A WORLD BANK COUNTRY STUDY 22357 May 2001 Peruvian Education at a Crossroads Challenges and Opportunities for the 21st Century A WORLD BANK COUNTRY STUDY Peruvian Education at a Crossroads Challenges and Opportunitiesfor the 21st Century The World Bank Washington, D. C. Copyright (C 2001 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing May 2001 123404030201 World Bank Country Studies are among the many reports originally prepared for internal use as part of the continuing analysis by the Bank of the economic and related conditions of its developing member countries and of its dialogues with the governments. Some of the reports are published in this series with the least possible delay for the use of governments and the academic, business and financial, and development communities. 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CONTENTS MAIN REPORT Preface ......................................... xi Acknowledgments .xii Task Team and Reviewers .xiv Acronyms and Abbreviations .xvi Executive Summary .xvii 1. Sector Overview. 1 1.1. Achievements. 1 1.2. The Evolving Education System. 3 2. Education Finance ..11 2.1. The Budget Process .11 2.2. Public Expenditure on Education .13 2.3. Household Expenditure on Education .24 3. System Performance Indicators .......................................... 29 3.1. Access, Repetition, and Retention .29 3.2. Labor Market Outcomes .34 3.3. Learning Outcomes .37 4. The Teaching Profession .47 4.1. Teacher Qualifications and Employment Status .47 4.2. Conditions of Service and Compensation .51 4.3. Incentives and Accountability .56 5. Second-Generation Reform .61 5.1. Improve Equity .61 5.2. Enhance Quality .64 5.3. Improve Efficiency of Resource Use .65 5.4. Conclusion .68 References .75 iii LIST OF TABLES IN THE MAIN REPORT 1. A Comparison of the Existing and New Education Structures .......................................6 2. Urban Peru: Rates of Return to Public Education, 1997 ............................................... 35 3. Fourth Grade Mathematics Outcomes, 1996 .......................................................... 38 4. Summary of Effects Crossing between Departments, Schools, and Students ............ 43 5. First Generation Reform and Its Implications for Second Generation Reform .......................................................... 70 6. Summary of Policy Options .......................................................... 72 LIST OF FIGURES IN THE MAIN REPORT 1. International Comparison of Enrollment of Students between the Ages of 3 and 23 ...........................................................2 2. International Comparison of Public Expenditure on Education as a Percentage of Gross Domestic Product ........................................................... 2 3. Trend of Enrollment in Public Institutions by Level, 1990 to 1997 ...............................7 4. Trend of Enrollment in Private Institutions by Level, 1990 to 1997 ..............................7 5. Ministry of Education Operational Structure .......................................................... 10 6. Public Expenditure on Education and Central Government Expenditure as a Percentage of Gross Domestic Product, 1970 to 1997 .................................... 14 7. Percentage Change of Gross Domestic Product, Central Government Expenditure, and Public Education Expenditure, 1970 to 1997 ............................. 14 8. Public Spending on Education and GNP Per Capita in Lower Middle- Income Countries .......................................................... 15 9. Total Recurrent and Capital Expenditures on Education, 1970 to 1997 (in constant 1997 soles) .......................................................... 17 10. Composition of Public Expenditures on Education, 1990 to 1997 (Percentage, Regrouped According to the Latest Classification) ........................... 17 1 1. Intergovernmental Transfer of Resources (Percentage of Total), 1990 to 1997 .......................................................... 17 12. Public Expenditure on Education by Level, 1990 to 1997 (Percentage) .................... 19 13. Per Student Recurrent Public Expenditure on Education by Level, 1990 to 1997 (Constant 1997 Soles) .......................................................... 19 14. Lorenz Curve for Incidence of Public Expenditure .................................................... 22 15. Lorenz Curves for Incidence with 5 Simulations ........................................................ 22 16. Lorenz Curves by Education Level .......................................................... 22 17. Lorenz Curve for Incidence of Private Expenditures - All Levels ............................. 27 18. Lorenz Curve for Incidence of Private Expenditures - Only Public Schools ............. 27 19. Lorenz Curve for Incidence of Private Expenditures - Only Primary Schools .......... 27 20. School Survival Rates by Gender, 1997 .......................................................... 32 21. School Survival Rates by Urban and Rural Areas, 1997 ............................................ 32 22. School Survival Rates by the Mother Tongue, 1997 .................................................. 33 23. School Survival Rates by Poorest and Richest Consumption Quintiles, 1997 ........... 33 24. Evolution of Estimated Premia by Educational Level, 1985 to 1997 ......................... 34 iv 25. Mathematics Outcomes and Recurrent Public Expenditure on Basic Education Per Student ......................................................... 39 26. Mathematics Outcomes and Household Expenditure on Basic Education Per Student ......................................................... 39 27. Poverty and Recurrent Public Expenditure on Basic Education Per Student ............. 40 28. Poverty and Household Expenditure on Basic Education Per Student .................. 40 29. Determinants of Effective Learning in Primary Education: Findings from Literature Review ......................................................... 44 30. Remuneration of Teachers in Real Terms, 1990 to 1997 ................................................ 54 31. Index of Remuneration of Government Employees 1970-1997 ................................. 55 32. Index of Private Sector Salaries in Metropolitan Lima, 1970-1997 (Based August 1990=100) ........................................................ 55 33. Index of Purchasing Power of Private Sector Salaries 1972, 1990, 1997 .................... 55 34. Index of Purchasing Power of Public Sector Salaries 1972, 1990, 1997 .................... 55 35. Estimates and Projections of School Age Population, 1995-2020 .............................. 67 BACKGROUND NOTES 1. The Structure of Education ......................................................... 85 2. Income Elasticity of Demand for Education and Engel's Curve .......................... 87 Table 1: Determinants of Household Budget Shares ................................................... 88 Table 2: Elasticity Estimates from Engel's Curves ............................................. ......... 89 3. Private and Social Returns to Public Education in Urban Peru ............................ 91 Table l: Earnings Functions Coefficients ................................... ..................... 93 Table 2: Linear Hypothesis on Regression Coefficients .............................................. 94 Table 3: Estimated Educational Premiums ......................................................... 95 Figure 1: Earnings by Age and Educational Level, Females ...................... ................. 94 Figure 2: Earnings by Age and Educational Level, Males ........................................... 95 4. Determinants of Achievement ......................................................... 97 Table 1: Descriptive Statistics of Student-Level Variables Used in the HLM Model ......................................................... 99 Table 2: Descriptive Statistics of School-Level Variables Used in the HLM Model ........................................................ 100 Table 3: Descriptive Statistics of Department-Level Variables Used in the HLM Model ........................................................ 101 Table 4: Effects of Student Characteristics on Student Outcomes ............. ............... 106 Table 5: Effects of School Characteristics on School Mean ...................................... 108 Table 6: Cross-Level Effects of School Characteristics on Mathematics Achievement Slopes ........................................................ 111 Table 7: Extent to which Variation in Math Achievement Is Accounted for by, Student-Level Characteristics and the Variation in True School Mean Mathematics Achievement Is Accounted for by School-Level Factors ......... ... 112 Table 8: Correlation Matrix ...................................................... 115 v Table 9: Effects of Departmental Characteristics on the Grand Mean of Math Test Scores ............................................................. 116 Table 10: Final Three-Level Model for Average Math Achievement with Interaction ............................................................. 120 Table 11: The Extent to which Mathematics Achievement Is Accounted for by Student, School, and Department-Level Characteristics ................ ................... 124 5. Teacher Education and Professional Development ................................................ 125 Table 1: A Comparison of Old and New Pilot Curriculum in Teacher Education ............................................................. 127 APPENDICES 1. Student Enrollment Statistics ............................................................. 135 1.1. Enrollment in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990-1997 1.2. Enrollment in Formal and Nonformal Education (Broadly Grouped) in Public Institutions by Level as Percentage of Total, 1990-1997 1.3. Enrollment in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 1.4. Enrollment in Formal and Nonformal Education (Broadly Grouped) in Private Institutions by Level as Percentage of Total, 1990-1997 1.5. Total Enrollment in Formal and Nonforrnal Education (Disaggregated by Minors and Adults) in Public and Private Institutions, 1990-1997 1.6. Public Enrollment by Level and by Department, 1997 2. Teacher Statistics .............. 143 2.1. Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990-1997 2.2. Teachers in Formal and Nonformal Education (Broadly Grouped) in Public Institutions by Level as Percentage of Total, 1990-1997 2.3. Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 2.4. Teachers in Formal and Nonformal Education (Broadly Grouped) in Private Institutions by Level as Percentage of Total, 1990-1997 2.5. Total Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public and Private Institutions by Level, 1990-1997 2.6. Teacher-to-Student Ratio in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990-1997 2.7. Teacher-to-Student Ratio in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 2.8. Teachers by Level and by Department, 1997 2.9. Teacher-to-Student Ratio by Level and by Department, 1997 2.10.Student Enrollment and Teachers in Public Pedagogical Institutes by Region, 1997 vi 2.11. Student Enrollment and Teachers in Private Pedagogical Institutes by Region, 1997 2.12. Changes in Student Enrollment and Student-to-Teacher Ratios in IEublic Pedagogical Institutes by Region, 1997 2.13. Change in Student Enrollment and Student-to-Teacher Ratios in Private Pedagogical Institutes by Region, 1997 3. School Statistics ............ 159 3.1. Public Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 3.2. Public Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 3.3. Private Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 3.4. Private Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 3.5. Total Public and Private Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 3.6. Total Public and Private Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 4. Indicators of Equity and Efficiency .......................... 167 4. la. Rural Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.1b.Urban Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.1 c. Rural Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1 997 4.1 d. Urban Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.2a. Rural Public Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.2b.Urban Public Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.2c. Rural Public Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.2d. Urban Public Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.3a. Rural Private Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.3b.Urban Private Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.3c. Rural Private Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.3d. Urban Private Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 4.4a. Simulation I - Distribution of Public Expenditure by Consumption Quintile, 1997 4.4b.Simulation 2 - Distribution of Public Expenditure by Consumption Quintile, 1997 vii 4.4c. Simulation 3 - Distribution of Public Expenditure by Consumption Quintile, 1997 4.4d. Simulation 4 - Distribution of Public Expenditure by Consumption Quintile, 1997 4.4e. Simulation 5 - Distribution of Public Expenditure by Consumption Quintile, 1997 4.5. Water and Sanitation in Public and Private Schools by Age and Income Group, 1994 4.6. Typology of Urban and Rural Schools, Based on School Characteristics, Infrastructure, Equipment, and Other Resources, Principals' and Teachers' Characteristics and Perceptions, 1994 4.7. Internal Efficiency of Public Education (Primary and Secondary) in Peru (Average 1994 to 1996) 5. International Comparison of Between-School Variation in Achievement ............ 188 5.1. International Comparison: Between-School Variation in Achievement by Selected Countries 5.2. International Comparison: Between-School Variance in IEA International Study on Reading, 1990 6. Public Expenditure on Education ......................................................... 195 6.1 Gross Domestic Product, Total Government Expenditure, and Total Public Expenditure on Education, 1970-1997 (Million Soles in Current Prices) 6.2 Gross Domestic Product, Total Government Expenditure, Total Public Expenditure on Education, and Tax Revenue of Central Government, 1970-1997 (Million Soles in Constant 1997 Prices) 6.3. Gross Domestic Product, Total Government Expenditure, and Total Public Expenditure on Education, 1970-1997 (Million US dollars at the 1997 Exchange Rate) 6.4. Recurrent and Capital Expenditure on Education, 1990-1997 (Constant 1997 Soles) 6.5. Public Expenditure on Education by Budgetary Entities, 1990-1997 6.6. Functional Composition of Public Expenditure on Education According to Pre- 1997 Classification, 1990-1996 6.7 Reclassified Functional Composition of Public Expenditure on Education According to the 1997 Classification, 1990-1997 6.8. Functional Composition of Public Expenditure on Education by Budgetary Entities, 1995-1997 6.9. Public Expenditure on Education by Level, 1990-1997 6.10. Per Student Recurrent Public Expenditure by Level, 1990-1997 6.1 1. Recurrent Public Expenditure by Level, by Function, and by Department from Central Government Allocation, 1997 6.12.Recurrent Public Expenditure by Level, by Function, and by Department from Own Resources, 1997 6.13.Recurrent Public Ependiture by Level, and by Department from Other Sources. 1997 viii 6.14. Total Public Expenditure on Education by Level, and by Department from All Sources of Funding, 1997 6.15. Departmental Revenues from Central Government Allocation as a Percentage of Total, 1997 6.16.Department's Own Resources as a Percentage of Total, 1997 6.17. Other Resources as a Percentage of Total, 1997 6.18.Per Student Recurrent Expenditure by Level and by Department, 1997 (Soles) 6.19.Teachers Salary Scale (July 1990-August 1997) (Soles in Current Prices) 7. Household Expenditure on Education .............................. 241 7.1. Average Household Expenditures on Preprimary Education by School Type, 1997 (Soles per Child) 7.2. Average Household Expenditures on Primary Education by School Type, 1997 (Soles per Student) 7.3. Average Household Expenditures on Secondary Education by School Type, 1997 (Soles per Student) 7.4. Average Household Expenditures on Tertiary Nonuniversity Education by School Type, 1997 (Soles per Student) 7.5. Average Household Expenditures on University Education by School Type, 1997 (Soles per Student) 7.6. Average Household Expenditures on Education by Education Level, 1997 (Soles per Student) 7.7. Average Household Expenditures on Education by School Type, 1997 (Soles per Student) 7.8. Total Household Expenditures on Preprimary Education by School Type, 1997 (Soles) 7.9. Total Household Expenditures on Primary Education by School Type, 1997 (Soles) 7.10.Total Household Expenditures on Secondary Education by School Type, 1997 (Soles) 7.1 l.Total Household Expenditures on Tertiary Nonuniversity Education by School Type, 1997 (Soles) 7.12.Total Household Expenditures on University Education by School Type, 1997 (Soles) 7.13.Total Household Expenditures on Education by Education Level, 1997 (Soles) 7.14.Total Household Expenditures on Education by School Type, 1997 (Soles) 8. Population Projection ................. 257 8.1. Assumptions of Population Projection 8.2. Population by Single Years of Age for Selected Age Ranges and Years, 1995- 2020 8.3. Projected School-Age Population, 1995-2020 ix 9. External Support for Education since 1990 ........................................ 263 10. Selected Indicators for International Comparison ........................................ 273 10.1. Educational Expenditure as a Percentage of GDP for All Levels of Education Combined, by Source of Funds (1997) 10.2.Educational Expenditure as a Percentage of GDP for Primary, Secondary, and Postsecondary Nontertiary Education, by Source of Funds (1997) 10.3.Educational Expenditure as a Percentage of GDP for Tertiary Education, by Source of Funds (1997) 10.4.Educational Expenditure from Public and Private Sources for Educational Institutions as a Percentage of GDP by Level of Education (1997) 10.5.Educational Expenditure by Resource Category for Public and Private Institutions, by Level of Education (1997) 10.6. Expenditure per Student (US Dollars Converted using PPPs) on Public and Private Institutions by Level of Education (Based on Fulitime Equivalents) (1997) 10.7. Expenditure per Student Relative to GDP per Capita on Public and Private Institutions by Level of Education (1997) x PREFACE The Government of Peru (GOP) has made poverty alleviation one of the cornerstones of its policy. Even so, in 1997 about 49 percent of Peru's population of 25 million people still lived in poverty, and 15 percent in extreme poverty. The World Bank's assistance program for Peru emphasizes support for the GOP's poverty reduction policies and investments. In order to guide its own work in helping the GOP deal with persistent poverty, to deepen its dialogue with government, and to inform public discourse, the Bank initiated a coordinated set of studies bearing on poverty reduction. The studies covered four topics: poverty itself, health, indigenous peoples, and education. This report conveys the findings of the education study. It seeks to inform discussion of potential policy options by examining the impact of public and private finance, and policies for resource use, on education and labor market outcomes. As such, it is one contribution to the larger discussion of human resource development and poverty reduction in Peru. The GOP has viewed investment in education as essential for social cohesion, for personal and moral development, and for improving individual economic productivity and employment prospects. Educational improvements thus underpin strategies both for poverty reduction and for long-term economic and social development. The current situation reflects important accomplishments. Almost all Peruvian children enroll in primary education, and opportunities for secondary and higher education well exceed what would be expected for a country of Peru's income level. Despite achievements to date, the new government inherits major problems that have received insufficient attention. Significant gaps remain-between the rich and the poor, between rural and urban areas, and between indigenous and nonindigenous populations-in school completion rates and learning outcomes. Overall challenges also remain for upgrading quality at all levels and for extending coverage of early childhood, secondary, and higher education. One path into the 21't century would pursue steady incremental improvements of the current situation. Another path for policy would seek a marked improvement in the intellectual and technical capacity of the population through a focussed commitment to closing gaps and meeting remaining challenges. The new government has thus arrived at a crossroads in education policy. This report reviews the period from 1990 to 1997. The study has limited its scope to analyzing data made available in or before 1997, but has not been able to take advantage of data that have been made public since the events in late 2000 and early 2001. While not incorporating newly released data, we have concluded after an intial review of this material that it does not change the general picture outlined herein. This World Bank document reports the work not only of its own staff but, also, to an unusual extent, that of Peruvian academics, policy analysts, and public officials. The document reflects a collective effort. xi ACKNOWLEDGMENTS The study is made possible by the support of the Ministry of Education (MED), Ministry of Economy and Finance (MEF), Ministry of the Presidency (PRES), and Regional Education Directorates. Special thanks are due to current and former officials of the following agencies: Ministry of Education Sr. Marcial Rubio Correa (Ministro), Sr. Idel Alfonso Vexler Talledo (Vice Ministro), Henry Anthony Harman Guerra (Vice Ministro), Sr. Juan Fernando Vega, Sr. Cesar Guadalupe, Sr. Jose Rodriguez (PLANMED), Sr. Jorge Ferradas (MECEP), Sra. Neride Sotomarino Maturo (MECEP), Sr.Maximo Silva Vargas (Unidad de Presupuesto), Dra. Blanca Encinas (Direcci6n Nacional de Educaci6n Inicial y Primaria), Hna. Rosario Valdeavellano (Direcci6n Nacional de Capacitaci6n y Formaci6n Docente), Sra. Raquel Villaseca Zevallos (PLANCAD), Juan Raul Borea Odria (Direcci6n Nacional de Educaci6n Secundaria y Superior Tecnologia), Sr. Jose Pezo de la Cuba (Unidad de Desarrollo Curricular y Recursos Educativos de Educaci6n Secundaria), Sr. Manuel Iguihiiz Echevarria (Direcci6n de Educaci6n de Adultos), Dra. Maggie Marquez (Oficina de Personal), Sra. Nery Luz Escobar (Unidad de Formaci6n Docente), Sr. Juan Carlos Godenzzi (Unidad de Educaci6n Bilingue Intercultural), Sr. Wolfarng Kiuper (GTZ- Proyecto Reforma de Formaci6n Magisterial), Srta. Karla Maria Daneri Preis (Directora General de Administraci6n). Ministry of the Presidency Dra. Mercedes Vergara Mejia (Secretario Interministerial de Asuntos Sociales), Dr. Humberto Rebagliati (Asesor del Despacho Ministerial Imagen Institucional), Dr. Ram6n Morante (Asesor Principal del Despacho Ministerial). Ministry of Economy and Finance Sr. Bruno Barletti Pascuale (SIAF), Dra. Heydi Huarcaya (Direcci6n de Sectores Sociales, DNNPP-MEF), Sr. Carlos Pichilingue (Direcci6n de Regiones, DNPP-MEF), Sra. Carlos Giesecke (Oficina de Inversi6n), Sr. Rafael Capristdn Miranda (Oficina de Inversiones). Direcci6n Regional de Educaci6n La Libertad: Sra. Rosa Neyra Orbegoso (Direcci6n Regional de Educaci6n), Sr. Estuardo Loyola Rabines (Oficina Asesoramiento Tecnico), Sra. Ren6 Vereau Orbegozo (Oficina de Administraci6n), Sr. Walter Rebaza Vasquez (Direcci6n Tecnica Pedag6gica), Sra. xii Regina Pacheco Ponce (Direcci6n, Aldea Infantil Santa Rosa de la Fundaci6n por los Nifios del Peru). Arequipa: Sr. Herman Robles Fernandez (Direcci6n T6cnica Pedag6gica), Sr. Juan Cabrera Zegarra (Relaciones Nblicas), Sr. Hector Rodriguez Alvarez, Sra. Marleny Rodriguez Arguelles, y Sr. Nelson Concha Serrano. Cusco: Sr. Juan C. Galvan (Director de Asesoramiento Tecnico). Puno: Padre. Teodoro Sakata Andrade (Direcci6n Regional de Educaci6n), Arq. Jose Manuel Pineda Barreda (Infraestructura Educativa), Sr. Nestor Marca Limachi, (Top6grafo), Prof. Florencio Madarriaga (Especialista en Educaci6n), Lic. Leticia Ramos Cuba (Relaciones Publicas). Loreto y Lima: Prof. Luis Veintemilla Soria (Direcci6n Regional de Educaci6n), Sra. Herman Rodriguez Flores (Area Estadistica), Prof. Bemardo Vasquez Cahuaza (Jefe de Presupuesto). Banco Central de la Reserva del Peru Sra. Maritza Guabloche Colunge (Apoderado General), Sr. Vfctor Hugo Diaz (Dpto. Entidades Gubernamentales), Sra. Judith Guabloche, Sra. Augusta Alfageme. Instituto Nacional de Estadistica e Informatica (INEI) Sra. Genara Rivera (Directora de Estudios Sociales), Sr. Alberto Padilla Trejo (Sub Jefe de Estadistica). The World Bank team is grateful to educators and researchers in universities, research centers, and nongovernmental organizations for sharing their work and perspectives. Universidades/Entidades de Investigaci6n/Organizaciones No Gubernamentales Sr. Jean Ansi6n (Pontificia Universidad Cat6lica del Peru), Sra. Carmen Montero (Instituto de Estudios Peruanos), Sr. Hugo Diaz (Instituto de Investigaci6n para el Desarrollo y la Defensa Nacional), Sr. Moises Ventocilla (Instituto Cuanto), Sr. Patricia Andrade (Foro Educativo), Sra. Cecilia Thorne (Pontificia Universidad Cat6lica del Peru), Sr. Fernando Bolanios (Foro Educativo), Sr. Santiago Cueto (GRADE - Grupo de Analisis para el Desarrollo), Sr. Julio Dagnino (Instituto de Pedagogia Popular), Sr. Eduardo Palomino (Colegio de Los Andes). xiii TASK TEAM AND REVIEWERS This report was prepared by: Kin Bing Wu, Team Leader With contributions from: Juan Pablo Silva and Arturo Review of Public Expenditure on Education Miranda Suhas Parandekar and Juvenal Diaz Analysis of Household Expenditure on Education Pete Goldschmidt Analysis of Academic Achievement Patricia Arregui and Sandro Analysis of School Survey in Lima/Callao and Cusco Marcone Jaime Saavedra and Eduardo Analysis of Social and Private Rates of Return to Maruyama Education and Labor Market Outcomes Kye Woo Lee and Hugo Diaz Teacher Supply and Demand and Compensation Maria Amelia Palacios Teacher Training Richard Webb Budget Process and Higher Education Livia Benavides External Support for Education Eduard Bos Projection of School-Age Population Supported by: Anna Maria Mayda, Roberto De Research Assistance Vogli, Olympia Icochea, Aude Damon, Yukiko Arai, and Atsuko Toi Aracelly Woodall, Douglas Task Assistance Flandro, and Pierre Sandoval Leslie Evans English Editing Luisa Maria Rojas, Berta Van Spanish Translation Zuiden, Ivonne Treneman Elizabeth Mestanz Word processing in Spanish Gladys L6pez, Gladys Del Valle, Support of various kinds from the staff of the World Ana Maria Arteaga, and Nancy Bank Resident Mission in Lima Escalante World Bank Reviewers: Peter Moock Marlaine Lockheed Ernesto Cuadra Fernando Reimers xiv Ministry of Education Reviewers: Juan Fernando Vega Cesar Guadalupe Jose Rodrfguez xv ACRONYMS AND ABBREVIATIONS ADE Areas de Desarrollo Educativo (Education Development Areas) AE Area de Ejecuci6n (Area of Execution) AFP Administraci6n de Fondo de Pensiones APP Authorized Pensionable Position CIAS Comite Interministerial de Asuntos Sociales (Interministerial Committee of Social Affairs) CORDELICA Corporaci6n de Desarrollo de Lima y Callao CTAR Consejo Transitorio de Administraci6n Regional (Transitional Council of Regional Administration) DRE Director Regional de Educaci6n FONAVI Fondo Nacional de Vivienda (National Housing Fund) FONCODES Fondo Nacional de Compensaci6n y Desarrollo Social (Social Fund) GRADE Grupo de Analisis para el Desarrollo INEI Instituto Nacional de Estadfstica e Informrtica INFES Infraestructura Nacional para Educaci6n y Salud (National Infrastructure for Education and Health) IPSS Instituto Peruano de Seguro Social (Peruvian Institute of Social Security) IST Institutos Superiores T6cnicos (Higher Technical Institutes) ISP Institutos Superiores Pedag6gicos (Higher Institutes of Pedagogy) MECEP Proyecto para Mejoramiento de la Calidad de la Educaci6n Primaria MED Ministerio de Educaci6n (Ministry of Education) MEF Ministerio de Economia y Finanzas (Ministry of Economy and Finance) MINSA Ministerio de Salud (Ministry of Health) OECD Organization for Economic Cooperation and Development ONP Oficina de Normalizaci6n Previsional (Pension Office) PLANMED Planning Unit in MED PROMUDEH Ministerio de Promocion de la Mujer y del Desarrollo Humano (Ministry for the Promotion of Women and Human Development) PRES Ministerio de la Presidencia (Ministry of the Presidency) USE Unidades de Servicios Educativos (Educational Service Units) UNESCO United Nations Educational, Scientific and Cultural Organization Exchange Rates (1997): Soles 2.66 = US$1 Fiscal Year: January 1 to December 31 School Year: April 1 to December 31 (180 days/year) Vice President David de Ferranti Country Director Isabel Guerrero Sector Director Xavier Coll Education Sector Manager Jamil Salmi Country Sector Leader Evangeline Javier Task Team Leader Kin Bing Wu xvi EXECUTIVE SUMMARY Peruvian education has achieved notable successes in the second half of the 20th century. Primary education now reaches almost all children. Secondary and tertiary education institutions enroll about 80 percent of the 12- to 16-year olds and 30 percent of the 17- to 25-year olds, respectively. Few countries in Peru's income range achieve comparable coverage. These accomplishments are particularly impressive given Peru's geographical and ethnic diversity. The government in the 1990s steadily increased public expenditure on education, despite constraints imposed by disciplined fiscal policies. Nonetheless, at 3 percent' of Gross Domestic Product (GDP) in 1997, Peru's public spending on education remained significantly less than the Latin American average of 4.5 percent. That Peru was able to attain high enrollment with a low level of public spending results from several factors: (i) achievement of near universal primary education before qualitative improvement; (ii) containment of the growth of personnel expenditure, channeling the additional public resources to build up infrastructure and capacity; and (iii) mobilization of high levels of household expenditure on education (total household spending on public and private education accounted for about 2 percent of GDP--much higher than the OECD's 1.3 percent). These factors operated in the context of what might be labeled a first generation of reform occurring in the early 1990s. This reform was characterized by rationalization of the public sector, regionalization of administration, deconcentration of social services, encouragement of private education, and extension of free and compulsory education. Despite these accomplishments, important challenges remain. Disparity between the rich and poor-and between rural and urban areas-remains pronounced in access to preschool, secondary education, and tertiary education, as well as in school completion rates. Disparity is also manifested in levels of achievement between indigenous and nonindigenous populations, and between public and private schools. Meanwhile, the earnings differentials between workers with different levels of education are growing. In urban areas, the largest increase in earnings differentials is between university educated and secondary educated workers. This signals a growing demand for higher skill levels in an open economy that is facing increasing international competition and technological change. These trends have serious implications for the employment prospects and future lifetime earnings of disadvantaged groups. Peru has thus reached a crossroads for education policy at the beginning of the 21st century. The status quo reflects major accomplishments. One direction for policy would, therefore, involve useful but relatively limited improvements in the current situation. This direction, however, may prove inadequate to fulfill the country's goal of having a highly iIn Peru, pensions are paid out of the recurrent budget of each rninistry, not out of a separate pension fund as in many other countries. Pensions accounted for about 21 percent of the total public expenditure on education. Net of pension, public spending on education accounted for only 2.4 percent of the GDP in 1997. xvii educated citizenry-fully competitive by international standards-to underpin poverty reduction and to facilitate economic and social development. A second generation of reform would be required if this direction were taken. This study explores these issues and lays out options for a second wave of reform. Either of the broad directions for policy will need to identify specific ways of improving educational quality. This report analyzed the factors affecting fourth graders' mathematics achievement levels in 1996 to identify options to improve quality. The findings are encouraging in indicating potential directions where change could make a difference: The analysis found that after controlling for a number of explanatory variables, the performance of poor and extremely poor departments was better than nonpoor departments. Some departments were doing a better job in educating over-aged students. Aymara students performed as well as Spanish-speaking students. Quechua students could achieve as much as others if they were not studying in predominantly Quechua- speaking schools, thereby indicating the potential for policy to reduce the disparity by increasing support to these schools. Teachers who graduated from universities and from Institutos Superiores Pedag6gicos, teachers who have had longer years of service, and teachers who have had more in-service training courses, were positively associated with higher student achievement, relative to those teachers who have not had these qualifications, years of service, and training opportunities. Nonavailability of textbooks was associated with lower achievement. Parental expectations-potentially modifiable through publication of assessment results and learning determinants-helped shape outcomes. Even within the limitations of this first assessment effort, the findings are sufficiently important to warrant attention for the policy possibilities to equalize educational outcomes and improve quality more generally. Because the burden for financing education is disproportionately heavier for poorer than richer households, public policy cannot rely on general increases in income to bring improved educational quality for disadvantaged populations. Increases in public sector investment will be required to ensure the equality of educational opportunity for all and improved economic opportunity for all-by directing more public resources to the poor. Past constraints on public expenditure allow room for new allocations to meet this mandate without exceeding reasonable overall public allocations to the sector. But increased public commitment to education will prove to be of little value unless the resources are committed not only to the right groups but also to the right interventions. To level the playing field for all Peruvian children, then, it is necessary to improve equity, quality, and efficiency. All countries face these challenges. In Peru, the policy options to meet them include the following: * Improving equity: Government support can come in the form of proven supply-side interventions. These include extending the current provision of each class with a set of instructional materials and supplies from primary education to preschool and secondary education; expanding provision of bilingual education programs and texts; xviii stepping up recruitment and strengthening training of indigenous teachers; training rural teachers in multigrade teaching; providing incentives to rural teachers; extending cost-effective health and nutrition programs for school-age children; and expanding access to secondary education (in part by establishing distance learning programs). Also important are demand-side financing measures (such as grants and scholarships targeted to rural areas and indigenous students, particularly girls). * Improving quality: Many of the interventions just listed to improve equity will also enhance quality. Additional options include: changing the system of incentives - such as adjusting the salary scale to reward higher levels of skills and competency and to compensate for the difficult working conditions in the rural areas, opportunity for professional development, and open-ended tenure to be determined by performance. Setting standards for learning and teaching, strengthening teacher pre- service and in-service training, teacher performance evaluation, and rewarding schools for improved performance (perhaps through formula-based funding) are among the options to sustain the efforts to enhance quality. These measures require complementary improvement in the frequency, quality, and transparency of student assessment (with results available to administrators, principals, teachers, parents, students, and the general public); the recently introduced program of national testing provides an excellent start in this direction. Building consensus with all stakeholders on the direction and means for change will be critical to build a culture of accountability. * Improving efficiency in planning and policy: Efficiency objectives can be advanced by (i) strengthening coordination of educational policy and financial matters between the budgetary entities that have responsibility for education (Ministry of Education, the Regions, universities, decentralized institutions, and Ministry of the Presidency, as well as the Ministry of Women and Human Development); (ii) proactive gathering of information on teachers and school-level finance (in both public and private schools) to guide policy; and (iii) using student achievement data systematically to target additional resources for schools falling below certain performance levels, while recognizing schools which have made above average progress over time. Some measures are easier to implement (such as provision of educational materials) than others (such as setting standards to drive development of teacher professionalism) b.ecause the latter involve institutional and cultural change. Therefore the timeframe of implementation will vary. Many countries have committed far more public resources to education than has Peru, but without achieving universal coverage for basic education. For these countries, increasingly binding fiscal constraints and continued needs to expand coverage of basic education sharply constrain the policy agenda. Peru, in contrast, has positioned itself at a crossroads. One direction to take involves continuation and improvement of the system much as it currently is. On the other hand, Peru has achieved the preconditions to initiate a major drive to consolidate equity gains and expand access while improving quality. Choosing this path would require, over time, substantially increased public expenditures xix on education. A gradual increase from 2.4 percent to 4.5 percent of GDP net of pension expenditures (that is, to the Latin American average) is, for Peru, feasible in the medium term, given its past demonstrated ability to maintain fiscal discipline and to improve macroeconomic performance. By increasing public expenditure levels to only the Latin American average, Peru has the opportunity to enhance markedly the intellectual ability and competitiveness of its labor force within a generation. No policy challenge is more significant. xx Chapter 1. Sector Overview As a lower middle-income country with a Gross National Product (GNP) per capita of $2,460 in 1997,' Peru has made impressive progress in extending education opportuni- ties over the last five decades. Between 1950 and 1997, enrollment expanded 6.6 times, more than double the three-fold increase of the population.2 Total enrollment grew from a mere 14 percent of the population in 1950 to 36 percent in 1997. As a consequence, over the period, the average education level of the population of age 15 and over increased from 1.9 years to 8.6 years,3 and the illiteracy rate was reduced from 58 percent to 11 percent. Female illiteracy was reduced from 70 to 18 percent, and rural illiteracy from over 60 to 29 percent. These are impressive accomplishments in a country as physically and ethnically diverse as Peru, particularly in view of its recent history of macroeconomic instability and civil unrest. Chapter 1 summarizes achievements to date and describes re- cent developments and issues in the sector. 1.1. Achievements In 1997, school enrollment was practically universal for the 6- to 11-year olds, about 80 percent of the 12- to 16-year olds, and over 30 percent of the 17- to 25-year olds.4 The education system now encompasses about 8 million children and adults, of whom 6.7 million are in the public, formal system. (See Background Note 1 for the struc- ture of the education system, Appendices I to 3 for education statistics, and Appendix 4 for gross and net enrollment ratios.) International comparison shows Peruvian enrollment ratio in a remarkably favor- able light, as is presented in Figure 1. Each bar in the figure stands for a particular coun- try in the World Bank's education database, and Peru is high up on the list, where the neighbors on the graph are mostly developed nations. When international comparison is made of the level of public spending on educa- tion as a percentage of Gross Domestic Product (GDP), however, the position of Peru changes, as is shown in Figure 2, which is also drawn from the same World Bank data- base. In the second graph, the neighboring countries include a number of low-income countries. I See World Bank, 1998b, World Development Report, p. 191. 2 See Diaz, Huayte, Farro, and Tavara (1995, p. 22), which cites Instituto Nacional de Estadistica e Informatica (INEI) and Ministry of Education (MED) statistics. The average of 1.9 years of education corresponds to the national census of 1940. The 8.6 years of education is based on the National Survey of Living Standards by MNEI (Encuesta Na- cional de Niveles de Vida, or ENNIV), 1997. 4The findings were based on analysis of a household survey conducted by Instituto Cuanto in 1997. The Cuanto dataset was more extensive in its questionnaire about education expenditure than the government's INEI dataset. Therefore, it was used for this study. I Figure 1. International Comparison of Enrollment of Students between the Ages of 3 and 23 100 Peru (Rank 15) 90 / . 80 60 (U 7 0. o 50 0 40 a. 30 20 10 0 Figure 2. International Comparison of Public Expenditure on Education as a Percentage of Gross Domestic Product 12 10 U E 6 DL Peru (Rank 118) .. 4 2 0 = 2- 0- Source: Edstats Database of The World Bank 2 These figures provide a compressed account of achievements to date and also pose a question: what can explain the puzzle that Peru has been able to achieve an unusually high participation rate with a relatively low level of public spending on education? How has this been accomplished? Answering this question raises the main policy issues that this report addresses. Several hypotheses for explaining the puzzle are investigated and their policy implications are discussed: (a) Is it because public resources have been well used and well targeted? (b) Is it because Peruvian households value education highly and spend heavily on educa- tion? (c) Has expansion of access to basic education come at the expense of qualitative im- provement? (d) Is the low level of public spending attributable to the ability to contain the salary cost of teachers? How does this and other policy on teachers affect the profession? The chapters in the report correspond roughly with the above questions. Chapter I sets the context for discussion by summarizing achievements to date and by describing recent developments in the sector. Chapter 2 reviews public expenditure on education in order to address the question of whether public resources have been used efficiently and equitably; it also examines private spending on education to assess the impact on equity. Chapter 3 analyzes indicators on access, school survival rates, quality, and labor market outcomes to evaluate the tradeoff between quantitative expansion and qualitative im- provement within the constraints of public and private finance. Chapter 4 reviews the pol- icy towards teacher employment and deployment, conditions of service and compensa- tion, incentives and accountability. Chapter 5 explores the options for improving equity, quality, and efficiency, and discusses the resource implications. 1.2. The Evolving Education System Many of the policy changes that have taken place in Peru in the 1990s represent, to a considerable extent, a break with the past. The evolution of this policy environment must be viewed against the economic and political crises of the late 1980s. Fiscal deficit (which included debt servicing) was equivalent to 10 percent of GDP in 1988. Hyperin- flation cumulated to a rate of over 7,000 percent in 1990. Insurgency was rampant in the countryside and at times in the cities. The year 1990 marked a turning point. Structural adjustment under the Fujimori Administration restored fiscal discipline. Sound macroeconomic management, in combi- nation with the ending of the Shining Path insurgency in 1994, gradually set the economy on a growth path. By 1997, a balanced budget (which included debt servicing) was achieved; savings in the current account progressively increased to 4.4 percent of GDP; and both GDP growth and inflation were estimated at around 7 percent each. Before the effects of the East Asian financial crisis was spread to Latin America in 1998, Peru's economic growth rate was second only to that of Chile in the region. Although the econ- omy suffered a downturn in subsequent years, its past demonstrated ability to turn the economy around indicates the potential once political and economic stability is restored. 3 In the first half of the 1990s, along with major reform of macroeconomic policy, a series of measures were implemented to contain public expenditure, to mobilize private resources, and to delegate social services to the regions. These measures and the changes they set into motion are as follows: * Rationalization of the public sector and introduction of a private personalized pension plan Retrenchment of government services between 1991 and 1993, which resulted in a reduction in employment in the central administration of the Ministry of Education (MED) by 72 percent.5 Vacant or new positions in the Ministry are filled by consultants on contract. To contain personnel expenditure, authorized pensionable positions (APP) for the entire sector have been frozen since 1995. In 1994, a new personalized pension plan (AFP) (Law 25897), modeled after the Chilean private pension plan, was introduced.6 People on public payroll, including teach- ers, are given the option of choosing whether they want their pension to be covered by the previous laws, or have the individualized account which they can invest with a private company for capitalization. Since it is a personalized plan, they can take the pension 5 World Bank, 1994b: Peru: Public Expenditure Review, Report No. 13190-PE, p. 50. 6 Before the reform, two laws governed the pensions for teachers: (1) Law 20530, which affects those teachers who joined the service before 1980, has no minimum retirement age. It allows female retirees to receive 7/25 and male retirees to receive 7/30 of their basic salaries after they have contributed 6 percent of their basic salaries for 7 years. Female retirees who have worked for 25 years and male retirees who have worked for 30 years are entitled to 100 percent of their basic salaries. Pension benefits are not only fully adjusted for inflation, but will be linked to the salary increments of current serving employees. When pension- ers were still in active service, they also contributed 9 percent of their salaries to social security (Instituto Peruano de Seguro Social or IPSS) and 5 percent to a housing fund (Fondo Nacional de Vivienda or FONAVI), while the employer (that is, MED) contributed 6 percent. Pensioners have to continue to contribute 4 percent of their pension to the Treasury in order to enjoy the benefits. (2) Law 19990, which affects those teachers who joined the service after 1980, imposes a minimum retirement age of 55 for women who had completed 25 years of service, and 60 for men who had completed 30 years of service. Pension benefits were fixed and not adjusted for infla- tion. Active teachers have to contribute 13 percent of their basic salaries, 9 percent to IPSS and 5 percent to FONAVI. There is no doubt that the pension provided under Law 20530 was very generous, and pro- vided coverage to those who might be as young as their late thirties and early forties (which are the age groups of the last cohort of ISP graduates who joined the teaching profession before 1980). In fact, Law 20530 provides a strong incentive for teachers to retire with pension and start a second career, such as teaching in or founding private schools. It was not surprising that the re- trenchment of the early 1990s resulted in launching the second career of many enterprising per- sons in this sector. The incomplete system of data collection left the Government unable to calcu- late the total number of teachers and their age stnicture governed under these three retirement laws. This has made it difficult to project the total pension obligations of the education sector. The pension issue has been a dominant one in public expenditure on education. The reform has stopped future drain on public expenditure. 4 wherever they are employed.7 Salaries paid to teachers who choose the personalized pen- sion plan are higher than those under previous laws in order to provide incentives for conversion (Appendix 6.19). Meanwhile, the Government has created a Pension Office (Oficina de Normalizacion Previsional, or ONP for short) which will eventually handle all pension matters. The new personalized pension plan is expected to lessen the burden of the state treasury and make employment more flexible by de-linking it with specific employers. These measures to contain public expenditure, along with other policies, helped re- store fiscal balance. These, however, came at the cost of low morale in the public sector, which could ultimately undermine high performance. Cost containment alone could not result in efficiency gain without concomitant use of transparent criteria for personnel re- cruitment and resource allocating, setting up of incentives to reward performance, and introduction of accountability. Towards the second half of the 1990s, there has been in- creased attention to these complementary needs. Competitive examinations were introduced in 1997 to select new principals and teachers to fill vacant authorized pensionable positions (APP). A national student assess- ment program was set up in 1996 to monitor achievement. These measures, although still in an early stage of implementation, provide the building blocks towards the establish- ment of a merit-based system. Further policy guidelines on standards for teachers and students, strengthened pre-service and in-service training, and incentives and accountabil- ity, backed up by resources to fund them, would counteract the morale issue, profession- alize the teaching force, and improve the quality of education. * Extension of free and compulsory education in the 1993 Constitution The Constitution of 1993 extends compulsory and free education from primary to secondary education. In 1997, a proposed structural change of the education system rede- fined basic education by extending downward by one-year to include initial education for 5-year-old children, and by shortening secondary education from five to four years. This will make the overall duration of basic education 11 years. The plan is to progressively extend universal initial education to cover children of four years of age, and then, those of three years of age. Since secondary education was shortened, it is compensated by the introduction of two years of preparatory course work (bachillerato) which is not compul- sory but free and will provide the transition to tertiary education or to the world of work. Certificate examinations will be held at the end of basic education, bachillerato, and ter- tiary education (Table 1). This structural change has far reaching implications on the resource requirements to implement the policy, the supply and demand for teachers at different levels and in dif- ferent subject specialties, and the content and methods of teacher in-service and pre- 7 Employees who choose private pensions have to contribute 11 to 13 percent of their basic salaries to future pension, and 5 percent to FONAVI, while the Government contributes 9 percent to IPSS to cover health insurance. 5 service training. How various components of the proposal can be synchronized remains to be worked out. Table 1: A Comparison of the Existing and New Education Structures Age Existing Structure Age New Structure Objective of Change 3-5 Nonuniversal 5 1 year of universal initial Facilitate the rticulation be. initial education education at the age of 5 tween initial and pimary education to improve effi; ciency of the systt 6-11 6 years of univer- 6-11 Duration of primary educa- Develop the capacity of sal primary tion remains unchanged, but learning education of un- emphasis is on improving the even quality qua;i,y 12-16 5 years of secon- 12-15 4 yer of universal secon- Guarantee ftee access and the dary education dary education use of distance education for that has uneven rural areas to extend cover- access between age. I year of preschool, 6 rural and urban years of primary, plus 4 years areas of secondary education will form 11 years of universal basic education. Certification of study at the end of basic education. 16-17 2 years of bachillerato is a Preparation for work and for new introduction. It is not tertiary education. Certifica- compulsory but free in public tion of study at the end of schools. bachillerato. 17- Tertiary education 18 and Tertiary education Remains unchanged. over I__ Source: MED, Nueva Estructura del Sistema Educativo Peruano: Fundamento de la Propuesta, 1997. * Encouragement of private schools Complementary to the need to contain public spending and to the constitutional mandate for expanded compulsory education is a new law that encourages establishment of private schools. This legal framework, combined with retrenchment of education ad- ministrators and teachers, has led to a rapid growth of private schools.8 This supply has met the demand of parents who have grown weary of frequent closing of public schools due to teachers' strikes in the early 1990s, who consider the quality of public education unsatisfactory, and who can afford to pay for private schools. (See Figures 3 and 4 for increase in enrollment in public and private schools.) 8 There are a number of private schools: secular schools, cooperative schools, Catholic and other religious schools, all of which are privately financed and privately run. In addition, there is Fe y Alegria, which is operated by the Catholic Church, but financed by the State. 6 Figure 3. Trend of Enrollment in Public Institutions by Level, 1990 to 1997 4,000,000 3,500,000 NW 3,000,000 ' 2,500,000 - 2,000,000 E 1,500,000 j -A z 1,000,000 500,000 0 1990 1991 1992 1993 1994 1995 1996 1997 Source: Ministry of Education Figure 4. Trends of Enrollment in Private Institutions by Level, 1990 to 1997 600,000 500,000 4 400,000 U, -0 300,000 - 'a0 E 100,000 0 - 1990 1991 1992 1993 1994 1995 1996 1997 Initial --*-Primary SecOndarv ---Sup No Univ' Source: Ministry of Education 7 Between 1990 and 1997, enrollment in private education grew by 62 percent in initial education, 9 percent in primary education, 28 percent in secondary education, and 37 per- cent in tertiary nonuniversity education (MED statistics). This outpaced the rate of in- crease at these levels in the public sector, which grew only by 34 percent in initial educa- tion, 8 percent in primary education, 10 percent in secondary education, 25 percent in ter- tiary nonuniversity education.9 Within the rapidly expanding tertiary nonuniversity sec- tor, private teacher training institutions accounted for a significant share. (See Appendi- ces 1.1 and 1.3.) The overwhelming majority of private school students are from the richest con- sumption quintile of the country. For example, in the urban areas, these students ac- counted for as much as 39 percent of net enrollment in the 6 to 11 age group, 27 percent of the 12 tol 8 age group, 21 percent of the 17 to 25 age group (Appendix 4.3d). A sig- nificant percentage of the fourth quintile also chose private schools. By contrast, there were less than 1.5 percent of students from the poorest quintile of all age groups in pri- vate schools. In the rural areas, overall, there was not even 1 percent of children of all age groups in private schools. In the public school system in urban areas, net enrollment of the richest quintiles in primary education (53 percent) was much lower than that of the poorest quintile (85 percent) (Appendix 4.2d). The implications for policy will be dis- cussed in Chapter 2. * Setting up of regional administration, deconcentration of education services, and creation of new ministries The 1993 Constitution also restructures the political system. It divides the country into regions, departments, provinces, and districts. ° At each region, the Transitory Coun- cil of Regional Administration (Consejos Transitorios de Administraci6n Regional or CTAR in short) coordinates all regional affairs and finances. In the case of Lima and Ca- llao, the Development Corperation of Lima and Callao (CORDELICA) serves a similar function as the CTAR. The CTARs and CORDELICA are directly under the Ministry of the Presidency (PRES). PRES was created at the same time. Not only does it coordinate the region's educa- tion budget but also has responsibility for most of the capital expenditure on education 9 The decline in public and private university enrollment in official statistics is inconsistent with reality. The most probable reason is due to nonreporting in the case of private universities. As for public universities, the dramatic decline in enrollment between 1996 and 1997 is most probably because only the registration in the first semester is taken into account. Normally, regis- tration in both semesters would be averaged out for the entire academic year. The figure for 1997 was probably not yet updated. An informal survey by the World Bank found that enrollment in public universities has remained stable in the 1990s, while that in private universities has grown rapidly. '° The political levels that have elected offices are the central government and the municipal government (provincial and district municipal governments). The central government has an elected president and congress. The provincial and district municipal governments have their re- spective elected mayors and councils. The CTAR each appoints a president and a regional coor- dination council. 8 through National Infrastructure for Education and Health (Infraestructura Nacional para Educaci6n y Salud, or INFES in short). While the central government and the municipal governments have their own reve- nue sources, the regional administrations do not, and depend on the central government for transfer of revenue. Regionalization of administration has affected the budget process, intragovemmental allocation of resources, and the balance of power between various ministries in the center and the regions. In education, MED retains the overall responsibility for setting education po]icy on preprimary, primary, secondary, vocational, and tertiary nonuniversity education, but not on university education. Public universities remain autonomous and outside the jurisdic- tion of the MED or regional administrations. They have their own coordinating body, the National Assembly of Rectors (Asamblea Nacional de Rectores). A new ministry, Minis- try for the Promotion of Women and Human Development (Ministerio de Promoci6n de la Mujer y del Desarollo Humano or PROMUDEH in short), which was created in 1996, is in charge of early childhood care for children from birth to four or five, and the literacy program. MED is charged with the missions of developing the character of the individual, improving the quality of life, and facilitating social development in Peru through promo- tion of culture, science and technology, physical education, and pursuit of excellence. The responsibility for provision of educational services from preschool to tertiary non- university education has been delegated to 23 Regional Education Directorates (Direc- ciones Regionales de Educaci6n or DREs in short) which sit within the regional admini- stration, and to the Directorate of Education in Lima and that of Callao. MED makes educational policy for the entire nation, and gives technical and normative directions to the DREs, which implement policies. The Education Director of Lima is appointed by the Minister of Education, and the other Regional Education Directors are also appointed by the Minister with the approval of the regional administration. Under the DREs are Areas of Execution (AEs), Educational Service Units (Uni- dades de Servicios Educativos or USEs for short), and Education Development Areas (Areas de Desarrollo Educativo or ADEs in short)."1 The USEs manage some 58,000 schools and about 18,000 nonformal educational programs, both public and private (Fig- ure 5). Both AEs and USEs are administrative units, but ADEs are educational supervi- sion and support units. USEs are line units executing the functions and budgets of the DREs or Sub-DREs. Each DRE is headed by a director, who is assisted by two committees: one com- posed of the heads of subordinate units (Sub-DREs), another composed of the heads of internal line units, such as internal control, administration, legal and technical advisors' 1 Decree 26011 provides the framework for decentralization of educational administration and management, and transfers the private right to Communal Councils of Education (Consejos Comunales de Educaci6n or COMUNED) for the administration of public schools. But months after the approval of the law, it was decided that it would not be implemented. 9 office, and technical pedagogic and technical cultural departments. Sub-DREs and USEs are organized essentially along the same lines. Figure 5: MINISTRY OF EDUCATION OPERATIONAL STRUCTURE 05-29-98 MINISTRY OF EDUCATION EDECATTON REGIONAL DIRECTORATE EDUICATION SUBREGIONS AREAS OF UNTOFEUATION iAREA OF EDUC-ATION EXECLMION SEVCS l DEVELOPMENT l TOTAL EDUCATION TOTAL NONFORMAL CENTfilERS (SCHlOOLS) PROGRAMS PUBBUC EDUCATION PRIVATE EDUCATION NONFORMAL PUBLIC NONFORMAL PRIVATE CENTERS CENTERS EDUCATON PROGRAMS EDUCATION PROGRAMS nit. Educ. 9,064 =nt. Educ. 4,936 nit. Edu. 17,029 lrItn EduC 69 Prim. Educ. 26, 963 Prim. Educ. 4,976 Prim. Educ. 309 Prim. Educ. 67 Sec. Educ. 6231 Sec. Educ. 2,060 Sec. Educ. 94 Sec- Educ. 169 Other levels 3,022 Other levels 1,76430 Other levels 4 Source: Ministry of Education Summary. Policies implemented since 1990 have irrevocably changed the educa- tion system. Containment of public spending and mobilization of private resources may be loosely considered as the first generation of reform. Important achievements ensued. Yet, in the course of implementation, many issues have arisen that must be addressed in order for the sector to move forward. These issues have largely defined the scope for a second wave of reform which must deal with remaining inequities, quality improvement, further expansion, and institutional issues. Table 5 at the end of Chapter 5 provides an overview of how these new measures cross cut with the issues of public and private fi- nance, quality, efficiency and equity, and the teaching profession. 10 Chapter 2. Education Finance Two key questions in the puzzle posed at the beginning of the report are whether Peru's ability to finance high level of enrollment is because public resources have been used efficiently and equitably and whether households spending on education is high. This chapter evaluates these questions by first reviewing public spending on education and then by looking at the magnitude and impact of household financing. It then dis- cusses the policy implications. 2.1. The Budget Process To understand public finance of education in Peru, it is important to first under- stand the budget process. The fiscal year in Peru coincides with the calendar year as well as the school year. The budget process begins in May every year when the lowest units submit their budget requests for the following year. The Ministry of the Economy and Finance (MEF) consolidates all requests in August and presents to the Congress in Sep- tember. The Congress approves the budget in November/December for funds to be allo- cated in January. There are five budgetary entities in education: (a) MED, which covers the greater Lima/Callao area, (b) regions, (c) public universities, (d) other decentralized institutions, and (e) PRES. The first four are entitled primarily to handle recurrent expenditure under their jurisdiction, while the last one is responsible for investment and, hence, controls most of the capital expenditure through INFES.12 Since the establishment of regional administration in 1991, each of these entities prepares their own budget and negotiates directly with MEF.13 The process begins when schools present their requests for recurrent budget to the USEs, which submit a consoli- dated request to the DREs which, in turn, forward the aggregated budget to the CTARs. These budgets are consolidated in PRES, and then presented to MEF. MED's budget which also covers greater Lima and Callao and some national programs are submitted directly to MEF. The universities and decentralized institutions submit their own respec- tive budgets directly to MEF. PRES also presents its budget for capital expenditure in education directly to MEF. Coordination is weak between MED and the regions, between MED and PRES, and between the regions and PRES.14 12 The budget for PROMUDEH, which has responsibility for literacy programs and early childhood care, is not consolidated with education. The budget for these activities is very small. Since PROMUDEH was created only in 1996, this report does not cover this new ministry. 13 In principle, this practice was to have changed after April 1, 1998, to have MED coordi- nate all recurrent budgetary matters for the regions. The new process was supposed to enable MED to have an overview of the nation's education budget and spending patterns. However, this was implemented for only three months and then there was a reversion to the old process. 14 DREs have little control over their own capital investment. For example, since all the education allocation to them covers only recurrent spending, if DREs want to buy a computer for 11 Under this process, MED does not have complete information about what the re- gions have requested and the regions do not report to MED about their actual expendi- ture. Therefore, much of the policy by MED which has national application has been made without clear information on the requirements and availability of resources in the country. MEF has the ultimate say over allocation of education resources but without having an overall view of priorities and strategies in the education sector. There is no co- ordinating body in education that can assure the coherence of policies and provide the necessary resources to support them. MEF's decisions for allocating resources are based on the availability of public funds to match with what have been requested by MED and the DREs to deliver services. The first obligation is to pay salaries and pensions, and then to meet the needs for provid- ing basic services of each of the entities. There are no funding formula to allocate re- sources other than the aforementioned priorities. Funds are allocated on a quarterly basis, but spent on a monthly basis. Funds not used as planned every month have to be returned to MEF at the end of the month, to be deposited back to the public treasury. There is no incentive to award savings. The main ground for allocating recurrent budgets to each DRE is the number of au- thorized pensionable positions (APPs) for teachers and administrative staff. These posi- tions, in turn, are based on the ratio of allocating, on average, one teacher for every 35 students in the urban areas, and one teacher for every 20 students in rural areas, with some variations by level and for remote areas. On the surface, this method of allocation has given special consideration to the rural areas. However, the 20 students in the rural areas may be of different ages and grade levels, and the teacher does not have a full range of skills to meet all of their educational needs. Moreover, since the freeze of pensionable positions in 1995, the departments that have high birth rates (usually poorer and with a larger indigenous population) have been more adversely affected than others. Teachers and administrators in the urban areas are paid monthly through deposit to their bank accounts although some are paid by checks; most of those in rural areas are paid by check. Textbooks, library books, and other educational materials and supplies are generally purchased by MED at the central level and are delivered to DREs, which dis- tribute them to all schools under their jurisdiction. Electricity and water for evening schools are paid by the USEs. For many schools, parents' contribution pay for water and electricity. Many rural schools have neither water nor electricity. Given the fragmentation of the budgetary process, there is a strong case for improv- ing coordination among various budgetary entities to assist the coherence of educational policy and raise efficiency in resource allocation to the sector as a whole. Equally strong is the case for improving the consistency, flexibility, and transparency of funding deci- sions through formula that reward efficiency and allow for adjustment to local needs. use in the office, they have to submit a separate request to the CTARs for incorporation into the regions' capital budget request. 12 2.2. Public Expenditure on Education'5 Historical trend. Government allocation constitutes the most important source of funding for education in Peru. Enrollment growth, however, has far exceeded the growth rates of either the GDP or public expenditure on education. Between 1970 and 1990, GDP increased by 85 percent in real terms, total govemment expenditure by 84 percent, public expenditure on education by 72 percent, while enrollment in public institutions by 130 percent (Appendix 6.2). Public spending on education fluctuated widely throughout the 27 years under re- view. Between 1970 and 1997, public expenditure on education16 peaked in 1972 at 3.7 percent of GDP, falling to 2.2 percent in 1988 at the lowest point, and recovering to 3 percent in 1997.17 The steep decline in public spending on education in the late 1980s re- flected the extremely volatile macroeconomic environment. In 1988, when GDP con- tracted by 8.4 percent in real terms and total government expenditure by 29 percent, total public spending on education declined disproportionately by 40 percent (Figures 6 and 7, Appendices 6.1 and 6.2). The recovery of public spending on education in the 1990s started from this ex- tremely low base in the late 1980s. After declining in real terms by 40 percent in 1988, 10 percent in 1989, and 7 percent in 1990, allocation to education increased annually by some 3.6 percent in 1991 and 1992, respectively, by 18 percent in 1993, by 23 percent in 1994, and by 20 percent in 1995. It was reduced by 7 percent in real terms in 1996 but rose by 18 percent in 1997. The overall trend in the 1990s is a reversal of that in the 1980s: education expenditure has increased at a higher rate than that of GDP or total gov- ernment expenditure (except for two years) (Figure 7). This trend indicates the govern- ment's commitment to education. Consideration for fiscal balance, however, has led to a gradual approach to increasing public spending on education. The enormous fluctuation of public expenditure on education over time, nonetheless, reflected deep-seated instabil- ity and unpredictability in resource allocation, which made it difficult for strategic plan- ning, and undermined continuity of projects. 15 This review of public expenditure on education by Juan Pablo Silva of the Ministry of Education and Arturo Miranda of Universidad de San Marcos has updated and deepened the analysis by Jaime Saavedra, Roberto Melzi, and Arturo Miranda (1998). Jaime Saavedra re- viewed the work to ensure consistency in methodology. 16 This review focuses on direct public expenditure for educational institutions, which coin- cides with the Government's official account of public education spending. It does not examine public subsidies to households such as school health and school meals, which is funded under the Ministry of Health and PRES, or early childhood care and the literacy program under PRO- MUDEH. The reason for doing so is to ensure that the scope of discussion remains focused. It is also consistent with OECD's classification, which divides public expenditure into three groups: (a) direct public expenditure for educational institutions, (b) total public subsidies to households and other private entities, and (c) financial aid to students not attributable to households (see Ap- pendix 10.1). When the scope of the review is clearly defined, it would be possible to compare across countries. 17 This includes external finance, but not interest payment from borrowing. 13 Figure 6. Public Expenditure on Education and Central Government Expenditure as a Percentage of Gross Domestic Product, 1970 to 1997 2 5 ___ ___- -__- -____- - _ _ . _____ 20 o i. 1 1 ngs$ 14e 4t lef le -Al "Al 'l ,t1 le leS 11# I'l I'll 'I ,I le se- ~ ot b C ExD_ on dTL jLtA.-aJ £AiiaiomExmeThiture Source: Ministry of Economy and Finance (MEF) Figure 7. Percentage Change of Gross Domestic Product, Central Government Expenditure, and Public Education Expenditure; 1970 to 1997 40 30 20 10 19 19 BB{<1 / 1 1993 1994 i995 t -30 -50 - - - -- - - -- - - - .GDP -A -Total Public Expenditurr -_--Total Education Expendituto Source: Ministry of Economy and Finance (MEF) 14 The level of public spending on education in Peru is low in comparison with other non-socialist lower-middle-income countries (Figure 8). It is substantially lower than the Latin American regional average of 4.5 percent of GDP (UNESCO, 1998), or the OECD average of 4.8 percent' (OECD, 2000). Because the school-age population of OCED is much smaller than that of Latin America, even if the level of public spending on educa- tion as a percentage is similar, the need for educational services is proportionally higher in the latter. In Peru, about one-third of the population are attending schools, in contrast to 16 percent in France and the United Kingdom, respectively, 14 percent in Japan, 28 percent in Mexico, 26 percent in Colombia, and 23 percent in Chile. This comparison makes Peru's level of public spending even lower in both relative and absolute terms. It should be noted that pensions of retired teachers and administrators are paid out of the recurrent expenditure on education. This accounted for 22 percent of the total edu- cation expenditure in 1997. Net of pensions, public expenditure on education was about 2.4 percent in 1997. Many countries'9 pay pensions out of a separate fund, such as a provident fund which may be invested in the capital or financial markets to increase the fund, not from the recurrent allocation to the sector. Although public education expendi- ture that includes pensions reflects the true cost of education, when comparison is made with other countries' spending levels, the proper way is to compare public expenditure net of pensions. This will make Peruvian public spending on education as a percentage of GDP less than half of the region's average for most of the years in the 1990s. While it is a tribute to MED and the teaching profession to be able to sustain such high enrollment ra- tios at all levels with so little resources, the situation highlights the predicament of the education sector, with adverse implications for quality. Figure 8: Public Spending on Education and GNP Per Capita in Lower-Middle-Income Countries c 10 9 Z 7 c0 3 ~~~PERU** 2 3 i 0- 0 2,000 4,000 6,000 8,000 10,000 GNP per capita PPP (Purchase Price Parity) in current tional $ Source: Edstats database of the World Bank 18 The OECD's average cited here refers to educational institution-related expenses, but ex- cludes educational subsidies to households, and student financial assistance. 19 Paying pensions out of the sector's recurrent expenditure is uncommon, except in social- ist countries such as China. The United States, Singapore, Hong Kong, South Korea, Jamaica, Trinidad and Tobago, Colombia, Chile, Mexico, and Argentina pay pensions from a separate fund. 15 Changes in composition of education spending. What did the additional public expenditure in the 1990s finance? Unlike many countries where most of the increase in public expenditure on education has been absorbed in personnel cost, Peru put the addi- tional resources in educational infrastructure, in quality enhancing inputs (such as text- books), in teacher training, and in capacity building. For example, the World Bank Pro- ject for Improvement of Quality of Basic Education (Mejoramiento de la Calidad de la Educacion Primaria or MECEP in short) finances a class-set of free textbooks for all grades in primary education throughout the country. Capital investment increased from 1.4 to 15 percent of total public expenditure on education between 1990 and 1994, and then gradually fell back to 8 percent in 1997. Spending on other capital goods also increased from 1.4 to 2.1 percent. Spending on goods and services as a percentage of total education expenditure more than doubled from 4 to 10 percent, and other recurrent costs also more than doubled from 0.7 to 1.8 percent. It should be noted, however, that the fluctuation in nonpersonnel education ex- penditure still bore the mark of unpredictability, which undermines planning and imple- mentation (Figures 9 and 10 and Appendix 6.7). By contrast, total personnel cost (remuneration and pensions) increased by 64 per- cent, substantially below the 94 percent increase of total public expenditure (Appendix 6.7). As a result, the percentage share of personnel cost was reduced from 92 to 78 per- cent of total public spending during the period. Net of pensions, compensation for teach- ers and administrators (which includes salaries, allowances, and contribution to future pensions) accounted for under 60 percent of total public expenditure. This is substantially below the personnel expenditure of most lower-middle-income countries. Intragovernmental transfer of resources is the area where the most far-reaching change in education finance has occurred. The Government in 1991 initiated a policy to transfer public funds directly to the regions. In 1990, the MED managed 71 percent of the public education expenditure, the regions 17 percent, the universities 10 percent, other public institutions 1.6 percent, and the PRES 0.3 percent. By 1997, only 25 percent of public expenditure was managed by MED, as 56 percent was transferred directly to re- gions, 16 percent to the universities, 2 percent to other decentralized public institutions, and nearly 4 percent to the PRES (Figure 11 and Appendix 6.5). Given that such a large share of public expenditure on education is transferred to the regions, the universities, decentralized institutions, and PRES, the case for strengthening coordination among these bodies for policy and resource allocation is even stronger. The departments can generate their own resources to invest in education, most of which are used to purchase goods and services, for administrative purposes, or for post- secondary education. However, the departments' capacity is limited, and they depend heavily on transfer from the central government (Appendices 6.11 to 6.17). In 1997, cen- tral transfer accounted for 100 percent of pensions, almost 100 percent of all salaries in administration, planning, initial education, primary education, secondary education, terti- ary education, and special education. PRES provided most of capital expenditure in pri- mary and secondary education in the country. 16 Figure 9. Total Recurent and Capital Expenditures on Education 1970 to 1997 (in constant 1997 soles) 6000 5000- ,,4000 0 E 3000 E c 2000 1000 0 A 4OCN 40 CO X O C N 0 0 co OC .0. 0~) 00 0 00 0~co 00 00 co -00 0 0 0 Total Expenditure ° Recurrent Expenditure | -Capital Expenditure Figure 10. Composition of Public Expenditures on Source: Ministry of Economy and Finance (MEF) Education, 1990 to 1997 (Percentage, Regrouped According to the Lastest Classification) 70 .- 1990 1991 1992 1993 1994 1995 1996 1997 I~~~~W ~PW w U ^ ,- 4 - G- S1. Figure 11. Inter-Govemrnental Transfer of Resources (Percentage Source: Ministry of Economy and Finance (MEF) of Total), 1990 to 1997 80 70 9 60 50 __- 40 > _ 30 20 10 o 1990 1991 1992 1993 1994 1995 1996 1997 -' Ministry of Education - Regional govemments -Public Institutions "-Public Universities Ministry of the Presidency Source: Ministry of Economy and Finance (MEF) 17 It should be noted that pensions are paid through the department where the retiree resides, not where he/she used to teach. That is why the share of pensions as a proportion of total public expenditure varies from one department to the next. In 1997, pensions ac- counted for 32 percent of MED's total expenditure, about 21 percent of the region's ex- penditure, but 14 percent of total university expenditure. Since MED has jurisdiction over Lima/Callao, 43 percent of the total pensions of the education sector were paid out of Lima/Callao, and the rest through other departments. (Appendix 6.8) Intrasectoral allocation. Among various subsectors, public universities are the only one which has benefited from uninterrupted increase in public expenditure (from about 10 to 16 percent) between 1990 and 1997. In 1997, about 6 percent of total public expendi- ture was spent on initial education, 27 percent on primary education, 19 percent on secon- dary education, 2 percent on nonuniversity tertiary education, 16 percent on university education, and 21 percent on administration. (Figure 12 and Appendix 6.9). It should be noted that administration expenditure includes compensation to all prin- cipals, school administrators, and inspectors at all levels of education. Disaggregated in- formation on administration is not available to pro-rate it to various educational levels. That makes spending by each level low and administration rather high. For comparison, OECD countries classify the salaries of all administrative personnel as personnel cost, not as administrative cost (Appendices 10.1-10.5). Per student spending. Between 1990 and 1997, per student recurrent public spend- ing steadily increased at all levels. It grew by 70 percent in initial education, 87 percent in primary education, 71 percent in secondary education, 79 percent in tertiary nonuniversity education, and 335 percent in university education. While the percentage increase was im- pressive, it started from a very low base (Figure 13 and Appendix 6.10). For university education, the very low per student spending in 1990 signaled poor quality. The rapid in- crease in per student spending throughout the 1990s, however, reflected not only addi- tional public allocation to this subsector, but also reduced enrollment in public universi- ties. Whether increased resources to improve quality of university education should come from the public or private sector will be discussed in Chapter 3. Converted to US dollars, per student public spending (inclusive of expenditure on pensions) in 1997 was US$175 in initial education, US$201 in primary education, US$260 in secondary education, US$324 in nonuniversity tertiary education, and US$1,255 in uni- versity education (Appendix 6.10). The difference in public spending per student between higher education and primary education in Peru was 6 times. Since the distribution of pen- sions differs by level of education, expenditure net of pension that goes to operating the university system is much higher than the gross figure, whereas expenditure net of pension that goes to basic education is lower than the gross figure. Net of pensions, per student spending on university education was 7 times higher than that of primary education in 1997. Nevertheless, this differential is still lower than that in many countries of Latin America (which may be as high as 20 times). In many countries in the region, public ex- penditure on higher education per student is often above $2,000. 18 Figure 12. Public Expenditure on Education by Level, 1990 to 1997 (Percentage) 30 25 201 sE ] ~~~~~~~~A ES o Al 1990 1991 1992 1993 1994 1995 1996 1997 -B-Initial Education -e- Primary Educabon *-Secondary Education _-+Tertiarv Non-Universitv -Administration -+Universities Source: Ministry of Economy and Finance (MEF) Figure 13. Per Student Recurrent Public Expenditure on Education by Level, 1990 to 1997 (Constant 1997 Soles) 3500 - 3000 - 2500 - 2000 - 1500 _ 1000 - 500 1990 1991 1992 1993 1994 1995 1996 1997 ~Inihial Education G Prirmary Education Se,ondary Education 4Tertiatv Non-University +Universitv Source: Ministry of Economy and Finance (MEF) 19 Equity of distribution of public expenditure. How equitable has the distribution of public expenditure been? A standard method to measure the incidence of public expendi- ture is to construct a Lorenz curve20 to show the proportion of education expenditure which accrues to each consumption or income quintile. (This report uses consumption quintiles throughout).21 Since capital expenditure varies from year to year, only recurrent expenditure for 1997 was used in the incidence analysis. Figure 16 shows a number of Lorenz curves with recurrent public expenditure dis- aggregated by level of education.22 This analysis included expenditure on pensions. Re- current public expenditure on preprimary and primary education was skewed toward the lowest consumption quintile (29 percent) and that on higher education was skewed to- ward the highest consumption quintile because the vast majority of students (47 percent) in higher education were from the top quintile and only 4 percent were from the bottom quintile. (Appendix 4.4a.) Public expenditure on pnrmary education is equity enhancing not only because of the universal enrollment in primary education, but also because many families in the top two quintiles have opted out of sending their children to public preprimary, primary, and secondary schools, leaving the public system mainly to the less well-off (Appendix 4.2a- 4.2d, and 4.3a-4.3d). However, the top quintile is the major user of public universities because children from that group have been better prepared for it and cand afford to forgo the income to pursue further education. That is why public spending on the preuniversity level is more equitable than that on university education. 20 The Lorenz curve is read as follows: the heavy straight black line joining the two corners as shown in Figures 14-16 is the line of "perfect equality" or the line which would obtain if each consumption quintile received an equal amount of educational expenditure-for instance, if 20 percent of expenditures accrued to the poorest quintile just as to the richest quintile. The curved line(s), the Lorenz curve(s) shown in these figures, represent the actual distribution of expendi- tures. The closer the curves are to the diagonal, the more equitable is the distribution of expendi- tures-in Figure 14 the curved line is very close to the diagonal, and the claim can be made, sub- ject to certain assumptions, that public education expenditures in Peru are equitable. 21 The methodology for undertaking this analysis is simple. A table is constructed which shows the enrollments from each quintile, separately for each level of education. The number of students in each of the cells in the table is then multiplied by public expenditure per student on that level. This method is to get around the lack of data on actual expenditure per student by quin- tile. It assumes that the same amount of public expenditure is spent on a child from a poor family as for a child from a rich family. It does not adjust for the difference in teacher-student ratios in rural and urban areas. 22 The heavy straight black line in Figure 16 shows the line of equality. This figure indicates that public expenditure for the preprimary and primary levels is not only equitable, it is actually biased towards the poor, so that more public expenditures accrue to the poor than to the rich. The diagram also shows that higher education expenditures are very inequitable, especially for univer- sity education. Interestingly, equity does not appear to be too much of a problem for secondary education-the broad dashed line for the Lorenz curve for secondary education falls close to the diagonal. 20 Figure 14 shows that when recurrent public expenditures per student at all levels were combined, overall recurrent public spending was distributed quite equitably. The three lowest quintiles each received over 21 percent of the recurrent public expenditure on education, while the top quintile received 17 percent (Appendix 4.4a). This curve, which includes pension expenditure, is also referred to as Simulation 1 in Figure 15. Since the distribution of pensions differs by level of education, Simulation 2 in Figure 15 tested what the Lorenz curve might look like without pension expenditure. The simulation took an average of 26.5 percent of pension expenditure out of preprimary, primary, secondary, and nonuniversity spending per student, and 13.5 percent out of uni- versity spending per student (Appendix 4.4b). The curve of Simulation 2 looks less equi- table, but is not significantly different from Simulation 1 (Appendix 4.4b). This simula- tion is closer to the true picture (assuming that similar proportion of teachers are retired from the various groups). Still, both Simulations 1 and 2 were built on the assumption that the public spend- 23 ing per student in each level of education was uniform across all quintiles. However, the variation of public expenditure per student by department indicates that this assumption is unlikely to hold. In 1997, for example, the average public recurrent expenditure on pri- mary and secondary education per student in the poor Department of Huancavelica was only 40 percent that of the national average, in contrast to richer Moquegua and Tumbes which had a level of per student spending that was about 160 percent of the national av- erage (Appendix 6.18). This variation may be attributable to three reasons: (i) the pension burden (which is included in the expenditure) is much smaller in the poorer, interior de- partment but much heavier in the richer, coastal departments; (ii) the ability to generate their own resources varies between departments; and (iii) since resources are based on student-to-teacher ratios, the freeze of pensionable positions in 1995 put departments with high birth rates at a disadvantage. Whatever the causes might be, the assumption of uniform per student spending is questionable. Simulation 3, therefore, varied the per student spending by quintile. The simulation held the public spending per student of the middle quintile constant for all levels of edu- cation, but reduced that of the second quintile to 15 percent below that of the middle quintile, and that of the first quintile 30 percent below that of the middle quintile. By the same token, per student expenditure of the fourth quintile was raised 15 percent higher than that of the third quintile and the top quintile was 30 percent higher (Appendix 4.4c). Although the choice of these percentages for the simulation already reduced the variation in per student spending in the Departments by more than half, the Lorenz curve of Simu- lation 3 is still dramatically more unequal. 23 An account of the criticisms and the relevance of studies about the incidence of benefits can be found in "Assessing the Welfare Impacts of Public Spending," by Dominique van de Walle, World Development, 1998. 21 Figure 14. Loreno Curm for Incince of Pubic Expenditure Figure 15. Lorenz Curves for Incidence with 5 Simulatios o~~~~~~~~~~~~~~.0 " ' ' ' . . ___ _- ____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~U. of P.,rScR Eq *_-:. o 0.2 o. c, o,^ 1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0. PnofteR"lullen ~ ~~~~~~~~~~~~~~~~~~~~~~0 0,1 0,2 0,3 0,4 0,5 O's 07 o,s D,9 Figuere 16. Lorenz Curves by Education Ledvel 030, _ _, , , 0~~~~~~~~~~~~~~~~~~~~~~~~, g04~~~~~~~, ==_ ___ ___ ___ St_ _ = or~~~~~~~~~, __ __ _ __ _ _ __ == =_ 0 0,1 0,2 0 0'4 OS 0,6 0,7 0, 2 0,9 , P.p.rd.r ol PqftiPn W----hel -Pnmtey -- 8o.dr, -OSt NoU-ov -Unit.i Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1997 22 Simulation 4 combined the principles of Simulations 2 and 3 and repeated the same experiment after taking out the pensions. Predictably, the distribution is the worst among all simulations (Figure 15 and Appendix 4.4d). Simulation 5 asked: if per student public expenditure on higher education was much higher than the current level, while per student spending at all other levels remained the same, how inequitably would public resources be distributed? The Lorenz curve of this simulation is almost as unequal as those in Simulations 3 and 4. This shows that the rela- tively low public expenditure on university education per student is an important reason for the overall Lorenz curve to look equitable in Simulations I and 2 (Figure 15 and Appendix 4.4e). However, if school resources are distributed inequitably across quintiles, no matter how high enrollment ratios are in basic education, the Lorenz curve will look worse., This exercise shows that the equitable overall showing of the Lorenz curve (Simula- tions I and 2) can be attributable to three reasons: (i) universal primary education benefits the poor more than the better-off; (ii) the better-off have opted out of public schools, thereby consuming less of public subsidies; and (iii) public spending on higher education per student is relatively low. The system could be extremely unequal, if per student alloca- tion in any given educational level is less for lower quintiles than upper quintiles, and if public expenditure allocated to universities continues to escalate. If capital investment is taken into account, the distribution of public expenditure is likely to be even more inequitable. The negligible historical capital investment has resulted in a highly inadequate learning environment. Historical investment in school infrastructure and equipment tended to favor large urban schools, the argument being to bring the great- est benefit to the largest number of pupils. As a result, urban schools are better endowed than rural schools. In the mid-1990s onwards, the National Social Fund (Fondo Nacional de Compensaci6n y Desarrollo Social or FONCODES in short) which was administered under PRES has responded to the demands of rural communities. INFES is also building schools in rural areas. Summary. The exercises in this chapter found that public resources have been very low, although relatively well-targeted and well-used within the overall constraints. The pattern that emerged in the 1990s of a modest but steady increase in public spending on education reflects both commitment to education and fiscal restraint. The Lorenz curve constructed from using uniform per student expenditure for all quintiles suggests equitable distribution of public expenditure, although the absence of actual cost data by quintile leaves lingering questions on the methodology. Future investigation should collect data on the actual amount of public resources spent on students from different quintiles to shed light on this question. Furthermore, the largest increase in per student spending in the 1990s is that on public universities, and yet the majority of students in this level are from upper quintiles. This trend should be monitored closely to ensure that public resources are targeted to the truly disadvantaged. Given that the level of public spending on education is still low in absolute and relative terms, to expand access and improve quality for the poor, additional and targeted investment needs to be sustained for a long time in order to equal- ize educational opportunity. 23 2.3. Household Expenditure on Education With respect to the second question of whether Peruvians value education and have invested heavily in the education of their children, the answer is unequivocally affirma- tive. Historically, communities built schools and organized learning long before the Gov- emment began to play a key role in financing or provision of education. This was borne out by the high level of household spending on education, totaling to about 2 percent of GDP,24 according to analysis of household surveys of 1994 and 1997. This level of household expenditure is higher than the 1.3 percent of GDP spent by OECD countries, also higher than Argentina's 0.75 percent and Mexico's 1.1 percent, but lower than Chile's 2.6 percent, Colombia's 3.6 percent, and Jamaica's 6 percent.25 It should be noted that composition of household expenditure on education may vary from country to country. Since household expenditure estimates are obtained from household surveys, how the questionnaires are phrased affect the information obtained. The cross- country information provided above is intended to show the range of household expendi- ture on education. It should be not be taken as comparable. The key question is not whether households in Peru spend too much or too little but what this level of spending by households implies for educational policy in Peru. Under- standing the breakdown of expenditures across consumption quintiles would provide an answer to the question of whether certain groups of Peruvians are deprived of educational benefits because they are too poor to afford the necessary expenditure. Secondly, it is im- portant to address the question of what variables determine the variation in expenditures across households-this would aid in understanding the implications for educational de- velopment in the future. Disparity in household spending on education by quintile. Figure 17 shows the Lorenz curve for total private expenditure, which looks very dissimilar to the equitable Lorenz curve for total public expenditures in Figure 14, but looks very similar to the very inequitable distribution of expenditure on public universities in Figure 16. The expendi- tures are indeed inequitable, with the lowest quintile accounting for only about 4 percent of the total household expenditures on education, and the upper quintile as much as 57 percent. This Lorenz curve for all private expenditures does include spending on private schools. Figure 18 shows the Lorenz curve of household expenditure which is spent only on public schools. However, the curve is only slightly better. Peruvian households spent ap- 24 In 1997, household spending on education amounted to about US$1,300 million (Appen- dix 7.14). Given Peru's GDP in that year of US$65,221 million, household expenditure on educa- tion was about 2 percent of GDP. This was consistent with the findings by Saavedra, Melzi, and Miranda in the analysis of 1994 household survey. 25 OCED, Education at a Glance, 1998; Colombia, Departamento Nacional de Planacion, 1996; Chile, Ministerio de Hacienda and Banco Central, 1998; World Bank, 1999d, Jamaica: Secondary Education: Improving Quality and Extending Access, Report No. 19069. 24 proximately $781 million (41 percent) for the education of children who were enrolled in public schools to complement the public spending on education that was about $1,932 million. These household expenditures include registration fees and contributions to par- ents' associations (Asociacion de Padres de la Familia or APAFA in short), uniforms, school lunches, and transportation.26 Figure 19 shows the Lorenz curve of household expenditure on public primary schools; the situation is only worse in regard to secondary schooling. The level of house- hold expenditure on education varies tremendously by income level-the total amount spent on education by the richest quintile in Peru was 13 times the total amount spent on education by the poorest quintile. (Appendix 7.14.) Even this figure is likely to be under- estimated because the household survey questionnaire which provided the data for analy- sis did not include spending on extra tutoring and other school activities such as field trips. It is far more difficult for poor families to provide sufficient educational inputs for their children. For example, spending on books accounted for 35 percent of total house- hold expenditure on public primary education for the poorest quintile, but only 11 percent for the richest quintile whose children attend private schools (Appendix 7.2). In the case of primary education, the average out-of-pocket cost for parents of the top quintile to send their children to a public primary school was 194 soles (US$73), or 2.2 times the amount spent by the poorest quintile, which was 88 soles (US$33). The average cost per child in the richest quintile in a private school was 1,645 soles (US$618), amounting to 19 times the average cost spent by households in the poorest quintile on public primary schools (Appendix 7.2). Since public expenditure covers mostly salaries, household contributions in school fees and to the APAFA are often used by schools for repair and maintenance, edu- cational materials and supplies, and water and electricity. The disparity in the ability of parents to pay, therefore, has contributed to the disparity in school resources. Appendix 4.6 presents the result of a survey of some 400 rural and urban public schools in Lima and Cusco by MED in 1994. It shows that the annual APAFA contribution to very large urban schools (with an average of over 1,600 students) amounted to 11,735 soles, in con- trast to only 279 soles of contribution to small rural schools (with an average of 96 stu- dents). This survey confirmed the disparity in school resources among very large, large, medium, and small urban and rural public schools27 (Appendix 4.6). To cite a few exam- ples, 81 percent of very large urban schools have a library, compared to 26 percent of small rural schools; 82 percent of very large urban schools have brick or cement walls, 26 Unfortunately, the data was recorded on the questionnaire instrument of the household survey in such a way that it is not possible to separate out items such as bus tickets from the direct transfer of resources to schools through items such as APAFA fees. 27 This was based on the MED's survey in Cusco and Lima conducted in 1994. Although the situation might be different now, it is unlikely to have changed so dramatically that it alters the picture. 25 compared to only 21 percent of small rural schools; 79 percent of very large urban schools have electricity, compared to 37 percent of small rural schools; and 76 percent of very large urban schools have latrines that work, compared to 32 percent of small rural schools. Appendix 4.5 also illustrates how the disparity in the ability of parents to pay is translated into inequity in resources of schools attended by children of different quintiles (proxied by water and drainage). Note the high percentage of both public and private schools attended by poor children in the first and second consumption quintiles that lacked either water or drainage or both, in contrast to those schools attended by children in the top quintiles. Poor children attend schools with little resources, be that public or private. Elasticity of demand for education. Notwithstanding the aforementioned fact, Pe- ruvians value education highly and would go to great lengths to make sure that their chil- dren have an education. Analysis of the behavioral aspect of household education expen- ditures (Engel's curves) found that the income elasticity of demand is a low 27 percent (See Background Note 2).28 This means that education expenditure is considered to be a necessity by Peruvian households and that there is a strong underlying demand for edu- cation, by both rich and poor.29 To make sure that the conclusion was not based just on one pooled set of regres- sions, the regressions were run separately for subsamples by indigenous and nondi- genous, rural and urban, and poor and rich. Consistently, the pattern is that the income elasticities are lower for the more disadvantaged groups. It was 12 percent for the poorest quintile, 14 percent for rural populations, and 10 percent for indigeneous people. The finding resonated with that of Rodriguez and Abler (1998) for a sample of Pe- ruvian children 6 to 16 years old. They found that even if there is a positive relationship between income of the family and the probability of school attendance, the estimated marginal effects are small. Moreover, the magnitude of the negative effect of family in- come over participating in the labor force is also small. That is why overall enrollment did not decline, and child labor did not increase, during the time of economic crisis. The study by Gertler and Glewwe (1989) had similar findings: that rural Peruvian households were willing to pay fees high enough to more than cover the operating costs of new sec- ondary schools in their villages. This is even true of the poorest quarter of the income dis- tribution. 28 See Deaton and Case (1988) and Sadoulet and de Janvry (1995). The specification used here also borrows heavily from a Yale University Working Paper by Mwabu (1994), Household Composition and Expenditures on Human Capital Inputs in Kenya. 29 Mwabu's work on Kenya indicated a much higher income elasticity of education expendi- tures of 73 percent. 26 Figure t17 Lorenz Curv for Incirlence of Private Expendftures All Levels Figure 18. Lorenz Curve for Incidence of Private Expenditures Figure 17. Lorenz Curve for Incidence Expenditures Only Public Schools 0,8 0.43 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~6 042-- 04 ,2__ _ _ _ 0 0,2 0,4 0,6 0.I 0 0.2 04 0.0 0.6 Proportion of Populotaln Proportoin o Populoon Figure 19. Lorenz Curve for Incidence of Private Expendftures Only Primary Schools .- …~~~~~~~~~~~~~~~~~~~~~~~~~~~~05 0,4_ _ _ _ _ _ _ _ _ _ _ _ _ 02 0.2~~~~~~~~00 0 0 0.2 0,4 0,6 0.6 Proportion of Popul.tion Source: World Bank Analysis of Household Survey by Insituto Cuanto, 1997 27 From the point of view of educational policy, however, the Government cannot rely on general increases in income to bring about greater expenditures on education to im- prove educational quality for disadvantaged groups. For every doubling of household in- come, the budget share spent on education would go up only by a quarter on average; for the poor, the rural people, and the indigenous people, their budget share in education would only go up by 10 percent or so. Given that levels of household expenditure on education vary vastly by income level, there is a great need for specific policy instru- ments that will address the inability of poorer households to incur additional expendi- tures. Conclusion. Because the burden for financing education is disproportionately heavier for poorer households than richer households, the public sector has a special mandate to ensure the equality of educational opportunity for all by directing more public resources to the poor. Past constraints on public expenditure allow exercising this man- date without exceeding reasonable overall public allocations to the sector. 28 3. System Performance Indicators This chapter reviews indicators on educational access, internal efficiency, quality, and labor market outcomes in order to address the question of whether expansion of edu- cation in the past has come at the expense of quality, and to assess the implications for policy in the future. 3.1. Access, Repetition, and Retention Household surveys have repeatedly found evidence of near universal enrollment in primary education for children between the ages of 6 and 11.30 This is reflected in very high gross enrollment ratios. Access to early childhood, secondary, and tertiary educa- tion, however, varied tremendously by socioeconomic status, gender, and urban or rural location. In general, both males and females between the ages of 12 and 17 in rural areas, irrespective of consumption quintile, are less likely to be in school than their counterparts in urban areas. For the 17 to 25 age group, girls in rural areas definitely have fewer op- portunities than boys of the same consumption quintiles in the rural areas or girls in the urban areas. (Appendices 4.la- 4.1b). It should be noted that these gross enrollment ratios indicate how many students of all ages are studying at a given level; it encompasses late entrants, under- and over-aged students, repeaters, and adult learners. Gross enrollment ratios, therefore, often exceed 100 percent. Net enrollment ratios, however, indicate what percentage of children of a particular age group are studying in the level designated for that age group. It never ex- ceeds 100 percent and is a more accurate measure of the amount of schooling acquired by the age group. Where large differences exist between net and gross enrollment ratios, they signal that a large proportion of students are late entrants and repeaters. It is, there- fore, very telling that gross enrollment rates in the rural areas among lower quintiles are higher (over 1 10 percent) than even their counterparts in the same quintile in urban areas. The gross enrollment of the top quintile in urban areas is under 99 percent, showing that 30 There are two major institutions in Peru that have conducted regular household surveys and which provide databases for analyzing education indicators: INEI, which is under the GOP, and Instituto Cuanto, which is a privately run organization. INEI conducted a school census in 1993 and another one in 1998. Most of the official education statistics including enrollment ratios and average years of schooling are drawn from INEI' s data. This report, however, draws from the household survey of 1997 conducted by Instituto Cuanto because its questionnaire is richer and also because the World Bank's other studies (on poverty and labor market) also drew from this dataset. It should be noted that Instituto Cuanto's sample size is much smaller than INEl's, and its sample frame is different. For this reason, indicators on access, repetition, and retention reported in this report are not the same as those reported in government statistics. Nonetheless, the broad picture revealed by data from Instituto Cuanto is similar to those by INEI. Subsequent to this analysis, the Ministry has undertaken other assessments and made the information public. The reader should also refer to information posted in the Ministry's new website: http:/lwww.minedu.gob.pe. 29 they move through the system rapidly without wasting time in it. (Appendices 4. Ia and 4.1b). Peru's net enrollment ratios are much lower than the gross ratios, averaging in the rural areas only 10 percent in initial education, 65 percent in primary education, 28 per- cent in secondary education, and 4 percent in tertiary education. These are lower than the urban areas' 12 percent in initial education, 71 percent in primary education, 57 percent in secondary education, and 22 percent in tertiary education. Net primary education en- rollment differs substantially by income quintile and gender within and between urban and rural areas. The difference was most pronounced at the tertiary level, where, in the rural areas, only 1.6 percent of girls and 2.5 percent of boys of the first quintile enrolled, in contrast to 12 percent of girls and 7 percent of boys in the same quintile in the urban areas. (Appendix 4.1c and 4.1d). Rural children tend to enter late into the school system because they often have to walk to school. As schools are usually established in population centers, allowing for 1.5 to two hours' walking distance from other settlements, only older children who can en- dure the journey can go to school. As a result of their late entrants and high absenteeism (due to the need to help their families and to vulnerability to climatic factors), they also tend to have high repetition rates. Research has found a clear association between late entry and high repetition rates on the one hand, and poverty, indigenous language speak- ers, and uneducated mothers on the other. About 63 percent of Quechua speaking chil- dren are over-aged. For children who work in the countryside, 68 percent are over-aged, and the dropout rate among them is as high as 55 percent (Montero, 1996). Official statistics on repetition3' and dropout are highly aggregated, without gender or urban and rural breakdown. In the aggregate, 80 percent of a cohort reached Grade 6, while 60 percent of the same cohort reached Grade 11. However, only 40 percent of Grade 6 students and 20 percent of Grade 11 students had not repeated during their course of study. Dropouts spent an average of 6.7 years in the system (Appendix 4.7). To obtain a picture of the differential repetition and dropout rates by the rich and poor, household survey data are used. The analysis by Saavedra and Felices (1997) of the 1994 Cuanto household survey confirmed the relationship between repetition and income-the percentage of repeaters went from 17 in Lima, to 24 in other urban areas, and rose further to 35 in rural areas. Repetition is also much higher in public schools than in private schools. The study also revealed the relationship between income and status dropout (defined as the proportion of individuals in a cohort that have not finished an educational level and are not enrolled in any educational institution). For those aged 17 to 24, the status dropout rates were 13 percent in Lima, 20 percent in other urban areas, and 54 percent in rural areas. 31 The MED has adopted a policy of automatic promotion in lower grades in primary educa- tion since 1998. Therefore, repetition rates are not good indicators of whether students have mas- tered the requisite skills for a given grade level. 30 An analysis of Instituto Cuanto's household survey of 1997 found a large disparity in school survival rates32 between urban and rural areas. Figure 21 shows that although urban and rural children started out the same in the first year of schooling, they rapidly diverged after the Fourth Grade. Figure 23 displays a similar pattern of school survival rates between children from the top and bottom quintiles. In both cases, disadvantaged children (that is, rural and poor) dropped out much earlier. School survival rates did not differ much by gender (Figure 20) but disparity was significant between children whose mother tongues are indigenous languages, and those who are Spanish speakers (Figure 22). In comparison with the school survival gap be- tween rural and urban areas, the indigenous gap appears to be smaller. However, this may simply reflect the reluctance of people to identify their own mother tongue (which is the variable often used to construct indigenous and nonindigenous populations). That the school survival rates of Spanish-speakers were much lower than those of urban dwellers seems to lend support to the above-mentioned point. Conclusion. High repetition and low retention rates indicate low internal effi- ciency of the system. This means that children spend time in the system without attaining the education level and mastering the skills commensurate with the number of years of enrollment. This is not only a waste of public and household resources but also has grave consequences for the future employment prospects and lifetime earnings of children. The solution is not to adopt a policy of automatic promotion but to ensure that children learn the skills relevant to the grade-level every year by a range of policy options to be dis- cussed in Chapter 5. 32 The survivor function plots in Figures 20 to 22 are known as "Kaplan-Meier Survivor Functions." They are nonparametric maximum likelihood estimates of the survivor function. (See The Statistical Analysis of Failure Time Data, by J.D. Kalbfleisch and R.L. Prentice, John Wiley and Sons, 1980). The survivor function uses information about the years of schooling completed and the current status of enrollment at school. It is the complement of the empirical cumulative distribution function. The Kaplan-Meier plot is not based on any regression model-the figure simply represents a count of people as they leave the educational system compared to the number of people who stay on. The numerator is the number of people who stay on, and the denominator is the number of people who have been in school up to the time. Hence, the plot always begins at a value of 1, since every one stays on at the first instant, and subsequent "steps" on the figure show people leaving. There is one underlying assumption, that if you have left school, you will not enroll again at a later date. This assumption is more valid in some cases than others, but it is a fairly standard one in the literature on educational attainment. 31 Figure 20: School Survival Rates by Gender, 1997 ....... Fe al CC? .M--a/Feal 0 $~~ 0 0~ ~~~~~ cli~~~~~~Ml 0 0 5I10 1 5 Grade Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1997 Figure 21. School Survival Rates by Urban and Rural Areas,1997 Urbann N _ ,~~~~Rua 0 5 10 15 Grade Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1997 32 Figure 22. School Survival Rates by Mother Tongue, 1997 Castellano o ~~~~~~~~Indigenous ---- CD 0 Cq n 0 5 1 0 1 5 Grade Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1997 Figure 23. School Survival Rates by Poorest and Richest Consumption Quintiles, 1997 -------------- ...... ~ ~ ~ ~ ~ ~ . ... Richest Quintile 0.6 ~~~Poorest Quintile 3 0.4 0.2 0.0 0 5 10 15 Grade Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1997 33 3.2. Labor Market Outcomes The consequence of low internal efficiency will impact on external efficiency (la- bor market outcomes). This will become even graver in the 21St century as the trend of the 1990s has already shown rapidly increasing wage differentials among workers with various education levels. After falling between 1985 and 1991 during the economic crisis, the premia of all education levels bounced back in recent years after the implementation of structural reform and opening of the Peruvian economy (Saavedra, 1998). The magni- tude of decline between 1985 and 1991 was different among workers of different educa- tion levels-it was minimal for university educated workers but was almost halved among workers with other levels of education. When the premia bounced back in the 1990s, the increase was also steepest for university graduates. Figure 24 shows that in urban areas, in 1997, the earnings differentials between workers who had no education and those who had primary education barely recovered to the level of 1985. Improving quality of primary education will provide some real benefit to those who would only have this opportunity to acquire the requisite skills that distin- guish them from workers with no education. The premia of secondary education (over that of primary education) has not grown as fast as that of the primary education premia and still has not reached the level of 1985. This might be related to the relatively slow growth of formal sector work, which normally employs secondary school graduates. Figure 24. Evolution of Estimated Premia by Educational Level, 1985 to 1997 160 - 3 140 - 148 University Higher/ 71 120 - < Secondary ~.00 c 4 y Secondary/ 80 Primary A 60 Non-university higher/ and secndary shool gaduatesalso rcoveredby 199, it wa onlySeturndagty hee .~340 20 1985 1991 1994 1997 Year Source: World Bank Analysis of Household Survey by Instituto Cuanto, 1985, 1991, 1994, 1997 Although the wage differential between nonuniversity tertiary education graduates and secondary school graduates also recovered by 1997, it was only returning to the level of 1985. Only the university premnium exceeded the level of 1985. This signals an in- creasing demand for a higher level of skills in an open economy that is facing growing 34 intemational competition and technological change. This trend is similar to those in many Latin American economies such as Colombia (Cardenas and Gutierrez, 1997), Costa Rica (Gindling and Robbins, 1995), Chile (Robbins, 1996) and Argentina (Pessino, 1995) where returns to education have also increased after structural reforms. Rising education premia has implications for policy. If education premia continue to increase, many people will have incentives to pursue further education, even in adult or evening classes. As they attain more education they are likely to climb in the earnings scale. This will probably increase their likelihood of not being poor. If repetition and dropout rates fall, people will have higher levels of education attainment, even if their years of schooling remain unchanged. Given the positive relationship between literacy of parents and school attendance of children, there is an intergenerational positive effect on education. This is the start of a virtuous cycle. In the case of workers that do not improve their skills through education and training, they are less likely to improve their income due to their lower productivity. In rural areas, Escobal, Saavedra, and Torero (1998), using a sample of rural fami- lies in household surveys from 1985 to 1996, found a positive relationship between years of education of the household head and of the other family members and per capita household expenditure. Taking the trend to its logical conclusion, an improvement in the quality of basic education will certainly have effects over the skill structure of the labor force and income distribution. Table 2 shows private and social rates of return to education in 1997 (See Back- ground Note 3 for methodology and explanation). Returns to primary education for men are very high. This is not inconceivable for this educational level. A paper by Psacharo- poulous shows the rates of 24 percent and 31 percent for Venezuela and Guatemala, re- spectively. On the other hand, the large difference between males and females might be attributable to the small number of noneducated males actually working in urban areas. That they comprised only 2.3 percent of the sample of males might bias the estimate. Table 2: Urban Peru: Rates of Return to Public Education, 1997 Female Male Private Social Private Social Primary education 5.9% 5.1% 37.8% 26.3% Secondary education 10.4% 7.4% 7.2% 6.1% Non-university higher education 12.1% 10.4% 9.4% 8.2% University higher education 13.9% 12.4% 12.1% 11.1% Source: World Bank analysis of Cuanto's 1997 household survey 35 Private returns to education increased with the level of education for both men and women (except for primary educated males). This is completely consistent with the trends observed in the labor market all over the world. What is peculiar to Peru is the very small difference between private and social returns to various levels of education (Table 2). This corroborates the point made in Chapter 2 about the low level of public spending on education per student at all levels, because the calculation of social returns used public expenditure to estimate the cost. When the costs are low, the returns would be high. Another feature special to Peru is that social returns to university education were higher than those to other levels of education. Again, this supports the point made in Chapter 2 about public spending on university education per student being much lower than that of other countries. Both the social and private rates of return to all levels of education, except primary, were higher for women than for men (Table 2). This indicates the profitability for public investment in girls and women's education. Given the very low enrollment ratio of fe- males in higher education in the rural area, and among the lower quintiles, the beneficiar- ies of these high private returns are urban women from upper quintiles. Since rural women are not likely to have access to such opportunities without specific government intervention, provision of scholarship to rural girls who have a good academic record would yield high benefits to society. Conclusion. The policy implications of these findings are that investment in basic education, both in terms of qualitative improvement and quantitative expansion of secon- dary education, will have a positive effect on alleviation of poverty, although returns to this level are probably lower because the initial general human capital is low. The rising private returns to higher education means that children from the upper quintiles who are the dominant consumers of university education, are the main benefici- aries of public investment in this subsector. (See Appendix 4.2a and 4.2b for enrollment in higher education by quintile and Appendices 4.4a, b, c, d, and e for incidence analy- sis). Although high social rates of return to education justify continuous government in- vestment in this level, the social returns will not remain high if public spending per stu- dent in this level keeps on rising. Additional resources needed to improve quality of higher education can come from cost sharing at the university level. Given that students will benefit from a high level of lifetime earnings, it is justifiable to ask them to contrib- ute their fair share to finance their own study. However, increased cost sharing also needs to be supported by student financial assistance such as student loans to ensure that the academically deserving will not be disqualified due to financial constraints. To ensure gender equity, a proactive policy by the Government is needed to sup- port women, particularly those in the rural areas, with good academic standing to access higher education. 36 3.3. Learning Outcomes While repetition, retention, and dropout rates are indirect indicators of quality, the most direct measure of learning outcomes is student achievement. Like other Latin American countries, Peru has only set up measures of student achievement in recent years.33 Notwithstanding startup problems-which are common to many countries, and which limit interpretation of the initial tests-information on the relative performance of students in the country provides the opportunity for a preliminary diagnosis of determi- nants of achievement. In addition, it provides a basis for improving the assessment in- strument and other technical aspects for policy research in future. (See Background Note 4 for description of the analytical procedures and preliminary findings.) Table 3 presents the national average Grade 4 mathematics test scores and scores of various subgroups of the 1996 assessment. It should be cautioned that such scores are not meaningful by themselves. Furthermore, validity (measuring what it should measure for students at that grade level) and comparability of the instrument across years have yet to be established. An assessment instrument that is set to be too difficult, even if it is based on the curriculum, can have the effect of making students perform badly; on the contrary, if it is too easy, it can make all students perform well.34 Also, because the test was not equated with other internationally known studies-such as the Third International Math and Science Studies, whose Population A of 9- and 10-year-olds were quite comparable with the Peruvian Fourth Graders-the test results cannot be interpreted as an indicator of how well students performed in comparison with students of other countries. What is informative for policy analysis purposes is the relative performance of stu- dents compared among themselves. Table 3 shows that the achievement gap between pri- vate and public schools was large. Among public schools, disparity existed between ur- ban and rural areas. The urban and rural outcome differential should be much bigger than the data show, because children in very small rural schools with only a single teacher were not included in the test, and yet these schools represented about 29 percent of all schools and about 6 percent of the population of Fourth Graders.35 Students on the coast performed on average better than those in the mountains (sierra), who in turn, fared better than those in the jungle (selva). There was also a gender difference in outcomes, as girls did less well than boys. The lowest score was among Quechua-speaking students. To the extent that the coefficient of variability was large on country average, and much larger 33 The first national standardized test of achievement in mathematics and language was conducted in 1996 among nearly 50,000 Fourth Graders in a national sample. Background Note 4 reports the findings of an analysis of determinants of achievement. The assessment program was expand to other grades in 1998 and 2000. However, because the findings from these subsequent exercises were released after the completion of this report, they are beyond the scope of this study. 34 A good test should have both easy and difficult items for both the average and the excep- tional students to score. It should be consistent in its difficulty level from year to year in order to measure progress. 35 According to the 1993 Census, 29 percent of all schools were single-teacher schools, 33 percent had at least some multigrade classrooms, and 38 percent were complete schools. 37 among certain subgroups (public rural schools, Quecha-speaking students, and in the jun- gle areas), variability of learning outcomes is a key issue in education. Table 3: Fourth Grade Mathematics Outcomes, 1996 Average Mathematics Test Coefficient Scores (Standard Deviation of Variability in parenthesis) Countrywide average 45.4 (21.5) 0.47 Male 47.1 (21.7) 0.46 Female 43.7 (21.1) 0.48 Public rural 38.7 (21.0) 0.54 Public urban 44.0 (20.2) 0.46 Private 62.4 (21.1) 0.34 Spanish-speakers 47.3 (21.6) 0.46 Quechua-speakers 33.1 (18.0) 0.54 Aymara-speakers 45.0 (20.0) 0.44 Coast 49.9 (21.6) 0.43 Mountain 45.4 (21.4) 0.47 Jungle 37.5 (18.9) 0.50 Source: Ministry of Education. Note: The coefficient of variability is computed by dividing the value of the standard deviation by the corre- sponding mean of the group. To assess the impact of public finance on school achievement, mathematics out- comes by department were regressed on per student public expenditure on primary educa- tion by department of 1994. The reason for using 1994 expenditure data to predict learn- ing outcomes in 1996 was because of the time lag between public spending and demon- stration of effects on learning. No relationship was found between public spending per student by department and outcomes by department, as the R-square was zero (Figure 25). A weak relationship was also found between poverty by department and per student public expenditure on primary education by department (R-square = 0.06) (Figure 27). This might be attributable to the formula of allocating teachers: in the rural areas, one teacher is allocated for every 20 students, whereas in the urban areas, one teacher is allocated for every 35 students. Given the high correlation between poverty and rural population as a percentage of total population in the department, it could well be that no relationship was found between poverty and public expenditure per pupil. One should also take into account that the massive increase in public spending on education began only in 1993 through 1995. It is not surprising to see little effect of public spending on outcomes during this short time period because of the natural time lag between delivery of infrastructure, goods, and services (including textbooks) to the schools, and when these facilities and goods are used for teaching and learning. 38 Figure 25: Mathematics Outcomes and Recurrent Public Expenditure on Basic Education Per Student High E ~ ~ ~~~~~~* * E X ~~~~~~~~~~~~~~~~~~~~~~~~~y =0.0209x + 42.029 in ~~~~~~~~~~~~~~~~~~~~~~~~RI = 0.0069 Low 0 so 1 00 1I50 200 250 FRecurr nt Publc Expenditure on Primary Education p rstude of 1 t94 (in US dollars) Source: World Bank analysis of data from MEF and MED. Figure 26: Mathematics Outcomes and Household Expenditure on Basic Education Per Student High .. ! E 0 0 E c y.O .1 186x +36.1531 Low'---- I 0 20 40 eo eo 100 120 140 160 Hoshold expenditure on Ict Education per student in LOS doll4rs Source: World Bank analysis of data from Cuanto's Household Survey and MED. 39 Figure 27: Poverty and Recurnet PuI)ic Experxdture on Basic Education Per Student 250 0 2 200 x 150 * w .0 EL 100 l X 5 50 y=-7.2356x + 163.28 0. Fe = 0.0663 0 0.0 0.5 1.0 1.5 20 25 3.0 3.5 4.0 4.5 5.0 Poverty Index Source: World Bank analysis of Cuanto's Household Survey and MEF. Rgure 28. Poertyand IHeldd Bperde an Basic Eation Per Sbder ,0 120 \ y=-26968x+t6.73 0- -C 80 :) W 60 U) a. 40 20 0 0.0 o.5 to 15 2.0 2.5 ao 3.5 4.0 4.5 s0 Poverty Index Source: World Bank analysis of data from Cuanto's Household Survey and FONCODES. 40 To examine the effects of household expenditure on achievement, family spending on basic education per capita in 1994, by department, was used to predict mathematics outcomes in 1996. It should be noted that the level of household spending on education reflected a very long tradition of family support for education, and hence, the level of private spending was likely to represent a continuity of this tradition, rather than an abrupt change as in public spending. When mathematics outcomes by department were regressed on household spending on basic education per capita by department, a positive relationship was found and the R- square statistic was a strong 0.38 (Figure 26). This indicates that the higher the level of household spending per capita by department, the higher the learning outcomes by de- partment. This raises the question of whether poorer departments were particularly disad- vantaged. To answer this question, poverty index by department was regressed on household spending on basic education per capita by department. A negative relationship was found, meaning that the poorer the department, the lower the departmental average of household spending per capita. The R-square statistic was a strong 0.54 (Figure 28). These reinforce the points made earlier about the inherent inequality in relying on households to finance basic education because this merely replicates the socioeconomic inequality in society. Nonetheless, some departments which had low levels of household spending on ba- sic education had departmental outcomes well above the predicted line (Figure 26). This raises the issue of whether household expenditure by department captures the effects of other variables. To disentangle these issues at the departmental level, the technique of hierarchical linear modeling (HLM) was used. (See Background Note 4 for details.) It was found that, although the combination of public and private expenditure per capita by department explained nearly half of the between-department variance in out- comes, public and private expenditure did not substitute for each other. Poverty alone had a negative effect on outcomes, but it did not have a linear relationship with them. Divid- ing departments into nonpoor, average, poor, and extremely poor categories provided a more precise measure of the effects of poverty on achievement. All of the above- mentioned variables, in combination with departmental percentage of female students, students in private schools, Quechua speaking students, and over-aged students, and the proportion of teachers graduated from universities and teachers trained in ISP, explained 94 percent of the variance in test scores between departments. Student-level variables (namely, age, gender, mother tongue, the availability and usage of textbooks, student attendance and study habits, and parental roles) cumulatively explained 5 percent of the within-school variance in achievement. School-level variables (namely, geographic factors, availability and usage of textbooks and homework assign- ments, teachers' characteristics, teachers' role, principals' characteristics, and parental role) cumulatively explained 35 percent of the variance in achievement between schools. (See discussion in Background Note 4 for details.) 41 When department-level variables were taken into consideration, in addition to stu- dent- and school-level variables, about 12 percent of the variance in math achievement was attributable to differences in characteristics between departments. Within depart- ments, 43 percent of the variance in test scores was attributable to characteristics between schools. Within schools, 45 percent of the variance was due to characteristics among stu- dents. A between-school variance in achievement above 30 percent is normally consid- ered as an indicator of inequity in learning outcomes. (See Appendices 5.1 and 5.2 for international comparison.) It should be noted that the relative weights of the above-mentioned variables re- flected more the imprecision in constructing the explanatory variables in the question- naires than the lack of predictive power of these variables. The relatively low percentage of variance explained at the student-level also reflected the absence of some crucial pre- dictors in the dataset, such as parental educational level, hours of study at home and home resources (which could be proxied by measurable materials such as type of dwell- ing and sanitary facilities, ownership of refrigerator or telephone). Even at the school- level, where explanatory power was higher, information was not collected on school- level resources (which could be proxied by material the school was constructed of, type of sanitary facilities, availability of water, electricity, and library, etc.); government allo- cation per student in the school; household contribution per student; whether the school is on shift; and student and teacher absenteeism. To inform policy for more precise inter- vention, it is desirable to collect these variables in the future, as well as to revise the questionnaire. Another limitation to be overcome in future is the appropriate sampling of rural schools, including single-teacher schools in the sample. Due to these limitations, the results obtained from analysis of determinants of learning outcomes should be viewed as suggestive rather than definitive. That said, the findings are encouraging in indicating potential directions where change could make a difference. Although there are gaps in mathematics outcomes between gender, region, and pri- vate and public schools, after controlling for a number of explanatory variables the pic- ture has changed. Students in poor and extremely poor departments performed better than those in nonpoor and average departments, holding other variables constant. Some departments were doing a better job in educating over-aged students. Aymara students performed as well as Spanish speakers. Quechua students could perform as well as others if they were not educated in predominantly Quechua schools, thereby indicating that the problem is not with the students themselves. Teachers who have had longer years of ser- vice, and teachers who have had more in-service training, were positively associated with higher student achievement nationwide. Nonavailability of textbooks was negatively as- sociated with mathematics achievement nationwide. Parental expectation for better per- formance in the relevant subject has been translated into higher student performance na- tionwide. 42 Table 4 summarizes the findings of cross-level HLM analysis to indicate which ex- planatory variables have positive or negative effects on math achievement and whether these effects varied across schools and departments. Table 4: Summary of Effects Crossing between Departiments, Schools, and Students Statistically significant effects Effects Effects did Effects Effects did (controlling varied not vary varied not vary for other across across across across variables) Department Department schools schools Between Department Household expenditure per capita + Poor departments + Extremely poor departments + Department % of teachers from ISP + Cross-Level between Schools and Departnents Female students x Over-aged students - x Quechua students - x Teachers' years of service + x Number of training courses at- + x tended Cross-Level between Students, Schools, and Departnent Female students - x x Over-aged students - x x Quechua students - x x Parental expectation for the + x x subject being tested No text materials x x Note: The coefficients of variables that had no statistical significance were not reported. Source: Background Note 4. Policy implications. These findings point to the opportunity for public policy to make a real difference for disadvantaged students. The policy interventions should be universal where the effects have nationwide impact (that is little variation at the school and departmental levels). These include textbook provision (by extending beyond the primary level which was provided by MECEP to preschool and secondary level); strengthening teacher pre-service and in-service training; providing incentives for experi- enced teacher to remain in the profession; deploying qualified and experienced teachers to the rural areas; specific training to teach more effectively to over-aged students; and 43 using the mass media and parents associations to enhance parents' role in supporting their children's education. Where the effects vary across departments or schools, targeted in- terventions are desirable. These include specific support for schools where Quechua- speaking (and other indigenous) people are predominant. This might require strengthen- ing bilingual education and text materials. In better schools, special attention might need to be paid to bring girls and over-aged students up to the standards of other students. Fi- nally, it should be remembered that there was no statistically significant difference in per- formance between Spanish speakers and Aymara speakers. Future research should find out what factors have enabled the latter to overcome the barriers facing speakers of this indigenous language. These findings were consistent with those from the literature about the effective- ness of several commonly used policy instruments to improve learning outcomes. These instruments include provision of instructional materials and facilities (such as textbooks, libraries, and laboratories); increasing the opportunity to learn through increasing instruc- tional time and homework; provision of teacher training; increasing teachers' salaries; and reducing class size. Figure 29 summarizes the findings from the literature re- view.36 Figure 29. Determinants of Effective Learning in Primary Education: Findings from Literature Review Class size Teacher salary Laboratones = Teacher experience 0. _Teacher knowledge tJ Textbooks Homework Instucsional tim6e Libraries 0 10 20 30 40 50 60 70 80 90 100 Percent of Studies Showing Positive Effect Source: Reproduced from World Bank, 1996, based on literature review by Fuller and Clarke. 36 World Bank, 1995; Lockheed and Verspoor, 1991; Harbison and Hanushek, 1992; Pos- tlethwaite and Ross, 1992; Warwick, Reimers, and McGinn, 1989; Tatoo et al., 1990; Fuller and Clarke, 1994. 44 It should be noted that the effectiveness of these instruments depends on specific country context and that some measures are administratively simpler to implement (such as provision of. a library, or textbooks) than others (such as improving teachers' knowl- edge). Therefore the graph should not be interpreted as a hierarchy of effective interven- tions, but rather it should be viewed as an indicator of degree of easiness for implementa- tion. Providing school libraries is found to be cost-effective in nearly 90 percent of the studies, and increasing instructional time (such as lengthening the school day or provid- ing additional instruction in a certain subject) almost equally effective. Asking students to do homework comes third, and provision of textbooks to children who do not have them comes fourth. Improving teachers' knowledge through training is found to be effective in the majority of cases, and having experienced teachers also comes close. Provision of laboratories, increasing teacher salaries, and reducing class size are also effective, but in fewer cases, most probably because they are more difficult to implement. A more recent review by Schiefelbein, Wolff, and Schiefelbein (1999) on cost- effectiveness of primary education policy in Latin American found six interventions with highest estimated impact on target population if fully implemented. These are: (i) adopt- ing multiple interventions of learning packages, school-based management, training, and testing; (ii) assigning best teachers to the first grade; (iii) decentralizing with supervision; (iv) paying rural teachers 50 percent more; (v) providing standard textbooks and training teachers; (vi) using developmentally oriented preschooling. Policymakers are well advised to evaluate which interventions are relevant for their country's conditions and to assess the feasibility of implementation and the recurrent cost implications. Conclusion. This chapter aims to address the question of whether expansion of education in the past has come at the expense of quality. A review of indicators on educa- tional access, internal efficiency, and quality has found that there has been a tradeoff be- tween access and quality as resources have been spread thinly to meet multiple demands. In light of rising premia for education, which signal a growing demand for higher skill levels in an increasingly competitive environment, the serious implications for the em- ployment prospects and future lifetime earnings of disadvantaged groups cannot be ig- nored. This calls for a focus on quality improvement particularly for disadvantaged groups as a centerpiece for education policy. 45 Chapter 4. The Teaching Profession The fourth question asks whether the ability to contain personnel cost in public ex- penditure on education has contributed to Peru's ability to extend educational access and how this and other policies toward teachers might affect the sector. Since education is a labor intensive enterprise because interaction between students and teachers is critical to learning, sound policy toward teachers that can enhance student learning will improve efficiency of resource use. The findings in the analysis of determninants of math achieve- ment in Grade 4 in Peru affirmed the positive impact of teacher qualification, experience, and professional development on achievement nationwide. Research evidence from the United States also found that skilled teachers are the most critical of all schooling inputs (in an environment where the needs for textbooks, instructional materials, and facilities have been met).37 These findings underscore that focusing on teachers and teaching is the only way in which an education reform can make an impact in the classroom and help improve student achievement. This chapter reviews the issues related to the teaching pro- fession with the aim of identifying options for improvement. 4.1. Teacher Qualifications and Employment Status Qualifications and deployment. In the Peruvian public education system, there are some 248,000 primary and secondary school teachers (Appendix 2). About 62 percent of these are qualified with titles (con titulos), that is, they have a diploma in pedagogy from one of the 318 tertiary-level teacher training institutes (Instituto Superior Pedag6gi- co or ISP for short) or from one of the 38 education faculties of a university. The rest (38 percent) are unqualified (sin titulos), that is, they do not have pedagogical titles. They either did not fully satisfy all requirements for the pedagogic diplomas, have other terti- ary-level diplomas, or have only completed secondary education. Teachers without titles have lower pay and lower status than those with titles in the profession (Appendix 6.19). There are no statistical breakdowns of the academic qualifications of teachers with- out titles by level of education. The only information comes from the sample survey of teachers that accompanied the 1996 national assessment. Among teachers of Grade 4 stu- dents in the sample, 15 percent graduated from university pedagogical programs, 51 per- cent graduated from ISPs; 1 percent graduated from Institutos Superiores T6cnicos (IST); 6 percent held university bachelor's degrees, 17 percent obtained their teaching qualifica- tions through part-time professional studies; 1 percent graduated from other programs; 6 percent had only secondary education plus teacher training; and 3 percent had only sec- ondary education without any training (see Table 2 in Background Note 4). Having the required pedagogical qualifications is not synonymous with being a good teacher. In fact, ISPs are alleged to be academically weak and also to attract poorly 37 According to Ferguson (1991), in the United States, 49 percent of learning outcomes is at- tributable to home and family factors (such as parental education, income, language background, ethnicity, and location), whereas 43 percent is attributable to teacher qualifications and experi- ence, and only 8 percent to class size. 47 prepared students. However, since teachers without titles have lower pay and lower status in the profession, those who remain in the profession signal that they have even lower opportunity cost than the trained teachers. Furthermore, the analysis of the 1996 test con- firmed that teachers who were graduates from university and ISPs were associated with higher student achievement than those who were not graduates from these programs. Although the number of teachers with titles seems low, this already represents a dramatic improvement even from the early 1990s. According to the 1993 census, only 52 percent of teachers in service had titles while 48 percent did not. By 1995, teachers with- out titles declined to 42 percent. By 1997, they were reduced to 38 percent.38 At present, to be appointed to an authorized pensionable position, one has to have a title and have passed a nationally competitive examination. The rapid growth in the supply of teachers with titles is due to a dramatic increase in teacher training institutions, particularly the private ones. Background Note 5 discusses the implications of the rapid growth of teacher training institutions and the issues of teacher training. While progress has been made in the supply of teachers with titles, the key question is whether there is a particular pattern in the deployment of teachers without titles and its potential impact on quality of education. According to the 1993 census, the vast majority (68 percent) of the unqualified teachers taught in the rural areas. The MED's 1994 survey of rural and urban public schools in Lima and Cusco found a positive relationship be- tween school size and the percentage of teachers with titles. In large and very large urban schools, as well as large rural schools, over 81 percent of teachers had titles. In medium- sized urban schools, teachers with titles dropped to 73 percent, while in small rural schools, they declined further to 50 percent. The percentage of teachers trained in regular programs (that is, in ISPs and the education faculties of universities) varied from a high of 85 percent in large and very large urban schools, to 64 percent in small urban schools, to 53 percent in small rural schools (Appendix 4.6). The same pattern is also observed among principals of schools. For example, over 92 percent of principals in very large and large urban schools, as well as in large rural schools, had titles, compared with only 74 percent in small rural schools. Over 86 percent of principals in these large urban and rural schools were trained in regular programs, in contrast to only 72 percent in small rural schools. (Appendix 4.6.) These patterns show that teachers who have the required qualifications are reluctant to take up hardship positions in remote rural areas. They still go to large rural schools which are located within reasonable distance from an urban area, and which tend to have better school resources, and easier access to health and other social services for the teacher and his or her family.39 The same is not true for small rural communities, where the poor working conditions, the lack of opportunities for additional part-time employ- 38 In the Inka Region, for example, unqualified teachers accounted for almost 60 percent of the teaching force in 1990, but now account for only 32 percent. 39 See the companion Health Sector Study by the World Bank for the inadequate and low quality rural health service (World Bank, 1999b). 48 ment or work for the teacher's spouse, and inaccessibility to various social services make it hard to attract teachers with titles. The findings of the 1994 survey of rural and urban schools illustrate just how large a disparity in working conditions exists between urban and rural schools. For example, electricity was available to over 97 percent of large urban schools, 89 percent of medium- sized urban schools, and 55 percent of small urban schools, in contrast to only 29 percent of small rural schools. All teachers of Second Grade who were surveyed in very large ur- ban schools had textbooks, compared to only 91 percent of teachers in small rural schools. The number of visits by an inspector from a USE, no more than 1.5 times per year at best, was twice as frequent in urban schools than in rural schools, reflecting both the difficulty of access as well as marginalization of rural schools. The average number of training courses attended by teachers was much higher for those in urban schools than rural schools (see Appendix 4.6). And yet, there was insignificant earnings differential between teachers who worked in difficult rural conditions and those who taught in urban areas. (See Appendix 6.19.) That is why the turnover rate of rural teachers is much higher than that of urban teachers. For example, the average year of teaching in the schools surveyed was much shorter (2.3 to 3.6) for teachers in small and medium-sized rural schools than for teachers in very large urban schools (5.9), although their average ages were within a narrow range of 32 to 36. For school principals, taking up postings in smaller schools appeared to be a channel for career advancement. The average age of principals in large urban and rural schools was between 45 and 48, but that of principals in small and medium sized rural schools was between 36 and 39. (Appendix 4.6.) Policy to address the inequity in learning outcomes cannot avoid tackling the issue of deployment of trained teachers to rural areas. This entails providing a larger rural al- lowance tied to positions in rural schools, and using job rotation every three years to at- tract teachers with titles to take up positing in remote communities because it will not be a permanent assignment. Meanwhile, public investments should be made in rural schools to improve school resources and working conditions, as well as to enable rural teachers to break their isolation and maintain professional contact with other teachers within a cluster of schools. Teachers who are going to teach in rural schools should be given additional training on how to handle multigrade teaching. Concomitantly, since many remote areas are populated by indigenous people, it makes sense both in terms of promoting multicul- turalism and bilingualism to recruit indigenous teachers for schools in their communities. Given the association of indigenous teachers (except the Aymara) with low achievement of students, this indicates indigenous teachers should be given additional pre-service and in-service training in order to prepare them better for the task. Employment status. Teachers are classified either as permanent staff who are ap- pointed to authorized pensionable positions (APP) (knows as nombrados), or on contract (known as contratados). Contracted teachers are not eligible for pensions, and can be dismissed without severance pay. There is no relationship between teachers' qualification and employment status. 49 APPs are the main means of allocating public education budgets to lower levels of educational authorities. Theoretically, APPs were distributed to each Regional Director- ate of Education on the basis of enrollments in each school (one APP for 35 students in the urban areas and 20 students in the rural areas), and incremental APPs are supposed to be allocated annually on the basis of the changes in enrollment. But the organic APPs have not been reviewed for a long time, and the number of annually adjustable APPs has been fixed since 1995. Therefore, no systematic relationships seem to exist between the number of APPs and enrollment, and the budget allocation system has lost its rationale. Since one APP can be used to hire more than one teacher by converting them into con- tract or part-time positions,40 there are no precise statistics as to how many teachers are in the system or how many are permanent or on contract. When a Regional Directorate of Education asks for additional APPs on the basis of incremental enrollments in one school, the MED has no information at hand on the possibility of redistributing the APPs in the same region or in its USEs, taking into account the APPs in nearby schools. The organic APPs are supposed to be filled by appointed teachers through the com- petitive selection examination, and the annually adjustable APPs by contracted teachers. But when fewer candidates than positions are selected, the organic positions are filled by contracted teachers as well.41 More than half of the contract teachers are estimated to have titles because most of them are recent graduates from ISPs. Of the 93,000 unqualified teachers nationwide, some 80 percent are estimated to be appointed teachers. In other words, it is possible that there is a higher percentage of unqualified teachers in permanent positions than those on contract, although in absolute numbers, they are minorities among permanent staff. The nonexistence of a relationship between qualification and employment status is largely due to historical school expansion and demand for teachers. This is probably a major reason why the determinant of achievement study using the 1996 test data did not find any asso- ciation between teachers' employment status and student achievement, but it did find a statistically significant positive relationship between teacher qualification and student achievement. The deployment of appointed and contracted teachers does not display a clear pat- tern because political considerations also may enter into the decision. For example, in the frontier areas, teachers in remote rural schools are often made permanent staff irrespec- tive of their qualifications. Their job security, status, and satisfaction are considered vital to national security. In remote internal areas, the pattern appears to hold as well. For ex- 40 Nationally selected teachers are appointed by the DRE for a specific school on the basis of teachers' preference, scores, and prior teaching experience. Therefore, there is no room for a school principal or a USE director to select the candidates. (However, a principal can hire con- tracted teachers with the DRE's approval.) 41 In the case of the Cusco Subregion in 1997, a total of 13,674 organizational APPs was budgeted. However, at the end of the year, only 12,320 teachers were appointed and the balance was contracted. In addition, 158 teachers were contracted as provided by the budget for annual incremental APPs, which has been fixed since 1995. 50 ample, the analysis of the 1994 survey of rural and urban schools in Lima and Cusco found that among teachers in small rural schools, 90 percent are permanent staff, even though only 50 percent of them have titles and their average age is about the same as those in large urban and rural schools. By contrast, only 84 percent of teachers in very large and large urban schools are permanent, reflecting largely the fact that 81 percent of them have titles. (Appendix 4.6.) In the old system, appointed teachers enjoyed generous benefits and life-long ten- ure (see Footnote 6 in Chapter 1 for the various laws that have regulated pensions). How- ever, the pension benefits are rapidly eroded for new entrants. The new individualized private pension system would enable even contracted teachers to contribute to their re- tirement benefits. At the same time, the institution of performance review in the public sector may soon erode the job security of appointed teachers as well. Nonetheless, ap- pointed teachers would be entitled to severance pay while contracted teachers will not. Given the rapid erosion of job security even for appointed teachers, the major difference between these two types of teacher may be the status conferred. This difference, however, could have a very negative impact on the morale and commitment of contracted teachers. Therefore, it is important for policy to address the issue of incentives for higher perform- ance. An option is to convert everyone into an open-ended contract with personalized pension plan. This could eliminate the two-tier system and allow performance to deter- mine duration of tenure.42 This may require a Congressional decision to change the law. 4.2. Conditions of Service and Compensation The differences are minimal in the conditions of service and compensation of ap- pointed and contracted teachers; they are also minimal between primary and secondary school teachers, and between teachers with titles and teachers who have academic de- grees in other professions or postgraduate degrees. This indicates a lack of incentives in the system to encourage conumitment, professional development, and higher perform- ance, which could translate into better student achievement. Conditions of service. Both appointed and contracted teachers are obligated to work for 40, 30, or 24 hours per week, but that distinction is largely artificial. Most teachers work about the same number of hours whether they were appointed to do it or not. The difference in pay between 40, 30, and 24 hours of weekly work is also minimal. There is also no difference between appointed and contracted teachers in working hours. The majority of initial and primary education teachers are appointed for 30 hours, and secondary teachers for either 30 or 40 hours. There is no difference in pay between pri- mary and secondary school teachers even though the latter are expected to have higher knowledge of subject content. Principals, deputy principals of all schools, and senior teachers at secondary schools are appointed for 40 hours a week. Teachers have the right to ask for reassignment to a different school in a different location after three years of 42 This is the option chosen by the World Bank for its staff in 1998 in order to address the inequity of the two-tier system of having pensionable staff and consultants doing the same job but with different compensation. 51 service, but in reality, it is hard to get reassignment, leading to a lot of dissatisfaction among teachers. Teachers' working conditions have not changed much over time. They usually have two months of vacation in the summer (January and February) and one week vacation in the winter. In remote rural areas, teachers do come to the town to collect their pay checks if they do not have a bank account and to take in-service training.43 On those occasions, they often take off a few days informally. Absenteeism is therefore often higher in rural and public schools than in urban and private schools. The academic year starts on April 1 and ends on December 15, but the net instructional period is often less than 180 days, as in many countries in Latin America, which is on the low side of the international range. There is also no difference in remuneration between appointed and contracted teachers. Remuneration.44 There is only one national salary scale for all regions and de- partments (Appendix 6.19). Teachers under different pension schemes (governed by Laws 20530, 19990, and individualized accounts) have different salary structures. Those under Law 20530, who enjoy the most generous pension benefits, have lower salaries, whereas those under the individualized pension scheme have the highest salaries. The basic salaries are adjusted for marital status (unmarried or married with up to 5 family members), rural allowance (s/45 per month, which has not changed since 1993), and three fixed bonuses (s/300 each in March, July, and December). The average salary for those under Law 20530 is 627 soles per month, those under Law 19990 receive 646 soles, and those under the individualized pension plan receive 689 soles. Rural allowance, however, is portable; that is, even after teachers leave the rural position and teach in the city, they will still be able to keep the monthly rural allowance permanently. This practice should be discontinued. Reform in salary scale should raise the rural allowance, but link it to the position and not to the person. There are five scales of salaries (I to V) for promotion. However, the difference be- tween scales is insignificant (only some s/.12 per month). There is only a 10 percent dif- ference in remuneration between the top grade for 40 hours work and the lowest grade for the same amount of work (Appendix 6.19). Promotion between scales has been frozen since 1991. Consequently, the majority of teachers are at levels I and II, and do not have strong motivation for better performance, professional development, and promotion. Ap- 43 To give an example, in Loreto Department, a teacher who has to work in a remote com- munity near the frontier with Ecuador has to travel 20 days upstream by boat from Iquitos to his/her posting and about the same number of days downstream to Iquitos. He/she usually has a few months' advance salary, and picks up his/her pay check in Iquitos three times a year. This means 120 travel days every year. During the teachers' travel to Iquitos, students would not have classes. Even though some of the travel time coincides with vacation days, a significant amount of time is lost. This again accounts for why the learning outcomes of rural children are lower than that of urban students. 44 Teachers' salaries have been increased by about 16 percent on average since April 1999. However, this report only refers to the salary scale prior to April 1999 because the latest informa- tion was made available too late to include in the analysis. 52 pointed teachers receive their salaries through a bank account, and contracted teachers by checks issued to them. The salaries for teachers without titles have also five scales (A to E) depending on their qualifications, but the difference among scales is more insignificant than among teachers with titles (Appendix 6.19). A is for those who completed pedagogic studies but have not earned the title yet. B is for those who have completed higher education studies with professional titles in other than pedagogy. C is for those who have not completed pedagogic studies at higher education level. D is for those who have studied at higher education level without any university titles. Finally, E is for those who have only secon- dary level education. The salary differential between a teacher without title who works for 40 hours and is in A scale and his/her counterpart in E scale is only 39 soles per month. The salary differential between teachers with titles and those without is more sub- stantial, ranging from 80 soles to 170 soles per month. However, the emphasis on peda- gogical titles at the expense of downgrading those who have other higher education de- grees (scale B) discourages talent from other fields from entering teaching. There are no statistics on the qualifications, conditions of service, and remuneration of private school teachers. Given the uneven conditions and quality of private schools, it is likely that teachers' salaries also vary a lot. Those private schools that serve the poor are most likely to pay teachers on an hourly basis (about 5 to 7 soles per hour), and teachers probably have to have two jobs at the same time in order to make ends meet. However, according to the preliminary results of a 1997 survey of 1,000 private and pub- lic school teachers, salaries in the top private schools may be as high as 4,000 soles per month (Saavedra and Dfaz, 1999). Teachers' average monthly salary of 646-689 soles is more than twice the mini- mum wage. This amounts to an average annual salary of about US$2,903-3097, or 1.5- 1.6 times the GDP per capita, which is lower than the 2 to 4 times prevalent in other countries at a similar level of development. This is one of the reasons why Peru has been able to provide such broad education coverage at such a low level of public expenditure. Teachers' salaries took a hard hit in the 1980s, but their remuneration in real terms steadily recovered in the 1990s (Figure 30). The key policy questions are not whether salaries were high or low relative to other countries, or whether the purchasing power has recovered, but (i) how teachers' salaries fare relative to other professionals who have similar years of tertiary education, which would impact on the ability of the sector to at- tract and retain academically capable individuals; (ii) whether the salary structure pro- vides incentives for teachers to take up hardship posts in rural areas, to continue to de- velop their professional skills, and to improve student learning; and (iii) what the recur- rent cost implications for restructuring the salary scale are. 53 Figure 30. Remuneration of Teachers in Real Terms, 1990 to 1997 700 -. 6 00 - ________ 3 0 0 - . . _ . 200- 0 ______ __ ___,,__ 1 ,- 1990 1991 1992 1993 1994 1995 1996 1997 1998 ' With data up to April 1998. Source: Minis Irt of Education Saavedra and Dfaz (1999)45 found that teachers' relative position eroded by 30 per- cent between 1986 and 1992, but other professionals' relative earnings declined by 16 percent between 1992 and 1996, so that for the whole decade 1986 to 1996 teachers' earnings deteriorated by 10 percent in comparison with other professionals. This salary differential between teaching and other professions could induce the best and most adapt- able teachers to leave the profession to take up jobs in other sectors. The data compiled by INEI show that real salaries of all sectors declined sharply between 1970 and 1990, but made some recovery in the 1990s. Average teacher salaries lost more than the private sector as a whole, but fared much better than the administrative staff of the public sector in general and nurses.446 This has not even taken into consideration the two months' vaca- tion enjoyed by teachers, which is not available to other employees in public or private sectors. In summary, although teachers salaries lost markedly their real purchasing power in the 1980s in comparison with the private sector, they gradually recovered after 1990 and fared better than the public sector as a whole. Nonetheless, better salaries in the pri- 45 Saavedra and Diaz compared the earnings of teachers and other professionals with univer- sity-level education, using panel data from the household surveys done in 1986, 1992, and 1996. The work is in draft. 46 The current mission's findings partly modify the conclusion of Psacharopoulos's 1996 study using household survey data during the 1980s; that is, that teachers are not underpaid in comparison with all other occupations. In fact, teachers are underpaid in comparison with the pri- vate sector workers, but not with the public sector workers. The current mission findings also modify the conclusion of the November 1994 Staff Appraisal Report on the Primary Education Quality Project, which states that teachers have suffered the worst fall in income among all cate- gories of government employees. In fact, teachers were better off among all government employ- ees in terms of falls in remuneration. 54 vate sector could induce migration of the more competent teachers to other sectors. (Fig- ures 31-34.) Figure 31. Index of Remuneration of Government Figure 32. Index of Private Sector Salaries in Metropolitan Employees, 1970-1997. Base August 1990=100 Uma, 197G-1997. Base August 1990=100 IBM0 1200 1500 --- --- -- -- --- -- -- -- 900 1200 - - - - - - - - - - - - - - - - - - - -- 900 -- - -- - - - _------------_-_--_--_ _ 600 600 12---------00 300 80 0 ---- - - - - - - - - - Figure 33. Index of Purchasing Power Figure 34. Index of Purchasing Power =af Private Sector Salaries 1972,1990, of Public Sector Salaries 1972, 1990, 1997 1997 120 - 100 - _ _120_- 80 20 - 20°°- 0 0 1970 1980 1990 2000 1970 1980 1990 2000 Figures 31-34 Source: INEI The unattractiveness of teaching is particularly serious for secondary education, where growth is expected to occur. The lack of salary differentials between primary and secondary school teachers does not reward knowledge of subject matter, which is more intense in secondary education than primary education. Furthermore, the current salary scale that rewards teachers with pedagogical titles more than those with other degrees (such as in arts and science or with postgraduate training) would not attract talent from other fields into the profession. In the short run, reforming teachers' salary scales to make remuneration to teachers with bachelor or masters degrees in nonpedagogical fields on a par with those with pedagogical titles will be the first step to widen the pool of talent in teaching. This will, of course, have implications for the type of training provided by ISPs, and the basic requirements for teaching. In the long run, the option should be considered of requiring all secondary school teachers teaching above Grade 7 to have the first degree in a subject area, and have additional pedagogical training. The salaries of these teachers would reflect this better academic preparation. 55 4.3. Incentives and Accountability Many countries in the world, including OECD countries and in Latin America,47 are pursuing education reform in order to meet the challenges of global economic integra- tion and technological changes of the 21t century. What distinguishes reform of the 1990s from those in previous decades is the focus on learning outcomes. In varying de- grees and at different paces, reforming countries are embracing a set of principles to overhaul their education systems. These are the needs to set standards for student learn- ing, to set standards for teaching, to improve teacher education and professional devel- opment, to provide incentives for teacher knowledge and skill upgrading, and to encour- age schools to organize for learning. The broad scope of the changes entailed makes this wave of reform one of the most ambitious in the history of education. In Peru, MED is in the process of building up a meritocratic-based education sector with some of the elements for accountability being set in place. For example, the first na- tional assessment test was implemented in 1996 (Background Note 4); a plan to modern- ize teacher pre-service and in-service training in primary education was also piloted in 1996 (Background Note 5); and a national competitive examination was introduced to select teachers for appointed teacher positions in 1997. The examination has the dual purposes of selection and quality assurance. Its setup was timely, given the rapid growth of private ISPs, and the diverse curricula offered by ISPs and the education faculty of universities. In the first administration of the exam, some 96,000 qualified teachers applied for the 34,000 appointed teacher positions, but only 12,000 candidates or 12.5 percent passed the exam. The results of the second examination are very similar to the first one. Since the examination is not a criterion reference test and the validity of the test has yet to be established, it is premature to judge whether the low pass rate reflects poor preparation of teachers or an administrative decision. To truly test the skills of teachers, there is room for modification. While Peru is heading in the right direction to pursue a systemic reform, it is neces- sary to ensure that a few key building blocks are in place.48 Specifically, what needs to be done is as follows: 47 In the 1990s, Latin American countries have devoted considerable financial and human resources to reform the educationsector. However, the recent experience of implementation of educational reforms throughout the region suggests that these programs have not been able to transform substantially and extensively the behaviors of teachers, the main actors, in the school level where it matters most. The Second Summit of the Americas held in Santiago, Chile, in 1998 outlined a Plan of Action committing all countries of the hemisphere to new reform efforts, in- cluding an increase in the level of professionalism among teachers that combines pre-service and in-service training, and the development of incentive mechanisms tied to updating their skills. 48 In the United States, 5 years of work has gone into improving the instruments that are making it possible today to pay teachers for what they know and do. 56 * Establishing overall goals and standards for students, and assessment of out- comes. As a first step, the education system has to have a clear set of goals spelled out in detail about what it expects students to know and be able to do. Standards are not the same as curriculum because the latter is usually very content- and topic- specific. Standards are more about skills and competency that could be developed in the course of learning various subjects. Without standards for students, it is impossi- ble to set standards for teachers or to measure whether the goals have been achieved. To be able to measure outcomes, an assessment system must be in place, and test va- lidity and reliability must be secured. The results of the assessment should be fed back not only to policymakers, but also to various levels of administrators, teachers, and parents so that they have a clear sense about their school's relative performance, and can gauge their value-added efforts. Peru might want to start with standard setting and provide the results of the 1996 assessment at least to Regional Education Direc- torates. * Setting standards for teachers. It is important to spell out the content teachers need to know, and the specific kinds of skills and behaviors that constitute good teaching need to be spelled out in meaningful detail. This means providing written documents on what excellence in teaching in a given subject and in a given level (such as science teaching in primary education) would be. Teachers would be assessed against these standards. This might be the next step for MED. * Aligning pre-service and in-service training programs with these standards. Vir- tually no country in the world has been able to do this yet. However, steady progress has been made. The development of meaningful teacher standards (such as PRAXIS and INTASC in the United States) is beginning to make an impact. Peru can shorten its development time by building on these materials, and by adding what is relevant for their conditions. * Making teacher assessments. Often, this begins with the use of paper and pencil ex- aminations to test teachers' knowledge of subject content, or for certification of teachers. This process has begun in Peru. However, even with the most carefully de- signed test items, this form of examination alone is inadequate to assess a broad range of skills (such as classroom management, pedagogical repertoire, and team work with other teachers) needed in order to be an effective teacher. New innovative instruments being experimented with in OECD countries include peer examinations of a portfolio of the teacher's work, videotapes of his/her teaching, interviews, competency tests, and other means to ensure a truly comprehensive assessment of a teacher's demon- strated competence as well as knowledge base. Such assessment is predictably expen- sive, but probably will be cost-effective in the long run when it can positively affect student learning. Assessment techniques are the vehicle to measure progress and in- form any corrections which should be made by the teacher and/or system. In future, Peru may want to start by modifying the methods of recruiting teachers to fill APPs by requiring candidates who perform well in the competency test to demonstrate classroom teaching to determine suitability for teaching. Similarly, the recruitment of 57 principals should be made beyond a paper-and-pencil test by having candidates visit schools and recommend plans for school improvement.49 * Rewarding teachers' knowledge and skills individually and schools collectively. How the incentive in the system is structured affects behaviors of teachers. Teachers should be rewarded for what they know and do-as measured by objective and multi- faceted performance assessments-rather than for how long they have been in ser- vice. Promotion from one level to the next should be based on demonstrated higher level of competency, not seniority. This could provide incentives for teachers to in- vest in their professional development, which is not necessarily restricted to in- service training, but could include doing more reading at home, more reflection on their practice, or networking with other teachers to keep abreast of the latest devel- opments in the profession. The results to be measured need not be restricted to stu- dent achievement, either, but could be extended to broader student intellectual, artis- tic, athletic, and moral development, and parent involvement. Whatever it is, it must be measurable, preferably in a value-added way. This is not easy, and that is why, de- spite the desirability of including more diverse measures, most school reward systems in countries that implement them are basically driven by student achievement tests. At the same time, there should be rewards to schools collectively in order to encour- age collaboration among teachers. Rewarding schools may not require more than one to two percent of the total education budget. This should be combined with public recognition of excellence. The amount should motivate teachers at the margin, but not be so central that teachers will focus their work exclusively on "the test" or whatever else is being measured.50 Building professionalism among teachers could be the center piece of reform, and standards should drive the change in each stage of professional development. The best way to build teacher professionalism is through the work of groups of teachers reflecting on what constitutes excellence in the practice of teaching. Since some countries have pio- neered the work, Peru can benefit from their experience without having to reinvent the wheel. It should be acknowledged, however, that this requires changing institutions and culture and will be the most difficult task to accomplish. 49 In New York City's District Two, which has attained national fame for being able to im- prove overall student achievement and reduce the variability of learning outcomes of an entire cohort, candidates for principal positions are asked to visit a school or watch a video tape of a school and then tell the hiring committee what problems they have identified and what solution they would propose. This would ensure that the principals selected have practical experience of running a school and of providing instructional leadership. 50 Colombia has introduced an innovative incentive system to reward and recognize good teaching. Students and parents in every school are asked to elect their best teacher based on a set of criteria provided by the Ministry of Education, while every municipality will elect the best schools, again based on provided criteria. Then the departments will elect the best schools from the list, based on the relative positions of the schools selected by the lower levels. The criteria focuses on efforts, rather than existing conditions. Then there are awards for the best schools in the nation, the best schools in the departments and municipalities, and the best teachers. Both teachers and schools would receive public recognition and a cash award. 58 Summary. The answer to the fourth research question is affirmative. However, this introduces a policy dilemma towards teachers. Because of the enormous recurrent cost financing implications, education ministries in the world are often constrained by what they can do about salaries across the board, particularly in view of the consensus on working to extend access to basic education. Yet it is important to provide incentives to attract and retain competent people in the profession because teachers are critical to qualitative improvement, as well as quantitative expansion of the system. The issues that need to be addressed in Peru are (i) the disparity in qualifications between rural and urban teachers; (ii) the lack of reward for subject matter knowledge, particularly in secondary school teaching, and the disincentive in the salary scale and promotion criteria for those who have had university and postgraduate education in fields other than pedagogy to en- ter and remain in the teaching profession; (iii) the morale problem related to different statuses of employment between appointed and contracted teachers; and (iv) the lack of incentives for higher performance. The first issue calls for increasing the salary differential between rural and urban teachers to compensate the rural teachers for hardship positions, combined with a job ro- tation system so that they have an opportunity to return after a few years, with investment in rural school inputs to make teaching there more attractive, and with recruitment of in- digenous teachers into teaching in their communities. The recent literature review on cost effective interventions in primary education in Latin America by Schiefelbein, Wolff, and Schiefelbein (1999) found that 50 percent increase in salaries of rural teadchers was asso- ciated with an increase in test scores by 19 percent. The second issue calls for (a) in the short run making the salary scale of teachers who teach in secondary education higher than those who teach in primary schools, and also raising the salaries of teachers who have university degrees in other subject areas at least to a par with teachers with peda- gogical titles so that they do not have a second-class status as teachers without titles; and (b) in the long run changing the academic requirement for secondary school teachers with stronger emphasis on subject matter knowledge. The third issue calls for unifying a two- tier employment status. The fourth issue really requires the support of a systemic reform geared to establishing an accountability system that rewards group and individual per- formance, preferably based on value-added efforts. This last will be a long-term endeavor and can only be done when the assessment tools are perfected and a culture of evaluation is accepted. 59 Chapter 5. Second-Generation Reform This report began by posing a puzzle as a guide to its inquiry of why Peru has been able to have high education participation rates with a relatively low level of public spend- ing on education. After reviewing the issues in the sector, the report found that progress made in Peruvian education is attributable to relatively equitable public expenditure that focuses on basic education; the ability to contain salary cost in the sector; and high value attached to education by Peruvian households and their high level of spending on educa- tion. However, expanding access under extreme resource constraints has come at the ex- pense of quality. The large gap in school survival rates between the rich and poor, and the rural and urban population; the large between-school variance in student achievement; and the ris- ing returns to higher levels of education signal that further policy changes can make a real difference, particularly to disadvantaged students. These changes may be loosely considered to be the second wave of reform because they will build on the foundation laid by the first-generation reform that rationalized the public sector, balanced the budget, and mobilized private resources. These changes would help realize the country's aspira- tion of having a highly educated citizenry to meet the challenges of the 21 " century. (Ta- ble 5 discusses the implications of the first generation of reform. Table 6 summarizes the objectives, issues, and suggested measures for the second wave of reform). Peru thus find itself at a crossroads with respect to education policy. The current situation, which partially reflects the success of the first generation of reform, represents important accomplishments. That path could be continued. A second generation of re- form, however, would take a path of focussed commitment to improving education as a more central goal in itself and means for accelerating growth and reducing poverty. The following paragraphs summarize this report's conclusions concerning the potential, the content, and the cost of second generation of reform. 5.1. Improve Equity Although public expenditure on education on the whole has been distributed rela- tively equitably, poor households still have a disproportionately heavier financial burden than rich households for their children's education. The disparity in household spending has led to inequitable learning outcomes. The indigenous people are among the poorest in the country. To equalize opportunity, resources have to target the disadvantaged groups. The options include a range of supply- and demand-side measures. On the supply side, the Ministry should extend the provision of a class set of textbooks to all levels of secondary education and supply an additional class set of enrichment reading materials throughout basic education. Given the major difference made by textbooks (as revealed in the 1996 test), this should be the first priority. Since the Ministry is already experi- enced in the development and provision of textbooks to primary education through the 61 MECEP project, it is both easy and logical for it to extend them downward to cover pre- school and upward to cover secondary education. Since this intervention does not involve changing the administrative or finance structure, it is feasible and achievable. This might raise achievement and help retain more children in the system. Meanwhile, the Ministry should also explore the feasibility of providing cost-effective educational technology such as interactive radio to supplement classroom teaching, or audio materials to assist second-language acquisition to support multigrade teaching. A number of countries in the world have experience with using interactive radio to reach rural communities. Some of experiences have been properly evaluated and found to be highly cost-effective when children are tested for what they learn in comparison with those who are in traditional classrooms. Given Peru's difficult terrain, radio sets can be given to families who have school-age children but live in remote communities. An esti- mated 6 percent of total students are in single-teacher schools in remote communities. Although radio broadcasts can be used for school instruction, the best use of this medium is during adverse conditions, such as rain, snow, and flood, when children who cannot travel to school can stay home and still learn the lesson. When this is done in combina- tion with programmed text, this will help them overcome the problem of their own and their teacher's absenteeism. Parenting education can also be broadcast by radio so that parents get more involved with their children's education. Specifically helpful to indigenous children is the expansion of bilingual and multi- cultural education through strengthening teacher training programs in these areas, as well as in multigrade teaching, and recruitment of indigenous teachers to these programs through affirmative action and scholarship. Arregui, Hunt, and Dfaz (1996) found that the vast majority of teachers are employed in the department they were trained in. Therefore, ISPs in departments heavily populated by indigenous people should offer bilingual and multicultural programs that are tailored to the groups within their jurisdiction. At present, offerings in these programs in ISPs are extremely limited. To seriously improve the learn- ing outcomes of indigenous children, the central government should support such pro- grams through its allocation of resources. Also, given the findings from the 1996 test that showed a relationship between indigenous teachers, except Aymara, and lower student test scores, it signals that indigenous teachers need better preparation during their train- ing. Therefore, not only should there be bilingual education teacher training programs, but there should also be some form of compensatory education in ISPs to assist indige- nous trainees whose performance might be weaker than average. The training of indigenous teachers needs to be complemented by the provision of bilingual textbooks and educational materials including interactive radio or audio and video tapes throughout primary education. Currently, the resources that have gone into these programs are negligible, and that is why the impact has yet to be demonstrated. Other countries that have adopted bilingual education, such as Guatemala, have positive results. It is worth examining the approaches in these countries in order to improve on them. Given that Peru has many indigenous groups who speak different languages, and that indigenous communities in the Amazon regions are small and dispersed, such inter- vention is predictably expensive. Nonetheless, given the cost of marginalizing indigenous 62 people, the benefit of poverty alleviation and social cohesion is high. Although it is unre- alistic to develop bilingual texts for all groups within a few years, this should be a long- term project, with targets to be met within a medium term time frame of, say, a decade. Training of teachers for rural schools should also emphasize multigrade teaching. Again, this should be complemented by the provision of program materials. The Government is already planning to provide distance learning through educa- tional television to enable children in remote areas or out-of-school children access to secondary education. While this is a commendable move, it should be noted that a range of support measures, such as face-to-face tutorials, needs to be put in place in order for distance education to work. Otherwise, dropout rates could be very high. To ensure that all children learn the requisite skills, formative and summative as- sessment could be used more systematically and frequently. Those students who are fal- ling behind should be provided remedial education during the school year as well as dur- ing holidays. The Ministry has adopted an automatic promotion policy from Grade I to Grade 2 in order to improve the promotion rates and reduce dropout rates. The impact of this measure on learning outcomes should be evaluated. Given that automatic promotion policy has been associated with failure for students to acquire the requisite skills in many countries, timely compensatory education might be a more cost-effective intervention in the long run. This might benefit particularly disadvantaged children. Another important issue is poor nutrition and health of children in poverty which has contributed to under achievement in many countries. Although this study on educa- tion has not devoted much discussion on the topic, the companion World Bank study on health has examined the issue (World Bank, 1999b). Addressing the health of school-age children either through services provided directly through the schools, or through targeted publicly financed insurance such as the on-going Seguro Escolar, would improve atten- dance and learning. At the same time, it is desirable to consider targeted expansion of the school feeding program (such as Desayunos Escolares), given initial positive findings of favorable outcomes with respect to nutrition, health, and attendance (Pollitt, Jacoby, and Cueto, 1996). Expansion of the school feeding program would need to be weighed against typically high costs of such programs, as well as the feasibility of reaching schools in remote areas. Obviously, such multisectoral interventions require a much more coordinated approach between ministries such as MINSA, PROMUDEH, and PRES. Demand-side financing measures such as scholarships and grants for indigenous or poor children should be explored to enable them to defray the direct costs of education. The mechanism for distributing such scholarships or grants needs to be worked out care- fully to prevent abuse. An indirect mechanism is to give schools that have an indigenous enrollment exceeding a certain percentage an additional per student instructional grant based on attendance. The money could be used by the school for purchase of instructional materials, to provide compensatory education, or to subsidize students' clothing or trans- portation costs. 63 5.2. Enhance Quality A key measure of qualitative improvement is reduction in variance in student achievement. The 1996 test shows that textbook availability and usage, homework as- signments, the characteristics and roles of teachers and principals, and parental role and expectations can reduce between-school differences in learning outcomes. Besides the interventions through textbook provision and teacher training, educating parents about good child-rearing practices and the positive effects on achievement of school attendance, after school studies, and parental involvement in their children's learning by means of the mass media could also help to improve learning outcomes. The policy of universalizing early childhood education to enhance students' school readiness might help reduce late entry and repetition and reduce the between-student differences. Grants for compensatory education or to defray the direct private cost of education to facilitate attendance of in- digenous students and girls, who tended to have lower average math scores, may also make a difference. These interventions could only have maximum impact if they are accompanied by systemic reform that focuses on standards. The necessary components include (i) setting standards for student learning, (ii) setting standards for teaching, (iii) strengthening teacher education and professional development, (iv) providing incentives for teacher knowledge and skill upgrading, and (v) encouraging schools to organize for learning. In other words, making the career of the teaching force more like that of other professions should be the center piece of reform, and standards should drive the change in each stage of professional development. Part and parcel of this reform is to make available indica- tors on perforrmance by school (test scores; repetition, promotion, and dropout rates by grade) available not only to the DREs, USEs, schools, and teachers, but also to parents, students, and the public. This will allow the families and public to benchmark the schools' performance against other schools. This will generate pressure for improvement, and will also build the groundwork to set up an accountability system. At the same time, studies on determinants of student achievement at all levels of education could be used to decide what qualifications should be required of teachers and at what level of education. This kind of study would be very similar to the one reported in Background Note 4. If teachers graduated from universities and from ISPs are found to be associated with higher student achievement even at Grade 4 levels, relative to teachers who entered the profession through a different ladder, then it is very important to ensure that teachers are appropriately qualified. To ensure that qualification matches all the req- uisite competencies to be a teacher of a given grade and subject, teacher pre-service and in-service training should be examined closely and reformed to ensure high standards. The broader issues that need to be addressed are: (i) the disparity in qualifications between rural and urban teachers; (ii) weakness in subject matter knowledge of teachers, particularly in secondary education; (iii) the morale problem related to different status of employment between appointed and contracted teachers; and (iv) the lack of incentives for higher performance. The first issue justifies increasing the salaries of rural teachers to compensate them for hardship positions, combined with a job rotation system so that they have an opportunity to return after a few years, with investment in rural school inputs to 64 make teaching there more attractive, and with stepped up efforts to recruit indigenous teachers into teaching in their communities. The second issue calls in the short run for raising the salary scale of teachers without title but who have university degrees in other disciplines to the same level as teachers with titles in order to enlarge the pool of teachers with stronger subject matter knowledge. In the long run, it requires changing the aca- demic requirements of secondary school teachers and reforming ISP curricula. The third issue calls for unifying a two-tier employment status to make open-ended contracts, with the tenure determined by evaluated performance. The fourth iggue really requires the sup- port of a systemic reform geared to establishing an accountability system that rewards group and individual performance, preferably based on value-added efforts. Given that data on teachers are not available, it would be useful to conduct a census of teachers, as part of the school census to be mentioned below, to review their age, qualifications and specialized areas, experience, subjects taught, and various types of compensation received. This would provide data that could serve as the basis of a review of the supply and demand for teachers and the financial implications of increasing the salary differentials between urban and rural teachers, tying promotions to demonstrated competency, and providing monetary incentives to reward schools. Concomitantly, it is necessary to review the legal framework and incentives for teachers in private and public schools so as to provide a benchmark for improvement. Inevitably, the policy towards teachers and their pre-service and in-service training will make a lasting difference in quality. These measures require institutional and cultural change, which take time. Consen- sus with stakeholders (namely, teachers) needs to be reached in order for the reform to take root. Therefore, as technical measures need to be put in place (such as spelling out teaching standards for each subject and grade, evaluation tools, and assessment of fiscal impact of changing salary structures), consensus building process should be set into mo- tion by reaching out to teachers, NGOs, and the business community. 5.3. Improve Efficiency of Resource Use The review of public expenditure in this report has only been able to access data down to the departmental level, but not to the USE level. Hence, the report was unable to evaluate the actual unit cost by level and by urban and rural areas, across departments. Even the MEF does not have the expenditure data; it only has the budget figures. To piece together a complete picture of the nation's public spending pattern, particularly to evaluate the equity and efficiency of resource use, in order to adjust policy, it is of utmost importance that monitoring continues to take place to cover the following areas. * The trend of public spending on education over time gross and net of pension. It would be desirable to update and extend the analysis of public expenditure on educa- tion to cover not only school-related expenditure, but also education-related subsidies to households (such as school health programs financed by the Ministry of Health, school feeding programs under the PRES), as well as other education-related expendi- tures under PROMUDEH (such as early child care and literacy programs). 65 * Differential spending by urban and rural areas at the USE level. This is equally im- portant so that interventions can be designed to equalize the resources allocated to different schools. This should take into account departments' own resources and capi- tal expenditure allocated to the USE level by PRES (through FONCODER). The growth of inequity in allocation of public resources should be watched closely. * Indicators of learning outcomes (e.g. test scores). These would be logical areas for monitoring so that input measures can be tied to outcomes. The existing questionnaire does not contain questions on school-level resources and public allocation per stu- dent. Expansion of the questionnaires is desirable to cover these and other variables on family background (parental education, income, family resources, amount of hou- sehold expenditure on education), study habits, and teacher characteristics, to assess determinants of achievement. It would help analysis if more questions are constructed for obtaining continuous variables rather than categorical variables. Studies of deter- minants of achievement would be helpful to identify effective policy interventions to help disadvantaged schools. * Household expenditure on education. This would be another key area to follow up. Given that INEI conducts such a survey annually, the data source for such an under- taking is available; only analysis needs to be undertaken. Closer cooperation between INEI and MED would help improve the questionnaire for data collection so that the education portion contains the relevant questions that can address issues for house- hold finance. For example the Cuanto dataset used by this report merges tuition fees with transportation and lunch. This does not allow assessment of the impact of fees alone on a household's decision to send their children to one type of school versus another. The question of extra tutoring should also be included because this area is where the rich tend to spend much more than the poor and it provides a proxy as to how much additional money is needed to ensure desirable learning outcomes. Analy- sis of the household survey would enable the Ministry of Education to monitor distri- bution of public expenditure by consumption or income quintile (Lorenz curve), changes in the elasticity of demand (Engel's curve) for education, and private and so- cial rates of return to education. Currently, responsibility for all aspects of education is fragmented across ministries and institutions-MED, universities, regions, decentralized institutions, PRES, as well as PROMUDEH and MINSA. There is an urgent need to improve the coordination of educational policy and financial matters between these budgetary entities. It is recommended that a sectorwide coordinating body be established that meets at least quarterly to review overall education policy, performance indicators of each subsector, and intrasectoral allocation of resources (gross and net of pension), in order to ensure consistency of overall education policy and finance. The minutes could be made available to all ministries concerned to keep them informed, as well as to CIAS to improve intersectoral coordination. Given that there is no accurate information on how many teachers (both appointed and contracted) are in the system, it is very difficult to assess whether resources have been used efficiently. At the same time, the lack of information in school-level finance in 66 both public and private schools impedes the formulation of policy towards regulating pri- vate schools to ensure standards and safety, and towards expanding secondary education through better use of private-school capacity and resources (such as the use of vouchers to buy places in private schools). The combined need to have accurate information on teachers and school-level finance in public and private schools, therefore, calls for con- ducting an education census. This needs to be taken at the school to obtain information on schools, teachers, and students. Information could provide a useful database to map out strategy to expand secondary education and to improve quality of primary education, as well as to explore options for formula-based funding. Currently there is an inflexible criteria for allocating resources, based on the num- ber of teachers on permanent payroll in 1995 without responding to the reality of chang- ing student populations. This is not an efficient mechanism in the long run. This inflexi- ble criteria has not taken into account the rural to urban migration and also disadvantages the populations with very high birth rates, namely, the indigenous and low income popu- lations. It is desirable to evaluate the option of using capitation grants in large public schools (but using a different formula for small rural schools) as a basis of allocations, to reflect the reality of changing student populations and to make allocation decisions trans- parent. Schools should be given the discretion of how best to use the capitation grants in terms of purchasing a mix of inputs, such as hiring teachers, buying instructional materi- als, or installing lab facilities. In addition, certain categorical grants should be provided to regional education directorates for them to earmark assistance for disadvantaged ar- eas/schools/persons. Increased international competition and technological change have led to growing demand for a higher level of skills in the labor force. This has translated into rising pri- Fig. 35. Estimates and Projection of School-Age Population, 1995-2020 4000 3500 3000 0 ° 2500 - +Age=5 M ^ * ^ - ^ * ^ ^ * ^ * ^ W _Age = 6-11 a 2000 A Age = 12-15 2_ 31tEXE~ - U31--K )1f 31t f f K )K B IK IK )K 3K )It )It 3 , Age = 16-17 . 1500 -*-Age =18-20 0 1000 1t- 500 - 0 1995 2000 2005 2010 2015 2020 Years 67 vate returns to higher education, which will fuel further demand for higher education. To properly support higher education and ensure quality, it is important to improve the transparency of the funding mechanism, with incentives to reward efficiency and quality, as well as to share costs with students, who are the chief beneficiaries of their own educa- tion. Increased cost sharing also needs to be supplemented by student financial assistance such as student loans to ensure the academically deserving will not be disqualified due to financial constraints. Although this report does not cover higher education, given that policy and expen- diture on higher education impact on lower levels of education, it will also point out areas for further investigation. In-depth review of funding of higher education is desirable to assess options for introducing funding formulas for higher education (such as fulltime equivalency based allocation) with incentives for improving efficiencies (such as that a certain allocation is based on graduation rates within a certain time frame), and examine the adequacy and impact of cost sharing (such as estimating the elasticity of demand and a survey of students' financial situation and expected earnings after graduation). This ex- ercise should involve MED, MEF, and higher education institutions, and inform overall education policy of the country. 5.4. CONCLUSION The 1993 Constitution enshrines the principle of compulsory and free preschool, primary, and secondary education. To the extent that there is strong evidence in many countries of the positive impact of preschool on subsequent student behavior and achievement, it is educationally very sound for the Ministry to include preschool in basic education. As for secondary education, the projected growth of the cohort between 12 and 15 years of age in the first 15 years of the 21" century highlights the need to address their educational needs (Figure 35). Given the association of high crime rates and poor, young males in many countries, the education of adolescents will provide many unmeas- urable social benefits. Population projections by the World Bank estimate that the cohort of ages 6 to 11 (the primary education age group) will rise modestly between the present and 2010 and then decline afterwards, while the 12-to-15, 16-to-17, and 18-to-20 age-groups will in- crease throughout the first two decades of the next century. There is likely to be increased pressure for education resources, including demand for qualified teachers in secondary and tertiary education. The reprieve provided by the declining primary school-age popu- lation would come only after 2010. If nothing is done immediately, many cohorts of stu- dents will miss their educational opportunity and sink into poverty. What might the resource requirement be to meet the Constitutional Mandate of universal basic education? Based on a rough estimate, it would probably cost the country an additional 2 percent of GDP, net of pensions, if the minimum standards are to be achieved for all students at all grade levels. This would still be within the range of the regional average of the Latin American Region. This would enable the government to simultaneously improve internal efficiency and extend access to the out-of-school popu- lation in order to ensure every child the opportunity to acquire the skills of a complete 68 secondary education cycle. This would also raise per student spending. Although the ex- act costing could only be done after knowing the Government's input mix in providing universal coverage, simply relying on efficiency gains or shifting resources in the margin could not provide the resources necessary to meet the Constitutional Mandate. Many countries have committed far more public resources to education than has Peru, but without achieving universal coverage for basic education. For these countries, increasingly binding fiscal constraints and continued needs to expand coverage sharply constrain the policy agenda. Peru, in contrast, has positioned itself to initiate a major drive to consolidate equity gains while improving quality. Peru has indeed reached a crossroads concerning education. The status quo reflects substantial progress, and one direction for the future would continue that path. The other and more ambitious direction would entail commitment to a reoriented human resource strategy for poverty reduction and economic growth. This path would require, over time, substantially increased public expenditures on education. An increase from 2.4 percent to 4.5 percent of GDP net of pension expenditure (to the Latin American average) is, for Peru, feasible in the medium term, given its past experience of limited public financial commitment to education. With such an increase in expenditures, financing a second- generation of reforms along lines discussed earlier, Peru has the opportunity to markedly enhance the intellectual ability and competitiveness of its labor force within a generation. No policy challenge is more significant. 69 Table 5: First Generation Reform and Its Implications for Second Generation Refonn First generation Household Equity, quality, The teaching reform Public finance finance and efficiency profession Rationalization Measures to con- There is no imme- Could be negative Negative effect of public service, tain cost provide diate impact on in the short run, on morale in the increased use of the starting point household finance but should im- short run. Fairer contract staff in to improve effi- of education. But prove quality in to offer an open- the central min- ciency in the use of if these measures the long run, if an ended contract istries, and resources. Poten- release more pub- accountability sys- for all and use privatization of tially, public re- lic resources to tem is properly set performance to pensions. sources can be improve quality or up. determine dura- used to improve extend access, tion of employ- quality or expand households will ment. Need to set access. It is impor- benefit from it and up incentives to tant to establish match public in- reward perform- transparent fund- vestment. ance and intro- ing criteria to im- duce accountabil- prove efficiency ity. Lack of and equity. timely statistics of appointed and contracted teach- ers on the payroll hinders develop- ment of policy. Increasing Need to establish If the quality of Monitoring and In the short run, attention to transparent evalua- education has evaluation should teachers will feel meritocracy and tion criteria and markedly im- underpin an ac- pressured. Need quality,. need policy and proved, house- countability sys- to support im- resources to re- holds are likely to tem that is central provement in pre- ward performance. be willing to invest to a merit-based service training This will ulti- even more and system. and in-service mately improve start the virtuous professional de- the efficiency of cycle. velopment. Meri- resource use. tocracy should raise status of teachers in the long run. Need to change salary scale to attract and retain compe- tent people in the profession. Expansion of Expanded mandate Increased public Need targeted sup- Affects the sup- constitutional needs to be sup- investment will port for the poor, ply and demand mandate for ported by in- have a matching indigenous, and for teachers. basic education. creased public effect to solicit rural students to Need to pay spe- investment to real- additional private ensure equity of cial attention to ize the goal. spending. learning outcomes. the quality of There must not be teacher pre- a trade-off between service and in- quality and quan- service training. tity. 70 Legal Opting out of the Led to mobiliza- Need to improve Need to improve encouragement public system by tion of more pri- quality of public incentives in the of private the urban rich and vate resources for education to avoid public system to education and middle class may education. Demand further deepening attract good the growth of free up resources for private educa- the socioeconomic teachers. Non- private for the working tion will induce divide. Need to monetary incen- education. class and rural supply in cities, accredit private tives include students. Lack of and greater avail- schools for quality making work supply of private ability of good assurance. environment at- schools in rural quality of educa- tractive and areas calls for in- tion may attract breaking teacher creased public more middle class isolation. investment there. to go for it. Regionalization Affects the budget Neutral at the be- Need to set na- Need to provide of administra- process, inter- ginning but can be tional standards, quality assurance tion and decon- governmental positive if the re- monitor perform- through accredi- centration of transfer of re- gions become ance, and under- tation of teacher services. sources, and bal- more effective in take measures to training pro- ance of power. meeting local reduce regional grams, and certi- Need to strengthen needs and in assist- variation in learn- fication of teach- the ability of the ing the poor. ing outcomes. ers. The regions regions to deliver should have more quality services by flexibility in the giving them more recruitment and discretionary deployment of power and re- teachers. sources. I I 71 Table 6: Summary of Policy Options Objectives Issues Suggested measures Improve Inequity in learning out- Provide teachers' guides, textbooks, workbooks, equity comes of poor, rural, and supplemental reading, and audiovisual materials indigenous students due and media (for example, interactive radio) to inadequate public re- throughout 11 years of basic education to all sources; unwillingness of schools. Provide radio sets to families with qualified teachers to serve school-age children in remote communities to en- in remote communities; able them to access lessons. and perhaps poor nutri- tion and health and lack Expand bilingual education programs; provide of preparedness of stu- bilingual instructional materials, recruit indige- dents themselves. nous teachers to serve their own communities, train rural teachers in multigrade teaching. Increase compensation to rural teachers and assure job rotation to induce teachers to teach in rural communities. Address the health of school children either through services provided directly through the schools, or through targeted publicly financed in- surance such as the on-going Seguro Escolar, to improve attendance and learning. Consider tar- geted expansion of the school feeding program given initial positive findings of favorable out- comes with respect to nutrition, health, and atten- dance. Weigh costs against scope of expansion of the school feeding program. Provide information (through the mass media) to parents about how good child-rearing practices and involvement in their children's learning en- hances children's interest in learning and im- proves their school achievement. Extend preschool education to disadvantaged communities in order to improve school readiness. Provide compensatory education to ensure all children learn all requisite skills relevant to their grade level. Inequity in access to sec- Establish distance learning programs (by a combi- ondary and tertiary nation of programmed text and communication education of poor, rural, media) to provide secondary education to rural and indigenous students communities. due to lack of supply and inability of households to Provide scholarships and grants to rural children pay. to enable them to attend secondary schools. 72 Table 6: Summary of Policy Options Objectives Issues Suggested measures Enhance Lack of incentives to at- Change salary scale to better reward secondary quality tract and retain competent school teachers, as well as those who have univer- people in teaching and to sity and postgraduate degrees in disciplines other improve performance. than education. Unify employment status and of- fer open-ended tenure to be determined by per- formance. Reward schools that show improve- ment. Set standards for student learning; set standards for teaching, strengthening teacher pre-service and in-service training (possibly changing the aca- demic requirement for teaching in secondary edu- cation); provide incentives for teacher knowledge and skill upgrading; and encourage schools to or- ganize for learning. This entails improving the tools for student assessment and teacher evalua- tion, as well as building consensus with all stake- holders. Lack of information for Publish information on performance by school benchmarking own per- and distribute to relevant actors (DREs, USE, formance to know how schools, and parents). much to do in order to improve. Improve Lack of sectoral overview Monitor the trend of public spending on education efficiency of of policy to assess its co- over time (gross and net of pension), particularly resource use. herence and consistency. differential spending by urban and rural areas at the USE level; track indictors of learning out- comes; survey household expenditures on educa- tion and follow changes in the elasticity of de- mand as well as the private and social rates of re- turn. Insufficient coordination Establish a sectorwide coordinating body that of educational policy and meets at least quarterly to review sectoral policy, financial matters between performance indicators of each subsector, and in- the budgetary entities that trasectoral allocations to ensure consistency of have responsibility for overall education policy and expenditure. education (MED, univer- sities, decentralized insti- tutions, Regions, PRES, PROMUDEH). Insufficient information Conduct an education census to obtain informa- on teachers and school- tion on teachers' profiles and school-level finance. level finance in public and private schools to guide policy to improve quality and expand ac- cess. 73 Table 6: Summary of Policy Options Objectives Issues Suggested measures Inflexible criteria for al- Clean up payroll list and set up a centralized data- locating resources based base. on the number of teachers on permanent payroll Consider using funding formula based on average without responding to daily attendance with grants to compensate for changing school-age various categories of disadvantage. Give schools population. greater discretion to use resources. Potential issue of intra- Given that policy and expenditure on higher edu- sectoral allocation be- cation impacts on lower levels of education, in- tween universities and depth review of funding for universities is desir- lower levels of education. able to assess options for introducing funding formulas with incentives for improving efficiency, and for cost-sharing. 74 REFERENCES Aitkin, M., and N. Longford. 1986. "Statistical Modeling Issues in School Effectiveness Studies." Journal of the Royal Statistical Society 149, pt. 1: 1-43. Arregui, Patricia; Barbara Hunt, y Hugo Diaz. 1996. "Problemas, Perspectivas y Re- querimientos de la Formaci6n Magisterial en el Peru." Informe Final del di- agn6stico elaborado a solicitud del Ministerio de Educaci6n y la GTZ. Lima, Peru, Octubre. Asamblea Nacional de Rectores. 1996. Universidades del Peru. Facultades y Carreras Profesionales. 1996. Lima, Peru. Asociaci6n Peruana de Administraci6n para el Desarrollo. 1991. 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It is estimated that about 20 percent of those under 5 are having some form of initial education (INEI, 1997). In 1997, 522,000 children enrolled in initial education in the public system, and 147,000 in private organizations. In a recent proposal for re- structuring education, one year of initial education is to be made compulsory and form part of basic education. Primary education comprises six grades, intended for the age group between 6 and 11, but also available to adults who have not received it. In 1997, about 3.7 million per- sons enrolled in public formal and nonformal programs, and 491,000 in private programs. The majority of primary schools are coeducational and the program of study comprises 25 hours per week during 36 weeks per year (900 hours per annum). Secondary education is offered to the age group between 12 and 16, as well as to adults who did not have it. In 1997, about 1.6 million enrolled in public secondary schools, and 318,000 in private schools. Secondary education is organized in two cycles: the first has a common curriculum for all students in Grades 7 to 8, and the second has a diversified curriculum of three years, divided into science and humanity streams. Secon- dary education is offered at 36 hours per week for 38 weeks in a year (1,368 hours per annum). Tertiary education includes nonuniversity and university education. Nonuniversity institutions include teacher training institutions (institutos superiores pedagogicos or ISPs for short), technical education institutions (institutos superiores tecnicos or ISTs), and schools for the arts. In 1997, 211,000 students attended public universities, and 129,000 private universities. Another 165,000 students enrolled in public tertiary institu- tions, and 139,000 in private institutions. In 1997, MED proposed major changes in the structure of the system, with the aims to improve the articulation between levels, to meet needs of a changing labor market, and to improve system efficiency and organizational flexibility. It pledged to universalize one year of initial education, improve the quality of primary education, reduce secondary education from five to four years, and introduce two years of preparatory course work (bachillerato) which will provide the transition to tertiary education or to the world of work. In other words, basic education will comprise 11 years of instruction, which in- cludes one year of preschool, six years of primary, and four years of secondary education. What is new is not only the structural change but the introduction of certifications of study at three levels: at the end of basic education, bachillerato, and tertiary nonuni- 85 versity education, respectively. Accreditable capacities of basic education will include: (a) comprehension of reading, editing, communication, and expression; (b) development of logic and mathematics; (c) management of the basics of technology and informatics; (d) facility for continuous learning and holistic reasoning; (e) creativity and imagination; (f) understanding of environment; (g) local, national, and universal culture; (h) basic work and organizational abilities; and (i) basic knowledge of an international language. Accreditable capacities of the bachillerato will include: (a) productive use of resources (time, space, skills, and technology); (b) abilities to search and select information; (c) fa- cility for analysis, synthesis, abstraction, and systematization; (d) proficiency in an inter- national language; (e) intermediate professional competency; (f) tools for management and self-employment. Students will be certified after having had no less than 2,500 hours of studies in tertiary nonuniversity education. The proposed bachillerato is divided into two streams: (a) scientific and techno- logical, and (b) scientific and humanistic. The former will prepare for studies in engineer- ing, medicine, mathematics, the sciences, and accounting in universities, and technical courses in tertiary nonuniversity education. The latter will prepare for studies in law, education, the social sciences, and humanities in universities, and tourism, graphic arts, translation, catering, and public relations in other tertiary education. In each stream, there will be a core curriculum and other subjects that prepare for the world of work. The core curriculum is shared by both streams and includes science and technology, earth science, oral and written communication, economics and management, informatics, history of Peru, natural philosophy, and international language. Bachillerato can be offered by (a) secondary colleges as add-ons to their four years of secondary education, (b) universities before the beginning of undergraduate studies, (c) postsecondary institutes before the be- ginning of two years of tertiary education, and (d) academic institutes specialized just in offering bachillerato. This ambitious plan requires investment in infrastructure, curriculum development, and teacher training. The implementation of bachillerato is sequenced as follows. In 1997, the proposed structural change was made public; the modernization of the secon- dary curriculum has begun; the transitional fifth year of secondary schooling was elabo- rated; and the bachillerato curriculum was proposed. Subsequently, a law was promul- gated to give the structural change legal force; new curriculum and training of principals and teachers was piloted; the development and distribution of education materials in sec- ondary education was initiated; a new administrative system was set up; and infrastruc- ture was planned. Thereafter, a second application of transition curriculum in the fifth year of secondary was implemented; training of teachers; and equipping institutions for implementation of bachillerato with followup and monitoring. Full scale implementation was expected to begin in 2000, affecting 200,000 young people each year. The following year will see the first batch of graduates from bachillerato. The effort to revamp the edu- cation system is expected to come to fruition in 2007. 86 Background Note 2. Income Elasticity of Demand for Education and Engel's Curve' The share of household expenditures for education were analyzed using an Engel equation framework. The explanatory variables include the logarithm of income (here proxied by total expenditure), the logarithm of the size of the household, and a set of variables intended to capture the gender and age composition of the household (with age brackets set up to correspond to the various levels of education in Peru). The explanatory variables also include a dummy for residence in the Lima metropolitan area, and three variables indicating, respectively, the education level of the household head, whether or not the household head is male, and whether or not the household head belongs to an in- digenous group. The focus of analysis in this section is the expenditure variable, but the other variables are included as "control" variables, so that the coefficient estimates re- ported in this section are not biased. In other words, it is important to be sure that what we call the effect of income is indeed the effect of income, and not, say, the effect mainly of the education of the household head. In addition to estimating the Engel function for expenditure, we also provide estimates for expenditures on Food, on Health, and on Other Expenditures. The object of the analysis is to compute income elasticities for each of the budget shares. It is an empirically established fact that the income elasticity for food shares is negative, because poor households need to spend larger shares on food, but an a priori judgment cannot be made about the income elasticities of the other budget shares. In par- ticular it is important to compare the income elasticity for education with those for health. The object of the analysis is to estimate a value for b, the slope on income in the budget share regression, as well as il, the income elasticity which tells us the percentage points by which the budget share goes up for a given percentage increase in income. The estimates from the Engel function analysis are presented in Table 1. It can be seen from the table that the average budget share for education is 0.0467 and the coeffi- cient on log total expenditures is 0.0128. The respective values for health related expen- ditures are 0.0411 for the average budget share and 0.0151 for the coefficient on log total expenditures. The income effect of food has the expected negative sign and the coeffi- cient on log total expenditure for food share is of the same order of magnitude as reported from other countries. It is of interest to note that the dummies for Lima, rural location, female head, and indigenous head of household are economically and statistically insignificant in the edu- cation share regression. Some of these dummy variables are important in the food share regression, such as the 0.1315 effect of a rural location. The lack of significance of the dummy variables for the education share, in contrast with the significance for food share, tells an important story about the stability of preferences for education across households lThis analysis was undertaken by Suhas Parandeker. 87 which vary across these measured variables. The estimates of elasticity, derived from the regression coefficients are reported in Table 2. Table 1: Determinants of Household Budget Shares Budget Shares Explanatory variables Education I Food I Health Other Mean OLS Coefficient (t-value for H0 Coeff. = 0) (Std. Dev.) Intercept -0.1424 0.8066 -0.0970 0.4328 (-10.02) (24.078) (-6.455) (13.52) Logarithm of total household expenditure 0.0128 -0.0377 0.0151 0.0098 9.2304 (8.113) (-10.139) (9.072) (2.750) (0.7202) Logarithm of total household size 0.0187 0.001 0.0046 -0.0242 1.5189 (8.755) (0.199) (2.035) (-5.052) (0.4920) Proportion of boys aged 0-5 years -0.0179 0.1294 0.0026 -0.1142 0.0706 (-2.209) (6.769) (0.307) (-6.249) (0.1174) Proportion of boys aged 6- 11 years 0.0746 0.1185 -0.0095 -0.1837 0.0660 (9.109) (6.133) (-1.091) (-9.951) (0.1130) Proportion of boys aged 12-16 years 0.1120 0.0489 -0.0131 -0.1477 0.0483 (12.43) (2.3000) (-1.377) (-7.279) (0.1000) Proportion of boys aged 17-22 years 0.0730 0.0322 -0.0119 -0.0932 0.0505 (8.875) (1.661) (-1.367) (-5.037) (0.1064) Proportion of girls aged 0-5 years -0.0321 0.1612 0.0118 -0.1409 0.0671 (-3.970) (8.443) (1.383) (-7.724) (0.1163) Proportion of girls aged 6-11 years 0.0717 0.0728 -0.0020 -0.1425 0.0607 (8.503) (3.661) (-0.234) (-7.498) (0.1085) Proportion of girls aged 12-16 years 0.0805 0.0899 -0.0169 -0.1535 0.0470 (8.711) (4.125) (-1.728) (-7.374) (0.0944) Proportion of girls aged 17-22 years 0.0718 0.0427 -0.0081 -0.1063 0.0560 (8.419) (2.125) (-0.906) (-5.537) (0.1069) Proportion of girls aged > 22 years 0.0209 -0.0008 0.0078 -0.0279 0.2815 (3.198) (-0.054) (1.122) (-1.891) (0.1848) Dummy for residence in metropolitan Lima -0.0032 -0.0130 -0.0071 0.0234 0.2893 (-1.646) (-2.812) (-3.430) (5.286) (0.4357) Dummy for residence in rural area -0.0010 0.1315 0.0075 -0.1380 0.3481 (-0.515) (27.00) (3.436) (-29.64) (0.4852) Female head of household -0.0020 -0.0148 -0.0007 0.0175 0.1563 (-0.776) (-2.434) (-0.248) (3.008) (0.3602) Indigenous head of household 0.0022 0.0022 -0.0035 -0.0009 0.2335 (1.182) (0.493) (-1.749) (-0.219) (0.4111) Education in years of the head of household 0.0019 -0.0049 -0.0011 0.0040 7.7645 (10.15) (-10.81) (-5.208) (9.257) (4.8485) Mean value of budget share 0.0467 0.5050 0.0411 0.4072 RI2 0.28 0.49 0.04 0.48 F value 91.8 233.5 8.94 220.8 Sample Size (N=3820 Households) 88 Table 2: Elasticity Estimates from Engel's Curves .__________ Expenditure Group Elasticity Food Health Other Budget share with respect to total expenditure 0.274 -0.0747 0.3674 0.0241 Specific expenditure with respect to total expenditure 1.274 0.9253 1.367 1.024 Budget share with respect to household size -0.6133 -0.0969 -0.0613 0.0769 Specific expenditure with respect to household size -0.6133 -0.0969 -0.0613 0.0769 The findings from the Engel's curve analysis are a mixed blessing. On the one hand, the income elasticity is a low 0.27, and education expenditures are considered to be a necessity by Peruvian households.2 This is a positive finding, as it indicates that there is a strong underlying demand for education in Peru. A high income elasticity would indi- cate that the item of expenditure is a luxury-households spend money on luxuries when they have the money, but simply do without it when they do not have money. The relative magnitudes are small, but the evidence also suggests that education expenditures are less responsive to changes in income as compared to expenditures on health. However, from the point of view of educational policy, the implication is that we cannot rely on general increases in income to bring about greater expenditures on educa- tion. For every doubling of household income, the budget share spent on education would go up only by a quarter. Add this finding to the fact that levels of household expenditure on education vary vastly by income level. (This fact can be seen from the Lorenz curve analysis reported in the main body of this report-the total amount spent on education by the richest quintile in Peru was more than 13 times the total amount spent on education by the poorest quintile.) The findings show the need for specific policy instruments that will address the inability of poorer households to incur additional expenditures.3 2 Mwabu's work on Kenya indicated a much higher income elasticity of education expendi- tures of 0.73. 3 To make sure that the conclusion was not based just on one pooled set of regressions, the regressions (not reported here) were run separately for subsamples by indigenous and nonindi- genous, rural and urban, and poor and rich. Consistently, the pattern is that the income elasticities are lower for the more disadvantaged groups. 89 Engel's Curves: Formulae for Elasticity Estimates The Engel curve estimates are based on the following equation, presented as Equa- tion (3) in the Working Paper by Germano Mwabu.4 wi ~' = a.i + log(x) + q. log(N) + EY(n .IN) + 5.z + E. wi I I tiJj I I where w, = the share of expenditure of the i th grouping of household expenditure items. i = household spending for the four groups, viz., education, health, food, and other expenses. (The share is conceptually equal to pi times q,, the price times the quantity, di- vided by the total expenditure x, but pi and qi are not empirically observed as separate en- tities in the actual estimation of the Engel curve.) ni = the number of family members in the age-by-gender group j. These groups range in the reported estimation from (boys aged 0 to 5 years) to (girls aged older than 22 years). N = the total family size, thus (n/N) represents the relative size of group j in the family. z - a set of control variables. These include (a) dummy for residence in metropoli- tan Lima, (b) dummy for residence in rural area, (c) female head of household, (d) in- digenous head of household, and (e) education in years of the head of household. ri = the error term in the regression equation, assumed to be i.i.d. normal. ai, A, t5 , yi = the parameters to be estimated. The equations for elasticities follow from the above equation. Letting Si represent the elasticity of the budget share for expenditure group i, and Ei represent the elasticity of specific expenditure with respect to total expenditure, x, and household size, N, the elas- ticities are: a) Si. =i/wi b) SiN = I/wi (mi- ;j yij (nj/N)) c) Ei, = I + (fi1wi) d) EiN = l/w i (Th - YjYii (n/N)) 4 Household Composition and Expenditures on Human Capital Inputs in Kenya. by Germano Mwabu, Department of Economics, Yale University, 1994. 90 Background Note 3. Private and Social Returns to Public Education in Urban Peru5 To estimate the private and social rates of return to public education, the rate of discount (r) was calculated. This discount rate equalizes the stream of discounted benefits to the stream of costs related to a given level of education at a given point in time. Thus, r can be determined by solving the following equation: E(W- + Cn)t(l + r) (1) tI(1+ r)' where n = Level of education T = Number of periods in the labor market of an individual with "n" education W, = Yearly labor income of individual with "n" education K = Number of periods taken to achieve "n" education C, = Direct costs of studying for level "n" education. The left hand side of the equation represents the benefits of achieving the addi- tional level of education, which is simply expressed by calculating the present value of the differential between the earnings with "n" education and "n-1" education. The cost of studying "n" education is expressed in the right hand side of the equation, and its two elements represent the foregone earnings (assuming that no one works while studying) and the direct costs of having achieved "n" education (basically, tuition). The data for this calculation was obtained from Instituto Cuanto's household sur- 6 vey of 1997. The analysis was restricted only to urban areas. The sample was con- strained to those individuals that had always studied in the public system, so the esti- mated rates of return would only capture the effect of public education. Instead of calcu- lating streams of average income by age and level of education, we decided to estimate an earnings function to calculate the yearly income associated with the educational level and age of the individual. Hence, the following equation was estimated separately for males and females in the sample: 5 This analysis was undertaken by Jaime Saavedra, with assistance of Eduardo Maruyama. 6 In Peruvian rural areas, household survey measurement of labor income is highly inaccu- rate due to high participation of self-employment, high seasonality, self-consumption, etc. Usu- ally, expenditure data is recommended instead of income data for these areas. 91 In Y = ,/0 + E3;nELn + /2AGE + /33AGE2 2 E /,i4n(AGE x ELn)+ E Z35n(AGE2 x ELn)+ /6HY n=1 n=1 n=l (2) where Y = Yearly labor income El, = Dummy variable for educational level "n" (1 = Primary education, 2 = Secondary education, 3 = Nonuniversity higher education, 4 = University higher education) HY = Hours worked per year. This specification allowed finding different life-cycle earnings patterns for all edu- cational levels (including no education), i.e. to find the streams of W, required in equa- tion (1). For the private rate of return, the basic assumption was that public education had no direct costs, so the only costs of a given level of education were the foregone earn- ings. To calculate social rates of return we used 1997 nationwide public expenditure data by level of education and student as the direct cost of education. Table 1 shows the re- gression coefficients obtained from equation (2). Table 2 in Chapter 3 shows the results of solving equation (1), for males and females. Given the low level of significance of many variables in the regression shown in Table 2 of Chapter 3, we tested the linear hypothesis that all the coefficients of a given level of education were equal to zero. For example, for primary education we tested the following hypothesis: Ho: /3PRIMARY = 0, JJPRIMARY x AGE = 0, 3PRJMARY x AGE Results of these tests are shown in Table 2. 7 It must be noted that even though tuition is free in the public system, families' expenditure in education mnight be important if we consider the amount spent in school uniforms, books, etc. 92 Table 1. Earnings Functions Coefficients (Dependent variable is the natural log of yearly earnings) Variable Coefficient Female Male Constant 6.0424 4.0796 Primary -0.8048 1.8678 Secondary 0.2877 1.7883 NU-Higher -0.9007 2.4524 U-Higher -2.2527 1.2498 Age 0.0089 0.1389 Age2*100 -0.0020 -0.1504 Primary*Age 0.0606 -0.0657 Secondary*Age 0.0274 -0.0477 NU-Higher*Age 0.1238 -0.0698 U-Higher*Age 0.1927 -0.0094 Primary*Age2* 100 -0.0695 0.0725 Secondary*Age2*100 -0.0390 0.0538 NU-Higher*Age2* 100 -0.1689 0.0812 U-Higher*Age2* 100 -0.2266 0.0311 Hours per year 0.0004 0.0002 Number of observations 1435 2535 R 2 0.27 0.26 p<0.01, **p<0.05, pF 3691) ob>F 1PR1-0, PiPRNAGE=0, 2.45 0. 10.45 0. 2 PPRIXAGE _0 0619 0000 OSEC=0, PSECXAGE=O, 6.12 0. 19.47 0. SECxAGE =0 0004 0000 ONUH * , ONUHXAGE=O, 12.96 0. 20.25 0. 2NHAG 0000 0000 pNUHxAGE =0 13UH0=o PUHXAGE=0, 27.11 0. 37.11 0. 2UxG 0000 0000 IUHxAGE =0 Graphs 1 and 2 show the earnings streams by educational level for males and fe- males calculated from the regression. Figure 1. Earnings by Age and Educational Level, Females 2500 . . __ _ _ _ __ _ 3000 1 2000 2500 g i000 ' , ,_ ;;f , ~~~~~~~~~~~~~~~~............. ----... 100.".. ' '... 2000 _ 1500- g 500 1000 iom~~~~~~~~~~~~~ 0 -, 5-0 , , , , , , , i 500~~~~~~~~~~~~~~~~~~~~ 0~~~~~~~~~~~~~~~~~~~~~~ 0~ 1 qqvv|21 '26 3'1 3'6 41 46 51 56 61 12 17 22 27 32 37 42 47 52 57 62 -50( - -500 -1000 Ag. Age .No.edloM - Pimiy ------ Ptmry -S-condary 6000 9000 5000 7000 4000 6000 3000~~~~~~~~~~~~~~~~~~50 3000~~~~~~~~~~~~~~~30 1000~~~~~~~~~~~~~~~~~~~10 0 _________22__2_____2__3__ 42 7 7 77 7 57 762 .1000 . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __~~-00 1 - 71 I 1 i 6 Age Age -- .-- Sewudary - Nw.universityhigh ----- Sodary - Univeruy highee 94 Figure 2. Earnings by Age and Educational Level, Males 4500 7000 3500 J1n 3000 /.J 2500 .' 4000 ......... 4 2000 4 5CO ,,1,. " 1000 -500 12 17 22 27 32 37 42 47 52 57 62 . . _.. . -1000-______________________ _ - 10001 16 21 26 31 36 41 46 51 36 61 AS. Age --No -- den.&- - Prv-,y - -.00W" -S.c dwy O 16000- 7000 14XO 6000 ,,..,,.. 12000 e 5000 / . 0 ' * ., t e ~~~~~~~~~~~~~~5100/ es 300 0J.-es 6000,, 2000 , 40i 1 ' 1000 2000 0 _10007 22 27 32 37 42 47 52 57 62 2000 7 22 n 32 37 42 47 12 57 62 Age Age ---- Secdwy - Nmuniv-rilyhigher.. S y Unv ltyI hgh Table 3 shows the regression coefficients used to construct the index of education premium in Chapter 3. The regression with which these coefficients were estimates were Mincerian earnings equations that include cumulative educational dummies, experience and its square, tenure and its square, marital status, gender, if living in Lima, and occupa- tional training. Table 3. Estimnated Educational Premiums 1985 1991 1994 1997 Primary/No education 0.418 0.230 0.275 0.427 Secondary/Primary 0.449 0.205 0.274 0.360 Non-university higher/Secondary 0.528 0.237 0.328 0.415 University higher/Secondary 0.581 0.502 0.698 0.864 95 Background Note 4. Determinants of Achievement8 Analysis of deterninants of achievement is an important tool to inform policy choice. This study uses the analytical approach of hierarchical linear modeling to identify the factors affecting math achievement at the levels of students, schools, and depart- ments. The findings cannot be used to evaluate the impact of education policy of the 1990s because of the usually long time lag between intervention and effects on teaching and learning in the classroom. 1. The dataset. The dataset was drawn from the first national standardized test of mathematics in Grade 4 in 1996 and the accompanying questionnaires. The sample comprised 50,479 students who were selected from a population of 618,719 Fourth Graders in 1,275 schools in 25 departments. Thirty students in each sample school were given the test, which lasted for an hour. The sample included private and public schools but under- sampled rural schools. Single-teacher schools in remote areas were excluded; these ac- counted for 29 percent of all schools in the country and enrolled about 6 percent of the population of Fourth Graders. This sample frame has resulted in a relatively narrow achievement gap between urban and rural areas. The dependent variable (also known as the outcome variable) relates to perform- ance on the mathematics test. For simplicity, this will be referred to as outcomes or achievement in this Note. The assessment instruments included multiple choice items in sets, natural numbers, fractions, decimals, geometry, and international units and money. Because the answers required were not dependent on interpretation, this outcome meas- ure can be considered a reasonable measure of performance in mathematics. This analysis applied reliability tests9 and found the instrument reliable. The scores are informative about the relative performance of students compared among themselves. 8 The analysis of data was undertaken by Pete Goldschmidt. 9 The reliability of a test is defined as the consistency of the information, or scores, obtained. Any test occasion will produce some errors of measurement, which are assumed to be random. That is, students taking the same test on different occasions will score slightly differently due to chance errors (e.g. accidentally marking the answer as B, when they mean C). If the analysis en- tails using the total test score, then what is of concern is whether any individual (or set of) item(s) scores are not related to the overall test score. There are several methods to estimate reliability. A simple method, which highlights what reliability is, is the spilt-half method. The split half method randomly divides the test in half and correlates the two halves. This would yield a reliability coef- ficient. More commonly, and the method used for this test, is to generate Cronbach's Alpha; which is to correlate all possible scores with n-I test items (i.e. remove item 1 from the score and correlate to the test with item 2 removed, etc.). The dependent variable in this study has passed these tests. 97 The independent variables (also known as the predictor or explanatory variables) were mostly drawn from, but not limited to, information collected by three questionnaires which accompanied the math test for the principal of the sample school, the teacher of the subject, and parents of the 30 students who took the test, respectively. The independent variables selected for this analysis are as follows: * At the student level (also known as level 1), the predictor variables were grouped into four categories: (a) ascriptive characteristics (gender, mother tongue, and student age), (b) availability and usage of text materials, (c) student attendance and study habits, and (d) parental roles and expectations. * At the school level (also known as level 2), the predictor variables were divided into seven groups: (a) geographic (such as urban and rural, and the coast, mountain, and jungle) , (b) public or private school type, (c) text usage, (d) teacher characteristics, (e) teacher roles, (f) principal characteristics, and (g) parent roles. * At the department level (also known as level 3), the predictor variables were drawn from four data sources: (a) variables which were aggregated from the student- and school-levels in the 1996 test dataset (such as departmental percentage of private school students, over-aged students, female students, Quechua speaking students, 4O Grade teachers with a Master's degree, and with a title from Institutos Superiores Pedagogicos); (b) government expenditure data on public spending on basic educa- tion per student by department in 1994; (c) household survey data on household ex- penditure on basic education per capita by department in 1994; and (d) FONCODE's 1993 Poverty Map, which provided information on departmental characteristics (such as poverty index, percentage of population in chronic malnutrition, mortality rates, il- literacy rates, and school nonattendance rates). This dataset has certain limitations. First, the assessment was undertaken at a single point in time so it is not possible to control for prior learning. Second, the questionnaires did not contain questions on parental education, number of siblings at home, family so- cioeconomic status (SES) or resources (e.g. family income or expenditure, type of dwell- ing, availability of water and electricity, etc.),'0 or school resources (e.g. public spending per student, parental contribution per student, availability of water, electricity, library and laboratory, etc.). In other words, some key predictors were not available to enable con- trolling for their effects. The only variables that may proxy public and private finance of education were public and private expenditure at the departmental level. 10 Although the questionnaire contains a question on parental occupation, the inclusion of the housewife category into the list confounded the effects because a large number of mothers checked this category. Therefore, it is not possible to even use occupation as a proxy for SES. 98 2. Descriptive statistics In the main text of this report, Table 3 presents the average scores and standard de- viation of the mathematics test. The outcome differentials were substantial, particularly between private and public schools, Spanish-speakers and Quechua-speakers, and be- tween the jungle and other regions. The coefficients of variability show large disparity within each subgroup. Table 1 below presents the mean, standard deviation, minimum and maximum value of variables at the student level. Most of the data were collected from a few ques- tions on students attached to the test and from the questionnaire for parents. Although the original sample had 50,479 students, only 40,766 returned the test, of whom, only 33,233 respondents had all the observations. The most common missing value was gender and type of school attended. Nonetheless, the mean did not change. In Table 1, the column which shows mean or percentage indicates either the aver- age value of the variable or the percentage share of each categorical variable (for exam- ple, girls accounted for 50 percent of the students in the sample). The percentage share of omitted variables, such as boys (which are used for comparison with predictors in the same categories) can be deduced from the percentage share of girls, and its standard de- viation can be derived from the formula in the footnote."t Table 1: Descriptive Statistics of Student-Level Variables Used in the HLM Model Mean or Standard Percentage deviation Minimum Maximum Ascriptive characteristics Girls (boys omitted) 0.50 0.50 0 1 Aymara 0.03 0.35 0 1 Quechua (Spanish speakers omitted) 0.15 0.16 0 1 Student over the age of IO for Grade 4 0.23 0.42 0 1 Materials (text books) No textbooks 0.15 0.36 0 1 School provided textbooks 0.06 0.24 0 1 Sibling's textbooks (dictated by teacher omitted) 0.21 0.41 0 1 Student attendance & study habits Daily attendance (sporadic attendance omitted) 0.07 0.26 0 1 No studying 0.01 0.08 0 1 Studies regularly 0.27 0.44 0 1 Studies for exams 0.16 0.37 0 1 Studies because expected 0.20 0.40 0 1 (Studies because of self-motivation omitted) 11 The equation for standard deviation is ((p(l-p))/n)A.5. p is the proportion of l's; so if the left out category is boys, for example, p for them would be 1-.493 (or it could be done as 100- 49.3). A.5 is the square root. In case of percentage being presented as decimals, the equation would need to be adjusted to 100-p. 99 Parental expectation, roles & home environment: Goal of schooling Develop literacy 0.19 0.39 0 1 Develop nothing 0.06 0.24 0 1 Develop comprehensively 0.23 0.42 0 1 Develop math (Learning well in general omitted) 0.13 0.34 0 1 Home academic support Environment for studying (through homework omitted) 0.09 0.28 0 1 None 0.22 0.42 0 1 Special education programs 0.01 0.11 0 1 Additional reading 0.19 0.39 0 1 Father assistance (Mother assistance omitted) 0.20 0.40 0 1 No assistance 0.25 0.43 0 1 Other family assistance 0.23 0.42 0 1 Sample size 33,233 Table 2 presents descriptive statistics of school-level predictors. Some of the vari- ables were aggregrated from the student level (such as the school means of students' ac- cessibility to text), while others were collected from surveys of teachers and principals. Table 2: Descriptive Statistics of School-Level Variables Used in the HLM Model Mean or Standard percentage deviation Minimum Maximum Geographic Rural (urban omitted) 0.19 0.39 0 1 Selva 0.21 0.41 0 1 Sierra (costa omitted) 0.37 0.48 0 1 School type Private (public omitted) 0.14 0.35 0 1 Text usage No text 15.41 14.93 0 100 School provided text 6.21 8.57 0 77 Siblings and/or other people's text 20.95 13.36 0 100 (Teacher's own text omitted) Teacher characteristics Number of years of service 12.17 7.59 1 57 Number of training courses (1990-96) 6.83 2.96 0 11 Teacher language: Aymara (Spanish omitted) 0.01 0.10 0 1 Teacher language: Quechua 0.08 0.27 0 1 Teachers graduated from universities 0.15 0.36 0 1 Teachers graduated from ISP 0.51 0.50 0 1 Teachers graduated from IST 0.01 0.11 0 1 Teachers graduated from professional courses 0.17 0.37 0 1 Professional titles in other specialties 0.01 0.11 0 1 University graduates 0.06 0.23 0 1 University leavers (finished courses without degree) 0.03 0.16 0 1 100 Table 2. (continued) Mean or Standard percentage deviation Minimum Maximum Secondary school graduates 0.00 0.06 0 1 (Secondary leavers with teacher training omitted) Condition of work (first of class omitted): 0.03 0.18 0 1 Titled Contracted 0.16 0.36 0 1 Teacher roles Explain materials 0.11 0.32 0 1 Invite specialized persons* 0.01 0.08 0 1 Student participation 0.79 0.41 0 1 (Assess performance omitted) Principal characteristics (Spanish omitted) Principal's language: Aymara 0.01 0.11 0 1 Principal's language: Quechua 0.10 0.30 0 1 Principal's language: Other 0.01 0.09 0 1 Parent roles (according to teachers) Check attendance 0.08 0.26 0 1 Check homework 0.21 0.41 0 ] Prepare children for exams 0.05 0.21 0 ] Provide nutrition 0.05 0.21 0 1 Stimulate leaming (no participation omitted) 0.26 0.44 0 1 Sample size 1,275 *The meaning of teacher's role being to invite specialized persons is unclear from the questionnaire. Table 3 presents the descriptive statistics of departmental-level variables. Some of the variables were aggregrated from the student level (such as percentage of students who are females, or in private schools), while others were collected from surveys of teachers and principals (such as percentage of teachers with various qualifications). Table 3: Descriptive Statistics of Departmental-Level Variables in the HLM Model Mean/ Standard percentage deviation Minimum Maximum Public expenditure on basic edu- cation per student (US$) 141.3 31.4 71.0 223.0 Household expenditure on basic education per capita (US$) 74.5 42.0 17.9 144.0 Poverty index 3.0 1.1 1.0 4.5 Female students 49.3 2.6 44.5 56.5 Over-aged students 23.5 10.1 10.1 42.3 Quechua students 14.8 21.3 0.0 67.5 Private school students 14.8 11.9 0.0 50.0 Teachers with MA degree 12.5 12.6 0.0 44.8 Teachers graduated from ISP 52.1 14.0 27.6 76.5 Teacher years of service 11.9 1.75 8.4 15.7 # of training courses attended 6.8 1.0 4.6 8.3 Sample size 25 101 3. The analytical approach of hierarchical linear modeling The appropriate approach to analyze this dataset is hierarchical linear modeling (HLM). This is because the structure of the data was hierarchical: student-level variables were nested within schools, and in turn, school-level variables were nested within de- partments. For example, students' accessibility to text materials is an indicator of stu- dents' home resource; but when it is aggregated to the school level, it became an indica- tor of school resource and the normative environment (Bryk and Raudenbush, 1992). Mixing individual and aggregated explanatory variables can lead to both statistical and substantive errors in interpretation of the effects of the group, such as the school or the department (Aitkin and Longford, 1986; Burstein, 1980). Group effects are truly important because students with the same characteristics might have different learning outcomes if they attend schools with different organization, quality, policies, and practices or if they live in different departments (Akin and Garfinkel, 1977). For this reason, the Ordinary Least Square (OLS) regression analysis cannot be applied to this dataset because it does not take into account the hierarchical structure of the data. If the variance in test scores attributable to differences between schools is large, OLS regression analysis will severely understate standard errors and overestimate their significance, thereby leading to falsely rejecting the null hypothesis. However, hierarchical linear modeling (HLM) allows personal and contextual (such as school and department) effects on an individual's score to be analyzed (Bryk and Raudenbush, 1992). Unconditional models. The first step in HLM was to estimate the fully uncondi- tional models, which can be at two levels (students and schools) or three levels (students, schools, and departments). The unconditional models for three-level analysis in this study are as follows: - ij "Ojk + e e.. - N(0, a2), (Equation 1) (Level 1) 7Cok =POOk + rojk rojk -N(O, TOk) (Equation 2) (Level 2) Pook = oo + uook, uook N(' l ). (Equation 3) (Level 3) where Yijk was math test score for student i in school j in department k; 7Ojk was the mean test score at school j in department k; 'iOOk was the departmental mean of the test score in department k, and yooo was the grand mean of the math test score. The eijk was the student-level random components in school j in department k; the rOjk was the school- level random components in school j in department k; and u00k was the departmental- level random component in department k. The a2 was the error term (residual) of the variance in test scores between students; the T was the error term of the total variance in Ok test scores between schools, and the T was the error term of the total variance in test scores between departments. 102 This unconditional model allowed for the calculation of the intraclass correlation. This provided estimates of (a) the total variance in test scores between students (within schools), (b) the total variance in test scores between schools (within departments), and (c) the total variance in test scores between departments: p = a2/( t + r + 62 ) (Equation 4) (Level 1, between students) Ok 00 p = T /( T +tX + U2 ) (Equation 5) (Level 2, between schools) Ok Ok 00 p =0 I/( T + X + a2 ) (Equation 6) (Level 3, between departments) oo Ok oo where p was the intraclass correlation, and the error terms (residuals) of the vari- ance on the right side have been described in Equations 1, 2, and 3. Subsequently, the un- conditional estimates of the errors in Equations 1, 2, and 3 provided the basis for comput- ing the proportion of variance in test scores explained by additional variables at each of the three levels. It should be noted that HLM does not generate R-squared statistics. The explanatory power of a model is indicated by how much of the proportion of variance in outcome it can explain. Conditional models. The next step was to specify a conditional model with random effects analysis of covariance (ANCOVA) for each of the three levels. At level 1, the model used student-level variables, and allowed the intercept and slopes to vary across schools and departments. The model was as follows: ijk "Ojk + iljk (Xijk Xjk) + eijk' eijk N(0, a2) (Equation 7) (Level 1) where X's were background characteristics of student i (such as girls, over-aged, and Quechua speakers) in school j and department k; and eijk was the student-level ran- dom effect. The intercept term of the conditional model was similar to that in the uncon- ditional model, except that the mean was now adjusted for the covariates (student-level variables). In this case, X's were centered on the school mean (the average value of a given variable of school j).'2 Centering allowed Tjk to be interpreted as the mean of school j in department k for test score of student i in the same school, adjusted for differ- ences among schools in student characteristics. In this manner, differences in student characteristics could be taken into account. 12 For example, if there is a continuous variable for the number of hours of studying per week, this could be centered around the mean hours of studying per week at school j, thereby ad- justing for the time of students actually studying. One advantage of school-mean centering all the variables is to easily identify the marginal effect of any single predictor, after controlling for the effects of other covariates. This would allow addressing the question of: if a student is an average in all respects at school j, what is the marginal effect of hours studying? For categorical variables, group-mean centering works in the same manner. At levels 2 and 3, grand-mean centering refers to the same procedure and effect. 103 Unlike OLS regression coefficients, the intercept and slope parameters were sub- scripted by j and k, indicating that each school could have a different intercept and slope(s). The student-level coefficients, it * could be specified as being either fixed, non- randomly varying, or randomly varying (Bryk and Raudenbush, 1992). A model with several student-level predictors could have any combination of the three specifications. If there is significant variation in intercepts and slopes between schools, then this can be modeled by including predictors at the school and student levels aggregated to the mean of school j. Thus the student-level intercepts and slopes became outcomes, and the school-level ANCOVA model was as follows: lT0jk = 300k + OIk (W1 - Oj ) + O0k' rOJk -N(0, ') t1jk = Ok + 'lk (Wj - VW ) + rlJks rIlk -N(O, t ) (Equation 8) (Level 2) where W's were school characteristics (for example, the average years of service of teachers in a school); rOjk was the school-level random effect; and 010 was the pooled within-school regression coefficient. The W's were centered on the grand mean (see the same footnote on mean-centering). The intercept and slope were modeled to vary ran- domly and to be affected by a characteristic, W, of school j. The interpretation of tjk would be how the adjusted school means of the outcome, Y, were affected by the school characteristics W's, given student characteristics, X's. Similarly, the slope coefficient could be described as being affected by W's, given X's. If there was significant variation in intercepts and slopes between departments, then this could be modeled by including department-level predictors, as well as school- and student-level predictors aggregated to the mean of department k. Thus the school- and student-level intercepts and slopes became outcomes. The department-level ANCOVA model was as follows: POOk YOOO + YOOI(Z.k Z ) +uOOk, uOOk - N(O, to) POk= 7100 + YIIO(Zk - Z ) +uook, uok - N(O, To ) (Equation 9) (Level 3) where Z's were department characteristics (for example, the poverty index). The Z's were centered on the grand mean (see the same footnote on centering). The intercept and slope were modeled to vary randomly and be affected by a characteristic, Z, of de- partment k. The interpretation of ,ook would be how the adjusted departmental mean of the test score were affected by the departmental characteristics Z's, given both student characteristics, X's, and school characteristics, W's. Similarly, the slope coefficient can be described as being affected by Z's, given X's and W's. In cases where student-level effects varied much between schools and departments, the next step was to analyze whether school and department variables have effects on student-level variables. This was known as the cross-level model. 104 At level 2, using information from the unconditional and the conditional models, the proportion of the variation in the it's is explained by the school-level variables. For example, the proportion of the variation of ix1 would be computed as follows: [ x1 (unconditional) - t 11 (conditional)]/t 11 (unconditional) (Equation 10) Additionally, a X2 test could be used to test whether the error (residual) variation tcc was significant; in which case additional variation in itc was left to be explained. This indicated that the relationship between the outcome and the student-level predictor varies significantly from school to school, even when controlling for the school-level variables modeling that particular coefficient. Similarly, at level 3, the proportion of variation in the Ps can be explained by de- partment-level variables and can also be determrined by Equation 10, using the error vari- ances at level 3. As with the level 2 analysis, the relationship between the outcome and department variables could be examined by using a X2 test to determine whether the school level predictor continued to vary from department to department after controlling for department-level variables. 4. Two-level analysis (student and school) The analysis began with the student and school levels in order to explore in depth the effects of variables at these two levels on mathematics outcomes. The approach was guided by four questions: (a) What were the marginal effects of various student charac- teristics on average student performance, after controlling for other covariates in the stu- dent-level model? (b) What were the marginal effects of school characteristics on average school outcomes, after controlling for other covariates in the school-level model? (c) What were the cross-level effects? In other words, what were the effects on a student who attended a particular school, after controlling for individual characteristics? (d) What proportion of variance in outcomes was attributable to differences between students (within schools) and between schools? (a) Effects of student characteristics on average student outcomes (Level 1 model) Table 4 shows the marginal effects of each of the above described student charac- teristics, controlling for other covariates in the model (see Equation 7 for the model)." When other concomitant variables were held constant, girls tended to do worse than boys. Students over the age of 10 performed significantly worse than younger children. This comes as no surprise because over-aged students tend to be repeaters. To a lesser extent than gender and age, the mother tongue also had an effect, but it was confined to Quechua speakers who did less well than Spanish speakers. There was no statistically significant difference in the outcomes of Aymara speakers and Spanish speakers. For pol- icy research, it is important to identify the variables that enable Aymara speakers to per- form so much better than other indigenous groups. 13 This model did not control for school-level variables. 105 Table 4: Effects of Student Characteristics on Student Outcomes Coefficient Standard error Intercept 45.1 0.45 Ascriptive characteristics Girls (compared with boys) -3.58 * 0.21 Mother tongue Aymara (compared with Spanish speakers) -0.65 0.71 Mother tongue Quechua (compared w/ Spanish speakers) -0.70 * 0.35 Student over the age of 10 for grade 4 -1.84 * 0.22 Text usage (compared with teachers' own text) No text -0.69 * 0.26 School provided text -0.38 0.36 Siblings' and/or others' text -0.06 0.22 Student attendance & study habits Daily attendance (compared with sporadic attendance) 1.62 * 0.33 No studying (compared with study because of self- -2.71 * 0.98 motivation) Study regularly (compared with self-motivation) -1.69 * 0.21 Study for exams (compared with self-motivation) -2.80 * 0.25 Study because expected (compared with self-motivation) -3.87 * 0.24 Parental expectations of school (compared with general learning) Develop literacy -1.13 * 0.23 Develop nothing -1.98 * 0.37 Develop comprehensively 0.42 0.23 Develop mathematics 1.09 * 0.26 Home academic support Provide environment for studying (compared with provide 0.46 0.31 support through homework) Provide no support -0.22 0.22 Special education programs -0.74 0.75 Provide additional reading 1.06 * 0.23 Father assistance ( compared with mother's assistance) 0.31 0.24 No assistance 0.86 * 0.23 Other family assistance -0.70 * 0.23 tp<--.05 Student attendance and study habits mattered. Students who attended school daily did better than those who attended sporadically. Motivation was important. Students who undertook their study because they were motivated had higher scores than students who studied for other reasons. Parental roles and expectations also affected achievement. Parents who expected school to develop mathematics skills saw their children performing better in math, com- pared to parents who expected schools to develop literacy, generally, or nothing. Interest- ingly, home academic support mattered only when parents provided additional reading 106 material, not simply through providing a general environment for studying, or through help with homework or other special programs. The assistance of mothers and other fam- ily members turned out not to be helpful in this sample. One mnight speculate as to whether this is due to lower educational level of mothers and other family members. (b) Effects of school characteristics on school mean (Level 2 model) Table 5 presents the marginal effects of each of the above described school charac- teristics, controlling for other covariates in the model'4 (See Equation 8 for the model). Holding other concomitant variables constant, rural and urban areas had no statistically different effects on achievement, but geographic region had big effects. Schools in the mountain region performed less well than those on the coast, whereas the jungle region did much worse than the coast. Students in private schools were associated with much higher achievement. The nonavailability of textbooks was negatively associated with learning outcomes. Schools with 50 percent or more of students who had no textbook, or who used their siblings' textbooks, did worse than those whose text was based on dicta- tion by teachers. Teachers who had more years of service had a positive impact on student achieve- ment, but in-service training did not. This, however, changed in a 3-level analysis. In this two-level analysis, there was also no statistically significant difference between teachers of various academic qualifications, conditions of service, and in-service (but this is not true in a three-level analysis). This may be because there is insufficient variance between schools in these variables to show the difference, but once aggregated to the departmental level, the difference has statistical significance. A more disturbing finding is that teachers whose mother tongue was Quechua were associated negatively with student math achievement, in comparison with teachers whose mother tongue was Spanish, but this was not true for teachers whose mother tongue was Aymara. Principals' characteristics also mirrored those of teachers. Even after controlling for students' mother tongue, Quechua speaking teachers were associated negatively with math performance. This may be due to Quechua speaking teachers being less prepared, and calls for special attention to the training of Quechua speaking teachers. That Aymara speaking teachers were indistinguishable from Spanish speaking teachers in terms of their impact on achievement disproves the notion that indigenous teachers are not effective. It also poses a very important research question as to why Aymara students and teachers were doing so much better than other indigenous groups. If the variables that enable them to overcome their disadvantage can be identified, they rnight also be used to help other indigenous peoples. Teachers' perception of their role made a difference. If teachers perceived that their role was to assess and improve performance, they had large positive effects on achieve- ment, in contrast to those who considered their role simply to explain materials, invite guests, and encourage student participation. This seemed to indicate that focusing on out- comes produced the desired results. 4 This model did not control for student-level variables. 107 Table 5: Effects of School Characteristics on School Mean Coefficient Standard error Intercept 45.10 0.37 Geographic Rural (compared with urban) -1.84 1.04 Selva (compared with costa) -5.65 * 1.07 Sierra (compared with costa) -2.77 * 0.92 School type Private (compared with public) 12.71 * 1.24 Text usage (compared with teacher's own text) Difference between % of student at school with (1): No text -0.18 * 0.03 School provided text -0.07 0.05 Sibling's and/or other's text -0.13 * 0.03 Teacher characteristics Number of years of service 0.14 * 0.06 Number of training courses taken (between 1990-96) 0.14 0.13 Mother tongue: Aymara (compared with Spanish) -0.85 3.83 Mother tongue: Quechua -5.17 * 1.51 University graduates with teacher's title (compared with 3.39 1.90 secondary school leavers with teacher training only) ISP graduates with teacher's title 1.69 1.67 IST graduates with teacher's title 2.43 3.80 Graduated from professional courses 0.62 1.82 Professional titles in other specialties -0.94 3.83 University graduates without teacher's title 2.61 2.22 University leavers who finished courses but had no degree -1.11 2.79 Appointed by manager (compared w/ officially appointed) -1.84 2.12 Contract -0.36 1.14 Teacher roles Explain materials (compared with focusing on learning -4.36 * 1.69 outcomes by assessing performance) Invite specialized persons (2) -9.50 * 4.71 Encourage student participation -3.48 * 1.31 Principal characteristics Mother tongue is Aymara (compared with Spanish) 6.78 3.62 Mother tongue is Quechua -4.44 * 1.38 Mother tongue is other languages -2.22 4.20 Parent roles (according to teachers) Check attendance (compared with no participation) 3.97 * 1.51 Check homework 3.05 * 1.06 Prepare children for exams 7.58 * 1.90 Provide nutrition 3.24 1.86 Stimulate learning 5.30 * 1.01 Notes: (I) Percent in 00.0% (i.e. to calculate the effect at 50%, the coefficient is multiplied by 50). (2) The meaning of teacher's role being to invite specialized persons is unclear from the questionnaire. *p <=.05 108 Parental role as perceived by teachers was also important. Parents who checked at- tendance and homework, prepared children for exams, and stimulated learning had chil- dren who performed significantly better than those parents who did not participate in their children's education. This might be an indicator that proactive teachers who tried to get parents more involved and communicate more have positive effects on children. At the school-level, this variable might be a proxy of community support. (c) Cross-level effects of school characteristics on achievement slopes This analysis examines whether or not the effects of student characteristics varied across school. In other words, were there school-level factors that had rnitigated the stu- dent-level effects? Ascriptive characteristics of students, accessibility of text, study habit, parental role and expectations, and home academic support were crossed with geographic variables of school location, availability of text, and other school characteristics such as private schools, teacher in-service training, and years of service. Only the coefficients and the standard errors of the group of variables which have statistical significanice in some of them are presented in Table 6. Those which has no significance at all were not recorded, leaving blank spaces in the table to make it easier to read. Table 6 shows that although girls in general performed less well than boys, those in the jungle and mountain regions did better relative to girls on the coast. Girls also did slightly better when schools provided the text. There was no significant difference in math achievement between boys and girls in the rural and urban areas, or in private and public schools. Overaged students performed worse in general and far worse in private schools, relative to achievement of overaged students in public schools. This rnight be attributable to a more competitive environment in private schools that did not help overaged students to catch up. With respect to the mother tongue of students, there was no significant difference in math achievement between Aymara and Spanish speakers, whether they were in pri- vate or public schools. Quechua speakers, however, not only performed less well than Spanish speakers in general, they performed significantly worse in private schools, rela- tive to their performance in public schools. With respect to study habits, students did better when they were self-motivated to study than if they studied because they were expected to. This had a greater effect than the replies on whether they studied only for exams, studied regularly, or did not study at all. However, students in the rural areas performed better if they studied because they were expected to. In private schools, students who did not study perforrned significantly worse. In fact, the biggest negative effect was found among private school students who did not study, relative to the performance of students who did not study in public schools. It might be because private schools have much higher expectations for studying hard and those who did not study fell behind. 109 Home academic support had positive effects on achievement only when the home provided additional reading material. The effect of additional student reading was strengthened when teachers had in-service training. If parental expectation was to develop math skills, versus general learning, there was a positive effect on math achievement. If it focused instead on other goals, such as developing literacy, comprehensive development, or lacked any definite goal, it did not produce higher math scores. However, in rural areas, even if the expectation was to de- velop literacy, there was a positive effect on math scores; but if the goal was to develop nothing in rural schools, the negative effect was washed out, possibly because it did not matter what the expectations were. No text was worse than having teachers' dictated notes. But the years of service negatively impacted on the effects of school-provided texts. The reason was unclear. In summary, the analysis of cross-level effects confirmed some common sense no- tions. For example, in private schools, students who did not study, were not high achiev- ers in general (those over-aged and Quechua speakers), and those who were not self- motivated did significantly worse than their counterparts in public schools. At the same time, the analysis also revealed many puzzles that require further investigation. For ex- ample, why were girls in the sierra and selva doing better, relative to boys, than girls in the costa? Why did Quechua speakers perform worse in private schools than public schools? The greatest puzzle of all is perhaps why experienced teachers were associated with higher math scores, but the score decreased when they could not use their own text and had to use school-provided texts? Could the school-provided text proxy a new cur- riculum which experienced teachers are less prepared to teach? Answers to these puzzles might help policymakers design more effective interventions. (d) Within-school and between-school variance in outcomes Applying Equations 1, 2, 4, and 5 to the unconditional models, it was found that some 54 percent of the variance in math achievement was attributable to between-school differences, while 46 percent was attributable to within-school differences (between stu- dents) (Table 7). The higher the between-school variance, the more inequality among schools there is. Normally, a 30 percent difference in variance is the cutoff point for iden- tifying serious equity problems (See Appendices 5.1 and 5.2). Student-level variables explained only 4.7 percent of the within-school variance in outcomes. 2.9 percent of the variance was explained by ascriptive characteristics, 0.1 per- cent by the availability of and usage of texts, 1.2 percent by student attendance and study habits, and 0.5 percent by parental roles. Between-school variables cumulatively explained 34.2 percent of the variance in outcomes-9.5 percent by geographic factors, 9.5 percent by text usage and homework assignments, 11.6 percent by teachers' characteristics, 0.7 percent by teachers' roles, 1 percent by principals' characteristics, and 1.9 percent by parental roles. 110 Table 6: Cross-Level Effects of School Characteristics on Mathematics Achievement Slopes Geographic Text provided by School characteristics Mean Rural Selva Sierra Sibling School None Private school In-ser. training Yrs. of service Difference between Effect S.E. Coef. SE Cf SE. Cof SE Cof SE Cof SE Coef S E Cof SE Coef. S E Coef. S E A Girls and boys -3.61 * 0.21 0.69 0.53 1.26 * 0.55 1.14 * 0.48 0.02 0.02 0.05 * 0.02 0.03 0.01 -0.53 0.68 B Student over age for grade (1) -2.15 * 0.26 -2.72 * 1.26 C Aymara and Spanish 0.95 0.73 3.94 2.60 D Quechua and Spanish -1.03 * 0.36 -4.28 * 1.37 E No studying vs. self motivated -3.14 * 1.04 -0.76 2.22 -8.99 * 4.01 F Studies regularly vs. self- -1.71 * 0.21 -0.22 0.59 -2.26 * 0.58 motivated G Studies for exams vs. self- -2.78 * 0.25 1.27 0.68 -2.04 * 0.74 motivation H Studies because expected vs. -4.02 * 0.24 2.08 * 0.60 -3.44 * 0.78 self-motivated I Environment for study vs. 0.47 0.31 0.18 0.10 through homework J None vs. through homework -0.20 0.22 0.06 0.07 K Special education prigrams vs. -0.71 0.75 0.11 0.26 through homework L Additional reading vs. through 1.02 * 0.23 0.16 * 0.08 homework MDevelop literacy vs. -1.10 * 0.23 1.87 * 0.60 -0.13 0.08 0.11 * 0.03 learning well, in general N Develop nothing vs. -2.01 * 0.38 2.08 * 0.94 -0.06 0.13 0.00 0.05 learning well, in general 0 Develop comprehensively vs. 0.43 0.23 0.98 0.64 0.04 0.08 0.01 0.03 learning well, in general P Develop nathematics vs. 1.06 * 0.26 -0.42 0.72 0.18 * 0.09 0.01 0.03 leamning well, in general Q No text versus teacher dictated -0.69 * 0.26 0.02 0.04 text R School provided text versus -0.45 0.36 -0.13 * 0.05 teacher dictated text S Sibling text versus teacher -0.08 0.22 -0.05 0.03 dictated text * p< 0.05 111 Table 7: Extent to which Variation in Mathematics Achievement Is Accounted for by Student-Level Characteristics and the Variation in True School Mean Mathematics Achievement Is Accounted for by School-Level Factors Mean S.E. True school mean mathematics 45.1 * 0.46 achievement Random effects ANOVA Level -1 (between students) (nij) 216.5 Level -2 (between schools) (UO) 258.3 Amount of variation in mathematics achievement attributable to schools 54.4% Variation in true school mean mathematics achievement between schools + Isd 61.19 - lsd 29.05 Cumulative Cumulative Variance Variance Change Within-school variables (1) Var(ni) (2) Explained var(UO) (2) Explained Signif. 1) Ascriptive chars 210.2 * 2.9% * 2) Materials (texts) 210.1 * 3.0% * 3) Student attendance & study habits 207.4 * 4.2% * 4) Parental roles and expectations 206.4 * 4.7% * Between-school variables (1) 5) Geographic 233.9 * 9.5% * 6) Text usage 209.2 * 19.0% * 7) Teacher characteristics 178.5 * 30.9% * 8) Teacher roles 177.5 * 31.3% * 9) Principal characteristics 175.0 * 32.3% * 10) Parent roles 169.5 * (3) 34.4% * Notes: (1) School-level (level-2) variables model the intercept only. (2) Indicates whether the variance varies significantly after variables are included in model. (3) The slope for "Gender" was the only one that had significant between-school variation after including level 2 variables * P<=.05. It should be noted that the relative weights of these variables reflected the way the ques- tions for principals, teachers, and parents were structured. A lot of the questions aimed at getting the perceptions of these stakeholders, rather than constructing measurable variables to capture the full impact of student attendance and study habits, parental roles, teachers' role, and princi- pals' characteristics. For exarnple, instead of asking how many days in the preceding week the student attended school, the question only asked whether the parent thought that the student at- tend regularly. These findings point to ways that future questionnaires could be improved. 112 In spite of the limitations, the analysis of determinants of achievement has policy implica- tions. It found that textbook availability, homework assignments, and teachers' characteristics and roles had effects on achievement. Since all of these variables are amenable to policy inter- vention, these call for concerted efforts in textbook provision and teacher training, particularly for Quechua teachers, in order to improve learning outcomes. 5. Three-level analysis (students, schools, and departments). From the perspective of national educational policy, inequality in leaming outcomes among departments is a serious concern. Since the Educational Directorates of the departments are responsible for delivery of education, identifying the determinants of variance in test scores at the department level is a requisite to addressing the issue. Because the 1996 assessment data are not in the public domain, this analysis is unable to report department-specific results. The three-level analysis was guided by several questions: (a) What were the marginal ef- fects of departmental characteristics on average departmental test score, after controlling for other covariates in the departmental-level model? (b) How much did school-level factors vary across departments, and how much did student-level factors vary across schools and depart- ments? In other words, did some departments do a better job on student achievement, after con- trolling for school-level, and student level, characteristics? (c) What was the proportion of vari- ance in test scores attributable to differences between students, between schools, and between departments? The following paragraphs address these questions. (a) Effects of departmental characteristics on departmental outcomes (level 3 model) In the Main Report, Figures 24 and 25 in Chapter 3 show the relationship between public and household expenditure by department and test scores by department, respectively. This raised the question of whether these two variables merely captured the effects of other variables. Since the issue of education finance has important policy implications, it is imperative to disen- tangle the effects of other variables from the expenditure variables. The level 3 analysis began by examining the correlation among the variables at the de- partmental level. Because of the very high correlation between poverty index, percentage of rural population, chronic malnutrition, mortality rates, illiteracy rates, and school nonattendance, only the poverty index was selected to proxy SES at the departmental level to avoid the problem of multicolinearity (Table 8). There were still four potential problems in analyzing the relationship between test scores and departmental level inputs. These problems did not permit the use of OLS regression, but could be partially addressed by using HLM: * Limited degrees offreedom: Given that there are only 25 departments, only a small number of predictors could be used. By applying HLM, the variation in student test scores was di- vided into three components: among students, among schools, and among departments. Only departmental variables were used to explain the variation in test scores between departments. 113 * Lack of variability in (and similarity of) departmental measures: Several of the departmental variables were highly correlated. This problem was addressed by centering the predictor variables. At the student level, the independent variables were centered on the school mean; at the school and the departmental levels, the independent variables were centered on the grand mean. This centering reduced multicolinearity and eased interpretation of results. * Ecological fallacy: This existed when group-level predictors were used to make inferences about individual effects. Using HLM explicitly took the nested nature into account, without ignoring the within-school and within-department variability of test scores. * Errors in interpreting the effects of departmental variables on individual student test scores: HLM enabled interpretation of analyses that simultaneously examined variables at different levels. School- and department-level independent variables identified in what context the ef- fects of student-level independent variables manifested themselves. Or, school- and depart- ment-level variables explained some of the heterogeneity among schools and departments in specific student-level effects. Table 9 presents 11 models which show the marginal effects of additional predictors at the departmental level on the mean of other existing predictors in the model (Equation 9). The major findings are as follows: Public and household expenditure per student by department together explained about 49 percent of the between-department variance in outcomes (Model 1). However, household expen- diture per capita by department has a high level of statistical significance, but not public expendi- ture. This finding was consistent with that observed in Figures 24 and 25. The lack of statistical significance for the interaction terms between public and private expenditure per student indi- cated that public and private expenditures did not substitute for each other (Model 2). Although poverty index has statistical significance, it only explained 18 percent of the variance in test scores (Model 3). Even when poverty index was combined with public and household expenditure, household expenditure per student is the only variable with statistical significance in predicting math test scores (Model 5). Meanwhile, these combined variables ex- plained over half of the variance of test scores between departments. To have more precise measure, departments were divided into the categories of nonpoor, average, poor, and extremely poor (Model 4). They were combined with public and household expenditure (Model 6). The results in these two models were similar to the above two, respec- tively. However, when interaction terms were created between poverty and household expendi- ture per capita, in extremely poor departments household expenditure alleviated the negative ef- fects of poverty on test scores (Model 7). When the percentage of students in private schools in the department was taken into con- sideration, the statistical significance of household expenditure disappeared. In extremely poor departments, however, household expenditure still alleviated the negative effects of poverty on test scores (Model 8). 114 Table 8: Correlation Matrix EXPPRIV OLDERKD3 TCHEMA3 SRVCSYR3 RURALP3 Poverty index MORTRATE SCHLNOAT P2 P4 Correlations EXPPUBLC OUECHUA3 TCHEN13 TCHRTRN3 PCTRURAL PCTMALNR ILITRATE Pi P3 MATH SScore Household expenditure per 1.00 capita by dept. Public expenditure per student 0.04 1.00 by dept. % of over-aged students in dept. -0.73 -0.33 1.00 % of Quechua students in dept. -0.45 -0.21 0.51 1.00 % of teachers from university in 0.46 0.04 -0.47 -0.12 1.00 dept. % of teachers from ISP in dept. -0.25 0.26 0.13 0.12 -0.45 1.00 % of teachers with long service 0.25 -0.07 -0.30 -0.22 0.20 -0.06 1.00 in dept. % of teachers with training in dept. 0.46 0.19 -0.50 0.06 0.51 -0.20 0.12 1.00 % of rural students in dept -0.40 0.03 0.32 0.10 -0.26 0.06 -0.22 -0.23 1.00 * of rural population in dept. -0.63 -0.33 0.58 0.41 -0.53 0.19 -0.25 -0.58 0.42 1.00 Poverty index in dept -0.73 -0.26 0.63 0.62 -0.26 0.14 -0.07 -0.39 0.41 0.77 1.00 % of malnutrition in dept. -0.69 -0.42 0.75 0.49 -0.43 -0.04 -0.21 -0.54 0.48 0.85 0.85 1.00 % of mortality in dept. -0.77 -0.18 0.71 0.70 -0.46 0.17 -0.21 -0.41 0.45 0.80 0.87 0.85 1.00 hliteracy rate in dept. -0.63 -0.39 0.75 0.78 -0.35 0.25 -0.17 -0.33 0.34 0.73 0.87 0.81 0.82 1.00 School nonattendance rates -0.60 -0.39 0.78 0.34 -0.52 0.04 -0.09 -0.63 0.26 0.72 0.74 0.84 0.73 0.73 1.00 in dept. Nonpoor department (PI) 0.30 0.05 -0.27 -0.20 0.25 -0.07 0.01 0.09 -0.29 -0.54 -0.63 -0.56 -0.51 -0.44 -0.49 1.00 Average department (P2) 0.49 0.32 -0.42 -0.30 0.09 0.13 0.02 0.41 -0.19 -0.38 -0.50 -0.48 -0.42 -0.50 -0.44 -0.25 1.00 Poor department (P3) -0.19 -0.21 0.15 -0.28 -0.30 -0.29 0.18 -0.33 -0.01 0.29 0.22 0.34 0.11 0.07 0.42 -0.23 -0.43 1.00 Extrerely poor department (P4) -0.54 -0.16 0.48 0.74 0.02 0.21 -0.20 -0.16 0.42 0.50 0.75 0.56 0.70 0.77 0.39 -0.23 -0.43 -0.39 1.00 Math test scores 0.60 -0.02 -0.63 -0.52 0.49 0.03 0.29 0.12 -0.03 -0.32 -0.32 -0.42 -0.53 -0.37 -0.44 0.16 0.15 -0.04 -0.22 1.00 115 Table 9: Effects of Departmental Characteristics on the Grand Mean of Math Test Scores Model I Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 Model II Intercept 44.5**** 44,46**** 44.43**** 44.50**** 44.5**** 44.2**** 44.47**** 44.62**** 44.57**** 44.72**** 44.44**** Public expenditure per student -0.02 0.5 0.01 0.02 -0.06 -0.03 -0.01 -0.04 -0.05 Household expenditure per capita 0.12**** 0.21 0.16*** 0.16**** 0.17* ** 0.09 0.10** -0.05 0.06 Interaction of public & private expenditure 0 Poverty index -2.65 1.83 Nonpoor dept. (Poverty index 1) 1.44 0.01 9.36 -5.69 -2.45 -2.60 -1.60 Poor dept. (Poverty index 3) -3.16 3.53 14.74 8.61 1.89 0.57 4.05 Extremely poor dept. (Poverty index 4) -5.35 5.21 -11.91 -10.49 9.32* 7.18* 6.42* (Average poverty index 2 omitted) Poverty I * Household expenditure p. c. -0.09 0.03 Poverty 3 * Household expenditure p. c. -0.2 -0.12 Poverty 4 * Household Expenditure p. c. 0.41* 0.3* (Poverty 2 omitted) % of private school students in dept. 0.31* 0.27* 0.18* 0.18* % of Quechua students in dept. -0.2*** -0.15** -0.14* % of female students in dept. -0.63* -0.66* % of over-aged students in dept. -0.39'** -0.30** % of teachers from university in dept. 0.19* % of teachers from ISP in dept. 0.20* Degree of freedom 22 21 23 21 21 19 16 1 5 17 15 13 Variance of Tau (random model) 28.79 27.93 45.55 48.9 27.42 26.96 18.S5 13.5 10.56 4.19 3.44 % of between dept. variance in test scores 48.50% 50.00% 18.40% 12.52% 51.00% 51.77% 66.28% 75.85% 81% 93% 94% explained Key: **** p<=0.000l; ***p<=0.OO1; **p<=0.Ol; *p<=Q.05 116 After adding the percentage of Quechua speaking students in departments, while the per- centage of private school enrollment was retained, the effects of poverty in extremely poor de- partments changed from negative to positive sign. This signaled that students in high poverty de- partments are doing marginally better once the effects of private schools and the proportion of Quechua students were controlled for (Model 9). The effects of Quechua speakers remained after the percentage of girls and the percentage of over-aged students were added (Model 10). When the proportion of teachers graduated from universities and teachers trained in Institu- tos Superiores Pedag6gicos (ISPs) were added, the effects of poverty remained. However, teacher's pre-service education played a significant role-when departments had a higher pro- portion of well-educated and well-trained teachers, departmental average test scores was higher. It should be noted, though, that teachers graduated from universities did not enhance the achievement of Grade 4t students more than teachers who graduated from ISPs. Students in ex- tremely poor departments performed marginally better after all of these variables were controlled for. The interpretation of this is that if an average student attended a school that was similar to all other schools in the country on average, and if the department in which this school was located only differed from other departments in its poverty level (i.e. extremely poor), this student would be expected to outperform another average student in another department, that only differed by the poverty level. Given the very small degree of freedom left by the full departmental model (Model I 1), in spite of the very large reduction in the percentage of between-department variation in mathemat- ics test scores, the equivalent of the R-squared statistics15 is reduced by approximately 3 to 15 points, depending on both the number of predictors in the model. For social science, this is very respectable. (b) Final 3-level model with interaction The 3-level model attempts to identify factors that accounted for variations in mathemat- ics achievement at the levels of students, schools, and departments simultaneously. Since the 2- level analysis had examined in details the determinants of achievement at the student and school levels, this final model focused mainly on the levels of schools and departments. This final model did not use the entire set of level-one predictors due to limitations with the data (the ex- tremely large number of categorical variables). Including all of the potential predictors would make it very difficult computationally to generate estimates. It also would muddle the results when the focus is on departments and schools because so many parameter estimates are unyield- ing to sort through, and lose meaning when too many extraneous variables are included. It is im- portant to consider the correlation among variables included and excluded in the model; if ex- cluded variables act as significant mediators or moderators between included variables and mathematics achievement, it would be improper to exclude these variables. The student level predictors used in this final model are not affected by those variables excluded from the model. '5 While the interpretation is roughly the same, what is normally referred to "R-squarec" is not tech- nically what is calculated. 117 It may be convenient to consider a student's mathematics score as a combination of a mean score, plus some deviation. This deviation can be broken into the student, school, and department components. The mean is itself a function of a grand mean plus school and department level variations. For example, the mean for Lima is equal to the grand (national) mean plus some vari- aton. Further, a student's individual score is some function of the grand mean plus the deviation associated with attending a particular school in a particular department plus effects due to per- sonal characteristics (e.g. being a girl or a Quechua speaker). The results in Table 10, Part A, indicate that department scores varied significantly, and that between-department results were not appreciably different from those presented in the de- partmental-level only models (Table 9). Both household expenditures and the proportion of ISP trained teachers were positively associated with mean departmental scores. Holding other vari- ables constant, the performance of extremely poor departments was significantly better than that in nonpoor and average departments. Table 10, Part B, displays the results of school-level factors that affected mean school mathematics achievement. School level factors could potentially vary among departments. Six school factors significantly impacted school mean achievement: * The percentage of girls in a school was inversely related to achievement. As Part B indicates, this effect did not vary from department to department (hence a national average effect). * The percentage of over-aged students was also inversely related to achievement. This effect varied significantly among departments, meaning that some departments have done a better job, on average, than others with over-aged students. * The proportion of Quechua speakers was also inversely related to achievement. This effect also varied among departments. However, neither the proportion of Quechua speakers in the department, nor the department average poverty level, directly affected this school-level phe- nomenon16. On average, schools attended predorninantly by Quechua speakers had lower scores than schools attended predominantly by Spanish speakers. 17 * The number of years of service a teacher had showed positive effects on math test scores. * The number of training courses a teacher had attended was positively associated with mathematics achievement. This effect did not vary among schools or departments. * Students attending private schools scored significantly better than students attending public schools. This effect did not vary among schools or departments. In other words, the effects of private schools were homogenous-on average they were all about the same. 16 Although, as a group these two variables reduce the variation by about 20 percent. 17 In this particular model, Aymara speakers, who performed about equal to Spanish speakers, were grouped together with Spanish speakers. 118 Table 10, Part C, presents student factors affecting mathematics achievement. Student fac- tors could potentially vary among schools, among departments, or both schools and departments. Another group of five variables were found to be associated with test scores, either negatively or positively: * There was a significant gender gap in mathematics achievement. On average girls scored about 3.4 points below boys. The gender gap varied significantly among schools. This varia- tion is highlighted by comparing the gender gap at a school where the effect was one stan- dard deviation (SD) above average with that in a school where the gender gap was one SD below average. The gender gap was approximately 7 points at schools where the gap was one SD above average, but disappeared at schools where the gap was one SD below average. Once the departmental poverty level was taken into account, the gender gap did not vary among departments. The gender gap was smaller in extremely poor departments. * Over-aged students also scored significantly below students who were at the right age for the grade. The average effect was about 2.6 points and varied significantly among schools. At schools where the over-age effect was one SD above average, over-aged students scored about 5 points lower; however, at schools where the over-age effect was one SD below aver- age, the over-aged students performed as well as the regular-aged students. The variation among schools was partially accounted for by the proportion of over-aged students in school. Over-aged students attending schools with a greater proportion of their peers performed bet- ter as the proportion of over-aged students increased. This might be related to a more effec- tive teaching-learning environment when teachers did not have to cover a wider age-range within the same class. The student-level effect is not dependent upon the departmental pov- erty level. * The student-level Quechua effect was not statistically significant at the 5 percent level, once the school and departmental effect were taken into account. Controlling for other variables, a Quechua speaking student would perform equal to a Spanish speaking student. However, the greater the proportion of Quechua speakers in a school, the poorer was the school's perforn- ance. This shows that Quechua speaking students are not inherently less able, but rather that schools that were attended predominantly by Quechua students, for reasons not identified in this model, performed poorly. Future research should investigate what might be the reasons for this phenomenon. * Four variables which proxied parental expectations (as in the 2-level model) were statistically significant and in the expected direction. The effects of parent expectations were homogene- ous throughout the country (the effects did not vary among schools or departments). * Text materials mattered. Students without texts performed below students who had some form of text. 8 Again, this effect was homogeneous among schools and departments. 18 In the two-level model this variable is broken into several possibilities, the level three model sim- ply contrasts students without texts against all other students. 119 Table 10: Final Three-Level Model for Average Student Achievement with Interaction Part A: Between department effects Effects S.E. Adjusted national grand mean 43.93 0.80 Public expenditure per student -0.04 0.02 Household expenditure per capita 0.08 0.04 Nonpoor department -3.15 2.81 Poor department 6.01 2.59 Extremely poor department 11.72 3.20 Departmental percentage of: Students in private school 0.05 0.09 Quechua students -0.06 0.06 Female students -0.32 0.34 Over-aged-students -0.02 0.13 Teachers from universities 0.17 0.10 Teachers from ISPs 0.21 0.08 Part B: Between schools and departments Modelfor % of girls in a school National average effect -0.05 0.02 Modelfor % of over-age students-in a school National average effect -0.18 0.02 Modelfor % of Quechua students in a school National average effect -0.20 0.04 Departmental % of Quechua students 0.00 0.00 Nonpoor department -0.11 0.15 Poor department 0.11 0.09 Extremely poor department 0.18 0.11 Modelfor % ofAymara students in a school National average effect -0.06 0.04 Modelfor teacherfrom universities National average effect 1.93 1.15 Modelfor teacher at schoolfrom ISP National average effect 0.36 0.81 Modelfor teacher at school years of service National average effect 0.11 0.05 Modelfor teacher at school # of training courses National average effect 0.27 0.12 Modelfor director at school Quechua National average effect -0.99 1.33 Modelfor school in jungle National average effect 1.09 2.29 Modelfor school in mountains National average effect -0.69 1.89 Modelfor school rural National average effect -0.71 1.02 120 Modelfor private school National average effect 10.77 1.11 Modelfor teacher in school Quechua National average effect 0.12 1.43 Part C: Between students, schools, and departments Model for gender gap Modelfor mean gender gap National average effect -3.41 0.24 Nonpoor department -1.46 0.78 Poor department 0.67 0.62 Extremely poor department 1.71 0.65 Modelfor teacher service year effect on gender gap National average effect 0.00 0.03 Model for over-age-student effect Modelfor mean over-age-student effect National average effect -2.59 0.30 Nonpoor department -0.14 0.98 Poor department -1.09 0.75 Extremely poor department -0.13 0.72 Modelfor school % over-age-student effect National average effect 0.06 0.01 Model for Quechua student disadvantage Modelfor mean Quechua student disadvantage National average effect -0.87 0.48 Departemental % Quechua effect -0.07 0.03 Nonpoor department 2.14 1.34 Poor department 0.37 1.21 Extremely poor department 1.51 1.46 Modelfor school % Quechua effect National average effect 0.03 0.02 Model for Aymara student effect National average effect 0.42 0.72 Model for Parent Expectations Modelfor gain in literature effect National average effect -1.19 0.23 Modelfor gain in mathematics effect National average effect 1.10 0.26 Modelfor gain in comprehensive way effect National average effect 0.62 0.23 Modelfor gain nothing effect National average effect -2.74 0.37 Model for no text effect National average effect -0.91 0.24 Key: **** p<-O.0001; ***p<=.001; **p<=O0.0; *p<-=.05 121 (c) Variance in outcomes attributable to differences between students, between schools, and be- tween departments The unconditional models for three-level analysis found that 12 percent of the variance in math test scores was attributable to differences in characteristics between departments. Within departments, 43 percent of the variance in test scores was attributable to characteristics between schools. Within schools, 45 percent of the variance was due to characteristics among students within schools. (Equations 1-6). Table 11 displays the amount of variance explained by variables at all three levels. Cumu- latively, student ascriptive characteristics, text materials, and parental roles and expectation ex- plained 3.8 percent of the variance in achievement between students; school enrollment charac- teristics, geographic, public or private sector, teacher and principal characteristics explained 33 percent of variance between schools; ascriptive characteristics, public or private sector, teacher characteristics, and household expenditure explained 43 percent of variance between depart- ments. That the amount of variance explained by student-level variables was reduced from those in Table 7 is because the variables have been reduced from the previous model. That the variance explained by department-level variables is substantially less than those percentages reported in Table 9 is because these are conditioned on the lower level variables in the model. Conclusion. The findings of this analysis yield positive messages. In spite of the gap in mathematics outcomes between gender, region, rural and urban areas, private and public schools, and the coast, mountain, and jungle, after controlling for a number of explanatory variables the picture has changed. Students in poor and extremely poor departments performed better than those in nonpoor and average departments, holding other variables constant. Some departments were doing a better job in educating over-aged students. The gender gap was less pronounced in extremely poor departments. Aymara students performed equally well as Spanish speaking stu- dents. Quechua students could perform equally well if they were not attending predominantly Quechua schools, thereby indicating that the problem lies in the schools, not in the students. Teachers graduated from universities, teachers graduated from ISPs, teachers with longer years of service, and teachers who have had more in-service training were associated positively with student achievement nationwide. Nonavailability of text books was negatively associated with outcomes. Parental expectation for better performance in the relevant subject was translated into higher student performance. It may be recalled that the existing sample excluded single-teacher schools in the remote areas which concentrated in the Sierra and Selva regions. If these schools and students were dis- tributed equally across all departments in the Sierra and Selva and were included in the sample, they might not pull down the averages at the student, school, or departmental levels, but they could increase the standard deviation. If they concentrated in a few departments, they might pull down the average of those departments. In either case, if these rural schools and students were included in the sample, the variance between departments and between schools would most likely increase, which, in turn, would proportionally reduce the variance within schools (between students). Given that teachers in remote schools have lower academic qualifications, fewer in- service trainings, and less years of services, the effects of these variables could be larger. 122 If single-teacher schools were included in the sample, would it change the optimistic ob- servations of this analysis? It would probably modify but not substantially change the outlook. Given the very large positive coefficient in the extremely poor departments (11.76 and statistical significance at p.<=0.01 level in the final model), even if the sample included single-teacher schools and students, the performance of the high poverty departments might still be marginally better than the other departments, when other explanatory variables were held constant. How- ever, the smaller coefficient in poor departments (6.01 and significance at p<=0.05 level) might be further reduced or lose its statistical significance. In this case, the observations for the ex- tremely poor departments might still hold, but not for others. The finding for the Aymara may not change because they live closer to urban areas than other indigenous peoples. The negative effects of Quechua schools is likely to still hold or grow bigger. If most of the remote schools are in the jungle areas, there would be new findings on the determinants of their achievement. The finding on over-aged students is also likely to hold because teachers probably teach to the stu- dents in the middle age range and neglect the needs of the older and younger kids. Gender is likely to remain a serious issue. Whatever the limitations of the existing sampling frame and dataset may be, they would not negate the importance of the findings and policy implications for the majority of the 4th Graders in the country. The findings point to the opportunity for public policy to make a real dif- ference for disadvantaged students. The interventions should be universal where the effects have nationwide impact (that is, little variation at the school and departmental levels). These include textbook provision, teacher pre-service and in-service training, providing incentives for experi- enced teacher to remain in the profession, deploying qualified and experienced teachers to the rural areas, specific training to teach more effectively to over-aged students, and using the mass media and parents associations to enhance parents' role in supporting their children's education. Where the effects vary across departments or schools, targeted interventions are desirable. These include specific support for schools where Quechua (and other indigenous people) are predominant. This might require strengthening bilingual education and text materials. In better schools, special attention might need to be paid to bring girls and over-aged students up to the standards of other students. (See Table 4 in Volume 1 for a summary of the effects crossing be- tween departments, schools, and students). 123 Table 11: The Extent to Which Mathematics Outcomes Is Accounted for by Student, School, and Department-Level Characteristics Mean S.E. True School Mean Mathematics Achievement 45.1 * 0.46 Random effects ANOVA Level -l (between students) (eijk) 216.6 Level -2 (between schools) (rjk) 206.6 Level -3 (between schools) (UO) 55.9 Amount of variation in mathematics achievement attributable to schools 4 3.1% Variation in True School Mean Mathematics Achievement between schools + lsd 59.49 - Isd 30.75 Amount of variation in mathematics achievement attributable to departments 11.7% Variation in True School Mean Mathematics Achievement between departments + lsd 52.60 - Isd 37.64 Cumula- tive Variance variance Within-school variables (1) component (2) explained 1) Ascriptive characteristics 209.7 * 3.2% 2) Materials (texts) 208.4 * 3.8% 3) Parental roles & expectations 208.3 * 3.8% Between-school variables (3) 4) School enrollment characteristics 151.3 * 26.8% 5) Geographic 151.5 * 26.7% 6) Private or public sector 139.6 * 32.4% 7) Teacher characteristics 137.9 * 33.3% 8) Principal characteristics 137.9* 33.3% Between-department variables (4) 9) Ascriptive (demographic) 19.1 * 26.0% 10) Private or public sector 16.3 * 37.0% 11) Teacher characteristics 14.6* 42.2% 12) Household expenditure 14.9 * 43.5% Notes: (1) Level-one model is a reduced form from that presented in the two-level analysis. (2) Indicates whether the intercept varies significantly after variables are included in model. (3) School-level (level-2) variables model the intercept only. This is also true for level 3 variables. (4) The variance explained is substantially less than those percentages reported in Table 9 because these are conditioned on the lower level variables in the model. * P<-.05. 124 Background Note 5. Teacher Education and Professional Development"9 System of pre-service training. Teacher education in initial, primary, and secondary edu- cation is under two modalities: (i) pre-service training with 10 semesters of academic studies in ISPs, Higher Education for the Arts, Physical Education, and Theology, and in the education faculties of universities; and (ii) professional studies of 12 semesters' duration for the currently serving teachers without credentials to attend classes during vacations and to use distance learn- ing methods during the year. There are 318 ISPs and 38 universities authorized to train teachers in the regular mode.20 Of the ISPs, 138 are public and 180 private. Private ISPs absorb 61 percent of the enroll- ment. At the college level, there are more faculties of education in public universities (21 of 29 universities) than private (17 of 35). MED authorizes course offerings and prescribes curriculum in public and private ISPs, while the National Council for Authorization of Function of Universi- ties (CONAFU), an autonomous organ under the National Council of Rectors, approves the course offerings of education faculties in universities. Since the universities are autonomous, they have more freedom to decide on the curriculum. The MED, the universities, and the ISPs do not conduct systematic consultation with each other regarding the curriculum or about the num- ber of entrants or graduates on the basis of the projected need for qualified teachers. One of the reasons for the low passing rate at the teachers' selection test may be due to the lack of agreed curriculum of teacher training. Public ISPs confer the professional title of "teacher" (Profesor) which is a nationwide title, with specification about the level and the specialty they can teach. Graduates from public ISPs are required to do a thesis on education research before they are conferred the professional title. Graduates from private ISPs are required to pass an exam set by the MED (according to a recent regulation, D.S. NO 008-98-ED). The universities can confer the academic title of Bachelor in Education (Bachiller en Educaci6n) and the professional title of Licensed in Education (Licen- ciado en Educaci6n). The teacher graduates from ISPs can obtain the title of Bachiller en Educa- ci6n in the universities without passing an entrance exam. To obtain this title, it is required to attend two more semesters of complementary studies that universities organize for this purpose. The title of Bachiller en Educaci6n allows continuation of studies in postgraduate work. The ti- tles of Profesor and Licenciado are equivalent and give equal status in the public career of teach- ing (Ley del Profesorado, 1984). The excess supply of teachers, accelerated by the rapid growth of private ISPs in the 1990s, may have compression effects on the level of remuneration for teachers. The proportion of university applicants who want to major in education declined from 27 percent in 1960-64 to 8-10 percent in the 1980s and 1990s. The quality of teacher education in the ISPs is alleged to be poor, and students in education faculties are alleged to have the lowest entrance scores among university aspirants. Not all of the students in ISPs enter teaching after graduation. To many, it is 19 This analysis was undertaken by Maria Amelia Palacios. 20 The sources are the preliminary results of the 1997 census of technical and pedagogical education, and information from the National Council of Rectors. 125 one of the relatively easy routes for entry into tertiary education. This also explains why students are willing to pay to enter private ISPs, in spite of the relatively low pay and prestige in educa- tion, as well as the instability of employment given the difficulty of getting appointed to a pen- sionable position. Characteristics of ISP students. According to the 1997 Census of ISPs, 227,942 students were enrolled in public and private ISPs. 61 percent of them studied in public ISPs and 59 per- cent were females. Of those who studied in private ISPs (39 percent), 70 percent were females. Only 83 percent of those who are in teacher training proceed to teach in public schools, generally in the same department where they were trained. Most of these are from poor families. This ex- plains why 30 percent of young people work when they study (Arregui, Hunt and Dfaz, 1996). About one-third of students have a mother tongue that is not Spanish. The entrance examination in the regular program of pre-service training is not demanding; about 52 percent of applicants are admitted, although public ISPs are more selective than private ones (Arregui, Hunt and Diaz, 1996). The demographic trend of a growing young population, combined with relatively easy access and growth in the supply of pre-service training, explains why the number of applicants has not diminished, in spite of low prestige of the profession, low salaries, and lack of job secu- rity. Professional studies (for teachers without titles). Since 1975, the training of teachers who do not have titles was considered an obligation of the state. After passing the Law of Teach- ers, the supply of public and private training programs increased significantly in the country since 1985. Created to be of a transitory nature, it has been ongoing due to the continuous de- mand. In 1996, the basic statistics of MED reported an enrollment of 30,753 persons in the pro- fessional program. Not all the participants, however, teach because many enter these programs in search of a profession. In 1996, due to irregular functioning of professional studies and criticism of poor quality, MED restricted professional studies' course offerings in ISPs (51 in 1995) and did not renew the permit with universities (15 universities in 1995). Recently, it has also suspended the intake of new students in professional studies in ISPs, and only 10 public ISPs can continue until their stu- dents graduate. In the case of universities, in 1997, only the National University of San Marcos, with its campuses in Lima, Huaraz, and Tarapoto, has an enrollment of 1,439 students, of which 73 percent were in secondary education. Curriculum of pre-service training. Pre-service training in public and private ISPs fol- lows the same curriculum that was approved in 1985 by MED (R.M. No 759-85-ED), unless it is authorized to experiment with an altemative curriculum. The universities are autonomous and as such follow their own curriculum. In 1996, MED provided a pilot plan of modernization of teacher training in primary education. The pilot plan began with participation of 13 ISPs (of which 11 are public and 2 are private). In 1998, another 11 public ISPs joined the pilot (sup- ported by the IDB); the new curriculum of teacher training in secondary education was added, with specialization in social science (7 ISPs) and natural science (10 ISPs). Meanwhile, there co- exist in ISPs two curricula for pre-service training: 126 * The basic curriculum for training teachers. This is applied in approximately 50 public and private ISPs for the specialty in primary education, in all ISPs with the specialty in initial education, and in all the ISPs with the specialty in secondary education (with the exception of 10 pilots). * The pilot curriculum for pre-service training in primary education. This is being piloted in 63 ISPs (of which 22 are public, 2 are private, and 39 are associated with public). The following table compares two curricular proposals in three key features: the curricular organization, the number of hours and the credit assigned to each component, and the weight of the practicum. The pilot curriculum introduces three important innovations: (a) the organization of content in six new major areas; (b) integration of education research with studies; and (c) the early initiation into practicum and extension to the last semester to account for 21 percent of the total time of the training program. Table 1: A Comparison of Old and New Pilot Curriculum in Teacher Education Old Curriculum (since 1985) Curriculum in the Pilot Plan (since 1996) # of as- # of % of #of sub- # of # of Curricular areas signment hours % credit areas hours % credit General education 16 1248 26 56 Professional education Basic 18 1216 25 56 Specialty 20 1728 25 79 Practicum 608 13 29 1152 21 Ecosystem 3 576 10 24 Society 3 648 12 24 Integrated communication 3 936 17 37 Mathematics 3 432 8 18 Education 5 1368 25 97 Religious education 3 288 5 8 Work and production 3 144 2 6 Total 54 4800 89 220 23 5544 100 238 Source: Ministry of Education Remuneration, qualifications, and professional development of teaching staff in ISPs. The remuneration of ISP trainers is very low. In public ISPs, the average monthly pay is 680 soles for a workload based on 24 hours per week, and 850 soles for a 40-hour workload. How- ever, the so-called workload is only a label and does not reflect the amount of work done. There is not much difference in private ISPs. These centers pay teachers by the hour, usually between 5 to 7 soles per hour. For a workload of 24 hours, a teacher in a private ISP would earn between 480 and 672 soles per month, less than those in a public ISP. Teaching staff in ISPs are almost uniformly trained in the field of education (as opposed to a liberal arts education with specialty in history, mathematics, or physics). According to the 1997 census of teacher-training institutions, 83 percent of the teaching staff in public ISPs and 85 per- cent of them in private ISPs had a major in pedagogical studies. There are others who studied psychology, sociology, law, or engineering. According to the evaluation report of the pilot plan, 40 percent of the ISP teachers in the sample declared that they studied other fields. In 1993, 70 127 percent of the 4,558 teacher trainers in service worked in public institutions (Census, 1993). In 1997, the number rose to 7,658, but only 49 percent taught in public ISPs (Census, 1997) due to a rapid increase in private ISPs. Between 1990 and 1995, private ISPs grew by almost 17 times, stimulated by the government policy to promote private investment in education (Diaz, 1996). The analysis of teacher training in Peru by Arregui, Hunt and Dfaz (1996) indicates that about half of the trainers in ISPs and universities were themselves trained in ISPs or Normal Schools, while the other half were trained in universities. Only 11 percent of ISP teachers in the sample were trained through professional studies. About 52 percent specialized in secondary education, and 23 percent in primary education. Of the total staff, about 21 percent specialized in math and sciences. Among those graduates of regular programs and professional studies, the 1993 census found that 20 percent specialized in secondary education with emphasis in mathematics or science. Among secondary teachers, 36 percent specialized in mathematics and/or science. MED has authorized recently (R.M. No 289- 98-ED) implementation of the pilot plan of Bachillerato, which is two years of postsecondary studies requisite for higher education. This proposal cut secondary education by a year. There is not yet an established curriculum to train teachers for this level of education. A major challenge to expansion of secondary education and the introduction of the new Bachillerato is undoubtedly the need to rapidly strengthen the academic and professional capacity of teacher educators. There exist 47 postgraduate programs of study in education-39 at the master's level and 8 at the doctoral level. However, few of the trainers have themselves completed such postgraduate education. About 37 percent of trainers are studying in a university to obtain the Bachelor's de- gree, a second specialty, or a higher degree (Arregui, Hunt and Dfaz, 1996). An evaluation study of the Pilot in 10 ISPs found that one-third of trainers in ISPs have a Bachelor's degree and one- fifth have a Master's degree. Although the offering of in-service training in universities, ISPs, and NGOs has increased notably in recent years, the cost is beyond the reach of many trainers. Since 1996, MED began to finance the professional development of teacher trainers. In 1997, it trained about 300 ISP train- ers through 13 courses and an international event in Lima (Evaluation of the Pilot Plan, 1998). The low salaries of ISP teachers is the main reason that they cannot access a more permanent form of training in the private sector. Diversification of pre-service training. The more important efforts to diversify in-service training are those linked with training of indigenous teachers. About 17 percent of the Peruvian population speak Quechua (3.5 million) and 3.2 percent (half a mnillion) speak other indigenous languages. However, teaching is conducted in Spanish in 95 percent of the education centers in the country. Of 2,706 bilingual schools in the country, 72 percent are in primary education. The majority of them (1,682 schools) teach in Quechua (1993 Census). The experience of diversifica- tion of teacher training can be summarized as follows: (a) The curricular model for pre-service and in-service training for teachers in bilingual and intercultural education. In 1993, MED authorized a five-year experiment in diversification in 10 selected institutions (8 ISPs and 2 universities). Only 3 public ISPs out of 8 have experi- mented with part of the models for as much as seven months. The two public universities 128 have not even done that. Recently, an analysis of the experience is being conducted. (b) The professional training of teachers in Andian rural areas. This has been ongoing since 1988, undertaken by the Technical and Pedagogical Higher Institute of Urubamba in con- junction with the Catholic University of Peru. Among the major contributions are the organi- zation of curricular content and improvement of the articulation between theory and action. (c) The program to train bilingual teachers of the Amazon region. This was developed by the Public ISP of Loreto in association with the AIDESEP in 1998. It is a curriculum of six years of study in intercultural and bilingual education in primary education for the Amazon region. One of the more interesting innovations is the intensive and supervised practicum in the last two years of training that leads to the employment of students either as permanent or contract staff by the MED to teach in communities in the jungle. Issues in teacher education. The main issues are as follows: (a) The quality of academic emphasis and pedagogy. The divorce between theory and practice in teacher education has undermined the quality of teacher preparation. The universities empha- size theory at the expense of practice while the ISPs prefer the instruments of teaching at the cost of a solid foundation in the content of teaching. (b) The difference in academic status between the Profesor and the Licenciado that requires the former to study for an additional year in university in order to complement this academic formation and to access postgraduate studies. This has contributed to the public perception that the Profesor is inferior to the Licenciado. (c) The lack of linkage between universities and ISPs in sharing innovations and research. The evaluation of the pilot plan found that the trainers tended to initiate students in social re- search but not in education research, and that, although there are exceptions, few teachers in ISPs have pursued research themselves, leading to a loss of prestige. (d) The absence of incentives for teachers to strive for excellence. The policy towards teacher remuneration is a major constraint (in terms of time and money) that does not enable teachers to use their own resources and outside of work hours for professional development. The ISPs do not have budgetary resources to support continuous development, and the state does not have a policy for professional development of the trainers of teachers, nor give much atten- tion to those working outside big cities. (e) The unsustainable increase in the supply of teacher training. The rapid growth in private ISPs in the 1990s has not been accompanied by evaluation of the quality of training. Since the MED has the only authority to grant permission to the operation of private ISPs, it can also plan a more adequate system of teacher training by taking into account vacancies, spe- cialties, and differential demand for teachers by departmentlareas. The mechanism of ac- creditation and supervision of the quality of service of private ISPs should be the first area of concern. 129 (f) The persistence of untrained teachers is a serious issue. They vary by level of education, ac- counting for 43 percent in initial education, 41 percent in primary education, and 55 percent in special education. In addition, there are untrained teachers in secondary education. Profes- sionals from other careers who do not have pedagogical titles but wish to become part of the teaching profession have followed the same complementary program in some universities in order to enter a public teaching career. However, it is not clear whether this is authorized by MED. (g) There is no evaluation of the quality of teacher education: availability of qualified human resources and support materials (such as libraries, instructional materials, access to com- puters), the relevance of teacher education to the reality and diversity of the student popula- tion, and the efficacy to develop in the majority of students the competencies teachers want in their professional profile. Options for improvement These are as follows: (a) Promote education research along with extending access to information about teacher train- ing. Teachers in different cultural and socioeconomic contexts should produce their own knowledge. This production can be advanced by interchange with educators in the world. ISPs should strengthen collaboration with education researchers. MED can promote and co- ordinate a research agenda for teacher education. (b) Support directors and trainers in the ISPs by making available the necessary conditions for innovation: continuous professional development in their work center and outside, scholar- ships for study abroad, teaching materials to support innovation, access to journals and publi- cations that inform trainers about innovation in their area of work, freedom to experiment, and opportunity and time to disseminate the results. (c) Progressively improve teacher incentives. Economic and noneconomic incentives should be designed to increase work satisfaction: such as stimulus for innovators, opportunity for pro- fessional development linked to classroom needs, supplying modem instructional materials, encouraging professional knowledge of students and parents, raising teachers' authority with the community, and fostering a climate favorable for group work. (d) Evaluate the results of training programs, particularly those where great resources and ef- forts have been invested. For example, the impact of the national plan to train teachers and education management (PLANCAD and PLANCGED) should be evaluated to assess the im- provement in the linkage between teacher training and reality of public schools. There should be a permanent system of evaluation of practicum in public schools. This needs to be a com- bined internal and external evaluation that takes account of the experience and knowledge of trainers, authorities, and students, and detects problems and proposes solution to the defi- ciencies. (e) Design a new strategy to qualify the teachers without titles. It should be offered exclusively to current serving teachers without titles; organized and executed by accredited public and 130 private institutions, supported with proper resources to ensure that it achieves its objective; and giving priority to those education levels and departments where unqualified teachers are most numerous (Loreto, Madre de Dios, Callao, Huancavelica, San Martfn, Amazonas, and Ucayali.) (f) Plan the supply of training collaboratively among the MED, the universities, and the ISPs. Since the state is the major employer of teachers, it is indispensable that it articulate supply and demand (Diaz, 1996). (g) Recruit and select diverse applicants for teacher training. New mechanisms should be de- signed to select candidates for teacher training by attracting youth from diverse social sectors and including professionals from other careers. (h) Organize continuous professional development of teacher trainers to be financed by the state by taking into account the following elements: (i) a process of professional development linked with the reflection of teaching practice; (ii) a training centered in the ISPs that can take account of the specific needs of teachers; (iii) encouragement of the participation of trainers in the process of reform of the teaching profession; (iv) involvement of teachers in planning and evaluation of the training program; and (v) incorporation of innovations and lessons of experience from the nation's classrooms. 131 APPENDICES 133 APPENDIX 1 STUDENT ENROLLMENT STATISTICS 135 Appendix 1.1: Enrollment in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990- 1997 PUBLIC Rates of change (percentage) Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 6,087,234 6,069,175 6,053,033 6,189,652 6,321,889 6,453,367 6,568,545 6,620,329 8.76 NONFORMAL 333,985 325,077 322,732 333,705 361,614 376,099 411,248 426,025 27.56 Initial education 702,791 707,873 721,190 775,396 806,804 841,924 908,070 938,539 33.54 Formal 412,699 419,542 431,644 470,452 475,528 494,254 521,607 537,777 30.31 Nonformal 290,092 288,331 289,546 304,944 331,276 347,670 386,463 400,762 38.15 Primary education 3,495,454 3,469,558 3,456,391 3,568,050 3,646,818 3,702,418 3,739,198 3,768,797 7.82 Minors 3,400,694 3,388,558 3,380,619 3,495,394 3,576,092 3,633,236 3,672,369 3,701,748 8.85 Formal 3,398,840 3,387,243 3,379,367 3,494,418 3,574,954 3,632,150 3,671,801 3,701,175 8.90 Nonformal 1,854 1,315 1,252 976 1,138 1,086 568 573 -69.09 Adults 94,760 81,000 75,772 72,656 70,726 69,182 66,829 67,049 -29.24 Formal 74,839 64,771 61,631 60,446 58,630 57,161 57,067 57,238 -23.52 Nonformal 19,921 16,229 14,141 12,210 12,096 12,021 9,762 9,811 -50.75 Secondary education 1,652,019 1,628,485 1,608,051 1,628,457 1,675,843 1,719,169 1,776,682 1,819,897 10.16 Minors 1,443,914 1,439,063 1,428,464 1,458,680 1,510,876 1,556,555 1,620,805 1,662,946 15.17 Formal 1,443,914 1,439,063 1,428,464 1,458,590 1,510,876 1,556,555 1,620,805 1,662,946 15.17 Nonformal 0 0 0 90 0 0 0 0 - Adults 208,105 189,422 179,587 169,777 164,967 162,614 155,877 156,951 -24.58 Formal 196,543 178,976 170,335 161,366 156,130 154,345 148,156 149,045 -24.17 Nonforrnal 11,562 10,446 9,252 8,411 8,837 8,269 7,721 7,906 -31.62 Tertiary nonuniversity education 131,876 143,352 150,732 159,318 169,122 174,358 162,188 165,068 25.17 University education 291,179 312,735 306,416 252,934 242,133 246,678 251,316 210,779 -27.61 Other 147,900 132,249 132,985 139,202 142,783 144,919 142,339 143,274 -3.13 Special education 16,952 16,054 16,312 16,943 18,527 19,833 20,962 21,291 25.60 Formal 16,952 16,054 16,312 16,943 17,259 18,292 19,246 19,477 14.89 Nonformal 0 0 0 0 1,268 1,541 1,716 1,814 - Vocational education 130,948 116,195 116,673 122,259 124,256 125,086 121,377 121,983 -6.85 Formal 120,392 107,439 108,132 115,185 117,257 119,574 116,359 116,824 -2.96 Nonformal 10,556 8,756 8,541 7,074 6,999 5,512 5,018 5,159 -51.13 |Source: Ministry of Education. 137 Appendix 1.2: Enrollment in Formal and Nonformal Educatdon (Broadly Grouped) in Public Institutions by Level as Percentage of Total, 1990-1997 FORMAL 1990 1991 1992 1993 1994 1995 1996 1997 Initial education 6.78 6.91 7.13 7.60 7.52 7.66 7.94 8.12 Primary education 57.06 56.88 56.85 57.43 57.48 57.17 56.77 56.77 Secondary education 26.95 26.66 26.41 26.17 26.37 26.51 26.93 27.37 Tertiary nonuniversity education 2.17 2.36 2.49 2.57 2.68 2.70 2.47 2.49 University education 4.78 5.15 5.06 4.09 3.83 3.82 3.83 3.18 Other 2.26 2.03 2.06 2.13 2.13 2.14 2.06 2.06 TOTAL 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 NONFORMAL Initial education 86.86 88.70 89.72 91.38 91.61 92.44 93.97 94.07 Primary education 6.52 5.40 4.77 3.95 3.66 3.48 2.51 2.44 Secondary education 3.46 3.21 2.87 2.55 2.44 2.20 1.88 1.86 Other 3.16 2.69 2.65 2.12 2.29 1.88 1.64 1.64 TOTAL 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 FORMAL AND NONFORMAL Initial education 10.94 11.07 11.31 11.89 12.07 12.33 13.01 13.32 Primary education 54.44 54.26 54.21 54.70 54.56 54.21 53.57 53.49 Secondary education 25.73 25.47 25.22 24.96 25.07 25.17 25.45 25.83 Tertiary nonuniversity education 2.05 2.24 2.36 2.44 2.53 2.55 2.32 2.34 University education 4.53 4.89 4.81 3.88 3.62 3.61 3.60 2.99 Other 2.30 2.07 2.09 2.13 2.14 2.12 2.04 2.03 TOTAL 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Source: Ministry of Education 138 Appendix 1.3: Enrollment in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 Rates of PRIVATE change (%) Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 1,222,386 1,251,568 1,271,364 1,285,169 1,292,480 1,335,264 1,343,105 1,363,661 11.6% NONFORMAL 13,687 15,918 18,345 27,784 24,132 24,837 34,774 37,660 175.2% Initial education 94,472 100,707 106,499 121,895 129,834 138,587 151,311 152,755 61.7% Formal 91,476 98,887 104,963 119,601 128,418 136,912 146,981 149,316 63.2% Nonformal 2,996 1,820 1,536 2,294 1,416 1,675 4,330 3,439 14.8% Primary education 461,807 475,285 478,323 487,251 492,293 504,580 494,269 504,773 9.3% Minors 456,442 470,222 473,731 482,449 486,933 499,085 488,384 498,288 9.2% Formal 456,442 470,222 473,731 482,275 486,767 498,935 488,134 498,180 9.1% Nonformal 0 0 0 174 166 150 250 108 Adults 5,365 5,063 4,592 4,802 5,360 5,495 5,885 6,485 20.9% Formal 4,598 4,000 3,531 3,190 3,088 3,011 2,808 3,183 -30.8% Nonformal 767 1,063 1,061 1,612 2,272 2,484 3,077 3,302 330.5% Secondary education 271,408 292,735 299,011 319,129 326,035 332,918 343,094 346,985 27.8% Minors 254,029 271,652 275,533 289,126 299,473 306,173 310,112 308,147 21.3% Formal 254,029 271,652 275,533 289,126 299,473 306,173 310,112 308,147 21.3% Nonformal 0 0 0 0 0 0 0 0 0 Adults 17,379 21,083 23,478 30,003 26,562 26,745 32,982 38,838 123.5% Formal 8,711 9,119 8,721 7,282 6,703 6,757 7,521 8,027 -7.9% Nonformal 8,668 11,964 14,757 22,721 19,859 19,988 25,461 30,811 255.5% Tertiary nonuniversity 103,428 109,605 118,457 104,558 115,472 124,536 138,669 141,634 36.9% education University education 151,753 162,974 157,083 150,157 123,894 126,230 128,603 142,130 -6.3% Other 153,205 126,180 130,336 129,963 129,084 133,250 121,933 113,044 -26.2% Special education 3,450 3,351 3,273 4,785 3,482 3,865 3,888 3,473 0.7% Formal 3,450 3,351 3,273 4,785 3,482 3,865 3,888 3,473 0.7% Nonformal 0 0 0 0 0 0 0 0 0 Vocational education 149,755 122,829 127,063 125,178 125,602 129,385 118,045 109,571 -26.8% Formal 148,499 121,758 126,072 124,195 125,183 128,845 116,389 109,571 -26.2% Nonformal 1,256 1,071 991 983 419 540 1,656 0 -100. Source: Ministry of Education 139 Appendix 1.4: Enrollment in Formal and Nonforinal Education (Broadly Grouped) in Private Institutions by Level as Percentage of Total FORMAL 1990 1991 1992 1993 1994 1995 1996 1997 Initial education 7.48% 7.90% 8.26% 9.31% 9.94% 10.25 10.94 10.9% Primary education 37.72% 37.89% 37.54% 37.77% 37,90% 37.59 36.55 36.8% Secondary education 21.49% 22.43% 22.36% 23.06% 23.69% 23.44 23.65 23.2% Tertiary nonuniversity education 8.46% 8.76% 9.32% 8.14% 8.93% 9.33 10.32 10.4% University education 12.41% 13.02% 12.36% 11.68% 9.59% 9.45 9.58 10.4% Other 12.43% 10.00% 10.17% 10.04% 9.95% 9.94 8.96 8.3% TO'I'AL 100.00% 100.00% 100.00% 100.00W, 100.00% 100.00 100.00 100.0% NONFORMAL Initial education 21.89% 11.43% 8.37% 8.26% 5.87% 6.74 12.45 9.1% Primary education 5.60% 6.68% 5.78% 6.43% 10.10% 10.61 9.57 9.1% Secondary education 63.33% 75.16% 80.44% 81.78% 82.29% 80.48 73.22 81.8% Other 9.18% 6.73% 5.40% 3.54% 1.74% 2.17 4.76 0.0% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00 100.00 100.0% Source: Ministry of Education 140 Appendix 1.5: Total Enrollment in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public and Private Institutions TOTAL Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 FORMAL 7,309,620 7,320,743 7,324,397 7,474,821 7,614,369 7,788,631 7,911,650 7,983,990 NONFORMAL 347,672 340,995 341,077 361,489 385,746 400,936 446,022 463,685 Initial education 797,263 808,580 827,689 897,291 936,638 980,511 1,059,381 1,091,294 Formal 504,175 518,429 536,607 590,053 603,946 631,166 668,588 687,093 Nonformal 293,088 290,151 291,082 307,238 332,692 349,345 390,793 404,201 Primary education 3,957,261 3,944,843 3,934,714 4,055,301 4,139,111 4,206,998 4,233,467 4,273,570 Minors 3,857,136 3,858,780 3,854,350 3,977,843 4,063,025 4,132,321 4,160,753 4,200,036 Formal 3,855,282 3,857,465 3,853,098 3,976,693 4,061,721 4,131,085 4,159,935 4,199,355 Nonformal 1,854 1,315 1,252 1,150 1,304 1,236 818 681 Adults 100,125 86,063 80,364 77,458 76,086 74,677 72,714 73,534 Formal 79,437 68,771 65,162 63,636 61,718 60,172 59,875 60,421 Nonformal 20,688 17,292 15,202 13,822 14,368 14,505 12,839 13,113 Secondary education 1,923,427 1,921,220 1,907,062 1,947,586 2,001,878 2,052,087 2,119,776 2,166,882 Minors 1,697,943 1,710,715 1,703,997 1,747,806 1,810,349 1,862,728 1,930,917 1,971,093 Formal 1,697,943 1,710,715 1,703,997 1,747,716 1,810,349 1,862,728 1,930,917 1,971,093 Nonformal 0 0 0 90 0 0 0 0 Adults 225,484 210,505 203,065 199,780 191,529 189,359 188,859 195,789 Formal 205,254 188,095 179,056 168,648 162,833 161,102 155,677 157,072 Nonformal 20,230 22,410 24,009 31,132 28,696 28,257 33,182 38,717 Tertiary nonuniversity education 235,304 252,957 269,189 263,876 284,594 298,894 300,857 306,702 University education 442,932 475,709 463,499 403,091 366,027 372,908 379,919 352,909 Other 301,105 258,429 263,321 269,165 271,867 278,169 264,272 256,318 Special education 20,402 19,405 19,585 21,728 22,009 23,698 24,850 24,764 Formal 20,402 19,405 19,585 21,728 20,741 22,157 23,134 22,950 Nonformal 0 0 0 0 1,268 1,541 1,716 1,814 Vocational education 280,703 239,024 243,736 247,437 249,858 254,471 239,422 231,554 Formal 268,891 229,197 234,204 239,380 242,440 248,419 232,748 226,395 Nonformal 11,812 9,827 9,532 8,057 7,418 6,052 6,674 Source: Ministry of Education 141 Appendix 1.6: Public Enrollment by Level by Department, 1997 Tertiary Initial Primary Secondary Nonuniversity Vocational Training Special TOTAL Amazonas 19,718 78,055 22,190 3,499 2,381 111 125,954 Ancash 45,788 183,130 81,421 9,955 4,298 1,172 325,764 Apurfmac 23,111 97,989 28,912 2,527 1,032 144 153,715 Arequipa 30,802 116,441 72,990 11,007 2,167 806 234,213 Ayacucho 25,801 126,265 37,980 5,675 3,441 397 199,559 Cajamarca 63,658 289,380 82,741 11,418 3,503 610 451,310 Cusco 52,636 217,512 80,422 6,388 3,989 643 361,590 Huancavelica 23,409 102,940 25,894 2,570 1,345 111 156,269 Huanuco 21,003 146,903 42,718 4,479 3,203 216 218,522 Ica 28,452 85,033 61,682 7,946 5,697 498 189,308 Junfn 37,979 203,690 102,040 11,064 5,263 653 360,689 La Libertad 37,466 194,442 93,898 12,390 5,765 692 344,653 Lambayeque 32,066 143,342 83,149 6,978 4,460 560 270,555 Lima - Callao 199,100 759,911 580,209 27,286 47,860 10,248 1,624,614 Loreto 42,871 180,477 58,709 4,712 5,505 328 292,602 Madre de Dios 3,872 16,673 7,147 871 231 45 28,839 Moquegua 6,266 16,553 11,602 1,852 1,750 109 38,132 Pasco 13,867 49,497 22,722 2,435 1,950 265 90,736 Piura 58,376 251,117 102,063 6,963 4,290 929 423,738 Puno 69,217 205,491 101,947 11,891 4,258 415 393,219 San Martin 26,681 122,735 45,325 3,988 1,304 298 200,331 Tacna 11,275 30,721 22,395 2,451 426 130 67,398 Tumbes 12,512 26,682 15,883 2,013 3,370 324 60,784 Ucayali 21,940 82,689 31,198 2,685 1,653 374 140,539 PERU 907,866 3,727,668 1,815,237 163,043 119,141 20,078 6,753,033 Source: Estadfsticas Basicas 1997. Ministerio de Educaci6n Note: Enrollment includes students in both formnal and nonformal education. 142 APPENDIX 2 TEACHER STATISTICS 143 Appendix 2.1: Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990-1997 Rates of change (%) Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 246,819 248,319 248,625 253,087 258,244 264,256 271,132 270,569 9.6% NONFORMAL 4,377 3,410 3,340 3,218 3,461 3,452 3,503 3,025 -30.9% Initial education 18,743 18,149 18,442 19,198 19,945 20,860 21,771 21,840 16.5% Formal 16,077 16,128 16,502 17,213 17,806 18,658 19,377 19,841 23.4% Nonformal 2,666 2,021 1,940 1,985 2,139 2,202 2,394 1,999 -25.0% Primary education 119,741 120,430 120,013 121,238 123,517 125,262 128,579 129,335 8.0% Minors 116,034 117,088 116,827 118,246 120,587 122,336 125,904 126,814 9.3% Formal 115,957 117,037 116,773 118,208 120,545 122,297 125,886 126,771 9.3% Nonformal 77 51 54 38 42 39 18 43 -44.2% Adults 3,707 3,342 3,186 2,992 2,930 2,926 2,675 2,521 -32.0% Formal 3,034 2,730 2,579 2,451 2,401 2,397 2,265 2,153 -29.0% Nonformal 673 612 607 541 529 529 410 368 -45.3% Secondary education 79,149 78,384 78,126 79,469 81,877 84,006 86,617 89,529 13.1% Minors 71,592 71,329 71,143 72,766 75,235 77,397 80,411 83,470 16.6% Formal 71,592 71,329 71,143 72,761 75,235 77,397 80,411 83,470 16.6% Nonformal 0 0 0 5 0 0 0 0 - Adults 7,557 7,055 6,983 6,703 6,642 6,609 6,206 6,059 -19.8% Formal 7,082 6,672 6,580 6,334 6,264 6,255 5,860 5,764 -18.6% Nonformal 475 383 403 369 378 354 346 295 -37.9% Tertiary nonuniversity 8,303 9,058 9,448 9,987 10,360 10,566 9,789 9,781 17.8% education University education 18,421 19,120 19,277 19,597 18,930 19,841 20,795 16,096 -12.6% Other 6,839 6,588 6,659 6,816 7,076 7,173 7,084 7,013 2.5% Special education 2,137 2,242 2,256 2,297 2,441 2,511 2,591 2,590 21.2% Formal 2,137 2,242 2,256 2,297 2,356 2,416 2,446 2,473 15.7% Nonformal 0 0 0 0 85 95 145 117 - Vocational education 4,702 4,346 4,403 4,519 4,635 4,662 4,493 4,423 -5.9% Formal 4,216 4,003 4,067 4,239 4,347 4,429 4,303 4,220 0.1% Nonformal 486 343 336 280 288 233 190 203 -58.2% Source: Ministry of Education 145 Appendix 2.2: Teachers in Formal and Nonformal Education (Broadly Grouped) in Public Insitutions by Level as Percentage of Total, 1990-1997 1990 1991 1992 1993 1994 1995 1996 1997 FORMAL Initial education 6.51% 6.49% 6.64% 6.80% 6.90% 7.06% 7.3% 7.3% Primary education 48.21% 48.23% 48.00% 47.67% 47.61% 47.19% 48.1% 47.6% Secondaryeducation 31.88% 31.41% 31.26% 31.25% 31.56% 31.66% 32.4% 33.0% Tertiary nonuniversity edu- 3.36% 3.65% 3.80% 3.95% 4.01% 4.00% 3.7% 3.6% cation University education 7.46% 7.70% 7.75% 7.74% 7.33% 7.51% 6.0% 5.9% Other 2.57% 2.51% 2.54% 2.58% 2.60% 2.59% 2.5% 2.5% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% 100.0% NONFORMAL Initial education 60.91% 59.27% 58.08% 61.68% 61.80% 63.79% 68.3% 66.1% Primary education 17.14% 19.44% 19.79% 17.99% 16.50% 16.45% 12.2% 13.6% Secondary education 10.85% 11.23% 12.07% 11.62% 10.92% 10.25% 9.9% 9.8% Other 11.10% 10.06% 10.06% 8.70% 10.78% 9.50% 9.6% 10.6% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% 100.0% Source: Ministry of Education 146 Appendix 2.3: Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 Rates of change (%) Levels and/or 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 modalities FORMAL 55,912 58,572 61,703 68,542 73,624 77,935 85,103 87,161 55.9% NONFORMAL 278 493 581 736 929 959 1,406 n.a. Initial education 4,426 4,763 5,237 6,736 7,952 8,744 10,081 10,931 147.0% Formal 4,392 4,736 5,224 6,720 7,939 8,720 10,021 10,895 148.1% Nonformal 34 27 13 16 13 24 60 36 5.9% Primary education 17,178 17,906 18,970 21,338 22,902 24,262 26,141 27,577 60.5% Minors 16,930 17,650 18,729 21,073 22,593 23,952 25,800 27,190 60.6% Formal 16,930 17,650 18,729 21,072 22,586 23,945 25,778 27,180 60.5% Nonformal 0 0 0 1 7 7 22 10 - Adults 248 256 241 265 309 310 341 387 56.0% Formal 217 202 188 169 159 156 143 163 -24.9% Nonformal 31 54 53 96 150 154 198 224 622.6% Secondary education 15,241 16,436 17,387 19,548 20,908 21,577 24,021 24,933 63.6% Minors 14,655 15,589 16,481 18,614 19,836 20,476 22,541 23,144 57.9% Formal 14,655 15,589 16,481 18,614 19,836 20,476 22,541 23,144 57.9% Nonformal 0 0 0 0 0 0 0 0 - Adults 586 847 906 934 1,072 1,101 1,480 1,789 205.3% Formal 391 456 417 326 329 348 404 440 12.5% Nonformal 195 391 489 608 743 753 1,076 1,349 591.8% Tertiary nonuniversity 5,927 6,085 6,610 7,162 7,806 8,497 9,731 9,867 66.5% education University education 9,158 9,599 9,504 9,716 9,871 10,345 10,842 9,699 5.9% Other 4,260 4,276 4,576 4,778 5,114 5,469 5,693 n.a. - Special education 307 371 399 423 460 516 561 531 73.0% Formal 307 371 399 423 460 516 561 531 73.0% Nonformal 0 0 0 0 0 0 0 0 - Vocational education 3,953 3,905 4j177 4.355 4,654 4,953 5,132 n.a. Formal 3,935 3,884 4,151 4,340 4,638 4,932 5,082 5,242 33.2% Nonformal 18 21 26 15 16 21 50 n.a. Source: Ministry of Education 147 Appendix 2.4: Teachers in Formal and Nonformal Education (Broadly Grouped) in Private Institutions by Level as Percentage of Total, 1990-1997 1990 1991 1992 1993 1994 1995 1996 1997 FORMAL Initial education 7.86% 8.09% 8.47% 9.80% 10.78% 11.19% 11.78% 12.5% Primary education 30.67% 30.48% 30.66% 30.99% 30.89% 30.92% 30.46% 31.4% Secondary education 26.91% 27.39% 27.39% 27.63% 27.39% 26.72% 26.96% 27.1% Tertiary nonuniversity education 10.60% 10.39% 10.71% 10.45% 10.60% 10.90% 11.43% 11.3% University education 16.38% 16.39% 15.40% 14.18% 13.41% 13.27% 12.74% 11.1% Other 7.59% 7.26% 7.37% 6.95% 6.92% 6.99% 6.63% 6.6% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% NONFORMAL Initial education 12.23% 5.48% 2.24% 2.17% 1.40% 2.50% 4.27% Primaryeducation 11.15% 10.95% 9.12% 13.18% 16.90% 16.79% 15.65% Secondary education 70.14% 79.31% 84.17% 82.61% 79.98% 78.52% 76.53% Other 6.47% 4.26% 4.48% 2.04% 1.72% 2.19% 3.56% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% Source: Ministry of Education 148 Appendix 2.5: Total Teachers in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public and Private Institutions by Level, 1990-1997 (percentage) Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 302,731 306,891 310,328 321,629 331.868 342,191 356,235 357,730 18.2% NONFORMAL 4,655 3,903 3,921 3,954 4,390 4,411 4,909 3,025 -35.0% Initial education 23,169 22.912 23,679 25,934 27,897 29,604 31,852 32,771 41.4% Formal 20,469 20,864 21,726 23,933 25,745 27,378 29,398 30,736 50.2% Nonformal 2,700 2,048 1,953 2,001 2,152 2,226 2,454 2,035 -24.6% Primary education 136,919 138,336 138,983 142,576 146,419 149,524 154,720 156,912 14.6% Minors 132,964 134,738 135,556 139,319 143,180 146,288 151,704 154,004 15.8% Formal 132,887 134,687 135,502 139,280 143,131 146,242 151,664 153,951 15.9% Nonformal 77 51 54 39 49 46 40 53 -31.2% Adults 3,955 3,598 3,427 3,257 3,239 3,236 3,016 2,908 -26.5% Formal 3,251 2,932 2,767 2,620 2,560 2,553 2,408 2,316 -28.8% Nonformal 704 666 660 637 679 683 608 592 -15.9% Secondary education 94,390 94,820 95,513 99,017 102,785 105,583 110,638 114,462 21.3% Minors 86,247 86,918 87,624 91,380 95,071 97,873 102,952 106,614 23.6% Formal 86,247 86,918 87,624 91,375 95,071 97,873 102,952 106,614 23.6% Nonformal 0 0 0 5 0 0 0 0 - Adults 8,143 7,902 7,889 7,637 7,714 7,710 7,686 7,848 -3.6% Formal 7,473 7,128 6,997 6,660 6,593 6.603 6,264 6,204 -17.0% Nonformal 670 774 892 977 1,121 1,107 1,422 1,644 145.4% Tertiary nonuniversity education 14,230 15,143 16,058 17,149 18,166 19,063 19,520 19,648 38.1% University education 27,579 28,719 28,781 29,313 28,801 30,186 31,637 25,795 -6.5% Other 11,099 10,864 11,235 11,594 12,190 12,642 12,777 7,013 -36.8% Special education 2,444 2,613 2,655 2,720 2,901 3,027 3,152 3,121 27.7% Formal 2,444 2,613 2,655 2,720 2,816 2,932 3,007 3,004 22.9% Nonformal 0 0 0 0 85 95 145 117 - Vocational education 8,655 8,251 8,580 8,874 9,289 9,615 9,625 4,423 -48.9% Formal 8,151 7,887 8,218 8,579 8,985 9,361 9,385 9,462 16.1% Nonformal 504 364 362 295 304 254 240 203 -59.7% Source: Ministry of Education 149 Appendix 2.6: Teacher-to-Student Ratio in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Public Institutions by Level, 1990-1997 Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 FORMAL 25 24 24 24 24 24 24 24 NONFORMAL 76 95 97 104 104 109 117 129 Initial education 37 39 39 40 40 40 42 42 Formal 26 26 26 27 27 26 27 27 Nonformal 109 143 149 154 155 158 161 184 Primary education 29 29 29 29 30 30 29 29 Minors 29 29 29 30 30 30 29 29 Formal 29 29 29 30 30 30 29 29 Nonformal 24 26 23 26 27 28 32 23 Adults 26 24 24 24 24 24 25 24 Formal 25 24 24 25 24 24 25 25 Nonformal 30 27 23 23 23 23 24 24 Secondary education 21 21 21 20 20 20 21 20 Minors 20 20 20 20 20 20 20 20 Formal 20 20 20 20 20 20 20 20 Nonformal Adults 28 27 26 25 25 25 25 25 Formal 28 27 26 25 25 25 25 26 Nonformal 24 27 23 23 23 23 22 21 Tertiary nonuniversity education 16 16 16 16 16 17 17 17 University education 16 16 16 13 13 12 12 13 Other 22 20 20 20 20 20 20 20 Special education 8 7 7 7 8 8 8 8 Formal 8 7 7 7 7 8 8 8 Nonformal 12 Vocational education 28 27 26 27 27 27 27 27 Formal 29 27 27 27 27 27 27 27 Nonformal 22 26 25 25 24 24 26 26 Source: Ministry of Education 150 Appendix 2.7: Teacher-to-Student Ratio in Formal and Nonformal Education (Disaggregated by Minors and Adults) in Private Institutions by Level, 1990-1997 Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 FORMAL 22 21 21 19 18 17 16 16 NONFORMAL 49 32 32 38 26 26 25 Initial education 21 21 20 18 16 16 15 14 Formal 21 21 20 18 16 16 15 14 Nonformal 88 67 118 143 109 70 72 96 Primary education 27 27 25 23 21 21 19 18 Minors 27 27 25 23 22 21 19 18 Formal 27 27 25 23 22 21 19 18 Nonformal 11 Adults 22 20 19 18 17 18 17 17 Formal 21 20 19 19 19 19 20 20 Nonformal 25 20 20 17 15 16 16 15 Secondary education 18 18 17 16 16 15 14 14 Minors 17 17 17 16 15 15 14 13 Formal 17 17 17 16 15 15 14 13 Nonformal Adults 30 25 26 32 25 24 22 22 Formal 22 20 21 22 20 19 19 18 Nonformal 44 31 30 37 27 27 24 23 Tertiary nonuniversity education 17 18 18 15 15 15 14 14 University education 17 17 17 15 13 12 12 15 Other 36 30 28 27 25 24 21 Special education 11 9 8 11 8 7 7 7 Fonnal 11 9 8 11 8 7 7 7 Nonformal Vocational education 38 31 30 29 27 26 23 Formal 38 31 30 29 27 26 23 21 Nonformal 70 5i 38 66 26 26 33 Source: Ministry of Education 151 Appendix 2.8: Teachers by Level and by Department, 1997 l'ertiary Vocational Initial Primary Secondary nonuniversity training Special 'FOTAL Amazonas 483 2,954 1,341 201 78 24 5,081 Ancash 1,072 7,030 4,750 505 200 91 13,648 Apurimac 581 3,439 1,467 136 59 30 5,712 Arequipa 722 4,469 3,613 587 88 140 9,619 Ayacucho 656 4,772 2,249 482 205 60 8,424 Cajamarca 1,423 9,822 4,449 557 123 83 16,457 Cusco 934 6,831 3,405 470 145 68 11,853 Huancavelica 491 3,407 1,380 147 76 24 5,525 Huanuco 586 4,555 2,022 264 117 29 7,573 Ica 723 2,976 3,182 518 163 74 7,636 Junin 938 7,250 5,725 781 168 88 14,950 La Libertad 957 6,249 5,190 773 192 85 13,446 Lambayeque 558 4,274 3,338 243 158 63 8,634 Lima - Callao 5,430 25,156 25,717 1,726 1,594 1,246 60,869 Loreto 1,215 6,228 2,759 242 215 63 10,722 Madre de Dios 155 661 382 59 14 11 1,282 Moquegua 230 886 768 125 62 18 2,089 Pasco 329 1,823 1,467 155 85 30 3,889 Piura 1,254 8,169 4,879 442 180 III 15,035 Puno 935 8,112 4,750 658 187 69 14,711 San Martin 902 4,578 2,230 250 62 49 8,071 Tacna 302 1,317 1,494 140 27 20 3,300 Tumbes 434 1,383 1,072 105 154 57 3,205 Ucayali 530 2,994 1,900 215 71 57 5,767 PERU 21,840 129,335 89,529 9,781 4,423 2,590 257,498 Source: Estadisticas Basicas 1997. Ministerino de Educaci6n Note: Includes teachers in both formal and nonformal education. 152 Appendix 2.9: Teacher-to-Student Ratio by Level and by Departnent, 1997 Tertiary Vocational Initial Primary Secondary nonuniversity training Special TOTAL Amazonas 41 26 17 17 31 5 25 Ancash 43 26 17 20 21 13 24 Apurnmac 40 28 20 19 17 5 27 Arequipa 43 26 20 19 25 6 24 Ayacucho 39 26 17 12 17 7 24 Cajamarca 45 29 19 20 28 7 27 Cusco 56 32 24 14 28 9 31 Huancavelica 48 30 19 1 7 18 5 28 Huanuco 36 32 21 17 27 7 29 Ica 39 29 19 15 35 7 25 Junin 40 28 18 14 31 7 24 La Libertad 39 31 18 16 30 8 26 Lambayeque 57 34 25 29 28 9 31 Lima - Callao 37 30 23 16 30 8 27 Loreto 35 29 21 19 26 5 27 Madre de Dios 25 25 19 15 17 4 22 Moquegua 27 19 15 15 28 6 18 Pasco 42 27 15 16 23 9 23 Piura 47 31 21 16 24 8 28 Puno 74 25 21 18 23 6 27 San Martin 30 27 20 16 21 6 25 Tacna 37 23 15 18 16 7 20 Tumbes 29 19 15 19 22 6 19 Ucayali 41 28 16 12 23 7 24 PERU 42 29 20 17 27 8 26 Source: Ministry of Education 153 Appendix 2.10: Student Enrollment and Teachers in Public Pedagogical Institutes by Region, 1997 Student Enrollment Teachers First semester (1997) Second semester (1997) with with no Number of pedagogical pedagogical pedagogical Department Total Males Females Total Males Females Total studies studies institutes Total Peru 71883 29494 42389 66955 27099 39856 3726 3086 640 138 Amazonas 1426 646 780 1378 634 744 68 61 7 3 Ancash 5324 2635 2689 5147 2512 2635 227 202 25 7 Apurimac 1584 778 806 1524 804 720 89 65 24 6 Arequipa 4319 1533 2786 3964 1375 2589 444 247 197 9 Ayacucho 3340 1506 1834 3237 1448 1789 166 125 41 7 Cajamarca 7157 3656 3501 6983 3577 3406 332 303 29 14 Callao 637 40 597 608 38 570 41 34 7 2 Cusco 3401 1630 1771 3296 1548 1748 251 223 28 11 Huancavelica 1261 608 653 1194 562 632 59 56 3 3 Huanuco 2333 985 1348 2241 954 1287 95 82 13 4 Ica 5830 1723 4107 3700 1164 2536 285 260 25 7 Junin 4849 1943 2906 4319 1746 2573 231 219 12 9 La Libertad 4822 1792 3030 4622 1685 2937 208 183 25 9 Lambayeque 2502 763 1739 2459 733 1726 127 106 21 3 Lima 5704 1071 4633 5491 1027 4464 303 253 50 9 Loreto 1525 657 868 1634 647 987 132 121 11 5 Madre de Dios 439 148 291 415 146 269 28 25 3 Moquegua 674 209 465 595 194 401 34 26 8 2 Pasco 851 476 375 1069 392 677 36 29 7 2 Piura 1689 460 1229 1782 496 1286 68 57 11 5 Puno 7249 4408 2841 6354 3513 2841 276 206 70 9 San Martin 2054 939 1115 2001 909 1092 97 90 7 6 Tacna 721 185 536 761 197 564 36 30 6 2 Tumbes 685 209 476 676 209 467 42 37 5 Ucayali 1507 494 1013 1505 589 916 51 46 5 2 Source: Ministry of Education, 1997. Censo Nacional de Educacion Tecnica y Pedagogica (Preliminary results) 154 Appendix 2.11: Student Enrollment and Teachers in Private Pedagogical Institutes by Region, 1997 Student Enrollment Teachers First semester (1997) Second semester (1997) with with no Number of pedagogical pedagogical pedagogical Department Total Males Females Total Males Females Total studies studies institutes Total Peru 45682 13907 31775 43422 13018 30404 3932 3339 593 180 Amazonas - - - - - - - - 0 Ancash 2148 924 1224 1993 840 1153 220 200 20 15 Apurimac - - - - - - - - - 0 Arequipa 3094 908 2186 2799 814 1985 204 185 19 10 Ayacucho 997 363 634 1078 413 665 70 35 35 3 Cajamarca 2379 908 1471 2226 813 1413 134 132 2 8 Callao 76 76 0 71 71 0 22 11 11 I Cusco 4075 1263 2812 3421 1038 2383 324 259 65 14 Huancavelica 129 37 92 115 35 80 17 15 2 Huanuco 1033 444 589 921 340 581 105 93 12 4 Ica 3450 1124 2326 3152 1017 2135 224 206 18 8 Junin 2814 906 1908 2871 911 1960 277 253 24 16 La Libertad 4771 1412 3359 4205 1249 2956 405 327 78 19 Lambayeque 3145 757 2388 2996 701 2295 278 244 34 10 Lima 11756 2809 8947 12452 3053 9399 1135 949 186 42 Loreto - - - - - - - 0 Madre de Dios - - - - - - - - 0 Moquegua 137 14 123 124 12 112 19 14 5 1 Pasco - - - - - - - - 0 Piura 1497 420 1077 1356 373 983 156 144 12 9 Puno 2371 1064 1307 2156 944 1212 223 173 50 11 San Martfn 459 183 276 343 129 214 19 12 7 2 Tacna 573 127 446 530 112 418 45 43 2 2 Tumbes 156 42 114 143 39 104 13 1 Ucayali 622 126 496 470 114 356 42 33 9 3 Source: Ministry of Education. 1997. Censo Nacional de Educacion Tecnica y Pedagogica, (Preliminary results). 155 Appendix 2.12: Changes in Student Enrollment and Student-to-Teacher Ratios in Public Pedagogical Institutes by Region, 1997 % Difference in Enrollment Student-to-Teacher Teachers with Pedagogical Average # of Students between First and Second Semester Ratio (total) Studies (15' and 2nd Semester) per Department Total Males Females lSt semester 2n semester as % of Total Pedagogical Insitute Total Peru -6.86% -8.12% -5.98% 19:1 18:1 82.82% 1006 Amazonas -3.37% -1.86% -4.62% 21:1 20:1 89.71% 935 Ancash -3.32% -4.67% -2.01% 23:1 23:1 88.99% 1496 Apurfmac -3.79% 3.34% -10.67% 18:1 17:1 73.03% 518 Arequipa -8.22% -10.31% -7.07% 10:1 9:1 55.63% 920 Ayacucho -3.08% -3.85% -2.45% 20:1 20:1 75.30% 940 Cajamarca -2.43% -2.16% -2.71% 22:1 21:1 91.27% 1010 Callao -4.55% -5.00% -4.52% 16:1 15:1 82.93% 623 Cusco -3.09% -5.03% -1.30% 14:1 13:1 88.84% 609 Huancavelica -5.31% -7.57% -3.22% 21:1 20:1 94.92% 818 Huanuco -3.94% -3.15% -4.53% 25:1 24:1 86.32% 1144 Ica -36.54% -32.44% -38.25% 20:1 13:1 91.23% 1361 Junin -10.93% -10.14% -11.46% 21:1 19:1 94.81% 1019 La Libertad -4.15% -5.97% -3.07% 23:1 22:1 87.98% 1049 Lambayeque -1.72% -3.93% -0.75% 20:1 19:1 83.46% 1654 Lima -3.73% -4.11% -3.65% 19:1 18:1 83.50% 1244 Loreto 7.15% -1.52% 13.71% 12:1 12:1 91.67% 632 Madre de Dios -5.47% -1.35% -7.56% 16:1 15:1 89.29% 854 Moquegua -11.72% -7.18% -13.76% 20:1 18:1 76.47% 635 Pasco 25.62% -17.65% 80.53% 24:1 30:1 80.56% 960 Piura 5.51% 7.83% 4.64% 25:1 26:1 83.82% 694 Puno -12.35% -20.30% 0.00% 26:1 23:1 74.64% 1511 San Martfn -2.58% -3.19% -2.06% 21:1 21:1 92.78% 676 Tacna 5.55% 6.49% 5.22% 20:1 21:1 83.33% 741 Tumbes -1.31% 0.00% -1.89% 16:1 16:1 88.10% 1361 Ucayali -0.13% 19.23% -9.58% 30:1 30:1 90.20% 1506 Source: Ministry of Education, 1997 Censo Nacional de Educacion Tecnica y Pedagogica (Prelimninary results). 156 Appendix 2.13: Changes in Student Enrollment and Student-to-Teacher Ratios in Private Pedagogical Institutes by Region, 1997 % Difference in Enrollment Student-to-Teacher Teachers with Pedagogical Average # of Students between First and Second Semester Ratio (total) Studies (15' and 2nd Semester) per Department Total Males Females I s" semester 2n semester as % of Total Pedagogical Institute Total Peru -4.95% -6.39% -4.31% 12:1 11:1 84.92% 495 Amazonas Ancash -7.22% -9.09% -5.80% 10:1 9:1 90.91% 276 Apurimac Arequipa -9.53% -10.35% -9.19% 15:1 14:1 90.69% 589 Ayacucho 8.12% 13.77% 4.89% 14:1 15:1 50.00% 692 Cajamarca -6.43% -10.46% -3.94% 18:1 17:1 98.51% 576 Callao -6.58% -6.58% - 3:1 3:1 50.00% 147 Cusco -16.05% -17.81% -15.26% 13:1 11:1 79.94% 535 Huancavelica -10.85% -5.41% -13.04% 8:1 7:1 88.24% 244 Huanuco -10.84% -23.42% -1.36% 10:1 9:1 88.57% 489 Ica -8.64% -9.52% -8.21% 15:1 14:1 91.96% 825 Junin 2.03% 0.55% 2.73% 10:1 10:1 91.34% 355 La Libertad -11.86% -11.54% -12.00% 12:1 10:1 80.74% 472 Lambayeque -4.74% -7.40% -3.89% 11:1 11:1 87.77% 614 Lima 5.92% 8.69% 5.05% 10:1 11:1 83.61% 576 Loreto Madre de Dios Moquegua -9.49% -14.29% -8.94% 7:1 7:1 73.68% 261 Pasco Piura -9.42% -11.19% -8.73% 10:1 9:1 92.31% 317 Puno -9.07% -11.28% -7.27% 11:1 10:1 77.58% 412 San Martin -25.27% -29.51% -22.46% 24:1 18:1 63.16% 401 Tacna -7.50% -11.81% -6.28% 13:1 12:1 95.56% 552 Tumbes -8.33% -7.14% -8.77% 12:1 11:1 84.62% 299 Ucayali -24.44% -9.52% -28.23%e 15:1 11:1 78.57% 364 Source: Ministry of Education, 1997. Censo nucional de Educacion Tecnica y Pedagogica, (Preliminary results). 157 APPENDIX 3 SCHOOL STATISTICS 159 Appendix 3.1: Public Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 Rates of change Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 40,683 41,176 41,591 40,648 43,479 44,308 45,260 45,564 12.0% NONFORMAL 13,885 14,328 14,566 15,073 16,045 17,562 23,536 19,199 38.3% Initial education 20,712 21,628 22,096 20,987 24,288 26,093 32,609 28,435 37.3% Formal 7,670 7,952 8,160 6,442 8,788 9,064 9,469 9,597 25.1% Nonformal 13,042 13,676 13,936 14,545 15,500 17,029 23,140 18,838 44.4% Primary education 26,896 26,814 26,862 27,309 27,586 27,947 28,263 28,385 5.5% Minors 25,769 25,756 25,805 26,326 26,616 26,981 27,440 27,596 7.1% Formal 25,718 25,717 25,767 26,302 26,593 26,963 27,434 27,580 7.2% Nonformal 51 39 38 24 23 18 6 16 -68.6% Adults 1,127 1,058 1,057 983 970 966 823 789 -30.0% Formal 696 703 716 680 676 675 620 602 -13.5% Nonformal 431 355 341 303 294 291 203 187 -56.6% Secondary education 5,464 5,495 5,575 5,743 5,909 6,064 6,184 6,230 14.0% Minors 4,550 4,620 4,704 4,901 5,067 5,231 5,403 5,476 20.4% Formal 4,550 4,620 4,704 4,900 5,067 5,231 5,403 5,476 20.4% Nonformal 0 0 0 1 0 0 0 0 Adults 914 875 871 842 842 833 781 754 -17.5% Formal 741 749 753 746 744 739 716 689 -7.0% Nonformal 173 126 118 96 98 94 65 65 -62.4% Tertiary nonuniversity education 283 334 369 395 402 409 403 415 46.6% University education 27 27 27 27 28 28 28 28 3.7% Other 1,186 1,206 1,228 1,260 1,311 1,329 1,309 1,270 7.1% Special education 310 307 318 330 354 359 380 356 14.8% Formal 310 307 318 330 332 336 332 327 5.5% Nonformal 0 0 0 0 22 23 48 29 Vocational education 876 899 910 930 957 970 929 914 4.3% Formal 688 767 777 826 849 863 855 850 23.5% Nonformal 188 132 133 104 108 107 74 64 -66.0% Source: Ministry of Education 161 Appendix 3.2: Public Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 1990 1991 1992 1993 1994 1995 1996 1997 Formal Initial education 18.85% 19.31% 19.62% 15.85% 20.21% 20.46% 20.92% 21.1% Primary education 64.93% 64.16% 63.67% 66.38% 62.72% 62.38% 61.98% 61.9% Secondary education 13.01% 13.04% 13.12% 13.89% 13.37% 13.47% 13.52% 13.5% Tertiary nonuniversity education 0.70% 0.81% 0.89% 0.97% 0.92% 0.92% 0.89% 0.9% University education 0.07% 0.07% 0.(6% 0.07% 0.06% 0.06% 0.06% 0.1 % Others 2.45% 2.61% 2.63% 2.84% 2.72% 2.71% 2.62% 2.6% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% i()00% Nonformal Initial education 93.93% 95.45% 95.67% 96.50% 96.60% 96.97% 98.32% 98,1% Primary education 3.47% 2.75% 2.60% 2.17% 1.98% 1.76% 0.89% .1% Secondary education 1.25%, 0.88% 0.81% 0.64 0/7 0.61(% 0.54% 0.28% 0.3% Others 1,35% 0.92% 0.91% 0.69% 0.81% 0.74% 0.52% 0.5% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% SourSce Ministry of Education 162 Appendix 3.3: Private Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 Rates of change Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 6,305 6,870 7,860 10,559 12,566 13,740 15,129 15,676 148.6% NONFORMAL 139 166 186 252 272 289 628 596 328.8% Initial education 2,056 2,257 2,527 3,746 4,535 4,995 5,728 5,837 183.9% Formal 1,979 2,196 2,484 3,672 4,489 4,936 5,461 5,632 184.6% Nonformal 77 61 43 74 46 59 267 205 166.2% Primary education 2,458 2,618 3,017 3,951 4,628 5,071 5,592 5,620 128.6% Minors 2,405 2,548 2,945 3,871 4,534 4,978 5,425 5,442 126.3% Formal 2,405 2,548 2,945 3,870 4,532 4,976 5,420 5,437 126.1% Nonformal 0 0 0 1 2 2 5 5 Adults 53 70 72 80 94 93 167 178 235.8% Formal 41 47 44 36 39 38 41 47 14.6% Nonformal 12 23 28 44 55 55 126 131 991.7% Secondary education 1,215 1,313 1,522 1,876 2,156 2,284 2,552 2,859 135.3% Minors 1,122 1,181 1.359 1,689 1,931 2,050 2,270 2,530 125.5% Formal 1,122 1,181 1,359 1,689 1,931 2,050 2,270 2,530 125.5% Nonformal 0 0 0 0 0 0 0 0 Adults 93 132 163 187 225 234 282 329 253.8% Formnal 49 57 55 58 59 65 63 74 51.0% Nonformal 44 75 108 129 166 169 219 255 479.5% Tertiary nonuniversity education 164 192 234 291 374 425 501 550 235.4% University education 19 19 22 24 24 24 29 29 52.6% Other 532 637 724 923 1,121 1,230 1,355 1,377 158.8% Special education 57 52 59 69 75 81 91 88 54.4% Formal 57 52 59 69 75 81 91 88 54.4% Nonformal 0 0 0 0 0 0 0 0 Vocational education 475 585 665 854 1,046 1,149 1,264 1,289 171.4% Formal 469 578 658 850 1,043 1,145 1,253 1,289 174.8% Nonformal 6 7 7 4 3 4 11 0 -100.0% Source: Ministy of Education 163 Appendix 3.4: Private Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 1990 1991 1992 1993 1994 1995 1996 1997 Formal Initial education 31.39% 31.97% 31.60% 34.78% 35.72% 35.92% 36.10% 35.9% Primary education 38.79% 37.77% 38.03% 36.99% 36.38% 36.49% 36.10% 35.0% Secondary education 18.57% 18.02% 17.99% 16.55% 15.84% 15.39% 15.42% 16.6% Tertiary nonuniversity education 2.60% 2.79% 2.98% 2.76% 2.98% 3.09% 3.31% 3.5% University education 0.30% 0.28% 0.28% 0.23% 0.19% 0.17% 0.19% 0.2% Others 8.34% 9.17% 9.12% 8.70% 8.90% 8.92% 8.88% 8.8% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% Nonformal Initial education 55.40% 36.75% 23.12% 29.37% 16.91% 20.42% 42.52% 34.4% Primary education 8.63% 13.86% 15.05% 17.86% 20.96% 19.72% 20.86% 22.8% Secondary education 31.65% 45.18% 58.06% 51.19% 61.03% 58.48% 34.87% 42.8% Others 4.32% 4.22% 3.76% 1.59% 1.10% 1.38% 1.75% 0.0% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% Source: Ministry of Education 164 Appendix 3.5: Total Public and Private Schools for Formal and Nonformal Education (Disaggregated by Minors and Adults) by Level, 1990-1997 Rates of change Levels and/or modalities 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 FORMAL 46,988 48,046 49,451 51,207 56,045 58,048 60,389 61,240 30.3% NONFORMAL 14,024 14,494 14,752 15,325 16,317 17,851 24,164 19,795 41.2% Initial education 22,768 23,885 24,623 24,733 28,823 31,088 38,337 34,272 50.5% Formal 9,649 10,148 10,644 10,114 13,277 14,000 14,930 15,229 57.8% Nonformal 13,119 13,737 13,979 14,619 15,546 17,088 23,407 19,043 45.2% Primary education 29,354 29,432 29,879 31,260 32,214 33,018 33,855 34,005 15.8% Minors 28,174 28,304 28,750 30,197 31,150 31,959 32,865 33,038 17.3% Formal 28,123 28,265 28,712 30,172 31,125 31,939 32,854 33,017 17.4% Nonformal 51 39 38 25 25 20 11 21 -58.8% Adults 1,180 1,128 1,129 1,063 1,064 1,059 990 967 -18.1% Formal 737 750 760 716 715 713 661 649 -11.9% Nonformal 443 378 369 347 349 346 329 318 -28.2% Secondary education 6,679 6,808 7,097 7,619 8,065 8,348 8,736 9,089 36.1% Minors 5,672 5,801 6,063 6,590 6,998 7,281 7,673 8,006 41.1% Formal 5,672 5,801 6,063 6,589 6,998 7,281 7,673 8,006 41.1% Nonformal 0 0 0 1 0 0 0 0 Adults 1,007 1,007 1,034 1,029 1,067 1,067 1,063 1,083 7.5% Formal 790 806 808 804 803 804 779 763 -3.4% Nonformal 217 201 226 225 264 263 284 320 47.5% Tertiary nonuniversity 447 526 603 686 776 834 904 965 115.9% education University education 46 46 49 51 52 52 57 57 23.9% Other 1,718 1,843 1,952 2,183 2,432 2,559 2,664 2,647 54.1% Special education 367 359 377 399 429 440 471 444 21.0% Formal 367 359 377 399 407 417 423 415 13.1% Nonformal 0 0 0 0 22 23 48 29 Vocational education 1,351 1,484 1,575 1,784 2,003 2,119 2,193 2,203 63.1% Formal 1,157 1,345 1,435 1,676 1,892 2,008 2,108 2,139 84.9% Nonformal 194 139 140 108 111 111 85 64 -67.0% Source: Ministry of Education 165 Appendix 3.6: Total Public and Private Schools for Formal and Nonformal Education (Broadly Grouped) by Level as Percentage of Total, 1990-1997 1990 1991 1992 1993 1994 1995 1996 1997 Forma Initial education 20.54% 21.12 % 21.52% 19.75% 23.69% 24.12% 24.72% 24,9% Primary education 61.42% 60.39% 59.60C/c 60.32% 56.81% 56.25% 55.50%"l 55,0% Secondaryeducation 13.75% 13.75% 13.89% 14.44% 13.92% 13.93% 14.00% 14.3% Tertiary nonuniversity education 0.95% 1.09% 1.22% 1.34% 1.38% 1.44% 1.50% 1,6% University education 0.10% 0.10% ().10% 0.10% 0.09% 0.09% 0.09% (0.1% Others 3.24% 3,55% 3.66% 4.0504 4.10% 4.18% 4.19% 4.2% TOTAI, 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% Nonformal Initial education 93.55% 94.78% 94.76% 95.39% 95.27% 95.73% 96.87% 96.2% Primary education 3.52% 2.88% 2.76% 2.43% 2.29% 2.05% 1.41% 1,7% Secondary education 1.55% 1.39% 1,53% 1.47% 1.62% 1.47%k 1,18% 1.6% Others 1.38% 0.96% 0.95% 0.70% 0.82% 0.75% 0.55% 0.5% TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.0% Source: Ministry of Education 166 APPENDIX 4 INDICATORS OF EQUITY AND EFFICIENCY 167 Appendix 4.1a: Rural Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education level Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Quintile Ql-poorest 40.61 97.21 74.34 26.23 68.52 49.85 91.94 75.04 29.21 67.72 45.71 94.59 74.70 27.77 68.11 Q2 54.19 97.15 80.32 20.78 68.87 43.02 96.34 78.27 27.21 66.66 48.13 96.77 79.24 23.79 67.76 Q3 53.39 97.35 83.86 26.44 70.99 57.85 97.17 91.87 46.37 78.81 55.52 97.26 88.22 36.87 75.07 Q4 60.47 99.12 81.03 28.79 68.87 55.89 98.72 85.15 51.27 78.94 58.09 98.89 83.29 37.92 74.08 Q5-fichest 69.42 100.00 88.98 44.40 75.91 59.58 100.00 88.50 45.09 72.09 63.99 100.00 88.79 44.72 74.16 ALL 50.86 97.62 79.89 27.42 69.73 50.88 95.46 81.60 36.87 71.55 50.87 96.53 80.78 31.98 70.65 source: Household Survey by Instituto Cuanto, 1997 Appendix 4.1b: Urban Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education level Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Quintile Ql-poorest 32.16 94.83 89.25 35.39 67.72 40.67 94.07 91.31 30.96 72.41 36.18 94.43 90.36 33.40 70.09 Q2 48.48 96.72 91.48 33.28 69.94 51.07 97.50 96.14 35.01 72.01 49.94 97.13 93.67 34.11 70.79 Q3 50.02 95.71 94.99 37.13 70.15 49.29 96.54 95.64 39.43 74.64 49.67 96.18 95.31 38.21 72.43 Q4 62.28 99.20 97.78 48.25 77.38 63.42 99.38 94.77 50.48 78.37 62.90 99.30 96.31 49.33 77.89 Q5-richest 62.43 99.20 94.39 70.04 82.98 66.14 98.72 98.05 64.73 81.48 64.29 98.97 96.19 67.35 82.24 ALL 51.15 97.17 93.83 46.58 73.82 54.53 97.21 95.18 46.79 75.90 52.90 97.19 94.50 46.48 74.87 Source: Household Survey by Instituto Cuanto, 1997 169 Appendix 4.1c: Rural Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary age group 3to 5 6 to ll 12 to 16 17 to 22 All 3 to 5 6 to ll 12 to 16 17 to 22 All 3 to 5 6 to ll 12 to 16 17 to 22 All Quintile Ql-poorest 4.83 66.76 20.59 1.62 33.39 9.34 56.05 17.73 2.45 28.90 7.28 61.39 19.15 2.02 31.11 Q2 7.07 67.68 29.82 0.86 32.73 8.87 70.19 23.78 1.76 31.75 8.03 68.90 26.65 1.29 32.24 Q3 13.42 64.91 24.30 3.88 34.38 14.84 67.07 41.75 2.28 39.19 14.13 66.03 33.48 3.03 36.88 Q4 26.06 65.69 31.80 8.51 35.76 6.18 70.58 44.96 10.64 43.45 16.65 68.45 39.03 9.37 39.67 Q5-richest 10.86 65.66 48.12 9.42 37.85 9.89 62.59 36.90 19.65 35.89 10.34 64.32 43.59 13.46 37.01 ALL 10.28 66.41 27.69 3.95 34.09 9.87 63.96 28.89 4.81 34.09 10.06 65.17 28.30 4.35 34.09 Source: Household Survey of Instituto Cuanto, 1997. Appendix 4.1d: Urban Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary age group 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All Quintile QI-poorest 3.57 70.20 34.46 11.45 35.20 6.66 62.05 40.29 7.43 37.04 4.98 65.70 37.48 9.52 36.14 Q2 9.24 71.12 56.27 9.70 39.83 15.94 71.36 55.96 12.52 42.44 13.01 71.25 56.13 11.04 41.14 Q3 14.23 75.38 57.37 19.78 43.76 6.70 64.11 50.33 14.27 40.01 10.81 69.04 53.85 17.26 41.88 Q4 12.64 70.78 64.75 23.52 46.91 22.28 72.50 66.56 18.48 48.20 18.00 71.70 65.65 21.07 47.57 Q5-richest 13.22 80.62 71.12 43.18 57.29 6.37 75.33 72.96 37.22 52.97 9.89 78.01 72.04 40.12 55.12 ALL 10.47 73.69 56.92 22.98 44.84 12.58 68.85 56.92 20.08 44.30 11.53 71.08 56.92 21.58 44.57-: Source: Household Survey of Instituto Cuanto, 1997. 170 Appendix 4.2a: Rural Public Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education level Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Quintile Ql-poorest 46.29 109.23 57.35 7.44 52.90 52.98 109.76 53.47 2.30 53.34 49.44 109.50 55.48 4.85 53.12 Q2 47.19 106.94 78.31 5.68 57.55 55.99 109.33 69.72 5.16 54.87 51.19 108.12 74.38 5.39 56.23 Q3 60.34 105.73 94.30 22.61 65.69 45.30 98.91 82.05 8.72 53.10 52.87 102.80 88.25 15.44 59.65 Q4 63.20 105.46 87.94 28.01 66.28 64.64 103.47 116.23 16.38 64.17 63.87 104.49 102.14 21.12 65.19 Q5-richest 56.94 127.91 69.84 28.74 54.63 116.22 117.62 80.34 26.36 68.57 84.34 121.21 75.65 27.67 61.99 ALL 50.12 107.99 71.83 12.91 57.50 55.05 107.88 69.66 6.94 55.35 52.43 107.94 70.79 9.77 56.43 Source: Household Survey by hnstituto Cuanto, 1997. Appendix 4.2b: Urban Public Gross Enrollment Ratio by Gender, Age and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All level -% % _ % _ % % _ % _ % % % _ % % Quintile Ql-poorest 58.18 101.04 80.92 12.99 58.07 53.46 105.45 72.75 16.43 55.18 55.89 103.25 77.36 14.80 56.66 Q2 69.45 98.76 94.99 15.83 59.51 37.80 95.45 103.84 14.56 56.59 53.83 97.23 99.20 15.21 58.11 Q3 46.74 90.82 91.43 26.12 57.64 38.36 103.46 86.62 25.09 56.39 42.63 96.14 88.82 25.55 57.01 Q4 46.49 78.33 90.28 37.10 56.04 43.21 77.98 88.90 34.65 54.30 44.91 78.16 89.54 35.77 55.14 Q5-richest 26.70 55.20 74.89 50.16 50.47 35.00 59.01 69.67 39.74 46.29 30.55 57.11 72.34 44.94 48.41 ALI, 48.29 84.10 87.20 32.85 56.03 40.48 86.36 85.93 29.11 53.47 44.51 85.16 86.56 30.89 54.75 Source: Household Survey by Instituto Cuanto, 1997. 171 Appendix 4.2c: Rural Public Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary age group 3 to 5 6 to ll 12 to 16 17 to 22 All 3 to 5 6 to ll 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All Quintile QI-poorest 9.74 83.50 37.40 5.78 39.50 12.47 85.25 34.03 2.30 40.49 11.03 84.40 35.77 4.03 39.99 Q2 10.86 84.43 58.24 5.09 45.19 15.09 89.46 49.91 5.16 44.18 12.79 86.91 54.44 5.13 44.69 Q3 15.08 91.54 56.54 15.11 51.15 21.50 76.12 59.63 8.72 41.67 18.27 84.90 58.07 11.81 46.60 Q4 19.13 92.23 62.66 23.54 54.72 13.31 90.85 72.01 14.36 49.74 16.43 91.55 67.35 18.11 52.15 Q5-richest 14.00 94.27 49.96 24.06 41.75 24.95 100.00 64.29 26.36 59.04 19.06 98.00 57.88 25.09 50.88 ALL 11.68 86.18 48.87 10.01 44.38 14.86 86.12 47.16 6.69 43.24 13.17 86.15 48.04 8.27 43.82 Source: Household Survey by Instituto Cuanto, 1997. Appendix 4.2d: Urban Public Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary age group 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to It 12 to 16 17 to 22 All % %EX 9% %S % 0/ %/ _ % % % _ % _ % _ % __6/ % Quintile Ql-poorest 14.61 83.95 64.97 11.69 48.26 7.37 86.94 54.98 16.43 45.51 11.10 85.44 60.62 14.18 46.91 Q2 17.15 83.92 72.56 15.60 49.72 4.57 77.69 69.79 11.73 42.75 10.94 81.04 71.24 13.71 46.38 Q3 7.53 79.99 75.01 19.76 48.69 14.03 84.73 69.86 17.75 45.21 10.72 81.98 72.21 18.65 46.93 Q4 11.75 69.13 68.00 25.59 44.56 11.58 69.79 74.78 28.08 46.48 11.67 69,45 71.61 26.94 45.55 Q5-richest 5.49 52.10 60.80 34.39 39.90 7.17 53,11 55.88 30.07 37.80 6.27 52.61 58.40 32.23 38.86 ALL 10.80 73.53 68.68 24.04 45.85 9.44 73.14 66.92 22.67 43.39 10.14 73.35 67.80 23.32 44.62 Source: liousehold Survey by Instituto Cuanto, 1997. 172 Appendix 4.3a: Rural Private Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education level Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Quintile Ql-poorest 0.36 0.23 0.35 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.19 0.11 0.1 0.00 0.08 Q2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Q3 0.00 0.00 5.32 0.00 1.03 2.63 4.48 0.00 0.00 1.35 1.31 1.93 2.69 0.00 1.18 Q4 0.00 0.00 2.92 0.00 0.71 0.00 1.20 0.00 0.00 0.35 0.00 0.59 1.45 0.00 0.52 Q5-richest 0.00 0.00 15.42 0.00 3.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.90 0.00 1.57 ALL 0.17 0.10 1.64 0.00 0.41 0.39 0.72 0.00 0.00 0.24 0.28 0.41 0.85 0.00 0.32 Source: Household Survey by Instituto Cuanto, 1997. Appendix 4.3b: Urban Private Gross Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education level Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Preschool Primary Secondary Tertiary All Quintile Ql-poorest 0.00 0.00 1.30 0.00 0.35 0.00 1.41 1.68 0.00 0.81 0.00 0.00 1.47 0.00 0.58 Q2 4.18 3.87 2.43 0.00 1.73 6.23 7.64 2.68 1.14 3.17 5.19 5.61 2.55 0.56 2.42 Q3 5.67 7.58 6.61 0.51 4.48 4.77 4.24 5.61 1.40 3.26 5.22 6.17 6.06 1.00 3.86 Q4 14.13 18.75 18.10 2.68 10.58 13.87 21.04 13.49 3.16 9.50 14.01 19.85 15.65 2.94 10.03 Q5-richest 15.40 39.03 28.85 25.42 26.50 27.94 41.37 34.31 24.84 28.95 21.21 40.20 31.52 25.13 27.71 ALL 8.76 14.43 12.17 8.24 10.05 11.11 16.61 12.20 8.05 10.44 9.90 15.45 12.19 8.14 10.24 Source: Household Survey by Instituto Cuanrto, 1997. 173 Appendix 4.3c: Rural Private Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary age group 3 to 5 6 to ll 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All Quintile Q I-poorest 0.00 0.23 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.04 Q2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Q3 0.0() 0.00 5.32 0.00 1.03 0.00 4.48 0.00 0.00 1.35 0.00 1.93 2.69 0.00 1.18 Q4 0.00 0.00 2.92 0.00 0.71 0.00 1.20 0.00 0.00 0.35 0.00 0.59 1.45 0.00 0.52 Q5-richest 0.00 0.00 10.28 0.00 2.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.60 0.00 1.05 Al L 0.00 0.10 1.33 0.00 0.34 0.00 0.72 0.00 0.00 0.24 0.00 0.41 0.69 0.00 0.29 Source: Household Survey by Instituto Cuanto, 1997. Appendix 4.3d: Urban Private Net Enrollment Ratio by Gender, Age, and Consumption Quintile, 1997 FEMALE MALE ALL Education Preschool Primary Secondary Tertiary Preschool Primary Secondary Tertiary Preschool Primary Secondary T'ertiary Age Group 3 to 5 6 to 11 12 to 16 17 to 22 All 3 to 5 6 to I1 12 to 16 17 to 22 All 3 to 5 6 to 11 12 to 16 17 to 22 All Y% % _ % % Y % / % / % %/ ° , ( %0/1° --' Quintile QI -pooresl (0.0 0.)0 1.3(1 0.00 0.35 0.0( 1 41 1.68 1) 0.81 0.00 0.7(0 1.47 0.00 0.58 Q2 0.00 3.28 2.43 0.00 1.56 (.00 6.96 2.68 1.14 2.98 (.00 4.98 2.55 0.56 2.24 Q3 3.36 7.58 5.71 0.51 4.29 2.59 4.24 4.21 1.41) 2.91 2.98 6.17 4.90 1.00 3.59 Q4 4.13 IX()7 14.27 2.68 9.59 1.16 20.37 11.22 316 8.83 2.70 19.17 12.65 2.94 9.20 Q5-richest 400 317.52 26.32 2! 73 24.00 10.79 38.56 27.58 19.57 24.56 7.15 38.05 26.93 20.65 24.28 [4. 6_ . -- I 3.86 1()5 7.15 9. 15 32.99 15.69 9.96 6.62 9.15 '2.Si 14.72 10.27 6.87 9'.15 Source:i Househo v ty hswilt) Clan. 1997 174 Appendix 4.4a: Simulation 1 - Distribution of Public Expenditure by Consumption Quintile, 1997 Quintile Preprimary Primary Secondary Nonuniversity University Total Enrollment Ql-poorest 278,534 1,069,606 282,818 13,932 8,273 1,653,164 Q2 238,944 976,715 399,087 26,144 12,178 1,653,068 Q3 178,470 817,723 398,787 29,280 38,153 1,462,413 Q4 161,117 586,191 430,363 48,503 53,660 1,279,834 Q5-richest 81,474 318,562 308,841 47,209 98,515 854,601 ALL 938,539 3,768,797 1,819,897 165,068 210,779 6,903.080 Per Student public expenditure with pension (US $) 175 201 260 324 1255 Distribution of public expenditure by quintile (US$) Ql-poorest 48,743,491 214,990,882 73,532,703 4,513,935 10,382,515 352,163,526 Q2 41,815,196 196,319,768 103,762,584 8,470,783 15,283,031 365,651,362 Q3 31,232,233 164,362,266 103,684,696 9,486,860 47,882,034 356,648,089 Q4 28,195,487 117,824,334 111,894,507 15,714,826 67,343,387 340,972.541 Q5-richest 14,257,919 64,030,948 80,298.729 15,295,628 123,636,678 297,519,902 ALL 164,244,325 757,528.197 473,173,220 53,482,032 264,527,645 1.712,955,419 Distribution of public expenditure by quintile (percentage) Ql-poorest 29.7% 28.4% 15.5% 8.4% 3.9% 20.6% Q2 25.5% 25.9% 21.9% 15.8% 5.8% 21.3% Q3 19.0% 21.7% 21.9% 17.7% 18.1% 20.8% Q4 17.2% 15.6% 23.6% 29.4% 25.5% 19.9% Q5-richest 8.7% 8.5% 17.0% 28.6% 46.7% 17.4% ALL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Source: Household Survey by Instituto Cuanto, 1997. 175 Appendix 4.4b: Simulation 2 - Distribution of Public Expenditure by Consumption Quintile, 1997 Quintile Preprimary Primary Secondary Nonuniversity University Total Enrollment QlI-poorest 278,534 1,069,606 282,818 13,932 8,273 1,653,164 Q2 238,944 976,715 399,087 26,144 12,178 1,653,068 Q3 178,470 817,723 398,787 29,280 38,153 1,462,413 Q4 161,117 586,191 430,363 48,503 53,660 1,279,834 Q5-richest 81,474 318,562 308,841 47,209 98,515 854,601 ALL 938,539 3,768,797 1,819,897 165,068 210,779 6,903,080 Per student public expenditure without pension (US $) 127 148 191 238 1084 Distribution of public expenditure by quintile (US$) Ql-poorest 35,826,466 158,018,299 54,046,536 3,317,742 8,970,493 260,179,536 Q2 30,734,169 144,295,029 76,265,499 6,226,026 13,204,539 270,725,262 Q3 22,955,691 120,806,265 76,208,252 6,972,842 41,370,077 268,313,128 Q4 20,723,683 86,600,886 82,242,463 11,550,397 58,184,686 259,302,115 Q5-richest 10,479,570 47,062,746 59,019,566 11,242,287 106,822,090 234,626,259 ALL 120,719,579 556,783,225 347,782,317 39,309,294 228,551,885 1,293,146,299 Distribution of public expenditure by quintile (percentage) Ql-poorest 29.7% 28.4% 15.5% 8.4% 3.9% 20.1% Q2 25.5% 25.9% 21.9% 15.8% 5.8% 20.9% Q3 19.0% 21.7% 21.9% 17.7% 18.1% 20.7% Q4 17.2% 15.6% 23.6% 29.4% 25.5% 20.1% Q5-richest 8.7% 8.5% 17.0% 28.6% 46.7% 18.1% ALL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Source: Household Survey by Instituto Cuanto. 1997. 176 Appendix 4.4c: Simulation 3 - Distribution of Public Expenditure by Consumption Quintile, 1997 Quintile Preprimary Primary Secondary Nonuniversity University TotW Enrollment Ql-poorest 278,534 1,069,606 282,818 13,932 8,273 1,653,164 Q2 238,944 976,715 399,087 26,144 12,178 1,653,068 Q3 178,470 817,723 398,787 29,280 38,153 1,462,413 Q4 161,117 586,191 430,363 48,503 53,660 1,279,834 Q5-richest 81,474 318,562 308,841 47,209 98,515 854,601 ALL 938,539 3,768,797 1,819,897 165,068 210,779 6,903,080 Per student public expenditure varying across quintile with pension (US $) Ql (0.7) 123 141 182 227 879 Q2 (0.85) 149 171 221 275 1,067 Q3 (1.0) 175 201 260 324 1,255 Q4 (1.15) 201 231 299 373 1,443 Q5 (1,3) 228 261 338 421 1,632 Distribution of public expenditure by quintile (US$) Ql -poorest 34,120,444 150,493,618 51,472,892 3,159,754 7,267,761 246,514,468 Q2 35,542,916 166,871,802 88,198,197 7,200,166 12,990,577 310,803,657 Q3 31,232,233 164,362,266 103,684,696 9,486,860 47,882,034 356,648,089 Q4 32,424,810 135,497,984 128,678,683 18,072,050 77,444,895 392,118,422 Q5-richest 18,535,294 83,240,232 104,388,348 19,884,316 160,727,681 386,775,872 ALL. 151,855,697 700,465,902 476,422,817 57,803,146 306,312,947 1,692,860,509 Distribution of public expenditure by quintile (percentage) Ql-poorest 22.5% 21.5% 10.8% 5.5% 2.4% 14.6% Q2 23.4% 23.8% 18.5% 12.5% 4.2% 18.4% Q3 20.6% 23.5% 21.8% 16.4% 15.6% 21.1% Q4 21.4% 19.3% 27.0% 31.3% 25.3% 23.2% Q5-richest 12.2% 11.9% 21.9% 34.4% 52.5% 22.8% ALL 100.0% 100.0% 100.0% 100.0% 100.0% 100,0% Source: Household Survey by Instituto Cuanto, 1997. 177 Appendix 4.4d: Simulation 4- Distribution of Public Expenditure by Consumption Quintile, 1997 Quintile Preprimary Primary Secondary Nonuniversity University Total Enrollment Ql-poorest 278,534 1,069,606 282,818 13,932 8,273 1,653,164 Q2 238,944 976,715 399,087 26,144 12,178 1,653,068 Q3 178,470 817,723 398,787 29,280 38,153 1,462,413 Q4 161,117 586,191 430,363 48,503 53,660 1,279.834 Q5-richest 81,474 318,562 308,841 47,209 98,515 854,601 ALL 938,539 3,768,797 1,819,897 165,068 210,779 6,9(03,080 Per student public expenditure varying across quintile without pension (US $) QI (0. 7) 90.04 103.41 133.77 166.70 759.02 Q2 (0.85) 109.33 125.57 162.44 202.42 921.67 Q3 (1.0) 128.63 147.74 191.10 238.14 1,084.32 Q4 (1.15) 147.92 169.90 219.77 273.86 1,246.97 Q5 k1.3) 167.21 192.06 248.43 309.58 1,409.62 Distribution of public expenditure by quintile (US$) QI-poorest 25,078,526 110,612,809 37.832,575 2,322,420 6,279,345 182,125,675 Q2 26.124,043 122,650,775 64,825,674 5,292.122 11,223,858 230.116,473 Q3 22,955,691 120,806.265 76,208,252 6,972,842 41,370.077 268,313,128 Q4 23,832,235 99,591,018 94,578,832 13,282,957 66,912,389 298,197,432 Q5-richest 13,623,441 61,181,570 76,725,436 14.614,972 138,868,717 305,014,137 ALI, 111,613,937 514,842,438 350,170,770 42,485,313 264,654,386 1,283,766,844 Distribution of public expenditure by quintile (percentage) QI-poorest 22.5% 21.5% 10.8% 5.5% 2.4% 14.2% Q2 23.4% 23.8% 18.5% 12.5% 4.2% 17.9% Q3 20.6% 23.5% 21.8% 16.4% 15.6% 20.9% Q4 21.4% 19.3% 27.0% 31.3% 25.3% 23.2% QS-richest 12.2% 11.9% 21.9% 34.4% 52.5% 23.8% ALL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Source: Household Survey by Instituto Cuanto, 1997. 178 Appendix 4.4e: Simulation 5 - Distribution of Public Expenditure by Consumption Quintile, 1997 Quintile Preprimary Primary Secondary Nonuniversity University Total 1,069,606Enrollment_ QI-poorest 278,534 1,069,606 282,818 13,932 8,273 1364 Q2 238,944 976,715 399,087 26,144 12,178 t,653,068 Q3 178,470 817,723 398,787 29.280 38,153 1.462,413 Q4 161,117 586.191 430,363 48,503 53,660 1,279,834 Q5-richest 81,474 318,562 308,841 47,209 98,515 854,601 ALL 938,539 3,768,797 1,819,897 165,068 210,779 6,903,080 Per student public expenditure (US$) 175 201 260 324 3500 Distribution of public expenditure by quintile (US$) Q 1-poorest 48,743,491 214,990,882 73,532,703 4,513,935 28,955,221 370,736,232 Q2 41,815,196 196,319,768 103,762,584 8,470,783 42,622,000 392,990,330 Q3 31,232,233 164,362,266 103,684,696 9,486.860 133,535,553 442,301,608 Q4 28,195,487 117,824,334 111,894.507 15,714,826 187,810,242 461,439,396 Q5-richest 14,257,919 64,030,948 80,298,729 15,295,628 344,803,484 518,686,708 ALL 164,244,325 757,528,197 473,173,220 53,482,032 737,726,500 2,186,154,274 Distribution of public expenditure by quintile (percentage) Ql-poorest 29.7% 28.4% 15.5% 8.4% 3.9% 17.0% Q2 25.5% 25.9% 21.9% 15.8% 5.8% 18.0% Q3 19.0% 21.7% 21.9% 17.7% 18.1% 20.2% Q4 17.2% 15.6% 23.6% 29.4% 25.5% 21.1% Q5-richest 8.7% 8.5% 17.0% 28.6% 46.7% 23.7% ALL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Source: Household Survey by Instituto Cuanto, 1997. 179 Appendix 4.5: Water and Sanitation in Public and Private Schools by Age and Income Group, 1994 6 to 11 Age Group 12 to 16 Age Group Only Only Only Only Quintile Both Neither drainage water ALL ALL Both Neither drainage water ALL ALL PUBLIC SCHOOLS Ql-poorest 61.34 22.94 1.62 14.08 100.00 643,397 69.32 17.31 0.65 12.70 100.00 480,969 Q2 70.35 17.28 1.52 10.83 100.00 549,940 76.66 13.67 1.46 8.19 100.00 478,102 Q3 75.52 14.33 0.95 9.18 100.00 390,693 83.89 7.74 0.61 7.73 100.00 436,591 Q4 77.55 11.24 1.41 9.78 100.00 359,016 87.56 8.07 0.70 3.65 100.00 397,799 Q5-richest 82.64 12.10 1.86 3.39 100.00 184,559 93.39 2.51 1.10 2.99 100.00 239,457 ALL 70.86 16.98 1.45 10.69 100.00 2,127,605 80.58 10.85 0.89 7.66 100.00 2,032,918 PRIVATE SCHOOLS Ql-poorest 61.55 10.74 - 27.70 100.00 11,126 59.28 - - 40.71 100.00 16,120 Q2 65.77 34.22 - - 100.00 18,760 77.38 18.45 - 4.16 100.00 17,788 Q3 100.00 - - - 100.00 34,901 100.00 - - - 100.00 21,718 Q4 98.32 - - 1.67 100.00 71,150 100.00 - - - 100.00 59,057 QS-richest 99.16 - - 0.83 100.00 143,370 99.17 - 0.82 - 100.00 182,423 ALL 95.31 2.72 - 1.95 100.00 279,307 95.93 1.10 0.50 2.45 100.00 297,106 Source: Household Survey by Instituto Cuanto, 1994 180 Appendix 4.6: Typology of Urban and Rural Schools, Based on School Characteristics, Infrastructure, Equipment, and Other Resources, Principals' and Teachers' Characteristics and Perceptions, 1994 C.E C.E C.E C.E C.E C.E C.E ANOVA Average number of cases very large large large medium medium small small F urban urban rural urban rural urban rural significance SCHOOL CHARACTERISTICS Average number of teachers in each school 61.92 24.84 21.00 12.75 13.13 4.59 3.85 0.000 62 93 10 83 8 49 34 Average number of teachers at the time of the survey (in the relevant shift) 29.10 13.83 14.50 9.08 12.13 4.45 3.85 0.000 59 94 12 86 8 49 34 Total average enrollment 1682.17 732.99 664.57 333.98 285.33 109.79 96.26 0.000 78 115 14 99 9 53 35 Average enrollment in the shift 908.05 466.20 526.77 264.74 275.22 108.19 96.26 0.000 58 98 13 96 9 53 35 Average number of students per class 37.45 34.43 39.10 28.61 33.94 24.62 25.32 0.000 56 96 13 93 9 52 34 Average number of students in the observed 2nd grade class 32.17 29.77 26.45 24.91 28.00 15.48 12.94 0.000 58 91 11 80 7 48 32 INFRASTRUCTURE, EQUIPMENT AND OTHER RESOURCES % of schools with own installations 97.44% 85.96% 100.00% 71.13% 100.00% 83.02% 97.14% 0.000 78 114 14 97 9 53 35 % of schools with telephone 17.95% 12.17% 0.00% 6.06% 0.00% 0.00% 0.00% 0.001 78 115 14 99 9 53 35 % of schools with electricity 98.70% 97.35% 71.43% 88.66% 55.56% 54.72% 29.41% 0.000 77 113 14 97 9 53 34 % of schools with a room for teachers 64.94% 46.90% 21.43% 44.33% 0.00% 11.32% 11.76% 0.000 77 113 14 97 9 53 34 % of schools with a management office 75.32% 52.21% 28.57% 38.14% 11.11% 9.43% 17.65% 0.000 77 113 14 97 9 53 34 % of schools with an office of the principal 96.10% 91.15% 92.86% 95.88% 100.00% 64.15% 88.24% 0.000 77 113 14 97 9 53 34 % of schools with a library 80.52% 56.64% 42.86% 51.55% 22.22% 15.09% 26.47% 0.000 77 113 14 97 9 53 34 % of schools with a gym or sports area 70.13% 62.83% 57.14% 43.30% 0.00% 15.09% 35.29% 0.000 77 113 14 97 9 53 34 % of schools with classrooms with brick or cement walls 84.42% 68.70% 50.00% 60.82% 11,11% 43.40% 20.59% 0.000 77 115 14 97 9 53 34 % of schools with classrooms with floors (as opposed to dirt floor) 97.37% 99.12% 100.00% 94.85% 100.00% 92.45% 97.06% 0.319 76 113 14 97 9 53 34 % average of visited classes with complete walls 60.68% 60.40 56.48 66.67 33.33 52.06 60.61 0.342 71 111 12 95 9 39 22 181 Appendix 4.6: (continued) CY C.E C.E CYE C.E C.E C.E very large large large medium medium small small urban urban rural urban rural urban rural % average of visited classes with walls without cracks 99.43 99.26 90.89 97.04 97.22 95.01 97.06 0.042 75 112 14 97 9 53 34 % average of visited classes with roof 90.28 89.35 77.38 83.35 51.28 64.81 61.51 0.000 75 112 14 96 9 53 34 % average of visited classes with roof in good condition 99.51 98.87 100.00 98.30 97.22 99.25 99.58 0.734 75 112 14 97 9 53 34 % average of visited classes with glass in the windows 88.76 85.72 72.49 77.18 53.83 64.51 62.88 0.000 75 112 14 96 9 53 34 % average of visited classes without broken window glass 83.89 81.07 66.44 68.27 79.80 67.21 60.58 0.000 75 112 14 96 9 53 34 % average of visited classes with electricity 78.80 59.88 58.83 61.30 25.16 52.00 36.81 0.000 75 112 14 96 9 53 34 %average of latrines that work in observed schools 75.90 76.69 38.57 79.01 27.78 52.25 31.87 0.000 75 112 14 96 9 53 34 % average of classrooms in use 94.81 92.56 93.92 92.24 92.95 87.22 89.67 0.281 77 113 14 96 9 52 34 % average of students in observed 3rd grades who have text 17.71 15.80 10.18 10.45 1.57 4.48 4.28 0.000 58 91 11 82 7 48 32 % of teachers of 2ld grade with text books 100.00% 93.27% 91.67% 98.89% 100.00% 95.65% 90.91% 0.131 62 104 12 90 8 46 33 % of teachers of 2nd grade who have maps or other visual materials 86.89% 83.33% 91.67% 86.52% 87.50% 63.04% 69.70% 0.008 61 102 12 89 8 46 33 % of teachers of 2'd grade who have a slide projector 5.00% 2.00% 0.00% 5.88% 0.00% 0.00% 0.00% 0.362 60 100 11 85 7 44 32 % of CE with maps or posters on the class walls 72.41% 72.92% 36.36% 65.88% 71.43% 68.75% 71.88% 0.321 58 96 11 85 7 48 32 Average number of visits made by USE to the teacher's class in the two last years 0.34 0.51 0.67 0.55 0.50 1.29 0.76 0.028 61 103 12 86 8 38 25 Average number of visits by USE to the school in the last year 1.34 1.08 0.92 1.11 0.43 1.33 0.52 0.117 56 93 12 83 7 46 33 % of directors that consider MED resources sufficient 2.63% 7.96% 0.00% 7.22% 0.00% 2.04% 0.00% 0.237 76 113 14 97 8 49 34 % of directors who consider that total CE resources are sufhicient 3.85% 5.31% 0.00% 10.20% 0.00% 3.77% 2.94% 0.358 78 113 14 98 9 53 34 Annual APAFA Average Expenditure in CE (Soles) 11735.42 4813.42 2204.55 2797.93 422.89 431.29 278.06 0.000 65 104 11 81 9 48 32 182 Appendix 4.6: (continued) C.E C.E C.E C.E C.E C.E C.E CHARACTERISTICS OF THE DIRECTOR very large large large medium medium small small urban urban rural urban rural urban rural Average years of experience as a director 5.92 6.18 6.71 6.70 6.67 5.32 5.31 0.879 78 115 14 99 9 53 35 Average of years working in the actual CE 11.00 8.18 8.79 8.71 6.00 4.92 4.46 0.000 78 115 14 99 9 53 35 % of appointed directors 46.75% 50.88% 28.57% 41.24% 33.33% 26.42% 20.00% 0.006 77 114 14 97 9 53 35 % of directors with university education 58.97% 52.63% 57.14% 61.86% 11.11% 54.72% 51.43% 0.134 78 114 14 97 9 53 35 % of directors with academic degree or professional certificate 94.87% 97.35% 92.86% 94.90% 100.00% 90.57% 74.29% 0.000 78 113 14 98 9 53 35 % of directors formed in formation programs 88.46% 92.86% 85.71% 92.78% 66.67% 69.81% 68.57% 0.000 Regular 78 112 14 97 9 53 35 Average number of training courses attended 5.85 6.77 6.93 5.68 3.56 3.74 3.60 0.066 78 113 14 97 9 53 35 Average age of directors 48.01 46.62 44.64 46.77 38.56 40.29 36.31 0.000 76 115 14 98 9 52 35 % of female directors 42.31% 40.87% 0.00% 44.44% 11.11% 54.72% 45.71% 0.007 78 115 14 99 9 53 35 % of directors satisfied or very satisfied with having decided to become a teacher 94.87% 92.11% 100.00% 94.95% 100.00% 94.34% 88.57% 0.664 78 114 14 99 9 53 35 CHARACTERISTICS OF 2ND GRADE TEACHERS Average years of experience as a teacher 10.58 10.49 10.00 10.77 10.25 9.20 8.67 0.630 62 104 12 90 8 46 33 Average years of permanency in the actual CE 5.85 4.41 5.08 5.22 2.25 3.26 3.61 0.000 62 104 12 89 8 46 33 % of appointed teachers 83.87% 83.65% 83.33% 87.64% 87.50% 82.22% 90.00% 0.952 62 104 12 89 8 45 30 % of teachers with university studies 59.68% 49,04% 33.33% 66.67% 14.29% 57.78% 50.00% 0.024 62 104 12 90 7 45 32 % of teachers with academic degree or professional certificate 80.65% 80.77% 83.33% 73.33% 85.71% 73.33% 50.00% 0.022 62 104 12 90 7 45 32 % of teachers formed in formation programs 85.25% 85.58% 41.67% 76.40% 57.14% 64.44% 53.13% 0.000 Regular 61 104 12 89 7 45 32 Average number of training courses attended 8.48 7.33 6.67 8.59 6.57 6.28 6.35 0.637 56 99 12 81 7 36 23 Average teacher's age 35.84 34.65 35.92 36.22 31.75 35.09 34.61 0.646 62 104 12 89 8 45 33 183 Appendix 4.6: (continued) C.E C.E C.E C.E C.E C.E C.E very large large large medium medium small small urban urban rural urban rural urban rural % of female teachers 77.42% 83.65% 66.67% 77.78% 25.00% 78.26% 60.61% 0.002 62 104 12 90 8 46 33 % of teachers satisfied or very satisfied with having decided to become teachers 90.32% 90.38% 91.67% 90.00% 87.50% 89.13% 93.94% 0.995 62 104 12 90 8 46 33 Total income for working teachers 466.82 499.73 476.67 520.60 444.00 465.00 474.88 0.444 62 104 12 88 8 45 33 % average of teaclhers that the director would consider to contract 59.74 65.79 69.25 65.79 56.76 67.66 68.94 0.775 again for this shift 54 89 12 83 8 49 33 % average of teachers that was finally terminated of those who 27.09 33.65 16.67 42.26 33.33 33.33 25.00 0.698 the director intended to tcrminate 39 51 8 42 6 9 8 % of directors who rely on the teacher's capacity of his school 94.87% 93.91% 92.86% 93.94% 88.89% 100.00% 85.29% 0.207 78 115 14 99 9 52 34 % of teachers that believe that salary problems don't affect 29.51% 36.89% 41.67% 30.34% 25.00% 39.47% 28.00% 0.820 their good performance 61 103 12 89 8 38 25 DIRECTORS' PERCEPTION ABOUT CLIMATE AND MANGEMENT OF THE SCHOOL %of directors that consider that there are no serious problems 69.23% 64.35% 57.14% 72.45% 88.89% 81.13% 82.86% 0.106 of indiscipline between teachers and students 78 115 14 98 9 53 35 % of directors who think that CE provides an adequate 96.15% 77.39% 71.43% 67.68% 55.56% 56.60% 51.43% 0.001 environment for students to study 78 115 14 99 9 53 35 % average of teachers commended or motivated this year 9.74 6.30 2.92 4.26 3.00 2.20 1.56 0.000 78 114 13 99 9 51 34 % of directors who think that students come to teachers for advice in 97.44% 93.91% 100.00% 100.00% 100,00% 92.31% 88.24% 0.040 pedagogical matters 78 115 14 99 9 52 34 % of directors that normally seek the teachers' opinion before 97.44% 96.52% 100.00% 96.97% 100.00% 100.00% 100.00% 0.732 taking important decisions pertaining to the school 78 115 14 99 9 52 34 % of directors that usually agree with the teachers in the pedagogical 52.56% 57.39% 42.86% 61.62%(, 88.89% 65.38% 64.71% 0.247 decisions that they make 78 115 14 99 9 52 34 % of directors who believe they can avoid the teachers' late arrival or absence 33.77% 41.96% 61.54% 36.73% 33.33% 21.15% 32.35% 0.094 77 112 13 98 9 52 34 % of directors that think that teachers back them up in what they do 97.33% 96.49% 100.00% 97.98% 100.00% 96.15% 100.00% 0.881 75 114 14 99 9 52 34 % of directors that think there is not much conflict among teachers in 66.23% 66.37% 64.29% 82.83% 77.78% 92.31% 97.06% 0.000 his or her school 77 113 14 99 9 52 34 % of directors that think that parents have the capacity to know what is 23.38% 22.61% 21.43% 21,21% 22.22% 15.09% 17.14% 0.932 best for their children's education 77 115 14 99 9 53 35 % of directors who think they can make important changes 93.59% 93.86% 92.86% 90,91% 100.00% 100.00% 100.00% 0.210 in their schools 78 114 14 99 9 53 35 184 Appendix 4.6: (continued) C.E C.E C.E C.E C.E C.E C.E Very Large Large Large Medium Medium Small Small Urban Urban Rural Urban Rural Urban Rural % of directors who think they are able to decide how to 85.90% 95.65% 92.86% 93.94% l0O.00'% 92.45% 100.00% 0.083 get funding for their schools 78 115 14 99 9 53 35 % of directors who think they can select the teachers 66.67% 58.26% 50.00% 55.56% 66.67%o 50.94% 48.57% 0.478 78 115 14 99 9 53 35 % of directors who think they can penalize the staffs absenteeism 93.51% 96.49% 92.86% 96.94% 100.001o 94.23% 91.43% 0,762 77 114 14 98 9 52 35 % of directors who think they can redistribute the number of hours 80.77% 78.26% 85.71% 80.61% 88.89% 83.02% 85.71% 0.940 assigned to each class 78 115 14 98 9 53 35 % of directors who think they can modify the dates of start and close 48.72% 44.35% 42.86% 39.39% 55.56% 50.94% 51.43% 0.760 of the school year 78 115 14 99 9 53 35 % of directors who think they can decide what goods the school 85.90% 93.04% 92.86% 87.88% 100.00% 90.57% 88.57% 0.643 could purchase 78 115 14 99 9 53 35 PERCEPTION OF TEACHERS OF 2ND GRADE OF PRIMARY ON THE CLIMATE AND MANAGEMENT OF THE SCHOOL % of teachers who think they have libertyto be able to introduce 88.71% 92.31% 75.00% 87.64% 100.00% 86.84% 76.00% 0.210 innovations 62 104 12 89 8 38 25 % of teachers who think the director consults their opinion 91.94% 92.31% 83.33% 91.01% 100.00% 86.84% 87.50% 0.810 for important decisions 62 104 12 89 8 38 24 % of teachers who think the director contributes to pedagogical 33.87% 32.04% 25.00% 35.23% 25.00% 23.68% 20.83% 0.752 enhancement 62 103 12 88 8 38 24 % of teachers that think that parents and teachers coincide on 82.26% 81.73% 83.33% 71.91% 100.00% 71.05% 64.00% 0.150 what is best for their children 62 104 12 89 8 38 25 % of teachers who don't think there are many conflicts among teachers 66.13% 66.02% 41.67% 71.11% 50.00% 91.11% 84.38% 0.002 in their schools 62 103 12 90 8 45 32 % of teachers who think that the director finds ways to stimulate 75.81% 79.61% 66.67% 79.78% 71.43% 71.05% 66.67% 0.714 or recognize their good performance 62 103 12 89 7 38 24 % of teachers who think the director is always available to counsel or 96.77% 91.35% 66.67% 94.38% 75.00% 94.74% 91.67%1 0.008 advise on pedagogical matters 62 104 12 89 8 38 24 % of teachers who think the director is a leader of those who work 70.49% 78.85% 66.67% 82.76% 62.50% 81.58% 72.00% 0.450 in his or her school 61 104 12 87 8 38 25 % of teachers who report that the updating or training in their school 30.65% 24.04% 8.33% 19.10% 0.00% 18.42% 8.33% 0.125 185 Appendix 4.6: (continued) C.E C.E C.E C.E C.E C.E C.E very large large large medium medium small small urban urban rural urban rural urban rural takes place with adequate frequency 62 104 12 89 8 38 24 % of teachers that report that pedagogical material in their school 19.67% 18.27% 0.00% 15.73% 0.00% 10.53% 8.00% 0.325 is distributed with adequate frequency 61 104 12 89 8 38 25 PEDAGOGICAL AND EFFICIENCY ASPECTS % of students of the period who graduated in 1993 93.68 92.27 91.70 92.35 85.12 87.15 83.38 0.000 53 94 13 94 9 51 33 % average of students of the period that attend in a typical day 96.02 95.45 95.62 93.36 93.85 91 .91 85.86 0.000 of classes 57 96 13 92 9 53 34 % average of students of the period that stay in the school 96.96 95.95 94.63 94.44 92.88 91 .22 91.22 0.000 since the beginning of the school year 54 93 13 96 9 53 35 % average of students of a section that the teacher thinks will 71.85 78.83 67.25 79.44 69.00 76.37 66.82 0.005 finish primary education 62 104 12 90 8 46 33 % average of students of a section that the teacher thinks will 71.95 72.98 49.33 114.09 51.25 77.20 39.82 0.162 finish secondary education 62 104 12 90 8 46 33 % of teachers of 2nd grade that think that the official curriculum 53.23% 38.24% 25.00% 46.67% 0.00% 28.26% 24.24% 0.004 is good 62 102 12 90 8 46 33 % ofteachers of 2nd grade that think that the official curriculum is not 29.03% 34.31% 58.33% 31.11% 87.50% 54.35% 72.73% 0.000 adequate to the regional and/or local realities 62 102 12 90 8 46 33 % average of students who comply with their homework in the 79.98 74.76 66.83 75.48 72.50 68.78 71.19 0.106 2nd grade teacher's opinion 62 102 12 90 8 46 32 %average of the official math curriculum of the 2nd grade which 85.98 83.94 79.50 84.22 70.63 81.13 73.70 0.000 comes to be completed within the school year 61 102 10 89 8 47 33 % average of the official language curriculum of the 2nd grade which 85.25 84.61 78.18 83.74 71.25 79.00 73.45 0.000 comes to be completed within the school year 61 103 11 89 8 47 33 Average number of weekly hours wasted in the period due to late 12.43 6.70 13.46 5.51 2.50 3.07 2.94 0.000 arrival or absence of teachers 68 101 14 90 8 47 33 Average annual number of days that the school had to close, 3.32 2.39 3.08 1.62 6.00 2.92 5.59 0.000 excluding official holidays 78 114 12 99 9 53 34 Source: Analysis of MED's Survey of Public Schools in Lima/Callao and Cusco, 1994 by Patricia Arregui and Sandro Marcone Notes: C.E. Very Large: more than 1,000 students C.E. Large: between 501 and 1,00() students C.E. Middle: between 201 and 500 students C.E. Small: between 30 and 200 students C.E. Urban: schools located in districts with more than 10,000 inhabitants or with less than 50% of the population economically active dedicated to agriculture. C.E. Rural: schools located in districts with less than 10,000 inhabitants or with 50%, or more of population economically active dedicated to agriculture. 186 Appendix 4.7: Internal Efficiency of Public Education (Primary and Secondary) in Peru (Average 1994 to 1996) Rates of Transition in Public Schools (average:1994195-1996/97) Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade 1 2 3 4 5 6 7 8 9 10 11 Repetition 0.17 0.17 0.15 0.10 0.08 0.04 0.14 0.08 0.07 0.05 0.04 Promotion 0.79 0.80 0.82 0.87 0.88 0.93 0.81 0.85 0.86 0.89 0.92 Drop out 0.04 0.03 0.03 0.03 0.04 0.03 0.05 0.06 0.06 0.06 0.05 Flow of a ReconatrLcted Cohort Year Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade Graduated Repetition Drop out 1 2 3 4 5 6 7 8 9 10 11 from G-1 1 by year by year 1 1000 1000 44 2 167 790 957 32 3 28 266 630 924 31 4 5 67 303 517 892 31 5 1 15 97 299 449 861 30 6 4 26 109 296 395 830 28 7 1 7 32 119 277 367 803 34 8 2 9 38 116 307 296 768 39 9 3 11 38 150 273 253 728 42 10 4 12 56 144 252 218 686 40 11 4 19 57 142 230 195 179 647 36 12 7 20 60 135 212 195 434 22 13 1 8 21 59 129 118 218 10 14 2 9 21 58 53 90 5 15 3 9 21 19 33 1 16 3 9 8 12 17 3 3 3 Total 575 9886 425 Indicators of Internal Efficiency Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade Grade Graduated Total 1 2 3 4 5 6 7 8 9 10 11 from G-11 student-year Student-year by grade 1201 1143 1065 969 917 842 907 800 740 675 627 9886 Promotion by grade 1000 949 912 873 841 807 783 732 683 639 603 576 8398 Desertion by grade 52 36 37 33 35 26 48 49 46 37 30 0 429 Repetition by grade 200 195 154 95 75 33 124 67 55 36 24 0 1034 187 Appendix 4.7: (continued) Primary education Percentage of students who reached Grade 6 81% Percentage of students who reached Grade 6 without repeating 40% Percentage of students who graduated to Grade 7 78% Total percentage of drop out after primary education 22% Secondary education Percentage of students who reached Grade 11 60% Percentage of students who reached Grade 11 without repeating 20% Percentage of which graduated from Grade 11 58% Total percentage of drop-outs after secondary education 43% Average number of years spent in the public education system Entire cohort 9.9 Those enrolled in Grade 6 7.3 Those enrolled in Grade I1 13.3 Dropouts 6.7 Student-years spent In primary education 7.6 In secondary education 16.4 Input-output ratio In primary education 1.3 In secondary education 1.4 In secondary education 1.4 Source: World Bank analysis of promotion repetition and dropout rates of MED. 188 APPENDIX 5 INTERNATIONAL COMPARISON OF BETWEEN-SCHOOL VARIATION IN ACHIEVEMENT 189 Appendix 5.1: International Comparison of Between-School Variation in Achievement by Selected Countries Country (year of study) % between schools With pretest After controlling for SES Peru (1998) Math Grade 4 58 Colombia (1992) Spanish Grade I 18 Spanish Grade 3 29 Egypt (1992) Math Grade 5 60 59 Science Grade 5 41 41 Arabic Grade 5 53 51 Honduras (1992) Reading Grade 1 33 Pakistan (1992) Math Grade 4 52 51 Science Grade 4 52 53 Math Grade 5 49 52 Science Grade 5 50 50 Thailand (1991a) Math Grade 3 31 Thai Grade 3 35 Thailand (1991b) Overall Grade 6 48 Zimbabwe (1988) English Language Grade 7 42 English Literature Grade 7 42 Math Grade 7 42 Zimbabwe (1991) English Grade 7 47 Math Grade 7 60 Zimbabwe (1992) English Grade 7 56 47 Math Grade 7 74 36 Botswana (1992) English Form II or Grade 8 12 Math Form II or Grade 8 16 Brazil (1990) Math Grade 9. 62 Portuguese Grade 9. 36 Egypt (1992) Math Grade 8 42 40 Science Grade 8 35 32 Arabic Grade 8 29 26 English Grade 8 43 . 39 191 Appendix 5.1: (continued) Philippines (1991) Math Grade 8 52 Science Grade 8 43 Thailand (1989) Math Grade 8 32 11 Zimbabwe (1988) Grade 8 English Form IV or Grade 10 42 27 21 English Literature Form IV 48 26 26 Math Form IV 44 23 18 Zimbabwe (1991) English Form 11 or Grade 8 65 47 Math Form 11 or Grade 8 61 51 . Source: Table courtesy of Abby Rubin Riddell, 1993 data. 192 Appendix 5.2: International Comparison of Between-School Variance in IEA International Study on Reading, 1990 Participating country 9-year-old level 14-year-old level Belgium (French) 16 40 Botswana 16 Canada (British Columbia) 21 27 Cyprus 13 15 Denmark 12 8 Finland 8 2 France 14 35 Germany (former West) 13 49 Germany (former East) 15 10 Greece 35 22 Hong Kong 33 43 Hungary 21 23 Iceland 9 8 Indonesia 37 Ireland 16 48 Italy 33 28 Netherlands 13 50 New Zealand 19 41 Nigeria * Norway 5 6 Philippines 61 Portugal 29 27 Singapore 22 52 Slovenia 10 12 Spain 18 22 Sweden 9 8 Switzerland 10 48 Thailand 66* Trinidad and Tobago 32 58 United States 19 42 Venezuela 33 29 Zimbabwe 46* Source: Andreas Schleicher and Jean Yip, Indicators of Between-School Differences in Reading Achievement, 1994, mimeo. * Nigeria was excluded from the calculation because it did not provide sufficient information on the identification of schools and also because of insufficient sampling information. Thailand and Zimbabwe also did not meet the IEA sampling standards. 193 APPENDIX 6 PUBLIC EXPENDITURE ON EDUCATION 195 Appendix 6.1: Gross Domestic Product, Total Government Expenditure, and Total Public Expenditure on Education, 1970-1997 (Million Soles in current prices) Public expen- Public Central diture on expenditure Public exp. on govern- Govt. ex- Public exp. Public exp. education on Public education as ment pend. as % on educ. as in educ. without pen- Total Public education Revenues of expenditure % of total Total govt. revenues of revenues % of reve- without sion as % of Gross Domestic government expenditure on without central gov. on education government expenditure as as % of of central nues of pension as % govt. Years Product expenditure education */ pensions */ ernment as % of GDP expenditure % of GDP GDP govt. central gov. of GDP expenditure. (1) (2) (3) (4) (5) (3/1) (3/2) (2/1) (511) (2/5) (315) (4/1) (4/2) 1970 0.00028 0.00005 0.00001 na. n.a. 3.22 18.83 17.12 n.a. n.a. n.a. n.a. n.a. 1971 0.00031 0.00006 0.00001 n.a. n.a. 3.11 16.84 18.44 n.a. n.a. n.a. n.a. n.a. 1972 0.00035 0.00007 0.00001 n.a. n.a. 3.73 19.45 19.19 n.a. n.a. n.a. n.a. n.a. 1973 0.00041 0.00008 0.00001 n.a. n.a. 3.59 17.86 20.09 n.a. n.a. n.a. n.a. n.a. 1974 0.00052 0.00010 0.00002 n.a. n.a. 3.44 18.20 18.90 n.a. n.a. n.a. n.a. n.a. 1975 0.00067 0.00013 0.00002 n.a. n.a. 3.28 16.62 19.75 n.a. n.a. n.a. n.a. n.a. 1976 0.00088 0.00018 0.00003 n.a. n.a. 3.24 16.11 20.09 n.a. n.a. n.a. n.a. n.a. 1977 0.00119 0.00027 0.00004 n.a. n.a. 3.00 13.38 22.39 n.a. n.a. n.a. n.a. n.a. 1978 0.00190 0.00043 0.00005 n.a. n.a. 2.55 11.29 22.62 n.a. n.a. n.a. n.a. n.a. 1979 0.00349 0.00071 0.00008 n.a. n.a. 2.19 10.75 20.38 n.a. n.a. n.a. n.a. n.a. 1980 0.00597 0.00137 0.00018 n.a. n.a. 2.95 12.82 22.99 n.a. n.a. n.a. n.a. n.a. 1981 0.01066 0.00228 0.00033 na. n.a. 3.06 14.32 21.37 n.a. n.a. n.a. n.a. n.a. 1982 0.01791 0.00363 0.00048 n.a. n.a. 2.71 13.35 20.29 n.a. n.a. n.a. n.a. n.a. 1983 0.03245 0.00766 0.00089 na. n.a. 2.75 11.66 23.61 n.a. n.a. n.a. n.a. n.a. 1984 0.07241 0.01696 0.00192 n.a. n.a. 2.66 11.33 23.43 n.a. n.a. n.a. n.a. n.a. 1985 0.19790 0.04431 0.00494 na. 0.03 2.50 11.15 22.39 12.69 176.51 19.67 n.a. n.a. 1986 0.37398 0.07669 0.01085 n.a. 0.04 2.90 14.16 20.51 11.07 185.23 26.22 n.a. n.a. 1987 0.73944 0.13122 0.02485 n.a. 0.06 3.36 18.94 17.75 8.55 207.60 39.32 n.a. n.a. 1988 4.94232 0.68087 0.10944 n.a. 0.38 2.21 16.07 13.78 7.74 177.91 28.60 n.a. n.a. 1989 115.11473 17.34800 2.59661 n.a. 7.35 2.26 14.97 15.07 6.39 236.00 35.32 n.a. n.a. 1990 6,789.94022 1,136.97100 150.86095 150.86 585.28 2.22 13.27 16.74 8.62 194.26 25.78 2.22 13.27 1991 32,937.32834 4,437.17000 737.44674 737.45 2,931.00 2.24 16.62 13.47 8.90 151.39 25.16 2.24 16.62 1992 52,060.93771 7,694.98100 1,227.87421 1,227.87 5,173.00 2.36 15.96 14.78 9.94 148.75 23.74 2.36 15.96 1993 80,010.14322 12,475.68800 2,080.73318 2,080.73 8,016.00 2.60 16.68 15.59 10.02 155.63 25.96 2.60 16.68 1994 109,315.76448 16,380.00000 3,080.56712 3,080.57 12,180.00 2.82 18.81 14.98 11.14 134.48 25.29 2.82 18.81 1995 132,598.96021 19,792.10000 4,188.69559 4,188.70 15,341.00 3.16 21.16 14.93 11.57 129.01 27.30 3.16 21.16 1996 149,780.37975 20,737.10000 4,291.16101 4,291.16 17,894.00 2.86 20.69 13.85 11.95 115.89 23.98 2.86 20.69 1997 171,375.00000 29,200.80000 5,150.26130 5,150.26 n.a. 3.01 17.64 17.04 n.a. n.a. n.a. 3.01 17.64 1998/** 195,000.00000 29,524.00000 5,589.70000 n.a. n.a. 2.87 18.93 15.14 n.a. n.a. n.a. n.a. n.a. Notes: *1 Include public expenditure in Ministry of Education, Regions, Universities, decentralized public institutions and PRES. **/ Preliminary Source: (a) ME - OSPP/DIPP/UFIC - Aspectos Financieros de la educaci6n Peruana, 1960- 1979. (b) ME - OA/ DIAF- Balances de Comprobaci6n, 1980 - 1988. (c) ME - OSPP /DIPP - Calendarios de Compromiso, 1989. (d) MEF - OFINE - Calendarios de Compromiso por Sub Programas y Programas, 1990 - 1997. (e) INEI - Pern: Compendio Estadistico, 1993-1994. (f) INEI - Direcci6n Nacional de Cuentas Nacionales. (g) MEF - Presu- puesto del Sector Publico 1994-1997. (h) Memoria del BCRP 1995. 197 Appendix 6.2: Gross Domestic Product, Total Government Expenditure, Total Public Expenditure on Education, and Tax Revenue of Central Government, 1970-1997 (Million Soles in Constant 1997 Prices) Total enroll- Gross Total Govern- Public expendi- Revenues of ment in public Percentage Change Domestic ment expendi- ture on Public exp. on central institutions Product ture education education with- government ('000) (1) (2) (3) (4) (5) Index of GDP Years (1) (2) (3) out pension (4) (5) (6) 1970 92,825 15,895 2,993 n.a. n.a. 2,891 4.24 0.00() 1971 96,762 17,843 3,005 n.a. n.a. 3,030 2.82 12.26 0.41 0.000 1972 99,489 19,092 3,714 n.a. n.a. 3,194 5.43 7.00 23.58 0.00( 1973 104,892 21,075 3,764 n.a. n.a. 3,426 9.24 10.39 1.36 0.00( 1974 114,588 21,663 3,942 n.a. n.a. 3,583 3.37 2.79 4.72 0.((0 1975 118,455 23,398 3,889 n.a. n.a. 3,797 1.95 8.01 -1.34 (.0()0 1976 120.767 24,266 3,909 n.a. n.a. 4,000 0.42 3.71 0.51 0.()00 1977 121,270 27,157 3,633 n.a. n.a. 4,140 0.28 11.92 -7.08 0.000 1978 121,615 27,505 3,105 n.a. n.a. 4,285 5.81 1.28 -14.53 0.000 1979 128,677 26,227 2,819 n.a. n.a. 4,318 4.46 -4.65 -9.20 0.000 1980 134,422 30,900 3,962 n.a. n.a. 4,398 4.44 17.82 40.53 0.000 1981 140,393 30,006 4,296 n.a. n.a. 4,812 0.21 -2.89 8.42 0.00° 1982 140,694 28,541 3,809 n.a. n.a. 5,107 -12.63 -4.88 -11.32 0.000 1983 122,926 29,027 3,385 n.a. n.a. 5,146 4.82 1.70 -11.15 0.000 1984 128,845 30,185 3,421 n.a. n.a. 5,346 2.27 3.99 1.09 0.00N) 1985 131,766 29,505 3,289 n.a. 16,716 5,475 9.24 -2.25 -3.88 0.0(N) 1986 143,943 29,516 4,178 n.a. 15,935 5,700 S.47 0.04 27.04 -4.67 0.000 1987 156,128 27,705 5,248 na, 13,346 5,852 -8.35 -6.13 25.60 -16.25 0.000 1988 143,098 19,713 3,169 n.a. 11,081 6,035 -11.66 -28.85 -39.61 -16.97 0.003 1989 126,409 19,050 2,851 n.a. 8,072 6,233 -5.39 -3.37 -10.02 -27.15 0.091 1990 119,594 20,026 2,657 2,657 10,309 6,087 2.80 5.12 -6.81 27.71 5.678 1991 122,939 16,562 2,753 2,753 10,940 6,069 -1.63 -17.30 3.59 3.59 6.12 26.792 1992 120,940 17,876 2,852 2,852 12,017 6,053 6.61 7.93 3.63 3.63 9.85 43.047 1993 128,938 20,105 3,353 3,353 12,918 6,190 13.06 12.47 17.55 17.55 7.50 62.053 1994 145,776 21,843 4,108 4,108 16,242 6,322 7.24 8.65 22.51 22.51 25.74 74.989 1995 156,335 23,335 4,938 4,938 18,087 6,453 2.61 6.83 20.22 20.22 11.36 84.817 1996 160,410 22,209 4,596 4,596 19,164 6,569 6.84 -4.83 -6.94 -6.94 5.95 93.374 1997 171,375 29,201 5,150 5,150 6,620 31.48 12.07 12.07 100.000 Source: a) ME - OSPP/DIPP/UFIC - Aspectos Financieros de la educaci6n Peruana, 1960 - 1979. b) ME - OA / DIAF - Balances de Comprobaci6n, 1980 - 1988. c) ME - OSPP / DIPP - Calendarios de Compromiso, 1989 d) MEF - Direcci6n Nacional de Presupuesto Pdblico - Calendarios de Compromiso por Sub Programas y Programas, 1990 - 1997 e) INEI - Peni: Compendio Estadistico, 1993-1994 198 Appendix 6.3: Gross Domestic Product, Total Government Expenditure, and Total Public Expenditure on Education, 1970-1997 (Million US dollars at the 1997 Exchange Rate) Gross Expenditure Public Recurrent Capital Domestic of central expenditure expenditure on expenditure Year Product government on education education on education (1) (2) (3) (4) (5) 1970 34,831.0 5,964.2 1,123.1 1,086.2 36.8 1971 36,308.4 6,695.2 1,127.7 1,088.2 39.5 1972 37,331.7 7,164.1 1,393.6 1,340.8 52.7 1973 39,359.2 7,908.2 1,412.5 1,354.8 57.7 1974 42,997.3 8,128.5 1,479.2 1,384.8 94.4 1975 44,448.4 8,779.6 1,459.5 1,402.8 56.6 1976 45,315.9 9,105.5 1,467.0 1,424.3 42.7 1977 45,504.9 10,190.4 1,363.1 1,323.3 39.8 1978 45,634.1 10,320.8 1,165.1 1,118.1 47.0 1979 48,284.0 9,841.1 1,057.9 1,017.5 40.5 1980 50,439.6 11,594.9 1,486.7 1,403.2 83.5 1981 52,680.3 11,259.3 1,611.9 1,536.1 75.8 1982 52,793.3 10,709.7 1,429.4 1,398.4 31.0 1983 46,126.1 10,891.8 1,270.1 1,248.6 21.5 1984 48,347.1 11,326.6 1,283.8 1,263.4 20.4 1985 49,443.3 11,071.2 1,234.1 1,201.3 32.8 1986 54,012.4 11,075.4 1,567.7 1,432.2 135.5 1987 58,584.7 10,396.0 1,969.1 1,862.4 106.6 1988 53,695.3 7,397.2 1,189.0 1,147.3 41.8 1989 47,432.9 7,148.2 1,069.9 1,017.4 52.6 1990 44,875.6 7,514.4 997.1 968.3 28.7 1991 46,130.8 6,214.5 1,032.8 940.6 92.2 1992 45,381.0 6,707.6 1,070.3 1,006.7 63.6 1993 48,382.1 7,544.0 1,258.2 1,123.5 134.7 1994 54,700.3 8,196.4 1,541.5 1,307.7 233.7 1995 58,662.2 8,756.1 1,853.1 1,568.3 284.8 1996 60,191.3 8,333.5 1,724.5 1,560.4 164.1 1997 64,305.8 10,957.1 1,932.6 1,734.6 198.0 Source: Ministry of Economy and Finance 199 Appendix 6.4: Recurrent and Capital Expenditure on Education, 1990-1997 (Constant 1997 Soles) Total Recurrent Capital Index Years Expenditure Expenditure Expenditure Percentages of GDP (1=2+3) (2) (3) (2)/(1) (3)/(1) 1970 2,992,929,996.38 2,894,790,273.45 98,139,722.93 96.72 3.28 0.000000302 1971 3,005,264,329.60 2,899,925,167.53 105,339,162.07 96.49 3.51 0.000000323 1972 3,713,882,819.18 3,573,362,183.26 140,520,635.92 96.22 3.78 0.000000348 1973 3,764,355,729.11 3,610,651,711.38 153,704,017.73 95.92 4.08 0.000000395 1974 3,942,098,683.83 3,690,601,434.39 251,497,249.44 93.62 6.38 0.000000456 1975 3,889,461,634.50 3,738,497,543.23 150,964,091.27 96.12 3.88 0.000000562 1976 3,909,489,799.42 3,795,697,494.72 113,792,304.70 97.09 2.91 0.000000725 1977 3,632,675,910.45 3,526,726,676.01 105,949,234.44 97.08 2.92 0.000000983 1978 3,104,928,455.58 2,979,687,428.92 125,241,026.66 95.97 4.03 0.000001563 1979 2,819,386,871.89 2,711,545,904.72 107,840,967.17 96.18 3.82 0.000002712 1980 3,962,000,862.59 3,739,594,950.01 222,405,912.58 94.39 5.61 0.00000444 1981 4,295,787,857.36 4,093,756,828.42 202,031,028.94 95.3 4.7 0.000007592 1982 3,809,483,279.78 3,726,798,546.90 82,684,732.88 97.83 2.17 0.000012729 1983 3,384,704,117.01 3,327,393,205.04 57,310,911.97 98.31 1.69 0.000026396 1984 3,421,448,012.79 3,367,088,022.90 54,359,989.89 98.41 1.59 0.000056199 1985 3,288,824,598.71 3,201,509,934.79 87,314,663.92 97.35 2.65 0.000150192 1986 4,178,012,206.15 3,816,805,651.24 361,206,554.91 91.35 8.65 0.000259808 1987 5,247,544,222.67 4,963,352,358.10 284,191,864.57 94.58 5.42 0.00047361 1988 3,168,766,165.26 3,057,491,622.22 111,274,543.04 96.49 3.51 0.003453802 1989 2,851,363,154.24 2,711,293,311.88 140,069,842.36 95.09 4.91 0.091065605 1990 2,657,167,015.60 2,580,549,605.23 76,617,410.37 97.12 2.88 5.677511128 1991 2,752,518,618.13 2,506,756,171.91 245,762,446.22 91.07 8.93 26.79170761 1992 2,852,420,824.38 2,682,818,235.95 169,602,588.43 94.05 5.95 43.04674102 1993 3,353,153,572.92 2,994,110,610.09 359,042,962.83 89.29 10.71 62.05302375 1994 4,108,042,988.56 3,485,108,506.54 622,934,482.03 84.84 15.16 74.98867786 1995 4,938,492,484.20 4,179,437,592.56 759,054,891.64 84.63 15.37 84.81729188 1996 4,595,691,835.04 4,158,395,903.39 437,295,931.65 90.48 9.52 93.37355866 1997 5,150,261,301.53 4,622,677,989.90 527,583,311.63 89.76 10.24 100 Source: a) ME - OSPP/DIPP/UFIC - Aspectos Financieros de la Educaci6n Peruana, 1960 - 1979. b) ME - OA / DIAF - Balances de Comprobaci6n, 1980 - 1988. c) ME - OSPP / DIPP - Calendarios de Compromiso, 1989 d) MEF - OFINE - Calendarios de Compromniso por Sub Programas y Programas, 1990 - 1994 e) ME - DE - Compendios Estadisticos 200 Appendix 6.5: Public Expenditure on Education by Budgetary Entities, 1990-1997 Years 1990 1991 1992 1993 1994 1995 1996 1997 Soles in current prices Ministry of Education 106,468,236 270,857,363 432,244,400 701,802,523 662,584,313 909,878,575 1,008,270,540 1,273,214,580 Regional governments 26,149,124 341,435,429 630,246,688 1,039,197,022 1,643,154,842 2,188,322,218 2,308,072,053 2,755,236,125 Decentralized public institutions 2,440,284 27,793,583 18,715,687 21,740,112 35,176,190 58,788,997 85,091,738 100,496,386 Public universities 15,402,778 95,803,399 138,911,762 244,408,996 473,071,329 636,504,831 735,346,482 808,248,984 Ministry of the Presidency 400,530 1,556,966 7,755,668 73,584,527 266,580,449 395,200,964 154,380,199 213,065,227 Total 150,860,952 737,446,740 1,227,874,205 2,080,733,180 3,080,567,123 4,188,695,585 4,291,161,012 5,150,261,302 Soles in constant 1997 prices Ministry of Education 1,875,262,480 1,010,974,616 1,004,128,047 1,130,972,321 883,579,137 1,072,751,269 1,079,824,475 1,273,214,580 Regional governments 460,573,716 1,274,407,119 1,464,098,496 1,674,691,996 2,191,203,911 2,580,042,547 2,471,869,002 2,755,236,125 Decentralized public institutions 42,981,580 103,739,498 43,477,593 35,034,734 46,908,668 69,312,514 91,130,443 100,496,386 Public universities 271,294,545 357,586,013 322,699,834 393,871,211 630,857,007 750,442,294 787,531,816 808,248,984 Ministry of the Presidency 7,054,676 5,811,373 18,016,853 118,583,306 355,494,265 465,943,860 165,336,099 213,065,227 Total 2,657,166,998 2,752,518,618 2,852,420,824 3,353,153,568 4,108,042,989 4,938,492,484 4,595,691,835 5,150,261,302 Percentage of total Ministry of Education 70.6 36.7 35.2 33.7 21.5 21.7 23.5 24.7 Regional governments 17.3 46.3 51.3 49.9 53.3 52.2 53.8 53.5 Decentralized public institutions 1.6 3.8 1.5 1 1.1 1.4 2 2 Public universities 10.2 13 11.3 11.7 15.4 15.2 17.1 15.7 Ministry of the Presidency 0.3 0.2 0.6 3.5 8.7 9.4 3.6 4.1 Total 100 100 100 100 100 100 100 100 Source: MEF/DNPP - Calendarios de Compromiso, 1990 - 1997 201 Appendix 6.6: Functional Composition of Public Expenditure on Education According to Pre-1997 Classification, 1990-1996 Years 1990 1991 1992 1993 1994 1995 1996 Soles in current prices 01.00 Remuneration 66,361,732 254,906,854 345,152,256 374,873,347 468,943,068 559,041,342 609,092,112 02.00 Goods 4,425,802 18,395,348 25,721,017 70,700,676 109,516,456 161,082,946 136,044,821 03.00 Services 1,541,650 9,342,189 15,996,394 30,355,544 79,656,424 155,589,420 226,709,902 04.00 Recurrent transfer 49,961,296 282,108,974 602,495,265 1,186,097,430 1,714,268,776 2,375,598,820 2,585,955,065 05.00 Pensions 24,220,513 106,849,419 165,500,886 195,909,171 241,052,067 293,507,587 325,040,338 06.00 Interests and commnissions 65,667 0 07.00 Studies 5,481 86,000 1,987,804 28,132,904 43,573,689 40,622,321 08.00 Works 1,913,770 17,954,923 23,084,821 146,213,058 382,806,236 475,157,344 262,577,233 09.00 Capital goods 262,179 14,787,297 2,454,841 18,956,888 48,311,348 68,115,761 75,736,255 10.00 Borrowing 1,411 47,600 27,500 84,579 358,210 2,378,393 4,497,041 11.00 Transfer of capital 2,164,621 32,968,136 32,861,825 53,932,758 10,000 60,000 14.00 E. of C. N.L.P.I. 2,500 14,579,400 1,621,928 7,521,634 54,574,616 24,825,924 Total 150,860,953 737,446,740 1,227,874,205 2,080,733,183 3,080,567,123 4,188,695,585 4,291,161,012 Soles in constant 1997 prices 01.00 Remuneration 1,168,852,517 951,439,370 801,808,099 604,117,776 625,351,828 659,112,463 652,317,552 02.00 Goods 77,953,203 68,660,603 59,751,369 113,935,908 146,043,988 189,917,577 145,699,514 03.00 Services 27,153,617 34,869,703 37,160,523 48,918,718 106,224,601 183,440,684 242,798,824 04.00 Recurrent transfer 879,985,867 1,052,971,233 1,399,630,380 1,911,425,678 2,286,036,806 2,800,842,573 2,769,472,538 05.00 Pensions 426,604,406 398,815,262 384,467,865 315,712,530 321,451,283 346,046,874 348,107,475 06.00 Interests and commissions 0 0 0 0 0 77,422 0 07,00 Studies 96,537 320,995 0 3,203,396 37,516,202 51,373,591 43,505,165 08.00 Works 33,707,911 67,016,717 53,627,337 235,626,000 510,485,378 560,212,822 281,211,551 09.00 Capital goods 4,617,851 55,193,559 5,702,734 30,549,499 64,424,856 80,308,814 81,111,030 10.00 Borrowing 24,852 177,667 63,884 136,301 477,685 2,804,137 4,816,183 11.00 Transfer of capital 38,126,226 123,053,508 76,339,867 86,913,989 0 11,790 64,258 14.00 E. of C. N.L.P.I. 44,033 0 33,868,766 2,613,778 10,030,360 64,343,738 26,587,745 Total 2,657,167,019 2,752,518,618 2,852,420,824 3,353,153,573 4,108,042,989 4,938,492,484 4,595,691,835 202 Appendix 6.6: (continued) 1990 1991 1992 1993 1994 1995 1996 Percentage of total 01.00 Remuneration 44 34.6 28.1 18 15.2 13.3 14.2 02.00 Goods 2.9 2.5 2.1 3.4 3.6 3.8 3.2 03.00 Services 1 1.3 1.3 1.5 2.6 3.7 5.3 04.00 Recurrent transfer 33.1 38.3 49.1 57 55.6 56.7 60.3 05.00 Pensions 16.1 14.5 13.5 9.4 7.8 7 7.6 06.00 Interests and commissions 0 0 0 0 0 0 0 07.00 Studies 0 0 0 0.1 0.9 1 0.9 08.00 Works 1.3 2.4 1.9 7 12.4 11.3 6.1 09.00 Capital goods 0.2 2 0.2 0.9 1.6 1.6 1.8 10.00 Borrowing 0 0 0 0 0 0.1 0.1 11.00 Transfer of capital 1.4 4.5 2.7 2.6 0 0 0 14.00 E. ofC. N.L.P.I. 0 0 1.2 0.1 0.2 1.3 0.6 Total 100 100 100 100 100 100 100 Rate of change Years 1990-1991 1991-1992 1992-1993 1993-1994 1994-1995 1995-1996 1990-1996 01.00 Remuneration -18.6% -15.7% -24.7% 3.5% 5.4% -1.0% -30.7% 02.00 Goods -11.9% -13.0% 90.7% 28.2% 30.0% -23.3% 176.6% 03.00 Services 28.4% 6.6% 31.6% 117.1% 72.7% 32.4% 426.1% 04.00 Recurrent transfer 19.7% 32.9% 36.6% 19.6% 22.5% -1.1% 166.0% 05.00 Pensions -6.5% -3.6% -17.9% 1.8% 7.7% 0.6% -13.2% 06.00 Interests and comrmissions 07.00 Studies 232.5% -100.0% 0.0% 1071.1% 36.9% -15.3% 15904.5% 08.00 Works 98.8% -20.0% 339.4% 116.7% 9.7% -49.8% 735.9% 09.00 Capital goods 1095.2% -89.7% 435.7% 110.9% 24.7% 1.0% 45.5% 10.00 Borrowing 614.9% -64.0% 113.4% 250.5% 487.0% 71.8% 1478.3% 11.00 Transfer of capital 222.8% -38.0% 13.9% -100.0% 0.0% 445.0% -100.0% 14.00 E. of C. N.L.P.I. -100.0% 0.0% -92.3% 283.7% 541.5% -58.7% 0.0% Total 3.6% 3.6% 17.6% 22.5% 20.2% -6.9% 79.4% Source: MEF/DNPP - Calendarios de Compromiso, 1990 - 1997 203 Appendix 6.7: ReclassifiJed Functional Composition of Public Expenditure on Eiducation According to the 1997 Classification, 1990 1997 Years 1990 1991 1992 1993 1994 1995 1996 1997 Soles in current prices 5.1 Personal cost and obligations (compcnsation) 95,838,896 421,351,149 700,624,462 1,074,670,831 1,480,361,646 1,960,644,646 2,134,805,601 2,941,364,687 5.2 Previous obligations (pensions) 43,705,418 216,871,919 400,474,039 658,487,169 909,616,890 1,219,991,127 1,333,562,813 1,078,918,396 5.3 Goods and services 5,967,451 27,737,537 41,717,411 101,056,220 189,172,880 316,672,366 362,754,723 509,213,129 5.4 Other recurrent expenditures 999,226 5,642,179 12,049,905 23,721,949 34,285,376 47,511,976 51,719,101 93,181,778 6.5 Investment 2,181,430 32,828,220 25,539,662 167,157,750 459,250,488 586,846,794 378,935,808 415,172,368 6.6 Financial investment 1,411 47,600 27,500 84,579 358,210 2,378,393 4,497,041 5,557,061 6.7 Other capital expenditures 2,167,121 32,968,136 47,441,225 55,554,686 7,521,634 54,650,283 24,885,924 106,853,883 Total 150,860,953 737,446,740 1,227,874,205 2,080,733,183 3,080,567,123 4,188,695,585 4,291,161,012 5,250,816,075 Soles in constant 1997 prices 5.1 Personal cost and obligations (compensation) 1,688,044,178 1,572,692,397 1,627,590,024 1,731,858,926 1,974,113,544 2,311,609,581 2,286,306,350 2,941,364,687 5.2 Previous obligations (pensions) 769,798,894 809,474,043 930,323,713 1,061,168,544 1,213,005,637 1,438,375,477 1,428,201,765 1,078,918,396 5.3 Goods and services 105,106,820 103,530,306 96,911,892 162,854,626 252,268,590 373,358,261 388,498,338 509,213,129 5.4 Other recurrent expenditures 17,599,717 21,059,425 27,992,608 38,228,514 45,720,736 56,016,851 55,389,451 93,181,778 6.5 Investment 38,422,299 122,531,272 59,330,071 269,378,895 612,426,437 691,895,227 405,827,746 415,172,368 6.6 Financial investment 24,852 177,667 63,884 136,301 477,685 2,804,137 4,816,183 5,557,061 6.7 Other capital expenditures 38,170,259 123,053,508 110,208,633 89,527,766 10,030,360 64,432,950 26,652,003 106,853,883 Total 2,657,167,019 2,752,518,618 2,852,420,824 3,353,153,573 4,108,042,989 4,938,492,484 4,595,691,835 5,150,261,302 Percentage of total 5.1 Personal cost and obligations (compensation) 67.7 63.3 65.6 60.9 56.8 55.6 59.1 56.8 5.2 Previous obligations (pensions) 24.8 23.2 24.1 22.4 20.8 20.4 21.7 21.6 5.3 Goods and services 4 3.8 3.4 4.9 6.1 7.6 8.5 9.8 5.4 Other recurrent expenditures 0.7 0.8 1 1.1 1.1 1.1 1.2 1.8 6.5 Investment 1.4 4.5 2.1 8 14.9 14 8.8 7.9 6.6 Financial investment 0 0 0 0 0 0.1 0.1 0.1 6.7 Other capital expenditures 1.4 4.5 3.9 2.7 0.2 1.3 0.6 2.0 Total 100 100 100 100 100 100 100 100o 204 Appendix 6.7: (continued) Rates of changes 1990-1991 1991-1992 1992-1993 1993-1994 1994-1995 1995-1996 1996-1997 1990-1997 5.1 Personal cost and obligations (compensation) -6.8 3.5 6.4 14 17.7 -1.1 28.7 74.2 5.2 Previous obligations (pensions) -5.2 14.9 14.1 14.3 18.6 -0.7 -24.5 40.2 5.3 Goods and services -1.5 -6.4 68 54.9 48 4.1 31.1 384.5 5.4 Other recurrent expenditures 19.7 32.9 36.6 19.6 22.5 -1.1 68.2 429.5 6.5 Investment 218.9 -51.6 354 127.3 13 -41.3 2.3 980.6 6.6 Financial investment 614.9 -64 113.4 250.5 487 71.8 15.4 22,260.20 6.7 Other capital expenditures 222.4 -10.4 -18.8 -88.8 542.4 -58.6 300.9 179.9 Total 3.6 3.6 17.6 22.5 20.2 -6.9 12.1 93.8 Source: MEF/DNPP - Calendarios de Compromiso, 1990 - 1997 205 Appendix 6.8: Functional Composition of Public Expenditure on Education by Budgetary Entities, 1995 -1997 Decentralized Public MED INFES Universities Regions Instit.utions TOTAL 1995 5.1 Remuneration 57.6 0.5 44.4 68.2 34.5 55.4 5.2 Pension 21.3 0.2 16.4 25.2 12.8 20.5 5.3 Goods and services 14.7 0.0 19.8 1.9 26.6 7.6 5.4 Other current expenditures 1.2 0.0 0.4 1.5 0.8 1.1 6.5 Investment 0.0 99.2 18.0 3.1 20.3 14.0 6.6 Financial investment 0.0 0.0 0.0 0.0 4.0 0.1 6.7 Other capital expenditures 5.1 0.0 1.0 0.0 1.0 1.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 (Soles in current prices) 909,878,575.0 395,200,964.0 636,504,831.0 2,188,322,218.0 58,788,997.0 4,188,695,585.0 1996 5.1 Remuneration 62.4 1.5 41.1 68.2 28.9 59.0 5.2 Pension 23.1 0.6 15,2 25.2 10.7 21.8 5.3 Goods and services 12.3 0.1 22.8 2.1 26.0 8.5 5.4 Other current expenditures 1.3 0.0 0.4 1.5 0.6 1.2 6.5 Investment 0.1 97.6 18.8 2.9 25.7 8.8 6.6 Financial investment 0.0 0.0 0.0 0.0 5.3 0.1 6.7 Other capital expenditures 0.8 0.3 1.7 0.1 2.9 0.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Soles in current prices 1,008,270,540.0 154,380,199.0 735,346,482.0 2,308,072,052.5 85,091,738.0 4,291,161,011.5 1997 5.1 Remuneration 48.9 2.1 38.7 71.0 13.8 56.8 5.2 Pension 31.9 0.0 13.6 21.4 7.1 21.6 5.3 Goods and services 13.6 0.4 26.0 3.3 34.7 9.8 5.4 Other current expenditures. 0.2 0.3 8.9 0.1 16.4 1.8 6.5 Investment 4.4 78.0 8.1 3.7 19.4 7.9 6.6 Financial investment 0.0 0.0 0.0 0.0 5.5 0.1 6.7 Other capital expenditures 1.0 19.3 4.7 0.4 3.0 2.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Soles in current prices 1,273,214,580 213,065,227 808,248,984 2,865,790,898 100,496,386 5,260,816,075 Source: MEF/DNPP - Calendarios de Compromiso, 1990 - 1997 206 Appendix 6.9: Public Expenditure on Education by Level, 1990-1997 Years 1990 1991 1992 1993 1994 1995 1996 1997 Soles in current prices Initial education 7,733,893 32,111,001 58,686,148 94,057,848 148,712,377 203,337,469 226,078,569 284,117.158 Primary education 40,791,358 161,362,221 296,107,972 480,980,342 719,310,676 961,137.528 1,048,724,737 1,398,613,025 Secondary education 30,300,217 115,488,352 214,220,096 344,710,338 512,958,213 685,959,743 768,086,960 958,899,509 Tertiary nonunivcrsity education 2,993,098 13,874,214 28,380,872 42,574,688 63,801,100 88,795,553 93,916,633 115,255,739 Vocational education 1,842,908 6,449,660 11,667,081 18,511,293 27,966,545 35,812,007 40,769.987 19,439,150 Special education 1,042,873 3.840,025 7,244,469 11,109,916 17,504,264 23,834,368 26,664,538 31,233,749 Literacy programs 1,032,020 4,977,281 8,252,521 11,298,131 18,652.939 22,585.648 22,059.850 0 Other/* 22,375 53,698 19,400 31,250 74.550 186,675 157.545 481,274 Administration 27.685,014 137.490,897 260,359,644 391.196,478 638,278,355 845,909,882 914.087.287 1,103,123,124 Universities 15,402,778 95,803,399 138,911,762 244.408,996 473,071,329 636,504,831 735,346.482 808,248,984 Inst. of Ministry of Education 19.173,604 136,645.443 177,552,885 312,483,949 64.943.865 162.654.057 108,480,201 0 Inst. of Education Sector 2,440,284 27,793,583 18,715,687 21.740.112 35.176.190 58.788.997 85.091.738 100,496,386 Other sectors /** 400.530 1,556,966 7,755,668 107.629,842 360,116.720 463.188,827 221696.485 330.353,204 Total 150,860,952 737,446,740 1,227,874,205 2,080,733,183 3,080,567,123 4,188,695,585 4,291,161,012 5,150,261,302 Soles in Current 1997 Prices Initial education 136.219,777 119.854.253 136,331,222 151,576.575 198.313.107 239.735.866 242.122.687 284.117.158 Primary education 718.472.533 602.284.197 687,875,470 775,111.853 959.225,708 1.133,185.824 1.123.149.585 1.398.613,025 Secondary education 533,688.377 431.060,064 497.645.329 555,5(09.332 684.047.549 808.749.876 822,595.787 958.899.509 Tertiary nonuniversity education 52.718,488 51,785,479 65,930,362 68.610,175 85.080.977 104,690,389 100,581.614 115.255.739 Vocational education 32,459.787 24,073,344 27,103,285 29.831.412 37.294.357 42,222.531 43.663.311 19.439.150 Special education 18.368,489 14.332.886 16,829.3(19 17,903.908 23.342,543 28,100.836 28,556,840 31,233,749 Literacy programs 18.177,331 18,577.692 19,17t,070 18.2(07,221 24.874,340 26,628,589 23.625.371 0 Other/* 394.()99 200.428 45,067 50,360 99,415 220,091 168,725 481,274 Administration 487,625.887 513.184,523 604,830,094 630.422.910 851,166,300 997,331,869 978,957,319 1.103,123,124 Universities 271,294,545 357,586,013 322,699,834 393.871,211 630,857,007 750,442,294 787,531,816 808.248.984 Inst. ofMinistry of Education 337,711,430 510.028,868 412,465.336 503,575,700 86.604.894 191.769,925 116,178,715 0 Inst. of Education Sector 42,981,580 103,739,498 43,477,593 35,034,734 46.908,668 69,312.514 91,130.443 100,496.386 Other sectors /** 7,054,676 5,811,373 18,016,853 173.448,183 480,228,123 546,101,882 237,429,620 330.353,204 Total 2,657,166,998 2,752,518,618 2,852,420,824 3,353,153,573 4,108,042,989 4,938,492,484 4,595,691.835 5,150,261,302 207 Appendix 6.9: (continued) Years 1990 1991 1992 1993 1994 1995 1996 1997 Percentage of total Initial education 5.1 4.4 4.8 4.5 4.8 4.9 5.3 5.5 Primary education 27 21.9 24.1 23.1 23.3 22.9 24.4 27.2 Secondary education 20.1 15.7 17.4 16.6 16.7 16.4 17.9 18.6 Tertiary nonuniversity education 2 1.9 2.3 2 2.1 2.1 2.2 2.2 Vocational education 1.2 0.9 1 0.9 0.9 0.9 1 0.4 Special education 0.7 0.5 0.6 0.5 0.6 0.6 0.6 0.6 Literacy programs 0.7 0.7 0.7 0.5 0.6 0.5 0.5 0 Other/* 0 0 0 0 0 0 0 0 Administration 18.4 18.6 21.2 18.8 20.7 20.2 21.3 21.4 Universities 10.2 13 11.3 11.7 15.4 15.2 17.1 15.7 Inst. of Ministry of Education 12.7 18.5 14.5 15 2.1 3.9 2.5 0 Inst. of Education Sector 1.6 3.8 1.5 1 1.1 1.4 2 2 Other sectors/** 0.3 0.2 0.6 5.2 11.7 11.1 5.2 6.4 Total 100 100 100 100 100 100 100 100 */ Extraescolar entre 1991-96, en 1997 asistencia a educandos, educaci6n fisica y deportes y Cultura I/ Incluye el gasto en INFES, CORDELICA y los gastos de capital ejecutados por los gobiemos regionales Nota: A partir de 1997 los gastos en las instituciones del MED se reparten entre los niveles y modalidades Source: MEF/DNPP - Calendarios de Compromiso, 1990 - 1997 208 Appendix 6.10: Per Student Recurrent Public Expenditure by Level, 1990-1997 Growth rate Years 1990 1991 1992 1993 1994 1995 1996 1997 1990-1997 Soles in 1997 Prices Initial education 275 256 291 298 381 440 415 468 70% Primary education 287 260 301 320 398 461 449 536 87% Secondary education 405 351 411 444 543 625 612 692 71% Tertiary nonuniversity education 481 448 539 533 638 755 769 863 79% University education 769 944 870 1286 2152 2464 2492 3232 335% In 1997 US Dollars Initial education 103 96 109 112 143 165 156 175 n/a Primary education 108 98 113 120 149 173 168 201 n/a Secondary education 152 132 154 167 204 235 230 260 n/a Tertiary nonuniversity education 181 168 202 200 239 283 288 324 n/a University education 289 354 326 483 807 925 935 1255 n/a Source: MEFIDNPP-Calendarios de Compromiso, 1990-1997 y Estadisticas Basicas del MED. NOTE: Includes pensions. 209 Appendix 6.11: Recurrent Public Expenditure by Level, by Function, and by Department from Central Government Allocattion, 1997 Department Piura 1'umbes Loreto Ucayali Aregnipa Moguegua Tacna Puno Amazonas Cajamarca l.ambayeque Cusco Pension 5.2 42.88X20,48 6,565,297 24,905.153 86,10,529 59,775.005 5,188,448 14,429,632 35176782 6,336.473 35,039,038 41,799,402 43,909,463 5.4 5,147 8,698 'rotal 42,887,195 6,565,293 24,913,851 8,160,529 59,775,()05 5,188,448 14,429,632 35,176,782 6,336,473 35,039,038 41,799,402 43,909,463 Administration 5.1 2,936,828 474,969 3,440,557 542,535 3,284,924 202,256 731,144 1453201 248,052 1,(X)2,900 915,182 3,210,007 5.3 827,387 2(02,470 1,885,768 484,382 3,126,368 59,303 126,700 511468 132,944 464,2()8 644,664 890,950 5.4 18,495 3,010 21,636 75,(XX) 3,739 6.5 i 50,000 6.6 6.7 77,773 33,847 180,00tX 2()0,(X0) 'Total 4.(10(,483 714,296 5,527,961 1,101,917 6,411,292 261,559 857,844 1,964,669 38(0,996 1,47(0,847 1,559,846 4,30(),957 Planning 5.1 52,824 511,029 664,S42 70)3,580 967,149 475,1(04 476,682 2128030 354,947 1.965J147 802,669 716,669 5.3 44,957 28,969 106,438 429,535 633,()07 48,130 74.500 363605 85,551 141,939 76,39() 355,776 5.4 2.:319 6.5 6.7 6.25() Total l(A),I(K) 78,998 771.280 1,i39,365 1,600,156 523,234 .551,182 2.491,635 44(),498 2,1()9,0(86 879,059 1,()72,445 Initial educ.ation 5.1 12,94S,142 5,407,740 14.604,0123 5,465.844 7,856,490 2,827,209 3,811.622 1490(4951 2.943,593 1453(i.587 6.(87,147 14,(0(9877 5.3 1.205M(13 384,690 517,780( S)3,470) 1,613,448 89,396 121,080 4(03130 404,264 2.582,'3'7 1,:381,374 98,825 5.4 ?4! 230 I ! 6.5 6.7 Total 14,17';.4()i 5.792.430 15,141,457 5,979,314 9,469.938 2,916.(605 3.932.70? 15.3(8,081 3,347,857 17.112.962 8.269.(21 14.4181.712 Primary education 5.1 77,415,427 15,426,863 64,586,636 21,019,9.6 45,SS,S167 9,80t),792 138-93.27() 81941()18 12,034.363 99,1172,220 43,685,588 73,002,873l 5.3 1,742,919 189,670 927,434 485,069 1,156,687 378,467 512,398 978647 560,540 3,216,423 1.406,492 366,015 5.4 10(5,169 79.222 6.5 6.7 Total 79,263,565 15.616,533 65,593,292 27,5()5,025 47,014,854 10,188,259 14.205.668 82.919,665 12,594,903 102,288,643 45,092,0(8() 73,368,848 Secondary education 5.1 53,456.924 13.908,622 37,856,057 17,878,936 41,868,312 9,304,497 14,611,508 57225685 6.906,950 49,445,673 33,336,861 33.657,2()9 5.3 193,023 103,873 386,032 163,134 25,360 140,846 279.838 567765 37,485 199.621 609,438 257,110 5.4 115,973 69,849 6.5 6.7 361 Total 53,765,920 14,012,495 38,311,938 18,0)42,070 41,893,672 9,445,343 14,891.346 57,793,450 6,944,435 49,645,294 33,946,299 33,914,680 Tertiary education 5.1 6,1(03,533 1,020,504 3.725,643 2,372,913 6,213,685 1,710,710 1.758,142 7533529 966,291 7,969,606 4,279,832 3,562,285 5.2 5.3 278,283 47,348 92,683 121,300 411,383 111,700 170041 110 47,132 42,181 112,214 5.4 13,0(X) 14,501 6.5 6.7 Total 6,394,816 1,067,852 3,832,827 2,494,213 6,213,685 1,751,093 1,869,842 7,703,57(1 966,401 8,016,738 4,322,013 3,674,499 Training 5.1 106,509 252,321 5.3 65,740 1,456 5.4 6.5 6.7 Total 172,249 253,777 Special education 210 Appendix 6.11: (continued) 5.1 1,498,188 637,559 659,179 469,204 2,236,227 312,093 865768 131,251 1,031,357 638,807 832,797 5.3 38,925 14,133 3,687 31,497 2,080 12,000 71796 3,996 11,202 24,774 33,813 5.4 1,216 11,527 6.5 6.7 Total 1,538,329 651,692 674,393 500,701 2,238,307 324,093 937,564 135,247 1,042,559 663.581 866,610 Assistance 5.1 67,994 5.3 62,690 5.4 6.5 6.7 Total 130,684 Physical education 5.1 34,653 5.3 7,100 5.4 6.5 6.7 30,000 Culture 71,753 5.1 16,162 5.3 28,590 5.4 6.5 6.7 Total 44,752 Military College 5.1 1,010,144 559,045 5.3 1,361,667 912,047 5.4 6.5 6.7 Total 2,371,811 1.471.092 Department Piura aggregate Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Amazonas Cajamarca Lambayeque Cusco 5.1 154,411,916 36,926,286 125,536,937 54,503,783 109.469,601 24,581,889 35.394,461 166,052,182 23,585,447 175,017,490 91,105.631 129,283,677 5.2 42,882,048 6,565,297 24,905,153 8,160,529 59,775,005 5,188,448 14.429.632 35,176,782 6,336,473 35,039,038 41,799.402 43,909,463 5.3 4,330,529 971,153 3,919,822 2,264,077 8,047,047 757,981 1,238.216 3,066,452 1,224,890 6,664,900 5,097,360 2,114,703 5.4 285.549 3,010 225,087 75.000 3,739 6.5 150,000 6.6 6.7 77,773 33,847 180,0(0 36,250 200,361 Total 202,137,815 44,499,593 154,766,999 65,039,639 177,291,653 30,528,318 51,062,309 204,295,416 31,146,810 216,725,167 138.002,393 175,508,204 Source: MEF/DNPP-Calendarios de Compromiso 211 Appendix 6.11: (continued) Madre de Department Apurimac DiOs La Libertad Ayacucho lea Huancavelica HuanuCo Pasco Junin Ancash San Martin Total Pension 5.2 11421581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458,919 53,888,645 40,440,064 20,908,096 555,698,358 5.4 10,420 24,265 Total 11,421,581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458,919 53,888,645 40,450,484 20,908,096 555,722,623 Administration 5.1 2691208 405,161 1,003,894 3,754,053 2,529.082 1,439,518 621,938 709,528 724,441 2,759,659 1,119,927 36,200.964 5.3 389350 129,363 477,177 564,870 521,623 722,608 375,866 373,506 805,202 572,262 403,099 14,691,538 5.4 8414 810 68.872 40,000 95,170 46,291 381,437 6.5 5,400 155,400 6.6 6.7 12078 13 35,000 55,000 593,711 Total 3,101,050 534,524 1,481,894 4,318,923 3,050,705 2,162,126 1,066,676 1,123,034 1,659,813 3,392,321 1,569,317 52,023,050 Planning 5.1 247256 92,070 664,784 533,354 396,195 637,940 456,225 378,346 137,079 2,914,811 919,245 16,734,977 5.3 131972 44,443 22,424 101,675 21,298 161,477 131,072 78,788 36,336 381,100 342,251 3,843,633 5.4 16,000 18,319 6.5 6.7 532 6,782 Total 379,228 136,513 687,208 635,029 417,493 799,417 603,297 457,134 173,415 3,295,911 1,262,028 20,603,711 Initial education 5.1 6012739 1,770,422 5,275,336 7,561,468 7,992,759 5,401,853 5,617,261 4,459,199 9,645,356 13,171,390 8,747,287 1I2,144,795 5.3 242014 68,013 576,093 468,367 511,362 453,529 991,485 879,758 1,229,753 857,655 733,580 16,326,476 5.4 9,480 53,364 6.5 6.7 Total 6,254,753 1,838,435 5.851,429 8,029,835 8,504,121 5,855,382 6,608,746 5,338,957 10,875,109 14,038,525 9,480,867 198,524,635 Prinmary education 5.1 31524074 6,089,880 41,046,343 46,390,001 31,808,754 33,000,244 43,283,173 17,647,761 69,919,143 70,259,200 43,645,516 998,160,272 5.3 577030 206,504 649,275 1,339,895 405,354 979,335 1,541,404 987,651 1,566,739 1,216,801 944,759 22,335,508 5.4 11,490 195,881 6.5 6.7 22 22 Total 32,101,104 6,296,384 41,695,640 47,729,896 32,214,108 33,979,579 44,824,577 18,635,412 71,485,882 71,487,491 44,590,275 1,020,691,683 Secondary education 5.1 15047815 3,736,113 24,542,162 24,429,814 35,348,778 14,858,481 21,738,275 14,172,554 52,731,293 51,626,277 21,441,162 649,129,958 5,3 209168 203,601 96,224 257,067 263,054 180,976 357,935 192,428 462,980 472,380 1,087,260 6,746,598 5.4 8,910 16,600 211,332 6.5 6.7 120 481 Total 15,256,983 3,939,714 24,638,506 24,686,881 35,611,832 15,039,457 22,096,210 14,364,982 53,194,273 52,107,567 22,545,022 656,088,369 Tertiary education 5.1 1520383 584,262 3.525,764 4,707,833 4,112,810 1,731,958 2,871,234 2,768,414 7,074,497 6,750,109 3,050,278 85,914,215 5.2 5.3 75445 39,230 22,719 69,216 109,532 3,116 98,424 29,845 93,068 129,242 156,618 1,889,830 5.4 27,501 6.5 6.7 140 140 Total 1,595,828 623,492 3,548,623 4,777,049 4,222,342 1,735,074 2,969,658 2,798,259 7,167,565 6,879,351 3,206,896 87,831,686 Training 5A1 29,670 24,571 19,134 432,205 5.3 71,554 10,101 54.000 202,851 5.4 6.5 6.7 Total 101,224 34,672 73,134 635,056 212 Appendix 6.11: (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Total Special education 5.1 260868 81,479 269,902 531,526 811.643 246,196 325,494 386,749 905,718 1,140,573 525,239 14,797,817 5.3 33062 7,261 14,170 12,914 38,063 2,502 21,907 8,672 27.772 25,314 141,539 581,079 5.4 12,743 6.5 6.7 7 7 Total 293,930 88,740 284,079 544,440 849,706 248,698 347,401 395,421 933,490 1,165,887 666,778 15,391,646 Assistance 5.1 67,994 5.3 62,690 5,4 6.5 6.7 rotal 130,684 Physical education 5.1 17,207 94,086 35,879 181,825 5.3 50,298 15,415 21,200 94,013 5.4 6.5 6.7 30,000 Total 67,505 109,501 57,079 305,838 Culture 5.1 16,162 5.3 28,590 5.4 6.5 6.7 Total 44,752 Military College 5.1 490,485 2,059,674 5.3 1,198,129 3,471,843 5.4 50 50 6.5 6.7 24,000 24,000 Total 1,712,664 5,555,567 Department Madre de Ayacucho aggregate Apurimac Dios La Libertad Ica Huancavelica Huinuco Pasco Junin Ancash San Martin Total 5.1 57,304,343 12.759,387 76,818,670 87,908,049 83,000,021 57,316,190 74,960,477 40,641,208 141,192,540 148,622,019 79,448,654 1,985,840,858 5.2 11,421,581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458,919 53,888,645 40,440.064 20,908,096 555,698,358 5.3 1.658.041 698,415 3,056,211 2,814,004 1,870.286 2,503,543 3,639,945 2,576,164 4,297,050 3,654,754 3,809,106 70,274,649 5.4 8,414 860 84,872 40,000 95,170 40,300 62,891 924,892 6.5 5,400 155,400 6.6 6.7 12,078 24,302 35,000 55,000 532 655,143 Total 70,404,457 15,137,822 94,597,317 112,653,194 120,573,586 67,609,193 96,297,903 48,716,291 199,508,405 192,817,537 104,229,279 2,613,549,300 Source: MEF/DNPPCalendanos de Compromiso 213 Appendix 6.12: Recurrent Public Expenditure by Level, by Function and by Department from Own Resources, 1997 Department Piur;i Tuimbes l.oreto Ucayali_ Arequipa Moquegua Tacna Puno Amiazonas Cajamarca Lambayeque ('usco Pension 5.2 5.4 Total Adrninistration 5.1 49,00() 13,500 41,867 108,838 5.3 81S,175 47,581 281,662 58,8() 4,897,259 133.Y74 45,000 71220 22,138 29(),619 634.434 274,776 5.4 176.337 139,315 25,000 37,1()1 2000 19,052 19,999 6.5 385,003 6.6 6,0t00 6.7 1 19,431 41,985 800,789 23,(X0 61663 179,663 264,329 249,636 Total 1,162,943 47,581 462,962 97,300 6,126,152 13.3,974 109,867 134,883 41,190 599,119 898,763 524,412 Planning 5.1 49,867 1000 5.3 12,000 36,000 49558 16,691 28,000 75,989 5.4 6.5 108860 6.7 22,000 18515 Total 12,000 107,867 177,933 16,691 28,000 75,989 Initial education 5.1 5.3 57,450 16500 5.4 2,800 6.5 6.7 Total 60,250 16,500 Primiary educationi 5.1 5,3 58,550 78800 5A4 750 6.5 6.7 Total 59,300 78800 Secondary educatiotn 5.1 5.3 440,350 138.258 1453(00 5.4 32,100 6.5 6.7 Total 472,450 138,258 145.300 Tertiary education 5.1 203,400 5.2 5.3 1,025,333 455,852 873,259 230,683 349463 635,673 5.4 5,000 23,848 1 51, 187 6.5 88,31 6 6.7 361,150 74,920 2763()9 05.400 Total 1,683,199 554,620 1,024,446 230,683 625772 701,073 Training 5.1 Souirce: MER/DNPP-Calendanos de ComProrniso 214 Appendix 6.12: (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin CORDELICA Total Pension 5.2 5.4 Total Administration 5.1 21,000 20,35s 254,563 5.3 95088 8,480 10,500 93,157 92,478 8,954 8954 17,262 258,240 193,609 162,039 8,524,399 5.4 418,804 6.5 385,003 6.6 40100 2,804 48,904 6.7 132443 19,250 60,280 31,070 17,270 17270 74,660 176,196 33,563 67,701 2,370,199 Total 267,631 27,730 31,500 153,437 143,906 26,224 26,224 91,922 434,436 229,976 229,740 12,001.872 Planning 5.1 50,867 5.3 9,240 6,033 6033 7,215 124,317 371,076 54 855 855 6.5 108,860 6.7 16,000 16000 1,950 74,465 Total 9,240 22,033 22,033 8,070 126,267 606,123 Initial education 5.1 5.3 35345 109,295 5.4 2,800 6.5 6.7 Total 35,345 112,095 Primary education 5.1 5.3 113159 250,509 5.4 750 6.5 6.7 Total 113159 251,259 Secondary education 5.' 5.3 207800 931,708 5,4 32,100 6.5 6.7 Total 207,800 963,808 Tertiary education 5.1 2,000 205,400 5.2 5.3 210,528 3,780,791 5.4 180,035 6.5 59.100 147,416 6.7 777,779 Total 271,628 5,091,421 Training 5.1 11076 11,076 Source: MEF/DNPP-Calendarios de Compromiso 215 Appendix 6.12: (continued) Departments Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Amazonas Cajamarca Lambayegue Cusco 5.3 5.4 6.5 6.7 Total Special education 5.1 5.3 5.4 6.5 6.7 Total Assistance 51 5.3 5.4 6.5 6.7 Total Physical education 5.1 5.3 5.4 6.5 6.7 Total Culture 5.1 5.3 5.4 6.5 6.7 Total Military College 5.1 5.3 660,090 1,566,000 5.4 6.5 6.7 Total 660,090 1,566,000 Depsrtment aggregate 5.1 252,400 13,500 91,734 1,000 108,838 5.2 5.3 2,515,598 503,433 281,662 1,488,409 6,463,259 502,915 81,000 710,841 38,829 318,619 1,346,096 274,776 5.4 181,337 23,848 139,315 211,837 37,101 2,000 19,052 19,999 6.5 88,316 385,003 108,860 6.6 6,000 6.7 480,581 74,920 41,985 800,789 45,000 356,487 179,663 329,729 249,636 Total 3,518,232 602,201 462,962 1,713,746 7,692,152 502,915 217,734 1,179,188 57,881 627,119 1,675,825 524,412 Source: MEP - DNPP / Calendros de Compromiso par Subprogram s Programun, 1997 216 Appendix 6.12: (continued) Madre de Huan- COR- Departments Apurimac Dios La Libertad Ayacucho Ica cavelica Huinuco Pasco Junin Ancash San Martin DELICA Total 5.3 151411 151,411 5.4 0 6.5 2200 2,200 6.7 49450 49,450 Total 214,137 214,137 Special education 5.1 5.3 2721 2,721 5.4 6.5 6.7 TotaI 2,721 2,721 Assistance 5.1 5.3 5.4 6.5 6.7 Total Physical education 5.1 5.3 5.4 6.5 6.7 Total Culture 5.1 5.3 5.4 6.5 6.7 Total Military College 5.1 1,464,661 1,464,661 5.3 2,226,090 5.4 6.5 6.7 1,464,661 1,464,661 Total 2,929,322 5,155,412 Department aggregate 5.1 11,076 1,485,661 20,358 2,000 1,986,567 5.2 5.3 605,524 8,480 10,500 102,397 92,478 14,987 14,987 17,262 258,240 411,352 286,356 1,523,575 17,871,575 5.4 855 635,344 6.5 2,200 59,100 17,204,294 17,847,773 6.6 40,100 2,804 48,904 6.7 181,893 19,250 1,464,661 60,280 31,070 33,270 33;270 74.660 176,196 33,563 69,651 2,982.702 7,719,256 Total 840,793 27,730 2,960,822 162,677 143,906 48,257 48,257 91,922 434,436 509,674 356,007 21,710,571 46,109,419 Source: MEF - DNPP / Calendarios de Compromiso por Subprograrmas Programas, 1997 217 Appendix 6.13: Recurrent Public Ependiture by Level, by Function, and by Department from Other Sources, 1997 Departments Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Cajamarca Amazonas Lambayeque Cusco Pension 5.2 5.4 Total Administration 5.1 5.3 5.4 6.5 6.6 6.7 Total Plarning 5.1 5.3 54 6.5 676,515 6.7 Total 676,515 Initial education 5.1 5.3 5.4 6.5 1,100,212 1,137,809 1,358,203 825,780 307,103 265,000 369,000 144,016 6.7 20,818 2,880 129,847 98,200 Total 1,100,212 1,158,627 1,358,203 828,660 436,950 265,000 467,200 144,016 Primary education 5.1 5.3 5,000 2,017 5.4 6.5 11,3S5,437 1,907,248 6,035,722 2,045,641 377,189 164,256 265,000 508,3v40 958,334 240,553 6.7 150,000 609,433 10,880 135,653 45,000 69,850 101,568 74,457 Total 11,535,437 1,907,248 6,650,155 2,056,521 512,842 164,256 310,000 578,190 1,059,902 317,027 Secondary education 5.1 5.3 5.4 6.5 6,725,526 2,586,797 8,179,434 3,223,421 873,195 637,834 148,000 99,600 957,471 165,735 6.7 73,545 533,800 106,618 149,501 55,000 112,000 Total 6,725,526 2,660,342 8,713,234 3,330,039 1,022,696 637,834 203,000 211,600 957,471 165,735 Tertiary education 5.1 5.2 5.3 5.4 6.5 2,064,935 570,654 395,258 569,713 345,782 629,051 10,0(0 6.7 29,391 Total 2,064,935 570,654 395,258 599,104 345,782 629,051 10,000 Training 5.1 53 5.4 Source: MEF - DNPP / Calendarios de Compromiso 218 Appendix 6.13: (continued) Madre de Departments Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Cordelica Total Pension 5.2 5.4 Total Administration 5.' 5.3 5.4 6.5 6.6 6.7 Total Planning 5.1 5.3 5.4 6.5 676,515 6.7 Total 676,515 Initial education 5.1 5.3 5.4 6.5 165,500 19.000 336,852 6,028,475 6.7 251,745 Total 165,500 19,000 336,852 6,280,220 Primary education 5.1 7,800 103,927 8,000 119,727 5.3 280,676 8,000 5,000 300,693 5.4 6.5 1.137,655 196,128 1,152,083 461,000 1,175,588 720,492 1,315,631 933,954 749,505 697.500 1,985,778 34,413,034 6.7 160,374 695,013 25,448 89,032 267,000 2,433,708 Total 1,305,829 196,128 1,847,096 871,051 1,264,620 720,492 1,315,631 933,954 757,505 977,500 1,985,778 37,267,162 Secondary education 5.1 5.3 4,517 4,517 5.4 65 745,468 228.551 548,808 360,000 48,899 948,827 2,083,251 1,063,855 3,121,959 1,063,750 2.226,961 36,037,342 6.7 136,182 273,000 1,439,646 Total 881,650 501,551 548,808 360,000 48,899 948,827 2,083,251 1,063,855 3,126,476 1,063,750 2,226,961 37.481,505 Tertiary education 5.1 5.2 5.3 1,489 1,489 5.4 6.5 72,333 25,000 278,041 1,015,260 286,917 6,262,944 6.7 6,720 36,111 Total 72,333 25,000 278.041 1,023,469 286,917 6,300,544 Training 5.1 5.3 5.4 Source: MEF - DNPP/ Calendarios de Compromiso 219 Appendix 6.13: (continued) Departments Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Cajamarca Amazonas Lambayegue Cusco 6.5 6.7 Total Special education 5.1 5.3 5.4 6.5 338,000 192,770 32,334 84,442 6.7 8,581 Total 338,000 192,770 40,915 84,442 Assistance 5.1 5.3 843,444 5.4 6.5 1,015,563 6.7 Total 1,859,007 Physical education 5.1 5.3 709,000 5.4 6.5 194,869 1,507,900 50,000 100,000 6.7 Total 194,869 2,216,900 50,000 100,000 Culture 5.1 5.3 236,850 5.4 6.5 102,000 6.7 Total 236,850 102,000 Military College 5.1 5.3 5.4 6.5 6.7 Total Department Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Cajamarca Amazonas Lambayeque Cusco aggregate 5.1 5.2 5.3 1,794,294 2,017 5.4 6.5 21,614,110 6,590,147 19,168,595 6,696,889 1,557,487 1,232,314 728,000 607,940 3,115,856 560,304 6.6 6.7 150,000 94,363 1,143,233 158,350 415,001 100,000 181,850 199,768 74,457 Total 21,764,110 6,684,510 22,106,122 6,855,239 1,972,488 1,232,314 828,000 789,790 3,315,624 636,778 Source: MEF - DNPP I Caleiodfrios de Compromiso por Subprorama y Programac, 1997 220 Appendix 6.13: (continued) Madre de Departments Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Cordelica Total 6.5 6.7 Total Special education 5.1 5.3 2,351 2,351 5.4 6.5 166,500 814,046 6.7 8,581 Total 168,851 824,978 Assistance 5.1 5.3 843,444 5.4 6.5 1,015,563 6.7 Total 1,859,007 Physical education 5.A 5.3 709,000 5.4 6.5 565.000 2,417,769 6.7 Total 565,000 3,126,769 Culture 5.1 5.3 236,850 5.4 6.5 102,000 6.7 Total 338,850 Military CoUege 5.1 5.3 5.4 6.5 6.7 Total Depart nent aggtgate 5.1 7,800 103,927 8,000 119,727 5.2 5.3 280,676 16,357 5,000 2,098,344 5.4 6.5 1,883,123 424,679 1,773,224 821,000 1,249,487 1,947,360 3,398,882 2,163,309 5,072,224 1,761,250 5,401,508 87,767,688 6.6 6.7 296,556 273,000 695,013 25,448 89,032 6,720 267,000 4,169,791 Total 2,187,479 697,679 2,468,237 1,231,051 1,338,519 1,947,360 3,398,882 2,163,309 5,095,301 2,041,250 5,401,508 94,155,550 Source: MEF - DNPPI Calendarios de Compromiso por Subprogramas Y Programas, 1997 221 Appendix 6.14 Total Public Expenditure on Education by Level, by Function, and by Department from All Sources of Funding, 1997 Department Piura i Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Amazonas Cajamarca Lambayegue Cusco Pension 5.2 42,882,048 6,565,297 24,905,153 8,160,529 59,775,005 5,188,448 14,429,632 35,176,782 6,336,473 35,039,038 41,799,402 43,909,463 5.4 5,147 8,698 Total 42,887,195 6,565,297 24,913,851 8,160,529 59,775,005 5,188,448 14,429,632 35,176,782 6,336,473 35,039,038 41,799,402 43,909,463 Administration 5.1 2,985,828 474,969 3,440,557 556,035 3,284,924 202,256 773,011 1,453,201 248,052 1,111,738 915,182 3,210,007 5.3 1.645,562 250,051 2,167,430 543,182 8,023,627 193,277 171,700 582,688 155,082 754,827 1,279,098 1,165,726 5.4 194,832 3,010 160,951 100,000 37,101 2,000 19,052 23,738 6.5 150,000 385,003 6.6 6,000 6.7 197,204 33,847 221,985 800,789 23,000 61,663 179,663 264,329 449,636 Total 5,173,426 761,877 5,990,923 1,199,217 12,537,444 395,533 967,711 2,099,552 422,186 2,069,966 2,458,609 4,825,369 Planning 5.1 52,824 50,029 664,842 703,580 967,149 475,104 526,549 2,129,030 354,947 1,965,147 802,669 716,669 5.3 56,957 28,969 106,438 429,535 633,007 48,130 110,500 413,163 102,242 171,939 152,379 355,776 5.4 2,319 6.5 676,515 108,860 6.7 6,250 22,000 18,515 Total 112,100 78,998 1,447,795 1,139,365 1,600,156 523,234 659,049 2,669,568 457,189 2,137,086 955,048 1,072,445 Initial education 5.1 12,948,142 5,407,740 14,604,023 5,465,844 7,856,490 2,827,209 3,811,622 14,904,951 2,943,593 14,530,587 6,887,647 14,301,877 5.3 1,205,035 384,690 517,780 570,920 1,613,448 89,396 121,080 419,630 404,264 2,582,375 1,381,374 98,825 5.4 24,230 19,654 2,800 6.5 1,1(00,212 1,137,809 1,358,203 825,780 307,103 265,000 369,000 144,016 6,7 20,818 2,880 129,847 98,200 Total 15,277,619 6,951,057 16,499,660 6,868,224 9,906,888 2,916,605 4,197,702 15,324,581 3,815,057 17,256,978 8,269,021 14,400,702 Primary education 5.1 77,415,477 15,426,863 64,586,636 27,019,956 45,858,167 9,809,792 13,693,270 81,941,018 12,034,363 99.072,220 43,685,5s8 73,002,833 5.3 1,742,9i9 189,670 932,434 543,619 1,156,687 378,467 512,398 1,()57,447 560,540 3,218,440 1,406,492 366,015 5.4 105,169 79,222 750 6.5 11,385,437 1,907,248 6,035,722 2,045,641 377,189 164,256 265,000 508,340 958,334 240,553 6.7 150,000 609,433 10,880 135,653 45,000 69,850 101.568 74,457 rotal 90,799,002 17,523,781 72,243,447 29,620,846 47,527,696 10,352,515 14,515,668 83,576,655 13,654,805 102,605,670 45,092,080 73,368,848 Secondary education 5.1 53,456,924 13,908,622 37,856,057 17,878,936 41,868,312 9,304,497 14,611,508 57,225,685 6,906,950 49,445,673 33,336,861 33,657,209 5.3 193,023 103,873 386,032 603,484 25,360 279,104 279,838 713,065 37,485 199,621 609,438 257,110 5.4 115,973 69,849 32,100 6.5 6,725,526 2.586,797 8,179,434 3,223,421 873,195 637,834 148,000 99,600 957,471 165,735 6.7 73,545 533,800 106,618 149,501 55,000 112,000 361 Total 60,491,446 16,672,837 47,025,172 21,844,559 42,916,368 10,221,435 15.094,346 58,150,350 7,901,906 49,811,029 33,946,299 33,914,680 Tertiary education 5.1 6,306.933 1,020,504 3,725,643 2,372,913 6,213,685 1,710,710 1,758,142 7,533.529 966,291 7,969,606 4,279,832 3,562,285 5.2 5.3 1,303,616 503,200 92,683 994,559 271,066 111,700 519,504 11o 47,132 677,854 112,214 5.4 18,(NN0 23,848 14.501 151,187 6,5 2,153,251 570,654 395,258 569,713 345,782 629.051 10,000 6.7 361,150 74,920 29,391 276,309 65,400 Total 10,142,950 2,193,126 4,228,085 4,117,763 6,213,685 2,327,558 1,869,842 8,329,342 1,595,452 8,026,738 5,023,086 3,674,499 Training 5,1 106,509 252,321 So-rce: MEF - DNPP I Calendariw,s de Compromriso 222 Appendix 6.14 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huinuco Pasco Junin Ancash San Martin Cordelica Total Pension 5.2 11,421,581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458.919 53,888,645 40,440,064 20,908,096 555,698,358 5 4 10,420 24,265 Total 11,421,581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458,919 53,888,645 40,450,484 20,908,096 555,722,623 Administration 5.1 2,691,208 405,161 1,024,894 3,754,053 2,549,440 1,439,518 621,938 709,528 724,441 2,759,659 1,119,927 36,455,527 5.3 484,438 137,843 487,677 658,027 614,101 731,562 384,820 390,768 1,063,442 765,871 565,138 23,215,937 5.4 8,414 810 68,872 40,000 95,170 46,291 800,241 6.5 5.400 540,403 6.6 40,100 2,804 48,904 6.7 144,521 19,250 13 60,280 31,070 17,270 17,270 74,660 211,196 88,563 67,701 2,963,910 Total 3,368,681 562,254 1,513,394 4,472,360 3,194,611 2,188,350 1,092,900 1,214,956 2.094,249 3,622,297 1,799,057 64,024,922 Planning 5.1 247,256 92,070 664,784 533,354 396,195 637,940 456,225 378,346 137,079 2,914,811 919,245 16,785,844 5.3 131,972 44,443 22,424 110,915 21,298 167,510 137,105 78,788 36,336 388,315 466,568 4,214,709 5.4 16,000 855 19,174 6.5 785,375 6,7 16,000 16,000 2,482 81,247 Total 379,228 136,513 687,208 644,269 417,493 821,450 625.330 457,134 173,415 3,303,981 1,388,295 21,886,349 Initial education 5.1 6,012,739 1,770,422 5,275,336 7,561,468 7,992,759 5,401,853 5,617,261 4,459,199 9,645,356 13,171,390 8,747,28-7 182,144,795 5.3 277,359 68,013 576,093 468.367 511,362 453,529 991,485 879,758 1,229,753 857,655 733.580 16,435,771 5.4 9,480 56,164 6.5 165,500 19,000 336,852 6,028,475 6.7 251,745 Total 6,290,098 1,838,435 5,851,429 8,029,835 8,504,121 5,855,382 6,608,746 5,504,457 10,894,109 14,038,525 9,817,719 204,916,950 Primary education 5.1 31,531,874 6,089,880 41,046,343 46,493.928 31,808,754 33,000,244 43,283,173 17,647,761 69,919,143 70,267,200 43,645,516 998,279,999 5.3 690,189 206,504 649,275 1,620,571 405.354 979,335 1,541,404 987,651 1,574,739 1,221,801 944,759 22,886,710 5.4 11,490 196,631 6,5 1,137,655 196,128 1,152,083 461.000 1,175,588 720,492 1,315,631 933,954 749,505 697,500 1,985,778 34,413,034 6.7 160,374 695,035 25,448 89,032 267,000 2,433,730 Total 33,520,092 6,492,512 43,542,736 48,600,947 33,478,728 34,700,071 46,140,208 19,569,366 72,243,387 72,464,991 46,576,053 1,058,210,104 Secondary education 5.1 15,047,815 3,736,113 24,542,162 24,429,814 35,348,778 14,858,481 21,738,275 14,172,554 52,731,293 51,626,277 21,441,162 649,129,958 5.3 416,968 203,601 96,224 257,067 263,054 180,976 357,935 192,428 467,497 472,380 1,087,260 7,682,823 5.4 8,910 16,600 243,432 6.5 745,468 228,551 548,808 360,000 48,899 948,827 2,083,251 1,063,855 3,121,959 1,063,750 2,226,961 36,037,342 6.7 136.182 273,000 120 1,440,127 Total 16,346,433 4,441,265 25,187,314 25,046,881 35,660,731 15,988,284 24,179,461 15,428,837 56,320,749 53,171,317 24,771,983 694,533,682 Tertiary education 5.1 1,520,383 584,262 3,525,764 4,707,833 4,112,810 1,731,958 2,871.234 2.768,414 7,074,497 6,7S2,109 3,050,278 86,119,615 5.2 5.3 75,445 39,230 22,719 69,216 109,532 3,116 98,424 29,845 94,557 339,770 156,618 5,672,110 5.4 207,536 6.5 72,333 25,000 278,041 1,015,260 59,100 286,9 17 6,410,360 6.7 140 6,720 814,030 Total 1.595,828 623,492 3,620,956 4,777,049 4,247,342 2,013,115 2,969,658 2,798,259 8,191,034 7,150,979 3,493,813 99,223,651 Training 5.1 11,076 29,670 24,571 19,134 443,281 Source: MEF - DNPP / Calendarios de Compromiso 223 Appendix 6.14 (continued) Department Piura Tumbes Loreto Ucayali Arequipa Moguegua Tacna Puno Amazonas Cajamarca L,ambayeque Cusco 5.3 65,740 1,456 5.4 6.5 6.7 Total 172,249 253,777 Special education 5.1 1,498,188 637,559 659,179 469,204 2,236,227 312,093 865,768 131,251 1,031,357 638,807 832,797 5.3 38,925 14,133 3,687 31,497 2,080 12,000 71,796 3,996 11,202 24,774 33,813 5.4 1,216 11,527 6.5 338,000 192,770 32,334 84,442 6.7 8,581 Total 1,876,329 844,462 674,393 541,616 2,238,307 84,442 324,093 937,564 135,247 1,042,559 663,581 866,610 Assistance 5.1 67,994 5.3 843,444 62,690 5.4 6.5 1,015,563 6.7 Total 1,859,007 130,6S4 Physical education 5.1 34,653 5.3 709,000 7,100 5.4 6.5 194,869 1,507,900 50,000 1OO,o00 6.7 30,000 Total 194,869 2,216,900 71,753 50,000 100,000 Culture 5.1 16,162 5.3 236,850 28,590 5.4 6.5 102,000 6.7 Total 236,850 44,752 102,000 Military College 5.1 1,010,144 559,045 5.3 660,090 2,927,667 912,047 5.4 6.5 6.7 Total 660,090 3,937,811 1,471,092 Department aggregate 5.1 154,664,316 36,926,286 125,536,937 54,517,283 109,469,601 24,581,889 35,486,195 166,053,182 23,585,447 175,126,328 91,105,631 129,283,677 5.2 42,882,048 6,565,297 24,905,153 8,160,529 59,775,005 5,188,448 14,429,632 35,176,782 6,336,473 35,039,038 41,799,402 43.909,463 5.3 6,846,127 1,474,586 5,995,778 3,752,486 14,510,306 1,260,896 1,319,216 3,777,293 1,263,719 6,985,536 6,443,456 2,389,479 5.4 466,886 26,858 364,402 286,837 37,101 2,000 19,052 23,738 6.5 21,852,426 6,590,147 19,168,595 6,696,889 1,942,490 1,232,314 728,000 716,800 3,115,856 560.304 6.6 6,000 6.7 708,354 203,130 1,365,218 194,600 1,215,790 145,000 538,337 199,768 254,120 329,729 449,997 Total 227,420,157 51,786,304 177,336,083 73,608,624 186,956,293 32,263,547 52,108,043 206,264,394 34,520,315 217,989,064 139,678,218 176,032,616 Source: MEP - DNPP I Calenducios de Compromiso 224 Appendix 6.14 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin CordelUca Total 5.3 151,411 71,554 10,101 54,<)00 354,262 5.4 6.5 2,200 2,200 6.7 49,450 49,450 Total 214,137 101,224 34,672 73,134 849,193 Special education 5.l 260,868 81,479 269,902 531,526 811,643 246,196 325,494 386.749 905.718 1,140,573 525,239 14,797,817 5.3 35,783 7,261 14,170 12,914 38,063 2,502 21,907 8,672 30.123 25,314 141,539 586,151 5.4 12,743 6.5 166.500 814,046 6.7 7 8,588 Total 296,651 88,740 284,079 544,440 849,706 24S,698 347,401 395,421 1,102,341 1,165,887 666,778 16,219,345 Assistance 5.1 67,994 5.3 906,134 5.4 6.5 1,015,563 6.7 Total 1,989,691 Physical education 5.1 17,207 94,086 35,879 181,825 5.3 50,298 15,415 21,200 803,013 5.4 6.5 565,000 2,417,769 6.7 30,000 Total 67,50s 109,501 57,079 565,000 3,432,607 Culture 5.1 16,162 5.3 265,440 5.4 6.5 102,000 6.7 Total 383,602 Military College 5.1 1,955,146 3,524,335 5.3 1,198,129 5,697,933 5.4 50 50 6.5 6.7 1,488,661 1,488,661 Total 4,641,986 10,710,979 Department aggregate 5.1 57,323,219 12,759,387 78,304,331 88,011,976 83,020,379 57,316,190 74,960,477 40,641,208 141,192,540 148,632,019 79,448,654 1,987,947,152 5.2 11,421,581 1,680,020 14,697,274 21,931,141 35,703,279 7,789,460 17,612,609 5,458,919 53,8S8,645 40,440,064 20,908,096 555,698,358 5.3 2,263.565 706,895 3,066,711 3,197,077 1.962,764 2.518,530 3,654.932 2,593,426 4,571,647 4,071,106 4,095,462 1,523,575 90,244,568 5.4 8,414 860 84,872 40,000 95,170 41,155 62,891 1,560,236 6.5 1,885.323 424,679 1,773,224 821,000 1,249,487 1,947,360 3,398,882 2,163,309 5,072,224 1,825,750 5,401,508 17,204,294 105,770,861 6.6 40,100 2,804 48,904 6.7 490,527 292,250 2,183,976 85,728 120,102 33,270 33,270 74,660 217,916 355,563 70,183 2,982,702 12,544,190 Total 73,432,729 15,863,231 100,026,376 114,046,922 122,056,011 69,604,810 99,745,042 50,971,522 205,038.142 195,368,461 109,986,794 21,710,571 2,753,814,269 I Source: MEF - DNPP / Calendarios de Compromism 225 Appendix 6.15 Departmental Revenues from Central Government Allocation as a Percentage of Total, 1997 Depart,inent Piura T'umbes Loeo Uaai Aeup ouga TcaPunoAmzna aj ara L byeuCuo Pension 5.2 100% I[(00% 100% 100% 100% 1()0% l00% [00% o(0% 100% 1I0% 100% 5.4 1 00% [00% Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Administration 5.1 98% JOO% 100% 98% 100% 100% 95% 100% 10)0% 90% 100% 100% 5.3 50% 81% 87% 89% 39% 31% 74% 88% S6% 61% 50% 76% 5.4 9% 100% 13% 75% 16% 6.5 l O0% 6.6 6.7 39% 100% 81% 44% Total 78% 94% 92% 92% 51% 66% 89% 94% 90% 71% 63% 89% Planning 5.1 100% 100% 100% 100% 100% 100% 91% 100% 100% 100% 100% 100% 5.3 79% 100% 100% 100% 100% 100% 67% 8S% 84% 84% 50% [00% 5.4 100% 6.5 6.7 100% Total 89% 100% 53% 100% 100% 100% 84% 93% 96 % 99% 92% 100% Initial education 5.1 100% 100% 100% 100% 1()0% 100% 100% I1(0% 100% 100% 10()% 100% 5.3 100% 100% 100% 9()% 100% I 00% 100% 96% 100% 100%V, 0100% 100/% 5.4 100% 1(51% 65 6.7 Total 93% 83% 92% 87% 96% 100% 94% 100% S8 % 99% 100% 100% Primary education 5.1 100% % 100% iO0% 100% 100% 100% I(00% 10(% I00% 100% 100% 5.3 100% 10()% 99% 89% 100% 100% 1(0% 93% DO% 100% 100% 100% 5.4 100% 1()0% 6.5 6.7 Total 87% 89% 91% 93% 99% 98% 98% 99% 92 %. 100% 100% 100% Secondary education 5.1 100% 100% 100% 1()0% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 100% 100% 100% 27% 100% 50% 100% 80% 100% 100% 10()% 100% 5.4 100% 100% 6.5 6.7 100% Total 89% 84% 81% 83% 98% 92% 99% 99% 88%7. 100% 100% 100% Tertiary education 5.1 97% 100% 100% 1(0% [00% iOD% I00% [00% 10D% 100% 100% 100% 5.2 5.3 21% 9% 100% 12% 15% 1(0% 33% 100% 100% 6% 100% 5.4 72% 100% 6.5 6.7 Total 63% 49% 91% 61% 100% 75% 100% 92% 61%T 100% 86% 100% Training 5.1 10% [00% Source: MEF - DNPP / C.loJdarmrn je C,,npromiso 226 Appendix 6.15 (continued) Madre de Department Apuriimac Dios La Libertad Ayacucho Ica Huancavelica Huainuco Pasco Junmn Ancashi San Martin Total Pension 5,2 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.4 100% 100% Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Administration 5.1 100% 100% 98% 100% 99% 100% I100% 100% 100% 100% 100% 99% 5.3 80% 94% 98% 86% 85% 99%7 98% 96% 76% 75% 71% 63% 5.4 100% 10O0% 100% 100% 100% 100% 48% 6.5 100% 29% 6.6 6.7 8% 100% 17% 62% 20% Total 92% 95% 98% 97% 95% 99% 98% 92% 79% 94% 87% 81% Planning 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 100% 100% I100% 92% 100% 96% 96% 100% 100% 98% 73% 9I% 5.4 1 00% 96% 6.5 6.7 21% 8% Total 100% 100% 100% 99% 100% 97% 96% 100% 100% 100% 91% 94% Initial education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 87% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 99% 5.4 100% 95% 6.5 6.7 Total 99% 100% 100% 100% 100% 100% 100% 97% 100% 100% 97% 97% Primary education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 84% 100% 100% 83% 10(0% 100% 100% 100% 99% 100% 100% 98% 5.4 100% 100% 6.5 6.7 0% 0% Total 96% 97% 96% 98% 96% 98% 97% 95% 99% 99% 96% 96% Secondary education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 50% 100% 100% 100% 100% 100% I100% 100% 99% 100% 100% 88% 5.4 100% 100% 87% 6.5 6.7 100% 0% Total 93% 89% 98% 99% 100% 94% 91% 93% 94% 98% 91% 94% Tertiary education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.2 5.3 100% 100% 100% 100% 100% 100% 100% 100% 98% 38% 100% 33% 5.4 13% 6.5 6.7 100% 0% Total 100% 100% 98% 100% 99% 86% 100% 100% 88% 96% 92% 89% {T .iin !000% 100% 100% 98% [Source: MEF- DNPP/ Ca1enant~osde comvromisc' 227 Appendix 6.15 (continued) Department Piura Turnbes Loreto Ucayali Arequipa Moguegua Tacna Puno Amazonas Cajamarca Lambayegue Cusco 5.3 100% 100% 5.4 6.5 6.7 Total 100% 100% Special education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.4 100% I00% 6.5 6.7 Total 82% 77% 100% 92% 100% 100% 100% 100% 100% 100% 100% Assistance 5.1 100% 5.3 100% 5.4 6.5 6.7 Total 100% Physical education 5.1 100% 5.3 100% 5.4 6.5 6.7 100% Total 100% Culture 5.1 100% 5.3 100% 5.4 6.5 6.7 Total 100% Military College 5.1 100% 100% 5.3 47% 100% 5.4 6.5 6.7 Total 60% 100% Department Piura Tumbes Loreto Ucayali Arequipa Moquegus Tacna Puno Amazonas Cajamarca Lambayeque Cusco aggregate 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.2 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 63% 66% 65% 60% 55% 60% 94% 81% 97% 95% 79% 89% 5.4 61% 11% 62% 26% 6.5 1% 6.6 6.7 11% 17% 13% 19% 45% Total 89% 86% 87% 88% 95% 95% 98% 99% 90% 99% 99% 100% Souwce: MEF - DNPP/ Cle.ndarios de Compromiso 228 Appendix 6.15 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Ruancavelica Huanuco Pasco Junin Ancash San Martin Total 5.3 100% 100% 100% 57% 5.4 6.5 6.7 Total 100% 100% 100% 75% Special education 5.1 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 92% 100% 100% 100% 100% 100% 100% 100% 92% 100% 100% 99% 54 100% 6.5 6.7 100% 0% Total 99% 100% 100% 100% 100% 100% 100% 100% 85% 100% 100% 95% Assistance 5.1 100% 5.4 7% 6.5 6.7 Total 7% Physical education 5.1 100% 100% 100% 100% 5.3 100% 100% 100% 12% 5.4 6.5 6.7 100% Total 100% 100% 100% 9% Culture 5.1 100% 5.4 11% 6.5 6.7 Total 12% Military College 5.1 25% 58% 5.3 100% 61% 5.4 100% 100% 6.5 6.7 2% 2% Total 37% 52% Department aggregate 5.1 100% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.2 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 5.3 73% 99% 100% 88% 95% 99% 100% 99% 94% 90% 93% 78% 5.4 100% 100% 100% 98% 100% 59% 6.5 0% 0% 6.6 6.7 2% 1% 16% 15% 1% 5% Total 96% 95% 95% 99% 99% 97% 97% 96% 97% 99% 95% 95% Souce: MEF - DNPP / Calendarkim de Compromiso 229 Appendix 6,16 Department's Own Resrouces as a Percentage of Total, 1997 Department Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Amazonas Cajaniarca l.ambaye4ue Cusco Pension 5.2 5.4 'rotal Administration 5.1 2% 2%h 5% 10% 5.3 50% 19% 13% 11% 61% 6395" 26%M 12% 14% 39% 50% 24% 5.4 91% 87% 25% look 100% 1(0gO 84% 6.5 100% 6.6 10 6.7 61% 19% 100% 100% I(Y)% 100% I100% 56%S Total 22% 6 % 8 % 8 % 49% 34% 11% 6 % 10% 29% 37% 11% Planning 5.1 9 % 0% 5.3 2 1 ek 33% 12% 16%/ 16% 50%f 5.4 6.5 100% 6.7 100% 100% Total 11% 16% 7% 4% 1% 8% Initial education 5.1 5.3 10% 4% S.4 100% 6.5 6.7 Total I /e 0% Primary education 5.1 5.3 11% 7% 5.4 100% 6.5 6.7 Total %0 Secondary education 5.1 5.3 73% 50% 20% 5.4 100% 6.5 6.7 Total 2% 1% 0% Tertiary education 5.1 3i7, -5.2 5.3 79°h 91% 88% S5%o 67%7 94% 5.4 28% 100% 100% 6.5 4% 6.7 1 OO% I00 OO00 O% I ()0% Total 17% 25% 2 5 te 10% 8%J 14% Training S.i Source: MEF - DNPP /Calendarios de Compromiso 230 Appendix 6.16 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Total Pension 5.2 5.4 Total Adniinistration 5.1 2% 1 % 1% 5.3 20% 6% 2% 14% 15% 1% 2% 4% 24% 25% 29% 37% 5.4 71% 6.6 100% 100% 100% 6.7 92% 100% 100% 100% 100% 100% 100% S3% 38% 100% 80% Total 8% 5% 2% 3% 5% 1% 2% 8% 21% 6% 13% 19% Planning 0 5.1 0% 5.3 8% 4% 4% 2% 27% 9% 5.4 100% 4% 6.5 6.7 100% 100% 79% 92% Total 1% 3% 4% 0% 9% 3% Initial education 5.1 l% 5.3 13% 1% 5.4 5% 6.5 6.7 0% Tota 1% Primary education 5.1 % 5.3 16% 0% 5.4 6.5 6.7 e* Total 0% Secondary education 5.112 5.3 50% 132% 5.4 6.5 6.7 e% Total 1% Tertiary education 5.2 5.23 62% 67% 5.4 87% 5.4 OD% 92%* 6.5 002%% 6.7 4% 5% Total4%5 Training 2% 5.1 100% Source: MEF - DNPP / Calendarios de Compromiso 231 Appendix 6.16 (continued) Departrent Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno A mazonas Caj3amarca lambayeque Cusco 5.4 6.5 6.7 Total Special education 5.1 5.3 54 6.5 6.7 Total Assistance 5.1 5.3 5.4 6.5 6.7 Total Physical education 5.1 5.3 5.4 6.5 6.7 Total Cultury 5.1 5.3 10 3 5.4 6.5 6.7 Total ltagry Coege 5.1 5.3 1 0%% 53% 6.5 6.7 Total 100* 40% Depar tmrnent aggregate 5.1 5.2 5.3 37% 34% 5 % 40% 45% 40% 6% 19% 3% 5% 21% 1l 5.4 39Sf 89% 38% 74% 100% 100% 100% 84% 6 5 O* 20% 15% 6.6 10 6.7 68% 37% 3% 66% 31% 66% 71% 100% 55% Total 2% 1% 0% 2% 4% 2% 0% 1% 0% 0% 1% 0% Source: MEF - DNPP / Calendarios de Compromiso 232 Appendix 6.16 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Total 5.3 100% 43% 5.4 6.5 100% 100% 6.7 lOo% 100% Total 100% 25% Special education 5.1 53 8% 0% 5.4 6.5 6.7 Total 1% 0% Asasisance 5.1 5.3 5.4 6.5 6.7 Total Physical education 5.1 53 5.4 6.5 6.7 Total Culture 5.1 5.3 5.4 6.5 6.7 ToWal Military College 5.1 75% 42% 5.3 39% 5.4 6.5 6.7 98% 98% Total 63% 48% Department aggregate 5.1 2% 0% 0% 0% 5.2 5.3 27% 1% 0% 3% 5% 1% 041% 1% 6% 10% 7% 20% 5.4 2% 41% 6.5 0% 3% 17% 6.6 100% 100% 100% 6.7 37% 7% 67% 70% 26% 100% 100% 100% 81% 9% 99% 62% Total 1% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 2% Source: MEF - DNiP / Calendrrios de Comprowiso 233 Appendix 6.17 Other Resources as a Percentage of Total, 1997 Department Piura Tumbes Loreto Ucayali Arequipa Moquegua Tacna Puno Amazonas Cajamarca Lambayegue Cusco Pension 5.2 5.4 Total Administration 5.1 5.3 5.4 6.5 6.6 6.7 Tow Planning 5.1 5.3 5.4 6.5 100% 6.7 Total 47% Initial education 5.1 5.3 5.4 6.5 100% 100% 100% 100% 100% 100% 100% 100% 6.7 100% 100% 100% 100% Total 7% 17% 8% 12% 4% 6% 12% 1% Primary education 5.1 5.3 1% 0% 5.4 6.5 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 6.7 100% 100% 100% 100% 100% 100% 100% 100% Total 13% 11% 9% 7% 1% 2% 2% 1% 8% 0% Secondary education 5.1 5.3 5.4 6.5 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 6.7 100% 100% 100% 100% 100% 100% Total 11% 16% 19% 15% 2% 6% 1% 0% 12% 0% Tertiary education 5.1 5.2 5.3 5.4 6.5 96% 100% 100% 100% 100% 100% 100% 6.7 100% Total 20% 26% 9% 15% 15% 39% 0% Training 5.1 Total 1% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 2% Source: MEF - DNPP / Calendarios de Compromniso 234 Appendix 6.17 (continued) Madre de Department Apurimac Dios La Libertad Ayacucho Ica Huancavelica Huanuco Pasco Junin Ancash San Martin Total Pension 5.2 5.4 Total Administration 5.1 5.3 5.4 6.5 6.6 6.7 Total Planning 5.1 5.3 5.4 6.5 86% 6.7 Total 3% Initial education 5.1 5.3 5.4 6.5 100% 100% 100% 100% 6.7 100% Total 3% 0% 3% 3% Primary education 5.1 0% 0% 0% 0% 5.3 17% 1% 0% 1% 5.4 6.5 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 6.7 100% 100% 100% l00% 100% 100% Total 4% 3% 4% 2% 4% 2% 3% 5% 1% 1% 4% 4% Secondary education 5.1 5.3 1% 0% 5.4 6.5 100% I00% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 6.7 100% 100% 100% Total 5% 11% 2% 1% 0% 6% 9% 7% 6% 2% 9% 5% Tertiary education 5.1 5.2 5.3 2% 0% 5.4 6.5 100% 100% 100% 100% 100% 98% 6.7 100% 4% Tetal 2% 1% 14% 12% 8% 6% Training 5.1 Source: MEF - DNPP / Calendarios de Compromiso 235 Appendix 6.17 (continued) Department Piura Tumbes Loreto Ucayali Arequipa Moguegua Tacna Puno Ansazonas Cajamarca Lambayeque Cusco 5.3 5.4 6.5 6.7 lTotal Special education 5.1 5.3 5.4 6.5 100% 100% 100% 100% 6.7 100% Total 18% 23% 8% 100% Assistance 5.I 5.3 100% 5.4 6.5 100% 6.7 Total 100% Physical education 5.1 5.3 100% 5.4 6.5 100% 100% 100% 100% 6.7 Total 100% 100% 100% 100% Culture 5.1 5.3 100% 5.4 6.5 100% 6.7 Total 100% 100% Military College 5.1 5.3 5.4 6.5 6.7 Total Department aggregate 5.1 5.2 5.3 30% 0.029% 5.4 6.5 99% 100% 100% 100% 80% 100% 100% 85% 100% 00% 6.6 6.7 21% 46% 84% 81% 34% 69% 34% 100% 29% Total 10% 13% 12% 9% 1% 4% 2% 0% 10% 0% Source: MEF - DNPP / Calendarios de Compromiso 236 Appendix 6.17 (continued) Madre de Department Apurimnac Dios La Libertad Ayacucho Ica Huancavelica Huinuco Pasco Junin Ancash San Martin Total 5.3 5. 4 6.5 6.7 Total Special education 5.1 5.3 8% 0% 5.4 6.5 100% 100% 6.7 100% Total 15% 5% Assistance 5.1 5.3 93% 5.4 6.5 100% 6.7 Total 93% Physical education 5.1 5.3 88% 5.4 6.5 100% 100% 6.7 Total 100% 91% Culture 5.1 5.3 89% 5.4 6.5 100% 6.7 Total 88% Military Colege 5.1 5.3 5.4 6.5 6.7 Total Departmnent aggregate 5.1 0% 0% 0% 0% 5.2 5.3 9% 0% 0% 2% 5.4 6.5 100% 100% 100% 100% 100% 100% 100% 100% 100% 96% 100% 83% 6.6 6.7 60% 93% 32% 30% 74% 3% 75% 33% Total 3% 4% 2% 1% 1% 3% 3% 4% 2% 1% 5% 3%I Source: MEF - DNPP / Calendarios de Compromiso 237 Appendix 6: 18: Per Student Recurrent Expenditure by Level and by Department, 1997 (Soles) Tertiary Vocational initial Primary Secondary nonuniversity training Special Amazonas 177 168 320 283 7 1,225 Ancash 328 411 661 733 21 1,016 Apurfmac 295 352 558 655 181 2,083 Arequipa 363 459 683 620 135 2,832 Ayacucho 337 406 675 867 25 1,397 Cajamarca 278 362 609 711 9 1,718 Cusco 289 352 437 590 15 1,363 Huancavelica 130 175 206 694 19 406 Huanuco 322 313 525 671 39 1,616 Ica 318 398 596 550 19 1,725 Junin 292 357 527 654 20 1,439 LaLibertad 300 315 530 750 8 2,171 Lambayeque 270 326 438 722 12 1,197 Lima- Callao 444 454 529 815 413 1,536 Loreto 376 386 675 836 22 2,078 Madre de Dios 498 401 575 739 24 1,996 Moquegua 490 640 850 1,094 169 24 Pasco 403 394 650 1,167 35 1,510 Piura 255 327 545 1,107 12 1,668 Puno 233 416 580 689 12 2,271 San Martin 371 379 513 820 16 2,253 Tacna 372 486 688 786 23 2,516 Tumbes 476 599 896 782 13 2,025 Ucayali 292 350 610 1,327 17 1,355 PERU 335 385 557 750 180 1,615 238 Appendix 6.19: Teachers Salary Scale (July 1990-August 1997) (Soles in current prices) LEVEL AND 1990 1991 1992 1993 1994 NOVEMBER - 1996 AUGUST - 1997 WORK SHIFT JUL AUG FEB AUG SEP JAN AUG MAY APR OCT 20530 19990 AFP 20530 19990 AFP With title V 40 Hrs. 17.58 35.16 73.78 121.06 138.31 163.21 223.21 293.21 413.21 557.21 642.88 662.49 703.65 745.74 768.49 816.23 30 Hrs. 16.49 32.97 65.87 106.22 123.47 149.86 209.86 279.86 394.86 536.86 619.28 638.04 677.45 718.36 740.13 785.84 24 Hrs. 13.65 27.30 60.20 97.60 114.85 129.31 189.31 259.31 369.31 502.31 579.20 597.11 634.72 671.87 692.65 736.28 IV 40Hrs. 16.66 33.31 69.68 111.66 128.91 153.42 213.42 283.42 399.42 539.42 622.25 641.18 680.95 721.81 743.77 789.9 30Hrs. 15.50 31.00 63.59 100.04 117.29 142.02 202.02 272.02 384.02 521.02 600.90 619.13 657.39 697.04 718.19 762.57 24 Hrs. 13.13 26.26 59.63 94.13 111.38 124.22 184.22 254.22 362.22 492.22 567.50 585.09 622.04 658.3 678.19 721.57 III 40 Hrs. 15.87 31.74 67.39 104.90 122.15 146.33 206.33 276.33 389.33 525.33 605.90 624.31 662.97 702.84 724.2 769.05 30Hrs. 15.01 30.01 61.25 93.00 110.25 135.32 195.32 265.32 374.32 508.32 586.17 603.89 641.11 679.96 700.51 743.69 24Hrs, 12.70 25.40 57.99 88.29 105.54 119.23 179.23 249.23 354.23 481.23 554.75 571.87 607.83 643.51 663.37 705.08 II 40Hrs. 15.59 31.17 65.25 99.64 116.89 139.99 199.99 269.99 379.99 512.99 591.59 609.57 647.32 686.24 707.1 750.89 30 Hrs. 14.50 29.00 59.85 88.70 105.95 129.98 189.98 259.98 365.98 499.98 576.50 593.94 630.57 668.74 688.97 731.46 24 Hrs. 12,27 24.54 56.71 84.11 101.36 114.97 174.97 244.97 347.97 471.97 544.01 560.78 595.99 631.05 650.5 691.35 1 40Hrs. 14.33 28.65 62.05 93.31 110.56 133.57 193.57 263.57 371.57 501.57 578.34 595.89 632.73 670.87 691.23 733.97 30 Hrs. 13.52 27.03 57.89 83.84 101.09 125.04 185.04 255.04 360.04 488.04 562.65 579.64 615.32 652.67 672.38 713.77 24Hrs. 11.60 23.20 55.06 79.56 96.81 111.11 171.11 241.11 343.11 465.11 536.05 552.53 587.14 621.82 640.93 681.08 Without title A 40Hrs. 13.54 27.07 56.48 78.98 96.23 116.98 176.98 236.98 326.98 442.98 510.38 525.76 558.08 592.04 609.88 647.37 30Hrs. 12.60 25.21 52.59 74.84 92.09 111.24 171.24 231.24 321.29 436.24 502.56 517.75 549.65 582.97 600.59 637.59 24 Hrs. 11.00 22.00 50.18 72.18 89.43 99.29 159.29 219.29 309.29 420.29 484.06 498.99 530.36 561.51 578.83 615.22 B 40-rs. 13.45 26.90 56.25 78.25 95.50 111.90 171.90 231.90 321.90 435.90 502.16 517.45 549.54 582.51 600.24 637.47 30Hrs. 12.55 25.09 52.37 74.12 91.37 106.36 166.36 226.36 316.36 429.36 494.58 509.66 541.35 573.71 591.21 627.97 24 Hrs. 10.95 21.90 49.99 71.49 88.74 95.41 155.41 215.41 305.41 415.41 478.40 493.27 524.50 554.94 572.19 608.42 C 40Hrs. 13.41 26.83 56.01 77.51 94.76 106.81 166.81 226.81 316.81 429.81 495,10 510.32 542.27 574.32 591.97 629.03 30Hrs. 12.49 24.97 52.17 73.42 90.67 101.51 161.51 221.51 311.51 423.51 487.79 502.81 534.36 565.84 583.26 619.86 24Hrs. 10.90 21.80 49.79 70.79 88.04 91.51 151.51 211.51 301.51 409.51 471.55 486.32 517.33 547 564.13 600.1 D 40 Hrs. 13.35 26.70 55.78 76.78 94.03 101.73 161.73 221.73 311.73 422.73 486.89 502.00 533.73 564.79 582.32 619.13 30 Hrs. 12.43 24.86 51.95 72.70 89.95 96.63 156.63 216.63 306.63 416.63 479.81 494.73 526.06 556.58 573.89 610.23 24Hrs. 10.85 21.70 49.61 70.11 87.36 87.63 147.63 207.63 297.63 404.63 465.89 480.59 511.47 540.43 557.48 593.31 E 40Hrs. 13.29 26.57 55.11 75.61 92.86 96.21 156.21 216.21 306.21 415.21 478.16 493.15 524.64 554.67 572.05 608.58 30Hrs. 12.38 24.76 50.12 70.37 87.62 90.14 150.14 210.14 300.14 408.14 469.96 484.71 515.69 545.15 562.26 598.2 24 Hrs. 10.80 21.60 49.40 69.40 88.65 86.65 146.65 206.65 296.65 402.65 463.59 478.23 508.97 537.76 554.75 590.41 TOTAL AVERAGE. 13.21 26.43 56.81 83.62 100.88 117.50 177.50 244.32 345.36 469.08 540.65 557.20 591.94 627.16 646.34 686.65 I/ The weighted average used the number of teachers and the structure by magisterial level of 1997, Lima Source: Payroll USE 04-EL AGUSTINO (Personrnel Office MED) 239 APPENDIX 7 HOUSEHOLD EXPENDITURE ON EDUCATION 241 Appendix 7.1: Average Household Expenditures on Preprimary Education by School Type, 1997 (Soles per Child) Consumption Fees, Books and School Tuition, food, Total Sample Projected quintile APAFA supplies uniforms and transport size population Ql-poorest Private 10.34 10.27 20.62 1 807 Public 3.82 23.58 20.73 14.22 62.35 226 260,964 Q2 Parochial 2.07 46.52 41.35 45.20 135.14 2 2,758 Private 61.17 138.60 78.56 163.20 441.54 5 6,634 Public 6.24 38.38 33.13 30.37 108.13 202 225,617 Q3 Parochial 18.25 42.13 126.26 204.07 390.71 2 2,193 Private 95.01 123.05 81.38 305.68 605.12 9 12,893 Public 12.62 62.50 46.56 57.50 179.18 145 170,802 Q4 Parochial 36.24 77.65 51.77 360.66 526.32 1 586 Private 59.11 140.71 122.01 440.61 762.44 23 33,850 Public 23.11 66.92 67.41 94.55 251.99 125 154,129 Q5-richest Parochial 46.62 207.20 207.20 401.97 863.00 1 1,648 Private 167.97 208.61 141.54 1,073.94 1,592.07 31 42,159 Public 39.21 102.01 81.03 208.66 430.91 64 78,465 All Parochial 20.01 84.58 106.16 201.23 411.98 6 7,185 Private 111.20 166.82 121.11 676.99 1,076.12 69 96,343 Public 12.58 49.22 42.23 57.68 161.71 762 889,977 Sum total All types 22.20 60.88 50.34 118.77 252.20 837 993,505 Source: Household survey by Instituto Cuanto 1997. 243 Appendix 7.2: Average Household Expenditures on Primary Education by School Type, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Sample Projected quintile APAFA supplies uniforms and transport Total size population Ql-poorest Private 20.78 83.13 226.09 330 1 1,648 Public 8.23 30.7 28.31 21.21 88.45 765 895,101 Q2 Parochial 12.08 78.01 51.44 89.95 231.47 7 10,192 Private 40.78 85.7 128.54 195.58 450.6 5 7,967 Public 11.03 45.9 41.36 44.11 142.4 700 827,612 Q3 Parochial 45.3 74.6 82.47 160.2 362.57 7 9,795 Private 81.06 131.59 86.75 516.47 815.88 18 25,835 Public 13.67 66.32 57.44 70.79 208.22 581 685,956 Q4 Parochial 182.3 91.45 88.75 378.14 740.64 7 8,099 Private 78.1 157.83 103.4 516.62 855.95 50 70,539 Public 20.02 85.04 78.72 123.92 307.7 409 494,873 Q5-richest Parochial 88.66 96.19 141.37 410.32 736.55 5 7,832 Private 346.04 209.34 137.69 1543.7 2236.76 111 150,271 Public 31.3 118.41 102.85 210.14 462.7 227 270,195 All Parochial 76.22 84.08 87.92 243.95 492.17 26 35,917 Private 233.99 182.67 121.94 1107.03 1645.63 185 256,259 Public 13.94 58.3 52.22 70 194.45 2682 3,173,737 Sum total All types 30.85 67.76 57.74 148.48 304.84 2893 3,465,914 Source: Household survey by 1nstituto Cuanto 1997. 244 Appendix 7.3: Average Household Expenditures on Secondary Education by School Type, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Total Sample Projected quintile APAFA supplies uniforms and transport size population Ql-poorest Private 23.90 57.55 15.17 285.58 382.20 3 3,572 Public 15.69 44.35 44.02 43.09 147.16 237 288,154 Q2 Private 83.72 110.79 49.17 100.40 344.09 4 5,811 Public 22.97 59.31 57.79 74.10 214.15 323 409,393 Q3 Parochial 88.26 102.52 40.72 74.86 306.37 6 9,131 Private 74.58 78.49 37.85 226.90 417.82 8 11,051 Public 34.15 79.52 57.20 141.94 312.81 323 402,306 Q4 Parochial 99.91 102.58 69.61 521.46 793.56 3 4,932 Private 87.05 110.77 96.49 375.88 670.19 33 48,807 Public 43.88 103.24 72.24 224.54 443.90 341 437,669 Q5-richest Parochial 57.61 95.71 136.78 905.40 1,195.50 7 8,387 Private 364.82 246.91 121.21 1,570.44 2,303.38 82 111,946 Public 102.79 138.91 77.93 454.88 774.51 239 318,495 All Parochial 79.37 99.99 82.95 483.24 745.55 16 22,450 Private 256.56 191.86 105.07 1,094.23 1,647.72 130 181,188 Public 42.89 85.38 62.39 184.81 375.47 1,463 1,856,017 Sum total All types 62.08 94.91 6637 268.06 491.42 1,609 2,059,656 Source: Household survey by Instituto Cuanto 1997. 245 Appendix 7.4: Average Household Expenditures on Tertiary Nonuniversity Education by School Type, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Total Sample Projected quintile APAFA supplies uniforms and transport size population Ql-poorest Public 41.13 46.27 36.69 121.12 245.21 18 21,213 Q2 Public 91.53 100.12 28.73 291.62 512.00 32 39,808 Q3 Private 100.44 401.78 16.07 1,622.45 2,140.75 1 1,379 Public 93.11 71.15 22.76 405.79 592.82 33 43,187 Q4 Private 146.04 143.51 53.41 1,172.44 1,515.41 4 6,329 Public 114.11 106.99 49.66 436.39 707.16 56 68,602 Q5-richest Parochial 47.78 73.77 12.29 1,773.78 1,907.63 2 2,899 Private 130.62 110.87 16.31 1,209.02 1,466.82 13 16,145 Public 174.50 163.57 51.97 933.55 1,323.58 55 69,103 All Parochial 47.78 73.77 12.29 1,773.78 1,907.63 2 2,899 Private 132.97 136.35 26.14 1,223.22 1,518.68 18 23,853 Public 117.50 110.30 40.94 521.47 790.21 194 241,914 Sum total All types 118.12 112.22 39.32 597.29 866.94 214 268,667 Source: HIousehold survey by Instituto Cuanto 1997. 246 Appendix 7.5: Average Household Expenditures on University Education by School Type, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Sample Projected quintile APAFA supplies uniforms and transport Total size population QI-poorest Public 73.99 69.79 13.64 197.03 354.45 11 16,159 Q2 Private 150.26 61.79 212.05 1 1,398 Public 96.08 91.50 168.80 356.39 20 23,786 Q3 Private 481.61 80.13 0.00 1,352.21 1,913.95 1 1,648 Public 124.90 142.42 9.91 361.53 638.76 59 73,714 Q4 Private 171.44 80.06 0.00 619.56 871.05 6 8,599 Public 132.99 166.65 5.16 433.58 738.39 72 101,796 Q5-richest Parochial 205.10 204.14 0.00 1,257.67 1,666.91 4 5,791 Private 403.13 305.94 7.90 2,829.92 3,546.89 96 126,701 Public 197.99 239.78 14.05 1,151.31 1,603.13 135 186,601 Parochial 266.36 176.66 0.00 1,278.62 1,721.64 5 7,439 All Private 385.97 289.23 7.32 2,661.94 3,344.47 103 136,698 Public 157.12 187.81 10.20 728.31 1,083.43 297 402,055 Sum total All types 215.88 213.04 9.34 1,219.74 1,658.01 405 546,192 Source: Household survey by Instituto Cuanto 1997. 247 Appendix 7.6: Average Household Expenditures on Education by Education Level, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Total Sample Projected quintile APAFA supplies uniforms and transport size population Ql-poorest Preprimary 3.81 23.54 20.66 14.21 62.22 227 261,772 Primary 8.26 30.80 28.26 21.58 88.90 766 896,749 Secundary 15.79 44.51 43.67 46.06 150.04 240 291,726 Sup. nouniv. 41.13 46.27 36.69 121.12 245.21 18 21,213 Sup. univ. 73.99 69.79 13.64 197.03 354.45 11 16,159 Q2 Preprimary 7.74 41.31 34.51 34.30 117.86 209 235,010 Primary 11.32 46.66 42.30 46.09 146.37 712 845,770 Secundary 23.82 60.03 57.66 74.46 215.97 327 415,204 Sup. no univ. 91.53 100.12 28.73 291.62 512.00 32 39,808 Sup. univ. 99.09 89.85 0.00 159.43 348.37 21 25,184 Q3 Preprimary 18.40 66.46 49.91 76.44 211.22 156 185,887 Primary 16.51 68.77 58.83 87.96 232.07 606 721,586 Secondary 36.37 79.99 56.34 142.71 315.42 337 422,488 Sup. no univ. 93.33 81.39 22.56 443.44 640.72 34 44,566 Sup. univ. 132.70 141.05 9.70 383.20 666.65 60 75,362 Q4 Preprimary 29.61 80.20 77.17 157.50 344.48 149 188,565 Primary 29.46 94.08 81.90 175.81 381.25 466 573,511 Secondary 48.73 103.98 74.62 242.56 469.88 377 491,409 Sup. no univ. 116.81 110.07 49.98 498.56 775.42 60 74,931 Sup. univ. 135.99 159.91 4.76 448.06 748.72 78 110,394 Q5-richest Preprimary 83.71 140.19 103.59 509.62 837.10 96 122,272 Primary 142.78 149.91 115.78 681.69 1,090.15 343 428,297 Secondary 168.77 165.63 90.10 748.07 1,172.57 328 438,828 Sup. no univ. 162.29 150.96 44.14 1,011.64 1,369.03 70 88,148 Sup. univ. 279.57 265.40 11.36 1,819.76 2,376.09 235 319,093 All groups Preprimary 22.20 60.88 50.34 118.77 252.20 837 993,505 Primary 30.85 67.76 57.74 148.48 304.84 2,893 3,465,914 Secondary 62.08 94.91 66.37 268.06 491.42 1,609 2,059,656 Sup. no univ. 118.12 112.22 39.32 597.29 866.94 214 268,667 Sup. univ. 215.88 213.04 9.34 1,219.74 1,658.01 405 546,192 Sum total All types 55.43 86.90 54.88 274.26 471.47 5,958 7,333,934 Source: Household survey by Instituto Cuanto 1997. 248 Appendix 7.7: Average Household Expenditures on Education by School Type, 1997 (Soles per Student) Consumption Fees, Books and School Tuition, food, Total Sample Projected quintile APAFA supplies uniforms and transport size population Ql-poorest Private 19.85 58.22 8.99 232.44 319.50 5 6,027 Public 10.10 32.75 29.99 27.58 100.42 1,257 1,481,592 Q2 Parochial 9.94 71.31 49.29 80.42 210.96 9 12,950 Private 65.44 106.94 83.95 147.84 404.17 15 21,810 Public 16.95 50.51 43.57 58.52 169.55 1,277 1,526,216 Q3 Parochial 91.51 83.07 63.98 216.48 455.04 16 22,767 Private 83.70 125.25 72.93 430.61 712.49 36 51,158 Public 27.98 73.94 52.39 116.03 270.33 1,141 1,375,965 Q4 Parochial 146.17 94.89 80.23 429.30 750.60 11 13,617 Private 84.21 136.20 97.97 490.41 808.79 116 168,124 . Public 42.99 96.96 67.54 197.48 404.97 1,003 1,257,069 Q5-richest Parochial 97.18 124.02 99.09 899.78 1,220.06 19 26,556 Private 342.35 242.49 92.78 1,858.42 2,536.03 333 447,223 Public 101.07 152.01 70.63 538.95 862.66 720 922,859 All Parochial 89.38 97.51 76.67 470.56 734.12 55 75,890 Private 249.29 202.26 91.57 1,354.14 1,897.25 505 694,342 Public 34.53 74.58 50.75 157.76 317.61 5,398 6,563,702 Sum total All types 55.43 86.90 54.88 274.26 471.47 5,958 7,333,934 Source: Household survey by Instituto Cuanto 1997. 249 Appendix 7.8: Total Household Expenditures on Preprimary Education by School Type, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniforms and transport Total population Ql-poorest Private 8,348 8,294 16,642 807 Public 997,511 6,153,643 5,409,215 3,711,363 16,271,732 260,964 Q2 Parochial 5,703 128,325 114,067 124,677 372,773 2,758 Private 405,786 919,441 521,177 1,082,643 2,929,047 6,634 Public 1,407,301 8,660,048 7,474,975 6,853,051 24,395,375 225,617 Q3 Parochial 40,028 92,385 276,861 447,494 856,768 2,193 Private 1,224,929 1,586,396 1,049,215 3,941,048 7,801,589 12,893 Public 2,154,922 10,675,767 7,952,087 9,821,412 30,604,186 170,802 Q4 Parochial 21,221 45,473 30,315 211,203 308,211 586 Private 2,000,737 4,762,970 4,130,151 14,914,804 25,808,662 33,850 Public 3,562,194 10,314,771 10,390,226 14,572,296 38,839,487 154,129 Q5-richest Parochial 76,831 341,471 341,471 662,449 1,422,222 1,648 Private 7,081,671 8,794,844 5,967,182 45,276,717 67,120,414 42,159 Public 3,076,362 8,004,475 6,358,016 16,372,644 33,811,497 78,465 All Parochial 143,783 607,654 762,714 1,445,823 2,959,974 7,185 Private 10,713,123 16,071,999 11,667,725 65,223,506 103,676,353 96,343 Public 11,198,290 43,808,704 37,584,518 51,330,766 143,922,278 889,977 Sum total All types 22,055,196 60,488,357 50,014,957 118,000,095 250,558,606 993,505 Source: Household survey by Instituto Cuanto 1997. 250 Appendix 7.9: Total Household Expenditures on Primary Education by School Type, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniforms and transport Total population Ql-poorest Private 34,249 136,997 372,594 543,841 1,648 Public 7,370,675 27,478,868 25,341,437 18,982,755 79,173,734 895,101 Q2 Parochial 123,084 795,089 524,238 916,737 2,359,148 10,192 Private 324,844 682,755 1,023,995 1,558,137 3,589,731 7,967 Public 9,127,476 37,984,802 34,228,377 36,507,590 117,848,245 827,612 Q3 Parochial 443,694 730,664 807,805 1,569,155 3,551,319 9,795 Private 2,094,285 3,399,769 2,241,203 13,343,133 21,078,390 25,835 Public 9,375,199 45,494,475 39,403,364 48,558,205 142,831,242 685,956 Q4 Parochial 1,476,395 740,674 718,782 3,062,484 5,998,335 8,099 Private 5,509,040 11,132,944 7,293,612 36,441,733 60,377,329 70,539 Public 9,907,406 42,082,922 38,957,651 61,325,669 152,273,648 494,873 Q5-richest Parochial 694,337 753,348 1,107,144 3,213,481 5,768,309 7,832 Private 51,999,106 31,457,963 20,690,406 231,972,208 336,119,682 150,271 Public 8,457,493 31,993,049 27,790,849 56,778,350 125,019,741 270,195 All Parochial 2,737,509 3,019,776 3,157,968 8,761,857 17,677,111 35,917 Private 59,961,524 46,810,427 31,249,216 283,687,805 421,708,973 256,259 Public 44,238,249 185,034,115 165,721,678 222,152,567 617,146,609 3,173,737 Sum total All types 106,937,283 234,864,318 200,128,862 514,602,230 1,056,532,693 3,465,914 Source: Household survey by Instituto Cuanto 1997. 251 Appendix 7.10: Total Household Expenditures on Secondary Education by School Type, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniforms and transport Total population QI-poorest Private 85,379 205,569 54,182 1,020,092 1,365,222 3,572 Public 4,521,983 12,779,775 12,685,371 12,417,217 42,404,346 288,154 Q2 Private 486,542 643,867 285,755 583,499 1,999,663 5,811 Public 9,402,559 24,279,302 23,656,967 30,334,272 87,673,100 409,393 Q3 Parochial 805,904 936,193 371,875 683,624 2,797,596 9,131 Private 824,104 867,415 418,240 2,507,373 4,617,132 11,051 Public 13,737,058 31,991,113 23,013,615 57,103,557 125,845,342 402,306 Q4 Parochial 492,777 505,974 343,337 2,572,013 3,914,101 4,932 Private 4,248,888 5,406,171 4,709,510 18,345,807 32,710,375 48,807 Public 19,202,940 45,183,285 31,616,994 98,276,186 194,279,404 437,669 Q5-richest Parochial 483,176 802,660 1,147,119 7,593,171 10,026,126 8,387 Private 40,840,419 27,640,164 13,569,061 175,804,912 257,854,556 111,946 Public 32,737,386 44,241,702 24,821,093 144,876,628 246,676,808 318,495 All Parochial 1,781,858 2,244,827 1,862,331 10,848,807 16,737,822 22,450 Private 46,485,332 34,763,186 19,036,748 198,261,682 298,546,947 181,188 Public 79,601,925 158,475,177 115,794,038 343,007,860 696,879,000 1,856,017 Sum total All types 127,869,114 195,483,190 136,693,116 552,118,349 1,012,163,770 2,059,656 Source: Household survey by Instituto Cuanto 1997. 252 Appendix 7.11: Total Household Expenditures on Tertiary Nonuniversity Education by School Type, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniforms and transport Total population Ql-poorest Public 872,611 981,466 778,410 2,569,300 5,201,787 21,213 Q2 Public 3,643,732 3,985,778 1,143,705 11,608.776 20,381,992 39,808 Q3 Private 138,530 554,120 22,165 2,237,640 2,952,455 1,379 Public 4,021,026 3,072,962 983,040 17,525,048 25,602,077 43,187 Q4 Private 924,260 908,247 337,995 7,419,999 9,590,501 6,329 Public 7,828,321 7,339,602 3,407,106 29,937,232 48,512,260 68,602 Q5-richest Parochial 138,530 213,876 35,646 5,142,641 5,530,693 2,899 Private 2,108,869 1,790,101 263,311 19,520,223 23,682,506 16,145 Public 12,058,199 11,303,118 3,591,499 64,511,210 91,464,026 69,103 All Parochial 138,530 213,876 35,646 5,142,641 5,530,693 2,899 Private 3,171,659 3,252,468 623,471 29,177,863 36,225,461 23,853 Public 28,423,889 26,682,927 9,903,760 126,151,567 191,162,142 241,914 Sum total All types 31,734,079 30,149,271 10,562,876 160,472,071 232,918,296 268,667 Source: Household survey by Instituto Cuanto 1997. 253 Appendix 7.12: Total Household Expenditures on University Education by School Type, 1997 (Soles) Consumption Fees, Books and Tuition, food, Projected quintile APAFA supplies School unif'orms and transport Total population QI-poorest Public 1,195,621 1,127,691 220,347 3,183,795 5,727,454 16,159 Q2 Private 210,064 86,378 296,441 1,398 Public 2,285,350 2,176,500 4,015,082 8,476,932 23,786 Q3 Private 793,692 132,056 2,228,440 3,154,187 1,648 Public 9,206,850 10,498,187 730,743 26,650,176 47,085,956 73,714 Q4 Private 1,474,118 688,428 5,327,370 7,489,915 8,599 Public 13,537,940 16,964,692 525,617 44,136,391 75,164,640 101,796 Q5-richest Parochial 1,187,759 1,182,148 7,283,179 9,653,086 5,791 Private 51,077,395 38,762,463 1,001,194 358,554,708 449,395,762 126,701 Public 36,944,862 44,743,189 2,622,583 214,835,285 299,145,918 186,601 All Parochial 1,981,451 1,314,203 9,511,619 12,807,273 7,439 Private 52,761,577 39,537,269 1,001,194 363,882,079 457,182,118 136,698 Public 63,170,622 75,510,259 4,099,290 292,820,730 435,600,901 402,055 Sum total All types 117,913,650 116,361,730 5,100,485 666,214,427 905,590,292 546,192 Source: Household survey by Instituto Cuanto 1997. 254 Appendix 7.13: Total Household Expenditures on Education by Education Level, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniformns and transport Total population Q Ipoorest Preprimary 997,511 6,161,991 5,409,215 3,719,656 16,288,374 261,772 Primary 7,404,925 27,615,865 25,341,437 19,355,348 79,717,575 896,749 Secondary 4,607,362 12,985,344 12,739,553 13,437,309 43,769,568 291,726 Sup. no univ. 872,611 981,466 778,410 2,569,300 5,201,787 21,213 Sup. univ. 1,195,621 1,127,691 220,347 3,183,795 5,727,454 16,159 Q2 Preprimary 1,818,790 9,707,814 8,110,219 8,060,371 27,697,194 235,010 Primary 9,575,404 39,462,646 35,776,610 38,982,464 123,797,123 845,770 Secondary 9,889,101 24,923,169 23,942,721 30,917,771 89,672,762 415,204 Sup. no univ. 3,643,732 3,985,778 1,143,705 11,608,776 20,381,992 39,808 Sup. univ. 2,495,413 2,262,878 0 4,015,082 8,773,374 25,184 Q3 Preprimary 3,419,879 12,354,548 9,278,163 14,209,954 39,262,543 185,887 Primary 11,913,177 49,624,908 42,452,372 63,470,493 167,460,950 721,586 Secondary 15,367,066 33,794,721 23,803,730 60,294,553 133,260,070 422,488 Sup. no univ. 4,159,556 3,627,082 1,005,205 19,762,689 28,554,531 44,566 Sup. univ. 10,000,541 10,630,243 730,743 28,878,616 50,240,144 75,362 Q4 Preprimary 5,584,152 15,123,214 14,550,692 29,698,303 64,956,361 188,565 Primary 16,892,841 53,956,540 46,970,045 100,829,886 218,649,312 573,511 Secondary 23,944,604 51,095,430 36,669,840 119,194,005 230,903,880 491,409 Sup. no univ. 8,752,581 8,247,849 3,745,100 37,357,231 58,102,761 74,931 Sup. univ. 15,012,057 17,653,120 525,617 49,463,761 82,654,556 110,394 Q5-richest Preprimary 10,234,864 17,140,789 12,666,669 62,311,811 102,354,133 122,272 Primary 61,150,936 64,204,360 49,588,399 291,964,038 466,907,732 428,297 Secondary 74,060,981 72,684,526 39,537,272 328,274,711 514,557,490 438,828 Sup. no univ. 14,305,598 13,307,096 3,890,456 89,174,075 120,677,225 88,148 Sup. univ. 89,210,017 84,687,800 3,623,777 580,673,172 758,194,765 319,093 All groups Preprimary 22,055,196 60,488,357 50,014,957 118,000,095 250,558,606 993,505 Primary 106,937,283 234,864,318 200,128,862 514,602,230 1,056,532,693 3,465,914 Secondary 127,869,114 195,483,190 136,693,116 552,118,349 1,012,163,770 2,059,656 Sup. no univ. 31,734,079 30,149,271 10,562,876 160,472,071 232,918,296 268,667 Sup. univ. 117,913,650 116,361,730 5,100.485 666,214,427 905,590,292 546,192 Sum total All types 406,509,322 637,346,866 402,500,297 2,011,407,172 3,457,763,657 7,333,934 Source: Household survey by Instituto Cuanto 1997. 255 Appendix 7.14: Total Household Expenditures on Education by School Type, 1997 (Soles) Consumption Fees, Books and School Tuition, food, Projected quintile APAFA supplies uniformns and transport Total population Ql-poorest Private 119,628 350,914 54,182 1,400,980 1,925,704 6,027 Public 14,958,401 48,521,443 44,434,780 40,864,430 148,779,053 1,481,592 Q2 Parochial 128,787 923,414 638,305 1,041,414 2,731,920 12,950 Private 1,427,235 2,332,440 1,830,926 3,224,279 8,814,881 21,810 Public 25,866,419 77,086,431 66,504,023 89,318,771 258,775,644 1,526,216 Q3 Parochial 2,083,318 1,891,298 1,456,541 4,928,713 10,359,870 22,767 Private 4,281,848 6,407,700 3,730,824 22,029,194 36,449,565 51,158 Public 38,495,054 101,732,503 72,082,849 159,658,398 371,968,804 1,375,965 Q4 Parochial 1,990,392 1,292,122 1,092,434 5,845,699 10,220,647 13,617 Private 14,157,043 22,898,760 16,471,268 82,449,713 135,976,783 168,124 Public 54,038,800 121,885,271 84,897,593 248,247,775 509,069,439 1,257,069 Q5-richest Parochial 2,580,633 3,293,502 2,631,380 23,894,921 32,400,436 26,556 Private 153,107,461 108,445,535 41,491,154 831,128,769 1,134,172,919 447,223 Public 93,274,301 140,285,533 65,184,040 497,374,117 796,117,991 922,859 All Parochial 6,783,131 7,400,336 5,818,659 35,710,747 55,712,873 75,890 Private 173,093,215 140,435,350 63,578,354 940,232,935 1,317,339,853 694,342 Public 226,632,976 489,511,181 333,103,284 1,035,463,490 2,084,710,931 6,563,702 Sum total All types 406,509,322 637,346,866 402,500,297 2,011,407,172 3,457,763,657 7,333,934 Source: Household survey by Instituto Cuanto 1997. 256 APPENDIX 8 POPULATION PROJECTION 257 Appendix 8.1. Assumptions of Population Projection PERU Projection (OOOs) with NRR=l by 2010 Age Group 1995 2000 2005 2010 2015 2020 Birth rate 24.9 22.1 19.4 18.2 17.7 Death rate 6.3 6.3 6.1 5.8 6 Rate of nat. inc. 1.86 1.58 1.33 1.25 1.17 Net migration rate -0.8 -0.4 -0.2 -0.1 0 Growth rate 1.78 1.55 1.31 1.24 1.17 Total fertility 3 2.6 2.25 2.12 2.11 NRR 1.37 1.2 1.05 1 1 e(0)-Both sexes 68.5 69 70.3 72.2 73 e(l5) - Both sexes 57.6 57.4 58.1 59.5 60 IMR-Both sexes 40 34 28 22.2 20.3 q(5)-Both sexes 0.05 0.04 0.03 0.03 0.02 DEP. RAT 67.4 61.5 55.9 50.7 47 45 259 Appendix 8.2.Population by Single Years of Age for Selected Age Ranges and Years, 1995-2020 (Units=1000's) Age Grou 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Males 5 296 296 296 296 296 293 297 298 299 299 301 300 296 292 288 284 287 282 278 273 269 275 278 280 283 285 6 296 296 296 296 296 293 297 298 299 299 301 300 296 292 288 284 287 282 278 273 269 275 278 280 283 285 6 294 295 296 296 296 295 293 297 298 298 299 300 300 296 292 28 2874 2 86 2 82 277 273 2 69 275 277 280 283 7 292 293 295 295 296 295 295 293 297 297 298 299 300 300 296 291 287 284 286 282 277 273 269 275 277 280 8 289 291 293 294 295 295 295 295 292 296 297 298 299 300 299 295 291 287 283 286 282 277 273 268 275 277 9 286 289 291 292 294 295 295 295 294 292 296 297 298 298 300 299 295 291 287 283 286 281 277 272 268 274 1 0 284 286 288 290 292 293 294 295 294 294 292 296 297 297 298 299 299 295 291 287 283 286 281 277 272 268 1 1 281 283 286 2%X 9a/5 ')O' 1(4 294 294 294 294 291 296 296 297 298 299 299 295 291 287 283 286 281 277 272 6.11 1726 1737 1749 17' .,>v YU87 1782 1770 1755 1742 1724 1706 1688 1669 1661 1650 1649 1654 12 278 280 283 285 288 290) 291 293 294 294 294 294 291 295 296 297 298 299 299 295 291 287 283 285 281 276 13 276 278 280 282 285 287 289 291 292 293 294 294 293 291 295 296 297 298 299 299 295 290 286 283 285 281 14 274 276 278 280 282 285 287 289 291 292 293 294 294 293 291 295 296 297 297 299 299 294 290 286 283 285 1 5 272 274 275 277 279 282 285 287 289 290 292 293 293 293 293 290 295 296 296 297 298 298 294 290 286 282 12-15 1100 1108 1116 1124 1134 1144 1152 1160 1166 1169 1173 1175 1171 1172 1175 1178 1186 1190 1191 1190 1183 1169 1153 1144 1135 1124 16 269 272 273 275 277 279 281 284 286 288 290 292 293 293 293 293 290 295 295 296 297 298 298 294 290 286 1 7 265 269 271 273 274 276 278 281 284 286 288 290 291 292 293 293 292 290 294 295 296 297 298 298 294 290 16-17 534 541 544 548 551 555 559 565 570 574 578 582 584 585 586 586 582 585 589 591 593 595 596 592 584 576 18 260 264 268 271 272 274 276 278 281 283 285 288 289 291 292 292 292 292 290 294 295 296 296 298 298 293 19 253 259 264 268 270 271 273 275 277 28(0 283 285 287 289 290 291 292 292 292 289 293 294 295 296 297 297 20 245 252 258 263 267 269 271 273 275 277 279 282 284 287 288 290 291 291 292 291 289 293 294 295 296 297 18-20 758 775 790 802 809 814 820 826 833 840 847 855 860 867 870 873 875 875 874 874 877 883 885 889 891 887 Total males 11833 12057 12279 12500 12720 12938 13159 13375 13584 13786 13982 14186 14383 14573 14756 14932 15123 15314 15504 15695 15886 16077 16269 16459 16649 16838 260 Appendix 8.2. (continued) Age Group 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Females 5 287 287 287 287 286 284 287 288 288 289 290 289 285 281 277 274 276 271 267 263 259 264 267 269 272 274 5 287 287 287 287 286 284 287 288 288 289 290 289 285 281 277 274 276 271 267 263 259 264 267 269 272 274 6 285 286 287 287 286 286 283 287 287 288 289 290 289 285 281 277 273 276 271 267 263 258 264 267 269 272 7 283 285 286 286 286 286 285 283 286 287 288 288 290 289 285 281 277 273 276 271 267 262 258 264 267 269 8 281 283 284 285 286 286 286 285 283 286 287 288 288 289 289 285 281 277 273 275 271 267 262 258 264 266 9 278 281 282 284 285 286 286 285 285 283 286 287 287 288 289 289 285 281 277 273 275 271 267 262 258 264 10 276 278 280 282 284 285 285 286 285 285 282 286 287 287 288 289 289 285 281 277 273 275 271 267 262 258 I 1 273 275 278 280 282 283 284 285 285 285 284 282 286 286 287 288 289 289 285 281 277 273 275 271 266 262 6-11 1676 1688 1697 1704 1709 1712 1709 1711 1711 1714 1716 1721 1727 1724 1719 1709 1694 1681 1663 1644 1626 1606 1597 1589 1586 1591 12 271 273 275 277 280 282 283 284 285 285 285 284 282 286 286 287 288 289 288 284 281 277 273 275 271 266 13 269 270 272 275 277 279 281 283 284 285 285 285 284 282 285 286 287 288 289 288 284 280 276 273 275 271 14 267 269 270 272 274 277 279 281 283 284 285 285 285 284 282 285 286 287 288 289 288 284 280 276 273 275 1 5 266 267 268 270 272 274 277 279 281 282 284 285 285 284 284 282 285 286 287 287 289 288 284 280 276 273 12-15 1073 1079 1085 1094 1103 1112 1120 1127 1133 1136 1139 1139 1136 1136 1137 1140 1146 1150 1152 1148 1142 1129 1113 1104 1095 1085 16 263 265 267 268 269 271 274 277 279 281 282 283 284 285 284 284 282 285 286 287 287 288 288 284 280 276 17 260 263 265 266 267 269 271 274 276 278 280 282 283 284 284 284 284 281 285 286 286 287 288 288 284 280 16-17 523 528 532 534 536 540 545 551 555 559 562 565 567 569 568 568 566 566 571 573 573 575 576 572 564 556 18 255 260 263 265 266 267 269 271 273 276 278 280 282 283 284 284 284 283 281 285 285 286 287 288 288 284 19 250 255 259 262 264 265 267 268 270 273 275 278 280 281 283 284 284 284 283 281 285 285 286 287 288 288 20 243 249 254 259 262 264 265 266 268 270 272 275 278 280 281 282 283 284 284 283 281 284 285 286 287 288 18-20 748 764 776 786 792 796 801 805 811 819 825 833 840 844 848 850 851 851 848 849 851 855 858 861 863 860 Total fe- 11986 12210 12432 12653 12873 13092 13328 13549 13757 13953 14139 14360 14565 14756 14933 15099 15310 15511 15702 15887 16066 16281 16484 16675 16858 17033 mnales TOTAI 23819 24266 24711 25154 25593 26030 26487 26923 27340 27739 28121 28546 28948 29329 29690 30032 30433 30824 31207 31582 31952 32359 32752 33135 33507 33871 |Source: World Bank Projection. 261 Appendix 8.3: Projected School-Age Population, 1995-2020 (Units=1000's) Age Cr011 1499 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Males 5 296 296 296 296 296 293 297 298 299 299 301 300 296 292 288 284 287 282 278 273 269 275 278 280 283 285 6-11 1726 1737 1749 1755 1763 1765 1765 1769 1769 1771 1776 1781 1790 1787 1782 1770 1755 1742 1724 1706 1688 1669 1661 1650 1649 1654 12-15 1100 1108 1116 1124 1134 1144 1152 1160 1166 1169 1173 1175 1171 1172 1175 1178 1186 1190 1191 1190 1183 1169 1153 1144 1135 1124 16-17 534 541 544 548 551 555 559 565 570 574 578 582 584 585 586 586 582 585 589 591 593 595 596 592 584 576 18-20 758 775 790 802 809 814 820 826 833 840 847 855 860 867 870 873 875 875 874 874 877 883 885 889 891 887 Females 5 287 287 287 287 286 284 287 288 288 289 290 289 285 281 277 274 276 271 267 263 259 264 267 269 272 274 6-11 1676 1688 1697 1704 1709 1712 1709 1711 1711 1714 1716 1721 1727 1724 1719 1709 1694 1681 1663 1644 1626 1606 1597 1589 1586 1591 12-15 1073 1 079 1085 1094 1103 1112 1120 1127 1133 1136 1139 1139 1136 1136 1137 1140 1146 1150 1152 1148 1142 1129 1113 1104 1095 1085 16-17 523 528 532 534 536 540 545 551 555 559 562 565 567 569 568 568 566 566 571 573 573 575 576 572 564 556 18-20 748 764 776 786 792 796 801 805 811 819 825 833 840 844 848 850 851 851 848 849 851 855 858 861 863 860 M+F 5 583 583 583 583 582 577 584 586 587 588 591 589 581 573 565 558 563 553 545 536 528 539 545 549 555 559 6-1 1 3402 3425 3446 3459 3472 3477 3474 3480 3480 3485 3492 3502 3517 3511 3501 3479 3449 3423 3387 3350 3314 3275 3258 3239 3235 3245 12-15 2173 2187 2201 2218 2237 2256 2272 2287 2299 2305 2312 2314 2307 2308 2312 2318 2332 2340 2343 2338 2325 2298 2266 2248 2230 2209 16-17 1057 1069 1076 102 1087 1095 1104 1116 1125 1133 1140 1147 1151 1154 1154 1154 1148 1151 1160 1164 1166 1170 1172 1164 1148 1132 18-20 1506 1539 1566 1588 1601 1610 1621 1631 1644 1659 1672 1688 17010 1711 1718 1723 1726 1726 1722 1723 1728 1738 1743 1750 1754 1747 Soturce: World Bank Projection. 262 APPENDIX 9 EXTERNAL SUPPORT FOR EDUCATION SINCE 1990 263 Appendix 9: External Support for Education since 1990 Loan or grant Implementation Implementation Organization amount (US$) dates agency Description of the project Problems/solutions World Bank 146,400,000 1995-2000 Ministry of Basic Education Quality Project. The objective of the Main problem identi- Education Project is to assist the MOE to improve the quality of its fied is the slow im- public primary education, to improve student achievement, plementation rate of and to reduce the incidence of repetition and dropout. It the infrastructure has the following components: (1) Education Quality Im- component. provement, including (a) curricular reform, (b) provision of textbooks and didactic materials, and (c) in-service teacher training; (2) Modernization of Educational Ad- ministration including (a) a program to improve the man- agement by MOE of the educational system, (b) the Pilot Plan for Modernization of School Management, (c) pro- gram for decentralization of the sector, (d) the establish- ment of an MIS, (e) the development of a student assess- ment system; and (3) Infrastructure Improvement through (a) the construction and rehabilitation of schools, (b) the provision of furniture for these schools, and (c) the devel- opment of a maintenance program. Inter-American 500,000 9/96-12/97 Ministry of Education Sector Reform Project (PPF). Preparation Development Education facility for Project below Bank Inter-American 100,000,000 12/96-12/2001 Ministry of Special Program for the Improvement of Education Main problem identi- Development Education Quality. Project components are: (1) Institutional fied is slow implemen- Bank strengthening of the Ministry of Education, including tation rate. Proposed Teacher Performance Improvement System, Education solution is to Quality Measurement System, strategic planning, strengthen Project strengthening of teacher pre-service training schools, man- Coordination Unit and agement training, and preschools; (2) strengthening of the hire consultants to 5-year-old program, including curriculum development, provide support to educational material, in-service teacher training, and infra- activities being carried structure; (3) strengthening of secondary education, in- out within the Minis- cluding curriculum development, educational materials, try. in-service teacher training, and quality networks; (4) edu- cation for work, including modernization of the public offer and curriculum development. 265 Appendix 9: (continued) UNFPA 246,500 6/96-12/97 Ministry of Support to Communication Activities on Population at Slow progress in Education the National Level. Review and publication of the Sex teacher training and in Education Guidelines for teachers and parents distribution of the Guidelines. UNFPA 144,728 12/95-6/97 Asociaci6n de Education on Responsible Parenthood and Information Main problem identi- Trabajo Laico on Natural Methods for Family Planning for Adoles- fied was the lack of Familiar (ATLF) cents in Lima and Callao. The objective of the Project sustainability of the was to contribute to the National Education Program on recipient institution Population by promoting and stimulating awareness on since it is completely population dynamics. The goal was to improve knowledge dependent on external and abilities in 1,500 teachers and 5,000 parents to provide funds. counsel to adolescents on planned parenthood and sexual education. UNFPA 515,657 10/96- ongoing Centro de Estu- Sex Education of Adolescents in the Arequipa Region. Main problem identi- dios Para el De- The objectives of the project are to contribute substantially fied is to attract and sarrollo Regional to the sexual education in Arequipa from a gender keep interested those (CEDER) and perspective through research, socialization, and initiation adolescents who are Regional Council of a training model for teachers and health professionals. not part of the school for Population The aim is to generate change in 1,082 teachers and system. from Arequipa 26,000 students from 973 schools, and in 20 health professionals and 800 adolescents not attending schools from rural and urban areas. UNFPA 222,230 11/97- ongoing Ministry of Preparation for the Project providing support to the The Guidelines should Education National Program on Sex Education. The objective of be drafted to respond the Project is to contribute to the National Population Pro- to the conditions of the gram 1996-2000, through the generalized training on Sex various regions of the Education for secondary school students and the develop- country. ment of the Family and Sex Education Guidelines. GTZ 1,984,802 3/1996-6/1 998 Ministry of Preparation for the Reform of the Teacher Training The main problem Education Program. The objectives of the Project were: to develop a identified was the dif- new concept and strategy for primary education teacher ficulty in communica- presence training and to integrate bilingual intercultural tion and coordination education and gender issues in the primary education re- between the various form, actors. 266 Appendix 9: (continued) European 5,000,000 12/97- 12/98 Ministry of Edu- Support to the Basic Social Expenditure "Educaci6n There are coordination Commission cation Basica para Todos." The project finances the production, difficulties at the tech- procurement and distribution of educational material in nical and financial Spanish for third and fourth grades and bilingual materials levels. There is over- for first and second grade to be used in single and multi- lap in the various pro- grade schools located in the rural sierra and jungle. It also grams under execution provides teacher training for the use of these materials. in the Ministry. The Project also finances a team of consultants in the defi- nition of guidelines and strategies to improve the quality of rural education. European 8,082,622 6/96 - 12/01 Ministry of Edu- Development and Insertion of Youth in Peru. The Pro- There is adequate co- Commission cation jects is targeted to a population group between the ages of ordination for this 15 and 24 who abandoned the education system without program at the central completing a professional education. It is based on the and regional level. network of the Public Higher Education Institutes in La There are problems Libertad and Cajamarca and includes the following activi- with counterpart funds ties: Professional training, available training resources, and administrative management of resource and centers, technology transfer, delays. The sustain- business orientation, pedagogical innovations. ability of this process depends on a flexible and agile training pro- gram linked to the productive environ- ment. European 8,082,622 1/98-1/03 Ministry of Edu- Professional Technical and Pedagogical Training Pro- There is good and Commission cation gram. This program is divided into two parts: The Hori- fluid coordination. zontal Project for Teacher Training in Technical Education has as its main objective the improvement of the quality of the technical and professional training system. This Pro- ject has national coverage and will be executed through 13 Magnet Schools. It will target to 10,650 teachers of tech- nical subjects. The second part is the Teacher Training Program in Bilingual Intercultural Education in the Ama- zon Basin and has as its main objective the integration of indigenous communities in the jungle through teacher training, applied research, and development of curricular and didactic materials. This Project will benefit 1,000 teachers and 100 teacher trainers. 267 Appendix 9: (continued) European 810,000 11/95 - 12/97 Ministry of Edu- Pedagogical and Technological Training and Research The Project concluded Commission cation in Education-Related Disciplines. The Project objective successfully. is to contribute to the improvement of technical education through training on science and technology of teachers from Technical Schools. The Project created Training Networks in eight cities in Peru. USAID 2,974,668 9/92 - 9/96 Partner for the Peruvian Program of Scholarships for Peace. The pro- Americas gram provided leaders and potential leaders with specific abilities, training, and academic education, as well as un- derstanding about the operation of the democratic process in a country with a free market economy. USAID 664,000 9/96 - 9/99 UNICEF Transit to Primary Education. Support to the MOE in Substantial improve- its efforts to improve the quality of public education. ment is needed in the quality of education in the first and second grades to be evaluated through entry and exit tests. USAID 362,000* 8/95 - 12/99 CARE Strengthening of Health Institutions. The southern com- ponent of this Project includes a subcomponent that pro- vides financing for basic education activities in the "Ay- mara" population in Puno. USAID 2,300,000 1/98 - 1/2 CARE Girls Education. The project aims toward the develop- ment of guidelines to identify barriers and long term solu- tions with respect to the education of girls and committing leaders from public and private sector organizations to promote policies and programs that will improve the op- portunities and quality of education for girls. 268 Appendix 9: (continued) DIACONIA 644,000 97-01 Ministry of Schools Agricultural Production Unit The Project aims High levels of malnu- Lutheran Education to improve the living conditions of the rural child and trition. Education in Evangelical his/her family through the improvement of diet and health rural schools is geared Association by promoting the harvest and consumption of vegetables towards memorization for Assis- produced with agroecological techniques. It also contrib- and not oriented to the tance to utes to the improvement of Primary Education through the learning process. Community development of a curriculum for rural areas. High rotation rate of Development teachers trained by the Project. Lack of iden- tification of teachers with the community. Lack of training mate- rials. UNESCO/ 311,400 6/96 - 6/00 Ministry for the Alphabetization and Civic Education of Indigenous Project had problems DANIDA Promotion of and Displaced Women in Peru. The objective is to pro- due to change in im- Women and Hu- mote the use of civic, cultural, and gender rights of indige- plementation institu- man Development nous and displaced women to overcome analphabetism tion (from MOE to (PROMUDEH) and exclusion. PROMUDEH). UNESCO/ 553,700 1/96 - 12/98 Ministry of Edu- Integration of Handicapped Children in Regular Lack of an institu- DANIDA cation Schools. The objective is to consolidate school integration tional framework in through teacher training, specialized support to teachers, the process. socialization activities, teacher supervision, and distribu- tion of educational materials Save the 172,635 3/93 - 7/96 EDUCA - Insti- Health and Development Education in Schools of the Difficulty in integra- Children tute for the Qual- INKA Region. The objective of the project was to pro- tion of project activi- ity of Education / mote the development and application of child integrated ties within the school. Arariwa attention in 40 schools in the Inka Region. The project benefited 1,600 children and 60 teachers. Save the 67,388* 4/97 - 3/99 Asociaci6n para el Community Development in Native and Mixed Com- Implementation of this Children Desarrollo munities of the Peruvian Amazon Basin. The objective type of project re- /European Amaz6nico Rural of the project is to train and provide assistance to commu- quires a strong moni- Union/ (ADAR) nity leaders and workers to manage a rural development toring and evaluation ADAR model based on primary health, basic sanitation, and nutri- system tional assessment. The Project includes a component of health education developed within the school setting. 269 Appendix 9: (continued) Spanish 817,458 93-98 Ministry of Design of the Technical Education and Professional Agency for Education Training System. The Project is developing a national International certification system for teachers and technical education, Cooperation the curriculum for a selection of professions, and the Pro- (AECI) gram for teacher training within the professional training system. Spanish 880,525 92-98 Ministry of Quality of Education and Regional Development. The Agency for Education Project aims to improve teacher training, certification and International quality in the Teacher Training Institutes. The Project con- Cooperation tributes to the development of the second specialization in (AECI) teacher training. Activities are developed through distance education to facilitate teacher participation. Spanish 1,331,267 92-98 Instituto Superior Bilingual Teacher Training in the Peruvian Amazon Agency for Pedag6gico Lo- Basin. The project has developed a bilingual intercultural International reto and AIDE- teacher training curriculum, and trains teachers from vari- Cooperation SEP ous indigenous nations on bilingual intercultural education (AECI) _ is to contribute to the development. Spanish 500,000 97-98 Ministry of Indus- Assistance to the "Instituto Superior Tecnol6gico del Agency for try, Ministry of Calzado." The Project provides support to development International Education, and activities of the above institution. Cooperation PROMPEX and (AECI) Leather and Shoe Associations Spanish 382,406 94-98 Asamblea Na- University Cooperation Program. The Program fi- Agency for cional de Rec- nances the exchange of professors, managers, and students International tores, Foreign between Latin America and Spain. To date it has benefited Cooperation Relations Minis- 678 students and 340 professors. (AECI) ___ _ try Spanish 1,600,000 96-98 Ministry of For- Program for Training of Human Resources. The Pro- Agency for eign Relations, gram provides study grants for Peruvian professionals to International Ministry of Edu- travel to Spain. To date it has benefited 102 people. Cooperation cation, INABEC, (AECI) CONCYTEC, Ministry of the I__ _ _ _ _ _ I_______ _ Presidency I * Amount reflects the education component of the larger project. 77(1 Appendix 9: (continued) Loan or grant Implementation Problemsl Organization amount (US$) dates Implementation agency Description of the Project solutions UNICEF 11,430,600 1992-1998 Ministry of Education Basic Education. The objective of the Project is to con- tribute to national efforts to reduce educational exclusion and to compensate for inequities in the use of basic educa- tion rights. Specifically the Project aims to (a) strengthen the educational management at the local and subregional levels in 10 departments through joint management initia- tives and improving social control of results in schools; (b) improve by 30 percent the student learning performance in communication, interpretation, and production of texts and problem resolution; and (c) implement the proposed in- crease of 20 percent of effective learning time through the development of complementary learning spaces. The project includes technical assistance, advocacy activities, provides support to local organizations, and provides schools with basic educational materials. 271 APPENDIX 10 SELECTED INDICATORS FOR INTERNATIONAL COMPARISON 273 Appendix 10.1: Educational Expenditure as a Percentage of GDP for All Levels of Education Combined, by Source of Funds (1997) Total expenditure Private payments to from public, pri- Financial aid to Public subsidies to educational institu- vate and intema- students not attrib- households and tions excluding Total expenditure tional sources for utable to household Direct public other private enti- public subsidies to from both public educational institu- payments to educa- expenditure for ties excluding pub- households and and private sources tions plus public Private payments tional institutions educational institu- lic subsidies for other private enti- for educational subsidies to house- other than to for educational tions student living costs ties institutions holds educational institu- services Country mean 5.1 0.09 0.76 5.8 6.1 tions 0.4 0.31 OECD total 4.8 0.10 1.23 6.1 6.5 0.3 0.21 IBRD members in OECD Korea 4.4 2.94 7.4 7.4 Mexico 4.5 0.95 5.5 5.6 0.3 0.11 Turkey Non-OECD countries Argentina 3.7 0.71 4.4 4.4 Brazil' 4.8 Chile 3.2 0.12 2.52 5.9 5.9 0.03 Israel2 7.5 0.12 1.74 9.4 9.4 0.6 Malaysia 4.4 0.32 4.7 4.7 0.1 Paraguay 3.7 Philippines 3.0 0.02 1.42 4.4 4.5 1.6 Thailand 4.5 Uruguay 2.6 Zimbabwe 6.5 . 6.5 6.8 0.29 Source: OECD, 2000. Education at a Glance, Table B .la. I. 1996 data. 2. 1995 data. 275 Appendix 10.2: Educational Expenditure as a Percentage of GDP for Primary, Secondary, and Postsecondary Nontertiary Education, by Source of Funds (1997) Private payments to Total expenditure Financial aid to Public subsidies to educational institu- from public, private students not attrib- households and tions excluding Total expenditure and international utable to household other private enti- public subsidies to from both public sources for educa- payments to educa- Direct public ex- ties excluding pub- households and and private sources tional institutions Private payments tional institutions penditure for educa- lic subsidies for other private enti- for educational plus public subsi- other than to educa- for educational tional institutions student living costs ties institutions dies to households tional institutions services Country mean 3.6 0.02 0.36 3.9 4.0 0.2 0.16 OECD total 3.4 0.02 0.38 3.7 3.8 0.1 0.10 IBRD members in OECD Korea 3.4 0.88 4.3 4.3 Mexico 3.3 0.62 3.9 4.0 0.2 0.04 Turkey Non-OECD countries Argentina 2.7 0.26 3.0 3.0 Brazil2 3.5 Chile 2.5 1.15 3.7 3.7 0.01 India' 1.9 0.09 2.0 2.0 Israel' 3 5.1 0.05 0.33 5.4 5.4 0.3 Jordan' 4.7 Malaysia 3.0 3.0 3.0 0.01 Paraguay 3.0 Philippines 2.4 0.02 0.49 2.9 2.9 1.2 2 Thailand' 2.4 Uruguay 1.8 Zimbabwe 5.0 5.0 5.1 0.11 Source: OECD, 2000. Education at a Glance, Table Bl.lb. 1. Excluding postsecondary nontertiary. 2. 1996 data. 3. 1995 data. 276 Appendix 10.3: Educational Expenditure as a Percentage of GDP for Tertiary Education, by Source of Funds (1997) Private payments to Total expenditure Financial aid to Public subsidies to educational institu- from public, private students not attrib- households and tions excluding Total expenditure and international utable to household other private enti- public subsidies to from both public sources for educa- payments to educa- Direct public ex- ties excluding pub- households and and private sources tional institutions Private payments tional institutions penditure for lic subsidies for other private enti- for educational plus public subsi- other than to educa- for educational educational institu- student living costs ties institutions dies to households tional institutions services Country mean tions 1.0 0.06 0.31 1.3 1.5 0.2 0.23 OECD total 1.0 0.08 0.70 1.7 2.0 0.1 0.14 lBRDmembers in OECD Korea 0.5 1.95 2.5 2.5 Mexico 0.8 0.27 1.1 1.2 0.07 Turkey 0.8 Non-OECD countries Argentina 0.8 0.29 1.0 1.1 I Brazil2 0.8 Chile 0.4 0.12 1.25 1.8 1.8 0.02 Israel3 1.2 0.05 0.78 2.0 2.0 Malaysia 1.1 0.28 1.4 1.6 0.27 Paraguay 0.7 Philippines 0.5 0.01 0.94 1.4 1.4 0.4 Thailand 1.0 Uruguay 0.6 Zimbabwe 1.5 1.5 1.7 0.19 Source: OECD, 2000. Education ata Glance, Table Bl.lc. 1. Includes postsecondary nontertiary data. 2. 1996 data. 3. 1995 data. 277 Appendix 10.4: Educational Expenditure from Public and Private Sources for Educational Institutions as a Percentage of GDP by Level of Education (1997) All levels of education (in- Primary and secondary education _Tertiary education cluding research) Pre- primary Primary & lower Upper Postsecondary Tertiary-type B Tertiary-type A education All secondary secondary nontertiary All (ISCED 5B) (ISCED 5A & 6) Country mean 0.4 3.9 2.5 1.3 0.1 1.3 0.2 1.1 5.8 OECD total 0.4 3.9 2.4 1.2 0.1 1.7 0.2 1.0 6.1 IBRD members of OECD Korea 0.1 4.3 3.0 1.3 a 2.5 0.7 1.8 7.4 Mexico 0.5 3.9 3.0 0.9 a 1.1 x 1.1 5.5 Turkey m m m m a m m m m Non-OECD countries Argentina 0.4 3.0 2.4 0.6 a 1.0 0.4 0.7 4.4 Chile 0.4 3.7 2.6 1.0 a 1.8 0.2 1.6 5.9 Israel2 0.9 5.4 2.8 2.6 x 2.0 x x 9.4 Malaysia 0.1 3.0 x x n 1.4 0.4 0.9 4.7 Philippines m 2.9 2.6 0.2 0.1 1.4 a 0.5 4.4 Zimbabwe x 5.0 5.0 x x 1.5 0.6 0.8 6.5 Source: OECD, 2000. Education at a Glance, Table B 1. Id. 1. Postsecondary nontertiary data included in tertiary education. 2.1995 data. 278 Appendix 10.5: Educational Expenditure by Resource Category for Public and Private Institutions, by Level of Education (1997) Primary, secondary, and postsecondary nontertiary education Tertiary education Percentage of total Percentage of total expenditure Percentage of current expenditure expenditure Percentage of current expendi ure Compensa- Compensa- Compensa- Other Compensa- Compensa- Compensa- tion of tion of tion of all current tion of tion of other tion of all Other cuffent Current Capital teachers other staff staff expenditure Current Capital teachers staff staff expenditure Country mean 91 9 64 14 80 20 87 13 42 23 67 33 IBRD members in OECD Korea 86 14 x x 83 17 69 31 38 15 53 47 Mexico' 93 7 77 12 89 1 1 89 1 1 66 18 84 16 Turkey" 3 87 13 91 9 73 27 67 33 Non-OECD coun- tries Argentina' 3 92 8 52 44 96 4 88 12 49 32 81 19 Brazil'-4 93 7 82 x 82 18 94 6 78 x 78 22 Chile' 3 94 6 x x 67 33 x x 89 11 India2'3 97 3 83 8 91 9 Israel3'5 89 11 x x 77 23 90 10 x x 76 24 Jordan".3 86 14 88 8 96 4 Malaysia' 89 1 1 68 16 84 16 66 34 x x x x Paraguay" 3 93 7 77 18 95 5 86 14 9 3 12 88 Philippines' 86 14 x x 83 17 86 14 x x 74 26 Uruguay"3 94 6 74 14 88 12 94 6 59 20 79 21 Source: OECD, 2000. Education at a Glance. Table B5.]. I Public institutions. 2 Public and govenmt-depeadent private institutions. 3 Excludes postsecondary nonteriary education. 4 1996data 5 1995 data. 6 Postscconday nonterfiary education included at t3e terfiary level. 279 Appendix 10.6: Expenditure per Student (US Dollars Converted using PPPs) on Public and Private Institutions by Level of Education (Based on Fulltime Equivalents) (1997) Tertiary Tertiary-type A Early Lower Upper All Postsecondary & Advanced research childhood Primary secondary secondary secondary nontertiary All Tertiary-type B programmes Country mean 3463 3851 4791 5790 5274 5337 8612 7295 8434 OECD total 3788 3769 4175 5312 5507 7084 10892 6765 8252 IBRD members of OECD Korea 1676 3308 3374 3652 3518 6844 4346 8512 Mexico 979 935 1443 2320 1726 4519 4519 Turkey' l 2397 Non-OECD countries Argentinal 1054 1224 1467 1781 1575 11552 3494 Brazil "3 820 859 921 1087 1002 10791 10791 Chile 1929 2115 2220 2337 2292 8775 4616 9820 India' 28 160 225 334 253 Jordan' 528 706 659 1176 807 Malaysia' 332 820 1334 6285 7793 6237 9129 Paraguayl 482 690 19271 19271 Philippines' 74 373 570 570 570 3189 2170 2170 Uruguay' 1104 974 979 1536 1221 2394 4062 2096 Zimbabwe 353 6471 Source: OECD, 2000. Education at a Glance, Table B4. 1. 1. Public institutions. 2. Public and govemment-dependent private institutions. 3. 1996 data. 280 Appendix 10.7: Expenditure per Student Relative to GDP per Capita on Public and Private Institutions by Level of Education (1997) Tertiary Tertiary-type A Early Lower Upper Postsecondary & Advanced research childhood Primary secondary secondary All secondary nontertiary All Tertiary-type B programmes Country mean 17 19 24 30 26 19 45 35 48 OECD total 17 18 23 29 25 33 49 34 47 IBRD members of OECD Korea 12 23 23 25 24 47 30 59 Mexico 13 12 19 30 22 59 59 Turkey' 37 Non-OECD countries Argentina' 10 12 14 17 15 112 34 Brazil'-3 13 13 14 17 16 167 167 Chile 15 17 17 18 18 69 36 77 Indial 2 10 14 20 15 Jordan' 15 21 19 34 23 Malaysia' 4 10 16 77 96 77 112 Paraguay' 12 17 484 484 Philippines' 2 11 16 16 16 91 62 0 62 Uruguay' 12 11 11 17 131 26 44 23 Zimbabwe . 15 28 . Source: OECD, 2000. Education at a Glance. Table B4.2. 1. Public institutions. 2. 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