WPS7073


Policy Research Working Paper                       7073




      CGE Analysis of the Impact of Foreign
       Direct Investment and Tariff Reform
           on Female and Male Wages
                                 María C. Latorre




Development Research Group
Trade and International Integration Team
October 2014
Policy Research Working Paper 7073


  Abstract
 This study analyzes the impact on male and female wages                            females. The most skilled (female and male) workers, who
 of tariff reform and the reduction of regulatory barriers                          are also the most intensively used in the business services
 faced by domestic and foreign firms operating in busi-                             sectors, benefit more from the real increases in wages. The
 ness services. The study applies the model to Tanzania and                         model illustrates that as the development process con-
 develops a data set that distinguishes labor and wages by                          tinues and developing countries become more business
 gender for 52 sectors and four skill categories. The model                         service oriented, these sectors demand more educated
 is the first to incorporate modern trade theory to assess                          workers and their wages will increase relative to those of
 the gender implications of trade reform. Given that the                            unskilled workers. The policy conclusion from this model
 Dixit-Stiglitz framework results in productivity gains                             is that it is crucial to invest in the education of females
 from additional varieties of services, the analysis finds that                     so their human capital increases and their skills are more
 real wages increase across all worker categories. However,                         marketable in business services and other more techno-
 the increase in wages is higher for males than for females,                        logically modern occupations. Otherwise, the wage gap
 because business services use males more intensively than                          between males and females would likely widen further.




  This paper is a product of the Trade and International Integration Team, Development Research Group. The paper is
  financed under BNPP Growth and Equity Trust Fund number TF012466: “Reducing Trade Costs in East Africa: Analytical
  Development and Capacity Building.” It is part of a larger effort by the World Bank to provide open access to its research
  and make a contribution to development policy discussions around the world. Policy Research Working Papers are also
  posted on the Web at http://econ.worldbank.org. The author may be contacted at mclatorre@ucm.es.




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                                                       Produced by the Research Support Team
CGE Analysis of the Impact of Foreign Direct Investment and Tariff Reform
                                 on Female and Male Wages



                                          María C. Latorre
                              (Universidad Complutense de Madrid)




Keywords: gender, FDI in services, trade, ad valorem equivalents, endogenous productivity
effects.

JEL codes: C68, F21, F23, F17, F16, J16

Acknowledgements: The author wishes to thank David Tarr and Jesper Jensen for very helpful comments and
suggestions. The paper was financed under BNPP Growth and Equity Trust Fund number TF012466: “Reducing
Trade Costs in East Africa: Analytical Development and Capacity Building.”
1. Introduction



As is well known, the promotion of gender equality is one of the “Millennium development
goals.” Female workers seem to be more exposed to vulnerability at work than males (e.g.,
United Nations, 2009; ILO, 2009). Despite recent improvements, women exhibit a higher
involvement in unpaid (but productive and time consuming) domestic and care activities in
developed and developing countries (Blau and Kahn, 2000). The main body of evidence
suggests the persistence of a gender wage gap (i.e., ratio of female to male wages) after
adjusted for worker characteristics (e.g., Aguallo-Tellez, 2011). However, the causes behind
these statistics are complex and multifaceted and a variety of factors other than discrimination
may bear to some extent on the different situations of female and male workers (Ganguli et al.,
2011; Fernandez, 2007). Discrimination seems to exist but a precise estimation of its
magnitude is difficult (Blau and Kahn, 2006). Although the first studies go back to Becker
(1957) and Arrow (1971), analysis of discrimination remains a challenging topic from a
theoretical and empirical perspective.

In this paper we extend previous analysis based on a computable general equilibrium (CGE)
model for Tanzania (Jensen et al., 2010), in order to focus on its gender implications. Given the
arguments in favor of a uniform tariff (Tarr, 2002), we analyze the effects of tariff reform, as
well as the reduction of regulatory barriers to the provision of specialized services by domestic
or foreign firms through foreign direct investment (FDI). How do foreign trade and the entry of
new domestic or foreign firms affect female and male workers? To what extent would men and
women be reallocated across sectors? What would be the effects on their wages? We aim to
shed light on these questions. To this end, we first work on the data that are publicly available
from the National Bureau of Statistics of Tanzania (NBS, 2002a). We produce an ambitious
three-dimensional (sex-sector-skill) data set for 52 sectors with factors of production
distinguished by four skill levels and by sex.

Our CGE methodology seems particularly well suited for in-depth analyses involving feedback
effects across sectors of the economy. Another advantage is its general equilibrium perspective,
which allows us to take into account the income and demand side of the economy, as well as
the interplay of goods and factor markets. We extend the model of Jensen et al. (2010), which
in turn builds on the more stylized theoretical model of Markusen et al. (2005), to include
actual gender and factors of production data on the Tanzanian economy. The model therefore
exhibits innovative features, compared to other CGEs, such as the presence of FDI and
multinational enterprises (MNEs) in advanced services sectors. This, together with the
incorporation of a Dixit-Stiglitz-Ethier mechanism in imperfectly competitive sectors, makes it
possible to capture potential increases in wages and workers’ reallocation throughout the



                                                                                               2
economy. As in Markusen et al. (2005), we find evidence contrasting with the predictions of
the Stolper-Samuelson Theorem. This aspect is critical to explaining the evolution of wages.
Furthermore, the model explicitly takes into account the different cost structures of foreign and
domestic firms in business services. Since foreign firms are generally less labor intensive than
national firms, a higher presence of foreign firms could reduce labor demand in business
services. There are both partial and general equilibrium effects of this phenomenon that need to
be taken into account simultaneously in order to analyze it. Indeed, we find that FDI
liberalization in services benefits both males and females, but it benefits males more, due to the
greater skill levels required in business services. Note that the few gender-aware CGE models
that have analyzed trade liberalization processes have included neither imperfect competition
nor FDI.

The rest of the paper is organized as follows. The next section presents the previous literature
on gender-aware CGE models and other studies related to them. Section 3 offers an overview
of data on gender and on the economic structure of the Tanzanian economy. Section 4 explains
the model, while section 5 describes the results obtained, including the evolution of output,
wages and factors’ reallocations. The final section offers the main conclusions. There are also
two appendices. The first one explains how the new three-dimensional data set has been
obtained and the second one presents a correspondence table between the sectors in Tanzanian
statistics and the 52 sectors of this paper.



2. Previous literature



Empirical studies of the impact of trade liberalization and deregulation on the wage gap have
found mixed results. Black and Brainerd (2004) find evidence that trade contributed to the
reduction in the wage gap in the United States during the 1976-93 period. Oostendorp (2009)
analyzes a large sample of developed and developing economies, looking at the effects of
growth, trade and FDI on gender gaps within particular occupational categories. While he finds
a decline in the gender gap for developed economies during the 1980s and 1990s, the effect is
less clear or insignificant for developing countries. Aguallo-Tellez (2011), after reviewing the
available empirical evidence on the impact of trade liberalization policies and FDI on wage
inequality, claims that they have increased wage inequality in both developed and developing
countries. Another review by Rama (2003), however, suggests that although the impact of
globalization is not evenly distributed across workers, there is no evidence of an increase in the
dispersion of wages by occupation. Black and Straham (2001) argue that deregulation in the
banking industry in the United States (and the subsequent increase in competition) results in
reductions of both female and male wages. However, given that the decrease in male wages
was greater, the gender wage gap was reduced.

From the theoretical point of view, among economists there are three main strands in the
literature on discrimination, leading to different policy prescriptions. They are: 1) the “taste for
discrimination model,” 2) the “theory of statistical discrimination,” and 3) the “crowding model


                                                                                                  3
of occupational segregation.” Chronologically, the first theory goes back to Becker’s (1957)
“Taste for discrimination model,” on which Arrow’s (1971) analysis is based. Employers’
preferences for discrimination against women are based on the idea that females impose
subjective or psychic costs on the employer, and that he (or his employees) wants to maintain a
certain physical or social distance from females. The strength of the “psychic cost” would be
reflected in a “discrimination effect” which could be measured in monetary terms. The
discriminatory employer would be indifferent between hiring a male or a female if female
wages were lower than those of males by an amount superior to the discrimination coefficient.
If there is indeed gender discrimination, so that female wages are lower than males’, a
discriminating employer will hire women only when those wage gaps are higher than his
discrimination coefficient. By contrast, non-discriminatory employers would hire every woman
independently of whether the wage gap has attained a particular level. Non- or less
discriminatory firms would have lower average total costs and product prices than
discriminatory producers. The more discriminatory firms would be driven out of business by
market forces. A problem for the Becker-Arrow theory is that, since market forces should
eliminate discrimination in the long run, it cannot explain the persistence of discrimination over
the long term.

A second strand of the literature is the “Theory of statistical discrimination” (Phelps, 1972;
Aigner and Cain, 1977; Fang and Moro, 2010). This holds that because it is costly to obtain
information about each job applicant, employers often use characteristics such as gender, race
or age as a proxy for productivity or other worker attributes that are not easily discernible. In so
doing they may well wrongly judge individuals on the basis of the average characteristics of
the group to which they belong, rather than on their own personal characteristics. As a result,
for example, married women who do not plan to have children (or do not plan to quit their job
if they do) would be discriminated against. In contrast to the taste for discrimination theory, in
this theory the employer minimizes hiring costs by practicing discrimination; so discrimination
may persist because it benefits those practicing it. Although this theory can explain
discrimination against an individual, since workers in a group are paid the average marginal
productivity of the group, it cannot explain a wage gap between groups.

Finally, the crowding model of occupational segregation (Bergmann, 2005; Sorensen; 1990;
Blau and Kahn, 2000, 2006) suggests that there are “women’s jobs” and “men’s jobs”, in the
sense that women are systematically excluded from men’s jobs that are high-paying and
crowded into low-paying occupations. Let us assume that women are as productive as men and
have homogeneous labor force characteristics. However, since women are crowded into a
rather small group of occupations, their supply exceeds demand and their wage rate is low. For
men, who are not confined to particular occupations, their supply does not exceed demand and
their productivity (and wage) is higher than those of women. In this model, the productivity of
women and men is a sort of “group” or “team” productivity. It depends on how supply
compares to demand across occupation. This theory predicts that if women are allowed to enter
“male” occupations, due to changes in social attitudes or in legislation, they will be less
crowded into the small group of low-paying occupations. Assuming that occupation shifts are



                                                                                                  4
costless, women will experience an increase in wages, while men will experience a fall.
However, the increase in women’s productivity and wages should prevail, since there is a
greater increase in their productivity and wages, given that they were disproportionately
crowded into certain occupations. Thus this theory predicts both equity and efficiency increases
when women are not excluded from certain occupations. This is a version of the Becker-Arrow
theory. If women are as productive as men but paid less, then firms could increase their
productivity by hiring women. The more productive firms should drive out the less productive
in the long run. Again, it is a challenge for this theory to explain the persistence of a wage gap
in the long term.

Only a handful of CGE models have dealt with discrimination and the majority have analyzed
scenarios of trade liberalization without FDI (e.g., Fontana and Wood, 2000; Fontana, 2003;
Siddiqui 2004, 2007; Arndt et al., 2006; Terra et al., 2007). However, their analysis of trade
firms’ production structure remains anchored in a setting of constant returns to scale. It is well
known that in modeling trade liberalization in CGEs, there are small welfare gains when
increasing returns to scale are absent (e.g., Francois and Reinert, 1997). Further, the previous
gender-aware CGE models, like many economic models, evaluate trade liberalization without
incorporating FDI. Among the few CGE models that include FDI some have found that FDI
liberalization has a stronger impact than trade liberalization (e.g., Jensen et al. 2007, 2010;
Brown and Stern, 2001; Bchir et al., 2002). Others have concluded that the result that trade
facilitation has more vigorous effects than FDI, (e.g., Jensen and Tarr, 2012) or that the results
vary according to a particular geographical area among those analyzed with the same model
(e.g., Petri, 1997). See Latorre (2009, 2010) for a review of these models and related literature.

In particular, the models of Jensen et al. (2007, 2010), Jensen and Tarr (2012) and Brown and
Stern (2001) incorporate a Dixit-Stiglitz-Ethier framework of monopolistic competition. In this
framework an increase in the number of firms (and product varieties) leads to potential
increases in both consumers’ welfare and producers’ productivity. The latter is due to the
possibility of obtaining a quality adjusted unit of services at a reduced price when there are
more varieties (i.e., more firms producing those services). These models apply to real world
data the stylized theoretical model of Markusen et al. (2005), which contradicts the predictions
of the Stolper-Samuelson Theorem due to the presence of the Dixit-Stiglitz-Ethier variety and
endogenous productivity gains effects. The present application to Tanzania also incorporates
these effects and therefore potentially captures factor remuneration adjustments absent in the
previous literature. It is of interest, therefore, to look precisely at how important this Dixit-
Stiglitz-Ethier framework is for the impact on female and male wages.

The few gender-aware CGE models available have focused on how to deal with “non-market
activities”, in which women account for a larger share than men. The issue is of relevance,
especially in developing countries, where the amount of work involved in household duties is
greater than in developed countries (clothes are washed by hand, food must be prepared on a
daily basis due to lack of refrigeration, water and firewood have to be carried over long
distances, etc.). These “non-market” activities were first analyzed in the papers by Fontana and
Wood (2000) and Arndt and Tarp (2000). The main contribution of Fontana and Wood (2000)



                                                                                                5
is to treat “domestic work” and “leisure” activities as two extra sectors in an otherwise standard
CGE. The rationale would be, again, that women are much more involved than men in these
two extra sectors. In order to add the sectors, information on time use is needed, to be able to
account for the amount of time allocated by women and men to leisure, household chores,
work, etc. The originality in Arndt and Tarp (2000) is that they develop a different treatment
for home consumption versus market consumption. Both types of consumption are possible for
all the commodities in the model.

Neither Fontana and Wood (2000) nor Arndt and Tarp (2000) disaggregate men and women
into different types of skill. Siddiqui (2007) does so, while including other improvements.
Apart from considering four different levels of skill (as in the present model), Siddiqui
introduces an extra interesting feature, namely, one household category for “all female-headed
households in rural areas”. This is a way of incorporating intra-household reallocation of
resources, capturing not only the “production” impact for women through wages but also
through the “consumption” side of the economy. There is considerable evidence, however,
suggesting that wages are the primary means through which women earn a living (United
Nations, 2009; Arndt et al., 2006).

In our model, we capture the impact of non-market consumption through the existence of a
factor called the subsistence factor, which is available in “Agricultural sectors” and in
“Fishing” and “Hunting and forestry”. We offer a revised ambitious four-skill-type
disaggregation of women and men. Our main contribution is, we believe, the innovative
modeling of potential sources affecting females’ and males’ remunerations and their sectoral
allocations through the presence of the Dixit-Stiglitz-Ethier framework and the entry of
multinational firms.



3. Data on gender and the Tanzanian economic structure



We use data (not typically available in most countries) from the Integrated Labour Force
Surveys (ILFS) of 2001 (NBS, 2002a) for Tanzania. In each sector, factors of production are
distinguished by four skill levels and by sex. At the top of Table 1, we show factor
remuneration in billions of current Tanzanian Shillings across all factor categories modeled for
four aggregates of sectors from the 52 sectors of our model. 1 The four broad sectors are: 1)
business services - those in which FDI is allowed to enter and regulatory barriers are going to
be reduced; 2) Dixit-Stiglitz goods, which produce under a Dixit-Stiglitz-Ethier mechanism in
imperfectly competitive sectors and with increasing returns to scale; 3) agriculture, and 4) other
CRTS (constant returns to scale sectors) - the remaining sectors that produce with constant
returns to scale. Exact correspondences between these four sectors, the 52 sectors of the model
and the sectors in Tanzanian statistics are given in Appendix 2.The sum of all factor

1
 Table 16A in Appendix 1 presents the value added shares of all factors across the 52 sectors. Note that the last
column of Table 16A offers the share in “Total value added” (or GDP).



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remunerations yields the “Total value added” column, whose value matches that in the
Tanzanian National Accounts. 2

Relying on the publicly available data of the ILFS (NBS, 2002a), we have undertaken a
thorough revision of labor shares in the SAM of Thurlow and Wobst using: 1) better proxies
for workers’ skill levels, 2) a wider coverage of wage data, and 3) more accurate information
on child labor. 3 In our new data set nine different occupational categories are converted into
four different skill levels, following the OECD suggestions (Elias, 1997, p. 7). The new skills
of workers are: “Unskilled”, “Laborers”, “Technicians” and “Professionals”. See the note in
Table 1 for the exact conversions. Therefore, we do not proxy skill levels with educational
categories, as Thurlow and Wobst (2003) do, because apart from “Traditional Agriculture”,
there is no information on the skill of workers regarding their wages in other sectors. By
contrast, we know occupations and wages for nine broad sectors, which are extrapolated into
52 sectors based on their value added weight. Furthermore, we assign different wages to the
self-employed, which is one of the main difficulties in estimating labor shares (Arpaia, et al.,
2009; Guerriero, 2012; ILO, 2010; Gollin, 2002). We also use children between 10 and 17
years old for whom there is data on their distribution across sectors with their corresponding
wages and occupations, instead of the children between 10 and 14 years old of Thurlow and
Wobst (2003), on whom that information is not available. The new category of children
between 10 and 17 years old is labeled “young workers” in our new data set. In addition to
labor, total value added is also composed of subsistence, 4 land and capital. Subsistence and
land in our model are the same as in the SAM of Thurlow and Wobst (2003), while capital
differs only slightly and is calculated (in our new data set) as a residual, subtracting from total
value added the values of subsistence, land and all labor categories.

There are considerable differences in factor intensity in the four broad sectors we analyze in
Table 1. As we move from the top to the bottom of that table, two measures of factor intensity
appear. There has been a considerable debate on the best way to proxy factor intensity in the
literature, particularly in the context of more than two factors of production (e.g., Bowen et al.,
1987). We present first a rather standard measure of “Factor intensity”, in which the
remuneration of each factor corresponds to its weight in total value added, the latter being
normalized to 100 (see column “Total value added”). The factor intensity of a sector, e.g.,
“Business services”, is calculated by comparing each factor share in the services with the
overall use of that factor in the whole economy (row “All sectors”). Thus, male Professionals
are used very intensively in Business services (4.37%) in comparison with their overall use

2
  This coincides with the total value added used in the Social Accounting Matrix (SAM) constructed by Thurlow
and Wobst (2003), which, except for its original factor shares, will be the base for our simulations. By keeping
total value added and using a model with one representative household, we ensure consistency with the rest of the
SAM.
3
 Appendix 1 offers a thorough explanation of how our new factor shares have been obtained, comparing them
with those previously available in Thurlow and Wobst (2003).
4
  The “subsistence factor” is a composite factor made up of labor, land and capital which produces the “home
production”. Its overall value is available in the Tanzanian National Accounts.




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throughout the economy (0.71%). Agriculture makes very intensive use of female Laborers, by
contrast. 5 The most capital intensive sectors are the “Dixit-Stiglitz goods” (83.12%>35.75%),
although Business services are also very capital intensive (67.07%>35.75%). Finally, “Other
CRTS” sectors also use female and male Professionals intensively, but make even more
intensive use of male and female Technicians.

We identify a considerable gender wage gap in Tanzania. Under “Labor intensity (%)”in Table
1, the columns of “Young worker” and “Adult (females+males)” add up to 100, since they
represent all the labor available for production in our model. A comparison of labor intensity
measured in value added terms with that available in Table 2, which presents the actual number
of workers (at the top) and their percentages over the total number of workers (below),
provides an indication of the existing wage gaps between females and males. Comparing the
row “All sectors” for labor intensities in value added and in physical units, we find that even
though the percentage of “all females” and “all males” in total physical workers in Tanzania is
very similar, 36.85% versus 36.10%, respectively, the former accounts for only 28.57%, while
the latter accounts for 67.81% of all workers’ remunerations. This indicates the presence of a
considerable gender gap.

However, the existence of this gender gap should be regarded with caution as evidence of
discrimination. Non-discriminatory factors may explain part or, less likely perhaps, all of the
wage differentials between men and women. Discrimination implies that some workers that
have the same abilities, education, training, motivation, experience, etc. as others are accorded
inferior treatment. Our broadly defined occupations, which are still more accurate than the
usual skilled versus unskilled distinction, lack the detail needed for careful assessment of the
exact portion of the pay gap due to labor market discrimination. For example, according to the
ILFS (NBS, 2002a), women exhibit lower levels of education than men in Tanzania. This
would support the idea of lower productivity (and therefore wages) of women with respect to
that of men. Another factor that could (at least partly) explain the wage gap is the higher
involvement of women in the low-paying sector of Agriculture. We do not know why women
are more involved in agriculture, perhaps they have freely chosen to do so. In addition, we do
not have detailed information on other factors: whether women are married, pregnant or have
child care duties, whether they have different work preferences than men or less opportunity to
migrate to urban areas, etc. Furthermore, in addition to individual attributes, sector sorting is
another factor in creating the wage gap between men and women.

We have updated the estimates on the barriers to the entry of multinationals and domestic
firms, while keeping the rest of model parameters and values in the SAM. Extensive
documentation on the existing inefficiencies in Tanzanian specialized services has been
developed elsewhere (Jensen and Tarr, 2010). The restrictiveness to the entry of multinationals
and other regulatory barriers faced by domestic firms and multinationals into these services
sectors have been translated into ad valorem equivalents by Jafari (2013), who updates the
previous estimates of Mircheva (2008). Jafari’s (2013) estimates suggest a lower level of ad
5
  Indeed, the category of “Laborers” is by far the most numerous in Tanzania because it includes “Skilled
agricultural and fishery workers”, which is the main occupation in the country.




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valorem barriers in business services sectors. Their exact levels are presented in Table 2,
together with the actual market shares controlled by multinationals in Tanzania. The table also
displays the tariff levels considered in the model, which stem from a particularly detailed data
set provided by the Tanzanian Revenue Authority. Due to the outstanding quality of the latter
data, Jensen and Tarr (2010) and Jensen et al. (2010) replaced the tariffs initially present in the
SAM developed by Thurlow and Wobst (2003). We also use the tariffs in the Jensen et al.
(2010) study in the present model. More information on the weight of sectors in private
consumption, aggregate exports and imports, as well as the weight of intermediates from
business services in their total costs, is also displayed in that table for future reference.



4. The model



Tanzania is modeled as a small open economy. In its present version the model is the successor
of a family of CGE models specialized in the analysis of trade, FDI and regulatory barriers.
The model was first applied to the accession of Russia to the World Trade Organization
(Jensen et al., 2007; Rutherford and Tarr, 2008). The findings suggest that FDI liberalization in
services (i.e., the reduction of barriers to the entry of multinationals) has a much stronger
impact than liberalization of tariffs in traditional competitive models of trade in goods. As
noted above, not many CGE models have considered the presence of multinationals. Further,
the modeling technique is very innovative due to the above mentioned Dixit-Stiglitz-Ethier
mechanism incorporating variety effects. Besides, it is also innovative because the model
incorporates different technologies of production of multinationals compared to domestic firms
operating in the same sector. Multinational service providers import some specialized inputs.
Only a few CGE models have incorporated ways of differentiating the technology used by
national firms from that of multinationals (e.g., Lakatos and Fukui, 2014; Latorre, 2013;
Gómez-Plana and Latorre, 2014; Hosoe, 2014; Latorre and Hosoe, 2013).

The model of Russia has been used in previous analyses of Tanzania (Jensen et al., 2010). As
in those analyses, we retain the 52-sector disaggregation, which, in turn, expanded the 43
sectors available in the original SAM for Tanzania (Thurlow and Wobst, 2003). There are 35
perfectly competitive sectors, 18 of which are in Agriculture. These goods and services,
produced under constant returns to scale, are differentiated in the demand functions of
Tanzanian consumers and firms through the Armington assumption. These sectors appear
under the headings of “Agriculture” and “Other CRTS”. The model also incorporates 17
sectors producing under increasing returns and imperfect competition. They are further split
into the advanced Business services sectors, which are central to the simulations, and the Dixit-
Stiglitz sectors, which include most manufacturing sectors. The demand from both firms and
consumers of products or services from the latter sectors is characterized as a Dixit-Stiglitz-
Ethier composite of domestic and imported varieties with firm-level product differentiation.
The Dixit-Stiglitz elasticities are obtained from Broda and Weinstein (2004) and Broda,
Greenfeld and Weinstein (2006). In the imperfectly competitive sectors marginal costs are



                                                                                                 9
constant and there is a fixed cost. Firms set prices such that marginal costs equal marginal
revenue and there is free entry, which drives profits to zero. There is Chamberlinian large-
group monopolistic competition, which, together with the assumption that the ratio of fixed to
marginal costs is constant, results in constant markups over marginal costs.

The 12-household version used for Tanzania has, however, been transformed into a one
representative agent version for this current application. In Jensen et al. (2010) there were six
rural and six urban households. Each category would receive the value added generated by the
land, labor and agricultural capital as reported in the labor-income shares and agricultural
capital and land returns available in the Household Budget Survey NBS (2002b). Further,
within each of the rural and urban types of households there was one group of households
below the food poverty line, another one between the food and basic poverty lines, while the
other four categories were sorted according to the education of the head of the household. As
can be seen this household categories did not include any information on gender. Therefore,
they were converted into a single representative, which was also necessary in order to use the
new factor shares we have derived with the rest of Thurlow and Wobst’s SAM (2003).

Jensen and Tarr (2010) have expanded the rest of the world region from the model of Jensen et
al. (2010) to account for different areas sourcing imports and FDI in Tanzania. Of particular
interest for our analysis is the fact that the multilateral liberalization scenario has the most
important impact for Tanzania (i.e., Tanzania gains more when it lowers regulatory barriers to
all regions, instead of lowering them to particular regions). Therefore, our single country
analysis focusing on gender aspects seems a “suitable simplification” of the greatest impact
that could be attained with the multiregional model.

Another aspect that merits comment is what could be the best approach to model gender
differences in our model. We have seen that previous CGE gender-aware models have mostly
concentrated on how to deal with “non-market activities” at the same time as they introduce
shocks of trade liberalization. It seems clear that despite recent changes women are still
primarily responsible for child care and housework duties. Although in developed countries
these tasks now tend to interrupt fewer women in work careers, due to the increasing
availability of child care facilities or better female pay (e.g., Light and Ureta, 1992;
Frederiksen, 2008), the story is different in developing countries. This is certainly an important
avenue for research. The task of trying to quantify its main determinants and effects, some of
them ranging beyond the boundaries of economics, remains for the future.

How do we model wage formation in the presence of a wage gap? As noted above, we find
evidence of the existence of a substantial wage gap. We do not know, however, to what extent
that gap is related to discrimination or to other characteristics of women that could make them
less productive than men, thus explaining their lower wages. One important question related to
the wage setting mechanism in our model is the following: Would the policies we implement
(increases in the number of foreign or domestic firms and changes in tariffs) directly impact on
the degree of discrimination or change those characteristics that make women less productive
than men? This does not seem to be the case. More openness could arguably reduce
discrimination itself through changes in customs, new ideas, etc., but not immediately. It would


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probably be rather a long-term process, not captured in our short- to medium-term time span.
Besides, if women are less productive due to, for example, fewer years of schooling than men,
this does not seem to be related either to our policy shocks. As Boeters and Savard (2012, p. 5)
put it, the crucial necessary condition for different types of labor to be modeled differently
arises when the wages of the different groups of workers do not move in parallel (i.e., when
they do not move in proportion, maintaining the initial wage difference). Boeters and Savard
(2012, pp. 2, 5, 39, 50-51 and 81) repeatedly touch upon this point throughout their chapter on
labor markets in CGE models.

We do not model wage discrimination. The model assumes that each labor type receives the
value of its marginal product. As suggested in the previous paragraph, the best way to model
discrimination would have been to include an endogenous wage gap. No CGE has modeled a
wage-setting mechanism explicitly targeting gender differences (Boeters and Savard, 2012, p.
51). Furthermore, to the extent that discrimination exists in Tanzania, it should be unaffected
by the shocks we consider. Another possibility would have been to include a discrimination
coefficient à la Becker, i.e., an exogenous wage gap between females and males or between
females and males of the same skill category. Previous evidence suggests, however, that the
introduction of an exogenous wage gap does not lead to any significant change in the results, 6
since wages would move in parallel.

Mathematically the model we are using has the following wage setting mechanism: MPLi,s,g =
ws,g, where MPL is the marginal product of labor and sets i, s and g denote industry, skill level
and gender, respectively. Workers of all skill levels and gender receive the value of their
marginal product without discrimination. Wage rates differ by gender and skill level, but this is
due to differences in marginal products. Alternatively, with an exogenous wage gap a
discrimination parameter, Di,s,g , could have been introduced. This discrimination parameter
could vary by gender, industry or skill level. However, to model discrimination by gender, but
not by industry or skill level, we could transform Di,s,g into Dg . In this latter case, we would
have the following: MPLi,s,g [1+ Dg ] = ws,g for all industries i. This says that for a particular
skill level, s, the wage rate will vary with the discrimination parameter Dg for gender. So if we
normalize on the female wage by setting Dfemales=0, we could have Dmales=.2. It says that firms
will pay males 20 percent above the value of their marginal product, but only pay females the
value of their marginal product. In our model, the discrimination parameter is zero for all
industries, skill levels and genders.

A related example dealing with differences in wages by sector may be of help in explaining the
model treatment of wages. There is abundant evidence of wage differentials across sectors,
which should a priori not be compatible with treating all labor as homogeneous. However, most

6
  One of the few CGEs which has compared workers’ reallocations and welfare improvements following the
elimination of constraints to trade due to import quotas (De Melo and Tarr, 1992) or Voluntary Export Restraints
(VERs) (De Melo and Tarr, 1993) has found very similar effects in the case of absence versus presence of
exogenous wage gaps. Interestingly, this similarity in results holds for the different market structures, such as
monopolistic competition, that are analyzed. The contrasts in results arise, however, when endogenous wage gaps
are introduced, which De Melo and Tarr (1992; 1993) model using a framework of labor unions that we believe is
not applicable to our developing country setting.




                                                                                                              11
CGE models treat labor supply as uniform with market clearing wages which balance labor
supply and demand. Why? Because if we regard the wage gap as remaining constant in the
simulations, we are in a case analogous to the well-known wage differences across sectors.
After all, as is the case with discrimination, it is also difficult to disentangle what share of the
wage differential across sectors is attributable to a different composition of the workforce and
what share is a pure sectoral wage differential (Boeters and Savard, 2012; Genre et al., 2011).

Our model fully develops mechanisms not present in previous CGE gender-aware models that
affect female (and male) wages and employment. In this regard, it is important to note that
when the policy shock run with the model is not directly labor market oriented, the impact on
the labor market occurs through shifts in labor demand (Boeters and Savard, 2012, p.5). Our
model has three important mechanisms affecting labor demand. The first mechanism is through
the endogenous productivity effects arising from product variety. The second stems from the
differences in cost structures of national firms and MNEs. The third is the traditional factor
intensity aspects present in CRTS models.

The Dixit-Stiglitz-Ethier mechanism implies that producers’ productivity goes up with
increases in the number of firms that supply intermediates for them, in line with the findings in
other empirical studies. We noted above that when more firms, for example, more foreign
multinationals, enter the market, producers can obtain more varieties of intermediates at a
quality-adjusted reduced price which raises their productivity. As developed in length in Tarr
(2012), the introduction of these endogenous productivity effects leads to estimations of
welfare gains that are consistent with the econometric literature on the productivity impacts of
the liberalization of services. Further, the welfare gains turn out to be several times larger than
those obtained in CGE models with no FDI and no endogenous productivity effects.

The model also captures whether the less labor-intensive technologies of foreign firms
(compared to domestic) could lead to decreases in labor demand. Interestingly, the predictions
of Markusen et al. (2005) and of previous full general equilibrium models calibrated to real
economies, recently summarized in Tarr (2012), suggest that generally fears for domestic labor
demand are not justified. Foreign multinationals use primary imported intermediates, such as
expatriates or specialized technical expertise (not available to national firms), making them
economize on labor. After the shock, there can be a partial equilibrium effect which may
decrease domestic labor demand in the sectors in which the entry of foreign firms crowds out
the more labor-intensive domestic firms. However, another force is at play at the same time. As
more foreign firms enter the market, the price of the services they sell goes down, thereby
inducing industries and consumers to expand their demand for services. If this latter general
equilibrium effect dominates the labor substitution effect, demand for labor in business services
will increase.




                                                                                                 12
5. Results

Sectoral production



The evolution of production, which is critical for labor demand, varies considerably across the
different scenarios we simulate. Table 4 presents the percentage changes in output with respect
to the benchmark of the 52 sectors in which the Tanzanian economy has been split. At the top
of the table appear the different scenarios considered. The scenario “Full reform” combines
three different shocks: 1) A 50% reduction of regulatory barriers to services faced by domestic
and foreign firms. These are inefficient barriers which raise the costs of domestic and foreign
service providers (i.e., the levels of “Regulatory barriers: All firms” presented in Table 3 would
be cut by half). 2) A 50% reduction of the barriers directed only at foreign firms
(discriminatory barriers against FDI) 7 (i.e., the levels of “Regulatory barriers: Additional
barriers for foreign firms” presented in Table 3 would be cut by half). 3) A change from the
heterogeneous import tariffs (available in Table 3), which are charged across the different
sectors, to a uniform tariff. The common import tariff modeled obtains exactly the same
revenues as the previous different import tariffs. These three shocks are combined in the “Full
reform” scenario and are also analyzed individually, i.e., one by one, in order to derive their
relative impact. The label “CRTS” refers to the outcome of the same “Full reform” shock in a
framework in which all sectors produce under constant returns to scale. Finally, in the “Steady
state” again the same three shocks are run simultaneously under an increasing returns to scale
framework, while the capital stock is allowed to adjust to its long-run equilibrium.

After the “Full reform” shock, there is a large increase in the number of domestic and foreign
service providers. Taking into account the output of the latter operating in Tanzania, production
goes up markedly in all Business sectors (with an average rise of 25.7%). The expansion is
much larger in Banking, Insurance and Professional Services, than in the Transport and
Telecommunication sectors. This is due to the greater non-discriminatory and discriminatory
barriers that the former sectors exhibit compared to the latter (as can be seen in Table 3).
Further, because the additional FDI barriers are considerably greater in Banking, Insurance and
Professional Services than the non-discriminatory barriers, facilitating FDI (i.e., running the
“Only barriers against FDI” scenario) expands output more than a shock concentrated only in
non-discriminatory barriers (i.e., the “Only non-discriminatory service barriers” scenario).

After the shock “Only uniform tariffs”, output falls for the sectors that were previously quite
protected and have lost the shelter of their remarkably high tariffs. These are Textile and
leather products, which has the highest tariff in Tanzania (29.7%), and Beverages and tobacco
(28.4%). These two sectors together, which are relatively substantial among manufacturing
sectors in terms of value added, explain the reduction in production in the Dixit-Stiglitz sectors.

7
  Note that foreign firms operating in Tanzania face an accumulation of regulatory barriers (“non-discriminatory
services barriers” which are also present for domestic firms) and additional discriminatory barriers (only “barriers
against FDI services” which are set for foreign multinationals).




                                                                                                                13
Paddy (20.5%) and Meat and dairy products (27.2%) also contract production. The fall in
barriers to FDI and, to a lesser extent, in non-discriminatory barriers dampens the downward
pressure on production stemming from the uniform tariffs, in the cases of Beverages and
tobacco, Textile and leather products, Paddy, and Meat and dairy products.

There is another group of sectors whose production goes down, even though they do not have
high tariffs. This is the group of agricultural sectors composed of cotton, coffee, tea, cashew
nuts, sisal fiber and sugar across several scenarios. This seems related to the very low weight in
private consumption of these sectors in Tanzania (Table 3), since most of them are very export
oriented. National income rises in all scenarios, resulting in an increase in private consumption.
The sectors whose weight in private consumption is higher tend to benefit from this upward
tendency. This is the case with maize, beans, oil seeds, other roots and tubers, fruits and
vegetables, poultry and livestock, hunting and forestry and, also, though to a lesser extent,
cassava.

The results in the “CRTS” and “Steady state” scenarios exhibit large contrasts. The “CRTS”
scenario runs the same simultaneous three shocks performed in the “full reform” package (i.e.,
lowering non-discriminatory and discriminatory barriers, as well as the change to a uniform
tariff) in a model without increasing returns to scale (i.e., the business services and Dixit-
Stiglitz sectors run with constant returns to scale in this simulation). The magnitude of the
shock is considerably reduced. The opposite applies to the “steady-state” simulation, which
magnifies the impacts. The increase in the varieties of services available makes capital more
productive, inducing a process of capital accumulation in the long run. With a higher capital
stock the economy will both produce and consume more. Our estimations for this scenario
represent, however, an upper limit of the possible outcomes since it does not take into account
the necessary forgone consumption to achieve a higher capital stock. With the “steady-state,”
we offer an insight into the long-run results. This contrasts with the rest of the results, which
are medium-term predictions stemming from comparative static simulations.

The sectors that expand more do so owing to their downstream (using) connections with
business services. These are petroleum and refineries (Table 2 shows that 5.1% of total costs
are intermediates from business sectors), manufacture of basic and industrial chemicals (4.7%),
rubber plastic and other manufacturing (3.8%), other services (11.8%), tobacco (9.2%), hotels
and restaurants (4.1%), as well as postal communication (15.3%). What is more, downstream
relationships are especially intense among business services themselves, which further
enhances their expanding tendency.



Labor market outcomes

Variations in factor earnings

Table 5 presents percentage changes in factors’ remunerations and in welfare with respect to
the benchmark. Recall that we assume full labor mobility and there are eight wage rates in the
model: one wage for each skill and female/male category. Wage rates do not differ across



                                                                                               14
sectors for the same skill and gender combination. The same scenarios from Table 4 are
analyzed.

In our increasing returns to scale comparative static model, all real factors’ remunerations
increase after the different shocks are considered. This is mostly due to Dixit-Stiglitz
externality dominating any Stolper-Samuelson effects. A clear upward trend in all factor
earnings emerges. Some factors of production stand out due to the high remunerations they
receive. “Professional” males experience the largest increase in wages across all factors of
production, followed by “professional” females. These are the factor most intensively used in
the business services sectors (Tables 1 and 2). Males in the categories “technicians” and
“laborers” come next in importance.

Note, however, that the overall increases in male wages (see row “Adult male wages”) are
higher than those of females (row “Adult female wages”) across almost all scenarios. That is,
males benefit more, even though women do also benefit. The key to understanding the gender
wage results is to recognize that the business services sectors expand the most (see Table 4). In
the “full reform” scenario or the “all services barriers” scenario, business services expand by
about 25%, while the other sector groups only change by between plus or minus 2%. Males
gain relatively more due to the fact that they are employed more intensively in the expanding
sectors. However, as shown in the factor intensity section of Table 1, all four types of male
labor are more intensively employed in business services compared with the same class of
female labor. In the case of “laborers”, “technicians” and “professionals,” the ratio of male to
female factor intensity is more than 10 to 1, a ratio higher than in the other sectors. As business
services expand, it must attract skilled male workers from sectors that do not use male workers
as intensively, which induces a rise in the relative wage of skilled male workers.

The above described patterns in factors’ remunerations hold across the different scenarios, with
the exception of the “Only uniform tariffs”. The contrasting factor earnings are mostly driven
by the reduction in barriers to FDI, which has a greater impact on the expansion of business
services. Even in the “CRTS” simulation the factors used intensively in business services
experience higher wage increases, although the increase is less than half of that experienced
with increasing returns to scale. The “steady state”, by contrast, leads to wage increases that are
more than double those arising from the “full reform” package. Since the capital stock expands
in the “steady state”, and the marginal productivity of labor increases with increases in the
capital stock, the wage increases are higher.

In the “only uniform tariffs” scenario, factors’ remunerations experience less intense increases
than in the “full reform” scenario. The uniform tariff scenario does not allow for services
liberalization. So there are little or no Dixit-Stiglitz endogenous productivity effects. Then
marginal productivity of labor increases only marginally and, as will be seen shortly, the
welfare gains are very small. Females in all categories experience rather small increases in their
wages, even though for all adult women the increase is slightly higher (0.74%) than the overall
increase experienced by adult men (0.69%). The latter wage increases are surpassed in the rest
of the scenarios, except for “only non-discriminatory barriers”.




                                                                                                15
Agricultural land and capital are the only two factors experiencing a mild reduction in their real
remuneration, but only in two scenarios. The price of land goes down in the scenario of “only
non-discriminatory barriers” and in the hypothetical “CRTS” simulation. Note that in these two
scenarios a relatively strong fall in production in Agriculture is experienced simultaneously
with a rather small increase in the “other CRTS” sectors, compared to the rest of scenarios.
These two sectors always adjust in those directions but they do so less vigorously than in the
two exceptional simulations, bringing about a small reduction in land remuneration. The
reduction in capital remuneration in the “steady state” simulation is natural after an intense
process of capital accumulation, which indeed raises the overall capital stock in Tanzania by
6.7%.

Even though foreign firms use less labor-intensive technologies than domestic firms, labor still
benefits in Tanzania from the entry of foreign firms. We find an overall improvement in real
wages. According to these results, in line with previous findings (reviewed in Tarr, 2012), the
outcomes of the model support the idea that the partial equilibrium effect of lower demand by
foreign firms is dominated by the general equilibrium effect, by which an overall increase in
the demand for cheaper business services will increase overall labor demand in these sectors.
That is why those factors most intensively used in the expanding business sectors benefit most,
in relative terms, from the higher wages. By contrast, those used most intensively in
agriculture, which reduces output, still benefit but less than the other worker categories. On the
other hand, in the “CRTS” scenario the absence of Dixit-Stiglitz-Ethier variety and of
endogenous productivity effects more than halved the positive outcomes on wages.

The highest increases in wages are experienced by the most skilled workers (“professional”
males and females), who are those used most intensively in the business services. Developing
countries, like industrialized countries, are opening up more to foreign investors in services and
becoming more business services oriented. Our model illustrates that with this change, these
sectors demand more educated workers, the relative wages of the better trained will increase
relative to those of the unskilled. The policy conclusion from this model is that it is important
to invest in the education of females so their human capital increases and their skills become
more marketable in business services and other more technologically modern occupations.
Otherwise the wage gap between males and females would likely widen further. (For a similar
conclusion see Arndt et al., 2006.)

Finally, at the bottom of Table 5 we present the positive outcomes on aggregate welfare
measured as Hicksian equivalent variations of consumption and GDP. They are parallel to the
impact on workers’ remuneration that we have just analyzed. Welfare improves across all
simulations. A “full reform” scenario, which brings about the most substantial increase in
wages, is the one that results in higher welfare, with increases of 2.23% of consumption and
2.03% of GDP. At the opposite extreme, the “only uniform tariffs” results in a marginal
increase in welfare. Note that in this last case, welfare improves despite output reductions in
some sectors that we had reported above. We also see that the welfare impact is three times
larger in the scenario of “only barriers against FDI in services” than in that of “only non-
discriminatory services barriers”.



                                                                                               16
Table 6 displays more detailed results on the differential evolution of female wages versus
those of men across occupational categories. Focusing merely on the analysis of pure gender
difference may be misleading in analyzing whether females and males benefit or not from
different policy shocks. The changes in percentage points of difference between female and
male wages appear at the top of the table. At the bottom is the wage gap measured as the ratio
of female over male wages in percentage terms. For the vast majority of estimations the
changes in percentage points of difference are negative, because female wage increases tend to
be of smaller magnitude than those of males. The largest differences between the sexes appear
in “professionals” followed by “technicians” and “laborers”. Nevertheless, as shown in Table 5
both female and male “professionals” experienced the highest increases in wages after the
shock. This implies that even though the change in percentage points of difference is greater (in
absolute value), both types of worker are better off. Similarly, the largest differences between
the sexes appear in the “steady state” followed by “all service barriers” and the “full reform”
scenario (see the row “all workers” in the middle of Table 6). However, those are precisely the
scenarios in which both females and males experienced the strongest wage increases (Table 5).
Paradoxically, the change in percentage points of difference is positive in the “only uniform
tariffs” scenario, in which the wage increases for both females and males are nearly the
smallest among all the scenarios.

At the bottom of Table 6 we see that the sizeable wage gaps existing in the benchmark remain
virtually unchanged across simulations and different occupational categories. In our benchmark
data set, the wage gap in Tanzania is 39.77% (see row “all workers” at the end of the table).
This means that in general women’s wages are 60% lower than men’s, which is a very wide
gap in international terms, even though we do not take into account workers’ characteristics for
this calculation. Ñopo et al. (2011) in their analysis of 64 countries across the world found that
gender earnings gaps ranged from 8% to 48% between individuals with the same
characteristics. They also established that wage gaps were higher in Sub-Saharan Africa and
South Asia compared with other regions. Note that the Tanzanian overall wage gap closely
resembles that of 39.43% of the occupation with most workers (“laborers”). In the other
occupations, the wage gap shrinks, particularly, in the case of “unskilled”, for which female
wages are around 30% lower than those of males in the benchmark. If we analyze the evolution
of the wage gap across occupational categories, we find a very small variation in this
indicator. 8 Only for “unskilled” workers is the wage gap very slightly reduced in some
simulations. Indeed, “unskilled” females experience higher wage increases than “unskilled”
males in some simulations. The higher intensive use of “unskilled” females versus “unskilled”
males in the expanding “Other CRTS” sectors explains why this occurs, particularly when the
output expansion in the Dixit-Stiglitz sectors is not very strong, since the latter make relatively
more intensive use of “unskilled” males than “unskilled” females.

In the “only uniform tariffs” scenario females across most categories undergo slightly higher
wage increases than males, even though females are not better off in this scenario compared to

8
  These outcomes further confirm that our assumption that the policy simulations do not significantly affect the
initial wage gap is correct.




                                                                                                             17
the rest. A very small improvement in the wage gap appears. For all female and male workers it
would turn from its benchmark value of 39.47% to 39.79%. However, again it is hard to argue
that women would be better off in this scenario since they would experience lower wage
increases than in the other scenarios (with the exception of the “Only non-discriminatory
service barriers”). With the change to a uniform tariff, Business services expand considerably
less than in the rest of scenarios, while the female-intensive Agriculture experiences a
relatively smaller fall in output. Thus, the demand for female workers is higher in this scenario,
compared to the rest, and women’s remunerations are slightly higher than those of men. The
“improvement” in the wage gap disguises both lower female and male remunerations.

The results displayed in Table 6 suggest that for the wage gap to improve, substantial female
wage increases (much larger than those of men) would be needed and that a broader
perspective going beyond differences in wages or wage gaps is necessary to address the
situation of women. The policies we have analyzed would always improve, in terms of the
evolution of wages, the status of women. However, it would improve even more that of men.



Reallocations of factors across sectors

In order to analyze women’s (and men’s) wellbeing we should also take into account “factor
adjustments”, i.e. the number of workers that change occupational sector. Traditionally, this
has been interpreted as a “cost” for workers. In principle, the higher the number of workers that
are reallocated, the more harmful to that economy the shock is (e.g., De Melo and Tarr, 1992).

Table 7 presents small “factor adjustments”. Across simulations female and male
“professionals” experience the highest increases in the percentage of workers reallocated, with
the sole exception of the “only uniform tariffs”. Recall that, except in this latter scenario, the
greatest increases in production take place in the business sectors where the number of firms
has gone up. This increases more sharply the demand for workers most intensively used in
those sectors. Across scenarios, as the “adult females” and “adult males” rows at the bottom of
Table 7 show, the percentage adjustment is slightly larger for men than for women. The
exception is, once more, the outcomes from the “only uniform tariffs”. In this latter case, two
forces coincide. First, there is still a small fall in output in female-intensive agriculture, which
releases more females than males in that sector. Second, output increases in male-intensive
sectors are smaller than in the other simulations, thus attracting a lower number of males to
them.

Due to the small share of business services in the Tanzanian economy (they provide 5.9% of
GDP, Table 1A in Appendix 1), the overall adjustment of female and male labor is limited. As
presented in the last rows, the percentage of “adult females” changing occupational sector in
the economy would be around 1.11%, while for “adult males” it would be 1.35%. Potential
higher adjustments could, however, occur if, for example, FDI inflows (and the number of
foreign firms) increase. This seems a plausible scenario in view of the remarkable growth of
FDI inflows to Africa in recent years (UNCTAD, several years). Our analysis points to the idea
that the resulting adjustment from FDI would contrast with a pattern following a shock on


                                                                                                 18
tariffs. As long as the shock makes the evolution of output in agriculture the prevailing force
behind the adjustment, such as in the “only uniform tariffs” scenario, agricultural technology is
what prevails. Similarly, the absence of Dixit-Stiglitz effects also reduces considerably the
potential for workers’ reallocation, due to the smaller output increases in the business services
sectors.



Sensitivity analysis



The results of the sensitivity analysis reveal that our findings of larger increases in wages and
factor adjustments for men in Tanzania (compared to women) remain under different elasticity
specifications. We also find that in all cases, again both women and men experience wage
increases after the shock. Table 8 presents the results of piecemeal sensitivity analysis for the
evolution of overall female and male wages, as well as factor adjustments. To simplify, we run
only the short-run scenario that has the strongest impact on model results, i.e., only the “full
reform” scenario. Elasticities and parameter specifications are changed one by one while
keeping the rest as in our central model. The results for this model, shown in Tables 5 and 7,
are displayed again here under the label “central”, to facilitate comparison.

The results are generally very close to those obtained with the central elasticity values.
However, with higher (lower) levels of elasticities increases in wages and factor adjustments
tend to be slightly larger (smaller) than in the central case. This is to be expected since higher
elasticities imply more flexibility in the economy, which facilitates the shift to products and
sectors that become cheaper after the shock. An exception to this tendency arises with the
elasticity of substitution between firm varieties in imperfectly competitive sectors (σ (qi,qj)).
This elasticity has a strong influence on the model results. When it is low, varieties are seen as
very different and as poor substitutes. Therefore, additional varieties have a stronger impact on
the economy, resulting in higher wages and factor adjustments than in the central case and vice
versa. The elasticity of multinational service firms’ supply with respect to the price of output (ε
(fi)) is also more influential for the outcomes than the rest, although to a lesser extent than σ
(qi,qj). If foreign multinationals expand their production more after the fall in output prices
(high value case of this elasticity), there will be a sharper increase in wages and worker
reallocations in the economy. This result further confirms that despite their relative low labor-
intensive technologies (compared to domestic firms), more activity of foreign multinationals is
beneficial for the wages of women and men in Tanzania. Finally, it is also interesting that with
a high elasticity of substitution between value added and business services (σ (va,bs)), adult
males’ wages and factor adjustments experience much higher percentage changes. When this
elasticity is high, it becomes easier to benefit from cheaper business services. This will increase
production in business services more markedly and, since these sectors are male intensive, this
type of labor will benefit more (than females).




                                                                                                19
Conclusions



Policies lowering regulatory barriers faced by both domestic and MNEs operating in the
business services sectors increase the number of firms in those sectors and their share of the
economy. Due to Dixit-Stiglitz endogenous productivity impacts from additional business
services, there is an increase in the demand for all labor categories, raising wages across all
worker categories, and, contrasting with the predictions of the Stolper-Samuelson Theorem, the
real remuneration of all factors of production rises simultaneously. Even though foreign MNEs
exhibit lower labor intensity in production than national firms, labor demand benefits from the
arrival of foreign firms. However, the increase in wages is higher for males than for females.
This is because the expanding business services exhibit higher male than female worker
intensity.

Developing countries, like industrialized countries, are opening up more to foreign investors in
services and becoming more business services oriented. This model illustrates that as this
development process continues, these sectors demand more educated workers and the relative
wages of the better trained will increase relative to the unskilled. The policy conclusion from
this model is that it is crucial to invest in the education of females so their human capital
increases and their skills are more marketable in business services and other more
technologically modern occupations. Otherwise the wage gap between males and females
would likely widen further.




                                                                                             20
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                                                                                                25
Table 1. Factor remunerations and factor intensities in the benchmark (in billions of current Tanzanian Shillings and %)

                                                                                                                                                                                                                     Adult     Total
                       Young       Female        Female        Female         Female        Male          Male           Male           Male
                       workers   (Unskilled)   (Laborers)   (Technicians) (Professionals) (Unskilled)   (Laborers)   (Technicians) (Professionals)
                                                                                                                                                     Subsistence    Capital     Land     All females   All males   (females    Value
                                                                                                                                                                                                                   +males)     Added
                                                                   Factors remuneration (in billions of current Tanzanian Shillings)
Business Services        47       1,631        12,030         1,126          1,899          5,618        91,143       13,437          19,402                        297,996               16,686        129,599     146,285     444,328
Dixit-Stiglitz Goods    466       1,110         9,776          567            315          10,849         54,574       5,434           1,039           33,572       579,777                11,768        71,896      83,665     697,479
Agriculture            72,280     4,494        363,150         377                          35,079       611,045       1,327            523          1,195,472      463,261    251,691    368,021       647,973    1,015,995   2,998,698
Other CRTS             21,876    93,790        202,511        47,782         5,747         107,334       603,313      178,600         32,698          719,918      1,369,809    58,549    349,830       921,946    1,271,776   3,441,928
ALL SECTORS            94,668    101,025       587,467        49,852         7,962         158,880      1,360,075     198,798         53,662         1,948,962     2,710,842   310,240    746,306      1,771,415   2,517,721   7,582,433
                                                                                                    Factor intensity (in %)
Business Services       0.01        0.37        2.71            0.25          0.43           1.26        20.51           3.02           4.37           0.00         67.07         -        3.76         29.17       32.92        100
Dixit-Stiglitz Goods    0.07        0.16         1.40           0.08          0.05           1.56         7.82           0.78           0.15            4.81        83.12        -          1.69        10.31       12.00        100
Agriculture             2.41        0.15        12.11           0.01          0.00           1.17        20.38           0.04           0.02           39.87        15.45       8.39       12.27        21.61       33.88        100
Other CRTS              0.64        2.72         5.88           1.39          0.17           3.12        17.53           5.19           0.95           20.92        39.80       1.70       10.16        26.79       36.95        100
ALL SECTORS             1.25        1.33         7.75           0.66          0.10           2.10        17.94           2.62           0.71           25.70        35.75       4.09        9.84        23.36       33.20        100
                                                                                                    Labor intensity (in %)
Business Services       0.03        1.11         8.22           0.77          1.30           3.84        62.28          9.18           13.26              -            -          -        11.40        88.57       99.97          -
Dixit-Stiglitz Goods    0.55        1.32        11.62           0.67          0.37          12.90        64.87          6.46            1.24             -            -          -         13.99        85.46       99.45          -
Agriculture             6.64        0.41        33.37           0.03          0.00           3.22        56.15          0.12            0.05              -            -          -        33.82        59.54       93.36          -
Other CRTS              1.69        7.25        15.65           3.69          0.44           8.30        46.64          13.81           2.53             -            -          -         27.04        71.27       98.31          -
ALL SECTORS             3.62        3.87        22.49           1.91          0.30           6.08        52.06           7.61           2.05              -            -          -        28.57        67.81       96.38          -


Source: Author's calculations based on NBS (2002a).

Notes: the conversion of occupations to labor categories is as follows: Unskilled (“Elementary occupations”); Laborers (comprised of five occupations: “Clerks”, “Services and shop
workers”, “Skilled agricultural and fishery workers”, “Craft and related workers” and “Plant and machine operators and assemblers”); Technicians (“Technicians and associate
Professionals”) and Professionals (“Professionals”). “Factor intensity” of each factor of production corresponds to the percentage weight of its remuneration in total value added, the latter
being normalized to 100 in the column “Total value added”. “Labor intensity” is the percentage weight of the remuneration of each category of labor over total labor remuneration. The
percentages in columns of “Young workers” and “Adult (females+males)” add up to 100, since they represent all the labor available for production in the model, excluding the labor
allocated to subsistence. In turn, the values in the column “Adult (females+males)” are the sum of the values in columns “All females” and “All males”.




                                                                                                                                                                                                                                       26
Table 2. Number of workers and labor intensity in the benchmark (in physical units and %)

                                                                                                                                                                                                                                       Ratio:
                                                                                                                                                                                                                   Adult      Total
                        Young        Female        Female        Female         Female        Male           Male           Male           Male                                                                                          All
                        workers    (Unskilled)   (Laborers)   (Technicians) (Professionals) (Unskilled)    (Laborers)   (Technicians) (Professionals)
                                                                                                                                                        Subsistence   Capital   Land   All females   All males   (females   Number of
                                                                                                                                                                                                                                      All males
                                                                                                                                                                                                                 +males)     workers

                                                                                                          Number of workers
Business Services        1,248      1,582          6,092         564            599           9,634         82,464         7,027         5,013              -           -        -       8,837    104,137   112,975   114,223            0.08
Dixit-Stiglitz Goods    12,497      3,108         34,609        1,129           566          12,527         68,826         4,235          610            22,965         -        -      39,412     86,198   125,610   138,107            0.46
Agriculture            2,858,933   30,730        3,825,359      2,537            0           117,406       3,199,983       4,202         1,571          3,939,135       -        -     3,858,627 3,323,162 7,181,788 10,040,722          1.16
Other CRTS              591,038    303,606        386,820      109,178         11,464        165,564        693,025       220,259        30,389          148,478        -        -      811,067 1,109,237 1,920,305 2,511,342            0.73
ALL SECTORS            3,463,716   339,026       4,252,880     113,408         12,629        305,131       4,044,297      235,723        37,583         4,110,578       -        -     4,717,943 4,622,734 9,340,678 12,804,394          1.02
                                                                                                      Lactor intensity (in %)
Business Services        1.09         1.39         5.33           0.49          0.52           8.43         72.20           6.15           4.39              -           -       -        7.74        91.17       98.91        100        -
Dixit-Stiglitz Goods     9.05         2.25        25.06           0.82          0.41           9.07         49.84           3.07           0.44             -           -        -       28.54        62.41       90.95        100        -
Agriculture             28.47        0.31         38.10           0.03          0.00           1.17         31.87           0.04           0.02              -          -        -       38.43        33.10       71.53        100        -
Other CRTS              23.53        12.09        15.40           4.35          0.46           6.59         27.60           8.77           1.21             -           -        -       32.30        44.17       76.47        100        -
ALL SECTORS             27.05        2.65         33.21           0.89          0.10           2.38         31.59           1.84           0.29              -          -        -       36.85        36.10       72.95        100        -


Note: For the conversions of occupations to labor categories see note in Table 1. “Labor intensity” is the percentage weight of the number of workers of each category in the total number of
workers. The percentages in the columns “Young workers” and “Adult (females+males)” add up to 100 as reflected in the column “Total number of workers”, since they represent all the
labor available for production in the model, excluding the labor allocated to subsistence. In turn, the values in the column “Adult (females+males)” are the sum of the values in columns
“All females” and “All males”.

Source: Author's calculations based on NBS (2002a).




                                                                                                                                                                                                                                                27
Table 3. Benchmark sectoral information (in %)

                                                                                       Market shares           Regulatory barriers
                                                        % in aggregate % in aggregate                                                   Weight in private Business services intermediates
                                               Tariff                                 Domestic Foreign              Additional barriers
                                                           exports        imports      firms    firms
                                                                                                         All firms                       consumption            (% of total costs)
                                                                                                                      for foreign firms
Business Services                                           12.5           10.9                                                               4.7
 Telecommunication                                           1.0            1.9        10.0     90.0      3.0             10.7                0.9                      15.3
 Insurance                                                   0.2            0.1        70.0     30.0      17.9            55.6                0.0                      11.8
 Banking                                                     4.4            2.2        60.0     40.0      14.7            50.9                0.8                      11.8
 Professional business services                              4.8            2.4        70.0     30.0      6.9             24.0                0.9                      11.8
 Air transport                                               0.2            0.4        60.0     40.0      0.0             30.0                0.2                      11.6
 Road transport                                              1.3            2.7        80.0     20.0      0.0             0.0                 1.3                      11.6
 Railway transport                                           0.1            0.1        40.0     60.0      0.0             10.7                0.1                      11.6
 Water transport                                             0.5            1.0        20.0     80.0      0.0             42.0                0.5                      15.3
Dixit-Stiglitz Goods                           7.4           3.9           60.7                                                              38.0
 Processed food                                11.1          0.5            3.4                                                              10.6                      2.0
 Beverages & tobacco products                  28.4          0.1            0.7                                                               3.9                      4.0
 Textile & leather products                    29.7          1.3            3.3                                                              10.7                      2.6
 Wood paper printing                           11.6          0.4            3.0                                                               0.8                      2.6
 Manufacture of basic & industrial chemicals    3.6          0.2            5.1                                                               3.6                      4.7
 Manufacture of fertilizers & pesticides                     0.0            0.6                                                               0.0                      0.5
 Petroleum refineries                           3.2          0.0           10.8                                                               4.4                      5.1
 Rubber plastic & other manufacturing           6.0          0.1            2.9                                                               1.4                      3.8
 Glass & cement                                 7.1          0.5            0.3                                                               0.3                      2.9
 Iron steel & metal products                    5.5          0.1            5.8                                                               1.3                      2.2
 Manufacture of equipment                       6.3          0.6           24.9                                                               1.0                      1.9
Agriculture                                    15.1         26.0            7.0                                                              21.1
 Maize                                          0.2          0.1            1.0                                                               3.5                      1.0
 Paddy                                         20.5          0.2            1.2                                                               0.7                      2.0
 Sorghum or millets                             4.6          0.0            0.0                                                               0.4                      3.6
 Wheat                                          8.7          0.0            1.1                                                               0.1                      2.4
 Beans                                         25.1          0.1            0.0                                                               2.5                      0.6
 Cassava                                       25.0          0.0            0.0                                                               1.0                      0.0
 Other cereals                                  8.8          0.0            0.0                                                               0.1                      0.5
 Oil seeds                                      1.1          0.3            0.0                                                               1.4                      0.6
 Other roots & tubes                            0.5                         0.0                                                               1.3                      0.0
 Cotton                                         1.2          2.9            0.0                                                               0.0                      4.9
 Coffee                                        11.8          6.6            0.0                                                               0.0                      1.7
 Tobacco                                       11.1          3.4            0.0                                                               0.0                      9.2
 Tea                                           18.9          1.8            0.0                                                               0.5                      3.1
 Cashew nuts                                   22.2          6.9            0.0                                                               0.0                      0.8
 Sisal fiber                                                                                                                                  0.0                      2.4
 Sugar                                         22.3          0.9           3.0                                                                0.2                      2.8
 Fruits & vegetables                            6.7          1.9           0.5                                                                6.8                      0.4
 Other crops                                    4.3          0.3           0.0                                                                0.6                      0.8
 Poultry & livestock                            4.4          0.5           0.2                                                                2.2                      1.2
Other CRTS                                     3.9          14.9           4.3                                                               36.1
 Fish                                          22.7          4.9           0.0                                                                5.5                      0.0
 Hunting & forestry                                          0.4           0.0                                                                2.8                      2.7
 Mining & quarrying                             3.2          1.5           0.9                                                                0.0                      2.5
 Meat & dairy products                         27.2          0.0           0.2                                                                5.9                      0.3
 Grain milling                                  8.6          0.5           0.9                                                               13.2                      0.3
 Utilities                                                                                                                                    1.3                      6.6
 Construction                                                               0.1                                                               0.0                      3.2
 Wholesale & retail trade                                                                                                                     0.0                      11.5
 Hotels & restaurants                                                                                                                         5.6                      4.1
 Postal communication                                        0.1            0.1                                                               0.1                      15.3
 Real estate                                                                                                                                  0.3                      2.8
 Other services                                              1.8           0.9                                                                0.3                      11.8
 Public administration health & education                   42.7           17.1                                                               1.1                      2.9
 Tourism                                                     5.6           1.0                                                                                         13.1


Source: Author's calculations based on Jensen and Tarr (2010) and Thurlow and Wobst (2003).



                                                                                                                                                                                     28
Table 4. Impacts on Sectoral Activity (% change from benchmark).
                                                                                       All services Only non-discrimina-     Only barriers against   Only uniform           Steady
                                                                         Full Reform                                                                                CRTS
   Scenario definition                                                                  barriers    tory services barriers     FDI in services          tariffs              State
   Liberalization of regulatory barriers for all services firms             Yes           Yes                Yes                     No                  No         Yes      Yes
   Liberalization of discriminatory barriers on foreign services firms      Yes           Yes                No                      Yes                 No         Yes      Yes
   Uniform import tariffs?                                                  Yes           No                 No                      No                  Yes        Yes      Yes
   Steady-state capital stock                                               No            No                 No                      No                  No         No       Yes
   Dixit-Stiglitz variety-induced productivity gains                        Yes           Yes                Yes                     Yes                 Yes        No       Yes
   IRTS Goods and Services                                                  8.3           9.5                2.5                    6.1                 -1.1        3.3     14.4
   CRTS Goods and Services                                                  1.5           0.1               -0.2                    0.3                  1.3        0.5      3.7
   Business Services                                                        25.7          24.8               6.7                    15.7                 0.9        9.4     34.5
     Telecommunication                                                      12.4          13.0               3.2                      8.8                -0.6        4.0     17.0
     Insurance                                                              61.5          63.4               20.5                    38.2                -1.3       25.0     78.3
     Banking                                                                59.1          61.0               17.9                    38.6                -1.3       24.6     75.7
     Professional business services                                         39.9          41.7               12.2                    24.0                -1.3       13.1     56.1
     Air transport                                                          10.8           6.0               0.4                      5.1                4.4         4.1     14.8
     Road transport                                                          9.1           4.2               0.4                      3.3                4.4         2.8     13.1
     Railway transport                                                       9.8           5.0               0.4                      4.2                4.3         3.5     13.7
     Water transport                                                        14.4          14.9               2.6                     11.3                -0.6        5.7     18.9
   Dixit-Stiglitz Goods                                                     -0.5          1.8                0.4                     1.2                -2.2        0.2      4.3
     Processed food                                                          2.8           1.1               0.3                      0.8                1.7         2.3     5.6
     Beverages & tobacco products                                           -2.4           2.0               0.4                      1.5                -4.3       -0.8     0.2
     Textile & leather products                                             -9.3           1.6               0.4                      1.1               -10.7       -3.3     -4.4
     Wood paper printing                                                     0.1           4.8               1.4                      2.9                -4.2        0.3     4.6
     Manufacture of basic & industrial chemicals                             5.2           3.9               0.9                      2.7                1.4         0.9     12.0
     Manufacture of fertilizers & pesticides                                 5.6          -5.7               -1.8                    -3.5                11.7        4.4     11.4
     Petroleum refineries                                                   11.6           3.0               0.7                      2.0                8.8         4.5     20.9
     Rubber plastic & other manufacturing                                    4.0           2.6               0.6                      1.8                1.5         1.3     11.3
     Glass & cement                                                          0.8           1.0               0.2                      0.7                -0.2        0.3     6.1
     Iron steel & metal products                                             3.7           1.2               0.3                      0.8                2.7         1.6     10.0
     Manufacture of equipment                                                2.6           1.3               0.3                      0.8                1.5         1.2     12.5
   Agriculture                                                              -1.9          -1.6              -0.6                     -1.0               -0.3        -1.1    -0.9
     Maize                                                                   1.1           0.7               0.2                      0.4                0.4         0.7     1.6
     Paddy                                                                  -2.5           0.7               0.2                      0.5                -3.1       -2.7     -1.8
     Sorghum or millets                                                      1.2           1.1               0.2                      0.8                0.1         0.5     2.4
     Wheat                                                                  -1.5           0.3               0.2                      0.1                -1.5       -0.9     -0.1
     Beans                                                                   0.8           0.7               0.2                      0.5                0.0         0.3     1.0
     Cassava                                                                 0.3           0.5               0.1                      0.3                -0.1        0.1     0.3
     Other cereals                                                           0.6           1.0               0.2                      0.7                -0.4        0.3     2.5
     Oil seeds                                                               0.9           0.6               0.2                      0.4                0.4         0.6     2.5
     Other roots & tubes                                                     0.6           0.6               0.2                      0.4                0.0         0.2     0.9
     Cotton                                                                 -7.3           0.0               -0.3                     0.4                -7.3       -2.4     -2.0
     Coffee                                                                 -9.0          -19.8              -6.0                    -12.9               9.4        -1.2    -10.1
     Tobacco                                                                 1.0          -0.2               -1.0                     1.2                0.8         0.9     5.1
     Tea                                                                    -3.6          -3.6               -1.2                    -1.9                -0.6       -2.4     1.4
     Cashew nuts                                                            -8.2          -26.4              -8.6                    -17.1               19.0        0.5     -5.2
     Sisal fiber                                                            -9.3           1.6               0.4                      1.1               -10.7       -3.3     -4.4
     Sugar                                                                  -13.7         -0.2               0.0                     -0.2               -13.1       -12.6   -12.1
     Fruits & vegetables                                                     0.5           0.3               0.1                      0.2                0.2         0.3     0.4
     Other crops                                                             1.0           0.4               0.1                      0.3                0.6         0.4     1.6
     Poultry & livestock                                                     0.4           0.1               0.0                      0.1                0.2         0.1     1.1
   Other CRTS                                                               2.0           1.7                0.4                     1.1                 0.2        0.7      4.4
     Fish                                                                    0.1          -0.9               -0.2                    -0.5                0.9         0.2     2.4
     Hunting & forestry                                                      0.9           1.2               0.3                      0.7                -0.3        0.4     1.6
     Mining & quarrying                                                      3.7           0.0               0.0                      0.0                3.7         2.8     11.5
     Meat & dairy products                                                  -0.4           0.6               0.2                      0.4                -1.0       -0.7     0.0
     Grain milling                                                           1.2           1.0               0.2                      0.7                0.2         0.6     2.9
     Utilities                                                               1.2           3.0               0.7                      2.0                -1.7        0.3     6.2
     Construction                                                            1.1           1.2               0.3                      0.8                -0.1        0.4     6.5
     Wholesale & retail trade                                                1.2           1.3               0.3                      0.9                -0.1        0.5     4.9
     Hotels & restaurants                                                    5.2           1.8               0.1                      1.6                3.1         1.8     8.5
     Postal communication                                                    6.4           6.9               1.7                      4.4                -0.5        2.1     11.2
     Real estate                                                             3.9           3.9               1.0                      2.5                0.0         1.2     6.5
     Other services                                                         10.0          11.0               2.8                      6.6                -0.8        3.4     10.7
     Tourism                                                                11.9          -12.4              -5.8                    -5.1                22.0        4.9     14.8
     Public administration health & education                                0.8           0.8               0.2                      0.5                0.0         0.3     1.0
   Source: Author's estimates.

                                                                                                                                                                                     29
Table 5. Variation in factor earnings and in aggregate welfare (% change from benchmark)

                                                      All services      Only non-discrimina- Only barriers against FDI in
                                      Full Reform                                                                                    Only uniform tariffs             CRTS      Steady State
                                                       barriers         tory services barriers        services



Variation in factor earnings
Subsistence Factor                      2.52            2.11                     0.49                        1.50                            0.37                      0.73        5.75
Young workers                           1.33            0.63                     0.04                        0.52                            0.64                      0.25        4.07
Female wages (Unskilled)                 2.53            1.33                    0.29                        0.96                            1.07                      1.03        5.23
Female wages (Laborers)                  1.79            1.03                    0.16                        0.78                            0.69                      0.49        4.57
Female wages (Technicians)               2.50            1.76                    0.48                        1.14                            0.68                      1.00        4.00
Female wages (Professionals)            7.96            7.23                     2.10                        4.59                            0.68                      3.14       11.25
Male wages (Unskilled)                  2.15            1.35                     0.28                        0.98                            0.71                      0.82        4.89
Male wages (Laborers)                   2.28            1.49                     0.29                        1.08                            0.71                      0.74        5.15
Male wages (Technicians)                3.90            3.21                     0.92                        2.05                            0.63                      1.56        6.00
Male wages (Professionals)              13.17           12.66                    3.77                        7.94                            0.48                      5.16       18.05
Return on subsistence Factor             2.52            2.11                    0.49                        1.50                            0.37                      0.73        5.75
Return on capital                        2.63            2.07                    0.50                        1.40                            0.50                      1.04       -0.70
Return on land                           0.52            0.04                    -0.15                       0.15                            0.43                     -0.27       3.13
Adult female wages                       2.00            1.18                    0.22                        0.87                            0.74                      0.62        4.69
Adult male wages                         2.78            2.01                    0.47                        1.39                            0.69                      0.97        5.61
Adult (female and male) wages            2.60            1.84                    0.43                        1.28                            0.71                      0.90        5.37

Aggregate welfare
Welfare (EV as % of consumption)         2.23            1.95                     0.46                       1.32                            0.26                     0.67         4.52
Welfare (EV as % of GDP)                 2.03            1.77                     0.42                       1.20                            0.24                     0.61         4.11

Source: Author's estimates.



Table 6. Difference in wages by sex (change in percentage points of difference) and evolution of the wage gap
(in %)
                                                                                             Only non-discrimina-tory   Only barriers against FDI in   Only uniform
                                      Benchmark     Full Reform      All services barriers                                                                              CRTS     Steady State
                                                                                                 services barriers               services                 tariffs


Change in percentage points of difference between females wages and males wages
  Female-male wages (Unskilled)           -         0.38          -0.02                               0.01                        -0.02                  0.36            0.21       0.34
  Female-male wages (Laborers)            -         -0.49         -0.46                              -0.13                        -0.30                  -0.02          -0.25      -0.58
  Female-male wages (Technicians)         -         -1.40         -1.45                              -0.43                        -0.91                  0.05           -0.56      -2.00
  Female-male wages (Professionals)       -         -5.21         -5.43                              -1.67                        -3.35                  0.20           -2.01      -6.81
  Female-male wages (All workers)         -         -0.78         -0.83                              -0.25                        -0.52                  0.05           -0.35      -0.92

Wage gap
  Female/male wages (Unskilled)        67.66          67.91                67.65                     67.66                        67.65                  67.90          67.80      67.88
  Female/male wages (Laborers)         39.43          39.24                39.25                     39.38                        39.31                  39.42          39.33      39.21
  Female/male wages (Technicians)      52.22          51.52                51.49                     52.00                        51.75                  52.25          51.94      51.24
  Female/male wages (Professionals)    41.46          39.55                39.46                     40.80                        40.18                  41.54          40.67      39.07
  Female/male wages (All workers)      39.77          39.47                39.45                     39.67                        39.56                  39.79          39.63      39.42



Source: Author's estimates.
Note: the wage gap is the ratio of female to male wages in percentage terms. Wages can be obtained by dividing total remuneration
of each type of worker by the corresponding number of physical workers based on NBS (2002a) for the benchmark and in the
simulation results for the remaining scenarios.




                                                                                                                                                                                          30
Table 7: Factor adjustments (% change from benchmark)

                                              All services Only non-discrimina- Only barriers against   Only uniform          Steady
                                Full Reform    barriers    tory services barriers FDI in services          tariffs     CRTS    State

Young workers                      1.12          1.08             0.34                  0.69                0.87       0.65    1.18
Female (Unskilled)                 1.09          0.61             0.17                  0.45                0.55       0.39    1.14
Female (Laborers)                  1.11          1.00             0.31                  0.65                0.82       0.57    1.19
Female (Technicians)               0.57          0.48             0.12                  0.31                0.17       0.20    0.66
Female (Professionals)             4.13          4.38             1.41                  2.77                0.47       1.70    5.08
Male (Unskilled)                   1.03          0.66             0.20                  0.46                0.70       0.42    1.18
Male (Laborers)                    1.12          0.91             0.29                  0.60                0.80       0.52    1.31
Male (Technicians)                 1.74          1.71             0.52                  1.07                0.26       0.68    2.13
Male (Professionals)               6.78          7.11             2.40                  4.54                0.51       2.88    8.28
Subsistence Factor                 0.30          0.15             0.04                  0.11                0.27       0.24    0.48
Capital                            1.98          1.64             0.51                  1.05                1.26       0.87    4.01
Land                               1.52          1.47             0.47                  0.95                1.27       0.95    1.58
Adult females                      1.11          0.95             0.29                  0.62                0.73       0.53    1.19
Adult males                        1.35          1.17             0.37                  0.76                0.72       0.60    1.60
Adult (female and male) wages      1.28          1.10             0.35                  0.72                0.73       0.58    1.48

Source: Author's estimates.




                                                                                                                                       31
Table 8: Piecemeal sensitivity analysis: Variation in Adult female and male wages and factor adjustments after “Full reform” (% change with respect to
benchmark)
                      Parameter value             Variation in Adult female and male wages (% change with respecto to benchmark)                         Factor adjustments (% change with respecto to benchmark)
                                                          Lower                      Central                       Upper                               Lower                       Central                     Upper
              Lower      Central        Upper
Parameter                                       Adult Females   Adult Males     Adult Females   Adult Males   Adult Females   Adult Males   Adult Females   Adult Males   Adult Females   Adult Males   Adult Females   Adult Males
σ (va, bs)      0.5        1.25         2.00        1.80              2.30           2.00          2.78           2.36           3.64           0.82           0.95           1.11           1.35           1.66           2.15
σ (qi, qj)     2.00        3.00         4.00        4.98              6.90           2.00          2.78           1.62           2.20           2.45           2.87           1.11           1.35           0.89           1.14
σ (D, M)       2.00        4.00         6.00        1.93              2.23           2.00          2.78           2.11           3.26           0.70           0.91           1.11           1.35           1.48           1.75
σ (L,K)        0.7         1.00         1.3         1.87              2.85           2.00          2.78           2.09           2.73           1.06           1.30           1.11           1.35           1.14           1.39
σ (A1,…,An)    0.00        0.00         0.25        2.00              2.78           2.00          2.78           2.01           2.79           1.11           1.35           1.11           1.35           1.11           1.35
σ (D,E)        2.00        4.00         6.00        2.03              2.66           2.00          2.78           1.98           2.88           0.95           1.20           1.11           1.35           1.25           1.51
ε (di)         2.00        4.00         6.00        1.98              2.73           2.00          2.78           2.02           2.82           1.04           1.27           1.11           1.35           1.15           1.41
ε (fi)         2.00        4.00         6.00        1.06              1.39           2.00          2.78           2.78           3.86           0.67           0.77           1.11           1.35           1.42           1.78
θm (i)                                              1.97              2.81           2.00          2.78           2.07           2.71           1.15           1.41           1.11           1.35           1.04           1.26
                      See table below
θfdi (i)                                            1.82              2.51           2.00          2.78           2.17           3.02           1.00           1.22           1.11           1.35           1.21           1.47

Source: Author's estimates.

Parameter       Definition of the parameter
σ (va, bs)      Elasticity of substitution between value-added and business services
σ (qi, qj)      Elasticity of substitution between firm varieties in imperfectly competitive sectors
σ (D, M)        Armington elasticity of substitution between imports and domestic goods in CRTS sectors
σ (L,K)         Elasticity of substitution between primary factors of production in value added
σ (A1,…,An)     Elasticity of substitution in intermediate production between composite Armington aggregate goods
σ (D,E)         Elasticity of transformation (domestic output versus exports)
ε (di)          Elasticity of national service firm supply with respect to price of output
ε (fi)          Elasticity of multinational service firm supply with respect to price of output
θm (i)          Share of value added in multinational firms in sector I due to specialized primary factor imports in the benchmark equilibrium
θfdi (i)        Share of output of service sector i captured by multinationals firms in the benchmark equilibrium

Parameters values for:                                     θfdi (i)                                           θm (i)
                                            Lower          Central           Upper          Lower             Central           Upper
Telecommunication                            0.85           0.9               0.95          0.025              0.05              0.1
Insurance                                    0.2            0.3               0.4           0.025              0.05              0.1
Banking                                      0.3            0.4               0.5           0.025              0.05              0.1
Professional business services               0.2            0.3               0.4           0.025              0.05              0.1
Road transport                               0.1            0.2               0.3           0.025              0.05              0.1
Railway transport                            0.5            0.6               0.7           0.025              0.05              0.1
Water transport                              0.7            0.8               0.9           0.025              0.05              0.1
Air transport                                0.3            0.4               0.5           0.025              0.05              0.1
                                                                                                                                                                                                                                  32
Appendix 1. Revision of labor shares


This Appendix puts together the main tables behind the new labor shares that have been used for the
results obtained in this study. Most of the information in the Tables reported below stems from the
analytical report of the Integrated Labour Force Survey of 2001 (NBS, 2002a). This source will be
denoted as ILFS (NBS, 2002a), henceforth 9. The sectoral classification in this Appendix necessarily
has to follow the one from Tanzanian statistical sources. Correspondences with the 52 sectors of our
model are presented in Appendix 2 below. We follow the “standard definition” of employment. This is
the definition that the authors from the IFPRI SAM have used (Thurlow and Wobst, 2003). As
documented in Kingdon et al. (2004), Tanzania is characterized by an important level of informal
employment and a low level of unemployment. This contrasts with the labor market structure of other
African economies, where unemployment is more important and the informal sector is small. The
“national definition” used in many data from the ILFS (NBS, 2002a) does not seem to be the most
suitable one. The “national definition” assumes that an important amount of people that respond as
being “employed” in the survey, have in reality an informal or insecure job, and consequently
classifies them as “unemployed” (Chapter 6 of the analytical report, NBS, 2002a). Since the ILFS
reports the numbers of workers in the informal economy by gender and sector and offers information
on their wages, we follow the “standard definition” taking into account their condition of workers in
the “informal sector” and do not considered them unemployed.

Table 1A provides the number of female and male workers, as well as female and male children based
mainly on the ILFS (NBS, 2002a). These figures are common to the authors of the IFPRI SAM
approach and ours. Nevertheless, the only exception is the fact that their measure of children is
between 10 and 14 years old. Ours is between 10 and 17 years old and therefore we label it “Young
workers”. There is available information on the sectoral allocation (Table 1A) and sectoral wages (see
Tables 4A and 5A) only for the latter group of young workers. However, note that we have data on
young workers only at the 9-sector (and not at the 16-sector) level. Note also that they are classified by
gender, so this factor could be split between males and females.

The number of workers reported in Table 1A combined with the information on their corresponding
remunerations (Tables 2-5), are the base for the compensation of employees (or labor remuneration)
provided in the subsequent tables. The first step has been to use the information on sectoral wages
from Table 2A and the number of workers from Table 1A. We have been careful to allocate different
wages to paid-employees and self-employed. This seems very valuable since difficulties in assigning
wages to self-employees is one of the main obstacles when trying to derive labor shares (Arpaia, et al.,
2009; Guerriero, 2012; ILO, 2010; Gollin, 2002). In two cases we have used the wages in Table 3A for
some of the sectors in Table 2A. In particular we have used the wage for paid-employees of
“traditional agriculture”, which is lower than the wage for “agriculture/forestry and fishing”. We do
the same with the particular wage of “housework duties”, which has been allocated to the workers
within the subsector of “other services”, since the workers in “housework duties” are mostly females.
The IFPRI authors comment they use the same wage for agricultural workers that we use. For the rest
of wages across sectors no comment is made.

The shares were initially calculated using medians (instead of averages) which are also available
across sectors and gender, except for the wages of young workers. The resulting shares were very
similar to the ones obtained with averages and, therefore, we have kept the average in order to be able
to use the average remuneration for young workers (Tables 4 and 5).



9
 Only for some information about the number of workers in the public sector we have also used a wage survey for the year
2001 from Tanzanian statistics (NBS, 2002c).

                                                                                                                     33
In Tables 6A-8A we provide the core results found. They offer factors’ remunerations at a 9-sector
level by gender. They stem from the publicly available data in the ILFS (NBS, 2002a) (Table 6A),
from the IFPRI SAM authors (Table 7A) and from the aggregation finally used in the simulations in
the model by Jensen and Tarr (2010) (Table 8A). Note that Jensen and Tarr (2010) expanded de 43
sectors available in the IFPRI SAM to a broader sectoral disaggregation of 52 sectors, which is the
same one we use in the present model. In constructing the data set presented in Table 6A, we keep the
total estimation on factors remunerations (i.e., value of gross domestic product at factor cost) of
Thurlow and Wobst (2003), which stems from the Tanzanian National Accounts. This total is
disaggregated into “the subsistence factor”, capital 10 and labor (considering females, males and
children). Regarding the “subsistence factor” we have also followed the approach of the IFPRI authors
(Thurlow and Wobst, 2003). The “subsistence factor” is the one that produces the “home production”.
Its value is available in the Tanzanian National Accounts and is very similar to the one reported in the
Household Budget Survey 2000/01 (NBS, 2002b). This latter source offers the allocation of home
consumption across sectors, since in that survey households were asked whether they had bought the
goods they consumed or whether they had produced or gathered them. Households had also been asked
to keep a journal of incomes and expenditures over a period of 30 days.

The rest of value added in Table 6A is disaggregated following the information from the ILFS (NBS,
2002a), where capital is calculated as a residual after subtracting from total value added the values of
subsistence and all labor categories. It is with regard to capital and labor categories where the changes
in shares arise. By comparing Tables 7A and 8A it can be seen that the modifications made by Jensen
and Tarr (2010) (Table 8A) with respect to the IFPRI SAM were very small for the 9 sector level we
are considering now. However, when we contrast the information in Table 6A (which contains the
factor shares used in this study) with the one of the IFPRI SAM (Table 7A), we find important
changes. The share of total female labor diminishes (from 11.06% to 9.84%) while the share of males
increases (from 19.12% to 23.36%), compared with the IFPRI SAM. Most of the effect behind this
reallocation takes place in the “Agriculture/Foresty/Fishing” sector which in the IFPRI SAM exhibits a
share of 7.08% of total labor for females and a share of 6.89% of total labor for males. Using the
information from the ILFS (NBS, 2002a), this turns out to be 5.82% and 10.25%. The share of women
in “rest of services” is also smaller, compared to the IFPRI SAM. Besides, the percentage of workers
in “Trade” and “Transport and communication” increases using the ILFS (NBS, 2002a). Table 1A
reported a rather similar percentage of total female (50.63%) and male workers (49.37%), including
female and male young workers. If we discount young workers, the number of workers remains quite
balanced 51.38% are women while 48.62% are men. However, when taking into account wages we
find that women account for 29.6% of total female and male remuneration, excluding young workers,
while men account for the remaining 70.36%, according to NBS (2002a). The same calculations
applied to the SAM derived by Thurlow and Wobst (2003) yield a 36.64% share of women (63.36% of
men) in total female and male remunerations (also excluding young workers remunerations).

We have analyzed whether women could be working less hours than men. This does not seem to be the
case. The issue of under-employment (i.e., working less than 40 hours per week) is explicitly analyzed
in chapter 7 of the analytical report of the ILFS (NBS, 2002a). It affects only 5.3% of the workers of
10 years and more (i.e., including young workers). The number of underemployed is bigger for men (it
affects 522,672 male workers which constitutes 6% of male workers) than women (427,755 or 4.7% of
female workers). Although not much further detail is given regarding the exact amount of hours
worked by gender, in principle, this would not support the fact that women work less than men.
Another reason explaining the wage gap could be different skill levels between men and women, with
women exhibiting lower skills. Table 9A gathers the mean monthly income of paid employees by
occupation and sex showing that there is a considerable wage gap between men and women with the
same occupation.

10
  Capital includes land. The latter exhibit the same factor remunerations as in the IFPRI SAM, the elaboration of Jensen
and Tarr (2010) and in our elaboration of the data.

                                                                                                                           34
Table 10A offers the number of workers (females, males, as well as, female and male young workers)
classified according to their occupations at the 9-sector level. The definitions of occupations in the
ILFS (NBS, 2002a) follow the Tanzanian National Standard Classification of Occupations (TASCO)
which, in turn, follows ILO classifications 11. According to the OECD (Elias, 1997, p. 7), those
occupations can be assigned to four different skill levels. Only for one of the categories there is not an
exact matching with skill levels. This category is “Legislators, administrators and managers”. In
Tanzania it gathers a very similar number of male and women. Furthermore, the total amount of
workers (male and women) included in that category is only 2.23% of all Tanzanian workers.
Therefore we drop the data for this category and assign the wages to the four skill level categories
following their weight in the total of the four categories. Wages by occupation (Table 9A) are only
available for paid-employees (and not for self-employees). Neither do we know how skill levels are
assigned across self-employees and paid-employees by sector and gender. So we provide just a crude
approximation by assigning to the percentage of paid-employees in each sector (by sex), wages
according to its corresponding occupations. This introduces a differentiation in levels of wages
depending on skills by sector and gender. Although, the higher the levels of paid-employees in the
sector, the stronger the different levels of wages according to skill levels are.

Looking at the country totals in percentages, at the bottom of the Table 10A, there is no big difference
in the distribution of skills between women and men. We should keep in mind that the 2nd level of
skills includes a category of “skilled agricultural and fisher workers”. Most labor is classified, then, in
this occupation (86.8% of the Tanzanian labor force, 37% being females, 33.77% males and the rest
young workers). Our skill level classification contrasts sharply with the one in the IFPRI SAM. As
shown in Table 11A, Thurlow and Wobst (2003) have used education levels to proxy skill levels. They
claim to be using information in the ILFS to assign men and women to different skill levels across
sectors. According to the public information of the ILFS, skill levels are available only for the
“traditional agriculture” sector, lacking any other detail for the rest of sectors. This can be seen in
Table 12A which summarizes the information available for education levels in the ILFS (NBS, 2002a).
Under this classification, the biggest percentages belong to the “not finished primary school” category,
although an important share of labor is also allocated to the category of “no formal education”. This
latter amount of workers appears in Table 10A behind the workers in agriculture.

Tables 13A and 14A concentrate on the shares of labor in total labor remuneration. They make even
more explicit that our data set exhibits a higher wage gap (Table 13A) compared to the one of the
IFPRI SAM (Table 14A). Looking at the bottom of Tables 13A and 14A (in Part 2 which offers the
percentages), males and females account for 67.81% (62.72%) and 28.57% (36.28%) using the ILFS
(original IFPRI SAM). Children between 10-14 years old also experience a considerable reduction
when transformed into the remuneration of young workers. Females and male young workers weight
around 20% on the total number of workers (Table 1A). However, their combined weight (i.e., for
female and male children) is of only 3.62% in total labor remuneration (Table 13A or Table 15A).

There is no information on skill levels beyond the 9 sectors. However, as reported in Table 1A, there
exists information for the number of females and males for a higher 16-sector disaggregation. We
have, thus, assumed that skill levels in the 16-sectors are as in their broader corresponding 9-sector
classification. Using this proportionality assumption we calculate the values in the Table 15A, which
shows the labor shares across the 16-sectors. The resulting overall value added shares for all factors of
production are presented in Table 16A. The one derived from the SAM of Thurlow and Wobst (2003)
appear in Table 17A.

Finally, as already noted, the table of conversions of the 9-sectors (or 16-sectors) in the ILFS to the 52
sectors from the SAM in Jensen and Tarr (2010) is presented in Appendix 2.

11
     The description of categories can be found in NBS (2012).

                                                                                                        35
    Table 1A. Workers (young worker, females and males) in Tanzania (absolute numbers and percentages)

                                                                            Number of workers                                              Percentage of workers
                                                                                  Female          Male                                             Female        Male
                                                        All                                                                    All        All
                                                                  All Males       Young           young          Total                             Young         young         Total
                                                      Females                                                                Females     Males
                                                                                  workers        workers                                           workers      workers
Agriculture/Forestry/Fishing                         7191237       6698817       1317936        1561141       13890054        42.51      39.60       7.79         9.23         82.12
a. Cattle. beef&dairy &small animals                  233066        545952                                     779018          1.38       3.23                                  4.61
b. Crop growing                                      6944679       6023079                                    12967758        41.06      35.61                                 76.67
c. Agricultural & forest services                       7446         17165                                      24610          0.04       0.10                                  0.15
d. Fishing                                              6047        112074                                     118121          0.04       0.66                                  0.70
Mining & quarrying                                     13771         15452            0           2279          29223          0.08       0.09       0.00         0.01          0.17
Manufacture                                            83750        161699          8582          5804         245448          0.50       0.96       0.05         0.03          1.45
i. Grainmill products&Food canning                     25578         47987                                      73564          0.15       0.28                                  0.43
ii. Apparel. spinning. weaving and finishing           47686         30680                                      78366          0.28       0.18                                  0.46
iii. Furniture making&Non-Metallic
                                                       10486        83032                                       93518          0.06       0.49                                  0.55
Minerals
Electricity. Gas and water                             1233          13464           0               0           14698         0.01       0.08                                  0.09
Construction                                           4196         147494           0             1977         151690         0.02       0.87       0.00         0.01          0.90
Trade                                                 697473        565495         50001          44877        1262968         4.12       3.34       0.30         0.27          7.47
A. Trade                                              515042        517069           0               0         1032112         3.04       3.06       0.00         0.00          6.10
B. Restaurants & Hotels                               182430         48427                                      230856         1.08       0.29                                  1.36
Transport & Communication                              7643         103929           0            1261          111571         0.05       0.61       0.00         0.01          0.66
Finance. Insurance & Business services                 4339          22162           0              0            26500         0.03       0.13       0.00         0.00          0.16
Personal services                                     559872        622779        276404         193455        1182651         3.31       3.68       1.63         1.14          6.99
Public services                                       145094        284151                                      429244         0.86       1.68                                  2.54
Other personal services                               414778        338628                                      753407         2.45       2.00                                  4.45
COUNTRY TOTAL                                        8563514       8351291       1652922        1810793       16914803        50.63      49.37       9.77        10.71         100.00
    Source: Author's own elaboration based on the ILFS (NBS, 2002a). Tables 34, 12A and 13.7. Note that the categories “All females” and “All males” include “Female young
    workers” and “Male young workers”, respectively,


                                                                                                                                                                          36
Table 2A. Mean monthly income of paid employees and self-employed by industry and sex (current Shillings)

                                                 Paid employees                            Self-employed
                                                  Sex                                     Sex
                                                                TOTAL                                    TOTAL
                                           Male       Female                        Male       Female
    Agriculture/Forestry/Fishing           16318       11193     15234              27523      14220      21291
    Mining & Quarry                        78800       27500     76277              27329       9173      17079
    Manufacture                           122435       42413    103407              49386      20532      38053
    Electricity & Gas                      89848       46122     86127              51482         0       51482
    Construction                           49885       44473     49693              54047      45749      53908
    Trade                                  37556       23422     31301              78105      28040      49933
    Transport                              82280      145972     87100              92310      64256      91143
    Finance                               144253      135863    142719             218064         0      218064
    Personal service                       69440       49949     61891              50026      38117      47112
    Total                                  54423       38888     49954              48988      21335      36005
       Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.4 and 9.8.

Table 3A. Mean monthly income of paid employees and self-employed by sector of employment and sex (current Shillings)

                                                          Paid employees                            Self-employed
                                                           Sex                                      Sex
                                                                         TOTAL                                    TOTAL
                                                    Male       Female                          Male        Female
    Central/Local Government                        80114       73376     77891                78951        36105  65075
    Parastatal Organization                        131316      121814    129546                42767        21429  41760
    Private-Traditional Agriculture                 15355        8232     13468                26946        14144  20891
    Private-Informal Sector                         25602       12527     22427                61450        26583  44788
    NGO/Party or Religion Organization
                                                    51564         33641        47679           124939     43476   95190
    & Private-Other
    Housework duties                                18236         10830        11862           15139       9876   10969
    Total                                           54423         38888        49954           48988      21335   36005
       Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.6 and 9.10.


                                                                                                                           37
Table 4A. Average monthly income of paid employees and self-employed by age group by age group and sex (current Shillings)

                              Paid employees                         Self-employed
                              Sex                                    Sex
                                                  Total                            Total
                      Male        Female                       Male     Female
     10-17             9339         7032           8360        11189     11046    11129
     18-34            33767        32027          33181        41853     21019    31359
     35-64            78470        60814          74567        58316     23059    42457
     65+              37013        11294          34152        71163     13606    55527
     Total            54423        38888          49954        48988     21335    36005
       Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.16 and 9.17.

Table 5A. Average monthly income of paid employees and self employed by age group and industry (current Shillings)

                                                       Paid employees                                     Self-employed
                                                         Age Group                                         Age Group
                                          10-17        18-34     35-64            65+           10-17   18-34      35-64    65+
     Agriculture/Forestry/Fishing          6447        14748     20796           18040           8258   19083      25223   16888
     Mining & Quarry                      41698       103503     46227                          10821   18653      16874    7714
     Manufacture                          18318        43883    172574           20000           8771   36598      44780   13395
     Electricity & Gas                                 84275     87128                                             51482
     Construction                         29879        40018     61284          125955                  61532      47055    43765
     Trade                                14247        25958     48332           38064          15408   40689      60673   183145
     Transport                            15000        59189    109341           36378           5571   46786     125660
     Finance                                           91827    159447                                   7714     264188
     Personal service                      7005        43822     77349           39866          12583   37297      60192   41769
     Total                                 8360        33181     74567           34152          11129   31359      42457   55527
       Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.19 and 9.22.




                                                                                                                                    38
Table 6A. 9-sector factors’ remunerations available in the ILFS (in current billions of Shillings and percentages)

                                                                                       Young                                                                         Young
                                            Subsistence     Female        Male                      Capital        Total      Subsistence      Female      Male                      capital        Total
                                                                                       workers                                                                       workers
  Agriculture/Forestry/Fishing                1388.87       441.211      776.837       86.654       810.370     3503.942         18.32          5.82      10.25       1.14             10.69       46.21
  Mining & quarrying                               0          1.58         7.66            0.46     100.710      110.404           0            0.02       0.10          0.01           1.33        1.46
  Manufacture                                    187.38      15.62        95.43            0.69     613.706      912.828          2.47          0.21       1.26          0.01           8.09       12.04
  Electricity. Gas and wáter                       0          0.68        13.98            0.00     117.536      132.199           0            0.01       0.18          0.00           1.55        1.74
  Construction                                     0          2.14        85.81            0.33     253.971      342.259           0            0.03       1.13          0.00           3.35        4.51
  Trade                                            0         170.10      344.17            5.40     471.737      991.403           0            2.24       4.54          0.07           6.22       13.07
  Transport & Communication                        0         11.58        97.65            0.05     329.116      438.390           0            0.15       1.29       0.001             4.34        5.78
  Rest of services                               372.72      103.40      349.87            1.09      323.94     1151.009          4.92          1.36       4.61          0.01           4.27       15.18
    Finance. Insurance & Business                  0          6.15        38.98             0        94.867      140.000           0            0.08       0.51           0             1.25        1.85
    Personal service                             372.72      97.24       310.89            1.09     229.068     1011.009          4.92          1.28       4.10          0.01           3.02       13.33
  ALL SECTORS                                1948.962       746.306     1771.415       94.668      3021.083     7582.433         25.70          9.84      23.36          1.25          39.84       100.00
 Source: Author's own calculations based on the ILFS (NBS, 2002a). The information merges the data from Tables on the number of employees by gender and sector (Table 3.4), young workers (Table 13.7), overall
 distribution of paid-employees and self-employees (Table 3.8), mean monthly income of paid-employees by gender and sector (Tables 9.4 and 9.6), mean monthly wages of self-employed by gender and sector
 (Tables 9.8 and 9.10). The young workers considered in this table are between 10-17 years old. Capital includes land, which exhibits the same factor remunerations in the different data sets.

Table 7A. 8-sector level factors’ remunerations in the original IFPRI SAM for Tanzania in 2001 (in current billions of Shillings and percentages)

                                       Subsistence        Female       Male        Children        Capital          Total         Subsistence      Female         Male        Children         Capital      Total
  Agriculture/Forestry/Fishing           1388.87          537.04      522.18         21.40        1034.449        3503.942             18.32           7.08       6.89          0.28           13.64        46.21
  Mining & quarrying                         0             0.24        1.60          0.09          108.466         110.404               0             0.00       0.02          0.001           1.43         1.45
  Manufacture                             187.38          73.68       179.62         0.55          471.601         912.828             2.47            0.97       2.37          0.01            6.22        12.03
  Electricity. Gas and water                 0             1.15        25.74           0           105.310         132.199               0             0.02       0.34           0              1.38         1.74
  Construction                               0             3.65       228.11         0.66          109.834         342.259               0             0.05       3.01          0.01            1.44         4.51
  Trade                                      0            41.58        67.71         0.18          881.931         991.403               0             0.55       0.89          0.002          11.63        13.07
  Transport & Communication                  0             7.80        48.06           0           382.533         438.390               0             0.10       0.63           0              5.04         5.78
  Rest of services                        372.72          173.20      376.45         0.21          228.438        1151.009             4.92            2.28       4.96          0.003           3.01        15.18
  ALL SECTORS                            1948.96          838.34      1449.47        23.10        3322.562        7582.433             25.70           11.06      19.12         0.30           43.81        100.00

 Source: Thurlow and Wobst (2003). Note: The original IFPRI SAM has 43 sectors which have been aggregated to this 8 sectors to make them comparable with the information available in the ILFS (NBS. 2002).
 The children considered in this SAM are from 10 and 14 years old. Capital includes land, which exhibits the same factor remunerations in the different data sets.

                                                                                                                                                                                                                     39
Table 8A. 9-sector level factors' remuneration in the 52-sector transformation of the IFPRI SAM by Jensen and Tarr (2010) (in current billions of Shillings
and percentages)

                                      Subsistence     Female       Male        Children    Capital        Total      Subsistence    Female      Male     Children     Capital    Total
Agriculture/Forestry/Fishing            1388.87       537.038     522.182      21.403      815.323     3284.816         19.10         7.38      7.18        0.29       11.21     45.17
Mining & quarrying                          0           0.24        1.60         0.09      112.821      114.759           0           0.00      0.02        0.00       1.55       1.58
Manufacture                              187.38        73.68      179.62         0.55      484.590      925.817          2.58         1.01      2.47        0.01       6.66      12.73
Electricity. Gas and water                  0           1.15       25.74            0      105.310      132.199           0           0.02      0.35         0         1.45       1.82
Construction                                0           3.65      228.11         0.66      109.834      342.259           0           0.05      3.14        0.01       1.51       4.71
Trade                                       0          41.58       67.71         0.18      955.078     1064.549           0           0.57      0.93       0.002       13.13     14.64
Transport & Communication                   0           7.80       48.06            0      200.929      256.786           0           0.11      0.66         0         2.76       3.53
Rest of services                          373           173         376             0        228          1151           5.13         2.38      5.18        0.00       3.14      15.83
 Finance. Insurance & Business              0          15.04       46.28         0.18      130.113      191.610           0           0.21      0.64       0.002       1.79       2.63
 Personal service                        372.72        158.16     330.16         0.03       98.325      959.399          5.13         2.17      4.54      0.0005       1.35      13.19
ALL SECTORS                             1948.96        838.34     1449.47       23.10     3012.322     7272.193         26.80        11.53      19.93       0.32       41.42    100.00
Source: Jensen and Tarr (2010). Note: The SAM in Jensen and Tarr has 52 sectors which have been aggregated to this 9 sectors to make them comparable with the information available in the ILFS (NBS.
2002). The children considered in this SAM are from 10 and 14 years old.

Table 9A. Mean monthly income of paid employees by occupation and sex

                                                                   Sex                    Total
                                                          Male             Female
Legislators, Administrators & Managers                   112551            107473          111705
Professionals                                            148253            94606           134261
Technicians & Associate Professionals                     83700            72689            79875
Clerks                                                   111060            76441            92760
Service & Shop Workers                                    42186            20384            30835
Skilled Agriculture & Fisheries Workers                   18173             9450            16003
Craft & Related Workers                                  110404            61206           105494
Plant & Machine Operators & Assemblers                    56908            37461            55939
Elementary Occupations                                    19578            14804            18740
Total                                                     54423            38888            49954
Source: Author's own calculations based on the ILFS (NBS, 2002a). Table 9.2.



                                                                                                                                                                                                   40
        Table 10A. Workers by occupation (absolute numbers and percentages)
                                                                                           Techni-    Profe-    All 4 occu-                                                Techni-    Profe-    All 4 occu-
                                                      TOTAL       Unskilled    Laborers                                       Legislators   TOTAL   Unskilled   Laborers                                      Legislators
                                                                                            cians    ssionals    pations                                                    cians    ssionals    pations
                               Female                5,873,300    46,775      5,822,663    3,862        0       5,873,300         0         34.72     0.28       34.42      0.02                  34.72

                               Male                  5,137,677    181,409     4,944,423    6,493     2,427      5,134,752       2,925       30.37     1.07       29.23      0.04      0.01        30.36          0.02

Agriculture/Forestry/Fishing   Female young worker   1,317,937    77,954      1,239,983                         1,317,937                   7.79      0.46       7.33                              7.79
                               Male young worker     1,561,140    204,444     1,356,696                         1,561,140                   9.23      1.21       8.02                              9.23

                               All labor             13,890,054   510,582     13,363,765   10,355    2,427      13,887,129      2,925       82.12     3.02       79.01      0.06      0.01        82.10          0.02

                               Female                 13,771        297        11,718        0        878        12,893          878        0.08     0.002       0.07                 0.01         0.08          0.01

                               Male                   13,173       4,005        7,519       618         0        12,142         1,031       0.08      0.02       0.04       0.00                   0.07          0.01

Mining & quarrying             Female young worker       0                                                          0
                               Male young worker       2,279        975         1,304                             2,279                     0.01      0.01       0.01                              0.01

                               All labor              29,223       5,277       20,541       618       878        27,314         1,909       0.17      0.03       0.12       0.00      0.01         0.16          0.01

                               Female                 75,168       5,469       60,898      1,986      996        69,349         5,819       0.44      0.03       0.36       0.01      0.01         0.41          0.03

                               Male                   155,896     21,885       120,244     7,399     1,066       150,594        5,302       0.92      0.13       0.71       0.04      0.01         0.89          0.03

Manufacture                    Female young worker     8,582       1,671        6,911                             8,582                     0.05      0.01       0.04                              0.05
                               Male young worker       5,803        952         4,851                             5,803                     0.03      0.01       0.03                              0.03

                               All labor              245,449     29,977       192,904     9,385     2,062       234,328       11,121       1.45      0.18       1.14       0.06      0.01         1.39          0.07

                               Female                  1,233         0          1,169        0         65         1,234           0         0.01                 0.01                 0.00         0.01          0.00

                               Male                   13,464       1,061        9,773      1,111     1,470       13,415           49        0.08      0.01       0.06       0.01      0.01         0.08          0.00

Electricity, Gas and water     Female young worker

                               Male young worker

                               All labor              14,697       1,061       10,942      1,111     1,535       14,649           49        0.09      0.01       0.06       0.01      0.01         0.09          0.00

                               Female                  4,196       1,501        2,646        49         0         4,196           0         0.02      0.01       0.02       0.00                   0.02          0.00

                               Male                   145,517     16,216       120,631     3,123      731        140,701        4,816       0.86      0.10       0.71       0.02      0.00         0.83          0.03

Construction                   Female young worker       0                                                          0
                               Male young worker       1,977                    1,977                             1,977                     0.01                 0.01                              0.01

                               All labor              151,690     17,717       125,254     3,172      731        146,874        4,816       0.90      0.10       0.74       0.02      0.00         0.87          0.03

                               Female                 647,472     216,153     256995.00    10,556     822        484,526      162,945       3.83      1.28       1.52       0.06      0.00         2.86          0.96

                               Male                   520,618     78,826       267,495     26,678    2,837       375,836      144,782       3.08      0.47       1.58       0.16      0.02         2.22          0.86

Trade                          Female young worker    50,001      24,308       18,736                            43,044         6,957       0.30      0.14       0.11                              0.25          0.04
                               Male young worker      44,877      17,084       21,812                            38,896         5,981       0.27      0.10       0.13                              0.23          0.04

                               All labor             1,262,968    336,371      565,038     37,234    3,659       942,302      320,665       7.47      1.99       3.34       0.22      0.02         5.57          1.90



                                                                                                                                                                                                                        41
 Table 10A. Workers by occupation (absolute numbers and percentages) (Cont.)
                                                                                           Techni-    Profe-     All 4 occu-                                                   Techni-    Profe-    All 4 occu-
                                                    TOTAL       Unskilled     Laborers                                         Legislators   TOTAL      Unskilled   Laborers                                      Legislators
                                                                                            cians    ssionals     pations                                                       cians    ssionals    pations
                             Female                  7,643       1,366         5,160        557        373         7,456          185         0.05        0.01       0.03       0.00      0.00         0.04          0.00

                             Male                   102,668      9,039        78,344       5,589      3,371       96,343         6,325        0.61        0.05       0.46       0.03      0.02         0.57          0.04

Transport & Communication    Female young worker

                             Male young worker       1,261       1,175          86                                 1,261                      0.01        0.01       0.00                              0.01

                             All labor              111,572      11,580       83,590       6,146      3,744      105,060         6,510        0.66        0.07       0.49       0.04      0.02         0.62          0.04

                             Female                  4,339        658          2,866         0         733         4,257           81         0.03        0.00       0.02                 0.00         0.03          0.00

                             Male                   22,162       1,494         9,516       4,462      5,255       20,727         1,435        0.13        0.01       0.06       0.03      0.03         0.12          0.01
Finance, Insurance &
                             Female young worker
Business
                             Male young worker

                             All labor              26,501       2,152        12,382       4,462      5,988       24,984         1,516        0.16        0.01       0.07       0.03      0.04         0.15          0.01

                             Female                 283,468      76,231       93,961       95,265     9,600      275,057         8,411        1.68        0.45       0.56       0.56      0.06         1.63          0.05

                             Male                   429,324      56,840       154,502     178,335     20,478     410,155        19,170        2.54        0.34       0.91       1.05      0.12         2.42          0.11

Personal service             Female young worker    276,404     239,439       36,965                             276,404                      1.63        1.42       0.22                              1.63
                             Male young worker      193,455     170,479       22,976                             193,455                      1.14        1.01       0.14                              1.14

                             All labor             1,182,651    542,989       308,404     273,600     30,078    1,155,071       27,581        6.99        3.21       1.82       1.62      0.18         6.83          0.16

                             Female                6,910,590    348,450      6,258,076    112,275     13,467    6,732,268      178,319       40.86        2.06       37.00      0.66      0.08        39.80          1.05

                             Male                  6,540,499    370,775      5,712,447    233,808     37,635    6,354,665      185,835       38.67        2.19       33.77      1.38      0.22        37.57          1.10

COUNTRY TOTAL                Female young worker   1,652,924    343,372      1,302,595       0          0       1,645,967        6,957        9.77        2.03       7.70                              9.73          0.04
                             Male young worker     1,810,792    395,109      1,409,702       0          0       1,804,811        5,981       10.71        2.34       8.33                             10.67          0.04

                             All labor             16,914,805   1,457,706   14,682,820    346,083     51,102    16,537,711     377,092       100.00       8.62       86.80      2.05      0.30        97.77          2.23


             Source: Author's own calculations based on the ILFS (NBS, 2002a). Table 9A.
             Note: The 9 categories of occupations from Table 9A have been converted into 4 skill levels following Elias (1997). Since there was no conversion for the occupation “Legislators, administratos and
             managers” (“Legislator” in the Table) this category has been omitted and wages have been allocated to the four remaining categories following their weight in the total of the four categories. The conversion
             is as follows: Unskilled (“Elementary occupations”); Laborers (is comprised of five occupations: “Clerks”, “Services and shop workers”, “Skilled agricultural an fishery workers”, “Craft and related
             workers” and “Plant and machine operators and assemblers”); Technicians (“Technicians and associate Professionals”) and Professionals (“Professionals”).




                                                                                                                                                                                                                            42
 Table 11A. Workers by education levels (absolute numbers and percentages)


                              Children     No formal         Not      Not finished   Secondary or                    Children    No formal      Not       Not finished    Secondary
                             (ages 10 to   education      finished     secondary        higher        TOTAL          (ages 10    education    finished     secondary       or higher     TOTAL
                                 14)                      primary        school       education                        to 14)                 primary        school       education
                                                           school                                                                              school

 COUNTR          Female                    1,527,131      672,474      2,344,897        143,315      4,687,817                    14.59         6.43         22.41          1.37          44.79
                             1,403,358                                                                                13.41
 Y TOTAL         Male                      788,193        928,912      2,407,857        249,685      4,374,647                     7.53         8.88         23.01          2.39          41.80
         Source: Table 5.2 of Thurlow and Wobst (2003).



 Table 12A. Workers by education levels (absolute numbers and percentages)


                                                            No formal          Not             Not       Secondary                        No formal        Not          Not            Secondary
                                                            education        finished        finished     or higher           TOTAL       education      finished     finished          or higher   TOTAL
                                                                             primary        secondary    education                                       primary     secondary         education
                                                                              school          school                                                      school       school


                                               Female          1004           54412          71697          1257              128370         0.01         0.32           0.42            0.01        0.76
Public sector
                                               Male            2844           130410         144805        16680              294739         0.02         0.77           0.86            0.10        1.74
                                               Female        2591190         4456276         113626           618          7161710           15.32        26.35          0.67            0.00       42.34
Private traditional agriculture
                                               Male          1560635         4796128         174841         1622           6533226           9.23         28.35          1.03            0.01       38.62
                                               Female          95379          524455         58132               0            677966         0.56         3.10           0.34            0.00        4.01
Private-informal sector
                                               Male            70292          615318         73883          2389              761882         0.42         3.64           0.44            0.01        4.50
NGO/Party or religion & Private                Female          18447          134887         47039            707             201080         0.11         0.80           0.28            0.00        1.19
other                                          Male            59835          400758         87581          6792              554966         0.35         2.37           0.52            0.04        3.28
                                               Female          69330          316375          8682               0            394387         0.41         1.87           0.05            0.00        2.33
Housework duties
                                               Male            20397          181656          4426               0            206479         0.12         1.07           0.03            0.00        1.22
                                               Female        2775320         5486408         299203         2582           8563513           16.41        32.44          1.77            0.02       50.63
TOTAL
                                               Male          1714003         6124270         485532        27486           8351291           10.13        36.21          2.87            0.16       49.37
    Source: Author's own elaboration based on the ILFS (NBS, 2002a). Table 10A.




                                                                                                                                                                                                        43
       Table 13A. (Part 1) 9-sector compensation of employees by skill and gender available in the ILFS (in current billions of Shillings)

                                          Young                          Females (without young workers)                                        Males (without young workers)
                                                                                                                                                                                                      TOTAL
                                          workers           Unskilled     Laborers
                                                                                      Techni-
                                                                                       cians
                                                                                                     Profe-
                                                                                                    ssionals
                                                                                                                  All 4 occu-
                                                                                                                   pations
                                                                                                                                  Unskilled      Laborers
                                                                                                                                                              Techni-
                                                                                                                                                               cians
                                                                                                                                                                           Profe-
                                                                                                                                                                          ssionals
                                                                                                                                                                                       All 4 occu-
                                                                                                                                                                                        pations
Agriculture/Forestry/Fishing               86.654           5.387        435.371      0.452         0.000         441.211         42.055        732.564       1.591       0.627        776.837        1304.702
Mining & quarrying                         0.459            0.037         1.427         0           0.116           1.579          1.767          5.479       0.409       0.000         7.655          9.694
Manufacture                                0.691            1.474        12.977       0.752         0.418          15.621         14.401         72.441       7.213       1.379         95.434        111.745
Electricity. Gas and water                   0                0           0.634         0           0.044           0.678          0.286         10.267       1.048       2.383         13.984         14.662
Construction                               0.331            0.369         1.741       0.034         0.000           2.144          7.167         76.318       1.805       0.522         85.813         88.288
Trade                                      5.395           71.068        92.754       5.758         0.516         170.096         60.252        249.482      30.183       4.258       344.175         519.666
Transport & Communication                  0.048            0.896         8.577       1.144         0.963          11.580          4.935         79.301       6.905       6.504         97.645        109.274
Rest of services                           1.090           21.795        33.987      41.711         5.904         103.397         28.017        134.223     149.645      37.988       349.872         454.358
 Finance. Insurance & Business             0.000            0.872         4.175       0.000         1.107           6.154          0.886         15.240       7.727      15.126         38.978         45.133
 Personal service                          1.090           20.923        29.811      41.711         4.797          97.242         27.131        118.983     141.918      22.862       310.893         409.225
ALL SECTORS                                94.668          101.025       587.467     49.852         7.962         746.306         158.880       1360.075    198.798      53.662       1771.415        2612.389

   Table 13A. (Part 2) 9-sector compensation of employees by skill and gender available in the ILFS (in percentage)

                                            Young                         Females (without young workers)                                        Males (without young workers)
                                                                                                                                                                                                       TOTAL
                                            workers          Unskilled    Laborers
                                                                                     Techni-        Profe-         All 4 occu-
                                                                                                                                    Unskilled    Laborers
                                                                                                                                                            Techni-        Profe-       All 4 occu-
                                                                                      cians        ssionals         pations                                  cians        ssionals       pations
Agriculture/Forestry/Fishing                   3.32           0.21        16.67       0.02          0.00             16.89            1.61        28.04      0.06         0.02           29.74           49.94
Mining & quarrying                             0.02           0.00         0.05       0.00          0.00              0.06            0.07         0.21      0.02         0.00            0.29            0.37
Manufacture                                    0.03           0.06         0.50       0.03          0.02              0.60            0.55         2.77      0.28         0.05            3.65            4.28
Electricity. Gas and water                     0.00           0.00         0.02       0.00          0.00              0.03            0.01         0.39      0.04         0.09            0.54            0.56
Construction                                   0.01           0.01         0.07       0.00          0.00              0.08            0.27         2.92      0.07         0.02            3.28            3.38
Trade                                          0.21           2.72         3.55       0.22          0.02              6.51            2.31         9.55      1.16         0.16           13.17           19.89
Transport & Communication                      0.00           0.03         0.33       0.04          0.04              0.44            0.19         3.04      0.26         0.25            3.74            4.18
Rest of services                               0.04           0.83         1.30       1.60          0.23              3.96            1.07         5.14      5.73         1.45           13.39           17.39
 Finance. Insurance & Business                 0.00           0.03         0.16       0.00          0.04              0.24            0.03         0.58      0.30         0.58            1.49            1.73
  Personal service                             0.04           0.80         1.14       1.60          0.18              3.72            1.04         4.55      5.43         0.88           11.90           15.66
ALL SECTORS                                    3.62           3.87        22.49       1.91          0.30             28.57            6.08        52.06      7.61         2.05           67.81          100.00
       Sources: Author's own elaboration based on the ILFS (NBS, 2002a). The information merges all the data used in Table 6 in this Appendix with the occupations by gender and sector (Table 10) and the mean
       monthly wage of paid employees by occupation and gender (Table 9).


                                                                                                                                                                                                              44
     Table 14A. (Part 1) 8-sector level labor remuneration in the original IFPRI SAM (in current billions of Shillings)
                                                               Females                                               Males
                                Child
                                              No       Not       Not    Secondary          No formal      Not        Not                           Secondary      No formal     Total labor
                               (age 10      formal   finished  finished  or higher TOTAL   education   finished    finished                         or higher     education
                                                                                                                                                                               remuneration
                                to 14)     education primary  secondary education                       primary   secondary                        education
                                                          school      school                                              school       school

Agriculture/Forestry/Fishing       21.403      62.824 68.947 400.732                 4.535      537.038      69.767      207.563 232.014            12.839        522.182        1080.623
Mining & quarrying                  0.093       0.069 0.005     0                    0.120        0.242       0.000        0.071   1.466             0.067          1.603           1.938
Manufacture                         0.550       2.539 3.739 48.443                  18.958       73.679       3.752       14.688 119.544            41.637        179.621         253.850
Electricity. Gas and water            0           0    0.000    1                    0.471        1.147       1.431        1.002  10.473            12.836         25.742          26.889
Construction                        0.663       0.208 0.000   1.109                  2.337        3.654       4.617       25.263 159.069            39.160        228.108         232.425
Trade                               0.180       3.279 5.419 28.131                   4.753       41.581       1.459        4.668  37.328            24.255         67.711         109.472
Transport &
                                    0.000      0.000      0.000      2.691          5.105           7.796    0.399        1.311      18.749         27.602         48.061         55.857
Communication
Rest of services                    0.210       0.843 2.899 49.844 119.614 173.199                            1.602       11.866 100.142           262.837         376.446        549.856
ALL SECTORS                        23.098      69.762 81.008 531.673 155.894 838.337                         83.026      266.431 678.785           421.232        1449.474       2310.909
     Table 14A. (part 2) 8-sector level factors' remuneration in the original IFPRI SAM for Tanzania in 2001 (in percentage)
                                                              Females                                            Males
                                     Child
                                     (age     No formal
                                              education
                                                            Not
                                                          finished
                                                                     Not finished
                                                                      secondary
                                                                                     Secondary or
                                                                                        higher       TOTAL
                                                                                                             No formal
                                                                                                             education
                                                                                                                           Not
                                                                                                                         finished
                                                                                                                                    Not finished
                                                                                                                                     secondary
                                                                                                                                                   Secondary or
                                                                                                                                                      higher
                                                                                                                                                                  No formal
                                                                                                                                                                  education
                                                                                                                                                                               Total labor
                                     10 to                primary       school        education                          primary       school       education                 remuneration
                                      14)                  school                                                         school


  Agriculture/Forestry/Fishing       0.93      2.72       2.98        17.34             0.20         23.24    3.02        8.98       10.04            0.56        22.60          46.76
  Mining & quarrying                 0.00      0.00       0.00         0.00             0.01          0.01    0.00        0.00        0.06            0.00         0.07           0.08
  Manufacture                        0.02      0.11       0.16         2.10             0.82          3.19    0.16        0.64        5.17            1.80         7.77          10.98
  Electricity. Gas and water         0.00      0.00       0.00         0.03             0.02          0.05    0.06        0.04        0.45            0.56         1.11           1.16
  Construction                       0.03      0.01       0.00         0.05             0.10          0.16    0.20        1.09        6.88            1.69         9.87          10.06
  Trade                              0.01      0.14       0.23         1.22             0.21          1.80    0.06        0.20        1.62            1.05         2.93           4.74
  Transport & Communication          0.00      0.00       0.00         0.12             0.22          0.34    0.02        0.06        0.81            1.19         2.08           2.42
  Rest of services                   0.01      0.04       0.13         2.16             5.18          7.49    0.07        0.51        4.33           11.37        16.29          23.79
  ALL SECTORS                        1.00      3.02       3.51        23.01             6.75         36.28    3.59       11.53       29.37           18.23        62.72         100.00
     Source: Thurlow and Wobst (2003). See note on Table 7 in this Appendix.
                                                                                                                                                                                           45
Table 15A. (part 1) 16-sectors labor remuneration (in billions of current Tanzanian Shillings)
                                      Young                  Females (without young workers)                               Males (without young workers)                  ALL
                                      workers                              Techni-    Profe-    All 4 occu-                           Techni-    Profe-    All 4 occu-    Labor
                                                 Unskilled      Laborers                                      Unskilled    Laborers
                                                                            cians    ssionals    pations                               cians    ssionals    pations

 Agriculture/Forestry/Fishing          86.654     5.387         435.371    0.452     0.000      441.211       42.055      732.564      1.591     0.627     776.837       1304.702
 a. Cattle. beef&dairy &small
                                       4.860      0.175         14.110     0.015     0.000       14.300        3.427       59.704      0.130     0.051      63.312        82.472
 animals
 b. Crop growing                       80.904     5.203         420.444    0.437     0.000      426.083       37.813      658.667      1.430     0.564     698.474       1205.461
 c. Agricultural & forest services     0.154      0.006          0.451     0.0005    0.000       0.457         0.111       1.937       0.004     0.002      2.054         2.664
 d. Fishing                            0.737      0.005          0.366     0.0004    0.000       0.371         0.704       12.256      0.027     0.010      12.997        14.105
 Mining & quarrying                    0.459      0.037          1.427     0.000     0.116       1.579         1.767       5.479       0.409     0.000      7.655         9.694
 Manufacture                           0.691      1.474         12.977     0.752     0.418       15.621       14.401       72.441      7.213     1.379      95.434       111.745
 i. Grainmill products&Food
                                       0.207      0.450          3.963     0.230     0.128       4.771         4.274       21.498      2.141     0.409      28.322        33.299
 canning
 ii. Apparel. spinning. weaving
                                       0.221      0.839          7.389     0.428     0.238       8.894         2.732       13.745      1.369     0.262      18.107        27.222
 and finishing
 iii. Furniture making&Non-
                                       0.263      0.185          1.625     0.094     0.052       1.956         7.395       37.198      3.704     0.708      49.005        51.224
 Metallic Minerals
 Electricity. Gas and water              0        0.000          0.634     0.000     0.044       0.678         0.286       10.267      1.048     2.383      13.984        14.662
 Construction                          0.331      0.369          1.741     0.034     0.000       2.110         7.167       76.318      1.805     0.522      85.813        88.254
 Trade                                 5.395     71.068         92.754     5.758     0.516      170.096       60.252      249.482     30.183     4.258     344.175       519.666
 A. Trade                              4.409     52.479         68.493     4.252     0.381      125.606       55.092      228.118     27.598     3.893     314.701       444.716
 B. Restaurants & Hotels               0.986     18.588         24.261     1.506     0.135       44.490        5.160       21.365      2.585     0.365      29.474        74.950
 Transport & Communication             0.048      0.896          8.577     1.144     0.963       11.580        4.935       79.301      6.905     6.504      97.645       109.274
 Financem Insurance & Business
                                       0.000      0.872          4.175     0.000     1.107       6.154         0.886       15.240      7.727    15.126      38.978        45.133
 services
 Personal services                     1.090     20.923         29.811     41.711    4.797       97.242       27.131      118.983     141.918   22.862     310.893       409.225
 Public services                       0.392      5.422          7.726     10.810    1.243       25.201       12.379       54.287     64.752    10.431     141.849       167.442
 Other personal services               0.698     15.501         22.086     30.901    3.554       72.041       14.752       64.695     77.166    12.431     169.044       241.783
 COUNTRY TOTAL                         94.668    101.025        587.467    49.852    7.962      746.271       158.880     1360.075    198.798   53.662     1771.415      2612.355



                                                                                                                                                                                   46
Table 15A. (part 2). 16-sectors labor remuneration (in percentage)
                                                                              FEMALES (without young workers)                             MALES (without young workers)
                                                            Young                                                                                                              All 4
                                                                                                                                                                                        ALL
                                                            workers                            Techni-    Profe-    All 4 occu-                          Techni-    Profe-              Labor
                                                                        Unskilled   Laborers                                      Unskilled   Laborers                         occu-
                                                                                                cians    ssionals    pations                              cians    ssionals
                                                                                                                                                                              pations

     Agriculture/Forestry/Fishing                             3.32       0.21       16.67       0.02      0.00        16.89        1.61       28.04       0.06      0.02      29.74     49.94
     a. Cattle. beef&dairy &small animals                     0.19       0.01        0.54       0.00      0.00        0.55         0.13        2.29       0.00      0.00      2.42       3.16
     b. Crop growing                                          3.10       0.20       16.09       0.02      0.00        16.31        1.45       25.21       0.05      0.02      26.74     46.14
     c. Agricultural & forest services                        0.01       0.00        0.02       0.00      0.00        0.02         0.00        0.07       0.00      0.00      0.08       0.10
     d. Fishing                                               0.03       0.00        0.01       0.00      0.00        0.01         0.03        0.47       0.00      0.00      0.50       0.54
     Mining & quarrying                                       0.02       0.00        0.05       0.00      0.00        0.06         0.07        0.21       0.02      0.00      0.29       0.37

     Manufacture                                              0.03       0.06        0.50       0.03      0.02        0.60         0.55        2.77       0.28      0.05      3.65       4.28
     i. Grainmill products&Food canning                       0.01       0.02        0.15       0.01      0.00        0.18         0.16        0.82       0.08      0.02      1.08       1.27
     ii. Apparel. spinning. weaving and finishing             0.01       0.03        0.28       0.02      0.01        0.34         0.10        0.53       0.05      0.01      0.69       1.04
     iii. Furniture making&Non-Metallic
                                                              0.01       0.01        0.06       0.00      0.00        0.07         0.28        1.42       0.14      0.03      1.88       1.96
     Minerals
     Electricity. Gas and water                               0.00       0.00        0.02       0.00      0.00        0.03         0.01        0.39       0.04      0.09      0.54       0.56

     Construction                                             0.01       0.01        0.07       0.00      0.00        0.08         0.27        2.92       0.07      0.02      3.28       3.38

     Trade                                                    0.21       2.72        3.55       0.22      0.02        6.51         2.31        9.55       1.16      0.16      13.17     19.89

     A. Trade                                                 0.17       2.01        2.62       0.16      0.01        4.81         2.11        8.73       1.06      0.15      12.05     17.02

     B. Restaurants & Hotels                                  0.04       0.71        0.93       0.06      0.01        1.70         0.20        0.82       0.10      0.01      1.13       2.87

     Transport & Communication                                0.00       0.03        0.33       0.04      0.04        0.44         0.19        3.04       0.26      0.25      3.74       4.18

     Finance, Insurance & Business services                   0.00       0.03        0.16       0.00      0.04        0.24         0.03        0.58       0.30      0.58      1.49       1.73

     Personal services                                        0.04       0.80        1.14       1.60      0.18        3.72         1.04        4.55       5.43      0.88      11.90     15.66

     Public services                                          0.02       0.21        0.30       0.41      0.05        0.96         0.47        2.08       2.48      0.40      5.43       6.41

     Other personal services                                  0.03       0.59        0.85       1.18      0.14        2.76         0.56        2.48       2.95      0.48      6.47       9.26

     COUNTRY TOTAL                                            3.62       3.87       22.49       1.91      0.30        28.57        6.08       52.06       7.61      2.05      67.81     100.00
Source: Author’s own elaboration based on the ILFS (NBS, 2002a). The information merges all the data from the Tables 1, 6, 9 and 13 in this Appendix


                                                                                                                                                                                                 47
Table 16A. Benchmark factors' remuneration with respect to total value added according to NBS (2002a) (in percentage)

                                                Young     Females   Females   Females   Females   Males   Males   Males   Males                                    All      All    Total Value
                                                                                                                                  Subsistence   Capital   Land
                                                workers    (1st)     (2nd)     (3rd)     (4th)    (1st)   (2nd)   (3rd)   (4th)                                  females   males     Added
IRTS Goods and Services                           0.0       0.0       0.3       0.0       0.0      0.2     1.9     0.2     0.3        0.4       11.6              0.4       2.7       15.1
CRTS Goods and Services                           1.2       1.3       7.5       0.6       0.1      1.9    16.0     2.4     0.4       25.3       24.2      4.1     9.5      20.7       84.9
 Business Services                               0.00      0.02      0.16      0.01      0.03     0.07    1.20    0.18    0.26       0.0        3.9       0.0     0.2      1.7        5.9
  Telecommunication                              0.000     0.003     0.025     0.003     0.002    0.014   0.232   0.020   0.014                  0.43             0.03     0.28       0.7
  Insurance                                                0.000     0.001               0.000    0.000   0.004   0.002   0.003                  0.05             0.00     0.01       0.1
  Banking                                                  0.005     0.022               0.004    0.005   0.080   0.041   0.057                  0.93             0.03     0.18       1.1
  Professional business services                           0.005     0.024               0.004    0.005   0.089   0.045   0.063                  1.03             0.03     0.20       1.3
  Air transport                                  0.000     0.001     0.009     0.001     0.001    0.005   0.086   0.007   0.005                  0.16             0.01     0.10       0.3
  Road transport                                 0.000     0.006     0.061     0.008     0.004    0.035   0.560   0.049   0.033                  1.04             0.08     0.68       1.8
  Railway transport                              0.000     0.000     0.003     0.000     0.000    0.002   0.028   0.002   0.002                  0.05             0.00     0.03       0.1
  Water transport                                0.000     0.001     0.014     0.002     0.001    0.008   0.126   0.011   0.007                  0.23             0.02     0.15       0.4
 Dixit-Stiglitz Goods                            0.01      0.01      0.13      0.01      0.00     0.14    0.72    0.07    0.01       0.4         7.6              0.2      0.9        9.2
  Processed food                                 0.001     0.003     0.028     0.002     0.001    0.031   0.157   0.016   0.002      0.33        1.44             0.03     0.21       2.0
  Beverages & tobacco products                   0.001     0.001     0.012     0.001     0.000    0.014   0.068   0.007   0.001      0.11        0.65             0.01     0.09       0.9
  Textile & leather products                     0.002     0.005     0.043     0.003     0.001    0.048   0.241   0.024   0.003                  2.71             0.05     0.32       3.1
  Wood paper printing                            0.001     0.002     0.014     0.001     0.000    0.015   0.076   0.008   0.001                  0.85             0.02     0.10       1.0
  Manufacture of basic & industrial chemicals    0.000     0.000     0.003     0.000     0.000    0.003   0.018   0.002   0.000                  0.20             0.00     0.02       0.2
  Manufacture of fertilizers & pesticides        0.000     0.000     0.001     0.000     0.000    0.001   0.003   0.000   0.000                  0.03             0.00     0.00       0.0
  Petroleum refineries                           0.000     0.000     0.003     0.000     0.000    0.003   0.014   0.001   0.000                  0.16             0.00     0.02       0.2
  Rubber plastic & other manufacturing           0.000     0.000     0.003     0.000     0.000    0.004   0.018   0.002   0.000                  0.21             0.00     0.02       0.2
  Glass & cement                                 0.000     0.001     0.006     0.000     0.000    0.006   0.033   0.003   0.000                  0.37             0.01     0.04       0.4
  Iron steel & metal products                    0.000     0.001     0.008     0.000     0.000    0.009   0.043   0.004   0.001                  0.49             0.01     0.06       0.6
  Manufacture of equipment                       0.000     0.001     0.009     0.001     0.000    0.010   0.051   0.005   0.001                  0.57             0.01     0.07       0.6




                                                                                                                                                                                             48
Table 16A. Benchmark factors' remuneration with respect to total value added according to NBS (2002a) (in percentage) (Continued)
                                              Young     Females   Females   Females   Females   Males   Males   Males   Males                                    All      All    Total Value
                                                                                                                                Subsistence   Capital   Land
                                              workers    (1st)     (2nd)     (3rd)     (4th)    (1st)   (2nd)   (3rd)   (4th)                                  females   males     Added
 Agriculture                                   0.95      0.06      4.79      0.00               0.46    8.06    0.02    0.01       15.8        6.1      3.3     4.9      8.5        39.5
   Maize                                       0.239     0.015     1.201     0.001              0.116   2.021   0.004   0.001      5.30        0.57     0.45    1.22     2.14       9.9
   Paddy                                       0.090     0.006     0.454     0.000              0.044   0.764   0.002   0.000      1.36        0.61     0.42    0.46     0.81       3.7
   Sorghum or millets                          0.032     0.002     0.160     0.000              0.015   0.270   0.001   0.000      0.70        0.08     0.07    0.16     0.29       1.3
   Wheat                                       0.006     0.000     0.028     0.000              0.003   0.047   0.000   0.000      0.01        0.10     0.03    0.03     0.05       0.2
   Beans                                       0.057     0.004     0.286     0.000              0.028   0.481   0.001   0.000      1.21        0.05     0.24    0.29     0.51       2.4
   Cassava                                     0.048     0.003     0.243     0.000              0.024   0.409   0.001   0.000      1.20        0.02     0.06    0.25     0.43       2.0
   Other cereals                               0.008     0.001     0.041     0.000              0.004   0.069   0.000   0.000      0.04        0.13     0.04    0.04     0.07       0.3
   Oil seeds                                   0.036     0.002     0.183     0.000              0.018   0.307   0.001   0.000      0.41        0.39     0.16    0.19     0.33       1.5
   Other roots & tubes                         0.039     0.002     0.196     0.000              0.019   0.331   0.001   0.000      0.86        0.06     0.11    0.20     0.35       1.6
   Cotton                                      0.018     0.001     0.090     0.000              0.009   0.151   0.000   0.000                  0.38     0.09    0.09     0.16       0.7
   Coffee                                      0.025     0.002     0.123     0.000              0.012   0.207   0.000   0.000      0.06        0.48     0.10    0.12     0.22       1.0
   Tobacco                                     0.016     0.001     0.081     0.000              0.008   0.136   0.000   0.000                  0.35     0.08    0.08     0.14       0.7
   Tea                                         0.008     0.001     0.042     0.000              0.004   0.071   0.000   0.000      0.00        0.18     0.04    0.04     0.07       0.3
   Cashew nuts                                 0.031     0.002     0.157     0.000              0.015   0.265   0.001   0.000                  0.67     0.15    0.16     0.28       1.3
   Sisal fiber                                 0.002     0.000     0.011     0.000              0.001   0.019   0.000   0.000                  0.05     0.01    0.01     0.02       0.1
   Sugar                                       0.039     0.002     0.197     0.000              0.019   0.331   0.001   0.000      0.02        0.78     0.23    0.20     0.35       1.6
   Fruits & vegetables                         0.161     0.010     0.808     0.001              0.078   1.359   0.003   0.001      3.17        0.52     0.57    0.82     1.44       6.7
   Other crops                                 0.020     0.001     0.098     0.000              0.009   0.165   0.000   0.000      0.35        0.10     0.07    0.10     0.18       0.8
   Poultry & livestock                         0.080     0.005     0.401     0.000              0.039   0.674   0.001   0.000      1.10        0.61     0.39    0.41     0.71       3.3
 Other CRTS                                    0.29      1.24      2.67      0.63      0.08     1.42    7.96    2.36    0.43       9.5        18.1      0.8     4.6      12.2      45.4
   Fish                                        0.101     0.006     0.507     0.001              0.049   0.852   0.002   0.001      0.25        1.86     0.56    0.51     0.90       4.2
   Hunting & forestry                          0.089     0.006     0.448     0.000              0.043   0.754   0.002   0.000      1.81        0.33     0.22    0.45     0.80       3.7
   Mining & quarrying                          0.006     0.000     0.019               0.001    0.023   0.072   0.005                          1.39             0.02     0.10       1.5
   Meat & dairy products                       0.002     0.004     0.033     0.002     0.001    0.036   0.182   0.018   0.003      2.03        0.02             0.04     0.24       2.3
   Grain milling                               0.001     0.001     0.010     0.001     0.000    0.011   0.054   0.005   0.001                  0.60             0.01     0.07       0.7
   Utilities                                                       0.008               0.000    0.004   0.136   0.014   0.023                  1.55             0.01     0.18       1.7
   Construction                                0.004     0.005     0.023     0.000              0.095   1.009   0.024   0.005                  3.36             0.03     1.13       4.5
   Wholesale & retail trade                    0.053     0.700     0.913     0.057     0.003    0.593   2.457   0.297   0.030                  5.37             1.67     3.38      10.5
   Hotels & restaurants                        0.018     0.240     0.313     0.019     0.001    0.203   0.842   0.102   0.010                  1.84             0.57     1.16       3.6
   Postal communication                        0.000     0.000     0.002     0.000     0.000    0.001   0.017   0.001   0.001                  0.03             0.00     0.02       0.1
   Real estate                                           0.002     0.008               0.001    0.002   0.028   0.014   0.020      5.42        0.48             0.01     0.06       6.0
   Other services                              0.001     0.020     0.028     0.039     0.003    0.026   0.113   0.134   0.016                  0.09             0.09     0.29       0.5
   Public administration health & education    0.013     0.257     0.366     0.512     0.037    0.333   1.460   1.742   0.202                  1.19             1.17     3.74       6.1
Total value added by factor                    1.2       1.3       7.7       0.7       0.1       2.1    17.9     2.6     0.7       25.7       35.8      4.1     9.8      23.4      100.0

Source: Authors’ calculations based on NBS (2002a, 2002c).
                                                                                                                                                                                               49
Table 17A. Benchmark factors' remuneration with respect to total value added in Jensen and Tarr (2010) (in percentage)

                                                                                            Female          Female                    Male            Male          Male                                                         Total
                                                  Child        Female       Female                                      Male
                                                                                          (not finished   (secondary              (not finished   (not finished (secondary or                                  All
                                                (age 10 to   (no formal (not finished
                                                                                           secondary       or higher
                                                                                                                     (no formal
                                                                                                                                    primary        secondary        higher
                                                                                                                                                                              Subsistence   Capital   Land
                                                                                                                                                                                                             females
                                                                                                                                                                                                                     All males   Value
                                                   14)       education) primary school)                              education)                                                                                                  added
                                                                                            school)       education)                school)         school)       education)
IRTS Goods and Services                            0.0          0.0           0.0             0.6            0.4         0.1          0.3             1.9          1.1           0.4        10.3              1.1      3.3       15.1
CRTS Goods and Services                            0.3          0.9           1.0             6.4            1.7         1.0          3.3             7.1          4.5           25.3       29.5      4.1     10.0     15.8      84.9
 Business Services                                0.00         0.00          0.01            0.13           0.15        0.02         0.08            0.56          0.58          0.0         4.3      0.0     0.3      1.2       5.9
  Telecommunication                                                                          0.011          0.020      0.002         0.005           0.076         0.107                     0.53             0.03     0.19       0.7
  Insurance                                       0.000        0.000         0.000           0.003          0.002      0.000         0.002           0.008         0.005                     0.04             0.01     0.02       0.1
  Banking                                         0.001        0.002         0.005           0.045          0.039      0.005         0.028           0.148         0.098                     0.80             0.09     0.28       1.2
  Professional business services                  0.001        0.002         0.006           0.050          0.043      0.005         0.031           0.164         0.108                     0.88             0.10     0.31       1.3
  Air transport                                                                              0.002          0.004      0.000         0.001           0.016         0.023                     0.23             0.01     0.04       0.3
  Road transport                                                                             0.015          0.028      0.002         0.007           0.104         0.148                     1.50             0.04     0.26       1.8
  Railway transport                                                                          0.001          0.001      0.000         0.000           0.005         0.008                     0.08             0.00     0.01       0.1
  Water transport                                                                            0.006          0.011      0.001         0.003           0.041         0.058                     0.29             0.02     0.10       0.4
 Dixit-Stiglitz Goods                             0.01         0.03          0.03            0.47           0.25        0.04         0.17            1.33          0.50          0.4         5.9              0.8       2.0       9.2
  Processed food                                               0.005         0.002           0.014          0.080      0.007         0.010           0.096         0.061         0.33        1.40             0.10     0.17       2.0
  Beverages & tobacco products                                                               0.001          0.003                    0.000           0.009         0.134         0.11        0.60             0.00     0.14       0.9
  Textile & leather products                      0.005        0.025         0.024           0.438          0.144      0.011         0.123           0.656         0.157                     1.49             0.63     0.95       3.1
  Wood paper printing                             0.000        0.000                         0.003          0.005      0.013         0.010           0.151         0.037                     0.74             0.01     0.21       1.0
  Manufacture of basic & industrial chemicals                                                                                                        0.175                                   0.05             0.00     0.17       0.2
  Manufacture of fertilizers & pesticides                                                                                                            0.028                                   0.01             0.00     0.03       0.0
  Petroleum refineries                                                                                                                               0.044         0.005                     0.13             0.00     0.05       0.2
  Rubber plastic & other manufacturing                                                       0.010          0.005                    0.001           0.026         0.009                     0.18             0.01     0.04       0.2
  Glass & cement                                               0.000         0.000           0.000                     0.001         0.001           0.074         0.014                     0.33             0.00     0.09       0.4
  Iron steel & metal products                                                                               0.003      0.006         0.017           0.058         0.044                     0.43             0.00     0.12       0.6
  Manufacture of equipment                                                                   0.003          0.002                    0.014           0.017         0.021                     0.59             0.00     0.05       0.6




                                                                                                                                                                                                                                   50
Table 17A. Benchmark factors' remuneration with respect to total value added in Jensen and Tarr (2010) (in percentage) (Continued)

                                                                                          Female          Female                    Male            Male          Male                                                         Total
                                                Child        Female       Female                                      Male
                                                                                        (not finished   (secondary              (not finished   (not finished (secondary or                                  All
                                              (age 10 to   (no formal (not finished
                                                                                         secondary       or higher
                                                                                                                   (no formal
                                                                                                                                  primary        secondary        higher
                                                                                                                                                                            Subsistence   Capital   Land
                                                                                                                                                                                                           females
                                                                                                                                                                                                                   All males   Value
                                                 14)       education) primary school)                              education)                                                                                                  added
                                                                                          school)       education)                school)         school)       education)
 Agriculture                                    0.28         0.75          0.91            4.74           0.06        0.61         2.12            2.01          0.14          15.9        8.7      3.3     6.5       4.9       39.5
   Maize                                        0.016        0.180         0.119           0.785          0.003      0.079         0.217           0.244         0.017         6.77        1.04     0.45    1.09     0.56        9.9
   Paddy                                        0.006        0.070         0.079           0.700          0.004      0.043         0.236           0.315         0.027         0.86        0.98     0.42    0.85     0.62        3.7
   Sorghum or millets                           0.005        0.052         0.008           0.068                     0.024         0.027           0.039         0.001         0.88        0.15     0.07    0.13     0.09        1.3
   Wheat                                                                                                                                           0.109                       0.01        0.08     0.03    0.00     0.11        0.2
   Beans                                                     0.090         0.058           0.479          0.002      0.024         0.095           0.078         0.005         0.72        0.57     0.24    0.63     0.20        2.4
   Cassava                                      0.003        0.022         0.012           0.067          0.000      0.012         0.040           0.032         0.003         1.63        0.13     0.06    0.10     0.09        2.0
   Other cereals                                0.002        0.019         0.008           0.066          0.000      0.009         0.021           0.023         0.001         0.04        0.10     0.04    0.09     0.06        0.3
   Oil seeds                                    0.009        0.050         0.024           0.255                     0.035         0.100           0.075         0.003         0.41        0.39     0.16    0.33     0.21        1.5
   Other roots & tubes                          0.012        0.026         0.021           0.193                                   0.069           0.059         0.003         0.86        0.26     0.11    0.24     0.13        1.6
   Cotton                                       0.033        0.007         0.020           0.077                     0.019         0.072           0.089         0.001                     0.33     0.09    0.10     0.18        0.7
   Coffee                                                    0.016         0.010           0.117                     0.018         0.068           0.117         0.005         0.06        0.50     0.10    0.14     0.21        1.0
   Tobacco                                                   0.015         0.012           0.080          0.001      0.018         0.056           0.076         0.012                     0.32     0.08    0.11     0.16        0.7
   Tea                                                                                                                             0.136                                       0.00        0.17     0.04    0.00     0.14        0.3
   Cashew nuts                                  0.010        0.032         0.007           0.108                     0.057         0.127           0.177                                   0.63     0.15    0.15     0.36        1.3
   Sisal fiber                                               0.005                                                   0.013         0.010           0.016         0.003                     0.03     0.01    0.01     0.04        0.1
   Sugar                                                                   0.436                                                   0.362                                       0.02        0.57     0.23    0.44     0.36        1.6
   Fruits & vegetables                          0.049        0.081         0.066           1.080          0.021      0.114         0.249           0.345         0.028         2.67        1.40     0.57    1.25     0.74        6.7
   Other crops                                  0.009        0.008                         0.096          0.001      0.028         0.056           0.023         0.002         0.35        0.17     0.07    0.10     0.11        0.8
   Poultry & livestock                          0.129        0.080         0.032           0.583          0.026      0.116         0.180           0.193         0.023         0.61        0.94     0.39    0.72     0.51        3.3
 Other CRTS                                     0.01         0.14          0.12            1.67           1.60        0.44         1.15            5.05          4.34          9.4        20.7      0.8     3.5      11.0       45.4
   Fish                                                      0.080                         0.134                     0.273         0.624           0.778                       0.25        1.49     0.56    0.21     1.68        4.2
   Hunting & forestry                                                                      0.409                     0.041                         0.279         0.030         2.20        0.52     0.22    0.41     0.35        3.7
   Mining & quarrying                           0.001        0.001         0.000           0.001          0.002                    0.001           0.019         0.001                     1.49             0.00     0.02        1.5
   Meat & dairy products                                                                   0.003          0.004      0.000         0.004           0.012         0.000         2.03        0.27             0.01     0.02        2.3
   Grain milling                                0.002        0.003         0.024           0.170                     0.011         0.015           0.235         0.050                     0.18             0.20     0.31        0.7
   Utilities                                                                               0.009          0.006      0.019         0.013           0.138         0.164                     1.39             0.02     0.33        1.7
   Construction                                 0.009        0.003                         0.015          0.030      0.061         0.334           2.103         0.499                     1.45             0.05     3.00        4.5
   Wholesale & retail trade                     0.002        0.012         0.025           0.136          0.040      0.018         0.053           0.310         0.226                     9.65             0.21     0.61       10.5
   Hotels & restaurants                         0.001        0.031         0.046           0.236          0.022      0.002         0.009           0.184         0.084                     2.97             0.33     0.28        3.6
   Postal communication                                                                    0.001          0.001      0.000         0.000           0.005         0.008                     0.04             0.00     0.01        0.1
   Real estate                                                                             0.001          0.035                    0.001           0.039         0.254         4.93        0.72             0.04     0.29        6.0
   Other services                               0.000        0.001         0.002           0.018          0.016      0.002         0.012           0.061         0.040                     0.33             0.04     0.11        0.5
   Public administration health & education                  0.007         0.024           0.542          1.416      0.009         0.083           0.903         2.845                     0.25             1.99     3.84        6.1
Total value added by factor                      0.3          0.9           1.1             7.0            2.1        1.1           3.5             9.0           5.6          25.7       39.7      4.1     11.1     19.1      100.0

Source: Jensen and Tarr (2010).
                                                                                                                                                                                                                                 51
Appendix 2. Sectors conversion.
     52 current sectors                                              9-sector                                    16-sector
Business Services
   Telecommunication                             1. Transport & Communication                1. Transport & Communication
   Insurance
   Banking                                       2. Finance, Insurance & Business services   2. Finance, Insurance & Business services
   Professional business services
   Air transport
   Road transport
                                                 1. Transport & Communication                1. Transport & Communication
   Railway transport
   Water transport
Dixit-Stiglitz Goods
   Processed food
                                                                                             i. Grainmill products&Food canning (3)
   Beverages & tobacco products
   Textile & leather products                                                                ii. Apparel, spinning, weaving and finishing (4)
   Wood paper printing
   Manufacture of basic & industrial chemicals
   Manufacture of fertilizers & pesticides       3. Manufacture (i+ii+iii)
   Petroleum refineries                                                                      iii. Furniture making&Manuf of Non-Metallic
   Rubber plastic & other manufacturing                                                      Mineral products Mineral products (5)
   Glass & cement
   Iron steel & metal products
   Manufacture of equipment
Agriculture
   Maize
   Paddy
   Sorghum or millets
   Wheat
   Beans
   Cassava
   Other cereals
   Oil seeds
   Other roots & tubes
                                                                                             b. Crop growing (6)
   Cotton
   Coffee
                                                 4. Agriculture/Forestry/Fishing (a+b+c+d)
   Tobacco
   Tea
   Cashew nuts
   Sisal fiber
   Sugar
   Fruits & vegetables
   Other crops
   Poultry & livestock                                                                       a. Cattle, beef&dairy &small animals (7)
Other CRTS
   Fish                                                                                      d. Fishing (8)
   Hunting & forestry                                                                        c. Agricultural & forest services (9)
   Mining & quarrying                            5. Mining & quarrying                       10. Mining & quarrying
   Meat & dairy products
                                                 3. Manufacture (a+b+c)                      a. Grainmill products&Food canning (3)
   Grain milling
   Utilities                                     6. Electricity, Gas and water               Electricity, Gas and water (11)
   Construction                                  7. Construction                             Construction (12)
   Wholesale & retail trade                                                                  Trade (13)
                                                 8. Trade
   Hotels & restaurants                                                                      Restaurants & Hotel (14)
   Postal communication                          1. Transport & Communication                1. Transport & Communication
   Real estate                                   2. Finance, Insurance & Business services   2. Finance, Insurance & Business services
   Other services                                                                            Other personal services (15)
                                                 9. Other Services
   Public administration health & education                                                  Public services (16)
   Tourism                                       1 Transport & Communication                 1. Transport & Communication


Note: Chapter 3 (p.26) of the Analytical report explains that the Tanzania Standard Classification of Industries code was assigned
to each employed person. These classification codes are compatible with those of the International Classifications of Industry Rev.
2, which is the one we follow for this conversion.




                                                                                                                                                52