 211,961 12 8 11 8 14 10 14 6 48 63 Central African Republic 111 2700 31 1 43 24 0 0 0 26 26 43 Chad 72 1340 4 81 0 . 0 . 15 Chile 4,584 16,296 15 25 10 9 1 0 64 48 9 16 China' 18,1360 182,792 71 .. 4 . 2 8 Hong Kong, Chinad . 19,703 .18,062.3 31 0 1 2 2 71 89 Colombia 3,945 11,455 72 32 5 5 3 31 0 1 20 31 Congo, Dem. Rep. 2,507 1,1400a 11 . 3 . 847 .. 6 Congo, Rep. 955 1,9380 1 1 2 8 90 88 0 0 7 3 Costa Rica 1.032 . 4,2730 . 64 66 1 7 1 1 0 1 28 25 C6te dIlvoire 2,979 4,2790- 64 28 .. 2 .. 0 .. 5 Croatia 4,341. 12 . 6 . 103. 69 Cuba 5,541 1,7550a 89 0 -. 3 . 5 . 0 Czech Repbi 2273533 85 .. .. . .. .. . .. .. . .I ... .. .. .. - .. . ...22 ,7 2 3. .. . .. .. . .. .. ..... ..5.. . .. . .. .. ..3.. . . .. ..4 Denmark 16,407 48,793 33 23 5 3 3 4 2 1 55 63 Dominican Republic 704 4,8020- 73 19 0 0 0 0 3 0 24 78 Ecuador 2,481 5,149 33 58 1 4 63 30 0 0 3 9 Egypt,Arab Rep. 3,046 3,908 7 8 16 4 64 43 2 5 11 40 El Salvador 720 1,352 47 55 12 1 3 3 3 2 35 39 Eritrea . .. Estonia .. 2,926 16 .. 9 6 . 3.. 6 Ethiopia' 424 5510 74 73 18 13 7 3 0 0 0 11 Finland 14,140 40,933 3 3 19 7 4 2 4 4 70 83 France 110,865 282,944 16 13 2 1 4 3 4 2 73 78 Gabon 2,189 3,1300 1 0 7 13 88 83 12 2 5 2 Gambia, The 361580 99 0 . . .. 3 -. 7 - Georgia .2301 0 . ..I Germany' 191,647 510,570 5 5 1 1 4 1 3 2 85 83 Ghana 942 1,737 0 78 . 4 017I. Greece 5,142 10.788 26 27 2 4 16 9 9 7 47 52 Guatemala 1,466 2,344 53 61 16 4 1 4 5 1 24 30 Guinea 374 9380 4 0 . 951 Guinea-Bissau 11 710 85 . 2 00 8 Haiti 376 2210 31 . 4 . 63 Honduras 813 1,033 75 65 5 3 0 0 6 3 12 27 t Data for Taiwan, China 19,837 121,318 . . . . . . . . . 204 1909 World Development Indicators 4.52 Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials $ millions % of total % of total % of total % of total % of total 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 Hungary 8.677 19,093 22 14 3 2 5 2 4 . 3 65 77 India 7,511 32,201A 28 19 5 3 0 2 7 3 59 72 Indonesia 21,909 53,220 8 11 14 5 72 25 4 5 2 42 Iran, Islamic Rep. 13,804 25,0791 1 93 0 . 5 Iraq 28,321 ' 2,309a 0 - 099 . 0 . Ireland 8,473 53,258 37 12 2 1 1 0 3 1 54 81 Israel 5,540 22,501 12 4 4 2 0 1 2 1 82 92 Italy 77,640 238,161 7 6 1 1 6 1 2 1 84 89 Jamaica 942 1,837 a 14 24 0 0 2 0 21 6 63 69 Japan 129,542.420,492.1 1.1.1 0 1 2 1 95 95 Jordan 402 1,453 a 25 25 1 2 0 0 40 24 34 49 Kazakhstan 6,366a. . Kenya 1,313 1,952 44 5 8. .33 9 2 3 12 25 Korea, Dem. Rep. 907 a . Korea Rep 17,446 135,986, 7 2 1 1 0 3 1 1 90 92 Kuwait 20,435 14,122 1 0 0 0 89 85 0 0 10 14 Kyrgyz Republic 551, 28 11 . 15 6. 38 Lao PDR 9192V 13 41 . . 4 .. 34 Latvia 1,672. 14 24I. 12 .. 58 Lebanon 930 711a 28 2 0 .. 958 Lesotho' Libya 21,910 9,824 a 100 ... 0 Lithuania 3,343 . 18 .. 5 .. 4 2 . 70 Macedonia, FYR 1,263a. Madag asa 387 278 80 54 4 6 6 3 4 7 6 28 Malawi 269 683 a 91 90 2 2 0 0 0 0 6 7 Malaysi'a 12,939 77,894 - 15 9 31 5 25 8 10 1 19 76 Mali 235 275 30 69 . 0 . 00 1 Mauritania 255 5361 a 16 1I . 0 . 830 Mauritiu 420 1,600 72 28 0 1 0 0 0 0 27 71 Mexico 15,442 109,890 12 6 2 1 67 10 6 2 12 81 Moldova 8751 . 72 2I. 3 23 Mongolia 430~ a 2 . 28 .. 0 .. 60 . 10 Morocc 2,403 4,674 28 31 3 3 5 2 41 5 24 9 Mozambique 511 272 68 69 7 9 2 1 5 4 18 17 Myanmar 460 1,161 a 40 33 -. 9 . 10 . Namibia' Nea 94.298 21 5 48 0 .0 0 30 95 Netherlands 73,871 ~184,295 20 16 3 3 22 7 4 2 50 71 New Zealand 5,262 13,983 a 48 47 26 17 1 2 4 4 20 29 Nicaragua 414 658 75 69 8 4 2 1 1 1 14 25 Niger 580 128 a 11 .. 11 85 . 2 Nigeria 25,057 16,028 a 2 . 0 97 0 . 0 Norwy 18,481 48,528 7 8 3 1 48 54 10 7 32 24 Oman 3,748 7,600 1 4 0 0 96 77 0 1 3 17 Pakistan 2,588 . 8,632 24 10 20 3 7 1 0 0 48 86 Panama 353 644 67 76 0 1 23 4 1 2 9 17 Papua New Guinea 1,133 a 2,685 a 33 7 0 -- 50 . Paraguay 310 1,141 38 72 50 13 0 0 0 0 12 15 Peru 3,266 6,115 16 35 4 3 21 7 43 39 17 17 Philippines 5,751 24,988 36 8 6 1 1 1 21 2 21 45 Poland 16,997 25,697 6 12 3 2 13 7 7 6 61 73 .otgl4,629 23,510 12 7 96 2 2 1 70 86 Puerto Rico Romania 12,230 8,420. 7 . 4 . 6 4 .. 79 Russian Federation 87,368 . 2 . 3 .. 46 11 .. 23 1999 World Development Indicators 205 4.5 Merchandise Food Agricultural Fuels Ores and Manufactures exports raw metals materials S millions % of total % of total % of total % of total % of total 1980 ±.997 1 1980 1997 1.980 1997 1980 ±997 1980 1997 1980 1997 Rwanda 138 100 a 827 0 .. 10 0 Saudi Arabia 109,113 61,603 - 0 1 0 0 99 90 0 0 1 9 Sierra Leone 302 2150 a 24 .. 1 034 40 - Singapore 19,375 124,431 8 4 10 1 25 7 2 2 47 84 Slovak Republic 8,790 53 . S4 . 79 Slovenia 8,3684 .. 21 .. 4.. 8 9 South Africag 25,539 31,2110 1 9 11 2 4 4 11 7 12 18 55 Spain 20,827 101,228 0 18 1 6 2 1 4 2 5 2 7 2 78 Sri Lanka 1,043 4,455 47 18 . 151 19 Sudan 584 518 47 68 51 28 1 0 1 0 1 3 Sweden 30,788 81,057 2 3 10 5 4 2 5 3 78 80 Switzerland 29,471 75,9993 3 1 1 0 0 5 3 90 93 Syrian Arab Republic 2,108 4,049 4 12 9 7 79 63 1 1 7 17 Tajikiatan 5888 Tanzania 528 7160 58 . 18 .. . 5 14 Thailand 6,369 57,567 47 19 11 4 0 2 14 1 25 71 Too 335 4070* 21 226 -.. 40 11 Trinidad and Tobago 4,077 2,545 2 9. . . . . 46 p . . s5 4*4. Tunisia 2,234 5,559 7 11 I1 1 52 9 4 1 36 78 Turkey 2,910 26,245 51 20 14 1 1 1 7 3 27 75 Turkmenistan I.....549.a . . - . . Uganda 465 6180 96 2 .. 1 . 1I. Ukraine 16,1320a United Arab Emirates 21,6280 30,4230 United Kingdom 114.422 278,784 7 7 1 1 13 6 5 2 71 83 United States 212,887 637,505 18 9 5 3 4 2 5 2 66 81 Uruguay 1,059 2,713 39 49 22 13 0 1 1 1 38 37 Uzbekistan .. 2,8930a Venezuela 19,293 21,658 0 2 0 0 94 83 4 2 2 12 Vietnam 123 8,758 a 30 23 . 32I. . 4 West Bank and Gaza ... . . .. . . Yee,Rep. 23 2,4790 45 3 4 1 0 95 0 1 47 1 Yugoslavia, FR (Serb./Mont.) .2, 368 16- .. 5 .. 2 -.. 14 . 61 Zambia 1,330 1,1780 1 .. 0 . 0 .. 82 . 16 Zimbabwe 433 2,119 40 46 3 11 3 1 17 10 36 32 Low income . . 30 1 97 .. 3 Middle income 1,135,665 .. 13 316 .. 5 58 Lower middle income . 580,191 . 11 3185 .. 5 Upper middle income 222,723 553,160 20 14 9 3 38 22 9 5 21 54 Low & middle income . 1,234,520 24 1 8.3 35.16 8 5 22 58 East Asia & Pacific 419,740.. 102 7 2.. 72 Euoe&Central Ais 7 . 3 .. 22 753 Latin America & Carib. 96,124 274,560 32 25 4 3 31 16 12 9 20 46 Middle East & N. Africa 154,377 3 4 1 0 87 75 3 2 8 19 South Asia 12,087 49,253 28 16 10 3 3 1 5 2 54 76 Sub-Saharan Africa .. 22 . . 27 . 9 .. 1 High income 1,284,906 4,030,189 11 7 4 2 7 4 4 3 73 81 Europe EMU . 519,571.1,492,584 11 9 3 1 6 2 3 2 75 81 a. Oats are from IMF, Direction of Trade Sratistics. b. I ncludes Luxembourg. c. I ncluded with South Africa. d. I ncludes reexports. a. Oats prior to 1992 i nclude Er'itrea. f. Data prior to 1990 refer to the Federal Republic of Germany before unification. g. Data are for the South African Customs Union, which comprises Botswana, Lesotho, Nawibia, and South Africa. 206 1999 World Development Indicators 4.5 Data on merchandise trade come from customs country, that move outward from customs storage; and * Merchandise exports show the f.o.b. value of reports of goods entering an economy or from reports (c) goods previously included as imports for domestic goods provided to the rest of the world valued in U.S. of the financial transactions related to merchandise consumption but subsequently exported without trans- dollars. * Food comprises the commodities in SITC trade recorded in the balance of payments. Because formation. Under the special system exports comprise sections 0 (food and live animals), 1 (beverages and of differences in timing and definitions, estimates of categories a and c. In some compilations categories b tobacco), and 4 (animal and vegetable oils and fats) trade flows from customs reports are likely to differ and c are classified as reexports. Because of differ- and SITC division 22 (oil seeds, oil nuts, and oil ker- from those based on the balance of payments. ences in reporting practices, data on exports may not nels). * Agricultural raw materials comprise SITC Furthermore, several international agencies process be fully comparable across economies. section 2 (crude materials except fuels) excluding divi- trade data, each making estimates to correct for unre- Total exports and the shares of exports by major sions 22, 27 (crude fertilizers and minerals excluding ported or misreported data, and this leads to other commodity group were estimated by World Bank coal, petroleum, and precious stones), and 28 (met- differences in the available data. staff from the COMTRADE database. Where neces- alliferous ores and scrap). * Fuels comprise SITC The most detailed source of data on international sary, data on total exports were supplemented from section 3 (mineral fuels). * Ores and metals com- trade in goods is the COMTRADE database maintained the IMF's Di rection of Trade database. The val ues of prise the commodities in SITC divisions 27, 28, and by the United Nations Statistical Division (UNSD). The total exports reported here have not been fully rec- 68 (nonferrous metals). * Manufactures comprise International Monetary Fund (IMF) also collects cus- onciled with the estimates of exports of goods and the commodities in SITC sections 5 (chemicals), 6 toms-based data on exports and imports of goods. services from the national accounts (shown in table (basic manufactures), 7 (machinery and transport The value of exports is recorded as the cost of the 4.9) or those from the balance of payments (table equipment), and 8 (miscellaneous manufactured goods delivered to the frontier of the exporting coun- 4.17). goods), excluding division 68. try for shipment-the f.o.b. (free on board) value. Many The classification of commodity groups is based on countries report trade data in U.S. dollars. When coun- the Standard International Trade Classification (SITC) Data sources tries report in local currency, the UNSD applies the revision 1. Most countries now report using later revi- average official exchange rate for the period shown. sions of the SITC or the Harmonized System. The United Nations Con- Countries may report trade according to the general Concordance tables are used to convert data ference on Trade and Develop- or special system of trade (see Primary data documen- reported in one system of nomenclature to another. ment (UNCTAD) publishes data tation). Underthe general system exports comprise out- The conversion process may introduce some errors of on the structure of exports and ward-moving goods that are (a) goods wholly or partly classification, but conversions from later to early sys- imports in its Handbook of produced in the country; (b) foreign goods, neithertrans- tems are generally reliable. Shares may not sum to , International Trade and De- formed nor declared for domestic consumption in the 100 percent because of unclassified trade. : velopment Statistics. Esti- mates of total exports of goods are also published in the IMFs Intemational Financial A strong trend toward manufactures among the top developing country exporters Statistics and Direction of Trade Statistics and in the United Nations' Monthly Bulletin of Statistics. Tariff-line I -, records of exports and imports are compiled in the United Nations' COMTRADE database. of manutacidree In their exporI.. Er,A E, 1 9 Wt Amn5h0 tp1evlnn C,Jr, exotr th trn is .;raigviwr aulcue xot.Ee ths.ha! eeprmrl fue eorer In 198.sc as Inoei n aii Arba ar inceain [h sa of~ maua,ie intei.xprs *~~~~~~~~~~~~~~~~~~~~~~~~~~~19 Wol Deelpmn I,d!c.tor. .0. 4.6 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures Imports raw metals materials $ millions % of total % of total % of total % of total % of total 1980 1997 ±L980 1997 ±980 1997 ±980 ±997 ±980 ±997 1980 1997 Albania 950 2713167 Algeria 10,524 8,688 21 32 3 3 2 2 2 1 72 62 Angola 873 2,268a 18 1 7 173 Argentina 10,539 30,349 6 5 4 2 10 3 3 2 77 88 Armna9320 Australia 19,870 61,243 5 5 3 1 14 6 2 1I 7 5 86 Austria 24,415 62,638 6 6 4 2 1 5 5 5 3 6 9 82 Azerbaijan 7910 Bangladesh 1,980 6,863 a 24 17 6 4 9 7 3 3 58 69 Belarus 8,639 0 BelgiUMb 71,192 0 151,973 Benin 302 9620 26 1 8 11 62 Bolivia 655 1,866 19 9 1 1 1 8 2 2 78 81 Bosnia and Herzegovirna... Botswana' Brazil ~~~~~~~~~24,949 65,074 . 10. 9 1 3 43 12 5 3 41 74 Bulgaria .9,650 3,816 a Burkina Faso 358 .......5060 20 . 2 13 .. 164. Burundi 106 1000 13 . 2 . 19 . 2 61 Cambodia 108 1,112a 51 . 3 .. 2 .. 0. 26 Cameroon 1,538 1,296 9 14 0 2 12 16 1 1 78 67 Canada 57,707 195,039 7 6 2 1 12 4 5 3 72 83 Central African Republic 80 1630 a 21 12 1 14 2 8 2 1 75 6 Chad 37 1410 a 23 24 2 1 2 18 1 1 72 56 Chile 5,123 18,111 15 7 2 1 18 10 2 1 60 79 Chinat 19,501 a 142,370 5575 77 .ong Kong. China 22,027.208,615 12 6 4 1 6 2 2 2 75 88 Colombia 4,663 15,379 12 11 3 2 12 3 3 2 6 9 79 Congo, Dem. Rep. .117.10480 9 .. 2 12 1 75 Congo, Rep. 418 93Q0 19 21 1 1 14 20 2 1 65 58 Costa Rica 1,596 4,3020 - 9 13 2 1 15 9 2 2 68 72 C6te d'lvoire 2,552 3,042 13 17 0 2 16 23 2 1 68 56 Croatia .. 9,121 . 10 .. 2 .. 9 .. 2 72 Cuba 1,656 2,642 a 32 .. I 11 2 53 Czech Republic .. 27,176 7 . 2 . 9 4 79 Denmark 19,315 44,472 11 13 5 3 22 4 3 2 57 75 Dominican Republic 1,426 7,112 0 17 .. 2 25 2 54 Ecuador 2,215 4,511 8 9 2 3 1 8 2 2 87 78 Egypt, Arab Rep. 4,860 13,095 32 26 6 6 1 2 1 3 59 63 El Salvador 976 2,961 18 17 2 3 18 11 2 1 61 67 Estonia . 4,438 16 2 .. 8 .. 2. 71 Ethiopia' 721 1,40908 14 .......3 2 25 11 11 64 ...... 72 Finland 15,632 30,991 7 7 3 2 29 10 5 6 56 73 France 134,328 266,165 10 10 4 2 27 8 5 3 54 76 Gabon 674 1,1550 19 19 0 1 1 4 1 1 78 75 Gambia, The 169 33Q0 26 I. 1 9 .. 0 .. 61 G eorgia .931 . . ..... ........................ Germany' 185,922 434,861 12 9 4 2 23 8 6........4 ......52 ........68 Ghana 1,129 3,295- 0 10 1I 27 2 59 Greece 10,531 25,191 . 9.. *5 2 23 7 2 3 60 73 Guatemala 1,559 3,852 8 13 2 2 24 11 1 1 65 73 Guinea 299 8130 12 1I . 19 .. 4 .. 62 Guinea-Bissau 55 1160 20 0 . 6 .. 2 69 Haiti 536 9260 22 I. 1 . 13 I. 1 . 62 Honduras 1,009 2,435 10 18 1 2 16 10 1 1 72 68 t Data for Taiwan, China 19,791 113,837 . . . . . . . . . 208 1999 World Development Indicators 4.6 Merchandise Food Agricultural Fuels Ores and Manufactures Imports raw metals materials $ millions % of total % of total % of total % of total % of total ±980 1997 1980 1997 1980 1997 1.980 ±.997 ±1980 1997 ±980 ±.997 India 13,819 36,293- 9 5 2 3 45 30 6 7 39 50 Indonesia 10,834 41,679 13 9 4 5 16 10 2 3 65 73 Iran, Islamic Rep. 9,330 14,705 a 21 4 2..1....2 . .... 72~. Iraq 11,534 7650 132 .. 0 . 1 .. 8 Ireland 11,133 39,192 12 8 3 1 15 3 2 2 66 79 Israel 8,023 28,880 11 7 3 1 26 8 4 2 57 8 1 Italy ~~~98,119 204,098 13 11 7 5 28 8 6 4 45 69 Jamaica 1,178 3,1830 20 15 1 2 38 15 2 1 39 65 Japan 139,892 337,501 12 15 9 5 50 19 10 6 19 55 Jordan 2,394 3,888- 18 21 2 2 17 1 3 1 3 6 1 61 ...e......a....2,590 3,278 8 17 1 ..... ..2 34 . ....16 .......1 ..... _2 ...... .56 ..... 63 Korea, Dem. Rep. .. 1,6868 Korea, Rep. 22,228 144,6040 10 6 11 5 30 17 6 5 43 67 Kuwait 6,554 8,102 15 16 1 1 1 1 1 2 81 81 Kyrgyz.Republic ..709 a..... . 2 1. 29 .. ........48 Lao PDR 85 4Q90 21 .. 0 ,.. 19 .. I . 56 Latvia .. 2,721 . . 13 . 2 .. 14 .. 2:. 69 Lebanon 3,132 7,4560 16 .. 2 1 .. 4 .. 63 Lesotho'. Libya 6,776 5,482 a 19I. . . . 78 Lithuania .. 5,469 . 11 .. 3 .. 14 .. . 6 Macedonia, FYR .. 1,8080 Madagascar 676 573 9 15 3 1 15 2 1 1 0 73 6 1 Malawi 440 7860 - 8 14 1 -1 15 11 1 1 75 73 Malaysia 10,735 79,6440 12 5 2 1 15 3 4 3 67 85 Mali 491 1,1260 19 .. 0 . 504 Mauritania 287 6010 30 .. 1 . 14 0 52 Mauritius 619 2,211 26 15 4 3 14 8 1 1 54 74 Mexico 19,591 111847 16 6 3 2 2 3 4 2 75 83 Moldova .. 1,164~ . 8 .. 3 46 .. 2 .. 42 Mongolia .. 5460 14 . I .. 19 . 1.. 65 Morocco 4,182 7,877 20 17 6 5 24 17 4 4 47 58 Mozamnbique 550 1,2810 14 22 3 2 9 11 3 1 T70 62 M yanm ar 577 2,739~ -6 .... . . .. .1 .... ... . ....3.... . . 2 ............. 87 ........ .. .. Namibia' . .. I... .. .. Nepal 226 640 4 14 1 6 18 20 1 5 73 54 Netherlands 76,889 162,155 15 1 3 2 2 4 9 4 3 53 7 New Zealand 5,515 14,551 - 6 8 2 1 22 6 4 2 65 83 Nicaragua 882 1,470 15 14 1 1 20 12 1 1 63 72 Niger *608 5661 ..... 14. 0 2 6 . 3 55....... Nigeria 13.408 6,734 a 15.. 0 ........7 ..... 7 ...... ..... ...2 ......... .... . 76 ... ....... Norway 16.952 35,751.8.7 3 2 17 3 5 5 67 82 Oman 1,732 4,866 15 17 1 1 11 2 0 2 66 75 Pakistan 5,350.11,182.13 19. .4 27 20 3 2 54 54 Panama 1,447 2,989 10 10 1 1 31 14 1 1 58 74 Papua New Guinea 958 1,885- 21 .. 0 .. 15 . 1 .. 61 Paraguay *615 4,267 a 11 21 1 0 28 8 .1 3 60 67 Peru 2,573 8,558 20 14 3 2 2 10 2 1 73 73 Philippines 8,295 38,576 8 8 2 2 28 8 3 2 48 61 Poland 19,089 42,271 14 8 5 2 18 9 6 3 51 77 Portugal . 9.293 34,338 14 13 7 3 24 8 4 2 52 74 Puerto Rico ... . .. Romania 13,201 11,270 6 2 .. 1 9 3: 68 Russian Federation .. 67,6190 .. 19I. 13 2.. 46 1999 World Development Indicators 209 4.6 Merchandise Food Agricultural Fuels Ores and Manufactures imports raw metals materials $ millions % of total % of total % of total % of total % of total 1980 1997 ±980 1997 ±980 1997 ±.980 ±997 1980 1997 1980 1997 Rwanda 155 3680 10 313. 0 7 2 Saudi Arabia 29,957 40,8370 14 18 1 1 1 0 1 4 82 76 Senegal 1,038 1,1610 25 32 1 2 25 10 0 2 48 53 Sierra Leone 268 239 a 24I1 2I171 Singapore 24,003 131,651 8 4 6 1 29 10 2 2 54 8 Slovak Republic .. 10,259 . . 8 2 12 3 6. 7 Slovenia ~ ~ ~ ~ ~ ~ ~~ ~ ........9,5 ... ....... . 7.. 3.......4...7 South Africa' 18,551 31,0200 3 6 3 2 0 10 2 1 62 72 Spain 33,901 118,4780 13 12 5 3 39 9 6 3 38 72 Sri Lanka 2,035 5,517a 20 . 1 42 5 Sudan 1,499 1,4930 26 1 7 1 2 13 19 1 0 60 60 Sweden 33.426 62,854 7 7 2 2 24 7 5 3 62 79 Switzerland 36,148 75,747 8 6 3 2 11 5 7 4 71 84 Syrian Arab Republic 4,124 5,929a 14 17 3 3 26 1 2 1 55 76 Tanzania 1,211 1,9580 13I1 21 .. 2 63 Thailand 9,450 62,084 5 5 3 3 30 9 4 3 51 78 Togo.550 1,0560 171 . 23 . . 59 Trinidad and Tobago 3,178 3,028 11.0.2.I 38 13 1 3 49 72 Tunisia 3,509 7,932 14 11 4 3 21 8 4 3 58 75 Turkey 7,573 48,585 4 5 2 5 48 10 3 5 43 72 Turkmenistan.. 1,2011 Uganda 417 825a 11I1 . 23 0 .. 65 Ukraine .. 26,902- United Arab Emirates 8,098 31,0500 11 . I .. 11 .. 2 74 United Kingdom 117,632 305,074 13 9 4 2 13 4 7 3 61 81 United States 250,280 894,995 8 5 3 2 33 9 5 2 50 78 Uruguay 1,652 3,716 8 10 4 3 29 9 3 1 56 76 Uzbekistan .. 4,842- Venezuela 10,669 16,2140 14 16 3 3 2 1 2 3 79 77 Vietnam 618 14,8140 37 . 2 . 5 0 5 West Bank and Gaza... . . .. . Yemnen, Re.1,853 1,8078a 28 29 0 2 7 8 1 1 63 59 Yugoslavia, FR (Srb./Mont.) 4,798 14 I.4 . 6 . 5 Zambia 1,100 10700 51. 221 . 71 Zimbabwe 193 ~3,090 6 7 2 2 12 10 1 2 73 77 Low income ...15 . 2 . 2 55 Middle income ..1,160,963 12 9 3 3 19 8 3 3 60 73 Lower middle income .. 59881 . 8 .. 3 .. 70 Upper middle income 210,421 602,676 11 9 2 2 18 8 4 3 62 77 Low &mIddl incoe..1,288,360 12. 10 3 3 20 8 3 3 60 72 East Asia & Pacific 380,545 '6 . 4 . 8 -. 4 75 Europe & Central Asia 11 . . 8. 36 Latin America & Carib. 99.026 319,219 13 9 2 2 19 7 3 2 63 79 Middle East & N. Africa 92,498 132,309 19 20 3 3 9 5 2 3 67 68 South Asia . 60,715 12 9 2 4 35 25 4 5 46 54 Sub-Saharan Africa ..! . .... ............... 10 .. ..............2~. . ...9 . . .. .. . .2- 64 High income 1,429,590 4,098,767 11 8 4 2 26 8 5 3 52 75 Europe EMU 589,631 1,352,916 12 10 4 3 25 8 5 4 52 72 a. Data are from IMF, Direction of Trade Sfatiatics. b. Includes Luxembourg. c. Included with Sooth Africa d. Oats priorto 1992 include Eritrea. a. Data priorto 1990 referto the Federal Republic of Germany before unification. f. Data are for the South African Customs Union, mhich comprises Botsanaa, Lesotho, Namibia, and Sooth Africa. 210 1999 World Development Indicators 4.6 Data on imports of goods are derived from the same * Merchandise Imports show the c.i.f. value of sources as data on exports. In principle, world exports goods purchased from the rest of the world valued in and imports should be identical. Similarly, exports The structure of imports looks much the U.S. dollars. * Food comprises the commodities in andimprtsshold e ientcal Siilaly,exprts same across regions from an economy should equal the sum of imports by SITC sections 0 (food and live animals), 1 (beverages the rest of the world from that economy. But differ- and tobacco), and 4 (animal and vegetable oils and ences in timing and definitions result in discrepancies fats) and SITC division 22 (oil seeds, oil nuts, and oil in reported values at all levels. For further discussion ; kernels). * Agricultural raw materials comprise SITC of indicators of merchandise trade see About the data section 2 (crude materials exceptfuels) excluding divi- for tables 4.4 and 4.5. i sions 22, 27 (crude fertilizers and minerals excluding The value of imports is generally recorded as the . coal, petroleum, and precious stones), and 28 (met- cost of the goods when purchased by the importer - alliferous ores and scrap). * Fuels comprise SITC plus the cost of transport and insurance to the fron- section 3 (mineral fuels). * Ores and metals com- tier of the importing country-the c.i.f. (cost, insur- - - prise the commodities in SITC divisions 27, 28, and ance, and freight) value. A few countries, including . 68 (nonferrous metals). * Manufactures comprise Australia, Canada, and the United States, collect , the commodities in SITC sections 5 (chemicals), 6 import data on an f.o.b. (free on board) basis and -;- (basic manufactures), 7 (machinery and transport adjust them for freight and insurance costs. Many . equipment), and 8 (miscellaneous manufactured countries collectand reporttrade data in U.S. dollars. goods), excluding division 68. When countries report in local currency, the United r.1,.icii, t, ,r r.,-.i,,, 9 Nations Statistical Division applies the average offi- . Data sources cial exchange rate for the period shown. Countries may report trade according to the gen- e s The United Nations Con- eral or special system of trade (see Primary data doc- ference on Trade and Dev- umentation). Under the general system imports elopment publishes data on include goods imported for domestic consumption the structure of exports and and imports into bonded warehouses and free trade -,r imports in its Handbook of zones. Under the special system imports comprise 5 .S. , Intemational Trade and Dev- goods imported for domestic consumption (including elopment Statistics. Esti- transformation and repair) and withdrawals for domes- mates of total imports of tic consumption from bonded warehouses and free goods are also published in the IMF's Intemational trade zones. Goods transported through a country en Financial Statistics and Direction of Trade Statistics route to another are excluded. and in the United Nations' Monthly Bulletin of Total imports and the shares of imports by major Statistics. Tariff-line records of exports and imports commodity groups were estimated by World Bank ,. m are compiled in the United Nations' COMTRADE data- staff from the COMTRADE database. Where neces- - base. F.- . sary, data on total imports were supplemented from F.::. the International Monetary Fund's (IMF) Direction of . , Trade database. The values of total imports reported , i .1 .. , ..i .-,,t here have not been fully reconciled with the estimates of imports of goods and services from the national accounts (shown in table 4.9) or those from the bal- Because nmanulactumea goods dominare xo.td ance of payments (table 4.17) :trade. the strLcture of Imports land exportsi tenao to look similar across regions. The excep The classification of commodity groups is based on tr,n s a.e the large snare of 'elt In South Asia's the Standard International Trade Classification (SITC) imports. and the large share of food in ihose ot revision 1. Most countries now report using later revi- the Middle EasT and North Atrica. sions of the SITC or the Harmonized System. Concordance tables are used to convert data reported in one system of nomenclature to another. The conversion process may introduce some errors of classification, but conversions from later to early sys- tems are generally reliable. Shares may not sum to 100 percent because of unclassified trade. 1999 World Development Indicators 2 11 4.7 Structure of service exports Service Transport Travel Communications, Insurance exports computer, and financial information, services and other services $ millions % of total % of total % of total % of total 1980 1997 1980 1997 1980 1997 ±980 1997 1980 1997 Albania 11 64 42.1 19.1 . . ...6.4 .. ...42.:5 46.8 36.3 ... ....4J 7....... 2.0O Algeria 476 41.0.................. ........... : 24......14 . 2 ..2.5 ... 29.5....::.... 5. .5 44 . Angola 268 30.4 0 6 60.7 83 Argentina 1,876 3,271 42.9 40.0 18.3 43.4 ..... 38.4 14.2 0.3 2..5 Armenia 97 52.4 20:2 .......... ........ 24.3 .. ...... .. ...3.1 Australia 3,862 18,838 49.3 26.7 29.4 48.2 20.0 19. .... g.7 1.... ..i3 ......... 5. Austria 9,423 26,697 7.3 15.3 68.9 41.5 . ......21.3 .......38.6 2 6 4.7 Azerbaijan 186 . Bangladesh 163 678 18.5 13.2 7.5 9.1 .......740.0 75.9 1 .9 Belarus .. 923. BelgiuMa 12,925 34,855 . Benin 62 56.8 14.1 . 268.8 I...... ... 23...... Bolivia 88 195 33 5 40.2 41.0 33:7.7...... 16.6 .. .... 16.3 .... 89 9.8 Bosnia and Herzegovina Botswana 101 163 41.7 24.6 22.2 57 2 32.0 17.3 4.1 0.8 Brazil 1,737 7,266 46.8 27.0 7.3 14.7 . ......38.1 ... ....52~6 ........7.9 ...... 5 7 Bulgaria 1.211 1.338 36.3 33.6 28.7 27.5 31.0 31 5 4.0 7 5 Burkina Faso 49 17.3 ...... 10..2 ..........: ....... 72.5 Burundi .9.......... 189 7170.2 ...... .... 3.8 Cambodia . 160 31.4 42.5 ....... .. :........ 26~.1 . . ........ ..... . Cameroon 374 475 48.4 38.5 15.5 11.8 30.9 44.0 ........5.2 5.....7.. Canada 7,445 30,018 34.1 20:0 34.2 292 2...... 31.7 508 .............. Central African Republic 54 6.5 51. 8612.4 Chad 0 100.0 Chile 1,263 3,685 32.2 37.8 13.9 28.2 .......51.9 ........307 7....... 2.1 3.4 China 2,512 24,581 52.3 12.1 28.0 49.1 11.7 380O....... 8.0 0.8 Hong Kong, China 5,794 41.056 . Colombia 1,342 4,180 . 1 .1 . 39.0 35.6 22 8 ........27 6 .......248 8....... 5.6 13.3 Congo, Dem . Rep..... .. ...... . ............... Congo, Rep. 111 56 46.0 46 2 67 4.6 ........42.7 .......492 ........46 Costa Rica 194 1,524 24.9 14 9 43.7 48.6 .. ....30.7 36. ..5 . S... ... 0 7 CMe dIlvoire 564 744 50.0 25.9 14.4 14.9 25.7 59.2 9 9 Croatia 3,994 166 63.3.. 20 Cuba .. Czeo Rpblic - 7,132 18:5 50:8 .......27.6.. 3.1l Denmark 5,853 15,146 44.4 45.0 21.1 20.8 31.8 342.2 2.7 Dominican Republic 309 2,132 7.8 1.7 55.8 86.4 35.9 11.9 0.5 Ecuador ............ 367 ... ....736....... 35.1 ....... 40 5 35.6 39.4 ........13.3 . .......96.6 160 10.5 Egypt, Arab Rep. 2,393 11,241 52.4 26.9 24.8 39.7 22.4....... 32.5 . ..... 0 4 0.9 El Salvador 139 292 18 3 16 1 9.6 34.9 50.5 43.4 . .. .21.6 .......5.5 Eritrea . 148 Estonia .. 1,318 49935.9. 131. 10 Ethiopia' 110 413 53.2 54.1 5 7 5.7 40 9 .......39O 0....... 0.2 1.2 Finland 2,733 7,168 35 1 27.3 25 0 26.5 37.0 47.5 2.9 -. France 43,506 81144 24.2 238.8..... 19.0 34.4 53.4 38.0. 3.4 ..... ..3.8 Gabon 325 273 21.5 35.2 5.2 2.6 67.7 56.2 5.6 6.0 Gambia, The 18 109 81 100.0 68.8 .... .. 22.9 . 0.1 Georgia .. 160 .. .. ~ : Germany' 33,062 79,904 26.6 23.6 15 1 20.6 57.4 50.2 0.8 5.6 Ghana 107 157 33.6 53.1 0.4 8.1 64.9 ........36.0 1.2 2.9 Greece 3,947 9.287 23.6 1.9 43.9 40 .6.... 324.4. 57.2 0.1 0.3 Guatemala 211 589 18.9 13.0 29.2 45.2 46.5 39.6 5.4 2.1 Guinea Ill11 42.9 0.1 .... .. .... ..... 558.8 1.2 Guinea-Bissau 6 8.9 12.5 .. 78.6... Haiti 90 109 5.9 5.5 85.0 87.6 7.8 . .......6.9 1.2 Honduras 82 335 36.9 19 7 301 43.5 18.5 34.6 145 22 212 1999 World Development Indicators 4.7~~~~~~~ Service Transport Travel Communications, Insurance exports computer, and financial Information, services and other services $ millions % of total % of total % of total % of total 1980 1.997 ±980 1997 1980 1997 1980 1997 1980 1997 Hungary 633 4,874 5.4 10.3 63.5 531.1. 30.5 .... 300 0........_.66.6 India 2,949 9,253 150. . . ... 21.7 .......522 32.6 .. 315.5 .... 43.1 ..... -1.2 ........ 2.6 Indonesia 449 6,941 15.1 0.0 50.8 95.8 34.1 4.2 Iran.Islmic Rep. 731 860 4.5 50.5 4.0 212 91.5 35.8 . 11.5 Iraq Ireland 1,381 6.159 36.6 ... . 18.7 42.0 419 9...... 21.4 ..... . 39 5........... Israel 2,722 8,426 38.1 ....... 24.6 .... 36.0 34:5.5 . ... 249.9 40~5.5 .... . 1.0 0..... O.3 Italy 19,192 72.310 23..9....... 22.1 ..... 46.7 .141.1 . . 22.9 ... 28~8.8 ..... 6.5 8.0 Jamaica 401 1,478 28.0 16 1 61.2 76.5 ........6.7 ..... 7.1 ........4 2 0.3 Japa 20,240 69.302 62.9 31.5 3.2 6.2 32.4 59.1 1.6 3.2 Jordan .... . 750 1,937 27.0 225 .. 51.9 44.6 21:1 32~7 '........ 0.2 Kazakhstan .. 842... Kenya 577 944 38.0 35.4 41.4 40.8 .......19.8 .... 22.2 0.8 1.6 Korea, Dem. Rep. ........ Korea, Rep. 2,570 26,301 60.1 41.8 14.4 .. .18.0 ...I.. 23:6 .....39 6 ..... 1.9 0.6 Kuwait 1,225 1,764 57.7 .....693 30.8 10.7 .... 11:5 .. .....16.1 ...... 31.9 Kyrgyz Republic .. 45 Lao PDR ---...........-100 ....... 17:6__ .--- 53-.8 281.. 0.4 Latvia .. 1,033 69.3. 18.6 ....8 2 ..... ...3. Lebanon . 776.... Lesotho 32 ..... 20 ........ . . 37.8 . ..... ........ 60:2 ..... .... Libya 164. 64562. 29 Lithuania 1,032 . 42.9. 34.8 20O.' 0 .................. 2 3 Macedonia, FYR 128 . 42:3 10.9445.2 Madagasca 79 293 49.4 27.1 6.3 221 1440 49.2 0.4 11~.6 Malawi 32. 49.8 29.5 . 18. . Malaysia 1,135 15,016 41.6 - 20.0 28.0 24.9 29.8 55.1 0 6 0.0 Mali 58 .... 82 30.9 .. 27.8...... 25 8 32.2 .....42.2 36.1 . ....1 0 4.0 Mauritani'a 56 28 26.3 6.3 11.9 40.1 61.8 .... ..536.6..... 0.0 ........ Mauritius 140 919 38.4 24.0 30.2 53.3 31.2 ......22!6.6..... 0.2 0.0O Mexico 4,591 11,400 9.7 12.4 69.7 66.6 10.4 17.9 10.2 3.1 Moldova .. 132 . 45.3 29.3 . 23 1.1 Mongolia 37 . ..57 ... .. 26.5 26.2 .8.6 36.0 .649.9 .. 33 .5 .. ..... 4... .44 Morocco 783 2,471 20.3 17.8 57.9 58 5 .......20.7 ......22-.4 1.1 1.2 Mozambique 118 253 78.5 23.3 0.0 21.5 76.77.... Myanmar 60 509 34.5 . 19.7 43.6 .......... 2. .......3 Namibia 367 .. 91 7.. 7.5 0......O.8 Nepal .........-127 898 .... 5.9 .... 6.3 40.8 16.8 53:3.3 .... 76 8.8 ................... ...... Netherlands 17,150 49,774 51.5 39.9 13 1 12 5 34.3 46.3 1.2 1.3 New Zealand 1,009 3,977 58.2 ......29A 1 .. .. 21.1 ........52.6 .......19.6 .......18.3 .. ... .1. . -0..1 Nicaragua 44 155 36~O.0 ...... 15.7 48 649.7 ......149.9 ..... 32.8 ........0.5 ........1.8 Niger 41 33 33.5 1.2 15.2 21.3 50.9 77.5 0.4 0.0 Nigeria 1,127 786 80.9 11 5 6.0 7.0 6.5 ........81.0 .......6.6 0.4 Norway .8,615 14,447 745.5... 62:4 88 14:5.5. 16.3....... 19.9 04.4. 3..1 Oman 9 18 100.0 100:0 Pakistan 617 1,761 41.3 50.1 22.8 63.3... 34 1.. 412. 1.8 2.5 Panama 9-02 1,600 47.0 494 .. 19.0 .23.4 ......258..... 21:1 82 6.1 Papua New Guinea 43 436 33.8 7.7 28.3 3.1 36 :9 86 0 1 0 3.1 Paraguay 118 576 2.0 55.3 . 42:6 ... .. 0.2 Peru 715 1,543 30.9 19:2 ......40.9 .. ..53.5. 24.9 ~ .....20.0 3.2........ 7.4 Philippines 1,447 15,137 14.2 2.4 .....22.1 15.5 63.6 82.0 ..... 0 2 Poland 2.018 8.986 59.2..... 34:6 11.9 256.6 24:1 29.5 . .... 4.8 ...... 10.3 Portuga 2,006 7,593 23.5 17.5 57.3 60-0 18:1 182 .......1.2 4. Puerto Rico .. . .. .. . Romania 1,063 1,422 37.6 34.2 30.5 37~.0.... 27:8. 23.5 ... 4.2 ..._...5..3 Russian Federation 13,520 25.5 . 16. 22.1 . 0.9 1999 World Development Indicators 213 4.7 Service Transport Travel Communications, Insurance exports computer, and financial information, services and other services $ millions % of total % of total % of total % of total 1980 1.997 1980 1997 1980 1997 1.980 1997 1980 1997 Rwanda 32 , 58 42.3 28.7 .....10.8 28.8 .... 463.3 . . .. 426.6 06.6 .. .... :.... Saudi Arabia 5,191 4,484 15.3 0.0 ...... 25.9 0..... . 0.. ~ 58:8.. .... 1000.0 ...... .... Senegal .337 556 19.1 10.1 29.3 30.2 5 1.3 59:3 0.3 0~4 Sierra Leone .....I....... 49 ...... .87 31.4 11:2 ...... 25.5 65-8 43:1 .......122 7. . 02 Singapore 4,856 30,472 26 9 16.9 . ... 29.5 20.9 42:5 ...... 60 7 ...... 1.1. 1 5 Slovak Republic . 2,167 . 34 1 .....25.1 .... ..... ......35:9 .4.8 Slovenia 2,043. 22 8 . 58.1 18.5. 0.6 South Africa 2,929 5,066...... 41.8 24.0 47.1 ..... 56.5 2. 5..... 10.3 . .8.6 9.1 Spain 11,593 43,902 25.9 15:5 ... ...60.0 ....- 60 9 .......116... .... 19.9 2.4 3.6 Sri Lanka 231 875 18.8 44:4.4.. .. 42.9 23.8 .... 37:4.4...... 283.3.. 1.0 3..4 Sudan 213 33 9.2 35.5..... 17.9 13.7 724.4..... 82.9 0.5 Sweden 7,489 17,848 40.5 32 1 .......12.9.. . 200.0 .. .. 44.0....... 45.9 2.. .6.. 2.0 Switzerland 6,888 26,225 18.8 .......9 4 46.0 34.2 30.5 269.9...... 4.7 29.5 Syrian Arab Republic ........365 1,604 ... 17.2 13.3 429 9.. ... 645.5 39.9 22~1.1......... Tajikistan..2 .. Tanzania 165 484 39.5 12.3 12.6 70.1 ........464.4 . 14.8.... 1.5 .... 2 8 Thailand 1,490 15,763 201 153 58.2 48.6 21.2 35.6 .....0.5 0 5 Togo ....... ..... 74 ... .. 38..3 ........... . 35.2 25.0 .. . .... :........ 1.4 Trinidad. and Tobago 411 343 27.7 56 6 37.3 22:6 35.0 12.0 8~9 Tunisia 1,067 2522 19.4 25.5 64.1 61:2.2 ...... 4: ...... 10.9 1.6 ....... 2.4 Turkey 711 19,373 37.4 11.3 45.9 36.1 16.3 50.5 0.4 2.0 Turkmenistan ' ...::.. Uganda ....... . .. 10....... 154 13.2 40.4 82.0 59.6 4~8.8 ......... Ukraine .. 4,937 81.6 . 5.5 ....... ...:.... .123.3 ... ..... 0.7 United Arab Emirates United Kingdom 36,452 87,239 38.9 21.0 19.0 23.7 .......421.1 40.9 .. ..... 14.4 United States 47,550 256,163 29.9 18.7 ..... .22 3 32.9 44:6.6..... 43.2 3.2 5.3 1 S 1475 1 :54 -1 1la 291 Venezuela 693 1,415 41.1 23.7 35.1 61 49.5 14. 14.4 0.1 Vietnam .. 2,530 West Bank and Gaza Yemen, Rep. .... ..I. ........... 260 16.4. 16. 32 3 ... .........51 2.2 ... Yugoslavia, FR (Serb./Mont .. . Zambia 152. 568 . 13.9 .. 25.3 .......... .... 4.1. . Zimbabwe 169 . 5 . 1. 26.1 . . Low income 9,757 25,593 35.4 24.6. 29.9 28.1 32.1 ......45:3 ... . 2.9 ........2.3 Middle income 54,746 242,215 . 2.6 2.3 36.4 41.4 30.3 36 0 ... 4.9 .......2..6 Lower middle income 25,880 131,509 29.7 20.3 36.7 43.9 30.9 34.3 3.4 1.7 Upper middle income 30,501 110,606 29.5 20.2 36.1 38 5 .....29:8. 379.9 6.0 3.8 Low & middle income 64,991 267,755 30.6. _20.6 .._35.2 .... ..404.4... 30.6 367.7 . 4.5 2..6 East Asia & Pacific 8,774 82,484 23.9 11.3 38.0 41.9 377.7...... 46.4. 0.6 .... 0.4 Europe & Central Asia ..... . 74,951 46.0 26.5 24.7 38-9 255 5...... 31~3.3 ..... 3.9 3.5 Latin America & Carib. 16,777 47,034 27.4 25.7 40 9 43.2 25.2 ........26.3 6.7 5.0 Middle East & N. Africa 14,875 34,369 25.1 19.8 33.0 39.3 41.4 40.0 ..1.2 South Asia 4,177 13,805 19.6 25.0 44.4 27.8 35.1....... 44~8. 1.4 ........2.5 -ubSaharan Africa 9,152 15,159 45.0 25.3 27.6 46.9 22.....1 ...... 24 0. 5.6 .......5.4 High income 327,151 1,104,142 34.1 23.9. .... 24.7 29!6.6...... 39.0 ...41j.7 2.6 5.6 Europe EMU 152,970 49,507 27.4 23.8 28.8 33.9 ........41.1_ 37-.7 ... 2.8 4.6 a. Includes Luxembourg. b. Data prior to 1992 include Eritrea. c. Data prior to 1990 refer to the Federal Republic of Germany before unification. 214 1999 World Development Indicators ; :6 . - 4.7 6 Balance of payments statistics, the main source of E. * Service exports refer to economic output of intan- information for international trade in services, have gible commodities that may be produced, transferred, Developing countries are garnering a many weaknesses. Until recently some large growing share of service exports and consumed at the same time. Intemational trans- economies-such as the former Soviet Union-did actions in services are defined by the IMF's Balance not report data on trade in services. Disaggregation of Payments Manual (1993), but definitions may nev- of important components may be limited and varies ertheless vary among reporting economies. * Trans- significantly across countries. There are inconsisten- port covers all transport services (sea, air, land, cies in the methods used to report items. And the 4 internal waterway, space, and pipeline) performed by recording of major flows as net items is common (for . . residents of one economy for those of another and example, insurance transactions are often recorded , involving the carriage of passengers, movement of as premiums less claims). These factors contribute to goods (freight), rental of carriers with crew, and related a downward bias in the value of the service trade - support and auxiliary services. Excluded are freight reported in the balance of payments. insurance, which is included in insurance services; Efforts are being made to improve the coverage, e*-.* goods procured in ports by nonresident carriers and quality, and consistency of these data. Eurostat and repairs of transport equipment, which are included in the Organisation for Economic Co-operation and I goods; repairs ofrallwayfacilities, harbors, and airfield Development, for example, are working together to facilities, which are included in construction services; improve the collection of statistics on trade in ser- a and rental of carriers without crew, which is included vices in member countries. In addition, the : - in other services. * Travel covers goods and services International Monetary Fund (IMF) has implemented acquired from an economy by travelers in that econ- the new classification of trade in services introduced omyfortheir own use during visits of less than one year in the fifth edition of its Balance of Payments Manual for business or personal purposes. * Comn- (1993). munications, computer, information, and other ser- Still, difficulties in capturing all the dimensions of vices cover international telecommunications and international trade in services mean that the record postal and courier services; computer data; news- is likely to remain incomplete. Cross-border intrafirm related service transactions between residents and service transactions, which are usually not captured nonresidents; construction services; royalties and inthe balance of payments, are increasing rapidlyas .n..n. _ - license fees; miscellaneous business, professional, foreign direct investment expands and electronic EIJI. , - r..,,,- , and technical services; personal, cultural, and recre- L* T-r, Xr ..r.- -.,r r . networks become pervasive. One example of such Er , , ational services; and government services not transactions is transnational corporations' use of i r j included elsewhere. * Insurance and financial ser- mainframe computers around the clock for data pro- * vices cover various types of insurance provided to non- cessing, exploiting time zone differences between E:.: rr. .rIr I. ,:rA,, Fu,.. 5 ,,. residents by resident insurance enterprises and vice their home country and the host countries of their P.m..-.i O ,.; versa, and financial intermediary and auxiliary ser- affiliates. Another important dimension of services vices (except those of insurance enterprises and pen- trade not captured by conventional balance of pay- Deheloping wunth the sarges galns in Eaxr Asia sion funds) exchanged between residents and ments statistics is establishment trade-sales in and the Pacific and Europe and Central Asla. nonresidents. the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise Data sources may be reported as exports or imports in the bal- ance of payments. Data on exports and imports - -: of services come from the IMF's balance of payments z: data files. The IMF publishes . balance of payments data in its International Fnancial - Statistics and Balance of Payments Statistics Yearbook. 1999 World Development Indicators 215 4.8 Structure of service imports Service Transport Travel Communications, Insurance imports computer, and financial information, services and other services $ millions % of total % of total % of total % of total 1980 1997 1.980 1997 1980 1997 1980 1.997 1980 1997 Albania 18 115 43:7.7..... 48.0 0.0 4.2 51.4 293_.3 . ... 4.9 ...... 18.5 Algeria 2,697 39.9. 12.4. 40 9.. 6.8 Angola. 2,423 . 12.8 . 3.0.. 81.20 Argentina 3,788 6,340 33.6 45 2 47 3 396..19..1..1292 32. Arm enia.... 159............. ................ ... . .. ..... 57:6 .......... ....... 26.0 .8.9 ..7.5 Australia 6,549 18,823 47.3 34.3 28.0 32.6 23.1 27.1 1..... .. 6 ........5 .9 Austria 6,204 24,942 ... 12:7.7...... 11.7 50.6 .... 40.6 32.4 .... 42.2 4.2 5 ~5 Azerbaijan 399 . Bangladesh ............. 173 557 .... 643 3...... 66 2 ........33 3....... 13.9 26.4 11.9 6.0 8.0 Belarus 385.... Belgium' 12,827 31,866 . Benin 109 569.9.... .......7.1 25:9 .. 10.1 ~.... ... Bolivia ............ 259 . .... 406...... .53 :3 60.6 21.3 140.. . . .... . 162 .......15.8 9.1 .. ... 9.5 Bosnia and Herzegovina ... Botswana 216 .344. .42:4 .......52,4 260.0...... 22.6 .... 278.8 _.. 20.8 3.7 4.2 Brazil 4,871 18,463 56.5 35.1 7.5 29.5 35.0 33.6 ........1.0........ 1.8 Bulgaria 549 1,167 51.3 ........428 8.6 19.0 344 29.1 5.......S.7 .......9.0 Burkina Faso ....... .... 209 . 58 0.......... :.... .. 15.3 ..........: .......21.9. 4.8 Burundi- 41. 480 .......... ........ 245 .25.1 ................ .... 2.3 Cambodia 188 . 490 ... .. ..... .........6.8 39.9 .................... 4.3 Cameroon 377 694 39.0 34 4 11:5.5...... 21.1 44.1 37.5 5.4 7.0 Canada 10,666 36,361 29.9 22.9 30!9 .... ..31.1 39.2 46.0'..... Central African Republic 142 .. 47 3 .. .... ...... 24.5 23:5 .......... ........ 4.7 ............... Chad 24.. 64. 57.535. 07 Chile 1.583 4,000 52.4 49:6 .... ...126 21.6 323 ........25.3 ........2.7 ........3.5 China 2,024....................... 30 30 6. 6 .. : . ._ . 33.8 .. ......33 .... .. 33.5 30.7 ....28:1 ........4-4 ........45 Colombia 1,170. 4,375 45.3 ........29.3 20.5 21.9 24.0 32.1 10.2 16.7 Congo, Dem. Rep .... .7 Congo, Rep. ............ 480 565 ... 270.0...... 42.0 6.1 9.1 63.5 45.6 3.5 3.3 Costa Rica ............. 286 1,152 582.2 41.8 21.1 29.0 13.9 23.7 6.7 5.4 CMe dIlvoire 1531 1,316 38.6 45.2 15.8 14.1 37.7 40.7 7.9 Croatia 1,972 . 20:2 ....... ... 264 53:3 Cuba Czech Republic 5,389 . 11.7 . 43J.7.. . ... 39.5 51. Denmark 4,663 14,990 47 7 45.0 27 4 27.5 22.9 27.5 2.0 Dominican Republic 399 962 39 6 ........568 .......416 ........21.1 .......147 7... ... 9.9 4.1 12.2 Ecuador 704 1,121 36 0_ 51.2 . ....32.4 20.2 19 1 12.2 12 4 16.3 Egypt, Arab Rep. 2,343 5,048 40.3 ........28. 2 72 2....... 19.9 49.1 49.0O 3.4 2.9 El Salvador 273 364 29:3 ........57.1 38.8 20.5 20.7 11:8.8..... 11..2....... 10.7 Eritrea 93. ... Estonia 727 .. 46.8 16.5 33.3 ..... ..... ....... 3.4 Ethiopia5 90 280 72:2 .......556 2.2 10.1 256.29.0...... Finland 2,555 8,377 39 :4 ...... 23.0 23.1 26.2 35 1 49,_.4 . 2.4 1.4 France 32,148 63,651 28.4 30.9 18.7 25,9 48.1 38.8 4 8 ........4.4 Gabon 789 949 22.0 29.6 12.2 18.3 60.1 48.3 5.7 3.8 Gambia, The 42 75 55.8 44.7 3.5 220.0.. ... 33.2 ....... 28:7.7....... 7.4 4.6 G eorgia . 245 . ... ....... ................. Germany' 42,378 119,507 251 1....... 19.3 41.2 38.5 33.2 39.6........ 06 6....... 2.6 Ghana 270 456 397 7...... 47.0 12.1 4.9....... 45.8 41.1 2.3 .........70 Greece 1,428. 4,650 41.5 26.2 21.6 285.5 31.1 40.7 5.8 4.5 Guatemala 487 650 37.0 50.4 33.6 18.2 26.0 24.8 3.3 6.5 Guinea ........322 . 38.7 7.2 ..........: ....... 52:0 2.1 uie-Bissau 14 21 47.9 52.6 11.0 .. 6. 41.5 50 5 Haiti 162 283 49.3 66.8 25.1 .... 13.1....... 23:0 20.1 2J.7 Honduras 174 361 53.3 59.7 178 172 16.6 211 1 2.3 1.9 218 1999 World Development Indicators 4.8 Service Transport Travel Communications, Insurance imports computer, and financial information, services and other services $ millions % of total % of total % of total % of total 1980 1997 1980 1997 ±980 1997 1980 1997 1980 1997 Hungary52 3,695 . 60.3 . 11.5 26.9 31.2 6.1 47:9 6.7 9.3 India ~~~1,516 8,110 60.9 .. .. 48 . . .. 38.... .10.8 .... 31.0 29.2 5-.3 ....... ' Indonesia 4,998 16,607 40 .1.... 32.5 1-1.9 .... 145.5 ..... 44.2 49.9 3 .8 3.1 Iran, Islamic Rep. 5,223 3,083 43:6. 46.5 32.5 8.4 17..4 35.4 ..... 6.4 ......9.8 Iraq - .~ Ireland 1,593 15,069 43:9 .... 13.4 ....36:6 .......14.7 16.1 70.7 3.4 1 2 Israel 2,310 11,068 44.1 .... 34.5 35.6 36A.4.. ...18 .5...... 26.5 1.8 2.6 Italy 16,249 70,429 43.8 .....352 11.8 ....23.6 31.3 31.5 131 9.6 Jamaica 370 1,175 554. 4 ... 44.6 89 15.4 23.8 33:.1 ..... 11.8 .... . 6.8 Japan 32,360 123,454 522.2 25.2 14.2 26.7 ..... 31.3 44.2 2.3 389 Jordan........ 819 1,698 .... 32.8 ... .. 44.6 33.1 25.9 28.1 24.2 6.0 5 .3 Kazakhstan 1,125 Kenya........ 502 837 .66.2 .... .43:7 4.6 23.2 18.0 22.9 11.2 10.3 Korea, Dem. Rep. Korea, Rep. 3,293 29,502 613.3 .. 34:9 .... 10:6.6... 23.7 27.6 . 40 6 0.5 0.8 Kuwait 3,067 5,238 38.8 31.3 43.7 488.8 169.9 18~6 0.6 1.3 Kyrgyz Republic .. 171 . Lao PDR 116 34.8. 18.2. 42.8 4.2 Latvia .. 662 30:4 . 49.2 17:3 3.2 Lebanon 574 .. ............. .............. Lesotho 50 ........31:6 158.8 ........ 49:7 ..2.8 ..... Libya 2,303 51.4 20.4 . 23.2. 5.0 Lithuania 897 40.1 .. ....... ... .30.9.. 26:3 . 2.7 Macedonia, FYR . 273 483. . 5 . 17.0 Madagascar 311 373 57.3. 46.7 9.9 19.3 28:0 33.2 4,8 ......0.8 Malawi 179.. .. 81.7 5~6 ......53 .... ... . 7A.4 ....... Malaysia 2,957 17,516 44.3 .....31.2 24.5 14.1 ......312 2... 546.6 ....... . .......... Mali 212 346 658.8 . 64.8 9.6 12.3 .... 18.3 21.4 ...... 6.2........ 1.5 Mauritania 128 217 59A.1 . ... 55 7.7 . .... 13.6 ....... 10.5 ..... 24.2 .. ... 32.4 ....... 3!.1 ...... 1.3 Mauritiu 174 684 64.7 36.8 12:9 ......25.9 .... 15.2 33.3 7.2 3.9 Mexico 6,514 12,616 28 2 .... 13.4 .....47.0 0... 30..8 ....16.3 21.2 8.5 ...... 34.5 Moldova . 195 .. 52.8 .. 24.8 . 21.2 . 1.2 Mongolia 31 95 48.4 ...... 63..7 . 0.3 20.4 5173.3 .. . 15.-8 . ................... Morocco 1,436 1,724 34.4 33.1 678 ......18.3 55.5 46 3 3.3 2.3 Mozambique 124 3-19 790.0 ... .. 32.6 .... ...00 0 . 145. 5 .... 65.0. ..... 6.. 5...... 2.4 Myanmar 85 343 56.8.. 4.6. 31.7 .... 6..9 ......... Namibia 506 . 33.5 . 19:6 38:0 8.8 Nepal 81 212 30:1.1...... 27.7 29.2 46.0 38.2 ..... 26.3 2.5 2.9 Netherlands 18,148 45,197 43.9 302.2 26.6 22.6 27:.1.... . 44 5 2.4 2 7 New Zealand 18 3 4,981 . 39.4 40.9 ..... 28.3 29..1 ..... 31.8 ........267 0..... ...O6 ........3.3 Nicaragua 104 238 50.7 .....32.4 299 27.3 .... 14.2 ... 37.8 5.2..2.5 Niger 279 -152 43:0 58.9 6.6 8.8 .......43.7 ........303 .......6.7 ........2.0 Nigeria 5,285 4,712 33 7 ...... .15.8 .....18.7 .... .. 38.5 44.8 .... 43:3 28 8 .. 2.3 Norway 6,996 14,582 522 2...... 35.6 21.1 30.5 23.3 29.1 3 .4 .......4.7 Oman 518 1,166 341.1 41.8 62.2 4.0O...... 55.9 49.5 3.8 4.6 Pakistan 853 3,259 64.5 65.3 9.6 13.3 22.8 17.5 3.0... .....3.9 Panama...I... 588 1,204 ... 65.4 63.3 9.5 13.6 1479.9 ...... 1077.7 ..... 10.2 .....12.4 Papua New. Gui'nea 302 747 ... 60:4 24.1 5.9 9.6 ...... 28.9.... 58.3 ...... 4.8 ..... . 7.9 Paraguay 260 746 58.0 21.0 . 32 . 7.9 Peru 880 2,290 55:4... .. 39.3 12.2 18.4 ...... 25:2 32.8 72.2. 9.5 Philippines 1,439 14,122 52 1 18. 9 7.4 13..7 39.8 66..6 0.8 0.8 Poland 2.023 5.814 599 9 .... 27.3 12.9 10.1 253 44:8.8. 2.0 17.7 Portugal 1,525 6,387 48.8 26.4 19.1 33.7 27.2 32.7 4.9 7.2 Puerto Rico . Romania 1,045 2,037 76:8 ..... 26.6 7.0 ...... 33.4 7.7 29.3 8-.5 4.7 Russian Federation .. 18,715 . 16.1. 53.0.. 28.8 . 2.0 1999 World Development Indicators 217 4.8 Service Transport Travel Communications, Insurance imports computer, and financial Information, services and other services $ millions % of total % of total % of total % of total ±.980 .1997 1980 1.997 1.980 ±1997 1L980 1997 1950 ±1997 Rwanda . .... ..... 123 243 635.5 I.... 26.4 ... ....9.3 .. 29.1 27.3 .44.5 Saudi Arabia 30,231 25,477 17.1 83.3 8.1 0.0 73.3 90.8 1.5 0.9 Senega 340 578 46.9 39.9 17.6 12.4 29.0 .......428 .......6.5 .... 4.9 Sierra Leone 85 92 54.8 15.0 9.8 .......53.9 23.4 27.8 11.9 3.3 Singapore 2,912 19,534 38.3 323 114 34 46.1 3. . Slovak Republic ....... 2,094. 16:3 20.9 56.6 6.1 South Africa 3,805 6,244 48.4 44.0 .....20:3 ...r...31.2 20.0 16.7 .......11..3 8.1 Spain 5,732 24.675...... 386.6. 28.9 21.5 18.2 34.6 46..2 5.4 6.7 Sri Lanka 351 1,301 60.4 ....... 58.4 9.5 13.8 23.5 .. . 21.8 6.5 6.0 Sudan 259 292 34:3 ... .. 73.1 ...168 ..... 19..2 44.5 7.4 ........4.5 .0 2 Sweden 7,018 19.559 35.9 24.0 3 1.6 33.6 28.1 41.2 4.4 1.2 Switzerland 4,885. . .15.387 30.4 24.9 48.8 49.0 19.3 25.1 1.6........ 1.0 Syrian Arab Republic 521 1.489 26.6 44.9 33.9 366.6 37.3 18:.5 . 2..2 ............ Tajikistan . . . .::... Tanzania............. 295....... 797. 62:.1 . .... 25.8 6.7 ... 51.1 25.6 20:4 5.5 2.7 Thailand 1,644 17,337 .... 64.4 39.6 ... 14.8 .... 19.7 14.8 35 5 .......5.9 .... 5 1 Togo167 . 62.7 14116.7 ... ........66 ........ Trinidad and Tobago . ..... 645 242 45.7 38.9 21.6 28.7 235.5. 25~1 9.2 7.3 Tunisia . ....I..... 600 ~1,129 .. 51.1 .... . 44.6 17.7 20.7 25.5 25~5 5.7 9.2 Turkey 569 8,507 50.5 22.1 18.3 20.2 27.1 51.3 4.2 6.4 Turkmenistan . Uganda......... ... 123 .... ... 405 .58.2 .. .....34.9 ...... 14.6 .... 19.8 22.8 41 4 ... ..4.4 ........3 9 Ukraine 2,268 . 2:1.0 ..14. 62035 United Arab Emirates United Kingdom .......... 27.933 72,032. 47:5.5...... 29.6 22 9 39:2 29.6 30.0. 1.2 United States 40,970 166,194 37.5_ 28 4.... _25 4 31.7 35.0 3. 5 5 Uruguay 476 947 31.8 50.4 42.6 27.9 18.1 14.8 7.5 6.9 Uzbekistan 650 .. .. . Venezuela 4,253 .5,350 .31:7 283 ...... 47.0 .....47.0 16.5 21.5 4.8 3.2 Vietnam .. 3,153...... West Bank and Gaza : : I: : ~ Yemen, Rep. ...... 692 47.5 . 12.9 396 ....... Yugoslavia, FR (Srb/Mont4.) Zambia 651 53:5 85.. .. 33:9. ..... ... ...... 4.0O .. ....... ZimbabweI..... 395. . .... .. .. . ..... .. . ...... ....43.3...... 40.. 3 ~ . ....... . 13.... . 9 .... 2.6..... Low income 18,825 3773 .. 46:5.5.... 45.5 13.8 . 19.7 35.2 3.9 4.8 4.3 Middle income 109,958 280,480 35.3 28.2 19.2 24.1 41.4 42.1 4.4 6.0 Lower middle income 35,891 146,180 -..47~.0.. .. 31.3 ... ...187.7~ .. 26.9 28.2 37 4 .......6-.1 ... 4 4 Upper middle income 72,140 134,314 309.9 25.1 ......19.4 .......21.3 46.4 46.9 .... 3~7.7...... 7.9 Low & middle income 129,097 318,330 37.0 . ... 29.7 184.4 23.8 40.5 .... 41.2 4.4 5 9 East Asia & Pacific 14,593 101,948 52.2 32.0 16.9 21.2 28.8 43.-8 3.8 3.7 Europe & Central Asia 59,171 61.6 20 5 115 34.3 22.4 39..5 ........44 4....... 5.9 Latin America & Carib. 29.427 66,252 40.8 33.8 .. 320.0 29:7.7... . 22:0.0..... 25.3 6 .1 .... 11.2 Middle East & N. Africa 54.134 51,371 .26.3 .. 18.3 12~6.6....... 7.3 581.1 72.4 3.0 2..1 South Asia 3,136 13,569 617.7... 57.6 6.5 12..6 27.0 .... .24.8 4.8 5.1 Sub-Saharan Africa 19,654 27,786 43.3 .... 35.5 16.3 29.2...... 345.5 30.4 6.2 5.3 High income 318,496 1,059,321 38.3 27.6 25.9 ....30.4 ..... 33:0.0 .... 38:2 3 .2. 4 2 Europe EMU 139,359 410,100 31.8 25.6 28.5 29.2 3.6. 40:1 5 ... 4.0 47. a. Includes Luxembourg. b. Data prior to 1992 include Eritrea. c. Oats prior to 199o refer to the Federal Republic of Germany before unification. 218 1999 World Developmest Indicators 4.8 The leading service exporters are also the leading ser- beginning from a much smaller base, has seen growth * Service imports refer to economic output of intangi- vice importers. High-income economies dominate world in its service trade accelerate from 5 percent a year in ble commodities that may be produced, transferred, and service trade, but developing countries' share has been the 1980s to 11 percent in the 1990s. Further reduc- consumed at the same time. International transactions increasing. In recent years service trade has grown most tions in barriers to service trade in the next round of in services are defined by the International Monetary rapidly in Asia, where nominal growth in 1990-97 sur- World Trade Organization negotiations could stimulate Fund's (IMF) Balance of Payments Manual (1993), but passed the rate in the previous decade. In East Asia and even faster growth early in the 21st century. definitions may nevertheless vary among reporting the Pacific, already the leading trader among developing Data on service imports are taken from balance of economies. * Transport covers all transport services regions, service trade grew 13 percent a year in payments statistics. For more information on trade in (sea, air, land, internal waterway, space, and pipeline) 1980-90, and 21 percent in 1990-97. South Asia, services see About the data for table 4.7. performed by residents of one economy for those of another and involving the carriage of passengers, move- ment of goods (freight), rental of carriers with crew, and A shifting dominance in service imports related support and auxiliary services. Excluded are freight insurance, which is included in insurance ser- 1980 vices; goods procured in ports by nonresident carriers and repairs of transport equipment, which are included in goods; repairs of railway facilities, harbors, and air- field facilities, which are included in construction ser- -.,: K. vices; and rental of carriers without crew, which is I,:, *- . : ;included in other services. * Travel covers goods and : c~ | *; > . .services acquired from an economy by travelers in that - . ' . ' i q ':i I 1. , rr, economy for their own use during visits of less than one ,,. U ' i I iU i im' year for business or personal purposes. * Com- -~I Fl . ;i . j ', f, f !z; ;amunications, computer, information, and other ser- ;vices cover international telecommunications and _ -. - :: -. - - -. --postal and courer services; computer data; news- related service transactions between residents and non- residents; construction services; royalties and license fees; miscellaneous business, professional, and tech- 1997 nical services; personal, cultural, and recreational ser- vices; and government services not included elsewhere. * Insurance and financial services cover various types of insurance provided to nonresidents by resident insur- -. O :ance enterprises and vice versa, and financial interme- diary and auxiliary services (except those of insurance . 3 | .. enterprises and pension funds) exchanged between res- I, u i i . I > | idents and nonresidents. Il F u Z sr>8i Sj s zji 'I El* Data sources ! Data on exports and imports -- - *- -; -- .- of services come from the IMF's balance of payments data files. The IMF publishes It T,.-,:.:.:.fl at T,, .balance of payments data in * ,: trr.m.Jn.-,,. .n ,-, - :r,;l.jI-r ,r,-o ,mlI. vmz-.r.2 -, ,,, t-' its Intemational Flnancial I Statistics and Balance of -n.,,-If,lM.c.,GI;.i.... ;l:.I . -,_, F.J1. e -r-_ L~- .dF-?I,rl.] i ................1l:~-~ Payments Statistics Yearbook Tran;por arca trapl, the dominanT cervice mport sector; in tne l990i are rine outpaced b) the rapidtg growing imports ot communications. compuier. and intormation ser0ices. 1999 World Development Indicators 219 4.9 Structure of demand Private General Gross domestic Exports Imports Gross domestic consumption government investment of goods of goods savings consumpytion and services and services % of GDP %ofGDP % of GDP % of GDP %ofGDP %ofGDP 1980 1997 1980 1997 1980 1997 1980 1.997 1980 1997 1980 1997 Albania 56 103 9 1 1 35 12 23 12 23 37 35 -13 Algeria 43 51 14 14 39 26 34 31 30 22 43 35 Angola .. 30 43 .. 25 68 .. 65 ...............2 7 Argentina 76 78 3 25 20 5 9........ 6 11 24 18 Arm enia .. ..... 116- ........ ....... ~ 13 ..9 ..... ....... 20... .~ ...... .. 5.... 8 .... ...... -29 Australia 59 63 18 17 2 5 20 16 20 18 20 24 21 Austria 55 57 18 20 29 24 36 41 38 41 27 23 Azerbaijan .. 83 . 8 .. 28 . 19 .. 37:. 10 Bangladesh 86 81 2 4 22 21 4 12 14 18 13 15 Belarus . 59 19 . 260. 64. 2 Belgium 64 63 18 15 22 18 57 68 60 64 19 22 Benin 96 79 9 10 15 18 23 25 43 33 -5 11 Bolivia 67 75 14 1 5 17 19 25 21 2 3 29 19 10 Bosnia and Herzegovina .. . ..: : : ..... ............... Botswana 47 28 18 2 7 35 26 50 56 49 38 36 45 Brazil 7 0 63 9 18 23 2 1 9 8 11 10 2 1 19 Bulgaria 55 70 6 12 34 1 2 36 6 1 3 1 5 6 39 17 Burkina Faso 95 79 10 12 17 25 10 14 33 30 -6 9 Burundi 91 83 9 15 14 7 9 10 23 14 -1 3 Cambodia .. 87 .9 .. 16 30 . 42 . Cameroon 70 71 10 8 21 16 27 27 27 22 20 21 Canada 53 58 22 21 23 18 28 40 26 36 25 21 Central African Republic 94 84 15 9 7 9 25 21 41 23 -9 7 Chad 100 92 4 7 3 19 17 17 29 35 -9 1 Chile 7 1 66 12 10 2 1 2 7 23 2 7 2 7 29 17 2 5 China 5 1 46 15 12 35 38 6 23 7 18 35 43 Hong Kong, China 60 61 6 9 35 34 90 132 91 135 34 31 Colombia 70 68 10 16 19 19 16 1 5 16 18 20 16 Congo, Dem.Rep. 82 83 8 8 10 7 16 24 16 22 10 9 Congo, Rep. 47 46 18 19 36 26 60 77 60 68 36 35 Costa Rica 66 63 18 12 27 27 26 46 37 48 16 25 CMe d'lvoire 63 65 17 12 27 16 35 47 41 40 20 23 Croatia : .66 :: 30 . 1 5 .. 42 53 .3 Cuba . . .. Czech Republic .. 51 . 20 31 3 . 58 63 .. 28 Denmark ............... 53 50 27 26 21 .... 19 33 35 35 31 20 24 Dominican Republic 77 70 8 8 25 25 19 48 29 51 15 22 Ecuador 60 67 15 12 26 20 25 30 25 29 26 21 Egt,Arab Rep. 69 77 16 10 28 18 31 20 43 25 15 13 El Salvador 72 86 14 9 13 15 34 24 33 35 14 4 Eritrea .... .... 85 . 33 . .. .. .......41 . ... .. _31 ........ ...... . 89 . .. ....-17 Estonia 59 .. 23 . 30 .. 7 . 89 18 Ethiopiab 83 80 14 11 9 19 11 16 17 263 9 Finland 54 53 18 22 29 17 33 38 34 30 28 25 France 59 61 18 19 24 17 22 24 23 21 23 20 Gabon 26 38 13 14 28 26 65 64 32 42 61 48 Gambia, The 63 85 31 11 27 18 43 47 64 616 4 Georgia 56 95 13 9 29 7 . 12 23 31 -4 Germany .. 58 . 20 .. 21 . 24 .. 23. 22 Ghana 84 80 11 10 6 24 8 24 9 38 5 10 Greece 62 75 12 14 33 19 16 15 22 24 27 11 Guatemala 79 87 8 5 16 14 22 18 25 24 13 8 Guinea .............. ..... 74 .. ....... ........7 22 18. .. 18..... 21 ...... .. 19 . Guinea-Bissau 73 88 28 7 28 24 13 21 42 40 -1 5 Haiti 82 97 10 7 17 10 22 8 31 23 8 -4 Honduras 70 63 -13 15 25 32 36 37 44 47 17 22 220 1999 Worili Development indicators 4.9 Private General Gross domestic Exports Imports Gross domestic consumption government Investment of goods of goods savings consumption and services and services % of GDP % of GDP % of GDP % of GDP % of GDP %ofGDP ±980 1997 1980 1997 1990 ±997 1980 1997 1980 1997 1980 1997 Hungary 61 63 10 10 31 27 39 45 41 46 29 27 India 73 70 10 10 21 24 7 12 10 16 17 20 Indonesia 51 63 11 7 24 31 34 28 20 28 38 31 Iran, Islamic Rep. 53 53 21 13 30 29 13 21 16 16 26 34 Iraq. Ireland 67 53 19 14 27 18 48 76 61 61 14 33 Israel 53 6 2 40 29 22 22 44 32 59 45 7 9 Italy ~~~61 61 15 16 27 17 22 27 25 21 24 22 Jamaica 64 61 20 18 16 35 51 51 51 64 16 22 Japan 59 60 10 10 32 30 14 10 15 9 31 30 Jordan 79 69 29 25 37 29 40 51 84 74 -8 6 Kazakhstan 81 516 .. 35 . 3.. 13 Kenya 62 72 20 17 29 19 28 29 39 37 18 11 Korea, Demn. Rep..** Korea, Rep. 64 55 12 11 32 35 34 38 41 39 24 34 Kuwait 31 47 11 28 14 13 78 53 34 4:1 58 25 Kyrgyz Republic. 69 .. 1 .... 7 ...... .. .. ..... 22. 38 .. ..... ........ 46 .. ......:..... 14 Lao PDR . 81 7 . 2 4 . 4 . 1 Latvia 59 67 8 23 26 205. 61 33 I1 Lebanon .. 101 16 . 27 10..... lo. 54 .. -7 Lesotho 133 82 26 28 43 86 20 33 122 128 -59 -10 Libya 21 22226 31 . 5 Lithuania . 65 . 19 .. 2.. 5. 65. 1 Macedonia,FYR . 85 . 12 .. 19 .. 40 . 56 3 Madagascar 89 89 12 7 15 12 13 22 30 30 -1 4 Malawi 70 85 19 13 25 12 25 24 39 35 11 2 Malaysia 51 45 17 11 30 43 58 94 55 93 33 44 Mali 89 74 10 12 16 23 16 25 31 35 1 14 Mauritani'a 68 79 25 12 36 18 37 40 67 49 7 9 Mauritius 75 64 14 12 21 28 51 62 61 65 10 24 Mexico 65 65 10 8 27 26 11 30 13 30 25 26 Moldova .. 74 . 26 . 2 . 5 6 . Mongolia 44 64 29 16 63 22 21 55 57 59 27 18 Morocco 68 65 18 18 24 21 17 28 28 32 14 17 Mozambique 94 76 15 10 8 30 15 18 32 34 -8 14 Myanmar 82 88 * *. 21 13 9 1 13 1 18 12 Namibia 47 55 17 31 29 20 7 6 53 68 58 3 7 14 Nepal 82 81 7 9 18 21 12 26 19 38 11 10 Netherlands 61 60 17 14 22 20 51 54 52 4 7 2 2 26 New Zealand 63 63 18 14 21 22 30 29 32 28 19 22 Nicaragua 82 84 20 13 17 28 24 41 43 66 -2 3 Niger 5 83 10 14 28 11 25 16 38 24 15 3 Nigeria 56 70 12 9 21 15 29 41 19 34 31 22 Norway 47 48 19 20 28 23 43 4.1 37 32 34 32 Oman 28 . 25 .. 2 263 38 47 Pakistan 83 78 10 12 18 15 12 16 24 21 7 10 Panama 45 53 18 15 28 29 98 94 89 91 38 32 Papua New Guinea 61 44 24 23 25 37 43 56 53 60 15 33 Paraguay 76 67 6 13 32 23 15 22 29 24 18 20 Peru 57 67 11 12 29 25 22 13 19 17 32 21 Philippines 67 7 3 9 13 2 9 25 24 49 28 59 24 15 Poland 67 64 9 18 26 22 28 26 31 30 23 18 Portugal 65 65 13 18 34 24 25 31 38 38 21 17 Puerto Rico 75 . 16 . 17-.. 65 . 73 . 10 Romania 60 75 5 10 40 21 35 30 40 37 35 14 Russian Federation . 63 . 12223205 1999 World Development Indicatora 221 4.9~ Private General Gross domestic Exports Imports Gross domestic consumption government investment of goods of goods savings consumption and services and services % of GDP %ofGDP % of GDP % of GDP % of GDP % of GDP 1980 1997 1980 1.997 1980 1997 1980 1997 1980 1997 1980 1997 Rwanda 83 99 12 9 16 11 14 6 26 24 4 -7 Saudi Arabia 22 35 16 30 22 20 71 45 30 31 62 35 Senegal 85 77 20 10 12 19 27 33 44 38 -5 13 Sierra Leone 98 21 10 -5 18 14 28 17 -8 Singapore 53 39 10 9 46 37 215 187 224 170 38 51 Slovak Republic 49 22 35 56 . 64 28 Slovenia 57 20 . 24 5 58 23 South Africa 50 62 13 21 28 16 36 28 28 27 36 17 Spain 66 62 13 16 23 21 16 26 18 25 21 2.1 Sri Lanka 80 72 9 10 34 24 32 36 55 44 11 17 Sudan 82 16 1 1 23 2 Sweden 51 52 29 26 21 15 29 40 31 33 19 21 Switzerland 62 61 12 14 29 20 35 36 38 32 25 24 Syrian Arab Republic 67 69 23 12 28 29 18 30 35 40 10 19 Tajikistan :. . ..... 71 ..... . ... 11 17 .114 114.. 18 Tanzaniac 83 13 20 . 22 3 Thailand 65 54 12 10 29 35 24 47 30 46 23 36 Too 54 80 22 10 28 16 51 31 56 37 23 10 Trinidad and Tobago 46 75 12 10 31 22 50 49 39 56 42 15 Tunisia 62 60 14 16 29 27 40 44 46 46 24 24 Turkey 77 68 12 12 18 25 5 25 12 30 11 19 Turkmenistan Uganda 89 83 11 10 6 15 19 13 26 20 0 8 Ukraine 62 22 20 41 44 16 United Arab Emirates 17 . 11 7 28 78 34 ... . ..... 72. United Kingdom 59 64 22 21 17 16 27 30 25 30 19 15 United States 64 68 17 16 20 18 10 12 11 13 19 16 Uruguay 76 74 12 14 17 13 15 23 21 23 12 12 Uzbekistan 61 . 21 . 19 . 38 .. 38 19 Venezuela 55 67 12 6 26 18 29 29 22 20 33 27 Vietnam .. 70 929 .. 46 . 54 . 21 West Bank and Gaza Yemen, Rep. ' 68 19 21 44 . 52. 13 Yugoslavia, FR (Serb./Mont.).. . .. . Zambia 55 78 26 12 23 15 . 41 33 45 38 19 10 Zimbabwe 68 72 19 16 17 19 23 36 27 43 14 12 .. . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . .6 . . . .. . . . . . . . .. . . . . .. . . . . Low income 74 73 10 10 20 22 11 18 17 23 15 17 Middle income 60 61 12 13 27 26 19 25 18 25 27 26 Lower middle income 56 58 14 12 31 29 18 28 19 27 30 30 Upper middle income 63 63 11 15 24 23 19 22 18 23 25 22 Low & middle income 62 62 12 13 26 25 18 24 18 25 26 25 East Asia & Pacific 53 51 14 11 32 36 17 .. ... 33 16... 31 33 38 Europe &Central Asia . 65 14 . 23 . 31 .. 33 .. 21 Latin America & Carib. 68 67 10 13 24 22 13 iS 14 16 22 20 Middle East & N. Africa 45 57 18 18 27 24 43 33 32 32 38 25 South Asia 75 72 9 10 21 23 8 13 14 18 iS 18 Sub-Saharan Africa 59 66 14 17 24 18 33 32 31 32 26 17 High income 60 62 16 16 25 21 20 21 21 20 .... 24 22 Europe EMU .. 60 18 . 19 .. 30 . 27 . 2 a. General government consumption figures are not available separately; they are included in private consumption. b. Data prior to 1992 include Eritrea. c. Dlata cover mainland Tanzania only. 222 1999 World Development Indicators i - ~~~4.9 Expenditures from GDP include private consumption, gen- ventions, adjustments should be made for appreciation of * Private consumption is the market value of all goods eral govemment consumption, gross domestic fixed capi- the value of inventories due to price changes, but this is and services, including durable products (such as cars. tal formation (private and public investment), changes in not always done. In highly inflationary economies this ele- washing machines, and home computers) purchased or inventories, and exports (minus imports) of goods and ser- ment can be substantial. received as income in kind by households and nonprofit vices. Such expenditures are generally recorded in pur- Exports and imports are compiled from customs institutions. It excludes purchases of dwellings but chasers' prices and so include net indirect taxes. returns and from balance of payments data obtained from includes imputed rent for owner-occupied dwellings. In Because policymakers have tended to focus on foster- central banks. Although the data on exports and imports practice, it may include any statistical discrepancy in the ing the growth of output, and because data on production from the payments side provide reasonably reliable use of resources relative to the supply of resources. are easier to collect than data on spending, many coun- records of cross-border transactions, they may not adhere * General government consumption includes all current tries generate their primary estimate of GDP usingthe pro- strictly to the appropriate valuation and timing definitions spending for purchases of goods and services (including duction approach. Moreover, many countries do not of the balance of payments or, more important, corre- wages and salaries) by all levels of government, excluding estimate all the separate components of national expen- spond with the change-of-ownership criterion. This issue mostgovernment enterprises. It also includes most expen- ditures or, if they do, derive some of the main aggregates has assumed greater significance with the increasing glob- ditures on national defense and security. * Gross domes- indirectly using GDP (output) as the control total. alization of international business. Neither customs nor tic investment consists of outlays on additions to the fixed Private consumption is often estimated as a residual, balance of payments data capture the illegal transactions assets of the economy plus net changes in the level of by subtractingfrom GDP all other known expenditures. The that occur in many countries. Goods carried by travelers inventories. Fixed assets include land improvements resulting aggregate may incorporate fairly large discrep- across borders in legal but unreported shuttle trade may (fences, ditches, drains, and so on); plant, machinery, and ancies. When private consumption is calculated sepa- further distort trade statistics. equipment purchases; and the construction of roads, rail rately, the household surveys on which much of the For further discussion of the problems of building and ways, and the like, including commercial and industrial estimates are based tend to be one-year studies with lim- maintaining national accounts see Srinivasan (1994), buildings, offices, schools, hospitals, and private residen- ited coverage. Thus the estimates quickly become Heston (1994), and Ruggles (1994). For a classic analy- tial dwellings. Inventories are stocks ofgoods held byfirms outdated and must be supplemented by price- and quan- sis of the reliability of foreign trade and national income to meet temporary or unexpected fluctuations in produc- tity-based statistical estimating procedures. Complicabng statistics see Morgenstern (1963). tion or sales. * Exports and imports of goods and ser- the issue, in many developing countries the distinction vices represent the value of all goods and other market between cash outlays for personal business and those for services provided to the woild. Included is the value of mer- household use may be blurred. General government con- chandise, freight, insurance, travel, and other nonfactor sumption usually includes expenditures on national services. Factor and property income (formerly called fac- defense and security, some of which are now considered tor services), such as investment income, interest, and to be part of investment. labor income, is excluded. Transfer payments are excluded Gross domestic investment consists of outlays on from the calculation of GDP. * Gross domestic savings additions to the economy's fixed assets plus net are calculated as the difference between GDP and total changes in the level of inventories. Under the revised consumption. (1993) guidelines for the United Nations System of National Accounts (SNA), gross domestic investment Data sources also includes capital outlays on defense establishments that may be used by the general public, such as schools The national accounts indica- and hospitals, and on certain types of private housing for tors for developing countries are family use. All other defense expenditures are treated as 9 r. >IC'V collected from national statisti- current spending. Investment data may be estimated - cal organizations and central from direct surveys of enterprises and administrative . banks by visiting and resident records or based on the commodity flow method using N Ticl.. .I,, World Bank missions. Data for data from trade and construction activities. While the w industral countries come from quality of data on public fixed investment depends on the Organisation for Economic Co- quality of government accounting systems (which tend to operation and Development (OECD) data files (see OECD, be weak in developing countries), measures of private National Accounts, 1960-1996, volumes I and 2). The fixed investment-particularly capital outlays by small, complete national accounts time series is available on the unincorporated enterprises-are usually very unreliable. World Development Indicators 1999 CD-ROM. The United Estimates of changes in inventories are rarely com- Nations publishes detailed national accounts for member plete but usually include the most important activities or countries in National Accounts Statistics: MainAggregates commodities. In some countries these estimates are and Detailed Tables; updates are published in the Monthly derived as a composite residual along with aggregate pri- Bulletin of Statistics. vate consumption. According to national accounts con- 1999 World Development Indicators 223 4.10 Growth of consumption and investment Private consumption Private General Gross consumption government domestic per capita consumption investment average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1980 1997 1980-90 1990-97 1.980-90 1990-97 ±980-90 1990-97 1980-90 1990-97 Albania . 2,523 6.2 . 6.0 -5.0 ......-0.3 ......26..9 Algeria 18,293 24,030 1.9 ...... ..1.0 -1..-.34 .7 3.. .. .8. -2..319 Angola . 2,278 0.1 -6.9 -2.7 -9.9 8 4 -0.7 -5.6 16.3 Argentina . 254,987 ..-5.2 12.6 Armenia. 1,88-86 -9.4 -2.1 -10.9 Australia 94,860 249,229 2.0 .... .... ± J.5 2.43. 2.4 27 ....... 5.4 Austria 43,264 129,921 2.4 2.1 2.2 -1.4 1.4 1.7 22 2.6 Azerbaijan 3,637 -~......... ...........:........ ...... 3079 . ... . ...... 4.4 Bangladesh 14,966 34,265 4.7 4.0 2.2 2.3 5.2 6.3 1.4 6.7 Belarus 1,363 -5-1 -2-. . -2 Belgium 76,629 169,285 1.7 10 16 0.7 0.5 1.2 3.2 -0.3 Benin 1,356 1,687 1.8 2.8 ..... -1.3 -0.1 0.5 3~4... -5~6.-.... 4.2 Bolivia 1,872 5,963 1.2 3.4 ......-0 9 1.0 -3~8 .........3.5 . .......1-.0 ........ 6 5 Bosnia and Herzegovina .. Botswana ~~~~~483 1,442 11.1 -27 7 ..... 7.4 -5.3 13.4 97 7... 11.4.. -0.7 Brazil 163,889 518,880 1.6 5.1 -0.4 3.6 7.3 -2 02 ........4.0 Bulgaria 11,089 7,237 2.5 -1.7 2.6 -1.09.1 -14.4 2~4. -12 8 Burkina Faso 1,631 1,880 2.6 2.8 0.1 0.46.2 2.4 8.6 3.2 Burundi 840 793 3.4 -1.9 0 5 -4.13.2 -2.5 6 9 -18.5 Cambodia 2.,645 . Cameroon 4,710 ... 6488 .... 3.9 2.2 1.0 -0.6 5.3 -3~7. ... -2.7 -3.7 Canada 141,521 341,855 3.3 1.8 .... 2.1...... 0.5 2.5 -0 25~2 ..1.. . ..5 Central African Republic 747 856 1 5. 2.1. --0.9 0.0 -1 7...... -13.9 .......10-.0 .......-.12.0 Chad 837 1,484 3.7 2.7 1.2 -0.6 16.5 -8.9 . 18.6 Chile 19,489 50,492 2.0 9.3 . 0.3 7.6 0.43 6.4 14.7 China 103,442 435.690 9.7......10.1 ..... 8.1 ... 8.8 7.4 9:9.9. 11.1. . 14.2 .Hong ong,.China 17,013 104,106 6.7 5.9 ..... 5.3 3.9 5.0 .... 5 1 ... 4~.0... 11.1 Colombia 23,442 64,846 2 6. 4.2 0.5 2.2 4.2 .......8 5....... 0.5 ...... 16.7 Congo, Dam. Rep. 12,167 5,038 3-6. 0.0-92 00 -18.5 -5.1 -4.9 Congo, Rep. 797 1,057 .... 2.5 4.6 -0.4 1.7 4 0 -10.0 -11~9.9...... -0.6 Costa Rica 3,156 5,644 2.9 3.4...... -0.1 1.5 1.1 2:8 ... 5 3.. ... 2.4 C6te dIlvoire 6,388 6,698 1.5 0.4 _-20.. . -2.5 -0.1 ......16 ..... -978...... 16.5 Croatia .. 12,647 . 3.5. 35.. 48.12 Cuba Czech Republic .. 26,726 3.2 . 3.2 . -6 2.3 5 0 Denmark 35,814 92,626 1:8 .........3.2 1.8 2.8 1..1 2.0 3.9 3,4 Dominican Republic 5,109 10,564 1~6 3.7 -0.6 1.8 1.9 3 5....... 3..5...... 10.8 Ecuador 6,995 13,294 1.9 2.4 -0.7 0.2 -1.~4 -0 7 -38 ..... .... 3.9...~! . . Egypt, Arab Rep. 15,848 56,844 .... 4.1 4.1 ... .1.5 21 2~7 ..... 2.0 .... 0.0 .........25 El Salvador 2,567 9,742 0.8 6.5 -0.2 4.20.1 3.2 2 28.3 Eritree 555 Estonia 2,733. -2.9 ...... .. ...... -1:7 4 -6.0 Ethiopia' 4,282 5,101 0.5 5.2 -2.5 3.2 4.5 -0.6. 1. Finland 27,761 68,455 ~ ~ ~ ~ ~ ~ ~ -0 3.8 04 34 -0.8 34 - 3.0 -5.4 France 391,263 935,923 2.6 . 1....I.2 . ....2.1 .0.7 2.2 2:1 .......2.8 -2.0 Gabon 1,119 1,945 1.5 ......---0.7 -1.8 -3.3 -06 6.4 -5.7 2.3 Gambia, The 152 346 -. 34 -6.0 --0.2 17 -.. 4.4 Georgia .. 4,964 .. .. Germany . 1,359,274 1.4.. 018 0.8 Ghana 3,730 5.500 2~8 3.7 -0.6 0.9 2.4 .. 4.6 3... .3 .... 25 Greece 32,706 91,847 2:4 1.5 1.9 1.0 ......2.7....1..I3 -0.83.4 Guatemala 6,217 15,454 1.2 4.4 -1.3 1.6 2.6 4.2 -. . Guinea .. 2,887. 4.1 .. ....... .......... 1.4 ... ......... -0.........6 .6.1..... Guinea-Bissau 109 233 0.8 7.1 -1.3 4.7 7.2 -0 2 12.9 -6.5 Haiti 1,197 2,743 0.9 .. -4.4 . -0.6 1.8 Honduras 1,806 2,827 2 7 35 -0.5 0.5 3.3 -1.2 2.9 7.9 224 1999 World Development Indicators 4. 10 Private consumption Private General Gross consumption government domestic per capita consumption investment average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1980 1997 ±980-90 ±990-97 1980-90 1990-97 ±.980-90 ±990-97 ±980-90 ±990-97 Hungary 13,574 28,459 1.3 -1.8 1.7 -1.5 1.9 1.0 -0.9 7 India 125,856 269 4.6 6.0 2.5 4.1 7j ......7-3:5 ..... ._6~5 ....... 8.4 Indonesia 40.821.133,448 5.6 .... 81.1 3.7 6.4 4-.6 ... 23 ......6.7 ... 10.4 Iran, Islamic Rep. 48,854 47,943 2.8 ......2.2 -0.6 0.6 -5~O.0.... 86.6.. -2. . 5 -0.8 Iraq : :..:-:~ Ireland 13,585 37,307. 2.2 . 4.2 . 1.9 . 3.6 -0.3 2.9 -0.4 1.9 Israel 11,547 60,685 5.3 ....... 6.9 .......3.5 3.7 0 5 .......-3.0 .........2.2 ..... -. 8.9 Italy 273,819 744,885 3.0 0.3 ........29 0.2 25. 0:21.9 -1 9 Jamaica 1,692 2,506 4.5 3.3 ... 3.3 24 6.3 1.2 ..... -04.1...... .7..6 AJar)............ 623,286 2,751,277 3.7 2.0 3.2 .. 1.7 2.4 ..... 23.3 .. . .5.3 .........0.5 Jordan 3,123 4,863. 2.2 5.0 -1.5 0.5 2.3 6:8 ..._-:15 ... 6.2 Kazakhstan 17,198 -11.4 -10.5 . -13.4. -17.5 Kenya 4,506 7,344 . 4.7 ........ 3.1 1.1 ... 0.3 2.6 .... 12:4 ... .....0.8 ..... .. 3.7 Korea, Rep. 40,473 237,333 8.0 7.0 6.8....... 5.9 ........5.2 4.9 11.9 6.3 Kuwait 8,836 14,322 -1.4 . 2.2. -4.5 Kyrgyz Republic 1,216 -8.8 -9.6 ... ................ -8.9 .. ...... 13.... . 1... ..... Lao PDR Latvia 3,712........ -0~2 .... .......... ....10 5.0 .34 ..... 34 ...... -25.1 Lesotho 492 .... 780...... 2:0 -29 -0.5 -5.1 2.9 4.9 .... 6~3 12.7.. Macedonia,. ........... ...... .. FYR...1,862. 7.8.7...-109...1. Madagascar 3,611 3,175 -0.6 ... ....1.2 ... .-3.3 -1.6 0-. 5...... -1.4 4.... . 9 . ...... -1.2 Malawi 866 2,148 1.5 7.6 _-17 4.7 6~3___-5.4 ..._._-2.8 -9.4 Malaysia 12,378 43,923 3.7 ...... 6~8 .. ....09 4.2 ....2.7 .. . ....7.2 .. .....2.6 ...._ 15.3 Mali 1,497 1,885 1.9 1.3 .-06 -1.5 ... 7..3 ...... -04 71.... ... .... 5.3 Mauritani'a 481 869 32 ......4~3 .... 05 1.5 -7..1 .........75 ......-41 2.0 Mexico 145,440 262,957 1.1 1.2 -1.0 -0.6 2.2 .........1.5 .-3j. 7 ..... 1.7 Moldova 1,378 11.8 12.0. 0.1 . -21.9 Mongolia .. 621 i.. ..~ Morocco 12,788 23,012 3.7 .....2 5.. . 1 5 0.7 5.5 ...... .0-.1 ........2..5 . ......0.2 Mozambique 2,672 2,092 -2.3 0.....O9....... -3.8 -1.5 -2.1 -9 3 3.8 ........8.2 Myanmar 0.6 4.8 ....... ..... ....... -4-1 .... 13.0 Namibia 950 1,791 1 1 08 -1.6 -1.8 3.7 28 8.. . . -3.5..... ..4.0 Nepal 1,600 3,985 4.5 4.6 1.8 2.17.2 6.06.07.3 Netherlands 104,571 236,355 1:7 .........2,1~ .. .. 1.2 1.5 2-.1 ..... 1. .11 ...... .31 1.... . . 0.6 New Zealand 13,801 41,064 2 1 3.0 1.3 1.4 1 5 ........0 8 2....... 8........ 8.8 Nicaragua1,768 1,661 -3.5 ...... 3 0 -6.1 0.12.3 -8.5 -61 ...... 9.8 Niger 1,883 . 1,541 0.0 1.9 -3 2 ...... -1.5 4.4 ..-3.1 -.......77.1.......- 1.2 Nigeria 36,258 27,743 -2.6 -0.8 -5.5 .... -3.7 -3.5 -32.2... .. -8-.5 ...... 10.4 Omen 1,657 . . ....... 255 5........ Pakistan 19,688 46,473 4.5 .......5.5 1.8 2.9 10' 3........ 0.8 6.0 ........ 3..1 Panama 1,709 4,201 4.2 ......2~8.8 . .... 2.1 ..... 1.0 1.2 ......:1 . .... -819.9 ...... 15.0 Papua New Guinea 1,568 . 2,038 0:4 2.1 -. -0.2 -0.1 1.7 -09_ ...... 8.2 Paraguay 3,467 6,836 2.4. ... 5!5. _ .. -0.7 .. ..... 2.8 1.5 .....93 .. _.-..0:8.-......3..8 Peru 12,000 ~~~43,070 1.5 4.6 -07 7 ..... 2.9 -1.8 ..... 52 2....... -4.2 .. ... 12.1 Philippines 20,910. 5,79 : 3.7 0.0 1.3 0.6..... 3.6 ....... -2.1 5.6 Poland 38,182 87,076 . 5~1 0.4. 4.9 12 4.4 09--.... 8.7 Portugal ..,16 70,645 25.5 .25 ........2.4 2.4 5.0 .. 2.5 3.0 2.6 Puerto Rico 10,756 . 3.5 .......... .. 5 .......1. .....6. 9 ... ....... ... Romania . 26,273 . 1 4'..1.8 . 1.7 .......... .... -8.6 Russia Federation ................ .. ............. ....I.... - . 283,195......8.I. 7.0.. ...... .... I..-14.2 ......... -1.9...... 1999 World Development Indicators 22S 4. 1 0 Private consumption Private General Gross consumption government domestic per capita consumption Investment average annual average annual average annual average annual $ millions % growth % growth % growth % growth 1.980 1997 1.980-90 1990-97 1980-90 1990-97 1980-90 ±990-97 1980-90 1990-97 Rwanda 969 1,837 1.4 -1.9 -1.6 -1.9 5.2 -1.4.3 -8.5 Saudi Arabia 34,538 49,746 . ..... S.enegal .2,528 3,480 2.1 2.9 -0.8 0.2 3.3 -2.6 ........5 2 2....... 2, Sierra Leone 809 0.2 -0.2 -1.9 -2.6 0.0 -0.3 --6.7 -17,2 Singapore 6,030 39,107 5. 7.2 4.1 5.1 6.6 8.3 3.1 9 8 S)ovak Republic...... .. ..... 3:8 ... -20 .. 4-8 0.2. 11 2.1 .......enia .. 10,288.............. -. . ..... 3 . 7 . ..... 3... . 7. . ..... .... . . 2.3... . ....... 9.. ...... 0. .... South Africa 39,543 79,577 2 3 1.9 -0.2 -0.2 3.5 2 9 -4.7 4.3 Spain 141,274 360,563 .... 2 5 ....I....0.9 2.2 0.. 7... ... 5.4 2.1 5. 7... -1.5 Sri Lanka I3,236 10,913 ... 3:8 5.5 24 42 7..3 ...... ..7.2 ........0-.6 ....... 5.8 Sudan 6,241 0:0 . . -0.5 . -. Sweden 64,624 13-1,982 1.8....0.2 1.5 -0.7 1.5 0........0 4.4 4-3. Switzerland 6,985 180,0.36 .. 1.6 0.4 . -0.5 3.1 1.0 ......-4-.0 ....... -0,9 Syrian Arab Republic 8,690 9585 3.6 2.4 0.2 -0.6 -3.6 4.4 -5.3 8.8 Tajilista .. .. .. . I .. .. . . .. .. .. ..... .. .. . . .. 1,191. .. .. .. ....... .. .... .. .... Tanzaniab 4.,882 2.3 -0.8 . -4.1 -4.1 Thailand 21,175 84,476 ... 5.9 69 4.1 5.6 4-.2 ........6:1. ..... 9.5....... 6.5 Togo619 1,177 4.7 0 7....... 1.6 -2.3 -1.2 -2.9 ... .....2..7 ...... 20 7 Trinidad and Tobago 2,860 4,415 -1.3 1.0 -2.6 0.2 -. -0.9 -10.1 4.8 Tunisia 5,380 11,364 2 9 .......3-.8 ..... ..0.3 2.0 3.8 .... 3.8 ...... -1.8 2.4 Turkey 42,088 129,188 ... 4 .0 ..I...... 2.2 .......... .... ..3.2 .. ....... 4..2 Turkmenistan Ukraine . . 30,627 . -12.3 . -12.1. -20.3 -15.4 United Arab Emirates 5,116 4 6 -9 ... .. -3.9.. . .... -8..7........ _..... United Kingdom 320,290 734,336 4.1 1.6 3.8 1.2 1.1 1.2 6.41.4 United States 1,720,600 5,043,000 ..3:4 2.7 2.5 1.6 2-.8..... -02 2.9 5.8 Uruguay 7,680 14,748 .05 6.2 -0.1 5.5 1-.8 .........23 .......-7.8 .........9.1 Uzbekistan .. 15,192 . .. -4.0 . -7.5 Venezuela 38,062 58,485 1 3 0.8 -1.2 -1.4 2.0 -1.5 .......-5.3 ........4.4 V ietnam .................... ......... - 1 7,413.... ..... .. 8.. .... . 4...... I................ ....... ....I.... .6 1.1.... .. . .. 2 . 4..... West Bank and Gaza . 7. Yem Re 3,875 I....1.3..... -2.5.....0.7..99 Yugoslavia, FR (Serb./Mont.) .. .. Zambia 2,145 3,024 . 1.8 0.9 -1.3 -1.9 -3-.4 ......-17 8 -.........8.7 ....... 12.1 Zimbabwe 4,622 6,408 .3.7 -1..1.0.3 -3.4 4.7 5.0 .....35 .... ..0.6 Low Income 313,070 257,482 .... 3.3 4.0 0.8 1.9 5.4 1.0 3.1 7.2 Middle incomne 1,353.957 3,288,259 ..3:0 4.3 1.3 3.0 4 .7 ..... -0.4 .......0.. O..... .. 4.0 Lower middle income 646,732 1,580,899 4.9 5.0 3.3 3.8 -0.9 4.6 2.9 Upper middle income 690,341 1,707,875 .. 1.6 3.7 -0.4 2.1 5.2 0.0 ........-1.6 .........5.6 Low & middle Income 1r9663173 3,547,470 3.0 4.3 1.0 2.6 4 7 -0.2 1.3 4.3 East Asia & Pacific21 ,6 8 325 6.9... 8.3 .... 5 .1 ... _. 6.9 5.3 ..... 7.7 8.3 ....... 12.3 Europe & Central..... Asia.... .......I... ..... ....... .7, 30.... 2.. 7.....I2.. ... - .0 ....... .. .~ Latin America & Carib. 553,947 1,391,133 ... 1.5 3.9 -0.5 2.1 5.5 -0 8 -1....... 7 - 5...... 8-~.... Middle East & N. Africa 148,839 283,370 South Asia 167,837 97,449 ..4.6 5.6 .....23 3.7 8.0 3:3 ........5.4 ........7.6 Su-Saharan Africa 15350 2630 16 14 -1 3 -1.2 2.6 1 2 -3.5 4.3 High Income 4,789.610 14,575,473 3.4 2.1 2.7 1-4 2.5 .........1.1 ........41.........2.0 Europe EMU .. 4,i2l,936 1.1 . 0.8 . 14 0.0 a. Oata prior to 1992 include Eritrea. b. Data cover mainland Tanzania only. 226 1999 World Development Indicators 4.10 Measures of consumption and investment growth are comparisons between countries in a given year, per- * Private consumption is the market value of all subject to two kinds of inaccuracy. The first stems haps even more than those over time, should be goods and services, including durable products (such from the difficulty of measuring expenditures at cur- treated with caution. as cars, washing machines, and home computers) pur- rent price levels, as described in About the data for chased or received as income in kind by households table 4.9. The second arises in deflating current price and nonprofit institutions. It excludes purchases of data to measure growth in real terms, where results Regional trends in gross domestic dwellings but incluudes imputed rentfor owner-occupied depend on the relevance and reliability of the price investment dwellings. In practice, it may include any statistical dis- indexes used. Measuring price changes is more diffi- I 9 l r crepancy in the use of resources relative to the supply cult for investment goods than for consumption goods of resources. e Private consumption per capita is cal- because of the one-time nature of many investments culated using World Bank population estimates. and because the rate of technological progress in cap- ,,,, * General government consumption includes all cur- ital goods makes capturing quality change difficult. rent spending for purchases of goods and services (An example is computers-prices have fallen as qual- * -. (including wages and salaries) by all levels of govern- ity has improved.) Many countries estimate invest- : C ment, excluding most government enterprises. It also ment from the supply side, identifying capital goods , - ' includes most expenditures on national defense and enteringan economy directlyfrom detailed production _ security. * Gross domestic investment consists of and international trade statistics. This means that the I, 19 outlays on additions to the fixed assets of the econ- price indexes used in deflating production and inter- omy plus net changes in the level of inventories. Fixed national trade, reflecting delivered or offered prices, *- assets cover land improvements (fences, ditches, will determine the deflator for investment expendi- -r - drains, and so on); plant, machinery, and equipment tures on the demand side. . purchases; and the construction of roads, railways, The data in the table on private consumption in r " and the like, including commercial and industrial build- current U.S. dollars are converted from national cur- ings, offices, schools, hospitals, and private residen- rencies using official exchange rates or an alterna- Gross aomestic in,estment in real terms) is tial dwellings. Inventories are stocks of goods held by tive conversion factor as noted in Primary data decllnirg or stagnant in Sub Saharan Alrica and firms to meet temporary or unexpected fluctuations in Europe and Central Asia. In East Asia anrd the Pacific documentation. (For a discussion of alternative con- it is beginni, to tape! off. South Asia is. ,hosirig production or sales. version factors see Statistical methods.) These slow but steady growth. exchange rates and conversion factors differ from the Data sources purchasing power parity conversion factors used to calculate private consumption per capita in table The national accounts indica- 4.11, which provide better estimates of comparative w tors for developing countries domestic purchasing power. Growth rates of private 0 . 1O' 7.1. are collected from national consumption per capita, general government con- 0 statistical organizations and sumption, and gross domestic investment are esti- central banks by visiting and mated using constant price data. Consumption and .- resident World Bank mis- investment as shares of current GDP are shown in sions. Data for industrial table 4.9. countries come from To obtain government consumption in constant Organisation for Economic Co-operation and prices, countries may adjust current values by apply- Development (OECD) data files (see OECD, National ing deflators that use a weighted index of government Accounts, 1960-1996, volumes 1 and 2). The com- wages and salaries, or simply take a government plete national accounts time series is available on the employment index as a measure of output. Neither World Development Indicators 1999 CD-ROM. The technique captures improvements in productivity or United Nations publishes detailed national accounts changes in the quality of government services. for member countries in National Accounts Statistics: Deflators for private consumption are usually calcu- Main Aggregates and Detailed Tables; updates are lated from consumer price series. Many countries published in the Monthly Bulletin of Statistics. estimate private consumption as a residual that includes statistical discrepancies accumulated from other domestic sources; thus these estimates lack detailed breakdowns of expenditures. Because the methods used to deflate consumption and investment can vary widely among countries, 1999 World Development Indicators 227 4.11 Structure of consumption in PPP terms Private Household consumption consumption per capita Transport Other Bread and Clothing and Fuel and Health and consump- All food cereals footwear power care Education communications tion ppp %%%% 1997 1997 1997 1997 1997 1997 1997 1997 1997 Antigua and Barbuda 4,611 33 9 32....... ..12 . .......17 627 Austria 14,530 13 2 7413 11 13 41 Belarus 3,137 16 06 415 21 _3 35 Belgium ............ 15..579. i....I.........I....5 2 ..... 3.... 14..11 .....40 . B elize 2 ,7 29.. .. . .. . . . .. . . . .. . .. . . . .. . . .. . . .. . . ... . . .... . . 28. . .. . . . . . 4. . .. . . ... ..0. . 2. ...510 . .. .. .. . 3 4.. .. . . . . . . B en. . . ... . . . . . . .. . . . . . . .. . . . .. . . . . .. . . . .. . . . ..1. . .. . . . .. ..0. . . . . . . 4 5. .. . .. . 1 3. . . ... . .. . .. . ..3. . 3.. 8 . 14.. .. . 17.. .. .. . . . B o ts. . .. . .. . . .. . ..an.. . .. . .. . .. . .. . . .. . .. . ..4. ...2 5 . .. . . 9. 4.. . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . 2 2 2 1.... 1 9.. . . . . . . B ul ari 3 ,1 30... . . . . .. . . . ... . .. . . .. . . . . 15.. .. 0.. . . . . .. . . . . . . . . . . . . .. . . . .. . . . .I . . . . . . . . . . . 5.. .. . .9. . 24 .. 6. 3 6.. . . .. .. .. I . . C am eroon .... . .... . .... ........ ......1 .... ...... ..... 1,277. ..... ..... 3....8...7. ....14... 2 6..... ...9 . 8. 24.. ..... .... C anada 15,439..... .. ... . .... . .. .. .. . .. .9 . . .. . . .. 2 . .. . . .. .... . .. ..5... .. . . . . . . . 4.. . . .. 11 9 .... 51.. . ... . . ...... . C ongo R ep.................. ............. ..... 791....36...5 3.... .... ..... ......... ... 1.....10... 15 ... 18.... 17.. . ... ..... C te. d o. .. .. .. ..e . ..1 ,3 93... .. 35.. . . .. . . . . .I . . . . . 8.. . . I . .. 9... .. . . . . ..8.. . 26.. . . .. . . . . . .. . .. . . .. . .. . . .. .. . . . .. . . . . . .1 1.. .. .. . . . . . . iEgypt, Arab Rep. 1,962 44 11 73 88 526 ...iji.... ... 2,669......... 30.............4.. 2......... 5.12 7...40 . Finland. 1 3 ,3 53... .. . ... . . . .. .. .. . . . .. . . . . . .. . . I . . .. . .. . . 11. . . .. .. . . .. .. . . . . ..2 . I . . . .. ...5.. . ..12. .. . .. . .. . 10... 4 9.. .. . . . .. . . . . France ... ..... .. .... .. .......... .... ... . .... .. ... 15,756. ....... ....12. ...2. ...4. 3.....21 .... 8..... 12... 40.. . .. .. .... Guinea 1,456 32 6 19 214 99 15 HlongKon, China . 16,496 10I ..1 .. 18 .... ....2 ..... 11 ...4... 10 .... 45 Hungar 5,372 14 04 510 17 643 Iran, Islamic Rep. 4,054 23 6 712 13 11 11 23 Ireland.. . 11,381. . .. ... ....... . 14.. . . .. ... . . .. .. .. ... .. . I. .... . . ..3.. .. . . . . 6...3 . 1... . ...1 . . 13... ..I . . ...8.. . 45... . ... .. . ... .. .Italy ...... .14,452... - ... 14... .. .. .. .. . ... .. . . .. .. I . .. 2 7.. .. .. .. ..... . 3....14 ... 7... ... ...... 46..... .. Jam aica..... 2,051.. .. ... .... .. . .. .. . .. .. .. .. 26 .. . ... .. . . ... .. . . ... ..7. ... .. .. . .. . ... ..8.. . 1....9 8.. .. 24.... 24.. .. . . .. . .. . Japan 15,554.. ... ...I. .. . .... ... .... 11... .. .... . .. ... .. .. ..3 .. ..... .. ..... .. ... . ... .. I..2. . .... 17 . .. ... ... 8 . .. 9. 47. . .. . . .. .. K enya 872.I.. ... . - .. .. ... .. .. .... . . .. . 38 I ... ..... ... . 8. . .. . .. . I .. . . .. . . . .. .. .. . .. . 2. .. ..5....22 .. . 10... 16 . . . ... .. K orea, R ep..... 6,590........................ ..21....... .....4.... ...... 3..... .... ...... ..... 5.....13 ......16 ... 17. 24.. .... ... ... Morocco 2,360 4511 92 510 10 18 Nigeria 692 48 15 863 4 527 Norway 14,741 132 5101411 838 Philippines 2,82833 93133 . ...4.. ...... 52 Poland.............5.,.....87......20......1..........5. 12.19.6.3 228 1999 World Development Indicators M,0 4.11 ~ Private Household consumption consumption per capita Transport Other Bread and Clothing end Fuel and Health and consump- All food cereals footwear power care Education Comm unications tione PpPp 1997 1997 1997 1997 1997 1997 1997 1997 1997 Senegal 1,536 52 U. 14 2 2 11 6 13 Sierra Leone 267 48 13 12 3 13 1482 Singapore 16,340 14 2 7 3 11 7 18 42 Slovak Republic 5,894 17 1 5 7 15 20 4 32 Slovenia 8,696 13 0 4 4 11 12 11 45 Spain 10,667 17 2 7 2 11 8 12 43 SriLanka 1,401 38 11 0 4 8 7 28 14 St. Kitta and Nevis 4,434 30 8 4 4 24 9 5 24 St. Lucia 3,414 39 5 5 3 13 7 6 26 St. Vin~cent and the Grenadines 2,833 24 7 4... 2...28..14...5..23. Swaziland 2,579 27 6 6 4 10 17 18 19 Sweden 13,583 10 2 5 5 11 9 11 50 Switzerland . 16,728 12 2 6 4 13 8 11 45 Thailand 3,162 23 7 8 3 22 10 17 17 Trinidad and Tobago 4,864 20 3 11 4 9 7 15 35 Tunisia 3,687 35 7 6 2 6 7 15 29 Turke 4,377 23 6 7 4 5 9 7 44 Ukraine 1,430 21 1 5 14 13 26 4 17 United Kingdom 15,490 11 2 6 3 10 8 11 52 United States 21,680 8 1 6 3 12 7 14 49 Vietnam 1,155 40 17 5 4 17 10 .20 Zambia 625 47 78 1 3 12 10 19 Zimbabwe 1,572 28 71 1 2 9 2 3 14 12 Note: Figures in italics refer to 1996. 1999 World Development Indicators 229 4.11 Cross-country comparisons of consumption expendi- government as well as private outlays. The * Private consumption includes the consumption tures must be made in a common currency. But when International Comparison Programme's (ICP) concept expenditures of individuals, households, and non- expenditures in different countries are converted to of enhanced consumption, or total consumption of governmental organizations. In the ICP. goods and a single currency using official exchange rates, the the population, focuses on who consumes goods and services accruing to households are included in pri- comparisons do not accountforthe sometimes sub- services ratherthan on who paysforthem. That is, it vateconsumptionwhethertheyarefinanced byindi- stantial differences in relative prices. Thus the emphasizes consumption rather than expenditure. viduals, governments, or nonprofit institutions. results tend to undervalue real consumption in This approach, adopted in the 1993 SNA, improves Thus private consumption as defined by the ICP economies with relatively low prices and to overvalue international comparability because aggregate mea- includes government expenditures on education, consumption in countries with high prices. Dif- sures based on consumption are less sensitive to dif- health, social security, and welfare services. ferences in the structure of prices also distort the ferences in national practices in financing health and * Household consumption shows the percentage apparent structure of consumption. For example, ser- education services. shares of selected components of consumption vices (such as health care or education) tend to be Because national statistical offices tend to con- computed from details of GDP converted using relatively cheaper than goods in low- and middle- centrate on the production side of national PPPs. * All food includes all food purchased for income economies, so when domestic prices are accounts, data on the detailed structure of con- household consumption. * Bread and cereals com- used to calculate consumption patterns, services sumption in low- and middle-income economies are prise the main staple products-rice, flour, bread, appear to be undervalued. The problem of making generallyweak. Consumption estimates aretypically all other cereals, and cereal preparations. consistent comparisons of real consumption across obtained through household budget surveys or * Clothing and footwear include purchases of new countries has led to the use of purchasing power par- other, similar surveys. These surveys are carried out and used clothing and footwear and repair services. ities (PPPs) to convert reported values to a common irregularly and may be targeted at specific income * Fuel and power exclude energy used for trans- unit of account. groups or geographic areas. In some countries sur- port (rarely reported to be more than 1 percent of PPPsmeasuretherelativepurchasingpowerofdif- veys are limited to urban areas or even to capital total consumption in low- and middle-income ferent currencies over equivalent goods and ser- cities and so do not reflect national spending pat- economies). * Health care and education include vices. They are international price indexes that allow terns. Urban surveys tend to show lower-than-aver- government as well as private expenditures. comparisons of the real value of consumption expen- age shares for food and higher-than-average shares * Transport and communications cover all per- ditures between countries in the same way that con- for gross rent, fuel and power, transport and com- sonal costs of transport, telephones, and the like. sumerpriceindexesallowcomparisonsofrealvalues munications. and other consumption. Controlled * Other consumption covers gross rent (including overtime within countries. To calculate PPPs, data on food prices and incomplete accounting of subsis- repair and maintenance charges); beverages and prices and spending patterns are collected through tence activities may also contribute to low measured tobacco; nondurable household goods, household surveys in each country. Then prices within a region, shares of food consumption. services, recreational services, services (including such as Africa, or a group, such as the Organisation The ICP collects price data from different outlets meals) supplied by hotels and restaurants, and pur- for Economic Co-operation and Development (OECD), on several hundred consumption items that are care- chases of carryout food; and consumer durables, are compared. Finally, regions are linked by compar- fully reviewed to ensure comparability. ICP surveys such as household appliances, furniture, floor cov- ing regional prices, to create a globally consistent set are conducted about every five years, but because erings, recreational equipment, and watches and of comparisons. The resulting PPP indexes measure not all countries have participated in all surveys, jewelry. the purchasing power of national currencies in "inter- regression methods are used to extrapolate results national dollars" that have the same purchasing from earlier surveys and to provide a complete set of Data sources power over GDP as the U.S. dollar has in the United estimates in a given year. See Ahmad (1994) for an States. extensive discussion of the ICP and its methods. PPP data come from the ICP, Because the goods and services that make up Although PPPs are more useful than official - *. . . which is coordinated by the consumption are valued at uniform prices, PPP-based exchange rates in comparing consumption patterns, . - - United Nations regional eco- expenditure shares also provide a consistent view of caution should be used in interpreting PPP results. nomic commissions and differences in the real structure of consumption PPP estimates are based on price comparisons of other international organiza- between countries. In other words, the shares shown comparable items, but not all items can be matched tions. The World Bank col- in the table reflect the relative quantities of goods perfectly in quality across countries and over time. lects detailed ICP benchmark and services consumed rather than their nominal Services are particularly difficult to compare, in part data from regional sources, cost. Table 4.12 provides the corresponding data on because of differences in productivity. Many ser- establishes global consistency across the regional the structure of prices within countries. vices, such as government services, are not sold on data sets, and computes regression-based esti- Private consumption refers to private (that is, the open market in all countries, so they are com- mates for nonbenchmark countries. For detailed household) and nonprofit (nongovernmental) con- pared using input prices (mostly wages). Because information on the regional sources and compilation sumption as defined in the United Nations System of this approach ignores productivity differences, it may of benchmark data see the World Bank's Purchasing National Accounts (SNA). Estimates of private con- inflate estimates of real quantities in low-income Power of Currencies: Comparing National Incomes sumption of education and health services include countries. Using ICP Data (1993b). 230 1999 World Development Indicators Relative prices in PPP terms 4.12 International Relative price level price level (price level of GDP =100) Private consumption Transport Gross ratio of PPP and Government fixed rate to $ Private All Bread and Clothing and Fuel and Health communi- connump- capital exchange rate consumption food cereals footwear power care Education cations tics form-atio3n 1L993 .1997 1993 1L993 1.993 1993 1993 1L993 1993 .1993 1993 1.993 Antilgus and Barbuda 83 84 106 105 107 178 187 116 43 154 63 112 Australia 95 104 100 77 97 113 75 88 83 102 93 105 Austria 118 115 100 102 97 108 101 92 82 115 90 102 Bahamas, The 117 100 90 93 103 112. 111 94 166 112 93 Bangladesh33...... 32 .... 101 154..... 153. .122 ...... 61 ......371766 52 ... . 201 Belgilum 108 104 101 95 . ..... 89. 125 114 72 80 110....... 90 102 Belize 63 65 95 90 80 75 167 80 71 118 79 128 Botswana 46 43 125 103 113 157 256 105 98 65 113 108 Bulgaria 31 30 85 128 136 103 62 58 21 149 70 133 Cameroon 53 34 93 87 98 102 173 88 60 97 123 141 Canada 97 88 103 96 93 101 77 110 104 109 115 85 Co9ngo,Rep, .................. 48 52 98 115 105 109 288 .... 58 .. 55 .... 67 ..... 78 ...... 193 C6e dIlvoire 52 39 98 101 112 989 60 7 1 5 Czech Republic 35 48 85 106 63 123 74 61 41 157 73 174 Denmark 137 135 105 108 107 104 130 111 85 123 90 87 Dominica 73 75 101 112 125 98 179 58 60 147 62 147 Egp,Arab Rep. 33 41 9 88 97 124 80 54 49 92 92 106 Fiji 58 64 90 100 112 124 146 89 66 140 59 170 Finland 104 115 108 121 138 112 79 99 79 126 82 85 France 113 107 101 102 95 124 124 71 94 116 101 97 Gabon 63 59 126 147 95 131 284 90 119 79 152 91 Germany 126 119 99 90 91 107 106 84 96 110 104 103 Greece 81 91 105 104 126 161 149 80 68 98 74 98 Grenada 65 66 96 120 110 109 191 47 53 189 65 142 Guinea 33 30 97 106 118 75 173 37 68 94 80 127 Hong Kong, China 95 107 91 76 78 73 57 59 145 80 136 106 Hungary 63 62 66 74 78 8 7 50 50 29 122 84 114 Iceland 122 103 120 109 136 42 89 68 115 87 94 Indonesia 32 30 93 93 82 99 98 38 31 89 34 163 Ireland 100 98 100 101 90 106 110 100 61 146 85 104 Italy 97 97 99 105 100 125 122 82 102 108 105 101 Jamaica48 47 101 119 100 118 181 57 64 85 69 111 Japan 163 137 9 9 130 135 113 Ill 61 83 90 86 104 Kenya 21 30 90 91 123 85 140 45 41 71 72 196 Korea, Rep 76 70 112 137 173 157 66 61 72 53 95 87 Luxembourg 123 95 93 84 128 86 80 109 97 119 109 Malawi 36 34 87 98 109 54 148 31 50 73 79 279 Mali 45 33 90 92 131 86 59 31 90 6 3 179 Mauritius 39 41 93 81 65 7 2 111 59 87 67 108 134 Moldova47 29 87 131 127 102 60 62 41 122 88 137 Morocco 36 37 93 83 77 68 282 81 81 77 106 182 Nepal20 20 93 129 129 116 94 25 30 61 61 Netherlands 115 108 97 92 79 101 100 71 86 119 92 112 New Zealand 82 98 98 91 93 102 65 96 74 100 78 119 1. 7 ci5 7 55 5'D 45 .1 Ai 11 Norway 138 141 106 117 114 108 50 97 87 139 98 88 Pakistan 32 31 101 115 103 133 113 35 60 108 42 167 Philippines 27 31 82 105 124 122 277 30 29 114 58 183 Portugal ~~71 7.1 107 125 102 162 175 117 54 140 56 116 Romania 31 36 82 132 74 106 59 59 22 116 78 132 Russian Federation 52 69 84 122 61 154 2 36 30 75 79 145 Senegal.33 30 90 87 119 74 227 75 45 79 81 237 Sierra Leone33 42 115 141 192 107 185 34 39 76 37 147 Singapore98 108 95 67 72 88 46 46 86 100 88 107 Slovak Republic 38 45 80 87 55 I11 68 53 33 123 74 124 1999 World Development Indicators 231 4.12 International Relative price level price level (price level of GDP = ±00) Private consumption Transport Gross ratio of PPP and Govemment fixed rate to $ Private All Bread and Clothing and Fuel and Health comrnmuni- consump- capital exchange rate consumption food cereals footwear power care Education ctadons ton formaton 1993 1997 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ................................... ................ - ............................... ............................... ............ ................................I ........ .............. ..................................................................................... .................... Slovenia 68 77 93 108 104 146 71 70 55 110 76 109 Spain 91 84 102 100 112 120 111 83 76 125 85 102 Sri Lanka 30 32 86 123 109 113 58 26 33 53 31 170 ... .... .. .. .. ... .... .... . .... .... ... .. ...I... .. .... .... . .... .. . .... . .... .. .... .. ... .... .. - ... . ... . .. .. ... ... .... . . . .... .... . .... ...I.. .. .... .... . .... .... .. . . . .. .... . .... .... . .. .... .... . ... . . . . .... .... .. . .... .... . .. . . .. .I. .... ............ .................... ..................................... St. Kitts and Nevis 76 80 95 105 118 108 121 47 81 145 69 129 St. Lucia 69 73 101 111 140 107 157 49 61 114 77 111 St. Vincent and the Grenadines 59 59 97 132 125 113 236 43 55 146 56 173 Swaziland 37 41 92 84 100 138 187 53 66 54 91 ............... ........ ................................... ................................. ................ .................. .. ............. .............. ......... ..................I.............................. ........... .............: ....................................................... Sweden 125 129 103 103 103 86 86 103 86 110 93 93 Switzerland 146 141 106 108 96 102 70 89 102 106 115 85 Thailand 41 38 122 106 79 158 74 40 71 103 68 93 ............. .................................. .............................. ................ . .... .............. .............. ................. ............................. ................................ .............................. - ................................................................ Trinidad and.Tobago 63 65 98 89 98 87 38 106 98 108 87 129 Tunisia 38 38 92 81 62 142 105 80 109 79 124 159 Turkey 55 47 106 123 100 140 141 75 53 116 62 106 United Kingdom 94 104 102 86 73 83 115 89 89 126 90 98 United States 100 100 100 88 82 84 90 145 125 89 113 94 .................. -. ............ .... ............ ...... . . ..................... ............ ........ .... .. .................. .. ............. . ............ ................. ............ . ..... ........ .. ..... ........ .............................................. ............................... Zambia 40 42 113 126 185 98 282 29 46 89 34 213 ................... .................................. .................. .................................... ... ........ ... .................. ............... ...I.... .......... ........................... .. ............. ..... ...........I. ........ ................................................... Zimbabwe 31 33 105 81 97 91 221 51 77 62 89 ........ 1................... .................................. a................... t................ ........... ... ... ........ ... ............................. ............ ........ .................I............. .............r....... 232 |1999 World Development Indicators 4.12 The International Comparison Programme (ICP) col- For example, Indonesia's relative price level of 163 * International price level is the ratio of a country's lects data on prices paid for a large set of comparable indicates thatthe price level of investmentgoods is 63 PPP rate to its official exchange rate for U.S. dollars. items in more than 100 countries. Purchasing power percent higher than the overall price level. This reflects * Private consumption includes the consumption parities (PPPs) computed from these data allow com- the fact that a large share of physical capital must be expenditures of individuals, households, and non- parisons of prices and real GNP expenditures across imported from high-income economies with higher governmental organizations. * All food includes all countries. PPPs are used in table 1.1 to measure GNP price levels. food purchased for household consumption. * Bread at internationally comparable prices and in table 4.11 and cereals comprise the main staple products-rice, to evaluate the structure of consumption. This table flour, bread, all other cereals, and cereal preparations. presents information on the relative prices of compo- Heatth care services are relatively * Clothing and footwear include purchases of new nents of GDP based on the most recent iCP data. expensive In high-income economies ... and used clothing and footwear and repair services. A country's international price level is the ratio of * Fuel and power exclude energy used for transport F i ,i r,n' 5i,r., e* C ,. its PPP rate to its official exchange rate for U.S. dol- l - (rarely reported to be more than 1 percent of total con- lars. PPPs can be thought of as the exchange rate of f,:'S. 6, sumption in low- and middle-income economies). dollars for goods in the local economy, while the U.S. 1 * * * * * Health care and education include government as dollar exchange rate measures the relative cost of I * well as private expenditures. * Transport and corn- domestic currency in dollars. Thus the international * * * -Ar munications cover all personal costs oftransport, tele- price level is an index measuring the cost of goods in ,, * _ phones, and the like. * Government consumption one country at the current rate of exchange relative to * * So C , ,,: includes spending on goods and services for collective a numeraire country, in this case the United States. An *' * * consumption less spending on recreational and other international price level above 100 means that the related cultural services, education, health, and hous- general price level in the country is higher than that in a l 4 ing. Expenditure on governmentfinal consumption con- the United States. For example, Japan's international 'ifl' , '" c_ .; ". r.: sists of compensation of employees, consumption of price level of 137 in 1997 impliesthatthe price ofgoods intermediate goods and services, and consumption of and services in Japan is 37 percent higherthan the price ... but capital investment is far more fixed capital and indirect taxes paid less proceeds from of comparable goods and services in the United expensive in low- and middle-income sales of goods and services to other sectors (such as States. By contrast, Kenya's price level of 30 means economies fees charged by municipalities and other government that a bundle of goods and services purchased for . . agencies, school fees, fees for medical and hospital $100 in the United States costs only $30 in Kenya. 12Ž3 treatment and drug sales, and sales of maps and The first two columns of the table present interna- - charts). * Gross fixed capital formation comprises tional price levels for 1993 and 1997. Price levels for ,,,,, . expenditures on construction, producer durables, and a country can be higher in 1997 than in 1993 if the BF;, 3dE 3, changes in stocks. Construction includes residential rate of inflation in the country over the period is higher and nonresidential buildings and roads, bridges, and than in the United States and if the exchange rate does :.: S. * other civil engineering activities. Producer durables not depreciate enough to offset the relative rate of l * , , , H30 include machinery and nonelectrical equipment, elec- inflation. Countries experiencing a fall in the price level eg . * trical machinery and appliances. and transport equip- are those that allowed their exchange rates to depre- * I . ment. Changes in stocks cover increases in the value ciate more than the relative rate of inflation. L. nI. v u12. Lf,,,E,4 -. j of materials and supplies, works in progress, and live- The relative prices of components of GDP shown in C, n I i : ,-, stock (including breeding stock and dairy cattle). the table are calculated from their international prices measured relative to each country's price level of GDR i .' B j, i I i -,r., i3- . Data sources A figure above 100 indicates that the price of that com- Pr'ce levels measurea using purchasing posse. ponent is higherthan the average price level of GDP. This parities demonstrate tne s%stematic differences PPP data come from the ICP, is not the same as saying that the component is more In the relailve prices of services and capital goods '.-.,. ,',. which is coordinated by the expensive in that country than in the United States. It In inoustial and deweloping economies. '" - - United Nations regional eco- indicates only that the price for that component is higher - nomic commissions and other than the general price level prevailing in the country. intemational organizations. Relative prices for consumption items tend to be The World Bank collects close to the overall price level of GDP. This is to be detailed ICP benchmark data expected because consumption accounts for a large , from regional sources, estab- share of GDR The data also indicate that the relative lishes global consistency across the regional data price of investment goods in developing countries sets, and computes regression-based estimates for tends to be higher than for other components of GDR nonbenchmark countries. 1999 World Development Indicators 233 4.13 Central government finances Current Total Overall Financing Domestic Debt revenue' expenditure budget deficit from abroad financing and Interest (including payments grants) Total Interest debt % Of % olf current % of GDP % of GDP 96 of GDP 96 of GDP 96 of GDP GDP revenue 1980 1996 1980 1996 1980 1996 1980 2.996 1980 1996 1996 1,996 Albania 21.2 .. 31.0 . -9.0 .. 19 . 7.1 35.3 10.8 Algeria.. 32~7 .............. 29.7 . 3.0 4.0. -70.0 65..2 ....10.4 Angola. Argentina 156.6 .. 11:9.9 .... 182 2 ... 14.0 -2.6 -2.0 0.0 2:8 ....2..6..... -0:8.8 .......... . 13.0 Armenia . . . . Australia 21.7 .. 243.3 ... 22:7.. 26.3 -1.5 -0.9 0.2 1:5 1.3 -0:6 223 ... 7.4 Austria .............33 9 . ...37.-2 . ....36:6 41.7 -3.3 -4.1 .0 .8 .......0.5 2 .5 3 :6 . ...... . ..... 10.7........... ... ... ...................I............ - ............... .... ...:... Azerbaijan. ... Bangladesh.84 ....... - 7.4 . 1.8 . 1.8 .... ... ..... 0.5 ........ ................. ... Belarus .... I 31.8 . 33.9 .. -1.9 .. --0.1~ ..2.0 .... 12.0 2.1 Belgiu 4..... .. ..... .. 3.0 44.1 50.1 48.2 ....-8.0 -3.2 2. -33 57 6 160 19.5 Benin. .... Bolivia ... ..... 16 ...9.I..22.8........ .-2. 6:3..........3.6 2. -.3....-1.36 47.1...... 7. 11.4 11. Bosnia and Herzegovina .. .. . Botswana 31.8..... 494.4.. 31:8.8.... 39.4 -0.2 9.4 1.3 0.6 -1.2 .... -1 0.0~.. . 12..3 ..... 1.3 Brazil 22.6 27.0 20:2 ...33.8 -2.4 -6.1 0-0 . 2.4 . 47.9 Bulgaria : ....32 .5 ...... .... 48.1 ... .... -1 4 -1. . 7.362:0 Burkina Faso 11.8 12.2 0.2 0.4 0.0 . 74.9 Burundi 139 .... 17.0.. 21.5 27.7 -3.9 -7.8 2.0 4.8 1.9 3.0 139.1 9.5 Cambodia.. . .. . . . Cameroon 164.4. .. 13.0 15.7 12.7 0.5 0,2 0.7 0.3 -1.2 -0.3 139.6 23.0 Canada . -............. 18.5 .... 20.6 .. . 21.1 24.2 -3.5 -3.7 0.6 2.9 ........ ....... ....... 21.3 Central African Republic 16.5 .. 22.0 - -3.5 2.1 1.5 Chad . . . . Chile 32~.0. .. 23..2 28.0 21.0 5.4 2.3 -0.8. -4.7 16.6 2.5 China . 5:5. 8.0 . -1.6. 0.1 . 1:5.......... .ogKong, China : ' Colombia 12.0 -16.3 13.4 . -1.8 . .. Congo, Demn. Rep.9.4 5.3 12.4 8.3 -0.8 0.0 0.3 0.0 0.5 0 0 232.9 1.0 Cong, Rep. 35.3 . 49.4 . -5.2 3.8 . 1.4 Costa Rica 17-8 26.7 25.0 30.6 -7.4 -3.9 1.1 -1-.1......6.3. 4:0....... 0.0 22.8 C6te d'lvoire 22 9 ....31.7 -10.8 6.5 . 4..4 .... ... ............ .... Croatia 45.5 . 46.7 . -0.5 0 9 . -0.4 . 2.6 Czech Republic .......35:0 364 . 0.0 .... ..... -018 .. . 08 1.5 3.1 Denmark 34.7 38.8 38.6 41.4 -2.6 -1.9 . .. 14.0 Dominican Republic..... 14.2 ......15.1 1J6.9 15.6 -26.6.... -0.3 1.4 -1.-0 ... ..1.2 1.3 26.9 3.8 Ecuador 12.8 15,7 14.2 15.7 -1.4 00 0.5 0.9 .. 21.8 Egypt,Arab Rep.44.1 .....35.4 50.3 34.3 -11.7 0.9 3.6 -0.9 8.0 0.0 20. El Salvador .. . .. .. .. .. . ............. Eritrea . . . .. Es ni ............ .... . ... .... 32.... 6.. ..33.8 .. -0.7 0.4. 0.3 ............ .. . 1.2 Ethiopia' .......... 16 6.6 ...... ..... 19.9 . -3 I. 1 2 1.9........ ........ .......... Finland 272 ..... 33.4 28.1 40.1 -2.2 -6.3 0.8 ......0.1 1.4 63.3..... 68.8 ... 15.6 Franca 39.6. 41..6 ..... 39.5 46.9 -0.1 -5.3 _ 00 ......0:2 0.1 5:1 .. .. ......... 7..7 Gabon 35.5 . 36.5 . 6.1 ...... ....... 0 0 -6.1 ........ ................. :... Gambia, The 23.1 . 31.7 ... -4.4 . 1.2.. 3.2 Georgia . . . . . .. Germany 31.3 . 33.7 . -2.1 1.5 0.6 38.8 7.7 G hana 6.9 10.9. -4.2 ..... ..... ...... 0.7 .. .5 .... .... :... ..............: ... Greece 25.3 21.6 29.3 32.8 -4.1 -8.6. 1.6 .......24 2.6 6:2..... 119.2.. .54.2 Guatemala 11.2 .. 143 . -3 9 .. 1.5 2.4 . : Guinea . ...... Guinea-Bissau Haiti 106 17.4 . 4.7 .... ........... .......... .. ... Honduras 14.6 . . . . . .. . 234 1999 World Development Indicators V~~~~~~~~~~~~~~~~~~ 4.13 Current Total Overall Financing Domestic Debt revenuea expenditure budget deficit from abroad financing and interest (includinlg payments grants) Total Interest debt % of % Of current % of GDP % of GDP % of GDP % of GOP % of GDP GDP revenue ±980 ±996 1980 1996 1980 1996 ±980 1996 1980 1996 1996 1996 Hungary 53.4 39.6 56.2 43.2 -2.8 0.1 2.1 -0.4 0.7 0.3 71.9 20.3 India 11.7 13.6 13.3 15.8 -6.5 -5.2 0.5 0.2 6.0 5.0 49.3 31.0 Indonesia 21..3. 17.0 22.1 14.6 -23.3.. . 1.2 ......2 1 -0.5 0.2 -0.7 23..9 7.1 Iran, Islamic Rep ........- 21..6 ... _24.5 35.7 ..... 23.2 ... -13.8 1.4 -0.6 0.0 14..4 ..... -1.4 ............... 0.0 Iraq Ireland 34.7 34.0 45.1 38.1 -12.5 -1.4. ... 14.5 Israel 52.2 40.3 72.8 ... 48.7 -16.2 -4.3 8.2 .1:8 ......8..1 .......2:4.4 .. 118.8 15:3 Italy ....... 31-.4 ..... 427.7 . . 41.3 49.5 -10.8.. . -7.1 0.2 10.6 . ...... ............... 23 8 Jamaica ........ 29.0 . 41.5 . -15.5 .. . ..... ...... Japan 11.6 . 84 . -70 . .. . 67 Jordan 17.9 28.6 41:3 35.0 -9.3 -1.4 5 .7 .......41 3.6 -27 7..... 93..4.... 11.2 Kazakhstan . Kenya 21.9 27.1 25.3 28.9 -4.5 --0.9 2.4 -0.2 2.1 1.1 . 27.9 Korea, Dem. Rep. . . ... :.. Korea, Rep. 17.7 ... 213.3.... 17.2 18.6 -2.2 0.1 0.9 -0:1 1.4 0:0 8.6 2.3 Kyrgyz Republic . Latvia ..... 29:8 31.0 -1.7 07 : .. 0.7....0 14.4 5.6 Lebanon.. 13 .. 3 20.6 .. 4.2 16.3 ......98.9 751 Lesotho 34'2 55.0 45.3 55.2 -774 . ....5.0 ......2 6 ......9.-5 4.8 ....-14.5...... 3.3 Lithuania. 22.5 25.0 -3.6 . 3.2 . 0.4 4.1 Macedonia, FYR . .. ... . Madagascar 13.2 .....8.-7 ............. .17.3 . -1.3 1.4 . . -0:.1 .... 119-8 54.0 M alaw i 19........... ... . . 1... . 346.....I -1 59.... ..... ........ 8 3................ 7.7........... . .... ........... Malaysia 26.3 24.9 28.5 21.9 -6.0 2.0 0.6 -0.9 5.4 -1.0 . 11.0 M ali 10!5 ... .... :. .... 20:6. -4.5 4.1.... 41... 0 4 ........ ...... ..... Mauritania Mauritius 2068..... 18.8 ....27.2 22.4 -10.3 -4.0 2.5 3.1 7.8 0:9...... 36.8 ... 13.5 Mexico 15.1 15.4 15.7 15 5 -0 -. 0 07 34 1.0 31.5 18.8 Moldova . . . . . Mongolia 24~1 . 21.6 . -6.6 53 ............... .1.3~ 51.3..... 3.0 Morocco 23.3 28.5 33:.1.... 33.3 -9.7 -4.4 5.3 -0.7 4.4 5 .1 79.4 20.5 Mozambique Myanmar 16.0 6 9 15.8 10.1 1.2 .-3.2 1.2 O. 0.0... -2.4 3.2 Namibia Nepal7.8 .....10.7 14.3 17.5 -3.0 -4.4 1.9 3. 1.2_ 14.4..... 65.2 14.5 Netherlands 49.4 45.1 52.9 48.0 -4.6 -2.3 0.0 -. . . 62.8 10. New Zealand 34.2 35.3 38.3 31.9 -6.7 5.1 3.6 . 3 1 .. 10.0 Nicaragua 23.3 25.4 30.4 33.2. .... -68 .-0 6 ..... 3.6 ...... 0.2 .. . .3.2. 0.5 . 15 5 Niger 14.6 . 18.6 . -4.8 . 4.1 . 0.7 Norway 37.2 41.7 34.4 36.8 -1.7 5.1 -0.7 -1.6 2.4 -3.5...... 24.5 ..... 5.1 Oman 382 ..... 31:7.... 38:5 42.4 0.4 -10.1 -3.6 9.1 3.1 1.0..... 31.3 7.6 Pakista n162 17 1 17 5 23.8 -. 78 2 9 3 9. 34.0 Panama 25.3 25:9. ...30.5 27.4 -2 -7 5 7 -0. 015.3 Papua New Guinea 23.0 22.0 ....344 29.4 -1.9 -4.1..... 25 5..... -0.2 -0.5 .4.3 ........ ... 12.2 Paraguay .10.7 9.9 0.3 2.2 I -2.5 Peru.... 17.1 .... 16.0 ..... 19 5 16.5 -2.4 2.3 06 0.7 1.8 -3.0 45..9.. 13.6 Philippines 14.0 18.6 13.4 18.5 -1.4 0.3 0.9 -0.3 0.5 0.0 53.2 18.7 Poland 39:7. 42.2 ....... ..... -2.2. -0.2. 24 ..... 51.1 .....9.9 Portugal 260 .....34:1 33.1 41.6 -8.4 -2.3 1.9 2..2 6.5 0.0...... 1.8 12.8 Puerto Rico Romania 45.3 27:8 44.8 31.4 0.5 .-40 .......... 0.0 . 2 5........58 Russian Federation 19.0 . 24.7 . -4.5. 1 5 . 3.0 . 15.8 1999 World Development Indicators 235 4.13 rvne xedtr ugtdfct fo bodfnnigaditrs Current Total Overall Financing Domestic Debt (including payments grants) Total Interest debt % of % Of current % of GOP % of GDP % of GOP % of GDP % of GOP GDP revenue 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 1996 1996 Rwanda 12.8 ....... 14:3 -1.7 2.6. -0.9 Saudi Arabia . . .. Senegal 24 3 . 23.30.-27. 18 Sierra Leone 15 .1 8.0 265 5 .... 14.8 -... 11.8 -5.8 3 ..5 .... ..2.8 ......8 . 3 .. .. 2.9 116.2 23.0 Singapore 254 29.0 20.0 21.0 2:.1 _ 10.4 -0.2 0~.0 .. -2.0 ....-104 75.5 2 2 Slovak Republic Slovenia. South Africa 23.5 28.8 22.1 347.7..... -2~3.3 .. -5.8 -0.2 0.2 2.5 5.5 . 21.7 Spain............ 24.0O..... 30.4 26.5 36.8 -42.2 ... -6.0...... 0.0 . .... 2.5 4.2 3.5 55.2 14.9 Sri Lanka ............ 202 2..... 19.0 ....41.4 ......277.7 -18.3 -7.8 4.5. 1~3.3 ... 13.8 6.5 92.3 33.4 Sudan 12.3 17.4-2.9-29 2.5 0.4 Sweden 35,0 41.6 393 ......46:1 -8:1..... -4.3 ......3.2 1.8 4.9 2.5 14.0 Switzerland 18~6 ......227..... 19:.2 26..3...... -0 .2 -1.2 . 0:0 1.2 24.2 3.8 Syrian Arab Republic ...... . 26 8 .....23 2. ... 48.2 23.8 -9:7. -0.2 -0.2 . 98 8.... ... Tajikistan Tanzania Thailand 14~3 19:0 18.8 16.5 .... -4.9 ......2.4 1.1 0.1 3 7 -2.4 3..7 1J.2 Togo 30.3 30:8 . -2.0 . 1.6 . 0.4 Trinidad and Tobago 43.2 27.3 30.9 28.3 7.4 0.2 2 6 -2.8 51. 7 17.8 Tunisia ............ 31.3 , 29.-6 31.6 326.6 . ... -2.8 -3.1 2.3 ......_2.8 0......5 04.4 .. .. 55.3 13.3 Turkey 18.1 18:3 21.3 26.9 -3.1 ....-8.4 . ...0 4 -0.9 2 .6 9.3 37.9 18.2 Turkmenistan . .. . Uganda 3.2 . 6.2 -3.1 . 0.0 . 3.1 Ukraine United Arab Emirates ........ 0.,2 2.5 12.1 11.8 2.1 0.2 0.0. 0.0 -2.1 -0.2. 0.0 United Kingdom 35.2 36.2 38.3 41.7 -4.6 -5.3 0.3 . 4.3 . 10.2 United States 20.2 20.7 ......22.0O 22.2 -2.8 -1.6 0.0 25.5 2.8. -0.9 51.1 16.5 Uruguay 22.3 ......297 . ...21:8 . .. 31:4 .......00 . .-1.6 ......0.9 .. ... 11. -0.9 ......1.7 26.3 4.4 Uzbekistan .: : Venezuela 22.3 19.7 18.7 16.9 00.0 1.4 1.8 -0.5 .....-1-.9.... -0.8. 19 1 Vietnam West Bank and Gaza . - . Yemen Rep.. 32:5 ....328 ...... -2.7 . 0.8 .. 1.8. 8.8 Yugoslavia, FR (Serb./Mont.) . .. .. ... .. Zambia 25.0 18.6 37.1 21.4 -18.-5.. 0.7 8.8 12.2..... 9.7 -1.9 8.7 Zimbabwe 19.3 . 27:9. -8..8 . 1.8 ......... 6.9 Low Income 12.9 14.4 14.8 17.4 . -5.6 . . . Middle Income .. 17.9 . 199-3.3 . 040.3.. 11 Lower middle income. 15.3 17.1.033 10.4 Upper middle income......... 20.8 24.1 20.1 27.9 -2.2 -3.4 0.9 0.0 1.6 0 6 ......32.6 11.0 Low & middle Income .. 17.4 . 19.5 -3.6. East Asia &Pacific . 11.0 11:6 ....12.0.0..03 00. .. 11.5 Europe & Central Asia 25.5 299 . -3.6 . Latin America & Carib. 19.8 ... 2-1.6 .....19.1 .....256 .....-20 -3.3 1 1.l. ........... 1,2 ........ 14:.7 Middle East &N. Africa .. . . . . 23 1.5 44 0. 12.4 South Asia 12.3 14.3 14.3 17.4 -5.8 2.1 1.6 4 . 5 32.2 Sub-ShrnArc 1322.5 . -3.6 2.4 1 9 . High Income 227 .... 287.7... 26~2 32.1 -43 -3.3 0.4 0.5 2.9 1.2-.. 10 Europe EMU 37,5. 41.7 ... :..... -4.2 0.5 0...... .3. 4.4 3.-7 .......594 10:7 a. Excluding grants. b. Oata prior to 1992 include Eritrea. 236 1999 World Development Indicators 4.13 I.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Tables 4.13-4.15 present an overview of the size and Central government can refer to one of two * Current revenue includes all revenue from taxes role of central governments relative to national accounting concepts: consolidated or budgetary. For and current nontax revenues (other than grants) economies. The International Monetary Fund's (IMF) most countries central government finance data have such as fines, fees, recoveries, and income from Manual on Govemment Finance Statistics describes been consolidated into one account, but for others property or sales. * Total expenditure includes the government as the sector of the economy respon- only budgetary central government accounts are avail- nonrepayable current and capital expenditure. It sible for 'implementation of public policy through the able. Countries reporting budgetary data are noted in does not include government lending or repayments provision of primarily nonmarket services and the Primary data documentation. Because budgetary to the government or government acquisition of transfer of income, supported mainly by compulsory accounts do not necessarily include all central gov- equity for public policy purposes. * Overall budget levies on other sectors" (1986, p. 3). ernment units, the picture they provide of central gov- deficit is current and capital revenue and official Data on government revenues and expenditures ernment activities is usually incomplete. A key issue grants received, less total expenditure and lending are collected by the IMF through questionnaires dis- is the failure to include the quasi-fiscal operations of minus repayments. * Financing from abroad tributed to member governments and by the the central bank. Central bank losses arising from (obtained from nonresidents) and domestic financ- Organisation for Economic Co-operation and monetary operations and subsidized financing can ing (obtained from residents) refer to the means by Development (OECD). Despite the IMF's efforts to result in sizable quasi-fiscal deficits. Such deficits which a government provides financial resources to systematize and standardize the collection of public may also result from the operations of other financial cover a budget deficit or allocates financial re- finance data, statistics on public finance are often intermediaries, such as public development finance sources arising from a budget surplus. It includes incomplete, untimely, and noncomparable. institutions. Also missing from the data are govern- all government liabilities-other than those for cur- In general, the definition of government excludes ments'contingentliabilitiesforunfunded pension and rency issues or demand, time, or savings deposits nonfinancial public enterprises and public financial insurance plans. with government-or claims on others held by gov- institutions (such as the central bank). Units of gov- Government finance statistics are reported in local ernment and changes in government holdings of ernment meeting this definition exist at many levels, currency. The indicators here are shown as percent- cash and deposits. Government guarantees of the from local administrative units to the highest level of ages of GDR Many countries report government debt of others are excluded. * Debt is the entire national government. Inadequate statistical coverage finance data according to fiscal years; see Primary stock of direct, government, fixed term contractual precludes the presentation of subnational data, how- data documentation for the timing of these years. For obligations to others outstanding at a particular ever, making cross-country comparisons potentially further discussion of government finance statistics date. It includes domestic debt (such as debt held misleading. see About the data for tables 4.14 and 4.15. by monetary authorities, deposit money banks, non- financial public enterprises, and households) and foreign debt (such as debt to international develop- Worsening fiscal balances in most of East Asia ment institutions and foreign governments). It is the gross amount of government liabilities not reduced r C, .z ,(,I,,l,r"-.,r,, :1 cI;LI. by the amount of government claims against others. Because debt is a stock rather than a flow, it is mea- l I sured as of a given date, usually the last day of the * *0 11 fiscal year. * Interest includes interest payments on government debt-including long-term bonds, long-term loans, and other debt instruments-to both domestic and foreign residents. - ........... ~~~~~~~~~~~~~~~~~~Data souarces Data on central government finances are from the IMFs Govemment Finance Statistics ^ > r.:e .. l;,lelr. jl .......:r,,l I.tnr. ........ 15l. ~.,n.1 I:-iCll-.,ner.lF,r,;,.:-, 1;l.:l. .1 jl jlil. t Yearbook, 1998 and IMF data files. Each country's accounts Except for Malaysia. the Easl Asian countiles In Inancial crisis showeo declining flacal surpIbses or emerging ,- are reported using the system aeficits In 1995-97. Bv corntrasl. India and China reduced Iheir deficits simghila. JM of common definitions and ------------- F classifications found in the IMFs Manual on Government Finance Statistics (1986). See these sources for complete and authoritative expla- nations of concepts, definitions, and data sources. 1999 World Development Indicators 237 4.14 Central government expenditures Goods and wages Interest Subsidies and Capital services and salarlesa payments other current expenditure transfers % of total % of total % of total % oftotal % of total expenditure expenditure expenditure expenditure expenditure 1980 19961 1980 1996 1980 1996 1980 1.996 1980 1996 Albana26 ':.........12 . ....... .8.48 ...18 Algeria 35 - 24-..10 26 26 Angola . Argentina 57 21 16 0 11 43 598 Australia 22 21 7 65 697 3 Austria 26 2 4 11 9 5 10 60 59 9 7 Azerbaijan Bangladesh. Belarus 2 9 82541 Belgium 23 18 16 15 10 18 59 59 8 5 Bosnia and H rzegovina.. .. .. Botswana 47 48 29 25 2 2 19 31 32 19 Brazil 20 13 16 8 8 44 64 44 83 Bulgaria .19 4.4236 Burkina Faso 67 ... 3 13 19 Burundi 3.9 47 25 29 2 6 7 12 4 628 Cambodia... .. Cameroon 55 53 32 3 7 1 23 1 1 13 33 8 Canada 22 . 10 . 12 18 65 10 Central African Republic 67.. 5 1. 16 6 Chad... Chile 41 29 29 19 3 3 46 5 2 10 17 China ... Ho.gKong, China Colombia 36 23 4~~~~~~~~.......I...... . .... ........... . 38.... I... .......... .... 31. ... Congo,Dem. Rep. 65 94 42 58 8 1 82 20 3 Congo, Rep. .. ........... 45 Costa Rica 53 47 44 35 9 20 24 23 21 10 CMe dIlvoire 39 28..8 ..13 28 Croatia .. 52 .23 3 34 1 2 Cuba . Czech Republic. 15 8 371 11 Denmark 22 19 13 11 7 13 65 64 7 4 Dominican Republic 50 36 39 27 6 4 12 17 31 41 Ecuador 28 47 26 42 9 22 34 9 16 21 Egypt, Arab Rep. 39 34 19 .18 8 22 32 25 21 19 El Salvador ... Eritrea .. Estonia ......... ..... ...... ..-44 . ... 13 ..........1 46 .. 9 Ethiopiab 86 . 363..4 15 Finland 22 18 11 7 2 13 66 65 11 4 France 30 24 20 16 2 7 62 65 54 Gabon Gambia, The46 23I. 14 4 Georgia Germany' 34 32 9 8 3 7 55 57 7 4 Ghana 48 : 27I. 626 10 Greece 45 29 29 24 8 36 35 22 16 13 Guatemala 53 .. 37 .......... .4 .38 Guinea .. .. Guinea-Bissau... Honduras . 238 1999 World Development Indicators 4.14 Goods and Wages Interest Subsidies and Capital services and salaries' payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1980 1996 1.980 ±996 1980 1996 ±980 1996 1980 1.996 Hungary 20 17 7 8 3 19 64 60 13 9 India 29 23 14 10 13 27 47 39 12 11 Indonesia 2 5 30 15 16 4 8 24 21 47 41 Ipran,slamic Rep. 57 53 45 39 1 0 19 14 22 33 Ireland 19 18 13 13 14 13 57 60 10 10 Israel 50 33 12 14 11 13 35 454 9 Italy 18 19 13 14 11 21 63 55 56 Jamaica . .. Japa 13 13 54 . 19 Jordan 43 59 44 3 9 17 11 29 20 Kazakhstan Kenya 57 45 27 28 7 26 13 18 23 12 Korea, Dem. Rep. Korea, Rep. 45 27 16 13 73 34 48 14 23 Kuwait 45 59 2 2 27 0 5 23 26 3 211 Latvia ':........ 36 ...... .... ..... 17.5. ........55 5 Lebanon 31............ .... ..... .. ...... ..23... 2 ...... . ...... 34 ......13. ..... 3....... . . 22.. 2 Lesotho 50 43 34 30 10 3 13 12 27 42 Libya...... Lithuania 43 17446.8 Macedonia, FYR.......... Madagacar 25 .. 1 7 . 83 Malawi 37 ..15.9 .6 48 Malaysia 38 45 28 29 10 12 19 24 35 19 M ali 46 ......: -..... ...33 .. ... 1... .. ..... .. 11 ......9.... Mauritania Mauritius 42 47 32 36 14 11 28 26 17 16 Mexico 32 26 25 17 11 19 32 43 32 12 Moidova Mongolia 36 11. .3 ....... .42 42 19 Morocco 47 49 33 34 7 18 15 12 31 22 Mozambique : I. Myanmar....... 24 54 Namibia ' ' Nepal .. . .9 Netherlands 16 16 11 9 4 9 72 71 9 4 New Zealand 29 49 21 10 11 55 376 2 Nicaragua 60 30 . 19 8 12 13 25 19 33 Niger 30......... 17 6 14 49 Nigeria Norway .20 20 9 8 7 6 67 69 65 Oman 71 75 13 27 36 56 21 12 Pakistan 47 46 6 4 12 24 23 15 17 15 Panama 50 48 33 37 18 14 14 25 18 13 Papua New Guinea58 48 37 28 59 23 32 15 11 Paraguay 61 . 431 . 2 Peru 45 38.. 1 18 13 14 33 23 16 Philippines 61 52 27 31 7 19 7 17 26 12 Poland 26 .. 39 61-4 Portugal 34 40 24 30 8 11 45 37 13 13 P ue rto .R ic ... . . . . . . ... . . . .. . . . .. . . . . . . . . . . .: .. . . . . . . . . . . . . . . . . Romania 11 33 2 16 0 5 55 51 33 11 Russian Federation 40 14 . 13 . 495. 1999 World Development Indicators 239 4.14 Goods and Wages Interest Subsidies and Capital services and salarlee~ payments other current expenditure transfers % of total % of total % of total % of total % of total expenditure expenditure expenditure expenditure expenditure 1980 1.996 1980 1996 1.980 1996 1980 1996 1980 1996 Rw nda 58 302 535 Sauidi Arabia ''. ........ Senegal 72 45 6 18 8 Sierra Leone 35 19 13 31 20 25 Singapore 58 45 29 23 15 4 6 21 22 29 Slovak Republic Slovenia South Africa 47 30 20 26 8 18 31 49 14 3 Spairn 40 16 32 11 1 12 48 66 11 5 Sri Lanka 31 38 13 18 8 23 20 22 40 I8 Sudan 46 12 6 28 23 Sweden 17 14 8 57 13 7 1 71 53 Switzerland 2 7 2 7 6 533 63 667 4 Syrian Arab Republic 42 . 18 . 21 37 40 Tajikistan Tanzania 52 19 7 4~ 40 Thailand 55 56 21 32 81 14 723 36 Too 52 28 9 12 . 27 Trinidad and Tobago 34 51 28 33 3 18 24 21 39 10 Tunisia 42 38 29 32 5 12 24 29 30 21 Turkey47 33 32 23 3 12 23 4 28 8 Turkmenistan Uigand . 1 Ukraine United Arab Emirates 80 87 . 35 00 12 98 5 United Kingdom 32 30 14 9 11 9 53 565 5 United States 29 22 11 9 10 15 54 596 3 Uruguay ~~47 29 30 16 24 43 618 5 Uzbekistan . . .. Venezuela 50 20 41 16 8 22 22 44 22 14 Vietnam.... West Rank and Gaza Yemen,Rep. 46 34.. 9 . 2617 Zambia 55 45 27 23 98 25 15 11 33 Zim babwe 56 31 ..... 7 ... 7 . ... .. .... .32 .. ........ ........... 5 . Low income...... Middle income 47 36 18 69 2 3 3424 13 Lower middle income 46 39 26 . 11 20 25 28 19 Upper middle income 47 30 25 18 3 11 31 46 21 12 Low & middle income.. ... East Asia & Pacific. 45 27 11 18 25 27 Europe & Central Asia . .. Latin America & Carib. 49 32 30 20 7 11 24 29 21 14 Middle East & N. Africa 43 49 32 11 19 15 2921 South Asia 31 38 13 10 12 24 23 22 17 15 Sub-Saharan Africa 53...... 1320 Hig income 28 25 13 11 7 10 56 587 5 Europe EMU 24 24 13 9 4 10 59 594 Note: Includes expenditures financed by grants in kind and other cash adjustments. a. Part of goods and services. b. Data prior to 1992 include Eritree. c. Oats prior to 1990 refer to the Federal Republic of Germany before unification. 240 1999 World Development Indicators 4.14 Government expenditures include all nonrepayable * Total expenditure of the central govemment payments, whether current or capital, requited or includes both current and capital (development) expen- unrequited. Total central government expenditure as ditures and excludes lending minus repayments. presented in the International Monetary Fund's (IMF) * Goods and services include all government pay- Govemment Finance Statistics Yearbook is a more ments in exchange for goods and services, whether in limited measure of general government consumption the form of wages and salaries to employees or other than that shown in the national accounts (see table purchases of goods and services. * Wages and 4.10) because it excludes consumption expenditures salaries consist of all payments in cash, but not in kind, by state and local governments. At the same time, the to employees in return for services rendered, before IMF's concept of central government expenditure is deduction of withholding taxes and employee contribu- broaderthan the national accounts definition because tions to social security and pension funds. * Interest it includes government gross domestic investment payments are payments made to domestic sectors and and transfer payments. to nonresidents for the use of borrowed money. Expenditures can be measured either by function (Repayment of principal is shown as a financing item, (education, health, defense) or by economic type and commission charges are shown as purchases of (wages and salaries, interest payments, purchases of services.) Interest payments do not include payments goods and services). Functional data are often incom- by government as guarantor or surety of interest on the plete, and coverage varies by country because func- defaulted debts of others, which are classified as gov- tional responsibilities stretch across levels of ernment lending. * Subsidies and other current trans- government for which no data are available. Defense fers include all unrequited, nonrepayable transfers on expenditures, which are usually the central govern- current account to prvate and public enterprises, and ment's responsibility, are shown in table 5.7. For more the cost to the public of covering the cash operating information on education expenditures see table 2.9; deficits on sales to the public by departmental enter- for more on health expenditures see table 2.13. prises. * Capital expenditure is spending to acquire The classification of expenditures by economic fixed capital assets, land, intangible assets, govem- type can also be problematic. For example, the dis- ment stocks, and nonmilitary, nonfinancial assets. Also tinction between current and capital expenditure may included are capital grants. be arbitrary, and subsidies to state-owned enterprises or banks may be disguised as capital financing. Data sowces Subsidies may also be hidden in special contractual pricing for goods and services. For further discussion Data on central government of government finance statistics see About the data -- expenditures are from the for tables 4.13 and 4.15. - IMF's Govemment Finance -'_. ' Statistics Yearbook, 1998 and IMF data files. Each country's accounts are reported using the system of common defini- tions and classifications found in the IMF's Manual on Govemment Finance Statistics (1986). See these sources for complete and authori- tative explanations of concepts, definitions, and data sources. 1999 World Development Indicators 241 4.15 Central government revenues Taxes on Social Taxes on Taxes on Other Nontax Income, profit, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1980 1998 1950 1996 1.980 1996 1980 1996 1980 1996 1980 1996 Albania 8 . . 15 38 14 I 21 Algeria 68 0 10 15 1 . 5 Angola Argentina 0 13 17 30 17 39 0 7 33 4 33 7 Armenia Australia6 66 0 0 23 21 5 2 0 2 10 8 Austria 21 22 35 38 26 25 2 0 9 7 8 8 Azerbaijan Banglaesh10 _ 0 25 . 29 . . 32 Belarus.. 8 .. 31 .. 37..S. 11.7 Belgium 38 35 31 34 24 25 0 0 2 2 4 2 B- i i .... .. .. . . .. .. . .. .. .. . .... .. ....13...8...6...0.. 1 Bosnia and Herzegovina.. . . .. Botswana 33 17 0 0 1 4 39 12 0 0 27 66 Brazil 11 12 25 27 32 21 7 2 4 4 21 22 Bulgari'a . 19 . 21 26..7. 2..5 Burkina FasoI1 . 8 16 . 44 . 4 . 11 Burundi 19 21 1 7 25 35 40 17 8 3 6 17 Cambodia Cameroon 22 17 8 0 18 25 38 28 5 3 8 27 Canada 53 48 10 19 17 19 7 2 0 0 14 11 Central African Republic .17 7 . 21 . 1. Chile 17 19 17 6 35 46 4 9 6 4 19 15 China 12 0 . 68 .8. 2.. 10 Hong Kong, China Colombia 25 33 11 0 23 39 21 8 7 0 14 16 Congo,Dem. Rep. 30 33 2 0 12 19 38 33 5 8 12 8 Congo, Rep. 39 . 4 . 6 10 . 2 .. 19 Costa Rica 14 11 29 27 30 40 19 8 2 2 6 12 C6e dIlvoire 12 5 24 41 . 67 Croatia 2 . 33 .. 40 ..8 .I ..6 Cuba Cze.hRepublic 14 42 . 33 .. 4 2 Denmark 35 39 2 4 46 40 0 0 3 3 11 13 Dominican Republic 19 16 4 4 21 33 31 36 2 1 22 8 Ecuador 45 50 0 0 17 26 31 11 3 1 4 12 Elpt Sralvador 16 16 9 10 15 13 17 10 8 9 34 38 Eritrea .. .. Estonia .. 16 32 . 40 .0.. 09 Ethiopia' 21 .... .. ........0 .. 24 - 36 ....... .. ......4 I ......15...... Finland 28 29 10 11 49 42 2 0 3 2 8 15 France 18 18 41 43 31 29 0 0 3 3 7 6 Gabon 37 .. 0. 4 18. 2 . 3 Gambia, The 15 0 3 65 . 2 .. 15 Georgia ... .. Germany' 19 15 54 49 23 21 0 0 0 0 4 6 Ghana 20 . 0 . 28 44 .. 0 . Greece 17 32 26 2 32 60 5 0 10 7 11 10 Guatemala 11 . 11 .. 26 . 30 1.. 10 G ui n e a . . . . .. . . . . .. . . . .. . . . . .. . . . . . . . . . . . .... . ... .. . . . . ... .... . . . . . . . . . .. .... .. . . . . .... . .. . . . .... . . . .. . . . . .. . . . . .... . . Guinea-Bissau Haiti 14 _. 0 .. 1S 48 .9 .. 13 Honduras 31 . 0 .. 24. 37 . 2 .. 7 242 1999 World Development Indicators 4.15 Taxes on Social Taxes on Taxes on Other Nontax Income, profit, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenue current revenue current revenue current revenue current revenue 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 1980 1996 Hungary 18 19 15 26 38 29 7 9 5 1 16 15 India 18 23 0 0 42 27 22 25 1 0 16 24 Indonesia 78 50 0 3 9 30 7 3 1 1 5 13 Iran, Islamic Rep. 4 11 7 6 4 5 12 6 5 4 68 68 Iraq Ireland 34 39 13 14 30 39 9 0 2 3 11 5 Israel 41 36 10 11 25 34 4 0 8 4 14 14 Italy 30 33 35 35 25 24 0 0 4 3 8 5 Jamaica 34 4 49 3 .. 6 4 Japan* 71 i0 21 2 5. 5 Jordan 13 13 0 0 7 30 47 26 9 9 22 22 Kazakhstan Korea, Dem. Rep. .. .. ..~ : Korea, Rep. 22 29 1 9 45 33 15 6 3 10 12 13 Kuwait 2 0 0 1. 0.. 97 Kyrgyz Republic Lao PDR Latvia 7 32 422 .. 0 .. 16 Lebanon 70 .. 4... .. 46 . 24 19 Lesotho 13 15 0 0 10 12 58 49 1 0 13 24 Lithuania 13 31 48 .. 3 0 4 Macedonia, FYR.. . .. . Madagascar 14 18 10 0 33 24 23 53 2 2 2 2 Malawi 34 0 31 220 13 Malaysia 37 36 0 1 17 26 33 12 2 5 10 20 Mali 18 0 37 .. 18 15.. 8 Mauritani'a.. .... Mauritius 15 14 0..... 6. 17 26 5 3447. .... 12 .... _.13 Mexico 34 27 12 13 50 57 7 4 3 2 7 16 Moldova . Mongolia 2222 . 21 ..: 11I1 2 Morocco 19 19 5 7 34 38 20 14 7 3 12 16 Mozambique.. . ... ... Myanmar 3 17 0 0 42 26 15 15 0 0 40 42 Namibia Nepal 6 12 0 0 37 37 33 28 8 4 16 18 Netherlands 30 26 36 40 21 24 0 0 3 4 11 6 New Zealand 67 62 0 0 18 26 3 2 1 2 10 Nicaragua 8 11 9 13 37 42 25 21 8 6 10 Niger 24 .. 4 .. 18 .. 36 315 Nigeria . Norway 27 19 22 21 39 36 1 1 1 1 9 22 Oman 26 21 0 0 0 1 1 3 0 2 71 72 Pakistan 14 21 0 0 34 27 34 24 0 7 18 21 Paaa2 18. 21 14 17 17 10 10 4 3 27 39 Papua New Guinea 60 50 0 0 12 10 16 24 1 2 10 14 Paraguay.15........... 13 18 25 19 9..... ................... .... ............. .. . ................. ....... Per 26 21 0 8 37 46 27 9 10 6 8 13 Philippines 21 33 0 0 42 27 24 26 2 4 11 9 Poland 26283718 Portugal 19 26 26 25 33 37 5 0 9 3 7 9 Puerto Rico Romania 0 27 13 27 0 23 0 6 9 4 78 12 Russian Federation 14 29 34 .. 9 .. 2 ..6 1999 World Development Indicators 243 4.15 Taxes on Social Taxes on Taxes on Other Nontax income, profit, security goods and international taxes revenue and capital taxes services trade gains % of total % of total % of total % of total % of total % of total current revenue current revenae carrent revenue current revenue current revenae current revenue 198o 1996 1990 1996 1980 1996 1980 1996 1980 1.996 1980 1996 Rwanda 18 . 4 19 42 .. 2 14 Saudi Arabia . Senegal 18 4 26 34 .. ......4 6 Sierra Leone 22 16 0 0 16 30 50 50 2 0 10 5 Singapore 31 18 0 0 15 13 7 1 13 11 30 33 Siovak Republic... Slovenia South Africa 56 53 1 2 24 34 3 2 3 3 13 7 Spain 23 31 48 39 13 24 4 0 4 0 8 7 Sr'iLanka 16 14 0 0 27 54 50 17 2 4 5 11 Sudan 14 0 26 421 16 Sweden 18 11 33 37 29 31 1 0 4 8 14 12 Switzerland 14 14 48 51 19 24 9 1 2 2 7 7 Syrian Arab Republic 10 23 0 0 5 29 14 12 10 7 61 29 Tajikistan . Tanzania 33 0 42 1-82 8 Thailand 18 32 0 1 46 41 26 15 2 2 8 9 Togo 34 6 15 32 . I .. 1 TrnddadTbg 71 49 1 2 4 25 7 6 1 1 16 14 Tunisia 15 16 9 17 24 21 25 26 4 5 22 16 Turkey 48 32 0 0 19 46 6 2 5 2 20 17 Turkmenistan Uganda 11 0 41 44 03 Ukraine United Arab Emirates 0 0 0 2 0 23 0 0 0 0 100 74 United Kingdom 38 36 16 17 28 33 0 0 6 7 13 8 United States 57 54 28 33 4 3 1 1 1 1 8 7 Urugay 11 13 23 31 43 33 14 3 8 il1 6 7 Uzbekistan . ........ Venezuela 67 38 5 2 4 28 7 7 2 0 15 29 Vietnam Went Bank and Gaza Yemen, Rep. .. 16 .. 0 . . 122 6 Yugoslavia, FR (Serb./Mont.). . .. . .. Zambia 38 36 0 0 43 44 8 12 3 0 7 8 Zimbabwe 46 .. 0 .. 28 .. 4 1. 20 Low Income 19 ...... ......... .0.. 26 .. 34..2.. 12 - ...... Middle Income 19 17 4 6 20 33 15 7 4 2 13 13 Lower middle income 18 16 4 7 21 34 21 10 5 3 11 12 Upper middle income 3 19 5 6 19 33 7 7 3 2 16 15 Low &middle income 19 1 .. 24 24 .. 3 13 East Asia &Pacific 29 32 0 1. 29 27 20 12 1 2 10 13 Europe & Central Asia..... . Latin America & Carib. 19 16 11 11 24 33 19 9 6 4 10 14 Middle East &N. Africa 15 16 5 0 7 8 17 14 7 4 34 25 South Asia 14 17 0 0 34 32 33 25 2 4 16 20 Sub-Saharan Africa 22 I. 1 . 25 .. 35 3 10 High Income 29 26 19 25 24 26 2 0 3 3 10 8 Europe EMU 26 26 35 38 25 25 1 0 3 3 8 6 Note: Components may not eaum to 100 percent an a resait of adjustments to tax revenue. a. Data prior to 1992 inciude Eritrea. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 244 1999 World Development Indicators 4.15 The International Monetary Fund (IMF) classifies gov- . , * Taxes on income, profit, and capital gains are ernment transactions as receipts or payments and levied on the actual or presumptive net income of indi- aGcording to whether they are repayable or nonre- Social security taxes account for a viduals, on the profits of enterprises, and on capital accodin towheherthe arerepyabe o nore- large share of current revenue in payable. If nonrepayable, they are classified as capital high-income countries ... gains, whether realized on land, securities, or other (meant to be used in production for more than a year) assets. lntragovernmental payments are eliminated in orcurrent, and as requited(involvingpayment in return : ,:,.,I a- I. e , : .:,.,,- 6 consolidation. * Social security taxes include for a benefit or service) or unrequited. Revenues r, employer and employee social security contributions include all nonrepayable receipts (other than grants), and those of self-employed and unemployed people. the most important of which are taxes. Grants are * Taxes on goods and services include general sales unrequited, nonrepayable, noncompulsory receipts .1- i and turnover or value added taxes, selective excises from othergovernments or international organizations. 9 6 on goods, selective taxes on services, taxes on the Transactions are generally recorded on a cash rather E 8 E . use of goods or property, and profits of fiscal monop- than an accrual basis. Measuring the accumulation of - olies. * Taxes on international trade include import arrears on revenues or payments on an accrual basis l duties, export duties, profits of export or import would typically result in a higher deficit. Transactions B l g monopolies, exchange profits, and exchange taxes. within a level ofgovernmentare notincluded, buttrans- * Othertaxes include employer payroll or labortaxes, actions between levels are included. In some .. taxes on property, and taxes not allocable to other cat- instances the government budget may include trans- egories. They may include negative values that are fers used to finance the deficits of autonomous, extra- . . and a large share in many adjustments (for example, for taxes collected on behalf budgetary agencies. middle-income countries of state and local governments and not allocable to The IMFs Manual on Govemment Finance individual tax categories). * Nontax revenue includes Statistics (1986) describes taxes as compulsory, unre- requited, nonrepayable receipts for public purposes, quited payments made to governments by individuals, such as fines, administrative fees, or entrepreneurial businesses, or institutions. Taxes traditionally have income from government ownership of property, and been classified as either direct (those levied directly voluntary, unrequited, nonrepayable receipts other on the income or profits of individuals and corpora- than from government sources. Proceeds of grants tions) or indirect (sales and excise taxes and duties i X and borrowing, funds arising from the repayment of levied on goods and services). This distinction may be previous lending by govemments, incurrence of liabili- a useful simplification, but it has no particular analyt- ties, and proceeds from the sale of capital assets are ical significance. For further discussion of taxes and not included. tax policies see About the data for table 5.5. For fur- Cl ther discussion of government revenues and expendi- Data sources tures see About the data for tables 4.13 and 4.14. .. _ - -- Data on central government revenues are from the IMF's .:.u r'l. Il.r _-,-, , ,r:. r,, 1 .r;13r I, ar, .] K. , c; ,r,n -r F,,-,.r,,:, ;131,:-. --ili 1al_ __ _ Government Finance Statistics Yearbook, 1998 and IMF data files. Each country's accounts x are reported using the system _ ,~ of common definitions and classifications found in the IMF's Manual on Government Finance Statistics (1986). The IMF receives additional information from the Organisation for Economic Co-operation and Development on the tax revenues of some of its mem- bers. See the IMF sources for complete and authorta- tive explanations of concepts, definitions, and data sources. 19S9 World Development Indicators 245 4.16 Monetary indicators and prices Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities annual % growth annual growth annual growth average annual average annual average annual of M2 asa%ofMV2 as %ofMV2 % growth % growth % growth 1990 1997 1990 1.997 1990 1997 1980-90 1990-97 1980-90 1990-97 1980-90 1990-97 Albania .. 28.9 ....... 1.4 . ... . ......31 9 -0.4 58.1 . .. 44.9 Algeria 11.4 18.6 12.2 -3.2 3.2 -45.9 8.1 23.8 9.1 24.8 6.8 26.0 Angola . 5.9 1,091.4 . ,1. Argentina 1,113 3 25.5 1,444.7 14.6 1,573.2 ......5 7 389.8 12.2 390.6 15.9 2069 9.. . 15 2 Armenia . 29.6 20.....-11.5....482.8... AUstralia .... 12:8 .....7-.3 1... 53 11.8 -22_ ...-4.8 7.3 1.7 ......7.9 .......2~3 ......74 2~4 Austhia 9.7 2.1 12.1 9:9 1..6... -1.4 33 2.6. 2.8.......26..... 1.9 Azeibaijan 41~4 ....... ..... 14.8 2.5 447.8 1:5 455.5 Belarus .._ . 111.4. 65:9 ........ . 55.5 .... .... 561.4 . . 2.4.. .611.7 Belgium 4.1 7:.1 ... 3.5 ...... 5.2 5.6 -42 ... 4.4 2.6 ......4.2 2.2 4.0 ......0.8 Bolivia 52.8 16.5 40.8 21.4 18!.0.... ..2 3. ..3279.9 ... 10.4.... 322 .5 10. 5.... 322.0_... 1 2 Bosnia and Herzegovina .. . ... ........ Botawana ......... -14.0 28.6 12.6 4.7 -52.4 -255.3 13.1 10.2 10.0 .... 11.9 .....10 7_ ....12.6 Brazil 1,289.2 18.4 1,566.4 9.5 3,0936.6 .... 3.6 284.0 475.7 285.6 456.6 238.2 ..646.5 Bulgaria 53.8 362.1 19.9 122.9 84.5 123.0 1.8 109..5 ............ ........... 147.9 Burkina Faso -0:5 ......176 3~6 .....246 -1.5 8.9 3.3 6.9 .1.0...... 6.8. -0.5 ......5.4 Burundi 10.4 9:4 .....163 ...... 4.4 -5.3 18.8 4.4 10.7 7.1 15.1 61 ..... 6.7 Cambodia. 16.6 22.2 . -8.0 . 37.8 . Cameroon -1.7 18~6 0.. O9...... -6.5 -3.0 13.3 5.6 6.4 8.7 8.6 Canada 7.8 8.6 9.2 14.9 0.7 -0.8 4.5 1.4 5.3 1.8 4.6 1.4 Central African Republic -3.7 -7.7 -1.6 0.7 2 3 -1.9 7.9 5.9 3.2 7.3 2.0 7.8 Chad ..-2.4 -. -13_ --0.7 -17.3 -0.3 2.9 7.3 0.6 9.1: Chile 23.5 16.3 21.4 23 5 164 -2.8 20J 7.... 10..2 20.6 11.4 20.8 11.3 China 28.9 20.7 26.5 16.9 1.5 0.3 5.9 11.2 -.. 12.6 Hong Kong, China 8.5 60~9 ..... 7.9 18.5 --1.0 -19..7.7 6.7 ... 6:8 6.8 Colombia 32.7 41.9 8J.7.... 38.2 -5 1..... 14 8 24.8 22.4 22.7 23.1 24.5 20 5 Congo, Dem. Rep. 195.4 357.6 18.0 59.6 4297 7...... -0.9 62.9 2,012.8 57.1 2,089.0 Congo, Rep. 18.5 9.5 5.1 4.6 -12.6 10.6 0.5 8.9 6.1 14.8 4.1 10.2 Coata Rica 27.5 16.4 7.3 126.6 82.2..... 13.3 23.6 18.0_ ..23.0 ..... 17.4 .23.0 14.9 C6te d lvoire -2.6 8.2 -3.9 8.4 -30 0.4 2. 93 54 91 Croatia . 37.7 . 39.0 . -18.5 . 218,1.... 304..1 171..7 ..._246.3 168.8 Cuba . .... .....C ......ech............... Republic. 1.7.... . 20.3 -7.7 ......1.5 17.1 .. 18.6 Denmark 6.5 ......6.8 3.0 3.9 -3.1 -0'9 5.6 1.9 5.5 2.0 4.8 1.8 Dominican Republic.......... 39.1 2... . 4:3 .....208.8 ..206 0.8 1.9 21.6 11 5 22.4 10.3 13.1 --1.6 Ecuador 101.6 32.8 .....46 7 ...39:6 ....-22.4 3.8 36.4 33.3 35.8 34.7 430 0. .. 34.6 Egypt, Arab Re. ..........28.7 10.8 .6-.3 .. 12.0 25.3 3.4 13 7 . ....10.5 17.4 11.4 19 0 9.0 El Salvador 32.4 ......18 8...8..88 22.4.9.6.-1.7216.399. 1. 1.6 2.4 14.1 Eritrea - . .. . .. 12.7 . Estonia 71-1 37.8 27,6 50.6 -11~3 -9.7 2.3 92.2.. 90:1 Ethiopia' 18.5 14.6 -10O ..... 85.5 .... 21 7..... -0 4 36.6 .... 8.9 ..... 4.0 6.9 3 7 .... 12 4 Finland 5.0 2.5 17.1 -3.6 -0:1 -0.2 6.8 ......1.8 6.2 1.7 5.8 -. France 3.3 7.3 157....... 2.4 0.3 2.5 60.0..... 1.8 5.8 2.0 5.7 1.1 Gabon 3.3 11.3 0 7... .18.5 -20.6 -3.6 1.8...... 8.5 5.1 5J 7..... 2.8. 5.1 Gambia, The 8.4 23.0 ......7..8 ......8.6 ....-35.4 2.6 17.9 .....4.7 .....20.0 .......5 1 20.4 5:0 Georgia . .: . . 1:.9 1,033.2 . Germany' .18.6 2.2 26.5 9.8 2.0 2.6 .. 2.5. . 2.2 ......3.0 2.0 Ghana 13.3 45.5 4.9 26.4 -0.8 61.5 42.1 29.2 39.1 31.2 33.1 27.5 Greece 14.3 10.9 4.6 8.9 16.3 -0.9 18.0 12.2 18.7 11.7 18.0 11.2 Guatemala 25.8 -4 150 49 05 12 1146 12 10 121 1.6. ...12.5 Guinea7. .......-17.4.. 1 .1.18.2 13.1. - . ......._-0.3....6 7.3.......-9.0 .I...........6.2.. ... ...... ....9.1... Guinea-Bissau 65.3 48.4 57.4 8.8 109.9 11.9 57.4 45.3 44.6 Haiti 25 .... 18~4. . .-0.6 ......182 0.4 0.2 7.5 25.3 5.2 26.2 471..... 19.2... 246 1999 World Development Indicatora 4. 16 Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit Price price and other deflatorine Index pubiic entities annual % growth annual growth annual growth average annual average annual average annual of M2 as %of M2 as %of M2 % growth % growth % growth 1.990 1997 1990 1997 1990 1997 1980-90 ±990-97 1.980-90 1990-97 ±980-90 1990-97 Hungary 29.2 18.4 22.8 5.5 2.3 -7.2 89.9.... 22.8 9.6 23.6 9.5 23.6 India 15.1 17.7 5.9 .......60.0 ... 10.5 5.8 8.0 8.8 8.6 9.6 8.4 10.2 Indonesia 44.6 25.2 66.9 31.2 -6.7 -9.0 8.5 .....8.6 8.3 8.6 8.6 9.6 I~r.!an,Ilmic Rep. 18.0 23j.7 ... -14.7 12.1... 1 : .. 5.8.. 2.5..........14.4. 325....I.........18.2...... ..289.. ... 16.3.... ....326.... Iraq 1 0 3............10.3... . . I ... . . . . . . . . . . ... . . . . . . . : . . . . . . . .. . . . Ireland 8.9 13.6 0.6 ...... 9.8 ......1.9 -2.6 6.6 ......1.8 6.8 2.2 10.5 2 0 Israel 19.4 15.0 18.5 . 13.7 ......49.9 . -6'.1... 101.1 11.4 ... 101.7 11.7 102.4 9.5 Italy 10.2 -5.8 11.3 3.8 2.6 -1.7 100 .0 .... 4.6 9.1 4.5 8.2...... 4.0 Jamaica 21.5 13.3 12.5 5.7 -16!0.0.... 17.6..... 18..6..... 32.8 15.1 32.7 16.2 32.9 Japan 8.2 3.1 9.7 0:.5 1.5 1.1 1.7 0.5 1.7 1.0 1.6 0.8 Jordan 8.3 7.6 ......4.7 .......3.6 ......1.0 .-2.1 4.3 3.3 5.7 4.1 4.7 4.4 Kazakhstan .. 8. . -21-0.2 .. 4404 . . . 7. Kenya 20.1 16.8 8.0 15.2 21.5 5.1 9.1 16.0 11.1 21.0 . 21.8 Korea, Dem. Rep.. .. . . . Kora Rep 17.2...... 14! .1 36..1 ..... 30.8 -1..5. 2.5 6.1 5-3.3.. . 4.9 5.6 5 0 5.7 Kuwait 0.7 3.9 3.3 15.7 -3.1 -1.7 -2.8 . 2.9 2.2 1.1 Kyrgyz Republic . . . .. .191 .. .. 171.8 Lao PDR 7.8 65 8.8 .... 3 6 6 ..... 53.0 7 0.0 ..33'8.8 ...... :..... 12, 2 .... ....... ..... ... ....... :... Latvia 37.8 ........ .....21 7.7 .......-3 3 .....0.0 ...87..7 ..... ........ .......... ..... 45 .9 Lebanon 55.1 19.6 27.6 10.4 .....18.5 13.6 . 27.7. Lesotho 8.4 9.4 6.8 4.8 -17.4 -44.7 13.8 8.0 13.6 12.1 13:2 13.0 Libya 203 3 20.7 0.9 7.0 8.5 -21.7 0.2 . Lithuania 34.1 5:4 ........: ...... 11.3 ....... ..... 140.3 . .. 114.1 M acedonia, FYR ........ .... ...... . ...... ...I,....... ...... 60..5 ........... . ...... . 24 . 1 6 3 Madagacar .. 4.5 20.8 23..8 7.4... -14.87 4-6.1 17.1 23.6 16.6 23.0 15.7 23.2 Malawi 11.1 2.1 15.8 3.6 -12.8 -7.4 14.6 33.8 16.9 34.2 16.3 39:.7 Malaysia 10.6 175_.5... 20-.8 2......5.3 -1.2 2 0 1.7 4.5 2.6 4.0 13 4 7 Mali -4.9 9.0 0.1 8.3 -13.4 4.6 3.6 10.0 . Mauritania 11.5 8.0 20.2 11.8 1.5 -44.5 8.4 6.0 7.1 6.6 Mauritius 21.2 16.4 10.8 15.3 0.8 .......6.6 9.5 6.4 6.9 7.1 7.4 7.5 Mexico 81.9 34.6 48.5 -0.3 13.6 -7.3 727.1.... 19.3 73.8 19.5 73.1 19.1 Moldova 358.0 34.5 53.3 1.2 469.1 33.8 . 222.5 . . . 230.3 Mongolia 31.6 42.2 40.2 -14:3.3... 38.5..... -5.7 -1.6 89.3 . Morocco 21.5 .......8,1 ......12.4 3 7 -.9 71 3.8 7.0 5.0 6.7...... 7.2 Mozambique 37.2 23.7 22.0 25.6 .....-5.1 .....-155_.5 .. 38.3..... 45.9 . 44.6:... Myanmar .37.7 25.0 12.8 7.0 23.4 15.1 12..2 24.2 11.5 25.3 11 9 26.8 Namibia 30.3 67 .7 .. 15.4 ..... 14.8 -4.2 -15.5 13.9 9.4 12.6 10.7 14.9 11.1 Neal18.5 15.8 5.7 10.0 7.3 1.5 11.1 9.7 10.2 9.6 10.0 10 5 .......... ................. ....... ....... ......Netherlands 6.9 6 8 6.7 15.8 -0.1...26 1.6 2.0 2.0 2.5 1.2 1.5 New Zealand 74.0 4.7 76.6 11.9 0.1 1.5 10.8 ..... 17.7 . . 10.9 2.0 99 9.......0.9 Nicaragua 7,677.8 54.4 4,932.9 28.7 12,679.2 41.1 422.6 67.7 535.7 62.9 Niger -4.1 -21.3 ...5.. 1~..... -6.-2 1~4.4 ... 22.8 1.9 7.2 0.7 7.3 Nigeria 32.7 16.9 7.8 1 4.4.... 27.1 -18.3 16.7 42.6 21.5 44.9 21.6 41.5 Norway 5.6 1.4 5.0 19.3 -0.6 -14.6 5.6 1.9 7.4 2.1 7.8 1.2 Oman 10.0 24.5 9. 6..... 37:1.1.. -10.9 -8.0 -3.-6 -2..9..... 2 90:2 Pakistan 11.6 19.9 .......5.9 ....I. 7.-6 ......77.7 ..... 3 3 6.7 11.3 6.3 11.1 6.6 .. 11.9 Panama 36.6 15~.0.....0..8 .....16~.9.... -25.7 -6.5 1.9 2.8 1.4 1.1 1.9 1.3 Papua New Guinea 4.3 7.7 1.3 12.0 7.2 6.9 53.3 6.7 5.6 7.6 4.6 6'8 Paraguay52.5 92.2.... 33~.1 .... 9.7 -9.5 8.0 24.4 16.1 21.9 15.6 24.9 .... 14.4 Peru 6.384.9 30.8 2,123.7 27.2 2,129.5 9 9 231.3 40.1 246.1 44.3 . 41.0 Philippines 22.5 26~1.1.. 15~7.7.... 25.9 3.4 9 9 14.9 8.6 14.4 8.9 14.1 8.1 Poland 1601 1..... 29.2 20.8..... 167.7.... 75 6 8.5 53.8 29.5 50.9 34.0 52.-4..... 29.8 Portugal 9.2 6.8 65 5..... 16.1 2.1 -5 5 18.0 6.3 17.1 5.7 16.9 4.3 Puerto Rico . ..3:5 . 2.8 7.6 Romania 26.4 ..105:0 ....... ........ .. 265.2 21.2 ....2:5 124.0 .. 1.8.... 127.5 Russian Federation -. 29 5 . 90 . 75288 . . . 390.9 1999 World Development Indicators 247 4.16 Money and Claims on Claims on GDP Consumer Food quasi money private sector governments Implicit price price and other deflator Index Index public entities annual % growth annual growth annual growth average annual average annual average annual of M2 asa%of M2 as %of M2 % growth % growth % growth 1990 1997 1990 1997 1990 1997 1980-90 1990-97 ±980-90 ±.990-97 1980-90 1990-97 Rwanda 5.:6 29.1~ -0.0 23..8 26.8 14.6 4.0 19.3 3.9 ..... 22.6 Saudi Arabia 4.6 5.2 -4~5 .......3.9 4:2 ....... 16..... -4.9 1.8 -0.8 1.7 -0:4 1.6 Senegal -4.8 3.5 -8.4 3.0 -5.3 -1.6 6.5 6.5 6.2 7.2 5.3 8.3 Sierra Leone 74.0 47.1 4~9...... 6.-2.... 228:7...... 50.4..... 64:0 34.5 72.4 33.7 Singapore 20.0 10.3 13~7 .....144 -4.9 07 2:2 2.9 1.6 2.3 0:9 .......21 Slovak Republic 87 .... 25...5. -20.1 18 16 .. - 6 I59 Slovenia 123.0 23.3 96.1 9.4 -10.4 2'5 . 323 . .. 252.3 41.7 South Africa 11.4 17.8 13 7 ......17.5 1.8 29 14.9 10.1 14.8 10.0 15 1 12..... 1 Spain 13.6 1.5 84 13.0 70 ~~~~ ~~~~ ~~~~ ~~~ ~~~~ ~~~~-1.7 9.3 4.6 9.0 4.6 93 5 Sri Lanka 21.1 13.8 162 2... .. 8.3 6.8 -1. 5.... 11:0 9.8 10.9 10.8 10.9 .... 11.2 Sudan 48:8 37.7 12.6 ....._6..5..... 29.4 9.3 41:6 81.6 37.6 100.4 Sweden 7:4 2.6 7.0 2.9 82 -. Switzerland 0.8 6.6 117 ......3.-5 ......10 0.0 3.4 1.9 2.9 2.3 3:1 .......0.5 Syrian Arab Republic 26 1 ~~~~~~-48.3 ......3~4 .......20.0.. . 11:4 -6.4 15.3 8.9 23.2 10.3 24:5 ..... 8.5 Sariktan Aa Re u lc2 1..... . .... ...394.3... Tanzania 41.9 12.9 22.6 6.1 80.6 -7.0 25.2 31.0 25.7 30.2 25.3 Thailand 26.7 16.5 300O..... 24.9 -4.0 2.1 3.9 4.8 3.5 4.9 2.7 6.3 Togo.9.5 5.1 1 .8 .......7.5 ......6:9 -......0.6 4.8 8.9 2.5 10.9 Trinidad and Tobago 6:2 ......11:3..... 27 ......13.-2 .....-1:9 .......96 2.4 7.2 10.7 6.5 14.6 14:0 Tunisia 7.6 16.5 5.9 13.1 1.8 3.4 7.4 5.0 7.4 5.1 8.3 5:0 Turkey 53.2 97 5 42.9 74:.1.... 10:0....... 9.1 45.2 79.3 44.9 81.4 82 8 Turkmenistan .. 449 1 . 665 . -520.5 1,074.2 . Uganda 60.2 20~8 1.4 19 4 113.8 17.5 102.5 14.9 13:4 Ukraine . 33.9 12.4 29.4 .. 591.0 United Arab Emirates .... ......-8.2 9.0 1.3 12.7 .-4.8 ~ 1.2 0.4 .............I,........:........ ... United Kingdom 5:7. ........5.7.3.2 5.8 3.0 4.6 2!4 United States 4.9 6.6 11 ...... 9.3 ......0:6 .......21 4.2 2.1 4.2 2.9 3.8 4.0 Uruguay . 118.5 28,4 56.2 23.4 25.8 -0.5 61.3 45.4 61.1 48.3 62.0 43.9 Uzbekistan . .442.5 Venezuela 71.2 58.5 17.0 49.9 42.7 -14.2 19.3 50.0 20.9 53.5 29.7 50.1 Vietnam . 24.3 . 12.8 7.3 210.8 19.7 West Bank and Gaza . . . . Yemen, Rep. 11.3 11.2 1.4 .......4.5 10.2 -2.9 26.7 .. Yugoslavia, FR (Serb./Mont.) Zambia 523.3..... 23~8..... 21 2 .......5.0 175.3 -5.0 42.2 72.4 72.5 80.8 42.8 98.4 Zimbabwe ............ 15 ..... 4:1.2 ... ..13.5_ 41.0 5.0 ......54.8 11.6 22.4 13.8 25.6 14.6 31..4 a. Data prior to 1992 include Enitrea. b. Data prior to 1990 refer to the Federal Republic of Germany before unification. 248 1999 World Development Indicators 4.16 Money and the financial accounts that record the sup- The quality of commercial bank reporting also may * Money and quasi money comprise the sum of cur- ply of money lie at the heart of a country's financial be adversely affected by delays in reports from bank rency outside banks, demand deposits other than system. There are several commonly used definitions branches, especially in countries where branch those of the central government, and the time, sav- of the money supply. The narrowest, Ml, encom- accounts are not computerized. Thus the data in the ings, and foreign currency deposits of resident sec- passes currency held by the public and demand balance sheets of commercial banks may be based tors otherthan the central government. This definition deposits with banks. M2 includes Ml plus time and on preliminary estimates subject to constant revision. of the money supply is frequently called M2; it corre- savings deposits with banks that require a notice for This problem is likely to be even more serious for non- sponds to lines 34 and 35 in the International withdrawal. M3includesM2aswellasvariousmoney bank financial intermediaries. Monetary Fund's (IMF) Intemational Financial market instruments, such as certificates of deposit Controlling inflation is one of the primary goals of Statistics (IFS). The change in money supply is mea- issued by banks, bank deposits denominated in for- monetary policy. Inflation is measured by the rate of sured as the difference in end-of-year totals relative eign currency, and deposits with financial institutions change in a price index. Which index is used depends to the level of M2 in the preceding year. * Claims on other than banks. However defined, money is a lia- on which set of prices in the economy is being exam- private sector (IFS line 32d) include gross credit from bility of the banking system, distinguished from other ined. The GDP deflator, the most general measure of the financial system to individuals, enterprises, non- bank liabilities by the special role it plays as a the overall price level, takes into account changes in financial public entities not included under net domes- medium of exchange, a unit of account, and a store government costs, inventory appreciation, and invest- tic credit, and financial institutions not included of value. ment expenditures. The GDP deflator reflects changes elsewhere. * Claims on govemments and other pub- The banking system's assets include its net for- in prices for all the final demand categories, such as lic entities (IFS line 32an + 32b + 32bx + 32c) usu- eign assets and net domestic credit. Net domestic government consumption, capital formation, and ally comprise direct credit for specific purposes such credit includes credit to the private sector and general international trade, as well as the main component, as financing of the government budget deficit, loans government, and credit extended to the nonfinancial private final consumption. It is usually derived implic- to state enterprises, advances against future credit public sector in the form of investments in short- and itly as the ratio of current to constant price GDP. It may authorizations, and purchases of treasury bills and long-term government securities and loans to state also be calculated explicitly as a Laspeyres price index bonds. Public sector deposits with the banking sys- enterprises; liabilities to the public and private sector in which the weights are base period quantities of out- tem also include sinking funds for the service of debt in the form of deposits with the banking system are put. and temporary deposits of government revenues. netted out. It also includes credit to banking and non- Consumer price indexes are constructed explicitly, * GDP implicit deflator measures the average bank financial institutions. based on surveys of the cost of a defined basket of annual rate of price change in the economy as a whole Domestic credit is the main vehicle through which consumer goods and services. Indexes of consumer for the periods shown. The least-squares method is changes in the money supply are regulated, with cen- prices should be interpreted with caution. The defini- used to calculate the growth rate of the GDP deflator. tral bank lending to the government often playing the tion of a household and the geographic (urban or rural) * Consumer price Index reflects changes in the cost most important role. The central bank can regulate and income group coverage of consumer price sur- to the average consumer of acquiring a fixed basket lending to the private sector in several ways-for veyscanvarywidelyacrosscountries. Inaddition,the of goods and services. The Laspeyres formula is gen- example, by adjusting the cost of the refinancing facil- basket of goods chosen varies by country. And the erally used. * Food price Index is a subindex of the ities it provides to banks, by changing market interest weights are derived from household expenditure sur- consumer price index. rates through open market operations, or by control- veys, which for budgetary reasons tend to be con- ling the availability of credit through changes in the ducted infrequently in developing countries, leading to Data sources reserve requirements imposed on banks and ceilings poor comparability over time. Although a useful indi- on the credit provided by banks to the private sector. cator for measuring consumer price inflation within a The IMF collects data on the Monetary accounts are derived from the balance country, the consumer price index is of less value in financial systems of its mem- sheets of financial institutions-the central bank, making comparisons across countries. Consumer . bercountries.Thedata in the commercial banks, and nonbank financial intermedi- price indexes should be distinguished from retail price table are published in the aries. Although these balance sheets are usually reli- indexes, which are used in a few countries. Retail monthly International Finan- able, they are subject to errors of classification and price indexes are based on prices at retail outlets rl cial Statistics and the annual valuation and differences in accounting practices. For weighted by sales turnover, so the weights may differ Intemational Financial Sta- example, whether interest income is recorded on an by country and over time. Like consumer price tistics Yearbook. The World accrual or a cash basis can make a substantial dif- indexes, the food price index too should be inter- Bank receives data from the IMF in electronic files that ference, as can the treatment of nonperforming preted with caution. may contain more recent revisions than the published assets. Valuation errors typically arise with respect to sources. GDP data are from the World Bank's national foreign exchange transactions, particularly in coun- accounts files. Food price index data are from the tries with flexible exchange rates or in those that have United Nations' Statistical Yearbook and Monthly undergone a currency devaluation during the report- Bulletin of Statistics. The discussion of monetary indi- ing period. The valuation of financial derivatives can cators draws from an IMF publication by Marcello also be difficult. Caiola, A Manual for Country Economists (1995). t999 World Development Indicators 249 4.17 Balance of payments current account Goods and services Net income Net Current GFOSS current account International transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $ millions $ millions 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 Albania 378 222 371 809 4 50 6 265 16 -272 . 309 Algeria 14,128 14,890 12,311 10,280 -1,869 -2.210 301 249249 3,773 8,047 Angola 5,286 4,681 .. -1,395 3,84.1 3,266 : Argentina 9,897 28,494 13,182 34,758 -1,512 -4,205 23 350 -4,774 -10,119 6,719 22,320 Armenia . 330 952 102 . 27 . -303 . 2 Australia 25,755 83,705 27,071 81,877 -2,695 -14,321 -425 -16 -4,436 --12,508 1,690 16,845 Austria 26,650 85,323 29,921 87,821 -528 -368 -66 -1,698 -3,865 -4,564 5,280 19,736 Azerbaijan 1,154 1,900 . -84 - 80 . -666 - 466 Bangladesh 885 5,096 2,545 7,677 14 -9 802 1,770 -844 -902 300 1,581 Belarus . 8,306 9.103 . -79 78 . -798 . 394 Belgiuma 70,498 185,404 74,259 174,267 61 6,315 -1,231 -3,803 -4,931 13,650 7,823 16,190 Benin 226 530 421 696 8 -30 151 . -36 . 8 253 Bolivia 1,030 1,362 833 2,049 -263 -266 60 248 -6 -705 106 1,087 Bosnia and Herzegovina Botswana 645 2,381 818 -1,770 -33 -91 5 5 89 -151 609 344 5,740 Brazil 21,869 60,256 27,826 79,817 -7,018 -16,091 144 1,812 -12,831 -33,840 5,769 50,826 Bulgaria 9,302 6,251 7,994 5,685 -412 -359 58 237 954 444 . 2,249 Burkina Faso 210 332 577 723 -3 - 2 -49 . 6 4 Burundi 9613 -12 . 60. 4 95 113 Cambodia 896 1,252 . -43 . 188 . -210 . 299 Cameroon 172 243 1829 2,041 -. 628. -609. 102 87. . -564 -120 189 1 Canada 74,977 247,438 70,259 236,225 -10,764 -20,913.-42 439 -6,088 -9,261 3,093 17,823 Central African Republic 201 213 327 237 3 -16 81 -43 55 179 Chad 71 271 79 587 - -5 24 . 12. 5 136 Chile 5,968 20,608 7,052 22,218 -1,000 -2,975 113 528 -1,971 -4,057 3,123 17,306 Chinat 23,637 207,251 18,900 166,754 451 -15,923 486 5,144 5,674 29,718 2,545 142,762 Hong Kong, China 25,585 228,877 27,017 231,485 .. . -1,432 -2,608 92,804 Colombia 5,328 15,861 5,454 18,784 -245 -3,371 165 612 -206 -5,682 4,831 9,507 Congo, Demn. Rep. 2371 1,445 2,353 1,385 -293 -752 . 2483 Congo Rep. 1,021 1800 1,025 1,368 -162 -664 -1 -20 -167 -252 86 60 Costa Rica 1,195 4,478 1,661 4,667 -212 -202 15 136 -661-54 14 C6e dIlvoire 3,577 4,927 4,145 3,694 -5 -849 -706 -350 -1,826 35 20 618 Croatia 8,198 11,402 -83 852852 -2,434 2,686 Cuba . . - Czech Republic 29,869 . 32,713 -791 365 -3,271 9,734 Denmark 21,989 63,680 21,727 57,971 -1,977 -3,635 -161 -1,190 -1,875 883 3,387 19,124 Dominican Republic 1.271 6,420 1,919 7,124 -277 -538 205 1,080 -720 -110 202 391 Ecuador 2,887 6,000 2,946 5,787 -613 -1,347 30 391 -642 -74 1,013 2,093 Egypt, ArabRep 6,246 1,171 9,157 18,296 -318 884 2,791 4,146 -438 2,905 1,046 18,665 El Salvador 1,214 2,706 1,170 3,885 -62 -87 52 1,363 34 96 78 1,308 Eritrea 201 . 583 - .. 364 . -21 Estni 3,609...4,142..-146. ......117............. -562..............758....... Ethiopiab 569 1,017 782 1,683 7 -43 80 259 -126 -450 80 501 Finland 16,802 48,228 17,307 37,976 -783 -2,906 -114 -852 -143 6,494 1,870.8,417 France 153,197 365,342 155,915 319,781 2,6890 2.693 -4,170 -8,780 -4,208 39,474 27,340 30,927 Gabon 2,409 3,295 1,475 2.165 -426 -674 -124 -198 384 100 108 283 Gambia, The 66 229 179 282 -2 -8 28 37 -87 -24 6 96 Germany 224,224 590,985 225.599 558,836 914 -2,436 -12,858 -32,487 -13,319 -2,774 48,592 77,587 Ghana 1,210 1,657 1,178 2,641 -83 -130 81 482 30 -324 180 829 Greece 8,122 14,863 11.145 25.601 -273 -1,632 1,087 7,510 -2,209 -4,860 1,346 12,595 Guatemala 1,731 3,187 1,960 4,193 -44 -224 110 607 -13 -2 45 1,1 Guinea 741 . 3 -114 . 116 -91 2 Haiti 306 218 481 809 -14 -12 89 463 -101 -138 16 77 Honduras 942 2,191 1,128 2,511 -152 -212 22 260 -317 -272 150 580 -~Data for Taiwan, China 21,495 139,396 22,361 132.739 48 2,391 -95 -1,327 -913 7,721 2,205 83,502 250 1999 World Development Indicators 4.17 Goods and services Net income Net Current Gross current account international transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $ millions $ millions 1980 1997 1980 1.997 1980 1997 1.980 .1997 1980 1997 1980 1.997 Hungary 9,671 24,514 9,152 25,067 -1,113 -1,426 63 973 -531 -1,007 . 8,476 India 11,265 44,102 17.378 59,236 356 -2,507 2,860 11,830 -2,897 -5,811 6,944 24,688 Indonesia 23,797 63,238 21,540 62,830 -3,073 -6,332 250 1,034 -566 -4,890 539 16,587 Irsn, Islamic Rep. 13,069 18,978 16,111 17,446 606 -379 -2 463 -2,438 5,232 10,223 Ireland 9,610 61,447 12.044 51,711 -902 -9,708 1,204 1,956 -2,132 1,984 2,860 6,526 Israel 8,668 30,320 11,511 38,810 -757 -2,791 2,729 6,266 -871 -5,014 3,351 20,332 Italy 97,298 310,550 110,26 261,885 1,278 -11,202 1,101 -4,040 -10,587 33,425 23,126 55,739 Jamaica 1,363 3,177 1,408 3,984 -212 -193 121 624 -136 -376 105 682 Japan 146,980 478,542 156,970 431,094.70 5,3 153 884-0750 94,354 24.636 219,648 . . ..... ........... ......770...55,739. ' -1 53 -8,834.... .. .............. Jordan 1,181 3,893 2,417 5,652 36 -282 1,481 1,834 281 -206 1,143 2,126 Kazakhstan .. 7,611 . 8,279 . -315 . 75 . -909 . 1,697 Kenya 2,007 2,994 2,846 3,771 -194 -232 157 632 -86 -377 492 580 Korea, Dem. Rep.. . . . . . . . Korea, Rep. 19,815 164,920 25,152 171,300 -512 -2,455 536 667 -5,312 -8,167 2,925 20,368 Kuwait 21,857 16,044 9,823 12,998 4,847 6,277 -1,580 -1,507 15,302 7,816 3,929 3,452 Kyrgyz Republic .. 676 . 1 . -5 . 68 . 19 7 Lao PDR.. 47 . 71 .. -9 .. 91 . -225 . 143 Latvia .. 2,871 . 3,348 . 55 . 77 . -345 . 704 Lebanon . 1,557 8. 053 380 . 2,635 -3,481 1,588 5,976 Lesotho 90 309 475 1,214 266 321 175 56 50 572 Libya 22,084 . .12,671 . -65 .. -1,134 . 8.214 . 13,091 Lithuania . 5,224 . 6,237 . -198 . 230 -981 . 1,010 Macedonia, FYR . 1,330 . 1,862 ....... -34.... .. .290 -275 .. .............. 257. Madagascar 516 772 1,075 1,063 -44 -103 47 210 -556 -153 9 282 Malawi........3'13 .615 487 1,153 -149 -88 63 ..:-260 . 68 162 Malaysia 14,097 92,897 13,525 91,522 -836 -5,074 -2 -1,094 -266 -4,792 4,387 20,788 Mali 263 644 520 897 -17 -51 150 126 -124 -178 15 415 Mauritania 253 463 449 387 -27 -52 90 76 -133 22 140 201 Mauritius 574 2,543 690 2,766 -23 -18 22 126 -117 -115 91 703 Mexico 22,622 121,829 27,601 122,424 -6,277 -12,106 834 5,247 -10,422 -7,451 2,960 28,797 Moldova . . 1,023 . . 1,430 . 34 77 . . -296 . 366 Morocco 3,233 9,510 5,207 10,627 -562 -1,175 1,130 2,205 -1,407 -87 399 3,993 Mozamnbique 399 499 844 1,007 22 -0 56 283 -367 -359 . 517 Myanmar 539 1,439 806 2,415 -48 -64 7 430 -307 -610 261 250 Namibia .. 1,726 . 1,908 . 54 .. 32. 193 . 251 Nepal 224 1,295 365 1,855 13 5 36 95 -93 -460 183 626 Netherlands 90,380 213,503 91,622 191,173 1.535 4,817 -1,148 -5,905 -855 21,242 11,645 24,865 New Zealand 6,403 17,835 6,934 18,434 -538 -5,322 96 355 -973 -5,566 352 4,451 Nicaragua 495 863 907 1,609 -14 -222 124 367 -411 -601 65 378 Niger 617 301 956 442 -33 -25 97 31 -276 -152 126 53 Nigeria 27.071 15,994 20,013 14,213 -1,304 -3,145 -576 1,916 5,178 552 10,235 4,075 Norway 27,264 63,213 23,749 52,286 -1,922 -1,391 -515 -1,424 1,079 8,112 6,048 23,400 Oman 3,757 7,649 2,298 ,1 -257 -460 -260 -1,430 942 -5 581 1,549 Pakistan 2,958 9,956 5,709 14,677 -281 -2,167 2,163 3,213 -868 -3,675 496 1,195 Panama 3,422 8,276 3,394 8,581 -397 -201 40 162 -329 -343 117 1,148 Papua New Guinea 1,029 2,462 1,322 2,656 -179 -335 184 72 -289 313 423 363 Paraguay .701 4,4 1,314 4,960 -4 87 0 47 -618 -483 762 695 Peru 4,631 8,356 3,970 10,842 -909 -1,602 147 681 -101 -3,407 1,980 10,982 Philippines 7,235 40,365 9,166 50,477 -420 4,729 447 1,080 -1,904 -4,303 2.846 .7,266 Poland 16,061 39,717 17,842 46,367 -2.357 -1,129 721 2,035 -3,417 -5,744 128 20,407 Portugal 6,674 32,339 10,136 40,684 -608 -245 3,006 6,713 -1,064 -1,877 795 15,660 Puerto R ico. .. . . . . ... . .. .. . ... .. ... .. . .. ..... .... .. .. . . .. .... Romania 12.087 9,853 13,730 12,448 -777 -322 0 579 -2,420 -2,338 323 3,803 Russian Federation . 102,196 90,065 . -9,200 . -362 .. 2,569 13,018 1999 World Development Indicators 251 4.17 - Goods and services Net income Net Current Gross current account international transfers balance reserves Exports Imports $ millions $ millions $ millions $ millions $ millions $ millions .1980 1997 1980 1997 1980 1997 1980 1997 1950 1997 1980 1997 Rwanda .............. 165 152 319 ...... 488 2 -16. ... 104...... 260. -48 .... . -93 187..... 153 Saudi Arabia 106,765 64,168 55,793 51,632 526 3,156 -9,995 -15,439 41,503 254 23,437 7,353 Senegal 807 1,475 1,215 1,716 -98 -136 120 382 -386 -58 8 386 Sierra Leone 275 92 471 146 -22 -3 53 26 -165 -127 31 38 Singapore 24,285 156,252 25,312 144,168 -429 3,906 -106 -1,187 -1,563 14,803 6,567 71,289 Slovak Republc 10,959 12,367 -12417 -1,5 ,3 Slovenia 1,449 . .0631 131 88 37 3,315 South Africa 28,627 35,440 22,073 34,625 -3,285 -2,602 239 -143 3,508 -1,931 726 4,799 Spain 32,140 148357 38,004 142,478 -1,362 -6,36 1,646 3,003 -5,580 2,486 11,863 68,398 Sri Lanka 1,293 5,514 2,197 6,569 -26 -165 274 832 -655 -388 246 2,024 Sudan 779 613 1,698 1,614 -143 -1,099 341 107 -721 -1,993 49 82 Sweden 38,151 100,989 39,878 84,779 -1,380 -6,174 -1,224 -2,736 -4,331 7,301 3,418 10,824 Switzerland 48,595 121,737 51,843 109,063 4,186 11,597 -1,140 -3,801 -201 20,470 15,656 39,028 Syrian Arab Republic 2,477 5,661 4,531 5,092 785 -504 1,520 499 251 564 337 Tajikistari .. ~~~~~~804 801 -7 20 -84 Tanzania .............. 748 1,200 1,384 1,962 -14 -124 129 341 -5... 721 -544 20 622 Thailand 7,939 72,415 9,996 72,437 -229 -3,480 210 479 -2,076 -3,024 1,560 26,179 Togo 550 632 691 724 -40 -23 86 -95 78 119 Trinidad and Tobago 3,139 2,698 2,434 2,857 -306.-339 -42 4 357 294 2,781 . 706 Tunisia 3,262 8,081 3,766.8,644 ?~ -863 410 785 -5 60 590 1,978 Turkey 3,621 52,004 8,082 56,536 -1,118 -3,013 2,171 4,866 -3,408 -2,679 1,077 18,658 Turkmenistan 759 1,004 0 43 Uganda 329 825 441 1,651 -7 -17 -2 322 -121 -521 3 633 Ukraine . 20,355 . 21,891 -644 845 -1,335 2,341 United Arab Emirates 2,015 8,372 United Kingdom 146,072 366,505 134,200 371,843 -418 19,742 -4,592 -6,486 6,862.7,918 20,651 3231 United States 271,800 937,434 290,730 1,043,477 29,580 -9,487 -8,500 -39,849 2,150 -155,375 15,596 58,907 Uruguay 1,526 4,256 2,144 4,450 -100 -208 9 81 -709 -321 384 1,556 Uzbekistan 3,980 4,417 -175 - 29 -583 Venezuela 19,968 25,120 15,130 18,282 329 -2,031 439 -123 4,728 4,684 6,604 14,378 Vietnam . 11,485 13,465 -602 713 -775 -1,870 . 1,986 Yemen, Rep. 2,522 . 3,005 -636 1,254 - 135107 Yugoslavia, F S M n) ..... .. Zambia 1,609 1,276 1,765 1,474 -205 -204 -155 -516 7 8 239 Zimbabwe 1,610 3,045 1,730.3,873 -61 -409 31 - ..... 149 .....214 .......160 .o ncm 7,1 140,087 9400 180.867 Middle Income 556,445 1,382,960 57,010 1,393,256 Lower middle income 206,993 714,567 218,759 699,107 Upper middle income 324,634 668,626 264,896 694,387 Low & middle Income 626,401 1,523,024 580,318 1,574,057 East Asia & Pacific 76,800 496,821 82,860 469,845 Europe & Central Asia .. 346,813 .. 370,167 Latin America & Canb. 114,161 335,490 129,051 375,905 Middle East & N. Africa 181,639 175,078 132,682 161,072 South Asia 17.314 66,540 28,820 90,646 Sub-Saharan Afilca 89,572 101,298 83,586 106,608 High Incomfe 1,698,786 5,346,334 1,756,498 5,186,801 Europe EMU . 727,473 2,041,478 765,073 1,866,612 a. Includes Luxembourg. b. Data prior to 1992 include Eritrea. c. Data prior to 1990 refer to the Federal Republic of Germany before unification. 252 1999 World Development Indicators 4.17 The balance of payments records transactions mentation). Values are in U.S. dollars converted at * Exports and imports of goods and services com- between countries. Balance of payments accounts market exchange rates. prise all transactions between residents of a country are divided into two groups: the current account refers Data in this table come from the IMF's Balance of and the rest of the world involving a change in owner- to goods and services, income, and current transfers; Payments and International Financial Statistics data- ship of general merchandise, goods sent for pro- the capital and financial account refers to capital bases, supplemented with estimates by World Bank cessing and repairs, nonmonetary gold, and services. transfers, the acquisition or disposal of nonproduced, staff for countries whose national accounts are * Net Income refers to employee compensation paid nonfinancial assets, and financial assets and liabili- recorded in fiscal years (see Primary data documen- to nonresident workers and investment income ties. This table presents data from the current tation) and countries for which the IMF does not col- (receipts and payments on direct investment, portfo- account with the addition of gross international lect balance of payments statistics. In addition, World lio investment, and other investments, and receipts reserves from the capital and financial account. Bank staff make estimates of missing data for the on reserve assets). Income derived from the use of The balance of payments is a double-entry account- most recent year. intangible assets is recorded under business ser- ing system that shows all flows of goods and services vices. * Net current transfers are recorded in the bal- into and out of a country; all transfers that are the coun- ance of payments whenever an economy provides or terpart of real resources or financial claims provided to Current account deficits as a corollary receives goods, services, income, or financial items or by the rest of the world without a quid pro quo, such to growth without a quid pro quo. All transfers not considered to as donations and grants; and all changes in residents' i be capital are current. * Current account balance is claims on, and liabilities to, nonresidents that arise from Republc of Korea the sum of net exports of goods and services, economic transactions. All transactions are recorded income, and current transfers. * Gross International twice-once as a credit and once as a debit. In princi- K' - u ,. reserves comprise holdings of monetary gold, special pie the net balance should be zero, but in practice the i - i_. i drawing rights, reserves of IMF members held by the accounts often do not balance. In these cases a bal- . IMF, and holdings of foreign exchange under the con- ancing item, net errors and omissions, is included in the -: trol of monetary authorities. The gold component of capital and financial account. ,..; 1 9J . :' - these reserves is valued at year-end (31 December) Discrepancies may arise in the balance of pay- London prices ($589.50 an ounce in 1980 and ments because there is no single source for bal- Indonesia $290.20 an ounce in 1997). ance of payments data and therefore no way to ! .1 ensure that the data are fully consistent. Sources -- Data sources include customs data, monetary accounts of the | banking system, external debt records, information about the provided by enterprises, surveys to estimate ser- Modesignandcompilation of the vice transactions, and foreign exchange records. 1'" 1991 I;:U ii' 1 ;:f : - --- balance of payments can be Differences in collection methods-such as in tim- found in the IMF's Balance of Thailand uv'ufudith MaBlnef ing, definitions of residence and ownership, and the , - - - Payments Manual, fifth edi- exchange rate used to value transactions-con- . ' tion (1993), Balance of tribute to net errors and omissions. In addition, - ' i ,. Payments Textbook (1996a), smuggling and other illegal or quasi-legal transac- - and Balance of Payments tions may be unrecorded or misrecorded. - . Compilation Guide (1995). The balance of payments The concepts and definitions underlying the data - ;data are'publishedintheIMF'sBalance ofPayments here are based on the fifth edition of the International Statistics Yearbook and International Financial Sta- Monetary Fund's (IMF) Balance of Payments Manual MalaysIa tistics. The World Bank exchanges data with the IMF (1993). The fifth edition redefined as capital transfers _ i - through electronic files that in most cases are more some transactions previously included in the current * timely and cover a longer period than the published account, such as debt forgiveness, migrants' capital -- sources. The International Financial Statistics and transfers, and foreign aid to acquire capital goods. - Balance of Payments databases are also available on Thus the current account balance now reflects more CD-ROM. accurately net current transfer receipts in addition to .. " 9 1'' I-i: 19. . i4. transactions in goods, services (previously nonfactor r . IF E ,iv..-- ..1 i services), and income (previously factor income). Many countries maintain their data collection systems Despile their reputation as formidable exporters. according to the fourth edition. Where necessary, the the East Aslan tigers ian large current accobnt delicits during their decade of growth. IMF converts data reported in such systems to con- form with the fifth edition (see Primary data docu- 1999 World Development Indicators 253 4.18 External debt Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions .1980 .1997 1980 1997 1980 1997 1980 1997 1.980 1997 1980 1997 Albania 706 60360140 55 Algeria 19,365 30,921 17,040 28,741 17,040 28,741 253 1,795 0 0 0 2,018 Angola . 10,160 8,885 I.. 8,885 0 136 :0 0 Argentina 27,157 123,221 16,774 99,365 10,181 73,955 404 5,494 6,593 25,411 0 5,868 Armenia ......... .... _666 . ... 512 ...... . 512 250 0l.. .....: ..... 132 Australia Auatria Azerbaijan 504 233 233 ............ 116 0267 Bangladesh 4,230 15,125 3,594 14,578 3,594 14,578 981 5,739 0 0 424 372 Belarus 1,162 67465 124 2325 Belgium . Benin 424 1,624 334 1,393 334 1,393 52 510 00 16 95 Bolivia 2,702 5,247 2,273 4,570 2,181 4,144 239 967 92 426 126 248 Bosnia and Herzegovina ..: :........ Botswana 147 562 143 522 143 522 6 6 58 0 0 0 0 Brazil 71,520 193,663 57.981 157,553 41,375 86,745 2,035 5,743 16,605 70,808 031 Bulgaria 9,858 8,144 7,721591.444 Burkina Faso 330 1,297 281 1,139 281 1,139 77 636 0 0 15 92 Burundi 166 1,066 118 -1,022 118 1,022 37 566 0 0 36 28 Cambodia 2,129 . 2,031 2,031 0 132 0 065 Cameroon 2,588 9,293 2,251 7,886 2073 7,688 298 1,020 178 198 59 93 Canada . Central African Republic 195 885 147 804 147 804 29 403 00 24 19 Chad 284 1,026 259 939 259 939 36 457 0 0 14 61 Chile 12,081 31,440 9,399 21,519 4,705 4,364 184 989 4,693 17,155 123 0 China 4,504 146,697 4,504 115,233 4,504 112,821 0 16,069 0 2,412 0 0 .ong Kong, China . Colombia 6,940 31,777 4,604 26,018 4,088 15,273 1,012 1,732 515 10,746 0 0 Congo, Darn. Rep. 4,770 12,330 . . . . 867 ,01 8,617 246 1,307 00 373 407 Congo, Rep. 1,526 5,071 1,257 4,284 1,257 4,284 61 238 0 0 22 34 Costa Rica 2,744 3,548 2,112 3,012 1700 2,840 183 193 412 172 57 0 C6te dIlvoire 7,462 15,609 6,339 12,498.4327 10,47.314 2,144 2,012 2,071 65 450 Croatia . 6,842 . 6,041 . . 4,217 . 276 1,824 233 Cuba ::7. ..... . ...0 0 Czech Republic ..21,456 1,389 . 12,275382 . 2,113-0 Denmark .. ...... Dominican Republic 2,002 4,239 1,473 3,460 1,220 3,460 83 225 254 0 49 29 Ecuador 5,997 14,918 4,422 12,716 3,300 12,376 146 875 1,122 340 0 133 Egypt, Arab Rep. 19,131 29,849 14,693 26,858 14,428 26,804 728 2.075 265 54 411 0 El Salvador 911 3,282 659 2,471 499 2,427 114 294 161 45 32 0 Eritrea 76 76 76 29 ..0 ..0 Estonia . 658 . 297 . 214 72 83 54 Ethiopia' 824 10,078 688 9,426 688 9,426 304 1,532 0 0 79 87 Finland... France . Gabon 1,514 4,284 1,272 3,671 1,272 3,671 19 77 0 0 15 131 Gambia, The 137 430 97 407 97 407 16 166 0 0 16 10 Georgia 1,446 . 1,6.. 1,168 2120... ...........255 G erm any ............................ Ghana 1.398 5,982 1,162 4,958 1,152 4,691 213 2,647 10 267 105 347 Greece... Guatemala 1,180 4,086 845 2,936 563 2,834 144 188 282 103 0 0 Guinea 1,134 3,520 1,019 3,008 1,019 3,008 87 922 00 35 99 Guinea-Bissau 140 921 133 838 133 838 5 221 0 0 112 Haiti 302 1,057 ~~242 897 242 897 66 458 00 46 43 Honduraa 1,472 4,698 .1.168 4,170 976 3,910 .216 772 191 259 33 46 254 1999 World Development Indicators 4.18 Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debyt debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 1980 1997 Hungary9,764 24,373 .. 6,41.6 20,856 6,416 14,94~1 0 1,520 0 5,915 0 160 India 20,581 94,404 18,333 88,694 17,997 79,486 5,969 26,050 336 9,208 977 664 Indonesia 20,938 136,174 18,163 97,199 15,021 55,869 1,606 10,706 3,142 41,330 0 2,970 Iran, Islamic Rep. 4,500 11,816 4,500 8,462 4,500 8,256 622 421 0 207 0 0 Iraq ... . . .0 . Ireland . .. I s r a e l . ..... . . . .. . . . .. . . . .. . . . . . . . . ... . . .. . . . .. . . . .. . . . ... . . . .. ... .. . . . . .... . .. . .. .. . . .... . . . .. . . . . Jamaica 1,913 3,913 1,505 3,109 1,430 2,921 176 431 75 188 309 118 Japan . . 7 . ... ' :. . Jordan 1,971 8,234 1,486 7,059 1,486 7,020 102 814 0 39 0 427 Kazakhstan . . 4,278 . 3,418 2,822 . . 648 596 . 511 Kenya 3,383 6,486 2,489 5,433 2,052 5,108 528 2,245 437 325 254 250 Korea, Dem. Rep. Korea, Rep. 29,480 143,373 18,236 78,517 15,933 40,241 1,836 4,580 2,303 38,276 683 11,064 Kuwait Kyrgyz Republic . 92873 730 251 0 . 6 Lao PDR 350 2,320 333 2,247 333 2,247 6 358 00 16 66 Latvia . 503 352 . 32 120 30 86 Lebanon 510 5.036 . 216.3,241 216 2,356 27 151 0 885 0 0 Lesotho 72 660 58 624 58 624 24 221 0 0 628 Libya Lithuania 1,540 . 1,107I....1.049 .. 114 58 ... 271 Macedonia, FYR :: 1,542 1,317 1,251 . 231 .. 67 . 88 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . .1 . . . . . I ... . .. . . . .. . . . . . . Madagascar 1,250 4,105 920 3,871 920 3,871 152 1,2-14 0 0 87 69 Malawi 830 2,206 634 2,073 634 2,073 156 1,409 0 0 80 106 Malaysia 6,611 47,228 5,256 32,289 4,008 16,807 54 758 1,248 15,482 00 Mali 727 2,945 664 2,687 664 2,687 121 939 0 0 39 176 Mauritani'a 840 2,453 713 2.037 713 2,037 38 378 0 0 62 113 Mauritius 467 2,472 318 1,976 294 1,187 55 126 24 789 102 0 Mexico57,365 149,690 41,202 112,095 33,902 84,775 2.063 11,356 7,300 27,320 0 9,088 Moldova .. 1,040 .. 785 .7 785 1700...0 .... ..233 Mongolia . 718 . 643 643 0 97 .0 048 Morocco 9,258 19,321 8,024 19,090 7,874 18,640 578 3.302 150 450 457 0 Mozambique .. 5,991 . 5,475 5,430 0 1,160 . 45 0 189 Myanmar 1,500 5,074 1,390 4,640 1,390 4,640 146 724 0 0 106 0 Npal205 2,398 156 2,340 156 2,340 76 1.047 0 0 42 30 Netherlands New Zealand Nicaragua 2,190 5,677 1,668 4,819 1,668 4.819 135 392 0 0 49 27 Niger 863 1.579. 687 1,427 383 1,331 66 625 305 96 16 61 Nigeria 8,921 28,455 5,368 22,926 4,271 22,631 554 2,783 1,097 295 0 0 Norway . Oman 599 3,602 436 2,570 436 2,567 14 13 03 0 0 Pakistan 9,931 29,664 8,520 25,902 8,502 23.565 1,150 6,572 18 2,338 674 1,281 Panama 2,975 6,338 2,271 5,418 2,271 5,074 133 209 0 344 23 142 Papua New Guinea . 719 2,272 624 2,071 486 1,311 110 371 139 760 31 48 Paraguay 954 2,052 780 1,537 630 1,488 124 176 151 49 0 0 Peru 9,386 30,496 6,828 22,653 6,218 20,177 359 1,920 610 2,476 474 1,011 Philippines 17,417 45,433 8,817 32.784 6,363 25,950 960 4,374 2,454 6,834 1,044 855 Poland . 39,889 . . 36,077 33,666 0 2,078 . 2.412 0 0 Portug.l Puerto Rico Romania 9,762 10,442 7,131 8,852 713~1.8422. 06.1,305 0 730 328 641 Russian Federation. 125,645 ..... .. 106,275 ...... 104,370.. 0 ...5,053 - 1,905 . 13,231 1999 World Development Indicators 255 4.18 Total Long-term Public and publicly Private Use of IMF external debt guaranteed debt nonguaranteed credit debt debt IBRD loans and Total IDA credits $ millions $ millions $ millions $ millions $ millions $ millions 1.980 .1997 1L980 1997 1980 1.997 1.980 1997 1980 1L997 1.980 .1997 Saudi Arabia Senegal .1,473 3,671 1,114 3,165 11.105 3,110 156 1,201 9 5 140 292 Sierra Leone 469 1,149 357 893 357 893 43 272 0 0 59 167 Singapore Slovenia 1400 South Africa 25,222 13,879 11,246 0 02,633 0 415 Spain Sri Lanka 1,841 7,638 1,230 6,725 1,227 6,641 129 1,544 3 84 391 433 Sudan 5,177 16,326 4,147 9,494 3,822 8,998 236 1,204 325 496 431 797 Sweden Switzerland Syrian Arab Republic 3,552 20,865 2,921 16,254 2,921 16,254 257 119 0 0 0 0 Tajikistan 901 797 . 6 50128 30 Tanzania 5,322 7,177 3,381 6,094 3,297 6,054 440 2,340 84 41 171 246 Thailand 8,297 93,416 5,646 56,151 3,943 22,009 703 1,826 1,702 34,142 348 2,429 Trinidad and Tobago 829 2,161 712 1,583 712 1,528 57 76 0 54 04 Tunisia 3,527 11,323 3,390 9,61 3210 9,427 337 1,480 180 183 0 173 Turke 19,131 91,205 15,575 67,977 15,040 47,633 1,347 3,705 535 20,344 1,054 594 United Arab Emirates... United Kingdom . .. United States :! Uruguay .1,660 6,652. 1,338. 4*751 1,127 4,528 72 393 211 223 0 0 Uzbekistan . - 2,760 . 2,119 . . 2,028 155 91 223 Venezuela 29,344 35,541 1795 *29,529 10,614 26,680 133 1,213 3,181 2,849 0 1,618 West Bank and Gaza . Yemen, Rep. 1,684 3,856 1,453 3,418 1,453 3,418 137 934 00 48 250 Yugoslavia, FR(eb.Mn. 18,486b 15,107 C 15,586b 10,9240 4,580 b 8,1650~ 1,359 b 1,1120 11,005 b 2,759 C 760 b 760- Zambia 3,261 6,758 2,227 5,246 2,141 5,233 348 1,555 87 13 447 1.138 Low Income 101,945 387,32 82629 334,546 77,541 318,237 13,845 80,781 5,088 16,309 5,310 11,097 Middle Income' 478,106 1,785,903 350,763 1,369,738 287,584 1,061,583 18,362 98,148 63,180 308,155 6,254 48,637 Lo'wer middle income . .. Upper middle income . . . . Lw& middle Income' 580,050 2,17329 43.9 . .7428 6,15139,820.32,20 178,929 68,268 324,46 1153 974 ......... .................... 3.. 2..................39 1,704,284............. .... . .... 365,125...... ..1.37....I......... 6........ ........... East Asia & Pacific 64,600 511,17 48438 371,212 39,687 270,171 4,077 36,102 8,751 101,042 1,551 6,933 Europe &Central Asia . 75,503.390,579 56,283 309,821 44,743 268,905 3,513 20,669 11,540 40,916 2,143 21,195 Latin America & Carib. 257,264 703,669 187,255 558,918 144,797 399,951.813 34,427 4248 1897 113 856 Middle East & N. Africa 83,805 193,11 61,745 149,320 61,150 145.210 3,053 11,103 595 4,110 916 2,868 South Asia 38,015 154,946 33,053 143,942 32,696 132,311 8,306 41,01-4 357 11,630 2,508 2,781 Sub-Saharan Africa 60,864 219,44 46619 171,070 42053 163,273 5125 35614 .,6 ,9 ,3 ,9 High Income 29,480 143,373 e 18,2360 78,5170 15,9330 40,241 0 1,8360 4,720 f 2,303 0 38,276 0 683 e 11,064f a. Data prior to 1992 include Eritrea. b. Data prior to 1993 refer to the former Socialist Federal Republic of Yugoslavia. c. Data for 1997 are estimates and reflect borrowing by tbe former Yugoslavia that are not yet allocated to the successor republics. d. Includes data for Gibraltar not included in other tables, a. Refers only to the Republic of Korea. f. Includes data for Slovenia. 286 1999 World Development Indicators 4.18 ... Data on the external debt of low- and middle-income i1 , i,--. . . * Total external debt is debt owed to nonresidents economies are gathered by the World Bank through its repayable in foreign currency, goods, or services. It is World Bank and IMF lending expands in Debtor Reporting System. World Bank staff calculate the regions most at risk of financial crisis the sum of public, publicly guaranteed, and private the indebtedness of developing countries using loan-by- nonguaranteed long-term debt, use of IMF credit, and loan reports submitted by these countres on long-term short-term debt. Short-term debt includes all debt hav- public and publicly guaranteed borrowing, along with IBRD loans ana IDA credits ing an original maturity of one year or less and interest information on short-term debt collected by the coun- in arrears on long-term debt. * Long-term debt is debt tries or from creditors through the reporting systems of i, that has an original or extended maturity of more than the Bank for International Settlements and the oneyear. It has three components: public, publicly guar- Organisation for Economic Co-operation and s anteed, and private nonguaranteed debt. * Public and Development (OECD). These data are supplemented by publicly guaranteed debt comprises long-term external information on loans and credits from major multilateral obligations of public debtors, including the national gov- banks and loan statements from official lending agen- I ernment and political subdivisions (or an agency of cies in major creditor countries and by estimates from , I I I I either) and autonomous public bodies, and external World Bank country economists and International - . obligations of private debtors that are guaranteed for Monetary Fund (IMF) desk officers. In addition, some . - repayment by a public entity. * IBRD loans and IDA countries provide data on private nonguaranteed debt. . - t .- .- : credits are extended by the World Bank Group. The In 1997, 25 countries reported their private nonguar- - . International Bank for Reconstruction and anteed debt to the World Bank; estimates were made Development (IBRD) lends at market rates. Credits for 45 additional countries known to have significant pri[ from the International Development Association (IDA) vate debt. For estimates of total financial flows to devel are at concessional rates. * Private nonguaranteed oping countries see table 6.8. debt comprises long-term external obligations of pri- The coverage, quality, and timeliness of debt data Use Of IMF cic-dii vate debtors that are not guaranteed for repayment by vary across countries. Coverage varies for both debt a public entity. * Use of IMF credit denotes repur- instruments and borrowers. With a widening spectrum , chase obligations to the IMF for all uses of IMF of debt instruments and investors and the expansion of resources (excluding those resulting from drawings on private nonguaranteed borrowing, comprehensive cov- the reserve tranche). These obligations, shown for the erage of long-term external debt becomes more com- end of the year specified, comprise purchases out- plex. Reporting countries differ in their capacity to standing under the credit tranches, including enlarged monitor debt, especially private nonguaranteed debt. . acoess resources, and all special facilities (the buffer Even data on public and publicly guaranteed debt are _ _ . I I stock, compensatory financing, extended fund, and oil affected by coverage and accuracy in reporting-again, . - _ _ facilities), trust fund loans, and operations under the because of monitoring capacity and, sometimes, will- - . - -' structural adjustment and enhanced structural adjust- ingness to provide information. A key part that is often - ment facilities. underreported is military debt. Vanations in reporting rescheduled debt also affect Data sources cross-country comparability. For example, rescheduling underthe auspices ofthe Paris Clubof official creditors * - - - -- The main sources of extemal may be subject to lags between the completion of the ~ ' | debt information are reports general rescheduling agreement and the completion of to the World Bank through its the specific, bilateral agreements that define the terms Debtor Reporting System from of the rescheduled debt. The World Bank estimates the 'Fmember countries that have effects of the general agreements and then revises the received IBRD loans or IDA data when countries report their bilateral agreements. credits. Additional information Other areas of inconsistency include country differ- has been drawn from the files ences in treatment of arrears and of nonresident of the World Bank and the IMF. Summary tables of the national deposits denominated in foreign currency. external debt of developing countries are published annually in the World Bank's Global Development Finance and Global Development Finance CD-ROM. 1999 World Development Indicators 257 Ou 4.19 External debt management incdebtedness Present value of Total debt service Public and Short-term classification' debt pubiclcy debt guaranteed debt service % of % of exports of exports of % of central % Of goocds end % Of goods and government % Of GNP services GNP services current revenue total debt 1997 1997 1997 1980 1997 ±980 1997 1980 1997 1980 1997 A lban a L..... .. .. .... . . ... . 22.. .. . 99. . . .. .. .. . ... 1. 6 . .. . .. .. . .. .. . . ... .. .1 . .. ... .. .. . . .. .. . . .. ... . 6. . 8.. . .. . Algeria M65 181 9.9 9.9 27.4 27.2 .. ........ 12:0 0.5 Angola S 200 165 . 19.2. 15.9. .. 12:6 Argntina S 38 352 5.5 6.3 37.3 587 6.1 34.2... 38.2 14..6 Armenia L 26 94 16. 5.8 .. .. 3.3 Australia Austria ... ... A zerbaijan L.. . .... .. I ... .. . .. .. 9. 32...... ... .. ..... .. .... . .. 1.. . 8.. .. .. .. .. .. . .. 6... . . 8... . ... .. .... .. .. . . .. ..... .. ... . ..0 8... ... .. Bangladesh M20 130 1.6 1 6 23.7 10.6 7.9 .. 5.0 ...... 1 2 Belarus L 5 13 0.7 1.8 . 19.8 Belgium Benin M46k' 160b .14 2.6 6.3 9.1 .17.3 .....8:4 Bolivia S 5lb 270b 6.1 35.0 32.5 23.5 11.2 8:2 Bosnia and Herzegovina Botswana L9....... .1........6 21 ...... 2:1. . .4.1 .2.7 7.1 Brazil S 23 277 6.5 4.7 63.3 57.4 15.3 .. 18.9 18.6 Bulgaria S 96 144 9.6. 14.4 . 22.6. 7.8 Burkina Faso M 29b 161b 1.3 2.2 579 11-8 8.4. 10:6 5.1 Burundi S ..... 58 ..546 ..... 0.9 3.1 . 29.0 4.8 15.8 72.2~ ..... 1..5 Cambodia M 53 175 . 0.3 . 1.1 . . . CamneroonS5 93 315 5 0 6.0 15.3...... 20.4. 17.0 . 10.8 14..1 Canada ... .. .. Central African Republic... S..........52 ..244 ...... 1.3 1.3 4.9 6.2 . . 1 . 7.1 Chad M 35 195 0.6 2.2 8.4 12.5 . . 4.0 2.6 Chile M41 140 10.2 ...... ..6.0 43.1 20.4 ...... 15.2 ..... 7.3 21.2 31.6 China L15 6305 .........2.1 8.6 ......... .................. 0.0 21.4 Hong Kong, China : :: Colombia M 33 178 2.9 4.9 16.0 26.6 13 2 33.7 18.1 CongO, Dem. Rep. S 215 783 ....... 3.8 0:2 226.. 0.....9 .... .29:2 . ........68 ..... 26:8 Con$g,Rep 5 247 249 7..1 6 2 ...... 10.6 ... ...6.2 ...... 9 5... ........... . 16..2...... 14:9 Costa Rica L35 69 7.7 60 29.1 118 ...... 23.9. ...... 21.0 15.1 M6e d'lvoire .....S 141" 268b 14.5 14.4 38.7 27.4 35.6_ . 14:2 17.0 Croatia L34 70. 5.8 . 11.9 ::. 7.3 8.3 Czech Republic..... ..L... . 40 . .....66 ..... . . ... 8.6. 14 1. 18.5 32.9 Denmark . . . Dominican Republic L.26 .......52 ... 5.9 3.2 25:3 ........6.2. 16.1 ........... ... 240 0..... 17 7 Ecuador S72 223 9.0 10.1 33:9 ..... .31.0 ......37..2 26 .3 ......13.9 Egypt, Arb .e .L ..... 28 ..... .99 5.8 2.5 13.4...... 9.0 5.9 . 21.1 10.0 El Salvador L25 70 2.7 2.5 7.5 7.0 .. 24:1 ......24.7 Eritrea L 4 9.. 0.1. 0.1 ... . 0.0 Estonia L14 17 .. 1.2. 1.4 .........: .. ....1.. . 46.8 Ethiopia' .5 ..... 131 791 1.6 ......7.6 9.5 4..4 69..... 6.9 5.6 Finland France . . .. GabonS594 128 11.2 9:7 ........177.7 .. .. 13.1 .......24.0 . 15..1 11.2 Gambia, The M57 97 1.7 6.76.2 11.6 1.4 . 17:0 3.0 Georgia M 20 149 . 0.9 64..1.6 Germany. Ghana S 58 t 229b 3.6 7:5 13.1 29.5 10:0 9.4 11.3 Greece. Guatemala L21 99 1.8 .........2 1 7.9 9.9 5 1 .............. 28:4.4 .... 28.1 Guinea .............. .67. ....330 . ..... ..... 44. .42.5 ......21.5 7.0.............11.77 ~ 1 . G ne -Bissau 52S3 1,136 44 3:9 ...... ....173.... .... .... ......... 3.7 ...... 7.7 HaitiS521 ..... 272 1.8 1.26:2.2 ..... 15.9..... 13.2 4.6 11.1 HondurasS5 83 157 8.5 11.1 21.4 20.9 26.0. 18 5 10 3 288 1999 World Development Indicators 4.19~~~~~~~~ Indebtedness Present v,alue of Total debt service Public and Short-term classification' debt publicly debt guaranteed debt service % Of % of exports of exports of % of central % of goods and % of goods and government % of GNP services GNP services current revenue total debt 1.997 1.997 1997 1980 1.997 1.980 1.997 1.980 1.997 1980 1997 Hungary M 52 89 8.8 17.3 . 29.7 . 24.3 34.3 13.8 India M ...... . 20...... 138 ....... 0.8 2.9 9.3 19.6 5.6 17.0 6.2 5.3 Indonesia .........M ...... ...62..... 195 4.1 9.5 . 30.0 10.6-.. 13.3 26.4 Iran, Islamic Rep.L 9 58 1.0 5.1 6:8 32.2 4.7 14.8 0:0 28.4 Iraq. Ireland Israel Italy .. Jamaica S 90 91 11.6 16.0 19.0 ... 16..2 26.6 5.1 17.5 Jordan S 110 134 5.2 9.1 11.2 11.1...... 25.3 ......24.6 ..... .9.1 Kazakthstan L 19 54 . . . 6.5.... 82 Kenya M 49 161 6.2 6.5 21.0 21.5 14.5 ..... 189 12.4 Korea, Dem. Reps. Korea, Rep. . ...-..... L. 33 .. ...84 .......7:3 .... ....33 20.2 86 24.7 5.5 35.8 37.5 Kuwait Kyrgyz Republic L39 97 ..... .. .. ... 2:5 ........ .. . ......6.3 .... .. ..... .... .. .... ..3.6 Lao PDR M 53 217 1.6 6-5.. 0.4 0.3 Latvia L 8 15 .. ..... ........2.4 4.4 ..4.7 12.9 Lebanon .......L 32 95'. ... . ... 4.8 . 14.4 ....57:6 35.6 Lesotho L 35 62 0.9 3.6 1.5 6.4 11.4 11.1 1.2 Libya777. Lithuania L 15 26 3.4 6.0 . 9.6 . 10.6 Macedonia, FYR M 75 119 5.5 8.8..... 8.9 Madagascar S 85 370 2.6 6.2 20.1 27.0 11.5 . 19.5 4.0 Malawi M 46 b 1821 777 3.1 27.8 12.4 28.7 .... 14.0 1.2 Malaysia M 47 46 4.0 7.6 6.3 7.5 5 8 12.0 20.5 31.6 Mali S 72b 2401 1.0 3.2 5.1 10.5 5:3 ..3.3 2.8 Mauritania S 169 377 7.1 10.9 17.3 24.2 ... 7.7 12.4 Mauritius M 55 92 4.7 . ......6 5 9.0 10.9 ... 14 7 19,5 10.1 20.1 Mexico ..........L ...... 37 ...... 110 .. .... 5.1 10.9 44..4 ...... 32.4 26.9 . 28.2. 19.0 M oldova L 52 83........................... . .......7 ..... . 6.8.. 6 1 10.97 . ..... . ..... . ... : 2.. . 1 M ongolia M 47... 89............ 6. 1..I...... 1...... . 11.7......3. Morocco M 53 149 7:9 9.5 334 26.6 271 .4 1.2 Mozambique S..... 1710 b 78504 1 186..... 5.5 Myanmar .... . S. ...... .. 289 ~ . . . . .25.4 8.0 11.9 . 0.3 . 8.6 Namibia.... Nepal L25 87 0.4 2.0 3:3 ..... 6.9 ........26.6 ..... 17..2 .......3.4 1.2 Netherlands New Zealand .. . . . Nicaragua .. .......S 244 b 441b 5.7 .. 175.5 ...... 223.3 .. .. 31.7 26.3 . 21.6 ... . 14.6 Niger S 56 b 329b 5.7 3.3 21.7 19.5 10.6 . 18.5 5.8 Nigeria M72 148 1.9 ... .....378 . .......4.1 . ......7.8 .... ..... .....398 ... 19:4 Norway... Oman L 42 4.7 ..... .... 6:4 5.9 9:7 88 .... 27:2.2 ... 28.7 Pakistan M 37 203 3.9 6.5 18.3 35.2 15.4 31.5 7.4 8.4 PanamaS5 87 76 14.4 18.9 6.2 16.4 48.3 . 22.9 12.3 Papua New Guinea L 46 74 6.0 9.3 .... ..13.8 15-.0. 10... 5: 8.9.....6.8.. ParaguayL 19 41 3.1 2.3 18.6 5.0 16.0 18.3 25.1 Peru S 45 293 10.9 . .......47 7...... 44.5 30.9 427.7 20.3 22.2 22.4 Philippines M51 88 . ..... 6.7 ..... ..5 3 26.6 .9.,2 13.1 20.5 .43.4 26.0 Poland L 27 85 19. 6.1 . 3.2 9.6 Portugal . .. .. Puerto Rico......... Romania L 29 98 4.6 12.6 15-.7 7.4 ...... : 23 6 9.1 Russian Federation L 27 114 . 1.5 . 6.5 . .. 4.9 1999 World Development Indicators 259 Indebtedness Present value of Total debt service Public and Short-term classification' debt publicly debt guaranteed debt service % Of % of exports of exports of % of central % of goods and % of goods and government % of GNP services GNP services current revenue total debt 1997 ±.997 1997 1980 1997 1980 1997 1980 1997 1980 1997 Rwanda S....... 33 373 0....7...O 1.2 4.1 13.3 2.8. 13.7 . ....6.9 Saudi Arabia ... . .. !.. - . .. ........ Senegal M 55 152 ..... 9.0 ...... 5.6 28.7 15 3 30.1 19.... 14.9 5.8 Sierra Leone .S 89 779 5.6 24.4 .23..8 21.2 22.7 20.9 11.3 7 7 Singapore.... ... Slovak Republic ..... M....... 48.. 82 7 1 . 12.2 . . 43.0 Slovenia South Africa L 19 65 3 8 12.8 5.6 . 3 Spain SriLanka L 35 79 4:5 .........2:9 12.0 6.4 103.3..... 11.6 11.9 6:3 Sudan 5 170 2,421 3.5 06 25:1 9.2 9.2 . 11.6 37.0 Sweden.. . Switzerland.. .. Syrian Arab Republic ..5.. 114 308 2.9 34 4 ...... 11.4 9.3 8:6. .17 8 22.1 Tajikistan -.......I.....L ....... . 34 .....86! . 1.8 4.6 ...... . . ..............- :.... .8.2 Tanzaniad 7 2 427 .. 22 ........21.2~ .... 12.9 ......8:3 .................33.3 11.7 Thailand ............. M. 61 ...... 120 .. _5.0 7.8 18.9 ......15.4. 9:5 ........70 .. 27.8 37.3 Togo M 60 129 4.8 3.8 9.0 8.1 11:0.......... 11.5 ..... 3.3 Trinidad and Tobago L.........38. .76 ........3.9 9.8 6~8 19.6 8:4 14.0. 26.-6 Tunisia ............. M ......... 58 .. .. 119 ....... 6.4 7.8 14 8.8 . 16 0 15.6 . 3.9. _13.6 Turkey-........... M... . 43...... 142 2.3 5.5 28.0 ..... 18.4 8.4 13.1 24.8 Turkm enistan..... . L. 59.. 219................ 9.3... 34...........I.. ....7 ..... ... .... .... ..... .... . .. . 2. 9. ... Uganda S 31b 239 b 4.6 2.9 17.3 22.1 6.7 . 9.1 3.0 Ukraine L 21 51 .2.8 . 613.6 ......... ... 10.0 United Arab Emirates . . . .. .. United Kingdom United States Uruguay............. M......... 32...... 133 .3.1 ........378 18.8 15.4 8.8 -10.1 19.4 28.6 Uzbekistan L 11 65 21 12.9 ... 15.2 Venezuela ........ .... M .........41 ..... 127 8.7 10.1 27.2 31~3 .19.1 35.7 53.0 12.4 Vietnam 5 81 168 . . . .. 0 West Bank and Gaza... Yem en, Rep.............. ..... M ..... . 56 . .... 75 .... . .. 1 9 ...926 .......... 4 .0 ......10.8 4.9 Yugoslavia, FR (Serb./M ont.) ........ .. .. . .........: ......... ..... .......: ........ ...... .. 11 6.6 ... 27:2 Zambia S 138 374 11.4 7.3 25.3 19.9 ......29 18.0 5.5 Zimbabwe ........... M . ..... 49 ...... 136 . : 1 ........0 80 3.8 22.0 .......39 .......... 11.5 19.7 Low Income 2.4 3.7 10:3 ......16.9 13.7 10.8 Middle income 4.8 52.2....... 13.5 f.g 17.0 f.9 25.9~ 21.8 f-5 Lower middle income 36_.6 ...... 3.9 .. ..... Upper middle income 5.8 6.5 Low & middle Income 4.3 5.0 13.1lf, 17.0Ofz 23.9fg 20.0fg Ea 7 ........ .3.......1:.~st Asia & Pacific,2443 1. 11.3~ 268.8 ... 28.5 5 Europe & Central Asia . 3.7 7.4 11.5 22.6 15.2 Latin America & Carib. 6.2 66 3.3 35.5 26.7 17.9 Middle East & N. Africa. 5:6 5.7 13.2 25:2.... 21.3 South Asia 1.3 3.2 1177.7..... 20.3 6 5 ......5.3 Sub-Saharan Africa . 4.6 7.3 12.8 18.4 18.7 Nigh income Europe EMU a. 5 = severely indebted, M = moderately indebted. L = less indebted. b. Data are from debt sustainability analyses undertaken as part of the Heavily Indebted Poor Countries (HIPC)Iinitiative. Present value estimates for these countries are for public and publicly guaranteed debt only, and export figures exclude workers' remittances. c. Data prior to 1992 include Eritrea. d. Data refer to mainland Tanzania only. a. Data prior to 1993 refer to the former Socialist Federal Republic of Yugoslavia. f. Includes data for Gibraltar not included in other tables. g. Includes data for the Republic of Korea not included in other tables. 260 1999 World Development Indicators 4.19 D The indicators in the table measure the relative bur- The World Bank classifies countries by their level of * Indebtedness is assessed on a three-point scale: den on developing countries of servicing external indebtedness for the purpose of developing debt man- severely indebted, moderately indebted, and less debt. The present value of external debt provides a agement strategies. In some cases the most severely indebted. * Present value of debt is the sum of short- measure of future debt service obligations that can indebted countries may be eligible for debt relief under term external debt plusthe discounted sum oftotal debt be compared with the current value of such indicators special programs such as the Heavily Indebted Poor service payments due on public, publicly guaranteed, as GNP and exports of goods and services. This table Countries (HIPC) Initiative. Indebted countries may also and private nonguaranteed long-term external debt over shows the present value of total debt service in the apply to the Paris Club and London Club for renegotia- the life of existing loans. * Total debt service is the most recent year (1997) both as a percentage of aver- tion of obligations to public and private creditors. In sum of principal repayments and interest actually paid age GNP in 1995-97 and as a percentage of average 1997 countries with a present value of debt service in foreign currency, goods, or services on long-term exports in the same three-year period. The ratios com- greater than 220 percent of exports or 80 percent of debt, interest paid on short-term debt, and repayments pare total debt service obligations with the size of the GNP were classified as severely indebted (S); countries (repurchases and charges) to the IMF. * Public and economy and its ability to obtain foreign exchange that were not severely indebted but whose present publicly guaranteed debt service is the sum of princi- through exports. Because workers' remittances are value of debt service exceeded 132 percent of exports pal repayments and interest actually paid on long-term an important source of foreign exchange for many or 48 percent of GNP were classified as moderately obligations of public debtors and long-term private oblig- countries, they are included in the value of exports indebted (M); and countries that did not fall into the ations guaranteed by a public entity. * Short-term debt used to calculate debt indicators. Public and publicly above two groups were classified as less indebted (L). includes all debt having an original maturity of one year guaranteed debt service is compared with the size of or less and interest in arrears on long-term debt. the central government budget. The ratios shown here may differ from those published elsewhere because A growing reliance on short-term Date sources estimates of exports and GNP have been revised to financing in East Asia incorporate data available as of I February 1999. I - The main sources of external The present value of external debt is calculated by Sr.-- r,.1. c , - r .D debt information are reportsto discounting the debt service (interest plus amortiza- the World Bank through its tion) dueon long-term external debtoverthe lifeofexist- D Debtor Reporting System from ing loans. Short-term debt is included at its face value. member countries that have Data on debt are in U.S. dollars converted at official - received IBRD loans or IDA exchange rates. The discount rate applied to long-term i, d credits. Additional information debt is determined by the currency of repayment of the " 4 has been drawn from the files loan and is based on reference rates for commercial of the World Bank and the IMF. Data on GNP and exports interest established by the Organisation for Economic of goods and services are from the World Bank's Co-operation and Development (OECD). Loans from the . :; national accounts files. Summary tables of the external Intemational Bank for Reconstruction and Develop- debt of developing countries are published annually in ment (IBRD) and credits from the Intemational Develop- .r i the World Bank's Global Development Finance and ment Association (IDA) are discounted using an SDR -- r.' v ir: , Global Development Finance CD-ROM. (special drawing rights) reference rate, as are obliga- tions to the International Monetary Fund (IMF). When the discount rate is greater than the interest rate of the loan, the present value is less than the nominal sum of The financial crisis that struck East Asia In 1997 lound many ol the reglon's fa.lest growing econ omles dependent on short-term borrowing. The ratios in the table are used to assess the sus- tainability of a country's debt service obligations, but there are no absolute rules that determine what val- ues are too high. Empirical analysis of the experience of developing countries and their debt service perfor- mance has shown that debt service difficulties become increasingly likely when the ratio of the pre- sent value of debt to exports reaches 200 percent and the ratio of debt service to GNP exceeds 40 per- cent. Still, what constitutes a sustainable debt burden varies from one country to another. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. 1999 World Development Indicators 261 III H - I I I I I - % , 0 II I.. B~~~~~~~~~~~~~~ 3W e see that in today's global economy countries can invest in education and health, can put the macroeconomic fundamentals in place, can build modern com- munications and infrastructure, can do all this, but, if they do not have an effective financial system, if they do not have adequate regulatory supervision or adequate bankruptcy laws, if they do not have effective competition and regulatory laws, if they do not have transparency and accounting standards, their development is endangered and will not last. James D. Wolfensohn Address to the Board of Governors of the World Bank Group and the International Monetary Fund Washington, D.C., 6 October 1998 Good policies are necessary for economic success and social progress-but they are not enough. Fiscal policy can achieve little if the tax administration is so weak and corrupt that it cannot collect revenue. When there is no confidence in government decisionmaking or no transparency in government budgets and monetary accounts, the economy can suffer. That is why governance-the way power is exercised in managing a country's economic and social resources for development-has come to be recognized as a major development issue. Neglected in the 1980s when policy reform seemed all-important, good gover- nance is now seen as critical to development progress. This point is driven home in the World Bank's new Comprehensive Development Framework, which takes a holistic approach to development. One pillar of the framework is the need for good government: an open, transparent legislative and regulatory system, a cadre of properly trained and remunerated officials, an educated and well-organized civil service, and an absolute commit- ment to clean government. Why governance matters The financial crisis in East Asia has shown that governance can be a prob- lem even in miracle economies with high education levels, admirable invest- ments in health care, good macroeconomic fundamentals, outward-looking policies, and solid infrastructure. There, as elsewhere, inadequately supervised financial systems, poor accounting standards, and public and pri- vate corruption have contributed to a temporary reversal of fortunes. The term governance covers a wide range of attributes- strategies to combat corruption and improve governance (box 5a). transparency, accountability, freedom of information, mainte- These countries are committed to open, transparent nance of civil peace, curbing of corruption, the quality of the civil policymaking-collecting detailed data on corruption and spon- service, and voice, the opportunity for citizens to participate in soring public workshops to discuss the data and the policy agenda. decisions that affect them. Governance also encompasses the reg- The first results show that in Georgia the highest levels of bribes ulatory and institutional foundations of a market economy- paid by enterprises to regulators are in tax and financial inspec- systems that secure property rights, enforce contracts, guard tions, in border crossings at customs, and in water and electricity against caprice, provide an effective judicial process, supervise services (figure 5a). modern financial systems, and insist on internationally recog- "Bank Insolvency: Bad Luck, Bad Policy, or Bad Banking?" nized accounting and auditing standards. (Caprio and Klingebiel 1997) reports that the primary causes of Capable institutions and good policies mean sound manage- the most severe cases of banking sector insolvency in the past 15 ment. According to the World Bank's World Development Report years were faulty supervision and regulation, deficient manage- 1997, income per capita grew only about 0.5 percent a year in ment, government intervention, or some degree ofpoliticallycon- 1964-93 in countries with poor management. By contrast, in nected or motivated lending. Why are these "microeconomic" countries with strong management capabilities and good poli- causes of banking crises considered governance issues? Because cies, income per capita grew at about 3 percent a year. Over 30 the regulatory framework and other institutional variables define years, these differences in income growth made a huge difference the incentive system in banks and in the environment. That in in the quality of people's lives. turn determines how well a banking system handles the macro- Another recent World Bank report, Assessing Aid (1998a), economic shocks that come its way (figure 5b). found that sound management-measured by the quality of the One thing that might have helped avert such crises is greater bureaucracy, the rule of law, and the pervasiveness of transparency in accounting. More reliable accounting informa- corruption-increases the effect of financial aid. It enhances tion could have brought on corrective action earlier, easing the growth and poverty reduction. And it improves social indicators crisis and allowing more discriminating responses by investors. beyond what good policies alone could bring. For poverty reduc- Even transparent accounting and reliable financial reporting are tion, the study reveals that in a country with sound management, no guarantee against crises-for Sweden and the United States, an extra 1 percent of real GDP in assistance adds 0.5 percentage considered to be among the world's most transparent, have point in growth and reduces the poverty rate (people living on less endured banking crises in recent years. than $1 a day) by 1 percentage point. But in countries with weak management, money has much less impact. In many cases poor countries' growth and development have been held back not by a financing gap but by an "institutional gap" and a "policy gap." The World Bank's anticorruption strategy Promoting good governanoe C,orrupicrin injerr,rn 'r- e tr.y ,:r In- V)rld Bni, anr] olr0cr i&.CIU)r car atrices To: coroi at ii. i,; wvoriu Sara iai Y- iti'ei Yorio- Corruption is the most notorious and noxious symptom of bad r;,er.* governance. In a recent survey of more than 150 high-ranking rt.crning r,a cr . Crrucr,rin a florId Banr prE.c.;r-s. r- na,e public officials and key members of civil society from more than ae nr.i;uer ann .ve rar .:.:,ni,nirr.e and in Sl,ule .n niernal corirl.;n ..r,ra ve nac,. ai a> '.' rn* oowE li'r.rcurene~ri r 'acIcE; Brd r.ric*.ed 60 developing economies, the respondents ranked public sector r r ;e: ni.rig.:rrengrr ererca ,ral r. corruption as the most severe impediment to development and ,Ie a'e eicariIr,g our ren-lea, r:.in.nde cl iu,ri, itrii ;ja.nur. f,rrn growth in their economies. a rcirjr.;r and de.elirr&g ., r,rd.aet anhi:rri srra'egi [. curiO lran.iparnrc, m.) rprupe:1 is .gr. ani sut:*o:r I n-;ounl', c Bvac r*..r*onrg. How does corruption lead to poor economic development HF'rinr ,:nrpres 3a' ;s r as ,aran.ne n VVCruOn re and growth? Corruption-defined as the use of public office for are oel*irg c rrulorcrrige.a aritici)rruplioni alratei,c ieririn private gain-includes briberyand extortion. Bribery can impede ,inl.lr,. or.al relirm-; c.ii ertice leral ;r,ano I i uei .rc.r,, long-term foreign and domestic investment, misallocate talent to pi.:,. a'r,na uiroaznclr.aa inluOC c i ra'ih arr e,.i ,,,er,r. Curnn rent-seeking activities, and drive businesses underground, under- 6olic. Elnico. aecr- a. Juriorn. Lvr,,a. .1;uru,us ri,caragua. T;anzar,. cutting the state's ability to collect taxes. It can also distort gov- Liganoa. l'rane, and1 eern.en Tn.ces luil Ds-gnr.ng, n0u.n BuiEnau. ernment spending because government officials may base Invic,,.er. rrnin .- E.n Trousr, .nil spending less on public welfare than on the opportunities for courart, a3 -iaac;e -jrvaegiea *:UiJrIi Innin. rl - rl:n a rl d extorting bribes. With lower revenues, the state's ability to provide Y raning. are rn n,ng anri.: r.rr upro.n Ba.; 1 'i ear Ci our regula r essential public goods, including the rule of law, is reduced. Surc:rlne nlerr.;nal elcirr; curt :orraouon m'crlde. We are Measuringgovernance-thatis, quantifying howgovernments ru.lr.n c.artnernr,s io, crosst croer i no.u.eardrincrir, cclIanra:t:n exercise power in managing resources for development-revealsa ,.rr n; Crcari.:, 'cr Ec:*nc'mc Cc-cue,.src.r an] erelcic.nrrr CEC'D,. Lnc L'nlcid raiarona Die.ei:.oren,ra Programnme L'rtDPr. DIcura troubling picture. Systemic corruption hurts public welfare, taxes i*--)r' Tuirnril Ire'ai .eic.p n-enar. za , nongoisrnrmenla t tr-aiz private sector activity, and is deeply ingrained in public institutions. riGoi,. ard o.r,er reiro o31 grB ani:aiior;n In the past year the World Bank has helped Albania, Georgia, and Latvia measure corruption with new surveys to provide the basis for 264 1999 World Development Indiceators Where does corruption occur most in Georgia? Microeconomic factors played the biggest part in recent banking crises .............- .,,r,r ... - . - .15 J4'1.,~~~~~~~~~~~~~~- Ur., morle voice. Ne comuictin tehooies; such ,asi th dvloe in the -Wor|ld Ban is to surve the prties to corp ji~~~~ ' !. Aohr app- roac to impovn governances to giv cItzn govrnnc in man counries? One sucssu tehiu being Internet enable people to come together and discuss concerns in tion directly and simultaneously-including household mem- "virtual meetings," sharing ideas and communicating quickly and bers, enterprise managers, and public officials. The surveys ask effectively wvith governments, them about the costs and private returns of paying bribes to obtain public services, special privileges, and government jobs. - r, at n governance Such information can identify activities and agencies where Promoting good governance is one of the toughest, most comn- corr-uption is concentrated, educate the public about the eco- plicated development challenges. But because corruption and nomic and social costs of corruption, and help in developing an financial crises flourish in the dark-and a lack of transparency anticorr-uption agenda. But governance needs to be assessed with imperils effective governance-the way forward is to build a social care, taking into account the historical and cultural factors that consensus based on good information, shape society. Data on governance from private rating agencies and pub- The mere act of quantifying poor governance is a great aid to lic policy research organizations are generally based on surveys public debate, for data are more than passive tools for research. or on polls of experts on governance issues. Because all coun- Data on poor govemance-well gathered, analyzed, and com- tries are measured by the same standard, cross-country corn- plemented with hard financial data-are almost impossible for parisons are encouraged. But for the same reason, such governments to ignore. A simple chart can be seized on by the measures are sometimes controversial, and knowledgeable media, opposition parties, and groups in civil society to stir pub- observers can disagree with the outcomes. All ratings are sub- lic debate. jective, reflecting external perceptions that do not always cap- Indeed, governments sometimes welcome data on what citi- ture the situation in a country. And they may not take into zens think of various schemes and services. A nongovernmental account the role of historical and cultural circumnstances in organization in Bangalore, India, devised a citizens scorecard on development. Moreover, the choice of what to rate and how to the quality of city services, which city authorities have accepted as rate it may reflect biases of the rating agency and the clients it a basis for improvements. Unresponsive government is some- serves. times the result of a lack of data. Recent advances in data colection-particularly in survey methods-have greatly improved our understanding of what Eoxenplary effoirts for the future causes corruption and mismanagement, how they affect devel- The opportunities-and the need-to address governance issues opmenr, and how they can he combated. But many questions are greater than ever. New global standards of behavior are remain. How does one estimate the size and scope of something emerging, driven partiy by changing attitudes toward bribery in as hidden as corruption? And how does one start to change the industrial countries and partiy by heightened awareness in devel- attitudes and incentives of public and private actors and the oping countries of the costs of poor management and corrup- entrenched ways of doing business that have brought about poor tion. The costs are high indeed. Macroeconomic stability may be 1999 World Development Indicators 265 undermined. Foreign private capital flows are affected. Small fraudulent activity. And for an evenhanded treatment of gov- enterprises are hurt by the rising cost of doing business. The envi- ernance issues in all member countries, it is collaborating ronment may be endangered. And the poor suffer. Unless gov- with other multilateral institutions, particularly the World ernments, working with civil society and the private sector, can Bank, to make better use of complementary expertise. blaze new trails in improving governance and combating cor- Other IMF initiatives include the Dissemination ruption, development will be endangered and will not last. The Standards Bulletin Board, which provides information about World Bank stands ready to do all that it can to help its member the Special Data Dissemination Standard and the General countries and partners increase their efforts in the fight against Data Dissemination System. The standard was established in corruption. 1996 to guide countries that have, or might seek, access to Ideas, information, analysis, and action plans are coming international capital markets in the dissemination of eco- from both the private and the public sectors. New empirical work nomic and financial data to the public. The system was estab- on governance is bringing researchers and practitioners closer lished in 1997 to guide countries in providing the public with together, providing new insights and informing and influencing economic, financial, and sociodemographic data that are policy. comprehensive, timely, accessible, and reliable. Consider these new efforts to improve governance and imple- The IMF's Code of Good Practices on Fiscal Transparency- ment anticorruption action programs: Declaration onPrinciples (1 998a) has several key objectives. The * The OECD's Convention on Combating Bribery of Foreign roles and responsibilities in government should be clear. Public Officials in International Business Transactions came Information on government activities should be provided to into force on 15 February 1999. The convention permits the public. Budget preparation, execution, and reporting OECD and other countries to move in a coordinated manner should be undertaken openly. And fiscal information should to adopt national legislation making it a crime to bribe for- be subject to independent assurances of integrity. The code eign public officials. It provides a broad definition of bribery, will facilitate surveillance of economic policies by country requires countries to impose dissuasive sanctions, and com- authorities, financial markets, and international institutions. mits them to providing mutual legal assistance. * The Asian Development Bank (ADB) outlines its three main * The Development Assistance Committee (DAC) of the objectives in "Governance: Sound Development Manage- OECD-in the DAC Orientations on Participatory Development ment" (1995). The first is to promote competitive markets and Good Governance (OECD 1995)-provides a framework to and efficient, effective, accountable, and transparent public help strengthen partner countries' capacities for good gover- administration. The second is to support promising anticor- nance. The building blocks of the framework emphasize the ruption efforts case by case and to improve the quality of dia- need for good govemance, the rule of law and respect for logue with developing member countries on a range of human rights, and the development of a strong civil society as governance issues, including corruption. The third is to a basis for long-term stability. ensure that ADB projects and staff adhere to the highest eth- * Transparency International is dedicated to increasing gov- ical standards. ernment accountability and curbing both international and * The United Nations Development Programme's Programme national corruption. Its annual Corruption Perception for Accountability and Transparency supports good gover- Index, ajoint initiative with G6ttingen University, is a "poll of nance efforts and initiatives in two main areas. Financial man- polls" prepared from seven sources. The results represent the agement and accountability systems to improve records subjective evaluation of the degree of corruption in the coun- management, accounting, and external auditing, and inte- try by businessmen, risk analysts, and (to a limited extent) the grity improvement initiatives aimed at institutional develop- public. Transparency International has also devised the ment and reform, create efficient public and private sector Integrity Pact, a concrete program to exclude corruption management systems, facilitate participation in decision- from procurement contracts involving multinational compa- making and governance, and encourage closer cooperation nies and the governments of developing countries. Such with international and local organizations. pacts have already been implemented in three Latin * The U.S. Agency for International Development has identi- American countries. fied anticorruption as a priority in its development agenda. * The International Monetary Fund's (IMF) approach to pro- Working with other bilateral and multilateral donors, its moting good governance is to maximize the transparency of Center for Democracy and Governance has developed the member governments' financial operations and to create sys- Handbook on Fighting Corruption (1999), which presents a tems that minimize the scope for making ad hoc decisions. In framework for addressing corruption and other forms of consultations with member governments on exchange rate criminal activity. USAID is raising awareness about the costs arrangements and in IMF-supported programs, governance of corruption by sponsoring integrity workshops and foster- issues within the IMF's mandate and expertise are treated ing anticorruption NGOs. It is promoting good governance more comprehensively. The IMF is also advocating policies by providing training and technical assistance for audit insti- and developing institutions and administrative systems to tutions and anticorruption agencies. It is strengtheningjudi- eliminate the opportunity for rent seeking, corruption, and cial systems by supporting the drafting of new criminal and 266 1999 World Development Indicators anticorruption laws and training prosecutors and judges. And it is discouraging the government's control over the economy where such controls create opportunities for abuse and impede economic growth. * The International Chamber of Commerce's Extortion and Bribery in International Business Transactions-Rules and Recommendations (1996) includes rules of conduct that are to be voluntarily accepted and self-regulated by interna- tional business-and supported by governments. The basic principles are that all enterprises should conform to the rel- evant laws and regulations of the countries in which they operate-and observe both the letter and the spirit of these rules of conduct. The rules cover extortion, bribery and kickbacks, payments to agents, financial recording and auditing, responsibilities of enterprises, political contribu- tions, and company codes. * The Guidelinefor Governmental Financial Reporting (exposure draft, 1998) of the International Federation of Accountants' Public Sector Committee assists in harmonizing the accounting principles and practices of governments around the world. The intention is to improve the comparability of information both within and between jurisdictions and to provide useful guidance for governments wishing to provide high-quality financial information. The committee's guide- lines should facilitate the choice and adoption of a particu- lar basis of accounting by a government. They should also ensure that standard-setters and preparers of financial state- ments have a common understanding of each basis. * The U.K. Committee on the Financial Aspects of Corporate Governance (also known as the Cadbury Committee), in its 19-point Code of Best Practice, requires listed companies incorporated in the United Kingdom to include a statement in their annual reports on whether they have complied with the code. The key safeguards: properly con- stituted boards, separated functions of chairman and of chief executive, audit committees, vigilant shareholders, and financial reporting and auditing systems that provide full and timely disclosure. The code recognizes that no sys- tem of corporate governance can be proof against fraud or incompetence; the test is how quickly such aberrations can be brought to light. The code-along with similar ones from the Toronto, New York, and Australian stock exchanges-can serve as a model for stock markets in emerging economies. All this is cause for optimism-and commitment. 1999 World Development Indicators 267 CCLrilries vitErte Ucter policies can erc efasier-5s Boliua and Ghana Shois. Annual percentage growth in GDP per capita Borsi'ara S 19,94-9 lndon1986-ES 4 19:;}3-9- ir1.lc,np',, 4han a Etc.3 * larS3a I G h& riIndi 2 197e-81 1 9-a-%fl> l Ta Economic management Ai-i targ9eied 9t countries watr; pgooi pul,cie. liftS rfore peoplc, Oul ut Dofsev-rtl --~~~~~~~~~~~~~~~~~~~~~~r .:~~~~~~~~~~~~~~~~~~~~t0 21!i il;i F- I-~~~~~~~~~~~~~~~- -w~~~ ~ ~ e 49*-2 . .w. R i-....d Z 7 million people , goo ois lifts morvep liu wo uid r.L - ei . O r.f- L.., 1l ., r-I1,,%r, r , ;, j 1. !a 0erSd b;, a 010 biliir. r, -re-,> Irl ao 3r,0 theheira l~l:e r. 3,n TaJ ,rDcTeJ at - ur,rn ,rr .: poiiocs 26Es . 99 '':1.1., u'1I l. -,l.;l Countries that pursue good policies with sound institutions perform better in economic growth and poverty reduction. That translates into improvements in the lives of 71 ~~~~~~~~~~~~~~people-more food on the table, .. _i _~~~~~~~~~ . ~~~healthier babies, and more children in school. , : ~~~~~~~~~~~~~Governance: the way power is exercised in managing a counitry's ! ~ ~~~~~~~~~~~~~economic and social resources for developrnent. ,,,1 -ea:e~~~~~~~~~~~~~inc. ;:-:cononnic stability, and efficient resource allocation play a major part 'e] ,,;~~~~~~~~~~~~W 1,,0;ot,|,i. .:o:r,..nnic performance. In Sub-Saharan Africa in the past few years, N,~~~~~ ~~ W_ cc-nr.e- mlat -_,stained good governance and good economic policies had higher growth ;~~~~~~~~~~~~~~~~~ at,r ru-.. -e ro.l. by conflict and instability. p$ g ~~~~~~~~~~~~~~~~~~All Sub-Saharan . .. those with . .. and with . .. and with resource g ~~ ~ ~~~~ ~ ~~~~~ ~~~~~ ~~coutre _socasailt macr stablt alloatio efiincy = l X n =~4 --_. -i.- ====Z W W J : . ,l 37counrie 30 ounrie ................................. 21 ounrie ~~ 1999 World Development Indicators| 269~~~~~~~~~~~~~~~~~~~14 5.1 Credit, investment, and expenditure Private Foreign direct investment Credit to Private non- Central investment private sector guaranteed government debt expenditure % of gross % of gross domestic domestic % of external fixed investment investment % of GDP % of GDP debt % of GDP 1980 1997 1980 1997 1980 1997 1980 1997 ±980 1997 1.980 1996 Albania .... 16.0 ........... 2.. .0... 3.8 . 0.0 . 1. Algeria 67.4 72.5 2.1 0.1 0.8 0.0 42 2 4.0 0.0 0.0 29.7 Angola 82:7 . 18:5 4:6 . . .. 0. Argentina 94.2 3.5 10.2 ......0.9 2 0 25.4 19.5 24.3 20 6...18202 14.0 Armenia 53.7 33:3 ..31 6.1 0 0 ................ Australia 73.5 81.8 4.6 6 9 1.2 2.3 51.9 80.6. 22-7 26~3 Austria . . . . . 74.2 103. 36.6 41.7 Azerbaijan . .52:6 148......:. . -. 2.1 0.0 Bangladesh ...I......... 577 .....67.8 0.0 1!6 0.0 0.3 6.0 . .. 23.3 0 0.0 OC 74 Belarus ... 34 9.. 862.0 3. Belgium ....................7..... . ................ . ... ...... . 28.8 . 67.4 . - 50.1I . 48.2 Benin ... 59... .. ......... .. . 5. _20 .......0 8 .. 0.3 ......0 1 .. 28.6 5.7 .. .... 0:0 0.0 Bolivia .. 58.1 10.2 40.3 . . 17.3 502 34 . 2. Bosnia and Herzegovina.. . .. .. Botswana 62.1 ~ ............. 30.5.. 76.6 ... 10.8 ......2.0 11.3 9.9 00...... 0.0 31.8 39.4 Brazil 89.8 88.7 3.5 11.3 ......0,8 .... 2.4 425 30.8 232 2... 36.6 20.2 33.8 Bulgaria 85.9 . 41.7 49.9 2 43 ...... .. 48.1 Burkina Faso . 524 .... 0.0 0.0 .......00 0 16.7 18 0.0 00O. 12.2 Burundi 8:1.1... 37.3..... 0.0 1.5 0.0 0.1. 9.8 16.5 0.0 0.0 21.5 27.7 Cambodia 68.9. 41:4..........I.... 6.7 7.0 0.0 Cam eroon ....... ....77:8 . . 93.7 9.2 3 .1 1.9 0.. .....O 5 . .....29.5 7 1 6 9.9 2.1 15.7 ..... ......... ..................12.7........ ...... ..I.......... ....... ... Canada 87.4 86.3 9.4 6.5 2.2 1.2 68.0 91.8 21.1 24.2 Central African Republic........ 46.5 . _40:2 9.5 6.0 .....0.7 0.5 13.9 4.1 0.0 0.0 22.0 Chad . 0.0 4.8 0.0 0.9 17.2 3.1 0. 0.0 Chile 80:9 .....3..7...... 26:1 0.8 7.0 46.9 61.5 38.8 54.6 28.0 21.0 China 43.4 49.1 0.0 12 8 0.0 4.9 53.4 103:1.1..... 0.0 1 6 8.0 Hong Kong, China . . ......85:1 ......86-8 ....I ... .... . ... :. ....... ..... 175.2 Colombia ...........58:2 .....59.1 2.5 332.2 _.0.5 ..... .6 2. .30.5 ....41.8 7.4 331 8 ..... 13:4.4 ...... Congo, Dem. Rep. 42.4 64.4 0.0 0.2 0.0 0.0 2.8 1.1 0.0 .......0.0..... 12.4 8.3 Congo, Rep. ................... 83.9 6.6 15 ..... 2.3 0.4 .15.5 7.9 0.0 0.0 494.4 Costa Rica 61:3.. 80:0 4.1 2:2. ' 1.1 0.6 27.9 19.5 15.0 4'8 250O..... 30.6 C6te d Ivoire 53:2..... 70.2 3.5 20:0.... 0.9 3.2 40.8 19.4 27 0.... 13.3 31.7 Croatia .. .... ............ 7...... 59.6 .. ......... 12 4 .. 1 .8 .......:...... 30.1 26.7 . . I 46.7 Cuba.. . Czech Republic7.......... ................ 7.3 2 67.5 9.8 36.4 Denmark ................... ........ ..... .. 10 .......2:2 .. ... 0.2 1.7 41.2 32.2 38:6.6 . . 41.4 Dominican Republic 68.4 83.0 5 6 11 0 ......1.4 .......27.7 30.8 28.2 12.7 0.0 16.9 15.6 Ecuador 59:7.7.. . 829.....9 23 3. ... 145.5... . 0. 6. 29.9..... 22.8 37.5 187.7..... 2.3 14.2 15 7 Egypt, Arab Rep. 30.1 ..... 68.4 8.7 .6.7 2.4...... 12 2..... 15.2 46.6 1:4.4. ... 0.2 50.3 34~3 El Salvador 44.8 ......77.0 ......12 ......0.7 0 2 .. ....0:1 .... 33.6 40.2 17.6 .......1.4 Eritrea .. 53.8 .. 0.000. . Estonia .......74.4 . 19.1 . .... .........5.7 ......... 256 ........ .....12.6 . 33.8 Ethiopia !.... . 56._6 00.... O....... 0:4.4 .... 0.0 0.1 20.1 23.1 0:0 ..... 0.0 19.9 Finland ... .. ..... ....... ...... 0.2 5.4 ~ 0.1 .... 18.8 ..... 48:5 54.5. 28.1 40.1 France . 2.0 8.4 0.5 1.7 104.8 80. 39.5 4. Gabon 80.1 ......7.25 2.7 -7.4 07 7..... -1:9.9 ... 15.8 9.0 0:0 ..... 0.0 36.5 Gambia, The 38.3 52.4 ... .0.0 16.6 0.0 2.9 23.9 10.7 0: 0...... 0.0 .....31.7 Georgia .. 84.0 13.31. ...... ....... 0.0 . 9.7 Germany .. . -0.5 . 0.0.. 108.7 ............. .... 33 .7 Ghana. 49.4 6.2 7.8 .....0.4 1.9 2.2 8.2 0.7 4.5 10.9 Greece 51:5 ........ ..59.9_.. 8. 64 14 0.9 43.9 35.1 29.3 32.8 Guatemala 63 8 80.4 8.9 3:7 .... 1.4 0.5 16.2 .... 20.1 23.9 2.5 14.3 Guinea . 68.5 01 . 0 . 4.4 0. 0.0 Guinea-B sa ................... ...... 25.5 0.0 3.1... . 0.0 08 .... 8 5.7 0 0.0 Haiti .. 1.0 5.3 1.0 0.9 0.1 15.7 15.5 0.0 0'0 174.4..... . Honduras 62.1 70.3 0 85 0.2 2 28.8 3. 3 5.5 270 1999 World Development Indicators 5.1 Private Foreign direct Investment Credit to Private non- Central investment private sector guaranteed government debt expenditure % of gross % of gross domestic domestic % of external fixed investment investment % of GDP % of GDP debt % of GDP 1980 1997 1980 1997 1980 1997 1980 1.997 1980 1997 1980 1.996 Hungary .......... .0.0.....16: ..6 ..0.0. 4.5...... 48.3......22.3 .... 004.0 243223 0 562... 2 .4362 32 India 555.5. .. 72.6 0.2 3.7 0.0 0.9 25.4 25.7 1.6 . ....9.8 13.3 15.8 Indonesia ............ .... 60.5 1.0. 7.0. 0.2 2.2 8.8 61.0 15:0.0 ... 30.4 22.1 14.6 Iran, Islamic Rep. 0.0 .1 0.0 0.0 43.8 23.2 0.0 1.7 35.7 23.2 Iraq . .. 0.0 Ireland . 20.7 1.4 3.6 44.0 7. 45 1 38.1 Israel . .. ... .............. :... . .... 1.0 12.7. .0.2 .... ..2 8.8 ... 70.8 75.3 - 72.8 48.7 Italy 0.5 1.7 0.1 03 55.9 51.5.. . 41.3 49.5 Jamaica 6.6 96 1 33 2. 294 3 48 15 Japan 69.8 70.5 0.1 0.0 0..0...O- .. .. 0.1 132.7 201.1 " - ... .... 184.4....... Jordan 513.3 84.0 2.3 .....1:1.1 .. . 0.9 ..... 03.3 ... . 51.0 77.8 0.0 .. ...0-.5 .... 41:3.3 . . 35.0O Kazakhstan 382 . 60.. 52. 13.9 Kenya 54.7 61:8 3.7 1:0 ......1.1 0.2 29.5 34.9 12.9..... 5.0 .... 25.3 28.9 Korea, Dem. Rep. Korea, Rep. 86:0 00.1.0..06 516 853 7.8 267 12 8. Kuwait........ ........... ... ..... . . 0.0. 0.4 0.0 0.1 37.7 56.8 27 7 45.2 Kyrgyz Republic 94:9 13.1 . 2.8 . .. . 0.0 Lao PDR ... .... .. 1:................J7 9.9 ... .. 5.1 . 12.7 0.0 0.0........ Latvia 89 2.. 47.8 .. 9.4 .. . ..10.7 ... .... : .....6 0 .. ..... .....31~0 Lebanon 79 3 .. 3.8 .. ~~~~~~ : 1.0 .. 69.6 0.0 17.6 3 Lesotho 81.8 2.9 3.6 1.2 3.1 9.8 16.7 0.0 0.0 45.3 55.2 Libya 8 3 -13.9 . -3.1 . 11.2 Lithuania . 88.2 13.9 . 3.7 . 9.6 37 7.... 25 0 Macedonia, FYR .. 91.23-5074 Madagascar. 46.5 -0.2 3.4 0.0 0.4 19.2 10.0 0.0 0 0 .0...... 17.3 Malawi .... ..2174.... 27 7 3.1 0.6 ... 0.8 0.1 .207.7 ... 3.9 0.0 0.0 34.6 Malaysia 62 ...... .6.... ..... 72:9.9 .. 12..5 ..... 12.1 ... ..3.8 ..... 5 2... 49.9 161.0 189.9 2. 28.5 21.9 Mali .... ... .......... .. . .. 54.6 0.9 25.5 0. 1 06.6 23.0 13.5 0.0 0.0 20.6 Mauritania . 78.3 10.6 ..1.6 ......3.8 .... 0:3 . ....31 .0 ... .23.0 0.0 0 0 Mauritius 640.0 . . 73.-4 0.5...... 4.4 ......0.1.. . 12 2 ... 21.6 50.5 5: 1.... 31 9 27:2.2 .. 22-.4 Mexico 57.0 81.5 3.6 11.7 1.0 3.1 19.7 15.3 12.7 18.3 15.7 15.5 Moldova .. 86.2 13:.3 .. 3.2 . 7.2 .. 0.0...... m... ..... Mongolia .7. ........ 3:6... ... 0:8 7.1 0.0 ..... ...: .... 21.6 Morocco . 70.4 2.0 17.4 0.5 3.6 27.0 33.5 1.6 . ....2.3 ....33. 33.3 Mozambique 66.1 4.3 ...... ....... 1.3 ......16.3. 0.8 Myanmar 20.6 55.0 . . 5.5 6.2 0.0 ... 0.0 15.8 10.1 Namibia 42.0 ......62.2 00 28.8 0.0 5.7 . 52.6 .. ... :..... . ......... Nepal 60:2.2.... 65.8 0.0 2.2 ....0.0 0.5 86 6.... 25.0 0.0 0.0 14.3 17.5 Netherlands... 85:.1 86.3 6.0 9.9 1.3 2.4 93.6 112.2 52.9 48. New Zealand 69.2 872 38 1. 4 18.4 100.6 3. 319 Nicaragua 38.6 0.0 15.6 0.0 4.3 48.3 39.6 0.0 0.0 30.4 33.2 Niger ......... 20:1. 45.3 7.0 1.0 2.0. .. 0.1 . ...17.1 .3.3 35.3 6.1. 18.6 .... .. Nigeria 44.0 -5.4 252.2... -1.2 3.9 12.2 8.2 12:3 1.0 Norway 70:3 ...... :...... 0.3 8.7 0.1 .. 2.3 .... 51.4 77.5 34-4 ... . 36.8 Oman ........ 34.1 .............7..4 . ..4O.0 . 1.6 0.4 13.7 . 29.2 0.0 0.1 38.5 42.4 Pakistan 36.1 65.4 1.4 7.6 ... 0.3 1 2 24.0 27.2 0.2 7.9 17.5 23.8 Panama . 83.8 -4.4 9.9 -1.2 2.9 58.1 80.7 0.0 5.4 30.5 27.4 Papua New Gui'nea 58.6 84.9 11.8 11.6 3.0 4.3 17.6 22.9 19.3 33 4 34~4 29.4 Paraguay.......... 85:1.1.. . 82.0 2.2 10.8 0.7 2.5 18.4 23.2 15.8 2.4 9 9 Peru ........ 75:6.6.... 84.-7...... 0.4 12.9 .0.1 3.2 12.9 22.2 6.5 8.1 19.5 16.5 Philippines 69.09.0 -1.1 6.0 -0.3 1.5 42.2 62.3 14 1 .15.0 13.4 18~5 Poland . .... 86.6 . 16.3. . 36 6.4 18.1 6.0 42.2 Portugal 1.6 2.7 ~~~~ ~~~~ ~~~~~~0.5 17 7 3.3 76.1 33.1 41.6 Puerto Rico .. . . ... Romania............. ....... . ............... 16.2 . 3 5.5 0:0 7 44.8 31.4 Russian Federation 76 6 .. 6.4 .. 1.4 8.7 . 1.5 24.7 1999 World Development Indicators 271 5.1 Private Foreign direct investment Credit to Private non- Central Investmeint private sector guaranteed government debt expenditure % of gross % of gross domestic domestic % of external fixed inesetment investment % of GDP % of GDP debt % of GDP 1.980 1997 1980 1997 1.980 1.997 1.980 1997 1980 1997 1980 1996 Rwanda . 16.7 8.7 0:5.5.... 1.4 0.1 5.7...... 8.1 0:0.0.... 0.0 14.3 Saud Arabia..... . .. ........7 '...... :...... -9.4. -4 4.4 . -2 0 -0.8 22.8 5... ..9.5 Senegal 581 .....70:2 .....4..1 35.5 05 0:7 42.7 _..16.3 ... 06 1.5 ....23:3.3 ..... ... Sierra Leone .. . .. -9. -1.6 0. 7.2 3.4 0 26.5 14~8 Singapore ..........75.6... .... .22.8.. . .24.02.8 24 10.5 .... 9.0 81.0.0..... 112.9 ... .20.. . 0.. 22101 Slovak Republic ..... 2.4 .. 08. 44.2 . 7.9 Slovenia .. 90.4 . . . 2. South Africa 508.8 .729.9................ 8.41.3. 56.9 135.7 . 10.4 22.1 34.7 Spain ....... 3.0.. .... 5.4 0.7 1.0 .775.5 80.7 ...... 26.5 36.8 Sri Lanka 77.4 77.6 3.2 11.7 1.1 2.8 17.2 24.1 0.2 1.1 41.4 27.7 Sudan 35.7 . 0.0 . 0.0 0.0 13.2 2.4 63.3 3.0 17.4 Sweden . 79.7 0.9 15.0 0.2 4.3 78.0 40.9 39.3 46.1 Switzerland . . . 5.9 . . 0.7 189.192.2 26.3 Syrian Arab Republic 36.1 . 0 .0....... 1.5 0.0 0.4 5.7 10.0 0.0 0. 0 ... 48.2 23.8 Tajikistan . .10 ...14.2 Tanzania .. ... .... .... ........ . ... 11.2 ..... 2.3 3.9 ..1.6 .... ..0.6 . Thailand 681 1.... 67:7 2.0 7.0 0.6 2.4 41.7 129.8 20.5 36.5 18.8 16.5 TogoI..a. ....... 283.3 85.7 13.1 0.0 3.7 0.0 27.5 ..18.0 0.0 0.0 3078 Trinidad and Tobago . 86.7 9.7 2. 3.0 5.8 28.7 4. . . 0 8 Tunisia 46.9 49.3 9.1 6.3 2.7 1.. 7 46.4 ....64.5 5.1 1.6 31.6 32.6 Turkey 785 0.1 1.7 0.0 0. 4 3. 26.3 . 23 2 69 Turkm enistan ........... .. ...... . ....... ....... -. .... 2.5 ..... ..... 1.7.5 0 ........ 0.0 Uganda 63 6 0.0 17.9 0.0 2.7 3.9 4.3 0.0 0.0 62 Ukraine 0.0 . 6.2 . 1.3 2.5 4.0 United Arab Emirates .. . .. . 22.9 50.0. 12.1 11.8 United Kingdomn 7.. 0.0 87.0 11.2 14.1 ....1.9 3.0 27.6 123.9 38.3. .41.7 United States....865.5 .85.9 ....3.1 ..6.0.... 0.6 1...J2 80.3 126.8 . . 22.0 22.2 Uruguay 7: .. 7 .1 . 16.5 6.3 2.9 0. 37.2 31.1 127.7 3. 2. 3. Uzbekistan . . . . . 11. . . 3.3 Venezuela 51:5.5... 43.6 0.3 32.9 0.1 5.8 48.2 14.3 10.8 8.0 18.7 16..9 Vietnam .. 79.7 5 . 7.2 106.0 W est Bank and Gaza ...................... Yemen, Rep. . 63.2 . -11.5 -2.4 4,6 0.0 0.0 328 Yugoslavia, FR (Serb./Mont.) . ... . .................... 595. ... 18. Zambia .. 62.6 6.8 12.2 .... 1.6 1.8 19.9..... 8.0 2.7 0.2 37.1 21.4 Zimbabwe 87:3.3.. . 85..5 0.1 4.2 0.0 0.8 26.6 .37.6 0.0 9.6 27.9 ... Low income 53.1 .....70:.1.... 0.0 6.0 0.0 1.3 20.7 . .. 21.6 5.0 4.2 14.8 17.4 Middle income . 70.1 1.0 108.8.... 0.5 1.3 ... 32.7. 50.8 12.9 18.0 . 19.9 Lower middle income .. 58.6 1.2 10.7 0.3 ....1.3 ......368.8 62.9 ........ 17.1 Upper middle income .. 83.1 0.9 11.0 0.6 1.3 .... 30.2 39.3 . . 2. 27.9 Low & middle income ........ :. ..-70.1 0.8 10.3 0.4 . .13.3.... 30.6 47.2 11.6 15.7 . 19.5 East Asia & Pacific 50.5 ......54.6 1..1 11.1 0.4 1.0 41.5 .... 99.0 .... 11:7.7.... 21.3 . 11.6 Europe & Central Asia .. 75.1 .. .. 8.3 . 1.9 .169 .....168 15.3 10.5- 29.9 Latin America & Carib. 70~.0.. . 84.1 3.2 13.1 0.8 1.4 321 1. .. 26.6 16.5 22.6 19.1 25 6 Middle East & N. Africa 0.9 .. 0.9 09 0.2 0.5 28.6 45.4 0.7 2.1 South Asia 54.2 71.7 0.4 3.8 0.1 0:9.9.... 23.1..... 25.6.... 0:9 7 5 14.3 17.4 Sub-Saharan Africa 52.1 676.6 . 7.7 ..1..9..6..75. 2. High income 78.9 79.4 2.9 3 9 0.7 1.1 81.1 125.4 26.2 32.1 Europe. EMU.. ............ .... .....:... 1.9-3.6 0 .5 ... 0.8 .... 79 1 86.3 ...... .. .. 4.7.. .. a. Oats prior to 1993 refer to the former Socialist FederaI Republic of Yugoslavia. 272 1999 World Development Indicators 5.1 - The indicators in the table measure the relative size erage, and cross-country comparability. (See About * Private investment covers gross outlays by the pri- of states and markets in national economies. There the data for table 6.7 for a detailed discussion of vate sector (including private nonprofit agencies) on is no ideal size for states, and size alone does not data on foreign direct investment.) additions to its fixed domestic assets. Gross domes- capture their full effect on markets. Large states may Data on domestic credit to the private sector are tic fixed investment includes similar outlays by the pub- support prosperous and effective markets; small taken from the banking survey of the IMF's lic sector. No allowance is made for the depreciation states may be hostile toward markets. The resources Intemational Financial Statistics or, when the broader of assets. * Foreign direct investment is net inflows of a large state may be used to correct genuine mar- aggregate is not available, from its monetary survey. of investment to acquire a lasting management inter- ket failures-or merely to subsidize state enterprises The monetary survey includes monetary authorities est (10 percent or more of voting stock) in an enter- making goods or providing services that the private (the central bank) and deposit money banks. In addi- prise operating in an economy other than that of the sector might have produced more efficiently. A large tion to these, the banking survey includes other bank- investor. It is the sum of equity capital, reinvestment share of private domestic investment in total invest- ing institutions, such as savings and loan institutions, of earnings, other long-term capital, and short-term ment may reflect a highly competitive and efficient pri- finance companies, and development banks. In some capital as shown in the balance of payments. Gross vate sector-or one that is subsidized and protected. cases credit to the private sector may include credit domestic investment (used in the denominator) is Because data on subnational units of govern- to state-owned or partially state-owned enterprises. gross domestic fixed investment plus net changes in ment-state, provincial, and municipal-are not read- stocks. * Credit to private sector refers to financial ily available, the size of the public sector is measured - - -- resources provided to the private sector-such as here by the size of the central government. While the Several transition economies rank high in through loans, purchases of nonequity securities, and central government is usually the largest economic foreign direct investment trade credits and other accounts receivable-that agent in a country and typically accounts for most pub- establish a claim for repayment. For some countries lic sector revenues, expenditures, and deficits, in - these claims include credit to public enterprises. some countries-especially large ones-state, * Private nonguaranteed debt consists of external provincial, and local governments are important par- I obligations of private debtors that are not guaranteed ticipants in the economy. In addition, activities attrib- for repayment by a public entity. Total external debt is uted to the "central government" may vary depending the sum of public and publicly guaranteed long-term on the accounting practice followed. Inmost countries ,- , ' debt, private nonguaranteed long-term debt, IMF central government finance data are consolidated into : - i credit, and short-term debt. * Central government one overall account, but in others only budgetary cen- ;. I expenditure comprises the expenditures of all gov- tral government accounts are available, which often . .* ' - : ernment offices, departments, establishments, and omit the operations of state-owned enterprises (see - - . - -- . other bodies that are agencies or instruments of the Primary data documentation). , central authority of a country. It includes both current When direct estimates are not available, private ,. and capital (development) expenditures. gross domestic fixed investment is estimated as the difference between total gross domestic investment * T;Llr Data sources and consolidated public investment. Total investment The figure shosS the top 10 developing countr" may be estimated directly from surveys of enter- recipients ol net roreign direct InLestment (FDI. Private investment data are World Bank estimates. prises and administrative records or indirectly using FDI isthelarEestsourceolcapital noswsTodeaelopinE Data on foreign direct investment are based on esti- countriles. Preliminary data for 1998 suggest that the commodity flow method. Consolidated measures ow asset prices and STrong efforts io 3ttract FDI mates compiled by the IMF in the Balance of of public investment may omit important subnational may hate lricreaaed rnflos to East Asia compared Payments Statistics Yearbook, supplemented by units of government. In addition, public investment with 1997. World Bank staff estimates. Data on domestic credit data may include financial as well as physical capital are from the IMF's Intemational Financial Statistics, investment. Asthe difference between two estimated and data on government expenditure are from the quantities, private investment may be undervalued or IMF's Government Finance Statistics Yearbook. overvalued and subject to large errors over time. External debt figures are from the World Bank's (See About the data for table 4.9 for further discus- Debtor Reporting System as reported in Global sion on measuring domestic investment.) Development Finance 1999. Statistics on foreign direct investment are based on balance of payments data reported by the International Monetary Fund (IMF), supplemented by data on net foreign direct investment reported by the Organisation for Economic Co-operation and Development and official national sources. The data suffer from deficiencies relating to definitions. cov- 1999 World Development Indicators 273 UP ~5.2 Stock markets Market capitalization Value traded Turnover ratio Listed domestic IFC Investable companies Index value of shares traded as % of % change in $ millions % of GDP % of GDP capital ization price indes ±990 ±.998 ±990 ±997 1990 1997 ±990 ±998 I±990 ±997 ±1997 1998 A lbania ........... ... ... .. . . ...... Algeria . .. Angola ... . ... Argentina 3,268 45,332 2.3 18.2 0.6 7.9 33.6 28.8 179 136 .... 174 4.... -28.5 A rm enia ... ..... 16 1: .... 1.0 ... .. 0.1 ... .. 0 8... :.. 59.................. Australia 107,611 696,666 36.2 177.0 i13.2 79.0 31.6 52.2 1,089 i,2±9 Austria 11,476 35,724 7.2 17.3 11.7 11.9 1 10. 18 9 1017 Azerbaijan .... ....... Bangladesh 321 1,034 1.1 3.7 0.0 0.9 1.5 24.2 134..... 202 ....-67.7 -38.5a Belarus :..: .. .. :..:: Belgium 65,449 136,965 33.4 56.5 3.3 12.3 . . 182 138 B enin ... ....... .. ... .. Bolivia 344 4.3 0 0 0.6 I 1 Bosnia and Herzegovina Botswana 261 724 6.6 12.1 0.2 1.5 6.1 9.0 9 12 99.8 a 9.2 a Brazil 16,354 160,887 3:5 ......31:1 1 2 .....24 8 .....23.6 70.9 581 536 21.8 . -43.0 Bulgaria ... .. 992 ....... ... ..0: ......... ......0 00 01 -1 .. 1 7 ..a ............. Burkina Faso . . . . . . Burundi Cambodia ..' Cameroon .. .......... Canada 241,920 567,635 42.2 93 4 12.4 .....58.5 26.7 62 2 1144 1,362 Central African Republic Chile 13,645 51,866 45.0 93.5 2.6 97......7 63.3 7:3.3 ... 215 295 3.6 -29.8 China 2,028 231,322 .. 0.5 ......229.9 0....2 ......41.0 158.9 130:.1...... 14. .764..... -.250.0. . -52.6 Hong Kong, China 83,397 413,323 111.5 241.1 46.3 285.5 . 43.1 44.2 284 658 Colombia 1,416 13,357 3.5 20.4 0.2 2.0 56.6 .... 9 6 .......80 .....189..... 304.4. . -46.8 Congo, Dam. Rep. Congo, Rep. . . .. Cosat Rica 475 820 7.0 8.6 0.2 0.2 5.8 3.5 82 114 C6te d'lvoire . .... ....... 549. ..1.818. . 571 12.0 0.2 0.2 3.4 2.2 23....... 35. -106.6 4.4 Croatia 3,190 32.2 . 0~3 ........: 0.0 1.......I..... .77.. -33 8 a Cuba : :_ Czech Republic .. 12,045 ......24.6 . . ...13 6........ .....38.0 ........ : .......276 ...-22.0 ......-7.3 Denmark 39.063 93,766 29 3 55.1 8.3 27.6 28.0 54.2 258 237 Dominican Republic .. 140 0.9 . 00 .6 Ecuador 69 1,527 0.5 10.9 0:9 .....0.0 ......5.2 65 41 .......8:7a ... .36.9 a Egypt, Arab Rep. 1,765 24,381 4.1 27.6 0.3 7.7 . . 22.3 573 650 . . -30.9 El Salvador 499 4 4. .. 01. 59 ... ............... Eritrea Eatonia ........519 .. 21.5 31.7 . 22.. . .. -64a Ethiopia .. .. ... ...... .. . .: Finland 22,721 73,322 16:9 ......61 2 2 ...... 2.9 303.3 ... .. ...... . . ..... .73 124 ........ ........... France 314,384 674,368 26 3 48.4 9.8 29.1 ..... ..... 578 683......... Gabon : : . . .......... Gambia, The . .7. G eorgia... ..... . ... ... .... Germany 355,073 825,233 22.9. 394 22.1 492 1393 .... 123:2 413 700 ........... ....... Ghana 76 1,384 -12 ......164 00. ....O ...... 0.7 00 1:1 .......13 .......21 .... 32.4a 17 3a Greece 15,228 79,992 18.4 19.7 4.7 6.7 36.3 .86.5 145 230 3 1 G uatem ala .. ...139 ... .... ....... 0.8 .. . . . .0.0. 2.5 ....7 ....... ........ . Guinea Guinea-Bissau Honduras 40 1.3 8 5 0.0 0.0 0.0 0.0 26 119 274 1999 World Development Indicators Market capitalization Value traded Turnover ratio Listed domestic IFC Investable companies Index value of shares traded as % of % change in $ millions % of GDP % of GDP capitalization price inden 1990 1998 1990 1997 ±990 1997 1990 1998 1990 1997 1997 1998 Hungary.......... 505 14,028 1.5 .... 3278.8. . . 0.3 16.8 6.3 113.9 21 .... 49 .... 600.0. . -10.8 India 38,567 105,188 12.9 33.7 7.3 14:.1.. . 65.9 56.0 6.200 5,843 5.8 -23.0 Indonesia 8,081 21,224 7 1 13.5 3.5..... 19:4..... 75.8 60.7 125..... 282 -.73.6 -28. Iran, Islamic Rep. 34,282 15,123 . 7.308 30.4 22.2 97 263 Iraq... ireland . 24,135 32.2 . 202 . 24.5 .. 83 Israel 3,324 39,628 6.3 46.2 10.5 10.9 95.8 26.4 216 640 21.7 -16.0 Italy 148,766 344,665 13.6 . .. 30:.1.... 3.9 17.3 26.8 43.8 220 235 Jamaica 911 2,139 215.5 53.4..... 0.8 1.8 3.4 .......2.5 44 49 ...... 7:2a -21.3 Japan 2,917,679 2,216,699 98.2 52.9 54.0O.. . 29~9.9.... 43.8 37.1 2,071 2.387 13.4 b 4.6 b Jordan 2,001 5,838 49.8 .. 77.6 .... 10.1 ......7.1 ......20.0 .. 11.6 ......105 .. 139 12:6.6 . 3.0 Kazakhstan Kenya 453 .2.024 5.3 17.7 0.1 1.0 ... 2.2 ......3.7 54 58 ~11.0 a 13.8n Korea, Dem. Rep. 110,594 41,881 ..669 776 Korea, Rep.. 114,593 61.3 184.7. -68:9 ....120.7 Kuwait . 25,880 . . 852.2. 113 .8 ........ .. ::........... 74 Kyrgyz Republic.. 5 03. 0.0 3.7 . 27 Lao PDR .. . .. .. Latvia . 382 . 6.1 .. 1.5 1476 50 .. ......... -.67.4 Lebanon .. 2,904 ..... .19.4 43 .. 9 Lesotho . Libya . . . Lithuania . 1,074 ....... 17.8 2589 .. 2.5...89. 607 ... 87 ....-.39.2 a Macedonia, FYR...... Madagascar Malawi Malaysia .48,611 107,104 113.6 95.1 ....25.4 .149.3 .24.6 .29 3 282 708 -72:9 .... -2.9 Mali Mauritania Mauritius 268 1,849 10.1 37.8 0.....O.2 ......3.1 1.9. 5.4 13 40 ..... 5.3 a. ...14.4 Mexico 32,725 91,746 12.4 389.9 4.6. 13:.1 .... 44.0 28.5 199 198 48.8 -38.9 Moldova . .. Mongolia . 54 673 1.7 434 Morocco 966 15,676 3:7 36.3 0.2 31 ..... ...... 10:1.1... 71 49 28.7 Mozambique Myanmar Namibia 21 ..... 689 .....07 21.0 0.0 07 0.0 12 .1 ... .3 ...... 13 ...... :... Nepal . 200 471 ..01 ...... ...... : 2.3 98 .... Netherlands 119,825 468,736 42.2 130.1 14.2 79.1 29.0 92.4 260 201 New Zealand 8,835 90,483 20.5 140.1 4.5 38.2 17.3 28.1 171 :190 Nicaragua Niger Nigeria 1,372 2,887 4.8 9.1 0.0 0.3 0.9 5.2 131 182 -1 9 a -8 Norway 26,130 66,503 22.6 . 43.4 12.1 30.3 54:4.4... 70.3 112 ... .196.. Oman 1,061 7,108 9.4 16.3 09 1.7 12.3 21.3 55 114 .. ................. Pakistan 2,850 . 5.418 7.1 178.8 .06 18.6 8.7 114.3 487 781..... 26.9 .....-61.9 Panama 226 2,175 3.4 15.5 00 03 0.9 . 13 21 Papua New Guinea Paraguay . 389 3.8 . 0 2 11~3 . 60 Peru 812 11,645 2.5 27.5 0.3 6.3 19.3 19 2 294 248 12.5 -39.3 Philippines 5,927 35,314 13.4 38.2 2.7 24.1 .... 136.6.... 31.1 153 221.... -61.6. 9.2 Poland 144 20,461 0.2 8 9 0.0 5.9 89.7 54.39 143 -18.5 -.....12.3 Portugal 9,201 62,954 13.3 38.1 2.4 20.5 16.9 95 7..... 181 148 44.4 38.4 Puerto Rico . .. ... .. Romania ,1 1~8 .......... 0.....8 7.2 76 . 677.71 Russian Federation 244 20.598 0.0 28.7 . 3.7 . 0.0 13 208 . -84.2 1999 World Development Indicators 27S 5.2 Market capitalization Value traded Turnover ratio Listed domestic IFC Investable companies index value of shares traded as % of % change in $ millions % of GDP % of GDP capitalization price index 1990 1998 ±.990 1997 1990 1997 1990 1998 1990 1997 1997 1998 Rwanda : : : : Saudi Arabia 48,213 42,563 40.8 42.3 -1.9 11.8 ........ 26 9 59....... 70 -26 ...8.... Sierra Leone. ..... Singapore 34,308 106,317 91.6 .....110.4 54.2 66.4 .. 28.7 150 303 .... Slovak Republic :........ 965 .. 9.4 .. . ...11.1 .... . . 73.7 ...872 .. . .... ... . -56.0 Slovenia 2,450 . 8:9 . 1.9 68.8 24 26...... 9:2 1. 190 a South Africa 137,540 170,252 128:.9 179.8 ......7 .6 ......348 .... ......304.4. . 732 642 -138 8... -30.3 Spain 111,404 290,383 22.6 54.6 8 3 .... .85 1 .... ......... ...... 427 384 Sri Lanka 917 1,705 11.4 13.9 0.5 2.1 5.8 14.8 175 239 22:3 ....-29 .2 Sudan .. ... .. Sweden 97,929 272,730 42.6 .....1198 .......7.6. . 77.4 .....14:9. 64:4 ....258 245 ........ Switzerland 160,044 575,338 70.1 225.4 29.6 1939.9. 0.0 94 0 182.. 216 Syrian Arab Republic .. . Tajikistan Tanzania Thailand 23,896 34,903 28.0 15.3 26.8 15.0 926.6 71 2 214 431 -788.8. 34.3 Togo .: .1 .. .:: :: Trinidad and Tobago . 696 3,922 13.7 52.9 1.1 2.3 10.0 8.3 ..... 30 24 109.3a 17 5a Tunisia 533 2,268 4.3 12.2 0.2 1.5 3.3 6.8 13 34 -470.0 .... -6.4 a Turkey 19,065 33,646 12.7 32.2 3.9 31.1 42.5 1549.9..... 110 257 109 9 -53.1 Turkmenistan Uganda .. ...... Ukraine :: ......570 _ ...... 7.4 ........... ... .. ..... ........ . .. -82 .3 a ...................... .......................United Arab Em iratea :~:: ::. United Ningdom 848,866 1,996,225 87.0 155.2 28.6 64-4.4.... 33.3 36.8 1,701 2,046 23.01 15.8 United States 3,059,434 11,308,779 55.1 144.4 31~5.5... 130.4 53.4 92.8 6,599 8,851 20.3 d 26.7d Uruguay :........ 2-12 . .. . . ...1.1 ............... ..0 0 0.0 1.7 36 16 Uzbekistan .. 465 .. 19 . 01 .. . . 4 Venezuela 8,361 7,587 17.2 ......167.7 4.6 44.4.... 43.0O..... 14.2 76 91 25.7 -50.5 Vietnam .. ......... .. .. Weat Bank and Gaza.. . . . .. . Yemen, Rep. Yugoslavia, FR (Serb./Mont.) Zam bia : -.. ..... 705 . .. ........... 18:2 . .. ... ..... 0:2 .1.0....... .6 ........ Zimbabwe 2,395 1,310 27.3 ......22:1 .....0.6 ......6 0 2.9 9 2 57 64 -46.8 -58.1 ... .. .. .. .. ... .. .. .. .. .. .. . . . . . . . . . .. . . . . . . . . .. .. . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . Low income 46,507 151,713 10.9 26.5 5.2 11.5 47.1 56.1 7,086 7,902 Middle Income 438,651 1,639,972 19.4 31.8 5.1 22.-1 44.2 5,039 10,239 Lower middle income 157,836 572,133 5.2 21.9 .... 21.2 . 117.4 2,517 5,634 Upper middle income 280,815 1,067,839 22.3 40.9 3.7 22.8 31.9 48:3 2,522 4,605 Low & middle Income 485,158 1,791,685 18.9 ..... 31.3 5.3 20.9 . 46.3 12,125 18,141 East Asia & Pacific 197,109 426,006 16.3 26.4 ........6 6..... 41.3 118.2 164.1 1,443 3,624 Europe & Central Asia 19,065 243,096 2.1 23.2 10.4 . 549 110 2,711 Latin America & Carib. 78,470 608,395 7.6 30.3 2-1 15.0 29.6 45.2 1,748 2,238 Middle Eaat & N. Africa 5,265 125,286 27.8 35.5 1.5 8.6. 817 1,328 South Asia 42,655 143,250 11 3 28.4 5.9 13.1 53.4 56.1 6.996 7,163 Sub-Saharan Africa 142,594 245,652 54.8 110.1 . 4.0 20.8 . 8.0 1,011 1,077 HIMh income 8,913,233 21,749,035 56.4...... 97.8 32.4 81.9 48.7 74.9 17,064 22,253 Europe EMU 1,168,755 2,994,299 22.4 46.5 7.5 39.8 .. . 2,485 2,853 a. Data refer to the IFC Global index. b. Data refer to the Nikkei index. c. Data refer to the FT 100 index. d. Data refer to the S&P 5OO index. 276 1999 World Development Indicators 5.2 Financial market development is closely related to The International Finance Corporation (IFC) has * Marketcapitalization(alsoknownasmarketvalue) an economy's overall development. At low levels of developed a series of indexes for investors inter- is the share price times the number of shares out- economic development, commercial banks tend to ested in investing in stock markets in developing standing. * Value traded refers to the total value of dominate the financial system. As economies grow, countries. The IFCG indexes are at the core of the shares traded during the period. * Tumover ratio is specialized financial intermediaries and equity mar- IFC family of emerging market indexes. The IFCG the total value of shares traded during the period kets develop. indexes are intended to represent the most active divided by the average market capitalization for the The stock market indicators presented in the stocks in the stock markets they cover and to be the period. Average market capitalization is calculated as table include measures of size (market capitalization broadest possible indicator of market movements. the average of the end-of-period values for the current and number of listed domestic companies) and liq- The IFCI indexes apply the same calculation method- period and the previous period. * Listed domestic uidity (value traded as a percentage of GDP, and ology as the IFCG indexes, but include only a subset companies are the number of domestically incorpo- turnover ratio). The comparability of such indicators of IFCG markets that IFC has determined to be rated companies listed on the country's stock between countries may be limited by conceptual and "investable." The indexes are designed to measure exchanges at the end of the year. This indicator does statistical weaknesses such as inaccurate reporting returns on emerging market stocks that are legally not include investment companies, mutual funds, or and differences in accounting standards. The per- and practically open to foreign portfolio investment. other collective investment vehicles. * IFC Investable centage change in stock market prices in U.S. dol- The indexes are widely used benchmarks for inter- index price change is the U.S. dollar prce change in lars, from the International Finance Corporation's national portfolio management. The IFCG indexes the stock markets covered by the IFCI country index, Global (IFCG) and Investable (IFCI) country indexes. cover 51 markets, providing regular updates on supplemented by the IFCG country index. are important measures of overall performance. more than 2,200 stocks, and the IFCI indexes more Regulatory and institutional factors that can boost than 1,250 stocks. See IFC's booklet The IFC Data sources investor confidence, such as the existence of a secu- Indexes: Methodology, Definitions, and Practices for rities and exchange commission and the quality of further information on the IFCG and IFCI indexes. Data are from IFC's Emerg- investor protection laws, may influence the func- Because markets included in IFC's emerging mar- ing Stock Markets Factbook tioning of stock markets but are not included in this kets category vary widely in level of development, it .1998, with supplemental table. is best to look at the entire category to identify the data from IFC. IFC collects Stock market size can be measured in a number most significant market trends. And it is useful to data through an annual sur- of ways, each of which may produce a different rank- remember that stock market trends may be dis- vey of the world's stock ing among countries. Market capitalization shows torted by currency conversions, especially when a exchanges, supplemented the overall size of the stock market in U.S. dollars currency has registered a significant devaluation. by information provided by and as a percentage of GDP. The number of listed - Reuters and IFC's network of correspondents. GDP domestic companies is another measure of market data are from the World Bank's national accounts size. Market size is positively correlated with the The top 10 emerging stock markets data files. About the data is based on Demirguc-Kunt ability to mobilize capital and diversify risk. i 1997 and Levine (1996b). Market liquidity, the ability to easily buy and sell I I;r [ , , ;. r,| .i LII. ,,|: securities, is measured by dividing the total value traded by GDP. This indicator complements the mar- ket capitalization ratio by showing whether market t size is matched by trading. The turnover ratio-the ' value of shares traded as a percentage of market cap- italization-is also a measure of liquidity as well as of transactions costs. (High turnover indicates low trans- I I actions costs.) The turnover ratio complements the , l ratio of value traded to GDP. because turnover is , i' , related to the size of the market and the value traded ratio to the size of the economy. A small, liquid mar- ket will have a high turnover ratio but a small value traded ratio. Liquidity is an important attribute of stock market development because, in theory, liquid markets improve the allocation of capital and enhance rhe market capitalizatlon ot emerging markefs prospects for long-term economic growth. A more accounted for 9 percent of the world s total market comprehensive measure of liquidity would include capitalization in 1997. trading costs and the time and uncertainty in finding a counterpart in settling trades. e999 World Development Indicators 277 qiN 5.3 Portfolio investment regulation and risk Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICRG risk Investor country sovereign sovereign long-term fating0 credit credit- long-term debt rating' rating' worthiness debt rating' rating' Foreign Dom estic Foreign Domestic Repatriation Repatriation currency currency currency currency Entry of income of capital December September September December December December December 1997 1997 1997 1998 ±.998 ±998 1998 ±998 1.998 1998 Albania . 57.0 12:0.0 ...... 13.8 .... ....... ....... ........... ....... Algeria ...56.5 258 263 . A ngola . .. . . .. . . .. . . .. . . . . . . .. . . . . . .... . : .. . .. .. ... . . I . . . . .49 . . 1. 3 1452 3 . .. . . . . . . .. . . . . . . . . . .. . . . . . . . . .. . . . . . Argentina Free Free Free 74.5 41.8 45.0 Ba3 Ba3 BB 888- Armenia .57.5 . 15.9 Austria 85.3 888 98 Aaa Aaa MAA AMA Azerbaijan ... .5. . 17.5 Bangladesh Free Free Free 62.3 26.1 23.4 Belarus ..610 14I17 . Belgium .813 ..... 835...94.3....... A.... ..+..... Benin .... 6. 8.4 . Bosnia and Herzegovina Botswana Free Free Free 81 0 51 9 ........40.. 2 ... ... ..................... ............... Brazil Free Free Free 65.3 38.1 35.9 82 Caal 88- BB+ Bulgaria 76.3 2. 65 B Burkina Faso 60.82 . 20. 5. ... .... ... ...... ..... ... .... Burundi Cambodia 5.4 Cameroon . 63.5 18.5 22.5 Canada 83.0 84.5 93.0 Aa2 Aal MA+ MAA Central African Republic . . 5.8 Chad ... ...6.2 Chile Rel. free Free Delayed b 74.8 62.0 59.8 AAM China Special . ..Free ..... . Free 75.5 57 7 7 . ..... 480 ....... A3 ..................... 888B+ Hong Kong, China ... .748 1 75.8 .A + Colombia Auth. only Free Free 55.0 46:2 .. . ..46.2 Baa3 Baa2 888- A Congo, Dam. Rep... .. 30.8. 8.6 ........... _..... .. . ...... :. .. Congo, Rep. ... .43.8 111 16.0 . Costa Rica Free Free Free 76.0 36.4 39.8 Bal Bal 88 88+ C6te dilvoire Free Free Free 65.8 22.2 30.3 Croatia Free Free Free 70.8 38 0 43.9 Baa3 13888- 888+ Cuba . . 60.8 12.5 17.3 . Czech Republic Free Free Free 76.8 . 23 . Al A-A- Denmark . .. 86.3 84.7 89.7 Aal Aaa AA+ AAA Dominican Republic ... ......72%3 ...... .26 4220 . 81 . ... +. 88..........B Ecuador Free Free ... Free ....... 60.8 8 ...... 26 1 28.4 8. . .. 3 83 Egypt, Arab Rep. Free Free Free 70.8 43.2 43 1 .................. ....888- A- ElSavaSalvador...... ..................... 75.... 47 288 7 ..7 ... 36. 6 7 .... .... .........88..Ba888+3113 Eritrea Estonia . ....... ...... ... ...73 0 ....... 40:8 .... ...41.2 ... .. ......8.....88+....... B ...... A- Ethiopia 593 16:6 .... ...24.7 ............ Finland 873 81:1 94.5 .... ...Aaa ........Anaa... .... AA...M. France 82.0 90.4 95.9 Aaa Aaa AMA MAA Gabon .69.0 2. 28.3 Gambia, The ..69.5 9.2 Georgia 19 27.2 Germany ... .838 2 97.1I .Aa AA Ghana . ........ . Free . ..... Free Free 62.5 30 3 3 ....... 29..4 . .. ............. Greece Free Free Free 79.3 54 6 96 Baal A2 Guatemala ... .67.5 266 3. BaBa Guinea 61.8 1618 Guinea-Bissau 47.0 148 . Haiti . 55.3 11:9. 9.6 Honduras ...60 3 201 28 . 2 278 1999 World Deveiopment Indicators Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICR6 risk Investor country sovereign sovereign long-term ratinga credit credit- long-term debt rating' rating' worthiness debt ratinga rating' Foreign Domestic Foreign Domestic Repatriation Repatriation currency currency currency currency Entry of income of capital December September September December December December December 1997 1997 1997 1998 1998 1998 1998 1998 1998 1998 Hungary .. - ..Free ..... Free ........ Free .. 78~O.0 ... .. 54.1 55.0 ... ....... Al . ....... BBB A India Auth. only Free Free 64.8 44 9..... 39.0 . 852 BB 888 Indonesia Rel. free Restricted Restricted 41.0 329.9 27 .2 ..8. ... 3 83 AA+ B Iran, Islamic Rep. ... 69.5 29.3 23.4 .......: Iraq .. ......... 4 5~8 .... ....7 :0 6 8......... ....... .... ..... .... Ireland 88~3 .... 80 4. 99 Aa As AM Israel . .. .. 65.8 53.1 7611 Ass . .... ..... A-. AA- Italy .. .......81.0 79:0. 91.1l....... Aa3 .. Aa3 ....... APAA Jamaica ....... Rel.. free.... Free. .... Free ....... 72.0 29 1 1 .... 42.6 Ba3 8aa3 :..........:.... Japan ....83.3 ...... .883.3 88.0 Aal l AAA .... AAA Jordan ... . Free ....... Free. Free.... ... 75.5 36.8 .... 40.3 .... 8.a3 B - .... 88-. 888- Kazakhstan . .. 70.3 27.7 27.5 8a3 .. + B8- Kenya Rel. free Free Free 61.5 25.9 24.6 . Korea, Dem. Rep. ..40O 0..... 5.9 27.4 .... Korea, Rep. .........Rel. fre.e Free ........Free .... 70 .0 .....536.6 . 64.5 .......Bal B 88+ .... 888+ Kuwait ..783.3 57:4 .......77.7 ...A A+ Kyrgyz Republic . .23.7 ..: Lao PDR Latvia 71O. .. 7 .0 ... 36:3 378 . 8 A- Lebanon 56.3 31.8 34.1 81 RB- 88 Lesotho ... . 3.8 . Libya . .643.3 286.. 216... Lithuania Rel. free Free Free 74.0 . 36.6 Bal 888- 888+ Macedonia, FYR ... .154 4 Madagascar . .65.5 20.2 Malawi . . 62.8 19.8 18. Malaysia Free Free Free 678.8 59 ....0...41.9 8aa3 A3 888- A Mal 660O ...... .170(.. ..... 249 ... .... ..... Mauritania . .9.2 Mauritius53 28.7 Baa2 Mexico Free Free Free 67.5 454 4... 45.4 Ba2 8sa3 88 888+ Moldova ... .57.8 . 1.7.3 82 Mongolia. 65.5 19.6 Morocco 72.5 42.4 41.0 BB. 8 8 Mozambique 58~3. 1. 0 Myanmar 55.0 20.7 17.4 Nam ibia.... ... Free Free ....Free .....77 8.. .36. 3........... ........ ..... Nepal ... .24.6 13.7 Netherlands ......I..... ..... ............. 88.5 919.9 96 .9 .. ........... Ass MAA MAA New Zealand . .. 76.5 73.7 91-3 Aa2 AsA- MAA Nicaragua .47 3 124B2. 2 Niger 55 .. 10 Nigeria . 59.5 1654. 17.2 Norway 89.5 88.2. 95.8 Ass Ass MAA MAA Oman Free Free Free 75.0 53:4. 61..5 ... .....888-.. . B Pakistan Free Free Free 53.8 25:3.. 18..6 .......Caal cc ..... ...:.... Panama Free Free Free .... 73.5 38:1.1...... 49.7 .....8al 88..... ...... B+ BB+ Papua New Guinea 67 .8 31 67.8.31.0 29 0 Paraguay . 64.0 32.7 31.1 81 88- 888- Peru Free Free Free 66~3.3 ..... 34.6. 40.1 ......... Baa3 88. ....B 888- PhilippinesSpecial Free ......Free .....690.0 43.0 .... 40.3 ..... Bal .......Baa3. 88+ 888± Poland Free Free Free 82.0 54.0 52.1 Baa3 A2 888- A- Portugal Free Free Free 84.5 755.5 .90.7 ... Aa2 Aa2 AA ...... AA Puerto Ric .. .. .... ....... Romania ... .60.3 33.8 .... 83 B. - 8+ Russian Federation ... .49.0 302 177 8 aCCC- 1599 World Development Indicators 279 5.3 Entry and exit regulations Composite Institutional Euromoney Moody's Standard & Poor's ICRG risk Investor country sovereign sovereign long-term rating' credit credit- long-term debt rating' rating' worthiness debt rating' rating' Foreign Domestic Foreign Domestic Repatriation Repatriation currency currency currency currency Entry of income of capital December September September December December December December 1997 1997 1997 ±998 1998 1998 ±998 ±.998 ±998 ±998 Rwanda Saudi Arabia 73~O.0 .......55.7 ........63 . 7 ... ... . ... ..... . .. ... . . Senegal ..63.3 2.355 . Sierra Leone .310 ...... .6:7 .. .....11.3 ... .... ...... ...... Singapore - 89.3 823 82 Aaa AAA AAM Slovak Republic ........... 780..... .. .................... . 41:7 . ......39.0 .... S... al Baa2 SBB- ...... .. BBB÷ Slovenia Closed Restricted Restricted 80.0 55.9 55.4 ....... A3..... . .....A. ....... MA South Africa Free Free Free 688.0 46.6 4. Baa3 Baal BB+ BBB÷ Spain -.. .79.8 80.0 920AA a Sri Lanka Rel. free Restricted Restricted 63.3 32.5 ...... 27.. 5 ... ... ..... .............. ......... .... Sudan ... .43.3 7 .27. Sweden ... ... ... ... 83.5 ...... 79.1 ........93.4 ........Aa2 ........Aal AA+ AMA Switzerland ,7 ...... . ......_:......... 87.0 93.4 96.4 .. Aaa AMA AMA Syrian Arab Republic 70.5 24.5 24.4 Tajikistan 17.8 Tanzania 60~8 ...... 199 21.0 .......: ..... ... Thailand Rel. free Free Free 69.3 47.5 41.2. Bal Baal 555-.. .A- Togo -.. .60.3 18.1 20.1 . Trinidad and Tobago Rel. free Free Free 76.8 42.9 47.0 Bal Baa3 BB+ BBS+ Tunisia .. .. . 72.5 49.0 45.7 Baa3 . 55- A Turkey Free Free Free 52.3 37.8 38.5 B5I-B Uganda -..64.0 1925- Ukraine 61.5 19.8 17.1 83 United Arab Emirates -78.3 61.8 79.4 -. : United Kingdom- 83.5 90.9 95.0 Aaa Aaa AMA AMA United States7.......... . ............83.5 ...... .91 2 .......97.8 .Aaa sAMA AMA Uruguay .....-..................................... 72.8 45.2 46.3 Baa3 Baa3 BBS- ..... .BBB+ Uzbekistan ... .19.5 . Venezuela ReL. free Restricted Restricted 62.3 36:1 ........30.0 ......52 Caal West Bank and Gaza Yugoslavia, FR (Serb./Mont.) ....38.8 . 13.9 Zam bia Free Free Free 59.0 17:2 ........21. 1 ....... ...... .............. ... ..... Zimbabwe ReL. free Free Free 51.0 29 .8 26.2 Low income .............59.2 ......18.3 17.6 Middle Income 69.5 36 .3 36.7 Lower middle income 69.0 30 2 29.0 Upper middle income 72.9 44 .0 42 0 Low & middle income 64.0 .....28.6 .......25.6 East Asia & Pacific 66.7 32.9 ..... 27.5 Europe & Central Asia 65.9 33.8 .......23.7 Latin America & Carib. 67.0 33 .7 36.3 Middle East & N. Africa 70.5 34 .3 37.2 South Asia 62.8 25.7 .......21.0 Sub-Saharan Africa .... ... . .... ........ ......I. ... ...... 60.8 . ...18.5 ........20.2 High income 83.3 80.4 ........91 3 Europe EMU 84.2 82.3 94.7 Note: For explanations of the terms used to describe entry and exit regulations see Definitions, a. This copyrighted material is reprinted with permission from the following data providers: PRS Group, 6320 Fly Road, Suite 102, P.O. Box 248, East Syracuse, N.Y. 13057: Institutional Investor. Inc., 488 Madison Avenue, New York, N.Y. 10022; Euromoney Publications PLC, Nester House, Playhouse Yard, London EC4V 5EX, U.K.; Moody's Investors Service, 99 Church Street, New York, N.Y. 10007; Standard and Poor's Rating Services, The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, New York, N.Y. 10020. Prior written consent from the original data providers cited must be obtained for third-party use of these data. b. After one year. 280 1999 World Development Indicators I ffl0kr" 5.3 As investment portfolios become increasingly global, gle numerical risk assessment ranging from O to 100. * Regulationsonentrytoemergingstockmarkets are investors and governments seeking to attract foreign Ratings below 50 are considered very high risk, and evaluated using the following terms: free (no significant investment must have a good understanding of coun- those above 80 very low risk. Ratings are updated restrictions), relatively free (some registration proce- try risk. Risk, by its nature, is perceived differently by every month. dures required to ensure repatriation rights), special different groups. This table presents information on Institutional Investor country credit ratings are classes (foreigners restricted to certain classes of country risk and creditworthiness from several major based on information provided by leading interna- stocks designated for foreign investors), authorized international rating services, and information on the tional banks. Responses are weighted using a for- investors only (only approved foreign investors may buy regulation of entry to and exit from emerging stock mula that gives more importance to responses from stocks), and closed (closed or access severely markets reported by the International Finance banks with greater worldwide exposure and more restricted, as for nonresident nationals only). - Regula- Corporation (IFC). sophisticated country analysis systems. Countries tions on repatriation of income (dividends, interest, and Entry and exit restrictions on investments are one are rated on a scale of 0 to 100, and ratings are realized capital gains) and repatriation of capital from of the mechanisms by which countries attempt to updated every six months. emerging markets are evaluated as free (repatriation reduce the risk to their economies associated with for- Euromoney country creditworthiness ratings are done routinely) or restricted (repatriation requires regis- eign investment. Yet such restrictions may increase based on nine weighted categories (covering eco- tration with or permission of a government agency that the risk or uncertainty perceived by investors, increas- nomic performance, political risk, debt, and access may restrict the timing of exchange release). ing their reluctance to participate in regulated mar- to financial and capital markets) that assess country * Composite international Country Risk Guide (ICRG) kets. Many countries close industries considered risk. The ratings, also on a scale of 0 to 100, are risk rating is an overall index, ranging from 0 to 100, strategic to foreign or nonresident investors. Or based on polls of economists and political analysts based on 22 components of risk. * Institutional national law or corporate policy may limit foreign supplemented by quantitative data such as debt Investorcreditratingranks,fromOtolOO,thechances investment in a company or in certain classes of ratios and access to capital markets. of a country's default. * Euromoney country credit- stocks. The regulations summarized in the table refer Ratings of sovereign foreign and domestic cur- worthiness rating ranks, from 0 to 100, the riskiness to 'new money" investment by foreign institutions; rency debt by Moody's Investors Service are pre- of investing in an economy. * Moody's sovereign for- other regulations may apply to capital invested sented for obligations that extend longer than one eign and domestic currency long-term debt rating through debt conversion schemes or to capital from year. These long-term ratings measure total expected assesses the risk of lendingto governments. Aaa bonds other sources. The regulations shown here are formal credit loss over the life of the security; they are not are judged to be of the best quality and C bonds of the ones. But even formal regulations may have very dif- intended to measure other risks in fixed income lowest quality. Numerical modifiers 1-3 are applied to ferent effects in different countries because of dif- investment, such as market risk. classifications from Aa to B. with 1 indicating that the ferences in the prevailing bureaucratic culture, the Standard & Poor's ratings of sovereign long-term obligation ranks at the high end of its rating category. speed with which applications are processed, and the foreign and domestic currency debt are based on cur- * Standard & Poor's sovereign foreign and domestic extent of red tape. The effect of entry and exit regu- rent information furnished by the issuer or obtained currency long-term debt ratings are categorized as lations may also be influenced by graft and corruption, by Standard & Poor's from other sources it considers investment grade (AAA through BBB) and speculative which are impossible to quantify. reliable. The ratings reflect several risk factors, such grade (BB through C). Ratings from M to CCC may be Most risk ratings are numerical or alphabetical as the likelihood of default and the capacity and will- modified by the addition of a plus (+) or minus (-) sign indexes. For numerical ratings, a higher number ingness of the debtor to make timely payments of to show relative standing within the rating category. means lower risk. For alphabetical ratings, a letter interest and repayments of principal in accordance closer to the beginning of the alphabet means lower with the terms of the obligation. The ratings measure Data sources risk. Readers should refer to the data sources for thecreditworthinessofthedebtoranddonottakeinto more details on the rating processes of the rating account exchange-related uncertainties for foreign Data on emerging stock mar- agencies. Risk ratings may be highly subjective, currency debt. kets' entry and exit regula- reflecting external perceptions that do not always tions are from IFC's Emerging capture the actual situation in a country. But these Stock Markets Factbook subjective perceptions are the reality that policy- 1998. Country risk and cred- makers face in the climate they create for foreign pri- _ w itworthiness ratings are from vate inflows. Countries that are not rated by credit several sources: the PRS risk rating agencies typically do not attract registered Group's monthly Intemational flows of private capital. The risk ratings presented Country Risk Guide; the monthly Institutional Investor; here are included for their analytic usefulness and the monthly Euromoney; Moody's Investors Service's are not endorsed by the World Bank. Sovereign, Subnational and Sovereign-Guaranteed The PRS Group's International Country Risk Guide Issuers; and Standard & Poor's Sovereign List in Credit (ICRG) collects information on 22 components of Week. risk, groups it into three major categories (political, financial, and economic), and converts it into a sin- 1-999 World Development Indicators 281 5.4 Financial depth and efficiency Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Spread over provided by liabilities liabilities liquid reserves to spread LIBOR banking sector bank assets Lending minus Lending rate deposit rate minus LIBOR percentage percentage % of GOP % of GDP % of GDP%points points 1990 1997 1990 1.997 ±1990 1L997 1990 1.997 1990 1997 1990 1997 Albania . 532.2 .. 58.8 . . 31.7 . 67.7 ... 2.1 .......72 16.7 18.4 Algeria 74~7 ... 42 9 73.6 .... 39.8 24.9 15.0 1.3 2.0. Angola Argentina 324 .... 272 2.... 11.5 23.9 71.1 17.3 7.4 26 . 2.3 . 3.5 Armenia............. 62 3 .... 8:2..... 83.5 8.9 42.9 .... 3.3 13.6 18.0. ...... 28.2 . . 48..5 Australia 103.5 87:5. 63.1 67.1 50:8 46.7 .......15 ......1:8 4.5 4.2 9.9 3.5 Austria ........ ...123.0 ....132.0 88 .8 . 89.8. 75.2 73.1 ... ..2.1 2. Azerbaijan 57.2 11.1 33.5 11.8 11.6 3.5 4.5 11.3 ........ . 156.5 Bangladesh 24.1 31.9 23.6 . 303 17.0 21.7 12.8 8.2 4.0 5.9 7.7 8.2 Belarus .. 17.7 ........_. 16.5 6._8 ....... .....17.9 16226. Belgium 73.6 147.0 47.9 85.6 29.0 68.2 0.2 1.0 6.9 4.2 4.7 1.3 Benin ....- ....I 22.4 7.5 26.7 23.4 5.9 I 8.0 ..29.3 12:1.1 ... 9.0 77....... 7.7... Bolivia......... ... 30-.7 ......555.5 24.5 ... 47.3 18.0 337.7 .... 18.8 10.1 18.0..... 35.3 33.5 44.3 Bosnia and Herzegovina.. ... . Botswana -49. -75:2.2.... 23.6 22.8 14.7 17.0 11.0 10.0 1.8 4.8 -0.4 8.3 Brazil 898.8 . 43.1 26.4 30.1 18.5 24.2 6.7 11.0. ........ ...... :.......: ... Bulgaria 118.5 30.0 71.9 33.6 53.6 20.2 -10.2 9.2 8.9 37.1 42.4 78.2 Burkina Faso 13.7 .... 13.4 21.3.... 25.2 7 5 6.0 12:7 ......5 4 .... 9.0 7 7 .7 ......: ... Burundi 24.5 .....25.4 18.2 19.8 6 5 5:5 2 8 5.0......... .... .... .....4.0 .....9.2 Cam bodia ..................... 7....... 7.. 11.7 . 7.5 . 16.5 ......... .. . . 1 .4........ .. _12 6 Cameroon 31:2 16:3 22.6 14.0 .... 10:1 5.4 34.4. . 11.3...... 11.0 10.5 10:2.2... 10.0. Canada ............. 85.9 103.3 74.3 79.8 59.8 59.4 16.6. 06.6 ..... 1.3 1.4 57.7 ..-0:8 Central African Republic 12.9.... 10.0 .... 15.3 19.6 1.8 1.5 2.8...... 6.3.. 11.0 10.5 10..2 .... 10.0 Chad 10:9 ......9~8.. 15.8 12.2 0.6 0:6 3.4 17.7...... 11.0 10,5 10.2, 10.0 Chile 73:0.0 ... 64.7 40.7 45.2 328.8 .... 35.2 3.8 2.8 ..... 8.6...... 37 .....40.5 9.....9 China 90.0 1063.3..... 79.2 122.9 41.4 71.6 15.7 ... 197.7...... 0.7 3.0 1.0..... 2.9 Hong Kong, China 1563 1677.7.... 181l.7 254.4 166.8 241.4 0.1 02 33 3.5 17 3.7 Colombia .... ..... 362 2.... 47 0 29.8 38.9.... 19.3 28.2 26 3 ....10A ..1.... 8.8 10.1 36 9 28.5 CongoDm.Re..2,3.Dam.. 17 ..Rep.29 .7 2 253490 ..17.. 129. ... ..6.7.. ..2.1. 2.0......... 490 . . 46.. Congo, Rep. 29.1 16.9~ 22.0 15.0 6.1 2.6 20 9.3 11.0 10.5 10.2 10.0 Costa Rica . . ........ 29.9 .... 37.4 ..... 42.7 ...42.4 30.0 30.0 71.0 65.1 ..... 114 9.5 242 .... 16:7 Cbe dIlvoire 44 5 .....288 28.8 26.9 10.9 8.8 2 1 2.6 9.0 7. 7 .7......... Croati.a .................... 464........... .. . .35.2 ..... ....... 24.2 6 3.3 .... 499.3 11.2 1,153.9 9.7 Cuba.. . .. Czech Republic 79:8 71.2 4-5:9 .... .... 12.7 5.5 7~4 Denmark 63~.0... .56.0 59.0 59.1 29.4 28Z 5..... 1.1 6.7 6.2.. 5.1 5.8 2.0 Dominican Republic........... 3-1.3 ..... 31.0 25.7 30.1 13.1 18.5 21.1 18.3 ...... 15.2 7.6 29.3.... 15 2 Ecuador 17.2 ..... 369.9. 23.3 36.0 12.9 27.2 23.1 7.9 -6.0 14.9 29.2 37.3 Egypt, Arab Rep. .... 1068.8 . 86.6 87.9 82.2 60.7 63.2 17.1 130 0...... 7.0 4.0 10 7 ......8.0 El Salvador 32.0 43.9 30.6 45.7. 19.6 ......35.9 33.4 28.7_ 3.2 4.3 12!9.9... 10.3 Eritrea.. ... ..... Estonia 65.0 29.6 136.2 30.0 93.5 ..9:7 .....43.1 14.9 ... ... 13.6 ... .266 6... 14.1 Ethiopia 67.3 44.3 42.0 44.2 12.5 19.8 23.3 12.7 3.6 3.5 -2.3 4.7 Finland 84.3 ..... 56.5 ..55.2 51.2 46. 164 41 4 41 33 33 -5 France ......... 106.3 1022.2 ..... 64.6 71.3 .38.7...... 47.4 1.0 ......0:4 . .....60 2.8 2:2.. _.0.6 Gabon 20.0 -166.6 17.8 .... 15.6 6.6 5.7 2.0 9 2 .....11.0 10.5 10 2.... 10.0 Gambia,The 3~4. 10.0 .. 20.7 28.6 8.8 13~5 8.8 13.2.... 15.2 13.0 182 .... 197 Georgia Germany ........... 110. 0 ... 1415 5..I. 64.4 71.4 44.2 46.6 3.5 1.5 4 5 6.4 3.3 3.4 Ghana 13.2 27.0 14.1 18.9 3.4 6.4 20.2 7.6..................... Greece 103.8 84.1 72.3 65.8 58.2 5-1.2 22.4 25.6 8.1 ......8.8 19:3 .....13:2 Guatemala 174.4 19.7...... 21.2 26.5 11.8 15..4..... 31.8 28.8 5.1 12.8 15.0 12.9 Guinea 5.4 6. 08 97 0.8 2. 62 1....4.7 ..0.2...... 4.0 12.9 15.5 Guinea-Bissau .......... 42.1 .. 7.4 .... 16.5 15.7 4.4 ......5.8 10.8 18.2 13.1 4.5 37.4 46.2 Haiti 329.9 25,8.8.... 31.4 31.6 159.9.... 206.6.... 74.9 30.7- 10.3 . 15.2 Honduras...... ...... 40..... . 9.... .29 .2 .... 33.6 39.9 18.8 25.3 66 2 . 83 10.8 8.7...... 26.3 282 1999 World Development Indicators 5.4 Domestic credit Liquid Quasi-liquid Ratio of bank Interest rate Spread over provided by liabilities liabilities liquid resefves to spread LIBOR banking sector bank assets Lending minus Lending rate deposit rate minus LIBOR percentage percentage % of GDP % of GDP % of GDP%points points 1990 1997 1990 1997 ±990 1997 1990 1997 1990 1997 1990 1997 Hungary ..... ........ 82.6 .. 49.1 .... 43.8 ..41.9 19.0 236.6 285.5 . .. 490O . 4.1. 3.2 20.5 ... 16.0 India 54.....7. ... 49.7 45.8 51.7 29.9 34.7 14.8 10.9 . . 8.2 8.1 Indonesia 45.5 ....58 0 40.4 55.6 29.1 44.6 4:5 ... 3:9...... 3.3 1.8 125 5... 16.1 Iran, Islamic Rep. 70 8 45 7 57.6 40.8 31.1 .... 22.2 ...66 0.... 589.9 I raq Ireland 58.1 44.6 44.5 75.6 32.4 61.9 4.8 28 .... 5.0 6.1 3.0 0.8 Israel 106:2.2... 82.3 70.2 86.9 63.6 80.2 11.9 13.4 12.0 5.6 18.1 12.9 Italy 90.6 ....92.9 71.0 54.2 35.7 21.1 12.9 5.1 7.3 4.9 5.8 4.0 Jamaica 34.8 39.2 51.0 51.7 ... 37.8 36.1 37.4 27.8 6.6 22.3 22.2 30.5 Japan 2668.8. . 296.0 187.5 210.5 159.6 170.2 1.5 1.2 3.4 2.1 -14.... -3.3 Jordan 117.... .9.... 90.9 131.2 102.0 ... 778.8. 69.3 20.5 369 9...... 2.2 3.2 2.0 6.5 K aza....khsta.. n ... ... ... . . . 6 .5........ . . .9 0. . .... . . .. 16.0....... . . ........ ... .... ......... Kenya 52.9 53.3 43.3 49.6 29.3 34.5 9.9 13.7 5.1 13.5 10.4 24.5 Korea, Dem. Rep. Korea, Rep. 65.3 ... 86.1 54.3 87.9 45.5 79.6 6.3 1.9 0.0 1.1 1.7 6.1 Kuwait 243.0 102 1 192.2 82.7 153.9 ... 69.1 .......1:2 0..7. ...... 0.4 2.9 4.1. 3.0 Kyrgyz Republic..... . .................. ......:...... ... . .. I., 9.843:6 Lao PDR 5...1 .161.1 7.2. ..18.4 3.1 14.8 .3.4 14.7 2.5 11.0 200.0... 21.5 Latvia .. 15.2 79 .. 10.3 . 69.. 9.4 . 9.5 Lebanon 132.6 122.5 193.7 150.1 170.9 141.-7 .....3.9 ....164.4.. .. 23.1 6.9 31.6 14. Lesotho 30 1 -25 3 38.8 33.1 22.4 157.7 24.1 23.1 7.4 6.2 12: 1. 12:3 Libya.... I:. .......... ..40.8.......... 54.0...... . 1.5....40 8 ......4 ........13 - . Lithuania 11.7 18.9 5.6 10.5.. 658 Macedonia, FYR.. . Madagascar 26 2 13.9 17.8 24.0 5.3 9.2. 8.5 24.4 5.3 15.6 175 5.. 24:2 Malawi 20.5 8.3 22.0 14.3 12.2 6.5 32..8 ....43.7 .......8.9 ... 18.0 12.7 22.5 Malaysia 77.9 -165.1 66.3 135.7 44.3 104.8 5.9 11.3 1.3 1.8 -1.1 3.8 Mali 13.4 12.1 20.0 23.4 5.4 60.0 50.8 11.2 .....9.0 77.7 Mauritania.......... 54.7 7.8 28.5 16.0 7.0 5.5 6 1 6.7 50 1.7 .......... Mauritius 45.1 72.2 63.3 79.6 49.1 67..2 8:8 5.5 5.4 9.8 9.7 13.2 Mexico 37.7 30.0 22.8 30.1 '16.4 21.5 4.2 49.4 9.9 . 18.8 Moldova .....62.8 .26:7.7... 70.3.. .22.2 35.4 7.2 8.3 2.6 9.9 27 6. Mongolia....... 68.5 .10.2. 52.4 .... 25.0 -13.8 13.8 2.0 87 7 ..... ..... 36.9 69.1 Morocco..... 60.1 59.8. . 61.0 67.3 18..4 19.0 11.~3 6.9 0.5 0.7 Mozambique 20.9 3.5 32.7 26.1 6.5 4.8 61.5 .....169.9... .... Myanmar 32.7 28.6 27.9 27.3 7.8 5.9 318.6 13.4 2.1 4.0 -0.3 10.7 Namibia 19.5 554.4... 23.4 . .. 48.2 13.7 29.0 ..... 4.4 3.5 10.6 7.5 17.4.... 14.4 Nepal 28.9 35.6 32.2 38.9 18.5 25.1 12.7 10.2 .6.1 8.8 Netherlands 1075.5. .132.6 84.1 86.0 60.1 56.2 0:3 ......0:2 8.4 3.0 3.4 0.4 New Zealand 74.3 98.3 66.0 87.1 32.5 52.5 2.9 15 4.4 ....4.1 7.7 5.6 Nicaragua 206.6 148.6 .. 56.9 44.1 23.1 34.1 20.2..... 253.3. ...12.5 8.6 13.7 ...15.3 Niger 162.2 10.2 19.8 9.1 8.3 2.3 42 9 10.6 9.0 . 7.7 - Nigeria 237.7 10.3 23.6 13.3 10.3 4.8 11:6 16.9 5.5 13.1 17.0 14.6 Norway 89.5 73.3 59.9 52.3 27.0 13:9 05 1 ' 4.6 2.3 5:9... 0:2 Oman 16.6 ... 29.2..... 28.9 32.5 193.3. .. 22.4 6.9 .....39.9. 1.4 2.0 1.4 ..3.5 Pakistan 50.9 518.8 39.8 49.5 1-0.0 20.4 8.9 9.4 -.. -.. ..... ....... Panama 52.7 .... 74.4 41.1 67.3 33.0 57.1 ,.... -1.... 36 6..... 3.6 3.7 4.9 Papua New Gui'nea 35.8 35.7 35.2 40.1 24.0 25.0 3.2 2.4 ..6.9 ..... 3.1 7.2 4.7 Paraguay 14.9 .. 25.5 .....21.4 ...28._9 12.8 ... 20.3 31.0 24.1. 8.1 14.0 22.7 21.3 Peru 16.2 17.7 19.9 25.8 9.5 171.1 22.0O 27.7 2,335.0 14.9 4,766.2 24.2 Philippines 26.9 84.1 36.8 66.5 28.2 55:5 11.7 5.7 4.6 6.1 15.8 ..10.5 Poland 19.5 36.9 .34.0 39.6 17:2 25.0 20.6 8.0 462.5 5.6 495.9 19.2 Portugal 71.7 99.3 ..... 82.2 97.0 57.9 66.8 17.5 2.1 7.8 4.6 13.5 3.4 Puerto Rico . .. . . Romania 79.7 9.7 .... 604 24.9 32.7 17.7 1.2 9.1 Russian.Federation 25 9 . 17.9 . 7.4 .. 13 3 15.3 ...........26.3 1999 World Development Indicators 283 5.4 Domestic credit Liquid Quasi-liquid Ratio of bank IntereSt rate Spread over provided by liabilities liabilities liquid reserves to spread LIBOR banking sector bank assets Lending mninus Lending rate deposit rate minus LIBOR percentage percentage % of GDP % of GDP % of GDP% points points 1.990 1997 1990 19S97 1990 1997 1990 1997 1990 1997 1990 1997 Rwanda 171 1..... 12.5 14.9 15.7 7.0 5.8 4.3 22.0 6.3 . 479.9.... Saudi Arabia ............ 58 7 ......34.0 47.9 51.8. 21.9 .24:9 5.6 3.7 .........:.......... Senegal 33~8.8 .. 22.0 22.9 21.9 9.7 9:0 14.1 5.2....... 9.0 77.... 7.7 . :.. . Sierra Leone 26.3 ......62.1 14.5 15.7 4.0 .5.3 .. . 64.1 ....46.9 ..... 12.0 14.0 44.2 ... 18.1 Singapore 74.1 8. 129 131 9.4 93.9 3.7 3.5 2. 1.0 06 Slovak Republic . 71:8 68.2 42.8 7.9 5.2 12.9 Slovenia ..............36 .8 35.5 34.2 42.5 25.8 34.6 ......2-.7 .....4:.1 ... 142.0 6.8 8-186.6 ... 14.3 South Africa 102.7 103 4. 5.4 28.5 21.3 3.3 2.8 2.1 46 12.7 14.2 Spain 109.0 .....1086.6 .... 76.6 75.5 45.3 45.5 9.2 ..... 3.0 .......5.4 ......2.1 7.7 0.3 Sri Lanka 43:1.1.... 322.2.... 35.2 41.1 22 9 31.5 979.9... 12-1.1 ... -6.4..... -2.2 4.7 6.2 Sudan 20.4 7. 01 9.8 2:9 36 79:5 ....333 .. .......:.......... ............... Sweden 145.5 73.0 46.6 45.0 . 19 05 6.8 4.5 8.4 1.2 Switzerland 179.0 .....184:2.2... 145.2 144.6 118.6 106.9 1.1I 0.9 -0.9 3.5 -0.9 -1.3 Syrian Arab Republic . ... . 56 6 ......568 54.7 23.6 10.5 23.6 46.0 8.1. ..........7 ..... Tajikistan Tanzania 39.2 ......13.8 22.6 21.9 7:2 10.3 5.3 ..... 9:2 .. ..... . :....21.4 . . 23.5 Thailand 91.1 1....4-0.4 74.9 89.9 66:0 81.0 3.1 ..... 6.1 ..... 2.2 3.1 6.1. 7.9 Togo 21.3 ......23.3 36.1 22.9 19.1 8.6 59.0 84.4...... 9.0 7:7.7 Trinidad and Tobago............ 58:5.5. . 57..8 54.6 48.4 42.7 36.2 13.5 153 3...... 6.9 8.4 46.6 . Tunisia 62.5 67.7 .... 51.5 51.7 26.7 .... 29.5 1.6 3.1 Turkey 25:9.. . 34 1. 24.1 36.6 16.4 31.4 16.3 10.3 Turkmen. stan ........................: .. . 1... .7 . 11.5 .. 1.0 . 11.4 Uganda 17.7 6.0 .......7.6 .....11.5 1.4 3 9 17.9 15.7. 7.4 9 30.4 15..6 Ukraine 83.2 17.2 50.1 13.6 9.0 3~8 49.0 8.1 ....30 9 ....... 43.4. 3 United Arab Emirates.......... 35.2 .....48.6 .47 0 .....56.7 382 42.2 4.4 73 3. .... ...7 ... ...:... United Kingdom 123.0 129.3 . 05 0 2.2 3.0 6.4 .....0:8 United States 114:4. . 148 7 67.8 61.3 51.2 45.0 2.3 1 7 ..................1.7 ......2.7 Uruguay.... ..... 60..I..... . 7...:..... 39.0 ..... 64.5 43.0 57:2 37.5 311 1 3... .1 7...... 76.6 51.9 166.1. . 65.8 Uzbekistan . . . : .. ..:7 : Venezuela 37 4. 18:5...... 41.1 23.4 29.4 10.3 21-9..... 28:9....... 0.5...... 4.4 19.9 13.4 Vietnam 15 9 22:6 ....22.7 23.9 9.3 .....10:4 .....13.3 121. ..1:15.3 . 9.4 West Bank and Gaza .. . .. ..~ -.. .. .. . Yemen, Rep. .......... 62:0 26.4 ..... 56.3 39.8 .10.7 ..... 173.3 121.2 21.5 ..... ... Yugoslavia, FR (Serb./Mont.) .. .. I.,: .. .: :.. . Zambia 64:5.. 44:2...... 23.8..... 17.5 12.6 10.6 33.7 10:0 .......9.4 122 2..... 26.8 40.9 Zimbabwe .... ..... 417 7 .... 613 3..... 41.8..... 46.8.. 30.3 26.7 122 2.... 10:2 .......2:9 .... 13.9 3.4 26.8 .. . .. . . .. . . .. . . .. . . . . . . . .. . . .. . . .. . ... . .. . . .. . . .. . . .. . .. ...... .. . . .. . . .. . . Low Income 46.0 .... 40:.1..... 37.8 41.5 21.6 25.2 14.1 12.1 Middle Income 64.5 60.1 43.7 55.2 26.5 36.6 13.5 10.1 Lower middle income 69.9 71.5 64.8 71.9 37.3 45.7 18.8 10.1 Upper middle income.. ........ 60 5 ......49 2 ... ..30.7 ..39:1. 19.9 ......280.0 8.1 10.1 Low & middle Income 61.3 57.6 42.7 53.5 25.6 35.2 13.8 11.2 East Asia & Pacific ...... 76.6 ..1034 67.6 105.0 41.6 68.5 ......5.2 11.3 Europe & Central Asia ..... 30:8. 28.2 .. 17.-5 .9.8 Latin America & Carib. 59.4 ....36 6 ......25.4 30.2 17:7 22.6 21.9 23.1 Middle East & N. Africa......... 69 5_ . 56.2...... 62.1 62.5 30.7 37.9 14.2 10.6 South Asia 514.4.... 47 8 43.1 49.2 26.5 31.7 12.7 10.2 Sub-Saharan Africa .......... 58.1 _ .78.8 33.3 34.9 17.7 15.7 11.9 10.9 High Income . ........ 138.7.... 160.9 94.6 99.6 . 73:.1 .... 74.6 2 3.. 1.9 Europe EMU 99.0 . 117.9 69.5 70.6 42.0 44.0 3.8 2.1 284 1999 World Development Indicators 5.4 Households and institutions save and invest inde- banking system. And where state-owned banks dom- * Domestic credit provided by banking sector pendently. The financial system's role is to interme- inate the financial sector, noncommercial considera- includes all credit to various sectors on a gross basis, diate between them and to cycle funds. Savers tions may unduly influence credit allocation. with the exception of credit to the central government, accumulate claims on financial institutions, which The indicators in the table provide quantitative which is net. The banking sector includes monetary pass these funds to their final users. As an economy assessments of each country's financial sector, but authorities, deposit money banks, and other banking develops, this indirect lending by savers to investors qualitative assessments of policies, laws, and regu- institutions for which data are available (including insti- becomes more efficient and gradually increases lations are needed to analyze overall financial condi- tutions that do not accept transferable deposits but do financial assets relative to GDP. This wealth allows tions. Recent events in East Asia highlight the risks incur such liabilities as time and savings deposits). increased saving and investment, facilitating and of weak financial intermediation, poor corporate gov- Examples of other banking institutions include savings enhancing economic growth. As more specialized ernance, and deficientgovernment policies, including and mortgage loan institutions and building and loan savings and financial institutions emerge, more procyclical macroeconomic policy responses to large associations. * Uquid liabilities are also known as financing instruments become available, reducing capital inflows. broad money, or M3. They are the sum of currency and risks and costs to liability holders. As securities mar- The accuracy of financial data depends on the deposits in the central bank (MO), plus transferable kets mature, savers can invest their resources quality of accounting systems, which are weak in deposits and electronic currency (MI), plus time and directly in financial assets issued by firms. some developing economies. Some of the indicators savings deposits, foreign currency transferable The ratio of domestic credit provided by the bank- in the table are highly correlated, particularly the deposits, certificates of deposit, and securities repur- ing sector to GDP is used to measure the growth of ratios of domestic credit, liquid liabilities, and quasi- chase agreements (M2), plus travelers checks, foreign the banking system because it reflects the extent to liquid liabilities to GDP, because changes in liquid currency time deposits, commercial paper, and shares which savings are financial. Liquid liabilities include and quasi-liquid liabilities flow directly from changes of mutual funds or market funds held by residents. bank deposits of generally less than one year plus in domestic credit. Moreover, the precise definition * Quasi-liquid liabilities are the M3 money supply less currency. Their ratio to GDP indicates the ease with of the financial aggregates presented varies by coun- Ml. * Ratio of bank liquid reserves to bank assets is which their owners can use them to buy goods and try. World Development Indicators 1999 introduces the ratio of domestic currency holdings and deposits services without incurring any cost. Quasi-liquid lia- an additional indicator, the ratio of bank liquid with the monetary authorities to claims on other gov- bilities are long-term deposits and assets-such as reserves to bank assets, which captures the banking ernments, nonfinancial public enterprises, the private certificates of deposit, commercial paper, and system's liquidity. In countries whose banking sys- sector, and other banking institutions. * Interest rate bonds-that can be converted into currency or tem is liquid, adverse macroeconomic conditions spread is the interest rate charged by banks on loans demand deposits, but at a cost. should be less likely to lead to banking and financial to primecustomers minusthe interestratepaid bycom- No less important than the size and structure of crises. Data on domestic credit and liquid and quasi- mercial or similar banks for demand, time, or savings the financial sector is its efficiency, as indicated by liquid liabilities are cited on an end-of-year basis. deposits. * Spread over LIBOR (London interbank the margin between the cost of mobilizing liabilities The indicators reported here do not capture the offered rate) is the interest rate charged by banks on and the earnings on assets. Small margins are cru- activities of the informal sector, which remains an loans to prime customers minus LIBOR. LIBOR is the cial for economic growth because they lower interest important source of finance in developing most commonly recognized international interest rate rates and thus the overall cost of investment. Interest economies. Personal credit or credit extended and is quoted in several currencies. The average three- rates reflect the responsiveness of financial institu- through community-based pooling of assets may be month LIBOR on U.S. dollar deposits is used here. tions to competition and price incentives. The inter- the only source of credit available to small farmers, est rate spread, also known as the intermediation small businesses, or home-based producers. And in Data sources margin, is a summary measure of a banking system's financially repressed economies the rationing of for- efficiency. It may not be a reliable measure of effi- mal credit forces many borrowers and lenders to turn Data on credit, liabilities, and ciency to the extent that information about interest to the informal market and self-financing. A - interest rates are collected rates is inaccurate, banks do not monitor all bank from central banks and finance managers, or the government sets deposit and lend- ministries and reported in the ing rates. The spread over LIBOR reflects the differ- 4 print and electronic versions of ential between a country's lending rate and the C the International Monetary London interbank offered rate (ignoring expected , ; Fund's Intemational Financial changes in the exchange rate). Interest rates are Statistics. expressed as annual averages. In some countries financial markets are distorted by restrictions on foreign investment, selective credit controls, and controls on deposit and lending rates. Interest rates may reflect the diversion of resources to finance the public sector deficit through statutory reserve requirements and direct borrowing from the 1999 World Development Indicators 285 5.5 Tax policies Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate, profits, and and services capital gains % of value Individual Corporate % of % Of added of industry % of % of rate on income rate GDP total taxes and services exports imports % over $ % 1997 1980 1997 1980 1997 1980 1997 1980 1997 1998 1998 1998 Albania 16.6 . 10.7 .. 19.0 .. 00 10.0 A lgeria 31.2 71.6 . 4 :8 .. . .... ...... .. .... ...... .... .......... .. ....... . ... .. .... Angola .M Argentina 11.2 0:0 14.9 2.8 5.3 0.0 0.5 00O 9.3 33 120,000 33 Armenia Aus.tralia 23.5 67.6 72.6 5.4 5.4 0.5 0.0 8.5 . ... 3.9 47 32,404'..36 Austria 34:4.4 .... 22.8 24.1 9. 4 02 0.0 1.6 0.3...... 50 .... 55,564 34 Azerbaijan .. . .. ... .. ....... 40 1,850 32 Bangladesh .. 14.8... 3.2 .. ..... ... .3.9 .16.4 . Belarus 29.4 . 8.3 . 16.0 Belgium 43 0 40.2 36.4 .. . 0 00 00 0.0 5 557 3 Benin 2.2 Bolivia 15.0 .. 8.4 .. 10.5 .. 0.0 . 5.5 13. 25 Bosnia and Herzegovina . .. . Botswana 16.5 45:5.5.... 51.3 0.3 1.9 0.1 0.0..... 21.8 18.9 30 21,008.... 15 Brazil 13.6 9.0 .0 0 1 25 19,459 1 Bulgaria 25~~~~~~~~~~... 2 ...... .... .2 . 0 9... 0. . 1........ . ... . . . 4.6. 40... 7,232.......... 30... ... Burgtina F2so202 2 0 3. .420. Burundi 127~~~~~~~. 204......: ..... 2841 0 14.5 .. .. ......... . 2 .3 .......... 18.9... .. .. 072 2 3 ..am b o d ia . .. . . . . . . . . . . . . . . .. .. .. .. ... .. . . . .. .. . . ... . . . .. . . . .. . . . .. . . . .... . CarknadFas 2. 60.8 3 5 . 2.9 1.3 .6 2901,70 3 Chuni 18: ....9 2.04 2413 121 11.35 0 2. 89... 7.0.. 45 ..... 6,748..... 15...... China 4..................... . 9.... . ... ... ....... .135 ...... 4. . 7.... . ........ 0.0.....28..45.12,077. 30 Colombin . ........ . .... 287.9.... 2 .4 341 5.4 7.2. 212.3 1 .7 35 38,7645 35 Congo, Dem Rep.. .. 4 9 34.5 ...358 ..... 1...6... 2.3............. .... . .... ...I...................... ...... ..50..13,167 ... Congoa Rp.a 63.8 ..... - 3 1 0 1 1...... ....4.0 9 4 ,7 ...3 Czechra Arepublepuic c 32.7 .7 14 9.2 00 3.9 17 4 370 3 Dominicn1Repubic 13. 224.8 2179 .... 38: 58. 6.2 071.0 11... .9...... 2.....5 16,1768 25 Chin,AabRp42.59 48 213 85 27 0 00 26 21.8 32 13,0749 40 E ritre s . . . .. . - . . .. . . . .. . . . . . . . . .. . . . .. . . . . .. . . . .. . . . . .. . . . .. . . .... . . . . . . . .. . . . . . .. . . . . .. . . . .. . . .. . . . .. . . .. . . . . . . . . . . . . . EsngKoia301, 1.1Ch175ina0.0 26 1168 26 E thiopia ... .. . .. ... . .. .... .. . . 2.7.. .. . . .. .. . . I. 9 2. . 35.1... ... ... .. ........ .. ... . .. 16.. . 4 .. . . .. .... ... .. ..... .. . I... . .... . .. .. . FCnlo band... ........... :..... 28.4 3 .9 343 1.54 6 0.0 0 0 .... .. . 12.93 38 586,45 28 Fran. e 39.2.......... .............. .... 19.1.20.9 12.8.12.2 0. 0 0.0... 0.1.. 0.... . 0........ ... ......... .. .. 33... Gaongo e.Rp 4.9 60.2 3.8 .8 1.1.738.3. 55 23,2906 4 G mI a,... . I ... .. . I .. ... .. .. . ..The. .. .. . .. .. .. ... .. . . ... .. .. ......18.1...1.2.. 12.. .. . 5.. 21... ... .. ... .....8 ... .. ........ . ... .. .. .. . .. .. .. .. .. .. . Germnyg268R19. 1608 .10.0 0 1400 0 5 698 3 G hana .......... ................. ........... ........... .. ... 22 0..4 30..... 5.. 14......... . 5...... .......... 35... 7,269....35... CotaReeca 19. 1.6 19. 23.7 10.2 163 0 0.0 6.3 0.1 45 55,923 35 Guinea.Bissau . .. .. .. .. .. .. ..~~~~~~~~~~~~~~~~~~~~~~~~......... .... - Hraiti a 1 598 92 7 1 . .... .. ....... .. 10.2 ' ...12.3..... .... ..... Honduras. ...... .......... ....... 32 . ................ 5............ ....7.5.. 7.8... ... .. 30..75,758...15 28C 99uolbDvlpmnandctr 5.5 Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate' profits, and and services capital gains % of value Individual Corporate % of % of added of industry % of % Of rate on income rate GDP total taxes and services exports imports % over $ % 1997 ±980 1997 ±980 1997 1980 1997 1.980 ±997 1998 ±998 1998 Hungary......... 325 5..... 22.1.... 21.2 28.2 151.1 . 0.1 0.0 7.5 4.0 42 5,394 18 India 10.8 219 9.... 29.5 8.9 58.8.. . 2.0 0.1..... 35.3 .... 29.4 40 5.059... 40 Indonesia 14.7 82.0 58.0 2.4 6.0 0.9 0.1. 5.1 2.7 30 8,938 30 Iran, Islamic Rep. ........ 67.... 12:2..... 32.8 1.0 1.7 0.0 00... 209 .... 11.1 54 173,227 12 Iraq ::7 Ireland 32-4 386 ..... 40.9 . 0.0 0.0 6.2 .... 00 46 14,493 32 Israel 36.8 47.3 42.1 . 0 0 4.5 0.7 50 57,387 36 Italy 422.2 321 1... 36 9 . 0 .. 00 ....0 0 0.1 0.0 46 181,801 37 Jamaica ':....-35.1 . 15.6 0.0 2.3 . 25 2,215 33 Japan . 74.7 . 2.5 . 0.0 . 2.3 ........ 50 230,592 38 Jordan 22.4 17.0 16.2 1.6 1.0.7 0.0 0.0 21.2 12.4 Kazakhstan .. . . 40 3 Kenya ......-. 23.4. . 33.3 38.9 14.8 16.8 1.3 0.0. 11.8 14.4 33 384 35 Korea, Dem. Rep. . . . ... Korea, Rep. 18.6 255 31 0 9.5 77 00 00 76 43 0 5,29 28 KuwaitI1.2 63.6 18.5 0.2 0.0 0.0 0:0 3.0 3.5 0. . ...... .6 Kyrgyz Republic 12.......... .... . 5... . 12. 2 ... ... ......19 0 ....0.0 2.2 40 250 ... .30 Lao PDR. Latvia 29:2 ........ ... ...13.9 17... . ... .0.. 00 1 2525 Lebanon 14.1 . 8 9 0.9 . . 14.9 Lesotho 30.7 .....15.6 19.4 5.4 579 6-2 21.6 Libya Lithuania 25.4 . 17.7 . 17.2. ..... 07 0 ....... :...... 1.3 33. 29 Macedonia, FYR . Madagascar 8.5 17.1 18.8 .....84.4 3.3 3.0 0.9 17.6 28 6 Malawi 38.9 11.7 . 0.0 16.8 . 38 1,969 38 Malaysia 19.3 41.9 44.4 5:7 7.1 9.1 0.5 90.0... 3.4 30 38,961 28 M alit. 20.5 . 7.9 3 0. .... ... ....... 8.0 .................. ............. Mauritania.. ... ..... Mauritius....... 17:6.6 . . 17.3 15.1 4.8 7.7 .8 6 .. ...0:0 ....16 2 13 0 30 2,471 15 Mexico 12.8 36.9 31,8 8.3 93.3 0.5 0.0...... 9:3.3.... 2.2 35 25,492 34 Moldova . . . . Mongolia 18.7 . 29.2 7.3 0.05. Morocco ......... 23:8 22.0 23 .7 .... 9:9 13.1 2.2 0.0 223.3. 14.8 44 6,203 35 Mozambique... Myanmar 4.0 4.9 29.4 12.7 4.3 0.0 0.0 19.3 74.2 Namibia .. . .. 35 1641 35 Nepal 8.9 ......6.6 .....15.0O . ... 8.0 7.4 .. 5.4 .....078 16.0 88 ...... ........... Netherlands 43.0 33.1 26.6 10.6 10.8 0.0 0.0 0.0 0.0 60 51,373 35 New Zealand 31:2.2... 75.0 66.3 6.9 . 0.1 0.0 4:4.4..... 4.5 33 19,922 33 Nicaragua 23.9 8.9 .118 11.3 16.4 3.9 0-0 871.. 11.2 30 18,083 30 Niger 28.1 4.6 26 .170. Nigeria . . . . . . 25 1,600 28 Norway 32-5 303 24.3 15.2 15.5 0.1 0.0 0.8 1.0 28 6,835 28 Oman 8.5 92.4 80.8 0.2 . 0.0 M00 1.4 2.5 0 30 Pakistan 129.9 16.8 26.2 86.6 6.7.... 1.9 .... 0.0 25.2 18.3 Panama 15.9 29.0 29.9 52.2 .. 0.5 .0.2 3.0 . 30 200,000 15 Papua New Guinea . 67.5 4.2 . 1.4 . 8.0 47 57,803 15 Paraguay .. 16.6 2.7 .. 0.5 8.40...0.. ...30 Peru 14.........0... .. ... 28 1 23.5 7.1 8.4 10.9 0.0 17.1 10.3 30 50,036 30 Philippines ...... . 17O. 0 23 6 39.8 7.8 6 8 10.... 0:0.0 .... 13:4.4.. .. 8.8 34 12,464 34 Poland 35.2 ...... ..7 ... .27..7. 123 ...0.0... ..... . 5.3 40 14,372 36 Portugal 31- .1.... 20.9 29.2 . 00 00.4 0.0 40 34,186 37 Puerto Rico . 33 50,000 20 Romania 24.4 0.0 31.0 8:7 00... ~O... 0 0 0 00 5.7 45 3,672 38 Russian Federation 17.9 ............ 15'.8 . . 8.0 4. 4 3....... 3.3 35 8,587 35 1999 World Development Indicators 287 Tax Taxes on Domestic taxes Export Import Highest marginal revenue income, on goods duties duties tax rate' profits, and and services capital gains % of value Individual Corporate % of ft of added of industry % of % of rate on income rate GDP total taxes and services exports imports % oeer $ %f ±.997 1980 1997 1980 1.997 1980 1997 1980 1997 1998 1998 1998 Rwanda .. 207.7 . 5.3 .. 21.3 . 17.7 Saudi Arabia .. . . . .. .. . 0.. 4 Senegal .. ... 1............ 21A.4 ... ...... . .. 7.8 ........ 28 . 26:9 ......... 50 20,821 35 Sierra Leone ........ -10:2 ......25.0 17.6 4.1 7:2 ......10A 1 ... 0.0 .... 172.2 .....20.3 Singapore 15 9 47 0 41.7 4.1 4.7 0.0 0.0 0.9 0. 28 3,95 6 Slovak Republic ... ..42 3,7 40 Slovenia South Africa 27.5 64 1 58.3 64.4 12.0~ .. .. 0.1 0.0 3.0 0.4 45 20,576 35 Spain..I........ 283.3 .. 252 2... . 32.8 ............ ..... 0.0: 0.0 6.0 0-0 48 69,216 35 Sri Lanka 162.2 16:4.4.... 14.5 8.0 14:6 22:0 0.0 9..6 ......8 6 30 4,862 .... 35 Sudan . 17.2 ............. ... 5:2 . 3:4 31:1 ..... Sweden ....... .. 37:0.0.... 21:1.1 12.8 11.7 0:0 0.0 1.:5 0. 31 27,198. 28 Switzerland 21:1.1... 15.1 15.5 . 0.0 0.0 4:0.0..... 0.6 13 46,382 45 Syrian Arab Republic 16.5 24.7 32.2 1.8 . 1.7 .......7.0 11.6 ... 2 29.7........... .. ..... .. .... . .................. .... ... ... ...... .. I ... . ... ...... Tajikistan Tanzania . 35.2 ~~~~~~~~~~~ ~~~~~~9.792 35 13,405 30 Thailand 16.1 19.3 35.5 8:6 8.6 4.4 0.1 11..1 .......6.0 37 84,836 30 To.go 38.6 6:4 3.3 15.2 Trinidad and Tobago .......... 23,4 65:7.7... 58.7 1.6 7 6. 00.~ 0.0 9:8 ..... 4.4 35 8,065 35 Tunisia 24.8 19.2 18.8 8.7 7.2 1.1 0.2 206 .... 19.9 Turkey 15 2 61.8 38.5 5.1 11.5 0:0 . 00 .......89 .1.8 45 59,259 25 Turkmenistan Uganda . 11.8 4:6 5578 1-5.8 30 4,316 30 Ukraine . . . .. 40 10,754 30 United Arab Emirateas. 00... . .. 20 United Kingdom 334 4 4 389 1. 39 00 00 01 0 1 40 44,580 31 United Statea ... 1........... 19:8 ... 616 ..... 59.4 0.9 07.7. 00 0.0 30 0... .. 2 1 40 278,450. 35 Uruguay 27 9 11:5 13.6 11.1 11:3 ......0.0 0.1 19.2 ... 6.0 0. 30 Uzbekistan . Venezuela 17.5 .... 79.4 53.0 10.0 7.5 .......0.0 0.0 9.6 11'O.0 ......: ... -...:...... Vietnam . .. 50 5,695 25 West Bank and Gaza :~: Yemen, Rep. 133.3. 50.8 2.7 ....... 00 9.7 Yugoalavia, FR (Serb./Mont.) . . .. .. ..~ :: Zambia 17.1 41.1 39.6 12~5 9.6 0.0 . 7.2 . 30 1,212 35 Zimbabwe . . 57.9 6:7 0.....0. .. ...:4.3 ........ 40 3,578 38 a. Thia copyrighted material is reprinted with permission from PricewaterhouseCoopera, 1301 Avenue of the Americas, New York, N.Y. 10019. Prior written consent from PricewaterhouseCoopers must be obtained for third-party use of these data. 288 1999 World Development Indicators I v -. 5.5 Taxes are compulsory, unrequited payments made to ation of these sectors is usually negligible. What is * Tax revenue comprises compulsory, unrequited, non- governments by individuals, businesses, or institu- missing here is a measure of the uniformity of these repayable receipts collected by central governments for tions. They are considered unrequited because gov- taxes across industries and along the value added public purposes. It includes interest collected on tax ernments provide nothing specifically in return for chain of production. Without such data no clear infer- arrears and penalties collected on nonpayment or late them, although they typically are used to provide ences can be drawn about how neutral a tax system payment of taxes and is shown net of refunds and other goods or services to individuals or communities. The is between subsectors. "Surplus" revenues raised by corrective transactions. * Taxes on income, profits, sources of the revenue received by governments and some governments by charging higher prices for and capital gains include taxes levied by central gov- the relative contributions of these sources are deter- goods produced under monopoly by state-owned ernments on the actual or presumptive net income of mined by policy choices about where and how to enterprises are not counted as tax revenues. individuals and profits of enterprises. Also included are impose taxes and by changes in the structure of the Export and import duties are shown separately taxes on capital gains, whether realized or not, on the economy. Tax policy may reflect concerns about dis- because their burdens on the economy (and thus sale of land, securities, and other assets. Social secu- tributional effects, economic efficiency (including cor- growth) are likely to be high. Export duties, typically rity contributions based on gross pay, payroll, or number rections for externalities), and the practical problems levied on primary (particularly agricultural) products, of employees are not included, but social security con- of administering a tax system. There is no ideal level often take the place of direct taxes on income and tributions based on personal income after deductions of taxation. But taxes influence incentives, and thus profits, but they reduce the incentive to export and and personal exemptions are included. * Domestic the behavior of economic actors and country encourage a shift to other products. High import taxes on goods and services include all taxes and competitiveness. duties penalize consumers, create protective barri- duties levied by central governments on the production, The level of taxation is typically measured by tax ers-which promote higher-priced output and ineffi- extraction, sale, transfer, leasing, or delivery of goods revenue as a share of GDP. Comparing levels of taxa- cient production-and implicitly tax exports. By and rendering of services, or on the use ofgoods or per- tion across countries provides a quick overview of the contrast, lower trade taxes enhance openness-to mission to use goods or perform activities. These fiscal obligations and incentives facing the private foreign competition, knowledge, technologies, and include general sales taxes, turnover or value added sector. In this table tax data measured in local cur- resources-energizing development in many ways. taxes, excise taxes, and motor vehicle taxes. * Export rencies are normalized by scaling variables in the The economies growingfastest over the past 15 years duties include all levies collected on goods at the point same units to ease cross-country comparisons. The have not relied on tax revenues from imports. Seeing of export. Rebates on exported goods-that is, repay- table refers only to central government data, which this pattern, many developing countries have lowered ments of previously paid general consumption taxes, may considerably understate the total tax burden, par- tariffs over the past decade, and this trend is excise taxes, or import duties-should be deducted ticularly in countries where provincial and municipal expected to continue. In some countries, such as from the gross receipts of the appropriate taxes, not governments are large or have considerable tax members of the European Union, most customs from export duty receipts. * Import duties comprise all authority. duties are collected by a supranational authority; levies collected on goods at the point of entry into the Low ratios of tax collections to GDP may reflect these revenues are not reported in the individual country. They include levies for revenue purposes or weak administration and large-scale tax avoidance or countries' accounts. import protection, whether on a specific or ad valorem evasion. They also may reflect the presence of a siz- The tax revenues collected by governments are the basis, as long as they are restricted to imported prod- able parallel economy with unrecorded and undis- outcomes of systems that are often complex, con- ucts. * Highest marginal tax rate is the highest rate closed incomes. Tax collection ratios tend to rise with taining many exceptions, exemptions, penalties, and shown on the schedule of tax rates applied to the tax- income, with more developed countries relying on other inducements that affect tax incidence and thus able income of individuals and corporations. Also pre- taxes to finance a much broader range of social ser- influence the decisions of workers, managers, and sented are the income levels above which the highest vices and social security than less developed coun- entrepreneurs. A potentially important influence on margnal tax rates for individuals apply. tries are able to provide. both domestic and international investors is a tax sys- As countries develop, they typically expand their tem's progressivity, as reflected in the highest mar- Data sources capacity to tax residents directly, and indirect taxes ginal tax rate on individual and corporate income. become less important as a source of revenue. Thus Figures for individual marginal tax rates generally refer *- ..*. - - - -. The deflnitions used here are the share of taxes on income, profits, and capital to employment income. For some countries the high- from the International Mone- gains is one measure of a tax system's level of devel- est marginal tax rate is also the basic or flat rate, and tary Fund's (IMF) Manual on opment. In the early stages of development govern- other surtaxes, deductions, and the like may apply. Government Finance Stafistics ments tend to rely on indirect taxes because the (1986). Data on tax revenues administrative costs of collecting them are relatively J are from print and electronic low. The two main indirect taxes are international , . editions of the IMF's Goven- trade taxes (including customs revenues) and domes- _ . ment Finance Statistics Year- tic taxes on goods and services. The table shows book.Dataonindividualandcorporatetaxratesarefrom these domestic taxes as a percentage of value added PricewaterhouseCoopers's Individual Taxes: A World- in industry and services. Agriculture and mining are wide Summary (1998b) and Corporate Taxes: A excluded from the denominator because indirect tax- Worldwide Summary (1998a). 1999 World Development Indicators 289 5.6 Relative prices and exchange rates Exchange rate Official Ratio of Real Purchasing Interest rate Key arrangements exchange official to effective power parity agricultural rate parallel exchange conversion producer exchange rate factor prices rate local local currency Wheat Maize currency units to Deposit Lending Real $ per $ per Classification Structure units to $ 1990 = 100~ international $% % metric ton metric ton 1997 1997 1.997 1997 1997 1990 1997 1997 1997 1997 1995 1.995 Albania I F U .148.9 0.9 2.5 48.1 16.8 24.0 8.2 347 178 Algeria MF U 57.7 .... 0.4 5.2 20.8 . :.......... ...... 399 336 Angola .. P U 229,040.1 ......0.8 0.0 106,505.1 ..... ........ :.......I,........ ...... Argentina P U 1 .0 1.0 0.3 ........ 0:9 ....7.0 9.2 9_.0 ..... 93 ........66 Armenia IF U 490.8 0.0 89~3 ... 26.1 54.2 31 5 Australia ~~~ ~~ ~~~~~IF U 1.3 1.0 92.9 1.4 1.4 ....5.1 .... ..9 3 7.4 158 176 Austria FL U 12.2 1.0 101.5 13.3 14.1 1.5 . . 7 5 Azerbaijan I F U 3,985.4 0.1 1,569.6 . - 162.5 ... -63.0 Banglaesh P. U 43.9 0.8 - 12.2 13.6 8.1 14.0 12.5 186 Belarus M F M . 0.1 7,055.1 1.6 31.8 -23.7 Belgium FL U...... 35:8 1.0 98.3 36.1 37.4 2.9 7.1 5.3 .............. Benin P U 583.7 108.5 169.4 ... 187 Bolivia MF U 5.3 1.0 99.2 1.1 1.9 14.7 50:0 42.4 187 137 B osnia and H erzegovina ... .. . ... ... .. .. .. ... ... . . .. ... ... .. . .. .. ... . ....P.. ...U.. .. ... . ... ... .... ... ... ... .... ... ... ... . . ... ... . Botswana P D 3.7 1.0 0.9 1.5 9.3 14.1 -0.9 .. 151 Brazil M F D 1.1 1.0 0 0 0.8 24.4- 155 122 Bulgaria P U.... 1,681.9 1.0 1 2 513.6 46.8 84.0 -825 5...... 55 .... . 61 Burktina Faso P U 583.7 108.8 132.6 .. Burundi P U 352.4 0. 119.5 45.8 83.5 . 15.3 0.1 Cambodia ME D 2,946.3 1.0 . . 74.5 674.4 8.0 18.4 8.4 Cameroon P U 583.7 64.3 146.5 186.8 5.5 16.0 -0.9 Canada I F U 1.4 1.0 80.3 1.3 1:2.2... 3.6 5.0 2.9 94 85 Central African Republic P U 583.7 1.0 62.1 111.0 1308 .....5.5 16 .0..... 23 543 Chad P U 583.7 108.7 135:2 .....55 16.0 6.5 ..... 318 127 Chile MF D 419.3 0.9 135.3 97.6 173.7 12.0 15.7 9.3 215 168 China ME F U 8.3 1.0 1.2 1.9 5.7 8.6 7.3 126 98 Hong Kong, China MF U 7.7 1.0 6.1 8.4 6.0 9.5 3.5 Colombia MF U 1,141.0 0.9 161.1 113.3 401.0 24.1 342_.2... 12.5...... 218 .185 Congo, Dem. Rep. IF U 7,024.4 101.7 0.0 18,567.1 . Congo, Rep. P U 583.7 .... 215.6 305.1 5.5 16.0 12.2 ........ .... 200 Costa Rica MF U 232.6 1.0 104.8 33.8 96.1 13.0 22.5 7.0 . 176 C6te dIlvoire P U 583.7 69.3 157.1 229.0 Croatia MF U 6.1 0.7 . 4:7.7... 4.3 15.5 16.6 Cuba . .. .. Czech R pblic ..............ME U 31.7 ..... 1.0.. 5.5 15.2 7.7 13.2 6..2...... 104 118 Denmark FL U 6.6 1.0 97.9 9.1 9.0 2.7 77.7..... 5.8 ..... 182 Dominican Republic ..... ME 0....... 14.3 1.0 121.0 2.5 55 5... 13.4_ . .21.0 11.7 . 311 Ecuador ME D 3,998.3 1.0 138.1 202.1 1,340.1 28.1 43.0 13.6 186 182 Egt,Arab Rep. ME M 3.4 1.0 0.8 1.4 9.8 13:8 7.5 157...... 142 El Salvador MIF .....U ........8.8 09 35 5.8............................ 11.8.. .....I....16.1............11.0 . . ..................164.. ... E ritre a IF 0.. . . . . . . . . . . .. . . . . . . .. . . . . . .... . . . . . . .I . . .... . . . . . . . . . . . . . . . ... 1.5 .. ..... .. .. . . . . . . Estonia P U 13.9 0.8 0.1 8.5 6.2 19.8 7.6 Ethiopia ME U 6.7 0.9- ..... 0.9 1.4 7.0 10.5 6.8....... ... ..... Finland FL U 5.2 1.0 75.7 6.1 6.0 2.0 53 ......4.0 ......199 France FL U 5.8 1.0 97.0 6.4 6.3 3.5 6.3 5.1 170 165 Gabon P U 583.7 53.5 270.2 345.5 5.5 16.0 16.6 Gambia, The IF U 102.2. .. 0.9 98.1 2 0 2.4 125 ..... 25:5..... 22.1 Georgia ME U . 0.0 0 6.6 _ ............7.........I.... .. ... Germany FL U 1.7 1.0 103.4 . 2 1 2 7 9.1 ......8.0 167 168 Ghana IF U 2,050.2 1.0 101.3 478.5 35 8 .. .. 144 Greece EL U 273.1 1.0 115.5 129.9 242.8 10.1 18.9 11.9 273 213 Guatemala ..............IF U 6.1 ..... 1.0 1 2 2~5.5 .. 5.9 18.6 9..5 ..... 203 ...... 176 Guinea IF U 1,095.3 1.0 231.1 327.1 17.5 21.5 16.9 . Guinea-Bissau P D 583.7 .. ,.. 4.6 51.8 2.1 . Haiti IF .......U...... 16.7 0.7 ........... 1.6 5.9 10.7 21.0 4.1. 328 Honduras ME D 13.0 101.3 4.4 21.3 32.1 11.l. 211 290 1999 World Development Indicators 5.6 Exchange rate Official Ratio of Real Purchasing Interest rate Key arrangement exchange official to effective power parity agricultural rate parallel exchange conversion producer exchange rate factor prices rate local local currency Wheat Maize currency units to Deposit Lending Reai $ per $ per Classification Structure units to $ 1990 10 international $% % metric ton metric ton 1997 1997 1997 1997 1997 1990 1997 1997 1997 1997 1.995 1995 Hungary MF U 186.8 1.0 132.3 31.1 116~.9. .18.5 .....21.8 2.8 89....... 95 India IF U 36.3 ....09 ......... ..... 56 ..... ..88.8 .... 13!8 . ...7.9 .. . ...134 . 104 Indonesia IF U 2,909.4 0.9 7 570.7 893.9 20.0 21.8 8.8 : 168 Iran, Islamic Rep. MEF D 1,752.9 .....0.3 7.....162.3 800.0 ........153 1138 IraqP D0 0.3 0.0 Ireland FL U 0.7 1.0 94.6 0'7 0j.7 0.5 6:6 ......50 .......1411 Israel MF U 3.4 1.0. 1'73.2 13.1 18:7 ......8-9........ ........ Italy FL U 1,703.1 1.0 84.9 1,403.5 1,671.3 4.8 9 7 6 3 1196 ... 1..189 Jamaica IF U 35.4 0.9 . 4 1 25.1 13.9 36.3 22 383 Japan IF.....I..I- U.. 121.0. 1.0. 11 .4 .....186 .....1 6.0......... 0.............3 .4 . 3.2 Jordan P U0.7 1.00 .... . .. ..... ................... ...... .... 0........ . ..O.3 0 .3 ... 9.1 12.3 9.3 ......2112 ......137 Kazakhstan MF U 75:4 09.. ..0 9 0.0 29.7 ...........7:......... ........... .. Kenya MF U 58.7 0~9 . 7.7 17.7 16.7 30.2 16.7 97 1140 Korea, Dem. Rep.. . .. Korea, Rep. ............IF .......U..... 951.3 1.0 535 8 673 7 10.8 11.9 ......9 .3 569 Kuwait P U 0.3 1.0 0.3 0.3 5.9 8.8 2.0 Kyrgyz Republic ...... .. ME .......U ....._ 17.4 .... . :.... .... ...0.0 2.9 39.6 49.4 25.2 Lao PDR MF D0 1,256.7 1.0 . 174 6 34. 116.0 27.0 112.6 Latvia ...... ..ME .......U ..... 0.6 .0.9. 0.0 0.3 5.9 ..... 15.2 . ....8..2. ... 73 ........ Lebanon IF U 1,539.4 1.0 185 5 936 0 13.4.I... 203. 3. 10.9 ...... . Lesotiho P U 4.6 1.0 102.7 0'8 1.2 11.8 18.0 7.7 1177 Libya P U 0.4 .... 0.2 Lithuania P U 4.0 0.9 . 0.0 2. 79 14.4 -0.4 Macedonia, EYR MF U 50.0 0.825 lvadaRascarIF U 5,090.9 0.9 . 423.0 1,376.9... 14.4 30.0 21.0 M alP U 583.7 ... .. :... . 129.5 192.1 I......... . .. ..... ........ ...... .. Mauritania MF U 151.9 1.0 .. .. 29.4 39 1.. ................ Mauritius MF U 20.6 10.. 6.0 8.0 ... 9.1 18:9. 12.3 283 Mexico IF U 7:9 ......1.0 1.3 4.0 14.7 245.5..... 4.7 181 1194 Moldova IF U 4.6 .. 1.3~ 23.5 33.3 19.5 Mongolia IF U 790:0 ......1.0 ...... 2.9 205~4.4.. 37.9 74.8 41.2 Morocco P U 9.5 10 114.1 3.1 35 274 246 Mozambique IE F U 11,543.6 0.9 . . 251.4 2,578.7 Myanmar P D0 6.2 0..0. .. .... 12-5 ...16:5 .. -45 Nepal.P U 58.0 0.9 . 7.0 11.6 15.... 14.5 . 5.6 Netheriands FL U 2.0 1.0 99.5 2.1 2~1.1.. 3.2 6.1...... 3.0 ........ New Zealand IF U 1.5 1.0 -10.0 1.6 1.5 7.3 11.3 9.6 Nicaragua ..........MF U 94.4 ... 1.0 81.9 0'.0.. 271.1.. 12.4 21.0 30.0 . 216 Niger P U 583:7 ....... .......... 104.6 130.6............ Nigeria MEF D 21.9 0.3 171.3 3.5 30.0 7.3 20.4 9.3 580 254 Norw ay.MF U 7:1 1.0 94.5 102 I 10.1 3 6.0 3.4 3 Oman P U 0.4 1.0 0.3 02 ....7.3 9.3 5.5 Pakistan ME F U 41.1 0.9 . 6.5 120O. ....... .. 126 Panama P U 1.0. 0.4 0.4 70 10.6 8.8 257 Papua New Guinea .i... ... I U 1.4 1.0 93.6 . 7 3 10.4 7~9.9.. Paraguay IE F U 2,191.0 0.9 124.1 467.7 1,101.7 ..13.0 27.0 17.6 1119 1145 Peru I F U 27 1.0 0.1 1.5 15.0 30.0 19.9 280 204 Philippines IF U 29.5 0.9 128.8 6.0 9.4 10.2 16.3 9.6 . 1194 Poland ME U 3-3 1:0 216.2 0.3 18.. 19.4 25.0 8.9 153 PortugaE L U 175.3 1.0 113.3 94.3 126.2 4.6 9.1 6.1 1187 1179 Romania ME F7,167.9 0.9 8.9 2,572.0 . 127 86 Russian Federation MF U 5.8 0 0 . 0.0 4.0 1. 32.0 13.3. 1999 World Development Indicators 291 5.6 Exchange rate Official Ratio of Real Purchasing Interest rate Key arrangement exchange official to effective power parity agricultural rate parallel exchange conversion producer exchange rate factor prices rate local local currency Wheat Maize currency units to Deposit Landing Real $ per $ per Classification Structure unita to $ 1990 = 100. internatFional $ % S % metric ton metric ton 1997 ±.997 1997 1997 1997 ±990 1997 1997 1997 ±.997 1995 ±995 Rwanda IF U 301.5 0.8 .. 38.8 108.6 9.5 .267 172 Saudi Arabia FL U 3.7 ......10 97.7 2.6 2.6 .....5-.8 ....... ........ ........ ........! SenegalP U 583.7 .. 40 174.140 Sierra Leone IF U 981.5 0.9 122.8 52.3 413.3 9.9 23 9 ......6.. 1........ ..... . Singapore MF U 1.5 1.. 1.6 1.6 3.5 6.3 4.8 Slovak Republic ...............P U 33:6 ......10 ... ...... .. . 7.4 15.4 13.4 18J 7 . 11.3 ......109 ..... 125 Slovenia MF U 159.7 ......09 9 . .. ...... ..... 124.0 13.2 20:0 ......9.4 ......194 144 South Africa IF U 4.6 1.0 96.6 1.2 2.0 15.4 200O 11.3 217 164 Sri Lanka MF U 59:0 .... .1.0 .... ...... .... 11.4 19.3 14.2 12.0 2.8 ........:....... 180 Sudan ME U 1,575.7 . .. 6.9 371.5 209 Sweden............... IF .......U........7:6.6 .. 1.0 86..8. 9.4 9~9 2.5 7:0. 5.1 142 Switzerland IF U 1.5 1.0 100.3 2.1 2 1 .....1.0 .......45 ......4.. 8 ..... 880 ......520 Syrian Arab Republic P M 11.2 0.2 . 100 15.3 Tajikistan MF U . . 00 94.8 Tanzania ........... ....IF .. ....U .....612.1 ... _0,9 . . ...... .... 53 6 ......234~1 ....7.8 ......29 2 .. ....7.8 .... ... ...... Thailand MF U 31.4 1.0 .. 9.9 11.9 10.5 13.6 7.8 .. 142 Togo P U 583.7 758 9. 33. .. 112 Trinidad and Tobago IF ........U.. ......6.3 1.0 84.4 3.2 4.1 . 6.9 .15.3 10.7... . .. . 371 Tunisia ME U 1.1 1.0 . 0.3 0.4 .. . .. 291 Turkey ME U 151,865.0 1.0 . 1,514.7 71,288.1 79.5 169 196 Turkmenistan ME D 404.4 .. 00 1,473.0 .. . Uganda ...............IF .......U 1,083.0 0.9 94.1 109.6 2948.8 . 11.8 21.4 13.8 .............. Ukraine ME D 1.9 9 .......090.0 0.8 18.2 49.1 27.2 United Arab Em irates ........... - L . UU3 7 3. .....3 2 ..7......3 1.........0....... 3 .......2. ......3.4:......... United Kingdom ........ IF U 0.6. ..... . 102,5 ......0'6 0.... ..... 6 .....3.6 .......66 ......4.2 186 United States ......... ......IF . ......U ........1:0 ....... .. .106..5 1 . . 0.. l .... ... 1.0 . .. ........... 8.4 6.7 127 ...... 89 Uruguay ME U 9:4 1.0. 168-.4 .......0.5 ........6.3 ...19.6 71.6 .....45J.7 ... 153 154 Uzbekistan ME M . .9.7 Venezuela .............. ME . .....U.. .. 488.6 154.5 16.0 211.8 14.7 19.1 -14.1 ....... 222 Vietnam MF U 11,359.4 1.0 . 481 2,359.3 9.9 15.2 9.6 West Bank and Gaza Yemen, Rep. IF U 129.3 0.9 . .. 14.3 57.2 ......................... 367 ... .. 416 Yugoslavia, FR (Serb./Mont.) .. .. . ... . Zimbabwe I F U 12.1 ......0.9 .......... 1.1 3.9 18.6 32.5 9..5 .....224 ......139 Note: Ecchange rate arrangements are given for the end of the year in 1997. Exchange rate classifications: FL = flexibility limited, IF = independent floating, MF = managed floating, P = pegged. Exchange rate structures: D = dual exchange rates, M = multiple exchange ratea. U = unitary rate. 292 1999 World Development Indicators 5.6 In a market-based economy the choices households, national currency estimates of GNP to a common cur- * Exchange rate arrangement describes the arrange- producers, and governments make about the alloca- rency by using conversion factors that reflect equiva- ment that an IMF member oountry has furnished to the tion of resources are influenced by relative prices- lent purchasing power. Purchasing power parity (PPP) IMF under article IV, section 2(a) of the IMF's Articies of the real exchange rate, real wages, real interest rates, conversion factors are based on price and expenditure Agreement. Exchange rate classification indicates how and commodity prices. Relative prices also reflect, to surveys conducted by the International Comparison theexchange rate is determined inthe main marketwhen a large extent, the choices of these agents. Thus rel- Programme (ICP) and represent the conversion fac- there is more than one market: pegged (to a single cur- ative prices convey vital information about the inter- tors applied to equalize price levels across countries. rency or composite of currencies), flexibility limited, or action of economic agents in an economy and with the See About the data for tables 4.11 and 4.12 for fur- floating (managed or independent). Exchange rate struc- rest of the world. ther discussion of the PPP conversion factor. ture shows whether countries have unitary, dual, or muk The exchange rate is the price of one currency in Many interest rates coexist in an economy, reflect- tiple exchange rates. * Official exchange rate refers to terms of another. Official exchange rates and ing competitive conditions, the terms governing loans the actual, principal exchange rate and is an annual aver- exchange rate arrangements are established by gov- and deposits, and differences in the position and sta- age based on monthly averages (local currency units rel- ernments. Parallel, or 'black market," exchange rates tus of creditors and debtors. In some economies inter- ativetothe U.S. dollar) determined by countryauthorities reflect unofficial rates negotiated by traders and are est rates are set by regulation or administrative fiat. or on rates determined largely by market forces in the by their nature difficult to measure. Parallel exchange In economies with imperfect markets or where legally sanctioned exchange market. * Ratio of official rate markets often account for only a small share of reported nominal rates are not indicative of effective to parallel exchange rate measures the premium peo- transactions and so may be both thin and volatile. But rates, it may be difficult to obtain data on interest ple must pay, relative to the official exchange rate, to in countries with weak policies and financial systems rates that reflect actual market transactions. Deposit exchange the domestic currency for dollars in the black they often represent the "going" rate. The parallel and lending rates are collected by the International market. * Real effective exchange rate is the nominal rates reported here are collected by Currency Data & Monetary Fund (IMF) as representative interest rates effective exchange rate (a measure of the value of a cur- Intelligence from a variety of sources, some within the offered by banks to resident customers. The terms rency against a weighted average of several foreign cur- country and some outside but doing business with and conditions attached to these rates differ by coun- rencies) divided by a prce deflator or index of costs. entities based in the country. try, however, limiting their comparability. Real interest * Purchasing power parity conversion factor is the Real effective exchange rates are derived by deflat- rates are calculated by adjusting nominal rates by an number of units of a country's currency required to buy ing a trade-weighted average of the nominal exchange estimate of the inflation rate in the economy. A nega- the same amounts of goods and services in the domes- rates that apply between trading partners. For most tive real interest rate indicates a loss in the purchas- tic market as $1 would buy in the United States. industrial countries the weights are based on trade in ing power of the principal. The real interest rates in * Deposit interest rate is the rate paid by commercial manufactured goods with other industrial countries the table are calculated as (i - P) /(1 + P), where i is or similar banks for demand, time, or savings deposits. during 1989-91, and an index of relative, normalized the nominal interest rate and P is the inflation rate (as * Lending interest rate is the rate charged by banks on unit labor costs is used as the deflator. (Normalization measured by the GDP deflator). loans to prime customers. * Real Interest rate is the smooths a time series by removing short-term fluctu- The table also shows prices for two key agricultural lending interest rate adjusted for inflation as measured ations while retaining changes of a large amplitude commodities, wheat and maize. Prices received by by the GDP deflator. * Key agricultural producer prices over the longer economic cycle.) For other countries, farmers, as used here, are important determinants of are domestic producer prices converted to U.S. dollars prior to 1990, the weights take into account trade in the type and volume of agricultural production. In the- using the official exchange rate. manufactured and primary products during 1980-82; ory these prices should refer to national average farm- from January 1990 onward weights are based on gate, or first-point-of-sale, transactions. But depending Data sources trade in manufactured and primary products during on the country's institutional arrangements--that is, 1988-90, and an index of relative changes in con- whether it relies on market wholesale prices, govern- Information on exchange rate sumer prices is used as the deflator. An increase in ment-fixed prices, or support prices-the data might arrangements is from the the real effective exchange rate represents an appre- not always refer to the same selling points. These data iMF's Exchange Arrangements ciation of the local currency. Because of conceptual come from the Food and Agriculture Organization a-.-- and Exchange Restrictions and data limitations, changes in real effective (FAO), and most originated from official country publi- Annual Report, 1998. Official exchange rates should be interpreted with caution. cations or from FAO questionnaires. As the data show, . _- - and real effective exchange The official or market exchange rate is often used prices received by farmers often are not equalized j ' rates and deposit and lending to compare prices in different currencies, but across international markets (even after adjusting for rates are from the IMF's because market imperfections are extensive and freight, transport, and insurance costs and differences Intemational Financial Statistics. Estimates of parallel exchange rates reflect at best the relative prices of in quality). Market imperfections such as taxes, sub- market exchange rates are from Currency Data & tradables, the volume of goods and services that a sidies, and trade barriers drive a wedge between Intelligence's Global Currency Report PPP conversion U.S. dollar buys in the United States may not corre- domestic and international prices. factors are from the ICP and World Bank staff esti- spond to what a U.S. dollar converted to another coun- mates. Real interest rates are calculated using World try's currency at the official exchange rate would buy Bank data on the GDP deflator. Agricultural price data in that country. The alternative approach is to convert are from the FAO's Production Yearbook. 1999 World Development Indicators 293 5.7 Defense expenditures and trade in arms Military expenditures Armed forces Arms trade personnel Exports Imports % of % of central Total % Of % of % Of GNP government expenditure thousands labor force total exports total imports 1985 1.995 .1985 1995 1.985 1.995 1985 1.995 1.985 .1995 1.985 1995 Albania 5.3 1.1 10'9 3:2 .... ....0.......52 ...... 0.0 3:3.3 . .. 0.0 00.....~O..... 00. . . 0. P 0 Algeria 2.5 3~2 ......6.3 6.9 170 . ....120, 2.9 . .1.4 00..... 0 00...... O.. .....5.1 ......2.2 Angola 199 30 . . . .. ...... .. :........ 66 .......82 .......17 . _16......60.0 0.0 1180 13. Argentina 3.8 1.7 12.4 270O 129 65 1. 1 0.5 1.0 0.3 5.5 0.2 Armenia . O0 9 60.... .....3.3 0...... . .0.. ~ ..... ..... 4.5 Australia 2.7 2.5 9.4 8.8 70 58 0.9 0.6 0.6 0.1 3 15 Austria 1.3 0.9 3.3 2.2 40 45 1.2 1.2 09 0.1 0.1 0.1 Azerbaijan .. 2.8 . 87 2.7 ......... O.... 0.0.... 0. 00 Bangladesh 1.7 1.7 13'0 9.9 91 115... ... 0.2 0.2....... 0.0 0 0........ 2 2..... 0.9 Belarus .. 0.8 . . 115 . 2.1 4.0 . 0.0 Belgium 3.1 1.7 5.3 3.5 107 47 2.7 1.1 0.7 0.1 .......0.7 0.2 Benin 2.2 1.2 . 8.6 6 6 0.3 0.2 0.0 0.0 3.0 0.0 Bolivi'a 3.3 2.3 22.6 9.5 28 28 1.2 0.9 0.0 0.0 0.7 0.7 Bosnia and Herzegovina . . . .50 .. 420 . 20 Botswana 2.5 5.3 5.8 12.7 3 8 0.6 12 00 00 ....... 1~7 ......0:0 Brazil 0.8 1.7 2.1 3.9 496 285 0.9 0.4 1.3 0.0 0.4 0.3 Bulgaria 14.1 2.8 32.5 6.3 189 86 4.1 2.0 4.7 28 7.3 0.0 Burkina Faso 1.9 2.9 18.7 12.0 9 9 0.2 0.2 0.0 0.0 6.0 0.0 Burundi 3.0 4.4 20.8 24.8 9 22 0.4 07 0.0 00 2.6 0.0 Cambodia .. 3.1 35 90 0.9 17 0.0 0.0 3 Cameroon 1.9 19 8 102 15 2 0.4 04 .0 .0 17 8 Canada 22~~............... 1.. . 7... .... 863 710. 83 .....2 .. .. . 70 .6 ... .04 .... ..06 ...... 0.1 .. . . 0. 1.7 ..... 018 Central African Republic 1.8 2.5 6.4 5 5 . 0.0 0.0 0.0 0 Chad 2'0 3.1 6.1 . 16 30 0.6 0.9 0.0 0.0 12.0 4.5 Chile... .........4.0 .......38 ...... 11.4 17.5 124 ..... 102 ...... 2.9 1.8 2.1 ...... 0.0 0.7 2:4 China 4.9 2~3.3.... 23.8 18.5 4,100 2,930 0.7 ......0!4 ...... . Colombia 1.6 2.6 10.3 16.2 .66 ..... 146 .... 0.6 0.9 0.0 0.0 0.5 0.4 Congo, Dam. Rep. 1.2 0.3 9.8 3.7 62 49 0.5 0:3 .....10.0 0.0 5.0 0 0 Congo, Rep. 4.0 2.9 9.2 . 15 10 1.9 1.0 0.0 0.0 6.7 1.5 Costa Rica 0.7 0.6 ~ 2.8 2.7 8 8 0.8 0.6 0.0 0.0 1.8 0.0 C6te d'lvoire . . .. . Croatia 10.5 .. 32.0 . 60 . 27 . 001 5 Cuba ... .... .....4.5 .....1.6 .... ....... ........7 ..297 ...... 70 ... ... 7.0 1.3 0.1 0.0 27.4 0.0 Czech Republic 2.3 . . 68 . 12. 0.6 0.0 Denmark ... ......... 2.3 .......18 ....... 5.2 4.1 29 27 1.0 0.9 ...... 0.1 0.0 0.6 ...... 0.2 Dominican Republic .. ... ..... 1.2 1.4 8.0 9.1 22 22 0'9 0.7 ......0.0 0.0 0.7 ...._0.3 Ecuador 2.8 3.7 16.9 18.3 43 58 1'4 1:4 ......0.0 0.0 40. ..... 6.2 Egypt, Arab Rep ....... ..... 12.8 5.7 22.1 13 .7 46 43 29 2.1 4.9 0.0 30.9 16:2 El Salvador 5.7 1.1 29.1 7.4 48 22 2'8 0.9 0.0 0.0 12.5 0.7 Eritrea . . . .... 0.0 Estonia .....1.1...29 . 6 07.7 . 0.0 ........0.2 Ethiopia 6.7 ...22 2..... 28.9 9.2 240 120 1.2 0.5....... 0.0 0.0 ...... 801.6 .....0 0 Finland 1.7 2.0 5.4 5.1 40 32 1.6 1.2 0.0 0.0 0.8 0:1 France 4.0 3.1 8.8 6.6 ..563..... 504 ..... 2.3 1.9 7:0 0......O.8 0.1 ..... 0.1 Gabon.... ......... 2.8 2.6 6.66 ..... .96 7 . .. 10 ...... 1.7 1.9 0.0 ......01.0 11.7 0.0 Gambia, The46 .. 4 .6 ........ 16.2 1.......1I .... 0.3 . .... 0.2 ......0.0 0.0 10.8 0.0 Georgia . 2~4 ....... . . . .5 . 02.2 . 0.0 4.0 Germany' . . . . .02. Ghana 1.0 1.4 7.2 5.8 15 7 0.3 0.1 0.0 0.0 0.0 0.0 Greece ............. 7 0 5.5 13.8 10.8 201. ..213...... 5.1...... 48.8 .. 0.7 0.1 2.9 2..... 22 Guatemala 1 6 1.3 17.0 14.2 ...... 43 36...... 1.6 1. .0 .IO 0.0 0.0 2.6. 0.2 Guinea 1.5. 28 12 11 04 .00.0 20.8 0.0 Guinea-Bissau 2.9 2.8 4.7 . 1 7 26 14 00 00 4 Haiti 1.5 2.9 7.5 21.6 6 0 0.2 0.0 00 0.0 2.3 0.0 Honduras 3.5 1.4 14.0 8.7 21 18 1.5 0.9 00 0.0 34 0.8 294 1999 World Development Indicators 5.7~~~~~~~~~~~..*. Military expenditures Armed forces Arms trade personnel Exports Imports t% of % of central Total % of % Of % of GNP government expenditure thousands labor force total exports total imports 1985 1995 1985 1998 1.985 1995 1985 1995 1985 1995 1985 1995 Hungary ........7.2 ......1:5 ..... 15.3 ......4.6 117 71 2.4 1.5 2.6 0..... 2 ........1.0 ......0.2 India 315 2.4 15.7 12.7 1,260 1,265 0.4 0.3 0.1 0.0......16~3._.. 1.2 Indonesia 2.4 1.:8..... 10.3 8.9 278 280 0.4 0.3 0.0 0.0 1 6 . . ..0.4 Iran, Islamic Rep. 7.7 26 3. 36 35 40 252506.6 17 2 . Iraq 41.I............ ...... . 2. ........ .. . ......... .... .... . .788 ......390 .... 19.8 7.0 0 .2 0.0 4....6 4 ... ..0.0 Ireland 1.7 1.3 2.9 ... 34.4 14. 13 ..... 1. ......0.9 .... 0.0 0......0 0 -.1 0......0 Israel 20.3 9.6 27.2 ... 21:1 195 185 12.1 7~9.9.... 10.8 4.1 109.9..... 1.1 Italy 2.2 1.8 4.6 3.9 504 435 2.2 1.7 1.7 0.1 0.3 0.1 Jamaica 0.9 0.8 1.8 14 2 3 0.2 0.2 0.0 00 09 0...... 0 Japan ........ '... 1.0 1.0..... 5.6 4.2 241 240 0.4 0.4 0.1 0.0 0.8 0.2 Jordan 15.5 7.7 397 ..21:7 ... 81 1.... 12 12.3 9.5 .00 0.0 22-.9 ......1.9 K azakhstan ..... ...... .......... .. . 0.. . 9... ... ............ . . 28.. ... ..I.... ......0... . 4.... ......... 0. 4 - ........ .. 7. 2... Kenya 2.3 2 3 8.4 ... 6.2 ..... ..19 ... ...22 ......0.2 0.2 ......0.0 0.0 0.7 ......0 3 Korea, Dem. Rep. 20.0 28.6 . . 784 1 ,00 888.8 25.4 3 6 22.1 5.8 Korea, Rep. 5.0 3~4....I. 26.6 13.6 600..... 655...... 3.4 3.0 0.6 0.0 1.5 ......0:8 Kuwait 5.7 11.6 13.6 25.5 16 20 2'4 3.4 0.0 0.0 6.2 11:6 Kyrgyz Republic.. 0.7 . . 13. 0.7 .. 2.5. 0 0 Lao PDR 7.4 4.2 . 22.3 54 50 . 0.0 0.0 48 8 0. Latvia 09 ... .......7..0.5.....0.0. ...03 Lebanon 3~........... ........7.. 9 7.7 .... 21 ......55 .. 21.. .. ..... 4.2 0.. . 0... . ...... 0.0 .......2.3 05.... Lesotho 5.3 1 9.. 2.5 ...... .2.... ...2 03 03 ...... 0.0 0.0 0.... . 0..OO 0.0 Libya 12...... . -.......... . 0...... 6.. . 0.......... .. . ........ ............91.768.0. 3..0.8 . 0....... 3 .0 0.0.... . Lithuania . 0.5. 2.1 12. 0.6 .......... 0.0 . 0.2 Macedonia, FYR 3.3_ ......... .... . ... 16. 1 8 0.0 .......0.0 Malawi 2.0 1.6 .. ....5.8 ...... 3.5 .... 6.... . 10 ...... 0.2 0.2 ...... 0.0 0.0 1 8 0.0 Malaysia 3.8 3.0 10.7 12.4 .....110. 122 ... 1.8 1.5....... 0.0 0.1 .... 3.8 ... 1.0 Mali 2.9 1~8 ... ...8.18 ....... 8 .......0.2 ~.....0.2 .. ....00 0.0 3.3 ......0.0 Mauritai '6.9 3.2 25.0 9.3 16 10 1.9 09.9. .. 0.0 0.0 0.0.OO .... 0.0 Mauritius 0.2 0.4 0.8 1.6 1 1 0.3 02.2 00O. 0.0 .......0.0 ......0.0 Moldova . 2.1 12 . 0.6 . 5.6 0. Mongolia 8.3 2.4 13.1 7.0 38 21 4.3 1.8 00 0.0 0.5 0.0 Morocco 6.0 43.3 ..... 20.0 13.8 165 195 2.1 19 9....... .........~ ........~ .......7 Mozambique 9.9 54 38.0 35 12 0.5 0:1 ......00 0:0. 63.7 0... . 0 Myanmar . .. Namibia .. 2.1. 5.5 8...1.3. ...0 0 ......... . .. 0.8 Nepal 1.1 0.9 .... 6.2 .. ..5.8 ..... ..25 40 .....~ 03 0.4 0.0...00....0.0.0 New Zealand .. 2.0 _.....1..3....... 4.5 3.3 13 10 0 9 0:6.6 ... . 0.0 0~.0 . .... ~ 1.3 0 3 Nicaragua 17.4 2.2 ..... 26.2 5.3 74 14 6.2 0.8 0.0 7:7.7... 29-.0 .... 0.0 Niger 0.8 1.2 5.0 7.9 5 . 9.... 0.2 0.2 0.0 ......0 0 0.0 0.0 Nigeria 1.5 0.8 9.4 3.5 134 89 0.4 0~2 0.0 0.0 3-.8 ... 0:0 Now y31 .7 ...... 7.5 ...... 6.5 3..... 6 .... . 38 . ... 1.8 1.7 0.2 0.0 1.2 .. .0.4 Oman 24.4 16.7 42.3 33.9 25 36 6.2 6.2 0.0 .. 4.4 ....10.8 Pakistan 6.2 6.1 .....28.1 25.3 483.... 587 1.4 1:3...... 1.5 ... 03.3 8.0 4.2 Papua.New Guinea 1.5 1.4 4.5 5.6 3 5 0.2 0.2 ....0.0 .....0.0........ 1.0 0.0 Paraguay 1.1 1.4 11.9 7.3 14 12 1.0 0.7 ....-10.0 0....0 0 4.0 0...0.. ~ Peru 6.7 1.7 36.3 9.3 128 115 2.0 1.4 0.0 0.0 3.8 3.0 Philippine.............1.4.....15 ..95. 85......115 ... 110 .... 0.5.4 0.0.4.....0200 .0.0 ....0.77 .... 0.3 . Poland 10.2 2~3 ...._407 5.4 439..~... 278..... 2.3 1.4 11.3 0.2 4.2 0.3 Portugal 2.9 2.6 6.5 5.9 102 78 2'1 1.6 37 0.03.1 0.3 Puerto Rico. Romnania 6.9 ....2~5._.. 200.0 ... 11.2 237 209 2.2 20.0 3:6 ....03 .......04 ....0:0 Russian Federation . 11.4 . 38.1 . 1,400 . 1.8 4.3 0.0 1999 World Development Indicators 295 517 Military expenditures Armed forces Arms trade personnel Exports Imports % Of % of central Total % of % of % of GNP government expenditure thousands labor force total exports total imports 1985 1995 1985 1995 ±985 1995 1985 1995 1985 1.995 1985 1995 Rw anda ......... ...1.7 . .5.2 9.4 .. 2323 5 . ..... 33 . 0 2 ..... .1.0 . . ...0.0 0.0 0.0 ................. ... ......... .. .. ........ 00...... .................... .: Saudi Arabia 22.7 13.5 27.0 41.0 80 175 2'0 2.8 0.0 0.0 30.1 31.3 Senegal 2.8 1.6 8.8 . 18 14 0'6 04.4..... 0.0 0.0 0.6 0.4 Sierra Leone 0.8 6 1 5.0 28.9 4 14 0.3 0.8 0.0 0.0 0.0 0:0 Singapore 5.9 4.7 17.0 24.0 56 60 4.6 3.9 0.2 0.0 0.5 0.2 Slovak Republic 30 . 6:8 52 1.8 0:8 -3.1 Slovenia . 1.5 35 .. 10 1.0 . 01 . 0:3 South Africa 3.8 2.2 11.6 6.7 95 100 0.8 0.7 0.5 0~4 0....2.. ..... 0.8 Spain 2.4 1.6 6.9 5.6 314 210 2.1 1.2 2.4 01 0.6 0.6 Sri Lanka 2.9 4.6 8.4 15.7 22 110 0.4 1.4 0.0 0.0 1.6 3.1 Sudan 3.2 6.6 37.6 65 89 0.8 0~9 .......00 00 7.8.~. . 8:4 Sweden 3.0 2.8 6.1 5.8 69 51 1.6 1.1 0.6 0.4 0.3 0.0 Switzerland 2.4 1.6 6,0 23 29 0.7 0.8 1.2 0 1 2.4 0.0 Syrian Arab Republic......... 21.8 ... .. 7..2 42 0 402 320 13.8 7.6 0.0 0~ 0...... 37..8. . 1..5 Tajikistan . 3.780.1400 Tanzania ............3.8 1......l.8. 12.8 84 43 35 0'4 0.2 .. ....00 .... 0.0 3-8 0...... 0 Thailand ... ..... . 4.2.. . .2.5 19.7 15.2 235 288 0.8 0:8 .......00 0:0 ........2.1 ......16 Togo 2-6 2.3 6.9 10.2 7 12 0.5 0.7 0.0 0.0 00...... ..... 070 Trinidad and Tobago 1.7 40.0 2........3...... 04 06 0. 0 00 00..... 0.0 Tunisia 3.6 20_ 8.8 6.3 38 3 5 1.5 1.1 0.0 0.0 11.2 0.5 Turkey 4.6 4.0 17.9 17.6 814 805 3.8 2.8 1.5 0.3 4.4 2.0 Turkmeniatan 1,7 .. 21 .. . ........... 11 .. . .. ...:.......0:.0 . .... .. . .42 Uganda........ .... 2.0 .....2.3 15.6 13.3 15 52 0.2 0.5 0:0 ....._00.0 3.1 0:0 Ukraine 2 .9 7.8 .. 476 . 1:8 .. ..... 1.... I4 .. ....... 0 .0 United Arab Emirates 6.7 4.8 43.5 38.4 44 60 6.1 5.1 0.0 0.0 3.4 1.8 United Kingdom ............ 5.1 ......30 ..... 12.6 7.2 334 233...... 1.2 0.8 1.6 2.1 0.7 071 United States 6 1 3.8 25.7 17.4 2,244 1,620 1.9 12.2. 6 3 2.7 0.5 ......0.1 Uruguay 2.9 2.4 10.6 7.3 30 25 2.4 .1.7 0.0 0.0 0.0 0.2 Uzbekistan 3~8 . . 21 02 ....... . 0~3 ... . ... . .... 0:0 Venezuela 2.1 1.1 9.2 6.3 71 75 1.2 0.9 0.0 0.0 6.5 0.8 Vietnam ........... 19.4 2.6 102.1 10.9 1,027 550 ... 3 6 . ...15 5...... 2.7 0.O~ 0.. . 94..3 ......2.7 Yemen, Rep.17_.. .. ...5.7 ... ..... 29.4 68 1:4 .......... 0.0 ...... 13.9 Yugoslavia, FR (Serb./Mont.) . . .. . Zambia. 2~8 ........ ...... 12:6.6 .. ... 16 22 ...... 0.6 0.6 0.0 0.0 4-.6 ......0.6 Zimbabwe 5.7 4.0 14.4 10.5 46 40 1.2 0.8 0.0 0.0 0.0 0.0 .. . .. . . . .. . . .. . . . .. . . . . . . .. . . .. . . - . .. . . . .. . . .. . . . .. . . . . . . .. . . .. . . . 1. .. . . .. . . .. . - . .. . . .. . . . .. . . .. . - . .. . . .. . . . .. . . .. . . . .. . . . Note: Data for some countries are based on partial or uncertain data or rough estimates; see ACDA 1997. a. Data prior to 1990 refer to the Federal Republic of Germany before unification. b. U.S. Arms Corntrol and Disarmament Agency aggregates. 296 1999 World Development Indicators Although national defense is an important function of ing through extrabudgetary accounts or unrecorded * Military expenditures for NATO countries are government and security from external threats con- use of foreign exchange receipts, or fail to include mil- based on the NATO definition, which covers military- tributes to economic development, high levels of itary assistance or secret military equipment imports. related expenditures of the defense ministry (includ- defense spending burden the economy and may Current spending is more likely to be reported than ing recruiting, training, construction, and the impede growth. Comparisons of defense spending capital spending. In some cases a more accurate esti- purchase of military supplies and equipment) and between countries should take into account the many mate of military spending can be obtained by adding other ministries. Civilian-type expenditures of the factors that influence perceptions of vulnerability and the value of estimated arms imports and nominal mil- defense ministry are excluded. Military assistance is risk, including historical and cultural traditions, the itary expenditures. This method may understate or included in the expenditures of the donor country, and length of borders that need defending, the quality of overstate spending in a particular year, however, purchases of military equipment on credit are relations with neighbors, and the role of the armed because payments for arms may not coincide with included at the time the debt is incurred, not at the forces in the body politic. deliveries. time of payment. Data for other countries generally Although the traditional secrecy shrouding data on Data on armed forces refer to active-duty military cover expenditures of the ministry of defense defense spending and trade in arms is gradually lift- personnel, including paramilitary forces. These data (excluded are expenditures on public order and safety, ing, data from governments are often incomplete and exclude payments to civilians from the defense bud- which are classified separately). * Armed forces per- unreliable. Even in countries where parliaments vigi- get and so are not consistent with the data on military sonnel refers to active-duty military personnel, includ- lantly review government budgets and spending, spending. Moreover, because they exclude payments ing paramilitary forces if those forces resemble defense spending and trade in arms often do not to personnel not on active duty, they underestimate regular units in their organization, equipment, train- receive close scrutiny. Finance ministries also may the share ofthe labor force that works for the defense ing, or mission. * Arms trade is exports and imports not exercise due oversight, particularly in countries establishment. Because governments rarely report of military equipment usually referred to as 'conven- where the armed forces have a strong political voice. the size of their armed forces, such data typically tional,' including weapons of war, parts thereof, For a detailed critique of the quality of such data see come from intelligence sources. The ACDA attributes ammunition, support equipment, and other com- Ball (1984) and Happe and Wakeman-Linn (1994). its data to unspecified U.S. government sources. modities designed for military use. See About the data The International Monetary Fund's (IMF) The Standard International Trade Classification for more details. Government Finance Statistics is the primary source does not clearly distinguish trade in military goods. of data on defense spending. It uses a consistent def- For this and other reasons, customs-based data on Data sources inition of defense spending based on the United trade in arms are of little use, so most compilers rely Nations' classification of the functions of government on trade publications, confidential government infor- Data on military expenditures, and the North Atlantic Treaty Organization (NATO) def- mation on third-country trade, and other sources. The armed forces, and arms trade inition. The IMF checks data on defense spending for construction of defense production facilities and are from the ACDA's World broad consistency with other macroeconomic data licensing fees paid for the production of arms are Military Expenditures and reported to it but is not always able to verify the accu- included in trade data when they are specified in mil- Arms Transfers 1996 (1997). racy and completeness of such data. Moreover, coun- itary transfer agreements. Grants in kind are usually try coverage is affected by delays or failure to report included as well. Definitional issues include treatment data. Thus most researchers supplement the IMF's of dual-use equipment such as aircraft, use of military l l l| l i data with independent assessments by organizations establishments such as schools and hospitals by civil- such as the U.S. Arms Control and Disarmament ians, and purchases by nongovernment buyers. ACDA Agency (ACDA), the Stockholm International Peace data do not include arms supplied to subnational Research Institute (SIPRI), and the International groups. Valuation problems arise when data are Institute for Strategic Studies (IISS). However, these reported in volume terms and the purchase price agencies rely heavily on reporting by governments, on must be estimated. Differences between sources confidential intelligence estimates of varying quality, may reflect reporting lags or differences in the period on sources that they do not or cannot reveal, and on covered. Most compilers revise their time-series data one another's publications. Data in this table are from regularly, so estimates for the same year may not be the ACDA. consistent between publication dates. Definitions of military spending differ depending on For more information see the ACDA's World Military whether they include civil defense, reserves and aux- Expenditures and Arms Transfers 1996 (1997). iliary forces, police and paramilitary forces, dual- purpose forces such as military and civilian police, military grants in kind, pensions for military person- nel, and social security contributions paid by one part of government to another. Official government data may omit parts of military spending, disguise financ- 1999 World Development Indicators 297 A*ffftb 5.8 State-owned enterprises Economic Investment Credit Net financial Overall Employment Proceeds activity flows from balance ftom government before privati- transfers zation % of gross % of gross domestic domestic % of GDP investment credit % of GDP % of GDP % of total $ millions 1985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1990-97 .. ..................... ....... .. .... ......................................................................................................... .........I........... .... .................................................................... ..... .. Albania 27.6 .. .................... ........I ....... ........ ............. ...... ................ ................................. :: ...... ..... . .......... ... ... ......!................................... ....................... .........- ... Algeria 38Z . ............ ... .......... :7 ............................ 7.2 1 9.3 .................... .. ......... ......................... ........... _ :: ... .. ....... 7.................. ........ .................. ...... - ......................... Angola 3.8 .. ....... ..... ................................. .......7......... .... .............. :: ............... 1. ........ . ....... ... .... ............ .............. ... .......... ............................ ....................................... Argentina 2:7 ....... _1:3 ........... 9:4 3.0 ......... ........ ......... 28 .......... ... .... ... -0.1 2.6 .. 27,921.0 . ..... - .... .....................I................... . .. .................. ........................................................... Armenia 182.1 .. ............ 1 1 ........ . ........ .................... ............. ........ .... ............ ........................ .. ..... .... ... .......... .................................. ................... ........ .......... Austra(ia J5.0b i2.9 b ..................... .. ........................ ............. ....... ....... - ........................I.................. ... ........ . . ........ ... .......... ............... ....... ...... 7: . ................. Austria ........... ....... ....... .......................... ........ ....... ...... 7: .................I ............ ............... :: ............ .............. ... ........ _ ': ...... ........................... .............. ................. Azerbaijan 65 4 .......................... ... ...... .. .................... 77 ....... ....... .. ..... .............................. ......... 7 .  . .. .. ::.. 8 ..6 ..... .. ............ ................:............................. .. .. ........ ................... .... Bangladesh' 3 1 3.4 33.5 23.5 17.4 12.0-0 7 -3 9 -3.6 59 5 ...................... Belarus 10.8 .... .. ... ............................. .............. :......................... ........ ............... :! ............ .............. ... .... ......... ............ I., ............... .................................. Belgium 2-8 a 7-8 ...................... ... ......I.. ...... ...... . . .... ...... . .. ........ ......... ............ O 5 ............ ................................................ ........... ..... ............... Benin 2 8 39.1 ..................I ....... ........ . ....... ......... 1 7 .................. ... ........ .......... -.7 ......... ..... :: .................................. ......... .............I......I.. .... .. ............... .... .. .... ............ Bolivia 1379'.. 13_8 .. ..... 25.-9 ......... 26..O. I..... 12.3. 6:1.. _7768 ....... -8:2 7.9 a 7.2 .. ...... 2.3 a 884.2 ........:.....I.. ........ .............................. .... .. ..... .. ... . ..... .. .. .... .... ..... ...... ........... ...... ........................................... Bosnia and Herzegovina ': :: : :: ............ I - ........................................ ..... ...... ... ........... .............. Botswana 56.1 ... ... 5 61 23-.21 ........ 9.4 ... . .... 6 0 ... ... -0,31 ....... .. - ... .6.3 5.8 1 .................I........ ........... ................ 16,5.a ... -0:3- -2.6 a ......... .... ...... .... ... .... ............... .......I........ ........................................... Brazil 7!6 . ... .... 8:0 ......... 13:0 ....I.... 8.6 14.0 ............ 4.2 -0.61 -2.2 0.9 b 3.3 34,301.4 I....... ... ...........I......... ............ ..... ........ ....I............... .... ........- .................................................. ......... .... ... ............. Bulgaria 38:9 877.7 . .................... .......... ........ ............. ............. ..... ........ 1- ... ............................................. .............. ............... :: ................. .......... .11- ... ................................. Burkina Faso 6 4 .. . ........ .................... ....... I....... .... ................. :.............. ... ...... ........ ... ........... ............ .......I...... . ........... .................................. ................................ Burundi 7:3 41.1 19.3 12.0 21.7 32.1 b,d 4.2 ....................... C a m b o d ia 1 :2 .... .. .............. ................................ .............. I....................... . .......................I........ ....... ........ ............. .............. :.. ........ ...... ......... ............ ... ........ Cameroon 180 8.5 13.0 7.5 ..... ... 1.3... ........ ............................... 41.1 . .. ....- .. ...................... ................ ... ... .............................. I........... ........ ........ .............. .. ..... .......... . .................... Canada . .. .... ........................I........ ........ ........ ..... .............. ........... ...... ................ ...............7 ............ ............ ... .............. ............... I., ............. :  ............... ....................... Central African Republic 4.1 : ................. ........ 12.5 11:1 ... ........ ............ .......... 73:1 ': ............................. ... ...... .. .................. ......... .. .................................... .... ........ ....... .... .. . ......... ................... Chad 32. ........ ............................ - I. ........ ............. .............. ......... ........ ...........5 ......... 1 5 :9 .... .. ........... .............. .................I...... ..... . ......................................... Chile 14-4 8:1 ......... 15:3 ....... . 6.1.. ...2.0 ............ 1:7 ......... 6 -4,8e 8.71 4.8 1 903.8 .... i.. ............................I .. .. ..... .. .... .. .... ................................................- ........ ..................... Chinl . ..........., 29.2 7.7 7.4 17,036.0 . ........................ ....... ...................... ...... ...... ......... ......... .2 4 .9 ........... ... ......... ...... ........... .............. ......... ................. ......................................I............. Hong Kong, China ........................ .. .... ............. .............. ........ ................................. ........... .............. .............. .......... ............... ........................................ Colombia 7.0 13.5 ................1 5.2 1:5 .......... 0:6 0:8 5,461.4 .... ..... ........ .......................................... ....... ................... .................. ........ .. ... . .......- ...... ............. ....................................................................... Congo, Dem. Rep. 18:8I.0 .. ....... 5:0 ............ .... ....... ....................I........ . ......................... ......... ... .......................... I....... ....... ...... ................................ .......... ............... Congo, Rep. 15,1a .. ........ ...................................... .................................. ........ ........ 1 2 ..8 .......... 8 : I .. .. ...... .............. .............. ......... ......... .............. I., ........ -1 50.8 C o sta R ica 8 :1 8 .5 . ...... 1 1 .0 ..................... ... . .... ........... :9 .............. ........... 1 :9 - ....................... ................ .......... ...... ...................... ........ ....... ......... ............ .. ....- ........ - ........ C6te d'lvoire 21:4 b 476.3 .................. ..... .. .......................... ....... ..... ....... ................... I................. ............ ..... .... ...........:............................. .......................... Croatia 4.2 246.3 .. ............ ........ ............................. . ......... ............ ............... .. ..... . ........................................... ..................... ................................ .... ....... 1. ........... ......... Cuba 706.0 ........... - ...... .. ............................. .. ........ .............. ............. ............ ........................ .......... ........ :: .............. 7: ................. ............. .................................- ... Czech Republic 22:4 ............ . .. ......... .............. ......................... ... .............. .... .. 4,277.4 ....................... I... . .. ..................... .............. ........................... ....................... .. ........................ Denmark ..................... ... ....... ........ ................ ...... ....... ............ .... ....... ...... . ............. ............ .............. ....... ...... ................................. ....... I., ....................... Dominican Republic 11.6 b15.0 11.1 - .... ........................ . .. ........ - ! .............. ............ I.. ........ ............................ .... ........ .. .... .............. Ecuador 10.2 12:7 ... .... 13.9 0.3 ............ 0:5.. 01 . ............ .......... _.1._2 .... .. .................. 169.4 ........- ........ ........ .............................. ....... ................... .. .................... .. .. ................ ............ Egypt, Arab Rep. 65 521.5 8 5 -2 6 13.8 1,510.2 ......... ....... .. ........ 7.............. ............. :....... .. I : .. . - 0 6 .............. ............ .. ........ ..................I.................................... ..... . ......... .... ....... ............... .... .. .... .. El Salvador 7.1 3-2. 0:0 ......... 01 ............ 7_0 ......................I...... 11 . ......................................... ... ........ ... ....................I ........ ........ . ...................... ..... ....... ..................... .. .... :3 Eritrea ...........I ........ ... ......................... ............ .............. .... .. ..... ... .................................. ............ .............. ........... .............................. ......... ...... ....................... E sto n ia 2 3 5 ............ ........... .. ............:............................... . .......... ......... 4 6 7 .3 .. ....... ........................ ........ ........ ............. ........... .. .... .......................... .. ........- ............ Ethiopia ....... ...........- ....................I ....... ..................... ....... ..... .................................. ........................................ . ....... .......................... ........... .................... ...... ............. Finland ....... 11 ........-. .............................. ....... France 11.2 .......I .............................. I....... ............ .............. Gabon 5.2 25.1 ... . ..........- ....... ..... ........ .......... :: ............. .. ...................... I....... ..... ................. 3 :3 ...... . .......... ............... . .................................... ......................... Gambia, The 3:8 ............. 2.9. ..........- ......... .......................... ....... ............ .......... ... .... ..... .. ....... ........ ................. ...................... .. ......... Georgia .......... .....-. ....... ..................... Germany ................... .................... ...... ................ ..... ...... ............... ........... ... .... ....... ............ ... ..... ......... ................. ......... ............................... Ghana 8:5 18.5 .......... ... . 12.4 .......... 10:0. 0.41 . .... ....... .......... 0:1 . . 34.3 d 864.8 .................... ..... ..................... ... ..... . .. ....... ... ... .... .. . ..... ...................................... .......................... Greece 11:5 . ...........7........ 2072 ........I ......... .. ....................I ..... . . . .... .. ................. .. ...... ........ :: ............ .. 1: ..... ....... :: ............... 1. ............. .. ............. .................. .. Guatemala 1.9 2.1 6.7 512 0 21 0 0.5 30.0 ......... ......... ...... .....I... ..................................... _ . ........... ................. ... ..... ..... 0 :1 ......................... .... ....- ... Guinea :: ! 45.0 ... . . .........- ................I.......... .... .... ........... ............ ... ..... ........ ................. ............ 0 :8 .... ....... ... ........ .. .......... Guinea-Bissau 13.2 8.9 0.5 - ................. .......I. ............. .. ..... ............. .... ...... ........ ............................. ........... ...... ........ I., ............. .................... ..... .7 ................I ..................... Haiti 11:2..- 7..7 ............ 3:9 ............ .......... ..........I........ ...................... .... ... ........ ........... ............. .. ............. .. ... .. ..... : ...... .......... ............. .............. I., ....... Honduras 5.5 is I0 0 -0 9 74.0 ............ ... ... ............................... I ........ . ...................................... ........ ........... . ...... .......... ...............:..............:............. .......... ......... ....................................... 298 1999 World Development Indicators 5.8 Economic Investment Credit Net financial Overall Employment Proceeds activity flows from balance from government before privati- transfers zation 96 of gross 96 of gross domestic domestic % of GDP investment credit % of GDP % of GDP 96 of total $ millions 1985-90 199G-96 1985-90 1990-96 1985-90 1990-96 1985-90 ±990-96 1985-90 1990-96 1985-90 1990-96 1990-97 Hungary .. . . . . . .. .. .. .. 12,293.3 India 13.4 134.4 3574.4 .... 32..4 ..... ....*:.... -0~3.3 -0.9 .....-25.5 ... -1.2 9 8.5 8.1 7,073.0 Indonesia 14.5 ... .. 89 9.... 15..7. ... ..... 3~4 ....1~3 ........... -05.5 -2 6..... 0.9 1.2 5,162.8 Iran, Islamic Rep.. . . Iraq..... Ireland . . . Israel . . . . Italy ..12.9 . ... Jamaica ..23.6 .. 3.3 1.8 -1.4 385.4 Japan . . 5.8 6.0 2.8 . .... 2~.0 ... . ... ...... Jordan. . . .. 9.5 7.7 . . ..58.7 Kazakhstan . . .. . ... .. .. ..5,797.6 Kenya 11.6 ....... ..... 20:4 ....... 5.2 32.02..... 3.20. 7.9 7.7 227.1 Korea, Dem. Rep... ..... .... .. . Korea, Rep. ~10.3 . 14.3 . -02 071.9 Kyrgyz Republic . . . . .. . .139.5 Lao PDR . . . . 29.8 . . . .32.0 Latvia . ...... .......I..... 53 . ... ..... . ... . 431.1 . Lebanon Lesotho Libya. Lithuania . . . . 99 . 899.8 Macedonia, FYR . 601.5 Madagascar . . . . . . Malawi 4.3~ .. 9.2 ..... .....12.8 ..10.2 ..... O! ..... ..... -1.2 a 10.8 Malaysia' . 25.9 -. . -1.9 10,029.6 Mali 21.9 M auritania 19.3 0.1 ...... . ....... .... .... ....... ......... ....I.... 1.1 M auritius.. 19.. ...... ... . .... ...... . .. . . . ..... .... . ........ . -0 3 ......I... Mexico' 6.7 . 49.9 . 14 6 10.5 6.5 ..... 0.9 -24..... -3.1 2.4 3.0 3.5 2.1 30,460.5 Moldova .38 9.......38.9 .. .. . 1.6 Mongolia . . . 3 2.8 Morocco 16.8 b 19.3 . .0 0 . 1,846.7 Mozambique . . . . 8.9 5.9 . . . 09.6 Myanmar .. 37.P .b. Netherlands New Zealand Nicaragua . 43.2 13.1 . 130.2 Nigeria ..0.5 . . ...730.2 Norway 25.8b .. 2.0 12 . Oman 1.3 0. ... . 60 Pakistan 288.8 . 25.9 ... . .. .. . 1,951.0 Panama 7.6 7.6 97 47 . ..-0.8 -2.0 21 3.6 . 823.8 Papua New Gui'nea. 7.8 . .. . . .. . 223.6 Paraguay 4:8 .... 4.5 11.2 5.1 1686 7.9 0.2 .... -0:4 -2.0 1.2 . .. . ............ 42.0 Peru 64 57 9.7 4.4 . ~~~~~~~~~~~~~ ~~~ ~~~~~~~~~~-3.1 -3.1 1.5 3.0 2.5 .. 7,477.5 Philippines 2.3 2 2 .....84 9.9 8.5 3:9 . .. -2:2 -3.7 0.8. 3,730.0 Poland 7 1... 76.0O 29.6 ........ . ..... .... ..... ............ . .... ......... 5,845.0 Portugal 15.1 . 16 6 . 12.2 6. .. . . Puerto. Ric ....... .... ..... . .. . .............. Romania 630'j . 72.9J . 75.2 . ..766.7 1999 World Development Indicators 299 5.8 Economic Investment Credit Net flnancial Overall Employment Proeeeds activity flows from balance from government before privati- transfers zation % of gross % of gross domestic domestic % of GDP investment credit % of GOP % of GOP % of total $ millions 1985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1.985-90 1990-96 1985-90 1990-96 1985-90 1990-96 1990-97 Rwanda ...... 0.4 .... ... . ...... 1.0 . Saudi Arabia 12.9 . . Senegal 6.9 28 2 ................ ...... 4.5 ......... -..... 1.0 . . 20.4 d * 191.4 Sierra Leone .. 04~~~~~~~~~~~~~~~~~~~~~~. 01 .... ..................... ......... I.......... .. . 1.6.. .. Singapore Slovak Republic ........... .27.9.... .......................... 7 . ...........1.....,979.4.. ..... . 1, 7 . Slovenia .. . . 521.1 South Africa 14.9 .. 1 . 2,481.9 Spain . .0 SriLanka 26.8 . 2.8 1.4 . 12.4 723.3 Sudan 16.5 5.0 . . Sweden 10.3 8.7 Switzerland Syrian Arab Republic Tajikistan Tanzania 12.9 30.0 ............. ....... . -7.4 ..... .. 21.4 140.8 Thailand.. . 11.5 10.4 1.5 ......2:3 ....-0:3 -......04 -0.3 -0.8 0.9 b128. Togo . 10.7 ..1.6 ..... .. .... . ... .:...... ..... ... . 38.2 Trinidad and Tobago. 9.1 . 16.4 ............. 10.8 6.5 0:0 .. .. . . ... 0.7 . 276.2 Tunisia ..31.0 . .. . 76 .. 150.2 Turkey 65.5 5.1 .....27.1 15.1 6.0 4.7 1.6 1.0 -3.2 .....-6.6 .....3.7 2.9 3,600.0 Turkmenistan.. . Uganda 151.6 Ukraine .. 49.2 .... .. . .... . .... ............. 31.5 United Arab Emirates .. _. .. . United Kingdom 3.6 2~8 6.4 United States .. . 2.1 2.0 . . Uruguay 5 0 14.2 11.1.. .....11.1. . -3~2 . ............3.3 . ... .. 17.0 Uzbekistan . . .. .. 212.0 Venezuela 22.3 . 5091.1 4.2 -11.1 .............. 9.3 5,914.1 Vietnam . . . . .. 44.0 . . .2.6 West Bank and Gaza. Yemen, Rep. 3:0 ......3.0 .. Yugoslavia, FR (Serb./Mont.) :.. ..: 921.7 Zam bia 32... ... ............2... ...... ................... ..............417.2......41 . Zimbabwe 10.8 113 29.7 7.5 . . 197.3 Note: Data are averages for the period shown except for proceeds from privatization, for which the data refer to the total for the period. a. Selected major state-owned enterprises only. b. Includes financial state-owned enterprises. c. Data for 1985-90 refer to the 10 largest state-owned enterprises. Data for 1990-96 refer to 210 enterprises. d. As a percentage of formal sector employment, e. Nonoperating revenue before 1989 is split between carrent transfers and grants. All nonoperating revenue since 1989 is classified as current transfers. f. Data refer to industrial state-owned enterprises. g. Data refer to central public enterprises only. h. Data prior to 1991 have not been shown because of lack of consistency. i. Data for economic activity and employment refer to nonfinancial enterprises in both the controlled and the noncontrolled sectors. Data on investment through 1986 refer to nonfinancial enterprises in both the controlled and the noncontrolled sectors. Data since 1987 include financial enterprises in the noncontrolled sector. Data on overall balances before trans- fers and net financial flows from government refer only to nonfinancial enterprises in the controlled sector. j. Data refer to state- and majority state-owned enterprises in 1990-97. 300 .1999 World Development Indicators State-owned enterprises are government-owned or -con overall balance before transfers. These indicators do a Economicactivityisthevalueaddedofstateenter- trolledeconomicentitiesthatgeneratemostoftheirrev- not, however, allow analysis of the relative efficiency of prises, estimated as their sales revenue minus the enue by selling goods and services. This definition state enterprises and private firms because not enough cost of their intermediate inputs, or as the sum of their encompasses commercial enterprises directly operated data are available on ownership by economic sector. operating surplus (balance) and wage payments. by a government department and those in which the gov- Data in the table are period averages for 1985-90 and * Investment refers to fixed capital formation by ernment holds a majority of shares directly or indirectly 1990-96. The updating of data has necessitated revi- state enterprises. * Credit is credit extended to state through other state enterprises. It also includes enter- sions to earlier years to ensure consistency over the enterprises by domestic financial institutions. * Net prises in which the state holds a minority of shares if the time series. For the more recent period World Develop financial flows from government is the difference distribution of the remaining shares leaves the govern- ment Indicators 1999 includes data for 11 more between total financial flows from the government to ment with effective control. It excludes public sector economies than last year's edition. state enterprises (including government loans, equity, activity-such as education, health services, and road Data on proceeds from privatization are included in and subsidies) and total flows from state enterprises construction and maintenance-that is financed in the table because privatization-that is, the transfer of to the government (including dividends and taxes). other ways, usually from the government's general rev- productive assets from the public to the private sector- Taxes paid by state enterprises are treated as a trans- enue. Because financial enterprises are of a different has been one of the defining economic changes of the fer of financial resources to the government. nature, they have generally been excluded from the data 1980s and 1990s. Direct sales are the most common * Overall balance before transfers is the sum of net on state enterprises. privatization method, accounting for more than half of operating and net nonoperating revenues minus net The definition of a state enterprise varies among privatization revenues in 1997. Direct sales enable gov- capital expenditure. Net operating revenues (or oper- countries and within countries overtime. In exceptional ernments to attract strategic investors who can transfer ating surplus or balance) refer to gross operating cases govemments include noncommercial activities, capital, technology, and managerial know-how to newly profits, or operating revenues, minus the costs of such as agricultural research institutes, in their data on privatized enterprises. Share issues in domestic and intermediate inputs, wages, factor rentals, and depre- state enterprises. But more often they omit activities internatonal capital markets are the second most com- ciation. * Employment for many countries refers to that clearly are state enterprises. The most common mon method, accounting for most of the remaining the share of full-time state enterprise employees in omissions occur when governments use a narrow defi- sales. total employment, but for some it refers to employ- nition of state enterprises-for example, by excluding Large sales proceeds do not necessarily imply major ment only in selected state enterprises, including those with a particular legal form (such as departmen changes in the control of stock of state-owned enter- financial ones, and for others it refers to employment tal enterprises), those owned by local governments (typ prises. Forexample, selling equity may not change effec- as a share of total formal sector employment. Thus ically utilities), orthose considered unimportant in terms tive control. It may only generate revenue, with no gains the data on state enterprise employment are not of size or need for fiscal resources. Accordingly, data on in efficiency. A preliminary analysis suggests that the directly comparable. * Proceeds from privatization state enterprises tend to underestimate their relative increase in proceeds from privatization in recent years include all sales of public assets to private entities importance in the economy. is due to a larger number of countries privatizing a few through public offers, direct sales, management and Although attempts have been made to correct for dif- firms rather than to a radical restructuring of ownership employee buyouts, concessions or licensing agree- ferences in definitions and coverage across countries, in many countries (Haggarty and Shirley 1997). ments, and joint ventures. inconsistencies remain. These cases are detailed in the country notes in the World Bank's Bureaucrats in Data sources Business (1995b). The state enterprises covered in the table are limited to central or federal government enter- -: Data on state enterprises were prises because data on enterpnses owned by local gov- collected from World Bank ernments are extremely limited. Another weakness in member country central the data is that many state enterprises do notfollow gen- banks, finance ministries, erally accepted accounting principles, so accountng - enterprises, and World Bank rules can vary by country and enterprise. In many cases and International Monetary small state enterprises are not audited by internation- _ --- Fund reports. These data were ally accredited accounting firms, so there may be no then collated into a database independent check on their record keeping and for the Worid Bank Policy Research Report Bureaucrats reporting. in Business: The Economics and Politics of Government To aid in assessing the importance of government Ownership (1995b). Updates to this database have ownership, the table includes three measures of the been made for several economies. Data on privatization economic size of state enterprises: share in economic are from the World Bank's Global Development Finance activity, share in investment, and share in employment. 1999 CD-ROM. Data on credit are from the International Indicators that measure the performance of state enter- Monetary Fund's International Financial Statistics. prises and their effect on the macroeconomy and growth include credit, net financial flows from government, and 1999 World Development Indicators 301 5.9 Transport infrastructure Roads Railways Air Passenger- Goods Goods km per transported Diesel transported PPP ton-km per PPP locomotives Aircraft Passengers Air freight Paved roads Normalized million $ million $ million available departures carried million % road index ton-km of GDP of GDP %thousands thousands ton-km 1997 1997 1997 1997 1997 1997 1996 1996 1996 Albania 30.0 3 22,092 5,523 1 13 Algeria 68.9 - 14,542 - 45 3,494 16 Angola.25.0. 2,187..- 8 585 62 Argentina 29_1 100...... 131 7,913 177 Armenia 100.0 80 23 9,397 . 30 Australia 38.7 123 128,000 ...31 30,05 1,834 Austria 100.O 195 73,200 54,166 78,419 89 118 4,719 192 Azerbaijan... 15,371 . 20 1,233 28 Bangladesh 12.3... . 13 1,252 136 Belarus 98.2 148 273 264,933 626,034 93 32 843 5 Belgium 79.7 85. 30,127 31,973 86 165 5,174 591 Benin 20.0 62 270 . . . 2 75 16 Bolivia 5.5 . 1 . 32 1,784 47 Bosnia and Herzegovina 52.3 . Botswana 23.5 242.....410 0 Brazil 9.3 130 384,000 . .. 484 22,012 1,645 Bulgaria 92.0 108 27 141,558 210,151 80 14 718 24 Burkina Faso 16.0 . .313817 Burundi 7.1 .. 1 9 0 Cambodia 7.5 . 0 Cameroon 12.5 . 11,588 34,031 68 4 362 42 Canada 35.3 . 300 2,084 . 306 22,856 1,781 Chile 13.8 44 4,110 4,274 5,999 65 94 3,622 806 Chinea.. 1,360,000 93,767 364,679 82 493 51,770 1,689 HogKon,Cia . 100.0 ..14 Colombia 12.0 39 . 56 . 195 8,342 311 Congo, Dem. Rep. 689 15 4 178 43 Congo, Rep. 9.7 113 70,625 35 5 253 17 Costa Rica 17.0 191 .5 21 918 45 MSe dIlvoire 9 7 79 7,824 13,487 53 5 179 17 Croatia 81.5 .. 45,137 86,589 63 15 727 2 Czech Republic 100.0 . 470 75,364 207,322 86 27 1,394 22 Denmark 100.0 91 . 38,993 14,518 108 5,892 172 Dominican Republic 49.4 115 . 1 30 0 Ecuador 18.9 103 54,671 23 1,925 33 Egypt, Arab Rep. 78.1 170 . 317,317 41 4,282 198 El Salvador 19.8 69 4 . 21 1,800 16 Estonia 50.8 90 49 63,128 536,076 80 5 149 1 Ethiopia 150 47. .. 257418 Finland 64.0 69 383 32,596 68,983 88 103 5,598 237 France 100.0 128 . 47,024 39,107 93 550 41,253 4,843 Gabon 8.2 38 . 9,487 61,975 89 7 431 35 Gambia, The 35.4 218 Georgia 93.5 .7 51,829 34 3 152 2 Germany 99 1 . - 319,436 34,780 39,350 92 567 40,118 6,036 Ghana 24.1 128 ............. .. .......... 3 ..........19.7 ........30 Greece 91.8 . 2.357 13,409 1,913 92 6,396 119 Guinea 16.5 150 ... 1 361 Guinea-Bissau 10.3 .. 1 21 0 Haiti 24.3 .. Honduras 20.3 118 302 1999 World Development Indicators 5.9 Roads Railways Air Passenger- Goods Goods km per transported Diesel transported PPP ton-km per PPP locomotives Aircraft Passengers Air freight Paved rosds Normalized million $ million $ million available depsrtures carried million % rend index ton-km of GDP of GDP %thousands thousands ton-km ±997 1997 1997 V997 1997 1997 1996 1.996 1996 Hungary 43.4 164 39 89,681 104,305 64 25 1,563 29 India 45.7 671 . . 230,668 176,994 90 151 13,395 565 Indonesia 46.3 180 18 20,636 .- 311 17,139 749 Iran, Islamic Rep. 50.0. - 22,650 - 47 63 7,610 110 Iraq 86.0.- Ireland 94.1 261- 17,558 9,124 82 95 7,677 102 Israel 100.0 91 9 2,713 11,947 92 50 3,695 1,113 Italy 100.0 54 78,061 42,403 18,421 7930 25,839 1,459 Jamaica 70.7 752 -- 18 1,388 25 Japan 74.3 68 8,9 8,656 88 564 95,914 6,801 Jordan 100.0 113 .- 47,605 90 17 1,299 297 Kazakhstan 82.8 169 621 10568 17 Kenya 13.9 124- 12,875 14 779 48 Korea, Dem. Rep. 6.4 -- 5 Korea, Rep. 74.0 118 2,772 53,034 24,826 88 207 33,003 6,551 Kuwait 80 6.. -- 19 2,133 34 Kyrgyz Republic 91.1. 110 13488 2 Lao PDR 13.8 _.- -4 125 1 Latvia 38.3 178 41 156,795 1,1.14,235 88 11 276 1 Lebanon 95.0 - -- 10 775 80 Lesotho 17.9 80. - 1 17 0 Libya 57.1 . -6 639 0 Lithuania 88.8 386 59 59,802 545,075 88 7214 2 Macedonia, FYR 63.8 - 18,932 45 5287 1 Madagascar 11.6 131- 17 542 25 Malawi 19.0 1951 2,733 9,875 . 4 134 75.1 -- 188~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.............. .. Malaysia 7. 8,456 9,425 65.18 15,118 .1,415. Mali 12.1 71 - .- 2751 Mauritani'a 11.3 50 5 235 17 Mauritius 930.0 145 9717.... .9.717 .129 Mexico 37.4 97 2,427 53,891 68 223 14,678 169 Moldova 87.3 155 41 . 190 1 Mongolia 3.4 2,564 ..- Morocco 51.8 140 2 7 19,060 55,524 75 32 2,301 57 Mozambique 18.7 136 . 3,770 . - - 4 163 5 Myanmar 12.2 .. . 15 335 1 Namibia 8.3 193 1,587 6,366 139,848 89 7237 29 Nepal 41.5 71 ... 28 755 18 Netherlands 90.0 62. 43,779 9,751 88 206 17,114 3,902 New Zealand 58.1 106 - 187 9,597 745 Nicaragua 10.1 .1 519 Niger 7.9 25 2 75 1 Nigeria 18.8 153 - 18 6221 5 Norway 74.0 95 261 .. 280 12,727 177 Oman 300 . 18 1,620 10 Pakistan 58.0 365. 94,544 26,598. 70 5,375 42 Panama 33.6 . 17 68918 Papua New Guinea 3.5 28 27 970 18 Paraguay 9.5 ..10210 Peru 10.1 32 1,604 . 35 2,328 14 Philippines 0.2 . ... 65 7,263 385 Poland 65.7 131 1,670 83,594 284.497 55 36 1,806 70 Portugal .. 369 32,171 13,596 88 81 4.806 211 Puerto Rico 100.0 .-- Romania 51 0 . 6604 171,983 231,591 78 17 91315 Russian Federation 887 .239,056 .- 465 22,117 854 1999 World Development Indicators 303 Roads Railways Air Passenger- Goods Goods km per transported Diesel transported PPP toe-km per PPP locomotives Aircraft Passengers Air freight Paved roads Normalized million $ million $ million available departures tarried million % road index ton-km of GDP of GDP%Nthousands thousands ton-km 2.997 1997 1.997 1.997 1.997 1997 1996 1.996 .1996 Saudi Arabia 42.7 131 - . 839 4,207 80 101 11,706 863 Seneg.l29.3 104 2,378 14,239 79 5 155 17 Sierra Leone 8.0 110 . 0 15 0 Singapore 97.3 .. 57 11,841 4,115 Slo-kRepublic . 98.9. 59.377 93,5.11 297,405 87 3 63 0 Slovenia 83 0 76 5 26,466 112,524 8 393 4 South Africa 41.5 34,211 338,015 96 88 7,183 329 Spain 99.0 95 593 26,469 15,985 87 333 27.759 ...... 740 Sri Lanka 40.0 543 3,020 72,287 9 1,171 159 Sudan 36.3. 5,556 -- 38,841 42 10 491 46 Sweden 77.2 128 .....35,890 103,253 184 - 9,879 - 5 Swvitzerland 340 218 10,468 1,511 Syrian Arab Republic ....23 1 - .. .......4,655 11,355 29,657.......100 9599. 16 Tajikistan 82 7 .-..- 3 594 3 Tanzania 4.2 63 . 44,792 87,390 66 6 224 3 Thailand 97.5 172 29,755 75 92 14,078 1,348 Togo 31.6 141 - 27516 Trinidad and Tobago 51.1 2731 89722 Tunisia 78.9 171 . 23,080 53,344 71 14 1,371 - 18 - Turke 25.0 36 135,781 13,906 17,557 74 85 8,464 207 Turkmenistan 81.2 . . 9 523 2 Uganda .- -.. 11,617 1 100I1 Ukraine 95.0 1,250 511,709 1,414,943 87 28 1,151 17 United Arab Emirates 100.0--- 38 - 4,064 651- United Kingdom 100.0 56 772 64,209 7,618 United States 60.5 98 1,071 361 .900 8,032- 571,072 - 21,676 Uruguay 90.0 139 2 7,414 16,123 7 504 4 Uzbekistan 87.3 12 1,566 8 Venezuela 39.4 105 .0 65 83 4,487 117 Vietnam 25.1 19,819 16,353 92 28 2,108 83 Wash Bank and Gaza . : Yemen Rep 8.1 12 588 9 Zambia . 32,611 56,428 82 4 235 16 Zimbabwe 47.4 22,561 -196,441 1064 153 Low income 18.7 529 34,119 Middle income 50.0 131 - 4,099 275,385 Lower middle income 50.0 159 2.313 165,105 Upper middle income 47.1 130 1,786 110,280 Low & middle income 29.6 126 4,628 309,504 East Asia & Pacific 9.9 . 1,294 110,201 Europe & Central Asia 82.7 . 846 46,014 Latin America & Carib. 26.0 104 1,513 7275 Middle East & N. Africa 50.2 . 397 -- 38,521 - South Asia 40.8 454 284 22,445 Sub-Saharan Africa 15.8 116 294 16,049 High income 90.9 95 14,118 1,080,439 Europe EMU 99.0 90 2,532 180,694 304 1999 World Development Indicators 5.9 Transport infrastructure-highways, railways, ports rail traffic indicators are normalized by a single * Paved roads are roads that have been sealed with and waterways, and airports and air traffic control indicator-the size of the economy-the normalized asphaltorsimilarroadbuildingmaterials. * Normalized systems-and the services that flow from it are cru- road index uses a multidimensional regression func- road index is the total length of roads in a country com- cial to the activities of households, producers, and tion to estimate a country's "normal," or expected, pared with the expected length of roads, where the governments. Because performance indicators vary stock of roads (Armington and Dikhanov 1996). expectation is conditioned on population, population significantly by transport mode and by focus (whether Normalizing variables include population, population density, percapita income, urbanization, and region-spe- physical infrastructure or the services flowing from density, per capita income, urbanization, and regional cific dummy vanables. A value of 100 is "normal." If the that infrastructure), highly specialized and carefully differences. The value of the normalized road index index is more than 100, the country's stock of roads specified indicators are required. The table provides shows whether a country's stock of roads exceeds or exceeds the average. * Goods transported by road are selected indicators of the size and extent of roads, falls short of the average for countries with similar the volume of goods transported by road vehicles, railways, and air transport systems and the volume characteristics. measured in millions of metric tons times kilometers of freight and passengers carried. One additional indi- traveled. * Railway passengers refer to the total pas- cator is included in World Development Indicators senger-kilometers per million dollars of GDP measured 1999: the percentage of diesel locomotives in PPP terms (see About the data for tables 4.11 and available. 4.12 for discussions of PPP). * Goods transported by Data for most transport sectors are not interna- rail are the tonnage of goods transported times kilome- tionally comparable. Unlike for demographic statis- ters traveled per million dollars of GDP measured in PPP tics, national income accounts, and international terms. * Diesel locomotives available are the diesel trade data, the collection of infrastructure data has locomotives in service as a percentage of all diesel loco- not been "internationalized." Data on roads are col- motives. * Aircraft departures are the number of lected by the International Road Federation (IRF), and domestic and international takeoffs of aircraft. dataon airtransport bythe International Civil Aviation * Passengers carried include both domestic and inter- Organization (ICAO). National road associations are nationalaircraftpassengers.- Airfreightisthesumof the primary source of IRF data; in countries where thetonsoffreight, express, and diplomatic bags carried such an association is lacking or does not respond, on each flight stage (the operation of an aircraft from other agencies are contacted, such as road direc- takeoff to its next landing) multiplied by the stage torates, ministries of transport or public works, or distance. central statistical offices. As a result the compiled data are of uneven quality. Data sources Even when data are available, they are often of lim- ited value because of incompatible definitions, inap- : Data on roads are from the propriate geographical units of observation, or lack International Road Feder- of timeliness. Data on passengers carried, for exam- ation's World Road Statistics. pie, may be distorted because of "ticketless" travel ' . $ The normalized road index is World Ra or breaks in journeys; in such cases the statistics Stathshcs 9 a i based on World Bank staff esti- may report the number of passenger-kilometers for j mates. Railway data are from a two passengers rather than one. Measurement prob- . database maintained by the lems are compounded because the mix of trans- i . World Bank's Transportation, ported commodities changes overtime, and in some Water, and Urban Development Department, Transport cases shorter-haul traffic has been excluded from Division. Air transport data are from the International intercity traffic. Finally, the quality of transport ser- Civil Aviation Organization's Civil Aviation Statistics of vice (reliability, transit time, and condition of goods the World. delivered) is rarely measured but may be as impor- tant as quantity in assessing an economy's transport system. Serious efforts are needed to create inter- national databases whose comparability and accu- racy can be gradually improved. Some form of normalization is required to mea- sure the relative size of an indicator over time or across countries. The table presents normalized indi- cators for railway passengers and goods transported by rail as well as the normalized road index. While the 1999 World Development Indicators 305 5.10 Power and communications Electric Telephone International power mainlines telecommunications Transmission and In largest Consumption Production distribution city Cost of Outgoing Cost of per average losses per per Waiting Waiting Revenue local call traffic call to U.S. capita annual % 1,000 1,000 list time per per line $ per minutes per $ per kwh % growth of output people people thousands years employee $ 3 minutes subscriber 3 minutes 1.998 1.980-96 1996 1997 1997 1997 1997 1997 1997 1997 1997 1997 Albania 904 -0:3 .......52 .. 23 .......68 .......74 0 4:9 19 430 ..... 0.04 471 7.19 Algeria 524 72 2...... 18....... 48 ....... 55 ..... 732.0 7.9 74 ...... 173 0.02 113...... 4.78 Angola 61 2 4 28 5 .. 29 1,81 0.09 351 2.92 Argentina 1,541...... 30 .......18 ... ..191 ..... 253 ...... 19.0 0.0 295 ...... 818 0.10 .. 32 7.08 Armenia 905 -6 5 38 150 . 89.9 55 74. 91 Australia 8,086...... 4:4 ..... .7...... 505 0.0 0.0 ill 1,572 ..... 0 19...... 104 2.94 Austria 5,952 1.9 6 49237 0. 235 940 020 243 3.11 Azerbaijan .1,822 0.9 22 87 157 162.8 >10 47 109 0.19 86 3.85 Bangladesh 97 111 ...... 30 ........3. .. .. 20 ......155.0 66 15 634 . ... 0:04 81 Belarus 2,476 0 0 16 227 284 514 0 3.6 88 102 0 01 ...... 64 3.53 Belgium 6,878 3.1 5 48 501 0. . 8 4 7 258 25 Benin 48 6.5 . .....87 ........6... . 36 34 ......0 8 30 1,023 ... . 013 ..... 221 ..... 7.24 Bolivia 371 4.0 12 69 96 .20 391 . 54 Bosnia and Herzegovina 772 -17.1 23 80 548 70.0 4.0 212 384 0.04 219 3 08 Botswana.. . 56 180 11.8 1.0 49 967 0.03 432 5 52 Brazil 1,660...... 4.6 ......17. 107 165 2,400.0 15 5..... 195 882 0.09 28 4.36 Bulgaria 3,577 -......02.2 ...... 13 323...... 378 ..... 423.0 6.6 101 ~..... 82 0.01 28 . ...... Burkina Faso .. ............ ........ .. .... ........3 .......29 . . ..:........ . ..... 29 1,158 0.10 ......216 .....11:62 Burundi . 3 51 51 >0 26 98 0.04 154 117 Cam bodia ..... .......... .... ... ...... ........2 ... ...... 13.4 ......2.8 19 1,381 0.....O.09.. ~ .. 431 ........ Cameroon 171 3.5 ..... .20 .....5.......... 45 0 7.9 41. .983 0.07 335 12.02 Canada 15,129 ......2.7 .......7... .. 609 .0.0 .....220 ...... 749 .... 0.00...... 232 ......1.16 Central African Republic : ........ ...... .......3....... 15. _0.3 . ... 0.4 24 1,260 0.20 368 20.56 Chad .............. 1... . ............. .6 .......1.0 1...... 1 22 2 1,193 0.... O17 _ ... 371 12.34 Chile -1,864 ..... 5:9 9 180......... 96.7 0.3 239 741 .... ...: .... 114 3.22 Hong Kong, China 5,013 7 6 14 565 584 0.0 101 2,088 0.00 467 26 Colombia ......... 922...... 5:0.0 .. 22 148 ..... 251 .... 800.0 1.3 288 ...... 700 0.01 30...... 3 82 Congo, Dem. Rep. 130 2:4. 3.........I.1 ......... 6.0 . 17.41 ....... Congo, Rep. 207 8.5 ........0 8........ .......0.8 0.8 23 2,195 0.12 . ... 191 Costa Rica 1.349 5 0 12 169 478 49 4 1.0 133 407 0.04 114 4.93 CMe dIlvoire -174 -0.1 ......16. 9...... 40 82.4 6.3 40 1,413 0.11 282 7.15 Croatia 2,291 -0.2 16 335 324 72.0 0.8 .....143 ......4S5 0.03 173 5.04 Cuba 966 1.0 .......13 .......34 4 .. ... 7 24 1,243 ..... 0.00....... 75 ..... 7:35 Czech Republic 4,875 .0.9 ........8.......318..... 594 406.0 1.1 121 .. 487 . 0.07_..... 96 3.97 Denmark 6,113 33.3 5...... 633 ......... ... . .. 0 0 195 1,012 0.17 182 2.19 Dominican Republic 608 4.0 ...... 25 . 88 . ... 129 .......... :.. .... .. _148 ......... ::....... .. .... .162 Ecuador 616 62.2 ... 21....... 75...... 194 ......60.2 0.8 158 395 0.02 73..... 5.32 Egypt, Arab Rep. 924 7.4 0 56 . 1,310.0 39 9.... 66 .......253 ......0.01 37 6.82 El Salvador 516 5.2 .. ...13 .......56 ... ..198 ......2000 4.0 60 .......689 _ .0.05 .......88 ......... Eritrea .............. .... ....... : ........6 ...... 34 . 42.0 >10 30 980 0:03_ . .. 95 ..... 8 24 Estonia 3,293..... -5.3 19 321 267 769.9 ..... 2 5...... 125 ...... 324 ... . 0:05 ..... 142 4.33 Ethiopia 18 ......4 1 1 3........37......207.0 >0 28 55 03 68 7.82 Finland 12,979...... 3.5 4 556...... 677 ............... 0.0 160 1,068 ..... 0 14 130 2.85 France 6,091...... 47.7 6~ ..... 575 ............. ........ 0.0 204 797 0.13 101 1.52 Gabon 742 35.5 ...... 10 ..... .33 .......85 1......0..0 .....I5 1 48 2,000 ..... 0.15...... 494 Gambia, The . 21 43 22.0 1 28 864 0:34...... 216 5.88 Georgia 1,020. -4.0 23 114 174 181:0 5.7 60 71 0.000........ Germany 5,596 1.1 ........5..... 550 ... ..5 1 .......... 0 0 206 1,021 0.14 106...... 2.49 . Ghana 275 4:2. 0... .....6 .......28 ...... 28.3 ..... 1:5 30 1,260 ..... 0.08 ..... 208 Greece 3,395 4.6 ........7.......S16 771 ...... 48.5 0.3 239. ... 605 0.04 110...... 3.18 Guatemala 364 S.S 13 41 103 100.0 1.....l.6 ......83 .......586 ......0.03 .....113 ....... Guinea ..3 9 . 4 1,572 0.1 20 78 Guinea-Bissau . . . 7 0.7 32 32 .1,447 0.09 384 Haiti 34 0.9 S~ ~ ~~ ~~~ ~~~4 8 17 100.0 >10 21 143 0.00 205 7.07 Honduras 350 86.6...... 27....... 37 95 259 5 ......7.6 50 624 0.06 180 6.40 306 1999 World Development Indicators Electric Telephone International power mainlines telecommunications Transmission and In largest Consumption Production distribution city Cost of Outgoing Cost of per average losses per per Waiting Waiting Revenue local call traffic call to U.S. capita annual % 1,000 1,000 list time per per line $ per minutes per S per kwh % growth of output people people thousands years employee $ 3 minutes subscriber 3 minutes 1L996 1980-96 1996 1.997 1.997 1.997 1L997 1997 1.997 1.997 1997 .1997 Hungary 2,814 2.3 .... .13..... 304 412. 40..4 0.1 170 ..... 448 ..... 0.12....... 93 ..... 3.21 India .......... ......347........ 9.......:.. 0 ... 18 ..... 19 104 2,710.0 .....1.0 ..... 34 .... ..300 .....0.02 . ....24 .....6.10 Indonesia 296 14.0 12 ... 25 105..... .. 131 .508 . . 0.04 ...... 71...... 4.69 Iran, Islamic Rep 1,142 .. 973.3..... 20...... 107 233 1,380.0 . ..1.9 137 199 0.01 25 .6.02 Ira .. .. .. .. ... . . 1,361.... 6.. . 1. 0.. 32.. .. .1 . .. .. . . .. .. . .. .. . . ... . . .. . -. . Ireland 4,363 3.7 ..... ..9 ... ...411. . .. ........... .... 136 1,247 ... 0:17 .... .. 463 .... 2:20 Israel 5,081 5.8 4 450 398 22.0 0:1 313 740 0.07 126..... 3 07 Italy 4,196 I..1.9 . 7 .447 498 32.0 0.1 273 881 0.20 90..... 2 28 Jamaica 2,108 .. 6:3 ......11 ..... 140 -183.0 .....3 8 88 995 0.06 . 183 .... 5:45 Japan 7,083 4.0 4 479 991 0.0 289 1,443 0.08 28 3.72 Jordan 1,187....................... . 110..... ......10 .... ..70 _ . 178~ .... 156.0 .....4.8 ... ..75 .. ....707 ......0.03 216 Kazakhstan 2,865 0 7 15 108 265 395:0 41 ..165 0.00 63 6.69 Kenya ........126 6.3 16 . .....8 ..... 71 ..... .77:2 ......5.6 20 1,139 ..... 0.06 ..... 104 11.17 K orea, Da... Rep.......248...I..... 0.8.... 84..... 49.. .. ............... ... ..... ..... . .... Korea, Rep. 4,453 11.7 5 . . 444 450 ..... 0.0 279 692 ..... 0:05....... 43...... 3 15 Kuwait 12,808 4.8 .......0... . 227 .. 2.6 ......0.2 49 579 ..... 0:00 ..... 389 .....5.44 Kyrgyz Republic 1,479 ... 2.4 33 76 235 57 5 >10 51 53 ......5 83 . ....8.73 Lao PDR.R....... !.........5...57.. 5 ..2.6. 7....1.2.2 6 23... 933..... 2 ..... .3263 ...... .7.16.. 6 7 1 Latvia 1,783 0.1 .......47...... 302 328 72.1 2.7 140 ......199 . . 0.08.. .... 59 6.09 Lebanhon.. 1,651...: 1.. . 2..... 13........ ....... 179 ......63.......93 .... . . 60 ........ 112 580. .1 0.05 1 0757.2 ..e ..oth .. ............10. 63..9.3. 6.0. 19 713 0.... . 04......... 1,593 ....... .... Libya, 7 3,579..... ..0 .... ....10.2...... 4 .0....68.. 84.... .... ... .....32 ..32 ...69619 ... 0 0.03 ... 9 ...93 .. Lithuania 1,785 -0.2 11 283 362 102:0 2.0 106..... 130 0.002. 5 7.88 Macedonia, FYR 2,443 . 204 235 40.0 17.7 117 245 0.01 127 4.50 Madgaca . . . 3 5 .6 3.0 15 1,144 0.10 176 23.22 M alawi .. ......4 ..... 4 . 33 . .... 30.9 .....>10 ..... .8 ...... 900 ..... 0.03 .... 2.14 ... . 11.02 Malaysia 2,078 9.9 11 195 . ..... .... 160.0 0.4 154 772 0.03 172 ... 5:33 Mali . . 2 17.17 2,336 0.17 477 15.42 Mauritania . . 5 13 1:2. 0:7 29 2,062 0.13 417........ Mauritius . 195 249 23.2 0.7 124 525 0.05 ill 5.11 Mexico 1.381 5.5 iS 96 . 9. 0.8 190 829. 0.14 115 ... ..3.79 Moldova 1.314 -4.4 23 145 277 179.0 6.6 .... 81 .. .....74..... 0.10....... 89.. .. . 6.36 M ongolia ........... ........: ....... :.......37 . 88 46.9 7..... .77... .... 17 ......254 .....0.02 ... ...34 .....10.25 Morocco 408 5.0 4 50 112 29.0 0.2 97 468 0.08 109 6.30 Mozambique 76 -19.3 0 4 24 16.9 5.9 30 1,073 0.04 248.... Myanmar......... 58 6.6 36 .. ....5 ..... 20 .... .55.0 2.2 29 1,802 0.17 ...... 76 26.86 Namibia ... ..... .. ... .. ....! ............. 58 . 308 6.5 0.8 55 873 0.04 661 Nepal 39 ..12:0....... 28 8 215.0..... 7.6 37 281 0.02 108 Netherlands 5,555 19 ......4..... 564 ... .. ....29.30 0.9 17.23 New Zealand 8,420 30 .0 ... 11 486. ...... .~ ..... 0.0 226 1,221 ... . 0.00 220 3.95 Nicaragua 256 40.0 ...... 28....... 29 ...... 57 ..... 29.3 ......2.1 41 561 0.11 316. Niger................2....18.. ....1.4...... .1.1... ...12. ....1,445.... . 17 0.15.. 323 ,4 5 . 5 2 Nigeria.......... 85...... 56.6 .... . 32 498.1 4.2 28 .1,929 0.26 121 Norway 23,487 2.1 ... ....8...... 621 ..... 732 ..... ... :.. ...O, 0.0 ....129 1,407 0.11 176 .. 1.64 Oman 2,973 .. 15:0 0. ....87. 165 ... ..3.9 0..2 95 1,371 0.08 371 Pakistan 333 96 6...... 23....... 19 61 303.0 1.2 48... ... 386 ..... 0.05 30 Panama 1,140 3.5 18 134 250 28.8 1.1 99 764 0.00 113 5.20 Papua New Gui'nea. 1 .. .... 1 200 .... . 0.3 ..... 0.1 23 2,418 0.13 572 Paraguay 914 40.5 7 43 114 . . 35 936 0.06 123 Peru S......... 98 .....25.5 . .. 15 68..... 132 SO.4 0.2 282 ....926 0.09 51 4.81 Philippines 405 3.2 17 . .. 29 .. . .. 900.0..... 2.8 132 582 .... 0.00 ... .108 6.22 Poland 2,420 0.9 ..... 13 ..... 194 .. .. 2,330.0 2.8. 103 . .... 415 0.06 70 . ... 4.12 Portugal 3,044 .. 6.3 .......10 .....402 701 9.4 .0.1 186...... 770 0.08 98 .... .3:35 Romania 1,757 -1.8 12 167 . 1,300.0 4.0 73 145 0.01 29 4.98 Russian Federation 4,165 0.8 .9...... 183 429 7,840.0 8.5 60 20S5-. 36 7.55 1999 World Development Indicators 307 fl~ 5. 10. Electric Telephone International power mainlines telecommunications Transmission and In largest Consumption Production d istribution city Cost of Outgoing Cost of per average losses per per Waiting Waiting Revenue local call traffic call to U.S. capita annual % 1,000 1,000 list time per per line $ per minutes per $ per kwh % growth of output people people thousands years employee $ 3 minutes subscriber 3 minutes 1996 1980-96 1996 1997 1997 1997 1997 1997 1997 1997 1997 1997 Rwanda .. . . 3 .. . 10 2 43 .. 90 Saudi Arabia 3,980 9.8 8 117 178 1,190.0 5.8 114 1,023 0.02 351 6.41 Senegal 103 3.6 16 13 37 16.7 1. 86 1.150 0.09 239 8720 Sierra Leone . ..4 20 1. >0 18 131 0.07 222 Singapore 7,196 8.3 4 543 461 . 0.0 203 1,751 0.03 689 2.42 Slovak Republic ........... 4,450 1.5 6 259 601 109.0 ... 0:.8... . 91 326 0.05 229...... 4.46 Slovenia 4,766 ..... 2.0 .6 ......364...... 661 48..7 . ... 1.0 229 434 ..... 0.03 .....157 ...4.71 South Africa 3,719 3.9 8 107 415 116.0 0.....O.4 80 942 0.07 88 ........ Spain 3,749 3.2 .9 .403 ..... 475 3.7 0.0 247 691 00.09....... 7 7 2.66 Sri Lanka ............. 203 . ... 63.3 ...... 17....... 17 271 284.0 6.3 30 5,943 0.04 106 ... 6.76 Sudan.............. 51 6.2 32 ........4....... 34 320:0 >10 46. 539 0.03 137 8.02 Sweden 14,239 3.1 7 679 844. 0.0 177 806..... 0.13 ......177 .......1.81 Switzerland 6,919 1.5 .7...... 661 966 0.3 0.0 212 1,447 0....O14 ......417...... 2.07 Syrian Arab Republic 755 8.7 0 88 132 2,950.0 >10 70 1,134 0 05 69 3341 Tajikiatan 2,292 1 9 ......12 .......38 .....221 53.1 1. 46 38 0.00 60 10.50 Tanzania 59 7:4 .. 12 .......3........23 .......37 2 . . ..6.7 22 778 0.10 97 4.46 Thailand 1,289 12.6 9 80 331 620:0 ...I..0 9 137 428 ..... 0 10....... 56 5.87 Togo . 6 2 130 >0 30 -1,557 0.10 316...11.56. Trinidad and Tobago 3,041 4.3 10 190 137 6 0 0.5 88 781 0.04 255 3.31 Tunisia 674 6.2 11 70 80 77 5 1.....3 105 451..... 0:06 ... 147 .... 5.70 Turkey 1,139 9.7 17~ ..250...... 367 413.0 0:4 215 201 0.07 36...... 2:34 Turkmenistan 1,020 3.5 11. 78...... 153 .......66:9 ......34 47 78 8 .. 28 ...... Ukraine 2,640 -0.7 10.... 186 416 2,962:2.2... 6.6 .72 . .... 116 0.10 52 ........ United Arab Emirates 8,030 7.6 0 351 333 06.6 .0.0 126 1,347 0.00 884 3.78 United Kingdom 5,198 1.3 9 540 ....... 0:0 214 1,015 0.20 172. 1.16 United States 11,796. 30.0 7...... 644 ..... ...... 0:0 187 1,280 0.09 134 ... Uruguay 1,605 ...... 3.6 .......20 ... 232 308 .. .. 0..... : ..... 0... 132 888 0.19 ......-90 4.98 Uzbekistan 1,657 ......2.1 .. 9 63 ......216..... 230.2 . 50 80 4 .. 42 ...... Venezuela 2,498. 5.1 20 116 ..... 329. 392.0 3.2 180 ..... 821 0.07 56 ..... 448 Vietnam.177.9.6.19.21 90.. ....20. 520. 011.3 West Bank and Gaza.. . 41 . 220 >0 102 50 00 199 0:61 Yemen, Rep. .............. 99 11.1 26 13 ..... 74 ...... 78:6 5.0 53 . .... 302 ..... 0.02 .....116 . .... Yugoslavia, FR (Serb./Mont.) 2,972 ..... 17.1 ........9..... 206 450 154.0 2.2 178 _247 0.01 103._ 6.19 Zambia 560 -2.8 11 9 24 11.6 . 24 1,780 0.09 167 3.91 Zimbabwe 765 6.0 7 17 62 109 0 4.2 33 639 0......O03...... 279 .....6.49 .. . . . . . - - . . . . .. . . . . . . .. . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . .. . . . . . .... . . . . . .. .... . .. . . . . .. . . . . ... . . . . Low Income 279 8.5 19 16 59 4,304.8 1.5 36 331 0.08 220 8.02 Middle Income 1,222 8.2 11 87 235 28,927.8 1.3 144 381 0.04 90 5.16 Lower middle income 1,020..... 109.9...... 10....... 71 228 22,572.6 2.0 126 286..... 0.02 58...... 5.87 Upper middle income 2,031. 4.44 ...... 13...... 150 248 8,233.6. 0.8 177 599 0......O05...... 112.. 4.46 Low & middle Income 851 8.2 12 60 177 34,710.9 1.3 140 375 0.05 114 6.22 East Asia & Pacific 624 ...... 8.3 .......10....... 50 378 1,90-1.9 1.2 .... 145 ...... 292 0.07 56 5.60 Europe & Central Asia 2,788.... 12.1 J1... .. 204 363 20,679.6 2.7 109 225 0.04 84 4.33 Latin America & Carib. 1,347 5.1 16 ......110 ......192 .....0.9 213. 796 .. ...0.04 .......82 ......4:42 Middle East & N. Africa 1,166 8.4 9 75 153 6,038.0 2.9 10.....i4 .....-445 0.03 1ll 6.02 South Asia ..... ....... 313...... 90.0 ...... 19 18 78 3,297.3 6.-3. 35 .. 1... 342 0.02 68 Sub-Saharan Africa..~.......... 439 3.3 10....... 16 60 1,439.9 079.9..... 68 961 0.09 249 . _8:11 High Income 7,941 3.1 6 5S06 ,......66 6 0.0 220 ..... 1.071 0.08 165 .... 2.63 Europe EMU 5,257_ .... 2.6 ........6...... 505 .. , ....... :...... 0.0 228 930 0.08 106...... 2:51 308 1999 World Development Indicators A country's production of electricity is a basic indica- Waiting time is calculated by dividing the number of * Electric power consumption measures the produc- tor of its size and level of development. Although a few applicants on the waiting list by the average number of tion of power plants and combined heat and power countries export electric power, most production is for mainlines added each year over the past three years. plants less transmission, distribution, and transforma- domestic consumption. Expanding the supply of elec- The number of mainlines no longer reflects a telephone tion losses and own use by heat and power plants. tricity to meet the growing demand of increasingly system's full capacity because mobile telephones pro- * Electric power production refers to gross production urbanized and industrialized economies without incur- vide an alternative point of access. (See table 5.11 for in kilowatt-hours by private companies, cooperative ring unacceptable social, economic, and environmen- data on mobile phones.) organizations, local and regional authorities, govem- tal costs is one of the great challenges facing The table includes two measures of efficiency in ment organizations, and self-producers. Electric power developing countries. telecommunications: mainlines per employee and rev- production growth is average annual growth in power Data on electric power production and consumption enue per mainline. Caution should be used in inter- production. i Electric power transmission and distri are collected from national energy agencies by the preting the estimates of mainlines per employee bution losses are losses in transmission between International Energy Agency (IEA) and adjusted by the because firms often subcontractpartoftheirwork.The sources of supply and points of distribution and in dis- lEA to meet international definitions. Adjustments are cross-country comparability of revenue per mainline tribution to consumers, including pilferage. * Tete- made, for example, to account for self-production by may also be limited because, for example, some coun- phone mainlines are telephone lines connecting a establishments that, in addition to their main activi- tries do not require telecommunications providers to customer's equipmentto the public switched telephone ties, generate electricity wholly or partly for their own submit financial information; the data usually do not network. Data are presented for the entire country and use. In some countries self-production by households include revenues from cellular and mobile phones or the largest city. * Waiting list shows the number of and small entrepreneurs is substantial because of radio, paging, and data services; and there are defini- applications for a connection to a mainline that have remoteness or unreliable public power sources, and in tional and accounting differences between countries. been held up by a lack of technical capacity. * Waiting these cases it may not be adequately reflected in time is the approximate number of years applicants these adjustments. Electricity consumption is equiva- must wait for a telephone line. * Mainlines per lent to production less power plants' own use and People in highmincome economies still have employee are calculated by dividing the number of main- transmission, distribution, and transformation losses. far better access to telephones lines by the number of telecommunications staff (with It includes consumption by auxiliary stations, losses in part-time staff converted to full-time equivalents) transformers that are considered integral parts of T-: employed by telecommunications enterprises poviding those stations, and electricity produced by pumping public telecommunications services. * Revenue per installations. It covers electricity generated by primary line is the revenue received per telephone line by firms sources of energy-coal, oil, gas, nuclear, hydro, geo- -,: for providing telecommunications services. * Cost of thermal, wind, tide and wave, and combustible local call is the cost of a three-minute call within the renewables-where data are available. Neither pro- same exchange area using the subscriber's equipment duction nor consumption data capture the reliability of - (that is, not from a public phone). * Outgolngtrafflc is supplies, including frequency of outages, breakdowns, the telephone traffic, measured in minutes per sub- and load factors. - scriber, that originated in the country and has a desti- Over the past decade privatization and liberalization : nation outside the country. * Cost of intemational call have spurred dramatic growth in telecommunications to U.S. is the cost of a three-minute peak rate call from in many countries. The table presents some common ..- the country to the United States. performance indicators for telecommunications, including measures of supply and demand, service Data sources quality, productivity, economic and financial perfor- T- ,, . ~ icrl E. z l . or:.: .ir , aT l mance, capital investment, and tariffs. : Data on electricity consump- Demand for telecommunications is often measured Among developing economies. those in Europe t tion, power growth, and losses and Central Asia have thve beist access to tele- ___ by the sum of telephone mainlines and the number of nI are from the IEA's Energ pnones. but access Is stiil only about a third that ~ -. r rmtelAsEeg registered applicants for new connections. (A mainline in high-income economies. _ - Statistics andBalances of Non- is normally identified by a unique number that is the OECD Countries 1995-96, the one billed.) In some countries the list of registered IEA's EnergyStatistics ofOECD applicants does not reflect real current pending Countries 1995-96, and the demand, which is often hidden or suppressed, reflect- * . United Nations' Energy Sta- ing an extremely short supply that has discouraged tistics Yearbook Telecommunications data are from the potential applicants from applying for telephone ser- International Telecommunication Union's (ITU) World vice. And in some cases waiting lists may overstate Telecommunication Development Report, except for demand because applicants have placed their names data on telephone traffic, which are from Direction of on the list several times to improve their chances. Traffic, published by TeleGeography and the ITU. 1999 World Development Indicators 309 5411 Th-e information age Daily Radios Television Mobile Fax Personal Intemnet newspapers phones machines computers hosts Sets Cable subscribers per 10,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 people people people people people people people people July 1996 1.996 1.997 1997 1.997 1996 1 1997 1998 Albania 35235161....... ...... .......I... 0.23...... Algeria 38 239 6 70.0 10.2 4.2 001 A I oa.1 154 9 1I. .070.00 Argentina 123 677 289 156.3 56 20 ...........39,2 ........15..92 Azerbaijan 28 20 211 0.1 5 .. 0.30 Belarus .. ......... ...... 174 290 ... ....... 314 11.5. 0.62 Belgium 160 792 510 361.8 95 186.6. .... . 235.3 150.65 Benin 2108 91 0.0 10.2 0.9 0.02 Bosnia and Herzegovina 202 248 412..2.. .. ... ... 1.41 Cam ...........odia.... ..... .. ..127. .124. ....... . . .. 3..... .....0 ... . 0. . 3.. 0.970.05 Cameroon 7 162.. ... .... 81 .. ............... 0...... ...... 1..5. 000.... Canada .. .......... 157 1,078 .... ... 708 ... .... 261.4 139 .... .. 33~O.0 ... .... 270.6 335.96 Central African Republic 284 5..00.1 0.00 Chad 0249 20.0 00.0.. 0.00 Chile 99 354 233 42:8 28 2!7 ...........54.1 .... 15.44 Hong Kong, China . 739 695 412 61.5 343 53.2 230.8 108.02 Colombia 46 565 217 5.5 ... . ......35 ...4 0.... .. .. 33 .4 . ... ...2.91 C ongo,. ..... ........... - .......I..... . ........ .em ..Rep.3.98.4 . ... ..... . . .. 0... 0.... . 1 . ..... .. 0...00 .. Costa Rica.... .... .... 94 271 ... . 403 .... .....14:4 ........... 19 2.5 ......8.08 C6te d'Ivoire 17 157 610.0 2 33 018 Croatia 108 333 267 11.0 27 11.3_ 22.0 12.84 Cuba 118 351 241 0. 0 00.0 .. 08 Czech Republic 254 806 447 68.0 51 10.0 82.5 63.84 Denmark 309 1,146 568 2386 ..........273_ . ..... 47.9 360.2 358.85 Doiominican c ..........Republic....... .... 452. ...177... ..6.84. .. 15....05 ...16.....03.......... 5.... 9659 Ecuador 70 342 ...294 ....... .. 11.8 13 .13.0 1:01 El Salvador 48461 250 4.4 7 .1 07 ... .. ..i. . .. ..r. . .. ..a. . .. . . .. 1 0 1. .. . .. .. .. . . . . . . . . . . . . . .... 0...0.4. . 0. ...00 Finland 455 1,385 534 170.0 417 38.5 310.7 996.13 Gabon 29 _.... ..182 ...........135.8 0.5 7..... . . 5. ... 0.01 G mI a,......The..... 2.................. 164.. ....4..... .1 ............... .... 4. 0 2 6. 0. 00........ Georgia . 553 473 2.46 0 11.16 Germany 311 946 570 210.5 99 681 ... 255.5 ......140..58 Ghana 14 238 1091 0.3 1.6 0.13 Guatemala -33 73 126 28 756.... 3.0 0.97 Guinea .. 47 41 00~~~~~~~.. . ...-- .... . . 0.0.1.03 0..... . 00.... .... H aiti 3 ..........55 . .... ..... 5 .. .... ....!... ... .0..0~00 310 1999 World Development Indicators Daily Radios Television Mobile Fax Personal Internet newspapers phones machines computers hosts Sets Cable subscribers per 10,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 per 1,000 people people people people people people people people July ±996 1996 1997 1997 1997 1996 1997 1995 Hungary186 697 436 146.1 69 .11:.8 49.0 73.14 India 105 69 18.8 10.1 2101 Iran, Islamic Rep. ......... 28 237 ... ..... 148 0.0 .... .... 4 . ... . .. 0 .5 .......32 7 ... 0.. 004 Ir q 19.... ....... ................ 228...83 . . ....... ...0.....00 Israel 290 530 .... 321 .........160.1 283 ....... 249.9 186.1 146.78 Japan 578 957 708 114:8 304 126:8.8........ 202.4 107.05 Korea, Dem.Rep..199.14 ..... ...........0......01..... Korea, Rep. ..... ..39 1,037 341 . ... 162.4 150 ......... 8'9 150.7 ...... 37.66 Kyr~'z Republic .. .15 115 44_ 0 ...-- .- .... 039 Lao PDR 4139 4 10.11.1 0 00 Latvia ....... 247 699 ..... .592 48.4 . . ... 31 ...... 0.3 7.9 .... 33.28 Lebanon...... 107 ....... ................-. 892.354 1.. . 4 135 .. . ........ . 3 .1.8 . 3.33..... Malawi 3 256 2 0 0.1~~~~~~~~~.... ............0....00... Malaysia 158 432 166 5.2 113 6 946.1 18.38 Mauritani'a 0150 89 0.... ..... .. 1.7 . ... ....5.3 ...... 0.09 Mauritius....... 75 368 .......... 228 32 24.5 78.9 3.19 Mexico 97 324 251 15.2 18 3.0 37.3 8.75 Morocco 26 241 160 0.0 30.6 2.5 0.17 M ozam bi ue 3 ..... ... .. ....... ...... .... ...... ,.. 39... 4. ... 0..0...4 . 6. 005 Myanrnar......10... 89...70000.0 .0:00 Nepal 11. ..... .37. 4. 0.2...0...00.......005.. Netherlands .. ......177 963 .... ... 541 ..... ..371.8 110. 38.2 ..... ....280.3 327.85 New Zealand 223 1,027 50:.149 17.9 263.9 468.39 Nicaragua 30 283 :190 40:2 .. I........2.... ...... .1.44 Oman 504 582 602 0.0 25 2.7 15.1 2.87 Peru 84 271 143 138. 8 ...... 180.6 12.3 .....1.52 Philippines 82 159 109 7.0 18 0.7 13.6 1.01 Portugal 75 306 523 38 5 ...... 152 ....... 7 0... 7O744 45.34 Puerto Rico 126 715 270 45 0.32 Romania ..... ..... 317 226 ......... 110 6 ....9... .0 9 .8 9 ..... ...6.09 1999 World Development Indicators 311 5. 11 Daily Radios Television Mobile Fax Personal Internet newspapers phones machines computers hosts Sets Cable subscribers per 10,000 per 1,000 per 1,000 per 1.000 per 1,000 per 1,000 per 1,000 per 1,000 people people people people people people people people July 1996 1996 ±997 ±997 ±997 ±996 I 1997 1998 Rwanda 0102... 0 0. .0.00 Saudi Arabia 57 319 260. 17 8.2 43.6 0.02 Senegal 5141 41 . 1 11.4 0.21 Sierra Leone 4251 20 0.0 0 0.4 .0 00 Singapore 360 739 354 17.3 273 32.2 399.5 187.98 Slovak Republic ...... 185 580 401 1050 0....... 37 ..... . 10.5 241.6 26.25 Slovenia 206 416 353 110 8 47 8~9 .........188.9 .. ....91.07 South Africa 32 316 125 3735 41.6 34.02 Spain 99 328 506 .10.8 110 17.8 122.1 61.90 Sri Lanka 29 210 91 0.0 6 4.1 0.31 Sudan .................. 27 ........ 270 143...... .... 0.0 0 0.4 1.1 0.00 Sweden 446 907 531 218.1 358 50.9 3503.3 429.86 Switzerland 330 969 536 346.0 147 29 2 394.9 289.32 Syrian Arab Republic 20 274 68 0 1.4_ 1.7 0.00 Tajikistan 20. 281. 0 0.3. .. .... ..... 0.... 09 O Tanzania...I... ... . 4 278 ..... 21 00... . : 1.. .. 1.6 0.04 Thailand ..I......... 63 .. .... .204 .... . ..... 234 ......... .3.5 33.. ... 2 5 5 ... . 19.8 4.17 Togo 4217 19 ..... 1 . ....I.. 3.9 5.8 .........019 Trinidad and Tobago 120 517 332 14 1 9 20.0 11.62 Tunisia 31 218 182 1 33 8.6 0:06 Turkey 109 178 286 871 26 17 ....... 20.7 4.30 Uganda2 123 26 0 0~2. 1.4 002 Ukraine 5 4872 493 15.8 1 00 05.6264 United Arab Emirates 156 354 294 . 132 21 0 84.0 50.61 United Kingdom 332 1,445 641 40.2 151 33.8 242.4 0.01 United States ..........2-15 2,115 ....... 847 . ..... 245.9 206 78A.4 ........ 406.7 975.94 Uruguay 293 610 242 21.9 46 3.4 21.9 49.67 Uzbekistan 3452 273:. 0 ..0.08 Venezuela 206 471 172 17.2 46 3:0.0......... 36.6 2.94 Vietnam 4106 180 0.0 2 0.3 4.6 00 W estB................. ..... .... ...15..65 Yemen Rep. 15 64 273 1 0.2 1.2 0.01 Zu osamb ia ,F S r .M n ) 112 ..... . 214 . 80.... .5 ...... ..... ... . .... .8 .... ..... 0 0.1......0 249 Zim babwe .........19 96 . ..... 29 0....... ....0 1.. ....... 0.4 ...........9.0 0...... O 72 . , -*~E; 1 nai, I I Low income 99 57..1211........ 0.2 2.2 0......O10 Middle income 244 256 35.6 15~~~~~~~~~~~~~~~~~~~~~~~~. ........... 0...I.....91.8.....3.. .96...... Lower middle income ........ .. ..... .... 230 ........ 249 37.4 10 0.4 . ..........9 6 6 ... .... 1.26 Upper middle income 92 36.1 283 28,9 34 2.536.5 14.51 Low &middle income ...... ...I...... 187 . ...... 190 .... . ... 27.59...... .... 06 115 2.41 East Asia & Pacific ...... .... ...... 184 229 _ 39 0 11 0.4 ... ... 7.4 0.60 Europe & Central Asia 104 ...... 411 384 13 1.1 17.7 10.55 Latin America & Carib. 74 398 264 31.3 26 1.9 32.8 7..65 Middle East & N. Africa .... 37 268 .... .... 140 . .... . ...: .... .......6 ....... .1.515 4.4 . .... 0.23 Sout As.......Ia ......... ........ :......99. . .. . ..... 69 .... ....16.4 1 ... .....0 2 .2 ...... 2.1 0.11 Sub-Saharan Africa 12 196 44 . 4 .. 2.32 High Income 287 647 165.3 189 49.7 264.4 374.89 Europe EMU 218 820 533 132.3 131 39.0 186.1 122.32 312 1999 World Development Indicators 5.11 1 The table includes indicators that measure the penetra- connected to a single mainframe computer; thus the num- Daily newspapers are the number of newspapers pub- tion of the information economy-newspapers, radios, ber of PCs understates the total use of computers. lished at least four times a week, per 1,000 people. television sets, mobile phones, fax machines, personal Internet hosts are computers connected directly to the * Radios are the estimated number of radio receivers in computers, and Internet hosts. Other important indica- worldwide network; many computer users can access the use for broadcasts to the general public, per 1.000 peo- tors of information and communications technology- Internet through a single host. Hosts are assigned to ple. * Television sets are the estimated number of tele- such as the use of teleconferencing or the use of the countries on the basis of the host's country code, though vision sets in use, per 1,000 people. * Mobile phones Internet in organizing and mobilizing conferences, dis- this does not necessarily indicate that the host is physi- refer to users of portable telephones subscribing to an tance education, and commercial transactions-are not cally located in that country. All hosts lacking a country automatic public mobile telephone service using cellular collected systematically and so are not reported here. code identification are assigned to the United States. technology that provides access to the public switched Data on the number of daily newspapers in circulation Because Network Wizards changed the methods used in telephone network, per 1,000 people. * Fax machines and radio receivers in use are obtained from statistical its Internet domain survey beginning in July 1998, the are the estimated number of facsimile machines con- surveys carried out by the United Nations Educational, data shown here are not directly comparable with those nected to the public switched telephone network, per Scientific, and Cultural Organization (UNESCO). In some published in lastyear's edition. The newsurvey is believed 1,000 people. * Personal computers are the estimated countries definitions, classifications. and methods of to be more reliable and to avoid the problem of under- number of self-contained computers designed to be used enumeration do not entirely conform to UNESCO stan- countingthat occurs when organizations restrict download by a single individual, per 1,000 people. * Intemet dards. For example, newspaper circulation data should access to their domain data. Nevertheless, some mee hosts are the number of computers with active Internet refer to the number of copies distributed, but in some surement problems remain, so the number of Internet Protocol (IP) addresses connected to the Intemet, per cases the figures reported are the number of copies hosts shown for each country should be considered an 10,000 people. All hosts without a country code identifi- printed. In addition, many countries impose radio license approximation. cation are assumed to be located in the United States. fees to help pay for public broadcasting, discouraging 77 , =a radio owners from declaring ownership. Because of these rz - !z ' z i Data surces and other data collection problems, estimates of the Latin America ranks highest among number of newspapers and radios vary widely in reliabil- developing regions in access to personal Data on newspapers and radios computers ...~~~~~~~~~~~~~~~~~~,**. ity and should be interpreted with caution. computers ... , - - are from UNESCO, which com- ;s, i:r lz :- r ; c'e l9: ,-;! Data presented for other electronic communications - ' piles data mainly from official and information technology are from the International Tele- : ' - replies by member states to communicabon Union (ITU) and Network Wizards. Data on - ; i UNESCO questionnaires and television sets and cable television subscribers are sup- special surveys, but also from plied to the ITU through annual questionnaires sent to ,, I official reports and publica- national broadcasting authorities and industry associa- - . tions, supplemented by infor- tions. Some countries require that television sets be reg- mation from national and international sources. Data on istered. To the extentthat households do not registertheir - television sets, mobile phones, fax machines, and per- televisions or do not register all of their televisions, the sonal computers are from the annual questionnaire sent number of licensed sets may understate the true number. - to member countries by the ITU. These data are reported Because of different regulatory requirements for the in the ITU's World Telecommunication Development provision of data, complete measurement of the ... but Europe and Central Asia has the Report or Telecommunications Indicators database. The telecommunications sector is not possible. Telecom- best access to the Internet text also draws on ITU sources. Data on Internet hosts munications data are compiled through annual ques- ,, - , - r r '. r- 1 . 'c- are from Network Wizards (http://www.nw.com). tionnaires sent to telecommunications authorities and operating companies. The data are supplemented by annual reports and statistical yearbooks of telecom- munications ministries, regulators, operators, and industry associations. In some cases estimates are derived from ITU documents or other references. Data on fax machines exclude fax modems attached to computers. Some operators report only the equip- ment they sell, lease, or register, so the actual number . is almost certainly much higher. Estimates of the number of personal computers (PCs) are derved from an annual questionnaire, supplemented ..: - r m ,- , I r,,; , .l, .:. by other sources. In many countries mainframe comput- ers are used extensively, and thousands of users can be 1999 World Development Indicators 313 5.12 Science and technology Scientists Technicians Expenditures High-technology Royatty and patenvt and in for exports license fees applications engineers R&D R&D filed' in R&D % of per million per million $ manufactured Receipts Payments Non- people people % of GNP millions exports $ millions $ millions Residents residents 1985595b 1985_95a 1995-95b 1997 1997 1990 1997 1990 1997 ±996 1996 Albania 2 1 .0 1 18,761 Algeria 87 22... 0.. ...1...15 Angoia . .0 .17 0 0 Argentina 671 149 0.4 1,355 15 46 409 240 Arm ni .....-......- ............. .162.... 20,268..... Australia 3,166 792 1.7 6,415 39 162 295 826 1,074 9,196 34,125 Austria 1,631 814 1.5 11,975 24 9 1 185 287 691 2,506 75,985 Azerbaijan.. 165 16,470 Belarus 2,339 308 1 1 2 2 701. 20,347 B liumII...... ...... ..... 1.814 2,20 17.. . .. . ..1,356...... 59,099..... Benin 177 54 0.7 0 0 Bolivia 250 154 17 9 00 3 5 Bosnia and HerzegovinaI.. .. . . 3,056 Botswana .. : . 0 0 8 6 556 Brazil 168 59 0.6 5,175 18 12 32 54 529 2,655 29,451 Bulgaria 965 0. 038 2,3 Burkina Faso .0 0 Burundi 32 31 0.-3..00 0 0 14 Cambodia . . Cameroon4 310 0 1 Canada 2,656 1,073 1.6 33,068 25 . 3,316 45,938 Chad .0 ..0 Chile 231 0.7 480 19 0 83 37 53 189 1,771 China 350 201 0.5 33,344 21 0 55 0 543 11,698 41,016 Hong Kong, China 98 105. 0.3 7,392 29 .. :..: ..: 41 2,059 Colombia 719 2 21 .61 13 51 87 1,172 Congo, Dam. Rep.. .. . . . 2 27 Conlgo,Rp. 5 16 000 0 Costa Rica . 0.2 95 14 .13 9 25 C6te d'ivoi.re . .0. 0 0 3 Croatia 1,978 696 1.2 562 19 . . 259 356 Cuba 1,606 1,117 ... . 84 4,834 Czech Republic 1,159 695 1.2 2,528 13 34 78 623 24,856 Denmark 2,647 2,656 1.9 8,174 27 0 0.. 2,452 72,151 Dominican Republic . . 450 230 0 0 11 Ecuador 169 215 04.1... .... 53 12 00 37 64 7 354 Egypt, Arab Rep. 458 340 0.5 112 7 0 54 0 365 504 706 El Salvador 19 299 82 16 00 1 0 Eritree Estonia 2,018 470 M06 456 24I1 5 12 21,144 Ethiopia .. . 0 0 0 0 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. 0................3. ..... Finland 2,812 2,005 2.5 8,797 26 50 93 317 504 3,262 61,556 France 2,584 2,874 2.4 68,655 31 1,295 2,046 1,629 2,476 17,090 81,418 Gabon 189 17 0~O.0 ... .... 20 320 ..0 .... 0 .. . ....0.... ..... Gambia, The0 0..54 Georgia ................... ............. 289 21,124 Germany 2,843 1,472 2.4 112,243 26 1,987 3,168 3,797 4,694 56,757 98,338 Ghana .. . .0 13 Greece 774 314 0.6 686 12 00 15 58 434 52,371 Guatemala 99 107 1.1 94 13 0 0 .2 102 Guinea.. . Guinea-Bissau . .. .00 . Haiti . .0 0 36 Honduras 12 .4 00 3 5 10 126 314 1999 World Development Indicators 5.12 Scientists Technicians Expenditures High-technology Royalty and Patent and in for exports license fees applications engineers R&D R&D filed' in R&D % Of per million per million $ manufactured Receipts Payments Non- people people % of GNP millions exports $ millions $ millions Residents residents 1955-95b 1.959..95b 1985595b 1997 1.997 1.990 1997 1990 1997 1996 1.996 Hungary 1,033 512 0.8 5,745 39 49 100 36 192 832 24,147 India 149 114 0.8 2,654 11 1 12 72 150 1.660 6,632 Indonesia.. .. 0. 4,474 20 0 . . 40 3,957 Iran, Islamic Rep. 521 154 0.5 . 0 00 0 Iraq ... ....68 1 Ireland 1,871 510 1.4 26,467 62 38 110 591 4,140 925 52,407 Israel ...... ....... . .. 2.2 6,870 33. 63 187 73 183 1,363 12,172 Italy 1,325 800 1.1 32,747 15 1,040 490 1,959 1,004 8,860 71,992 Jamaica 86 0.0 619 67 3 77... 28 Japan 6,309 828 2.9 152,431 38 2,866 7,303 6,051 9,620 340,861 60,390 Jordan .106 . ....7. 0.3 .. 183 26 0.... .0 Kazakhstan 1,024 2,6 Kenya . 52 11 9 4 6 39 15 3,4 Korea, Dem. Rep. ... ......... 20,575 Korea, Rep. 2,636 317 2.8 44,433 39 37 252 1,364 2,413 68,446 45,548 Kuwait . . .76 4 Kyrgyz Republic 703 69 0~.0 ...... 47 24. ..... ... ... 26 20,179 Lao PDR ........... 0 0 Latvia 1,189 390 0.5 149 15 . 0 .. 1 197 21,498 Lebanon..2 . .. Lesotho... ...... 0......... 2 37,043 Libya .. 493 0.2 . ..0 .0.. 12. 23 Lithuania ..~- I.... . .. .. 629... . .:... .. 494. . 21 1. I....5 ... 101 21,249 Macedonia, FYR .. ~~~~~334 ... _..2 .. .....2 53 18,934 Madagascar 11 34 0.2 1 2 0 1 0 3 7 20,800 Malawi..1 3. 0.0..3 39,031 Malaysia 87 88 0.4 39,490 6 7 0 0 0 0 Mali ... . Mauritania ... .. .0000 Mauritius 361 158 0.4 20 2 0 0 0 0 3 19 Mexico 213 73 0.4 29,692 33 73 130 380 501 389 30,305 Moldova 1,539 224 07.7.. 15 9 .. 0O..0 90 20,245 Mongolia 943 182 0.0I1 2 0 .. 0.... .114 20,882 Morocco . . .. ..... ... 622 .27 4 460 123 90 237 Mozambique 3 8 0 . Myanmar..0. 0 Namibia I.. .1024 Nepal 5 ~~~~~~~~~~~~~~~~~~..... . .0 0.0.0 04 Netherlands 2,656 1,357 2.1 57,082 44 1,086 2,085 1,751 2,455 4,884 61,958 New Zealand 1,778 .822 1.1 428 11 0 0 0 0 1,421 26,947 Nicaragua 214 89 . 61 38 0 .. 0 Nigeria 15 69 0.1. .000 0 Norway 3,678 1,859 1.8 2,703 24 133 85 148 336 1,550 25,628 O m an . -...... .. . . .... . ... 157 1.2 0.. 0... . ..... ... . ...... Pakistan 54 76 0.9 281 4 0 2 0 19 16 782 Panama 0.0 16 14 9 15 31 142 Papua New Guinea . 0 0 0 0 Paraguay 8. 4....0.......80 Peru 625 188 0.6 99 10 . 0 5 80 52 565 Philippines 157 22 .. 02.2 6,249 56 1 18.... 38 158 163 2,634 Poland 1,299 510 0.7 2,235 12 0 27 0 175 2,414 24,902 Portugal 1.185 167 0.6 2,185 11 14 26 117 285 105 71,544 Puerto Rico . . . . . .. .. Romania 1,382 .. 579 0... ...O7 .479 .7 ....0 0 0 29 1,831 22,139 Russian Federation 3.520 688 0.7 3,809 19 . 176 . 11 18,138 28,149 1999 World Development Indicators 315 s1o4e0!pul wuawdoliA9aa PIJOM 666T 9TC ,jaaR (AAjns jol uoyWlelawnoop elepGAewud eas aiaj)eAae je&A 4saela 0144 10; aae aba( *q Liep asaaL 40 uo?ewWjojsuLjaq; 014 pJeRaJ 144Dm Al!Ipta -uodsa jo f0 Al!qe!f ou saawnsse OdIM 4o neaain IeuOI4auJEluI aia '(OdIM) U0!4az!UaO0 valjdojd lenboallabul plioM aLn Aq PaP!Aoid semuo eO4wjo4uI jeu!6ijo ala (sbuapisaluou Aq 9O'ST 'sbuap!saJ Aq 6C) U0!42Z!UBJ0 4UaWed uo!Bjnl3 pue '(sluapisaiuou Aq gglt sjuap'sai Mi 9t'9'89) ao!;J0 Wualed ueadoin14 '(abuaplsajuou Aq LtE906 's4uapisal lq OT) uo!;azuua1o AlJadOjd leisn4pul jeuoijai ueo.ijIV '(suap!sajuou Aq E9806Z, 'siuapIsaj Mq 9L) uo!iez!ue?jo ATladOld jenpoaialul ue3pj4 aqj 40 sao!dsne aL14 japun palIb asoDO apn14ou! 966I u! Pa4 suopeo!idde buawed )Oet1o e 17817 Os 17 9 8S amqeqwlZ S6 9 0 0 8qa t756 9L9~~~~~~~~~~~~~~~~6 e 'aa ezeE) pue bjueq Isom 96'6e6 L IV' 0 80S WeUPIOA *z 8'7 81 0 0O7 6to6 9 0 i6S 806 SIOOnzauaA 8816 t176 STES 09L17 ULlsi)(aqznl 6817 96 L 0 0 0 8 8 889 MiAenfnl 6ES9'1171 688"T17 T17716 9SVE' 9L9'SS 16,T t 99Z'617 96Z 6S2'S s1PS Pai!un t'80V017 69i'96 655'9 99£E 1706'9 sso0S 17V 99L96 Z66.pO14t7z;wou!i iun S91WiuW3 qeiV pai!un 2617'86 0 0 0 0.epuanr 8t76'1 T 99 ua14sIawb4jnj 899'617 295 6 . 2L'1 9 96 ..... 796 ..... . .. ... 8617 9V V 7 1 1 1717 T L8t7 50O T L 88SSis.iunli 608'06 6 0 2 0 L z 966 ........... o~aqoj pue papUji S06t, co 1708 0OLT 17V 0E517 q,L ' Ot7 61717pue)!a4i 0 0 S 0 Bu8zue1 029'617 6S 602 UISSO!M&j O 0 17 9 ... u!lqndaH qejV ue!)AS 9S99L 6696Z 86 9tVL'617 86Z pua)pazj!Ms t'9C'9L 2202L 296 St'L 00017 S99 V7S 696176 175S ETs tTLSupm T790'6 0 0 0 0617 4 uepS() V766'17 6896Z 99917 ZO'T7 TTZ7 06 11 6T17'S17 6 6O VE O017Tu1e. 896 017 EL VS9 L0O 986 8SC6 24V t44ln0 989'TZ6 TOS 69 917 T7SZ'T L17 t7S.. t7t1. V'g6. !UAI S9p8'6 1706 98 L17 91 0t70'T1 017 99L.T8, ojqnda4) bMJSAI 0 0 0 0 aua aJ! 0 0 T T9 9I :....,e~Ua 0178 Li, 0 0 0 0 66 909'1 e!qejV lpn6s *: T7 0 00 00 O 9 176 pum 9661 9661: L66T 066T ±66T: 0661 L66T 466T 996-S96T qS6-986T q96-S86T sjUap!SaJ s1uap!Sa8 sUoIl!lu $ suoHIPi $ s;jodxa su0141w dNS 40 % adoad aldoad -UON SIUaWABd sid!aoaJ pa)jnloejnuew $ uo!IIw iad uo!IiIJJ jad 40 % ~~~peig uw~~~~~~~~~~~~~i 6J99~~~~~~~au!u9 suor Ide 99 eUa!jSiOd PUB IUBWud PUB f4linAou f~IOUWDIOO-IIH somnp.puadx3 suelopupejL s4sn4ueslo ZT9g 5.12 Rapid progress in science and technology is changingthe ers as well as indirect R&D expenditures made by sup- * Scientists and engineers in R&D are people trained to global economy and increasing the importance of knowl- pliers of intermediate goods used in producing the final workin anyfield of sciencewho are engaged in professional edge as a factor of production. It is also driving rapid good. Industries classified on the basis of the U.S. R&D activity (including administrators). Most such jobs shifts in comparative advantage between countries. The Standard Industrial Classification (SIC) were ranked require completion of tertiary education. * Technicians in table shows several key indicators that provide a partial according to their R&D intensity, and the top 1D SIC R&D are people engaged in professional R&D activity who picture of the "technological base": the availability of groups (three-digit classification) were designated as have received vocational ortechnical training in any branch skilled human resources (scientists, engineers, and high-technology industries. The industry ranked tenth of knowledge or technology of a specified standard. Most technicians employed in research and development, or had an R&D intensity index 30 percent greater than that of these jobs require three years beyond the first stage of R&D), the competitive edge countries enjoy in high- for the industry ranked eleventh and more than 100 per- secondary education. * Expenditures for R&D are current technology exports, sales and purchases of technology cent greater than the average for manufacturing. and capital expenditures (including overhead) on creative, through royalties and licenses, and the number of patent To translate Davis's industry classification into a def- systematic activity intended to increase the stock of knowl- applications filed. inition of high-technology trade, Braga and Yeats (1992) edge and on the use of this knowledge to devise new appli- The United Nations Educational, Scientific, and used the concordance between the SIC grouping and the cations. This includes fundamental and applied research Cultural Organization (UNESCO) collects data on scien- Standard International Trade Classification (SITC) revi- and experimental development work leading to new tifc and technical workers and research and develop- sion I classification proposed by Hatter (1985). devices, products, or processes. * High-technology ment expenditures from member states, mainly from Because of the imperfect match between SIC and SITC exports are goods produced by industries (based on U.S. official replies to UNESCO questionnaires and special codes, Hatter estimated high-technology weights (the industry classifications) that rank among a country's top surveys, as well as from official reports and publica- share of U.S. high-technology imports and exports in 10 in terms of R&D expenditures. Manufactured exports tions, supplemented by information from other national each SITC group, based on 1975-77 U.S. trade data) to are commodities in the SITC revision 1, sections 5-9 and international sources. UNESCO reports either the highlight the relative importance of high-technology prod- (chemicals and related products, basic manufactures, stock of scientists, engineers, and technicians (all qual- ucts in SITC groups. In preparing the data on high- manufactured articles, machinery and transport equip- ified persons in science, engineering, and technical posi- technology trade, Braga and Yeats considered only SITC ment, and other manufactured articles and goods not else- tions on a given reference date) or the number of groups (at a four-digit level) that had a high-technology where classified), excluding division 68 (nonferrous economically active persons (people engaged in or weight above 50 percent. Examples of high-technology metals). * Royalty and license fees are payments and actively seeking work in any branch of the economy on a exports include aircraft, office machinery, pharmaceuti- receipts between residents and nonresidents forthe autho- given date) qualified to be scientists, engineers, or tech- cals, and scientific instruments. It is worth noting that rized use of intangible, nonproduced, nonfinancial assets nicians. Stock data generally come from censuses and this methodology rests on the somewhat unrealistic and proprietary rights (such as patents, copyrights, trade- are less timelythan measures of the economically active assumption that using U.S. input-output relations and marks, industrial processes, and franchises) and for the population. UNESCO supplements these data with esti- trade patterns for high-technology production does not use, through licensing agreements, of produced originals mates of the number of qualified scientists and engi- introduce a bias in the classification. of prototypes (such as manuscripts and films). * Patents neers by counting the number of people who have Most countries have adopted systems that protect are documents, issued by a government office, that completed education at ISCED (International Standard patentable inventions. Under most legislation relating to describe the invention and create a legal situation in which Classification of Education) levels 6 and 7; qualified inventions, to be protected by law ("patentable"), an idea the patented invention can normally be exploited (made, technicians are estimated using the number of people must be new in the sense that it has not already been used, sold, imported) only by, or with the authorization of, who have completed education at ISCED level 5. The published or publicly used; it must be nonobvious the patentee. The protection of inventions is limited in time data on scientists, engineers, and technicians, normally ("involve an inventive step") in the sense that it would (generallyto 20 yearsfrom the filingdate ofthe application calculated in terms of full-time-equivalent staff, cannot not have occurred to any specialist in the industrial field, for the grant of a patent). Information on patent applica- take into account the considerable variations in quality had such a specialist been asked to find a solution to tons filed is shown separately for residents and nonresi- of training and education. the problem; and it must be capable of industrial appli- dents of the country. Data on R&D expenditures may reflect differences in cation in the sense that it can be industrially manufac- the tax treatment of such expenditures. In some coun- tured or used. The table shows data on patent Data sources tries they may also reflect large and possibly unproduc- applications filed by residents and nonresidents. The tive outlays by governments or state-owned research World Intellectual Property Organization (WIPO) esti- o_ _ _.. Data on technical personnel and establishments, mates that at the end of -996 about 3.8 million patents n R&D expenditures are collected High-technology exports are those produced by a were in force in the world. by UNESCO and published in its country's 10 most R&D-intensive industries. Industry Statistical Yearbook. Information rankings are based on a methodology developed by on high-technology exports are Davis (1982). Using input-output techniques, Davis esti- from the United Nations' COM- mated the technology intensity for U.S. industries in TRADE database. Data on roy- terms ofthe R&D expenditures required to produce a cer- = alty and license fees are from tain manufactured good. This methodology takes into the IMF's Balance of Payments Statistics Yearbook. Data account direct R&D expenditures made by final produc- on patents are from WIPO's Industrial Property Statistics. 1999 World Development Indicators 317 AtmN ¾'" """' 'V ¾ rr'r '"''4 4 A' *:. '4' ' ½';' Globalization-the integration of trade, finance, people, and ideas in one global marketplace-increases the ability of firms to do business across national boundaries. The barriers to crossing those boundaries are coming down. And the costs of transport, computing, and communications are all plummeting-to small fractions of what they were a few decades ago. What once took days now takes hours, and what once took hours now takes minutes, even seconds. All this is opening new opportunities for people everywhere- opportunities to improve human welfare. But as recent collapses in East Asia, Russia, and Latin American show, globalization is far from risk-free. Globalization increases the opportunities for people in poor countries to prosper, but ill-considered policies and weak institutions may expose them to greater risks, such as financial shocks transmitted across the world economy. The adjustments to globalization-like closing uncompetitive firms and transferring resources to newly profitable sectors-can be painful. The opening of labor mar- kets and the free flow of information, which transfers knowledge and increases pi of dtictivity, may also disrupt social and political institutions. But the alterna- ri x c i r not attractive: globalization has largely bypassed some countries, many of clitrlr in Africa, because of their domestic weakness, which isolates them from glofi.ii markets, discourages investment, and limits the spread of innovation. F.-:iei* n aid by itself cannot correct these deficiencies (box 6a). The challenge i. r,a developing countries to strengthen policies and institutions to make the mn vq of globalization's opportunities while managing the risks. Tvrade Thlet past half century has brought unprecedented prosperity and better liv- inm Xt.indards to most parts of the world. World GNP rose from $1.3 trillion in li'360 to $29 trillion in 1997. The liberalization and rapid expansion of irade have underpinned this remarkable achievement. Between 1987 and I 9'197 world trade nearly doubled, and the ratio of trade to GDP in purchas- ilng power parity dollars rose from 20.6 to 29.6 percent. Trade in services is o T 1 g even faster, aided by the revolution in telecommunications and com- plnies. World service exports, having more than tripled from $392 billion in 19-S-I LO $1.4 trillion in 1997, now amount to a quarter of world merchandise exp)l ts (see table 4.7). Not all countries have integrated at the same pace, ic,-,ever. Growth in real volumes of trade ran below that of GDP in 44 of 93 d t%cloping countries in 1985-94, including many of the poorest. . =__= =.__P ---- =1 are lost. On balance, then, the benefits of trade liberalization tend to greatly outweigh the risks. Those benefits should con- The dependence on aid tinue to rise because of falling import tariffs and the liberaliza- o.r.i inre p,wreai r.ara a cr r,e ...c'rl. --Dae:, Sur $ur-.; 3 _ir,:; tion of trade in textiles, agriculture, and services now under way e r.earlr, ;-e r, ir,e -ge ar r,,. Be* ,iI i:....1 I:,¢ h.eIr,g through the World Trade Organization (table 6.6). cc''L''e D ,n irre 1 1-l:'a .ii. FI r1l:i ,-, lI.I,i,. e ; ,r-,Ei,Lr,.:r,a ;rn.l r9a,..r p.l:o f re ir-erJ r ' , r_p ) ,n_r.- nl3r .-p r; . z n1 d S .::r,r,,,e' r; -ed [2.(' ru,ll,:., ,r, 1S7 : ,r,ig u : ~oFregin direct investmient r .r ,1i al ,.J ,i-, a. .le.m r.1 J, Bui ir le P',. , . .;,.eri Foreign direct investment is often (but inadequately) called a cap- I aeel. I; i r,ir,.ij :1 .:,r,Il,,e ir'e i.e-I rer,;;, hiil r.-rer,Jer,l. a' *:r,e in~ie ;,.1. i -e-nrJ ; n.JEr.lert yr'.r.:n .n.: ;.lr;,-T; ital flow. In fact, it is a package of capital, trade, technology, and ri,e,r-.eilrril ,,r ,r,.: uaau . ..... 2,1el. lo ue-rr.rT * -uerroin rr managerial skills. Increasingly, foreign direct investment makes ..:r, _.J,.:i.;ar,.-n;,1 I.;i, :'1 earlerer,: --, rs,,;_ 1-; C-ull l;the globalization of production easier, separating production rr,,;n,:;,,r,i,e a',:..e a eedi LCu reil: rr .1*r r.J.- c,n,eal,: ,r;, - 1 |IV rr.e 1 i i 24 rrell:erlr ,r, h r. h. 2processes and locating them in different countries according to ,.ir iri I.r. elrer ,r, i.l,r,e s,- s;, ie.I comparative advantage. TL,' rL, e r, - n I rasr ir, ,1 .ari,,: i, er, e 1n 6au - r.,... A third of the world's external trade is now the internal trade :-ron r& C':a :,~.Jiir of multinational companies: parts of a car or computer can be rl;r ,iJ ....r-n ri I ,,; ,.:.,rr, ..lr rn r, l',r p:,:e ar. r-,lJ, r, .;r,-r,, r eew. 'c airir. ': i rr:e r I, 3;l 5rle1. arc.r manufacturedin several countriesand assembledinyetanother. p .a 31 il i, ir. 'C *:nnrmea. rinm. ri.;r,,3,1e 'elii.,...... Falling transport and communications costs have aided this -n iez- Irr-, r .r r.. nr~,,lr.-: D. -, process. Sea freight rates fell 70 percent in real terms between 1980 and 1996. The cost of international phone calls in the 1990s has fallen by 4 percent a year in developing countries and 2 per- Trade is good for economic welfare in several ways. It encour- cent a year in industrial ones. ages specialization in line with a country's comparative advan- Foreign direct investment in developing countries was mainly tage while broadening the set of opportunities available to in mineral extraction in the 19th century-creating development consumers. It enhances efficiency by stimulating competition enclaves with few spillover effects. But most today is in manufac- with the best in the world, and enables firms to reap economies turing and services. Valuable intangible assets-technology, orga- of scale by catering to global demand. nizational skills, and research and marketing know-how-get Exports also facilitate the creation of specialized centers of pro- transferred as part of foreign direct investment. Such knowledge duction (such as the computer software industry in Bangalore, often spills over from foreign ventures to the local economy. In India) that can reap externalities arising from the clustering ofinno- Malaysia the local affiliate of Intel now subcontracts a range of vators, skilled labor, and ancillary suppliers. And being in the global activities to firms set up by its former engineers. Some studies sug- market is a powerful way of acquiring the latest knowledge about gest that foreign direct investment tends to encourage additional new ideas, technologies, and markets. Recent research shows a pos- domestic investment: an increase of one dollar of foreign invest- itive association between trade openness and economic growth. ment is associated with an additional $0.50-1.30 of domestic But trade liberalization can entail costs. Uncompetitive investment. Foreign direct investment in previously restricted industries will close down, causing unemployment and output areas such as banking can strengthen domestic management losses in some sectors as labor and capital shifts to newly rising capacity and reduce vulnerability to financial crises. industries. The fiscal deficit may widen if import duty collections Foreign direct investment is generally considered less volatile fall. Without adequate long-term financing, infant industries that than other financial flows. That to East Asia declined only mod- could be viable in the long run may succumb to the pressure of estly during the crash in financial flows, mitigating the real eco- international competition. nomic adjustment. For all developing countries, the sum of inward Global experience suggests, however, that fears about the and outward foreign direct investment as a share ofGDPwentfrom costs of trade liberalization tend to be exaggerated. Major stud- 0.2 percent in 1986 (PPP$) to 0.8 percent in 1996-still modest ies find no systematic link between trade liberalization and unem- when compared with the 2.7 percent in high-income countries. ployment, which is often much smaller than expected (Harrison In past years many countries discouraged foreign direct invest- and Revenga 1995b; Papageorgiou, Choksi, and Michaely 1990). ment on political grounds. Nationalists wished to build strong Moreover, the adverse effects on vulnerable groups can be miti- home-grown champions and feared foreign direct investment gated by carefully designed social safety nets. would erode sovereignty and local culture. Today such fears look Another problem is that undertaking trade reforms in a poor vastly overblown. China and Singapore, which welcome foreign macroeconomic policy environment can magnify the adjustment direct investment, are far stronger than Myanmar or the costs. Reform is less likely to succeed in countries with large fis- Democratic Republic of Korea. cal deficits, high inflation, and unrealistic and volatile exchange rates (World Bank 1997d). Unreformed loss-making parastatal linancial flows firms and weak financial sectors can also boost the adjustment Economic theory suggests that the international mobility of cap- costs. In a favorable policy environment, however, trade liberal- ital can bring several benefits. It enables capital to flow to areas ization usually generates far more employment and revenue than with the highest returns. It facilitates portfolio diversification for 320 1999 World Development Indicators both foreign investors and locals. And it helps improve the abil- remittances back home are an important source of foreign ity of individuals to smooth their consumption across time. exchange for many countries. Take India, which benefited from Evidence on the actual size of these benefits is quite limited, private current transfers in 1997 of $11.8 billion, more than its however, and tends to suggest that the gains may be relatively small. current account deficit of $5.8 billion (see table 4.17). One study suggests that a 1.0 percentage point increase in the ratio The revolution in telecommunications is now reducing the of financial flows to GDP is associated with a 0.1 percentage point incentive to migrate. Some of the traditional benefits of migration increase in the GDP growth rate, much less than the 0.3-0.4 per- can be realized without labor movement. Clerical jobs, like billing centage point for foreign direct investment (Wacziarg 1998). customers, can be done electronically in low-wage countries with- Another study looking at 18 countries finds no significant asso- out the clerks moving to the countries where the customers are. ciation between financial inflows and the growth of total factor pro- ductivity (World Bank 1999a). A third study finds no significant Knowledge association between open capital accounts and GDP growth (Rodrik Knowledge can bridge gaps between rich and poor countries, forthcoming). One reason could be that recipient countries think helping developing countries accelerate their growth. But to it prudent to keep part of the inflows in reserves-to guard against enjoy these benefits, developing countries have to facilitate a speculative attack. But the yields on reserves tend to be much lower inflows of knowledge through open economic and political than interest rates on inflows, creating social losses. regimes-and they have to create skilled labor forces that can The benefits of greater international financial integration absorb and use global knowledge. need to be weighed against its risks. These arise in part from Ghana and the Republic of Korea had roughly the same imperfections in international capital markets that create the income per capita in the 1950s, but Korea was six times better off possibility of large, sudden swings from euphoria to panic. by the 1990s. World Bank economists estimate that half the dif- Reversals in flows can force huge, wrenching economic adjust- ference was Korea's greater success in using global knowledge. ments-as in East Asia, where financial sector regulation and Knowledge travels in channels other than trade and foreign supervision are weak and where local banks and firms are in poor direct investment. International cooperation and the exchange financial condition (World Bank 1999a). of ideas, journals, and personnel transmit knowledge in every Recent research provides evidence for contagion-for the field. Knowledge of how to develop local high-yielding varieties transmission of financial effects across countries more strongly has spread the green revolution. Knowledge of how to prevent than economic fundamentals alone can explain. Capital flow and cure disease has greatly reduced infant mortality globally. volatility is sometimes associated with volatility in international Knowledge of how to operate microcredit schemes has empow- interest rates-and can increase the probability of a crisis ered poor women across the world. (Frankel and Rose 1996; Kaminsky and Reinhart 1997). Even the The communications and computer revolutions are greatly withdrawal of sums that are small for overseas investors can have increasing the scope for global transmission of knowledge. Indeed, large, destabilizing effects in shallow emerging markets. they can enable developing countries to leapfrog old technologies East Asia suggests that financial integration may not be the that still hold in industrial countries. The ratio of cellular to total cause of financial crises-but that it can magnify the ill effects of phone use is much higher in the Philippines and Sri Lanka than domestic policy failings and institutional weaknesses. One lesson: in France or Belgium (see tables 5.10 and 5.11). Sub-Saharan developing countries need to proceed with capital account liber- African countries such as Botswana, Djibouti, and Ghana have 100 alization in a measured way after careful preparation. They need percent digital telephone networks, compared with the OECD to create a framework of sound macroeconomic policies. They average of less than 70 percent. need to restructure weak financial systems and strengthen systems The globalization of knowledge carries risks, too. Technical of financial supervision and regulation. They need to strengthen assistance programs have often failed. Inappropriate technology is corporate governance. And they need to eliminate distortions sometimes spread by donor-driven or government-driven pro- (such as tax incentives) that encourage short-term flows. grams, in isolation from what consumers want. Using local people to determine the suitability of new technology is vital. The selec- Labor tions by local women farmers of bean varieties for propagation in Nations define themselves by citizenship. In theory, flows of labor Rwanda outperformed selections by bean breeders by 60-90 per- could have the same efficiency effects as flows of goods or capi- cent (World Bank 1998e). The chances of success are greatest tal. But governments that espouse free flows of goods and invest- where the flow of technology responds to local demand-and is ment often oppose free flows of labor, since they can raise serious spread by commercial markets rather than subsidized agencies. questions of national identity. In sum: globalization is offering unprecedented opportunity to Workers feel differently, with annual flows of temporary and rich and poor, and countries have little choice but to develop the permanent migrants ranging up to 3 million a year in recent policies and capabilities thatwill allow them tojump onto the global years, mainly to OECD and OPEC countries. Most migrants have bandwagon. If people use the new global economy merely to dump traditionally been laborers, but today they include skilled profes- moneyintobad investments-if the lessons of 1997-98 do notresult sionals, such as software developers. More than 120 million peo- in better financial management-then more destructive shocks lie ple now live in countries they were not born in, and their ahead. 1999 World Development Indicators 321 I ril-r -51 ,;-,-,rr-.: cr;'1 an- :er nerrn-r, l r T1,in.er 7|: 1 t 1 -;91 15: 1 _ 1 1 ) ^ 1 Ž9| -{4 1 ' ,IC 199 , 1997 1 | , . ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~T,,r l olt,%- rr.,al rJl,;r.e lTical corces.siorI31 irsrce OiebeIOflarmn eet Sci4lCrdr,re ri: 1al | ' : \ rze,] e-:l,jriJnz ie~~~~~I C chidng chr *?i'.oer3ttin r n i S.;r loL nrra tmiroarre :omprises rrji jlii rr.ierl -ornlai al r , o $ billions 300 1990 1991 1992 1993 [ 1994 1995 1996 1997 1998 250 ' i Bank and trade-related lending 200 Portfolio bonds _!0 Portfolio equity flows 100 j Foreign direct investment 50 Source: Wrld Book, Debtor Reportig System. 322 1999 World Development Indicators g i '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i w . n 1 - X2-Kt'~~~~~~~tgr' -.f'j- ,?'w'':ur*;|;,' ',n'1;gso--it ' '; '''4z':0 ''< '-:i- - . % , ;-2 - ,; t~~~~~~~~~~~~~~~~~~~~~~~~t ffit > r i r 1 - s t r ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ N i;~~~~~~7 -9 v ; r $ | r b , , X ~~~~~~~~~~~~~~0 d = - - w; | | - s ^ - I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~t -1 L ;ab - 1 l : i l' '1 .-]W - - -' *;j ',, .,., r " - . L v>> 1 * - , ,, ,, . , ,1 . s !,1; . . _ Ii 10 | .~~~~~~~~~~~~~~~~~D 6.1 Integration with the global economy Trade In goods Growth Gross private Gross foreign direct in reai capital flows investment trade iess growth In real GDP % Of % Of percentage % of % Of PPP GDP goods GOP points PPP GOP PPP GDP 1987 1997 1987 1997 1987-97 1987 1997 1987 1997 Albania 10.8 27.9 38.3 ..........20.9 ..2.4 ..0.7 Algeria ..... ...... 16:0 17.4 .... 37.8 .......... 71.2 -1.3 .......... 0.4 ............ . 0.0 Angola 26:0 38.5,..................... ...... 9.2 0.9 4.2 0-9 .........1.0 Argentina 5 5 15.1 23.8 33.8 9.2 1 0 6.5 .... 0.5 .........1.8 Armenia .. 12.3 ... 12 9 2.2 0.6 Australia 24.4 35.0 80.1 116.8 4.7 13.1 13.5 5.6 4.5 Aus.tria 53.1 69.0 125.1l. ..... 153..8........... 3.1 7.9 ..... .. 21.0O 0.6 2.5 Aerbaijan .. 13.4 ..93.2 Bangladeh57.7 ........ 8.3 32.1 50.7 7.5 0.1 0.3 0. 0... .. 0.1 Belarus .. 31.8 ..109.1. Belgium ... ..3.0 Benin 11.5 ......16.6 65.1 109.1 -3.6 1 1 . 0-.0.......... Bolivia 8.5 14.2 ..80.8 3.2 1:6 ..........4.8 0.5 2.7 B osnia and H erzegovina : .. . ... .. . 7. .. . .... . ..... ... .. .. .. . ...I,.. . .. ..... .. .... ... .. .. . . . ... .. .. .. . .. :. . .. .. ... .! .. .. ... Botawana .. .. .. -3~~ ~ ~~~~~~~ ~~~~ ~~~~~~~~~~~.8... 84 2...........7.2.4 0.8.... Brazil 6:0 11.5 32.5 28.9........... 6.4 1.7 3.0 0 ..2 ... .....2.0 Bulgaria 207 7........ 24.6 37.3 134.3. -16.7 3.7 3.7 0.0 1.5 Burkina Faso 9 7 ..........6.5 48.3 46.5 -2 81!0 ..... ......7....I.... 0-.0 ......... Burundi ....... -. ....... .8 6 5.2 ......36.4 30.2 -16.. 0:7 ..........0.6 0.0 .......0.0 Cambodia 0.4 12.8 4.3 86.8 ...........................: ..... 2.1 . 1.5 Cameroon 10.1 12.2 36.4 54.1 1.2 46.6......... 5.1 0.5 0.1 Canada 44.0 62.9 109.5 . 4.8 8.5 13.9 3.7 .......3.8 Central African Republic 6.0 9.5 25.8 57.5 -0.3 1.5.. 0..4 Chad 5.4 3.4 35.3 26.5 ......... -5.5 .. .... 2.5 . . ....0..5.......... Chile 12.8 19.3 89.9 93.4 4.4 ........4 5 .........10.7 1.2 4.0 China 6:8 8.5 43.6 53.1 ... .. .. 28 .. ........0:6 ..........1.8 ........0.2 ....... .1.2 Hong Kong, China 125.0 . _ _250.4 .......596.6 8. 8 2 ........ . .. ........... ......... Congo, Dem. Rep ..............3.2 5.7 40.4 51.0 -5.6 : Congo, Rep. 33.3 64.5 93.0 185 1 1.4 9.8 5.2 1.4 0.0 Costa Rica 19.8 37.2 120.8 237 5 5.4 2.7 3.6 0.7 0.3 C6e dIlvoire 32.8 28.0 103.5 147.2 1.0 2.8 4.3 0-.5 .........1.3 Croatia . 53,9 ..132.2 ... 26.1 5 Cuba . .. Czech Republic .. 48.2 ..11.0 .....9.7... . 1.2 Denmark 62.1......... 74.5 114.0 116.6 23 3........ 17.6 . ....-38.1 0. 5.2 Ecuador 12.8 18.4 84.4 116.4 2 1 3.3 2.1 0 4 1.0 Egypt, Arab Rep. 10.0 ..........9.3 47.7 42.8 -0 62.8 4.......1.... . . 1.0 0.6... El Salvador 17.8 32.7 86.1 122.6 6.6 0 7 1.4 0.3 0.0 Eritrea. Estonia ..91.8 .342.3 .. . ........... ......... 25 4 ..........5.5 Ethiopia' 8.0 6 5 ...... 29.1 .-1.4 ...........0.3 1.4 0~ ........0 .0: Finland 56.2 66:7.. 94.8 121.3 ...... ....3 2 ..........17.2 ........ 27.2 2 .0 8.2 France 36.2 43 9 93.2 112.3 ...........2 7 ..........12.8 12.1 1-.7 .. ..... 4.5 Gabon 47.5 49.5 95.2 123.1 -0.2..12.3.7.4..2.2 4.6 Gambia, The 24.2 28.1 205.3 230.4 0.2 0.5 1.3 0..3 ........0.7 Georgia .10:9 ............... ........ 38.9 .. Germany . 54.3 60.4 24..... ...25.4... 2.2 Ghana 13.4 17.0 60.7 118.4 3.4 0.7 1.3 0.0 0.4 Guatemala 10.4 16:5 ........75.1 91.5 ...........4,1 ..... .....2.4 7.7 0.6 .........2:7 Guinea 13.7 13.5 ........72-.5 ..........75..6 ..........-2.2 ...........1.7 ..........1.4 .......0.2 . .......0.1 Guinea-Bissau ... 79.5 89 -3.9 . .. Haiti 9. 2. .6.5 0.6 0.1 0.1 0.0 Honduras 20.2 50.7 77.6 238.2 -0.2 1.9....... 3.......2 ......0.5 .........0.9 324 1999 Worid Development Indicators 6.1 Trade in goods Growth Gross private Gross fofeign direct In real capital flows Investment trade less growth in real GDP % of % of peroentage % of % Of PPP GDP goods GDP points PPP GDP PPP GDP 1987 1997 1987 1.997 1987-97 1987 1997 1987 1997 Hun ary 301.1 65.2 .......112..1 .. 181.4 3.8 ..........0.3 ......11.6 0.. .. . 0..O ..... .. 3.4 India 3.9 4 1 16.7 31.0 3.5 0.2 1.2 0.0 0.2 Indonesia 11.1 13.7 66.475.9 1.9 0.6 2.1 0.1._ 0.7 Iran, Islamic Rep. 10.9 ......... 9.5 30. .... 3O.3... .... 57.6 0.3 .......... 0!7.7 ........ 0.8 0.0 0.0 Iraq Ireland 94.4 121.0 189.3 238.1 3.2 108.8 129.9 0........O.3 .........4.9 Israel 43.3 48.6 .. 1.7 5.2 11.9 0.6 3.2 Italy 30.3 38.2 80.0...... 107.3 4.1 3.2 17.0 0.8 ........ 1.2 Jamaica 32.1 56.0 152.9.. 330.2 ......... 1.6 3.1 8.5 173.3....... 2.2 Japan 20.8 25.0 40.8 46.5 3.1 10:5 ........ 104 1.2 1.0 Jordan 37.0 34.5 133.8 ....... 190.8 ......... .6.5 ....... ...1.6 2.5 0:4.4 ....... 2.4 Kazakhstan ':....- ...18.-9 ....... ....... 115..4 . Kenya .. .......... .13.2 153 .... 59.4 .... .96..9 45........ .......... 1~0 2.2 0 3.3 ....... 0.1 Korea, Dem. Rep.' : Korea, Rep. 36.6 44.9 1195 5........ 131.3 5.0 3.9 8.9 0.5 _..... 1.2 Kuwait 84.3 66.7 135.4 134.4 14.3 15.0 4.1 3.6 Vr,~~~~~~~~Z R~~~~~~~~ojl:.tc 12 1 101.9 .. 2 S .. 0.5~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ...... .. .. ...I... Lao PDR 5.6 9.5 42.4 46.3 . 1.5 2.6 0.1 1.4 Latvia 45.3 ..5.8 .17.9 ..... 3.8 Lebanon 32.4 33.2 150.4 .. Lesotho ........ .... .............. -3.9 1.0 .. 0 4 Libya . 96.1 ... Lithuania 56.9 185.6 ..11.2. Macedonia, FYR 45.0 .... . .0.2 Madagascar 7.6 10.5 38.9 2.5 0.8 2.4 0.0 0.1 Malawi 16.0 20.0 71.8 99.5 1.0 0:4 0.0 ........ Malaysia 49.4 90.0 176.7 271.4 7.6 2.5 ..........6.2 0.7 2.9 Mall 12.8 18.2 44.6 80.7 0.3.. ...0:3 1.0 ..... 04.1.. .... 0.5 Mauritani'a 32.6 26.4 140.6 175.6 -4.9 2 3 11.6 0.1 .........0.2 Mauritius 36.8 36.1 180.2 177.8 0.8 1.8 4.1 0.3.........0.6 Mexico ....... 7.3 29.3 .... 55.4 .. 144.8 9.2 ......... 27 7....... . 3.8 0.6 1.6 Moldova 31..15 1 . 5 7 ..................._ 0.9 Mongolia 6.2 29.5. 142.1 36 1... ...... 1.6 0... .0O........ 0.3 Morocco 13:3 13.9 72.8 77.2 4.0..... 0:9 1........80.11.2 Mozambique ....... 15.7 .... 11.9 73.6 89.1 -1.2 0.1 0.7 0-.1 ........ 0.7 Myanmar... ..... N am ibia ...... .. ... ........... .. . ...... ...... .... .. -1.4 8~ _.......... 8 6..... 1.6 Nepal....... 5.2. .... 4.1 30.4 30.5 8.0 ..........0:7 0.4 .. 0.0O........ 0.1 Netherlands 92.0 113.4 221.5 238.1 1.9 ......... 24.8 .........52.8 .......5..8. 8.8 New Zealand 33.4 43.6 106.2. 3.2 10.0 4.6 4.1 3.1 Nicaragua 177 7........ 20.3 . ..... 52.1 163.3 .. ...... 6 . 7 .......... 0.5 3.0 0-.0 ... 1.1 Niger 13.4 84.4 _ .. 62.9 66.8 -4.4 0.9 0.8 0.4 0.3 Nigeria 20.5 210.0 ... .. 68.1 70.8 -0 ........ O.1 ..........9 1 6.5 1-.1 ...... 1.4 Norway 68.7 77.1 114.9 116.4 1.3 23.4 37.7 3 .0 7.0 Oman 47.1 43.9 110.0... 1.1 0.7 0-.3 ........ 0.2 Pakistan 9.7 ... 10.1 59.9 60-.5 0.... .... 6........ .0.8 1.8 0..1.. 0.4 Panama 14.0 27.2 107.8 254.4 0.7 157.8 42.9 4..9 .. 1.5 Paraguay 8.5 .........27 3 516 94-.3 .........10 .8....2... . 9. 0.0 Peru 7.6 1378........ 47.4 569.9 ...... . 4.2 0..... .. .O.7 ........ .54 01 18 Philippines 8.8 30.8 65.2 190.7 7.4 1.4 3.4 0 2 ........0.5 Poland 13.9 27.0 58.7 96.2 9.3 5.34.2 0.02.0 Portugal 29.0 41.1 125.6 136.2 4.6 3.8 .........21.4 0.62.4 Puerto Rico ..~ Romania 20.8 20.1 76.1 83.6 4.6 1.9 2~9 0.0 1.3 Russian Federation . 21 3 64.4 74.43.. 1999 World Development Indicators 325 6.1 Trade in goods Growth Gross private Gross foreign direct in real capital flows investment trade less growth in real GDP % of % Of percentage % of % of PPP GOP goods GDP points PPP GDP PPP GOP 1987 1997 1987 1997 1987-97 1987 1997 1987 1997 Rwanda 9.7 9.0 .......135..3 39.0 4.2...... .... 0:7.7 ... .. 0.1 0.3 0.0 Saudi Arabia .... .. ... 36.1 .. 50.3 ..... .113-.8 . ..... .................... 19.5 7.... .. . 4. 1.0 1.3 Senegal 20:7.7 ....... 10.5 108.4 .... 86..6..... ..... -1.4 . 2.4 1.4 0.3 0.4 Sierra Leone 12.3 23.1 45.9 76.1 7.7 3.7 ... .....1.6 ... 1.9_ . -0.3 Singapore .... ....200.7 .......290 7. 7 .. 7830 ..... ....753..9 ...4.9 ... .....24.1 109.3 ... 10,3.01 . Slovak Republic 51:4 260.8 9.0 . 10.3 D. .6 Slovenia 75 6 197-1.7..76. 15 South Africa 17.6 20.8 86..1........ 97.7 4.2 .. .....1.2 8.1 0.1 1.4 Spain 21.3 36:2 69.9 44.7 .. ...... 57. 4:4 17.1 1..4........ 2.7 Sri Lanka 14.5 21:6.6 ...... 86.2 121.5 . ..... . 2.5 ...... 3.1 5.2 0.3 ...... 1.0 Sudan 77.5 ....... ...46.6 14.8. .. .... 0.5 .. 05.5 ..... . 0.0 0.2 Sweden 66.9 84.6 126.3 . 3.0 138.8...I... 39.4 4.3 12.2 Switzerland 75.7 84.7... 1.2 36.9 89.6 4.0 9.2 Tajikistan .. 22 1 Tanzania .. 9.1 ..... 14.8 28.8 59.1 2.4 0.0 1.7 0. . ....0 0 0 9 Thailand 16.9 29.7 99.0 153.2 6.0 15.5. 5.6 0.4 1.0 Togo 16.2 22.6 100.4 158.2 -5.8 2.7 0.2 Trinidad and Tobago.. 40.7 .... 59.8 134.1 191.4 -2.4 2.6 9.2 0.6 3.6 Tunisia 19.0 2 5.7 87.9 122.3 1.3 18.8 3.8 0..4 ......0.7 Turkey 11.5 18.5 52.7 79.6 6.1 1.3 .........3.5 0.1 0.3 Turkm enistan 3-1.8 . .... .. ......... . ....:. . ...... 1.8 1 1........ Uganda 8:5.......... 6.0 19.1 33.3 1.0O 0.5 09 00 0:7 Ukraine 38.8 . 149.9 7.8 . 3.7 0.6 United Arab Emirates 65.6 106.2 150.8 United Kingdom 35.5 47.6 92.5 81.2 2.5 24.7 53.5 5.9 8.0 United States 140.0 .. ..... 20.4 50..3 ..... ... 75.3 4 .6 ..........6:8 ... .... 14.5 2.1 .... ...2.9 Uruguay.... ....... 12.8 21.5 64.3.......... 99.9..6 2 . . .... 4.2 6.3 0 30 5 Uzbekistan .. 13.2 . 49.0 Venezuela...... ... 15.7........ 20.6 86.4 85.5 2.6 3 9 ..........8.0 0.0 2.9 Vietnam 9.5 18.7 . ......13.3 ... . . .. 20.0 ......... .......... Yemen, Rep.......... 33.1 .... 124.0O 0.5 521.1 Yugoslavia, FR (Serb./Mont.) ... Zambia 23.0 24.6 100.6 96.6 -1.2 6.1 1.2 Zimbabwe 14.6.. 19.9 65.8......... 117.5 6.7 . .... 1.2 0.2 . ... Low income ............ 7:0.0.... 8.4 31.3 52.0 . 1.1 1.5 0.1 0.3 Middle income 10.3 18.6 53.8 80.0 2 3 3.7 0.3 1.4 Lower middle income 8.9 14.4 47.8 78.9 ..1.6 2.8 0..3. 1.2 Upper middle income 12.0 26.0 59 ..4 ...... 81..2 ...... .... 3.1 5.4 0.3 , 1.8. Low & middle income 9 7 16.7 49.5 79.6 2 1 3.4 0.3 1.2 East Asia & Pacific 9.8 14.6 61.2 89.9 0.9 2.3 03 1.2 Europe & Central Asia 8.2 26.6 35.2 89.0 2 6 5.0 0.0 1.2 Latin America & Carib. 8.0 18:.5 47J.7... .....66.3 ..2:9. 4.70.4 2.0 Middle East & N. Africa 19 0 26.1........ 63.8 72 0 4.6..........52.20.4 0.9 South Asia 4.9 5.4 23.8 39.6 0.4 1.3 0.0 0.2 -ubSaharan Africa 15.6 17.8 67.6 94 .4 2.5., 5.6.. 0.4 1.. . 0 High income 27.4 38.7 72.5 78.7 9 9 19.1 2.2 3.1 Europe EMU 38.3 51.3 1012.2 86.3 90 0...... 22.5........ 1.8 3.1 a. Oata priorto0 1992 include Eritrea. 326 1999 World Development Indicators 6.1 The growing importance of trade in the world's * Trade in goods as a share of PPP GDP is the sum economies is one indication of increasing global eco- of merchandise exports and imports measured in cur- nomic integration. Another is the increased size and Large capital flows relative to GDP rent U.S. dollars divided bythe value of GDP converted reflect extensive integration with global importance of private capital flows to developing coun- capital markets to international dollars using purchasing power parity tries that have liberalized their financial markets. The conversion factors. * Trade in goods as a share of indicators inthe table highlight keyfeatures of the ongo- ';,:.: C. ;, -.:, . .-- "C goods GDP is the sum of merchandise exports and ing expansion of global markets in goods and capital. imports divided by the current value of GDP in U.S. dol- For three of the indicators GDP measured in purchasing lars after subtracting value added in services. power parity (PPP) terms has been used in the denom- . I, * Growth in real trade less growth in real GDP is the inator to adjust for differences in domestic prices. (No P difference between annual growth in trade of goods adjustment has been made to the numerators because and services and growth in GDP. Growth rates are cal- goods and capital exchanged on international markets p culated using constant price series taken from are assumed to be valued at international prices.) This '1 national accounts, expressed in percentages. is a conservative measure: because the GDP of many -.: I i * Gross private capital flows are the sum of the developing countries is larger in PPP terms than when * j ., .. il f absolute values of direct, portfolio, and other invest- converted at official exchange rates, the resulting ment inflows and outflows recorded in the balance of ratios tend to be lower. Still, there is ample evidence of - - . - - : .- ; payments financial account, excluding changes in the the increasing importance of trade and international assets and liabilities of monetary authorities and gen- capital flows. -:, Tvi- I eral government. The indicator is calculated as a ratio The growth of services has also affected the histor- to GDP converted to international dollars using pur- ical record. Compared with the levels achieved at the The figuic shovws tibe couritres vwhose grozs p'imate chasing power parities. * Gross foreign direct invest. capital fIoV, exceeded 20 Percent ol PPP GDP end of the last century, trade in goods appears to have .r 1997. ment is the sum of the absolute values of inflows and declined in importance relative to GDP, especially in outflows of foreign direct investment recorded in the economies with growing service sectors. Deducting balance of payments financial account. It includes value added by services from GDP thus provides a bet- equity capital, reinvestment of earnings, other long- ter measure of the relative size of merchandise trade term capital, and short-term capital. Note that this than physical output, although it neglects the growing indicator differs from the standard measure of foreign services component of most goods output. direct investment (see table 6.7), which captures only Trade in services, traditionally called invisibles, is inward investment. The indicator is calculated as a becoming an important element of global integration. ratio to GDP converted to international dollars using The difference between the growth of real trade in purchasing power parities. goods and services and the growth of GDP helps to identify economies with dynamic trade regimes. Data sources In the financial account of the balance of payments inward investment is recorded as a credit and outward Data on merchandise trade investment as a debit. Thus net flows, the sum of cred- are from the International itsanddebits, representa balance inwhich manytrans- Monetary Fund's (IMF) Direc- actions are canceled out. Gross flows are a better tion of Trade database. Data measure of integration because they show the total on GDP in PPP terms come value of financial transactions during a given period. from the World Bank's The investment indicators in the table were constructed I International Comparison Pro- from data recorded at the most detailed level available. gramme database. Data on Higher-level aggregates tend to be affected by the net- real trade and GDP growth come from the World Bank's ting out of credits and debits and so produce a smaller national accounts files. Gross private capital flows and total. The comparability of these indicators between foreign direct investment were calculated from the countries and overtime is affected bythe accuracy and IMF's Balance of Payments database. completeness of balance of payments records and by their level of detail. 1999 World Development indicators 327 6.2 Direction and growth of merchandise trade High-income importers Other All United European Other All high high States Union Japan industrial industrial income income Source of exports High-income economies 10.7 29.7 3.5 8.3 52.2 7.8 60.0 Industrial economies 8.4 28.2 2.4 7.9 46.8 5.8 52.7 United States 2.5 ..... ...... 1.2 3 .1 .. .6.8 1.684 European Union 2.8 21.1 ........... 0.7 4.1 ...... ... .28.7 ........ .....1.8 .. .... ......30..5 Japan 2.1 1.2 0.3 3.6 1:9 ........ 55 Other industrial economies 3.5 . .......... 3.4 .... ...... 0 5 0.4 7.7 0.5 ... ...... 8 .2 Other high-income economies 2.2 1.6 1 1 0.4 5.4 2 0 ... ........7..3 Loin- & middle-income economies 4.8 5.4 . ..... 2 0 0.7 12.9 31J......... .. 16.0 Sub-Saharan Africa 0.3 0.5 .............0.0 0.1 ..... ... ..0.9 . .... . .. 0 1 . .......... 1 0 East Asia & Pacific 1.4 1.1 1.3 0.3 4.0 2.2 6.2 Europa & Central Asia 0.2 20 0.1 0.1 ........... _2.5 0 2 .. .... . ...2 .6 Middle East & N. Africa 0.2 08 ... ...... ..0.4 0.1 ..... ........1.5 ... ....... 0 4 . 1 9 Latin America & Caribbean 2.5 0.7 0.2 0.2 3.6 0.1 3.7 World 15.5 35.1 5.4 9.1 65.1 10.9 70 Low- and middle-income importers Europa Middle Latin All lcw & Sub-Saharan East Asia South & Central East & America middle - - ~~~Africa & Pacific Asia Asia N. Africa & Caribbean income World Source of exports High-icm economies ..... 0.9 6..... . 3... 0.8 .3.8 1.8 ...........4.2 ...........17j.7 77.7 Industrial economies 0.8 3.4 .........0.5 3.5 ... .......1.6 3.9 13.7. ......66.3 United States0.08 0 10 20.32.4 3.9 12.3 European Union 0.5 1.0 0.3 3.0 1.1 1.0 6.9 37.4 .Japa 0.1 1,3 0.1 0.1 0.1 0.4 2.0 7.5 Other industrial economies 0.1 0.3 0.1 0.2 0.1 0.2 0.9 9.1 Other high-income economies 0.2 2.8 0.3 0.3 0.2 0.4 4.1 11.4 Loin- & middle-income economies 0.4 .. ... 1.5 .... ..... 0.3 2.0 0.6 1.4 .......... 6~2.2 ...... . 22.3 Sub-Saharan Africa 0.2 0.1 0.0 0.0 0.0 0.0 0.4 1.4 East Asia &Pacific 0.1 0.8 0.1 0.1 0.1 0.1 1.4 8.5 South Asia ....... .. ...0:0.0 ... .... . 0.1 ... .......00 0.0 0.0 0.0 0.2 .... .... ..0.8 *Europe & Central Asia 0.0 0.1 0.0 1.7 0.1 01l 2.1 4.7 Middle East & N. Africa 0.1 0.3 0.1 0.0 0.2 0.1 0.7 2.6 Latin America & Caribbean 0.0 ......0.1 ..........0.0 0.1 0.1 1.1 1.4 5.1 W orld 1.3 7.8 1.1 5.8 2 3 5.6 24.0 100.0...... .... ..... .... . . .... .....I ..... ... ........... . .... ....:.... 328 1999 World Development Indicators 6.2 High-income importers Other All United European Other All high high _____ Staten Union Japan industrial industrial income income Source of exports Hig..h-in.come economies 646.7 8... ..... . 2.. ...... 9.9 7.2 1-2.7 ....... 7.7 Industrial economies 6.3 6.5 .....8.1 9.9 7.1 11.5 7.5 United States 8.4 8.89.5 ....9.0... 12.2 9.5 European Union 5.9 58.8 9 1 1 ... ....6.5 13.1 6.8 Japan 3.4 4.5 2.2 3.6 9.6 5.3 Other industrial economies 9.2 12 2 5.3 ...... 12 1 10.2 12 3 10:3 Other high-income economies 6.6 9 9 8 5 9.0 80 17.3 9.8 Low- & middle-income economies 13.5 8.6 10 1 12.4 10.6 1-5.5 11.4 Sub-Saharan Africa 7.3 4.6 1.7 3.3 5.1 16.5 5.7 East Asia & Pacific 18..5 16.3 ......... 12.7 .. .. 16.3 15.6 167.1 ......I.... 15.8 South Asia ........ 12.4 .... ... 10.5 .... .. .. 32 2... 9.3 10.1 1-54.4 ...... 11.0 Europe & Central Asia . ... 13.0 ......... 10.2 3.4 13 2 10.3 22. 10.7 Middle East & N. Africa .... 4.3.... ..... . 3.8 8.4 10:8.8 .........I. 5.1 11.5 6.1 Latin America & Caribbean 13.9 6:3 7.0..... 12.3 .... 11.5 13.9 11.5 World 8.1 7.0 8.9 10.0 7.8 13.4 8.4 Low- and middie-income importers Europe Middle Latin All low & - .' ~~~~~~~Sub-Saharan East Asia South & Centra I East & America middle ILf4..~. RRI,M., .~ Africa & Pacific Asia Asiaa N. Africa & Caribbean income World Source of exports High-income economies 3.7 16.5 6.9 13.3 4.6 12.4 11.6 8.5 Industrial economies 2.8 14.3 4 9 ..... 13 0 4.6 ..... 12,1.1 ..... .. 10.4 ..........80O United States ~~~~~7.0 16.4 771. 6.8 ..14.4 13.1_ 10.5 European Union 2.6 13.9 4.7 14.3 4.7 9 7 9.7 7.3 Japan -1914.3 03141 . Other high-income economies 9.7 20.1 12.6 18.4 4.9 18.2 174.4. 11.9 Low- & middle-income economies 11.7 16.7 10.1 7.1 3.9 14.5 102.2 11.1 Sub-Saharan Africa 12.4 13.2 23.0 6.1 .. ... 13:6.6.... ... 10.3 123.3......... 7.1 East Asia & Pacific 11.0 ..........18.5 14.0.....7.5. 5.6 27.2 14.7.... 15.6 South.Asia 14.3 17.8 11.3 1 .. . . 5.. ....... . 6.0 . ...... 31.8 8.5 10..3. Europe & Central Asia ..... .1 2 . ......I..8 1 -1...... ..._ 4.. 8.6 1.0 . ...... .10.9 7.5 9.2 Middle East & N. Africa 19.2 20.6 10.0 -18.7 3.2 -1.4 6.6 6.2 Latin America & Caribbean 7.9 15.1 9.9 3.9 7.217 13.6 12.1 World 5.6 16.6 7.8 10.7 4.412911.2 9.0 1999 World Development Indicators 329 6.2 Most industrial countries and about 22 developing * Merchandise trade includes all trade in goods. countries report their trade data to the International Trade in services is not included. * Regional group- Monetary Fund (IMF) each month. Together these ings are based on World Bank definitions and maydif- countries account for about 80 percent of world fer from those used by other organizations. exports. Trade from less timely reporters and from * European Union comprises Austria, Belgium, countries that do not report at all is estimated using Denmark, Finland, France, Germany, Greece, Ireland, reports of partner countries. Because the largest Italy, Luxembourg, the Netherlands, Portugal, Spain, exporting and importing countries are reliable Sweden, and the United Kingdom. * Other Industrial reporters, a large portion of the missing trade flows economies include Australia, Canada, Iceland, New can be estimated from partner reports. Even so, a Zealand, Norway, and Switzerland. * Other high- small amount oftrade between developing countries, income economies include Cyprus, Hong Kong particularly in Africa, is not captured in partner data. (China), Israel, the Republic of Korea, Kuwait, Qatar, Inter-European trade estimates have been signifi- Singapore, Taiwan (China), and the United Arab cantly affected by changes in reporting methods fol- Emirates. Some small high-income economies such lowing the creation of a customs union. as Aruba, the Bahamas, and Bermuda have been Most countries report their trade data in national included in the Latin America and Caribbean group. currencies, which are converted using the IMF's pub- lished exchange rate series rf (official rate, period Data sources average) or rh (market rate, period average). Because imports are reported at c.i.f. (cost, insurance, and - Intercountry trade flows are freight) valuations and exports are reported at f.o.b. - published in the IMF's (free on board) valuations, the IMF divides partner Direction of Trade Statistics country reports of import values by 1.10 to estimate Yearbook and Direction of equivalent export values. This approximation is more - Trade Statistics Quarterly; or less accurate, depending on the set of partners the data in the table were cal- and the items traded. Other factors affecting the . culated using the IMF's accuracy of trade data include lags in reporting, Direction of Trade database. recording differences across countries. and whether the country follows the general or special system of trade. (See About the data for tables 4.5 and 4.6 for further discussion of the measurement of exports and imports.) The regional trade flows shown in this table were calculated from current price values. Growth rates therefore include the effects of changes in both vol- umes and prices. 330 1999 World Development Indicators OECD trade with low- and middle-income economies 6.3 High-income European United States Japan OECD countries Union 1990 1997 1990 1997 1990 1L997 1990 1997 Food 32.7 .... 59.1 .16.1 28.4 . .......102.2 19.5 ........ 0.3 0.4 Cereals 132.2 14.6 .... 4.2 5.3 ... 5.5 ......... 6.2 0.1 ....0.0 Agricultural raw materials 8.8 ...... ....15..4. .. .. 3.1 4.5 ........ ..3.5. 5... .5:5 .... .... 0.6 .... ......1.1 Ores and nonferrous metals 7.2 137 7... 2.8 5.5.. 2 1 . ........ .3.2 0.5 1.4 Fuels 6.7 16 6 .... 2 6 6.2 ...2.6 ..... .... 4.9 ....I....0.3 1.1 Crude petroleum 0.3 0.9 .... 0.0 0 .3 .. .... 0.0 ..... 0:1 0.0 0.0 .Petroleum products ..... .. .4.7 .12.2 ..2.4 5.5 1.7 ...... ... 3.6 . .........0.2.. 1.0 Manufactured goods 2861.1. 638~.0.... ... 145.4 292.5 .... 64.2. 164.7 53.0 104.6 .Chemical products 41.3 81.7 22.9 42.1 10.M4 21.1 3.6 7.1 Mach. and transport equip. 163.1 378.5 78.1 160 3 39...... .3 ..........103.8 35.0 74.0 Other 81.7 177.5 .........44.4 90 3 14.5 .. ... . 39..8 14.5 23..5 Miscellaneous goods 10.3 .... 26.8 3.5.. 113.3... ......4.3 82.2. 0.5 1....... 7 Total 351.7 769.6 135344 86.9 206. 55.2 110.4 % of total exports Food 9.3 7.7 9.3 8.2 11J.7 ......... 9.5 ....... .0.6 0.4 .C~ereals 3.8 19 2.4 1.5 6.3. 3:0 0.1 00. Agricultural raw materials 2.5 2.0 1.8 1.3 4A.1.......... 2.7 1.0 1.0 Ores and nonferrous metals 2.0 1.8 1.6 1 6 .... ....2A.4 ....... 1.5 .........0.9 1.3 Fuels 192.2 1.5 8 3.0 2.40.10 Crude petroleum 0.1 0.1 0.0 0 .1 0.0 0.1 na. 0.0 Petroleum products 1.3 1.6 ......... 1.4 1. .... .6 2.0 1 7. 0 4 0.9 Manufactured goods 81.3 82.9 83.6 84.0 73.9 79 9 ... ......96 1 94.7 Chemical products 11.7 10.6 13.2 12.1 ..........12~.0 ..........10.2 ... .......6 4 6.4 Mach. and transport equip. 46.4 49.2 45.0 46.0 45.2 ... . . ..50.4 63.4 67.1 Other 23.2 231.1 25.6 25.9 .... 16.6 19.3 . 26.2 21.3 Miscellaneous goods 2.9 3.5 2.0 3.2 5.0 4.0 ......... 0.9 1.6 Total ........ 100.0 ........ 100.0 100.... .0I1.0 l 0 10.0 100.0 100.0 100.0 1.999 World Development indicators 331L High-income European United States Japan OECD countries Union 1990 1997 ±990 ±997 1990 1997 1990 1997 $ blillions Food 63.8 95.3 ..........34.9 42.5 15.4 ..........23.7 ...........9.4 18.4 Cereals 1.3 2.3 0.5 0.8 0.2 0.6 0.5 0.4 Agricultural raw materials 18-.6 26.4 9.9 12.4 2.3 4:5 4.7 5.9 Ores and nonferrous metals 31.1 46.8 15020.2 5.1 8 1 8.9 1. Fuels 149.1 .........166.5 58.6 55.4 48.7. 53.4 33.1 31.8 Crude petroleum . 111.1 .........121.7 ..........46.6 38.8 37.3 43.8 20.8 .. . .19.5 Manufacture goods ..........174..8 .........513.9 76.9 178.5 66.7' .221.9 15.4 .59.7 Chemical products 14 .0 29.6 7.8 13.4 ..........2.7.. 7 8 1.7 3.5 Mach. and transport equip. 46.9 187.0 15. 54.0 24.8 96.6 2.0 18.3 Other 113.9 296.9 54.1 111.2 39.1 117.6 11.7 37.9 Miscellaneou good 5.2 11.5 .........2.0 ...... .....4.0 ...... 2.5 6.0 0.5 1.2 Total 442.6 860.5 197.3 312.8 140.6 317.6 71.9 ±28.5 % ovf total exports Food .............14.4 .... 11.1 . ........ 17.7 13.6 ..........10.9 7.5 13.1 ........14.3 Cereals 0.3 ...........03 ...........0.3 0.2. .0.1 0.2 . .... ....0.7 0.3 Agricultural raw materials 4..2 3 1 .. ... ....5.0 4.0 ...........1.6 1.4 6......... 66 4.6 Ores and nonferrous metals 7.0 5.4 7.6 6.4 3.6 2.6 12.3 9.0 Fuels 33.7 19:3 ...........29.7 17.7. 34.7 16.8 46.0. .24.7 Crude petroleum 25 .1 14 .1 23.6 12.4 26.5 13.8 28.9 15.2 Petroleum products 5.4 .. ..... ..2 6 ...I.....-3. 1 2.7 ...........7.7 2.7 7 5 2.3 Manufactured goods 39.5 59.7 .... 39.0 .57.0 47.4 ..........69.9 21.5 ........46.5 Chemical products 3.2 .. ......3 4 ...........3.9 4.3 ........ ..2.0.. 2.5 2.4 .........2.7 Mach. and transport equip. 10.6 .. . ....21 7 ...........7.6 17.3 .. . ... 17.7 30.4 ..........2.7 14.2 Other 25.7 .345 ..........27.4 35.5 ........_27.8 37.0 ... ....16.3 29.5 Miscellaneou goods 1.2 ..........1:3 ... ..... .1.0 1 3 1.8 ...........1.9 0 6 0.9 332 1999 World Development Indicators 6.3 Trade flows between high-income members of the r:.-. ._ The product groups in the table are defined in accor- Organisation for Economic Co-operation and ..a dance with the Standard International Trade Development (OECD) and low- and middle-income trade with developing economies Classification (SITC) revision 1: food (0, 1, 22, and economies reflect the changing mix of exports to 4) and cereals (04); agricultural raw materials (2 and imports from developing economies. While food excluding 22, 27, and 28); ores and nonferrous met- and primary commodities have continued to fall as Euc.-; | '-: r.:* 1 als (27, 28, and 68); fuels (3), crude petroleum a share of OECD imports, the share of manufac- (331), and petroleum products (332); manufactured tured goods supplied by developing countries has _ I goods (5-8 excluding 68), chemical products (5), grown. At the same time, developing countries have machinery and transport equipment (7), and other increased their imports of manufactured goods manufacturedgoods (6 and 8 excluding 68); and mis- from high-income countries-particularly capital- cellaneousgoods (9). * Exports are all merchandise intensive goods such as machinery and transport exports by high-income OECD countries to low- and equipment. Although trade between developing middle-income economies as recorded in the United countries has grown substantially over the past Nations COMTRADE database. * Imports are all decade (see table 6.5), high-income OECD coun- E , :.Ge ,r u.-,,.;,.,v. ..: l - merchandise imports by high-income OECD countries tries remain the developing world's most important from low- and middle-income economies as recorded partners. I - in the United Nations COMTRADE database. * High- The aggregate flows in the table were compiled Income OECD countries in 1997 were Australia, from intercountry flows recorded in the United 2 . Austria, Belgium, Canada, Denmark, Finland, France, Nations Statistical Division's Commodity Trade ,- Germany, Greece, Iceland, Ireland, Italy, Japan, the (COMTRADE) database. Partner country reports by I r ;.l Data on financial flows are compiled by the Development Si3uo- Ar r.. 3 54i ::1 192 251 Assistance Committee of the OECD and published in its UrlUre.] Em, I.Tre -: ,, 31 annual statistical report, Geographical Distribution of Other ronors Financial Flows toAid Recipients, and the DAC chairman's annual report, Development Co-operation. I rd .I-,,r.; l- . I .r...,. - ;,Jc..,l- : '' .r :11, .:.r-., rrj.,:rr,, ,iv:..n,;.r, . ,:.,,, ;,.,J.33ru,Idr;,,±..I c.L:ir.lrnri I;,. ,i.-,,i-,Ci,,:,?A.J dl... -.i-, r;I,;,,1;, World . Ii o 3 i.,.Jv.:1. l.ILl 3999 World Development Indicators i349 Aid flows from Development Assistance 6.9 Committee members Net official Aid Untied development assistance appropriations aid annual average % Per capita of change in volume, donor country' % of central % of total $ millions % of GNP 1991-92 to $$ government budget ODA commitments 1992 1997 1992 1997 1996-97 1992 1997 1992 1.997 1992 1997 Australia 1,015 1,061 0.37 0.28 -1.2 57 -1 .... 0 0.0.......1,1..1... 2871.1.. . 28.3 Asria 556 527 0.30 0.26 -2.8 71 -3 0.8 0.0 71.5 .......0.0 Belgium 870 764 0.39 0.31 -2.7 7.-3. 0 009.3 0.0 Canada 2,515 2,045 0.46 0.34 -4.0 92 -4 1:7.7.. .. 1.3 ... .245.5... 15.2 Denmark 1,392 1,637 1.02 0.97 3.7 270 ..... .4 .. 2.6 .. ...27.7 0.0 34.7 Finland 64.4 379 0.64 0.33 -11.9 127 -12 1.8 1.1 37.8 17.9 France 8,270 6,307 0.63 0.45 -4.2 144 -4 0.0 .......0,0.0... . 22.8 .......0.0 Germany 7,583 5,857 0.38..... 0.28. -3.9 94 -4 0.0 0.0 30.5 24.5 Ireland 70 187 0.16 0.31 20.1 20 20 0.0 0.0 0.0 0.0 Italy 4,122 1,266 0.34 0.11 -12.3 72 -12 0.8 0.0 16.5 0.0 Luxembourg 38 95 0.26 0.55 15.0 97 15 0.0 0.0 37.7 0.0 Netherlands 2,753 2,947 0.86 0.81 1.4 181 1 0.0 0.0 19.5. 80.7 New Zealand .............. 97 154 0.26 0.26 0.9 28 1 0:4.4..... 0.6 63.9 0.0 Norway 1,273 1,306 1.16 0.86 0.5 296 0 1.7 1.8 44.2 47.9 Portugal 293 250 0.35 0.25 -2.6 29 -3 0.0 0.0 1.7 72.8 Spain 1.518 1,234 0.27 0.23 -0.7 39 -1 1.0 0.90.0 0.0 Sweden 2,460 1,731 1.03 0.79 -2.5 283 -3 0.0 0.0 61.3 0.0 Switzerland 1,139 911 .0.45 0.34 .-3.. 1.....165....... -3 32.2 3.2 50.4 0.0 United Kingdom 3,243 3,433 .0.91.26 -0.3 56 0 0.0 1.1 17.7 ......23.7 United States 11,709 6,878 .0.20 0.09 -8.9 ..... . 46 ....-.9. 1.5 1.1 25.9 0.0 Total 62,711 48,324 0.34 0.22 -4.6 78 -5 1.3 12 3. 31.0 Net official aid Per capita of annual average % donor country' $ millions % of GNP change in volumea$ $ 1992 1997 1992 1.997 1991-92 to 1996-97 1992 1997 Australia 5 0 0.00 0.00 -107.70 0 Austria 349 181 0.19 0.09 -... .... ..13.4 44 .22 Belgium 135 59..-......... 0.06 ........ . 0.02-2 6 13 6 Canada 260 157 0.05 0:03 -2.69 5 Denmark 82 133 0 06 0.08 12.1 16 ... ... 25 Finland 40 7 1 0 040.06 -2.5 8 14 France 364 308 0.03 0.02 2.7 6 5 Germany 3,344 660 ........ ....0.17 .0.03 -26. .... . 6.42 .... ....... ..8 Ireland 10I1 0.02 0.00 -48.63 0 Italy 334 241 0.03 0.02 -5.7.. ..6 4 Japan 238 84 0.01 0.00 -9:62 1 Luxembourg .. 2 0.04 0.01 -198 . 13 6 Netherlands 152 7 0.05 0.00 -50.5 10 ..... ..0 New Zealand 1I0 0.00 0.00 -0900 Norway 64 55 0.06 0.043. 15 1 Portugal 18 18 0.02 0.02 -4.6 2....... .2 Spain 102 3 0.02 0.00 -21.3 ..3 0 Sweden 337 148 0.14 0.07. . ...-1.9 39 17 Switzerland 90 75 0.04 0.03 1.0 13 11 United Kingdom 337 337 0............ ........ ..... ... .... . . 1 . .. . 03 ... ..I... . 0.. . 03..... .. . . 0.3.. ...6.....6.. United States 682 2,516 0.01 0.03 ............10.2 ........... ..3 9 Total 6,948 5,056 0.04 0.02 -9.29 6 a. At 1997 eschange rates and prices. 350 1999 World Development Indicators 6.9 As part of its work the Development Assistance ient country when the multilateral institution makes * Net official development assistance and net offi- Committee (DAC) of the Organisation for Economic a disbursement. cial aid record the actual international transfer by the Co-operation and Development (OECD) assesses the Aid-to-GNP ratios, aid per capita, and aid appro- donor of financial resources or of goods or services aid performance of member countries relative to the priations as a percentage of donor government bud- valued at the cost to the donor, less any repayments size of their economies. As measured here, aid com- gets are calculated by the OECD. The denominators of loan principal during the same period. Data are prises bilateral disbursements of concessional used in calculating these ratios may differ from cor- shown at current prices and dollar exchange rates. financing to recipient countries plus the provision by responding values elsewhere in this book because of * Aid as a percentage of GNP shows the donor's donor governments of concessional financing to mul- differences in timing or definitions. contributions of ODA or official aid as a share of its tilateral institutions. Volume measures, in constant The proportion of untied aid is reported here GNP. * Annual average percentage change in vol- prices and exchange rates, are used to measure the because tying arrangements require recipients to pur- ume and aid per capita of donor country are calcu- change in real resources provided over time. Aid chase goods and services from the donor country or lated using 1996 exchange rates and prices. * Aid flows to part I recipients-official development assis- from a specified group of countries. Tying arrange- appropriations are the share of ODA or official aid tance (ODA)-are tabulated separate from official aid ments may be justified on the grounds that they pre- appropriations in the donor's national budget. to part 11 recipients (see About the data for table 6.8 vent a recipient from misappropriating or mismanaging * Untied aid is the share of aid that is not subject for more information on the distinction between ODA aid receipts, but they may also be motivated by a to restrictions by donors on procurement sources. and official aid). desire to benefit suppliers in the donor country. The Measures of aid flows from the perspective of same volume of -aid may have different purchasing Data sources donors differ from aid receipts by recipient countries. power, depending on the relative costs of suppliers in This is because the concessional funding received by countries to which the aid is tied and the degree to iv1)X ifji ]t The data in this table appear multilateral institutions from donor countries is which each recipient's aid basket is untied. Thus tying 1 in the DAC chairman's report, recorded as an aid disbursement by the donor when arrangements may prevent recipients from obtaining -. Development Co-operation. the funds are deposited with the multilateral institu- the bestvalue for their money and so reduce the value 4. ''I L tr. - The OECD also makes its tion and recorded as a resource receipt by the recip- of the aid received. f , data available on diskette, Pt t12 tM , magnetic tape, and the I' i.it,i, t zja -t i |Internet Aid flows fell again in 1997 Ala fell bol' in dollar amount and as a shate of GrIP lot most donois. 1~~~~~~~~. . .; 1999 World Development Indicators 351 kg 6.10 Aid dependency Net official Aid per capita Aid dependency ratios development assistance and official aid Aid as Aid as Aid as % of % Of % Of Aid as gross domestic imports of central government $ millions $ % of GNP investment goods sand services expenditures ±1992 1.997 1992 ±997 1992 1997 1992 1997 1992 1997 1992 ±997 Albania 430.1 ... 169.3 135 51 63.9 6.7 1,156.7 56..6..... 64..5..... 20.6.......... ... Algeria 406.4 248.2 15 . ......8 .. .. 0.9 . 0.6 2.8 2.0 ...... 3..2 .. ....1.9 ........ ..... .. ... Angola 350.7 435.7 .....35. 37 10.0 9.9 167.7 23.0 7.3 71 Argentina 292 6 ..221.6 9 6 0.1 0.1 0.8 0.3 ......1.3 ......0-.5 ......1.1 0.5 Armenia 22.5 169.5 6 45 2.4 97.7... . 120 .7.10 .. 17.19 A u stra lia . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . I . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . ... 7 1 .. . . . . ..: . . . . . . Austraia Azerbaijan 37.0 182.4 5 24 0.6 4.2 . 14.8 4 92 Bangladesh 1,824.8 1,010.2 16 8 5.6 2.3 30.8 11.7 44.4 12.9 Belarus 273.1 42.6 26 ...... .4 ... .. 0.9 0.2 2.8 0.. . .7 7..... . 7 0.5.- I ..:. Belgium Benin 2707 7.... 225.4 54 39 17.1 10:7.7. ... 121:1.1... 56.9. .34..7 _ 30.8 Bolivia 677.5 716.8 98 9 2 12.7 9.2 73.7... 48..1 43.2 30.3.~.. 592.2... 410O Bosnia and Herzegovinaa 9.7 874.4 3 373 . Botswana 113.8 124.8 84 81 2.8 2 6 9.8 9..5 ......4~8 ...I ..........75 5.......... Brazil -234.4 487.4 -2 3 -01 0 -0.3 0.3 -0.6 05 -0.2 Bulgaria 147.9 209.9 17 25 1.4 2.2 7.2 17.6 2.7 3.4 4.2 6.2 Burkina Faso 438.9 370.5 47 35 22.1 15.5 103.4 60.8 64.0 50 .1 .....8672.2....... ... Burundi 311.9 119.3 54 19 29.1 12.6 192.0 182.7 90.0 76.5 85.4 52.0 Cambodia 206.8 373.8 ......22 ........36 ......10. 5 .. . 12.3 106..7 .... 76.. 3.... 39.2 28.5 Cameroon 716.2 501.2 59 36 6.7 5.9 43.9 34.0 26.0 18.8 32.8 Canada Central African Republic 180.4 91.9 59....... 27 12.7 9:2.2 ..... 103..5 100.4 496 6.... 360 0. ......... Chad ..I..........1240.9 224.9 40 31 13.5 14.3 .158.5 .72.4 50.0 37.4 Chile 135.7 136.6 10 9 0.3 0.2 1.4 0..7 .......10 ......05 1. 0.9 China 3,055.3 2,054.4 3 2 0.7 0.2 2.0 0.6 3.9 11 7:9.9 .......... Hong Kong, China -39.0 8.5 -7. 1 0.0 0.0 -0.1 0. Colombia 246.4 274.2 7 7 0.5 0.3 2.9 1I.5 2.3 1. 2 3.7 Congo, Dem. Rep. 269.8 168.0 7 4 3.3 3.2 47.6 38.7 12.5 7.8 190.0....... Congo,Rep. ~1137.7... 268.1 48 99 4.4 14.7 17.9 44.8 7.3 13.2......... Costa Rica 142.6 -1.9 45 -1 2.2 0.0 7.3 -0O.1 ......44 .4 .... 0.0 ......89 9....... :... C6te dl~Ivo'ire 758.2 444.7 61 31 7.7 4.7 ........98.-2 .... 27.1 16.7 9J.7 ...... ..... . Croatia' 0.0 44.0 0........9 ...... .... . ... 0.2 ..... ...0.0 . 0.4 . 0 5 C ub..................24...... . 9.... 67.. . 3....... 2.... 6.... . Czech Republic 130.6 107.2. 13 .10 0.5 0.2 .........1.7 0.....O.6 ...............03 ........... 0.... O6 Denmark Dominican Republic 56.1 75.7 8. . . ..9 . .... 0:7 0.5 2.8 .....2.1 ...... 1.8 ...... 1.0 ...... 4~8 ... ... Ecuador 244.8 175.0 23 15 20.0 .0.99.1 4.4 6 .1 ......24 ......119 9........... Egpt, Arab Rep. 3,604.0 1,949.2 66 32 9.0 2.5 47.3 14.6 22.4 10.0 21:9........ El Salvador 407 0 299.5 77 51 6.9 2.7 36..8 17.7 19.8 74.4. .. ......-... Er'itrea . 123.1 33..14.8.....46. 20. 6 ....... ........ :... Estonia 104.4 64.6 68 44 2:5 1.4 9.2 4.6 ......14.4 15 44 Ethiopia 1,1827.7.... 637.2 22 11 21.4 10.1 231.1 52.3 101.4 36.3 89.4 Franca Gabon 69.5 39.8 68....... 35 1.4 0.9 5.5 2.9 ......2.6 ......1.4 ....... ..... . Gambia, The 113.1 41.3 113 35 33.0 10.3 146.7 57.3..... 44.9 .....14..1........ ............ Georgia 21.0 247.2 .......4 46 0.4 4.7 1... ..J 3 65.9 ...... 19,.7 . Ghana 615.6 497.7 39 . .... 28 ... ...9..8 7.4 75 0 . ...30.0 .....31..4 ....17..8 .....539 9 .... Guatemala 199.6 303.0 22. .29 1.9 1.7 10.4 12.4 6.5 ......6 7 Guinea 4503 3.... 381 9 74 55 14.3 10.3 78.6 45.2 41.7 40.0 72.6 Guinea-Bissau 108.5 125.5 107 110 51.0 49.9 99.0 197.6 85.2 103.7 Haiti 1027.7 . .. 3322.2 ..... 15....... 44 5.3 118.8 ..... 149._1 ... 11-5..7..... 39.4 40.5 Honduras 379.4 375 73 .51 11.7 6.7 42.8 21.4 24.1 1. 352 1999 World Development Indicators 6.10 Net official Aid per capita Aid dependency ratios development assistance and official aid Aid as Aid as Aid as % of % of % Of Aid as gross domestic imports of central government $ millions $% of GNP investment goods and services expenditures 1992 ±.997 1992 1997 1992 1997 ±992 ±997 1992 1997 1992 1997 Hungary 223.0 164.2 22. 16 0.6 0.4 3.7 1.3 .......1.5 0.......6 1 .... 1. .... 0:8 India 2,395.6 1,678.3 32 10 0.4 4.1 1.8 ......7j.7 .... 2.7 .......5 2 ......2 6 Indonesia 2,091.9 849.5 11 ......4 ..... .1.6 0.4 5.3 1.3 ......511.1 ... .1.2 ..... .8..1..... ... Ia,IlmcRep 106.8 _196.6 2 30.2 O4..... 0.4 1.1 0.1 0.5 Iraq 140.0 280.8 7 13 ...... ... ....... ....... .. ............ ...... Ireland Israel 2,065.8 1,191.6 403 204 3.2 1.2 12. 6 ......5:6 .......74.4.... 2.7 6.5 2.5 Jamaica 122.8 70.9 50 ..... .28 4.3 1.8 11.7 5~.0 ..... 4.7 .......1,3.6 .. ............ ... Japan Jordan 425.2 463.0 114..... 104 8.7 6.8 23.9 231.1~.... 8.9 7 6 26.7 Kazakhstan 11.6 132.2 18 0.0 0.6 0.1 3.8 0.. ....O2 ......1.5 ................. ... Kenya 886.7 458.9 35 16 11.7 4.6 63.8 23.4 35.0 11.4 44.7 Korea, Dem. Rep. 32.2 201.6 2 9..... ...... . ..... ...... Korea, Rep. -3.0 -158.3 0 -3 0.0 0 ..... 0 O 00.... -01 00..... -0-1 .......00 .....-0.2 Kuwait 2.4 2.4 2 . .1 0.0 0.0 .. . ... 0 1 0.. ..... 1 ...... 00... 0-.0 - 0.0. . .. 0.0 Kyrgyz Republic 23.8 240.4 5 52 1.1 14.1 5.3 62:9 ......60. ....27.1 Lao PDR . ... 1653.3 342.2 39 71 14.7 19.5 ..I.... 68.1 ... 53.6 45. Lebanon 1235.5. . 239.3 33 58 2.1 1.6. 8.9_ 6.0. 2 8. 2.8. Lesotho .146.7 .....93.5 .....81 46 13.5 ... .7.4.. .... 28-.3... . 115.5 .... 14.. 0 ..... 7. 2 ..... 39..8 .... 24 7 Libya 6.4 9.2 1 2 Lithuania 93.9 ... 101.8 25 27 0.8 1 .1 ...... .5 3 . ...40 ........ . .... 1.6 ...... ........ 3.9 Macedonia, FYRI .. 149.6 . ..... 75 .. 69.... 349 ..... ....7.7 Madagascar 362.8 837.8 30 59 12.7 24.3 107.0 200.8 41.1 71.4 64.4 Malawi 579.1 348.3 64 34 32.9 14.1 161.4 112.7 69.2 27.8 Malaysia 205.8 -240.9 11 -11 0.4 -0.3 1.0 ....-0.6 0......O.4 .....-0..2 .. .. 1.2 -1.2 Mal 4353 3.... 4554 4..... 49 44 15.5 18.4 69.8 77.2 43.0 47.5,......... Mauritani'a 202.2 250.1 94 102 18.0 23.9 83.0 130.3 30.2 56.8 Mauritius 52~6... .. 418 ..... 49 36 1.6 1.0 5~6 ......374 .......2.5 1.......5 7.1 4.2 Mexico 316.8 107.7 4 1 01 00.... 0-..4 ......0:1 0.4 ......0.1 0.6 ...... Moldova 9.7 63.0 2 15 0.3 3.5 0... .. O 6 .....139 ......... ...... 4.2 ................. ... Mongolia 123.4 2525.5 53 . 99 . 25.6 . 130.7 .....25,5 6 ... 44 .9 .....51.5 ........... Morocco 947.1 462.1 38 17 3.5 1.4 14.3 6.7 ......9.1 ......3.9 ..... 11.1 .......... Mozambique 1,474.2 .963.2 100 58.. 105.2 37.4 343.9 118.5 .121.5 81.8 Myanmar 115:3 61.7 3 1 .. 9.1 ..... 2.5 2.5 Namibia 144.3 165.8 101 .....102 .... _4.9 5.0 23.3 25.6 7.3 7-9 12 ,4 Nepal... ... 462:0 414.7 23 ..... .19..... 13.1 8.4 62~8.... 39:3 ...49~5 ...22.0 78.5 50..6 Netherlands New Zealand Nicaragua .... 660 6 421.7 163 ...... 90 49.0 22.7 186.1 ....:..... 46-. 5.... 22 8 119.6 Niger........ 370~5.5 ... 341 0 45 35 16.0 18.6 2919 9 . 17012 ... 569 9 .... 725 5............ Nigeria 262.0 .....201.6 32 0.9 0.5 3..7 ......33 ... ...2.3 1..... 1........ Norway Oman ........-37.5 19.7 20 .9 ... .0.4 1-9..... 1.9 .:. 0.7 0.3 0.8 0.4 Pakistan 1,007.4 597.0 ....9 5 2.1 1.0 12.4. 64 7.9 3.5 ... 8..6. ..4.5 Panama 163.3 128.8 66 47 2.6 1.5 10.4 2.1 1.3 9 9 Papua New Guinea 448.9 349.4 112 ......78 11.5 8.7.. 44-.0 203 ..... 18.4 11.6 32.1 Paraguay 105.0 122.1 23 2 1.7 1.2 7.8 5.3 39 2-4 12.0 Peru 410 2 489.0 18 20 1.0 0.8 5.9 31. 5.7 3.7 6.1 4.8 Philippines 1,718.7 . 694.2 26 9 3.2 0.8 15.2 3.4 . .....9. 0.. . 1.3 16.5 4.4 Poland 1,438.6 641.4 37 17 1.7 0.5 11.2 21.1 6.3 1.3 . . 1.1 Portugal. Puerto Rico Romania ........ 238:3 203.4 10- 9 10 0-6 ...... .30 27 3.6 1.6_..... 3..1 Russian Federation 1,938.1 719.0 13 5 0 5 0 2 1.3 0.7 3.6 0.7 . 1999 World Development Indicators 353 6.10 Net official Aid per capita Aid dependency ratios development assistance and official aid Aid as Aid as Aid as % of % of % Of Aid as gross dom estic imports of central government $ millions $ % of GNP investment goods and services expenditures 1992 1997 1992 1997 ±992 1997 1992 1997 1992 1997 1992 1997 Rwanda 353.8 591.5 48...... 75 17.5 32.0 124.9 293.9 95.3 115.5 86.0 Saudi Arabia . .. ....1 . 56.0 14.8 3 1 .. 0.0 0.0 0.2 0.1 0.1 0.0 Senegal 675 7 .....4270.0 88 49 17.4 9.6 118.2 50.3 33.5 22.7 Sierra Leone 135:1 .....130 .1 . . 32 27 21.7 16.0 237.3 -31077.7... 622 2.... 86.8 118.3 89..1 Singapore 19.9 ..... 0.8 ... ...7 0 0.0 0.0 0.1 0 . 0... 00.... 0. ...... 0.0 0.2 0..0 SlovkRep.ublic . 63.8 674.4 . .. 12 13 0.5 0.3 1.9 1.0 0.5 ... Slovenia' 9 6:8 .. . . ... 49 ...... ... .... 0 .5. 2.2 . .... .... . ..... 0:.9 ........ ...... ... SouthAfrica........I................ .. 497.. ...12...0. . ..... 2.4....13 11 Spain Sri Lanka 641.8 346.6 37.. 19 6.7 2:3.3.... 27.2 9.4 15.7 5.0 24.5 8..9 Sudan ...... ... 550 1 . .187 7.7 ..... 22 7 9:9 2 1 1 .... ..~ ......... .. 26-.3 ..... 6.9 Sweden Switzerland Syrian Arab Republic ... ...... 117.7. 199.3 . ... 9 ...... 13 1.0 1.2 3.8 .3.8 2 .... 2-2 .. ... 3.3 1.5 .. !... Tajikistan ...- ...... .... . 1.9. 101... . 71 2 17 0.4 5.0 .. ..... .... . .... 11.6 ..... ... Tanzania 1,349.7 963:8.8. .. 50 31 28.8 13.1 102.6 68.4 70.5 45.3............. Thailand 772.2 626.4 13 10 .... 0.7 0.4 . .....1 7 .....1 2 .... 1.5 0,8 ......4.6 2.... 22 Togo 223.9 123.8 60. .29 134.4 . .. 8.6 83.6 5376 27.7 16.2 !... Trinidad and Tobago ............91.1 33 1 7 25 0.2 06.6 11 2 6 0.4 1.0 :... Tunisia 39073.3 .. 19473.3 ... 46 21 2,6.6 ..... 1.1 8.6 3.9 5.. 0 .... 2.0 7.9.... Turkey............. 269:0.0 . . 2.2 5 0 02.2 .. .. 0.0 0.7 0.0 .......0-9 .......00 0.8 TurkmenistanM86 10.9 2........2...... 0.1 0.4 0-.7. .1.1 Uganda ... ... 738.0 840.2 42 41 26.6 12.8 162.0 83.6 109.8 49.3 Ukraine 559.2 .....178.8 11 4 0.6 0.4 1-.8 1...8 .... . 0.8 United Arab Emirates ...........-8:3 ..... 3.8 -4 1 0.0 -0 .1 ... .. ....-0:2.2 .... United Kingdom United States Uruguay 73~.1... 56.9 23 17 0.6 0.3 4.6 2.2 25 .5. .. 1.1 2.2 0~9 Uzbekistan 61.7 131:3 3 6 0.3 0:.5. 2~3 .....28 ... ... 2:8. Venezuela 41.0 27.6 2 1 0.1 0 . .0... 0:...... O3 0.2 0.2 ...... 0.1 0.3 0.2 Vietnam............. 576.2 1,007.3 8. .13 ..... 5.8 4.2 33.1 14.0. . 17.1 7.1 W est Bank and Gaza ....... 630.0 . 245 .. . .. ... ......... ...... ....... . ........ ....... ... Yemen Rep. 283.2 366.5 20 23 5.3 7.3 26-6.6 .. 30.6 ......8..2 ......9.9 6 2 18.3 Yugoslavia, R(Serb./Mont.)e .. 97.4 .. 9 Zambia 1,036.2 .. . 618.3 125 65 36.1 16.9 273.8 107.4 . .. 538.8 35.3 1042.2.. .. Zimbabwe 796.5 327.5 77....... 29 12.3 3.9 58.3 19.7 .... 29.0 7..6 36.2. Low income 26,719.7 22,403.5 15 . 11...j 5.0 2.9 24.0 13.2 21.2 106.6..... . ... .. Middle income' 33,611.3 26,033.3 .13 ........9 1.0 0.5 3.7 .. 4.0..... 1.6 Lower middle incomeb 29,815.2 22,827.8 14 10 1.7 0.9 5.4 2.9 6 9 . .....2.8 Upper middle income' 3,796.1 3,205.5 8 5 0.2 0.1 1.1 . 09 . Low & middle income' 65,036.7 53,713.2 15 11 1.7 0.9 6.2 3.3 .... 6 7 . ....2 9 East Asia & Pacificb 9,941.7 7,157.5 6 4 1.2 0.5 . . 4 1:3. Europe & Central Asia' 8,600.9 7,188.6 19 15 0.9 0.5 2.9 2.2 1 5 Latin America & Carib.b 5,507.3 6,168.1 12 13 0.4 0.3 2.1 . 2 3 .....1.4 Middle East & N. Africab 7,000.8 5,379.9 28 19 .....2.2 . 9-.3. . 40. .3.1 South Asiab 6,657.3 4,460.1 6 3 1.9 0.8 8.5 3.6 .... 12.1 4.3 Sub-Saharan Africab 19,712.8 15,860.0 39 26 12.0 5.0 66.1 27.7 ....27.9 ......12.7 High income 2,897.8 2,037.6 a. Aid to the states of the former Yugoslavia, sot otherwise specified, is included in regional and income group aggregates. b. Inc udes data for economies not specified elsewhere. 354 1999 World Development Indicators 6.10 Ratios of aid to GNP, investment, imports, or public The values for population, GNP, gross domestic * Net official development assistance consists of dis- spending provide a measure of the recipient country's investment, imports of goods and services, and central bursements of loans (net of repayments of principal) and dependency on aid. But care must be taken in drawing government expenditures used in computing the ratios grants made on concessional terms by official agencies policy conclusions. Forforeign policy reasons some coun- are taken from World Bank and International Monetary of the members of DAC and certain Arab countries to pro- tries have traditionally received large amounts of assis- Fund databases. The ratios shown may therefore differ mote economic development and welfare in recipient tance. Thus aid dependency ratios may reveal as much somewhat from those computed and published by the economies listed as developing by DAC. Loans with a aboutthe interests of donors as they do about the needs Organisation for Economic Co-operation and Develop- grant element of more than 25 percent are included in of recipients. In general, aid dependency ratios in Sub- ment (OECD). ODA, as are technical cooperation and assistance. Saharan Africa are much higher than those in other Aid not allocated by country or region-including * Net official aid refers to aid flows, net of repayments, regions. and they increased during the 1980s. These administrative costs, research into development issues, from official donors to the transition economies of high ratios are due only in partto the volume of aid flows. and aid to nongovernmental organizations-is included Eastern Europe and the former Soviet Union and to cer- Many African countries experienced severe erosion in in the world total. Thus regional and income group totals tain advanced developing countries and territories as their terms of trade during the 1980s, which, along with do not sum to the world total. determined by DAC. Official aid is provided under terms weak policies, contributed to falling incomes, imports, and conditions similarto those for ODA. * Aid per capita and investment. Thus the increase in aid dependency ' includes both ODA and official aid. * Aid dependency ratios reflects events affecting both the numerator and Countries receiving aid amounting to ratios are calculated using values in U.S. dollars con- the denominator. more than 15 percent of their GNP verted at official exchange rates. See Definitions for As defined here, aid includes official development tables 1.1, 4.9, and 4.14 for definitions of GNP, gross assistance (ODA) and official aid. The data cover bilat- domestic investment, imports of goods and services, eral loans and grants from Development Assistance and central government expenditures. Committee (DAC) countries, multilateral organizations, and certain Arab countries. They do not reflect aid given Data sources by recipient countries to other developing countries. As a result some countries that are net donors (such as r, .:. Data on aid are compiled by Saudi Arabia) are shown in the table as aid recipients. - DAC and published in its (See table 6.8a for aid disbursement by some non-DAC ,'' i. .* .' , -r annual statistical report, countries.) ti F .,i r ! ' Geographical Distribution of The data in the table do not distinguish among dif- Financial Flows to Aid ferent types of aid (program, project, or food aid, emer- - o Recipents, and in the DAC gency assistance, peacekeeping assistance, ortechnical . chairman's report, Develop- cooperation), each of which may have a very different :E ; r,. -.r..-r, I - t. . 1 J ment Co-operation. The OECD effect on the economy. Technical cooperation expendi- also makes its data available on diskette, magnetic tures do not always directly benefit the economy to the Tile mor.t aid dependenl countries a'e in Sub- tape, and the Internet. Saharan Ahica. extent that they defray costs incurred outside the coun- try on the salaries and benefits of technical experts and the overhead costs of firms supplying technical services. Because the table relies on information from donors, it is not consistent with information recorded by recipi- ents in the balance of payments, which often excludes all or some technical assistance-particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Although ODA estimates in balance of payments statistics are meant to exclude purely military aid, the distinction is sometimes blurred. The definition used by the country of origin usually pre- vails. The nominal values used here tend to overstate the amount of resources transferred. Changes in intema- tional prices and in exchange rates reduce the purchas- ing power of aid. The practice of tying aid, still prevalent though declining in importance, also reduces the pur- chasing power of aid (see About the data for table 6.9). 1999 World Development Indicators 355 Distribution of net aid by Development 6.11 Assistance Committee members Total Ten major DAC donors Others United United $ millions, 1997 Japan States France Germany Netherlands Kingdom Canada Sweden Denmark Norway Albania......I... 100.0 10.8 6.0 2.2 .. 26.5...... 7.7 ...... 1:4.4 .... 0.4 ~.... 1.0 1.5 1:3 41.2 Algeria 19. 21 00 367 13..5 18 01 1 7 0 013. Anola 227.0 12.0 22.0 5.6 15.7 21.7 54 46 27.8 0.8 24.5 86:8 Argntina 56:9..... 22.8 0.0 6~6. . 10.8 07 0.2... 1.4 0.6 0.1 0.0 13 7 Armenia 46.4 4.3 22.0 4~3 ..... 4.1 5.1 1:6.6 .... 0.0 1.3 0.3 ......22 .2 ... .1.2 Australia Austria Azerbaijan ....... .... 15.4 . ...2.8 2.0 0.4 3.6 1.3 2.0 0-.1... 0.3 0.0 2.2 0... .O7 Bangladesh 5390.0. 1300.0.. 30.0 16.1 47.3 63.7 703.3.... 52.4.... 33.0 39.0 324.4... 24.8 Belarus 609.3..... 0.3 17.0 0.0 8.6 0.0 1.7 0 .0 ... 1.0 0.7 0.2. 31.0 Belgium Ben ........n.... 148.... .0. ... 18:8 22.0 26, _.6 .. 20.3..... 11.1 ...... 0:0 4.2 0.0 19.0 -0:1 26~2 Bolivia 452.5 65.0 163.0 13 9 47 5 59.8 86 .....12 4 .....20.1 1-4.7 38.8 43.7 Bosnia and Herzegovina 510.1 .....34.2 185.0 5. 0..... 33.1 84.1 1 8 .......8.2 ..... 31.2 4.0 40.8 82.9 Botswana 55.8 .98 9.0 0.7 7.2 4.5 6.5 .....0.6 8.5 0.8 7.3 1.1 Brazil 184:9.9 ... 61.8 -8.0 184 4..... 51.7 18.0 9.0 . .... 3.7 ......26 .6 ... -2.-4 1 .....I.9. 28.1 Bulgari 11.I 4. . 0.0 22.7 1.1 5.2 ..... 0.0 0.1 1.5 0.0 6. Burkina Faso 217.9 8.2 14.0 56.3 31.4 36.6 1:.0 6.0 1.4 25.-9 ....3.2 .....33 8 Burundi 3872.2 ..... 0.0 .... 3.0 5.9 6 .1 4.0 1.7 .... 1.0.... 3.0 00 0 54 .4 .... 8.0 Cambodia 226.0 61.6 30.0 27.1 17.0 11.5 7.4 3 .0 23.0 2.4 8.8 34.3 Cameroon 330.2 ......4.9 2.0 199.8 57.1 8.0 3.8 9 .1 .......00 0.8 0:.5. 44.3 Canada Central African Republic 61.3 20.0 0.0 31.1 .8.8. .. 0.5 ......00 .......0 1 . ....0.4 0.1 0.1 .. 0.4 Chad 96:4 .....0.4 ... 50 48.2 27.6 1.9 0.2 .......0 .8 ..... 0.5 0.0 0.2 11.7 Chile 113.1 43.2 -3.0 17.4 33.8 8.5 2.4 1.3 3.0 0:1 1.2 5.4 China 1,2286.6... 576.9 .......0.0 50.1 381.9 7.7 462.2 .35.4 6.8 1-3:7.7 ... 15.0O..... 94.8 Hong Kong, China 3.8 1.5 0.0 0.0 1.7 0.1 01 0 0.0 0.0 . . Colombia 171.2 35.7 23.0 14.0 32.0 8.9 4.1 -1 2 4.8 0 1 5.5 44..4 Congo, Dem. Rep.. . 104:5.5.8. 0.0 1.9 58 73..0..... 15 .7..5. 3.8 6.7 0.1..... 7.9..... 39.2 Conpgo,.Rep. 260.0 . 0.0 242.4 8. 0.. .. 0 .2 0.0 .......3.8 ...... 1.2 -0.3 0.8 .. 3.7 Costa Rica -2.4 -7.4 -32.0 3.6 -5.4 14.3 1.1 1.7 3.1 3.3 2.0 13.3 MSe d 'lvoire 232.7 33.4 .10.0 133.7 21.1 5.5 1.6 4.3 0.2 1.0 0.2 21.9 Croatia 22.9 -0.9 3.0 2-.6 -3.8 0.6 1.5_ ....0.4_ .... 3.0 0.1 89.9 7.4 Cuba 31~9_ _0.9 0.0 5.3...... 1.7...... 0.1 3:6 41.. 1:1. 0:0 1.1 14.1 Czech Republic ........ ... 84.7 ..... 2.2 1.0 0 0.0 .... 24.0 0.0 4.6.. .... 1 5 0. 1.I . 2.9 0.2 48.3 Denmark Dominican Republic ...........313.3,... 17.3 -18.0.4.4 6..7 2,6 0.0 0.9 0.1 0.1 04.. 4 16:7 Ecuador 139.8 25:6.6.... 13.0 ...... 96.6.... 16.0 14.5 131.1. 3.0 3.2 0.1 I 1.6 40.2 Egypt, Arab Rep. 1,496.3 125.4 542.0 283.9 397.2 22.9 88.8..... 17.1 ..... 2.3. 30.-7 .......33.3... 62.9 El.Salvador 234.0 68.3 89.0 6.4 ..... 25.7 8.5 0:4. 6.6. 4.8 1.3 2 3 20.8 Entrea 809.9.... 12.-4 12.0 18.8.... 10..2 ......5.9 1.7 1.4 5.0 4.6 8.8 17.1 Estonia 54.6 0.2 0.0 0.0 3.2 0.0 1.0 0.3 7:2 .....6.8 ......1.8 .....34 1 Ethiopia 372.5 37.3 60.0 7-.6 ..... 58.3..... 35.4 21.6 12. 3 36.0 3.5 28.4 72 1 Gabon 302.2 . .. 0.4 ......20 20.7 1.0 0.1.. 0:0 .......39 0.0 0.0 0.0 2..... 22 Gambia,The 17.4 0.2 50 0.6 3.8 2.2 2.4 02 0.7 0.2 08.8..... 1.3 Georgia 69 7 ..... 44 .4 .... 32:0 .......20 ..... 15.4 5.3 2 9 ......0. 0 .......0.6 00 1.6.......... ............. . ..................... .. .. 5.5.. . ............ .... .... Ghana 2919 .. 702 ..... 44.0 12.5 42.4 17.3 38:1.1.... 14.1 3.8 39:0 1.1 .....9.4 Guatemala ........... 212.2 .....49.8 35.0 1-.6 .... 31..7 16.5 ... ...0:.8 ..... 5.5 13.6 10.6 15.2 31.9 Guinea 125.5 4.4 22.0 .48.6 .15.9.... 0.2 0:7 9.8 0.2 2.0O... .1.0 .....20.6 Guinea-Bissau 58.5 ......7.5 11.0 6-.0 ..... 13 3 .I.. 8.0 0.1 0..2 .. 5.5 1.1 0.0_... 17:9 Haiti 175.4 14:.1.... 88.0 24.8 5.2...... 4.1 0.2 30 8 0.1 0.0 0.9 7.2 Honduras 155.0 42.0 28.0 12.2 24.1 14.3 1.9 ... 4..9 ..... 1.8 1.4 2.0 33.4 356 1999 Worild Deveiopment Indicators 6.11 Total Ten major DAC donors Others United United $ millions, 1997 Japan States France Germany Netherlands Kingdom Canada Sweden Denmark Norway Hungary 141.0 24~2.2 ..... 1.0 0.0 34.1 0.0 6.3 ..... .19...... 0.0 -1.0 0:3..... 74:2 India 927.6 491.8 29.0 36.4 55.0 32.8 15. 160 26 34 13.8 38.8 Indonesia 790.5 496.9 -51..0.12. 115.2 -48.8 57.2. 18.7 ...... 1.3 1.7 9.2 177..5 Iran, Islamic Rep. 165.2 .....70.3 0.0 _11~1.1 .... 56-.6 7.8. 0.6 ...... 0.1 ...... 3.7 0.0 2.2 12.7 I raq 180:0 ......0:8 ..... 00 3.4 64.1 42.8 6.3 ...... 0.7 44.6 0.0 5.2 12.2 Ireland Israel 1,181.2 0.5 1,248.0 00. O~ .. -70..4 20 0.0 0.2 ...... . 0.0 ..... . 0.0 0.0 ......0.8 Italy Jamaica...... . -50.0 . .. 15.5 -23.0 -03.3 .... -5.8 ..... 1.2 -4.8 3.4 3.2 0O.......0 0.3 ......5.4 Jordan 288.1 139.6 65.0 11..3 ......34.8. 8.2 12:2 .......2.5 0... .O.9 1..1 0.6 .....12.1 Kazakhstan 93.7 43.1 .....37.0 1.5 6..9 0.4...... 3:8 0.2 0.1 0.0 0.0- .....0.8.... Kenya 301.0 66.8 17.0 6.0 43.7 31.7 46:6 ......80 ......17.3 165 2:8 .....42.6 Korea, Rep. ...... -158.3 -140.6 -44.0 9.5 .. 14.3 ...0.2 0.0 0.0 0:0 0.0 0.0 2.3 Kuwait 0.1 0.0 0.0 0.0 01I 0.0 0.0 0.0 0.0 0.0 0.0- ....00 Kyrgyz Republic 50.4 18:1.1 ..... 8.0 1.2 8.7 ......0.7 1.3 0.0 .....0.0 2.6 0.6..... 9.3 Lao PDR 1648.8 .... 78.-6... . 2.0 14.8 16 ..6...... 4.2 1.1 1..2 ..... 15.5 0.8 5.4..... 24..7 Latvia 80.2 0:1.1..... 2.0 0.0 7.6 0.0 1.1 0..4..... 15:5 5.7 17.7 .... 46.2 Lebanon 69:2 08.-1.0.39.7 96 0.6 0.5 2..3 3.8 0.0 6:1.. 6.8 Lesotho 44.6 5.7 2.0 2.0 5.8 0.4 7.4 0.2 6.5 1.1 4.0 9.5 Libya 1:8 ..... 0.0 0.0 0.4 1.0 0.0 0:0.0 ..... 0.0 0. 1. 0.0 ......0.0 0...... 3 Lithuania 98.3 03.3 .... 1.0 0.. . 0.. ~ ... .. 8.2 .....0.0 1.3 0. 7..... 21.-1 .... 11.2 28 8.... 51.7 Macedonia, FYR 30.2 10.0 0.0 2 4 3.1 7:5 .......1:.......0 0.0 1.0 .... 0.3 0.0 4.9 Madagascar 549.0 29.5 33.0 311.0 47.5 2.9 1 2 0.3 0.0 0.8 6.2 116.8 Malawi 174.0 35.1 27.0 1..7...I ..... 32.8 12.4 284 ...... 5.6 5.3 17..7 .......4.6 ......3.4 Malaysia -243.7 -258.9 .......0.0 .... .0.9 6.4 .2.0 .......52 2.. . 2... 0.1.9 .......0.1 -......1.2 Mali....... 2566.6 .... 26.1 38.0 63..2' 55..7..... 33.5 ...... 1: 5...... 9.3 ... 0.8 3.5 8.0..... 17.1 Mauritania... 9.55 5.... 3-5 5 2.0 30-.7. 16.8 1.0 0.4 ...... 06 6...... 0.2 0.9 0.6 .... .6.9 Mauritius 2 7 3.5 ~~~~~~~~~~~~~~-1.0 10.6 -15.3 0.1 I14 0 1 0. 0.0 0.033 Mexico 88.7 41.4 8.0 10.3 12.5 3.9 5.5 2.4 0.3 1.4 0.2 ......2.8 Mldova 14.3 0.1 8.0 0.0 2.7 2.2 1.0 0.0 0.1 0.0 .....0:.0..... 0.2 Mongolia 118:1.1 ... 780.0 .... 12.0 0.6 14.0. .1.8 0.7 0. 2...... 0.8 6.6 0.5...... 2.9 Morocco 215.2 23.7 -.... 27.0 154.1 15.0O 0.7 0.6 .......4. 6...... 0.3 -0.6 0.1.. 43.7 Mozambique 621.6 38.1 71.0.... 45-.0..... 40..5 42.7 72.5 9.1 52.1 30.1 54.7 165.9 Myanmar 23:6. 14:8 ......0.0 .......1.9 ... ...1..4 1.2 0.5 0.1 ......0 .1 0.3 116 . . .1.6 Namibia 122.9 4 9 17.0 6.6 26.6 8.1 7.3 0.3 14.9 6.3 11.8 19.1 Nepal.233:5 ... 86 1 21.0 3.5 24..6..... 10.9 28:6.6 ..... 4.7 1.2 18.0 ......77 .7 ... 27.3 Netherlands New Zealand Nicaragua 258:3..... 490 0..... 41.0 1.2 29.0 23.6 1.2 5.0 21.5 25.8 ..... 19:4.4... 41.7 Niger 181.2 13.6 16.0 94.6 17.4 6.4 1.3 2.6 0.3 2.9 1.9 24.2 Nigeria 52:2 ......0:6 12.0 4.0 14.1 1.1 14.2 1.0 0.6 -0.-3 ..... .0.7 ......4.3 Norway Oman 20.3 ......7.3 12.0 0.4 0.....O.4 0.0 0.1 0.0 0. 00 00 00 Pakistan 73...1 92.2 -7 51 -266 17.2 42.5 12.5 .......1.8 -2.7 6.4 10.8 Panama 42.7 42.6 -13.0 0.3 3.8 0.4 0.8 1.0 0.0 0.2 0.0 6 5 Papua New Guinea 291.9_ 49.2 1.0 0.4 4.5 1.5 0.0 0.1 0.0 0.0 0.0 235 2 Paraguay.70.1 .....31.4 2.0 0-.2 ......20.1 ......0.8 -0..1.02.2.5 0 . 2 Peru 364.2 38.4 119.0 17-.4 ......85. 1... . 29.8 ...... 7.1 .....17.3...... 4 6 2.2 24.4 .... 40:8 Philippines 567.3 319 0 15.0 12-.2 .... 566 6. ... 22.4 18.5 16.5 16.7 3.9 2.6 840' Poland 673.6 4.8 26.0 0-.0 72.3 0.0 23.5. 121.4 7.9 12.8 3.0 402.0 Portugal Puerto Rico Romania 109.6 5.9 0.0 0.0 24.8 3.1 10.2 1.4 0.0 2.5 0.3 .....61:5 Russian Federation 1,033.2 5.6 273.0 0.0 90.5 0.4 56.2 14.1 14.3 17.5 29.8 631.9 1999 World Development Indicators 357 6.11 Total Ten major DAC donors Others United United $ millions, 1.997 Japan States France Germany Netherlands Kingdom Canada Sweden Denmark Norway Rwanda 178.7 8.1 9.0 10.7 .... 26.0 29.2 1070.0.... 21.4 1.9 1.8 11.1 49.4 Saudi Arabia -2.4 -4.8 0.0 1.6 08 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Senegal 292.0 25.4 30.0 1422.2 34.2..... 12.0 1.0 15.5 1.8 -1.-4 .... 1.3 30.1 Sierra Leone 41.4 1.2 130.0 2.9..... 2.9 1.4 8.0 5.2 ... 0:8 0....~ 1 39 2.1 Singapore -0.4 3.1 0.0 0.0 -4.4 0.2 073.3.. .. 0.4 0...... 00.. 0.. 0. O0 0 0.1 Slovak Republic 51.0 1.7 4.0 0.0 10.8 0.1 5.5 086 0.1 0.9 0.2 27.2 Slovenia -9.7 ....1.0 0.0 0.7 -15.5 0.2 1:3 0.0 0.0 0:1 0.0 2.6 South Africa 415.0 2879.9... 104.0 34.0 29..6 .... 35.9 388.8 10.9 .... 41.1 35.7 19.8 36.4 Spain Sri Lanka 228.3 ....134.6 .....5.0 1.6 9.8 14.0 17.4 3-.6 . .. 13.9 1.5 159.9.. 11.1 Sweden Switzerland Syrian Arab Republic. .93.0 66.3 0.0 11-.1 ..._12.0 1.2 0:1.1... 0.2 1.4 0.0 0.5 .....0.1 Tajikistan 35.9 0.3 13.0 04.1 .... 5.1 .. 5. 5 ......3.2 ..... 0.0 1.3 0.0 0.9 6.5 Tanzania 569.1 55.4 13.0 79.6 59.3 52.4 67.6 78.8 48.2 64.0 50.9 70..9 Thailand 600.8 468.3 8.0 82.2 35.3 6.1 3.0 9..9 5.4 28.9 0.5 27.3 Togo 75.7 27.5 ....- 2.0 ~ ... 32.6 8.7 0.4 . .... 0.5. 0.4 0.1 0.0 .....O0.0 3.5 Trinidad and Tobago -0.5 2:0 0.0 0.5 -4 0.2 . 07 03 0.0 0.0 0.0 0.1 Tunisia 69.3 11.5 -.... 19..0 60.3 9.9 3.4 03.3 . 1.1 ......0.5 00 0 01 ......1.3 Turkey -59.3 -21:7.7... -86.0 22.4 24.3 -0.6 1.7 -1..2 2 2 -0.2 0.7 -0.8 Turkmenistan 2.2 ......0.8 0.0 0.2 0.5 0.0 0.6 0_.0 ...... 0.0 0.0 0~ ......0 0.1 Uganda 438.8 2679.9. 36.0 5.2 38..6.. 33.4.... 78.2 3..2... 31.3 58.4 278.8. 100.0 Ukraine 309.6 0.5 72.0 0.0 35.6 0.1 18.8 12.6 6.9 3.2 0.1 159:9.9.. United Arab Emirates 0.5 . ...0.1 ..... 0.0 0.0 0.4...... 0.0 0.0 0-.0 ...... 0.0 0.0 O...... 0.00.0 ... United Kingdom United States Uruguay 29.5 ... 11.6 1.0 3_.1 .8.4.. 0-8 .......0.5 1......6 .... 0,9 0.0 0:0 1~5 Uzbekistan 110.9 . ...83 2 2...0.. 1.6 21.4 0.2 1.0 0.0 0.1 0.0 0.0.. 1.4 Venezuela -1.8 5.9 0.. . 0 3..7. 5.0 0.2, 0.3 ..... 09 ......0.1 0.0 01.1 . .-17.9 Vietnam 585.5 232.5 48.0 63.9 40.1 19.1 84 15.8 35.7 34.3 65.5. . 81.3 West Bank and Gaza 320.6 45.5 70.0 12~8.8. 35.1 25.3 10.2 5-8 20.8 5.1 41.0 49.2 Yemen, Rep. 1745.5. 36.9 4.0 12.8 .... 37.9 52.4 12:4 .......3.8 0.6. 0.0 0.2 13.5 Yugoslavia, FR (Serb./Mont.) 77.0 0.1 00 3.1 28.4 4.3 ... .0.8. .....0.2 22..7 0.1 5.4 12.1 Zamnbia 367.0 43.5 48.0 3 1 16.9.. 24.3 93.7 11.7 21.3 24.0 37.2 43.2 Zimbabwe 222.5 387.7. 19.0 -0.8 38..4... 24.1 222.2 4.3 ... 22.9..:.. 198.8 159.9.... 18.0 Low income 12,021* 2,228.1,194.1914 1,240 922 906 361 533 522 497 1,703 Middle income 12,333 3,233 1,673 1,236 .2,169 458 605 374 381 244 252 1,716 Lower middle income 10,782 3,228 1,578 931 1,836 382 496 217 300 177 201 1,437 Upper middle income 1,552 5 95 305 324 76 109 157 82 68 52 279 Low& mddl inome 34,295 6,753 629 406 4024 2,995.2119 1,371 1,329 1,135 973 4,271 East Asia & Pacific 5,010 2,250 224 . .l 24. 47 159 26 119 III 71 968 Europe & Central Asia 3,994 276 1,599 50 571 149 218 179 208 134 128 483 Latin America & Carib. 3,794 715 916 174 473 318 301 149 117 67 71 493 Middle East & N. Africa 9,901 1,746 2,084 1,408 1,17. 68 .91 558 378 331. 204.850. South Asia 221 964 38 54 13 18 32 103 88* 103 8 144 Sub-Saharan Africa 9,375 803 1,388 2,169 955 624 628 255 419 390 413 1333 High income 1.850 -132 1,202 710 -75 135 1 1 0 0 0 8 Europe EMU Nate: Regional aggregates include data for economies not specified elsewhere, World and income group totals include aid not allocated by country or region. 358 1999 World Development Indicators 6.11 The data in the table show net bilateral aid to low- and Because these data are based on donor country * Net aid comprises net bilateral ODAto part I recip- middle-income economies from members of the reports of bilateral programs, they cannot be recon- ients and net bilateral official aid to part 11 recipients Development Assistance Committee (DAC) of the ciled with recipientcountryreports. Nordothey reflect (see About the data for table 6.8). * Other DAC Organisation for Economic Co-operation and the full extent of aid flows from the reporting donor donors are Australia, Austria, Belgium, Denmark, Development (OECD). The DAC compilation includes aid countries or to recipient countries. A full accounting Finland, Ireland, Luxembourg, New Zealand, to some countries and territories not shown in the table would include donor country contributions to multilat- Portugal, Spain, and Switzerland. and small quantites to unspecified economies that are eral institutions and the flow of resources from multi- recorded only at the regional or global level. Aid to coun- lateral institutions to recipient countries as well as Data sources tries and territories not shown in the table has been flows from countries that are not members of DAC. In assigned to regional totals based on the World Bank's addition, the expenditures countries report as official Data on aid are compiled by regional classification system. Aid to unspecified development assistance (ODA) have changed. For DAC and published in its economies has been included in regional totals, but not example, some DAC members providing aid to annual statistical report, in totals for income groups. Aid not allocated by country refugees within their own borders have reported these Geographical Distribution of or region-including administrative costs, research into expenditures as ODA. Financial Flows to Aid development issues, and aid to nongovernmental orga- Some of the aid recipients shown in the table are Recipients, and in the DAC nizations-is included in the world total; thus regional themselves significant donors. See table 6.8a for a chairman's report, Develop- and income group totals do not sum to the world total. summary of ODA from non-DAC countries. ment Co-operation. The OECD also makes its data available on diskette, magnetic tape, and the Internet. Poor economies were not the sole recipients of aid from the top donors In 1997 C' Japan United Stales France I 1s,j:; , ________j _ .r ,-| j Germana Nerherlands United Kirngdor These ligures show tne distributior. of aid tiom the Lop six donors to their top five recipienits in 1997. Among the top recipients. many are middle,-income econom'es. and lsiael a nigh incom2 countrV. recei,es s.bstantlal aid liom the United States. 1999 World Development Indicators 359 Net financial flows from multilateral 6.12 institutions International financial inistitutions United Nations Total Regional development World Bank IMF banks Others Conces- Non- Conces- Non- $ millions, 1L997 IDA IBRD sienal contessional sional concessional WFP UNDP UNFPA UNICEF Others Albania 18.9 0.0 0.0 4.2 .... .......-0-0.0.... 2:2 3.5 0.8 0.2 2.-4 0.8 33.0 Algeria 0.0 16.5 114.2 . 267.6 -171.5 1.0 1.2 0.7 2.0 4.8 236.5 Angola .......... 27.9 .....00 ......... .. . . ...... .. 0.0 0 0 0 - . 5 . 5. 14.9 ....17.4 136.9 Argentina 0.0 497.8 . -36.8 -1.9 604.5 0.0 .. 124.8 0.1 3.9 6.6 1,198.9 Armenia 76.5 0.9 23.2 0.0 . -0.2 -0 8 29 2.3 0.5 1.6 2.3 109.2 Australia .. .. . .. ..~~~~~~~~~~~~~~~~~~~~~~. ...................... ............... ...... Austria . .. . . Azerban "55.4 0.0 76.5 28.2 ....... ........ 1267_ ..-576 3.5 4.5 0.7 2.8 03 ....140.9 Banglades 245.0 -4.5 -113.6 0.0 140:4..... -69_ .82 .. 60..5 11.7 6.8 23.3 8.6 393.2 Belarus 0.0 13.3 0.0 431 00 . 0.8 0.0 0.6 1.1 58.9 Belgium . . . Benin 15.9 0.0 1.959 ..5.9 0:.0.... 2.3 ....44.4... 6.7 2.9 3.0 3.0 45.9 Bolivia 132.2 -19.4 -11.2 0.0 69.3 -15.6 .....383.3 .10.9.... 7.8 2.2 9.7 3.1 227.4 Bosnia.and.Herze$qvina 65-2 ......... ....... -1.0 . ........6 - ....... .. ...... .11.7 .....0.2 7.5 1.0 . . 84 7 Botawana -0.5. ...-16.5......... 1.6 -9.3 .....-1.6 3.5 00.9 . 1.5 -19. Brazil 0.0 367.5 . -2.6 0.0 1,015.8 0.0 . 200.6 2.2 20.1 22.5 1,596.1 Bulgaria 0.0 85.0 400.1 . 19 3 -117.7 . 1.4 0.3 0.1 1.4 389.9 Burkina Faso 30.4 0.0 16.5 12.9 -. -2.0 5.6 7.0 2.1 4.5 2.4 77.9 Burundi 7.4 0.0 -8.0 0.0 1.3 -3.8 -1.1 7.3 1.4 8.5 59.4 72.5 Cambodia 30.4 0.0 0.0 0.0 10.0 12 39 39 11.5 4.2 112.1 Cameroon 114.5 -70.4 37 2 -11.3 1.6 -14.2 -48.0 0.8 0.9 1.9 2.1 2.2 17.2 Canada. ........ . Central rianRe ubiAfrican Republic ... - 3.3...... 0.0......... 0.-3. ..8...-3.. .7...0.0.....00 ..1...1... ............. -3.1 3: Chad 46.6 0.0 5,5 -5.3 21.1. -3.4 6.2 4.6 1.4 3.7 1.7 82.0 Chile -0~............ . 7.... -24.....7. ... 00 ....-099 -3429 9.. 0.-8 . 10.7 0.1 0.7 ... ..2.1 -354.9 China 687.1 1,210.8 0.0 0.0 495.6 -8.5 38.1 43.1 0.1 20.5 10.1 2,496.9 Hong Kong, China .. Colombia --0.7 -248.1 .. 1 -1 489 60.6 0.1 80.8 0.8 2.6 3.3 -261.1 Congo, Dem. Rep. 0.0 0.0 0.0 0.0 0 0 0.0 0.0 .. 15.3 0:1.1... 21.4 7.3 44:1 Congo, Rep. .....I....... 0.6 _ -2.8 ....0.0 ....... -2.2. 0.0.OO...... -13.9 0.0 . . 0.2 0.3 1.4 .. 1,6.6 .. -14.9 Costa Rica -0..2 -34.2 . -0.7 -10.2...... 77.63 62.1 . 6.5 0.3 1.0 2.3 104.5 CMe dIlvoire 140. 1-157.6 0.0 -22.1 66.6 -17.7 -54.0 3.1... 2.6 1.9 2.6 12.6 -81.9 Croatia 0,0 100.4 37.5... 4.7 . 1.52.5 03 146.7 Cuba . . 7.3 2.0 0.9 1.8 3.0 15.0 Czech Republic 0.0 -16.7 0.0 0:0 -59.4 04......... ......... .... 1.3 -7 . Denmark Dominican Republic . ....... -0.7 -15.2 -62.4 6.9........ 274 0.5 1. 8 5.2 1.5 1.4..... 2.0 -56.6 Ecuador -1.1. .-55.6 -2.7 -2:9 ........51 5 .....75.1 1.8 17.4 1.6 ... 39.9 .... 1.9 90.9 Egypt, Arab Rep. 141.4 .-122.8 0.0 -15.0 153.3 -.162 2.... 82:1 ..6.2 .. 14.9 3.1 5.6 .....79.9. 122.7 El Salvador -0.8 11.8 0.0 0.0 10.7 93.1 51.5 1.5 23-9 1.0 ......14..... 1.1 195.1 Eritrea 3.8 0.0 .... 147.7 1.1 6.3 2.1 31.4 Estonia 0.0 10.9 .. -19.5 -3:4 4. 3.4 . 1.1 04.4 _-7.0 Ethiopia...... ..... 49.7 0.0 0.0........ 0.0-..... 20.8 37.3 -4.4 83.5 .27.4 5.8 14.8 18.5 253.4 Finland .. . . . France . ... Gabon............. 0.0 ..-7.1 . 19.2 0.1 4.3 -5:3 ...... 1.1 0.4 0.6 ... 1.-3.... 14:7 Gambia, The 7.8 0.0 -6.4 0.0 10:3. -1.5 -0.1 2.1 4.2 0.7 1.7 1.9 20 7 Georgia ....... ... 64.3 .... 0.0 . .76.4 0.0... .... 005.0 0.0 4.8 3.8 0.9 2 1 2.1 159.3 Geriany Ghana 225.2 -10.9 -118.2 -47.4 32.1 -12.7 5.6 1.1 6.7 1 9 ......6..2 ..3.5 93.0 G r e e c e .... .. . . . .. . . . .... . .. . . . . .. . . . . . . .. . . . .. . . . . .. . . . . . . . . .. . . . .. . . . . .. ..... . . . . . . . . .. . . . . .. . . . . . . .. . . . . Guatemala 00............OO.... 43 0-0 4:6.6 ...... 17:0 1-4 ....68... 332 06 ......3:2 8.4 79.6 Guinea 105.1 0.0 22.1 0 0 17.9 4 7 34.7 . 4~8 1.0 3.2 27.9 221.4 Guinea-Bissau 16.4 0.0 5.1 0.0 11.6 -0.4 -0.7 2.0 2.9 0.6 2.5 1.6 41.5 Haiti 35.1 0.0 19.9 0.0 42.3 . -0.8 4.7 23.3 -1.7 4.0 .....13 ... 131:6 Honduras -.......... 93.1 .-49.7 0.0 -8.8 19:6 70O.... 262.2 .4.0._114 .....1.5 2.6 .....0.8 343.7 360 1999 World Development Indicators 6.12 International financial Institutions United Nations Total Regional development World Bank IMF banks Others Conces- Noni- Conces- Non- $ mililons, £.997 IDA IBRD sional concessional sional concessional WFP UNDP UNFPA UNICEF Others Hungary 00........ O. ..-32.8 0.0. -29.9 -38.2 0 3 ...... 20................ ... ..... ........................ ... .-987................. . '................. India ............. 579.9 -278.1 0.0 -613.0.... ..480~3 29.2 23.8.. 23.8 8.8 52:9 13.2 320.8 Indonesia -20.4 -245.2 . 3,029.4 18.9 ...... 173 6 -7.7 . 11.4 2.3 14.6 9.2 2,986.1 Iran, Islamic Rep. 0.0 49.0 .. -40.3 3.7 1.9 1.8 1.7 15.0 32.9 Iraq . . 37.6 12.8 0.1 17.9 7.0 7. Ireland. ...... . Israel Italy Jamaica 0.0 -45.7 -34.6 -46.6.... . 19.9 -19.8 1... I.5.... 1.9 0.3 1.0 1.3 -78.9 J a p a n ... . . . .. . . . . ... . .. . . . I .. . . . . .. . . . . .. . . . . . . . . . . .. . . . . .. . . . . . ... .... . . .. .. . . . . .. . . ..... . . . . .. . . . . .. . . . . . Jordan -2.3 25. 0 ........... .... 110..7 .. . .... ..... ... 137.2 4.8 27.7 . 1.1 1.3 79.8 ... 360:4 Kazakhstan 0.0 201.7 --6.4 16.9 .......54 6 10.3 . 2.2 0.3 1.6 1.4 282.6 Kenya 72.3 -73 8 -67.3 0.0 42.0 -6.4 -12.5 31.0 6.8 2.0 5.8 21.6 21.4 Kore , Oe. R ep....... ...... . ..... .............. . .... ................ ..... ... ..104. 7 .. ..5.9 2.3 6.9 3.2 123.0 Korea, Rep. -3.5 2,797.9 11,283.9 1,938.5 0.0 1.8 . 1.8 16,020.4 Kuwait Kyrgyz Republic 66.5 0.0 44.4 -9.8 .....51.9 ........156.6 0.4 4.1 . .1.1 1.3 0.7 176.1 Lao PDR 40.9 0.0 3.2 84.6 . ....-I. . 5.8 12.0 11.6 0.4 3.3 1.3... 163.1 Latvia 0.0 52.8 -36.7 -15.9 133 . . 2.0 0.1 . 0.2 15.8 Lebanon 0.0 31.5 31.4. 3145.7 0.3 2:9 45.1 116.8 Lesotho ... .... ..... 10.0.... 6.0.. -4.3 4.8 -2.1......54 3.4 ... 5.6 0.7 1.2 1.3 32.1 Libya . .................... 2.4 0.. . 0.. I. ...I ...4.8.73 Lithuania 0.0 18.1 14.2 4:4 17.6 .. 10 0.0. 0.5 55.8 Macedonia, FYR 29.2 13.0 25.0-0 . . ....19. 6 .....180.0 47..1 ....0.1 5..... !.... 1..... 1.....7 5.5...... Madagascar 119.7 -3.6 0.9 0.0 71.5 . .....-22.0 -20.2 2.8.. 6.1 1.7 4.7 3.3 164.8 Malawi 98.1 -6.0 -6.4 0.0 29.2 -1.4 -3.5 0.9 12.5 2.8 6.7 3.6 136.5 Malaysia 0.0 -75.10. 00 24:2 -2.4 . . 5.2 0.2 0.6 2.4 -44.9 Mali.............. 67.5 0.0 21.5 00 ......O. ... 12:1.1 ...... -0.3 8.3 7.4 .. 19.0 2.3 6.7 11.7 156.1 Mauritania 31.8 ~~~~~~~~~-1.9 12.1 00 1. -62.2..... 3.1 10.1 7.8 ... 1.0 2.0 3.7 77.1 Mauritius -0.6 -3.5 0.0 0.0 -0.3 ...... -2.7 .....19 0 0.0.... 0.9 0.3 0.6 1.0 14.7 Mexico 0.0 -316.1 -3,439.2 -4.7 240.9 0..... 0.08.8 ....1.8 4.3 10.4 -3,493.7 Moldova 35.0 5.4 0.6. 15 0.0 2.8 0.2 0.6 07 58.8 Mongolia .... ....... 33..8 0....0O 7.7 -0.9 67.4 . .. 0.0... ..... 0 5.2 0.8 1.0 2.2 117.4 Mrcc -1.4 -152.6 0.0 -3 2 5.5 -150.4 215.9 4. . . . . 68.4 Mozambique . 146.6 0.0 19.6 555.5 .-63.3.... 31.8 5.0 25.9 .... 3:0.0 .. 10.3 2.8 294..2 Myanmar -7.0 0.0 0.0 0.0 -8.2 -0.8 -2.6 1.6 14.0 0.9 8.4 12.7 19.0 Namibia . .. . . 10 .4 2.7 3.2 8.3 Nepal 45.2 0.0 -7.1 0.0 84.7 0.0 0.6 10.3 11.9 2:0.0 ... 8.4 10.2 166.2 Netherlands .. . . New Zealand Nicaragua 49.4 .-16.7 0.0 0.0 51.3 .24 -44... "9 6.9 8.3... 2:7 .. 3.4 1.0 63.9 Niger 44.9 0.0 17.3 -5.7 18:0 -0.4 -2.6 8.2 6.0 2.2 4.5 6-8 .'99:2 Norway .. .. . ..-.. .. Oman 0.0 -4.2 ........ -8.2....0.020.1 0.8 .....12 -10.3 Pakistan 190.7 284.0 35.9 -64.5 2173 3..... -10 7 ... -61.9 16.4 10.7 .4.6 12.5 15.2 .650:1 Panama 0.0 24.7 ........ 19.8 ......-8.8 115.3 -0.3 0.8 81.4 0.3 0.8 1.9 235:9 Papua.New Guinea I.-2-.2 ....19.7. 0.0 .........00 ... ...78 ...... 3 8 -4.5 . 3.6 1 9 0.7 2-4.4 ... 33.0 Paraguay .-15 15. .. 266 53 14.3 .5 14 0 1. 11 128 Peru 0.0 425.0 . 147.2 -6.6 437.8 0.0 3.2 96.0 3.1 6.8 3.3 1,115:7 Philippines 7.6 -112.1 0.0 1.484..2 ......47 9 .. 91 0 0.0 . . 7.8. 6 1 8.8 4.4... 545.8 Poland 0.0 83.6 - 0.0 -- 0.0 . 1.6 02-. 18 87.2 Portugal .. .. Puerto Rico . . . Romania 0.0 371.0 . 30.5. 275.6 .....436.6 ..... 1.....................I.......8.516 1 2.4.. Russian Federation 0.0 2,690.6 ..1,524.4 .........381 1 -41.1 1.2 1.7 0:3 0.1 9.5 4,224.7 ip99 World Development Indicators 361 6.12 International financial institutions United Nations Total Regional development World Bank IMF banks Others Conces- Non- Conces- Non- $ millions, 1997 IDA IBRD sional concessional sional concessional WFP UNDP UNFPA UNICEF Others Rwanda 47.5 0.0 -2.4 2075.5.. ... 7.9 0.0 3.4 .. 145.0 29.1 1. .... 18.3 115.6 386.3 Saudi Arabia ... 5.3 . . 11.8 17.1 Senegal 52.3 -7:2 2.2 -16.0 14.3 -9.6 5.1 2.4 4.3 31 3.9 4.5 59.3 Sierra Leone 264 -02 700 9.7 0.0 3.7 1. 39 0 33 57 77 Singapore Slovak Republic ....0.0 -2.2 .. -517.7 ..... ... -83 3 .. 83.8 ... ....0:5 1,.5 . 23:6 Slovenia 0.0 -2.3 .. ......... . .. . .... .... 0.2 . 2.3 0.2 South Africa 0.0 0:0 -42278 .....I.....5.3 13 2.3 5.1 -408.9 Spain Sri Lanka 67.8 -5.9 -66.1 0.0 79.0 0.0 -1.1 3.9 8.1 0.9 5.1 9.9 101.6 Sudan 0.0 0 0 -7.1 -35.0 -0.2 4.7 00.. ..O...32.4 11.1 18.8... 29.0 11.8 48.3 Sweden . .... . . Switzerland Tajikistan 22.0 0.0 10.3 . 12.9 3.8 0.8 2.4 1..5 ....53 7 Tanzania 167.5 -20.1 53.6 00.0.... . 45.9 -0.8 -10.8 12.0 14.9 4.2 12.4 5.6 ...284.4 Thailand -1.8 251.0 0.0 2,477.0 -1.6 532.6 17.7 0.9 6.7 1.5 4.4 8.6 3,296 .9 Too 12.9 0.0 3.3 0:0.0.... . 17.0 -0.1 -10.3 .. 4.1 0.7 1.7 30 3. Trinidad and Tobago 0.0 3.6 -18.3 -. 268 16 .. 03 . .. 0.6 25.5 Tunisia -21 -58-0 98 12.0 2.8 16 0.7 10 2.2 -8.2 Turkey -5.9 -426.4 . -27.6 . -79.7 . 2207 2. 63 -52. Turkmenistan ... . ........00 0 .... 3.2 ..8.0. 4 .9 ..... .....1.4 0.8 1.1 03 1 . Uganda 207.3 0.0 2.3 0.0 330 04 3.7 31.3 19.0 3.0 18.7 25.8 344.6 Ukraine 0.0 305.5 285.2 326 135 . 28 02 08 3.4 744.0 United Arab Emirates United Kingdom United States . Uruguay 0.0 -17.5 -8.2 -1.7 133.4 .....2..6 . 24.5 0.. .O.3 .... 0.8 ....0.9 ...135.1 Uzbekistan 0.0 12.9 0........ . 0: .................... 8!5. .-66.9 ... .... 3.4 0.4 2.5 ..... 0.5 ..-3878 Venezuela 0.0 -81.3 . -452:.1..... -1.3 -12.9 0019. 0.5 16 33-2. Vietnam 180.2 0.0 0.0 -54.0 143.6 0.0 15 9 13.9 15.8 4.8 .....9.7 3.7 333.6 West Bank and Gaza . .. ... ... 1.9 28.0 1.8 3.0 125.6 160.3 Yemen, Rep. 79.0 0.0 60.5 7970.0 ......I. 6.6 10.3 10.7 ... 2.2 .... 34.4 8.8.. 260.5 Yugoslavia, FR(Serb./Mornt.) 0.0 0.0 .. 0.00 0.0 . Zambia .........I. 165.6 -34.5 .13.8 00...... ~O........ 8.5 -15.7 18.2 2.0 11.5 2.6 7.2 . ...6.3 185.4. Zimbabwe 82.7 3.1 0.0 -25.5 3.1 -2.9 61.4 . 0.0 . . 0.8 2. Low Income 4,172 -596 168 -784 1,455 402 194 757 622 110 447 535 7,482 Middle income 1,116 4,525 29 3,956 298 4,289 497 316 992 61 200 582 16,860 Lower middle income 1,122 4,455 29 8,417 308 2,637 522 316 564 52 155 445 19,021 Upper middle income -6 70 0 -4,461 -10 1,652 -25 0 428 9 45 137 -2,160 Low & middle income 5,223 3,929 197 3,172 1,753 4,691 691 1,073 1,629 178 653 1,364 24,553 East Asia & Pacific 952 1,050 11 5,936 372 1,319 8 185 172 27 93 84 10,209 Europe & Central Asia 427 3,490 246 2,182 69 540 -12 7 62 1 39 249 7,248 Latin America & Carib. 325 431 17 -3,948 204 2,378 528 53 817 27 82 114 1,027 Middle East & N. Africa 205 -466 61 235 21 151 279 83 98 17 43 357 1,084 South Asia 1,133 -5 -151 -677 531 477 -21 167 94 25 115 84 1,772 Sub-Saharan Africa 2,246 -S2 1 -56 57 -7 38 09 86 1 22 49 3,2 High income -4 2,796 . 11,284 .. 1,939 0 0 3 0 0 4 16,022 Note: The aggregates for the regional development banks, United Nations, and total net financial flows include amounts for economies not specified elsewhere. 362 1999 World Development Indicators 731 6.12 IC.7T' -77= This table shows concessional and nonconcessional $925, measured in 1997 using the Atlas method (see * Netfinancialflowsaredisbursementsofloansandcred- financial flows from the major multilateral institutions- Users guide). In exceptional circumstances IDA extends its less repayments of principal. * IDA is the International the World Bank, the International Monetary Fund (IMF), eligibility temporarily to countries that are above the cut- Development Association, the soft loan window of the regional development banks, United Nations agencies, off and are undertaking major adjustment efforts but are World Bank Group. * IRD is the Intemational Bank for and regional groups such as the Commission of the not creditworthy for IBRD lending. An exception has also Reconstruction and Development, the founding and largest European Communities. Much of these data come from been made for small island economies. member of the World Bank Group. * IMF is the the World Bank's Debtor Reporting System. The IMF makes concessional funds available through International Monetary Fund. Nonconcessional lending is The multilateral development banks fund their non- its Enhanced Structural Adjustment Facility (ESAF), the the credit provided by the IMF to its members, principally concessional lending operations primarily by selling low- successor to the Structural Adjustment Facility, and to meet their balance of payments needs. Concessional interest, highly rated bonds (the World Bank, for through the IMF Trust Fund. Low-income countries that assistance is provided through the Enhanced Structural example, has a AAA rating) backed by prudent lending face protracted balance of payments problems are eli- Adjustment Facility. * Regional development banks and financial policies and the strong financial backing gible for ESAF funds. include the African Development Bank, based in Abidjan, of their members. These funds are then on-lent at Regional development banks also maintain conces- Cote d'lvoire, which lends to all of Africa, including North slightly higher interest rates, and with relatively long sional windows for funds. According to the DAC defini- Africa; the Asian Development Bank, based in Manila, maturities (15-20 years), to developing countries. tion, concessional flows contain a grant element of at Philippines, which serves countries in South Asia and East Lending terms vary with market conditions and the poli- least 25 percent. (The grant element of loans is evalu- Asia and the Pacific; and the Inter-American Development cies of the banks. ated assuming a nominal, market interest rate of 10 per- Bank, based in Washington, D.C., which is the principal Concessional, or soft, lending by the World Bank Group cent. The grant element of a loan carrying a 10 percent development bank of the Amercas. * Others is a residual is carried out through the International Development interest rate is nil, and for a grant, which requires no category in the World Bank's Debtor Reporting System. It Association (IDA), although some loans by the repayment, it is 100 percent.) In the World Development includes such institutions as the Caribbean Development International Bank for Reconstruction and Development Indicators loans from the major regional development Bank, European Investment Bank, and European (IBRD) are made on terms that may qualify as conces- banks-the African Development Bank. Asian Development Fund. * United Nations includes the Worid sional under the Development Assistance Committee Development Bank, and Inter-American Development Food Programme (WFP), United Nations Development (DAC) definition. Eligibility to receive IDA resources is Bank-are recorded accordingto each institution's clas- Programme (UNDP), United Nations Population Fund based on GNP per capita; countries must also meet per- sification. In some cases nonconcessional loans by (UNFPA), United Nations Children's Fund (UNICEF), and formance standards assessed by World Bank staff. Since these institutions may be on terms that meet DAC's def- other United Nations agencies such as the United Nations 1 July 1998 the GNP per capita cutoff has been set at inition of concessional. High Commissioner for Refugees, United Nations Relief and Works Agency for Palestine Refugees in the Near East, >14- V ;-oB _ and United Natons Regular Program for Technical Nonconcessional lending by the World Bank increased sharply in 1997 Assistance. * Concessional financial flows cover dis- bursements made through concessional lending facilities. Nonconcessional financial flows cover all other disbursements. Data sources tzrnrg, ijData on net financial flows from I,:tG-ggg | intemational financial institu- * I t4iIirI ,tions come from the World :-d . --=------- - -'aw, t Bank's Debtor Reporting System. These data are pub lished in the World Bank's Global Development Finance 1999. I , ; '~- : , i . ; 4 1 -, - > t 1?'W 9 9- l|11! _ -- D a ta on aid from United Nations _IEIL ,I, L..,i,,,,, .-; , ...¼". agencies come from the DAC chairman's report, IliFO-,xl.u, I-.,, C...... ,;, -,- - o-iii-ii 11. .Sc1 !.:I ,;\:r,: Development Co-operation. Lending la countries aflecTee b) the financial crisis accounted Yor a laige share of the increase in ihe World Bank s nonconcessronal aisburse menats. 1999 World Development Indicators 363 6.13 Foreign labor and population in OECD countries Foreign population' Foreign labor forceb Inflows of foreign population % of total % Of total Total Asylum seekers thousands population labor force thousands thousands 1990 1996 1990 ±996 1990 1996 1990 ±996' 1990 1996' Austri 456' 728 5.9 9.0 .. 10.0 224.2 637 Belgium 905 912 9 1 9075 8.1 519 51.9 ........13. ... 11:6 Denmark 161 238......23 . 31......3147.... .. 2.0........203.0....... . 15.. ... 5.... ... 5 .. .....1.5 Finland 26 74 0.5 1.4 .0.8 7 7.5 3 1.0 France 3,597h 3,597 h 6.3 6.3 6.4 6.3 102 74.0 47 21.4 Germany 5,343' 7,314 8.4 8.9 8-.4 .........9 1 842 708.0 .......193. 104.4 Ireland 801 118 2.3 J 3.2 2.6 3.5 ...3.9 Italy 781k 1,096k 1.4 2.0 ......... ...... 1:7 .. ........ .... . 5 ..... 1.4 Japan i,0751 1,415 0.9 1.1 .09 224 225.4 Luxembourg 113 143 29.4 34.1 33.4 53.8 9 ~............. 0.4 Netherlands 692 680 4.6 4.4 3.7 .........3.1 81J 77.2 21 34.4 Norway 143'" 158 3.4 m 3.6 .2:6 16 17.2 42. Portugal 108' 173 1.1" 1.7 ..1. .. 00. Spain 279' 539 0 7' 1.3 10.........9........3.7 Sweden 484 527 5.6 60 5 6 ...... 5.1 .... ...53 .... 29.3 29 9.7 Switzerland 1il0OP 1,338 16.3 P 19 0 . 17. 101 74.3 36 23.9 United Kingdom 1,723J 1,972 3.21 3.4 3.5 3452 216.4 381 41.5 Foreign-born population' Foreign-born Inflows of foreign population labor force' % of total % of total Total Asylum seekers thousands population labor force thousands thousands 1990 1996 1990 1996 1990 1996 1990 1996 1990 1997 Australia 4,125t 3,908 22.7t 21.1 .......25.8 ..... 246 ........121 .. . . .99..1 .........4"u.... .. 7:7 Canada 4,343t 4,971 15.6t 17.4 .......1841.4 ....... 18. ~r 214 226.1 ........37 ........23.9 United States 19,767 24,600 7.9 9.3 9.4 10.8 1,537 915.9 7'798 a. Escept for France, Ireland, Portugal, and the United Kingdom, data are from population registers. Unless otherwise noted, they refer to the population on 31 December of the years indicated. b. Data include the unemployed except for Italy, Luxembourg, the Netherlands, Norway, and the United Kingdom. Data for Austria, Germany, and Luxembourg aer from social security registers, data for Denmark are from the population register, and data for Norway are from the register of employees. Data for Italy, Portugal. Spain, and Switzerland are from residence or work permits. Figures for Japan and the Netherlands are estimates from national statistical offices. Data for other countries are from labor force surveys. C. Data are from population registers except for France (census), Ireland and the United Kingdom (labor force survey), Japan and Switzerland (register of foreigners), and Italy. Portugal, and Spain (residence permits). d. January to September 1997 for Italy, Luxembourg, Poland, and Spain. e. Annual average. f. Data do not include de facto refugees from Bosnia and Herzegovina. g. Includes some asylum seekers. h. Data are from the 1990 population census. i. Data refer to the Federal Republic of Germany before unification. j. Estimated from the annual labor force survey. k. Data are adjusted to take account of the regularizations in 1987-ge and 1.990. Data for 1996 do not include permits delivered under the 1995-96 regularization program. I. Data refer to registered foreign nationals, who include for- eigners staying in Japan for more than 90 days. m. Includes asylum seekers whose requests are being processed. n. Includes all foreigners who hold a valid residence permit. o. Data refer to foreigners with a residence permit. Those with permits for fewer than six months and students are excluded. p. Data refer to foreigners with an annual residence permit or with a settlement permit (permanent permit). q. Data adjusted to include dependents. r. Data are from the latest population census, a. Data are from labor force surveys except for Canada and the United States, for which data are from the latest population census. t. Data refer to 1991. a. Data refer to principal applicants and do not include dependents. v. Data refer to 1986. w. Data refer to the fis- cal year (October to September of years shown). 364 1999 World Development Indicators * v i s 1~6.13 The data in the table are based on national defini- issue a single permit for residence and work, while * Foreign population is the number of foreign or tions and data collection practices and are not fully others issue separate residence and work permits. foreign-born residents in a country. * Foreign labor comparable across countries. Japan and the Differences in immigration laws across countries, force as a percentage of total labor force is the share European members of the Organisation for Economic particularly with respect to immigrants' access to the of foreign or foreign-bom workers in a country's work- Co-operation and Development (OECD) traditionally labor market, greatly affect the recording and mea- force. * Inflows of foreign population are the gross have defined foreigners by nationality of descent. surement of migration and reduce the comparability arrivals of immigrants in the country shown. The total Australia, Canada, and the United States use place of raw data at the international level. The data does not include asylum seekers, except as noted. of birth, which is closer to the concept of the immi- exclude temporary visitors and tourists (see table * Asylum seekers are those who apply for permission grant stock as defined by the United Nations. Few 6.14). to remain in the country for humanitarian reasons. countries, however, apply just one criterion in all cir- OECD countries are not the only ones that receive cumstances. For this and other reasons data based substantial migration flows. Migrant workers make up Data wonces on the concept of foreign nationality and data based a significant share of the labor force in Gulf countries on the concept of foreign-born cannot be completely and in southern Africa, and people are displaced by Intemational migration data reconciled. wars and natural disasters throughout the world. - are collected by the OECD Data on the size of the foreign laborforce are also Systematic recording of migration flows is difficult, through information provided problematic. Countries use different permit systems however, especially in poor countries and those p by national correspondents to to gather information on immigrants. Some countries affected by civil disorder. the Continuous Reporting Sys- - tem on Migration (SOPEMI) =- 'IX E - t . ;t network, which provides an OECD countries drew Immigrants from around the world in 1997 annual overview of trends and policies. Data appear in the OECD's Trends in r -----.------- ---- --- --.- _ _ _ International Migration (1997d). |- I ,f.L11 ,1 -I ' 'LrJ-' 1Jr- 1 B'i'1:i I France Ge,many Japan r| ..| 11~,' - c-11--- Irl ir,.' Skitzerland UnnKed Kingdom United States S.:, E OCD The graphs show the distribution ol immlgants In 1997 by natlonaItrV. The definition of immigrants differs by reporting coentry and may Include transient visitors. For the United State; the aata are for permanent settlers classilied by country of birth. 1999 World Development Indicators 365 6.14 Travel and tourism International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % Of S of thousands thousands $ millions exports $ millions imports ±.980 1997 1.980 1997 1980 1997 1980 1997 1980 1997 1980 1997 Albania 4 50 . 15 .. 10 . .5 . 0.6 Algeria 946 635 698 1,800 115 20 0.8 0.1 333 64 2.7 0 6 Angola 8 . 9 .. 0.2 ....73 16.. Argentina 1,120 4,540 . 4,465 345 5,069 3.5 17.8 1.791 2,680 13.6 7.7 Armenia . . . . . 12 . 3.6.. 41 . . Australia 905 4.286 1,217 2,933 967 9,324 3.8 I11.1 1,749 6,129 6.5 7.5 Austria 13,879 16,646 3,525 13,263 6,442 12,393 24.2 14.5 2,847 10,992 9.5. 12.5 Azerbaijan 146 . . . 59 . 138. 72 . 38 Bangladesh 57 184 . 866 15 42 1.7 0: 8. 16.. 170 0.6 2.2 Belarus . ...I.... ....... 250 . 703 ._ . 49.. 0.6.. 114 1.3 Belgium 3,777 5,875 9.565 6,658 1,810 5,997 2.6 3.2 3,272 8,275 4.4 4.7 Bolivia 155 375 . 285 40 180 3.9 13.2 52 172 6.2 8.4 Bosnia and Herzegovina 100 is 1 Botswana 236 728 . 460 22 181 3.4 7.5 1 4 1 8 Brazil 1,271 2,995 427 4,852 1.794 2,602 8 2 4.3 1,160 6,583 4 2. 8 2 Bulgaria 1,933 2,684 759 3,006 260 391 2.8 6.3 . 222 3.9 Burundi 34 11 30 22 1.. 1.0 ..... 17 10 .... ... .. 7.. . 2.. C sm bod.....I ...... ...219........... ....... 45......... ....... 145... .. ...... ... 16 .2 .. . .... .... 12 . .... . . .... 1.0 Cameroon 86 102 14 . 62 39 3.5 1.6 82 107 4.5 5.2 Canada 12,876 17,610 12,833 19,111 2,284 8,928 3.0 3.6 3,122 11,284 4.4 4.8 Central African Republic 7 22 .: .......... ..... 3. 5 1.5 2:3 18 39 5..5 16.5 Chad ..... .... 7 8 .7.... ... 10 .. ..3 ....9 4.2 3.3 14 24 . 17.6 4.1 Chile 420 1,693 379 1,263 166 1,080 2.8 5.2 195 946 2.8 4.3 China 3,500 23,770 . 5,324 617 12.074 3.6 5.8 66 10,166 0.3 6.1 Hong Kong. China 1,748 10,406 916 3,758 1,317 9,242 5.1 4.07 Colombia 553 1,193 781 1,366 357 955 6.7 6:0 250 961 4.6 5.1 Congo, Dam. Rep. ... ... 23 . ... 30 ........ 22 2 0.9 .....0.1_ ... 38 7 1.6 05 Congo, Rep. 48 19 10 3 1.0 02.2 29.. 36 2.8 2.6 Costa Rica 345 811 133 288 87 752 7.3 16.8 62 348 3:7 .. -17.5 MSe dIlvoire 194 274 5 7 8 22 18 270 8 6.5 7 6 Croatia . 3,834 . . . 2,165 . ..... 26.4 . 521 . 4.6 Cuba 101 1,152 7 55 40 1,338 . Czech Republic .. 16,830 . . .. 3,700 . 12.4 . 2,380 . 7.3 Denmark 950 1,815 5,064 1,337 3,159 5.6 5.0 1,560 4,128 5.8 7.1 Dominican Republic. 383 2,211 257 185 168 2,106 13.2 328.8 166..... 242 8.7 3.4 Ecuador 243 525 300 9 8 . . 228 227 77 3. Eglypt, Arab Rep. 1,253 3,657 1,180 2,945 808 3,847... 12.9 23.8 573 1,347 6 3. 7.4 El Salvador 118 385 464 627 7 67 0.6 2.5 .....106.... 75 9.1I 1.9 E I .. . . ..re. .. . .. . . 1 . . .. . . . ... 4 9 . . . . I . . . . . I ... . . . . . . . . . .. . . . . . . . . .. . 7 5... . . . . .I ... 3 7.2.. .. . . . . I. . . . . . . . Estonia 620 .. 28. 475 . 13.2 118 2 8 Ethiopia 42...... .... .... 115 25 140 11 36 1.9 3.5 5. S 40 0 6 2 Finland 350 1,828 291 5,233 677 1,630 4.0 3.4 544 2,270 3.1 6.0 France 30,100 66,864 7,930 17,115 8,235 28,316 5.4 7.8 6,027 16,755 ... 3.9 5.2 Gabon 17 137 ...... ... .. 17_ . ..7 . 0.7 0.2 96 178 6.5 8.2 Gambia, The 22 80...... 18 22 27.2 9.6 1.... 16 0.6 5.7 Germany' 11,122 15,837 22,473 77,517 6,566 16,418 2.9 2.8 20,599 45,536 9.1 8.1 Ghana .. ........... 40 325..... 321 266 0.1 16.1 27 .... 22 2.3 0.8 Greece 4,796 10,246 1,374.... 1,765 1,734 3,800 21.3 .... 25:6.6... 190. . 1,325 1.7 5.2 Guatemala ....1..........466.... 576 178 331 183 325 10.6 -102.2.. 183... 119 9.3 2.8 Guinea 95 . .5 0 . 2. 2.8 Guinea-Bissau . . :: ' Haiti 138 152 .. . 65 80 21.3 36.6 41 37 8.5 4.6 Honduras 122 257 . 165 27 120 2.9 5.5 31 62 2 7 2.5 366 1999 World Developmant Indicators 6.14 International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % of % Of thousands thousands $ mnillions exports $ millions imports 1980 1997 1.980 1997 1980 1997 1.980 1.997 1980 1997 1980 1997 Hungary 9,413 17,248 5,164 12,173 160 2,570 2.5 10.5 88 1,153 0.9 4.6 India 1,194 2,374 1,017 3,500 1,150 3,152 10.2 7.1 113 1,342 0.7 2.3 Indonesia 527 5,036 635 2,200 246 6,589 -1.2 10.4 375 2,436 30O 3.9 Iran, Islamic Rep ...... 156 ..580 428 1,354 54 248 0.4 1.3 1,700 258 10 63 1 5 Iraq 1,222 346 443 .. 170 13 Ireland 2,258 5,540 669 3,053 472 3,250 4.9 5.3 742 2,223. 62. .4.3 Israel 1,116 2,003 513 2,755 903 2,800 10.4 9.2 533 3.570 .4 6 9.2 Italy 22,087 34,087 23,994 18,039 8,213 30,000 8.4 9.7 1,907 16.000 1.7 6.1 Jamaica 395 1,192 .. . 242 1,131 17.7 35.6 12 181 0.9 4.5 Japan 844 4,223 5,224 16,803 644 4,322 0.4 0.9 4,593 33,041..... 2.9 7.7 Jordan 393 1,127 720 1,233 431 760 36.5 195.5 301 398 12.5 7.0 Kazakhstan . . .. .. 445 . 5.4 Kenya 372 700 . 350 220 400 11.0 13.4 33. 194 1.2 5.1 Korea, Rep. ......... 976 3,908 436 4,542 369 5,200 .1.9 3.2 350 6,262 .. 1.4 3.7 Kuwait 108 35 230 . 7 4 . . 1,339 2,558 136 9. KyrgyzRepublic . 13~...... .I ..... 4 . ...... 4.... 06......... ... - ... ....... 4.... 0.5..... Lao.PDR 115 . . . 54 . 12.9 . 21 ..2.9 Latv a 98 . . 2,013 . 183 64 326 9 7 Lebanon 558 .. 1,000 64.2 Lesotho 73 112 . 12 20 13.3 65 8 8 17.7 0.7 L .Ibya . ...... ... 126. 94.. 95.... 185.....10....6. 0 ...0. 470.. 215.3.7 Lithuania ....... ...... 288 .. 2,981 . 360 . 6.9 290 . 4.6 Macedonia, FYR Madagascar 13 84 . 35 5 67 1.0 8.7 31 48 2.9 4~5 Malawi 46 .. 250 9 7 2.9 1. 10 17 2.1 1.5 Malaysia 2,105 6,211 1,738 26,165 265 3,850 1.9 4.1 470 2,478 3 5 2.7 Mali 27 102..... 15_ 21 5.7 3.3 20... 42 3.8 4.7 Mauritania . .7 1 . . 7 2 . . Mauritius 123 536 33 132 45 459 7.8 180.0. 27 177 . 3.9 . .....6.4 Mexico 11,945 19,351 3,322 8,910 5,393 7,593 23.8 6.2 4,174 3,892 15.1 3.2 Moldova 33 . 7.. 34 33.3 . 4 . 3.4 Mongolia 195 82 22 .. .. 2 ...4.-4 . 21 . 3.9 Morocco 1,425 3,115 578 1,203 397 1,200 . 12.3 12.6 98 315 1.9 3 0 Mozambique . . .. Myanmar .....I .38. . 185 .. . .10....... 32 ..1.9 2.2 325 0.4 .... .1.0 Namibia ..... .... ... . :..... 410: ..... 210 ... 12.2 .... . .99 5.2 Nepal 163 418 23 110 45 119 20.1 9.2 26 103 7.1 5 6 Netherlands 2,784 6,674 6,749 10,200 1,668 6,597 1.8 3.1 4,664 10,232 5.1 5.4 New Zealand 465 1,615 454 1.132 211 2,510 3 3 14 1 534 1,451 ... 77.7 7.9 Nicaragua.. 350 . 330 22 80 4.4 9.3 . 65 4 0 Niger 20 1810 3 18 0.5 6.0 18 24 1!9 54... Nigeria............. 86 611.. .. 6148 86 0.2 0.5 780 1,816 ... 3~9.9. . 12.8 Norway 1,252 2,702 246 3,120 751 2,497 2.8 4.0 1,310 4,496 5.5 ... 8.6 O m an. ~.... ..... ...... ... 60..... 375... .. . ... ...... . ....... ... . ...... ....108 .. .. . .. 1. . 4. - ...... ... I.. 47 . ... 0.8.. ... Pakistan 299 351 104 . 154 117 5.2 1.2 90 364 1.63 2.5 Panama 392 402 113 200 167 374 4.9 4.5 56 164 1.7 1.9 Papua New Guinea 40 66 . 63 12 72 1.2 2.9 18 81 1. 3.0 Paraguay 302 387 . 369 91 759 13.0 17.5 35 193 2.7 3.9 Peru 373 635 127 577 208 682 4.5 8.2 107 485 2.7 4.5 Philippines 1.008 2.223 461 1.930 320 2,831 4.4 7.0 105 1,936 1.1 3.8 Poland 5,664 19,514 6,852 48,610 282 8,700 1.8 21.9 357 6,900 2.0 14.9 Portugal 2,730 10,100 . 2,425 1,147 4,264 17.2 13.2 290 2,164 2 9 5 3 Puerto Rico 1,639 3,332 2,758 1,325 619 1,996 .400 9 Romania . 2,741 1,711 6,243 . 55056 . 9 . 5.5 Russian Federation . 15,350 . 11,182 . 6,669 6.5 . 10,40-1 11.5 1999 World Development Indicators 367 6.14 International tourism International tourism receipts International tourism expenditures Inbound tourists Outbound tourists % of % of thousands thousands $ millions exports $ millions imports 1980 1997 1980 .1997 ±980 1997 1980 1997 1980 ±.997 ±980 1997 Rwanda 30 1 . . 4 1± 2.4 0.7 11 71 3.4 14.6 Saudi Arabia 2,475 3,594 .. . 1,344 1,420 1.3 2.2 2,453 44.4........... Senegal.186 300 . 68 160 8-4 10. 45 77 37 45 Sierra Leone 46 48 . 10 10 3.6 10.9 8 2 1.7 1.4 Singapore 2,562 6,542 . 3,671 1,433 7,993 5.9 5.1 322 6,139 1.3 4.3 Slovak Republic . 808 . 405349 . 439 3.5 Slovenia . 974 1,275 12.2. 544 . 5.1 South Africa 700 5,530 572 3,080 652 2,297 2.3 6.5 756 1,947 3.4 5.6 Spain 22,388 43,378 18,022 12,313 6,968 26,595 2.1.7 17.9 1,229 4,467 3.2 3.1 Sri Lanka 322 366 138 531 111 212 8.6 3.8 34 180 1.5 2.7 Sudan 25 64 . 200 52 8 6.7 1.3 74 34 3.9 2.1 Sweden 1,366 2,386 2,94:1 10,818 962 3,785 2.5 3.7 .1,235 6,579 3.1 7.8 Switzerland 8,873 11,077 4,451 12,100 3,149 7,960 6.5 7.3 2,357 6,731 4.5 6.9 Syrian Arab Republic 1.239 842 1,189 1,900 156 1,250 ... 6 3 . 22.1 177 545 3.9 10.7 Tajikistan . ... Tanzania 84 350 . - 150 20 360 2.7 30.0 20 407 1.4 20.7 Thailand 1,859 7,263 497 1,660 867 8,700 10 9 12.0 244 1,888 2.4 2.6 Togo 92 92 . . 13 13 2.4 2.1 22 9 32 2.6 Trinidad and Tobago 199 324 206 250 151 108 4.8 4.0 140 75 5.8 2.6 Tunisia 1,602 4,263 478 1,381 601 1,540 ... 18.4 19.1 55 160 15...5... 1.9 Turkey 921 9,040 1,795 4,633 327 7,000 9.0 13.5 115 1,363 14.4 2.4 Turkmenistan . - 238 . 31 70.9 125 . 12.5 Uganda 36 227.. . 5 103 1.5 12:5.5 . 18... ... 137 ....4..1 ......8.3 Ukraine . 818 . . .. 205 . 1:0 305 . 1.4 United Arab Emirates 300 1,792 .. . . . .. . United Kingdom 12,420 25,960 15,507 45,550 6,932 20,569 4.7 5.6 6,893 27,710 5.1 7.5 United States 22,500 48,409 22,721 52,735 10,058 75,056 3.7 8.0 10,385 54,183 3.6 5.2 Uruguay 1,067 2,316 640 562 298 759 19.5 17.8 203 264 9.5 5.9 Uzbekistan Venezuela 215 796 747 460 243 1,063 1.2 4.2 1,880 2,381 12.4 13.0 Vietnam . ' 1,716 . . 88 0:8 ... . .... -......... West Bank and Gaza .. . . Yeien.Rep.39 84 . . 24 69 . 2.7 53 8 2.7 Yugoslavia, FR (Serb./Mont.) . 298 . . .. 42 Zambia 87 278 .................20.......65......1.2.........5.1.........57. ....59...... 32.......40 1 2 Zimbabwe 243 1,894 326.. 123 38 250 2.4 8.2 ......140 ...... 120 8.1 3.1 lil.0:' * *.w :. i. - ~ * S * SOL-EAIM IQ. I. Low Income 4,319 14,114 1,486 6,647 2,323 6,618 3.3 4.8 2,153 6,424 2.7 4.0 Middle Income 63,181 216,194 33,531 169,443 20,120 117,766 4.7 8.2 20,333 72,353 5.7 5.5 Lower middle income 21,887 90,333 9,782 51,045 7,070 60,180 6.1 8.1 5,356 36,096 4.7 5.3 Upper middle income 41,294 125,861 23,749 118,398 13,050 57,586 4.3 8.3 14,977 36,257 6.2 5.7 Low & middle Income 67,500 230,308 35,017 176.090 22,443 124,384 4.5 7.9 22,486 78,777 5.2 5.3 East Asia & Pacific 9,570 49,784 3,339 37,471 2,480 37,017 5.1 7.0 1,234 19,147 2.4 4.2 Europe & Central Asia 17,935 91,125 15,522 89,019 1,029 33,835 3.0 10.3 560 25,962 7.4 Lati Ameica& Caib. 22,766 48,213 9,907 27,212 11,262 .. 31,629 9.2 8.0 11,338 21,257 8.7 5.5 L atin. .. .. A m eric . .. . . . . . .. . . . . . .. . . . . . .. . ..a. .. . . . . . .. . . . . .. . . . . ..&I . . . . . .. . . . . . .. . . . . ..arib. . .. . . . . . .. . . . . .. . . . . . .. . . . . .. . . . . .. . . . . . . Middle East & N. Africa 11,815 22.229 4,620 12,173 4,589 12,388 2.5 7.8 6,366 3,750 5.0 South Asia 2,086 4,068 1,259 5,040 1,485 3,936 8,5 5.9 283 2,198 1.0 2.4 Sub-Saharan Africa 3,328 14,889 370 5,175 1,598 5,579 1.9 5.5 2,705 6,463 3.7 6.2 High Income 193,391 381,320 123,635 359,421 78,573 319,879 4.6 5.8 79,580 296.730 4.6 6.2 Europe EMU 112,095 207,544 68,933 165,816 42,198 135,757 5.8 6.6 42,121 118,914 5.5 6.4 a. Data prior to 1990 rarer to the Federal Republit of Germany before unification. 388 1999 World Development Indicators 6.14 The data in the table are from the World Tourism makes several trips to a country during a given period * International inbound tourists are the number of Organization. They are obtained primarily from ques- is counted each time as a new arrival. visitors who travel to a country other than that where tionnaires sent to government offices, supplemented Regional and income group aggregates are based they have their usual residence for a period not with data published by official sources. Although the on the World Bank's classification of countries and exceeding 12 months and whose main purpose in vis- World Tourism Organization reports that progress has differ from those shown in the Yearbook of Tourism iting is other than an activity remunerated from within been made in harmonizing definitions and measure- Statistics. Countries not shown in the table but for the country visited. * International outbound ment units, differences in national practices still pre- which data are available are included in the regional tourists are the number of departures that people vent full international comparability. and income group totals. World totals are calculated make from their country of usual residence to any Data on international inbound and outbound by the World Tourism Organization and include all other country for any purpose other than a remuner- tourists refer to the number of arrivals and depar- reporting countries as well as countries not sepa- ated activity in the country visited. * International tures of visitors within the reference period, not to rately reported on by the organization. Thus world tourism receipts are expenditures by international the number of people traveling. Thus a person who totals may differ from the sums of the group totals. inbound visitors, including payments to national car- riers for international transport. These receipts "MrIM = 0 M. - should include any other prepayment made for goods Countries with tourism receipts accounting for more than 15 percent of exports in 1997 or services received in the destination country. They also may include receipts from same-day visitors, -- oir ...c-nr- Ou.. n] i er? (Ei except in cases where these are so important as to e.C justify a separate classification. Their share in exports is calculated as a ratio to exports of goods eo ,; and services. * International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carri- , t1 ! Na i ; ers for international transport. These may include r7'. 'i* l t L , expenditures by residents traveling abroad as same- G, . i .- e. 5 -~ N g S g i j | X i ;-i important as to justify a separate classification. Their : es '> '; .P ^4> S; ,sS *sfi - qs ,,,,s +? share in imports is calculated as a ratio to imports of goods and services. Data sources SowcFl. Wo'rliTu;o rantl: an-3 mwia., Bria; z31, Ce-.,31Es - ' . The visitor and expenditure data are available in the World Tourism Organization's Year- book of Tourism Statistics and Compendium of Tourism Sta- tistics, 1992-1996. Data in the table were updated from - .- _ .electronic files provided by the World Tourism Organization. Export and import data are from the International Monetary Fund's Intemational Financial Statistics and World Bank staff estimates. 1999 World Development Indicators 369 S .4' l I Statistscaf methods This section describes some of the statistical procedures used in prepar- some cases, another indicator as a weight. The aggregate ratios are ing the World Development Indicators. It covers the methods employed based on available data, including data for economies not shown in the for calculating regional and income group aggregates and for calculating main tables. Missing values are assumed to have the same average growth rates, and it describes the World Bank's Atlas method for deriv- value as the available data. No aggregate is calculated if missing data ing the conversion factor used to estimate GNP and GNP per capita in account for more than one-third of the value of weights in the benchmark U.S. dollars. Other statistical procedures and calculations are described year. In a few cases the aggregate ratio may be computed as the ratio in the About the data sections that follow each table. of group totals after imputing values for missing data according to the above rules for computing totals. Aggregation rules Aggregate growth rates are also generally calculated as a weighted Aggregates based on the World Bank's regional and income classifica- average of growth rates (and indicated by a w). In a few cases growth tion of economies appear at the end of most tables. The World Bank's rates may be computed from time series of group totals. Growth rates income and regional classifications are shown on the front and back are not calculated if more than one-third of the observations in a period cover flaps of the book. This year's World Development Indicators are missing. For further discussion of methods of computing growth includes an aggregate of the member countries of the European rates see below. Monetary Union (EMU). Members of the EMU on I January 1999 were Aggregates denoted by an m are medians of the values shown in the Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, table. No value is shown if more than one-third of the observations are the Netherlands, Spain, and Portugal. Other classifications, such as the missing. European Union and regional trading blocs, are documented in About the Exceptions to the rules occur throughout the book. Depending on the data for the tables in which they appear. judgment of World Bank analysts, the aggregates may be based on as Because of missing data, aggregations for groups of economies should little as 50 percent of the available data. In other cases, where missing betreated as approximationsfor unknowntotals oraveragevalues. Regional or excluded values are judged to be small or irrelevant, aggregates are and income group aggregates are based on the largest available set of data, based only on the data shown in the tables. including values for the 148 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules Growth rates are intended to yield estimates for a consistent set of economies from one Growth rates are calculated as annual averages and represented as period to the next and for all indicators. Small differences between the val- percentages. Except where noted, growth rates of values are computed ues of sums of subgroup aggregates and overall totals and averages may from constant-price or real-value series. Three principal methods are occur because of the approximations used. In addition, compilation errors used to calculate growth rates: least squares, exponential endpoint, and data reporting practices may cause discrepancies in theoretically iden- and geometric endpoint. Rates of change from one period to the next tical aggregates such as world exports and world imports. are calculated as proportional changes from the earlier period. There are four principal methods of aggregation. For group and world totals denoted in the tables by a t, missing data are imputed based on Least-squares growth rate. Least-squares growth rates are used wher- the relationship of the sum of available data to the total in the year of ever there is a sufficiently long time series to permit a reliable calcula- the previous estimate. The imputation process works forward and back- tion. No growth rate is calculated if more than half the observations in ward from 1995. Missing values in 1995 are imputed using one of sev- a period are missing. eral proxy variables for which complete data are available in that year. The least-squares growth rate, r, is estimated by fitting a linear The imputed value is calculated so that it (or its proxy) bears the same regression trendline to the logarithmic annual values of the variable in relationship to the total of available data as it did in the benchmark year. the relevant period. More specifically, the regression equation takes the Imputed values are usually not calculated if missing data account for form more than one-third of the total in the benchmark year. The variables In X, = a + bt, used as proxies are GNP in U.S. dollars, GNP per capita in U.S. dollars, total population, exports and imports of goods and services in U.S. dol- which is equivalent to the logarithmic transformation of the compound lars, and value added in agriculture, industry, manufacturing, and ser- growth equation, vices in local currency. Xt = XO (1 + r)t. Aggregates marked by an s are sums of available data. Missing values are notimputed. Sums are notcomputed if morethan one-third of the obser- In this equation, X is the variable, t is time, and a = log XO and b= vations in the series or a proxy for the series are missing in a given year. In (1 + r) are parameters to be estimated. If b* is the least-squares Aggregates of ratios are generally calculated as weighted averages estimate of b, the average annual growth rate, r, is obtained as of the ratios (indicated by w) using the value of the denominator or, in [exp(b*) - 1] and is multiplied by 100 to express it as a percentage. 1999 World Development Indicators 371 The calculated growth rate is an average rate that is representative exchange rates for each currency change. The SDR deflator is calcu- of the available observations over the entire period. It does not neces- lated in SDR terms first and then converted to U.S. dollars using the sarily match the actual growth rate between any two periods. SDR to dollar Atlas conversion factor. This three-year averaging smooths annual fluctuations in prices and Exponential growth rate. The growth rate between two points in time for exchange rates for each country. The Atlas conversion factor is then certain demographic indicators, notably labor force and population, is cal- applied to a country's GNR The resulting GNP in U.S. dollars is divided culated from the equation by the midyear population for the latest of the three years to derive GNP r= ln(p0/p1)/n, per capita. When official exchange rates are deemed to be unreliable or unrepresentative of the effective exchange rate during a period, an alter- where pn and pi are the last and first observations in the period, n is the native estimate of the exchange rate is used in the Atlas formula (see number of years in the period, and In is the natural logarithm operator. below). This growth rate is based on a model of continuous, exponential The following formulas describe the calculation of the Atlas conver- growth between two points in time. It does not take into account the inter- sion factor for year t: mediate values of the series. Note also that the exponential growth rate does not correspond to the annual rate of change measured at a one- year interval, which is given by (p, - _p)/p,n1. 3[ (Pt-2 S-2) + e -1 / + e 3 Pt-2 Pt-2 )Pt-i Pts$j Geometric growth rate. The geometric growth rate is applicable to com- pound growth over discrete periods, such as the payment and reinvest- ment of interest or dividends. Although continuous growth, as modeled and the calculation of GNP per capita in U.S. dollars for year t: by the exponential growth rate, may be more realistic, most economic phenomena are measured only at intervals, a case in which the com- Yt$= (YI/N,)/e, pound growth model is appropriate. The average growth rate over n peri- ods is calculated as where et* is the Atlas conversion factor (national currencyto the U.S. dol- lar) for year t, et is the average annual exchange rate (national currency r= exp[ln(pr/p1)/n] - 1. to the U.S. dollar) for year t, p, is the GNP deflator for year t, ps$ is the SDR deflator in U.S. dollar terms for year t, y,$ is the Atlas GNP in U.S. Like the exponential growth rate, it does not take into account inter- dollars in year t, Y, is current GNP (local currency) for year t, and N, is mediate values of the series. midyear population for year t. World Bank Atlas method Alternative conversion factors In calculating GNP and GNP per capita in U.S. dollars for certain opera- The World Bank systematically assesses the appropriateness of official tional purposes, the World Bank uses a synthetic exchange rate com- exchange rates as conversion factors. An alternative conversion factor is monly called the Atlas conversion factor. The purpose of the Atlas used when the official exchange rate is judged to diverge by an excep- conversion factor is to reduce the impact of exchange rate fluctuations tionally large margin from the rate effectively applied to domestic trans- in the cross-country comparison of national incomes. actions of foreign currencies and traded products. This applies to only a The Atlas conversion factor for any year is the average of a coun- small number of countries as shown in Primary data documentation. try's exchange rate (or alternative conversion factor) for that year and Alternative conversion factors are used in the Atlas methodology and its exchange rates for the two preceding years, after adjusting for dif- elsewhere in the World Development Indicators as single-year conversion ferences between the rate of inflation in the country and the G-5 coun- factors. tries (France, Germany, Japan, the United Kingdom, and the United States). A country's inflation rate is measured by its GNP deflator. The inflation rate for G-5 countries is measured by changes in the SDR deflator. (Special drawing rights, or SDRs, are the IMF's unit of account.) The SDR deflator is calculated as a weighted average of the G-5 countries' GDP deflators in SDR terms. The weights are determined by the amount of each currency included in one SDR unit. Weights vary over time because both the composition of the SDR and the relative 372 1999 World Development Indicators P-rimary data documentation The World Bank is not a primary data collection agency for most areas other than living standards surveys and debt. As a major user of socio- economic data, however, the World Bank places particular emphasis on data documentation to inform users of data in economic analysis and pol- icymaking. The tables in this section provide information on the sources, treatment, and currentness of the principal demographic, economic, and environmental indicators in the World Development Indicators. Differences in the methods and conventions used by the primary data collectors-usually national statistical agencies, central banks, and customs services-may give rise to significant discrepancies over time both among and within countries. Delays in reporting data and the use of old surveys as the base for current estimates may severely compro- mise the quality of national data. Although data quality is improving in some countries, many devel- oping countries lack the resources to train and maintain the skilled staff and obtain the equipment needed to measure and report demographic, economic, and environmental trends in an accurate and timely way. The World Bank recognizes the need for reliable data to measure living stan- dards, track and evaluate economic trends, and plan and monitor devel- opment projects. Thus, working with bilateral and other multilateral agencies, it has funded and participated in technical assistance projects to improve statistical organization and basic data methods, collection, and dissemination. The World Bank is working at several levels to meet the challenge of improving the quality of the data that it collates and disseminates. At the country level the Bank is carrying out technical assistance, training, and survey activities-with a view to strengthening national capacity- in the following areas: * Poverty assessments in most borrower member countries. * Living standards measurement and other household and farm sur- veys with country partner statistical agencies. * National accounts and inflation. * Price and expenditure surveys for the International Comparison Programme. * Statistical improvement projects in the countries of the former Soviet Union. * External debt management. * Environmental and economic accounting. 1999 World Development Indicators 373 National currency Fiscal National accounts Balance of Government IMF year payments finance special end and trade data dissemi-li nation Balance SNA Alternative PPP of Payments System Reporting Bane price conversion surrey Manual External Of Accounting period year valuation factor year in ace debt trade concept Albania Albanian 1ek Dec. 31 CY 1993 VAP 1996-97 BPIV1 Actual G Algeria Algerian diner Dec. 31 CY 1980 VAB BPM5 Actual S Angola Angolan adjuated kwanza Dec. 31 CY 1997 VAP 1986-97 BPM4 Actual S Argentina Argentine peso Dec. 31 CY 1986 VAP 1972-81 1980 BPM5 Actual S C S* Armenia Armenian dram Dec. 31 CY 1993 VAB 1985-95 1990 BPM5 Actual Australia Australian dollar Jun. 30 FY 1989 VAP 1993 BPMS G C S Austri'a Austrian schillinga Dec. 31 CY 1983 VAP 1993 BPM5 S C S Azerbaijan Azeri manat Dec. 31 CY 1995 VAP 1987-97 1990 BPM4 Preliminar Bangladesh Bangladesh taka Jun. 30 'Iy l990b VAP 1971-97 1993 BPM5 Actual G Belarus Belarussian rubel Dec. 31 CY 1990 VAB 1987-97 1993 BPMV5 Actual C Belgium Belgian franca Dec. 31 CY 1990 VAP 1993 BPM5 S C S Benin CFA franc Dec. 31 CY 1985 VAP 1992 1993 BPM4 Actual S Bolivia Boliviano Dec. 31 CY 1990 VAP 1985 1980 BPM4 Actual S C Bosnia and Herzegovina Convertible mark Dec. 31 CY 1987 VAP BPM5 Preliminary Botswana Botswana pula Jun. 30 FY 1986 VAP 1975-97 1993 BPM5 Actual G B Brazil Brazilian real Dec. 31 CY 1995 VAB 1980 BPM5 Preliminary S C . Bulgari'a Bulgarian lava Dec. 31 CY 1990 VAP 1985-92 1993 BPM5 Preliminary G C Burkina Faso CFA franc Dec. 31 CY 1985 VAB 1992-93 BPM4 Actual S C Burundi Burundi franc Dec. 31 CY 1980 VAB BPM5 Actual S Cambodia Cambodian riel Dec. 31 CY 1989 VAP 1987-93 BPM5 Preliminary S Cameroon CFA franc Jun. 30 FY 1980 VAP 1970-97 1993 BPM5 Preliminary S C Canada Canadian dollar Mar. 31 CY 1992 VAB 1993 BPM5 G C S Central African Republic CFA franc Dec. 31 CY 1987 VAB BPM4 Preliminary S Chad CFA franc Dec. 31 CY 1995 VAB BPM4 Preliminary S C Chile Chilean peso Dec. 31 CY 1986 VAP 1993-96 1980 BPM5 Actual S C S China Chinese yuan Dec. 31 CY 1990 VAP 1987-93 1986 BPM5 Estimate G B Hong Kong, China Hong Kong dollar Dec. 31 CY 1990 VAB 1993 BPM4 G S Colombia Colombian peso Dec. 31 CY 1975 VAP 1991-94 1980 BPM5 Estimate S C S* Congo, Dem. Rep. New zaire Dec. 31 CY 1987 VAP 1993-97 BPM5 Estimate S C Congo, Rep. CFA franc Dec. 31 CY 1978 VAP 1993 1993 BPM4 Estimate S Costa Rica Costa Rican colon Dec. 31 CY 1966 VAP 1980 BPM4 Estimate S C C6te dIlvoire CFA franc Dec. 31 CY 1986 VAP 1993 BPM5 Estimate S C Croatia Croatian kuna Dec. 31 CY 1994 VAB 1993 BPM5 Preliminary C 5* Cuba Cuban peso Dec. 31 CY -. .S Czech Republic Czech koruna Dec. 31 CY 1984 VAP 1993 BPM5 Preliminary G C S Denmark Danish krone Dec. 31 CY 1990 VAB 1993 BPM5 G C S Dominican Republic Dominican peso Dec. 31 CY 1970 VAP 1980 BPM5 Actual G C Ecuador Ecuadorian sucre Dec. 31 CY 1975 VAP 1980 BPM5 Actual G B S Egypt ,Arab Rep. Egyptian pound Jun. 30 FY 1992 VAB 1970-91 1993 BPM5 Actual S C El Salvador Salvadoran col6n Dec. 31 CY 1990 VAP 1982-90 1980 BPM5 Actual S B S Eritrea Eritrean nakfa Dec. 31 CY 1992 VAB BPM4 Actual Estonia Estonian kroon Dec. 31 CY 1995 VAB 1987.-95 1993 BIPM5 Actual C S Ethiopia Ethiopian birr Jul. 7 FY 1981 VAB 1989-97 1985 BPM5 Actual G B Finland Finnish markkaa Dec. 31 CY 1990 VAR 1993 BPM5 G C S France French franc' Dec. 31 CY 1980 VAP 1993 BPM5 S C 5 Gabon CFA franc Dec. 31 CY 1991 VAP 1993 1993 BPM5 Actual S B Gambia, The Gambian dalasi Jun. 30 CY 1987 VAB BPM5 Estimate G B Georgia Georgian sari Dec. 31 CY 1994 VAB 1990-97 1990 BPM4 Actual.. Germany Deutsche mark' Dec. 31 CY 1991 VAP 1993 BPM5 S C S Ghana Ghanaian cedi Dec. 31 CY 1975 VAP 1973-87 BPM5 Actual G C Greece Greek drachma Dec. 31 CY 1990 VAB 1993 BPM4 Estimate S C Guatemala Guatemalan quetzal Dec. 31 CY 1958 VAP 1985-86 1980 BPM5 Actual S B Guinea Guinean franc Dec. 31 CY 1994 VAB 1993 BPMS Estimate S C Guinea-Bissau CFA franc Dec. 31 CY. 1986 VAP 1970-86 BPM5 Actual S Haiti Haitian gourde Sep. 30 FY 1976 VAP 1991-97 BPM5 Preliminary G Honduras Honduran lempire Dec. 31 CY 1978 VAB 1988-89 1980 BPM5 Actual S 374 1999 World Development Indicators Latest Latest household or Vital Latest Latest Latest Latest Latest population demographic survey registration agricultural industrial water survey of survey of census complete census data withdrawal scientists expenditure data and for R&D engineers engaged in R&D Albania 1989 LSMS, 1996 V1995 1993 1970 Algeria .1987 PAPCHILD, 1992 1973 1993 1990 Angola 1901964-65 1987 Argentina . 1991 /1988 1993 1976 1995 1995 Armenia 1989 /1991 1994 Australia 1996 / 1990 1992 1985 1994 1994 Austria 1991 i 990 1994 1991 1993 1995 AzerbaUjan 1989 /1995 Bpangaesh . 199-1 OHS, 1996-97 1977 1992 1987 1993 1993 Belarus .1989 /1994 1990 1995 1995 Belgium .1991 /1990 1994 1980 1991 1991 Benin 1992 OHS, 1996 1992 1981 1994 1989 1989 Bolivia 1992 DHS, 1997 1994 1987 1991 1991 Bosnia and Herzegovina 1991 /1991 Botswana 1991 OHS, 1988 1993 1994 1992 Brazil 1991 OHS, 1996 1996 1992 1990 1995 1995 Bylgaria 1992 LSMS, 1995 / 1994 1988 1996 1996 Burkina Faso 1996 OHS, 1998 1993 1983 1992 Burundi 1990 1991 1987 1989 1989 Cambodia 1998 1987 Cameroon 1987 OHS, 1997 1972-73 1994 1987 Canada 1991 / 1991 1993 1991 1993 1995 Central African Republic 1988 OHS, 1994-95 1992 1987 1990 1990 Chad 1993 OHS, 1996-97 1987 Chile 1992 / 1997 1993 1975 1995 1995 China 1990 Population, 1995 1996 1994 1980 1995 1995 Hong Kong, China 1996 /1993 1995 1995 Colombia 1993 OHS, 1995 1988 1992 1987 1982 1982 Congo, Oem. Rep. 1984 1990 1990 Cono .e.1984 1986 1998 1987 1984 1984 Costa Rica 1984 CDC, 1993 / 1973 1994 1970 1996 1991 CMe dIlvoire 1998 OHS, 1997 1974-75 1993 1987 Croatia 1991 /1992 1995 1995 Cuba 1981 /1989 1975 1995 1995 Czech Republic 1991 CDC, 1993 /1990 1993 1991 1995 1995 Denmark 1991 / 1989 1991 1990 1993 1993 Dominican Republic 1993 OHS, 1996 1971 1985 1987 Ecuador 1990 LSMS, 1995 1997 1994 1987 1990 1990 Egypt, Arab Rep. 1996 OHS, 1997 / 1989-90 1992 1993 1991 1995 El Salvador 1992 CDC, 1994 1970-71 1994 1975 1992 1992 Eritree 1984 OHS, 1995 Estonia 1989 /1994 1995 1995 1995 Ethiopia 1994 Family and fertility. 1990 1989-92 1990 1987 Finland 1990 1 1990 1994 1991 1995 1995 France 1990 Income, 1989 / 1988 1994 1990 1994 1994 Gabon 1993 1974-7 1982 1987 1987 1986 Gambia, The 1993 1982 1982 Georgia 1989 /1992 1990 1991 1991 Germany /1993 1993 1991 1993 1993 Ghana 1984 OHS, 1993 1984 1987 1970 Greece 1991 11993 1993 1980 1993 1993 Guatemala 1994 OHS, 1995 1 1979 1990 1970 1988 1988 Guinea 1996 SDA, 1994-95 1989 1987 1984 1984 Guinea-Bissau 1991 SDA, 1991 1988 1991 Haiti 1982 OHS, 1994-95 1971 1987 Honduras 1988 CDC, 1994 1993 1994 1992 1999 World Development Indicators 375 National currency Fiscal National accounts Balance of Government IMF year payments finance special end and trade dda dfissemI- nation Balance SNA Alternative PPP of Payrnents System Reporting Base price conversion -survey Manual External off Accounting .period year valuaaton factor year in use debt trade concept Hungary Hungarian forint Dec. 31 CY 1994 VAB 1993 BPM5 Actual G C S India Indian rupee Mar. 31 FY 1980 VAB 1985 BPM5 Preliminary G C S Indonesia Indonesian rupiah Mar. 31 CY 1993 yAP 1993 BPM5 Preliminary S C S* Iran, Islamic Rep. Iranian rial Mar. 20 FY 1982 VAB 1980-97 1993 BPM5 Actual S C Iraq Iraqi dinar Dec. 31 CY 1969 VAB S Ireland Irish pound' Dec. 31 CY 1990 VAB 1993 BPM5 G C S Israel Israeli new shekel Dec. 31 CY 1995 VAB 1980 BIPM4 S C S Italy Italian lira' Dec. 31 CY 1990 VAP 1993 BPM5 S C S Jamaica Jamaica dollar Dec. 31 CY 1986 VAP 1.995-97 1993 BPM5 Preliminary G Japan Japanese yen Mar. 31 CY 1990 VAP 1993 BPM5 G C S Jordan Jordan dinar Dec. 31 CY 1985 VAB 1993 BPM5 Actual G B Kazakhstan Kazakh tenge Dec. 31 CY 1993 VAB 1987-95 1990 BPM5 Actual Kenya Kenya shilling Jun. 30 CY 1982 VAB 1993 BPM5 Preliminary G B Korea, Dam. Rep. Democratic Republic of Korea won Dec. 31 CY 1990 VA? BPM5 Korea, Rep. Korean won Dec. 31 CY 1990 VA? 1993 BIPM5 Estimate S C S Kuwait Kuwaiti dinar Jun. 30 CY 1984 VA? BPM5 S C Kyrgyz Republic Kyrgyz som Dec. 31 CY 1995 VAB 1985-95 1990 BPM5 Actual Lao PDR Lao kip Dec. 31 CY 1990 VAB 1965-89 BPM5 Preliminary Latvia Latvian let Dec. 31 CY 1993 VAB 1979 93 BM ctua Lebanon Lebanese pound Dec. 31 CY 1994 VAB 1993 BPM4 Actual G Lesotho Lesotho loti Mar. 31 CY 1980 VAB BPM5 Actual G C Libya Libyan dinar Dec. 31 CY 1975 VAB 1993 BPM5 G Lithuani'a Lithuanian litas Dec. 31 CY 1993 VAB 1987-95 BPM5 Preliminary C S Macedonia, FYR Macedonian denar Dec. 31 CY 1996 VAP 1992-97 BPM5 Preliminary Madagascar Malagasy franc Dec. 31 CY 1984 VAB 1993 BPM5 Actual S C Malawi Malawi kwacha Mar. 31 CY 1978 VAB 1985 BPM5 Estimate G B Malaysia Malaysian ringgit Dec. 31 CY 1978 VAP 1993 BPM4 Preliminary G C S Mali CFA franc Dec. 31 CY 1987 VAB 1985 BPM4 Actual S Mauritania Mauritanian ouguiya Dec. 31 CY 1985 VAB BPM4 Actual S Mauritius Mauritian rupee Jun. 30 CY 1992 VAB 1985 BPM5 Actual G C Mexico Mexican new peso Dec. 31 CY 1993b VAp BPM5 Actual G C S* Moldova Moldovan leu Dec. 31 CY 1996 VAB 1987-95 1993 BPM5 Actual Mongolia Mongolian tugrik Dec. 31 CY 1986 VAP 1993 BPM5 Preliminary C Morocco Moroccan dirham Dec. 31 CY. 1980 VAP .1985 BPM5 Preliminary S C Mozambique Mozambican metical Dec. 31 CY 1995 VAB 19.92-95 EIPM5 Preliminary S Myanmar Myanmar kyat Mar. 31 FV 1985 VA? BPM5 Actual G C Namibi'a Namibia dollar Mar. 31 CY 1990 VAB EBPM5 Esti'mate C Nepal Nepalese rupee Jul. 14 FY 1985 VAB 1973-97 1993 BPM5 Actual G C Netherlands Netherlands guildera Dec. 31 CY 1990 VAP 1993 BPM5 S C S New Zealand New Zealand dollar Mar. 31 FV 1990 VA? 1993 BPM4 G B Nicaragua Nicaraguan gold cordoba Dec. 31 CY 1980 VAP 1970-93 EBPM5 Actual G C Niger CFA franc Dec. 31 CV 1987 VAP 1993 BPM5 Actual S Nigeria Nigerian naira Dec. 31 CV 1987 VAB 1971-97 1993 BPM5 Estimate G Norway Norwegian krone Dec. 31 CV 1990 VAP 1993 BPM5 G C S Oman Rial Omani Dec. 31 CV 1978 VAP 1993 BPM5 Actual G B Pakistan Pakistan rupee Jun. 30 FV 1981 VAB 1972-97 1980 BPMS Actual G C Panama Panamanian balboa Dec. 31 CV 1982 VAB BPM5 Actual S C Papua New Guinea Papua New Guinea k(ina Dec. 31 CV 1983 VAP 1980 BPM5 Actual G B Paraguay Paraguayan guarani Dec. 31 CV 1982 VAP 1982-86 1980 BPM5 Actual S C Peru Peruvian new aol Dec. 31 CV 1979 VA? 1986-91 1993 BPM5 Actual S C 5* Philippines Philippine peso Dec. 31 CV 1985 VA? 1993 BPM5 Actual G B S Poland Polish zloty Dec. 31 CV 1990 VAP 1978 1993 BPM5 Actual G C S Portugal Portuguese escudoa Dec. 31 CV 1990 VA? 1993 BPM5 S C S Puerto Rico U.S. dollar Dec. 31 CY 1954 VA? 1993 Romania Romanian leu Dec. 31 CV 1993 VAB 1987-96 1993 BPM5 Estimate G C Russian Federation Russian ruble Dec. 31 CV 1996 VAB 1989-94 199 BPM Estimate C 376 1999 World Developrnent Indicators Latest Latest household or Vital Latest Latest Latest Latest Latest population demographic survey registration agricultural industfial water survey of survey of census complete census data withdrawal scientists expenditure data and for R&D engineers enigaged in R&D Hungary 1990 Income, 1995 / 1994 1994 1991 1995 1995 India 1991 National family health, 1992-93 1986 1992 1975 1994 1994 Indonesia 1990 DHS, 1997 1993 1994 1987 1995 1995 iran, Islamic Rep. 1991 Demographic, 1995 1988 1993 1993 1994 1994 Iraq 1997 1981 1992 1970 1993 1993 Ireland 1996 / 1991 1994 1980 1993 1993 Israel 1995 /1983 1993 1989 1992 1992 Italy 1991 / 1990 1991 1990 1994 1994 Jamaica 1991 LSMS, 1994 /1979 1992 1975 1986 1986 Japan 1995 /1990 1993 1990 1994 1994 Jordan 1994 OHS. 1997 1994 1993 1986 1989 Kazakhstan 1989 DHS, 1995 V1 1993 Kenya 1989 DHS, 1998 1981 1994 1990 Korea, Darn. Rep. 1993 1987 Korea, Rep. 1951991 1994 1994 1994 1994 Kuwait 1995 / 1970 1993 1974 1984 1984 Kyrgyz Republic 1989 DHS, 1997 / 1994 1994 1995 Lao PDR 1995 1987 Latvia 1989 /1994 1994 1994 1995 1995 Lebanon 1970 1970 1994 1980 1980 Lesotho 1996 DHS, 1991 1989-90 1994 1987 Libya 1995 PAPCHILD, 1995 1987 1989 1994 1980 1980 Lithuania 1989 *' 1994 1994 1993 1996 1996 Macedonia, FYR 1994 /1994 1995 1995 Madagascar 193DS 981984 1988 1984 1994 1995 Malawi 1987 OHS. 1996 1981 1989 1994 Malaysia 1991 / 1993 1975 1992 1992 Mall 1987 OHS, 1995-96 1978 1981 1987 Mauritania 1988 PAPCHILD, 1990 .. 1985 1985 Mauriti'us 1990 COC, 1991 /1993 1974 1992 1992 Mexico ~~~~~~1990 Population, 1995 1991 1991 1991 1995 1995 Moldova 1989 / 1992 1995 1995 Mongolia 1991994 1987 1995 1995 Morocco 1994 OHS, 1995 1962 1994 1992 Mozambique 1997 OHS, 1997 1992 Myanmar 1931993 1993 1987 Namibia 1991 OHS, 1992 1960 1991 Nepal 1991 OHS, 1996 1992 1993 1987 1980 1980 Netherlands 1991 / 1989 1993 1991 1991 1994 New Zealand 1996 / 1990 1992 1991 1993 1993 Nicaragua 1995 LSMS, 1993 1963 1985 1975 1987 1987 Niger 1988 OHS, 1997 1980 1982 1988 Nigeria 1991 Consumption and expenditure, 1992 1960 1990 1987 1987 1987 Norway 1990 / 1989 1992 1985 1995 1995 Oman 1993 Child health, 1989 1979 1993 1991 Pakistan 1998 LSMS, 1991 1990 1990 1991 1990 1987 Panama 1.990 .1990 1992 1975 1986 1986 Papua New Guinea 1989 OHS, 1996 1989 1987 Paraguay . 1992 OHS, 1990; CDC, 1992 1991 1981 1987 Peru 1993 OHS, 1996 1994 1992 1987 1994 1995 Philippines 1995 OHS, 1998 1991 1992 1975 1992 1992 Poland 1988 /1990 1994 1991 1995 1995 Portugal 1991 /1989 1994 1990 1995 1995 Puerto RICO 1990 /1987 1994 Romania 1992 LSMS, 1994-95 / 1993 1994 1994 1995 Russian Federation 1989 LSMS, 1992 / 1994-95 .1994 1994 1995 1995 1999 World Development Indicators 377 National currency Fiscal National accounts Balance of Government IMF year payments finance special end and trade data dissemi- nation Balance SNA Alternative PPP of Payments System Reporting Base price conversion survey Marcsal External of Accounting period year valuation factor year is ase debt trade concept Rwanda Rwanda franc Dec. 31 CY 1985 VAB 1994 1985 BPM5 Actual G C Saudi Arabia Saudi Arabian riyal Hijri year Hijri year 1970 VAP BPM4 Estimate S Senegal CFA franc Dec. 31 CY 1987 VAP 1992-93 1993 BPM5 Preliminary S Sierra Leone Sierra Leonean leone Jun. 30 CY 1990 VAB 1974-79 1993 BPM5 Estimate G 8 Singapore Singapore dollar Mar. 31 CY 1990 VAP 1993 BPM5 G C 55 Slovak Republic Slovak koruna Dec. 31 CY 1993 VAP 1993 BPS Preliminary.S Slovenia Slovenian tolar Dec. 31 CY 1993 VAB 1993 BPM5 Actual S South Africa South African rand Mar. 31 CY 1990 VAB BPMS Estimate C S* Spai'n Spanish peseta' Dec. 31 CY 1986 VAP 1993 BPM5 S C S Sri Lanka Sri Lanka rupee Dec. 31 CY 1982 VAB 1985 BPM5 Actual G C Sudan Sudanese pounds Jun. 30 CY 1982 VAB 1980-91 BPM4 Estimate G Sweden Swedish krona Jun. 30 ..CY 1990 VAB 1993 BPM5 G C S Switzerland Swiss franc Dec. 3.1 CY 1990 VAP 1993 BPM5 Estimate S C S* Syrian Arab Republic Syrian pound Dec. 31 CY 1985 VAP 1970-97 1993 EBPM5 Estimate S C Tajikistan Tajik ruble Dec. 31 CY 1993 VAP 1987-97 BPM4 Actual Tanzania Tanzania shilling Jun. 30 CY 1992 VAB 1981-94 1993 BPM5 Actual G Thailand Thai baht Sep. 30 CY 1988 VAP 1993 BPM5 Preliminary G C S* Togo CFA franc Dec. 31 CY 1978 VAP 1983 BPME Actual S Trinidad and Tobago Trinidad and Tobago.dollar Dec,.31 CY 1985. VAB 1992 BPM5 Preliminary S Tunisia Tunisian dinar Dec. 31 CY 1990 VAP 1993 BPM5 Preliminary G C Turkey Turkish fira Dec. 31 CY( 1994 VAB 1993 BPM5 Actual S C S Turkmeniatan Turkmen manat Dec. 31 CY 1987 VAP 1990-97 1990 BPM5 Actual Uganda Uganda shilling Jun. 30 FY 1991 VAB 1980-97 BPM4 Estimate G Ukraine Ukraine hryvnia Dec. 31 CY 1990 VAB 1987-95 1993 BPM5 Actual United Arab Emirates U.A.E,.di.rham Dec. 31 CY 1985 VAB BPM4 G B United Kingdom Pound sterling Dec. 31 CY 1990 VAB 1993 BPM5 G C S United States U.S. dollar Sep. 30 CY 1992 VAP 1993 BPM5 G C S Uruguay Uruguayan peso Dec. 31 CY 1983 VAP 1980 BPM5 Actual S C Uzbekistan Uzbek sum Dec. 31 CY 1987 VAB 1991-96 1990 BPM4 Actual Venezuela Venezuelan bolfvar Dec. 31 CY 1984 VAP 1980 BPM5 Estimate G C Vietnam Vietnamese dong Dec. 31 .CY 1989 VP 1991-97 .1993 BPM4 Estimate West Bank and Gaza Israeli new shekel Dec. 31 CY ..VAP Yemen. Rep. Yemen rial Dec. 31 CY 1990 VAB 1990-97 1993 BPMS Estimate G C Yugoslavia, FR (Serb./Mont.) Yugoslav new dinar Dec. 31 CY 1984 VAP 1985 Estimate S Zambia Zambian kwacha Dec. 31 CY 1994 VAP 1990-92 1993 BPM5 Preliminary G C Zimbabwe Zimbabwe dollar Jun. 30 CY 1990 VAB 1993 BPM5 Actual G C Note: For explanation of the abbreviations used in the table see the notes. a. European Monetary Union member currency linked to tbe euro. b. Country uses the 1993 System of National Accounts methodology. 378 1999 World Development Indicators Latest Latest household or Vital Latest Latest Latest Latest Latest population demographic survey registration agricultural industrial water survey of survey of census complete cenisus data withdrawal scientists expenditure data and for R&D engineers engaged In R&D Rwanda 1991 DHS, 1992 1984 1986 1993 1995 1995 Saudi Arabia 1992 Maternal and child health, 1993 1983 1989 1992 Senegal 1988 DHS. -1997 1960 1994 1987 1981 1981 Sierra Leone 1985 SHEHEA, 1989-90 1985 1993 1987 Singapore 1990 General household. 1995 /1994 1975 1995 1995 Slovak Republic 1991 /1994 1991 1995 1995 Sloveni'a 1991 /1991 1994 1995 1995 South Africa 1996 DHS, 1997 1992 1990 1993 1993 Spain 1991 / 1989 1992 1991 1994 1994 Sri Lanka 1981 OHS, 1993 /1982 1993 1970 1985 1985 Su'dan, 1993 OHS, 1989-90 1995 Sweden 1990 / 1981 1994 1991 1993 1993 Switzerland 1990 /1990 1994 1991 1990 1990 Syrian Arab Republic 1994 PAPCHILD, 1995 1981 1992 1993 Tajiki:stan 1989 /1994 1994 1992 1992 Tanzania 1988 OHS, 1996 1995 1988 1994 Thailand 1990 OHS, 1987 1988 1991 1987 1995 1995 Togo 1981 OHS, 1998 1983 1984 1987 Tri'nidad and Tobago 1990 OHS, 1987 / 1982 1993 1975 1984 1984 Tunisi'a 1994 PAPCHILD, 1994-95 1961 1993 1990 1992 1992 Turkey 1997 OHS, 1993 1991 1993 1992 1995 1995 Turkmenistan 1995 / 1994 Uganda 1991 OHS, 1995 1991 1989 1970 Ukraine 1991 / 1992 1995 1995 United Arab Emirates 1995 1985 1995 United Kingdom 1991 / 1993 1994 1991 1993 1993 United States 1990 Current population, 1996 / 1987 1994 1990 1993 1995 Uruguay 1996 1990 1993 1965 1987 1987 Uzbekistan 1989 OHS, 1996 / 1994 1992 1992 Venezuela 1990 LSMS, 1993 ,/ 1997-98 1993 1970 1992 1992 Vietnam 1989 OHS, 1997 1994 1992 1985 1985 West Bank and Gaza 1997 Demographic, 1995 1971 Yemen, Rep. 1994 OHS, 1997 1982-85 1990 Yugoslavia, FR (Serb./Mont.) 1991 /1981 1994 1995 1995 Zambia 1990 OHS., 1996 1990 1994 1994 Zimbabwe 1992 OHS, 1994 1960 1993 1987 1999 World Development Indicators 379 * Fiscal year end is the date of the end of the fiscal for tables 4.11, 4.12, and 5.6 for further details, or are seeking access to international capital markets year for the central government. Fiscal years for other * Balance of Payments Manual in use refers to the in providing economic and financial data to the public. levels of government and the reporting years for sta- classification system used for compiling and reporting The SDDS is expected to enhance the availability of tistical surveys may differ, but if a country is desig- data on balance of payments items in table 4.17. timely and comprehensive data and therefore to con- natedasafiscalyearreporterinthefollowingcolumn, BPM4 referstothe fourth edition of the MF's Balance tribute to the pursuit of sound macroeconomic poli- the date shown is the end of its national accounts of Payments Manual (1977), and BPM5 to the fifth edi- cies; it is also expected to contribute to the improved reporting period. * Reporting period for national tion (1993). Since 1995 the IMF has adjusted all bal- functioning of financial markets. Although subscription accounts and balance of payments data is designated ance of payments data to BPM5 conventions, but isvoluntary,itcommitsthesubscribertoobservingthe as either calendar year basis (CY) or fiscal year (FY). some countriescontinueto report usingthe oldersys- standard andto providing informationtothe IMFabout Most economies report their national accounts and tem. * External debt shows debt reporting status for its practices in disseminating economic and financial balance of payments data using calendar years, but 1997 data. Actua/ indicates data are as reported, pre- data. * Latest population census shows the most some use fiscal years, which straddle two calendar liminary indicates data are preliminary and include an recent year in which a census was conducted. years. In the World Development Indicators fiscal year element of staff estimation, and estimate indicates * Latest household or demographic survey gives data are assigned to the calendar year that contains data are staff estimates. * System of trade refers to information on the surveys used in compiling house- the larger share of the fiscal year. If a country's fiscal the general trade system (G) or the special trade sys- hold and demographic data presented in section 2. year ends before June 30, the data are shown in the tem (S). For imports under the general trade system, PAPCHILD is the Pan Arab Project for Child first year of the fiscal period; if the fiscal year ends on both goods entering directly for domestic consumption Development, DHS is Demographic and Health Survey, or after June 30, the data are shown in the second year and goods entered into customs storage are recorded, WFS is World Fertility Study, LSMS is Living Standards of the period. Saudi Arabia follows a lunar year whose at the time of their first arrival, as imports; under the Measurement Study, SDA is Social Dimensions of starting and ending dates change with respect to the special trade system goods are recorded as imports Adjustment, CDC is Centers for Disease Control and solar year. Because the International Monetary Fund when declared for domestic consumption whether at Prevention, and SHEHEA is Survey of Household (IMF) reports most balance of payments data on a cal- time of entry or on withdrawal from customs storage. Expenditure and Household Economic Activities. endar year basis, balance of payments data for fiscal Exports under the general system comprise outward- * Vital registration complete identifies countries year reporters in the World Development Indicators are moving goods: (a) national goods wholly or partly pro- judged to have complete registries of vital (birth and based on fiscal year estimates provided by World Bank duced in the country: (b) foreign goods, neither death) statistics by the United Nations Department of staff. These estimates may differ from IMF data but transformed nor declared for domestic consumption in Economic and Social Information and Policy Analysis, allow consistent comparisons between national the country, that move outward from customs storage; Statistical Division, and reported in Population and accounts and balance of payments data. * Base year and (c) nationalized goods that have been declared Vital Statistics Reports. Countries with complete vital is the year used as the base period for constant price from domestic consumption and move outward with- statistics registries may have more accurate and more calculations in the country's national accounts. Price out having been transformed. Under the special sys- timely demographic indicators. * Latest agricultural indexes derived from national accounts aggregates, tem of trade exports comprise categories (a) and (c). census shows the most recent year in which an agri- such as the GDP deflator, express the price level rela- In some compilations categories (b) and (c) are clas- cultural census was conducted and reported to the tive to prices in the base year. Constant price data sified as re-exports. Direct transit trade, consisting of Food and Agriculture Organization. * Latest industrial reported in the World Development Indicators are goods entering or leaving for transport purposes only, data refer to the most recent year for which manufac- rescaled to a common 1995 reference year. See About is excluded from both import and export statistics. See turing value added data at the three-digit level of the the data for table 4.1 for further discussion. * SNA About the data for tables 4.5 and 4.6 for further dis- International Standard Industrial Classification (revi- price valuation shows whether value added in the cussion. * Government finance accounting concept sion 2 or 3) are available in the UNIDO database. national accounts is reported at basic or producers' describes the accounting basis for reporting central * Latest water withdrawal data refer to the most prices (VAB) or at purchasers' prices (VAP). government financial data. For most countries govern- recent year for which data have been compiled from a Purchasers' prices include the value of taxes levied on ment finance data have been consolidated (C) into one variety of sources. See About the datafor table 3.5 for value added and collected from consumers and thus set of accounts capturing all the central government's more information. * Latest surveys of scientists and tend to overstate the actual value added in production. fiscal activities. Budgetary central government engineers engaged in R&D and expenditure for R&D SeeAboutthedatafortable4.2forfurtherdiscussion accounts (B) exclude central government units. See refer to the most recentyearfor which data are avail- of national accounts valuation. * Alternative conver- Aboutthedatafortable4.13forfurtherdetails. * IMF able from a data collection effort by UNESCO in sci- sion factor identifies the countries and years for which special data dissemination shows the countries that ence and technology and research and development a World Bank-estimated conversion factor has been subscribe to the International Monetary Fund's (IMF) (R&D). SeeAboutthedatafortable 5.12formore infor- used in place of the official (IFS line rf) exchange rate. Special Data Dissemination Standard (SDDS). S refers mation. See Statistical methods for further discussion of the to countries that subscribe; S* indicates subscribers use of alternative conversion factors. * PPP survey that have posted data on the Internet. (Posted data year refers to the latest available survey year for the can be reached through the IMF Dissemination International Comparison Programme's estimates of Standard Bulletin Board at http://dsbb.imf.org/). The purchasing power parities (PPPs). See About the data IMFestablished the SDDS to guide members that have 380 1999 World Development indicators Acronyms and abbreviations gbO biochemical oxygen demand ACDA Arms Control and Disarmament Agency btu British therm'al units ADB Asian Development Bank CFC chiorofluorocarbon AfDB African Development Bank c.i.f- cost nsrance ad freight APC Asia-Pacific Economic Cooperation co, carbon dioxide. CDC Centers for Disease Control and Prevention CPI consumer price index CDIAC Carbon Dioxide Information Analysis Center cu. m cubic meter EC Comission of the EturopenComninty DHS . demographic and health survey DAC Development Assistance Committee DMTU drmerctnui EEBRD European Bank for Rec'onstruction and Development OPT diphthe'ria, pertussis, and tetanus EDF European Develo'pment Fund isAF Enhanced Structural Adjustment Facility EFTA European Free Trade Area FIDI foreign' di'rect investment' EIB Euro pe'a n Invest'ment bank f.o.b. free on board EmU European Monet'ary Union FYR former YugoslavReulcEroeaUnn GDO 'gross domestic prod'uc't EUROSTAT Statistical Ofice of the Eu'ro'pean Community GEMS Gobal Enviro'n ment Monitoring Sy'ste'm FAO Food and Agriculture Organizati'on GIS 'geographic information sy'stem G-5 Franc'e. Germany', Japan, United Kingdom, and United States GNI gr'oss 'national income G-7 G-5 plus Canada and Ital GNP gro'ss'national product GATT Gene'ral'Agreement on Ta'rif'fs and Trade ha hectare GEF Global Environment Facility HIC high-income countries IBRD International Bank for Reconstruction and Development HIV human immunodeficiency virus ICAO International Civ'l Aviation Organisation ICD . international classification of diseases ICCO International Cocoa Organization ICRG International Count'ry'Risk Guide.ICO International Coffee Organization HOSE interba~tionba absifcation.of Statue in Employment lCP Intentoa oparison Programme IMR infant mortality rate ICSE International Classification of Status in Employment ISCED Internati:onal Standard Clas'sifi'cation of Education' IDA International Development Association isic International Standard Industrial Classification D13B inter-American Dev'elopment Bank kg kilogram.lIEA Internationa nryAec km kilometer IFAC Intern'ational Feceration of Accountants kwh kilowatt-hour IF6 International Finance Corporation LiBiOR Londo'n interbank offered rate IFCI International'Fin'ance Corporatio'n I'nvestable MO6 currency and coins (monetary base).110 Inter'national Labour Org'anisation Ml narrow 'money (currency and demand deposits iMF In'ternational Monetar'y Fund M2 money plus quasi money IRF International Road Federto M3 . broadi m'oney or liquid liabilities ITu International .Telecommunication U'ni'on MIIFA Multifibre Arrangement IUCN world Conse'rva'tion Union mmbtu millions of British thermal units LME London M'etals Exchange . mt metric ton MIGA MulItilateral Investment Guarantee Agency Muv manu'factures unit value NAFTA North American Free Trade Agre'em'ent NEAP national environmental action plan NATO North Atlantic Treaty Organization NG0 nongovernme'nt'al organization' OECD Organisation for Economic Co-operation and Developme'nt NTBs nontariff barriers PAHO Pan American Health Organization ODA official develo'pment assistan'ce UIN United Nations' PC personal computer UNAIDS Joint United Nations Programme on HIV/AiDS PP' purcha'sing power parity U.NCED United Nation's Conference on Environment and Development R&D researc'h and development UNCHS United Na'tio'ns Center for'Hu'man Settlements SAF Structural Adjustment Facility UNCTAD United Nations Conference on Trade and Development SDR s'pecial drawing .rig'ht UNDP Unit'ed Nations Development Programm-e SITO Standard Interna-tional Trade Classification UNECE Unit'ed Nations Economic Commission for Europe SNA U.N. System of Natio'nal Accounts UNEP Unite'd Nations Envir'onment Progr'am'me SOPEMI Contin'uous Reporting Syste. on Migration UNESC'O United Natio'ns Educational, Scientific, and Cultural Organ~ization SO, Sulfur dioxide UNFPA: United Nations Population Fund sq. km square kilometer UNICEF United Nations Children's Fund STD sex'uall'y transmitted disease UNIDO United Nations Industrial Development Organization TB. .tu'berculosis' UNRISD United Nations Research Institute for Social Development f~P tota'l factor produc'tivity UNSD United Nations Statistical Division ton-km metric ton-kilomneters USAID U.S. Agency for International Development TSP total suspended particula'tes WCMC World'Conservation ..Monitoring Cent're UN5MR chilId (under'-5') m'ortality r'at'e WFP Wo~rld Food Pr'ogramme . WHO ~ Wo rld Health Organizat!on WIPO World Intellectual Property Organization WRI World Resources Insti'tute WTO W~o-rld Trade Oirga'nization WWF World Wide Fund for Nature 1999 World Development Indicators 381 Credits This book has drawn on a wide range of World Bank Indicators thematic group under the leadership of Ahmad and Yonas Biru (structure of consumption and reports and numerous external sources. These are Eduard Bos and Dena Ringold facilitated the data relative prices in PPP terms), and Soong Sup Lee and listed in the bibliography that follows this section. review by the Bank's regional staff. Comments and K. M. Vijayalakshmi (balance of payments and OECD Many people inside and outside the World Bank suggestions were received from Michael Walton, K. national accounts). The national accounts and bal- helped in writing and producing the World Sarwar Lateef, and Eric Swanson at various stages of ance of payments data for low- and middle-income Development Indicators. This note identifies those the production. economies are gathered from the World Bank's who made specific contributions. Numerous others, regional staffthrough the annual Unified Survey under too many to acknowledge here, helped in many ways 3. Environment the direction of Monica Singh and Mona Fetouh. Maja for which the team is extremely grateful. was prepared by M. H. Saeed Ordoubadi in partner- Bresslauer, Raquel Fok, Mona Fetouh, Soong Sup ship with the World Bank's Environmentally and Lee. and Monica Singh worked on updating, estimat- 1. World view Socially Sustainable Development Network and in col- ing, and validating the databases for national was prepared by the members of the WDI team. K. laboration with the World Bank's Development accounts and balance of payments. The national Sarwar Lateef wrote the introduction with contribu- Research Group and Transportation, Water. and accounts data for OECD countries were processed by tions from Sulekha Patel and Eric Swanson; Masako Urban Development Department. Eric Rodenburg and Mehdi Akhlaghi and Soong Sup Lee; Demet Kaya pre- Hiraga and Asieh Kehyari assisted in the preparation Robin White of the World Resources Institute, Laura pared estimates for 1997. The external debt tables oftables and figures. The introduction drew heavilyon Battlebury of the World Conservation Monitoring were prepared by the Financial Data Team, led by East Asia: The Road to Recovery (World Bank 1998b) Centre, and Christine Auclair of the UNCHS Urban Punam Chuhan, and reviewed by Ibrahim Levent and and Global Economic Prospects 1998/99 (World Indicators Programme made important contributions. Gloria Reyes; Shelley Fu provided systems support. Bank 1999a). Crucial ideas and suggestions were pro- Demet Kaya assisted with research and data prepa- vided by Martin Ravallion and Michael Walton. Lant ration. John Dixon, Kirk Hamilton, and Michael Ward 5. States and markets Pritchett, Martin Ravallion, Shaohua Chen, Branko provided invaluable comments and guidance. The was prepared by David Cieslikowski in partnership Milanovic, and Giovanna Prennushi provided valuable Environment Department devoted substantial staff with the World Bank's Finance, Private Sector, and inputs on the introduction. Substantial assistance in resources to the book, for which we are very grateful. Infrastructure Network, the Poverty Reduction and preparing the data for this section was received from John Dixon and Kirk Hamilton wrote the introduction Economic Management Network, and the Inter- Sultan Ahmad and Yonas Biru, who prepared the esti- to the section with contributions from Lisa national Finance Corporation. Lavan Sujarittanonta mates of GNP in PPP terms. Segnestam. Other contributions were made by Derek helped prepare the data for this section, David Byerlee (land use and agriculture). Susmita Dasgupta, Cieslikowski and Swaminathan Aiyar drafted the intro- 2. People Craig Meisner, and David Wheeler (water pollution), duction to this section with substantial inputs from was prepared bySulekha Patel in partnership with the and Charles Di Leva and Junko Funahashi (govern- Victoria Elliot, Daniel Kaufmann, Philip Keefer, Homi World Bank's Human Development Network, the ment commitment). The team received valuable com- Kharas, Cheryl Gray, Richard Messick, and Michael Development Research Group, and the Gender Anchor ments at various stages from Christine Kessides, Stevens. Other contributors included Carol Gabyzon of the Poverty Reduction and Economic Management William Denning, Eduard Bos. Carl Richard Bartone, (privatization data), Graeme Littler (stock markets), Network. Substantial help in preparing the data for John Vernon Henderson, Lawrence Hannah, and Colin Sultan Ahmad and Yonas Biru (relative prices and PPP this section came from UNESCO's Division of Gannon. conversion factors), Asli Demirgoc-Kunt and Gerard Statistics, the WHO's Department of Reproductive Caprio (financial sector), Yann Burtin and Bjorn Health and Research, the Global Programme for 4. Economy Wellenius, and Maria Concetta Gasbarro and Michael Evidence and Information for Policy, the ACC Sub- was prepared by K. M. Vijayalakshmi and Eric Minges of the ITU (communications and information), Committee on Nutrition, and UNAIDS. Masako Hiraga Swanson in close collaboration with the Macro- and William Denning, Colin Gannon, Ian Heggie. Marc assisted with data and table preparation. economic Data Team of the Development Data Group, Juhel, Christine Kessides, and Louis Thompson Consultations on content and tables were held with led by Robin Lynch. Swaminathan Aiyar prepared the (transport). counterparts from the ILO, WHO, UNICEF, and introduction to this section with advice from Milan UNESCO. Sulekha Patel wrote the introduction, with Brahmbhatt and K. Sarwar Lateef. Valuable sugges- 6. Global links valuable comments and suggestions from Jee-Peng tions were received from the members of the Bank's was prepared by Eric Swanson with assistance from Tan and Jeffrey Waite. Inputs to the section were pro- Economic Policy sector board: Homi Kharas, director, Lavan Sujarittanonta. Swaminathan Aiyar prepared vided by Eduard Bos (demography), Amit Dar, Monica and Deepak Bhattasaii. Wei Ding, Hafez Ghanem, the introduction with valuable input from Milan Fong, Dena Ringold, Zafiris Tzannatos, Martin Rama, Eliana Cardoso, Daniela Gressani, and Zoubida Brahmbhatt. Substantial help in preparingthe data for and Raquel Artecona (labor force and employment), Allaoua. Special acknowledgment is made to the thissectioncamefromAzitaAmjadi, MickRiordan,Aki Shaohua Chen and Martin Ravallion (poverty and Global Economic Prospectsteam, led byUri Dadush- Kuwahara of UNCTAD, and Jerzy Rozanski (tariffs), income distribution), Lianqin Wang, Harry Patrinos, especially Robert Lynn and Mick Riordan for use of Betty Dow (commodity prices), Shelly Fu, Ibrahim Ayesha Vawda, Mitesh Thakkar, and Clemetina Acedo their projections. Substantial contributions to the sec- Levent, and Gloria Reyes (financial data), Bill Shaw (education), and Martha Ainsworth, Masako Hiraga, tion were provided by Robin Lynch and Michael Ward and Malvina Pollock (official finance and aid), and and Eduard Bos (health and nutrition). The HNP (national accounts), Azita Amjadi (trade), Sultan Celine Thoreau of the OECD (migration). We wish to 382 1999 World Development Indicators acknowledge the considerable assistance of Jean- Publishing and dissemination Louis Grolleau of the OECD, who provided data on aid The Office of the Publisher, under the direction of Dirk flows, and Augusto Huescar and Rosa Songel of the Koehler, provided valuable assistance throughout the World Tourism Organization. production process. Jamila Abdeighani and Carolyn Knapp coordinated production, and Alan Donovan and Other parts Maya Brahmam supervised marketing and distribution. The maps on the inside covers were prepared by the Lawrence MacDonald of Development Economics and World Bank's Map Design Unit. The Partners section Geoffrey Bergen and Phillip Hay of External Affairs man- was coordinated and edited by Eric Swanson. The aged the communications strategy, and the regional Users guide was prepared by David Cieslikowski. operations group headed by Paul Mitchell helped coor- Primary data documentation was coordinated by K. M. dinate the overseas release. Helen Meade of the Vijayalakshmi. who served as database administra- General Services Department coordinated the printing. tor. Statistical methods was written by Eric Swanson. Acronyms and abbreviations was prepared by Estela The Atlas Zamora. The index was collated by Richard Fix and Eric Production was managed by Richard Fix with guidance Swanson. from David Cieslikowski and Elizabeth Crayford. The preparation of data benefited from the work on corre- Systems support sponding sections in the World Development Mehdi Akhlaghi was responsible for database man- Indicators. William Prince assisted with systems sup- agement and programming and overall systems sup- port and production of tables and graphs. Greg G. port. Reza Farivari and Tariqul Khan provided valuable Prakas and Jeffrey Lecksell from the World Bank's systems support. Demet Kaya provided assistance in Map Design Unit coordinated map production. updating the databases. World Development Indicators CD-ROM Administrative assistance and office technology Design, programming, and testing were carried out by support Reza Farivari and his team: Mehdi Akhlaghi, Azita Estela Zamora provided administrative assistance. Amjadi, Ying Chi, Elizabeth Crayford, Yusuf Harun, and She was supported by Karen Adams, Premi Rathan Angelo Kostopoulos. Ming-Kuen Lin producedthe social Raj, and AlexanderWilliams. Office technology support indicators tables; Aslam Ansari and Liangin Wang pro- was provided by Nacer Megherbi and Shahin Outadi. duced the education tables. William Prince coordinated production and provided quality assurance. Design, production, and editing Richard Fix coordinated all aspects of production with Client feedback the Communications Development Incorporated team We are also grateful to the many people who took the led by Terry Fischer. Bruce Ross-Larson edited the trouble to complete our questionnaire. Their com- chapter introductions and provided overall direction to ments helped us to improve this year's edition. the design and planning process. We would like to thank the design team of Peter Grundy and Tilly Northedge, the production team led by Laurel Morais and including Garrett Cruce, Megan Klose, Suzanne Luft, and Donna McGreevy, and the editing team led by Alison Strong and including Wendy Guyette, Daphne Levitas, and Alison Smith. Client services The Development Data Group's Client Services Team, led by Elizabeth Crayford, contributed to the design and planning of the books and helped coordinate work with the Office of the Publisher. The help of John Raines in responding to numerous client queries over the past year is gratefully acknowledged. 1999 World Development Indicators 383 Bibliography ACDA (Arms Control and Disarmament Agency). Bos, Eduard, My T. Vu, Ernest Massiah, and Reproductive Health. Washington, D.C.: 1997. World Military Expenditures and Arms Rodolfo Bulatao. 1994. World Population Population Action International. Transfers 1996. Washington, D.C. Projections 1994-95. Baltimore, Md.: Johns Council of Europe. Various years. Recent Ahmad, Sultan. 1992. 'Regression Estimates of Hopkins University Press. Demographic Developments in Europe and North Per Capita GDP Based on Purchasing Power Bourguignon, Francois, and Christian Morrison. America. Strasbourg: Council of Europe Press. Parities." Policy Research Working Paper 956. 1992. Adjustment and Equity in Developing Currency Data & Intelligence, Inc. Various issues. World Bank, International Economics Countnes. Development Centre Study. Paris: Global Currency Report. Brooklyn, N.Y. Department, Washington, D.C. Organisation for Economic Co-operation and Dasgupta, Partha. 1993. An Inquiry into Well-Being - . 1994. 'Improving Inter-Spatial and Inter- Development. and Destitution. Oxford: Clarendon Press. Temporal Comparability of National Accounts.' Braga, C.A. Primo, and Alexander Yeats. 1992. Dasgupta, Partha, and Martin Weale. 1992. 'On Joumal of Development Economics 44:53-75. "How Minilateral Trading Arrangements May Measuring the Quality of Life." World Ahuja, Vinod, Benu Bidani, Francisco Ferrera, and Affect the Post-Uruguay Round World." World Development 20:119-31. Michael Walton. 1997. Everyone's Miracle: Bank, International Economics Department, Davis, Lester. 1982. Technology Intensity of U.S. Revisiting Poverty and Inequality in East Asia. A Washington, D.C. Output and Trade. Washington, D.C.: U.S. Directions in Development book. Washington, Brown, Lester R., Christopher Flavin, Hilary F. Department of Commerce. D.C.: World Bank. French, and others. 1998. State of the Word Demery, Lionel, and Michael Walton. 1997. "Are American Automobile Manufacturers 1998. Washington, D.C.: Worldwatch Institute. Poverty and Social Targets for the 21st Century Association. 1997. World Motor Vehicle Brown, Lester R., Michael Renner, Christopher Attainable?" Paper presented at a Data. Detroit, Mich. Flavin, and others. 1998. Vital Signs1998. Development Assistance Committee- Anker, Richard. 1998. Gender and Jobs: Sex Washington, D.C.: Worldwatch Institute. Development Centre seminar. Organisation for Segregation of Occupations in the World. Bulatao, Rodolfo A. 1998. The Value of Family Economic Co-operation and Development, 4-5 Geneva: International Labour Office. Planning Programs in Developing Countries. December, Paris. Armington, Paul, and Yuri Dikhanov. 1996. Santa Monica, Calif.: RAND. Demirguc-Kunt, Ashl, and Enrica Detragiache. "Multivariate Normalization of Infrastructure (e.g. Calola, Marcello. 1995. A Manual for Country 1997. "The Determinants of Banking Crises: Roads) for Comparative Purposes." World Bank, Economists. Training Series 1, vol. 1. Evidence from Developed and Developing International Economics Department, Washington, D.C.: International Monetary Fund. Countries." Working paper. World Bank and Washington, D.C. Caprio, Gerard, Jr., and Daniela Klingebiel. 1997. International Monetary Fund, Washington, D.C. Asian Development Bank. 1995. "Governance: "Bank Insolvency: Bad Luck, Bad Policy, or Bad Demirguc-Kunt, Ashl, and Ross Levine. 1996a. Sound Development Management." 12151-95. Banking?" In Michael Bruno and Boris Pleskovic, 'Stock Markets. Corporate Finance, and 17 August. Washington. D.C. eds., Proceedings of the Annual World Bank Economic Growth: An Overview." The World Bank Azariadis, Costas, and Allen Drazen. 1990. Conference on Development Economics 1996. Economic Review 10(2):223-39. "Threshold Externalities in Economic Washington, D.C.: World Bank. . 1996b. "Stock Market Development and Development.' Quarterly Journal of Economics Cassen, Robert, and associates. 1986. Does Aid Financial Intermediaries: Stylized Facts.' The 105(2):501-26. Work? Report to Intergovernmental Task Force World Bank Economic Review 10(2):291-321. Ball, Nicole. 1984. "Measuring Third World Security on Concessional Rows. Oxford: Clarendon Dixon, John, and Paul Sherman. 1990. Economics Expenditure: A Research Note." World Press. of Protected Areas: A New Look at Benefits and Development 12(2):157-64. Centro Latinoamericano de Demografia. Various Costs. Washington, D.C.: Island Press. Barro, Robert J. 1991. "Economic Growth in a years. Boletin Demogr6fico. Santiago, Chile. DKT International. 1998. 1997 Contraceptive Cross-Section of Countries." Quarterly Journal of Chamie, Joseph. 1994. "Demography: Population Social Marketing Statistics. Washington, D.C. Economics 106(2):407-44. Databases in Development Analysis." Joumal of Drucker, Peter F. 1994. 'The Age of Social Becker, Gary. 1964. Human Capital: A Theoretical Development Economics 44:131-46. Transformation." Atlantic Monthly 274 (November). and Empirical Analysis, with Special Reference Chellaraj, Gnanaraj, Olusoji Adeyl, Alexander S. Edwards, Sebastian. 1995. Crisis and Reform in to Education. General Series 30. New York: Preker, and Ellen Goldstein. 1996. Trends in Latin America: From Despair to Hope. New York: Columbia University Press. Health Status, Services, and Finance: The Oxford University Press. Behrman, Jere R., and Mark R. Rosenzweig. 1994. Transition in Central and Eastem Europe. Vol. 2. Euromoney. 1998. September. London. "Caveat Emptor: Cross-Country Data on World Bank Technical Paper 348. Washington, Eurostat (Statistical Office of the European Education and the Labor Force," Journal of D.C. Communities). Various years. Demographic Development Economics 44:147-71. Coale, A.J., and P. Demeny. 1983. Regional Model Statistics. Luxembourg. Bhattasali, Deepak. 1998. "Economic Outcomes Life Tables and Stable Populations. Princeton, . Various years. Statistical Yearbook. and Policies in Sub-Saharan Africa." World Bank, N.J.: Princeton University Press. Luxembourg. Africa Region, Economic Management and Conly, Shanti R., and Joanne E. Epp. 1997. Falling Evenson, Robert E., and Carl E. Pray. 1994. Social Policy Unit, Washington, D.C. Short: The World Bank's Role in Population and "Measuring Food Production (with reference to 384 1999 World Development Indicators South Asia)." Joumal of Development Center for Development Information and Systems. A Directions in Development book. Economics 44:173-97. Evaluation, Washington. D.C. Washington, D.C.: World Bank. Faiz, Asif, Christopher S. Weaver, and Michael P. Frankel, Jeffrey. 1993. "Quantifying International Haggarty, Luke, and Mary M. Shirley. 1997. "A Walsh. 1996. Air Pollution from Motor Vehicles: Capital Mobility in the 1990s." In Jeffrey New Data Base on State-Owned Enterprises." Standards and Technologies for Controlling Frankel, ed., On Exchange Rates. Cambridge, The World Bank Economic Review Emissions. Washington, D.C.: World Bank. Mass.: MIT Press. 11(3):491-513. Fallon, Peter, and Zafiris Tzannatos. 1998. Child Frankel, Jeffrey, and Andrew K. Rose. 1996. Happe, Nancy, and John Wakeman-Linn. 1994. Labor: Issues and Directions for the World Bank. "Currency Crashes in Emerging Markets: An "Military Expenditures and Arms Trade: Alternative Washington, D.C.: World Bank. Empirical Treatment." Joumal of International Data Sources." IMF Working Paper 94/69. Fankhauser, Samuel. 1995. Valuing Climate Economics 41:351-66. International Monetary Fund, Policy Development Change: The Economics of the Greenhouse. Fredricksen, Birger. 1993. Statistics of Education and Review Department, Washington, D.C. London: Earthscan. in Developing Countries: An Introduction to Their Harbison, Ralph W., and Eric A. Hanushek. 1992. FAO (Food and Agriculture Organization). 1986. Collection and Analysis. Paris: UNESCO. Educational Performance of the Poor: Lessons "Inter-Country Comparisons of Agricultural Freedom House. 1998. Freedom in the World from Rural Northeast Brazil. New York: Oxford Production Aggregates." Economic and Social 1997/1998. New Brunswick, N.J., and London: University Press. Development Paper 61. Rome. Transaction Publishers. Harrison, Ann, and Ana Revenga. 1995a. "The 1990. "Tobacco: Supply. Demand and French, Kenneth, and James M. Poterba. 1991. Effects of Trade Policy Reform: What Do We Trade Projections, 1995 and 2000." Economic "Investor Diversification and International Equity Really Know?" NBER Working Paper 5225. and Social Development Paper 86. Rome. Markets." American Economic Review 81: National Bureau of Economic Research, 1 1996. Food Aid in Figures 1994. Vol. 12. 222-26. Cambridge, Mass. Rome. Furstenberg, George Von. 1997. 'Where to Look . 1995b. 'Factor Markets and Trade Policy - 1997. State of the World's Forests 1997. for International Financial Integration? A Review Reform." World Bank, Washington, D.C. Rome. Essay." Paper presented at an International Hatter, Victoria L. 1985. U.S. High-Technology - . Various years. Fertilizer Yearbook. FAO Monetary Fund Research Department seminar, Trade and Competitiveness. Washington, D.C.: Statistics Series. Rome. 29 July, Washington, D.C. U.S. Department of Commerce. - Various years. Production Yearbook. FAO Gannon, Colin, and Zmarak Shalizi. 1995. "The Heck, W.W. 1989. "Assessment of Crop Losses Statistics Series. Rome. Use of Sectoral and Project Performance from Air Pollutants in the U.S." In J.J. McKenzie - Various years. Trade Yearbook. FAO Indicators in Bank-Financed Transport and M.T. El Ashry, eds., Air Pollution's Toll on Statistics Series. Rome. Operations." TWU Discussion Paper 21. World Forests and Crops. New Haven, Conn.: Yale Feldstein, Martin, and Charles Horioka. 1980. Bank, Transportation, Water, and Urban University Press. "Domestic Savings and International Capital Development Department, Washington, D.C. Heritage Foundation and the Wall Street Journal. Flows." Economic Journal 90(358):314-29. Gardner-Outlaw, T., and R. Engelman. 1997. 1999. 1999 Index of Economic Freedom. Edited Filmer, Dean, and Lant Pritchett. 1998. "The "Sustaining Water, Easing Scarcity: A Second by Bryan T. Johnson, Kim R. Holmes, and Melanie Effect of Household Wealth on Educational Update." Population Action International, Kirkpatrick. Washington, D.C., and New York. Attainment: Demographic and Health Survey Washington, D.C. Heston, Alan. 1994. "A Brief Review of Some Evidence." Policy Research Working Paper GATT (General Agreement on Tariffs and Trade). Problems in Using National Accounts Data in Level 1980. World Bank, Development Research 1966. International Trade 1965. Geneva. of Output Comparisons and Growth Studies.' Group, Washington, D.C. . 1989. International Trade 1988-89. Journal of Development Economics 44:29-52. Filmer, Dean, Elizabeth King, and Lant Pritchett. Geneva. Hettige, Hemamala, Muthukumara Mani, and David 1998. "Gender Disparity in South Asia." Policy Goldfinger, Charles. 1994. L'utile et le futile: Wheeler. 1998. "Industrial Pollution in Economic Research Working Paper 1867. World Bank, L'economie de l'immateriel. Paris: Editions Odile Development: Kuznets Revisited." Policy Research Development Research Group, Washington, D.C. Jacob. Working Paper 1876. World Bank, Development Finger, J. Michael, Merlinda Ingco, and Ulrich Goldstein, Ellen, Alexander S. Preker, Olusoji Adeyl, Research Group, Washington, D.C. Reincke. 1996. The Uruguay Round: Statistics and Gnanaraj Chellaraj. 1996. Trends in Health Heyneman, Stephen P. 1996. "The Quality of on Tariff Concessions Given and Received. Status, Services, and Finance: The Transition in Education in the Middle East and North Africa." Washington, D.C.: World Bank. Central and Eastern Europe. Vol. 1. World Bank EMT Working Paper 3. World Bank, Europe and Fischer, Stanley. 1993. "The Role of Technical Paper 341. Washington, D.C. Central Asia and Middle East and North Africa Macroeconomic Factors in Growth." Joumal of Gray, Cheryl W., and Daniel Kaufmann. 1998. Technical Department. Washington, D.C. Monetary Economics 32:485-512. "Corruption and Development." Finance & Hill, M. Anne, and Elizabeth M. King. 1993. Fox, James W. 1995. "What Do We Know about Development 35(1):7-10. "Women's Education in Developing Countries: World Poverty?" USAID Evaluation Special Study Greaney, Vincent, and Thomas Kellaghan. 1996. An Overview." In Elizabeth M. King and M. Anne 74. U.S. Agency for International Development, Monitoring the Learning Outcomes of Education Hill, eds., Women's Education in Developing 1999 World Development Indicators 385 Countries. Baltimore, Md.: Johns Hopkins of Governors of the IMF, 16 April. Washington, Isard, Peter. 1995. Exchange Rate Economics. University Press. D.C. Cambridge: Cambridge University Press. ICAO (International Civil Aviation Organization). . 1998b. Exchange Arrangements and IUCN (World Conservation Union). 1996. 1996 1998. Civil Aviation Statistics of the World: Exchange Restrictions Annual Report, 1998. IUCN Red List of Threatened Animals. Gland, 1997. ICAO Statistical Yearbook. 22d ed. Washington, D.C. Switzerland. Montreal. . Various years. Balance of Payments . 1998. 1997 IUCN Red List of Threatened IEA (International Energy Agency). Various years. Statistics Yearbook. Parts 1 and 2. Washington, Plants. Gland, Switzerland. Energy Balances of OECD Countries. Paris. D.C. Journal of Development Economics. 1994. Special - Various years. Energy Statistics and . Various issues. Direction of Trade issue on database for development analysis. Balances of Non-OECD Countries. Paris. Statistics. Quarterly. Washington, D.C. Edited by T.N. Srinivasan. Vol. 44, no. 1. - Various years. Energy Statistics of OECD . Various years. Direction of Trade Statistics Kaminsky, Graciela L., and Carmen M. Reinhart. Countries. Paris. Yearbook. Washington, D.C. 1997. "The Twin Crises: The Causes of Banking IFAC (International Federation of Accountants). . Various years. Government Finance and Balance-of-Payments Problems." Working 1998. Guideline for Governmental Financial Statistics Yearbook. Washington, D.C. Paper in International Economics 37. University Reporting. Exposure draft. Public Sector . Various issues. International Financial of Maryland, College Park. Committee. New York. Statistics. Monthly. Washington. D.C. Kaminsky, Graciela L., Saul Lizondo, and Carmen IFC (International Finance Corporation). 1997. . Various years. International Financial M. Reinhart. 1997. "Leading Indicators of Trends in Private Investment in Developing Statistics Yearbook. Washington, D.C. Currency Crises." Policy Research Working Countries 1997. Washington, D.C. Intergovernmental Panel on Climate Change. Paper 1852. World Bank, Latin America and the - 1998. Emerging Stock Markets Factbook 1996. Climate Change 1995. Cambridge: Caribbean Region, Office of the Chief 1998. Washington, D.C. Cambridge University Press. Economist, Washington, D.C. ILO (International Labour Organisation). 1990a. International Association for the Evaluation of Kanbur, Ravi. 1997. "Income Distribution and ILO Manual on Concepts and Methods. Geneva: Educational Achievement. 1997. Third Development." Cornell University, Ithaca, N.Y. International Labour Office. Intemational Mathematics and Science Study; Kaufmann, Daniel. 1998. "Challenges in the Next - 1990b. Yearbook of Labour Statistics: 1994-95. Boston, Mass.: Boston College, Stage of Anti-Corruption." In Transparency Retrospective Edition of Population Censuses TIMSS International Study Center. International and World Bank, Economic 1945-89. Geneva: International Labour Office. Intemational Chamber of Commerce. 1996. Extortion Development Institute, New Perspectives on - Various years. Sources and Methods: and Bribery in International Business Trans- Combating Corruption. Washington, D.C. Labour Statistics. (Formerly Statistical Sources actions-Rules and Recommendations. Paris. Kaufmann, Daniel, Sanjay Pradhan, and Randi and Methods.) Geneva: International Labour International Telecommunication Union. 1998. Ryterman with James Anderson. 1998. "New Office. World Telecommunication Development Report. Frontiers in Diagnosing and Combating - . Various years. Yearbook of Labour Geneva. Corruption." PREM Note 7. World Bank, Statistics. Geneva: International Labour Office. International Working Group of External Debt Development Economics Vice Presidency and IMF (International Monetary Fund). 1977. Balance Compilers (Bank for International Settlements, Poverty Reduction and Economic Management of Payments Manual. 4th ed. Washington, D.C. International Monetary Fund, Organisation for Network, Washington, D.C. 1986. A Manual on Government Finance Economic Co-operation and Development, and Klugman, Jeni, and George Schieber. 1996. A Statistics. Washington, D.C. World Bank). 1987. External Debt Definitions. Survey of Health Reform in Central Asia. World - 1993. Balance of Payments ManuaL 5th Washington, D.C. Bank Technical Paper 344. Washington, D.C. ed. Washington, D.C. Inter-Secretariat Working Group on National Knetter, Michael. 1994. "Why Are Retail Prices in - 1995. Balance of Payments Compilation Accounts (Commission of the European Japan So High? Evidence from German Export Guide. Washington, D.C. Community, International Monetary Fund, Prices.' NBER Working Paper 4894. National - 1996a. Balance of Payments Textbook. Organisation for Economic Cooperation and Bureau of Economic Research. Cambridge, Mass. Washington, D.C. Development, United Nations, and World Bank). Kravis, Irving B. 1970. "Trade as a Handmaiden of - 1996b. Manual on Monetary and Financial 1993. System of National Accounts. Brussels. Growth." Economic Journal 80(323):850-72. Statistics. Washington, D.C. Luxembourg. New York, and Washington, D.C. Krueger, Anne 0., Constantine Michalopoulos, and - . 1997a. Good Govemance: The IMF's Role. IRF (International Road Federation). 1998. World Vernon W. Ruttan. 1989. Aid and Development. Washington, D.C. Road Statistics 1998. Geneva. Baltimore, Md.: Johns Hopkins University Press. - 1997b. Why Worry about Corruption? Irwin, Douglas A. 1996. 'The United States in a Krugman, Paul. 1991. Geography and Trade. Washington, D.C. New Global Economy? A Century's Perspective." Cambridge. Mass.: MIT Press. - 1998a. Code of Good Practices on Fiscal Papers and Proceedings of the 108th Annual Kunte, Arundhati, Kirk Hamilton, John Dixon, and Transparency-Declaration on Principles. Meeting of the American Economic Association. Michael Clemens. 1998. "Estimating National Adopted by the Interim Committee of the Board American Economic Review (May). Wealth: Methodology and Results." Environmental 3s6 19g9 World Development Indicators Economics Series, no. 57. World Bank, Murray, Christopher, Ramesh Govindaraj, and . Various years. National Accounts. Vol. 1, Environment Department, Washington, D.C. Gnanaraj Chellaraj. 1994. "Global Domestic Main Aggregates. Paris. Leamer, Edward. 1987. 'Measures of Openness." Expenditures in Health." Background paper 13 . Various years. National Accounts. Vol. 2, Working Paper 447. University of California at to World Development Report 1993. World Detailed Tables. Paris. Los Angeles, Department of Economics. Bank, Washington, D.C. Papageorgiou, Demetris, Armeane Choksi, and Levine, Ross, and Sara Zervos. 1996. 'Stock Obstfeldt, Maurice. 1995. "International Capital Michael Michaely. 1990. "Liberalizing Foreign Market Development and Long-Run Growth." The Mobility in the 1990s.' In P.B. Kenen, ed.. Trade in Developing Countries: The Lessons of World Bank Economic Review 10(2):323-40. Understanding Interdependence: The Experience.' World Bank, Washington, D.C. Lewis, Karen K. 1995. "Puzzles in International Macroeconomics of the Open Economy. Pearce, David, and Giles Atkinson. 1993. 'Capital Financial Markets.' In Gene Grossman and Princeton, N.J.: Princeton University Press. Theory and the Measurement of Sustainable Kenneth Rogoff, eds., Handbook of International Obstfeldt, Maurice, and Kenneth Rogoff. 1996. Development: An Indicator of Weak Economics. Vol. 3. Amsterdam: North Holland. Foundations of International Macroeconomics. Sustainability.'" Ecological Economics 8:103-08. Lewis, Stephen R., Jr. 1989. "Primary Exporting Cambridge, Mass.: MIT Press. PricewaterhouseCoopers. 1998a. Corporate Taxes: Countries." In Hollis Chenery and T.N. OECD (Organisation for Economic Co-operation A Worldwide Summary. New York. Srinivasan, eds., Handbook of Development and Development). 1985. 'Measuring Health . 1998b. Individual Taxes: A Worldwide Economics. Vol. 2. Amsterdam: North Holland. Care 1960-1983: Expenditure, Costs, Summary. New York. Lockheed, Marlaine E., Adriaan M. Verspoor, and Performance.' OECD Social Policy Studies 2. Pritchett, Lant. 1996. "Measuring Outward associates. 1991. Improving Primary Education Paris. Orientation in Developing Countries: Can It Be in Developing Countries. New York: Oxford . 1989. "Health Care Expenditure and Other Done?" Policy Research Working Paper 566. University Press. Data: An International Compendium from the World Bank, Country Economics Department, Lovei, Magdolna. 1997. 'Toward Effective Pollution OECD." In Health Care Financing Review. Annual Washington, D.C. Management.' Environment Matters (fall): supplement. Paris. Pritchett, Lant, and Geeta Sethi. 1994. "Tariff 52-53. . 1995. DAC Orientations on Participatory Rates, Tariff Revenue, and Tariff Reform-Some Low, Patrick, and Alexander Yeats. 1994. Development and Good Govemance. New Facts." The World Bank Economic Review "Nontariff Measures and Developing Countries: Development Co-operation Guidelines Series. 8(1):1-16. Has the Uruguay Round Leveled the Playing Paris. PRS Group. 1998. International Country Risk Field?" Policy Research Working Paper 1353. . 1996a. DevelopmentAssistance: Efforts Guide. December. East Syracuse, N.Y. World Bank, International Economics and Policies of the Members of the Psacharopoulos, George. 1994. "Returns to Department, Washington, D.C. Development Assistance Committee. Paris. Investment in Education: A Global Update." Lucas, R.E. 1988. "On the Mechanics of Economic . 1996b. Education at a Glance. Paris. World Development 22(9):1325-43. Development.' Journal of Monetary Economics . 1996c. Shaping the 21st Century: The . 1995. Building Human Capital for Better 22:3-22. Contribution of Development Cooperation. Paris. Lives. Washington, D.C.: World Bank. Lustig, Nora, ed. 1995. Coping with Austerity: . 1997a. "DAC Guidelines on Conflict, Peace Psacharopoulos, George, and Zafiris Tzannatos. Poverty and Inequality in Latin America. and Development Co-operation." Development 1992. Case Studies on Women's Employment Washington, D.C.: The Brookings Institution. Assistance Committee. Paris. and Pay in Latin America. A World Bank Regional Mani, Muthukumara, and David Wheeler. 1997. . 1997b. Employment Outlook. Paris. and Sectoral Study. Washington, D.C. "In Search of Pollution Havens? Dirty Industry in . 1997c. OECD Environmental Data: Rama, Martin, and Raquel Artecona. Forthcoming. the World Economy, 1960-95." World Bank, Compendium 1997. Paris. "A Database of Labor Market Indicators across Policy Research Department, Washington, D.C. . 1997d. Trends in International Migration: Countries.' Policy Research Working Paper. Midgley, Peter. 1994. Urban Transport in Asia: An Continuous Reporting System on Migration, World Bank, Washington, D.C. Operational Agenda for the 1990s. World Bank 1997. Paris. Ravallion, Martin. 1996. "Poverty and Growth: Technical Paper 224. Washington, D.C. - . 1998. Trends in Intemational Migration: Lessons from 40 Years of Data on India's Poor." Milanovic, Branko. 1998. Income, Inequality, and Continuous Reporting System on Migration, DECNote 20. World Bank, Development Poverty during the Transition from Planned to 1998. Paris. Economics Vice Presidency, Washington, D.C. Market Economy. A World Bank Regional and . Various years. Development Co-operation. Ravallion, Martin, and Shaohua Chen. 1996. 'What Sectoral Study. Washington, D.C. Paris. Can New Survey Data Tell Us about the Recent Moody's Investors Service. 1998. Sovereign, - . Various years. Geographical Distribution of Changes in Living Standards in Developing and Subnational and Sovereign-Guaranteed Issuers. Financial Flows to Aid Recipients: Transitional Economies?" World Bank, Policy December. New York. - Disbursements, Commitments, Country Research Department, Washington, D.C. Morgenstern, Oskar. 1963. On the Accuracy of Indicators. Paris. . 1997. "Can High-lnequality Developing Economic Observations. Princeton, N.J.: . Various issues. Main Economic Indicators. Countries Escape Absolute Poverty?" Economic Princeton University Press. Monthly. Paris. Letters 56:51-57. 1999 World Development Indicators 387 . Forthcoming. "What Can New Survey Data Standard & Poor's. 1998. Credit Week. December. . 1998b. World Education Report Paris: Tell Us about Recent Changes in Distribution New York. UNESCO Publishing and Bernan Press. and Poverty?" The World Bank Economic Review. Standard & Poor's DRI. 1998. Global Risk Service. UNICEF (United Nations Children's Fund). Various Rodrik, Dani. Forthcoming. 'Who Needs Capital Lexington, Mass. years. The Progress of Nations. New York: Convertibility?" Princeton Essays in International Stephenson, Sherry M. 1996. "Standards and Oxford University Press. Finance. Conformity Assessments as Nontariff Barriers to . Various years. The State of the World's Rogoff, Kenneth. 1996. "The Purchasing Power Trade." Policy Research Working Paper 1826. Children. New York: Oxford University Press. Parity Puzzle." Journal of Economic Literature World Bank, Development Research Group. UNIDO (United Nations Industrial Development 34:647-68. Washington, D.C. Organization). 1996. International Yearbook of Romer, P.M. 1986. "increasing Returns and Long- Swanson, Eric V. 1988. Achievement and Wastage: Industrial Statistics 1997. Vienna. Run Growth.' Joumal of Political Economy The Retention of Literacy and Numeracy Skills in United Nations. 1947. Measurement of National 94:1002-37. Primary School. Ph.D. dissertation. State Income and the Construction of Social Accounts. Ruggles, Robert. 1994. 'Issues Relating to the UN University of New York at Buffalo, Economics New York. System of National Accounts and Developing Department. Ann Arbor, Mich.: University . 1968. A System of National Accounts: Countries." Journal of Development Economics Microfilms. Studies and Methods. Series F, no. 2, rev. 3. 44(1):87-102. Syrquin, Moshe. 1988. "Patterns of Structural New York. Ryten, Jacob. 1998. "Fifty Years of SIC: Historical Change." In Hollis Chenery and T.N. Srinivasan, . 1985. National Accounts Statistics: Origins and Future Perspectives." United eds., Handbook of Development Economics. Vol. Compendium of Income Distribution Statistics. Nations Department of Economic and Social 1. Amsterdam: North Holland. New York. Affairs, Statistics Division, New York. Taylor, Alan M. 1996a. "International Capital . 1990a. Assessing the Nutritional Status of ECA/STAT.AC.63/22. Mobility in History: Purchasing Power Parity in Young Children. National Household Survey Schultz, T. Paul. 1993. "Returns to Women's the Long Run.' NBER Working Paper 5742. Capability Programme. New York. Education." In Elizabeth M. King and M. Anne Hill, National Bureau of Economic Research, . 1990b. Intemational Standard Industrial eds., Women's Education in Developing Countries. Cambridge, Mass. Classification of All Economic Activities, Third Baltimore, Md.: Johns Hopkins University Press. . :1996b. 'International Capital Mobility in Revision. Statistical Papers Series M, no. 4, rev. Schultz, Theodore W. 1961. "Education and History: The Saving-Investment Relationship." 3. New York. Economic Growth.' In N.B. Henry, ed., Social NBER Working Paper 5743. National Bureau of . 1991. The World's Women, 1970-90: Forces Influencing American Education. Chicago: Economic Research, Cambridge, Mass. Trends and Statistics. New York. University of Chicago Press. TeleGeography and International . 1993a. Integrated Environmental and Sen, Amartya. 1988. "The Concept of Telecommunication Union. 1997. Direction of Economic Accounting. New York. Development.' In Hollis Chenery and T.N. Traffic 1996. Washington, D.C. . 1993b. International Trade Statistics Srinivasan, eds., Handbook of Development Transparency International and World Bank, Yearbook. Vol. 1. New York. Economics. Vol. 1. Amsterdam: North Holland. Economic Development Institute. 1998. New . 1993c. Report on the World Social Serageldin, Ismail. 1995. Toward Sustainable Perspectives on Combating Corruption. Situation, 1993. New York. Management of Water Resources. A Directions in Washington, D.C. - . 1997. World Urbanization Prospects: The Development book. Washington D.C.: World Bank. UNAIDS and WHO (World Health Organization). 1996 Revision. New York. Shapiro, Harvey. 1998. "A High-Level Stall." 1998. Report on the Global HIV/AIDS Epidemic. . Various years. Energy Statistics Yearbook. Institutional Investor 23(September):9. Geneva. New York. Shiklovanov, Igor. 1993. "World Fresh Water UNCTAD (United Nations Conference on Trade and . Various years. International Trade Statistics Resources." In Peter H. Gleick, ed., Water in Development). Various years. Handbook of Yearbook. New York. Crisis: A Guide to Fresh Water Resources. New International Trade and Development Statistics. - . Various issues. Monthly Bulletin of York: Oxford University Press. Geneva. Statistics. New York. South Pacific Commission. 1997. Pacific Island UNEP (United Nations Environment Programme). - . Various years. National Accounts Statistics: Populations Data Sheet. Noumea, New 1991. Urban Air Pollution. Nairobi. Main Aggregates and Detailed Tables. Parts 1 Caledonia. UNEP (United Nations Environment Programme) and 2. New York. Srinivasan, T.N. 1991. "Development Thought, and WHO (World Health Organization). 1992. . Various years. National Income Accounts. Policy, and Strategy, Then and Now." Urban Air Pollution in Megacities of the World. Statistics Division. New York. Background paper to World Development Report Cambridge, Mass.: Blackwell. . Various years. Statistical Yearbook. New York. 1991. World Bank, Washington, D.C. . 1995. City Air Quality Trends. Nairobi. . Various years. Update on the Nutrition 1 1994. "Database for Development UNESCO (United Nations Educational, Scientific, and Situation. Administrative Committee on Analysis: An Overview." Journal of Development Cultural Organization). 1998a. Statistical Yearbook. Coordination, Subcommittee on Nutrition. Economics 44(1):3-28. Paris: UNESCO Publishing and Bernan Press. Geneva. 388 1999 World Development Indicators United Nations Department of Economic and . 1994. Biodiversity Data Sourcebook. . 1992. World Development Report 1992: Social Affairs. 1996. Intemational Migration Cambridge: World Conservation Press. Development and the Environment. New York: Policies 1995. New York. WHO (World Health Organization). 1977. Internatonal Oxford University Press. - . 1998. World Population Prospects: The Classification of Diseases. 9th rev. Geneva. - . 1993a. The Environmental Data Book: A 1998 Revision. New York. . 1991. Matemal Mortality: A Global Guide to Statistics on the Environment and - . Various years. Levels and Trends of Factbook. Geneva. Development. Washington, D.C. Contraceptive Use. New York. . 1996. Evaluating the Implementation of the - . 1993b. Purchasing Power of Currencies: - . Various years. Population and Vital Strategy for Health for All by the Year 2000. Comparing National Incomes Using ICP Data. Statistics Report. New York. Geneva. Washington, D.C. United Nations Economic and Social Commission for - . 1997a. Coverage of Maternity Care. Geneva. - . 1993c. World Development Report 1993: Westem Asia. 1997. Purchasing Power Parities: - . 1997b. Global Tuberculosis Control Report Investing in Health. New York: Oxford University Volume and Price Level Comparisons for the Middle 1997. Geneva. Press. East, 1993. E/ESCWA/STAT/1997/2. Amman. - . 1997c. Monitoring Reproductive Health: -. 1994a. Global Economic Prospects and the UNRISD (United Nations Research Institute for Selecting a Short List of National and Global Developing Countries 1994. Washington, D.C. Social Development). 1977. Research Data Indicators. Geneva. . 1994b. Govemance: The World Bank's Bank of Development Indicators. Vol. 4, Notes - . 1997d. Tobacco or Health: A Global Status Experience. Development in Practice series. on the Indicators. Geneva. Report, 1997. Geneva. Washington, D.C. - . 1993. Monitoring Social Progress in the - . 1998. EPI Information System: Global - . 1994c. World Development Report 1994: 1990s: Data Constraints, Concems, and Summary, September 1998. Geneva. Infrastructure for Development. New York: Priorities. Avebury, England. . Various years. World Health Statistics Oxford University Press. U.S. Agency for International Development. 1999. A AnnuaL. Geneva. - . 1995a. Advancing Social Development: A Handbook on Fighting Corruption. Washington, D.C. WHO (World Health Organization) and UNICEF World Bank Contribution to the Social Summit. U.S. Department of Health and Human Services. (United Nations Children's Fund). 1992. Low Washington, D.C. 1997. Social Security Systems throughout the Birth Weight: A Tabulation of Available - . 1995b. Bureaucrats in Business: The World. Washington, D.C. Information. Geneva. Economics and Politics of Government U.S. Environmental Protection Agency. 1995. - . 1996. Revised 1990 Estimates on Ownership. A World Bank Policy Research National Air Quality and Emissions Trends Maternal Mortality: A New Approach. Geneva. Report. New York: Oxford University Press. Report 1995. Washington, D.C. Windham, Douglas M. 1988. Indicators of . 1995c. Global Economic Prospects and the Wacziarg, Romain. 1998. 'Measuring the Dynamic Educational Effectiveness and Efficiency. Developing Countries 1995. Washington, D.C. Gains from Trade." Policy Research Working Tallahassee, Fla.: Florida State University, - . 1995d. Priorities and Strategies for Paper 2001. World Bank, Development Educational Efficiency Clearinghouse. Education: A Review. Washington, D.C. Prospects Group, Washington, D.C. Wolf, Holger C. 1997. "Patterns of Intra- and Inter- - . 1995e. Private Sector Development in Low- Walsh, Michael P. 1994. "Motor Vehicle Pollution State Trade." NBER Working Paper 5939. Income Countries. Washington, D.C. Control: An Increasingly Critical Issue for National Bureau of Economic Research, - . 1995f. Toward Gender Equality: The Role of Developing Countries." World Bank, Washington, Cambridge, Mass. Public Policy-An Overview. Washington, D.C. D.C. Wolfensohn, James D. 1997. 'The Challenge of - . 1995g. World Development Report 1995: Walton, Michael. 1997. 'Will Global Advance Inclusion." Address to the Board of Governors of Workers in an Integrating World. New York: Include the World's Poor?' Paper presented at the World Bank Group and the International Oxford University Press. the Aspen Institute conference on Poverty Monetary Fund, 23 September, Hong Kong, China. - . 1996a. From Vision to Action in the Rural Persistence in Developing Countries: - . 1998. "The Other Crisis." Address to the Sector. Agriculture Department. Washington, D.C. Determining the Causes and Closing the Gaps, Board of Governors of the World Bank Group and -. 1996b. Global Economic Prospects and 1-4 December, Broadway, England. the International Monetary Fund, 6 October, the Developing Countries 1996. Washington, WCEFA Inter-Agency Commission (United Nations Washington, D.C. D.C. Development Programme, United Nations World Bank. 1990. World Development Report - . 1996c. Livable Cities for the 2Ist Century. Educational, Scientlflc, and Cultural 1990: Poverty. New York: Oxford University Washington, D.C. Organization, United Nations Children's Fund, Press. . 1996d. National Environmental Strategies: and World Bank). 1990. World Conference on - . 1991a. Developing the Private Sector: The Learning from Experience. Environment Education for All: Meeting Basic Learning World Bank's Experience and Approach. Department. Washington, D.C. Needs-Final Report. Jomtien, Thailand. Washington, D.C. - . 1997a. Can Environment Wait? Priorities WCMC (World ConservaUon Monitorlng Centre). . 1991b. World Development Report 1991: for East Asia. Washington, D.C. 1992. Global Biodiversity: Status of the Earth's The Challenge of Development. New York: Oxford - . 1997b. Confronting AIDS: Public Priorities Living Resources. London: Chapman and Hall. University Press. in a Global Epidemic. A World Bank Policy 1999 World Development Indicators 389 Research Report. New York: Oxford University World Intellectual Property Organization. 1998. Press. Industrial Property Statistics. Publication A. - . 1997c. Expanding the Measure of Wealth: Geneva. Indicators of Sustainable Development. ESD World Resources Institute, International Institute Studies and Monographs Series, no. 17. for Environment and Development, and IUCN Washington, D.C. (World Conservation Union). Various years. - . 1997d. Global Economic Prospects and the World Directory of Country Environmental Developing Countries 1997. Washington, D.C. Studies. Washington, D.C. - . 1997e. Helping Countries Combat World Resources Institute, UNEP (United Nations Corruption: The Role of the World Bank. Poverty Environment Programme), and UNDP (United Reduction and Economic Management Network. Nations Development Programme). 1994. Washington, D.C. World Resources 1994-95: A Guide to the - . 1997f. Private Capital Flows to Developing Global Environment. New York: Oxford University Countries: The Road to Financial Integration. A Press. World Bank Policy Research Report. New York: World Resources Institute, UNEP (United Nations Oxford University Press. Environment Programme), UNDP (United - . 1997g. Sector Strategy: Health, Nutrition, Nations Development Programme), and World and Population. Human Development Network. Bank. 1998. World Resources 1998-99: A Washington, D.C. Guide to the Global Environment. New York: - . 1997h. World Development Indicators Oxford University Press. 1997. Washington, D.C. World Tourism Organization. 1997. Yearbook of - . 1997i. World Development Report 1997: Tourism Statistics. Vols. 1 and 2. 49th ed. The State in a Changing World. New York: Oxford Madrid. University Press. - . 1998. Compendium of Tourism Statistics - . 1998a. Assessing Aid: What Works, What 1992-96. 18th ed. Madrid. Doesn't, and Why. A World Bank Policy Research Zimmermann, Klaus F. 1995. "European Report. New York: Oxford University Press. Migration: Push and Pull.' In Michael Bruno - 1998b. East Asia: The Road to Recovery. and Boris Pleskovic, eds., Proceedings of the Washington, D.C. World Bank Annual Conference on Development - . 1998c. Education Sector Strategy Paper. Economics 1994. Washington, D.C.: World Washington, D.C. Bank. -. 1998d. 1998 Catalog: Operational Documents as of July 31, 1998. Washington, D.C. -. 1998e. World Development Report 1998/99: Knowledge for Development. New York: Oxford University Press. -. 1999a. Global Economic Prospects and the Developing Countries 1998/99: Beyond Financial Crisis. Washington, D.C. -. 1999b. Health, Nutrition, and Population Indicators: A Statistical Handbook. Human Development Network. Washington, D.C. -. Forthcoming. Poverty Reduction and the World Bank: Progress in Fiscal 1998. Washington, D.C. -. Various issues. Global Commodity Markets. Quarterly. Washington. D.C. - Various years. Global Development Finance. (Formerly published under the title World Debt Tables.) Washington, D.C. 390 1999 World Development Indicators Index of indicator rs References are to table numbers. Asylum seekers-See Migration Agriculture cereal area under production 3.2 Balance of payments yield 3.3 current account balance 4.17 fertilizer consumption 3.2 goods and services 4.17 labor force gross international reserves 4.17 as share of total labor force 1.5 net current transfers 4.17 share of total labor force, male and female 2.4 net income 4.17 land See also Exports; Imports; Investment; Private capital flows; Trade arable, per capita 3.2 irrigated, as share of cropland 3.2 Biological diversity machinery assessment, date prepared, by country 3.14 tractors per thousand agricultural workers 3.2 species 3.4 tractors per thousand hectares of arable land 3.2 threatened species 3.4 producer prices 5.6 treaty 3.14 production indexes crop production 3.3 Birds food production 3.3 species 3.4 livestock production 3.3 threatened species 3.4 value added annual growth of 1.4, 4.1 Birth rate, crude 2.2 as share of GDP 1.5, 4.2 per agricultural machine 3.3 Births attended by health staff 2.15 per worker 3.3 wage per worker 2.6 Birthweight, low 2.16 Aid appropriations by DAC members 6.9 net concessional flows Carbon dioxide emissions from international financial institutions 6.12 per capita 3.8 from United Nations agencies 6.12 per 1995 $ of GDP 3.8 net official development assistance and official aid by DAC members total 1.6, 3.8 as share of GNP of donor country 6.9 average change in volume 6.9 Cities by recipient 6.10 air pollution 3.13 by type 6.8 environment 3.11 major donors, by recipient 6.11 population per capita of donor country 6.9 in largest city 3.10 total 6.8, 6.9, 6.11 in selected cities 3.13 untied aid 6.9 telephone mainlines in largest city 5.10 by recipient See also Urban indicators aid dependency ratios 6.10 per capita 6.10 Commodity prices and price indexes 6.4 total 6.10 Computers, personal 5.11 Air pollution-See Pollution Condoms Air transport reported use 2.17 aircraft departures 5.9 socially marketed sales 2.17 air freight 5,9 passengers carried 5.9 Consumption distribution of-See Income distribution Amphibians government, general species 3.4 annual growth of 4.10 threatened species 3.4 as share of GDP 4.9 Anemia, pregnant women 2.16 1999 World Development Indicators 391 private Distribution of income or consumption-See Income distribution annual growth of 1.4, 4.10 as share of GDP 4.9 per capita, annual growth of 1.2, 4.10 per capita, on PPP terms 4.11 Education relative price level 4.12 attainment total 4.10 expected years of schooling, male and female 2.11 See also Purchasing power parity share of cohort reaching grade 5, male and female 2.11 enrollment ratio Consumption of iodized salt 2.16 gross, by level 2.10 net primary Contraceptive prevalence rate 2.15 gender differences 1.3 male and female 1.2 Credit, domestic net, by level 2.10 from banking sector 5.4 primary level to private sector 5.1 duration 2.9 to state-owned enterprises 5.8 pupil-teacher ratio 2.9 public spending on Current account balance 4.17 as share of GNP 2.9 See also Balance of payments per student, by level 2.9 teaching materials, by level 2.9 pupil, hare female, by level 2.12 teachers, share female, by level 2.12 DAC (Development Assistance Committee)-See Aid Electricity Death rate, crude 2.2 consumption 5.10 See also Mortality rate distribution losses 5.10 production Debt, external annual growth of 5.10 debt service, total 4.19 total 3.9 IMF credit, use of 4.18 source of 3.9 long-term 4.18 present value 4.19 Employment private nonguaranteed agriculture, male and female 2.4 as share of external debt 5.1 industry, male and female 2.4 total 4.18 services, male and female 2.4 public and publicly guaranteed state-owned enterprises 5.8 debt service 4.19 IBRD loans and IDA credits 4.18 Endangered species-See Biological diversity, threatened species total 4.18 short-term 4.19 Energy total 4.18 commercial use GDP per unit of energy 3.8 Defense growth 3.7 armed forces personnel per capita 3.7 share of labor force 5.7 total 3.7 total 5.7 efficiency 3.8 arms trade emissions-See Pollution exports 5.7 imports, net 3.7 imports 5.7 production, commercial 3.7 military expenditure traditional fuel use 3.8 as share of central government expenditure 5.7 See also Electricity as share of GNP 5.7 Entry and exit regulations Deforestation 3.1 freedom of entry 5.3 Density-see Population density repatriation of capital 5.3 Development assistance-see Aid of income 5.3 392 1999 World Development Indicators Environmental profile, date prepared 3.14 annual withdrawal as share of total resources 3.5 Environmental strategy, year adopted 3.14 for agriculture 3.5 for domestic use 3.5 Euromoney country creditworthiness rating 5.3 for industry 3.5 volume of 3.5 Exchange rates resources per capita 3.5 arrangements 5.6 See also Safe water official, local currency units to U.S. dollars 5.6 ratio of official to parallel 5.6 real effective 5.6 See also Purchasing power parity Gender differences child mortality 1.3 Exports education 1.3, 2.11, 2.12 arms 5.7 employment 2.4, 2.5 goods and services illiteracy 1.3 annual growth of 1.4 labor force participation 1.3 as share of GDP 4.9 life expectancy 1.3 total 4.17 mortality 2.18 merchandise smoking 2.16 by high-income OECD countries, by product 6.3 women per 100 men aged 65 and above 2.1 by regional trade blocs 6.5 direction of trade 6.2 Genuine savings 3.15 high technology 5.12 structure of 4.5 Gini index 2.8 total 4.5 See also Consumption, private, per capita, annual growth of value, annual growth of 4.4, 6.2 volume, annual growth of 4.4 Government, central services debt structure of 4.7 as share of GDP 4.13 total 4.7 interest as share of current revenue 4.13 travel 4.7, 6.14 interest as share of total expenditure 4.14 See also Trade expenditures as share of GDP 4.13, 5.1 by economic type 4.14 military 5.7 Fax machines 5.11 financing domestic 4.13 Fertility rate from abroad 4.13 adolescent 2.15 overall deficit 4.13 total 2.15 revenues, as share of GDP 1.5, 4.13 revenues, current Financial depth and efficiency-See Liquidity; Monetary indicators nontax 4.15 taxes, by source 4.15, 5.5 Financial flows, net from DAC members 6.8 Gross domestic investment (GDI) from multilateral institutions 6.12 annual growth of 4.10 official development assistance and official aid as share of GDP 4.9 grants from NGOs 6.8 fixed, annual growth of 1.4 other official flows 6.8 private flows 6.8 Gross domestic product (GDP) total 6.8 annual growth of 4.1 See also Aid implicit deflator-See Prices total 4.2 Foreign direct investment, net-See Investment Gross domestic savings (GDS) as share of GDP 4.9 Forest area 3.1 Gross foreign direct investment-see Investment Freshwater 1999 World Development Indicators 393 Gross national product (GNP) goods and services annual growth of 1.1, 1.4, 1.6 as share of GDP 4.9 in 1997 U.S. dollars 1.1, 1.6 total 4.17 per capita merchandise annual growth of 1.1, 1.4, 1.6 by high-income OECD countries, by product 6.3 in 1997 U.S. dollars 1.1, 1.6 structure of 4.6 rank 1.1 total 4.6 purchasing power parity value, annual growth of 4.4, 6.2 in 1997 international dollars 1.1, 1.6 volume, annual growth of 4.4, 6.2 per capita, in 1997 international dollars 1.1, 1.6 services rank 1.1 structure of 4.8 rank 1.1 total 4.8 travel 4.8, 6.14 See also Trade Health care Income distribution average length of hospital stay, days 2.13 differentials, selected cities 3.11 hospital beds per 1,000 people 2.13 Gini index 2.8 immunization 2.14 percentage shares of 2.8 inpatient admission rate, share of population 2.13 survey year 2.8 outpatient visits, per capita 2.13 physicians, per 1,000 people 2.13 Income, urban, selected cities average, per person 3.11 Health expenditure differential 3.11 per capita house price to income ratio 3.11 current U.S. dollars 2.13 purchasing power parity 2.13 Industry, value added private 2.13 annual growth of 1.4, 4.1 public 2.13 as share of GDP 4.2 total 2.13 Inflation-See Prices HIV high-risk group, share infected 2.17 Institutional Investor credit rating 5.3 number of people infected 2.17 share of population 2.17 Integration, global economic, indicators of 6.1 stage of epidemic 2.17 survey year 2.17 Interest payments-See Government, central, debt women attending urban antenatal clinic, share infected 2.17 Interest rates Hospital beds-See Health care deposit rate 5.6 interest rate spread 5.4 Housing, selected cities lending rate 5.6 average floor space per person 3.11 real 5.6 price to income ratio 3.11 spread over LIBOR 5.4 International Bank for Reconstruction and Development (IBRD) IBRD loans and IDA credits 4.18 Illiteracy rate, adult net financial flows from 6.12 gender differences 1.3 total, for other economies 1.6 International Country Risk Guide (ICRG), composite risk rating 5.3 youth, male and female 2.11 International Development Association (IDA) Immunization, child net concessional flows from 6.12 DPT, share of children under 12 months 2.14 measles, share of children under 12 months 2.14 International Finance Corporation (IFC) Investable index 5.2 Imports arms 5.7 International Monetary Fund (IMF) net financial flows from 6.12 394 1999 World Development Indicators use of IMF credit 4.18 Internet hosts 5.11 Malnutrition, children under 5 2.16 Investment Mammals by state-owned enterprises 5.8 species 3.4 entry and exit regulations-See Entry and exit regulations threatened species 3.4 foreign direct gross Manufacturing, labor cost per worker 2.6 as share of PPP GDP 6.1 net Manufacturing, value added as share of GDP 5.1 annual growth of 4.1 as share of gross domestic investment 5.1 as share of GDP 4.2 total 6.7 per worker 2.6 government capital expenditures 4.14 structure of 4.3 portfolio total 4.3 bonds 6.7 equity 6.7 Merchandise private 5.1 direction and growth of merchandise trade 6.2 See also Gross domestic investment (GDI) exports agricultural raw materials 4.5 food 4.5 fuels 4.5 Labor cost, per worker in manufacturing 2.6 manufactures 4.5 ores and metals 4.5 Labor force growth of merchandise trade 4.4 agricultural 1.5 imports annual growth of 1.4, 2.3 agricultural raw materials 4.6 armed forces 5.7 food 4.6 children 10-14 2.3 fuels 4.6 female 2.3 manufactures 4.6 foreign, in OECD countries 6.13 ores and metals 4.6 participation gender differences 1.3 Migration population aged 15-64 2.3 foreign labor force in OECD countries as share of labor force 6.13 total 2.3 foreign population in OECD countries 6.13 See also Employment; Migration inflows of foreign population asylum seekers 6.13 Land area total 6.13 arable-See Agriculture, land selected cities 3.11 Monetary indicators See also Protected areas; Surface area claims on governments and other public entities 4.16 claims on private sector 4.16 Land use, by type 3.1 Money and quasi money (M2) Life expectancy at birth annual growth of 4.16 gender differences 1.3 as share of GDP 1.5 total 1.6, 2.18 Moody's sovereign foreign currency long-term debt rating 5.3 Liquidity bank liquid reserves to bank assets 5.4 Mortality rate liquid liabilities 5.4 adult, male and female 2.18 quasi-liquid liabilities 5.4 child 1.3, 2.18 See also Monetary indicators child, male and female 2.18 children under five 1.2, 2.18 Literacy-See Illiteracy rate infants 1.2, 2.18 maternal 1.2, 2.15 1999 World Development Indicators 395 Motor vehicles total 3.10 passenger cars 3.12 women per 100 men aged 65 and above 2.1 per kilometer of road 3.12 See also Migration per 1,000 people 3.12 two-wheelers 3.12 Poverty See also Roads; Traffic international poverty line population below $1 a day 2.7 population below $2 a day 2.7 poverty gap at $1 a day 2.7 Nationally protected areas-See Protected areas poverty gap at $2 a day 2.7 survey year 2.7 Newspapers, daily 5.11 national poverty line population below the poverty line national 2.7 rural 2.7 Official aid-See Aid urban 2.7 survey year 2.7 Official development assistance-See Aid Power-See Electricity, production Pregnancy, risk of unwanted 2.15 Passenger cars per 1,000 people 3.12 Prices Patent applications filed 5.12 agricultural producer prices maize 5.6 Physicians-See Health care wheat 5.6 commodity prices and price indexes 6.4 Plants, higher consumer, annual growth of 4.16 species 3.4 food, annual growth of 4.16 threatened species 3.4 GDP imp!icit deflator, annual growth of 4.16 international price level 4.12 Pollution, air terms of trade 4.4 nitrogen dioxide, selected cities 3.13 organic water pollutants, emissions of Privatization, proceeds from 5.8 by industry 3.6 per day 3.6 Private capital flows per worker 3.6 gross, as share of PPP GDP 6.1 sulfur dioxide, selected cities 3.13 net suspended particulate matter, selected cities 3.13 bank and trade-related lending 6.7 foreign direct investment 6.7 Population from DAC members 6.8 age dependency ratio 2.1 portfolio investment 6.7 age groups total 6.7 15-64 2.3 See also Investment 65 and above 2.1 annual growth of 1.4, 2.1, 2.2 Productivity density agriculture rural 3.1 value added perworker 3.3 total 1.1, 1.6 wage per worker 2.6 female, share of total 1.3 average hours worked per week 2.6 foreign, in OECD countries 6.13 labor cost per worker, manufacturing 2.6 momentum 2.2 value added per worker, manufacturing 2.6 projected population by 2030 2.2 total 1.1, 1.6, 2.1 Protected areas urban as share of total land area 3.4 as share of total population 1.5, 3.10 size of 3.4 in largest city 3.10 in selected cities 3.11 Purchasing power parity in urban agglomerations 3.10 conversion factor 5.6 396 1999 World Development Indicators gross national product 1.1, 1.6 Schooling-See Education health expenditure, per capita 2.13 household expenditures by type 4.11 Services private consumption per capita 4.11 exports relative price levels structure of 4.7 government consumption 4.12 total 4.7 gross fixed capital formation 4.12 imports international price level 4.12 structure of 4.7 private consumption 4.12 total 4.8 value added annual growth 4.1 as share of GDP 4.2 Radio sets 5.11 Sewerage, selected cities 3.11 Railways diesel locomotive availability 5.9 Smoking, prevalence of, male and female 2.16 goods transported 5.9 passengers 5.9 Standard & Poor's sovereign long-term debt rating 5.3 Regional development banks, net financial flows from 6.12 State-owned enterprises economic activity of 5.8 Relative prices (PPP)-See Purchasing power parity, relative price levels emp!oyment by 5.8 gross domestic credit held by 5.8 Reptiles gross domestic investment by 5.8 species 3.4 net financial flows from government to 5.8 threatened species 3.4 overall balances of, before transfers 5.8 Research and development Stock markets expenditures for 5.12 IFC Investable index 5.2 scientists and engineers 5.12 listed domestic companies 5.2 technicians 5.12 market capitalization as share of GDP 5.2 Reserves, gross international-See Balance of payments total 5.2 turnover ratio 5.2 Roads value traded 5.2 goods transported 5.9 normalized road index 5.9 Sulfur dioxide emissions-See Pollution paved roads 5.9 traffic 3.11 Surface area :.1, 1.6 See also Land area Royalty and license fees payments 5.12 Suspended particulate matter-See Pollution receipts 5.12 Tariffs Safe water, population with access all products as share of total 1.2, 2.14 mean tariff 6.6 households with potable water 3.11 standard deviation 6.6 rural 3.5 manufactured goods urban 3.5 mean tariff 6.6 standard deviation 6.6 Sanitation primary products households with sewerage connections, selected cities 3.11 mean tariff 6.6 population with access standard deviation 6.6 as share of total 2.14 See also Taxes and tax policies, duties urban 3.10 Taxes and tax policies Savings-See Gross domestic savings duties 1999 World Development Indicators 397 on exports 5.5 import value 4.4, 6.2 on imports 5.5 import volume 4.4 See also Tariffs nominal growth, by region 6.2 goods and services, domestic 4.15 OECD trade 6.3 highest marginal tax rate real, growth less growth in real GDP 6.1 corporate 5.5 See also Balance of payments; Exports; Imports individual 5.5 income, profits, and capital gains 4.15 Trade blocs, regional international trade 4.15 exports within bloc 6.5 other 4.15 total exports, by bloc 6.5 social security 4.15 tax revenue, share of GDP 4.15, 5.5 Trade policies-See Tariffs Technology-See Computers, personal; Exports, merchandise, high Traffic technology; Fax machines; Internet hosts; Research and development; accidents, people injured or killed 3.12 Telecommunications, international road traffic 3.12 See also Roadls Telecommunications, international cost of call to U.S. 5.1 0 Transport-See Air transport; Railways; Roads; Traffic; Urban indicators outgoing traffic 5.10 Treaties, participation i'n Telephones biological diversity 3.14 cost of local call 5.10 CFC control 3.14 mainlines climate change 3.14 per employee 5.10 Law of the Sea 3.14 per 1,000 people ozone layer 3.14 in largest city 5.10 national 5.10 Tuberculosis 2.16 revenue per line 5.10 waiting list 5.10 waiting time in years 5.10 mobile 5.11 Unemployment rate male and female 2.5 Television total 2.5 cable subscribers per 1,000 people 5.11 sets per 1,000 people 5.11 UN, others, net concessional flows from 6.12 Terms of trade, net barter 4.4 UNDP, net conces-sional flows from 6.12 Tetanus vaccination 2.14 UNFPA, net concessional flows from 6.12 Threatened species-See Biological diversity UNICEF, net concessional flows from 6.12 Tourism, international Urban indlicators expenditures 6.14 selected cities .inbound tourists, by country 6.14 area 3.11 outbound tourists, by country 6.14 average income, per capita 3.11 receipts 6.14 crowding 3.11 population 3.10, 3.1 1 Trade travel time to work 3.11 arms 5.7 work trips by public transportation 3.11 exports plus imports See also Pollution, air; Population; Safe water; Sanitation as share of GDP 1.5 as share of PPP GOP 6.1 merchandise as share of goods GDP 6.1 Value added direction of trade, by region 6.2 as share of GDP export value 4.4, 6.2 agriculture 1.5, 4.2 export volume 4.4 industry 4.2 398 ±999 World Development Indicators manufacturing 4.2 minimum 2.6 services 4.2 growth Waste collection, households with access 3.11 agriculture 1.4, 4.1 industry 1.4, 4.1 Water-See Freshwater; Safe water manufacturing 4.1 services 1.4. 4.1 WFP, net concessional flows from 6.12 per worker agriculture 3.3 Workweek, average hours 2.6 manufacturing 2.6 total World Bank, net financial flows from 6.12 manufacturing 4.3 See also International Bank for Reconstruction and Development; International Development Association Wage agricultural 2.6 1999 World Development Indicators 309 DISTRIBUTORS CZECH REPUBUC HUINGARY KOREA, REPUBLIC OF PERU SRI LANKA, THE MALDIVES BOOKSELLERS ISIS, NIS Prodeiva Euro Into Service Dayang Backs Trading Co. Editorial Desarrollo PA Lake House Rookshop OF WORLD BANK Cavelkava22 Margitsageti Europa Hoe International Division Apartado 3824. 100, Sir Chittampalaro OF WORLD BANK 13I000 Prague 3 H-1138 Budapest 7R3-20, Pangbe Ban-Dovg. loa 242 OF. 106 Gardinr,e PUBLICATIONS Tell: (4120 2)2423 1.486 Tel: (36 1) 350 80 24, 350 Pohoh-ka Lime P Mawataha PUBLICATIONS Prices and ceras: terms vary Peax: i420 2) 2423 1114 80 25 Seoul Tel: (51 14) 285380 Colambo 2 Prices vry fronm cso,try to fram country to cocvtry. Wahoite: wwwn.is.vz/ Faa: (38 1) 35090 32 Tel: (82 2) 536-9555 Tao: (53 14) 286628 Tel: (94 1) 32105 cooctry Consulr yourlecai Tcosoultyour localodistrinutor Email: Faa: (82 2) 536-0025 Fax: (94 1) 432104 bocoksller for prices end ,ibraTe Piecioga- arder. DENMARK euroa,fta@mil.matav.hu Email: searnap@vholliareeet PHILIPPINES Email: LHL@s4.lanka.net availability SamfundsLitterautr INDIA Internationial Booaksource ARGENTINA Rosenoeres Aill 11 Allied Publishers Ltd. EaIyoo Pablishing Ce., Ltd. Caeate Inc. SWEDEN B3ULGARIA Wodd Fublications PA DK-1970 Fredmfiksberg C 751 Moses Road 46-1, Susong-Dong 1127-A AntSpolo Pt, Wennergren-Williams AB Hamanities Research Caster An. Cordoba 1677 Tel: (45 35) 351942 Madras - 600 002 Jongra-Gu Barue,gay, Venezuela P. 0. Boo 1305 P.O. Boo 1784 1120 Ciadad de Tao: (45 35) 357822 Tel: (61 44) 852-3938 Seeul Makei City S-171 28 PoIse 1764 Sofia 6senos Aires Website: wwve.sL.cbs.ck Faa: (91 44) 852-0649 Tel: (82 2) 734-3515 Tel: (63 2)6896 6001: 6505: Tel: (46 8) 705-97-50 Tel: (356 2) 76 61 57 Tel/Tao: (54 11) 4815-8156 Tao: (82 2) 732-9154 6507 Faa: (46 6) 27-00-71 Tao: (359 2) 76 35 34: 76 Email: ECUADOR INDONESIA Faa: (63 2)6896 1741 Email: mailinasise 2784 vrpbooks@ivtfva.a.m.ar :Libro Moedi Pt. lodira Limited LEBANON Email: chrSmgu.bg blinaria Internecional Jalat Borohadur 20 Libraide du Liban POLAND SWITZERLAND AUSTRALIA, FIJI, PAPUA P.O. Baa 17-01-3029 P.0. Bay 181 P.0. Baa 11-9232 Intereatioval Publishing Librairle Payer Service CHINA NEW GUINEA, SOLOMON Juas Lean Maca 851 Jakarta 10320 Beirut Service Institutionnel Chine hatiotal Publications ISLANDS, VANUATU, Quite Tel: (62 21) 390-4290 Tel: (961 9) 217 944 U). Plakna 31/37 C6tes-de-tatntbenon 30 :import & Expart Corporation SAMOA Tel: (593 2) 521-606: (893 Tao: (62 21) 350-4269 Tao (961 9i 217 434 00-677 Warzawa 1002 Lausatee 16 Gongti East Read D.A. Itfermation Services 2) 544-185 Email: hsayeghsS:SIbr:e,-du- Tel: (48 2) 628-6089 Tel: (41 21) 341-3229 :Post Coda 100020 648 Whitehorse Road Fax: (593 2) 504-209 IRAN liban.com.lb Fax: (48 2) 621-7285 Fax: (41 21) 341-3235 Bej,ng Mitcham 3132. Victoria Email: Ketab Sara Ca. Pubi,shers Website: aowssibrairde-da- Email: Tel: (61) 389210 7777 librdm"'l@:enmund:.com.ec Khaled Esiamboli Ave., liban.comfib booksSips3-ikp.atm.corm.pl ADECO Mev Diermen HUNGARY Faa: (61) 3 9210 7788 Email: 6th Street -Welbsit.: EditionsTechviqu-s Fvuvdation tar Market Email: librim,u2@libr:mund:.com.ec Delatrooz Alley ho. 8 MALAYSIA wvwsmccg.,wa,,,.pl/ipo/eepvr Ch. de Lacauez 41 Economy serviaedadirect.com.au RO 'Bee 15745-733 University of Malaya t/ CH1807 Blenay 112 Pft 249 Websita: CODEU Tehrat 15117 Cooperathve Tel: (41 21) 943 2673 1519 Budapest a"m`w.dadire&ct.c*m.au Rule de Castilla 763. EdifL Tel: (99 21) 8717819: Bookshop, Limited PORTUGAL Faa: (41 21) 94383605 Tel: (36 1) 204 2951: 204 Expocolor 8716104 P.0. Baa 1127 Livraria Portugal 2948 AUPSTRIA Pdmer pbso, 0f. 62 Faa: (98 21) 8712479 Jaien Pantal Bare Apartado 266.1, THAILAND F am: (36 1) 204 2953 Gerald and Co. Quito Email: ketab- 59700 Koala Lompur Rua D0 Cormo 70-74 Central Books Distributiot Email: ipargaod@hungary.net WeihbSurggasse 26 Tel: (593 2(507-383 sara@neda.netj:r Tel: (60 3) 796-5000 1200 Lishon 306 Shaom Road A-lOll Wiv Past (593 2) 293-091 Faa: (60 3) 756-4424 Ta): (0) 347-4952 Bangkook 10500 JORDAN Tel: (43 1(512-47-31-0 Email: oadeu6-,mpsat.net.ao KSokeb Publishers Emnail: amlaopStm.eet.my Fee (1) 347-0264 Tel: (66 2) 2336930-9 Glehal Develapavent Farom Faa: (43 1) 512-47-31-29 P.O. Bat 19575-51:1 Faa: (66 2) 237-8321 P.0. Rev 941488 WesoSa: ~~~~~~EGYPT, ABAB REPUBUIC OF Tehran MEXICO ROMANIA Amman 11L194 w-a.gerold.ca/at.onl:ne Al Abramn Disnbtriaot Agency Tel/tao: (98 21) 258-3723 INFTOTEC Compavi De L:Errfi, TRINIDAD & TOBAGO Tel: (962 6) 8837701 Al Gale. Street An. San Fernando ha. 37 Bucuresti PS.A AND THE CARRIBBEAN Pot: (962 6) 5537702 BANGLADESH cairo IRELAND Cvi. Tadello Gcerra Str. Lipscani to- 26, Pyotematics Studies Ltd. Email: gdhSindex.com.)e Micro Induostes Tel: (20 2) 578-6083 Government Supplies 14050 Mealc.OF. seaterl3 St. Agugstine Shopping Developmvent Tao: (20 2) 578-6833 Agency Tel: (52 5(624-2800 Bucharest Center KOREA, Republic of Assistance Satiety (MIDAS) OilOg av tSvidthair Faa: (52 5) 624-2822 Tel: (40 1( 313 9645 Eastern Malt Road, Sejovg Books, Inc. Cease 5, The Middle East Observer 4-5 H-arcoart Read Email: intotec@-rtnnt.mnx Tax: (40 1) 312 4000 Pt. Augustine 636-7 Junggok-Dong Road 16 41. Shedtf Street Dsblin 2 Wesaite: rts.vet.mx Trividad A Toabgo, West Kwnagia-Ku Ohanmondi R/Area Carro: Tel: 1353 1) 661-311.1 RUSSIAN FEDERATION Indies Seoul 143-220 Dhaka 12o9 Tel: (20 2) 353-6519 F-ao (353 1) 475-2670 :Mc-d-Prassa Medico S.A. isdatalst- V3es Mire Tel: (868) 945-466 Tel: (82 2) 498-0300 Tel :(880 2) 126427 Tao: (20 2) 393-9732 de C.V. 9a, Kolpachnly Feemalok Tao: (868) 645-8467 F ao: (82 2) 3409-321 Tao: (880 2) 811188 Email: ISRAEL c/Rio Panuco. 141-Colonia Mascow 101831 Email: tobenterleidad.net IEmail: danielchtvi@se)ong- malaauc@me-bseroer.com. Yoemet Literature Ltd. C.aahtemoc Tel: (7 095) 517 87 49 :bavks.cam B3ELGIUM ag P.O. Baa 56055 06500 Mexico, D.T. Faa: (7 0951 917 92 59 UGANDA IWebsohe: Jean Dn Lannoy URL: 3 Tohavan Hasandlar Street Tel: (52 5) 533-5658 ozimahe@nglasnet.rc Gcstro Ltd. : wwesej ongbvaks.com Se, du Rei 202 aoaameolbserser.com.eg Tel Aviv 61560 Tao: (52 5) 514-6799 P.O. ass 9997, Madhvaer 1000 Brussels Tel/Tao: (972 31 5288-397 SINGAPORE; TAIWAN. Building NEPAL Tel: (32 2)8538-5169 FINLAND NEPAL CHINA Plot 16/4 Jinja Rd. Bazaar International Faa: (32 2) 538-0841 Aketeemiene Kidakauppa R.OY. Intemational Everest Media Isternational MYANMARt BRUNEI Kampala GOo Boa 2480 P.O. Baa 128 P.0. Bax 11056 Services (P.) Ltd. Hemisphere PsAlicaatio Tel: (256 41) 251 467 Kathmandu BRAZIL FIN-00101 Helsinki Tel Aviv 61130 GPO Baa 9403 Services Tao: (256 41) 251 468 Tel: (977 1) 22-29-83 Pablieacdes Tecnicas Tel: (358 0) 121 4418 Tel: (972 3)9649 9469 Kathtmwdu 41 Rallaeg Podding Read Email: Faa: (977 1123-94-37 Internacionais Ltda. Pay: (358 0) 121-4435 Faa (972 3) 648 6039 Tel: (977 1) 416 026 604-03 gus@smwftganda.com Rua Pexoata Gomide, 209 Email: Email: royIiatvetnisin.set.:l Fec: 1977 1) 224 431 Golden Wheel Bailding SLOVAK REPUBLIC 01409 Say Paulo, SP. akaIdaus@stockmann.vi mebs,ite: wwaoymoait.av.Ji Singapore 349316 UNITED KINGDOM Slasart 0.T10. Ltd. Tel: (55 11) 259-6644 Website: eswakateemi- NETHERLANDS Tel: (65) 741-5166 Microeina Ltd. Krapinski§ 4 Tao (55 11) 258-6990 nev-m Falestiviat Authority/ DeLindeboom/ Tao: (65) 742-9356 P.0. BooS3, P.0. Baa 152 Email: Middle East Internationale Email: aohgate@asianaon- Omega Park. 852 99 Bratislava 5 postmaster~tIp.uol.br FRANCE Index Information Services Pablin-ties Au. n.at.-m Alto., Tel: (42 7) 839471: Webohe.: vau.ol.br Editions Eska: DEJ PROB. 19502 Jerusalem P.O. Baa 202. 7480 AE Hampshire GU34 2P5 839472: 839473 48. rca Gay Lussac Tel: (572 2(6271219 Haalksbargev SLOVENIA England Faa: (42 7) 839485 CANADA 75005 Pads Tao: (972 2) 6271.634 Tel: (31 53) 574-0004 Gospodaroki vestnik Tel: (44 1420( 86848 Email: gtg@intemst.sk Reteuf Publishing Ca. Ltd. Tel: (33-1) 55-42-73-08 Faa: (31 53) 572-9296 Pablishing Group Fa- (44 1420) 89889 5369 ranotek Road Tao: (33-1) 43-29-91-67 ITALY, LIBERIA Email: l:rndebe@o9vdrvlo- Duvajska aesta 5 Email: THAILAND Ottawa, Ontada KI 9.13 Licosa Cemmissionadia Ive-i 1000 Ljubljana a,bank@-m:croinfo.ce.ah Chciaiovgkorv University Tel: (613) 748-2665 GERMANY Sansmai SPA Wehsite: Tel: (385 61( 133 83 47: Website: Book Center Fax: (613) 748-7660 UNO-Verdag Via Dove Di Calabria, 1/1 wwemworIdonlIne.nI/-limde- 132 12 30 eac.micrvinfo.co.uk Phyathai Road Email: order.dept@reveufh Poppelsdorfer Alien 55 Casella Pastael 552 boo Tao: (386 61) 133 80 33 Bangkok 10330 baoks.com 53115 Boon 50125 Rrance Email: The Stationery Office Tel: (66 2(218 7292 Wehsite: ~~~~~Tel: (48 228) 949020 Tel: (39 55) 645-415 NEW ZEALAND repansekjWgvestnik.si 51 hive Elms LaneTa:(6225441 Wawuea.fsobovks.cam Tao: (49 228) 217492 Faa: (39 S51)641-257 EBSCO NZ Ltd. Londan 5W8 508 Website: wwwunoesvedag.de :Email: IIcvseaLftb-u it Privt MalBg:91 OUHARCA, Tel: (44 171) 873-8372 TURKEY CHINA Emal: noverlg~aol.co Website;-.ftbcc.t/licosa Nem Market BOTSWANA T ax: (44 171) 873-242 Dsvya Intotel, A.S. Chive Financial A Ecana.mic Auckland Tar single tites: : Website: wwmathe-st:ationery- 100 Ti: Mahallesi Pubi,shing Cease GHANA JAMAICA Tel: (64 9) 524-8119 Oxtard Eviversity Press otfice.co.uh/ 34440 Bagvilar-lstanbul 6, Da Ta Si Doag one tpp Boaka S-ri.as Ian Ruadle Publishers Ltd. Fas (64 9(524-8067 S-uthers Africa Tel: (90 212) 625 0808 Be)ivig RO. Boo 44 208 Old Hope Reed, Viasco Boulev,ard, Goadwaed VENEZUELA TFo: (90 212) 629 4689: Tel/Tao: (86 10) 6401-7365 TUC Kingston 6 Oasis Official P.O. Boa 12119, hI City Tecni-Ciencia dbrs, 5.A. 629 4627 Acra Tel: 878-927-2085 P.O. Box 3627 7463 Centre Caidad Camermial Email: daunyaWduncl-y China Book Import Centre Tel: 223 21 778843 Tao: 878-677-0243 hhCiliinga Cape Town Tamanca gaeete.com.tr P0O. Baa 2825 Tao: 223 21 779099 Email: irpld'aolis.com Tel: (84 4) 499 1551 Tel: (27 21) 595 4400 hivel C2, Caracas Website: wwssclurpy.aa.m/ Be(ivig Tao: (644( 459 1972 Tao: (27 211 5956 4430 Tel: (5882) 959 5547: 5035: GREECE JAPAN Email: oasis@aatrIsges.nz Email: ectard@oup.se.za 0018 UNITED ARAB EMIRATES Chinese Corporatian tor Papasotiriau S.A. Easterv Beak Service Website: Tao: (58 2) 959 5636 Al Hamim Stationery A Promotion at Cumanities 35, Stourvara Str. 3-13 Congo 3-chanme, mwwaoas:sbooks.ce.nz/ Far suakeaaptior, erdero: Bookoshop 52, You Fang Hu Tang, 100 82 Athays Bonhpo-ku Internatioval Subscription ZAMBIA P0O. Baa 5027 Xuaan Nei Da Jie Tel: (30 1) 364-1826 Toktyo 113 NIGERIA Service University Boakhbop, Shaqah Beijing Faa: (30 1) 364-8254 Tel: (81 3) 38180861 University Press Limited P0O. Boo 41095 Unvirsaity of Zam,bia Tel: (971 6( 734687 Tel/tas: (86 10) 660 72 494 Tat: (80 31 3818-0864 Three Crowns Buildling Cruighall Great East Read Campus Tao: (971 6) 384473 HAITI Email: orders@svt-ebs.co.lp Jerichv Johantesburg 2024 P0O. Box 32375 Pager. (871 6) 8760576 COLOMBIA Culture Ditffusion Website: PHvate Mail Bag 5095 Tel: (27 11) 880-1448 Lusaka nfoeenlace Ltd.. 5, Rue Capois amm.bekkaame.orjp/.-svt- lbadan Faa: (27 11.)880-6248 Tel: (260 1) 252 576 URUGUAY Carmve 6 No. 51-21 C.P. 257 ebs Tel: (234 22( 41-1356 Email: iss@is.co.za Faa: (260 1) 253 952 Librerg T8cnica Uruguaya Apartade Arem 34270 Part-auc-ranse Tac: (234 22) 41-2056 Cofonfa 1543, Piso 7, OT. Santafk de Bagat6, D.C. Tel: (509( 23 8260 KENYA :SPAIN ZIMBABWE [702 Tel/tao: (57 1) 288-2798 Tao: (509) 23 4858 Aft.c Book Sarsime PAKISTAN I Mandi-Presa Libros, P.A.. Academic and Baobab Caoilla de Correo 1518 (E.A.) Ltd. Mvirza Revk Agency Castello 37 :Bvoks 1Pvt.) Ltd. Montevidev 11000 C6TE D'IVOIRE HONG KONG, CHINAt Q.aara House, 65, SRahrah-eQua:d-aAzam 28001 Madrid 4 Conald Road, Graviteside Tel: 1598 21490072 Center dEdation at de MACAO Mtangano Street Lahore 84000 Tel: 134 91) 4 363700 P.O. Boo 567 Tao: (898 2(41 34 48 Diff.sio Asia 2000 Ltd. PDO Boo 45248 Tel: (92 42) 735 3601 Tao: (34 91( 5753998 Harem Africamnes (CEDA) Sales A Cir-cation Nairobi Tao: (92 42( 578 3714 Email: Tel: 263 4755035 04 B.P. 541 Department Tel: 1254 21 223 641 Oxford Eniversity Press Iibreda@muncdiprensa.es Tao: 263 4 781913 Abidjan 04 302 Seabird House Tao: (254 21 330 272 5 Ba-galore Taon Webshte: Tel: (225) 2489510; 24 22-28 Wyvuhaim Street, Legacy Books Sharae Faisal -se.und:prensa.aorm/ 6511 CentralHvng Kong, Loita Hvase P.O. Bay 13034 Teax: (225( 25 0567 Chive Me-canin I Karalhi-75350 Munai-Prensa Barcelana Tel: (852) 2530-1409 P.O. Boo 68077 Tel: 192 21) 446307 Conseil da Cast, 391 CYP'RUS Teax: (852) 2526-1107 Nairobi Test (92 23) 4547540 08009 Barcelona Center for Applied Reseamch Email: Tel: (254) 2-330853, Email: Tel: (34 3) 488-3492 Cyprus College sales@asia2000.cvm.hk 221426 ouppak@Threffice.net Taa: 134 3( 487-7659 6, Diogenes Winest, Engormi Website: Tao: 1254) 3-330854, Email: P.O. ass 2006 a-c.asia2000.cam.hk 561654 Pak Beak Corporatian barcaiona@mundipransa.as hicosia Email: Leg-pTafmnrmeetvcen dAic Chambers 21. Queens. Tel: (387 2) 59-0780 Road Tao: (3572( 68-2051 Lahosre Tel: (92 42) 836 3222: 636 0885 Teax: (92 42) 636 2328 Email: pbcrSbra:e.net.pk NY -S-a : .- ;^. .;:. The third annual edition of the World Bank's flagship statistical reference-World Development Indicators 1999. This award-winning publication provides an expanded view of the world economy for 148 3j it s> countries-with chapters focusing on world view, people, environment, economy, states and markets, and 0' iEjig-,i Fi-r global links, as well as introductions highlighting recent research on major development issues. The 1999 . .C01Bl edition includes some key indicators for 1998. >* gtt , . April 1999 420 pages Stock no. 14374 (ISBN 0-213-4374-2) $60.00 :V001@ey g @i; Also available on :'i:' t.3 *,.3iS,r3 ] This comprehensive database contains underlying time-series data for the World Development Indicators and World Bank Atlas, now covering 1960-1997 for most indicators with some - extending to 1998. Powerful features allow you to generate maps and charts and down _ i load your results to other software programs. April 1999 Individual Version: Stock no. 14375 (ISBN 0-8213-4375-0) $275.00 Network Version: Stock no. 14376 (ISBN 08213 4376-9) $550.00 This year, for the first time, World Development Indicators is available as a book and sint .-- user CD-ROM package. Stock #: 31671 Price: $295.00 _ndicators. Tb e r a tffi2 ,,1~X, . ,One of the Bank's most popular offerings, the Atlas is designed as a companion to the World Development r 3|11Yi-it {t N,,. s,Indicators. Tables, charts, and colorful maps address the development themes of world view, people, 4 Ei3s'g a..3 , .environment, economy, states and markets, and global links. This easy-to-use book is an international standard in statistical compilations and an ideal reference for office or classroom. Text, maps, and references appear in English, French, and Spanish. April 1999 64 pages Stock no. 14377 (ISBN 0-8213-4377-7) $20.00 §W§*=tr bit t3L,,j,t3,3,3ePyPtt;~~~~~~~ustmer inth U.S.:F Title Stock# Price Copies Subtotal c Customerst in the U S: return it in the enclosed business reply envelope. To charge by credit card call (800) 645-7247 or (703) 661-1580 or send this completed coupon via fax to (703) 661-1501. Customers outside the U.S.: Contact Subtotal your local distributor for information on Shipping & handling prices in local currency and payment terms. If no distributor is listed for your country, fax Total US$ this order form to the number above or mail it to World Bank Publications, P.O. 960, Herndon, VA 20172-0960, USA. Name (Please Print)//IIII/IJJ I_/ I / I J J J I I I/I_11_I Address J _/ J J / J I J J *Shipping and handling charges are $8.00 per order. If a purchase order is used, _ __ __ …………/DJDJDJX X XJX X XJ-/1/ / actual shipping will be charged. For air mail City J … I J J J J J I I / IJ J J I _/ IJ J J State J J delivery outside the United States, charges are $13.00 for one item plus $6.00 for each Zip/Postal Code __I __1/J J/ Phone __X __I __I- __I __/ J- J _/ J J - Ext. _ - - - I additional item. Method of Payment i Enclosed is my check drawn on a U.S. bank made payable to the World Bank Institutional Customers in the U.S. Only II Charge my LI Visa O MasterCard O American Express O Bill me (Purchase Order must be included) Credit Card Account Number _J / i / J I J / J J J I _//I Exp. Date X -1 D OBegin Standing order Signature WD99 ulLi® zwo WSy Wci ow East Asia and the Argentina Maldives Belgiume Pacific Barbados Nepal Canada American Samoa Belize Pakistan Denmark Cambodia Bolivia Sr Ladnka Finlando China Brazil France* Fiji Chile Sub-Saharan Africa Germany* Indonesia Colombia Angola Greece Kiribati Costa Rica Benin Iceland Korea, Dem. Rep. Cuba Botswana Ireland* Lao PDR Dominica Burkina Faso Italy* Malaysia Dominican Republic Burundi j Japan Marshall Islands Ecuador Cameroon Korea, Rep. Micronesia, Fed. Sts. El Salvador Cape Verde Luxembourge Mongolia Grenada Central African Republic Netherlands* Myanmar Guadeloupe Chad New Zealand Palau Guatemala Comoros Norway Papua New Guinea Guyana Congo, Dem. Rep. Portugal* Philippines Haiti Congo, Rep. Spain* Samoa Honduras Cote d'lvoire Sweden Solomon Islands Jamaica Djibouti Switzerland Thailand Mexico Equatorial Guinea United Kingdom Tonga Nicaragua Eritrea United States Vanuatu Panama Ethiopia Vietnam Paraguay Gabon Other high-income Peru Gambia. The Andorra Europe and Central Asia Puerto Rico Ghana Aruba Albania St. Kitts and Nevis Guinea Bahamas, The Armenia St. Lucia Guinea-Bissau Bermuda Azerbaijan St. Vincent and the Kenya Brunei Belarus Grenadines Lesotho Cayman Islands Bosnia and Herzegovina Suriname Liberia Channel Islands Bulgaria Trinidad and Tobago Madagascar Cyprus Croatia Uruguay Malawi Faeroe Islands Czech Republic Venezuela Mali French Guiana Estonia Mauritania French Polynesia Georgia Middle East and [ Mauritius Greenland Hungary North Africa Mayotte Guam Isle of Man Algeria Mozambique Hong Kong, China Kazakhstan Bahrain Namibia Israel Kyrgyz Republic Egypt. Arab Rep. Niger Kuwait Latvia Iran, Islamic Rep. Nigeria Liechtenstein Lithuania Iraq Rwanda Macao Macedonia. FYR Jordan Sao Tom6 and Principe Martinique Moldova Lebanon Senegal Monaco Poland Libya Seychelles Netherlands Antilles Romania Malta Sierra Leone New Caledonia Russian Federation Morocco Somalia Northern Mariana Islands Slovak Republic Oman South Africa Qatar Tajikistan Saudi Arabia Sudan Reunion Turkey Syrian Arab Republic Swaziland ISingapore Turrmenistan Tunisia Tanzania II Slovenia Ukraine West Bank and Gaza Togo United Arab Emirates Uzbekistan Yemen, Rep. Ugandal Virgin Islands (U.S.) Yugoslavia, FR Zambia (Serbia/Montenegro) South Asia Zimbabwe * Member of the Afghanistan European Monetary Union. Latin America and the Bangladesh HEgh-tncore OECD Caribbean Bhutan Australia Antigua and Barbuda India Austria* Th, world by region o- L 00d riddle- Hroom 000n0m 00 high Income I otor es C d d h Lt At a sod tno Poe fi, SI ddli ELOt and North Af, so OECD D Nho dtt Classified according to World Benk analytical C) E-opo aod Control As o Sooth AOis 3D Otri grouping. Lotor A-rir - l roe Carlhbb-os C Siih-Ssrro At to = - . -Ic .,-. --5 ,, -. . _ . K A , I . S.I .* -ffi~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- -- ~ ~ ~ -= II I = 101 2 t . , r _ ~ ~ ~ ~ ~ ~ ~ . i ., , , I . I, , . , - -~ ~~~~~~~~~ .' I ti.i;; , i The World Bank 1818 H Street N.W. Washington, D.C. 20433 USA Telephone: 202 477 1234 - Fax: 202 477 6391 Telex: MCI 64145 WORLDBANK or MCI 248423 WORLDBANK Cable address: INTBAFRAD WASHINGTON DC Website: http://www.worldbank.org Email: books@worldbank.org Th- World Development Indicators * Includes more than 800 indicators for 148 economies * Includes definitions, sources, and other information about the data Alt * Organizes the data into six thematic areas: SB~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 3 7 @ The World Development Indicators Is printed on recycled papefr I i 9 780821 343746 ISBN 0-8213-4374-2