Report No. 19945-ME Mexico Earnings Inequality after Mexico's Economic and Educational Reforms (In Two Vclumes) Volume 1: Main Document May 16, 2000 Mexico Country Management Unit Poverty Reduction and Economic Management Division Latin America and the Caribbean Region Document of the World Bank CURRENCY EOUIVALENTS Currency Unit - Mexican Peso (mxp$) MP$I .0=$0.105 WEIGHTS AND MEASURES Metric System FISCAL YEAR July I - June 30 MAIN ABBREVIATIONS & ACRONYMS AMCM: Metropolitan Area of Mexico City (Area Metropolitana de Ia Ciudad de Mexico) ANUIES: National Association of Universities and Institutions of Tertiary education (Asociaci6n Nacional de Universidades e Instituciones de Educaci6n Superior) CBTA: Center of Technological Agricultural Baccalaureate (Centro de Bachillerato Tecnol6gico Agropecuario) CBTIS: Center of Technological Industrial and Services Baccalaureate (Centro de Bachillerato Tecnol6gico Industrial y de Servicios) CBTF: Center of Technological Forester Baccalaureate (Centra de Bachillerato Tecnol6gico Forestal) CECYT: Center of Scientific and Technological Studies (Centro de Estudios Cienffficos y Tecnol6gicos) CENEVAL: National Center of Evaluation for Tertiary Education (Centro Nacional de Evaluacion para la Educaci6n Superior A.C.) CETAC: Center of Technological Studies of Continental Water (Centro de Estudios Tecnol6gicos de Aguas Continentales) CETIS: Center of Technological Industrial and Services Studies (Centro de Estudios Tecnol6gicos Industrial y de Servicios) CETNIAR: Center of Technological Studies of Sea (Centro de Estudios Tecnologicos del Mar) COMIPENIS: letropolitan Commission of Public Institutions of Upper Secondary Education (Comisi6n Nietropolitana de Instituciones Publicas de Educaci6n Media Superior) CONAPO: National Council or Population (Consejo Nacional de Poblaci6n) CONALEP: National College of Technical Professional Education (Colegio Nacional de Educaci6n Profesional Tecnica) COSNET: Council of the National System or Technological Education (Consejo del Sistema Nacional de Educacion Tecnol6gica) DGAIR: General Direction of Accreditation, Incorporation and Revalidation (Direcci6n General de Acreditaci6n, Incorporaci6n y Revalidaci6n) DCETA: General Direction of Technological Agricultural Education (Direcci6n General de Educaci6n Tecnol6gica Agropecuaria) IBRD Vice President David de Ferranti Chief Economist: Guillermo Perry Country Director: Olivier Lafourcade Lead Economist/Manager: Marcelo Giugale Task Manager: Marcelo Giugale Operation's Lead Specialists: Steven Webb Fernando Rojas William Dillinger Team Production Support: Michael Geller This operation was prepared by a World Bank team composed of MessrslMmnes. Dillinger, Rojas (LCSPS); Webb (LCSPE); Marquez (LCSHH): Brizzi, Giugale, Nguyen, Velez, Everhart, Draaisma, Ordonez, Duval, Urbiola, Geller, Toxtle (LCCIC); Genta-Fons (LEGLA); Sherman and Vetter (consultants). The team was led by Mr. Giugale (Lead Economist, LCCIC), and worked under the general guidance of Mr. Olivier Lafourcade (Director, LCC IC). DGETI: General Direction of Technological Industrial Education (Direcci6n General de Educaci6n Tecnol6gica Industrial) DGPPP: General Direction of Planing, Programing and Budgeting (Direcci6n General de Planeaci6n, Programaci6n y Presupuesto) EGCP: General Test of Profesional Quality (Examinationen General de Calidad Profesional) ENIGH National Household Survey of Income and Expenditures (Encuesta Nacional de Ingresos y Gastos de los Hogares) ENEU National Urban Employment Survey (Encuesta Nacional de Empleo Urbano) FOMES: Fund for Modernize the Tertiary Education (Fondo para Modernizar la Educaci6n Superior) IPN: National Polytechnic Institute (Instituto Politecnico Nacional) ITESM: Technological Institute of Higher Studies of Monterrey (Instituto Tecnol6gico de Estudios Superiores de Monterrey) OECD: Organization for Economic Cooperation and Development (Organizaci6n para la Cooperaci6n y Desarrollo Econ6mico) PROMEP: Program of Improvement of Professors (Programa de Mejoramiento del Profesorado) SEIT: Vice Ministry of Technological Research and Education (Subsecretaria de Educaci6n e Investigaci6n Tecnol6gicas) SEP: Ministry of Education (Secretarfa de Educaci6n Publica) SESIC: Vice Ministry of Tertiary education and Scientific Research (Subsecretaria de Educaci6n Superior e Investigaci6n Cientifica) SNTE: National Union of Education Workers (Sindicato Nacional de Trabajadores de la Educaci6n) UAEM: State of Mexico Autonomous University (Universidad Aut6noma del Estado de Mexico) UAM: Metropolitan Autonomous University (Universidad Aut6noma Metropolitana) UECyTM: Educational Unit of Science and Sea Technology (Unidad Educativa de Ciencia y Tecnologia del Mar) UNAM: National Autonomous University of Mexico (Universidad Nacional Aut6noma de Mexico) ii MEXICO Earnings Inequality after Mexico's Economic and Educational Reforms List of Contents Executive Summary Resumen Ejecutivo Introduction Chapter 1. Earnings Inequality, Education Attainment and Rates of Return to Education I. The Evolution of Total Income Inequality 7 II. The Evolution of Earnings Inequality 9 III. Static Decomposition 13 IV. The Evolution of Educational Attainment 15 V. The Dynamic Decomposition 18 VI. Returns to Education 20 VII. Concluding Remarks 25 Chapter 2. Education and Public Policy I. The Public Education System 27 II. Enrollment and Public Expenditures in the Benefit Incidence Analysis 27 11.1 Enrollment rates 28 II. 2 Public Educational Expenditures 29 11.3 Benefit Incidence Analysis 30 III. Private Expenditures in Education and the Determinants of Enrollment 32 IV. Estimating the Effect of Government Spending on Household Educational Expenditures 34 V. Concluding Remarks 39 Chapter 3. Educational Policy in Intermediate and Tertiary Level of Education' 1. Upper Secondary and Tertiary System Organization and Enrollment Rates 41 II. Quality of education and Access to Upper Secondary and Tertiary Educational Levels 46 III. Dissemination of the Available Study Programs and Diversity in the Curricula 49 IV. Credit and Financing Education 51 IV. 1 Supply-Side Financing of Higher Education Services 51 IV.2 Demand-Side Financing of Higher Education Services 52 V. Concluding Remarks 54 References 56 Annex 61 Vice President; David de Ferranti Director: Olivier Lafourcade Lead Economist: Marcelo Giugale Task Manager: Gladys Lopez-Acevedo The Bank team that produced this report was headed by Gladys Lopez-Acevedo (LCC IC). Members of the Bank team include Angel Salinas (LCCIC), Monica Tinajero (U.N.A.M) and Lauro Ramos (I.P.E.A). Peer reviewers were Rodolfo de la Torre (Universidad Iberoamenricana), Suhas D. Parendekar (LCSHD) and Ricardo Rocha Silveira (LCSHD). The Lead Economist for Mexico, Marcelo Giugale, provided overall guidance. This study was undertaken under the general direction of Mr. Olivier Lafourcade (Director, LCC1C). The Bank team expresses its deepest appreciation to the support of Carlos Mancera (Subsecretario de Planeaci6n y Coordinaci6n), as well as to the staff of the Secretaria de Educacion Publica, the Secretaria de Hacienda y Credito Publico, the Secretaria del Trabajo y Prevision Social, and the Instituto Nacional de Estadistica, Geografia e Informatica. The views or findings in this report not necessarily reflect the Mexican government opinion. Finally, the team is thankful for the invaluable support and advice from Paulo Vieira Da Cunha and Vicente Paqueo. Preliminary comments from the Mexican Government were received in August. iii List of Tables Table I Ratio of Income Share of the Highest 10 Percent to the Lowest 40 Percent Household Income Distribution Table 2 Lorenz Curves for Total Current Income Table 3 Share of Total Income by Source(%) Table 4 Inequality Measures for Total Current Income Table 5 Decomposition of Total Current Income Table 6 Inequality Indices for the Distribution of Earnings (1988-1997) Table 7 Contribution to the Explanation of Earnings Inequality(%) Table 8 Contribution of Education to Earnings Inequality (International Comparison) Table 9 Evolution of Education Distribution by Gender (°/O) Table 10 Evolution of Education Distribution by Economic Sector (%) Table 11 Evolution of Education Distribution by Age Groups (%) Table 12 Synthetic Indicators of Schooling Distribution and Income Profile Table 13 Results of the Dynamic Decomposition Table 14 Education and Inequality Variation: Brazil, Argentina and Peru Table 15 Change in Differentials Controlling for Economic Sector, Labor Market Status and Region Table 16 Marginal Value of Education by Level Table 17 Percentage Change in the Marginal Value of Education, 1988-1997 Table 18 Percent Earnings Differentials by Country Table 19 Total share in the Labor Market by Level of Education Table 20 Total and Public Enrollment Rate by Poverty Status, Location and Level of Education 1996, INEGI/CEPAL Poverty Line Table 21 Federal and State Expenditures on Public Education, 1994 Table 22 Federal and State Expenditures on Public Education, 1996 Table 23 Household Expenditure in Education by Poverty Status, 1996 Table 24 Expenditures in Education per Student (Fees/tuition/unforeseen expenses) by Level of Education, 1996 Table 25 Individual Educational Expenditures plus Subsidy in Public Schools by Poverty Status Table 26 Individual Educational Expenditures in Private Schools by Poverty Status Table 27 Determinants of Upper Secondary School Enrollment, 1996 Table 28 Probit on Private School Attendance Table 29 Effect of Public Schools Provision on an Average'Household Education Spending Table 30 Effect of Public Schools Provision on Household Education Spending by Area Table 31 Effect of Public Schools Provision on Household Education Spending by Poverty Status Table 32 Effect of Public Schools Provision on Household Education Spending by Educational Level Table 33 Effect of Public Schools Supply on Household Education Spending through out Expenditure Distribution Table 34 Enrollment in Upper Secondary Education by type of school 1997 (Upper-Secondary and Middle Professional) Table 35 Public Enrolment in Tertiary Education 1997-1998. Normal, Bachelor and Graduate Table 36 Failing, Desertion and Terninal Efficiency in Upper Secondary 90-97 Table 37 Dropout in Tertiary Education Table 38 Average of Correct Answers Candidates Accepted by School Table 39 Hours of Education for Work and Study Table 40 Number of Specialties by Institution Table 41 Number of Specialties by Learning Areas Table 42 Public Scholarships Granted 1997-1998 iv List of Figures Figure 1 a Mexican Economy Openness Degree and Inequality Figure lb Conditional Median Real Hourly Earnings by Educational Level Figure 2 An Stylized View of the Interaction Between Education and the Labor Market Figure 3 Median Real Hourly Earnings Figure 4 Conditional Median Real Hourly Earnings Figure 5 Conditional Median Real Hourly Earnings Quantile .90 Figure 6 Education Spending per Student Figure 7 Distribution of Education Expenditure by Source, 1988-1998 Figure 8 Cumulative Distribution of Total Education Expenditures, 1996 National Figure 9 Cumulative Distribution of Federal Education Expenditures, 1996 National Figure 10 Structure of Upper Secondary and Tertiary Schooling Figure 11 Net Enrollment Rate in Upper Secondary by Various Sources Figure 12 Net Enrollment Rate at 17 Years Old Figure 13 Tertiary Education Structure Figure 14 Net Enrollment Rate at 20 Years Old v vi Mexico: Earnings Distribution after Mexico's Economic and Educational Reforms EXECUTIVE SUMMARY This study reviews the factors and mechanisms, which have been driving inequality in Mexico particularly in terms of educational policies. More specifically, the observed expansion in earnings inequality in the recent period is examined with emphasis on the roles of education, aiming at (i) establishing an analytical framework that permits the analysis of interaction between education and labor market; (ii) examining the evolution of earnings inequality after the macro economic and educational policies followed in the 80's and 90's; (iii) exploring ways to improve the public educational resource use and allocation in the face of possible further increases in earnings inequality; and (iv) identifying specific aspects of educational public policy that have an important impact in the production of graduates. The report consists of Volume 1 (main document), which summarizes the findings of the background papers (Volume II). A concise description of Volume I is given as follows. The first chapter relates the recent evolution in earnings inequality to the changes in the distribution of education, as well as to the way the labor market interacts with the distribution of schooling in the labor force. This chapter also examines the structure of the rates of returns to education and its impact on inequality in overall terns and with regard to formal and technical education. The second chapter focuses on the role of educational policy in the face of further increases in earnings inequality. Thus, it discusses educational distribution issues in Mexico; private educational expenditures and the determinants of school enrollment as well as the marginal willingness to pay for educational services. The final chapter examines policy aspects of upper secondary and tertiary levels of education. The issues identified are a) low educational quality from students who finish lower and upper secondary education; b) lack of information and diversity of curricula; and c) poor financial support. Concluding remarks are provided at the end of each chapter. Achieving sustainable economic growth with a more egalitarian income distribution is at the core of Mexico's development challenge. Yet, the country does not perform well in terms of equity when compared with other Latin American countries. According to a recent study developed by the International Development Bank (1998), Mexico has the sixth most unequal total household income distribution in Latin America. In a broader international context, Mexico's ratio between the income share accruing to the 10 top percent to the bottom 40 percent of the population is higher than what is observed for other high-income countries and for the vast majority of the low income countries. Moreover, Mexico's income and earnings distribution has deteriorated in recent times, both according to the data made available by ENIGH and ENEU household surveys. The methodology includes the computations of the Gini index, Theil index and Lorenz Curves. Income distribution became unequal from 1984 through 1996. The Gini coefficient, which is more sensitive to changes in the middle of the distribution, rises from 0.473 to 0.519. On the other hand, the Theil T index, which is extremely sensitive to changes in the upper and lower tails, went up from 0.411 in 1984 to 0.524 in 1996. Nonetheless, most of the deterioration of the total current income distribution happened in the mid-eighties (1984-1989). The early nineties displayed little variation in total current income inequality except for a small trend towards deterioration. From 1989 to 1994, the total current income share accruing to the 20% poorest decreased slightly (it went down from 3.9% to 3.8%), whereas the richest 10% were the only ones that increased theirs (by one percentage point), and therefore, those in the middle also experienced losses. Between 1994 and 1996, there was an improvement in the income distribution, an interval of time that entailed a severe financial crisis in the Mexican economy. The 10% richest experienced relative losses (their total current income share dropped 1.6% points) and, accordingly, total current income inequality went down. The Gini coefficient came down from 0.534 in 1994 to 0.519 in 1996, whereas the drop in the Theil T was from 0.558 to 0.524. Labor earnings is the source of income that contributes to most of the overall inequality. The Gini coefficient on earnings distribution jumped from 0.395 in 1988 to 0.442 in 1997, after reaching a peak of 0.464 in 1996. Similarly, the Theil T index went up from 0.327 in 1988 to 0.372 in 1997, with 0.474 in 1996. Another index, the RIO120, increased from 4.48 to 6.04 over the period, reaching a maximum of 6.74 in 1996. There is a clear worsening in the earnings distribution in the present decade throughout 1996. The drop in earnings inequality from 1996 to 1997 is a surprising finding. Among the most persuasive hypotheses to explain the deterioration in the earnings distribution is the one that points towards an increase in the rate of skill-biased technological change, whose transmission to Mexico might be facilitated by the increased openness of the economy. This hypothesis is based on the following facts. First, the worsening in the income distribution does not appear to be the result of a deterioration in the distribution of education, whereas the income profile, which is related to the returns to schooling, has become much steeper. Second, demand increases for a more educated labor force "within" the economic sectors, particularly metal products and machinery, explain the increase in their premium when compared to the demand shifts for less educated workers "between" economic sectors. And third, after 1990, conditional real earnings for workers with high school level education increased substantially, while conditional real earnings for lower educational levels remained steady up to 1994. In short, demand and supply, interacting within a context of economic modernization and globalization generated a trend toward greater wage disparity. As the Mexican economy continues to integrate globally, especially into NAFTA, technological progression will likely continue raising the premiums to education even further. The decomposition of the Theil index indicates that education is a key variable for the understanding of income and earnings inequality in Mexico. The marginal contribution of education, i.e., the increase in the explanatory power when it is added to a model that already has the other variables, is almost equal to the joint contribution of other relevant variables such as age, economic sector, and labor market status. The gross contribution results, i.e. the explanatory power of a variable when considered alone, and the marginal contribution of education to inequality indicate that as the Mexican 2 economy progresses, education becomes even more important in determining the choices of sectors and occupations. This is, the marginal contribution of education by itself remains the same, but the gross contribution increases. The contribution of education to income distribution in Mexico is the second highest in Latin America, next only to Brazil. Moreover, what seems to be particularly interesting in the Mexican experience is that the significance of education has been increasing over time. The decomposition of the variation in the Theil T index shows that education has the highest gross contribution to the explanation of changes in earnings distribution. In addition, the income effect is always the prevalent one, and the significance of changes in the distribution of education remains high even when one controls for changes in other relevant variables such as age, economic sector, region and labor market status. The significance of education as an explanatory source for income distribution changes seems to be a common pattern in Latin American countries. Moreover, the relevance of changes in the relative eamings among groups over changes in the distribution of the labor force is also a trait shared by all countries where a similar analysis was carried out. What deserves attention in the Mexican case is the fact that the figures are well above those for other countries. In other words, the structural changes in the supply and demand for labor, which are greatly affected by the country's educational and macroeconomic policies as well as by the supply and demand interaction in the labor market, were particularly relevant to the eamings distribution. The increase in earnings inequality, however, does not appear to be the result of a worsening in the distribution of education, whereas changes in the structure of payments of the labor force, which is related to the returns to schooling, has become much steeper. In fact, it is shown that the returns to education have increased in Mexico in recent times, especially for the higher levels of education and in the upper tail of the conditional earnings distribution. There are two developments that could bode well for the distribution of earnings in Mexico in future years. The 1997 data suggests a possible trend towards a more evenly distribution of education and earnings across the labor force. Nevertheless, one should wait for more recent data to reinforce this preliminary evidence. The other development is that the supply of workers with higher education is increasing, which is the desired market response to the increased wage premium on higher education. An increase in the supply of educated workers could "eventually" reduce the wage premium received by them, i.e. if the right number of tertiary school students were supplied, there would be a tendency to equalize the earnings distribution. However, inequality might go up even in the midst of egalitarian policies. In view of the long gestation periods associated with investments in education, the income-equalizing dynamic in both cases operates only in a long-term horizon. The second part of this report identifies educational policies that can bring about equality in the short and long run. An interesting finding is that women are undoubtedly more educated than men. More educated women, at a time of greater female labor force participation, points in one direction -a relaxation of the supply constraint which has kept wage differentials high. A policy implication is to make it easier for women to work in the labor force through greater investment in early childhood care or other options, which would allow women to transform their education into productive gains for the society. 3 One alternative to improve worker's opportunities in the labor market is through the development of a more demand-driven, financially sustainable vocational training system with stronger links to industry and increased private sector participation, within the framework of a national system of labor competency norms and certification. Furthermore, technical education after completing basic education 'may be another alternative for those individuals that face both a high opportunity cost to continue formal education and need to acquire skills that enable them to participate in the job market. The implication from the rates of returns to education and the benefit-incidence analysis is that the government should allocate more resources and improve the use of those resources in basic education. It was shown that at this level of instruction education is working to weaken the inequality. A large share of public resources is given to tertiary level of education, which has a tendency to favor non-poor students in urban areas. A strategy to reallocate the public education expenditures from a higher to a lower level of instruction, in order to favor the poor groups, would have to involve the development of higher educational credit markets. That is to say, the Govemment's appropriate role in that context is to help overcome market failures in the financial sector, which limit the availability of long-term finance for investments in higher education. These failures can be corrected through student loan programs, or means-tested financial aid and scholarship programs for the poor students in the tertiary level. Since the Mexican private sector is not able to finance higher education, public institutions should play a central role in this issue. This report argues that, in order to finance higher education for needy students, some targeted government financial assistance programs have to be designed. Experiences, up to this date, with existing loan schemes in some fifty industrial and developing countries have been disappointing, i.e., in some cases the programs either have had poor financial performance or are quite small in scale. Despite the poor performance of many loan programs, the experience of the Colombian and Canadian province of Quebec programs shows that it is possible to design and manage financially sustainable programs. The financial support, provided by the government and private educational institutions of Mexico, faces three main problems: targeting, centralized regulation and small coverage. It will be important to assess the status of the current Sonora Program in order to compare and take into account the characteristics of successful programs. With respect to the public educational expenditures by income strata and region, using the unit cost per student by state and educational level, the results indicate that at national level the poorest income groups receive the bulk of primary education subsidy (federal plus state expenditures). This same group, at higher levels of education receives progressively smaller subsidies and the pattern changes across regions. In the North Region, primary education is almost neutral (benefits equally all income groups) and regressive (benefits high income groups) for other levels of instruction. In the Central Region, primary schooling benefit the low income groups while lower secondary is almost neutral. Upper secondary and tertiary instruction benefit the richest income deciles. In the South Region, basic education benefits the bottom income groups, upper 4 secondary is neutral and tertiary education level benefits the top income groups. In Mexico City, the cumulative distribution at all levels of education, except primary, highly benefits the high income groups. The benefit-incidence analysis assumed that the subsidy and the quality of education are the same for all income deciles. This is a strong assumption that has the tendency to minimize the distributional inequity within educational levels. In addition, this report estimates how much the parents are willing to spend on educational services for their children. Thus, the marginal willingness to pay. for educational services complements the benefit-incidence analysis. Controlling for relevant individual characteristics, this methodology allow us to answer the following questions: What would an average household h with a given set of characteristics willing to spend on an individual child i with certain characteristics, if subsidized public education facilities were not available? What would the household have "saved" by sending the child to public schools instead of private schools? How large are these "savings" for various income groups?. The results from the marginal willingness to pay methodology show that the non- poor and those in urban areas get a large share of the subsidy or "savings" from the government provision of education services. This could be explained by the following factors: (i) poor location of public educational services; (ii) distance that an individual has to travel to the nearest school; (iii) the population dispersion and lastly the opportunity cost of the children in rural areas. Another interesting implications from the marginal willingness to pay analysis are; ii) the valuation for private educational services is higher for the wealthy as compared to for the poor; and, iii) differences of school quality are higher in primary level. In light of these results, some natural alternatives for the government include i) to better target public educational services; ii) charge a fee for public educational services to the non- poor; and iii) increase the quality of education in basic education. The last policy recommendation is important because as it was shown, the main cause that drives inequality among people who have the same education and who work in similar occupations and sectors is the socioeconomic status. The government could help to overcome the effect of family socioeconomic status but above all to improve educational policies, which would work to reduce inequality. On the demand side, household school enrollment and transition patterns are highly dependent on the cost of schooling, head of household's educational level, dwelling's services and household income per capita and. On the supply side, government effort greatly affects the probability of enrollment and transition. The probability of school attendance is much higher for the top 40% of the income distribution in urban areas when compared to those in the bottom 40% in rural areas. The variable government effort has a significant marginal impact which is many times larger for the 'Poor' as compared to the 'Wealthy' (in elasticity terms, this variable is more effective for the poor by a factor of 12 and by of factor of 15 in rural areas). The differential impact suggests that the goal of efficiency in terms of maximizing enrollments in secondary school level does not have a trade-off with the goals of greater equity of educational opportunity. Indeed, these findings indicate that increases in enrollment will be more readily obtained if resources are successfully targeted towards the poorer income group. 5 Finally, it is argued that the student's decision-making of which field to pursue at secondary and tertiary school level is clearly influenced by several factors as tastes, abilities, family background, information available, etc. Some of these factors are intrinsic to each particular student, and others can be used as policy tools in order to advice students of the best study option to take. In this regards, information available plays an important role on school completion and transition, since it allows students to make their own choice of study compatible with their particular interests and available study opportunities. Thus, insufficient effort on the part of educational institutions and the lack of information could not permit students to take their best option. Regarding the diversity on curricula, revalidation and lessening the numbers of specialties at secondary and tertiary levels should focus on contents of subjects rather than only on the course. This is so because many of the differences could be artificial, which impedes the transition from one program to another instead of providing mobility throughout the fields, On the other hand, curricula diversity could enhance student's vocational aptitudes and allow them to insert in the labor market more easily. Thus, it becomes extremely important to assess empirically the net impact of curricula diversity on education attainment and transition. All findings discussed throughout this report and summarized here provide theoretical support to the objectives in the World Bank Group Country's assistance strategy (CAS). The objectives in the area of education are: i) Development of basic education, and increased access to these programs for the poor. ii) Support to secondary education to gradually bring Mexico to the level of other OECD countries. iii) In higher education, continue implementing a market-based program of student loans (Sonora program) to improve access to higher education, particularly for academically qualified but financially needy students, and to develop more effective, financially viable student loan institutions. iv) Improve worker's opportunities in the labor market, primarily through the development of a more demand-driven, financially sustainable vocational training system with stronger links to industry and increased private sector participation, within the framework of a national system of labor competency norms and certification. 6 La Distribuci6n del Ingreso despues de las Refomas Econ6micas y Ed ucativas en Mexico Resumen Ejecutivo 22 de Marzo de 2000 Mexico ocupa el sexto lugar en desigualdad del ingreso a nivel de hogar ( y el tercero en Areas urbanas). En un contexto intemacional la situaci6n de Mexico no es favorable, la proporci6n del ingreso de los individuos en el decil mas alto respecto al ingreso de los cuatro deciles mas bajos es mayor a lo observado en otros paises con alto ingreso, asi como en la mayoria de los paises con bajo ingreso. Este estudio muestra que la distribuci6n del ingreso en Mexico empeor6 en el periodo comprendido entre 1984-1997, de acuerdo a nuestros calculos con base en la encuestas de los hogares publicadas por el INEGI. La metodologia incluye el computo de los indices Gini, Theil, RIo/40 y las curvas de Lorenz. La descomposici6n del indice de Theil indica que la educaci6n es una variable clave para explicar la desigualdad del ingreso en Mexico. La contribuci6n marginal de la educaci6n es decir, el incremento en el poder explicativo cuando esta se agrega a un modelo que ya contiene otras variables, es casi igual a la contribuci6n conjunta de otras variables relevantes como edad, sector econ6mico, y ocupaci6n en el mercado laboral. Los resultados de la contribuci6n bruta es decir, el poder explicativo de una variable unica, y la contribuci6n marginal de la educaci6n a la desigualdad indican que al avanzar la economia mexicana la educaci6n se vuelve sumamente importante para determinar las altemativas de inserci6n laboral por sector y ocupaci6n. Esto es, la contribuci6n marginal de la educaci6n se mantiene igual pero la contribuci6n bruta aumenta. La contribuci6n de la educaci6n a la desigualdad en Mexico es la segunda mas alta en America Latina, despues de Brasil. Mas aun, lo que es particularmente interesante en el caso de Mexico es que la importancia de la educaci6n aumenta en el tiempo. La descomposici6n de la variaci6n en el indice de Theil muestra que la educaci6n tiene la contribuci6n bruta mas alta para explicar los cambios en la distribuci6n del ingreso. El efecto ingreso es siempre el que prevalece, y la importancia de los cambios en la distribuci6n de la educaci6n es alta auin cuando uno controla por cambios en otras variables relevantes como edad, sector econ6mico, regi6n y ocupaci6n en el mercado laboral. La importancia de la educaci6n para explicar los cambios en la desigualdad es comrnn a otros paises de America Latina. La relevancia del efecto ingreso sobre el efecto poblaci6n es tambien una caracteristica de los paises donde se han realizado ejercicios similares. Sin embargo lo que merece la atenci6n en el caso mexicano es que estos resultados mas importantes en comparaci6n a otros paises. Es decir, los cambios estructurales en la oferta y la demanda de trabajo, que se ven afectados por las politicas educativas y macroecon6micas en el pais asi como, por la interacci6n de la oferta y la demanda en el mercado laboral, fueron particularmente importantes para explicar la distribuci6n del ingreso. El aumento en la desigualdad del ingreso no puede atribuirse a un aumento en la desigualdad de la distribuci6n de la educaci6n, mientras que las brechas de ingreso relacionadas a los retornos a la educaci6n son las que han aumentado considerablemente. Este estudio muestra que los rendimientos a la educaci6n se han incrementado en Mexico 7 en los ultimos tiempos, particularmente para los niveles de educaci6n mas altos y en la parte superior de la distribuci6n del ingreso. La distribuci6n del ingreso empeor6 para el periodo de estudio 1984-1996. El coeficiente Gini, que es el mas sensible a los' cambios en la parte media de la distribuci6n, aument6 de 0.473 a 0.515. El indice de Theil T, que es extremadamente sensible a cambios en los extremos de la distribuci6n, aument6 de 0.411 en 1984 a 0.524 en 1996. Sin embargo, la mayor parte del deterioro en la distribuci6n del ingreso total ocurri6 a mediados de los 80s (1984-1989), a principios 'de los 90s la variaci6n en la distribuci6n del ingreso corriente total fue pequefia, excepto por un leve deterioro. Entre 1989 y 1994, la proporci6n del ingreso corriente total de los dos deciles mas bajos (pobres) cay6 ligeramente ( pas6 de 3.9% a 3.8%), mientras que el decil mAs alto (rico) fue el uinico que increment6 su participaci6n (cerca de un punto porcentual) por tanto los grupos en la mitad de la distribuci6n experimentaron una perdida. Entre 1994 y 1996, hubo un mejoramiento en la distribuci6n del ingreso, en un periodo de tiempo que comprende una crisis financiera en la. economia mexicana. E1 10% de la poblaci6n mas rico experiment6 perdidas (su participaci6n en el ingreso corriente total cay6 en 1.6%) y, por lo tanto el ingreso corriente total disminuy6. El coeficiente de Gini pas6 de 0.530 en 1994 a 0.515 en 1996, mientras que el indice de Theil T vari6 de 0.558 a 0.524. Los salarios laborales son la fuente de ingreso que mas contribuye a la desigualdad. El coeficiente de.Gini aument6 de 0.395 en 1988 a 0.442 en 1997, despues de alcanzar un maximo de 0.464 en 1996. De igual manera, el indice de Theil T subi6 de 0.327 en 1988 a 0.372 en 1997, con 0.474 en 1996. Otro indice, el R1O,20, aument6 de 4.48 a 6.04 durante el periodo de analisis, alcanzando un.maximo de 6.74 en 1996, es innegable el deterioro en la distribuci6n del ingreso en la presente decada. La caida en la desigualdad del ingreso de 1996 a 1997 es un resultado sorprendente aunque explicable. Este estudio muestra que la hip6tesis mas factibles para explicar el deterioro en la distribuci6n del ingreso es la que versa en un carnbio tecnol6gico sesgado cuyo efecto en Mexico se propag6 a traves de la apertura comercial. Algunos resultados que respaldan esta hip6tesis son: i ) el perfil de ingreso, que esta relacionado a los rendimientos a la educaci6n, se ha vuelto mas pronunciado en la parte alta de la distribucion. ii ) Cambios en la demanda de la fuerza laboral mAs educada 'dentro' de los sectores economicos (sobre todo en el comerciable) explica el aumento en los retornos a la educaci6n. en comparaci6n con los cambios en la demanda de una fuerza de trabajo menos educada 'entre' sectores econ6micos. iii ) Por illtimo, despuds de 1990 los salarios reales condicionados de los trabajadores con 'nivel de educaci6n medio y superior aumentaron substancialmente, mientras que los salarios reales condicionados de los trabajadores con ti nivel educaci6n basico permanecieron constantes hasta 1994. Por lo tanto la interacci6n de la demanda y oferta dentro de un contexto de modernizaci6n econ6mica y globalizaci6n, generaron una creciente dispersi6n salarial. Los resultados recientes muestran que la distribuci6n del ingreso en Mexico puede mejorar en los pr6ximos anos: los datos de 1997 sugieren una posible distribuci6n mas equitativa de la educaci6n y del ingreso en el mercado laboral. Sin embargo, se necesita informaci6n mas reciente para reforzar esta evidencia preliminar. El otro hecho interesante es que la oferta de trabajadores con riivel de educaci6n medio y superior estA aumentando que es la respuesta natural del mercado ante un incremento de los retomos a la educaci6n superior. Un aumento en la oferta de trabajadores con educaci6n media y superior podria 'eventualmente' reducir el premio salarial recibido en este nivel, siempre 8 y cuando el numero necesario de estudiantes con educaci6n superior se incorpore al mercado laboral, con esto habria una tendencia a mejorar la distribuci6n salarial. Sin embargo, la desigualdad podria aumentar auin cuando las politicas para disminuir la desigualdad se implementaran, porque la inversi6n en educaci6n tiene un horizonte de tiempo largo y la dinamica de igualaci6n de los salarios en ambos casos opera s6lo en el largo plazo. La segunda parte de este estudio identifica las politicas educativas y laborales que pueden mejorar la desigualdad tanto en el corto como en el largo plazo. Un resultado interesante es que las mujeres estan mas educadas que los hombres, esto implica que en una 6poca de mayor participaci6n de las mujeres en el mercado laboral podria relajar la oferta laboral. Un resultado de politica es facilitarle a la mujer incorporarse a la fuerza de trabajo a traves de un mayor numero de guarderias u otras opciones, que permitan a la mujer transformar su educaci6n en una ganancia productiva para la sociedad. Otro resultado importante es que la educaci6n t6cnica puede ser una alternativa para los individuos que enfrentan un alto costo de oportunidad para continuar su educaci6n superior asi como una verdadera necesidad por adquirir habilidades que les permitan participar en el mercado laboral. Especialmente, las habilidades o conocimientos adquiridos a traves de la educaci6n tecnica despues de haber completado el nivel secundaria es un factor clave en la formaci6n de ingresos laborales. Sin embargo los resultados pueden ser mayores si los recursos se enfocan adecuadamente. La implicaci6n de las tasas de rendimiento a la educaci6n y el anglisis de incidencia es que el gobiemo debe canalizar mas recursos y mejorar como se enfocan estos a la educaci6n basica. Se demostr6 que este nivel de educaci6n es un instrumento redistributivo del ingreso. Otro resultado importante es que una parte importante de los recursos piiblicos asignado a la educaci6n superior tiende a favorecer a los estudiantes no pobres en areas urbanas. Una estrategia para reasignar el gasto puiblico en educaci6n superior a la educaci6n basica (esto es para favorecer a los grupos mas pobres), tendria que incluir la creaci6n de un mercado de credito para la educaci6n superior. El papel del gobierno es ayudar a reducir las fallas de mercado en el sector financiero, que limitan la disponibilidad de financiamiento a largo plazo para invertir en educaci6n superior. Estas fallas, pueden ser corregidas a traves de programas de prestamos a estudiantes y programas financieros dirigidos asi como becas para los estudiantes pobres en nivel de educaci6n superior. Dado que el sector privado mexicano no puede financiar la educaci6n superior, las instituciones puiblicas deberan desempefiar un papel central. Este reporte sugiere que, con el fin de financiar la educaci6n superior para estudiantes de bajos recursos, se disefie programas financiados por el gobiemo. La experiencia hasta esta fecha, con esquemas de prestamo existentes en cerca de 50 paises con alto ingreso y en desarrollo ha sido decepcionante, en algunos casos los programas han tenido un financiamiento muy limitado o son pequeflos en escala. A pesar de los resultados poco favorables de muchos programas de prestamo, la experiencia de los programnas en las provincias de Columbia y Quebec en Canada demuestran que es posible disef ar y manejar programas financieramente sostenibles. El estudio indica que los problemas que limitan el funcionamiento de este tipo de programas son los siguientes: objetivo claro, regulaci6n centralizada y cobertura pequefna. En este contexto, sera importante evaluar el Programa Sonora con el fin de incorporar y considerar las carateristicas de programas exitosos. 9 Con respecto al gasto publico educativo por estrato de ingreso y regi6n, utilizando el costo unitario por estudiante, por estado y nivel educacional; los resultados indican que a nivel nacional los grupos con bajo ingreso reciben la mayor parte del subsidio en educaci6n primaria (el gasto federal mas el estatal). Este mismo grupo, a niveles mas altos de educaci6n recibe progresivamente un menor subsidio y este patr6n cambia segun la regi6n. En el norte, la educaci6n primaria esta cerca de la linea de igualdad mientras que el gasto puiblico en educaci6n es regresivo para otros niveles educativos. En la regi6n central, el gasto en educacion primaria esta por arriba de la linea de igualdad mientras que el nivel de secundaria esta muy cerca de dicha linea. El bachillerato y la educaci6n superior benefician a los deciles de ingreso mas altos. En la regi6n sur, la educaci6n basica es progresiva, el bachillerato esta en la linea de igualdad y la educaci6n superior estA por debajo de la linea de 45 grados. En la Ciudad de Mexico, la distribuci6n acumulada en todos los niveles de educaci6n, excepto la primaria, estin muy por debajo de la linea de 45 grados. El analisis de incidencia del gasto supone que el subsidio y la calidad de los servicios educativos son los mismos para todos los deciles de ingreso, esta es una suposici6n fuerte que tiende a minimizar la desigualdad distributiva dentro de los niveles educativos. El analisis de la valuaci6n marginal por los servicios educativos subsana esta desventaja, esta metodologia mide el efecto del aprovisionamiento de escuelas puiblicas por parte del gobierno en el patr6n del gasto educativo de una familia promedio. Algunos de los resultados mas interesantes son i ) los no pobres y aquellas personas que residen en areas iurbanas reciben una mayor parte del beneficio o 'ahorro' del aprovisionamiento de servicios educativos por parte del gobiemo. ii ) El valor del servicio educativo privado es mayor para el rico en comparaci6n con el pobre y iii ) la diferencia en la calidad escolar es mayor en el nivel primaria. A la luz de estos resultados algunas alternativas para el gobierno serian i ) Focalizar los servicios puiblicos educativos; ii ) cobrar una cuota a los ricos por los servicios puiblicos educativos; iii ) mejorar la calidad de los servicio piublicos educativos en la educaci6n basica. Esto es importante porque como se demostr6 en este estudio la principal causa de la desigualdad en individuos con la misma educaci6n y que trabajan en ocupaciones y sectores similares es el perfil socioecon6mico familiar. El gobierno no puede cambiar este, pero puede mejorar las politicas educativas que influyen para reducir la importancia de este perfil. Otro hecho interesante es, que las tasas de asistencia escolar para los niveles de secundaria y educaci6n superior son bajas, especialmente para las personas pobres. Este reporte muestra que la probabilidad para asistir a la secundaria es mucho mayor para aquellos individuos que se estan en los 4 deciles mas altos de ingreso y que viven en zonas urbanas, en comparaci6n con aquellos individuos que estan en los 4 deciles mAs bajos y que viven en zonas rurales. El nivel educativo del jefe de familia, el ingreso per capita familiar y el esfuerzo del gobierno tienen todos un impacto positivo sobre la probabilidad de asistencia escolar. La variable del esfuerzo gubemamental tiene un impacto marginal significativo mayor para los pobres que para los ricos (en termninos de elasticidad, esta variable es mas efectiva para los pobres por un factor de 6). Esto sugiere que la meta de eficiencia en terminos de maximizar la asistencia escolar para el nivel medio de educaci6n no se contrapone con los objetivos de alcanzar una mayor equidad y oportunidad educativa. Los resultados indican que el aumento en la asistencia escolar se podria obtener si los recursos se dirigen exitosamente hacia el grupo mas pobre. 10 INTRODUCTION Achieving sustainable economic growth with a more egalitarian income distribution is at the core of Mexico's development challenge'. Yet, the country does not perform well in terms of equity when compared with other Latin American countries. According to a recent study developed by the IDB (1998), Mexico has the sixth most unequal overall household income distribution (and the third worst in urban areas). In the broader international context, Mexico's ratio between the income share accruing to the 10 top percent to the bottom 40 percent of the population is higher than what is observed for the high-income countries and for the vast majority of the low-income countries (table 1). Moreover, as it will be shown later, Mexico's income and earnings distribution has deteriorated in recent times, both according to the data made available by ENIGH and ENEU household surveys.2 Table 1. Ratio of Income Share of the Highest 10 Percent to the Lowest 40 Percent Household Income Distribution Low Income Countries"' High Income Countries1/ Latin American Countries2' China 1.6 Australia 1.7 Argentina 2.8 Egypt 1.3 Belgium 1.0 Bolivia 3.6 India 1.4 Canada 1.4 Brazil 5.6 Ivory Coast 1.6 France 2.1 Chile 4.4 Kenya 4.7 Germany 1.3 Costa Rica 2.5 Madagascar 2.2 Italy 1.4 Ecuador 4.9 Nigeria 2.4 Japan 1.0 El Salvador 3.5 Pakistan 1.2 New Zealand 1.8 Mexi -. 4.4 Sri Lanka 1.1 Spain 1.0 Panama 4.9 Tanzania 1.7 Sweden 1.0 Paraguay 5.7 Uganda 2.0 Switzerland 1.8 Peru 2.6 Vietnam 1.5 United Kingdom 1.9 Uruguay 2.2 Zimbabwe 4.6 United States 1.6 Venezuela 2.7 Sources: "World Development Report (1996). 2 IDB (1998). The second half of the eighties and the present decade is an especially meaningful period for the Mexican economy, as it encompasses a major structural change from a protected, public- sector driven economy, to a globally integrated private-sector-led one. This change has resulted in sizable economic growth but, besides being increasingly unequal, Mexico's income distribution seems resilient to both growth and public policy. Mexico's growth since 1987 (albeit interrupted by a financial crisis in 1995) did little to close the gap between the rich and the poor; if anything that gap has expanded. In turn, the In the Country Assistance Strategy of the World Bank Group, for The United Mexican States, a Mexico's Development Challenge evaluation is included. This assessment takes into account the quest for socially sustainable adjustment and growth, which translates into three deeply interrelated goals: social sustainability, removal growth obstacles and maintenance of macroeconomic stability in the framework of globalization, and more effective public governance. This report is involved with the goal of social sustainability, more specifically to the point evaluated in providing better access to education for the poor. See annex I for a brief description of these surveys. 11 Government has increased its social expenditure3 continuously since 1988, both as proportion of GDP (from 5.7 to 8.5 percent), in real terms (by 97.3 percent), as percentage of programmable budget expenditures (from 32 to 52.5 percent), and per capita (today, some US$363.12 per person are spent in social programs). While that expenditure may have partially cushioned the deterioration in income distribution, it has clearly fallen short of reversing it. Most remarkably, the level, deterioration, and policy resilience of Mexico's inequality has over the past decade co-existed with ver7 rapid progress in education attainment, both in terms of coverage and distribution of schooling. This phenomenon, which in recent years has also been observed in other developing as well as developed countries, is somewhat surprising, given the powerful equalizing properties generally attributed to education. Various hypotheses have been advanced to explain the parallel rise in earnings, inequality in developed and developing countries, among which the most persuasive ones point toward increases in the rate of skill-biased technological change. Whatever the primary cause may be, however, the resulting increase in earnings inequality presents policymakers with a difficult tradeoff in the allocation of public resources in education, especially in the advent of the educational decentralization process in Mexico. In this context, this study will review the factors and mechanisms, which have been driving inequality in Mexico particularly in terms of educational policies. More specifically, the observed expansion in earnings inequality in the recent period is examined with emphasis on the roles of education,5 aiming at (i) establishing an analytical framework that permits the analysis of interaction between education and labor market; (ii) examining the evolution of earnings inequality after the macro economic and educational policies followed in the 80's and 90's; (iii) exploring ways to improve the public educational resource use and allocation in the face of possible further increases in earnings inequality; and (iv) identifying specific aspects of educational public policy that have an important impact in the production of graduates. The report consists of Volume I, which summarizes the findings of the background papers (Volume II). A concise description of Volume I is given as follows. The first chapter relates the recent evolution in earnings inequality to the changes in the distribution of education, as well as to the way the labor market interacts with the distribution of schooling in the labor force. This chapter also examines the structure of the rates of returns to education and its impact on inequality in overall terms and with regard to formal and technical education. The second chapter focuses on the role of educational policy in the face of further increases in earnings inequality. Thus, it discusses educational distribution issues in Mexico; private educational expenditures and the determinants of school enrollment as well as the marginal willingness to pay for educational services. The final chapter examines policy aspects of upper secondary and tertiary levels of education. The issues identified are a) low educational quality from students who finish lower and upper secondary education; b) lack of information / diversity of curricula; and c) poor financial support. Concluding remarks are provided at the end of each chapter. 3 Social Expenditure includes Education, Health, Social Security, Labor, Rural and Urban Development and Basic Food Supply and Social Assistance. 4De ia Torre, Rodolfo, (1997). 5 Wages are directly related to individual characteristics and do not depend upon family structure. Besides, the acquaintance with their distribution brings one most of the way to understand the distribution of welfare in society. 12 Chapter 1. Earnings Inequality, Education Attainment and Rates of Return to Education6 The first part of this chapter relates the recent evolution in earnings inequality to the changes in the distribution of education, as well as to the way the labor market interacts with the distribution of schooling in the labor force. The second part examines the structure of the rates of returns to education and its impact on inequality both in overall terms as in what regards to technical education. This chapter is organized as follows: Section I describes the evolution of total current income inequality between 1984-96, based on the ENIGHs household surveys, and using the household per capita current income as the unit of analysis. Section 2, focuses on the evolution of individual earnings inequality, using the information of the ENEU survey. Section 3, investigates how much of the earnings inequality can be explained by education, as well as other control variables, both in gross and marginal terms.' Then, one takes a closer inspection at the evolution of educational attainment and distribution in recent times. Section 5, relates the changes in the distribution of education to the changes in earnings inequality. Section 6 focuses on the computations of rates of returns to education and the analysis of their evolution over a time; this section also examines the impact of technical education on earnings formation. L THE EVOLUTION OF TOTAL INCOME INEQUALITY This evaluation of the evolution of income inequality in Mexico is based on the information available in the ENIGHs. The reason for doing so is that this survey captures total current income of the households, including non-monetary income, besides earnings and other sources of monetary income. The unit of analysis is the household, and the concept of income is the household per capita total current income.8 The main results of this evaluation are shown in table 2. It indicates that a very sizable deterioration in the income distribution has taken place between 1984 and 1996. While the poorest 20% of the population lost almost one seventh of their income share (0.6 percentage points), the richest 10% increased theirs by something close to one seventh (5.2 percentage points). Moreover, this last group was the only one that gained over that period, as not only the poorest, but also those in the middle lost in relative terms. Looking at the results of this comparison, one can say that the 1984-1996 period in Mexico was marked by a series of regressive income transfers from almost the entire population spectrum to the richest stratum. Accordingly, the most commonly used inequality index points to a worsening in income inequality over this span of time. The Gini coefficient, which is more sensitive to changes in the middle of the distribution, rises from 0.473 in 1984 to 0.519 in 1996. 6 The findings presented in this chapter are based on background papers I and 2. 7 Educational attainment has not only monetary impacts, but can also affect other outcomes, which are important for the individual well being, but that are not necessarily measured in monetary terms. This study, however, will not consider the non-monetary impacts of education. An interesting methodology for the estimation of these impacts can be found in Wolfe, Barbara and Samuel Zuvekas (1997). s This means that total current income of the household divided by its number of household members. That is, we are considering the household as a unit characterized by a flow of income transfers and disregarding aspects related to equivalence scale. 13 On the other hand, the Theil T index, which is extremely sensitive to changes in the upper and lower tails, goes up from 0.411 in 1984 to 0.524 in 1996. Even though the worsening of the distribution is indisputable, there are, nevertheless, two points that must be stressed. The first one is that, according to the ENIGH survey, most of the worsening of the total current income distribution happened in the mid-eighties (1984-1989). The early nineties display little variation in total current income inequality except for a small trend towards deterioration. From 1989 to 1994. the total current income share accruing to the 20% poorest decreased slightly (it went down from 3.9% to 3.8%), whereas the richest 10% were the only ones that increased theirs (by one percentage point), and, therefore, those in the middle also experienced losses. Table 2. Lorenz Curves for Total Current Income" (accumulated income share %) Population Share 1984 1989 1992 1994 1996 10 1.66 1.39 1.32 1.39 1.39 20 4.47 3.88 3.68 3.76 3.89 30 8.19 7.29 6.92 6.98 7.29 40 12.85 11.65 11.09 11.08 11.63 50 18.76 17.05 16.26 16.28 17.08 60 26.15 23.78 22.83 22.79 23.86 70 35.51 32.25 31.13 31.10 32.39 80 47.64 43.12 42.14 41.93 43.44 90 64.53 58.75 58.32 57.68 59.33 92 68.79 63.06 62.81 62.03 63.61 94 73.73 68.03 68.03 67.26 68.68 96 79.38 73.82 74.47 73.70 74.95 98 86.68 81.60 82.81 82.49 83.32 100 100.0 100.0 100.0 100.0 100.0 Bottom 20% 4.5 3.9 3.7 3.8 3.9 Middle40% 21.7 19.9 19.2 19.0 20.0 Middle high 30% 38.4 35.0 35.5 34.9 35.5 Top 10% 35.5 41.3 41.7 42.3 40.7 Gini 0.473 0.519 0.529 0.534 0.519 Theil T 0.411 0.566 0.550 0.558 0.524 Source: Own calculations based on ENIGH. " Based on household per capita income The second fact to be emphasized is very surprising and hard to be explained: the observed improvement in the income distribution between 1994 and 1996. an interval of time that entails a severe financial crisis in the Mexican economy.9 Usually one would expect inequality to go up during recessive times, as it seems plausible to admit that the rich have more ways to protect their assets than the poor do, especially when it comes to labor which is basically the only asset of the poor (the labor-hoarding hypothesis). The fact, however, is that the 10% richest experienced relative losses (their total current income share dropped 1.6% points) and, accordingly, total current income inequality went down. The Gini coefficient came down from 0.534 in 1994 to 0.519 in 1996, whereas the drop in the Theil T was from 0.558 to 0.524. In principle, it could be argued that the richest experienced severe capital losses due to the crisis, in such a way that their total current income was affected compared to the poor. This hypothesis, however, is not supported by the data shown in table 3, as monetary income other than wages and salaries, and financial income as well, increased their share in total income in that time interim, particularly so for the urban areas. Therefore, the fall in inequality remains somewhat puzzling. 9 In 1994, current account deficit was 30 billion dollars. about 7 percent ofGDP. The main effects of the financial crisis were i) GDP and domestic demand felt 6.2 percent and 14 percent respectively each. ii) the unemployment rate rose from 3.7 percent in 1994 to 6.2 percent in 1995; and, iii) the GDP per capita decreased 7.8 percent and workers experienced a significant reduction in their real wage, nearly 17 percent in 1995. 14 Table 3. Share of Total Income by Source (%) 1994 1996 Source Total Urban Rural Total Urban Rural Monetary Current Income Total Labor Earnings 47.12 49.01 32.07 44.51 46.08 33.75 Property (Business) Income 16.96 16.23 22.75 17.74 17.11 22.07 Property Income and Rents 1 10 1 13 0.87 1.35 1.47 0.51 Income from cooperative firms 0.22 0.24 0.12 0.06 0 03 0.32 Monetary Transference 5.44 4.72 11.23 6.55 5.89 11.11 Other Current Income 0.64 0.67 0.36 0.69 0.66 0.91 No Monetary Current Income Self-Consumption 1.44 0.81 6.46 1.20 0.69 4.72 Non Monetary Payment 1.55 1.58 1.28 2.25 2.32 1.82 Gifls 5.04 4.73 7.57 6.07 5.86 7.55 Housing Imputed Rent 16.02 16.60 11.39 13.76 14.28 10.20 Financial Income 4.46 4.28 5.91 5.80 5.62 7.04 Total Income 100.00 100.00 100.00 100.00 100.00 100.00 Source: Own calculations based on ENIGH. In table 4 the results for the Gini and Theil T are displayed for urban and rural areas using total current income. There, one can see, for both indices that inequality in rural areas was lower than in urban areas and remarkably stable until 1992. After a small decrease in 1994, it increased in 1996, contrary to the aggregate result discussed before. In light of these outcomes, it seems pertinent to state that the leading force behind the behavior of current income distribution in Mexico in the urban areas. Table 4. Inequality Measures for Total Current Income Gini Coefficient Theil T Index Year National Urban Rural Year National Urban Rural 1984 0.473 0.442 0.448 1984 0.411 0.356 0.375 1989 0.519 0.498 0.444 1989 0.566 0.526 0.361 1992 0.529 0.498 0.434 1992 0.550 0.483 0.353 1994 0.534 0.508 0.419 1994 0.558 0.499 0.325 1996 0.519 0.493 0.452 1996 0.524 0.470 0.390 Source: Own calculations based on ENIGH. II. THE EVOLUTION OF EARNINGS INEQUALITY How much of total income inequality is due to earnings inequality? Table 5 presents the results of total current income inequality for each of its components: earnings"', monetary income excluding earnings, and non-monetary income by urban and rural areas." Earnings are the source of income that contributes for most of overall inequality, being responsible for almost half of it at national level. It is clear that these figures may be affected by a possible underreporting of capital gains, but it seems valid to state that if one understands better the mechanisms that leads earnings inequality, this will be a large step towards understanding the behavior of total inequality. Besides, as long as labor is the main, if not the only asset of the poor, a better knowledge of earnings inequality is a valuable input for the assessment of poverty and welfare issues. '° Earnings as defined in the ENIGH survey include salaries and wages, paid over-time, tips, contract workers' earnings, Christmas or New Year gifts and other gifts, and other monetary compensations (non-regular earnings). Earnings as defined in the ENEU survey include salaries and wages, self-employed workers' earnings, contract workers' earnings, and implicit firm owners' salaries, as well as non-monetary earnings. " Although the results are shown for the Gini coefficient, these could have been obtained for the Theil T index, as both of them satisfy' the six propositions listed in Shorrocks (1982). 15 Table 5. Decomposition of Total Current Income (Percentage Share in Overall Gini) Income Source Earnings Monetary income No monetary TOTAL Excluding earnings Current income National 1984 46.0 32.9 21.0- 100.0 1989 41.0 36.0 23.0 100.0 1992 42.9 31.9 25.2 100.0 1994 50.2 25.9 23.9 100.0 1996 46.7 29.4 23.9 100.0 Urban 1984 45.6 32.2 22.2 100.0 1989 38.6 37.3 24.1 100.0 1992 41.4 33.1 25.5 100.0 1994 50.0 26.0 24.0 100.0 1996 46.1 29.8 24.1 100.0 Rural 1984 30.7 49.5 19.8 100.0 1989 35.7 43.5 20.8 100.0 1992 29.6 42.2 28.2 100.0 1994 31.9 43.8 24.2 100.0 1996 35.7 41.2 23.1 100.0 1996 35.7 41.2 23.1 100.0 Source: Own calculations based on ENIGH. To examine the behavior of eamings distribution in recent times the household survey ENEU and ENIGH were used"2. The main reasons for presenting in this volume the results based on ENEU'3 are that its sample size is bigger than the ENIGH survey and it has richer information about personal attributes and other economic variables, which are essential for the decomposition exercises to be carried out in the next sections. This survey is collected quarterly. For the purposes of this study the third quarter of each year was chosen, in an attempt to avoid the influence of seasonal factors that could make results non-comparable to those obtained from ENIGH.'4 By examining the results shown on table 6, one also reaches the conclusion that the distribution of earnings has become more unequal in recent times. The Gini coefficient jumps from 0.395 in 1988 to 0.442 in 1997, after reaching a peak in 1996 of 0.464. Similarly, the Theil T index went up from 0.327 in 1988 to 0.372 in 1997, with 0.474 in 1996. Another index, the R1o020,`5 increased from 4.48 to 6.04 over the period, reaching a maximum of 6.74 in 1996. Table 6. Inequality Indices for the Distribution of Earnings (1988-1997) Population Earnings Share (%) Share (%) 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Bottom 20% 7.54 7.62 7.19 6.84 6.47 6.13 5.98 5.91 5.72 5.95 Middle 40% 25.23 24.45 23.86 23.41 23.37 22.86 22.36 22.59 22.09 23.01 MHigh 30% 33.44 . 34.15 33.96 33.77 33.52 33.37 32.94 33.42 33.61 35.13 Top 10% 33.78 33.78 34.98 35.98 36.64 37.63 38.72 38.08 38.58 35.91 Gini 0.395 0.398 0.414 0.426 0.434 0.447 0.458 0.455 0.464 0.442 Theil T 0.327 0.328 0.350 0.380 0.396 0.414 0.470 0.427 0.474 0.372 R10/20 4.48 4.43 4.87 5.26 5.66 6.14 6.47 6.44 6.74 6.04 Source: Own calculations based on ENEU (3Id quarter). 12 The results based on ENIGH are provided in background paper #I.. 13 For seeing the impact of the ENEU's geographic coverage change on the results inferred, refer to annex 1. 14 In order to reduce the heterogeneity of the sample and also aspects related to self selection, the population under analysis are individuals living in urban areas, between 16 and 65 years old, working 20 hours a week or more and no -seasonal workers. Also, the two highest observations were dropped from the sample, as there was clear evidence of the presence of outliers in some years. 5 This index is the ratio of the income share accruing to the 10% richest and 20% to the poorest. 16 There are two main differences in the pattem shown by the earnings and total current income distribution. First, the gains are not limited to the richest 10%. As those in the seventh, eight, and nine tenths of the distribution also improved their relative earnings over the period by almost two percentage points; the biggest losers were the middle 40%, who lost more than two percentage points of their income share. Second, there is a clear worsening in the earnings distribution in the present decade throughout 1996. On the other hand, the inequality associated with the total current income was moderately stable in the nineties, displaying an improvement in 1996. The different behavior between total current income and earnings inequalities from 1994 to 1996 gives support to the idea that the poor, who mostly rely on labor as a source of income, were the least able to protect themselves during the recession. However, the substantial drop in earnings inequality from 1996 to 199716 is, once more, a surprising finding. It is true that the Mexican economy as a whole had a strong and impressive performance in 1997. The aggregate growth rate was around 7%, real investment grew by 24% and exports by 17%, industrial production increased 9.7%, and the civil construction sector, which is highly intensive in less skilled labor, experienced a growth close to 11%. Under such a scenario, an improvement in distribution of earnings itself is not unlikely, but the magnitude and quickness of the recovery calls for a detailed inspection of the mechanisms responsible for it. Three broad hypotheses are frequently advanced to explain the similar increases in earnings inequality experienced in Mexico and other countries."7 These link the increase of earnings inequality to (i) the increased openness of the economy, (ii) institutional changes in the labor market, and (iii) skill-biased technological change. We will only outline here the first and third hypotheses (for details see background paper 1). The first of these hypotheses argues that as trade barriers are reduced, an economy is placed under increased competitive pressures to specialize along its lines of comparative advantage. A developed country that is relatively high skill-abundant, like the United States, will be induced to specialize in high skill- or education-intensive activities as its low-skilled industries come under increased competitive pressure from low skill-abundant, low-wage countries. Hanson and Harrison (1995) examined the impact of the Mexican trade reform on the structure of wages using information at firm level. They tested whether trade reform had shifted employment toward industries that are relatively intensive in the use of skilled labor force [the Stolper-Samuelson-Type (SST) effect]. Their main conclusion is that the wage gap is associated to changes within industries and firms, which cannot be explained by the SST effect. Thus, the increase in wage inequality should be due to other factors.'8 Hanson's (1997) paper examines a trade theory based on increasing returns, which has important implications for regional economies. Hanson's conclusion is that employment and wage patterns are consistent with the idea that access to market is important for industry location. This first hypothesis has several problems when applied to the United States, and becomes even less persuasive when applied to Mexico. Mexico greatly liberalized its trade regime since 1984. However, the reduction of its trade barriers has mostly been vis-a-vis imports from the developed countries, notably the United States and Canada, whose share in total Mexican merchandise imports increased from 68 percent in 1985 to 73 percent in 1993 and to 77.5 percent in 1996. Since Mexico is a low skill-abundant country compared to its two northern neighbors, it would be expected that the liberalization of trade would have induced a specialization pattern that would raise the relative demand (and hence wages) for the lesser-educated members of the labor force. This did not happen. Instead, the increase in earnings inequality observed in Mexico follow '6 The R10/20 index, for instance, was 6.74 in 1996 and went down to 6.04 in 1997. 17 See, for example, the "Symposium on Wage Inequality" (1997) and the "Symposium on How International Exchange, Technology and Institutions Affect Workers" (1997). Ix The Stolper-Samuelson effect was also examined under NAFTA in Burfisher, Mary E., Robinson, Sherman, and Thierfelder (1993). 17 the same pattern as that observed in the United States: less educated workers experienced real wage declines, while highly educated workers experienced real wage improvements. The trade- based explanation may still be relevant, however, to the extent that greater openness facilitates the transfer of ideas and technology, which is identified below as the more persuasive explanation of increase in earnings inequality. A variant on the globalization/technology nexus explanation, advanced by Feenstra and Hanson (1994), involves outsourcing behavior where multinational enterprises in the developed country relocate their lower skill-intensive activities to the less skill- abundant developed countries. However, what is referred to as a low skilled activity in the United States may be a high-skilled activity in Mexico, which could explain the similar evolution of earnings inequality in both countries. A persuasive explanation, both for the United States and Mexico, seems to be one which links earnings inequality to skill-biased technological changes that raise the relative demand for higher-skilled labor. Cragg and Epelbaum (1996) exarnined the shift demand in Mexico. They pointed out that the major source of rising inequality is a biased shift demand rather than a uniform demand growth when there are different labor supply elasticities. Meza (1998) also investigated demand shifts. The author 's hypothesis is that the demand shifts for more educated labor force, "within" economic sector, explains the increase in their premium when compared to the demand shift for less educated workers "between" economic sector. Tan and Batra (1997) studied the skill-biased technical change hypothesis as a plausible explanation of wage inequality using data at the firm level for Colombia, Mexico, and Taiwan. They obtained the following results: (i) firm investments in technology have the largest impact on wage size distribution for skilled workers. (ii) It had the smallest impact on wages paid to unskilled workers. And (iii) a decomposition of wage effects by sources of technology revealed that wage premiums paid to skilled workers are led primarily by firm investment in R&D and training. Such conclusions seem to support the skilled-biased technological change hypothesis.'9 According to the typology used by Johnson (1997), the type of technological change that drives wages up for the more highly skilled workers and drives wages down for the less skilled workers (as occurred in both the United States and Mexico) is extensive skill-biased technological change. Under this type of technological change, skilled workers become more efficient in jobs that were traditionally performed by unskilled workers. Figure la Mexican Economy Openness Degree and Inequality 55- 45 35 25 GATT A cement NAFTA,kgreement 15 - 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 -O Oppeness Degree - - R1 0/20 Source: Own calculations based on ENIGH and INEGI data 19 Note that these results should be considered carefully, since the analysis is based on data at the firm level and just for the manufacturing industry. 18 Figure lb Conditional Median Real Hourly Earnings by Educational Level'/ 20.00 1988=100 18.00 16.00 _ - * - _- - _ _ _ - 16.00 4.00 - 12.00- 10.00 I 9RR 19R9 1 99n 1991 I99 199I 3 19944 199s 1996 1 Q97 _ Primary Incomplete - -- Primary Complete ----L. Secondary Comp.| 0 _- U. Secondary Comp. --- --Universityl Source: Own estimations based on ENEU survey. I/ The medians were calculated conditional on experience, experience squared, gender, economic sector, labor market status, and region. As it is shown in. figure 1 b20, all series have the same trend for all period. However, from 1990 conditional real earnings for University increased substantially, while conditional real earnings for lower educational levels remained steady up to 1994. The drastic fall in earnings in 1995 is mainly due to the crisis. After that, it seems that the earnings differentials among all educational levels remained constant21. This suggests that other factors rather than supply of new basisc education comers drove earnings differentials among level of schooling. In sum, demand and supply, interacting within a context of economic modernization and globalization generate the trend towards greater earnings disparity. It should be noted however that none of these explanations deal explicitly with the changes in the distribution of education, as well as the interaction between the educational policies that induced them and the workings of the labor market. IIL. STA TIC DECOMPOSITION This section aims at evaluating the contribution to earnings inequality in Mexico of a set of variables, either related to individual attributes, as schooling and age, or form of participation in the labor market, as number of hours worked or status, for selected years from 1988 to 1997. The idea is to measure the reduction in inequality that results from excluding the differences in average earnings among workers in different groups formed by those variables. When the exercise is conducted for a single variable, this reduction is said to be the gross contribution, of such a variable to the overall earnings inequality. When a variable is added to a model that contains all the remaining ones, the change in the gross contribution of these two models is called the marginal contribution of the added variable. In other words, the gross contribution can be regarded as the uncontrolled explanatory power of a given variable, and the marginal contribution as its explanatory power controlled by a set of other seemingly relevant variables. 20 Median real hourly earnings were estimated using quantile regression models (0='0.5) and conditioned on experience, gedr labor market status, economic sector and region (see annex 1 for groups definition). 2 Section VI provides an extensive analysis about educational earnings differentials using the rate of returns to education 19 Background paper I reviews all the different decomposition methods and results generated in the Mexican case. Before proceeding to the decomposition exercise, it is worth to review the conclusions of other recent studies in relation to the evolution of earnings inequality and some variables that are important in the process of earnings formation. Cragg and Epelbaum (1996) show that both average wage and education skill premium, which is defined as the percentage increase in wages over the primary schooling group, have increased substantially for more educated workers. In other words, the higher the level of education the larger the increase in the average wage is, which in turn leads to an increase in inequality. They also examined whether the high demand for skilled labor is industry specific, task specific or simply general education. In order to assess the marginal contribution of other factors that are not related to education, these factors are controlled by a set of dummy variables that describe the industry and task specific effects. The authors concluded that the industry- specific effect was small and that the task-specific effect (occupational variable) explained half of the growing wage dispersion from 1987 to 1993. This conclusion, however, may not be correct, as occupation might be considered an endogenous variable, which is determined by education. As shown on background paper 1, educational level and occupational variables are highly correlated. In contrast, the correlation between education and other variables are low. Hence the occupation variable should be carefully handled in any kind of analysis. The results for the exercise of static decomposition are shown on table 7 22 Education (the result of the interaction between demand and supply) is by far the variable that accounts for the largest share of earnings inequality in Mexico, both in termns of its gross and marginal contributions. The gross contribution, i.e., its explanatory power when it is considered alone, amounts to one fifth of total inequality in 1988 and one third in 1997.23 The marginal contribution, i.e., the increase in the explanatory power when it is added to a model that already has the other variables, is remarkably stable and meaningful, staying around 21% throughout the whole period. It is worth pointing out that the difference between the two contributions has been increasing over time, indicating that the degree of correlation and other variables has been going up, i.e., the "indirect" effects are becoming more important. Table 7. Contribution to the Explanation of Earnings Inequality (%) 1988 1992 1996 1997 Variables Gross Marginal Gross Marginal Gross Marginal Gross Marginal Education 20.2 20.8 26.9 21.6 29.3 21.2 32.6 21.2 Age 5.4 8.3 7.2 6.1 6.6 6.2 7.3 5.4 Economic Sector 2.3 8.1 4.0 5.2 6.8 5.2 8.6 4.4 Status 12.8 11.2 13.7 8.9 13.7 7.4 15.6 7.5 Source: Own calculations based on ENEU (3"d quarter). The other variables considered seemn to be much less important. The three of them, and particularly the economic sector and status in the labor market, display an upward trend in their gross contribution, and a declining one in their marginal contribution. This can be interpreted as evidence that the interaction between these variables and education has become more intense. 22 Since this exercise is very intensive in the number of observations (which constitutes its main handicap) the variable "hours worked" was dropped in order to avoid the problems with cells with too few observations. The decision was made through the comparison among different combinations of variables, where hours worked ended up being the least relevant. 23 In most earnings equations for any country, the set of measure observable variables explains at most 60% of the total variance. In United States, education accounts for 10% of the total variance (see David Lam and Deborah Levison). 20 That is, workers' skills are becoming increasingly more relevant towards the determination of -their type of participation in the labor market, as well as for their position across different economical segments of the economy. The analysis of these results leads to the conclusion that education is a key variable for the understanding of earnings inequality in Mexico.24 Even though this is to some extent a remarkable finding, it comes as no surprise in the Latin American context. The results for some countries in the region, where similar exercises were carried out, are reported on table 8. Mexico stays on the average range for Latin American countries, and displays a situation close to that observed in Colombia and Peru. However, education seems to be more important for inequality in Brazil, and much less important in Argentina and Uruguay. It is important to stress the fact that this is a comparison in relative terms. Given that in Colombia and Peru, where education has a similar explanatory power, there is a lower degree of inequality compared to Mexico, the absolute contribution of education is higher in Mexico. As a matter of fact, in absolute terms, the contribution of education to inequality in Mexico is the second highest in Latin America, next only to Brazil. Moreover, what seems to be particularly interesting in the Mexican experience is the fact that the significance of education has been increasing over time. Therefore, the inspection of the evolution of the educational distribution and the income profile associated to it, as well the link between changes in this distribution and changes in earnings inequality will be addressed in the next sections. Table 8. Contribution of Education to Earnings Inequality. International Comparison Country Author(s) Period Gross Contribution (%) Latin America Altimir and Pifiera (1982) 1966/74 17-38 Argentina Fiszbein (1991) 1974/88 16-24 Brazil Ramos and Trindade (1992) 1977/89 30-36 Vieira (1998) 1992/96 30-35 Colombia Reyes (1988) 1976/86 29-35 Moreno (1989) 1976/88 26-35 Costa Rica Psacharapoulos et alt. (1992) 1981/89 23-26 Peru Rodriguez (1991) 1970/84 21-34 Uruguay Psacharapoulos et alt. (1992) 1981/89 10-13 Venezuela Psacharapoulos et alt. (1992) 1981/89 23-26 IV. THE EVOLUTION OF EDUCATIONAL A TTArAvmENT Education attainment levels increased rapidly in most developing countries since the 1950s Schultz (1988). While Mexico also partook in that development, earlier studies had identified a significant lag in its education indicators. Londofio (1996) for example, points to an "education deficit", according to Latin American countries in general, and Mexico in particular, have approximately two years less of education than would be expected for their level of development. 5 Elias (1992) found that education was the most important source of labor quality improvement in Latin America between 1950 and 1970, but points out that such improvements did not take place to the same extent in Mexico as in other countries in the region. This changed 24 Additional evidence is that the explanatory power of the complete model was 42.5% in 1988, 45.0% in 1992, 45.5 in 1996, and 48.3% in 1997. This means that the marginal contribution of education is almost equal to the joint contribution of age, economic sector, and status in the labor market. The same pattern holds when hours worked, instead of sector, is considered. 25 On the other hand, Behrman (1987) classifies Mexico as an overachiever in what comes to the relation between economic development and educational progresses in the context of developing countries. 21 dramatically in the 1980s, figure 2 in background paper 1 shows that although Mexico's education attainment increased steadily since the 70's, it continued to remain below the international trend line. In the 1980s, however, the growth of education attainment in Mexico accelerated, permitting it to catch up with international standards by 1990; where its placement in figure 2 is slightly above the trend line. The closure of Mexico's education gap vis-A-vis the rest of the world was hastened in part by the country's economic stagnation. Mexico's real GDP per capita in the mid-1990s was roughly the same as it had been in the first half of the 1980s. Nevertheless, the preceding observation should not detract from the remarkable increase in schooling that occurred during the 1980s. While the level of average schooling in Mexico increased by roughly one year per decade during 1960-1980 (from 2.76 to 4.77 years), it increased by two years in the 1980-1990 decade. The acceleration in schooling during the 1980s, in turn, was the product of concerted efforts to increase basic education coverage combined with advances made in the reduction of primary school repetition and dropout rates. With respect to changes in the distribution of schooling by socioeconomic groups, there are several aspects to be considered. In particular, three of them are examined here: the changes in this distribution related to gender, economic sector and age. Table 9 shows the schooling distribution by gender from 1988 to 1997 (See background paper 1 for the evolution of the labor force participation by gender). There one can see that, even though there were clear improvements for both males and females, which translates in an upgrade of educational attainment, women achieved a better performance during that period, especially at the top of the distribution. Improvements for males, on the other hand, were more evenly spread over the entire distribution. Nevertheless, in 1997 it is possible to state that women were undoubtedly more educated than men, as their cumulative distribution dominates that of men.26 Table 9. Evolution of Education Distribution by Gender (%) Educational Group Primary Primary Lower Secon. Upper Secon. University Incomplete Complete Complete Complete Complete 1988 Male 19.0 30.1 24.5 14.6 11.8 Female 17.3 22.2 23.2 29.1 8.2 Total 18.5 27.7 24.1 18.9 10.7 1997 Male 13.0 25.7 28.4 18.0 14.9 Female 12.2 20.0 22.3 30.1 15.5 Total 12.7 23.7 26.3 22.1 15.1 Source: Own calculations based on the ENEU (3rd quarter). With respect to the distribution of schooling by economic sector, table 10 shows that there has been a significant upgrade from 1988 to 1997. Three points, nonetheless, deserve to be stressed. First, financial and social services industries became relatively more intensive in the use of high-skilled labor. Second, the primary sector, together with non-manufacturing industry and other services, were characterized by more intensive use of low-skilled labor. Third, in a surprising way, the manufacturing industry, in contrast to what seems to be the common wisdom, cannot be characterized as a sector that intensively uses high-skilled labor. Using transition probabilities, the evolution of educational composition by economic sector is analyzed in background paper 2 in order to assess changes "between" and "within" economic sectors. 26 This remark is true for the 1997 overall distribution relative to the 1988 one. 22 Table 10. Evolution of Educational Distribution by Economic Sector (%) Educational Group Primary Primary Lower Secon. Upper Secon. University Incomplete Complete Complete Complete Complete 1988 Primary Sector 41.1 21.0 13.3 14.3 10.3 Manufacturing Industry 16.2 33.3 27.8 14.7 8.0 Non Manufacturing Industry 36.6 285 14.7 9.0 11.2 Commerce 18.0 28.7 28.8 18.7 5.8 Finance Services/Rent 4.8 6.1 19.5 47.1 22.5 Transportation/communication 14.4 35.7 26.0 18.9 5.0 Social Services 11.3 17.6 21.7 28.2 21.2 Other Services 32.8 36.6 20.2 8.1 2.3 Total 18.5 27.7 24.1 18.9 10.7 1997 Primary Sector 28.1 27.4 17.7 10.9 15.9 Manufacturing Industry 11.0 29.5 32.7 18.2 8.7 Non Manufacturing Industry 28.6 31.7 18.4 10.0 11.4 Commerce 12.4 23.4 30.6 24.1 9.5 Finance Services/Rent 2.7 5.4 16.1 40.3 35.6 Transportation/communication 9.1 26.8 32.2 23.9 8.0 Social Services 6.0 13.2 21.1 29.6 30.0 Other Services 26.2 35.7 24.6 11.1 2.4 Total 12.7 23.7 26.3 22.1 15.1 Source: Own calculations based on the ENEU (3d quarter) Another relevant observation is that the age groups also experienced upgrades in their education attainment, as the distribution by educational level in 1997 is above the one in 1988 (table I1). In an attempt to reach a better understanding of this event, it is interesting to contrast the time and cohort effects27. In order to do this, one can look at the first age groups, 14-25 and 26-34, like synthetic cohorts. Namely, the 26-34 age group in 1997 can be directly compared to the 16-25 in 1988, and, in to a lesser extent, the 35-49 in 1997 to the 26-34 in 1988. From 1988 to 1997 the percentage of those in the primary incomplete level decreased, this reduction was higher than that experienced by the 16-25 age group (later being the 26-34 in 1997). The opposite took place for the highest level of instruction. In other words, it seems that the improvements throughout the educational process in Mexico are significant, both for those entering the system (higher coverage) and for those already in there (higher efficiency). Table 11. Evolution of Educational Distribution by Age Groups (%) Educational Group Primary Primary Lower Secon. Upper Secon. University Incomplete Complete Complete Complete Complete 1988 16-25 8.5 26.5 36.7 23.7 4.6 26-34 12.6 23.7 23.1 22.5 18.2 35-49 24.0 33.3 16.8 14.3 11.6 50-65 46.1 27.2 9.9 9.0 7.8 Total 18.5 27.7 24.1 18.9 10.7 1997 16-25 5.8 23.8 38.7 25.5 6.2 26-34 6.9 19.5 28.1 27.0 18.5 35-49 14.8 25.8 19.5 19.1 20.7 50-65 37.3 27.6 11.5 10.6 13.0 Total 12.7 23.7 26.3 22.1 15.1 Source: Own calculations based on the ENFU (3r quarter). 27 The time effect refers to the comparison of the same age group in two different points of time. 23 Also concerning the interaction between age and education, one can argue that the effect of developments in the educational system is more important for the new generations than for the elderly. To investigate this, it is necessary to contrast the behavior of inequality between different age groups to that of inequality within synthetic cohorts and in relation to education. As seen above, the younger cohorts are in fact better educated. At the same time the "within" income dispersion for the youngest cohorts seems to increase over time, compared to the internal Theil in 1997 and 1988. Thus, it becomes easier to understand why the gross contribution of age to inequality has been going up and at the same time its marginal contribution has been decreasing. In other words, differences in both educational attainment and distribution among cohorts have become pronounced in recent times, leading to a higher correlation (negative) between education and age. V. THEDYNAMICDECOMPOSITIONM In order to address the relationship between education (the result of the interaction between supply and demand)) and earnings inequality it is necessary to explain the role of the labor market, since the way it works determines the earnings differentials among workers with different educational attributes. Thus, this relationship can be viewed as being determined by two elements: (i) the distribution 'of education itself, and (ii) the way the labor market rewards educational attainment. The first element reflects a pre-existing social stratification that already entails some inequality, due to reasons other than the workings of the labor market itself. The second is associated to the degree of growth of this pre-existing inequality into earnings inequality due to the performance of the labor market (i.e. demand behavior). The diagram below shows the distribution of education in the horizontal axis (ml is an indicator of the average schooling of the labor force and i, represents its dispersion) while the vertical axis has the distribution of earnings. The first quadrant depicts the interaction between the pre-existing conditions (the distribution of education) and the workings of the labor market, through the steepness s, of the income profile related to education. Therefore, at a point of time: (i) the higher m, is, the larger the average earning will be; (ii) the lower i, is, the smaller the earnings inequality will be; and (iii) the higher s, is, the bigger the growth of pre-existing disparities, and, accordingly, the higher the earnings inequality will be. As these indicators change over the time, there are going to be alterations in the income distribution induced by them: changes in i,, assuming s, constant, will change earnings inequality due to changes in the composition of the labor force (the so-called allocation/population effect), whereas changes in s, will produce alterations in the earnings differentials (the income effect). Figure 2. An Stylized View of the Interaction Between Education and the Labor Market / ~ ; / 28 The results are based on the methodology described in Background paper # I. 24 Barros and Reis (1991) developed three synthetic measures for the indicators m, (average schooling), i, (schooling inequality), and s, (income profile), based directly on the definition of the Theil T index. The figures for Mexico from 1988 to 1997 are presented in the table below. As it can be seen, there was some improvement on average schooling, but the inequality of the distribution of education has deteriorated over the period studied, whereas the income profile, which is related to the returns to schooling, has become much steeper. Meaning that, there was a shift in demand towards high skilled labor that was not met by the increase in supply probably due to the increased rate of skill-biased technological change, whose transmission to Mexico may be facilitated by the increased openness of the economy (this is explored in the next section). The same pattern observed for the overall sample holds for the 16-25 years old age group: the m, goes up from 0.561 to 0.574 in 1988 through 1997; the i, increases from 0.0196 to 0.0218, whereas the s, doubles going from 0.0196 to 0.0383. Table 12. Synthetic Indicators of Schooling Distribution and Income Profile Year 1988 1992 1996 1997 mt 0.476 0.491 0.511 0.510 i, 0.066 0.069 0.076 0.075 * s, 0.066 0.102 0.122 0.111 Source Own calculations based on the ENEU (3rd quarter). The methodology applied here is the dynamic decomposition. This tool permits translating this stylized view in quantitative results, giving one a better understanding of the socio-economic transformations responsible for changes in the earnings distribution. Besides permitting the identification of the relevant individual variables, it also helps understand the nature of their contribution for the evolution of earnings inequality over time. The results of the decomposition of the variations in the Theil T index for different intervals of time are shown in the table below. The first point to highlight is the fact that, when the variables are considered alone, education has the highest gross contribution to the explanation of changes in earnings distribution. Secondly, both changes in the distribution of education (the allocation effect) and the changes in the relative earnings among educational groups (the income effect) were positive in all periods. This means that the changes in the distribution of education and in the relative earnings among educational groups were always in phase with the alterations in the earnings distribution. Namely, when the income profile related to education became steeper and the inequality of education increased, the earnings distribution worsened (as in the 1988- 1992, 1992-1996, and 1988-1997 periods), and vice-versa (as in the 1996-1997 period). Third, the income effect is always the prevalent one. If one considers, for instance, the 1988-1997 period, the changes in the relative earnings among educational groups alone, would have generated a deterioration in the earnings distribution higher than the one observed. To lesser extent, the same holds true for the other periods.29 Even the decrease in inequality observed between 1996 and 1997 is partially explained by the changes in relative earnings (it is possible to see, in table 12, that the income profile related to education became less steep in this period). Therefore, it seems reasonable to conclude that the income effect is the leading force underlying the increase in inequality, and that, in turn, suggests that the workings of the labor market, and its interaction with the educational policies, should be thoroughly examined. Fourth, it is worth pointing out that the significance of changes in the distribution of education remains high even when one controls for changes in other relevant variables. As a matter of fact, with the exception of the 1996-1997 transitional period, the marginal contribution of age, economic sector and status in the labor market are usually negative. This means that the changes in these variables contributed to reduce the effects induced by changes related to 29 Of course the explanation for such a phenomenon is that the changes in the other variables worked in the direction of attenuating the changes in the rewards to education. 25 education, as most of the time they work in the direction of reducing inequality after the influence of education is accounted for. Table 13. Results of the Dynamic Decomposition Period Variable Allocation Income Gross Marginal Education 11.4 58.8 70.2 30.5 1988-1992 Age -1.8 21.9 20.2 -5.2 Sector -0.6 7.8 7.1 -17.7 Status 3.9 15.1 19.0 -7.4 Education 23.9 32.8 56.7 27.6 1992-1996 Age 11.1 10.5 21.6 10.5 Sector -5.4 25.4 20.0 10.5 Status 1.2 12.4 13.6 -4.2 Education 2.2 15.5 17.7 24.2 1996-1997 Age -0.4 5.9 5.5 12.5 Sector 0.4 1.0 1.4 18.4 Status 1.4 6.1 7.5 7.8 Education 35.8 108.4 144.1 33.7 1988-1997 Age 7.4 32.7 40.1 -19.9 Sector -6.6 43.2 36.6 -40.6 Status 9.0 20.2 29.2 -35.6 Source: Own calculations based on the ENEU (3Yd quarter). The last period, from 1996 to 1997, deserves special comment. First because inequality was substantially reduced. Secondly because, once more, there were alterations associated with education, now working in the other direction, and such alteration appear to be the main factor responsible for the reduction in inequality. As it can be seen from the synthetic indicators, there was a small improvement in the distribution of schooling during the period and, a sizable decrease in the steepness of income profile related to education. All other variables, as observed for other periods, also contributed to an improvement in earnings inequality. Because the reasons for their reversal are not clear, a closer analysis of the returns to education will be carried out in section VI. The next table shows the results of the same kind of decomposition for Brazil, Argentina and Peru. The significance of education as an explanation of changes in inequality seems to be a common pattern in Latin American countries. Moreover, the relevance of the income effect over the allocation (population) effect is also a trait shared by all countries where a similar analysis was carried out. Interestingly, in the Mexican case the figures are above those for other countries (in a shorter period of time length, one should stress). That means that the changes in the structure of supply and demand for labor, which are greatly affected by the educational and macroeconomic policies followed by the country and/or their interaction with the workings of the labor market, were particularly relevant for the earnings distribution. Table 14. Education and Inequality Variation: Brazil, Argentina and Peru Country Author(s) Period Explanatory Income Power (%)l Effect (%) Brazil Ramos and Trindade (1992) 1977/1989 6-20 10-17 Argentina Fiszbein (1991) 1974/1988 54-56 38-46 Peru Rodriguez (1991) 1970/1984 32-47 34-43 "The explanatory power is the income plus the allocation/population effect. VI. RETURNS TOEDUCATION In light of the evidence that the income effect associated with education seems to be the most important factor for the explanation of changes in earnings inequality in Mexico, it is relevant to follow-up and to pursue the analysis of rates of return to education. Though this is a 26 common procedure, there is an important caveat, as the international comparison becomes cumbersome because the differences in the structure of the educational system in Mexico and other countries. The earnings functions, for estimating the rates of return to education, can be fitted using "least squares" estimation. Also, a robust estimation technique has been developed recently, the quantile regression model, which is a special case of a location model.30 The objective function in the quantile regression estimation is a weighted sum of absolute deviations (the weights are given by the chosen quantiles), which gives a robust measure of location, in such a way that the estimated coefficient is less sensitive to outliers. Moreover, when the error term is non-normal, quantile regression estimator may be more efficient than OLS estimators.31 Thus, both ordinary least squares and quantile regression models were estimated.32 However, before analyzing the rates of return to education, it is worth investigating the role of each explanatory variable in the determination of earnings. For such purpose, several regressions were fitted adding the explanatory variable one by one. This exercise has two advantages: (i) it clarifies the marginal contribution of each explanatory variable; and (ii) it highlights the role of each explanatory variable throughout the conditional earnings distribution. Cragg and Epelbaum (1996) performed a similar exercise. As it was shown in the dynamic decomposition, education is the most important variable in the explanation of earnings inequality. However, one can also assess the importance of other explanatory variables using the estimates of educational level differentials. If the changes of such differentials in a given period of time have been smoothed by other explanatory variable, then such variable is a measure of some specific-skill. The relative change in the differentials by educational level in 1988-1992 and 1992-1997 periods were computed. The estimates are presented below. Table 15. Change in Differentials Controlling for Economic Sector, Labor Market, Status and Region Education level Controlling for Economic Sector Status Economic Sector Economic Sector, none and Status Status and Region Period 1988-92 1992-97 1988-92 1992-97 1988-92 1992-97 1988-92 1992-97 1988-92 1992-97 Primary Complete -0.03 0.05 -0.01 0.02 -0.04 0.03 -0.02 0.02 -0.02 0.01 Lower-Secondary -0.06 0.08 -0.05 0.03 -0.06 0.03 -0.03 0.00 -0.03 0.00 Complete Upper-Secondary -0.02 0.11 0.02 0.04 -0.02 0.04 0.01 0.00 0.01 0.00 Complete University Complete 0.14 0.18 0.15 0.08 0.12 0.09 0.15 0.04 0.15 0.04 Source: Own calculations based on ENEU (3d quarter). Note: Least squares estimates. The reference group is "Primary incomplete'. Table 15 shows that the changes in earnings differentials were "smoothed" by the introduction of the economic sector variable in the regression for 1992-1997 period, particularly for tertiary education,33 while in the 1988-1992 period the "smooth" effect was very small. Labor 30 For a brief review of this technique, see background paper 2. 31 This technique has been usually applied to analyze the determinants of wages structure as well as rates of returns to investment in education throughout the earnings distribution. Buchinsky (1994), (1995), and (1998) applies this technique to the U.S. labor market in order to assess the wage structure and its changes. Poterba (1994) also use the quantile regression to study the pattem of U.S. wage differentials between state and local govemment employees and their private counterparts. The quantile regression analysis has also been applied to other countries: Shultz and Mwabu (1996) to South Africa, Muller (1998) to Canada, Abadie (1997) to Spain, and, Montenegro (1998) to Chile. 32 The Os parameters in the quantile regression were 0.1, 0.25, 0.5, 0.75, and 0.9, following a common procedure in the literature. 33 For a reference see Cragg and Epelbaum (1996). 27 market status seems to have the same "smooth" effect pattern as the economic sector variable. These results suggest that the degree of correlation between education and economic sector, as well as labor market status, changed (this had been already detected in table 7, as the relation between gross and marginal contributions of economic sector as well as labor market status changed). In addition, table 15 shows that the "smooth' effect of the region variable was almost zero for both periods. In other words, the inclusion of this variable in the regression only produced a very small change in the earnings differentials (see the last four columns in table 15). At this point, one tentative conclusion emerges: the smooth effect of both economic sector and labor market status variables were significantly larger in 1992-1997 period than in 1988-1992 period (before the trade agreement). This means that the relationship between those types of specific skills acquired through such variables and education changed in the labor market. This implies that worker's insertion into the labor market and economic sector variables were a consequence of skill's differentials and not solely attributed to education. Hence, in order to have a precise assessment of the marginal value to educational level the analysis must incorporate this based on the earnings regression conditional on economic sector, labor market status, region, as well as age, age squared and gender. In the regression estimates, all the coefficients for education were significant at the 5% level, and the results for the marginal value of each educational level are reported in table 16. In general the OLS estimates are quite similar to the ones obtained by the quantile regression approach for 8=0.5, 0.75. It is also true, nevertheless, that the estimates through the latter technique tend to increase as one moves from the right to the left of the conditional earnings distribution, particularly for the upper levels of education. In summary, the results above have two strong implications: (i) education does play a crucial role in the process of earnings formation; and (ii) its effect is not the same throughout the conditional earnings distribution. Specifically, one can say that the rewards to education display a log-convexity for all years investigated. This log-convexity, however, has become pronounced in the 1988-1996 period, as the marginal value for the higher levels increased relatively more. There was a reversal in this trend in 1997, basically due to the gains associated with primary complete and losses associated with the upper secondary, though in a slight way.34 Table 16. Marginal Value of Ed cation by Level 1988 1992 Quantile 0.10 0.25 0.50 0.75 0.90 OLS 0.10 0.25 0.50 0.75 0.90 OLS Primary Complete 1.15 1.15 1.16 1.18 1.19 1.19 1.12 1.13 1.13 1.14 1.16 1.16 Lower-SecondaryComp 1.11 1.11 1.14 1.17 1.20 1.17 1.10 1.12 1.15 1.18 1.21 1.15 Upper-Secondary Comp 1.13 1.18 1.23 1.26 1.26 1.27 1.20 1.25 1.30 1.35 1.39 1.32 University Complete 1.34 1.39 1.44 1.46 1.52 1.49 1.46 1.54 1.66 1.70 1.69 1.69 1996 1997 Quantile 0.10 0.25 0.50 0.75 0.90 OLS 0.10 0.25 0.50 0.75 0.90 OLS Primary Complete 1.14 1.14 1.15 1.17 1.20 1.15 1.15 1.16 1.17 1.18 1.18 1.18 Lower-Secondary Comp 1.12 1.13 1.15 1.18 1.20 1.16 1.11 1.12 1.14 1.18 1.22 1.14 Upper-Secondary Comp 1.21 1.25 1.31 1.40 1.48 1.34 1.20 1.25 1.31 1.39 1.47 1.32 University Complete 1.60 1.71 1.80 1.78 1.70 1.74 1.63 1.76 1.80 1.77 1.70 1.75 Source: Own calculations based on ENEU (3Q quarter). Note 1: The marginal value is with respect to the previous education level Note 2: The asymptotic covariance matrix of the estimated coefficient vector in quantile regression is computed using the bootstrap method Note 3: All the coefficient are statistically significant at 5%, and conditioned to age, squared age, gender, status in the labor market, economic sector and region (North, Center, South and Mexico City). Regarding the accumulated changes in the marginal value of education by level (table 17), these are not significant for the primary complete and lower secondary instruction levels, along 34 Needless to say, this pattern had already been detected through the synthetic indicator s,. 28 the conditional earnings distribution for OLS estimates. In the case of upper secondary education the changes were substantial and very progressive across quantiles (8% at the median and 23% at the top decile) and even more so for the university level. Table 17. Percentage Change in the Marginal Value of Education, 1988-1997 Quantile 0.1 0.25 0.5 0.75 0.9 OLS. Primary Complete 0 1 1 0 -I -I Lower-Secondarv Complete I I 0 1 2 -3 Upper-Secondary Complete 7 7 8 14 23 5 University Complete 34 45 43 36 20 30 Source Own calculations based on ENEU. In sum, the returns to education have increased in Mexico in recent times, especially for the higher levels of education and in the upper tail of the conditional earnings distribution. Given the previous remark and using the transition probability results (see background paper 2), it is plausible to assume that the relative demand shifts within economic sectors dominate the relative demand shifts between sectors. Finally, with the goal of putting the rate of returns results in perspective, table 18 shows the return to education for other Latin American countries. Mexico's level of inequality is above the average, only after Brazil (the country that has the highest inequality in Latin America) suggesting that educational policies must be at the core of any effort aimed at reducing inequality, and by extension poverty, in Mexico. Table 18. Percent Earnings Differentials by Country Latin America Mexico Brazil Argentina Peru Primary Complete 50% 100°'o 100% 35% 40% Upper Secondarv Complete 120% 170% 170% 80% 80% University Complete 200% 260% 280% 160% 145% Reference group non schooling Source: IDB (1998). The pattern observed in the rates of return for secondary level, including technical education, suggest that it is necessary to address more carefully the impact of this type of education on the earnings distribution. TECHNICAL EDUCA TION One alternative to improve worker's opportunities in the labor market is through the development of a more demand-driven, financially sustainable vocational training system with stronger links to industry and increased private sector participation, within the framework of a national system of labor competency norms and certification. Moreover, technical education and on the job training may be an alternative for those individuals that face both a high opportunity cost to continue formal education and need to acquire skills that enable them to participate in the job market. This section provides answers to the following questions: Is there a significant change in earnings between secondary schooling and secondary schooling plus technical instruction? How much more does the labor market reward the skills or knowledge acquired through technical instruction or additional schooling? Is this premium growing or decreasing through time? Which income levels benefit more from technical instruction, training or additional schooling in terms of salary? 29 For the purpose of the analysis, we have selected the group of individuals in the labor force, non-seasonal workers are considered, between 16 and 65 years old with secondary instruction. This group is of interest because our results show that the greatest returns to schooling are at the top tail of the earnings distribution among those of lower and upper secondary levels. This group is divided into four categories, a) lower secondary complete (LS); b) lower secondary complete plus technical education or training (LSTE); c) upper-secondary complete (US); and, d) upper-secondary complete plus technical education or training (USTE). As indicated on figure 4, for all periods the conditional median hourly earning35 for LSTE was much higher than for LS. USTE earnings are fractionally lower than US median hourly earnings, from 1988 to 1990. Yet, after 1990, conditional median hourly earnings for USTE were substantially higher than US. In addition, it seems there has not been a significant difference in conditional earnings between LSTE and US. The above results suggest that the skills or knowledge acquired through technical education after completing lower-secondary level is a key factor in the formation of earnings. Another noteworthy observation is that there is a significant difference between conditional median hourly earnings for workers with lower-secondary (LS) compared to those workers with either upper secondary plus technical education (USTE) or upper secondary (US), holding constant other characteristics such as experience. Figure3 Figure 4 Median Real Hourly Earnings Conditional Median Real Hourly Earnings 3.50 1.00 Upp.,S..o.d.rywihThTh. Edo. 0 . , .Upp.S onywithr-ch. Ed.. 3.00 - UppeXotOO5Sy _ --- _............ _ 00~~~~~~~~~~~~~~~~~~~09 00 0 c* e0.80 Low-rStc... d.rywitch T..Ed 250o 0 0.70 Upp-,S-o.dary ' } 0 L-S .w...d. ryw ithTeh. . Ed.40 1.00 - , , , . , , , , , , 0.60 j2.00 '00 rSndy o U /' ~~~~~~~~~~~~~~~~~0.50 LowerSecond.ry 1.50 0.40 1.00 0.30 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1S ------ LSTE - US - - - -USTE LS ---- LSTE - US - - - -USTE Source: Own estimates based on ENEU (3" quarter). Source: Own estimates based on ENEU (3rd quarter). As can be seen in the table below, the lower secondary share in the labor market has increased dramatically (form 0.19 to 0.30). The upper secondary has also increased but not at the same rate. On the other hand, both primary incomplete and primary complete have reduced their relative participation in the labor market. Table 19. Total Share of Labor Market by Level of Education Education 1984 1989 1992 1994 1996 Primary Incomplete 0.32 0.20 0.23 0.21 0.19 Primary Complete 0.32 0.29 0.27 0.27 0.25 L-Secondary Complete .0.19 0.26 0.28 0.28 0.30 U-Secondary Complete 0.10 0.15 0.13 0.15 0.16 University Complete 0.07 0.09 0.09 0.09 0.10 Source: Own estimates based on ENIGH. 35 Earnings regression conditioned on age, squared age, gender, economic sector and labor marked status. 30 The change of the structure in the labor market share for lower secondary and upper secondary educational level and the pattern of the earnings differentials among LS, LSTE, US, USET could possibly be the result of a combination of several factors: (i) during the 90's there was a high demand for skilled workers in Mexico. (ii) During the 80's there was a substantial increase in basic education coverage. And, (iii) the youngsters are facing several restrictions on continuing education after completing upper secondary instruction. Quantile regressions at different mean values are shown next. These are attempted to assess which income groups lead the earnings gap found in the previous figures. The results from the conditional hourly earnings evaluated at the first decile show that there is not a significant difference in conditional hourly earnings for those individuals with US, USTE or LSTE levels of education. While there is a major disadvantageous gap for those individuals with LS instruction versus the other three levels of schooling. The conditional hourly earnings, evaluated at the 25 percentile, resemble the leveling off and gap difference observed for the first decile. The figure below shows that all series fluctuate considerable. However, after 1992, the way in which US and USTE condition hourly earnings resemble each other first reaches a peak and then decreases steadily. The same applies to LS with substantially lower earnings fluctuating and then decreasing steadily up to 1996 when they all increase marginally. Meaning that, the increased earnings premium on technical education at the top tail of the income distribution raises the rate of return from investing in that level of education. The significant increases in private rates of return in higher education observed in recent years makes compelling the case for leaving decisions of how much to invest to the private sector. Figure 5 Conditional Real Hourly Earnings Quantile 0.90 1 70 /51eadr S ~ ~ ~ ~ p 1 10 ,, , ~0.90 + 0.50 S988 S989 S990 S91 l1S92 S993 S994 S995 S996 1997 S. p-STE - Us- - E USTE _ Source: Own estimates based on ENEU (3'd quarter). 3' VII. CONCLUDING REMARKS * Even though the education attainment levels expanded very rapidly, Mexico also experienced a pronounced increase in the degree of income and earnings inequality over the period of analysis. Most of the worsening of income distribution happened in the mid-eighties. The early nineties, display little variation in earnings inequality except for a small trend towards deterioration. * It was shown that education is by far the variable that accounts for the largest share of earnings inequality in Mexico, both in terms of its gross and marginal contributions. The results of the gross contribution and the marginal contribution of education to inequality indicate that as the Mexican economy progresses, education becomes even more important in determining the choices of sectors and occupations. This is, the marginal contribution of education by itself remains the same, but the gross contribution increases. * The gross contribution of age to inequality has been going up and at the same time its marginal contribution has been decreasing. In other words, differences in both educational attainment and distribution among cohorts have become pronounced in recent times, leading to a higher correlation (negative) between education and age. The education contribution to income distribution in Mexico is the second highest in Latin America, next to Brazil. Moreover, what seems to be particularly interesting in the Mexican experience is the fact that the significance of education has been increasing over time. * The increase in earnings inequality does not appear to be the result of a worsening in the distribution of education, whereas the income profile, which is related to the returns to schooling, has become much steeper. Meaning that, there was a shift in demand towards high skilled labor that was not met by the increase in supply probably due to the increased rate of skill-biased technological change, whose transmission to Mexico may be facilitated by the economy's increased openness. * Women are undoubtedly more educated than men. More educated women at a time of greater female labor force participation points in one direction -a relaxation of the supply constraint which has kept earnings differentials high. The policy implication is to make it easier for women to work in the labor force through greater investment in early childhood care or other options, which would permit women to transform their education into productive gains for the society. * It was shown that the returns to education have increased in Mexico in recent times, especially for the higher levels of education groups and in the upper tail of the conditional earnings distribution. * One alternative to improve worker's opportunities in the labor market is primarily through the development of a more demand-driven, financially sustainable vocational training system with stronger links' to industry, and increased private sector participation, within the framework of a national system of labor competency norms and certification. In parallel, technical education may be an alternative for those individuals that face both'a high opportunity cost to continue formal education and need to acquire skills that enable them to participate in the job market. The skills or knowledge acquired through technical education, after completing lower-secondary level, is a key factor in the formation of earnings. 32 Chapter 2. Education and Public Policy36 Chapter 1 concluded that education does play a crucial role in the process of earnings formation and that the returns to education have increased in Mexico in recent times, especially for higher levels of education and in the upper tail of the conditional earnings distribution. Namely, educational policies must be at the core of any effort aimed at reducing inequality, and by extension poverty, in Mexico. In light of such findings, this chapter examines the government's educational policy response in the face of possible further increases in earnings inequality (technological changes in the same direction). In particular, it is analyzed both public and private educational expenditure patterns: How are federal and total subsidies distributed across income groups and by level of schooling? How have federal subsidies evolved through time? Which are the deterninants of school enrollment by income groups and location? How do individuals' educational expenditures affect school enrollment patterns? What would an average household with a given set of characteristics be willing to spend on an individual child, if subsidized public education facilities were not available? What would the household have "saved" by sending the child to public schools instead of private schools? How large are these "savings" for various income groups?. The chapter is structured as follows: Section 1 has a brief review of the education system in Mexico. Section 2 discusses the two elements of the benefit-incidence analysis: enrollment and education expenditures in Mexico, also examines the distribution of total subsidies allowance for each state, across the levels of education and income deciles. Section 3 examines private expenditures on education and the determinants of upper secondary enrollment. Section 4 analyses the marginal willingness to pay for educational services. Last section presents the concluding remarks. . PUBLIC EDUCA TIONAL SYSTEM The structure of Mexico's educational system has the following main characteristics. First, there is basic education, which is the government's priority. The basic education system consists of (i) early childhood education (or pre-school), which is optional for children 3 to 5 years old and (ii) mandatory primary education where the official entry age is 6 and ideally should be completed in 6 years. In fact, due to late enrollment and grade repetition, however, the target population is 6 to 14 years; (iii) mandatory lower secondary school consist of a 3-year cycle, and it is intended for children ages 12 to 16. At this level, the structure is divided in two areas: general and vocational/technical. In parallel, the system also includes the telesecundaria, a distance education program designed to reach remote areas through the transmission of recorded lessons via television network supported by face to face assistance from tutors. The next level, following basic education, there is middle level education with options available to students who may choose technical schools and upper secondary education. The duration of these programs is 3 years. A high percentage of the students go for bachillerato also called upper-secondary which allows them to pursue tertiary instruction. On the other hand, a demand for technical studies has been increasing steadily in recent times. Finally, there is tertiary education. This level of education encompasses three lines of study: a system of federal technological institutes, state and autonomous universities, and teacher-training institutes. There is at least one university for each state, and the large universities have campuses in various cities. 36Based on background papers 3 and 4. 33 II. ENROLLMENTAND PUBLIC EXPENDITURmES IN THE BENEFIT INCIDENCE ANALYSIS Two general approaches for measuring public education. expenditure benefit can be identified. Although neither is able to adequately resolve the various difficulties related to variables such as individual or household characteristics and quantity constraints. The first approach is based on benefit-incidence and. assumes that the value of the benefits of education equals the unit cost of providing the service. The second approach, the marginal willingness to pay, attempts to measure the benefits by using a related notion of consumer surplus (examined in section IV). This paper discusses both and applies the corresponding methodologies to the ENIGH household income and expenditure surveys. The benefit-incidence methodology applied in this section, ranks individuals into groups by income deciles. It then draws information on individual public school enrollment by state and decile to tally up numbers of beneficiaries of each group. These numbers are then multiplied by the government's unit cost of provision allowance for each state and educational level. This provides a profile of distribution for a specific category of educational public expenditures throughout the distribution of income or the "benefit incidence". Thus this technique assumes that the benefit derived from education is equal to the government cost of providing this service; The incidence analysis brings together two sources of information. First, data from income-expenditure surveys (ENIGH) used to construct the deciles and the enrollment. The ENIGH surveys identify the educational level, type of school and total income/expenditure. Second, government expenditures (Federal plus State) on education assigned to the different levels of schooling for each state from the Direccion General de Planeacion, Programacion y Presupuesto, DGPPyP, (Ministry of Education) used for calculating unit costs. Equity issues are then analyzed using the Lorenz Curves based on the pattern of government subsidies to education received by different population groups, highlighting the results of changes in the use of educational services and change in government's expenditures for education by levels and by region.37 1R.1 ENROLLMENTRA TES As shown on table 20 variability of enrollment between poor and non-poor individuals is not substantial at the primary level. However, urban areas show slightly larger primary enrollment rates than in rural areas, which might be explained by higher accessibility and affordability to the private system. Enrollment rates for the educational levels beyond primary and probable lower-secondary levels decrease dramatically, particularly for the extremely poor, thus resulting in an increase in the educational gap between poor and non-poor. Background paper 3 shows enrollment by educational level and types of schools used in the benefit incidence analysis. 37 For a review see Dominique Van de Walle and Kimberly Nead (1995). 34 Table 20. Total and Public Enrollment Rate by Poverty Status, Location and Level of Education 1996,_NEGI/CEPAL Poverty Line Urban Rural Total Poverty Status All Public All Public All Public Primary (6-11 years old) Extreme 93.2 93.2 93.5 93.5 93.3 93.3 Moderate 96.4 96.4 94.6 94.6 96.0 96.0 Non-poor 96.1 95.7 96.4 96.3 96.1 95.7 Total 95.4 95.2 93.9 93.9 94.9 94.7 Lower Secondary (12-14 years old) Extreme 49.1 48.9 29.0 28.8 37.9 37.6 Moderate 68.7 68.8 51.0 51.2 64.8 64.9 Non-poor 81.4 81.3 59.5 59.8 79.1 78.8 Total 68.5 67.7 36.8 36.6 58.4 57.4 Urban Rural Total Poverty Status All Public All Public All Public Upper Secondary (15-17 years old) Extreme 23.5 21.4 6.9 5.9 14.5 12.9 Moderate 39.6 36.8 22.2 21.7 36.0 33.5 Non-poor 61.7 54.0 24.5 21.8 58.0 50.1 Total 45.7 39.8 12.8 11.7 36.4 31.2 University (18-24 years old) Extreme 3.4 2.9 0.4 0.4 1.8 1.6. Moderate 7.4 7.0 2.3 2.2 6.4 5.9 Non-poor 24.0 17.6 5.9 5.4 22.0 16.1 Total 15.3 11.5 2.0 1.8 12.0 8.9 Source: Own calculations based on ENIGH96. Given that coverage at primary level and the first years of lower secondary is already sizable and decreasing due to demographic factors which cause the population in this group to stagnate and start to shrink at the beginning of the next century.3 This in turn frees some resources so that coverage may be increased at the upper-secondary level. II 2 PUBLICEDUCATIONALEXPENDITURES Total public education spending per student in Mexico increased steadily up to 1994 and peaked in 1998, even though the total student population increased from 26 million in 1994 to 28 and a half million in 1998. By 1998, total spending in education increased by 5.2 percent of GDP, less than a full percentage point above the 4.9% of GDP reached in 1995. The federal government currently accounts for close to 80% of total sector spending. 3 From 1973-1994, there was a change in the population structure: the population ages between one year through 14 dropped 36%, those between 15 and 64 increased 59.8% and the age group over 65 rose 4.2%. 35 Figure 6 Figure 7 Education Spending per Student Distribution of Education Expenditure by 5000 Source 4500 0% 4000 | *Federal&State Oprivate | |EDd,ral U State &MunictpaI aPrivate| Source: Iv Informe de Gobierno, 1998 Source: IV Inforrne de Gobierno, 1998 A desegregation of public expenditures in education by instruction level for 1994 and 1996 is shown below. Public expenditures in primary and lower secondary absorb a large proportion (59% in 1996) of federal budgetary resources for forrnal education services. Yet, public expenditures in upper secondary and tertiary level were 13.7% and 27.3% each respectively. Another observation about the evolution of educational public spending is that it seems it has become more egalitarian in per-capita terms across different schooling categories.39. In the early 1980s, the amount of federal spending per university student was 10 times the amount spent per primary student. This ratio fell to around 7 times in the early 1990s. Federal spending on the other levels relative to the primary level indicates a similar decline, even though the absolute amounts increased at all levels. In 1996, upper-secondary received 1.5 as much as each primary school student and each university student received five times as much as a primary student. Table 21. Federal and State Expenditures on Public Education, 1994 (Thousands of current pesos) Primary Lower Secondary Upper Secondary Tertiary Federal Expenditure 17,947,229 8,603,383 6,610,913 13,141,420 State Expenditure N/A N/A N/A N/A Total Expenditure 17,947,229 8,603,383 6,610,913 13,141,420 Enrollmen (enigh)t 13,593,797 4,661,522 2,386,758 1,461,189 Subsidy per Student (pesos) 1,320 1,846 2,770 8,994 Primary Student equivalence 1.00 1.40 2.10 6.81 (only Federal subsidy). Sources: ENIGH 94 and DGPPyP (1999), SEP Table 22. Federal and State Expenditures on Public Education, 1996 (Thousands of current pesos) Primary Lower Secondary Upper Secondary Tertiary Federal Expenditure 33,328,323 13,394,898 10,884,850 21,651,986 State Expenditure 8,920,249 4,747,407 1,869,710 2,210,962 Total Expenditure* 42,248,572 18,142,304 12,754,560 23,862,948 Enrollment(enigh) 13,802,395 4,972,116 2,767,993 1,459,820 Subsidy per Student (pesos) 3,061 3,649 4,608 16,347 Primary Student equivalence 1.00 1.19 1.51 5.34 (Federal plus State subsidy) Source: ENIGH 96 and DGPPyP (1999), SEP 39 IV Informe de Gobierno, 1998. 36 L3 BENEFIT INCIDENCE ANALYSIS Next, a comparison, was made between the cumulative distribution of the various educational sub-sectors and the distribution of per capita annual total and federal public educational expenditures. Beforesaid, in order to derive the cumulative distribution for various educational levels, individual public school enrollment by state and decile is multiplied by the government's unit cost of provision allowance for each state. This is also done subsequently by region and state. Figures 8 and 9 show the cumulative distribution by total and federal educational expenditures for all of Mexico. One of the main messages is that the poorest income/expenditures deciles receive the bulk of the primary education subsidy. This same group, at higher levels of education receives progressively smaller subsidies. This indicates that primary education is very progressive and lower-secondary education is basically neutral. Upper-secondary schooling, benefits the middle and upper classes. Finally, the tertiary level is strongly regressive in that it mainly benefits the richest deciles. At national level, public expenditures seem quite equal, as shown by the fact that the expenditure line lies very close to the 45 degree diagonal. Figure 8 Figure 9 Cumulative Distribution of Total Education Cumulative Distribution of Federal Education Expenditures, 1996 National Expenditures, 1996 National 100 90~~~~~~~~~~~~~~~~~0 90 |9,3 0 - o 0,0 - 70 - 70 60 6 50 5~~~~~~~~~~~~~0 20 ~~~~~~~~20- 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 - - Primary ----- Lower Sec - - Upper Sec - Primary ----- LoAer Sec - -Upper Sec -- --- Tertiary -Total expend 0 Equality - -- Tertiary Fed expend Equality Source: ENIGH 96 and DGPPyP, SEP Source: ENIGH 96 and DGPPyP, SEP When desegregated by region, it becomes evident that the educational inequality in the Ceniral Region of Mexico leads the national pattern. Still, in the Central Region, the curve for total and federal schooling expenditures lies above the equality line. This implies that on average total schooling expenditures for that region are more uniformly distributed that the national pattern. The distribution of the average subsidy in the South Region and Tabasco State lies above the average distribution for the North Region. One plausible explanation is the higher concentration of the enrollment in the lower deciles (mainly in primary) in the South Region and Tabasco compared to the concentration in,the North, where the students are in the medium and top deciles. In the South, public enrollment is highly progressive particularly for primary school, as shown by the fact that public school enrollment is above and far from the 45 degree diagonal. It should also be mentioned that public education spending in upper-secondary in Tabasco is basically neutral at high level of income, while progressive at the bottom of the distribution. In the North Region, the cumulative distribution of education subsidy lies below the 45- degree diagonal, except for primary schooling, which is near the equality line. In general, both larger populations in the medium and top deciles, and higher enrollment rates in the higher levels can explain this, which probably reflects higher incomes in the North Region and easier access to schools. 37 The distribution of per capita public expenditures in Mexico City is far below the 45- degree diagonal indicating that it is very regressive. Public expenditures in primary level are progressive for the highest income deciles, in that the primary curve lies above the 45-degree.axis and it is much more progressive than the distribution of per capita expenditures, reflecting the fact that fewer higher income children attend public primary schools. Spending at the lower and upper secondary level is more progressive than the public expenditures, although the curves still lie below the 45-degree diagonal. Only university instruction is more regressive than the average distribution of total expenditures. Interestingly, public expenditures in education in Nuevo Leon are far below the 45-degree diagonal following a pattern similar to Mexico City. The evidence presented suggests that public subsidies for education, particularly at the tertiary level, are regressive. A large share of public resources is given to the high-income level students. A strategy to reallocate public expenditures from tertiary to secondary level in order to favor the poor would involve a comprehensive agenda that would meet the challenges posed in upper-secondary level such as financing and the quality of education, as discussed in chapter 3.4° III PRIVATEExPENDITURESINEDUCATIONAND THE DETERMINANTS OF ENROLLMENT On the demand side, household enrollment patterns are highly dependent on the cost of schooling. This section shows that, in addition to the cost of schooling, there are other factors that affect the probability of enrollment. The total monetary cost for the household, without considering the opportunity cost, comprises school fees, tuition and unforeseen expenses, transportation cost, textbooks, stationery, and uniforms. Private costs of education could be considered modest in terms of average earnings, but they are significant when compared to the earnings of workers without experience, which has an impact on student's decision to continue his studies to a certain level or begin working without completing his degree. Table 23 below illustrates the fact that most students in private schools spend more than twice the amount that public school students spend in education. As it can observed in this table, the share of the expenditures in services and materials are similar for both private and public school, while the fees and unforeseen expenses constitute the differences in total school expenditures between private and public schools. In private schools, fees and unforeseen expenses account for 70% of the school expenditures compared to 38% in public schools. Moreover, the educational expenditures in the urban areas are twice as high as in rural areas. Table 23. Household Expenditure in Education by Poverty Status, 1996 Poverty Status Expenditures per student (%) Educational Services, materials. Number of Fees/Unforeseen Services Materials 1/ Total Expenditures Expenditures Households expenses )%2t (%)2U Private schools Extreme 70.3 1.0 28.7 100.0 14.6 4.3 12 Moderate 75.1 4.3 20.7 100.0 11.1 2.8 50 Non poor 70.8 5.5 23.7 100.0 16.7 4.9 499 Total 70.9 5.4 23.7 100.0 16.6 4.8 561 Public schools Extreme *32.2 1.2 66.6 100.0 6.3 4.3 2825 Moderate 35.2 2.4 .62.4 . 100.0 7.0 4.5. .2511 Non poor 41.8 5.4 52.7 100.0 6.7 3.9 2544 Total 38.3 3.8 57.8 100.0 | 6.7 4.2 7880 Source: Own calculations based on ENIGH. "Textbooks, stationery, etc. 21As percentage of household's expenditures. 40 The comparison of the distribution of the federal subsidy across years is reviewed in background paper 3. 38 Table 24 compares the expenditure of poor and non-poor students by education level, * showing significant disparities. At primary level, non-poor students in public school spend four times the amount than extremely poor students spend in education. While at the university level non-poor individuals spend 1.4 times as much as poor students. These differences might be partly explained by scholarships or discounts on tuition fees among the poor. (Absolutes amounts are in background paper 4). Information at the individual level on schooling expenditures is available only for school fees, tuition and unforeseen expenses, but assuming that the amount spend on materials and services is fixed for all levels of education, the individual total educational expenditures are much lower than the government subsidy. In fact, the public subsidy compared to the average student expenditure is 2.8 times for primary level, 2.3 for lower secondary, 2.2 for upper secondary and 5.2 for university. Table 24. Expenditures in Education per Student (Fees/tuition/unforeseen expenses)" by Level of Education, 1996 Poverty Status Prirmary Lower Secondary Upper Secondary Tertiary Public and Private schools Extreme 76.7 268.2 851.9 1828.7 Moderate 186.3 491.6 975.9 835.9 Non poor 1378.9 1404.7 2965.1 5448.7 Total 425.4 750.1 1996.2 4466.8 Public schools Extreme 74.6 262.4 760.1 1828.7 Moderate 179.4 485.2 883.7 817.4 Non poor 307.4 712.5 1292.1 2577.7 Total 156.8 492.9 1057.4 2141.6 Private schools Extreme 1422.5 1252.5 2845.0 0.0 Moderate 1739.0 1088.7 2540.3 1179.4 Non poor 6468.1 6539.0 8515.2 12950.5 Total 6241.9 5915.4 7495.3 12451.3 Source: ENIGH96 "Annual pesos per student The total cost (student expenditure plus government subsidy) per student in primary public school corresponds to about 35% of the private primary school cost. For students in lower and upper secondary it represents 43% and 53%, respectively. On the other hand, the cost of tertiary level is 13% higher in public schools as compared to private (see tables below). An interesting question that arises is why the cost at tertiary level is higher in public than in private schools. Is it because the subsidy is not being used efficiently, or because the infrastructure (research institutes, libraries, museums, entertainment centers, etc.) they offer is costly?. In the next section a technique is applied that will allow us to evaluate the impact of public expenditures on household spending patterns. Table 25. Individual Educational Expenditures"/ plus Subsidy in Public Schools by Poverty Status2' Poverty Status Primary Lower Secondary Upper Secondary Tertiary Extreme 2799 3266 5002 16970 Moderate 3267 3852 5489 16322 Non poor 3941 4626 6444 18629 Total 3268 3883 5686 17670 Source: ENIGH 96 and DGPPyP, SEP " Fees/tuition/unforeseen expenses, services and materials, 2Annual pesos 39 Table 26. Individual Educational Expenditures" in Private Schools by Poverty Status2/ Poverty Status Primary Lower Secondary Upper Secondary Tertiary Extreme 2295 2125 3717 Moderate 2390 1740 3191 1831 Non poor 9851 9922. 11898 16334 Total 9387 9060 10640 15596 Source: ENIGH 96 and DGPPyP, SEP " Fees/tuition/unforeseen expenses, services and materials, 21 Annual pesos The preliminary results on the pattern of individual expenditures with children in public schools suggest that the burden on poor households can be substantial, and that it is unlikely that a poor household would afford to attend private school.4' Determinants of Enrollment and Transition Other factors that affect the enrollment decision after lower secondary, as estimated from the probits for enrollment, show that the probability of enrollment is positively influenced by the household's head educational level, dwelling services such as sewage and per capita household income". Aside from being sewage an important indicator of family wealth, the absence of sewage suggest the possible necessity for children to be involved in a greater number of household chores. The positive marginal effects from such variable increases by 7% the probability of enrollment. Variables with a negative influence include student's age and family size. On the other hand, gender is not significant. Note the 12 % negative impact of some primary schooling for the household head in the poorer group, as compared to the statistically insignificant impact of this variable on enrollment probability amongst the richer group. Similarly, household head income per capita has a 5% positive impact on the probability of enrollment in urban areas, and the impact of household head income per capita is absent among the rural. Table 27. Determinants of Upper Secondary School Enrollment, 1996 Full Sample Poorest 40% Richest 40% Urban Rural Probability of enrollment 0.66 0.49 0.82 0.73 0.39 Mean Income 7.44 6.63 8.14 7.56 7.03 Mean teachers 1.89 1.80 1.95 1.90 1.84 Income Elasticity 0.58 -1.11 0.44 0.53 0.11 Teachers Elasticity 0.20 0.49 0.04 0.08 1.19 Source: Own calculations based on ENIGH. As can be seen on table 27, the probability of enrollment in upper-secondary is much higher for both the top 40% of the income distribution and in urban areas when compared to those in the bottom 40% and in rural areas. The variable teachers (govermment effort) has a significant marginal impact which is many times larger for the 'Poor' as compared to for the 'Wealthy' and for rural areas as compared to urban areas. In elasticity terms, the teacher's variable is more effective for the poor and for the rural areas by factors of 12 and 15, respectively. The differential impact suggests that the goal of efficiency in terms of maximizing enrollments in upper- secondary school does not have a trade-off with the goals of greater equity of educational opportunity. Indeed, the above findings indicate that increases in enrollment will be more readily obtained if resources are successfully targeted towards the poorer income group. It is of interest to note the negligible impact of educational transfers, which could probably be explained by the null variance of transfers among states. 41 An illustration of the disproportional burden of education on the poor individuals can be obtained by comparing household expenditures on education with non-food expenditures per capita. 42 The probability.being modeled is enrollment in upper secondary school for individuals age 15 to 19 and conditional to lower secondary completed. For more details see background paper 4, section 4. 40 IV ESTIMATING THE EFFECT OF GOVERNIMENT SPENDING ON HOUSEHOLD EDUCATIONAL EXPENDITURES In section II it was assumed that the subsidy and the quality of education are uniformly the same for all income deciles. This is a strong assumption that tends to minimize the distributional inequity within educational levels. The marginal willigness to pay methodology prevents this drawback. The methodology estimates a willingness to pay equation for private school services corrected for self-selection bias, using standard Heckman methodology. In analyzing the impact of public spending on household behavior, this section focuses in the following questions: What would an average household h with a given set of characteristics (Xh) be willing to spend on an individual child i with traits (Xc), if subsidized public education facilities were not available? What would the household have "saved" by sending the child to public schools instead of private schools? How large are these "savings" for various income groups? Intuitively, one would think that household "savings" could be estimated as the difference in household education spending on public versus private schooling of children of comparable characteristics. While the concept appears straightforward, the estimation is not. The challenge is to ensure that these two groups of children are comparable. One can argue that due to observable and unobservable factors, the two groups of children are in fact different. Examples of measurable variables are family income and parents' education. Examples of unobserved variables that can generate self-selection bias is preference for religious instruction, high rate of return to quality due to child's exceptional intelligence, and taste for individualized instruction. Lack of control for these unobservable factors would overstate the potential household "savings" associated with the provision of subsidized public education. Households send their children to private schools despite availability of public school places, because they want higher quality and additional services that they cannot find in public schools. The marginal willigness to pay methodology starts by estimating a probit equation. The probit equation or step I has as dependent variable whether child i is attending private school (value of 1) or public school (value of 0). The explanatory variables are per capita household income, years of school of household head (hh), area (urban/rural), age, gender, number of rooms, type of floor and number of children in household. The trigger variable, that identifies the model, is the amount of students per classroom, by type of education (public versus private) and level of instruction at municipal level. Table 28 provides the results of the estimation. Table 28. Probit on Private School Attendance Explanatory Coefficient Marginal Variable _ Effect Per capita income 0.78 * 0.064 Years of schooling of head 0.03 * 0.002 Area (rural) 0.80 * 0.046 + Age 0.16 * 0.014 Age squared 0.00 * 0.000 Gender (female) -0.12 ** -0.010 + Number of rooms 0.09 * 0.008 Floor (notfinishedfloor) 0.40 * 0.037 + Sewage (not sewage) 0.32 * 0.024 + Number of Children -0.10 * -0.008 Trigger Variable -0.09 * -0.008 Constant -7.56 * Source: Own calculations based on ENIGH 96 and DGPyP, SEP * Significant at 5% ** Significant at IO% Italics: indicate the reference category for dichotomous variables (+) dF/dx stands for the discrete change in the dummy variable from 0 to I 41 The first column on table 28 has the coefficient and the second shows the marginal effects as estimated from the probit.43 Notice that all explanatory variables are significant at 5% level, except for the gender. In addition, all explanatory variables show the expected sign on the probability to attend private school. For instance, the probability to attend private school is positively influenced by per capita household income and area. The household's willingness to pay for private education (Pv) of child i ( step 2) in this methodology is estimated using total educational expenditures on private schools (fees, tuition, unforeseen expenses and school materials44) as dependent variable.45 Explanatory variables are Mills' ratio and the variables from the probit estimation except the trigger variable. Using the estimates from step 2 and step 3 and the mean of all explanatory variables, one can compute the amount of money that households would be willing to pay for the child's private education (MM WPv, step 4) or public education (MMWPu, step 5). Notice that the difference between MMWPv and MA'IWPu measure the effect of the government provision of public schools on the education spending behavior of an average household. In other words, this difference reflects the relative quality and payments (fees and unforeseen expenses) associated with public and private schools. Next, how large the household "savings" is computed for different population subgroups by area, poverty status, level of schooling and total educational expenditures quantiles. Background paper 4 shows the average values of the explanatory variables for the different population subgroups. Beforesaid, these values are used to compute the marginal willingness to pay corrected for self-selection bias.46 All the explanatory variables turn out significant in the process of computing the marginal willingness to pay for public educational service except for gender. In the case of private education, the relevant variables are income, years of schooling of head of the household and sewage. Table 29. Effect of Public Schools Provision on an Average Household Education Spending I' MMWPv 6274.88 MMWPu 1080.92 Effect or "savings" 5193.96 Source: Own calculation based on ENIGH 96 'Annual 1996 pesos Assuming that there are no differences in the quality of education between a public and a private school is seen in table 29 (after controlling for observed and unobserved factors) that family's savings from sending a child to a public school amounts to approximately $5,000 pesos per year (0.56 minimum wages in 1996). In addition, the results suggest that such savings are correlated with the schooling of the household head, location and number of children at home. Now, assuming that the difference between private and public schools students' scores is only 10%, then, ninety percent of the effect or "savings" is due to relative payments and unforeseen expenses. The rest will reflect the amount that the average child would have to pay for "quality difference" in moving from a public to a private school. 4' The marginal effect for continuous variables is the marginal effect as evaluated at the mean of the particular exogenous variable. Dichotomous variables have been coded as '0' or '1', and the marginal effect for such variables represents the impact of the probability of having a 'I' value for the exogenous variable, as compared to a '0' value, the other variables being held constant at their mean values. 44 Included only those students with positive fees, tuition and unforeseen expenses. 45 Step 3 uses public educational expenditures instead of private ones. 46 Note that it is not possible to compare directly actual average payments with the marginal willingness to pay since the later controls for observed factors. 42 The marginal willingness to pay desegregated by area is shown on table 30. Assume that the quality between public and private schools in both rural and urban areas is the same, then, it turns out that the government provision of public schools is higher in urban areas than in rural areas. This result could be explained among other things, by the following factors: (i) poor location of public educational services; (ii) distance that an individual has to travel to the nearest school; (iii) the population dispersion and lastly the opportunity cost of the children in rural areas. On the other hand, assuming that there is not a quality difference between private-public schools in rural areas and the quality difference between public-private schools in urban areas is a little above 50%, the relative payments and unforeseen expenses would be the same in both areas. Finally, assuming that the quality difference between public and private rural schools is zero, but the quality difference in urban areas is 10%, then the relative payments and unforeseen expenses (as part of the "effect") in urban areas is higher than in rural areas (urban effect $4,016 pesos and rural effect $2,245 pesos). In summary, such scenarios suggest that students in urban areas get a larger share of the subsidy or "savings" from the government provision of educational services compared to these in rural areas. Table 30. Effect " of Public Schools Provision on Household Education Spending by Area Urban Rural MMWPv 6459 2674 MMWPu 1438 429 Effect 5021 2245 Source: Own calculation based on ENIGH 96 "Annual 1996 pesos As shown on table 31, the government provision to public schools has a smaller impact on the poor as compared to the non-poor. For both poor and non-poor, gender is not important in the determination of the AMWPu, while per capita household income, age, and number of children do have an impact. It is important to note that while the education of the. household head determines the MMWPu for the poor, it does not affect it for the non-poor. With respect to MMWPv by poverty status, the educational level of the household head affects the MMWPv for both groups, while the per capita household income does affect the non-poor. The age of the children impacts on the MMWPv of the poor, but it does not have any effect on the non-poor. Finally, again gender is not important in the MMWPv for the poor as well as for the non-poor. The analysis by poor/non-poor and region suggest the following: (i non-poor, and those who live in urban areas, get a large share of the subsidy or "savings" from the government provision of education services. And (ii the valuation for private educational services is higher for the wealthy as compared to the poor. In light of these results, plausible alternatives for the government include: (i) to better target public educational services; (ii) charge a fee for public educational services to the non-poor; and (iii) increase the quality of education for the poor. Table 31. Effect "1 of Public Schools Provision on Household Education Spending by Poverty Status47 Extreme Moderate Non Poor MMWPv 2114.63 2963.22 7229.76 MMwPu 849.23 1241.22 1073.99 Effect 1265.40 1722.00 6155.77 Source: Own calculation based on ENIGH 96 "Annual 1996 pesos At the primary level, all explanatory variables turned are significant for explaining the MAWPu. In lower secondary level, the variables household per capita income, region, number of Assuming that the income distribution for the extreme poor is uniform, it turns out that an average poor household earns $9510.00 annual 1996 pesos . Note that it is assumed that the family size is 5 members and that the extreme poverty line is $317.00 monthly per capita 1996 pesos. 43 rooms, type of floor and number of children are important in the determination of the MMWPu. While for upper secondary instruction, household per capita income and age are relevant. Interestingly, household per capita income is also a significant variable in explaining MMVWPv. This suggests that parent's valuation for private educational services relies solely on income while for public educational services there are other important factors in addition to 'income that determnines the parent's valuation for public educational services. In table 32, the effect of government provision of school services' is very similar for both primary and lower secondary school levels (basic education).48 This effect is higher in basic education as compared to upper secondary or technical education. Assuming that the quality difference between private and public schools is only 10% for all levels of education, relative payments and unforeseen expenses ("savings") will be much higher in basic school as compared to upper secondary level. As derived in section 3, for primary through upper-secondary level, there is a positive decreasing relationship between the payments difference (in private versus public schools) and instruction level. Allowing such difference to be a proxy for quality, then, the quality difference between private and public schools in primary level is 70%, in lower secondary is 60% and in upper secondary is 50%. This indicates that quality differences are higher in primary level. The remaining savings are due to payments and provisions. Table 32. Effect" of Public Schools Provision on Household Education Spending by Educational Level Pre Primary Lower Upper Technical Primary Secondary Secondary Education MMWPv 5856.12 6920.00 8024.88 7156.88 3688.08 MMWPu 880.36 714.68 1725.68 2541.96 2539.2 Effect 4975.76 6205.32 6299.2 4614.92 1148.88 Source: Own calculation based on ENIGH 96 -Annual 1996 pesos In addition to Least Squares Regression (LSR), quantile regressions were computed to test the robustness of the above results. The aim is to assess how large are the "savings" for various educational expenditure groups. Results of this test indicate that the distribution of "savings" might be right-skewed since the MMWPu, MMWPv and the savings effect, evaluated at the median of the total educational expenditure distribution, are lower than in the LSR. Note that at the median of the public educational expenditure distribution all explanatory variables are significant for explaining MAilWPu. Yet, at the tails of this distribution, variables such as gender, number of children and schooling of the household head are not significant in explaining MMWPu. Another observation is that at the median and lower tail of the private educational expenditures distribution, variables such as income, household head schooling and housing facilities are important to determine the MAfA!Pv. At the upper tail of the distribution, household per capita income is the only relevant variable that determines MMWPv. Table 33. Effect" of Public Schools Supply on Household Education Spending through out the Conditional49 Expenditure Distribution Quantile Quantile Quantile Quantile Quantile 0.1 0.25 0.5 0.75 0.9 MMWPv 1101.48 2843.56 5781 16291.04 39533.6 MMWPu 16.52 252.12 1636.2 6241.2 12897.04 Effect 1084.96 2591.44 4144.84 10049.88 26636.56 Source: Own calculation based on ENIGH 96 "Annual 1996 pesos 48 The analysis could not be done for tertiary level because there was not a "trigger variable" at this level of education. 49 Conditional to Per capita income, Years of schooling of head, Area (rural), Age, Age squared, Gender, Number of rooms, Type of Floor, Sewage, Number of Children, Trigger Variable. 44 I I I It follows from the above analysis and from table 33 that those households with a high level of educational expenditures receive the largest subsidy from public educational services. Given that there is a strong positive relationship between educational expenditures and per capita household income, a reasonable conclusion might be that the govermnent should charge a fee to those in the upper tail of the income distribution especially considering that the wealthy individuals have both a high valuation for quality of schooling and are able to pay for the educational service 45 V. CONCLUDINGREMARKS * Enrollment rates for the educational levels beyond primary and lower-secondary levels are dramatically low, particularly for the extremely poor, thus resulting in an increase in the educational gap between poor and non-poor. Given that coverage at primary level and the first years of lower secondary is already sizable and that demographic pressure is decreasing, the population of this group is virtually stagnated and will start to shrink at the beginning of the next century. This in turn frees some public resources, which can eventually be used to increase coverage at the upper-secondary. level. * It was shown that the enrollment rates for secondary and tertiary levels are extremely low, particularly for the poor. The probability of enrollment in secondary level is much higher for both the top 40% of the income distribution and for urban areas when compared to those in the bottom 40% of the income distribution and for rural areas. Head of household's educational level, household income per capita and government effort have a positive influence on the probability of enrollment. The variable government effort has a marginal impact which is many times larger for the 'Poor' as compared to for the 'Wealthy' (in elasticity terms, this variable is more effective for the poor by a factor of 12). The differential impact suggests that the goal of efficiency in terms of maximizing enrollments in secondary school does not have a trade-off with the goals of greater equity of educational opportunity. Indeed, these findings indicate that increases in enrollment will be more readily obtained if resources are successfully targeted towards the poorer income group. * Government spending per student steadily increased until 1994 and stayed the same until 1995; peaked again in 1998. On the other hand, after 1994, government spending per student became better distributed. Nevertheless, government spending still favors tertiary education. Spending on education continues to be concentrated in the federal sector, which accounts for over 80 percent of total sector spending. * Another noteworthy observation about the evolution of public spending on education in Mexico is that it has become somewhat more egalitarian in per-capita terms across different schooling categories. By moving towards a more evenly distribution of per capita spending across different levels, equity seems to have improved. At the same time, the external environment changed in a manner that raised the relative returns to higher education, thereby tending to make more efficient what had initially been an inefficient allocation of resources. * With respect to the public educational expenditures by income strata and region, using the unit cost per student by state and educational level, the results indicate that at national level the poorest income groups receive the bulk of primary education subsidy (federal plus state expenditures). This same group, at higher levels of education receives progressively smaller subsidies and the pattern changes across regions. In the North Region, primary education is almost neutral (benefits equally all income groups) and regressive (benefits high-income groups) for other levels of instruction. In the Central Region, primary schooling benefit the low income groups while lower secondary is almost neutral. Upper secondary and tertiary instruction benefit the richest income deciles. In the South Region, basic education benefits the bottom income groups, upper secondary is neutral and tertiary education level benefits the top income groups. In Mexico City, the cumulative distribution at all levels of education, except primary, highly benefits the high income groups. Public expenditure at the tertiary level is more regressive than the pattern of household expenditure. A large share of public resources given to this level of education tends to favor non-poor students in urban areas. A strategy to reallocate the education public expenditures from a higher to a lower level of instruction in order to favor the poor groups, would have to 46 involve the development of higher educational credit markets. Meaning that, the government's appropriate role could be to help overcome market failures in the financial sector, which limit the availability of long-term finance for investments in higher education. These failures can be corrected through student loan programs, or means-tested financial aid and scholarship programs. These programs are rarely devoid of subsidy components, but they are preferable to a direct, cost-free provision of services because the subsidy is more closely targeted to the source of market failure. (Chapter 3 has a brief discussion on this). * The total cost (student expenditure plus government subsidy) per student in primary public school corresponds to about 35% of the private primary school cost. For students in lower and upper secondary it represents 43% and 53%, respectively. On the other hand, the cost of tertiary level is 13% higher in public schools as compared to private (see tables below). An interesting question that arises is why the cost at tertiary level is higher in public than in private schools. Is it because the subsidy is not being used efficiently, or because the infrastructure (research institutes, libraries, museums, entertainment centers, etc.) they offer is costly?. In the next section a technique is applied that will allow us to evaluate the impact of public expenditures on household spending patterns. * The benefit-incidence analysis assumed that the subsidy and the quality of education are uniformly the same for all income deciles. This is a strong assumption that tends to minimize the distributional inequity within educational levels. The marginal willingness to pay analysis prevents this drawback. This methodology measures the effect of the government provision of public schools on the educational spending behavior of an average household. The results suggest the following: i) the non-poor and those in urban areas get a large share of the subsidy or "savings" from the government provision of education services. ii) The valuation for private educational services is higher for the wealthy as compared to for the poor. And, iii) quality differences are higher in primary level. In light of these results, plausible alternatives for the government include i) to better target public educational services; ii) charge a fee for public educational services to the non-poor; and iii) increase the quality of education in basic education. T The public education system can improve its targeting to the poor by increasing itsfocus on the secondary (lower and upper) levels versus university levels, especially technical education. The later is of special relevance since as shown on Chapter 1, the skills or knowledge acquired through technical education after completing lower-secondary level is a key factor in the formation of earnings. * Preliminary evidence suggests that the burden of educational expenditures on poor households is high. This finding suggests that actions aimed at increasing the participation of poor children should comprise subsidies for secondary textbooks, scholarships for transports and schools materials, to reduce the burden on other schooling costs (i.e., unforeseen expenditures). 47 Chapter 3 Educational Policy in Intermediate and Tertiary Level of Education It has been shown in previous chapters that rates of return to education for basic educational levels have not changed significantly in the last ten years. Conversely, the rates of return to education for upper secondary and university educational levels have increased dramatically. In addition, the enrollment rates both for upper secondary, and for tertiary education are too low, i.e. while primary and lower secondary educational levels show high enrollment rates (about 95 and 60 percent for primary and lower secondary respectively), high-educational levels display much lower enrollment rates (about 36 and 12 percent for upper secondary and university respectively). Moreover, household head's age, his schooling, his sector of activity, household per capita income, and government effort seem all relevant variables in explaining the probability of enrollment in upper secondary (a requirement to reach tertiary education). Hence, the interaction of educational demand factors and educational supply factors determines school enrollment. In light of the above results, a natural step is to investigate some specific educational policy aspects, which may have a significant impact on the individual's decision-making of whether to continue or not studying. This decision-making will bring a change in the skilled labor supply, and then a change in the earnings of skilled labor relative to unskilled labor. Consequently, a change in earnings distribution may occur. Finally, since there seems to be a problem with enrollment for higher educational levels, this part will examine such specific educational policy aspects particularly for upper secondary and university educational levels. This chapter addresses the following questions: What is the structure of upper secondary and tertiary education in Mexico? What is the enrollment rate for these educational levels? How do these numbers compare with other countries? What is the quality status of students that have finished lower secondary educational level and want to reach higher educational levels? What are the program options that these students have? Is this information readily available or fully disseminated? Can students from low-income families get any credit or loan to finance their higher education in Mexico? What is the international experience in this respect? Thus, this chapter is structured as follows: Section I covers the system organization, length of studies and enrollment. Section II examines the quality of education in upper secondary and tertiary instruction; it also describes the process of professional quality certificatioh, as well as procedures and criteria used to assign places. Section III examines the dissemination of study programs, the limited promotion of study programs, their contents, and curricular diversity. Section IV analyzes the available grants or financial support for upper secondary and tertiary schooling and scholarships' assignation criteria in public and private schools. Finally, Section V presents the concluding remarks. I. UPPERSECONDARYAND TERTLARYSYSTEM ORCANIZA4TIONVANDENROLLMENTRATES Upper Secondary System Organization and Enrollment Rate The age group, which typically attends upper secondary education, is from 15 to 18 years old. Upper secondary education includes General Baccalaureate, Technical Baccalaureate and Pedagogical Baccalaureate, as well as professional education not requiring upper-secondary degrees (technicians). On the other hand, while general curriculum provides studies in all areas of knowledge, technological curriculum provides, in addition to the general baccalaureate education, training as a technician. Figure 10 shows this upper secondary educational structure. 48 Figure 10. Structure of Upper Secondary and Tertiary Schooling | Univrst | ehooia Teachers' School| Technological Technologicalchoo Labor Market University I Market Tertiary Education: Bachelor Degree and Graduate General Technological Technician Baccalaureate Baccalaureate Pedagogial(Middle Upper Secondary -Bynd -Industrial, Baccalaureate Professional Education Cooperation* -Agriculture, Conalep) Conalep and Cattle *Parents and government pay the -Fishing expenses of the school (salaries, -Forester ............................................................................... ..................................................... .... Basic Education The upper-secondary is classified according to its type of service in: General or Propedeutical Technological (bivalent equal to (2 and 3 years) baccalaureate and technician) -Preparatory -Industrial -By cooperation -Agriculture and cattle -Pedagogical -Fishing -Tele Upper-Secondary -Forester -Of Art Programs and study at this level take from two to three years, depending on the program and the type of institution. Once the studies are completed a certificate is obtained. This certificate permits the student to continue into tertiary education level. At upper secondary there is a bivalent option, which allows students both to be certified as a middle professional technician and to have access to the labor market. The general upper secondary widens and consolidates lower secondary education and prepares the student to choose a professional career. As shown on figure 10, the upper secondary education level allows access to tertiary education, either the Bachelor Degree (Teacher's School, University and Technological) or Higher Technical Degree (Technological University), the technician being inserted into the labor market. 49 Table 34. Enrollment in Upper Secondary Education by type of school 1997 (Upper-Secondary and Middle Professional) Support Enrollment Percentage enrollment Federal (SEIT, SESIC) 1,015,636 39% * General Upper-secondary 20,781 0.8% * Upper-secondary by cooperation 68,441 2.6% * Preparatory 83,946 3.2% * Technical Upper-secondary 597,416 22.9% * Technician (CETIS and CBTIS) 45,073 1.7% * Technician CONALEP 197,906 7.6% * Technician (Others) 2,073 0.08% State 703,515 27% Autonomous (University) 374,201 14.3% Private 512,747 19.7% Total 2,606,099 100% Source: SEP, "Compendio Estadistico por Entidad Federativa 1997", DGPPP. Upper secondary level is covered by federal, state, autonomous and private educational institutions. Table 34 shows that public schools covered 80.3 percent of enrollment in 1997. On the other hand, it is important to note that the general upper-secondary education is administered under the care of the Under Ministry for Tertiary Education and Scientific Research (SESIC), while the technical upper secondary education is administered by the Under Ministry for Technological Education and Research (SEIT).50 With respect to the student body, figure 11 shows the enrollment rate for- the upper secondary level from various sources.31 As it can be seen all estimates are between 32 and 40 percent. Additionally, both ENIGH and SEP estimates present the same tendency: an increase in the enrollment rate from 1994 to 1996. Although there has been an improvement on this issue, one has to put this in an international context. Figure 11 Net Enrollment Rate in Upper Secondary by various sources 50- 45- 38.4 42.2 40 38 3694 3 35- 32.2 * ;30 -I 25- ~20 * 15 10 . 5 ,, 0 Estimates based on SEP estimates" OCDE esiats ENIGH* ___ __ 011994 El9976 Se=. wne Ot eswiniw based on, ENiGH suvey. 11infom,a de Labours 1997.1998, SEP -Analisis del panomm edurakso, OCDE, this figure cotrespend to 1995 'o Autonomous and State Universities also offer the service. 51 In the ENIGH estimates the age group is 15 to 17 years, while in the SEP estimates is 16 to 18. OCDE estimate includes only the group of 17 years old. 50 As it is shown on figure 12, while the highest income countries have an enrollment rate of 75 percent or higher, Mexico is rated as being last in place, just before Turkey. These results point out to an educational deficit in the production of potential graduate students. Figure 12 Net Enrollment Rate in upper Secondarv at 17 Years Old Tu rke y Portugal United Kingdom Spain N ew Zer land . ^ iJi-__________?^_iR2Pr 1 @ 2 0lPercent| A actnm a r ___k __w. ___ . _._;_-_^_-_i_ ^_ -: _ i j A ustria -73 ¢ts r§ ;sb SotkkBib t sE~s Norway ,, ,>*=+,,L , ,* ; <~ , , Finland 3__ _ _ ____ __ _ __ _ 2 ______.' Australia -f;Yi>S;+m.4.rd;ti%vMi E, .JO_>;..z,S+ . S.cdcn S <..>.|>.e.--N 0 20 40 60 80 100 . Source: Analisis del Panorama Educatiovo 1997, OCDE According to the Informe de Labores (1997) from Sep, in 1997 1.25 million of students graduated from lower secondary education and 1.18 million registered in upper secondary for the school cycle 97-98. Thus, 94.4 percent of the graduates from lower secondary education continued to upper secondary level. However, this conclusion is unwarranted since table 27 in chapter 2 shows that the probability of enrollment to upper-secondary level is about 66 percent at national level and only 39 percent in rural areas52 (for more details see background paper 4, section 4). Tertiary Educational System Organization and Enrollment Rate Tertiary education is accessible after upper-secondary or equivalent studies. Instruction is provided by universities, (autonomous, state or technological); technological institutions (federal or state), teachers' schools (teacher's training) including studies at bachelor degree level, graduate (specialty, master degree and doctorate) terminal options before the bachelor degree (higher technical, specialty) and normal education in all its levels and specialties. Figure 13 shows the tertiary educational structure. 52 The probability being modeled is enrollment in upper secondary school for individuals age 15 to 19 and conditional to lower secondary completed. 51 Figure 13. TertiaryvEducational Structure Labor Market ~~~~~~~~~~~~~~~~~~~~~~~.........................."T'''''''; Graduate Doctorate Master Degree - Specialization H ig h er 1. .................. .. ...................... ................ ................. ........................... .................... .................... ............... ............................. E ducation ..... ..................... .......................................... .................................. Normal Bachelor University Technologicai - Pedagogical T l Bachelor .. University.Tehogia University ~~~~University Degree -Autonomous -Normal School (Higher Universities -T~~~~~echnological Public and State Federal, Prvat Technician) -Private State *(reacher sTraining school) F _ Upper Secondary Education 7 The age group attending tertiary education is from 19 to 24 years old. This educational service is administrated by SEIT for Federal Technological and State Institutes. Autonomous, State, Technological and Private Universities also offer tertiary education services. According to the "Informe de Labores 1997-1998", 26.5 percent of enrollment, equivalent to 458.6 thousand students, attended private institutions (see table 35). The remaining, 1.26 million, were distributed in public institutions as follows: Table 35. Public Enrolment in Tertiary Education 1997-1998 Normal, Bachelor and Graduate Institution 97-98 Autonomous Universities*. 799,600 Technological Institutes 196,700 Technological Universities 11,800 Normnal School 145,800 Other tertiary education institutions 115,000 Total Public 1,268,900 Source: SEP "Informe de Labores 1997-1998" *Includes Universities of Guadalajara, Guanajuato and Veracruz With regards to enrollment rate, this educational level has displayed a very low performance, i.e. given that the national population for this age group was 11.51 million (CONAPO) and the enrollment of tertiary education for this same age group was 1.168 million of 53 students in 1997, consequently the enrollment rate just reached the 10.1 percent in that year53. Alternatively, the Mexican position in relation to other countries in this educational level is not much better. Figure 14 shows that the enrollment rate in the highest income countries is 2 or 3 times Mexico's enrollment rate for tertiary education. Moreover, since the Mexican economy is ranked as a middle-income country and the average enrollment rates for tertiary education in 53 This percentage is lower than the one obtained in section 11 of chapter 2 because the age group considered was those in the 18-24 age range. 52 OCDE countries and in middle-income countries are 51% and 21%, respectively54, we can conclude that the service coverage in Mexico in tertiary education has shown a very low performance. Figure 14 Net Enrollment Rate in u iversitv at 20 Years Old I_._. Turrey Mexico Hungary , Austfia Ps g _l< Sa_o _ss Sweden - SwiGzeoand = , U . S .A .s;_.,......... .- 9b W+7 5Ar O/I Greece . . . . .. Komea Unkied Kingdom . I Finland _.g_=_____ New Zealod - ________ _ Portugal - i Noreay ___________, .... ,,..... ; .; s E Ig i , '_ _>a Germany + .____ _......* Austraia ,. - _' > CE i a a s 7 K Spain - -6~---_ -= -;I FranceL. - _ - Canada 7 - -. . 0 10 20 30 40 s0 60 Source: Analisis del Panorama Educaovo, OCOE II. THE QUALITY OF EDUCATiON AND ACCESS TO UPPER SECONDARY AND TERTIARY EDUCAT'IONAL LEVELS The quality of education might play an important role in earnings distribution because of several reasons. One of them is the quality level. in basic educational services, which might facilitate the entrance into higher educational levels. This factor would perform negatively if there were significant differences in the quality, of education among schools and generations. Hence, these differences could become an institutional restriction in order to enter higher educational levels, thus having an effect on earnings distribution in the medium and the long run. Another reason is that the differences in the quality of education in upper secondary and tertiary education may emerge at the end in the labor market, both in the medium and in the long run. In other words, the higher the differences in quality of education, the greater the differences in productivity among individuals, and, therefore, a change in the earnings distribution may occur". In order to evaluate the quality of educational service in upper secondary and tertiary levels, some indirect indicators can be used. Table 36 shows such indicators, which are failing, desertion, terminal efficiency (number of students finishing upper-secondary in the time required), student/group and student/teacher indexes, which give an idea of the educational quality in upper-secondary.56 54 The World Bank Group (1995). 5 For a review of how public policy can affect the household decision-making regarding the quality of educational service differences, see section IV in Chapter 2. 56 The COSNET, in a survey carried out in 1995 with 50 Technological Institutes, found some relevant problems associated with the quality of education. The most important are: (i) failing, dropouts and low terminal efficiency; (ii) poor teachers' education; and, (iii) low academic level of newly registered students. 53 Table 36. Failing, Desertion and Terminal Efficiency in Upper Secondary 90-97 School Cycles 90-91 91-92 92-93 93-94 94-95 95-96 96-97 e Total Enrollment (Thousands) 1,721.6 1,725.2 1,767.0 1,837.6 1,936.3 2,050.6 2,157.857 Absorption Percentage of Lower Secondary 61.0 62.2 63.7 65.8 71.1 74.1 72.3 School Graduates Failing 47.6 43.5 46.6 44.5 44 44 44 Terminal Efficiency (Traditional method) 57.0 57.0 57.4 57.1 57.2 56.7 57.1 Dropouts 16.4 17.4 15.7 14.6 16.6 16.6 16.8 Student/Teacher 15.7 15.3 15.3 15.2 15.0 14.8 14.9 Student/Group 39.4 38.0 37.7 37.1 37.1 36.9 36.9 e Estimated Source: SEP "Inforne de Labores 1996" One of the aspects that may have a positive or negative effect on educational quality is the number of students per group as well as per teacher. Regarding this, for the 1996-97 school cycle, the average number of students per group was 37 and the average number of students per teacher was 15. These could be considered as acceptable, according to the maximum number of students recommended by the SEP (40 and 28 respectively). However, other indicators have a poor performance: failing is high, 44 percent, compared with failing in lower secondary that is 23 percent; the dropout rate is 16.8 percent and the terminal efficiency, 57.1 percent, is low. All these indexes may be a sign of the low background level of students when they enroll into this level of education and/or a doubtable educational quality from the institution. For tertiary education, the SEP has only statistical data regarding dropout rates, which are shown in table 37. As it can be seen in such table dropout rates do not show a clear trend (i.e. increase or decrease). However, if we compare 1995-1996 with respect to 1997-1998 there is a clear increase in the dropout rate for all tertiary educational levels. Table 37. Dropout in Tertiary Education School Teachers University Tertiary Cycle School And Technological Graduate (All levels) 1995-1996 8.2% 4.3% N.A.* 4.7% 1996-1997 3.8% 7.5% 5.0% 7.1% 1997-1998 4.8% 7.1% 8.5% 6.8% Source: DGPPP 1997 *N.A.= Not available Another way to evaluate the quality of education is through some direct outcomes, for instance, exams in order to assess the students performance. Regarding this, the only information available are the results of EXANI-I exam, which are generated by The National Evaluation Center for Tertiary Education A.C. (CENEVAL). This exam is a requirement for enrollment into upper secondary education. It is applied in the Metropolitan Area of Mexico City and in 157 institutions through out the country (except for SEIT institutions). The criteria for assigning a place are to have a general average minimum proficiency (6 or 7 out of a 10 scale) and to answer correctly at least 31 out of 128 questions on the EXANI-I examination. The EXANI-I is structured as follows: 24 for verbal skill, 24 for mathematical knowledge and 10 for each area of learning in Spanish, History, Geography, Civism, Mathematics, Physics, Chemistry and Biology. The Council of the National System of Technological Education (COSNET) applies other tests in the SEIT schools to measure students' formal reasoning and the ability to learn mathematics. In addition,. each institution designs its own proficiency examination. The "technological" area applies as criterion 7 points in the learning examination (in a 0 to 10 scale) and a minimum 18 correct answers out of 32 in the over-all knowledge examination and 12 correct answers out of 24 in the test to assess capacity for learning mathematics. SEP refers in the 57 In the ENIGH 96 there were 2,767.9 thousands enrollments in upper secondary. 54 "Informe de Labores 1997-1998" that 234,925 students took the examination. Of them 3,231 (1.3 percent) were rejected, not having the knowledge and capacities requested, by the institutions, to enter upper secondary education. Table 38 shows the results of these examinations for the 1992- 1993 and 1993-1994 cycles. Table 38. The COSNET Examination Results Minimum DGETI" DGETA" UECyTM" Total Average Examination Required 92-93 93-94 92-93 93-94 92-93 93-94 92-93 93-94 cycle cycle cycle cycle cycle cycle cycle cycle Capacity for formal 56.20% 33.06%o 36.25% 31.50% 31.25% 33.00% 31.25% 32.21% 34.37% reasoning Capacity for 50.000/o 47.00%'o 39.58% 36.90% 33.330/o 44.00% 37.50% 45.70% 37.50% mathematical learning Learning examination 7.00 5.17 n.a. 4.86 n.a. 4.70 n.a. 5.03 n.a. General Direction ol Technological Industrial Education I DGETI): General Direction of Technological Agriculture and Cattle Education (DGETA); Educational Unit of Science and Technology of the Sea (UECyTN). In the learning results of 92-93 and 93-94 school cycles, table 38 indicates that on average the candidates admitted did not obtain a 7 in a 0 to 10 scale. Despite this just 11.5% and 10.9% of the students examined were rejected58 The CENEVAL applies the EXANI-Il at national level (including the Metropolitan Area of Mexico City) as an examination for admission to tertiary education. Tertiary institutions design their own examinations including 120 questions, as per the menu offered by CENEVAL, starting with a common module which includes questions comprising verbal and mathematical reasoning; contemporary world knowledge, natural science and humanities, mathematics and Spanish. This is followed by specific modules covering knowledge of mathematics, biology and health sciences, physics, chemistry, Mexico's geography and history, humanities, literature and advanced Spanish, law, administration, social sciences and English. According to data from the 1997-1998 CENEVAL report, the examination was applied to 126,124 candidates in 94 institutions. There have not been any public statistical data on the results of this level, therefore the academic criteria of acceptance into these 94 institutions are not known. The CENEVAL promotes and applies a "General Examination of Professional Quality" (EGCP) at tertiary education institutions, but few have decided to incorporate this examination to their professional degree requisites. In summary, there are two factors that do not permit the institutions to retain their students. The first one refers to the low academic level of pupils entering the upper secondary education (as shown by the COSNET examination). And secondly, the poor educational service (terminal efficiency of 57.1 percent) of these institutions compared to basic education. Access to Upper Secondary Level At present there is no official information on the total places available to candidates for upper secondary educational institutions. This allows institutions to accept a different number of candidates in every registration period. The available places or each institution's capacity is linked to the teacher/student ratio, the resources available for operating workshops and laboratories, and the number of school teachers, among other factors. Based on the dimensions of classrooms, the SEP's recommendation is to have a minimum of 40 teacher/student ratio and the authorized budget depends on the GDP of the country and the appropriations that the congress authorizes for education. As in upper secondary education, in tertiary education there is a set of norms for place assignation. These norms request some knowledge and skills from the precedent 58 The number of candidates who took the examination were 246,316 and 250,254, in 1992-93 and 1993-94 cycles respectively from which 2 18,078 and 222,927 were accepted in each cycle. 55 educational level and a certificate attesting previous termination of an educational level of studies. The admission tests by area of learning are announced through a poorly informative campaign, which basically states the names and careers. It is important to know that, as in the upper secondary level, the total available capacity for each career in Tertiary institutions is not known. This phenomenon is aggravated by the fact that some tertiary institutions also provide upper secondary education and allow their graduates to enter the tertiary level through an automatic or regulated pass.59 For example, in the Metropolitan Area of Mexico City, the places available in the UNAM, the National Polytechnics Institute (IPN) and the Metropolitan Autonomous University (UAM), are usually never officially stated and this might allow for discretional action in the selection process and place assignation. Access to Tertiary Educational Level About the procedures for new admissions, the tertiary educational level faces the same conditions and situations as upper secondary educational level. An example of this is the UTNAM's note published in the newspaper "Reforma" on Sunday May 16, 1999. This note informed the pre-requisites, dates, career space availability, campuses, and the places assigned in the previous selection examination (June 1998). Once this has been obtained, they use it as reference in order to select the career and school where they want to attend. Ill. DISSEMINATION OF THE AVAILABLE STUDY PROGRAMS AND DIVERSITY IN THE CURRICULA The student's decision-making of what career she or he wants to or should study is clearly influenced by several factors as tastes, abilities, family background, information available, etc. Some of these factors are intrinsic to each particular student, and others can be used as policy tools in order to advice students of the best study option to take. In this regards, information available plays an important role on school completion, since it allows students to make their own choice of study compatible with their particular interests and available study opportunities. Regarding this, outside the Metropolitan Area of Mexico City the promotion of programs for upper secondary level and their contents is very limited. In Mexico City, the COMIPES attempts to identify those professional careers, which can be studied in the upper secondary education, admission pre-requisite, support to vocational orientation through visits to the specific educational institutions. In the rest of the country, local newspapers publish information regarding public and private institutions their dates of test, kinds of upper secondary education and pre- requisites required. This information is the only one available for students and parents regarding upper secondary educational options. Upper secondary education schools provide vocational orientation services. In some instances, at the students request, a study is carried out in order to assess their capabilities and skills. This might help those students who have problems with selecting a professional career. Most tertiary educational institutions do not promote themselves and they assume that the academic prestige will entice applicants. Generally, private institutions advertise their services, and highlight the advantages of studying with them. However, these campaigns usually give very little information about their study program contents. In tertiary educational level, there has not been any effort made to promote study programs nor have their contents been accurate, nation wide, because tertiary education schools conduct 59 Annex 7 indicates the criteria for the assignation of places. 56 very limited upper secondary school campaigns on careers offered, academic contents and job areas become accessible at the end of their careers. For upper secondary education level, there is a great diversity in the curricula; 300 programs according to the Organization for Economic Cooperation and Development (OECD) in the National Policies Report published in 1997. Student options are further complicated by the five administrative and normative areas (SEIT, SESIC, States, Autonomous Universities and State Universities), which not only make it more difficult for the potential candidate to choose an adequate option in accordance with his/her abilities but also complicates the process of eventual -attendance due to the problems regarding the revalidation of studies. As shown on table 39, general upper secondary differs from bivalent technological upper secondary schools mainly by the number of educational work hours (theory and practices in workshops, laboratories, companies) leaving out the number of theoretical study hours, which makes them having more education instructional in common. Table 39. Hours of Education for Work and Study Hours of Theory and Practice in Hours of Theoretical Institution Workshops and/or Companies Study Colegio de Bachilleres 6 in 3' and 4 . Semester 27 average (Upper-secondary College) 10 in 5th and 6'h Semester Conalep 17 average in 1' Semester up to 70% class 33 average hours weekly Centros de Estudios de Bachillerato 14 in 5h and 6" Semester 26 to 29 (Centers of Baccalaurate's studies) CBTA 11 average 23 average CBTIS Y CETIS (Upper-secondary) 11 average 23 average CBTIS Y CETIS (Technical) 24 average 10 average CECYT (IPN) 13 27 Colegio de Bachilleres (Upper-secondary 14. 17 a 20 College-State of Mexico) CECYT (State of Mexico) 15 average 21 average Centros de Bachillerato Tecnol6gico 13 a 14 26 a 27 Technological Upper-secondary Centers- State of Mexico) Enfermeria (Nurse Training School) (Technical 32 12 UNAM) Preparatory School (UNAM) 30 Colegio de Ciencias y Humanidades (College of 28 Sciences and Humanities)(UNAM) Preparatoria, Universidad Aut6noma del Estado 37 de Mexico (Preparatory from the Autonomous University of the State of Mexico)(UAEM) Preparatorias Oficiales.y Anexas a las Normales 36 a 38 (Official Preparatories and Attached to the Teaching Schools) (State of Mexico). Source: COMIPEMS 1998. CONALEP; CBTA; CBTIS; CETIS; CECYT. The SEIT and the IPN in the Metropolitan Area of Mexico City offer 125 specialties, from which 70 have a technical level and 55 are of bivalent upper-secondary. At national level the supply by institution is: 57 Table 40. Number of Specialties by Institution Institution Number of Type of Studies Specialties CONALEP 29 Technical Professional DGETI (CETIS, CBTIS) 42 Technical Professional 12 Bivalent Upper-secondary CETI. Techno-Industrial Teaching Center (Centro de Ensefianza 12 Bivalent Upper-secondary Tecnico Industrial) UECYTM (CETMAR and CETAC) 5 Technical Professional DGETA (CBTA, CBTF) 18 Bivalent Upper-secondary CECyTE'S. Scientific and Technological Studies' Center in the 48 Bivalent Upper-secondary States (Centros de Estudios Cientificos y Tecnol6gicos en los Estados) Source: COSNET 1997. In this broad spectrum, we have to include the existing upper secondary two year-programs in Coahuila, Nuevo Le6n, San Luis Potosi and Tamaulipas, provided by the Autonomous Universities of those states, in which service to 94,627 students is given (4%) of a national total 2,323,069 in 1997. There is great curricular diversity for the upper secondary levels both of technical and general type. Should be important to compare the contents and programs in order to outline the differences and find the common points to assess the curricula diversity optimun. The tertiary educational institutions also have a wide curricular diversity and offer 323 specialties or professional careers. The great majority are graduate and engineering studies grouped in 6 learning areas. Table 41. Nuimber of Specialties by Learning Areas Learning Areas Number of Specialties Percentage of the Enrolment Agriculture & Cattle 45 specialties 3% Of Health 25 specialties 9% Exact and Natural 23 specialties 2% Social and Administrative 81 specialties 51% Education and Humanities 46 specialties 3% Engineering and Technology 103 specialties 32% TOTAL 323 Specialties 100% Source: Anuario Estadistico 1997, ANUIES. In 1997 the social and administrative sciences area had the higher demand (33.9%) as shown in annex 7. Public schools covered 76% of the demand, while private schools covered 24% of it. Out of 76%, 56% of the students were enrolled in universities, 18.2% in technological institutes of the SEP and 1.8% in other public schools (ANUIES 1997). - In tertiary education, each specialty requests an upper-secondary instruction oriented towards that area of learning and the great diversity of careers presents a wide range of pre- requisites for the bachelor's degree. Such situation might create a problem for the student who wishes to change his field and may need to reassign his/her upper secondary studies. In other words, a series of revalidation procedures and acknowledgment of studies could force the student to start again. On the other hand, curricula diversity could enrich student's decision and allow him/her to insert in the job market more easily. IV. CREDITAND FINANCING EDUCATION This section reviews scholarship coverage at upper secondary and university and examines whether these scholarships are targeted to students with academic abilities, but with low 58 family income. In particular, this section briefly examines the financial support for supplying educational services to higher education as well as financial assistance to individuals who are in higher education. We examine government and other institutions' financial assistance to individuals, i.e. characteristics and allocation of public and private scholarships in upper secondary and tertiary instruction level. IV. SUPPL Y-SIDE FINANCING OF HIGHER EDUCA TION SER VICES In most countries, public institutions are still providing the major percentage of total tertiary education supply (World Bank 1995). In Mexico, public institutions provide most of the supply to higher education, 80.3 and 73.5 percent in upper secondary and tertiary education,. respectively in 199760. Thus, the upper secondary and tertiary educational supply depends primarily on public financing. Because the enrollment rates for upper secondary and tertiary education are low, the apparent public institutions' efforts along with their high relative participation in the educational supply services do not seem to be enough. This fault can be partially explained by the fact that Mexico, as almost all less developed countries (LDC's), have reduced their financial support to educational services within higher education, since the country has had to face its economic crisis by reducing its fiscal expenditures, among other economic policy tools. - In other countries, the previous phenomenon has lead upper secondary, tertiary schools to find other financial alternatives. For example, external transference (from ex-alumni or other private agents) and some generated income activities, such as short courses; technical assistance to private firms, etc. (World Bank 1995). Another way to solve the lack of financial support to supply educational services at higher educational levels is to allow and promote the private institutions participation in this market. One advantage of private institution participation is due to the fact that these institutions are more flexible regarding changes in the demand. However, as it will be analyzed in the next section (from the demand side), there are economic justifications not only in the government's financial assistance participation for higher educational services, but also to increase this participation. This is basically due to the social benefits generated by higher education (basic research, technology development, etc.) and by the inherent imperfections in capital markets. IV2 DEMAND-SIDE FINANCING OFHIGHER EDUCA TIONSER VICES On the demand-side for higher education, capital market imperfections limit individual form getting loans to finance their education, specially those individuals with middle or low income. We can address two types of problems in order to find some answers to the question of why the financial private sector might not provide educational financing. On the one hand, if there were no imperfect-information then individuals with high abilities and academically qualified would be able to get loans in order to finance their education, due to their high rate of return. However, imperfect-information like adverse selection and moral hazard bring credit constraints on financing education. On the other hand, if the financial sector chose the second best option (i.e. signing contracts) then the financial sector should be working on an efficient and transparent manner in order to be able to finance any kind of investment on education. In Mexico, the private financial sector seems to have both problems. The first one is inherent to a general point of view of imperfect-information models. The second one is the most 60 Informe de Labores 1997-1998, SEP. 59 pernicious. The country's financial sector has suffered several institutional changes (nationalization in 1982 and privatization in 1991-92). Additionally, the financial sector faced a full-fledged crisis in 1995, which led to a domestic credit crunch. Given this scenario, the private financial sector has not been able to finance any educational investment (human capital), and will probably not do in the near future. Thus, since the Mexican private sector is not able to finance higher education, public institutions should play a central role in this issue. Hence, in order to finance higher education for needy students, some government financial assistancm programs have to be designed. Some alternatives are:' (i) Fixed Repayment Loan Scheme. Covering tuition or student living expenses, this loan will be repaid through subsequent earnings after graduation; (ii) Income-Contingent Loans. The repayment of these loans will be fixed proportions of a graduate's annual income; and, iii) Grants and Work-Study Schemes. In this scheme the public institution guarantee access to academically qualified low-income students (The World Bank, 1995) Experiences, up to this date, with existing loan schemes in some fifty industrial and developing countries have been disappointing, i.e. in some cases the programs either have had poor financial performance or are quite small in scale. Despite the poor performance of many other loan programs, the experience of the Colombian and Canadian province of Quebec programs show that it is possible to design and manage financially sustainable programs. The Success of the Colombian program resides in its decentralized structure (there are twenty-one regional offices, each one manages its own portfolio, appoints its own staff, allocates its own budget, and develops a regional student loan trust fund (The World Bank, 1995). In Mexico, government, through the Ministry of Education (SEP), and some private educational institutions provide financial assistance for upper secondary and tertiary students. With regards to upper secondary, financing is achieved through scholarships which cover 100% registration fees (private schools) scholarships and financial support, from the Ministry of Education in order to reduce drop-outs and failing grades. Table 42. Public Scholarships Granted 1997-1998 L Level Scholarships Granted Monthly Amount (pesos) Upper Secondary 49,831 $ 270.90 Tertiary 17,616 $ 325.10 Source: Direcci6n General de Acreditaci6n, Incorporaci6n y Revalidaci6n (DGAIR) SEP, 1997. In 1997 public institutions, as per Table 42, granted 49,831 scholarships to upper secondary education students, which represents 4.19% of the total enrollment. This was done to reduce the drop out and failing grade indexes. In each public school, a scholarship committee or technical board (teachers and director) selects scholarship holders in accordance with *an application stating the general proficiency average of 8 or 8.5. In addition, it is requested that the student get a socioeconomic study made and not have a brother or sister hold a scholarship granted by the SEP (one scholarship per family). Financial aid is paid twice a year and its monthly installment is 270.90 pesos (230.2 1996 pesos). From chapter 2, fees, tuition and unforeseen expenses paid by an average individual enrolled in public upper secondary instruction level represent 38.3% of the public scholarships granted in upper secondary level and 32% for a moderately poor individual. Textbooks, stationery, etc, represent 25.2% of the public scholarship grant. This in turn suggests that SEP could lower the amount given for scholarships and target them to the poor. Thus, increase the number of students eligible for scholarships based on academic achievement regardless of parenthood. In 1997, private upper-secondary schools covered 512,747 students. According to a circular issued May 24, 1992 by the Under-Ministry of Educational Coordination, scholarships 60 had to be, granted to a minimum 5% of this enrollment. The. scholarships covered .100% registration and tuition fees.6 Private schools can increase the number of scholarships by reducing the percentage of expenses covered (if the scholarship is granted to 10% of the enrollment the scholarship covers 50% of expenses).. This circular states that a scholarship committee, formed by directives and parents in private schools, selects scholarship holders in accordance with an application stating, the general proficiency average; 8 as minimum and a socioeconomic study providing the family's low income.62 Students receiving a scholarship, according to a circular of May 24, 1992, should not pay tuition. But in some cases they do some work to maintain their general proficiency averages of 8 as a minimum, and in addition not fail any subject. With regards to tertiary education, the Ministry of Education granted 17,616 scholarships in 1997 in order to keep students in institutions and reduce the failing subject indexes (see table 42). The monthly installment was 325.10 pesos. From chapter 2; fees, tuition and unforeseen expenditures in tertiary school level represent 64.6% of the public scholarship grant in tertiary level (276.2 pesos of 1996) for an average individual and 24.7% for a moderately poor individual. textbooks, stationery, etc, represent 21% of the public scholarship grant. Public and private institutions follow the same mechanisms to grant scholarships as mentioned above. When.the percentage of academic scholarships does not cover 100% of the registration costs or tuition fees, private educational institutions grant their students credit. These 63 students pay after having finished their studies. In parallel to the general financing support granted in upper secondary and tertiary schooling levels, the government established a demand-side higher education financing project in Mexico. This financial assistance program was begun in 1998 and its specific goals are i) improving access to higher education for qualified but financially needy students, and ii) developing a more effective and financially sustainable student loan institution. This program is currently being applied in the State of Sonora, Mexico. The student loan will be executed by two agencies, Sonora Student Loan Institute (ICEES) and The Society for the Promotion of Higher Education (SOFES). SOFES will coordinate the development of the private sector student loan scheme component and ICEES will coordinate the component aimed at strengthening the Sonora student loan scheme. It is expected that at the end of the project SOFES will have provided loans to a minimum of 26,600 students, of which approximately 70% would come formt middle-low to low income families, while ICEES would be providing credits to 21,000 students by the year 2002. Component 1, which is administrated by SOFES, would be aimed at improving access to private higher education students who are academically qualified but whose financial conditions limit their opportunities for higher education: In the first phase of the program, the loans would only be used to cover registration, insurance and tuition fees. In the second phase, books and supplies, living allowances, and transportation would also be covered by the loan. The repayment term for graduating students would be double the study period for which the loan was taken, in addition to a six-month grace period. 61 Beforesaid, average individuals paid, 624.60 monthly pesos (1996=100), for fees and tuition in a private upper secondary. 62 In private schools, there are also sport scholarships granted to students with an overly.performance level in representative sports organized by the institutions. Some service clubs or foundations and enterprises, also grant scholarships as a service to the community or benefits to their employees. 63 Insurance companies offer financing service for tertiary education. This service works as a combination of life insurance and academic financing insurance for the beneficiary upon covering the quotas. These costs make up the sum of a trust, which delivers annual amounts established upon signing the contract. .61 In component 2 which is administrated by ICEES the beneficiaries would be academically qualified, low- and middle-income students born or residents of the State of Sonora who were to enroll in a recognized public higher education institution. Each individual contract will cover a maximum study period of 12 months. In the targeting strategy, both SOFES and ICEES are using the same methodology for classifying students by socioeconomic background. Two remarks can be derived from the previous description of the financing assistance programs in Mexico. First, the financial support provided by the SEP and private education institutions faced two problems: centralized regulation and small coverage. Secondly, given that the successful of the Colombian financing assistance program resides in its decentralization and that the Sonora program seems to have the same nature, it is plausible to believe in its feasibility both in the short and long run terms. Then, it would be important to assess the status of the current Sonora Program in order to compare and take into account the characteristics of successful programs. 62 V. CONCLUDING REMARKS The above analysis gives us a general view of the situation that upper secondary and tertiary education presents in Mexico, from which the following conclusions can be derived. * Coverage and Quality in Upper-secondary and Tertiary education. Chapters 2 and 3 showed that enrollment rates for upper secondary and tertiary levels are extremely low, particularly for the poor. Moreover, Mexico's performance on this issue has been very low in an international context. Regarding educational quality services and according to its indirect and direct indicators, academic results are low and students have poor learning abilities. The funds assigned to improve the quality of tertiary education are insufficient with respect to the needs. In addition, there is a need for new rules to be agreed upon by the tertiary institutions and the SEP, in order to assign financial resources to ensure effective use of the money and provide information on academic achievements. * Information dissemination and curricula. The student's decision-making of what career she or he wants to or should study is clearly influenced by several factors as tastes, abilities, family background, information available, etc. Some of these factors are intrinsic to each particular student, and others can be used as policy tools in order to advice students of the best study option to take. In this regards, information available plays an important role on school completion, since it allows students to make their own choice of study compatible with their particular interests and available study opportunities. Thus, insufficient effort from the part of educational institutions and the lack of information could not permit students to take their best option. Regarding the diversity on curricula, revalidation and lessening the numbers of specialties at secondary and tertiary levels should focus on contents of subjects rather than only on the course. This is so because many of the differences could be artificial, which impedes the transition from one program to another instead of providing mobility throughout the fields. On the other hand, curricula diversity could enhance student's vocational aptitudes. Thus, it becomes extremely important to assess empirically the net impact of curricula diversity on education attainment and transition. * Scholarships and grants. In most countries, public institutions are still providing the major percentage of total tertiary education supply. Mexico is not the exception. However, both the public institutions' effort and their high relative participation in the supply do not seem to be enough. This phenomenon has lead upper secondary and tertiary schools in other countries to find some financial alternatives. Another way to solve the lack of financial support to provide educational services to higher education is to allow and promote the private institutions' participation in this market. One advantage of private institutions' participation is that these institutions are more flexible regarding changes in the demand. However, there are economic justifications not only in the government financial assistance participation for higher educational services, but also to increase this participation. This is basically due to the social benefits generated by higher education (basic research, technology development, etc.) and by the inherent imperfections in capital markets. Since the Mexican private sector is not able to finance higher education, public institutions should play a central role in providing financial assistance support to needy students. Hence, it would be convenient, in principle, that the government continues providing financial assistance to those students academically qualified but whose financial conditions limit their opportunities for higher education. However, this should be done in a different way. In other words, public funds should be reallocated in order to create a decentralized structure that would provide that kind of financial assistance, i.e. regional public agencies that manage their own resources. It might be preferable that the loan program be channeled through existing commercial credit institutions to ensure loan recovery, increase credibility and maintain cost-effectiveness. 63 * The analysis suggests that SEP could lower the amount of financing given per scholarship and target it to the poor or increase cov,erage. By doing so it would be possible to increase the number of students receiving scholarships based on academic achievement and economic background. In addition, it would be important to investigate the nature of the unforeseen expenditures paid by households with children enrolled in public schools. * In addition to the problems outlined in this section, there are union problems that affect the quality of educational service. Upper secondary and tertiary education institutions face union * pressures that make it difficult to impart service on time, thus affecting quality. All upper secondary education and tertiary educational institutions have a union of professors and administrative workers affiliated to the "Sindicato Nacional de Trabajadores de la Educaci6n" (National Union of Education Workers) or are Independent (Autonomous or State Universities). Their activities at bargaining better working conditions (better wages, positions and reduction of hours) cause interruptions in the academic life of these institutions. Unions use strikes and suspension of work as a way of pressuring to achieve their goals which is reflected in the student academic performance. Most of the tertiary education institutions also have affiliated upper secondary schools. Out of 36 institutions, there are 31 affiliated to ANUIES and are subject to having student conflicts at the upper secondary. This linkage affects academic life at the tertiary level since it can cause a strike as a way of pressuring to obtain their aims (the problem at the UNAM for increasing fees is an example). * Finally, the results found, which have been described through out the two previous chapters as well as this one, provide theoretical support to the objectives in the WBG's assistance strategy (CAS). These objectives are: i) Development of basic education, and increased access to these programs for the poor, which will continue to be the central element of the WBG's sector strategy for the next three years. The objective is to achieve major improvements in equity, service quality, and institutional capacity for efficient delivery. Special attention will be given to assisting the states, under their newly decentralized educational responsibilities. ii) Support to secondary education will focus on increased educational attainment, to gradually bring Mexico to the level of other OECD countries. iii) In higher education, the WBG will continue to implement a market-based program of student loans to improve access to higher education, particularly for academically qualified but financially needy students, and to develop more effective, financially viable student loan institutions. iv) Finally, the WBG assistance will seek to improve worker's opportunities in the labor market, primarily through the development of a more demand-driven, financially sustainable vocational training system with stronger links to industry and increased private sector participation, within the framework of a national system of labor competency norms and certification. 64 ANNEX 1. DATA SOURCES The National Household Income and Expenditure Survey (ENIGH) and the National Urban Employment Survey (ENEU) were used in this study. 1.1 ENIGH The National Household Income and Expenditures Survey is collected by the Instituto Nacional de Estadistica, Geografia e Informatica (INEGI). This survey is available for 1984, 1989, 1992, 1994 and 199664. Each survey is representative at the national level, urban area and rural area. For 1996. the ENIGH is also representative for the states of Mexico, Campeche, Coahuila, Guanajuato, Hidalgo, Jalisco, Oaxaca and Tabasco. For each year the survey design was stratified, multistage and clustered. The final sampling unit is the household and all the members within the household were interviewed. In each stage, the selection probability was proportional to the size of the sampling unit. Then, it is necessary to use the weighs65 in order to get suitable estimators. The table below shows the sample size for each year. Table 1. Sample Size by Year Year Numberof Number of households persons 1984 4,735 23,756 1989 11,531 56,727 1992 10,530 50,378 1994 12,815 59,835 1996 14,042 64,359 The available information can be grouped into three categories: * Income and consumption: the survey has monetary, no monetary and financial items. * Individual characteristics: social and demographic, i.e., age, schooling attendance, level of schooling, position at work, sector, etc. * Household characteristics. Category Selection For the purpose of the analysis, the individuals in the sample were classified according to their educational level, position in occupation, sector of activity and geographical region in the following categories: 64 The sample in a given year is independent from another. 65 The weights should be calculated according to the survey design and corresponds to the inverse of the probability inclusion. 65 a) Educational level i) Primary incomplete: no education and primary incomplete (one to five years of primary) ii) Primary complete: primary complete and secondary incomplete (one or two years) iii) Secondary complete: secondary complete and preparatory incomplete (one or two years) iv) Preparatory complete: preparatory complete and university incomplete v) University complete: university complete (with degree) and postgraduate studies b) Position in occupation i) Worker or employee ii) Employer iii) Self employed c) Sector of activity i) Agriculture ii) Manufacturing iii) Construction iv) Commerce v) Services vi) Other (utilities, extraction, transports, financial services, communications, etc) d) Geographical regions i) North: Baja California, Baja California Sur, Coahuila, Chihuahua, Durango, Nuevo Leon, Sinaloa, Sonora, Tamaulipas and Zacatecas ii) Center: Aguascalientes, Colima, Guanajuato, Hidalgo, Jalisco, Mexico, Michoacan, Morelos, Nayarit, Puebla, Queretaro, San Luis Potosi and Tlaxcala iii)South: Campeche, Chiapas, Guerrero, Oaxaca, Quintana Roo, Tabasco, Veracruz and Yucatan iv) Distrito Federal. Group Selection The labor force was limited to individuals who are: i) working as employee, employer or self employer66; ii) between 12 and 65 years old; iii) living in urban areas; iv) working 20 hours or more per week; v) with positive income; vi) having the attributes of interest defined. The number of persons in the survey that belong to the labor force is shown in the next table. Table 2. Sample size for the labor force Year Number of % of the total persons sample 1984 3,892 16.4 1989 10,401 18.3 1992 8,752 17.4 1994 10,982 18.4 1996 12,996 20.2 66 The respective categories: workers without payment and cooperative's member were excluded because of the sample size. 66 According to the groups mentioned before, the number of cases is presented next. Table 3. Sample size by variable and year Variable 1984 1989 1992 1994 1996 Education Level Primary Incomplete 1,246 1,951 1,879 2,387 2,736 Primary Complete 1,299 3,006 2,501 2,975 3,411 Secondary Complete 803 2,875 2,489 3,014 3,734 Preparatory Complete 389 1,614 1,168 1,617 1,915 University Complete 245 955 715 989 1,200 Position in Occupation Employee 3,175 8,604 7,188 8,843 10,207 Employer 126 311 393 450 610 Self employed 681 1,486 1,171 1,689 2,179 Total 3,982 10,401 8,752 10,982 12,996 1.2. ENEU The National Urban Employment Survey is also a micro-level data set collected by INEGI and contains quarterly wage and employment data on the last ten years (1987-1997). Currently, the data is representative of the 41 largest urban areas in Mexico, covering 61% of the urban population following the 2500 inhabitants or more criteria and 92% of the population who live in metropolitan areas with 100,000 or more inhabitants. In 1985, the ENEU included 16 urban areas: Mexico City, Guadalajara, Monterrey, Puebla, Leon, San Luis Potosi, Tampico, Torreon, Chihuahua, Orizaba, Veracruz, Merida, Ciudad Juarez, Tijuana, Nuevo Laredo and Matamoros, covering 60% of urban population in that year. In 1992, 18 more urban areas were included in the survey: Aguascalientes, Acapulco, Campeche, Coatzacoalcos, Cuernavaca, Culiacan, Durango, Hermosillo, Morelia, Oaxaca, Saltillo, Tepic, Toluca, Tuxtla Gutierrez, Villahermosa, Zacatecas, Colima and Manzanillo. In 1993 and 1994, Monclova, Queretaro, Celaya, Irapuato and Tlaxcala entered to ENEU. Finally, Cancun and La Paz joined the survey in 1996. As can be seen in the previous description, the ENEU have always covered about 60% of the national urban population. Therefore, the results deduced from this survey allow one to know and assess the socioeconomic and employment characteristics of the national urban areas. The data is from household surveys, which fully describe family composition, human- capital acquisition, and experience in the labor market (the variables contain information about social household characteristics, activity condition, position in occupation, unemployment, main occupation, hours worked, earnings, benefits, secondary occupation, and searching for another job). As the ENIGH, the sampling design was stratified, in several stages (where the final selection unit was the household), and with proportional probability to size6'. So this statistical construction allows us to do comparisons among different years. Moreover, this survey is structured to generate a panel data set which has the characteristic to conform to a rotator panel (a 67 For this it was necessary to use weights or expansion factors. 67 fifth of the total sample goes out and a new one comes in every quarter). Hence, the panel data follows the same household through oi4t the five quarters. Category Selection The individuals in the sample were classified according to their educational level, age, sector of activity, position in occupation, hours worked and geographical region in the following categories: a) Educational level i) Primary incomplete: no education and primary incomplete (one to five years of primary) ii) Primary complete: primary complete and secondary incomplete (one or two years) iii) Secondary complete: secondary complete and preparatory incomplete (one or two years) iv) Preparatory complete: preparatory complete and university incomplete v) University complete: university complete (with degree) and postgraduate studies b) Age i) 12 to 25 years old ii) 26 to 34 years old iii)35 to 49 years old iv) 50 to 65 years old c) Sector of activity i) Primary sector ii) Manufacturing industry iii)Not manufacturing industry (construction, utilities) iv) Commerce v) Finance services and rent vi) Transportation and communication vii)Social services (Tourism, education, health, public administration, embassy) viii)Other services d) Status i) Employer ii) Self employed iii) Informal salaried: people that work in an enterprise with 15 workers or less and no receive social security (IMSS, ISSTE, private, etc.) iv) Formal salaried: people that works in an enterprise with 16 workers or more or receive social security (IMSS, ISSTE, private, etc.) v) Contract e) Hours worked i) 20 to 39 hours per week ii) 40 to 48 hours per week iii)At least 49 hours per week i Geographic regions i) North:, Baja California, Baja California Sur, Coahuila, Chihuahua, Durango, Nuevo Leon, Sinaloa, Sonora, Tamaulipas and Zacatecas 68 ii) Center: Aguascalientes, Colima, Guanajuato, Hidalgo, Jalisco, Mexico, Michoacan. Morelos, Nayarit, Puebla, Queretaro, San Luis Potosi and Tlaxcala iii)South: Campeche, Chiapas, Guerrero, Oaxaca, Quintana Roo, Tabasco, Veracruz and Yucatan iv) Distrito Federal. Group Selection Analogous to the ENIGH, the universe of study are those individuals, i) between 16 and 65 years old; ii) living in urban areas (localities with at least 2,500 inhabits); iii) regular workers (non-seasonal workers); iv) working 20 hours or more per week; v) with positive earnings68; vi) having the attributes of interest defined. The table below shows the sample size and labor force. Table 4. 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Country Economic Memorandum, "Factor Productivity and Growth". 75 Minutes of Meeting with Government Officials to Discuss the "Earnings Inequality after Mexico's Economic and Educational Reforms" Report Tuesday, March 28th, 2000 A meeting to discuss the above-referenced green cover World Bank Report with Mexican Government officials was held in Mexico City on Tuesdavy March 28, 2000. The meeting was chaired b) Mr. Carlos Mancera Corcuera (Underminister of Planning, Ministry of Education). The Bank's delegation was led by Mr. Marcelo Giugale (Lead Economist). A list of the participants is attached. Format of meeting. Mr. Carlos Mancera called the meeting to order and proposed the agenda for the discussions, also attached . NIr. Marcelo Giugale followed with a brief introductory statement on the importance of education and sector work in the Bank's assistance program, and the task manager presented the results of the document. The remainder of the meeting took place in a seminar format, with some parts of the report discussed individually, beginning with some introductorv comments by Mr. Eduardo Velez (HD sector leader). The presentation on each one of the chapters that comprises the study was followed by a general round-table discussion and reply from the task manager of the report. The report was very well received by the participants of the meeting, giving rise to an animated and wvell-focused discussion. The main points raised by government officials are summarized below (grouped by chapter), together with the responses offered. Comments on Chapter 1.- (For reference, Chapter I presents the factors and mechanisms driving earnings inequality; the evolution of education attainment; the evolution and structure of the rate of returns to education). One of the participants indicated that the increase in the rates of returns to education at the bottom of the conditional earnings distribution suggests that education is a good mechanism to redistribute income. Other commentator elaborated on certain aspects of the education sector, including recent progress made in improving education quality, targeting education spending on the poor, Progresa and the role of compensatorv programs on school attendance. The discussants made clear that if the information could be updated to 1999, Mexico will clearl) lie above the trend line in the cross-countrv relationship between the levels of economic development and education attainment of the labor force. Some participants felt that the relative decline of rates of return for primary education in the recent past is largely due to poorer job prospects for students with lower education attainment levels. Even so, the production of graduates with higher education levels will still require that they complete primary education first, which limits the flexibility in shifting resources from the primary to higher education levels. Another commentator noted that the evolution of rates of return in education is also likely to reflect problems on the supply side of the education sector. Other participants mentioned that the document explains clearly the mechanisms driving the increase in the rates of returns to education in the upper tail of the income distribution. In general, it was agreed that technical education only after completing basic education is important for reducing earnings inequality. Technical education could be an alternative for those individuals that face both a high opportunity cost to continue formal education and need to acquire skills that enable them to participate in the job market. It was also argued by one commentator that a better focalization of public expenditures on technical education is needed as well as an assessment of the unit cost of provision of technical educational services. A participant mentioned that the change of the structure in the share of the labor market for lower secondary and upper secondary educational level and the pattern of the earnings differentials among the different educational levels (technical and formal level) could possibly be the result of a combination of several factors: (i) during the 90's there was a high demand for skilled workers in Mexico; (ii) during the 80's there was a substantial increase in basic education coverage; and (iii) the youngsters are facing several restrictions on continuing education after completing upper secondary instruction. Another commentator 76 indicated that conditional real earnings dramatically felt after 1994. As explained, additional work is needed to assess to what degree the move in the level and' distribution of wages could be explained entirely by labor supply shocks without recourse to trade related issues. Comments on Chapter 2.- (This chapter analyzes the patterns of public and private educational expenditures; the determinants of enrollment in upper secondary and universit) levels and the marginal willingness to pay for educational services). One commentator indicated that the results from the incidence analysis and the determinants of enrollment support the role of the compensatory programs. Various discussants expressed uneasiness about the marginal willingness to pay approach. It was explained that the difference betv een the marginal willingness to pay for public educational services and the marginal willingness to pay for private services measures the effect of the government provision of public schools on the education spending behavior of an average household. This difference reflects the relative quality and payments (fees and unforeseen expenses) associated with public and private schools. One commentator expressed that the difference between the marginal willingness to pay for pre- primary versus primary educational level reflects higher fees in the first level. Another discussant suggested to do the marginal willingness to pay analysis by state and family size. In addition, it was suggested to indicate in the document which poverty line was used to compute the marginal willingness to pay for educational services. Also, questions were raised about the government's current emphasis on subsidizing education on the supply side, and it was recommended that consideration be given to shifting subsidies increasingly to the demand side or find a balance between the two. In this context, one participant also noted that supply-side subsidies may influence locational decisions by beneficiaries, encouraging greater population dispersion, which feeds back into raising the unit costs of subsidization. Several participants took the opportunity of this meeting to raise questions about the SEP's approach to measuring education quality. 77 Comments on Chapter 3.- (This chapter examines institutional factors that limit the production of graduates). It was recognized that the study does not show how the curricula diversity affect student's performance. In addition, the study does not assess the optimal number of specialties. One participant mentioned that the diversity in the curricula in higher education is good because generates a flow and creates a link with basic education. Moreover, it helps the students to acquire skills and adapt to different labor market conditions. A commentator indicated that the results from the rates of returns and the incidence analysis show that better targeting of public educational resources in higher education is needed. It was suggested that more resources should be devoted to vocational education and training. In his concluding statements, the Chairman thanked all participants and indicated that the meeting had provided a great opportunity for a fruitful exchange of ideas. He also indicated that it will be important to moderate the tone of some of the paragraphs in the document. In addition, he suggested to narrow down and qualified the conclusions derived from the analysis of the private rates of return to higher education, the benefit incidence analysis in higher education and the calculations of marginal willingness to pay for basic educational services. 78 Earnings Inequality after Mexico's Economic and Educational Reforms - Meeting with Mexican Government Brasil 31, piso 2, Sala de Juntas March 28th, 2000 18:00 - 20:15 Government staff participating in the meeting: Secretaria de Educaci6n Publica (SEP) Lic. Carlos Mancera Corcuera (Subsecretario de Planeaci6n y Coordinaci6n, SEP) Lic. Arturo Villalobos (Director General de Educaci6n, SEP) Lic. Rafael Miramontes Lomeli (Subsecretaria de Educaci6n Superior e Investigaci6n Cientifica, SEP) Lic. Antonio Sauri Lomeli (Subsecretaria de Educaci6n Superior e Lnvestigaci6n Cientifica, SEP) Lic. Cesar Ortiz (Asesor del Subsecretario de Planeaci6n y Coordinaci6n, SEP) Lic. Fernando C6rdoba (Asesor del Subsecretario de Planeaci6n y Coordinaci6n, SEP) Lic. Mario Alberto Oliva (Asesor, SEP) Lic. Jorge Enrique Juarez Barba (Economista, Unidad Desarrollo Educativo, Aguascalientes, SEP) Lic. David Diaz Romo (Economista, Unidad de Desarrollo Educativo, Aguascalientes, SEP) Lic. Rodolfo Navarro Ochoa (Director de Planeaci6n Educativa, Estado de Colima, SEP) Lic. Eugenio Flores Villasuso (Director de Planeacion Educativa, Estado de San Luis Potosi, SEP) Lic. Serafin Aguado Gutierrez (Consejo del Sistema Nacional de Educaci6n Tecnol6gica, SEP) Lic. Guillermo Betancourt (Direcci6n General de Programaci6n Planeacion y Presupuesto, SEP) Lic. Diego Gaspar (Secretaria de Planeacion y Coordinaci6n, SEP) Secretaria de Trabajo y Prevision Social (STPS) Lic. Alfredo Narvaez (Coordinador General de Politicas, Estudios y Estadisticas del Trabajo, STPS) Lic. Rodolfo Mendoza Cedillo (Coordinador General de Normatividad, STPS) Lic. Oscar Margain Pitman ( Director General de Empleo, STPS) Lic. Sergio Sierra Romero (Director de Normatividad, STPS) Lic. Sandra Barajas Beltran (Directora de Informacion Ocupacional, STPS) Secretaria de Hacienda y Credito Publico (SHCP) Lic. Evelyn Rodriguez (Directora General Programacion y Presupuesto Agropecuario, SHCP) Lic. Debora Schiam (Directora General Adjunta de Analisis y Educaci6n Sectorial) Lic. Ignacio Chavez (Coordinador de Programas Especiales, SHCP) Lic. Miguel Angel Gonzalez ( Director de Proyectos Especiales, SHCP) Lic. Ana Laura Terrazas (Jefe de Departamento Programas Sociales, SHCP) Lic. Paola Pereznieto (Jefe de Departemento de Proyectos Sociales, SHCP) Lic. Miriam Matamoros (Asesor del Subsecretario de Egresos, SHCP) Instituto Nacional de Estadistica, Geografia e Informatica (INEGI) Lic. Antonio Nieblas (Subdirector de Estadistica, INEGI) Lic. Ernesto Tapia Cupil (Economista, INEGI) Programa de Alimentaci6n, Salud y Educaci6n (Progresa) Dra. Susan Parker (Asesora del Coordinador de Progresa) Bank Staff participating in the meeting: Mr. Marcelo Giugale (Encomista en Jefe, PREM) Mr. Eduardo Velez (Gerente del Sector Social, HD) Ms. Anna Sant'Anna (Especialista en Sectores Sociales) 79 Ms. Gladys L6pez-Acevedo (Economista, PREM) Mr. Angel Salinas (Consultor, PREM) Ms. Mariana Urbiola (Consultor, PREM) Ms. M6nica Tinajero ( Estadistica, U.N.A.M) Seminario Banco Mundial Marzo 28, 2000 ORDEN DEL DIA 18:00 Presentaci6n Carlos Mancera Corcuera ( SEP) Introducci6n Marcelo Giugale (Banco Mundial) Presentaci6n general del documento Gladys L6pez-Acevedo (Banco Mundial) 18:45 Discusi6n Factores institucionales que afectan la educaci6n media y superior Tasas de retorno a la educaci6n Valuaci6n por los servicios educativos Calidad de los servicios educativos Desigualdad y Educaci6n Educaci6n Tecnica 80 Minutes of Meeting with Government Officials to Discuss the "Earnings Inequality after Mexico's Economic and Educational Reforms" Report Monday, April 10th, 2000 A second meeting to discuss the above-referenced green cover revised World Bank Report with Mexican Government officials was held in Mexico City on Monday, April 10th, 2000. The meeting was chaired by Lic. Cesar Ortiz (Advisor to the Underminister of Planning, Ministry of Education). Gladys Lopez -Acevedo attended the meeting on the part of the Bank. A list f the participants is attached. Objective of the meeting. Mr. Cesar Ortiz called the meeting to discuss the revised version of the document. In addition, each government official representing the Ministries of Education, Labor and Hacienda verified that the comments received on March 28th had been incorporated in the document. It was agreed that if there are no further comments after May 28h, formal authorization will be given on the part of Hacienda for the report to go into gray cover. Earnings Inequality after Mexico's Economic and Educational Reforms - Meeting with Mexican Government Brasil 31, piso 2, Sala de Juntas April 10th, 2000 10:00 - 13:00 Government staff participating in the meeting: Secretaria de Educaci6n Publica (SEP) Lic. Cesar Ortiz (Asesor del Subsecretario de Planeaci6n y Coordinaci6n, SEP) Secretaria de Trabajo y Previsi6n Social (STPS) Lic. Rodolfo Mendoza Cedillo (Sub coordinador de analisis y politica laboral, STPS) Lic. Sandra Barajas Beltran (Directora de Informaci6n Ocupacional- DGE, STPS) Lic. Amparo Munoz (Subdirectora de Informacion Ocupacional- DGE, STPS) Secretaria de Hacienda y Credito Publico (SHCP) Lic. Debora Schlam (Directora General Adjunta de Analisis y Educaci6n Sectorial) Lic. Ana Laura Terrazas (Jefe de Departamento Programas Sociales, SHCP) Lic. Socorro Elizalde (Directora de Analisis y Educaci6n Sectorial, SHCP) Bank Staff participating in the meeting: Ms. Gladys L6pez-Acevedo (Economista, PREM) 81