Source document: https://docs-lawep.s3.us-east-2.amazonaws.com/1696588486420.pdf (Vanuatu)
EFA Global Monitoring Report 2 0 0 9 Overcoming inequality: why governance matters l l A r o f n o i t a c u d E Overcoming inequality: why governance matters Overcoming inequality: why governance matters This Report is an independent publication commissioned by UNESCO on behalf of the international community.
It is the product of a collaborative effort involving members of the Report Team and many other people, agencies, institutions and governments.

The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.
The EFA Global Monitoring Report Team is responsible for the choice and the presentation of the facts contained in this book and for the opinions expressed therein, which are not necessarily those of UNESCO and do not commit the Organization.

Great Clarendon Street, Oxford OX2 6DP Oxford University Press is a department of the University of Oxford.
It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong (China) Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Republic of Korea Singapore Switzerland Thailand Turkey Ukraine Viet Nam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries.
Published jointly by the United Nations Educational, Scientific and Cultural Organization (UNESCO), 7, Place de Fontenoy, 75352 Paris 07 SP, France and Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, United Kingdom.
© UNESCO, 2008 All rights reserved First published 2008 Published in 2008 by the United Nations Educational, Scientific and Cultural Organization 7, Place de Fontenoy, 75352 Paris 07 SP, France Graphic design by Sylvaine Baeyens Layout: Sylvaine Baeyens and Hélène Borel Library of Congress Cataloging in Publication Data Data available Typeset by UNESCO Printed on acid-free paper by Rotolito Lombarda SpA OUP ISBN 978-0-19-954419-6 UNESCO ISBN 978-92-3-104089-4 F O R E W O R D Foreword When a majority of the world’s countries committed at the turn of the new century to achieve Education for All (EFA) by 2015, they did so with the confidence that the EFA goals would stand the test of time.
They are making a difference.

Remarkable gains have been registered in many of the world’s poorest countries towards universal primary education and gender parity.

But we still have a long way to go.

Progress has been too slow and too uneven in many countries.

There is now a clear and present danger that some key goals will not be achieved.
Averting that danger is vital, not just because education is a basic human right, but also because it is crucial for improving child and maternal health, individual incomes, environmental sustainability and economic growth, and for driving progress towards all the Millennium Development Goals.

This seventh edition of the EFA Global Monitoring Reportoffers a warning to governments, donors and the international community.

On current trends universal primary education will not be achieved by 2015.
Too many children are receiving an education of such poor quality that they leave school without basic literacy and numeracy skills.

Finally, deep and persistent disparities based on wealth, gender, location, ethnicity and other markers for disadvantage are acting as a major barrier to progress in education.

If the world’s governments are serious about Education for All, they must get more serious about tackling inequality.
This Report persuasively argues that equity must be at the centre of the EFA agenda, to offset rising inequalities.

Financing and governance reforms have an important role to play.

Developing countries are not spending enough on basic education and donors have not lived up to their commitments.

Stagnating aid to education is a serious concern for educational prospects in a large number of low-income countries.

This clearly has to change in order to achieve EFA.
But increased financing without equity will not benefit the most vulnerable and disadvantaged groups.

A pro-poor approach to education policy is imperative for the goals to have meaning for the world’s out-of-school children and 776 million adult illiterates.

The Report presents some of the public policy and governance reforms that can break the cycle of disadvantage, improve access, raise quality, and enhance participation and accountability.
At the September 2008 United Nations High-Level Event on the Millennium Development Goals, world leaders and a broad range of partners stressed the key role of education for achieving anti-poverty targets and pledged additional resources.

It is crucial that governments and donors do not renege on these commitments if education is going to become a reality for all the world’s children.
This Report, which tracks progress annually towards the EFA goals, offers a comprehensive overview of the state of education in the world today.

It provides national and international policy-makers with the analysis of complex issues, lessons learned and recommendations to provide equal chances in learning for all children, youth and adults.

We are now more than halfway to 2015.

The diagnosis is clear; so are the most effective strategies for addressing the most pressing educational challenges.
By publishing this authoritative annual report, UNESCO, as lead United Nations agency charged with coordinating efforts towards EFA, aims to inform and to influence policy in order to steer the right course to 2015.

Koïchiro Matsuura Acknowledgements This report is the product of a cooperative effort.
It has been researched and written by the Global Monitoring Report team with the support of many people and organizations worldwide.

The team owes a special debt of gratitude to the members of the International Editorial Board and its chair, Marcela Gajardo.

Nicholas Burnett, the former director of the team and now Assistant Director-General for Education at UNESCO, provided constant support and guidance.

The Report depends greatly on the work and expertise of the UNESCO Institute for Statistics (UIS).
We thank its director, Hendrik van der Pol, along with Claude Apkabie, Saïd Belkachla, Georges Boade, Michael Bruneforth, Brian Buffet, Weixin Lu, Adriano Miele, Albert Motivans, Juan Cruz Persua, José Pessoa, Pascale Ratovondrahona, Ioulia Sementchouk, Saïd Ould Voffal and Yanhong Zhang.

The UIS team made important contributions in many areas, particularly in the preparation of Chapter 2 and the statistical tables.

Thanks also to Amy Otchet for helping disseminate the findings of the Report.
The analysis in the Report is informed by commissioned background papers from eminent researchers.
We thank all the authors who prepared background papers for this year’s Report: Rashid Ahmed, Samer Al-Samarrai, Nadir Altinok, Massimo Amadio, Vaidyanatha Ayyar, Masooda Bano, Elisabet Jané Camacho, Sergio Cardenas, Ketevan Chachakhiani, Luis A. Crouch, Anton De Grauwe, Nader Fergany, Hanspeter Geisseler, Alec Ian Gershberg, Katharine Giffard- Lindsay, Gabriele Göttelmann-Duret, Pablo González, Christine Harris-Van Keuren, Ken Harttgen, Janine Huisman, Ulla Kahla, Jackie Kirk, Stephan Klasen, Karine Kruijff, Xin Ma, Athena Maikish, Ben Mead, Mark Misselhorn, Mario Mouzinho, Karen Mundy, Juan-Enrique Opazo Marmentini, Supote Prasertsri, Claude Sauvageot, Yusuf Sayed, Ai Shoraku, Iveta Silova, Jeroen Smits, Devi Sridhar, Gita Steiner-Khamsi, Eszter Szucs, Tuomas Takala, Barbara Tournier and Paul Vachon.
Tragically, Jacqueline Kirk, one of our background paper authors, died in August 2008 during a mission to Afghanistan.

Jackie was a passionate advocate for education in conflict and post-conflict situations, as well as a great scholar.

Over the years she made important contributions to the Report in many areas.

Her courage, commitment and insights will be greatly missed.

Many colleagues at UNESCO supported the work of the Global Monitoring Report team.
We are indebted to numerous individuals within divisions and units of UNESCO’s Education Sector, the International Institute for Educational Planning, the International Bureau of Education and the UNESCO Institute for Lifelong Learning.

UNESCO regional bureaux provided helpful advice on country-level activities and helped facilitate commissioned studies.
We are grateful to the Education Policy and Data Center at the Academy for Educational Development, and particularly to Karima Barrow, Ania Chaluda, Joseph Goodfriend, George Ingram, HyeJin Kim, Sarah Oliver, Ben Sylla and Annababette Wills for their valuable contribution and data for Chapter 2.
We are also grateful to Desmond Bermingham and Luc-Charles Gacougnolle of the Fast Track Initiative Secretariat and to Julia Benn, Valérie Gaveau, Cecile Sangare and Simon Scott of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) for their continuing support and helpful advice on international cooperation and aid data.
A C K N O W L E D G E M E N T S Special thanks to Paul Bennell, Vittoria Cavicchioni, Louis Crouch, Rob Jenkins, Thomas Kellaghan, Marlene Lockheed, Albert Motivans, Steve Packer and Abby Riddell, all of whom provided valuable comments on draft chapters.

We would like to thank François Leclercq for his contribution to the development of the financing section in Chapter 3.
The production of the Report benefited greatly from the editorial expertise of Rebecca Brite and Wenda McNevin as well as from the support of Anaïs Loizillon, who assisted in developing graphic material and the governance mapping Annex.

We wish to acknowledge the work of Jan Worall, who prepared the Report’s comprehensive index.

We also thank our colleagues Fouzia Belhami, Lotfi Ben Khelifa and Judith Roca of the UNESCO Education Knowledge Management Service for their help.
Nino Muñoz Gomez, Sue Williams and the staff of UNESCO’s Bureau of Public Information have provided constant support in bringing the report to global media attention and giving advice in matters relating to web, audiovisual and print publication.
The EFA Global Monitoring Report Team Director Kevin Watkins Samer Al-Samarrai, Nicole Bella, Aaron Benavot, Philip Marc Boua Liebnitz, Mariela Buonomo, Fadila Caillaud, Alison Clayson, Cynthia Guttman, Anna Haas, Julia Heiss, Keith Hinchliffe, Diederick de Jongh, Leila Loupis, Isabelle Merkoviç, Patrick Montjourides, Claudine Mukizwa, Ulrika Peppler Barry, Paula Razquin, Pauline Rose, Suhad Varin.
For more information about the Report, please contact: The Director EFA Global Monitoring Report Team c/o UNESCO 7, place de Fontenoy, 75352 Paris 07 SP, France e-mail: efareport@unesco.org Tel.

: +33 1 45 68 10 36 Fax: +33 1 45 68 56 41 www.efareport.unesco.org Previous EFA Global Monitoring Reports 2008.

Education for All by 2015 – Will we make it?

2007.

Strong foundations – Early childhood care and education 2006.

Literacy for life 2005.

Education for All – The quality imperative 2003/4.


Gender and Education for All – The leap to equality 2002.

Education for All – Is the world on track?

Any errors or omissions found subsequent to printing will be corrected in the online version at www.efareport.unesco.org i i i C O N T E N T S Contents Foreword .

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i Acknowledgements .

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ii List of figures, tables, text boxes and map .

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vi Highlights of the Report .

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1 Overview .

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6 Chapter 1 Education for all: human right and catalyst for development .

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23 Introduction .

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24 Educational opportunity: highly polarized .

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26 Unlocking the wider benefits of education .

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29 Conclusion .

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37 Chapter 2 The Dakar goals: monitoring progress and inequality .

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39 Introduction .

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40 Early childhood care and education: a long way to go .

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42 Progress towards UPE: nations at the crossroads .

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56 Secondary education and beyond: some gains .

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84 Meeting the lifelong learning needs of youth and adults .

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91 Adult literacy: still neglected .

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93 Assessing gender disparities and inequalities in education .

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97 Ensuring both equity and the quality of learning .

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108 Education for All: measuring composite achievement .

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122 Chapter 3 Raising quality and strengthening equity: why governance matters .

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127 Introduction .


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128 Financing education for equity .

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132 Choice, competition and voice: school governance reform and EFA .

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152 Strengthening teacher governance and monitoring .

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171 An integrated approach to education and poverty reduction: the missing link .

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185 Chapter 4 Increasing aid and improving governance .

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203 Introduction .

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204 Aid for education .

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205 Governance and aid effectiveness .

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219 Chapter 5 Policy conclusions and recommendations .

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235 What governments can do .

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236 The role of aid donors .

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240 The role of non-government actors .

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241 Annex .

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243 The Education for All Development Index .

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244 Global and regional patterns in education decision-making .

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252 Statistical tables .

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256 Aid tables .

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389 Glossary .

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407 References .

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414 Abbreviations .

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440 Index .

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442 List of figures, tables, text boxes and map Figures 1.1: Age-specific attendance rates by level in OECD countries and sub-Saharan Africa, 2000–2006 .

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27 1.2: Grade attainment among 10- to 19-year-olds in Latin America and the Caribbean, South and West Asia, and sub-Saharan Africa, 2000–2006 .

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27 1.3: Under-5 mortality rate by mother’s level of education, selected countries, most recent year .

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33 1.4: Child vaccination and mother’s level of education, selected countries, most recent year (% of 1-year-olds having received selected vaccines by the time of the survey) .

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33 1.5: Antenatal care by mother’s level of education, selected countries, most recent year .

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34 1.6: Severe stunting among children under 3 by mother’s level of education, selected countries, most recent year .

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35 2.1: Rates of decline in under-5 mortality in 1990—2006 and required rates for 2007—2015 to meet the MDG .

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44 2.2: Under-5 mortality rates by location and income group, selected countries, most recent year .

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45 2.3: Ratio of under-5 mortality rates of the richest 20% compared with those of the poorest 20% of households, selected countries, most recent year .

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45 2.4: Low birth weight and moderate and severe stunting worldwide .

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47 2.5: Change in pre-primary gross enrolment ratios between 1999 and 2006 in countries with GERs below 90% in 2006 .

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52 2.6: Percentage of children aged 4 and 5 in Egypt attending kindergarten, by place of residence and wealth quintile, 2005—2006 .

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53 2.7: Disparities in pre-school attendance of 3- and 4-year-olds, selected countries, most recent year .

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54 2.8: School-age population in 2006 and 2015 as a percentage of school-age population in 1995, by region .

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57 2.9: Change in primary net enrolment ratios in countries with NERs below 97% in 1999 or 2006 .

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60 2.10: Number of out-of-school children in selected countries, 1999 and 2006 .

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62 2.11: Distribution of out-of-school children by school exposure, by region, 2004 .

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63 2.12: Percentage of out-of-school children unlikely to enrol, by gender, 2006 .

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65 2.13: Primary school progression without repetition or dropout, selected countries, 2006 .

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67 2.14: Percentage of pupils relative to the official primary-school age group, most recent year .

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68 2.15: Survival rates to grade 9 for three age groups: expected age for grade, one year over age and two or more years over age, most recent year .

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69 2.16: Under-age pupils as a percentage of grade 1 enrolment, total repetition rate and under-age repetition rate for grade 1, most recent year .

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69 2.17: Primary net enrolment ratios and survival rates to the last grade of primary education, 2005 and 2006 .

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71 2.18: Changes in net enrolment ratios and survival rates in primary education, selected countries .

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72 2.19: Opportunity gaps: population reaching tertiary education in OECD countries and chances of primary school completion in developing countries .

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73 2.20: Primary net attendance and primary attainment rates for poorest and richest, selected countries, most recent year .

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74 2.21: Primary net attendance rates by wealth quintile, selected countries, most recent year .

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75 2.22: Grade survival in primary school for 10- to 19-year-olds, by wealth quintile, Cambodia and Senegal, 2005 .

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77 C O N T E N T S 2.23: Primary net attendance rates by location, wealth quintile and gender, selected countries, most recent year .

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79 2.24: School attendance disadvantage for economically active children .

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80 2.25: Proportion of children aged 6 to 11 with and without disabilities who are in school .

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83 2.26: Secondary attainment rates among adults and youth, by region, circa 2000 .

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86 2.27: Survival rates to each grade of primary and secondary education, by gender, selected countries, most recent year .

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87 2.28: Net attendance rates in secondary education, by wealth quintile, selected countries, most recent year .

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88 2.29: Survival rates to each grade of primary and secondary education, by wealth quintile, selected countries, most recent year .

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94 2.31: Changes in gender disparities in primary gross enrolment ratios between 1999 and 2006 .

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99 2.32: Changes in gender disparities in access to primary education between 1999 and 2006, by region .

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100 2.33: Change in gender disparities in secondary gross enrolment ratios between 1999 and 2006, by region .

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101 2.34: Changes in gender disparities in secondary gross enrolment ratios between 1999 and 2006 .

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102 2.35: Gender parity index of secondary school enrolment, Bangladesh .

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103 2.36: Change in gender disparities in tertiary gross enrolment ratio by region between 1999 and 2006 .

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103 2.37: Gender parity index of net attendance rates, by education level and wealth quintile, selected countries, most recent year .

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104 2.38: Percentage of female teachers by level of education and by region, 2006 .

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107 2.39: Percentage of low performing students (at or below level 1) in science literacy, PISA 2006 .

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110 2.40: Disparities in mean learning achievement in primary education among developed countries, developing countries and countries in transition, 1995–2006 .

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112 2.41: Comparison of PTRs with ratios of pupils to trained teachers in primary education, 2006 .

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119 2.42: Public-to-private sector disparities in primary PTRs, 2006 .

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120 2.43: EDI in 2006 and change since 1999 .

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124 2.44: EFA Inequality Index by wealth quintile, selected countries, most recent year .

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125 3.1: Change in total public expenditure on education as a percentage of GNP between 1999 and 2006 (percentage points) .

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134 3.2: Distribution of public current expenditure on education by level, sub-Saharan Africa, 2006 .

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135 3.3: Inequality among countries in public expenditure per primary school pupil, 2006 .

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136 3.4: Distribution of global public education expenditure by region, 2004 .

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137 3.5: Distribution of global public education expenditure by country, 2004 .

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137 3.6: Trends in primary net enrolment ratios in Ghana before and after introduction of the Capitation Grant Scheme .

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144 3.7: Gross enrolment ratios in Pakistan by location and gender, 2004/2005 .

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169 3.8: Education equity-associated targets in eighteen PRSPs .

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189 4.1: Total ODA net disbursements, 1999–2007 .

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207 4.2: Aid as a percentage of GNI, net disbursements, 2005–2010 .

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207 4.3: Total aid commitments to education and basic education, 1999–2006 .

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208 4.4: Components of aid to education and to basic education, 2006 .

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209 4.5: Share of social sectors in total aid commitments, 1999–2006 .

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210 4.6: Total aid disbursements to education and basic education, 2002–2006 .

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210 4.7: Aid to education and basic education by income group, commitments .

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211 4.8: Total aid to basic education (commitments) and out-of-school children of primary-school age, 1999–2000 and 2005–2006 .

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212 4.9: Aid to basic education (commitments) and progress towards UPE .

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213 4.10: Total aid to education and basic education (commitments), by donor, 2006 .

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214 4.11: Change in aid to education and basic education between 2005 and 2006, by donor, commitments .

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215 4.12: Non-concessional loans for education, 1999–2006, commitments .

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217 4.13: Donor priority to low income countries and to basic education, aid commitments, 2005–2006 annual average .

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218 4.14: Distribution of aid to education by level, France and Germany, commitments, 2005–2006 annual average .

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218 4.15: Categories of aid to basic education, 1999–2006, commitments .

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222 4.16: Donor involvement in education sector programmes in five countries .

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223 Tables 1.1: Average years of education for poorest and richest 20% of 17- to 22-year-olds, selected countries, most recent year .

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28 2.1: Pre-primary enrolment and gross enrolment ratios by region, 1999 and 2006 .

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51 2.2: New entrants to grade 1 and gross intake rates by region, 1999 and 2006 .

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56 2.3: Primary enrolment by region, 1991, 1999 and 2006 .

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57 2.4: Estimated number of out-of-school children by region, 1999 and 2006 .

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61 2.5: Projections of out-of-school populations in 2015 for countries with more than 500,000 children out of school in 2006 .

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66 2.6: Distribution across wealth quintiles of children not attending primary school .

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76 2.7: Rates of transition to, and participation in, secondary education, 1999 and 2006, worldwide and by region .

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85 2.8: Percentage of technical and vocational education and training in secondary education, 1999 and 2006 .

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85 2.9: Gross enrolment ratios in lower and upper secondary education, 1999 and 2006 .

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86 2.10: Change in tertiary gross enrolment ratios between 1999 and 2006 .

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90 2.11: Estimated number of adult illiterates (age 15+) in 1985–1994 and 2000–2006, with projections to 2015, by region .

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93 2.12: Estimated adult literacy rates (age 15+) in 1985–1994 and 2000–2006, with projections to 2015, by region .

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95 2.13: Distribution of countries according to their distance from the gender parity goal in primary, secondary and tertiary education, 2006 .

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98 2.14: Gender disparities in survival rates to last grade, 1999 and 2005 .

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100 2.15: Percentage of Indian students in standards 3, 4 and 5 who successfully demonstrated basic skills .

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109 2.16: Achievement among grade 4 students in Egypt, by cognitive level and content domain, 2006 .

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110 C O N T E N T S 2.17: Percentage of students in grade 10 in well-equipped schools, by parental socio-occupational status .

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116 2.18: Teaching staff and pupil/teacher ratios in primary and secondary education, by region, 1999 and 2006 .

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118 2.19: Distribution of countries by EDI scores and region, 2006 .

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123 3.1: Total public expenditure on education as a percentage of GNP, by region and income group, 2006 .

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133 3.2: Total public expenditure on education as a percentage of total government expenditure, by region and income group, 2006 .

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134 3.3: Poverty and public education expenditure in Indonesia, 2004 .

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142 3.4: Ethnicity and public education expenditure in the former Yugoslav Republic of Macedonia, 2005 .

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143 3.5: Free textbooks distributed by governments in selected Central American countries, percentage per income quintile, 2000–2004 .

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143 3.6: Inequality in per-student education expenditure in China following decentralization .

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147 3.7: Ethiopia’s Southern Nations, Nationalities and Peoples Region: woreda-level spending on education before and after decentralization .

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150 3.8: Functions transferred to schools in three Latin American programmes .

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154 3.9: Responsibilities of the public and private sectors in provision and financing of education service delivery .

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159 3.10: Strategies to address education inequity in eighteen PRSPs .

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200 4.1: Share of education and basic education in aid commitments, 2000–2006 .

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209 4.2: Governance components of recent education projects and programmes supported by the World Bank .

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231 Text boxes 2.1: Malnutrition compromises India’s progress in primary school enrolment .

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48 2.2: Country evidence: health and nutrition interventions can enhance cognitive development .

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49 2.3: Pre-school benefits for equity and efficiency .

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50 2.4: In Egypt, national progress but the poor are being left behind .

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53 2.5: The equity gap in early childhood provision in the United States .

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55 2.6: Ethiopia — moving into the UPE fast lane .

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58 2.7: Nepal — also on fast-forward towards UPE .

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59 2.8: Nigeria off track — the price of weak governance .

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62 2.9: United Republic of Tanzania — remarkable progress .

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63 2.10: Rebuilding statistical capacity in the Democratic Republic of the Congo .

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64 2.11: Achieving UPE — lessons from strong performers .

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77 2.12: Uganda’s good example of integrating disabled students .

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83 2.13: Bangladesh’s triumph: achieving gender parity by 2005 .

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103 2.14: How to measure quality in education?

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108 2.15: New international evidence on learning outcomes: what it reveals about quality .

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111 2.16: Unequal learning time, unequal outcomes .

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115 2.17: How many teachers are needed to achieve EFA?

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118 3.1: Tracking public expenditure in Bangladesh .

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140 3.2: Public expenditure tracking, information campaigns and the fight against corruption in Uganda .

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141 3.3: Supporting school fee abolition: school grants in Ghana .

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144 3.4: Fiscal decentralization in Nigeria — reinforcing regional disparities .

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149 3.5: Financial decentralization with equity in Ethiopia .

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150 3.6: Planning for strengthened school autonomy in Mexico .

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156 3.7: Community involvement in Uttar Pradesh .

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158 3.8: Chile’s experience with choice and competition: no advertisement for the governance reform blueprint .

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162 3.9: Swedish lessons in competition: not readily exportable .

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163 3.10: Why poor households choose low-fee schools in an Indian district .

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165 3.11: Government schools for the rich, private schools for the poor in Kenya’s slums .

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167 3.12: In Pakistan, a questionable public-private partnership .

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169 3.13: Weighing the costs of lower teacher wages in Togo .

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173 3.14: ‘Marginal and frustrated’: a contract teacher’s view from Cameroon .

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174 3.15: Contract teaching in India: reaching the underserved .

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174 3.16: Recruiting ethnic minority teachers in the Lao People’s Democratic Republic and Cambodia .

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176 3.17: Teacher deployment in a fragile state: the experience of Afghanistan .

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177 3.18: Problems in Mongolia’s teacher bonus system .

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178 3.19: Incentives to reduce absenteeism in India: a randomized experiment .

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179 3.20: No Child Left Behind in the United States: the jury is still out .

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181 3.21: Assessments inform teacher support in Uruguay’s schools .

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183 3.22: Reforming school supervision in Uganda .

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184 3.23: Strengthening equity through sector-wide approaches: Sri Lanka’s experience .

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186 3.24: Building capacity for pro-poor reform in the United Republic of Tanzania .

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187 3.25: Decentralization in Nepal: a difficult journey .

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190 3.26: Intersectoral planning on HIV/AIDS and education in Cambodia .

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192 3.27: Getting pastoralist concerns onto the PRSP agenda in Kenya .

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193 3.28: New York City is learning lessons from Mexico’s Oportunidades programme .

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196 3.29: Social protection in Pakistan’s poverty reduction strategy: the effects of fragmentation .

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197 4.1: Aid supports the abolition of school fees in Kenya .

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206 4.2: Assessing the amount of total aid to the education sector .

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209 4.3: France and Germany focus on aid to post-secondary education .

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218 4.4: Aid effectiveness — falling short of the 2010 targets .

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221 4.5: India’s education aid: standing firm in negotiations .

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224 4.6: Fragile states and the new aid agenda .

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227 Map 3.1: Net attendance rates and education spending per primary school-age child, Nigeria, most recent year .

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149 H I G H L I G H T S O F T H E E FA R E P O R T 2 0 0 9 Highlights of the EFA Report 2009 Headline messages There has been remarkable progress towards some of the EFA goals since the international community made its commitments in Dakar in 2000.

Some of the world’s poorest countries have demonstrated that political leadership and practical policies make a difference.
However, business as usual will leave the world short of the Dakar goals.

Far more has to be done to get children into school, through primary education and beyond.

And more attention has to be paid to the quality of education and learning achievement.

Progress towards the EFA goals is being undermined by a failure of governments to tackle persistent inequalities based on income, gender, location, ethnicity, language, disability and other markers for disadvantage.
Unless governments act to reduce disparities through effective policy reforms, the EFA promise will be broken.

Good governance could help to strengthen accountability, enhance participation and break down inequalities in education.

However, current approaches to governance reform are failing to attach sufficient weight to equity.
Progress on the six EFA goals Goal 1 — Early childhood care and education Child malnutrition is a global epidemic that affects one in three children under the age of 5 and undermines their ability to learn.

Slow progress in tackling child malnutrition and ill health – especially in sub-Saharan Africa and South Asia – is undermining progress towards universal primary education.

Progress indicators for the well-being of children in their pre-school years are a source for concern.
The development targets set in the Millennium Development Goals for child mortality and nutrition will be missed by wide margins if current trends continue.

Major global disparities in provision continue to divide the world’s richest and poorest children.

In 2006, pre-primary gross enrolment ratios averaged 79% in developed countries and 36% in developing countries, falling as low as 14% in sub-Saharan Africa.
Global disparities are mirrored in wide gaps within countries, especially between the richest and poorest children.

In some countries, children from the wealthiest 20% of households are five times more likely to attend pre-school programmes than those from the poorest 20%.

Goal 2 — Universal primary education The average net enrolment ratios for developing countries have continued to increase since Dakar.
Sub-Saharan Africa raised its average net enrolment ratio from 54% to 70% between 1999 and 2006, for an annual increase six times greater than during the decade before Dakar.

The increase in South and West Asia was also impressive, rising from 75% to 86%.

In 2006, some 75 million children, 55% girls, were not in school, almost half in sub-Saharan Africa.

On current trends, millions of children will still be out of school in 2015 – the target date for universal primary education.
Projections for 134 countries accounting for some two-thirds of out-of-school children in 2006 suggest that some 29 million children will be out of school in 2015 in these countries alone FA R E P O R T 2 0 0 9 Children from poor households, rural areas, slums and other disadvantaged groups face major obstacles in access to a good quality education.
While children from the wealthiest 20% of households have already achieved universal primary school attendance in most countries, those from the poorest 20% have a long way to go.

Trends in primary education are susceptible to public policy.

Ethiopia and the United Republic of Tanzania are making remarkable progress in increasing enrolment and reaching the poor, thanks to policies such as the abolition of school fees, the construction of schools in underserved areas and increased teacher recruitment.
In Nigeria and Pakistan, poor education governance is holding back progress and keeping millions of children out of school.

In 2006, some 513 million students worldwide – or 58% of the relevant school-age population – were enrolled in secondary school, an increase of nearly 76 million since 1999.

Despite progress, access remains limited for most of the world’s young people.

In sub-Saharan Africa, 75% of secondary-school-age children are not enrolled in secondary school.
Goal 3 — Meeting the lifelong learning needs of youth and adults Governments are not giving priority to youth and adult learning needs in their education policies.

Meeting the lifelong needs of youth and adults needs stronger political commitment and more public funding.

It will also require more clearly defined concepts and better data for effective monitoring.

Goal 4 — Adult literacy An estimated 776 million adults – or 16% of the world’s adult population – lack basic literacy skills.
About two-thirds are women.

Most countries have made little progress in recent years.

If current trends continue, there will be over 700 million adults lacking literacy skills in 2015.

Between 1985–1994 and 2000–2006, the global adult literacy rate increased from 76% to 84%.

However, forty-five countries have adult literacy rates below the developing country average of 79%, mostly in sub-Saharan Africa, and South and West Asia.

Nearly all of them are off track to meet the adult literacy target by 2015.
Nineteen of these countries have literacy rates of less than 55%.

Major disparities in literacy levels within countries are often linked with poverty and other forms of disadvantage.

In seven sub-Saharan African countries with low overall adult literacy rates, the literacy gap between the poorest and wealthiest households is more than forty percentage points.
Goal 5 — Gender In 2006, of the 176 countries with data, 59 had achieved gender parity in both primary and secondary education – 20 countries more than in 1999.

At the primary level, about two-thirds of countries had achieved parity.

However, more than half the countries in sub-Saharan Africa, South and West Asia and the Arab States had not reached the target.

Only 37% of countries worldwide had achieved gender parity at secondary level.
There is a confirmed trend towards more female than male enrolments in tertiary education worldwide, in particular in more developed regions and in the Caribbean and Pacific.

Poverty and other forms of social disadvantage magnify gender disparities.

For example, in Mali girls from poor households are four times less likely to attend primary school than those from rich households, rising to eight times at secondary level.
Once girls are in school, their progress is often hampered by teacher attitudes and gender-biased textbooks that reinforce negative gender stereotypes.

These school-based factors interact with wider social and economic factors that influence school performance along gender lines.

Goal 6 — Quality International assessments highlight large achievement gaps between students in rich and poor countries.

Within countries too, inequality exists between regions, communities, schools and classrooms.
These disparities have important implications not just in education but for the wider distribution of opportunities in society.

In developing countries there are substantially higher proportions of low learning achievement.

In a recent Southern and Eastern Africa Consortium for Monitoring Educational Quality assessment (SACMEC II) in sub-Saharan Africa, fewer than 25% of grade 6 pupils reached a desirable level of reading in four countries and only 10% in six others.
H I G H L I G H T S O F T H E E FA R E P O R T 2 0 0 9 International aid Commitments to basic education are stagnating.

In 2006, for developing countries, they amounted to US$5.1 billion, a little below the 2004 level.

Half of all commitments to basic education came from just a handful of donors.

Total aid for basic education for low-income countries in 2006 was US$3.8 billion.
The amount will have to be tripled to reach the estimated US$11 billion required annually to finance a narrow range of goals in low-income countries.

The Fast Track Initiative (FTI) is failing to galvanize additional bilateral donor support for EFA.

Current commitments to its Catalytic Fund fall short of those required to meet financing requests in the pipeline.

By 2010, countries with plans approved by the FTI could be facing a financing shortfall of US$2.2 billion.
An ambitious new agenda governing aid hopes to make aid more efficient and effective.

To date progress is mixed: though some donors are willing to encourage national ownership, work through national systems and cooperate with other donors, others are more reticent.
Top policy recommendations Meeting the EFA goals Early childhood care and education Strengthen the links between education planning and child health provision, using cash transfer programmes, targeted health interventions and more equitable public spending in health sectors.

Prioritize early childhood education and care in planning for all children, with incentives provided to include those who are vulnerable and disadvantaged.
Strengthen wider anti-poverty commitments by tackling child malnutrition and improving public health systems, using innovative social welfare programmes which target poor households.

Student background, the organization of the education system and the school environment explain learning disparities within each country.

Many essential resources taken for granted in developed countries remain scarce in developing countries – including basic infrastructure such as electricity, seats and textbooks.
More than 27 million teachers work in the world’s primary schools, 80% of them in developing countries.

Total primary school staff increased by 5% between 1999 and 2006.

In sub-Saharan Africa alone, 1.6 million new teacher posts must be created and teachers recruited by 2015 to achieve UPE, rising to 3.8 million if retirement, resignations and losses (due to HIV/AIDs, for example) are taken into account.
There are large national and regional disparities in pupil/teacher ratios, with marked teacher shortages in South and West Asia, and sub-Saharan Africa.

But it is within countries that the greatest disparities exist, with teachers unevenly distributed across regions.

Financing education National finance In the majority of countries with data, national spending on education has increased since Dakar.

In some countries, increased spending has been associated with substantial progress on the EFA goals.
However, the share of national income devoted to education decreased in 40 of the 105 countries with data between 1999 and 2006.

Low-income countries are still spending significantly less on education than are other countries.

In sub- Saharan Africa, eleven out of the twenty-one low- income countries with data spend less than 4% of their GNP.

In South Asia, several high-population countries continue to spend under or only just over 3% of their GNP on education.
This appears to reflect low political commitment to education.

Global wealth inequalities are mirrored by inequalities in education spending.

In 2004, North America and Western Europe alone accounted for 55% of the world’s spending on education but only 10% of the population aged 5 to 25.

Sub-Saharan Africa accounts for 15% of 5- to 25–year-olds but just 2% of global spending.

South and West Asia represents over one- quarter of the population and just 7% of spending.
3 H I G H L I G H T S O F T H E E FA R E P O R T 2 0 0 9 Universal primary education Fix ambitious long-term goals supported by realistic planning and sufficient medium- to long-term budgetary allocations to ensure progress in access, participation and completion in primary education.

Support equity for girls, disadvantaged groups and underserved regions by setting clear targets for reducing disparities, backed by practical strategies for achieving more equitable outcomes.
Raise quality while expanding access by focusing on smooth progression though school and better learning outcomes, increasing textbook supply and quality, strengthening teacher training and support, and ensuring that class sizes are conducive to learning.

Education quality Strengthen policy commitments to quality education and create effective learning environments for all students, including adequate facilities, well-trained teachers, relevant curricula and clearly identified learning outcomes.
A focus on teachers and learning should be at the heart of this commitment.

Ensure that all children attending primary school for at least four to five years acquire the basic literacy and numeracy skills that they need to develop their potential.

Develop the capacity to measure, monitor and assess education quality, in areas that affect learning conditions (infrastructure, textbooks, class sizes), processes (language, instructional time) and outcomes.
Revise existing policies and regulations to ensure that children have sufficient instructional time and that all schools minimize the gap between intended and actual instructional time.

Participate in comparative regional and international learning assessments and translate lessons learned into national policy, and develop national assessments that best reflect each country’s particular needs and goals.
Overcoming inequality — lessons for national governance reforms Commit to the reduction of disparities based on wealth, location, ethnicity, gender and other indicators for disadvantage.

Governments should develop well- defined targets for reducing disparities and monitor progress towards their achievement.
Sustain political leadership to reach education targets and tackle inequality through clear policy objectives and improved coordination within government through active engagement with civil society, the private sector and marginalized groups.

Strengthen policies for reducing poverty and deep social inequalities that hinder progress towards education for all.

Governments should integrate education planning into wider poverty-reduction strategies.
Raise quality standards in education and work to ensure that disparities in learning achievement between regions, communities and schools are reduced.

Increase national education spending, especially in developing countries that chronically underinvest in education.

Put equity at the centre of financing strategies, in order to reach disadvantaged children, with more accurate estimates of the costs of reducing disparities and the development of incentives for reaching the most marginalized.
Ensure that decentralization has an inbuilt commitment to equity through financing formulas that link resources to levels of poverty and deprivation in education.

Recognize that school competition and choice, and private-public partnerships have their limits.

If a public education system works poorly, the priority must be to fix it.
Strengthen the recruitment, deployment and motivation of teachers to ensure that there are enough qualified teachers in all regions and schools, especially in remote and underserved communities.
FA R E P O R T 2 0 0 9 Aid donors — delivering on commitments Increase aid for basic education, especially to low-income countries, by providing around US$7 billion to cover current financing gaps in priority EFA areas.

Enlarge the group of donor countries committed to providing aid to basic education, in order to ensure that the financial support for the EFA goals is sustainable.
Commit to equity in aid for education by providing more funds to basic education in low-income countries.

Several donors – including France and Germany – should urgently review their current aid allocations.

Get behind the Fast Track Initiative and close the projected financing gap – estimated at US$2.2 billion for 2010 – for countries with approved plans.
Improve aid effectiveness and reduce transaction costs, as set out in the Paris Declaration, through greater alignment of aid behind national priorities, better coordination, increased use of national financial management systems and greater predictability in aid flows.
Overview E ight years have passed since representatives of more than 160 governments gathered at the World Education Forum in Dakar, Senegal, to adopt an ambitious Framework for Action aimed at expanding learning opportunities for children, youth and adults.

At the heart of the Framework is a pledge to achieve six Education for All (EFA) goals.
The Dakar promise extends from early childhood care and education (ECCE) and universal primary education (UPE) to gender equality, the spread of adult literacy, the expansion of skills programmes for youth and adults, and improvements in the quality of education.

Underpinning the Framework is a commitment to inclusive and equitable education provision and opportunity for all the world’s citizens.

This edition of the EFA Global Monitoring Reportcomes at a critical moment.
With the 2015 deadline for some key goals just over the horizon, there are worrying signs of a large-scale shortfall.

Remarkable gains have been registered in many of the world’s poorest countries,1 but the distance remaining is great.

Governments and aid donors have to act with a renewed sense of urgency and shared commitment to deliver on the pledges they made in 2000.

These promises cannot wait and time is running out.
The Report, titled Overcoming Inequality: Why Governance Matters, identifies deep and persistent disparities based on income, gender, location, ethnicity and other markers for disadvantage as a major barrier to progress in education.

Inequity in education is linked to wider disparities in the distribution of power, wealth and opportunity.
And it is perpetuated by policies that either tolerate or actively exacerbate an unfair distribution of life chances – policies that fuel the transmission of poverty across generations.

Inequalities in education of the magnitude observed in many countries are unacceptable.

The circumstances into which children are born, their gender, the wealth of their parents, their language and the colour of their skin should not define their educational opportunities.
Apart from being inequitable, large disparities in education are inefficient: they hold back economic growth and progress in other areas.

Governments 1.

Throughout the Report, the word ‘countries’ should generally be understood as meaning ‘countries and territories’.

and aid donors can do a great deal to equalize opportunity in education, working with civil society and local movements for change.

The starting point is to put equity squarely at the centre of the EFA agenda.
Extreme inequalities in education are linked to wider disparities in society.

Overcoming these inequalities requires effective and committed government leadership and a public sector with the human and financial resources to break down disadvantage.

More than that, it requires good governance.

In its broadest sense, governance is about the processes, policies and institutional arrangements that connect the many actors in education.
It defines the responsibilities of national and subnational governments in areas such as finance, management and regulation.

Governance rules stipulate who decides what, from the national finance or education ministry down to the classroom and community.

Good governance practices can help foster development of more inclusive, more responsive education systems that address the real needs of the marginalized.

Bad governance practices have the opposite effect.


Education has been at the forefront of a wider governance reform agenda.

Outcomes to date have not been encouraging, especially when it comes to equity.

Approaches to financial decentralization, choice and competition in school management, and the integration of education planning with wider strategies for poverty reduction have not given the required impetus to EFA.
One reason is that equity considerations have typically been bolted onto governance reforms as an afterthought.

Government responsibility for acting on the Dakar Framework extends to international aid partnerships.

Having signed up for the Framework, donors in rich countries have underperformed.

Aid flows are falling far short of the required levels, calling into question donors’ commitment to ensure that no developing country would fail in its planning for EFA for want of finance.
Donors are also falling short of commitments to increase aid by 2010.

Besides keeping their promises on aid, donors need to address governance problems that are undermining the quality and effectiveness of development assistance.

Chapter 1 Education for all: human right and catalyst for development The EFA agenda is rooted in a commitment to human rights and social justice.

It recognizes that expanding and equalizing opportunities for education are development goals in their own right.
But the Dakar Framework for Action also defines a public policy agenda linking education to wider development goals.

Progress towards equitable education can act as a powerful catalyst for progress in other areas, including public health, poverty reduction, gender equality, participation and democratization.

The Millennium Development Goals (MDGs), also adopted in 2000, are the world’s time-bound and quantitative targets for addressing extreme human deprivation in its many dimensions.
The targets range from halving extreme poverty to cutting child and maternal death rates and reducing malnutrition.

Education is part of the MDG framework.

However, the MDG targets for education are far less ambitious and more restrictive than the EFA agenda.

The MDG project is at a watershed.

While there has been progress in many areas, it has been uneven and too slow to achieve the targets.
In September 2008, governments from around the world met at a United Nations summit in New York to reaffirm their MDG commitments – but reaffirmation alone does not bring the targets within reach.

Accelerated progress in education could play an important role in getting the world on track to achieve the wider MDG goals.

Recent research has reinforced earlier evidence on the key role of education as a catalyst for human development.

The links run two ways.


Progress in education can unlock progress in health, nutrition and poverty reduction, and vice versa.

This has important implications in areas where the MDG outcomes are lagging far behind target levels: O V E R V I E W Halving extreme poverty.

Broad-based and equitable economic growth is the key to cutting income poverty.

There is strong evidence linking education to higher growth and productivity.
The increasing importance of knowledge for economic growth may be strengthening the links.

When educational opportunities are broadly shared, with marginalized groups participating, prospects for shared economic growth are strengthened.

Child mortality and nutrition.

In many countries, having a mother with secondary or higher education more than halves the risk of child mortality, relative to mothers with no education.
Controlling for other factors, when a Bangladeshi mother has completed primary education, it cuts the risk of child stunting by 20%.

These outcomes reflect the empowering effects of education in expanding access to information and to health service use.

The case for gender equality in education is important in its own right.

It is also true that no country can afford the prohibitive human, social and economic costs that come with gender inequality.
The potential benefits of the EFA agenda extend far beyond the MDGs.

Recent evidence from sub-Saharan Africa points to the important role of education in building support for multiparty democracy and in challenging autocracy.
As the latest learning assessment by the Organisation for Economic Co-operation and Development’s Programme for International Student Assessment (OECD-PISA) shows, education also equips children with the learning skills they need to understand complex environmental problems – including climate change – and to hold political leaders to account for resolving them.

Chapter 2 The Dakar goals: monitoring progress and inequality Monitoring of progress towards the EFA goals serves many purposes.
It provides global, regional and national measures of how close the Dakar Framework is to being fulfilled.

Effective monitoring can also pick up early warning signals, alerting governments and the international community to potential failures.

And it is an essential element for holding governments to account for their actions and performance.
Building on the previous Report’s systematic midterm assessment of progress towards EFA, the EFA Global Monitoring Report 2009draws on data for the school year ending in 2006.

It highlights the extraordinary progress made in many areas, notably by some of the poorest countries.

That progress bears testimony to the fact that the EFA goals are attainable.
With strong political commitment, the right public policies and sufficient financial commitment, all countries have the potential to move rapidly towards meeting the six goals.

The bad news is that the world is not on track for achieving several key targets, including UPE by 2015.

Changing this picture requires urgent action.

It takes time to build classrooms, train teachers and put in place policies to remove barriers facing the disadvantaged – and time is running out.
Goal 1: Early childhood care and education ECCE is the foundation of the EFA agenda.

The health and nutritional status of children, especially during the first two years of life, has a profound influence on their cognitive development and learning achievements in school.

Early childhood malnutrition affects brain development and diminishes prospects for success in school and beyond.

Pre-primary education and health provision can counter early childhood disadvantage.
Good-quality ECCE programmes have a strong track record in reducing dropout rates in primary school, improving learning achievements and narrowing inequalities.

Childhood malnutrition and poor health are two of the greatest barriers to EFA.

Progress in both areas has lagged far behind progress in getting children into school.
The upshot is that millions of children entering school have had their brains, their cognitive development and their education potential permanently damaged by hunger and ill health.

This runs counter to the commitments made in the Dakar Framework for Action: filling classrooms with malnourished and sick children is not what UPE is about.

The facts of childhood deprivation make their own case for a strengthened focus on early childhood: 8 Child mortality.
Around 10 million children a year die in developing countries before their fifth birthday.

Survival prospects are improving – but far too slowly.

Estimates for 2015 based on current trends show that the gap between the MDG target of a two-thirds reduction in child deaths and actual outcomes will amount to 4.3 million deaths.

Already significant inequalities in child death rates between rich and poor are widening in many countries.

Stunting and low birth weight.


Around one in three children under 5 – 193 million in total – suffer moderate to severe stunting.

The vast majority of these children live in South Asia, where almost half of all children are affected, and in sub-Saharan Africa.

Low birth weight is a risk factor for ill-health and stunting and an indicator for poor maternal health.

Some 16% of children in developing countries were delivered with low birth weight in 2006, rising to 29% in South Asia.
Vitamin and mineral deficiencies.

Millions of children are affected by micronutrient deficiency.

Iron deficiency anaemia, which affects around half of pre-school children in developing countries, impairs cognitive development and increases vulnerability to infectious diseases.

More rapid economic growth alone will not overcome these deficits.

Over the past two decades, India has been among the world’s fastest-growing economies.

By contrast, child health and nutrition have been improving very slowly.
Rising food prices could undermine international efforts to counteract malnutrition in many countries, with damaging consequences for the EFA goals.

The record on pre-school provision is discouraging.

Enrolments are increasing but the vast majority of the world’s children continue to lack access to quality pre-schools.

Gross enrolment ratios (GERs) in 2006 averaged 79% in developed countries and 36% in developing countries.
Of the thirty-five countries in sub-Saharan Africa for which data are available, seventeen had coverage rates below 10%.

Coverage rates are lowest for precisely those children who stand to gain the most: namely, the poor and disadvantaged.

Weak public policies in ECCE are holding back accelerated progress towards wider EFA goals and reinforcing education disparities.

Evidence from several countries demonstrates what can be achieved.
Countries such as Bangladesh, Ethiopia, Nepal and the United Republic of Tanzania have made rapid progress in O V E R V I E W reducing child mortality and improving child health.

In the Philippines an integrated ECCE programme has registered strong improvements in cognitive development.

In Mexico a conditional cash transfer programme linked to early childhood health and education has achieved tangible gains in primary school progression and learning achievement.
These figures can be traced to political leadership and effective public policies.

Increased public investment, ambitious school construction programmes, the abolition of school fees, measures to strengthen quality and – critically – the targeting of disadvantaged groups have all played a role.

So have increased recruitment and training of teachers.

It is not just developing countries that face problems in ECCE.
While most developed countries have high levels of early childhood provision, this is not the case in the United States, which has relatively low and highly unequal levels of coverage.

The evidence suggests that inequalities in early childhood education are an important source of disparities in primary and secondary school.

Goal 2: Universal primary education UPE is not just about getting children into school at an appropriate age.
It is also about ensuring that they stay in school to complete a full cycle of quality basic education.

The report card is mixed.

Some impressive gains have been registered.

The net enrolment ratio (NER) for developing countries as a group increased between 1999 and 2006 at twice the rate of the 1990s.

In sub-Saharan Africa, it increased from 54% to 70%.

This is six times the rate of the 1990s – and it was achieved despite rapid population growth.
In South and West Asia the NER climbed from 75% to 86%.

Behind these regional figures are some remarkable achievements: Ethiopia more than doubled its NER to 71%.

The NERs for Benin and the United Republic of Tanzania moved from around 50% to more than 80%.

In the midst of a civil conflict, Nepal increased its NER from 65% to 79% (in 2004).

Among the Arab States, Djibouti, Mauritania, Morocco and Yemen registered strong gains.
Post-Dakar progress is also reflected in a decline in the number of children out of school.

There were 28 million fewer out-of-school children in 2006 than when governments met in Dakar in 2000.

In sub-Saharan Africa, the number of primary-school-age children not in school dropped by 10 million while the population in that age group increased by 17 million.

South and West Asia more than halved its out-of-school population, from 37 million to 18 million.
The distance travelled towards the EFA goals since 1999 should not obscure the distance that remains.

The yardstick is not the record of the 1990s but the target of UPE by 2015.

On current trends, it will be missed: In 2006 some 75 million children of primary school age were not in school.

This is 12% of the developing world’s primary-school-age population.

In sub- Saharan Africa, nearly one-third of that age group is out of school.
At the start of the twenty-first century, in an increasingly prosperous, knowledge-based global economy, millions of children do not even have a foot on the first rung of the EFA ladder.

Girls still account for the majority of the world’s out- of-school children (55%).

Importantly, out-of-school girls are also more likely never to have been to school than boys.

This Report provides a partial projection for the out-of-school population in 2015.
It is partial because, for reasons of data limitation, it covers countries that are home to just two-thirds of out-of-school children in the relevant age group.

Countries that are not covered include Sudan and the Democratic Republic of the Congo, both of which have large populations affected.

Even with the exemptions, a business-as-usual trajectory suggests that there will still be 29 million children out of school in 2015.
Slow progress towards UPE in Nigeria and Pakistan is pushing these countries towards the top of the out-of-school league table.

By 2015, more than 10 million children could be out of school in these two countries alone.

Out-of-school figures and projections capture just one aspect of the challenge that has to be addressed to bring UPE within reach by 2015.

In many countries, primary school students are locked into cycles of repetition and early dropout.
In Malawi, just over six in ten children enter primary school at the official age – and half of them either drop out or repeat grade 1.

Of the thirty-one countries in sub-Saharan Africa with data, eleven have grade 1 and 2 repetition rates in excess of 20%.

The problem is also widespread in Latin America.

This year’s Report highlights the inefficiencies and inequalities associated with grade repetition.
Combining data on enrolment and completion highlights the scale of global inequality in education.

Children in Britain or France are more likely to enter tertiary education than children in the Niger or Senegal are to complete primary school.

Such inequalities in the international distribution of opportunity for education have important implications for future patterns of globalization.
Today’s inequalities in education are tomorrow’s inequalities in the distribution of wealth and wider opportunities for human development.

Inequality as a barrier to progress Inequalities within countries are also marked.

When it comes to primary school attendance, children from rich and poor households move in different worlds.

National averages can obscure this point.


If the richest 20% in countries including Bangladesh, Bolivia, Ghana, India and Nigeria were a country, they would almost have achieved UPE.

The poor have a long way to go.

Simple UPE arithmetic points to a strong case for greater focus on equity.

In countries with school attendance rates above 80%, children from poor households are heavily over-represented among out- of-school children.
They account for more than 40% of the non-attending school population in countries from Cameroon and Kenya to Indonesia and Nicaragua.

Even in countries with lower levels of attendance reported in household surveys, such as Ghana, India, Mozambique, Nigeria and Zambia, the poorest quintile accounts for 30% to 40% of the out-of-school population.

Income-based disparities intersect with wider inequalities.
Rural children in many developing countries are less likely to attend school and more likely to drop out.

In Senegal, children in urban areas are twice as likely as those in rural areas to be in school.

Slum dwellers face a distinctive set of challenges, with high levels of poverty, ill health and limited provision restricting access.

Socio-cultural inequalities linked to ethnicity and language are also important.
Disadvantage in each of these areas is related to, and compounded by, poverty and income- based inequalities – but they are also important in their own right.

achieved.

Child labour is one of the most formidable.

There are around 218 million child labourers in developing countries, and numbers are coming down slowly in sub-Saharan Africa and parts of Asia.

Ill health and malnutrition undermine school attendance and learning capacity for millions of children.
And childhood disability is strongly associated with inequalities in participation, reflecting a widespread failure to implement policies for inclusive education.

Post-primary education Increasing participation in secondary education is part of the Dakar commitment.

Progress in this area is vital.

Expanded access to secondary school is needed to absorb the increase in numbers of children emerging from primary schools, to create incentives for primary school completion and to train teachers.
Secondary and post-secondary education is also important for the development of skills needed in an increasingly knowledge-based global economy.

There are large regional disparities in participation in secondary schools.

At one end of the spectrum, most developed and transition economies are nearing universal secondary education.

At the other, the secondary NER for sub-Saharan Africa is just 25%, implying that nearly 78 million children of the relevant age group are not enrolled in secondary school.
The transition point from primary to secondary is marked by high levels of dropout in many countries.

As at primary level, progression through the secondary school system is characterized by rising inequalities.

In Latin America, 88% of children from the wealthiest decile move steadily through the secondary school system without repetition or dropout – twice the share for the poorest decile.

Global disparities are strongly apparent at tertiary level.

The global tertiary GER is around 25%.


Regional GERs, however, range from 70% in North America and Western Europe to 32% in Latin America and 5% in sub-Saharan Africa.

Beyond the quantitative gaps are large qualitative disparities fuelled by differences in financing capacity.

In equivalent dollar terms, France spends sixteen times as much per university student as Peru.
In 2005, the top American universities spent over twenty-five times as much per student as Dar-es-Salaam University in the United Republic of Tanzania.

Other barriers to UPE also have to be removed if the 2015 targets are to be Tertiary education is the point at which the cumulative effects of disparities at the primary and secondary level become apparent.

In Brazil, the university participation for black people is 6% – just under one-third of the rate for white Brazilians.
Goals 3 and 4: Lifelong learning and literacy Achieving UPE would establish a basis for lifelong learning and literacy for future generations.

But there is an immense backlog of unmet need.

Millions of teenagers have never attended primary school and millions more leave without the skills they need.

Limited access to educational opportunities in the past has also left 776 million adults –two-thirds of them women – lacking basic literacy skills.
Many governments have paid insufficient attention to youth and adult learning needs.

Public funding remains inadequate and provision highly unequal.

The fact that some of the goals in the Dakar Framework were vaguely worded may have contributed to a lack of urgency.

The sixth International Conference on Adult Education, scheduled for 2009, provides an important opportunity to change this picture.

Illiteracy continues to receive inadequate attention from policy-makers.
Although there were 95 million fewer illiterates worldwide in 2000–2006 than in 1985–1994, absolute numbers have increased in sub-Saharan Africa and the Arab States.

On current trends there will still be over 700 million adult illiterates in 2015.

Many factors contribute to low literacy levels, including gender disparities, poverty, location and ethnicity.

The problem is not restricted to developing countries.
Many OECD countries also record high levels of literacy problems: 1 million native Dutch speakers in the Netherlands are classified as functionally illiterate, for example.

In metropolitan France, some 10% of the population aged 18 to 65 – more than 3 million people – lacks basic reading, writing, arithmetic and other fundamental skills despite having attended French schools.
Goal 5: Gender disparities and inequalities in education The Dakar Framework sets out an ambitious two-part agenda on gender equity.

The first part aims at gender parity in school participation and the second at wider progress towards equality between girls and boys in educational opportunities and outcomes.

The world has made sustained progress towards gender parity, but deficits remain large.

Of the 176 countries in 2006 with data, 59 had achieved gender parity in both primary and secondary education.
Over half the countries of sub-Saharan Africa, South and West Asia, and the Arab States have yet to achieve parity at primary level.

O V E R V I E W There are large regional variations in progress towards gender parity.

Advances in sub-Saharan Africa have been slow and uneven.
The regional gender parity index (GPI), which measures the ratio of girls to boys primary GER, rose from 0.85 in 1999 to 0.89 in 2006, though several countries – including Ghana and the United Republic of Tanzania – have achieved parity.

The GPI for South and West Asia rose from 0.84 to 0.95.

However, Pakistan still enrols only 80 girls for every 100 boys in primary school.

Expansion of secondary school enrolment has led to reductions in gender disparities in most regions.
However, gender disparities remain larger in secondary education than in primary.

In many countries in sub- Saharan Africa, and South and West Asia, participation rates for girls remain low and disparities high.

One major exception is Bangladesh, which has achieved gender parity.

Public policy, notably the creation of financial incentives through stipend programmes, has played a key role.

Underparticipation by boys is marked in many countries, especially in Latin America.
Gender disparities are unequally distributed across societies.

Being born into a household that is poor, rural or indigenous, or speaks a minority language, reinforces gender disadvantage in many countries.

In Mali, the GPI for the poorest 20% of households was 0.60 in 2001, whereas many more girls in the richest 20% were attending primary school.

The secondary GPI is 0.50 for the poorest households and 0.96 for the wealthiest.
Such facts demonstrate how poverty often magnifies the effects of gender disparities.

Gender equality is more difficult to measure than parity.

Learning achievements provide one benchmark.

Four broad themes emerge from international assessments.

First, girls often outperform boys in reading and literacy.

Second, boys outperform girls in mathematics, though the gap is closing.

Third, boys maintain a small advantage in science.


Fourth, at the tertiary level women remain under-represented in science and engineering and ‘over-represented’ in areas such as education and health.

Goal 6: Education quality and learning achievements The ultimate goal of education is to equip children with the knowledge, skills and opportunities they need to realize their potential and to participate in social and political life.

Many education systems are failing to achieve this goal.
Real learning divides are larger than those captured in international assessments.

This is because assessments measure learning outcomes among children in school and do not include children who are currently or permanently out of school.

Given that out-of-school children would be expected to score at lower levels than children in school, the real national averages may be well below those indicated.
Within-country inequalities in outcomes often mirror global disparities in scale.

In countries including Morocco and South Africa, the top 5% of pupils covered in the Progress in International Reading Literacy Survey (PIRLS) assessment registered scores comparable to those of the best pupils in high-achieving countries.

But the scores of the bottom 5% were less than one-fifth of those for top performers.
Research in the Indian states of Rajasthan and Orissa also points to extremely large learning disparities.

Many factors influence learning achievement levels.

Student characteristics play a significant role.

Socio-economic status, family size and composition, immigrant status and home language are all important variables.

System-level variables, such as access to early childhood provision, selection and the social composition of schools, are also influential.
School-based factors have a strong effect on learning.

Insufficient instructional time is one source of underachievement.

A study in Bangladesh found that 10% of government schools provided fewer than 500 hours of instruction, compared with 860 hours at the other end of the spectrum.

In many cases, children and teachers lack access to basic learning materials.
SACMEQ II found that over half of grade 6 pupils in many countries – including Malawi, Mozambique, Uganda and Zambia – did not have a single book.

A poor learning environment can exacerbate social disparities.

O V E R V I E W Progress in quantitative headcount indicators has masked problems in qualitative learning achievement.

In many developing countries, absolute levels of average learning are exceptionally low.
International learning assessments draw attention to the very large disparities between rich and poor countries.

Within countries, too, there are often large differences in test scores based on socio-economic status, school performance and other variables.

Getting children into school and through a full cycle of basic education remains a major priority.
But evidence from many countries suggests that, once in school, many children are acquiring only the most rudimentary skills, as the following examples demonstrate: One recent assessment in the Punjab province of Pakistan found that over two-thirds of grade 3 students could not write a sentence in Urdu and a similar proportion could not solve a simple subtraction problem.

In India, a large-scale assessment found that 45% of children in standard 3 could not read a text designed for standard 1 students.
Results from the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) II assessment in Africa indicated that the share of grade 6 children reaching the ‘desirable’ level of literacy was less than 25% in Botswana, Kenya and South Africa, and less than 10% in Malawi, Mozambique, Uganda and Zambia.

A recent assessment in Peru found that as few as 30% of children in grade 1, and 50% in grade 2, could read a simple passage from a grade 1 textbook.
These examples, which could be multiplied many times over, draw attention to the sheer scale of the learning achievement deficit in many countries.

International assessments reinforce this picture.

They draw attention to the low average level of learning in many developing countries relative to developed countries.
To take one illustration from the PISA 2001 assessment, the median scores for students in Brazil, Indonesia and Peru would be situated in the lowest 20% of the distribution in France or the United States.

PISA 2006 showed that over 60% of students from Brazil and Indonesia scored at or below the lowest level in science, compared with fewer than 10% in Canada or Finland.

Other international assessments confirm the scale of global inequalities.
The state of a nation’s schools can have an important bearing on prospects for success in education.

Dilapidated school buildings, overcrowded and under- resourced classrooms, and an inadequate supply of teaching materials can all hurt learning prospects – and dilapidation is widespread.
One of the most comprehensive recent surveys of the state of primary schools, overseen by the UNESCO Institute for Statistics, found that more than one-third of students in India, Peru and the Philippines attended schools with insufficient toilets.

More than half the school heads in some countries surveyed felt their schools needed complete rebuilding.

As in other areas, the poor bear the brunt.


Evidence from Latin America shows that badly equipped schools are disproportionately attended by children from the poorest households.

Teachers are the front-line providers in education.

Delivery of quality education is critically dependent on having a sufficient supply of properly trained and motivated teachers.

How teachers are deployed also has an important bearing on equity and learning outcomes.

Acute teacher shortages remain a problem in many countries.
If the world is to achieve UPE by 2015, it will need to recruit an estimated 18 million additional teachers.

In sub-Saharan Africa, an additional 145,000 recruitments are needed annually – 77% above the observed increase between 1999 and 2006.

South and West Asia will need an additional 3.6 million teachers.

National pupil/teacher ratios (PTRs) sometimes mask very large disparities.
There are large variations in ratios within countries, often reflecting differences between rich and poor, rural and urban, and indigenous and non-indigenous areas.

Inequalities in access to trained teachers reinforce these differences.

In India, the majority of untrained teachers are concentrated in rural areas.

In Ghana, they are concentrated in the north, the poorest part of the country.

Reported PTRs are often a misleading indicator for what happens in schools.
Teacher absenteeism has an important bearing on learning in many countries.

In a recent study covering six developing countries, absenteeism rates averaged 19%, rising to 25% for India.

Absenteeism was more pronounced in poorer and rural areas – and it disproportionately affected children from low socio-economic backgrounds.

Low teacher morale and weak motivation, linked to inadequate pay, poor conditions of service and weak support systems, are systemic problems in many countries.
O V E R V I E W Chapter 3 Raising quality and strengthening equity: why governance matters Education governance is not an abstract concept.

It is something that affects the lives of parents, the school experience of children, and the efficiency and equity of education provision.

If the precise meaning of ‘good governance’ can be debated, the consequences of bad governance are readily observable.
They include chronically underfinanced schools, service providers and government agencies that are unresponsive to local needs and unaccountable to parents, large disparities in school access, participation and completion, and low levels of learning achievement.

Governance reform is a prominent part of the EFA agenda.

Within the vast array of country experience, several themes recur.
Many governments have moved towards decentralized provision, shifting the locus of decision-making from central to local level.

The umbrella category of decentralization, however, covers multiple patterns.

The Report maps decision-making in a large group of countries (see annex on education decision-making), revealing a variety of possible arrangements.

Many of the central currents in governance reform span the developed and developing world.
School-based management, which aims at giving schools and communities more autonomy in decision-making, is one illustration.

Another is the growth of education provision models emphasizing the virtues of choice and competition, either within the state sector or through an expanded role for the private sector.

In many developing countries, low-fee private schools are emerging as another source of choice and competition, often outside government regulation.
In teacher management, governance issues focus on concerns over pay and policies for allocating teachers to different areas Other governance issues have received less attention.

One striking example is the integration of education planning with broader poverty reduction strategies.

This is a key issue for the Dakar Framework for Action.
Many of the most entrenched barriers to EFA are rooted beyond the school in underlying structures of poverty and social disadvantage.

Effective education governance can make some difference.

But ultimately, sustained progress towards EFA depends on the effective integration of education planning in wider poverty reduction strategies, for an obvious reason: poverty, poor nutrition and ill health are formidable barriers to success in education.

Governance reform has delivered highly variable results.
While the progress achieved in some countries has to be acknowledged, overall outcomes have been disappointing.

One reason is that governance reforms have often been designed with scant regard for their impact on the most disadvantaged people and regions.

Choice and competition have their merits – but also their limits, notably for the poor.

Governance reform design problems have sometimes been compounded by a tendency to embrace fixed blueprints, many of them originating in developed countries.
The Report explores four central themes in national governance reform, principally as they relate to basic education: financing; ‘voice’, participation and choice; governance of teachers and monitoring of learning; integration of EFA and poverty reduction strategies.

Financing for basic education It costs nothing to set ambitious goals in education.

However, achieving those goals requires financial resources, along with policies that maximize efficiency and equity in the management of those resources.
While many of the issues appear technical, financial governance has a critical bearing on prospects for achieving EFA.

Countries vary enormously in their capacity to finance education.

Increased public spending is not guaranteed to improve access, equity or learning outcomes.

But chronic and sustained underfinancing is a sure route to limited, poor-quality provision.

Most countries have increased the share of national income allocated to education since 1999.
In some cases, such as those of Ethiopia, Kenya, Mozambique and Senegal, the share has climbed sharply.

In others, as in India and Pakistan, it has stagnated at a relatively low 3% of gross national product or less.

While cross-regional comparisons have to be treated with caution, spending patterns in South and West Asia would appear to indicate a limited public spending commitment to education.

Global wealth inequalities are mirrored by inequalities in education spending.
In 2006, per-student expenditure for primary school (expressed in constant dollars) ranged from less than US$300 in much of sub-Saharan Africa to over US$5,000 in most developed countries.

As a region sub-Saharan Africa accounts for 15% of 5- to 25-year-olds but just 2% of global spending on their education.

South and West Asia represent over one-quarter of the population and 7% of spending.

As in any area of public financing, efficiency is an important determinant of outcomes.
Technical efficiency provides a crude indicator of the cost associated with turning finance into quantitative and qualitative outcomes.

In many countries, corruption is a major source of both inefficiency and inequity – the former because it means more public money provides fewer inputs and the latter because the costs of corruption invariably fall most heavily on the poor.

Public spending on education has the potential to redress inequalities but often reinforces them instead.
Wealthier regions and advantaged groups often attract more financing than poorer regions and disadvantaged groups.

Public spending is often not pro-poor.

Governments have developed various approaches aimed at strengthening equity, including school grants and formula funding linked to need – with mixed outcomes.

Financial decentralization has important implications for equity.
There is nothing intrinsically equitable or inequitable about reforms in this area: outcomes depend on the rules governing issues such as revenue raising and resource transfer.

One obvious danger is that, in the absence of redistributive transfers from richer to poorer areas, decentralization will widen financing gaps in education, with damaging consequences for equity.

Another is that subnational governments will seek to mobilize revenue through charges on local services, including education.
Evidence from many countries highlights the risks associated with financial decentralization.

In China, Indonesia and the Philippines, decentralization appears to have exacerbated inequalities.

In Nigeria, financial decentralization has consolidated large disparities in education financing, often to the detriment of the states facing the most serious problems.
However, countries including South Africa, Uganda and Viet Nam have developed models aimed at greater equity, with rules on financial decentralization geared towards the attainment of national goals in education and other areas.

‘Voice’, participation and choice in school governance Schools are on the front line of the campaign to bring high-quality education to all children.
They are also at the centre of debates on education governance in which choice, competition, participation and ‘voice’ are buzzwords.

Behind these terms are crucial questions about the role of governments, parents, communities and private providers in managing and financing schools.

Many countries with poorly performing education systems suffer from institutional problems.

The Dakar Framework does not set out a blueprint for resolving these problems.
But it does call on governments to ‘develop responsive, participatory and accountable systems of educational governance and management’.

Translating these widely shared objectives into practical strategies that tackle institutional weaknesses, expand access, raise quality and strengthen equity is far from straightforward.

The Report focuses on three broad reform currents in school governance.

School-based management, the first current, aspires to anchor education in the social fabric of communities.
Transferring authority to front-line providers is presented as a vehicle for increasing parental influence in decisions affecting children’s education – and for ensuring that schools reflect local priorities and values.

The second reform current focuses on choice and competition.

Expanding parental choice in the selection of schools is widely viewed as a key to driving up standards, with competition creating powerful incentives for improved performance.
In some countries, public- private partnerships are seen as a route to enlarged choice.

Governments are using vouchers and other instruments to facilitate transfers from public to private providers, or contracting out the management of government schools to private providers.

The locus for the third thematic area is outside the public education system.

Low-fee private schools have spread rapidly in many countries.
Some commentators see these schools as a vehicle for improving access and quality for poor households.

O V E R V I E W Proponents of all three approaches claim various benefits from governance reform.

These range from gains in efficiency to increases in participation, accountability and equity.

There is a widely shared underlying assumption that devolution of authority, competition and the growth of low-fee private schools will strengthen the voices of the poor and increase their choices.
Are the claims and assumptions backed by evidence?

There are no simple answers to that question.

In some cases, school-based management reforms have improved learning achievements and strengthened equity.

The EDUCO schools in El Salvador are an example.

More widely, though, there is limited evidence either of systematic benefits in learning outcomes or of changes in teaching practices.

Effects on ‘voice’ are also ambiguous.


More localized decision-making may bring authority closer to parents and communities, but it does not follow that this will overcome wider disadvantages.

An obvious danger is that local power structures associated with poverty and social inequality will limit the real influence of the poor and marginalized.

Choice and competition are at the centre of sometimes polarized debates in both developed and developing countries.
Underlying these debates are strongly held views about the role and responsibilities of government.

The idea that increased parental choice leads to improved learning outcomes and greater equity may have intuitive appeal.

But once again the evidence is not clear cut.

Evidence from PISA data does not point to strong effects of school competition on learning outcomes.
In the United States, neither the still limited recourse to voucher programmes nor the more expansive development of charter schools has unambiguously raised academic achievement standards or tackled disparities.

Evidence from Chile is also instructive.

Over more than two decades, Chile has introduced education governance reforms, aimed at increasing choice, that are broader and deeper than in most countries.
Yet private schools with state subsidies do not register any advantage over municipal schools once adjustments are made for socio-economic status.

Overall improvements in education quality have been limited – as has progress towards greater equity.

While Chile is widely cited as a model governance reformer, it is not clear from the outcomes that it merits this description.

Others countries have a stronger claim to successful governance reform.

Sweden is a case in point.


Since the mid-1990s it has allowed parents to choose non-public education providers and take state funding with them.

There is a broad consensus in Sweden behind the reforms.

However, the exportability of the Swedish model is unproven.
Increased competition in this case was introduced against the backdrop of a public education system that meets high achievement standards, with relatively low inequality and a highly developed institutional capacity for regulation.

These are not the prevailing conditions in most countries, developed or developing.

Serious questions have to be asked about current approaches to school-based management.
Parental participation is important and, under the right conditions, choice and competition can help raise standards and equalize opportunity.

But the overwhelming priority, especially in the poorest countries, is to ensure that a properly financed public education system is available to all citizens.

The rapid emergence of low-fee private schools raises a different set of concerns.

In many countries, these schools are outside state auspices.
There is no question that low-fee private schools are catering for real demand.

Countries as diverse as Ghana, India, Kenya, Nigeria and Pakistan have experienced increases in enrolment in such schools.

But to what extent have they raised standards and enhanced equity?

While international evidence remains patchy, it offers little cause for optimism.
In many countries, parents select private schools not as a positive choice but as a negative response to perceived – and usually real – failures of the public system.

In the case of slum areas, as in Nairobi, public schools often simply do not exist.

In India evidence does not suggest that poor parents are more actively involved in decision-making in low-fee schools, or that teachers are less likely to be absent.
While the fact that parents meet school charges may be taken as evidence of willingness to pay, the costs impose a considerable burden on household budgets.

Efforts to integrate low-fee private schools into private-public partnerships through voucher-type programmes, as advocated by some, do not appear to offer a short cut to greater equity.

Basic education is a fundamental human right, not a tradable commodity.

It follows that provision must be available to all, regardless of ability to pay.
Moreover, the public sector must govern provision, underwriting finance, providing management and setting a clear policy framework.

Public sector leadership does not mean that actors such as non-government organizations and the private sector have no role or responsibilities.

In the right conditions, properly regulated choice and competition can strengthen standards, especially at secondary level.

However, there are acute dangers for equity.


Where government failure leads to creeping commercialization through the low-fee private sector, it poses the risk of rising inequity, and the fragmentation of services and standards.

The real challenge for governments with basic education systems that are broken is to fix the system.
Governance of teachers — improving motivation and monitoring The effectiveness of any school is heavily influenced by the quality of teaching, and the skills, motivation and commitment of its teachers.

Ensuring that children – including the most disadvantaged – have access to enough trained and motivated teachers is vital to the delivery of good and equitable education.

The effectiveness and equity of school systems are also linked to national monitoring of standards.
Good monitoring systems can help inform policy and so raise quality and enhance equity.

Weak systems have the opposite effect.

The governance of teachers raises issues that go far beyond administrative technicalities.

One recent assessment of teacher morale in sub-Saharan Africa concludes that school systems catering for tens of millions of children face a ‘teacher motivation crisis’ over issues ranging from employment conditions to training and support.
How teachers are distributed within countries has profound implications for equity and access: deployment patterns in many countries reinforce disparities.

The rapid growth of low-fee private schools is in large measure a symptom of state failure.

Chronic underfinancing, often combined with weak accountability, low levels of responsiveness and poor quality of provision, has led millions of poor households to vote with their feet – and their income – to exit public provision.
This is not a prescription either for equity or for accelerated progress towards EFA.

Teacher salaries are at the centre of polarized exchanges in public policy debate.

Some commentators say salary levels in many countries are too high and crowd out spending on other aspects of education.

Apart from cost factors, hiring teachers centrally on permanent civil service contracts is also viewed as a source of weak accountability and poor performance.
The problem with these perspectives is that they overlook wider issues.

These include the low absolute salary levels of many teachers.

In Malawi, average teacher salaries are too low to meet basic needs.

There, and in many other countries, teachers often have to supplement their income with a second job, with damaging consequences for the quality of their teaching.

Teacher recruitment to reduce PTRs and address shortages confronts governments with tough choices.
Some governments have attempted to contain costs by recruiting teachers on contract outside the civil service pay structure.

Hiring contract teachers can expand access to basic education at lower cost, often benefiting areas that might otherwise not have enough teachers, as in parts of India.

On the other side of the coin are potential threats to quality and equity.
Seeking to reduce recruitment costs through contract arrangements can weaken quality by lowering the standard of new entrants or reducing teacher morale.

In Togo, expanded use of contract teachers is associated with reduced learning achievement.

And if contract teachers are deployed principally in poor and marginalized areas, it can also weaken equity.
There are no easy answers, but it is important for governments to be aware of potentially damaging trade-offs between lower-cost recruitment and wider education goals with respect to equity and quality.

Teacher deployment is often inequitable within countries, which can exacerbate disparities.

The rural- urban divide is particularly marked.

In Uganda, two- thirds of urban teachers are qualified, compared with 40% in rural areas.
Urban bias in deployment reflects many teachers’ aversion to working in hard-to-reach, remote, rural and sparsely populated areas, often for both professional and personal reasons.

Public policies can overcome inequalities in deployment.

In Brazil, central government redistribution of financial resources has been used to support teacher recruitment and training in poor states.
In Cambodia and the Lao People’s Democratic Republic, institutional incentives encourage the recruitment of teachers from marginalized areas and groups; a lesson from their experience is that very strong incentives may be needed.

One symptom of poor teacher motivation is absenteeism.

In many developing countries absenteeism is endemic (see Chapter 2).

Motivation is not always the culprit: in parts of Africa HIV-related health problems are heavily implicated.
Some governments see performance-related pay as a strategy to address O V E R V I E W motivation problems and so raise quality.

But there is little evidence from cross-country experience that performance-related pay produces positive results – and some evidence that it creates perverse incentives for teachers to focus on the best-performing students.

The importance of monitoring in raising quality standards and addressing equity concerns is widely overlooked.
Information is one of the keys to improved learning outcomes – and the flow of information is increasing.

Between 2000 and 2006, around half the world’s countries conducted at least one national learning assessment.

Regional assessments have also expanded: thirty-seven sub-Saharan African countries and sixteen Latin American countries now participate in major regional assessments.
While large gaps in coverage remain, many governments have access to more national and international learning assessment information than their predecessors had in the 1990s.

Many of these assessments are ‘high stakes’ – so called because they have direct consequences for student progression, and sometimes for teachers and schools.

Others are ‘low stake’ exercises that provide information, with no direct consequences for students, teachers or schools.
The value of ‘high stakes’ assessment as a vehicle for holding schools and teachers to account is widely contested.

In the United States, the No Child Left Behind Act offers a particularly high-profile example of high stakes testing – and one with a mixed record in terms of its effects on achievement.

How information is used is as important as the flow of information.

Education authorities in many developing countries increasingly use assessments to inform policy design.
In Kenya, SACMEQ results were a basis for benchmarks on minimum classroom facilities.

In Senegal, data from a PASEC assessment showed that grade repetition imposed high costs on school systems with no tangible benefits for learning outcomes – a finding that prompted a prohibition on repetition for some primary grades.

Viet Nam has used learning assessments to identify disparities in achievement and guide the framing of regulations aimed at raising input provision for disadvantaged groups and areas.
Uruguay has applied carefully designed national assessment programmes to strengthen pedagogical management.

Learning outcomes improved in some grades by as much as 30% in six years.

These positive examples are the exception rather than the rule.

In many cases, the findings from assessments have no influence on resource allocation or teacher support programmes.
Even where good assessment systems are in place, their effects are often limited.

The reason, in many cases, is weak institutional capacity.

Thus, Bolivia has a first-rate assessment system and strong expertise, but they have had limited impact on policy design or what happens in the classroom.

Integrated planning to advance EFA Progress in education is contingent on wider social conditions influencing inequalities based on income, gender, ethnicity and location.
The Dakar Framework for Action calls for EFA policies to be promoted ‘within a sustainable and well-integrated sector framework clearly linked to poverty elimination and development strategies’.

While education planning has been strengthened, a failure to join education strategies to general poverty reduction strategies, along with high levels of fragmentation and weak coordination, continues to hamper progress.
Education planning within the framework of sector-wide approaches (SWAps) has been instrumental in clarifying priorities, broadening the EFA agenda and allowing governments to develop longer-term planning horizons.

However, many education SWAps suffer from continued weaknesses.

Financial costing is often inadequate, education targets are not reflected in national budgets and there is a tendency to adopt blueprint models.
Even more serious is the widespread tendency to delink education planning from wider strategies for overcoming poverty and inequality.

Poverty reduction strategy papers (PRSPs) provide a vehicle for addressing this problem.

Fifty-four countries – just over half of them in sub-Saharan Africa – have operational PRSPs.

While PRSPs are ‘nationally owned’ documents, they also set out the terms of the aid partnership with donors.
PRSPs have brought poverty closer to the centre of the development agenda but they are not yet facilitating effective integration in education planning, for at least four reasons: Weak linkage to the EFA agenda.

The point of reference for PRSPs is the MDGs.

One consequence is an overwhelming emphasis on quantitative targets related to primary education, often to the exclusion of wider EFA goals.

When wider goals are considered, they are typically not linked to a broader poverty reduction agenda.
For example, in a review of eighteen recent PRSPs the Report finds that ECCE is regarded primarily as a mechanism for increasing primary school enrolment rather than as a strategy to improve the health and nutrition of young children.

Poor targeting and limited consideration of equity in target-setting.

Targets rarely include the narrowing of equity gaps, with the partial exception of gender parity goals; they invariably address access rather than learning achievement.
No link between education and broader governance reforms.

PRSPs often incorporate national commitments to wide-ranging governance reforms.

However, the implications of the reforms for equity in education are seldom considered in any detail, even where the reforms have potentially significant consequences.

Decentralization is one prominent example.


More generally, few PRSPs set out practical strategies for ensuring that governance reforms strengthen the link between education planning and wider poverty reduction efforts.

Poor integration of cross-sectoral policies.

As Chapter 2 notes, there are deep and persistent inequalities in education linked to poverty, gender, nutrition, health, disability and other forms of marginalization.
Addressing these inequalities requires policies that extend far beyond the education sector.

Evidence from PRSPs suggests that education strategies are often disconnected from such policies.

While PRSPs have thus far seldom provided an integrated framework, there are positive experiences to draw upon.

Social protection programmes are making a strong contribution to education by addressing problems in health, nutrition and child labour.
Targeted cash transfers in Latin America have been particularly successful – so much so that one has been adopted on a pilot basis in New York City.

There are strong grounds for considering an increase in public investment and aid for cross-sectoral programmes of this kind in other contexts.

Planning is not just about technical documents.

It is also about the political process through which priorities are set.

Consultation processes are a central part of PRSPs.
They provide opportunities for civil society organizations to participate in policy discussions.

They also help ensure that education figures in debates over national poverty strategies.

The challenge is to extend participation to ensure that the voices of the poor and vulnerable are heard.

This in turn will help focus more attention on ECCE, adult literacy and skills development.
It will also inform policy-makers about factors beyond the education sector that are holding back progress towards equitable education.

Sustained political commitment is crucial for priorities set out in consultation processes to become reality.

Chapter 4 Increasing aid and improving governance The Dakar Framework for Action is based on an international partnership.

Developing countries pledged to strengthen national education planning, tackle inequalities and enhance accountability.
Rich countries also made an important commitment, pledging that no credible national plan would be allowed to fail for want of finance.

Increased and more effective aid is vital to achieving the goals and targets set at Dakar.

Are donors delivering on their promises?

Not in the area of financing.

On a highly conservative estimate, the aid financing required for a narrow range of basic education goals in low-income countries is around US$11 billion annually.
In 2006, aid in support of basic education to these countries was just one-third of the estimated requirement, leaving a financing gap of around US$7 billion.

These large aid deficits are holding back progress.

Debates over the achievements and effectiveness of development assistance continue.

Pessimists claim aid has had a modest impact at best, and a negative effect in many cases.

Evidence in education does not support this view.


In the United Republic of Tanzania aid has supported a national education strategy that has cut the number of out-of-school children by 3 million since 1999.

In Cambodia, Kenya, Mozambique and Zambia, aid helped finance the abolition of school fees, extending educational opportunities to previously excluded children.

In Bangladesh and Nepal, aid supported national strategies providing incentives for girls and disadvantaged groups.
Development assistance is not a panacea or a corrective for bad policy – but it makes a difference.

Aid levels for education are linked to overall development assistance flows.

In 2005 donors made a number of important commitments to increase aid O V E R V I E W flows, notably at the Gleneagles summit of the Group of Eight (G8) and at a European Council meeting.

This was a backdrop to the United Nations ‘Millennium +5’ summit.
Delivery on these commitments would lead to an increase of around US$50 billion in development assistance by 2010 (at 2004 prices), with around half going to sub-Saharan Africa.

Prospects for delivery, however, are not encouraging.

As a group, donors are not on track to meet their commitments.

Taking into account increased aid and programmed commitments to 2010, there is a shortfall of US$30 billion against the pledges made in 2005 (again in 2004 prices).
The aid gap for sub-Saharan Africa is US$14 billion – a financing shortfall that has damaging implications for progress towards the MDGs and EFA.

Most individual donor countries are not on track to meet their Gleneagles commitments and two G8 countries – the United States and Japan – continue to invest a very low share of gross national income in development assistance.

Commitments to education have followed the overall trend.
The average annual aid commitments in 2005–2006 were below the level for 2003–2004, and there is a real danger that this will be reflected in slower growth of disbursements, or even stagnation.

Donors have a mixed record on aid for basic education.

In 2006, half of all aid commitments for basic education came from just three sources – the Netherlands, the United Kingdom and the World Bank’s International Development Association.
These sources accounted for 85% of the overall increase in aid commitments to basic education in 2006.

However, the combined effort of a few committed donors could not counteract an overall fall in aid commitments from 2004.

The profile of donor aid commitments varies widely.

Some countries, such as Canada, the Netherlands and the United Kingdom, allocate more than three-quarters of their education aid to low income countries – and at least half to basic education.
By contrast, France and Germany, both major donors to education, attach less weight to basic education in the poorest countries.

Only 12% of French aid and 7% of German aid is devoted to basic education in low income countries.

Both countries put greater priority on subsidizing attendance at their universities by foreign students, mostly from middle income developing countries, than on supporting basic education in low income countries.
In France two-thirds of education aid is absorbed by imputed costs for students studying at French tertiary institutions The continued aid financing gap in education raises important questions about the future of the Fast Track Initiative (FTI).
Established in 2002, the FTI was seen as a multilateral mechanism to encourage broad donor support for EFA and, through the Catalytic Fund begun in 2003, an element of financing.

Unfortunately, the FTI has not developed a sufficiently deep donor base and it faces an uncertain future.

In mid-2008, the thirty-five countries with endorsed FTI plans faced a financing gap of US$640 million.

Factoring in the eight countries with plans in the pipeline would push that figure up to around US$1 billion.
By the end of 2009, the financing deficit for countries with approved FTI plans could be as high as US$2.2 billion.

Assuming that the Catalytic Fund might be expected to cover around 40% to 50% of the deficit, around US$1 billion would still have to be mobilized.

Current aid financing trends do not augur well for achieving the goals and targets in the Dakar Framework.

Yet there are some positive signs.

In 2007, the G8 reaffirmed its pledge that no national strategy would fail for want of finance.
It also promised to meet shortfalls in FTI-endorsed plans.

In June 2008, the European Council also reaffirmed its support for EFA.

However, reaffirmations of long-standing commitments do not put children into school or deliver a good-quality education.

If donors are serious about their pledges to education, they cannot afford more years of underperformance.

Increased aid is just part of the equation.


Ultimately, the case for increased commitments will be accepted only if aid is perceived to deliver real results.

Much depends on governance in developing countries.

But the governance and management of aid are also important.

In 2005, donors and developing country governments pledged to strengthen the effectiveness of development assistance.
That promise, in the Paris Declaration, envisages the harmonization and alignment of donor practices behind nationally owned development strategies.

The approach signals a shift in emphasis away from project-based support and towards programme support – a shift already strongly evident in education.

Targets were set for 2010 to measure progress.

It is too early to fully assess the extent to which new aid principles are being translated into practice.
In terms of financial commitment, there has been a strong push away from projects towards programme-based support.

Best estimates suggest that just over half of all aid is now delivered through education sector programmes – up from around one-third in 1999–2000.

Preliminary assessment suggests that some Paris Declaration targets will be hard to achieve.

Monitoring results for fifty-four countries accounting for half of all aid are not entirely encouraging.
Use of national systems remains limited, with only 45% of aid channelled through national public financial management systems (the 2010 target is 80%).

In some cases, donors are not using national systems even when they have been strengthened.

Donor coordination is often still rudimentary.

In 2007, the fifty-four countries received more than 14,000 donor missions, of which only 20% were jointly coordinated.

Progress towards greater coordination has been more evident in education than many other areas.
Even so, the rate of progress has been both erratic and uneven – and far more needs to be done.

In Cambodia, only 39% of donor missions in education in 2007 were jointly conducted, raising transaction costs for the host government.

While all donors stress their commitment to the alignment of aid with national priorities and the use of national systems, outcomes have been variable.

Progress has proved far from straightforward, with frustrations and concerns on both sides.
Donors often point to worries over corruption and weak capacity.

For their part, many aid recipients complain about what they see as unrealistic donor demands and onerous reporting requirements.

Emerging aid modalities have the potential to resolve these problems.

In the best cases, improved national management systems, greater sector coherence, better oversight and coordination of donor activity, and more innovative approaches to finance are coming to the fore.
Important achievements have already been made in some countries, including Burkina Faso, Cambodia, India and Mozambique.

Successful implementation of the Paris agenda will require commitment and flexibility on both sides, with donors avoiding the use of financial support to leverage change.

O V E R V I E W Reinforce the commitment to quality education for all.

Progress on expanded access to schools is outstripping improvements in quality.
Policy-makers should renew and strengthen the Dakar commitment to quality in education and put in place the infrastructure, teacher support and monitoring programmes needed to deliver results.

Act on the commitment to equity in financing.

Many governments have failed to develop pro-poor public spending patterns and decentralization reforms have often exacerbated inequalities in education.

Looking to the future, it is important for governments to develop approaches that avoid these outcomes.
Central government needs to retain its capacity for redistribution from wealthier to poorer regions and subnational bodies need to ensure that spending plans reflect a national commitment to EFA.

Recognize the limits to choice and competition.

The development of quasi-markets in education and the rapid emergence of low-fee private providers are not resolving underlying problems in access, equity or quality.
While many actors have a role to play in education provision, there is no substitute for a properly financed and effectively managed state education system, especially at primary level.

Deliver on aid commitments.

The donor community needs to recognize the wide-ranging benefits of accelerated progress towards EFA and to close the aid financing gap.

At a conservative estimate, this means increasing aid to basic education by around US$7 billion annually and acting on the commitments undertaken in 2005.
Closing the projected 2010 financing gap of US$2.2 billion in countries with plans approved by the Fast Track Initiative is another priority.

Strengthening the commitment of some key donors to equity in aid allocations would help to cut the financing deficits.

Chapter 5 Conclusions and recommendations Delivering on the pledges set out in the Dakar Framework for Action will require strong political leadership, a sense of urgency and practical strategies.
The final chapter of this Report sets out some of the key priorities.

While avoiding blueprints, it identifies principles for good practice, including the following: Get serious about equity.

Many governments have not given sufficient weight to policies aimed at overcoming inequalities in education.
Setting time- bound ‘equity targets’ aimed at reducing disparities based on wealth, gender, language and other markers for disadvantage, and carefully monitoring progress, would help to focus political attention.

At the same time, education planning has to put far higher priority on pro-poor public spending and the development of incentives targeted at the poorest and most disadvantaged.

Strengthen the links between education planning and poverty reduction strategies.
Education policies can make an important difference in equalizing opportunity and reducing disadvantage.

However, progress in education depends critically on progress in other areas, including poverty reduction, nutrition and public health.

While education sector planning has become stronger, it remains weakly integrated with wider poverty reduction strategies.
2 1 9 0 0 2 t r o p e R Planting the seeds for success: teaching young children about healthy eating, in Zimbabwe / i z z o r i P o m o c a G © i 2 2 Education for All Global Monitoring Report 9002 Chapter 1 Education for all: human right and catalyst for development The international community has adopted ambitious targets for human development.
Objectives set under the Millennium Development Goals (MDGs) include the halving of extreme poverty, a two-thirds reduction in child mortality, universal primary education and greater gender equality.

The deadline for delivering results is 2015.

On current trends, most of the targets will be missed.

Accelerated progress towards Education for All, with a strengthened focus on equity, could change this picture.

But governments must act with a renewed sense of urgency and political commitment.
This chapter looks at the issues at stake.
Introduction .......................................................................................................... 24 Introduction Educational opportunity: highly polarized .................................................................................... 26 Unlocking the wider benefits of education .................................................... 29 Conclusion .................................................................................................................. 37 Almost two decades have passed since governments gathered at the World Conference on Education for All (EFA) in Jomtien, Thailand, to reaffirm the human right to education.
They set bold targets – but outcomes fell far short of ambition.

In 2000, the 164 governments assembled at the World Education Forum in Dakar, Senegal, adopted another set of ambitious goals on education.

The Dakar Framework for Action pledges to expand learning opportunities for every child, youth and adult, and to meet targets in six areas by 2015.

With the deadline now just six years away, will it be different this time around?
Accelerating progress towards education for all is one of the defining development challenges of the early twenty-first century.

The right to education is a basic human right.

Like any human right, it should be protected and extended as an end in itself.

But education is also a means to wider ends.

Prospects for reducing poverty, narrowing extreme inequalities and improving public health are heavily influenced by what happens in education.
Progress towards the equalization of opportunity in education is one of the most important conditions for overcoming social injustice and reducing social disparities in any country.

It is also a condition for strengthening economic growth and efficiency: no country can afford the inefficiencies that arise when people are denied opportunities for education because they are poor, female or members of a particular social group.

And what is true at a national level also applies internationally.
Prospects for achieving more equitable patterns of globalization are heavily influenced by developments in education.

In an increasingly interconnected and knowledge-based world economy, the distribution of opportunities for education will inevitably have an important bearing on future patterns of international wealth distribution.

Some benefits of education are less tangible and harder to quantify than others.

Schools are not just institutions for imparting information.
They are a place where children can acquire social skills and self-confidence, where they learn about their countries, their cultures and the world they live in, and where they gain the tools they need to broaden their horizons and ask questions.
People denied an opportunity for achieving literacy and wider E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY S T F O R D E V E L O P M E N T I n t r o d u c t i o n education skills are less equipped to participate in societies and influence decisions that affect their lives.
That is why broad-based education is one of the foundations for democracy and government accountability, and why it is such a vital input for informed public debate in areas – such as environmental sustainability and climate change – that will have a bearing on the well-being of future generations.

The Dakar Framework is not the only pledge on the international development agenda.

At the United Nations Millennium Summit, also in 2000, world leaders adopted eight Millennium Development Goals (MDGs).
These wide-ranging goals extend from the reduction of extreme poverty and child mortality to improved access to water and sanitation, progress in cutting infectious diseases and strengthened gender equality.

The goals are linked to the achievement of specific targets by 2015.

In the area of education, the MDGs offer a highly restricted version of the goals adopted at Dakar.

They include a commitment to achieve universal primary school completion and gender parity at all levels of schooling by 2015.
At one level the MDG framework is too narrow.

EFA means more than five or six years in primary school and more than gender parity, vital as both goals are.

The quality of education and learning achievement, access to secondary and post- secondary opportunities, literacy and gender equality, in a broader sense, are all important as well.

Yet the Dakar Framework targets and the MDGs are complementary.
Progress in education depends on advances in other areas, including the reduction of extreme poverty, the achievement of gender equity and improvements in child health.

The links in this direction are obvious but often forgotten.

Children whose lives are blighted by hunger, poverty and disease are clearly not equipped to realize their potential in education.

Without advances across the broad front of MDG targets, the ambition of education for all cannot be realized.
By the same token, progress towards many of the MDG targets depends critically on progress in education.

Halving poverty or cutting child mortality by two-thirds by 2015 is not a serious proposition in a situation of slow and unequal progress towards the policy objectives set out at Dakar.

The goals adopted by the international community are mutually interdependent – failure in any one area increases the likelihood of failure in all areas.
The interdependence between the MDGs and the Dakar Framework has taken on a new importance.

In 2008 the world entered the second half of the commitment period for both undertakings.

Now just seven years remain before the 2015 deadline – and the world is off track on many of the targets.

On current trends, the goal of universal primary education (UPE) by 2015 will not be achieved and the pledges made at Dakar will be broken.
Using a partial projection covering countries that account for just two-thirds of the 75 million primary school age children out of school today, this Report estimates that the countries will still have 29 million out of school in 2015.

That number has consequences for the children and countries most immediately affected.

But it also has consequences for the entire MDG project.
Bluntly stated, the targets set for cutting child and maternal death, reversing the spread of infectious disease and reducing poverty will not be achieved unless governments act decisively on education.

Conversely, accelerated progress on the wider MDGs would strengthen prospects in education by lessening the poverty, nutrition and health handicaps that millions of children take with them into school.
In September 2008, governments from around the world gathered at a United Nations summit in New York to reaffirm their commitment to the MDGs.

The summit was prompted by a recognition that, without fundamental change, the development goals will not be achieved.

Averting that outcome and restoring the momentum behind international partnerships for development will require more than encouraging communiqués.

What is needed is a sense of urgency, political leadership and practical strategies.
Strengthening the commitment to the education goals set out in the Dakar Framework for Action is one of the most pressing priorities.

Much has been achieved since 2000.

Indeed, education has a strong claim to being counted as an MDG success story.

Progress towards UPE and gender parity has been far more rapid than advances in other areas, such as nutrition or child and maternal mortality.
One of the problems for EFA identified in Chapter 2 is precisely the failure of many countries to move more rapidly towards the MDG targets in these areas.

But the relative success of education should not deflect attention from the size of the potential 2015 shortfalls in UPE.

Making up these shortfalls would act as a powerful catalyst for accelerated progress towards the MDGs.
Children whose lives are blighted by hunger, poverty and disease are clearly not equipped to realize their potential in education 2 5 9 0 0 2 t r o p e R i g n r o t n o M i Few governments treat the crisis in education as an urgent priority, in stark contrast to their response to financial market problems The backdrop for the September 2008 MDG summit was an unprecedented crisis in international financial markets.
The fallout from that crisis remains uncertain.

Governments are taking far-reaching measures to stabilize banking systems.

The scale and urgency of their actions were guided by a recognition that, when financial markets fail, the contagion effects can spread rapidly across all aspects of society and the real economy.

Analogies with education system failure are inexact but instructive.
When education systems fail to reach large sections of the population, when children are denied opportunities by virtue of their gender, the income of their parents, their ethnicity or where they happen to live, or when schools deliver chronically substandard learning outcomes, there are also contagion effects.

Those effects are not reflected in highly visible bank collapses, fluctuating share prices or mortgage failures.

But there are real human, social and economic consequences.
Education system failures weaken the real economy, holding back productivity and growth.

They undermine efforts to reduce child and maternal mortality, contributing to loss of life and increased health risks.

And they contribute to social polarization and the weakening of democracy.

Yet despite the high stakes and the costs of inaction, few governments treat the crisis in education as an urgent priority – in stark contrast to their response to financial market problems.
This is an area in which national and international leadership is needed to place education firmly at the centre of the political agenda.

The EFA Global Monitoring Reportwas first published in 2002 to track progress towards the six EFA goals enshrined in the Dakar Framework for Action.

Since its inception it has covered each of the goals.

This year the Report looks beyond the goals to a range of issues in education governance, finance and management.
It focuses on the critical importance of equity in educational opportunity because equity should be an overarching public policy goal – and because deep inequalities in education threaten to undermine progress towards both the EFA goals and the MDGs.

Educational opportunity: highly polarized The distribution of educational opportunity plays a key role in shaping human development prospects.
Within countries, governments and people increasingly recognize that unequal opportunities for education are linked to inequalities in income, health and wider life chances.

And what is true within countries is true also between countries.

Large global disparities in education reinforce the extreme divides between rich and poor nations in income, health and other aspects of human development.

The full extent of the gulf in opportunities for education is not widely appreciated.
Education is a universal human right.

However, enjoyment of that right is heavily conditioned by the lottery of birth and inherited circumstance.

Opportunities for education are heavily influenced by where one is born and by other factors over which children have no control, including parental income, gender and ethnicity.

From a global perspective, being born in a developing country is a strong indicator for reduced opportunity.
School attainment, measured in terms of the average number of years or grade reached in education, is one (admittedly limited) measure of global inequality.

While almost all member countries of the Organisation for Economic Co-operation and Development (OECD) have achieved universal school attainment to grade 9, most countries in developing regions are far from this position.
Age-specific school attendance pyramids that plot the distribution of age and grades graphically illustrate the contrast in average life-chances for education associated with being born in the OECD countries or in sub-Saharan Africa (Figure 1.1).

By age 7, almost all children in the OECD countries are in primary school, compared with 40% for sub-Saharan Africa.
At age 16, over 80% of the population of the OECD countries is in secondary school while one-quarter of sub-Saharan Africa’s population is still in primary school.

Four years later, at age 20, around 30% of the OECD population is in post-secondary education.

The figure for sub-Saharan Africa is 2%.

Stark as they are, these figures tell only part of the story.
One way of thinking about unequal opportunity is to consider the chance that a child E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY : h i g h l y p o l a r i z e d born in one country has of achieving a given level of education relative to a child born somewhere else.

Chapter 2 draws on international data to compare educational opportunities across countries.

The results are striking.


They show that children in countries such as Mali and Mozambique have less chance of completing a full primarycycle than children in France or the United Kingdom have of reaching tertiaryeducation.

The gulf in attainment is not restricted to sub-Saharan Africa.

Around one in five pupils entering primary school in Latin America and in South and West Asia does not survive to the last primary grade.

Global inequalities in education mirror inequalities in income.
The association is not coincidental.

While the relationship between education and wealth creation is complex, knowledge is an important driver for economic growth and productivity (see below).

In an increasingly knowledge-based international economy, disparities in education are taking on more importance.
There is a growing sense in which today’s inequalities in education can be seen as a predictor for tomorrow’s inequalities in the global distribution of wealth, and in opportunities for health and employment.

The fact that in half the countries of sub-Saharan Africa the survival rate to the last grade of primary school is 67% or less is not irrelevant to prospects for overcoming the region’s marginalization in the global economy.
Inequalities within countries create an even starker picture of disparities in opportunity.

Data on national average life chances in education have the effect of masking the distribution of life chances across different groups in society.

When within- country distribution is superimposed on cross- country disparity, the effect is to magnify the scale of inequality.
To illustrate this point the EFA Global Monitoring Report 2009 has created a composite regional picture of the distribution of attainment across income groups using national household survey data.

Figure 1.2 presents attainment curves at the polar ends of the distribution for the richest and poorest 20%.

Once again the results are striking.


They show that only around half of the poorest 20% in sub-Saharan Africa, and South and West Asia progress to grade 5, compared with over 80% for the wealthiest quintile.
Being born into the poorest 20% of the wealth distribution in sub-Saharan Africa, or in South and West Asia, more than halves Figure 1.1: Age-specific attendance rates by level in OECD countries and sub-Saharan Africa, 2000–20061 OECD countries Sub-Saharan Africa age 24 age 23 age 22 age 21 age 20 age 19 age 18 age 17 age 16 age 15 age 14 age 13 age 12 age 11 age 10 age 9 age 8 age 7 age 6 age 24 age 23 age 22 age 21 age 20 age 19 age 18 age 17 age 16 age 15 age 14 age 13 age 12 age 11 age 10 age 9 age 8 age 7 age 6 100% 80% 60% 40% 20% 0% 20% 40% 60% 80% 100% Proportion enrolled by age and level of education Primary Secondary Post-secondary 1.
Weighted averages.

Data are for the most recent year available during the period.

Sources: Calculations based on OECD (2008b); World Bank (2008b).
Figure 1.2: Grade attainment among 10- to 19-year-olds in Latin America and the Caribbean, South and West Asia, and sub-Saharan Africa, 2000–20061 ) % ( t n e m n i a t t a e d a r G 100 80 60 40 20 Latin America/Caribbean, Q5 South and West Asia, Q5 Latin America/Caribbean, average Sub-Saharan Africa, Q5 Latin America/Caribbean, Q1 South and West Asia, average Sub-Saharan Africa, average Sub-Saharan Africa, Q1 South and West Asia, Q1 Grade Note: Q1 is the poorest quintile, Q5 the richest.
1.

Weighted averages.

Data are for the most recent year available during the period.

Source: World Bank (2008b).

 The human costs of economic, social and educational inequalities are cumulative and cross- generational 2 8 C H A P T E R 1 the chance of school attendance at grade 9.
While the wealthiest 20% in Latin America achieve attendance levels close to those in the OECD countries at grade 9, the poorest 20% are closer to the average for sub-Saharan Africa.

These income-based disparities are mirrored in differences in average years of education attained by the people aged 17 to 22.

In Mozambique, someone in the poorest 20% has on average 1.9 years of education, compared with 5 years for someone from the richest 20%.
In Peru, the gap between rich and poor is 4.6 years of schooling, rising to 6.7 years in India (Table 1.1).

Income-based disparities such as those charted above are not the only type of disparity in education.

Inherited disadvantages linked to gender, ethnicity, location and other factors are also important.

These disadvantages intersect with income-based differences, restricting opportunity and transmitting educational disadvantage and poverty across generations.
One of the central messages of this Report is that national governments and international development agencies need to strengthen the focus on equity in order to achieve the core goals in the Dakar Framework for Action.

Unequal distribution of education has wider consequences.

Income-based gaps in educational opportunity reinforce income inequalities and the social divisions that come with them.
They also mean the benefits associated with education in areas such as public health, employment and participation in society are unequally distributed.

The human costs of these inequalities are cumulative and cross-generational.

For example, the fact that women account for the majority of illiterate people in the world today is a reflection of historical gender disparities in access to education.
But when women who have been denied an education become mothers, their children also inherit diminished life chances: they are less likely to survive, more likely to experience ill health and less likely to go to school than the children of mothers who have education.

Quality counts Some inequalities are easier to measure than others.

Headcount indicators covering the number of children in school or completing grades allow for relatively straightforward comparisons from country to country.
Learning achievement indicators and comparisons pose more of a problem.

Although Table 1.1: Average years of education for poorest and richest 20% of 17- to 22-year-olds, selected countries, most recent year Poorest 20% Richest 20% (years) Bangladesh, 2004 Burkina Faso, 2003 Ethiopia, 2005 Ghana, 2003 Guatemala, 1999 India, 2005 Mali, 2001 Mozambique, 2003 Nicaragua, 2001 Nigeria, 2003 Peru, 2000 Philippines, 2003 U.R.
Tanzania, 2004 Zambia, 2001 3.7 0.8 1.6 3.2 1.9 4.4 0.4 1.9 2.5 3.9 6.5 6.3 3.9 4.0 Source: Demographic and Health Surveys, calculations by Harttgen et al.

(2008).

8.1 5.6 7.4 9.2 8.3 11.1 4.8 5.0 9.2 9.9 11.1 11.0 8.1 9.0 global and regional learning assessments are expanding to cover more countries, information remains sparse and insufficiently available in forms that allow for straightforward global comparisons.
Put differently, quantity is easier to measure than quality – yet in the last analysis, it is quality that counts.

Ultimately, what matters is the degree to which schooling supports cognitive development, facilitates skills acquisition and enriches children’s lives.

Qualitative inequalities are probably narrowing far more slowly than quantitative gaps.

Large though it remains, basic headcount inequality is falling at the primary and secondary levels.

Convergence is the order of the day.


Developing countries are catching up on enrolment, attendance and completion, albeit unevenly and often from a low base.

One reason for this is obvious: rich countries cannot exceed universal coverage at the primary and secondary levels, so any gain by developing countries narrows the gap.

However, school attainment has to be adjusted for the quality of education.
When it comes to learning achievements and outcomes, an average school year in Zambia is clearly not the same as an average school year in, say, Japan or Finland.
There is compelling international evidence (discussed further in Chapter 2) that completing six or even nine years of schooling in developing countries does not assure the development of E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY basic cognitive skills or even functional literacy and numeracy (Filmer et al., 2006; Pritchett, 2004a).
International assessment tests provide a pointer to the scale of global inequalities in learning achievement.

To take one example, the OECD Programme for International Student Assessment (PISA) survey of reading and literacy skills places the median achievement in developing countries such as Brazil and Peru in the lowest 20% of the distribution for many OECD countries.
One recent study of basic educational achievement found very high levels of functional illiteracy in mathematics and science among secondary school students in many developing countries.

In Brazil, Ghana, Morocco, Peru and South Africa, fewer than 60% of children in school reached basic competency thresholds (Hanushek and Wößmann, 2007).

Factoring in children out of school would be expected to lower the average performance.
At primary level, recent surveys in Ghana and Zambia have found that fewer than 60% of young women who completed six years of primary school could read a simple sentence in their own language.

Similarly, assessment exercises in countries including India and Pakistan found that over two-thirds of pupils at grade 3 level were unable to write a simple sentence in Urdu.

Incorporating data on qualitative achievement magnifies the inequalities associated with quantitative attainment.
Education quality is important both in understanding the distribution of life chances in society and in charting the scale of global inequality in education.

The bottom line is that EFA cannot be interpreted, as the MDGs sometimes are, as a simple matter of getting all children into school.

It goes without saying that this is important.

But it is what children get out of school that will shape their life chances.
Unlocking the wider benefits of education There are many good reasons for governments committed to the MDGs to renew their commitment to the Dakar Framework for Action.

First and foremost, education is a human right and an important goal in its own right.

It is central to the development of human capabilities – people’s potential to choose lives that they value (Sen, 1999).
Beyond this intrinsic importance, there are strong two-way links between education and progress in areas where the world is off track on the MDG targets.

None of this is to imply that the links between education and social or economic benefits are automatic.

The impact of education is strongly conditioned by other factors, from macroeconomic and labour market conditions to the state of public health provision and levels of inequality based on wealth, gender and other factors.
The benefits of education are likely to be greatest in contexts marked by broad-based economic growth, a strong political commitment to poverty reduction, high levels of equity in access to basic services, and a commitment to democratic and accountable governance.

Economic growth, poverty reduction and equity The links between education and economic growth, income distribution and poverty reduction are well established.
Education equips people with the knowledge and skills they need to increase income and expand opportunities for employment.

This is true for households and for national economies.

Levels of productivity, economic growth and patterns of income distribution are intimately linked to the state of education and the distribution of educational opportunity.
Increasing global economic interdependence and the growing importance of knowledge-based processes in economic growth have raised both the premium on education and the cost associated with education deficits.

All this has important implications for the international development goal of halving extreme poverty (MDG 1).

The rate of poverty reduction is a function of two variables: the overall rate of economic growth and the share of any increment in growth that is captured by the poor (Bourguignon, 2000).
Education has a bearing on both sides of the equation.
Improved access to good quality learning It is what children get out of school that will shape their life chances Broad-based access to good quality basic education is one of the foundations for broad-based growth 3 0 C H A P T E R 1 opportunities can strengthen economic growth by raising productivity, supporting innovation and facilitating the adoption of new technology.
And broad-based access to good quality basic education is one of the foundations for broad-based growth, since it enables poor households to increase their productivity and secure a greater stake in national prosperity.

Recent research, discussed in the following subsections, confirms earlier findings on the key role of education in poverty reduction and highlights the critical importance of quality.
Economic growth No country has ever reduced poverty over the medium term without sustained economic growth.

Education plays a critical role in producing the learning and skills needed to generate the productivity gains that fuel growth.

One recent research exercise draws attention to the importance for economic growth of both years in school and learning outcomes.
Modelling the impact of attainment in fifty countries between 1960 and 2000, the study found that an additional year of schooling lifted average annual gross domestic product (GDP) growth by 0.37%.

The impact of improved cognitive skills was considerably larger, with the combined effect adding, on average, a full percentage point to GDP growth (Hanushek et al., 2008; Hanushek and Wößmann, 2007).
There is also some evidence that the impact of gains in education quality on cognitive skills may be larger in developing than in developed countries.

Education quality has a significant impact on economic returns for households as well.
Research in fifteen countries participating in the International Adult Literacy Survey (IALS) found that a standard deviation in literacy (an indicator for quality) had a larger effect on wages than an additional year of schooling – confirmation that it is outcomes which count (Denny et al., 2003).

Individual earnings.A large body of evidence points to high returns on investment in education.

The scale of these returns is a matter for debate.


One cross-country exercise found each additional year of education increasing earnings by 10%, with variations that reflect underlying conditions: returns are higher for low-income countries, for lower levels of schooling and for women (Psacharopolous and Patrinos, 2004).

Other research has generated different results both overall and by level of education (Bennell, 1998).
As these differences indicate, findings on returns to education are influenced both by methodological factors, and by economic conditions.

Broadly, as countries move towards UPE, returns at the primary level tend to fall as the national skills deficit shifts to the secondary and tertiary levels – a phenomenon widely observed in Latin America (Behrman et al., 2003).

In terms of public policy, there are limits to relevance of rate of return analysis.
The case for investment in basic education is rooted in human rights and ideas about citizenship, not in monetary calculation.

That said, there is compelling evidence that private and public rates of return to education at the primary and secondary levels are sufficiently high to mark this out as a good investment for society.
In the agricultural sector, increases in education are strongly associated with higher wages, agricultural income and productivity – all critical indicators for poverty reduction (Appleton and Balihuta, 1996).

In contrast to these potential benefits, education inequalities based on gender and other factors inflict real economic costs.

In Kenya it was found that increasing the education and input levels of female farmers to those of male farmers could increase yields by as much as 22% (Quisumbing, 1996).
Income distribution.The distribution of educational opportunity is strongly associated with income distribution, though the underlying relationship is highly variable and complex.

This has important implications for poverty reduction and the MDGs.

Economic growth matters because it raises average income.

The rate at which growth is converted into poverty reduction depends on the share of any increment to national income going to people living in poverty.
By raising the productivity of the poor, more equitable education can increase overall growth andthe share of growth that accrues to those below the poverty line.

Less equitable education can have an equal and opposite effect.

Evidence from the developed world points towards inequality in education as a cause of wider income inequalities.
For example, over the past three decades, growing wage differentials between secondary school graduates and secondary school dropouts has been a major source of rising inequality and social polarization in the United States (Heckman, 2008).

With a greater proportion of young Americans graduating from college and a greater proportion dropping out of secondary school, the skills gap is fuelling inequality.
Patterns of income inequality are conditioned by private returns from different levels of education, which in turn reflect developments in labour E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY markets.
Rapid increase in demand for people with higher skills in countries with limited secondary school completion and restricted access to tertiary education can lead to pronounced increases in inequality.

In India, Indonesia, the Philippines and Viet Nam rising wage inequalities are closely linked to widening wage gaps between people with tertiary education and those at lower attainment levels (Asian Development Bank, 2007).
Similarly, evidence from Latin America suggests that returns to secondary and tertiary education are rising more rapidly than those to primary education (Behrman et al., 2003).

Prevailing patterns of income distribution reinforce the case for progress towards equalization of educational opportunity.

At global level, the poorest 40% of the world’s population, living on less than US$2 a day, accounts for 5% of world income – and the poorest 20% (living on less than US$1 a day) for 1.5% (Dikhanov, 2005).
Even small shifts in the share of global income going to the world’s poor could have very significant effects for poverty reduction.

Measured in financial terms, it would take around US$300 billion – less than 1% of world GDP – to lift the billion people surviving on less than US$1 a day above the poverty line (UNDP, 2005).

Given the prevailing level of global inequality, this would represent a modest degree of redistribution for a large impact on poverty.
Greater equity in the distribution of educational opportunity could facilitate that redistribution.

What appears clear is that more equitable patterns of global integration cannot be built on the vast educational disparities in evidence today.

The same broad conclusion holds true at the national level.

Over the past two decades there has been a clear trend towards rising income inequality within countries.
Of the seventy-three countries for which data are available, inequality has risen in fifty-three, which account for 80% of the world population.

Many factors are involved, with inequality in education linked to technological change and wider forces.

But the importance of inequality in education as a driver of wider inequality is increasingly recognized.
When education is broadly shared and reaches the poor, women and marginalized groups, it holds out the prospect that economic growth will be broadly shared.

Greater equity in education can help fuel a virtuous cycle of increased growth and accelerated poverty reduction, with benefits for the poor and for society as a whole.

The relationship between education on the one side and economic growth and poverty reduction on the other illustrates the importance of context.
Schools and education systems are not guarantors of faster growth or greater equity.

Problems in macroeconomic management and other policy spheres may reduce the benefits of education.

In the Arab States, to take a case in point, regional evidence points to a weak association between the expansion of education and productivity (World Bank, 2008d).
Increasing the supply of skilled labour in an economy marked by low productivity, stagnation and rising unemployment markedly diminishes the private returns to schooling.

It can also give rise to large populations of educated unemployed youths and graduates.

In Egypt, adults with secondary education account for 42% of the population but 80% of the unemployed (World Bank, 2008d).

Other labour market factors are also important.


Education can benefit individuals by facilitating entry into higher-earning occupations and raising earnings within an occupation.

To the extent that these two benefits accrue equally to women and men, education can help promote gender equality in earnings.

However, discrimination and distortions in the labour market based on gender can negate the equalizing effects of education.
In Pakistan, women lag far behind men in labour force participation, are concentrated in a much narrower set of occupations, perform mostly unskilled jobs and have substantially lower earnings.
While women’s earnings are lower than men’s at all levels of education, the economic returns to education and skills defined in terms of the earnings increment from an extra year of schooling are greater for Pakistani women than for men in all occupations (except agriculture), so that education is associated with reduced gender gaps in earnings.
But women’s participation in the labour market increases only after ten years of education – and only about 10% of Pakistani women have had ten or more years of education (as of the early 2000s).

Thus gender barriers to labour market entry, the narrowness of female occupations and limited opportunities for education are diluting the equality-promoting benefits of education in Pakistan (Aslam et al., forthcoming).
Many factors can weaken the relationship between more education on the one side and faster, broader-based growth on the other.

An increase in the average number of years in school is not always Discrimination in the labour market based on gender can negate the equalizing effects of education good proxy for human capital formation.
Where education quality is poor and levels of learning achievement are low, the real skills base of the economy may not increase.

Rising enrolment and school completion can have a marginal bearing on human capital.

Similarly, increases in the average number of years spent in education will not result in more equitable income distribution if large sections of the population are left behind.

What matters in this context is the degree to which the poor are catching up in education with the non-poor.
The bottom line is that average years in school is an important indicator of human capital but not the only indicator.

Quality and equity are also critical.

It is important to recognize the limits to the current state of knowledge on the emerging relationship between education on the one side and economic growth and poverty reduction on the other.

Economic modelling exercises can tell us something important about this relationship on the basis of past evidence.
The future is always uncertain – but it will not look like the past.

Globalization and the increased weight of knowledge-based factors in driving economic growth have important consequences for wealth distribution and poverty reduction nationally and internationally.

If knowledge is increasingly recognized as the key to competitiveness, employment and long-term growth prospects, learning endowments become ever more important.
In the context of rapidly changing national and international economic structures, there is a premium on the acquisition of transferable skills and knowledge.

Lifelong learning, a core EFA goal, is the critical condition for adjustment to knowledge-based economic life.

People and countries need formal education systems that give them opportunities to build their learning skills.

And they need opportunities to continually renew their skills and competencies.
While literacy and numeracy remain the foundations for all education systems, human development and prosperity in the twenty-first century will rest increasingly on the spread of secondary and post-secondary learning opportunities.

Public health and child mortality: both linked to education The links between education and public health are well established.
Improved education is associated with lower levels of child mortality and better nutrition and health, even when controlling for factors such as income.

The transmission mechanisms from education to benefits in these areas are often complex and imperfectly understood.

However, empowerment effects are important.

Education can equip people with the skills to access and process information, and with the confidence to demand entitlements and hold service providers to account.
Whatever the precise channels of influence, there are compelling grounds for placing EFA at the centre of strategies for getting the world on track towards achieving the health-related MDGs.

Child mortality.One of the international development targets is to reduce the child mortality rate by two-thirds (MDG 4).

The developing world is so far off track that very deep cuts in death rates will be required to bring the 2015 goal within reach.
At current rates of progress, many countries in sub-Saharan Africa and South Asia will not achieve the target until 2050 or later.

Failure to close the gap between existing trends and the target will cost lives: the projected gap for 2015 is equivalent to 4.7 million deaths (see Chapter 2).

Overcoming gender gaps and getting young girls into school, an imperative in itself, is also one of the most effective strategies for closing the gap.
The association between maternal education and child mortality is irregular.

Having a mother with primary education reduces child death rates by almost half in the Philippines and by around one- third in Bolivia.

In other countries, such as Ghana and the Niger, primary education has more modest effects.

The strongest effects are at post-primary level (Figure 1.3).
Having a mother with secondary education or higher dramatically reduces the risk of child death in almost all countries, often far more so than having a mother with just primary schooling.

This reinforces the argument for education and gender equity goals that look beyond the primary level.
Leaving aside rights-based arguments and the efficiency case for expanded female access to secondary school, it is increasingly clear that failure to expand opportunity in this area will have grave consequences for public health – and for progress towards the targets identified in the MDGs.

What are the reasons behind lower death rates for children of more educated women?
Transmission mechanisms vary by country, but they include nutrition, birth spacing and the use of preventive health interventions (Malhotra and Schuler, 2005).
To take one illustration, levels of education are Improved education is associated with lower levels of child mortality and better nutrition and health 3 2 E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY Figure 1.3: Under-5 mortality rate by mother’s level of education, selected countries, most recent year positively associated in many countries with vaccination levels among children (Figure 1.4).
The developing world is off track for cutting child deaths and maternal mortality Jordan, 2002 Colombia, 2005 Honduras, 2005 Morocco, 2003/2004 Viet Nam, 2002 Egypt, 2005 Zimbabwe, 2005/2006 Dominican Rep., 2002 Indonesia, 2002/2003 Nepal, 2006 Philippines, 2003 India, 2005/2006 Bangladesh, 2004 Eritrea, 2002 Haiti, 2005 Ghana, 2003 Kenya, 2003 Cambodia, 2005 Ethiopia, 2005 Bolivia, 2003 Madagascar, 2003/2004 Senegal, 2005 U. R. Tanzania, 2004 Lesotho, 2004 Uganda, 2006 Malawi, 2004 Cameroon, 2004 Guinea, 2005 Zambia, 2001/2002 Burkina Faso, 2003 Mozambique, 2003 Congo, 2005 Rwanda, 2005 Niger, 2006 Nigeria, 2003 Maternal mortality.Levels of education also have an important bearing on maternal mortality.
Complications in pregnancy and childbirth are a leading cause of death and disability among women of productive age, claiming over 500,000 lives a year.

Trend analysis in maternal mortality is problematic because of large margins of uncertainty around the estimates.

Nevertheless, the best estimates for 1990–2005 show that mortality rates are falling at a pace far below that needed to achieve the target (MDG 5) of a 75% reduction (WHO et al., 2007).
Risk factors include poor nutrition, anaemia and malaria.
Figure 1.4: Child vaccination and mother’s level of education, selected countries, most recent year (% of 1-year-olds having received selected vaccines by the time of the survey) Nigeria, 2003 Chad, 2004 Dominican Rep., 2002 Indonesia, 2002/2003 Ethiopia, 2005 Zimbabwe, 2005/2006 India, 2005/2006 Cameroon, 2004 Haiti, 2005 Viet Nam, 2002 Burkina Faso, 2003 Philippines, 2003 Bolivia, 2003 Mozambique, 2003 Lesotho, 2004 Cambodia, 2005 Malawi, 2004 U. R. Tanzania, 2004 Bangladesh, 2004 Nepal, 2006 0 50 100 150 200 250 300 Under-5 mortality rate per 1,000 births 0 20 40 60 80 100 Share of children with all vaccinations (%) No education Primary Secondary or higher No education Primary Secondary or higher Source: Macro International Inc. (2008).
Note: ‘All vaccinations’ = BCG (tuberculosis), measles and three doses of DPT and polio (excluding polio 0).

Source: Macro International Inc. (2008).

 Good antenatal care can significantly reduce risk.
Apart from the direct benefits of pregnancy monitoring, women who receive antenatal care are more likely to use other health services, opt for institutional delivery and seek professional advice for post-delivery health complications (Ram and Singh, 2006).
It should be emphasized that the relationship between antenatal care and maternal welfare is heavily influenced by the quality of the care, but effective provision can sharply reduce both maternal and infant mortality (Carolli et al., 2001; Osungbade et al 2008).

Education is important because it is positively associated with recourse to antenatal services.
This is true for both primary and secondary education, though once again some of the most pronounced effects are to be found at secondary level (Figure 1.5).

The benefits of education are transmitted through channels that range from access to information to empowerment effects and demand for entitlements.

As in other areas, the point to be stressed is not that improved access to antenatal care justifies a strong public policy emphasis on female education.
The case for gender equity is rooted in the fundamental human right to education and not in incidental benefits.

But any country with a concern for accelerated progress in child and maternal well-being should view the evidence in Figure 1.5 as a useful measure of some of the hidden costs of gender disparity in education.

Nutrition.Around one-third of children under 5 are stunted, with damaging consequences for cognitive development and health, and often fatal consequences for life (Chapter 2).
Stunting is one proxy for hunger, which the development goals aim to halve by 2015 (MDG 1).

Here, too, the world is off track, and sub-Saharan Africa and South Asia, the regions with the highest rates of stunting, have made the least progress.

Cross-country evidence suggests education is powerful protection against stunting.
Recent research using household survey data found that having a mother who had completed primary education reduced the risk of stunting by 22% in Bangladesh and 26% in Indonesia (Semba et al., 2008).

This was after controlling for factors such as household wealth, location and family size.

Higher levels of parental education in both countries are associated with greater uptake of a range of health inputs, including childhood immunization, Vitamin A intake and use of iodized salt.
Other empowering effects mediating between maternal education and the physical growth of children have been observed.

One potential pathway Around one-third of children under age 5 are stunted, with damaging consequences for cognitive development and health 3 4 involves the association between increased maternal education and the decision-making authority of mothers in claiming resources within the household.
In many contexts, mothers are more likely than fathers to allocate household resources in ways that promote child nutrition (Huq and Figure 1.5: Antenatal care by mother’s level of education, selected countries, most recent year Ethiopia, 2005 Chad, 2004 Bangladesh, 2004 Nigeria, 2003 Niger, 2006 Viet Nam, 2002 Egypt, 2005 Morocco, 2003/2004 Cambodia, 2005 Cameroon, 2004 Bolivia, 2003 India, 2005/2006 Eritrea, 2002 Madagascar, 2003/2004 Nepal, 2006 Mali, 2006 Congo, 2005 Philippines, 2003 Burkina Faso, 2003 Kenya, 2003 Colombia, 2005 Haiti, 2005/2006 Mozambique, 2003 Lesotho, 2004 Honduras, 2005 Guinea, 2005 Benin, 2006 Indonesia, 2002/2003 Jordan, 2002 0 10 20 30 40 50 60 70 80 Children under 3 born without any antenatal care (%) No education Primary Secondary or higher Source: Macro International Inc. (2008).
E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY Tasnin, 2008).

As Figure 1.6 shows, the inverse relationship between stunting and maternal education holds across a large group of countries and all developing regions.

HIV/AIDS.The development goals call for countries to ‘halt and begin to reverse the spread of HIV/AIDS’ (MDG 6).
There is strong evidence that primary education has a significant positive impact on knowledge of HIV prevention, with secondary education having an even stronger impact (Herz and Sperling, 2004).

One study, covering thirty-two countries, found that women with post-primary education were five times more likely than illiterate women to know about HIV/AIDS (Vandemoortele and Delmonica, 2000).
Education systems could play a far more active and effective role in combating HIV/AIDS through teaching and awareness-raising about risky behaviour.

Each of the areas considered above illustrates the potential for education to accelerate progress towards the MDG targets.

In important respects, though, static pictures of the potential benefits hide some of the dynamic gains over time.
For example, increased female access to education generates cumulative benefits linked to cross-generational effects because the level of maternal education is one of the strongest determinants of whether daughters enrol in school (Alderman and King, 1998; UN Millennium Project, 2005a).

Unfortunately, costs are also cumulative.


Just as the world today would have far lower levels of child mortality and stunting had there been greater progress in education during the 1990s, so the education deficits of today will result in human costs in the future.

Improving educational opportunity, especially for girls, is not only a priority in its own right but also essential for improving educational outcomes in the next generation – and for reaching wider goals in public health and nutrition.
Democracy and citizenship — from local to global Education is about much more than what happens in schools.

Through education, societies inculcate their values and ideas, and equip their citizens with skills.

This year’s Report focuses on education governance.

Yet education itself is intimately linked to wider governance issues in society – and to the empowerment of people.
As Nelson Mandela has put it: ‘Education is the most powerful weapon which you can use to change the world.’ Figure 1.6: Severe stunting among children under 3 by mother’s level of education, selected countries, most recent year Sub-Saharan Africa Senegal, 2005 Uganda, 2006 Ghana, 2003 U. R. Tanzania, 2004 Cameroon, 2004 Kenya, 2003 Mali, 2006 Eritrea, 2002 Burkina Faso, 2003 Benin, 2006 Congo, 2005 Mozambique, 2003 Rwanda, 2005 Ethiopia, 2005 Chad, 2004 Madagascar, 2003-2004 Malawi, 2004 Zambia, 2001/2002 Nigeria, 2003 Niger, 2006 Lesotho, 2004 Jordan, 2002 Morocco, 2003/2004 Egypt, 2005 Nepal, 2006 Bangladesh, 2004 India, 2005/2006 Dominican Rep., 2002 Colombia, 2005 Haiti, 2005/2006 Bolivia, 2003 Honduras, 2005 Arab States South and West Asia Latin America and Caribbean 0 5 10 15 % of severely stunted children1 20 25 The education deficits of today will result in human costs in the future 30 No education Primary Secondary or higher 1.
Severe stunting is defined as a height-for-age score below minus 3 standard deviations from the reference median (see glossary).

Source: Macro International Inc. (2008).

 There are strong links between education, citizenship and informed decision-making 3 6 C H A P T E R 1 Some of the most powerful effects of education operate through the channels of democracy and participation.
History provides plenty of evidence that the effects are neither universal nor straightforward.

There are numerous examples, past and present, of societies with a well-educated citizenry that might not be considered model democracies.

And there are countries with relatively low levels of education, as measured by indicators for literacy and average years in school, that have a well-developed democratic tradition.

India is an example.

Yet education is conducive to democracy.


It has the potential to equip people with the skills, attitudes and norms needed to hold governments to account, to challenge autocracy and to assess policies that affect their lives (Glaeser et al., 2006).

At an individual level, education is a crucial determinant of whether people have the capabilities – the literacy, the confidence, the attitudes – that they need to participate in society (Sen, 1999).
As a concrete example, when poor and marginalized people are educated, they are often more likely to participate in meetings of local political bodies and devolved bodies managing education, health and water resources (Alsop and Kurey, 2005).

It is not just education that matters for democracy.
Cross-country research has drawn attention to the importance both of the average level of education and the education attained by the majority of society in creating the conditions for democracy (Castello- Climent, 2006).

Recent evidence from sub-Saharan Africa is instructive.

Analysis of national survey data in Malawi found that even primary schooling promotes citizen endorsement of democracy and rejection of non-democratic alternatives (Evans and Rose, 2007b).
Research into relationships between education and democratic attitudes in eighteen countries of sub-Saharan Africa strongly reinforces this finding (Evans and Rose, 2007a).

Controlling for a wide range of factors, including religion, age, gender and political preference, schooling emerged as by far the strongest social factor explaining adherence to democratic attitudes.

Moreover, the education effects increase in a linear form with the levels of education attained.
People of voting age with a primary education are 1.5 times more likely to support democracy than people with no education, rising to three times more likely for someone with secondary education.

Here, too, the democratizing effects of education appear to operate through the channels of participation and information: more education is significantly associated with increased political discussion, political knowledge and access to political information from the media.
Due caution has to be exercised in extrapolating lessons from research in a group of countries in one region and applying them to other regions.

There is no one model for democratic governance, let alone a universal blueprint for the development of democratic institutions.

Even so, the evidence for Africa strongly suggests that investment in education of good quality may be among the most effective antidotes to autocracy and unaccountable governance.
Links between education and citizenship go beyond public attitudes towards democracy.

One reason education is conducive to democracy is that it can facilitate the development of informed judgements about issues that have to be addressed through national policies.

In any country, public debate and scrutiny can help strengthen policy-making.

And once again, what is true at national level applies internationally as well.
One feature of global integration is that governments and populations worldwide face problems – in finance, trade, security, environmental sustainability – that do not respect national borders.

Education has a key role to play in fostering national and international support for the multilateral governance needed to address such problems.

Climate change provides an illustration.


The role of science in developing the skills and technologies on which productivity, employment and prosperity increasingly depend is well known.

Less attention has been paid to the role of scientific education in increasing children’s awareness of the great environmental challenges their generation faces.

Climate change poses a particularly stark set of threats for humanity, in general over the long term and for the poor in particular over the medium term.
Understanding the causes of climate change is difficult because of the complex processes that influence the build-up of greenhouse gases in the atmosphere.

Evaluating the effects is even more challenging because of the time horizon involved and the uncertainties about when and where effects will be felt and how ecosystems will respond.
Similarly, any evaluation of policy responses at national or international level has to grapple with issues that range from energy policy to approaches to burden-sharing in any multilateral agreement.

E D U C AT I O N F O R A L L : H U M A N R I G H T A N D C ATA LY S T F O R D E V E L O P M E N T C o n c l u s i o n Understanding the science behind climate change is a vital first step in raising the awareness needed to drive political solutions to the threat.
This is true both technically speaking and in terms of people having a sufficient grasp of evidence to assess the action – or inaction – of their governments.

The PISA 2006 assessment of scientific literacy among 15-year-old students offers some important lessons (OECD, 2007b).

When the assessment was published, international attention focused on the ranking of countries.

Less emphasis was placed on an innovative survey of the relationship between scientific literacy and global environmental problems.
The results of that survey point to: A strong association between student levels of environmental awareness and science performance, in all participating countries.

On average, an increase of one unit on the PISA composite index of environmental awareness was associated with a performance difference of forty-four score points.

A significant relationship between science knowledge and environmental awareness on the part of the general public.
The majority of citizens in countries with a mean score in science below the basic literacy threshold (of 450 score points) were less aware of environmental issues.

An association, in all OECD countries surveyed, between higher science performance and a stronger sense of responsibility for sustainable development.

That is, students demonstrating higher science knowledge reported feeling more responsible for the environment.

These findings point to the potential for a double dividend.
Strong performance in science and awareness of global environmental problems tend to go hand in hand, and both are associated with a sense of responsibility supporting sustainable environmental management.

Conversely, weak performance in science is associated with lower awareness of environmental problems.

Failure in scientific education will mean less widespread – and less informed – public debate on issues such as climate change and wider environmental problems.
This in turn will reduce the pressure on governments to act.

In facing up to the challenge of global warming and wider problems, EFA is a vital part of the toolkit for national and international change.

Conclusion Much has been achieved since governments signed the Dakar Framework for Action.

Perhaps more than in any other area, progress in education bears testimony to the fact that international commitments can make a difference.
That does not diminish the case for a greater sense of urgency and stronger political leadership.

The bottom line is that ‘business as usual’ will leave the world far short of reaching the commitments made.

And as this chapter shows, shortfalls in education come at a high price.

Breaking with business as usual will require change at many levels.

Equity has to be put at the centre of the EFA agenda.
As Chapter 2 demonstrates, inequalities in opportunity for education represent a formidable barrier to the achievement of the Dakar goals.

Removing that barrier will require political leadership and practical strategies that tackle the underlying causes of disadvantage.

Governance is a central concern.


The aim of good governance in education, as in other areas, is to strengthen accountability and give people a voice in decisions that affect their lives so as to enable the delivery of good-quality services.

Good governance is also about social justice and fairness.

Education for all, as the term itself makes clear, is about all citizens enjoying an equal right to quality education.
Translating good governance principles into practice involves reforms in institutional arrangements that link children and parents to schools, local education bodies and national ministries.

Unfortunately, the design of governance reform is often guided by blueprints that produce limited benefits, especially from the perspective of the poor, the marginalized and the disadvantaged.
Accelerated progress towards EFA and the goals set in the Dakar Framework for Action is a condition for accelerated progress towards the MDGs.

More than that, it is a condition for the development of more equitable and more sustainable patterns of globalization.

But accelerated progress towards EFA cannot take place without a far stronger commitment on the part of national governments and international donors to equity in education.

Inequality has to be brought to the centre of the EFA agenda.
This Report explores why equity matters, and what can be done nationally and internationally to overcome disparities.
Accelerated progress towards EFA requires a stronger commitment by countries and donors to equity in education 3 7 A nutritious meal, in Lesotho Signing up for literacy classes, in Honduras / l e d n e M n o e d G © i S O N A P / i z z o r i P o m o c a G © i Support and encouragement from a primary school teacher, in Djibouti 3 8 S O N A P / r e p o o C l i e N © s i b r o C / l e d n e M n o e d G © i A disabled child joins the game, in the United Kingdom Education for All Global Monitoring Report 9002 Chapter 2 The Dakar goals: monitoring progress and inequality This chapter provides an overview of progress towards the six Education for All (EFA) goals as set out in the 2000 Dakar Framework for Action.
Using the most recent international data, it measures performance against targets, compares the state of education across countries and identifies trends.

Looking beyond national averages, the chapter turns the spotlight on inequalities based on wealth, gender, location and other markers for disadvantage.

Overcoming inequality would accelerate progress towards the goals, unlocking wider benefits for societies in the process.
Introduction .......................................................................................................... 40 Introduction Early childhood care and education: a long way to go ............... 42 Progress towards UPE: nations at the crossroads .......................... 56 Secondary education and beyond: some gains .................................... 84 Meeting the lifelong learning needs of youth and adults ....................... 91 Adult literacy: still neglected ............................................................................................... 93 Assessing gender disparities and inequalities in education ....... 97 Ensuring both equity and the quality of learning ..................... 108 Education for All: measuring composite achievement ....................................................................................................... 122 The maxim ‘to improve something, first measure it’ encapsulates the importance of monitoring progress towards the EFA goals.
Effective measurement can serve as a guide to policy, focusing attention on the targets, giving early warning of failure, stimulating debate, informing advocacy and strengthening accountability.

At the international level, cross-country monitoring can help to identify areas of good practice and cases of underperformance.
Above all, EFA monitoring is important because it charts progress towards goals that are ultimately about improving the quality of people’s lives, extending opportunity and overcoming inequalities.

Monitoring has a special role to play when it comes to international goals.

Too often in the past governments have convened high-level summits on development, adopted bold sounding targets and then failed to deliver.

Education is no exception to the rule.


A decade before the Dakar World Education Forum, the World Conference on Education for All held in Jomtien, Thailand, adopted the target of attaining universal primary education (UPE) by 2000, along with a wider range of similarly impressive goals.

Results were less impressive than the targets.

National governments and donors fell far short of their commitments, but did so in the absence of intense scrutiny.
One of the differences between the commitments made at Jomtien and those undertaken at Dakar is that the latter have been subject to close monitoring since 2002 by the EFA Global Monitoring Report.

In this year’s Report we identify areas of progress and offer an early warning of impending failure.

As the 2015 deadline for achieving some of the Dakar goals draws nearer, the urgency of breaking with business-as-usual approaches becomes starker.
One of the most important time-bound targets in the Dakar Framework is the commitment to achieve UPE by 2015 – a commitment restated in the Millennium Development Goals (MDGs).

The present Report argues that progress towards this goal has been hampered by a systematic failure to place equity at the heart of the EFA agenda and by problems in improving the quality of education.
The Report focuses on four areas that are central to achieving EFA by 2015: T H E D A K A R G O A L S : Early childhood care and education (ECCE).

What happens in the years between birth and primary school is crucial.

In this area the Report makes for bleak reading.


Around one in three children in developing countries enter primary school with their cognitive development damaged, often irreparably, by malnutrition or disease.

This is not a viable foundation for UPE.

Most governments are failing to act with sufficient urgency to break the link between child malnutrition and lost educational opportunity.
Progress towards high-quality pre-school provision and care, a vital condition for lifelong learning and enhanced equity, remains slow and uneven.

And those with most to gain from ECCE programmes are the least likely to have access.

Universal primary enrolment and completion.

Progress towards UPE has accelerated since Dakar.

Sub-Saharan Africa has made particularly impressive strides, with many governments increasing the priority attached to basic education.
Numbers of out-of-school children are coming down.

Ultimately, though, progress has to be measured against the benchmark established in Dakar of achieving UPE by 2015.

Under a business-as-usual scenario, that target will be missed.

The EFA Global Monitoring Report 2008has developed projections for 134 countries that accounted in 2006 for 64% of out-of-school children of primary school age.

The results indicate that some 29 million will still be out of school in 2015 in these countries alone.
Importantly, the projection does not cover countries such as the Democratic Republic of the Congo or the Sudan because of data limitations.

While trends can be changed, the current trajectory is worrying.

If the targets are to be achieved, governments must attach greater urgency to meeting the triple challenge of getting all children into school, ensuring that they do not drop out and providing the support needed for them to complete the cycle.
Several of the world’s poorest countries have demonstrated that rapid progress is possible.

But deep-rooted and persistent inequalities in opportunity, based on wealth, gender, location, language and other markers for disadvantage, constitute a formidable barrier to UPE.

For countries that are close to UPE, going the final mile will require practical strategies for reaching the most marginalized.

A strengthened focus on equity will accelerate progress in all countries.

The quality imperative.


The ultimate aim of EFA is to ensure that children receive an education that enriches their lives, expands their opportunities and empowers them to participate in society.

Much of what currently passes for education fails to meet these criteria.

Despite serious data constraints in cross-country monitoring of education quality, the scale of the problem is increasingly apparent.
Absolute learning levels are so low in many developing countries that millions of children complete primary school without acquiring basic literacy and numeracy skills.

International learning assessments point to very large gaps between developed and developing countries.

These gaps are mirrored by large within-country disparities in learning achievements.

Education quality problems are often exacerbated by the dilapidated physical state of schools in many countries and by severe shortages of teachers.
Progress towards gender parity.

There has been impressive progress towards gender parity at primary and secondary levels.

Yet many countries failed to achieve the goal of parity by 2005.

Countries in South Asia and sub-Saharan Africa feature strongly in this group.
Gender gaps in education are often reinforced by other markers for disadvantage, such as poverty and ethnicity, but country experience suggests that parity can be achieved given strong national commitment accompanied by policies targeting the main constraints.

The decision to focus on four priority areas does not detract from the importance of the larger EFA package.

Indeed, a defining feature of the EFA agenda is that it treats the six goals as part of a single comprehensive, integrated framework.
In this respect, the Dakar Framework is far broader than the Millennium Development Goal framework, which addresses only UPE and gender parity – an unduly restrictive approach.

This chapter also looks at post-secondary education, youth learning opportunities and adult literacy.

In addition, its final section updates the EFA Development Index (EDI), a composite measure of overall progress.
Partial projections indicate that well over 29 million children of primary school age will still be out of school in 2015 Malnutrition or micronutrient deficiency in the first two years of life can impair brain development, with irreversible consequences 4 2 C H A P T E R 2 Early childhood care and education: a long way to go Goal 1: Expanding and improving comprehensive early childhood care and education, especially for the most vulnerable and disadvantaged children.
The path towards Education for All starts long before primary school.

Adequate nutrition, good health and an emotionally secure, language-rich home environment during the earliest years are vital for later success in education and life.

Yet millions of children lack these advantages and are locked at an early age into long-term cycles of deprivation.

Failure to deliver on the early childhood goal is hampering overall progress towards the EFA targets set in Dakar.
Well-designed early childhood care and education policies are a powerful antidote to inherited disadvantages.

Monitoring evidence suggests, however, that many governments are failing to apply that antidote in two key areas.

The first is child health.

One in three children below the age of 6 in the developing world will start primary school with their bodies, brains and long- term learning prospects permanently damaged by malnutrition and ill health.
This has important but widely ignored implications for education.

Getting children into primary school is an important part of the Dakar promise.

When so many of the children entering school have had their lives blighted by sickness and hunger, improved access alone is not a secure foundation for education for all.

That is why governments urgently need to strengthen the link between child health and education.

The second area of concern is pre-school provision.
While coverage rates are increasing worldwide, early childhood services of good quality remain inaccessible to the majority of the world’s children.

This is especially true for children in the poorest countries – and for the most disadvantaged among them.

The upshot is a perverse outcome for equity: those with the most to gain from ECCE are least likely to participate.

This section builds on the comprehensive analysis set out in the EFA Global Monitoring Report 2007 on ECCE.
It is divided into three parts.

After a brief overview of childhood development stages, the second subsection looks at child health and nutrition, two foundations for early childhood development and lifelong learning.

Using the MDGs as a benchmark for assessing performance, a stark message emerges: governments are failing children on an international scale.

The third subsection focuses on ECCE delivery and provision.
The crucial early years Child development starts in the womb, where it is affected by the state of the mother’s health and nutrition.

The period between birth and age 3 is one of rapid cognitive, linguistic, emotional and motor development, with explosive growth in vocabulary starting around 15 to 18 months.
Development from age 3 is marked by the emergence of increasingly complex social behaviour, problem-solving and pre-literacy skills that build on earlier achievements (Harvard University Center on the Developing Child, 2007; National Scientific Council on the Developing Child, 2007).

This is a critical period for acquisition of the cognitive skills that will carry children through school and influence their life chances in adulthood.

Many factors affect cognitive development.


Genetic factors interact with social and environmental influences in shaping the physiological processes through which neurons in the brain form sensing pathways which in turn shape cognitive development and behaviour (Abadzi, 2006).

Neurological research continues to shed light on the processes at work.

Physiological factors are important.


Malnutrition or micronutrient deficiency in the first two years of life can impair brain development and the functioning of the central nervous system, with irreversible consequences (Grantham-McGregor and Baker- Henningham, 2005; The Lancet,2008).

Other processes are linked to the quality of the home environment, including care provision and cognitive stimulation.
Childhood poverty is one of the most powerful negative influences on the home environment (Farah et al., 2005; Noble et al., 2007).

Its impact is cumulative, with poor cognitive development leading to weaker academic outcomes and more limited life chances.

The simple message to emerge from the complex field of neurocognitive research is that early experience is critical.

There are no rapid-rewind buttons through which deprivation can be offset and no quick fixes for the injury to cognitive development.
Early childhood cognitive damage is for life.

T H E D A K A R G O A L S : : a l o n g w a y t o g o Damage prevention is better than cure for reasons of both equity and efficiency.

It is unfair for children to be held back in life because of circumstances – such as having poor parents – over which they have no control.
The efficiency argument for ECCE is backed by evidence pointing to high private and social returns: not just improved academic performance, higher productivity and higher income, but also improved health and reduced crime.
As the Nobel Prize-winning economist James Heckman has put it: ‘Early interventions in children from disadvantaged environments raise no efficiency-equity trade-offs; they raise the productivity of individuals, the workforce and society at large, and reduce lifetime inequality by helping to eliminate the accident of birth’ (Heckman and Masterov, 2004, p. 5).
Child health and nutrition: slow and uneven progress Rapid progress towards UPE cannot be sustained as long as progress in tackling child health problems remains slow.

High levels of child mortality and malnutrition represent a formidable development challenge in their own right.

They are also symptoms of wider problems that directly affect education.

There is good news: most indicators for child welfare are improving in most countries.
In some cases the rate of progress has been impressive: Child survival:In 2006, there were 3 million fewer deaths of children under age 5 than in 1990 – a decline of one-quarter.

In 1990, one South Asian child in every eight died before their fifth birthday.

The figure is now one in twelve.

Bangladesh, Ethiopia, Mozambique and Nepal are among countries having reduced under-5 mortality by 40% or more (UNICEF, 2007).

Vaccination:Increased immunization is saving lives.
World Health Organization projections for 2007 indicated that 75% of children in the seventy-three countries covered by the GAVI Alliance (formerly the Global Alliance for Vaccines and Immunisation) had been immunized with three doses of the diphtheria, pertussis and tetanus vaccine (DPT3) – up from 64% in 2000 (GAVI Alliance, 2008).

Vaccination against measles is estimated to have cut deaths worldwide by 60% and in sub-Saharan Africa by 75% (UNICEF, 2007).
HIV/AIDS.At the end of 2007, some 3 million people in developing countries were receiving antiretroviral therapy, up from 30,000 in 2002.

Improved access to drugs intended to prevent mother-to-child transmission – a major cause of the 370,000 annual new cases of HIV/AIDS among children – is starting to have an impact (UNAIDS, 2008).

In each of these areas strong national policies backed by global initiatives are making a difference.
One example is the Global Fund to Fight AIDS, Tuberculosis and Malaria, established in 2002.

As of mid-2008, it was providing 1.75 million people with antiretroviral treatment (a 59% increase in one year) and 59 million antimalarial bed nets (doubling provision over the course of the year) (Global Fund to Fight AIDS, Tuberculosis and Malaria, 2008).

While many targets have been missed and insufficient attention has been paid to strengthening national health systems, these are real achievements.
The bad news is that current efforts fall far short of what is required.

Notwithstanding the ready availability and affordability of interventions with proven effectiveness, key targets set under the MDGs for child health will be missed.

Child mortality: slow progress and large inequalities Child mortality is one of the most sensitive barometers of well-being for children under 5.
While the measure itself captures premature death, it also provides an insight into the health and nutritional condition of the next generation of primary school-age children.

Each year around 10 million children die before they reach the starting age for primary school (UNICEF, 2007).

The vast majority of these deaths result from poverty-related infectious diseases and inadequate access to basic services, such as clean water and sanitation.
Around 1.8 million children die annually in developing countries for want of these latter two commodities that people in rich countries take for granted (UNDP, 2006).

Sub-Saharan Africa accounts for half of all under-5 deaths, and its share is growing.

South Asia accounts for one-third of such deaths.

Childhood mortality figures represent the tip of an iceberg.
The diseases that account for the bulk of child deaths, such as pneumonia (19% of the total), Around 1.8 million children die annually in developing countries for want of basic services, such as clean water and sanitation 4 3 9 0 0 2 t r o p e R i g n r o t n o M i In many countries being poor and rural dramatically reduces the prospect of surviving to the fifth birthday diarrhoeal infections (17%), malaria (8%) and measles (4%), inflict far wider and often lasting damage on children’s development prospects (Patrinos, 2007; WHO, 2008).
For example, diarrhoea is both a cause and a consequence of micronutrient deficiency.

Pneumonia not only claims the lives of some 2 million children a year but is also a major opportunistic infection associated with diphtheria, whooping cough and measles (Simoes et al., 2006).

Maternal malaria is a significant cause of intrauterine growth retardation and low birth weight, and, in Africa, of childhood anaemia (Breman et al., 2006).
The major diseases implicated in child mortality also have consequences for education through long- term effects on nutrition and cognitive development, as well as on school attendance and learning.

In the MDGs the world’s governments have pledged to cut under-5 deaths by two-thirds, from 1990 levels, by 2015.

Without a greatly intensified effort, that goal will be missed by a wide margin (Figure 2.1).

The situation in sub-Saharan Africa is particularly worrying.
The region as a whole has been reducing child mortality at one-quarter the required rate and only three out of forty-six countries are on track for the MDG target.

In South Asia, the observed rate of decline for 1990–2006 is around one-third what is required to achieve the MDG.

These are large statistical deficits, with large associated human costs.
At global level, the projected gap between the MDG target and outcome in 2015 can be measured in terms of the 4.3 million child deaths that would be averted if the goals were achieved (UNICEF, 2007).

Childhood health and survival, and their effects on cognitive development and education, are heavily influenced by patterns of inequality.

In many countries being poor and rural dramatically reduces the prospect of surviving to the fifth birthday.
For example, in Bolivia and Nigeria, child death rates among the poorest quintile are over three times those of the wealthiest 20% (Figure 2.2).

These disparities reflect underlying inequalities in nutrition, vulnerability and access to health services.

Reducing health disparities would deliver a high pay-off in terms of lives saved.

Cutting child death rates among the poorest quintile of households to the levels prevailing among the richest 20% would reduce overall deaths by some 40% (UNICEF, 2007).
Unfortunately, mortality data suggest that many countries are moving in the wrong direction (Figure 2.3).

Disaggregating child mortality data for twenty-two countries for which household survey data by income quintile are available shows that: In nine of the seventeen countries that have made progress in reducing child deaths, the mortality gap between the richest and poorest quintiles has widened.
In Nicaragua, the Philippines and Zambia the rate of improvement for the poorest 20% fell far behind that for the richest.
Figure 2.1: Rates of decline in under-5 mortality in 1990—2006 and required rates for 2007—2015 to meet the MDG e n i l c e d f o e t a r d e v r e s b O ) % ( 6 0 0 2 – 0 9 9 1 , y t i l a t r o m 5 - r e d n u n i 12 10 8 6 4 2 0 k c a r O n t Latin America and Caribbean CEE/CIS1 East Asia and Pacific Middle East and North Africa South Asia Sub-Saharan Africa 0 2 4 6 8 10 12 Required rate of decline in under-5 mortality, 2007–2015 (%) Notes: The area of each circle represents the relative number of current under-5 deaths.
The closer the circle is to the on-track line, the closer it is to meeting the MDG.

Regions presented are those used by UNICEF, which differ to some extent from the EFA regions.

1.

Central and Eastern Europe, and the Commonwealth of Independent States.

Source: UNICEF (2007).
T H E D A K A R G O A L S : : a l o n g w a y t o g o Figure 2.2: Under-5 mortality rates by location and income group, selected countries, most recent year Colombia Colombia Colombia Viet Nam Viet Nam Viet Nam Philippines Philippines Philippines Dominican Rep. Dominican Rep. Dominican Rep. Nicaragua Nicaragua Nicaragua Morocco Morocco Morocco Indonesia Indonesia Indonesia Namibia Namibia Namibia Bolivia Bolivia Bolivia Bangladesh Bangladesh Bangladesh Nepal Nepal Nepal Ghana Ghana Ghana Kenya Kenya Kenya U. R. Tanzania U. R. Tanzania U. R. Tanzania Cameroon Cameroon Cameroon Uganda Uganda Uganda Benin Benin Benin Zambia Zambia Zambia Mozambique Mozambique Mozambique Burkina Faso Burkina Faso Burkina Faso Chad Chad Chad Nigeria Nigeria Nigeria Mali Mali Mali Figure 2.3: Ratio of under-5 mortality rates of the richest 20% compared with those of the poorest 20% of households, selected countries, most recent year Countries where national under-5 mortality rate has increased Countries where national under-5 mortality rate has decreased Uganda Nigeria Chad Kenya Cameroon U. R. Tanzania Zambia Nepal Benin Colombia Nicaragua Philippines Viet Nam Morocco Burkina Faso Mali Mozambique Bangladesh Ghana Dominican Rep. Indonesia Bolivia 0 50 100 150 200 250 300 Under-5 mortality rates (per 1 000 live births) 0 Richest to poorest ratio in under-5 mortality rates Rural Urban Poorest 20% Richest 20% Average Pre-Dakar Post-Dakar (increase) Post-Dakar (decrease) Stable Source: Demographic and Health Surveys, calculations by Gwatkin et al.
(2007a).

Source: Demographic and Health Surveys, calculations by Gwatkin et al.

(2007a).

Among the five countries in which child mortality has increased, the gap between rich and poor widened in Nigeria and Uganda.

The trends in child mortality point in a worrying direction for education on two counts.

First, there is a widening disjuncture between the rapid progress in primary school enrolment and the slow progress on child mortality.
The implication: childhood diseases will corrode the potential benefits of improved access to education.

Second, to the extent that child mortality disparities mirror wider health status, there is a danger that child health inequalities will reinforce the other educational disadvantages facing children from poor rural households once they enter school.
Child malnutrition undermines potential and impedes progress Malnutrition is the world’s most serious health epidemic and one of the biggest barriers to UPE.

The epidemic affects one-third of children less than 5 years old.

It also accounts for around one-third of the global disease burden for the age group and some 3.5 million child deaths annually (Black et al., 2008).

Yet its importance is understated – not least in relation to education.
In 2006, around 193 million children under 5 suffered moderate to severe stunting.

By the time they enter primary school, malnutrition will have damaged the brains and cognitive development of many of these children.

There is compelling evidence that poor nutrition in early childhood affects cognitive development, fine motor skills, learning acquisition and behaviour.
Even moderate malnutrition results in altered behaviour, including lower activity levels, greater apathy and less enthusiasm for play and exploration (Grantham- McGregor, 1995; Grantham-McGregor et al., 2007).
Of particular importance is the period between birth and 24 months, during which nutritional Even moderate malnutrition results in altered behaviour, including lower activity levels, greater apathy and less enthusiasm for play and exploration 4 5 9 0 0 2 t r o p e R i g n r o t n o M i India accounts for one in three malnourished children in the world l a b o G l l l A r o f i n o t a c u d E 1.
Children are classified as suffering from stunting if their height for their age is between two and three standard deviations (moderate stunting) or three or more standard deviations (severe stunting) below the reference median (see glossary).

4 6 C H A P T E R 2 deficits can have irreversible physical and cognitive effects (The Lancet,2008).

Malnourished children are less likely to start school at the official age and less equipped to learn.
Research in the Philippines found that malnourished children performed more poorly in school, partly as a result of delayed entry and resultant loss of learning time and partly because of diminished learning capacity (Glewwe et al., 2001).

The impact of malnutrition persists into adult life.

In Guatemala early stunting is associated with deficits in literacy, numeracy and educational attainment at age 18 (Maluccio et al., 2006).
The scale of malnutrition can be captured in three key indicators: Low birth weight: The nutrition crisis starts in the womb and is linked to the health status of women.

In much of sub-Saharan Africa and South Asia women suffer poor nutrition before and during pregnancy in near epidemic proportions.

Low birth weight is a proxy measure for this phenomenon.

In 2006, around 16% of children in developing countries – some 19 million – were born underweight and the share reached 29% in South Asia.
Such children are twenty times more likely to die in infancy and those who survive are more susceptible to infectious disease.

Around 42% of pregnant women in developing countries are anaemic, a primary cause of low birth weight (UNICEF, 2007).

Child stunting: Moderate and severe stunting are indicators of persistent undernutrition.1 For all developing countries, around one child in three suffers from moderate or severe stunting (The Lancet,2008).
The vast majority of these children live in South Asia and sub-Saharan Africa.

Almost half of all children in South Asia and one-third in sub-Saharan Africa are affected by stunting.

These regional averages mask large differences between countries.

Over 40% of the children living in Angola, Burundi, Chad, Ethiopia and Malawi will reach primary school entry age having suffered the debilitating effects of stunting (Figure 2.4).
Of the twenty-two countries with a child stunting prevalence of 40% or more, thirteen are in sub-Saharan Africa, six in Asia and two in the Arab States.

Many observational studies have shown associations between child stunting or low weight for age, and poor mental and motor development later in life (Grantham-McGregor and Baker-Henningham, 2005).

Micronutrient deficiency: Damage caused by insufficient calorie intake is compounded by nutrient deficiency.
Micronutrients such as iodine, iron and vitamin A have a profound effect on a child’s development.

For example, clinical deficiency of iodine is the single greatest cause of mental retardation.

It restricts development of the central nervous system, leading to an average loss of around thirteen IQ points.

Iron deficiency anaemia, which affects 47% of pre-school children, impairs concentration and increases vulnerability to infectious disease (Black et al., 2008; Grantham- McGregor et al., 2007).
Measured against internationally agreed benchmarks, progress in reducing child malnutrition has been limited.

The MDG target is to halve undernutrition (from 1990 levels) by 2015.

Fewer than one-quarter of the 143 countries for which data are available, and only three of the twenty countries that account for 80% of global malnutrition, are on track (The Lancet,2008).

In many countries the situation is deteriorating.


Malnutrition rates have increased in twenty-six countries, half of them in sub-Saharan Africa.

By one estimate, the number of undernourished people in the region increased from 169 million to 206 million between 1990 and 2003 (World Bank, 2006b).

Much of South Asia is also off track for the MDG target, including countries with high rates of economic growth.
The experience of India, which accounts for one in three malnourished children in the world, is instructive and disconcerting in equal measure.

For two decades, the country has been in the fast lane of globalization, registering one of the world’s highest economic growth rates.

Yet this economic breakthrough has not translated into similar progress in tackling child malnutrition (Box 2.1).

The international food crisis could dramatically worsen prospects for achieving the MDGs.
In many countries people living on less than US$1 a day spend over 60% of their income on food, leaving them highly vulnerable to even modest price increases (Minot, 2008).

Over the past year, international price changes have been anything but modest.

Grain prices have doubled since 2006, with prices of other staples increasing even more – by a factor of three in the case of rice (Minot, 2008).

Vulnerable households are already suffering the consequences.
In Yemen, for instance, rising food prices have reduced the real income of the poorest 20% of households by 12% (World Bank, 2008a).
T H E D A K A R G O A L S : : a l o n g w a y t o g o Figure 2.4: Low birth weight and moderate and severe stunting worldwide1 60 50 40 30 20 10 ) % ( e t a r g n i t n u t s e r e v e s d n a e t a r e d o M Burundi Zambia Guatemala Niger Timor-Leste Rwanda Malawi Angola Madagascar Nepal Ethiopia U. R. Tanzania DPR Korea Cambodia Benin Pakistan Mali Lao PDR Mozambique Bhutan Equat.
Guinea Somalia Eritrea Nigeria C. A. R. Lesotho D. R. Congo Djibouti Guinea Burkina Faso Côte d‘Ivoire Uganda Myanmar Swaziland Kenya Viet Nam Cameroon Zimbabwe Philippines Bangladesh Comoros Chad Guinea-Bissau Sierra Leone Yemen India Sudan Bolivia Tajikistan Honduras Kuwait Sao Tome/Principe Mongolia Syrian A. R. Peru Botswana Ghana Saudi Arabia El Salvador Belize Congo South Africa Togo Namibia Ecuador Gabon Nicaragua Iraq Panama Egypt Morocco U.
A. Emirates Libyan A. J. Paraguay Mexico Tunisia Brazil Uruguay Bahrain Oman Venezuela, B. R. Colombia Thailand Azerbaijan Guyana Suriname Jordan Mauritius Qatar Dominican Rep. Turkey Turkmenistan Iran China Algeria Lebanon Palestinian A. T. Montenegro Belarus Ukraine Cuba Costa Rica Argentina Singapore United States 0 0 Chile 5 Jamaica Trinidad/Tobago Gambia Senegal Sri Lanka Maldives Haiti ) % ( e t a r g n i t n u t s e r e v e s d n a e t a r e d o M 40 Least developed countries South Asia Sub-Saharan Africa East Asia/ Pacific 20 World Developing countries Arab States Latin America/Caribbean CEE/CIS 10 20 30 Low birth-weight rate (%) 10 15 20 25 30 35 Low birth-weight rate (%) Note: Regions shown are those used by UNICEF, which differ to some extent from the EFA regions.
For example, UNICEF includes the Islamic Republic of Iran in the Arab States region, rather than South and West Asia.

Data points for countries in Central and Eastern Europe are shown in green, in Central Asia in cyan and in South and West Asia in orange.

1.

Data are for the most recent year available.

Sources: Annex, Statistical Table 3A; UNICEF (2007).
On one estimate, food price inflation could push 105 million more people below the poverty line, 30 million of them in sub-Saharan Africa (Wodon et al., 2008).

The slow progress on child well-being indicators is difficult to justify.

Priority interventions for child health are well known, effective and affordable.

Detailed stategies drawn up by the African Union suggest that additional financing of US$2-3 per capita could cut child death by 30% and maternal mortality by 15%.
Expanded immunization, treatment for diarrhoea and pneumonia, use of anti-mosquito bed nets and preventive drugs for malaria, distribution of key micronutrients and measures to prevent mother-to-child HIV transmission could dramatically cut child sickness and death.
In rural parts of the United Republic of Tanzania, the incidence of underweight children was reduced by 7% between 1999 and 2004 through integrated maternal and child health interventions, including improved water and sanitation provision, mass immunization and malaria prevention (Alderman et al., 2005).
Ethiopia has embarked on a major programme to extend antenatal care and to ensure that essential drugs and vaccines are Additional financing of US$2-3 per capita could cut child death by 30% and maternal mortality by 15% in sub-Saharan Africa Children’s achievement in school is affected by what happens to them before they even get to school 4 8 C H A P T E R 2 Box 2.1: Malnutrition compromises India’s progress in primary school enrolment In recent years India has made impressive progress towards universal enrolment in primary school.
Progress on child health indicators is less impressive.

While India has sustained one of the world’s highest economic growth rates for two decades, social indicators for child mortality, nutrition and child health lag far behind: Child mortality has been falling at around one- third the rate required for India to achieve the MDG target.

Bangladesh and Nepal, with lower levels of income and economic growth, have both outperformed India on this key indicator of child welfare.
If India had reduced child mortality to Bangladesh levels, it would have had 200,000 fewer deaths in 2006.

Rising average income has done little to enhance child nutrition.

According to the 2005–2006 National Family Health Survey, the prevalence of underweight children was 46% in 2005, the same level as in 1998.

Micronutrient deficiencies are pervasive.

Iodine deficiency in pregnant women causes congenital mental impairment in an estimated 6.6 million children annually.
One-third of all children in the world born with mental damage related to iodine deficiency live in India.

In addition, around 75% of pre-school children in India suffer iron deficiency anaemia and 60% have subclinical vitamin A deficiency.

Health provision is lacking in many areas.

More than one-quarter of children with diarrhoea are never treated.

Around 45% of children do not receive the full DPT3 vaccination, the same share as in 1998.

Vaccination coverage has dropped in ten states since 1998.
This marked disconnect between success in the economy and failure in child nutrition is the product of deep inequalities linked to income, caste, gender and state — and of wide-ranging public policy failures.

The Integrated Child Development Services (ICDS) programme is the institutional spearhead of India’s efforts to combat child malnutrition.

However, its effectiveness is undermined by serious problems in targeting.
The five states with the highest prevalence of malnutrition have the lowest level of coverage from the ICDS.

In addition, older children (aged 3 to 6) participate much more than younger ones, so the crucial window of opportunity for tackling malnutrition is being missed.

Many children from the poorest households are not covered.

And the programme fails to preferentially target girls, children from lower castes and the poor, all of whom face higher risks of malnutrition.
The Government of India’s publicly declared aspiration is to create a world-class education system that delivers good-quality schooling for all its children.

Achieving that goal will require stronger political leadership and practical policies that link the EFA agenda with policies to improve public health and enhance equity.

Sources: Deaton and Drèze (2008); Gragnolati et al.

(2006); International Institute for Population Sciences and Macro International Inc. (2007).
available at primary health clinics.

To underpin the plan, the government is training and deploying 30,000 female health extension workers recruited from the communities they will serve (UNICEF, 2007).

Box 2.2 illustrates the case for such interventions.

Unfortunately, decisive action is the exception rather than the rule.

Nutritional security seldom figures among key development priorities and is rarely well integrated into national poverty reduction strategies.
A review of malnutrition policy carried out by the medical journal The Lancet recently concluded that ‘leadership is absent, resources are too few, capacity is fragile, and emergency response systems are fragmentary’ (The Lancet,2008, p. 179).

Good-quality ECCE provision: a foundation for equity Two children are born in Ecuador on the same day.

One is born into a household in the top 20% of the wealth distribution in the country, the other into the bottom 20%.
At age 3 both score at roughly equivalent levels in tests of vocabulary recognition.

By age 5 the child from the richest household is scoring around 40% higher.

When they enter primary school, children from the poorest households are so far behind that they are unlikely to ever catch up.

This story summarizes the findings of an important study of cognitive development in Ecuador (Paxson and Schady, 2005).
It illustrates that what children achieve in education is profoundly affected by what happens to them before they even get to school.

T H E D A K A R G O A L S : : a l o n g w a y t o g o Box 2.2: Country evidence: health and nutrition interventions can enhance cognitive development Exploiting the window of opportunity for combating malnutrition can deliver high returns.
Programmes in many countries make a powerful case for early intervention.

For example: In the Philippines, a pilot child nutrition programme focused investments on a wide range of nutrition and preventive health interventions.

For children aged 2 to 3, exposure to the programme for seventeen months was associated with significantly higher expressive and receptive language skills (0.92 to 1.80 standard deviations higher), as well as higher weight-for-height scores.
Children under 4 also recorded significant lowering of worm infestation and diarrhoea incidence.

Bolivia’s Integrated Project for Child Development provides 70% of recommended nutrient inputs and systematic learning environments for poor urban children aged 6 months to 6 years.

Controlled comparisons point to large positive effects on cognitive development and language skills, as well as improved weight for height in children under 3.
The Oportunidades programme in Mexico provides some of the most compelling evidence for the effectiveness of health interventions.

Because the programme has been progressively implemented, it has been possible to conduct a randomized evaluation looking at a range of outcomes.

Among the findings: Reduced prevalence of stunting.

At age 2, children in the programme had a 1-cm height advantage over non-participants.

Enhanced school attendance and progression.


Those who participated between birth and 6 months were more likely to enter school on time, progress steadily through the system and acquire more years in school.

Enrolment rates at secondary level increased from 67% to 75% for girls and from 73% to around 78% for boys.

Strengthened cognitive development.


A recent study using administrative data to look at the cumulative benefits of cash and nutrition transfers on health, cognitive development and motor skills found that a doubling of cash transfers was associated with better height-for-age scores and higher scores on three scales of cognitive development and receptive language.
Two of the cognitive development domains positively associated with cash transfers — short-term working memory and language — are among the most sensitive to social and economic status.

Sources: Armecin et al.

(2006); Behrman and Hoddinott (2005); Behrman et al.

(2004); Fernald et al.

(2008); Schady (2006).

Provision for under-3s: exploiting the window of opportunity Institutional arrangements, capacity and quality of service for children under the age of 3 vary enormously.
In most developed countries, provision includes regular health visits, immunization, nutritional advice and universal access to child care services.

However, there are important exceptions to this rule – and poor children often have the most limited access.

In developing countries, interventions are usually far more limited and poorly coordinated.

Households act as the frontline carer in developing countries, although government agencies also have child well-being remits.
Maternal and child health services typically fall under the authority of health ministries or dedicated child development services.

In Latin America day care centres are widely used to deliver nutritional support to vulnerable households.

Governments in the region have also expanded social protection programmes with early childhood components.
Sometimes these programmes provide conditional cash transfers: eligible households receive payments if they meet conditions such as presenting their children for growth monitoring and vaccinations, and assuring their attendance in school.

The largest such programme is Oportunidades in Mexico, which in 2007 had a budget of US$3.7 billion and reached 5 million families (Fernald et al., 2008).

Other social protection programmes provide unconditional cash transfers.
An example is Ecuador’s Bono de Desarrollo Humano, which provides a cash transfer to women designated as eligible solely on the basis of a composite deprivation index (Paxson and Schady, 2007).

Research from a large group of countries points to very high returns from investing in good quality ECCE.

Evaluations of the Bono de Desarrollo Humano programme have identified a range of positive effects on fine motor control, long-term memory and physical well-being.
Children of participants in the poorest quartile measure 25% higher in cognitive outcomes than the average for a control group.
For the poorest half of these families, the transfer – amounting to US$15 per month – Research from a large group of countries points to very high returns from investing in good quality ECCE 4 9 9 0 0 2 t r o p e R i g n r o t n o M i ECCE coverage is lowest in sub-Saharan Africa and the Arab States increases school enrolment from 75% to 85% and reduces child labour by seventeen percentage points (Oosterbeek et al., 2008; Schady and Aranjo, 2006).
Other evaluations demonstrate cumulative benefits over time in the form of improved indicators for achievement and learning.

Formal pre-school access from age 3: uneven expansion, deep inequalities Around thirty countries have laws making at least one year of pre-school compulsory though few are stringently enforced.

In most cases, ministries of education oversee national provision.

Such experiences confirm that ECCE has the potential to make a big difference.
Cross-country research suggests there are three conditions for unlocking that potential (Armecin et al., 2006; Grantham-McGregor and Baker-Henningham, 2005; Schady, 2006): Start early.

Effective exploitation of the narrow window of opportunity up to the third birthday diminishes vulnerability to stunting and enhances cognitive development.

Operate long term.
Operate long term.

Intervention needs to be continuous and to take a variety of forms deter- mined by circumstances, with nutritional, health and behavioural interventions all playing a role.

Undertake multiple actions.

For example, feeding programmes that incorporate cognitive stimulation, as in Bolivia and the Philippines, are more effective than either nutrition or stimulation alone.
Good-quality ECCE provision can equip children with cognitive, behavioural and social skills that generate large benefits in terms of access to primary school, progression through school and learning outcomes (Box 2.3).

There is no simple template for determining what constitutes good quality.

International research points to the importance of class or group size, the adult/child ratio, the quality of teaching and the availability of materials and curriculum.
Interaction among children, carers and teachers is probably the key determinant of quality (Young and Richardson, 2007).

Worldwide access to pre-school facilities has been steadily increasing.

Some 139 million children were in ECCE programmes in 2006, up from 112 million in 1999.

The global pre-primary gross enrolment ratio (GER) in 2006 averaged 79% in developed countries and 36% in developing countries (Table 2.1).

Coverage was lowest in sub-Saharan Africa and the Arab States.
Of the thirty-five Box 2.3: Pre-school benefits for equity and efficiency Improved access to pre-school can enhance both education outcomes and equity.

Much of the evidence comes from extensively researched pilot programmes in the United States, where outcomes included higher test scores, better secondary school graduation rates and increased college enrolment.
Two programmes that targeted African-American children provide examples: the Perry Preschool Program was associated with 44% higher pre-school graduation and the Abecedarian Project achieved an increase of one grade in reading and mathematics achievement.

Research from developing countries is more limited but no less compelling: In Argentina pre-school attendance from age 3 to age 5 increased performance in language and mathematics (by 0.23 to 0.33 standard deviation).
Measured through third-grade test scores, the effect was twice as large for students from poor backgrounds.

In Uruguay, pre-school attendance had a positive effect on completed years of schooling, repetition rates and age-grade distortion.

By age 10, children who had attended pre-school had an advantage of about one-third of a year over children who had not attended.

By age 16, they had accumulated 1.1 additional years of schooling and were 27% more likely to be in school.
Household survey data in Cambodia showed that the availability of pre-school facilities increased the probability of successful school completion from 43% to 54%.

The strongest impact was found for remote rural areas and the two poorest income quintiles.

Probability of cohort graduation at Grade 6 increased by 13% for the poorest — almost double the increase for the richest cohort.
A programme in India’s Haryana state resulted in a 46% decline in dropout among lower-caste children, though it did not significantly change the dropout rate for children from higher castes.

Wider evidence from India covering eight states and based on tracking of cohorts found significantly higher rates of retention for children who had been enrolled in ECCE.

Sources: Berlinski et al.

(2006); Nores et al.

(2005); Schweinhart et al.

(2005); UNESCO (2006); Vegas and Petrow (2007).


T H E D A K A R G O A L S : : a l o n g w a y t o g o countries in sub-Saharan Africa for which data are available for 2006, seventeen had coverage rates below 10%.

Out of eighteen Arab States with data, six had coverage rates below 10% and three others below 20%.
Participation in pre-primary education tends to rise with income, although the association is not clear-cut.

Several high-income Arab States have lower coverage than low-income countries including Ghana, Kenya and Nepal.

Looking within regions, the Philippines provides lower levels of pre-primary education access than does Cambodia and Bolivia has a higher pre-school GER than wealthier Colombia (Figure 2.5).

These comparisons underline the importance of public policy choices.
While provision in low-income countries is of course constrained by resource availability, often it is also limited by government neglect – notably with respect to the poor.

Aid donors’ priorities reinforce such neglect: ECCE accounts for just 5% of total aid for education.

This share is hard to justify given the enormous potential benefits of ECCE for primary education goals and the MDGs.
The very low level of provision and high level of need in sub-Saharan Africa in particular suggest a strong case for placing a higher priority on ECCE in education strategies (Jaramillo and Mingat, 2008).

Within-country disparities in pre-school attendance.

There are marked disparities in pre-primary education provision within countries.
Although vulnerable children from poor households stand to benefit most from ECCE interventions that counteract home disadvantage, international evidence points to an inverse relationship between need and provision.

Preliminary analysis of data from the latest round of Multiple Indicator Cluster Surveys (MICS3) for seventeen countries points to large gaps in pre-school attendance, with children who are poor and rural at the bottom of the distribution range (Figure 2.7).
Attendance rates for children from poor households fall far below those for children from wealthy households.

In the Syrian Arab Republic the attendance rate for the wealthiest 20% is five times the level for the poorest 20% (Figure 2.7).

Wealth disparities go beyond attendance indicators.
In Brazil, where the average enrolment rate in pre- school is 29% for the poorest households and above 50% for the richest, children from wealthier homes overwhelmingly attend better-resourced private facilities (Azevedo de Aguiar et al., 2007).

Research in Rio de Janeiro suggests that average spending per child in private pre-schools is twelve times that in government pre-schools.
Table 2.1: Pre-primary enrolment and gross enrolment ratios by region, 1999 and 2006 Total enrolment Gross enrolment ratios School year ending in 1999 2006 Change between 1999 and 2006 (millions) (millions) (%) School year ending in 1999 (%) 2006 (%) Change between 1999 and 2006 (%) World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe 112 80 25 7 5 2 1 37 37 0.4 21 16 1 16 19 9 Note: Change is computed using non-rounded figures.
Source: Annex, Statistical Table 3B.

139 106 26 7 9 3 1 37 36 1 39 20 1 20 20 10 24 32 3 2 73 26 8 -1 - ECCE accounts for just 5% of total aid for education.
This share is hard to justify Notes: The apparent decrease in the United Kingdom is due to the reclassification into primary of some programmes formerly considered as pre- primary.

See source table for detailed country notes.

1.


The GER is 90% or above in forty-one countries or territories: ten in Latin America and the Caribbean, fourteen in Western Europe, nine in East Asia and the Pacific, five in Central and Eastern Europe and three in sub-Saharan Africa.

2.

Change in duration between 1999 and 2006.


Compared to 1999, pre- primary duration is reported to be one year shorter in Mongolia, Nepal, Nicaragua and Slovenia; one year longer in Chile, Costa Rica and the Marshall Islands and two years longer in Guatemala.

Source: Annex, Statistical Table 3B.


5 2 C H A P T E R 2 Figure 2.5: Change in pre-primary gross enrolment ratios between 1999 and 2006 in countries with GERs below 90% in 20061 Sub-Saharan Africa Central Asia Chad C. A. R. Burundi Niger Burkina Faso Togo Ethiopia Mali Comoros Uganda Côte d’Ivoire Sierra Leone Benin Guinea Madagascar Congo Senegal Nigeria Eritrea Botswana Gambia Swaziland Lesotho Cameroon Namibia U. R. Tanzania S. Tome/Principe South Africa Equat.
Guinea Kenya Cape Verde Ghana Yemen Djibouti Mauritania Iraq Oman Libyan A. J. Syrian A. R. Saudi Arabia Algeria Egypt Sudan Palestinian A. T. Jordan Qatar Bahrain Morocco Lebanon Kuwait U.

A. Emirates Arab States Tajikistan Kyrgyzstan Uzbekistan Azerbaijan Armenia Kazakhstan Mongolia 2 Georgia Myanmar Timor-Leste Cambodia Lao PDR Fiji Tonga Vanuatu Indonesia China Philippines Marshall Is 2 Samoa Brunei Daruss.
Palau Kiribati Japan Macao, China Nauru Afghanistan Bhutan Bangladesh Nepal 2 India Pakistan Iran, Isl.

Rep. Maldives East Asia and the Pacific South and West Asia 0 20 40 60 80 100 0 20 40 60 80 100 Gross enrolment ratios (%) Gross enrolment ratios (%) 1999 2006 (increase since 1999) 2006 (decrease since 1999) Stable Rural-urban gaps and other geographic disparities are also marked in many countries.
For example, in Côte d’Ivoire attendance rates range from less than 1% in the remote north-west to 19% in the capital city, Abidjan.

Viet Nam’s Red River delta region, with the country’s highest average income, has a pre-primary attendance rate of 80%, compared with 40% for the Mekong River delta region, which has some of the worst social indicators.

In Bangladesh, slum dwellers are at the bottom end of the distribution for access to ECCE (Figure 2.7).
Factors such as language, ethnicity and religious associations play a part in shaping the distribution as well.
In several countries of the former Soviet Union, attendance rates are higher for Russian T H E D A K A R G O A L S : : a l o n g w a y t o g o Latin America and the Caribbean North America and Western Europe Central and Eastern Europe 2 2 2 2 Guatemala Dominican Rep. Belize Paraguay Honduras Colombia Bolivia El Salvador Nicaragua Chile Venezuela, B. R. Argentina Panama Peru Saint Lucia Brazil Costa Rica Dominica Uruguay Grenada Suriname Trinidad/Tobago St Vincent/Grenad.
Ecuador Finland United States Cyprus Canada Greece Portugal Luxembourg United Kingdom Austria Turkey TFYR Macedonia Albania Croatia Poland Serbia Lithuania Rep. Moldova Romania Slovenia Bulgaria Hungary Russian Fed.

Latvia 2 0 20 40 60 80 100 Gross enrolment ratios (%) Box 2.4: In Egypt, national progress but the poor are being left behind Egypt has embarked on an ambitious programme to expand pre-school provision, focusing on children aged 4 and 5.
The increase in coverage has been impressive, but has not significantly reduced pre-school disparities that threaten to aggravate inequalities at the primary level and beyond.

The GER for pre-primary education increased from 11% in 1999 to 17% in 2007.

However, the 2005–2006 Egypt Household Education Survey revealed that only 4% of children from the poorest 40% of households ever attended pre-school (Figure 2.6).
By contrast, 43% of children from the richest quintile had completed two years in kindergarten.

Two factors stand out as barriers to enhanced and more equitable access.

First, for parents in the poorest three quintiles, lack of access is the most commonly cited reason for not sending children to pre-school.

Second, around one-third of parents in the poorest 40% cite affordability as a major problem.

Achieving greater equity will require public policy action on several fronts.
Providing kindergartens in the poorer districts of cities, small towns and rural areas is an urgent priority.

Removing cost barriers will require either targeted transfers to poor households or free provision, or some combination of both.

Free school meals could provide another incentive: only 10% of 4- and 5-year-olds in kindergarten receive free food at school.

Source: El-Zanaty and Gorin (2007).


Figure 2.6: Percentage of children aged 4 and 5 in Egypt attending kindergarten, by place of residence and wealth quintile, 2005—2006 ) % ( 50 45 40 35 30 25 20 15 10 5 0 By place of residence By wealth quintile Urban Rural Poorest 40% Q1 and Q2 Q3 Q4 Richest 20% Q5 Source: El-Zanaty and Gorin (2007).

speakers.
speakers.

Roma people living in Serbia have participation levels less than one-sixth of those for Serb nationals in pre-school programmes (Figure 2.7).

Why are children from poor households less likely to go to pre-school?

The answer varies by country (see Box 2.4 for one example).

In some cases it is because there are no local facilities.

In others it is because of cost, or because parents believe the quality is inadequate.
Detailed household surveys from Egypt provide an insight into the barriers facing disadvantaged households, highlighting the importance of cost (see Box 2.4).
Figure 2.7: Disparities in pre-school attendance of 3- and 4-year-olds, selected countries, most recent year ) % ( 90 80 70 60 50 40 30 20 10 0 Poorest 20% Richest 20% Average Urban Rural Red River delta North Capital city Russian South Capital city Russian Montenegrin Mekong River delta Serbian Jola Russian Capital city Tribal Capital city Christian Muslim Kazakh Urban slum Uzbek Kyrgyz Fula Bosnian/Muslim Countryside North- west e r i o v I ’ d e t ô C .
g e z r e H / a i n s o B Fula .

R .

A n a i r y S 1 u a s s i B - .

p e R n a c i n m o D i Roma 1.

The Fula markers refer to the ethnic group.

2.

In Kazakhstan, Kyrgyzstan and Uzbekistan, all markers except capital city refer to languages.

3.
3.

In Montenegro and Serbia, markers refer to ethnic groups.

Sources: Bangladesh Bureau of Statistics and UNICEF (2007); Bosnia and Herzegovina Directorate for Economic Planning et al.
(2007); Côte d’Ivoire National Institute of Statistics (2007); Dominican Republic Secretary of State for Economy, Planning and Development (2008); Gambia Bureau of Statistics (2007); Guinea-Bissau Ministry of Economy (2006); Kyrgyz Republic National Statistical Committee and UNICEF (2007); Macedonia State Statistical Office (2007); Mongolia National Statistical Office and UNICEF (2007); Montenegro Statistical Office and Strategic Marketing Research Agency (2006); Serbia Statistical Office and Strategic Marketing Research Agency (2007); Sierra Leone Statistics and UNICEF (2007); Syrian Arab Republic Central Bureau of Statistics (2008); Thailand National Statistical Office (2006); UNICEF and Agency of the Republic of Kazakhstan on Statistics (2007); UNICEF and State Statistical Committee of the Republic of Uzbekistan (2007); Viet Nam General Statistics Office (2006).
There are large disparities in pre-school provision among rich countries Rich countries have a mixed record on equity.

Developing countries are not the only ones struggling to make ECCE more equitable.

There are large disparities in pre-school provision among Organisation for Economic Co-operation and Development (OECD) countries.
While France and Scandinavian countries (except Finland) have achieved near-universal pre-school enrolment, the pre-primary GER of the United States is 61% (see annex, Statistical Table 3A).

And within the United States, the disadvantaged lag behind the national average.
5 4 T H E D A K A R G O A L S : : a l o n g w a y t o g o Unlike most rich countries, the United States has no national standard or regulatory structure for ECCE.

Provision is left to individual states, and both coverage and quality vary widely among and within states.

Federal programmes targeting the poor have a mixed record.
The largest such programme is Head Start, begun in the mid-1960s under President Johnson’s ‘War on Poverty’ legislation as an effort to break the link between poverty and educational disadvantage.

Operated through local agencies, with funds supplied directly by the federal government, it reaches around 11% of children aged 3 and 4.

Eligibility is determined by poverty, but not all eligible children take part, and quality indicators are discouraging (Belfield, 2007).
Compared with other pre-school programmes, Head Start is modest in terms of absolute size and relative impact – a finding that points to its low value added (Haskins, 2008).

While many wider factors drive education inequalities in the United States, disparities in access to good-quality pre- school provision contributes to a persistent school readiness gap between disadvantaged and other children.

That gap widens as children progress through the education system (Magnuson and Waldfogel, 2005) (Box 2.5).
Unlike most rich countries, the United States has no national standard or regulatory structure for ECCE Box 2.5: The equity gap in early childhood provision in the United States Almost one in five American children lives in poverty — twice the OECD average.

One in eight lives in overcrowded housing and one in ten in households lacking health insurance.

This social backdrop is closely related to high levels of inequality in educational outcomes.
Early childhood interventions have the potential to weaken the link between social deprivation and education inequality.

However, current programmes appear to be failing on several counts.

Measured in terms of equity, they are often failing to reach those in greatest need.

In 2008, the American Human Development Report examined inequalities in pre-school enrolment by social group, ethnic background, state and congressional district.
It found a striking disparity between levels of need as reflected in a human development index (HDI) (a composite indicator for health, education and income) and provision: Only 45% of 3- to 5-year-olds in low-income families are enrolled in pre-school, compared with 75% among high-income families.

There are marked ethnic disparities.

The enrolment ratio for Hispanic and Latino children is 45%, compared with 62% for white children.
For the twenty congressional districts with the highest HDI scores, the average pre-school enrolment rate was 76%, compared with 50% in the bottom twenty.

Of the top twenty HDI districts, only two had pre-school enrolment rates below 60%, while only three of the bottom twenty had rates above 60%.

The quality of ECCE programmes is also a cause for concern.

In the absence of a well-defined federal framework and regulatory structure, management and quality control are highly variable.
Another problem is the lack of coherence across programmes covering child poverty and social welfare.

The Committee on Education and Labor of the House of Representatives has identified fragmentation in this area as a major problem in most states, counties and cities.

Another concern is that the overall level of investment under Head Start is around one-third lower per pupil than in the best performing programmes.

Sources: Burd-Sharps et al.


(2008); Haskins (2008); Maghnuson and Waldfogel (2005); UNICEF (2007).
More children are entering primary school, but too many fail to complete the cycle Progress towards UPE: nations at the crossroads Goal 2: Ensuring that by 2015 all children, particularly girls, children in difficult circumstances and those belonging to ethnic minorities, have access to and complete, free and compulsory primary education of good quality.
With only seven years to the target date, will governments fulfil their pledge to achieve UPE by 2015?

Not if they continue on a business-as-usual trajectory.

Some 75 million children of primary school age are still out of school, and their numbers are coming down too slowly and too unevenly to achieve the 2015 target.

The twin challenge is to accelerate increases in access and to strengthen retention so that all children enter school and complete a full primary cycle.
Since its inception the EFA Global Monitoring Reporthas charted progress towards UPE and the wider goals adopted at Dakar.

If there is one central message to emerge from the reporting set out below it is that this is a make-or-break moment for the commitments to achieve UPE by 2015.

Without an urgent drive to get children into school, increase survival and completion rates and strengthen quality, the promise made at Dakar will be broken.
Access and participation: increasing, but a long way to go The numbers of children entering primary school have climbed sharply since Dakar.

In 2006, just over 135 million children stepped through a classroom door for the first time – an increase of about 5 million over the level in 1999.
The developing country gross intake rate (GIR), which registers the number of new entrants regardless of age, has increased by just under eight percentage points over the period, with the Arab States, South and West Asia, and sub-Saharan Africa registering the biggest increases (Table 2.2).

Some regions have seen their intake levels stagnate or even decline, as in East Asia and the Pacific, Latin America, and North America and Western Europe.
This typically reflects a combination of demographic change and a better match between school starting age and progression through the system in countries that started with high GIRs.
Table 2.2: New entrants to grade 1 and gross intake rates by region, 1999 and 2006 New entrants Gross intake rates School year ending in 1999 (000) 2006 (000) World 130 195 135 340 Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe 113 366 12 380 4 449 16 397 6 297 1 795 37 045 36 513 533 40 522 13 176 565 12 612 9 328 5 635 Note: Changes are computed using non-rounded figures.
Source: Annex, Statistical Table 4.
120 589 11 575 3 175 23 230 7 191 1 416 31 830 31 288 542 44 823 13 142 585 12 557 8 932 4 370 Change between 1999 and 2006 (%) 4 6 -6 -29 42 14 -21 -14 -14 2 11 -0.3 4 -0.4 -4 -22 School year ending in 1999 (%) 2006 (%) Change between 1999 and 2006 (percentage points) -0.2 1 22 10 1 -5 -5 -1 13 -0.1 1 -0.2 -0.2 0.3 5 6 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s As intake rates have risen, so has overall enrolment.
Worldwide, some 40 million more children were in primary school in 2006 than in 1999 (Table 2.3).

Sub-Saharan Africa, and South and West Asia accounted for the bulk of the increase, with enrolment in the former increasing by 42% and in the latter by 22%.

Elsewhere, total enrolment fell slightly, owing in part to declining school age populations.
Demographic trends: a key factor in education planning For some regions, slower growth or even contraction of the primary school-age cohort creates an oppor- tunity to increase per capita financing.

For others, continued increases in the primary school-age population mean incremental pressure on financial, physical and human resources.
East Asia and the Pacific will have some 15 million fewer children of primary school age in 2015; in sub-Saharan Africa the cohort will grow by 26 million, and in the Arab States by 4 million (Figure 2.8).

One consequence of such demographic pressure is that governments have to work harder to maintain existing gains.

Sub-Saharan Africa, for example, has to expand participation by over two percentage points a year just to stand still in terms of enrolment ratios.
Table 2.3: Primary enrolment by region, 1991, 1999 and 2006 Figure 2.8: School-age population in 2006 and 2015 as a percentage of school-age population in 1995, by region 126 153 2006 2015 Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.
Europe Central and Eastern Europe 99 108 89 87 93 85 108 109 102 104 77 77 77 72 60 80 100 120 140 160 School-age population as a % of the 1995 school-age population Base year (1995) = 100 Source: UIS database.
Where there is demographic pressure, governments have to work harder to maintain existing gains Total enrolment Gross enrolment ratios Net enrolment ratios School year ending in 1991 1999 2006 Change between 1991 and 1999 Change between 1999 and 2006 School year ending in 1991 1999 2006 (millions) (% per year)1 (%) (%) (%) Change between 1991 and 1999 Change between 1999 and 2006 (percentage points per year) School year ending in 1991 1999 2006 (%) (%) (%) Change between 1991 and 1999 Change between 1999 and 2006 (percentage points per year) World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America/Caribbean Caribbean Latin America N. America/W.
Europe Central/Eastern Europe Average annual growth rate based on compound growth.

Sources: Annex, Statistical Table 5; UIS database.
1.0 1.3 -0.4 -0.9 3.3 1.9 3.1 0.6 0.6 2.3 1.9 -0.9 7.1 -1.1 0.7 -2.3 0.8 1.0 -0.7 -2.8 4.5 1.6 -1.8 -1.5 -1.6 -0.1 2.5 -0.3 -0.4 -0.3 -0.4 - -0.7 -0.7 -0.4 0.2 2.2 5.3 2.1 -0.1 0.5 1.0 1.2 -0.2 -0.9 2.9 1.3 0.3 -0.5 -0.5 -0.8 3.0 -0.6 -0.7 -0.6 -0.2 - -0.1 0.3 0.6 0.3 -0.1 -0.1 0.0 0.6 0.8 2.9 0.8 0.0 0.1 0.6 0.7 -0.2 0.3 2.0 0.9 0.3 -0.3 -0.3 -0.9 1.5 0.2 -0.4 0.3 -0.2 0. Sub-Saharan Africa has made remarkable advances towards UPE Net enrolment ratio: a benchmark for UPE The net enrolment ratio (NER) is one of the most robust instruments for measuring distance from UPE.
It captures the share of primary school age children officially enrolled in school.

Countries that consistently register NERs of around 97% or more have effectively achieved UPE since it means that all children of the appropriate age are in primary school and are likely to complete the cycle.

Post-Dakar progress on NERs has mirrored advances in other areas (Table 2.3).

The NER for developing countries as a group has increased since 1999 at double the average annual rate registered in the 1990s.
This is a remarkable achievement.

Particularly remarkable by recent historical standards has been the progress of sub-Saharan Africa.

During the 1990s the region’s NER increased at an average of 0.3 percentage points a year to 56% at the end of the decade.

In 2006 it stood at 70% – an average annual increase of two percentage points, or six times the rate of the pre-Dakar decade.

South and West Asia also recorded an impressive increase in NER, from 75% to 86%.
The sharp rise of enrolment rates despite rapid population growth reflects the higher priority being attached to primary education in many countries.

These achievements prove that rapid progress towards UPE is possible, even under difficult circumstances.

Several countries in sub-Saharan Africa have registered some particularly impressive progress.

For example, Benin, Madagascar, the United Republic of Tanzania and Zambia have moved since 1999 from NERs of between 50% and 70% to levels in excess of 80%.
Starting from an even lower baseline, Ethiopia has doubled its NER, reaching 71% (Box 2.6).

While the country still has a long way Box 2.6: Ethiopia — moving into the UPE fast lane Ethiopia faces daunting development challenges, including high levels of poverty, chronic malnutrition and recurrent drought.

Yet the country has sustained an impressive push towards UPE.
The push started in 1997 with the adoption of the first Education Sector Development Plan (ESDP I), which prioritized increased access, greater equity and improved quality.

Through the subsequent ESDP II and III, overall enrolment has increased from 3.7 million to 12 million in 2007.

Ethiopia has registered one of the fastest NER increases in sub-Saharan Africa.

It has cut the number of out-of-school children by over 3 million.

Efforts to improve equity have also produced results.
The GER in rural areas increased from 45% to 67% between 2000–2001 and 2004–2005.

Secondary education has expanded too with numbers doubling since ESDP I.

What are the policy factors behind Ethiopia’s success?

The priority attached to education in public spending has increased steadily since 1999: the education budget grew from 3.6% of GNP to 6%.

Within the education budget, more weight has been attached to the primary sector.
It accounts for 55% of spending under ESDP III compared with 46% under ESDP I.

International aid accounts for around 17% of projected spending to 2010.

A key target for increased public spending in education has been rural school construction.

Of the nearly 6,000 schools built since 1997, 85% are in rural areas.

This has reduced distance to school and unlocked demand for education, especially for girls (distance being a significant barrier to girls’ participation in education).
Textbook distribution has improved and contents revised to enhance quality and relevance: schoolbooks are now published in twenty-two local languages.

Much remains to be done if Ethiopia is to achieve the target of UPE by 2015.

Old problems persist — and success has brought new challenges.

Regional variations in access remain wide.

The two predominantly pastoral regions, Afar and Somali, have GERs of less than 20%.

While gender disparities are falling they remain large.
And the country still has more than 3 million children out of school.

The substantial expansion of enrolment has created systemwide pressures.

Instead of going down as planned, the average pupil/teacher ratio (PTR) increased from 42:1 in 1997 to 65:1 in 2006.

A national learning assessment conducted in 2004 recorded no improvement in quality.

Dropout rates remain high, with nearly one in four students leaving school before grade 2.
Households’ contributions to financing are high, both for school construction and recurrent costs, leading to concerns that this could further foster inequality.

Ambitious targets and strategies have been adopted to address these problems.

Goals for 2010 include a GER of 109%, a GPI of the GER at 0.94 and a 64% primary school completion rate.

Classroom construction is being scaled up, with an emphasis on building near marginalized communities in areas with large out-of-school populations.
Financial incentives for girls’ education are being strengthened, with targeted interventions in areas where gender gaps are wide.

Ethiopia envisages recruiting almost 300,000 teachers by 2010 to bring down PTRs while accelerating progress towards UPE.

Sources: Annex, Statistical Table 5; Ethiopia Ministry of Finance and Economic Development (2006, 2007).
5 8 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Nepal has succeeded in increasing enrolment in the face of civil conflict to go to UPE, it has made dramatic advances in improving access and tackling inequalities.
One important factor has been an ambitious school construction programme in rural areas, which has spurred demand by reducing the distance to school and addressing security concerns for girls.

There are other striking success stories.

Amidst a destabilizing civil conflict, Nepal has increased its NER from 65% to 79% since 1999.
Governance reforms involving transfer of resources and authority to local communities and incentives aimed at overcoming gender and caste inequalities played an important role in improving access (Box 2.7).

Among the Arab States, Djibouti, Mauritania, Morocco and Yemen, with the region’s four lowest NERs, have all registered strong progress (Figure 2.9).

The policies behind increases in NER vary by country but some consistent features emerge.
While there are no blueprints, there are some useful guides to good practice.

In several countries across sub- Saharan Africa – including Ethiopia, Kenya, Lesotho, the United Republic of Tanzania and Zambia – the elimination of school fees has propelled enrolment rates upwards.

This was also a factor in Nepal.

Increased public spending and investment in schools, teachers and teaching materials has been critical.
So has an increased focus on equity through measures to remove barriers and create incentives aimed at overcoming disadvantages based on wealth, gender, social standing or caste.

International aid partnerships have played an important role in some of the best performing countries, including Ethiopia, Nepal and the United Republic of Tanzania.

Consistent and predictable financial support for nationally-owned strategies has made an important difference.
The important contribution that aid has made in many countries casts into sharp relief the high costs associated with the collective failure of donors to honour the pledges undertaken at Dakar (see Chapter 4).

Out-of-school children: still a long way to go In 2006 there were 28 million fewer out-of-school children than when governments met in Dakar in 2000.

Viewed against the backdrop of the 1990s, when out of school numbers were rising in some regions, progress has been dramatic.
In sub- Saharan Africa the number of primary school-age Box 2.7: Nepal — also on fast-forward towards UPE In recent years Nepal has registered rapid progress towards UPE.

The NER for 2004 stood at 79% — up from 65% in just five years.

Numbers of children out of school have fallen from 1 million to 700,000.

And survival to grade 5 has increased from 58% to 79%.

The fact that this progress was sustained during a civil conflict that ended only in 2006 points to a remarkable achievement.
Nepal’s experience demonstrates that even the most deeply-rooted problems and inequalities are susceptible to public policies.

Reforms in the following areas have been particularly important: Strengthened local accountability.

In 2001, reforms were initiated to increase school accountability and strengthen community management.
Devolution of authority to district and community level insulated education from a general breakdown in centralized planning and service provision, and from the impact of civil conflict.

Around 13% of public schools have been transferred to school management committees.

Each committee is provided with a start-up grant.

Schools receive salary grants to help them recruit teachers.

A shift to financing linked to enrolment diluted political influence over resource allocation.

Improved equity.
Improved equity.

Reforms have scaled up scholarship programmes for girls, Dalits and disabled children at primary and secondary level.

The number of scholarship recipients increased to 1.7 million and the aim is to reach 7 million by 2009.

Progress towards greater equity is reflected in a shrinking gender gap: the GPI of the primary GER has increased from 0.77 in 1999 to 0.95 in 2006.

And enrolment and survival rates for low-caste groups are increasing.

Infrastructure expansion and a focus on quality.


The country has embarked on an ambitious programme to increase the number of schools and classrooms, expand teacher recruitment and improve the supply of textbooks.

Effective donor support.

Nepal has been at the forefront of efforts to improve donor governance.

Aid harmonization began in 1999, with five donors pooling resources to finance a primary education subsector programme.
Building on this, a sector-wide approach was developed to support the 2004–2009 Education for All Programme.

Its success resulted in a steady increase in the share of pooled aid finance, reducing transaction costs and enhancing predictability.

Sources: Annex, Statistical Tables 5 and 7; Nepal Ministry of Education and Sports (2006); World Bank (2007d).

 Note: See source table for detailed country notes.

1.
1.

The NER exceeded 97% in both years in sixteen countries: nine in Western Europe, three in Latin America and the Caribbean, three in East Asia and the Pacific, and one in South and West Asia.

2.

Countries where the duration of primary education changed between 1999 and 2006.

3.

The increase in the Islamic Republic of Iran is due to the recent inclusion of literacy programmes.

Source: Annex, Statistical Table 5.

2.


The UNESCO Institute for Statistics (UIS) has revised the out-of-school population series using more up-to-date population estimates from the United Nations Population Division.

The revisions show that in 2005 there were 77 million children out of school.
6 0 C H A P T E R 2 Figure 2.9: Change in primary net enrolment ratios in countries with NERs below 97% in 1999 or 20061 Sub-Saharan Africa Central Asia 2 2 2 Liberia Niger Eritrea Burkina Faso Mali Gambia Nigeria Senegal Ethiopia Ghana Guinea Lesotho Kenya Mozambique Namibia Swaziland Togo Benin Botswana Zimbabwe Cape Verde South Africa Malawi Zambia Mauritius Madagascar S. Tome/Principe U. R. Tanzania 2 2 2 Djibouti Oman Yemen Palestinian A. T. Mauritania Lebanon Kuwait U.
A. Emirates Morocco Iraq Jordan Qatar Algeria Egypt Tunisia Bahrain Arab States Azerbaijan Kyrgyzstan Georgia Mongolia Cook Islands Lao PDR Vanuatu Cambodia Samoa Macao, China Fiji Philippines Tonga Australia Rep. of Korea Myanmar Bhutan Nepal Bangladesh Iran, Isl.
Rep. 3 East Asia and the Pacific South and West Asia 20 40 60 80 100 20 40 60 80 100 Net enrolment ratios (%) Net enrolment ratios (%) 1999 2006 (increase since 1999) 2006 (decrease since 1999) Stable children not in school has fallen by 10 million since 1999, while the population in that age bracket has increased by 17 million.

Over the same time-frame, South and West Asia almost halved its out-of- school population, from 37 million to 18 million.
Encouraging as these trends may be there is a long way to go.

Some 75 million children of primary school age are still not in school – and on current trends the 2015 target will not be achieved.2 The circumstances and characteristics of out-of- school children vary.

Over four out of five live in rural areas, mostly in South and West Asia, and sub-Saharan Africa.

The vast majority are poor and many are the victims of a cross-generational transfer of deprivation.
Having a mother with no education doubles the probability of a child’s being out of school (UIS, 2005).

Measured in terms of scale and impact on life chances, the out-of-school problem represents a crucial human development challenge.

More than that, it represents an indictment of national and international policy failures.
In an increasingly knowledge-based global economy, where national and individual prosperity is linked more and more to education, 12% of the developing world’s primary- school-age population is not in school.

In sub- Saharan Africa the share is almost one in three.
T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Latin America and the Caribbean North America and Western Europe Central and Eastern Europe Dominica Dominican Rep. Trinidad/Tobago Bahamas Colombia Nicaragua Jamaica Venezuela, B. R. Brazil Guatemala Br.
Virgin Is Bolivia Barbados Peru Cuba Ecuador Belize Switzerland Malta United States Ireland Sweden Denmark Finland Israel Luxembourg Cyprus Greece Rep. Moldova Hungary Lithuania Latvia Croatia TFYR Macedonia Bulgaria Czech Rep. Romania Albania Estonia Slovenia Poland 20 40 60 80 100 Net enrolment ratios (%) These children are being deprived of the opportunity to get their foot on the first rung of a ladder that could give them the skills and knowledge to climb out of poverty and break the transmission of disadvantage across generations.
While the initial costs are borne most directly by those affected, slow progress in getting children into school has wider and longer-term consequences.

The loss of human potential behind the out-of-school numbers undermines economic growth, deepens social divisions, slows progress in public health, and weakens the foundations for social participation and democracy – and these are costs borne by society as a whole.
Table 2.4: Estimated number of out-of-school children by region, 1999 and 2006 1999 2006 Total (000) % by region % female Total (000) % by region % female World 103 223 100 Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America/Caribbean Caribbean Latin America N. America/W.
Europe Central/Eastern Europe Note: The UIS has revised out-of-school numbers using new United Nations Population Division estimates.
The revisions increased estimates of the number of out-of-school children, so figures for 1999 reported here are higher than those in the 2008 Report (UNESCO, 2007a).

Source: Annex, Statistical Table 5.

The out-of-school population is heavily concentrated geographically (Table 2.4).
With around 19% of the world’s primary school-age population, sub-Saharan Africa accounts for 47% of out-of-school children worldwide – a stark reminder of the scale of global inequalities in the distribution of opportunities for education.

South and West Asia account for a further one-quarter of the out-of-school population.

Within regions there is a heavy concentration by country.
Eight countries have more than 1 million out-of-school children each – and four in ten children not in school live in these countries (Figure 2.10).

The post-1999 record of countries with large out- of-school populations is mixed.

Some have failed to make a dent in the numbers.

This group includes Nigeria – with more out-of-school than any other country – along with Burkina Faso, Mali and the Niger.

Trends in Nigeria are cause for global concern.


The country accounts for around one in nine of the world’s out-of-school children (Box 2.8).

And there is little evidence to suggest that, on current policies, the country is set for an early breakthrough.

In other countries with large out-of-school populations in 1999, the picture is more encouraging.
For example, Bangladesh, Ethiopia, Eight countries have more than 1 million out-of-school children each Box 2.8: Nigeria off track — the price of weak governance Figure 2.10: Number of out-of-school children in selected countries,1 1999 and 2006 Nigeria had 8 million children out of school in 2005 — 23% of the total for sub-Saharan Africa — and is not on track to achieve UPE by 2015.
Its NER increased slowly between 1999 and 2005, from 58% to 63%, well below the regional average.

To change this picture the government will have to renew its commitment to equity by addressing the following inequalities head-on: Wide geographical differences in primary school enrolment.

In the south-west, the average primary NER was 82% in 2006, compared with 42% in the poorer north-west.

Substantial gender gaps in primary school, particularly in the north.
Only 40% of primary school-age girls are enrolled in some northern states, compared with 80% in the south-east.

Major income inequalities in school access.

Children who have never attended primary school come mainly from the poorest households.

In Kaduna state, 48% of girls from the poorest 20% of households have never attended, compared with 14% in the richest quintile.

Low enrolment and attendance rates among disadvantaged groups have many causes.

Cost is a significant barrier for many.


Primary education in Nigeria is supposed to be free, but about half of parents report paying formal or informal fees.

Average education-related costs represent about 12% of average household expenditure, a burden especially great on poor households.

Other demand-side barriers are less tangible.


Cultural attitudes, such as perceptions that girls’ education is of lesser value than that of boys, have a powerful bearing on the distribution of opportunity, especially in the north.

Parents in northern states often prefer schools offering Islamic education, which do not all teach the core subjects of the national curriculum.

Supply-side factors are also important.

Serious quality deficits in education exist across Nigeria.
An assessment of fifth-grade students in 2003 found that only 25% knew the answer to more than a quarter of the test questions in core subjects.

Average class size ranges from 145 pupils in the northern state of Borno to 32 in the southern state of Lagos.

The national ratio of students to core textbooks is 2.3 to 1, the ratio of students to toilets 292 to 1.

A significant proportion of teachers lack the minimum requirement of three years of post-secondary education.
Many have limited mastery of the subjects they teach.

The Nigerian Government has been forthright about the scale of the challenge it faces, calling for ‘nothing less than major renewal of all systems and institutions’ (World Bank, 2008e, p. 1).

Top priorities include improved quality, intensified efforts to recruit and deploy teachers, strengthened budget management and the development of financing mechanisms that can help allocate resources more equitably.
Rapid improvement along these lines will be needed if Nigeria is to achieve UPE by 2015.

Sources: Annex, Statistical Table 5; World Bank (2008e; 2008f).

Nigeria India Pakistan Ethiopia U. R. Tanzania Bangladesh Kenya Iran, Isl.
Rep. Mozambique Yemen Ghana Niger Burkina Faso Morocco Nepal Brazil Philippines Mali Madagascar Senegal Guinea Zambia Iraq Benin 0 3 000 6 000 9 000 Number of out-of-school children (000) 1999 2006 (increase since 1999) 2006 (decrease since 1999) Note: Estimates are for 2006 or the most recent year available.

Data for India and Pakistan for 1999 are not available.

1.

Countries listed had more than 500,000 out-of-school children in 1999 or 2006.

Source: Annex, Statistical Table 5.


Ghana, Kenya, Nepal and the United Republic of Tanzania have all been making rapid progress towards UPE.

The performance of the United Republic of Tanzania is particularly striking.

Since 1999 the country has reduced its out-of-school population from over 3 million to less than 150,000 through policy interventions including the abolition of primary school fees in 2001, increased public investment and measures to enhance education quality (Box 2.9).
This Report’s data on out-of-school children come with some important caveats.

In some countries with large school-age populations (e.g.

China, the Sudan, Uganda), data are not available or publishable for 2006.

Estimates for these countries are an approximation of the real picture.

There are also questions in some cases about the size of the school-age population and the accuracy of administrative data on enrolment.
T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Box 2.9: United Republic of Tanzania — remarkable progress A strong partnership of government, donors and civil society has been instrumental in the rapid improvement in access to and completion of primary education in the United Republic of Tanzania since Dakar.
In 2001 the government abolished primary school fees and launched a programme to simultaneously improve access and quality at the primary level.

The main components of the programme were: Increased spending on education, with a focus on primary education.

Public education spending rose from 3% of GDP in 2000 to 4.5% in 2005.

School construction and rehabilitation through school development grants.

Between 2002 and 2004 some 30,000 new classrooms were built.

Introduction of double shifts.


Splitting shifts made it possible to accommodate the large, rapid enrolment increases after fee abolition.

Recruitment of teachers and upgrading of current staff.

An additional 32,000 primary school teachers were recruited between 2002 and 2004.

Introduction of school capitation grants.

At school level, grants have paid for teaching and learning materials, including textbooks, to help defray school operating expenses and to support teachers’ professional development.
Between 1999 and 2006 the number of out-of-school children of primary school age decreased dramatically, from over 3 million to under 150,000.

The primary NER went from 50% in 1999, before the programme, to 98% in 2006.

Completion rates also improved rapidly, partly due to improved teacher training and increased availability of teaching and learning materials.
With the introduction of school capitation grants, non-salary spending at school level increased from just 4% of the primary education budget to 27% in 2004.

This significantly improved the availability of teaching and learning materials in schools, though pupil/textbook ratios remain high.
Sources: Annex, Statistical Table 5; HakiElimu (2005); United Republic of Tanzania Ministry of Education and Vocational Training (2007); United Republic of Tanzania Research and Analysis Working Group (2007); United Republic of Tanzania Vice President’s Office (2005); World Bank (2005e).
Between 1999 and 2006 the number of out-of-school children fell by over 3 million in the United Republic of Tanzania Expected never to enrol Enrolled but dropped out Expected to enter late Reporting systems themselves often provide an imperfect measure of the out-of-school population.

This is especially true of countries marked by civil conflict or going through post-conflict recovery (Box 2.10) – as in the Democratic Republic of the Congo.
Acknowleging those limitations and omissions, the Report brings together the best of currently available data.

Characteristics of the ‘missing’ schoolchildren ‘Out-of-school children’ is a blanket category with a complex underlying story.

Not all children in the category are in the same position.

Analysis of enrolment data by age suggests that around 31% of the world’s out-of-school population may eventually enrol as late entrants (Figure 2.11).

A further 24% were previously enrolled but dropped out.
This means that nearly half the children currently out of school have never had any formal education and are unlikely to enrol unless new policies and additional incentives are put in place.

Here, too, the regional variations are wide.

In sub- Saharan Africa about two-thirds of the out-of-school population is expected never to enrol.
In South and West Asia a similar share has enrolled but dropped Figure 2.11: Distribution of out-of-school children by school exposure, by region, 2004 World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/ W. Europe Central/Eastern Europe 0 20 40 60 80 100 % of total number of out-of-school children Source: Bruneforth (2008).

out.
out.

In Latin America and in East Asia, the overwhelming majority of out-of-school children have entered late or dropped out early.

As these contrasting experiences suggest, tackling the out- of-school problem requires policy responses that address specific structures of disadvantage.
Box 2.10: Rebuilding statistical capacity in the Democratic Republic of the Congo After many years of conflict, the Democratic Republic of the Congo has begun an important process of political, social and economic reconstruction.

Information on the education system was urgently needed to help decision-makers in planning.
With the help of the African Development Bank, the country revitalized its education management information system so that reliable information can be regularly produced.

After a pilot phase, data collection was extended countrywide in the 2006–2007 school year.

Information was gathered from about 90% of all education institutions and statistical yearbooks were produced.

Data collection and analysis continued in 2007–2008.
While progress has been impressive, a population census is needed so that important education indicators such as enrolment ratios and completion rates can be computed more accurately.

Source: Sauvageot (2008).

Gender also has a bearing on the profile of out-of- school children.

In 2006, girls accounted for 55% of the world’s out-of-school children.

In addition, they are far more likely than boys never to enrol.
Globally, 53% of out-of-school girls have never been to school, compared with 36% of out-of-school boys.

Just over half the girls who were not enrolled in school in 2006, in other words, had never been enrolled and might never go to school without additional incentives.

On the other hand, 25% of girls who are out of school may enter late – a lower share than for boys (38%) – and 22% have dropped out (26% for boys).
Significant regional and national differences characterize each of these areas: Girls’ limited access to school is of particular concern in sub-Saharan Africa, where 72% of those not in school have never been enrolled, compared with 55% for boys.

Dropout seems to be the main reason children are not in school in South and West Asia, with boys particularly affected: 79% of out-of-school boys in the region have dropped out, compared with 53% of girls.
In East Asia and in Latin America, most out-of- school children, but particularly boys, may eventually enrol late: 88% of boys in East Asia and 76% in Latin America, compared with 67% and 71% of girls, respectively.

Girls’ access to school remains a big issue in India, Nigeria and Pakistan.

These countries have very wide gender gaps in the out-of-school Only integrated approaches can remove the structural barriers that keep children out of school 6 4 population profile.
For example, in Nigeria 31% of out-of school boys are unlikely ever to enrol, compared with 69% of out-of-school girls (Figure 2.12).

Similar if somewhat smaller gender differences (about twenty percentage points or more) are found in Burundi, Guinea and Yemen.

If the goal of UPE is to be achieved by 2015, many countries will have to strengthen their focus on out-of-school children.

There is more to UPE than getting children into school: retention, completion and learning outcomes are also critical.
But universal access is the first step.

This is an area in which a strengthened commitment to equity is vital.

Public investment has to be targeted and distributed to bring education of good quality to marginalized populations and rural areas.

For the hardest to reach, free education may not be enough, given the large indirect costs often associated with school attendance: paying for transport, uniforms, books and other items may remain an obstacle.
And clearing the backlog of out-of-school children will require more than education policies.

The majority of those out of school face disadvantages associated with chronic poverty, gender, ethnicity and disability.

Overcoming these disadvantages will require integrated policy approaches aimed at removing the structural barriers that keep children out of school.

The gender profile of out-of-school children highlights areas of great concern for UPE and the 2015 gender parity goals.
The fact that out-of-school girls are far more likely never to enrol underscores the resilience of gender disadvantage.

Given that the social and economic background of out-of-school girls and boys is broadly similar, it would appear that the low social value ascribed to women’s education is at the heart of the problem.

If attitudes are part of the problem, part of the solution has to be changing attitudes – an area where political leadership and public campaigning can make a difference.
That is a long-term project, but in the meantime governments can lower other gender barriers by providing incentives for girls’ education and addressing parental fears for their daughters’ safety by building schools in local communities (as the Ethiopian case above shows) and by providing adequate sanitation.

Projections for 2015 — heading towards a broken promise A year is a long time in politics and seven years is a lifetime when it comes to assessing education scenarios.
Someone looking at the United Republic T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Figure 2.12: Percentage of out-of-school children unlikely to enrol, by gender, 2006 100 Female Male l o r n e o t y l e k i l n u n e r d l i h c l o o h c s - f o - t u o f o % 80 60 40 20 - .
R .

A .

C i t u o b i j D l a p e N Note: Countries are sorted by the highest percentage, independent of gender.

Source: Bruneforth (2008).

of Tanzania in 1999 on the basis of education performance since 1990 would have been justified in acute pessimism.

Projecting trends of that decade to 2015 would have led to the overwhelming conclusion that millions of children would still be out of school and that UPE was a Utopian dream.
Re-running the projection on the basis of data from 1999 would produce a very different set of conclusions – and UPE is now very much within reach.

As the United Republic of Tanzania has demonstrated, governments have the option of altering their course and choosing a different future.

Any projection to 2015 has to start by acknowledging uncertainty and recognizing that change is possible.

Trend-based projections simply draw attention to one possible outcome among many.
They do not define a country’s destiny.

Changes in public policy can dramatically change trends in education.

Any global projection is also highly sensitive to data quality and coverage.

Data constraints mean that projections regarding children out of school can provide only a partial picture.

With these caveats in mind, research updating trend analysis carried out for the 2008 Report has been used to develop an out-of-school projection.
The projection uses data from 1999 through to 2006 to derive out-of-school populations for 2015 on the basis of (i) predicted school-age populations and (ii) total primary net enrolment ratios (TNERs) derived from trend projections (Education Policy and Data Center, 2008a).

Data availability limited the projection of out-of-school children to 134 countries.

While the list is partial, these countries were home to 48 million children, or 64% of the out-of-school population in 2006.
It also includes all but one of the countries in Figure 2.10 with out-of-school populations in excess of 500,000 in 1999 or 2006.

However, countries not covered include the Sudan and the Democratic Republic of the Congo.

The results point in a direction that should set alarm bells ringing for all governments that signed the Dakar Framework for Action (Table 2.5).

Projections for 2015 provide a clear early warning sign of impending deficits.
The main findings are: Some 29 million children will be out of school in the countries covered.

Nigeria will have the largest out-of-school population (7.6 million), followed by Pakistan (3.7 million), Burkina Faso (1.1 million), Ethiopia (1.1 million), the Niger (0.9 million) and Kenya (0.9 million).

Of these children, 20 million (71% of the total) will be in the seventeen countries that had more than 500,000 children out of school in 2006.
Just three of these seventeen countries – Bangladesh, Brazil and India – are on track to achieve TNERs in excess of 97% by 2015.

The projection highlights the very different trends associated with current policies and outcomes in this key country grouping, and the large gap separating weak and strong performers.
The largest projected out-of-school populations are in Nigeria, followed by Pakistan, Burkina Faso, Ethiopia, the Niger and Kenya Table 2.5: Projections of out-of-school populations in 2015 for countries with more than 500,000 children out of school in 2006 TNER for latest year (2004–2007) Children out-of-school in 2004–2007 (000) Projected TNER (2015) Estimated out- of-school children in 2015 (000) Average annual change in out-of- school population (%) On track to achieve UPE in 2015 India Bangladesh Brazil 94 92 96 Not on track to achieve UPE in 2015 Nigeria Pakistan Burkina Faso Ethiopia Philippines Niger Kenya Ghana Turkey Mali Mozambique Yemen Iraq Senegal 65 66 48 72 92 44 76 65 91 61 76 75 89 72 Subtotal Remaining 117 countries included in projection Total – – – – – – 626 322 248 7 605 3 707 1 062 1 053 919 873 859 712 710 628 289 265 246 228 20 352 8 341 28 693 -24 -15 -9 -1 -7 -1 -13 -0.4 -4 -5 -3 -0.3 -3 -12 -13 -8 -9 – – – Notes: Countries are included if available information indicates they had out-of-school populations of over 500,000 in 2006.
Countries are ranked according to the size of their estimated out-of-school populations in 2015.

See Annex, Statistical Table 5, for detailed country notes.

Sources: TNER projections: Education Policy and Data Center (2008a); population projections: UIS database.

Another three of these seventeen countries – Ethiopia, Mozambique and Yemen – also perform strongly in terms of projected percentage declines in out-of-school populations, with annual declines of over 10%.
However these countries will not achieve the 2015 target without increased effort.

The remaining eleven out of seventeen countries reduce out-of-school numbers by less than 10% annually and will miss the 2015 target.

What of countries not covered in the projection?

In terms of population, the major absent players are China, the Democratic Republic of the Congo and the Sudan.

China is well placed to ensure that all children are in school by 2015.
Prospects for the two others are less certain, but hardly encouraging.

The Democratic Republic of the Congo had about 10 million children of primary school age in 2005.
Using the latest information on primary school enrolment, a conservative estimate would put the number out of school at 3.5 million.3 The limited data on enrolment expansion between 1999 and 2003 suggest that progress has been slow and the country is unlikely to meet the 2015 goal.4 A similar pattern emerges in the Sudan, which in 2005 had around 6 million children of primary school age.

Extrapolation from GERs would suggest that about 2 million of these children were out of school.
While the Sudan has made steady progress since 1999 the country is not on course to enrol all primary school aged children by 2015.

Without additional effort approximately 1.3 million children would still be out of school by 2015.5 In both the Democratic Republic of the Congo and the Sudan, three of the most vital requirements for changing the current trajectory are peace, stability and reconstruction.

3.
3.

These figures are intended to demonstrate the likely magnitude of the out-of-school population and are not as precise as those in Table 2.5.

They are calculated using GERs, which underestimate the out-of-school population of primary school age because GERs include enrolled children outside the official age range.

4.

Primary gross enrolment ratios increased from 48% to 61% between 1999 and 2003.

5.

Between 1999 and 2006 the GER in the Sudan rose from 49% to 66%.
Estimates for 2015 are based on a linear projection of all GER information between 1999 and 2006.

6 6 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Out-of-school trends and the projections to 2015 highlight once again the importance of public policies.
On average, the percentage of children out of school in developing countries is inversely related to income: as wealth rises, the incidence of children not in school declines.

But income is not an absolute constraint.

Nigeria is far wealthier than Ethiopia and has access to large revenue flows from oil exports, yet Ethiopia is greatly outperforming Nigeria in progress towards UPE and reduction of out-of-school numbers.
Similarly, Pakistan is wealthier than the United Republic of Tanzania or Nepal, yet its slow progress towards UPE will leave it second only to Nigeria in terms of projected out-of-school population in 2015.

While the underlying causes of variable performance are complex, governance figures prominently.

Ethiopia, Nepal and the United Republic of Tanzania have increased overall investment in education and strengthened their commitment to equity.
Nigeria and Pakistan combine weak governance with high levels of inequity in finance and provision (see Chapter 3).

Progression through school: dropout, repetition and low survival rates Getting children into school is a necessary condition for achieving UPE, but not a sufficient one.

What counts is completion of a full cycle.


Depending on the length of the primary or basic education cycle, this means all children must be in school by around 2009 at an appropriate age, and progress smoothly through the system, to make the 2015 goal.

Even getting within range of this objective will require rapid and far-reaching change.

In many developing countries smooth progression through the primary school system is the exception rather than the rule.
Students are locked into cycles of repetition and dropout.

The cycles are mutually reinforcing because repetition is often a prelude to dropout.

Tracking cohorts through primary school serves to demonstrate the scale of the problem (Figure 2.13).

Take the case of Malawi.

Just over 60% of children enter primary school at the official age.
Around half of these drop out or repeat grade 1 Figure 2.13: Primary school progression without repetition or dropout, selected countries, 2006 In many developing countries, students are locked into cycles of repetition and dropout ) % ( t u o p o r d r o n o i t i t e p e r t u o h t i w 100 80 60 40 20 0 94 Cuba 59 Pakistan 56 U. R. Tanzania 27 Dominican Rep. 24 Ethiopia 23 Cambodia 19 Benin 18 Lao PDR 10 Madagascar 8 Mali 7 Malawi Net intake rate Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Notes: Primary school progression is calculated using net intake and grade-specific drop-out and repetition rates.
Source: Annex, Statistical Tables 4, 6 and 7 Grade repetition is costly, and a source of inefficiency and inequity and only 7% progress smoothly to grade 5.
Country patterns for progression through the primary school system vary: some countries, including Cambodia, the Lao People’s Democratic Republic and Madagascar, follow the Malawi model in registering very high levels of interruption in the early grades.

In others, among them Benin, the pattern is more uniform, with disruption occurring on a more regular basis through the system.

At the other end of the scale, 94% of Cuban children progress smoothly through the system.
Cohort tracking is an important tool because it can help policy-makers to identify stress points in the primary cycle.

Figure 2.14: Percentage of pupils relative to the official primary-school age group, most recent year Haiti Cambodia Ethiopia Mozambique Uganda Malawi U. R. Tanzania Rwanda Nepal Madagascar Ghana Kenya Dominican Rep.
Guinea Cameroon Benin Lesotho Zambia Nigeria Nicaragua D. R. Congo Mali Senegal Honduras Burkina Faso Namibia India Niger Zimbabwe Colombia Armenia Peru Philippines Rep. Moldova Egypt 40 20 0 20 40 60 80 100 Share of primary school pupils (%) Under age Expected age for grade One year over age Two or more years over age Source: Demographic and Health Surveys, calculations by Education Policy and Data Center (2008b).

6 8 High repetition rates are endemic in many countries.
Educationists are divided on approaches to repetition.

Some see it as a necessary device for improved learning and greater resilience at higher grades.

Others see grade repetition as an over-used tool with limited education benefits.

Much depends on national and local education contexts.

But it is clear that high levels of repetition are a major barrier to UPE.

Of the countries in sub-Saharan Africa with data available, eleven have grade 1 and nine have grade 2 repetition rates over 20%.
In Burundi and Cameroon repetition rates in grade 1 exceed 30%.

Several countries in Latin America and the Caribbean have repetition rates in grade 1 above 10%, including Brazil, El Salvador, Guatemala, Honduras and Nicaragua.

In South and West Asia, grade 1 repetition rates are below 10% for all countries except Nepal, where they exceed 30% (Annex, Statistical Table 6).

Apart from its damaging consequences for UPE, grade repetition is a source of inefficiency and inequity.
Efficiency losses are associated with the costs of repetition.

The financing required to provide additional school places for repeaters can be substantial.

Repetition consumes an estimated 12% of the education budget in Mozambique and 16% in Burundi (UIS, 2007).

High costs are also reported for other regions.

Governments in Latin America and the Caribbean spend an estimated US$12 billion annually as a result of grade repetition (UN Economic Commission for Latin America and the Caribbean, 2007).
Repetition is a source of inequity: it imposes an increased burden on households in terms of direct financial costs and opportunity costs.

As the burden is heaviest for the poorest households, it is more likely to result in dropout.

Late school entry and grade repetition means that only a small proportion of children actually attend the appropriate class for their age in many developing countries (Figure 2.14).
Household surveys in thirty-five countries demonstrate the point: for many countries – including Cambodia, Ethiopia, Ghana, Honduras, Mozambique and the United Republic of Tanzania – over 60% of children in primary school are over the expected age for their grade.

The presence of over-age children tends to increase by grade as repetition’s negative effects are strengthened.

At the other end of the spectrum, many countries have a large number of under-age children in primary school.
They represent more than 20% of primary school pupils in Egypt, India, Nicaragua, the Niger and Peru.

T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Why do age profiles for primary school matter?

Figure 2.15 provides part of the answer: it shows that over-age children are far less likely to survive through to grade 9.
In countries such as India, Mozambique, Peru and the Philippines, being two years over age more than halves the chances of survival.

For the thirty-five countries examined, the pattern confirms a well-established trend: over-age children are far more likely to drop out, especially in the later grades.

Less attention has been paid in policy debate to under-age children.

The survey evidence suggests this may be a mistake.
In many countries, under- age children are far more likely to repeat early grades – an outcome with important implications for class size and education quality.

In Cameroon and Uganda, under-age pupils represent a large share of grade 1 pupils and have high repetition rates.

Under-age pupils account for the bulk of repeaters in countries with low repetition rates, including India, the Niger and Nigeria (Figure 2.16).
The overall pattern to emerge from monitoring evidence is that being over age strongly predisposes children to drop out, while being under age makes repetition more likely.

The prevalence of under-age children in many countries has important implications for education planning.

It suggests that, in many countries, parents are using the first primary grade to make up for inadequate pre-school provision.
Expanding pre-school participation in such cases could reduce repetition in the early primary grades, with important efficiency and equity benefits.
Under-age children tend to repeat, while over-age children tend to drop out Figure 2.15: Survival rates to grade 9 for three age groups: expected age for grade, one year over age and two or more years over age, most recent year Figure 2.16: Under-age pupils as a percentage of grade 1 enrolment, total repetition rate and under-age repetition rate for grade 1, most recent year Rep. Moldova Ghana Guinea Egypt Namibia Cambodia Dominican Rep. D. R. Congo Nepal Madagascar Benin Mali Nigeria Peru Cameroon Philippines Senegal Honduras India Colombia Mozambique Uganda Ethiopia Kenya Lesotho Zimbabwe Nicaragua Zambia Niger 0 20 40 60 80 100 Survival rate (%) Expected age for grade Two or more years over age One year over age Peru Colombia Nicaragua Zambia Senegal Burkina Faso Zimbabwe Guinea Mali Niger Ghana Benin Dominican Rep. D. R. Congo Philippines Ethiopia Nigeria India U. R. Tanzania Rwanda Cameroon Kenya Malawi Mozambique Madagascar Haiti Uganda Lesotho Nepal Cambodia Under-age pupils as a % of grade 1 pupils Under-age grade 1 repetition rate Total grade 1 repetition rate 0 20 40 (%) 60 80 Source: Demographic and Health Surveys, calculations by Education Policy and Data Center (2008b).


 High dropout rates for over-age pupils point to a wider set of policy problems.

Some are linked to education quality: dropout is more likely when children fail grades.

Non-school factors are also important.


In higher grades over-age pupils may face growing pressure to get a job, to take over household work or, in the case of girls, to marry.

To the extent that such pressure is linked to poverty, social protection programmes and financial incentives to keep children in school can make a difference.

Mixed patterns of access and survival The rate of progress to UPE is a function of advances – or setbacks – on two fronts: enrolment and completion.
Enrolment matters for a very obvious reason: being in school is a requirement for receiving a primary education.

But getting through a full cycle of primary education is a necessary, though far from sufficient, condition for achieving the level of learning needed to equip children with the skills they need.

The relationship between enrolment and completion is not clear cut.

Figure 2.17 illustrates four broad patterns that can be identified using international data.
It locates countries on the basis of their NER and survival rates to the last grade of primary school.

1) Low enrolment, low survival:this group has the furthest to travel to UPE.

It comprises twenty countries, all but three of them in sub-Saharan Africa.

school-age children were out of school in 2005.

Of those in school only around one-third make it through to the last grade.

To varying degrees, countries in group 3 have succeeded in raising NER levels but face problems in survival.
For example, Madagascar, Malawi and Nicaragua have achieved NERs of 90% and above, but fewer than half of those who enrol survive to the last grade of primary school.

Experience since Dakar powerfully demonstrates that past trends do not dictate destiny.

Some countries have moved a long way since 1999, as the experiences of Burundi, Ethiopia, Mozambique and the United Republic of Tanzania demonstrate (Figure 2.18).

Each has dramatically increased primary net enrolment.
Performance in improving survival to the last grade has been more mixed, with limited progress in Ethiopia and Mozambique but more striking advances in Burundi and, from a higher starting point, the United Republic of Tanzania.

The experience of Nepal is also encouraging.

Unfortunately, experience since 1999 also demonstrates that less favourable outcomes are possible.

As Figure 2.18 shows, Malawi rapidly increased NER levels in the 1990s, then failed to improve survival rates.
The Philippines has sustained high enrolment levels but experienced a decline in survival.

Madagascar has registered dramatic progress towards universal enrolment with an equally dramatic decline in survival to the last grade.

2) Low enrolment, high survival:only a small group of countries fit into this category.

This group includes Kenya and the Palestinian Autonomous Territories.
3) High enrolment, low survival:this category covers twenty-one countries, from Malawi in sub-Saharan Africa to Nicaragua and Guatemala in Latin America and Cambodia and the Philippines in East Asia.

In one important respect Figure 2.18 understates the distance to UPE.

Survival to the last grade is not the same as completion of the last grade.

Many children reaching the final grade prove unable to negotiate the last hurdle.
In Burundi, Mauritania, Nepal and Senegal, for instance, only about half the children who survive to the last grade actually complete it (see annex, Statistical Table 7).

What that means in Senegal is that only 30% of primary school-age children complete the full primary cycle.

4) High enrolment, high survival:this group includes a diverse array of countries that have achieved or are close to achieving UPE.
The global gulf in educational opportunity Countries in groups 1 and 3 face overlapping but distinctive challenges.

For group 1, the twin priority is to increase enrolment rapidly while improving retention levels.

In Rwanda, one in five primary Progress towards UPE should not deflect attention from the vast disparities in opportunity that divide rich and poor nations.

If UPE is a first rung on the ladder, progression up the ladder is heavily influenced not by innate ability but by where a child is born.
A full cycle of primary education is essential for equipping children with the skills they need 7 0 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Figure 2.17: Primary net enrolment ratios and survival rates to the last grade of primary education, 2005 and 20061 Group 3: High enrolment, low survival Group 4: High enrolment, high survival 100 90 80 70 60 50 40 ) % ( s o i t a r l t n e m o r n e t e N 30 20 Madagascar Myanmar Panama Argentina S. Tome/Principe Ecuador Guatemala El Salvador U. R. Tanzania Honduras Indonesia Brazil Uruguay Aruba Mexico Belize Saint Lucia Tunisia Peru Bolivia Tonga Algeria Paraguay Qatar Bermuda Fiji Barbados Brunei Daruss.
Anguilla Malaysia Bahrain Rep. of Korea Cuba Mauritius Egypt Venezuela, B. R. Jordan Colombia Bahamas Cape Verde U.
A. Emirates Malawi Nicaragua Cambodia Philippines Bangladesh Iraq Zambia India Morocco South Africa Lao PDR Botswana Trinidad/Tobago Kuwait Rwanda Mozambique Mauritania Dominican Rep. Yemen Benin Togo Swaziland Ethiopia Lesotho Senegal Lebanon Bhutan Dominica Kenya Palestinian A. T. Oman Nepal Namibia Burundi Guinea Pakistan Nigeria Mali Group 3 Group 4 C. A. R. Niger Burkina Faso Eritrea East Asia South/West Asia 80 Sub-Saharan Africa ) % ( s o i t a r l t n e m o r n e t e N Central/ Eastern Europe Latin America/ Caribbean Central Asia Arab States N. America/ W. Europe Group 1: Low enrolment, low survival Group 1 80 Group 2 Survival rates to last grade (%) Group 2: Low enrolment, high survival 30 40 50 60 70 80 90 100 Survival rates to last grade (%) 1.
Survival rates are for 2005, net enrolment ratios for 2006.

Source: Annex, Statistical Tables 5 and 7.

Consider the education prospects of an average child born in a developed country compared with those for a child born in selected developing countries shown in Figure 2.19.

These two children are moving on very different tracks.
While one transits smoothly from primary to secondary school with a strong chance of reaching tertiary education, the other is marked by high levels of attrition from primary school on.

Simple enrolment and school attendance data do not capture the full extent of the resulting inequalities.
But attainment rates and cohort completion data can be used to measure the opportunity gap that divides children in some of the world’s richest and poorest countries: In rich countries such as Canada and Japan, over half the population aged 25 to 34 reaches university level.

Some 40% to 50% of the children in poor countries such as Bangladesh and Guatemala will not even complete primary school.
In rich countries, such as Canada and Japan, over half the population aged 25 to 34 reaches university level Figure 2.18: Changes in net enrolment ratios and survival rates in primary education, selected countries1 100 90 80 70 60 50 40 ) % ( s o i t a r l t n e m o r n e t e N 30 20 Madagascar Malawi Nicaragua Cambodia U. R. Tanzania Indonesia Zambia Guatemala Philippines Iraq Mauritania Mozambique Nepal Bhutan Burundi Ethiopia Burkina Faso Eritrea 30 40 50 60 70 80 90 100 Survival rates to last grade (%) Start year End year 1.
The start year is 1999 for all countries except Burundi and the United Republic of Tanzania, where it is 2000.

The end year is 2005 for survival rates and 2006 for net enrolment ratios.

Source: Annex, Statistical Tables 5 and 7.

Children in France are twice as likely to enter tertiary education as children in Benin or the Niger are to complete primary school.

in the level of provision, state of infrastructure and learning outcomes would reveal much larger inequalities.
Children in the United Kingdom have a greater probability of entering tertiary education than their counterparts in countries such as Mozambique, Senegal or Uganda have of completing primary education.

There are limitations to the use of probability indicators for measuring disparity.

One limitation is that they heavily understate the scale of the problem, as they measure only quantitative gaps.
Introducing quality-adjusted indicators that factor Disparities within countries: a barrier to EFA Children do not select the wealth of the households they are born into, or choose their race, language, ethnic group or gender.

Yet these predetermined circumstances powerfully influence the distribution of opportunity for education within countries.
In the Dakar Framework countries pledged to try to reach the most disadvantaged and equalize T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s opportunity.

Underpinning this commitment to equity are two central ideas: first, that equal opportunity is a hallmark of fairness and social justice; and, second, that equity is central to avoiding deprivation and abject poverty.
Greater equity in education, valuable for its own sake, also matters because unequal opportunity in education is linked to the transmission of inequalities and deprivation in other areas, including health, employment, gender disparities and participation in society.

Many countries with high levels of absolute deprivation in education are also marked by extraordinary inequalities of opportunity.

This subsection charts the scale of those inequalities in primary education.
The picture it provides is partial and limited in important respects.

Because cross- country data from Demographic and Health Surveys provide comprehensive information on inequalities linked to wealth, this domain is highlighted.

Economic inequalities, however, are only part of the story.

They operate alongside, and intersect with, inequalities based on other inherited characteristics which play a role in predetermining life chances.

Why does inequality matter?


Unequal opportunities in education, especially those of an extreme nature, are problematic for at least three reasons.

First, they are intrinsically unfair.

They run counter to basic precepts about what a socially just society should look like – and they violate the idea of education as a basic, universal human right.

Second, inequalities in education are undermining progress towards Education for All and the specific goal of UPE by 2015.
Third, and apart from considerations of fairness, equity and compliance with global development commitments, extreme inequalities in education are inefficient.

They contribute to reduced opportunities for social and economic progress in many areas, as underlined in Chapter 1.

In short, overcoming inequality in education is not just the right thing to do, it is also the smart thing to do.

Wealth-based inequalities: one country, several worlds When it comes to UPE, rich and poor live in different worlds.
National data reveal the average distance a country must travel to achieve universal primary education, but averages conceal large wealth-based disparities.

In many of the world’s poorest countries the richest households already enjoy UPE while the poor lag far behind.
Figure 2.19: Opportunity gaps: population reaching tertiary education in OECD countries and chances of primary school completion in developing countries Population that has attained tertiary education (ISCED 5A+B+6) as % of age 25-34 Canada Japan Rep. of Korea Norway Denmark France United States Australia Iceland Netherlands United Kingdom Share of children who will complete primary education1 Morocco Lebanon Kuwait Bangladesh Pakistan Guatemala El Salvador U.
A. Emirates Namibia Lao PDR Philippines2 Guinea Togo Malawi2 Madagascar Botswana Mozambique2 Nicaragua Senegal Benin Uganda2 Niger Rwanda Burundi Mauritania ) % ( n o i t a l u p o p t n a v e l e r e h t f o e r a h S 60 50 40 30 20 10 0 Sources: Annex, Statistical Tables 4, 7 and 8; OECD (2007a); UIS database.

Children in France are twice as likely to enter tertiary education as children in Benin or the Niger are to complete primary school 1.
The share of children who will complete primary education is calculated by combining net intake and cohort completion rates (or survival to last grade).

2.

Data for these countries refer to the survival rate to the last grade of primary school, which will always be greater than or equal to the completion rate 6.
Attendance rates recorded in household surveys are used to look at participation in school, and attainment levels to look at levels of education among people likely to have completed their education.

Households are ranked using an index of household assets, then grouped into quintiles according to their level of wealth.
C H A P T E R 2 Figure 2.20 traces the border separating the worlds of rich and poor using household survey data for primary school attendance.6 While their nations may have a long way to go to UPE, the wealthiest 20% in countries such as Bangladesh, Ghana, India and Nigeria do not: most have already arrived.

The wealth gaps reflected in attendance data are often very large.
In Bolivia, Burkina Faso, Chad, Ethiopia, Mali and the Niger, children from the richest 20% are two to three times more likely to attend school than children from the poorest quintile.

A striking feature to emerge from the data is that, irrespective of the overall wealth position of their country, children born into the richest quintile in all of these countries have similar attendance and attainment rates.
For example, attendance rates for the richest quintile in India and Nigeria are the same, even though Nigeria’s average attendance rate is far lower.

Patterns of inequality are conditioned by attendance levels (Figure 2.21).

Disparities tend to be far larger in countries with low average attendance rates, for statistical reasons.

As countries progress towards 100% attendance at the top end of the distribution, any average increase in attendance narrows inequalities and produces convergence by definition.
Thus, attendance inequalities are far higher in Côte d’Ivoire, for instance, than in Uganda.

However, the relationship is not uniform.

There are some marked differences between countries at a considerable distance from UPE.

To take one example, Nigeria has far wider inequalities in attendance than Senegal, despite having higher average attendance rates.

This is an outcome that points to problems of extreme marginalization.
The poorest 20% in Nigeria have attendance levels far below those that might be predicted given the national average attendance rate – an indication that some groups or regions are being left far behind.

One word of caution has to be applied to wealth- based cross-country comparisons.

The poorest 20% denotes a position in the national distribution and not a common level of income.

The poorest 20% in, say, Viet Nam, have higher levels of average income than the poorest 20% in Burkina Faso.
The incidence and depth of poverty within the Figure 2.20: Primary net attendance and primary attainment rates for poorest and richest, selected countries, most recent year 100 80 60 40 20 ) % ( Nicaragua Madagascar Indonesia Rwanda Bangladesh Malawi Bangladesh Niger Benin Indonesia Nigeria India Ethiopia Ghana Burkina Faso India Nigeria Poorest 20% Richest 20% Rwanda Malawi Madagascar Nicaraga Benin Ghana Niger Ethiopia Burkina Faso 20 40 60 80 100 Primary education attainment for ages 17 to 22 (%) Source: Demographic and Health Surveys, calculations by Harttgen et al.
(2008).

7 4 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s poorest 20% also varies with average income and income distribution.

Nevertheless, cross-country comparison raises some important questions.

Why, for example, does Côte d’Ivoire have a lower attendance rate and higher inequality in attendance than Mozambique despite having a higher average income and lower level of poverty?
At the other end of the scale, why does Viet Nam (average income PPP$202) register higher attendance and greater equity than the Philippines (average income PPP$352)?

The answers would require detailed cross-country analysis.

But the disparities draw attention to the fact that the national income does not dictate education outcomes and that public policy plays a role in shaping the distribution of opportunity.
The most immediate reason that inequality matters for the Dakar target of achieving UPE by 2015 is that the distribution of children not attending school is skewed towards the poor.

Table 2.6 illustrates this point for thirty-five countries.

The corollary of a higher non- attendance incidence among the poor is that, other things being equal, progress in raising school attendance among the poor has a greater impact on national attendance than progress among other groups.
The UPE arithmetic in favour of greater equity is most starkly apparent for countries at higher attendance levels.

Table 2.6 includes data for eighteen countries with average attendance equal to or over 80%.

For many of them – including Cameroon, Colombia, Indonesia, Madagascar, Kenya, Nepal, Nicaragua, the Philippines and Uganda – the share of out-of-school children from the poorest quintile is above 40%, rising to 51% in Indonesia and 60% in Viet Nam.
Reaching UPE in these countries will require the development of policies targeting the very poor.

This is a population that generally includes many hard- to-reach households – in remote rural areas, for instance, and urban slums – facing multiple disadvantages, including chronic poverty, high mortality, and poor health and nutritional status.

For countries that are further from universal primary attendance, the concentration of disadvantage is less marked – but still significant.
In all seventeen countries with average attendance rates below 80%, the poorest quintile still accounts for a disproportionate share of non-attendance.

Conversely, in none of these countries does the richest quintile account for more than 10% of non-attendance.

In several countries with very high non-attendance levels – including Burkina Faso, Ethiopia and Mali – the problem is broadly distributed across the four bottom quintiles.

However, this does not imply that equity is unimportant.
The poorest quintile accounts for 30% to 40% of non-attendance in many countries with low overall attendance, including Cambodia, Ghana, India, Mozambique, Nigeria and Zambia.

The challenge here is to increase participation across society but with a strengthened focus on the poorest groups.
This has potential implications Figure 2.21: Primary net attendance rates by wealth quintile, selected countries, most recent year Both national income and public policy shape the distribution of education opportunity Burkina Faso Burkina Faso Burkina Faso Ethiopia Ethiopia Ethiopia Mali Mali Mali Chad Chad Chad Niger Niger Niger Côte d'Ivoire Côte d'Ivoire Côte d'Ivoire Guinea Guinea Guinea Ghana Ghana Ghana Senegal Senegal Senegal Mozambique Mozambique Mozambique Benin Benin Benin Zambia Zambia Zambia Nigeria Nigeria Nigeria Cambodia Cambodia Cambodia U. R. Tanzania U. R. Tanzania U. R. Tanzania India India India Malawi Malawi Malawi Madagascar Madagascar Madagascar Nicaragua Nicaragua Nicaragua Haiti Haiti Haiti Namibia Namibia Namibia Cameroon Cameroon Cameroon Uganda Uganda Uganda Bangladesh Bangladesh Bangladesh Rwanda Rwanda Rwanda Nepal Nepal Nepal Kenya Kenya Kenya Zimbabwe Zimbabwe Zimbabwe Dominican Rep. Dominican Rep. Dominican Rep. Philippines Philippines Philippines Colombia Colombia Colombia Indonesia Indonesia Indonesia Peru Peru Peru Viet Nam Viet Nam Viet Nam 0 20 40 60 80 100 Net attendance rates in primary education (%) Q1 (poorest) Q5 (richest) Q2 Average Q3 Q4 Source: Demographic and Health Surveys, calculations by Harttgen et al.
(2008).
Table 2.6: Distribution across wealth quintiles of children not attending primary school Country Survey year Net attendance rate (%) % of primary school-age group not attending Distribution of those not attending primary school (%) Q1 poorest quintile Q2 Q3 Q4 Q5 richest quintile High primary school attendance (NAR greater than or equal to 80%) Viet Nam Peru Indonesia Colombia Philippines Dominican Rep. Zimbabwe Kenya Nepal Rwanda Bangladesh Uganda Cameroon Guatemala Namibia Haiti Nicaragua Madagascar Low primary school attendance (NAR less than 80%) Malawi India U. R. Tanzania Cambodia Nigeria Zambia Benin Mozambique Senegal Ghana Guinea Côte d’Ivoire Niger Chad Mali Ethiopia Burkina Faso Source: Data for calculations from Harttgen et al.
(2008).

See Filmer and Pritchett (1999) for a similar analysis of attainment rates.

for financing and planning.

It cannot be assumed that the future marginal costs of reaching children from the poorest households will reflect the past average costs of getting children into school – nor that business-as-usual policy design will suffice.

New incentive structures, and stronger integration of education into wider strategies for reducing poverty and inequality, may be required.
Box 2.11 outlines key lessons learned from countries that have moved strongly towards UPE.

Household wealth also has a marked bearing on how far children progress in education.

Grade survival indicators provide insight into the ways inequalities constrain progress towards UPE.

In looking at countries with low survival rates, two broad patterns can be identified (Figure 2.22).
For those with low attendance such as Senegal, gaps between wealth groups tend to remain relatively constant as children progress through the primary cycle.

This implies that dropout rates are not markedly widening inequalities.
Countries including Chad, Ethiopia, Mali and the Niger T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Box 2.11: Achieving UPE — lessons from strong performers There is no blueprint for accelerating progress towards UPE.

Countries have differing problems and constraints — and differing financial, institutional and human resources.
Blueprints in any case are no substitute for practical policies.

Still, five broad thematic lessons can be drawn from the experience of strong performers.

Set ambitious targets — and back them with strong political commitment and effective planning.

Political leadership is vital in placing education squarely at the centre of the national policy agenda and the international aid agenda.

Successful governments have fixed ambitious long-term goals supported by clear medium-term ‘stepping stone’ targets.
They have underpinned the targets with strengthened pubic spending commitments and a predictable budget framework.

Realistic planning requires targets to be reflected in resource allocation decisions and linked to policies for classroom construction, teacher recruitment, textbook provision and other factors.

Get serious about equity.

Disparities in education are holding back progress towards UPE.
Ensuring that all children participate in education advancement requires practical measures to overcome structural inequalities.

Reducing the cost of education for poor households by abolishing fees and wider charges is one strategy for enhancing equity.

Another is creating financial incentives for the education of girls and children from disadvantaged backgrounds.
More equitable public spending patterns are also critical, to ensure that schools, teachers and resources are skewed towards those with greatest need rather than those with the greatest wealth.

Raise quality while expanding access.

Improving the quality of education is one of the most effective strategies for strengthening demand.

Enhanced quality requires a focus on smooth progression and learning outcomes, rather than pupil headcounts.
Increasing textbook supply and quality, strengthening teacher training and support, and ensuring that class size is conducive to learning and that children are taught in an appropriate language are key elements in raising quality.

Strengthen wider anti-poverty commitments.

More efficient and more equitable school systems can only do so much if wider structures perpetuate disadvantage.
Eradicating child malnutrition and strengthening public health systems are conditions for accelerated progress towards UPE.

Social welfare programmes and cash transfers can shield poor households from economic pressures that force children out of school and into labour markets.

Develop an agenda for equitable governance.

‘Good governance’ is an imperative that goes beyond UPE.
While developing more accountable, transparent and participative education systems is important in its own right, successful governments have also strengthened governance more generally and addressed equity concerns.

Ensuring that decentralization does not widen disparities in finance requires a commitment to redistributive public spending.

It is now clear that devolving authority does not automatically strengthen equity or participation by the poor — and, in fact, can weaken them.
Figure 2.22: Grade survival in primary school for 10- to 19-year-olds, by wealth quintile, Cambodia and Senegal, 2005 Cambodia Senegal 9 1 - 0 1 d e g a s l i p u p f o e r a h S ) % ( e d a r g h c a e d e n i a t t a e v a h o h w 100 80 60 40 20 0 9 1 - 0 1 d e g a s l i p u p f o e r a h S ) % ( e d a r g h c a e d e n i a t t a e v a h o h w 100 80 60 40 20 0 Grade Grade Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Source: World Bank (2008b).
General principles provide a framework that individual countries must adapt to their own circumstances There are many benefits to using the child’s mother tongue as the medium of instruction during the early years 7 8 C H A P T E R 2 broadly conform to this pattern.
The second pattern emerges in countries with high attendance, where children from poor households are often almost as likely to start school as their richer counterparts but far more likely to drop out.

Inequalities widen progressively as children progress through the system, as in Cambodia.

While the extent of divergence differs, Benin, India, Malawi, Myanmar and Togo broadly conform to this pattern.
Wealth-based inequalities interact with wider disparities Disparities based on wealth do not exist in isolation.

They interact with wider inequalities and markers for disadvantage related to gender, location, language and other factors.

Breaking down these inequalities is a key to accelerated progress towards UPE.

Rural-urban inequalities.In many countries living in a rural area carries a marked handicap in terms of opportunities for education.
Rural children are less likely to attend school, and more likely to drop out, than their urban counterparts.

In Senegal, children in urban areas are twice as likely to be in school as their counterparts in rural areas.

Poverty provides part of the explanation: some two-thirds of the rural population live in poverty, compared to around half of urban households (IMF, 2006).

In addition, correlates of poverty such as the prevalence of child labour and malnutrition are often much higher in rural areas.
Demand for different types of schooling may also vary between more traditional rural areas and less traditional urban districts.

In Senegal, instruction in Arabic is important for many rural communities, potentially limiting demand for government schools, where the medium of instruction is French (IMF, 2006).

Disparities faced by slum dwellers.Slums are typically characterized by high levels of poverty, poor child health status and limited participation in education.
UN-HABITAT recently analysed primary school attendance rates for slums in cities of eighteen countries (UN-HABITAT, 2006).

In Benin and Nigeria, children who live in slums had attendance rates some twenty percentage points lower than those of other city children.

In six countries, including Bangladesh and Guatemala, attendance rates for the children of slum dwellers were lower even than average rates in rural areas.
Socio-cultural inequalities.Cultural factors such as religion and ethnicity can affect both the demand for schooling and its supply.

On the demand side, households from various religious backgrounds may attach differing weight to education, or they may demand schools and curricula different from those provided through the formal education system.
In north-western Nigeria, some 15% of children aged between 6 and 16 were not in formal school because their parents preferred them to attend Quranic schools (Nigeria National Population Commission and ORC Macro, 2004).

Language-based disparities.There are large differences in school attendance and completion among linguistic groups.

Analysis of household data for 22 countries and over 160 linguistic groups has attempted to identify the weight of different factors behind disparities.
It is estimated that socio-economic factors such as household wealth and location account for less than half of observed differences in education outcomes among linguistic groups.

So what factors account for the balance of the disparities?

The medium of instruction had statistically significant effects: if at least half of schools offer the opportunity to learn in a home language, attendance rises by approximately 10% (Smits et al., 2008).
Children living in rural areas were found to be at a particular disadvantage if they did not have access to school instruction in their mother tongue.

These results add further weight to the growing body of evidence on the benefits of using the mother tongue in schools, at least in the early years.

Household survey data make it possible to observe and measure inequalities in education as if they fit into neat compartments.
In the real world, disparities in educational opportunity and other areas combine, interact and are reproduced through dynamic political and socio-cultural processes that involve complex and unequal power relationships.

Disadvantage spans many dimensions.

Being poor is a universal marker for restricted opportunity in education.

Being rural and poor represents a double disadvantage in many countries.

Being poor, rural and female is a triple barrier to equal opportunity.
Figure 2.23 captures the multidimensional scale of disadvantage by locating where groups stand in the distribution of educational opportunity, as measured by school attendance.
T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Three barriers to UPE: child labour, ill health and disability Figure 2.23: Primary net attendance rates by location, wealth quintile and gender, selected countries, most recent year Every country faces its own distinctive set of challenges in achieving UPE, but high levels of poverty and low average incomes are pervasive in most of the countries furthest from the target.
So are the three barriers to UPE considered in this subsection: child labour, ill health and disability.

Child labour Progress towards universal enrolment and completion of primary education is inextricably bound up with the progressive elimination of child labour.

Not all economic activity carried out by children is a barrier to education.
But activity that keeps children out of school, limits their mental and physical development or exposes them to hazardous conditions violates children’s right to education, along with international conventions.

There were around 218 million child labourers in 2004, of whom 166 million were aged between 5 and 14.
In this younger age group, around 74 million were engaged in hazardous work (ILO, 2006).7 The reported number of child labourers globally has fallen by 11% since 2000 – and by 33% in the hazardous category.

However, progress has been uneven.

It has been most rapid in Latin America and the Caribbean and slowest in sub-Saharan Africa.

Around one-quarter of the region’s 5- to 14-year-olds are engaged in child labour.
Because population growth has increased faster than child labour rates have fallen, there were some 1 million more child labourers in 2004 than in 2000.

In absolute terms, most child labourers – 122 million in total – live in Asia and the Pacific.

Here, too, progress towards elimination has been slow, with a decline from 19.2% to 18.8% between 2000 and 2004 (ILO, 2006).

School attendance figures provide stark evidence of the trade-off between child labour and UPE.
The Understanding Child Work programme has used household survey data to examine school attendance in some sixty countries (Guarcello et al., 2006).

Its findings indicate that working children face an attendance disadvantage of at least 10% 7.

The concept of ‘child labour’ is based on the ILO Minimum Age Convention of 1973.

It excludes children aged 12 and older who work a few hours a week in permitted light work, and those aged 15 or over whose work is not categorized as hazardous.
‘Economic activity’, a broader concept sometimes used in discussions of child labour, refers to any labour lasting more than one hour per day during a seven-day reference period. )

% ( s e t a r e c n a d n e t t a t e n y r a m i r P 100 90 80 70 60 50 40 30 20 10 0 Boys urban richest 20% Girls urban richest 20% Boys urban poorest 20% Girls urban poorest 20% Boys rural richest 20% Girls rural richest 20% Boys rural poorest 20% Girls rural poorest 20% Average d a h C a i p o i h t E a i n a z n a T .

R .
R .

 .

p e R n a c i n i m o D Sources: Demographic and Health Surveys, calculations by Gwatkin et al.

(2007a, 2007b, 2007c, 2007d, 2007e, 2007f, 2007g, 2007h, 2007i, 2007j, 2007k, 2007l, 2007m, 2007n); Macro International Inc. (2008).
Offering school meals and cash incentives can tilt the balance between school and work 8 0 C H A P T E R 2 in twenty-eight countries, at least 20% in fifteen countries and at least 30% in nine countries (Figure 2.24).

Child labour is also associated with delayed school entry.

In Cambodia, for example, a working child is 17% less likely to enter school at the official age and thus runs a higher risk of dropout.
While the trade-off between child labour and primary eduction is clear cut, there is wide cross- country variation in the relationship.

Moreover, evidence of trade-offs says little about the direction of influence: association is not causation.

Are children not attending school because they are working, or are they working because they are not in school?

The answer varies among and within countries.


When schools are unavailable or distant, when the cost of schooling is high and the perceived quality low, disincentives to send children to school may push them into work.

In other cases, household poverty and associated labour demands ‘pull’ children into labour markets: that is, they are not in school because they are working.

These ‘pull’ factors are often triggered by inability to cope with a crisis, such as a drought.
Household survey evidence from Pakistan shows that for around 10% of poor households, withdrawing children from school is a deliberate coping strategy in times of economic and environmental shock (World Bank, 2007c).

How should governments tackle the trade-off between school and work that is slowing progress towards UPE?
Practical measures are needed, first to reduce the pressures that force poor households to augment income or labour supply through child work and, second, to strengthen incentives for sending children to school.

Removing formal and informal fees and strengthening education quality are first steps.

In many countries, including Cameroon, Ghana, Kenya and the United Republic of Tanzania, abolishing school fees has helped reduce child labour.
Other interventions, such as school meal programmes, financial incentives for disadvantaged groups, social protection measures to better enable vulnerable households to manage risk, and conditional cash transfer programmes can also play an important role (see Chapter 3).

Health barriers to UPE The early childhood section of this chapter highlights health handicaps that can affect children from birth to age 5.

Such handicaps do not disappear after entry to primary school.
Inadequate nutrition and poor health continue to track children Figure 2.24: School attendance disadvantage for economically active children Zambia Uzbekistan Cameroon Guinea-Bissau Iraq Mexico Honduras Panama Nicaragua Ghana Mali Swaziland Ecuador Guatemala Paraguay Côte d’Ivoire Colombia Jamaica Romania Togo C. A. R. Gambia Belize Philippines Senegal Turkey Yemen Sierra Leone El Salvador Bolivia Lesotho Argentina Peru Sri Lanka Angola Brazil Burundi Chile Dominican Rep. Namibia Portugal Chad Congo Guyana Zimbabwe U. R. Tanzania Comoros Kenya Malawi Mongolia Trinidad/Tobago Azerbaijan Bosnia/Herzeg.
Rep. Moldova South Africa Rwanda Viet Nam Albania 0.2 0.4 0.6 0.8 1.0 1.2 School attendance disadvatange index Note: The school attendance disadvantage index is the school attendance rate of economically active children expressed as a ratio of the school attendance rate of children who are not economically active.

The smaller the index value, the higher the disadvantage.

Source: Guarcello et al.

(2006).


T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s after they enter school, trapping them in a vicious cycle of cumulative disadvantage.

Reversing this cycle requires public health interventions, some of which can be initiated through schools.

Getting more children into school is an important indicator for progress in education.
In many countries, though, it has to be deflated to take into account the consequences of hunger, micronutrient deficiency and infection.

On one estimate, as many as 60 million school-age children have iodine deficiency, which limits cognitive development.

Some 200 million are anaemic, which affects concentration levels (Pridmore, 2007).

Water-related infectious diseases impose an enormous toll on health and learning, costing an estimated 443 million school days per year in absenteeism (UNDP, 2006).
Almost half these days are lost as a result of intestinal helminths, such as roundworm, hookworm and whipworm.

Over 400 million children are infected with parasitic worms that leave them anaemic, listless and often unable to concentrate (Miguel and Kremer, 2004).

Observational studies in the Philippines and the United Republic of Tanzania found a strong negative association between helminth infection and cognitive domains including learning and memory (Ezeamama et al., 2005; Jukes et al., 2002).
Schools can make a difference in all these areas.

Of course, they cannot fully compensate for damage caused in early childhood, but they can provide some level of protection.

In India, school meal programmes have been used in some states, such as Tamil Nadu, to improve pupils’ nutritional status (Sridhar, 2008).

Public health programmes can use schools to deliver vaccinations, vitamins and treatment for infectious diseases.
In Kenya, a randomized evaluation of a school-based mass treatment campaign for intestinal helminths found marked reductions in infection rates (Edward and Michael, 2004; Kremer and Miguel, 2007).

The programme also reduced school absenteeism by one-quarter.

This meant children who attended primary school and underwent regular deworming every six months ended up with the equivalent of an extra year of education.
School-based treatment costs were very low – around US$0.50 per child – and returns very high: for every dollar spent on deworming Kenya gained an estimated US$30 through the higher income associated with more education.

Linking health and education policies can yield high returns.

In the United Republic of Tanzania, the rapid drive towards UPE has been supported by a public health programme aimed at tackling the debilitating effects of helminth infection among schoolchildren.
In an initiative launched in 2005, the ministries of health and education undertook a joint risk-mapping exercise, identifying eleven regions as having the highest burden of infection.

Teachers from every school in selected districts were trained to identify symptoms, advise parents on causes of infection and deliver medicine with local health workers.

As part of the campaign, which is supported by aid donors, free drugs are reaching 5 million children annually.
In this example, regional and district school health coordinators have played a pivotal role in facilitating progress in education (Schistosomiasis Control Initiative, 2008).

Investments in public health offer some of the most cost-effective routes to increased school participation.

Conversely, failure to invest in health can have large hidden costs for education.

Malaria provides a particularly striking example.

Exposure to malaria has grave implications for achievement in school.
After controlling for other factors, researchers have found that endemically intensive malaria cuts school completion rates by around 29% and increases repetition by 9% (Thuilliez, 2007).

In Sri Lanka another research exercise found that children aged 6 to 14 who had more than five bouts of malaria in a year scored 15% lower in language tests than children who had fewer than three, controlling for factors such as income and location (Fernando et al., 2003).
Simple preventive measures in the form of insecticide-treated bed nets and low-cost treatment can dramatically reduce the incidence of malaria.

Yet coverage remains limited.

Fewer than one in ten children living in malarial areas of sub-Saharan Africa have access to insecticide- treated bed nets, for example.

This is one area in which scaled-up global initiatives and strengthened national health systems have the potential to deliver rapid results in education.

HIV/AIDS prevention is another.


Previous Reports have documented in detail the devastating impact of the disease on education in areas ranging from teacher attrition to child health.

The pandemic is stabilizing, but at very high levels, and progress is uneven.
Globally, an estimated 33 million people live with the disease, two-thirds of them (and Investing in public health and using schools to deliver vaccinations, vitamins and treatment for infectious diseases is one of the most cost-effective ways to increase school participation 8 1 C H A P T E R 2 almost three-quarters of deaths) in sub-Saharan Africa.

Beyond the immediate human costs, the HIV/AIDS crisis represents a formidable obstacle to UPE.
Around 1.9 million children under 15 in sub-Saharan Africa live with HIV/AIDS and some 9% of the region’s children have lost one or both parents to the disease.
While the evidence is mixed, there is evidence from several countries – including Kenya, Rwanda and the United Republic of Tanzania – that HIV/AIDS orphans enter school later and are more likely to repeat grades (Bicego et al., 2003; Siaens et al., 2003).8 In fifty-six countries from which recent household survey data are available, orphans who had lost both parents were 12% less likely to be in school (UNAIDS, 2008) More broadly, the grief, trauma, isolation and depression that can accompany the death of parents also have a destructive impact on education (Kelly, 2004; Pridmore and Yates, 2005).
Progress in combating HIV/AIDS will have powerful spin-off benefits for education.

Beyond prevention, the most immediate challenge is to improve access to antiretroviral drugs.

Household-level research in western Kenya has documented significant increases in weekly hours of school attendance by children from households affected by HIV/AIDS when the parents have access to medicine.
In the six months after treatment is initiated, attendance increases by 20% with no significant drop-off thereafter (Thirumurthy et al., 2007).

This is just one example, but it highlights the costs associated with current treatment deficits.

While the number of people receiving antiretroviral medicine has increased tenfold in the past six years to 3 million, 30 million are still untreated.
Similarly, although mother-to-child transmission rates are falling with an increase in antiretroviral drug coverage, two-thirds of HIV-positive pregnant women are not covered by antiretroviral programmes (UNAIDS, 2008).

Links between public health and education operate in both directions.

Strengthened health systems can enhance equity and opportunity in education; progress in education can act as a catalyst for gains in public health (see Chapter 1).
From a public policy perspective the important lesson is that planning frameworks have to avoid compartmentalized approaches and integrate a wide range of interventions.

Disabled learners The promise of EFA, as the phrase implies, applies to all children.

It does not differentiate between able- bodied and disabled children.
The Convention on the Rights of Persons with Disabilities, adopted by the United Nations General Assembly in December 2006 and in force since May 2008, is the latest legal tool supporting integration of disabled people and the most recent reaffirmation of the human rights of disabled learners.

It recognizes a clear link between inclusive education and the right to education.

Yet children with disabilities are still among the most marginalized and least likely to go to school.
Data constraints make cross-country comparison of the impact of disability difficult.

There is no internationally agreed definition of ‘disability’,9 and few governments closely monitor the impact of disability on school attendance.

However, evidence from household surveys indicates that disabled children have lower rates of school participation.

Figure 2.25 shows the proportions of children with and without disabilities at primary school age (6 to 11) in thirteen countries.
The difference in primary school attendance rates between children with disabilities and those with none ranges from ten percentage points in India to almost sixty in Indonesia.

The barriers for disabled children vary.


Physical distance to school, the layout and design of school facilities, and shortages of trained teachers all play Children with disabilities are among the most marginalized and least likely to go to school 8.

See Bennell (2005a) for a counterargument.

9.


Although no satisfactory international working definition of ‘disability’ exists, the consensus is that any eventual definition must (i) be broad, to encompass the complexity of disability in all its visible and non-visible forms; (ii) be based on the World Health Organization’s International Classification of Functioning, Disability and Health; and (iii) reflect the social, rather than medical, model of disability.
8 2 T H E D A K A R G O A L S : : n a t i o n s a t t h e c r o s s r o a d s Figure 2.25: Proportion of children aged 6 to 11 with and without disabilities who are in school Box 2.12: Uganda’s good example of integrating disabled students Chad Burundi Mozambique Mongolia Zambia Cambodia India Romania Indonesia Colombia Bolivia South Africa Jamaica 0 20 40 60 80 100 Attendance rate (%) With disabilities Without disabilities In Uganda the human rights of disabled people are enshrined in the Constitution and sign language is recognized as an official language.
Deaf children attend their local schools, with appropriate support, such as sign language interpreters, to enable them to learn (Rustemier, 2002).

The Uganda National Institute of Special Education has been training teachers in inclusive and special needs education since 1991.

It received legal status and parliamentary recognition as an educational institution in 1996.
It is involved in research, community service, and development of educational materials and adaptive devices for learners with special needs.

It makes a graphic design/illustration and desktop publishing facility available and offers distance learning opportunities, leading to a certificate or a diploma, open to teachers, parents, social workers, community development personnel, health workers, caregivers and law enforcement personnel.
Uganda has also employed media successfully to advocate for the needs of persons with disabilities and to spread awareness of educational opportunities.

The 2006 Convention on the Rights of Persons with Disabilities recognizes the disabled learner’s right to education Source: Filmer (2008).

Source: The Communication Initiative Network (2002).

a role.
a role.

Among the most serious obstacles, however, are negative attitudes towards the disabled, which affect both the school participation and the self- confidence of disabled children (Dutch Coalition on Disability and Development, 2006).

Speeding up progress towards UPE will require a far stronger focus on public policy facilitating access for the disabled – and on political leadership to change public attitudes (Box 2.12).
The starting point is that disabled children should be treated as an integral part of the learning community rather than as a ‘special’ group requiring separate classes or institutions.
Post-primary education is often too academic and removed from social and economic realities 8 4 C H A P T E R 2 Secondary education and beyond: some gains Increasing participation in secondary education is an explicit part of the Dakar commitment to EFA and of the MDG on gender parity and equality.

Secondary education is also important for wider reasons.
Where opportunities for secondary education are scarce, parents may see less reason to ensure that their children complete primary school, undermining progress towards UPE.

There is another link between primary and secondary schools: namely, secondary school graduates represent the primary school teachers of the future.

Secondary school is of value for personal development and civic participation as well, and it is a stepping stone to tertiary education.
Expanded access to both these levels is essential to equip young people with the skills, know-how and training they and their countries need to succeed in an increasingly integrated and knowledge-based global economy.

While participation in post-primary education is expanding, access remains limited for most of the world’s young people.

Disparities in opportunity reinforce persistent inequalities in society.
And problems go beyond access to school because many post-primary programmes do not meet real needs.

Too often they are overly-academic, selective, stratified and disconnected from social and economic realities (World Bank, 2005c).

This section reports on recent developments in secondary and tertiary education while emphasizing global, regional and national disparities at these levels.
Assuring the transition from primary to secondary education Most governments today are committed to providing universal access to basic education, which includes lower secondary as well as primary education.10 It follows that universal basic education requires completion of primary school 10.

By international convention, primary and lower secondary education are the first two stages of basic education (UNESCO, 1997).
While most countries organize basic education according to the international definition, a significant number define it differently.

In twenty-two countries, basic education includes at least one year of pre-primary education, in fifteen countries it consists exclusively of primary education and in twelve countries it includes lower secondary and part of upper secondary education (UNESCO, 2007a; UNESCO-IBE, 2007).

and a successful transition to lower secondary.


Enforcement of compulsory schooling laws and elimination of primary school-leaving examinations are just two of the measures being taken to improve transition rates.
All developed countries, some countries in transition and most countries in Latin America and the Caribbean, and in East Asia and the Pacific consider primary and lower secondary education part of compulsory schooling (UIS, 2006a).11 Prolonging compulsory schooling has increased access to, and participation in, secondary education.

The median transition rate from primary to secondary is above 90% in all regions except South and West Asia, and sub-Saharan Africa.
Transition rates remain especially low (70% or less) in twenty-two countries, nineteen of them in sub-Saharan Africa (see annex, Statistical Table 8).

Expanding enrolment in secondary education Enrolment in secondary education is rising.

In 2006, some 513 million students worldwide were enrolled in secondary school, an increase of nearly 76 million since 1999.

However, enrolment ratios vary enormously by region (Table 2.7).
Worldwide, the average net enrolment ratio (NER) in secondary education increased from 52% in 1999 to 58% in 2006.

Developed countries and most transition countries are moving closer to universal enrolment, but developing regions much less so.

In sub-Saharan Africa, for example, the secondary NER was just 25% in 2006.

This implies that nearly 78 million of the region’s secondary school-age children are not enrolled in secondary school.

Regional figures conceal significant differences between countries.
In the Arab States, secondary NERs ranged from less than 22% in Djibouti and Mauritania to nearly 90% or more in Bahrain, the Palestinian Autonomous Territories and Qatar in 2006.

In South and West Asia they ranged from 30% in Pakistan to 77% in the Islamic Republic of Iran.

Secondary NER levels in sub-Saharan Africa were less than 20% in Burkina Faso, Madagascar, Mozambique, the Niger and Uganda, but over 80% in Mauritius and Seychelles (see annex, Statistical Table 8).

11.
11.

Some countries, including Bahrain, Malaysia, Mauritius, Oman and Tokelau, have achieved near universal participation in lower secondary education (GERs of at least 90%) even without compulsory school laws (see annex, Statistical Tables 4 and 8).
T H E D A K A R G O A L S : : s o m e g a i n s Table 2.7: Rates of transition to, and participation in, secondary education, 1999 and 2006, worldwide and by region Transition rates from primary to secondary education (median) Gross enrolment ratios Net enrolment ratios School year ending in School year ending in School year ending in Secondary education Total (%) 93 88 99 100 62 92 99 … 91 … 87 93 94 92 99 98 2005 Male (%) 92 93 … 100 66 90 99 … … … 90 … … 92 99 98 Female (%) 94 83 … 99 57 93 99 … … … 83 … … 92 99 99 1999 2006 (%) 60 52 100 90 24 60 83 65 64 111 45 80 53 81 100 87 (%) 66 60 101 89 32 68 91 75 75 107 51 89 57 91 101 88 1999 (%) 2006 (%) World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe Source: Annex, Statistical Table 8.
Between 1999 and 2006, secondary GERs increased in 118 of the 148 countries with data available.

Fourteen of the countries that started with enrolment ratios of less than 80% made significant progress with increases of at least fifteen percentage points.12 In many Western European countries, GERs in secondary education declined as systems became more age- standardized, with fewer under- and over-age students.
Technical and vocational education and training (TVET) occupy an important position in secondary education.

Of the more than 513 million students enrolled in secondary schools worldwide in 2006, 10% were in TVET programmes (Table 2.8), mainly at upper secondary level (UNESCO-UNEVOC/UIS, 2006).13 The percentage had declined slightly since 1999.
The relative shares of secondary-level TVET enrolment were highest in Central and Eastern Europe, North America and Western Europe, and the Pacific, and lowest in South and West Asia, the Caribbean and sub-Saharan Africa.
Table 2.8: Percentage of technical and vocational education and training in secondary education, 1999 and 2006 Enrolment in technical and vocational education % of total secondary School year ending in 1999 2006 World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe Source: Annex, Statistical Table 8.
Secondary-school enrolment has risen by nearly 76 million since 1999 12.

Cambodia, Costa Rica, Cuba, Ethiopia, Guatemala, Guinea, Macao (China), the Maldives, Mexico, Mongolia, Morocco, Saint Lucia, the Syrian Arab Republic and the Bolivarian Republic of Venezuela.

13.

In some countries TVET programmes are post- secondary non-tertiary education (ISCED level 4).
14.

In the Pacific subregion, however, the rates are uncertain because the small population base makes it difficult to estimate population reliably.

15.

The secondary attainment rate is the percentage of a population that has participated in secondary education though not necessarily completed a full cycle.
C H A P T E R 2 Transition to upper secondary — a dropout point Disparities in secondary education attainment Countries increasingly make the distinction between lower secondary education (ISCED level 2) and upper secondary education (ISCED level 3) (UNESCO, 1997).

The former is frequently part of a compulsory basic education cycle, whereas the onset of the latter typically marks the end of compulsory schooling and consists of diverse programmes and more specialized instruction (UIS, 2006a).
The transition from lower to upper secondary is a dropout point in many education systems.

At a global level, the average GER in 2006 was much higher in lower secondary education (78%) than in upper (53%) (Table 2.9).

Differences in the participation rates between the two levels are especially prominent in East Asia,14 Latin America and the Caribbean, the Arab States and sub-Saharan Africa (where rates are relatively low at both levels).
By contrast, participation levels in lower and upper secondary education are quite similar in North America and Western Europe, Central and Eastern Europe, and Central Asia.

Household surveys help capture the scale of international inequalities at the post-primary level.

They provide evidence that, while global primary education disparities may be narrowing, inequalities at the secondary level remain large (Barro and Lee, 2000; Bloom, 2006).

Regional disparities are particularly marked.


The average secondary education attainment rate15 in developed countries is 70% among the population aged 25 or more, but just 40% in East Asia and the Pacific and around 20% in sub-Saharan Africa (Figure 2.26).

Attainment rates among the group aged 15 to 24 are higher, pointing to improved access and reduced inequality over time.

The exception is sub-Saharan Africa.


This is the only region in which 15- to 24-year-olds were less likely to have attended secondary school, indicating a need for urgent action to close the gap with the rest of the world.

Detailed attainment figures broken down by grade level help pinpoint where students face critical transition hurdles.
Several patterns Table 2.9: Gross enrolment ratios in lower and upper secondary education, 1999 and 2006 Figure 2.26: Secondary attainment rates among adults and youth, by region, circa 2000 Gross enrolment ratios (%) Lower secondary Upper secondary School year ending in School year ending in 1999 2006 1999 2006 World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe Source: Annex, Statistical Table 8.
World Developed countries Sub-Saharan Africa Middle East and North Africa East Asia and the Pacific South Asia Latin America/Caribbean Eastern Europe/Central Asia 0 20 40 60 80 100 Share of adults and youth having attained secondary education (%) Aged 25 or older Aged 15 to 24 Note: The regional classification in this figure follows that used by the World Bank, which differs to some extent from the EFA classification used in this Report.
Developed countries include OECD countries and other high income countries, such as Bahrain, Cyprus, Israel and Kuwait.

Source: Barro and Lee (2000), as reported in Bloom (2006).
T H E D A K A R G O A L S : : s o m e g a i n s emerge from comparing survival rates to the end of secondary education (Figure 2.27).16 The profile for Armenia, where almost all students complete eight years of schooling (roughly the end of lower secondary education) and only begin to drop out during the upper secondary grades, is typical for most developed and transition countries.
For these countries, inequalities in grade attainment become more marked in the post-compulsory years.17 Patterns in low-income developing countries look very different.

Some countries register steep declines at specific grades, as in the United Republic of Tanzania (grade 7) and Ghana (grade 9).

In other cases, such as Malawi and Nicaragua, survival rates decline more gradually, with secondary education marking a broad continuation of the pattern established at the primary level.
Finally, some countries, such as Mali, maintain a consistent level of survival at a low level of attainment.
Grade-specific attendance data of this Inequalities at the secondary level remain large Figure 2.27: Survival rates to each grade of primary and secondary education, by gender, selected countries, most recent year Last year of primary Armenia Last year of lower secondary Female Male Nicaragua Last year of primary Last year of lower secondary Female Male ) % ( e d a r g o t l a v i v r u S Grade Ghana Last year of primary Last year of lower secondary ) % ( e d a r g o t l a v i v r u S 100 80 60 40 20 0 Male Female Grade Malawi Last year of primary Last year of lower secondary Male Female Grade United Republic of Tanzania Last year of primary Last year of lower secondary ) % ( e d a r g o t l a v i v r u S 100 80 60 40 20 0 Male Female Grade Mali Last year of primary Last year of lower secondary Male Female ) % ( e d a r g o t l a v i v r u S ) % ( e d a r g o t l a v i v r u S ) % ( e d a r g o t l a v i v r u S Grade Grade Source: Demographic and Health Surveys, calculations by Education Policy and Data Center (2008b).
16.

Survival rates by grade portray pupils’ ability to progress through the school system and reach higher grades, which is largely determined by cumulative dropout rates.

The reported survival rates were calculated based on promotion, repetition and dropout rates for primary- and secondary-age pupils who were currently in school.

They are different from the survival rates of cohorts of pupils who start school in a particular age group.

17.
17.

Using a different methodology, the World Bank has constructed national profiles of attainment by grade level that indicate the proportion of people aged 15 to 19 who successfully reach each grade of primary and secondary education.

These profiles reflect similar patterns to those discussed here.

(Both methods use data from household surveys.)

See the World Bank Educational Attainment and Enrolment around the World database (World Bank, 2008b) and Pritchett (2004b).
type are important because they help identify critical moments for public policy intervention.

Disparities within countries are greater than those among countries Within-country inequalities in secondary education are often more marked than inequalities between countries.

This is another area in which wealth matters for the distribution of opportunity.
As Figure 2.28 shows, in many developing countries, secondary attendance rates are significantly lower among poorer households Figure 2.28: Net attendance rates in secondary education, by wealth quintile, selected countries, most recent year Burkina Faso Burkina Faso Burkina Faso Niger Niger Niger Mali Mali Mali Senegal Senegal Senegal Côte d’Ivoire Côte d’Ivoire Côte d’Ivoire Chad Chad Chad Guinea Guinea Guinea U. R. Tanzania U. R. Tanzania U. R. Tanzania Ethiopia Ethiopia Ethiopia Rwanda Rwanda Rwanda Zambia Zambia Zambia Bangladesh Bangladesh Bangladesh Benin Benin Benin Nicaragua Nicaragua Nicaragua Indonesia Indonesia Indonesia Zimbabwe Zimbabwe Zimbabwe Madagascar Madagascar Madagascar Ghana Ghana Ghana Nigeria Nigeria Nigeria Mozambique Mozambique Mozambique Uganda Uganda Uganda Cameroon Cameroon Cameroon Cambodia Cambodia Cambodia India India India Kenya Kenya Kenya Malawi Malawi Malawi Viet Nam Viet Nam Viet Nam Namibia Namibia Namibia Nepal Nepal Nepal Haiti Haiti Haiti Colombia Colombia Colombia Philippines Philippines Philippines Peru Peru Peru Dominican Rep. Dominican Rep. Dominican Rep. 0 20 40 60 80 100 Net attendance rates in secondary education (%) Q1 (poorest) Q5 (richest) Q2 Average Q3 Q4 Source: Demographic and Health Surveys, calculations by Harttgen et al.
(2008).

8 8 than among richer ones.

As at primary level, wealth-based inequalities contract as attendance rates rise to 80% or above.

More surprising is the extent to which wealth-based inequalities in secondary attendance vary among countries with similar average attendance.

For example, Bangladesh, Benin, Nicaragua and Zambia all have secondary attendance rates of about 60%.
Yet the ratios of attendance rates between the richest and poorest households range from 1.4 in Bangladesh to 2.4 in Nicaragua (Benin is at 1.8 and Zambia at 1.7).

This cannot be explained by differences in poverty levels or average income: Bangladesh has a higher incidence of extreme poverty and a lower average income than Nicaragua.

The evidence from this comparison would suggest that public policies in Nicaragua could be doing far more to narrow disparities.
The same would apply to Indonesia and Ghana.

Analysis of the relationship between household wealth and survival rates by grade level reveals a number of patterns (Figure 2.29).

A dominant one is exemplified by Colombia and India, where the relationship between household wealth and survival rates is fairly muted in the early grades of primary education but much more salient in the upper grades of secondary education.

Lesotho, Peru and the Philippines also follow this pattern.
In another common pattern, illustrated by Benin and Cambodia, the relationship between household wealth and survival rates remains fairly consistent throughout primary and secondary education (EPDC, 2008b).

In some regions there has been a displacement effect with greater equity at the primary level shifting disparities to the secondary level.

The experience of Latin America is instructive.


Household surveys taken between 1990 and 2005 show a steady increase in the percentage of students achieving timely promotion through the education system at both primary and secondary level.

The overall percentage of students aged 15 to 19 having achieved timely promotion at the primary level increased from 43% to 66%.

Advances for the cohort aged 10 to 14 were proportionately more beneficial for low-income students, with the rich- poor gap narrowing.
Convergence is much less evident at the secondary level.

In 2005, some 88% of children in the richest decile moved steadily through school without interruption, compared with 44% of the poorest decile (UN Economic Commission for Latin America and the Caribbean, 2007).
T H E D A K A R G O A L S : : s o m e g a i n s Figure 2.29: Survival rates to each grade of primary and secondary education, by wealth quintile, selected countries, most recent year India Benin ) % ( e d a r g o t l a v i v r u S ) % ( e d a r g o t l a v i v r u S 100 80 60 40 20 0 100 80 60 40 20 0 Last year of primary Last year of lower secondary ) % ( e d a r g o t l a v i v r u S 100 80 60 40 20 0 Last year of primary Last year of lower secondary Grade Colombia Grade Cambodia Last year of primary Last year of lower secondary ) % ( e d a r g o t l a v i v r u S 100 80 60 40 20 0 Last year of primary Last year of lower secondary The relationship between household wealth and survival rates is most apparent in the post- primary years Grade Grade Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Note: Survival rates for some quintiles and grades could not be calculated because too few observations were available.
Source: Demographic and Health Survey, calculations by Education Policy and Data Center (2008b).

Disadvantages based on characteristics other than household wealth also cross the divide between primary and secondary school.

Speaking an indigenous or non-official language remains a core marker for disadvantage.

When home language and official national language differ, the chances of completing at least one grade of secondary school are reduced.
For example, in Mozambique, 43% of people aged 16 to 49 who speak Portuguese (the language of instruction) have at least one grade of secondary schooling; among speakers of Lomwe, Makhuwa, Sena and Tsonga, the shares range from 6% to 16%.
In Bolivia, 68% of Spanish speakers aged 16 to 49 have completed some secondary education while one-third or fewer of Aymara, Guaraní and Quechua speakers have done so; in Turkey the corresponding shares are 45% for Turkish speakers and below 21% for Arabic and Kurdish speakers (Smits et al., 2008).

Tertiary education: global patterns of inequality Tertiary education has expanded rapidly since the Dakar Forum.
Worldwide, some 144 million students were enrolled in tertiary education in 2006 – 51 million more than in 1999.

Over the same period, the global tertiary GER increased from 18% to 25%.

A large majority of the new places in tertiary institutions were created in developing countries, where the total number of tertiary students rose from 47 million in 1999 to 85 million in 2006 (see annex, Statistical Table 9).
Even with rapid growth in tertiary education in developing countries, global disparities remain large (Table 2.10).

Tertiary GERs range from 70% in North America and Western Europe to 32% in Latin America, 22% in the Arab States and 5% in sub-Saharan Africa.
These disparities capture just At global level there are huge gaps in spending per student and in university enrolment rates Table 2.10: Change in tertiary gross enrolment ratios between 1999 and 2006 Gross enrolment ratios in tertiary education (%) School year ending in 1999 2006 World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe Source: Annex, Statistical Table 9A.
the quantitative side of the equation.

Qualitative gaps are also important.

In equivalent dollar terms, France spent over sixteen times as much per university student in 2004 as did Indonesia and Peru.
In 2005, top private universities in the United States, such as Harvard, Princeton and Yale, spent US$100,000 or more per student; the equivalent figure for a student at Dar-es-Salaam University was US$3,239 (Kapur and Crowley, 2008).

Of course, spending per student is not the only indicator for quality at the tertiary level, any more than it is at the primary level.

But financing gaps on this scale have implications for disparities in learning opportunities and the provision of teaching materials.
Global inequalities are often magnified at national level.

It is at the entry point to tertiary education, that the compound effects of inequalities in access to and completion of basic education, and progression through secondary education, become most visible.

Brazil’s universities provide a microcosm of a wider problem.

The university participation rate for black Brazilians aged 19 to 24 is 6%, compared with 19% for white Brazilians (Paixão and Carvano, 2008).
In other words, being born with black skin in Brazil reduces your chance of reaching university by a factor of three.

This is the culmination of disadvantage rooted in poverty, social discrimination and the filtering effect of inequality at lower levels of the education system.
9 0 T H E D A K A R G O A L S : Meeting the lifelong learning needs of youth and adults Goal 3: Ensuring that the learning needs of all young people and adults are met through equitable access to appropriate learning and life-skills programmes.
Goal 4: Achieving a 50 per cent improvement in levels of adult literacy by 2015, especially for women, and equitable access to basic and continuing education for all adults.

Fixing early childhood provision, achieving UPE and expanding post-primary education will create the conditions under which future generations can realize their potential.

But what of people who have been failed by current systems?
This Report highlights the fact that governments across the world have to address an immense backlog of unmet need.

Millions of teenagers have never attended primary school and many millions more have left school lacking the skills they need to earn a livelihood and participate fully in society.

To this constituency can be added about 776 million adults who lack basic literacy skills and many others without access to adult education or skills training.
To take one priority area, whole sections of the adult population in some countries have no access to the information and communication technology that is pervasive in today’s knowledge economy.
Systematic monitoring of EFA goal 3 and the latter part of goal 4 has been stymied by problems of definition and lack of data.18 There is little agreement about how to define the notions of ‘adult learning’ and ‘life skills’, and which learning activities to include (Ellis, 2006; Hargreaves and Shaw, 2006; Hoffmann and Olson, 2006; King and Palmer, 2008; Merle, 2004).
‘Life skills’ and ‘livelihood skills’, both aspects of adult learning, have taken on different meanings in different countries (Maurer, 2005).

At Dakar ‘livelihood skills’ was thought to be subsumed within the broader concept of ‘life skills.’ Adult learning activities are found in a myriad of formal, informal and non-formal programmes and institutions.

In some cases they involve programmes aimed at youth or adults who wish to return to school – that is, equivalency education or second- chance programmes.
Many governments have given little if any priority to youth’s and adults’ learning needs in their education strategies and policies.

Inadequate public funding hampers provision and inadequate monitoring obscures other problems (Hoppers, 2007; UNESCO, 2004).

The fact that no clear quantitative targets were established at Dakar, apart from the main literacy target, may have contributed to a lack of urgency.

In addition, the language of the commitment is ambiguous.
Some read goal 3 as calling for universal access to learning and life-skills programmes, but others, including the drafters of the Dakar Framework, understand no such intent.19 The EFA Global Monitoring Report 2008explored a range of issues in non-formal education, where much adult learning activity takes place.20 It found evidence in several countries of significant disparities in provision by location, age group and socio-economic status (UNESCO, 2007).
It also found that national history heavily influenced approaches to provision.

While Mexico, Nepal and Senegal, for instance, see non-formal provision principally in terms of adult education, Bangladesh and Indonesia take a broader view, stressing flexibility and programme diversity to complement formal education.
Burkina Faso, Ghana, Kenya, Nigeria, the Philippines, the United Republic of Tanzania and Zambia, meanwhile, largely conceive of non-formal education as any structured learning activity outside the formal education system.

There is a strong case to be made for clarifying the purpose of lifelong learning provision, improving data flows and, critically, strengthening political commitment in this area.
As a first step towards more effective monitoring, improved information is needed in the following areas: National conceptions and commitment: How do government agencies understand the learning needs of out-of-school youth and adults?

To what extent do authorities address these needs by articulating a clear vision, setting policy priorities, providing for resource mobilization and allocation, and enabling partnerships with non-government and international organizations?
How long do various adult learning programmes last?

To what extent are specific lifelong learning opportunities put in place?

Lifelong learning needs are great, but rarely reflected in national education strategies and policies 18.

A future EFA Global Monitoring Reportwill examine these issues as part of an overarching theme.

19.

Based on exchanges with Steve Packer and Sheldon Shaeffer, who helped draft the EFA goals.

20.
20.

UNESCO’s working definition of non-formal education states that it ‘may cover education programmes to impart adult literacy, basic education for out-of-school children, life skills, work skills and general culture’ (UNESCO, 1997, p. 41).
Effective monitoring of lifelong learning provision requires better information Demand: What is the demand for youth and adult learning programmes, which populations are involved and how has demand changed over time?

Flexibility and diversification: Are youth and adult learning programmes highly standardized, or do they incorporate flexibility so as to better address the learning needs of diverse groups?
Nature of provision: What are the character and focus of existing youth and adult learning programmes?

Do they include frameworks oriented towards re-entry into formal education?

Basic literacy programmes (reading, writing and numeracy)?

Literacy programmes to promote life skills or livelihood skills?

Other skills development programmes (especially related to labour market participation)?

Rural development?

Target groups: Which groups do existing youth and adult learning programmes target?
Which target groups do the biggest, most established adult learning programmes serve?

To what extent does existing provision create or worsen disparities based on age, gender, educational attainment, wealth, residence, ethnicity or language?

Sustainability: How long have youth and adult learning programmes been in existence?

Which agencies and stakeholders provide funding?

Has funding been constant and/or increasing over time?

How long have educator/ facilitator training frameworks existed?
An important and potentially rich source of information is the Sixth International Conference on Adult Education (CONFINTEA VI), scheduled for May 2009 in Belém, Brazil.
Its overall aim is ‘to draw attention to the relation and contribution of adult learning and education to sustainable development, conceived comprehensively as comprising a social, economic, ecological and cultural dimension.’ Five regional preparatory conferences will have examined policies, structures and financing for adult learning and education; inclusion and participation; the quality of adult learning and education; literacy and other key competencies; and poverty eradication.
9 2 T H E D A K A R G O A L S : : s t i l l n e g l e c t e d Adult literacy: still neglected Overcoming inequalities in literacy’s reach Goal 4: Achieving a 50 per cent improvement in levels of adult literacy by 2015, especially for women, and equitable access to basic and continuing education for all adults.

Reading and writing are essential skills for today’s world.
Literacy expands people’s choices, gives them more control over their lives, increases their ability to participate in society and enhances self- esteem.

It is a key to education that also opens the way to better health, improved employment opportunities and lower child mortality.

Despite these advantages for individuals, and the wider benefits in terms of broader social and economic development, literacy remains a neglected goal.
As the EFA Global Monitoring Report 2008noted: ‘Illiteracy is receiving minimal political attention and remains a global disgrace, keeping one in five adults (one in four women) on the margins of society’ (UNESCO, 2007a, p.1).

Multiple barriers restrict the achievement of widespread literacy.
They include insufficient access to quality education, weak support for young people exiting the education system, poorly funded and administratively fragmented literacy programmes, and limited opportunities for adult learning.

Many of these barriers disproportionately affect marginal and vulnerable groups, and exacerbate socio- economic inequalities.
In developing countries in particular, lower literacy levels are commonly associated with poverty, low socio-economic status, gender discrimination, ill health, immigration, cultural marginalization and disabilities (UNESCO, 2005).

Even in highly literate and schooled societies significant pockets of illiteracy and low literacy remain, leaving those affected marginalized and with diminished life chances.
Illiteracy in global perspective An estimated 776 million adults – 16% of the world’s adult population – are unable to read and/or write, with understanding, a simple statement in a national or official language (Table 2.11).21 Most live in South and West Asia, East Asia and sub-Saharan Africa, and nearly two in every three are women.

The global progress report on literacy is not encouraging.
Between 1985–1994 and 2000–2006, the number of adults lacking literacy skills fell by almost 100 million, primarily due to a marked Table 2.11: Estimated number of adult illiterates (age 15+) in 1985–1994 and 2000–2006, with projections to 2015, by region 1985–19941 2000–20061 2015 Percentage change World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe Total (000) Female (%) 63 63 64 84 61 63 74 69 69 56 61 55 50 55 63 78 Total (000) Female (%) 64 64 62 71 62 67 68 71 71 55 63 55 48 56 61 80 Total (000) Female (%) 1985–1994 to 2000–2006 2000–2006 to 2015 64 64 59 59 60 69 50 71 71 52 63 54 45 55 59 79 -11 -11 -12 -59 21 4 -18 -51 -51 35 -1 -7 -2 -7 -11 -31 -9 -9 -8 -51 -8 -9 -58 -28 -28 11 -3 -15 -2 -17 -10 -17 1.
Data are for the most recent year available.

See the web version of the introduction to the statistical tables in the annex for explanations of national literacy definitions, assessment methods, sources and years of data.

Source: Annex, Statistical Table 2A.

The global literacy progress report is not encouraging.

Some 776 million adults — 16% of the world’s adult population — are unable to read or write with understanding 21.
This figure is based on conventional approaches that define and measure literacy in dichotomous terms using indirect measurement methods.

Other approaches conceive of literacy as a multidimensional phenomenon, embracing a variety of skill domains that need to be directly assessed using wider scales.

In general, direct assessments show that conventional approaches understate actual literacy levels, especially in poor countries (UNESCO, 2005).
On current trends, over 700 million adults will still lack basic literacy skills in 2015 22.

China experienced a dramatic increase in numbers of adult literates between 1985–1994 and 2000–2006 and its adult literacy rate rose from 78% to 93%, due to the combined impact of mass literacy campaigns organized in previous decades, expansion of primary education and the spread of text-laden literate environments (UNESCO, 2005).
23.

The projection in this Report of 706 million illiterate adults in 2015 is more optimistic than the estimate of 725 million published in the 2008 Report.

24.

The number of literate persons expressed as a percentage of the total population aged 15 and over.

25.

Afghanistan, Bangladesh, Benin, Bhutan, Burkina Faso, the Central African Republic, Chad, Côte d’Ivoire, Ethiopia, Guinea, Liberia, Mali, Morocco, Mozambique, Niger, Pakistan, Senegal, Sierra Leone and Togo.

26.
26.

Bangladesh, Burundi, Cambodia, the Central African Republic, Ethiopia, Ghana, India, Madagascar, Niger, Nigeria, Rwanda, the United Republic of Tanzania and Zambia.

9 4 C H A P T E R 2 reduction in East Asia, especially China.22 The net effect obscures large regional variation.

In sub-Saharan Africa, the Arab States and the Pacific, absolute numbers of illiterates increased, reflecting continued population growth.

Moreover, global progress has slowed in recent years.
The upshot is that, on current trends, over 700 million adults will still lack basic literacy skills in 2015.23 Changing this picture will require a renewed sense of urgency, on the part of national governments and the international community, particularly in highly populous developing countries.

In terms of absolute numbers, adult illiteracy is heavily concentrated in a relatively small group of countries.
Some 80% of those affected worldwide live in twenty countries (Figure 2.30), with Bangladesh, China, India and Pakistan accounting for over half of the total.

While significant reductions have occurred in Algeria, China, Egypt, India, Indonesia, the Islamic Republic of Iran and Turkey since 1985–1994, progress elsewhere has been less promising.
Figure 2.30: Countries with the greatest numbers of adult illiterates (age 15+) as of 2000–20061 Adult literacy rates Between 1985–1994 and 2000–2006, the global adult literacy rate24 increased from 76% to 84% (Table 2.12).

Progress was especially marked in the developing countries as a group, where the average adult literacy rate increased from 68% to 79%.
During the period adult literacy levels improved in almost all regions, though not sufficiently in some to cut the number of those lacking literacy skills.

Regional adult literacy rates remained below the world average in sub-Saharan Africa, South and West Asia, the Arab States and the Caribbean.

In 45 countries out of 135, mostly in sub-Saharan Africa, and South and West Asia, adult literacy rates are below the developing country average of 79% (see annex, Statistical Table 2A).
The most populous countries in this group include Bangladesh, Ethiopia, India, Nigeria and Pakistan.

Nineteen countries in the group have very low literacy rates, less than 55%;25 thirteen are low-income countries in which severe poverty prevails – that is, where 75% or more of the population lives on less than US$2 per day.26 Current projections indicate a large proportion of countries in the group will not meet the adult literacy goal by 2015.
270.1 Youth literacy 73.2 48.4 47.1 28.9 23.5 India China Bangladesh Pakistan Ethiopia Nigeria Indonesia Brazil Egypt D. R. Congo Afghanistan Morocco Sudan Iran, Isl.

Rep. Nepal Mozambique Turkey U. R. Tanzania Mexico Algeria Rest of the world 14.8 14.2 14.2 10.5 9.9 9.8 8.7 8.1 7.6 6.6 6.3 6.2 6.0 6.0 155.9 0 25 50 75 100 Number of adult illiterates (millions) Notes: See source table for detailed country notes.

1.

Data are for the most recent year available.


See the web version of the introduction to the statistical tables in the annex for explanations of national literacy definitions, assessment methods, sources and years of data.

Source: Annex, Statistical Table 2A.

Adult illiteracy is the product of past exclusion from educational opportunities.

Tomorrow’s illiteracy figures will reflect current patterns of access to learning.
With the continued expansion of formal education, the global number of youth illiterates (aged 15 to 24) declined from 167 million in 1985–1994 to 130 million in 2000–2006 (see annex, Statistical Table 2A).

Declines occurred in most regions, but in sub-Saharan Africa the number of youth lacking basic literacy skills increased by 7 million due to continuing high population growth and low school participation and completion rates.
There were also relatively small increases in Central Asia, the Pacific, and North America and Western Europe, partly due to changing population estimates.

The global youth literacy rate also improved during the period, from 84% to 89%, most notably in South and West Asia, sub-Saharan Africa, the Caribbean and the Arab States.

Low youth literacy rates (under 80%) were recorded in sub-Saharan Africa, and South and West Asia.
T H E D A K A R G O A L S : : s t i l l n e g l e c t e d Table 2.12: Estimated adult literacy rates (age 15+) in 1985–1994 and 2000–2006, with projections to 2015, by region 1985–19941 2000–20061 Projected 2015 Literacy rates (%) Total World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America and the Caribbean Caribbean Latin America North America and Western Europe Central and Eastern Europe 76 68 99 98 53 58 98 82 82 94 48 87 66 87 99 96 GPI (F/M) 0.85 0.77 0.99 0.98 0.71 0.66 0.98 0.84 0.84 0.99 0.57 0.98 1.02 0.97 0.99 0.96 Literacy rates (%) Total 84 79 99 99 62 72 99 93 93 93 64 91 74 91 99 97 GPI (F/M) 0.89 0.85 1.00 1.00 0.75 0.75 0.99 0.94 0.94 0.99 0.71 0.98 1.05 0.98 1.00 0.97 Literacy rates (%) Total 87 84 99 100 72 79 99 95 96 93 71 93 78 94 99 98 GPI (F/M) 0.92 0.90 0.99 1.00 0.86 0.81 1.00 0.96 0.96 1.00 0.78 0.99 1.07 0.99 1.00 0.98 1.
Data are for the most recent year available.

See the introduction to the statistical tables in the annex for explanations of national literacy definitions, assessment methods, sources and years of data.

Source: Annex, Statistical Tables 2A and 12.

Literacy, inequality and exclusion National literacy rates conceal major disparities in literacy levels within countries.

These disparities are linked to gender, poverty, place of residence, ethnicity, language and disabilities.
Age is another important dimension: younger adults tend to have higher literacy rates than older adults.27 Gender disparities in adult literacy are widespread, especially in the countries facing the greatest literacy challenge.

Worldwide, women account for 64% of the adults who cannot read and write, with understanding, a simple statement from their everyday life.

This share is virtually unchanged from the 63% recorded during 1985–1994 (Table 2.11).
The global literacy rate is lower for women than men, as reflected in the global GPI of 0.89 in 2000–2006, up from 0.85 in the previous period.

Gender disparities to the disadvantage of women are especially marked in South and West Asia, the Arab States and sub-Saharan Africa.

Gender disparities in these regions improved between the two periods (Table 2.12).
Gender and poverty often interact in relation to literacy: for example, in the Gambia, literacy rates ranged from 12% among extremely poor women to 53% for non-poor men (Caillods and Hallak, 2004).

The close link between poverty and illiteracy is observed not only from one country to another but also among regions and households within a country.

In India, for example, literacy levels are lower in the poorest states.
Evidence from thirty developing countries indicates that literacy levels are substantially lower in the poorest households than in the wealthiest (UNESCO, 2005).

In seven sub-Saharan African countries with particularly low overall adult literacy rates, the literacy gap between the poorest and wealthiest households is more than forty percentage points.28 Literacy rates also vary by place of residence.

They are almost always lower in rural areas than in urban areas.
Countries where overall literacy rates are comparatively low show large regional disparities: Pakistan census figures report adult literacy rates of 72% in the Islamabad Capital Territory but 44% in rural Balochistan and Sindh (Choudhry, 2005).

In Ethiopia regional disparities in literacy rates range from 83% in the Addis Ababa region to 25% in the Amhara region (Shenkut, 2005).
Pastoralists and nomads, who number in the tens of millions across the African drylands, the Middle East and parts of Asia, have lower literacy levels than other rural populations (UNESCO, 2005).

In the Afar region of Ethiopia, In seven countries in sub-Saharan Africa, the literacy gap between the poorest and the wealthiest households is more than forty percentage points 27.

The relationship between age and literacy rates is sometimes curvilinear; see UNESCO (2005).

28.
28.

The countries are Côte d’Ivoire, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Sudan and Togo.

9 5 C H A P T E R 2 for example, the literacy rate for adults was 25% in 1999, but in pastoralist areas it was only 8%.

Indigenous populations, many of them characterized by proficiency in non-official languages, tend to have lower literacy rates than non-indigenous populations.
The national literacy rate in Ecuador, for example, was 91% in 2001 but that of indigenous groups was 72%; in Viet Nam the rates in 2000 were 87% nationally, 17% for ethnic minorities and merely 5% for some indigenous groups.

Nepal’s Dalit population has a significantly lower adult literacy rate than the rest of the population.

The Roma in Central Europe also have lower literacy levels than those of majority populations (UNESCO, 2005).
The household surveys and censuses used in determining literacy rates often overlook other excluded groups and individuals (Carr-Hill, 2005).

People who are homeless, institutionalized in prisons or care homes, or unregistered may not figure.

In developing countries, refugees or internally displaced persons often go uncounted.

The same is true for street children.

In all these cases, official literacy figures are likely to understate the scale of the problem.
Pockets of illiteracy and low literacy in developed countries In highly schooled countries that achieved UPE some time ago, illiteracy is considered a problem of the past.

Yet international and national literacy surveys often reveal substantial pockets of illiteracy and low literacy.
International assessments show that many OECD countries have large groups with low levels on key literacy indicators: An assessment in Canada (2003) established that 9 million Canadians of working age (42% of people aged 16 to 65) scored at level 2 or below on the prose literacy scale, a figure that had changed little since the previous assessment in 1994 (Grenier et al., 2008).29 A 2004–2005 assessment in metropolitan France found that 3.1 million French adults of working age, some 59% of them men, faced literacy problems.
(National Agency to Fight Illiteracy, 2007).

Older French adults were more affected than younger ones, with the rates of low literacy being 14% among 46- to 65-year- olds but about 5% for those aged 18 to 35.

In the Netherlands some 1.5 million adults, of whom roughly 1 million are native Dutch speakers, are classified as functionally illiterate.

One-quarter of these native Dutch speakers are almost completely illiterate.
In addition, one in ten Dutch-speaking adults functions at the lowest level of literacy.

Among employed people, 6%, or one in fifteen workers, have great difficulty reading and writing (Reading and Writing Foundation, 2008, drawing on the 1998 International Adult Literacy Survey).

As these examples suggest, illiteracy and low literacy are not confined to poor countries.
Incomplete formal education, high unemployment or underemployment and lack of access to adult education all contribute to a weakening of literacy skills.

While largely hidden, low literacy affects sizable populations in rich countries, acting as a barrier to greater social mobility and equality.

9 0 0 2 t r o p e R i g n r o t n o M i Official literacy figures generally understate the problem in rich and poor countries alike l a b o G l l l A r o f i n o t a c u d E 29.
The International Adult Literacy and Skills Survey defined levels 1 and 2 as low proficiency and levels 3, 4 and 5 as medium to high proficiency.
9 6 T H E D A K A R G O A L S : Assessing gender disparities and inequalities in education Goal 5: Eliminating gender disparities in primary and secondary education by 2005, and achieving gender equality in education by 2015, with a focus on ensuring girls’ full and equal access to and achievement in basic education of good quality.
The Dakar Framework for Action sets out an ambitious two-part agenda on gender equity.

Achieving gender parity in school participation is one part.

The other is progress towards gender equality in educational opportunities and outcomes.

The combination of the two makes the Dakar Framework far broader in scope than other international development targets, including the MDGs (Colclough, 2007).

How is the world performing against these Dakar benchmarks?

The record is mixed.


There has been sustained progress towards parity as captured by the gender parity index (GPI), the ratio of male to female enrolment rates.

However, the 2005 target for eliminating gender disparities was missed in many countries.

Ensuring that the same fate does not befall the 2015 targets will require renewed urgency and commitment.

Although progress towards equality is inherently more difficult to measure, clearly much remains to be done.
Gender disparities: still deeply entrenched The world has made continued progress towards gender parity but many countries still have a long way to travel.

In 2006, only 59 of the 176 countries with data available had achieved gender parity (defined as a GPI of GER ranging from 0.97 to 1.03) in both primary and secondary education.

That is twenty more than in 1999.

But the fact that over half the countries have not achieved gender parity is a source of concern.
About two-thirds of the countries with data available had achieved gender parity at the primary level by 2006.

However, more than half the countries in sub-Saharan Africa, South and West Asia, and the Arab States had yet to achieve gender parity.

These three regions also account for most of the countries furthest away from achieving the goal (Table 2.13).

At the secondary level many more countries have failed to achieve gender parity.
In 2006, only 37% of countries with data, mostly in North America and Europe, had achieved parity.

Gender gaps in secondary schools existed in almost all the countries in sub-Saharan Africa, and South and West Asia, in three-quarters of the countries in East Asia and the Pacific, and in half the countries in Latin America and the Caribbean.

Worldwide there are about as many countries with gender disparities at the expense of girls (fifty-eight) as at the expense of boys (fifty-three).
Countries in the first group are mostly from less developed regions, including sub-Saharan Africa, and South and West Asia.

Boys’ underparticipation, particularly in upper secondary education, is increasingly marked in Latin America and the Caribbean.

At the tertiary level only a handful of the countries for which data are available have achieved gender parity.

In around two-thirds of countries, female enrolment tended to be higher than male enrolment, particularly in the more developed regions (e.g.
North America and Western Europe, and Central and Eastern Europe) and in the Caribbean and Pacific.

In sub-Saharan Africa, and South and West Asia, the majority of countries have enrolment gaps favouring male students.

Primary education: substantial progress but more effort needed to reach gender parity Globally, most of the seventy-one countries with data that had not achieved gender parity in primary education by 2006 had nonetheless made progress since 1999 (Figure 2.31).
On a less positive note, some countries were moving in the wrong direction.

For example, the Dominican Republic, the Libyan Arab Jamahiriya, Mauritania, Niue and Saint Lucia registered gender parity in 1999 but not 2006.

In the Congo, gender disparities increased significantly.

Though some countries in South and West Asia failed to meet the gender parity goal, there has been significant progress since Dakar.

The region’s average GPI rose from 0.84 to 0.95 between 1999 and 2006.
Bhutan, India and Nepal have all achieved gender parity in primary education since Dakar, or are close.

However, Pakistan, with a large overall school-age population (see the UPE section) still enrols only eighty girls for every hundred boys at primary level.
The 2005 target for eliminating gender disparities was missed in many countries Table 2.13: Distribution of countries according to their distance from the gender parity goal in primary, secondary and tertiary education, 2006 Disparities in favour of boys/men Parity Disparities in favour of girls/women Far from the goal: GPI below 0.80 Intermediate position: GPI between 0.80 and 0.94 Close to the goal: GPI between 0.95 and 0.96 Goal achieved: GPI between 0.97 and 1.03 Close to the goal: GPI between 1.04 and 1.05 Intermediate position: GPI between 1.06 and 1.25 Far from the goal: GPI above 1.25 Number of countries in the sample Primary education Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America and the Caribbean North America and Western Europe Central and Eastern Europe Total Secondary education Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America and the Caribbean North America and Western Europe Central and Eastern Europe Total Tertiary education Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America and the Caribbean North America and Western Europe Central and Eastern Europe Total Source: Annex, Statistical Tables 9A and 12.
Progress towards gender parity in sub-Saharan Africa has been slow and uneven.

The mean regional GPI rose from 0.85 in 1999 to 0.89 in 2006.
But the Central African Republic, Chad, Côte d’Ivoire, Mali and the Niger had fewer than eighty girls enrolled in primary school for every hundred boys in 2006.

On the other hand, parity has been achieved in many other countries, including Ghana, Kenya and the United Republic of Tanzania.30 These outcomes demonstrate that gender differences in education can be overcome through public policy action and changes in attitude.
Access to school: where gender disparities begin Disparities at the entry point to formal education run counter to the principles of human rights.

One of the characteristics of universal rights is that they do not differentiate between children on the basis of their gender – and education is a universal right.

Beyond the issues raised by basic rights provision, gender disparities at school entry are reflected in future disparities as children progress through school.
Intake disparities and trends broadly mirror those for total enrolment (Figure 2.32).

Gaps are widest in South and West Asia, and sub-Saharan Africa 30.

The full list of countries having achieved gender parity in primary education in the region: Botswana, Gabon, Ghana, Kenya, Lesotho, Mauritius, Namibia, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Uganda, the United Republic of Tanzania, Zambia and Zimbabwe.
9 8 T H E D A K A R G O A L S : Figure 2.31: Changes in gender disparities in primary gross enrolment ratios between 1999 and 2006 Central Asia Gender parity East Asia/Pacific South/West Asia Latin America/Caribbean GPIs for Ethiopia, Liberia and Nepal increased by 30% between 1999 and 2006 Sub-Saharan Africa Gender parity Arab States Chad C. A. R. Niger Côte d’Ivoire Mali Eritrea Burkina Faso Benin Nigeria Cameroon Guinea Togo Mozambique Ethiopia Comoros Congo Sierra Leone Liberia Burundi Swaziland Cape Verde Equat.
Guinea South Africa Madagascar Kenya Zambia Senegal Ghana Lesotho Uganda Rwanda Malawi Gambia Yemen Djibouti Iraq Sudan Morocco Algeria Egypt Libyan A. J. Syrian A. R. Lebanon Tunisia Qatar Oman Mauritania Tajikistan Mongolia Armenia Papua N. Guinea Lao PDR Timor-Leste Cambodia Macao, China Palau Tonga Niue Solomon Is Indonesia Cook Islands Tokelau Afghanistan Pakistan Nepal India Bhutan Iran, Isl.

Rep. 1 2 Bermuda Guatemala Saint Lucia Brazil Dominican Rep. Chile Grenada El Salvador Br.
Virgin Is Paraguay Panama Cuba Dominica Turks/Caicos Is St Vincent/Grenad.

St Kitts/Nevis Western Europe Centr./East.

Europe Portugal Turkey 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 GPI of GERs GPI of GERs 1999 2006 (increase since 1999) 2006 (decrease since 1999) Stable Notes: Excludes countries with GPI between 0.97 and 1.03 in both years.

See source table for detailed country notes.

1.

In 1999, the GPI for Afghanistan was 0.08.

2.


The high increase in female enrolment in the Islamic Republic of Iran is due to recent inclusion in enrolment statistics of literacy programmes in which women are overrepresented.

Source: Annex, Statistical Table 5.

(average GPIs of 0.94 and 0.92, respectively, in 2006).

On a more positive note, in South and West Asia the GPI registered an 11% gain between 1999 and 2006.

Some countries in the region reported spectacular progress.
For example, the GPI for Nepal increased by 30% and the country achieved gender parity.

While overall progress in sub-Saharan Africa has been less marked, Ethiopia and Liberia each achieved a 30% increase in GPI.

Burundi had attained gender parity in school access by 2006 (see annex, Statistical Table 4).

School progression In many countries, girls are less likely to repeat grades, have a greater chance of reaching the final grade and are more likely to complete the primary school cycle.
Grade repetition.In 114 of the 146 countries with data for 2006, girls repeated less than boys (see annex, Statistical Table 6).

However, lower repetition rates for girls are not necessarily related to progress in gender parity in enrolment.
Figure 2.32: Changes in gender disparities in access to primary education between 1999 and 2006, by region Gender parity World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.

Europe Centr./East.
Europe Centr./East.

Europe 0.70 0.80 0.90 1.00 1.10 GPI of GIRs 1999 2006 (decrease since 1999) 2006 (increase since 1999) Stable In Afghanistan, which had fewer than seventy girls per hundred boys entering school in 2005, the percentage of primary school repeaters was 14% among girls but 18% for boys.

Most of the small number of countries where the percentage of female primary-school repeaters was higher were in sub-Saharan Africa.
Their ranks included Chad, Guinea, Liberia, Mali, the Niger, Nigeria and Sierra Leone.

School retention.In 63 countries out of the 115 with data, there was gender parity in survival rates to the last grade of primary education in 2005 (again defined as GPI between 0.97 and 1.03).

In 36 of the other 52 countries where gender disparities remained, the GPI for the survival rate to the last grade favoured girls – in some cases by a wide margin (Table 2.14).
On the other hand, girls’ survival rates to the last grade were much lower than boys’ in the Central African Republic, Chad, Iraq and Togo.

Source: Annex, Statistical Table 4.
Table 2.14: Gender disparities in survival rates to last grade, 1999 and 2005 Higher survival for boys (16 countries) GPI 1999 2005 Higher survival for girls (36 countries) GPI 1999 2005 GPI 1999 2005 Sub-Saharan Africa Sub-Saharan Africa South/West Asia … … 0.82 … 0.95 … 0.88 0.93 … 0.82 0.92 … … 1.01 0.81 0.83 0.85 0.90 0.90 0.92 0.93 0.93 0.93 0.96 0.78 0.93 0.93 0.95 C. A. R. Togo Chad Niger Eritrea Guinea Zambia Mali Benin Mozambique Arab States Iraq Yemen Mauritania Morocco Central Asia Azerbaijan 1.02 0.94 Latin America/Caribbean Guatemala 1.08 0.96 Pakistan Bangladesh Bhutan Nepal … 1.16 1.10 1.10 Latin America/Caribbean 0.99 … El Salvador Uruguay 1.01 Argentina 1.06 Paraguay 1.09 Venezuela, B. R. … Bahamas Trinidad/Tobago … … Honduras Colombia Dominican Rep. Nicaragua 1.08 1.13 1.20 1.07 1.07 1.08 1.10 1.07 1.04 1.04 1.06 1.07 1.07 1.09 1.09 1.10 1.12 1.18 N. America/W.
Europe Luxembourg 1.11 1.04 1.09 … 1.02 1.07 … Ethiopia Nigeria Madagascar Burkina Faso U. R. Tanzania South Africa Cape Verde Comoros Rwanda Botswana Namibia Burundi Swaziland 1.06 S. Tome/Principe … 1.32 Lesotho 0.96 … … … 1.09 1.06 … 1.04 1.04 1.05 1.05 1.05 1.06 1.06 1.07 1.08 1.10 1.10 1.12 1.13 1.29 1.32 Arab States Lebanon 1.07 1.09 East Asia/Pacific Indonesia Cambodia Philippines Kiribati … 0.87 … … 1.05 1.06 1.14 1.18 Notes: Excludes countries with GPIs between 0.97 and 1.03 in 2005.
Countries with the highest disparities (GPI below 0.90 and above 1.10 in 2005) are highlighted.

See source table for detailed country notes.

Source: Annex, Statistical Table 7.


Gender disparities narrowed in more than half of the 142 countries with data 1 0 0 T H E D A K A R G O A L S : Gender disparities in secondary education: different scales, different patterns Figure 2.33 documents global progress on gender parity at secondary level.
Developed and transition countries had generally achieved gender parity in secondary education by 2006, while the average GPI for developing countries was 0.94, below the world average.

Among the developing regions, the Arab States, South and West Asia, and sub-Saharan Africa combined low participation with low GPIs.
In several countries in these regions – including Afghanistan, Benin, Eritrea, Ethiopia, Iraq, Mali, the Niger and Yemen – the secondary GERs for girls were less than 70% of those for boys (see annex, Statistical Table 8).

Conversely, in many countries in Latin America and the Caribbean, more girls were enrolled than boys at secondary level.

Socio- economic context, occupational practices and gender identity in school all appear to play a role in keeping boys away from school.
Particularly among disadvantaged and excluded groups, boys are more likely to leave school early to earn a living, opting for shorter and less academic secondary education programmes that do not offer the chance to continue to the tertiary level (UNESCO, 2007a).

Expansion of secondary school enrolment has led to reductions in gender disparities in almost all regions.

Several countries in South and West Asia have registered rapid progress.
Many factors have contributed, including increased primary enrolment and completion for girls, rising average incomes and falling poverty rates.

Public policy has also played a key role.

In Bangladesh, which has transformed patterns of gender disparity within a decade or so, the creation of financial incentives for girls’ education has been critical (Box 2.13).
The notable exception to the generally improving situation with respect to gender parity is sub- Saharan Africa, where the GPIs of secondary GERs fell slightly in 2006 (Figure 2.33).

The overall positive trend towards gender parity is also evident at country level.

Gender disparities narrowed in more than half of the 142 countries with data (see annex, Statistical Table 8).
Progress was striking in many countries, particularly those where gender disparities were still substantial in 1999 (Figure 2.34; see countries above the line).

GPIs rose by more than 20% in Benin, Cambodia, Chad, the Gambia, Guinea, Nepal, Togo, Uganda and Yemen.
While girls’ secondary school Figure 2.33: Change in gender disparities in secondary gross enrolment ratios between 1999 and 2006, by region Gender parity World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.

Europe Centr./East.
Europe Centr./East.

Europe 0.70 0.80 0.90 1.00 1.10 GPI of GERs The Arab States, South and West Asia, and sub-Saharan Africa combined low participation with low GPIs 1999 2006 (decrease since 1999) 2006 (increase since 1999) Stable Source: Annex, Statistical Table 8. participation has worsened in several countries,31 gender disparities at the expense of boys have increased in some, including Argentina, El Salvador, Georgia, the Republic of Moldova and Tunisia.
Gender disparities in tertiary education: large differences between regions The world GPI of the tertiary GER rose from 0.96 in 1999 to 1.06 in 2006 (Figure 2.36).

This shows that more women than men are enrolled in tertiary education worldwide.

However, there are large differences among regions.

More women are enrolled in developed and transition countries (GPIs of 1.28 and 1.29, respectively, in 2006), while on average men retain an advantage in developing countries (0.93).
The situation of developing regions varies, with higher rates of female participation in the Caribbean (1.69) and the Pacific (1.31), and far fewer female students in tertiary education in South and West Asia (0.76), and sub-Saharan Africa (0.67).

In some countries, including Afghanistan, the Central African Republic, Chad, Eritrea, the Gambia, Guinea and the Niger, fewer than thirty women were enrolled for every hundred men in 2006.

31.
31.

Azerbaijan, Cameroon, the Comoros, Djibouti, Eritrea, Kenya, Mozambique, Nigeria, Oman, Rwanda and Tajikistan.
Figure 2.34: Changes in gender disparities in secondary gross enrolment ratios between 1999 and 2006 Gender parity Disparities in favour of girls widened Gender parity Disparities in favour of girls narrowed Disparities in favour of boys narrowed Disparities in favour of boys widened 1.4 1.2 1.0 0.8 0.6 0.4 0.2 There is a strong association between poverty and gender inequalities in education 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 GPIs in 1999 Developing countries Developed countries Countries in transition Line indicating no change between 1999 and 2006 Source: Annex, Statistical Table 8.
Gender disparities within countries Global and regional data provide an insight into the position of the average girl or woman.

But, as in other areas highlighted in this chapter, averages can conceal as much as they reveal.

Within countries, some girls face greater disadvantage than others.

Being born into a poor household, living in a rural area or being a member of a particular ethnic or language group can multiply and reinforce disadvantages that come with gender.
Gender parity and poverty Cross-national research using household survey data carried out for this Report underlines the strong association between poverty and gender inequalities in education (Harttgen et al., 2008).

Children in poor households are less likely to attend school than their wealthier counterparts, irrespective of whether they are boys or girls.

Cutting across the wealth divide is an important gender dynamic.
Gender disparities are inversely related to wealth: they rise for girls born into the poorest households.
This disadvantage also tends to be greater at the secondary level than at the 1 0 2 T H E D A K A R G O A L S : Box 2.13: Bangladesh’s triumph: achieving gender parity by 2005 Bangladesh is one of the few countries in the world to have met the Dakar and MDG target of achieving gender parity in primary and secondary education by 2005 — and it did so ahead of schedule.
At the start of the 1990s, boys in Bangladesh were three times more likely to get to secondary school than girls.

By the end of the decade, that immense gap had been closed.

Bangladesh is the only country besides Sri Lanka in South and West Asia to have achieved the EFA gender parity goal (Figure 2.35).

Good governance has played a major role, with public policies helping create an enabling environment for gender parity.
Programmes aimed at creating incentives for girls’ education have been particularly important.

In the mid-1990s, rural girls entering secondary school were exempted from tuition fees and given a small stipend or scholarship.

Successive reforms strengthened the programme.

To keep receiving the benefits, girls must demonstrate attendance rates of 75% or above, pass twice-yearly exams and remain unmarried.

Funding for schools also is conditional upon the participation of girls in the stipend programme.
Thus the incentives extend from the home to the school.

The impact of the stipend programme reaches well beyond education.

Improved levels of secondary education among girls are associated with declines in child mortality, better nutrition, expanded employment opportunities and a narrowing of the gender gap in wages (Al-Samarrai, 2007; World Bank, 2005g).

Bangladesh’s success provides important lessons for countries making slower progress towards gender parity.

Nevertheless, important challenges remain.


Only one child in five who start secondary school will successfully pass the school certificate exam, and girls still lag behind boys on this indicator.

Source: Bangladesh Bureau of Educational Information and Statistics (2006).
Figure 2.35: Gender parity index of secondary school enrolment, Bangladesh Introduction of nationwide stipend programme 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1980 1985 1990 1995 2000 2005 Note: The GPI calculations include general secondary schools and madrasas.

Source: Bangladesh Bureau of Educational Information and Statistics (2006).

primary level (Figure 2.37).


While the transmission mechanisms are often complex, poverty has a generalized effect of exacerbating gender inequality.

Figure 2.36: Change in gender disparities in tertiary gross enrolment ratio by region between 1999 and 2006 As for the wealth-based disparities affecting girls, gender differences in net attendance rates tend to be wider for poorer households in countries with relatively low levels of school attendance.
Countries such as Burkina Faso, Chad, Guinea, Mali, Nepal, the Niger and Zambia illustrate this point (Figure 2.37).

In Mali, the GPI of the primary school net attendance rate in 2001 was only 0.60 for the poorest quintile, whereas many more girls in the richest 20% of households were attending primary school.

If these findings were placed on a global scale, Mali’s poorest households would rank at the bottom of the international league table.
The gap is even more striking at the secondary level, with the GPI about 0.50 for the bottom quintile compared with an average value of 0.96 for the richest group.
In some countries where average net attendance rates are higher for girls than boys, the relationship between poverty and gender disparities works the other way.32 For example, in the Philippines the GPI Gender parity World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.

Europe Centr./East.
Europe Centr./East.

Europe 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 GPI of GERs 1999 2006 (increase since 1999) Stable Source: Annex, Statistical Table 9A.

32.

This is the case in primary education for Cambodia, Haiti, Madagascar, Malawi, Rwanda and Senegal; in secondary education for Colombia and the Philippines; and at both levels in Ghana and Nicaragua.
1 0 3 9 0 0 2 t r o p e R i g n r o t n o M i School proximity has a positive effect on attendance, especially for girls Figure 2.37: Gender parity index of net attendance rates, by education level and wealth quintile, selected countries, most recent year Mali Chad Niger Burkina Faso Guinea Benin Mozambique Nigeria Côte d'Ivoire Kenya India Cameroon Nepal Ethiopia Zambia Uganda Peru Viet Nam U. R. Tanzania Indonesia Namibia Colombia Zimbabwe Bangladesh Ghana Philippines Dominican Rep. Rwanda Cambodia Senegal Haiti Malawi Madagascar Nicaragua Primary education Secondary education Gender parity Gender parity Mali Chad Niger Burkina Faso Guinea Benin Mozambique Nigeria Côte d'Ivoire Kenya India Cameroon Nepal Ethiopia Zambia Uganda Peru Viet Nam U. R. Tanzania Indonesia Namibia Colombia Zimbabwe Bangladesh Ghana Philippines Dominican Rep. Rwanda Cambodia Senegal Haiti Malawi Madagascar Nicaragua 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.2 0.4 0.6 0.8 1.0 1.2 1.4 GPI of NARs GPI of NARs Source: Demographic and Health Surveys, calculations by Harttgen et al.
(2008).

Poorest 20% Richest 20% of the secondary school net attendance rate for the poorest quintile was 1.24 compared with 0.98 for the richest.

When it comes to school attendance, poverty weighs more heavily on girls than boys.

In some cases it weighs far more heavily.

The attendance disparity ratios of the richest to poorest quintile are significantly higher for girls than for boys in Burkina Faso, Chad, Guinea, Mali and the Niger.
These ratios say something important about the unequal distribution of opportunity.

For example, in Mali girls from the richest households are four times more likely to be attending primary school than the poorest girls, an advantage rising to eight times at the secondary level.

Other drivers of gender disadvantages Wealth disparities interact with wider social, economic and cultural factors to disadvantage girls.
As an important cross-country research exercise shows, being born into a group that is indigenous, a linguistic minority, low caste or geographically isolated can magnify disadvantage (Lewis and Lockheed, 2008): Indigenous girls in Guatemala are less likely to be enrolled than other demographic groups (Hallman et al., 2007).

At age 7, only 54% of indigenous girls are in school, compared with 71% of indigenous boys and 75% of non- indigenous girls.
By age 16, only one-quarter of indigenous girls are enrolled, compared with T H E D A K A R G O A L S : 45% of boys.

Poverty has a magnifying effect, with only 4% of ‘extremely poor’ indigenous girls aged 16 attending school, compared with 45% of their ‘non-poor’ counterparts.
India’s caste system has a major effect on participation, with 37% of girls aged 7 to 14 belonging to scheduled castes or tribes not attending school, compared with 26% of girls from the majority Hindu group (Lewis and Lockheed, 2006).

Poor rural girls in Pakistan are among the most deprived in the country.

Girls in urban areas and from the highest income group are almost as likely as their male counterparts to attend school or complete the five primary grades (Lloyd et al., 2007).
By contrast, one girl for every three boys attends school among the poorest rural households.

In the Lao People’s Democratic Republic, poor rural non-Lao-Tai girls aged between 6 and 12 have the lowest attendance rate of any group.

In 2002/2003 their attendance rate was 46%, compared with 55% for poor non-Lao-Tai boys and 70% among poor rural Lao-Tai girls.

Poverty and the need to work seem to be the main reasons children do not go to school or drop out early (King and van de Walle, 2007).
Cultural attitudes and practices that promote early marriage, enforce seclusion of young girls or attach more value to boys’ education can form a powerful set of barriers to gender parity.

In Nepal, 40% of girls are married by age 15 – a barrier to school completion.

Norms that keep girls at home during the menses reduce their time in school and lower their school performance (Lewis and Lockheed, 2006).
Distance from school is also associated with strong gender disparity effects, especially in rural areas (UN Millennium Project, 2005b).

In the Lao People’s Democratic Republic, distance to school is negatively related to enrolment (King and van de Walle, 2007).

Similarly, research in Pakistan reports that having a state school in a village has a strong positive effect on the probability that girls aged 10 to 14 will be enrolled (Lloyd et al., 2007).
Public policy and governance initiatives can help overcome gender inequalities.

Removing fees and providing incentives for girls to be in school can counteract financial pressures on households.

Building schools close to rural communities and recruiting local teachers can help narrow gender disparities in rural areas.

Removing cultural barriers to equity is more difficult.

It requires long-term public education, committed political leadership and legislation enforcing the equal rights of girls.
Gender equality in education: more difficult to achieve In addition to the target of eliminating gender disparities in primary and secondary education by 2005, the EFA gender goal calls for achieving gender equality in education by 2015, with a focus on ensuring girls full and equal access to and achievement in basic education of good quality.

That part of the goal is more challenging, as this Report’s monitoring of learning outcomes and school practices reveals.
Learning outcomes and subject choice: gender differences persist Girls and boys achieve very different outcomes in school, not just in overall performance but also by subject.

Features of education systems and classroom practices partly explain these differences, but such school-based factors interact with wider social, cultural and economic forces that structure expectations, aspirations and performance along gender lines.

Student assessment results show wide-ranging gender differences.
While the disparities vary, four distinctive patterns emerge: Girls continue to outperform boys in reading literacy and language arts.

This effect holds across a diverse group of countries, including those with significant gender disparities in school participation, such as Burkina Faso in sub- Saharan Africa and Morocco in the Arab States (UNESCO, 2007a).
In one of the most recent international student assessments, the 2006 Progress in International Reading Literacy Study (PIRLS), average scores on the combined reading literacy scale were significantly higher for girls than for boys in forty-three of the forty-five countries participating (Mullis et al., 2007).

Girls’ average score across all forty-five countries was seventeen points higher than that for boys, although variations by country were wide.
Elsewhere, the 2006 Segundo Estudio Regional Comparativo y Explicativo (SERCE) conducted in Latin America found that girls did significantly Student assessment results show large gender differences better than boys in reading achievement in grades 3 and 6 in half of the sixteen countries participating (UNESCO-OREALC, 2008).33 Historically, boys have outperformed girls in mathematics in all grades of primary and secondary education – but that picture is changing.
Girls increasingly perform at levels equal to or better than those of boys (Ma, 2007).
For example, in Francophone Africa, among students tested in the Programme d’analyse des systèmes éducatifs de la CONFEMEN (PASEC), there were no appreciable gender differences in mathematics achievement at second-grade level; in the fifth grade, small gender differences in favour of boys were reported in half of the eight participating countries (Michaelowa, 2004b).34 Among sixth graders tested in fourteen countries or territories by the Southern and Eastern Africa Consortium for Monitoring Educational Quality (in SACMEQ II, 2000–2003), significant male advantages in mathematics were present in Kenya, Mozambique, the United Republic of Tanzania, Zambia and Zanzibar.
In the recent SERCE assessment, eight countries35 demonstrated gender differences, most of them small, in favour of boys in grade 3 (UNESCO- OREALC, 2008).
Moreover, girls are outperforming boys in mathematics in a growing number of countries, including Seychelles (SACMEQ II); Cuba (2006 SERCE); Armenia, the Philippines and the Republic of Moldova (grade 4, in the 2003 Trends in International Mathematics and Science Study, or TIMSS); Bahrain and Jordan (eighth grade, TIMSS, 2003); and Iceland (2003, in the OECD-sponsored Programme for International Student Assessment, or PISA).
In TIMSS 2003, as many countries showed gender differences in favour of girls as in favour of boys (Ma, 2007).

The science gap is often small, though boys tend to maintain an advantage.

Recent science assessments continue to report cases in which boys hold an advantage over girls, but more often than not the difference is statistically insignificant (Ma, 2007).

In Latin America, sixth-grade boys outperformed girls in science in Colombia, El Salvador and Peru.
In the remaining countries (Argentina, Cuba, the Dominican Republic, Panama, Paraguay and Uruguay) gender differences were mixed and not statistically significant (UNESCO-OREALC, 2008).

In TIMSS 2003 boys outperformed girls in some countries while the reverse was true of a smaller group of countries.

The evidence indicates a slightly greater male advantage in the higher grade levels: boys outperformed girls in proportionally more countries in grade 8 than in grade 4.
In PISA 2006, which tested reading, mathematics and science, gender differences in science were the smallest among the three (OECD, 2007b).

Subject choice in tertiary education is still marked by strong gender selection effects.

Despite the increase in female participation, some subject areas remain male domains.

Globally, women’s median share of tertiary science enrolment in 2006 was 29% and their share in engineering was lower still at 16%.
On the other hand, in half the countries with the relevant data women accounted for more than two-thirds of students in fields long considered ‘feminine’, such as education, health and welfare (see annex, Statistical Table 9B).

Social scientists have long sought to understand the forces underlying women’s under-representation in scientific fields.
Recent studies indicate socialization processes may influence girls’ orientation to specific disciplines; examples include poor career counselling, lack of role models, negative attitudes from families, fear of mathematics and fear of being in the minority (Morley, 2005).

Course and stream selection in upper secondary is also important.

Why do girls perform differently in achievement tests?
The scope and magnitude of the differences point to a conditioning environment that extends from school policies and classroom practices to ascribed gender roles and perceptions in society (UNESCO, 2007a).

Social conditioning and gender stereotyping can limit ambition and create self-fulfilling expectations of disparities in outcomes.
Recent research underlines a strong association between the degree of gender equality in society at large and the size of gender gaps in mathematics achievement (Guiso et al., 2008).

How children are taught is important not just in relaying knowledge, but also in moulding expectations and building self-confidence.

Teacher attitudes and practices that translate into different treatment of boys and girls can affect cognitive development and reinforce gender stereotyping (Carr et al., 1999; Tiedemann, 2000).
So can textbooks.

Content analyses of textbooks in many Girls are outperforming boys in mathematics in a growing number of countries 33.

The eight countries concerned were Argentina, Brazil, Cuba, the Dominican Republic, Mexico, Panama, Paraguay and Uruguay.

In addition there was a statistically significant female advantage among sixth graders in Chile.

34.

This was the case in Burkina Faso, Mali, the Niger and Senegal.
The four other countries participating were Cameroon, Chad, Côte d’Ivoire and Madagascar.

35.

Brazil, Chile, Colombia, Costa Rica, El Salvador, Guatemala, Nicaragua and Peru.

1 0 6 T H E D A K A R G O A L S : countries continue to reveal gender biases, with girls and women under-represented.
Despite the general movement towards gender equality, both sexes continue to be shown in highly stereotyped household and occupational roles, with stereotyped actions, attitudes and traits (UNESCO, 2007a).

Progress towards eliminating gender bias in textbooks seems very slow.

While unrefined examples of sexism have largely disappeared, unbalanced and inappropriate learning material remains prevalent (Blumberg, 2007).
Female teachers can serve as role models for young girls, potentially countering gender stereotypes.

Globally, female teachers are overrepresented in lower levels of education while the reverse is true at higher levels (Figure 2.38).
In many countries, particularly in the developing world, female teachers tend to be clustered in Figure 2.38: Percentage of female teachers by level of education and by region, 2006 Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.

Europe Centr./East.

Europe 0 20 40 60 80 100 Female teachers (%) Pre-primary Primary Secondary Tertiary Source: Annex, Statistical Tables 10A and 10B.

urban schools.
urban schools.

A recent survey in eleven middle- income countries shows that pupils in rural primary schools are more likely than urban pupils to be taught by male teachers.

This is particularly the case in India, Paraguay, Peru and Tunisia (Zhang et al., 2008; see the section on quality below for further survey results and the full list of countries).

Rural girls thus have less chance of contact with female role models who might raise their expectations and self-confidence.
The presence of female teachers may also help increase girls’ access to school in countries where high gender disparities prevail.

Yet it does not always guarantee gender equality in socialization and learning processes (UNESCO, 2007a).

Teachers of either sex may discriminate informally, reinforcing gender disparities and undermining learning outcomes for disadvantaged groups.
Such behaviour can affect learning opportunities if, for example, girls or minority students are seated far from the teacher, do not receive textbooks or are not called on in class.

In Yemen, researchers observed that primary school girls were typically seated at the rear of the classroom – an arrangement not conducive to effective participation (Lewis and Lockheed, 2006, pp.

70-1; World Bank, 2003).


Greater attention to gender training for teachers would help, but in many countries the gender dimension in teacher training takes a back seat to the teaching of reading and mathematics when it comes to efforts to improve classroom and teacher practices (UNESCO, 2007a).
Progress towards eliminating gender bias in textbooks is slow Ensuring both equity and the quality of learning Getting all children through a full basic education cycle and into secondary school is an important goal.
But the ultimate purpose of schooling is to provide children with an education that equips them with the skills, knowledge and wider perspectives they need to participate fully in the social, economic and political lives of their countries.

Education quality is harder to measure than quantitative indicators – but quality and learning is what counts.

Evidence from many developing countries paints a worrying picture on learning achievement.
Recent progress in quantitative indicators of school participation has distracted attention from the glaring need to improve education quality at the same time.

Many children attend primary school, and even graduate, without ever acquiring a minimum toolkit of literacy and numeracy skills.

It would be a Pyrrhic victory for EFA if countries achieved UPE but failed to give children real opportunities to learn.
Clearly, then, assuring quality in education – as manifested by the design, scope and depth of learning experiences children encounter in school – is vital, going to the heart of what constitutes good governance in education.

National ‘average’ learning levels and global disparities A primary education of good quality should enable children to acquire at a minimum basic skills in language and mathematics, and to aspire to continued learning (Box 2.14).
But what level of knowledge and skills do children actually attain in school?

While international assessments consistently spark intense political debate, less attention is paid to the absolute level of learning, especially in developing countries.

Recent studies, many based on national assessments, point to deep deficits in student knowledge in many developing countries.

What do learners learn?

In many countries children are acquiring only the most rudimentary skills in school.
A recent Box 2.14: How to measure quality in education?

Measuring quality in education is fraught with difficulty.

While indicators exist to measure enrolment, grade attainment and school completion, there is no ready-made yardstick for quality and no globally agreed benchmark for measuring progress.
Participants at the Dakar Forum identified several elements as necessary for quality in education, among them: well-nourished, motivated students; well-trained teachers using active learning techniques; adequate facilities and materials; a relevant, local language curriculum that builds on teachers’ and learners’ knowledge and experience; a welcoming, gender-sensitive, healthy, safe environment that encourages learning; and a clear definition and accurate assessment of learning outcomes (UNESCO, 2000).
Until recently, monitoring of quality primarily meant tracking input measures, such as educational expenditure, and teacher supply and qualifications.

Now, however, with the growth of learning assessments, monitoring increasingly focuses on learning outcomes.1 Still, as measures of observed teaching and learning remain few and are rarely examined,2 the bias towards measuring inputs continues (Alexander, 2008).

Another measurement issue concerns equity.


Improved quality is typically equated with higher average achievement levels.

Student knowledge and competencies are ranked in content domains (e.g.

language, mathematics, sciences) based on reported country mean scores on standardized tests.

Information about the uneven dispersion of learning across regions, households, ethnic groups and, most importantly, schools and classrooms often goes under-reported.3 1.
In theory, learning outcomes include subject-based knowledge, broader skills and competencies, social attitudes, moral values and behaviours.

In practice, student learning is mainly assessed in terms of either cognitive understanding or skills and competencies.

In the past, most international assessments involved high-income countries and a few middle-income ones.

Since Dakar more middle- and low-income countries have participated in international and regional assessments.
At the same time more national assessments are being conducted, in all regions (Benavot and Tanner, 2007).

2.

Studies based on teacher self-reports of teaching processes are more common than those based on classroom observation.

See, for example, Anderson et al.

(1989).

3.

Gender disparities are the exception, having received considerable attention.

Disparities based on poverty, ethnicity, language, race, caste, residence and religion are less examined.
Many children graduate from primary school without acquiring minimum literacy and numeracy skills 1 0 8 T H E D A K A R G O A L S : assessment in Punjab, Pakistan, demonstrates the point: more than two-thirds of grade 3 students could not form a sentence in Urdu and a similar percentage was unable to subtract three-digit numbers (Das et al., 2006).
While most children could recognize and write the English alphabet, large percentages found it difficult to place a word like ‘ball’ near a picture of a ball.

Complicated words and sentences were beyond the reach of the vast majority.

South Asia’s problems in education achievement are not confined to Pakistan.

Learning assessments in India also point to low levels of literacy and numeracy.
Since 2005 a large-scale, non- government initiative has carried out household surveys of rural Indian children to determine their school enrolment status and assess their abilities in reading, arithmetic and English (Pratham Resource Center, 2008).
The most recent survey (2007) found that fewer than half the children in standard 3 could read a text designed for standard 1 students, and only about 45% of standard 4 students could read simple words or sentences in English.36 Just 58% of the students in standard 3 and 38% in standard 4 could subtract or divide.
Another recent school-based assessment in India involving over 20,000 students in the states of Andhra Pradesh, Madhya Pradesh, Orissa, Rajasthan and Tamil Nadu confirmed the low level of learning in many primary schools.

Many students in standards 3, 4 and 5 were found to lack basic reading, writing and arithmetic skills (Table 2.15).37 Assessment exercises elsewhere in the developing world suggest that the situation in Pakistan and India may be less the exception than the rule.
Table 2.15: Percentage of Indian students in standards 3, 4 and 5 who successfully demonstrated basic skills % of students who can: Standard 3 Standard 4 Standard 5 Read Write Add Subtract Multiply Divide 59 47 52 45 30 12 62 47 53 47 40 28 71 60 67 59 54 41 Note: In reading, children were expected to read ten short, simple sentences at standard 2 level.

In writing, they were expected to write ten words and five short, simple sentences of standard 2 difficulty.
In arithmetic, they were given five problems each in addition, subtraction, multiplication and division and were defined as competent if they received a score of seventy or above.

Source: Aide et Action (2008).

Research indicates many countries face an immense challenge in helping children acquire minimum language skills: In Cambodia a grade 3 assessment of the Khmer language involving almost 7,000 students found that 60% had ‘poor’ or ‘very poor’ skills in reading (e.g.
as regards pronunciation and word recall) and writing (e.g.

punctuation and sentence structure) (Cambodia Education Sector Support Project, 2006).

In the Dominican Republic, Ecuador and Guatemala half or more of grade 3 students were found to have very low reading levels: they could not recognize the addressee of a family letter or decipher the meaning of a simple text in Spanish (UNESCO-OREALC, 2008).
A recent assessment in Peru found that as few as 30% of children in grade 1 and only about half in grade 2 could read simple passages from a grade 1 textbook (Crouch, 2006).

Results from SACMEQ II indicate that fewer than 25% of grade 6 children reached the ‘desirable’ level of reading literacy in Botswana, Kenya, South Africa and Swaziland, and fewer than 10% in Lesotho, Malawi, Mozambique, Namibia, Uganda and Zambia.

These examples draw attention to the sheer scale of the quality challenge in education.
Millions of children in the developing world attend primary schools, many for several years, without mastering basic skills.

Assessments of more complex abilities, such as conceptualizing, critical thinking and problem-solving, are equally disturbing.

For example, in Egypt close to 10,000 fourth grade students in seven governorates were assessed in Arabic, mathematics and science.
In all three subjects only one-quarter to one-fifth of students demonstrated an ability to answer questions involving critical thinking and problem-solving (Table 2.16).

Overall, deep learning deficits are too common among schooled children in many developing countries.

The policy challenge is clear: creating school systems in which a significant segment of each school-age cohort reaches a minimal learning threshold (Filmer et al., 2006).
The sheer scale of the quality challenge in education is daunting 36.

Standards 1 through 5 are equivalent to primary grades 1 through 5.

The 2007 assessment indicated a slight improvement over earlier tests in reading and no change in mathematics.

English is introduced by standard 3 in all states except Gujarat; it is the medium of instruction in Jammu and Kashmir State, and Nagaland State.

37.

The Children’s Competency Assessment tested students in standards 2 through 5.
Table 2.16: Achievement among grade 4 students in Egypt, by cognitive level and content domain, 2006 Figure 2.39: Percentage of low performing students (at or below level 1) in science literacy, PISA 2006 % of students who correctly answered items assessing: Factual knowledge Conceptual understanding Critical thinking and problem-solving Arabic Mathematics Science 50 43 21 36 31 17 60 34 27 Source: Egypt Ministry of Education (2006).
l a b o G l l l A r o f i n o t a c u d E There are glaring gaps in student achievement between developed and developing country students 38.

The association is stronger at the lower end of the income scale than at the upper end.

In TIMSS 1999, the association between per capita GNP and scores in mathematics and science was about 0.60 (Barber, 2006).

39.

Bahrain, Egypt, Jordan, Lebanon, Morocco, the Palestinian Autonomous Territories, Saudi Arabia, the Syrian Arab Republic and Tunisia.
1 1 0 The international divide in learning outcomes Beyond the concern about low learning levels based on national surveys, international assessments show most developing countries still far behind developed countries.

In an increasingly integrated, knowledge-based world economy, these disparities have important implications for development prospects – and for future patterns of globalization.
With more developing countries participating in international assessments over the past twenty years, two consistent findings have emerged.

First, there are glaring gaps in achievement between developed and developing country students at similar levels of schooling.

Second, the gaps are only partially associated with differences in per capita income.38 Other differences – linked to school quality, teaching policies and system-wide governance – are also significant.
International assessments illustrate the extent of low learning levels among ‘average’ students in participating countries.

Results from PISA, which tests 15-year-old students in several competencies, are instructive because they include many non- OECD countries.

They highlight striking disparities.


The median PISA 2001 scores of Brazilian, Indonesian and Peruvian students, placed on a scale alongside those of students from Denmark, France and the United States, are situated in the lowest 20% of the latter countries’ distribution (Filmer et al., 2006).

PISA 2006 science results show students from developing countries being much more likely to figure in the lowest achievement levels (Figure 2.39).
Over 60% of students from Brazil, Indonesia and Tunisia, but fewer than 10% in Canada and Finland, scored at or below level 1, the lowest level in the PISA science ranking.

And fewer than 2% to 3% of students from developing countries attained proficiency levels 5 and 6, whereas 15% or more did so in several OECD countries.
Kyrgyzstan Qatar Azerbaijan Tunisia Indonesia Brazil Colombia Argentina Mexico Montenegro Romania Turkey Thailand Jordan Bulgaria Uruguay Chile Serbia Israel Italy Portugal United States Greece Russian Fed.
Luxembourg France Norway Iceland Lithuania Slovakia Spain Denmark Latvia Belgium Poland Croatia United Kingdom Sweden Austria Switzerland Czech Rep. Ireland Germany Hungary Slovenia New Zealand Netherlands Australia Japan Rep. of Korea Macao, China Canada Estonia Finland 0 20 40 60 80 100 Share of students (%) At or below level 1 Levels 2 to 4 At or above level 5 Source: OECD (2007c).

Other international assessments point in a similar direction.
In the 2003 TIMSS, half of all grade 8 students achieved the intermediate benchmark (475), but only 17% from the nine participating Arab States39 did so (UNDP Arab TIMSS Regional Office, 2003).

The 2006 PIRLS, testing fourth graders in reading, revealed large disparities between T H E D A K A R G O A L S : developed and developing countries.
The five middle- and low-income countries outside Europe40 achieved a mean score of 377 – almost 125 achievement points below the international mean (500).41 Box 2.15 discusses more findings.

While the number of developing countries participating in international assessments has increased, many gaps remain, limiting the scope for cross-country comparison.
Exploratory research for this Report attempts to address the problem by standardizing national achievement data in primary education from different assessments to place countries on a single international scale.42 The exercise shows the achievement scores of many developing countries clustered far below those of developed and transition countries (Figure 2.40).43 It also suggests that the learning gaps tend to be more pronounced in science than in mathematics and reading (Altinok, 2008).
International assessments can understate the divide between developed and developing countries since they assess learning outcomes only among children in school.

They do not include similarly aged children who are currently – or permanently – out of school.

Especially in countries where school participation rates are low and dropout rates high, exclusion of out-of-school children can distort national learning profiles.
In rural India, for example, when out-of-school children were tested they were half as likely as in-school children to listen to and answer a subtraction problem (Pratham Resource Center, 2008).

Similarly, in Ghana, Indonesia and Mexico, tests of language and mathematics among out-of-school youth found lower achievement levels than among enrolled students (Filmer et al., 2006).
Box 2.15: New international evidence on learning outcomes: what it reveals about quality Since the release of the EFA Global Monitoring Report 2008 (UNESCO, 2007a), results from three major international assessments have been published.

They provide important insights on a range of qualitative indicators of education performance.

PIRLS 2006 measured grade 4 reading skills.

The percentage of students demonstrating basic reading ability — i.e.
reaching level 1, the lowest international benchmark — varied from 22% in South Africa and 26% in Morocco to more than 95% in most of North America and Western Europe.

The share of students performing at or above the intermediate benchmark, level 2, was over 75% in most OECD countries but less than 20% in developing countries including Indonesia, Morocco and South Africa (Mullis et al., 2007).1 PISA 2006 tested 15-year-olds in science, mathematics and reading (OECD, 2007b).
Twenty of the thirty participating OECD countries had science scores within twenty-five points of the OECD average of 500.

Among countries scoring significantly below the OECD average, the variation was considerably greater, from a low of 322 in Kyrgyzstan to a high of 493 in Croatia.

PISA 2006 results can be compared with those from 2000 in reading and from 2003 in science and mathematics.
For most countries with comparable data, average scores changed relatively little — despite increases in national investment in education (OECD, 2007b).
In Latin America the 2006 Segundo Estudio Regional Comparativo y Explicativo (SERCE) assessed reading and mathematics in grade 3, and reading, mathematics and science in grade 6 (UNESCO-OREALC, 2008).2 Overall, countries fell into four categories: 1) Cuban students outperformed those from other countries in almost all subjects and grade levels; 2) a small group of other consistently high performing countries included Chile, Costa Rica and Uruguay; 3) a large group of relatively poor performing countries included the Dominican Republic, Ecuador, El Salvador, Guatemala, Nicaragua, Panama, Paraguay and Peru; and 4) in countries in the middle — Argentina, Brazil, Colombia and Mexico — pupil achievements varied by subject and grade.
For the last three groups, within- country differences in achievement by location and gender were often marked.

For example, among poor-performing countries, rural-urban differences were considerably more pronounced in El Salvador, Guatemala and Peru than in the Dominican Republic, Nicaragua and Panama.

1.
1.

Of the twenty-seven countries and territories with reading achievement data for both PIRLS 2001 and 2006, eight showed significant gains: Germany, Hong Kong (China), Hungary, Italy, the Russian Federation, Singapore, Slovenia and Slovakia.

In Germany, the Russian Federation and Slovakia, improvements in reading achievement were at the expense of equity: gains were made among higher-performing students but not lower-performing ones.
Average reading levels declined over time in England (United Kingdom), Lithuania, Morocco, the Netherlands, Romania and Sweden.

2.

Sixteen Latin American countries took part in SERCE.

The number of countries participating in learning assessments varies: there were forty countries in PIRLS 2006, fifty-seven countries and territories in PISA 2006.

Within-country differences in achievement by location and gender are often marked 40.
Indonesia, the Islamic Republic of Iran, Morocco, South Africa, and Trinidad and Tobago.

41.

In addition, two high- income Arab States, Kuwait and Qatar, scored below 350.

42.

Another study of this type is Hanushek et al.

(2008).

43.

Achievement scores for some countries in sub- Saharan Africa, and South and West Asia were excluded due to data limitations.
1 1 1 9 0 0 2 t r o p e R C H A P T E R 2 Figure 2.40: Disparities in mean learning achievement in primary education among developed countries, developing countries and countries Developed countries Developing countries and countries in transition a c i r f A h t u o S o c c o r o M a i s i n u T .

R .

B , .

p e R .
p e R .

l s I , n a r I y e k r u T o g a b o T / d a d n i i r T 20 25 30 35 40 45 Composite achievement score based on pupil performance in international learning assesments Note: The composite achievement score is the arithmetic mean of all scores of a given country on international assessments between 1995 and 2006.

Standardized with a mean of 50 and a standard deviation of 10, it ranges from 0 to 100.
Achievement data are only compiled from international and regional assessments, and not from national assessments.

Source: Altinok (2008).

Beyond national averages — huge inequalities in achievement of the best pupils in high-achieving countries (Mullis et al., 2007).

Unequal learning outcomes, typically related to socio-economic status and other indicators for disadvantage, are most pronounced within countries.

They exist at every level: between regions, communities, schools and classrooms.
Such unequal outcomes are a source of intense political debate.

Parents, policy-makers and often children themselves perceive such disparities as evidence of an unfair and inequitable education system.

Improving and equalizing the provision of education of good quality is at the core of the wider EFA governance challenge.

Among the huge within-country differences that learning assessments continue to document: Reading scores of fourth graders from developing countries varied widely in PIRLS 2006.
The gap between the top 5% and bottom 5% was 454 scale points (562 minus 108) in South Africa, 359 points in Morocco and 340 points in Trinidad and Tobago.

In all three, high-scoring pupils reached reading levels comparable to some In SERCE, third-grade reading scores varied extensively in both high- and low-performing countries.

In Cuba the point difference between students in the top 10% and bottom 10% of the distribution was 295 scale points (779 minus 484).
Most other countries in the region had smaller differences, among them Argentina (236 points), Costa Rica (231), El Salvador (219) and Paraguay (241) (UNESCO-OREALC, 2008).

Using items based on the 2005 TIMSS, tests were administered to 6,000 ninth-grade students in the Indian states of Rajasthan and Orissa.
Not only were average scores very low, with 30% to 40% of the children unable to reach a low international benchmark, but the score distribution was highly unequal: the difference between the top 5% and bottom 5% was among the highest in the world.

Students in the top 5% scored higher than the top students in other low-performing countries, and higher than the median students in all but the best-performing countries (Das and Zajonc, 2008; Wu et al., 2007).
Unequal learning outcomes are most pronounced within countries 1 1 2 T H E D A K A R G O A L S : in transition, 1995–2006 Developed countries d n a l e c I e c e e r G ) r F ( ) l F ( m u i g l e B n a p a J Developing countries and countries in transition .
p e R .

d e F n a i s s u R e r o p a g n S i 45 50 55 60 Composite achievement score based on pupil performance in international learning assesments a e r o K f o .

p e R 65 70 The underlying causes of inequality in learning outcomes are enormously varied.
However, research drawing on data from international, regional and national assessments identifies three sets of key factors influencing within-country disparities: student background, school context and system-level characteristics.44 Student-related factors What students bring with them to school influences how well they perform.

Some student endowments, such as ability, are inherent and randomly distributed.
Others are the product of social, economic and cultural circumstances, such as parents’ education, occupation and income; gender (see previous section); home language; and other family characteristics.

Socio-economic-related gaps in achievement are a dominant, recurring theme in national and international research.

Students of lower socio- economic status generally score lower than students from more advantaged backgrounds.
The level and slope of the socio-economic gradient of learning vary considerably among countries – a key fact, as it shows the influence of public policies in this area (Ma, 2008; Willms, 2006).
Interestingly, recent assessments suggest that larger performance gaps linked to socio-economic status exist in Central and Eastern Europe and in North America and Western Europe than in developing countries (Ma, 2008).45 Research for this Report attempted to clarify the degrees to which occupation, parents’ education, family income/ household wealth and ‘home literacy’ were each associated with pupil achievement in various countries (Ma, 2008).
Occupation was found to be the most important socio-economic status component in North America and Europe, while household wealth (family possessions) was the most important in East Asia and the Pacific, and in Latin America and the Caribbean.46 Parental education, while significant, was found to have less impact.

‘Home literacy’, defined as the possession of over ten books, had strong positive effects on learning outcomes in most middle- and low-income countries.
Family size and composition also influence learning achievement.

Recent research confirms that children with fewer siblings tend to outperform those with more siblings (Dronkers and Robert, 44.

See, for example, Fuller (1987), Fuller and Clarke (1994), Keeves (1995), Lockheed and Verspoor (1991), Michaelowa (2004a), Mullis et al.

(2000), Mullis et al.

(2003), Postlethwaite (2004), Riddell (2008), Scheerens (2004) and Wößmann (2003).

45.
45.

PISA, employing an index of economic, social and cultural status, found few differences in its effects among the different subject domains: reading, mathematics and science.

46.

The author argues that, when it comes to student learning outcomes, in more developed regions social capital at home outweighs material resources at home, whereas in less developed regions the opposite is the case.
1 1 3 9 0 0 2 t r o p e R i g n r o t n o M i Extensive early childhood education boosts equity and helps overcome disadvantage 2008; Park, 2008).47 Rapid changes in family structure due to divorce, separation, parental death or migration are equally influential.
The PISA 2003 mathematics assessment found that in twenty countries with relevant information, students from two-parent homes performed best, on average – a result that held even after controlling for socio- economic status (Hampden-Thompson and Johnston, 2006).48 Immigrant status influences learning in many countries.
Results from PISA 2003 indicated that first-generation immigrant students (those born abroad) and second-generation students (those whose parents were born abroad) scored lower in reading, mathematics and science than their native counterparts, except in Canada (OECD, 2006b).49 As language proficiencies in the host country improve, achievement disparities among second- generation immigrants decline (Schnepf, 2008).
Characteristics of immigrant children’s countries of origin and destination also influence achievement (de Heus et al., 2008; Levels et al., 2007).

First-generation children from countries where compulsory education lasted longer performed better in science (OECD, 2007a) than other, similar children (de Heus et al., 2008).
With migration flows increasing worldwide, closing immigration-related inequalities is important not just for achieving equity in education, but also for addressing concerns over social cohesion (International Organization for Migration, 2005; OECD, 2006b).

Home language is related to classroom success.


In eighteen of the twenty OECD countries participating in PISA 2003, students whose home language differed from the language of instruction had significantly lower scores in mathematics than those who spoke the test language at home (Hampden-Thompson and Johnston, 2006).50 In many Latin American countries, including Bolivia, Chile, Ecuador, Guatemala, Mexico and Peru, 47.
See also results from national assessments in Cambodia, Ethiopia, Madagascar and Mongolia (Cambodia Education Sector Support Project, 2006; Academy for Education Development and USAID Ethiopia, 2004; Madagascar Ministry of National Education & Scientific Research and UNESCO, 2004; Mongolia Ministry of Education, Culture and Science and UNICEF, 2008).

48.

See also Bradshaw and Finch (2002), Downey (1994), Duncan and Brooks-Gunn (1997), Hampden-Thompson and Pong (2005), Haveman et al.
(1991), McLanahan and Sandefur (1994) and Pong et al.

(2003).

49.

This study focused on seventeen countries or territories with large immigrant populations: Australia, Austria, Belgium, Canada, Denmark, France, Germany, Luxembourg, the Netherlands, New Zealand, Norway, Sweden, Switzerland and the United States, among OECD countries, and three non-OECD PISA participants: the Russian Federation, Hong Kong (China) and Macao (China).
children from households where indigenous languages were spoken scored significantly lower in reading and mathematics than those from non-indigenous households (Flores-Crespo, 2007; Lewis and Lockheed, 2006; McEwan, 2004; McEwan, forthcoming; McEwan and Trowbridge, 2007).51 In most studies the impact of language remained after adjusting for factors such as poverty, location and other home background indicators.
System-level factors The way an education system is organized can have a significant bearing on learning outcomes.

Rules on promotion between grades, school- leaving exams, institutional differentiation (between different types of school) and instructional differentiation (through ability grouping, streaming, multigrade teaching) all have an impact on learning outcomes.
While the impact varies by context, some broad findings emerge from international research (Fuchs and Wößmann, forthcoming; OECD, 2007a): Sorting students into non-equivalent tracks or streams is associated with reduced equity (unequal learning outcomes) and sometimes lower learning levels.

Education systems with very selective academic streams are associated with larger gender gaps in mathematics and science at both primary and secondary level (Bedard and Cho, 2007).
Extensive early childhood education (of longer duration and higher enrolment coverage) increases equity in education for children from dissimilar family backgrounds (Schütz et al., 2005).

Public policies and attitudes towards immigration, residency, gender and language are associated with differences in educational opportunities.

Where measures encouraging gender equality have been taken, gender gaps in mathematics tend to be smaller (Baker and Jones, 1993; Guiso et al., 2008; Marks, 2008).

50.
50.

More recently, poor language achievement by third- and sixth-grade children in the Dominican Republic, Ecuador, Guatemala, Nicaragua, Panama, Paraguay and Peru was reported in SERCE, further illustrating the influence of home language (UNESCO-OREALC, 2008).

51.

The ‘effect size’ of belonging to an indigenous household was, on average, over one-third of a standard deviation; i.e.
indigenous children’s scores were one-third of a standard deviation lower in Spanish and mathematics than those of non-indigenous children.

The indigenous disadvantage was higher for language scores than in mathematics.

T H E D A K A R G O A L S : Policies governing the group composition of schools significantly affect learning.
The mix of students attending a school – in terms of socio-economic status, ethnicity or race – affects learning not only independently but also indirectly through managerial, pedagogical and psycho-social processes (Dumay and Dupriez, 2007).

Systems with more privately funded and academically selective schools tend to attain higher learning outcomes, but the achievement advantages tend to be reduced once student background factors are taken into account (OECD, 2007a).
In some assessments, learning outcomes are related to the extent to which schools in a system have autonomy over teacher appointments, budget formulation and allocation, and/or instructional content (see Chapter 3).

Evidence from PISA 2006 indicates that learning outcomes tend to be higher in countries that encourage public posting of student performance.

School-based factors Properly resourced schools, effective teachers and dynamic classrooms are crucial for learning.
Even after adjustments for student background and other factors, international research consistently points to large school-based differences in learning outcomes (Willms, 2006).

Inequalities in school context and quality are especially pronounced in developing countries and typically account for considerable variation in learning outcomes (Baker et al., 2002; Heyneman and Loxley, 1983).

What makes for an effective learning environment?

Dynamic processes are important.


Professional leadership, shared vision and goals, teachers who motivate students, and the use of monitoring and evaluation to improve performance are considered key ingredients of ‘effective’ schools (Creemers, 1997; Reynolds et al., 2002).52 Sufficient instructional time is also vital (Box 2.16).

Dilapidated buildings, overcrowded and under- resourced classrooms, and inadequate supplies of textbooks and workbooks are not conducive to learning.
More students from rich families attend well-equipped schools (Table 2.17).

The poor state of the school environment in many countries is Box 2.16: Unequal learning time, unequal outcomes Learning gaps in many countries are linked to inadequate and unequal provision of instructional time.

While almost all countries set official guidelines and rules on the amount of time children should be in school, actual time varies enormously within and among countries (Abadzi, 2007).
* Many factors influence the delivery of instructional time.

Armed conflict, ethnic violence, natural disasters and inclement weather can affect the number of days schools are open in some regions and communities and not others (Abadzi, 2007; O’Malley, 2007).

Teacher absenteeism and lateness significantly reduce time available for teaching and learning (Abadzi, 2007).
The PASEC and SACMEQ surveys report that teacher turnover and late teacher postings leave many African schools unable to follow the official school year (Bonnet, 2007).

Significant disparities in instructional time between schools are reported.

An in-depth study of Bangladesh’s government primary schools and registered non-government primary schools found large disparities in annual lesson time (Financial Management Reform Programme, 2006b).
The bottom 10% of government schools provided fewer than 500 lesson hours per year in classes 3, 4 and 5 whereas the top 10% provided more than 860; the equivalent range at the non-government schools was 470 to 700 hours.

In several developing countries school heads report that village schools operate fewer days a year than town/city schools.
Similarly, despite uniform countrywide guidelines, grade 4 teachers in village schools in Paraguay, the Philippines and, to a lesser extent, Brazil, Malaysia and Tunisia report teaching significantly fewer annual hours of mathematics and reading than teachers in city/town schools.

In some countries the most instructed 10% of pupils receive 50% more instructional time per year than the least instructed 10% (Zhang et al., 2008).
In PIRLS 2006 grade 4 teachers also report considerable in-country differences in weekly hours spent on reading (Mullis et al., 2007).

* International agencies recommend 850 to 1,000 hours per year, or about 200 days on a five-day school week (Lockheed and Verspoor, 1991; UNESCO, 2004; World Bank, 2004).

In many countries even the official instructional time falls short of this (UNESCO, 2007a).

Use of double, triple or split shifts significantly reduces yearly instructional time (Abadzi, 2007).
Learning gaps in many countries are linked to inadequate and unequal provision of instructional time 52.
Alexander (2008) argues, on the other hand, that the ‘effective school’ approach aggregates findings from studies conducted by differing methods, at different times and places; inadequately addresses deeper cultural differences having to do with the aims and purposes of each education system; presents teaching as value-neutral, content-free and entirely devoid of the dilemmas of ideal and circumstance; and employs rather arbitrary variables to describe effective schools.
Table 2.17: Percentage of students in grade 10 in well-equipped schools, by parental socio-occupational status1 % of students from highest quartile who attend well-equipped schools % of students from lowest quartile who attend well-equipped schools Argentina Brazil Chile Mexico Peru Latin American countries OECD countries2 61 64 64 47 39 59 65 25 38 38 23 10 32 58 1.
Schools were divided into two groups on the basis of how well they were equipped with libraries, multimedia tools, computer laboratories, chemistry laboratories, etc.

2.

Data are for twenty-seven OECD countries, not including Mexico.

Regional totals are weighted.

Source: UN Economic Commission for Latin America and the Caribbean (2007).

linked to insufficient financing.

Of course, increased spending does not automatically lead to enhanced quality (Hanushek and Luque, 2003).
Research in developing countries in recent years underlines the importance of the school environment.

Learning assessments in Madagascar and the Niger found that having electricity in the school significantly improved outcomes (Fomba, 2006; Madagascar Ministry of National Education & Scientific Research and UNESCO, 2004).

In Guinea, access to books was shown to significantly improve learning (Blondiaux et al., 2006).
The parlous state of the education infrastructure, documented in past Reports (UNESCO, 2004; UNESCO, 2007a), is still of concern.

For example, poor and unequal provision of school resources is endemic in sub-Saharan Africa: SACMEQ II found that over half the grade 6 students in Kenya, Malawi, Mozambique, Uganda, the United Republic of Tanzania and Zambia attended classrooms that did not have a single book (UNESCO, 2005).
In these and other countries, 25% to 40% of teachers did not possess a manual in the subjects they taught (Bonnet, 2007).

Significant percentages of Nigerian students in grades 4 and 6 reported lacking textbooks: 30% in English, 50% in mathematics, 65% in social studies and 75% in science (Nigeria Federal Ministry of Education et al., 2005).
Many schools lack sufficient toilets, drinking water, desks and books 1 1 6 Under such circumstances teachers spend much class time writing lessons and problems on the board while students copy them into exercise books – if they have any.

Poor school infrastructure is also widespread in Latin America.
Ecuador, Guatemala, Nicaragua, Panama, Paraguay and Peru have many primary schools lacking several or all of the following: sufficient toilets, potable water, libraries, books and computer rooms (UNESCO- OREALC, 2008).

Poorly equipped schools tend to be attended by children from poorer households, exacerbating underlying inequalities in opportunity.
Recent monitoring work underlines the appalling and unequal state of education infrastructure and quality in eleven developing countries (Zhang et al., 2008).53 Chile, Malaysia and Uruguay were found to have the best-resourced schools, and India, Peru and Sri Lanka the worst.

Among key findings: School resources are unequally distributed within countries.

Schools in cities and towns have significantly more resources (from a list of thirty-one items) than schools in villages and rural areas.
Schools attended by more socially advantaged students also have greater resources and private primary schools are better resourced than public sector schools.

Many schools and classrooms are in a state of disrepair.

In Peru, the Philippines and Sri Lanka, half or more of school heads say that the ‘school needs complete rebuilding’ or ‘some classrooms need major repairs’.

In all countries except Malaysia, village schools are reported to be in greater need of repair than city/town schools.
In India, Peru, the Philippines, Sri Lanka and Tunisia, one-third or more of students attend schools with insufficient toilets.

Distance and student well-being are serious problems.

In the Philippines, Sri Lanka and Tunisia, teachers report that one in seven children has to walk more than 5 kilometres to attend school.

Teachers in all countries report that at least 9% of children come to school with an empty stomach, and in some countries the share is as high as 18%.

53.
53.

Argentina, Brazil, Chile, India (in the states of Assam, Madya Pradesh, Rajasthan and Tamil Nadu), Malaysia, Paraguay, Peru, the Philippines, Sri Lanka, Tunisia and Uruguay.

The response rate in parts of Sri Lanka was low because of armed conflicts and the 2004 tsunami, so results should be interpreted with caution.
T H E D A K A R G O A L S : Many countries and schools lack fundamental resources for learning.

India, Peru, the Philippines and Sri Lanka suffer an acute shortage of seating.

Nearly half of students in Paraguay, the Philippines, Sri Lanka and Tunisia attend schools with no libraries; in Argentina, Chile, Malaysia and Uruguay the share is down to 20% or less.
Pupil access to a classroom book corner varies considerably within countries.

Textbook provision and content remain problems.

About 15% to 20% of grade 4 pupils do not have a textbook or have to share one.

In Argentina, Paraguay and the Philippines the percentage is higher.

Schools in Asian countries and Tunisia rely on materials focusing on basic decoding skills, though most schools in Latin America use more challenging continuous texts (e.g.

fables), and imaginary and real-life narrative texts.
In general the difficulty and appropriateness of grade 4 texts, and the frequency of their use, varied greatly within and among countries.

Overall, such student, school and system characteristics affect learning outcomes in all countries, with the relative weight of each category varying according to context.
New multilevel analyses of student achievement that mostly involve developed and transition countries underscore the overriding importance of student- level factors, followed by school- and system- related factors (Riddell, 2008).

Analyses of learning outcomes in developing countries emphasize school resources and teacher-related factors.
Clearly governance decisions concerning school infrastructure, classroom processes, and the recruitment, deployment and effectiveness of teachers, as well as the student composition of schools, matter a great deal for learning.

Teacher supply and quality Delivery of good-quality education is ultimately contingent on what happens in the classroom, and teachers are in the front line of service.
To improve student outcomes, having enough teachers and reasonable pupil/teacher ratios (PTRs) is not sufficient: the teachers need to be well trained and motivated.

The profile of teachers, and the governance systems through which they are recruited, trained and deployed, have a critical bearing on learning outcomes and on equity.

Numbers and needs worldwide Good-quality education depends in part on reasonable class sizes.
Pupils in large classes have fewer opportunities for participation and interaction with teachers, and generally less access to instructional materials.

More than 27 million teachers were working in primary education institutions worldwide in 2006, 80% of them in developing countries (Table 2.18 and annex, Statistical Table 10A).54 Total primary teaching staff increased by 5% between 1999 and 2006.

The largest increases occurred in sub- Saharan Africa.
Teacher numbers also increased in Latin America and the Caribbean.

The number of secondary school teachers increased by 5 million over the period, to 29 million.

While these increases are impressive, achieving EFA will still require vast efforts in terms of teacher recruitment (Box 2.17).

PTRs are a more useful benchmark for measuring teacher provision than global and regional numbers.
There is a broad consensus that a PTR of 40:1 is an approximate ceiling for a primary school learning environment of good quality.55 Very low ratios point to inefficient allocation of teachers.

As secondary education is often organized by subject units, more teachers are needed than in primary school, so global benchmarks are less easily established and compared for this level.
Regional and national PTRs show marked variation and little change.There are large regional and national disparities in PTRs, with marked teacher shortages in South and West Asia, and sub-Saharan Africa (see annex, Statistical Table 10A).

In Afghanistan, Chad, Mozambique and Rwanda, national primary PTRs exceed 60:1.

In sub-Saharan Africa, and South and West Asia, the supply of new teachers has failed to keep pace with increases in primary school enrolment.
Particularly sharp increases in PTRs are evident in some countries, including Afghanistan, Kenya, Rwanda and the United Republic of Tanzania.

By contrast, PTRs in Latin America and the Caribbean, and in North America and Western Europe have declined as enrolment decreased and/or teacher numbers increased.

In secondary education, the highest PTRs are again observed in sub-Saharan Africa, and South and West Asia.
Eritrea, Nigeria and Pakistan, for instance, have ratios above 40:1 (see annex, Statistical Table 10B).

As in primary education, there has been no discernible shift in secondary PTRs since 1999.

The most acute teacher shortages are in South and West Asia, and sub-Saharan Africa 54.
More than a third of teachers worldwide are in East Asia and the Pacific (mostly in China) and a further fourth are in the most populous countries in other regions: Bangladesh, Brazil, Egypt, India, the Islamic Republic of Iran, Mexico, Nigeria, Pakistan, the Russian Federation and the United States.

55.
55.

The PTR is a rough measure of class size because it is calculated by taking the total number of teachers (including some who may not be in classrooms) and dividing it by the total number of pupils enrolled, including those not attending classes.

As the indicator is based on teacher headcounts, it does not reflect part-time teaching or double-shifting.
1 1 7 C H A P T E R 2 Table 2.18: Teaching staff and pupil/teacher ratios in primary and secondary education, by region, 1999 and 2006 Primary education Secondary education Teaching staff School year ending in 1999 (000) 2006 (000) Pupil/teacher ratio1 School year ending in 1999 2006 Change between 1999 and 2006 (%) Change between 1999 and 2006 (%) 5 7 3 -11 29 18 -4 -4 -4 8 13 12 7 13 7 - -9 -10 10 -4 -10 -8 -8 -8 8 -13 -10 -13 -9 -6 Teaching staff School year ending in 1999 (000) 2006 (000) Pupil/teacher ratio1 School year ending in 1999 2006 Change between 1999 and 2006 (%) Change between 1999 and 2006 (%) 20 30 5 -4 42 28 6 22 23 10 40 31 26 31 8 - -2 -4 -5 -10 13 -3 11 -1 -1 -3 -10 -15 -12 -15 -4 -10 World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific East Asia Pacific South and West Asia Latin America/Caribbean Caribbean Latin America N. America/W.
Europe Central/Eastern Europe 1.

Based on headcounts of pupils and teachers.

Source: Annex, Statistical Tables 10A and 10B.

Box 2.17: How many teachers are needed to achieve EFA?

The 2008 Report (UNESCO, 2007a) emphasized that national governments had to recruit and train teachers on a vast scale to achieve the EFA goals.
It is estimated that the world will need approximately 18 million additional primary teachers by 2015.1 The most pressing need is in sub-Saharan Africa, where an estimated 1.6 million additional posts must be created and teachers recruited by 2015 (on the basis of 2004 data) if UPE is to be achieved.

Taking teacher retirement, resignations and losses into account pushes that figure up to 3.8 million.
This represents about 145,500 posts and teachers annually, 77% higher than the annual increase observed between 1999 and 2006.

In Ethiopia and Nigeria the annual requirement for new posts is more than 11,000.

Burkina Faso, the Congo, Chad, Mali and the Niger all need to increase posts and teachers by more than 10% a year.

East Asia and the Pacific will need an estimated 4 million teachers by 2015 and South and West Asia 3.6 million, with the largest increases required in China, India and Indonesia.
Teacher needs in these regions, however, are mostly to fill posts left by retiring or otherwise departing teachers.

These estimates do not take account of additional investments (e.g.

for teacher training) required to ensure that teaching is effective.

Moreover, comprehensive estimates of teacher needs are available only at primary level.
Factoring in the number of teachers and other staff needed to meet all the EFA goals increases still further the scale of necessary investment in teacher recruitment and training: A study for Senegal, for example, shows that non-formal education will need an additional 1,900 instructors yearly between 2008 and 2010, nearly as many additional posts as are required at primary level.
Projections in Ghana, Kenya, Malawi, Senegal, Uganda and Zambia2 show that 321,561 new lower-secondary teachers would be needed between 2006 and 2015 to reduce student dropout and repetition at all levels by 25% and increase primary to lower-secondary transition rates by 25%.

Kenya and Malawi, for example, would have to double teacher numbers to meet these goals.

1.

Estimated on the basis of 2004 teacher supply and PTRs.

2.

Based on constant PTRs at 2006 levels, not disaggregated by subject.
Sources: Diagne (2008); Schuh Moore et al.

(2008); UIS (2006b).


 : Trained teachers are in short supply in many countries.While differences in teacher training limit the scope for simple cross-country comparisons,56 large regional variations are apparent.
In primary education, the median shares of trained teachers in the total teaching force range from 68% in South and West Asia to 100% in the Arab States (see annex, Statistical Table 10A).

Variations by country are also marked.

In Lebanon, for example, just 13% are trained, for an average of one trained teacher per 110 students (Figure 2.41).
In Mozambique the percentage of trained teachers is higher (65%) but because the total number is insufficient the ratio of pupils to trained teachers is very high, 104:1.

Nearly half the forty countries with data for both 1999 and 2006 increased the presence of trained teachers (see annex, Statistical Table 10A), in some cases by considerable margins.
The Bahamas, Myanmar, Namibia and Rwanda raised the proportion of trained primary school teachers by more than 50%.57 However, more than a third of countries, including Bangladesh, Nepal and the Niger, moved in the opposite direction, with percentages of trained primary school teachers declining.

Excessive PTRs, shortages of trained teachers and questions about teachers’ skills point to wide-ranging problems in governance.
Teacher shortages often result from inadequate investment in education and questionable incentive structures for teacher recruitment and retention.

At the primary level in particular, teacher training is often fragmented and incomplete – in some cases non-existent.

Many countries have had trouble increasing the number of primary education teachers because they have not yet expanded secondary education sufficiently to produce enough candidates for teacher-training programmes.
Within-country disparities The total number of teachers and the national PTR shed some light on the state of a given education system, but they can obscure disparities in teacher assignment associated with location, income and school type.

These disparities affect the extent to which a country truly gives everyone the opportunity to receive an education of good quality.

In many countries teachers are unevenly distributed, resulting in major disparities in PTRs.
In Nepal in 2005, the PTR in the Dhanusa district, in the central region, was 82:1 – double the national average (Sherman and Poirier, 2007).

Among the country’s seventy-five districts, nearly half had ratios at or above 40:1 while the rest were well staffed, Figure 2.41: Comparison of PTRs with ratios of pupils to trained teachers in primary education, 2006 Bermuda Cuba Kuwait Andorra Qatar Cayman Is Azerbaijan Brunei Daruss.

Lebanon Oman Br.
Virgin Is Bahamas Belarus Cook Islands Maldives Montserrat Anguilla Ukraine Dominica Aruba Uzbekistan Costa Rica Viet Nam Macao, China Mauritius Tajikistan Dominican Rep. Ecuador Belize Saint Lucia Kyrgyzstan Algeria Panama Cape Verde Bhutan Myanmar Lao PDR Nicaragua Djibouti Sudan Gambia Ghana Pakistan Niger El Salvador Lesotho Mauritania Benin Guinea Cameroon Burkina Faso Eritrea Cambodia U. R. Tanzania Congo Rwanda Mozambique 0 20 40 60 Ratio 80 100 120 Pupil/teacher Pupil/trained teacher Source: UIS database.
providing very small class sizes in some cases.

Similarly, PTRs in the Nigerian state of Bayelsa were five times higher than in Lagos.

Large variations in PTRs can exist even within local administrative areas: a 2004 survey of 10 of the 493 upazilas(subdistricts) in Bangladesh found Many countries have had trouble increasing the number of primary education teachers because they have not yet expanded secondary education sufficiently 56.
Wide variations exist in the institutional quality of pre-service education, programme selectivity and professional development opportunities and requirements.

57.

Myanmar’s Basic Education Long-term Development Plan (2001/02–2030/31) focused for the first five years on reducing the number of uncertified teachers and expanding teacher-training colleges.
It introduced two-year pre-service training programmes and increased the intake of primary and lower secondary teachers to in-service teacher training in twenty education colleges.

Also during this period Myanmar’s two Institutes of Education, in Yangon and Sagaing, provided more teacher-training programmes for the upper secondary level ratios ranging from 36:1 to 93:1 (Ahmed et al., 2007).
Geographic disparities in teacher distribution often coincide with socio-economic variation in the populations served.

Compared with poorer pupils, wealthier children often attend schools with better PTRs and larger shares of trained teachers.

While urban PTRs tend to be higher than in rural areas, untrained teachers are often concentrated in poor rural areas.

Lower PTRs in rural areas reflect many factors, from population dispersal to lower demand for education.
They do not necessarily indicate greater equity, as a more detailed look at the composition of the teaching force shows: In Bolivia many teachers are interinos, hired on contract.

They need not have a teaching degree or even any teaching experience.

Interinosmake up 19% of the total teaching force but 56% of all teachers in rural areas (World Bank, 2006a).

In Ghana untrained teachers are concentrated in the Northern region, which has the lowest economic development and the most out-of- school children.
In 2004–2005, the percentage of trained teachers was a third lower in the forty most deprived districts of the country than in other districts (Akyeampong et al., 2007).

In India the majority of untrained or undertrained teachers are concentrated in rural areas and cater to the poorest and most deprived children (Govinda and Bandyopadhyay, 2008).

PTRs also depend on whether schools are publicly funded.

Many countries show a marked gap between government and non-government providers.
In Bangladesh average ratios are 64:1 in government schools, 40:1 in non-government ones.

Public sector school teachers in Djibouti and Rwanda work in classes that on average are more than two and a half times the size of classes in private schools (Figure 2.42).

Because children from poorer households are more likely to attend government schools, unequal PTRs both reflect and reinforce wider inequalities.
The PTR offers an important, if deceptive, headcount indicator showing teacher distribution countrywide.

For children in the classroom, however, other factors also affect the quality of teaching and learning.

Thus, even favourable headcount indicators can obscure wider problems.

Other factors affecting the quality of teaching and learning Teacher absenteeism.

Teacher counts in employment statistics do not guarantee their presence in the classroom.
In a recent study of six countries, teacher absenteeism rates in primary schools averaged 19% and ran as high as 25% in India and 27% in Uganda (Chaudhury et al., 2006).
Schools attended by wealthier children tend to have smaller classes and more trained teachers than those attended by poorer children Figure 2.42: Public-to-private sector disparities in primary PTRs, 2006 80 70 60 50 40 30 20 10 0 o i t a r r e h c a e t / l i p u P Average Public sector Private sector .
.

r B s e t a r i m E .

A .

U .

 .

p e R .

l s I , n a r I a n i h C , o a c a M .


 .

 1.

Data are for the school year ending in 2007.

Source: UIS database.
1 2 0 In Mozambique, HIV/AIDS kills 1,000 teachers a year T H E D A K A R G O A L S : Data for Ghana, India, South Africa and the United Republic of Tanzania suggest that teacher absenteeism is more pronounced in public sector schools, in schools with poorer infrastructure, in rural areas, in poorer states and in schools serving children from lower socio-economic backgrounds (Kremer et al., 2005; Sumra, 2006; van der Berg and Louw, 2007; World Bank, 2004).
High levels of teacher absenteeism directly affect learning time and outcomes as well as national education costs and spending.

In countries participating in SACMEQ II, teacher absenteeism was shown to have significant negative effects on mathematics tests (van der Berg and Louw, 2007).

In Peru the economic costs of teacher absenteeism represent 10% of current expenditure in primary education; in Uganda the figure is 24%.
A recent study put the cost of absenteeism in India at around US$2 billion per year (Patrinos and Kagia, 2007).

HIV/AIDS.

Although teacher mortality rates due to HIV/AIDS are decreasing or are reasonably stable (Bennell, 2006), the epidemic continues to damage lives and education systems.

In South Africa, HIV prevalence among teachers was 13% in 2004; projections show it declining slightly by 2015 (Bennell, 2005b).
In Kenya, where 14,500 teachers are estimated to be HIV positive, between four and six teachers die each day due to AIDS (Bennell, 2005b; UNESCO, 2007b).

In Mozambique, HIV/AIDS kills 1,000 teachers a year: it is estimated that 19,200 teachers and 100 education officials will have died during the current decade (Reuters, 2007).

Teachers suffering from HIV/AIDS are more likely to be absent or transferred (particularly in rural areas further from medical facilities) as a result of opportunistic infections.
Poor morale and weak motivation undermine teacher effectiveness.

Teacher retention and absenteeism and the quality of teaching are heavily influenced by whether teachers are motivated and their level of job satisfaction.

Evidence suggests many countries face a crisis in teacher morale that is mostly related to poor salaries, working conditions and limited opportunities for professional development (Bennell and Akyeampong, 2007; DFID and VSO, 2008).
The surveys in eleven developing countries discussed above (see pp.

116-7) found professional satisfaction among grade 4 teachers to be low.

In some cases, salary concerns were paramount: in Argentina, Brazil, Peru, the Philippines and Uruguay, for example, fewer than a third of fourth graders had teachers who thought their pay was adequate (Zhang et al., 2008).

Motivation tends to be lower among teachers with large classes and in schools that are poorly resourced or attended by disadvantaged pupils.
Issues raised in this section are at the heart of education governement challenges.

The recruitment, training, allocation and motivation of teachers are issues that we turn to in Chapter 3.
80 70 60 50 40 30 20 10 0 o i t a r r e h c a e t / ' .

T .
T .

 While each EFA goal is important in its own right, what ultimately matters is progress on all fronts Education for All: measuring composite achievement The EFA goals represent more than the sum of their parts.

While each is important in its own right, what ultimately matters is progress on all fronts.
Achieving UPE without advancing policies to strengthen early childhood development, gender parity in post-primary education or progress in adult literacy would put overall EFA achievement at risk.

Advancing on all fronts generates cumulative, mutually reinforcing benefits.

By contrast, slow progress in one area can erode the benefits of strong performance in others.

The EFA Development Index The EFA Development Index (EDI) is a composite measure that captures overall progress.
Ideally, it should reflect all six Dakar goals, but there are serious data constraints.

Reliable and comparable data relating to goal 1 (ECCE) are not available for most countries and progress on goal 3 (learning needs of youth and adults) is not easy to measure or monitor.
The EDI thus focuses only on the four most easily quantifiable EFA goals, attaching equal weight to each measure: UPE (goal 2) is proxied by the total primary NER.58 Adult literacy (goal 4) is proxied by the literacy rate for those aged 15 and above.59 Gender parity and equality (goal 5) are proxied by the gender-specific EFA index (GEI), an average of the GPIs of primary and secondary GERs, and of the adult literacy rate.
Quality of education (goal 6) is proxied by the survival rate to grade 5.60 The EDI value for a given country is the arithmetic mean of the four proxy indicators.

It falls between 58.

The total primary NER measures the proportion of children of primary school age who are enrolled in either primary or secondary education.

59.
59.

The literacy data used are based on conventional assessment methods – either self- and third-party declarations or educational attainment proxies – and thus should be interpreted with caution; they are not based on any test and may overestimate actual literacy levels.

60.

For countries where primary education lasts fewer than five years, the survival rate to the last grade of primary is used.
0 and 1, with 1 representing full EFA achievement.61 For the school year ending in 2006 it was possible to calculate values for 129 countries.

Coverage varies substantially by region, ranging from fewer than 40% of countries in East Asia and the Pacific to about 80% or more in Central and Eastern Europe, and North America and Western Europe (Table 2.19).

Data limitations preclude a global look at overall EFA achievement.
Many countries are excluded, among them a majority of what the OECD identifies as fragile states,62 including those in conflict or post- conflict situations.

Of the 129 countries for which the EDI could be calculated for 2006: Fifty-six – five more than in 2005 – have either achieved the four most easily quantifiable EFA goals or are close to doing so, with EDI values averaging 0.95 or above.

Most of these high- achieving countries are in more developed regions.
With a few exceptions,63 they have achieved balanced progress on the four EFA goals included in the index.

Forty-four countries, mostly in Latin America and the Caribbean, the Arab States and sub- Saharan Africa, are midway to achieving EFA as a whole, with EDI values ranging from 0.80 to 0.94.

Most of these countries show uneven progress.


Participation in primary education is often high, with deficits in other areas, such as adult literacy (Algeria, Belize, Egypt, Kenya, Swaziland, Tunisia, Zambia) education quality as measured by the survival rate to grade 5 (Ecuador, El Salvador, the Dominican Republic, Myanmar, the Philippines, Sao Tome and Principe), or both (Guatemala).

Twenty-nine countries, more than a fifth of those in the EDI sample, are lagging behind with EDI values below 0.80.
Sub-Saharan Africa is overrepresented in this group, with EDI values below 0.60 in Burkina Faso, Chad, Ethiopia, Mali and the Niger.

Countries in other regions, including four Arab States and five out 62.
The fragile states not included are Afghanistan, Angola, the Central African Republic, the Comoros, the Congo, Côte d’Ivoire, the Democratic Republic of the Congo, the Gambia, Guinea-Bissau, Haiti, Kiribati, Liberia, Papua New Guinea, Sierra Leone, the Solomon Islands, Somalia, the Sudan, Timor-Leste, Uzbekistan and Vanuatu.

61.

For further explanation of the EDI rationale and methodology, see The EFA Development Index in the annex, which also includes detailed values and rankings for 2006.

63.
63.

The total primary NER remains around 90% in Armenia, Belarus and Georgia, as does the average adult literacy rate in the United Arab Emirates.
T H E D A K A R G O A L S : : m e a s u r i n g c o m p o s i t e a c h i e v e m e n t Table 2.19: Distribution of countries by EDI scores and region, 2006 Far from EFA: EDI below 0.80 Intermediate position: EDI between 0.80 and 0.94 Close to EFA: EDI between 0.95 and 0.96 EFA achieved: EDI between 0.97 and 1.00 Subtotal sample Total number of countries Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America and the Caribbean North America and Western Europe Central and Eastern Europe Total 17 4 2 5 1 29 Source: Annex, The EFA Development Index, Table 1.
of six South Asian countries, are also in this category.

Except in a few cases where participation of primary school-age children is relatively high (e.g.

Bangladesh, Cambodia, India, Madagascar, Malawi, Nicaragua), these low EDI countries face multiple challenges: low education participation, widespread adult illiteracy, gender inequalities and poor education quality.
Progress towards EFA as a whole Analysis of changes in the EDI between 1999 and 2006 could be carried out for only forty-five countries.

Thirty-one of these recorded increases – significant ones, in several cases (Figure 2.43).

Though absolute EDI values remained low in Ethiopia, Mozambique and Nepal, they increased by more than 20%.

The EDI decreased in fourteen countries.

Chad experienced the largest fall: it was in last place in 2006, well behind the others.
Increased school participation was the primary driver of progress in the EDI.

The total primary NER increased on average by 7.3% across the forty-five countries.

In Ethiopia, the level of participation in school more than doubled, from 35% in 1999 to 72% in 2006.

Ethiopia also experienced gains in adult literacy (+35%) and school retention (+14%).
In Yemen, significant increases in the total primary NER, adult literacy, and gender parity and equality more than compensated for a large drop in the survival rate to grade 5 (-24%), leading to overall EDI improvement of 10%.

For most of the fourteen countries where the EDI declined between 1999 and 2006, the education quality component was an important factor.

Overall EFA achievement: inequalities within countries remain the rule The EDI provides a snapshot based on national averages.
But progress towards EFA, as the word ‘all’ implies, should be shared equally across the whole of society.

One drawback of the standard EDI is that it does not capture variation based on wealth and other indicators of disadvantage.

To address this shortcoming, an EFA Inequality Index for Income Groups (EIIIG) was constructed for thirty- five developing countries, using household survey data (Harttgen et al., 2008).
The EIIIG uses a different set of indicators to provide a measure similar to the EDI, showing distribution of overall EFA achievement within countries by wealth and by rural/urban location.64 The EIIIG shows large disparities in overall EFA achievement between wealth groups in most of the thirty-five countries.

These gaps are almost as large as those between nations (Figure 2.44).
They are particularly wide in Benin, Burkina Faso, Chad, Ethiopia, Mali, Mozambique and the Niger: the EIIIG for the richest group in those countries is more than twice that of the poorest group.

In Ethiopia, which had the widest inequality in overall EFA achievement, the EIIIG for the highest wealth quintile was 0.873 in 2003, compared with 0.344 for the lowest quintile.

Disparities within income groups were less pronounced in seven countries, 64.

The EIIIG differs from the EDI in three main ways.


The total primary net attendance rate is used rather than the total primary NER.

As many household surveys do not include literacy rates, this EIIIG component is based on the proportion of 15- to 25-year-olds with five or more years of education.

Finally, the survival rate for the EIIIG is defined as the proportion of 17- to 27-year-olds who report having at least five years of education among those who reported having at least one year of education.
Increased school participation was the main driver of progress in the EDI 1 2 3 C H A P T E R 2 Figure 2.43: EDI in 2006 and change since 1999 Italy Italy Italy Italy Croatia Croatia Croatia Croatia Cyprus Cyprus Cyprus Cyprus Cuba Cuba Cuba Cuba Aruba Aruba Aruba Aruba Estonia Estonia Estonia Estonia Hungary Hungary Hungary Hungary Kyrgyzstan Kyrgyzstan Kyrgyzstan Kyrgyzstan Latvia Latvia Latvia Latvia Lithuania Lithuania Lithuania Lithuania Romania Romania Romania Romania Bulgaria Bulgaria Bulgaria Bulgaria Bahrain Bahrain Bahrain Bahrain Argentina Argentina Argentina Argentina U.
A. Emirates U.

A. Emirates U.

A. Emirates U.
A. Emirates Mongolia Mongolia Mongolia Mongolia Albania Albania Albania Albania Rep. Moldova Rep. Moldova Rep. Moldova Rep. Moldova Azerbaijan Azerbaijan Azerbaijan Azerbaijan Mauritius Mauritius Mauritius Mauritius Saint Lucia Saint Lucia Saint Lucia Saint Lucia Panama Panama Panama Panama Paraguay Paraguay Paraguay Paraguay Venezuela, B. R. Venezuela, B. R. Venezuela, B. R. Venezuela, B. R. Fiji Fiji Fiji Fiji Ecuador Ecuador Ecuador Ecuador Bolivia Bolivia Bolivia Bolivia Belize Belize Belize Belize South Africa South Africa South Africa South Africa Namibia Namibia Namibia Namibia Swaziland Swaziland Swaziland Swaziland Zambia Zambia Zambia Zambia Dominican Rep. Dominican Rep. Dominican Rep. Dominican Rep. Guatemala Guatemala Guatemala Guatemala Nicaragua Nicaragua Nicaragua Nicaragua Lesotho Lesotho Lesotho Lesotho Iraq Iraq Iraq Iraq Bangladesh Bangladesh Bangladesh Bangladesh Nepal Nepal Nepal Nepal Malawi Malawi Malawi Malawi Mauritania Mauritania Mauritania Mauritania Yemen Yemen Yemen Yemen Mozambique Mozambique Mozambique Mozambique Ethiopia Ethiopia Ethiopia Ethiopia Chad Chad Chad Chad -1.1 -0.3 -1.1 -2.1 -1.3 -0.8 -0.7 -0.8 -1.2 -0.3 -0.2 -1.6 0.8 2.0 1.6 0.8 0.8 1.1 1.6 7.8 3.5 2.1 2.2 2.9 2.7 0.7 2.3 0.1 2.2 5.5 5.0 -3.1 12.5 11.6 6.7 6.2 3.2 3.7 0.5 4.5 9.9 0.408 -4.2 Italy Italy Italy Italy Croatia Croatia Croatia Croatia Cyprus Cyprus Cyprus Cyprus Cuba Cuba Cuba Cuba Aruba Aruba Aruba Aruba Estonia Estonia Estonia Estonia Hungary Hungary Hungary Hungary Kyrgyzstan Kyrgyzstan Kyrgyzstan Kyrgyzstan Latvia Latvia Latvia Latvia Lithuania Lithuania Lithuania Lithuania Romania Romania Romania Romania Bulgaria Bulgaria Bulgaria Bulgaria Bahrain Bahrain Bahrain Bahrain Argentina Argentina Argentina Argentina U.
A. Emirates U.

A. Emirates U.

A. Emirates U.
A. Emirates Mongolia Mongolia Mongolia Mongolia Albania Albania Albania Albania Rep. Moldova Rep. Moldova Rep. Moldova Rep. Moldova Azerbaijan Azerbaijan Azerbaijan Azerbaijan Mauritius Mauritius Mauritius Mauritius Saint Lucia Saint Lucia Saint Lucia Saint Lucia Panama Panama Panama Panama Paraguay Paraguay Paraguay Paraguay Venezuela, B. R. Venezuela, B. R. Venezuela, B. R. Venezuela, B. R. Fiji Fiji Fiji Fiji Ecuador Ecuador Ecuador Ecuador Bolivia Bolivia Bolivia Bolivia Belize Belize Belize Belize South Africa South Africa South Africa South Africa Namibia Namibia Namibia Namibia Swaziland Swaziland Swaziland Swaziland Zambia Zambia Zambia Zambia Dominican Rep. Dominican Rep. Dominican Rep. Dominican Rep. Guatemala Guatemala Guatemala Guatemala Nicaragua Nicaragua Nicaragua Nicaragua Lesotho Lesotho Lesotho Lesotho Iraq Iraq Iraq Iraq Bangladesh Bangladesh Bangladesh Bangladesh Nepal Nepal Nepal Nepal Malawi Malawi Malawi Malawi Mauritania Mauritania Mauritania Mauritania Yemen Yemen Yemen Yemen Mozambique Mozambique Mozambique Mozambique Ethiopia Ethiopia Ethiopia Ethiopia Chad Chad Chad Chad 22.5 25.6 31.7 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -5 0 EDI Change since 1999 (%) Note: Only countries with EDI values for both 1999 and 2006 are included.
Source: Annex, The EFA Development Index, Table 3.
: : m e a s u r i n g c o m p o s i t e a c h i e v e m e n t Figure 2.44: EFA Inequality Index by wealth quintile, selected countries, most recent year Mali Mali Mali Burkina Faso Burkina Faso Burkina Faso Chad Chad Chad Ethiopia Ethiopia Ethiopia Mozambique Mozambique Mozambique Niger Niger Niger Guinea Guinea Guinea Benin Benin Benin Côte d’Ivoire Côte d’Ivoire Côte d’Ivoire Senegal Senegal Senegal Cambodia Cambodia Cambodia Rwanda Rwanda Rwanda Madagascar Madagascar Madagascar Haiti Haiti Haiti Malawi Malawi Malawi Zambia Zambia Zambia Ghana Ghana Ghana Nicaragua Nicaragua Nicaragua Nepal Nepal Nepal Nigeria Nigeria Nigeria Bolivia Bolivia Bolivia Uganda Uganda Uganda India India India Cameroon Cameroon Cameroon Bangladesh Bangladesh Bangladesh Dominican Rep. Dominican Rep. Dominican Rep. Kenya Kenya Kenya Peru Peru Peru Namibia Namibia Namibia Viet Nam Viet Nam Viet Nam Colombia Colombia Colombia Philippines Philippines Philippines Indonesia Indonesia Indonesia U. R. Tanzania U. R. Tanzania U. R. Tanzania Zimbabwe Zimbabwe Zimbabwe 0.2 0.4 0.6 0.8 1.0 1.2 EIIIG Poorest 20% Richest 20% Average Source: Demographic and Health Surveys, calculations by Harttgen et al.
(2008).

including Colombia, Indonesia, the Philippines, the United Republic of Tanzania and Zimbabwe.

In these best-performing countries the richest quintile achieved the maximum EIIIG score of 1.00.

The higher the average EIIIG, the fewer the education inequalities.
Although cross-country inequalities by income group are highly variable, a general trend is visible: countries with better- functioning education systems have not only higher overall EFA achievement as measured by the EIIIG, but also fewer inequalities.

Progress towards overall EFA achievement has benefited the poorest in most countries.

The EIIIG ratio of the richest to poorest population quintile decreased in about three-quarters of the thirty-five countries in the sample.
The reductions were particularly significant in Benin, Ethiopia, India and Nepal, with declines of 15% or more.

On the other hand, inequalities in overall EFA achievement between the poorest and the richest households increased in the remaining countries, particularly Kenya and Nigeria, where the mean EIIIG decreased slightly.

Overall EFA achievement is greater in urban than in rural areas, whatever the wealth group.
Rural residents are particularly disadvantaged in Burkina Faso, Chad, Ethiopia and Mali, where the ratio of urban to rural EIIIG is about 2 or more.

A close look at the interaction between wealth and place of residence highlights the impact of poverty in rural areas.

Education inequality between wealth groups as measured by the EIIIG ratio of richest to poorest quintile tends to be higher in rural areas than urban in about two-thirds of the thirty-five countries.
In Chad, Ghana, Haiti and Nicaragua the ratio of richest to poorest is close to 2 in rural areas.

In other countries, however, including Benin, Cambodia and Mozambique, the urban poor are more disadvantaged.
Education inequality between wealth groups tends to be higher in rural than in urban areas in about two-thirds of the thirty-five countries / P A / n a s O r e d n i r u G © Going their separate ways: school girls and street children in New Delhi 1 2 6 Education for All Global Monitoring Report 9002 Chapter 3 Raising quality and strengthening equity: why governance matters Many countries have introduced far-reaching governance reforms in education.
This chapter looks at governance problems and reform measures in four important areas: finance, school management, teacher recruitment and allocation, and education planning.

Two key findings emerge.

The first is that there is no blueprint for good governance: each country has to develop national and local strategies.

The second finding is that governments across the world have attached insufficient weight to equity in the design of governance reforms.
Introduction .......................................................................................................... 128 Introduction Financing education for equity ......................................................................................................................... 132 Choice, competition and voice: school governance reform and EFA ...................................................................................... 152 Strengthening teacher governance and monitoring ............... 171 An integrated approach to education and poverty reduction: the missing link ..................... 185 Governance is a word that conjures up an image of abstract political, administrative and management processes.
It is easy to lose sight of the real impact that governance practices in education can have on the lives of ordinary people, the hopes and aspirations of parents and children, and the human development prospects of nations.

To understand why good governance matters in education, consider the alternative.

Bad governance leaves parents and communities facing education provision that is unaccountable and unresponsive to their needs.
It contributes to education systems that are ineffective in raising learning achievements.

It leaves communities and regions with children sitting in classrooms lacking basic teaching materials, and in the charge of untrained and demotivated teachers.

In some cases, bad governance also means that financial resources allocated to schools do not arrive.

Poor governance practices in education affect the whole of society.

But invariably it is the poor who bear the brunt.


Good governance implies not just transparency and accountability, but also a commitment to equal opportunity for all citizens.

Unlike the wealthy, who can opt for private provision, poor households depend on governments to deliver education services.

When those services are of poor quality, inaccessible or unaffordable, it is the poor who lose.
Indicators for bad education governance include large financing gaps between rich and poor areas, provision that is unaffordable for the poor and a lack of attention to strategies for reaching the disadvantaged.

Failure to tackle corruption, another hallmark of bad governance, has particularly damaging consequences for poor households.

When resources do not reach schools, or when schools levy unauthorized fees, it is the poor who are least able to pay.
The good governance agenda Governance describes the institutions, rules and norms through which policies are developed and implemented – and through which accountability is enforced.
Governance reform in its broadest sense is concerned with changing the rules of the game – that is, changing the processes through which decisions are made and implemented on behalf : W H Y G O V E R N A N C E M AT T E R S I n t r o d u c t i o n of members of an organization or a society (Rodrik, 2008).

However, governance is not just about abstract institutional processes or formal rules.
It is also about power relationships in society.

At its most basic level, governance systems define who decides on policies, how resources are distributed across society and how governments are held accountable.

Good governance is now a central part of the international development agenda.

Beyond education, it is seen as a condition for increased economic growth, accelerated poverty reduction and improved service provision.
The most widely used data on governance indicators show that objectives range from strengthening multiparty democracy to reducing corruption, strengthening the rule of law, increasing the accountability of public institutions and enhancing the participation and voice of citizens (Kaufmann et al., 2007).

Few people would take issue with the intrinsic importance of improving governance in these various dimensions – and most would argue that progress is intrinsically important to development (Rodrik, 2008).
As one set of commentators put it: ‘Good governance is an ideal in which political processes translate the will of the people into public policies and establish the rules that efficiently and effectively deliver services to all members of society’ (Crouch and Winkler, 2007, p. 1).

More controversy surrounds the choice of policies to achieve the desired end of good governance.

Achieving good governance may require far-reaching political reforms and the reordering of institutional arrangements.
This has often been forgotten by donors, who sometimes present their aid partners with lengthy shopping lists for good governance reform, with little regard for prioritization or political feasibility (Grindle, 2004).

More broadly, the good governance narrative is often silent on the power relationships and vested interests that may be affected by governance reform.

There has been a parallel failure to acknowledge that national and local contexts are important.
When it comes to governance reform, what works in one setting may not work in another.

And there is no guarantee that progress towards good governance, as measured by the standard indicators, will resolve wider problems in development.

Education governance: the Dakar Framework and beyond Education governance is not simply the system of administration and management of education in a country.
In its broadest sense, it is concerned with the formal and informal processes by which policies are formulated, priorities identified, resources allocated, and reforms implemented and monitored.

Governance is an issue not only for central government but also for every level of the system, from the education ministry down to the classroom and community.

It is ultimately concerned with the distribution of power in decision-making at all levels.
As with any service, education provision is affected by wider governance conditions.

When democracy, transparency and respect for the rule of law is weak, accountability and participation suffer.

Within the education sector, governance structures link many actors and define the terms of their interactions.
The ability of parents to participate in school decisions, hold schools and teachers to account, and secure access to information is conditioned by the allocation of rights and responsibilities under governance systems.

Governance rules also define the terms on which governments recruit, allocate and train teachers.

They have an important bearing on the skills and motivation that teachers bring to the classroom.
Beyond the classroom, governance systems shape the relationship between school bodies, local government and central government.

They define who sets priorities and makes decisions in key areas ranging from the curriculum to teacher management, and the monitoring and supervision of schools.

In the area of finance, education governance is about how priorities are set and how resources are mobilized, allocated and managed.
As this non-exhaustive list suggests, governance involves a broad array of actors and many layers of government, affecting virtually all decisions made in education.

Within any country, the relationships between actors and government agencies can be enormously complex and varied.

Similarly, change in governance can mean very different things in different contexts.
For example, decentralization might reallocate authority in one area (say, teacher recruitment) but not in another (say, teacher pay or curriculum design).

It might devolve political authority but keep financial responsibility highly centralized.
Bad governance affects the whole of society, but invariably the poor bear the brunt Education is often inaccessible, inefficient, unaffordable and of questionable quality 1 3 0 C H A P T E R 3 The Dakar Framework for Action did not set out a comprehensive agenda on governance reform.

However, it did define some broad principles.


It committed governments to ‘develop responsive, participatory and accountable systems of educational governance and management’.

Apart from being intrinsically important, progress in these areas was identified as a strategy for ensuring that governments ‘can respond more effectively to the diverse and continuously changing needs of learners’ (Unesco, 2000, Expanded Commentary, para.

55).
55).

While the Dakar Framework stops well short of offering a blueprint for good governance, it does advocate moving towards ‘more decentralized and participatory decision-making, implementation and monitoring at lower levels of accountability’.

Much of this is consistent with central themes in wider governance debates.

The development of more accountable and participatory systems for delivering services has been a broad goal in public service reform.
Decentralization – the transfer of political, administrative and fiscal authority to lower levels of government – is one of the most pervasive governance reforms of the past two decades.

While decentralization has often been driven by fiscal motives, governments invariably present it as an exercise in bringing decision- making closer to the people affected.

An underlying assumption in approaches to governance reform involving devolution of authority is that they are intrinsically beneficial for equity.
Making service providers more accountable to the communities they serve, and giving those communities a greater role in decision-making, is widely presented as a source of empowerment.

The widely held conviction is that moving decision-making away from remote government agencies, and making the process more localized and transparent will change incentive structures, prompting education service providers to be more responsive to the needs and concerns of the poor.
After some two decades of far-reaching governance reform in education the jury is still out on the results.

Despite continuing enthusiasm, there is surprisingly little evidence that governance policies implemented thus far have actually improved education quality and led to greater equity.
This is true not just of countries that have introduced reform on a piecemeal basis, but also of such widely cited models of radical governance reform as undertaken in Chile, South Africa and Uganda (Crouch and Winkler, 2007).

Evaluation of governance reform is a difficult exercise.
The EFA Global Monitoring Report team has examined the locus of decision-making in 184 countries, looking at areas ranging from curriculum design and school infrastructure to teacher recruitment and pay to finance and resource allocation (see mapping exercise in annex, p. 252).

The sheer complexity of governance systems makes the mapping of decision-making in these areas difficult, even within one country.

Cross-country comparison is an even more hazardous enterprise.
Nevertheless, despite the complexity of layers of decision-making, some broad patterns emerge.

One of the most striking is that, notwithstanding frequent government declarations in favour of decentralization, many decisions in education are still taken by central government authorities.

While governance systems across the world vary, many have one thing in common.

They are delivering education services that are often inaccessible, inefficient, unaffordable and of questionable quality.
Changing such systems is crucial if countries are to accelerate progress towards the goals set out in the Dakar Framework for Action.

Some countries have demonstrated what is possible, but current approaches to governance reform are often failing.

Why have outcomes to date been so disappointing?

No generalized answer is possible, as every country faces different constraints and problems in governance reform.

Two broad problems can be identified nonetheless.


First, there has been a tendency in many developing countries to apply governance reform ‘blueprints’ borrowed uncritically from rich countries – a practice that some donors have encouraged – and to extend wider public service reforms to education without paying sufficient attention to their appropriateness for education, or to real institutional constraints and local context for reform.
Second, many governments have failed to place poverty reduction and equity at the centre of governance reform.

Too often the interests of the poor have been a rhetorical afterthought : W H Y G O V E R N A N C E M AT T E R S I n t r o d u c t i o n symptom of failure in public provision, not a solution to the problem.
The lesson: transferring responsibility to communities, parents and private providers is not a substitute for fixing public-sector education systems.

3.

Teacher governance and monitoring.

Teachers have figured prominently on the governance reform agenda.

Problems to be addressed range from recruitment to motivation and deployment.

Low teacher morale, often linked to poor pay and working conditions, is a major impediment to high-quality learning.
In many countries, problems in teacher recruitment are compounded by large disparities in access to well-qualified teachers, with children from poor households and remote rural areas losing out.

Monitoring of learning achievements can play an important role in informing policy design – but institutional capacity for effective monitoring is often limited.
The lessons: improve teacher recruitment, deployment and motivation through appropriate incentives and accountability mechanisms to improve learning and enhance equity; and strengthen the use of regional, national and school-level assessments to support policy design aimed at these same ends.

4.

An integrated approach to education and poverty reduction.

The planning processes through which priorities are set are a key aspect of governance reform.
Education-sector planning in developing countries has been strengthened in recent years, reflecting increased political and financial commitment.

However, progress towards greater equity in education requires governance reform beyond the education sector itself.

Broader strategies are needed to remove barriers to EFA associated with disparities based on wealth, gender, ethnicity and other factors.

Coordination across sectors is needed to influence health, nutrition and livelihood opportunities.
Poverty reduction strategy papers have potential in this effort, but most are not delivering.

The lesson: integrate education with wider strategies for overcoming poverty and inequality.

If there is one clear lesson from experience with governance reform, it is that changes in administrative processes and shifts in the locus of decision-making do not automatically generate ‘pro-poor’ outcomes.
Tackling the root causes of educational disadvantage requires political commitment and policy processes that take account of the problems and priorities of the poor and vulnerable.

It also calls for an integrated, cross-sectoral approach to planning.

This chapter does not attempt to cover all aspects of education governance.

It focuses selectively on some of the most important currents in governance reform and on themes neglected in wider education reform debates.

It is divided into four sections.


1. Financing strategies for closing the equity gap.

Many governments have increased their public finance commitment to education since Dakar.

But many could – and should – be doing much more.

Governance reforms have focused on improving efficiency, with scant regard for equity.

Decentralization is a case in point.

Devolving revenue mobilization in countries marked by large regional wealth gaps can lead to increased inequality in education financing.
In many cases, decentralization has reinforced and magnified disparities in education.

The lesson: while decentralization is important, central government should retain a strong role in equalizing the distribution of education finance.

2.

Choice, competition and voice.

Choice and competition are central themes in education governance reform.

The idea is that competition drives gains in efficiency, with wide-ranging advantages for learning achievement and equity.
Weak evidence of presumed benefits has not diminished the enthusiasm of many reformers for public-private partnerships involving an enlarged role for private schools and voucher systems to allocate public finance to such schools.

‘School-based management’, or the devolution of authority to school and community level, has been another powerful reform current.

Its stated aim is to make education providers more responsive to local needs.
Meanwhile, low-fee private schools are seen by some as a viable alternative to state provision.

Positive outcomes associated with reforms in these areas have been muted.

Public-private partnerships have a mixed and modest record on learning achievements and equity.
And low-fee private schools are a Tackling the root causes of educational disadvantage requires political commitment and policy processes that take account of the poor and vulnerable Failure to tackle corruption reduces efficiency and erodes equity 1 3 2 C H A P T E R 3 Financing education for equity Introduction In the Dakar Framework for Action governments promise to ‘enhance significantly investment in basic education’ (UNESCO, 2000, para.
8).

Implicit in this pledge is the conviction that additional financing is needed if the world is to achieve UPE and the wider EFA goals.

But resource mobilization is just one part of a broader set of governance challenges.

How governments mobilize, distribute and manage investment in education has a crucial bearing on the efficiency and equity of their school systems.

Countries vary enormously in their capacities for financing education.
Differences in wealth contribute to vast disparities in spending per student, which in turn fuel the global inequalities in access and quality discussed in Chapter 2.

These inequalities have important implications not just for education, but also for the future distribution of wealth and opportunity in an increasingly integrated global economy.

While low average incomes and high levels of poverty impose obvious budget constraints, patterns of spending on education also reflect political choices.
Some governments are far more committed to financing education than others.

This has important consequences: higher spending does not lead automatically to improved or more equitable education outcomes, but sustained and chronic underfinancing is definitely not conducive either to the development of high-quality education systems or to equity.

Financial management figures prominently in debates on education governance.

The issues at stake have important consequences for equity.
Rules governing allocation of funds between students, schools and regions can determine whether the disadvantaged receive more or less financing than the more advantaged.

Governance practices also affect the efficiency of government spending, strongly influencing the availability of classrooms, teachers and teaching materials.
Failure to tackle corruption, a key governance concern, imposes a double economic burden on education: it penalizes efficiency and, because the burden of corruption falls disproportionately on the poor, it erodes equity.

Decentralization has been widely advocated and adopted as a governance reform.

The main argument for decentralization is that it brings decisions closer to people.
By devolving decision- making and financial management to local government agencies, the argument runs, decentralized structures offer greater accountability and responsiveness to local problems.

However, decentralization can also weaken education provision and widen inequalities.

Outcomes depend on the design of decentralization strategies and the commitment of central government to equalizing opportunity.
Government spending on education In recent years, two contrasting viewpoints have emerged on the importance of increased financing for achieving the EFA goals.

Some commentators treat increased spending on education as an automatic indicator of progress.

Others point to the harsh lesson provided by analysis of cross-country data: namely, that the relationship between education spending and student performance is weak at best and sometimes non-existent (Hanushek, 2003; Pritchett, 2004; Wößmann, 2003).
The latter group has stressed the importance of improving efficiency, viewing it as the best way to progress towards the goals.

Reality is less clear-cut.

Dismal learning outcomes and high levels of inequality are possible at low, medium and high levels of spending.

Rapid increases in spending do not necessarily lead to improved achievement levels.

Yet financing thresholds are important.

Students need access to a minimum level of resources and materials.
Schools have to be built and buildings maintained.

Teachers have to be recruited and paid.

Even with improved efficiency, chronic financing gaps in many countries contribute to inadequate access, poor quality, insufficient teacher recruitment and low teacher morale.

Underfinancing is not neutral in its effects.

Middle- and high-income groups can compensate for inadequate state provision.

They can send their children to private schools and hire private tutors.
They can also buy supplementary textbooks and teaching materials.

Low-income households are likely to find these choices impossible.

For the poorest groups, public investment and provision constitute the only viable route to an education that meets basic quality standards.
: W H Y G O V E R N A N C E M AT Patterns and trends in public spending around the world Any country’s public investment in education is linked to four factors: national wealth, the share of wealth converted into budget revenue, the proportion of public expenditure dedicated to education, and external support.
In addition, the distribution of spending on the various education levels has important implications for equity within countries.

This subsection provides a brief snapshot of global, regional and national financing for education.

Education spending and national income: an irregular association.The share of national income devoted to education differs substantially among regions and income groupings (Table 3.1).
On average, the share increases with national wealth, largely because tax revenue collection rises with per capita national income.

Low-income countries in sub-Saharan Africa, and South and West Asia, where some 80% of the world’s out-of-school children live, tend to invest the smallest proportion of GNP in education.

In sub-Saharan Africa, about half of all low-income countries (eleven of the twenty-one with relevant data) spend less than 4% of their national income on education.
In South Asia, Bangladesh devotes only 2.6% of its national income to education and Pakistan 2.7% (see annex, Statistical Table 11).

More surprisingly, India invests a smaller share of GNP in education – around 3.3% – than the sub-Saharan Africa median, even though average incomes are around one-third higher.

While low income countries spend significantly less on education than others, large differences exist within this group.
For example, the Central African Republic allocates 1.4% of GNP to education while Ethiopia devotes 6%.

Public spending is rising, but not across the board.

In the majority of countries with data, public spending on education as a share of GNP has increased since Dakar (Figure 3.1).

Large increases in spending have been associated in some countries with substantial progress on EFA goals (though association is not causation).
For example, Ethiopia, Kenya, Mozambique and Senegal have sharply increased the share of GNP invested in education and all have seen significant declines in numbers of out-of-school children (see Chapter 2, Figure 2.10).

On a more negative note, the share of national income devoted to education decreased between 1999 and 2006 in 40 of the 105 countries with data.
In twelve of them it dropped by more than a Table 3.1: Total public expenditure on education as a percentage of GNP, by region and income group, 2006 Minimum Median Maximum Countries with data (%) World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.
Europe Central and Eastern Europe Income group Low income countries Lower middle income countries Upper middle income countries High income countries 1.2 1.4 1.2 2.4 1.4 1.6 2.4 1.8 2.6 1.2 2.3 3.6 1.4 2.3 1.4 1.2 4.9 4.4 5.3 3.9 4.4 4.6 3.4 … Note: Country groupings in the first part of this table follow the classification used in the statistical tables in the Report’s annex.
The income classification in the second part of the table follows that used by the World Bank.

Source: Annex, Statistical Table 11. percentage point.

Worryingly for prospects of achieving UPE by 2015, this group includes several countries with relatively large out-of-school populations, low levels of participation, or both, including the Congo (-3.5 percentage points), Eritrea (-2.9) and India (-1.3).
The lack of significant change in Pakistan and Bangladesh, and in some sub-Saharan African countries, is equally worrying.

These countries need simultaneously to increase the level, efficiency and equity of public spending on education.

National commitment to education varies.


To the extent that budget priorities reflect political priorities, how governments allocate resources says something important about their ordering of concerns.1 The share of education in total public expenditure is a more direct measure of government commitment to education than the share in GNP.

The median share of government spending on education in sub-Saharan Africa is among the highest for any region (Table 3.2).
On the other hand, South and West Asia devotes a smaller share of government resources to education than countries in the Arab States and sub-Saharan Africa.

Whatever their resource constraints, many The large share of teacher remuneration in education financing is not an indicator that teachers are overpaid 1.

The share of national income devoted to education depends on governments’ ability to collect revenue.
To devote the same share of GNP to education, a country with a higher revenue share in overall GNP can allocate a smaller share of government resources to education compared with a country with the same national income but a lower revenue share.
Figure 3.1: Change in total public expenditure on education as a percentage of GNP between 1999 and 2006 (percentage points) P N G f o % ) s t n i o p e g a t n e c r e p ( 6 0 0 2 d n a 9 9 9 1 n e e w t e b 6 5 4 3 2 1 0 -1 -2 -3 -4 o g n o C a e r t i r E Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific .
p e R Source: Annex, Statistical Table 11.

Table 3.2: Total public expenditure on education as a percentage of total government expenditure, by region and income group, 2006 Minimum Median Maximum Countries with data (%) World Developing countries Developed countries Countries in transition Sub-Saharan Africa Arab States Central Asia East Asia and the Pacific South and West Asia Latin America/Caribbean N. America/W.
Europe Central and Eastern Europe Income group Low income countries Lower middle income countries Upper middle income countries High income countries 4 4 6 9 4 10 9 9 11 9 9 6 10 4 8 9 15 16 12 17 18 21 … … Note: Country groupings in the first part of this table follow the classification used in the statistical tables in the Report’s annex.
The income classification in the second part of the table follows that used by the World Bank.

Source: Annex, Statistical Table 11. countries in South and West Asia would appear to suffer from a lack of political commitment to education.

Once again, regional averages hide large differences across countries.

For example, in sub-Saharan Africa, Madagascar devotes 25% of its government budget to education compared to only 10% in Chad (see annex, Statistical Table 11).
Allocation of education finance Overall resource mobilization patterns are just one side of the financing equation.

How governments allocate resources within the education sector is also important.

The selection of priorities often says something important about education governance.

Allocations to primary education vary with enrolment patterns.The allocation of funds to a particular education level is influenced by enrolment patterns.
Countries with low levels of post-primary enrolment tend to have higher proportions of their overall budget allocated to primary.

In thirteen of the twenty-nine sub- Saharan African countries with data, the proportion of government spending allocated to primary education is over 50% (Figure 3.2).
Countries in other regions with low post-primary participation rates – Guatemala, Mauritania and the Philippines are examples – demonstrate a similar concentration of resources towards primary education (see annex, Statistical Table 11).
The lowest allocations to primary education are : W H Y G O V E R N A N C E M AT South and West Asia Latin America and the Caribbean North America and Western Europe Central and Eastern Europe a i d n I n a t s i k a P l a p e N h s e d a l g n a B .

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p e R 6 5 4 3 2 1 0 -1 -2 -3 -4 P N G f o % ) s t n i o p e g a t n e c r e p ( 6 0 0 2 d n a 9 9 9 1 n e e w t e b found in countries and regions where secondary enrolment is almost universal and tertiary enrolment high.
As always, averages obscure important variations: of the 108 countries with data in all regions, 60 spend more on secondary education than on primary education.

Spending on teachers dominates, especially in the poorest countries.About half the countries with data for 2006 spent more than three-quarters of their primary education recurrent budgets on teacher remuneration in public institutions (see annex, Statistical Table 11).
The share of teachers in budget allocations sometimes leaves little space for the financing of other inputs, including learning materials and the professional development of teachers.

In Zambia, 93% of the primary recurrent budget goes to teacher salaries and less than 4% to textbooks, and other teaching and learning materials – and this example is no anomaly.
The large share of teacher remuneration in education financing is not, as is sometimes assumed, an indicator that teachers are overpaid; many have salary levels close to the poverty line (see section below on teachers and monitoring).

Rather, it indicates that the primary education sector is under-resourced, suggesting a need for increased commitment from governments and aid donors.

Weak commitment and inequitable allocation have consequences.
Financial governance decisions reflected in resource mobilization and allocation In Zambia, 93% of the primary education budget goes to teacher salaries Figure 3.2: Distribution of public current expenditure on education by level, sub-Saharan Africa, 2006 Botswana Angola Seychelles Congo Mauritius Cameroon Ghana Swaziland Lesotho Eritrea South Africa Rwanda Senegal Madagascar Chad Benin Ethiopia Burundi Sierra Leone C. A. R. Kenya Cape Verde Zambia Mali Uganda Malawi Niger Burkina Faso Mozambique 0 20 40 60 80 100 Share in public current expenditure on education (%) Primary education Secondary education Source: Annex, Statistical Table 11.
Low spending can lead to dilapidated schools and poor outcomes C H A P T E R 3 matter for the experiences of children in classrooms.

The case of Nigeria is illustrative.


In 2005 between 3.5% and 4.2% of GDP was allocated to education.2 However, education represents only 11% to 13% of total government spending, which compares unfavourably with the regional average for sub-Saharan Africa (World Bank, 2007b, 2008f, Table 3.2).
The consequences of underfunding are powerfully captured in the following assessment: ‘Spending on essential inputs, such as textbooks, instructional materials, in-service teacher training, and operations and maintenance are inadequate.

About half of primary schools require major rehabilitation, with an additional 251,000 classrooms needed countrywide’ (World Bank, 2008e, p. 15).
Raising government spending on education in Nigeria to the regional average would release substantial additional resources to address the many difficulties the sector faces.

Global and regional inequalities in the distribution of public education expenditure Wealth inequalities among countries are mirrored by disparities in education spending.

These disparities are closely associated with the large global differences in opportunity for education documented in Chapter 1.
The links between national wealth and education financing operate in both directions.

Differences in national wealth reflect the impact of education attainment and quality on growth and productivity.

And differences in education attainment and quality reflect the financing capacities of countries with different levels of national wealth.
Huge gaps in per-student spending between developed and developing countries.In terms of spending per student, children in developed and developing countries live in different worlds.

In 2006, per-student expenditure in primary education varied between US$39 in the Congo and US$9,953 in Luxembourg, at purchasing power parity (PPP) in constant 2005 dollars (Figure 3.3).
While the transmission mechanisms between education spending and education quality are complex, the very low absolute spending in many developing countries is implicated in the abysmal learning outcomes and dilapidated school infrastructure that Chapter 2 documents.

When per-pupil spending is less than PPP US$300 a year and largely absorbed by teacher salaries, the consequences are registered in classrooms with leaking roofs, no books and no chairs.
Global public expenditure on education is highly skewed.Differences in per-student spending translate into an extremely uneven global distribution of public expenditure on education (Figures 3.4 and 3.5).
In 2004, North America and Figure 3.3: Inequality among countries in public expenditure per primary school pupil, 2006 $ S U 5 0 0 2 t n a t s n o c n i P P P t a ) t s o c t i n u ( l i p u p r e p 10 000 8 000 6 000 4 000 2 000 .
.

 .

A .

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d a n e r G / t n e c n i V t S Source: Annex, Statistical Table 11.

2.
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A range is provided because results differ depending on the estimating technique used.

See World Bank (2008f) for details : W H Y G O V E R N A N C E M AT Western Europe alone accounted for 55% of the world’s spending on education but only 10% of the population aged 5 to 25.
At the other extreme, sub- Saharan Africa was home to 15% of 5- to 25-year- olds but accounted for just 2% of global spending, and South and West Asia for 28% of the age group but 7% of the spending (UIS, 2007).

For the poorest countries, increased aid flows could play an important role in reducing the public education expenditure gap (see Chapter 4).

Both finance and governance matter Governance reforms can unlock efficiency gains that can expand access and improve quality.
The previous subsection showed the wide variation by country in education financing.

The extent to which differences in levels of financing explain disparities in outcomes such as those outlined in Chapter 2 is partly determined by education system efficiency.
Raising efficiency to increase the flow of benefits, Differences in per-student spending translate into an extremely uneven global distribution of public expenditure on education Figure 3.4: Distribution of global public education expenditure by region, 2004 Figure 3.5: Distribution of global public education expenditure by country, 2004 0.3% Central Asia 2% Sub-Saharan Africa 3% Arab States 7% Central/Eastern Europe 7% South and West Asia 8% Latin America/Caribbean 18% East Asia/Pacific 37% Other countries 3% Brazil 3% Italy 4% United Kingdom 4% France 4% Germany 5% India 6% Japan 6% China 55% N. America/W.
Europe Note: Distribution is calculated using PPP US$.

Source: UIS (2007, Figure 1, p. 11).

28% United States Note: Distribution is calculated using PPP US$.

Source: UIS (2007, Figure 1, p. 11).

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 10 000 8 000 6 000 4 000 2 000 $ S U 5 0 0 2 t n a t s n o c n i P P P t a ) t s o c t i n u ( Governments can stretch limited resources by being more efficient 3.

The average for Africa was taken from an average based on World Bank projects.
The original figure, US$8,000 in constant 2000 prices, was inflated to 2007 prices using the US GDP deflator.

1 3 8 C H A P T E R 3 measured in terms of access, attainment and quality, is an important public policy goal.

Improving efficiency and reducing corruption Efficiency in public spending is about how effectively governments use revenue to advance social welfare.

Defining efficiency is not straightforward.


Education outcomes cannot be measured simply in terms of numbers, whether of children in classrooms, books or teachers.

Qualitative indicators are critical.

There are also important questions about how much weight should be attached to equity-related goals.

Should an additional year in education for a high-income child in secondary school count for the same as an additional year in primary school for a low-income child?
There are no simple answers – but no government can afford to neglect efficiency.

Technical efficiency, at its most basic level, can be thought of in terms of rates of conversion.

What level of financing is associated with a specific output?

Consider the following example.

Senegal and Ethiopia both had primary NERs of 71% in 2006 and 2007, respectively.

However, Senegal spent PPP US$299 per primary pupil, compared with Ethiopia’s PPP US$130 (2005 dollars).
On this simple indicator, Ethiopia’s education system is more efficient in translating resources into school places.

If the costs of a school place were similar in the two countries, Senegal could easily provide sufficient school places for all primary school-age children with its current levels of public spending.

In this comparison differences in efficiency may be driven by many factors, including teacher salary levels and class size.
Higher levels of efficiency on the narrow technical indicator of an expenditure/ enrolment ratio may not indicate better quality provision.

The real question for policy-makers, therefore, is whether better qualitative outcomes (e.g.

attainment of basic literacy) can be achieved for less.

Classroom construction provides an example of where efficiency gains can make a big difference.
In the Nigerian state of Kano the average building cost per classroom in 2007 was US$14,000, while the average cost estimated by the World Bank for Africa was US$10,000 (FTI Secretariat, 2006; Kano State Ministry of Education, 2008).3 If improvements in efficiency could reduce the cost in Kano to the average for Africa, an additional 40 classrooms could be built for every 100 currently being constructed.
The efficiency saving could result in more school places and reduced overcrowding, within current levels of education spending.

Comparisons of this type do not provide grounds for sweeping conclusions.

Classroom construction costs may vary across countries in sub-Saharan Africa and other regions for many reasons.

These include the cost of materials, wage levels and the quality of the classrooms constructed.

Even so, it would be wrong to understate the critical importance of efficiency.
In low-income countries facing tight budget constraints and with large deficits in classroom availability, efficiency is one of the most critical requirements for expanded access to education and enhanced equity.

Cross-country evidence can provide education planners with insights into policies for achieving the central goal of maximizing the number of good-quality classrooms available, within their resource envelope.

Improving efficiency is not just an issue for the very poorest countries.
Nor is it just about infrastructure and inputs.

Efficiency gains can also be reflected in learning achievement indicators.

A recent study explored differences in public education spending efficiency across regions in Argentina and Mexico.

The study used NERs and test scores as output indicators.

It measured efficiency after controlling for levels of regional income, literacy rates and education spending per capita.
For Mexico, the analysis estimated that improvements in efficiency alone could increase the primary NER by five percentage points and the secondary NER by fifteen percentage points.

For Argentina, it estimated that improving efficiency had the potential to increase mathematics scores by seven percentage points in primary school and by nine percentage points in secondary (Jayasuriya and Wodon, 2007).

Corruption is a source of inefficiency and inequality.
Analysis of public education expenditure accounts provides insights into the official picture of resource flows.

It reveals the level of resource mobilization and the flow of funds through national budgets to lower levels of government and down to schools.

In some countries there is a gap between budget provision and delivery of real inputs to education.

Corruption is often implicated.

Tackling corruption in education is important for the sector and for society in a broader sense.
Education receives a large share of total public expenditure – in most countries it is the largest : W H Y G O V E R N A N C E M AT single area of government activity and the largest public employer.

Efforts to limit corruption in general are unlikely to succeed if they do not address the education sector in particular.
Moreover, ‘lack of integrity and unethical behaviour within the education sector is inconsistent with one of the main purposes of education itself, which is to produce “good citizens” respectful of the law, of human rights and of fairness.

It is also incompatible with any strategy that considers education as one of the principal means of fighting corruption’ (Hallak and Poisson, 2004, p.7).

Corruption is difficult to measure and its effects are hard to evaluate.
Because it is illegal it is not recorded in official data and, with government agencies often implicated, its full extent may be hidden.

Corruption has adverse consequences for efficiency and equity.

Efficiency suffers because corrupt practices mean part of the benefit of public investment is captured in the form of private rent.

Equity suffers because corruption acts as a regressive tax that hurts the poor the most.
Notwithstanding the problems with monitoring corruption, cross-country and within-country research has provided interesting insights.

One example comes from Nicaragua.

Monitoring of six major school upgrade and repair projects that were undertaken by the education ministry demonstrates how corrupt practices diminish resource flows to education (Transparency International, 2005).

Comparison of the buildings before and after project completion revealed widespread irregularities.
Substandard materials and overpricing contributed to substantial financial losses.
In Brazil, the otherwise highly effective FUNDEF programme (Fundo de Manutenção e Desenvolvimento do Ensino Fundamental e de Valorização do Magistério) was affected in the past by illegal appropriation of funds meant for teacher salaries and training (Transparency International, 2005).4 On average, around 13% of the total was lost in the course of transfer from the federal budget to municipal bank accounts, rising to 55% for some municipalities.
The governance problem was linked to the inability of local councils charged with monitoring the grants to ensure that they were properly received and used.

Measuring cross-country corruption is intrinsically difficult.

However, one study using a data set of fifty-seven countries reached a conclusion that has important implications for EFA.
It found that increased public spending on education was associated with a significant increase in primary education completion rates onlyin the least corrupt countries and those with better-quality bureaucracies (Rajkumar and Swaroop, 2008).

Corruption creates setbacks for equity because the efficiency losses linked to corruption are not distributed equally across society.

The greatest burden falls on the poor and disadvantaged, for three reasons.
First, the poor tend to be more reliant on public services.

Lacking financial resources, they may not have the luxury of responding to corruption by opting out of the public system and putting their children into private schools.

Second, the poor are more likely to be susceptible to corrupt practices because they have limited recourse to formal or informal channels through which to seek redress and they often lack a strong enough voice to hold service providers to account.
Third, when informal payments are required to secure access to education, the cost is likely to represent a higher proportion of household income for the poor, making it difficult for them to send children to school.

In Mexico, every two years the National Survey on Corruption and Good Governance records informal payments by households for thirty-eight public services in all thirty-two federal states, making it possible to quantify what amounts to a tax (Transparency International, 2005).
Estimates based on the survey indicate that households pay almost US$10 million in bribes to secure access to public education, which is legally free.

In 2003 households paid an average of US$30 each to meet illegal demands from service providers.

In a country where around one-quarter of the population was living on less than $2 per day, this is a significant financial burden.
There are also indications that informal payments for access to basic services may be charged more frequently to poorer households.

Tackling corruption through information, institutional reform and monitoring.

Because corruption represents a regressive transfer of public funds away from the poor and powerless, reducing it is intrinsically good for equity – in the education sector and elsewhere.

In rich and poor countries alike, corruption is rooted in political cultures of non-accountability.
Rooting out corruption may be a long-term process, but rapid progress is possible in the short term.

Corruption is rooted in political cultures of non-accountability 4.

In 2007 the law governing FUNDEF was amended to add coverage of pre-school and secondary education and to rename the programme FUNDEB – Fundo de Manutenção e Desenvolvimento do Ensino Básico e de Valorização do Magistério (Fund for the Maintenance and Development of Basic Education and Valorization of Teachers).
In Indonesia, public availability of monitoring reports transformed corruption in the education system from a well-hidden activity to a highly visible subject for public debate 1 4 0 C H A P T E R 3 Governments acting with resolve can put policies in place that make an immediate difference.

An important first step is to acknowledge the scale of the problem and develop a commensurate institutional response.
Information has a key role to play because governments and the public alike are often insufficiently aware of the scale of the corruption problem.

A recent study in Bangladesh provides a detailed tracking analysis of the flow of resources through the system – an approach that other countries could usefully follow (Box 3.1).

The analysis reveals a broadly positive picture while identifying areas requiring greater scrutiny.
Box 3.1: Tracking public expenditure in Bangladesh In 2005 a public expenditure tracking and quantitative service delivery survey was conducted to assess the primary education sector in Bangladesh.

Among the key findings: Records for allocation and expenditure were fairly consistent among various sources, both at national level and at lower levels.
The teacher payment system appeared to be robust, with no evidence that resources for the payment of teacher salaries were leaking between the centre and local offices, or between local offices and schools.

Textbook leakage varied, with 98% of allocated books reaching children in government and registered non-government schools compared with 76% in madrasas.
Poor record-keeping by schools made it hard to track contingency payments and expenditure on small repairs and construction, though no major leakage of funds was found.

Nearly 20% of stipend payments were misallocated due to exaggerated attendance figures and payments to ineligible children; 5% of stipend resources were unaccounted for.

While the results point to a relatively effective financial management system, some serious problems were identified.
For example, informal payments to local education offices were reported by 16% of recently transferred teachers and about 40% of head teachers.

Around one in ten households reported making informal payments to get their children into the primary school stipend programme.

Source: Financial Management Reform Programme (2006a).

Another positive example comes from Indonesia.


Here the School Improvement Grant Programme (SIGP) provided cash grants to primary and junior secondary schools between 2000 and 2004, targeting in particular those in the poorest districts and with large populations of children from households displaced by conflict or natural disaster.

A large programme, it covered some 8,000 schools in 130 districts and had a budget of around US$60 million, of which 70% was for physical rehabilitation of school buildings.
Recognizing that corruption was a systemic problem, government and donors created an institutional structure aimed at strengthening governance.

Among the central features: Decision-making was decentralized: district and local committees including non-government representatives selected beneficiary schools.

School committees were involved in determining needs and construction work involved local people.
Details of block grants were announced publicly and finance was directly transferred through the banking system, thus avoiding interference from intermediaries.

Comprehensive guidelines were issued for programme procedures.

The programme was independently monitored through a Central Independent Monitoring Unit (CIMU).

The monitoring report of the CIMU, which was publicly released, sparked an intense national debate on corruption.
It documented forty apparent cases of corruption, from construction consultants illegally charging for services to diversion of funds by local government officers, attempted bribery and collusion on prices between officials and building contractors or textbook suppliers.

Measured against past practices, overall levels of corruption in the SIGP were modest – institutionalized transparency made a clear difference.
The CIMU report transformed corruption in the education system from a well-hidden activity to a highly visible subject for public debate (Baines, 2005).

Institutionalized public-expenditure tracking is one of the most effective anti-corruption devices.
Monitoring real delivery of funds compared with : W H Y G O V E R N A N C E M AT budget provisions can turn a spotlight on problem areas.
Since the mid-1990s the World Bank and other donors have conducted Public Expenditure Tracking Surveys (PETS) to evaluate the effectiveness of financial management systems and identify where leakage occurs between ministries and the classroom (Reinikka and Smith, 2004; Winkler, 2005).

Building on an exercise undertaken initially in the mid-1990s in Uganda, PETS are now a widely used monitoring tool (Box 3.2).
Their impact is strengthened when it is combined with improved public access to information – a point illustrated by the experiences of Indonesia and Uganda.

Not all public expenditure tracking exercises have been successful.

When corruption is deeply entrenched and political leaders do not create conditions for strengthened accountability, such exercises can deliver limited results.

The PETS on education conducted in Peru in 2002 is an example.
Opaque budget planning made it impossible to establish real allocation levels, providing extensive opportunities for corruption.

Over 90% of the resources earmarked for education were devoted to payroll, but data lapses on teacher numbers limited the scope for assessing delivery (Reinikka and Smith, 2004).

As Transparency International puts it: ‘in many countries, anti-corruption laws and regulation have been in place for years, but citizens do not know about them – often because they are rarely applied.
With no visible sanctions, people are inclined to believe that corruption cannot be resisted and therefore will not report it’ (Transparency International, 2005, p. 14).

Part of the problem is that those who benefit from corruption have much to lose from information campaigns and much to gain from the maintenance of reporting systems that lack transparency.

On the other hand, most citizens – particularly the poor and parents with children in school – stand to lose a great deal from failure to act.
Box 3.2: Public expenditure tracking, information campaigns and the fight against corruption in Uganda Public information and budget monitoring are two of the most powerful antidotes for corruption.

Uganda’s experience illustrates their effectiveness in strengthening public financial management systems.

In the mid-1990s official budget records were a weak guide to the financing of education in Uganda.

In 1996, the World Bank conducted a PETS in 250 schools located in 19 districts.
It showed that, according to school records, only 13% of central government capitation grants actually reached schools.

Most schools reported having received no funds, and most teachers and parents were unaware that the grants existed.

Financing earmarked for education was diverted to other sectors, used for political activities or stolen.

When Uganda introduced free primary education in 1997, donor support was required to replace parental contributions with public spending.
Aid partners made support conditional on implementation of an anti- corruption programme, including measures to raise awareness about leakage and give parents a voice.

Schools were instructed to publicly post detailed information about funds received from local government.

Several national and regional newspapers published information about grant transfers from central to local government, including dates and amounts.

Institutional changes were also introduced.


Instead of transferring funds for education and other sectors to districts as a single block grant, the government decided to transfer them as twenty-two separate conditional grants with payments linked to specific actions.

A second PETS in 2002 showed that schools were receiving on average 80% of their capitation grant, and that all schools were receiving at least part of the grant.

Evaluations of the Uganda experience have provided some important insights.
Detailed statistical analysis indicates that the reduction in grant leakage was greatest in schools that were closest to newspaper outlets — an indication that information had the effect of empowering communities.

However, the benefits of information campaigns were not equitably distributed.
Their impact was less marked in communities with the lowest literacy levels, thus underlining yet again the importance of education — and literacy skills in particular — to help people make informed choices and to create an enabling environment for responsible and accountable governance.

Sources: Crouch and Winkler (2007); Hubbard (2007).
Without visible sanctions corruption often goes unreported Unequal spending reinforces disparities Resource mobilization, efficiency and measures to tackle corruption have system-wide benefits for education.

That is why governance in these areas is of such importance for achieving EFA.

Equity also matters.


Overcoming the disadvantages and disparities documented in Chapter 2 requires financing strategies that aim explicitly at equalizing opportunity, with public financing being used to counteract social deprivation.

schools whose students are of Albanian ethnicity receive almost 20% less in per-student funding than the national average.

In rural areas they receive almost 37% less than schools whose students are of Macedonian ethnicity (Table 3.4).
In China, per student spending at the primary level varies by a factor of ten between the lowest spending and highest spending provinces, broadly reflecting differences in provincial wealth (Tsang, 2002).

Equitable financing is not an easy concept to define.

It clearly means something more than equal per- student financing.

Providing equivalent support to children in very unequal circumstances is not the same as equalizing opportunity.
Children who are disabled, who lack home advantages associated with parental literacy, who are poor or are disadvantaged by virtue of their gender or ethnicity are not competing on a level playing field.

For children in cases like these, achieving a particular outcome in education is likely to entail higher costs than for children from social groups that are not disadvantaged.

Whatever the precise definition of equity, government spending patterns around the world are often highly inequitable.
Analysis of the distribution of benefits from public spending across populations suggests inequity is the rule, not the exception.

A study of countries in sub-Saharan Africa, Asia and the Pacific, the Middle East and North Africa, and countries in transition, discussed in the 2008 Report, found that total expenditure on education was not pro-poor in any region.
In many cases public spending was strongly ‘pro-rich’, with pro-poor expenditure on primary education outweighed by a bias towards higher income groups in secondary and tertiary education.

National data broadly confirm this picture.

In some cases, public financing allocations fail to counteract poverty-related disadvantage.

One illustration comes from Indonesia, where per-capita expenditure on education in the poorest 20% of districts amounts to only 54% of expenditure in the richest 20%.
The gap in per-student expenditure is smaller because enrolment is lower in the poorest districts (Table 3.3).

In a more equitable system the poorest districts, which face the most severe deprivation in education, would receive the highest per-student allocations.

In some cases, the contours of unequal financing follow ethnic lines.

In the former Yugoslav Republic of Macedonia, Spending patterns are not fixed in stone.

They change with patterns of enrolment and as a result of public policy decisions.
During the 1990s, Brazil had one of the world’s most inequitable patterns of public spending.

Per-student spending in the poorest states of the north-east averaged around half the level in the wealthier states of the south- east.

More recently, redistributive financing programmes have significantly changed this picture.
Many governments are attempting to redress social inequalities in education directly through new approaches to financial resource allocation, attaching more weight in allocation formulas to disadvantaged groups and regions, or to special programmes.

Targeted interventions focused on specific inputs have also been used.

For example, spending on free textbook programmes targeting the disadvantaged in three Central American countries was found to successfully redirect resources to the poor (Table 3.5).
Many factors determine how governments allocate resources in education.

Technical financing formulas can be useful, but the real drivers of distribution patterns lie elsewhere.
Governments with a weak commitment to equity in general are unlikely to attach a great deal of weight to the interests of disadvantaged groups or regions in Table 3.3: Poverty and public education expenditure in Indonesia, 2004 Total district education expenditure (Constant 2006US$) District quintile Per capita Per public school student Quintile 1 (poorest) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (richest) 53 75 63 71 98 147 132 125 151 169 Source:Calculations based on Arze del Granado et al.
(2007, Table 10, p. 17).

In many countries public education spending tends to benefit the relatively well-off : W H Y G O V E R N A N C E M AT education.

Similarly, in situations where the educationally disadvantaged have a weak political voice, they are unlikely to exercise a strong claim on public financial resources.
Political leadership can make an enormous difference.

For example, in Senegal, Uganda, the United Republic of Tanzania and Zambia the decision of political leaders to abolish user fees in education while increasing spending at the primary level has strengthened equity in public spending and had important positive effects on enrolment.

Strategies for greater equity in financing There are no ready-made formulas for equitable financing in education.
Any strategy aimed at equalizing opportunity has to take into account specific patterns of disadvantage.

Some of these patterns might be rooted in regional disparities.

Others will involve factors such as the incidence and depth of poverty, or disadvantage based on gender, ethnicity and language.

In an equitable system, allocation of education finance would be inversely related to current outcomes, with those in greatest need receiving the most support.
Moving from statements of principle to practical measures raises multiple challenges for government.

For all the egalitarian rhetoric of public policy discourse on education, it is relatively rare to find a clear link between student needs, as determined by equity criteria, and per-student expenditure.

Governance rules for resource allocation vary enormously.

Most countries have funding formulas for allocating money, teachers and teaching materials across education systems.
These formulas can include a per-student component, a fixed capital cost component and compensatory elements aimed at redressing disadvantage.
Compensatory finance can address wide-ranging sources and types of inequality, such as: Table 3.4: Ethnicity and public education expenditure in the former Yugoslav Republic of Macedonia, 2005 School ethnicity type Rural Small city Large city Skopje (capital) Total Municipality type Albanian Dominant Albanian Macedonian Dominant Macedonian Other Total 289 341 457 467 454 391 (Constant 2006 US$) 261 298 364 295 375 332 263 367 347 386 … 352 255 238 290 360 296 285 274 301 359 372 395 342 Source: Calculations based on World Bank (2008c, Table 2.7, p. 24).
Table 3.5: Free textbooks distributed by governments in selected Central American countries, percentage per income quintile, 2000–2004 Country El Salvador Guatemala Nicaragua Quintile 1 (poorest) 29 29 29 Quintile 2 Quintile 3 Quintile 4 (% of total free textbooks) 24 21 26 26 27 23 14 16 15 Quintile 5 (richest) 7 8 8 Source: Porta Pallais and Laguna (2007).
to finance transportation to school or to buy school supplies, textbooks, uniforms and school meals; lack of provision for students in remote schools, where small class sizes, transport problems, boarding and dormitory costs, and problems in attracting and retaining teachers restrict opportunity; the additional financing and specialized teaching that may be required to assist students who are disabled or have special needs.
The link between student needs and per-student expenditure is often weak disadvantage associated with racial, ethnic, caste and other characteristics where cultural norms, political disenfranchisement and systemic discrimination hold back achievement; problems facing linguistic minorities or students whose native language is not the national or official language of instruction; restricted opportunities facing children from poor households whose parents may lack the resources to keep them out of the labour force, One common application of formula funding in developing countries has been the introduction of school grants.
Such grants represent a transfer of resources and spending authority from central, regional or district education authorities to local communities and schools (Fredriksen, 2007).

They can address a wide range of equity-related goals.

On the supply side, grants can be used to increase finance and the flow of teaching materials to schools in areas marked by high concentrations of poverty or with large numbers of disadvantaged students.
On the demand side, they can be used to In Ghana, a government grant enables schools to cover losses from the withdrawal of student fees C H A P T E R 3 reduce barriers to access.

Compensating schools for the loss of revenue that occurs when user charges are withdrawn is an example, as illustrated by Ghana’s Capitation Grant Scheme (Box 3.3).

Other countries have also introduced school grants into education financing.
In Kenya, the government established a school grant of US$14 per student to enable schools to cover losses from the withdrawal of student fees and to increase spending on materials, maintenance and operations.

The programme has improved availability of textbooks and other materials.

It has also been used to fund boarding schools to improve access for children living in sparsely populated areas (Fredriksen, 2007).
Similarly, the United Republic of Tanzania introduced school grants soon after abolishing fees in 2001.

In 2002 its capitation grants for primary schools amounted to about US$10 per pupil for textbooks, teaching and learning materials, school operations and administration, and teacher training (Fredriksen, 2007).5 Formula funding and school grant design are often viewed as narrowly technical matters.

They seldom figure in public debate or political programmes.
Box 3.3: Supporting school fee abolition: school grants in Ghana Under the Free Compulsory Universal Basic Education policy introduced in 1996, Ghana officially eliminated tuition fees for grades 1 to 9.

Five years later, the policy had not had its intended effect on enrolment and retention.

Policy reviews identified part of the problem as a substitution effect: faced with a loss of revenue, communities and parent-teacher associations authorized the introduction of informal fees.
School grants were introduced in 2004/2005 as a response.

They targeted schools in the forty most deprived districts and were linked to school improvement plans.

Intended to cover the cost of fee abolition, they were calculated based on student numbers, with a higher per-student grant for girls (the grants were equivalent to US$2.70 per boy and US$3.88 per girl per year).
Central government funds were channelled through dedicated bank accounts in each district and then on to individual school bank accounts.

Complaints and extensive lobbying from other districts led to the programme being extended to all primary schools from 2005/2006.

How successful has it been?

Enrolment figures covering the school years 2002/2003 to 2006/2007 point to sharp increases in overall enrolment ratios (Figure 3.6).
For example, the primary NER for girls living in deprived districts increased by twenty-four percentage points over this five year period.

While these improvements have been impressive, it is too early to tell whether the programme will have an impact on two central problems facing education in Ghana: high dropout rates and low rates of transition to secondary school.
Figure 3.6: Trends in primary net enrolment ratios in Ghana before and after introduction of the Capitation Grant Scheme Introduction of Capitation Grant Scheme Male living in non-deprived district Female living in non-deprived district Male living in deprived district Female living in deprived district ) % ( s o i t a r t n e m l o r n e t e n y r a m i r P 90 80 70 60 50 40 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 Sources: Fredriksen (2007); Gershberg and Maikish (2008).

5.
5.

Other sub-Saharan African countries experimenting with school grants include Ethiopia, Madagascar, Mozambique and Uganda : W H Y G O V E R N A N C E M AT This is unhelpful.

Financial resource management is complex, but that does not mean it should be the sole preserve of technocrats and administrators.
Formula funding design provides a strong indicator of whether governments are intent on translating commitments on equity into practical policies.

Chapter 2 suggests that governments could attach more weight to the mapping and monitoring of disparities in education.

Approaches to formula funding should be evaluated in the light of this exercise, with governments providing full public disclosure not just of the formula used but also of the equity rationale for its selection.
Decentralization: a potential driver of inequality The decentralization of public services to local government control has been a major feature of governance reform worldwide.

Arguments for decentralization extend from efficiency to equity.

Bringing decisions closer to the people affected, and devolving authority to local governments they elect, is seen as a route to more responsive service provision.

Education has figured prominently in decentralization reform.
In the developing world, a growing number of countries have transferred responsibility for education to lower levels of government, typically as part of wider public service reform.

Decentralization in education redistributes authority not only from central to local government but also from political authorities to school providers (King and Cordeiro Guerra, 2005).

What this means in practice varies by country.


Governments seldom devolve power wholesale and devolved education systems operate through multilayered governance structures that link many actors and agencies.

Central governments may transfer authority in some areas and either retain authority in others or set rules putting limits on local government choice.
Despite widespread advocacy for decentralization, central governments often retain high levels of control, as the governance mapping exercise in this Report’s annex shows.

Patterns of financial decentralization are particularly complex and have important implications for education provision.

The rules and policies governing fiscal decentralization also shape the ways in which it affects education.
Any assessment has to take account of three key areas: the assignment of spending authority,which defines the level of government responsible for making decisions on spending; the assignment of revenue-raising authority, which defines the powers of the various levels of government to impose taxes and charges; formulas for intergovernment resource transfers,which determine how revenue is allocated among regions and sectors.

Policies in each of these areas can have an important impact on equity.
It cannot be taken as axiomatic that the impact will be positive.

The devolution of revenue-raising authority, for example, can give local government greater autonomy but also lead to the introduction of user charges and taxes that may hurt the poor.

In Viet Nam, fiscal decentralization has gone a long way but central government controls revenue mobilization and transfers to local government (see below).
The only form of revenue autonomy for district and commune governments is in the introduction of fees in areas such as education and health.

User charges have been increased in both areas, with damaging implications for equity (Huong, 2006).

The experience in Viet Nam epitomizes some of the tensions inherent in decentralization.

Part of the rationale for decentralization is to improve efficiency and strengthen autonomy by devolving real authority.
But devolving revenue mobilization in countries with high levels of inequality comes with grave risks for equity.

Governments can mitigate those risks by ensuring that intergovernment resource transfers equalize opportunity.

The formulas used by governments in designing these transfers are highly technical – and highly political.
The following cases are instructive because equity has been a primary concern in each: Viet Nam: Provincial governments now have extensive tax collection powers in areas such as land and property, and responsibility for transfers to communes.

However, transfers from central government remain the largest revenue stream.

Transfers are determined by a formula based on population, but with weighting for poverty, remoteness, health and education norms, and the presence of disadvantaged populations.
A 2003 law recalculated the education norm on the basis of all children, rather than in-school Despite widespread advocacy for decentralization, central governments often retain high levels of control children.

Since the shares of school-age children enrolled are lower in poorer provinces, this has increased equity.
Similarly, the education norm for a child living in mountainous areas (which have the worst education indicators) is 1.7 times that of an urban child.

The commitment to equity is reflected in spending: richer regions such as the Red River Delta have some twenty-five times the income of the poorest regions such as the North West, but budget spending per capita is roughly equivalent, reflecting large transfers from rich to poor regions (Adams, 2005; Huong, 2006).
Uganda: Decentralization reforms in Uganda have been among the most ambitious in sub-Saharan Africa.

However, decentralized governance has proceeded faster in the sphere of service management and delivery than in finance.

While district authorities are allowed to collect local taxes, they are not entitled to charge fees for basic services such as education.

They are also constrained in defining spending priorities.

Around 90% of revenue in most years comes from central government.
Over two-thirds of the funding transferred is a conditional grant, linked to achievement of goals from the national poverty reduction strategy, including UPE, secondary education and teacher recruitment.

Most of the rest is an unconditional grant calculated on the basis of population and land area.

In addition, a small equalization grant is aimed at reducing the gap between richer and poorer districts (Obwona et al., 2000; Steiner, 2006; Uganda Local Government Finance Commission, 2000).
South Africa: The end of apartheid in 1994 brought a new democratic government and a radical move towards decentralization, with provincial and local governments taking on extensive new responsibilities in areas such as health, education and housing.

The financing formula for fiscal decentralization incorporated a strong redistributive component aimed at overcoming inequalities inherited from the apartheid era.

Around 95% of provincial government expenditure comes from central government.
The largest component is known as an equitable share transfer, weighted to reflect levels of poverty and the costs of achieving minimum national norms in areas such as health and education.

In education, financing is based on student numbers, with some additional weight given to poor and rural provinces.

Provincial authorities are also required to rank schools by a poverty index, which is used to allocate funding for non-personnel inputs (Gershberg and Winkler, 2003; Momoniat, 2003).
As a consequence of these reforms, resource allocations to schools have become more equitable (Crouch and Winkler, 2007).

Colombia: Decentralization of government finance in the 1990s significantly improved equity of intergovernment transfers.

Before decentralization, transfers from central government were based on historic transfers – an arrangement that favoured wealthier provinces.
Under the reforms, historic allocations were replaced by a formula allocating resources on the basis of population, with adjustments for health and education provision (Bossert et al., 2003).

The motivation for financial decentralization can be important.

Where reform is prompted by fiscal pressures on central government, it can result in reduced central government financing.

In these circumstances local governments, communities and schools are likely to seek supplementary funding from parents.
This is broadly what happened in China in the 1990s (King and Cordeiro Guerra, 2005).

It is sometimes forgotten that decentralization is a highly political process.

It is one thing to devolve authority in countries characterized by high levels of national cohesion, strong national, regional and local government institutions, and well-defined processes for conflict resolution.
It is quite another to shift the locus of decision-making in countries marked by weak governance systems and high levels of tension.

In a country such as Nigeria – where public confidence in institutions is weak, political relationships between regions are tense and democracy is still under construction – decentralization is a fraught political exercise (World Bank, 2008f).

Decentralized finance in education In education, as in other areas, decentralization has to be evaluated on its outcomes.
However, in the context of financing EFA, two broad dangers can be identified.

First, devolution of finance can act as a powerful driver for disparities in provision.
Decentralization with equity requires central governments to retain a strong role in redistributing Decentralization is a highly political process : W H Y G O V E R N A N C E M AT finance, with a commitment to equalizing opportunity.
Second, financial and political devolution to weak local governance structures can have negative consequences for the coverage and quality of education, again with damaging consequences for equity.

Here too, successful decentralization requires an active role for central government in building capacity.

China’s experience with fiscal decentralization provides a cautionary tale for education equity.
During the 1990s the central government reduced its share in overall education financing, giving more responsibility to local governments, schools and communities.

This decentralization effort had unintended consequences.

Overall resource mobilization for education lagged behind economic growth, leading to a decline in the share of GDP allocated to education from 2.9% in 1991 to 2.2% in 1997.

Decentralization also generated major geographic and income-based disparities in per- student spending.
The ratio of highest-spending to lowest-spending province in per-student expenditure in primary education almost doubled from 5 to 9 (Table 3.6).

Many schools and local authorities resorted to formal and informal household charges, so equity suffered: in effect, fiscal decentralization acted as a regressive education tax on the poor (King and Cordeiro Guerra, 2005; Tsang, 2002).
Recognition that the lack of a strategy to equalize financing has compromised equity and the quality of education in poorer areas has prompted the Chinese Government to rethink its initial strategy.

It has removed some tax powers from local government, continued to finance teacher salaries and maintained responsibility for parts of the capital budget.

Concerns over inequality are cited as a primary motivation.

However, these efforts have met with limited success.
While the central government formally prohibits the charging of fees, still many local governments informally encourage it (Wang, 2004) and large gaps in the quality of provision remain.

More fundamentally, China still lacks a system of transfers between provinces, and between rich and poor areas within provinces, consistent with a more equitable pattern of expenditure while preserving the principle of decentralized decision-making.
Table 3.6: Inequality in per-student education expenditure in China following decentralization Primary education Lower secondary education (constant 2006 US$) (constant 2006 US$) 1989 1997 2000 1989 1997 2000 Highest-spending province Mean Lowest-spending province Ratio of highest-spending to lowest-spending province 157 66 30 5 357 90 39 9 419 75 40 11 314 141 69 5 520 166 75 7 424 103 64 7 Source: Calculations based on Tsang (2002, Tables 1-3, p. 19).
Decentralization can generate major geographical and income-based disparities in per-student spending remained less decentralized, local authorities are permitted to raise revenue for education through a Special Education Fund (SEF) tax on property (King and Cordeiro Guerra, 2005).

Spending per student from the SEF in the poorest municipalities with the lowest property values is only 13% of the levels in the richest municipalities and 3% of that in the richest cities.
Here too, the absence of a strong formula for redistributive public finance has hampered efforts to strengthen equity.

In Indonesia, decentralization has gone hand in hand with a large increase in the share of GNP allocated to education, from less than 2% before decentralization to over 4% today (King and Cordeiro Guerra, 2005).
It transfers resources from central to local government via a block grant system incorporating a strong equity component, with the poorer districts receiving the largest transfers.

However, central government also requires local districts to mobilize their own resources – and it has devolved tax raising authority.

This has an inbuilt danger for equity: in the richest provinces, such as Jakarta, per-capita GDP is some nine times greater than in the poorest provinces, such as South Sulawesi.
Enrolment ratios at junior secondary level range from 68% in South Sulawesi to 93% in Jakarta.

The lesson from East Asia is that governments need to plan for equity.

The push towards financial devolution has brought a risk of widening disparities between regions, with attendant dangers for the inequalities outlined in Chapter 2.

Central government resource transfers hold the key to making financial decentralization work for the poor.
Tensions between the goals of equity and political decentralization are not limited to China.

In the Philippines, where education financing has Evidence from Latin America is also instructive.
The decentralization of education from federal to 1 4 7 9 0 0 2 t r o p e R i g n r o t n o M i Central transfer mechanisms and targeted redistribution have proved helpful in redressing education inequalities l a b o G l l l A r o f i n o t a c u d E C H A P T E R 3 provincial governments was an important feature of institutional reform in the region during the 1990s.
In Argentina, the transfer of responsibility for secondary schools from federal to provincial level was accompanied by a system of federal tax transfers.

Detailed evaluations of the decentralization process have identified many benefits.

Nationally, decentralization appears to have improved local participation, strengthened monitoring and improved learning standards.

However, the results have not been uniform.


Test scores point to a widening gap between wealthier provinces with strong government capacity and poorer provinces with low administrative and institutional capacity; the latter performed worse under decentralization.

National efficiency has improved, but at the expense of equity (Galiani et al., forthcoming; Rhoten, 2000).

Experience in other regions is more limited.


In sub-Saharan Africa, financial decentralization is less advanced than political decentralization.

Some governments have integrated equity into decentralized financing (as in Uganda).

In other cases, fiscal decentralization appears to have had damaging consequences for education equity.
This is demonstrated by the experience of Nigeria, whose financial governance system combines a ‘worst of two worlds’ approach: low overall commitment and highly unequal financing (Box 3.4).

In marked contrast, decentralization policies in Ethiopia attach far more weight to equity.

Apart from highlighting innovative pro-poor financing approaches in education, Ethiopia’s experience demonstrates the importance of flexible policy responses to unanticipated problems (Box 3.5).
Many governments now recognize the importance of central transfer mechanisms to redress education inequalities arising from decentralization.

These mechanisms can take various forms.

The primary one is often the block grant.

Its size can be linked to levels of deprivation, as in Uganda.

Central governments can introduce a conditional element into such grants, requiring local governments to meet specified standards for overall sector financing and equity.
In other cases, financing can be linked to specified inputs.

Two examples from Latin America illustrate the scope for redistribution: (covering teacher salaries and administrative costs per student) supplemented by a compensatory component which includes weighting for geographic dispersion, poverty and the share of rural households in the population.

In 2006 the seven poorest and most rural departments received extra funding amounting to between 39% and 112% of the average basic cost.
In addition, legislation provides for teachers in rural and remote areas to receive 15% more than the base salary (Meade and Gershberg, 2008).

Preliminary evidence, while less than clear-cut, suggests that the move has strengthened equity on some indicators.

Transfers to Bogotá, the capital, have increased at a rate below the national average.
Meanwhile, in 2006 two of the poorest departments, Chocó and La Guajira, received 30% more than Antioquia, one of the most urban and developed departments (Meade and Gershberg, 2008).

Brazil – targeted redistribution through FUNDEF.

When Brazil devolved authority from a highly centralized system to states and municipalities in the mid-1990s, it created FUNDEF to reduce the large national inequalities in per-student spending (de Mello and Hoppe, 2005; Gordon and Vegas, 2005).
State and municipal governments were required to transfer a proportion of their tax revenue to FUNDEF, which redistributed it to state and municipal governments that could not meet specified minimum levels of per-student expenditure.

FUNDEF has not prevented wealthier regions from increasing their overall spending more rapidly than poorer regions, but it has played a highly redistributive role.
It has also increased both the absolute level of spending and the predictability of transfers, notably for poor states and municipalities in the north and north-east.

There is strong evidence that FUNDEF has been instrumental in reducing class size, improving the supply and quality of teachers, and expanding enrolment.
At municipal level, data show that the 20% of municipalities receiving the most funds from FUNDEF were able to double per-pupil expenditure between 1996 and 2002 in real terms (Gordon and Vegas, 2005).

Colombia – the use of compensatory financing.

In 2004 Colombia introduced allocation rules using a funding formula based on the number of students enrolled, with a basic cost component Are there rules of good practice in decentralization that can be derived from experience to date?
As in many other areas of governance, the diversity of country experience militates against drawing simple lessons with universal application : W H Y G O V E R N A N C E M AT Box 3.4: Fiscal decentralization in Nigeria — reinforcing regional disparities Improved governance and the return of democracy to Nigeria have done little to narrow inequalities in education.
One reason is that insufficient attention has been paid to the development of a more equitable financing system.

Primary net attendance rates (NARs) in Nigeria range from 85% in Anambra and Ondo states to less than 30% in Jigawa and Zamfara states (Map 3.1).

These disparities are linked to substantial differences in poverty rates.

In 2004 the poverty headcount ratio in Anambra was 20%, compared with 95% in Jigawa.
Under an equitable financing system more resources would be allocated to states with low levels of participation and high rates of poverty.

In Nigeria the equity principle is turned upside down: the wealthiest states and regions with the highest education participation secure the lion’s share of federal resources.

For example, Lagos receives around five times as much as Jigawa, which has attendance rates less than half of those in the commercial capital.
Fiscal decentralization has reinforced regional disparities in education.

Since the return to multiparty democracy in 1999, an increasing share of federal revenue (predominantly from oil and gas) has been allocated to state and local governments.

Since 2002 about half the federal budget has been allocated to states and local government areas (LGAs).
Of this share, a third is reserved for the four oil-producing states in the Niger delta and the remainder is distributed under a complex formula that produces a simple result: large financing inequalities.

In 2005, Kano, a poor state with a low primary NAR and limited revenue-generating capacity, received 20% less in federation account revenue than Enugu state, with similar revenue raising capacity, lower poverty rates and a higher primary NAR.
Unlike most states that depend on federal allocations, Lagos generates two-thirds of its revenue from local sources — an arrangement that reinforces regional financing gaps.

Not all the problems in Nigeria’s education financing can be traced to unequal fiscal decentralization.

National planning is also weak.

No statutory accountability mechanisms exist to ensure that state and LGA plans, where they exist, are aligned with national goals in education.
As a result, the priority that LGAs give to primary education varies enormously, even within states.

In Kano, 28% of the Dala local government budget was allocated to primary education, compared with 12% in Bichi.

Recognizing the need for additional financing in education, the federal government created the Universal Basic Education Commission (UBEC) intervention fund, which channels federal resources directly to basic education.
Between 2005 and early 2008 about US$750 million was made available to states through the fund.
Unfortunately, this has done little to enhance equity or efficiency: Map 3.1: Net attendance rates and education spending per primary school-age child, Nigeria, most recent year Sokoto Katsina Zamfara Kebbi 28 Jigawa Yobe 26 Kano 21 Borno Kwara Oyo 65 Osun Ekiti Ondo Ogun 132 Lagos 28 Kaduna Bauchi Gombe Niger Adamawa 63 FCT, Abuja Plateau Kogi Nassarawa Benue Taraba Edo 34 Enugu Anambra Ebonyi Delta Imo Abia Bayelsa Rivers Akwa Ibom 28 Cross River Public spending per primary school-age child (constant 2006 US$) Primary net attendance rate Less than 50% 50% - 79% 80% and above The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by UNESCO.
Based on United Nations map.

Sources: Calculations based on Kano State Ministry of Education (2008); Nigeria National Bureau of Statistics (2006a).

Equal allocations lead to unequal effect.

Some 70% of available resources are allocated equally across states without regard for differences in need.

Only 9% of resources are directed to the most disadvantaged states and to activities promoting education for physically and mentally challenged children.

Disbursements have been much lower than expected.


Only 60% of allocated funds had been disbursed by mid-2007.

Problems range from inadequate policy coordination to complex bureaucratic procedures and weak capacity in state education bodies.

Use of funds is inflexible.

The UBEC has strict guidelines on the proportion of funds that can be spent on pre-primary, primary and junior secondary education, as well as the type of expenditure.
For example, 70% of funds must be spent on construction, regardless of need.

This makes it more difficult to use resources effectively to support state plans for the development of basic education.

Sources: Adediran et al.

(2008); Bennell et al.

(2007); Kano State Ministry of Education (2008); Nigeria National Bureau of Statistics (2006a, 2006b); World Bank (2007b, 2008f).
1 4 9 9 0 0 2 t r o p e R i g n r o t n o M i Much depends on how governments use financing arrangements to improve service in poor areas Box 3.5: Financial decentralization with equity in Ethiopia Since the late 1990s Ethiopia has witnessed rapid improvement in education outcomes.

The country has also been implementing far-reaching education reform, including radical decentralization, in the context of wider governance reform.
Equity is a central concern.

Ethiopia’s decentralization has involved a radical overhaul of government structures.

In the first phase, a four-tier governance structure was created: the centre, the regions (nine ethnic-based states plus the cities of Addis Ababa and Dire Dawa), the zones and the woredas (districts).

In the second phase, legal, fiscal and administrative reforms devolved responsibility for management of social services to the woredas.

Education is now financed through a two-step process.
The first involves fiscal transfers from the federal government to the regions and the second from regional governments to woredas, which now manage about 45% of regional public expenditure.

Most transfers operate through large federal block grants which regional and woreda governments may allocate freely.

A ‘three parameter’ formula for grant allocation to the regions takes into account population size, poverty and development levels, along with an index of revenue effort and sector performance.
While funding formulas have an equity component, they have tended to produce strong biases in favour of regions with smaller populations, even though they are not necessarily the poorest.

Most regional governments use the three-parameter formula to allocate their grants to woredas, but there is sufficient flexibility for them to experiment with other approaches.
The Southern Nations, Nationalities and Peoples Region, one of the country’s poorest, has taken advantage of that flexibility to develop innovative new approaches.

Between 2003/2004 and 2006/2007, authorities have experimented with a unit cost approach that distinguishes between recurrent and capital expenditure.
Reduced to its essentials, it allocates higher per capita funding for recurrent expenditure to the woredas with the more developed social services, so those services can be adequately staffed and function effectively.

Meanwhile, higher per capita funding for capital expenditure is allocated to woredas having less developed social services so they can expand infrastructure and reduce the gap with the other woredas.
Data on education expenditure suggest that the unit cost approach had equalizing effects between woredas.

For instance, mean per-student recurrent expenditure on primary education increased by 18% between 2001 and 2004, per-student funding became more equal among woredas, the mean woreda-level primary GER increased from 63% to 73% and enrolment gaps between woredas narrowed.

Decentralization in the region seems to have disproportionately favoured remote, food-insecure and pastoral woredas (Table 3.7).
Despite these outcomes, the unit cost approach has led to some woredas receiving lower funding, prompting demands for further reform.
Table 3.7: Ethiopia’s Southern Nations, Nationalities and Peoples Region: woreda-level spending on education before and after decentralization Type of woreda (district) Remote (more than 50 km from a zone head city) Non-remote Food insecure Food secure Pastoral Non-pastoral Total education 2001 2004 Change between 2001 and 2004 Constant 2006 US$ (%) 266 516 431 288 126 389 361 530 528 320 221 453 36 3 22 11 75 16 Source: Calculations based on World Bank (2007b, Table 4.3, p. 43).
Decentralization has been described as a process rather than a destination (Bird and Smart, 2001).

The way the process unfolds is heavily influenced by public policy choices, institutional capacity and government commitment to deal with poverty issues.

In the case of education, much depends on how governments use financing arrangements to equalize opportunity and improve service provision in poor areas.
From an equity perspective, the important question would seem to be not whether to decentralize, but how and what to decentralize.

Four broad rules would appear to be of particular importance for progress towards EFA.

First, revenue-raising powers for local government should be clearly defined.

Subnational authorities should not be permitted to mobilize budget resources through user charges in basic education, which have regressive and damaging effects on the poor.
: W H Y G O V E R N A N C E M AT Second, central government should retain redistributive capacity.

Intergovernment transfers are needed to prevent the growth of regional financing inequalities and the widening gaps in opportunity that can result.

Third, equity goals should be built into intergovernment financing formulas.
Transfers should be weighted to provide larger per-capita transfers to regions marked by high levels of poverty and marginalization, with education indicators as a central part of the formula.

In addition, national rules need to provide a framework for ensuring that lower levels of government prioritize equity in delivery of financing.

Conclusion Approaches to education finance will continue to exercise a critical influence over prospects for achieving the goals set out in the Dakar Framework for Action.
Increased financing is not a sufficient condition for delivering on the commitment to education for all – but in many countries it is a necessary condition.

In some cases, national governments are not demonstrating sufficient levels of commitment either to resource mobilization or to equity.

Much of South and West Asia falls into this category.

In other cases, stronger national commitment will need to be accompanied by scaled-up donor support.
Finally, central governments should carefully assess the implications of decentralization for the achievement of national goals in education.

Ensuring that local governments have the resources and capacity to manage progress towards inclusive education is critical.

Financial governance challenges vary enormously across regions and countries.

Improving efficiency and facing up to corruption are two immediate priorities for many governments.
It is also important for governments to take stock of the experience of decentralization.

While the case for avoiding overly centralized decision-making and for devolving political authority under appropriate conditions remains strong, decentralization is not a panacea.

In the area of financing, there is an urgent need to place equity at the centre of the decentralization agenda.
That means central government retaining a strongly redistributive role consistent with commitments to inclusive education and equal opportunity for education.
Central government should retain a strongly redistributive role consistent with commitments to equal opportunity for education Debates over school governance go to the heart of wider questions about the role of government in education 1 5 2 C H A P T E R 3 Choice, competition and voice: school governance reform and EFA Introduction Governments around the world repeatedly emphasize their commitment to providing good- quality schooling for all citizens.
Outcomes often fall short of the commitment.

Persistent problems with education equity and quality, even in countries with high levels of coverage and strong public spending, have brought the management of school systems to the centre of education governance debates.

The evident failure of current education strategies to provide high-quality school systems accessible to all in many countries has prompted calls for wide-ranging reforms.
This section looks at some of the central currents in approaches to school governance reforms, with a focus on school-based management and reforms to promote choice in education through public policies.

Looking beyond the formal public policy framework, the section also explores the implications of the growth of low-fee private schools for EFA.

‘Voice’, ‘participation’, ‘competition’ and ‘choice’ are buzzwords in debates on education governance worldwide.
Devolving authority away from central government and towards schools – the core principle behind school-based management – is seen as a means of holding providers to account and increasing participation.

Giving parents an opportunity to choose among education providers is widely portrayed as a way to strengthen education provision, with competition acting as a spur to improved quality.
While no government treats the education sector as a pure market, many have introduced what have been called ‘quasi-market’ principles into provision.

For some commentators, the entry of low-fee private providers into the education marketplace is important precisely because it provides an impetus towards greater accountability and competition (Tooley, 2007).

The motivation behind quasi-market reform has been to raise standards rather than to address inequality.
However, advocates for reform often attach to their arguments claims of wide-ranging benefits for equity.

There is a bewildering array of approaches to school governance reform.

Countries at very different levels of development have taken up the mantle of reform.

The design, scope and depth of reform also come in a multitude of variations.

Evaluating outcomes against this backdrop is inherently difficult.

Even so, two broad conclusions emerge from the evidence presented in this section.
The first is that context matters.

Governance debates are frequently characterized by bold assertions on the presumed benefits of school management reform for learning outcomes and for equity.

Evidence to back these assertions is often lacking.

Moreover, there is a widespread tendency to generalize findings and to assume that a policy that works in one context will deliver the same results elsewhere.

Looking ahead, it is important that policy-makers develop more evidence-based approaches.
It is also important that they identify the broader institutional conditions and enabling factors needed to strengthen education quality and equity.

The second conclusion is that competition and choice have the potential to reinforce inequality.

Choice is important in education, as in other areas.
The Universal Declaration of Human Rights (Article 26) enshrines the right of parents ‘to choose the kind of education that shall be given to their children.’ Under certain conditions, competition can act as a force to drive up standards and improve efficiency.

But choice and competition are not abstract concepts.

For people living in chronic poverty, choice is often constrained by a lack of purchasing power, limited access to information and, in many cases, by an absence of responsive providers.
Introducing choice and competition into an environment characterized by high levels of inequality without effective public action to equalize opportunity is a prescription for widening disparities.

As in many other areas, markets – and quasi-markets – in education are unlikely to prove effective in strengthening equity in the absence of pro-poor regulation.

The issues raised in debates over school governance go to the heart of wider questions about the role of government in education.
To what extent should governments finance andprovide education services?

If private providers are to play an expanded role, how should governments manage and regulate their operations?
The answers to these questions will vary across countries and across levels of the education system, with a distinction drawn between primary and post : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA primary education.
The critical issue facing policy- makers is to work out strategies through which competition, incentives and accountability can be harnessed to enhance overall quality andequity.

than under more remote centralized systems, empowering them to play a role in framing priorities and in holding school providers to account (World Bank, 2007f).

The bottom line is that governments have the ultimate responsibility to ensure that everyone has access to basic education systems of acceptable quality.
Discharging that responsibility effectively means different things in different places, but it invariably requires placing a premium on the equalization of opportunity across the education system.

School-based management: a broad spectrum of approaches and outcomes Conventional education governance structures allow schools little control over their affairs.
Principals, teachers, parents and even local education bodies have been bound by centralized rules and procedures, leaving them limited scope for influence over staff selection, teaching methods and wider practices.

School-based management reforms are challenging this model.

School-based management in its broadest sense aims at increasing school autonomy and empowering teachers and parents to make decisions.
It aims to strengthen incentives for schools to deliver services that are responsive to the needs of the communities they serve, and to address problems facing disadvantaged groups (Caldwell, 2005).

Advocates of school-based management point to a wide range of potential benefits.

They argue that the devolution of decision-making authority to schools can facilitate and enhance participation – a core strategy in the Dakar Framework for Action.
A stronger parental voice and more participation in school management, the argument runs, will lead to greater incentives for education providers to offer more efficient services (World Bank, 2007f).

Moving decisions away from remote planners and closer to those who know the most about the learners and their educational needs, as well as about local values and realities, is seen as a route to a more responsive system.

Equity is another important benefit cited for school-based management.
It is assumed that poor households will have a stronger and more effective voice on school management committees and in local community institutions School-based management is not a recent innovation.

Its origins can be traced to the United States in the 1980s and Australia, Canada and the United Kingdom in the 1990s.

School-based management programmes have also been adopted in some developing countries.
Most of these programmes are in Latin America and South Asia, though sub-Saharan Africa also figures with increasing prominence.

While much of the reform impetus in developing countries is home grown, aid donors have played an important role.

For example, some 11% of all education projects supported by the World Bank between 2000 and 2006 included school-based management components.
These programmes represent around US$1.74 billion in education financing, or just under one-quarter of the World Bank’s education portfolio (World Bank, 2007f).

School-based management reform is an umbrella description for a diverse range of country experiences.

In some countries the schools involved have broad coverage.

El Salvador’s Educación con Participación de la Comunidad (EDUCO) schools are an example.
They account for half of enrolment in public rural pre-schools and 37% in rural basic education.

EDUCO is the main schooling option for about 80% of the municipalities with extreme poverty in El Salvador (Meza et al., 2006).

In other contexts, the reforms operate on a smaller scale.

In some cases, authority is delegated to principals and teachers, with weak community participation included as part of the design.

In other cases, decision-making authority is also given to parents or school committees.
Similarly, while some programmes transfer authority for hiring and firing teachers, others do not.

Variations are found in the degree of devolution of budgetary authority as well.

The experience of three Latin American countries with school-based management reform illustrates this diversity (Table 3.8).

The context in which community schools are formed is important.

In some cases, the move towards school-based management has been driven from below.
In Bolivia, indigenous schools emerged in the 1980s in the context of an intense political struggle over national education policies.
The decision of the Quechua communities in some School-based management aims to move decisions closer to those who know more about learners, local values and realities Table 3.8: Functions transferred to schools in three Latin American programmes El Salvador EDUCO1 Mexico PEC2 Nicaragua autonomous schools3 Personnel management Paying staff salaries Hiring/firing teaching staff Supervising and evaluating teachers Pedagogy Setting school calendar, classroom hours Selecting some textbooks/curriculum Method of instruction Maintenance and infrastructure Building/maintaining school Buying school materials Budget Oversight Allocation √ √ √ √ √ √ 1.
EDUCO - Educación con Participación de la Comunidad.

2.

PEC - Programa Escuelas de Calidad.

3.

The policy ended in 2007.

Note: Empty cells indicate that the function was not transferred to schools.

Source: World Bank (2007f).


√ √ √ √ √ √ √ √ √ √ √ areas to withdraw their children from state schools started out as a protest against teacher absenteeism but rapidly became part of a movement against the imposition of the dominant ‘Criollo’ culture (the culture and language of the Creole Spanish descendents).

Communities themselves took over the running of schools and recruitment of teachers, and they initiated a curriculum for indigenous language teaching.
Under the 1994 Education Act and subsequent legislation, indigenous communities have been given greater autonomy within the state system and indigenous language teaching has been brought to the centre of the national curriculum (Albó and Anaya, 2003; Regalsky and Laurie, 2007).

Learning achievement: a mixed record Assessing the impact of school-based management reforms on learning outcomes presents serious methodological problems.
Cross-country comparisons are of limited relevance and within- country assessments point in various directions.

In some cases positive results have been registered but the association between school- based management and improved education quality is weak.

Several factors contribute to the difficulty in extrapolating clear lessons for education quality.

In Latin America and East Asia, school-based management has not greatly affected teaching practices 1 5 4 Diversity of context is one obvious factor.
More broadly, it is difficult to identify or isolate the ‘school-based management effect’ in achievement, not least because school-based management is usually part of a broader package of political, administrative or educational change.

Selection bias is another problem.

Schools and communities either self-select into school-based management programmes or are selected to participate by government authorities, often on the basis of specific characteristics that differentiate them from others.
This makes it difficult to tell how, or whether, school autonomy in particular had an influence on outcomes.

Most detailed school-based management evaluations come from Latin America.

The regional evidence points to some positive effects on attainment.
Some studies have found an association between delegation of management functions and reduction of school repetition and dropout (Gertler et al., 2006; Jimenez and Sawada, 2003; Murnane et al., 2006; Paes de Barros and Mendonça, 1998; Skoufias and Shapiro, 2006; World Bank, 2007e).

Learning outcomes are more variable, with marked differences among countries.

A study of mathematics and language performance among grade 3 students found that EDUCO schools in El Salvador scored lower than traditional schools.
However, after controlling for background, the differences disappeared and EDUCO pupils actually scored slightly higher in language tests, on average (Jimenez and Sawada, 1999).

On the other hand, evaluations in Honduras of schools in the Programa Hondureño de Educación Comunitaria (PROHECO) concluded that the delegation of decision-making was not associated with significant changes in learning achievement (Di Gropello and Marshall, 2005).

Autonomy and pedagogy: a loose connection.


An important assumption behind school-based managment is that greater autonomy will permit more flexible, responsive and innovative teaching.

That assumption is not strongly backed by evidence.

Findings from Latin America show that school- based management reforms can result in improved teacher motivation.
Reduced absenteeism, more time meeting with parents and more hours spent at school are among the key indicators for improved motivation (Di Gropello, 2006; Sawada and Ragatz, 2005).
However, evidence from a wide range of country experiences suggests that teaching : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA practices in schools with more autonomy do not differ significantly from those in other schools (Di Gropello and Marshall, 2005; Fuller and Rivarola, 1998; Gunnarsson et al., 2004; Jimenez and Sawada, 1999; King and Ozler, 1988; Parker, 2005).
Why has improved motivation not led to new teaching practices?

Once again the explanation varies by country and context.

An important factor is that school-based management reforms do not always increase the autonomy of schools and teachers in areas such as pedagogy, as Table 3.8 shows.

Even where reforms do provide for greater flexibility, schools and teachers often have not had an opportunity to acquire the capacity and skills to introduce innovative practices.
This is borne out by evidence from East Asia.

In Indonesia, legislation allows schools to devote 20% of instruction to locally designed subject matter.

In Thailand, 30% of the curriculum in basic education can be locally determined.

Yet these windows of opportunity for greater flexibility are not fully exploited in either country, partly because teachers have no training or experience in developing innovative approaches to instruction and curriculum design (Bjork, 2004; Shoraku, 2008).
Enabling environments are important Devolving authority to schools shifts the locus of decision-making and transfers new responsibilities to parents, teachers and principals.

Such governance reforms can change incentives and influence relationships between key actors in the delivery of education.

Under what circumstances is devolved authority likely to produce positive results?
Outcomes invariably depend on local factors but case studies have identified four broad conditions influencing equity and efficiency (Cárdenas, 2008): voluntary participation of the school and the surrounding community; organizational and technical capacity in, or available to, the school; strong and committed school leadership; support from upper levels of government.

Voluntary participation.Public-sector schools managed by communities need the motivation and capacity to generate demand for schooling.
School-based management initiatives are likely to be most successful when they are driven by demand from below.

However, community participation can be a double-edged sword from an equity perspective, especially when it involves competition for resources.

Schools with committed principals and organized communities are in a stronger position to exploit opportunities.
Evidence from Mexico’s Programa Escuelas de Calidad (PEC) illustrates the point: voluntary participation by itself resulted in a selection of schools that were neither located in the poorest communities nor among the lowest performers (Cárdenas, 2008).

An important lesson is that voluntary participation has to be supported by measures that strengthen equity.

Organizational and technical capacity of schools.

Schools must have sufficient financial and human resources to take on new responsibilities.
Drawing up school plans, budgets, and requests for financial and material inputs from central government may require new skills.

Evidence suggests that technical capacity of this kind on the part of the head teacher and staff is an important condition for overall school improvement (Abu-Duhou, 1999; Briggs and Wohlstetter, 2003; UNESCO, 2004).
The delegation of management functions to schools in Central Asian countries in recent years has been hampered by a lack of programmes to develop school staff capacity for the additional responsibilities involved (Chapman et al., 2005).

One danger for equity that comes with school-based management derives from the unequal capacities of schools.
In some cases, schools that select themselves for school-based management may have stronger planning capacity than other schools and thus be better able to secure access to resources (Cárdenas, 2008; Reimers and Cárdenas, 2007).

The upshot is that schools with weak capacity and the greatest needs may fall further behind.

Strong and committed school leadership.


The EFA Global Monitoring Report2005argued that strong school leadership was a prerequisite for creating a culture of school improvement (UNESCO, 2004).

Because school-based management increases their responsibilities, head teachers often end up spending more time on administration than on leadership to support pedagogical initiatives and quality improvements.
In Nicaragua a common feature of autonomous schools that succeeded in reducing school failure and improving learning Successful devolution to schools requires strong school leadership and high-level government support School grants need to be predictable, timely and adequately funded outcomes was the principal’s leadership abilities (PREAL and Foro Educativo Nicaragüense EDUQUEMOS, 2008).
Skilled head teachers can take advantage of the opportunities autonomy provides rather than getting buried in administrative burdens.

But the skills needed to maintain a balance between such responsibilities are often lacking, pointing to a need for any move towards school autonomy to be accompanied by training of head teachers for their new roles.

Sustained support from upper levels of government.


If the goal is to reduce disparities in learning, upper levels of government need to focus their efforts on schools with disadvantaged learners.

This means strengthening the schools’ institutional and technical capacity and ensuring that teachers use their increased autonomy effectively.
There should be feedback mechanisms that link monitoring through school supervision to the provision of pedagogical support, including staff training (an issue discussed in the section below on governance of teachers and monitoring).

Building financial capacity: the role of school grants Autonomy without financial capacity is a general prescription for weak governance.

To be effective, schools taking on new responsibilities need sufficient financial and human resources to meet those responsibilities.
Some countries have attempted to build school capacity through school grant programmes.

Disbursed and allocated in a variety of ways, grants can be used to achieve a wide range of goals in areas such as education quality and equity.

In some cases disbursement is tied to development of a strategic plan to achieve agreed goals in areas such as quality (Espínola, 2000; Nielsen, 2007).

In others, grants are geared towards the provision of specific services and inputs.
Uses for school grants range from upgrading infrastructure to contracting additional teachers.

Grants provided under the PEC in Mexico have been used mainly for improving infrastructure and acquiring school materials rather than changing teaching practices or working with parents (Yoshikawa et al., 2007).

Participation in the programme is associated with overall improvement in school progression although differences in capacity have contributed to inequalities (Skoufias and Shapiro, 2006; Box 3.6).
In Brazil there is evidence that the School Development Plan and School Improvement Projects under the Fundo Box 3.6: Planning for strengthened school autonomy in Mexico Introducing school-based management in an environment marked by deep capacity inequalities between schools is unlikely to enhance equity.

The Programa Escuelas de Calidad (PEC) in Mexico has attempted to strengthen support for its most disadvantaged areas, but has encountered problems linked to weak capacity.
The PEC aims to increase school autonomy and strengthen performance.

Schools compete for grants, which are provided for up to five years to improve pedagogical practices, encourage collaborative work between teachers, parents and school authorities, and improve planning in pre-schools and primary schools.

Though the intent is to encourage participation by disadvantaged schools, in practice the initial allocations were skewed against the poorest communities and the worst-performing schools.
The schools were often the least equipped to make successful applications, even though they were in greatest need of support.

To apply to the PEC, a school must prepare a Strategic Transformation Plan.

This requires a level of organizational capacity often lacking in schools with many disadvantaged students.

Differences in the priorities set out in school plans also have important implications for outcomes.
Rural and indigenous schools participating in the programme are likely to use the funds for infrastructure and materials instead of pedagogical improvement.

As a result, the quantity and quality of physical inputs and materials have generally improved more than the quality of the education process.

The overall record of the PEC remains problematic.


While it has made available technical and supervisory support that has improved quality in participating schools, the support has been greater in wealthier states and at more advantaged schools.

Instead of reducing gaps between less and more advantaged children, the initiative risks amplifying inequalities.

Sources: Bracho (2006); Murnane et al.

(2006); Reimers and Cárdenas (2007); Yoshikawa et al.

(2007).
(2007).

 : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA de Fortalecimento da Escola (FUNDESCOLA) are associated with increased availability of learning materials.

For schools in FUNDESCOLA that have managed to increase spending, evidence indicates some improvement in learning outcomes (Carnoy et al., 2008).
School grants do not automatically produce positive results.

To be effective, they need to be predictable, timely and large enough to cover the activities in the strategic plan.

These conditions are not always in place.

In Nepal, a school improvement plan is a condition for the release of government block grants, but funds are very limited.

Inadequate grant transfers can have adverse implications for equity.
In the case of Nepal, there is evidence that underfinancing has led to parents being asked for the funds to recruit teachers and meet other basic needs (Vaux et al., 2006).

Involving parents and communities in school management In the Dakar Framework for Action, governments pledge to ‘develop responsive, participatory and accountable systems of educational governance and management.’ The devolution of authority to schools and local communities is seen by many as a means to this end.
Whatever the intrinsic merits of devolution, its implications for parental and community participation are not straightforward.

Moves towards greater school autonomy are often accompanied by the creation of formal structures, such as school committees, village education committees and parent-teacher associations, to facilitate parental and community involvement in school management.
The terms of engagement and the distribution of authority between schools and parents vary, with important implications for decision-making structures.

But whatever the arrangements, formal devolution does not override deeply entrenched imbalances in power linked to wealth, gender and other factors.

local elites dominating decision-making and capturing the lion’s share of resources (Mosse, 2004).

Education has not been immune to the effects of ‘elite capture’.
Parental participation: some voices are louder than others While schools may officially have formal structures designed to facilitate community and parental involvement, there is often a large gap between intent and outcome.

Membership of these bodies may or may not be representative.

And they may or may not facilitate influence over decision-making.
To the extent that cross-country evidence is available, it suggests that in both developed and developing countries the direct involvement of parents in school affairs is limited (OECD, 2007b; Zhang et al., 2008).

Even when parents nominally participate in school management, they may have a limited say.

In some contexts ‘participation’ is confined to raising money, with limited influence over how it is used.

Research in some West African countries is instructive.
It shows that parent associations have only nominal control over the use of financial resources – much of which they have contributed – because they lack the capacity to exercise control (Lugaz and De Grauwe, 2006).

Evidence from Cambodia points in a similar direction.

There the devolution of authority to schools is backed by the creation of local school support committees.
Comprising community members and the school principal, the committees are charged with monitoring children’s progress, increasing enrolment, developing school improvement plans and monitoring the management of operational budgets allocated by the Priority Action Programme.

However, a Public Expenditure Tracking Survey reveals that the committees have not been effective, that few parents know about the funds and that parental representation is limited (Shoraku, 2008; World Bank, 2005a).
The transfer of decision-making responsibility from central governments to ‘user groups’ has been a recurrent theme in areas such as health and water provision as well as school management.

Numerous development programmes have aimed to empower the poor by transferring authority to village-level associations.

In many cases, the effect has been to concentrate power in the hands of affluent and powerful members of society, with Representation is an important component of participation.
Having a voice on a school management committee implies either a direct presence or the delegation of authority through a democratic process.

In practice, community representation is often just one of the considerations shaping the committee profile.

Formal and informal eligibility rules can create a barrier to equitable representation.
In Pakistan’s Transferring decision-making to village-level associations often concentrates power in the hands of local elites Punjab province, membership of school councils for rural public-sector elementary schools is divided along socio-economic status and gender lines: most members are men of high socio-economic status, even for girls’ schools.
Members of school management councils are meant to be elected, but they are often appointed by principals, who choose people with relatively high education, wealth or social status, partly because of the social networks they can bring to benefit the schools (Khan, 2007).

If participation is to enhance equity, the poor, marginalized and disadvantaged need to be not just adequately represented but actively engaged.

They have to be able to articulate their concerns and to influence decisions.
In many cases this implies a change in power relationships.

It also requires the design of governance structures that empower poor households.

Unfortunately, school- based management reforms seldom address this issue of ‘voice’ explicitly.

Programme design often just assumes that devolved authority is inherently more equitable.

Evidence from several countries suggests that far more attention needs to be paid to the conditions for participation.
Many factors influence ‘voice’, including parental socio-economic status, education level, race, caste and gender (Dunne et al., 2007; Poor, illiterate parents may lack the expertise and confidence to evaluate approaches to teaching or curriculum design Box 3.7: Community involvement in Uttar Pradesh The Sarva Shiksha Abhiyan (Universal Elementary Education) programme in India gives a prominent role to village education committees.
Each committee comprises three parents, the principal of the village school and the head of local government.

Its tasks include monitoring school performance.

Despite the committees’ prominence in education policy, most parents are either unaware that the committees exist or do not realize that they can be involved in school affairs.

Furthermore, many committee members are not aware of the options they have to improve school quality.
Would improved access to information make a difference in their effectiveness?

A project by the Indian NGO Pratham in Uttar Pradesh suggests that information is only part of the story.

Pratham carried out interventions aimed at encouraging greater participation by village members in the monitoring and improvement of education.
It reported, however, that even mobilizing communities, spreading information about the village committees and informing people about their potential to improve the quality of schooling, were not enough to induce effective participation and improve children’s learning.

Far more effective was the training of volunteers to conduct reading classes for village children.

Sources: Banerjee et al.

(2006); Banerjee et al.

(2008); Pritchett and Pande (2006).


1 5 8 Educational Research Network for West and Central Africa and USAID, 2002; Khan, 2007; OECD, 2006a).

For example, people who are chronically poor, of low caste or from an indigenous minority may have little experience of articulating concerns in a forum including wealthier community members.

A study in India of participatory decision- making in local government found landless labourers far less likely than others to participate in meetings (Alsop and Kurey, 2005).
Two factors were critical in weakening their voice.

First, economic dependence on landed groups, combined with their low caste, was seen as a constraint on dissent.

Second, education and access to information were significantly associated with participation.

On a constructed scale of participation, someone with ten years of education was 27% more active than someone with no education.

The terms of dialogue on school management boards can reinforce the marginalization of the poor.
One study reviewing parental participation in the management of rural schools in South Africa found that the language employed, the use of technical jargon and ways of addressing the parents all affected participation.

This might explain why a survey in Gauteng province found that, despite a general view that parental participation had increased, real participation remained limited: only 10% of parents had voted in elections for the boards (Naidoo, 2005).
Any assessment of the role of participation has to start by asking what is being assessed.

Participation is viewed by many as a goal in its own right.

But for most parents the ultimate aim of any involvement in school management is to improve children’s education.

Formal participation and consultative arrangements may not facilitate achievement of this goal.

Participants may have limited knowledge about issues under discussion, such as school performance and teaching practices.
Parents may lack the expertise or confidence to appraise approaches to pedagogy or curriculum effectively.

Poor, illiterate parents with limited school experience are at a particular disadvantage.

One possible approach, when many parents lack the time and basic literacy skills to participate effectively, is to train community volunteers to support children’s learning (Box 3.7).
If real participation, rather than the creation of formal participatory structures, is the ultimate aim of policy, then many current approaches to school : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA management have to be rethought.
The idea that the devolution of authority to parents, schools and communities is inherently pro-poor is not well grounded.

One of the defining characteristics of poverty and marginalization in many contexts is precisely that those affected lack an effective voice.

That is why central and local governments should ensure that moves towards devolution are backed by measures aimed at facilitating real participation.
Such measures might include affirmative action in areas such as representation of, say, women or people of low caste.

At the same time government agencies should manage devolution to ensure that powerful groups with a strong voice do not introduce policies – on school fees, for example – that might have damaging implications for equity.
Choice and competition in education provision In standard economic theory, choice and competition are two of the most powerful drivers of efficiency, with the spur of the market acting to raise productivity and enhance welfare.

Few people see education provision as directly comparable with the production of market goods and services.
But competition and its corollary, choice, are increasingly viewed as antidotes for the failings of public education systems in relation to learning standards and equity gaps.

This theme is at the centre of some of the most heated controversies about education governance reform.

In the United States, much of Europe and parts of the developing world, the topic divides political parties and can generate polarized debates.
Underlying the debates are strongly held views and questions about the proper role of government in education provision, the place of non-state providers and the rights of parents to choose.

What do choice and competition in education mean in practice?

In almost all countries, the ultimate responsibility for school systems resides with the state.

Governments set policy, curriculum and standards, and are responsible for assessment and the regulation of the system as a whole.
Within this framework, however, many approaches are possible.

In broad terms, education service delivery can be broken down into four types, depending on who owns and manages schools, and who finances them (Table 3.9).

Governments play a key role in defining the parameters of choice.

They can provide financial support to private providers, either directly or in the form of financing arrangements that allow parents to send children to private schools.
Since the early 1990s Sweden has used a voucher-type system to give parents the right to take children out of state schools, put them in independent schools and take state funding with them.

In some states in the United States, authorities distribute vouchers to parents who can use them to finance the transition of their children to private schools.

Another approach is to contract the management of government provision to the non-state sector.
For example, other American states have sought to increase competition by encouraging the development of charter schools.

Several European Union countries, including parts of the United Kingdom, also follow this model, in effect substituting private management for state management while retaining public finance.

In all these cases, governments have developed public-private partnerships to facilitate choice and competition.
Not all competition involves such partnerships (see the discussion below of low-fee private schools that operate independently of state control or support), but they are a powerful force in governance reform.

To what extent is this good news in terms of improving the overall quality of education and enhancing equity?

As in the case of school-based management, there is no simple answer.

Experiences and outcomes have varied.

Once again, context is important.


It is one thing to introduce vouchers in Sweden, which offers high-quality public education for all, and quite another in Pakistan, which does not.

What makes sense in Chile may be entirely inappropriate for Burkina Faso.
Institutional capacity, levels of Competition and choice are increasingly viewed as antidotes for the failings of public education systems Table 3.9: Responsibilities of the public and private sectors in provision and financing of education service delivery Public provision Public finance Purely government schooling Private finance Private provision • Vouchers to parents • Subsidies to private schools • Contracting management to private operators (e.g.
charter schools) User fees for government schooling • Purely private schooling: low-fee to elite Source: Adapted from Patrinos and Sosale (2007).

 Advocacy for increased competition and choice in the developing world makes repeated reference to programmes in high-income countries 1 6 0 C H A P T E R 3 inequality and the effectiveness of education planning all play an important role in defining governance reform options.
One problem with the current debate on education governance is that insufficient attention is paid to evidence and context.

There is a widespread tendency to draw far-reaching public policy conclusions from a weak evidence base (Lubienski, 2008).

The importance of national circumstance is often forgotten.
Advocacy for increased competition and choice in the developing world makes repeated reference to programmes in high-income and some middle-income countries – voucher systems, charter schools and other public-private partnership arrangements (Patrinos and Sosale, 2007).

Quite apart from the fact that such governance reforms have been highly contentious in rich countries and that the evidence on their impact is uncertain, little attention is paid to key questions of institutional capacity in poor countries.
School choice and achievement — strong claims, weak association The idea that increased parental choice leads to improved learning outcomes has intuitive appeal but is not well supported by evidence.

While there may be good reasons to allow parents greater flexibility in selecting schools, the assumption that this will raise standards is questionable.

The evidence in favour of public-private partnerships is not clear-cut even in the developed world.
One study using data from thirty-five countries claimed that private providers using public funding delivered the largest gains in learning outcomes (Wößmann, 2006).

However, interpretation of this exercise is open to question since the results were largely driven by scores in just a few countries, notably Belgium, Denmark and the Netherlands.
Another study, based on analysis of data from the PISA 2006 assessment, found that around 60% of students in mainly OECD countries had a choice between two or more schools.

Results of a modelling exercise showed that students at the schools competing with other schools in the same area did perform better in terms of average test scores.

The effect disappeared when demographic and socio-economic factors were accounted for, however; and effects on both equity and quality were muted.
As the PISA analysis puts it: ‘Whether students are in competitive schools or not does not matter for their performance when socio-economic factors are accounted for.

... None of the factors related to parent’s pressure and choice were found to have a statistically significant association with educational equity’ (OECD, 2007b, p. 236).

One of the most detailed reviews of the impact of choice and competition for part of the United Kingdom reaches a broadly similar conclusion.
Focusing on primary schools in the South-East of England, the study assessed test scores on the basis of parental choice (defined in terms of location) and school competition for a fixed pool of students.

It found that neither choice nor competition had a bearing on test results (Gibbons et al., 2006).

Some of the most detailed evidence on school competition and learning achievement comes from the United States and Chile.
Both countries have been in the forefront of governance reforms aimed at expanding choice.

Measured in terms of learning achievements, the outcomes have been mixed.

Assessing these learning achievements is difficult.

Consider first the charter school experience in the United States.
Such schools represent a hybrid approach to provision in which the public sector gives funds to private organizations to establish and manage schools independently of state administration while meeting certain conditions set by the state (Lubienski, 2008).
During the 2004/2005 school year charter schools served around 1 million students in forty states and the District of Columbia (Education Commission of the States, 2008; US Department of Education, National Center for Education Statistics, 2007), or just over 2% of total American public-sector school enrolment (Center for Education Reform, cited in Lubienski, 2008).

The broad aim is to improve performance by removing many of the rules binding regular public-sector schools and by introducing competition.
Because the schools’ characteristics are determined by state law, the diversity of arrangements is immense – making comparison far from straightforward.

Although charter schools cater disproportionately for African-American students in several states, the percentage of those from better-off households is also higher than in regular public-sector schools.

Evaluations of the impact of programmes aimed at increasing choice, including through charter schools, have found widely disparate results.
Some commentators have identified positive effects on learning in some states (Hoxby and Murarka, 2008).

Other research finds little benefit.
: W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA Using a national data set to examine mathematics achievement at grade 4, one evaluation found charter school students to be performing significantly below their public-sector school counterparts (Lubienski and Lubienski, 2006, cited in Lubienski, 2008).
Another study found similar results for reading (Braun et al.

2006, cited in Lubienski, 2008).

Still another found the overall charter school effect on African-American students to be negative (Carnoy et al., 2005).

To the extent that any conclusion can be drawn, it is that generalizations are not warranted on the basis of the available evidence.

Findings are heavily influenced by localized contexts and by the evaluation methodology used.
Flagship programmes aimed at expanding choice have also produced little compelling evidence that choice makes a difference.

The 2001 No Child Left Behind Act (see section below on teachers and monitoring) contains a federal mandate in favour of school choice: parents can transfer their children from schools that repeatedly fail to meet targets of academic progress to non-failing public-sector schools in the same district.
Students who opted to change schools under this provision showed no significant gains after two or more years (Zimmer et al., 2007).

The United States experience with school vouchers is also ambiguous.

Research has identified positive effects on student achievement in some subjects after children switched schools, but not in others, with effects usually emerging after some time (Molnar, 1999; Rouse, 1998).
Meanwhile, small private voucher programmes introduced in Dayton (Ohio), New York City and Washington, DC, in the late 1990s resulted in improved test scores for African-American students but not for other groups (Peterson and Howell, 2006).6 One review of the Washington, DC, district-wide voucher programme, conducted two years after its initiation, found no significant impact on the academic achievement of public schools (Winters and Greene, 2007).
It might be argued that the outcomes of small voucher programmes are sensitive to levels of competition and to time horizons.

Such programmes could generate small initial effects, with benefits increasing with the level of competition and over time.

Evidence from the Milwaukee Parental Choice Programme, the longest-running voucher scheme in the country, lends some weight to the proposition that there are long-run competition effects.
In this case, there is some evidence that public-sector schools with high levels of student eligibility for vouchers have raised their standards – an outcome interpreted by some as evidence that the risk of losing students has created incentives for more efficient teaching (Chakrabarti, 2007; Hoxby, 2003).
Evidence from other states, however, is inconclusive: partly positive in Florida, negative in Michigan, insignificant in California, North Carolina and Texas (Arsen and Ni, 2008; Miron et al., 2008; Ni, 2007).

At best, the overall results are muted.


As one commentator put it: ‘If any general finding is available it is that advantages to academic outcomes stemming from voucher programmes are at most notably modest, and also certainly do not rise to the level anticipated by the early optimistic assumptions’ (Lubienski, 2008).

In the developing world Chile is often viewed as a standard-bearer for choice-based governance reform.

It has had a nationwide system of school vouchers for over two decades.
Yet here, too, the results have been disappointing (Box 3.8).

The United States and Chilean experiences provide no definitive evidence in favour of choice and competition.

Experience in Sweden has been more positive (Box 3.9).

There, increased choice and competition have led to expansion of independent private schools, albeit from a low base.

Importantly, though, reform in Sweden was not prompted by a chronically underperforming public system.
Moreover, it was introduced in a country with relatively low levels of inequality and strong regulatory institutions.

While the Swedish model provides useful insights and lessons, there are limits to its exportability to developed countries with greater social polarization and failing public education systems – and even stronger limits to its relevance for developing countries.
Choice, competition and inequality Choice and competition are often presented in education governance debates as drivers not only of efficiency improvements but also of enhanced equity.

The fact that competition by its nature creates losers as well as winners is sometimes forgotten.
This has specific consequences in education where losers are students remaining in underperforming schools while the winners are those with parents who have the motivation, information, resources or connections to secure transfers to schools of better quality (Arsen and Ni, 2008).

One obvious question that arises is whether Flagship programmes aimed at expanding choice have produced little compelling evidence that choice makes a difference 6.
Other studies have shown that the results are sensitive to the way race and ethnicity are measured and to how the sample for the baseline data is constructed (Krueger and Zhu, 2002).

 Box 3.8: Chile’s experience with choice and competition: no advertisement for the governance reform blueprint Increased competition between schools has been just one element in Chile’s education governance reforms.
A partial list of wider measures includes devolution to municipal level in many areas of management, increased use of exams and assessment to monitor performance, increased funding (since the return of democracy in 1990), performance-related incentives for teachers and the lengthening of the school day.

The reforms have led to large gains in education coverage, especially at secondary level, and they have made Chile a widely-cited ‘model’ for governance reform.
Outcomes for learning achievement and equity have been far from impressive.

Private schools with public subsidies do register an advantage over municipal schools on the yardstick provided by fourth-grade standardized tests.

However, the findings are reversed when the socio-economic characteristics of schools are taken into account.

In other words, there is no equalizing effect.

Municipal schools do a better job than private schools of lifting the achievement of students in the lowest group.
Only among students in the middle socio- economic group do private subsidized schools have higher associated test scores.

Analysis of international assessment data over time also calls into question Chile’s credentials as a governance success story.

Governance reforms have certainly done little to close the gap between Chile and the developed world.
For example, while the PISA reading assessment shows 32% of 15-year-old students in OECD countries scoring in the top two levels, only 6% of students in Chile do so.

National standardized achievement tests show little improvement over time, even in primary education, where enrolment has been near universal since the early 1970s.
The TIMSS assessment of 2003 told roughly the same story of poor performance as in 1999, with Chile failing to catch up with countries such as Egypt and Thailand — neither of which has been in the front rank of reformers.

And Chile retains some of the starkest education disparities in Latin America, with large gaps in test scores persisting between students in municipal schools, which serve students primarily from socio-economically disadvantaged backgrounds, and private school students.
While Chile’s experience with education governance reform is often held up as a model, Chileans themselves have been more circumspect.

The government is embarking on a new wave of reforms with a more explicit focus on equity.

Secondary school students have responded not with enthusiasm for past governance reforms, but with street protests over poor quality and highly unequal education provision.
After more than fifteen years of education reform under a democratic government and ten years before that under the military government, Chile remains a weak advertisement for the governance reform blueprint favoured by many governments and aid donors.

Sources: Bellei (2005); Contreras (2001); Crouch and Winkler (2007); González (2008); Mizala and Romaguera (2000); Mizala et al.

(1998); Sapelli and Vial (2002); Tokman Ramos (2002); World Bank (2007a).
governments should allow choice to be exercised on terms that leave many behind.

Governance reform that aims to expand school choice without building in protection for equity carries an inherent risk of school systems becoming sources of widening disparities.

School choice can exacerbate inequalities in many ways.

If high-performing schools are allowed to select students on the basis of measured ability at a given age, disadvantages linked to income or ethnic background are magnified.
Students with high levels of inherited disadvantage end up concentrated in the worst-performing schools.

The same thing can happen if high-performing schools are allowed to select students when total applications exceed places (Epple et al., 2004).

Evidence from the OECD is instructive: cross- country research shows that countries with highest levels of school choice tend to be more socially segmented (OECD, 2007b).

In Chile too, the student composition of schools is marked by rigid divides.
Municipal schools largely enrol students from lower socio-economic groups, while private independent schools draw pupils almost entirely from higher ones (González, 2008).

Parent surveys show that active choice in school selection is strongly associated with higher socio-economic status and that parents making active choices tend to choose schools with a more homogeneous demographic composition (Elacqua, 2004).

Inequalities associated with school choice interact with wider inequalities in society.
People who are poor, marginalized or illiterate may lack access to information to enable them to make choices.

Research in the United States shows that parents with wider social networks and more access to information are more likely to take advantage of choice policies and that they are better able to ensure that their children enter the higher- quality schools they select (Goldring and Rowley, 2006; Lacireno-Paquet and Brantley, 2008).
Where private school enrolment is expanding and attracting a large share of children from the middle class, which can include many of the most motivated students and most active parents, public education stands to lose a powerful constituency with a strong political voice in claiming financial resources.
The economist Albert Hirschman identified the phenomenon of middle-class exit from public education systems as a threat to school quality and equity as a major problem some four : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA Box 3.9: Swedish lessons in competition: not readily exportable The Swedish school system is marked by strong achievement in international learning assessments and high levels of equity.
Since the early 1990s, the country has introduced radical and wide-ranging education governance reforms, with more extensive power devolved to the local level as a core objective.

The ‘Swedish model’ is frequently held up as a blueprint for others to follow.

Is this justified?

Expanded parental choice is a central pillar of reform in Sweden.

Since the early 1990s parents have had the right to send their children to independent schools.

Public funding follows the children.


Independent schools are closely regulated: they cannot select pupils by ability or charge fees and they follow national curricula.

Independent schools have been spreading in some parts of the country.

By 2007 there were nearly 1,000 of them, providing for about 9% of children aged 7 to 16 and 17% of those aged 16-plus.

At lower levels, many of these schools provide for children with learning difficulties.

Higher-level schools often provide vocational training.
Initially a source of intense political controversy, independent schools today enjoy broad support.

Private school providers have clearly responded to parental demands in important areas of education.

Evaluation results on the standard academic curriculum suggest that growth in independent school enrolment has been associated with improved achievement in mathematics.

The impact on equity is less clear-cut.


Positive achievement effects have not been observed for students with less educated parents, or for foreign-born students.

The Swedish experience of governance reform provides valuable insights and lessons.

There have clearly been important benefits.

However, none of this means that the ‘Swedish model’ is an exportable blueprint, or that the model itself is as far-reaching as is sometimes assumed.
Despite the incentives for the private sector provided by the reforms, independent schools still cover only a minority of students and their presence varies widely by municipality.

Many municipalities have no independent schools; their presence is most visible in urban centres.

The transferability of the reforms is questionable.

Sweden has relatively low levels of inequality: an emphasis on equality is deeply embedded in society.
Governance reform and competition were not introduced in the context of a national crisis in public education but rather were driven by a desire for diversity in the school system.

Public schools continue to offer all children the option of a good education.

The country also has a highly developed institutional capacity for regulation and oversight of private providers at the central level.

Many of these conditions are absent in other developed countries, let alone much of the developing world.
Sources: Björklund et al.

(2004); Böhlmark and Lindahl (2007); Sandström and Bergström (2005); Swedish National Agency for Education (2008).

decades ago (Hirschman, 1970).

Evidence from many countries suggests that he was right.
Today there is a real danger in many countries that poorly managed ‘quasi-markets’ in education with an enlarged role for choice and competition will leave public education systems trapped in a downward spiral of underinvestment, poor quality of provision and widening inequalities.

There are important respects in which choice and competition have enjoyed an exaggerated press.
In most countries, governments continue overwhelmingly to dominate education provision, finance and management, especially at the primary level.

However, advocates of choice and competition continue to exercise a marked influence on education governance reform debates in the developed world and – increasingly – the developing world.

The evidence and issues at stake need to be carefully weighed up.
While analogies with markets may have some effect in the context of political debate, their relevance to the real world of education is questionable.

Schools are not allowed to go ‘bankrupt’ and no government can allow schools to fail – the social, economic and political stakes are too high.

Similarly, no government with a concern to protect basic citizenship rights can allow disadvantaged children to be further marginalized through competitive choice.
Assertions to the effect that school competition creates ‘a rising tide that lifts all boats’ (Hoxby, 2003, p. 288) are not substantiated by cross-country evidence.

Simultaneously raising achievement and strengthening equity needs good governance supported by strong institutional arrangements – and it requires political leadership in tackling poverty and inequality.
There is a real danger that an enlarged role for choice and competition will leave public education systems in a downward spiral of under- investment, poor quality and widening inequalities In some developing countries, low-fee private schools are changing the education landscape 1 6 4 C H A P T E R 3 Low-fee private schools: symptom of state failure Debate over the role of public-private partnerships can divert attention from pressing concerns.
Unplanned growth in private schooling for the poor in some parts of the world is symptomatic of an underlying malaise: underperformance, or outright failure, of public providers.

The previous subsection looked at choice within the formal education governance structure.

In developing countries, however, millions of households are exercising choice outside that structure.
While private schools affordable only to middle-class and high-income groups continue to play an important role, new patterns of private provision are emerging.

Even a cursory observation of education provision in slums from Hyderabad to Nairobi demonstrates that private provision in some developing countries is no longer the sole preserve of the rich.
Private primary schools charging modest fees and operating as small businesses, often with neither regulation nor support from government, are changing the education landscape.

Whatever the formal education policy may be, a growing marketplace in education provision is appearing by default.

The rapid emergence of low-fee private schools is reflected in wider education governance debates.
Some observers see the growth in this sector as a potentially powerful force for greater equity and expanded opportunity.

Guidelines written for USAID to inform its investment in private primary schooling provide an illustration: ‘The private sector… has played a critical role in meeting the needs of disadvantaged groups and has the potential to further increase access and equity.
Private provision of education is more effective in terms of student achievement on standardized tests and is an effective alternative to publicly provided education’ (Chandani et al., 2007, p. 6).
In a similar vein, the World Bank’s 2006 Education Sector Strategy Updatesignals a commitment to promote an enlarged role for the private sector in reaching the poor: ‘Increased competition among public and private education institutions (for example, through new methods of public finance that shift education decisions to private households) is providing more incentives to improve quality.
The Bank can help countries investigate the market for education, develop an enabling environment for private participation and competition, and align private provision whenever possible with equity principles laid out in national education strategies’ (World Bank, 2005b, p. 34).
Some advocates of more radical privatization options have called on governments and donors to use public financing, vouchers and other public-private partnership arrangements to open the door to a large-scale exit from public provision (Tooley, 2007).

As in other areas, sweeping recommendations have been weakly grounded in evidence.

Clearly, unplanned growth in low-fee private primary schools is responding to real demand.
Many poor people are voting with their feet and their meagre incomes to leave public provision.

The important question for public policy is whether governments should use financial resources to accelerate that trend, or resolve the underlying problem driving it: namely, the failure of public education systems to meet the needs of the poor.
Given that nine out of ten primary school children in developing countries attend public-sector schools, the overwhelming priority should be to improve their standards and accessibility rather than to channel public finance into the private sector.

Provision expanding but difficult to measure Estimating the size of the low-fee private sector is intrinsically difficult because documentation is poor, institutions are typically unregistered and national administrative data provide only a very partial account.
Even so, observation and anecdotal evidence suggest the sector is growing rapidly in many developing countries.

The extent of its expansion varies.

Evidence from countries as diverse as Ghana, India, Kenya, Nigeria and Pakistan points to rapid growth.

In urban India, around 96% of the total increase in primary enrolment between 1993 and 2002 is estimated to be due to growth in private schools unaided by government.
While growth in private enrolment was slower in rural India, it still accounted for 24% of the increase in rural areas (Kingdon, 2006).

In Pakistan’s Punjab province, one in every three children enrolled in primary school studies in a private school (Andrabi et al., 2006).

Nigeria has also witnessed prolific growth in low-fee private schooling.
It is estimated that in parts of Lagos state, three-quarters of the children in school are enrolled in registered and unregistered private : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA schools.
According to one (admittedly speculative) study, incorporating students enrolled in unregistered private schools into administrative data would reduce the proportion of those out of school from 50% to 26% (Tooley and Dixon, 2007).

These figures should be interpreted with caution.

The fact that a country has many low-fee private school providers in slums is not a sound basis for extrapolation to rural areas with more dispersed, and often much poorer, populations.
National averages can also give a distorted picture of coverage.

One study in India finds that 28% of rural people have access to a private school in their village, and that half of the schools are unrecognized.

Variation among states, however, is considerable: fewer than 1% of villages have a private school in rural Gujarat and Maharashtra, compared with over 50% in Rajasthan, Bihar, Uttar Pradesh, Punjab and Haryana.
In general, richer states are more likely to have rural private schools (Muralidharan and Kremer, 2006).

Questions of quality, accountability and affordability Whatever the precise dimensions of the phenomenon, low-fee private schools are clearly an important element in education provision for many poor households.

And the sector is expanding.


Advocates of a bigger role for the private sector see in these two observations evidence that such schools are cost-effective, affordable, less prone to teacher absenteeism than public schools, better equipped to provide a good- quality education and more accountable to parents (Tooley and Dixon, 2007).7 Evidence to support these wide-ranging claims is less emphatic than the claims themselves.
Available data does not provide a robust base for meaningful large-scale comparisons across or even within countries.

The findings of the most credible assessments point to large grey areas in which parental motivation, perceptions of quality and the availability of alternative providers intersect to inform choice.

Parental perceptions and motivations.


Parents clearly would not pay to send children to private schools if they believed government providers offered better provision at an equivalent or lower price, let alone for free.

Parents send their children to low-fee private schools because they perceive an advantage, whether in the form of reduced teacher absenteeism, greater pupil and teacher discipline, and smaller class sizes.

These are not the only attractions, however.


The choice of low-fee private schools may also be associated with aspirations for social mobility, especially if the schools use English as the medium of instruction (Rose, 2006; Srivastava, 2007).

Detailed work on attitudes in Uttar Pradesh state, India, shows parental motivation to be complex (Box 3.10).
Other research in the same state finds that recourse to private providers is not In India, some families seek access to a low-fee private school in order to distinguish themselves from others within their community Box 3.10: Why poor households choose low-fee schools in an Indian district Why do low-income households, many with children who are first-generation learners, choose for-profit, low-fee private schools even where a less costly state alternative exists?
The reasons are complex, according to a qualitative study on low-fee private schools in Lucknow District of Uttar Pradesh state in India.

Not all parents, particularly in rural areas, are convinced of the quality at low-fee private schools.

While such schools are seen as a better option than government provision, they were not necessarily seen as being of acceptable quality.
Rather than basing choice on the quality of education provided, however, some families seek access to a low-fee private school in order to distinguish themselves from others within their communities.

Lalita Bai, a rural migrant and wife of a labourer, whose family belongs to a scheduled caste, explained why she sent her daughters and sons to a low-fee private school: ‘Only those who are absolutely penniless, the lowest of the low in society, can send their children to government schools.
Most people cannot bring themselves to send them there.’ Some higher-caste families had a similar explanation for why they found low-fee private schooling important.

Rambha Devi, a rural grandmother of four, said: ‘Only low-caste children attend government schools so no real schooling takes place there.’ The concerns expressed in these views are revealing.
They point to a disconcerting lack of confidence in public provision on the part of the poor, coupled with a concern to maintain social divides on the part of some households.

The overall findings suggest that there is an urgent need to examine more closely the role of low-fee private schools in the context of achieving India’s EFA goals, including the wider social impact of household choices within increasingly socially and economically segmented schooling arenas.

Source: Srivastava (2006, 2008).

7.


Teachers in low-fee private schools are usually recruited under conditions similar to those of contract teachers in government schools, for whom similar arguments are made regarding accountability.

See the section on teachers and monitoring Even low fees are a burden for poor families 1 6 6 C H A P T E R 3 the same as trust, or an indicator for preferences between providers.
Interviews with parents reveal a high level of mistrust of the private sector.

The most widely stated parental preference in this case was for a properly functioning government system; parents resorted to private school because they felt they had no alternative (Härmä, 2008).

Assessing quality.

Do low-fee private schools offer an efficient route to improved education quality?

Data constraints rule out a general answer to the question, but country evidence suggests that caution is in order.
There is evidence that in many contexts private schools are outperforming state schools.

In parts of India and Pakistan, children enrolled in low-fee private schools perform better, on average, than those in government schools, once adjustments are made for socio-economic status and other variables (Andrabi et al., 2008; Aslam, 2007; Das et al., 2006; Muralidharan and Kremer, 2006; Schagen and Shamsen, 2007).
This does not mean government provision is necessarily worse than private provision across the board.

Even in Pakistan, where the poor condition of government education in general is widely recognized, the top-performing public-sector providers outperform private schools.

The problem is that there are many more poorly performing government schools, in which learning outcomes are considerably lower than in the worst private schools.
As one study concludes: ‘The only reason the private schools look so good is that the poorly performing public schools are so disastrous: if at some future date, children actually started demanding something more than the most rudimentary education, the semi-educated teachers in the private schools would actually find it hard to cope’ (Andrabi et al., 2008, p. xiii).

Teacher accountability and parental participation.


It is widely argued that dependence on parental finance makes low-fee private schools and their teachers more accountable.

Available evidence does not lend clear support to this view.

In Pakistan, a survey in Punjab has suggested that teacher absenteeism (one indicator for accountability) is less of a problem in private schools than in government schools.

The study found that head teachers reported 13% absenteeism in the former and 8% in the latter.
By contrast, a more rigorous analysis of teacher absenteeism in rural India, based on data collected during unannounced visits to schools, reported very little difference in teacher absenteeism – around one-quarter of teachers were absent from both types of school (Muralidharan and Kremer, 2006).
In addition, a qualitative study in Lucknow District of Uttar Pradesh, India, found low parental participation and interest in the private schools, which proprietors and households attributed to parents’ low education levels and inexperience with schooling.

Interaction with the school was limited to fee-related complaints rather than dealing with concerns to do with education (Srivastava, 2007).

Affordability in perspective.


Advocates for low-fee private schools claim that they are affordable to the poor.

However, affordability is not a straightforward concept.

When poor households pay for education, they divert income from other areas, including nutrition, health, shelter and savings for emergencies.

Education expenditure by poor households for low-fee private schools can be viewed, as it is by some, as a market preference freely expressed.
Alternatively, it can be seen, with more credibility, as an entry charge to education paid by vulnerable households with two options: paying for education through severe sacrifices in other areas, or accepting that their children have no opportunity for an education meeting minimum quality standards.
Evidence from a variety of contexts illustrates the real trade-offs facing poor households when they have to pay low-fee providers: In Hyderabad, India, a city with a fast growing market for low-fee private primary schools, it is estimated that a family living on the minimum wage would have to spend roughly one-quarter of its income to put three children through such a school, even before taking account of additional related costs for nutrition and other household needs (Watkins, 2004).
In rural Uttar Pradesh, India, one survey puts the total cost of educating four children (the average family size) in a low-fee school at half the mean annual salary for households in the lowest two income quintiles.

Unsurprisingly, most of these households send their children to government schools.

Choice is limited to better-off households.

Those in the richest 20% of the sample were almost eleven times more likely to choose private schooling than families in the poorest 20% (Härmä, 2008).
In urban Malawi, even the relatively modest fees cited by owners of low-fee private schools : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA (around US$3 per term in 2004) are beyond the reach of poor households, even before taking other costs of education into account.
For the two-thirds of the population living below the poverty line, fees at this level would translate to over one-third of available resources per person per household (Kadzamira et al., 2004).

In Ghana’s capital, Accra, around 17% of total enrolment in primary education is in the private sector.

But households in the rural north and other areas where enrolment is already low are far less likely to opt for private schools, since school costs are already the major reason their children are out of school.
While private schools are spreading in rural Ghana, it is mainly in areas where fishing and trading are the main occupations, not areas dependent on subsistence farming (Akyeampong, 2008).

Households with livelihoods in the latter area tend to be poorer.

Access and equity.

Recourse to private schools on the part of the poor is not an indicator for equitable access.

As noted, chronically poor households may not be able to finance even relatively low fees without suffering adverse consequences.
Locality is also a limitation on equity.

By definition, low-fee private schools will be established only where enough parents are willing to pay fees.

As such markets are far more likely in high-density urban areas than remote rural areas, the schools could exacerbate the rural-urban divide.

In addition, significant gender disparities have been observed with low-fee private schooling.

Parents lacking the resources to send all their children to private school often choose to send only some of them.
Studies in India and Pakistan find significant pro-male bias in this choice (Aslam, 2007; Härmä, 2008; Mehrotra and Panchamukhi, 2007).

In India’s Bihar state, 10% of all enrolled scheduled-caste girls are in private schools that receive no public funds, compared with 21% of upper-caste girls.

In Uttar Pradesh the respective shares are 16% and 37% (Mehrotra and Panchamukhi, 2007).

Where there is no choice.


While many poor households are rejecting public provision by switching to private providers, the extent of choice is often exaggerated.

People in some slums of the Kenyan capital, Nairobi, do not have the option of sending their children to government schools for a very simple reason: there are none.

The residents of these informal settlements lack formal property rights, so the government provides no basic services in education (Box 3.11).
State failure in education provision in slum areas across many countries has created a strong impetus for the development of private school markets.

Even where children can travel to a school in a neighbouring area, they often cannot enrol as they lack the Box 3.11: Government schools for the rich, private schools for the poor in Kenya’s slums Does the high incidence of low-fee private schools in slum areas reflect the power of choice in a competitive market?

Not in the Kenyan capital, Nairobi.


Over 60% of Nairobi’s population lives in slums.

This population is crowded into just 5% of the residential area of the city.

Slums are marked by high levels of poverty and deprivation, and are not at first sight an obvious location for private education provision.

Yet a longitudinal study covering two slum and two non-slum areas, with a total sample of over 13,000 children, finds that children living in slum areas are more likely to attend private school.
Conversely, children of higher socio-economic status were more likely to attend a government school: the richest 20% of households were more than twice as likely as the poorest to send their children to a government school.

The Kenyan study suggests that government schools are the preferred choice for richer, non- slum residents, while private providers are the only viable option for the poor.

From the perspective of the poor, however, the choice is highly constrained.
There are no government providers in some slums.

Where there are government schools on the periphery of the slums, they require an official residency title for entry.

Because most slum dwellers lack legal property status, their children are excluded.

In this context, ‘choice’ is an inappropriate description of the parameters for decision-making.

Parents ‘choose’ low-fee private providers because there is no alternative.
It is not a positive choice based on an assessment of the relative merits of different providers.

Indeed, household surveys show that parents complain about the private schools, with staff shortages, congested classrooms and lack of teaching materials identified as common problems.

Source: Mugisha et al.

(2008).


People in some slums of Nairobi do not have the option of sending their children to government schools for a very simple reason: there are none necessary paperwork and residential eligibility.

Thus poor slum dwellers may find the only schooling available to them is a low-fee private school, while richer households have the choice of attending a government school.
Governing low-fee private schools Low-fee private schools are the subject of an often polarized debate.

Some advocates of increased competition and of an expanded role in education for the private sector see them as an alternative to publicly financed and delivered provision.

Critics see them as a symptom of state failure.


Whatever the perspective, it is clear that the low-fee private school sector is a response to demand and that it is unlikely to shrink rapidly, let alone disappear, in the foreseeable future.

Governance of the sector to advance the EFA goals is therefore a priority.

In many countries low-fee private schools currently operate as a governance-free zone.
The schools have increased in number far faster than the capacity of regulatory and management regimes to ensure that their activity is aligned with national policies.

Malawi has only one person in the education ministry responsible for school registration, so small private schools effectively remain outside the system (Kadzamira et al., 2004).
In India, compliance of low-fee private schools with the rules and norms governing teacher qualifications, teaching practices, the curriculum and infrastructure is haphazard at best, non-existent at worst (Kingdon, 2006).

Even where governments do regulate low-fee private providers, the focus is often on assessment at the time of school registration rather than regular monitoring of performance and outcomes.
More effective management and regulation are easier to advocate in principle than to deliver in practice.

Low-fee private providers tend to expand most rapidly in areas where many government schools are struggling to meet standards and levels of poverty are often high.

Nigeria, for example, has stringent legislation on private- sector regulation, including fines and even imprisonment if providers do not comply with regulations.
Yet the states in which private provision is most prevalent, such as Lagos, find it almost impossible to enforce the legislation, not least given the government failure to provide alternatives for children whose schools would be closed (Rose and Adelabu, 2007).

Public-private partnerships offer another regulatory option.

In principle, education authorities can use financing and other measures to generate incentives and enforce rules while addressing concerns over equity.
Pakistan’s programme of public-private partnership is an example.

The government, with support of donors, has embarked on a range of public- private partnership projects aimed at addressing long-standing problems in access and equity.

The problems are acute: Pakistan’s NER is 73% for boys and 57% for girls.
Not only are overall enrolment levels lower than in poorer countries such as Nepal and the United Republic of Tanzania, but Pakistan is near the bottom of the international league table for gender parity.

Large disparities between states, between urban and rural areas and between richer and poorer households are at the heart of Pakistan’s slow progress in basic education.
Low-fee private providers are widely presented as a dynamic force for change, though experience and evidence point to the case for a more cautious appraisal (Box 3.12).

Countries with more developed institutional capabilities might be well placed to oversee effective partnerships with low-fee private schools.

But the countries in which such schools are flourishing are those with weaker institutional capacity and tighter financial constraints.
For these countries, it is not obvious that public- private partnerships involving management relationships with large numbers of small private providers will deliver progress towards a national system based on uniform standards and equal access for all.

The question remains: why are governments not using their capabilities to deliver equitable and affordable public education?

There is no ‘one-size-fits-all’ model for effective governance of low-fee private schools.
The overarching challenge for governments is to develop strong national strategies for achieving EFA and to ensure that all providers operate within these strategies.

The bottom-line obligation of all governments, especially at the primary school level, is to develop a publicly financed and operated education system that offers the option of good-quality, free education to all citizens.
The bottom-line obligation for all governments is to develop publicly financed and operated primary schooling of good quality for all children : W H Y G O V E R N A N C E M AT T E R S C h o i c e , c o m p e t i t i o n a n d v o i c e : s c h o o l g o v e r n a n c e r e f o r m a n d E FA Box 3.12: In Pakistan, a questionable public-private partnership The Government of Pakistan, with support of aid donors, has made public-private partnerships the ‘anchor’ of its strategy to address the challenges of education access, quality and equity.
A 2004 policy paper spelled out the premise underpinning the current policy framework: ‘Government has officially recognized that the public sector on its own lacks all the necessary resources and expertise to effectively address and rectify low education indicators.’ Low-fee private schools figure prominently in this strategy.

Such schools are expanding rapidly in parts of Pakistan.
Coverage is variable: there are more of the schools in the relatively prosperous Punjab province (where enrolment is already higher) than in rural Sindh or Balochistan, which have the lowest enrolment rates overall and particularly wide gender gaps (Figure 3.7).

Although equity concerns have figured in the design of public-private partnerships, experience in Punjab illustrates just how difficult it can be to achieve more equitable outcomes.
The Punjab Education Foundation has been running two different but overlapping public-private partnership models.

Under an education voucher programme for selected slums, parents can use state funding for entry to low-fee private schools.

Meanwhile, a Foundation Assisted Schools programme provides a per-child subsidy for children enrolled directly in private schools in selected high-priority areas.
While there is some initial evidence of positive influence on enrolment and learning outcomes, serious problems have been identified: Fragmented authority and inequality of financing.

Responsibility for running public-private partnerships rests not within the Ministry of Education but with semi-autonomous education foundations that depend on their ability to raise external funds.
Provinces such as the Punjab that are already in a stronger position in terms of education can benefit more because they have the possibility to recruit qualified staff, have more potential NGO and private sector partners, and are a priority client for most donors.

Financial sustainability.

Public-private partnership models have been an important component of education-sector World Bank loans in Punjab and Sindh.
Their continuation and expansion is contingent on sustained donor support, as the Ministry of Education has so far not decided to mainstream the models.

That support cannot be taken for granted.

Limited scope.
Limited scope.

Notwithstanding the international attention Pakistan’s public-private partnership programme is receiving as a potential model for other countries to follow, the school voucher programme reaches only 10,000 students and the Foundation Assisted Schools programme only 50,000 (Punjab Education Foundation, 2008).

This is in a country with 2.7 million boys and 4.1 million girls out of school.
Whatever the course of public-private partnership projects, the majority of children from poor households in Pakistan rely on government provision — and will continue to do so.

Reaching children who are not in school will require expansion of the public education system, with a far stronger focus on wealth, gender and regional inequalities.

Chronic underfinancing of education is an immediate problem, with just 2.7% of GNP (12% of total government expenditure) allocated to education.
Sources: Andarabi et al.

(2006, 2008); Aslam (2007); Bano (2008).

Figure 3.7: Primary gross enrolment ratios in Pakistan by location and gender, 2004/2005 ) % ( s o i t a r t n e m l o r n e s s o r G 120 100 80 60 40 20 0 Urban Rural Urban Rural Urban Rural Urban Rural Balochistan NWFP1 Punjab Sindh 1.

North West Frontier Province.

Source: Pakistan Ministry of Education (2006).


Male Female Conclusion Governance reform in school management has been widely cited as a positive force promoting a wide range of important goals in basic education, including improved quality and enhanced equity.
Strengthened choice, competition between schools, devolved authority and increased public participation have all been identified as drivers of more accountable education provision.

Disadvantaged households are commonly presented as first among equals in the list of beneficiaries.
Evidence presented in this section calls into question some of the more optimistic assessments of school governance reform, particularly with regard to the ability to promote free, good-quality, equitable education for all citizens.

Increasing accountability and participation are important ends in themselves in the design of education policy.
But devolving authority to schools does not automatically confer increased voice in school management on parents or communities, especially if they are poor and marginalized.

Similarly, while choice and competition between providers may have the potential to play a role in improving education quality, there is little evidence of that potential being realized on a significant scale.
For marginalized, vulnerable and impoverished households, choice remains highly constrained – and access to basic education remains contingent on public education provision.

The rapid emergence of low-fee private schools may be a response to real demand, but there is little evidence to suggest that low-fee providers offer a genuine choice of affordable, accessible, quality education.
All this points to a strong case for governments to focus their energies and resources on public provision of quality basic education for everyone.

Private finance and private providers have a role to play, and governments need to ensure that they are integrated into properly managed national strategies.
However, transferring responsibility to schools, parents, communities and private providers will not address the underlying problems faced by education systems in providing equitable opportunities for quality education.

These will only be revealed through governance systems that combine strong institutional arrangements with a commitment to equity.
For marginalized, vulnerable and impoverished households, choice remains highly constrained : W H Y G O V E R N A N C E M AT Strengthening teacher governance and monitoring Recruitment, deployment and motivation Introduction Getting children into school, through a full primary education cycle and into secondary school is a priority for public policy.
But education is about more than putting bodies in classrooms.

It is about engaging minds, expanding horizons and ensuring that students have access to real opportunities for learning.

The ultimate aim of any education system is to ensure that children develop their cognitive, emotional and social capacities – and that they acquire the skills they need to realize their potential (UNESCO, 2004).

Schools are the primary institution for achieving this aim.

And teachers are on the front line of delivery.


Chapter 2 documents serious problems in the quality of education.

In many countries absolute levels of learning are so low as to raise questions about the value of primary schooling.

There is disconcerting evidence that the gap in average performance between rich and poor countries may be widening.

Moreover, that average gap obscures large disparities in learning achievement within countries.
In short, many school systems are failing to deliver services that meet even the most basic standards for quality and equity.

Improved governance in teacher management is vital for changing this picture.

Education systems need to attract qualified people into the teaching profession, retain them, provide the skills and knowledge they need, and ensure that they are motivated.

But how should poor countries with limited financial resources set about achieving these goals?
And what mechanisms are available to ensure that disadvantaged children living in marginalized areas have access to good teachers?

This section addresses these questions.

It also looks at the crucial role of monitoring as a vehicle for raising standards.

In the absence of effective monitoring, problems relating to education quality and equity often remain invisible to the public and policy-makers alike.
When integrated into policy formulation, monitoring can play a key role in raising quality and strengthening equity.

If the world’s poorest countries are to achieve UPE by 2015, millions of additional teachers have to be recruited, trained and deployed, the majority of them in marginalized areas characterized by high levels of poverty.

The problem is not just a quantitative one of recruitment.
A recent cross- country survey on teacher motivation in sub- Saharan Africa and South Asia concludes: ‘[V]ery sizeable proportions of primary school teachers, particularly in sub-Saharan Africa, have low levels of job satisfaction and are poorly motivated.

Many tens of millions of children are not being taught properly and are not receiving even a minimally acceptable education.
… [T]he unavoidable conclusion is that most schooling systems are faced with what amounts to a teacher motivation crisis’ (Bennell and Akyeampong, 2007, p. 25).

Increasing recruitment, strengthening motivation and improving qualifications are issues at the heart of the teacher governance challenge.

Equity concerns are also paramount.

The distribution of more experienced, better-qualified teachers is often skewed towards the best performing schools and students from higher socio-economic backgrounds.
Marginal rural areas and low- income urban settlements are more likely to attract unqualified teachers or to experience large deficits in teaching staff.

This subsection looks at four important governance themes relating to teachers: salaries and living standards; recruitment and contract teachers; deployment patterns; motivation and performance-related pay.

Salaries and living standards Teacher salaries figure prominently in education governance debates.

This is for good reason.


Remuneration for teachers absorbs the lion’s share of education budgets, especially in low- income countries.

Pay levels also influence recruitment.

Salary has an important bearing on the number of people entering the profession and their qualifications.

Higher salary levels are likely to be positively associated with levels of recruitment, experience and morale.

By the same token, the higher the recruitment costs, the fewer teachers can be recruited within a fixed budget.
Many school systems are faced with a teacher motivation crisis There is no simple formula for determining an appropriate level for teacher salaries.

As in any labour market, costs are determined partly by supply and demand, and partly by political factors.

Decisions on recruitment levels, qualification requirements, and pay and conditions are all important.
Average teacher wages as a multiple of GNP tend to decline as a country develops economically (Bruns et al., 2003).

But the ratio of teacher salaries to GNP is of questionable relevance in determining what salary corresponds to the attainment of specified goals in areas such as recruitment and motivation.

In any country, policies on teacher pay and recruitment have to take into account average incomes, relative pay with comparable professions and wider labour market conditions.
Whatever the national ratio of pay to GNP, it is clear that many teachers in developing countries have very low income levels.

In some countries pay levels do not cover basic living costs and this is a major factor in the teacher motivation crisis.

In much of sub-Saharan Africa and South Asia teacher pay levels are perilously near, or even below, the poverty line (Bennell and Akyeampong, 2007; Benveniste et al., 2008; Sinyolo, 2007).

In some cases salaries have fallen precipitously.


In Malawi, average teacher salaries were 30% lower in real terms in 2004 than in 1992.

At the equivalent of just US$3.50 per day, a teacher’s average pay is below the amount needed to cover the most immediate household needs (Kadzamira, 2006).

Late payment, a widespread problem in many countries, adds to the pressures associated with low salaries (Benveniste et al., 2008; Sherry, 2008; VSO, 2007).

It is not just absolute salary levels that are important.
Relative pay matters in terms of both recruitment and morale.

In Latin America, teacher salaries are generally well above the poverty threshold but compare unfavourably with pay in other professional and technical occupations (Morduchowicz and Duro, 2007).

Similarly, teacher salaries in much of Central Asia are considered unattractive.

This is true even in countries such as Armenia and Tajikistan where teacher salaries increased markedly between 2003 and 2007 (Steiner-Khamsi et al., 2008).
One consequence of low relative pay in Central Asia has been an increase in the number of teachers seeking to supplement their income through a second job – a phenomenon that has been extensively documented in most Central Asian countries (Education Support Program, 2006).

This practice can have damaging consequences for the quality of education, with some teachers withholding curriculum to pressure students into private tutoring (Bray, 2003).
The students least able to pay for private tutoring stand to lose the most.

Debates over teacher pay have to be viewed in a broader context.

Governance reforms have often increased teachers’ level of responsibility and workload.

In many countries teachers are being asked to use demanding new ‘learner-centred’ curricula which entail major changes in teaching practice, and frequently increased preparation and marking time.
Yet these new responsibilities are seldom reflected in pay and conditions, helping explain why many teachers lack enthusiasm for reform efforts (Bennell and Akyeampong, 2007).

Contract teachers: increasing recruitment at the expense of quality and equity?

All governments operate under real budget constraints in education.

The constraints are particularly severe in many of the poorest countries.
The fact that these countries need to increase recruitment on a large scale poses an obvious public spending problem: namely, how to increase the supply of teachers within a sustainable budget framework.

Many governments have attempted to resolve the problem by increasing the recruitment of contract teachers.

Teachers have traditionally been recruited as civil servants.

This influences the structure of their pay and benefits – and the costs of recruitment.
Recruiting teachers on a contract basis, outside the civil service scale, has the potential to reduce costs.

It also gives education authorities greater flexibility with respect to hiring and firing.

One feature of civil service employment in many countries is that teachers have a high level of immunity when it comes to being fired.

According to one study in India, only one in 3,000 head teachers surveyed has ever fired a teacher (Chaudhury et al., 2006).
Such findings confirm that education authorities have trouble dismissing tenured teachers for substandard performance.

This is seen by some as a factor in the high levels of absenteeism in many countries noted in Chapter 2.

Contract teaching arrangements are seen by some as a vehicle for greater flexibility.

While civil service employment is a long-term arrangement, contracts are time- bound and can be revoked swiftly.
Increasing the Poor training, poor pay and poor working conditions contribute to teacher discontent : W H Y G O V E R N A N C E M AT opportunity for communities and head teachers to hire and fire teachers is widely cited as a governance benefit of private schools, discussed earlier in this chapter.
Recruitment on a contract basis has increased the supply of teachers in many countries.

This is particularly the case in West Africa (Göttelmann- Duret and Tournier, 2008).

Over a third of teachers in Guinea, the Niger and Togo are contract teachers (UNESCO, 2007a).

Many other countries have also stepped up recruitment of contract teachers, among them Cambodia, China, India, Nepal, Nicaragua, Pakistan and Sri Lanka (Duthilleul, 2005; Göttelmann-Duret and Tournier, 2008; Govinda and Josephine, 2004).
To what extent has this enabled governments to achieve EFA goals?

The evidence on contract teachers is mixed.

The increase in the supply of contract teachers has enabled governments to reduce pupil/teacher ratios (PTRs) in many countries.

However, that superficially positive outcome has to be weighed against concerns that there may be a trade-off between the supply of contract teachers and overall education quality.

For example, contract teachers in Togo appear to provide lower-quality education.
That is not entirely surprising since they have less experience and training than civil service teachers (Box 3.13).

There is also evidence from West Africa that recourse to contract teaching may in some cases compound problems of teacher morale.

The testimony of one contract teacher in Cameroon provides an eloquent account of the impact of contract teaching arrangements on self-esteem in one particular context (Box 3.14).
The broader concern with contract teaching is that what might appear as an advantage from one perspective (more flexibility and reduced cost) might be seen from another perspective as a threat to livelihood security and a source of low morale.

Evidence from other regions also varies.

In some cases, increased recruitment of contract teachers can have positive effects on equity.

This is especially true where contract teachers are recruited from regions and communities that are not well served.
There is also some evidence – admittedly mixed – that contract teachers are less likely to be absent.

This appears to be the case in India, where the practice of employing contract teachers has expanded rapidly since 2002 (Box 3.15).
Most contract teachers in the country work in rural areas, often teaching in schools used by very Box 3.13: Weighing the costs of lower teacher wages in Togo Public sector reforms in Togo in the 1980s and 1990s resulted in pay and recruitment freezes for teachers, leading to a sharp rise in PTRs.

The country responded by recruiting contract teachers.

One-half of public primary school teachers are now contract teachers.
Their wages are some 60% below those on the civil service salary scale, and they are not entitled to promotion, pension rights or other non-wage benefits.

While contract recruits often receive teacher training and may have as many years of general education as civil service teachers, they tend to have fewer years of teaching experience.

What difference has the introduction of more flexible contracts made to accountability and quality?
An analysis of data from the PASEC achievement survey found no difference in absenteeism between contract and civil service teachers.

The threat of not having a contract renewed does not appear to have changed teachers’ behaviour, probably because the threat is seldom acted upon.

However, the analysis did find that, controlling for student background and for teacher education and experience, students in classes taught by contract teachers performed worse than those taught by civil service teachers.
In other words, low-wage contracts do appear to have attracted a needed pool of teachers, as intended, but with the unintended consequence of potentially long-term detrimental effects on education quality.

Source: Vegas and De Laat (2003).

poor communities.

However, even here the implications for equity are ambiguous.

An obvious concern is that contract teaching arrangements will leave some of India’s most marginalized children to be taught by its least qualified and experienced teachers.
The issues raised by contract teaching are far from straightforward.

All governments need to assess carefully the potential risks, in terms of equity and education quality, of a recruitment strategy that lowers the standards for recruited teachers.

From an EFA perspective, increasing teacher supply while lowering quality standards is a false economy.

The first objective of teacher governance should be raising learning achievement.
At the same time, governments have to operate within a defined resource envelope.

In the poorest countries, increased national effort and increased aid to meet recurrent costs in education may be needed for meeting recruitment goals.

One way of reducing the pressure for recruitment is to strengthen teacher retention.
In many countries, large numbers of teachers are leaving the profession not just because of poor pay and conditions, but also because of inadequate support, There is a potential trade-off between the recruitment of contract teachers and education quality In Cameroon, recruitment of contract teachers is allowing an expansion in enrolment at the expense of teacher morale 1 7 4 C H A P T E R 3 Box 3.14: ‘Marginal and frustrated’: a contract teacher’s view from Cameroon In Cameroon, contract teachers make up more than half of the teaching staff at primary level.
The experience of one teacher, Mr Bikono, who works in a government school in Yaounde, illustrates the potential trade-off between teacher supply on the one hand and teacher morale on the other.

Unable to get a job after qualifying as a lawyer, Mr Bikono took the qualifying exam to enter the teaching profession.

After eight years of experience as a substitute teacher, he achieved contract teacher status.

Being a contract teacher in Cameroon makes Mr Bikono ‘feel marginal and frustrated’.
His monthly salary amounts to 99,000 CFA francs (about US$158), a long way from the starting salary of 140,000 CFA francs for a teacher with a civil service job.

He also lacks benefits attached to civil service posts, such as a pension.

Mr Bikono sees it as discrimination.

‘We’re doing the same job and we have the same amount of work.

In fact, contractors are sometimes better qualified than civil servants.’ He is indignant about his paltry salary, which forces him to live in his father’s home.
His wife has left him, tired of waiting for a ‘supposed improvement of the situation’.

The mass recruitment of contract teachers has provided a short cut to expanding the teaching force.

This is in a national context where one-third of teachers are untrained and there is a need to more than double teacher numbers to achieve UPE by 2015.

While the recruitment of contract teachers is allowing an expansion in enrolment, the effect on the morale of teachers such as Mr Bikono is damaging.
The danger is that contract teaching will lead to further deterioration of quality, which in turn would have worrying implications for the number of children successfully completing the primary cycle.

Sources: Ekwè (2007); UIS (2006b).

Box 3.15: Contract teaching in India: reaching the underserved The recruitment of contract teachers in India aims to address the dual challenges of teacher shortages and high absenteeism in some states.

Outcomes for access, equity and quality have been mixed.
Contract teachers have been a feature of the education system in several Indian states since the 1990s.

The practice has expanded rapidly since 2002, when states were permitted to recruit such teachers through central government grants.

By 2004 half a million contract teachers had been hired.

Their recruitment is aimed at reaching villages not served by regular government schools and increasing the number of teachers in single-teacher schools.
Most contract teachers, accordingly, work in remote rural schools, particularly in the states of Madhya Pradesh (which accounts for 46% of all contract teachers) and Rajasthan (21%); contract teachers make up half the teaching force in the former and a third in the latter.

Their pay averages around one-fifth to half of what civil service teachers make.

The least qualified, lowest-paid contract teachers are concentrated in rural tribal areas serving deprived children.
What impact has the increased recruitment of contract teachers had on education?

There is insufficient evidence to provide a clear answer to that question.

Contract teachers are often recruited from marginalized communities, increasing the supply of teachers in areas where civil service teachers often do not want to work.

Indeed, the policy of hiring teachers under contract has been supported in some cases by civil service teachers, who benefit by not having to transfer to less attractive areas.
Clearly, many children being taught by contract teachers would not otherwise receive an education.

The fact that these teachers may be of a similar background to the children they are teaching may help to address problems of caste stigma.
There is also some evidence that contract teachers are less likely to be absent, thus helping schools open more regularly and for longer hours; and that learning outcomes are at least as good for children taught by contract teachers as for those taught by civil service teachers.

From a broader public policy perspective the questions facing India are similar to those raised elsewhere.
While contract teachers have brought real benefits for many communities, increased the equity of teacher deployment and cut average recruitment costs, they are often less qualified and experienced than civil service teachers.

The obvious danger is that children who are poor, low caste and living in remote rural areas will be taught by lower-quality teachers — an outcome that will reinforce wider inequalities in India.
Sources: Govinda and Josephine (2004); Muralidharan and Sundaraman (2006); Pandey (2006).

 : W H Y G O V E R N A N C E M AT large class sizes and low job status.

Reducing the outflow requires an approach that looks beyond pay to the wider factors affecting the morale of teachers who, it must be remembered, provide a critical public service.
Tackling equity gaps in teacher deployment Average PTRs can mask serious problems in deployment of teachers within a country.

Areas that are remote, poor and home to disadvantaged ethnic, racial or caste groups are often underserved, especially in having their share of experienced teachers.
This is not surprising: where teachers have a choice, they may be unwilling to work in hard-to-reach locations offering poor housing, no water or electricity and few public services, especially if they must also be separated from their spouses.

But the skewed allocation of teachers is a factor in the large equity gaps in access and learning outcomes discussed in Chapter 2.

Urban bias is a systemic problem.


In countries where most teachers come from urban areas and there are few recruits from disadvantaged groups, filling posts serving rural areas and minority groups is often difficult.

Most teachers want to be posted to urban schools for both professional and personal reasons, including the education of their own children.

But the effect in many countries is to reinforce the rural-urban gulf in educational opportunity.
In Pakistan, lack of transport, security problems and poor housing in remote rural areas form a major deterrent to equitable deployment of teachers, especially women (Khan, 2007).

In Namibia, 40% of teachers in rural schools in the north are qualified, compared with 92% in the capital.

Two-thirds of urban teachers in Uganda are qualified, but only half of rural teachers (Bennell and Akyeampong, 2007).

Public policies can create incentives that weaken the urban bias.
One strategy is to change recruitment patterns so that more teachers from underserved areas join the profession.

Another is to provide special incentive packages, such as accelerated career advancement, eligibility for study leave and better housing aimed at drawing teachers towards underserved areas.

Giving teachers bonuses for accepting rural postings is another incentive that can change location preferences.

All these measures are widely, if haphazardly, used in sub-Saharan Africa.
The problem is that the incentives are usually insufficient to outweigh the perceived disadvantages of living in isolated areas (Bennell and Akyeampong, 2007).

Recruitment of teachers from under-represented groups offers several potential benefits.

Most immediately, it helps target increases in teacher supply on the areas where it is most needed.

There are also motivational benefits.


Some evidence suggests that locally recruited teachers in sub-Saharan Africa and South Asia tend to be more satisfied with their jobs, which should help reduce attrition (Bennell and Akyeampong, 2007).

In addition locally recruited teachers are more likely to be familiar with the cultural context in which they are working, with potential benefits for the quality and relevance of their teaching.
Where teachers are part of the community, there is also greater opportunity for closer monitoring by parents, which can increase teacher effort and reduce absenteeism.

In El Salvador, for example, parental oversight of teacher attendance and working hours resulted in increases in the time teachers spent on work (Di Gropello, 2006).

Various strategies have been developed to create incentives for the recruitment of teachers from under-represented groups.
Some countries have set teacher training quotas, including for women and people from ethnic minorities or low castes.

Further incentives can be generated by waiving fees for entry to training on condition that candidates agree to teach for a stipulated period in their local areas.

Devolving authority for teacher hiring to communities or regional and district governments can also facilitate the recruitment of teachers from under-represented groups.
None of this implies that increased local recruitment is straightforward.

Locally recruited teachers are often untrained initially and may have less education than other teachers – some may have completed only primary school.

Increasing the cohort of secondary-school graduates in underserved areas is often a first step in ensuring that teachers from under-represented groups meet required national standards.

This may be difficult in educationally disadvantaged areas, however.
Experience in the Lao People’s Democratic Republic and Cambodia demonstrates the problems that governments face (Box 3.16).

Both countries have succeeded in getting more teachers from ethnic minority groups into teacher training, partly by reducing eligibility requirements.
However, Areas that are remote, poor and home to disadvantaged ethnic, racial or caste groups often lack experienced teachers Box 3.16: Recruiting ethnic minority teachers in the Lao People’s Democratic Republic and Cambodia The Lao People’s Democratic Republic and Cambodia are accelerating progress towards UPE, but teacher shortages in remote areas are holding back their efforts to expand access and overcome marked regional disparities.
In response, authorities in both countries are trying to recruit teachers from ethnic minority groups.

In the Lao People’s Democratic Republic initiatives emerged as a response to the failure of incentives to increase teacher supply in remote, mountainous areas.

Under a previous policy, the government offered supplements equivalent to between 15% and 20% of salary, but these proved insufficient to outweigh teacher preferences for urban postings.
The emphasis has now shifted to a programme aimed at recruiting ethnic minorities into teacher training.

Entry requirements have been adjusted and financial inducements provided.

Numbers passing through the programme have increased.

However, serious administrative problems have been identified.

Some of the students recruited do not actually come from targeted villages but are enrolling to receive the benefits offered.
Language problems in teacher training have resulted in high dropout levels for indigenous minorities.

And many of the students who graduate do not go back to teach in their home area, suggesting that the pull of urban employment is stronger than the incentives on offer to return to the local area to teach.

Public policy in Cambodia has followed a similar trajectory.

In the past, transferring teachers into areas of high need, coupled with incentives for rural hardship postings, met with limited success.
Salaries were too low to support the transfer of teachers lacking an extended family, housing or land in the area.

Special resettlement allowances also proved insufficient.

There has also been an increased emphasis on local recruitment.

Entry requirements for teacher training (set at grade 12 for national recruitment) have been waived in districts and provinces where secondary education is not widely available.
This has opened the door to students from those areas who have only a lower secondary education.

Teacher training scholarships for students from poor and ethnic minority (non-Khmer) backgrounds have helped.

Affirmative action targets have been set for the recruitment of minority students into teacher-training colleges, with one in four places reserved for non-Khmer students.

Evidence suggests that the strategy may be starting to pay dividends, although problems remain.
Teaching posts in many remote areas remain unfilled.

Moreover, it has not been possible either to fill all quota places with ethnic minority students or to prevent abuse of the quota system.

Even so, local recruitment has helped rapidly expand the supply of teachers to isolated rural areas.

Source: Benveniste et al.

(2008).


In Cambodia, local recruitment of ethnic minority teachers is helping tackle shortages in remote areas 1 7 6 they have faced problems in ensuring that trained teachers return to teach in their home areas.

Cambodia’s approach, combining quotas for such groups in teacher training with local recruitment, has been more promising.
Some countries have developed national programmes aimed at overcoming disparities in teacher allocation through financing mechanisms to support teacher recruitment in underserved areas.

One particularly striking example comes from Brazil.

During the 1990s, high levels of inequality in education attainment and achievement in the country were linked to deep disparities in teacher allocation.

The FUNDEF programme used national education financing strategies to change this picture.
Under FUNDEF, a share of subnational tax revenue was pooled and used to supplement spending per student in poor states.

Around 60% of these resources were used to hire and train teachers or to increase teacher salaries.

The highest salary increases were in the poorer states of the north-east with the greatest education needs.

After FUNDEF began in 1998, the percentage of teachers having completed more than a primary education rose sharply, especially in poor areas such as the north-east.
The programme has been associated with sharp increases in school attendance, particularly in the upper grades of basic education (Gordon and Vegas, 2005).

Fragile states affected by conflict face particularly acute problems in teacher allocation.

Restoring education systems is a critical part of post-conflict reconstruction.

Yet teachers may have good reasons for wishing to avoid placement in areas recently or currently affected by security problems.

The experience of Afghanistan is instructive.


The country is showing signs of success in its programmes to improve school access, but large disparities in teacher allocation remain.

Now that the governance system is being rebuilt, the country must ensure that qualified teachers are deployed to the areas where they are most needed.

Bringing community-recruited teachers into the system is one response (Box 3.17).
: W H Y G O V E R N A N C E M AT Local recruitment is not a quick fix for inequalities in teacher deployment.

Training and support programmes also have to be developed and made accessible.

Several countries are using teacher resource centres8 to address this challenge and to break the isolation of teachers in rural areas.
Teacher resource centres offer an alternative to central or regional teacher training, enabling teachers to develop their capacity while remaining in the community.

In India, Kenya, Malawi, Mali and Uganda, teacher resource centres have been an important mechanism for rapid teacher upgrading, accreditation and professional development, and for generating local solutions to local problems (Giordano, 2008; Global March Against Child Labour and International Center on Child Labor and Education, 2006).
According to one review, teachers participating in teacher resource centres report increased professional dialogue and commitment, increased awareness of child-centred teaching methods and increased access to materials and resources.

There is also some evidence of wider benefits, with teacher resource centres reportedly having helped to narrow the achievement gap between urban and rural schools (Chile and Kenya) and to reduce repetition and improve retention (Cambodia).
Nevertheless, teacher resource centres are not universally effective.

In the worst cases, they reproduce many of the problems of national teacher-training programmes.

Often they are underfinanced and training is disconnected from teachers’ and pupils’ real classroom needs (Giordano, 2008).

Teacher deployment patterns are not just a reflection of incentives and teacher preferences.

Weak management capacity and corruption also play a role.
Bribery of politicians and officials by teachers to secure favoured postings is reported to be common in some countries (Hallak and Poisson, 2007).

In a study in Bangladesh, for example, over 40% of secondary school teachers believed that teacher appointment procedures were unfair and that informal payments were needed to secure a post.
Many head teachers also saw promotion and transfer procedures for government teachers as unfair and reported that informal payments were commonly required to secure a transfer (Financial Management Reform Programme, 2005).

Contrary to some widely held views, devolution of authority to parent-teacher associations is not an automatic cure for such practices.
For example, there is evidence in West Africa of school principals and parent-teacher Box 3.17: Teacher deployment in a fragile state: the experience of Afghanistan Given the massive increases in student enrolment taking place in Afghanistan since 2002 and the high numbers of school-age children who remain out of school, rapid teacher training, recruitment and targeted deployment are critical.
The government took the important step of deciding to build a comprehensive system of thirty-eight teacher training colleges in a context where schools have long relied on teachers with little or no professional training.

However, training is a route to increased supply rather than equitable deployment.

Over 20% of teacher-training students — and almost 40% of women students — are in Kabul, the capital.
One way to redress the imbalance is by integrating community schools into the government system and improving their status.

Under the Ministry of Education’s Community-based Education Policy, teachers previously paid by communities on a largely ad hoc basis, often with small cash or in-kind contributions, are being brought onto the government payroll.

Achieving the transition has been a major undertaking.
In collaboration with provincial Ministry of Education officers, a partnership of four non-government organizations called PACE-A (Partnership for Advancing Community Education in Afghanistan) has been collecting from teachers the signatures and photos needed to include them on the payroll.

Source: Kirk (2008).

association chairs appointing relatives or friends as teachers (De Grauwe et al., 2005).
In Rajasthan, India, local recruitment combined with a lack of performance or duty incentives encourages teachers to network with political leaders and local bureaucrats to secure posts and awards (Ramachandran et al., 2005).

As both these cases demonstrate, governance problems associated with teacher recruitment and deployment seldom have simple solutions.

The limits to performance-related pay Teacher pay in most countries is tied not to learning outcomes but to qualifications and years of experience.
The weakness of the link between pay and student learning achievement has prompted some to advocate a shift towards performance-related pay.

Paying teachers for what they deliver rather than their qualifications and years of experience, the argument runs, could create new incentives that might significantly raise learning outcomes while improving motivation and retention among the best teachers (Sander, 2008).
Teacher resource centres can help to make training accessible and break the isolation of teachers in rural areas The ideas behind performance-related pay are not new.

Nor are they restricted to education.

8.

Also known as teacher development centres.
8.

Also known as teacher development centres Linking teacher pay to performance can create perverse incentives, including teachers focussing on the best-performing students C H A P T E R 3 The concept has been widely applied in a variety of public sector reforms.
The broad idea is that less weight should be attached to the fixed salary component of teacher pay (usually linked to qualifications and experience) and more to actual teacher performance payments (linked to student or school results).

The issues at stake are highly contentious, with teacher unions frequently opposing what they see as market-based incentives that are inappropriate to education (Umansky, 2005).
Despite the controversy and the enthusiasm for performance-related pay in some quarters, evidence of the benefits claimed is limited.

One reason is that the measurement issues involved are enormously complex.

Measuring performance is challenging for many reasons, not least because of the difficulty in separating teacher performance from the multitude of other home-based factors, school-based factors and random events that influence learning outcomes.
Another problem with performance-related pay systems is their potential for producing perverse outcomes in at least two areas.

First, they can lead to a focus on the development of a narrow range of subjects and skills needed to pass tests, at the expense of creative thinking.

Second, they can encourage teachers and schools to exclude from tests the children who are least likely to do well (Glewwe et al., 2003).
Box 3.18: Problems in Mongolia’s teacher bonus system Introducing performance-related pay is not a simple administrative matter.

In Mongolia, large bonuses, up to 25% of annual salary or three months’ pay, were introduced in 2006 with the aim of acknowledging outstanding teacher performance.

In the first year of the reform, schools received central funding with which to give bonuses to selected teachers.
In subsequent years schools were to raise their own funds or deduct money from salary supplements for some teachers to reward others.
The idea of bonuses was abandoned a year after its inception, for several reasons: a strongly held belief in social redistribution that prohibits rewarding a few at the expense of others; concerns that the plan would emphasize a hierarchical structure between those who are monitored (teachers) and those who monitor (head teachers); the heavy load of documentation and paperwork that resulted from close and continuous monitoring over the course of a year.

Source: Steiner-Khamsi et al.

(2008).


1 7 8 Relatively few countries have introduced performance-related pay on a large scale.

Moreover, the different contexts in which pilot programmes have been introduced make meaningful cross-country comparison difficult.

In many cases the evidence is inconclusive.


For example, extensive evaluations of experience in the United States and other developed countries do not indicate a clear cause-effect relationship between performance-related pay and teacher performance (Umansky, 2005).

Evidence of perverse incentives comes from several countries.
In Chile a national performance-related pay system, the Sistema Nacional de Evaluación del Desempeño, awards the schools that show the greatest progress in student achievement, giving them a financial bonus for teachers equivalent to about half a month’s salary.

Schools are stratified within regions by socio-economic status and other external factors that affect school performance.

This ensures that competition is among comparable establishments.

However, the design has some inherent flaws.


It rewards schools that are already doing well rather than those that are improving yet still need to do better (Carnoy et al., 2007).

Similar problems have emerged in Mexico.

In this case, a long-standing programme, the Carrera Magisterial, allows teachers to move up a pay level based on assessment of a range of criteria, including their students’ performance.

The approach encourages teachers to focus on the best-performing students (Vegas and Petrow, 2007).
The experience of Chile and Mexico is instructive in a wider sense.

While the introduction of performance-related pay was highly controversial in both countries, the impact of the pay incentives on learning achievement has been minimal.

This is partly because only a small minority of teachers has any real likelihood of receiving a reward in the form of a bonus in Chile or promotion in Mexico (Vegas and Petrow, 2007).
Salary increments for performance have emerged as a popular governance reform in a number of post-socialist countries in Central Asia.

Political and administrative obstacles, however, have often prevented their effective implementation, as Mongolia’s experience demonstrates (Box 3.18).

There is even less experience with performance- related pay in poor countries with very low teacher pay.
Some small-scale randomized experiments have been conducted, mainly through NGOs, with : W H Y G O V E R N A N C E M AT indeterminate results.

Studies in the Indian state of Andhra Pradesh and in Kenya found that student test results were better in schools exposed to performance-related pay than in others.
Both programmes gave teachers a financial bonus if students achieved higher-than-average scores on standardized tests.

However, a key factor behind improved results in both countries was a tendency of teachers to train students for the test, often excluding other aspects of the curriculum.

In Kenya the learning achievement improvement was found to be short-lived – an outcome that raises questions about sustainability.
In addition, teachers receiving incentives were as likely to be absent from school as those receiving none, raising further questions about the strength of incentive effects associated with performance-related pay (Glewwe et al., 2003; Muralidharan and Sundararaman, 2006).

By contrast, evidence from another randomized experiment in India found a positive effect on teacher attendance and learning outcomes.
In this case, what appears to have been important is a mix of close monitoring and financial incentives (Box 3.19).

Monitoring of the intensity carried out in this case, however, is neither feasible nor desirable on a national scale.

The idea that teacher earnings should reward good teaching and not just qualifications and seniority has intuitive appeal.

Yet learning processes are very complex and so is the attribution of improvement in student performance to teachers alone.
This makes it extremely difficult to develop a policy framework that links pay to improved learning outcomes.

Another problem with some performance-related pay proposals is that they take a highly reductionist view of teacher motivation.

Factors such as job satisfaction, status, an ethos of public service and work conditions may have as much bearing on teacher motivation as monetary incentives (Bennell and Akyeampong, 2007).
Monitoring education systems for enhanced quality and equity Broad-based learning and the acquisition of skills defined in national curricula are the ultimate education policy objectives.

Monitoring these qualitative outcomes is more difficult than counting heads.
Yet it is vital for policy-makers on four counts: to chart progress and identify disparities in learning; to influence and monitor policy measures aimed at improving learning (related to teacher Box 3.19: Incentives to reduce absenteeism in India: a randomized experiment Combining teacher incentives with close monitoring of teachers can reduce absenteeism and enhance quality, a randomized experiment in India has shown.
The project selected 60 one-teacher non-formal education centres from among 120 operated by an NGO in villages.

The other centres served as a control group.

Instructors were given a camera with a tamper-proof time and date function, which children were asked to use at the beginning and the end of each school day.

The teacher’s salary was linked to proven hours in school.

Absenteeism fell immediately, from 44% to 24%, in the schools supplied with cameras, while staying the same in the other schools.
The programme also resulted in higher test scores and, one year after the experiment, higher rates of student transition into regular schools.

Source: Duflo et al.

(2007).


training, curriculum development and textbook revision, for example); to determine the allocation of resources to support poorly performing schools; and to provide information to parents and policy- makers, ensuring that schools are held to account for student performance.9 From classroom to system level, the weakness of existing monitoring mechanisms in many countries undermines efforts to address the learning needs of the most disadvantaged schools and students.
Two strategies have been adopted to address this problem: more extensive use of large-scale learning assessments and reform of school supervision services.10 A key motive for large-scale assessments has been to track performance of education systems as a whole, while supervision reforms have aimed to improve monitoring and support quality at school level.

This subsection discusses the role of these two strategies for improving quality and equity.
Learning assessments: more coverage, but weak links to planning Recent growth in the number of large-scale learning assessments indicates the increased emphasis on learning outcomes.

Between 2000 and 2006, around half of all countries conducted at least one national learning assessment (UNESCO, 2007a).
In addition, an increasing number of developing countries are participating in international learning assessments primarily designed for OECD countries, although their involvement remains limited (three sub-Saharan Recent growth in the number of large-scale learning assessments indicates an increased emphasis on learning outcomes 9.
Monitoring is also important in building public confidence in the education system, and giving parents and communities an opportunity to hold schools to account – aspects of which are reviewed earlier in this chapter.

10.

The terms ‘school inspection’ and ‘inspectorate’ are used in many countries Translating lessons from assessments into policy design and implementation remains a pressing challenge 11.
Chapter 2 gives fuller description of learning outcomes from these assessments.

1 8 0 C H A P T E R 3 African countries participated in the most recent round of the PIRLS, PISA or TIMSS assessments – Botswana, Ghana and South Africa).

Regional learning assessments, more explicitly designed to address concerns in developing countries, have expanded.
Thirty-seven African countries participate in SACMEQ and PASEC, and sixteen Latin American countries in LLECE (Lockheed, 2008).11 The limits to assessment have to be recognized: many developing countries have never carried out a countrywide learning assessment and many other have only recently done so (Benavot and Tanner, 2007).

But there has been an exponential increase in the flow of information on learning outcomes.

How countries use information from different types of assessments varies greatly.
At one extreme, yearly census-based test results in Chile are widely disseminated.

Public access to information is seen as a mechanism for holding schools and municipalities to account, informing parental choice and creating competition between schools.

By contrast, in Uruguay assessment results from individual schools are not made public and there are no school rankings.
The government’s stated policy is to use assessments not to create competition in the education system but to inform policies and resource allocation, and guide the targeting of support to teachers (Benveniste, 2002).

These differing approaches are rooted partly in different governance agendas.

Some countries see testing as a mechanism for promoting an agenda that emphasizes competition, choice and public information to hold service providers to account.
Others view testing results as an input to public policy design.

The optimal design is to combine both.

Even where data from assessments are available, it does not follow that they are widely used.

For example, South Africa has seen a proliferation of national and international assessments generated through large investments of human and financial resources.

These assessments have provided a better understanding of how learning occurs by developing some key indicators.
But the use of test results remains limited.

Education authorities seldom use them to inform approaches to equity in addressing the learning needs of students from disadvantaged backgrounds.

Reporting of information to schools as part of a strategy to improve their performance also remains uncommon (Kanjee, forthcoming).

South Africa’s experience is a microcosm of a common problem.
While education policy-makers are equipped with an increasing amount of information, learning assessment data often have a relatively weak impact on policy design.

One reason is weak institutional capacity, reinforced in some cases by institutional segmentation between assessment agencies and education planning.
While the need for information on achievement is widely recognized, translating the lessons that emerge from assessments into policy design and implementation remains a pressing challenge (Postlethwaite and Kellaghan, Forthcoming).

High-stakes testing Most recent national, regional and international learning assessments have been conducted to measure the performance of education systems as a whole.
These sample-based assessments are often described as ‘low-stakes’ because they are not directly linked with incentives for participants (students or schools) to perform well, or with sanctions for those performing badly.

In ‘high- stakes’ assessments, measured outcomes have direct consequences, most commonly for the pupil.

Tests can also serve as accountability measures for schools and teachers, and the results used as the basis for rewards and sanctions.
High-stakes assessment is most frequently associated with consequences for student progression and certification.

It is also used in some cases to inform approaches to performance-related pay for teachers (see above).

Cross-country evidence on the implications for student learning is limited.

Standardized exit examinations at the end of secondary school are the most studied accountability measure.
Findings suggest that students perform significantly better in countries with such exams than in countries lacking them.

On the other hand, this association is variable: taking demographic and socio- economic factors into account, exit exams are positively associated with an increase in average scores for students from both poor and rich households, but students from rich households improve their scores by a greater amount (Schütz et al., 2007).
One implication, then, is that high- stakes testing may reinforce inequalities in learning achievement.

Another problem is that high-stakes testing can have unintended consequences for the quality of education and for : W H Y G O V E R N A N C E M AT weaker students.
Recent evidence on the impact of England’s rigorous testing system shows that the overall rise in test scores since the mid-1990s has been achieved at the price of a narrowed curriculum and extensive time devoted to test preparation.

Particularly worrying is the finding by some researchers that the intensified focus on passing tests has lowered the self-esteem of poorly performing students (Harlen, 2007; Wyse et al., 2008).
Most developing countries have long-standing traditions of high-stakes testing through public examinations.

Results are primarily used for student certification and selection.

Here, too, there have been unintended effects for efficiency and equity.

In some developing countries, such exams have contributed to increased grade repetition and lower levels of transition from primary to secondary school (Kellaghan and Greaney, 2004; N'tchougan-Sonou, 2001).
A study in Kenya, for example, found that the transition rate from grade 6 to grade 7 was reduced partly because poor- performing pupils were discouraged from taking the final primary examination.

The reason: schools’ average scores were made public in league tables and school officials did not want the poorer pupils to pull down their average (Ackers et al., 2001).

As a mechanism for holding schools and teachers to account, high-stakes testing has strengths and weaknesses.
The strengths include the generation of simple and comparable results.

The weaknesses are also results-related.

The assumption is that teachers and schools strive harder when they can work towards standardized goals, with incentives attached to meeting specified targets.

But if the driving concern is to maximize average school scores, underperformers or hard-to-teach children may be viewed as potential liabilities (as in Kenya).
There will be incentives in this case to support the students who are most likely to pass the tests and devote less time to their weaker classmates.

Moreover, where selection to schools is related to socio-economic status, which in turn is closely correlated with performance, rewarding schools for test scores can be tantamount to penalizing the schools that enrol less wealthy students.

Recent international debate on high-stakes testing has been heavily influenced by experience in the United States.
The 2001 No Child Left Behind Act, which was introduced expressly to close equity gaps in learning achievement, uses high-stakes testing as a device aimed at strengthening accountability, extending choice and improving school management (Box 3.20).

There is a great deal Box 3.20: No Child Left Behind in the United States: the jury is still out The No Child Left Behind Act has given a push to high- stakes testing in the United States.
Legislation now requires all states to put in place accountability systems, including mandatory testing in mathematics and reading, annually for all pupils in grades 3 through to 8 (corresponding roughly to ages 8 to 13) and once in secondary school.

Test results are used as the basis for decisions on a range of important questions, such as whether to adopt special improvement measures for a school or district and whether pupils receive subsidized tutoring.
Sustained low performance can result in interventions that range from the replacement of teaching staff to the contracting out of school management to a private operator.

There are obvious difficulties associated with evaluating outcomes at this relatively early stage.

One of the most comprehensive studies so far concludes that average learning achievements in mathematics and reading improved between 2002-2006.
However, it notes that changes in this area cannot be directly attributed to the 2001 legislation because implementation has coincided with other national and state level programmes.

The study finds that achievement gaps between ethnic groups have narrowed in some states, though once again stopping short of attribution.

Moreover, in twenty-four of thirty-eight states with comparable data, differences in reading test scores remained unchanged between white and African-American pupils.
Given that closing equity gaps is one of the reform’s key objectives, this evidence suggests that it may be under-performing.

The No Child Left Behind legislation has generated extensive debate in the United States.

For instance, 144 major education and labour organizations in 2008 together called for major corrections to make the Act fairer and more effective.

They questioned the overwhelming reliance on standardized test and advocated for wider measures to hold states and school districts accountable.
More broadly, critics of the Act suggest that the tests are too narrowly focused, encourage ‘teaching to the test’ and exclude low-scoring children to boost test results.

They also say implementation of the Act is underfunded.

Sources: Center on Education Policy (2007); Forum on Educational Accountability (2008).
In the United States, high-stakes testing has yielded uncertain outcomes of controversy surrounding the record to date.

However, the evidence does not suggest that the legislation has been an unequivocal success story.
Using monitoring to improve policy-making Whatever the problems associated with high-stakes testing, information from learning assessments can play a critical role in informing policy design.

The following examples identify some key areas: Defining minimum learning standards.

In Lesotho and Sri Lanka, national learning assessments have been used to establish minimum learning standards against which pupils’ achievements are monitored (Greaney and Kellaghan, 2008).
Kenya has used SACMEQ results to set benchmarks for classroom facilities, such as textbooks and desks per pupil (Nzomo and Makuwa, 2006).

Informing curriculum reform.

An evaluation of the value of participating in PIRLS and TIMSS found that twenty out of twenty-four low- and middle-income countries participating indicated that taking part in the assessments had influenced changes in curriculum.

In Romania, for instance, poor results in TIMSS were a ‘wake-up call’ spurring curriculum changes.
Topics were added to the mathematics curriculum, an integrated science curriculum was approved, new teacher guides in science were developed for some grades, and several new chemistry and mathematics textbooks were written (Gilmore, 2005).

Reviewing policy.

High repetition rates have been cause for concern in Senegal.

Data from the PASEC learning assessments from 1995 to 2000 were used to shed light on the effects of grade repetition for primary school outcomes.
The results consistently showed that, on average, Senegalese students who repeated a grade did not perform better than those who did not repeat, taking aspects such as family background, school environment and initial achievement levels into account.

This gave further weight to the education ministry’s desire to reduce repetition.

As a result, the government has prohibited repetition for some primary grades since 2003 (Bernard and Michaelowa, 2006).

Contributing to education planning and reform.


Results from the SACMEQ cross-national learning assessments in sub-Saharan Africa have been used in national reviews and commissions on the status of the education systems in Mauritius, Namibia, Zambia, Zanzibar (United Republic of Tanzania) and Zimbabwe.

These analyses of learning conditions have played a role in formulation of sector or subsector reform programmes (Greaney and Kellaghan, 2008).
Information from learning assessments can also play an important role in addressing equity goals.

One example comes from Viet Nam.

In 2001 the country conducted a national grade 5 learning assessment in mathematics and reading.

The results provided a basis for understanding the problems and identifying ways to improve education quality in some of the country’s most deprived areas.
After controlling for socio-economic background and school location, the assessment showed strong correlations between pupil achievements and both teacher qualifications and availability of school resources.

In 2003 Viet Nam adopted new regulations for primary schools, specifying minimum levels for several education inputs, including learning materials, school infrastructure, teacher qualifications and in-service training.
By 2005 the concerted efforts to raise the quality of the learning environment had begun to show results, with reduced gaps in quality inputs between the poorest and richest districts (Swinkels and Turk, 2006; World Bank, 2005f).

The experience in Viet Nam demonstrates a strong link between assessment and policy design.

Such linkage is not always evident.

By definition, ‘low- stakes’ assessments generate weak incentives to change.
A survey covering Ethiopia, Malawi, the Niger, Nigeria, South Africa and Uganda showed that only one country had used the findings from assessment exercises as a basis for allocating resources to schools and only two had undertaken campaigns to inform teachers or schools about the assessment process (Kellaghan and Greaney, 2004).

Limited public awareness is one factor that may have weakened incentives in these cases: nowhere were the assessment results subject to parliamentary debate.
Good quality assessment systems are no guarantee of effective integration into public policy.

Institutional structures and capacity are also important.

Bolivia, for example, has an evaluation system called SIMECAL that is of very high standard.
It uses nationally developed test items In Viet Nam, using information from learning assessments has helped to reduce quality gaps between the poorest and the richest districts : W H Y G O V E R N A N C E M AT that reflect Bolivia’s culture in both Spanish and indigenous languages.

The SIMECAL staff has high levels of expertise.


Yet despite the technical excellence, inadequate funding has resulted in sporadic and irregular testing.

Moreover, weak links between SIMECAL evaluations and policy management units in the education ministry has meant that policy development in several critical areas, from pedagogy to curriculum development and teacher training, has not been tied to assessment results (World Bank, 2006a).

What conditions can facilitate better use of assessment results?
An environment promoting close interaction between the various actors in the education system is important.

So is an overall focus on supporting teachers’ professional development.

Recent practices in Uruguay are instructive (Box 3.21).

As concern has shifted towards the poor quality of education in many countries, monitoring is emerging as a central governance theme.

The experiences of two countries at the forefront of education reform are instructive.
In post-apartheid South Africa, the school management and public financing systems have been transformed to expand access and address equity concerns.

Yet the record on quality gains has been disappointing.

A national assessment in 2004 revealed that learner performance on grade 6 tasks was worse than that on grade 3 tasks in 2001 and that proficiency levels were low in absolute terms (only 40% of answers were correct).
In Chile, sweeping reforms during the 1990s produced disappointing outcomes with respect to quality.

National assessments point to a very slow rate of improvement and international assessments suggest that Chile has not overtaken developing countries with more centralized systems.
One key problem identified in both countries has been the lack of an effective pedagogical management system – extending from the setting of curriculum standards to supervision, information management, school inspection and support, and in-service training – to address problems identified in monitoring exercises (Crouch and Winkler, 2007).

Box 3.21: Assessments inform teacher support in Uruguay’s schools Uruguay has managed to improve learning outcomes rapidly in recent years.
Its quality improvement efforts have been informed by sample-based assessments aimed at strengthening pedagogical management in schools.

By combining the assessments with cluster-based teacher training and support, spread over the whole school year, education authorities have turned information into policy practice.

Evidence suggests that learning outcomes improved in certain grades by 30% over six years.

Special measures have been taken to improve the functioning of weaker schools.
Important moves to redress learning disparities have included targeting financial resources primarily on the basis of poverty rather than test results and using test results to provide targeted support to teachers in weaker schools and districts.

Sources: Crouch and Winkler (2007); Ravela (2005).

an essential aspect of monitoring, not only to check teacher and school performance but also to identify and support needed quality improvements.
External assessment can be reinforced by school- level assessment as part of broader quality improvement strategies.

This is an area in which South Africa’s District Development Support Programme has been attempting to strengthen national commitment to equity.

The programme aims to improve education quality in grades 1 to 9 at the weakest schools.

Since its inception in 2000, the programme has focused on improving classroom learning, and school and district management.
To improve classroom assessment practices, resource materials have been developed, and extensive training and support provided to teachers.

External supervision and learning assessments further underpin these school-level efforts.

Evaluations of the programme are largely positive.


They suggest that a gain in learning achievements between 2000 and 2003 resulted partly from the increased supervisory support available to schools and teachers, and partly from increased use of classroom assessments (Schollar, 2006).12 Combining national assessment with school-level monitoring To understand the realities facing schools, information from international, regional and national assessments needs to be combined with monitoring at school level.
School supervision is As the only direct institutional link between classrooms and education ministries, school supervision plays a crucial role in education system management.

School visits can allow supervisors not only to support and monitor implementation of official policies but also to bring school realities to Linking classrooms and education ministries, school supervision plays a crucial role in education system management 12.
The District Development Support Programme ended in 2003, and was succeeded by the Integrated Education Programme the attention of policy-makers.

School supervision systems in developing countries are under- researched, though anecdotal evidence suggests they are overstretched.
With demanding mandates, and limited human and financial resources, few developing countries have supervision services that are fit for the task at hand.

However, in their quest for quality education, many countries have changed and clarified the role and structure of supervision in recent years (De Grauwe, 2008).

The experience of Uganda shows that supervision can be used to foster more cooperative approaches aimed at raising learning achievement and reducing inequality (Box 3.22).
The very large gaps in learning outcomes between schools in many developing countries mirror other inequalities in education and in society at large.

Supervision has a key role to play in closing these gaps.

What weak schools need is not just inspection but also consistent pedagogical support, including regular visits by support-oriented supervisors.
This implies radical institutional change, with supervisors finding the right balance between allowing schools sufficient autonomy and intervening to identify performance problems (De Grauwe, 2008).

What weak schools need is not just inspection but also consistent pedagogical support Box 3.22: Reforming school supervision in Uganda Uganda’s recent strides towards improving the quality of education have included a strengthened inspection service.
After a slow start, the Education Standards Agency began operating in 2001, replacing an outdated inspectorate in the education ministry.

Efforts have been made to tailor the service to what is feasible with limited resources.

Where the former body covered such disparate areas as policy, curriculum development, exams, troubleshooting, staff development and independent school registration, the new one focuses on school visits.
The inspection service reform drew on experience in Masindi, one of Uganda’s poorest districts, with many internally displaced families from conflict-affected northern Uganda and refugees from neighbouring countries.

In 2000 Masindi scored among the lowest districts in the national primary-school leaving exam.
An extensive district-based programme of school improvement, combining internal school evaluation and external district-based supervision, produced remarkable results: Masindi went from one of the poorest-performing districts in 2000 to one of the top five in 2007.

Know-how from Masindi was fed into the revised national inspection approach, which was subject to a national consultation in 2005.

Sources: Penny et al.

(2008); Roebuck (2007).


1 8 4 One model based on a more collaborative and supportive approach has been developed in Chile, where each supervisor visits a limited number of carefully selected schools, giving priority to the weakest ones.

To improve teaching and school functioning, school plans and projects are developed in collaboration with the supervisor.

Learning assessments allow the education ministry and the supervision service to know which schools to focus on.
The most intricate challenge has been changing the culture of the supervision service from one of control over many schools to one of supporting a few selected schools.

That challenge has been addressed through training, new job descriptions with removal of all control functions and the elaboration of new working tools.

Supervisors have found it difficult nonetheless to abandon their tradition of control and to adopt a support-oriented approach (De Grauwe, 2008).
Conclusion Delivering high-quality education for all will require far-reaching governance reforms in the areas covered by this section.

There are no ready-made solutions to the problems identified.

Clearly, governments need to recognize that declines in teacher pay and conditions have the potential to damage morale, quite apart from reducing quality in recruitment and the quantity of applicants seeking to join the profession.
It is important that governments recognize the potential risks for equity and education quality of scaling-up contract teacher recruitment.

In the case of teacher allocation, far more emphasis has to be placed on the development of incentives for greater equity, in some cases through a stronger commitment to the training and local recruitment of teachers from marginalized groups and areas.

Learning assessments provide a valuable and increasing flow of information.
That information could – and should – be used to identify the factors behind low levels of learning achievement and to map disparities in achievement.

The limits of high-stakes testing in strengthening accountability, performance and equity have to be recognized.

At the same time, it is important for governments to reinforce the institutional links between assessment exercises, on the one hand, and public policy development, monitoring practices and school supervision on the other.
: W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k An integrated approach to education and poverty reduction: the missing link Introduction Accelerated progress towards EFA requires more than increased public investment, more and better equipped schools, and an increase in the number of well-trained and motivated teachers.
It also requires progress towards poverty reduction and a reduction in social disparities.

Education reforms can make an important contribution in both areas.

But good policies in the education sector cannot compensate for weak policies on poverty reduction or for the failure of political leaders to tackle extreme inequality.

Achieving EFA requires an integrated approach to planning for education and poverty reduction.
When they met in Dakar, governments recognized that their ambition could not be achieved through education reform alone.

That is why they called for EFA policies to be promoted within ‘a sustainable and well-integrated sector framework clearly linked to poverty elimination and development strategies’.

The engagement and participation of civil society in the formulation, implementation and monitoring of strategies was seen as an important means to this end.
This section asks whether governments have acted on their Dakar commitment.

More specifically, it examines how education has been integrated into wider strategies for overcoming poverty and inequality.

The issues involved are highly political.

They relate directly to the power relationships that sustain social inequities.

Chapter 2 documents the barriers to EFA created by disparities based on wealth, gender, ethnicity and wider disadvantages.
In principle, the Dakar Framework commits governments to rapid removal of these barriers.

Yet the disparities themselves indicate past and present failure to address the underlying causes of unequal educational opportunities.

In short, government tolerance of extreme inequality has been, and remains, part of the problem.

This section explores the link between education planning and wider policies for combating poverty and inequality.
It focuses on the treatment of education within poverty reduction strategy papers (PRSPs).

These documents set out governments’ broad development priorities and provide a framework for international cooperation.

Clearly, PRSPs are not the only measure of policy coherence.

But they do reveal something important about the degree to which education is being integrated into the wider public policies that shape prospects for attaining the Dakar goals.
PRSPs are a vital link in the governance chain for education.

Apart from providing a broad framework for poverty reduction policies, they represent a vehicle for dialogue between a wide range of actors.

Planning is about more than producing technical documents.

National plans provide an opportunity for governments to set out their goals and their strategies for achieving them.

They define a purpose against which governments can be held to account.
Hence, plans – and planning processes – are a vital part of the governance architecture.

The stated intent is to give civil society opportunities to shape priorities and strategies, in the expectation that this will assure greater responsiveness to the marginalized.

But have PRSPs provided a coherent framework and facilitated real dialogue?

The central message of this section is that education remains poorly integrated into poverty reduction planning.
With some exceptions, governments have not acted on their commitments.

PRSPs in general fail to articulate clear strategies either for overcoming poverty-related barriers to education or for reducing inequalities.

Most take a narrow and reductionist approach to education, rarely reflecting the broad EFA agenda.

There are promising experiences that PRSPs could draw on.
The development of integrated social protection programmes in several countries shows that interventions aimed at tackling social inequality and reducing vulnerability have large potential benefits for education.

The record on participation in planning processes is more encouraging: PRSPs have widened the space for dialogue with civil society.
However, participation and ‘voice’ are not the same as influence and outcome – and there have been distinct limits to the policy influence of the poor and marginalized.

Education planning: stronger, but still not strong enough Since the Dakar Forum many countries have strengthened their education planning capabilities.

The 2008 Report provided an overview of achievements in this area.
It highlighted the greater Education remains poorly integrated into poverty reduction planning Cross-sectoral planning is characterized by fragmentation and weak political leadership 13.

Boxes on Ethiopia, Nepal and the United Republic of Tanzania in Chapter 2 further illustrate the benefits of SWAps in addressing equity challenges.
1 8 6 C H A P T E R 3 clarity evident in many national education plans with respect to the formulation of clear objectives and time-bound targets.

Strategic priorities are also more apparent: UPE is a well-defined core priority and there is a strengthened focus on gender parity.

constraints faced by marginalized groups (UNESCO-IIEP, 2006).

This is unhelpful because overcoming marginalization requires the delineation of practical strategies within a particular context.
The development of sector-wide approaches (SWAps) has played an important role in strengthening national education planning.

Experience with SWAps over the past decade indicates they are potentially more effective than previous planning approaches in addressing education quality and equity problems.

Sri Lanka provides an illustration (Box 3.23).13 While much has been achieved in education planning, continued systemic challenges remain in three areas.

One of these is finance.


Education plans may set out medium-term targets but they rarely include plausible cost estimates for achieving them.

That partly explains why education goals are commonly absent from the medium-term financial frameworks that shape real budget allocations (FTI Secretariat, 2007b).

One lesson from the lengthy and not particularly encouraging history of goal-setting in development is that targets that are not backed by finance are seldom attained.
Another weakness has been the tendency of planning documents to follow a highly generalized blueprint.

One recent assessment of forty-five national education plans found remarkable similarity in policy approach, with limited attention paid to social and political context or to the Cross-sectoral planning weaknesses constitute another area for concern.
Education planners know they are not operating in an insulated sector; they recognize the enormous importance of poverty, public health, child nutrition, social marginalization and other factors in shaping prospects for education.

Yet the cross-sectoral planning processes needed to address these problems continue to be characterized by high levels of fragmentation and weak political leadership.

The standard education-plan blueprint also tends to downplay the importance of progress in some key areas.
For example, early childhood education, literacy and non-formal education are often EFA ‘orphans’ (UNESCO, 2007a).

Poverty reduction strategies: new generation, old problems When they were launched in 2000, PRSPs were seen as a bold innovation in development cooperation.

The aim was to provide a comprehensive integrated framework for placing poverty reduction strategies at the centre of macroeconomic policy.
Each country was expected to identify clear goals, which would be reflected in short-term budget allocations and long-term financial planning.

In line with a broader shift away Box 3.23: Strengthening equity through sector-wide approaches: Sri Lanka’s experience Sri Lanka has a long-standing commitment to equity in education planning.

Even so, persistent widespread poverty (estimated to affect one-quarter of the population) and the impact of ethnic conflict in the north and east remain challenges.
The devastating effects of the 2004 tsunami are also still being felt.

The development of the Education Sector Development Framework and Programme for 2006–2010 has helped strengthen Sri Lanka’s approach to tackling inequality.

This SWAp recognizes that equity is a matter not just of access but also of quality and resourcing, and it attempts to mainstream various aspects of equity from the outset.
It provides a clear strategic approach and monitoring framework, linked to a medium-term budgetary framework allowing resources to be aimed at the most disadvantaged schools.

One important aspect is that the SWAp also sets quantifiable goals for reducing disparities.
Equity- based targets extend from the number of disabled students enrolled in regular schools, the number of special education centres and centres for street children, and the presence of professionally qualified teachers in difficult schools to learning outcomes by school, district, urban/rural area and gender.

Source: Jayaweera and Gunawardena (2007).


 : W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k from loan conditionality and project-based approaches, PRSPs defined a new set of core principles.
They were to be country owned, developed through dialogue with civil society, results oriented, long term, and comprehensive and multidimensional in their approach to poverty.

PRSPs remain a core poverty reduction planning document.

While some commentators downplay their importance in public policy, PRSPs play a key role in setting and reflecting national priorities and strategies.
They also define the terms and broad goals of the ‘aid partnership’ between developing country governments and aid donors – an issue explored in Chapter 4.

Fifty-four countries now have operational PRSPs.

Most are low-income countries, twenty-eight of them in sub-Saharan Africa.

It would be a mistake to overstate the significance of PRSPs or to exaggerate the level of country ownership they imply.

Donors retain a strong influence in framing PRSP priorities in many countries.
However, given their scope and the intensity of the dialogue surrounding their development, PRSPs provide important insights into the place of education in national poverty reduction processes.

The implementation period for second-generation PRSPs is now well advanced.

Have the lessons of the first generation been absorbed?

Has there been a significant improvement in quality?

In some countries both these questions can be answered in the affirmative.
In Uganda, the first country to adopt a PRSP, the PRSP built on the existing Poverty Eradication Action Plan to set out well- defined goals and budget commitments aimed at accelerating progress in health, education and the development of rural infrastructure, with positive results (see Chapter 4).

These strong foundations have been built upon, facilitating a marked increase in programme-based aid from many donors.
Moreover, in countries with well- developed education sector plans, there is evidence that education planning and poverty reduction strategies are mutually supportive.

For example, the United Republic of Tanzania has steadily strengthened its institutional capacity, resulting in better integration of education within a poverty reduction framework (Box 3.24).

Wider experience is less encouraging.


Cross- country evidence suggests that second-generation PRSPs suffer from many of the same problems as their predecessors.

They continue to focus on Box 3.24: Building capacity for pro-poor reform in the United Republic of Tanzania The relationship between education and poverty has been a policy focus in the United Republic of Tanzania since independence.

It was central to President Nyerere’s policy of Education for Self-Reliance.
The PRSP process has built on this existing political commitment to strengthen institutional capacity for pro-poor reform.

The country’s first PRSP in 2000 was relatively narrow, focusing on macroeconomic policy and key social investments.

However, as implementation unfolded, the strategy was steadily broadened.

Sector strategies were further developed and incorporated into the overall development agenda.
With the second PRSP, the 2005 National Strategy for Growth and Reduction of Poverty, the medium-term development programme was clearly articulated across a broad range of sectors, incorporated cross-cutting issues and was linked to medium-term countrywide goals.
Attention within the education sector has also broadened: while the first plan focused on primary schooling, the second PRSP includes attention to secondary education, in accordance with priorities set in the country’s Secondary Education Sector Development Plan.

Source: Wedgwood (2007); World Bank and IMF (2005).

a narrow range of education goals and targets, often limited to those associated with the MDGs.

Attainment of UPE heavily outweighs wider education goals in priority-setting.
Moreover, surprisingly little attention has been paid to the interaction between deprivation in education and other areas in explaining the intergenerational transmission of poverty.

Detailed evidence relating to the types of policies that might break the vicious cycle of education deprivation and poverty is in similarly short supply (Rose and Dyer, 2006).

As a result, many PRSPs fail in their core purpose.
There is also little evidence to suggest that the development of second-generation PRSPs has helped break down the fragmentation in planning between education and other line ministries.

Such fragmentation has real consequences for poor and vulnerable people – and for progress towards the goals set in the Dakar Framework for Action.
In exploring the link between education planning and poverty reduction, this EFA Global Monitoring Reporthas carried out a detailed review of eighteen second-generation PRSPs.14 Part of the aim was to examine whether there has been a change from the approaches set out in the first PRSPs.

In particular, the review considered whether the latest PRSPs 14.
The eighteen countries with two PRSPs included in the review are: Burkina Faso, Cambodia, Ethiopia, the Gambia, Ghana, Guinea, Madagascar, Malawi, Mali, Mauritania, Mozambique, Nicaragua, Rwanda, Senegal, the United Republic of Tanzania, Uganda, Viet Nam and Zambia.

Table 3.10 at the end of this Chapter presents a selected summary of this review.
The narrow focus on primary education has eclipsed other EFA goals and the broader development agenda 1 8 8 C H A P T E R 3 were less prone to blueprint approaches and more geared towards addressing the underlying causes of disparity in education.

Table 3.10, at the end of this chapter, summarizes some of the key findings.

The conclusions to emerge from the review are not encouraging.
They point to a broad failure on the part of governments and donors to articulate a more integrated approach to education planning.

With some exceptions, PRSPs also downplay the issues raised by extreme inequalities in opportunity.
Four areas stand out as meriting an urgent rethink of current approaches: the weak link with the EFA agenda; problems in defining credible equity-based targets; the separation of education from broader governance reforms; limited attention to wider drivers of education disadvantage.

Weak link to the EFA agenda The point of reference for most first-generation PRSPs was the MDGs and associated targets for 2015 (Caillods and Hallak, 2004).

This focus appears to have strengthened over time.


One practical consequence is that most PRSPs attach far more weight to the quantitative target of UPE by 2015 than to other EFA goals.

Where education equity is identified as needing attention, it is almost exclusively associated with strategies for improving access to primary schooling.

While UPE is undeniably important, this is a highly limited approach.

The need for particular attention to disadvantaged learners is rarely recognized in PRSPs.
Meanwhile, wider EFA goals are either downplayed or separated from a broader poverty reduction agenda.

To the extent that the eighteen second-generation PRSPs mention ECCE at all, it is still frequently seen as a means of improving learning in primary schools rather than as a source of progress in child health, nutrition and cognitive development with potential benefits for primary and secondary education – and for wider opportunities (see Chapter 2).
Strategies concerning technical and vocational education and training (TVET) and skills development are often considered principally in relation to the role of the private sector.

The distribution of benefits in TVET provision is rarely considered – and the linkage to poverty reduction is often vague.

Other intersectoral links between education and employment strategies are barely visible.

Literacy is another part of the EFA agenda for which vital intersectoral links are absent.
This is surprising in view of the critical role of literacy in overcoming poverty, inequality and political marginalization.

One consequence of the overwhelming weight attached to primary education is the neglect of secondary schooling.

This is counterproductive at many levels.

As primary school completion rates increase, demand for secondary school places will grow.
Indeed, improving access to secondary school is one of the conditions in many countries for creating incentives to complete primary education.

Some recent PRSPs pay more attention to equity considerations at secondary level, although the focus is primarily on building schools (Table 3.10).

The barriers facing poor households in getting children into and through secondary school seldom figure in PRSPs.
This is despite the fact that public policies in this area can have a powerful impact on gender equity – as witnessed by the experience of Bangladesh (see Chapter 2).

Fragmentation is at the heart of many of these problems.

To take one example, progress in literacy requires coordination across a wide range of government bodies.

PRSPs rarely acknowledge this, even though most successful policies explicitly address the problem of institutional fragmentation.
Madagascar’s programme on non-formal education is an example.

It is based on cooperation between different parts of government and various United Nations agencies, and integrates literacy into several specialized areas of development.

The programme is noted for contributing to a ‘strong literacy lens’ in the national PRSP (UNESCO, 2008, p. 10, cited in Robinson-Pant, 2008).

Such experience is, however, extremely rare.


Problems in target-setting For targets to be meaningful guides to policy, they must be both credible and consistent.

Those set in many PRSPs are neither.


For example, Senegal aims to achieve a primary school NER of 90% according to an MDG follow-up document, while its PRSP puts the objective for this indicator at 98%; the United Republic of Tanzania sets a target of primary school NER at 99% by 2010, but aims for only 30% of orphans and vulnerable children enrolled or having completed primary education by that year (UNESCO-IIEP, 2006).
Such inconsistencies send confused signals to budget planners and other policy-makers involved in the development of national strategies : W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k Another concern is that few countries provide specific targets to enable monitoring of equity in education (Figure 3.8).
Among the eighteen countries with two PRSPs, only Nicaragua, in its first PRSP, included a poverty-disaggregated education indicator; the United Republic of Tanzania includes a focus on orphans and vulnerable children in its second PRSP; and, most unusually, both PRSPs of Viet Nam include an indicator related to ethnicity, linked with the attention to strategies to provide support within the education sector and beyond.

Target-setting is stronger with respect to gender (see below).
This may be due to the attention generated by virtue of the MDGs.

Even so, six of the eighteen countries still do not include gender- disaggregated targets in their second PRSP.

Where equity targets are set, they invariably address access rather than learning, revealing a limited focus on education quality.

There is no discernible improvement between the first and second PRSPs in this area.
The mismatch between targets, strategies and financing commitments is another area in which progress has been limited.

One detailed review of four second-generation PRSPs identified problems at several levels.

In Ghana and Nepal budgets were not aligned with planned activities.
Cambodia and Ethiopia manifested a clear mismatch between stated planning intentions and budgetary feasibility; a World Bank-IMF assessment found that in Ethiopia the assumptions of economic growth underpinning national financing projections were unrealistic (Giffard-Lindsay, 2008).

A separation between education strategies and governance reform Many PRSPs emphasize the importance of governance reform, often presenting it as a separate pillar of poverty reduction.
Governance reform has also become increasingly prominent in education sector planning, where it commonly reflects strategies on the broader governance agenda, particularly decentralization and participation.

Since governance strategies, such as decentralization, usually originate outside the education sector, PRSPs provide an opportunity to strengthen the linkage between education and broader governance reform.
Governance reform is seldom neutral in its implications for people who are poor, marginalized and disadvantaged in education.

In principle, the PRSP process could be used to look at how emerging approaches to governance might Figure 3.8: Education equity-associated targets in eighteen PRSPs s e i r t n u o c f o r e b m u N 12 10 8 6 4 2 0 PRSP 1 PRSP 2 PRSP 1 PRSP 2 Gender Location PRSP 1 PRSP 2 Special needs PRSP 1 PRSP 2 PRSP 1 PRSP 2 PRSP 1 PRSP 2 Poverty Ethnicity OVC1 1.
Orphans and vulnerable children.

Sources: See Table 3.10. help – or hinder – efforts to reduce disparities in education.

In practice, the opportunity for strengthening coherence is rarely pursued.

One evaluation of seventeen PRSPs identified a marked bias towards technocratic planning approaches (Grant and Marcus, 2006).
One consequence – ironically, given the nature of the documents – is that the implications of governance reform for distribution and poverty reduction are largely ignored (Grindle, 2004).

The treatment of governance suffers from wider problems.

One aim of PRSPs was to move away from development blueprints and focus on country-specific problems.

Yet many PRSPs reflect a blueprint approach to governance.
This is apparent from the education governance agenda set out in the eighteen second-generation PRSPs reviewed by this Report.

The review identified decentralization and participation as common PRSP themes in education.

However, the governance reforms in education were typically delinked from the wider governance agenda, with scant regard directed to their implications for equity in education.

As shown earlier in this chapter, financial decentralization can have a major impact on equity.
Yet PRSPs seldom consider the potentially negative outcomes of devolving finance to subnational government.

In terms of the education sector itself, the governance priorities in PRSPs closely resemble those frequently provided in education planning documents.

A recent review of forty-five education plans identifies three recurring themes.
First, Many PRSPs tend to be technocratic blueprints that separate education from broader governance reform Real progress in education depends on addressing the underlying causes of poverty and inequality outside the school 1 9 0 C H A P T E R 3 decentralization, accompanied by aspirations for grass-roots participation, features prominently in thirty-six plans, including all those in Latin America and in South and West Asia, and all but one in sub-Saharan Africa (Zimbabwe being the exception).
The second theme, school-based management and school autonomy, figures in seventeen plans, most notably in Latin America.

The third theme, appearing in thirty plans, is increased recourse to and support for private providers, particularly in South and West Asia and in sub-Saharan Africa (UNESCO-IIEP, 2006).

Apart from this striking uniformity, some obvious equity-related questions arise from the governance content of PRSPs.
How will decentralization be pursued without widening financing gaps between richer and poorer regions?

If authority is devolved to regional and local governments and schools, what measures will be taken to facilitate participation by marginalized groups?

If the private sector is to play an expanded role, what regulatory measures will be put in place to prevent the development of a two-tier system and to keep poor households from being priced out of provision?
How will the education ministry fulfil its mandate of assuring EFA if it lacks control over a wide variety of private providers and no institutional mechanism for monitoring them exists?

These are some of the challenges raised in earlier sections of the chapter that PRSPs have the potential to address – but they are not doing so.

The experience of Nepal draws attention to the importance of country-specific circumstances.
As Chapter 2 notes, Nepal has made great strides in education planning within a sector-wide framework, resulting in improvements in access and equity.

However, governance challenges remain.


Drawing up uniform ‘good governance’ blueprints is a simple enough exercise but, under the conditions prevailing in Nepal, decentralization and devolution can have adverse consequences linked to a lack of capacity or imbalances in political power and can as a result have the effect of disadvantaging certain groups (Box 3.25).

Education missing in cross-sectoral approaches Recognition that poverty is multidimensional is at the heart of the PRSP concept.
Yet strategies for tackling multidimensional poverty are often conspicuous by their absence.

An earlier review Box 3.25: Decentralization in Nepal: a difficult journey Central to the education strategies in Nepal’s PRSP (which corresponds to its tenth national plan, 2002–2007), and education planning in the country more generally, is the devolution of school management, including teacher recruitment, to communities.
Initiatives are being funded through District Development Councils but implementation has not been straightforward for a number of reasons: Line ministries’ reluctance to relinquish control of budgets and programmes is stalling the process.

Political uncertainty, resistance from some groups, security constraints and weak monitoring have also hampered implementation.

Many schools lack the necessary financial and technical capacity.
Parents have trouble judging school quality and influencing government decisions, which affects school management committee activities and weakens accountability by government decision-makers.

Because the central government handed down a fixed programme framework and budget allocation, communities feel burdened rather than empowered.

Sources: Acharya (2007); International Development Association and IMF (2006); Nepal National Planning Commission (2005).
of PRSPs carried out in 2003 found that the education component was little more than a ‘copy-summary’ of education ministry plans.

More broadly, education sector planning was weakly integrated into poverty reduction strategy formulation and, to an even greater degree, into budget planning (Caillods and Hallak, 2004).

One more recent review finds that PRSPs continue to present a summary of education plans (Giffard- Lindsay, 2008).
While this has the advantage of ensuring that priorities developed in the education sector are aligned with those in PRSPs, it means the potential of PRSPs to address causes of education disadvantage originating outside the sector is not being realized.

This is arguably the most serious of all PRSP failings.
While education policy can make a real difference in extending opportunity, progress in education depends critically on addressing the underlying causes of poverty and inequality outside of the school.
: W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k The problem can be illustrated by reference to six areas highlighted in Chapter 2, in which initiatives outside education are critical to EFA progress: The relevant conclusion that can be drawn for PRSPs is that what happens in employment is an education issue.
tackling gender equality; reducing child malnutrition; responding to HIV/AIDS; addressing disability; overcoming marginalization; responding to problems associated with conflict.

Gender equality features less prominently than gender parity.Gender is more visible in PRSPs than other dimensions of education inequity.

Attention to gender parity has been growing: twelve of the eighteen countries in this Report’s analysis have a second PRSP that includes gender-disaggregated targets (Figure 3.8).
There are also promising signs that some of these countries’ strategies now go beyond targeting headcount parity in school and are seeking to address wider issues of inequality, such as violence and abuse in schools (Table 3.10).

Nevertheless, gender equity in PRSPs focuses on improving girls’ access to education.

This narrow approach can be traced to education plans and, to some degree, the MDG framework.
A review of twenty-eight education plans by the EFA Fast Track Initiative (FTI) showed that half lacked a strategy for girls’ education.

Where ‘strategies’ were included, they took the form of a list of unprioritized interventions (FTI Secretariat, 2007b).

Restricted approaches to gender have important analytical and wider policy consequences.

Consider the interface between female education and the position of women in labour markets.
In Bangladesh, a range of education policies – including increased spending, stipends for girls’ secondary education and recruitment of more female teachers – have played an important role in strengthening gender parity.

However, one of the most critical drivers of change has been the income and empowerment effects associated with mass female employment in the garment industry (Hossain, 2007; Schuler, 2007).
In this context, it could be argued that change in education has been driven to a large degree by changes in employment and labour markets.

From a policy perspective, one conclusion might be that education planning should consider the potential benefits of policy interventions in areas that shape women’s lives and aspirations, including the strengthening of employment rights and minimum wage provision.
Malnourished children.EFA cannot be achieved while mass childhood malnutrition continues at current levels (Chapter 2).

In countries where stunting affects 30% to 40% of the population, the goal of UPE by 2015 is out of reach.

This is a challenge that cannot be addressed through compartmentalized policies.

Achieving breakthroughs requires secure access to adequate food, a sanitary environment, adequate health services and education.
It also requires political commitment from a variety of sectors, including agriculture, local government, health, water and sanitation, environment, public works and education, as well as links with finance, economic planning and justice.

Most PRSPs point to a combination of neglect and highly fragmented approaches to malnutrition (Grant and Marcus, 2006; Shekar and Lee, 2006).
The neglect is related to the insufficient attention that has been directed towards malnutrition under the MDG framework, which often guides priorities in PRSPs.

In turn, this problem can be traced to the absence of a visible constituency in a position to put malnutrition on the political agenda (Benson, 2004).

The malnourished are not just widely dispersed – they are overwhelmingly poor and marginalized.
Yet the neglect of policy options associated with nutrition affects the lives of young children and the educational opportunities of those who survive.

This neglect was captured in a recent review of forty PRSPs for countries where malnutrition is particularly acute: Only thirteen countries included activities to address vitamin A deficiency and anaemia, despite recognition that they are public health problems in the vast majority of the forty countries.
Only 35% of the PRSPs allocated budget resources specifically for nutrition.

Yet more than 90% mentioned food security interventions, even when food security was not necessarily the main problem (Shekar and Lee, 2006).

PRSPs often identify malnutrition as an important symptom of poverty yet fail to include actions or budgets for improving nutrition.
Where budgets are included, they may cover only micronutrient Achieving breakthroughs in childhood nutrition requires political commitment from a variety of sectors There is slow progress in integrating HIV/AIDS and children with disabilities into multisectoral planning 1 9 2 C H A P T E R 3 programmes or specific interventions.
School feeding is one common intervention, although such programmes are not always found to have much impact on nutrition – particularly for children who are too ill to attend school in the first place (Shekar and Lee, 2006).

There are exceptions to this picture of fragmentation.
Nutrition is among the six pillars of Bangladesh’s PRSP, which has helped institutionalize nutrition in the country’s development agenda, building on the earlier Bangladesh Integrated Nutrition Project and National Nutrition Project.

In Madagascar nutrition is being mainstreamed and scaled up after project experience.

The Ethiopian government has developed a national nutrition strategy with coordinated support from development partners (Shekar and Lee, 2006).
Nevertheless, the more common institutionalized failure of PRSPs to address the crisis in malnutrition points to a deeper obstacle to progress towards EFA.

HIV/AIDS: the high price of uncoordinated responses.The devastating impact of HIV/AIDS on education systems in highly affected countries has been extensively documented.

Yet many of these countries have not developed an effective planning response to prevent new infections and to limit the effect of HIV/AIDS on families, communities and schools.
In many cases the focus has been on curriculum reform in education to include teaching on HIV/AIDS prevention rather than an integrated response aimed at addressing the multiple disadvantages faced by children affected by HIV/AIDS (Table 3.10).

There are exceptions to this rule, as the experience of Cambodia shows (Box 3.26).

The approach to HIV/AIDS and education in PRSPs is closely associated with wider failures in education planning.
A review of twelve FTI- endorsed education plans found considerable variability in how HIV/AIDS was addressed: five made no mention of it and only four had specific cost estimates (Clark and Bundy, 2004).

Follow-up research found that the FTI appraisal and endorsement process was still uneven despite amendments to the guidelines following the first report (Clark and Bundy, 2006).
Three of the eight plans endorsed had no HIV/AIDS component, even though two of the countries involved have a generalized HIV epidemic (HIV prevalence above 1% in the general adult population); and two had only a limited set of interventions.

More promisingly, the other three – Ethiopia, Kenya and Lesotho – were moving towards a comprehensive response and provided good examples of what could be achieved.
Disabled children: little evidence of inclusive approaches.Disability is a significant source of inequality and marginalization in education (see Chapter 2).

If governments are to get the remaining out-of-school children into school, removing barriers facing disabled children is one priority.

Another is the creation of inclusive education systems that respond to varying needs.
A shift towards more inclusive systems is supported by the Convention on the Rights of Persons with Disabilities, which came into force in May 2008.

It not only recognizes that inclusive education is a right, but calls for an improved educational environment for the disabled and measures to break down barriers and stereotypes related to disability (United Nations, 2006).

Progress in recognizing disability as an area needing policy attention has been limited.
Only ten of the twenty-eight education plans endorsed by the Box 3.26: Intersectoral planning on HIV/AIDS and education in Cambodia Cambodia’s Ministry of Education, Youth and Sports (MOEYS) established an Interdepartmental Committee on HIV/AIDS in 1999 to coordinate mainstreaming of HIV/AIDS issues in the education sector.

The committee, chaired by the MOEYS secretary of state, comprises representatives of fifteen departments and institutes.
It has ensured that priority is given to HIV/AIDS, which since 2001 has been referred to as a key cross-cutting priority in Education Strategic Plans (2001–2005 and 2006–2010), in the annual Education Sector Support Programme and in the PRSP (the National Strategic Development Plan, 2006–2010).

In terms of the curriculum, HIV/AIDS is integrated as a regular topic in primary and secondary schools, and in non-formal education settings, and is part of pre- and in-service teacher training.
MOEYS has a strategic plan on HIV/AIDS (2008–2012) and recently became the first Cambodian ministry to adopt a workplace policy on the issue.

Political commitment is reported to have contributed to a rapid decline in the prevalence of HIV, from 3% in 1997 to 1.9% in 2005.

Source: Cambodia Ministry of Education, Youth and Sports, and Interdepartmental Committee on HIV/AIDS (2007).
: W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k FTI between 2002 and 2006 included a strategy for children affected by disability.

While thirteen others mention disability, there is little detail of strategies for the inclusion of disabled children in education, and five make no mention at all (World Vision, 2007).
Ensuring that disabled children receive an inclusive education demands a multisector approach.

PRSPs could play an important role in coordinating health and social welfare issues that affect educational opportunities and outcomes for disabled children, such as nutrition, access to health services, early childhood care and social assistance.
The United Republic of Tanzania, with its 2003 National Policy on Disability, is a rare example of a country that includes targets and strategies aimed at increasing educational opportunities for children with disabilities in its second PRSP (Figure 3.8) (World Vision, 2007).

PRSPs that include strategies aimed at supporting children with disabilities tend to focus on school infrastructure and sometimes curriculum relevance (Table 3.10).
Few PRSPs and education plans address the interlocking forms of social exclusion that children with disabilities often face.

The ‘invisible’ marginalized.Chapter 2 shows that simply living in a particular part of a country can reinforce disadvantage in educational opportunities.

An Oxfam review of first-generation PRSPs found that only a few had education strategies for the special needs of marginalized or impoverished areas of countries (Oxfam International, 2004).
Second- generation PRSPs continue to pay sparse attention to geographic factors limiting the visibility of particular groups (Table 3.10) (Chronic Poverty Research Centre, 2008).

Uniform strategies are commonly identified, with insufficient attention to the ways in which forms of disadvantage vary for different population groups geographically; targets are usually not differentiated by location (Figure 3.8).
Where PRSPs do address geographic imbalances, they usually focus on disadvantaged rural areas, often failing to recognize the plight of slum dwellers (Chronic Poverty Research Centre, 2008).

There is, moreover, almost no mention in recent PRSPs of the educational needs of child migrants (whether with their families or alone) (Black, 2004).

Given that children of both domestic migrants and, in many cases, cross-border migrants are among the most disadvantaged in education, this is a serious omission.
Similarly, children living on the street are seldom considered as a distinctive group facing disadvantage.

Also neglected are ethnic minorities.

Where they do appear in PRSPs, the main strategy aimed at overcoming inequalities is associated with educational access (Grant and Marcus, 2006).

But children from minority groups face exclusion beyond the school environment.
Kenya provides a rare illustration of an integrated approach to the needs of marginalized people with its Pastoralist Thematic Group, which influenced the PRSP (Box 3.27).

Attention to religious minorities is rarer still – none of the eighteen second-generation PRSPs refers to education of religious minority groups (Table 3.10).

Children in conflict-affected states are too often an absent constituency.Many of the world’s children without any opportunity to attend school live in fragile states.
In some cases, their lives are directly affected by violence and civil conflict.

In others, their countries are undergoing post-conflict reconstruction.

Either way, with weak institutions, limited resources and often restricted government authority, fragile states face distinctive problems in planning for education and poverty reduction.
Box 3.27: Getting pastoralist concerns onto the PRSP agenda in Kenya The pastoralists of Kenya’s arid and semi-arid regions make up about a quarter of the total population.

In 2000 only 20% of their children had the opportunity to attend school.

Yet the interim PRSP totally neglected pastoralist issues.

This picture started to change from early 2001 when a Pastoralist Thematic Group was included in PRSP consultations.
There was wide-ranging discussion on whether to present these concerns as a cross-cutting theme in a separate chapter or fit them to each ministry’s priorities.

In the final PRSP (Investment Programme for the Economic Recovery Strategy for Wealth and Employment Creation, 2003–2007), pastoralist issues were discussed under the theme of human resource development.
The discussion combined a number of interlinked aspects, including closing the gap with the rest of the country by developing a creative schooling programme for pastoralist children, strengthening community-based health care systems and preventive medicine, and improving food security through community-based early warning systems.

A target of increasing primary enrolment among pastoralists to 40% was adopted.

Source: Abkula (2002).


Children from marginalized or impoverished areas and ethnic minorities need greater visibility in planning Afghanistan has shown political commitment to developing conflict-sensitive education strategies 1 9 4 C H A P T E R 3 Many lack the technical capacity to develop plans.

Political commitment is often constrained and likely to result in particular groups being ignored in the planning process.
Recent experiences with PRSPs developed in Afghanistan and the Democratic Republic of the Congo show nonetheless that, even in particularly challenging contexts, it is possible to develop conflict-sensitive education strategies, to varying degrees of success.

Afghanistan demonstrates the importance of developing an education plan that can provide the basis for conflict-sensitive strategies in the PRSP.
The development of the country’s Education Strategic Plan under the leadership of the education minister is a considerable achievement associated with a series of key changes in management of the sector.

The plan is incorporated under Social and Economic Development in the 2008 Afghanistan National Development Strategy, the country’s PRSP.

Given the technical work already undertaken for the sector strategy, the education sector was well advanced for inclusion in the national development strategy.
Conflict and reconstruction define the context for education planning.

Afghanistan faces some of the world’s highest estimated rates of disability and gender inequality.

It is estimated that half the school-age population is out of school; thus, the government’s aim to achieve primary NERs of at least 60% for girls and 75% for boys by 2010 is laudable.
However, as the PRSP notes: ‘threats to schools, destruction of school buildings, killing and maiming of students and teachers is increasing, particularly in the southern provinces’.

Schools are not always considered safe, a fact affecting the enrolment of girls in particular.

Against this backdrop, Afghanistan still has far to go to achieve EFA and to narrow the gender gap.
While the Education Strategic Plan and PRSP provide a clear basis for moving in the right direction, along with clear signals of political commitment, some commentators have pointed to the need for greater consideration to be given to strategies that can address the impact of security issues on the education sector (Greeley, 2007a).

The specific challenges posed for education by conflict vary across countries.
In the Democratic Republic of the Congo, education is at the heart of a reform process whose urgency is underscored by recognition that broader state legitimization depends in large measure on the perceived strength of government commitment to improving education provision (Greeley, 2007b).

As a marker of such commitment, the government elected in 2006 has developed a PRSP, the Poverty Reduction and Growth Strategy Paper.
Education appears under the pillar of improving access to social services and reducing vulnerability.

The paper identifies core problems in the sector, including deterioration in the primary GER from 92% in 1972 to 64% in 2002 and a stagnant secondary GER at 30%.

However, while there are some similarities to Afghanistan in terms of the formal PRSP approach, the substantive differences are also marked.
In contrast to Afghanistan, the government has not yet developed a more detailed education sector strategy that takes into account the realities in the country.

Thus the PRSP deals with governance, provision of free education and equity issues, but lacks details of how to achieve the goals set out.

Given the devastating impact of conflict on nutrition, health, poverty and security, there is an urgent need for policies that address the real problems facing the Democratic Republic of the Congo.
Integrated social protection for the poor and vulnerable The compartmentalized approach to planning evident in many PRSPs contrasts strongly with emerging approaches to tackling poverty and inequality.

One example is social protection.

Many of these approaches place an emphasis on linkages between education, health and employment – and on policy integration across sectors.
They also stress the importance of equipping poor households with the capabilities they need to break the cross-generational transmission of poverty.

Recognizing that poverty is multidimensional, many governments are introducing programmes that target reductions in risk and vulnerability at several levels in health, nutrition, education and employment.
‘Social protection’ describes a broad set of policies that can help poor and vulnerable households manage risk through transfers of cash, food or entitlements to key services during critical periods (Marcus, 2007).

For households lacking assets or insurance, a drought, a flood, a shift in labour market conditions or an illness can give rise to coping strategies that lead to long-run cycles of deprivation.
For example, in East Africa drought is often the catalyst for reduced nutrition and withdrawal of children from school.

Poor households may also withdraw children from : W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k school in difficult times, partly to save on schooling costs but also to send them to work.
Successful programmes in several Latin American countries combine social protection and enhancement of investments in children’s education and health with alleviation of pressure to send children to work.

The objective is to go beyond traditional social welfare transfers by equipping vulnerable households with assets that will break the cycle of poverty.

Cash transfer has played an important role in the design of social protection programmes, some of which now operate on a national scale.
For example, the Bolsa Família programme in Brazil reaches around 11 million families.

It provides a cash transfer of up to US$35 per month to poor families with children, conditional on their keeping the children in school and taking them for regular health checks (Lindert et al., 2007).

Social protection programmes have far-reaching aims.
Rather than responding to poverty through welfare payments, they aim to meet immediate needs and break the intergeneration transmission of poverty through their impact on education and child health.

Evaluations point to some positive results.


A recent study of targeted social protection programmes in Honduras, Mexico and Nicaragua found them effective not only at increasing school attendance and but also at keeping poor children in school when households faced shocks to their livelihoods (de Janvry et al., 2006b).

They have also had significant positive effects on children’s health and nutritional status, particularly in the early years (Gertler, 2004).
In Nicaragua, the Red de Proteción Social programme increased visits to health centres and improved diet, resulting in a five percentage point decline in the stunting of children under 5 compared with control areas (Maluccio and Flores, 2004).15 The Oportunidades programme in Mexico, which provides poor households with a cash transfer of up to US$55 per month if they send their children to school and visit nutrition monitoring centres regularly, is often held up as a successful cross- sectoral social protection programme.
A recent study showed that unemployment or illness of the household head reduced the chances of poor children enrolling in school by some two percentage points.

For Oportunidades beneficiaries, however, the drop was almost completely non- existent (de Janvry et al., 2006a).

Social protection programmes in Latin America show positive impacts on poverty, health and education Social protection programmes are also having an impact on child labour.
The employment of children is both a consequence of poverty and a cause of restricted opportunity in education.

Few PRSPs pay explicit attention to the trade-off between education and child labour (World Bank, 2005d).

Yet social protection programmes have demonstrated that the links between poverty and child labour can be broken.

Bono de Desarrollo Humano in Ecuador illustrates what can be achieved.

Under this programme, households identified as extremely poor receive a cash transfer of US$15 per month.
Unlike Oportunidades, the programme does not make the transfer conditional on changes in household behaviour.

A recent evaluation based on an experimental research design found that the programme had a large positive impact on school enrolment (by about 10%) and a large negative impact on child labour (a reduction of around 17%) (Schady and Araujo, 2006).
Other programmes that provide unconditional cash support targeting families of poor children have also led to marked improvement in the children’s educational and nutritional status.

For example, a recent study of the child support grant in South Africa found that children who had been in the programme for a large part of their childhood had significantly higher height-for-age ratios, a measure of improved nutrition (Agüero et al., 2006).
The programme has also had a significant impact on school enrolment (Case et al., 2005).

Part of the success of social protection programmes in improving educational outcomes for the poor and disadvantaged comes from their effectiveness at channelling resources to target groups.

A recent study on programmes in Brazil (Bolsa Família), Chile (Solidario) and Mexico (Oportunidades) found that about 60% of transfer funds flowed to the poorest 20% of the population.
Conditional cash transfers have materially increased equity in the income distribution (Soares et al., 2007).

The success of social protection programmes is increasingly recognized.

Mexico’s Oportunidades programme even offers a rare example of policy transfer from a developing country to a developed country (Box 3.28).

These examples provide a practical demonstration of how integrated approaches to reducing vulnerability benefit education.
The good news is that social protection has emerged as a greater 15.

Stunting is defined as a height-for-age z-score two or more standard deviations below the reference median (see glossary).
Mexico’s Oportunidades programme offers a rare example of policy transfer from a developing country to a developed country 1 9 6 C H A P T E R 3 Box 3.28: New York City is learning lessons from Mexico’s Oportunidades programme Ideas for combating extreme deprivation in education usually travel a one-way street, from North to South.
Now one of the most successful programmes is moving in the opposite direction, from Mexico to the United States.

In an effort to help some of its most deprived people escape poverty traps that cross generations, New York City is experimenting with a model based on Mexico’s Oportunidades programme.

The Opportunity NYC programme was introduced in late 2007 after the city’s mayor led a team of officials to Mexico to study Oportunidades.
While its Mexican counterpart covers 25 million people, Opportunity NYC is currently a small pilot programme covering just over 5,000 families in parts of the Bronx, Harlem and Brooklyn.

The districts included are marked by high levels of social deprivation.

Poverty rates average around 40%, compared with a 21% average for the city as a whole; and unemployment rates are 19%, compared with 5% for the city.

Families covered by the programme are drawn mainly from the Latino and African-American communities.
They can receive as much as US$4,000 to US$6,000 per year in transfers every two months, as long as they meet conditions in health (including regular medical and dental visits), job training and education.

Education targets include regular school attendance, parental attendance at parent-teacher conferences and the obtaining of a library card.

Improvements in test scores and secondary school graduation attract additional bonuses.
The overall approach is to provide financial transfers not just to address immediate hardship, but to create incentives that will induce behavioural change.

Opportunity NYC is an innovative attempt to apply this model.

The two-year, US$53 million programme is privately funded by the Rockefeller Foundation and other donors.

Will it succeed?

It is too early to tell: the first payments were made at the end of 2007.
Half the families covered will be part of a control group and implementation is designed to facilitate a random assignment evaluation.

By building evaluation into the implementation of the project from the outset, policy- makers should have access to a steady flow of data and information that can inform future policy design.

Whatever the outcome in New York, there is sufficient evidence from programmes of this kind to draw two broad conclusions for education.
The first is that integrated poverty reduction planning is far more effective than the compartmentalized models evident in many PRSPs.

The second is that, if governments are serious about achieving the goals in the Dakar Framework and the MDGs, they are heavily underinvesting in cash transfer programmes.

Sources: Jack (2008); MDRC (2007); Seedco (2007).

priority and is increasingly forming part of the PRSP agenda.
One review of eighteen recent PRSPs found that seventeen included sections on social protection and those of Bolivia, Nepal, Pakistan and Senegal made it a core pillar.

However, formal endorsement in PRSPs for social protection sometimes obscures what remains a piecemeal, project-based approach (Grant and Marcus, 2006).

Pakistan’s experience demonstrates the point: social protection is used as an umbrella for a wide range of unconnected strategies, to limited effect (Box 3.29).
Positive lessons from the more successful social protection programmes include the importance of sustained political commitment, large-scale programmes with allocation of significant and predictable resources, careful targeting and the coordination of planning across sectors.

Social protection is not a panacea for the poverty and inequality that are holding back progress towards EFA.

Outcomes depend on policy design, financing and implementation.
By focusing on the development of a policy framework that integrates health, education, employment and wider concerns, social protection has facilitated more effective, integrated planning of the type envisaged – but not delivered – under the Dakar Framework for Action.
Strengthening participatory planning for the most vulnerable The Dakar Framework calls on governments to engage in consultation on policy with ‘learners, teachers, parents, communities, non-governmental organizations and other bodies representing civil society’ (UNESCO 2000, Expanded Commentary, para.

53).
53).

PRSPs are widely seen as having made a positive contribution in this area by extending consultation to civil society organizations and coalitions, some of which explicitly aim to represent the disadvantaged (Chronic Poverty Research Centre, 2008).
: W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k Box 3.29: Social protection in Pakistan’s poverty reduction strategy: the effects of fragmentation Pakistan’s 2003 PRSP, called Accelerating Economic Growth and Reducing Poverty: The Road Ahead, reflects the growing importance of vulnerability in poverty analyses.
Social protection is identified as a central priority, but programme implementation has been dogged by institutional fragmentation, inadequate financing and poor targeting.

Why is this relevant to Pakistan’s efforts to achieve EFA?

First, the children are highly vulnerable as a result of high levels of poverty.

Second, ill health, unemployment and natural disaster are a recurring theme in the lives of the poor, often leading to children being taken out of school.
Around 10% of poor households in one study reported taking children out of school and putting them to work during such crises.

Because poor households have fewer resources to support coping strategies, crises tend to widen social and economic disparities.

In response, Pakistan has established a range of social protection initiatives, including microfinance, public works, pensions and various social safety nets.
Such measures are intended to protect the households at greatest risk and help them regarding income in times of financial crisis so they may eventually escape poverty.

Some initiatives are directly linked to education while others have an indirect impact.
Examples specific to education include: central government stipends to girls in middle school from poor districts; provincial stipend programmes, such as one in Punjab; free textbooks for poor students who attend government schools; a pilot child support programme in five districts (since 2006/2007); the Tawana Pakistan Project, a school-feeding programme aimed at improving health, nutrition and enrolment; non-formal education provision for vulnerable children, such as child labourers.
The list is impressive, but it also highlights a series of problems: Programmes overlap in their intended scope, with uncoordinated financing and delivery modes (e.g.

via federal or provincial government, quasi- government and non-government organizations).

Coordination is lacking among bodies responsible for implementation, including the ministries of education, labour, social welfare and special education, and science and technology, along with the National Technical and Vocational Education Commission.
Many initiatives are experiments or relatively small in scale.

In a context where government commitment to education and other social sector spending is already low (education spending amounts to just 2.7% of GDP), measures often depend on external resources and so are unlikely to be sustainable.

Targeted stipend programmes are extremely limited in scale, fail to pay beneficiaries regularly and do not show a positive impact on schooling.

Sources: Bano (2007, 2008); World Bank (2007c).


Whether consultation translates into action depends on political actors’ willingness to listen and respond, which is affected in turn by the influence of the electorate on political priorities and by the extent of support from elites.

A convergence of interests among a range of stakeholders on access to primary schooling has helped keep this topic high on the agenda in many countries.
In some contexts, concerns about the need to develop a skilled workforce have also raised awareness of the stakes.

However, where priorities are set according to whose voices are heard, other areas of the EFA agenda are at risk of being further sidelined.
Amplifying the voice of civil society including the unheard poor As the EFA Global Monitoring Report 2008noted, civil society organizations are increasingly influential in the formulation of national education plans, a trend further strengthened by the formation of national coalitions of such organizations since 2000 in many countries, in response to commitments made at Dakar.
However, as the Report observed, challenges remain: Opportunities to participate systematically in agenda-setting and final drafting remain limited.
New concerns have arisen as a result of broadening consultation, including rising Whether consultation translates into action depends on political actors’ willingness to listen and respond stakeholder expectations that plans will reflect their concerns more prominently.

Some civil society organizations lack the analytical capacity to engage in consultation productively and confidently.
Consultation can serve to validate decisions already taken rather than to facilitate genuine engagement.

As in any process of political dialogue and consultation, issues of representation are important in education planning.
A detailed analysis of civil society participation in Burkina Faso, Kenya, Mali and the United Republic of Tanzania identified a wide range of actors interested in participating, including national and international non-government organizations, faith-based groups, national parent-teacher associations, teachers’ unions, private provider groups and research networks.

The analysis found a lack of transparency in the processes for selecting which actors to invite to the policy dialogue table.
Those most likely to represent critical viewpoints are excluded.

Teachers’ unions, many of which oppose education reforms that affect employment and pay conditions, are often not invited to participate in policy dialogue (Mundy et al., 2007).

The shortcomings of participation in education sector planning are also evident in PRSP consultations, with participation by civil society organizations and coalitions working on education remaining restricted (Commonwealth Education Fund, 2007).
Despite the important role of civil society organizations and coalitions in mobilizing public concern over the policy decisions most likely to affect the disadvantaged, such groups do not usually include the voices of the poor directly.

To address this deficiency, participatory poverty assessments have been undertaken in many countries as part of the PRSP consultation process.

There have also been attempts to involve the marginalized more directly in consultation.

These are laudable aims.


There have also been some important results.

National participatory poverty assessments have given new insights into the underlying causes of poverty and vulnerability.

In some cases – Uganda is an example – the evidence collected has had a direct bearing on the framing of national poverty reduction priorities.

Efforts have been made to increase the accessibility of PRSP documents (for example, by making them available in national languages).
And there have been efforts to extend consultation.

Nepal’s recent PRSP process provided opportunities for extensive consultation in the fragile economic and political context of an ethnically, geographically and linguistically diverse country emerging from years of conflict between government and Maoist forces.

It went far beyond the consultation process for education plans, which has been top-down with little real involvement of minority rights groups (Giffard- Lindsay, 2008; Vaux et al., 2006).
For all these advances, the limits to consultation have to be acknowledged.

Some of the limits relate to representation.

PRSP consultation exercises have led to engagement with national and civil society organizations on a very large scale.

Engagement with organizations of the poor, as distinct from organizations claiming to speak on their behalf, has been far more limited.

Marginalized groups face many barriers to meaningful engagement, including lack of time, literacy and organizational capacity.
Even when their views seem to be invited, information asymmetry can mean that they remain weakly involved (Goetz and Jenkins, 2005).

Marginalized groups may simply lack access to the information they need to develop policy inputs.

There is also a wider point to be made.

PRSPs do not override everyday political realities that perpetuate deep inequalities in society.
Governments that turn a deaf ear to the concerns of the poor in everyday public policy formulation are unlikely to undergo political transformation as a result of PRSP exercises.

Tackling poverty and reducing inequality require policies and public spending priorities that are likely to call into question prevailing power relationships in many countries.
That is precisely why many political elites prefer to ensure that PRSPs are pitched at a very high level of generality with a restricted process of dialogue and consultation.

PRSPs are part of a wider process of public policy development and political debate – a process that involves donors as well as national governments and political constituencies.

Outcomes will be shaped by the interactions and power relationships between actors.
In many cases, PRSP consultation exercises have led to engagement with civil society organizations on a large scale : W H Y G O V E R N A N C E M AT : t h e m i s s i n g l i n k was marked by a broad-based consensus in favour of education.
Another important factor was a recognition by national elites of the benefits of education for poverty reduction and development (Hossain, 2007).

The starting point for political consensus is a shared recognition that greater equity in education is not a zero-sum game.

No section of society has to lose out – and society as a whole stands to gain from progress towards UPE and wider education goals.
As Chapter 1 argues, equitable education is a powerful force for economic growth and rising living standards, as well as gains in other areas.

Viewed from a different perspective, large-scale disparities are a source of inefficiency.

This can also hamper advances in areas such as public health and fuel social polarization.

Conclusion There is evidence that education planning has improved since Dakar.

Education planning within a sector-wide framework is leading to greater coherence in priority-setting.
However, serious problems remain.

Far more has to be done to integrate education planning into wider poverty reduction strategies and to back priorities with budget commitments.

Particular attention has to be paid to the interlocking disadvantages that are holding back progress towards EFA.

Social protection programmes provide important lessons.
They demonstrate that broad-based strategies for reducing poverty and vulnerability can generate important gains for education, creating new opportunities for the poor.

Achieving this outcome will require the development of high-level political commitment, supported by strong national consensus, in favour of education for all.

PRSPs may give rise to tensions.

Take the twin commitment to national ownership and equity.

These goals might be attainable.


But what if national governments are not committed to equity, or if they are less committed than sections of their society – or aid donors – might desire?

(Booth and Curran, 2005).

Priorities in poverty reduction strategies are not set in a political vacuum.

They are formulated by governments that assess constraints, opportunities and political pay-off.

Experience in education is instructive.


Undertaking highly visible reforms such as the abolition of user fees often generates a high and fast political return.

The Kenyan Government announced the abolition of secondary school fees at a moment that coincided with the controversy surrounding the 2008 election.

In Burundi, school fees were abolished in 2005 following a controversial one-party election.
Elsewhere, too, user-fee abolition has been seen as a quick route to enhanced political legitimacy (Rose and Brown, 2004).

Interventions in other areas with a longer payback period have been less enthusiastically taken up.

For example, the development of strategic policy frameworks to strengthen education standards and monitoring has not received a great deal of prominence in political discourse (Giffard-Lindsay, 2008).
The same is true in areas that are likely to raise questions of social division, such as the narrowing of regional inequalities or transfers from higher-income to lower-income groups and areas.

Reforms that have less immediately visible outcomes, such as improvements to education quality, or ones that might challenge political authority and patronage systems, such as those supporting girls’ education, might gain less popularity in election processes (Rose and Brown, 2004).
Moving towards integrated and more equitable education planning poses challenges at many levels.

Potentially, many of the issues involved are highly divisive.

This is especially the case in societies marked by high levels of social polarization.

Part of the challenge is to create a political discourse that looks beyond narrow self- interest to national interest and to shared goals – including the goals of equal citizenship and shared opportunity.
One reason Bangladesh has progressed so rapidly in primary enrolment is that the return of multiparty democracy in the 1990s Far more has to be done to integrate education planning into wider poverty reduction strategies and to back priorities with budget commitments Table 3.10: Strategies to address education inequity in eighteen PRSPs Curriculum relevance School building/ infrastructure Stipends Community sensitization School feeding Fee abolition Primary education Type of inequity PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 Gender Poor/vulnerable HIV/AIDS Disabled/special education needs Geographic (e.g.
region) Conflict-affected areas Ethnicity Out-of-school children Rural/urban Religion Not specified Total number of strategies 13 17 18 13 11 13 School building/ infrastructure Curriculum relevance Stipends Quotas Counselling Total secondary Secondary education Type of inequity PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 Gender Poor/vulnerable HIV/AIDS Disabled/special education needs Geographic (e.g.
region) Conflict-affected areas Ethnicity Out-of-school children Rural/urban Religion Not specified Total number of strategies Table 3.10 presents information on PRSPs for the eighteen countries that have prepared two plans.
Most of the first PRSPs were prepared around 2000, with the second prepared in most cases between 2004 and 2007.

Based on the information provided in the PRSPs, the table indicates the number of countries that propose strategies aimed at addressing educational inequalities in the areas of ECCE, primary and secondary education, TVET and adult literacy, as well as the forms of disadvantage addressed.

The table identifies ten broad sources of inequality identified in PRSPs.
It then summarizes the number of plans with proposed actions, comparing the first- and second-generation PRSPs.

The broad headline message is that PRSPs pay insufficient attention to addressing educational inequalities and the change between the first- and second-generation PRSPs was limited.
Among the findings to emerge from an analysis of the information in the table: Attention to primary education strategies aimed at tackling disparities increased slightly between the first- and second- generation PRSPs, with a total of seventy-one strategies presented in the first and eighty in the second.

This increase is due to the inclusion of a wider range of strategies aimed at achieving gender parity and equality.
In total, twenty-five gender-related strategies are mentioned in the more recent PRSPs, compared with eleven in the early ones.

Encouragingly, this is due in part to a focus on strategies aimed at addressing gender inequality within the school environment (including recruitment of female teachers and addressing gender abuse in schools) — an area that did not appear in early PRSPs.
: W H Y G O V E R N A N C E M AT T E R S Female teachers Teacher sensivity training Addressing abuse in school Water and sanitation Language of instruction Total primary Primary education PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 Type of inequity 11 16 11 10 10 0 3 1 5 0 4 71 25 14 12 8 5 3 2 1 1 0 9 80 Gender Poor/vulnerable HIV/AIDS Disabled/special education needs Geographic (e.g.
region) Conflict-affected areas Ethnicity Out-of-school children Rural/urban Religion Not specified Total number of strategies ECCE TVET Adult literacy Relevance Non formal Type of inequity PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 PRSP1 PRSP2 Gender Poor/vulnerable HIV/AIDS Disabled/special education needs Geographic (e.g.
region) Conflict-affected areas Ethnicity Out-of-school children Rural/urban Religion Not specified 0 1 0 0 1 0 0 0 1 0 7 0 3 1 1 1 0 0 0 2 0 7 Total number of strategies 10 15 1 0 0 0 3 0 0 2 1 0 1 8 2 2 1 3 0 0 0 1 1 0 0 10 Note: ‘Not specified’ indicates that the strategy included in the PRSP does not specify the type of disparity that is being targeted.
Sources: Burkina Faso Ministry of Economy and Development (2004); Burkina Faso Ministry of Economy and Finance (2000); Cambodia Council for Social Development (2002); Cambodia Government (2005); Ethiopia Ministry of Finance and Economic Development (2002, 2006); Gambia Department of State for Finance and Economic Affairs (2002, 2006); Ghana National Development Planning Commission (2003, 2005); Guinea Government (2002); Guinea Ministry of the Economy, Finances and Planning (2007); Madagascar Government (2003, 2007); Malawi Government (2002, 2006); Mali Government (2006); Mali Ministry of Economy and Finance (2002); Mauritania Government (2000, 2006); Mozambique Government (2001, 2006); Nicaragua Government (2001, 2005); Rwanda Ministry of Finance and Economic Planning (2002, 2007); Senegal Government (2002, 2006); Uganda Ministry of Finance, Planning and Economic Development (2000, 2004); United Republic of Tanzania Government (2000, 2005); Viet Nam Government (2003, 2006); Zambia Government (2006); Zambia Ministry of Finance and National Planning (2002).
Some countries include strategies in both first- and second- generation PRSPs to increase participation by poor and vulnerable households, notably through stipends and school feeding programmes.

Given the potential of such strategies in supporting educational opportunities, the number of countries including them remains low.

For example, stipends for the poor and vulnerable are mentioned in only six of the eighteen PRSPs.
Moreover, there is limited emphasis on fee abolition (five of the eighteen second-generation PRSPs mention it as a strategy), even though informal fees continue to be an important barrier to enrolment of poor and vulnerable children.

Approaches to supporting children with disabilities are mainly focused on improving accessibility of school infrastructure rather than paying attention to curriculum reform.
Strategies aimed at tackling regional and rural-urban marginalization have declined, mainly because less attention is given to school construction.

Strategies aimed at supporting ethnic or religious minorities remain extremely limited.

More positively, greater attention is being given to education strategies aimed at overcoming inequalities at secondary level.

As with primary schooling, the focus is on school infrastructure and curriculum relevance.
Inequality in ECCE continues to receive limited attention.

Even where strategies are mentioned, they often do not specify how they intend to address particular forms of disadvantage.

Similarly, there is limited attention in PRSPs to addressing inequalities in TVET and adult literacy (the main focus of the latter is on women’s literacy).
2 0 1 9 0 0 2 t r o p e R i g n r o t n o M i S O N A P / e l a t i V i m A © l a b o G l l l A r o f i n o t a c u d E Empowering local communities: low caste women meet to exchange information and voice their concerns, in Uttar Pradesh, India 2 0 2 Education for All Global Monitoring Report 9002 Chapter 4 Increasing aid and improving governance Increased and more effective aid is vital to achieving the EFA goals.
At the 2000 World Education Forum in Dakar, rich countries pledged that no credible national plan would be allowed to fail for want of finance.

The pledge has yet to be honored.

This chapter examines the most recent evidence on aid flows.

That evidence points to continued shortfalls in development assistance — and to worrying signs that promises made in 2005 are not being met.
The chapter also explores the significant shift in approaches to aid governance under the 2005 Paris Declaration on Aid Effectiveness.

Drawing on recent evidence, it asks whether the strengthened focus on country ownership, sector-wide approaches and harmonization is creating an enabling environment for more effective aid.
Introduction ............................................................................................................. 204 Introduction Aid for education ............................................................................... 205 Governance and aid effectiveness ....................................................... 219 The Dakar Framework for Action is built on a compact between developing countries and rich countries.
Like any compact, it involves two-way responsibilities and obligations.

Developing countries pledged to strengthen national education plans, tackle inequality and enhance accountability to their citizens.

Governments of developed countries pledged to provide the aid needed to ensure that no credible strategy in the poorest countries would fail for want of finance.

Since Dakar, both groups of countries have reinforced these pledges on numerous occasions.
As Chapters 2 and 3 show, the record of developing countries in translating their pledges into actions has been mixed.

The record of donors is also mixed, but it is one of collective failure.

As a group, donors have failed to act on their commitments – and they have failed to close a large financing gap.
On conservative estimates, US$11 billion of aid is needed annually in low income countries to achieve three of the targets set in the Dakar Framework for Action: universal primary education (UPE), early childhood programmes and literacy.

In 2006 aid in support of basic education in low income countries was just one-third of the estimated requirement.

Several major donors appear to have almost abandoned support for Education for All in spite of explicit promises.
During 2005, the Gleneagles summit of the Group of Eight (G8) and commitments made by donors outside this group raised expectations of a sharp increase in aid by 2010 to achieve the Millennium Development Goals (MDGs).

There is now a clear danger that donors will not deliver and current trends point in the direction of a large shortfall against the target.

All of this has grave implications for progress towards the goals set at Dakar.
Having been encouraged to draw up ambitious national plans, many developing countries will be left without the resources required for their full implementation.

Changing this picture is an urgent priority because of the time lag between investment and outcome.

If countries are to achieve UPE by 2015, they cannot wait to put in place the financing needed to build schools, recruit and train teachers, and provide incentives needed to reach marginalized social groups.
More broadly, progress in education is contingent on delivery of the aid needed to achieve the MDGs in areas such as child health, water and sanitation, and the reduction of extreme poverty.

The bottom line message is that time is running out.

While developing country governments have to redouble their efforts, in the absence of a concerted drive by donors to close the gap between aid pledges and aid delivery, the targets set at Dakar will not be achieved in many countries.
Both donors and the governments receiving aid have recognized that there are serious problems in aid governance.

Too often, national ownership is weak, transaction costs are high and development assistance is delivered in ways that erode, rather than build, the institutional capacity of aid recipients.

New approaches to aid governance are emerging, slowly.
The emphasis is shifting away from aid projects to support for sector programmes and national budgets – and education is in the forefront of this transition.

In addition, donors have taken on other important commitments to enhance aid effectiveness and cut transaction costs.

All of these commitments have quantifiable targets for 2010.

Early monitoring suggests that, while some progress has been made, without acceleration most of the 2010 targets will be missed.
There are also concerns that the agenda may turn out to be a double-edged sword, if stronger collective action on the part of donors leads to a weakening of national ownership.

Good governance is at the heart of the emerging aid dialogue in education.

Commitment by governments to accountability, transparency, participation and equity is vital for achieving the targets set under the Dakar Framework for Action.
These are intrinsically important goals in their own right, as well as a means to education progress.

Unfortunately, as Chapter 3 shows, too many governments have not taken this commitment seriously enough.

As for donors, there is a danger they might seek to advance a good governance blueprint geared towards a narrow set of policies of questionable relevance to the needs of developing countries.

This chapter is divided into two parts.


The first provides a monitoring overview of developments in the level of aid.

Looking beyond current trends in commitments and disbursements, it also explores issues of equity in aid distribution.

The second part turns to the evolving agenda concerning the delivery of aid.

It examines how the strengthened donor commitment to supporting sector programmes is playing out in the education sector.
It also looks at what donors understand by ‘good governance’ in education and the types of programmes they and governments are developing together to achieve it Aid for education International aid is at the centre of an increasingly polarized debate.
Most governments in rich countries, backed by United Nations agencies and international financial organizations, and prompted by non-government organizations, see increased development assistance as a condition for achieving the MDGs and wider development goals.

Aid ‘pessimists’ respond with the claim that big increases in aid have achieved small results at best and that at worst aid is a barrier to progress.

In reality, the situation is more complex than either view suggests.
No amount of international aid will override the consequences of widespread corruption, inefficient service delivery or inequitable patterns of public spending.

And how donors provide aid is also important.

The benefits of development assistance are certainly contingent on good governance, not only in the receiving country but also on the part of the donor community.

That being said, aid can – and does – make a large difference.
And it makes the biggest difference when it is aligned behind nationally owned country strategies.

In the case of EFA, international aid has played a crucial role in supporting policies that have improved access to education, enhanced equity and addressed the quality issue.
While disentangling the precise effects of aid is difficult, it seems clear that many countries that have achieved rapid progress towards some or all of the Dakar goals would have progressed more slowly without aid, as the following examples illustrate: In Cambodia, Ghana, Kenya, Mozambique, the United Republic of Tanzania and Zambia, the increase in international aid has facilitated the abolition of primary school tuition fees, leading to a large expansion of primary school enrolment.
While the main responsibility for financing basic education lies with governments, external assistance can make a difference (Box 4.1).

In the case of the United Republic of Tanzania, aid has supported an education sector strategy that has cut the number of out-of-school children by 3 million since 1999.

In Ethiopia, education’s share of the budget increased from 3.6% of gross national product (GNP) to 6% between 1999 and 2006. International aid was a critical component of overall financing.
As a group, donors have failed to act on their commitments to close a large financing gap In the absence of aid, many more children would be out of school or sitting in even more overcrowded classrooms 2 0 6 C H A P T E R 4 Box 4.1: Aid supports the abolition of school fees in Kenya In 2003, a new government in Kenya abolished primary school tuition fees, resulting in 1.3 million additional pupils pouring into the country’s schools, overwhelming school infrastructure and catching ill-prepared teachers by surprise.
Schools in slums found it especially difficult to cope with the large numbers.

The government disbursed US$6.8 million in emergency grants to provide for basic items such as chalk and exercise books.

But this was insufficient to meet the overwhelming need for extra textbooks, classrooms, and water and sanitation facilities.
In the following year grants totalling US$109 million were made by the Organization of the Petroleum Exporting Countries, Sweden, UNICEF, the United Kingdom, the World Bank and the World Food Programme.

Bolstered by this support, the decision to scrap primary school tuition fees has advanced Kenya’s quest to provide free primary education for all children.

Between 2002 and 2006, enrolment increased by 25%, repetition rates tumbled and more pupils completed school.
Despite this progress, however, challenges remain.

In some areas there are as many as 100 pupils for every teacher.

Source: Chinyama (2006).

The number of out-of-school children dropped from 7 million to 3.7 million over the same period.

International aid has played a central role in stipend programmes for girls in secondary education in Bangladesh.

One effect has been to stimulate a parallel programme in primary education for girls from poor families.
Taken together, these programmes have pushed Bangladesh rapidly towards gender parity in school participation at primary and secondary levels.

Donors in Nepal have pooled financial support for an education strategy that has empowered local communities to expand access while scaling up teacher recruitment, school construction and targeted incentive programmes aimed at children from low-caste backgrounds.

As a result, the out-of-school population fell from 1.0 million in 1999 to 0.7 million in 2004.
It is hard to escape the conclusion that, in the absence of aid, many more children would be out of school or sitting in even more overcrowded classrooms, without books or desks.

Yet, none of these examples owes its success to aid alone.

The case for increased aid remains dependent on recipients’ ability to deliver positive results.
That outcome, in turn, ultimately depends on enhanced capacity, strengthened systems and the integration of education into wider strategies for tackling poverty and extreme inequality.

The remainder of this section focuses on aid as a source of finance for accelerated progress towards the Dakar goals.

The amount available for basic education is a function of the overall mobilization of development assistance, the share of aid allocated to education and the distribution of aid within the education sector.
Total aid flows: donors are not delivering on their commitments The overall levels of aid, as well as the trends, directly affect the degree of progress made towards the Dakar targets and goals, on two accounts.

First, the education sector is vulnerable to shifts in aid availability.
Second, more rapid and more equitable progress towards EFA is intricately linked with developments in other areas in which aid plays an important role – especially efforts to combat child mortality and infectious diseases, improve access to clean water and sanitation, and reduce extreme poverty.

It is in this wider context that aid trends point in a worrying direction, with most donors falling far short of their commitments.
In 2005 at the Gleneagles G8 meeting and the UN ‘Millennium +5’ and European Union summits, the donor community undertook to increase aid.

Combining the pledges, the Development Assistance Committee of the Organisation for Economic Co-operation and Development (OECD- DAC) estimated that meeting these commitments would increase official development assistance (ODA) from US$80 billion in 2004 to US$130 billion by 2010, at 2004 prices (OECD-DAC, 2008d).

Half the increase was earmarked for sub-Saharan Africa.
The commitments were made at a time when aid was on a rising trend.

Total net disbursements of ODA increased significantly between 1999 and 2005, from US$64 billion to US$110 billion, or 8% per year.

Much of this growth was driven by debt relief.

Total ODA then fell for two consecutive years to less than US$97 billion, with a decline of 8.4% in 2007 (Figure 4.1).1 As a share of OECD gross national income (GNI), ODA declined from 0.33% in 2005 to 0.28% in 2007.
The OECD recently completed its first comprehensive survey of donors‘ spending plans (OECD-DAC, 2008d).

It reports that of the promised increase in programme aid of US$50 billion by 2010, about US$5 billion was delivered in 2005 and an additional US$16 billion was either committed by donors to the multilateral development agencies or included in their own spending plans for 2010.

Almost US$30 billion (in 2004 prices) remains to be committed if the overall promise on aid is to be met.
Behind the overall deficit are wide variations in donor performance.

They relate to current disbursements measured as a share of GNI, and to both initial promises and progress to date (Figure 4.2).

Between 2005 and 2007, Denmark, Luxembourg, the Netherlands, Norway and Sweden each maintained a level of aid above 0.8% of GNI.

At the other end of the scale, Japan and the United States allocated a very low share of GNI and made only modest commitments in 2005 to increase this.
Having set the bar low, both countries are likely to achieve their targets.

All countries in the European Union have set the bar higher and in many cases, particularly those of Greece, Italy, Portugal and Spain, achieving the target level will take a sustained increase in aid.

Only Ireland and Spain significantly increased their share of national income devoted to aid between 2005 and 2007.
Overall, most donors are not on track to fulfil their promises and will need to make unprecedented increases to meet the targets they have set themselves for 2010 (OECD-DAC, 2008d).

Global trends in aid financing give serious cause for concern not just for education but for a wide range of development goals: donor performance in 2006 and 2007 may reflect weakening commitment to the Gleneagles pledges and, by extension, to the MDGs.
With economic growth slowing in many OECD countries and governments facing mounting fiscal pressure, there is additional danger that aid budgets will be cut still further.
Figure 4.1: Total ODA net disbursements, 1999–2007 110 105 97 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 80 83 76 64 63 68 63 61 64 66 63 72 83 87 89 19991999 20002000 20012001 20022002 20032003 20042004 20052005 20062006 20072007 Total ODA Total ODA minus debt relief Source: OECD-DAC (2008c).
Figure 4.2: Aid as a percentage of GNI, net disbursements, 2005–2010 Greece United States Japan Italy Portugal New Zealand Canada Australia United Kingdom Germany Switzerland France Finland Spain Belgium Austria Ireland Denmark Netherlands Luxembourg Sweden Norway 0.0 0.2 0.4 0.6 0.8 1.0 1.2 ODA as % of GNI 2005 2007 (increase) 2007 (decrease) 2010 Sources: OECD-DAC (2008b, 2008c).

Aid to education is stagnating Domestic resource mobilization is the key to sustainable financing for EFA.
Even in the poorest countries, national finance is far more important than aid.

Nonetheless, for a significant number of low income countries, external assistance is needed to help them reach the Dakar goals.

Millions of children from poor backgrounds and rural communities are still deprived of access Almost US$30 billion remains to be committed if the overall promise on aid is to be met 1.
These figures refer to contributions made by OECD-DAC countries either directly (bilateral aid) or indirectly through multilateral organizations (multilateral aid).

Other donors are increasing their ODA but information remains sketchy.

Contributions from donors other than DAC members and from private foundations are discussed later in the chapter.
Aid to basic education in 2006 was below 2004 levels C H A P T E R 4 to primary education because many governments cannot make adequate provision and continue to charge school tuition fees or impose other costs on primary school attendance.

In most low income countries, early childhood programmes remain largely underdeveloped and illiteracy is still widespread, especially for women.
Challenges also remain in increasing access to post-primary education as well as in improving education quality and addressing threats to education systems from pandemics, natural disasters and civil conflict.

The Dakar Framework for Action sets ambitious targets and goals in all these areas.

It also incorporates an important commitment.


When developed countries signed on to the Framework, they affirmed that ‘no countries seriously committed to education for all will be thwarted in their achievement of this goal by a lack of resources’ (UNESCO, 2000).

The G8 reaffirmed this at its 2007 summit in Heiligendamm, Germany (Group of 8, 2007).
Two years earlier at Gleneagles, G8 leaders declared: ‘We support our African partners’ commitment to ensure that by 2015 all children have access to and complete free and compulsory primary education’ (Group of 8, 2005).

To what extent are donors acting on their promises?
The EFA Global Monitoring Report 2007estimated that an annual US$11 billion of aid was required for low income countries to achieve UPE, make significant gains in reducing adult illiteracy and expand early childhood programmes (UNESCO, 2006).

For the full set of EFA goals, including providing basic life skills for all youth and adults and reaching the literacy goal, the requirement would be higher.

Aid commitments to the education sector have broadly followed overall aid trends.
The period between 1999 and 2004 was marked by a significant increase, from US$7.3 billion to US$11.0 billion.

However, commitments fell by 23% in 2005 to US$8.5 billion (Figure 4.3).

Commitments to basic education followed the same pattern, increasing to US$5.2 billion by 2004 and then falling to US$3.7 billion in 2005.

(Box 4.2 details the assumptions made to compute aid to education and to basic education.)
Overall commitments increased again in 2006, but only to a level slightly above that of 2004 – and aid to basic education did not regain even its 2004 level.

Annual variability alone does not explain the pattern.

The slowdown in the growth of aid for education, and even more so for basic education, is confirmed by calculating two-year averages to reduce the effect of volatility in year-on-year commitments.
As Figure 4.3 shows, the average annual aid commitments in 2005 and 2006 were below those made in 2003 and 2004 for both education and basic education.

The shares of education and basic education in total ODA are indicators of the priority they receive.

There has been little change.

Table 4.1 shows that education’s share in total aid remained broadly stable at around 9% between 2000 and 2006, with the exception of a fall in 2005.
The share for basic education was also maintained at around 4%, suggesting that its position within the education sector remained about the same.
In other words, all the growth in aid commitments to both Figure 4.3: Total aid commitments to education and basic education, 1999–2006 11.0 11.3 9.4 8.3 8.5 7.3 6.6 7.0 2.8 2.8 3.0 3.0 5.2 4.1 5.1 3.7 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 10.2 9.9 7.7 7.0 2.8 3.0 4.7 4.4 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 1999 1999 2000 2000 2001 2001 2002 2002 20032003 20042004 20052005 20062006 1999–2000 1999–2000 2001–2002 2001–2002 2003–2004 2003–2004 2005–2006 2005–2006 Annual Two year averages Total aid to education Total aid to basic education Source: OECD-DAC (2008c).
Box 4.2: Assessing the amount of total aid to the education sector The OECD-DAC statistical reporting system distinguishes three main levels of education: basic, secondary and post-secondary.

Aid to basic education is divided into early childhood education, primary education and basic life skills for youth and adults, including literacy.
However, not all aid for education is specified as going to a particular level of education.

Since 2006, the EFA Global Monitoring Report and the Secretariat of the EFA Fast Track Initiative (FTI) have assumed that half of ‘level unspecified’ aid for education benefits basic education.

In addition, the education sector receives aid as part of general budget support.

It is assumed that one-fifth of this is allocated to education, with half of that benefiting basic education.
* Hence: Total aid to the education sector = direct aid to education + 20% of general budget support.

Total aid to basic education = direct aid to basic education + 50% of ‘level unspecified’ aid to education + 10% of general budget support.

Figure 4.4 shows the components of total aid commitments and disbursements to education and to basic education in 2006 for all recipient countries.
Figure 4.4: Components of aid to education and to basic education, 2006 Education Basic education 11.3 1.1 9.0 0.8 10.2 8.2 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 5.1 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 0.5 1.1 3.5 3.5 0.3 0.6 2.6 Total Budget support Level unspecified Direct aid to education Direct aid to basic education * A review of World Bank Poverty Reduction Support Credits suggests that between 15% and 25% of general budget support typically benefits the education sector (FTI Secretariat, 2006).
Source: OECD-DAC (2008c).

Commitments Commitments Disbursements Disbursements Commitments Commitments Disbursements Disbursements education and basic education over this period resulted from the general increase in aid commitments rather than from any shifts in priority.

How has the education sector fared in comparison with other social sectors?
Increased commitments from multilateral agencies and the growth of global funds led to a rise in the share of health and population programmes in total aid commitments from 7% to 9% between 1999–2000 and 2005–2006 (Figure 4.5).

The share for water and sanitation remained at 5%.

Overall, the share of total ODA allocated to these social sectors (education, health, population programmes, and water and sanitation), which are at the heart of the MDGs, remained constant at 21% between 1999–2000 and 2005–2006.
Discussion so far has centred on aid commitments.

These are important since they reflect current priorities given to aid in general and to individual sectors.

However, the aid committed in a given year is usually disbursed over several years.

Disbursements reflect the amount of aid actually made available to countries in any given year.
Table 4.1: Share of education and basic education in aid commitments, 2000–2006 2000 2001 2002 2003 2004 2005 2006 9% 9% 10% 9% 10% 7% 9% 42% 43% 36% 43% 48% 44% 45% 4% 4% 3% 4% 5% 3% 4% Education as a share of total ODA Basic education as a share of total aid to education Basic education as a share of total ODA Source: OECD-DAC (2008c).

Because of the time lag between the decision to commit aid and its disbursement, total disbursements reflect commitments in previous years.
Figure 4.6 shows total aid disbursements to education and to basic education between 2002 (the first year for which the data are available) and 2006, pointing to a continual increase.

Disbursements for education reached US$9.0 billion in 2006, up from US$5.5 billion in 2002 – an average increase of 11% per year.

Aid disbursements for basic education grew at the same rate, reaching US$3.5 billion in 2006 compared with US$2.1 billion in 2002.
Figure 4.5: Share of social sectors in total aid commitments, 1999–2006 32% 33% 5% 7% 5% 2% 5% 9% 36% 4% 10% 4% 4% 5% 9% 35% 4% 9% 5% 4% 5% 7% 4% 8% 4% 3% 4% 8% Other Government and civil society Water supply and sanitation Population programmes Health Education All social sectors 1999–2000 1999–2000 average average 2001–2002 2001–2002 average average 2003–2004 2003–2004 average average 2005–2006 2005–2006 average average Note: This figure shows only direct contribution to sectors.
It excludes general budget support.

Source: OECD-DAC (2008c).

Figure 4.6: Total aid disbursements to education and basic education, 2002–2006 9.0 8.1 7.2 7.2 2.7 2.8 3.2 3.5 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 5.5 2.1 2002 2002 2003 2003 2004 2004 2005 2005 20062006 Total aid to education Total aid to basic education Note: The European Commission was the only multilateral agency reporting data on disbursements to the OECD-DAC Secretariat, although the IDA provided unofficial data.
Source: OECD-DAC (2008c).

If commitments increase year on year, disbursements will tend to be lower than commitments in any given year.

Provided the commitment/disbursement ratio remains roughly constant, education planners can expect an increased flow of real funding.

The opposite also holds: any contraction of commitments signals shrinkage in future flows.
In the current aid context, there is a risk that the slowdown in growth in commitments since 2004 will soon be reflected in slower growth, or even stagnation, of disbursements.

There is a risk that the slowdown in growth in commitments since 2004 will soon be reflected in slower growth of disbursements 2 1 0 Allocating aid to those in greatest need: is equity improving?

As financial aid is scarce, its distribution is important.
Achieving maximum impact and reaching those in greatest need are twin imperatives, but combining efficiency with equity is often a difficult balancing act: there is no guarantee that aid to those in greatest need will achieve the greatest impact.

While there is no formula for determining the right balance, increasing attention is being paid to how aid is distributed, especially given the slowdown in the growth of aid commitments for education and basic education since 2004.
Previous EFA Global Monitoring Reports, particularly the 2008 Report, have described in detail the amounts of aid received by individual countries and changes since 1999.

In the aid tables of the annex to this year’s report, monitoring information has been updated for 2006.

This subsection goes beyond descriptions of aid levels by country.

It attempts to evaluate the degree to which allocations are equitable, as defined by indicators for need, and whether they are related to progress towards EFA.
The analysis draws on data for total aid committed to education and basic education in 2006 across sixty-eight low income countries.

What share of aid to education goes to the poorest countries?

Aid for education was allocated to 147 countries in 2006.

The OECD-DAC defines seventy-nine of these as middle income developing countries and sixty-eight as low income developing countries (OECD-DAC, 2007a).

The latter group includes fifty countries categorized as least developed.
In 2006, the low income countries received US$6.4 billion in aid to education, slightly below the amount received in 2004 but higher than in earlier years (Figure 4.7).

Their share of total aid to education in 2006 was 57%.

While this aid share may appear relatively low, it is higher than for any year since 2000 apart from 2004.

Low income countries received a higher share of overall aid to basic education: 75% in 2006.
Again, apart from 2004, this was higher than in any other recent year, and almost ten percentage points higher than during 2001–2003.

These positive trends notwithstanding, middle income developing countries received over two-fifths of aid to education and a quarter of aid to basic education in 2006.

Given their Dakar and G8 commitments, donors may need to ask themselves whether this allocation is consistent with distributional equity and the achievement of the Dakar goals.
Figure 4.7: Aid to education and basic education by income group, commitments Education Basic education 6.5 6.4 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C l a t o t n i e r a h S 3.4 1.9 46% 23% 4.6 4.6 3.7 3.7 4.1 2.4 2.2 2.7 3.0 4.1 3.8 3.2 57% 53% 49% 49% 60% 55% 57% s n o i l l i b $ S U 6 0 0 2 t n a t s n o C l a t o t n i e r a h S Low income countries Least developed countries Source: OECD-DAC (2008c).
Since 2000, there has been very little increase in the share of aid for education, including basic education, directed to the fifty least developed countries.

With per capita income below US$900, these are the world’s poorest countries.

From 2000 to 2005 their share of overall aid to education increased slightly, from an average of 33% in 2000–2002 to almost 36% in 2003–2005.

During the same period, their share in total aid to basic education barely increased, from 45% to 46% (Figure 4.7).
The data for 2006 are uncertain since two donors made relatively large commitments of aid for basic education through the Fast Track Initiative (FTI) in that year, but the amounts that least developed countries will receive are not yet known.

Allocation across low income countries remains inconsistent Aid allocations to individual low income countries are inevitably influenced by historical and political factors.
But to what extent are allocations also shaped by a country’s relative need and proven ability to use aid effectively?

The evidence is mixed.

The low income group itself is diverse.

These sixty-eight countries range from Kenya and Viet Nam, which are close to reaching at least some EFA goals, to Chad, the Niger and Pakistan, which have a long way to go.

There are also significant differences in per capita income, which affects countries’ potential ability to finance EFA programmes.
Recent performance indicators vary as well.

Diversity of country characteristics and outcomes points to a need for caution in cross-country comparisons.

Nevertheless, such comparisons can provide useful insights into aid efficiency and equity.

This subsection looks first at the relationship between aid levels and the scale of education challenges as measured by the number of children out of school.

It then examines the relationship between aid and progress in moving towards the EFA goals.
While need can be measured in many ways, the number of out-of-school children might be considered a useful first approximation.
Accordingly, if aid flows reflect need, aid levels Since 2000, there has been very little increase in the share of overall aid allocated to education, including basic education to the least developed countries Countries making more progress received, on average, slightly more aid should rise with the number of out-of-school children and countries with similar numbers of out-of-school children should receive broadly similar amounts.
Yet Figure 4.8 suggests that the relationship between the level of aid and the number of out-of-school children is not consistent.

For example, prior to Dakar, Ethiopia had twice as many out-of-school children as the United Republic of Tanzania but received three-fifths of the amount of aid for basic education.

Similarly, while the number of out-of-school children in Kenya was over three times as high as that in Zambia, the latter received twice as much aid.
In 2006, Kenya still received a small amount of aid to basic education relative to the number of out-of-school children compared with countries such as Mozambique and the United Republic of Tanzania.

However, while the relationship between aid and number of out-of-school children is weak, simple regression analysis indicates there is some movement towards needs-based provision.
Aid to basic education was more concentrated on the countries with the highest number of out-of-school children in 2006 than in 2000.

If the average association between aid and out- of-school children is positive but weak, did poorer countries receive more aid than countries with similar numbers of out-of-school children but higher per capita incomes?

The answer appears to be negative.


For example, while the per capita income of Bangladesh was three times that of the Niger, it received five times as much aid for basic education, though both countries had around 1.3 million children out of school (see annex, aid tables and Statistical Table 5).

While donors have increasingly allocated more aid for basic education to countries with more out-of-school children, there is no evidence that they have given greater priority to the poorest countries.
Figure 4.8: Total aid to basic education (commitments) and out-of-school children of primary-school age, 1999–2000 and 2005–2006 1 000 100 10 e g a r e v a 0 0 0 2 – 9 9 9 1 , s t n e m t i m m o c , n o i t a c u d e c i s a b o t d i a l a t o T 1999–2000 average 2005–2006 average 1 000 R2 = 0.242 R2 = 0.3309 Zambia Bangladesh Nigeria Kenya U. R. Tanzania Ethiopia e g a r e v a 6 0 0 2 – 5 0 0 2 , s t n e m t i m m o c , n o i t a c u d e c i s a b o t d i a l a t o T 100 10 U. R. Tanzania Ethiopia Zambia Bangladesh Kenya Nigeria 1 10 000 100 000 1 000 000 10 000 000 1 10 000 100 000 1 000 000 10 000 000 Out-of-school children, 1999–2000 average Out-of-school children, 2005–2006 1999–2000 average 2005–2006 average Bangladesh Ethiopia Kenya Nigeria U. R. Tanzania Zambia Bangladesh Ethiopia Kenya Nigeria U. R. Tanzania Zambia Total aid to basic education, US$ millions Out-of-school children, millions 82 2.3 26 40 42 7.0 1.8 8.1 42 3.1 93 0.6 Total aid to basic education, US$ millions Out-of-school children, millions 101 169 81 12 1.4 4.8 1.3 8.0 133 0.3 111 0.2 Notes: Logarithmic scale.
Size of bubbles reflects total aid to basic education (commitments).

Sources: Annex, Statistical Table 5; OECD-DAC (2008c).

2 1 2 Is a country’s recent performance in educational development reflected in the allocation of aid?

Figure 4.9 plots the amount of aid committed to basic education per primary school-age child in 2005–2006 against the change in net enrolment ratios since Dakar in forty-one countries.

The relationship overall is positive but weak.


Countries making more progress (shown towards the right side of the figure) received, on average, slightly more aid.

The relationship, however, is far from perfect.

Countries including Ethiopia and the United Republic of Tanzania, where enrolment rates have almost doubled since Dakar, received less aid per capita than Burkina Faso, Mali, Senegal and Zambia, where progress was slower.
Overall, progress in increasing enrolment explained only about 15% of the variance in the distribution of the amounts of aid to basic education per child.

Measured against common indicators of progress, the aid distribution reveals some apparently arbitrary outcomes.

For example, if aid is partly intended to reflect strong performance on shared goals, it is not clear why Malawi received somewhat more aid than Ethiopia, since the enrolment rate fell in the former and doubled in the latter.
Clearly, donor perceptions of countries’ capacity to absorb increased aid are important, although this would appear to provide a limited explanation.

The Niger, for instance, has a stronger record than Burkina Faso on getting children into school, but received only one-third the amount of aid per primary school-age child.

Fragile states are a distinctive subset of countries.

By almost any standard they have high need relative to domestic financing capacity.
Many also face tight constraints in terms of institutional capacity.

Is it possible to judge whether donors are giving sufficient attention to such countries?
In 2006, the thirty-five countries that the OECD-DAC defines as fragile received US$1.6 billion in aid to education, of which US$0.9 billion was allocated to basic education.2 These amounts represented 14% of all education aid and 17% of aid to basic education, only slightly higher than the share of fragile states in the combined population of all developing countries.
Given the low levels of access to education, low completion rates and severe problems in education quality in the fragile states, combined with their domestic financing and capacity constraints, these aid levels would appear to reflect a very limited commitment to needs- based aid financing.
Figure 4.9: Aid to basic education (commitments) and progress towards UPE 80 70 60 50 40 30 20 10 Domincan Rep. Malawi , d l i h c e g a - – 5 0 0 2 , s t n e m t i m m o c Burkina Faso Senegal Ghana Bolivia Nigeria Nepal More rapid progress towards UPE Mali Zambia Mozambique R2 = 0.1472 Yemen Niger Guinea U. R. Tanzania Ethiopia -20 0 20 40 60 80 100 120 Change in NER between 1999–2000 and 2005–2006 (%) Total aid to basic education per primary school-age child (Constant 2006 US$) Out-of-primary school children (000) Change in net enrolment ratios between 1999–2000 and 2005–2006 2005–2006 average 2005–2006 average (%) Dominican Rep. Malawi Bolivia Nigeria Nepal Ghana Mali Senegal Yemen Burkina Faso Zambia Mozambique Guinea Niger U. R. Tanzania Ethiopia -7 -7 0 9 22 26 30 31 34 35 36 45 61 66 97 108 Note: Size of bubbles reflects total aid to basic education per primary school-age child (commitments).
Sources: Annex, Statistical Table 5; OECD-DAC (2008c).

The overall record can be briefly summarized.

In recent years there has been a slight shift towards targeting aid to basic education in the countries most in need.

Low income countries’ share of total aid to education marginally increased, though within this group the share going to the least developed countries remained constant.
Within the low income country group there is a weak but positive trend in aid commitments towards the countries with the greatest educational needs, as defined by the size of their out-of-school population.

There is also some evidence – albeit limited and inconsistent – that countries that have performed 2.

The education sectors of fragile states also received 2% of all emergency aid allocations (Save the Children, 2008b).
Financing to education is dominated by a small core of donors C H A P T E R 4 relatively well in expanding access to basic education have been rewarded with aid.

Yet none of this points to any overarching commitment to greater efficiency or equity in aid flows.

It is important to exercise caution in interpreting these results.

The concept of need is broad and can be measured in various ways.
Similarly, there is no single yardstick for progress towards EFA.

Nevertheless, there appear to be strong grounds for strengthening both the focus on equity and the level of aid commitments to those countries registering progress.

Donor performance: a mixed record Although several countries have made significant aid contributions to education relative to the size of their economies (e.g.

Canada, Denmark, Ireland, Luxembourg), overall financing to education is dominated by a small core of donors.
In 2006, France was the largest (US$1.9 billion), followed by Germany (US$1.4 billion), the Netherlands (US$1.4 billion), the United Kingdom (US$1.2 billion) and the International Development Association (IDA) of the World Bank (US$1.0 billion) (Figure 4.10).

Apart from Japan, the European Commission and the United States, no other donor committed more than US$300 million to education.

Because of its relatively small economy, the performance of the Netherlands stands out.
In their dialogue with developing countries, donors have strongly emphasized the importance of equity in public spending.

The share of basic education in overall spending is widely used as an indicator for equity.

Were they to apply the same standards to themselves, many donors would regard their own aid programmes as highly inequitable.

Few give high priority to supporting the EFA goals, either through their own programmes or in contributions to the Fast Track Initiative Catalytic Fund.
When it comes to distribution among different levels of education, large donors have a mixed record.

For example, in 2006 France directed only 17% of total aid to education to the basic levels and Germany only 11%.

Both countries’ education aid is skewed towards tertiary education.

In contrast, the Netherlands allocated 83% of its total education aid to basic education and the United Kingdom 71%.
The Netherlands was the largest donor to basic education in 2006 at US$1.1 billion, almost one- Figure 4.10: Total aid to education and basic education (commitments), by donor, 2006 Education 1 026 683 IDA EC AfDF AsDF FTI UNICEF 206 190 85 40 France Germany Netherlands United Kingdom Japan United States Canada Norway Spain Sweden Belgium Australia Austria Switzerland Ireland New Zealand Finland Denmark Luxembourg Greece 553 270 266 233 211 164 155 104 69 68 60 46 44 30 24 1 862 1 388 1 357 1 170 920 Total aid to education IDA EC AfDF AsDF FTI UNICEF 70 26 85 39 Netherlands United Kingdom United States France Japan Germany Sweden Norway Spain Australia Ireland Belgium Switzerland Denmark New Zealand Finland Luxembourg Portugal Austria Greece 155 153 135 88 74 45 42 28 27 20 19 11 9 5 3 Basic education 597 302 1 129 834 403 308 243 Total aid to basic education 0 500 1 000 1 500 2 000 0 200 400 600 800 1 000 1 200 Constant 2006 US$ millions Constant 2006 US$ millions Note: Italy did not report aid data in 2006.
Source: OECD-DAC (2008c).

2 1 4 quarter of the total.

Other major donors were the United Kingdom (US$843 million), IDA (US$597 million), the United States (US$403 million), France (US$308 million) and the European Commission (US$302 million).

Half of all aid commitments to basic education came from just three donors – the Netherlands, the United Kingdom and IDA.
These three were also responsible for 60% of all aid for basic education to low income countries, a situation which poses troubling questions about the commitment of many individual donors to EFA.

A majority of bilateral donors increased their overall aid to education in 2006 (Figure 4.11).

The largest increase was by the United Kingdom, which almost tripled its aid to education that year.
German aid to education also nearly tripled, though this might be explained in part by an under-reporting of tertiary education costs in 2005.

The Netherlands more than doubled its aid to education, as did Switzerland.

Other bilateral donors marginally increased their aid to education, while Denmark, Finland, Greece, Luxembourg, New Zealand, Portugal and the United States actually reduced theirs.
Among the multilateral agencies, IDA and Figure 4.11: Change in aid to education and basic education between 2005 and 2006, by donor, commitments UNICEF AsDF EC IDA AfDF FTI Denmark Greece United States Finland Luxembourg New Zealand Portugal Canada Spain Ireland Austria Australia Belgium Japan Sweden Norway France Netherlands Switzerland Germany United Kingdom Education Basic education -100% 0% 100% 200% 300% 400% 500% Change between 2005 and 2006 Note: Italy did not report aid data in 2006.
Source: OECD-DAC (2008c).

 the African Development Bank increased commitments to education while some other agencies decreased them.

The picture for basic education is more mixed.


Only seven of twenty-one OECD-DAC bilateral donors significantly increased3 their aid for basic education in 2006 (Australia, France, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom), while significant decreases were recorded in six countries (Denmark, Finland, Germany, Japan, New Zealand and the United States).

In the remaining eight countries, there was no significant change.
Aid to basic education was lower in 2006 than in 2005 for UNICEF, the Asian Development Bank and the European Commission.

Ultimately, what matters is the overall level of aid for basic education and its distribution among developing countries, rather than the performance of individual donors.

However, when growth is driven by a handful of donors, there is a greater danger of a sudden reduction in aid, with damaging consequences for progress towards EFA.
To summarize the balance sheet for 2005–2006: The overall growth in aid to basic education was more the result of action by a very few donors than of a broad-based effort by the international community.

Twelve donor countries and agencies decreased their aid (by US$0.49 billion) while fifteen increased it (by US$1.86 billion).

The Netherlands, the United Kingdom and IDA were responsible for 85% of the increase in aid to basic education.
The increased effort of a small number of donors in 2006 was insufficient to counteract the large fall in commitments in 2005, so total aid to basic education was still lower in 2006 than in 2004 (Figure 4.3).

Fast Track Initiative: not meeting expectations The continued aid financing gap in education raises important questions about the future of a major post-Dakar multilateral initiative.
The FTI was created in 2002 as a mechanism to encourage broad donor support for EFA and, in late 2003, the Catalytic Fund was established (see the 2008 Report for a detailed description).

At the centre of the FTI are governments’ education sector plans, The Netherlands, the United Kingdom and IDA were responsible for 60% of all aid for basic education to low income countries 3.

Change was considered significant when its absolute value was more than US$10 million.
whose endorsement by local donor representatives serves as an indicator of readiness for scaled-up aid.

The Catalytic Fund is not intended as a first call for aid for basic education – bilateral and multilateral agency programmes continue to play that role.

The Fund’s initial purpose was to provide short-term support to countries without bilateral programmes.
The rules for eligibility and length of support have since been expanded.

Currently, thirty-five country plans have been endorsed, eight others were expected to be by the end of 2008 and a further thirteen during 2009.

While the FTI’s central role is to leverage bilateral programmes, several countries see its Catalytic Fund as an important source of finance in its own right.

The balance sheet raises some important questions about the current and prospective role of the Catalytic Fund.
One concerns the failure to develop a deep donor support base.

Pledges to the Fund for 2004–2011 amount to US$1.3 billion from seventeen donors.

Pledges for 2007 and 2008 were below those for 2006 (US$265 million and US$383 million, against US$439 million).

So far the Netherlands has pledged 43% of the total and the United Kingdom 21%.

Together with the European Commission and Spain, these donors are responsible for 79% of all pledges.
Eight of the sixteen bilateral donors that are members of the FTI and regularly take part in its meetings have so far pledged less than US$20 million each.

This suggests either a low level of commitment or a low level of confidence in the Catalytic Fund, or both.

When measured in terms of overall financing, the Catalytic Fund is of limited relevance.

Of the US$1.3 billion pledged, US$1.1 billion has been notionally allocated but not all of it has been transferred yet.
Agreements totalling just US$329 million have been made with countries, with total disbursements by the end of February 2008 amounting to US$270 million.

Eighteen countries have received grants; the largest amounts went, in descending order, to Kenya, Yemen, Madagascar, Ghana and Nicaragua.

No other country has received above US$10 million.

Of the total commitments of aid to basic education in low income countries in 2006, the Catalytic Fund accounted for just over 2%.
Looking to the future, the Fund faces a large and imminent shortfall.

For 2008, the projected needs for endorsed programmes are estimated at US$1.0 billion.

By 2010, the estimated financing gap for the fifty-six countries expected to have had their plans endorsed is US$2.2 billion per year.

Without more pledges, projected needs will not be met.
Some countries receiving support from the Catalytic Fund will see it interrupted while those with newly endorsed plans will not be able to get support from the fund at all.

Non-DAC and other kinds of aid The data on aid presented so far are those reported by bilateral and multilateral agencies to the OECD-DAC Secretariat.

Non-DAC bilateral donors also support education in developing countries.
For instance, the number of African students on government scholarships in China is expected to double from 2,000 in 2006 to at least 4,000 by 2009.

China has also agreed to train 15,000 African professionals between 2007 and 2009 in several technical, scientific and administrative fields, and to construct 100 rural schools (Forum on China-Africa Cooperation, 2006).

Private foundations increasingly provide support for basic education.
In 2006 the Hewlett and Gates foundations announced they would provide a series of grants totalling US$60 million to improve the quality of primary and secondary school education in developing countries (William and Flora Hewlett Foundation, 2006).

In 2007 they allocated US$9.1 million over three years to the Indian non-government organization (NGO) Pratham for its Read India programme, which works to improve reading skills across 100 districts in India (William and Flora Hewlett Foundation, 2007).
Another significant initiative in 2007 was the launch of Dubai Cares.

This foundation has raised nearly US$1 billion from individuals and businesses in Dubai and entered a partnership with UNICEF to educate 1 million children (UNICEF, 2008).

The first activities of this partnership are a programme in Djibouti to build and rehabilitate primary schools to benefit 30,000 children and to improve the quality of education.
In addition, the foundation has allocated US$16.6 million to Save the Children to support education in Sudan (Save the Children, 2008a).

As well as providing ODA through grants and concessional loans, several multilateral agencies provide non-concessional loans for education.

The World Bank is the largest source of such loans.
It provided US$1.3 billion a year on average between 1999 and 2006 to support education development, mainly in middle income countries, including about US$700 million for basic education, which is slightly above the amount of aid allocated to basic Looking to the future, the Fast Track Initiative’s Catalytic Fund faces a large and imminent shortfall 2 1 6 education through IDA.
Around 60% of the loans were made to Latin American countries in 2005 and 2006, the largest recipients being Brazil, Colombia, Mexico and the Bolivarian Republic of Venezuela (plus the Philippines).

Regional development banks are also active.

Over 1999–2006, the African Development Bank committed US$16 million a year, the Asian Development Bank US$83 million a year and the Inter-American Development Bank about US$283 million a year, on average (Figure 4.12).
About half these loans were specifically for basic education.

Are donors adequately supporting EFA in low income countries?

International aid for education is at a watershed.

As Chapter 2 shows, achieving the goals and targets of the Dakar Framework for Action will require a significant increase in momentum.

National policies will determine prospects for success.
But if donors do not renew their commitment to act on their Dakar pledge that no country should fail in its efforts to achieve EFA for want of resources, the UPE goal will not be reached.

An earlier section cites the estimate of US$11 billion needed annually to achieve a subset of basic education goals in low income countries.

Achieving all the EFA goals requires more than that.
To close the financing gap, overall aid to basic education in low income countries will have to rise by a factor of three from the current level of US$3.8 billion a year.

Whether this is done through an increase in total aid or redistribution of total aid to education, or both, it is clear that current practices have to change – and soon.

The Dakar UPE commitment is time-bound.


To achieve UPE by 2015, governments need to put the long-term plans in place today for building schools, recruiting and training teachers, and providing incentives for marginalized groups.

In reality, it does not make sense to talk of ‘the donors’ for basic education in aggregate, because there are vast differences among the countries and organizations supporting EFA.
Some are clearly giving it high priority – increasing overall aid for education, focusing on the poorest countries and allocating a high share to the basic levels – and some are not (Figure 4.13).
In 2005 and 2006, Canada, IDA, the Netherlands and the United Kingdom each allocated Figure 4.12: Non-concessional loans for education, 1999–2006, commitments 2.6 2.3 2.2 1.6 1.4 0.9 1.5 1.2 AsDB AfDB EC IDB IBRD Total s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 3.0 2.5 2.0 1.5 1.0 0.5 0 19991999 20002000 20012001 20022002 20032003 20042004 20052005 20062006 Source: OECD-DAC (2008c).
on average, more than three-quarters of their education aid to low income countries and basic education made up at least half of their total aid to education.

To a lesser degree, Norway and Sweden have demonstrated similar priorities.

Several other major donors are falling short on one or more counts.

France, Germany and Japan have shown a relative neglect of basic education and low income countries.
France and Germany maintain aid programmes that are weakly aligned with their international commitments to the Dakar Framework for Action and the education MDGs.

Both give greater weight to subsidizing the entry into their universities of foreign students, mainly from middle income developing countries, than to supporting basic education in low income countries (Box 4.3).
The recent commitment by the French Government to work with the United Kingdom in a partnership aimed at getting 16 million sub-Saharan African children into school by 2015 is welcome if it signals a more thoroughgoing reassessment of aid priorities (France Ministry of External and European Affairs, 2008).
France, Germany and Japan have shown a relative neglect of basic education and low income countries Figure 4.13: Donor priority to low income countries and to basic education, aid commitments, 2005–2006 annual average 100% 80% 60% 40% 20% 0% 20% More priority for low income countries United States Netherlands Canada EC Norway Sweden United Kingdom IDA Japan Germany France n o i t a c u d e c i s a b r o f y t i r o i r p e r o M 30% 40% 50% 60% 70% 80% 90% 100% Share of low income countries in total aid to education Notes: Size of bubbles reflects total aid for basic education to low income countries, 2005–2006 average (commitments).
Only donors with education aid above US$100 million on average in 2005 and 2006 are shown.

Source: OECD-DAC (2008c).

Some donors have a strong record on prioritizing aid to basic education but a weak record on targeting low income countries.

For example, the United States and the European Commission allocate less than half their education aid to low income countries.
Missed opportunities, collective underperformance and a weak voice for EFA Current trends in aid raise serious concerns about prospects for delivering on the promises made at Dakar.

Commitments to education were no higher in 2006 than in 2004 – and in basic education they were a little lower.

Distribution remains a serious concern: less than half of aid for basic education was allocated to the fifty least developed countries.
It is not just the level of aid that is less than what is needed – and what was promised.

Central parts of the post-Dakar aid architecture are falling short.

The FTI was created to support the development of the credible EFA plans called for in the Dakar Framework, to harmonize donor efforts in basic education and to encourage more bilateral aid.

The subsequent creation of the Catalytic Fund, initially for countries not linked with bilateral donors but later expanded to others, was an important addition.
Yet, having created these institutions, donors are failing to deliver.

Bilateral support for basic education is stalling and, unless the Catalytic Fund is replenished sufficiently, its credibility and that of the FTI itself will be diminished.

Box 4.3: France and Germany focus on aid to post-secondary education In 2005 and 2006, France’s aid to education averaged US$1.7 billion annually and Germany’s US$0.9 billion, making them the largest and third-largest donors to the sector (Figure 4.14).
However, only 12% of France’s aid to education supported basic education in low income countries, while for Germany the share was 7%.

They allocated a large share of their overall education aid to the imputed cost of students from developing countries studying in their tertiary education institutions.

Imputed student costs accounted for 62% of France’s aid to education and 50% of Germany’s.

Imputed student costs were a significant share of all aid to education for some recipient countries.
In Algeria, for example, they accounted for 80% and in Tunisia for 40%.

In Morocco, where the net enrolment ratio was below 90% in 2006 and the adult literacy rate was just over 50%, two-thirds of all aid to education took the form of imputed student costs while only 7% supported the EFA goals.
Figure 4.14: Distribution of aid to education by level, France and Germany, commitments, 2005–2006 annual average 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 s n o i l l i b $ S U 6 0 0 2 t n a t s n o C 0.2 1.1 0.2 0.3 France Germany Other post-secondary education Imputed student costs Secondary education Basic education 0.1 0.3 0.1 0.2 0.1 0.4 0.2 0.2 0.1 0.3 0.0 0.1 All developing All developing countries countries Low income Low income countries countries All developing All developing countries countries Low income Low income countries countries Source: OECD-DAC (2008c).
2 1 8 An opportunity to help galvanize donors as a whole behind the EFA agenda will have been lost.

Set against this discouraging background are some positive signs.

The reaffirmation in the 2007 G8 communiqué of the Dakar commitment to ensure that lack of resources does not undermine national EFA efforts was encouraging (Group of 8, 2007).

So was the promise to work ‘to meet shortfalls in all FTI-endorsed countries’, repeated in 2008 in Hokkaido.
In June 2008, the European Council reaffirmed its support for EFA and pledged an increase in education aid of ¤4.3 billion by 2010 (Council of the European Union, 2008).

But promises are only as good as their realization.

If donors are serious, they cannot afford another two years of collective underperformance.

Accelerated progress towards EFA will not be possible without a strengthened international commitment to increase overall aid to the levels pledged in 2005.
In the current international environment, that commitment will require renewed international leadership – part of which must come from the education sector itself.

The most obvious source of guidance is the High-Level Group on Education for All, the intent of which is to bring together heads of state or government, ministers of education and of international cooperation, heads of development agencies, and representatives of civil society and the private sector.
Its role is to reinforce political will in order to accelerate progress towards EFA, strengthen partnerships, identify priorities and highlight the resources to be mobilized.

Meetings of the High-Level Group have so far failed to drive the Dakar Framework forward and to galvanize international action, partly because they have not attracted sufficient ministerial attendance from donor countries.
The broader problem is that, with the notable exception of a few bilateral donors, EFA has lacked a strong and consistent voice to keep it at the centre of the international development agenda.

An important challenge for the High-Level Group meeting in 2008, and for UNESCO, is to provide that voice and to lay the base for reinvigorating donor support to EFA.
Governance and aid effectiveness Increased aid is one part of the equation for delivering on the commitments made at Dakar.

More effective aid is the other.

Ultimately, the case for more aid will be won only if it is perceived as delivering positive results.

Whether aid is effective is partly a function of governance in developing countries.
High levels of corruption, low levels of transparency and accountability, and an absence of effective development strategies add up to an environment not conducive to effective aid.

The governance of aid itself is also important.

Ensuring that development assistance builds, rather than erodes, national capacity to deliver change, that it is predictable and that it supports national strategies for achieving well-defined goals is critical to its effectiveness.
Recent years have witnessed a growing concern to address problems in aid quality.

Some of that concern originates with aid recipients.

Developing country governments point to the high transaction costs, undue donor influence in policy design and a failure to use national systems as problems that reduce aid effectiveness.

For their part, donors have recognized that traditional aid delivery systems are flawed.
Increasingly, developed country governments have acknowledged that aid conditionality is less effective in delivering results than national ownership of development strategies.

Wider problems in aid governance have also been recognized.

These include the channelling of aid through stand-alone projects rather than through national budgets, financial systems and programmes; weak coordination between donors; and unpredictable aid financing.
Accordingly, in 2005 donors set out a new vision for the governance of development assistance in the Paris Declaration on Aid Effectiveness (OECD-DAC, 2005).

In it, rich countries resolve ‘to take far- reaching and monitorable actions to reform the ways we deliver and manage aid’.

The watchwords of the new approach are harmonization, alignment and national ownership.
Specific commitments have been undertaken in the form of targets for enhancing aid predictability, using national institutions and financial systems, and cutting transaction costs through improved donor coordination.
EFA has lacked a strong and consistent voice to keep it at the centre of the international development agenda 2 1 9 9 0 0 2 t r o p e R i g n r o t n o M i The Paris Declaration marks an important acknowledgement by donors of real failings in their performance Is the new governance model delivering results?

In some cases, changes in aid delivery mechanisms are lowering transaction costs and reducing fragmentation.
In others, new delivery mechanisms have not been able to overcome existing problems.

The emerging aid governance system is struggling to produce benign outcomes, partly because genuine national ownership requires a real capacity to develop, implement and evaluate strategies, which has not yet emerged in all countries; and partly because some old donor habits die hard and many donors find it difficult to remain at arms length.
One example, touched on at the end of this section, is that donors tend to develop their own positions on what constitutes ‘good governance’ in the education sector and elsewhere.

This may be giving rise to a rather narrowly defined range of good governance measures that governments perceive as necessary to attract donor support.

In the rest of this section the emerging aid governance agenda is examined to see what difference, if any, it has made to the quality and effectiveness of aid to education.
The recent approach of donors to education governance in developing countries is also described.

Improving the quality of aid Viewed from one perspective, the importance of international aid is exaggerated.

In the case of the Dakar commitments, as in other areas, real progress ultimately depends on whether goverments address problems, mobilize domestic resources and tackle inequalities in their education systems.
But as this chapter has made clear, for many countries aid matters: even the best plans will not deliver UPE and wider goals by 2015 in the poorest countries without a large increase in development assistance.

How aid is delivered can be as important as how much aid is delivered.

Unpredictable flows do not provide a secure foundation for long-term investment in schools, teacher recruitment and training, and targeted support for marginal groups.
Similarly, when donors provide aid in ways that bypass national systems and overstretch national management capacity, the outcomes are seldom sustainable.

The new aid paradigm is intended to address governance problems in aid delivery and highlight government responsibility.

Few people would contest the objectives.

Respect for national ownership and the pursuit of greater efficiency in donors’ contributions to poverty reduction are intrinsically laudable.
In practice, though, it is more difficult to change procedures than it is to change the language of aid governance.

The Paris Declaration marks an important acknowledgement by donors of real failings in their performance.

Donor proliferation, the use of projects to bypass government structures, weak coordination and disparate reporting systems are hallmarks of poor aid governance that have left a deep imprint on many countries.
Consequences have included weakened policy- making and budgeting processes, fragmentation of service delivery, and erosion of capacity and national institutions.

The OECD-DAC, in its 2008 survey of aid practices in fifty-four countries, revealed the scale of the problem.

Only 43% of donor-supported projects and programmes evaluated were using partner country procurement systems.

The Niger hosted over 600 donor missions in 2007, fewer than 100 of which were joint missions (OECD-DAC, 2008a).
OECD-DAC members have adopted indicators to measure progress towards more effective aid.

Goals include quantified reductions in the share of aid not reported or included in national budgets, and targets for increasing joint missions.

There is also a commitment to increase the share of aid delivered through programme-based, rather than project-based, approaches to two-thirds of the total.
Recognizing that short- as well as medium- term aid predictability is central to sound public finances, the Paris agenda includes a commitment to halve the proportion of aid not disbursed in the fiscal year for which it was scheduled.

Despite good intentions, progress in all these areas has been variable (Box 4.4).

Of course, not all the problems and shortfalls can be attributed to donor failings.
It makes little sense, for instance, to channel resources through national budgets in countries where egregious corruption is known to exist.

Still, donors’ performance against their own benchmarks raises important governance questions.

Donors can hold aid recipients to account for good governance by simply restricting aid flows, but how might aid recipients who have delivered on their side of the compact hold donors to account?

Do any incentives exist to encourage good behaviour by donors?
Monitoring the Paris agenda commitments is an important step towards genuinely shared accountability.

Yet monitoring exercises are only as effective as their follow-up.

The 2008 OECD-DAC survey provides clear evidence of serious problems in aid partnerships.

National ownership remains weak, transaction costs are high, donor commitment to national systems is still lacking and donor coordination remains rudimentary.
If aid is scaled up while governance arrangements remain largely the same, transaction costs can be expected to rise without a commensurate increase in aid effectiveness, as measured in real development results.

The following subsections examine what the emerging agenda on the governance of aid has meant for aid to education and for prospects of achieving the goals and targets set under the Dakar Framework for Action.
Four areas are considered: the shift from projects to programmes; action on ownership; alignment of aid with national priorities and systems; donor coordination.

Shifting from projects to programmes One benchmark used to assess progress on the new approach to delivering aid is the level of donor commitment to programme-based aid.
Project- based support is widely perceived as an underlying cause of fragmentation, involving high transaction costs and, especially where projects are administered and evaluated through separate units, potentially undermining efforts to strengthen national capacity.

The criteria for measuring commitment to programme-based aid include growth in the share of aid provided as pooled funding and budget support.

The target is to provide two-thirds of the total by 2010.
There has been a particularly strong trend towards the adoption of sector-wide approaches (SWAps) in social sectors, with education figuring prominently.

The trend has been driven by a conviction that coordinated provision of aid from all donors to support broad sector programmes will strengthen national ownership and improve development results.

Experience to date suggests that this view is broadly justified, with some important caveats.
In terms of financial commitment, there has been a strong push towards programme-based support in education.

Although the data represent only Box 4.4: Aid effectiveness — falling short of the 2010 targets The Paris Declaration marked a departure for aid governance.

For the first time, donors and recipient governments set targets for 2010 for measuring aid effectiveness.
Early monitoring results from fifty-four developing countries and twenty-seven donors, accounting for half of all aid delivered in 2007, suggest that the targets will not be achieved in most countries and that progress has been slow and uneven: Country ownership remains weak.

Fewer than one-quarter of the surveyed countries have national development strategies that are clearly linked to the national budget.

This is up from 17% in 2005, but far short of the 2010 target of 75%.
Monitoring capacity is limited.

Fewer than 10% of the aid recipients covered are assessed as having systems capable of monitoring development results — a slight increase from 7% in 2005, but the 2010 target is 35%.

Progress on aligning aid with government programmes has been minimal.

46% of all aid was delivered through common aid delivery arrangements such as SWAps.

This is roughly the same proportion as in 2005.

The target for 2010 is 66%.

Use of national systems remains limited.


Only 45% of aid is channelled through national public financial management systems.

This is not a major increase over the 40% level registered in 2005 and is only just over halfway towards the 2010 target of 80%.

Even in countries with good systems, donors are not necessarily making more use of them, indicating that quality is not the only factor influencing donor choices.
For example, although Mongolia’s financial management system was ranked one of the highest among the fifty-four countries monitored, only 17% of all aid to the country is managed through its national system.

Donor coordination is still rudimentary.

In 2007, the fifty- four countries received more than 14,000 donor missions, of which only one in five was coordinated on a joint-donor basis.

The 2010 target is 40%.

Source: OECD-DAC (2008a).


rough guide because of definition problems, the best estimate suggests that the share of aid delivered through sector programmes increased from 31% in 1999–2000 to 54% in 2005–2006 (Figure 4.15).

Examples of education sector programmes developed and implemented in five countries over the past decade are summarized in Figure 4.16.

From a narrow base, the number of donors involved in these programmes has increased.
However, some major donors, including Japan and the United States, as yet provide no financial support directly through SWAps.
There has been a strong push towards programme– based support in education Figure 4.15: Categories of aid to basic education, 1999–2006, commitments 5% 19% 2% 22% 4% 32% 45% 42% 22% 31% 34% 42% 4% 18% 24% 54% 1999–2000 1999–2000 2001–2002 2001–2002 2003–2004 2003–2004 2005–2006 2005–2006 Source: OECD-DAC (2008c).
Others Investment project Technical cooperation Sector programme The strength of the momentum towards programme-based support varies.

In broad terms, it has been strongest in low income, more aid dependent countries.

While it was initially driven largely by donors, governments in these countries expect that programme-based support will allow for greater flexibility, more predictability and reduced transaction costs, and that national priorities will dictate programme content.
In contrast, governments in middle income countries, which tend to have fewer donors and are less aid dependent, often prefer to negotiate with donors separately rather than with a coordinated group.

Coordinated aid to education sector programmes is also uncommon in fragile states, mainly because of the lack of government capacity to lead the process.

Acting on ownership One aim of programme-based support is to strengthen national ownership.
In the language of the new aid governance paradigm, country ownership requires governments to ‘exercise leadership in developing and implementing their national development strategies through broad consultative processes’ and donors to ‘respect partner country leadership and help strengthen the capacity to exercise it’ (OECD-DAC, 2005).

Is this happening in education?

ownership.


In a world of good governance, education sector plans would be developed by governments, with clear priorities reflected in national budgets and wider strategies, and they would be supported through coordinated donor actions.

Early SWAps did not conform to this model.

Most governments lacked capacity to develop effective SWAps, and donor influence weighed heavily in design and implementation.
Over time, ownership and government leadership have strengthened, but progress has been neither universal nor uniform.

An obvious dilemma for aid dependent countries is that donors control the purse strings and have a ‘right of last refusal’.

Paradoxically, SWAps can weaken aid recipients’ negotiating position and strengthen donors’ policy leverage.

Project-based aid, whatever its wider limitations, was based on a bilateral relationship between recipients and individual donors.
The strength of any donor was contingent on the size of its financial commitment or strategic role.

With programme-based aid, donors act collectively, effectively pooling their resources in national budgets.

Collective action in this context can increase the negotiating strength of the donor community in SWAp discussions.

The prospect of increased donor power may be one reason why some governments continue to prefer projects.
Programme aid might offer a textbook route to greater efficiency, but it can also entail greater intrusion into national policy.

As one commentator puts it, ‘recipients now face a more intimate supervision of all aspects of national planning, budgeting and development programme implementation than at any time since independence’ (Fraser, 2006).
Governments vary, of course, in their ability to take responsibility for education sector programme development, to lead the dialogue with donors and to restrict donor influence.

Countries face different types of problems, and governments have different levels of policy-making, administrative and financial capacity.

How these differences affect the relationships that frame the development and implementation of SWAps is a complex matter.
The contrasting experiences of India and Mozambique illustrate the point and provide a pointer to some conditions for more effective government leadership.

In the education sector as in other areas, SWAps are widely presented as a vehicle for strengthened The Government of India’s current programme to achieve UPE is Sarva Shiksha Abhiyan (SSA).
Sector-wide approaches can weaken aid recipients’ negotiating position and strengthen donors’ policy leverage Figure 4.16: Donor involvement in education sector programmes in five countries 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Mali Nepal Nicaragua United Republic of Tanzania Zambia 2001-2005: PISE I Launch of Mali’s 10-year education sector plan PRODEC in 2000, accompanied by the SWAp PISE I in 2001, with pooled funding from three donors (the Netherlands, Sweden and the World Bank) and non-pooled funding from ten donors.
A Partnership Framework sets the principles for donor coordination with rotating lead donor and joint review missions.

2006-2009: PISE II Second phase of PRODEC/PISE supported by fourteen donors through sector budget support from six donors and eight donors financing specific components of the sector plan through projects.

Joint Financial Arrangement signed by the six budget support donors.
1999-2003: BPEP II Donor harmonization started with pooled financing from five donors (Denmark, EC, Finland, Norway and the World Bank) to the Second Basic and Primary Education Programme.

Use of IDA management procedures.

2004-2009: National EFA programme Supported by a SWAp including pooled financing from seven donors and three non-pooled donors.

Joint Financial Arrangement signed by the pooled donors, specifying use of the government’s management procedures.
Code of Conduct sets principles for all donor coordination and alignment.

The government, all donors and NGO representatives participate in annual joint review missions of the national EFA programme.

2004-2008: PCT The Common Work Plan (PCT) was adopted by the Ministry of Education as a sector programme and accepted as a SWAp by all donors.

Funded by a majority of aid regardless of aid modality.
Budget support from the European Commission and a pooled fund with support from three donors introduced in 2005.

Sector-wide committee monitors implementation.

1997-2001: ESDP Launch of the government’s Basic Education Master Plan in 1997, with the supporting Education Sector Development Programme, including non-pooled support from eighteen donors.

Intensive government-donor consultations and sector analysis to move towards programme aid.

Donor group formed under an appointed lead donor.


2001-2006: PEDP I The government’s Primary Education Development Programme launched in 2001, accompanied by a subsector SWAp, including pooled funding from nine donors governed by a Memorandum of Understanding (MoU).

The PEDP steered by a committee, with attached technical working groups.

2007-2011: ESDP SWAp covering the whole education sector.

All previous donors contributing to the pooled fund moved to budget support.
Launch of the Joint Assistance Strategy for the United Republic of Tanzania in 2006, stating government’s preference for general budget support and concerns about pooled funds as parallel mechanisms.

1999-2002: BESSIP I Launch of the Basic Education Sub-Sector Investment Programme, with pooled aid from four donors (Ireland, the Netherlands, Norway and the United Kingdom).

By the end of BESSIP, fourteen donors involved in the SWAp.
2003-2007: MoESP The government’s second education plan, covering the whole education sector.

Nine donors and the government signed a MoU to specify the principles of government-donor interaction and pooled financing.

The Joint Assistance Strategy for Zambia to coordinate all aid to the country was launched in 2007, indicating the government’s preference for general budget support.

At the end of MoESP, four donors left the pool to move to general budget support.
Launched in 2001, it received donor support two years later and has become the world’s largest SWAp in basic education.

While donors have a large stake in absolute financial terms, they represent a relatively small part of the overall financing envelope: 4% of the total cost.

Evaluations of SSA leave little doubt that it is country led, with the government firmly in control of priority-setting and implementation.
SSA developed out of a long process of national policy development and donor dialogue, and its origins can be traced to the success of the District Primary Education Programme (DPEP), which began in 1994 and was developed under strong government leadership, including strict national management of donor participation (Box 4.5).
Negotiating authority was delegated to the federal department of education, which brought together the relevant national organizations to forge Box 4.5: India’s education aid: standing firm in negotiations India’s District Primary Education Programme is in many respects a prototype of what sector programmes aim to achieve.
It was unique at the time of its inception, not only in its design, which emphasized participatory planning and outcomes, but also because of the level of government leadership.

From the outset, the central government adopted a firm position on limiting donor influence.

Agreement on aid was reached only after donors had given clear assurances that they accepted the programme’s goals and approach, and that the federal government alone had the right to develop and oversee its implementation in the states.
To manage the donors, DPEP pioneered common financial procedures, joint reporting and joint review mechanisms.

Prolonged negotiations were required on the scope and composition of review missions and the participation of the federal government as an equal partner.

Late in 1994, agreement was reached on modalities and on the limited areas of implementation donors could assess.
Two years after DPEP was scaled up and relaunched as the SSA in 2001, DFID, the European Commission and the World Bank provided support within all existing parameters.

Source: Ayyar (2008).

agreement on the framework of the programme.

Intense negotiations between the central and state governments reconciled divergent interests and perceptions prior to discussions with donors.
Capacity-building was identified early on as a major priority, with many national institutes mobilized to provide support programmes.

The situation in Mozambique has been less conducive to government leadership in the aid relationship.

Even so, its experience shows that ownership can develop over time.

Twenty-two multilateral and bilateral donor agencies and around a hundred NGOs are involved in the education sector, making aid effectiveness a daily preoccupation.
Some 42% of the education budget is provided externally.

Since 1998 most of this assistance has been given in the context of SWAps, first focusing mainly on primary education (to 2005) and then adopting more comprehensive coverage (2006–2011).

Preparation of the current plan took more than three years.

It tested the ability of the government and donors to come to agreement.
On several occasions, including the sensitive area of teachers’ The Mozambique experience shows that ownership can develop over time 2 2 4 pay and conditions, donors collectively challenged government positions.

However, an assessment of the relationship suggests that donors have primarily pressed for clarity and more dialogue on priorities, rather than exerting direct pressure on policies and strategies (Takala, 2008).
With regard to implementation, despite an agreement in 1998 to follow the national planning cycle, donors initially continued to bypass planning and budgeting procedures, and to micro-manage the activities they funded.

However, in 2003, fifteen donors collectively recognized the negative effects of their behaviour and agreed to change it.

This resulted in an increase in the government’s ability to take charge of the programme (Takala, 2008).
The Indian example shows it is possible for a recipient government to lead the aid relationship.

Low levels of aid dependence, high levels of government capacity and strong national institutions for capacity development enabled India to engage with donors on its own terms.
In addition, the federal government had a clearly defined strategy for education and it instigated domestic political processes through which differences among states, and between the states and the central government were resolved before negotiations with donors began.

Mozambique faced a very different set of circumstances, having emerged from a protracted civil war with very weak capacity in key areas of administration, a collapsed education system and high dependence on donors.
Inevitably, donors had considerable policy leverage capacity.

However, the picture changed over time, with government capacity strengthening and donor confidence in national systems increasing.

Aligning aid with national priorities and using government systems The Paris Declaration envisages an aid relationship in which donors support the strategies, institutions and procedures of their partner governments.
This vision reflects a belief that when aid is aligned with country priorities and systems it is likely to be more effective than when aid is donor driven, fragmented and administered through donor systems.

As with ownership, assessing alignment of aid in this broad sense is difficult.

While many statements have been made in support of aligning aid with government priorities and practices, donor behaviours vary.

They range from very loose support to fully shared acceptance.
Progress towards alignment has proved far from straightforward, with frustrations on both sides.

The issues involved raise complex questions.

Using national procedures to channel aid makes donors much more dependent on national systems.

Being answerable to their citizens and legislative bodies for ensuring that aid is used effectively, donors are very concerned about corruption and the speed of delivery.
For their part, many aid recipients complain about the administrative and reporting requirements of programme aid.

Such challenges notwithstanding, efforts to align aid to education sector programmes and national management systems can have positive outcomes.

Greater sector coherence is one example.

Closer collaboration in areas such as joint planning and monitoring can provide better oversight of donor activities, averting fragmented service delivery.
Programme aid, including budget support and pooled funds, can also help increase flexibility.

Traditionally, aid for the education sector funded development expenditure such as classroom construction.

Yet much of the incremental cost of expanding the education system and improving its quality requires increased recurrent expenditure, notably for teacher salaries.

Programme aid has the advantage of being able to cover both development and recurrent costs.
Millions of additional primary school teachers are needed to achieve UPE by 2015 and it will be difficult for aid dependent governments to finance them without relying on more, and more flexible, external aid (Foster, 2008).

Some countries have indeed increased the proportion of programme aid to the education sector with positive results.

In Uganda, for example, an initial surge in primary school enrolments after the withdrawal of tuition fees severely compromised aid effectiveness.
The government responded by developing the Education Strategic Investment Plan, which reflected a strong national commitment to education, enshrined in the national poverty reduction strategy.

The plan, covering the whole education sector, became a central tool for more strategic decision-making.

The government integrated aid flows into sector-wide planning.

Donor support enabled Uganda to strengthen its public finance management system, which in turn encouraged donors to channel aid through it.
From a donor perspective, the key factor enabling the effective use of aid to the education sector was the government’s capacity for strategic decision- making, which provided a basis for reduced fragmentation and increased flexibility of aid.
The stability and predictability of funding was enhanced by a medium-term budget framework guaranteeing the availability of budget funds to the sector (Ward et al., 2006).

While donors and aid recipients may share a commitment to alignment in their policy pronouncements, differences do arise.

The complex history of aid to Rwanda is a case in point.

Today, almost half the national recurrent budget and over 95% of the development budget for the education sector come from external resources.
The past fifteen years have seen an extraordinary evolution of the aid relationship, from a huge number of emergency aid and relief projects in the mid-1990s to a sector-wide approach.

The transition has not been without tension.

Donors have attached overwhelming priority to primary and lower secondary education, whereas the government has also wanted to expand tertiary education.
Several donors put pressure on Rwanda to lower its allocation to tertiary education (from 37%) and give higher priority to basic education.

The result has been a package of cost savings at tertiary level, including higher charges for boarding facilities (Hayman, 2007).

Project-based aid is often a response to a negative risk assessment.

However, it is possible to strengthen alignment, at least in policy areas, through project aid provided a strong sector plan is in place.
The experience of Cambodia is instructive.

The introduction of the Education Strategic Plan in 2001 marked the beginning of closer government-donor cooperation.

The plan has given the government more focus internally and has gradually become the key reference for almost all donor support to primary education.

With sixty separate basic education projects from fourteen donors in 2007, national planning and monitoring remain complex.
Yet the Ministry of Education’s leadership within the education reform process has strengthened, and ministry officials report that the increased importance given to the plan by donors has helped improve the ministry’s knowledge of, and influence on, donor actions (Pirnay, 2007; Prasertsri, 2008).

In addition to governments developing strong sector programmes, donor support for improving national management systems and efforts to increase aid predictability are two key conditions for successful alignment.
Progress towards alignment has not been straightforward, with frustrations on both sides Developing management systems.The new aid agenda assumes that increased recourse to national management systems will create incentives for their improvement and that, rather than circumventing national systems to facilitate rapid delivery of aid, many donors will recognize that effective aid ultimately depends on improved institutionalized capacity for its delivery.
The overall record of progress on the use of national systems is mixed, as Box 4.4 showed.

Their use is also arbitrary.

The 2008 OECD-DAC survey of aid practices reported that donor policies on the use of national systems are often very slow to respond to successful reforms.

Even when countries register improvement in their capacity for financial management, many donors still prefer their own systems.

Others are sending more consistent signals.


In Burkina Faso, for example, Canada, the Netherlands and the World Bank have worked with the Ministry of Basic Education to improve government management structures so as to disburse funds through them.

As a result, the predictability of external funds has improved and the number of donors accepting common funding arrangements has increased.

In 2007, 57% of the country’s total aid to basic education was disbursed through such arrangements (Vachon, 2007).
Not all donors agree on the benefits of channelling aid through government management systems.

A group including Canada, the Netherlands and the United Kingdom has been willing to use deficient systems while supporting efforts to strengthen them.

Most of these donors share the view that aligned aid delivery stands a better chance of developing sustainable institutions and that administering aid flows through continued reliance on parallel project implementation units is ineffective and unsustainable.
Another group of donors takes the more cautious position that systems have to function better before they are ready to channel aid through them.

Australia, Portugal and the United States are prominent in this group.

Increasing aid predictability.Unpredictable aid flows make national planning in education a hazardous affair.

Hiring teachers has financial implications over several years.
In dialogue with the OECD-DAC, aid recipients have stressed the importance of aid predictability in making their budget management and planning more effective (OECD-DAC, 2007b).

Programme aid is not an automatic route to greater medium-term predictability.

A recent survey of donor support to fourteen countries for 2006 showed that 94% of committed sector budget support was delivered within the year (Strategic Partnership with Africa, 2008).
However, while commitments for 2007 had been made for 90% of the budget support programmes, the share fell to 68% for 2008 and to 47% for 2009.

While short-term predictability of sector budget support is high, medium- to long-term predictability still tends to be low.

Progress on aid predictability has been limited.

Some of the reasons can be traced to developing country governance practices.

It is clearly legitimate for donors to withhold support when faced with systematic underperformance.
But many donors have been slow to address weaknesses in their own institutional practices.

Bilateral donors often use an annual funding cycle linked to their budgeting processes.

National legislation may prevent them from signing binding medium- or long-term financing agreements, thus precluding predictable multiyear provision.

Recent initiatives have started to address this problem.
The European Commission’s MDG contracts, the United Kingdom’s ten year memoranda of understanding and the United States Millennium Challenge Corporation all provide for multiyear commitments.

The MDG contracts become operational in 2008, with the aim of committing general budget support for six years.

Monitoring will mainly focus on outcomes in the education and health sectors.
The initial contracts will be made with countries that have shown good performance in managing budget support and strong commitment to achieving the MDGs.

Cross-country evidence on efforts to strengthen national leadership and improve alignment points to the importance of several enabling conditions: Recipient governments’ political will to lead the education agenda is fundamental – and the development of well-structured education sector plans can facilitate their task.
National commitment to improve public management, especially financial management, is a prerequisite for increasing donor confidence in national systems and procedures, though donors can do much to create incentives and provide support.
Progress on aid predictability has been limited, and many donors have been slow to improve their own institutional practices 2 2 6 For aid alignment to be effective, donors must be willing and able to adapt to country circumstances and to set aside many of their own agendas.

Mutual trust is another key ingredient.


For SWAps and programme aid to be sustained over time, governments must be confident of donors’ commitment and donors must have a degree of trust in government policy direction and management capacities.

Tailoring the new aid agenda to national realities is critical.

Countries vary widely in institutional capacity to meet donor standards.

Fragile states in particular face deeply entrenched problems (Box 4.6).
Improving coordination among donors For countries with a narrow base of skilled administrators, inefficient time allocation has high costs.

One source of inefficiency is the management of donors.

When multiple donors arrive separately to assess performance in the same programmes, or local donor representatives require separate meetings with government, the transaction costs of aid and the opportunity costs associated with the diversion of human resources are high.
Better management and coordination of donors can reduce these costs.

Having to service multiple missions when a single joint mission might suffice is one source of inefficiency.

The Paris agenda sets targets for increasing the number of joint missions.

While donors vary in their preference for joint missions, collectively they are far short of the target level.

In 2007 only 20% of missions were conducted on a joint basis, while the target is 40% (OECD-DAC, 2008a).
Education is widely cited as the sector in which donors have made the most progress on this count.

For instance, a recent survey by the FTI Secretariat (2008) showed that in Honduras, 73% of education donor missions in 2007 were conducted jointly, and in Ethiopia 55%.

However, just 20% of donor analytic work on Nicaragua’s education sector was undertaken in joint exercises.

Some aid recipients are beginning to cut transaction costs and manage the flow of demands from donors.
Several have introduced ‘mission-free’ periods.

Ghana, for instance, does not accommodate missions during the one-month period when it finalizes the national budget Box 4.6: Fragile states and the new aid agenda Fragile states present particularly challenging environments for aid.
In some states, such as Sudan and Afghanistan, violent conflict continues to hold back development.

In others, post-conflict reconstruction confronts governments with enormous political challenges.

Two common features link all fragile states: limited institutional capacity and vast unmet needs.

In education, such states are dealing not just with a backlog of deprivation but also with school systems that fail to reach many citizens.
In Somalia, for instance, two generations have reached adulthood with practically no access to education (Netherlands Ministry of Foreign Affairs, 2006).

Few fragile states are in a position to meet the demands of the new aid agenda, with its emphasis on country ownership, donor alignment with education sector plans and the use of national systems.

Most lack the capacity to plan, implement and report through national systems.
Emergency assistance and project aid thus continue to play an important role, and the road from emergency aid to development aid is not straightforward.

The FTI has encountered difficulty, for example, in supporting fragile states.

Its endorsement process requires credible education sector plans, which many fragile states are unprepared to develop.

Where government capacity is weak, early engagement by donors is crucial to a managed transition to long-term development aid.

Afghanistan is an example.


In 2002, the Afghanistan Reconstruction Trust Fund (ARTF) was established as a coordinated aid mechanism, pooling contributions from twenty-five donors.

By mid-2007 the ARTF had mobilized US$1.45 billion to finance the government’s recurrent budget and priority reconstruction projects (Berry, 2007).

In the education sector, the ARTF pays the salaries of around 100,000 teachers and provides financial support to community initiatives to build and renovate schools.
The priority given to external financing of recurrent salary expenditure is seen as crucial to strengthening the country’s long-term effort to reconstruct its public service delivery system.

Although the Ministry of Education’s capacity is weak, it has shown some willingness and ability to engage with and coordinate donors to rebuild the education system.

Bridging emergency and development aid requires such qualities.
The emergence of new donor coordination practices across the education sector could play an important role in lowering transaction costs.

In many countries donor groups are being formed with appointed lead donors.

Among the countries which had received funds from the FTI Catalytic Fund by the end of 2007, all but the Republic of Moldova had such arrangements (FTI Secretariat, 2007a).

These donor groups have considerable responsibility, for instance in managing appraisals of education sector plans.
Some are more effective than others, however, and capacities vary among countries.

Donors move at different speeds, and some groups are restricted to general information sharing rather than joint aid management.
Ghana does not accommodate donor missions when it finalizes the national budget The aid system has become increasingly complex, with ever more donors and financing mechanisms C H A P T E R 4 Duplication frequently persists, notably in textbook provision and classroom construction.
The Netherlands, the United Kingdom and some Scandinavian countries have taken the lead in harmonizing their donor procedures; others, including Japan and the United States, prefer to continue working through parallel structures.

In some cases, the incentive systems for donor staff are holding back progress.

Pressure for tangible and visible results, rigid administrations and lack of support for staff members’ coordination of work across agencies have limited their interest (De Renzio et al., 2005).
A large number of donors each providing small amounts of aid is a prescription for higher than necessary transaction costs.

The Paris Declaration stresses the need to reduce this kind of fragmentation, for good reason: in 2006, fourteen countries each had to deal with at least twelve donors for basic education.4 To look at it the other way round: on average, each OECD-DAC donor had aid programmes in basic education with thirty- three countries (counting only programmes of at least US$100,000).
France topped the list with programmes in seventy-two countries.

As the aid system has become increasingly complex, with ever more donors and financing mechanisms, donors are beginning to recognize the need to rationalize their delivery of aid.

However, progress has been limited.

Between 2002 and 2006, fourteen out of twenty-one major donors increased the number of countries to which they provided aid for basic education.
The largest increases were for the European Commission, Greece, Japan and the United States.

While most of these fourteen donors also increased their total aid to basic education during the period, several – including Austria, Greece, Ireland, Japan and Spain – increased recipient countries more rapidly than aid levels, thereby reducing the average amount of aid per country.
In sharp contrast, five donors reduced the number of recipient countries while increasing aid, thereby raising the amount of aid per country.

In particular, the two largest bilateral donors to basic education, the Netherlands and the United Kingdom, more than doubled total disbursements to basic education while each reduced the number of recipient countries by five.

4.

This understates the problem as it includes only OECD-DAC donors.
In 2007 the European Union adopted a code of conduct to address skewed aid distribution.

The code encourages a division of labour among 2 2 8 donors.

Where appropriate, one donor may provide resources to another to administer alongside its own aid programme.

The code also encourages more equitable distribution of donors among countries.
The European Commission, after surveying donors on the challenges of applying the code in the education sector, indicated that Denmark, Finland, Ireland, the Netherlands, Norway and Spain, along with the Commission itself, would withdraw from active participation in some countries.

The decisions resulted partly from exercises analysing the division of labour within countries and partly from limited capacity in the field.
Finland, Ireland and Norway reported that they had withdrawn direct support to education but secured continued aid for the sector through general budget support.

Denmark and the Netherlands similarly chose to redirect some of their education aid through the FTI Catalytic Fund (European Commission, 2007b).

Donors are not alone in addressing fragmentation.

Some governments in ‘donor-dense’ developing countries are attempting to rationalize the assistance they receive.
For example, the Government of Afghanistan has introduced a rule to reduce the number of donors in each sector, including education.

A donor wishing to provide funding to more than three sectors must contribute at least US$30 million per sector (Rocha Menocal and Mulley, 2006).

In the United Republic of Tanzania, one aim of the Joint Assistance Strategy is to develop a more effective division of labour among donors.
Among its eighteen donors in education, Finland, Ireland, the Netherlands and Norway recently decided to withdraw following consultations with government and other donors on overall financing.

Similarly, after the Joint Assistance Strategy in Zambia reviewed the involvement of donors in education, two left the sector and four moved to general budget support.

In India the government is strongly selective, accepting aid for the SSA programme from only three donors.
Most efforts towards more effective division of labour among donors are very recent and it is too early to evaluate their impact on the quality and quantity of aid to education.

Again, though, recent work by the OECD-DAC points to the importance of government leadership in low income countries (OECD-DAC, 2008d).

Looking ahead Sector-wide approaches have driven the new model of aid emphasizing country ownership and leadership, alignment and harmonization.
The Paris agenda in turn has further reinforced SWAps as the default model of aid to education in many low income countries.

That model can point to significant achievements, including large enrolment increases in several countries having sector-wide programmes, such as Burkina Faso, Ethiopia, India, Nepal, Uganda, the United Republic of Tanzania and Zambia.

Yet SWAps are far from straightforward and major challenges remain.
Recipient government leadership stands out as the most critical determinant of success.

Strong education plans cannot be carried through with weak political leadership.

Nor can they be managed by governments lacking capacity.

Where education ministries remain unconvinced of their advantages or unable to develop capacity to take the lead, SWAps have not facilitated education reform.

The new modalities face common tensions in country level aid management.
The tensions are not new but have become more explicit through increased harmonization and alignment.

Among them: Long-term capacity-building versus short-term impact:SWAps typically emphasize long-term institutional capacity development, mainly through more extensive use of national management systems.
The argument is sometimes made that investment in these areas of capacity-building, mainly channelled as programme aid, comes at the expense of short- term effects, for instance in terms of numbers of schools built and textbooks distributed.

Others see short-term achievements through project aid as undermining efforts to build and sustain national capacity.
While the trade-off between short-term delivery and long-term capacity may be overstated, it is important to consider whether countries are best served by adopting one model rather than a mix of models thinking and implementation of education activities.
Yet at the same time programme support has provided opportunities for donors to strengthen their collective influence over strategic decisions.

Differences between donors may counteract this effect to some degree by giving rise to ‘lowest common denominator’ positions.

But a government in a very poor, highly aid dependent country is unlikely to risk a breach with a group of major donors.

Does collective action by donors weaken the potential for real national ownership?
To prevent such trade-offs, donors must exercise a high level of self- restraint when policy differences arise.

Process-oriented consensus building versus the drive for results:At country level, much energy goes into coordination and consultation.

These are clearly important, as making SWAps effective requires substantial ‘process’ work.
However, without a firm managerial hand and a focus on results, SWAp processes can become ends in themselves, absorbing large amounts of technical assistance and diverting government resources.

The focus on results, though, carries its own risks because of a growing tendency to assess performance on the basis of common sets of key indicators – an approach that can reduce complex processes to static and reductionist yardsticks.
More aid for better governance The restructuring of aid relationships is leading to less direct involvement of donors in designing programmes and monitoring implementation.

Recourse to aid conditionality as a lever for reform is also more limited.

However, ‘aid dialogue’ remains a source of donor influence.

Increasingly central to that dialogue are issues of good governance.
Donors advocate their own approaches to governance reform, in terms of what areas are important and what policies are effective, and may use aid programmes to leverage change.

The question is whether donors’ approaches to governance are consistent with the needs of poor countries and the spirit of the Paris agenda.
Recipient government leadership stands out as the most critical determinant of success Sector coherence versus donor influence on national policies:SWAps have potentially opened recipients’ doors to increased donor influence.

Sector analysis and government-donor discussions on sector policies and strategies have undeniably contributed to more coherent Governance is climbing the aid agenda Financial flows provide one indicator of the growing profile of governance issues in aid programmes.
The share of all sector-allocable ODA supporting projects and programmes in the ‘governance and civil society’ category was 9% in 2006 – the highest The European Commission and the World Bank are promoting a broad good-governance agenda through their aid programmes 2 3 0 C H A P T E R 4 share of any single sector (Figure 4.5).
This does not include programmes in other sectors, such as education and health, that have governance components.

Donors have invested heavily in developing their approaches to governance reform.

In 2006 and 2007 several major donors (including the European Commission, France, the Netherlands, the United Nations Development Programme, the United Kingdom, the United States and the World Bank) adopted new strategies on governance.
A 2006 United Kingdom white paper on aid policy captures the emerging mood by committing the government to ‘[p]ut support for good governance at the centre of what we do, focusing on state capability, responsiveness and accountability’ (DFID, 2006).

The European Commission and the World Bank have been particularly active in promoting good governance through their aid programmes.
The broad governance agenda covers a multitude of areas ranging from public financial management, decentralization, transparency and accountability (linked to corruption) to participation and reform of public sector employment, to mention a few.

Donors have also developed quantitative and qualitative tools to measure the status of a country’s governance arrangements (Advisory Board for Irish Aid, 2008).
DFID produces Country Governance Analyses, the European Commission prepares Governance Profiles for its main partner countries, the Netherlands carries out Strategic Governance and Anti-Corruption Assessments, and the World Bank is piloting a Governance and Anti- Corruption Assessment instrument.

Increasingly, these measures are being used to inform decisions on aid allocation and assessment.
They also play an important role by highlighting donor concerns and priorities that governments must bear in mind when developing programmes they hope donors will support.

Education figures prominently in governance reforms.

Any broad public sector reform, whether in employment conditions, budget management or financial management, affects education because the sector accounts for a large share of public expenditure and a commensurately large share of the public sector wage bill.
Donors have also supported governance reforms in the education sector directly through specific projects and SWAps.

The most recent World Bank Education Sector Strategy Update sends a clear institutional signal on key elements of good governance to be included in projects and programmes.
In a section on maximizing the effectiveness of education aid, it calls for more support to decentralized local authorities, increased devolution of power to schools and more public-private partnerships, as part of a strategy for integrating education into a broader policy framework (World Bank, 2005b).

The emphasis on governance reform is stronger in the update than in its 1999 predecessor.

Most bilateral donors have also followed this trend.
How has increased interest in governance among donors influenced policies and practices in the education sector in recipient countries?

This is not a simple question to answer.

In a traditional project almost wholly funded by a donor, it is reasonable to assume that the donor strongly influences the design and that implementation is closely monitored.

With programme support, influence is more difficult to untangle because it is embedded in complex processes of dialogue and bargaining.
However, by examining the components of recently prepared projects and programmes supported by donors, it is possible to discern the contours of a distinctive governance agenda.

The EFA Global Monitoring Report team reviewed eighteen projects or programmes in basic education supported by the World Bank since 2006.

The focus was on activities relating to governance and on loan conditions.
Thirteen of the operations are relatively conventional arrangements in which financial support is provided for specific activities.

However, reflecting the change in aid modalities, these are generally components of the government’s sector plan.

Four of the operations provide budget support for sector programmes.

The remaining one is an example of the treatment of education sector reform in a recent programme of general budget support.

Table 4.2 describes the components of each operation.
The World Bank was chosen partly because it is one of the largest sources of development assistance for education, especially in the poorest countries, partly because it makes more information available than most donors, and partly because the terms and conditions of its operations reflect and inform the views of the wider donor community.

Other donors are involved in several of the sector programmes being supported.
What broad conclusions about approaches to governance can be drawn from these eighteen operations?

In the thirteen that can be categorized as conventional projects, seven cover governance as a peripheral issue, their main focus being on direct financial support for school buildings, learning materials and teacher training.
These seven projects are in Afghanistan, Burundi, the Democratic Republic of the Congo, the Gambia, Haiti, Mali and Nigeria; all but Mali are categorized as fragile states.
Improvements in governance are sought largely through capacity development in the Ministry of Education and through Table 4.2: Governance components of recent education projects and programmes supported by the World Bank Conventional basic education projects Burundi 2007: Education Reconstruction Project Components: expansion of primary enrolment by financing school buildings; support for teaching and learning through in-service training programmes, provision of textbooks for all primary and lower secondary students, and measurement of reading adequacy; and capacity-building in policy and planning in the ministry.
Democratic Republic of the Congo 2007:Education Sector Project Components: increasing access and equity in primary schooling through the rehabilitation of school infrastructure and support for eliminating school fees; improving quality through the provision of textbooks; and building capacity to assess learning achievement and strengthening institutional capacity in the education system by preparing policies for teacher training, and strengthening education sector policy making and planning, and the project management unit.
Nigeria 2007:State Education Sector Project Components: school development grants to improve the quality of teaching and learning; development of model ‘whole’ schools through infrastructure and furniture grants plus basic teaching inputs to a small selection of schools; institutional development of state and local government authorities particularly via the EMIS and the Inspectorate plus project management and monitoring, and evaluation support to all states.
Mali 2006:Second Education Sector Investment Programme Project Haiti 2007:First Phase of the Education for All Project Gambia 2006:Third Education Phase Two Sector Programme Components: improving the quality of basic education through establishing reading areas in classrooms and providing libraries in teacher-training colleges, funding schemes to purchase school supplies, and providing in-service and accelerated training programmes for teachers; increasing access to education through financing new classrooms in basic education and one new secondary school; strengthening institutional management capacity in the education sector in human resources, EMIS, budgetary and financial management, and programme coordination.
Components: improving access and equity in primary education; operationalizing partnerships between public and non-public sectors; and building capacity to assess learning outcomes.
Components: construction/rehabilitation of urban and multigrade classrooms; support for in-service teacher training and mentoring; developing monitoring tools for tracking instructional time, observations of classroom instruction, attendance of school personnel and students, and parental involvement; technical assistance for functional analysis of the education ministry and for the development of management and teacher in-service training modules, monitoring and evaluation, and support to the project coordination unit.
Afghanistan 2008:Second Education Quality Improvement Programme Source: Group of 8 (2005, Annex 2).

Components: quality enhancement and social awareness; teacher training and increases in female teachers; school grants for infrastructure; and project management, monitoring and evaluation.
Uzbekistan 2006: Basic Education Project Components: providing modern, low-cost learning materials in selected schools to improve learning; strengthening community participation in school decision-making; building capacity in the education sector for budget planning and formulation, and management and accounting; enhancing the capacity of line units in the ministry to implement the project and, in doing so, to strengthen capacity overall.
Philippines 2006:National Program Support for Basic Education Project Components: develop and strengthen school-based management; improve teacher effectiveness through refining teacher competency standards and using them for appraisal, training needs and promotion, and through a more equitable distribution of teachers across schools; increase equity and quality by applying a standards-based approach to address growing disparities in inputs and outcomes; improve budget planning and management in the education department.
Pakistan 2006: Balochistan Education Support Project Components: establish community schools in rural areas; support private schools; and provide capacity- building activities for education NGO staff, members of parent education committees and teachers including through training, school monitoring and supervision.
Indonesia 2007: Better Education through Reformed Management and Universal Teacher Upgrading Components: reform teacher pre-service training; strengthen structures for teacher improvement at the local level; reform teacher accountability and incentive systems for performance appraisal and career advancement; and monitoring and evaluation, including the development of a teacher data base.
Honduras 2008:Education Quality, Governance and Institutional Strengthening Components: scale up interventions in pre-school and primary schools in poor areas; increase community participation within a new integrated school management system; governance and institutional strengthening of the ministry; and project administration.
Colombia 2008:Rural Education Project Components: improve the capacity of a set of departmental and municipality secretariats to deliver rural education; strengthening rural education at the school level in these municipalities; and strengthening the Ministry of Education in the area of rural education.
Improvements in governance are sought through capacity development in the Ministry of Education 2 3 1 C H A P T E R 4 Table 4.2 (continued) Education sector/subsector-wide plan Kenya 2006:Education Sector Support Project Bangladesh 2007:Third Education Sector Development Support Project Pakistan 2007:Fourth Punjab Education Development Policy Credit Project Support for the government’s programme to provide basic education and improve the quality of education for all children through twenty-three investment programmes in four areas; equity of access to basic education; improving quality and learning achievement; providing opportunities for post-primary education and training; and strengthening education sector management.
‘All of the investment programmes emphasise information dissemination, transparency, accountability and addressing corruption, within the context of the sector’s strategy for good governance.’ This loan directly focuses on governance and a general sector reform progammme which ‘attempts to address systemic governance issues in order to raise the quality and cost effectiveness of service delivery’.
The reform agenda focuses on accountability and systemic improvement through enforcing the criteria for establishing new schools, linking school grants to measures of school performance and strengthening school management committees; building administrative capacity through further development of oversight measures and devolving more responsibility to lower levels of government; improving monitoring and evaluation through expenditure tracking surveys and impact evaluations plus greater dissemination of information on examination outcomes, school performance and programme effectiveness; increasing teacher quality through the establishment of an autonomous teacher registration and accreditation authority; and improving the efficiency of textbook production and curriculum development through production being opened up to competition and transparent textbook evaluation and approval mechanisms.
Triggers for the loan are in areas related to accountability and systemic improvements in school financing, teacher effectiveness and textbook production.
The reform programme being supported has three pillars – improve fiscal sustainability and the fiduciary environment through ensuring increases in education expenditure and increasing transparency of financial management and procurement practices;improve equitable access to primary education through participation of the private sectorand its quality through better teaching practices and textbooks, and a credible examination system; and improve public education sector governance and management through strengthening district departments and expanding school-based management and the monitoring of schools by communities.
Triggers include performance formulae for school grants, a draft law for establishing a procurement regulatory authority, approval of policy and implementation modalities to scale up government financial support to private schools, textbooks printed and published through open competitive bidding, and a performance monitoring index, including for teacher absenteeism, approved with quarterly ranking of districts against the indicators.

Source: Group of 8 (2005, Annex 2).
Pakistan 2007:First Sindh Education Sector Development Policy Credit Project The reform programme being supported has four components: improving fiscal sustainability and the effectiveness of public expenditures in education, partly through improving financial management and procurement reforms to increase credibility, transparency and accountability of public resources; improving education sector management through reforms to strengthen the functioning, capacity and accountability of provincial and district management in line with devolution objectives and to strengthen the role of school management committees;improving access to quality education with a focus on rural areas and girls through infrastructure and reducing implementation bottlenecks of incentive programmes (free textbooks and stipends)and by launching partnerships with the non-government/private sector; and improving the quality of teaching and learning through merit-based recruitment of teachers and improved accountability and through a competency-based system of teacher education and continuous professional development.
General budget support and the education sector India 2007:First Bihar Development Policy Loan/Credit This loan is part of a wider framework of support to India’s poorest and second largest state by the World Bank, DFID, the Asian Development Bank and Japan.
The overall objective is to ‘support the implementation of critical fiscal, governance, administrative and service delivery reforms’.Bihar’s Eleventh Plan (2007) emphasizes three pillars – increasing public investment and strengthening public financial management and governance;raising economic growth through agriculture, investment climate reforms and infrastructure; and improving public service delivery in the social services.The last of these focuses strongly on teachers.
Huge numbers of teachers are required to move towards universal primary and elementary schooling.
The plan emphasizes the need to refine recruitment criteria for teachers, selection processes, contracting terms and the overall management.The current situation is summed up as ‘Lack of comprehensive data on teachers leads to problems such as corruption and political manoeuvring in teacher recruitment, irrational deployment and transfer of teachers, high teacher absenteeism and ineffective overall management’.
Prior actions for the release of the first tranche of the loan include devolution to panchayats(local governments) of the responsibility for all new teacher hiring, with a first round of approximately 100,000 teachers hired.
Conditions for the release of the second tranche include hiring the second round of 100,000 teachers, incorporating lessons learned in terms of standardization of recruitment criteria, increased transparency of candidate review process with proper registries and third party monitoring of selection processes and sample-based evaluation of the recruitment process by an independent agency.Indicative triggers for a potential follow-up loan include the development and piloting of a teacher competence assessment tool and an assessment of the 100,000 new teacher hirings in basic education levels and pedagogical skills plus the development and implementation of a monitoring system to record and improve teacher attendance in both primary and secondary schools.
Note: Governance components are displayed in italics government-donor negotiations on the grant or loan, and the triggers required for the release of financing tranches, provide mechanisms for leveraging reform.
Sector plans give a great deal of emphasis to teachers, including recruitment, terms of service, lines of accountability, transfers and absenteeism.

Measures to support greater community participation in schools through village or school education committees, and the auditing of school funds, are also common.
So, too, is the encouragement of private sector participation, both through private schools – often supported by public funds – and expansion of the private sector in producing, printing and distributing learning materials.

What conclusions can be drawn from this exercise?

Clearly, caution has to be exercised in deriving general lessons from these particular projects and programmes.
Yet even with this caveat, there is compelling evidence that governance now figures prominently in aid dialogues on education, as well as strong grounds for predicting it will figure even more prominently in the future.

At one level, this is entirely justified.

The need for good governance in the education sector cannot be contested.

Indeed, it is a condition for achieving the goals and targets set in the Dakar Framework for Action.
Furthermore, as aid is increasingly directed towards broad sector programmes rather than specific activities, and more of it is managed through government systems, it is not surprising if donors pay greater attention to the broad governance framework.

At the same time, donors have no monopoly on insights into what constitutes good governance for education.
There is a risk that policies reflecting particular currents of education policy debates in rich countries – such as shifting powers from local authorities to schools, expansion of voucher programmes, performance-related pay for teachers, and an increased role for private sector provision – will become routinely promoted.

As Chapter 3 shows, the evidence for their relevance in poor countries is not always strong.

the strengthening of monitoring and evaluation units.
A deeper governance agenda is nonetheless discernible in the components for school autonomy in Afghanistan and public-private partnerships in Haiti.

In the remaining conventional projects, governance issues are predominant.
This is reflected in support for community participation in the running of schools (Honduras, the province of Balochistan in Pakistan and Uzbekistan), school-based management (the Philippines), development of formulas for school funding (the Philippines), teacher recruitment, deployment and monitoring (Indonesia, the Philippines), support for private schooling (Balochistan) and significant efforts to improve overall administration and governance across the sector (all countries, but particularly Colombia).
The education sector programmes being supported are associated with the promotion of a yet more ambitious governance agenda.

Programmes in Bangladesh, Kenya and the provinces of Punjab and Sindh in Pakistan set wide-ranging goals.

They include accountability, transparency, decentralization, school autonomy, information sharing, reforms of teacher recruitment and placement, teacher performance monitoring, and private sector involvement in schools and textbook production.
In addition, there is a common focus on improving broad aspects of public financial management.

Triggers for the loans include government actions intended to improve criteria for school financing, teacher effectiveness and textbook production, and to provide support to private schools.

In the case of general budget support to the state of Bihar in India, required actions concern the rules for hiring teachers and monitoring teacher performance.

This examination reveals some patterns.


Traditional education projects focusing on activities directly aimed at expanding access (such as through provision of school buildings) and improving the quality of schooling (such as through provision of learning materials and support for pre-service and in-service teacher training) are typical in countries with weak governments or bureaucracies.
In other countries where aid is still tied to specific activities, the focus has shifted towards improving education service delivery, accountability and transparency.

When it comes to SWAps, the governance agenda dominates even more strongly.
The initial Donors have no monopoly on insights into what constitutes good governance for education / h t i m S - r e l y a r T e b b A © i The long road to education: walking through the desert to get to school, in Yemen 2 3 4 Education for All Global Monitoring Report 9002 Chapter 5 Policy conclusions and recommendations Inequality is one of the major barriers to the goals set out in the Dakar Framework for Action.
Governments across the world need to act with far greater resolve to reduce the disparities that restrict opportunity in education.

One of the central lessons to emerge from this Report is that there is no quick fix for enhanced equity, or for accelerated progress towards education for all.

However, it is possible to identify some of the broad principles and approaches needed to guide policy.

This chapter sets out key priorities for national governments, donors and civil society.
What governments can do ....................... 236 The role of aid donors ................................................ 240 The role of non-government actors .............................................................................................................................................. 241 What governments can do Every country faces a different set of constraints and challenges in education.
That is why effective national planning is the starting point for governance reform and for the development of national strategies to accelerate progress towards EFA.

With the 2015 target date for key EFA goals drawing closer, the early warning indicators for failure are clearly visible.

Particularly disconcerting is the fact that, on current trends, the goal of universal primary education (UPE) will not be achieved.
Governments need to act with far greater urgency in tackling the inequalities holding back progress in this area.

Simultaneously, education quality and learning achievement must be brought to the centre of national education planning at the primary level and beyond.

In taking forward the EFA agenda, eight broad thematic lessons can be drawn from the experience of strong national performances.
1 Get serious about equity Education planners need to ensure that the benefits of expanded provision are shared by disadvantaged groups and underserved regions.

Practical strategies for strengthening equity include the removal of user fees, the introduction of financial incentives for the education of girls and children from disadvantaged backgrounds, targeted support to keep children in school and the deployment of well-trained teachers proficient in local languages.
More equitable public spending patterns are also critical to ensure that schools, teachers and resources are skewed towards those with the greatest need rather than those with the greatest wealth.

Setting clear equity targets is one of the most important things governments can do in rethinking planning approaches.

Current EFA targets, such as those in the Millennium Development Goals (MDGs), are set in terms of national average goals.
The problem, as Chapter 2 shows, is that national averages can mask deep underlying disparities.

Average progress that leaves whole sections of society behind is not consistent with the spirit of the Dakar Framework for Action: the EFA goals are for everyone.

Going beyond national average targeting to identify well-defined equity goals would make a difference at a number of levels.
Even taking the political decision to adopt equity goals in education and monitor progress towards them would send an important signal.

It would place inequality of opportunity where it deserves to be: at the centre of the political agenda.

Concrete targets for reducing disparities could also provide a benchmark for holding political leaders to account.

Equity goals in education need to be well-defined.

As in other areas discussed in this Report, there are no blueprints.
The starting point is an assessment of current disparities.

For purposes of illustration, the overall commitment to UPE by 2015 could be supported by interim 2010–2012 targets for, say, halving the school attendance gap between the richest and poorest 20%, or between rural and urban areas, or majority and minority ethnic populations.

Specific targets could be set for particularly marginalized groups or regions with high concentrations of deprivation.
Progress towards the equity targets could be monitored through household surveys and education reporting systems.

Equity targets could also play a role in informing approaches to national planning.

Meaningful targets would have to be backed by financing and wider policy commitments.

Particular attention would have to be paid to estimating the cost of reaching disadvantaged groups and areas, not least since the marginal costs are likely to be far higher than average costs.
Similarly, extending opportunity to children from households marked by poverty, ill- health and acute vulnerability might require higher levels of per capita spending than for children from more advantaged households.

This is an area that continues to receive insufficient consideration in education planning.
An important priority for EFA is for governments and donors to develop estimates of the costs of reaching the marginalized and reducing disparities – and to make provisions for these costs in national budgets.

Moreover, strategies for achieving equity targets in education would have to consider not just school-based policies, but also wider strategies on nutrition, health and poverty.
The targets themselves could provide an opportunity to develop the type of integrated approach to education planning and poverty reduction set out in Chapter 3.

The commitment to equity has to start before primary school – and continue afterwards.

Good-quality early childhood care and education P O L I C Y C O N C L U S I O N S A N D R E C O M M E N D AT I O N S W h a t g o v e r n m e n t s c a n d o (ECCE) strengthens cognitive development and helps prepare children for school.
The benefits are reflected in improved attainment and achievement levels in school.

Progress towards ECCE has been disappointing and highly unequal, both among and within countries.

National governments should prioritize ECCE in planning, with incentives provided to improve coverage of disadvantaged children.

Similarly, it is increasingly apparent that progress in many EFA areas – and towards many of the MDGs – depends on a rapid scaling up of opportunities for good-quality secondary education.
2 Provide leadership, set ambitious targets and forge effective partnerships International evidence provides insights into the specific policies that can accelerate progress towards EFA.

But the crucial ingredient for success is leadership.

There is no substitute for sustained political commitment.

Political leaders need to put education at the centre of national development strategies and use their influence to make equity a shared goal throughout society.
They also need to reach beyond government agencies to involve civil society, the voluntary and private sectors, and groups representing the poor in policy processes.

Ensuring that the voices of the poor and marginalized are heard in policy formulation is a condition for strengthened equity.

Setting clear policy objectives is crucial.


Successful governments have fixed ambitious long-term goals that are supported by clear medium-term ‘stepping stone’ targets and backed by commitments on inputs ranging from classroom construction to teacher recruitment and textbook supply.

Governments with less successful track records have often set ambitious goals but failed to underpin them either with coherent strategies for delivery or with predictable budget commitments.
Another crucial condition for progress in education is ensuring that the policies of departments whose work affects education are complementary rather than contradictory.

Many countries have improved policy coherence within the education sector and between education and other line ministries.

Sector-wide approaches have played an important role in this regard.

Even so, problems remain.

Education targets are often weakly integrated in national budgets and financing strategies.
Strategies for achieving equity targets in education should also consider wider constraints linked to nutrition, health and poverty Conditional cash transfer programmes in Brazil and Mexico have demonstrated that strategies for tackling child labour, poor health and weak nutrition can yield large benefits for education 2 3 8 C H A P T E R 5 3 Strengthen wider anti-poverty commitments It is a widely overlooked fact that sustained progress in education cannot be built on the foundations of mass poverty and deep social inequality.
One in three pre-school children has what amounts to brain damage as a result of malnutrition, which constitutes a formidable obstacle to UPE.

The associated facts that 10 million children die before the age of 5 and that tens of millions more suffer life-threatening diseases represent further limitations on the human right to education.

National governments should strengthen policies for combating the poverty, inequality and wider structural factors that produce such outcomes.
Cash transfer programmes, targeted health interventions and more equitable public spending in health service provision all have a role to play.

Child malnutrition must also be accorded a far higher priority.

The bottom line is that progress in education is being held back by the failings of current national strategies for poverty reduction.

The Dakar Framework envisages the integration of education planning within effective national poverty reduction strategies.
This is an important goal because many of the most insurmountable barriers to access and learning are located beyond the school.

Unfortunately, progress towards coherent national strategies linking education and poverty reduction has been limited.

Many countries urgently need poverty reduction strategies to address the health, nutrition and wider poverty- related constraints on progress towards EFA.
Conditional cash transfer programmes in countries such as Brazil and Mexico have demonstrated that strategies for tackling child labour, poor health and weak nutrition can yield large benefits for education.

All governments and donors should actively explore the potential for strengthening and expanding social protection as part of the strategy for advancing the EFA agenda.
Within the education sector, far more weight should be attached to targeted interventions such as incentive programmes for children who are disadvantaged as a consequence of poverty, gender, caste, ethnicity or location.

Ensuring that schools and classroom are built and teachers allocated so they are within reach of marginalized communities is also important.

4 Raise quality standards Senior policy-makers should renew and strengthen the Dakar pledge on education quality.
Policies should emphasize new approaches to teaching and learning, improved provision of learning materials and strong incentives to raise standards.

National authorities, community officials and local school leaders must work together to ensure that every school becomes an effective learning environment.
Such an environment requires well-nourished and motivated students, well-trained teachers using adequate facilities and instructional materials, a relevant, local-language curriculum, and a welcoming, gender-sensitive, healthy, safe environment that encourages learning.

This must be accompanied by a clear definition and accurate assessment of learning outcomes.

It is also important that students receive the threshold international benchmark of 850 hours per year in instructional time.
These are all areas in which clear norms and policy rules have to be defined and enforced.

5 Strengthen capacity to measure, monitor and assess education quality, and inform parents and policy-makers The methods used and the information collected in monitoring and assessment exercises should be transparent and accessible to diverse education stakeholders.

Successful monitoring is not just about generating information.
It is also about creating institutional mechanisms through which monitoring can inform the development and implementation of policy.

The monitoring of education quality should include three dimensions: (i) input or enabling conditions for learning (from infrastructure and learning materials to qualified, trained teachers and adequate budgets); (ii) pedagogy and the learning process, including an appropriate language of instruction, and learning time; and (iii) learning outcomes.
Official reporting in these areas can be supplemented by monitoring undertaken by civil society organizations.

6 Scale up education financing with a commitment to equity High levels of education financing do not guarantee universal access or strong learning achievements.

Nevertheless, sustained underfinancing is unequivocally bad for efficiency, equity and education quality.

Many developing countries, especially (though not exclusively) in South Asia, chronically underinvest in education.
Under- financing is not consistent with a commitment to EFA or the targets set in the Dakar Framework for Action.

To make matters worse, current spending patterns are often pro-rich rather than pro-poor.

Decentralization, under the right conditions, can help foster political accountability, but it is not a panacea for inequality.

On the contrary, financial decentralization can widen disparities to the detriment of poor regions and disadvantaged communities.
Avoiding this outcome requires a built-in commitment to equity in the financing formulas adopted for decentralization.

It is important for central government to retain a strong redistributive role, facilitating the transfer of resources from richer to poorer subnational regions.
In developing rules for transfers to subnational authorities, central governments must also attach sufficient weight to equity indicators – such as poverty levels, health status and children out of school – in allocating transfers.

The guiding principle should be that those in greatest need receive the most per capita support.

Too often the inverse is the case, with the wealthiest regions receiving the highest levels of per capita public spending in education.
While one aim of decentralization is the devolution of authority, in the area of finance governments should recognize the limits to fiscal autonomy.

In particular, fiscal autonomy for local governments should not mean authority to mobilize revenue through user charges in basic education.

As Chapter 3 suggests, the real issue raised by decentralization is not whetherto do it, but how to do it.

And the starting point has to be a commitment to decentralization with equity.
7 Recognize the limits to competition and choice Under the right conditions, competition and choice can support EFA goals.

At the same time, policy- makers need to recognize that education provision cannot be reduced to oversimplified market principles.

Imperfect and asymmetric information, time and distance constraints, and institutional capacity failings all impose limits on competition.

Meanwhile, poverty and social disadvantage limit choice.
Public-private partnership models aim to P O L I C Y C O N C L U S I O N S A N D R E C O M M E N D AT I O N S W h a t g o v e r n m e n t s c a n d o expand choice by separating education finance and management.

Voucher programmes, state funding for private schools and the development of independent schools are all public-private partnership strategies – and each has a limited record of success, even in the developed world.
Private schools in Sweden are one exception to this rule – but the ‘Swedish model’ is not readily transferable to other developed countries, let alone developing ones.

Choice and competition are often presented as a solution to the failings of public provision.

Some commentators view low-fee private schools in the same light.

The failings of public provision are strongly evident in many countries.

And millions of poor households are voting with their feet, switching to low-fee private providers.
However, the vast majority of the world’s children – especially those from poor and disadvantaged households – will depend on public provision for the foreseeable future.

Low-fee private schools will continue to play a role, but they are symptoms of state failure and the entry costs impose a considerable burden on poor households.

Introducing choice and competition into a system in which all parents have the option of sending their children to a good-quality public provider is one thing.
Using private providers to compensate for state failure is quite another – and in most cases will not be the best option when it comes to efficiency and equity.

The bottom line, for governments in countries where public-sector basic education is failing the poor, is to fix the system first and consider options for competition between providers second.
8 Strengthen the recruitment, deployment and motivation of teachers An adequate supply of motivated, qualified and properly trained teachers is a foundation of good-quality education for all.

All countries have to assess remuneration levels consistent with building that foundation.

Poverty-level wages and poor conditions, moreover, are not consistent with strong motivation.

Improving the teaching environment through the provision of learning materials, training and support is vital for raising morale.
Hiring contract teachers can reduce the marginal costs of recruitment and thereby release resources for investment in other areas.

But it can also reduce the quality of recruited teachers and weaken Policy-makers need to recognize that education provision cannot be reduced to oversimplified market principles motivation, with damaging consequences for children in classrooms.
There is a case for recourse to contract teacher recruitment as a strategy for reaching marginalized groups and underserved areas.

However, it is important for governments to recognize the potential trade-off between teacher quantity and quality that can come with contract teaching.

Other strategies for reaching marginalized groups include incentives for teachers to locate in underserved areas and measures to increase recruitment from disadvantaged communities.
Performance-related pay for teachers is a popular topic in discussions of governance.

In practice, however, it is difficult to implement and unlikely to create incentives for improved learning achievement.

It may also have damaging consequences for equity as schools and teachers focus on the students most likely to attain high scores.

In some countries, especially in sub-Saharan Africa, expanding teacher recruitment is an urgent priority.
UPE by 2015 will not be attainable without a marked increase in the rate of recruitment and retention.

In many cases, donors will need to increase support to achieve these goals.

If teacher retirement is taken into account, sub-Saharan Africa alone will need to recruit around 3.8 million teachers by 2015.

Donors need to increase aid for basic education to at least US$11 billion annually The role of aid donors National governments carry the main responsibility for achieving the EFA goals.
Many of the developing countries that are most off track are highly dependent on aid, and will remain so for the foreseeable future.

Developed countries can support progressive strategies by increasing their level of financial commitment, improving aid practices and ensuring that aid is used to support national priorities.

Delivering on commitments and expanding the donor base Donors pledged in 2005 to ‘double aid to halve poverty’.

Since then they have instead cut development assistance.
Debt relief explains only part of the reduction.

Meeting the 2005 commitment requires an additional US$30 billion (at 2004 prices) – some three times the increases currently set out in aid spending plans.

The shortfall for sub-Saharan Africa is around US$14 billion (2004 prices).

Failure to act on the Gleneagles commitment will hamper global poverty reduction efforts, with damaging consequences for education.
More detailed national and international efforts to update estimates of education financing gaps are required; however, having promised that no national strategy would fail for want of finance, donors need to increase aid for basic education to at least US$11 billion annually.

In 2006, commitments to basic education in low-income countries totalled US$3.8 billion – around a third of the level required.
The two year average for 2005 and 2006 points unmistakably towards a reduction in commitments for basic education, compared with the previous two years.

This is true for developing countries in general and the poorest countries in particular.

Failure to reverse the trend will adversely affect future disbursements.

As an immediate priority, donors should commit to an increase of US$7 billion annually in aid financing for basic education.
Aid flows to basic education are heavily concentrated in a small group of donors.

Just three donors – the Netherlands, the United Kingdom and the International Development Association – accounted for half of all aid commitments and 85% of the increase in disbursements in 2006.

The narrowness of the donor base is a source of underfinancing.

It is also a source of potential instability and unpredictability in aid.
Strengthening the commitment to equity UPE will not be achieved without effective aid partnerships.

What is needed is the renewal and the realization of the compact embodied in the Dakar Framework for Action.

There are responsibilities and obligations on both sides.

But donors need to demonstrate a far greater level of resolve and political leadership.

This Report proposes action in four areas.

Several donors appear to attach a low priority to equity in their education aid.
France, Germany and Japan, for example, have shown neglect for basic education and low-income countries.

Calculations for this Report suggest that France and Germany devote far more aid to bringing students to study in their domestic tertiary education systems than they spend on aid to basic education.
If developing AT I O N S T h e r o l e o f a i d d o n o r s / T h e r o l e o f n o n - g o v e r n m e n t a c t o r s country governments followed this practice and allocated well over half their education budget to the tertiary level, they would – justifiably – stand accused of questionable governance practices.
To avoid potential double standards, it is important for donors to consider whether their aid allocation patterns are consistent with a commitment to equity and to the spirit of the Dakar Framework for Action.

Getting behind the Fast Track Initiative The balance sheet of the Fast Track Initiative (FTI) is a source of growing concern.
In mid-2008, there were thirty-five countries with plans endorsed by the FTI, entailing programme costs estimated at US$8 billion and external financing needs of US$2 billion.

The gap between current aid pledges and external financing requirements was around US$640 million.

With eight countries expected to join the FTI by the end of the year, that gap could climb to US$1 billion.
Another thirteen countries are scheduled to join in 2009, which means the total annual financing gap could reach US$2.2 billion.

Closing that gap is the responsibility of a wide range of bilateral and multilateral donors.

However, working on an assumption that the Catalytic Fund of the FTI might be expected to cover around 40% to 50%, a prospective 2010 financing gap of around US$1 billion will remain.
These looming shortfalls pose a real and imminent threat to efforts to achieve the targets set in the Dakar Framework for Action.

They also call into question donors’ commitments to ensure that no viable plan for achieving UPE and wider education goals would be allowed to fail for want of financial support.

Addressing the FTI deficit is an urgent priority.

At the same time, it is important for the FTI to broaden its currently narrow base of donor support.
Delivering on the Paris agenda Progress towards the goals set in the Paris Declaration for improved aid quality has been limited and uneven.

Donors could do far more to reduce transaction costs and improve aid effectiveness through greater alignment of aid behind national priorities, better coordination, increased use of national financial management systems and improved predictability in aid flows.

Increased emphasis on programme-based aid creates opportunities and threats.
The opportunities lie in the potential for more effective national planning and donor alignment behind national priorities.

The threats derive from donors’ ability to use collective action through programme aid to assert their priorities.

There are no easy answers – but donors must engage in genuine dialogue.

The role of non-government actors This Report has emphasized the central importance of government leadership and public policy.
That is not to minimize the responsibilities and capacities of other actors.

Achieving EFA requires partnerships at many levels – between schools and parents, between civil society organizations and governments, between state and non-state education providers.

Civil society has a critical role to play in strengthening equity in education.
Organizations of the marginalized – slum dwellers, child labourers, members of low castes, indigenous people – have been in the forefront of international efforts to extend education to all, often in the face of government indifference or outright hostility.

National and international non-government organizations have also emerged as key EFA actors, holding governments to account, supporting provision and building capacity.
Governance reform provides an opportunity to strengthen the voice and effectiveness of civil society organizations and to enhance participation and accountability.

Governance in education cannot be treated in isolation from wider governance issues.

Democracy, transparency and the rule of law are enabling conditions for effective participation and accountability.
When citizens lack a voice in choosing their government, or when they face arbitrary laws, they are unlikely to have an effective voice in framing education priorities.

Within the education sector, governance reforms can play a role in devolving authority to parents and communities.

Yet devolution is not an automatic ticket to empowerment; there is a danger that poor and marginalized communities will lack the capacities and resources needed for effective management.
To ensure that devolved responsibilities do not result in a further widening of equity gaps, schools that are in disadvantaged areas or serving disadvantaged groups need to be provided with extra resources and support.
Governance reform provides an opportunity to strengthen the voice and effectiveness of civil society organizations, and to enhance participation and accountability / s e h g u H n p s i r C © i A committed teacher opens young minds to the excitement and benefits of education, in rural Mali 2 4 2 Education for All Global Monitoring Report 002 9 Annex The Education for All Development Index Introduction .

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244 Table 1: The EFA Development Index (EDI) and its components, 2006 .

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248 Table 2: Countries ranked according to value of EDI and components, 2006 .

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250 Table 3: Change in EDI and its components between 1999 and 2006 .

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251 Global and regional patterns in education decision-making .

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252 Table 1: Levels of decision-making in primary education, by function and region, circa 2006/2007 .

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253 Statistical tables Introduction .

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256 Table 1: Background statistics .

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260 Table 2: Adult and youth literacy .

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268 Table 3A: Early childhood care and education (ECCE): care .

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276 Table 3B: Early childhood care and education (ECCE): education .

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284 Table 4: Access to primary education .

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292 Table 5: Participation in primary education .

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300 Table 6: Internal efficiency: repetition in primary education .

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308 Table 7: Internal efficiency: primary education dropout and completion .

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316 Table 8: Participation in secondary education .

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324 Table 9A: Participation in tertiary education .

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332 Table 9B: Tertiary education: distribution of students by field of study and female share in each field, school year ending in 2006 .

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340 Table 10A: Teaching staff in pre-primary and primary education .

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348 Table 10B: Teaching staff in secondary and tertiary education .

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356 Table 11: Commitment to education: public spending .

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364 Table 12: Trends in basic or proxy indicators to measure EFA goals 1, 2, 3, 4 and 5 .

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372 Table 13: Trends in basic or proxy indicators to measure EFA goal 6 .

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380 Aid tables Introduction .

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389 Table 1: Bilateral and multilateral ODA .

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391 Table 2: Bilateral and multilateral aid to education .

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392 Table 3: ODA recipients .

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394 Table 4: Recipients of aid to education .

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398 Glossary .

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407 References .

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414 Abbreviations .

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440 Index .

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442 2 4 3 9 0 0 2 t r o p e R A N N E X The Education For All Development Index Introduction The EFA goals represent more than the sum of their individual parts.
While each is individually important, it is also useful to have a means of indicating achievement of EFA as a whole.

The EFA Development Index (EDI), a composite of relevant indicators, provides one way of doing so.

Ideally, it should reflect all six EFA goals but, due to data constraints, it currently focuses only on the four most easily quantifiable EFA goals: universal primary education (UPE), adult literacy, the quality of education and gender parity.
The two goals not yet included in the EDI are goals 1 and 3.

Neither has a quantitative target for 2015.

Goal 1 (early childhood care and education) is multidimensional and covers both the care and education aspects.

The indicators currently available on this goal cannot easily be incorporated in the EDI because national data are insufficiently standardized and reliable, and comparable data are not available for most countries (see Chapter 2 and EFA Global Monitoring Report 2007).
Goal 3 (learning needs of youth and adults) has not yet been sufficiently defined for quantitative measurement (see Chapter 2).

In accordance with the principle of considering each goal to be equally important, one indicator is used as a proxy measure for each of the four EDI components1 and each component is assigned equal weight in the overall index.

The EDI value for a particular country is thus the arithmetic mean of the observed values for each component.
Since the components are all expressed as percentages, the EDI value can vary from 0 to 100% or, when expressed as a ratio, from 0 to 1.

The closer a country’s EDI value is to the maximum, the greater the extent of its overall EFA achievement and the nearer the country is to the EFA goal as a whole.

Choice of indicators as proxy measures of EDI components In selecting indicators, relevance has to be balanced with data availability.

1.

The EDI’s gender component is itself a composite index.
2 4 4 Universal primary education The UPE goal includes both universal access to and universal completion of primary education.

However, while both access and participation at this level are relatively easy to measure, there is a lack of consensus on the definition of primary school completion.
Therefore, the indicator selected to measure UPE achievement (goal 2) in the EDI is the total primary net enrolment ratio (NER), which reflects the percentage of primary school-age children who are enrolled in either primary or secondary school.

Its value varies from 0 to 100%.

A NER of 100% means all eligible children are enrolled in school in a given school year, even though some of them may not complete it.
However, if the NER is at 100% for many consecutive years, it may imply that all children enrolled do complete school.

Adult literacy The adult literacy rate is used as a proxy to measure progress towards the first part of goal 4.2 This has its limitations.

First, the adult literacy indicator, being a statement about the stock of human capital, is slow to change and thus it could be argued that it is not a good ‘leading indicator’ of year-by-year progress.
Second, the existing data on literacy are not entirely satisfactory.

Most of them are based on ‘conventional’ non-tested methods that usually overestimate the level of literacy among individuals.3 New methodologies, based on tests and on the definition of literacy as a continuum of skills, are being developed and applied in some countries to improve the quality of literacy data.

Providing a new data series of good quality for even a majority of countries will take many years, however.
The literacy rates now used are the best currently available internationally.

2.

The first part of goal 4 is: ‘Achieving a 50 per cent improvement in levels of adult literacy by 2015, especially for women’.

To enable progress towards this target to be monitored for all countries, whatever their current adult literacy level, it was decided as of the EFA Global Monitoring Report 2006to interpret it in terms of a reduction in the adult illiteracy rate.

3.
3.

In most countries, particularly developing countries, current literacy data are derived from methods of self-declaration or third-party reporting (e.g.

a household head responding on behalf of other household members) used in censuses or household surveys.

In other cases, particularly as regards developed countries, they are based on education attainment proxies as measured in labour force surveys.
Neither method is based on any test, and both are subject to bias (overestimation of literacy), which affects the quality and accuracy of literacy data.

T H E E D U C AT Quality of education There is considerable debate about the concept of quality and how it should be measured.
Several proxy indicators are generally used to measure quality of education, among them measures of students’ learning outcomes, which are widely used for this purpose, particularly among countries at similar levels of development.

However, measures of learning achievement are incomplete, as they are often limited to basic skills (reading, numeracy, science) and do not include values, capacities and other non-cognitive skills that are also important aims of education (UNESCO, 2004, pp.

43-4).
43-4).

They also tell nothing about the cognitive value added by schooling (as opposed to home background) or the distribution of ability among children enrolled in school.4 Despite these drawbacks, learning outcomes would likely be the most appropriate single proxy for the average quality of education, but as comparable data are not yet available for a large number of countries, it is not yet possible to use them in the EDI.
Among the feasible proxy indicators available for a large number of countries, the survival rate to grade 5 seems to be the best available for the quality of education component of the EDI.5 Figures 1, 2 and 3 show that there is a clear positive link between such survival rates and learning achievement across various regional and international assessments.

The coefficient of correlation (R2) between survival rates and learning outcomes in reading is 37% (Figure 1).
Education systems capable of retaining a larger proportion of their pupils to grade 5 tend to perform better, on average, in student assessment tests.

The survival rate to grade 5 is associated even more strongly with learning outcomes in mathematics (with a coefficient of 45%; Figure 2) and science (42%; Figure 3).

Another possible proxy indicator for quality is the pupil/teacher ratio (PTR).
Among Latin American countries participating in the 2006 Segundo Estudio Regional Comparativo y Explicativo (SERCE) assessment, the association between this indicator and learning outcomes in mathematics is strong (45%), about the same as for the survival rate to grade 5.

Many other studies, however, produce much more ambiguous evidence of the relationship between PTRs and learning outcomes (UNESCO, 2004).
In a multivariate context, PTRs are associated with higher learning outcomes in some studies, but not in many others.

In addition, 4.

Strictly speaking, it would be necessary to compare average levels of cognitive achievement for pupils completing a given school grade across countries with similar levels and distributions of income, and with similar levels of NER, so as to account for home background and ability cohort effects.

5.

See EFA Global Monitoring Report 2003/4,Appendix 2, for background.
Figure 1: Survival rates to grade 5 and learning outcomes in reading at lower secondary level, 2006 Countries participating in PISA, 2006 100 80 60 40 20 ) % ( 5 e d a r g o t e t a r l a v i v r u S y = -0.138x + 101.03 R2 = 0.3732 0 0 20 40 60 80 100 15-year-old students performing below or at level 1 in reading literacy (%) Sources: Annex, Statistical Table 7; OECD (2007b).
Figure 2: Survival rates to grade 5 and learning outcomes in mathematics at primary level, 2006 100 Cuba Costa Rica Uruguay Countries participating in SERCE, 2006 Chile Mexico Argentina Peru Panama Paraguay Colombia Brazil El Salvador ) % ( 5 e d a r g o t e t a r l a v i v r u S 80 60 Ecuador Guatemala Nicaragua Dominican Rep. y = -0.7222x + 97.26 R2 = 0.4538 40 0 10 20 30 40 50 Sixth-grade students performing below or at level 1 in mathematics (%) Sources: Annex, Statistical Table 7; UNESCO-OREALC (2008).
Figure 3: Survival rates to grade 5 and learning outcomes in science at lower secondary level, 2006 Countries participating in PISA, 2006 100 80 60 40 20 ) % ( 5 e d a r g o t e t a r l a v i v r u S y = -0.1438x + 100.97 R2 = 0.424 0 0 20 40 60 80 100 15-year-old students performing below or at level 1 in science literacy (%) Sources: Annex, Statistical Table 7; OECD (2007b).
the relationship seems to vary by the level of mean test scores.

For low levels of test scores, a decrease in the number of pupils per teacher has a positive impact on learning outcomes, but for higher levels of test scores, additional teachers, which leads to lower PTRs, have only limited impact.
For these reasons, the survival rate is used as a safer proxy for learning outcomes and hence for the education quality component of the EDI.6 Gender The fourth EDI component is measured by a composite index, the gender-specific EFA index (GEI).
Ideally, the GEI should reflect the whole gender-related EFA goal, which calls for ‘eliminating gender disparities in primary and secondary education by 2005, and achieving gender equality in education by 2015, with a focus on ensuring girls’ full and equal access to and achievement in basic education of good quality’.
There are thus two subgoals: gender parity (achieving equal participation of girls and boys in primary and secondary education) and gender equality (ensuring that educational equality exists between boys and girls).

6.

Another reason is that survival rates, like the other EDI components, but unlike PTRs, range from 0% to 100%.

Therefore, the use of the survival rate to grade 5 in the EDI avoids a need to rescale the data.
2 4 6 The first subgoal is measured by the gender parity indexes (GPIs) of the gross enrolment ratios (GERs) at primary and secondary levels.

Defining, measuring and monitoring gender equality in education is difficult, as it includes both quantitative and qualitative aspects (see Chapter 2; UNESCO, 2003).

Essentially, measures of outcomes, which are also part of gender equality, are needed for a range of educational levels, disaggregated by sex.
No such measures are available on an internationally comparable basis.

As a step in that direction, however, the GEI includes the gender parity measure for adult literacy.

Thus, the GEI is calculated as a simple average of three GPIs: for the GER in primary education, for the GER in secondary education and for the adult literacy rate.

This means the GEI does not fully reflect the equality aspect of the EFA gender goal.
The GPI, when expressed as the ratio of female to male enrolment ratios or literacy rates, can exceed unity when more girls/women than boys/men are enrolled or literate.

For the purposes of the GEI the standard F/M formula is inverted to M/F in cases where the GPI is higher than 1.

This solves mathematically the problem of including the GEI in the EDI (where all components have a theoretical limit of 1, or 100%) while maintaining the GEI’s ability to show gender disparity.
Figure 4 shows how ‘transformed’ GPIs are arrived at to highlight gender disparities that disadvantage males.

Once all three GPI values have been calculated, and converted into ‘transformed’ GPIs (from 0 to 1) where needed, the composite GEI is obtained by calculating a simple average of the three GPIs, with each being weighted equally.

Figure 5 illustrates the calculation for Uruguay, using data for the school year ending in 2006.
The GPIs in primary education, secondary education and adult literacy were 0.973, 1.161 and 1.007, respectively, resulting in a GEI of 0.943.

GEI = 1/3 (primary GPI) + 1/3 (transformed secondary GPI) + 1/3 (transformed adult literacy GPI) GEI = 1/3 (0.973) + 1/3 (0.862) + 1/3 (0.993) = 0.943 Calculating the EDI The EDI is the arithmetic mean of its four components: total primary NER, adult literacy rate, GEI and survival rate to grade 5.
As a simple average, the EDI may mask important variations among its components: for example, results for goals on which a country has made less progress can offset its advances on others.
Since all the EFA goals are equally important, a synthetic indicator such as the EDI is thus very useful to inform the policy T H E E D U C AT Figure 4: Calculating the ‘transformed’ GPI Figure 5: Calculating the GEI Transformed secondary education GPI (M/F) GPI (F/M) 1.161 0.862 1.20 1.00 0.800 0.600 0.400 0.200 0.00 (F/M) Primary education 0.973 GPIs (M/F) Secondary education (M/F) Adult literacy GEI 0.993 0.943 0.862 Example used: Uruguay 1.00 0.800 0.600 0.400 0.200 0.00 Example used: Uruguay debate on the prominence of all the EFA goals and to highlight the synergy among them.
Figure 6: Calculating the EDI Components Total primary NER Adult literacy rate GEI Survival rate to grade 5 1.00 0.978 0.943 0.931 EDI 0.963 Figure 6 illustrates the calculation of the EDI, again using Uruguay as an example.

The total primary NER, adult literacy rate and GEI are for 2006 while the survival rate to grade 5 is for 2005.

Their values were 1.00, 0.978, 0.943 and 0.931, respectively, resulting in an EDI of 0.963.
EDI = 1/4 (total primary NER) + 1/4 (adult literacy rate) + 1/4 (GEI) + 1/4 (survival rate to grade 5) EDI = 1/4 (1.00) + 1/4 (0.978) + 1/4 (0.943) + 1/4 (0.931) = 0.963 Data sources and country coverage Example used: Uruguay All data used to calculate the EDI for the school year ending in 2006 are from the statistical tables in this annex and the UNESCO Institute for Statistics (UIS) database, with one exception.
Adult literacy data for some OECD countries that did not answer the annual UIS literacy survey are based on European Labour Force Survey data.

Only the 129 countries with a complete set of the indicators required to calculate the EDI are included in this analysis.

Many countries thus are not included in the EDI, among them a number of fragile states and countries with weak education statistical systems.
This fact, coupled with the exclusion of goal 1 and 3, means the EDI does not yet provide a fully comprehensive global overview of EFA achievement.
1.00 0.800 0.600 0.400 0.200 0.00 Table 1: The EFA Development Index (EDI) and its components, 2006 Ranking according to level of EDI High EDI Countries/Territories EDI Total primary NER1 Adult literacy rate Gender-specific EFA index (GEI) Survival rate to grade Medium EDI 57 58 59 60 61 62 63 64 65 2 4 8 Kazakhstan Japan3 Germany2 Norway2 United Kingdom2 Italy Denmark2 France2 Luxembourg2 Croatia New Zealand3 Iceland2 Slovenia Finland2 Austria2 Cyprus2 Netherlands2 Spain Sweden2 Republic of Korea3 Greece Cuba Aruba Poland2 Estonia Israel2 Belgium2 Hungary2 Czech Republic2 Switzerland2 TFYR Macedonia Kyrgyzstan Ireland2 Seychelles Latvia Brunei Darussalam Tajikistan Slovakia2 Lithuania Georgia3 Belarus Portugal Armenia Tonga Malaysia Romania Uruguay Bulgaria Maldives Bahrain Argentina United Arab Emirates Mexico Malta Mongolia Albania Republic of Moldova Azerbaijan Macao, China Mauritius Barbados3 Jordan Saint Lucia2 Trinidad and Tobago Panama 0.988
T H E E D U C AT I O N F O R A L L D E V E L O P M E N T I N D E X Ta b l e 1 Table 1 (continued) Ranking according to level of EDI Medium EDI Countries/Territories EDI Total primary NER1 Adult literacy rate Gender-specific EFA index (GEI) Survival rate to grade Low EDI 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 Kuwait Qatar Paraguay Venezuela, B. R. Peru Indonesia Fiji3 Bahamas3 Ecuador Bolivia Belize3 Palestinian A. T. Turkey Colombia Brazil St Vincent/Grenad.2 Tunisia South Africa Myanmar Algeria Philippines Lebanon3 Honduras Oman Cape Verde Egypt Botswana El Salvador Namibia Sao Tome and Principe Swaziland Zambia Dominican Republic Guatemala Kenya Nicaragua India Lesotho Cambodia
 Bhutan Iraq Burundi Lao PDR Bangladesh Nepal Madagascar Malawi Nigeria Rwanda Mauritania Togo Djibouti3 Pakistan Senegal Benin Yemen Mozambique Eritrea3 Guinea Ethiopia Mali Burkina Faso Niger Chad 0.651
0.730 0.737 0.622 0.932 0.806 0.879 0.620 0.651 0.785 0.358 0.442 0.726 0.458 0.574 0.746 0.899 0.697 0.650 0.715 0.663 0.576 0.737 0.809 0.644 0.812 0.725 0.565 0.332 Notes: Data in blue indicate that gender disparities are at the expense of boys or men, particularly at secondary level.
1.

Total primary NER includes children of primary school age who are enrolled in either primary or secondary schools.

2.

The adult literacy rate is a proxy measure based on educational attainment; that is, the proportion of the adult population with at least a complete primary education.

3.

Adult literacy rates are unofficial UIS estimates.

Sources: Annex, Statistical Tables 2, 5, 7 and 8; UIS database; European Commission (2007a) for proxy literacy measure for European countries.
Notes: 1.

Total primary NER includes children of primary school age who are enrolled in either primary or secondary schools.

2.

The adult literacy rate is a proxy measure based on educational attainment; that is, the proportion of the adult population with at least a complete primary education.

3.

Adult literacy rates are unofficial UIS estimates.


Sources: Annex, Statistical Tables 2, 5, 7 and 8; UIS database; European Commission (2007a) for proxy literacy measure for European countries.
2 5 0 A N N E X Table 2: Countries ranked according to value of EDI and components, 2006 Countries/ Territories High EDI Kazakhstan Japan3 Germany2 Norway2 United Kingdom2 Italy Denmark2 France2 Luxembourg2 Croatia New Zealand3 Iceland2 Slovenia Finland2 Austria2 Cyprus2 Netherlands2 Spain Sweden2 Republic of Korea3 Greece Cuba Aruba Poland2 Estonia Israel2 Belgium2 Hungary2 Czech Republic2 Switzerland2 TFYR Macedonia Kyrgyzstan Ireland2 Seychelles Latvia Brunei Darussalam Tajikistan Slovakia2 Lithuania Georgia3 Belarus Portugal Armenia Tonga Malaysia Romania Uruguay Bulgaria Maldives Bahrain Argentina United Arab Emirates Mexico Malta Mongolia Albania Medium EDI Republic of Moldova Azerbaijan Macao, China Mauritius Barbados3 Jordan Saint Lucia2 Trinidad and Tobago Panama Total primary NER1 Adult literacy rate EDI Gender- specific EFA index (GEI) Survival rate to grade 5 Countries/ Territories Total primary NER1 Adult literacy rate EDI Gender- specific EFA index (GEI) Survival rate to grade Medium EDI Kuwait Qatar Paraguay Venezuela, B. R. Peru Indonesia Fiji3 Bahamas3 Ecuador Bolivia Belize3
 Palestinian A. T. Turkey Colombia Brazil St Vincent/Grenad.2 Tunisia South Africa Myanmar Algeria Philippines Lebanon3 Honduras Oman Cape Verde Egypt Botswana El Salvador Namibia Sao Tome/Principe Swaziland Zambia Dominican Republic Guatemala Kenya Low EDI Nicaragua India Lesotho Cambodia Bhutan Iraq Burundi Lao PDR Bangladesh Nepal Madagascar Malawi Nigeria Rwanda Mauritania Togo Djibouti3 Pakistan Senegal Benin Yemen Mozambique Eritrea3 Guinea Ethiopia Mali Burkina Faso Niger Chad T H E E D U C AT I O N F O R A L L D E V E L O P M E N T I N D E X Ta b l e s 2 a n d 3 Table 3: Change in EDI and its components between 1999 and 2006 Countries/ Territories Italy Croatia Cyprus2 Cuba Aruba Estonia Hungary2 Kyrgyzstan Latvia Lithuania Romania Bulgaria Bahrain Argentina United Arab Emirates Mongolia Albania Republic of Moldova Azerbaijan Mauritius Saint Lucia Panama Paraguay
 Venezuela, B. R. Fiji3 Ecuador Bolivia Belize South Africa Namibia Swaziland Zambia Dominican Republic Guatemala Nicaragua Lesotho Iraq Bangladesh Nepal Malawi Mauritania Yemen Mozambique Ethiopia Chad EFA Development Index 1999 2006 Variation 1999–2006 (in relative terms) Change in EDI components between 1999 and 2006 (% in relative terms) Total primary NER1 Adult literacy rate Gender-specific EFA index (GEI) Survival rate to grade -1.1 -0.3 1.1 -1.1 -2.1 -1.3 -0.8 1.6 -0.7 7.8 3.5 -0.8 -1.2 -0.3 2.1 2.2 -0.2 2.9 2.7 -1.6 0.7 2.3 5.5 5.0 0.1 2.2 12.5 -3.1 11.6 6.7 6.2 3.2 3.7 22.5 0.5 4.5 9.9 25.6 31.7 -4.2 -0.4 7.6 1.6 -2.2 1.6 -3.0 -2.5 -0.8 -6.4 -6.2 -4.4 -5.1 0.8 -0.6 16.6 6.4 -0.9 -6.7 -0.1 4.9 1.6 2.2 -1.7 7.1 -4.6 0.4 0.4 3.8 -5.3 4.1 5.0 37.1 -6.3 15.1 14.2 26.0 -8.4 6.7 19.7 -7.2 24.3 34.0 45.1 107.9 18.1 0.4 0.5 0.8 0.0 0.8 0.0 0.0 0.6 0.0 0.0 0.3 0.1 2.0 0.4 6.9 - -0.2 2.0 -1.7 1.6 4.1 -0.2 -0.8 -2.3 1.3 0.7 -0.2 1.0 2.5 -0.1 0.5 2.4 0.8 1.4 0.1 -0.6 4.2 -0.2 6.1 0.2 4.6 2.0 0.0 20.6 9.4 3.3 32.0 12.8 4.4 14.0 3.0 0.0 3.1 3.7 -0.1 -2.2 1.1 4.3 1.2 -2.6 -2.1 2.0 1.5 -0.6 7.3 4.2 -2.7 1.7 0.8 -0.6 6.5 -4.3 12.3 1.4 -1.7 0.4 3.1 18.6 27.3 -5.9 5.2 10.8 -8.8 23.1 10.8 -0.4 22.9 0.2 35.3 -9.6 -15.4 -24.2 35.0 14.0 -39.7 Notes: 1.
Total primary NER includes children of primary school age who are enrolled in either primary or secondary schools.

2.

The adult literacy rate is a proxy measure based on educational attainment; that is, the proportion of the adult population with at least a complete primary education.

3.

Adult literacy rates are unofficial UIS estimates.

Sources: Annex, Statistical Tables 2, 5, 7 and 8; UIS database; European Commission (2007a) for proxy literacy measure for European countries.
2 5 1 9 0 0 2 t r o p e R A N N E X Global and regional patterns in education decision-making overnance reforms in education involve the reallocation of decision-making authority across levels of government.

These arrangements affect the roles of parents, teachers, civil servants and politicians at local and national levels.
The issues at stake range from financing to school supervision, curriculum development, and teacher recruitment and management.

Decentralization has been a dominant theme in governance reforms.

What does this mean in practice for the locus of decision-making?

A mapping of 184 countries, described in the accompanying box, finds that some broad patterns in levels of decision-making are discernable, as Table 1 shows.
One prominent finding is that, even in nominally decentralized structures, central government continues to play a key role in various areas of education service delivery – notably in designing curricula and instructional materials, in teacher governance and management, and in financing arrangements.

Other actors – including local government, schools and communities – play a highly variable role.
The following are among the findings to emerge: In most countries, the central government continues to take overall responsibility for curriculum development and the design of instructional materials.

In two-thirds of countries in Central and Eastern Europe, and North America and Western Europe, the curriculum is jointly developed by schools and teachers based on a general framework established by the central government.
The central government remains strong in many aspects of teacher governance and management, especially with regard to training standards, salary levels and conditions of service.

Teacher training is organized and run by the central government in sixty-eight of the seventy-six countries with the relevant information.
When teacher recruitment, appointment and deployment are not centralized, as in around half the cases covered, this administrative role is usually undertaken by a mix of provincial or municipal governments.

In one-quarter of Latin America and Caribbean countries, decisions in this area are shared 2 5 2 among various government levels.

Generally, schools have little control over teacher management.
With respect to infrastructure, school mapping and other decisions regarding the opening and closing of schools occur at all levels of government.

The decisions may originate at one government level and the budget to implement them at another.

In Croatia, Ethiopia, the Gambia, Latvia, Nepal and the Syrian Arab Republic, for example, the largest source of capital funds is the central government, while school infrastructure decisions are taken at municipal or provincial levels.
School inspection and supervision occurs at either national or municipal level in most countries.

The central government is the main source of funding for primary schools in more than three-quarters of the countries surveyed.

Although financial responsibility in some countries is located at the level of provinces (7% of cases) or municipalities (13%), this does not preclude high levels of central government involvement.
Municipalities are responsible for operating expenditure in one-third of the countries surveyed, mostly developed countries or countries in transition.

Parental involvement in school governance and management tends to be limited in most countries to providing additional funding and, particularly in sub-Saharan Africa, supporting school construction.
While these broad patterns are informative, there is a need to understand in more detail how decisions are made, by whom and for what purpose, within a given context.

Decisions are sometimes made at more than one level and patterns are not static – countries that have decentralized aspects of service delivery may later recentralize them.
Of particular concern for this Report, as Chapter 3 highlights, is that the ways in which decisions are made within a particular context can significantly affect educational opportunities available to the poor.
G L O B A L A N D R E G I O N A L P AT T E R N S I N E D U C AT I O N D E C I S I O N - M A K I N G Ta b l e 1 Box 1: Mapping levels of decision-making in primary education UNESCO’s International Bureau of Education (UNESCO- IBE) has compiled data for 184 countries on governance, management and financing of formal education, focusing on who makes key decisions.
The regional compilations of country reports, commissioned for this Report and available online (www.efareport.unesco.org), were used to develop a database of levels of decision- making and responsibility for primary education in six key areas: curriculum and learning materials; teacher training, management and employment conditions; school infrastructure; school supervision and inspection; financing arrangements; administration/management.
For each function, the database identifies the level at which decisions are made, including whether decision-making is shared by multiple levels.

Five levels of decision-making authority are identified: central government; subnational government (state, province, region or governorate); local government (district, municipality or other locality); school boards or other school authorities; and non-state (including the private sector, non-government organizations, communities and aid donors).
Table 1 presented in this Annex presents a summary of some indicators available in the database.

UNESCO-IBE compiled the information for the database from an array of sources, including its own 2006/2007 World Data on Education database and 2004 series of National Reports, as well as sources such as websites of ministries of education, World Bank public expenditure reviews, regional development bank reports and education reports by various international organizations.
Table 1: Levels of decision-making in primary education, by function and region, circa 2006/2007 Central government Subnational government 1 Local government 2 School3 responsibility4 Non-state5 Joint Number of countries in the sample Curriculum and learning materials Curriculum development/revision Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Development of instructional materials Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Table 1 (continued) Central government Subnational government 1 Local government 2 School3 responsibility4 Non-state5 Joint Number of countries in the sample Teachers Teacher training Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total In-service training Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total 7 7 4 9 17 3 5 16 68 9 6 2 3 7 4 2 15 48 Teacher management (recruitment, appointment, dismissal, deployment,
 promotion, transfer, discipline) Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total 0 0 0 1 10 2 2 11 26 Establishment of teacher salary levels and other conditions of service (allowances, vacations, promotions) Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Infrastructure 3 4 0 1 12 6 1 9 36 0 0 0 1 0 2 1 0 4 Establishment/opening and closure of schools, school mapping Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total G L O B A L A N D R E G I O N A L P AT T E R N S I N E D U C AT I O N D E C I S I O N - M A K I N G Ta b l e 1 Central government Subnational government 1 Local government 2 School3 responsibility4 Non-state5 Joint Number of countries in the sample 0
Table 1 (continued) Supervision School supervision/inspection practices6 Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Financing arrangements7 General Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Capital expenditure Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Personnel expenditure Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total Current or operating expenditure Arab States (20) Central and Eastern Europe (20) Central Asia (9) East Asia and the Pacific (33) Latin America and the Caribbean (41) North America and Western Europe (26) South and West Asia (9) Sub-Saharan Africa (45) Total 1.
State, province, region or governorate.

2.

District, municipality or other locality.

3.

School authorities or school boards.

4.

Only where explicitly indicated.

5.

Private sector, non- government organizations, communities, aid donors and other non-state entities.

6.

Supervision/inspection practices, unlike standards, imply direct involvement of schools.

7.

Indicates the level providing the largest share of funding.

Sources: UNESCO-IBE (2008a, 2008b, 2008c, 2008d, 2008e, 2008f, 2008g, 2008h).
2 5 5 9 0 0 2 t r o p e R A N N E X Statistical tables* Introduction The most recent data on pupils, students, teachers and expenditure presented in these statistical tables are for the school year ending in 2006.1 They are based on survey results reported to and processed by the UNESCO Institute for Statistics (UIS) before the end of May 2008.

Data received and processed after this date will be used in the next EFA Global Monitoring Report.
A small number of countries2 submitted data for the school year ending in 2007, presented in bold in the statistical tables.

These statistics refer to all formal schools, both public and private, by level of education.

They are supplemented by demographic and economic statistics collected or produced by other international organizations, including the United Nations Development Programme (UNDP), the United Nations Children’s Fund (UNICEF), the United Nations Population Division (UNPD) and the World Bank.
A total of 204 countries and territories are listed in the statistical tables.3 Most of them report their data to the UIS using standard questionnaires issued by the Institute.

For some countries, however, education data are collected via surveys carried out under the auspices of the World Education Indicators (WEI) or are provided by the Organisation for Economic Co-operation and Development (OECD) and the Statistical Office of the European Communities (Eurostat).
Population The indicators on school access and participation in the statistical tables were calculated using the 2006 revision of population estimates produced by the UNPD.

Because of possible differences between national population estimates and those of the United Nations, these indicators may differ from those published by individual 1.

This means 2005/2006 for countries with a school year that overlaps two calendar years and 2006 for those with a calendar school year.

2.
2.

Egypt, Ethiopia, Ghana, Kazakhstan, the Marshall Islands, the Federated States of Micronesia, Nauru, the Republic of Korea, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Sao Tome and Principe, Serbia, Seychelles, Sierra Leone, the United Republic of Tanzania, Uzbekistan and Vanuatu.

3.

Serbia and Montenegro are now presented separately as two independent entities.
countries or by other organizations.4 The UNPD does not provide data by single year of age for countries with a total population of fewer than 80,000.

Where no UNPD estimates exist, national population figures, when available, or estimates from the UIS were used to calculate enrolment ratios.

ISCED classification Education data reported to the UIS are in conformity with the 1997 revision of the International Standard Classification of Education (ISCED).
In some cases, data have been adjusted to comply with the ISCED97 classification.

Data for the school year ending in 1991 may conform to the previous version of the classification, ISCED76, and therefore may not be comparable in some countries with those for years after 1997.

ISCED is used to harmonize data and introduce more international comparability among national education systems.

Countries may have their own definitions of education levels that do not correspond to ISCED.
Some differences between nationally and internationally reported enrolment ratios may be due, therefore, to the use of these nationally defined education levels rather than the ISCED standard, in addition to the population issue raised above.

Adult participation in basic education ISCED does not classify education programmes by participants’ age.

For example, any programme with a content equivalent to primary education, or ISCED 1, may be classed as ISCED 1 even if provided to adults.
The guidance the UIS provides for respondents to its regular annual education survey, on the other hand, asks countries to exclude ‘data on programmes designed for people beyond regular school age’.

As for the guidance for the UIS/OECD/Eurostat (UOE) and WEI questionnaires, until 2005 it stated that ‘activities classified as “continuing”, “adult” or “non-formal” education should be included’ if they ‘involve studies with subject content similar to regular educational programmes’ or if ‘the underlying 4.
Where obvious inconsistencies exist between enrolment reported by countries and the United Nations population data, the UIS may decide to not calculate or publish the enrolment ratios.

This is the case, for example, with China, publication of whose NER is suspended pending further review of the population data.

* For more detailed statistics and indicators, please consult the website: www.efareport.unesco.org 2 5 6 programmes lead to similar potential qualifications’ as do the regular programmes.
Since 2005, however, the countries involved in the UOE/WEI survey have been requested to report data for such programmes separately so that the UIS can exclude them when calculating internationally comparable indicators.

Despite the UIS instructions, data from countries in the annual survey may still include pupils who are substantially above the official age for basic education.
Literacy data UNESCO has long defined literacy as the ability to read and write, with understanding, a short simple statement related to one’s daily life.

However, a parallel definition arose with the introduction in 1978 of the notion of functional literacy.
A definition approved in the UNESCO General Conference that year stated that a person was considered functionally literate who could engage in all activities in which literacy is required for effective functioning of his or her group and community, and also for enabling him or her to continue to use reading, writing and calculation for his or her own and the community’s development.
In many cases, the current UIS literacy statistics rely on the first definition and are largely based on data sources that use a ‘self-declaration’ method: respondents are asked whether they and the members of their household are literate, as opposed to being asked a more comprehensive question or to demonstrate the skill.
Some countries assume that persons who complete a certain level of education are literate.5 As definitions and methodologies used for data collection differ by country, data need to be used with caution.

Literacy data in this report cover adults aged 15 and over as well as youth aged 15 to 24.

They refer to two periods, 1985–1994 and 2000–2006.

Data for the first period are mostly based on observed information obtained from national censuses and surveys taken during that period.
For the second period, most of the literacy data in the table are UIS estimates.

They refer to 2006 and are based on the most recent observed national data.

For countries indicated with an asterisk (*), for which estimates could not be made, national observed literacy data are used.

The reference years and literacy definitions for each country are presented in a longer version of this introduction, posted on the EFA Global Monitoring Report 5.
For reliability and consistency reasons, the UIS has decided no longer to publish literacy data based on educational attainment proxies.

Only data reported by countries based on the ‘self-declaration method’ and ‘household declaration’ are included in the statistical tables.

However, in the absence of such data, educational attainment proxies are used to calculate the EDI for some countries, particularly developed ones.

S TAT I S T I C A L TA B L E S I n t r o d u c t i o n website.
Both UIS estimates and projections to 2015 presented in the literacy statistical table are produced using the Global Age-specific Literacy Projections Model.

For a description of the projection methodology, see p. 261 of the EFA Global Monitoring Report 2006, as well as Global Age-specific Literacy Projections Model (GALP): Rationale, Methodology and Software, available at www.uis.unesco.org/TEMPLATE/pdf/Literacy/GALP.pdf.
In many countries, interest in assessing the literacy skills of the population is growing.

In response to this interest, the UIS has developed a methodology and data collection instrument called the Literacy Assessment and Monitoring Programme (LAMP).

Following the example of the International Adult Literacy Survey (IALS), LAMP is based on the actual, functional assessment of literacy skills.
It aims to provide literacy data of higher quality and is based on the concept of a continuum of literacy skills rather than the common literate/illiterate dichotomy.

Estimates and missing data Both actual and estimated education data are presented throughout the statistical tables.

When data are not reported to the UIS using the standard questionnaires, estimates are often necessary.

Wherever possible, the UIS encourages countries to make their own estimates, which are presented as national estimates.
Where this does not happen, the UIS may make its own estimates if sufficient supplementary information is available.

Gaps in the tables may also arise where data submitted by a country are found to be inconsistent.

The UIS makes every attempt to resolve such problems with the countries concerned, but reserves the final decision to omit data it regards as problematic.
To fill the gaps in the statistical tables, data for previous school years were included when information for the school year ending in 2006 was not available.

Such cases are indicated by a footnote.

Data processing timetable The timetable for collection and publication of data used in this report was as follows.

June 2006 (or December 2006 for some countries with a calendar school year): the final school year in the data collection period ended.
November 2006 and June 2007: questionnaires were sent to countries whose data are collected directly either by the UIS or through the WEI and UOE questionnaires, with data submission deadlines of 31 March 2007, 1 August 2007 and 30 September 2007, respectively June 2007: after sending reminders by e-mail, fax, phone and/or post, the UIS began to process data and calculate indicators.
September 2007: estimation was done for missing data.

October 2007: provisional statistical tables were produced and draft indicators sent to member states for their review.

End February 2008: the first draft of statistical tables was produced for the EFA Global Monitoring Report.

April 2008: the final statistical tables were sent to the Report team.
Regional averages Regional figures for literacy rates, gross intake rates, gross and net enrolment ratios, school life expectancy and pupil/teacher ratios are weighted averages, taking into account the relative size of the relevant population of each country in each region.

The averages are derived from both published data and broad estimates for countries for which no reliable publishable data are available.
The figures for the countries with larger populations thus have a proportionately greater influence on the regional aggregates.

Where not enough reliable data are available to produce an overall weighted mean, a median figure is calculated for countries with available data only.

Capped figures There are cases where an indicator theoretically should not exceed 100 (the NER, for example), but data inconsistencies may have resulted nonetheless in the indicator exceeding the theoretical limit.
In these cases the indicator is ‘capped’ at 100 but the gender balance is maintained: the higher value, whether for male or female, is set equal to 100 and the other two values – the lower of male or female plus the figure for both sexes – are then recalculated so that the gender parity index for the capped figures is the same as that for the uncapped figures.

Footnotes to the tables, along with the glossary following the statistical tables, provide additional help in interpreting the data and information.
Symbols used in the statistical tables (printed and web versions) * National estimate ** UIS estimate … Missing data — Magnitude nil or negligible .

Category not applicable ./.

Data included under another category Composition of regions World classification6 Countries in transition (12): Countries of the Commonwealth of Independent States, including 4 in Central and Eastern Europe (Belarus, Republic of Moldova, Russian Federationw, Ukraine) and the countries of Central Asia minus Mongolia.
Developed countries (44): North America and Western Europe (minus Cypruso and Israelo); Central and Eastern Europe (minus Belarus, the Republic of Moldova, the Russian Federationw, Turkeyo and Ukraine); Australiao, Bermuda, Japano and New Zealando.

Developing countries (148): Arab States; East Asia and the Pacific (minus Australiao, Japano and New Zealando); Latin America and the Caribbean (minus Bermuda); South and West Asia; sub-Saharan Africa; Cypruso, Israelo, Mongolia and Turkeyo.
EFA regions Arab States (20 countries/territories) Algeria, Bahrain, Djibouti, Egyptw, Iraq, Jordanw, Kuwait, Lebanon, Libyan Arab Jamahiriya, Mauritania, Morocco, Oman, Palestinian Autonomous Territories, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisiaw, United Arab Emirates and Yemen.
Central and Eastern Europe (21 countries) Albaniao, Belarus, Bosnia and Herzegovinao, Bulgariao, Croatia, Czech Republico, Estoniao, Hungaryo, Latviao, Lithuaniao, Montenegro, Polando, Republic of Moldova, Romaniao, Russian Federationw, Serbia, Slovakia, Sloveniao, The former Yugoslav Republic of Macedoniao, Turkeyo and Ukraine.

6.

This is a United Nations Statistical Division country classification revised in 2004.
Central Asia (9 countries) Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan and Uzbekistan.
East Asia and the Pacific (33 countries/ territories) Australiao, Brunei Darussalam, Cambodia, Chinaw, Cook Islands, Democratic People’s Republic of Korea, Fiji, Indonesiaw, Japano, Kiribati, Lao People’s Democratic Republic, Macao (China), Malaysiaw, Marshall Islands, Micronesia (Federated States of), Myanmar, Nauru, New Zealando, Niue, Palau, Papua New Guinea, Philippinesw, Republic of Koreao, Samoa, Singapore, Solomon Islands, Thailandw, Timor-Leste, Tokelau, Tonga, Tuvalu, Vanuatu and Viet Nam.
East Asia (16 countries/territories) Brunei Darussalam, Cambodia, Chinaw, Democratic People’s Republic of Korea, Indonesiaw, Japano, Lao People’s Democratic Republic, Macao (China), Malaysiaw, Myanmar, Philippinesw, Republic of Koreao, Singapore, Thailandw, Timor-Leste and Viet Nam.
Pacific (17 countries/territories) Australiao, Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Zealando, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu and Vanuatu.
Latin America and the Caribbean (41 countries/territories) Anguilla, Antigua and Barbuda, Argentinaw, Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazilw, British Virgin Islands, Cayman Islands, Chilew, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaicaw, Mexicoo, Montserrat, Netherlands Antilles, Nicaragua, Panama, Paraguayw, Peruw, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguayw and the Bolivarian Republic of Venezuela.
Caribbean (22 countries/territories) Anguilla, Antigua and Barbuda, Aruba, Bahamas, Barbados, Belize, Bermuda, British Virgin Islands, Cayman Islands, Dominica, Grenada, Guyana, Haiti, Jamaicaw, Montserrat, Netherlands Antilles, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, and Turks and Caicos Islands.
S TAT I S T I C A L TA B L E S I n t r o d u c t i o n Latin America (19 countries) Argentinaw, Bolivia, Brazilw, Chilew, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexicoo, Nicaragua, Panama, Paraguayw, Peruw, Uruguayw and the Bolivarian Republic of Venezuela.
North America and Western Europe (26 countries/territories) Andorra, Austriao, Belgiumo, Canadao, Cypruso, Denmarko, Finlando, Franceo, Germanyo, Greeceo, Icelando, Irelando, Israelo, Italyo, Luxembourgo, Maltao, Monaco, Netherlandso, Norwayo, Portugalo, San Marino, Spaino, Swedeno, Switzerlando, United Kingdomo and United Stateso.

South and West Asia (9 countries) Afghanistan, Bangladesh, Bhutan, Indiaw, Islamic Republic of Iran, Maldives, Nepal, Pakistan and Sri Lankaw.
Sub-Saharan Africa (45 countries) Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Swaziland, Togo, Uganda, United Republic of Tanzania, Zambia and Zimbabwew.
o Countries whose education data are collected through UOE questionnaires w WEI project countries Least developed countries (50 countries)7 Afghanistan, Angola, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Cape Verde, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, Sudan, Timor-Leste, Togo, Tuvalu, Uganda, United Republic of Tanzania, Vanuatu, Yemen and Zambia.
7.

Fifty countries are currently designated by the United Nations as ‘least developed countries’ (LDCs).

The list of LDCs is reviewed every three years by the Economic and Social Council of the United Nations, in the light of recommendations made by the Committee for Development Policy.

The LDCs grouping is not presented in the statistical tables but is discussed in the main text.
Table 1 Background statistics Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China 20 530 382 14 197 1 320 864 2 6 0 DEMOGRAPHY1 HIV/AIDS2 Average annual growth rate (%) total population Average annual growth rate (%) age 0-4 population Total population (000) 2006 2005-2010 2005-2010 Life expectancy at birth (years) Total 2005-2010 Male Female Total fertility rate (children per woman) 2005-2010 HIV prevalence rate (%) in adults (15-49) % of women among people (age 15+) living with HIV Orphans due to AIDS (000) 2007 Total 2007 2007 1.5 1.8 1.7 1.8 1.8 3.0 2.4 1.1 2.0 2.5 1.2 2.0 3.2 2.1 2.2 2.2 2.5 1.1 2.8 3.0 0.6 -0.6 0.1 -0.7 -0.1 0.0 -0.3 -0.3 -0.5 -0.5 -0.3 -0.2 -0.9 -0.4 -0.5 0.1 0.0 0.0 0.1 1.3 -0.8 -0.2 0.8 -0.8 0.7 1.1 1.0 1.5 1.3 1.4 1.0 2.1 1.7 0.6 1.7 -0.4 0.3 0.9 0.0 1.6 2.3 0.0 1.5 1.2 1.0 1.2 1.7 1.6 1.4 0.8 1.6 0.8 3.4 2.7 0.0 -0.3 -3.1 -0.4 -0.2 0.4 1.7 -0.7 0.8 -0.2 0.5 0.2 -0.8 -0.8 1.1 0.8 0.6 0.2 -1.7 0.3 1.0 2.1 3.3 -1.5 4.2 1.9 -0.2 0.1 0.5 0.6 0.5 0.3 1.2 -0.1 72 76 55 71 60 73 78 72 74 64 71
0.5 0.3 1.2 - … 3.1 … … … … 0.1 … 0.8 0.1 … … … … 1.4 … 0.1 … … … 0.2 <0.1 … <0.1 … 1.3 0.1 0.8 0.1 … 0.1 0.4 0.1 1.1 0.1 <0.1 <0.1 <0.1 … 1.6 0.1 0.2 0.1 0.1 0.1 0.1 0.3 <0.1 0.1 0.2 … 0.8 0.1 29 … 58 29 … … … <33 … 28 28 … … … … 59 … 28 … … … 30 … … … <33 24 <30 27 <45 … 29 30 50 26 28 … … … … 44 <42 17 <37 28 26 <20 21 … 29 7 … 29 29 … … 5 … … … … … … 3 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … S TAT I S T I C A L TA B L E S Ta b l e 1 GNP, AID AND POVERTY INEQUALITY IN INCOME OR EXPENDITURE4 GNP per capita3 Current US$ PPP US$ Population living on less than US$1 per day4 (%) Population living on less than US$2 per day4 (%) Net aid per capita (US$)4 Share of income or expenditure % Inequality measure Poorest 20% Richest 20% Richest 20% to poorest 20%6 Gini index7 1998 2006 1998 2006 2005 1990-20055 1990-20055 1992-20055 1992-20055 1992-20055 1992-20055 Country or territory 1 570 9 940 730 1 240 … 1 590 17 770 4 250 … 560 1 310 6 270 … … 8 030 310 920 2 050 20 020 380 890 1 550 1 430 1 270 4 600 5 580 3 730 4 320 2 650 2 600 … 4 300 460 1 520 2 140 … 4 100 10 530 1 930 3 070 850 590 510 770 1 390 350 460 180 560 620 3 030 … 1 060 1 360 … 2 650 … 5 580 7 290 760 2 160 … … … 13 980 800 1 560 2 970 … 760 2 930 3 470 3 230 3 990 9 310 12 790 11 400 10 870 8 100 7 930 4 130
… 610 4 110 22 020 1 590 3 360 … 2 960 40 180 7 330 … 1 350 2 480 13 590 … … … 8 950 1 260 5 730 6 000 … 10 660 14 990 5 450 5 970 2 870 1 830 1 850 1 970 4 000 1 150 1 700 760 … 1 320 5 940 … 2 180 4 940 … 4 820 … 9 600 11 630 1 970 3 860 … … … 22 300 1 780 4 110 6 490 … … … 99 13 … 115 … 68 … 62 22 12 304 … 1 51 4 38 … 16 102 … 140 … 28 … … … … … … … 46 … … … … … 113 6 … 64 27 69 15 52 83 37 6 7 … … 38 1 … … … 3 … … … … … 26 … … … … … … … … … 16 … … … … … … … … … … … … … … … … … … … 3 … … 4 7 … … 11 7 … … … … 34 10 15 … … 44 … 7 … … … 63 14 … … … … … … 7 … 45 10 … … 6 … … 8 … 5 8 … … 21 13 12 … 29 … … 19 5 31 33 25 16 21 45 43 … … … … 78 35 7 … … 9 … 7 … … … 6 7 … … … … … … 6 … 7 8 9 10 9 8 10 7 10 7 7 … 8 8 8 6 … 9 9 6 5 9 9 7 6 7 9 8 8 6 7 6 … 7 4 43 … … 44 … 46 … … … 46 47 … … … … … … 47 … 41 40 38 36 38 40 36 43 37 45 43 … 42 41 39 47 … 35 36 46 50 38 43 45 46 42 39 41 41 48 45 41 … 50 52 6 … … 5 … 7 … … … 7 7 … … … … … … 8 … 6 5 5 4 4 5 4 6 4 7 6 … 6 5 5 8 … 4 4 8 9 4 5 6 8 6 4 5 5 8 6 7 … 7 12 35 … … 34 … 39 … … … 39 40 … … … … … … 40 … 33 31 30 26 29 29 25 36 27 38 36 … 35 33 31 40 … 26 28 39 44 28 34 37 40 34 30 33 33 41 37 35 … 42 47 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania
 Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Table 1 (continued) DEMOGRAPHY1 HIV/AIDS2 Average annual growth rate (%) total population Average annual growth rate (%) age 0-4 population Total population (000) 2006 2005-2010 2005-2010 Life expectancy at birth (years) Total 2005-2010 Male Female Total fertility rate (children per woman) 2005-2010 Country or territory Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama -2.2 0.3 0.6 1.2 0.0 1.6 1.7 0.7 1.7 2.2 0.5 0.9 0.3 0.9 - -0.3 1.5 1.1 1.4 0.0 2.5 -0.2 1.6 1.9 0.5 1.1 1.2 1.3 1.3 1.6 … -2.1 -1.1 -0.6 -1.4 … 0.8 1.1 -0.1 … -1.4 -0.3 … 0.3 … … -0.5 0.4 -1.8 -2.5 -3.0 0.7 0.0 4.6 …
 0.9 … 1.1 0.0 … … 0.6 -1.7 -0.1 -1.2 -0.1 … 0.1 0.0 … … 0.2 -1.0 0.2 -2.9 … 0.2 -0.8 -0.3 -3.4 1.2 -4.2 0.5 0.5 -1.2 -1.0 … -1.3 0.3 0.1 … 67 69 71 83 … 64 81 74 … 69 62 … 80 … … 57 72 79 71 80 64 71 61 … 73 … 70 74 … … 75 74 73 77 76 … 66 72 … … 79 73 79 78 … 72 75 72 69 70 67 61 70 73 76 … 75 73 76 … 65 67 69 79 … 63 79 72 … 68 59 … 78 … … 55 70 75 69 78 63 66 60 … 72 … 68 72 … … 72 71 71 74 73 … 63 69 … … 75 69 76 76 … 69 72 69 67 67 64 59 67 70 74 … 71 70 73 … 69 71 73 86 … 66 83 77 … 69 65 … 82 … … 60 74 82 75 82 64 75 62 … 74 … 72 76 … … 79 77 76 80 79 … 68 76 … … 82 77 81 80 … 75 78 75 70 74 70 63 74 75 79 … 79 76 78 … 1.9 2.8 2.2 1.3 … 3.2 0.9 2.6 … 3.7 2.1 … 2.0 … … 3.8 3.2 1.2 3.9 1.3 3.9 1.9 6.5 … 3.8 … 3.7 2.1 … … 2.3 2.0 2.0 1.5 2.9 … 3.5 2.2 … … 1.9 2.2 2.1 1.5 … 2.8 2.6 2.7 2.3 4.2 2.3 3.5 3.3 2.4 2.2 … 1.9 2.8 2.6 HIV prevalence rate (%) in adults (15-49) % of women among people (age 15+) living with HIV Orphans due to AIDS (000) 2007 Total … … 0.1 0.2 … … 0.2 … 0.5 … … 0.7 … 0.1 … … 1.5 … <0.1 … 0.2 … 1.4 … … … … … 0.5 … … 0.5 … 3.0 1.2 2.1 … 0.2 0.6 … … 0.3 0.6 0.4 0.1 … 1.1 0.3 0.8 … 0.8 2.5 2.2 0.7 1.6 0.3 … … 0.2 1.0 2007 2007 … … … 20 24 … 24 … 27 … … 42 … <36 … … 40 27 28 … 29 … 42 … … … … … 27 … … 27 … 26 <45 59 … 28 34 … … 28 29 28 29 … 51 28 29 … 98 59 53 28 29 29 … … 28 29 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … S TAT I S T I C A L TA B L E S Ta b l e 1 GNP, AID AND POVERTY INEQUALITY IN INCOME OR EXPENDITURE4 GNP per capita3 Current US$ PPP US$ Population living on less than US$1 per day4 (%) Population living on less than US$2 per day4 (%) Net aid per capita (US$)4 Share of income or expenditure % Inequality measure Poorest 20% Richest 20% Richest 20% to poorest 20%6 Gini index7 1998 2006 1998 2006 2005 1990-20055 1990-20055 1992-20055 1992-20055 1992-20055 1992-20055 Country or territory … … 2 290 670 32 970 1 150 310 15 260 3 630 2 070 2 030 … … 15 480 … …
 810 1 080 9 200 1 330 23 490 870 2 120 … … 1 720 … 1 300 350 … 7 810 8 020 … 12 920 7 680 2 710 … 1 000 4 880 … … 5 270 2 440 3 500 … 3 300 1 770 1 810 1 870 3 020 1 670 880 400 750 2 660 4 020 … … 670 3 550 … … 3 720 1 420 38 630 1 240 500 … 5 620 2 980 2 390 … … 26 750 … 7 990 740 1 390 17 690 2 270 28 730 690 3 050 840 … 2 250 … 1 690 700 … 11 050 5 150 … … … 3 740 … 1 100 4 710 … … 6 810 3 120 4 980 … … 2 910 2 910 2 680 … 2 590 1 150 430 1 270 3 560 7 830 … … 930 5 000 … … 3 030 2 140 24 240 5 520 1 100 20 880 7 630 6 490 5 020 410 … 17 020 … … 1 480 2 260 12 590 3 300 28 130 1 880 4 410 … … 3 790 … 3 270 1 220 … 10 490 9 160 … … … 4 650 … 3 000 6 540 … … 8 700 4 720 6 180 … 5 660 3 410 4 760 4 340 6 010 4 060 2 420 1 130 2 520 5 590 8 440 … … 1 820 5 960 … … 4 450 3 310 32 840 6 230 1 740 … 12 160 8 040 6 070 … … 25 750 … 14 340 1 630 3 430 22 990 5 090 43 300 1 850 7 440 5 100 … 5 470 … 3 480 2 310 … 15 130 11 670 … … … 7 080 … 3 810 8 700 … … 11 300 6 130 9 220 … … 5 550 6 810 5 610 … 5 120 3 410 1 070 3 420 7 050 11 990 … … 2 720 8 690 … … 76 11 … … 50 … 1 … … 3 … … … … 45 7 … 238 … 415 -3 189 … 310 … 187 23 … 89 3 … … -8 44 … 64 1 … … 9 11 7 8 211 9 16 29 421 20 182 60 95 14 2 … … 135 6 … … … 8 … … 27 … … … … … … … … … … 15 … … … … … … … … … … … … … 7 … … … … … 23 8 … … … 7 3 … … 3 18 19 … 14 … 54 15 … 3 … … 45 7 … … … 52 … … 74 … 9 … … … … … … … … 43 … … … … 25 … … … … … … … … 17 … … … … … 42 21 … … 6 18 10 … … 16 41 41 … 32 … 78 36 14 12 … … 80 18 … … … 8 11 … 8 … 4 … … … … 6 … … 5 5 8 … 5 … 6 … … … … … 9 … … 3 … … … … … 2 3 … … 4 3 4 … … 4 3 3 … 3 … 2 3 5 4 … … 6 3 … … … 43 36 … 43 … 54 … … … … 44 … … 57 51 38 … 49 … 49 … … … … … 44 … … 55 … … … … … 63 61 … … 60 63 54 … … 57 58 56 … 60 … 63 58 52 55 … … 49 60 … … … 5 3 … 5 … 12 … … … … 7 … … 13 9 5 … 10 … 8 … … … … … 5 … … 18 … … … … … 42 22 … … 16 25 16 … … 14 17 21 … 20 … 27 17 10 13 … … 9 24 … … … 34 25 … 35 … 49 … … … … 36 … … 51 45 32 … 43 … 42 … … … … … 34 Cook Islands DPR Korea Fiji Indonesia
 Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean … … 51 … … … … … 60 57 … … 55 59 50 … … 52 54 52 … 55 … 59 54 46 46 … … 43 56 Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Table 1 (continued) Country or territory Paraguay Peru Saint Kitts and Nevis Saint Lucia St Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 6 016 27 589 50 163 120 455 1 328 25 3 331 27 191 North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo DEMOGRAPHY1 HIV/AIDS2 Average annual growth rate (%) total population Average annual growth rate (%) age 0-4 population Total population (000) 2006 2005-2010 2005-2010 Life expectancy at birth (years) Total 2005-2010 Male Female Total fertility rate (children per woman) 2005-2010 HIV prevalence rate (%) in adults (15-49) % of women among people (age 15+) living with HIV Orphans due to AIDS (000) 2007 Total 2007 2007 1.8 1.2 1.3 1.1 0.5 0.6 0.4 1.4 0.3 1.7 0.4 0.4 0.2 0.9 1.1 0.2 0.3 0.5 - … 1.1 -0.1 -1.0 0.9 … -0.8 0.5 … -0.3 -0.5 0.3 1.5 -1.1 0.6 -0.3 -1.2 0.2 0.6 2.2 0.4 -0.1 0.3 0.0 … -2.0 -0.1 0.0 … 1.8 1.2 -0.8 1.0 0.8 3.6 -0.3 -1.6 -0.1 3.0 3.1 0.8 1.9 -1.1 2.5 2.4 0.7 2.4 5.3 0.4 1.1 1.0 2.3 1.0 1.2 72 71 … 74 72 70 70 … 76 74 … 80 79 81 79 78 79 81 79 79 82 79 81 81 79 79 … 80 80 78 … 81 81 82 79 78 44 64 66 65 71 68 64 65 72 43 57 51 52 50 50 72 45 51 65 55 70 69 … 72 69 67 68 … 73 71 … 77 76 78 76 76 76 77 77 77 80 76 79 78 76 77 … 78 78 75 … 78 79 79 77
 76 44 63 64 63 69 68 63 65 69 41 56 50 51 48 50 68 43 49 63 54 74 74 … 76 74 74 72 … 80 77 … 83 82 83 82 81 82 84 82 82 83 81 83 83 82 81 … 82 83 81 … 84 83 84 82 81 44 65 67 66 73 69 64 66 76 44 58 51 54 51 51 74 46 52 67 57 3.1 2.5 … 2.2 2.2 2.4 1.6 … 2.1 2.5 … 1.4 1.6 1.5 1.6 1.8 1.8 1.9 1.4 1.3 2.1 2.0 2.8 1.4 1.7 1.4 … 1.7 1.8 1.5 … 1.4 1.8 1.4 1.8 2.1 7.1 2.8 2.2 2.8 2.0 2.6 3.3 3.5 1.9 6.4 5.4 2.9 6.0 6.8 4.3 3.4 4.6 6.2 4.3 4.5 0.6 0.5 … … … 2.4 1.5 … 0.6 … … 0.2 0.2 0.4 … 0.2 0.1 0.4 0.1 0.2 0.2 0.2 0.1 0.4 0.2 0.1 … 0.2 0.1 0.5 … 0.5 0.1 0.6 0.2 0.6 … … 0.1 0.3 0.2 … 0.5 0.1 … 2.1 1.2 23.9 1.6 2.0 5.1 … 6.3 3.5 <0.1 3.5 29 28 … … … 28 59 … 28 … … 30 27 27 … 23 <42 27 29 27 <40 27 59 27 … … … 27 <33 28 … 20 47 37 29 21 … 17 <20 38 28 … 25 29 38 61 63 61 51 59 60 … 65 61 <50 59 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 50 29 95 100 120 300 … 72 85 <0.1 69 S TAT I S T I C A L TA B L E S Ta b l e 1 GNP, AID AND POVERTY INEQUALITY IN INCOME OR EXPENDITURE4 GNP per capita3 Current US$ PPP US$ Population living on less than US$1 per day4 (%) Population living on less than US$2 per day4 (%) Net aid per capita (US$)4 Share of income or expenditure % Inequality measure Poorest 20% Richest 20% Richest 20% to poorest 20%6 Gini index7 1998 2006 1998 2006 2005 1990-20055 1990-20055 1992-20055 1992-20055 1992-20055 1992-20055 1 650 2 240 6 150 3 880 2 620 2 500 4 440 … 6 610 3 360 … … 25 820 35 400 11 560 … 15 220 28 930 41 560 22 860 30 620 … 340 600 420 1 730 1 930 210 470 810 460 340 3 350 240 140 630 1 240 280 220 420 560 1 410 2 980 … … … 4 210 12 500 … 5 310 6 070 … … 43 050 68 440 17 850 45 130 27 340 43 530 58 050 40 560 44 710 … 450 1 430 820 2 930 3 010 320 800 1 310 1 970 530 5 570 440 100 990 2 130 350
 450 660 … 3 480 4 630 9 990 6 870 4 670 5 840 7 610 … 7 880 8 430 … … 26 340 35 710 15 620 … 19 500 22 470 30 210 23 090 31 650 … … … … 7 720 16 800 … 9 940 10 970 … … 37 940 50 070 19 960 … 28 200 34 310 40 840 33 650 44 070 … … 8 14 73 67 41 98 - 2 … 4 2 … … … … … … … … … … … … … … … … … … … … … … … … … … … 9 98 2 2 203 16 11 61 28 41 40 50 48 25 317 24 39 42 362 14 11 … … … … 12 … … 19 … … … … … … … … … … … … … … … … … … … … … … … … … … … 41 … 34 … … 24 17 6 … 31 28 27 55 17 … 67 … … … 30 31 … … … … 39 … 6 40 … … … … … … … … … … … … … … … … … … … … … … … … … … … 84 … 80 7 … 69 74 42 … 74 56 72 88 51 … 84 … … … 2 4 … … … … 6 … 5 3 … 9 9 7 … 8 10 7 9 7 … 7 6 7 … … … 8 10 6 … 7 9 8 6 5 … 9 … 8 5 … 6 9 7 … 7 3 7 5 6 … 2 … … … 62 57 … … … … 45 … 51 52 … 38 41 40 … 36 37 40 37 42 … 42 45 42 … … … 39 37 46 … 42 37 41 44 46 … 43 … 45 50 … 55 40 48 … 45 65 47 48 51 … 65 … … … 26 15 … … … … 8 … 10 16 … 4 5 6 … 4 4 6 4 6 … 6 8 7 … … … 5 4 8 … 6 4 6 7 8 … 5 … 6 10 … 9 4 7 … 6 20 7 10 9 … 33 … … … Country or territory Paraguay Peru Saint Kitts and Nevis Saint Lucia St Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 58 52 … … … … 39 … 45 48 North America and Western Europe … 29 33 33 … 25 27 33 28 34 … 34 39 36 … … … 31 26 39 … 35 25 34 36 41 … 33 … 37 43 … 47 31 40 … 37 61 40 42 45 … 61 … … … Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Table 1 (continued) DEMOGRAPHY1 HIV/AIDS2 Average annual growth rate (%) total population Average annual growth rate (%) age 0-4 population Total population (000) 2006 2005-2010 2005-2010 Life expectancy at birth (years) Total 2005-2010 Male Female Total fertility rate (children per woman) 2005- Sum -0.1 0.4 1.4 2.0 -0.1 1.0 0.7 0.7 1.2 1.2 1.1 1.3 0.6 1.6 2.4 0.8 3.5 2.0 3.1 1.6 0.4 1.3 0.6 1.5 3.1 2.9 -0.4 4.7 1.5 1.5 3.2 -0.5 0.6 0.4 3.1 1.2 4.0 0.3 1.3 … 1.9 2.0 -0.5 0.2 1.4 3.1 1.2 0.9 0.3 0.5 1.2 0.2 0.5 1.2 0.5 1.5 -0.2 -0.2 0.1 -0.2 0.0 -0.2 0.4 0.3 1.8 48 46 52 58 53 57 59 60 56 46 54 43 46 59 48 54 73 42 53 57 47 46 66 63 … 43 48 49 40 58 52 53 42 43 48 45 50 56 52 56 59 60 54 45 53 43 45 58 48 52 70 42 52 58
 46 45 64 61 … 41 47 49 40 57 51 51 42 44 Weighted average … … 6.5 6.0 2.6 3.4 4.8 6.5 5.2 5.2 3.2 2.6 1.6 1.7 2.8 3.2 1.5 2.3 1.9 1.9 2.3 2.2 3.0 2.2 1.8 2.9 5.2 HIV prevalence rate (%) in adults (15-49) % of women among people (age 15+) living with HIV Orphans due to AIDS (000) 2007 Total 3.9 … 3.4 1.3 2.1 5.9 0.9 1.9 1.6 1.8 … 23.2 1.7 0.1 11.9 1.5 1.7 12.5 15.3 0.8 3.1 2.8 … 1.0 … 1.7 0.5 18.1 26.1 3.3 5.4 6.2 15.2 15.3 0.8 … … … … … … … 0.1 0.4 … 1.1 0.5 … … 5.0 2007 2007 60 … 60 60 60 59 60 60 59 58 … 58 59 26 58 60 29 58 61 30 58 60 … 59 … 59 28 59 59 58 59 58 57 57 Weighted average 50 … … … … … … … 27 30 … 50 32 … … 59 420 … 5 18 650 18 3 160 25 6 … 110 15 3 560 44 <0.5 400 66 25 1 200 220 … 8 … 16 9 1 400 56 68 1 200 970 600 1 000 15 000 … … … … … … … … … … … … … … 11 592 Country or territory Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 1.

UN Population Division (2007), medium variant.

2.

UNAIDS (2008).

3.

World Bank (2008).

4.

UNDP (2007).

5.

Data are for the most recent year available during the period specified.

For more details see UNDP (2007).
2 6 6 S TAT I S T I C A L TA B L E S Ta b l e 1 GNP, AID AND POVERTY INEQUALITY IN INCOME OR EXPENDITURE4 GNP per capita3 Current US$ PPP US$ Population living on less than US$1 per day4 (%) Population living on less than US$2 per day4 (%) Net aid per capita (US$)4 Share of income or expenditure % Inequality measure Poorest 20% Richest 20% Richest 20% to poorest 20%6 Gini index7 1998 2006 1998 2006 2005 1990-20055 1990-20055 1992-20055 1992-20055 1992-20055 1992-20055 Country or territory 730 110 1 120 210 130 4 070 300 370 470 140 440 650 130 250 200 280 3 760 220 2 030 200 270 260 … 510 7 320 160 … 3 280 1 460 300 280 220 310 570 … … … … … … … … … … … … … … … … … 5 390 2 400 350 300 350 630 … 7 448 … … … … … … 1 856 … … 4 785 … … … … … 1 140 12 770 340 … 6 140 3 820 680 600 630 800 … … … … … … … … … … … … … … … … … … 8 900 4 700 770 880 980 1 140 … 7 32 78 81 27 39 38 51 19 50 22 38 … 50 45 51 26 65 61 37 49 64 204 59 223 62 … 16 41 14 42 39 81 28 Weighted average 9 209 … … … … … … 4 359 … … 8 682 … … … … 1 681 16 … … 17 94 … … 5 … … 11 … … … … 42 15 … … … 23 … 59 45 … … 23 36 … 61 21 36 … 36 35 61 71 60 … 17 … 57 … 11 48 … … 58 64 56 … … … … … … … … … … … … … … … … 49 … … … 78 … 83 79 … … 58 56 … 85 63 72 … 74 56 86 92 88 … 56 … 75 … 34 78 … … 90 87 83 … … … … … … … … … … … … … … … … 5 … … … 9 … 5 6 7 5 6 2 … 5 7 6 … 5 1 3 5 5 … 7 … 1 … 4 4 … 6 7 4 5 … … … … … … … … … … … … … … … … 51 … … … 39 … 53 47 46 53 49 67 … 54 47 47 … 54 79 53 49 53 … 48 … 63 … 62 56 … 53 42 55 56 10 … … … 4 … 11 8 7 10 8 44 … 11 7 8 … 10 56 21 10 10 … 7 … 58 … 18 13 … 9 6 15 12 Weighted average … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 6.
Data show the ratio of income or expenditure share of the richest group to that of the poorest.

7.

A value of 0 represents perfect equality and a value of 100 perfect inequality.
45 … … … 30 … 50 41 39 47 43 63 … 48 39 40 … 47 74 51 44 47 … 41 … 63 … 58 50 … 46 35 51 50 … … … … … … … … … … … … … … … … Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa Table 2 Adult and youth literacy ADULT LITERACY RATE (15 and over) (%) ADULT ILLITERATES (15 and over) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Country or territory Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan2 Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea 2 6 8 50.
* 84.

* … 44.

* … … 74.

* … 76 … 42.

* … … 76.

* 71.

* … … … 71.

* 37.

* … 98.

* … … 97.

* … 100.

* … 99.

* 98.

* … … 96.

* 97.

* 98.

* … … 100.

* 94.

* 79.

* … 99.

* … … 98.

* … … 98.

* … … … 88.

* … 78.

* … … 63.

* 89.

* … 57.

* … … 78.

* … 88 … 55.

* … … 77.

* 80.

* … … … 72.

* 57.

* … 99.

* … … 99.

* … 100.

* … 100.

* 99.

* … … 99.

* 99.

* 99.

* … … 100.

* 97.

* 90.

* … 99.

* … … 99.

* … … 99.

* … … … 92.

* … 87.

* … … 36.

* 77.

* … 31.

* … … 69.


* … 63 … 29.

* … … 72.

* 57.

* … … … 69.

* 17.

* … 97.

* … … 95.

* … 100.

* … 99.

* 98.

* … … 94.

* 95.

* 97.

* … … 99.

* 91.

* 69.

* … 98.

* … … 96.

* … … 97.

* … … … 82.

* … 68.

* … … 75 88 … 71 74.

* 93 93.

* … 86 55 55 84 92 90 84 61.

* 83 77 90 57 99 100 97.

* 98 99 … 100 … 100 100 … … 99 98 100 … … 100 97 88.

* 100 99 99 … 100 99 97 100 99 97.

* … 95 76 93 … … 84 90 … 83 84.

* 96 95.

* … 94 63 68 89 97 90 89 71.


* 89 86 90 76 99 100 99.

* 99 99 … 100 … 100 100 … … 100 98 100 … … 100 99 96.

* 100 100 100 … 100 99 97 100 100 98.

* … 96 86 96 … … 65 86 … 60 64.

* 89 91.

* … 78 47 42 76 88 90 78 52.

* 76 68 89 39 99 100 94.

* 98 98 … 100 … 100 100 … … 99 97 99 … … 100 95 80.

* 100 99 99 … 99 99 98 99 99 96.
* … 93 67 90 … … 81 92 … 77 … 95 96 … 91 61 62 89 95 93 89 … 87 83 94 70 99 100 … 98 99 … 100 … 100 100 … … 100 98 100 … … 100 98 91 100 100 100 … 100 99 96 100 100 … … 97 81 96 … … 88 93 … 86 … 98 96 … 97 66 74 93 98 93 92 … 92 90 95 85 99 100 … 98 100 … 100 … 100 100 … … 100 98 100 … … 100 99 97 100 100 100 … 100 100 95 100 100 … … 98 88 98 … … 74 90 … 68 … 93 95 … 84 55 51 84 93 93 85 … 82 76 92 55 99 100 … 98 99 … 100 … 100 100 … … 100 97 100 … … 100 97 86 100 100 100 … 100 99 98 100 100 … … 96 75 93 … … 6 572 56 … 16 428 … … 276 … 685 … 9 602 … … 68 2 907 … … … 473 4 686 … 166 … … 120 … 3 … 11 44 … … 113 589 2 290 … … 7 87 7 640 … 31 … … 278 … … 68 … … … 21 … 184 214 … … 64.
* 56.

* … 62.

* … … 48.

* … 73 … 62.

* … … 30.

* 59.

* … … … 31.

* 66.

* … 87.

* … … 82.

* … 79.

* … 80.

* 76.

* … … 82.

* 78.

* 88.

* … … 60.

* 77.

* 75.

* … 77.

* … … 82.

* … … 74.

* … … … 67.

* … 70.

* … … 6 030 64 … 14 213 4 327 266 145 … 580 817 9 826 278 161 66 2 506 8 674 2 169 1 764 347 5 076 24 25 110 114 52 … 2 … 4 9 … … 26 443 604 … … 6 52 6 285 129 13 42 … 46 27 49 16 18 565 … 15 2 188 73 232 … … 68 49 … 71 69.

* 74 50.

* … 78 59 66 60 78 28 58 63.


* 69 69 31 72 66 68 86.

* 63 81 … 50 … 54 52 … … 78 67 72 … … 57 77 83.

* 70 73 78 … 74 67 44 70 71 68.
* … 65 72 73 … … 5 392 55 … 13 822 … 215 114 … 472 934 9 458 242 135 54 2 176 … 2 037 1 464 259 4 961 19 16 … 118 31 … 2 … 4 8 … … 12 394 398 … … 5 36 5 282 83 8 24 … 34 21 85 11 12 … … 11 2 146 49 848 … … 68 49 … 70 … 73 48 … 81 57 67 62 76 30 60 … 70 71 39 75 59 50 … 58 74 … 47 … 51 52 … … 63 58 61 … … 57 73 84 58 63 76 … 65 55 32 62 61 … … 65 69 74 … … S TAT I S T I C A L TA B L E S Ta b l e 2 YOUTH LITERACY RATE (15-24) (%) YOUTH ILLITERATES (15-24) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Country or territory 74.
* 97.

* … 63.

* … … 87.

* … 95 … 58.

* … … 90.

* 88.

* … … … 82.

* 60.

* … 100.

* … … 100.

* … 100.

* … 100.

* 100.

* … … 100.

* 99.

* 100.

* … … 100.

* 99.

* 93.

* … 100.

* … … 100.

* … … 100.

* … … … 98.

* … 94.

* … … 86.

* 97.

* … 71.

* … … 91.

* … 99 … 71.

* … … 89.

* 94.

* … … … 81.

* 83.

* … 100.

* … … 100.

* … 100.

* … 100.

* 100.

* … … 100.

* 99.

* 100.

* … … 100.

* 99.

* 97.

* … 100.

* … … 100.

* … … 100.

* … … … 98.

* … 97.

* … … 62.

* 97.

* … 54.


* … … 84.

* … 91 … 46.

* … … 91.

* 81.

* … … … 85.

* 35.

* … 100.

* … … 100.

* … 100.

* … 100.

* 100.

* … … 100.

* 99.

* 100.

* … … 100.

* 99.

* 88.

* … 100.

* … … 100.

* … … 100.

* … … … 98.

* … 91.

* … … 92 100 … 85 85.

* 99 99.

* … 99 66 74 98 99 97 97 77.

* 93 95 97 79 99 100 100.

* 98 100 … 100 … 100 100 … … 100 98 100 … … 100 99 96.

* 100 100 100 … 100 100 96 100 100 94 100 … 90 89.

* 99 99.

* … 100 70 83 99 99 96 98 85.


* 95 97 98 93 99 100 100.

* 98 100 … 100 … 100 100 … … 100 97 100 … … 100 99 98.

* 100 100 100 … 100 100 94 100 100 90 100 … 80 80.

* 99 98.

* … 95 100 … 91 … 99 100 … 100 98 71 62 83 64 99 98 99 99 99 98 96 99 71.

* … 96 91 98 94 99 96 90 64 99 100 100.

* 98 100 … 100 … 100 100 … … 100 98 100 … … 100 99 94.

* 100 100 100 … 100 100 97 100 100 99 100 … 96 100 … 100 … 100 100 … … 100 96 100 … … 100 99 97 100 100 100 … 100 99 91 100 100 … … 100 91 100 … … 99.
* 99.

* 99.
* … 100 85 99 … … … 100 89 99 … … … 99 81 99 … … 95 100 … 92 … 99 100 … 100 73 89 100 99 99 99 … 97 98 100 97 99 100 … 96 100 … 100 … 100 100 … … 100 96 100 … … 100 99 99 100 100 100 … 100 99 86 100 100 … … 100 93 100 … … 95 100 … 90 … 100 100 … 100 70 78 99 100 99 98 … 95 97 98 83 99 100 … 96 100 … 100 … 100 100 … … 100 97 100 … … 100 98 96 100 100 100 … 100 100 96 100 100 … … 100 89 100 … … 1 215 3 … 3 473 … … 37 … 55 … 2 239 … … 6 369 … … … 36 1 122 … 3 … … 2 … 0.3 … 0.8 2 … … 2 35 56 … … 0.7 4 867 … 0.5 … … 8 … … 3 … … … 0.9 … 14 352 … … 73.
* 53.

* … 60.

* … … 62.

* … 89 … 65.

* … … 31.

* 74.

* … … … 38.

* 78.

* … 43.

* … … 53.

* … 35.

* … 40.

* 44.

* … … 48.

* 53.

* 44.

* … … 44.

* 62.

* 76.

* … 49.

* … … 44.

* … … 56.

* … … … 49.

* … 73.

* … … 593 0.5 … 2 238 877 11 7 … 18 207 1 704 10 7 3 150 1 659 298 100 17 1 003 4 3 1 25 2 … 0.4 … 1.0 1 … … 2 81 72 … … 0.4 4 507 15 1 1 … 5 4 25 2 2 39 … 0.3 495 1 703 … … 63 42 … 66 63.

* 45 45.

* … 88 54 68 62 52 28 66 64.

* 63 65 60 82 44 37 38.


* 50 48 … 37 … 41 46 … … 48 47 40 … … 37 57 78.

* 41 38 28 … 38 38 30 47 41 52.
* … 53 62 58 … … 320 0.1 … 1 435 … 7 0.05 … 0.7 211 1 017 3 6 1.05 75 … 163 39 5 587 4 3 … 28 2 … … 20 0.8 0.8 … … 2 86 53 … … 0.3 4 388 12 1.3 0.6 … 5 6 46 2 2 … … 0.12 313 907 … … Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan 2 Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea 48 45 … 55 … 30 37 … 67 52 67 63 36 60 76 … 60 57 78 87 41 33 … 46 44 … … 33 42 50 … … 49 42 36 … … 30 52 75 39 33 18 … 36 31 24 44 33 … … 57 59 51 … … Table 2 (continued) ADULT LITERACY RATE (15 and over) (%) ADULT ILLITERATES (15 and over) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Country or territory Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam … 82.
* … … … … 83.

* … … … … … … … … 94.

* … 98.

* 89.

* … … … … … … … 88.

* Latin America and the Caribbean … … 96.

* … … … 70.

* … 80.

* … … … 94.

* 81.

* … … … … 88.

* 74.

* … 64.

* … … … … 88.

* … 95.

* … 89.

* 90.

* 87.


* … Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis 2 7 0 … 88.

* … … … … 89.

* … … … … … … … … 94.

* … 98.

* 95.

* … … … … … … … 93.

* … … 96.

* … … … 70.


* … 88.

* … … … 95.

* 81.

* … … … … 90.

* 77.

* … 72.

* … … … … 90.

* … 95.

* … 89.

* 92.

* 93.

* … … 75.

* … … … … 77.

* … … … … … … … … 93.

* … 97.

* 83.

* … … … … … … … 83.

* … … 96.

* … … … 70.

* … 72.

* … … … 94.

* 81.

* … … … … 86.

* 71.

* … 57.

* … … … … 85.

* … 95.

* … 88.

* 89.

* 82.

* … … 91 … … 72 93 92 … … 90.

* … … … … 57 93 … 99 94 … 94 … … 99 … … … 95 … … 80 96 94 … … 94.
* … … … … 62 93 … 99 97 … 96 … … 99 … … … 87 … … 66 90 89 … … 86.

* … … … … 53 94 … 98 91 … 92 … … 99 … … 90.

* 94.

* 87.

* … … 98 98 … … … … 90 90.

* … … 96 92.

* 96 100 … 89 92 84.

* … 72 … … 83 85 92.

* … 96 80 93 94 89.

* … … … 98 98 … … … … 95 89.

* … … 96 92.

* 96 100 … 88 93 87.

* … 78 … … 82 80 94.

* … 96 79 94 94 94.

* … … … 98 98 … … … … 85 90.

* … … 96 92.

* 96 100 … 89 91 81.

* … 67 … … 83 91 90.

* … 96 81 93 93 83.
* … … 94 … … 78 95 94 … … … … … … … 61 94 … 99 96 … 96 … … 99 … 84 … … … 98 98 … … … … 93 93 … … 97 95 97 100 … 92 94 89 … 79 … … 86 89 94 … 97 84 95 95 93 … … 96 … … 83 97 96 … … … … … … … 63 94 … 99 98 … 97 … … 99 … 85 … … … 98 99 … … … … 97 92 … … 97 95 96 100 … 91 95 91 … 83 … … 85 85 96 … 97 82 95 95 96 … … 92 … … 73 93 93 … … … … … … … 60 95 … 99 94 … 94 … … 99 … 82 … … … 98 98 … … … … 90 93 … … 97 95 97 100 … 92 93 87 … 74 … … 88 94 93 … 97 85 94 95 90 … … 21 577 … … … … 1 989 … … … … … … … … 2 325 … 2 259 … … … … … … … 4 789 … … 889 … … … 32 … 825 … … … 547 4 458 … … … … 731 830 … 1 915 … … … … 6 397 … 7 … 175 255 1 848 … … 68.
* … … … … 66.

* … … … … … … … … 53.

* … 60.

* 78.

* … … … … … … … 72.

* … … 53.

* … … … 49.

* … 71.

* … … … 53.

* 52.

* … … … … 59.

* 58.

* … 61.

* … … … … 62.

* … 54.

* … 52.

* 59.

* 72.

* … … 14 772 … … 967 28 1 527 … … 3 529 … … … … 1 579 3 711 … 1 207 … 3 022 … … 0.5 … … … 71 … … 64 74 64 … … 70.

* … … … … 55 48 … 58 76 … 66 … … 46 … … 5 892 69.

* … … 701 2 … … … … 597 14 242 … … 447 2 461 133 19 … 718 678 729 … 2 047 … … 734 269 6 037 … 6 693 156 250 2 126 … … … 51 54 … … … … 76 50.
* … … 52 52.

* 47 53 … 49 57 61.

* … 63 … … 49 33 64.

* … 55 49 55 55 74.
* … … 11 158 … … 993 22 1 244 … … … … … … … 1 831 4 073 … 1 157 … 2 387 … … 0.4 … … … … … 602 1 … … … … 471 11 275 … … 367 1 876 124 17 … 641 636 594 2 106 … … 747 218 4 880 … 5 685 150 242 1 578 … … 71 … … 62 73 63 … … … … … … … 52 46 … 54 74 … 65 … … 45 … … … … … 50 54 … … … … 77 49 … … 51 51 46 54 … 48 56 60 63 … … 46 30 64 … 54 46 55 53 75 … S TAT I S T I C A L TA B L E S Ta b l e 2 YOUTH LITERACY RATE (15-24) (%) YOUTH ILLITERATES (15-24) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Country or territory … 96.
* … … … … 96.

* … … … … … … … … 97.

* … 99.

* 99.

* … … … … … … … 94.

* … … 98.

* … … … 76.

* … 94.

* … … … 98.

* 91.

* … … … … 96.

* 85.

* … 76.

* … … … … 95.

* … 97.

* … 95.

* 96.

* 95.

* … … 97.

* … … … … 96.

* … … … … … … … … 96.

* … 99.

* 99.

* … … … … … … … 94.

* … … 98.

* … … … 76.

* … 96.

* … … … 98.

* 89.

* … … … … 97.

* 85.

* … 82.

* … … … … 96.

* … 97.

* … 95.

* 96.

* 97.

* … … 95.

* … … … … 95.

* … … … … … … … … 97.

* … 99.

* 99.

* … … … … … … … 93.


* … … 99.

* … … … 77.

* … 92.

* … … … 99.

* 92.

* … … … … 96.

* 85.

* … 71.

* … … … … 95.

* … 97.

* … 95.

* 95.

* 94.

* … … 99 … … 82 100 98 … … 95.

* … … … … 64 94 … 99 100 … 98 … … 100 … … … 99 … … 85 100 98 … … 96.

* … … … … 63 94 … 99 100 … 98 … … 100 … … … 99 … … 79 100 98 … … 93.

* … … … … 65 95 … 99 100 … 98 … … 100 … … 94.

* 94.

* 94.

* … … 99 99 … … … … 98 98.

* … … 99 98.

* 98 100 … 96 96 95.

* … 85 … … 90 94 98.

* … 98 88 96 96 98.


* … … … 99 99 … … … … 99 97.

* … … 99 98.

* 98 100 … 95 96 94.

* … 88 … … 88 90 98.

* … 98 85 97 96 99.

* … … … 99 99 … … … … 98 98.

* … … 99 98.

* 98 100 … 97 97 95.

* … 82 … … 93 98 98.

* … 98 91 96 96 97.
* … … 99 … … 87 100 99 … … … … … … … 68 95 … 100 100 … 99 … … 100 … … … … … 99 100 … … … … 99 99 … … 99 98 98 100 … 97 97 97 … 89 … … 92 96 99 … 99 92 97 97 99 … … 99 … … 89 100 99 … … … … … … … 63 94 … 99 100 … 99 … … 100 … … … … … 99 99 … … … … 99 98 … … 99 98 98 100 … 97 96 96 … 90 … … 89 94 99 … 99 88 97 97 99 … … 99 … … 85 100 99 … … … … … … … 74 96 … 100 100 … 99 … … 100 … … … … … 99 100 … … … … 99 99 … … 100 99 99 100 … 98 97 98 … 88 … … 95 99 99 … 99 95 97 97 98 … … 1 421 … … … … 155 … … … … … … … … 428 … 0.3 6 … … … … … … … 831 … … 92 … … … 9 … 83 … … … 38 693 … … … … 79 173 … 462 … … … … 828 … 0.9 … 25 37 215 … … 65.
* … … … … 53.

* … … … … … … … … 45.

* … 49.

* 44.

* … … … … … … … 53.

* … … 43.

* … … … 49.

* … 70.

* … … … 41.

* 43.

* … … … … 54.

* 51.

* … 62.

* … … … … 56.

* … 44.

* … 52.

* 52.

* 67.

* … … 506 … … 228 0.2 90 … … 509 … … … … 434 950 … 0.2 2 … 187 … … 0.1 … … … 53 … … 58 37 47 … … 60.

* … … … … 48 42 … 42 38 … 52 … … 40 … … 1 771 39.

* … … 60 0.1 … … … … 30 853 … … 28 181 18 0.4 … 76 89 64 … 396 … … 146 30 402 … 0.4 140 22 47 121 … … … 39 44 … … … … 68 33.

* … … 41 42.
* … … 41 42.

* 38 52 … 37 46 45.

* … 60 … … 38 19 57.

* … 50 36 52 47 67.
* … … 335 … … 196 0.05 51 … … … … … … … 490 997 … 0.2 1 … 132 … … 0.1 … … … … … 48 0.07 … … … … 18 375 … … 17 139 13 0.2 … 52 88 45 … 362 … … 140 20 215 … 0.3 109 21 39 79 … … 42 … … 58 22 43 … … … … … … … 40 38 … 37 31 … 49 … … 45 … … … Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam … … 63 26 … … 39 34 35 64 … 37 40 … … … … Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis 49 29 50 44 54 … 31 17 45 … 34 41 35 … 56 … … Table 2 (continued) ADULT LITERACY RATE (15 and over) (%) ADULT ILLITERATES (15 and over) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Country or territory Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. … … … 97.

* … 95.

* 90.

* North America and Western Europe … … … … 94.

* … … … … 93.

* … … … … … 88.

* … … … 88.

* … 96.

* … … … … … 35.

* … 48.

* 66.

* 96.

* 33.

* … … … 27.

* 69.

* 14.

* 37.

* … 63.

* 34.

* 12.

* … 74 34.
* … … … Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India2 Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka2 Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea 2 7 2 … … … 98.
* … 95.

* 91.

* … … … … 98.

* … … … … 96.

* … … … … … 88.

* … … … 92.

* … 98.

* … … … … … 44.

* … 62.

* 74.

* 96.

* 49.

* … … … 40.

* 65.

* 20.

* 48.

* … 75.

* 48.

* … … 83 44.

* … … … … … … 96.

* … 96.

* 89.

* … … … … 91.

* … … … … 89.

* … … … … … 88.

* … … … 85.

* … 95.

* … … … … … 26.

* … 34.

* 56.

* 96.

* 17.

* … … … 17.

* 71.

* 8.

* 28.

* … 53.

* 20.

* … … 65 23.

* … … … … … 90 99 … 98.

* 93.

* … … 93 99 … 97.

* 93.

* … … 88 98 … 98.

* 93.


* … … … … 98 … … … … 97 … … … 99 … 91 … … … 95 … 97 … … … … 28.

* 52 54 65 84 97 55 54.

* 91.

* 67.

* 40 82 26 59.

* 68.

* 83 49.

* 26.

* … 86 49.

* 67.

* 87.

* … … … … … 99 … … … … 98 … … … 99 … 90 … … … 96 … 99 … … … … 43.

* 58 66 76 89 97 69 68.

* 93.

* 83.

* 52 82 34 67.

* 77.

* 89 65.

* 41.

* … 92 61.

* 81.

* 93.

* … … … … … 96 … … … … 96 … … … 99 … 93 … … … 93 … 96 … … … … 13.

* 47 40 53 78 97 42 40.

* 89.

* 54.


* 27 82 18 52.

* 60.

* 78 33.

* 13.

* … 81 39.

* 54.

* 80.
* … … … 92 99 … 98 … … … … … 99 … … … … 98 … … … 99 … 93 … … … 97 … 98 … … … … … 61 64 72 89 98 66 62 93 … 47 87 36 … … 89 … … … 92 … … … … … … 94 99 … 98 … … … … … 99 … … … … 99 … … … 99 … 92 … … … 98 … 99 … … … … … 64 73 81 93 97 77 73 94 … 59 87 43 … … 93 … … … 95 … … … … … … 91 99 … 99 … … … … … 98 … … … … 97 … … … 99 … 95 … … … 96 … 97 … … … … … 58 54 62 85 98 56 49 92 … 35 88 30 … … 86 … … … 89 … … … … … … … 26 … 102 1 242 … … … … 29 … … … … 615 … … … … … 31 … … … 965 … 1 103 … … … … … 44 458 … 283 848 11 124 5 7 619 … … … 2 131 247 4 136 1 945 … 70 1 085 3 177 … 404 4 180 … … … … … … 70.
* … 46.

* 54.

* … … … … 81.

* … … … … 74.

* … … … … … 50.

* … … … 67.

* … 73.

* … … … … … 56.

* … 61.

* 62.

* 47.

* 63.

* … … … 59.

* 47.

* 55.

* 61.

* … 70.

* 63.

* ….

… 67 55.

* … … … … … 32 14 … 56 1 318 … … … … 17 … … … … 289 … … … 596 … 29 … … … 486 … 988 … … … … … 299 5 541 10 486 38 … … … 63 67 … 45.

* 52.

* … … … … 78 … … … … 70 … … … 63 … 41 … … … 68 … 72 … … … … 59.
* 55 60 65 67 48 67 64.

* 61.

* 74.

* 61 51 56 61.

* 62.

* 69 67.

* 60.

* … 70 60.

* 71.

* 75.
* … … … 27 10 … 45 … … … … … 9 … … … … 200 … … … 386 … 24 … … … 268 … 787 … … … … … 48 189 198 261 687 6 504 6 7 346 49 588 1 061 … 3 476 176 6 567 … … 45 … … … 216 … … … … … … 62 62 … 42 … … … … … 75 … … … … 67 … … … 62 … 37 … … … 68 … 72 … … … … … 53 60 65 69 46 67 64 59 … 61 49 56 … … 68 … … … 72 … … … … S TAT I S T I C A L TA B L E S Ta b l e 2 YOUTH LITERACY RATE (15-24) (%) YOUTH ILLITERATES (15-24) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Total Male Female Total Male Female Total Male Female Total (000) % Female … … … 99.
* … 99.

* 95.

* … … … … 100.

* … … … … 99.

* … … … … … 98.

* … … … 99.

* … 100.

* … … … … … 45.

* … 62.

* 87.

* 98.

* 50.

* … … … 40.

* 89.

* 20.

* 54.

* … 88.

* 48.

* 17.

* … 94 49.

* … … … … … … 99.

* … 98.

* 95.

* … … … … 100.

* … … … … 99.

* … … … … … 97.

* … … … 99.

* … 100.

* … … … … … 52.

* … 74.

* 92.

* 98.

* 68.

* … … … 55.

* 86.

* 27.

* 59.

* … 90.

* 63.

* … … 96 60.

* … … … … … … 99.

* … 99.

* 96.

* … … … … 100.

* … … … … 99.

* … … … … … 99.


* … … … 99.

* … 100.

* … … … … … 38.

* … 49.

* 81.

* 98.

* 33.

* … … … 27.

* 92.

* 14.

* 48.

* … 86.

* 35.

* … … 91 38.

* … … … … … 95 99 … 99.

* 97.

* … … 96 99 … 98.

* 96.

* … … 94 99 … 99.

* 98.

* … … … … 100 … … … … 99 … … … 100 … 97 … … … 100 … 100 … … … … 34.

* 71 76 81 98 98 78 69.

* 97.

* 72.

* 51 94 34 73.

* … 97 59.

* 38.

* … 98 61.

* 70.

* 95.

* … … … … … 100 … … … … 99 … … … 100 … 96 … … … 100 … 100 … … … … 51.
* 70 81 86 98 98 85 79.

* 97.

* 84.

* 63 93 40 77.

* … 96 70.

* 56.

* … 99 71.

* 78.

* 95.

* … … … … … 100 … … … … 99 … … … 100 … 99 … … … 100 … 100 … … … … 18.

* 72 70 76 97 98 71 58.

* 98.

* 63.

* 40 95 28 70.

* … 98 47.

* 23.

* … 97 52.

* 63.

* 95.
* … … … 96 100 … 99 … … … … … 100 … … … … 99 … … … 100 … 98 … … … 100 … 100 … … … … … 83 88 88 99 98 88 78 99 … 60 95 45 … … 99 … … … 100 … … … … … … 96 100 … 99 … … … … … 100 … … … … 100 … … … 100 … 97 … … … 100 … 100 … … … … … 80 90 90 99 98 91 83 98 … 69 95 47 … … 98 … … … 100 … … … … … … 96 100 … 99 … … … … … 100 … … … … 99 … … … 100 … 99 … … … 100 … 100 … … … … … 85 87 86 99 99 85 72 99 … 51 96 43 … … 100 … … … 100 … … … … … … … 2 … 6 176 … … … … 0.4 … … … … 16 … … … … … 1 … … … 13 … 29 … … … … … 12 833 … 63 893 1 399 1 1 847 … … … 612 31 1 495 495 … 8 270 1 042 … 35 1 054 … … … … … … 50.
* … 37.

* 39.

* … … … … 44.

* … … … … 49.

* … … … … … 26.

* … … … 46.

* … 47.

* … … … … … 55.

* … 64.

* 70.

* 45.

* 67.

* … … … 62.

* 35.

* 54.

* 56.

* … 58.

* 64.

* ….

… 69 60.

* … … … Total (000) … … 4 1 … 6 148 … … … … 0.2 … … … … 10 … … … 7 … 2 … … … 5 … 17 … … … … 3 324 9 175 36 41 644 423 1 1 228 11 151 90 888 870 26 1 912 473 … 4 365 1 235 … 14 1 587 3 512 5 … % Female Total (000) % Female Country or territory … … 56 49 … 34.

* 34.
* 34.

* … … … … 37 … … … … 50 … … … 46 … 24 … … … 44 … 48 … … … … 60.

* 48 59 62 61 42 64 65.

* 40.

* 70.

* 61 39 54 56.

* … 40 65.

* 63.

* … 67 62.

* 63.

* 49.

* … … … 3 0.8 … 6 … … … … … 0.1 … … … … 6.0 … … … 4 … 0.9 … … … 2 … 12 … … … … … 5 908 17 29 320 169 1 819 8 771 43 … 895 20 2 068 … … 1 … … … 3 … … … … … … 53 48 … 30 … Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 56 … … … … … … … 36 … … … … North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States 50 … … … … 21 … … … 42 … 46 … … 41 55 58 52 37 60 60 35 … 61 43 51 … … 20 … … … 60 … … … … South and West Asia Afghanistan Bangladesh Bhutan India 2 Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka 2 Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Table 2 (continued) ADULT LITERACY RATE (15 and over) (%) ADULT ILLITERATES (15 and over) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Country or territory Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 27.

* 72.

* … … … … … … 41 … 49.

* … 80.

* … 76.

* … 55.

* 58.

* 73.

* 27.

* 88.

* … … … 67.

* … 56.

* 59.

* 65.

* 84.

* 76 98 99 68 58 96 98 82 82 94 87 66 87 99 48 53 36.

* 79.

* … … … … … … 52 … 65.

* … 85.

* … 78.

* … 68.

* … 85.

* 37.

* 87.

* … … … 70.

* … 68.

* 71.

* 73.

* 89.

* 82 99 99 77 70 98 99 89 89 94 88 65 88 99 60 63 19.

* 65.

* … … … … … … 30 … 34.

* … 75.

* … 74.

* … 44.

* … 62.

* 18.

* 89.

* … … … 65.


* … 45.

* 48.

* 57.

* 79.

* 70 97 99 59 46 94 97 75 75 93 86 67 86 99 34 45 36.

* 85 … 64 29.

* … 74.

* 82.

* 54 71.

* 71 23 87 44 88 30 71 65.

* 87 42 92.

* 37 … 88 80.

* 53.

* 73 72 68.

* 91 50.

* 90 … 71 43.

* … 78.

* 74.

* 60 77.

* 79 31 90 57 88 44 79 71.

* 93 53 91.

* 49 … 88 81.

* 69.

* 81 79 76.

* 94 23.

* 81 … 57 18.

* … 70.

* 90.

* 49 65.

* 63 16 84 32 87 16 63 60.

* 82 32 92.

* 26 … 87 78.

* 38.

* 64 65 60.


* 88 … 91 … 71 … … … … 64 … 79 27 90 49 90 36 79 … 91 47 … 47 … 91 … … 81 74 … 94 … 94 … 76 … … … … 65 … 83 34 92 58 90 48 85 … 94 56 … 58 … 92 … … 86 79 … 96 … 88 … 66 … … … … 64 … 74 20 89 41 91 23 74 … 88 38 … 37 … 91 … … 75 70 … 93 23 045 165 … … … … … … 652 … 2 197 … 150 … 198 … 23 296 1 468 17 2 964 … … … … 126 … 4 185 5 217 1 541 990 57.

* 64.

* … … … … … … 60 … 68.

* … 63.

* … 56.

* … 64.

* … 73.

* 56.

* … … … … 59.

* … 64.

* 65.

* 62.


* 66.

* 28 859 124 … 5 053 3 628 … 5 473 205 865 3 154 2 094 4 832 124 6 566 156 5 014 23 451 1 871 11 4 067 … 2 066 … 4 088 141 1 706 4 154 6 157 2 039 754 64.

* 65 … 59 59.

* … 58.

* 31.

* 56 60.

* 64 58 62 64 54 60 65 61.

* 74 60 … 61 … 55 56.

* 67.

* 66 63 63.
* 67 … 92 … 5 152 … … … … 946 … 2 009 6 146 103 7 112 150 6 334 21 577 … 10 4 802 … 2 080 … 3 107 … … 4 045 7 185 … 532 … 66 … 58 … … … … 51 … 61 57 60 60 49 60 63 … 68 59 … 61 … 55 … … 64 59 … 65 Weighted average Sum % F Sum % F Sum % F Note: For countries indicated with (*), national observed literacy data are used.
For all others, UIS literacy estimates are used.

The estimates were generated using the UIS Global Age-specific Literacy Projections model.

Those in the most recent period refer to 2006 and are based on the most recent observed data available for each country.

The population used to generate the number of illiterates is from the United Nations Population Division estimates (2007), revision 2006.
For countries with national observed literacy data, the population corresponding to the year of the census or survey was used.

For countries with UIS estimates, populations used are for 1994 and 2006.
2 7 4 S TAT I S T I C A L TA B L E S Ta b l e 2 YOUTH LITERACY RATE (15-24) (%) YOUTH ILLITERATES (15-24) 1985-19941 2000-20061 Projected 2015 1985-19941 2000-20061 Projected 2015 Total Male Female Total Male Female Total Male Female Total (000) % Female Total (000) % Female Total (000) % Female Country or territory 34.

* 93.

* … … … … … … 51 … 59.

* … 91.

* … 88.

* … 71.

* 75.

* 94.

* 38.

* 99.

* … … … 84.

* … 70.

* 82.

* 66.

* 95.

* 84 100 99 80 76 98 100 95 95 92 94 78 94 99 61 64 39.
* 94.

* … … … … … … 56 … 70.

* … 91.

* … 86.

* … 81.

* … 96.

* 49.

* 98.

* … … … 83.

* … 77.

* 86.

* 67.

* 97.

* 88 100 99 85 84 99 100 97 97 93 93 75 94 100 72 70 28.

* 92.

* … … … … … … 47 … 49.

* … 92.

* … 90.

* … 62.

* … 92.

* 28.

* 99.

* … … … 84.

* … 63.

* 78.

* 66.

* 94.

* 79 100 99 75 67 97 100 93 93 92 94 81 95 99 49 58 50.

* 97 … 77 47.

* … 80.

* … 70 70.

* 82 29 96 52 93 38 86 78.

* 95 51 99.

* 52 … 95 88.

* 74.

* 85 78 69.

* 98 62.

* 98 … 79 59.

* … 80.


* … 67 73.

* 83 36 95 58 91 53 88 79.

* 95 58 99.

* 63 … 94 87.

* 84.

* 88 79 73.

* 98 39.

* 96 … 75 34.

* … 81.

* … 74 68.

* 81 22 97 46 94 25 84 77.

* 95 43 99.

* 42 … 96 90.

* 64.

* 83 76 66.
* 98 Weighted average … 98 … 84 … … … … 80 … 90 33 97 57 94 46 92 … 95 56 … 67 … 98 … … 91 77 … 99 92 100 99 91 93 99 99 99 99 90 98 91 98 99 87 81 … 99 … 84 … … … … 72 … 89 38 96 59 91 56 92 … 93 61 … 76 … 97 … … 92 76 … 99 93 100 99 92 95 99 99 99 99 89 97 87 98 100 89 82 … 97 … 84 … … … … 87 … 91 27 98 56 96 36 91 … 96 51 … 59 … 98 … … 91 77 … 100 91 100 99 90 90 98 100 99 99 92 98 95 99 99 85 80 7 404 13 … … … … … … 196 … 616 … 18 … 35 … 5 091 305 1 849 … … … … 24 … 1 061 831 543 102 54.
* 59.

* … … … … … … 54 … 64.

* … 46.

* … 40.

* … 67.

* … 65.

* 58.

* … … … … 51.

* … 62.

* 62.

* 51.

* 62.

* 8 068 9 … 1 111 967 … 1 588 … 212 1 108 484 1 732 8 1 977 34 1 493 4 171 524 2 1 224 … 522 … 491 33 346 893 1 795 758 62 62.

* 66 … 54 61.

* … 49.

* … 44 54.

* 53 55 37 56 39 64 58 52.

* 48 58 … 61 … 41 44.

* 69.

* 58 53 55.
* 41 … 5 … 867 … … … … 200 … 380 2 175 6 2 225 34 1 884 3 078 … 2 1 322 … 430 … 218 … … 711 2 306 … 25 … 72 … 48 … … … … 31 … 45 55 31 52 32 60 51 … 35 55 … 63 … 35 … … 52 49 … 16 Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe Sum % F Sum % F Sum % F World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 1.

Data are for the most recent year available during the period specified.

See the web version of the introduction to the statistical tables for a broader explanation of national literacy definitions, assessment methods, and sources and years of data.

2.

Literacy data for the most recent year do not include some geographic regions.
Table 3A Early childhood care and education (ECCE): care CHILD SURVIVAL1 CHILD WELL-BEING2 % of children under age 5 suffering from: % of children who are: Infant mortality rate Under-5 mortality rate Infants with low birth weight (‰) (‰) (%) Underweight moderate and severe Wasting moderate and severe Stunting moderate and severe Exclusively breastfed Breastfed with complementary food Still breastfeeding (<6 months) (6-9 months) (20-23 months) 2005-2010 2005-2010 1999-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000- … … 1 … … … … … 3 … 4 3 3 2 … … 2 4 1 4 7 3 4 3 6 17 11 5 … … 36 3 5 21 4 5 2 11 5 3 13 9 7 1 2 11 16 9 2 15 12 7 1 3 … 1 … … … … … 3 … 4 2 4 3 … … 2 1 0 5 2 2 4 4 2 7 6 3 … … 7 11 10 33 18 21 9 24 11 15 35 18 10 10 8 20 43 22 12 17 53 22 3 7 … 1 … … … … … 5 … 8 10 13 6 … … 9 12 3 13 13 12 13 14 21 27 15 15 … … 37 7 34 1 38 25 27 12 27 … 20 31 … 27 12 31 16 29 47 34 12 2 9 18 … 23 … … … … … 19 … 46 16 … 15 … … 37 21 6 33 7 18 17 32 57 25 11 26 … … 60 39 65 23 67 51 70 26 35 … 78 66 92 … 48 60 47 37 … 52 76 38 38 29 … … … … … … … 35 … 18 41 … 39 … … 8 38 83 57 39 12 39 49 57 15 54 45 … … 82 22 41 18 37 36 12 9 11 23 57 15 73 … 21 30 40 16 22 29 … 20 4 10 … … … … … … … 13 … 2 … … 8 … … 10 24 11 15 16 12 16 26 65 34 37 38 … … 54 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab
 Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia6 Brunei Darussalam Cambodia 2 7 6 S TAT I S T I C A L TA B L E S Ta b l e 3 A PROVISION FOR UNDER-3s WOMEN’S EMPLOYMENT AND MATERNITY LEAVE CHILD WELL-BEING2 % of 1-year-old children immunized against Tuberculosis Diphtheria, Pertussis, Tetanus Polio Measles Hepatitis B Corresponding vaccines: Official programmes targeting children Youngest age group targeted in programmes Female labour force participation rate (age 15 and above)4 BCG 2006 DPT3 2006 Polio3 2006 Measles 2006 HepB3 2006 under age 3 2005 (years) c. 2005 (%) 2003 99 … 88 99 91 95 … … 99 86 95 99 99 99 95 77 99 99 98 70 98 99 97 98 98 99 99 99 99 99 98 94 99 99 97 99 98 … 92 88 97 91 99 95 99 99 98 94 99 98 … … 98 75 98 99 88 98 68 95 99 97 96 96 60 98 99 92 85 98 98 82 96 … 98 95 … 97 95 90 98 98 99 98 93 99 … 89 82 96 78 93 83 99 90 98 86 98 97 94 99 80 … Yes … Yes … Yes No Yes … … No No Yes … … Yes Yes No No No No … Yes No … No Yes Yes No No … … … No … … … Yes No Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes … Yes … 0-2 … 2-3 … 0-3 .
0-2 … … .

.

0-4 … … 0-6 0-2 .

.

.

.

… 0-3 .

… .

1-6 0-2 .

.

… … … .

… … … 1-3 .

0-2 0-3 2 0-2 0-2 1-6 1-3 2-3 .
0-2 2-3 1-4 … 0-6 34 29 53 21 20 26 45 30 28 54 27 20 … 36 17 23 37 27 36 29 50 53 55 45 45 51 53 43 51 53 … 48 57 49 54 … 53 50 43 27 51 50 60 57 64 55 54 49 61 56 55 44 74 Duration of paid maternity leave5 (weeks) 2005-20073 14 … … 13 … … … … 12 14 14 … … … … 0 … 4 … … 52 18 … 19 58 28 20 24 16 18 … 16 18 17 20 … 28 15 … 12 18 20 18 8 18 18 … … 16 18 52 … … Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia 6 Brunei Darussalam Cambodia Table 3A (continued) CHILD SURVIVAL1 CHILD WELL-BEING2 % of children under age 5 suffering from: % of children who are: Country or territory Infant mortality rate Under-5 mortality rate Infants with low birth weight (‰) (‰) (%) Underweight moderate and severe Wasting moderate and severe Stunting moderate and severe Exclusively breastfed Breastfed with complementary food Still breastfeeding (<6 months) (6-9 months) (20-23 months) 2005-2010 2005-2010 1999-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 China Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands7 Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize
 Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua 2 7 8 23 … 48 20 27 3 … 51 7 9 … 34 66 … 5 … … 61 23 4 22 3 55 11 67 … 19 … 28 20 … … 13 17 14 10 16 … 46 24 … … 7 19 10 5 … 30 21 22 34 30 43 49 28 14 17 … 15 21 29 … 62 24 32 4 … 67 8 11 … 42 97 … 6 … … 84 27 5 27 4 72 15 92 … 22 … 34 23 … … 16 20 17 11 20 … 61 29 … … 9 26 11 7 … 33 26 29 41 39 57 72 42 17 20 … 17 26 2 3 7 10 9 8 5 14 … 9 12 18 15 … 6 0 9 11 20 4 4 8 13 9 … … 3 5 6 7 … 5 7 … 7 13 6 … 7 8 … … 6 9 7 5 10 11 16 7 9 12 13 25 10 12 8 … … 12 7 10 23 … 28 … 13 40 … 8 … 15 32 … … … … … 28 … … 3 … 9 … … … … … 25 … … 4 … … … 7 … 8 6 … … 1 7 5 4 … 5 9 10 … 23 14 22 11 4 5 … … 10 … … 7 … … … … 15 … … … … 9 … … … … … 6 … … 2 … 4 … … … … … 7 … … 1 … … … 1 … 1 2 … … 0 1 2 2 … 1 2 1 … 2 11 9 1 4 2 … … 2 11 … 37 … … … … 42 … … … … 32 … … … … … 30 … … 2 … 12 … … … … … 30 … … 4 … … … 18 … 27 11 … … 1 12 6 5 … 7 23 19 … 49 11 24 25 3 13 … … 20 51 19 65 47 40 … 80 23 … 29 63 60 15 … … … 59 59 34 … … … 65 5 … … 62 … 50 17 … … … … … … 24 … 54 … … … 63 47 35 41 … 4 40 24 39 51 11 41 30 15 38 … … 31 32 … 31 … 75 … … 10 … … … … 66 … … … … 74 58 … … … … 43 … … … … … 70 … … … … … … 54 … 74 30 … … 47 65 47 42 … 36 77 76 … 67 42 87 69 36 36 … … 68 15 … 37 … 59 … … 47 … 12 … … 67 … … … … 66 32 … … … … 19 … … … … … 23 … … … … … … 23 … 46 17 … … … 32 12 9 … 15 23 43 … 47 31 35 48 24 21 … … 39 S TAT I S T I C A L TA B L E S Ta b l e 3 A PROVISION FOR UNDER-3s WOMEN’S EMPLOYMENT AND MATERNITY LEAVE CHILD WELL-BEING2 % of 1-year-old children immunized against Tuberculosis Diphtheria, Pertussis, Tetanus Polio Measles Hepatitis B Corresponding vaccines: Official programmes targeting children Youngest age group targeted in programmes Female labour force participation rate (age 15 and above)4 BCG 2006 DPT3 2006 Polio3 2006 Measles 2006 HepB3 2006 under
 age 3 2005 (years) c. 2005 (%) 2003 92 99 96 93 82 … 99 61 … 99 92 55 85 99 … 99 … 75 91 98 84 98 84 99 72 … 99 99 92 95 … … 99 … … … 97 … 93 99 … … 98 88 88 99 99 95 99 93 … 96 96 75 90 90 99 … … 99 93 99 89 81 70 99 86 57 … 96 74 67 82 72 89 99 98 75 88 98 56 95 91 98 67 … 99 97 85 94 … 99 91 … 95 84 98 … 81 99 … … 94 86 91 89 95 81 98 96 91 80 93 53 87 85 98 … … 87 94 99 98 83 70 97 86 56 … 96 95 81 82 45 89 99 98 75 88 98 57 95 91 98 66 … 99 97 85 94 … 99 92 … 94 85 98 … 79 99 … … 94 86 91 99 88 85 97 96 91 81 92 52 87 86 98 … … 88 93 99 96 99 72 99 61 48 … 90 96 83 78 99 82 99 98 65 92 99 54 93 84 96 64 … 99 84 99 93 … 99 97 … 88 92 99 … 81 99 … … 91 88 89 96 99 99 97 98 98 95 90 58 91 87 96 … … 99 91 99 96 81 70 … 88 57 … 87 97 84 75 99 87 99 98 70 77 99 56 94 93 96 … … 99 97 85 93 … 99 84 … 96 84 98 … 81 97 … … 94 86 90 89 7 74 98 96 91 80 93 … 87 87 98 … … 87 Yes … Yes No Yes Yes No Yes No Yes … … … … Yes … … No No Yes … Yes No Yes … … … … … Yes … … Yes … … Yes … … Yes Yes Yes … Yes Yes Yes Yes … … Yes Yes Yes Yes No Yes Yes No Yes … … Yes 0-3 … 0-3 .
0-6 0-6 .

0-2 .

0-3 … … … … 0-5 … … .

.

0-5 … 2-6 .

0-5 … … … … … 0-2 … … 0-5 … … 0-2 … … 0-4 0-3 0-3 … 0-2 0-5 0-3 1-6 … … 0-4 0-3 0-2 0-6 .

0-3 0-3 .
0-3 … … 0-3 70 … 51 50 51 49 … 54 54 45 … … 68 … 59 … … 72 52 49 40 50 55 65 54 … 46 … 79 72 … … 52 … 64 65 42 … 63 57 54 … 37 60 42 43 … 44 54 47 … 33 43 55 44 57 39 … 50 36 Duration of paid maternity leave5 (weeks) 2005-20073 Country or territory 13 … … … 0 14 … 12 … 0 0 … 12 … 14 … … … 9 12 … 12 0 13 … … … … 12 17 … 13 13 … 13 12 14 4 13 17 13 … 18 12 17 18 12 12 12 12 12 12 13 … 12 8 12 … … 12 China Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands 7 Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Table 3A (continued) Country or territory Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of 18 32 21 … 13 23 28 12 … 13 17 North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States7 South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros 2 8 0 … 4 4 5 6 4 4 4 4 7 3 5 5 5 5 6 … 5 3 5 … 4 3 4 5 6 157 52 45 55 31 34 54 67 11 132 98 46 104 99 88 25 97 119 48 CHILD SURVIVAL1 CHILD WELL-BEING2 % of children under age 5 suffering from: % of children who are: Infant mortality rate Under-5 mortality rate Infants with low birth weight (‰) (‰) (%) Underweight moderate and severe Wasting moderate and severe Stunting moderate and severe Exclusively breastfed Breastfed with complementary food Still breastfeeding (<6 months) (6-9 months) (20-23 months) 2005-2010 2005-2010 1999-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 24 38 29 … 16 28 35 18 … 16 22 … 5 5 6 7 6 5 5 5 8 4 6 6 6 7 8 … 6 4 7 … … 8 9 … 7 8 6 … 5 4 7 7 8 4 6 8 6 8 6 … … 5 8 … 6 4 6 8 8 … 22 15 30 7 22 21 19 22 12 16 10 16 11 11 13 13 22 25 8 5 8 … … … 13 6 … 5 5 … … … … … … … … … … … … … … … … … … … … … … … … … 2 39 48 19 43 11 30 39 38 29 31 23 13 37 39 19 … 29 37 25 1 1 1 … … … 7 4 … 2 4 … … … … … … … … … … … … … … … … … … … … … … … … … 0 7 13 3 20 5 13 13 13 14 6 7 5 23 7 6 … 10 14 8 18 14 24 … … … 10 4 … 11 13 … … … … … … … … … … … … … … … … … … … … … … … … … 1 54 43 40 48 15 25 49 37 14 45 38 23 35 53 30 … 38 41 44 25 22 64 56 … … 9 13 … … 7 … … … … … … … … … … … … … … … … … … … … … … … … … … … 37 … 46 44 10 53 16 53 11 70 34 7 45 21 57 23 2 21 38 60 81 … … … 25 43 … …
 50 … … … … … … … … … … … … … … … … … … … … … … … … … … 29 52 … 56 … 85 75 31 … 77 50 57 50 88 64 64 55 77 34 21 … 41 … … … 11 22 … … 31 … … … … … … … … … … … … … … … … … … … … … … … … … … 54 89 … … 0 … 95 56 73 37 57 11 85 … 21 13 47 65 45 S TAT I S T I C A L TA B L E S Ta b l e 3 A PROVISION FOR UNDER-3s WOMEN’S EMPLOYMENT AND MATERNITY LEAVE CHILD WELL-BEING2 % of 1-year-old children immunized against Tuberculosis Diphtheria, Pertussis, Tetanus Polio Measles Hepatitis B Corresponding vaccines: Official programmes targeting children Youngest age group targeted in programmes Female labour force participation rate (age 15 and above)4 BCG 2006 DPT3 2006 Polio3 2006 Measles 2006 HepB3 2006 under age 3 2005 (years) c. 2005 (%) 2003 Duration of paid maternity leave5 (weeks) 2005-20073 99 75 99 99 94 99 … … … 99 83 … … … … … … 98 84 … 88 … 93 … … … … 90 … … 89 … … 17 … … … 90 96 92 78 99 99 93 89 99 65 99 99 99 84 85 70 70 40 84 99 73 94 99 85 99 84 92 … … … … 95 71 84 83 78 14 93 … … 29 86 88 … … 95 96 95 86 99 … … 94 95 81 … … … 92 … 88 95 6 99 98 69 83 98 … 93 85 76 74 81 69 … … 69 Yes Yes Yes … Yes … … Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No … … Yes Yes Yes … Yes Yes Yes Yes Yes … No No Yes Yes Yes No Yes … … Yes Yes … … Yes … Yes … … 2-4 0-4 0-5 … 0-2 … … 0-5 2 0-3 0-2 0-3 1-3 1-3 0-6 0-5 0-2 0-6 0-3 0-2 0-3 0-6 0-5 0-4 0-2 … … … 0-3 0-5 0-3 … 0-3 1-6 0-5 1-3 0-4 … .
.

0-6 0-6 0-3 .

0-6 … … 2-5 0-4 … … 1-6 … 2-5 … … 47 64 58 … 52 52 35 49 … 55 53 … 50 43 61 54 60 57 48 50 41 70 49 49 37 44 30 … 55 62 55 … … 13 … … 12 16 … 7 12 12 … 14 12 14 12 14 6 14 14 … Country or territory Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States 7 South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Table 3A (continued) Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe 70 117 114 92 55 87 54 74 57 103 113 64 65 133 66 89 129 14 96 42 111 109 112 72 66 … … 278 193 66 114 126 127 118 157 94 Weighted average World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa CHILD SURVIVAL1 CHILD WELL-BEING2 % of children under age 5 suffering from: % of children who are: Infant mortality rate Under-5 mortality rate Infants with low birth weight (‰) (‰) (%) Underweight moderate and severe Wasting moderate and severe Stunting moderate and severe Exclusively breastfed Breastfed with complementary food Still breastfeeding (<6 months) (6-9 months) (20-23 months) 2005-2010 2005-2010 1999-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 2000-20063 13 17 12 13 14 20 14 20 9 12 … 10 13 … 17 13 23 14 15 14 13 14 6 8 19 … 24 11 15 9 12 12 10 12 11 15 … … 16 16 … … 6 … … 9 … … … … 14 14 20 31 19 40 38 12 20 18 26 … 20 20 26 42 19 33 15 24 24 44 29 23 9 17 … 30 36 12 10 26 20 22 20 17 7 7 13 7 13 11 3 6 5 9 … 6 4 6 13 3 11 14 4 9 10 9 4 8 8 … 9 11 3 1 14 5 3 6 6 Weighted average 25 … … 26 17 … … 14 … … 7 … … … … 28 11 … … 11 8 … … … … … 2 … … … … 9 26 34 38 39 38 47 21 22 22 35 … 30 38 39 48 46 38 10 41 24 50 38 45 23 16 … 40 38 25 30 24 32 38 50 29 31 … … 32 25 … … 16 … … 16 … … … … 38 19 4 24 24 52 49 6 41 54 27 … 13 36 35 67 56 25 21 30 19 14 17 88 60 34 … 8 9 7 24 28 60 41 40 22 38 … … 38 28 … … 43 … … … … … … … 30 78 54 79 … 43 54 62 44 58 41 … 84 79 70 78 89 32 … 80 57 62 64 69 60 61 … 52 15 46 60 35 80 91 87 79 Weighted average 56 … … 56 57 … … 45 … … … … … … … 67 21 37 52 … 62 … 9 53 56 71 … 57 60 45 64 73 69 … 65 37 62 34 77 18 42 … 57 35 … 25 44 54 55 58 28 39 … … 40 25 … … 27 … … … … … … … 50 1.
UN Population Division (2007), median variant.

2.

UNICEF (2007).

3.

Data are for the most recent year available during the period specified.

4.

Employed and unemployed women as a share of the working age population, including women with a job but temporarily not at work (e.g.

on maternity leave), home employment for the production of goods and services for own household consumption, and domestic and personal services produced by employing paid domestic staff.
Data exclude women occupied solely in domestic duties in their own households (ILO, 2008).
2 8 2 S TAT I S T I C A L TA B L E S Ta b l e 3 A PROVISION FOR UNDER-3s WOMEN’S EMPLOYMENT AND MATERNITY LEAVE CHILD WELL-BEING2 % of 1-year-old children immunized against Tuberculosis Diphtheria, Pertussis, Tetanus Polio Measles Hepatitis B Corresponding vaccines: Official programmes targeting children Youngest age group targeted in programmes Female labour force participation rate (age 15 and above)4 BCG 2006 DPT3 2006 Polio3 2006 Measles 2006 HepB3 2006 under age 3 2005 (years) c. 2005 (%) 2003 Duration of paid maternity leave5 (weeks) 2005- … … 86 92 … … 91 … … 96 … … … … … … 78 91 … … 89 … … 92 … … … … Weighted average 80 … … 79 91 … … 89 … … 92 … … … … … … 78 89 … … 89 … … 93 … … … … 72 … 77 … … 97 … 38 95 84 … … 80 85 … 61 99 90 97 72 … … 41 99 75 89 99 … … 99 68 … 80 90 80 90 60 … … 59 88 … … 86 … … 89 … … … … 48 … … … … Yes No … … Yes Yes … … No Yes Yes … Yes Yes … Yes Yes Yes … … Yes Yes No … Yes Yes … … … Yes … … … … … … … … … … … … … … … … … … … … … 0-6 .
… … 0-2 0-3 … … .

2-6 0-3 … 0-3 0-2 … 0-1 2-6 0-3 … … 0-5 0-3 .
… 0-5 0-6 … … … 0-6 … … … … … … … … … … … … … … … … … 61 39 61 50 59 71 61 59 71 79 62 69 47 55 79 85 72 41 85 47 71 46 81 30 57 … 56 59 47 31 51 80 86 66 63 52 55 51 53 30 51 55 55 55 … 54 54 54 54 40 60 15 14 14 12 … 6 14 … 0 … … 8 … … 14 0 14 12 … … 14 12 8 9 14 10 0 … 26 … 14 … 12 0 13 14 18 16 12 … 19 18 … 12 … 13 13 12 16 12 13 Median Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 5.

Refers to paid employment-protected leave duration for employed women around the time of childbirth.

6.

Maternity leave duration refers to unpaid parental leave, as no specific maternity leave policy exists (except for special medical cases).

7.

Maternity leave duration refers to unpaid maternity leave.

Sources: For women’s maternity leave status, US Social Security Administration (2006, 2007a, 2007b, 2008); OECD (2008).
Table 3B Early childhood care and education (ECCE): education ENROLMENT IN PRE-PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) % F Total (000) % F 36 14 0.2 328 68 74 57 143 10 … 805 7 77 8 … 366 108 78 64 12 82 263 … 219 81 312 55 376 58 94 … 958 103 625 4 379 175 169 59 33 261 1 103 57 111 74 165 48 74 56 … 616 … 11 58 24 030 49 48 60 48 48 46 49 48 48 … 34 45 48 48 … … 46 47 48 45 50 47.
* … 48 48 50 48 48 48 48 … 49 48 49 … 46 … 46 49 47 48 … 46 48 48 43 54 42 … 47 … 49 50 46 166 19 1 580 93...z 95 67 148 22 5...z 705 10 77 16 … 505 155 … 90 18.z 80.y 271 … 206 90 284 45 327 65 89 … 840 102 648 4 530 173 145 43 33.z 550 1 032 49 109 76 331 57 95 62 … 562 263 12 106 21 790 47 48 47 47 49...z 47 48 48 48 … 40 47 48 49 … 49 47 … 48 45.z 48.y 48 … 48 48 48 48 48 48 48 … 49 48 49 47 49 48 48 49.z 48 48 51 47 46 48 49 52 46 … 48 48 48 51 45 Total Male Female 3 36 0.4 3 37 0.3 3 36 0.5 11 5 29 78 61 5 … 62 6 39 25 … 19 8 14 64 11 5 30 78 62 5 … 82 6 40 25 … … 9 14 65 10 5 27 79 60 5 … 43 6 39 25 … … 8 13 63 0.7 0.7 0.6 40 75 … 67 40 90 87 78 53 50 … 50 48 62 68 54 82 75 27 6 50 26 21 36 14 10 25 8 … 24 … 50 5 38 39 77.
* … 41 73.

* … 67 40 87 88 79 54 50 … 50 49 61 … 57 … 78 27 6 50 … 22 36 15 11 23 9 … 24 … 49 5 38 66 39 93 87 77 51 49 … 50 48 63 … 51 … 71 28 6 49 … 20 36 14 9 27 7 … 23 … 51 5 37 GPI (F/M) 1.01 0.96 1.50 0.95 0.98 0.91 1.02 0.97 0.97 .… 0.52 0.88 0.96 0.98 .… .… 0.90 0.95 0.97 0.86 1.06 0.95* .… 0.99 0.98 1.07 0.99 0.98 0.95 0.97 .… 1.01 0.96 1.02 .… 0.90 .… 0.91 1.01 0.94 0.98 .… 0.89 1.00 0.96 0.80 1.21 0.76 .… 0.94 .… 1.04 1.03 0.97 .

100 100 54 .

100 24 78 .

… 100 100 100 100 … 90 67 88 68 37 .


– … 0.1 5 2 1 3 1 0.3 … 3 … 1 … … 0.4 1 .

6 0.04 – – 0.1 10 1 4 .

… … … 66 22 … 45 100 72 30 .z 94 40 78 17 78...z 100 100 100 90 … 70 75 … 77 49.z 5.y 5 … 0.3.z 10 1 2 5 3 0.2 … 9 0.2 1 2 0.1 2 2 .z 9 3 2 0.1 – 5 1 1.z .
… 4 67 66 29 31 Country or territory Age group 2006 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova1,2 Romania Russian Federation Serbia1 Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China 4-5 3-5 4-5 4-5 4-5 4-5 4-5 3-5 4-5 3-5 4-5 4-5 4-5 3-5 3-5 4-5 3-5 3-5 4-5 3-5 3-5 3-5 3-5 3-6 3-6 3-5 3-6 3-6 3-6 3-6 … 3-6 3-6 3-6 3-6 3-6 3-5 3-5 3-6 3-5 3-5 3-6 3-5 3-5 3-6 3-6 3-6 3-6 3-6 3-6 4-4 3-5 3-5 4-6 2 8 4 S TAT I S T I C A L TA B L E S Ta b l e 3 B GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRE-PRIMARY EDUCATION (%) GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY AND OTHER ECCE PROGRAMMES (%) NEW ENTRANTS TO THE FIRST GRADE OF PRIMARY EDUCATION WITH ECCE EXPERIENCE (%) School year ending in School year ending in School year ending in School year ending in 2006 2006 2006 2006 Total Male Female 15 52 2 17 6...z 32 75 64 9 2...z 59 8 30 43 … 24 11 … 15 52 2 18 6...z 33 76 65 9 … 70 9 31 43 … 24 11 … 14 51 2 17 6...z 31 73 63 9 … 48 8 30 43 … 24 11 … 78 0.9.z 79 1.0.z 77 0.8.z 49.y 103 … 82 50 114 93 86 89 69 … 57 71 72 87 59 93 81 33.z 13 90 36 32 55 38 14 54 9 … 27 104 51 11 39 49.y 104 … 82 50 116 93 87 90 70 … 57 71 72 90 59.
95 82 33.z 14 91 33 32 56 38 14 51 10 … 27 106 51 11 40 49.y 102 … 82 49 112 92 86 88 68 … 58 70 73 85 59.

92 80 34.z 13 88 40 32 54 38 14 57 9 … 26 103 51 11 38 GPI (F/M) 0.93 0.98 0.92 0.94 1.00...z 0.94 0.96 0.97 0.97 .… 0.69 0.94 0.98 1.00 .… 1.00 0.93 .… 0.98 0.85.z 1.00.y 0.98 .… 0.99 0.98 0.96 0.99 0.99 0.98 0.98 .… 1.01 0.99 1.01 0.95 1.00.
0.97 0.97 1.03.z 0.96 0.97 1.21 1.02 0.96 0.98 1.00 1.13 0.88 .… 0.94 0.97 1.00 1.06 0.95 Total Male Female 6.z 51 1 16 6...z 30 50 62 8 … 52 7 25 40 … 24...z 11 … 56 … 47.y 90 … 79 50 … 88 85 87 68 … 56 69 71 70.y … 86...z 79 32.z 13 68 26.

* 22 39 38 11 47 7 … 21 63 46 10 … 6.z 51 1 17 6...z 31 50 63 8 … 62 7 25 39 … 24...z 11 … 57 … 47.y 90 … 79 50 … 88 86 87 68 … 55 69 71 … … 88...z 79 31.z 14 69 24.
* 22 40 38 11 … 7 … … 63 45 10 … 6.z 50 1 16 6...z 29 49 61 7 … 43 7 25 40 … 24...z 10 … 56 … 47.y 89 … 78 49 … 88 85 86 67 … 56 68 72 … … 85...z 78 32.z 13 67 29.

* 23 38 37 11 … 7 … … 62 46 10 … GPI (F/M) 0.96.z 0.98 0.99 0.93 1.00...z 0.95 0.98 0.97 0.96 .… 0.70 0.95 0.98 1.02 .… 1.00...z 0.93 .… 0.98 .… 1.00.y 0.99 .… 0.99 0.98 .… 1.00 1.00 0.99 0.98 .… 1.01 0.99 1.02 .… .… 0.96...z 0.98 1.02.z 0.96 0.97 1.20.
* 1.04 0.95 0.98 1.00 .… 0.91 .… .… 0.97 1.02 1.07 .… Total Male Female … 55 2 17 6...z 32 75 64 … … 59 8 30 43 … 24 11 … 78 … 49.y 120 … 82 50 114 … 86 89 69 … 57 71 72 … … 93 81 … … 90 36 32 55 … 14 … … … … … 56 2 18 6...z 33 76 65 … … 70 9 31 43 … 24 11 … 79 … 49.y 122 … 82 50 116 … 87 90 70 … 57 71 72 … … 95 82 … … 91 33 32 56 … 14 … … … … … 54 2 17 6...z 31 73 63 … … 48 8 30 43 … 24 11 … 77 … 49.y 119 … 82 49 112 … 86 88 68 … 58 70 73 … … 92 80 … … 88 40 33 54 … 14 … … … … 104 55 12 39 106 55 12 40 103 55 13 38 GPI (F/M) .… 0.97 0.92 0.94 1.00...z 0.94 0.96 0.97 .… .… 0.69 0.94 0.98 1.00 .… 1.00 0.93 .… 0.98 .… 1.00.y 0.98 .… 0.99 0.98 0.96 .… 0.99 0.98 0.98 .… 1.01 0.99 1.01 .… .… 0.97 0.97 .… .… 0.97 1.21 1.02 0.96 .… 1.00 .… .… .… .… 0.97 1.01 1.06 0.95 Total Male Female Arab States 4 82 2 … … 50 87 95 … 50 … … … … … 51 12.z … 83 … 4 83 2 … … 52 87 95 … 52 … … … … … 47 12.z … 83 … 4 81 2 … … 49 87 95 … 48 … … … … … 55 12.z … 83 … Central and Eastern Europe … … … … … … … … … … … … … … … … … … … … … … 7 2.y … 13 … … … … … 99 16 … … … … … … … … … … … … … … … … … … … … … … … 7 2.y … 13 … … … … … … … … … … … … … … … … … … … … … … … … … Central Asia … 7 2.y … 13 … … … … East Asia and the Pacific … 99 15 … … 99 17 … Table 3B (continued) Country or territory Age group 2006 Cook Islands1 DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands1 Micronesia Myanmar Nauru New Zealand Niue1 Palau1 Papua New Guinea Philippines Republic of Korea Samoa Singapore3 Solomon Islands Thailand Timor-Leste Tokelau1 Tonga Tuvalu1 Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands1 Cayman Islands3 Chile Colombia Costa Rica Cuba Dominica1
 Dominican Republic Ecuador El Salvador Grenada1 Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama 4-4 4-5 3-5 5-6 3-5 3-5 3-5 3-5 5-5 4-5 3-5 3-4 3-5 3-4 4-4 3-5 6-6 5-5 5-5 3-4 3-5 3-5 3-5 4-5 3-4 3-4 3-5 3-5 3-5 3-4 3-4 3-5 4-5 3-4 3-4 3-4 4-4 4-5 4-6 3-4 4-4 3-5 3-5 4-5 3-5 3-4 3-5 5-5 4-6 3-4 3-6 4-5 3-5 3-5 3-5 4-5 3-4 4-5 3-5 4- ENROLMENT IN PRE-PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) 0.4 … 9 1981 2 962 … 37 17 572 2 3 41 … 101 0.1 0.7 … 593 535 5 99 13 2 745 … … 2 … … 2 179 0.5 … 1 191 3 1 6 4 … 208 5 733 0.5 0.5 450 1 034 70 484 3 195 181 194 4 308 37 … … 138 3 361 0.1 7 161 49 % F 47 … 49 49 49 … 52 47 50 50 … … … 49 44 54 … 50 47 53 32 48 49 … … 53 … … 48 52 … 50 49 51 49 50 … 49 49 53 48 49 50 49 50 52 49 50 49 50 49 49 … … 51 50 52 50 50 49 Total (000) 0.5.z … 9 3 143 3 073 5...y 49 10 668.z 1 … 93 1 102 0.03.z 1...z … 912 547 5...y … … 2 462 7.z 0.1...y 1...z 0.7 5 2 713 0.5 … 1 334.z 3 … 6 5 … 241 7 298.z 0.7 0.7 402 1 084 110 465 2.z 212 261 240 3...z 451 33 … 211 154.z 4 463 0.1 … 210 92 % F 45.z … 49 50 … … 51 49 51.z 48 … 50 49 49 58.z 53...z … 49 48 54...y … … 49 51.z 48...y 56...z 52 48 48 49 … 50.z 49 … 49 52 … 49 49.z 52 52 50 49 49 48 50.z 49 49 50 52...z 50 49 … 50 50.z 49 56 … 49 49 25 … … 99 65 … 18 94 49 19 … 90 … … .
24 … 47 75 100 … … 19 … … … … … 49 100 … 28 83 … … … … … 28 100 88 45 45 10 .

100 45 39 22 … 22 1 … … 88 9 .

75 17 23 19.z … 100...z 99 67 … 30 95 43.z … … 50 … 98 … 20...z … 43 78 … … … 21 … … 12.y … 83 61 100 … 30.z 75 … 16 81 … 10 29.y 100 92 52 39 11 .
100.z 44 43 18 … 20 8 … 14 91.z 15 – … 16 17 Total Male Female 86 … 16 23 83 … 8 87 108 59 37 2 … 85 154 63 … 30 80 53 … 35 97 … … 30 … … 39 … … 57 99 12 74 27 … 45 58 62 … 77 37 84 109 80 32 64 43 93 46 124 … … 78 74 … 111 27 39 87 … 16 23 82 … 7 89 105 57 … … … 85 159 56 … 30 83 48 … 35 96 … … 27 … … 41 … … 56 99 11 75 27 … 44 58 57 … 77 37 84 107 76 31 63 42 93 46 125 … … 75 73 … 110 27 39 85 … 16 23 84 … 8 85 110 60 … … … 85 147 69 … 31 77 58 … 35 97 … … 33 … … 38 … … 57 99 12 73 27 … 45 58 66 … 76 38 85 111 85 32 66 43 93 45 124 … … 81 75 … 112 28 40 GPI (F/M) 0.98 .… 1.02 1.01 1.02 .… 1.11 0.95 1.04 1.04 .… .… .… 1.00 0.93 1.23 .… 1.05 0.92 1.21 .… 1.02 1.01 .… .… 1.24 .… .… 0.94 .… .… 1.02 1.00 1.09 0.98 1.03 .… 1.01 1.00 1.16 .… 0.99 1.02 1.01 1.04 1.11 1.01 1.04 1.01 1.01 0.97 0.99 .… .… 1.08 1.02 .… 1.02 1.04 1.01 S TAT I S T I C A L TA B L E S Ta b l e 3 B GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRE-PRIMARY EDUCATION (%) GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY AND OTHER ECCE PROGRAMMES (%) NEW ENTRANTS TO THE FIRST GRADE OF PRIMARY EDUCATION WITH ECCE EXPERIENCE (%) School year ending in School year ending in School year ending in School year ending in 2006 2006 2006 Total Male Female 94...z … 16 37 86 75...y 11 87 125.z 45 … 6 89 92 119.z 64...z … 45 101 48...y … … 92 10.z 125...y 23...z 107 29 … 103 … 66.z 99 … 94 34 … 50 69.z 93 … 55 40 70 113 77.z 32 90 51 81...z 29 99 … 38 92.z 106 91 … 52 67 99...z … 16 36 … … 11 87 120.z 45 … 5 89 91 108.z 59...z … 44 100 43...y … … 91 10.z 126...y 19...z 98 29 … 110 … 65.z 99 … 94 32 … 50 69.z 88 … 54 41 70 113 72.z 32 89 51 77...z 29 99 … 37 91.z 106 76 … 52 67 89...z … 16 38 … … 12 86 131.z 45 … 6 88 93 129.z 68...z … 45 103 54...y … … 92 11.z 125...y 26...z 116 29 … 97 … 66.z 98 … 94 35 … 51 68.z 97 … 55 40 70 113 82.z 32 90 52 84...z 29 100 … 39 94.z 106 108 … 53 67 GPI (F/M) 0.90...z .… 1.01 1.03 .… .… 1.06 0.99 1.10.z 1.00 .… 1.02 0.99 1.02 1.19.z 1.16...z .… 1.02 1.03 1.26...y .… .… 1.01 1.09.z 1.00...y
 1.37...z 1.18 0.98 .… 0.88 .… 1.02.z 1.00 .… 1.00 1.09 .… 1.01 0.98.z 1.11 .… 1.03 0.99 1.00 1.00 1.13.z 1.00 1.02 1.03 1.09...z 1.01 1.01 .… 1.05 1.03.z 1.00 1.42 .… 1.02 1.01 Total Male Female 88...z … 15 26 85 … 11 82 74.z 9 … 6 … 91 … … … 35 54 … … … 84 … … … 92 20 … 93 … 65.z 96 … 83 32 … 42 53.z 84 … … 35 … 100 … 28 74 45 80...y 27 84 … 28 91...z 93 79 … 52 60 92...z … 15 26 … … 10 82 73.z 9 … 5 … 89 … … … 36 54 … … … 84 … … … 84 20 … 100 … 65.z 96 … 82 31 … 41 53.z 80 … … 35 … 99 … 28 74 44 76..y 27 83 … 27 90...z 93 65 … 52 59 83...z … 15 27 … … 11 81 77.z 10 … 6 … 92 … … … 35 55 … … … 85 … … … 100 20 … 87 … 66.z 97 … 83 34 … 42 53.z 88 … … 35 … 100 … 28 75 46 83...y 27 84 … 28 93...z 93 95 … 53 60 GPI (F/M) 0.91...z .… 1.01 1.03 .… .… 1.07 0.99 1.05.z 1.05 .… 1.02 .… 1.02 .… .… .… 0.96 1.03 .… .… .… 1.01 .… .… .… 1.19 1.02 .… 0.87 .… 1.02.z 1.00 .… 1.01 1.10 .… 1.02 1.00.z 1.10 .… .… 1.00 .… 1.01 .… 1.02 1.01 1.05 1.09...y 1.01 1.00 .… 1.04 1.04...z 1.00 1.47 .… 1.02 1.01 Total Male Female 94...z … 16 37 101 75...y 11 87 125.z 45 … … … … 119.z 64...z … 45 101 48...y … … … 10.z 125...y 23...z … 29 … 103 … 66.z 99 … 94 34 … 50 … 166 … 55 40 74 193 77.z … 193 51 81...z 29 99 … 44 92.z 106 91 … 74 67 99...z … 16 36 … … 11 87 120.z 45 … … … … 108.z 59...z … 44 100 43...y … … … 10.z 126...y 19...z … 29 … 110 … 65.z 99 … 94 32 … 50 … 158 … 54 41 74 193 72.z … 187 51 77...z 29 99 … 43 91.z 106 76 … … 67 89...z … 16 38 … … 12 86 131.z 45 … … … … 129.z 68...z … 45 103 54...y … … … 11.z 125...y 26...z … 29 … 97 … 66.z 98 … 94 35 … 51 … 175 … 55 40 74 193 82.z … 200 52 84...z 29 100 … 45 94.z 106 108 … … 67 GPI (F/M) 0.90...z .… 1.01 1.03 .… .… 1.06 0.99 1.10.z 1.00 .… .… .… .… 1.19.z 1.16...z .… 1.02 1.03 1.26...y .… .… .… 1.09.z 1.00...y 1.37...z .… 0.98 .… 0.88 .… 1.02.z 1.00 .… 1.00 1.09 .… 1.01 .… 1.11 .… 1.03 0.99 1.00 1.00 1.13.z .… 1.07 1.03 1.09...z 1.01 1.01 .… 1.05 1.03.z 1.00 1.42 .… .… 1.01 2006 Male 100.z … … 42 … … 9 97 74.z … … 11 … … … … … 57 … … … …
 … … … … … … … Female 100.z … … 42 … … 10 97 79.z … … 11 … … … … … 60 … … … … … … … … … … … Total 100.z … … 42 … … 10 97 76.z … … 11 … … … … … 58 … … … … … … … … … … … Latin America and the Caribbean 100 … 94.z 90.z … 100 … 94.z 90.z … 100 … 94.z 90.z … 100 .
… 66 … 99 90.

* … … 86 99 100.y … 58 68 … … … … … … … 78.z … 42 69 100 .

… 66 … 102 90.

* … … 86 99 100.y … 57 66 … … … … … … … 114.z … 42 68 100 .

… 66 … 97 90.
… 66 … 97 90.

* … … 86 99 100.y … 59 69 … … … … … … … 48.z … Table 3B (continued) Country or territory Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of Age group 2006 3-5 3-5 3-4 3-4 3-4 4-5 3-4 4-5 3-5 3-5 North America and Western Europe Andorra1 Austria Belgium Canada Cyprus1 Denmark Finland France4 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco3 Netherlands Norway Portugal San Marino3 Spain Sweden Switzerland United Kingdom5 United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of6 Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo 3-5 3-5 3-5 4-5 3-5 3-6 3-6 3-5 3-5 4-5 3-5 3-3 3-5 3-5 3-5 3-4 3-5 4-5 3-5 3-5 3-5 3-5 3-6 5-6 3-4 3-5 3-6 3-5 4-5 3-5 5-5 3-5 3-4 3-4 4-4 3-5 4-5 3-5 4-6 4-6 4-5 3-5 3-5 3-5 3-5 3- ENROLMENT IN PRE-PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) 123 1 017 … 4 … … 23 0.8 100 738 … 225 399 512 19 251 125 2 393 2 333 143 12 … 355 1 578 12 10 0.9 390 139 220 … 1 131 360 158 1 155 7 183 … 1 825 0.3 13 869 220 12 238 … … … 18 … 20 5 104 … … … 1 6 % F 50 50 … 50 … … 50 54 49 50 … 49 49 49 49 49 49 49 48 49 48 … 48 48 49 48 52 49 50 49 … 49 49 48 49 48 … 50 48 48 50 48 41 … … … 48 … 50 50 48 … … … 51 61 Total (000) 148.z 1 131 2 4 4...z 16 30.
*,z 1.z 122 1 011 3 217 412 494...y 20 253 140 2 628 2 418 143 12 … 362 1 662 15 9.z 1.y 355 159 262 1.y 1 490 333 156 990 7 342 25...y 1 109.y 0.4 29 254.z 549 14 392 4 075.z … … 31 20.z 27 12 195 21 6...y 8...z 2...z 28 % F 49.z 49 52 50 49...z 47 49.
*,z 47.z 49 49 47 48 49 49...y 48 49 49 49 48 49 49 … 49 48 48 50.z … 49 … 49 … 49 50 48 50 49 43...y 49.y 47 49.z 51 49 46 46.z … … 50 50.z 48 52 50 50 51...y 33...z 48...z 52 29 15 … … … … 100 47 … 20 … 25 56 8 54 27 10 13 54 3 5 … 7 30 5 37 26 69 40 52 … 32 10 6 6 34 … … 100 … … 30 … … … … 20 … 34 49 57 … … … 100 85 Total Male Female 29 55 … 70 … … 58 … 60 45 … 82 111 64 60 90 48 112 94 68 88 … 105 95 73 103 … 97 75 69 … 100 76 89 77 58 … 17 1 18 13 54 11 … … … 4 … 29 54 … 69 … … 57 … 59 44 … 82 112 64 59 90 49 112 94 67 88 … 106 96 73 103 … 98 73 69 … 100 76 89 77 59 … 17 1 18 13 54 13 … … … 4 … 30 56 … 71 … … 58 … 60 45 … 82 110 64 60 90 48 112 93 68 87 … 105 95 73 102 … 97 77 69 … 100 76 88 77 57 … 17 1 19 14 54 10 … … … 4 … 2 0.8 11 … … … 2 2 2 0.8 11 … … … 2 2 2 0.8 11 … … … 2 3 GPI (F/M) 1.03 1.02 .… 1.03 .… .… 1.01 .… 1.02 1.03 .… 0.99 0.99 0.99 1.02 1.00 0.99 1.00 0.98 1.01 0.99 .… 0.98 0.98 1.00 0.99 .… 0.99 1.06 0.99 .… 1.00 1.01 0.99 1.00 0.97 .… 1.04 0.93 1.02 1.05 1.00 0.73 .… .… .… 0.97 .… 1.04 1.01 0.95 .… .… .… 1.07 1.59 28.z 22 68 100 100...z 48 100.
*,z 65.z 43 19 2 27 53 … 50 … 9 13 63 3 8 … 3 30 7 39.z 19.y 70.y 44 47 .y 35 12 9 29 37 … 52.y 100 … 8 38.z … … … … 37.z 96.z … 54 62 – … 47.y 62...z 79 S TAT I S T I C A L TA B L E S Ta b l e 3 B GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRE-PRIMARY EDUCATION (%) GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY AND OTHER ECCE PROGRAMMES (%) NEW ENTRANTS TO THE FIRST GRADE OF PRIMARY EDUCATION WITH ECCE EXPERIENCE (%) School year ending in School year ending in School year ending in School year ending in 2006 2006 2006 2006 Total Male Female 34.z 68 99 69 88...z 84 85.
*,z 118...z 79 60 34.z 67 91 67 89...z 87 86.

*,z 132...z 79 60 34.z 68 107 70 86...z 80 84.

*,z 106...z 80 60 GPI (F/M) 1.01.z 1.02 1.18 1.05 0.97...z 0.93 0.97.

*,z 0.80...z 1.01 1.01 Total Male Female 30.z 67 … 53 … 83...z 68.

*,z 73...z 79 54 30.z 66 … 53 … 82...z 68.

*,z 80...z 79 54 31.z 68 … 54 … 84...z 67.

*,z 68...z 80 54 GPI (F/M) 1.03.z 1.02 .… 1.03 .… 1.02...z 1.00.

*,z 0.85...z 1.01 1.02 Total Male Female 34.z 68 143 … 88...z … 85.

*,z 118...z 94 80 34.z 67 133 … 89...z … 86.


*,z 132...z 94 80 34.z 68 153 … 86...z … 84.

*,z 106...z 95 80 GPI (F/M) 1.01.z 1.02 1.15 .… 0.97...z .… 0.97.

*,z 0.80...z 1.02 1.01 Total Male Female 75.y 61 … … 100...z 100 81.

*,y 100.z 96 75 74.y 61 … … 100...z 100 80.

*,y 101.z 96 74 76.y 62 … … 100...z 100 82.
*,y 100.z 96 76 102 90 121 68...y 79 95 62 116 105 69 96 … 91 104 88 97.z … 90 90 79 … 121 95 99 72 61 102 90 122 68...y 80 95 62 116 106 68 96 … 91 105 88 95.z … 90 … 79 … 121 93 99 71 61 102 89 121 68...y 78 96 62 115 105 70 96 … 91 104 88 100.z … 90 … 80 … 120 98 98 73 61 0.8...y 10.y 2 39.z 53 82 27 52.z … 0.9...y 10.y 2 38.z 50 82 28 55.z … 0.7...y 10.y 2 40.z 56 82 26 50.z … … … … 6 15.z 2 2 19 53 2...y 0.8...z 3...z 9 6 15.z 2 2 19 53 2...y 1.1...z 3...z 8 6 15.z 2 2 19 53 2...y 0.5...z 3...z 9 1.00 0.99 0.99 1.00...y 0.98 1.01 0.99 1.00 0.99 1.02 1.00 .… 1.00 0.98 1.00 1.05.z .… 0.99 .… 1.01 .… 1.00 1.05 1.00 1.03 1.00 0.80...y 1.01.y 0.93 1.04.z 1.11 1.00 0.91 0.90.z .… .… 1.05 1.00.z 0.97 1.09 1.01 1.00 1.05...y 0.49...z 0.96...z 1.11 86 87 100 … 71 92 61 100.y … 69 96 … 86 99 86 83.z … 90 90 78 … 100 95 74 67 56 … 9.y … … … 69 … 43.z … … 3...z 11.z … … … 50 2...y … … 9 86 87 100 … 72 90 61 100.y … 68 96 … 85 100 86 82.z … 90 … 78 … 100 92 74 67 56 … 9.y … … … 69 … 45.z … … 3...z 11.z … … … 49 2...y … … 8 87 86 100 … 70 93 61 100.y … 70 96 … 87 98 86 85.z … 90 … 79 … 100 98 74 68 57 … 9.y … … … 69 … 40.z … … 3...z 11.z … … … 50 2...y … … 9 1.00 0.99 1.00 .… 0.98 1.04 1.00 1.00.y .… 1.02 1.00 .… 1.02 0.98 1.01 1.04.z .… 0.99 .… 1.02 .… 1.00 1.06 0.99 1.03 1.02 .… 1.01.y .… .… .… 0.99 .… 0.89.z .… .… 1.03...z 1.01.z .… .… .… 1.01 1.05...y .… .… 1.11 102 90 121 … 79 95 62 116 105 69 96 … 91 104 88 97.z … 90 90 79 … … 95 99 72 61 0.8...y … 1.6 39.z 53 82 27 … … … … 15.4.z 2 2 19 53 2...y … … 9 102 90 122 … 80 95 62 116 106 68 96 … 91 105 88 95.z … 90 … 79 … … 93 99 71 61 0.9...y … 1.7 38.z 50 82 28 … … … … 15.4.z 2 2 19 53 2...y … … 8 102 89 121 … 78 96 62 115 105 70 96 … 91 104 88 100.z … 90 … 80 … … 98 98 73 61 0.7...y … 1.5 40.z 56 82 26 … … … … 15.3.z 2 2 19 53 2...y … … 9 1.00 0.99 0.99 .… 0.98 1.01 0.99 1.00 0.99 1.02 1.00 .… 1.00 0.98 1.00 1.05.z .… 0.99 .… 1.01 .… .… 1.05 1.00 1.03 1.00 0.80...y .… 0.93 1.04.z 1.11 1.00 0.91 .… .… .… .… 1.00.z 0.97 1.09 1.01 1.00 1.05...y .… .… 1.11 North America
 and Western Europe … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … South and West Asia … … … … 34.z 94 19.z 52.z … … … … … 29.z 93 18.z 63.z … Sub-Saharan Africa … … … 3.y 1 … 84 … … … 11.z … … … 3.y 1 … 86 … … … 13.z … … … … 31.z 94 19.z 57.z … … … … 3.y 1 … 85 … … … 12.z Table 3B (continued) Country or territory Age group Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles1 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe I World II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin America North America and Western Europe South and West Asia Sub-Saharan Africa 3-5 3-5 3-6 5-6 4-6 3-5 3-6 3-5 3-6 4-6 3-5 3-5 3-5 3-5 3-5 3-6 3-4 3-5 3-5 4-6 3-5 4-6 3-6 4-6 4-5 3-5 3-5 6-6 3-5 3-5 4-5 5-6 3-6 3-5 … … … … … … … … … … … … … … … … ENROLMENT IN PRE-PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total
 (000) % F 36 … 17 12 90 … 29 667 … 4 1 188 33 112 50 … 21 42 … 35 12 … … 4 24 3 … … 207 … 11 66 … … 439 49 … 51 47 49 … 47 49 … 51 50 52 42 51 … 51 50 … 53 50 … … 52 50 49 … … 50 … 50 50 … … 51 Total (000) 54 … 25.z 36 219 … 30...y 1 105 80 … 1 672 30 358 146 … 51 37 … 33 24 1 753.y … 6 95 3 25 … 387.y 15...z 13...y 69 795 … … % F 50 … 45.z 50 49 … 50...y 50 49 … 48 64 51 51 … 49 50 … 50 51 49.y … 51 52 48 52 … 50.y 49...z 50...y 50 51 … … 46 … 37 97 100 … … 33 … 62 10 100 39 93 … … 85 … 100 33 … … – 68 5 … … 26 … 53 100 … … … Total Male Female 2 … 34 5 1 … 18 39 … 3 44 21 41 3 … 2 96 … 21 1 … … 25 3 109 … … 21 … 2 4 … … 41 2 … 33 6 1 … 19 39 … 3 44 20 47 3 … 2 95 … 19 1 … … 24 3 107 … … 20 … 2 4 … … 40 2 … 34 5 1 … 17 39 … 3 43 22 35 3 … 2 97 … 22 1 … … 26 3 111 … … 21 … 2 4 … … 41 53 … 49.z 44 95 … 100...y 19 84 … 31.z 100 32 94 … … 83 … 100...y 31 … … –...z 76 6 50 … 7.y .z 59...y 100 2 … … Sum % F Sum % F Median Weighted average … 29 88 23 26 … 53 34 2 9 49 76 2 1 55 50 … 39 86 20 27 … GPI (F/M) 0.96 .… 1.04 0.89 0.97 .… 0.91 1.02 .… 1.05 1.00 1.08 0.74 1.02 .… 1.06 1.02 .… 1.15 1.04 .… .… 1.12 1.00 1.04 .… .… 1.01 .… 0.99 1.00 .… .… 1.03 0.97 0.94 0.99 0.96 0.77 0.97 0.92 0.98 0.98 1.00 1.02 1.05 1.01 0.98 0.94 0.98 1.
National population data were used to calculate enrolment ratios.

2.

Enrolment and population data exclude Transnistria.

3.

Enrolment ratios were not calculated due to lack of United Nations population data by age.

4.

Data include French overseas departments and territories (DOM-TOM).

5.

The decline in enrolment is essentially due to a reclassification of programmes.
From 2004, it was decided to include children categorized as being aged ‘4 rising 5’ in primary education enrolment rather than pre-primary enrolment even if they started the school year at the latter level.

Such children typically (though not always) start primary school reception classes in the second or third term of the school year.
2 9 0 S TAT I S T I C A L TA B L E S Ta b l e 3 B GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRE-PRIMARY EDUCATION (%) GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY AND OTHER ECCE PROGRAMMES (%) NEW ENTRANTS TO THE FIRST GRADE OF PRIMARY EDUCATION WITH ECCE EXPERIENCE (%) School year ending in School year ending in School year ending in School year ending in 2006 2006 2006 2006 Total Male Female Total Male Female GPI (F/M) 0.99 .… 0.83.z 1.03 0.96 .… 1.04...y 1.04 1.01 .… 0.94 1.79 1.05 1.04 .… 0.99 1.02 .… 1.01 1.10 0.98.y .… 1.07 1.11 0.97 1.07 .… 1.02.y 0.99...z 0.98...y 1.03 1.07 .… .… Total Male Female … … … 7 2 … … 42 7 … 27 12 35 8 … … 90 … … 1 10.y … 34 6 95 4 … … 11.y 2...y 2 32 … … … … … 7 2 … … 41 7 … 27 7 34 7 … … 89 … … 1 10.y … 32 6 97 4 … … 11.y 2...y 2 31 … … … … … 7 2 … … 43 7 … 27 16 36 8 … … 91 … … 2 10.y … 35 7 92 4 … … 11.y 2...y 2 33 … … GPI (F/M) .… .… .… 1.01 0.96 .… .… 1.05 1.01 .… 0.98 2.19 1.08 1.05 .… .… 1.02 .… .… 1.09 0.95.y .… 1.08 1.12 0.95 1.07 .… .… 0.99.y 0.98...y 1.01 1.07 .… .… Total Male Female 3 … 44.z 18 3 … … … 7 … … … … 8 … 3 101 … … 2 … … … … 109 … … 58.y 17...z 2...y … 32 … … 3 … 48.z 17 3 … … … 7 … … … … 8 … 3 100 … … 2 … … … … 110 … … … 17...z 2...y … 31 … … 3 … 40.z 18 3 … … … 7 … … … … 8 … 3 101 … … 2 … … … … 107 … … … 17...z 2...y … 33 … … GPI (F/M) 0.99 .… 0.83.z 1.06 0.96 .… .… .… 1.01 .… .… .… .… 1.04 .… 0.99 1.02 .… .… 1.10 .… .… .… .… 0.97 .… .… .… 0.99...z 0.98...y .… 1.07 .… .… 3 … 48.z 13 3 … 16...y 59 7 … 51 13 97 8 … 3 100 … 22 2 14.y … 33 9 110 4 … 37.y 17...z 2...y 3 31 … … 3 … 40.z 14 3 … 17...y 62 7 … 48 23 102 8 … 3 101 … 22 2 13.y … 35 10 107 5 … 38.y 17...z 2...y 3 33 … … … … 70.z … … … … … 17.z … … … … … … 3 100 … .y … … … 42 4.y … … … … … … … … 17 … … … 67.z … … … … … 17.z … … … … … … 0.4 100 … .y … … … 42 4.y … … … … … … … … 16 … Weighted average Weighted average Weighted average Median 0.89
44 73 65 82 64 81 39 14 0.98 0.95 1.00 0.98 0.89 0.96 0.96 0.97 0.97 0.98 1.00 1.06 1.00 1.00 1.01 0.97 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 3 … 44.z 14 3 … 17...y 60 7 … 49 18 100 8 … 3 101 … 22 2 14.y … 34 9 109 5 … 38.y 17...z 2...y 3 32 … … 41 62 79 36 18 62 28 45 44 74 65 79 64 81 39 14 6.
The apparent increase in the gender parity index (GPI) is due to the recent inclusion in enrolment statistics of literacy programmes in which 80% of participants are women.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.
… … 72.z … … … … … 18.z … … … … … … 7 100 … .y … … … 43 5.y … … … … … … … … 17 … … … … … … … … … … … … … … … … … I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI 2 9 1 9 0 0 2 t r o p e R A N N E X Table 4 Access to primary education Compulsory education (age group) Legal guarantees of free education1 New entrants (000) School year ending in 1999 2006 GROSS INTAKE RATE (GIR) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Country or territory Arab States Algeria2 Bahrain Djibouti Egypt3 Iraq Jordan2 Kuwait2 Lebanon2,3 Libyan Arab Jamahiriya2 Mauritania3 Morocco Oman Palestinian A. T. Qatar3 Saudi Arabia Sudan3 Syrian Arab Republic2 Tunisia United Arab Emirates3 Yemen3 Central and Eastern Europe Albania3 Belarus3 Bosnia and Herzegovina3 Bulgaria2,3 Croatia3 Czech Republic Estonia Hungary Latvia3 Lithuania2 Montenegro Poland2,4 Republic of Moldova3,5,6 Romania3 Russian Federation3 Serbia Slovakia2 Slovenia2 TFYR Macedonia2,3 Turkey3 Ukraine3 Central Asia Armenia3 Azerbaijan3 Georgia3 Kazakhstan Kyrgyzstan3 Mongolia3 Tajikistan3 Turkmenistan3 Uzbekistan3 East Asia and the Pacific Australia Brunei Darussalam Cambodia3 China3,7 6-14 6-15 6-15 6-14 6-11 6-15 6-14 6-15 6-14 6-14 6-14 6-15 6-15 6-17 6-11 6-13 6-14 6-16 6-14 6-14 6-13 6-15 … 7-14 7-14 6-15 7-15 7-16 7-15 7-15 … 7-15 7-15 7-14 6-15 7-14 6-15 6-14 7-14 6-14 6-17 7-14 6-16 6-14 7-17 7-15 7-15 7-15 7-15 7-15 5-15 5-16 … 6-14 2 9 2 Total Male Female 101 105 29 92 102 101 97 93 … … 112 87 103 108 … … 106 100 93 76 96 131 … 101 94 100 100 102 98 104 … 101 105 94 96 … 102 98 102 … 97 … 94 99 … 100.
* 109 99 … 102 … 107 109 … 102 103 33 94 109 100 97 97 … … 115 87 103 109 … … 109 100 95 88 97 132 … 102 95 101 101 104 99 105 … 101 105 95 … … 102 98 102 … 97 … 94 99 … 99.

* 109 102 … … … 107 112 … 100 107 25 91 95 101 98 90 … … 108 87 104 107 … … 103 100 92 63 95 131 … 100 93 99 100 101 98 104 … 100 104 94 … … 101 97 102 … 97 … 95 100 … 100.
* 109 96 … … … 106 106 … GPI (F/M) 0.98 1.04 0.74 0.96 0.88 1.00 1.01 0.92 .… .… 0.94 1.00 1.01 0.98 .… .… 0.94 1.00 0.97 0.71 0.98 0.99 .… 0.98 0.98 0.98 0.99 0.97 1.00 0.99 .… 0.99 1.00 0.99 .… .… 0.99 0.99 1.00 .… 1.00 .… 1.01 1.02 .… 1.02.

* 1.00 0.95 .… .… .… 0.99 0.95 .… Total Male Female 98 125 52 103 108...z 90 94 86 … 127 102 76 78 108 … 77 123 98 102 112 99.y 101 … 100 97 109 96 97 95 96 … 97 98 97 100 … 101 96 99.z 94 99.
* 104 98 100 110 97 122 101 … 93 106...z 105 131 88 99 124 57 105 111...z 89 96 86 … 124 104 76 78 107 … 83 125 97 103 122 100.y 102 … 100 97 109 96 97 95 98 … 97 98 97 101 … 102 95 99.z 95 99.

* 102 99 97 110 98 122 103 … 95 106...z 105 135 88 97 126 48 102 105...z 90 93 86 … 129 100 76 78 109 … 72 122 100 101 102 99.y 100 … 100 97 108 95 96 95 94 … 98 97 97 100 … 101 96 99.z 92 99.
* 106 97 103 110 97 122 99 … 92 105...z 105 127 87 GPI (F/M) 0.98 1.02 0.85 0.97 0.94...z 1.01 0.97 0.99 .… 1.04 0.96 1.01 1.01 1.02 .… 0.87 0.97 1.02 0.99 0.83 0.99.y 0.98 .… 1.00 1.00 1.00 0.99 1.00 1.00 0.96 .… 1.00 0.99 1.00 0.99 .… 0.99 1.00 1.01.z 0.97 1.00.
* 1.05 0.98 1.06 1.00 0.99 1.00 0.95 .… 0.97 1.00...z 1.00 0.94 0.99 Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes … Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes … Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes 745 13 6 1451 709 126 35 71 … … 731 52 95 11 … … 466 204 47 440 67 173 … 93 50 124 18 127 32 54 … 535 62 269 1 866 … 75 21 32 … 623 … 175 74 … 120.
* 70 177 … 677 … 8 404 … 569 15 11 1 702 844...z 131 41 68 … 103 615 44 95 13 … 795 568 158 56 730 56.y 91 … 64 46 92 12 97 18 35 … 386 40 217 1 288 … 56 17 26.z 1311 395.
* 40 124 49 235 103 56 173 … 505 269...z 8 435 16 764 S TAT I S T I C A L TA B L E S Ta b l e 4 NET INTAKE RATE (NIR) IN PRIMARY EDUCATION (%) School year ending in SCHOOL LIFE EXPECTANCY (expected number of years of formal schooling from primary to tertiary education) School year ending in 1999 2006 1999 2006 Total Male Female GPI (F/M) Total Male Female 77 89 21 … 79 67 62 69 … … 51 70 … … … … 60 … 49 25 … 76 … … 68 … … … … … … … … … … … … … … … 69 … … 69 … 58.
* 81 93 … … … … 64 … 79 86 24 … 83 67 63 70 … … 53 70 … … … … 60 … 49 30 … 77 … … 69 … … … … … … … … … … … … … … … … … … 68 … 59.

* 81 95 … … … … 65 … 76 91 18 … 74 68 61 67 … … 48 71 … … … … 59 … 49 20 … 76 … … 66 … … … … … … … … … … … … … … … … … … 69 … 58.

* 81 90 … … … … 63 … 0.97 1.06 0.75 .… 0.90 1.02 0.97 0.95 .… .… 0.92 1.01 .… .… .… .… 0.98 .… 1.00 0.68 .… 0.99 .… .… 0.97 .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… 1.02 .… 0.99.
* 0.99 0.95 .… .… .… .… 0.97 .… 85 99 37 93...y 83...z 62.y 55.z 60 … 38 84 54 58 … … … 52 88 38 … … 84 … … … … … 65...z … … … … 71 … … … … … … 74...z 75.

* 55 70 74 59 59 79 98 … 77 72...z 68 82 … 86 99 41 93...y 86...z 63.y 54.z 61 … 38 86 53 58 … … … 52 88 39 … … 85 … … … … … 67...z … … … … 71 … … … … … … 75...z 75.

* 53 71 73 61 60 79 100 … … 69...z 67 83 … 84 100 33 92...y 79...z 62.y 55.z 59 … 39 82 54 58 … … … 52 89 37 … … 83 … … … … … 63...z … … … … 71 … … … … … … 73...z 75.
* 58 69 76 57 58 79 95 … … 75...z 69 82 … GPI (F/M) 0.98 1.01 0.81 0.99...y 0.92...z 0.99.y 1.01.z 0.97 .… 1.04 0.96 1.01 0.99 .… .… .… 1.00 1.02 0.94 .… .… 0.98 .… .… .… .… .… 0.94...z .… .… .… .… 1.00 .… .… .… .… .… .… 0.97...z 1.00.
* 1.08 0.98 1.05 0.93 0.96 1.00 0.95 .… .… 1.08...z 1.03 1.00 .… Total Male Female Total Male Female Country or territory … 13 3 13 8 … 14 12 … 7 8 … 12 13 … 5 … 13 11 8 11 14 … 13 12 13 14 14 14 14 … 15 11 12 … … 13 15 12 … 13 11 10 12 12 11 9 10 … 11 20 14 … … … 13 4 … 9 … 13 12 … … 9 … 12 12 … … … 13 10 10 11 13 … 13 12 13 14 14 13 13 … 14 11 12 … … 13 14 12 … 13 … 10 12 12 11 8 11 … 11 20 13 … … … 14 3 … 7 … 14 12 … … 7 … 12 14 … … … 13 11 5 11 14 … 13 12 13 15 14 14 14 … 15 12 12 … … 13 15 12 … 13 … 10 12 12 12 10 9 … 10 20 14 … … 13...z 15 4 12...y 10...z 13 13 13 … 8 10 12 14 13 … … … 14 … 9 11...y 15 … 14 14 15 16 15 16 16 … 15 12 14 14 … 15 17 12.z 11 14 11 11 12 15 12 13 11 … 11 20 14 10 11 13...z 14 5 … 11...z 13 12 13 … 8 … 12 13 13 … … … 13 … 11 11...y 14 … 13 13 15 15 15 14 15 … 15 12 14 13 … 14 16 12.z 12 14.
* 11 11 12 15 12 12 12 … 12 20 14 10 11 13..z 16 4 … 8...z 13 13 13 … 8 … 11 14 14 … … … 14 … 7 11...y 15 … 14 14 15 17 16 17 17 … 16 13 14 14 … 15 17 12.z 11 15.
* 12 11 13 16 13 14 10 … 11 21 14 9 11 Arab States Algeria 2 Bahrain Djibouti Egypt 3 Iraq Jordan 2 Kuwait 2 Lebanon 2,3 Libyan Arab Jamahiriya 2 Mauritania 3 Morocco Oman Palestinian A. T. Qatar 3 Saudi Arabia Sudan 3 Syrian Arab Republic 2 Tunisia United Arab Emirates 3 Yemen 3 Central and Eastern Europe Albania 3 Belarus 3 Bosnia and Herzegovina 3 Bulgaria 2,3 Croatia 3 Czech Republic Estonia Hungary Latvia 3 Lithuania 2 Montenegro Poland 2,4 Republic of Moldova 3,5,6 Romania 3 Russian Federation 3 Serbia Slovakia 2 Slovenia 2 TFYR Macedonia 2,3 Turkey 3 Ukraine 3 Central Asia Armenia 3 Azerbaijan 3 Georgia 3 Kazakhstan Kyrgyzstan 3 Mongolia 3 Tajikistan 3 Turkmenistan 3 Uzbekistan 3 East Asia and the Pacific Australia Brunei Darussalam Cambodia 3 China 3, Table 4 (continued) Compulsory education (age group) Legal guarantees of free education1 New entrants (000) School year ending in 1999 2006 GROSS INTAKE RATE (GIR) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Country or territory Total Male Female Cook Islands5 DPR Korea Fiji Indonesia Japan4 Kiribati5 Lao PDR Macao, China Malaysia Marshall Islands2,5 Micronesia Myanmar3 Nauru New Zealand4 Niue5 Palau2,5 Papua New Guinea Philippines3 Republic of Korea2,4 Samoa Singapore Solomon Islands Thailand Timor-Leste3 Tokelau5 Tonga Tuvalu5 Vanuatu Viet Nam3 5-15 6-15 6-15 7-15 6-15 6-15 6-10 5-14 … 6-14 6-13 5-9 … 5-16 5-16 6-14 6-14 6-12 6-14 5-14 6-16 … 6-14 7-15 … 6-14 7-14 6-12 6-14 Latin America and the Caribbean Anguilla3 Antigua and Barbuda Argentina2,3 Aruba5 Bahamas Barbados Belize Bermuda5 Bolivia3 Brazil3 British Virgin Islands5 Cayman Islands8 Chile2,3 Colombia2 Costa Rica3 Cuba Dominica5 Dominican Republic3 Ecuador3 El Salvador3 Grenada5 Guatemala3 Guyana3 Haiti Honduras2,3 Jamaica Mexico3 Montserrat Netherlands Antilles Nicaragua3 Panama3 2 9 4 5-17 5-16 5-14 6-16 5-16 5-16 5-14 5-16 6-13 7-14 5-16 5-16 6-13 5-14 6-15 6-14 5-16 5-13 5-14 7-15
 5-16 7-15 6-15 6-11 6-11 6-11 6-15 5-16 6-15 6-12 6-11 … Yes No No Yes No No … No No No Yes No Yes … Yes No Yes Yes No No No Yes Yes … No No No Yes Yes Yes Yes … No Yes Yes … Yes Yes … … Yes No Yes Yes No Yes Yes Yes No Yes Yes No Yes No Yes … … Yes Yes 0.6 … … … 1 222 3 180 6 … 1 … 1 226 … … 0.05 0.4 … 2 551 711 5 … … 1037 … … 3 0.2 6 2 035 0.2 … 781 1 7 4 8 … 282 … 0.4 1 284 1 267 87 164 2 267 374 196 … 425 18 … … … 2 509 0.1 4 203 69 0.3.z … 18 5 122 1 205 3...z 186 4 520.z 1.6 … 1 173 0.2 58...z 0.02...z 0.3.y … 2 547 606 6...y … … … 37.z 0.04...y 3 0.3 7.y 1355 0.2 … 743.z 1 6 4 9 1 287 4 323.z 0.4 1 252 1 129 86 145 1 217 399 183 2...z 460 20...y … 252 52...z 2 355 0.1 … 228 78 131 … … … 101 109 114 88 … 123 … 132 … … 105 118 … 133 106 105 … … 110 … … 104 89 109 106 … … 112 109 116 99 128 … 124 … 106 … 95 137 104 106 111 132 134 134 … 131 126 … … … 111 … 112 141 112 … … … … 102 106 121 88 … 122 … 130 … … 79 120 … 136 109 106 … … 111 … … 107 94 109 110 … … 111 112 122 99 129 … 124 … 109 … 95 140 104 109 118 137 134 138 … 135 123 … … … 111 … 109 144 113 … … … … 101 113 108 89 … 123 … 133 … … 137 115 … 129 103 104 … … 107 … … 100 83 109 103 … … 112 106 111 98 126 … 125 … 103 … 94 134 105 104 104 128 134 129 … 127 128 … … … 111 … 115 137 111 GPI (F/M) .… .… .… .… 1.00 1.06 0.89 1.02 .… 1.01 .… 1.03 .… .… 1.73 0.96 .… 0.95 0.94 0.98 .… .… 0.96 .… .… 0.94 0.89 1.00 0.93 .… .… 1.00 0.94 0.91 0.99 0.98 .… 1.01 .… 0.95 .… 0.99 0.96 1.01 0.95 0.88 0.94 1.00 0.94 .… 0.94 1.05 .… .… .… 1.00 .… 1.06 0.95 0.99 Total Male Female 68...z … 96 121 99 120...z 124 95 98.z 100 … 138 71 104...z 81...z 87...y … 126 107 101...y … … … 112.z 78...y 116 112 120.y … 116 … 109.z 98 107 111 123 103 122 125.z 113 … 99 125 108 103 82 101 137 119 100...z 124 126...y … 137 93...z 111 110 … 168 115 67...z … 96 123 99 119...z 129 94 98.z 105 … 139 65 105...z 69...z … … 131 106 101...y … … … 118.z 48...y 118 120 122.y … 101 … 109.z 93 106 112 122 … 122 … 110 … 100 127 108 102 79 102 138 121
 102...z 125 126...y … 139 94...z 112 108 … 173 116 70...z … 96 119 99 121...z 120 96 98.z 96 … 136 77 104...z 93...z … … 121 109 101...y … … … 105.z 109...y 114 104 117.y … 137 … 108.z 103 108 109 124 … 122 … 115 … 99 123 108 104 85 100 137 116 99...z 122 127...y … 134 92...z 110 113 … 163 114 GPI (F/M) 1.04...z .… 1.00 0.96 1.01 1.02...z 0.93 1.02 0.99.z 0.91 .… 0.98 1.19 1.00...z 1.34...z .… .… 0.93 1.02 1.00...y .… .… .… 0.89.z 2.28...y 0.97 0.86 0.96.y .… 1.36 .… 0.99.z 1.10 1.01 0.97 1.02 .… 1.00 .… 1.04.
* .… 0.98 0.97 1.00 1.02 1.07 0.98 0.99 0.96 0.96...z 0.98 1.02...y .… 0.96 0.98...z 0.98 1.04 .… 0.94 0.98 S TAT I S T I C A L TA B L E S Ta b l e 4 NET INTAKE RATE (NIR) IN PRIMARY EDUCATION (%) School year ending in SCHOOL LIFE EXPECTANCY (expected number of years of formal schooling from primary to tertiary education) School year ending in 1999 2006 1999 2006 Total Male Female GPI (F/M) Total Male Female … … … … … … 52 63 … … … 90 … … … … … 46 97 77 … … … … … 48 … … 79 … … … 90 84 77 78 … 69 … 73 … … 60 … 98 80 58 84 … … 56 91 … … … 89 … 77 39 84 … … … … … … 53 61 … … … … … … … … … 47 100 77 … … … … … 50 … … … … … … 91 85 77 80 … 68 … 70 … … 61 … … 83 58 83 … … 58 90 … … … 89 … 72 40 84 … … … … … … 51 65 … … … … … … … … … 45 94 77 … … … … … 47 … … … … … … 89 82 76 76 … 69 … 76 … … 59 … … 78 58 84 … … 54 93 … … … 89 … 82 38 84 .… .… .… .… .… .… 0.96 1.07 .… .… .… .… .… .… .… .… .… 0.95 0.94 1.00 .… .… .… .… .… 0.94 .… .… .… .… .… .… 0.98 0.96 0.99 0.95 .… 1.03 .… 1.09 .… .… 0.96 .… .… 0.94 1.00 1.01 .… .… 0.92 1.03 .… .… .… 1.01 .… 1.14 0.95 1.00 51...z … 70...z 41 … … 66 78 … … … … … 100...z … … … 45 98 … … … … 39.z … … … 55.y … 78...z … 98.z 81...z 70 87 68 … 71 … 70...z … … … … 99 46.y 68.z 89 60 … 71 … … 70 75...z 90.y 56...z … 67 88...y 49...z … 70...z 42 … … 66 78 … … … … … 100...z … … … 42 97 … … … … 39.z … … … 56.y … … … 100.z 80...z 67 87 68 … 71 … 66...z … … … … 98 46.y 68.z 89 60 … 72 … … 69 74...z 90.y 42...z … 66 87...y 53...z … 70...z 41 … … 66 79 … … … … … 100...z … … … 47 100 … … … … 38.z … … … 54.y … … … 97.z 82...z 73 87 68 … 72 … 74...z … … … … 100 46.y 68.z 89 61 … 70 … … 72 76...z 89.y 73...z … 68 89...y GPI (F/M) 1.08...z .… 1.00...z 0.97 .… .… 1.00 1.02 .… .… .… .… .… 1.00...z .… .… .… 1.12 1.04 .… .… .… .… 0.97.z .… .… .… 0.97.y .… .… .… 0.97.z 1.02...z 1.09 1.00 1.00 .… 1.01 .… 1.12...z .… .… .… .… 1.02 1.01.y 1.00.z 1.00 1.01 .… 0.97 .… .… 1.05 1.03...z 0.99.y 1.76...z .… 1.04 1.02...y Total Male Female Total Male Female Country or territory 11 … … … 14 12 8 12 12 … … 8 … 17 12 … … 12
 15 12 … 7 … … … 13 … 9 10 … … 15 13 … 13 … … 13 14 16 … 13 11 10 12 12 … … 11 … … … … … … 12 … 15 … 13 11 … … … 15 11 9 12 12 … … 7 … 17 12 … … 11 16 12 … 8 … … … 13 … … 11 … … 14 13 … 13 … … … 14 15 … 13 11 10 12 12 … … 11 … … … … … … 12 … 14 … 12 11 … … … 14 12 7 12 12 … … 8 … 18 12 … … 12 14 13 … 7 … … … 14 … … 10 … … 16 14 … 14 … … … 14 17 … 13 11 10 13 13 … … 11 … … … … … … 12 … 15 … 13 10...z … 13...z 12 15 12.z 9 15 13.z … … … 8.
* 19 12.z … … 12 17 … … 8.z 14 … 11...y 13...y … 10...y … 11 … 15.z 14 … … 13...y 13.z … 14.z 17...z … 14 12 12.z 16 13...z 12...y … 12 12...z 10 13...z … 11...y … 13 16 … … 13 10...z … 13...z … 15 12.z 10 16 12.z … … … 8.

* 19 12.z … … 11 18 … … 9.z 13 … 10...y 13...y … 11...y … 11 … 14.z 13 … … 13...y 13.z … 14.z 15...z … 14 12 12.z 15 13...z 12...y … 12 12...z 11 13...z … 11...y … 14 15 … … 13 10...z … 13...z … 15 13.z 8 14 13.z … … … 9.
* 20 12.z … … 12 15 … … 8.z 14 … 11...y 13...y … 10...y … Cook Islands 5 DPR Korea Fiji Indonesia Japan 4 Kiribati 5 Lao PDR Macao, China Malaysia Marshall Islands 2,5 Micronesia Myanmar 3 Nauru New Zealand 4 Niue 5 Palau 2,5 Papua New Guinea Philippines 3 Republic of Korea 2,4 Samoa Singapore Solomon Islands Thailand Timor-Leste 3 Tokelau 5 Tonga Tuvalu 5 Vanuatu Viet Nam 3 Latin America and the Caribbean 11 … 16.z 14 … … 13...y 14.z … 15.z 19...z … 14 13 12.z 17 14...z 13...y … 12 12...z 10 14...z … 12...y … 13 16 … … 14 Anguilla 3 Antigua and Barbuda Argentina 2,3 Aruba 5 Bahamas Barbados Belize Bermuda 5 Bolivia 3 Brazil 3 British Virgin Islands 5 Cayman Islands 8 Chile 2,3 Colombia 2 Costa Rica 3 Cuba Dominica 5 Dominican Republic 3 Ecuador 3 El Salvador 3 Grenada 5 Guatemala 3 Guyana 3 Haiti Honduras 2,3 Jamaica Mexico 3 Montserrat Netherlands Antilles Nicaragua 3 Panama Table 4 (continued) Compulsory education (age group) Legal guarantees of free education1 New entrants (000) School year ending in 1999 2006 GROSS INTAKE RATE (GIR) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Country or territory Paraguay3 Peru3 Saint Kitts and Nevis5 Saint Lucia Saint Vincent/Grenad.
Suriname3 Trinidad and Tobago2,3 Turks and Caicos Islands Uruguay3 Venezuela, B. R.3 6-14 6-16 5-16 5-15 5-15 6-12 5-11 4-16 6-15 6-15 North America and Western Europe Andorra2,5 Austria2,4 Belgium4 Canada Cyprus2,5 Denmark Finland France9 Germany Greece2 Iceland Ireland Israel3 Italy2 Luxembourg Malta2 Monaco2,8 Netherlands2,4 Norway Portugal2 San Marino2,8 Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan3 Bangladesh3 Bhutan3 India3 Iran, Islamic Republic of3,10 Maldives Nepal3 Pakistan Sri Lanka2 Sub-Saharan Africa Angola2 Benin Botswana Burkina Faso Burundi Cameroon Cape Verde2 Central African Republic Chad2,3 Comoros2 Congo3 2 9 6 6-16 6-14 6-18 6-16 6-14 7-16 7-16 6-16 6-18 6-14 6-16 6-15 5-15 6-14 6-15 5-15 6-15 5-17 6-16 6-14 6-16 6-16 7-16 7-15 5-16 6-17 6-15 6-10 6-16 6-14 6-10 6-12 5-9 5-9 5-13 6-9 6-11 6-15 6-16 7-12 6-11 6-11 6-15 6-11 6-13 6-16 Total Male Female 179 676 … 4 … … 20 0.3 60 537 … 100 … … … 66 65 736 869 113 4 51 … 558 5 5 … 199 61 … … 403 127 82 … 158.z 615 0.9.z 3 2...z 11 17.
*,z 0.4.z 52 561 1 87...z 114 362...y 9 67 57 … 820 104 4 57 124 555 6 4.z 0.4.y 202 60 119 0.3.y 414 93...z 74 … 4 322 4 142 … 4 005 12 29 639 1 563 8 879 … … … … 50 154 146 335 13 … 175 13 32 742.z 4 318.y 16 32 366 1400 6 1 155.
* 4 425 324...z … 291 53.z 306 366 517 11 74 287.z 16...z 91 131 110 … 107 … … 94 … 107 98 … 106 … … … 100 100 102 100 106 99 100 … 100 97 102 … 99 100 … … 104 104 93 … 104 … 113 79 120 91 102 132 … … … … 114 45 71 74 101 … 72 70 37 134 110 … 109 … … 94 … 107 99 … 107 … … … 100 100 103 101 107 101 101 … 101 … 103 … 100 100 … … 104 105 91 … 107 … 115 83 129 91 101 150 … … … … 115 52 78 82 102 … 84 76 36 128 110 … 106 … … 93 … 107 97 … 105 … … … 100 100 101 100 105 97 99 … 99 … 102 … 99 99 … … 104 103 95 … 101 … 112 75 111 91 102 113 … … … … 113 37 64 67 100 … 60 64 37 GPI (F/M) 0.96 1.00 .… 0.97 .… .… 0.98 .… 1.00 0.98 .… 0.98 .… .… .… 1.00 1.00 0.98 1.00 0.98 0.96 0.98 .… 0.99 .… 0.99 .… 0.99 0.99 .… .… 1.00 0.98 1.04 .… 0.95 .… 0.98 0.90 0.86 0.99 1.01 0.76 .… .… .… .… 0.99 0.72 0.83 0.81 0.98 .… 0.71 0.84 1.02 Total Male Female 111.z 109 95.z 107 95...z 113 94.
*,z 83...z 101 101 113.z 109 92.z 104 101...z 115 96.

*,z 83...z 100 102 110.z 110 99.z 110 90...z 111 92.

*,z 84...z 101 99 GPI (F/M) 0.97.z 1.01 1.08.z 1.05 0.89...z 0.97 0.96.

*,.z 1.01...z 1.01 0.97 96 101...z 99 96...y 106 98 96 … 104 100 98 98 96 105 99 94.z … 102 100 109 … 104 95...z 90 … 104 96.z 123.y 118 130 130 100 160.
* 113 109...z … 115 122.z 73 164 107 86 61 94.z 70...z 90 96 102...z 98 97...y 108 97 97 … 104 100 96 97 95 105 97 93.z … 103 100 108 … 104 96..z 88 … 105 113.z 122.y 119 133 112 100 160.

* 125 109...z … 122 124.z 79 164 114 86 70 109.z 74...z 92 96 100...z 99 95...y 105 98 96 … 103 99 99 99 98 104 100 95.z … 101 100 109 … 104 95...z 92 … 102 79.z 124.y 117 126 150 99 160.
* 100 109...z … 108 120.z 67 164 100 85 52 79.z 66...z 87 1.01 0.98...z 1.02 0.99...y 0.97 1.01 1.00 .… 0.99 0.99 1.03 1.02 1.03 0.99 1.03 1.02.z .… 0.98 1.00 1.01 .… 1.00 0.99...z 1.04 .… 0.97 0.70.z 1.02.y 0.98 0.95 1.35 0.99 1.00.
* 0.80 1.00...z .… 0.89 0.97.z 0.85 1.00 0.88 0.99 0.73 0.73.z 0.89...z 0.94 Yes Yes No No No Yes Yes … Yes Yes … Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes No No No No No No No No No No Yes No Yes S TAT I S T I C A L TA B L E S Ta b l e 4 NET INTAKE RATE (NIR) IN PRIMARY EDUCATION (%) School year ending in SCHOOL LIFE EXPECTANCY (expected number of years of formal schooling from primary to tertiary education) School year ending in 1999 2006 1999 2006 Total Male Female … 79 … 76 … … 67 … … 60 … … … … … … … … … 97 98 … … … … … … … … … … … … … … … … 74 20 … 44 87 … … … … … 23 19 … … 65 … 22 16 … … 79 … 76 … … 66 … … 60 … … … … … … … … … 97 100 … … … … … … … … … … … … … … … … 74 21 … 45 86 … … … … … 21 22 … … 64 … 25 18 … … 79 … 75 … … 67 … … 60 … … … … … … … … … 96 96 … … … … … … … … … … … … … … … … 74 19 … 43 87 … … … … … 25 16 … … 66 … 18 13 … GPI (F/M) .… 1.00 .… 0.99 .… .… 1.02 .… .… 1.01 .… .… .… .… .… .… .… .… .… 0.99 0.97 .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… 1.00 0.91 .… 0.97 1.01 .… .… .… .… .… 1.20 0.71 .… .… 1.03 .… 0.72 0.70 .… Total Male Female 69.z 82 66...y 75 62...z 86 65...z 54...z … 65 47.z … … … … 73.z 93...z … … 94...z 96.z … … … … … … … … … … … … … … 70...z … 86.y 43 … 94.z 78 … 88 97...y … 48...z 31.z 27 54 … 70 … … … 53 68.z 81 66...y 73 66...z 86 64...z 57...z … 65 48.z … … … … 69.z 91...z … … 93...z 98.z … … … … … … … … … … … … … … 69...z … 83.y 44 … … 78 … 97 … … 51...z 28.z 30 54 … 70 … … … 54 70.z 82 67...y 77 58...z 86 66...z 51...z … 65 46.z … … … … 77.z 95...z … … 94...z 95.z … … … … … … … … … … … … … … 72...z … 88.y 42 … … 79 … 78 … … 45...z 34.z 25 55 … 71 … … … 52 GPI (F/M) 1.04.z 1.02 1.00...y 1.05 0.88...z 1.00 1.03...z 0.90...z .… 1.00 0.97.z .… .… .… .… 1.11.z 1.04...z .… .… 1.00...z 0.97.z .… .… .… .… .… .… .… .… .… .… .… .… .… .… 1.03...z .… 1.07.y 0.95 .… .… 1.01 .… 0.80 .… .… 0.89...z 1.22.z 0.83 1.02 .… 1.01 .… .… .… 0.96 Total Male Female Total Male Female Country or territory 11 …
 … … … … 11 … 14 … … 15 18 … 13 16 17 16 16 14 17 16 15 15 14 … … 16 17 16 … 16 19 15 16 16 … 8 7 … 12 12 … … … 4 6 11 3 … 7 … … … 7 … 11 … … … … … 11 … 13 … … 15 18 … 12 16 17 15 16 13 16 16 15 15 13 … … 17 17 15 … 15 17 15 16 … … 9 8 … 12 12 … … … 4 8 11 4 … … … … … 7 … 11 … … … … … 12 … 15 … … 15 18 … 13 17 18 16 16 14 17 17 15 15 14 … … 16 18 16 … 16 20 14 16 … … 8 7 … 11 12 … … … 3 5 12 3 … … … … … 6 … 12...z 14 12...z 14 12...z … 11...z 11...z 15 13 11 15 16 17...y 14 17 17 16 16 17 18 18 15 16 14 15.z … 16 17 15 … 16 16 15 16 16 8...y 8.y 10 10...z 13...z 12 … 7 … … 8...z 12...z 5 7 9 12 … 6...z 8...y … 12...z 14 12...z 13 12...z … 11...z 11...z 14 … 11 15 16 17...y 13 16 17 16 16 17 17 17 15 16 13 15.z … 17 17 15 … 16 15 15 16 15 11...y 8.y 11 11...z 13...z 12 … 7 … … … 12...z 5 8 10 11 … 7...z 9...y … 12...z 14 13...z 14 12...z … 11...z 12...z 16 … Paraguay 3 Peru 3 Saint Kitts and Nevis 5 Saint Lucia Saint Vincent/Grenad.
Suriname 3 Trinidad and Tobago 2,3 Turks and Caicos Islands Uruguay 3 Venezuela, B. R. 3 North America and Western Europe 11 16 16 17...y 14 17 18 17 16 17 19 18 16 17 14 15.z … 16 18 16 … 17 17 15 17 16 4...y 8.y 10 9...z 13...z 12 … 6 … … … 12...z 4 7 8 12 … 4...z 7...y … Andorra 2,5 Austria 2,4 Belgium 4 Canada Cyprus 2,5 Denmark Finland France 9 Germany Greece 2 Iceland Ireland Israel 3 Italy 2 Luxembourg Malta 2 Monaco 2,8 Netherlands 2,4 Norway Portugal 2 San Marino 2,8 Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan 3 Bangladesh 3 Bhutan 3 India 3 Iran, Islamic Republic of 3,10 Maldives Nepal 3 Pakistan Sri Lanka 2 Sub-Saharan Africa Angola 2 Benin Botswana Burkina Faso Burundi Cameroon Cape Verde 2 Central African Republic Chad 2,3 Comoros 2 Congo 3 2 9 7 9 0 0 2 t r o p e R A N N E X Table 4 (continued) Compulsory education (age group) Legal guarantees of free education1 New entrants (000) School year ending in 1999 2006 GROSS INTAKE RATE (GIR) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Total Male Female Country or territory Total Male Female Côte d’Ivoire D. R. Congo3 Equatorial Guinea Eritrea Ethiopia Gabon Gambia3 Ghana2,3 Guinea Guinea-Bissau3 Kenya Lesotho Liberia2 Madagascar3 Malawi Mali3 Mauritius3 Mozambique Namibia3 Niger3 Nigeria3 Rwanda3 Sao Tome and Principe Senegal3 Seychelles5 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania3 Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 6-15 6-13 7-11 7-14 7-12 6-16 7-12 6-14 7-12 7-12 6-13 6-12 5-11 6-10 6-13 7-15 6-11 6-12 6-15 7-12 6-14 7-12 7-12 7-12 6-15 6-12 … 7-15 6-12 6-15 6-12 7-13 7-13 6-12 … … … … … … … … … … … … … … … … No Yes Yes No No Yes Yes Yes No Yes No No No Yes No Yes Yes No Yes Yes Yes Yes Yes Yes Yes No No No Yes No No No No No … … … … … … … … … … … … … … … … 309 767 … 57 1 537 … 28 469 119 35 892 51 50 495 616 171 22 536 54 133 3 714 295 4 190 2 … … 1 157 31 139 … 714 252 398 344 … 15.z 61 3 221 … 30 627 231 … 1113...z 56 117 1 000 664 301 19 930 53 279 4 431.z 527 5 313 1 296 … 1 173.y 31.z 178 1 448 1267 435 … Sum Sum … 54 78 … 78 85 52 92 102 99 60 107 175 58 98 104 97 43 102 127 106 66 117 … … 115 99 91 … 75 84 111 104 99 102 105 90 97 101 103 103 102 119 156 118 103 114 90 71 49 … 60 92 … 80 87 58 106 104 99 73 108 174 67 96 112 96 50 114 129 108 68 116 … … 117 101 97 … 75 84 113 109 100 103 110 93 99 101 103 103 104 122 153 121 104 123 96 57 52 … 49 63 … 76 83 46 79 101 100 46 106 177 50 99 95 98 35 89 126 105 65 118 … … 114 97 86 … 74 84 109 100 99 101 100 87 96 101 102 102 101 116 159 115 101 104 84 GPI (F/M) 0.80 1.07 .… 0.81 0.69 .… 0.95 0.96 0.80 0.74 0.97 1.01 0.63 0.98 1.02 0.75 1.04 0.84 1.02 0.71 0.79 0.97 0.97 0.96 1.02 .… .… 0.97 0.96 0.88 .… 0.99 1.01 0.97 67 … 112.z 49 136 … 68 107 91 … 110...z 102 108 178 150 83 104 148 104 68 108.z 208 114 97 127 180 … 115.y 107.z 98 146 107 122 … Weighted average … 116.z 53 144 … 65 105 94 … 112...z 105 109 181 145 89 104 153 104 76 116.z 209 113 95 131 188 … 118.y 111.z 101 145 108 119 … 114 101 103 115 102 98 103 99 99 103 122 157 120 103 130 116 61 … 107.z 46 128 … 71 110
 87 … 108...z 99 106 176 156 76 104 143 105 59 99.z 206 114 98 124 172 … 112.y 103.z 95 147 106 125 … 108 100 101 109 98 97 100 97 97 99 117 156 115 102 123 106 GPI (F/M) 0.83 .… 0.92.z 0.86 0.89 .… 1.09 1.05 0.92 .… 0.96...z 0.94 0.98 0.97 1.07 0.85 1.00 0.93 1.01 0.78 0.85.z 0.99 1.01 1.03 0.94 0.92 .… 0.95.y 0.92.z 0.94 1.02 0.99 1.05 .… 0.95 0.99 0.99 0.95 0.96 0.99 0.98 0.98 0.98 0.96 0.96 0.99 0.96 0.99 0.94 0.92 1.
Source: Tomasevski (2006).

2.

Information on compulsory education comes from the Reports under the United Nations Human Rights Treaties.

3.

Some primary school fees continue to be charged despite the legal guarantee of free education (Bentaouet-Kattan, 2005; Tomasevski, 2006; World Bank, 2002).

4.

No tuition fees are charged but some direct costs have been reported (Bentaouet-Kattan, 2005; Tomasevski, 2006; World Bank, 2002).

5.

National population data were used to calculate enrolment ratios.

6.
6.

Enrolment and population data exclude Transnistria.

7.

Children can enter primary school at age 6 or 7.

8.

Enrolment ratios were not calculated due to lack of United Nations population data by age.
2 9 8 S TAT I S T I C A L TA B L E S Ta b l e 4 NET INTAKE RATE (NIR) IN PRIMARY EDUCATION (%) School year ending in SCHOOL LIFE EXPECTANCY (expected number of years of formal schooling from primary to tertiary education) School year ending in 1999 2006 1999 2006 Total Male Female GPI (F/M) Total Male Female 26 23 … 17 20 … 46 29 20 … 30 26 … … … … 72 18 55 27 … … … 37 75 … … 44 42 37 … 14 37 … … … … … 65 … … … … … 77 … 69 … … 27 29 22 … 18 23 … 47 29 21 … 29 25 … … … … 71 19 54 32 … … … 38 74 … … 45 40 40 … 13 36 … … … … … 65 … … … … … 72 … 68 … … 29 23 24 … 16 18 … 45 29 18 … 31 27 … … … … 73 17 57 22 … … … 36 77 … … 43 43 35 … 15 38 … … … … … 64 … … … … … 82 … 69 … … 27 0.79 1.09 .… 0.89 0.80 .… 0.97 1.00 0.87 .… 1.05 1.06 .… .… .… .… 1.03 0.93 1.06 0.68 .… .… .… 0.96 1.03 .… .… 0.95 1.06 0.87 .… 1.16 1.07 .… … … … 22 59 … 38 33 40 … … 48.z … 82 62 29 91 53 59 45 67...y 96 45 59 96 … … 52.y 48.z 42 62 86 44 … Median … … … … 0.99 … … … … … 1.14 … 1.03 … … 0.92 68 … … 66 60 … 72 … 67 … 70 70 71 … 87 52 … … … 23 61 … 36 32 41 … … 48.z … 82 60 32 91 53 58 51 73...y 97 44 58 97 … … 53.y 48.z 44 62 86 43 … 67 … … 66 61 … 72 … 66 … 66 67 72 … 90 53 … … … 21 57 … 40 34 40 … … 49.z … 82 65 26 91 53 61 39 61...y 95 46 59 94 … … 51.y 47.z 41 63 87 46 … 68 … … 67 59 … 73 … 67 … 74 73 71 … 83 51 GPI (F/M) .… .… .… 0.91 0.94 .… 1.09 1.06 0.98 .… .… 1.01.z .… 1.01 1.09 0.83 1.00 0.99 1.05 0.76 0.85...y 0.99 1.04 1.02 0.97 .… .… 0.98.y 0.99.z 0.94 1.02 1.01 1.08 .… 1.01 … … 1.00 0.97 … 1.02 … 1.01 … 1.12 1.09 0.99 … 0.92 0.98 Total Male Female Total Male Female Country or territory 6 4 … 4 4 13 7 … … … … 9 8 … 11 5 12 5 … … 7 6 … 5 14 … … 13 10 9 10 5 7 10 10 12 15 9 10 12 11 10 10 15 13 11 13 16 8 7 7 … … 5 5 14 8 … … … … 9 10 … 12 6 12 … … … 8 … … … 14 … … 13 10 11 11 5 7 … 10 12 15 10 11 12 11 11 11 15 12 11 12 15 9 7 5 … … 3 3 13 6 … … … … 10 7 … 10 4 12 … … … 7 … … … 14 … … 14 9 7 9 5 6 … … … … 5...y 8 … 7...y 9 8 … 10...y 10 … 9 9...y 7 14...z 8...z 11 4 8...y 9...z 10 7...z 15 … … 13...y 10...z … 10...y … … … Weighted
 average … … … 6...y 8 … 7...y 10 10 … 10...y 10 … 10 10...y … 14...z 9...z 11 5 9...y 8...z 10 … 14 … … 13...y 10...z … 11...y … … … 11 13 15 11 11 13 12 12 12 14 13 11 13 15 10 9 … … … 4...y 7 … 7...y 9 7 … 9...y 10 … 9 9...y … 13...z 7...z 11 3 7...y 9...z 10 … 15 … … 13...y 10...z … 10...y … … … 11 13 16 10 10 13 12 12 11 14 14 11 14 16 9 8 Côte d’Ivoire D. R. Congo 3 Equatorial Guinea Eritrea Ethiopia Gabon Gambia 3 Ghana 2,3 Guinea Guinea-Bissau 3 Kenya Lesotho Liberia 2 Madagascar 3 Malawi Mali 3 Mauritius 3 Mozambique Namibia 3 Niger 3 Nigeria 3 Rwanda 3 Sao Tome and Principe Senegal 3 Seychelles 5 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania 3 Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 9.

Data include French overseas departments and territories (DOM-TOM).

10.

The apparent increase in the gender parity index (GPI) is due to the recent inclusion in enrolment statistics of literacy programmes in which 80% of participants are women.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.
Table 5 Participation in primary education ENROLMENT IN PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) % F Total (000) 4 779 76 38 8086 3 604 706 140 395 822 346 3 462 316 368 61 … 2513 2 738 1 443 270 2 303 292 632 … 412 203 655 127 503 141 220 … 3 434 262 1 285 6 743 387 317 92 130 … 2 200 255 707 302 1 249 470 251 690 … 2 570 1 885 46 2 127 … 47 49 41 47 44 49 49 48 48 48 44 48 49 48 … 45 47 47 48 35 48 48 … 48 49 49 48 48 48 48 … 48 49 49 49 49 49 48 48 … 49 … 49 49 49 49 50 48 … 49 4 197 90 54 9 988 4430...z 805 203 448 755 466 3 944 288 382 71 … 3 881 2 280 1 134 272 3 220.z 250.y 368 … 273 195 473 80 416 79 150 … 2 602 171 938 5 165 297 235 93 110.z 7950 1 754 121 538 327 948 424 250 688 … 2 165 1 939 49 46 47 46 2 582 … 108 925 % F 47 49 44 48 44...z 49 49 48 48 50 46 49 49 49 … 46 48 48 49 42.z 48.y 48 … 48 49 48 48 48 48 48 … 49 49 48 49 49 48 48 48.z 48 49 48 47 49 49 49 50 48 … 49 49 48 47 47 Total Male Female GPI (F/M) 105 107 33 102 92 98 100 105 120 89 86 91 105 102 … 49 102 113 90 71 103 111 … 106 92 103 102 102 100 102 … 98 100 105 108 112 103 100 101 … 109 100 94 98 97 98 97 98 … 98 100 114 97 … 110 106 39 106 101 98 99 108 121 89 95 93 105 104 … 53 107 116 92 91 104 111 … 108 93 104 103 103 101 103 … 99 100 106 109 112 103 100 102 … 110 … 94 98 97 98 96 101 … 99 100 115 104 … 100 108 28 97 83 98 101 103 118 88 77 89 106 100 … 45 98 111 89 51 102 110 … 105 92 103 100 101 99 101 … 97 100 104 107 111 102 99 100 … 109 … 94 98 98 97 99 96 … 98 100 112 90 … 0.91 1.01 0.71 0.91 0.82 1.00 1.01 0.95 0.98 0.99 0.81 0.97 1.01 0.96 .… 0.85 0.92 0.95 0.97 0.56 0.98 0.99 .… 0.98 0.98 0.99 0.97 0.98 0.98 0.98 .… 0.98 1.00 0.98 0.98 0.99 0.99 0.99 0.98 .… 0.99 .… 1.00 1.00 1.01 0.99 1.04 0.95 .… 1.00 1.00 0.97 0.87 .… – 25 13 8
 ....z 31 34 67 5 7 7 5 9 63 … 5.z 4 1 65 2.z 4.y 0.1 … 0.4.z 0.2 1 3 7 1 0.5 … 2 0.9 0.2 0.6 … 5 0.1 ..z 2 0.5 2 0.2 6 0.8 0.6 3.z .. … ... 29 36 0.9 4 ... 19 9 … ... 29 32 66 ... 2 4 5 9 37 … 2 4 0.7 44 1 ... 0.1 … 0.3 0.1 0.8 1 5 1 0.4 … … … .
… … 4 0.1 ... … 0.3 … – 0.5 0.5 0.2 0.5 .. … … 27 36 2 … Age group 2006 School-age population1 (000) 2005 6-11 6-11 6-11 6-11 6-11 6-11 6-10 6-11 6-11 6-11 6-11 6-11 6-9 6-11 6-11 6-11 6-9 6-11 6-10 6-11 6-9 6-9 6-9 7-10 7-10 6-10 7-12 7-10 7-10 7-10 … 7-12 7-10 7-10 7-10 7-10 6-9 6-10 7-10 6-11 6-9 7-9 6-9 6-11 7-10 7-10 7-11 7-10 7-9 7-10 5-11 6-11 6-11 7- … 2 666 … 895 5 381 … 235 93 109 8 438 1 717 124 559 341 932 438 248 686 295 2 313 1 849 43 2 113 97 931 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova3,4 Romania Russian Federation5 Serbia3 Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China6 S TAT I S T I C A L TA B L E S Ta b l e 5 GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in 2006 Total Male Female 110 120 44 105 99...z 97 96 94 110 102 106 82 83 105 … 66 126 108 104 87.z 105.y 96 … 100 99 100 99 97 95 95 … 98 97 105 96 97 100 100 98.z 94 102 98 96 96 105 97 101 100 … 95 105 107 122 111 114 120 49 107 109...z 96 97 96 113 99 112 82 82 105 … 71 129 110 104 100.z 106.y 97 … 101 99 100 100 98 96 95 … 98 97 105 96 97 101 100 98.z 96 102 96 98 94 105 97 99 103 … 97 105 107 126 112 106 119 39 102 90...z 98 96 93 108 104 100 83 83 104 … 61 123 107 103 74.z 105.y 95 … 99 99
 100 98 96 93 94 … 97 96 104 96 97 99 100 98.z 92 102 100 95 97 105 96 102 98 … 94 105 106 118 111 GPI (F/M) 0.93 1.00 0.81 0.95 0.83...z 1.02 0.99 0.97 0.95 1.05 0.89 1.01 1.00 0.99 .… 0.87 0.96 0.97 0.99 0.74.z 0.99.y 0.98 .… 0.99 1.00 0.99 0.98 0.98 0.96 0.99 .… 1.00 0.99 0.99 1.00 1.00 0.98 0.99 1.00.z 0.95 1.00 1.04 0.97 1.03 1.00 0.99 1.02 0.95 .… 0.97 1.00 0.99 0.93 0.99 NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Total Male Female 91 96 27 94 85 91 87 86 … 64 70 81 97 92 … … 92 93 79 56 94 … … 97 85 97 96 88 97 95 … 96 93 96 … … … 96 93 … … … 85 77.
* … 88.

* 89 … … … 94 … 83 … 93 95 32 97 91 91 86 88 … 65 76 81 97 92 … … 95 94 80 70 95 … … 98 86 96 96 88 98 96 … 96 … 96 … … … 97 94 … … … 85 77.

* … 89.

* 87 … … … 94 … 87 … 89 97 23 90 78 91 87 85 … 64 65 81 97 92 … … 88 92 79 41 94 … … 96 85 97 95 88 96 95 … 96 … 95 … … … 95 92 … … … 86 77.

* … 87.
* … 87.

* 90 … … … 94 … 79 … GPI (F/M) 0.96 1.03 0.73 0.93 0.85 1.01 1.01 0.96 .… 0.99 0.85 1.00 1.00 1.01 .… .… 0.93 0.98 0.99 0.59 0.98 .… .… 0.98 0.98 1.00 0.98 0.99 0.98 0.99 .… 1.00 .… 0.99 .… .… .… 0.99 0.98 .… .… .… 1.01 1.00.

* .… 0.99.
* .… 0.99.

* 1.04 .… .… .… 1.01 .… 0.91 .… Total Male Female 95 98.z 38 96 89...z 90 83 82 … 79 88 74 76 94 … … … 96 88 75.z 94.y 89 … 92 90 93...z 94 88 90...z 89 … 96 88 93 91 95 92...z 95 92.z 91 90 82 85 89 90 86 91 97 … … 96 94 90 … 96 98.z 42 98 95...z 89 84 82 … 78 91 73 76 93 … … … 96 88 85.z 94.y 90 … 93 91 91...z 95 89 89...z 90 … 96 88 93 91 95 92...z 96 92.z 93 90.
* 80 86 88 90 86 90 99 … … 96 94 91 … 94 98.z 34 94 82...z 90 83 82 … 82 85 75 76 94 … … … 97 88 65.z 93.y 88 … 92 90 94...z 94 88 92...z 89 … 96 88 93 91 95 92...z 95 92.z 89 90.

* 84 83 91 90 85 93 95 … … 97 94 89 … GPI (F/M) 0.98 1.00.z 0.82 0.96 0.86...z 1.02 0.99 0.99 .… 1.05 0.94 1.02 1.00 1.01 .… .… .… 1.01 1.00 0.76.z 0.99.y 0.98 .… 0.99 0.99 1.03...z 0.99 0.99 1.03...z 0.99 .… 1.01 1.00 1.00 1.00 1.00 1.01...z 1.00 1.00.z 0.96 1.00.
* 1.05 0.97 1.03 1.00 0.99 1.02 0.96 .… .… 1.01 1.00 0.98 .… OUT-OF-SCHOOL CHILDREN (000)2 School year ending in 1999 2006 Total (000) % F Total (000) % F 357 1.0 83 285 605 40 10 44 … 139 1 183 61 4 1.9 … … 139 82 55 1 410 61 6 53 97 71 46 46 55 … 49 59 48 31 50 … … 84 55 50 65 Arab States 88 0.4.z 75 232 508...z 53 24 81 … 70 33.z 53 96 78...z 42 50 50 … 92 429 82 94 1.2 … … … 27 13 906.z 44 61 47 49 35 … … … 34 47 70.z Central and Eastern Europe 16 … … 4 18 21 0.1 15 2 4 … 133 11 2 … … … 1.7 1.4 … … … 109 70.
* … 28.

* 22 … … … 108 … 366 … 55 … … 77 52 46 66 46 56 44 … 48 … 100 … … … 58 95 … … … 47 49.

* … 50.

* 38 … … … 15.y 39 … 17 2 37...z 2 23 7...z 13 … 100 17 40 337 15 19...z 3 3.z 729 161 51.y 53 … 51 7 41...z 46 48 37...z 48 … 45 48 47 44 48 47...z 48 45.z 60 49.
* Central Asia 12 82 33 9 29 7 19 … … 3 53 41 29 49 15 89 … … East Asia and the Pacific 63 46 … 61 … 1.1 213 … 44 42 54 … Table 5 (continued) Country or territory Age group 2006 Cook Islands3 DPR Korea Fiji Indonesia Japan Kiribati3 Lao PDR Macao, China Malaysia Marshall Islands3 Micronesia Myanmar Nauru New Zealand Niue3 Palau3 Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau3 Tonga Tuvalu3 Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda3 Bolivia Brazil British Virgin Islands3 Cayman Islands7 Chile Colombia Costa Rica Cuba Dominica3 Dominican Republic Ecuador El Salvador Grenada3 Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama 5-10 6-9 6-11 7-12 6-11 6-11 6-10 6-11 6-11 6-11 6-11 5-9 6-11 5-10 5-10 6-10 7-12 6-11 6-11 5-10 6-11 6-11 6-11 6-11 5-10 5-10 6-11 6-11 6-10 5-11 5-11 6-11 6-11 5-10 5-10 5-10 5-10 6-11 7-10 5-11 5-10 6-11 6-10 6-11 6-11 5-11 6-11 6-11 7-12 5-11 7-12 6-11 6-11 6-11 6-11 6-11 5-11 6-11 6-11 6- ENROLMENT IN PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) 3 … 116 … 7 692 14 828 47 3 040 8 … 4 733 … 361 0.3 2 … 12 503 3 845 27 300 58 6 120 … … 17 1 34 10 250 2 … 4 821 9 34 25 44 … 1 445 20 939 3 3 1 805 5 162 552 1 074 12 1 315 1 899 940 … 1 824 107 … … 316 14 698 0.4 25 830 393 % F
 46 … 48 … 49 49 45 47 48 48 … 49 … 49 46 47 … 49 47 48 48 46 48 … … 46 48 48 47 50 … 49 49 49 49 48 … 49 48 49 47 48 49 48 48 48 49 49 48 … 46 49 … … 49 49 44 48 49 48 Total (000) 2.z … 110 28 983 7 229 16.z 892 35 3 202.z 8 19 4 969 1 351 0.2.z 2...z 532 13 007 3 933 32...z 285 75.z 5 844 178.z 0.2...y 17 1 38 7 318 2 … 4 651.z 10 36 22 51 5 1 508 18 661.z 3 3 1 695 5 296 547 890 9 1 234 2 006 1 035 16...z 2 405 117.z … 1 293 326.z 14 595 0.5 … 966 437 % F 48.z … 48 48 49 49.z 46 47 49.z 48 49 50 49 49 51.z 48...z 44 49 47 48...z 48 47.z 48 47.z 57...y 47 48 48 48 49 … 49.z 49 49 49 49 46 49 48.z 48 48 48 49 48 48 49 48 49 48 49...z 48 49.z … 49 49.z 49 46 … 48 48 15 … … … 0.9 … 2 95 … 25 … ... … … ... 18 … 8 2 16 … … 13 … … 7 … … 20.z … 99...z 16 1 … 3 97 0.8.z … 8 ... … 12 … 21...z … 8 1 17...z … … 17 … … 9.y … 27 0.3 0.5 5 … 20 83 … … … … … 8 13 36 45 20 7 .. 24 14 21 11 … 15 1 … … 4 7 38 74 16 10 8 … 22.z 79 26 12 83 35 8 10.y 23 35 53 19 7 ... 31 17 29 10 … 11 2.z … 7 8.z 8 34 … 15 11 Total Male Female 96 … 109 … 101 104 111 100 98 101 … 100 … 100 99 114 … 113 95 99 … 88 106 … … 108 98 111 108 … … 117 114 95 98 118 … 113 154 112 … 101 114 108 111 104 113 114 112 … 101 121 … … 92 111 … 131 100 108 99 … 109 … 101 104 120 102 99 102 … 101 … 100 99 118 … 113 97 99 … 91 107 … … 110 97 112 112 … … 116 114 96 99 120 … 114 159 113 … 102 114 109 113 107 114 114 114 … 108 122 … … 93 112 … 135 100 110 94 … 108 … 101 105 102 97 97 100 … 100 … 100 98 109 … 113 94 98 … 86 105 … … 106 99 110 104 … … 117 114 94 98 116 … 112 150 110 … 99 114 107 109 102 111 114 109 … 94 120 … … 92 109 … 127 101 106 GPI (F/M) 0.95 .… 0.99 .… 1.00 1.01 0.85 0.96 0.98 0.98 .… 0.99 .… 1.00 1.00 0.93 .… 1.00 0.97 0.98 .… 0.94 0.99 .… .… 0.96 1.02 0.98 0.93 .… .… 1.00 0.99 0.98 0.98 0.97 .… 0.98 0.94 0.97 .… 0.97 1.00 0.98 0.97 0.95 0.98 1.00 0.96 .… 0.87 0.98 .… .… 1.00 0.98 .… 0.95 1.01 0.97 School-age population1 (000) 2005 … 1 602 110 25 394 7 231 … 769 33 3 201 … 17 4 342 … 345 … … 965 11
 877 3 857 32 … 76 5 417 186 … 15 … 34 … … … 4 119 9 37 22 42 … 1 386 13 752 … … 1 624 4 568 491 880 … 1 257 1 717 908 … 2 117 96 1 389 1 094 342 12 951 … 17 834 392 GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in 2006 Total Male Female 80...z … 100 114 100 113.z 116 106 100.z 93 110 114 79 102 105.z 104...z 55 110 105 100...z … 101.z 108 99.z 93...y 113 106 108 … 93 … 112.z 115 98 103 123 100 109 137.z 112 … 104 116 111 101 86 98 117 114 93...z 114 124.z … 118 95.z 113 114 … 116 112 79...z … 100 116 100 112.z 123 109 101.z 94 109 114 78 102 107.z 107...z 60 110 107 100...z … 102.z 108 103.z 79...y 116 106 110 … 94 … 113.z 116 98 104 125 108 109 141.z 114 … 107 117 112 102 85 101 117 116 94...z 118 125.z … 119 95.z 114 114 … 117 113 80...z … 99 112 100 114.z 109 102 100.z 92 110 115 80 102 102.z 101...z 50 109 103 100...z … 98.z 108 95.z 107...y 110 105 106 … 92 … 112.z 113 98 102 121 92 109 133.z 110 … 102 115 111 100 87 96 117 112 91...z 109 124.z … 118 95.z 111 114 … 114 110 GPI (F/M) 1.01...z .… 0.98 0.96 1.00 1.01.z 0.89 0.94 1.00.z 0.97 1.01 1.01 1.03 1.00 0.95.z 0.94...z 0.84 0.99 0.97 1.00...z .… 0.96.z 1.00 0.92.z 1.35...y 0.95 0.99 0.97 .… 0.99 .… 0.99.z 0.98 1.00 0.98 0.97 0.85 1.00 0.94.z 0.97 .… 0.95 0.99 0.99 0.97 1.02 0.95 1.00 0.96 0.96...z 0.93 0.99.z .… 0.99 1.00.z 0.97 1.00 .… 0.98 0.97 S TAT I S T I C A L TA B L E S Ta b l e 5 NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 OUT-OF-SCHOOL CHILDREN (000)2 School year ending in 1999 2006 Total Male Female 85 … 99 … 100 97 76 85 98 … … 92 … 99 99 97 … 92 94 92 … … … … … 88 … 91 95 … … 99.
* 98 89 94 94 … 95 91 96 … … 89 … 97 94 84 97 … … 82 … … … 88 97 … … 76 96 87 … 98 … 100 96 79 84 99 … … 92 … 99 99 99 … 92 96 92 … … … … … 90 … 92 … … … 99.

* 97 90 94 94 … 95 … 95 … … 89 … 97 95 83 97 … … 86 … … … 87 97 … … 76 96 83 … 99 … 100 98 73 85 97 … … 91 … 99 98 94 … 92 93 91 … … … … … 86 … 91 … … … 99.
* 100 89 94 94 … 95 … 97 … … 90 … 98 93 84 98 … … 78 … … … 88 97 … … 77 96 GPI (F/M) 0.96 .… 1.01 .… 1.00 1.01 0.92 1.01 0.98 .… .… 0.99 .… 1.00 1.00 0.94 .… 1.00 0.97 0.99 .… .… .… .… .… 0.96 .… 0.99 .… .… .… 1.00.
* 1.03 0.99 0.99 0.99 .… 1.00 .… 1.02 .… .… 1.01 .… 1.01 0.98 1.01 1.01 .… .… 0.91 .… .… .… 1.00 1.00 .… .… 1.01 0.99 Total Male Female 74...z … 91 96 100 … 84 91 100.z 66 … 100 … 99 … … … 91 98 90...y … 62.z 94 68...z … 96.z … 87 … 92 … 99.z 100 88 96 97 92 95 94.z 95 … … 88 … 97 77 77 97 94 84...z 94 … … 96 90...z 98 99 … 90 98 73...z … 91 97 100 … 86 92 100.z 67 … 99 … 99 … … … 90 … 90...y … 62.z 94 70...z … 97.z … 88 … 92 … 99.z 99 87 97 97 … 94 93.z 95 … … 89 … 96 75 76 96 94 84...z 96 … … 96 90...z 98 … … 90 99 75...z … 91 94 100 … 81 90 100.z 66 … 100 … 99 … … … 92 … 91...y … 61.z 94 67...z … 94.z … 86 … 91 … 98.z 100 89 96 97 … 95 95.z 95 … … 88 … 97 79 78 97 94 83...z 92 … … 97 90...z 97 … … 90 98 GPI (F/M) 1.03...z .… 1.00 0.96 1.00 .… 0.94 0.98 1.00.z 0.99 .… 1.01 .… 1.00 .… .… .… 1.02 .… 1.00...y .… 0.99.z 0.99 0.96...z .… 0.97.z .… 0.99 .… 1.00 .… 0.99.z 1.00 1.03 0.99 1.01 .… 1.01 1.02.z 1.00 .… .… 1.00 .… 1.01 1.06 1.03 1.01 1.00 0.99...z 0.96 .… .… 1.02 1.00...z 0.99 .… .… 1.00 0.99 Total (000) 0.4 … 1.4 … 3 0.1 178 7 70 … … 387 … 2.0 0.0 0.05 … 895 215 1.6 … … … … … 1.8 … 2.5 447 % F 54 … 30 … 100 … 56 47 70 … … 51 … 45 50 91 … 48 62 50 … … … … … 56 … 51 … Total (000) 0.7...z … 6 418 16 … 125 2.9 4.z 3 … 16 … 2 … … … % F 45...z … 47 100 … … 57 55 100.z 49 … … … 44 … … … 42 953 … 57 0.3...y ….y … … 29.z 48.z 100 0.9 51...z 57..z … … 100.z … 0.2.z … 4 … 51 … Latin America and the Caribbean … … 10.
* 0.1 4 1.6 1.7 … 52 1 033 0.04 … … 369 … 9 0.4 174 17 … … 299 … … … 38 55 … … 165 11 … … 52.
* 7 50 51 49 … 51 … 42 … … 46 … … 61 47 16 … … 61 … … … 49 17 … … 47 53 0.1 … 36.z 0.04 4 0.8 0.4 0.3 52 597.z 0.1 … … 367 … 27 1.9 255 11 39 2...z 82 … … 33 31...z 73 0.0 … 72 3.7 48 … 86.z 24 43 54 12 … 43 36.z 46 … … 48 … 44 39 45 … 45 49...z 75 … … 35 48...z 84 … … Table 5 (continued) Country or territory Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of Age group 2006 6-11 6-11 5-11 5-11 5-11 6-11 5-11 6-11 6-11 6-11 North America and Western Europe Andorra3 Austria Belgium Canada Cyprus3 Denmark Finland France8 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco7 Netherlands Norway Portugal San Marino7 Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of9 Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo 6-11 6-9 6-11 6-11 6-11 7-12 7-12 6-10 6-9 6-11 6-12 4-11 6-11 6-10 6-11 5-10 6-10 6-11 6-12 6-11 6-10 6-11 7-12 7-12 5-10 6-11 7-12 6-10 6-12 6-10 6-10 6-12 5-9 5-9 5-9 6-9 6-11 6-12 7-12 7-12 6-11 6-11 6-11 6-11 6-11 6- ENROLMENT IN PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Total (000) 951 4 350 … 26 … … 172 2 366 3 261 … 389 763 2 429 64 372 383 3 944 3 767 646 30 457 722 2 876 31 35 2 1 268 412 815 … 2 580 763 530 4 661 24 938 957 17 622 81 110 986 8 667 74 3 588 … … 1057 872 322 816 702 2 134 92 … 840 83 276 % F 48 49 … 49 … … 49 49 49 49 … 48 49 49 48 49 49 49 49 48 48 49 49 48 49 49 50 48 49 48 … 48 49 49 49 49 7 49 46 43 47 49 42 … … 46 39 50 40 44 45 49 … 37 45 49 Total (000) 934.z 4 026 6 24 15 66 130.
*,z 2.z 365 3 452 % F 48.z 49 54 48 51 48 49.
*,z 51.z 48 48 4 355 733 2389...y 60 416 372 4 052 3 329 645 30 462 803 2 790 35 30.z 2.y 1 277 430 750 1.y 2 501 627 517 4 518 24 319 4 319.z 17 953.y 102 139 170 7 274 55 4 503 16 688 1635...z … 1 357 327.z 1 391 1 325 2 998 81 419 1 262.z 107...z 617 47 49 49 49...y 49 49 49 48 49 49 49 48 49 48 49 48.z … 48.2 48.8 48 … 48 49 49 49 49 36.z 50.y 49 47 55 48 47 42 49...z … 44 49.z 44 48 45 49 41 40.z 46...z 47 15 13 … 2 … … 72 18 … 15 … 4 55 6 4 11 1 15 2 7 1 0.9 … 7 7 36 31 68 1 9 … 33 3 3 5 12 … 37 2 … … 3 … … … 5 7 5 11 1 28 – … 25 12 10 Total Male Female 119 122 … 109 … … 96 … 111 100 … 103 105 99 97.
* 101 99 107 106 94 99 104 112 103 101 107 … 108 101 123 … 106 110 102 101 101 28 102 75 93 96 134 114 … … 64 74 104 43 60 84 119 … 63 76 56 121 123 … 110 … … 96 … 112 101 … 103 105 99 98.

* 102 99 107 106 94 100 104 112 103 100 106 … 109 101 126 … 106 108 102 101 100 51 102 81 100 99 134 128 … … 69 89 104 51 67 92 122 … 79 82 58 116 121 … 108 … … 95 … 111 99 … 102 105 99 97.
* 101 99 106 105 95 98 103 111 102 102 107 … 107 101 121 … 105 111 102 101 102 4 102 69 85 94 135 98 … … 59 59 104 36 54 75 116 … 46 69 55 GPI (F/M) 0.96 0.99 .… 0.98 .… .… 1.00 .… 0.99 0.98 .… 0.99 0.99 1.00 1.00* 1.00 1.00 0.99 0.99 1.00 0.98 0.99 0.99 0.99 1.02 1.01 .… 0.98 1.00 0.96 .… 0.99 1.03 1.00 1.00 1.03 0.08 0.99 0.85 0.84 0.95 1.01 0.77 .… .… 0.86 0.67 1.00 0.70 0.80 0.82 0.96 .… 0.58 0.85 0.95 17.z 18 20 3 4 47 70...z 30.z 14 14 2 5 54 … 6 12 1 15 3 7 1 1 ... 7 7 38.z 26.y 69.y 2.z 11 ..y 33 7 4 5 10 … 42.y 2 … 5 2 15.z 34 2...z … 13 5.y 14 1 23 0.3 10.z 31.z 10...z 38 School-age population1 (000) 2005 844 3 459 … 20 16 55 133 … 318 3 309 … 349 718 2 329 … 421 379 3 690 3 224 634 31 446 730 2 697 35 29 … 1 199 438 652 … S TAT I S T I C A L TA B L E S Ta b l e 5 GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in 2006 Total Male Female 111.z 116 94 118 97 121 95.
*,z 90...z 115 104 113.z 116 86 121 94 121 96.

*,z 88...z 117 106 110.z 117 103 114 100 121 94.

*,z 92...z 113 103 GPI (F/M) 0.97.z 1.01 1.20 0.94 1.06 1.00 0.98.
*,z 1.04...z 0.97 0.98 90 102 102 100...y 102 99 98 110 103 102 98 104 110 103 102 100.z … 107 98 115 … 105 96 97 105 98 101.z 103.y 102 112 118 116 126 84 108...z … 96 107.z 60 103 107 106 61 76.z 85...z 108 90 102 102 100...y 103 99 98 110 103 102 98 104 109 104 101 101.z … 108 98 118 … 106 96 98 105 98 126.z 101.y 103 114 104 118 129 94 108...z … 105 107.z 66 108 117 108 72 90.z 91...z 113 90 101 102 99...y 102 99 98 109 103 102 97 103 111 103 102 99.z … 105 98 112 … 104 95 97 106 99 75.z 105.y 101 109 132 114 123 73 108...z … 87 106.z 54 98 98 103 49 61.z 80...z 102 1.00.
0.99 0.99 0.99...y 1.00 1.00 1.00 0.99 1.00 1.00 0.99 0.99 1.02 0.99 1.01 0.98.z .… 0.98 1.01 0.95 .… 0.98 1.00 0.99 1.01 1.01 0.59.z 1.03.y 0.98 0.96 1.27 0.97 0.95 0.78 1.00...z .… 0.83 0.99.z 0.82 0.91 0.84 0.95 0.69 0.68.z 0.88...z 0.90 NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 Total Male Female 96 98 … 96 … … 87 … … 86 … 97 99 99 95 97 99 99 … 92 99 94 98 99 97 95 … 99 100 … … 100 100 94 100 94 … 83.

* 56 … 82 98 65.

* … … … 50.


* 80 35 … … 99 … 51 49 … 96 98 … 97 … … 87 … … 85 … 97 99 99 95 97 99 99 … 92 100 93 98 99 96 94 … 100 100 … … 100 100 94 100 94 … 83.

* 60 … 83 97 72.

* … … … 59.

* 79 41 … … 99 … 63 54 … 96 97 … 96 … … 88 … … 86 … 98 99 99 95 97 98 99 … 93 98 94 98 99 98 96 … 99 100 … … 100 100 94 100 94 … 83.

* 53 … 81 98 57.

* … … … 40.


* 82 28 … … 98 … 39 45 … GPI (F/M) 1.00 1.00 .… 0.99 .… .… 1.01 .… .… 1.01 .… 1.01 1.00 1.00 1.00 1.00 1.00 1.00 .… 1.01 0.98 1.01 1.00 0.99 1.03 1.02 .… 0.99 1.00 .… .… 1.00 1.00 1.00 1.00 1.00 .… 1.00.

* 0.89 .… 0.97 1.01 0.79.

* .… .… .… 0.68.

* 1.04 0.70 .… .… 0.98 .… 0.62 0.85 .… Total Male Female 94.z 96 71 98 90.z 96 85.

*,z 78...z 100 91 94.z 96 64 99 92.z 95 85.

*,z 75...z 100 91 95.z 97 78 97 88.z 98 85.


*,z 81...z 100 91 83 97 97 … 99 96 97 99 98 99 98 95 97 99 97 91.z … 98 98 98 … 100 95 89 98 92 … 89.

*,y 79 89 94 97 79...y 66 97...y … 80 84.z 47 75 … 88 46 … … 55 83 97 97 … 99 95 97 98 98 100 98 94 96 99 96 92.z … 99 98 98 … 100 95 89 98 91 … 87.

*,y 79 90 … 97 84...y 73 … … 87 83.z 52 76 … 88 53 … … 58 83 98 97 … 99 96 97 99 98 99 97 95 97 98 98 91.z … 97 98 98 … 99 95 89 99 93 … 90.
*,y 79 87 … 97 74...y 57 … … 73 85.z 42 73 … 87 38 … … 52 GPI (F/M) 1.01.z 1.01 1.22 0.98 0.96.z 1.03 1.00.

*,z 1.07...z 1.00 1.00 1.01 1.01 1.00 .… 1.00 1.01 1.00 1.00 1.00 1.00 0.99 1.01 1.01 0.99 1.01 0.99.z .… 0.99 1.01 0.99 .… 1.00 1.00 0.99 1.01 1.02 .… 1.04.
*,y 1.00 0.96 .… 1.00 0.87...y 0.78 .… .… 0.84 1.03.z 0.82 0.97 .… 0.99 0.72 .… .… 0.90 OUT-OF-SCHOOL CHILDREN (000)2 School year ending in 1999 2006 Total (000) % F Total (000) 28 6 … 0.7 … … 16 … … 424 46 100 … 52 … … 46 … … 47 43.z 33 2 0.2 1.2.z 1.9 15.

*,z 0.5...z 0.1 226 % F 46.z 9 35 75 61.z 26 48.

*,z 42...z 100 46 North America and Western Europe … 10 6 30 1.3 8 5 9 … 31 0.3 28 15 7 0.6 1.7 … 6.4 0.6 … … 6 2 10 2.0 1 215 … 2 350.

* 47 … 1616 1.1 1 043.

* … … … 586.


* 55 1 231 … … 0.8 … 654 53 … … 38 43 42 49 42 57 34 … 44 100 45 51 100 16 41 … 99 60 … … 69 100 37 25 49 0.8 9 18 … 0.3 16 11 27 12 1.9 0.7 23 22 17 0.4 2.6.z … 21 8 5 … 7 33 35 16 1 683 46 38 47 … 49 40 45 34 … 71 63 44 40 72 19 51.z … 69 42 63 … 86 50 48 0.1 42 South and West Asia … 1 371.

*,y 20 7 208 391 … 39.

*,y 49 64 … 0.9 702...y 6821 51...y 46 62...y 60 … … 48.

* 53 … 52 41 61.

* … … Sub-Saharan Africa … 59.
* 44 54 … … 90 … 62 54 … … 244 49.z 1 215 324 … 9 375 … … 243 … 71 45.z 54 53 … 52 57 … … Table 5 (continued) Country or territory Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria10 Rwanda Sao Tome and Principe Senegal Seychelles3 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe I World II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin America North America and Western Europe South and West Asia Sub-Saharan Africa 6-11 6-11 7-11 7-11 7-12 6-11 7-12 6-11 7-12 7-12 6-11 6-12 6-11 6-10 6-11 7-12 5-10 6-12 6-12 7-12 6-11 7-12 7-12 7-12 6-11 6-11 6-12 7-13 6-12 6-11 6-12 7-13 7-13 6-12 … … … … … … … … … … … … … … … … ENROLMENT IN PRIMARY EDUCATION Enrolment in private institutions as % of total enrolment GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 Age group 2006 School-age population1 (000) 2005 Total (000) % F … … … … 49 49 43 47 50 48 49 Total
39 44 50 49 46 49 … … 49 49 43 47 50 48 49 Total (000) 2 112 … 76.z 364 12 175 281...y 182 3 366 1 258 … 6 101 425 538 3 699 2 934 1 610 121 4 173 403 1 127 22 267.z 2 020 31 1 473 9 1 322 … 7 444.y 222.z 1 052 7 364 8 317 2 679 2 446 % F 44 … 49.z 44 47 49...y 51 49 45 … 49 50 47 49 50 44 49 46 50 41 45.z 51 49 49 49 48 … 49.y 48.z 46 50 49 49 50 12 19 33 11 … 17 3 13 15 19 … … 38 22 … 22 24 … 4 4 4 … – 12 5 … … 2 – 36 … 0.2 … 88 12 … 30.z 8 … 29...y 3.y 16 22 … 4.z 0.4 … 19 1 38 26 2 4 4 … 1 –.
z 12 6 3 … 2.y ..z 43 9 1 3 … Total Male Female 69 48 142 52 48 148 77 75 57 70 93 102 85 93 137 59 105 70 104 31 88 92 108 64 116.y … … … … … … … 871 1 520 7 116 207 1 027 6 309 7 217 2 292 2 417 Sum Sum % F Sum % F Median Weighted average … 15 21 15 7 … GPI (F/M) 0.74 0.90 0.79 0.82 0.61 1.00 0.87 0.92 0.64 0.67 0.97 1.08 0.74 0.97 0.96 0.70 1.00 0.74 1.01 0.68 0.79 0.98 0.97 0.86 0.99 .… .… 0.97 0.95 0.75 0.92 1.00 0.92 0.97 0.92 0.99 1.00 0.91 0.87 0.96 0.99 0.99 0.99 0.97 0.97 0.98 0.97 1.01 0.84 0.85 1.
Data are for 2005 except for countries with a calendar school year, in which case data are for 2006.

2.

Data reflect the actual number of children not enrolled at all, derived from the age-specific enrolment ratios of primary school age children, which measures the proportion of those who are enrolled either in primary or in secondary schools (total primary NER).

3.

National population data were used to calculate enrolment ratios.

4.

Enrolment and population data exclude Transnistria.

5.
5.

In the Russian Federation two education structures existed in the past, both starting at age 7.

The most common or widespread one lasted three years and was used to calculate indicators; the second one, in which about one-third of primary pupils were enrolled, had four grades.

Since 2004, the four-grade structure has been extended all over the country.

6.

Children enter primary school at age 6 or 7.


Since 7 is the most common entrance age, enrolment ratios were calculated using the 7-11 age group for population.
3 0 6 S TAT I S T I C A L TA B L E S Ta b l e 5 NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION (%) School year ending in 1999 2006 OUT-OF-SCHOOL CHILDREN (000)2 School year ending in 1999 2006 GROSS ENROLMENT RATIO (GER) IN PRIMARY EDUCATION (%) School year ending in 2006 Total Male Female GPI (F/M) 0.79 .… 0.95.z 0.81 0.88 0.99...y 1.08 0.99 0.84 .… 0.97 1.00 0.90 0.96 1.04 0.79 1.00 0.86 1.00 0.73 0.83.z 1.04 1.00 0.98 0.99 0.90 .… 0.96.y 0.93.z 0.86 1.01 0.98 0.98 0.99 Total Male Female 52 … 89 33 34 … 64 57 45 45 63 57 42 63 98 46 91 52 73 26 58 … 86 54 … … … 94 74 79 … 50 68 83 60 … 100 36 41 … 68 58 52 53 63 54 47 63 99 55 90 58 71 31 64 … 86 57 … … … 93 73 89 … 49 69 83 45 … 79 31 28 … 61 55 36 37 64 61 36 63 97 38 91 46 76 21 52 … 85 50 … … … 94 75 70 … 50 67 83 GPI (F/M) 0.75 .… 0.79 0.86 0.69 .… 0.89 0.96 0.69 0.71 1.01 1.12 0.77 1.01 0.98 0.70 1.01 0.79 1.07 0.68 0.82 .… 0.99 0.88 .… .… .… 1.01 1.02 0.79 .… 1.04 0.96 1.01 Total Male Female … … … 47 71 … 62 72 72 … 75 72 39 96 91 61 95 76 76 43 63...z 79...z 98 71 99.y … … 88.y 78.z 80 … 98 92 88 … … … 50 74 … 59 73 77 … 75 71 40 96 88 67 94 79 74 50 68...z 76...z 97 71 99.y … … 88.y 78.z 86 … 98 90 87 … … … 43 68 … 64 71 66 … 76 74 39 96 94 54 96 73 79 37 59...z 81...z 98 70 100.y … … 88.y 79.z 75 … 97 94 88 GPI (F/M) .… .… .… 0.87 0.92 .… 1.09 0.97 0.86 .… 1.02 1.04 0.97 1.00 1.06 0.79 1.02 0.93 1.06 0.73 0.86...z 1.06...z 1.01 0.98 1.01.y .… .… 1.00.y 1.01.z 0.87 .… 0.99 1.03 1.01 Total (000) 1 290 … 6 335 7 069 … 68 1349 698 114 1 859 152 268 796 20 862 12 1 574 98 1 255 8218 … 2.7 740 … … … 97 54 148 … 3 148 616 406 % F 58 … 100 52 55 … 55 50 56 57 49 46 55 50 100 58 47 56 45 52 57 … 50 54 … … … 2 48 81 … 49 52 49 Total (000) … … … 308 3 721 … 90 967 389 … % F … … … 53 55 … 46 51 59 … 1 371 101 356 106 202 793 6 954 89 1 245 8097...z 303...z 0.6 48 47 50 49 33 59 41 56 45 55 56...z 45...z 38 51 0.04.y ….y … … … … 469.y 45.z 176 … 44.
49.z 68 … 513 143 150 281 65 36 47 79 … 125.z 69 97 153...y 71 98 96 … 107 115 96 142 117 90 102 113 107 58 105.z 137 128 81 126 155 … 108.y 110.z 110 116 113 118 102 62 … 119.z 56 85 152...y 77 97 81 … 104 114 87 137 121 71 102 97 107 43 87.z 142 127 79 125 139 … 103.y 102.z 95 117 111 116 101 Weighted average Weighted average Weighted average Sum % F Sum % F … 122.z 62 91 152...y 74 98 88 … 106 114 91 139 119 80 102 105 107 51 96.z 140 127 80 125 147 … 106.y 106.z 102 117 112 117 101 105 99 101 106 97 97 100 109 110 91 118 108 118 101 108 95 7.
Enrolment ratios were not calculated due to lack of United Nations population data by age.

8.

Data include French overseas departments and territories (DOM-TOM).

9.

The apparent increase in the gender parity index (GPI) is due to the recent inclusion in enrolment statistics of literacy programmes in which 80% of participants are women.

10.
10.

Due to the continuing discrepancy in enrolment by single age, the net enrolment ratio in primary education is estimated using the age distribution of the 2004 DHS data.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.
I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI 3 0 7 9 0 0 2 t r o p e R A N N E X Table 6 Internal efficiency: repetition in primary education Duration1 of primary education REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Grade 1 Grade 2 Grade 3 Grade 4 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea … 6 4 4 4 4 4 5 4 6 4 3 4 6 4 4 5 4 3 4 7 6 6 5 6 4 12.6 3.0.y 3.9 – 9.2...y 0.5 2.6 5.4 … 8.9 16.2 0.3 0.0.y 1.4 … 1.4.y 10.7 1.0 2.6 4.3 3.2.x 0.2 … 0.8 0.7 1.1 1.6 3.7 4.8 1.2 … 0.8 0.5 4.0 0.8 … 5.0 0.5 0.3.y 4.1 0.3 – 0.3 0.4 0.1 0.2 0.5 0.0 … – … 0.5 22.2 … … … 14.6 2.4.y 4.1 – 10.3...y … 2.7 6.4 … 9.1 17.3 0.3 0.0.y 1.9 … 1.1.y 11.7 1.2 2.5 4.3 3.7.x 0.2 … 0.9 0.7 1.3 2.0 4.1 6.4 1.4 … … 0.6 4.5 0.8 … 5.3 0.6 0.3.y 4.4 0.3.
* – 0.3 0.5 0.1 0.2 0.5 0.0 … – … 0.6 23.0 … … … 10.4 3.5.y 3.5 – 7.9...y … 2.5 4.4 … 8.7 14.9 0.2 0.0.y 1.0 … 1.8.y 9.5 0.9 2.8 4.3 2.7.x 0.2 … 0.6 0.6 1.0 1.1 3.2 3.0 0.9 … … 0.3 3.5 0.8 … 4.7 0.4 0.2.y 3.7 0.3.
* – 0.3 0.4 0.0 0.2 0.4 0.0 … – … 0.3 21.3 … … … 10.6 3.2.y 9.6 1.8 7.7...y 0.4 1.5 6.6 … 9.4 13.7 0.1 0.0.y 1.0 … 1.6.y 7.3 9.4 1.9 … 2.1.x 0.0 … 2.8 0.3 0.6 1.1 1.5 2.0 0.6 … 0.4 0.1 1.8 … … 2.3 … 0.2.y 1.9 0.1 0.3 0.3 0.2 0.1 0.1 … 0.4 … … … 0.5 14.7 … … … 12.9 3.7.y 9.5 … 8.7...y … 1.5 8.1 … 9.6 15.4 0.1 0.0.y 1.3 … 1.4.y 8.4 10.8 2.0 … 2.5.x 0.0 … 3.3 0.4 0.7 1.4 1.7 2.7 0.6 … … 0.1 2.2 … … 2.5 … 0.2.y 1.9 0.1 0.2 0.3 … 0.2 0.2 … 0.4 … … … 0.8 15.8 … … … 8.1 2.6.y 9.7 … 6.5...y … 1.6 5.0 … 9.3 11.8 0.0 0.0.y 0.7 … 1.9.y 6.0 7.9 1.7 5.6 3.4.y 6.3 2.5 6.4...y 0.4 1.9 6.3 … 10.4 13.8 0.1 0.4.y 1.6 … 1.8.y 4.3 2.1 1.8 7.1 4.0.y 6.0 … 7.4...y … 2.2 7.8 … 10.4 16.0 0.1 0.4.y 2.2 … 1.6.y 5.1 2.6 2.0 3.9 2.8.y 6.8 … 5.2...y … 1.5 4.7 … 10.5 11.2 0.1 0.4.y 1.0 … 2.1.y 3.4 1.6 1.6 … … … … 1.6.x 0.0 … 1.5.x 0.0 … 1.9.x 0.0 … 1.1.x 0.0 … 2.3 0.2 0.5 0.8 1.2 1.3 0.5 … … 0.1 1.4 … … 2.0 … 0.2.y 1.9 0.1 0.4 0.3 … 0.1 0.1 … 0.3 … … … 0.3 13.3 … … … 2.3 0.2 0.5 1.3 1.1 1.8 0.4 … 0.4 0.1 1.3 … … 1.6 … 0.1.y 1.9 … 0.2 0.2 0.2 0.1 0.1 … 0.2 … … … 0.3 12.0 … … … 2.8 0.2 0.6 1.8 1.3 2.4 0.5 … … 0.1 1.6 … … 1.9 … 0.1.y 1.7 … 0.2 0.2 … 0.1 0.1 … 0.2 … … … 0.5 13.5 … … … 1.8 0.1 0.4 0.9 0.8 1.1 0.2 … … 0.1 1.0 … … 1.4 … 0.1.y 2.2 … 0.2 0.2 … 0.1 0.1 … 0.2 … … … 0.2 10.3 … … … 10.1 2.5.y 5.5 4.1 7.2...y 1.4 1.7 16.6 … 10.2 11.3 0.0 2.2.y 0.8 … 2.1.y 2.8 12.1 2.0 … 1.7.x 0.0 … 2.7 0.1 0.6 1.9 1.1 2.2 0.3 … 0.9 0.1 1.4 … … 1.9 … 0.2.y 2.3 … … 0.3 0.2 0.1 0.1 … 0.2 … … … 1.5 8.6 … … … 12.8 3.2.y 5.6 … 8.5...y … 2.1 19.0 … 10.1 13.8 0.0 2.4.y 0.8 … 1.7.y 3.4 14.4 2.8 … 2.0.x 0.0 … 3.0 0.1 0.7 2.5 1.4 3.0 0.5 … … 0.1 1.8 … … 2.1 … 0.2.y 1.8 … … 0.3 … 0.1 0.1 … 0.2 … … … 2.1 9.9 … … … 7.1 1.8.y 5.4 … 5.5...y … 1.3 14.1 … 10.3 8.4 0.0 2.1.y 0.7 … 2.5.y 2.1 9.4 1.2 … 1.4.x 0.0 … 2.5 0.1 0.5 1.2 0.8 1.4 0.2 … … 0.0 1.1 … … 1.6 … 0.1.y 2.7 … … 0.2 … 0.0 0.0 … 0.2 … … … 0.8 7.3 … … … REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) Grade 5 Grade 6 Grade 7 School year ending in 2005 Total Male Female Total Male Female Total Male Female Total
 12.1 2.8.y … 3.9 13.1...y 2.0 2.7 11.1 … 12.3 8.6 1.4 … 3.3 … 1.8.y … 2.3 1.8 … .
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… 0.5 4.8 … … … 17.3 1.9.y … 6.8 4.2...y 2.1 … 10.1 … 18.2 9.0 1.3 … 1.5 … 1.9.y … 7.0 … 5.0 .

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11.9 3.8 16.6 6.0 10.0 0.7 3.3 9.1 … … 12.4 8.0 2.1 2.7 … 11.3 6.5 18.3 3.5 10.6 3.9 0.5 … 3.2 0.4 1.2 2.5 2.2 2.1 0.9 … 1.2 0.9 3.4 1.4 … 2.3 1.0 0.0 … 0.8 … 0.4 0.3 0.3 0.3 0.9 0.5 … 0.1 … .
24.6 … 2.6 … S TAT I S T I C A L TA B L E S Ta b l e 6 REPEATERS, ALL GRADES (%) School year ending in Female Total 1999 Male 14.6 4.6 16.9 7.1 10.7 0.7 3.4 10.5 … … 14.1 9.5 2.2 3.5 … 10.9 7.2 20.0 4.4 11.7.

* 4.6 0.5 … 3.7 0.5 1.5 3.5 2.1 2.7 1.3 … … 0.9 4.1 … … 2.6 1.3 0.1 … … … 0.4 0.5 … 0.4 1.0 0.5 … … … .

25.4 … … … 8.7 3.1 16.1 4.6 9.2 0.7 3.1 7.7 … … 10.2 6.4 2.0 1.9 … 11.8 5.6 16.4 2.5 8.7.

* 3.2 0.5 … 2.7 0.3 1.0 1.4 2.2 1.3 0.5 … … 0.9 2.6 … … 2.0 0.7 0.0 … … … 0.4 0.2 … 0.2 0.8 0.6 … … … .
23.5 … … … 2006 Male 14.4 3.0 7.6 3.9 9.1...z 1.1 2.5 11.2 … 10.1 14.6 0.4 0.7.z 2.0 … 1.4.z 7.3 7.3 2.3 5.3 Female Arab States 9.2 2.3 7.2 2.2 6.5...z 1.1 1.6 7.9 … 10.3 10.2 0.8 0.7.z 1.0 … 2.1.z 5.4 4.7 1.7 4.3 12.0 2.7 7.5 3.1 8.0...z 1.1 2.1 9.6 … 10.2 12.6 0.6 0.7.z 1.5 … 1.7.z 6.4 6.1 2.0 4.9 Central and Eastern Europe 2.6.y 0.1 1.7.y 0.1 2.1.y 0.1 … … … 2.3 0.3 0.7 2.1 1.9 2.8 0.6 … 0.7 0.2 2.2 0.6 … 2.8 0.5 0.2.z 2.9 0.1 0.2 0.3 0.3 0.1 0.1 0.3 0.2 … – 2.6 0.4 0.8 2.9 2.2 3.8 0.8 … 1.0 0.2 2.6 0.6 … 3.0 0.6 0.2.z 2.7 0.1 2.0 0.2 0.6 1.3 1.5 1.8 0.5 … 0.3 0.1 1.8 0.6 … 2.5 0.3 0.2.z 3.1 0.1 Central Asia 0.1 0.3 0.4 0.1 0.1 0.3 0.2 … – 0.2 0.3 0.2 0.1 0.1 0.3 0.2 … – East Asia and the Pacific … 1.6 12.7 0.3 – z … … 2.2 13.8 0.3 – z … … 0.9 11.4 0.2 – z … t r o p e R A N N E X Table 6 (continued) Duration1 of primary education REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Grade 1 Grade 2 Grade 3 Grade 4 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam 6 6 6 6 5 6 6 6 6 5 6 6 6 5 6 6 6 6 6 6 6 6 6 6 6 6 5 Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis 6
– ..y 32.9 2.3 ...y ...x … 0.8 … … … … … 5.6 0.0 2.6...x … … … … .
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13.2...x 2.9 1.4.y … 9.8.y 15.5 – .

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1.4...x 27.3.x 8.3 1.3.y 2.5 6.6 13.3 – 12.2 6.5 3.1 14.7 1.7 24.4 1.1...y … 16.4 3.9...y 6.7 12.3.y … 18.0 9.5 10.2.y 5.1 ..y 4.7 7.5 – ...y 33.7 2.7 ...y ...x … 0.8 … … … … … 6.6 0.0 2.9...x … … … … .

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13.4...x … 3.2.y … 11.2.y 18.8 – .

19.0 … 1.5...x … … 2.1.y 3.0 7.2 14.9 – 15.8 7.8 3.5 16.1 2.6 25.7 1.2...y … 17.6 5.1...y 7.8 … … 19.2 10.7 11.5.y 5.3 ..y 3.2 5.4 – ...y 31.9 1.9 ...y ...x … 0.8 … … … … … 4.5 0.0 2.2...x … … … … .

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14.0 … 1.4...x … … 0.4.y 2.1 5.9 11.5 – 8.7 5.0 2.8 13.1 0.8 22.9 1.0...y … 15.1 2.6...y 5.4 … … 16.5 8.2 8.7.y 4.9 ..y 2.1 5.0 … ...y 18.0 2.5 ...y 0.0.x … 0.5 … … … … … 3.0 0.0 … … … … … .

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1.3...x 20.5.x 4.2 –.y 2.2 4.1 8.3 1.5 3.8 8.0 2.0 6.7 3.0 13.5 … … 9.5 … 6.6 … … 9.6 8.2 6.9.y 14.0 ..y 2.7 6.1 … ...y 19.3 3.4 ...y ...x … 0.6 … … … … … 3.9 0.0 … … … … … .

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10.9 … 1.4...x … … –.y 2.6 4.6 9.5 2.0 … 9.8 2.3 7.9 3.3 14.6 … … 10.8 … 7.8 … … 10.9 9.3 8.3.y 14.3 ..y 1.4 3.7 … ...y 16.5 1.6 ...y 0.0.x … 0.5 … … … … … 2.0 0.0 … … … … … .

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8.3 … 1.2...x … … –.y 1.8 3.5 6.9 0.9 … 5.9 1.7 5.5 2.6 12.4 … … 8.2 … 5.3 … … 8.2 7.0 5.5.y 13.6 ..y 1.3 4.2 … … 12.1 4.5 ...y 0.0.x … 0.5 … … … … … 2.3 0.0 … … … … … .

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… … 0.7 0.5.y … 5.9.y 8.5 – .

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1.6...x 15.4.x … –.y 2.0 3.2 7.2 – 2.7 12.4 1.4 5.4 2.9 10.1 … … 6.4 … 4.5 … … 8.4 6.0 5.1.y 11.2 … 1.7 5.2 … … 13.6 5.7 ...y ...x … 0.6 … … … … … 3.0 0.0 … … … … … .

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… … … 0.9.y … 7.0.y … – .

11.6 … 1.6...x … … –.y 2.3 3.6 8.3 – … 15.3 1.6 6.2 3.7 11.0 … … 7.2 … 5.5 … … 9.5 7.2 6.1.y 11.6 … 0.8 3.2 … … 10.4 3.1 ...y 0.0.x … 0.4 … … … … … 1.4 0.0 … … … … … .

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… … … –.y … 4.7.y … – .


9.5 … 1.5...x … … –.y 1.6 2.7 5.9 – … 9.1 1.1 4.6 2.0 9.1 … … 5.5 … 3.5 … … 7.2 4.7 4.0.y 10.9 … 1.2 3.1 … … 7.8 … ...y 0.0.x … 0.4 … … … … … 1.7 0.0 … … … … … .

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… … 0.6 –.y … 5.8.y 8.0 – .

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1.5...x 15.4.x … –.y 1.8 2.5 8.8 0.8 2.6 7.4 1.0 5.3 … 7.0 … … 4.1 … 3.7 … … 6.6 4.4 3.4.y 8.4 1.8 3.7 … … 9.0 … ...y ...x … 0.7 … … … … … 2.4 0.0 … … … … … .

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… … … –.y … 7.0.y … – .


9.8 … 1.6...x … … –.y 2.1 2.9 10.3 1.1 … 9.6 1.1 6.4 … 7.8 … … 4.6 … 4.7 … … 7.8 5.3 4.2.y 8.7 0.6 2.4 … … 6.3 … ...y 0.0.x … 0.1 … … … … … 1.0 0.0 … … … … … .

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… … … –.y … 4.6.y … – .


7.1 … 1.3...x … … –.y 1.4 2.1 7.2 0.4 … 5.1 0.8 4.2 … 6.2 … … 3.5 … 2.8 … … 5.3 3.3 2.5.y 8.1 … … … REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) Grade 5 Grade 6 Grade 7 School year ending in 2005 Total Male Female Total Male Female Total Male Female Total 1.0 2.1 … … 4.5 … ..y 0.0.x … 0.2 … … … … … 1.5 0.0 … … … … … … … .

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0.1 –.y … 5.4.y 7.5 – .

9.3 .


1.4...x … … –.y … 2.1 6.4 0.4 2.1 5.4 0.7 4.5 … 4.7 … … 2.6 … 2.6 … … 4.7 3.1 2.0.y 7.2 1.4 2.5 … … 5.5 … … … ..x 0.3 … … … … … 2.1 0.0 … … … … … … … … .

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… … 0.7 1.6 … … 3.3 … … 0.0.x … 0.2 … … … … … 0.8 0.0 … … … … … … … … .

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… … 6.5.y 4.2.y … – … 10.0 … 1.6...x … … … … 2.6 7.3 0.5 … 7.2 0.8 5.5 … 5.2 … … 3.1 … 3.4 … … 5.5 4.0 2.5.y 7.5 … – … 8.4 … 1.3...x … … … … 1.7 5.3 0.2 … 3.6 0.5 3.6 … 4.0 … … 2.1 … 1.8 … … 3.9 2.2 1.4.y 6.9 3.4 0.2 … … .

… ..y ..x … .
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… 0.8 0.0 0.3...x … … … … … 20.2 .

13.5...x .

0.5.y … 4.4.y 3.9 – .

7.6 .

2.9...x .

… –.y … .

0.7 0.1 2.3 4.3 0.4 4.2 … 1.5 0.7...y … 0.7 … 0.6 … … 2.5 1.3 1.0.y 4.0 3.5 0.2 … … .

… … ..x … .

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… 1.1 0.0 0.4...x … … … … … 22.7 .

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3.4 0.1 … … .

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… 0.4 0.0 0.1...x … … … … … 17.3 .

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… … … … 5.5.y 3.4.y … – … 8.7 … 3.3...x .

… … … .

0.8 0.2 … 5.9 0.4 5.1 … 1.7 0.8...y … 0.9 … 0.7 … … 3.2 1.5 1.3.y 4.3 … – … 6.5 … 2.5...x .
… … … .

0.5 0.1 … 2.7 0.3 3.3 … 1.2 0.6...y … 0.6 … 0.5 … … 1.9 1.0 0.7.y 3.7 … … … … … … .

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20.9 6.3 .

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… 1.7 … … – … .

1.9 – 1.0 … … 3.5 … … 8.8 .

10.6 3.8 0.3 … 6.1 7.7 .

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9.7 … 2.4 24.0 3.8 0.2 2.4 5.2 9.2 1.9 3.6 4.1 2.7 7.1 … 14.9 3.1 … … … 6.6 0.8 12.0 4.7 6.4 7.8 10.2 … S TAT I S T I C A L TA B L E S Ta b l e 6 REPEATERS, ALL GRADES (%) School year ending in 1999 Male … … … .

22.4 7.3 .

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… 1.7 … … .


– … 2.4 – 1.1 … … 3.4 … … 8.5 .

11.1 4.2 0.4 … 7.1 9.5 .

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10.8 … 2.6 24.0 4.1 0.2 2.9 5.8 10.4 2.6 3.8 4.5 3.0 7.7 … 15.8 3.6 … … … 7.6 1.4 14.5 5.3 7.4 8.8 10.5 … Female Total … … … .

19.1 5.1 .

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… 1.7 … … .

– … 1.4 – 0.9 … … 3.5 … … 9.2 .

9.9 3.2 2.2 3.7 … ..z 18.2 5.7 ..z .

… 0.5 … … ..z 4.7.y … 2.7 0.0 0.9...y … … … … ..y 5.2 .

10.7.y 1.0 2006 Male 2.7 4.5 … ..z 19.3 7.0 ..z .

… 0.6 … … ..z … … 3.5 0.0 1.1...y … … … … ..y 5.9 .
11.5...y … Female 1.7 2.9 … ..z 17.0 4.2 ..z .

… 0.4 … … ..z … … 1.9 0.0 0.7...y … … … … ..y 4.4 .

9.7...y … Latin America and the Caribbean 0.3 … 5.0 5.9 .

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8.4 … 2.3 24.0 3.6 0.1 1.9 4.6 7.9 1.1 3.5 3.7 2.4 6.4 … 13.8 2.5 … … … 5.5 – 9.3 4.1 5.2 6.7 9.9 … – … 6.4.z 8.8 – .

10.4 .

0.9 18.7.z 6.8 – 2.3 3.8 7.3 0.5 3.8 7.8 1.5 7.5 3.4...z 12.1 0.9.z … 7.4 2.8...z 4.2 3.1 … 9.5 5.6 5.1.z 8.7 .

– … 7.6.z 9.8 – .

11.7 .


0.9 … 8.5 – 2.8 4.4 8.4 0.7 4.6 9.9 1.7 8.7 4.1...z 13.0 1.1.z … 8.3 3.3...z 5.1 3.3 … 10.8 6.6 6.1.z 9.0 .

– … 5.2.z 7.7 – .

8.9 .

0.8 … 4.9 – 1.7 3.3 6.1 0.3 3.0 5.5 1.3 6.3 2.8...z 11.1 0.8.z … 6.5 2.3...z 3.3 3.0 … 8.2 4.6 4.1.z 8.4 .
Table 6 (continued) Duration1 of primary education REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Grade 1 Grade 2 Grade 3 Grade 4 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 7 7 6 7 6 6 6 6.2 5.3.y … 10.8.

*,y 0.9.x 13.9 10.0 7.6 6.5.y … 12.8.

*,y 1.8.x 16.0 11.8 4.8 3.9.y … 8.6.
*,y –.x 11.6 8.0 North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea … 1.2.x 6.5 … 1.1 ..y 0.9 … 1.2 1.3 – 1.8 1.7 0.4 5.4 0.8.y –..x .
– – … – … … … – 8.8...y 7.1.x 8.1 3.7 4.0 0.5 37.0 2.3 0.3...y … 1.4 ..y 6.4 36.5 31.8 1.4 29.6 23.2.y 33.3...y 27.7.y … … … 13.7 … 1.4.x 6.9 … 1.4 ..y 1.1 … 1.3 1.4 – 2.0 2.1 0.5 6.3 0.8.y … .

– – … – … … … – 8.1...y 6.8.x 8.6 3.7 4.9 … 36.8.

* 2.5 … … 1.6 ..y 6.5 36.1 33.4 1.6 29.8 22.8.y 35.0...y … … … … 13.9 … 1.1.x 6.1 … 0.8 ..y 0.6 … 1.1 1.2 – 1.7 1.2 0.3 4.6 0.8.y … .

– – … – … … … – 10.5...y 7.4.x 7.5 3.7 3.2 … 37.3.


* 2.1 … … 1.1 ..y 6.3 37.0 29.8 1.2 29.3 23.7.y 31.2...y … … … … 13.5 … … … … … … … … … … … … … … … … … … … … … … … … … … … 3.5.

*,y 2.7.

*,y 4.3.

*,y 4.1.

*,y 5.1.

*,y 3.0.

*,y 4.1.

*,y 4.9.

*,y 3.2.


*,y … 9.5 7.8 … 1.3.x 4.4 … 0.1 ..y 0.9 … 1.2 0.7 – 1.2 0.8 0.2 5.2 0.8.y –..x … – … … … … … … – … 6.7.x 7.8 … … 0.4 19.3 … 0.7...y … 9.3 ..y 9.6 35.1 … 24.8 23.5 21.9.y 28.9...y … … … … 14.2 … 10.7 9.4 … 1.5.x 4.4 … 0.2 ..y 1.1 … 1.2 0.7 – 1.4 1.0 0.2 6.3 0.9.y … … – … … … … … … – … 6.6.x 8.9 … … … 18.5.
* … … … 9.4 ..y 9.8 35.4 … 29.0 23.9 21.2.y 27.5...y … … … … 14.4 … 8.3 5.9 … 1.2.x 4.4 … 0.1 ..y 0.7 … 1.1 0.6 – 1.1 0.6 0.1 4.1 0.7.y … … – … … … … … … – … 6.7.x 6.6 … … … 20.1.
* … … … 9.2 ..y 9.3 34.7 … 19.8 23.0 22.7.y 30.4...y … … … … 14.0 … 6.8 7.5 … 1.3.x … … 0.0 ..y 0.3 … 1.4 0.5 – 0.7 0.9 0.1 5.1 … –..x … – … … … … … … – … 9.2.x 7.9 … … … 15.0 … … … 11.2 ..y 11.9 32.7 … 13.4 31.3 21.5.y 28.5...y … … … … 13.5 … 8.2 9.2 … 1.5.x … … 0.0 ..y 0.4 … 1.5 0.6 – 0.9 1.2 0.2 5.2 … … … – … … … … … … – … 9.4.x 8.7 … … … 15.0.
* … … … 11.1 ..y 12.1 32.5 … 15.7 30.3 19.5.y 30.4...y … … … … 13.8 … 5.5 5.5 … 1.0.x … … 0.0 ..y 0.2 … 1.3 0.4 – 0.6 0.6 0.1 5.1 … … … – … … … … … … – … 8.9.x 7.1 … … … 15.1.

* … … … 11.3 ..y 11.6 32.9 … 11.0 32.7 24.7.y 26.2...y … … … … 13.2 … 5.4 5.4 … 1.0.x 2.5 … 0.0 ..y 0.2 … 0.8 0.4 – 0.4 1.2 0.1 4.0 … –..x … – … … … … … … – … 7.7.x 5.9 … … … 15.9 … … .
11.5 ..y 14.0 31.8 … 16.8 27.6 21.3.y 24.1...y … … … … 14.8 … 6.5 6.9 … 1.1.x 2.7 … 0.0 ..y 0.2 … 0.8 0.5 – 0.5 1.5 0.2 4.5 … … … – … … … … … … – … 8.2.x 6.5 … … … 15.9.

* … … .

11.0 ..y 14.0 31.5 … 19.1 27.1 20.3.y 26.0...y … … … … 14.8 … 4.2 3.9 … 0.9.x 2.3 … – ..y 0.1 … 0.7 0.4 – 0.4 0.9 0.1 3.5 … … … – … … … … … … – … 7.3.x 5.4 … … … 16.0.

* … … .
* … … .

12.2 ..y 13.9 32.2 … 14.2 28.4 22.8.y 21.9...y … … … … REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) Grade 5 Grade 6 Grade 7 School year ending in 2005 Total Male Female Total Male Female Total Male Female Total … … … … … … … … … … … … … … … … … … 4.2.
*,y … 5.0.

*,y … 3.3.

*,y … 5.2.

*,y … 6.5.

*,y … 4.0.

*,y … 4.2 3.6 … .

2.5 … 0.0 ..y 0.2 … .

0.4 – 0.5 1.3 0.3 3.9 … –.x … – … … … … … … – … 5.1.x 9.1 4.0 … … 12.0 2.2 … … 12.2 ..y 15.1 42.2 … 10.6 27.3 22.6.y 22.7...y … … … … 8.9 5.1 4.6 … .

2.7 … – ..y 0.2 … .

0.4 – 0.5 1.7 0.3 4.7 … … … – … … … … … … … … 5.5.x 10.4 4.1 … … 11.8.

* 2.4 … … 11.5 ..y 14.6 40.7 … 12.6 27.9 21.1.y 23.6...y … … … … 9.1 3.2 2.6 … .

2.3 … 0.1 ..y 0.1 … .


0.3 – 0.5 0.9 0.2 3.1 … … … – … … … … … … … … 4.7.x 7.7 3.8 … … 12.3.

* 1.8 … … 13.2 ..y 15.8 44.1 … 8.7 26.5 25.1.y 21.7...y … … … … 8.7 2.0 1.5 … .

1.1 … 0.1 ..y 0.2 … .

0.4 – 0.4 0.9 … 0.9 … … … – … … … … … … – … .

5.8 .

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4.4 ..y 32.1 46.3 26.0 12.2 33.7 23.2.y 26.2...y … … … .

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2.6 1.9 … .

1.2 … 0.1 ..y 0.2 … .

0.4 – … 1.2 … 1.0 … … … – … … … … … … … … .

6.1 .

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4.6 ..y 31.0 44.2 28.7 13.7 34.3 22.9.y 27.9...y … … … .

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1.4 1.0 … .
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1.4 1.0 … .

1.1 … 0.1 ..y 0.1 … .

0.3 – … 0.7 … 0.9 … … … – … … … … … … … … .

5.5 .

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4.1 ..y 33.4 48.9 22.5 10.8 32.9 23.9.y 24.3...y … … … .

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2.9.

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3.4.

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2.4 … … 4.7 … 7.9 7.0 … 1.5 … … 0.4 .

0.4 4.2 1.7 – – 1.8 … 0.4 … 2.1 – .

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… … … … 1.8 – – … 6.5 12.1 4.0 … … 22.9 … … 29.0 … 3.3 17.7 20.3 26.7 11.6 … 25.9 26.0 39.1 23.7 … 11.8 19.4 S TAT I S T I C A L TA B L E S Ta b l e 6 REPEATERS, ALL GRADES (%) School year ending in 1999 Male 2.8 … … 4.9 … 9.3 8.5 … 1.8 … … 0.5 .

0.6 4.2 1.9 – – 2.1 … 0.5 … 2.4 – .

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… … … … 1.9 – – … 6.8 12.5 4.0 … … 22.2 … … 29.0 … 3.9 17.5 20.3 26.8 12.8 … 25.7 26.4 40.0 22.8 … 9.3 18.2 Female Total 2.0 … … 4.4 … 6.5 5.5 2.2 4.1.z 20.3.z 5.2.
*,z 2.9.z 7.0 6.1 2006 Male 2.6 5.0.z 22.3.z 6.0.

*,z 3.2.z 8.2 7.5 Female 1.7 3.0.z 18.1.z 4.4.

*,z 2.6.z 5.7 4.6 North America and Western Europe … – 1.2.y 3.2 – 1.4.y 3.3 – 1.1.y 3.1 … 0.2 – 0.4 … 1.1 0.6 – 0.7 1.4 0.2 4.2 2.6.z –.y .
– 10.2...z –.y 2.3...z … 1.4 – – 16.3.z 7.0.y 6.9 3.4 2.0 4.7 20.6 2.2 0.8...z … 7.8 ..z 12.0 28.8 25.1 14.0 28.2 22.5.z 27.1...z 21.2 23.5 … 25.6.z 13.7 … 0.3 – 0.5 … 1.2 0.7 – 0.8 1.7 0.3 4.8 2.9.z … .

– … … 2.6...z … 1.5 – – … 0.2 – 0.3 … 1.1 0.5 – 0.7 1.0 0.2 3.6 2.2.z … .

– … … 1.9...z … 1.3 – – South and West Asia 17.6.z 7.2.y 7.6 3.4 2.8 5.5 20.8.

* 2.4 0.9...z 13.9.z 6.9.y 6.1 3.4 1.4 3.7 20.4.


* 1.9 0.7...z Sub-Saharan Africa … 7.9 ..z 12.1 29.2 26.8 16.4 28.2 21.8.z 28.2...z 21.5 23.4 … 25.5.z 13.9 … 7.8 ..z 11.8 28.4 23.1 11.5 28.2 23.5.z 25.9...z 20.9 23.7 … 25.6.z 13.5 1.3 … … 0.3 .

0.3 4.2 1.5 – – 1.6 … 0.3 … 1.8 – .

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… … … … 1.6 – – … 6.2 11.7 4.1 … … 23.8 … … 29.0 … 2.7 18.0 20.4 26.5 10.3 … 26.3 25.5 38.2 24.9 … 14.9 20. Table 6 (continued) Duration1 of primary education REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Grade 1 Grade 2 Grade 3 Grade 4 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe I World2 II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa … … … … … … … … … … … … … … … … 7.1 … … 9.7.y 3.2 … 6.2...y 28.1 … 12.5 25.7 12.2 .

3.6 19.1 0.2 1.2.y 17.7 28.0 5.3 ..y … … 10.2.x 20.5.y 27.6 12.3.y 8.5 6.4 … 2.9 0.2 0.8 5.4 2.8 1.0 0.1 0.8 2.3 … 6.6 2.8 9.8 0.8 4.0 12.2 7.4 … … 10.1.y 3.2 … 6.4...y 31.5 … 12.9 26.1 12.1 .
3.7 21.3 0.2 1.3.y 15.8 30.4 5.4 ..y … … 10.7.x 22.9.y 28.1 11.1.y 8.6 6.4 … … 0.2 … 6.4 2.5 1.1 0.1 0.8 2.7 … 7.2 3.8 11.2 0.8 4.9 12.1 6.8 … … 9.3.y 3.3 … 5.9...y 24.1 … 12.1 25.4 12.4 .

3.5 16.7 0.2 1.2.y 19.4 25.5 5.2 ..y … … 9.6.x 17.9.y 27.1 13.6.y 8.4 6.4 … … 0.2 … 4.4 3.1 0.9 0.1 0.8 1.9 … 5.9 1.7 8.2 0.8 3.2 12.4 5.2 5.6 4.8 5.8 6.4 5.1 7.6 8.2 6.8 … … … 12.3 … 5.8...y 24.5 … 27.8 21.6 11.5 .
8.2 13.4 3.0 … … 28.7 9.6 ..y … … 8.0.x 18.1.y … 12.2...y 4.9 5.9 … 2.2 0.1 0.7 6.3 3.2 0.8 0.3 … 1.7 … 6.6 1.5 7.8 0.7 6.7 11.8 … … … 11.9 … 6.0...y 28.2 … 28.9 22.0 11.1 .

8.4 16.1 3.0 … … 29.9 9.7 ..y … … 8.6.x 21.1.y … 12.5...y 5.1 6.0 … 2.6 0.2 0.7 6.9 3.7 1.0 … … … … 7.8 1.7 9.4 0.8 6.6 11.8 … … … 12.8 … 5.6...y 20.1 … 26.6 21.2 11.9 .
8.0 10.6 3.1 … … 27.5 9.5 ..y … … 7.4.x 14.6.y … 11.9...y 4.7 5.8 … 1.9 0.1 0.6 5.6 2.6 0.6 … … … … 5.3 1.3 5.9 0.6 6.7 11.9 … … … 4.3 … 6.1...y 21.0 … 28.4 22.6 18.4 .

4.4 12.0 4.7 … … 25.5 9.3 ..y … … 9.1.x 19.7.y … 14.3...y 4.2 6.0 … 2.3 0.1 0.5 5.1 2.5 1.1 0.2 … 1.5 … 5.1 … 6.0 0.2 … … … … 4.0 … 6.4...y 25.0 … 29.2 22.9 18.0 .

4.6 14.1 4.5 … … 26.3 9.4 ..y … … 9.8.x 22.7.y … 15.2...y 4.2 6.1 … 2.9 0.1 0.6 6.1 … 1.3 … … … … 6.1 … 7.2 0.3 … … … … 4.7 … 5.8...y 16.6 … 27.5 22.3 18.9 .
4.1 9.8 4.9 … … 24.7 9.3 ..y … … 8.3.x 16.4.y … 13.4...y 4.2 5.9 … 1.6 0.1 0.4 4.0 … 0.8 … … … … 4.0 … 4.7 0.2 … … … … 13.3 … 6.2...y 21.1 … 8.2 17.3 20.5 .

3.5 14.4 5.6 … … 16.6 11.3 ..y … … 9.5.x 17.6.y … 13.2...y 8.7 6.8 … 2.5 0.1 0.5 5.4 2.8 1.1 0.2 … 1.5 … 4.1 … 5.3 0.2 … … … … 12.5 … 6.5...y 24.9 … 8.4 17.8 19.5 .

3.7 17.2 5.3 … … 18.5 11.1 ..y … … 9.9.x 19.9.y … 13.2...y 8.7 7.1 … 2.9 0.1 0.6 6.7 3.4 1.4 … … 2.1 … 4.8 … 6.4 0.2 … … … … 14.5 … 5.9...y 17.1 … 8.0 16.9 21.7 .
3.3 11.5 6.1 … … 14.5 11.5 ..y … … 8.9.x 15.1.y … 13.2...y 8.8 6.5 … 2.1 0.0 0.4 4.0 2.1 0.8 … … 0.8 … 3.4 … 4.2 0.1 … 11.9 13.1 10.7 13.2 13.2 13.2 1.

Duration in this table is defined according to ISCED97 and may differ from that reported nationally.

2.

All values shown are medians.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2006 for repetition rates by grade, and the school year ending in 2007 for percentage of repeaters (all grades).
(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(x) Data are for the school year ending in 2003.

(*) National estimate.

3 1 4 REPETITION RATES BY GRADE IN PRIMARY EDUCATION (%) Grade 5 Grade 6 Grade 7 School year ending in 2005 Total Male Female Total Male Female Total Male Female Total 9.0 … … … 4.2 … 5.9...y 17.6 … 22.1 16.5 25.4 .

9.4 22.5 8.0 … … 24.7 12.0 ..y … … 7.3.x 18.3.y … 13.8...y 0.1 6.6 … 1.8 .

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.

4.2 3.1 … … … 1.0 … 2.9 … 3.6 0.0 5.1 12.2 9.6 … … … 3.7 … … 20.4 … 22.0 16.8 24.3 .

9.4 25.6 7.6 … … 24.7 11.8 ..y … … 7.8.x 20.0.y … 13.7...y 0.1 6.8 … 1.5 .

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5.1 3.8 … … … 1.5 … 3.7 … 4.6 0.0 5.5 11.5 8.3 … … … 4.8 … … 14.9 … 22.1 16.1 27.0 .

9.4 19.3 8.7 … … 24.7 12.2 ..y … … 6.7.x 16.6.y … 13.9...y 0.1 6.5 … 2.2 .

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3.2 2.3 … … … 0.5 … 2.0 … 2.6 0.1 4.7 13.2 7.1 … … … 17.8 … 5.5.y 13.4 … .

13.3 25.5 20.1 1.4 15.1 16.8 1.9...y … 33.6 22.2 ..y … … 5.8.x 16.8.y 17.6 13.2...y 0.0 7.6 … 0.5 .

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1.9 5.0 … … 0.0 0.0 … 1.1 … 1.4 0.1 .

8.1 … … … 16.3 … … 15.1 … .

13.2 24.4 23.2 1.6 16.8 16.1 1.9...y … 33.1 21.8 ..y … … 5.7.x 18.0.y 16.8 11.9...y 0.0 7.8 … … .

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3.1 5.7 … … 0.0 0.0 … 1.4 … 1.7 0.1 .

5.7 … … … 20.0 … … 12.1 … .

13.4 27.0 16.7 1.2 13.5 18.0 1.9...y … 34.1 22.7 ..y … … 5.8.x 15.6.y 18.8 14.5...y 0.0 7.4 … … .

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0.8 3.7 … … 0.0 0.0 … 0.8 … 1.0 0.1 .

13.3 13.2 13.4 .

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16.0 .

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S TAT I S T I C A L TA B L E S Ta b l e 6 REPEATERS, ALL GRADES (%) School year ending in Female Total 1999 Male 9.8 … 12.1 4.3 25.5 23.6 … 22.9 … 27.7 14.4 17.2 4.1 23.2 13.9 12.4 … 29.2 32.6 14.5 .

… … 11.6 19.5 30.9 … 3.1 6.4 .

4.2 0.5 … 8.1 9.5 1.5 0.5 1.7 2.9 0.0 5.3 3.6 7.5 0.5 … 10.6 … 12.2 4.2 26.2 24.0 … 20.3 … 28.3 14.4 17.4 3.8 23.8 12.3 12.2 … 29.1 30.7 14.4 .

… … 10.4 17.1 31.2 … 3.2 6.1 .


3.8 0.5 1.2 6.8 8.0 1.2 0.3 1.7 2.7 0.0 4.7 3.1 6.5 0.4 … 11.9 … 12.3 4.1 27.4 24.5 … 17.9 … 28.9 14.4 17.7 3.5 24.7 10.7 11.8 … 29.0 28.7 14.2 .

… … 9.2 14.5 31.6 … 3.2 5.8 .

3.2 0.5 … 5.5 6.4 1.0 0.2 1.7 2.4 0.0 4.1 2.5 5.4 0.3 … 6.0 … 6.1 5.8.z 8.6 … 5.8...z 18.6 5.7 19.7 20.8 17.0 4.4 5.3 16.4 4.9 2.9...z 14.6 25.9 10.6 .
9.9 … 8.0.y 17.3.z 22.9 13.1...z 4.2 6.9 … 2.9 0.2 0.7 5.3 4.0 0.7 0.2 1.0 1.6 0.0 4.1 2.9 6.4 0.4 4.7 13.1 17.4 17.2 17.7 2006 Male 6.6 … 6.3 6.0.z 8.3 … 6.0...z 21.3 5.6 20.4 21.1 16.7 5.2 5.5 18.6 4.7 2.8...z 14.6 27.4 10.8 .

9.7 … 8.4.y 19.5.z 22.6 13.0...z 4.3 7.2 … 2.7 0.2 1.0 5.5 4.6 0.9 0.2 … 2.2 0.0 5.1 3.2 7.6 0.5 5.5 13.0 Female 5.4 … 5.9 5.7.z 9.0 … 5.6...z 15.8 5.8 19.0 20.4 17.3 3.6 5.1 14.1 5.0 3.0...z 14.6 24.5 10.5 .
10.2 … 7.5.y 15.0.z 23.3 13.3...z 4.2 6.5 … 3.1 0.1 0.3 5.1 3.3 0.4 0.2 … I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Table 7 Internal efficiency: primary education dropout and completion Duration1 of primary education Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female DROPOUT RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China … 6 4 4 4 4 4 5 4 6 4 3 4 6 4 4 5 4 3 4 7 6 6 5 1.9 –.y 1.0 1.5 0.2.y 0.7 3.0 … 5.2...y 0.7 … 4.5 3.8 3.2...y 3.9 0.1.y 1.0 0.5.y 0.4 … … – 11.2...y 7.8...y … 3.7 … 2.5 3.8 3.6 … … 0.3 –.y 2.0 0.7 0.0.y 2.3 0.3 … –.y 1.1...y … – – 1.1 – 1.4 … 13.1 4.3 – 1.2.y 3.2 … 4.9.y 1.4 0.4 – 6.7.y … 12.1 3.8 – 1.2.y 0.6 … 4.8.y 1.7 0.3 – 5.4.y 1.2 0.4.y 2.8 … 2.9...y … – 0.6 … … 17.3 14.0 5.9 4.9 – – 1.4.y … – 5.9 … … 5.7.y 5.0.y 1.0 … 0.4 – 8.5.y 1.7 – 7.9.y 5.3 4.9 … 17.0 5.3 – … – … 6.6.y … 1.9 – 6.7.y 1.2 –.y 4.1 1.9 11.1...y – 0.7 2.2 … 7.6 5.4 0.2 0.9.y 4.9 1.7 –.y 1.5 2.4 9.1...y … 1.5 2.5 … 7.7 5.2 0.3 0.9.y 5.5 0.6 –.y 7.0
 1.3 13.4...y … – 1.8 … 7.6 5.8 0.1 0.9.y 4.3 0.7 –.y 2.7 1.0 –.y 2.7 … – –.y 1.4...y 0.2 … – – 1.6 1.3 … … 8.3 2.7 – –.y 5.5 7.7 2.4 – –.y 6.1 1.4 –.y 2.7 … 3.7...y … – 1.1 … 8.8 3.2 – –.y 5.0 … … … … … … 6.3.y 1.5 0.7 – 8.9.y 3.4.x 0.2 5.6.y 1.7 0.8 – 8.5.y 3.8.x 0.5 … … 1.9 – – 0.7 0.2 0.3 1.0 1.8 – – 0.8 0.2 – 0.9 … … 0.2 … 1.3 1.7 2.0 1.9 6.1.y 4.4 0.2 0.8 13.5.y 6.7.y 4.1 0.2 1.2 15.1.y 5.3.y 4.7 0.3 0.4 11.5.y 3.5.x 0.0 4.1.x 0.2 … … 2.8.x – … 2.0 – 0.5 1.0 1.7 1.4 1.5 2.0 – 0.5 1.1 1.9 1.6 1.8 … … 0.6 … 0.7 3.1 2.7 0.9 3.2 3.4 … … 1.5 … 1.0.y 0.1.y 1.2 1.8 … 1.5.y 0.5.y 1.4 1.1 1.2 – 0.5 0.3 … – – 1.0 0.3 5.5.x 5.6.x – – … … 0.2 0.7 … 0.3 9.4 … … 0.9 9.3 … 2.1 – 0.5 0.8 1.4 1.2 1.2 … … 0.4 3.0 2.0 … … … 1.3 … 0.5.y –.y 1.0 1.3 1.6 … – – 5.7.x – … – … – 9.6 … 0.4 … 0.1.y 0.8.y 0.7 – 0.9 – 0.3 0.5 2.0.x – … 0.7 … 0.6 9.5 … … … 0.2 … 0.0.y 0.7.y 0.7 – 0.2 – 0.2 0.4 1.9.x – … 0.9 … 0.1 9.8 … 7.1.y 1.3 0.6 – 9.4.y 1.9 – – 0.5 0.2 0.8 1.0 … … 0.7 1.4 … … 0.5 … 3.1.x – … 3.3.x 0.1 3.5.x 0.4 … … 1.2 – – 0.6 0.2 0.1 0.8 1.1 – – 0.6 0.3 0.4 0.6 … … 0.3 … 1.0 1.5 1.2 1.6 … … 0.6 … … … 0.6 … 0.8.y 0.9.y – 1.6 – 0.3 0.5 2.0.x – … 0.5 … 1.2 9.2 … … 0.3 – – 0.6 1.7.x 0.6 … 0.4 … – 9.2 … … – – – 1.3 2.2.x – … 0.4 … – 9.8 … – 0.6 – 0.9 … … … … … 3.0.x … … – … … 1.3 … … – – 0.5 … 0.1 … … … – … 1.0 … … … … 0.3 … … … 0.8 … … 1.3 … … … … … … … … 0.5 … … … … 0.5.y … … 1.2.y 1.1.y 0.6.y .
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… … 1.5 … 0.9 … – … … – … … – … 1.2.x … … 1.3 … … … … … 0.5 … 0.6.y 1.0.y 0.3.y 0.9.y 0.7 … … … … … 4.5 –.y … … 8.8...y 5.1 … 18.9 6.6 1.2 … – … … 19.6 7.5 1.9 … – … 5.5.y 5.8.y … 2.3 … … 2.5 … 9.6.y 8.4.y .

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3.3 0.2.y … … 14.6...y … … 2.4 … 20.4 8.7 2.6 … – … 5.0.y … 2.2 … 11.7.y .

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… 17.6 6.6 – … – … 4.6.y … 1.4 – 9.9.y … … … … … – 1.3 … … … … … … … … … … … … 1.6.y … .
… … … … … … … … … – 8.5 … … – 10.1 … … – 10.6 … … – 9.5 … … 1.5 10.8 … … 1.5 11.0 … … 1.5 10.5 … S TAT I S T I C A L TA B L E S Ta b l e 7 PRIMARY EDUCATION COMPLETION SURVIVAL RATE TO GRADE 5 (%) SURVIVAL RATE TO LAST GRADE (%) PRIMARY COHORT COMPLETION RATE (%) School year ending in School year ending in School year ending in 1999 Male Total Female Total 95 97 77 99 66 98 … 91 … 68 82 94 … … … 84 92 92 92 87 … … … … … 98 99 … … … … 99 … … … … … … … … … … … … … … … … … … … … 56 … 94 97 71 99 67 98 … 88 … 70 82 94 … … … 81 92 91 93 … … … … … … 98 99 … … … … … … … … … … … … … … … … … … … … … … … … … 58 … 96 98 85 99 63 97 … 95 … 66 82 94 … … … 88 91 93 92 … … … … … … 99 99 … … … … … … … … … … … … … … … … … … … … … … … … … 54 … 95 99.y 90 … 81...y … 96 91 … 57 80 100 … … … 79.y … 97 99 66.y … … … … … 100 97 … … … … 99 … … … … … … … 97.y … … … 100 … … … … … … … 100 62 … 2005 Male 95 100.y 92 … 87...y … 95 88 … 59 82 100 … … … 78.y … 96 98 67.y … … … … … 100 97 … … … … … … … … … … … … 97.y … … … … … … … … … … … 100 61 … Female Total 1999 Male Female Total 96 98.y 87 … 73...y … 97 94 … 56 79 100 … … … 79.y … 97 100 65.y … … … … … 100 97 … … … … … … … … … … … … 97.y … … … … … … … … … … … 100 64 … 91 92 … 99 49 97 94 91 … 61 75 92 99 … … 77 87 87 90 80 92 99 … 93 100 98 99 97 97 99 … 98 95 96 95 … 97 … 97 … 97 … 97 99 … 90 91 … 99 51 97 93 88 … … 75 92 100 … … 74 87 86 90 … 90 99 … 93 99 98 98 96 97 99 … … … 95 … … 96 … 96 … … … 96 99 … 95.
* 87 97 … 100 … … 49 … 95.

* 85 100 … 100 … … 52 … 93 93 … 99 47 97 95 95 … … 76 92 99 … … 81 87 88 89 … 95 99 … 93 100 99 99 98 97 100 … … … 96 … … 98 … 99 … … … 98 100 … 94.
* 90 94 … 99 … … 45 … 91 99.y … 97 70...y 96 96 87 … 45 74 99 98.y 89 … 74.y 92 94 99 59.y 90.x 99 … 95 100 100 96 98 98 97 … 98 97 94 … … 97 … 98.y 94.y … 99 97 100 100 99 91.x 99 … 99 … 98 55 … 2005 Male 90 100.y … … 78...y … 95 83 … 46 76 100 99.y 89 … 73.y 92 94 98 61.y 89.x 99 … 95 99 100 96 97 98 97 … … 96 93 … … 97 … 98.y 95.y … 100 100 … 100 97 91.x 100 … 98 … 98 54 … Female Total 92 97.y … … 61...y … 97 91 … 43 72 99 97.y 89 … 75.y 93 95 100 57.y 91.x 100 … 95 100 100 97 98 98 97 … … 98 94 … … 98 … 99.y 93.y … 99 94 … 100 100 91.x 97 … 99 … 99 57 … 86.y … … … 68...y … 86 83 … 21.y 60 98.y … … … … … … 98 … … 97 … … … … … … … … … … … … … … … … … … … … 96 84 99 95 … 97.y … … … 83 … … 2005 Male 84.y … … … 75...y … 83 78 … 21.y 63 98.y … … … … … … … … … 96 … … … … … … … … … … … … … … … … … … … … 100 … 99 92 … … … … … 81 … … Female Country or territory 88.y … … … 60...y … 90 88 … 20.y 56 97.y … … … … … … … … … 100 … … … … … … … … … … … … … … … … … … … … 93 … 100 97 … … … … … 84 … … Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Table 7 (continued) Duration1 of primary education Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female DROPOUT RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Cook Islands DPR Korea
 Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam 6 4 6 6 6 6 5 6 6 6 6 5 6 6 6 5 6 6 6 6 6 6 6 6 6 6 6 6 5 … … 7.1 4.1 … 12.0.x 13.0 – –.y … … 11.3 … … … … … 14.4 – … … … … … … 4.2 … … 6.9 4.7 … 11.4.x 12.7 – –.y … … 12.0 … … … … … 15.9 – … … … … … … 4.3 … … … … 2.1 … … … 7.4 3.5 … 12.5.x 13.2 – –.y … … 10.6 … … … … … 12.8 – … … … … … … 4.0 … … … … … – 1.6 … 2.9.x 6.8 – –.y … … 5.9 … … … … … … … – 1.8 … 4.2.x 6.7 – –.y … … 4.9 … … … … … … … – 1.4 … 1.6.x 6.9 – –.y … … 6.9 … … … … … … … 4.3 3.0 … 0.8.x 7.7 – –.y … … 6.7 … … … … … … … 4.6 3.6 … 1.6.x 7.8 – –.y … … 7.3 … … … … … … … 3.9 2.2 … 0.1.x 7.7 – –.y … … 6.2 … … … … … … … 4.6 7.0 … 3.3.x 7.2 … … 4.6 7.4 … 8.7.x 6.5 … … 0.0.y 0.0.y … … … … 7.9 8.1 … … … … … … … … … … … … … … 0.6.x 5.7 5.7 … 0.0.y 4.7 6.7 … –.x 8.0 … … … –.y … … … … 7.7 … … … … … … … … … … … … … 5.7 5.9 … 1.5.x … … … … … … … … … … … 4.9 0.2 5.7 0.2 4.1 0.2 4.0 0.3 5.0 0.3 2.9 0.2 4.0 0.4 5.0 0.4 2.9 0.3 4.7 0.4 6.0 0.4 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 1.6 0.2 3.1 2.8 3.8 1.5 … … … … 1.6 … … … … … … … … 2.0 … … … … – – … … … … 2.3 … 1.3 2.3 0.2 Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama 3 1 8 1.0.y … 3.2.y … –.y … –.y … –.y … 8.2...x 3.7.y 0.9 10.6 1.6 2.5 3.4.y 0.2 8.7 3.2 4.0 1.6 … 7.9...x 8.4.x … 1.4 … 5.6.y 0.4.x 10.9 2.3 1.4 4.6 8.0 13.1 11.2 … 4.9.y 0.4.x 12.0 2.5 1.9 3.5 8.0 13.2 11.7 … 3.1.y – 6.7 4.8 5.6 …
 7.7...x … … 6.3.y 0.5.x 9.5 2.0 1.0 5.6 8.0 12.9 10.7 … 2.2.y 2.7 5.3 1.6 0.7 1.5...x 1.9.y 1.5.x 4.4 1.3 2.1 1.9.y 2.2 3.0 0.5 0.7 1.4 … 1.6...x 2.0.x … 0.7 … 5.7.y 1.3.x 3.4 0.8 1.3 – 7.1 3.5 6.1 … … 8.7 8.5 8.8 6.1 … … 6.9 … 1.8 … … 17.1 4.5 … … 8.0 … 2.1 … … 17.8 4.5 … … 5.7 … 1.6 … … 16.3 4.5 … … 2.7 … 0.9 … … 10.0 2.3 7.5 3.5 6.4 … 5.6 … … 3.5 … 1.0 … … 11.3 2.8 –.y … 1.6.y 1.6 0.7 – 0.7 … 1.6...x … … 9.9.y 1.1.x 2.3 0.3 0.4 … 6.7 3.4 5.7 … 6.6 … … 1.9 … 0.8 … … 8.6 1.8 0.9.y … –.y … 2.2.y 0.7 – 3.3 … 1.9.y – 2.0 0.8 1.4 9.8 … 3.8...x 5.5.x … … … 3.6...x 7.8.y 6.6.y –.x –.x 4.9 4.1 0.2 0.4 – – 3.5 … 8.9 3.7 4.8 10.2 3.8 5.3 … 7.2 … … 2.8 … 1.6 … … 9.1 1.9 … 6.4 … … 3.4 … 1.7 … … 10.2 2.4 1.9.y … 1.6.y … 3.2 1.6 – … 4.0...x … … 5.4.y –.x 3.3 0.6 – … 7.4 3.5 4.3 … 8.1 … … 2.3 … 1.4 … … 7.8 1.3 1.0.y … 2.7.y – 2.0 1.0 0.8 – 2.6...x … … 0.9.y … 3.0.y … 2.3 2.6 1.3 – 2.4...x … … 9.8.y 6.4.y –.x –.x – – 2.3 2.2 – 0.1 1.3 … 9.0 3.8 6.1 9.7 4.2 6.8 … 8.3 … … 3.2 … 1.2 … … 14.4 3.0 … 7.8 … … 3.5 … 1.4 … … 15.4 3.1 … … 5.8 5.6 … –.x … … … … … … … … … … … 3.4 0.4 … … … … … … … … … … … 2.4.y … 4.3 … – … 3.8...x … .
… 0.3.x … 2.9 0.1 … 8.9 2.3 6.1 … 8.0 … … 2.1 … 2.0 … … 4.6 3.2 … … … … … … – … … … 1.3.y … 2.4.y … 1.7 – 0.2 – 2.8...x … … 2.0.y –.x – 2.2 0.3 … 8.2 3.4 5.4 … 8.8 … … 2.9 … 0.9 … … 13.4 2.8 3.7.y 4.2 … … … … … … … … … … … … … … … –.y … … … … … … … … 3.0.y – 4.3 – – – 3.7 … 2.7...x … 1.7...x … .

–.y 0.5.x … .
… 0.7.x … 3.7 0.2 3.3 0.1 3.5 … 10.1 2.2 6.3 11.3 2.1 6.6 … 7.8 … … 2.9 … 2.3 … … 6.1 3.0 … 7.6 … … 3.7 … 2.6 … … 7.7 2.9 S TAT I S T I C A L TA B L E S Ta b l e 7 PRIMARY EDUCATION COMPLETION SURVIVAL RATE TO GRADE 5 (%) SURVIVAL RATE TO LAST GRADE (%) PRIMARY COHORT COMPLETION RATE (%) School year ending in School year ending in School year ending in 1999 Male Total Female Total … … 87 … … … 54 … … … … … … … … … … … 100 94 … … … … … … … 72 83 … … 90 … … … 78 … 82 … … 74 100 67 91 94 91 75 77 65 … 56 95 … … … 89 … … 48 92 … … 89 … … … 55 … … … … … … … … … … … … … 86 … … … 54 … … … … … … … … … … … 100 100 91.
* … … … … … … … 72 80 … … 90 … … … … … 83 … … … 100 64 90 94 … 71 77 64 … 55 … … … … 88 … … 44 92 96.
* … … … … … … … 72 86 … … 90 … … … … … 81 … … … 100 69 93 94 … 79 77 66 … 58 … … … … 90 … … 53 92 … … 86 84 … 82.x 62 … 99.y … … 72 … … … … … 74 99 … … … … … … 92 … … 92 97.y … 90.y 97 85 … 92 90 85...x … … … 99.x 82 94 97 92 68 77 72 … 69 … … 83 … 94 … … 54 88 2005 Male … … 85 83 … 76.x 62 … 99.y … … 71 … … … … … 70 99 … … … … … … 92 … … … 94.y … 88.y … 82 … … … 85...x … … … 99.x 78 93 96 … 66 77 70 … 70 … … 80 … 94 … … 50 87 Female Total 1999 Male Female Total … … 87 86 … 88.x 62 … 100.y … … 72 … … … … … 78 99 … … … … … … 92 … … … 100.y … 91.y … 88 … … … 85...x … … … 99.x 86 95 98 … 71 78 74 … 68 … … 87 … 95 … … 57 89 … … 82 … … … 54 … … … … … … … … … … … 100 92 … … … … … … … 69 83 … … 89 97 … 94 77 … 80 … … … 100 67 88 93 … 71 75 62 … 52 93 … … … 87 … 84 46 90 … … 82 … … … 55 … … … … … … … … … … … … … 82 … … … 54 … … … … … … … … … … … 100 100 91.
* … … … … … … … 67 80 … … 88 99 … 95 77 … 82 … … … 99 64 86 92 … 66 74 63 … 50 … … … … 86 … 78 42 90 94.
* … … … … … … … 71 86 … … 89 95 … 93 76 … 77 … … … 100 69 89 93 … 75 75 62 … 54 … … … … 88 … 91 50 91 … … 81 79 … 81.x 62 … 99.y … … 72 … … … … … 70 99 … … … … … … 91 … … 92 93.y … 87.y 96 81 97 92 86 82...x 80.x … 78.y 98.x 82 90 97 88 61 76 67 … 63 … … 81 … 92 … … 50 85 2005 Male … … 80 78 … 75.x 62 … … … … 71 … … … … … 66 99 … … … … … … 90 … … … … … 85.y … 79 … 91 … 83...x … … … 98.x 78 89 96 … 58 75 65 … 65 … … 77 … 91 … … 46 84 Female Total 2005 Male Female Country or territory … … 82 81 … 89.x 62 … … … … 72 … … … … … 75 99 … … … … … … 92 … … … … … 89.y … 84 … 94 … 81...x … … … 98.x 86 92 98 … 65 76 70 … 62 … … 85 … 93 … … 55 86 … … … … … … 58.y … … … … 70.y … … … … … … … … … … … … … … … … … 88.y … … 95.y … … … … … … … … … 75.y 81 … 83.x … 70...x 63 … 61.y … … … … … … … 47.y 84 … … … … … … 58.y … … … … 68.y … … … … … … … … … … … … … … … … … … … … 93.y … … … … … … … … … 73.y 80 … 83.x … 70...x 60 … 63.y … … … … … … … 44.y … … … … … … … 57.y … … … … 71.y … … … … … … … … … … … … … … … … … Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean … Anguilla … Antigua and Barbuda … Argentina 97.y Aruba … Bahamas … Barbados … Belize … Bermuda … Bolivia … Brazil … British Virgin Islands … Cayman Islands … Chile 77.y Colombia Costa Rica 83 … Cuba 83.x Dominica … Dominican Republic 71...x Ecuador El Salvador 65 … Grenada 59.y Guatemala … Guyana … Haiti … Honduras … Jamaica … Mexico … Montserrat … Netherlands Antilles 51.y Nicaragua … Panama Table 7 (continued) Duration1 of primary education Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female DROPOUT RATES BY GRADE
 IN PRIMARY EDUCATION (%) School year ending in 2005 Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 6 6 7 7 7 6 7 6 6 6 North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo 4.9.y 2.6 … 1.5.y … … –..*,y … 4.2 2.8 5.2.y 2.4 … 1.8.y … … –..*,y … 4.7 3.4 … … 1.4.x 1.7 3.0.x 2.1 … … 0.0 1.0.y 0.0 – 0.8.y 0.4 … – 1.8 – 0.4 – – – … … … – … … –.y … … … … – 2.6 – 1.5 – – – … … … – … … –.y … … … 4.5.y 2.8 … 1.1.y … … –..*,y … 3.7 2.1 … –.x 1.3 … 0.1 1.3.y – … – 0.9 – – – – – … … … – … … –.y … … … 1.7.y 2.5 … 1.1.y … … 2.1.y 2.3 1.2.y 2.7 2.0.y 2.4 2.4.y 2.0 1.5.y 2.8 3.4.y 2.4 4.0.y 2.2 2.8.y 2.7 4.2.y 4.7 4.7.y 4.6 3.6.y 4.9 … –.y … … … … … … … … … … … … 2.3.y 0.2.y 0.1.y 0.3.y 1.2.y 1.8.y 0.6.y 2.0.y 3.3.y 0.7.y … … … … … … … … … … … … … … … … … … … … 3.8..*,y 5.1..*,y 2.4..*,y 3.9..*,y 4.7..*,y 3.1..*,y 1.6..*,y 1.4..*,y 1.9..*,y 4.1..*,y 4.3..*,y 3.8..*,y … … … … … … … … … … … … 0.4 0.9 0.5 1.2 0.3 0.5 0.5 1.7 0.8 2.1 0.2 1.1 1.0 2.2 1.3 2.9 0.6 1.5 0.7 2.1 0.9 2.7 0.5 1.4 … –.x 0.8 … – 4.8.y – … 1.2 – – – – – – … … … – … … –.y … … … … –.x 0.8 … – 4.8.y – … 1.3 – – – – – – … … … – … … –.y … … … … –.x 0.7 … – 4.9.y – … 1.0 – – – – – – … … … – … … –.y … … … – … –.x 0.4 … 0.3 1.6.y – … 0.6 – – – – – – … … … – … … –.y … … … 0.8 … –.x 0.4 … 0.5 1.6.y – … 0.5 – – – – – – … … … – … … –.y … … … – … –.x 0.4 … … … 0.5 … 0.1 0.1 1.6.y –.y – – … … 0.6 … – – – – – – – – – – – 1.8 … … … … … … – … … –.y … … … – … … –.y … … … 1.8 0.0 … … 0.6 … 0.3 –.y – … … – – – – – 2.3 … … … – … … –.y … … … – … … 0.5 … – –.y – … … – – – –
 – 1.3 … … … – … … –.y … … … 1.3 … .
2.8 … – 1.4.y – … .

– – – – … 11.6 … … … – … … –.y … … … – 2.2 3.7 0.7 0.0 2.0 … 14.6.x – 14.0.y 1.8 – 10.8 15.3.y … … 10.0 8.8.y 9.5 2.8 10.2 – 9.3 20.0.y 1.4...y … … 17.6.x – 14.3.y 4.0 … 12.2.

* 15.4.y … … 10.3 9.2.y 9.4 3.3 8.6 – 7.9 18.9.y 1.7...y … … 11.2.x – 13.6.y – … 9.3.

* 15.1.y … … 9.9.x 1.8 6.8.y … – 0.3 4.7.y … 11.4.x 1.8 6.6.y … … 1.1.

* 6.1.y … … … … … … 8.3.x 1.8 7.0.y … … –.


* 2.5.y 5.8.x 3.1 6.0.y … … 5.2.x 3.9 6.2.y … … 6.4.x 2.2 5.9.y … … 7.2.x 1.5 1.9.y … … 5.5.x 2.4 1.4.y … … 1.3 3.8.y 1.5.

* 4.7.y 1.0.

* 2.5.y 2.1 9.2.y 2.8.

* 9.1.y … … … … … … … … 4.4 … … 8.9.x … 0.7 2.4.y … … … … … 1.4.
* … 9.4.y … … … … … … 9.7 8.3.y 9.7 2.2 6.6 2.6.y 5.0 0.1 12.1 … – 11.2 21.6.y 1.2...y … 2.1 5.0 12.2.y 2.2...y … 6.9 2.9.y 4.7 0.6 … 2.6 3.9 11.2.y 2.3...y … … 6.4 2.4.y 5.5 – … 1.6 6.7 13.5.y 2.2...y … … … … … 5.0 –.y 8.1 1.9 … 1.0 14.6 22.4.y 3.2...y … 4.7 –.y 9.0 2.7 … 1.4 14.1 25.3.y 4.1...y … 5.5 –.y 6.9 1.0 … 0.7 15.2 17.8.y 2.3...y … 7.9 8.5.y 5.0 0.3 … 4.4 14.8 19.7.y 7.0...y … … 7.3 10.4.y 5.7 1.2 … 5.7 14.2 18.3.y 6.1...y … … 8.7 6.4.y 4.1 – … 3.0 15.8 21.9.y 8.2...y … .
7.6 3.5.y 9.4 2.8 … 2.9 14.2 17.6.y 7.4...y … … .

3.1 … – 1.5.y – … .

– – – – … 12.7 … … … – … … –.y … … … … … .

2.5 … – 1.3.y – … .

– – – – … 10.3 … … … – … … –.y … … … … … … … … 5.1 3.8 … … … … … … .

5.9 3.8.y 9.9 3.9 … 3.2 12.9 15.9.y 8.8...y … … … … … … … .
10.1 3.1.y 8.8 1.4 … 2.7 16.2 20.3.y 5.9...y … S TAT I S T I C A L TA B L E S Ta b l e 7 PRIMARY EDUCATION COMPLETION SURVIVAL RATE TO GRADE 5 (%) SURVIVAL RATE TO LAST GRADE (%) PRIMARY COHORT COMPLETION RATE (%) School year ending in School year ending in School year ending in Female Total 1999 Male Female Total 1999 Male Total Female Total 78 87 … 90 … … … … … 91 … … … … 96 … … 98 … … … 95 … 97 96 99 … 100 … … … … … … … 94 … 65 90 62 … … 58 … … … … 87 68 … 81 … … 55 … … 76 88 … … … … … … … 88 … … … … 95 … … 98 … … … 94 … … 93 100 … 100 … … … … … … … … … 60 89 63 … … 56 … … … … 84 67 … … … … 58 … … 80 87 … … … … … … … 94 … … … … 97 … … 97 … … … 97 … … 100 99 … 100 … … … … … … … … … 70 92 60 … … 61 … … … … 89 70 … … … … 50 … … 88.y 89 … … … … 91..*,y … 93 92 … … 96 … 99 93.y 99 … … 99 99 99 … 100 100 … … … 100 … … 100...y … … … 97 … 65.x 93 73.y … … 79 70.y … … 72 83.y 72 88 … 92 50 33.y 80...y … 2005 Male 86.y 90 … … … … 90..*,y … 92 90 … … 96 … 98 93.y 99 … … 97 98 97 … 99 99 … … … 100 … … 100...y … … … 96 … 63.x 91 73.y … … 75.
* 68.y … … 72 80.y 71 84 … 89 53 34.y 79...y … 90.y 73 83 89 … … … … … … … … 92..*,y … … … … 95 94 88 … … … … 96 100 100 98 99 … 100 … … 97 89 99 … 100 100 … … … … … … 92 … 65 81 62 … … 58 … … … … 82 61 … 78 … … 47 … … … … 97 … 100 93.y 100 … … 100 100 100 … 100 100 … … … 100 … … 100...y … … … 98 … 67.x 95 73.y … … 83.
* 72.y … … 71 85.y 74 92 … 94 45 32.y 81...y … 71 84 … … … … … … … 84 … … … … 95 100 100 98 99 … … … … … 84 … … 100 100 … … … … … … … … 60 78 63 … … 56 … … … … 79 59 … … … … 50 … … 76 82 … … … … … … … 92 … … … … 97 100 100 97 100 … … … … … 94 … … 100 100 … … … … … … … … 70 86 60 … … 61 … … … … 86 63 … … … … 41 … … 84.y 85 … 96.y … … 84..*,y … 92 90 … 98.x 94 … 99 92.y 99 … 99 98 99 … 100 100 88 … … … 100 … … 100...y … … … 96 … 65.x 84 73.y … … 79 70.y … … 65 75.y 64 78 … 89 39 26.y 72...y … 2005 Male 82.y 86 … 95.y … … 80..*,y … 91 87 … 97.x 93 … 98 92.y 99 … 98 97 98 … 100 99 86 … … … 99 … … 100...y … … … … … 63.x 81 73.y … … 75.
* 68.y … … 67 71.y 63 74 … 86 43 27.y 69...y … Female Total 2005 Male Female Country or territory … 86.y … 84 … … … 97.y … … … … 87.

*,y … … … … … 94 93 … 100.x 94 … 100 92.y 100 … 99 100 100 … 99 100 90 … … … 100 … … 100...y … … … … … 67.x 88 73.y … … 83.
* 72.y … … 63 78.y 66 83 … 92 35 23.y 74...y … … … … … … … … … … … … … … … … … … … … … … … … … … … … 55.x … … … … 38.6...y 48.y … … 36.y 69.y … 36.y … 82.y … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States … … … … … … … … … … … … … … … … … … … … … … … … … … … 52.x … … … … 35.4...y 47.y … … 38.y … … 38.y … … … … … … … 58.x … … … … 42.8...y 51.y … … 34.y … … 32.y … … … … … … South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Table 7 (continued) Duration1 of primary education Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Country or territory 2006 Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female DROPOUT RATES BY GRADE IN PRIMARY EDUCATION (%) School year ending in 2005 Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World2 Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.Europe South and West Asia Sub-Saharan Africa … … … … … … … … … … … … … … … … … … … 6.9 15.7 … … … – … 9.1...y 9.3 … 24.5 22.1 2.9 – 14.4 5.5 16.1 8.7...x 21.0.x 11.6 17.4 … … … 10.0.x 5.5.y 6.5.y 31.6...y 1.2 5.7 … … … … 7.0 15.8 … … … – … 9.9...y 9.8 … 24.7 21.7 2.9 – 13.7 6.4 15.2
 9.1...x 21.4.x 10.7 17.6 … … … 10.6.x 5.7.y 6.0.y 32.8...y 2.3 5.1 … 2.6 0.3 1.0 5.2 2.4 1.0 1.1 5.3 2.5 1.4 2.0 1.2 0.3 … … … 3.1 … … … 3.6 4.1 1.6 … 4.9 – 10.8 9.3 5.2 – 12.2 7.9 … … … 6.8 15.6 … … … – … 8.3...y 8.8 … 24.3 22.4 3.0 – 15.3 4.6 17.4 8.3...x 20.5.x 12.5 17.2 … … … 9.4.x 5.3.y 7.1.y 30.5...y 0.1 6.3 … 2.8 0.0 0.9 5.0 1.6 1.0 … … … … 4.6 … 4.5 – 9.3 11.2 … … … 5.3 12.9 … … … 5.2 … 5.9...y 2.0 … 18.4 6.4 3.5 – 12.9 1.9 18.9 2.7...x 11.7.x 4.2 6.6 … … … 2.9.x 0.6.y 2.5.y 3.9...y 2.1 – … 1.4 0.4 0.1 2.1 1.2 0.5 0.4 … 1.6 … 1.8 1.1 2.0 – 3.2 4.0 … … … 4.8 13.2 … … … 3.9 … 6.6...y 2.9 … 18.4 5.7 2.9 – 12.2 2.3 17.9 3.0...x 11.7.x 7.4 6.2 … … … 3.1.x 0.8.y 1.7.y 4.7...y 2.5 – … … 0.3 … 2.6 1.2 0.5 0.3 … … … 2.1 0.0 … – 4.0 6.1 … … … 5.9 12.5 … … … 6.8 … 5.1...y 0.9 … 18.4 7.0 4.1 – 13.6 1.5 20.3 2.3...x 11.7.x 0.6 7.0 … … … 2.7.x 0.5.y 3.5.y 3.0...y 1.7 – … … 0.4 … 1.6 1.2 0.5 0.4 … … … 1.4 2.3 … – 2.2 1.8 … … … 4.9 8.5 … … … 7.9 … –.y 3.7 … 11.6 14.9 5.1 0.4 9.6 2.3 8.9 7.1...x 10.8.x 3.2 8.7 … … … 1.7.x 4.0.y 6.0.y 7.1...y 1.1 3.0 … … … 3.0 8.7 … … … 7.6 … –.y 4.8 … 11.8 15.3 4.7 0.9 9.0 2.8 8.4 7.4...x 13.4.x 7.7 8.9 … … … 1.1.x 4.8.y 5.2.y 4.5...y 2.4 2.0 … … … 7.2 8.3 … … … 8.2 … –.y 2.5 … 11.3 14.5 5.6 – 10.3 1.8 9.5 6.7...x 8.3.x – 8.6 … … … 2.3.x 3.0.y 7.0.y 9.6...y – 4.0 … … … 1.5 0.4 0.3 2.8 1.1 0.6 0.4 … 2.5 … 2.0 … 2.4 – 3.8 5.1 1.7 0.2 … 3.6 0.7 0.6 0.4 … … … 0.7 … 2.0 – 4.7 4.7 1.4 1.0 … 1.9 1.8 0.5 0.5 … … … 3.2 … 2.8 – 2.5 5.5 … … … 8.2 1.4 … … … 6.4 … 4.0...y 6.3 … 18.4 12.2 5.3 0.2 12.0 1.8 7.3 11.1...x 12.4.x 12.0 4.3 … … … 2.2.x 3.3.y 5.5.y 11.4...y 7.9 2.9 … 2.3 … … 3.3 2.3 … … … 4.0 … 2.1 … 2.6 – 2.1 6.7 … … … 6.8 1.8 … … … 6.4 … 4.2...y 7.8 … 18.6 12.5 5.0 0.3 11.0 2.2 7.0 12.0...x 13.8.x 10.6 4.4 … … … 2.7.x 4.3.y 4.1.y 11.7...y 7.2 2.6 … … … … 4.3 … … … … 5.0 … 2.3 … 2.7 – 2.8 6.6 1.
Duration in this table is defined according to ISCED97 and may differ from that reported nationally.

2.

All regional values shown are medians.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2006.

(y) Data are for the school year ending in 2004.

(x) Data are for the school year ending in 2003.

(*) National estimate.


3 2 2 … … … … … … 10.0 … 0.9 9.2 … … … 6.5 … 3.8...y 4.7 … … … 6.3 … –.y 7.1 … … … … … … … … 9.7 8.7 … … … … … … 5.4 7.5 … … … … 9.7 4.6 … … 18.1 … 11.8 5.6 0.0 13.2 1.4 7.7 9.9...x 11.0.x 13.4 4.1 … … … 1.6.x 2.2.y 7.3.y 11.1...y 8.5 3.2 … … … … 2.2 … … … … 2.9 … 2.0 … 2.6 – 1.4 6.9 … … 15.3 6.3 1.3 21.5 7.8 5.1 13.3...x 23.9.x 5.0 15.2 … … … 2.9.x 5.8.y 4.0.y 14.4...y 1.3 7.9 … 2.3 .

.

… 5.1 .

.
.

… 5.1 .

.

… … … 2.7 … 3.7 – … 9.7 … … 16.0 8.2 1.0 20.9 5.4 7.1 13.7...x 25.9.x 1.3 16.2 … … … 2.2.x 4.5.y 8.7.y 16.0...y 1.2 8.3 … 1.6 .

.

… 2.4 .

.

… … … 3.0 … 2.4 – … 4.6 15.6 7.1 1.2 21.3 6.6 5.9 13.5...x 24.9.x 3.2 15.6 … … … 2.6.x 5.2.y 6.0.y 15.2...y 1.3 8.1 … 2.0 .

.

3.5 3.8 .

.
… … … 2.8 – 3.0 – … 7.1 S TAT I S T I C A L TA B L E S Ta b l e 7 PRIMARY EDUCATION COMPLETION SURVIVAL RATE TO GRADE 5 (%) SURVIVAL RATE TO LAST GRADE (%) PRIMARY COHORT COMPLETION RATE (%) School year ending in School year ending in School year ending in 1999 Male Total Female Total 2005 Male Female Total 1999 Male Female Total 2005 Male Female Total 2005 Male Female Country or territory 69 … … 95 56 … … … … … … 74 … 51 49 78 99 43 92 … … 45 … … … … … 65 80 … … … 81 … … … … … 92 … … … … … 87 … 85 … … … 73 … … 97 55 … … … … … … 67 … 51 55 79 100 47 92 … … … … … … … … 65 72 … … … 83 … … … … … 90 … … … … … … … 86 … … … 65 … … 93 59 … … … … … … 80 … 52 43 77 99 37 93 … … … … … … … … 64 88 … … … 78 … … … … … 93 … … … … … … … 84 … … … … … … 74 64 … … … 81 … … 74 … 36 44 81 99 58 87 56 73...x 46.x 64 65 … … … 82.x 84.y 75.y 49...y 87 89 … … … … 83 … … … … 84 … 90 … 88 … 73 74 … … … 77 64 … … … 83 … … 68 … 35 44 83 98 60 84 58 71...x 43.x 58 65 … … … 82.x 81.y 79.y 49...y 85 92 … … … … 80 … … … … 83 … 88 … 86 … 73 77 … … … 70 65 … … … 78 … … 80 … 37 44 79 100 55 90 54 75...x 49.x 71 65 … … … 83.x 87.y 70.y 49...y 89 87 … … … … 87 … … … … 86 … 91 … 90 … 73 70 62 … … 95 51 … … … … … … 58 … 51 37 66 99 28 82 … … 30 … … 99 … … 57 64 … … … 66 … … 97 98 … 90 97 97 … … … 84 … 81 … … … 67 … … 97 49 … … … … … … 50 … 51 39 67 100 31 79 … … … … … 99 … … 59 62 … … … 70 … … … 98 … 89 96 98 … … … 78 … 83 … … … 56 … … 93 54 … … … … … … 66 … 52 34 63 99 25 84 … … … … … 100 … … 56 66 … … … 62 … … … 99 … 92 98 96 … … … 91 … 80 … … … … … … 74 58 … … … 76 … 84...y 62 … 36 36 73 99 40 77 53 63...x 31.x 61 53 … … … 77.x 71.y 68.y 25...y 83 76 … 88 99 98 81 92 97 99 … 79 … 85 … 84 99 73 67 … … … 77 57 … … … 79 … … 53 … 35 36 75 97 41 73 55 61...x 30.x 54 54 … … … 75.x 66.y 74.y 26...y 81 79 … 88 100 98 79 92 97 99 … 78 … 84 … 82 98 73 71 … … … 70 59 … … … 72 … … 71 … 37 36 70 100 39 80 50 64...x 32.x 69 53 … … … 79.x 75.y 62.y 25...y 85 73 … 89 97 98 83 93 98 98 … 81 … 86 … 86 100 73 62 … … … … … … … … 65.x … 71...y … … 27 … … … … 63...x
 39.y … 13.x … 30 … … … … … 63.y … … … … … … … … … … … … … … … … … … … … … … … … … … … … 69.x … … … … … … … … … 59...x 40.y … 15.x … 24 … … … … … 70.y … … … … … … … … … … … … … … … … … … … … … … … … … … … … 59.x … … … … … … … … … 67...x 36.y … 12.x … 37 … … … … … 55.y … … … … … … … … … … … … … … … … … … … … Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World 2 Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa Table 8 Participation in secondary education 1 TRANSITION FROM PRIMARY TO SECONDARY GENERAL EDUCATION (%) ENROLMENT IN SECONDARY EDUCATION School year ending in 2005 Total Male Female Age group 2006 School-age population2 (000) 2005 Total enrolment School year ending in 1999 2006 Total (000) % F Total (000) 76 96 73 86....x 70...y 96 98 86 … 48 77 98 98 98 … 97 96 88 99 83.y 100...x 100 … 96 100 99 98 99 97 98 … 92 99 98 … … 98 … 100.y 92.y … 100 99 99 100 100 97 98 … 100 … 94 81 74 95 75 83...x 73...y 97 95 83 … 51 78 99 98 96 … 94 95 86 99 83.y 100...x 99 … 96 100 99 … 99 97 98 … … 98 98 … … 97 … 100.y 93.y … 100 100 98 100 100 95 … … 100 … 92 83 79 98 70 89...x 66...y 96 100 88 … 45 77 98 99 100 … 100 97 90 100 82.y 99...x 100 … 96 100 99 … 99 97 99 … … 99 98 … … 98 … 99.y 90.y … 99 98 100 100 100 99 … … 100 … 96 80 12-17 12-17 12-18 12-17 12-17 12-17 11-17 12-17 12-18 12-17 12-17 12-17 10-17 12-17 12-17 12-16 10-17 12-18 11-17 12-17 10-17 10-16 10-17 11-17 11-18 11-18 13-18 11-18 11-18 11-18 … 13-18 11-17 11-18 11-17 11-18 10-18 11-18 11-18 12-16 10-16 10-16 10-16 12-16 11-17 11-17 12-17 11-17 10-16 11-17 12-17 12-18 12- … 3 332 … 2 344 13 746 … 679 183 251 6 851 4 171 398 1 267 370 2 091 832 368 1 209 804 4 528 1 687 47 2 162 … 59 16 7671 1 105 579 235 372 … 63 1 470 229 444 44 … 965 1 030 1 059 202 1 042 364 978 … 700 416 928 116 1 007 255 407 … 3 984 415 2 218 … 737 674 220 219 … … 51 42 47 38 49 49 52 … 42 43 49 50 50 … … 47 49 50 26 48 50 … 48 49 50 50 49 50 49 … 49 50 49 … 49 50 49 48 … 5 214 50.
* 347 929 442 1 966 633 205 769 … 3 411 2 491 34 318 … 49 49 49 50 55 46 … 49 49 51 34 3756...z 74 30 8330...y 1751...z 649 236 366 733 99.

* 2 061 299 686 59 … 1 447 2 465 1 247 298 1 455.z 397.y 879 … 667 396 966 120 949 258 411 … 3 317 382 2 013 11 548 616 640 174 214.z 5388 3 896 356 1 052 314 1 874 719 329 999 … 4 598 2 537 46 825 % F 51...z 50 40 47...y 39...z 49 50 52 53 45.

* … 48 50 49 … 48 48 51 49 32.z 48.y 49 … 48 50 49 49 49 49 49 … 48 50 49 48 49 49 49 48.z 44 48.
* 50 48 50 49 50 52 45 … 49 47 49 43 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova3,4 Romania Russian Federation Serbia3 Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia5 Brunei Darussalam Cambodia Enrolment in private institutions as % of total enrolment Enrolment in technical and vocational education School year ending in 2006 School year ending in 2006 Total (000) 464...z 15 2 2525...y 140...z 32 5 50 … 3.
* 119 .

6 0.6 … 44 114 113 1 10.z 24.y 6 … 203 151 381 19 129 38 38 … 779 38 683 1 958 220 220 60 58.z 1 112 311 2 3 6 106 29 22 25 … 1 075 1 048 3 26 % F 39...z 39 37 44...y 32...z 35 8 40 … 34.

* … .

31 – … 47 42 39 .

6.z 34.y 27 … 39 47 46 34 38 39 35 … 36 43 44 37 47 46 42 43.z 38 34.

* 33 29 31 31 35 46 28 … 49 43.6 40 43 –.z 17 17 5...y ..z 17 29 55 2 17.

* 5 1 4 32.z … 8 4 5 46 2.z 3.y 0.1 … 0.9.z 1 7 2 10 1 0.4 … 3 1 0.7 0.6 0.2 8 1 0.6.z 2 0.4 1 0.4 4 0.8 1 4.z .

… .
27 13 3 S TAT I S T I C A L TA B L E S Ta b l e 8 GROSS ENROLMENT RATIO (GER) IN SECONDARY EDUCATION (%) Lower secondary Upper secondary School year ending in 2006 School year ending in 2006 Total secondary School year ending in 1999 2006 Total Male Female 108...z.z 104 27 98...y 58...z 94 91 88 116 27 69 94 100 101 … 47 92 107 97 51.z 97.y 109 … 91 98 100 110 97 103 100 … 101 93 98 80 97 96 92 94.z 88 93 93 90 93 103 91 94 94 … 97 114 116 54 111...z 104 32 102...y 70...z 94 91 85 117 29 … 96 98 103 … 49 95 107 97 67.z 98.y 111 … 93 97 100 113 98 104 101 … 102 92 99 80 97 97 92 93.z 94 93.
* 93 92 92 102 91 90 99 … 98 114 118 59 105...z 104 22 95...y 45...z 95 92 92 115 26 … 92 102 100 … 45 89 106 96 34.z 97.y 107 … 88 99 100 107 96 101 99 … 100 93 98 80 96 96 92 94.z 83 93.

* 94 88 94 103 91 98 88 … 96 114 114 49 GPI (F/M) 0.95...z 1.00 0.69 0.93...y 0.64...z 1.01 1.01 1.09 0.99 0.88 .… 0.95 1.04 0.97 .… 0.92 0.94 1.00 0.99 0.52.z 0.98.y 0.97 .… 0.95 1.02 1.00 0.95 0.98 0.97 0.98 .… 0.98 1.01 0.98 1.00 0.99 0.99 1.00 1.01.z 0.88 1.00.
* 1.02 0.96 1.03 1.00 1.01 1.09 0.89 .… 0.98 0.99 0.96 0.84 Total Male Female 58...z 100 16 77...y 32...z 78 85 74 77 22.

* 36 83 73 101 … 25 33 70 81 40.z 56.y 71 … 123 85 92 92 94 93 95 … 98 82 77 91 80 92 98 75.z 72 94 81 66 74 71 75 81 55 … 50...z 96 20 79...y 38...z 75 80 70 65 24.

* … 84 68 103 … 24 33 63 78 54.z 58.y 65 … 125 83 91 87 94 91 93 … 99 77 76 94 77 91 98 77.z 80 96.

* 78 68 72 74 74 74 68 … 67...z 104 13 75...y 26...z 80 90 78 91 20.
* … 82 78 100 … 25 33 77 85 25.z 53.y 77 … 122 86 94 96 95 96 98 … 98 87 78 88 82 93 99 72.z 63 91.

* 85 65 76 69 76 88 42 … 115 116 114 223 84 21 234 79 25 211 90 16 GPI (F/M) 1.36...z 1.08 0.63 0.95...y 0.70...z 1.06 1.12 1.12 1.41 0.84.

* .… 0.97 1.16 0.97 .… 1.01 0.99 1.22 1.09 0.46.z 0.93.y 1.19 .… 0.97 1.04 1.03 1.10 1.00 1.06 1.06 .… 0.99 1.13 1.03 0.94 1.07 1.03 1.00 0.95.z 0.79 0.95.
* 1.09 0.95 1.06 0.94 1.03 1.19 0.61 .… 0.98 0.90 1.14 0.65 Total Male Female … 95 14 82 34 89 98 74 … 19 37 75 80 87 … 26 40 72 76 41 71 85 … 91 84 83 93 94 88 95 … 99 83 79 … 93 85 100 82 … 98 91 76 79 92 83 58 74 … 86 … 91 16 86 41 88 98 70 … 21 41 75 79 83 … … 42 72 74 58 72 83 … 92 84 81 91 93 87 95 … 100 84 79 … 93 84 98 83 … … 98 12 79 26 90 99 77 … 16 32 75 82 92 … … 38 73 78 22 70 87 … 90 85 84 95 94 90 96 … 99 82 80 … 94 86 101 81 … 97.

* 100.
* 100.

* … 76 80 92 83 51 80 … 87 … 76 78 92 84 65 68 … 86 GPI (F/M) .… 1.08 0.72 0.92 0.63 1.02 1.02 1.09 .… 0.77 0.79 1.00 1.04 1.11 .… .… 0.91 1.02 1.06 0.37 0.98 1.05 .… 0.98 1.02 1.04 1.04 1.02 1.04 1.01 .… 0.99 0.98 1.01 .… 1.01 1.02 1.03 0.97 .… 1.03.

* .… 1.00 0.98 1.00 1.02 1.27 0.86 .… 0.98 Total Male Female 83...z 102 22 88...y 45...z 89 89 81 94 25.
* 52 89 94 101 … 34 70 85 90 46.z 77.y 96 … 106 91 96 100 96 99 99 … 100 89 86 84 88 94 95 84.z 79 93 90 83 85 93 86 89 83 … 80...z 100 27 91...y 54...z 88 87 78 86 27.

* … 90 91 103 … 34 71 81 89 61.z 78.y 95 … 108 90 95 99 96 98 99 … 100 87 86 85 87 94 95 85.z 86 94.

* 88 85 83 93 86 84 90 … 86...z 104 18 85...y 36...z 90 91 85 101 23.

* … 87 97 100 … 33 68 89 91 30.z 75.y 97 … 104 93 97 101 95 99 99 … 99 91 86 83 89 95 95 83.z 71 93.
* 91 81 86 92 87 95 75 … 102 103 101 157 85 17 158 81 22 157 89 12 1.00 1.09 0.53 150 98 38 154 96 43 146 100 34 GPI (F/M) 1.08...z 1.04 0.67 0.94...y 0.66...z 1.03 1.05 1.10 1.17 0.86.

* .… 0.96 1.06 0.97 .… 0.96 0.95 1.10 1.02 0.49.z 0.96.y 1.02 .… 0.96 1.03 1.01 1.02 0.99 1.00 1.00 .… 0.99 1.04 1.00 0.98 1.03 1.01 1.00 0.98.z 0.83 0.98.
* 1.04 0.96 1.04 0.99 1.01 1.12 0.83 .… 0.98 0.95 1.04 0.79 NET ENROLMENT RATIO (NER) IN SECONDARY EDUCATION (%) Total secondary School year ending in 2006 Total Male Female GPI (F/M) 66.y 93 22...z … 38...z 82 77 73 … 16 … 77 90 91 … … 63 … 79 37...z 65...y 91 26...z … 45...z 81 75 70 … 16 … 77 87 91 … … 64 … 78 48...z Arab States 68...y 1.06...y 1.05 96 0.66...z 17...z .… … 0.70...z 32...z 1.03 83 1.05 78 1.10 76 .… … 0.90 .… 0.99 1.06 0.99 .… .… 0.95 .… 1.02 0.53...z 77 92 90 … … 80 26...z 61 … 15 … 91 90 … 90 90 … 92 90 … 90 86 … 88 88 … 89 87 … 72...y 89 … 74...y 87 … Central and Eastern Europe 73...y 0.97...y 1.02 88 .… … 0.98 1.02 .… 1.02 1.00 .… 1.01 .… 1.02 1.04 0.98 .… .… .… 1.01 0.98.z 0.86 1.01.
* 90 82.z 74 83.

* 91 80.z 64 84.
* 94 83 73 … … … 93 80 74 … … … 94 81 73 … … … 90 81.z 69 84 93 … 92 … 92 … 86 78 79 86 80 82 80 … … 84 79 77 86 80 77 87 … … Central Asia 88 76 81 86 81 87 74 … … 1.04 0.96 1.05 1.00 1.02 1.13 0.84 .… .… East Asia and the Pacific Table 8 (continued) TRANSITION FROM PRIMARY TO SECONDARY GENERAL EDUCATION (%) School year ending in 2005 Total Male Female Age group 2006 School-age population2 (000) 2005 Country or territory China Cook Islands3 DPR Korea Fiji Indonesia Japan Kiribati3 Lao PDR Macao, China Malaysia Marshall Islands3 Micronesia Myanmar Nauru New Zealand Niue3 Palau3 Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau3 Tonga Tuvalu Vanuatu Viet Nam … … … 99 88 … … 77 90 100...y … … 74 … … … … … 99 99 96...x … … … … … 62 … 64 93 Latin America and the Caribbean 98.y … 93.y 99 99 99 86 95 90...x 81.x 94 … 97.y 99 98 98 93 84 78 91 … Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda3 Bolivia Brazil British Virgin Islands3 Cayman Islands6 Chile Colombia Costa Rica Cuba Dominica3 Dominican Republic Ecuador El Salvador Grenada3 Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua 91 … … 71 99...y 94 … … … 3 2 6 … … … 99 88 … … 79 87 100...y … … 76 … … … … … 100 99 95...x … … … … … 62 … 63 … 100.y … 92.y … 99 100 85 … 90...x … 100 … 96.y 99 100 98 … 81 80 91 … 92 … … 68 100...y 95 … … … … … … 100 89 … … 75 93 99...y … … 72 … … … … … 98 99 97...x … … … … … 62 … 65 … 96.y … 94.y … 99 97 88 … 90...x … 89 … 98.y 100 97 99 …
 87 75 92 … 90 … … 74 97...y 93 … … … 12-17 11-17 10-15 12-18 13-18 12-17 12-17 11-16 12-17 12-18 12-17 12-17 10-15 12-17 11-17 11-16 11-17 13-18 12-15 12-17 11-17 12-15 12-18 12-17 12-17 11-15 11-16 12-17 12-18 11-17 12-16 12-16 12-17 12-16 11-16 11-15 11-16 11-17 12-17 11-17 12-16 11-16 12-17 11-16 12-16 12-17 12-16 12-17 12-17 13-18 12-16 13-17 12-16 12-18 12-16 12-16 12-17 12-16 12-17 12-16 134 016 … 2 456 119 25 575 7 456 … 910 47 3 634 … 16 5 503 … 437 … … 808 7 582 3 958 31 … 77 5 802 147 … 15 … 36 … … … 4 138 7 36 20 38 … 1 264 23 439 … … 1 792 5 453 436 991 … 1 150 1 633 820 … 1 513 68 1 512 854 285 12 486 … 17 681 ENROLMENT IN SECONDARY EDUCATION Total enrolment School year ending in 1999 2006 Enrolment in private institutions as % of total enrolment Enrolment in technical and vocational education School year ending in 2006 School year ending in 2006 Total (000) 77 436 2 … 98 … 8 959 9 240 32 2 177 6 … 2 059 … 437 0.3 2 … 5 117 4 368 22 172 17 … … … 15 … 9 7 401 1 … 3 722 6 27 22 22 … 830 24 983 2 2 1 305 3 589 235 740 7 611 904 406 … 435 66 … … 231 8 722 0.3 15 321 % F Total (000) … 101 195 2.z 50 … … 51 … 49 53 40 51 51 50 … 50 … 50 54 49 … 51 48 50 48 41 … … … 50 … 45 47 53 … 51 51 49 51 51 … 48 52 47 48 50 52 51 50 57 55 50 49 … 45 50 … … 50 50 47 54 54 100 16 424 7 561 11.z 395 46 2 489.z 5 15 2 696 0.7 522 0.2.z 2...z … 6 302 3 864 24...z 215 22.z 4 530 75.z 0.2...y 14 … 14.y 9 975 1 … 3 476.z 7 33 21 30 5 1 043 24 863.z 2 3 1 634 4 484 374 928 7 794 1 103 529 14.
*,z 809 71 … 635.z 246.z 10 883 0.3 … 448 % F 48 49.z … 51 49 49 52.z 43 49 51.z 49 … 49 51 50 48.z … … 52 47 51...z 48 43.z 51 49.z 45...y 48 … 45.y 49 52 … 52.z 50 50 50 51 51 48 52.z 53 49 50 52 50 49 50 54 50 50 50.

*,z 48 50 … 56.z 50.z 51 46 … 53 Total (000) 15 306 ..z … 2 2 232 961 –.z 5 2 146.z 0.2 … – … .

.

… … .

494 ..z 26 ..z 703 3.z ..y … … 3.y 500 … … 1 234.z 1 .

.

2 .

.

754.z 0.3 .

395 254 56 271 0.2 35 251 107 0.7.

*,z 239 … … 241.z –.z 1 602 .


… 23 % F 51 ..z … 34 42 43 –.z 35 44 43.z 50 … – … .

.

… … .

46 ..z 36 ..z 45 40.z … … … 30.y 54 … … 54.z 38 .

.

40 .

.

50.z 58 .

47 54 50 44 67 61 51 53 46.

*,z 51 … … 58.z –.z 56 .

… 54 7 14.z … 92...z 44 19 … 1 95 3.z … … .

… 21 … 27...z … 20 32 32...z … … 15 … … … … … 10...z .

… 28.z 92 28 5 71 42 13 12.y 11 30 54 24 10 .

32 23 33 18 … 74 … … 33.z 6.z 15 .
… 26 S TAT I S T I C A L TA B L E S Ta b l e 8 GROSS ENROLMENT RATIO (GER) IN SECONDARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN SECONDARY EDUCATION (%) Lower secondary Upper secondary School year ending in 2006 School year ending in 2006 Total secondary School year ending in 1999 2006 Total Male Female 98 … … 99 78 101 112...z 52 115 90.z 82 100 56 … 104 … 107...z … 86 101 100...z … 46.z 98 68.z … 99 … 46...y … 82 … 102.z 119 95 100 87 91 93 114.z 115 … 99 90 104 96 125 79 77 80 102...z 58 … … 65.z 93.z 112 131 … 73 98 … … 96 77 101 109...z 58 118 89.z 82 100 56 … 104 … … … 83 106 100...z … 49.z 96 67.z … 100 … 46...y … 83 … 99.z 126 95 101 85 93 95 111.z 110 … 101 87 102 98 134 74 78 80 104...z 62 … … 60.z 93.z 109 146 … 71 98 … … 102 79 101 115...z 46 113 91.z 83 99 56 … 103 … … … 90 97 100...z … 44.z 100 69.z … 99 … 47...y … 81 … 104.z 111 95 99 89 89 91 116.z 121 … 98 94 105 95 117 83 76 80 100...z 54 … … 70.z 93.z 114 116 … 76 GPI (F/M) 1.00 .… .… 1.06 1.02 1.00 1.06...z 0.80 0.96 1.02.z 1.01 0.99 0.99 .… 1.00 .… .… .… 1.09 0.91 1.00...z .… 0.89.z 1.04 1.02.z .… 1.00 .… 1.04...y .… 0.98 .… 1.05.z 0.88 0.99 0.99 1.05 0.96 0.96 1.05.z 1.10 .… 0.97 1.08 1.03 0.97 0.87 1.13 0.98 1.01 0.96...z 0.87 .… .… 1.18.z 1.00.z 1.04 0.80 .… 1.07 Total Male Female 55 … … 63 51 102 65...z 35 83 53.z 59 … 35 … 141 … 97...z … 73 93 72...z … 17.z 59 37.z … 81 … 31...y … 84 … 67.z 88 86 105 61 79 77 95.z 93 … 87 66 60 91 78 64 58 48 97.z 46 68 … 93.z 77.z 61 115 … 54 54 … … 57 51 102 57...z 39 80 48.z 59 … 35 … 134 … 92...z … 66 94 66...z … 19.z 55 38.z … 75 … 39...y … 80 … 61.z 80 85 100 59 74 78 87.z 85 … 85 60 56 89 68 57 56 46 89.z 46 68 … 75.z 73.z 61 98 … 47 56 … … 69 50 102 74...z 29 86 58.z 60 … 36 … 148 … 103...z … 80 92 79...z … 14.z 64 37.z … 88 … 23...y … 88 … 73.z 96 88 111 64 85 75 103.z 101 … 89 72 65 94 88 71 60 50 104.z 47 69 … 110.z 81.z 61 136 … 61 GPI (F/M) 1.03 .… .… 1.19 0.97 1.00 1.30...z 0.75 1.07 1.22.z 1.02 .… 1.04 .… 1.11 .… 1.12...z .… 1.22 0.97 1.20...z .… 0.74.z 1.18 0.96.z .… 1.17 .… 0.58...y .…
 1.10 .… 1.21.z 1.20 1.03 1.11 1.09 1.15 0.96 1.19.z 1.18 .… 1.05 1.19 1.15 1.07 1.29 1.25 1.07 1.10 1.17.z 1.01 1.01 .… 1.45.z 1.11.z 1.00 1.39 .… 1.29 Total Male Female 62 60 … 80 … 102 84 33 76 65 72 … 36 … 113 98 101 … 76 100 79 … 25 … … … 102 … 30 62 … … 94 99 79 100 64 … 78 99 99 … 79 70 57 77 90 57 57 52 … 33 82 … … 88 70 … 92 52 … 58 … 76 … 101 77 39 73 63 70 … 36 … 110 93 98 … 72 100 76 … 28 … … … 97 … 32 65 … … 91 96 79 98 62 … 80 94 103 … 78 67 55 75 77 51 56 52 … 36 82 … … 87 69 … 85 47 … 63 … 84 … 102 91 27 79 68 74 … 36 … 115 103 105 … 79 100 84 … 21 … … … 108 … 28 58 … … 97 103 78 103 67 … 75 104 94 … 81 74 60 80 104 63 57 51 … 30 83 … … 88 70 … 99 56 GPI (F/M) .… 1.08 .… 1.11 .… 1.01 1.18 0.69 1.08 1.07 1.06 .… 1.01 .… 1.05 1.10 1.07 .… 1.09 1.01 1.10 .… 0.76 .… .… .… 1.11 .… 0.87 0.90 .… .… 1.07 1.07 0.99 1.05 1.08 .… 0.93 1.11 0.91 .… 1.04 1.11 1.09 1.07 1.35 1.24 1.03 0.98 .… 0.84 1.02 .… .… 1.02 1.01 .… 1.16 1.19 Total Male Female 76 72...z … 84 64 101 88.z 43 98 69.z 66 91 49 46 120 99.z 102...z … 83 98 81...z … 30.z 78 53.z 101...y 94 … 40.y … 83 … 84.z 100 91 102 79 84 82 105.z 107 … 91 82 86 94 106 69 68 65 100.z 53 105 … 76.z 87.z 87 125 … 66 75 71...z … 80 64 101 82.z 49 98 66.z 66 … 49 42 117 96.z … … 79 100 76...z … 32.z 75 53.z 107...y 92 … 43.y … 82 … 80.z 98 90 100 77 82 84 100.z 100 … 90 78 83 93 107 63 67 63 99.z 56 105 … 66.z 86.z 86 127 … 62 76 74...z … 88 64 101 94.z 38 98 72.z 67 … 49 50 123 102.z … … 88 94 86...z … 27.z 82 53.z 94...y 96 … 37.y … 84 … 89.z 102 91 104 81 87 81 111.z 113 … 92 87 89 94 105 75 68 66 102.z 51 104 … 86.z 88.z 88 124 … 70 Total secondary School year ending in 2006 Total Male Female … … … 79 59 99 68...z 35 77 69.z 45 … 46 … … … … … 60 96 66...y … … 71 … … 60 … 38...y … … … … 76 59 99 65...z 38 76 66.z 43 … 46 … … … … … 55 99 62...y … … 68 … … 54 … 41...y … … … … 83 59 99 72...z 32 79 72.z 47 … 46 … … … … … 66 93 70...y … … 75 … … 67 … 35...y … GPI (F/M) .… .… .… 1.10 1.00 1.00 1.11...z 0.86 1.05 1.10.z
 1.08 .… 1.00 .… .… .… .… .… 1.21 0.94 1.14...y .… .… 1.11 .… .… 1.25 .… 0.87...y .… GPI (F/M) 1.01 1.04...z .… 1.10 1.00 1.00 1.14.z 0.78 1.00 1.10.z 1.02 .… 1.00 1.19 1.05 1.07.z .… .… 1.11 0.94 1.13...z .… 0.84.z 1.09 1.00.z 0.88...y 1.04 .… 0.86.y .… 65 … 72 75.z 82...z … … 71 79.z 88...z … … 70 83.z 95...z … … 79...z … 82.z 77 85 89 69 … 81...z … 78.z 74 84 89 67 … Latin America and the Caribbean 0.96...z 83...z .… … 1.10.z 75.z 1.10 70 1.02 83 1.02 88 1.08 64 .… … 0.98 1.11.z 1.16...z .… .… 1.11 .… 1.03 1.10 1.22 1.03 1.05 1.02...z 0.92 .… .… .… 1.05...z 0.99 .… .… 1.16 86 87 77 81 47 52 57 57 53 54 78...z 79...z 40 38 … … … … … … 76...z 78...z 70 71 96.
*,y … … … 88 85 57 58 55 80...z 37 … … … 80...z 70 … … 61 … 68 … 47 43 40 1.02 .… 1.11.z 1.04 1.01 1.04 1.06 1.06 0.96 1.10.z 1.13 .… 1.02 1.11 1.06 1.02 0.98 1.20 1.02 1.04 1.03.z 0.92 0.98 .… 1.30.z 1.03.z 1.02 0.98 .… A N N E X Table 8 (continued) TRANSITION FROM PRIMARY TO SECONDARY GENERAL EDUCATION (%) School year ending in 2005 Total Male Female Age group 2006 School-age population2 (000) 2005 Country or territory Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B R. 94 89.y 95 90...x 71...y 84.y … 93.

*,y 88.y 81 99 92 89.y 97 … 63...y 79.y … 94.

*,y 84.y 76 99 95 89.y 94 … 79...y 88.y … 92.
*,y 92.y 87 99 North America and Western Europe Andorra3 Austria Belgium Canada Cyprus3 Denmark Finland France7 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco6 Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros 96.x … … … 100 100.y 99 … 99 99 100 99.y 73 99 … 94.y … 98...x 100 … … … … 100 … … … 89.x 93 85.y 88 81 77...x 72 98...y … 71 97.y 44 34 33 84 47 51...y 63...y 95.x … … … 100 100.y 98 … 99 100 100 … 73 100 … 93.y … 96...x 99 … … … … … … … … 86.x 92 87.y 93 76 79...x 69 … … 72 97.y 45 37 32 81 44 56...y 70...y 96.x … … … 100 99.y 99 … 99 99 100 … 72 99 … 94.y … 100...x 100 … … … … … … … … 92.x 94 83.y 83 85 74...x 75 … … 70 98.y 43 31 34 87 51 42...y 55...y 12-17 12-17 12-16 12-16 12-16 12-16 12-18 12-16 12-16 12-17 12-16 12-17 10-17 12-17 12-17 12-17 13-18 13-18 11-17 10-18 12-17 13-19 12-16 12-17 11-18 12-18 11-17 11-17 12-17 13-18 12-17 11-18 12-17 13-18 13-19 11-17 12-17 13-18 11-17 13-18 11-17 11-17 13-17 10-16 10-16 10-17 10-16 12-18 13-17 13-19 13-19 12-18 12-17 12-18 12-18 12-18 368 803 2 921 … 16 13 60 121 … 320 2 734 … 768 749 2 592 … 388 387 5 260 8 128 683 31 281 665 4 519 38 38 … 1 204 365 679 … ENROLMENT IN SECONDARY EDUCATION Total enrolment School year ending in 1999 2006 Enrolment in private institutions as % of total enrolment Enrolment in technical and vocational education School year ending in 2006 School year ending in 2006 Total (000) 230 425 2 278 … 12 … … 117 1 284 1 439 … 748 1 033 … 63 422 480 5 955 8 185 771 32 346 569 4 450 33 … 3 1 365 378 848 … 3 299 946 544 5 192 22 445 … 9 912 20 67 090 9 727 15 1 265 … … 300 213 158 173 … 626 … … 123 29 % F 51 50 48 …
 56 … … 52 51 53 54 … 48 51 … 49 50 51 49 48 49 50 50 49 49 50 … 51 48 49 51 … 50 55 47 49 … … 49 44 39 47 51 40 … … 43 31 51 38 … 45 … … 21 44 Total (000) 257 529.z 2 760 5 14 10.z 47 97.
*,z 2...z 323 2 105 % F 51 50.z 50 50 54 55.z 56 50.

*,z 48...z 53 52 4 783 822 2999...y 65 464 433 5 994 8 185 705 34 313 613 4 532 37 38.z 3.y 1 423 412 662 … 3 091 751 584 5 358 24 552 651.z 10 355.y 45 89 462.z 9 942.z 33 1984 8 421 2332...y … 435...z 169.z 320 192 698 61 … 237...z 43...z 50 48 48 48...y 49 49 50 49 48 48 49 51 49 48 50 49.z … 48 49 51 … 50 49 47 49 49 23.z 50.y 48 43.z 47.z 50 45 42 49...y … 35...z 51.z 41 43 44 53 … 25...z 43...z Total (000) 48 47.z 220 .
0.6 0.4.z 20 0.9...z 0.1...z 50 115 0.2 303 335 … 4 124 125 1 165 1 813 125 7 50 124 1 676 12 4.z 0.5.y 657 134 111 … 482 210 182 976 .

9.z 168.y 0.7 742.z 876.z … 22 284 … … 58...z 11.z 23 12 118 2 … 3...z 0.2...z % F 50 47.z 61 .

37 34.z 51 28...z 48...z 46 51 50 44 43 … 17 44 46 42 42 37 41 54 43 40 48 33.z … 46 43 42 … 50 44 40 49 .

10.z 27.y 36 15...z 38.z … 22 39 … … 43...z 38.z 49 49 39 42 … 41...z 7...z 16 21.z 24 3 4 25.z 20 24.

*,z 16...z 11 25 4 10 68 … 14 14 7 25 8 6 6 0 .


5 19 28.z 23.y 83.y 7.z 16 … 28 11 7 25 8 … 96.y 8 … 8...z 12 27.z 32 … … 25...z … 39 11 28 14 … … 41...z S TAT I S T I C A L TA B L E S Ta b l e 8 GROSS ENROLMENT RATIO (GER) IN SECONDARY EDUCATION (%) Lower secondary Upper secondary School year ending in 2006 School year ending in 2006 Total Male Female 84 79.z 109 … 94 90.z 96 79.

*,z 86...z 109 87 83 79.z 107 … 87 83.z 89 78.

*,z 89...z 105 84 86 80.z 112 … 100 96.z 105 80.


*,z 84...z 113 91 GPI (F/M) 1.03 1.01.z 1.05 .… 1.15 1.16.z 1.18 1.03.

*,z 0.95...z 1.08 1.08 Total Male Female 55 53.z 72 … 78 54.z 54 73.

*,z 85...z 93 61 51 52.z 73 … 69 44.z 36 70.

*,z 89...z 82 55 60 55.z 71 … 87 64.z 72 76.

*,z 82...z 104 68 GPI (F/M) 1.17 1.05.z 0.97 .… 1.26 1.46.z 1.97 1.07.
*,z 0.92...z 1.26 1.23 91 103 114 100...y 96 116 102 112 101 101 102 107 76 104 107 104.z … 128 100 114 … 116 104 109 99 100 25.z 60.y 59 71.z 86.z 124 66 42 103...y … 41...z 89.z 19 19 30 99 15.

* 19...z 41...z 92 103 116 101...y 96 115 102 113 101 103 103 105 76 106 107 103.z … 131 100 114 … 116 105 108 99 101 37.z 57.y 60 75.z 90.z 117 70 47 100...y … 51...z 86.z 22 22 33 93 18.
* 28...z 47...z 91 102 111 100...y 97 117 102 112 101 100 101 108 76 102 108 105.z … 125 101 115 … 117 104 110 100 99 13.z 63.y 58 66.z 82.z 132 63 36 106...y … 30...z 92.z 17 17 26 104 12.

* 10...z 35...z 0.99 0.99 0.95 0.99...y 1.00 1.02 1.00 0.99 1.00 0.97 0.98 1.03 1.00 0.97 1.01 1.03.z .… 0.96 1.00 1.01 .… 1.00 0.99 1.03 1.01 0.99 0.35.z 1.10.y 0.97 0.88.z 0.91.z 1.13 0.89 0.76 1.05...y .… 0.58...z 1.07.z 0.75 0.77 0.80 1.11 0.68.
* 0.36...z 0.75...z 72 101 108 134...y 97 123 121 116 100 105 116 119 109 98 88 89.z … 108 126 81 … 123 102 80 97 88 12.z 31.y 29 41.z 77.z … 24 21 73...y … 20...z 58.z 7 7 15 61 … 10...z 27...z 65 104 109 137...y 95 121 117 115 103 106 113 112 109 98 85 92.z … 108 127 74 … 114 103 85 95 88 18.z 32.y 34 46.z 79.z … 26 24 74...y … 27...z 58.z 9 9 17 55 … 15...z 30...z 78 98 106 132...y 98 126 126 117 96 104 120 126 108 98 91 87.z … 108 125 89 … 133 102 75 99 88 5.z 30.y 24 35.z 76.z … 22 19 72...y … 14...z 58.z 5 6 13 67 … 4...z 24...z 1.20 0.94 0.97 0.97...y 1.03 1.04 1.08 1.02 0.94 0.98 1.06 1.13 0.99 1.00 1.08 0.94.z .… 1.01 0.99 1.20 .… 1.17 1.00 0.87 1.04 1.00 0.28.z 0.94.y 0.70 0.75.z 0.96.z .… 0.87 0.80 0.97...y .… 0.52...z 1.00.z 0.61 0.64 0.78 1.22 .… 0.26...z 0.78...z Total secondary School year ending in 1999 2006 Total Male Female GPI (F/M) Total Male Female 67 58 84 … 71 … … 77 … 92 56 … 99 143 … 93 125 121 111 98 90 110 107 90 92 98 … … 124 120 106 … 108 157 94 101 95 … 45 37 44 78 43 34 … … 13 19 74 10 … 25 … … 10 25 65 57 87 … 62 … … 74 … 84 51 … 101 138 … 92 121 116 111 99 89 107 104 90 92 96 … … 126 118 102 … 105 137 98 101 … … 45 41 52 81 42 40 … … 15 26 72 12 … 27 … … 16 28 69 59 81 … 79 … … 81 … 99 62 … 97 148 … 95 128 126 111 97 92 113 111 90 91 99 … … 121 121 110 … 112 177 90 101 … … 45 33 36 75 44 28 … … 11 12 76 7 … 23 … … 4 22 1.07 1.04 0.94 .… 1.29 .… .… 1.10 .… 1.17 1.22 .… 0.96 1.07 .… 1.03 1.06 1.09 1.00 0.98 1.04 1.06 1.06 1.00 0.99 1.04 .… .… 0.96 1.02 1.08 .… 1.07 1.29 0.92 1.00 .… .… 1.01 0.81 0.71 0.93 1.07 0.70 .… .… 0.76 0.47 1.07 0.62 .… 0.83 .… .… 0.26 0.81 70 66.z 94 105 87 75.z 77 76.
*,z 86...z 101 77 67 66.z 93 110 80 67.z 66 75.

*,z 89...z 94 73 73 67.z 96 100 95 83.z 90 78.
*,z 83...z 109 82 85 102 110 117...y 97 120 112 114 101 103 110 112 92 100 96 99.z … 118 113 97 … 119 103 93 98 94 19.z 44.y 49 54.z 81.z 83 43 30 87...y … 32...z 76.z 15 14 24 80 … 15...z 35...z 83 104 111 119...y 96 118 109 114 102 104 108 108 93 101 94 99.z … 119 113 94 … 115 104 95 97 94 28.z 43.y 51 59.z 83.z 80 46 34 86...y … 41...z 75.z 17 16 26 75 … 23...z 40...z 87 100 108 116...y 97 121 114 114 99 102 111 116 92 100 98 100.z … 117 112 102 … 122 103 90 99 94 9.z 45.y 46 49.z 78.z 86 40 26 88...y … 23...z 78.z 12 12 21 86 … 8...z 30...z 1.04 0.96 0.97 0.97...y 1.02 1.03 1.04 1.00 0.98 0.97 1.03 1.07 0.99 0.99 1.04 1.00.z .… 0.98 0.99 1.09 .… 1.06 0.99 0.95 1.03 0.99 0.33.z 1.03.y 0.91 0.82.z 0.94.z 1.07 0.89 0.78 1.02...y .… 0.57...z 1.05.z 0.72 0.74 0.79 1.15 .… 0.33...z 0.76...z NET ENROLMENT RATIO (NER) IN SECONDARY EDUCATION (%) Total secondary School year ending in 2006 Total Male Female 64 57.z 72 65 73...z 64...z 68...z 65...z 70...z … 66 61 56.z 72 70 65...z 57...z 57...z 64...z 72...z … 62 67 59.z 72 61 80...z 71...z 79...z 66...z 69...z … 71 GPI (F/M) 1.11 1.06.z 1.00 0.87 1.24...z 1.23...z 1.38...z 1.04...z 0.96...z .… 1.14 GPI (F/M) 1.09 1.03.z 1.03 0.91 1.19 1.24.z 1.37 1.05.
*,z 0.94...z 1.16 1.12 North America and Western Europe 73 … 75 … 74 … .… 0.96 .… 1.02 1.03 1.00 1.02 .… 1.01 1.02 1.05 1.00 1.01 1.05 1.07...z .… 1.01 1.01 1.10 .… 1.03 1.00 0.96 1.04 1.00 89 … 93 88 96 98 … 92 89 85 88 93 82 84...z … 88 96 78 … 92 99 84 90 88 85 … 95 90 96 100 … 93 91 90 89 94 86 90...z … 89 97 86 … 96 99 80 94 88 South and West Asia … 42.y 39 … 75.z 70 … … 40.y 38 … 79.z 64 … .… 1.04.y 1.01 .… 0.94.z 1.09 .… 0.77 .… 33 … 26 … Sub-Saharan Africa … … … … 60...z 52...z 10 14 … … … … .… .… 1.14...z 0.71 .… .… 1.13 .… .… .… 56 … … … 63 … … … 87 … 94 89 96 99 … 92 90 87 89 94 84 87...z … 88 96 82 … 94 99 82 92 88 … 41.y 38 … 77.z 67 … 30 … … … 56...z 12 … … 59 … … … Table 8 (continued) Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles3 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe I World II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 58...y … … … 83 89 … … … 71 … … 68 … 55 72 57 67 54 75 60 … … 55 50 95.x … … 90.x 88.y 65 43 46 54 … 93 100 99 88 92 98 99 … 91 … 93 94 92 99 87 62 TRANSITION FROM PRIMARY TO SECONDARY GENERAL EDUCATION (%) School year ending in 2005 Total Male Female Age group 2006 School-age population2 (000) 2005 58...y … … … 86 90 … … … 75 … … 68 … 56 74 63 61 52 72 61 … … 53 52 93.x … … 89.x 88.y 68 42 47 49 … 58...y … … … 79 87 … … … 66 … … 68 … 54 71 48 72 56 77 58 … … 56 48 97.x … … 91.x 89.y 61 43 45 60 … 12-18 12-18 12-17 12-18 12-18 13-18 12-18 13-18 12-17 13-19 13-17 12-17 13-17 12-17 11-17 12-17 13-18 11-17 13-17 13-17 13-19 12-17 13-18 13-17 13-19 12-16 12-17 13-17 14-18 13-17 12-18 13-18 14-19 14-18 13- … 738 876 4 886 153 1 014 4 294 5 252 1 377 2 080 ENROLMENT IN SECONDARY EDUCATION Total enrolment School year ending in 1999 2006 Enrolment in private institutions as % of total enrolment Enrolment in technical and vocational education School year ending in 2006 School year ending in 2006 Total (000) … 592 1 235 20 115 1 060 87 50 1 024 172 … 1 822 74 114 … 556 218 104 103 116 105 3 845 105 … 237 8 … … 4 239 62 232 318 271 237 835 % F … 35 34 27 41 40 46 39 44 26 … 49 57 39 … 41 34 49 41 53 38 47 51 … 39 50 … … 53 50 29 40 45 43 47 Total (000) 235...y … … … 228 3 430 … 90 1581 483 … 2 584 94 … 730 565 463 128...z 367 153 217 6 398.z 204...z 8 447 8 240 … 4 593.y 71.z 399...z 760...z … 409...z 831 % F 46...y … … … 38 40 … 47 46 34 … 48 56 … 49 45 38 49...z 42 53 39 45.z 48...z 51 43 50 41 … 52.y 50.z 34...z 44...z … 45...z 48 Total (000) 43...y … … … 2 191 … – 32 4 … 23 2 … 32 .
51 18...z 26 .

6 –.z 73...z 0.1 … .

12 … 276.y ..z 22...z 32...z … 8...z .

% F 48...y … … … 43 44 … – 50 14 … 62 53 … 34 .

41 31...z 31 .

48 –.z 48...z 18 … .

60 … 40.y ..z 18...z 32...z … 8...z .
22...y … … … 5 … … 39.y 13 16 … 6...z 3 … 43 10 24 … 13 5 10 … … –.z 23.z 6 7 … 3.y ..z 28...z 45.y … 4.y … Median Sum Sum % F Sum % F Median Sum % F 92 100 … 93 90 98 99 … … … … … 92 99 90 66 94 99 … 83 93 99 99 … … … … … 92 99 83 57 … … … … … … … … … … … … … … … … … 1.
Refers to lower and upper secondary education (ISCED levels 2 and 3).

2.

Data are for 2005 except for countries with a calendar school year, in which case data are for 2006.

3.

National population data were used to calculate enrolment ratios.

4.

Enrolment and population data exclude Transnistria.

5.

Enrolment data for upper secondary education include adult education (students over age 25), particularly in pre-vocational/vocational programmes, in which males are in the majority.
This explains the high level of GER and the relatively low GPI.
3 3 0 S TAT I S T I C A L TA B L E S Ta b l e 8 GROSS ENROLMENT RATIO (GER) IN SECONDARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN SECONDARY EDUCATION (%) Lower secondary Upper secondary School year ending in 2006 School year ending in 2006 Total secondary School year ending in 1999 2006 Total Male Female Total secondary School year ending in 2006 Total Male Female Total Male Female 57.y … … … 46 39 … 60 69 43 … 89 45 … 32 39 39 99.z 22 74 15 35.z 18...z 70 32 116 46 … 98.y 56.z 54.z 22...z … 47.z 58 78 89 103 75 81 89 95 92 92 89 102 72 103 103 66 38 60.y … … … 56 47 … 62 72 54 … 91 40 … 33 42 48 98.z 25 68 18 38.z 19...z 65 36 111 54 … 96.y 55.z 69.z 24...z … 50.z 59 80 89 104 77 85 90 96 92 92 91 100 71 101 104 70 43 53.y … … … 36 32 … 59 66 31 … 87 51 … 32 36 30 100.z 18 79 12 32.z 17...z 74 28 121 37 … 100.y 56.z 39.z 20...z … 44.z 58 76 88 103 72 77 89 93 92 92 88 104 72 105 103 62 34 GPI (F/M) 0.88.y .… .… .… 0.63 0.67 .… 0.95 0.91 0.58 .… 0.96 1.29 .… 0.96 0.87 0.63 1.02.z 0.72 1.16 0.65 0.84.z 0.89...z 1.13 0.78 1.09 0.69 .… 1.05.y 1.02.z 0.57.z 0.84...z .… 0.87.z 0.99 0.95 0.99 0.99 0.94 0.90 0.98 0.97 1.00 1.00 0.97 1.04 1.02 1.05 0.99 0.89 0.79 Total Male Female 23...y … … … 19 11 … 28 28 23 … 31 24 … 11 17 17 80...z 5 30 5 30.z 10...z 28 12 106 17 … 92.y 33.z 20...z 10...z … 18...z 31 53 88 99 46 54 85 84 58 57 139 74 43 76 98 39 24 27...y … … … 25 13 … 31 31 32 … 32 22 … 12 20 21 81...z 6 29 6 33.z 10...z 28 15 96 20 … 89.y 34.z 31...z 12...z … 21...z 33 54 91 99 48 55 88 87 58 57 143 70 42 71 98 43 27 19...y … … … 14 8 … 25 26 13 … 29 27 … 11 15 12 78...z 4 32 4 26.z 9...z 28 10 116 14 … 96.y 32.z 10...z 8...z … 16...z 28 51 86 99 45 53 83 81 59 58 135 79 44 80 98 34 21 GPI (F/M) 0.69...y .… .… .… 0.54 0.64 .… 0.80 0.82 0.42 .… 0.91 1.22 .… 0.89 0.77 0.56 0.96...z 0.66 1.12 0.61 0.79.z 0.89...z 0.97 0.67 1.21 0.69 .… 1.08.y 0.94.z 0.31...z 0.68...z .… 0.73...z 0.87 … 22 18 33 21 12 49 32 37 14 … 38 31 29 … 36 16 76 5 55 7 23 9 … 15 113 … … 89 45 28 10 6 20 43 Weighted average 0.95 0.94 1.00 0.93
10 6 20 43 Weighted average … 28 24 48 25 15 53 38 41 21 … 39 26 35 … 42 22 76 6 52 9 24 10 … 19 111 … … 83 45 40 12 7 23 46 62 90 100 55 63 88 84 66 65 111 78 53 78 101 51 26 … 15 12 18 17 10 46 25 33 8 … 37 35 23 … 30 11 75 4 58 5 22 9 … 12 115 … … 94 45 16 8 5 18 40 57 91 100 49 57 87 82 64 63 111 83 54 84 100 38 21 GPI (F/M) .… 0.54 0.52 0.37 0.69 0.68 0.86 0.66 0.80 0.37 .… 0.96 1.35 0.65 .… 0.70 0.52 0.98 0.69 1.12 0.60 0.89 0.99 .… 0.64 1.04 .… .… 1.13 1.00 0.40 0.66 0.82 0.77 0.88 0.92 1.01 1.00 0.89 0.89 0.98 0.98 0.96 0.96 0.99 1.07 1.03 1.07 0.99 0.75 0.82 Total Male Female 43...y … … … 31 30 … 45 49 35 … 50 37 … 24 29 28 88...z 16 57 11 32.z 13...z 46 24 112 32 … 95.y 47.z 40...z 18...z … 30...z 40 66 89 101 60 68 88 91 75 75 107 89 57 91 101 51 32 47...y … … … 39 36 … 47 52 45 … 52 33 … 24 32 35 89...z 18 53 14 36.z 14...z 44 27 105 37 … 92.y 47.z 54...z 20...z … 33...z 41 67 90 101 62 70 89 93 75 74 109 86 56 87 101 55 35 39...y … … … 23 24 … 43 46 24 … 49 41 … 23 27 21 88...z 13 61 9 29.z 13...z 47 20 119 26 … 98.y 47.z 27...z 16...z … 27...z 38 64 87 101 58 65 86 90 76 75 104 93 57 94 101 46 28 GPI (F/M) 0.84...y .… .… .… 0.60 0.67 .… 0.90 0.88 0.53 .… 0.93 1.27 .… 0.95 0.84 0.61 0.99...z 0.72 1.15 0.63 0.82.z 0.89...z 1.07 0.76 1.13 0.69 .… 1.07.y 1.00.z 0.51...z 0.81...z .… 0.82...z 0.93 0.95 0.97 1.00 0.94 0.92 0.96 0.96 1.01 1.01 0.96 1.07 1.03 1.07 1.00 0.85 0.80 6.
Enrolment ratios were not calculated due to lack of United Nations population data by age.

7.

Data include French overseas departments and territories (DOM-TOM).

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2006 for transition rates, and the school year ending in 2007 for enrolment and enrolment ratios.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(x) Data are for the school year ending in 2003.


(*) National estimate.
… … … … 25 24 … 38 45 28 … 42 24 … 17 24 … 82...z 4 35 9 26.z … 33.z 20 94 23 … … 32.z … 16 … 28...z 37 58 82 91 53 59 81 83 69 69 66 70 40 71 91 45 25 GPI (F/M) .… .… .… .… 0.67 0.64 .… 0.94 0.91 0.57 .… 0.97 1.55 .… 1.04 0.93 .… 1.02...z 0.89 1.31 0.63 0.84.z .… 1.11.z 0.76 .… 0.71 .… .… 1.21.z .… 0.91 .… 0.80...z … … … … 30 29 … 40 47 35 … 43 19 … 17 25 … 81...z 4 30 12 28.z … 31.z 23 … 27 … … 29.z … 17 … 31...z 38 … … … … 20 19 … 37 43 20 … 42 29 … 18 23 … 82...z 4 40 7 23.z … 34.z 18 … 19 … … 35.z … 15 … 25...z 36 Weighted average I 0.98 1.01 0.95 0.94 0.97 0.97 1.01 1.02 0.99 1.07 1.07 1.07 1.01 0.86 0.83 II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI Table 9A Participation in tertiary education ENROLMENT IN TERTIARY EDUCATION Total students enrolled (000) School year ending in Gross enrolment ratio (GER) (%) School year ending in 1999 2006 1999 2006 Country or territory Total (000) % F Total (000) % F Total Male Female Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova2,3 Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan 456 11 0.2 2447 272 … 32 113 308 13 273 … 66 9 350 201 … 157 40 164 39 387 … 270 96 231 49 279 82 107 … 1 399 104 408 … … 123 79 35 1 465 1 737 61 108 130 324 131 65 76 … 296 … 60 51
 … 34 … 68 50 49 … 42 … 46 72 57 47 … 48 67 21 60 56 … 59 53 50 58 54 62 60 … 57 56 51 … … 52 56 55 40 53 54 39 52 53 51 65 25 … 45 818 18 2 2594...z 425...z 220 38 173 … 10 385 68 169 10 615 … … 325 … 209 53.y 544 … 243 137 338 68 439 131 199 … 2 146 144 835 9 167 … 198 115 49.z 2 343 2 740 99 132 145 773 233 138 133 … 289 East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands 846 3.7 … 6 366 .
54 66 … … .

1 040 5 76 23 361 .

3 3 2 55 68 40 … 36...z 52 65 53 … 26 45 50 54 68 59 … … 58 … 26 62.y 57 … 53 54 54 62 58 63 60 … 57 57 55 57 … 58 58 57.z 42 54.

* 55 47 52 58 56 61 27 … 41 55 66 33 47 .

14 22 0.3 … 16 0.3 … 28 0.3 37 11 … 23 33 50 5 9 … 25 23 20 6 … 17 18 10 15 51 … 45 31 26 50 33 50 44 … 45 33 22 … … 26 53 22 22 47 24 15 36 24 29 26 14 … 13 65 12 … 6 .
… 15 … 14 33 51 … 11 … 26 11 16 6 … 17 10 16 12 44 … 36 28 26 42 30 38 35 … 38 29 21 … … 25 45 19 25 44 22 19 35 23 28 18 20 … 14 59 8 … … .

… 8 … 33 33 50 … 8 … 23 41 24 6 … 17 29 4 17 58 … 55 33 27 59 37 63 53 … 52 37 23 … … 28 61 24 17 50 25 12 37 26 30 34 7 … 12 72 16 … … .
GPI (F/M) .… 1.76 1.05 .… 0.54 .… 2.40 1.00 0.98 .… 0.71 .… 0.89 3.82 1.50 0.92 .… 0.97 2.97 0.28 1.43 1.30 .… 1.54 1.16 1.03 1.40 1.24 1.65 1.53 .… 1.38 1.29 1.09 .… .… 1.11 1.36 1.28 0.68 1.15 1.11 0.64 1.07 1.15 1.04 1.88 0.35 .… 0.82 1.22 1.98 .… .… .

Total Male Female 22 32 2 35...z 16...z 39 18 48 … 4 12 25 48 19 29 … … 31 … 9 19.y 66 … 46 44 50 65 69 74 76 … 66 39 52 72 … 45 83 30.z 35 73 32 15 38 51 43 47 19 … 10 73 15 5 22 .
19 19 3 … 20...z 37 11 45 … 5 13 25 44 10 23 … … 26 … 14 15.y 56 … 41 40 45 49 56 53 60 … 55 33 46 61 … 38 68 25.z 39 65.

* 29 15 36 42 38 37 27 … 11 64 10 6 22 .

24 47 2 … 12...z 41 26 51 … 2 11 26 53 33 35 … … 37 … 5 23.y 76 … 50 49 55 82 82 95 93 … 77 46 59 83 … 53 99 35.z 30 81.

* 34 14 41 61 48 58 10 … 8 82 20 3 21 .


GPI (F/M) 1.26 2.46 0.68 .… 0.59...z 1.11 2.32 1.16 .… 0.36 0.81 1.04 1.22 3.41 1.50 .… .… 1.42 .… 0.37 1.60.y 1.37 .… 1.21 1.23 1.22 1.67 1.47 1.80 1.56 .… 1.40 1.38 1.30 1.36 .… 1.42 1.46 1.38.z 0.75 1.23.

* 1.18 0.94 1.13 1.44 1.27 1.57 0.37 .… 0.71 1.28 1.99 0.50 0.98 .
S TAT I S T I C A L TA B L E S Ta b l e 9 A DISTRIBUTION OF STUDENTS BY ISCED LEVEL (%) FOREIGN STUDENTS Total students Percentage of females at each level School year ending in School year ending in School year ending in 2006 2006 1999 2006 Level 5A Level 5B Level 6 Level 5A Level 5B Level 6 Total (000) % F Total (000) % F Country or territory 82 92 68 … 78...z 87 97 84 … 97 75 80 90 97...z 84 … … … … … 99.y 71 … 88 66 84 63 92 85 70 … 97 89 94 77 … 93 54 94.z 69 80 98 99 99 99 99 95 99 … 99 81 62 100 … .
13 8 32 … 17...z 12 .

15 … 3 19 20 10 3...z 14 … … … … … 1.y 28 … 10 33 9 34 6 14 29 … 1 10 3 21 … 1 45 6.z 29 18 .

.

.

.

.

3 .

… .

15 38 46 .

.

5 0 .

… 5...z 1 3 1 … .

6 1 .

1.z 2 … … … … … ./.1,y 1 … 2 1 7 3 2 1 1 … 2 1 3 2 … 5 1 –.z 1 1 2 1 1 1 1 1 1 … 1 4 0 – … .

58 70 37 … 39...z 51 66 54 … 26 46 51 54 … 65 … … … … … 62.y 58 … 54 55 53 62 58 64 60 … 57 58 56 58 … 58 62 57.z 43 55.

* 55 48 52 58 56 61 27 … 41 55 69 33 … .

36 51 46 … 22...z 61 .


49 … 12 47 46 47 … 21 … … … … … 73.y 54 … 54 52 68 62 66 60 59 … 80 56 58 53 … 67 54 50.z 41 52.

* .

.

.

.

.

66 .

… .

53 62 49 .

.

45 – .

… 35...z 31 51 38 … .

33 25 .

… 40 … … … … … ./.1,y 54 … 50 47 38 53 47 60 57 … 49 63 48 43 … 43 46 –.z 39 54.

* 37 27 63 66 60 58 36 … 47 50 19 – … .


… … – … … … … 16 … … 4 … 3 … 6 … … 3.j … … 0.8 3 … 8 0.5.j 5 0.8 9.j 2.j 0.5 … 6.j 2 13 … … … 0.7 0.3 18.v 18 … 2 0.3 8 1 0.3 5 … … … … – … … … … … … … 16 … 29 … 25 … … … … … 27 … … 42 … 41 58 54 … 22 … 48 … 40 … … … 40 43 28 … … 35 … … 51 50 25 … … 117 0.1 0.02 … .

49 53 25 … .

6 0.7 – … 4.y 22 … 17 … 0.2...y 6 0.2 – 2.z 13.z … … 2.y … … 0.5.y 4 … 9 3 21 1 14 2.z 0.9.z … 11 2 9 77 … 2 1 0.3.z 19 27 4 3 0.1 12 27 1 1 … 0.1 207.z 0.2 0.1 36 .
… 49 – … 19.y 28 … 54 … … 26 … – 61.z 33.z … … … … … 25.y … … 41 … 51 56 48 … 48.z … 52 35 … … … 46 54 49.z 32 … 42 20 … … 62 50 31 … … 46.z 54 10 45 .
Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova 2,3 Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands Table 9A (continued) Country or territory DPR Korea Fiji Indonesia Japan Kiribati Lao RDP Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands2 Cayman Islands4 Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru 3 3 4 ENROLMENT IN TERTIARY EDUCATION Total students enrolled (000) School year ending in Gross enrolment ratio (GER) (%) School year ending in 1999 2006 1999 2006 Total (000) … … … 3 941 .
12 7 473 … 2 335 .

167 .

… 10 2 209 2 636 1.9 … 1 814 … .

.

0.4 .

0.6 810 .

.

1 601 1.4 … 7 … … 253 2 457 0.9 0.4 451 878 59 153 .

… … 118 .

… … … 85 … 1 838 .

2 … 109 66 … % F … … … 45 .

32 46 50 … … 61 .

59 .

… 35 55 35 47 … … 53 … .

55 … … 43 .

.

62 54 … 69 … … … … 70 74 47 52 53 53 .

… … 55 .

… … … 56 … … .

53 … 61 57 … Total (000) … 13...z 3 657 4 085 .

57 23 697.z … … … .

.

238 … … 2 484 3 204 … … .

2 339 … .

0.7...y .


1...y 1 355.z 0.05 ..y 2 083.z 2 … … 0.7.y 0.9 346...y 4 572.z 1...z 0.6 661 1 315 111...z 682 ..z 294...y … 125 ..z 112.

* 7 … 123...y … 2 447 .

… … 131 156...z 952 % F … 53...z … 46 .

40 46 56.z … … … .

.

59 … … 54 37 … … .

51 … .

60...y .

36...y 41.z 83 ..y 59.z 60 … … 70.y 71 … 56.z 69...z 72 49 51 54...z 61 ..z 61...y … 55 ..z 46.

* 69 … 59...y … 50 .

… … 61 52...z 51 Total Male Female … … … 45 .

2 28 23 … 14 7 .

64 .

… 2 29 66 11 … .


33 … .

3 .

4 11 .

.

49 27 … 33 … … 33 14 60 … 38 22 16 21 .

… … 18 .

… … … 14 … 18 … 19 … 41 13 … … … … 49 .

3 32 23 … … 6 .

52 .

… 3 25 83 11 … .

31 … .

.

3 … 12 .

.

37 25 … 20 … … … 13 36 … 39 21 15 19 .

… … 16 .

… … … 13 … 19 … 18 … 31 11 … … … … 41 .

2 24 23 … … 9 .

77 .

… 1 32 47 12 … .

36 … .

4 .

… 9 .

.

60 29 … 45 … … … 16 86 … 36 23 17 22 .

… … 20 .

… … … 16 … 17 … 20 … 50 15 … GPI (F/M) .… .… .… 0.85 .

0.49 0.76 1.02 .… .… 1.61 .


1.46 .

.… 0.55 1.26 0.57 1.04 .… .

1.16 .… .

1.29 .

.… 0.76 .

.

1.63 1.19 .… 2.28 .… .… .… 1.26 2.40 .… 0.91 1.11 1.17 1.19 .

.… .… 1.24 .

.… .… .… 1.24 .… 0.91 .… 1.11 .… 1.59 1.38 .… Total Male Female … 15...z 17 57 .

9 57 29.z … … … .

.

80 … … 28 93 … … .

46 … .

6...y .

5...y … 5 ..y 64.z 32 … … 3.y … 41...y 25.z 75...z … 47 31 25...z 88 ..z 35...y … 21 ..z 9.

* 12 … 17...y … 26 .

… … 45 26...z 35 … 14...z … 61 .

11 64 25.z … … … .

64 .

… … 25 111 … … .


44 … .

5...y .

6...y … 2 ..y 52.z 25 … … 2.y … … 22.z 46...z … 47 30 23...z 67 ..z 27...y … 19 ..z 10.

* 7 … 14...y … 27 .

… … 35 24...z 34 … 17...z … 54 .

7 51 32.z … … … .

96 .

… … 32 72 … … .

47 … .

8...y .

4...y … 8 ..y 76.z 39 … … 4.y … … 29.z 106...z … 46 32 28...z 110 ..z 42...y … 23 ..z 8.

* 16 … 20...y … 25 .

… … 56 27...z 36 GPI (F/M) .… 1.20...z .… 0.88 .

0.68 0.81 1.29.z .… .… .… .

1.51 .

.… .… 1.24 0.65 .… .… .

1.07 .… .

1.68...y .


0.59...y .… 4.86 ..y 1.45.z 1.56 .… .… 2.43.y .… .… 1.30.z 2.28...z .… 1.00 1.09 1.26.z 1.65 ..z 1.59...y .… 1.21 ..z 0.82 2.17 .… 1.41...y .… 0.93 .
.… .… 1.61 1.13...z 1.06 S TAT I S T I C A L TA B L E S Ta b l e 9 A DISTRIBUTION OF STUDENTS BY ISCED LEVEL (%) FOREIGN STUDENTS Total students Percentage of females at each level School year ending in School year ending in School year ending in 2006 2006 1999 2006 Level 5A Level 5B Level 6 Level 5A Level 5B Level 6 … 86...z 78 74 .

47 85 59.z … … … .

71 .

… … 89 62 … … .

83 … .

30...y .

… 67.z 72 ..y 74.z 31 … … 100.y .

… 93.z 67...z 11 66 72 … 99 ..z 91...y … 87 ..z 96.

* 43 … 91...y … 96 .


… … 91 90...z 60 … 12...z … 24 .

53 13 40.z … … … .

.

27 … … 10 37 … … .

17 … .

42...y .

… 30.z 28 ..y 26.z 69 … … ..y 100 … 5.z 33...z 89 33 27 … .

..z 8...y … 13 ..z 4.

* 57 … 9...y … 3 .

… … 9 10...z 40 … 1...z … 2 .

.

2 1.z … … … .

2 .

… … 0 1 … … .

0 … .

28...y .

… 3.z .

..y 0.z .

… … ..y .

… 3.z ..z .

0 0 … 1 ..z 1...y … 0 ..z ..* .

… 0...y … 1 .

… … 0 … … … 52...z 47 41 .

40 44 59.z … … … .

59 .

… … 55 37 … … .

52 … .

34...y .


… 47.z 82 ..y 55.z 74 … … 70.y .

… 57.z 75...z 90 52 53 … 61 ..z 65...y … 55 ..z 45.

* 67 … 58...y … 51 .

… … 61 51.z 47 … 63...z … 61 .

40 62 52.z … … … .

.

59 … … 53 38 … … .

48 … .

95...y .

… 29.z 85 ..y 69.z 54 … … ..y 71 … 36.z 56...z 69 44 47 … .

..z 25...y … 53 ..z 70.

* 70 … 67...y … 43 .

… … 58 66.z 57 … 43...z … 30 .

.

25 38.z … … … .

.

51 … … 61 34 … … .

54 … .

36...y .

… 28.z .

..y 56.z .

… … ..y .

… 55.z ..z .


41 34 … 43 ..z 40...y … 10 ..z ..* .

… 33...y … 41 .

… … 63 … … Total (000) … … 0.3 57 .

0.1 … 4 … … … .

7 .

… 0.3 4 3 0.1 … .

2.j … .

… .

… 0.5 .

.

… … … … … … … … .

… 2 … … … .

… … … … … … … … 0.6 2 .

… … … … … % F … … … 43 .

14 … … … … … .

.

51 … 32 … 38 39 … .

55 … … .

.

… 15 .

.

… … … … … … … … .

… … … … … .

… … … … … … … … … … … … … … … .

Total (000) … 4.y 0.4.y 130 ..y 0.2 12 40.z … … … ..y 41.z ..y … … 5.z 22 … … … … … … ..z .
… 2.z – … … 0.2 … … –.y … … 1.y ..y 0.2 2.z … 1 15 ..y … … 0.6 ..y … 0.04.z … … … … .

… … … … … % F … 53...y … 49 ..y 28 34 … … … … ..y 50.z ..y … … … 47 … … … … … … ..z .

… 21.z – … … 59 … … –.y … … … ..y 71 … … … … ..y … … 47 ..y … 51.z … … … … .
… … … … … Country or territory DPR Korea Fiji Indonesia Japan Kiribati Lao RDP Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands 2 Cayman Islands 4 Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru 3 3 5 A N N E X Table 9A (continued) 9 0 0 2 t r o p e R ENROLMENT IN TERTIARY EDUCATION Total students enrolled (000) School year ending in Gross enrolment ratio (GER) (%) School year ending in 1999 2006 1999 2006 Country or territory Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of Total (000) .

.

… … 7.6 .
91 … North America and Western Europe Andorra2 Austria Belgium Canada Cyprus2 Denmark Finland France5 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo 3 3 6 … 253 352 1 221 11 190 263 2 012 … 388 8 151 247 1 797 2.7 6 .
470 187 357 … 1 787 335 156 2 081 13 769 … 709 1.5 … 1 308 .

… … … 8 19 5.5 10 5 67 0.7 6 … 0.6 11 97 60 % F .

… .

… 57 .

63 … … 50 53 56 56 56 54 54 … 50 62 54 58 55 52 51 .

49 57 56 .

53 58 42 53 56 … 32 36 … 43 .

… … … 39 20 44 23 30 … … 16 … 43 21 26 … Total (000) % F Total Male Female ..z 1.6 ..z … 17...z ..z 113 1 381.

* 0.4 253 394 1327...y 21 229 309 2 201 … 653 16 186 310 2 029 3 9.z .
580 215 367 … 1 789 423 205 2 336 17 487 28.y 912.z 4 12 853 2 399 – 147.y 820 … 48.z 43 11.z 30 17 120 4.6 4 10...z 1.8...y … … … 85 ..z ..z … 56...z ..z 62 … 53 54 55 56...y 51 57 54 55 … 51 64 55 55 57 52 56.z .

51 60 55 … 54 60 47 57 57 20.y 33.z 33 40 52 – 28.y 45 … … … 50.z 31 31 42 52 22 13...z 43...y … … … .

… .

… 6 .

34 … … 54 57 60 21 56 82 52 … 47 40 46 48 47 11 20 .

49 66 45 … 57 64 36 60 73 … 5 3 … 19 .

… … … 0.6 3 3 0.9 1.0 5 2 2 … 1.0 4 6 1 .

… .

… 5 .
… .

… 5 .

25 … … 52 53 52 19 48 74 47 … 45 30 42 40 41 10 18 .

49 55 39 … 52 53 41 55 63 … 7 3 … 21 .

… … … 0.7 5 3 1.4 1.4 … … 3 … 1.1 6 9 … .

… .

… 7 .

44 … … 55 61 69 23 64 91 58 … 49 50 50 57 53 11 21 .

50 77 51 … 62 75 30 64 83 … 4 2 … 17 .

… … … 0.5 1 3 0.4 0.6 … … 0.6 … 0.9 1 3 … GPI (F/M) .

.… .

.… 1.38 .

1.76 .… .… 1.05 1.15 1.34 1.25 1.33 1.23 1.24 .… 1.11 1.69 1.20 1.44 1.28 1.10 1.13 .

1.01 1.40 1.30 .… 1.18 1.41 0.73 1.16 1.31 .… 0.51 0.58 .… 0.80 .
.… .… .… 0.63 0.25 0.79 0.30 0.41 .… .… 0.18 .… 0.75 0.26 0.36 .… Total Male Female 10 ..z ..z … 11...z ..z 46 52.

* 10 50 63 62...y 33 80 93 56 … 95 73 59 58 67 10 32.z .

60 78 55 … 67 79 46 59 82 1.y 6.z 6 12 27 – 6.y 5 … 3.z 5 5.z 2 2 7 8 1 1...z 2...y … … … ..z 3 ..z … 10...z ..z 35 … 9 45 56 53...y 33 67 84 50 … 89 51 52 51 56 10 27.z .
58 61 48 … 61 62 48 50 68 2.y 8.z 7 14 25 – 8.y 5 … … … 5.z 3 3 8 8 2 2...z 3...y … … … ..z 16 ..z … 13...z ..z 58 … 11 55 70 72...y 34 93 103 63 … 101 96 66 65 78 11 36.z .

62 94 61 … 74 96 43 69 96 1.y 4.z 4 10 28 – 3.y 4 … … … 5.z 1 1 6 8 0.5 0.3...z 2...y … … … GPI (F/M) ..z 5.46 ..z .… 1.28...z ..z 1.68 .… 1.25 1.21 1.25 1.36...y 1.05 1.39 1.22 1.27 .… 1.13 1.87 1.27 1.29 1.38 1.12 1.35.z .
1.08 1.54 1.28 .… 1.23 1.55 0.90 1.40 1.41 0.28.y 0.53.z 0.59 0.72 1.11 – 0.40.y 0.85 .… .… .… 1.00.z 0.46 0.43 0.72 1.09 0.28 0.14...z 0.77...y .… .… .… DISTRIBUTION OF STUDENTS BY ISCED LEVEL (%) FOREIGN STUDENTS Total students Percentage of females at each level School year ending in School year ending in School year ending in 2006 2006 1999 2006 Level 5A Level 5B Level 6 Level 5A Level 5B Level 6 ..z 75 ..z … 51...z ..z … 64.

* 40 84 46 73...y 22 85 93 72 … 59 97 68 79 97 68 85.z .
99 97 93 … 82 90 74 74 77 … 91.z 100 100 69 – 99...y 94 … 100.z … 94.z 70 35 87 100 77 … 68...y … … … ..z 25 ..z … 34...z ..z … 36.

* 60 9 52 24...y 76 12 0 24 … 37 2 29 18 1 … 14.z .

.

1 1 … 13 5 17 22 21 … 9.z – – 30 – ..y 5 … ..z … 6.z 30 64 12 .

23 … 32...y … … … ..z .

..z … … ..z 0 … .

7 2 3...y 1 2 7 3 … 3 1 3 3 2 … 1.z .

1 2 6 … 4 5 8 4 2 … 0.z .

0 1 – 1...y 1 … –.z … –.z – 0 2 – .

… ..y … … … ..z 91 ..z … 60...z ..z … … 59 53 51 58.y 73 59 54 56 48 53 65 58 55 57 … 56.z .
51 60 55 … 54 61 49 55 57 … 35.z 33 40 56 – 28...y 45 … … … 52.z 33 39 … 52 20 … 39...y … … … ..z 64 ..z … 48...z ..z … … 49 68 58 52...y 44 46 16 56 61 48 39 49 55 60 … 57.z .

.

57 57 … 51 50 42 66 60 … 20.z – – 43 – ..y 45 … … … 16.z 27 26 … .

30 … 52...y … … … ..z .

..z … … ..z 40 … .

46 41 46.y 49 46 52 46 … 44 58 48 52 52 … 30.z .

41 46 56 … 51 49 40 45 52 … 28.z .

40 28 – 23...y 27 … … … –.z – 19 … – .

… ..y … … … Total (000) .

.

… … 1 .


0.9 … … 30 36 115 2 12 5 131.± 178 … 0.2 7.eo … 23 0.7.j 0.3.j .

14 9 … … 33 24 25 233 452 … … … … … .

… … … … … … … 0.1 … … … … … … … .

% F .

… .

… 46 .

… … … 49 48 … 39 61 41 … 46 … 72 51 … 50 … 53 .

46 53 … … 51 45 44 47 42 … … … … … .

… … … … … … … … … … … … .


… … … Total (000) ..y 0.1 ..z … 1.0.y ..y … 2.y … 39 25 … 5 12 12 248 260.z 17 0.7 13 … 49 1 0.6.z ..y 27 14 17 … 18 21 28 330 585 … 1.z – 8.y 3 … … … … … … 0.7.z 0.9.z … 2 … 0.5 … … 0.1.y … … 24 59 43 49 50.z … 64 51 … 58 … 57.z ..y 56 58 49 … 54 48 47 48 … … … – … 28 … … … … … … … 38.z … … … 9 … … … … … S TAT I S T I C A L TA B L E S Ta b l e 9 A % F ..y 33 ..z … 55...y ..y … … Country or territory Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of North America and Western Europe … Andorra 2 Austria Belgium Canada Cyprus 2 53 … … Denmark Finland France 5 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo 3 3 7 A N N E X Table 9A (continued) 9 0 0 2 t r o p e R ENROLMENT IN TERTIARY EDUCATION Total students enrolled (000) School year ending in Gross enrolment ratio (GER) (%) School year ending in 1999 2006 1999 2006 Country or territory Total (000) % F Total (000) % F Total Male Female Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa … 4.0 52 7.5 1.2 … … 0.5 … 4 21 31 3 19 7.6 10 … … 699 6 .

29 .

… … 633 5 15 41 19 23 43 … 14 19 36 23 … … 16 … 64 19 46 28 32 46 … … … 43 … … .

.

… … 54 48 17 35 21 32 … … 5.y 210 … 1.5.y 140 43 … 103.y 9 … 50 5.y 33.z 17 28.z 13 11 1 392.z 26...z .

59.

*,z .

… … 741 6 … 88.y 55 … … … 13.y 25 … 19.y 34 21 … 38.y 55 … 47 35.y 31...z 53 33.z 47 27 41.z 39...z .

… .

… … 55 50 … 38.y 32 … … … 1.0 0.9 7 1 … … 0.4 … 2 8 2 0.3 2 7 0.6 … … 6 1 .

3 .
3 .

… … 14 5 3 2 0.6 2 3 … 1.7 1.4 9 2 … … 0.7 … 2 13 2 0.4 3 7 … … … 7 … … … … .

.

13 5 5 2 1.0 3 … … 0.3 0.3 5 0.5 … … 0.1 … 3 3 2 0.2 1 6 … … … 5 … .

… .

… … 15 4 1 1 0.3 1 … GPI (F/M) .… 0.16 0.23 0.54 0.30 .… .… 0.18 .… 1.65 0.24 0.84 0.37 0.45 0.88 .… .… .… 0.76 .… .

.… .

.… .… 1.16 0.86 0.21 0.53 0.27 0.46 .… Total Male Female … 1.y 3 … 1.y 6 5 … 3.y 4 … 3 0.4.y 3.z 17 1.z 6 1 10.z 3...z .

6.

*,z .

… … 15 4 … 3.y 1 … … … 2.y 4 … 2.y 8 8 … 3.y 3 … 3 0.5.y 4...z 16 2.z 6 2 12.z 3...z .

.
.

… … … 14 4 … 4.y 2 … … … 0.3.y 1 … 0.4.y 4 2 … 2.y 4 … 3 0.3.y 2...z 18 1.z 5 0.5 8.z 2...z .

.
… … … 17 4 … 3.y 1 … … Sum %F Sum %F Weighted average Weighted average GPI (F/M) .… 0.15.y 0.34 .… 0.24.y 0.54 0.28 .… 0.60.y 1.19 .… 0.87 0.55.y 0.45...z 1.15 0.49.z 0.88 0.29 0.69.z 0.62...z .
.… .

.… .… 1.24 0.98 .… 0.62.y 0.48 .… .… 1.06 1.29 1.28 0.93 1.00 1.25 1.10 0.94 0.94 1.31 1.16 1.69 1.15 1.33 0.76 0.67 1.

Data are included in ISCED level 5A.

2.

National population data were used to calculate enrolment ratios.

3.

Enrolment and population data exclude Transnistria.

4.

Enrolment ratios were not calculated due to lack of United Nations population data by age.

5.

Data include French overseas departments and territories (DOM-TOM).
3 3 8 S TAT I S T I C A L TA B L E S Ta b l e 9 A DISTRIBUTION OF STUDENTS BY ISCED LEVEL (%) FOREIGN STUDENTS Total students Percentage of females at each level School year ending in School year ending in School year ending in 2006 2006 1999 2006 Level 5A Level 5B Level 6 Level 5A Level 5B Level 6 … 77.y 100 … 100.y 73 … … 66.y 79 … 76 100.y 95...z 56 100.z 61 80 52.z 65...z .

… .

… … 62 99 … 62.y … … … 78 99 82 75 84 84 99 62 74 .

66 .

90 78 94 76 … 23.y .


… ..y 26 … … 34.y 21 … 19 ..y 5...z 43 ..z 39 20 47.z 35...z .

… .

… … 36 .

… 36.y … … … Median … .

13 22 15 12 – 37 24 33 10 19 5 21 .

.

… ..y 0 … ..y 0 … … –.y .

… 5 ..y ..z 1 ..z 0 – 1.z ..z .

.

… … … 1 1 … 2.y … … … … 1 4 3 1 4 1 1 2 .

0 .

0 3 1 2 … 12.y 25 … 19.y 35 … … 35.y 51 … 47 35.y 31...z 53 33.z 43 22 36.z 41...z .

… .

… … 55 50 … 41.y … … … 52 55 56 45 53 58 54 40 47 .

55 .

55 55 35 36 … 16.y .


… ..y 33 … … 43.y 70 … 45 ..y 51...z 54 ..z 52 45 46.z 35...z .

… .

… … 56 .

… 35.y … … … … ..y 2 … ..y 26 … … –.y .

… 42 ..y ..z ..z 38 45 – 24.z ..z .

… .

… … 42 50 … 37.y … … … Total (000) … 0.1 … 0.4 … … … … … 1 … 1 … 1 … … … … … 0.1 .

1 .

… … … 0.1 0.5 … … … … % F … 16 … … … … … … … 46 … … … … … … … … … … .

… .

… … … … 33 … … … … Total (000) … … … … –.y 2 0.9 … … 0.1 … 1 … … 0.1.y … 0.2 0.2...z … … ..z … .
… … 54 0.1 … … 0.3.y … … % F … … … … –.y 52 26 … … … … 23 … … 53.y … … 25...z … … ..z … .

… … 48 … … … 20.y … … Median Sum %F Sum %F .

.

.

39 57 57 47 55 – 51 29 53 53 61 20 23 22 37 30 13 31 58 53 15 28 10 10 45 28 12 .

.
… … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … Country or territory Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa (eo) Full-time only.

(j) Data refer to ISCED levels 5A and 6 only.

(v) Data do not include ISCED level 6.

± Partial data.

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.

3 3 9 9 0 0 2 t r o p e R A N N E X Table 9B.
Tertiary education: distribution of students by field of study and female share in each field, school year ending in 2006 Country or territory (000) % F Total enrolment Education PERCENTAGE DISTRIBUTION BY FIELD OF STUDY Social sciences, business and law Humanities and arts Engineering, manufacturing and Science construction Agriculture Health and welfare Not known or unspecified Services Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A.T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea 3 4 0 818 18 1.9 2594...z 425...z 220 38 173 … 10 385 68 169 10 615 … … 325 … 209 53.y 544 … 243 137 338 68 439 131 199 … 2 146 144 835 9 167 … 198 115 49.z 2 343 2 740 99 132 145 773 233 138 133 … 289 1 040 5 76 23 361 .
… 55 68 40 … 36...z 52 65 53 … 26 45 50 54 68 59 … … 58 … 26 62.y 57 … 53 54 54 62 58 63 60 … 57 57 55 57 … 58 58 57.z 42 54.

* 55 47 52 58 56 61 27 … 41 55 66 33 47 .

… 1.7 2.1 .

… 20.1.y 14.2 27.8 3.4 … 3.6.z 1.3 29.7...z 34.5 12.6.y 23.8.z … … 1.0 … … 16.6.y 12.8 … 7.0 4.3 14.7.z 7.6 13.4 12.2 12.3 … 14.5 … 2.3 … … 16.5 8.8 13.3.z 12.3 8.9 19.6 … 2.8 … 23.9 9.3 7.0 … 32.8 9.0 51.8 14.6 … .
… 17.5 8.8 23.3 … 10.7.y 15.6 … 16.2 … 13.0.z 17.6 8.3...z 10.7 6.4.y 32.4.z … … 20.0 … … 13.0.y 5.5 … 7.9 9.9 9.5.z 11.6 8.0 7.0 7.0 … 9.2 … 10.5 … … 6.0 7.5 10.9.z 6.9 5.1 4.6 … 39.1 … 12.8 11.8 31.2 … 12.0 11.7 9.3 0.9 … .

… 38.9 51.8 43.9 … 21.3.y 26.0 … 44.5 … 19.8.z 53.0 20.5...z 31.7 48.3.y 14.6.z … … 17.5 … … 40.2.y 38.6 … 42.5 40.5 28.1.z 39.0 41.6 54.2 41.8 … 40.9 … 50.0 … … 28.3 43.5 32.8.z 47.4 42.2 27.1 … 30.3 … 34.5 39.4 26.4 … 21.0 37.9 13.9 52.6 … .
… 8.3 9.2 22.6 … 5.2.y 9.8 … 12.8 … 6.2.z 16.2 10.7...z 9.6 14.5.y 14.1.z … … 14.8 … … 4.2.y 2.4 … 5.0 7.4 9.5.z 10.0 5.2 5.2 6.1 … 9.7 … 4.7 … … 9.0 5.4 7.4.z 7.5 4.1 0.2 … 4.6 … 8.1 6.5 13.3 … 5.8 10.6 6.8 12.2 … .

… 9.9 8.6 5.9 … 19.0.y 12.5 … 11.6 … –.z 5.6 9.3...z 6.6 4.7.y 3.3.z … … 10.7 … … 8.0.y 25.4 … 21.0 16.3 19.7.z 12.3 12.4 10.0 18.0 … 12.6 … 18.2 … … 16.4 15.6 18.1.z 13.3 22.1 6.2 … 7.4 … 9.7 16.3 14.4 … 15.3 10.4 6.6 3.6 … .

… 2.2 .

.

… 4.0.y 1.7 .


0.3 … –.z 0.6 0.2...z 0.6 0.2.y 0.4.z … … 2.7 … … 7.6.y 7.9 … 2.5 3.8 3.8.z 2.5 2.9 1.2 2.3 … 2.2 … 2.9 … … 2.8 3.1 4.0.z 3.5 4.6 3.2 … 3.2 … 1.1 2.9 2.7 … 3.8 1.4 .

3.4 … .

… 6.6 7.0 .

… 8.1.y 12.8 … 9.5 … –.z 4.4 3.1...z 6.1 3.9.y 5.2.z … … 7.7 … … 8.0.y 4.0 … 6.4 7.5 9.8.z 8.5 8.2 5.2 9.2 … 5.7 … 5.7 … … 15.2 7.4 9.0.z 5.6 5.3 6.9 … 9.4 … 3.3 7.8 3.5 … 7.0 15.4 6.8 6.0 … .

… 1.0 3.0 4.3 … 11.8.y 0.4 .


1.0 … –.z 1.1 –.z 0.2 –.y 0.1.z … … 12.9 … … 1.7.y 3.3 … 7.6 10.2 4.5.z 8.5 8.3 4.9 3.4 … 5.4 … 3.0 … … 5.8 8.7 4.5.z 3.5 6.0 3.4 … 3.2 … 6.7 5.0 1.5 … 2.2 3.5 .

– … .

… 14.0 9.6 – … –.y 7.1 4.5 0.6 … 57.4.z 0.2 18.2...z 0.0 9.3.y 6.1.z … … 12.6 … … –.y – … … … 0.5.z … … … … … … … 2.8 … … – … –.z – 1.7 28.8 … 0.0 … 0.0 1.0 – … – … 4.9 6.7 100.0 .
… S TAT I S T I C A L TA B L E S Ta b l e 9 B PERCENTAGE FEMALE IN EACH FIELD Education Humanities and arts Social sciences, business and law Engineering, manufacturing and construction Agriculture Health and welfare Science Services Not known or unspecified 70 51 .

… 50.y 84 80 95 … 17.z 41 69...z 70 89.y 71.z … … … … … 82.y 78 … 68 92 74.z 90 73 85 78 … 73 … 75 … … 75 80 74.z 53 … 94 … 55 … 83 77 … … 57 74 71 39 … .
… 74 83 48 … 38.y 66 … 66 … 24.z 47 60...z 66 73.y 64.z … … … … … 71.y 75 … 63 71 63.z 75 66 77 73 … 70 … 69 … … 59 73 68.z 46 … 55 … 62 … 60 71 … … 63 63 59 29 … .

… 58 70 47 … 33.y 40 … 52 … 26.z 47 41...z 40 65.y 43.z … … … … … 60.y 70 … 60 64 60.z 65 65 67 68 … 62 … 62 … … 63 66 60.z 45 … 48 … 44 … 53 64 … … 24 54 63 40 … .

… 57 75 22 … 51.y 41 … 49 … 21.z 40 53...z 46 75.y 60.z … … … … … 74.y 51 … 49 42 36.z 39 31 30 34 … 37 … 54 … … 36 33 55.z 40 … 27 … 57 … 48 47 … … 56 35 58 14 … .
… 31 21 21 … 19.y 27 … 21 … –.z 27 20...z 28 16.y 15.z … … … … … 26.y 29 … 32 25 21.z 27 19 21 25 … 27 … 30 … … 29 24 32.z 19 … 30 … 28 … 29 39 … … 12 21 37 6 … .

… 47 .

.

… 30.y 55 .

48 … –.z 32 25...z 18 –.y 0.z … … … … … 39.y 30 … 43 45 54.z 53 45 49 47 … 53 … 37 … … 40 55 34.z 44 … 30 … 33 … 21 61 … … 15 53 .

24 … .

… 59 85 .

… 41.y 47 … 66 … –.z 66 67...z 57 100.y 44.z … … … … … 74.y 81 … 67 74 75.z 89 76 86 84 … 73 … 67 … … 81 80 74.z 61 … 36 … 75 … 50 80 … … 46 76 78 35 … .
… 26 69 49 … 37.y 54 .

50 … –.z 47 –.z 31 –.y 27.z … … … … … 21.y 42 … 47 26 38.z 51 59 52 43 … 49 … 46 … … 44 47 38.z 31 … 13 … 13 … 20 36 … … 30 52.z .

– … .

… 38 72 – … –.y 65 64 60 … 25.z 1 40...z 40 94.y 45.z … … … … … –.y – … … … 11.z … … … … … … … 47 … … – … –.z – … 52 … 22 … – 53 … … – 67.z 75 10 47 .
… Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A.T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea Table 9B (continued) Country or territory (000) % F Total enrolment Education PERCENTAGE DISTRIBUTION BY FIELD OF STUDY Social sciences, business and law Humanities and arts Engineering, manufacturing and Science construction Agriculture Health and welfare Not known or unspecified Services Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam 13...z 3 657 4 085 .
57 23 697.z … … … .

238 .

… … 2 484 3 204 … … 2 339 … .

.

0.7...y .
1.0...y 1 355.z Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia 3 4 2 0.05 ..y 2 083.z 2.1 … … 0.7.y 0.9 346...y 4 572.z 1.2...z 0.6 661 1 315 111...z 682 ..z 294...y … 125 ..z 112.
* 7 … 123...y … 2 447 .

… … 131 156...z 952 ..z 1.6 53...z … 46 .

40 46 56.z … … … .

59 .

… … 54 37 … … .

51 … .

60...y .

36...y 41.z 83 ..y 59.z 60 … … 70.y 71 … 56.z 69...z 72 49 51 54...z 61 ..z 61...y … 55 ..z 46.

* 69 … 59...y … 50 .

… … 61 52...z 51 ..z 85 … … 7.2 .

21.6 4.3 13.2.z … … … .

10.2 .

… … 16.9.y 6.3 … … .

… … .

… .

… … 40.4 … 10.7.z 17.9 … … 25.3.y 4.1 … 19.8.z … .

13.8 8.8 26.5.y 16.6 … … … 8.3 ..z 13.1.

* 30.5 … … … 10.6 .

… … 14.9 … 10.3 … 15.5 … … 15.8 .


14.2 6.9 9.8.z … … … .

17.5 .

… … 3.2.y 18.3 … … .

… … .

… .

… … .

… 12.5.z .

… … 3.7.y 9.3 … 3.4.z … .

7.7 4.2 4.0.y 1.3 … … … 4.4 ..z 0.7.

* 2.9 … … … 4.3 .

… … 9.8 … … … 0.3 … … 29.3 .

15.3 69.3 27.1.z … … … .

34.8 .

… … 28.0.y 21.6 … … .

… … .

… .

… … 59.6 … 39.6.z 44.2 … … 29.4.y 33.0 … 40.5.z … 81.0 25.7 42.8 25.7.y 24.0 … … … 47.1 ..z 46.0.

* 41.7 … … … 39.6 .

… … 39.6 … 5.9 … 19.1 … … 2.9 .

1.9 3.7 19.4.z … … … .

13.9 .

… … 11.8.y 8.6 … … .


… … .

… .

… … .

… 10.0.z .

… … 9.1.y 12.5 … 8.3.z … 16.4 8.0 3.3 8.5.y 2.5 … … … 11.2 ..z 2.3.

* 9.9 … … … 12.6 .

… … 8.0 … 6.7 … 0.1 … … 16.1 .

7.7 2.2 18.4.z … … … .

6.6 .

… … 15.5.y 28.9 … … .

… … .

… .

… … .

… 8.1.z 19.5 … … 0.1.y 6.0 … 7.5.z … .

18.5 32.3 14.9.y 2.1 … … … 11.9 ..z 18.6.

* 6.5 … … … 18.6 .

… … 11.2 … 0.6 … .

… … 2.1 .

6.8 – 2.9.z … … … .

1.0 .

… … 3.2.y 1.3 … … .

… … .

… .

… … .

… 3.5.z .

… … –.y – … 2.1.z … .


4.5 – 3.0.y 1.0 … … … 1.1 ..z 2.9.

* 2.2 … … … 2.5 .

… … 1.1 … 1.2 … .

… … 12.2 .

2.0 5.4 6.7.z … … … .

12.6 .

… … 13.2.y 8.8 … … .

… … .

… .

… … .

… 12.6.z 18.5 … … 8.9.y 7.7 … 13.6.z … .

14.8 8.5 11.1.y 17.3 … … … 16.0 ..z 7.0.

* 4.9 … … … 8.4 .

… … 8.0 … 9.0 … .

… … 5.7 .

2.8 8.3 2.5.z … … … .

2.7 .

… … 0.7.y 6.2 … … .

… … .

… .

… … .

… 2.9.z .

… … –.y 3.3 … 2.1.z … .

7.1 – 3.3.y 6.7 … … … 0.0 ..z –.

* 0.8 … … … 2.8 .

… … 6.9 … … … .


… … 8.7 .

27.7 – –.z … … … .

0.7 .

… … 7.3.y – … … .

100.0 … .

… .

… … .

… 0.2.z .

… … 23.4.y 24.3 … 2.7.z … 2.6 … – 3.0.y 28.5 … … … – ..z 9.4.

* 0.7 … … … … .

… … 0.5 … 66.3 … 37.8 S TAT I S T I C A L TA B L E S Ta b l e 9 B PERCENTAGE FEMALE IN EACH FIELD Education Humanities and arts Social sciences, business and law Engineering, manufacturing and construction Agriculture Health and welfare Science Services Not known or unspecified … … 70 .
49 63 61.z … … … .

82 .

… … … 70 … … .

.

.

… … … … … 89 … 81.z 79 … … … 89 … 74.z … .

69 66 73.y 71 … … … 75 ..z 56.

* 85 … … … 71 .

… … 77 … 63 … 84 … … 67 .

41 74 55.z … … … .

64 .

… … … 56 … … .

.

.

… … … … … .

… 65.z .

… … … 72 … 60.z … .

50 45 57.y 65 … … … 54 ..z 68.

* 75 … … … 56 .

… … 60 … … … 100 … … 35 .

38 40 60.z … … … .

56 .

… … … 36 … … .

.

.

… … … … … 79 … 58.z 63 … … … 78 … 52.z … 74 52 57 57.y 66 … … … 57 ..z 51.

* 71 … … … 57 .

… … 65 … 55 … 75 … … 25 .


40 14 56.z … … … .

43 .

… … … 29 … … .

.

.

… … … … … .

… 47.z .

… … … 59 … 34.z … 60 32 49 35.y 45 … … … 37 ..z 61.

* 44 … … … 40 .

… … 46 … 42 … – … … 12 .

11 14 39.z … … … .

25 .

… … … 16 … … … … … … … .

.

.

.

… 31.z 12 … … –.y 4 … 26.z … .

24 37 29.y 25 … … … 25 ..z 25.

* 16 … … … 25 .

… … 31 … 19 … .

… … 39 .

24 – 79.z … … … .

58 .

… … … 32 … … … … … … … .

.

.

.

… 42.z .

… … –.y – … 40.z … .

47 – 41.y 31 … … … 36 ..z 17.


* 36 … … … 37 .

… … 24 … 30 … .

… … 60 .

57 74 66.z … … … .

80 .

… … … 63 … … .

.

.

… … … … … .

… 68.z 87 … … … 93 … 71.z … .

69 71 55.y 78 … … … 73 ..z 59.

* 73 … … … 64 .

… … 76 … 80 … .

… … 80 .

22 66 70.z … … … .

48.z .

… … … 31 … … … … … … … .

.

.

.

… 57.z .

… … –.y 62 … 66.z … .

46 – 50.y 30 … … … 59 ..z – 68 … … … 59 .

… … 58 … … … .

… … 49 .

46 – –.z … … … .

63 .

… … … – … … .

51 … .

.

… … … .

… 54.z .


… … … 75 … 54.z … 80 … – 61.y 52 … … … – ..z 43.

* 73 … … … … .
… … 58 … 46 … 67 Country or territory Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia 3 4 3 9 0 0 2 t r o p e R A N N E X Table 9B (continued) Country or territory (000) % F Total enrolment Education PERCENTAGE DISTRIBUTION BY FIELD OF STUDY Social sciences, business and law Humanities and arts Engineering, manufacturing and Science construction Agriculture Health and welfare Not known or unspecified Services i g n r o t n o M i Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. ..z … 17...z ..z 113 1 381.
* ..z … 56...z ..z 62 … North America and Western Europe l a b o G l l l A r o f i n o t a c u d E Andorra Austria Belgium Canada Cyprus Denmark Finland France1 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia 3 4 4 0.4 253 394 1327...y 21 229 309 2 201 … 653 16 186 310 2 029 3 9.z .
580 215 367 … 1 789 423 205 2 336 17 487 28.y 912.z 4 12 853 2 399 – 147.y 820 … 48.z 43 11.z 30 17 120 5 4 10...z 2...y … … … … 5.y 210 53 54 55 56...y 51 57 54 55 … 51 64 55 55 57 52 56.z .
51 60 55 … 54 60 47 57 57 20.y 33.z 33 40 52 – 28.y 45 … … … 50.z 31 31 42 52 22 13...z 43...y … … … … 13.y 25 ..z … 4.9.y ..y 20.2.z … – 12.8 10.2 … 9.4 11.4 5.3 3.1 7.3 6.5.z 17.4 5.3 13.8 6.4 22.7 15.7.z ..y 14.6 14.7.z 7.2 … 9.2 15.2 10.3 8.9 9.4 … 2.7.z 38.3 1.3.z 6.5 … … 4.6 … … … 21.4.z 1.4 … 0.9 … … … … … … … … 21.9.y 26.8 ..z … 8.4.y ..y 4.5.z … 6.7 14.9 10.5 … 8.5 15.0 14.5 16.5 15.6 11.6.z 14.8 15.7 11.1 15.5 8.2 13.5.z ..y 8.3 11.5.z 8.6 … 10.4 12.6 13.0 17.0 10.6 … 24.2.z 17.4 36.0.z 12.3 … … 11.5 … … … 25.7.z 11.5 … 7.7 … … … … … … … … 1.8.y 2.9 ..z … 26.7.y ..y 40.0.z … 55.1 35.0 27.5 … 47.4 29.5 22.5 34.5 27.4 31.9.z 38.0 23.1 38.7 36.5 45.2 41.6.z ..y 37.5 32.2.z 31.5 … 31.9 26.2 37.1 27.0 27.3 … 33.9.z 14.8 13.5.z 26.9 … … 18.3 … … … 24.8.z 53.2 … 64.5 … … … … … … … … 23.7.y 36.9 ..z … 13.7.y ..y 5.3.z … 24.7 12.4 6.9 … 12.7 8.0 11.4 12.3 15.2 15.7.z 8.0 11.6 9.6 7.9 8.4 5.9.z ..y 6.6 9.4.z 7.3 … 11.4 9.7 10.7 13.7 8.9 … 15.5.z 3.2 14.3.z 11.0 … … 4.6 … … … 11.8.z 19.9 … 19.7 … … … … … … … … 9.3.y 7.0 ..z … 22.6.y ..y 11.1.z … – 11.8 10.6 … 6.1 10.1 25.9 11.5 15.7 16.5.z 7.3 10.4 17.9 15.6 15.0 7.8.z ..y 8.2 6.9.z 21.9 … 17.8 16.3 13.4 8.2 6.7 … 5.0.z 14.4 5.9.z 30.3 … … 5.6 … … … 5.5.z 5.6 … 4.9 … … … … … … … … 27.9.y 8.0 ..z … 3.6.y ..y 2.9.z … – 1.6 2.5 … 0.1 1.5 2.2 1.0 1.4 5.9.z 0.5 1.2 0.6 2.3 … 0.8.z ..y 1.2 0.9.z 1.9 … 3.4 0.9 1.2 0.9 0.6 ..z … 9.9.y ..y 11.5.z 1.7.
* 13.5 9.4 22.1 … 6.6 22.2 13.3 14.2 14.7 6.9.z 12.4 12.8 7.2 12.5 0.4 14.5.z ..y 16.2 19.0.z 16.0 … 9.9 17.2 10.2 18.8 13.9 ..z … 4.2.y ..y 0.6.z … – 2.1 1.5 … 9.2 2.3 4.8 3.5 2.5 5.0.z 1.5 4.5 .

2.6 … 0.2.z ..y 5.8 3.8.z 5.6 … 5.6 1.8 3.8 0.7 5.1 … … … 0.9.z 6.1 –.z 5.3 … … 1.5 … … … –.z – … 0.6 … … … … … … … … 9.0.y 8.5 2.4.z 5.8 2.2.z 5.6 … … 7.5 … … … –.z 8.2 … 1.3 … … … … … … … … 6.5.y 9.1 0.2.z – –.z 2.0 … … .

… … … 0.3.z 0.2 … 0.4 … … … … … … … … –.y .

..z … 5.9.y ..y 3.6.z 98.3.
* – … … … … – … … … –.z … … … … … … ..y 1.8 1.6.z … … 0.5 0.2 … 4.9 … … 15.1.z – 26.8.z – … … 46.3 … … … 10.6.z – … 0.1 … … … … … … … … –.y 0.8 S TAT I S T I C A L TA B L E S Ta b l e 9 B PERCENTAGE FEMALE IN EACH FIELD Education Humanities and arts Social sciences, business and law Engineering, manufacturing and construction Agriculture Health and welfare Science Services Not known or unspecified ..z … 69.y ..y 78.z … – 75 73 … 88 71 81 75 69 70.z 83 78 83 87 … 72.z ..y 74 75.z 82 … 78 76 71 74 79 … 36.z 36 44.z 71 … … 65 … … … 58.z 20 … … … … … … … … … … 9.y 24 ..z … 78.y ..y 67.z … 78 66 56 … 76 62 71 69 66 73.z 66 64 62 72 … 57.z ..y 54 62.z 61 … 61 62 59 62 58 … 41.z 37 44.z 71 … … 43 … … … 62.z 40 … … … … … … … … … … 41.y 32 ..z … 70.y ..y 63.z … 63 55 53 … 48 50 63 61 49 55.z 59 56 56 57 … 56.z ..y 47 56.z 59 … 59 61 46 55 56 … 33.z 33 36.z 56 … … 22 … … … 56.z 31 … … … … … … … … … … 16.y 31 ..z … 51.y ..y 49.z … 11 34 32 … 36 33 40 36 35 39.z 38 42 40 50 … 35.z ..y 16 32.z 49 … 34 43 29 37 39 … 26.z 32 40.z 70 … … 21 … … … 9.z 23 … … … … … … … … … … 21.y 23 ..z … 21.y ..y 36.z … – 21 24 … 14 33 19 23 18 28.z 32 16 27 28 … 28.z ..y 15 24.z 26 … 28 28 15 20 16 … 15.z 20 24.z 26 … … 15 … … … 12.z 43 … … … … … … … … … … 10.y 15 ..z … 55.y ..y 41.z … – 61 51 … – 52 51 41 47 44.z 43 45 56 45 … 31.z ..y 50 57.z 56 … 54 60 49 61 50 … 17.z 20 –.z 41 … … 16 … … … –.z – … … … … … … … … … … 6.y 15 ..z … 64.y ..y 72.z … 78 67 72 … 69 80 84 71 74 74.z 87 79 76 66 … 67.z ..y 74 81.z 77 … 76 81 69 78 80 … 38.z 45 35.z 76 … … 47 … … … –.z 31 … … … … … … … … … … 20.y 26 ..z … 66.y ..y 12.z … – 51 45 … 39 22 70 40 51 44.z 82 48 .
48 … 33.z ..y 49 49.z 49 … 57 59 51 65 53 … 33.z – –.z 57 … … .

… … … 87.z 75 … … … … … … … … … … –.y .

Country or territory Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of ..z … 67.y ..y 62.z … North America and Western Europe – … … … … – … … … –.z … … … … … … ..y 41 59.z … … 45 74 … 62 … … 36.z – 38.z – … … 58 … … … 53.z – … … … … … … … … … … –.y 26 Andorra Austria Belgium Canada Cyprus Denmark Finland France 1 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia 3 4 5 9 0 0 2 t r o p e R A N N E X Table 9B (continued) Country or territory (000) % F Total enrolment Education PERCENTAGE DISTRIBUTION BY FIELD OF STUDY Social sciences, business and law Humanities and arts Engineering, manufacturing and Science construction Agriculture Health and welfare Not known or unspecified Services … ..y 4.3.y 10.9 … … 4.2 … 2.7 … … 1.9 5.2.z … … 0.0.z … ..z … .
… … 1.8 6.1 … 1.6.y 4.7...z … … 2 8 1 1 0.1 3 3 1 3 .

3 2 4 0 2 … … 15.1.y 3.7.y 7.8 … … 4.2 … 7.1 … … 0.1 5.2.z … … 0.0.z … ..z … .

… … 5.9 7.0 … 4.4.y 6.6...z … … 22 4 14 9 2 7 7 9 6 .

10 9 11 10 8 … … ..y 1.8.y 1.1 … … – … 0.1 … … 0.3 2.7.z … … 0.0.z … ..z … .

… … 1.2 0.8 … 3.7.y 1.7..z … … 2 3 5 … .

3 4 6 1 .

1 4 4 7 .

… … 7.4.y 1.5.y 9.5 … … 21.0 … 0.3 … … 0.4 0.5.z … … 99.5.z … ..z … .

… … 0.0 .

… 2.1.y 22.4...z … … … – … 66 33 – 15 – 3 .


5 15 … … 46 … Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe … 2 140 43 … 103.y 9 … … 19 34 21 … 38.y 55 … 50 5.y 33.z 17 28.z 13 11 1 392.z 26...z .

47 35.y 31...z 53 33.z 47 27 41.z 39...z .

59.

*,z … .

.
59.

*,z … .

.

… … 741 6 … 88.y 55 … … … … 55 50 … 38.y 32 … … … 3.6.y 11.4.y 4.3 … … 17.2 … 2.9 … … 18.6 7.6.z … … 0.0.z … ..z … .

… … 13.3 10.7 … 32.1.y 12.9...z … … … 34.6.y 39.1.y 11.1 … … 9.0 … 11.2 … … 19.3 11.1.z … … 0.0.z … ..z … .

… … 4.9 21.1 … 5.3.y 7.1...z … … … 18.8.y 12.0.y 32.0 … … 34.0 … 57.7 … … 35.2 43.9.z … … 0.2.z … ..z … .

… … 52.9 45.5 … 40.3.y 20.2...z … … … 20.5.y 14.6.y 19.4 … … 10.5 … 12.1 … … 8.9 13.9.z … … 0.2.z … ..z … .
… … 10.4 5.7 … 3.3.y 15.2...z … … … ..y 11.6.y 3.9 … … – … 6.0 … … 15.4 9.9.z … … 0.0.z … ..z … .

… … 9.5 3.1 … 7.2.y 9.0...z … … Sum % F Median World 143 723 Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa .

12 4 13 9 5 … 11 6 11 … 10 7 8 18 5 7 9 4 10 12 … .

27 39 27 6 34 45 33 22 40 43 30 36 37 18 … .

7 2 9 7 10 7 3 9 16 .

6 13 5 11 5 … 11 25 7 1 2 16 11 29 11 .

13 14 19 6 6 … 1.

Data include French overseas departments and territories (DOM-TOM).

Data in italic are UIS estimates.


Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.


3 4 6 S TAT I S T I C A L TA B L E S Ta b l e 9 B PERCENTAGE FEMALE IN EACH FIELD Education Humanities and arts Social sciences, business and law Engineering, manufacturing and construction Agriculture Health and welfare Science Services Not known or unspecified … 2.y 36.y 30 … … … … 40 … … 57 33.z … … 49.z … ..z .

… … … 72 53 … 39.y 38.z … … 70 … 75 55 70 75 77 … … .

71 69 72 75 44 … … 19.y 37.y 20 … … … … 59 … … 63 36.z … … 38.z … ..z .
… … … 61 63 … 41.y 56.z … … 56 … 66 66 74 59 71 … … .

65 78 61 62 44 … … 14.y 42.y 24 … … … … 50 … … 58 41.z … … 31.z … ..z .

… … … 57 49 … 41.y 41.z … … 36 … 59 50 58 63 64 … … .

66 70 61 56 36 … … 14.y 27.y 16 … … … … 34 … … 47 21.z … … 4.z … ..z .

… … … 44 36 … 24.y 24.z … … 29 … 43 49 57 36 47 … … .

45 51 40 32 40 … … ..y 8.y 12 … … … … 18 … … 27 10.z … … 11.z … ..z .

… … … 26 9 … 19.y 10.z … … Median 16 … 27 17 31 29 39 … … .

25 21 27 24 24 … … ..y 20.y 17 … … … … 37 … … 57 27.z … … 23.z … ..z .
… … … 43 18 … 22.y 26.z … … 32 … 47 17 47 40 61 … … .

31 55 36 57 – … … 13.y 37.y 33 … … … … 51 … … 21 54.z … … 33.z … ..z .

… … … 67 65 … 40.y 29.z … … 63 … 74 62 59 81 80 … … .

78 64 67 81 35 … … ..y 22.y 15 … … … … 43 … … 9 21.z … … 29.z … ..z .

… … … 66 62 … 53.y 16.z … … 31 … 48 31 26 44 36 … … .

30 66 40 49 – … … 68.y 33.y 20 … … … … 61 … … 78 23.z … … 41.z … ..z .

… … … 50 .

… 55.y 32...z … … – … 53 22 38 – 53 … … .
52 67 57 59 38 … Country or territory Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 3 4 7 9 0 0 2 t r o p e R A N N E X Table 10A Teaching staff in pre-primary and primary education PRE-PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) % F Total Male Female Total Male Female % F 93 100 100 99 100 100 100 95 100 … 40 100 100 96 … 84 96 95 100 93 100 … … 100 100 100 100 100 99 99 … … 100 100 … 98 100 99 99 99 100 … 100 100 … 100 100 100 … 96 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Total (000) 1 0.7 0.01 14 5 3 4 11 1 … 40 0.4 3 0.4 … 12 5 4 3 0.8 4 53 … 19 6 17 7 32 7 13 … 77 13 37 642 8 16 3 3 17 143 8 12 6 19 3 3 5 … 66 … 0.6.
* 2 875 3 4 8 7 1 0.05 23 6...z 5 5 10 2 0.3...z 40 0.5 3 0.9 … 17 7 … 5 1.z 4...y 44 … 18 6 24 6 31 6 11 … 49 10 36 628 10 11 2 3.z 21 124 5 11 7 31 2 3 5 … 61 69 100 45 99 100...z 99 100 99 96 100...z 57 100 99 99 … 95 96 … 100 97.z 100...y 99 … 100 99 100 100 100 100 100 … 98 100 100 100 98 100 100 99.z 94 99 100 100 100 99 100 99 100 … 95 … 95 96 98 … 18 … … … … 100 … … … … 93 … … … … 87 … 59 … … … … … 76 … … … … … … … 92 … … … … … … … … … 78 … … 32 99 … … … … … … … … – … … … … 100 … … … … .
… … … … 84 … 71 … … … … … 86 … … … … … … … .

… … … … … … … … … .

… … – 75 … … … … … … … … 18 … … … … 100 … … … … 93 … … … … 87 … 59 … … … … … 76 … … … … … … … 92 … … … … … … … … … 78 … … 32 99 … … … … … … … … 58 100.z … 100.y … 100 9 … 100.y 100.z 100 100 36 … 60 24 … 50.z … … 64 … … … … … … … … … … 90 … … … … … … … … 56.y 90 … … 41 … 82 … 100 … 69 85 … … 100 100.z … ..y … 100 9 … ..y 100.z .

100 67 … 60 26 … 80.z … … 64 … … … … … … … … … … .

… … … … … … … … 20.y 100 … … 40 … .


… 100 … 93 … … … 58 100.z … 100.y … 100 9 … 100.y 100.z 100 100 35 … 60 24 … 50.z … … 64 … … … … … … … … … … 90 … … … … … … … … 56.y 90 … … 41 … 82 … 100 … 67 … … 28 21 29 24 15 22 15 13 8 … 20 20 29 21 … 30 24 20 19 17 20 5 … 11 13 18 8 12 9 7 … 12 8 17 7 21 10 18 10 15 8 7 9 13 9 18 25 11 … 9 … 20.
* 27 27 24 16 16 25 16...z 20 12 16 9 19...z 17 18 26 18 … 29 24 … 18 15.z 21...y 6 … 11 14 12 8 11 10 8 … 17 10 18 7 17 14 18 11.z 26 8 9 10 11 11 24 29 13 … 9 … 19 24 23 … 83.
* 99 94 … 0.6 4 952 S TAT I S T I C A L TA B L E S Ta b l e 1 0 A PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) % F 170 … 1 346 141 … 10 28 … 7 123 12 10 5 … … 110 60 17 103 13 32 … 23 11 36 8 47 9 13 … … 12 69 367 23 17 6 6 … 107 … 37 17 … 19 8 31 … 123 46 … 28 52 72 … 73 82 … 26 39 52 54 75 … … 65 50 73 20 75 99 … 91 89 85 86 85 97 98 … … 96 86 98 … 93 96 66 … 98 … 83 92 … 95 93 56 … 84 Total (000) 171 … 2 369 216...z … 20 32 … 11 146 20 12 7 … 113 … 59 18 … 12...y 23 … 17 11 30 8 41 7 11 … 232 10 56 301 22 14 6 6.z … 102 6 43 25...y 57 18 8 31 … 119 105 3.
* 45 … … 66.
* 37 … … 4 51 5 968 % F 52 … 27 56 72...z … 87 85 … 32 47 65 67 85 … 68 … 52 84 … 76...y 99 … 93 90 95 89 96 97 98 … 84 97 87 98 … 89 97 70.z … 99 99 86 95...y 98 97 95 65 … 85 … 73 42 55 Total Male Female Total Male Female Country or territory 94 … … … … … 100 15 … … … 100 100 … … … 81 … … … … … … … 100 … … … … … … … … … … … … … … … … … 100 … … 48 … … … … … … … … 92 … … … … … 100 … … … … 100 100 … … … … … … … … … … … 100 … … … … … … … … … … … … … … … … … 100 … … 49 … … … … … … … … 96 … … … … … 100 … … … … 99 100 … … … … … … … … … … … 100 … … … … … … … … … … … … … … … … … 100 … … 48 … … … … … … … … 99 … 79 … 100.y … 100 13 … 100 100.z 100 100.z 52 … 59 … … 60.z … … 100 … … … … … … … … … … … … … … … … … … 100 77.z 100 … … 61 … 93 … 100 … 85 98 … 99 … 80 … 100.y … 100 14 … 100 100.z 100 100.z 51 … 73 … … 69.z … … 100 … … … … … … … … … … … … … … … … … … … 22.z … … … 61 … … … 100 … 77 … 100.y … 100 12 … 100 100.z 100 100.z 53 … 52 … … 58.z … … 100 … … … … … … … … … … … … … … … … … … … 78.z … … … 61 … … … 100 100 28 … 40 23 25 … 13 14 … 47 28 25 38 13 … … 25 24 16 22 23 20 … 18 19 18 16 11 15 17 … … 21 19 18 17 19 14 22 … 20 … 19 17 … 24 32 22 … 21 … 92 … … … 82 … … 18 14.
* 48 … 24 … 34 27 21...z … 10 14 … 41 27 14 32 11 … 34 … 19 15 … 21...y 16 … 16 17 16 11 10 12 14 … 11 17 17 17 13 17 15 19.z … 17 21 13 15...y 17 24 33 22 … 18 … 13 50 18 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Table 10A (continued) Country or territory Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama 3 5 0 PRE-PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) 0.03 … … 118 96 … 2 0.5 21 0.1 … 2 … 7 0.01 … … 18 23 … … … 111 … … 0.1 … … 94 0.03 … 50 0.1 0.2 0.3 0.2 … 5 304 0.03 0.1 … 59 4 26 0.1 8 10 … 0.2 12 2 … … 5 150 0.01 0.3 6 3 % F 100 … … 98 … … 100 100 100 … … … … 98 100 … … 92 100 … … … 79 … … 100 … … 100 100 … 96
 100 97 93 98 … 93 98 100 96 … 94 97 98 100 95 90 … 96 … 99 … … … 94 100 99 97 98 Total (000) 0.02.z … 0.5 202 107 … 3 0.4 30.z … … 6 0.04 7 … … … 28 28 0.1...y … … 99 0.2.z 0.01...y … … 0.5 160 0.04 … 69.z 0.1 … 0.4 0.3 … 6...z 396.z 0.05 0.05 20 50 7 27 0.2.z 8 15 7 0.3...z 18.
* 2 … 8 7...z 159 0.01 … 9 5 % F 91.z … … … 98 … 99 100 96.z … … 99 97 99 … … … 97 99 94...y … … 78 97.z 100...y … … 91 98 98 … 96.z 99 … 95 99 … 92..z 97.z 100 100 98 96 94 100 100.z 94 87 91 99...z 91.

* 99 … … 98...z 96 100 … 92 95 Total Male Female Total Male Female … … … … … … 86 93 … … … … … … … … … 100 … … … … … … … … … … 44 38 … … 100 53 … … … … … 29 92 … … 92 98 75 54 … … … … 38 … … … … 100 100 32 36 … … … … … … 100 .

… … … … … … … … … … … … … … … … … … … … .

.

… … .

50 … … … … … .


50 … … … – .

59 … … … … 41 … … … … .

100 19 35 … … … … … … 86 93 … … … … … … … … … … … … … … … … … … … … 44 38 … … 100 53 … … … … … 29 94 … … … 100 75 53 … … … … 38 … … … … 100 100 33 36 61.z … … … … … 80 98 … … … 50 82 … … … … … … … … … … … … … … … 71 54 … … 100 … 63.z 10 … … … … 100 … .

82 100 78...y 76 73 90 … … 48.z … 64.y … … 100 … 33 45 –..z … … … … … 36 .

… … … 29 – … … … … … … … … … … … … … … … … – … … 100 … 29.z 33 … … … … … .

.

66.

* .
.

66.

* .

..y 69 61 62 … … 21.z … 53.y … … .

… 33 8 67.z … … … … … 81 98 … … … 51 84 … … … … … … … … … … … … … … … … 55 … … 100 … 65.z 9 … … … … 100 … .

83.
83.

* 100 78...y 76 75 93 … … 49.z … 65.y … … 100 … 33 47 14 … … 17 31 … 18 31 27 11 … 22 … 15 11 … … 33 24 … … … 25 … … 18 … … 23 18 … 24 26 9 18 19 … 42 19 13 9 … 18 19 19 18 24 18 … 18 26 18 … … 25 22 12 21 26 19 21.z … 19 16 29 … 16 23 23.z … … 16 18 14 … … … 33 20 42...y … … 25 29.z 14...y … … 11 17 11 … 19.z 21 … 18 17 … 41...z 18.z 15 13 20 22 15 17 14.z 26 17 33 10.z 25.
* 16 … 26 22...z 28 11 … 25 19 S TAT I S T I C A L TA B L E S Ta b l e 1 0 A PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) 0.1 … … … 367 0.6 27 2 143 0.6 … 155 … 20 0.02 0.1 … 360 124 1 11 3 298 … … 0.8 0.1 1 337 0.1 … 221 0.5 2 1 2 … 58 807 0.2 0.2 56 215 20 91 0.6 … 71 … … 48 4 … … … 540 0.02 1 24 15 % F 86 … … … … 62 43 87 66 … … 73 … 82 100 82 … 87 64 71 80 41 63 … … 67 … 49 78 87 … 88 78 63 76 64 … 61 93 86 89 77 77 80 79 75 … 68 … … … 86 … … … 62 84 86 83 75 Total (000) 0.1.z … 4...z 1428 386 0.7.z 29 2 190.z … 1 166 0.05 22 0.02...z 0.2...z 15 376 148 1...y 13 … 320 5.z 0.04...y 0.8 0.1.y 2.y 354 0.09 … 279.z 0.6 2 1 2 0.6 64...y 887.z 0.2 0.3 66 188 28 89 0.5 55 89 26.
* 0.9...z 78.

* 4.z … 46 12...z 521 0.03 … 29 18 % F 77.z … 57...z … 65 75.z 46 87 66.z … … 82 91 83 100...z … 43 87 76 73...y 83 … 60 31.z 69...y … … 54.y 78 93 … 88.z 82 81 78 71 89 61...y 88.z 88 88 78 76 80 77 84 76 70 70.

* 76...z 64.
* 86.z … … 89...z 67 100 … 74 76 Total Male Female Total Male Female Country or territory … … … … … … 76 81 … … … 60 … … … … … 100 … … … … … … … … … … 78 76 … … 100 58 … … … … … 72 98 … … 93 100 64 … … … … … 52 … … … … 100 100 79 79 … … … … … … 69 62 … … … 60 … … … … … … … … … … … … … … … … 75 78 … … 100 57 … … … … … 55 96 … … … 100 46 … … … … … 52 … … … … 100 100 63 86 … … … … … … 85 84 … … … 60 … … … … … … … … … … … … … … … … 78 76 … … 100 59 … … … … … 75 98 … … … 100 70 … … … … … 52 … … … … 100 100 82 77 95.z … … … … … 86 89 … … … 98 … … … … … … … … … … … … … … … … 96 64 … … 99 89 73.z 39 100 … … 74 97 … … 88 100 64 88 71 94.
* 67...z … 57.z … 87.y … … 77 … 74 91 … … … … … … 81 72 … … … 98 … … … … … … … … … … … … … … … … 93 17 … … 97 71 78.z 37 100 … … 30 94 … … 88.

* 100 44 81 70 92.

* 65...z … 52.z … 86.y … … – … 59 94 … … … … … … 91 91 … … … 98 … … … … … … … … … … … … … … … … 96 67 … … 100 93 72.z 40 100 … … 80 98 … … 88.

* 100 68 90 71 95.
* 100 68 90 71 95.

* 68...z … 58.z … 88.y … … 77 … 79 90 18 … … … 21 25 31 31 21 15 … 31 … 18 16 15 … 35 31 24 27 19 21 … … 21 19 24 30 22 … 22 19 14 18 24 … 25 26 18 15 32 24 27 12 20 … 27 … … 38 27 … … … 27 21 20 34 26 16.z … 28...z 20 19 25.z 31 21 17.z … 17 30 23 16 12...z 13...z 36 35 27 25...y 23 … 18 34.z 6...y 22 19.y 20.y 21 17 … 17.z 18 15 15 23 8 24...y 21.z 15 12 26 28 20 10 17 23 23 40.

* 18...z 31.
* 28.z … 28 28...z 28 17 … 33 25 Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama 3 5 1 A N N E X Table 10A (continued) 9 0 0 2 t r o p e R PRE-PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Country or territory Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of Total (000) … … … 0.3 … … 2 0.1 3 … North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo 3 5 2 … 14 … 30 1 45 10 128 … 9 2 … … 119 … 0.9 0.1 … … … … 68 … … … 327 … 68 0.01 … 9 0.4 10 … … … 0.6 … … 0.2 4 … … … 0.1 0.6 % F … … … 100 … … 100 92 98 … … 99 … 68 99 92 96 78 … 100 98 … … 99 … 99 100 … … … … 93 … … … 95 … 33 31 … 98 90 31 … … … 61 … … 99 97 … … … 94 100 Total (000) 6...y 50 0.3.z 0.3 0.3...z 0.5 2.
*,z 0.1...z 5 63.z 0.2 16 29 … 1 … 12 142 208 12 2 … … 134 1 0.9.z 0.1...y … … 17 0.1.y 111 34 11 44 458 4...y 33.y 0.02 717.z 19.z 0.6 … 86...y … … 0.6 0.9.z 0.9 0.4.

* 9 1.0 … 0.2...z … 1 % F 88...y 96 100.z … 100...z 99 100..*,z 95...z … 94.z 95 99 98 … 99 … 97 81 98 99 97 … … 100 98 99.z 100...y … … 98 … 89 96 98 97 91 100...y 90.y … 100.z 89.z 98 … 45...y … … 78 55.z 71 93.
* 99 100 … … … 97 Total Male Female Total Male Female … … … … … … 20 61 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 100 … … 47 – … … … 100 … … … … … … … … … … … … … … … – 40 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 100 … … 46 – … … … 100 … … … … … … … … … … … … … … … 20 63 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 100 … … 47 – … … … 100 … … … … … … … … … … … 46...z 56...z 59...z … 25...y 76...z … 86.z 100 … … … … … … … … … … … … … … … … … … … … … … … … … … 41.y … … … 45 … … … … 100.y 50.z 38 66.y 48 11 … … … 53.z … … ..z –....z ..z … ..y 25...z … 70.z 100 … … … … … … … … … … … … … … … … … … … … … … … … … … 50.y … … … 46 … … … … 100.y … 96 25.y 51 .
… … … –..z … … 46...z 56...z 59...z … 25...y 78...z … 87.z 100 … … … … … … … … … … … … … … … … … … … … … … … … … … 40.y … … … 45 … … … … 100.y … 14 69.y 48 11 … … … 62.z … … … 13 … … 13 13 31 … … 16 … 17 19 6 12 19 … 16 5 … … 13 … 12 18 … … … … 17 … … … 22 … 27 22 … 23 31 24 … … … 28 … … 28 23 … … … 26 10 26...y 23 6.z 11 11.z 31 14..*,z 12...z 23 15.z 12 14 14 … 18 … 12 18 12 12 6 … … 12 12 10.z 17...y … … 15 8.y 13 10 14 22 16 7...y 34.y 23 41.z 27.z 23 … 41...y … … 49 22.z 29 29.
* 21 22 … 38...z … 19 S TAT I S T I C A L TA B L E S Ta b l e 1 0 A PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) % F … … … 1 … … 8 0.1 18 … … 29 … 141 4 37 22 209 221 48 3 21 54 254 … 2 0.1 … … … … 172 62 … 244 1 618 26 312 2 3 135.
* 327 3 92 … … … 16 12 17 12 41 3 … 12 2 5 … … … 84 … … 76 92 92 … … 89 … 68 67 63 71 78 82 57 76 85 … 95 … 87 87 … … … … 68 80 … 76 86 – 33 32 33.

* 53 60 23 … … … 23 81 25 54 36 62 … 9 26 42 Total (000) 33...y 184 0.4 1 1...z 4 8.

*,z 0.1...z 19 184.z 0.4 29 65 … 4 … 24 218 238 61 3 27 60 264 3 3.z 0.1...y … 41...y 71 0.2.y 184 63 41 250 1 761 52.z 353.y 4 3388...y 374 3 113 428 75...z … 31 13.z 30 24 67 3 … 20.z 3...z 11 % F 72...y 64 87 86 73...z 91 72.
*,z 89...z … 81.z 76 89 79 … 83 … 76 82 84 64 80 85 86 96 72 86.z 80...y … 73...y 81 … 70 81 79 81 89 34.z 34.y 50 44...y 62 70 30 45 79...z … 17 78.z 30 55 40 66 … 12.z 33...z 47 Total Male Female Total Male Female Country or territory … … … … … … 71 81 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 64 100 … … 67 46 … … … 58 90 … … … … … … … … … … … … … … 74 63 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 64 100 … … 70 50 … … … 52 81 … … … … … … … … … … … … … … 71 82 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 64 100 … … 65 35 … … … 77 92 … … … … … … … … … … 64 80 74...z … 81.
*,y 82...z … 84.z … … … 98 68...z … 72.

*,y 81...z … 70.z … … … 77 76...z … 84.

*,y 83...z … 87.z 100 … … … … … … … … … … … … … … … … … … … … … … … … … 36.z 48.y 92 … 100.z 68 31.z 85 … … 72 87.z 87 88.z 62.

* 81 … 27.z … 89 100 … … … … … … … … … … … … … … … … … … … … … … … … … … 47.y 92 … 100.z 70 32.z 92 … … 71 89.z 85 83.z 58.

* 77 … 21.z … 84 100 … … … … … … … … … … … … … … … … … … … … … … … … … … 52.y 92 … 100.z 67 27.z 75 … … 76 86.z 91 91.z 67.
* 84 … 70.z … 95 … … … 22 … … 21 18 20 … … 13 … 17 18 10 17 19 17 14 11 22 13 11 … 20 16 … … … … 15 12 … 19 15 36 56 42 35.

* 27 24 39 … … … 53 27 49 57 52 29 … 68 35 61 28...y 22 15 24 18...z 16 17.

*,z 15.z 20 19.z Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of North America and Western Europe 10 12 11 … 16 … 16 19 14 11 10 17 13 11 11 12.z 14...y … 11...y 11 6.y 14 10 13 18 14 83.z 51.y 29 40...y 19 16 40 39 22...z … 44 24.z 46 54 45 25 … 63.z 35...z 55 Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Table 10A (continued) PRE-PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Country or territory Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe Total (000) 2 … 0.4 0.3 2 … … 26 … 0.2 44 … 6 … … … 3 … 1 0.6 … … 0.1 1 0.2 … … 6 … 0.6 3 … … … % F 96 … 36 97 93 … … 91 … 73 55 … 19 … … … 100 … 88 98 … … 95 78 100 … … 80 … 97 70 … … … Total (000) 3 … … 1 8 … 0.8...y 34 3 … 75 2 25 4 … … 3 … … 0.9 … … 0 3 0.2 1 … 11...y 0.5...z 0.7...y 2 18 … … % F 90 … … 96 … … 56...y … 50 … 87 99 22 97 … … 100 … … 94 … … 48 82 100 79 … 78...y 75...z 91...y 70 56 … … Sum %F Sum %F World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin
 America North America and Western Europe South and West Asia Sub-Saharan Africa Total Male Female Total Male Female … … … 65 63 … … 24 … … … … … … … … 100 … 77 96 … … … … 86 … … … … … … … … … … … … … … … … … … … … 61 … … … … … … … 22 37 … … 14 … … … … … … … … .
… 12 91 … … … … .

… … … … … … … … … … … … … … … … … … … … 40 … … … … … … … 66 65 … … 25 … … … … … … … … 100 … 86 96 … … … … 86 … … … … … … … … … … … … 65 76 … … 22.z 34 … 71 –..z … 13 … … 88 … … 95 … … … 100.z … 52 … … … … … 14 … … … … … 54 … … … 25.z 31 … 55 –..z … 10 … … .
… … … … … … 100.z … 53 … … … … … 8 … … … … … 66 … … … 22.z 38 … 73 –..z … 13 … … 88 … … … … … … 100.z … 52 … … … … … 19 … … 23 … 43 36 36 … … 25 … 21 27 … 18 … … … 16 … 27 21 … … 28 19 16 … … 36 … 20 25 … … … 16 … … 35 27 … 38...y 32 29 … 22 19 14 37 … … 15 … … 27 … … 23 36 15 20 … 34...y 32...z 18...y 42 43 … … Median Weighted average … … … … … … … … … … … 63 … … … … … 82 … … 100 … 82 … … … 64 59 75 … … … .

.

.
.

.

… … … 100 … … … … 53 65 … … … … 82 … … 100 … 82 … … … 65 59 76 … … … Data on trained teachers (defined according to national standards) are not collected for countries whose education statistics are gathered through the OECD, Eurostat or the World Education Indicators questionnaires.

2.

Based on headcounts of pupils and teachers.

Data in italic are UIS estimates.


Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.

3 5 4 S TAT I S T I C A L TA B L E S Ta b l e 1 0 A PRIMARY EDUCATION Teaching staff School year ending in Trained teachers (%)1 School year ending in Pupil/teacher ratio2 School year ending in 1999 2006 1999 2006 1999 2006 Total (000) % F Total (000) 45 155 1 6 112 6 5 80 16 3 148 8 10 43 … 15.
* 5 37 12 13 440 24 0.7 21 0.7 … … 227 6 23 110 104 33 60 20 21 28 35 28 42 29 32 25 20 42 80 19 58 … 23.

* 54 25 67 31 47 55 … 23 85 … … 78 75 13 33 45 49 47 46 … … 8 … 8.y 5.

* 95 28 … 154...z 11 28 77 … 29 6 62 13 28 599.z 31 1 38 0.7 30 … 209...y 7.z 28 150 157 52 64 % F 23 … … 43 … 45.y 34.
* 37 25 … 45...z 78 27 57 … 30 64 26 67 40 51.z 53 55 25 85 26 … 76...y 73.z 12 39 49 48 … Sum %F Sum %F … 49 … … 82 … … 62 91 … … … 94 … … … … … … … … … … … … 76 … … … … 27 98 … 52 … … 76 … … 65 89 … … … 93 … … … … … … … … … … … … 78 … … … … Total Male Female Total Male Female Country or territory … … … 73 … … 72 72 … … … 78 … … … … … … … 75 … … 72 64 … … … 68 … … … … … … … 69 … … 72 89 … … … 81 … … … … 100 … 100 … 100 … … … … 88 … … 76.
* 59 68 … 99.y 66 … 36.z … … 100 65 92.z 92 50.z 98 … 100.z … 49 … … 91.z 37.z 85.z 100 … … … … … 92 … … 75.

* … 65 … 98.y 49 … 30.z … … 100 57 83.z 92 39.z 98 … 100.z … 45 … … 89.z 37.z 84.z 100 … … … … … 82 … … 78.

* … 74 … 99.y 71 … 40.z … … 100 86 97.z 92 60.z 98 … 100.z … 63 … … 91.z 38.z 86.z 100 … … 43 26 57 47 46 44 33 30 47 44 32 44 39 47 … 62.

* 26 61 32 41 41 54 36 49 15 … … 35 33 41 57 40 47 41 46 … … 47 … 36...y 35.
* 35 44 … 40...z 40 19 48 … 56 22 67 31 40 37.z 66 31 39 12 44 … 36...y 33.z 38 49 53 51 38 Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe 30 98 … 46 … … 83 … … 61 92 … … … 95 … Median Weighted average … … … … … … … … … … … 76 … … … … … 100 … 85 100 … 93 … … … 80 74 88 … 68 85 … 100 … 92 99 … … … … … 85 30 87 … 70 84 … 100 … 79 100 … … … … … 81 80 88 … World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin America North America and Western Europe South and West Asia Sub-Saharan Africa Table 10B Teaching staff in secondary and tertiary education SECONDARY EDUCATION Teaching staff Trained teachers (%)1 Lower secondary Upper secondary Total secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 2006 Country or territory Total (000) % F Total (000) % F Total (000) % F Total (000) % F Total (000) % F Total (000) % F Total Male Female Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic3 Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine
 Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan … … 0.5 207 34 … 11 27 … 1 53 7 14 2 … … … 27 8 29 16 … … 27 16 … 5 47 16 24 … … 25 104 … 24 29 7 8 … … … … … … … 8 … … … … … 24 44 77 … 58 57 … 11 35 48 49 56 … … … 46 54 20 51 … … 76 67 … 85 86 83 81 … … 74 67 … … 77 77 46 … … … … … … … 69 … … … East Asia and the Pacific Australia Brunei Darussalam Cambodia … 2.
* 14 … 48.
* 28 3 5 6 113...y … … 231...y 61...z … … 19 … 2 60...y 12 19 3 … 30 … 33 13 … … … … 24 18 43 5 49 15 39 … 128 23 89 … 25 27 … 9.z … … … … … … … 11 … … … … … 21 51...y … … 45...y 59...z … … 61 … 11 36...y 54 51 56 … 67 … 52 56 … … … … 80 71 74 82 78 85 82 … 73 77 68 … … 76 … 51.z … … … … … … … 75 … … … … … 33 … … 0.2 247 23 10 11 15 … 1 35 5 3 2 … 18 … 30 8 19 6 … … 29 18 … 6 53 9 12 … … 8 73 … 24 25 9 5 … … … … … … … 3 … … … … … 17 38 57 48 53 42 … 10 29 51 38 57 … 47 … 35 55 18 54 … … 70 62 … 78 59 76 76 … … 68 60 … … 66 62 53 … … … … … … … 67 … … … … 1.
* 4 … 47.

* 24 64...y … … 257...y 32...z 10.y … 22 74 2.

* 40...y 7 5 3 … 36 40 32 11 … … … … 32 23 48 3 41 10 … … 134 8 68 … 27 23 … 6.z 168 … … … … … … 5 … … … … … 9 46...y … … 38...y 56...z 35.y … 47 71 11.
* 29...y 48 46 57 … 42 47 45 53 … … … … 76 64 57 81 64 85 … … 65 73 65 … 63 69 … 56.z 42 … … … … … … 70 … … … … … 31 … … 0.7 454 56 … 22 42 … 2 88 13 18 4 … … 54 56 16 48 22 107 … 56 33 … 11 100 25 36 … … 33 177 … 48 54 17 13 … 400 … 118 59 … 48 11 47 … 307 … 3 18 176...y … 1.0 488...y 93...z … 24 41 … 4.
* 100...y 19 25 6 … 66 … 65 24 … 23...y 103 … 56 41 92 8 90 25 41 … … … 22 41 69 … 56 51 … 10 33 50 48 57 … … … 40 55 19 52 77 … 73 64 … 81 71 80 79 … … 261 31 72 157 64 … 1 284 … 52 50 16 15.z … 72 69 49 … 76 349 … 63 77 … 68 69 43 … 57 … 48 27 43 129 34...y 180 53 16 61 … 352 … 4 29 49...y … 23 41...y 58..z … 53 53 … 11.

* 33...y 52 50 57 … 54 … 49 55 … 56...y 80 … 77 67 65 82 72 85 81 … 69 76 67 80 … 73 71 53.z … 79.

* 83 66 82...y 85 73 73 47 … 63 … 59 32 … … … … 100.y … 100.z 12 … 100.
* … 100 … 56 … 80 … … 46.z … … … … … … … … … … … … … … … … … … … … … … … … … … 78 … 92.y … … … … … 100.y … 100.z 14 … 100.

* … 100 … 45 … … … … 47.z … … … … … … … … … … … … … … … … … … … … … … … … … … 76 … … … … … … … 100.y … 100.z 11 … 100.
* … 100 … 65 … … … … 46.z … … … … … … … … … … … … … … … … … … … … … … … … … … 78 … … … 100 100 100 … 90 95 … 91 … … 90 … S TAT I S T I C A L TA B L E S Ta b l e 1 0 B SECONDARY EDUCATION TERTIARY EDUCATION Pupil/teacher ratio2 Teaching staff Lower secondary Upper secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 1999 2006 Total (000) % F Total (000) … … 26 22 22 … 12 9 … 28 19 19 26 13 … … … 23 14 22 16 … … 13 14 … 11 11 10 11 … … 13 12 … 17 13 14 16 … … … … … … … 19 … … … … 12.
* 16 21...y … … 20...y 19...z … … 10 … 28 20...y 13 29 10 … 28 … 20 14 … … … … 12 11 11 11 10 10 8 … 13 12 11 … 13 13 … 14.z … … … … … … … 21 … … … … … 30 … … 16 13 16 17 9 8 … 24 14 16 19 8 … 22 … 15 10 21 17 … … 12 11 … 10 9 10 11 … … 12 13 … 14 12 13 16 … … … … … … … 17 … … … … 10.

* 21 20...y … … 14...y 19...z 17.y … 8 5 24.


* 17...y 20 22 9 … 17 11 19 10 … … … … 11 8 10 19 12 11 … … 13 14 15 … 11 13 … 16.z 17 … … … … … … 19 … … … … … 25 … … 23 17 20 … 11 9 … 26 17 18 24 10 … … 19 19 12 22 16 9 … 13 12 … 10 10 10 11 … … 13 13 … 15 13 13 16 … 13 … 8 8 … 13 19 16 … 11 … 11 18 21...y … 31 17...y 19...z … 10 9 … 26.
* 19...y 16 28 10 … 22 … 19 12 … 18...y 9 … 12 10 11 14 11 10 10 … 13 12 13 9 12 13 11 15.z … 11 8 8 9...y 10 14 20 16 … 13 … 11 28 … … 0.02 … 12 … 2 9 12 … 16 … 3 0.7 20 4 … 6 … 5 2 30 … 24 7 19 6 21 6 15 … 76 7 26 … … 11 2 3 60 133 9 13 14 27 8 6 6 … 17 … 0.5 1 … … 30 … 31 … … 28 13 … 23 … 13 32 36 23 … 41 … 1 36 51 … 41 35 38 49 38 52 50 … … 50 37 … … 38 21 42 35 … 42 36 49 58 32 47 29 … 36 … 32 19 30 0.8...z 0.1 81...y 19...z 8 2 21 … 0.4 19 3 6 0.7.z 27 … … 17 … 6...z 2...y 42 … 22 9 23 6 25 6 13 … 98 8 32 656 … 13 5 3.z 85 192 13 16 12 43 13 8 9 … 23 … 0.6 3 % F 34 41...z 16 … 35...z 21 27 37 … 4 24 34 17 32.z 33 … … 41 … 16...z 41...y 56 … 46 41 38 48 39 57 53 … 42 55 43 57 … 42 34 44.z 39 … 46 41 39 63 56 55 32 … 36 … 39 11 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic 3 Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia Table 10B (continued) SECONDARY EDUCATION Teaching staff Trained teachers (%)1 Lower secondary Upper secondary Total secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 2006 Country or territory Total (000) % F Total (000) % F Total (000) % F Total (000) % F Total (000) Total Male Female Total (000) 3 649 … … 3.y 748 259 0.3...z 10 1 … … … 59 … 17 … … … 118 100 0.4...y … … 126 2.z … … … … 306 … … 120.z … 1 … 1 0.4 19...y 966.z 0.1 0.1 23 … 15.
* 46 0.2 13 44 12.

* 0.6.z 31.

* … … 11.y … % F 46 … … 50.y … … 52...z 42 63 … … … 84 … 66 … … … 76 65 74...y … … 56 26.z … … … … 68 … … 72.z … 59 … 61 68 61...y 88.z 67 61 78 … 57.

* 64 63 76 50 51.

* 60.z 43.


* … … 56.y … 49 … … 50 … … … … … 362 0.3 3 0.5 … 0.2 … 14 … 15 0.0 0.1 … 50 102 0.8 … … … … … 0.3 … … 64 … … … 0.2 0.6 0.5 0.2 … 25 401 0.05 0.1 29 48 4 25 0.1 14 23 … … 13 0.9 … … … … 2117 … … … … … 2...y … 600 … 350 38 40 49 … … … 73 … 54 50 49 … 76 27 49 … … … … … 48 … … 51 … … … 49 75 58 60 … 48 70 57 41 54 50 54 49 67 47 50 … … … 63 … … … 0.3...z 6 1 … … … 20 … 19 … … … 51 114 0.8...y … … 83 1.z … … … … 133 … … 137.z … 1 … 0.5 0.4 25...y 646.z 0.08 0.2 44 … 6.
* 42 0.2 20 33.

* 7.

* 0.3.z 18.

* 1.0 … 5.y … 198 0.01 0.4 3.

* 40 60 66 56.

* 245 … … 4 43 … … 50...y … … 42...z 44 54 … … … 79 … 58 … … … 77 40 53...y … … 53 24...z … … … … 54 … … 63.z … 58 … 52 67 47...y 70.z 68 56 54 … 59.

* 48 65 53 49.

* 44.

* 57.z 42.

* 63 … 52.y … 43 … … 58 … … … … … 630 0.5 12 1 … 0.3 … 68 … 28 0.03 0.2 … 150 192 1 9 1 … … … 1 … 0.4 258 0.07 … … 0.4 1 1 0.9 … 39 1 104 0.2 0.2 45 187 13 65 0 … 54 … … 33 4 … … … 519 0.03 1 10.
* 46 40 56 … … … 76 … 58 44 51 … 76 40 57 65 33 … … … 48 … 47 65 63 … … 49 74 58 62 … 52 79 63 46 62 50 52 60 68 … 50 … … … 63 … … … 44 62 53 56.

* 0.1.z … 5766 … … … … 5...y … 1347 … 610 % F 45 61.z … 50...y … … 47.z 43 59 63.z … … 82 81 61 68.z … … 76 52 60...y 67 … 0.7.z 16 2 147.z … … 80 0.04 36 0.03.z … … 169 214 1...y 12 … … 97.z … … … … … 95 68 … … … 95 … … … … … … … … … … … … … … … … 55 209 3.z 25...z 0.03...y 44...y … … … … … … 439 64 98 0.1 … 257.z 0.5 3 1 2 0.7 45...y 1 612.z 0.2 0.3 67 165 21.
* 89 0.5 33 77.

* 19.

* 0.9.z 50.

* 4 … 17.y 13...z 610 0.03 … 14 69 … 68.z 54 59 59 58 67 53...y 81.z 67 58 63 52 58.

* 56 64 62 49.

* 48.

* 59.z 43.

* 63 … 55.y 68...z 47 63 … 53 67...z … … 93 90 57 36 100 … … … 100 … … 80.

* 100 34 80 70.

* 89.

* 35.z … 55...z … 64.y … … 52 … 53 … 100.z … … … … … 95 55 … … … 96 … … … … … … … … … … … … … … … … … 71...z … … 95 89 57 29 100 … … … 99 … … 81.

* 100 29 72 64.

* 87.


* 39.z … 46...z … 59.y … … 20 … 46 … 95.z … … … … … 95 77 … … … 95 … … … … … … … … … … … … … … … … … 65...z … … 92 90 57 41 100 … … … 100 … … 79.

* 100 37 85 77.

* 90.

* 33.z … 60...z … 69.y … … 71 … 59 46 63 46 56.
* 366 … … 9 41 … … … … … 59 40 59 65 … … 77 … 63 43 54 … 76 54 76 … … … … … 49 … … 70 … … 73 49 73 58 63 … 59 84 64 52 78 50 51 68 68 … 49 … … … 63 … … … China Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam 3 213 … … … … 268 0.2 9 0.9 76 0.1 … 54 … 13 0.02 0.1 … 100 90 0.3 … … … … … 0.7 … … 194 Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua … … 171 0.2 0.6 0.7 0.7 … 14 703 0.2 0.1 16 138 9 40 0.3 … 31 … … 20 3 … … … 321 0.0 0.7 7.
* 3 5 8 S TAT I S T I C A L TA B L E S Ta b l e 1 0 B SECONDARY EDUCATION TERTIARY EDUCATION Pupil/teacher ratio2 Teaching staff Lower secondary Upper secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 1999 2006 17 … … … … 16 21 20 24 18 28 … 28 … 18 6 14 … 41 22 26 … … … … … 15 … … 29 … … 13 16 23 18 24 … 24 23 6 11 32 19 18 12 21 … 17 … … 15 19 … … … 18 11 12 31.
* 17 … … 22.y 13 14 21...z 23 23 … … … 34 … 15 … … … 42 21 25...y … … 22 28.z … … … … 21 … … 18.z … 13 … 18 6 … 16.z 11 11 25 … 18.

* 10 21 24 14 29.

* 14...z 17.

* … … 28.y … 20 … … 33 … … … … … 13 19 22 21 … 18 … 38 … 13 21 12 … 21 23 17 … … … … … 13 … … 29 … … … 16 23 18 23 … 20 21 10 7 27 20 18 10 15 28 17 … … 11 19 … … … 14 10 21 31 18 … … 22...y 11 11 13...z 27 20 … … … 32 … 14 … … … 26 16 19...y … … 21 18.z … … … … 27 … … 10.z … 12 … 15 6 24...y 15.z 8 7 24 … 17.

* 11 11 24 14.

* 26.

* 18.


*,z 14.

* 18 … 45.y … 15 … … 32 … … … … … 14 20 20 23 … 22 … 30 … 15 11 13 … 34 23 20 19 13 … … … 15 … 23 29 15 … … 16 23 18 24 … 21 23 7 9 29 19 18 11 19 … 17 … … 13 19 … … … 17 10 15 31 Total (000) 504 .

… … … 465 .

1 0.7 … … 0.1 9 .

11 .

… 1 94 127 0.2 … 50 … .

.

0.07 .

… 28 .

.

102 0.2 … 0.6 … … 13 174 0.08 18 16.z … 22...y 12 12 17.z 25 22 17.z … … 34 16 15 8.z … … 37 18 21...y 18 … 22 24.z 7...y … … … 23 10 … 14.z 14 13 15 17 6 … 15.z 10 90.02 42 24 27 18.
* 10 16 24 14.

* 28.

* 15.

*,z 16.

* 18 … 33.y 18...z 18 12 … 33 … 86 … 24 .

… … 7 .

… … … … … 192 .

0.2 … % F … .

… … … … .

31 … … … … 76 .

43 .

… 20 … 25 41 … 53 … .

.

21 .

… 37 .

.

54 43 … 41 … … … 41 49 0.05 … .

.

34 … 48 … … 32 … … … … … … .

42 … Total (000) 1 332 .

… … 272.z 511 .

3 2 45.z … … … .

.

15 … … 113...z 193 … … ..z 70 … … .

.

… 48.z 0.02 ..y 139...z 0.2 … … 0.1.z 0.1 18.y 293.z 0.1...z 24 … 87.

* … 116 ..z 11...y … 9 ..z 4 0.5 … 7...y … 262 .

… … % F 42 .
… … % F 42 .

… … 39...z 18 .

34 32 48.z … … … .

.

50 … … 56...z 31 … … ..z 51 … … .

.

… 40.z Country or territory China Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean 50 ..y 50...z 46 … … 49...z 55 … 44.z 55...z … 35.
* … 58 ..z 41...y … 33 ..z 31 48 … 38...y … … .
… … Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua 3 5 9 9 0 0 2 t r o p e R A N N E X Table 10B (continued) SECONDARY EDUCATION Teaching staff Trained teachers (%)1 Lower secondary Upper secondary Total secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 2006 Country or territory Total (000) % F Total (000) Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 8 … … … 0.4 … … 3 0.1 14 … North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros 3 6 0 … 43 … 71 2 20 20 255 365 37 1 … 19 177 … 3 0.2 … … … … … 28 … 142 764 … 136 0.4 … 179 0.8 22 … … … 6 … 5 … 13 … … 2 … 55 … … … 65 … … 61 61 75 … … 64 … 68 54 63 71 … 57 64 78 … … 73 … 50 69 … … … … … … … 55 60 … 13 32 … 45 25 12 … … … 12 … … … 28 … … 5 … 9 … … … 0.8 0.4...z 2 3...z 0.1...z 14 116.z 0.4.z 43 43 … 3 … 21.z … 412 42 1 … 21 178 … 3.z … … 20...y 47 0.1.y 161 40 32 153.z 921 32.z 186.y 1 1 312.y 236.z 3 30.y … 67...y … 10...y … … … … 2 … … 2...z % F 60 … … … 64 58...z 64 62...z 61...z … 65.z 61.z 69 60 … 68 … 72.z … 61 66 80 … 79 76 … 60.z … … 73...y 67 69.y 62 66 49 61.z 68 ….z 17.y 49 37.y 49.z 39 16.y … 64...y … 11...y … … … … 40 … … 16...z Total (000) % F Total (000) % F Total (000) % F 6 … … … 0.3 … … 2 0.05 5 … … 30 … 68 2 24 … 240 168 38 1 … 36 245 … 0.2 0.2 … 26 … … … 35 … 212 740 … 129 0.2 … 143 0.05 18 … … … 3 … 1 … 13 … … 1 … 55 … … … 62 … … 55 63 65 … … 49 … 68 49 30 … … 39 49 44 … … 59 … 31 54 … 44 … … … 50 … 56 51 54 7 … … … … … … … … 57...z 0.2...z 52 1 2...z 62...z 0.07...z 64...z … 7 60.z 72.z 0.07.z 29 … … 3 … 14...z … 182 44 2 … 30 249 … … … … 26...y 47 … 121 39 … 235.z 758 51.z 51 … … 55 … 59...z … 47 48 53 … 65 60 … … … … 47...y 65 … 50 51 … 61.z 56 … 19.y 28 31.y 47.z … … … 62...y … 15...y … … … … 36 … … 9...z … … 192.y 13 32 0.8 … 1 274.y 294.z 44 … 27 … 7 … … … 52...y … 14 … … … 28 … … 6 … … 4...y … … … … 1 … … 1...z 14 … … … 0.7 … … 6 0.1 19 … … 73 … 139 5 44 … 495 533 75 3 … 55 422 …
 4 0.4 … … … … … 63 … 355 1 504 … 265 0.6 1995 322 0.9 40 … … 16 9 9 6 … 26 … … 4 … 55 … … … 64 … … 59 62 72 … … 57 … 68 51 45 … 57 51 56 58 … … 65 … 48 61 … … … … … … … 56 56 … 13 32 34 45 25 9 … … 33 12 45 … … 28 … … 5 … Total (000) 16 44...y 173 0.4 1 1...z 3 6...z 0.2...z 21 188.z 0.5.z 72 82 … 6 … 35...z 524 594 86 3 30 51 427 4 4.z 0.4...y 107 46...y 94 … 282 79 42 388.z 1 680 … 378.y 2 2 586.y 530.z … … 197.
*,y 119...y … 14...y 12...z 11 8...y 43 3 … 7...z 3...z % F 58 62...y 45 66 64 58...z 59 62...z 62...z … 63.z 59.z 62 57 … 61 … 67...z 58 57 57 65 62 71 67 47 57.z 66...y 46 58...y 66 … 57 59 47 61.z 62 … … … … … … … … … … … … … … … … … … … … … … … … … … … … 32.y 18.y 92 41 … 34.y 100.z 48.z … … … … 51.
*,y … … 63...y … 12...y 54...z 17 21...y 26 38 … … 13...z … … … 26 … … 62.z … … … Total Male Female 90 … … 46 57 55...z … 56...y 100...z … 83.z 88 … … … 53 58...z … 58...y 100...z … 76.z 92 … … … 59 53...z … 54...y 100...z … 86.z … … … … … … … … … … … … … … … … … … … … … … … … … … … 31.y 92 … 100.z … … … … … … … 25 … … 60.z … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 35.y 92 … 100.z … … … … … … … 30 … … 65.z … … … S TAT I S T I C A L TA B L E S Ta b l e 1 0 B SECONDARY EDUCATION TERTIARY EDUCATION Pupil/teacher ratio2 Teaching staff Lower secondary Upper secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 1999 2006 17 … … … 19 … … 22 9 12 … … 9 … 17 14 10 10 13 15 10 11 … 12 10 … … 10 … … … … … 12 … 16 16 … 43 35 … 30 18 38 … … … 27 … 29 … 26 … … 41 … 17 … … … 11 18...z 16 16...z 9...z 13 12.z 7.z 9 7 … 11 … 10.z … 13 8 10 … 12 10 … 8.z … … 9...y 8 6.y 12 10 9 15.z 14 14.z 34.y 30 37.y 19.z 11 40.y … 20...y … 27...y … … … … 21 … … 16...z 15 … … … 16 … … 19 9 23 … … 12 … … 12 9 … 11 16 10 14 … 9 11 … … 7 … 8 … … … 18 … 14 14 … 32 27 … 31 9 24 … … … 15 … 23 … 21 … … 23 … 15 … … … … 18...z 11 16...z 9...z 20 9.z 14.z 14 … … 11 … 17...z … 16 8 12 … 12 11 … … … … 8...y 6 … 9 9 … 14.z 15 … 21.y 12 28.y 19.z … … … 19...y … 16...y … … … … 17 … … 11...z 16 … … … 18 … … 21 9 15 … … 10 … … 13 10 … 12 15 10 13 … 10 11 … … 8 … … … … … 15 … 15 15 … 37 32 34 30 17 32 … … 18 24 18 28 … 24 … … 34 … Total (000) % F 8 … … .
.

… … 0.5 .

11 … … 26 … 129 1 … 18 102 272 17 1 10 … 73 … 0.7 .

… 14 … … 108 29 8 92 992 … 45 0.2 … 65 .

… … … 0.8 0.7 0.5 0.8 0.4 3 … 0.3 … 0.1 … … … … .

.

… 31 .

… … … … … 41 34 … 46 40 30 31 43 33 … 28 … 25 .

… 36 … … 35 … 16 32 41 … 14 … … 17 .

… … … 20 9 28 … … … … 5 … 10 Total (000) 12 … … ..z 0.2 ..z … 2...z ..z 14 109.

* 0.08 40 26 … 2 … 19 136.y 288 29 2 12 … 100 … 0.7...z .
44 18 37 … 146 36 33 126 1 290 2.y 52.z 0.4 539.y 122 – … 45 … … … 0.5.z 1 0.7...z 3.z 0.5 … 1...z 0.1...y % F 46 … … 47 ..z ..z … 33...z ..z … … Country or territory Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.

Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe 40 35 41 … 40 … 46 39.y 35 35 44 38 … 34 … 23...z .
36 40 43 … 39 43 31 41 45 12.y 15.z … 40.y 20 – … 37 … … … 37.z 7 14...z … 43 … 3...z 15...y Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros 16 12...y 16 11 17 18...z 14 16...z 9...z 15 11.z 8.z 11 10 … 11 … 12...z 11 14 8 11 11 12 11 10 10.z 9...y 13 9...y 7 … 11 9 14 15.z 15 … 27.y 23 33.y 19.z … … 42...y 20...y … 24...y 14...z 30 19...y 16 19 … 34...z 14...z 3 6 1 9 0 0 2 t r o p e R A N N E X Table 10B (continued) SECONDARY EDUCATION Teaching staff Trained teachers (%)1 Lower secondary Upper secondary Total secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 2006 Total Male Female Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe Total (000) … 13 … 0.7 1 … 2 2 40 4 … … 2 4 … … 5.
* … … 4 2 … … … 6 0.4 … … … … 5 … … 4 … % F Total (000) % F Total (000) % F … … … 5 12 … 17 16 24 11 … … 51 16 … … 17.

* … … 45 23 … … … 14 54 … … … … 13 … … 28 … 4.y … … … 2 … … 3.

* 67 9 … … … … 22 … 10 … 8 … 5 … … … … … … … … … … … … … … 15.y … … … 10 … … 18.
* 23 5 … … … … … … 17 … 16 … 18 … … … … … … … … … … … … … … … 7 … 0.1 1 … 0.7 0.6 12 1 … … 1.0 3 … … 3 … … 1 2 … … … 3 0.2 … … … … 2 … … 6 … … 13 … 7 11 … 15 12 16 10 … … 53 16 … … 10 … … 49 12 … … … 13 55 … … … … 15 … … 27 … Total (000) 3...y … … … 2 … … 0.9.

* 18 … … … … … 8 10 … … 2 … 2 … … … … … … … … … … … … … … % F 11...y … … … 12 … … 12.

* 19 … … … … … … 18 … … 15 … 13 … … … … … … … … … … … … … … Total (000) % F … 20 89 0.9 2 … 3 2 52 6 … … 3 7 … … 8.
* 5 … 5 4 … … … 9 0.6 … … 145 … 7 … … 10 31 … … 10 5 12 … 16 15 22 11 … … 51 16 … … 14.

* 47 … 46 18 … … … 14 54 … … 50 … 13 … … 27 37 Total (000) 7...y … … … 4 … … 4.

* 85 … … 78...z 4 … 31 … … 7.z 10 6 7 159.z 8...z 0.4 15...z 0.6 10 … 149.y 4.z 13...z 36...z … … … % F 13...y … … … 11 … … 16.

* 22 … … 38...z 55 … 47 … … 55.z 16 50 17 36.z 20...z 13 14...z 55 16 … 52.y 46.z 7...z 22...z … … … … … … … 49 … … 89.
* … … … … 87 … … … … … 64 97.z 21 … … … 51.y … 82 … … 99.z 47.y 82.y … … … … … … … 47 … … 90.

* … … … … 78 … … … … … 62 … 21 … … … 50.y … 81 … … 98.z 47.y 81.y … … … Sum % F Sum % F Sum % F Sum % F Sum % F Sum % F Median World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 66 56 … … … … … … … … … … … … … … 65 56 … … … … 1.
Data on trained teachers (defined according to national standards) are not collected for countries whose education statistics are gathered through the OECD, Eurostat or the World Education Indicators questionnaires.

2.

Based on headcounts of pupils and teachers.

3.

Teaching staff in upper secondary includes full- and part-time teachers.

3 6 2 … … … … 64 … … 83.
* … … … … 95 … … … … … 80 … 20 … … … 55.y … 89 … … 99.z 39.y 86.y … … … … … … … … … … … … … 67 56 … … … … S TAT I S T I C A L TA B L E S Ta b l e 1 0 B SECONDARY EDUCATION TERTIARY EDUCATION Pupil/teacher ratio2 Teaching staff Lower secondary Upper secondary Total secondary School year ending in School year ending in School year ending in School year ending in 1999 2006 1999 2006 1999 2006 1999 2006 … 21 … 15 45 … 28 25 19 26 … … 17 18 … … 24 … … 21 12 … … … 19 14 … … … … 23 … … 19 … 18...y … … … 47 … … 28.
* 25 … … … … … 16 16 … … 24 … 20 … … … … … … … … … … … … … … … 29 14 23 51 … 28 22 20 30 … … 22 17 … … 28.

* 20 … 24 24 … … … 25 14 … … 29 … 35 … … 23 27 34...y … … … 54 … … 24.

* 19 … … 32...z 25 … 24 … … 17...z 36 25 30 40.z 26...z 22 26...z 13 24 … 31.y 17.z 30...z 21...z … … … Total (000) % F 0.4 … 4 … 0.2 2 0.6 0.1 2 … 0.03 … 0.4 0.6 1 0.5 1.0 0.6 … … … 52 0.4 .

.

… … … … 0.2 0.4 2 2 … … 5 … 6 … 13 6 17 15 13 … 18 … 45 15 31 25 … 26 … … … 31 10 .

… .
… .

… … … 32 10 17 14 … … Total (000) … … … … 0.4.y 8 … 0.1.y 4 1 … … 0.6 … 2 0.4.y 1...z … 3.z 0.8 1 37.y 2...z .

.

… … … 44 0.5 … 4.y 3 … … % F … … … … 14.y 9 … 16.y 11 3 … … 47 … 28 32.y … … 21.z 42 6 17.y 12...z .

… .
… … 51 40 … 19.y 18 … … Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe Weighted average Sum % F Sum % F … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 45.y … … … 59 … … 23.

* 17 40 … … … … 27 … 35 … 39 … 33 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 34 … 25 55 … 28 20 20 31 … … 24 17 … … 31.

* … … 25 34 … … … 29 14 … … … … 40 … … 29 … … … … … … … … … … … … … … … … … Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.
3 6 3 9 0 0 2 t r o p e R A N N E X Table 11 Commitment to education: public spending Total public expenditure on education as % of GNP Total public expenditure on education as % of total government expenditure Public current expenditure on education as % of total public expenditure on education Public current expenditure on primary education as % of public current expenditure on education Public current expenditure on primary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on primary education as % of GNP Country or territory 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea … … 7.5 … … 5.0 … 2.0 … 2.8 6.2 4.2 … … 7.0 … … 7.2 … … … 6.0 … … … 4.1 7.0 5.0 5.8 … … 4.7 4.6 3.6 … … 4.2 … 4.2 4.0 3.7 3.1 4.3 2.0 4.0 3.7 6.0 2.2 … … 4.9 … 1.0 1.9 0.4 … … … 7.6 4.2 … … 3.4 2.8 … 2.8 6.8.z 5.0 … … 6.7.y … … 7.7.z 1.6.
*,y … … 6.2 … 4.5.z 4.6.y 4.7.y 5.4.y 5.8.z 5.2.y 5.3.z … 5.7.z 6.6 3.6.z 3.9.z … 4.1.z 6.0.z … 4.1.y 6.4 … 2.4 3.2 2.5.z 5.0.z 5.3.y 3.5 … … 4.7.z … 1.8.y … … … … … … … … 21 … 10 … … 26 21 … … 26 … … … … … … … … … … 10 … 13 … … … 11 16 … … … 14 … … … 14 … 24 10 14 … … 12 … … … 9 9 13 13 … 3 6 4 … … 23 13 … … 13 11.z … 10 27.z 31 … 20.z 28.y … … 21.z 28...z … … 13 … 6.y 10.x 10.y 15.y 11.z 14.y 15.z … 13.y 20 9.y 13.y … 11.y 13.z … … 19 … 17 9 … … … 19 … … … … … … … … … … … … … … … … 68 … 91 … … … … … … … … … … … … … … 91 … 91 … … … 93 … … … … 96 … … … … … 99 … … 99 … 90 … … 96 97 … 93 99 … … … … … … … 92 93 … 99 95.z 92 … 88...y … … … 87.z … … … 94 … 91.z 95.y 90.y 91.y 93.z … 94.z … 95.z 86 94.z … … 94.z 93.z … 90.y … … 98 97.y … 95.z 94.y 88.z … … 96.z … … … … … … … … … … … … … 12 … 39 … … … … … … … … … … … … … … 18 … 20 … … … … … … … … 14 … … … … … … … … … … … … … 33 … … 34 53 … … … … … … … 21 33...z … 62...z 45.z 50.z … … … … … 35...z … … … 9 … 20.z 18...y 15.y 26.y 21.z … 15.z … 30.z … 14...z … … 17.z 20...z … 40...y … … 17 … … … 24.y 27...z … … 33.z … … … … … … … … … … 568 … … … … 697 … … … … … 349 … 1 997 … … … … … … 1688 … 2339 … … … … … … … … 1245 … … … … … … … … … … … … … 4637 … … … … … 692...x … … … … 695.z 2 204.z 402...z … 224...z 1 005.z … … … … … 611 1581...z 1 636.y … … 1196 … 2 045.z 2197...x 2 242.y 2 511.y 4 479.z … 2 166.z … 3 155.z … 941.z … … 2 149.z 5206...z … 1059....y … … 356 … … … 261.y 106....z … … 5 181.z … … … … … … … … … … 1.9 … … … … 2.2 1.4 … … … … 1.7 … 0.7 … … … … … … 0.7 … 0.9 … … … … … … … … 0.6 … … … … … … … … … … … … … 1.6 … … 0.6 0.2 … 1.6...x … … … … 1.8.z 0.7 0.9...z … 1.4...z 2.9.z 1.8.y … … … … 1.9 2.4...z 0.4.y … … 0.5 … 0.8.z 0.8...y 0.6.y 1.3.y 1.1.z … 0.7.z … 1.7.z … 0.5...z … … 0.6.z 1.1...z … 1.5...y … … 0.4 … … … 1.2.y 0.9...z … … 1.5.z … … … … … S TAT I S T I C A L TA B L E S Ta b l e 1 1 Public current expenditure on primary education per pupil as % of GNP per capita Public current expenditure on secondary education as % of public current expenditure on
 education Public current expenditure on secondary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on secondary education as % of GNP Public current expenditure on secondary education per pupil as % of GNP per capita Primary teachers’ compensation as % of current expenditure on primary education, in public institutions 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 Country or territory … … … … … 13 … … … … 18 11 … … … … 10 … 8 … … … … … … 10 … 18 … … … … … … … … 10 … … … … … … … … … … … … … 16 … … … 2.
* … 11...x … … … … 12.z 8 8...z … 10...z 22.z 15.y … … … … 16 20...z 7.y … … 14 … 22.z 19...x 12.y 18.y 26.z … 16.z … 23.z … 11...z … … 14.z 24...z … 13...y … … 6 … … … 13.y 8...z … … 16.z … … … … … … … … … … … … … 10 … 44 … … … … … … … … … … … … … … 50 … 41 … … … … … … … … 56 … … … … … … … … … … … … … 40 … … 38 40 … … … … … … … 38 30...z … 33...z 38.z 41.z … … … … … 43...z … … … 41 … 46.z 51...y 52.y 47.y 41.z … 51.z … 36.z … 46...z … … 50.z 49...z … 34...y … … 50 … … … 32.y 50...z … … 39.z … … … … … … … … … … 658 … … … … 1 830 … … … … … 602 … 2 605 … … … … … … 3328 … 2435 … … … … … … … … 2246 … … … … … … … … … … … … … 4218 … … 455 … … 1049...x … … … … 858.z 3 280.z 449...z … 564...z 1738...z … … … … … … 1832...z 2 115.y … … 2247 … 1 955.z 2777...x 4 221.y 3 362.y 3 983.z … 2 784.z … 2 979.z … 1407...z … … 2 336.z 6711...z … 1313...y … … 547 … … … 241.y 138...z … … 4 675.z … … … … … … … … … … 1.8 … … … … 2.5 2.0 … … … … 1.1 … 0.7 … … … … … … 1.8 … 1.8 … … … … … … … … 2.2 … … … … … … … … … … … … … 1.9 … … 0.7 0.2 … 1.9...x … … … … 1.7.z 1.2 0.8...z … 0.7...z 2.5.z 1.6.y … … … … … 2.9...z 0.6.y … … 2.4 … 1.9.z 2.2...y 2.2.y 2.3.y 2.2.z … 2.5.z … 2.0.z … 1.5...z … … 1.9.z 2.7...z … 1.2...y … … 1.2 … … … 1.6.y 1.6...z … … 1.8.z … … … … … … … … … … 15 … … … … 47 21 … … … … 18 … 10 … … … … … … 20 … 19 … … … … … … … … 18 … … … … … … … … … … … … … 14 … … 11 2.
* … 17...x … … … … 15.z 13 8...z … 24...z 38...z 14.y … … … … … 23...z 9.y … … 26 … 21.z 24...x 23.y 25.y 23.z … 20.z … 22.z … 16...z … … 15.z 30...z … 16...y … … 10 … … … 12.y 11...z … … 14.z … … … … … … … … … … 78 … 69 … … … 75 … … … … … … … … … … … … … 45 … … … … … … … … … … 62 … … … … … … … … 47 … … … … 60 … … … … … Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia China Cook Islands DPR Korea … … … … … 85.z 77 84.z … … … 91.y … … … … … … 77.y … … … … 55.z … 47.y … … … … … … … … … … 54.z 41...z … … … … … … … … … … … … 63.z … … … … … Table 11 (continued) Total public expenditure on education as % of GNP Total public expenditure on education as % of total government expenditure Public current expenditure on education as % of total public expenditure on education Public current expenditure on primary education as % of public current expenditure on education Public current expenditure on primary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on primary education as % of GNP Country or territory 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu
 Viet Nam 5.7 … 3.6 7.7 1.0 3.6 6.1 13.3 6.5 0.6 … 7.2 … … … … 3.8 4.5 … 3.3 5.1 … … 6.7 … 6.7 … Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia 3 6 6 … 3.5 4.6 … … 5.3 5.7 … 5.8 4.0 … … 4.0 4.5 5.5 7.7 5.5 … 2.0 2.4 … … 9.3 … … … 4.5 … … 4.0 5.1 5.1 3.4 5.6 8.0 6.5.y 3.8 3.5.z … 3.4 … 6.6.y 9.5...y … … … 6.1 … … … 2.3.z 4.6.y … … … 4.3.z … … 4.9.y … 10.0.x … 4.0.z … 4.0.y 5.1.z … 7.2.z 5.8.y 1.2 6.6.x 4.1.y 4.0 2.9 3.6 4.9 4.9 9.3 … 3.9 … 3.2 6.0.x 2.6 8.6 … … 5.6.z 5.6.z … … 3.3...x 4.1...y 4.1.y 2.7 10.8.z 7.1 18 … 9 … … 14 25 … … 8 … … … … … … 13 13 … … 28 … … … … 17 … … … 13 14 … 15 17 … 16 10 … … 16 17 … 14 … … 10 17 … … 18 … … … 23 11 14 6 … 9 21 13 21 … 18 9.z … 14 14.z 25.y 16.x … … … 15.z … … … 15.z 16.y … … … 25.z … 15.x 13.x … … … 14.z … 13.y 15.z … 16.z 18.x … 18.x … 12.z … 16 11.z 21 14 … 17 … … 13.x … 15 … … 9.z 26.y … … … 9...y 10.y 15 13.x 19 … … … … … … … … … 64 … 95 100 … … … 80 99 … … … … … … … 84 … … 100 94 90 … 92 … … 84 95 … … 88 … 100 … … … 93.
* … … … … … … … 95 47 94 … … 88 88 … 79 97.y … … … 37 89.y 88.y … … … … 100 … … … 93.z 88.y … … … … … … … … … … 90.z … 99.y 84.z … 96.z 88.y 97.z 96.x 94.y 95 … 95 99 79.y 88 … 96 … 89 87...x 92 91 … … 97.z 97.z 65.y … … … 96.y 93 37.z 74 … … … … … … … … … … … 27 32 … … … 44 32 … … … … … … … 39 … … … 37 30 … 21 … … 41 33 … … 45 … 47 … … … … … … … … … … … 41 … … … … … 40 … 53 40.y … … … 46.z … 29.y … … … … 24 … … … 54.z 34.y … … … … … … … … … … 30.z … 37.y 30.z … 28...z 47.y 41...z 46.x 32.y 27 … 36 41 56.y 32 … … … 48 35...x 66 27 … … 34.z 39.z … … … … 46.y 42 … 39 … … … … … … … … … … … 3971 … … … … 2621 443 … … … … … … … 409 … … … 1 637 … … … … … 295 788 … … 1 256 … 1 469 … … … … … … … … … … … 1 114 … … … 867 … 366 … 1197 1 143.y … … … 61.z … 1 324.y … … … … 4 831 … … … 418.z 3 379.y … … … … … … … … … … … … 1 703.z … … … 846.y … 435.x 1 005.y … … 1 287 1 257 1 623.y … … 644...z … 478 766...x 390 752.z … … 547.z 1 604.z … … 331 … 518.y 446 … 949 … … … … … … … … … … … 1.8 … … … … 1.3 1.4 … … … … … … … 2.2 … … … 1.6 … … 1.0 … … 2.0 1.3 … … 1.5 … 2.6 … … … … … … … … … … … 1.8 … … … 1.9 … 1.2 … 3.3 2.5.y … … … 0.5.z … 1.7.y … … … … 1.5 … … … 1.2.z 1.4.y … … … … … … … … … … 1.1.z … 1.5.z 1.3.z … 2.0...z 2.4.y 0.8...z 2.9.x 1.3.y 1.0 … 1.2 2.0 2.3.y 2.6 … 1.2...z … 1.4 1.8...x 1.6 2.1 … … 1.8.z 2.2.z … … 1.6 … 1.8.y 1.1 … 2.0 S TAT I S T I C A L TA B L E S Ta b l e 1 1 Public current expenditure on primary education per pupil as % of GNP per capita Public current expenditure on secondary education as % of public current expenditure on education Public current expenditure on secondary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on secondary education as % of GNP Public current expenditure on secondary education per pupil as % of GNP per capita Primary teachers’ compensation as % of current expenditure on primary education, in public institutions 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 Country or territory … … … … … … … … … … … 19 … … … … 16 9 … … … … … … …
 12 … … … 12 … … 11 … … 11 10 … … 13 … 18 … … … … … … … … … … … 12 … … … 14 … 7 … 20 19.y … … … 3.z … 13.y … … … … 17 … … … 7.z 16.y … … … … … … … … … … 9.z … 12.z 13.z … 24...z 13.y 11...z 17.x 12.y 8 … 12 17 17.y 33 … 9...z … 9 11...x 8 18.z … … 15.z 15.z … … 9 … 11.y 8 … 14 … … … … … … … … … … … 40 59 … … … 38 27 … … … … … … … 52 … … … 35 32 … 31 … … 22 36 … … 36 … 29 … … … … … … … … … … … … … … … … 30 28 … 33 33.y … … … 30.z … 34.y … … … … 42 … … … 27.z 43.y … … … … … … … … … … 17.x … 38.y 32.z … 30.z 44.y 52...z 25.x 40.y 36 … 38 29 34.y 36 … … … 24 35...x 10 35 … … 38.z 30.z … … … … 30.y 36 … 30 … … … … … … … … … … … 4947 … … … … 2177 468 … … … … … … … 2 081 … … … 2 044 … … … … … 278 714 … … 1 424 … 2 127 … … … … … … … … … … … … … … … 1 236 816 491 … 1 601 1 060.y … … … 91.z … 1 923.y … … … … 5 617 … … … 435.z 4 814.y … … … … … … … … … … … … 2 789.z … … … 1 226.y … 350.x 926.y … … 1 435 1 052 1 632.y … … 460...z … 457 841...x 179 1327 … … 820.z 1 722.z … … 146 … 600.y 554 … 1 231 … … … … … … … … … … … 2.7 … … … … 1.2 1.2 … … … … … … … 2.9 … … … 1.5 … … 1.5 … … 1.1 1.4 … … 1.3 … 1.6 … … … … … … … … … … … … … … … 1.5 1.3 0.9 … 2.0 2.1.y … … … 0.3.z … 2.0.y … … … … 2.6 … … … 0.6.z 1.7.y … … … … … … … … … … … … 1.8.z 1.4.z … 2.1.z 2.2.y 1.0...z 1.6.x 1.5.y 1.4 … 1.3 1.4 1.4.y 3.0 … 0.5...z … 0.7 1.8...x 0.2 2.8 … … 2.0.z 1.7.z … … 0.3 … 1.2.y 0.9 … 1.6 … … … … … … … … … … … 24 … … … … 13 10 … … … … … … … 61 … … … 15 … … 18 … … 11 9 … … 15 … 26 … … … … … … … … … … … … … … … 19 16 10 … 27 17.y … … … 5.z … 19.y … … … … 20 … … … 8.z 23.y … … … … … … … … … … … … 20.z 20.z … 26.z 19.y 14...z 13.x 11.y 15 … 13 14 17.y 36 … 6...z … 9 13...x 4 29 … … 22.z 16.z … … 4 … 13.y 9 … 18 … … … … … … 70 … … … … … … … … … 78 … … … … … … … … 94 … … 66 … … … … … … … … … … … 91.
* … … … … … … … … … … … … 86 … … … … … 88 … 88 … … … … … … 64.y … … … … … … … … 94.y 64.y … … … … … … … … … … Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean … … 57.z … … … 86.y … … … 81.z 89 85 81 … 69.z … 71.z … Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia 73 93.x 88...z 75.z … … 87.z 84.z … … 88 99.z 82.y 66 68.z 79 3 6 7 9 0 0 2 t r o p e R A N N E X Table 11 (continued) Total public expenditure on education as % of GNP Total public expenditure on education as % of total government expenditure Public current expenditure on education as % of total public expenditure on education Public current expenditure on primary education as % of public current expenditure on education Public current expenditure on primary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on primary education as % of GNP Country or territory 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 i g n r o t n o M i Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 7.2 … 3.9 … 2.8 … North America and Western Europe l a b o G l l l A r o f i n o t a c u d E Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon 3 6 8 … 6.4 … 6.0 5.4 8.2 6.3 5.7 4.5 3.5 … 4.9 7.5 4.7 3.7 4.9 … 4.5 7.2 5.4 … 4.4 7.5 5.0 4.6 5.0 … 2.3 … 4.5 4.5 … 2.9 2.6 … 3.4 3.0 … … 3.5 2.1 … … 1.7 … 6.0 5.6 … … 5.3 3.6 3.5 8.8.z … … … 3.0 3.7 2.3 5.5.z 6.0.y 5.1.z 6.5.y 8.3.z 6.4.z 5.7.z 4.6.y 4.4.z 7.9.y 5.6.z 7.1.y 4.5.z … 5.2.y … 5.2.z 7.1.z 5.5.z … 4.3.z 7.2.z 5.3.z 5.5.z 5.3.z … 2.6.z 7.2.z 3.3.z 5.2 8.3 3.2.x 2.7 … 2.7.z 4.4.y 9.3 4.2 5.2.z 3.3 6.6 1.4 2.3.z … 2.5.z … … 1.4...x 2.4 6.0 … … … 16 17 … … … 12 … … … 15 12 11 10 7 … 13 14 10 8 … 5 10 16 13 … 11 14 15 11 … … 15 … 13 19 … 12 … … 6 16 … … … 10 … … … … 22 … … … … … … 16.z … … 12.z 12 … … 11.z 12.y … 15.z 16.z 13.z 11.z 10.y 9.z 17.y 14.z 14.x 9.z … 11.y … 11.z 17.z 11.z … 11.z 13.y 13.x 12.z 14.z … 14.z 17.z 11.x 19 15.z 15.x 12 … … 17.y 21 15 18.z 17 16 … 10.z … 8.z … … 4...x … 18 … … … 96 73 92 … … 94 … 98 86 … 94 91 … 78 … 91 94 94 … … 92 96 90 93 … 91 … 90 … … … 64 … 98 91 … 74 89 … 89 88 … … 94 … … … … … 93 74 … … 70 … 87 68.z … … 88.z … 92 94 96.z 98.y 95.z 87.z 95.z 94.z 91.z 98.y 78.z 90.y 92.z 95.y 94.z … 95.y 91.y 94.z 92.z 98.z … 91.z 100.z 92.z 93.z … … 79.z 59.z … 93 81...z 77.x 75 … 42.z 82.y 75 95 98.z 74 74 98 50.z … 91.z … … 90...x 80 65 … … … 40 30 32 … … 19 … … 34 … 21 20 … 25 … 32 34 26 … … 18 26 25 31 … 28 … 32 … … … 39 … 30 … … 53 … … … … … … 39 … … … … … 36 43 … … … … … 50.z …
 … 20...z … 30 25 19.z 24.y … 30.z 22.z 20.z 21.z 15.y 26...z 34.y 33.z 36.y 25.z … 22.y 17.y 26.z 24.z 31.z … 26.z 26.z 29.z 26.z … … 35...z 27.z … 29 54...z 49...x … … 20.z 50.y 19 66 52.z 34 58 52 48.z … 27.z … … … 39 51 … … … 1 012 … 748 … … 7112 … … … 7345 4615 4697 … 2148 … 3112 4835 6425 … … … 4606 6456 3872 … 4112 … 7 066 … … … 64 … 288 … … 97 … … … … … … 85 128 … … … … 191 274 … … … … … 1 227.z … … … … 583 … 7 596.z 6 303.y … … 7 949.z 5 373.z 5 224.z 4 837.y 3562...z 7 788.y 5 100.z 5 135.y 6 347.z 9953...z 2 549.y … 5 572.z 7 072.z 4 908.z … 4 800.z 8 415.z 7 811.z 5 596.z … … 115...z … … 927 … 119...x … … … 120...z 1 158.z 328 132.z 107 1 052 88 54.z … 39.z … … … 99 130 … … … 1.5 … 0.8 … … 1.1 … … 1.6 1.6 1.2 1.1 … 0.7 … 1.4 2.4 1.2 … … … 1.1 1.6 1.5 … 1.1 … 1.4 … … … 0.6 … 1.3 … … 1.1 … … … … … … 1.3 1.0 … … … … 2.0 1.8 … … … … … 3.0.z … … … … 1.0 0.5 1.0.z 1.4.y … 1.7.y 1.8.z 1.2.z 1.1.z 0.7.y 0.9...z 2.4.y 1.7.z 2.4.y 1.0.z 1.4...z 1.1.y … 1.3.z 1.6.z 1.7.z … 1.0.z 1.9.z 1.4.z 1.3.z … … 0.7...z 1.1.z … 1.4 3.5...z 1.2...x … … 0.2.z 1.7...z 1.3 2.6 2.7.z 0.8 2.8 0.7 0.6.z … 0.6.z 0.1...z … … 0.8 2.0 … Public current expenditure on primary education per pupil as % of GNP per capita Public current expenditure on secondary education as % of public current expenditure on education Public current expenditure on secondary education per pupil (unit cost) at PPP in constant 2005 US$ 1999 2006 1999 2006 1999 2006 … … 11 … 8 … … 23 … … 17 23 17 16 … 12 … 12 20 23 … … … 14 17 19 … 17 … 19 … … … 4 … 12 … … 7 … … … … … … 12 7 … … … … 24 16 … … … … … 20.z … … … … 8 9 23.z 19.y … 21.y 23.z 17.z 16.z 16.y 16...z 23.y 15.z 21.y 22.z 18...z 14.y … 16.z 17.z 24.z … 18.z 26.z 20.z 17.z … … 6...z … … 13 20...z 8...x … … … 11...z 11.z 25 19.z 5 18 7 4.z … 4.z … … … 9 13 … … … 31 40 37 … … 45 … … 53 … 39 50 … 38 … 37 30 47 … … 51 39 32 44 … 47 … 40 … … … 42 … 38 … … 29 … … … … … … 37 … … … … … 24 36 … … … … … 30.z … … 30.z … 18 22 47.z 43.y … 49.z 36.z 41.z 47.z 49.y 37.z
 35.y 35.z 30.y 47.z … 42.y 46.y 40.z 35.z 41.z … 41.z 38.z 38.z 35.z … … 47.z 44.z … 47 … 28.x … … 66.z 28...y 48 12 33.z 55 36 24 29.z … 41.z … … … 13 10 … … … 1 163 … 1 100 … … 8768 … … … 11578 6858 7678 … 2674 … 4685 5422 7398 … … … 6619 9082 5280 … 5435 … 8 790 … … … 140 … 600 … … 151 … … … … … … … 279 … … … … … 743 … … … … … 1 235.z … … … … 588 … 8 608.z 10 662.y … … 11 440.z 9 755.z 7 774.z 6 427.y 4 578.z 7 556.y 7 731.z 5 429.y 7 429.z 12 142.z 3 622.y … 7 861.z 11 072.z 7 224.z … 5 909.z 10 973.z 9 382.z 6 096.z … … 265.z … … 720.z … 144.x … … … 267...y 3732...z 264 506...z 746 861 … 177...z … … … … … 52 95 … S TAT I S T I C A L TA B L E S Ta b l e 1 1 Public current expenditure on secondary education as % of GNP Public current expenditure on secondary education per pupil as % of GNP per capita Primary teachers’ compensation as % of current expenditure on primary education, in public institutions 1999 … … 1.2 … 1.0 … … 2.7 … … 2.4 2.9 2.3 2.6 … 1.0 … 1.6 2.1 2.1 … … … 1.7 2.1 2.2 … 1.9 … 1.8 … … … 0.6 … 1.7 … … 0.6 … … … … … … 1.2 0.6 … … … … 1.3 1.5 … … … … … 2006 1999 2006 1999 2006 Country or territory 1.8.z … … … … 0.6 0.5 2.5.z 2.5.y … 2.9.y 2.9.z 2.5.z 2.4.z 2.2.y 1.3.z 2.5.y 1.8.z 2.0.y 2.0.z 1.7.z 2.0.y … 2.0.z 2.3.z 2.2.z … 1.6.z 2.7.z 1.9.z 1.8.z … … 1.0.z 1.9.z … 2.3 … 0.7.x … … 0.8.z 1.0...y 3.4 0.5 1.7.z 1.4 1.8 0.3 0.3.z … 0.9.z 0.5.z … … 0.2 0.4 … … … 13 … 11 … … 29 … … 27 37 25 26 … 14 … 18 23 26 … … … 20 24 26 … 23 … 24 … … … 8 … 24 … … 11 … … … … … … … 15 … … … … … 42 … … … … … 20.z … … … … 8 9 26.z 33.y … 34.y 33.z 30.z 24.z 22.y 20.z 22.y 23.z 22.y 26.z 22.z 20.y … 23.z 27.z 35.z … 22.z 34.z 24.z 18.z … … 13.z … … 11.z … 10.x … … … 24...y 37...z 20 74...z 32 15 … 14...z … … … … … 5 9 … … … 78 63 71 … … 71 … … … 49 59 … … … … 83 … … … … … … … … … 78 50 72 52 56 … … … 79 … … … … … … … … … … … … … … … … … … … … … … 85.z … … … 52.y … Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe 50 55.z 66.y … 78.z 51.z 58.z 53.z … 91.z … 76.z … 65.z 74...z 58.y … … 79.z 85.z … 73.z 54.z 72.z 53.z 55.z … … … 80.y … … … … … … … … … … … 86 … … … … … … … … … … Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Table 11 (continued) Total public expenditure on education as % of GNP Total public expenditure on education as % of total government expenditure Public current expenditure on education as % of total public expenditure on education Public current expenditure on primary education as % of public current expenditure on education Public current expenditure on primary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on primary education as % of GNP Country or territory 1999 2006 1999 2006 1999 2006 1999 2006 1999 2006 1999 Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World1 Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 3.1 4.2 2.1 5.6 5.4 10.2 … 2.5 4.7 3.0 4.2 2.5 7.9 … … … … 3.5 5.5 … … 6.2 5.7 4.3 … 2.2 2.0 … 4.5 3.7 4.9 4.5 … 4.4 3.7 4.7 3.6 6.5 4.9 … 4.5 5.0 2.9 3.6 2.1...y 5.5.z 1.7.z … 6.9 10.8 … 3.1 5.9.x 4.4 3.9 5.3.z 6.8.x 3.3 … 3.8.z … 5.0 6.8 3.9...z … 5.5 6.9.z … 5.3...y … 2.1.z … 4.9 3.9 5.3 4.4 4.6 5.3 3.4 … 3.6 … 4.1 5.8 4.0 5.5 3.3 4.4 14 … … 12 … 26 … … 25 … 18 … … … … … … … … … … 22 … 26 … … … … … 14 11 … … … … … 13 … 16 … 15 12 … … … … … … 18.z 30.z … 25.z … 17 13 23.y … 18 … 19 … 26 13 … … 18 … … 18...y … 15.y … 15 17 12 16 21 13 … … 16 … 15 15 15 12 15 18 87 … … 41 95 74 … … 82 90 91 … 94 … … … … … … … … 98 100 97 … … … … … … … … … … … … … … … … 93 92 89 … 86...x 86.z … … 94 91 … 84 82.x 73 88 77.z … 81 … 94 … 92 88 99...z … 97 100.y … 75...y … 99.z … 92.1 94.2 93.8 89.5 … 93.0 94.6 … … … 91.9 88.0 94.5 93.6 77.9 86.4 … … … … … 43 … … … 49 32 … 59 … … … … … … … … 45 33 43 … … … … … … … … … … … … … … … … … 26 … … … 34.z … … 55 38 … 46 63.x 60 28 70.y … 64 … 45 … 46.z 21 52...x … 45 38...y … 62...y … 59.z … 33 … 24 … … 19 … … … … 37 32 41 25 35 48 … … … … … 566 … … … 136 1 067 … 1 416 … … … … … … … … 1 403.
* 437 150 … … … … … … … … … … … … … … … … … 4 697 … … … 300.z … … 237 663 … 57 90.x 183 1 205 156.y 944.x 178 … 109.z … 299.z 2399...y … … 1 383.z 484...y … 110...y … 55.z … 1 005 … 5 100 … … 2 182 … … … … … … 614 5 584 … 167 … … … … … 3.2 … … … 1.3 1.2 … 4.4 … … … … … … … … 2.7 1.9 1.8 … … … … … … … … … … … … … … … … … 1.3 … … 2006 … 1.6.z … … 3.6 3.8 … 1.2 3.0.x 1.9 1.0 2.6.y 3.9.x 1.7 … 1.9.z … 2.1.z 1.3 2.3...x … 2.4 2.3...y … 2.5...y … 1.3.z … 1.4 … 1.1 1.7 1.7 0.8 … … … … 1.7 1.8 1.6 1.3 1.2 1.9 1.
All regional values shown are medians.

Data in italic are UIS estimates.

Data in bold are for 2007.

(z) Data are for 2005.

(y) Data are for 2004.

(x) Data are for 2003.

(*) National estimate.
3 7 0 S TAT I S T I C A L TA B L E S Ta b l e 1 1 Public current expenditure on primary education per pupil as % of GNP per capita Public current expenditure on secondary education as % of public current expenditure on education Public current expenditure on secondary education per pupil (unit cost) at PPP in constant 2005 US$ Public current expenditure on secondary education as % of GNP Public current expenditure on secondary education per pupil as % of GNP per capita Primary teachers’ compensation as % of current expenditure on primary education, in public institutions 1999 2006 1999 2006 1999 2006 1999 … … … … … 15 … … … 16 11 … 21 … … … … … … … … 14.
* 9 10 … … … … … … … … … … … … … … … … … 17 … … … 12.z … … 20 16 … 6 13.x 17 10 14.y 19.x 22 … 9...z … 17.z 15...y … … 14.z 12...y … 9...y … 6.z … 14 … 17 13 12 17 … … … … 12 13 12 18 … 13 … … … … … 24 … … … 34 37 … 28 … … … … … … … … 34 27 34 … … … … … … … … … … … … … … … … … 42 … … … 37.z … … 23 19 … 21 10.x 27 43 17.y … 25 … 20 … 26.z 21 27...x … 31 28...y … 20...y … 15.z … 36 … 42 … … 46 … … … … 32 33 30 40 … 26 … … … … … 1 655 … … … 412 1 576 … 2 313 … … … … … … … … 1 973.
* 1 237 484 … … … … … … … … … … … … … … … … … 6 858 … … … 707...z … … 245 1 439 … 134 80...x 291 1778 538.y 1 140.x 366 … 197...z … 590.z 2828...y … … 1 726.z 1203...y … 376...y … 84...z … … … 6 427 … … 1 203 … … … … … … 594 7 753 … 376 … … … … … 1.9 … … … 0.9 1.4 … 2.1 … … … … … … … … 2.0 1.5 1.4 … … … … … … … … … … … … … … … … … 2.1 … … 2006 … 1.8.z … … 1.5 1.8 … 0.6 0.5.x 0.9 1.5 0.6.y 1.6.x 0.7 … 0.4.z … 1.2.z 1.3 1.2...x … 1.6 1.7...y … 0.8.y … 0.3.z … 1.6 … 2.2 1.4 … 1.4 … … … … 1.4 … 1.3 2.1 … 1.0 1999 2006 1999 2006 Country or territory … … … … … 45 … … … 48 16 … 34 … … … … … … … … 20.
* 25 31 … … … … … … … … … … … … … … … … … 25 … … … 29...z … … 21 35 … 14 12...x 27 14 49.y 23.x 45 … 17...z … 34.z 18...y … … 17.z 31.y … 30...y … 9...z … 20 … 23 … … 21 … … … … 14 … 13 24 … 20 … … … … … 84 … … … … … … … … … … … … … … … … … 79 … … … … … … … … … … … … … … … … … … … … 75.y … … … … … … … … … … 93.y … … … … … … 68 … … 78 … … … … 93.y … … … … … … … … … … … 82 … 81 65 … … Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World 1 Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa Table 12 Trends in basic or proxy indicators to measure EFA goals 1, 2, 3, 4 and 5 GOAL 1 GOAL 2 GOAL 3 Early childhood care and education Universal primary education Learning needs of all youth and adults GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION YOUTH LITERACY RATE (15-24) School year ending in 1999 Total (%) 3 36 0.4 11 5 29 78 61 5 … 62 6 39 25 … 19 8 14 64 1991 Total (%) … 27 0.6 6 8 21 33 … … … 58 3 21 28 7 18 6 8 56 0.7 0.7 59 84 … 91 28 95 76 113 47 58 … 47 70 76 74 … 86 66 … 4 86 37 19 59 73 34 39 16 … 73 71 48 4 40 75 … 67 40 90 87 78 53 50 … 50 48 62 68 54 82 75 27 6 50 26 21 36 14 10 25 8 … 24 … 50 5 2006 Total (%) 15 52 2 17 6...z 32 75 64 9 2...z 59 8 30 43 … 24 11 … 78 0.9.z 49.y 103 … 82 50 114 93 86 89 69 … 57 71 72 87 59 93 81 33.z 13 90 36 32 55 38 14 54 9 … 27 104 51 11 Country or territory Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan2 Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova3,4 Romania Russian Federation5 Serbia3 Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia 3 7 2 1991 School year ending in 1999 2006 1985-19941 2000-20061 Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) 89 99 29 86 94 94 49 66 93 36 56 69 … 89 59 40 91 93 99 50 95 85 79 85 79 87 100 91 94 … … 97 86 81 98 … … 96 94 89 81 … 89 97 88 92 90 77 … 78 99 92 72 0.88 1.00 0.72 0.84 0.88 1.01 0.93 0.97 0.96 0.78 0.70 0.95
 … 0.98 0.80 0.75 0.91 0.93 0.98 0.38 1.01 0.96 1.00 1.00 1.00 1.00 0.99 1.01 0.99 … … 1.00 1.01 1.00 1.00 … … 1.01 0.99 0.92 1.00 … 0.99 1.00 0.99 1.00 1.02 0.98 … 0.99 1.00 0.98 0.84 91 96 27 94 85 91 87 86 … 64 70 81 97 92 … … 92 93 79 56 94 … … 97 85 97 96 88 97 95 … 96 93 96 … … … 96 93 … … … 85 77.
* … 88.

* 89 … … … 94 … 83 0.96 1.03 0.73 0.93 0.85 1.01 1.01 0.96 .… 0.99 0.85 1.00 1.00 1.01 .… .… 0.93 0.98 0.99 0.59 0.98 .… .… 0.98 0.98 1.00 0.98 0.99 0.98 0.99 .… 1.00 .… 0.99 .… .… .… 0.99 0.98 .… .… .… 1.01 1.00.

* .… 0.99.
* .… 0.99.

* 1.04 .… .… .… 1.01 .… 0.91 95 98.z 38 96 89...z 90 83 82 … 79 88 74 76 94 … … … 96 88 75.z 94.y 89 … 92 90 93...z 94 88 90...z 89 … 96 88 93 91 95 92...z 95 92.z 91 90 82 85 89 90 86 91 97 … … 96 94 90 0.98 1.00.z 0.82 0.96 0.86...z 1.02 0.99 0.99 .… 1.05 0.94 1.02 1.00 1.01 .… .… .… 1.01 1.00 0.76.z 0.99.y 0.98 .… 0.99 0.99 1.03...z 0.99 0.99 1.03...z 0.99 .… 1.01 1.00 1.00 1.00 1.00 1.01...z 1.00 1.00.z 0.96 1.00.
* 1.05 0.97 1.03 1.00 0.99 1.02 0.96 .… .… 1.01 1.00 0.98 74 97 … 63 … … 87 … 95 … 58 … … 90 88 … … … 82 60 … 100 … … 100 … 100 … 100 100 … … 100 99 100 … … 100 99 93 … 100 … … 100 … … 100 … … … 98 … 0.72.

* 0.99.

* .… 0.76.

* .… .… 0.93.

* .… 0.92 .… 0.64.

* .… .… 1.03.

* 0.86.

* .… .… .… 1.04.

* 0.43.

* .… 1.00.

* .… .… 1.00.

* .… 1.00.

* .… 1.00.

* 1.00.

* .… .… 1.00.

* 1.00.

* 1.00.

* .… .… 1.00.

* 0.99.

* 0.92.

* .… 1.00.

* .… .… 1.00.

* .… .… 1.00.

* .… .… .… 1.00.


* .… 92 100 … 85 85 99 99 … 99 66 74 98 99 97 97 77 93 95 97 79 99 100 100 98 100 … 100 … 100 100 … … 100 98 100 … … 100 99 96 100 100 100 … 100 100 96 100 100 99 … 100 85 0.95 1.00 .… 0.89 0.91.

* 1.00 1.00.

* .… 0.98 0.88 0.78 0.99 1.00 1.01 0.98 0.84.

* 0.96 0.97 0.98 0.69 1.00 1.00 1.00.

* 1.00 1.00 .… 1.00 .… 1.00 1.00 .… .… 1.00 1.00 1.00 .… .… 1.00 1.00 0.96.

* 1.00 1.00 1.00 .… 1.00 1.00 1.03 1.00 1.00 1.00.
* .… 1.00 0.92 S TAT I S T I C A L TA B L E S Ta b l e 1 2 GOAL 4 GOAL 5 Improving levels of adult literacy Gender parity in primary education Gender parity in secondary education ADULT LITERACY RATE (15 and over) GROSS ENROLMENT RATIO (GER) GROSS ENROLMENT RATIO (GER) 1985-19941 2000-20061 1991 School year ending in 1999 2006 1991 School year ending in 1999 2006 Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Country or territory 50 84 … 44 … … 74 … 76 … 42 … … 76 71 … … … 71 37 … 98 … … 97 … 100 … 99 98 … … 96 97 98 … … 100 94 79 … 99 … … 98 … … 98 … … … 88 … 0.57.
* 0.87.

* .… 0.55.

* .… .… 0.88.

* .… 0.71 .… 0.52.

* .… .… 0.94.

* 0.72.

* .… .… .… 0.95.

* 0.30.

* .… 0.97.

* .… .… 0.96.

* .… 1.00.

* .… 0.99.

* 0.99.

* .… .… 0.96.

* 0.96.

* 0.97.

* .… .… 1.00.

* 0.94.

* 0.76.

* .… 0.99.

* .… .… 0.97.

* .… .… 0.98.

* .… .… .… 0.89.

* .… 75 88 … 71 74 93 93 … 86 55 55 84 92 90 84 61 83 77 90 57 99 100 97 98 99 … 100 … 100 100 … … 99 98 100 … … 100 97 88 100 99 99 … 100 99 97 100 99 97 … 95 76 0.78 0.95 .… 0.72 0.76.

* 0.92 0.96.
* 0.92 0.96.

* .… 0.82 0.76 0.62 0.86 0.91 1.00 0.89 0.73.

* 0.85 0.79 0.98 0.51 0.99 1.00 0.95.

* 0.99 0.98 .… 1.00 .… 1.00 1.00 .… .… 0.99 0.98 1.00 .… .… 1.00 0.97 0.84.

* 1.00 1.00 0.99 .… 1.00 1.00 1.01 1.00 1.00 0.98.
* .… 0.96 0.78 96 110 34 94 108 101 60 97 101 52 64 85 … 101 73 49 101 113 114 63 100 95 … 97 85 97 112 95 98 92 … 98 90 91 108 … … 100 99 99 89 … 111 97 89 … 97 91 … 81 108 114 90 0.85 1.00 0.72 0.83 0.83 1.01 0.95 0.97 0.94 0.77 0.69 0.92 .… 0.93 0.85 0.77 0.90 0.90 0.97 0.35 1.00 0.96 .… 0.98 0.99 1.00 0.97 1.00 1.00 0.95 .… 0.99 1.02 1.00 1.00 .… .… .… 0.98 0.92 1.00 .… 0.99 1.00 0.99 .… 1.02 0.98 .… 0.98 0.99 0.94 0.81 105 107 33 102 92 98 100 105 120 89 86 91 105 102 … 49 102 113 90 71 103 111 … 106 92 103 102 102 100 102 … 98 100 105 108 112 103 100 101 … 109 100 94 98 97 98 97 98 … 98 100 114 97 0.91 1.01 0.71 0.91 0.82 1.00 1.01 0.95 0.98 0.99 0.81 0.97 1.01 0.96 … 0.85 0.92 0.95 0.97 0.56 0.98 0.99 … 0.98 0.98 0.99 0.97 0.98 0.98 0.98 … 0.98 1.00 0.98 0.98 0.99 0.99 0.99 0.98 … 0.99 … 1.00 1.00 1.01 0.99 1.04 0.95 … 1.00 1.00 0.97 0.87 110 120 44 105 99...z 97 96 94 110 102 106 82 83 105 … 66 126 108 104 87.z 105.y 96 … 100 99 100 99 97 95 95 … 98 97 105 96 97 100 100 98.z 94 102 98 96 96 105 97 101 100 … 95 105 107 122 0.93 1.00 0.81 0.95 0.83...z 1.02 0.99 0.97 0.95 1.05 0.89 1.01 1.00 0.99 .… 0.87 0.96 0.97 0.99 0.74.z 0.99.y 0.98 .… 0.99 1.00 0.99 0.98 0.98 0.96 0.99 .… 1.00 0.99 0.99 1.00 1.00 0.98 0.99 1.00.z 0.95 1.00 1.04 0.97 1.03 1.00 0.99 1.02 0.95 .… 0.97 1.00 0.99 0.93 60 100 11 71 44 63 43 … 80 14 36 45 … 84 44 21 48 45 68 … 78 93 … 75 76 91 100 79 92 92 … 81 78 92 93 … … 89 56 48 94 … 88 95 100 100 82 102 … 99 83 77 25 0.80 1.04 0.66 0.79 0.63 1.04 0.98 .… .… 0.49 0.72 0.81 .… 1.06 0.80 0.79 0.73 0.79 1.16 .… 0.86 .… .… 1.04 1.10 0.97 1.08 1.01 1.02 .… .… 1.05 1.10 0.99 1.06 .… .… .… 0.99 0.63 .… .… 1.01 0.97 1.03 1.02 1.14 .… .… 0.91 1.03 1.09 0.43 … 95 14 82 34 89 98 74 … 19 37 75 80 87 … 26 40 72 76 41 71 85 … 91 84 83 93 94 88 95 … 99 83 79 … 93 85 100 82 … 98 91 76 79 92 83 58 74 … 86 157 85 17 .… 1.08 0.72 0.92 0.63 1.02 1.02 1.09 .… 0.77 0.79 1.00 1.04 1.11 .… .… 0.91 1.02 1.06 0.37 0.98 1.05 .… 0.98 1.02 1.04 1.04 1.02 1.04 1.01 .… 0.99 0.98 1.01 .… 1.01 1.02 1.03 0.97 .… 1.03.
* .… 1.00 0.98 1.00 1.02 1.27 0.86 .… 0.98 1.00 1.09 0.53 83...z 102 22 88...y 45...z 89 89 81 94 25.

* 52 89 94 101 … 34 70 85 90 46.z 77.y 96 … 106 91 96 100 96 99 99 … 100 89 86 84 88 94 95 84.z 79 93 90 83 85 93 86 89 83 … 102 150 98 38 1.08...z 1.04 0.67 0.94...y 0.66...z 1.03 1.05 1.10 1.17 0.86.

* .… 0.96 1.06 0.97 .… 0.96 0.95 1.10 1.02 0.49.z 0.96.y 1.02 .… 0.96 1.03 1.01 1.02 0.99 1.00 1.00 .… 0.99 1.04 1.00 0.98 1.03 1.01 1.00 0.98.z 0.83 0.98.
* 1.04 0.96 1.04 0.99 1.01 1.12 0.83 .… 0.98 0.95 1.04 0.79 Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan 2 Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova 3,4 Romania Russian Federation 5 Serbia 3 Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia Table 12 (continued) GOAL 1 GOAL 2 GOAL 3 Early childhood care and education Universal primary education Learning needs of all youth and adults GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION YOUTH LITERACY RATE (15-24) Country or territory China6 Cook Islands3 DPR Korea Fiji Indonesia Japan Kiribati3 Lao PDR Macao, China Malaysia Marshall Islands3 Micronesia Myanmar Nauru New Zealand Niue3 Palau3 Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau3 Tonga Tuvalu3 Vanuatu Viet Nam 1991 Total (%) 22 … … 14 18 48 … 7 89 37 … … … … 76 … … 0.3 12 55 … … 36 49 … … … … … 28 Latin America and the Caribbean … … Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda3 Bolivia Brazil British Virgin Islands3 Cayman Islands7 Chile Colombia Costa Rica Cuba Dominica3 Dominican Republic Ecuador El Salvador Grenada3 Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua School year ending in 1999 Total (%) 38 86 … 16 23 83 … 8 87 108 59 37 2 … 85 154 63 … 30 80 53 … 35 97 … … 30 … … 39 … … 57 99 12 74 27 … 45 58 62 … 77 37 84 109 80 32 64 43 93 46 124 … … 78 74 … 111 27
 2006 Total (%) 1991 School year ending in 1999 Total (%) GPI (F/M) Total (%) GPI (F/M) 39 94...z … 16 37 86 75...y 11 87 125.z 45 … 6 89 92 119.z 64...z … 45 101 48...y … … 92 10.z 125...y 23...z 107 29 … 103 … 66.z 99 … 94 34 … 50 69.z 93 … 55 40 70 113 77.z 32 90 51 81...z 29 99 … 38 92.z 106 91 … 52 98 … … … 96 100 … 62 81 93 … 98 99 … 98 … … 66 96 100 … 96 84 88 … … 97 … 71 90 … … 94 … 90 79 94 … 91 85 … … 89 68 87 94 … 56 98 75 100 64 89 21 88 96 98 … … 70 0.96 … … … 0.96 1.00 … 0.86 0.98 0.99 … 1.04 … … 1.00 … … 0.86 0.99 1.01 … 0.99 0.86 0.99 … … 0.97 … 1.01 0.92 … … 1.00 … 1.03 0.98 0.99 … 0.92 0.95 … … 0.98 1.15 1.01 1.00 … 2.15 1.01 1.01 1.00 0.91 1.00 1.05 1.01 1.00 0.97 … … 1.03 … 85 … 99 … 100 97 76 85 98 … … 92 … 99 99 97 … 92 94 92 … … … … … 88 … 91 95 … … 99.
* 98 89 94 94 … 95 91 96 … … 89 … 97 94 84 97 … … 82 … … … 88 97 … … 76 .… 0.96 .… 1.01 .… 1.00 1.01 0.92 1.01 0.98 .… .… 0.99 .… 1.00 1.00 0.94 .… 1.00 0.97 0.99 .… .… .… .… .… 0.96 .… 0.99 .… .… .… 1.00.
* 1.03 0.99 0.99 0.99 .… 1.00 .… 1.02 .… .… 1.01 .… 1.01 0.98 1.01 1.01 .… .… 0.91 .… .… .… 1.00 1.00 .… .… 1.01 2006 1985-19941 2000-20061 Total (%) … 74...z … 91 96 100 … 84 91 100.z 66 … 100 … 99 … … … 91 98 90...y … 62.z 94 68...z … 96.z … 87 … 92 … 99.z 100 88 96 97 92 95 94.z 95 … … 88 … 97 77 77 97 94 84...z 94 … … 96 90...z 98 99 … 90 GPI (F/M) Total (%) .… 1.03...z .… 1.00 0.96 1.00 .… 0.94 0.98 1.00.z 0.99 .… 1.01 .… 1.00 .… .… .… 1.02 .… 1.00...y .… 0.99.z 0.99 0.96...z .… 0.97.z .… 0.99 .… 1.00 .… 0.99.z 1.00 1.03 0.99 1.01 .… 1.01 1.02.z 1.00 .… .… 1.00 .… 1.01 1.06 1.03 1.01 1.00 0.99...z 0.96 .… .… 1.02 1.00...z 0.99 .… .… 1.00 94 … … … 96 … … … … 96 … … … … … … … … 97 … 99 99 … … … … … … … 94 … … 98 … … … 76 … 94 … … … 98 91 … … … … 96 85 … 76 … … … … 95 … 97 … GPI (F/M) 0.94.
* .… .… .… 0.98.

* .… .… .… .… 0.99.

* .… .… .… .… .… .… .… .… 1.01.

* .… 1.00.

* 1.00.

* .… .… .… .… .… .… .… 0.99.

* .… .… 1.00.

* .… .… .… 1.01.

* .… 0.95.

* .… .… .… 1.01.

* 1.03.

* .… .… .… .… 0.99.

* 1.00.

* .… 0.87.

* .… .… .… .… 0.99.

* .… 1.01.

* .… Total (%) 99 … … … 99 … … 82 100 98 … … 95 … … … … 64 94 … 99 100 … 98 … … 100 … … 94 … … 99 99 … … … … 98 98 … … 99 98 98 100 … 96 96 95 … 85 … … 90 94 98 … 98 88 GPI (F/M) 1.00 .… .… .… 1.00 .… .… 0.93 1.00 1.00 .… .… 0.98.
* .… .… .… .… 1.03 1.02 .… 1.00 1.00 .… 1.00 .… .… 1.00 .… .… 0.99.

* .… .… 1.00 1.00 .… .… .… .… 0.99 1.02.

* .… .… 1.00 1.01.

* 1.01 1.00 .… 1.02 1.01 1.01.

* .… 0.94 .… .… 1.06 1.08 1.00.
* .… 1.00 1.07 50 … … … 23 … 32 48 … … 72 13 65 102 … … 42 21 … 25 74 33 13 78 63 … … 13 S TAT I S T I C A L TA B L E S Ta b l e 1 2 GOAL 4 GOAL 5 Improving levels of adult literacy Gender parity in primary education Gender parity in secondary education ADULT LITERACY RATE (15 and over) GROSS ENROLMENT RATIO (GER) GROSS ENROLMENT RATIO (GER) 1985-19941 2000-20061 1991 School year ending in 1999 2006 1991 School year ending in 1999 2006 Total (%) 78 … … … 82 … … … … 83 … … … … … … … … 94 … 98 89 … … … … … … … 88 … … 96 … … … 70 … 80 … … … 94 81 … … … … 88 74 … 64 … … … … 88 … 95 … GPI (F/M) 0.78.
* .… .… .… 0.86.

* .… .… .… .… 0.87.

* .… .… .… .… .… .… .… .… 0.99.

* .… 0.99.

* 0.87.

* .… .… .… .… .… .… .… 0.89.

* .… .… 1.00.

* .… .… .… 1.00.

* .… 0.82.

* .… .… .… 0.99.

* 1.00.

* .… .… .… .… 0.95.

* 0.92.

* .… 0.80.

* .… .… .… .… 0.94.

* .… 1.00.


* .… Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) 93 … … … 91 … … 72 93 92 … … 90 … … … … 57 93 … 99 94 … 94 … … 99 … … 90 … … 98 98 … … … … 90 90 … … 96 92 96 100 … 89 92 84 … 72 … … 83 85 92 … 96 80 0.93 .… .… .… 0.92 .… .… 0.83 0.94 0.95 .… .… 0.92.

* .… .… .… .… 0.85 1.01 .… 0.99 0.94 .… 0.96 .… .… 1.00 .… .… 0.93.

* .… .… 1.00 1.00 .… .… .… .… 0.89 1.01.

* .… .… 1.00 1.00.

* 1.00 1.00 .… 1.01 0.98 0.93.

* .… 0.86 .… .… 1.01 1.13 0.96.
* .… 1.00 1.02 126 … … 133 114 100 … 103 99 93 … … 114 … 102 … … 65 109 105 124 103 88 113 … … 112 … 95 107 … … 108 … 96 92 112 … 97 104 … … 101 103 103 100 … 91 116 81 117 81 94 46 107 101 112 … … 91 0.93 .… .… 1.00 0.98 1.00 .… 0.79 0.96 0.99 .… .… 0.97 .… 0.99 .… .… 0.85 0.99 1.01 1.02 0.97 0.87 0.98 .… .… 0.98 .… 0.96 0.93 .… .… .… .… 1.03 1.00 0.98 .… 0.92 .… .… .… 0.98 1.02 0.99 0.97 .… 1.00 0.99 1.01 0.85 0.87 0.99 0.95 1.04 0.99 0.97 .… .… 1.06 … 96 … 109 … 101 104 111 100 98 101 … 100 … 100 99 114 … 113 95 99 … 88 106 … … 108 98 111 108 … … 117 114 95 98 118 … 113 154 112 … 101 114 108 111 104 113 114 112 … 101 121 … … 92 111 … 131 100 … 0.95 … 0.99 … 1.00 1.01 0.85 0.96 0.98 0.98 … 0.99 … 1.00 1.00 0.93 … 1.00 0.97 0.98 … 0.94 0.99 … … 0.96 1.02 0.98 0.93 … … 1.00 0.99 0.98 0.98 0.97 … 0.98 0.94 0.97 … 0.97 1.00 0.98 0.97 0.95 0.98 1.00 0.96 … 0.87 0.98 … … 1.00 0.98 … 0.95 1.01 Total (%) 111 80...z … 100 114 100 113.z 116 106 100.z 93 110 114 79 102 105.z 104...z 55 110 105 100...z … 101.z 108 99.z 93...y 113 106 108 … 93 … 112.z 115 98 103 123 100 109 137.z 112 … 104 116 111 101 86 98 117 114 93...z 114 124.z … 118 95.z 113 114 … 116 GPI (F/M) 0.99 1.01...z .… 0.98 0.96 1.00 1.01.z 0.89 0.94 1.00.z 0.97 1.01 1.01 1.03 1.00 0.95.z 0.94...z 0.84 0.99 0.97 1.00...z .… 0.96.z 1.00 0.92.z 1.35...y 0.95 0.99 0.97 .… 0.99 .… 0.99.z 0.98 1.00 0.98 0.97 0.85 1.00 0.94.z 0.97 .… 0.95 0.99 0.99 0.97 1.02 0.95 1.00 0.96 0.96...z 0.93 0.99.z .… 0.99 1.00.z 0.97 1.00 .… 0.98 Total (%) 49 … … 64 45 97 … 23.
* 65.

* 57 … … 23 … 90 … … 12 71 90 33 67 15 33 … … 98 … 18 32 … … 72 … … … 44 … … 40 … … 73 50 45 94 … … 55.

* 25 100 23 79 21.

* 33 65 53 … 93 42 GPI (F/M) 0.75 .… .… 0.95 0.83 1.02 .… 0.62.

* 1.11.

* 1.05 .… .… 0.99 .… 1.02 .… .… 0.62 1.04 0.97 1.96 0.93 0.61 0.96 .… .… 1.04 .… 0.80 .… .… .… .… .… .… .… 1.15 .… .… .… .… .… 1.07 1.19 1.06 1.15 .… .… .… 1.22 1.16 .… 1.06 0.94.
* 1.23 1.06 0.99 .… 1.19 1.20 Total (%) GPI (F/M) 62 60 … 80 … 102 84 33 76 65 72 … 36 … 113 98 101 … 76 100 79 … 25 … … … 102 … 30 62 … … 94 99 79 100 64 … 78 99 99 … 79 70 57 77 90 57 57 52 … 33 82 … … 88 70 … 92 52 .… 1.08 .… 1.11 .… 1.01 1.18 0.69 1.08 1.07 1.06 .… 1.01 .… 1.05 1.10 1.07 .… 1.09 1.01 1.10 .… 0.76 .… .… .… 1.11 .… 0.87 0.90 .… .… 1.07 1.07 0.99 1.05 1.08 .… 0.93 1.11 0.91 .… 1.04 1.11 1.09 1.07 1.35 1.24 1.03 0.98 .… 0.84 1.02 .… .… 1.02 1.01 .… 1.16 1.19 Total (%) 76 72...z … 84 64 101 88.z 43 98 69.z 66 91 49 46 120 99.z 102...z … 83 98 81...z … 30.z 78 53.z 101...y 94 … 40.y … 83 … 84.z 100 91 102 79 84 82 105.z 107 … 91 82 86 94 106 69 68 65 100.z 53 105 … 76.z 87.z 87 125 … 66 GPI (F/M) 1.01 1.04...z .… 1.10 1.00 1.00 1.14.z 0.78 1.00 1.10.z 1.02 .… 1.00 1.19 1.05 1.07.z .… .… 1.11 0.94 1.13...z .… 0.84.z 1.09 1.00.z 0.88...y 1.04 .… 0.86.y .… Country or territory China 6 Cook Islands 3 DPR Korea Fiji Indonesia Japan Kiribati 3 Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue 3 Palau 3 Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand Timor-Leste Tokelau 3 Tonga Tuvalu 3 Vanuatu Viet Nam British Virgin Islands 3 Cayman Islands 7 Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda 3 Bolivia Brazil Latin America and the Caribbean 1.02 .… 1.11.z 1.04 1.01 1.04 1.06 1.06 0.96 1.10.z 1.13 .… 1.02 1.11 1.06 1.02 0.98 1.20 1.02 1.04 1.03.z 0.92 0.98 .… 1.30.z 1.03.z 1.02 0.98 .… 1.14 Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Dominican Republic Ecuador El Salvador Chile Colombia Costa Rica Cuba Dominica 3 Grenada Table 12 (continued) GOAL 1 GOAL 2 GOAL 3 Early childhood care and education Universal primary education Learning needs of all youth and adults GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION YOUTH LITERACY RATE (15-24) Country or territory Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. 1991 Total (%) 57 31 30 … 51 45 79 8 … 43 40 North America and Western Europe Andorra3 Austria Belgium Canada Cyprus3 Denmark Finland France8 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco7 Netherlands Norway Portugal San Marino7 Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India2 Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka2 Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros 3 7 6 … 69 105 61 48 99 34 83 … 57 … 101 85 94 92 103 … 99 88 51 … 58 65 60 52 63 … … … 3 12 … … … … 47 2 … 0.8 … 12 … 6 … … School year ending in 1999 Total (%) 39 29 55 … 70 … … 58 … 60 45 … 82 111 64 60 90 48 112 94 68 88 … 105 95 73 103 … 97 75 69 … 100 76 89 77 58 … 17 0.9 18 13 54 11 … … … 4 … 2 0.8 11 … … … 2 2006 Total (%) 67 34.z 68 99 69 88...z 84 85.
*,z 118...z 79 60 102 90 121 68...y 79 95 62 116 105 69 96 … 91 104 88 97.z … 90 90 79 … 121 95 99 72 61 0.8...y 10.y 2 39.z 53 82 27 52.z … … 6 15.z 2 2 19 53 2...y 1...z 3...z 1991 School year ending in 1999 2006 1985-19941 2000-20061 Total (%) GPI (F/M) Total (%) 92 94 88 99 95 91 81 89 … 91 87 … 88 96 98 87 98 98 100 84 95 100 90 92 100 … 97 … 95 100 98 … 100 100 84 98 97 25 76 55 … 92 87 63 33 84 50 41 88 27 53 69 91 52 34 57 1.00 0.99 0.99 0.99 0.97 0.99 1.06 1.00 … 1.01 1.03 … 1.02 1.02 1.00 1.00 1.00 1.00 1.00 1.03 0.99 0.99 1.02 1.03 1.00 … 0.99 … 1.04 1.00 1.00 … 1.00 1.00 1.02 1.00 1.00 0.55 0.87 … … 0.92 1.00 0.50 … 0.95 0.95 0.54 1.08 0.65 0.85 0.88 0.95 0.66 0.45 0.73 96 96 98 … 96 … … 87 … … 86 … 97 99 99 95 97 99 99 … 92 99 94 98 99 97 95 … 99 100 … … 100 100 94 100 94 … 83.
* 56 … 82 98 65.

* … … … 50.

* 80 35 … … 99 … 51 49 GPI (F/M) 0.99 1.00 1.00 .… 0.99 .… .… 1.01 .… .… 1.01 .… 1.01 1.00 1.00 1.00 1.00 1.00 1.00 .… 1.01 0.98 1.01 1.00 0.99 1.03 1.02 .… 0.99 1.00 .… .… 1.00 1.00 1.00 1.00 1.00 .… 1.00.

* 0.89 .… 0.97 1.01 0.79.

* .… .… .… 0.68.

* 1.04 0.70 .… .… 0.98 .… 0.62 0.85 Total (%) GPI (F/M) Total (%) 98 94.z 96 71 98 90.z 96 85.

*,z 78...z 100 91 0.99 1.01.z 1.01 1.22 0.98 0.96.z 1.03 1.00.
*,z 1.07...z 1.00 1.00 83 97 97 … 99 96 97 99 98 99 98 95 97 99 97 91.z … 98 98 98 … 100 95 89 98 92 1.01 1.01 1.00 .… 1.00 1.01 1.00 1.00 1.00 1.00 0.99 1.01 1.01 0.99 1.01 0.99.z .… 0.99 1.01 0.99 .… 1.00 1.00 0.99 1.01 1.02 … 89.

*,y 79 89 94 97 79...y 66 97...y .… 1.04.


*,y 1.00 0.96 .… 1.00 0.87...y 0.78 .… … 80 84.z 47 75 … 88 46 … … .… 0.84 1.03.z 0.82 0.97 .… 0.99 0.72 .… .… 95 96 95 … … … … 99 … 99 95 … … … … 100 … … … … 99 … … … … … 98 … … … 99 … 100 … … … … … 45 … 62 87 98 50 … … … 40 89 20 54 … 88 48 17 … GPI (F/M) 0.99.

* 0.99.

* 0.97.

* .… .… .… .… 1.00.

* .… 1.01.

* 1.02.

* .… .… .… .… 1.00.

* .… .… .… .… 1.00.

* .… .… .… .… .… 1.02.

* .… .… .… 1.00.

* .… 1.00.

* .… .… .… .… .… 0.73.

* .… 0.67.

* 0.88.


* 1.00.

* 0.48.

* .… .… .… 0.48.

* 1.07.

* 0.53.

* 0.81.

* .… 0.96.

* 0.56.

* .… .… Total (%) 96 96 98 … … … 95 99 … 99 97 … … … … 100 … … … … 99 … … … 100 … 97 … … … 100 … 100 … … … … 34 71 76 81 98 98 78 69 97 72 51 94 34 73 … 97 59 38 … GPI (F/M) 1.00 1.00 0.98.

* .… .… .… 0.99 1.00 .… 1.01.

* 1.02.

* .… .… .… .… 1.00 .… .… .… .… 1.00 .… .… .… 1.00 .… 1.03 .… .… .… 1.00 .… 1.00 .… .… .… .… 0.36.

* 1.02 0.86 0.88 0.99 1.01 0.84 0.74.

* 1.01.


* 0.75.

* 0.63 1.03 0.69 0.92.

* .… 1.01 0.67.

* 0.42.
* 0.42.

* .… S TAT I S T I C A L TA B L E S Ta b l e 1 2 GOAL 4 GOAL 5 Improving levels of adult literacy Gender parity in primary education Gender parity in secondary education ADULT LITERACY RATE (15 and over) GROSS ENROLMENT RATIO (GER) GROSS ENROLMENT RATIO (GER) 1985-19941 2000-20061 1991 School year ending in 1999 2006 1991 School year ending in 1999 2006 Total (%) 89 90 87 … … … … 97 … 95 90 … … … … 94 … … … … 93 … … … … … 88 … … … 88 … 96 … … … … … 35 … 48 66 96 33 … … … 27 69 14 37 … 63 34 12.
* … GPI (F/M) 0.99.

* 0.96.

* 0.88.

* .… .… .… .… 0.98.

* .… 1.01.

* 0.98.

* .… .… .… .… 0.93.

* .… .… .… .… 0.93.

* .… .… .… .… .… 1.01.

* .… .… .… 0.92.

* .… 0.97.

* .… .… .… .… .… 0.58.

* .… 0.55.

* 0.76.

* 1.00.

* 0.35.

* .… .… .… 0.42.

* 1.09.

* 0.42.

* 0.57.

* .… 0.71.

* 0.42.


* .… .… Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Country or territory 93 94 89 … … … 90 99 … 98 93 … … … … 98 … … … … 97 … … … 99 … 91 … … … 95 … 97 … … … … 28 52 54 65 84 97 55 54 91 67 40 82 26 59 68 83 49 26 … 0.99 0.98 0.89.

* .… .… .… 0.95 0.99 .… 1.01.

* 0.99.


* .… .… .… .… 0.97 .… .… .… .… 0.98 .… .… .… 0.99 .… 1.04 .… .… .… 0.96 .… 0.98 .… .… .… .… 0.29.

* 0.81 0.61 0.70 0.88 1.00 0.61 0.59.

* 0.96.

* 0.65.

* 0.52 1.00 0.52 0.78.

* 0.78.

* 0.88 0.52.

* 0.31.


* .… 105 106 118 119 139 112 104 94 … 108 95 … 101 100 104 90 98 99 108 101 98 101 102 98 104 91 108 … 102 100 119 … 106 100 90 105 103 29 … … 94 109 … 110 … 115 80 54 107 33 71 94 111 63 51 75 .… 0.97 0.97 1.02 0.94 0.98 1.03 1.00 .… 0.99 1.03 .… 1.00 1.01 0.98 1.00 1.00 0.99 0.99 1.01 0.99 0.99 1.00 1.03 1.00 1.08 0.96 .… 1.03 1.00 0.95 .… 0.99 1.00 1.01 1.01 0.98 0.55 .… .… 0.77 0.90 .… 0.63 .… 0.96 0.92.
* 0.51 1.07 0.64 0.84 0.86 0.94 0.64 0.45 0.73 108 119 122 … 109 … … 96 … 111 100 … 103 105 99 97 101 99 107 106 94 99 104 112 103 101 107 … 108 101 123 … 106 110 102 101 101 28 102 75 93 96 134 114 … … 64 74 104 43 60 84 119 … 63 76 0.97 0.96 0.99 … 0.98 … … 1.00 … 0.99 0.98 … 0.99 0.99 1.00 1.00 1.00 1.00 0.99 0.99 1.00 0.98 0.99 0.99 0.99 1.02 1.01 … 0.98 1.00 0.96 … 0.99 1.03 1.00 1.00 1.03 0.08 0.99 0.85 0.84 0.95 1.01 0.77 … … 0.86 0.67 1.00 0.70 0.80 0.82 0.96 … 0.58 0.85 112 111.z 116 94 118 97 121 95.
*,z 90...z 115 104 0.97 0.97.z 1.01 1.20 0.94 1.06 1.00 0.98.
*,z 1.04...z 0.97 0.98 90 102 102 100...y 102 99 98 110 103 102 98 104 110 103 102 100.z … 107 98 115 … 105 96 97 105 98 101.z 103.y 102 112 118 116 126 84 108...z … 96 107.z 60 103 107 106 61 76.z 85...z 1.00 0.99 0.99 0.99...y 1.00 1.00 1.00 0.99 1.00 1.00 0.99 0.99 1.02 0.99 1.01 0.98.z .… 0.98 1.01 0.95 .… 0.98 1.00 0.99 1.01 1.01 0.59.z 1.03.y 0.98 0.96 1.27 0.97 0.95 0.78 1.00...z .… 0.83 0.99.z 0.82 0.91 0.84 0.95 0.69 0.68.z 0.88...z 62 31 67 85 53 58 58 82 … 84 34 … 102 101 101 72 109 116 98 98 94 100 100 88 83 75 83 … 120 103 66 … 105 90 99 87 92 16 … … 42 57 … 34 25 71 11 10 48 7 5 26 21.
* 11 7 18 .… 1.05 0.94 1.11 1.45 1.24 1.16 1.04 .… .… 1.38 .… 0.93 1.01 1.00 1.02 1.01 1.19 1.05 0.97 0.98 0.96 1.09 1.08 1.00 .… 0.94 .… 0.92 1.03 1.16 .… 1.07 1.05 0.95 1.04 1.01 0.51 .… .… 0.60 0.75 .… 0.46 0.48 1.09 .… 0.42 1.18 0.54 0.58 0.71 .… 0.40 0.20 0.65 67 58 84 … 71 … … 77 … 92 56 … 99 143 … 93 125 121 111 98 90 110 107 90 92 98 … … 124 120 106 … 108 157 94 101 95 … 45 37 44 78 43 34 … … 13 19 74 10 … 25 … … 10 25 1.07 1.04 0.94 .… 1.29 .… .… 1.10 .… 1.17 1.22 .… 0.96 1.07 .… 1.03 1.06 1.09 1.00 0.98 1.04 1.06 1.06 1.00 0.99 1.04 .… .… 0.96 1.02 1.08 .… 1.07 1.29 0.92 1.00 .… .… 1.01 0.81 0.71 0.93 1.07 0.70 .… .… 0.76 0.47 1.07 0.62 .… 0.83 .… .… 0.26 0.81 1.04 0.96 0.97 0.97...y 1.02 1.03 1.04 1.00 0.98 0.97 1.03 1.07 0.99 0.99 1.04 1.00.z .… 0.98 0.99 1.09 .… 1.06 0.99 0.95 1.03 0.99 85 102 110 117...y 97 120 112 114 101 103 110 112 92 100 96 99.z … 118 113 97 … 119 103 93 98 94 19.z 44.y 49 54.z 81.z 83 43 30 87...y … 32...z 76.z 15 14 24 80 … 15...z 35...z 70 66.z 94 105 87 75.z 77 76.
*,z 86...z 101 77 1.09 1.03.z 1.03 0.91 1.19 1.24.z 1.37 1.05.

*,z 0.94...z 1.16 1.12 Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe Andorra 3 Austria Belgium Canada Cyprus 3 Denmark Finland France 8 Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco 7 Netherlands Norway Portugal San Marino 7 Spain Sweden Switzerland United Kingdom United States 0.33.z 1.03.y 0.91 0.82.z 0.94.z 1.07 0.89 0.78 1.02...y .… 0.57...z 1.05.z 0.72 0.74 0.79 1.15 .… 0.33...z 0.76...z South and West Asia Afghanistan Bangladesh Bhutan India 2 Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka 2 Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Table 12 (continued) GOAL 1 GOAL 2 GOAL 3 Early childhood care and education Universal primary education Learning needs of all youth and adults GROSS ENROLMENT RATIO (GER) IN PRE-PRIMARY EDUCATION (%) NET ENROLMENT RATIO (NER) IN PRIMARY EDUCATION YOUTH LITERACY RATE (15-24) Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles3 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 1991 Total (%) 3 0.9 … … … 1 … … … … … 35 … … … … … … … 13 1 … … … 2 … … … 21 … 3 … … … … … … … … … … … … … … … … … … … … School year ending in 1999 Total (%) 2 2 … 34 5 1 … 18 39 … 3 44 21 41 3 … 2 96 … 21 1 … … 25 3 109 … … 21 … 2 4 … … 41 Weighted average 33 46 73 27 15 49 21 40 40 61 56 65 55 75 21 9 2006 Total (%) 9 3 … 44.z 14 3 … 17...y 60 7 … 49 18 100 8 … 3 101 … 22 2 14.y … 34 9 109 5 … 38.y 17...z 2...y 3 32 … … 41 62 79 36 18 62 28 45 44 74 65 79 64 81 39 14 1991 School year ending in 1999 2006 1985-19941 2000-20061 Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) … 52 … 89 33 34 … 64 57 45 45 63 57 42 63 98 46 91 52 73 26 58 … 86 54 … … … 94 74 79 … 50 68 83 .… 0.75 .… 0.79 0.86 0.69 .… 0.89 0.96 0.69 0.71 1.01 1.12 0.77 1.01 0.98 0.70 1.01 0.79 1.07 0.68 0.82 .… 0.99 0.88 .… .… .… 1.01 1.02 0.79 .… 1.04 0.96 1.01 55 … … … 47 71 … 62 72 72 … 75 72 39 96 91 61 95 76 76 43 63...z 79...z 98 71 99.y … … 88.y 78.z 80 … 98 92 88 0.90 .… .… .… 0.87 0.92 .… 1.09 0.97 0.86 .… 1.02 1.04 0.97 1.00 1.06 0.79 1.02 0.93 1.06 0.73 0.86...z 1.06...z 1.01 0.98 1.01.y .… .… 1.00.y 1.01.z 0.87 .… 0.99 1.03 1.01 94 49 … … … 34 93 … … … … … … 51 … 59 … 91 … 88 … 71 75 94 38 99 … … … 84 … 70 82 66 95 GPI (F/M) 0.95 0.63.
* .… .… .… 0.71.

* 0.98.

* .… .… .… .… .… .… 0.84 .… 0.70.

* .… 1.01.

* .… 1.06.

* .… 0.77.

* .… 0.96.

* 0.57.

* 1.01.

* .… .… .… 1.01.

* .… 0.82.

* 0.90.

* 0.97.

* 0.98.
* Total (%) 98 61 70 95 … 50 97 … 77 47 … 80 … 70 70 82 29 96 52 93 38 86 78 95 51 99 52 … 95 88 74 85 78 69 98 Weighted average Weighted average GPI (F/M) 0.99 0.74.
* 0.81.

* 1.00.

* .… 0.62.

* 0.98 .… 0.94 0.57.

* .… 1.01.

* .… 1.10 0.94.

* 0.98 0.61 1.02 0.79 1.04 0.46 0.95 0.98.

* 1.00 0.74 1.01.

* 0.66 .… 1.02 1.03.

* 0.76.

* 0.94 0.96 0.91.
* 0.94 0.96 0.91.

* … 64 49 25 91 42 86 24 55 67 96 45 … … 1.00 0.93 0.60 1.00 0.79 1.08 0.61 0.77 0.94 0.94 0.75 … 0.73 0.55 1.03 1.05 0.71 0.83 1.02 0.96 1.00 0.88 0.99 1.00 0.86 0.81 0.98 0.99 0.97 0.97 0.97 0.99 1.02 0.99 1.00 0.67 0.86 1.
Data are for the most recent year available during the period specified.

See the web version of the introduction to the statistical tables for a broader explanation of national literacy definitions, assessment methods, and sources and years of data.

For countries indicated with (*), national observed literacy data are used.

For all others, UIS literacy estimates are used.

The estimates were generated using the UIS Global Age-specific Literacy Projections model.
Those in the most recent period refer to 2006 and are based on the most recent observed data available for each country.

2.

Literacy data for the most recent year do not include some geographic regions.

3.

National population data were used to calculate enrolment ratios.

4.

Enrolment and population data used to calculate enrolment rates exclude Transnistria.

5.

In the Russian Federation two education structures existed in the past, both starting at age 7.
The most common or widespread one lasted three years and was used to calculate indicators; the second one, in which about one-third of primary pupils were enrolled, had four grades.

Since 2004, the four-grade structure has been extended all over the country.
3 7 8 S TAT I S T I C A L TA B L E S Ta b l e 1 2 GOAL 4 GOAL 5 Improving levels of adult literacy Gender parity in primary education Gender parity in secondary education ADULT LITERACY RATE (15 and over) GROSS ENROLMENT RATIO (GER) GROSS ENROLMENT RATIO (GER) 1985-19941 2000-20061 1991 School year ending in 1999 2006 1991 School year ending in 1999 2006 Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) GPI (F/M) Total (%) 74 34 … … … 27 72 … … … … … … 41 … 49 … 80 … 76 … 55 58 73 27 88 … … … 67 … 56 59 65 84 76 98 99 68 58 96 98 82 82 94 87 66 87 99 48 53 GPI (F/M) 0.79 0.53.
* .… .… .… 0.51.

* 0.82.

* .… .… .… .… .… .… 0.57 .… 0.51.

* .… 0.88.

* .… 0.95.

* .… 0.65.

* .….

* 0.73.

* 0.48.

* 1.02.

* .… .… .… 0.94.

* .… 0.66.

* 0.67.

* 0.79.

* 0.88.

* 86 49 67 87 … 36 85 … 64 29 … 74 82 54 71 71 23 87 44 88 30 71 65 87 42 92 37 … 88 80 53 73 72 68 91 Weighted average * 0.67.

* 0.86.

* .… 0.46.

* 0.91 .… 0.80 0.43.

* .… 0.90.


* 1.23.

* 0.83 0.85.

* 0.80 0.50 0.94 0.56 0.98 0.36 0.79 0.84.

* 0.88 0.60 1.01.

* 0.52 .… 0.98 0.97.

* 0.56.

* 0.79 0.83 0.78.
* … 93 66 30 109 60 128 28 83 71 … 55 … 53 … .… 0.98 0.84 0.59 1.00 0.74 1.03 0.61 0.79 0.93 .… 0.73 .… 0.70 .… 0.99 0.99 0.65 0.84 0.98 .… … … … … 0.97 0.95 0.75 0.92 1.00 0.92 0.97 Weighted average Total (%) 108 71 … 122.z 62 91 152...y 74 98 88 … 106 114 91 139 119 80 102 105 107 51 96.z 140 127 80 125 147 … 106.y 106.z 102 117 112 117 101 105 99 101 106 97 97 100 109 110 91 118 108 118 101 108 95 GPI (F/M) 0.90 0.79 .… 0.95.z 0.81 0.88 0.99...y 1.08 0.99 0.84 .… 0.97 1.00 0.90 0.96 1.04 0.79 1.00 0.86 1.00 0.73 0.83.z 1.04 1.00 0.98 0.99 0.90 .… 0.96.y 0.93.z 0.86 1.01 0.98 0.98 0.99 0.95 0.99 1.00 0.94 0.90 0.98 0.98 0.99 0.99 0.97 0.97 0.99 0.97 1.00 0.95 0.89 Total (%) GPI (F/M) Total (%) GPI (F/M) 46 21.
* … … … 13 … 17 34 10 … 28 24 … 17 8 8 55 7 45 7 24 9 … 15 … 17 … 69 42 20 11 5 23 49 51 95 93 42 51 82 98 52 51 66 51 44 52 94 39 21 0.72 0.48 .… .… .… 0.75 .… 0.50 0.65 0.34 .… 0.77 1.42 .… 0.97 0.46 0.50 1.04 0.57 1.22 0.37 0.72 0.73 .… 0.53 .… 0.57 .… 1.18 0.96 0.34 0.59 0.77 .… 0.79 0.83 1.03 1.01 0.75 0.76 0.98 0.99 0.83 0.83 1.00 1.09 1.03 1.09 1.02 0.60 0.75 … 22 18 33 21 12 49 32 37 14 … 38 31 29 … 36 16 76 5 55 7 23 9 … 15 113 … … 89 45 28 10 6 20 43 .… 0.54 0.52 0.37 0.69 0.68 0.86 0.66 0.80 0.37 .… 0.96 1.35 0.65 .… 0.70 0.52 0.98 0.69 1.12 0.60 0.89 0.99 .… 0.64 1.04 .… .… 1.13 1.00 0.40 0.66 0.82 0.77 0.88 Weighted average Total (%) 43...y … … … 31 30 … 45 49 35 … 50 37 … 24 29 28 88...z 16 57 11 32.z 13...z 46 24 112 32 … 95.y 47.z 40...z 18...z … 30...z 40 66 89 101 60 68 88 91 75 75 107 89 57 91 101 51 32 GPI (F/M) 0.84...y .… .… .… 0.60 0.67 .… 0.90 0.88 0.53 .… 0.93 1.27 .… 0.95 0.84 0.61 0.99...z 0.72 1.15 0.63 0.82.z 0.89...z 1.07 0.76 1.13 0.69 .… 1.07.y 1.00.z 0.51...z 0.81...z .… 0.82...z 0.93 0.95 0.97 1.00 0.94 0.92 0.96 0.96 1.01 1.01 0.96 1.07 1.03 1.07 1.00 0.85 0.80 Country or territory Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles 3 Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America/Caribbean Caribbean Latin America N. America/W.
Europe South and West Asia Sub-Saharan Africa 6.

Children enter primary school at age 6 or 7.

Since 7 is the most common entrance age, enrolment ratios were calculated using the 7-11 age group for both enrolment and population.

7.

Enrolment ratios were not calculated due to lack of United Nations population data by age.

8.

Data include French overseas departments and territories (DOM-TOM).

Data in italic are UIS estimates.

Data in bold are for the school year ending in 2007.
(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(*) National estimate.
Table 13 Trends in basic or proxy indicators to measure EFA goal 6 GOAL 6 Educational quality SURVIVAL RATE TO GRADE 5 School year ending in 1999 2005 PUPIL/TEACHER RATIO IN PRIMARY EDUCATION1 School year ending in 1999 2006 1991 1991 Country or territory Total (%) GPI (F/M) Total (%) GPI (F/M) Arab States Algeria Bahrain Djibouti Egypt Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Bulgaria Croatia Czech Republic Estonia Hungary Latvia Lithuania Montenegro Poland Republic of Moldova Romania Russian Federation Serbia Slovakia Slovenia TFYR Macedonia Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Australia Brunei Darussalam Cambodia … … … … … … 75 75 97 … 64 83 94 96 86 80 … … … … 91 … … … 98 … … … 98 … … … … … … … 98 … … … … … … … … … … 99 … … 0.99 1.01 1.81 … … … … … … 0.99 1.02 0.99 … 1.02 1.03 1.09 0.98 0.83 0.99 … … … … 0.99 … … … 1.26 … … … 1.08 … … … … … … … 0.99 … … … … … … … … … … 1.01 … … 95 97 77 99 66 98 … 91 … 68 82 94 … … … 84 92 92 92 87 … … … … … 98 99 … … … … 99 … … … … … … … … … … … … … … … … … … … … 56 1.02 1.01 1.19 1.01 0.94 0.99 .… 1.07 .… 0.94 1.00 1.00 .… .… .… 1.10 0.99 1.02 0.99 .… .… .… .… .… .… 1.01 1.01 .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… .… 0.93 Total (%) 95 99.y 90 … 81...y … 96 91 … 57 80 100 … … … 79.y … 97 99 66.y … … … … … 100 97 … … … … 99 … … … … … … … 97.y … … … 100 … … … … … … … 100 62 GPI (F/M) 1.01 0.98.y 0.94 .… 0.84...y .… 1.02 1.06 .… 0.96 0.97 1.00 .… .… .… 1.02.y .… 1.01 1.02 0.96.y .… .…
 .… .… .… 1.00 1.00 .… .… .… .… .… .… .… .… .… .… .… .… 0.99.y .… .… .… .… .… .… .… .… .… .… .… 1.00 1.05 28 19 43 24 25 25 18 … 14 45 27 28 … 11 16 34 25 28 18 … 19 … … 15 19 23 … 12 15 18 … 16 23 22 22 … … … 21 30 22 … … 17 21 … 28 21 … 24 17 15 33 28 … 40 23 25 … 13 14 … 47 28 25 38 13 … … 25 24 16 22 23 20 … 18 19 18 16 11 15 17 … … 21 19 18 17 19 14 22 … 20 … 19 17 … 24 32 22 … 21 18 14.
* 48 24 … 34 27 21...z … 10 14 … 41 27 14 32 11 … 34 … 19 15 … 21...y 16 … 16 17 16 11 10 12 14 … 11 17 17 17 13 17 15 19.z … 17 21 13 15...y 17 24 33 22 … 18 … 13 50 S TAT I S T I C A L TA B L E S Ta b l e 1 3 % FEMALE TEACHERS IN PRIMARY EDUCATION School year ending in 1999 2006 1991 TRAINED PRIMARY-SCHOOL TEACHERS2 as % of total PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION as % of GNP PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION PER PUPIL (unit cost) at PPP in constant 2005 US$ School year ending in 1999 2006 School year ending in 1999 2006 1991 School year ending in 1999 2006 1991 GOAL 6 Educational quality 39 54 37 52 70 62 61 … … 18 37 47 … 72 48 51 64 45 64 … 55 … … 77 75 … … 84 … 94 … … 97 84 99 … … … … 43 98 … … 92 96 81 90 49 … 79 72 57 31 46 … 28 52 72 … 73 82 … 26 39 52 54 75 … … 65 50 73 20 75 99 … 91 89 85 86 85 97 98 … … 96 86 98 … 93 96 66 … 98 … 83 92 … 95 93 56 … 84 … 66.
* 37 52 … 27 56 72...z … 87 85 … 32 47 65 67 85 … 68 … 52 84 … 76...y 99 … 93 90 95 89 96 97 98 … 84 97 87 98 … 89 97 70.z … 99 99 86 95...y 98 97 95 65 … 85 … 73 42 94 … … … … … 100 15 … … … 100 100 … … … 81 … … … … … … … 100 … … … … … … … … … … … … … … … … … 100 … … 48 … … … … … … … 99 … 79 … 100.y … 100 13 … 100 100.z 100 100.z 52 … 59 … … 60.z … … 100 … … … … … … … … … … … … … … … … … … 100 77.z 100 … … 61 … 93 … 100 … 85 98 4.5 … 1.8 … … … 1.5 … … … 1.6 1.5 … … … … … … … … … 1.8 … 2.8 … … … 2.4 … … … 1.8 … … … … … 1.0 … 1.3 … … … … … … … … … … … 0.5 … … … … … … 1.9 … … … … 2.2 1.4 … … … … 1.7 … 0.7 … … … … … … 0.7 … 0.9 … … … … … … … … 0.6 … … … … … … … … … … … … … 1.6 … … 1.6...x .… .… .… .… 1.8.z 0.7 0.9...z .… 1.4...z 2.9.z 1.8.y .… .… .… .… 1.9 2.4...z 0.4.y .… .… 0.5 .… 0.8.z 0.8...y 0.6.y 1.3.y 1.1.z .… 0.7.z .… 1.7.z .… 0.5...z .… .… 0.6.z 1.1...z .… 1.5...y .… .… 0.4 .… .… .… 1.2.y 0.9...z .… .… 1.5.z .… .… 1 560 … 1 006 … … … … … … … 607 … … … … … … … … … … … … … … … … 2 480 … … … 1 011 … … … … … 2 487 … 657 … … … … … … … … … … … … … … … … … … 568 … … … … 697 … … … … … 349 … 1 997 … Arab States 692...x … … … … 695.z 2 204.z 402...z … 224...z 1 005.z … … … … … 611 1581...z 1 636.y … Central and Eastern Europe … … … … … 1688 … 2339 … … … … … … … … 1245 … … … … … … … … … … … … … … 1196 … 2 045.z 2197...x 2 242.y 2 511.y 4 479.z … 2 166.z … 3 155.z … 941...z … … 2 149.z 5206...z … 1059...y … Central Asia … 356 … … … 261.y 106...z … … East Asia and the Pacific 4637 … … 5 181.z … … Table 13 (continued) Country or territory China Cook Islands DPR Korea Fiji Indonesia Japan Kiribati Lao PDR Macao, China Malaysia Marshall Islands Micronesia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Republic of Korea Samoa Singapore Solomon Islands Thailand
 Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bermuda Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Total (%) 86 … … 87 84 100 92 … … 97 … … … … … … … 69 … 99 … … 88 … … … … … … … … … … … 84 … 67 … … 73 … … 92 76 84 92 75 … … 58 … … … … … … 80 … … 44 GOAL 6 Educational quality SURVIVAL RATE TO GRADE 5 School year ending in 1999 2005 PUPIL/TEACHER RATIO IN PRIMARY EDUCATION1 School year ending in 1999 2006 1991 1991 GPI (F/M) Total (%) 1.36 … … 0.97 2.27 1.00 … … … 1.00 … … … … … … … 0.97 … 1.00 … … 1.28 … … … … … … … … … … … … … 0.96 … … … … … 0.97 … 1.02 … … … … 1.08 … … … … … … 2.06 … … 3.33 … … … 87 … … … 54 … … … … … … … … … … … 100 94 … … … … … … … 72 83 … … 90 … … … 78 … 82 … … 74 100 67 91 94 91 75 77 65 … 56 95 … … … 89 … … 48 GPI (F/M) .… .… .… 0.96 .… .… .… 0.98 .… .… .… .… .… .… .… .… .… .… .… 1.00 1.05.
* .… .… .… .… .… .… .… 0.99 1.08 .… .… 1.00 .… .… .… .… .… 0.97 .… .… .… 1.00 1.08 1.03 1.00 .… 1.11 1.01 1.02 .… 1.06 .… .… .… .… 1.02 .… .… 1.19 Total (%) … … … 86 84 … 82.x 62 … 99.y … … 72 … … … … … 74 99 … … … … … … 92 … … 92 97.y … 90.y 97 85 … 92 90 85...x … … … 99.x 82 94 97 92 68 77 72 … 69 … … 83 … 94 … … 54 GPI (F/M) .… .… .… 1.02 1.04 .… 1.16.x 0.99 .… 1.01.y .… .… 1.01 .… .… .… .… .… 1.11 1.00 .… .… .… .… .… .… 1.00 .… .… .… 1.06.y .… 1.03.y .… 1.07 .… .… .… 1.00...x .… .… .… 1.00.x 1.10 1.02 1.02 .… 1.09 1.02 1.06 .… 0.96 .… .… 1.08 .… 1.02 .… .… 1.14 22 … … 31 23 21 29 27 … 20 … … 48 … 17 20 … 31 33 36 26 26 21 22 … … 23 … 29 35 … … … … … 18 26 … 24 23 19 … 25 30 32 13 29 … 30 … … 34 30 23 38 34 31 … … 36 … 18 … … … 21 25 31 31 21 15 … 31 … 18 16 15 … 35 31 24 27 19 21 … … 21 19 24 30 22 … 22 19 14 18 24 … 25 26 18 15 32 24 27 12 20 … 27 … … 38 27 … … … 27 21 20 34 18 16.z … 28...z 20 19 25.z 31 21 17.z … 17 30 23 16 12...z 13...z 36 35 27 25...y 23 … 18 34.z 6...y 22 19.y 20.y 21 17 … 17.z 18 15 15 23 8 24...y 21.z 15 12 26 28 20 10 17 23 23 40.
* 18...z 31.
* 28.z … 28 28...z 28 17 … 33 S TAT I S T I C A L TA B L E S Ta b l e 1 3 % FEMALE TEACHERS IN PRIMARY EDUCATION School year ending in 1999 2006 1991 TRAINED PRIMARY-SCHOOL TEACHERS2 as % of total PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION as % of GNP PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION PER PUPIL (unit cost) at PPP in constant 2005 US$ School year ending in 1999 2006 School year ending in 1999 2006 1991 School year ending in 1999 2006 1991 GOAL 6 Educational quality 43 … … 57 51 58 58 38 … 57 … … 62 … 80 … … 34 … 50 72 … … … … … 67 … 40 … … … … … … 72 70 … 59 … … … 73 … 80 79 81 … … … … … 76 45 74 … … … … 86 … 86 … … … … 62 43 87 66 … … 73 … 82 100 82 … 87 64 71 80 41 63 … … 67 … 49 78 87 … 88 78 63 76 64 … 61 93 86 89 77 77 80 79 75 … 68 … … … 86 … … … 62 84 86 83 55 77.z … 57...z … 65 75.z 46 87 66.z … … 82 91 83 100...z … 43 87 76 73...y 83 … 60 31.z 69...y … … 54.y 78 93 … 88.z 82 81 78 71 89 61...y 88.z 88 88 78 76 80 77 84 76 70 70.
* 76...z 64.

* 86.z … … 89...z 67 100 … 74 … … … … … … … 76 81 … … … 60 … … … … … 100 … … … … … … … … … … 78 76 … … 100 58 … … … … … 72 98 … … 93 100 64 … … … … … 52 … … … … 100 100 79 … 95.z … … … … … 86 89 … … … 98 … … … … … … … … … … … … … … … … 96 64 … … 99 89 73.z 39 100 … … 74 97 … … 88 100 64 88 71 94.
* 67...z … 57.z … 87.y … … 77 … 74 … … … … … … … … … 1.5 … … … … 1.7 … … … … 1.3 … … 2.2 1.5 … … … … … … … … … … … … 2.7 1.1 … … … … … … 1.2 … … … … … … … … 0.7 … 1.5 0.8 … … … 0.6 0.2 … … … … … … … … … … … … 1.8 … … … … 1.3 1.4 … … … … … … … 2.2 … … … 1.6 … … 1.0 … … 2.0 1.3 … … 1.5 … 2.6 … … … … … … … … … … … 1.8 … … … .… .… .… 2.5.y .… .… .… 0.5.z .… 1.7.y .… .… .… .… 1.5 .… .… .… 1.2.z 1.4.y .… .… .… .… .… .… .… .… .… .… 1.1.z .… 1.5.z 1.3.z .… 2.0...z 2.4.y 0.8...z 2.9.x 1.3.y 1.0 .… 1.2 2.0 2.3.y 2.6 .… 1.2...z .… 1.4 1.8...x 1.6 2.1 .… .… 1.8.z 2.2.z .… .… 1.6 … … … … … … … … … 671 … … … … 3 255 … … … … 1 379 … … 349 637 … … … … … … … … … … … … 528 … … … … … … … 552 … … … … … … … … 194 … 421 431 … … … … … … … … … … … … … … … … … 3971 … … … … 2621 443 … … … … … … … 409 … … … … 1 143.y … … … 61.z … 1 324.y … … … … 4 831 … … … 418.z 3 379.y … … … … … … … … … … Latin America and the Caribbean … … 1 637 … … … … … 295 788 … … 1 256 … 1 469 … … … … … … … … … … … 1 114 … … … … … 1 703.z … … … 846.y … 435.x 1 005.y … … 1 287 1 257 1 623.y … … 644...z … 478 766...x 390 752.z … … 547.z 1 604.z … … A N N E X Table 13 (continued) 9 0 0 2 t r o p e R GOAL 6 Educational quality SURVIVAL RATE TO GRADE 5 School year ending in 1999 2005 PUPIL/TEACHER RATIO IN PRIMARY EDUCATION1 School year ending in 1999 2006 1991 1991 Country or territory Total (%) GPI (F/M) Total (%) Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia Saint Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, Bolivarian Rep. of North America and Western Europe Andorra Austria Belgium Canada Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland United Kingdom United States South and West Asia Afghanistan Bangladesh Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros … 74 … … 96 … … … … 97 86 … … 91 97 100 94 100 96 … 100 … 100 … … … 99 83 … 100 … 88 … 100 … … … … … … … 90 … 51 … 92 … 55 84 70 62 … … 23 51 … … 1.02 … … … … … … … 1.03 1.09 … … 1.02 1.04 1.00 1.00 1.00 1.37 … 1.00 … 1.01 … … … 1.01 0.81 … 1.01 … … … 1.00 … … … … … … … 0.98 … 0.99 … 1.01 … 1.02 1.06 0.96 0.89 … … 0.90 0.74 … 92 78 87 … 90 … … … … … 91 … … … … 96 … … 98 … … … 95 … 97 96 99 … 100 … … … … … … … 94 … 65 90 62 … … 58 … … … … 87 68 … 81 … … 55 … GPI (F/M) 1.01 1.05 0.98 .… .… .… .… .… .… .… 1.08 .… .… .… .… 1.03 .… .… 0.99 .… .… .… 1.03 .… .… 1.08 0.99 .… 1.00 .… .… .… .… .… .… .… .… .… 1.16 1.04 0.95 .… .… 1.10 .… .… .… .… 1.06 1.05 .… .… .… .… 0.86 .… Total (%) 88 88.y 89 … … … … 91.
*,y … 93 92 … … 96 … 99 93.y 99 … … 99 99 99 … 100 100 … … … 100 … … 100...y … … … 97 … 65.x 93 73.y … … 79 70.y … … 72 83.y 72 88 … 92 50 33.y 80...y GPI (F/M) 1.03 1.05.y 0.98 .… .… .… .… 1.03.

*,y .… 1.03 1.05 .… .… 1.01 .… 1.02 1.00.y 1.01 .… .… 1.03 1.02 1.03 .… 1.01 1.01 .… .… .… 1.00 .… .… 1.00...y .… .… .… 1.03 .… 1.07.x 1.04 1.00.y .… .… 1.10.
* 1.07.y .… .… 0.98 1.05.y 1.03 1.09 .… 1.06 0.86 0.94.y 1.02...y … 25 29 22 29 20 22 26 … 22 23 … 11 … 15 21 … … … … 19 … 27 15 12 13 21 … 17 … 14 6 22 10 … 20 … … … … 47 31 … 39 … 31 32 36 30 57 67 51 … 77 66 37 26 … … … 22 … … 21 18 20 … … 13 … 17 18 10 17 19 17 14 11 22 13 11 … 20 16 … … … … 15 12 … 19 15 36 56 42 35.

* 27 24 39 … … … 53 27 49 57 52 29 … 68 35 25 28...y 22 15 24 18.z 16 17.
*,z 15...z 20 19.z 10 12 11 … 16 … 16 19 14 11 10 17 13 11 11 12.z 14...y … 11...y 11 6.y 14 10 13 18 14 83.z 51.y 29 40...y 19 16 40 39 22...z … 44 24.z 46 54 45 25 … 63.z 35...z S TAT I S T I C A L TA B L E S Ta b l e 1 3 % FEMALE TEACHERS IN PRIMARY EDUCATION School year ending in 1999 2006 1991 TRAINED PRIMARY-SCHOOL TEACHERS2 as % of total PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION as % of GNP PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION PER PUPIL (unit cost) at PPP in constant 2005 US$ School year ending in 1999 2006 School year ending in 1999 2006 1991 School year ending in 1999 2006 1991 GOAL 6 Educational quality … … … 74 83 67 84 70 … … 74 … 82 … 69 60 … … … … 52 … 77 82 91 51 79 … 53 … 81 89 73 77 … 78 … … … … 28 53 … 14 27 … … 25 78 27 46 30 … 25 6 … 75 … … … 84 … … 76 92 92 … … 89 … 68 67 63 71 78 82 57 76 85 … 95 … 87 87 … … … … 68 80 … 76 86 – 33 32 33.
* 53 60 23 … … … 23 81 25 54 36 62 … 9 26 76 72...y 64 87 86 73...z 91 72.

*,z 89...z … 81.z 76 89 79 … 83 … 76 82 84 64 80 85 86 96 72 86.z 80...y … 73...y 81 … 70 81 79 81 89 34...z 34.y 50 44...y 62 70 30 45 79...z … 17 78.z 30 55 40 66 … 12.z 33...z 79 … … … … … … 71 81 … … … … … … … … … … … … … … … … … … … … … … … … … … … … … 64 100 … … 67 46 … … … 58 90 … … … … … … … 91 … … 64 80 74...z … 81.
*,y 82...z … 84.z 100 … … … … … … … … … … … … … … … … … … … … … … … … … 36.z 48.y 92 … 100.z 68 31.z 85 … … 72 87.z 87 88.z 62.
* 81 … 27.z … 1.7 … … 1.1 2.5 3.0 … … … 0.9 … … 0.9 1.1 … 1.2 … 1.7 0.9 … 0.6 … 1.5 1.9 0.8 … 0.9 … 0.9 2.5 1.7 … 0.8 3.1 2.1 1.2 … … … … … … … … … … … … … … 1.5 … … 1.2 0.7 … 1.9 … 1.2 … 3.3 … … 1.5 … 0.8 … … 1.1 … … 1.6 1.6 1.2 1.1 … 0.7 … 1.4 2.4 1.2 … … … 1.1 1.6 1.5 … 1.1 … 1.4 … … … 0.6 … 1.3 … … 1.1 … … … … … … 1.3 1.0 … … … … .… 1.8.y 1.1 .… 2.0 3.0.z .… .… .… .… 1.0 0.5 1.0.z 1.4.y .… 1.7.y 1.8.z 1.2.z 1.1.z 0.7.y 0.9...z 2.4.y 1.7.z 2.4.y 1.0.z 1.4...z 1.1.y .… 1.3.z 1.6.z 1.7.z .… 1.0.z 1.9.z 1.4.z 1.3.z .… .… 0.7...z 1.1.z .… 1.4 3.5...z 1.2...x .… .… 0.2.z 1.7...z 1.3 2.6 2.7.z 0.8 2.8 0.7 0.6.z .… 615 … … … 536 724 … … … 634 … 867 … 366 … 1197 … … 1 012 … 748 … … 518.y 446 … 949 1 227.z … … … … 583 North America and Western Europe … … … … … 7345 4615 4697 … 2148 … 3112 4835 6425 … … … 4606 6456 3872 … 4112 … 7 066 … … 64 … 288 … … 97 … … … 7 596.z 6 303.y … … 7 949.z 5 373.z 5 224.z 4 837.y 3562...z 7 788.y 5 100.z 5 135.y 6 347.z 9953...z 2 549.y … 5 572.z 7 072.z 4 908.z … 4 800.z 8 415.z 7 811.z 5 596.z … … 115...z … … 927 … 119...x … … South and West Asia … Sub-Saharan Africa … … … … 85 128 … … … … … 120...z 1 158.z 328 132.z 107 1 052 88 54.z … 4 812 4 158 … … … 5 132 3 099 … 1 248 … 2 082 2 609 3 770 … 1 306 … 3 315 9 534 2 779 … 2 253 10 960 12 056 3 440 … … … … … … … … … … … … … … 140 … … 167 82 … A N N E X Table 13 (continued) 9 0 0 2 t r o p e R GOAL 6 Educational quality SURVIVAL RATE TO GRADE 5 School year ending in 1999 2005 PUPIL/TEACHER RATIO IN PRIMARY EDUCATION1 School year ending in 1999 2006 1991 1991 Country or territory Total (%) GPI (F/M) Total (%) 60 73 55 … … 18 … … 80 59 … 77 66 … 21 64 70 97 34 62 62 89 60 … 85 93 … … … 77 48 36 81 … 76 … … … … 86.9 … … … … … … … 79.5 … … 63.4 1.16 0.93 0.86 … … 1.47 … … 0.98 0.76 … 1.04 1.26 … 0.96 0.80 0.95
 1.01 0.87 1.08 1.06 … 0.97 … … 1.03 … … … 1.09 0.80 … 1.02 … 1.12 … … … … 1.00 … … … … … … … … … … 0.93 … 69 … … 95 56 … … … … … … 74 … 51 49 78 99 43 92 … … 45 … … … … … 65 80 … … … 81 … … … … … 92 … … … … … 87 … 85 … … … GPI (F/M) .… 0.89 .… .… 0.95 1.06 .… .… .… .… .… .… 1.20 .… 1.02 0.77 0.97 0.99 0.79 1.02 .… .… .… .… .… .… .… .… 0.99 1.22 .… .… .… 0.94 .… … … … … 1.03 … … … … … … … 0.98 … … … Median Total (%) … … … … 74 64 … … … 81 … … 74 … 36 44 81 99 58 87 56 73...x 46.x 64 65 … … … 82.x 84.y 75.y 49...y 87 89 … … … … 83 … … … … 84 … 90 … 88 … 73 74 GPI (F/M) .… .… .… .… 0.90 1.03 .… .… .… 0.94 .… .… 1.18 .… 1.05 1.00 0.96 1.02 0.93 1.07 0.92 1.05...x 1.13.x 1.22 1.00 .… .… .… 1.02.x 1.08.y 0.89.y 0.99...y 1.05 0.95 .… … … … 1.08 … … … … 1.04 … 1.03 … 1.04 … 1.00 0.90 65 37 40 … 38 36 … 31 29 40 … 32 54 … 40 61 47 21 55 … 42 39 57 … 53 … 35 … 27 32 58 33 36 … … 62.
* 26 61 32 41 41 54 36 49 15 … … 35 33 41 57 40 47 41 Weighted average 25 20 16 27 23 19 21 22 22 20 26 24 26 15 37 41 55 46 … … 47 … 36...y 35.
* 35 44 … 40.z 40 19 48 … 56 22 67 31 40 37.z 66 31 39 12 44 … 36...y 33.z Congo Côte d’Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe I World Countries in transition Developed countries Developing countries Arab States Central and Eastern Europe Central Asia East Asia and the Pacific East Asia Pacific Latin America and the Caribbean Caribbean Latin America North America and Western Europe South and West Asia Sub-Saharan Africa II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI 3 8 6 1.
Based on headcounts of pupils and teachers.

2.

Data on trained teachers (defined according to national standards) are not collected for countries whose education statistics are gathered through the OECD, Eurostat or the World Education Indicators questionnaires.
S TAT I S T I C A L TA B L E S Ta b l e 1 3 % FEMALE TEACHERS IN PRIMARY EDUCATION School year ending in 1999 2006 1991 TRAINED PRIMARY-SCHOOL TEACHERS2 as % of total PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION as % of GNP PUBLIC CURRENT EXPENDITURE ON PRIMARY EDUCATION PER PUPIL (unit cost) at PPP in constant 2005 US$ School year ending in 1999 2006 School year ending in 1999 2006 1991 School year ending in 1999 2006 1991 GOAL 6 Educational quality 32 18 24 … 45 24 … 31 36 22 … 38 80 … … 31 25 45 23 … 33 43 46 … 27 … … … 58 78 19 … 40 … … 23.
* 54 25 67 31 47 55 … 23 85 … … 78 75 13 33 45 49 47 Weighted average 58 93 81 52 52 82 84 55 55 71 76 50 77 81 35 43 47 23 … … 43 … 45...y 34.

* 37 25 … 45...z 78 27 57 … 30 64 26 67 40 51.z 53 55 25 85 26 … 76...y 73.z 12 39 49 48 … 62 94 83 57 58 81 87 60 60 75 77 57 78 85 45 45 … … … … 73 … … 72 72 … … … 78 … … … … 100 … 29 98 … 49 … … 82 … … 62 91 … … … 94 … … … … … … … … … … … … 76 … … … … 89 … … … 88 … … 76.
* 59 68 … 99.y 66 … 36.z … … 100 65 92.z 92 50.z 98 … 100.z … 49 … … 91.z 37.z 85.z 100 … … … 100 … 85 100 … 93 … … … 80 74 88 … 68 85 Median … … … … … 1.1 … 1.3 … … … 3.1 … … … 1.1 … 1.3 … … … … … … 1.7 … … … … 1.4 … … … … 4.3 … … … … … … … … … … … … … 1.2 … … 2.0 1.8 … … … … … … … … … … 3.2 … … … 1.3 1.2 … 4.4 … … … … … … … … 2.7 1.9 1.8 … … … … 0.6.z 0.1...z .… .… 0.8 2.0 .… .… 1.6.z .… .… 3.6 3.8 .… 1.2 3.0.x 1.9 1.0 2.6.y 3.9.x 1.7 .… 1.9.z .… 2.1.z 1.3 2.3...x .… 2.4 2.3...y .… 2.5...y .… 1.3.z .… … … … … … 168 … 254 … … … 172 … … … 38 … 696 … … … … … … 303 … … … 1 843 301 … … … … 625 191 274 … … … … … … … … … … 566 … … … 136 1 067 … 1 416 … … … … … … … … 1 403.
* 437 150 … … … … Median Median … … … … … … … … … … … … … 1.3 … … 1.4 … 1.1 1.7 1.7 0.8 … … … … 1.7 1.8 1.6 1.3 1.2 1.9 … … … … … … … … … … … … … … … … … … … … … … … … … … 3 378 … … 4 697 … … 39.z … … … 99 130 … … 300.z … … 237 663 … 57 90.x 183 1 205 156.y 944.x 178 … 109...z … 299.z 2399...y … … 1 383.z 484...y … 110...y … 55.z … 1 005 … 5 100 … … 2 182 … … … … … … 614 5 584 … 167 Data in italic are UIS estimates.
Data in bold are for the school year ending in 2006 for survival rates to grade 5, and the school year ending in 2007 for the remaining indicators.

(z) Data are for the school year ending in 2005.

(y) Data are for the school year ending in 2004.

(x) Data are for the school year ending in 2003.

(*) National estimate.
I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI 3 8 7 A I D TA B L E S I n t r o d u c t i o n Aid tables Introduction Most of the data on aid used in this Report are derived from the OECD’s International Development Statistics (IDS) database, which records information provided annually by all member countries of the OECD Development Assistance Committee (DAC).
The IDS comprises the DAC database, which provides aggregate data, and the Creditor Reporting System, which provides project- and activity-level data.

The IDS is available online at www.oecd.org/dac/stats/idsonline.

It is updated frequently.

The data presented in this Report were downloaded between March and June 2008.

The focus of this section of the annex on aid data is official development assistance.
This term and others used in describing aid data are explained below to help in understanding the tables in this section and the data presented in Chapter 4.

Private funds are not included.

Aid recipients and donors Official development assistance (ODA) is public funds provided to developing countries to promote their economic and social development.
It is concessional: that is, it takes the form either of a grant or of a loan carrying a lower rate of interest than is available in the market and, usually, a longer than normal repayment period.

ODA may be provided directly by a government (bilateral ODA) or through an international agency (multilateral ODA).

ODA can include technical cooperation (see below).

Developing countries are those in Part I of the DAC List of Aid Recipients, which essentially comprises all low- and middle-income countries.
Twelve central and eastern European countries, including new independent states of the former Soviet Union, plus a set of more advanced developing countries are in Part II of the list, and aid to them is referred to as official aid (OA).

The data presented in this Report do not include OA unless indicated.

Bilateral donors are countries that provide development assistance directly to recipient countries.
The majority (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom and the United States) are members of the DAC, a forum of major bilateral donors established to promote the volume and effectiveness of aid.

Non-DAC bilateral donors include the Republic of Korea and some Arab states.
Bilateral donors also contribute substantially to the financing of multilateral donors through contributions recorded as multilateral ODA.

The financial flows from multilateral donors to recipient countries are also recorded as ODA receipts.

Multilateral donors are international institutions with government membership that conduct all or a significant part of their activities in favour of developing countries.

They include multilateral development banks (e.g.


the World Bank and the Inter-American Development Bank), United Nations agencies (e.g.

UNDP and UNICEF) and regional groupings (e.g.

the European Commission and Arab agencies).

The development banks also make non-concessional loans to several middle- and higher- income countries, and these are not counted as part of ODA.
Types of aid Unallocated aid: some contributions are not susceptible to allocation by sector and are reported as non-sector- allocable aid.

Examples are aid for general development purposes (direct budget support), balance-of-payments support, action relating to debt (including debt relief) and emergency assistance.

Basic education: the definition of basic education varies by agency.

The DAC defines it as covering primary education, basic life skills for youth and adults, and early childhood education.
Education, level unspecified: the aid to education reported in the DAC database includes basic, secondary and post-secondary education, and a subcategory called ‘education, level unspecified’.

This subcategory covers aid related to any activity that cannot be attributed solely to the development of a single level of education.

3 8 9 Current and constant prices: aid figures in the DAC database are expressed in US$.
When other currencies are converted into dollars at the exchange rates prevailing at the time, the resulting amounts are at current prices and exchange rates.

When comparing aid figures between different years, adjustment is required to compensate for inflation and changes in exchange rates.

Such adjustments result in aid being expressed in constant dollars, i.e.

in dollars fixed at the value they held in a given reference year, including their external value in terms of other currencies.
Thus, amounts of aid for any year and in any currency expressed in 2006 constant dollars reflect the value of that aid in terms of the purchasing power of dollars in 2006.

In this Report, most aid data are presented in 2006 constant dollars.

The indices used for adjusting currencies and years (called deflators) are derived from Table 36 of the statistical annex of the 2007 DAC Annual Report (OECD- DAC, 2008b).
In previous editions of the EFA Global Monitoring Report, amounts of aid were based on the constant prices of different years (the 2007 Report used 2003 constant prices), so amounts for a given country for a given year in these editions differ from the amounts presented in this Report for the same year.

For more detailed and precise definitions of terms used in the DAC database, see the DAC Directives, available at www.oecd.org/dac/stats/dac/directives.

Source:OECD-DAC (2008c).


A N N E X Sector budget funding: funds contributed directly to the budget of a ministry of education are often reported by donors in this subcategory.

Although in practice this aid will mainly be used for specific levels of education, such information is not available in the DAC database.

This reduces accuracy in assessing the amount of resources made available for each specific level of education.
Technical cooperation (sometimes referred to as technical assistance): according to the DAC Directives, technical cooperation is the provision of know-how in the form of personnel, training, research and associated costs.
It includes (a) grants to nationals of aid recipient countries receiving education or training at home or abroad; and (b) payments to consultants, advisers and similar personnel as well as teachers and administrators serving in recipient countries (including the cost of associated equipment).

Where such assistance is related specifically to a capital project, it is included with project and programme expenditure, and is not separately reported as technical cooperation.
The aid activities reported in this category vary by donor, as interpretations of the definition are broad.

Debt relief: this includes debt forgiveness, i.e.

the extinction of a loan by agreement between the creditor (donor) and the debtor (aid recipient), and other action on debt, including debt swaps, buy-backs and refinancing.

In the DAC database, debt forgiveness is reported as a grant.

It raises gross ODA but not necessarily net ODA (see below).
Commitments and disbursements: a commitment is a firm obligation by a donor, expressed in writing and backed by the necessary funds, to provide specified assistance to a country or multilateral organization.

The amount specified is recorded as a commitment.

Disbursement is the release of funds to, or purchase of goods or services for, a recipient; in other words, the amount spent.

Disbursements record the actual international transfer of financial resources or of goods or services valued by the donor.
As the aid committed in a given year can be disbursed later, sometimes over several years, the annual aid figures based on commitments differ from those based on disbursements.

Gross and net disbursements: gross disbursements are the total aid extended.

Net disbursements are the total aid extended minus amounts of loan principal repaid by recipients or cancelled through debt forgiveness.
TA B L E S Ta b l e 1 Table 1: Bilateral and multilateral ODA Total ODA Constant 2006 US$ millions Net disbursements as % of GNI Sector-allocable ODA Constant 2006 US$ millions Debt relief and other actions relating to debt Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 2007 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total DAC African Development Fund 818 1 563 1 670 Asian Development Fund 1 282 1 450 1 175 European Commission 8 908 11 670 12 311 Fast Track Initiative 0 52 85 International Development Association Inter-American Development Bank Special Fund UNICEF 6 824 8 957 8 716 350 199 508 760 362 775 Total multilaterals 19 102 26 613 27 394 0.27 0.24 0.33 0.27 1.04 0.32 0.34 0.27 0.18 0.30 0.14 0.28 0.69 0.82 0.26 0.82 0.26 0.23 0.75 0.35 0.28 0.10 0.22 … … … … … … … … 0.25 0.52 0.53 0.34 0.81 0.46 0.47 0.36 0.17 0.42 0.29 0.28 0.86 0.82 0.27 0.94 0.21 0.27 0.94 0.44 0.47 0.23 0.33 … … … … … … … … 0.30 0.47 0.50 0.29 0.80 0.40 0.47 0.36 0.17 0.54 0.20 0.25 0.89 0.81 0.27 0.89 0.21 0.32 1.02 0.39 0.51 0.18 0.31 … … … … … … … … 0.30 0.49 0.43 0.28 0.81 0.40 0.39 0.37 0.16 0.54 0.19 0.17 0.90 0.81 0.27 0.95 0.19 0.41 0.93 0.37 0.36 0.16 0.20 … … … … … … … … 1
… … … … … … … … … … … … … … … … … … … … … … … … Total 75 410 124 987 127 384 … … … … 50 588 70 248 79 254 … … … Notes: (…) indicates that data are not available.
All data represent commitments unless otherwise specified.

Source: OECD-DAC (2008c).
Table 2: Bilateral and multilateral aid to education Total aid to education Constant 2006 US$ millions Total aid to basic education Constant 2006 US$ millions Direct aid to education Constant 2006 US$ millions Direct aid to basic education Constant 2006 US$ millions Direct aid to secondary education Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 0
0 29 52 74 0 69 85 216 0 38 62 0 55 0 0 63 105 0 20 23 1 0 48 0 0 Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total DAC African Development Fund Asian Development Fund European Commission Fast Track Initiative International Development Association Inter-American Development Bank Special Fund UNICEF Total multilaterals 1 791 2 241 2 231 976 1 027 1 119 1 395 1 535 1 774 541 532 565 226 379 358 Total 6 958 8 464 11 289 2 771 3 689 5 063 6 287 7 337 10 190 1 624 2 395 3 376 919 854 893 Notes: (…) indicates that data are not available.
Data for sector-allocable aid include general budget support.

All data represent commitments unless otherwise specified.

Source: OECD-DAC (2008c).
3 9 2 A I D TA B L E S Ta b l e 2 Direct aid to post-secondary education Education, level unspecified Constant 2006 US$ millions Constant 2006 US$ millions Share of education in total ODA Share of education in total sector-allocable ODA Share of basic education in total aid to education (%) (%) (%) 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average … … 27 12 4 … 21 … 14 17 21 10 7 13 5 14 12 11 13 … … … 51 28 37 … 65 … 62 26 30 65 43 74 55 35 62 7 64 … 52 50 100 55 26 4 25 77 55 54 16 39 15 62 … 31 44 66 73 56 16 38 37 21 74 77 43 50 10 50 48 5 25 79 62 41 17 11 12 66 … 26 36 83 33 51 14 38 73 41 71 73 44 34 14 44 Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total DAC African Development Fund Asian Development
 Fund European Commission 100 100 Fast Track Initiative 48 0 99 46 58 … 97 50 International Development Association Inter-American Development Bank Special Fund UNICEF Total multilaterals 14 12 14 40 44 45 Total Table 3: ODA recipients Total ODA Constant 2006 US$ millions Per capita ODA Sector-allocable ODA Constant 2006 US$ Constant 2006 US$ millions Debt relief and other actions relating to debt Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Arab States 6 706 29 567 16 653 Algeria Bahrain Djibouti Egypt Iraq Jordan Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Saudi Arabia Sudan Syrian Arab Republic Tunisia Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Croatia Republic of Moldova TFYR Macedoniao Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Cambodia China Cook Islands DPR Korea Fiji Indonesia Kiribati Korea, Rep. Lao PDR Malaysia Marshall Islands Micronesia Myanmar Nauru Niue … 311 21 38 189 12 0 26 72 36 60 13 53 109 25 5 7 7 40 2 140 9
A I D TA B L E S Ta b l e 3 Table 3 (continued) Total ODA Constant 2006 US$ millions Per capita ODA Sector-allocable ODA Constant 2006 US$ Constant 2006 US$ millions Debt relief and other actions relating to debt Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominica
 Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia St Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe Malta South and West Asia 6 764 14 476 13 893 Afghanistan Bangladesh 187 2 059 3 476 2 038 3 257 2 374 1 887 107 21 202 264 23 437 … … … 81
Table 3 (continued) Total ODA Constant 2006 US$ millions Per capita ODA Sector-allocable ODA Constant 2006 US$ Constant 2006 US$ millions Debt relief and other actions relating to debt Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka 73 2 314 150 33 495 857 598 80 3 656 62 76 517 2 928 1 608 69 4 518 117 56 481 2 205 769 Sub-Saharan Africa 20 023 35 882 37 772 Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde C. A. R. Chad Comoros Congo Côte d'Ivoire D. R. Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda U. R. Tanzania Zambia Zimbabwe Unallocated by countries Total 13
15 41 62 54 42 94 72 28 5 67 347 98 84 71 15 13 30 23 49 39 113 19 … … … 3482 8264 12307 57 73 1779 22 50 807 70 347 79 296 4 132 27 548 17 072 A I D TA B L E S Ta b l e 3 Table 3 (continued) Total ODA Constant 2006 US$ millions Per capita ODA Sector-allocable ODA Constant 2006 US$ Constant 2006 US$ millions Debt relief and other actions relating to debt Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Upper middle income countries Low middle income countries High income countries Unallocated by income Least developed countries
 Low income countries Middle income countries Total Arab States Central and Eastern Europe Central Asia East Asia and the Pacific Latin America and the Caribbean North America and Western Europe South and West Asia Sub-Saharan Africa 20 023 35 882 37 772 Unallocated by region Total 9257 14777 24235 73 705 124 681 121 566 11 11 3 … 29 15 11 15 25 39 26 7 18 13 18 0 … 44 23 17 24 97 34 27 7 16 5 33 … 15 9 51 … 24 11 13 0 … … Notes: (…) indicates that data are not available.
All data represent commitments unless otherwise specified.

Totals may not match those presented in Table 1 due to the use of different databases.

Source: OECD-DAC (2008c).
Table 4: Recipients of aid to education Total aid to education Constant 2006 US$ millions Total aid to basic education Constant 2006 US$ millions Total aid to basic education per primary school-age child Constant 2006 US$ Direct aid to education Constant 2006 US$ millions Direct aid to basic education Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Arab States 1 094 1 310 1 672 319 479 Algeria Bahrain Djibouti Egypt Iraq Jordan Lebanon Libyan Arab Jamahiriya Mauritania Morocco Oman Palestinian A. T. Saudi Arabia Sudan Syrian Arab Republic Tunisia Yemen Central and Eastern Europe Albania Belarus Bosnia and Herzegovina Croatia Republic of Moldova TFYR Macedoniao Turkey Ukraine Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Mongolia Tajikistan Turkmenistan Uzbekistan East Asia and the Pacific Cambodia China Cook Islands DPR Korea Fiji Indonesia Kiribati Korea, Rep. Lao PDR Malaysia Marshall Islands Micronesia Myanmar Nauru Niue 16
A I D TA B L E S Ta b l e 4 Direct aid to secondary education Constant 2006 US$ millions Direct aid to post-secondary education Constant 2006 US$ millions Education, level unspecified Constant 2006 US$ millions Share of education in total ODA Share of education in total sector- allocable ODA Share of basic education in total aid to education (%) (%) (%) 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 103
… 3 20 6 7 27 … 5 4 2 7 8 3 5 5 14 9 8 7 6 3 1 29 10 29 … 14 7 7 … 5 52 32 4 32 … … 7 2 25 … 10 8 14 10 40 … … 8 75 23 … 14 9 4 20 54 97 57 10 46 5 33 93 23 31 9 11 55 32 31 27 18 12 8 … 6 25 8 11 36 … 7 5 3 9 8 5 9 10 17 10 10 9 7 3 16 30 19 30 … 15 7 16 … 8 52 35 11 38 … … 7 2 26 … 19 9 30 15 44 … … 8 77 25 … 19 10 4 29 31 2 28 27 8 4 20 11 28 24 12 51 13 26 10 25 76 32 34 … 30 2 37 43 38 … 23 15 32 22 11 35 36 40 10 16 27 38 16 0 4 15 35 43 … 14 1 45 … 58 0 0 37 14 … 60 80 69 58 10 3 78 15 6 49 19 56 5 8 94 11 19 7 8 2 7 26 4 1 48 17 52 37 28
… 39 14 49 … 47 27 48 33 10 … … 24 2 51 … 80 50 10 Table 4 (continued) Total aid to education Constant 2006 US$ millions Total aid to basic education Constant 2006 US$ millions Total aid to basic education per primary school-age child Constant 2006 US$ Direct aid to education Constant 2006 US$ millions Direct aid to basic education Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam Latin America and the Caribbean Anguilla Antigua and Barbuda Argentina Aruba Barbados Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Nicaragua Panama Paraguay Peru Saint Kitts and Nevis Saint Lucia St Vincent/Grenad.
Suriname Trinidad and Tobago Turks and Caicos Islands Uruguay Venezuela, B. R. North America and Western Europe Malta South and West Asia Afghanistan Bangladesh … … … 7
A I D TA B L E S Ta b l e 4 Direct aid to secondary education Constant 2006 US$ millions Direct aid to post-secondary education Constant 2006 US$ millions Aid to education, level unspecified Constant 2006 US$ millions Share of education in total ODA Share of education in total sector- allocable ODA Share of basic education in total aid to education (%) (%) (%) 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average … … 3 3 12 12 18 3 3 6 2 11 7 5 47 12 0 7 4 8 8
28 … 3 3 12 12 18 3 3 6 2 11 7 5 47 12 0 7 4 8 8 … 6 8 22 6 0 2 6 29 1 36 4 17 0 … 8 7 18 8 11 9 49 2 10 15 13 57 1 10 8 9 0 4 22 … 2 3 6 21 36 3 3 8 4 34 15 10 1 4 10 9 5 8 10 … 11 5 5 9 1 10 3 5 91 20 16 29 0 … 7 4 10 7 19 11 23 16 2 4 93 12 16 30 9 8 54 21 26 … … 4 4 16 13 21 3 4 8 2 12 8 5 53 15 0 11 11 13 8 … 10 9 24 8 0 2 7 29 1 36 4 20 0 … 12 10 22 13 11 11 52 2 11 17 31 62 2 10 9 10 0 5 23 … 2 3 7 23 40 3 3 9 5 51 17 12 1 7 12 11 8 9 10 … 13 6 5 10 1 11 3 6 92 … 17 31 0 … 9 5 13 40 65 35 46 29 9 29 0 14 34 4 16 45 12 50 15 … … 0 50 … 25 63 45 21 17 18 29 12 20 46 21 49 99 73 29 33 68 78 14 … 73 10 32 34 0 60 40 44 4 100 11 6 50 … 49 71 35 57 62 50 29 30 5 65 43 68 50 44 16 36 … 0 14 … 0 55 40 20 15 18 20 12 7 46 32 44 29 41 79 23 78 81 9 … 72 22 47 20 0 50 28 1 0 … 27 11 0 … 49 73 32 Table 4 (continued) Total aid to education Constant 2006 US$ millions Total aid to basic education Constant 2006 US$ millions Total aid to basic education per primary school-age child Constant 2006 US$ Direct aid to education Constant 2006 US$ millions Direct aid to basic education Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 Bhutan India Iran, Islamic Republic of Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa 2 352 2 840 3 811 1 186 1 474 2 070 Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde C. A. R. Chad Comoros Congo Côte d’Ivoire D. R. Congo Equatorial
 Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Togo Uganda U. R. Tanzania Zambia Zimbabwe Unallocated by countries Total … … … 6
A I D TA B L E S Ta b l e 4 Direct aid to secondary education Constant 2006 US$ millions Direct aid to post-secondary education Constant 2006 US$ millions Aid to education, level unspecified Constant 2006 US$ millions Share of education in total ODA Share of education in total sector- allocable ODA Share of basic education in total aid to education (%) (%) (%) 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 5
Table 4 (continued) Total aid to education Constant 2006 US$ millions Total aid to basic education Constant 2006 US$ millions Total aid to basic education per primary school-age child Constant 2006 US$ Direct aid to education Constant 2006 US$
 millions Direct aid to basic education Constant 2006 US$ millions 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average … … … Total 6 958 8 464 11 289 2 771 3 689 5 063 Arab States … Sub-Saharan Africa 2 352 2 840 3 811 1 186 1 474 2 070 12 17 1 842 2 207 3 051 452 765 1451 98 354 930 … … Upper middle income countries Low middle income countries High income countries Unallocated by income Least developed countries Low income countries Middle income countries Central and Eastern Europe Central Asia East Asia and the Pacific Latin America and the Caribbean North America and Western Europe South and West Asia Unallocated by region Total Notes: (…) indicates that data are not available.
All data represent commitments unless otherwise specified.

Source: OECD-DAC (2008c).
4 0 4 A I D TA B L E S Ta b l e 4 Direct aid to secondary education Constant 2006 US$ millions Direct aid to post-secondary education Constant 2006 US$ millions Aid to education, level unspecified Constant 2006 US$ millions Share of education in total ODA Share of education in total sector- allocable ODA Share of basic education in total aid to education (%) (%) (%) 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average 2005 2006 1999–2000 annual average … … … … … … / r e l l i m e n n a D h t i e K © Mexico’s Oportunidades programme reaches out to poor and indigenous households 4 0 6 G L O S S A R Y Glossary Achievement.
Performance on standardized tests or examinations that measure knowledge or competence in a specific subject area.

The term is sometimes used as an indication of education quality within an education system or when comparing a group of schools.

Adult education.

Educational activities, offered through formal, non-formal or informal frameworks, targeted at adults and aimed at advancing, or substituting for, initial education and training.
The purpose may be to (a) complete a given level of formal education or professional qualification; (b) acquire knowledge and skills in a new field (not necessarily for a qualification); and/or (c) refresh or update knowledge and skills.

See also Basic education and Continuing education.

Adult literacy rate.

Number of literate persons aged 15 and above, expressed as a percentage of the total population in that age group.
Different ways of defining and assessing literacy yield different results regarding the number of persons designated as literate.

Age-specific enrolment ratio (ASER).

Enrolment of a given age or age group, regardless of the level of education in which pupils or students are enrolled, expressed as a percentage of the population of the same age or age group.

Basic education.
Basic education.

The whole range of educational activities taking place in various settings (formal, non-formal and informal) that aim to meet basic learning needs; in the Dakar Framework the term is synonymous with the broad EFA agenda.

Similarly, the OECD-DAC and standard aid classifications use a definition that includes early childhood education, primary education, and basic life skills for youths and adults, including literacy.
According to the International Standard Classification of Education (ISCED), basic education comprises primary education (first stage of basic education) and lower secondary education (second stage).

Basic learning needs.


As defined in the World Declaration on Education for All (Jomtien, Thailand, 1990): essential learning tools (literacy, oral expression, numeracy, problem-solving) and basic learning content (knowledge, skills, values, attitudes) required by human beings to survive, develop their full capacities, live and work in dignity, participate fully in development, 4 0 7 A N N E X improve the quality of their lives, make informed decisions and continue learning.
The scope of basic learning needs and how they should be met varies by country and culture, and changes over time.

Disability.

A temporary or permanent physical or mental condition that may limit a person’s opportunities to take part in the community on an equal level with others.

Charter school.
Charter school.

A public school that is not subject to some of the local and state regulations applied to conventional public schools, allowing parents, community leaders, educational entrepreneurs or others greater autonomy over decisions in defined areas.

Charter schools are sponsored by local, state or other organizations, which monitor their quality and hold them accountable for academic results and good financial practice as specified in their charters.

Child labour.
Child labour.

A term often defined as work that deprives children of their childhood, their potential and their dignity, and that is harmful to their physical and mental development.
The term refers to work that is mentally, physically, socially or morally dangerous, that harms children and that interferes with their schooling by depriving them of the opportunity to attend school, obliging them to leave school prematurely or requiring them to try to combine school attendance with excessively long and heavy work hours.

Child- or under-5 mortality rate.

Probability of dying between birth and the fifth birthday, expressed per 1,000 live births.

Cognitive development.


Development of the mental action or process of acquiring knowledge through thought, experience and senses.

Compulsory education or attendance.

Educational programmes that children and young people are legally obliged to attend, usually defined in terms of a number of grades or an age range, or both.

Constant prices.

A way to express financial values in real terms, that enables comparisons over time.
To measure changes in real national income or product, economists calculate the value of total production in each year at constant prices using a set of prices that are applied in a chosen base year.

Continuing or further education.

A general term referring to a wide range of educational activities designed to meet the learning needs of adults.

See also Adult education.

Dropout rate by grade.

Percentage of pupils or students who drop out of a given grade in a given school year.
It is the difference between 100% and the sum of the promotion and repetition rates.

Early childhood.

The period of a child’s life from birth to age 8.

Early childhood care and education (ECCE).

Programmes that, in addition to providing children with care, offer a structured and purposeful set of learning activities either in a formal institution (pre-primary or ISCED 0) or as part of a non-formal child development programme.
ECCE programmes are normally designed for children from age 3 and include organized learning activities that constitute, on average, the equivalent of at least 2 hours per day and 100 days per year.

Education attainment rate.

The percentage of a population belonging to a particular age group that has attained or completed a specified education level (typically primary, secondary or tertiary) or grade in school.

EFA Development Index (EDI).

Composite index aimed at measuring overall progress towards EFA.
At present, the EDI incorporates four of the most easily quantifiable EFA goals – universal primary education as measured by the total primary net enrolment ratio, adult literacy as measured by the adult literacy rate, gender parity as measured by the gender-specific EFA index and quality of education as measured by the survival rate to grade 5.

Its value is the arithmetic mean of the observed values of these four indicators.

EFA Inequality Index for Income Groups (EIIIG).


A composite index measuring inequality in overall EFA achievement across different population groups.

The EIIIG measures the (unequal) distribution of overall EFA achievement within countries according to household wealth and other socio-demographic markers, using a set of indicators from household surveys that differs from those in the EDI.

Elementary education.

See primary education.
Elementary education.

See primary education Enrolment.

Number of pupils or students enrolled at a given level of education, regardless of age.

See also Gross enrolment ratio and Net enrolment ratio.

Foreign students.

Students enrolled in an education programme in a country of which they are not permanent residents.

Entrance age (official).


Age at which pupils or students would enter a given programme or level of education, assuming they had started at the official entrance age for the lowest level, studied full time throughout and progressed through the system without repeating or skipping a grade.

The theoretical entrance age for a given programme or level may be very different from the actual or even the most common entrance age.

Equity.
Equity.

As used in the report, the term describes fairness in the distribution of opportunities for education.

Enhanced equity implies a reduction in disparities based on gender, poverty, residence, ethnicity, language or other characteristics and circumstances that should not influence education outcomes.

Equivalency education.

Programmes primarily organized for children and youth who did not have access to, or who dropped out of, formal primary/basic education.
Typically, these programmes aim at providing equivalency to formal primary/basic education and at mainstreaming the target groups into the formal system upon successful completion of the programme.

Fields of study in tertiary or higher education.

Education: teacher training and education science.

Humanities and arts:humanities, religion and theology, fine and applied arts.

Social sciences, business and law:social and behavioural sciences, journalism and information, business and administration, law.
Science:life and physical sciences, mathematics, statistics and computer sciences.

Engineering, manufacturing and construction: engineering and engineering trades, manufacturing and processing, architecture and building.

Agriculture: agriculture, forestry and fishery, veterinary studies.

Health and welfare: medical sciences and health- related sciences, social services.

Services: personal services, transport services, environmental protection, security services.

Gender parity index (GPI).


Ratio of female to male values (or male to female, in certain cases) of a given indicator.

A GPI of 1 indicates parity between sexes; a GPI above or below 1 indicates a disparity in favour of one sex over the other.

Gender-specific EFA index (GEI).

A composite index measuring gender parity in total participation in primary and secondary education, and in adult literacy.
The GEI is calculated as the arithmetic mean of the gender parity indices of the primary and secondary gross enrolment ratios and of the adult literacy rate.

General education.

Programmes designed to lead students to a deeper understanding of a subject or group of subjects, especially, but not necessarily, with a view to preparing them for further education at the same or a higher level.

These programmes are typically school-based and may or may not contain vocational elements.
Their successful completion may or may not provide students with a labour-market- relevant qualification.

Grade.

Stage of instruction usually equivalent to one complete school year.

Graduate.

A person who has successfully completed the final year of a level or sub-level of education.

In some countries completion occurs as a result of passing an examination or a series of examinations.

In other countries it occurs after a requisite number of course hours have been accumulated.
Sometimes both types of completion occur within a country.

Gross enrolment ratio (GER).

Total enrolment in a specific level of education, regardless of age, expressed as a percentage of the population in the official age group corresponding to this level of education.

For the tertiary level, the population used is that of the five-year age group following on from the secondary school leaving age.

The GER can exceed 100% due to early or late entry and/or grade repetition.

Gross intake rate (GIR).


Total number of new entrants to a given grade of primary education, regardless of age, expressed as a percentage of the population at the official school entrance age for that grade.

4 0 9 A N N E X Gross domestic product (GDP).

The value of all final Infectious diseases.

Diseases that are caused by goods and services produced in a country in one year (see also Gross national product).
GDP can be measured by aggregating an economy’s (a) income (wages, interest, profits, rents) or (b) expenditure (consumption, investment, government purchases), plus net exports (exports minus imports).

The results should be the same because one person’s expenditure is always another person’s income; the sum of all income must equal the sum of all expenditure.

Gross domestic product per capita.

GDP divided by the total population at mid-year.

Gross national product (GNP).


The value of all final goods and services produced in a country in one year (gross domestic product) plus income that residents have received from abroad, minus income claimed by non- residents.

GNP may be much less than GDP if much of the income from a country’s production flows to foreign persons or firms.
But if the people or firms of a country hold large amounts of the stocks and bonds of firms or governments of other countries, and receive income from them, GNP may be greater than GDP.

pathogenic micro-organisms, such as bacteria, fungi, parasites or viruses, and that can be spread directly or indirectly from one person to another.

They include influenza, dengue, hepatitis, malaria, measles, tuberculosis and yellow fever.

Informal education.
Informal education.

Learning that takes place in daily life without clearly stated objectives.

The term refers to a lifelong process whereby all individuals acquire attitudes, values, skills and knowledge from daily experience, and from the educative influence and resources in their environment.

International Standard Classification of Education (ISCED).
Classification system designed to serve as an instrument for assembling, compiling and presenting comparable indicators and statistics of education both within countries and internationally.

The system, introduced in 1976, was revised in 1997 (ISCED97).

Labour force participation rate.

The share of employed plus unemployed people in comparison with the working age population.

Gross national product per capita.

GNP divided by Least developed countries (LDCs).


Low-income countries the total population at mid-year.

HIV prevalence rate.

Estimated number of people of a given age group living with HIV/AIDS at the end of a given year, expressed as a percentage of the total population of the corresponding age group.

Household survey.
Household survey.

Survey whose purpose is to compile socio-economic and demographic information on households and individual household members in such areas as education, health, income, employment, mortality and fertility.

In the area of education, large-scale household surveys supplement information derived from administrative sources, censuses and school surveys.

They are conducted using standard sampling procedures.

Illiterate.

See Literate.

Indigenous language.


A language that originated in a specified territory or community and was not brought in from elsewhere.

Infant mortality rate.

Probability of dying between birth and the first birthday, expressed as deaths per 1,000 live births.

that, according to the United Nations, have human resource weaknesses (based on indicators of nutrition, health, education and adult literacy) and are economically vulnerable.
The category is used to guide donors and countries in allocating foreign assistance.

Life expectancy at birth.

Approximate number of years a newborn infant would live if prevailing patterns of age-specific mortality rates in the year of birth were to stay the same throughout the child’s life.

Literacy.

According to UNESCO’s 1958 definition, the term refers to the ability of an individual to read and write with understanding a simple short statement related to his/her everyday life.
The concept of literacy has since evolved to embrace multiple skill domains, each conceived on a scale of different mastery levels and serving different purposes.

Many today view literacy as the ability to identify, interpret, create, communicate and compute, using printed and written materials in various contexts.

Literacy is a process of learning that enables individuals to achieve personal goals, develop their knowledge and potential, and participate fully in the community and wider society.
Literate/illiterate.

As used in the statistical tables, the term refers to a person who can/cannot read and write with understanding a simple statement related to his/her everyday life.

Literate environment.


The term can have at least two meanings: (a) the availability of written, printed and visual materials in learners’ surrounding environment, enabling them to make use of their basic reading and writing skills; and/or (b) the prevalence of literacy in households and communities, enhancing the prospects of successful literacy acquisition by learners.

Lower-secondary education (ISCED level 2).

See Secondary education.

Net attendance rate (NAR).


Number of pupils in the official age group for a given level of education who attend school in that level, expressed as a percentage of the population in that age group.

Net enrolment ratio (NER).

Enrolment of the official age group for a given level of education, expressed as a percentage of the population in that age group.

Net intake rate (NIR).


New entrants to the first grade of primary education who are of the official primary school entrance age, expressed as a percentage of the population of that age.

New entrants.

Pupils entering a given level of education for the first time; the difference between enrolment and repeaters in the first grade of the level.

New entrants to the first grade of primary education with ECCE experience.
Number of new entrants to the first grade of primary school who have attended the equivalent of at least 200 hours of organized ECCE programmes, expressed as a percentage of the total number of new entrants to the first grade.

Non-formal education.

Learning activities typically organized outside the formal education system.

The term is generally contrasted with formal and informal education.
In different contexts, non-formal education covers educational activities aimed at imparting adult literacy, basic education for out-of-school children and youth, life skills, work skills and general culture.

Such activities usually have clear learning objectives, but vary in duration, in organizational structure and in conferring certification for acquired learning.

G L O S S A R Y Out-of-school children.


Children in the official primary school age range who are not enrolled in either primary or secondary school.

Post-secondary non-tertiary education (ISCED level 4).

Programmes that lie between the upper secondary and tertiary levels from an international point of view, even though they might clearly be considered upper secondary or tertiary programmes in a national context.
They are often not significantly more advanced than programmes at ISCED level 3 (upper secondary) but they serve to broaden the knowledge of students who have completed a programme at that level.

The students are usually older than those at ISCED level 3.

ISCED 4 programmes typically last between six months and two years.

Pre-primary education (ISCED level 0).


Programmes at the initial stage of organized instruction, primarily designed to introduce very young children, aged at least 3 years, to a school-type environment and provide a bridge between home and school.

Variously referred to as infant education, nursery education, pre-school education, kindergarten or early childhood education, such programmes are the more formal component of ECCE.
Upon completion of these programmes, children continue their education at ISCED 1 (primary education).

Primary cohort completion rate.

The number of pupils who complete the final year of primary school, expressed as a percentage of the number who entered the first year.

Primary education (ISCED level 1).


Programmes normally designed on a unit or project basis to give pupils a sound basic education in reading, writing and mathematics, and an elementary understanding of subjects such as history, geography, natural sciences, social sciences, art and music.

Religious instruction may also be featured.

These subjects serve to develop pupils’ ability to obtain and use information they need about their home, community or country.

Also known as elementary education.


Private enrolment/institutions.

Number of pupils/students enrolled in private institutions, that is, in institutions that are not operated by public authorities but are controlled and managed, whether for profit or not, by private bodies such as non- government organizations, religious bodies, special interest groups, foundations or business enterprises.
Public enrolment/institutions.

Number of students enrolled in public institutions, that is, institutions controlled and managed by public authorities or agencies (national/federal, state/provincial or local), whatever the origins of their financial resources.

Public expenditure on education.


Total current and capital expenditure on education by local, regional and national governments, including municipalities.

Household contributions are excluded.

The term covers public expenditure for both public and private institutions.
Current expenditure includes expenditure for goods and services that are consumed within a given year and have to be renewed the following year, such as staff salaries and benefits; contracted or purchased services; other resources, including books and teaching materials; welfare services and items such as furniture and equipment, minor repairs, fuel, telecommunications, travel, insurance and rent.
Capital expenditure includes expenditure for construction, renovation and major repairs of buildings, and the purchase of heavy equipment or vehicles.

Pupil.

A child enrolled in pre-primary or primary education.

Youth and adults enrolled at more advanced levels are often referred to as students.

Pupil/teacher ratio (PTR).

Average number of pupils per teacher at a specific level of education, based on headcounts for both pupils and teachers.

Pupil/trained-teacher ratio.


Average number of pupils per trained teacher at a specific level of education, based on headcounts for both pupils and trained teachers.

See also Trained teacher.

School-age population.

Population of the age group officially corresponding to a given level of education, whether enrolled in school or not.

School life expectancy (SLE).

Number of years a child of school entrance age is expected to spend in school or university, including years spent on repetition.
It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education.

Secondary education (ISCED levels 2 and 3).

Programme made up of two stages: lower and upper secondary.

Lower secondary education (ISCED 2) is generally designed to continue the basic programmes of the primary level but the teaching is typically more subject- focused, requiring more specialized teachers for each subject area.
The end of this level often coincides with the end of compulsory education.

In upper secondary education (ISCED 3), the final stage of secondary education in most countries, instruction is often organized even more along subject lines and teachers typically need a higher or more subject-specific qualification than at ISCED level 2.

Sector-wide approach (SWAp).


A development approach in which all significant donor funding for a given sector supports a single sector policy and expenditure programme, under the leadership of the recipient government.

Donor support for a SWAp may take the form of project aid, technical assistance, basket/pooled funding or budget support.

There is commonly a commitment to progress towards reliance on government procedures to disburse and account for donor funds.

Purchasing power parity (PPP).


An exchange rate Stunting rate.

Proportion of children in a given age that accounts for price differences among countries, allowing international comparisons of real output and incomes.

Quintile.

In statistics, any of five equal groups into which a population can be divided according to the distribution of values of a variable.

Repeaters.
Repeaters.

Number of pupils enrolled in the same grade or level as the previous year, expressed as a percentage of the total enrolment in that grade or level.

group whose height for their age is between two and three standard deviations (moderate stunting) or three or more standard deviations (severe stunting) below the reference median established by the National Center for Health Statistics and the World Health Organization.

Low height for age is a basic indicator of malnutrition.
Survival rate by grade.

Percentage of a cohort of students who are enrolled in the first grade of an education cycle in a given school year and are expected to reach a specified grade, regardless of repetition.

Repetition rate by grade.

Number of repeaters in a given grade in a given school year, expressed as a percentage of enrolment in that grade the previous school year.

4 1 2 G L O S S A R Y Teacher compensation.

A base teaching salary plus Total fertility rate.


Average number of children that bonuses.

Base salary refers to the minimum scheduled gross annual salary for a full-time teacher with the minimum training necessary to be qualified at the beginning of his or her teaching career.

Reported base salaries are defined as the total sum of money paid by the employer for the labour supplied, minus the employer contribution to social and pension funding.
Bonuses that are a regular part of the annual salary, like a thirteenth month or holiday bonus, are usually included in the base salary.

would be born to a woman if she were to live to the end of her childbearing years (15 to 49) and bear children at each age in accordance with prevailing age-specific fertility rates.

Total primary net attendance rate (TNAR).


Number of pupils of the official primary school age group who attend school in either primary or secondary education, expressed as a percentage of the population in that age group.

Total primary net enrolment ratio (TNER).

Enrolment of children of the official primary school age group in either primary or secondary schools, expressed as a percentage of the population in that age group.

Trained teacher.
Trained teacher.

Teacher who has received the minimum organized teacher training normally required for teaching at the relevant level in a given country.

Transition rate to secondary education.

New entrants to the first grade of secondary education in a given year, expressed as a percentage of the number of pupils enrolled in the final grade of primary education in the previous year.

Undernutrition/malnutrition.


The condition of people whose dietary energy intake is below that needed to maintain a healthy life and carry out light physical activity.

Malnutrition refers to food deficiencies either in terms of quantity or quality (lack of specific nutrients or vitamins).

Upper-secondary education (ISCED level 3).

See Secondary education.

Variance.

A measure of dispersion of a given distribution.

Youth literacy rate.


Number of literate persons aged 15 to 24, expressed as a percentage of the total population in that age group.

Teachers/teaching staff.

Number of persons employed full time or part time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism (i.e.

face to face and/or at a distance).

Excludes education personnel who have no active teaching duties (e.g.
headmasters, headmistresses or principals who do not teach) and persons who work occasionally or in a voluntary capacity.

Technical and vocational education and training (TVET).

Programmes designed mainly to prepare students for direct entry into a particular occupation or trade (or class of occupations or trades).
Successful completion of such programmes normally leads to a labour- market-relevant vocational qualification recognized by the education ministry, employers’ associations or other authorities in the country in which it is obtained.

Tertiary or higher education (ISCED levels 5 and 6).

Programmes with an educational content more advanced than what is offered at ISCED levels 3 and 4.
The first stage of tertiary education, ISCED level 5, includes level 5A, composed of largely theoretically based programmes intended to provide sufficient qualifications for gaining entry to advanced research programmes and professions with high skill requirements; and level 5B, where programmes are generally more practical, technical and/or occupationally specific.
The second stage of tertiary education, ISCED level 6, comprises programmes devoted to advanced study and original research and leading to the award of an advanced research qualification.

Total debt service.

Sum of principal repayments and interest paid in foreign currency, goods or services on long-term debt, or interest paid on short-term debt, as well as repayments (repurchases and charges) to the International Monetary Fund.
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PIRLS 2001 International Report: IEA’s Study of Reading Literacy Achievement in Primary Schools in 35 Countries.
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Dutch Humanitarian Assistance: An Evaluation.

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Oxford, UK, Oxford University Press.

Senegal Government.

2002.

Poverty Reduction Strategy Paper.


Dakar, Government of the Republic of Senegal.

_ 2006.

Poverty Reduction Strategy Paper II.

Dakar, Government of the Republic of Senegal.

Serbia Statistical Office and Strategic Marketing Research Agency.

2007.

Republic of Serbia Multiple Indicator Cluster Survey 2005, Final Report.

Belgrade, Statistical Office of the Republic of Serbia/Strategic Marketing Research Agency.

Shekar, M. and Lee, Y.-K. 2006.


Mainstreaming Nutrition in Poverty Reduction Strategies: What Does it Take?

A Review of the Early Experience.

Washington, DC, World Bank.

(Health, Nutrition and Population Discussion Paper.)

Shenkut, M. K. 2005.

Ethiopia: where and who are the world's illiterates?Background Paper for EFA Global Monitoring Report 2006.

Sherman, J. D. and Poirier, J. M. 2007.

Sub-national disparities in participation in quality primary education: draft report.
Background paper for EFA Global Monitoring Report 2008.

(Through the American Institutes for Research.).

Sherry, H. 2008.

Teachers’ Voice: A Policy Research Report on Teachers' Motivation and Perceptions of their Profession in Nigeria.

London, Voluntary Service Overseas .

Shoraku, A.

2008.

Educational movement toward school-based management in East Asia: Cambodia, Indonesia and Thailand.

Background paper for EFA Global Monitoring Report 2009.

Siaens, C., Subbarao, K. and Wodon, Q. T. 2003.


Are Orphans Especially Vulnerable?

Evidence from Rwanda.

Washington, DC, World Bank.

Sierra Leone Statistics and UNICEF.

2007.

Sierra Leone Multiple Indicator Cluster Survey 2005, Final Report.

Freetown, Statistics Sierra Leone/UNICEF.

Simoes, E. A. F., Cherian, T., Chow, J., Shahid-Salles, S. A., Laxminarayan, R. and John, T. J.

2006.

Acute respiratory infections in children.


Jamison, D. T., Breman, J. G., Measham, A. R., Alleyne, G., Claeson, M., Evans, D. B., Jha, P., Mills, A. and Musgrove, P. (eds), Disease Control Priorities in Developing Countries.

New York/Washington, DC, Oxford University Press/World Bank, pp.

483–97.

Sinyolo, D. 2007.


Teacher Supply, Recruitment and Retention in Six Anglophone sub-Saharan African Countries: A Report on a Survey Conducted by Education International in the Gambia, Kenya, Lesotho, Tanzania, Uganda and Zambia.

Brussels, Education International.

Skoufias, E. and Shapiro, J.

2006.

Evaluating the Impact of Mexico's Quality Schools Program: The Pitfalls of Using Nonexperimental Data.

Washington, DC, World Bank.

(Impact Evaluation Series, 8.

Policy Research Working Paper, 4036.)


Smits, J., Huisman, J. and Kruijff, K. 2008.

Home language and education in the developing world.

Background paper for EFA Global Monitoring Report 2009.

Soares, S., Osório, G., Soares, R., Veras, F., Medeiros, M. and Zepeda, E. 2007.

Conditional Cash Transfers in Brazil, Chile and Mexico: Impacts upon Inequality.

New York, United Nations Development Program.

(International Poverty Centre Working Paper, 35.)

Sridhar, D. 2008.
Sridhar, D. 2008.

Linkages between nutrition, ill-health and education.

Background paper for EFA Global Monitoring Report 2009.

Srivastava, P. 2006.

Private schooling and mental models about girls' schooling in India.

Compare, Vol.

36, No.

4, pp.

497–514.

_ 2007.

For philanthropy or profit?

The management and operation of low-fee private schools in India.

Srivastava, P. and Walford, G. (eds), Private Schooling in Less Economically Developed Countries: Asian and African Perspectives.
Oxford, UK, Symposium Books, pp.

153–86.

_ 2008.

School choice in India: disadvantaged groups and low-fee private schools.

Forsey, M., Davies, S. and Walford, G. (eds), The Globalisation of School Choice?Oxford, UK, Symposium Books, pp.

185–208.

Steiner-Khamsi, G., Harris-van Keuren, C., Silova, I. and Chachkhiani, K. 2008.

Decentralization and recentralization reforms: their impact on teacher salaries in the Caucasus, Central Asia and Mongolia.
Background paper for EFA Global Monitoring Report 2009.

Steiner, S. 2006.

Decentralisation in Uganda: Exploring the Constraints for Poverty Reduction.

Hamburg, Germany, German Institute of Global and Area Studies.

(GIGA Working Paper, 31.)

R E F E R E N C E S Strategic Partnership with Africa.

2008.

Strategic Partnership with Africa: Survey of Budget Support, 2007.

Volume I – Main Findings.Prepared for SPA Annual Plenary Meeting, Tunis, 21–22 February.

Sumra, S. 2006.
Sumra, S. 2006.

The Living and Working Conditions of Teachers in Tanzania.

A Research Report.

Dar es Salaam, HakiElimu.

Swedish National Agency for Education.

2008.

Barn, Elever Och Personal: Riksnivå: Sveriges Officiella Statistik om Förskolverksamhet, Skolbarnomsorg, Skola Och Vuxenutbildning.

Del 2.

[Official Statistics for Pre-School Activities, School-Age Childcare, Schools and Adult Education].

Stockholm, National Agency for Education.

(In Swedish.)

Swinkels, R. and Turk, C. 2006.


Explaining Ethnic Minority Poverty in Vietnam: A Summary of Recent Trends and Current Challenges.Presented at MPI Meeting on Ethnic Minority Poverty, Hanoi, Committee for Ethnic Minorities, 28 September.

Syrian Arab Republic Central Bureau of Statistics.

2008.

The Syrian Arab Republic Multiple Indicator Cluster Survey (MICS) 2006.

Damascus, Central Bureau of Statistics.

Takala, T. 2008.
Takala, T. 2008.

Government and donor efforts for improved aid effectiveness in the education sector: a case study of Mozambique.

Background paper for EFA Global Monitoring Report 2009.

Thailand National Statistical Office.

2006.

Thailand Multiple Indicator Cluster Survey, December 2005–February 2006, Final Report.

Bangkok, National Statistical Office.

The Communication Initiative Network.

2002.

Speaking Up on Disability.

Country: Uganda.

Victoria, BC, The Communication Initiative Network.
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The Lancet.

2008.

Maternal and child undernutrition series launched.

The Lancet, Vol.

371, No.

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Thirumurthy, H., Graff Zivin, J. and Goldstein, M. 2007.

AIDS Treatment and Intrahousehold Resource Allocations: Children’s Nutrition and Schooling in Kenya.

Washington, DC, Center for Global Development.

(Working Paper, 106.)

Thuilliez, J.

2007.


Malaria and Primary Education: A Cross-Country Analysis on Primary Repetition and Completion Rates.

Paris, University of Paris 1 Pantheon-Sorbonne, Sorbonne Economics Centre/National Centre for Scientific Research.

(CES Working Papers, 2007.13.)

Tiedemann, J.

2000.

Parents’ gender stereotypes and teachers’ beliefs as predictors of children's concept of their mathematical ability in elementary school.

Journal of Educational Psychology, Vol.

92, No.

1, pp.
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Tokman Ramos, A.

2002.

Is Private Education Better?

Evidence from Chile.

Santiago, Central Bank of Chile.

(Working Papers, 147.)

Tomasevski, K. 2006.

The State of the Right to Education Worldwide.

Free or Fee: 2006 Global Report.

Copenhagen.

Tooley, J.

2007.

Could for-profit private education benefit the poor?

Some a priori considerations arising from case study research in India.

Journal of Education Policy, Vol.

22, No.

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Tooley, J. and Dixon, P. 2007.

Private education for low-income families: results from a global research project.

Srivastava, P. and Walford, G. (eds), Private Schooling in Less Economically Developed Countries: Asian and African Perspectives.

Oxford, UK, Symposium Books, pp.

15–40.

Transparency International.

2005.

Stealing the Future: Corruption in the Classroom.

Ten Real World Experiences.

Berlin, Transparency International.

Tsang, M. 2002.
Tsang, M. 2002.

Establishing a substantial and regularized scheme of intergovernmental grants in compulsory education.

Harvard China Review, Vol.

3, No.

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Uganda Local Government Finance Commission.

2000.

Equalisation Grant for Urban Local Governments: Analysis and Recommendations.

Kampala, Local Government Finance Commission.

Uganda Ministry of Finance Planning and Economic Development.

2000.
2000.

Poverty Reduction Strategy Plan: Uganda Poverty Eradication Action Plan Summary and Main Objectives.

Kampala, Ministry of Finance, Planning and Economic Development.

_ 2004.

Poverty Eradication Action Plan (2004/5–2007/8).

Kampala, Ministry of Finance, Planning and Economic Development.

UIS.

2005.

Global Education Digest 2005: Comparing Education Statistics across the World.

Montreal, Que, UNESCO Institute for Statistics.

_ 2006a.
_ 2006a.

Global Education Digest 2006: Comparing Education Statistics across the World.

Montreal, Que, UNESCO Institute for Statistics.

_ 2006b.

Teachers and Educational Quality: Monitoring Global Needs for 2015.

Montreal, Que, UNESCO Institute for Statistics.

_ 2007.

Global Education Digest 2007: Comparing Education Statistics across the World.

Montreal, Que, UNESCO Institute for Statistics.

_ 2008.

Data Centre: Culture and Communication.

Montreal, Que, UNESCO Institute for Statistics.


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Umansky, I.

2005.

A literature review of teacher quality and incentives: theory and evidence.

Vegas, E.

(ed.

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Washington, DC, World Bank, pp.

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2006.
2006.

The State of the World's Cities Report 2006/7: The Millennium Development Goals and Urban Sustainability.

30 Years of Shaping the Habitat Agenda.

Nairobi, United Nations Human Settlements Programme.

UN Economic Commission for Latin America and the Caribbean.

2007.

Social Panorama of Latin America 2007.

Santiago, United Nations Economic Commission for Latin America and the Caribbean.

UN Millennium Project.

2005a.

Taking Action: Achieving Gender Equality and Empowering Women.
Achieving the Millennium Development Goals.

London, United Nations Development Programme, UN Millennium Project, Task Force on Education and Gender Equality.

_ 2005b.

Towards Universal Primary Education: Investments, Incentives and Institutions.

London, United Nations Development Programme, UN Millennium Project, Task Force on Education and Gender Equality.

UN Population Division.

2007.

World Population Prospects: The 2006 Revision Population Database.
New York, United Nations, Department of Economic and Social Affairs, Population Division.

(ESA/P/WP.202.)

UNAIDS.

2008.

Report on the Global AIDS Epidemic 2008.

Geneva, Switzerland, Joint United Nations Programme on HIV/AIDS.

(UNAIDS/08.25E / JC1510E.)

UNDP.

2006.

Human Development Report 2006.

Beyond Scarcity: Power, Poverty and the Global Water Crisis.

New York, United Nations Development Programme.

_ 2007.

Human Development Report 2007/2008.


Fighting Climate Change: Human Solidarity in a Divided World.

New York, United Nations Development Programme.

UNDP Arab TIMSS Regional Office.

2003.

Achievements of Arab Countries that Participated in the Trends in International Mathematics and Science Study.

Amman, United Nations Development Programme, Arab TIMSS Regional Office.

UNESCO.

1997. International Standard Classification of Education (ISCED) 1997.

Paris, UNESCO.

(BPE.98/WS/1.)

_ 2000.


The Dakar Framework for Action: Education for All – Meeting our Collective Commitments.World Education Forum, Dakar, UNESCO.

_ 2003.

EFA Global Monitoring Report 2003/4.

Gender and Education for All: The Leap to Equality.

Paris, UNESCO.

_ 2004.

EFA Global Monitoring Report 2005.

Education for All: The Quality Imperative.

Paris, UNESCO.

_ 2005.

EFA Global Monitoring Report 2006.

Education for All: Literacy for Life.

Paris, UNESCO.

_ 2006.


EFA Global Monitoring Report 2007.

Strong Foundations: Early Childhood Care and Education.

Paris, UNESCO.

_ 2007a.

EFA Global Monitoring Report 2008.

Education for All by 2015: Will We Make It?Paris, UNESCO/Oxford University Press.

_ 2007b.

Supporting HIV-Positive Teachers in East and Southern Africa: Technical Consultation Report.

30 November–1 December 2006.

Nairobi, UNESCO.

UNESCO-IBE.

2007.


Recent estimates of intended instructional time over the first nine years of schooling (prepared by Massimo Amadio).

Background paper for EFA Global Monitoring Report 2008.

_ 2008a.

A compilation of background information about educational legislation, governance, management and financing structures and processes: Arab States.

Background paper for EFA Global Monitoring Report 2009.

_ 2008b.
_ 2008b.

A compilation of background information about educational legislation, governance, management and financing structures and processes: Central and Eastern Europe.

Background paper for EFA Global Monitoring Report 2009.

_ 2008c.

A compilation of background information about educational legislation, governance, management and financing structures and processes: Central Asia.

Background paper for EFA Global Monitoring Report 2009.

_ 2008d.
_ 2008d.

A compilation of background information about educational legislation, governance, management and financing structures and processes: East Asia and the Pacific.

Background paper for EFA Global Monitoring Report 2009.

_ 2008e.

A compilation of background information about educational legislation, governance, management and financing structures and processes: Latin America and the Caribbean.

Background paper for EFA Global Monitoring Report 2009.

_ 2008f.
_ 2008f.

A compilation of background information about educational legislation, governance, management and financing structures and processes: North America and Western Europe.

Background paper for EFA Global Monitoring Report 2009.

_ 2008g.

A compilation of background information about educational legislation, governance, management and financing structures and processes: South and West Asia.

Background paper for EFA Global Monitoring Report 2009.

_ 2008h.
_ 2008h.

A compilation of background information about educational legislation, governance, management and financing structures and processes: sub-Saharan Africa.

Background paper for EFA Global Monitoring Report 2009.

UNESCO-IIEP.

2006. Review of national education planning documents in 45 countries.

Background paper for the EFA Global Monitoring Report 2006.

UNESCO-OREALC.

2008.


Los Aprendizajes de los Estudiantes de América Latina y el Caribe: Primer Reporte de los Resultados del Segundo Estudio Regional Comparativo y Explicativo [Student Achievement in Latin America and the Caribbean: Results of the Second Regional Comparative and Explanatory Study].

Santiago, UNESCO Regional Bureau for Education in Latin America and the Caribbean, Latin American Laboratory for Assessment of the Quality of Education.

(In Spanish.)

R E F E R E N C E S UNESCO-UNEVOC/UIS.


2006.

Participation in Formal and Vocational Education and Training Programmes Worldwide.

An Initial Statistical Study.

Bonn, Germany/Montreal, Que, UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training/UNESCO Institute for Statistics.

UNICEF.

2007.

The State of the World’s Children 2008: Child Survival.

New York, UNICEF.

_ 2008.


At a Glance: United Arab Emirates: UNICEF and Dubai Cares Support Quality Education to Break the Cycle of Poverty.

New York, UNICEF.

http://www.unicef.org/infobycountry/uae_42888.html (Accessed 27 September 2008.)

UNICEF and Agency of the Republic of Kazakhstan on Statistics.

2007.

Kazakhstan Multiple Indicator Cluster Survey 2006: Final Report.

Astana, UNICEF Kazakhstan/Agency of the Republic of Kazakhstan on Statistics.
UNICEF and State Statistical Committee of the Republic of Uzbekistan.

2007.

Uzbekistan Multiple Indicator Cluster Survey 2006, Final Report.

Tashkent, UNICEF.

United Nations.

2006.

Convention on the Rights of Persons with Disabilities.New York, United Nations.

(Adopted by the Sixty-First Session Item 67 [b].

Human Rights Questions: Human Rights Questions, Including Alternative Approaches for Improving the Effective Enjoyment of Human Rights and Fundamental Freedoms.)
United Republic of Tanzania Government.

2000.

Poverty Reduction Strategy Paper (PRSP).

Dar es Salaam, Government of the United Republic of Tanzania.

_ 2005.

National Strategy for Growth and Reduction of Poverty (NSGRP).

Dar es Salaam, Government of the United Republic of Tanzania.

United Republic of Tanzania Ministry of Education and Vocational Training.

2007.

Basic Education Statistics in Tanzania (BEST) 2003–2007 National Data.

Dar es Salaam, Ministry of Education and Vocational Training.
United Republic of Tanzania Research and Analysis Working Group.

2007.

Poverty and Human Development Report (PHDR) 2007.

Dar es Salaam, Ministry of Planning, Economy and Empowerment, Research and Analysis Working Group, MKUKUTA Monitoring System.

United Republic of Tanzania Vice President's Office.

2005.

National Strategy for Growth and Reduction of Poverty (NSGRP).

Dar es Salaam, Vice President's Office.

US Department of Education, National Center for Education Statistics.

2007.
2007.

The Condition of Education 2007.

Washington, DC, Government Printing Office.

(NCES 2007-064.)

US Social Security Administration.

2006.

Social Security Programs Throughout the World: Europe, 2006.

Washington, DC, Office of Policy, Office of Research, Evaluation and Statistics.

(SSA Publication, 13-11801.)

_ 2007a.

Social Security Programs Throughout the World: Africa, 2007.

Washington, DC, Office of Policy, Office of Research, Evaluation and Statistics.

(SSA Publication, 13-11803.)


_ 2007b.

Social Security Programs Throughout the World: Asia and the Pacific, 2006.

Washington, DC, Office of Policy, Office of Research, Evaluation and Statistics.

(SSA Publication, 13-11802.)

_ 2008.

Social Security Programs Throughout the World: The Americas, 2007.

Washington, DC, Office of Retirement and Disability Policy, Office of Research, Evaluation and Statistics.

(SSA Publication, 13-11804.)

Vachon, P. 2007.

Country case studies: Burkina Faso.


Background paper for EFA global Monitoring Report 2008. van der Berg, S. and Louw, M. 2007.

Lessons Learnt from SACMEQ II: South African Student Performance in Regional Context.

Metieland, South Africa, University of Stellenbosch, Department of Economics and Bureau for Economic Research.

(Working paper, 16/07.)

Vandemoortele, J. and Delamonica, E. 2000.

The ‘education vaccine’ against HIV.

Current Issues in Comparative Education, Vol.

3, No.

1, pp.

6–13.


Vaux, T., Smith, A. and Subba, S. 2006.

Education for All – Nepal: Review From a Conflict Perspective.

London, International Alert.

Vegas, E. and De Laat, J.

2003.

Do Differences in Teacher Contracts Affect Student Performance?

Evidence from Togo.

Washington, DC, World Bank.

Vegas, E. and Petrow, J.

2007.

Raising Student Learning in Latin America: The Challenge for the 21st Century.

Washington, DC, World Bank.

Viet Nam General Statistics Office.

2006.


Viet Nam Multiple Indicator Cluster Survey 2006, Final Report.

Hanoi, General Statistics Office.

Viet Nam Government.

2003.

The Comprehensive Poverty Reduction and Growth Strategy.

Hanoi, Government of the Socialist Republic of Vietnam.

_ 2006.

The Five Year Socio-Economic Development Plan 2006–2010.

Hanoi, Government of the Socialist Republic of Vietnam.

VSO.

2007.
VSO.

2007.

Teachers Speak Out: A Policy Research Report on Teachers' Motivation and Perceptions of their Profession in the Gambia.

London, Voluntary Service Overseas.

Wang, Y.

2004.

Governance of Basic Education: Service Provision and Quality Assurance in China.

Washington, DC, World Bank Ward, M., Penny, A. and Read, T. 2006.

Education Reform in Uganda – 1997 to 2004.


Reflections on Policy, Partnership, Strategy and Implementation.

London, UK Department for International Development.

(Researching the Issues, 60.)

Watkins, K. 2004.

Private education and ‘Education For All’ – or how not to construct an evidence-based argument: a reply to Tooley.

Economic Affairs, Vol.

24, No.

4, pp.

8–11.

Wedgwood, R. 2007.

Education and poverty reduction in Tanzania.

International Journal of Educational Development, Vol.

27, No.
27, No.

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383–96.

WHO.

2008.

World Health Statistics 2008.

Geneva, Switzerland, World Health Organization.

WHO, UNICEF, UNFPA and World Bank.

2007.

Maternal Mortality in 2005: Estimates Developed by WHO, UNICEF, UNFPA and the World Bank.

Geneva, Switzerland, World Health Organization.

William and Flora Hewlett Foundation.

2006.

Gates and Hewlett Foundations Join to Improve the Quality of Education in Developing Nations.

Menlo Park, Calif., William and Flora Hewlett Foundation.
http://www.hewlett.org/Programs/GlobalAffairs/News/Gates+and+Hewlett+Foundations+Join+to+Improve+the+Quality +of+Education+in+Developing+Nations.htm (Accessed 27 September 2008.)

_ 2007.

The Hewlett and Gates Foundations Award $9 million to Pratham’s Read India Campaign.

Santa Clara, Calif., William and Flora Hewlett Foundation.

http://www.pratham.org/documents/Pratham%20Hewlett%20Gates%20Grant %20Release%20Final%20July%205%202007.pdf (Accessed 27 September 2008.)

Willms, J. D. 2006.
Willms, J. D. 2006.

Learning Divides: Ten Key Policy Questions about the Performance and Equity of Schools and Schooling Systems.

Montreal, Que, UNESCO Institute for Statistics.

(UIS Working Paper, 5.)

Winkler, D. 2005.

Public Expenditure Tracking in Education.

Washington, DC, USAID/Academy for Educational Development, EQUIP2: Educational Policy, Systems Development and Management.

(Policy Brief.)

Winters, M. A. and Greene, J. P. 2007.


Second Year Evaluation of the Systemic Effects of the DC Voucher Program.

Fayetteville, Ark., University of Arkansas.

(School Choice Demonstration Project.)

Wodon, Q. T., Tsimpo, C., Backiny-Yetna, P., Joseph, G., Adoho, F. and Coulombe, H. 2008.

Measuring the Potential Impact of Higher Food Prices on Poverty: Summary Evidence from West and Central Africa.

Washington, DC, World Bank.

(Mimeo.)

World Bank.

2002.

User Fees in Primary Education.


Washington, DC, World Bank.

(Mimeo, review draft.)

_ 2003.

World Development Report 2004: Making Services Work For Poor People.

Washington, DC, World Bank/Oxford University Press.

_ 2004.

Books, Buildings and Learning Outcomes: An Impact Evaluation of World Bank Support to Basic Education in Ghana.

Washington, DC, World Bank, Operation Evaluation Department.

_ 2005a.

Cambodia – Public Expenditure Tracking Survey (PETS) in Primary Education.
Washington, DC, World Bank, East Asia and Pacific Region, Human Development Sector Unit.

(Human Development Sector Reports, 34911-KH.)

_ 2005b.

Education Sector Strategy Update.

Achieving Education For All, Broadening Our Perspective, Maximizing Our Effectiveness.

Washington, DC, World Bank.

(Final Draft.

Copyedited version of document presented to the Board of Directors on 7 November 2005.)

_ 2005c.
_ 2005c.

Expanding Opportunities and Building Competencies for Young People: A New Agenda for Secondary Education.

Washington, DC, World Bank.

_ 2005d.

Going to School/Going to Work: A Report on Child Labor and EFA in World Bank Projects and Policy Documents.Third Roundtable on Child Labor and Education for All (EFA), Beijing, Global Task Force on Child Labour and Education, 28 November.

_ 2005e.
_ 2005e.

Implementation Completion Report (IDA-35700 TF-50588) on a Credit in the Amount of SDR 119.1 Million (US$150 Equivalent) and a Grant in the Amount of US$50 Million to the United Republic of Tanzania for the Primary Education Development Program.

Washington, DC, World Bank, Africa Region, Human Development, Country Department.

(32071.)

_ 2005f.

Project Appraisal Document Vietnam.

Targeted Budget Support for National Education for All Plan Implementation Program.

Washington, DC, World Bank.


_ 2005g.

World Development Report 2006: Equity and Development.

Washington, DC, World Bank.

_ 2006a.

Bolivia.

Basic Education in Bolivia: Challenges for 2006–2010.

Washington, DC, World Bank; Latin America and the Caribbean Region; Bolivia, Ecuador, Peru and Venezuela Country Management Unit; Human Development Department.

(35073-BO.)

_ 2006b.

Repositioning Nutrition as Central to Development: A Strategy for Large-Scale Action.

Overview.

Washington, DC, World Bank.


(Directions in Development.)

_ 2007a.

Chile: Institutional Design for an Effective Education Quality Assurance.

Washington, DC, World Bank; Latin America and the Caribbean Regional Office; Argentina, Chile, Paraguay and Uruguay Country Management Unit; Human Development.

(39830-CL.)

_ 2007b.

Ethiopia: Enhancing Human Development Outcomes through Decentralized Service Delivery.

Washington, DC, World Bank, AFTH3, Human Development Department, Africa Region.
(32 675-ET.)

R E F E R E N C E S _ 2007c.

Pakistan.

Social Protection in Pakistan: Managing Household Risks and Vulnerability.

Washington, DC, World Bank, Human Development Unit, South Asia Region.

(35472-PK.)

_ 2007d.

Project Paper on a Proposed Additional Financing (Grant) in the Amount of SDR 38.7 Million (US$60,0 Million Equivalent) to Nepal for the Education For All Project, World Bank, Human Development Unit, South Asia Regional Office.

(41346-NP.)

_ 2007e.


What Do We Know About School-Based Management?Washington, DC, World Bank, Education, Human Development Network.

_ 2007f.

What Is School-Based Management?Washington, DC, World Bank, Education, Human Development Network.

_ 2008a.

Addressing the Food Crisis: The Need for Rapid and Coordinated Action.Group of Eight: Meeting of Finance Ministers, Osaka, Japan, World Bank, 13–14 June.

_ 2008b.

Educational Attainment.

Washington, DC, World Bank.


http://www.worldbank.org/research/projects/edattain/ (Accessed 27 September 2008.)

_ 2008c.

FYR Macedonia: Public Expenditure Review.

Washington, DC, World Bank, Poverty Reduction and Economic Management Unit, Europe and Central Asia Region.

(42155-MK.)

_ 2008d.

MENA Development Report.

The Road Not Travelled: Education Reform in the Middle East and North Africa.

Washington, DC, World Bank.

_ 2008e.

Nigeria: A Review of the Costs and Financing of Public Education.
Volume 1: Executive Summary.

Washington, DC, World Bank, Human Development Unit, Africa Region.

(42418-NG.)

_ 2008f.

Nigeria: A Review of the Costs and Financing of Public Education.

Volume 2: Main Report.

Washington, DC, World Bank, Human Development Unit, Africa Region.

(42418-NG.)

_ 2008g.

World Development Indicators.

Washington, DC, World Bank.

World Bank and IMF.

2005.

2005 PRS Review: Balancing Accountabilities and Scaling Up Results.
Washington, DC, World Bank/International Monetary Fund.

World Vision.

2007.

Education's Missing Millions: Including Disabled Children in Education through EFA FTI Processes and National Sector Plans.

Main report of Study Findings.

Milton Keynes, UK, World Vision.

Wößmann, L. 2003.

Schooling resources, educational institutions and student performance: the international evidence.

Oxford Bulletin of Economics and Statistics, Vol.

65, No.

2, pp.

117–70.

_ 2006.


Public-Private Partnerships and Schooling Outcomes across Countries.Paper prepared for the conference Mobilizing the Private Sector for Public Education, Cambridge, Mass., Harvard University, Kennedy School of Government, Program on Education Policy and Governance/World Bank, 5–6 October.

Wu, K. B., Goldschmidt, P., Boscardin, C. K. and Azam, M. 2007.

Girls in India: poverty, location and social disparities.
Lewis, M. and Lockheed, M. (eds), Exclusion, Gender and Education: Case Studies from the Developing World.

Washington, DC, Center for Global Development, pp.

119–43.

Wyse, D., McCreery, E. and Torrance, H. 2008.

The Trajectory and Impact of National Reform: Curriculum and Assessment in English Primary Schools.

Cambridge, UK, University of Cambridge.

(Interim Reports.

The Primary Review.

Research Survey, 3/2.)


Yoshikawa, H., McCartney, K., Myers, R., Bub, K. L., Lugo-Gil, J., Ramos, M. A. and Knaul, F. 2007.

Early Childhood Education in Mexico: Expansion, Quality Improvement and Curricular Reform.

Florence, Italy, UNICEF Innocenti Research Centre.

(Innocenti Working Paper, IWP-2007-03.)

Young, M. E. and Richardson, L. M. (eds).

2007.

Early Child Development from Measurement to Action: A Priority for Growth and Equity.
Washington, DC, World Bank, Children and Youth Unit, Human Development Network.

Zambia Government.

2006.

Fifth National Development Plan 2006–2010.

Lusaka, Government of the Republic of Zambia.

Zambia Ministry of Finance and National Planning.

2002.

Zambia Poverty Reduction Strategy Paper 2002–2004.

Lusaka, Ministry of Finance and National Planning.

Zhang, Y., Postlethwaite, T. N. and Grisay, A.

2008.

A View Inside Primary Schools: A World Education Indicators (WEI) Cross-National Study.
Montreal, Que, UNESCO Institute for Statistics.

Zimmer, R., Gill, B., Razquin, P., Booker, K. and Lockwood III, J. R. 2007.

State and Local Implementation of the No Child Left Behind Act.

Volume I – Title I School Choice, Supplemental Educational Services, and Student Achievement.

Washington, DC, US Department of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service.
Abbreviations ADB Asian Development Bank AfDF African Development Fund ARTF Afghanistan Reconstruction Trust Fund AsDF Asian Development Fund CONFEMEN Conférence des Ministres de l’Éducation des pays ayant le français en partage DAC Development Assistance Committee (OECD) DPT Diphtheria Pertussis Tetanus vaccine DFID Department for International Development (United Kingdom) DPEP District Primary Education Programme (India) E-9 Nine high-population countries (Bangladesh, Brazil, China, Egypt, India, Indonesia, Mexico, Nigeria, Pakistan) EC European Commission ECCE Early childhood care and education EDI EFA Development Index EDUCO Educación con Participación de la Comunidad (El Salvador) EIIIG EFA Inequality Index for Income Groups EFA Education for All EMIS Education Management Information System(s) ESDP Education Sector Development Programme (Ethiopia) EU European Union FTI Fast Track Initiative FUNDEB Fundo de Manutenção e Desenvolvimento da Educação Básica e de Valorizaçãodos Profissionais da Educação (Brazil) (formerly FUNDEF) FUNDEF Fundo de Manutenção e Desenvolvimento do Ensino Fundamental e de Valorização do Magistério (Brazil) (renamed FUNDEB in 2007) FUNDESCOLA Fundo de Fortalecimento da Escola ( Brazil) G8 Group of Eight (Canada, France, Germany, Italy, Japan, Russian Federation, United Kingdom and United States, plus EU representatives) GDP Gross domestic product GEI Gender-specific EFA index GER Gross enrolment ratio GIR Gross intake rate GNI Gross national income GNP Gross national product GPI Gender parity index HIV/AIDS Human immuno-deficiency virus/acquired immune deficiency syndrome IBE International Bureau of Education (UNESCO) ICT Information and communication technology IDA International Development Association (World Bank) IDB Inter-American Development Bank IEA International Association for the Evaluation of Educational Achievement IIEP International Institute for Educational Planning
 (UNESCO) ILO International Labour Organization IMF International Monetary Fund ISCED International Standard Classification of Education LAMP Literacy Assessment and Monitoring Programme A B B R E V I AT I O N S LDCs Least developed countries LGA Local Government Area (Nigeria) LLECE Laboratorio Latinamericano de Evaluación de la Calidad de la Educación MDG Millennium Development Goal MICS Multiple Indicator Cluster Surveys (UNICEF) MOEYS Ministry of Education, Youth and Sports (Cambodia) NAR Net attendance rate NER Net enrolment ratio NGO Non-government organization NIR Net intake rate ODA Official development assistance OECD Organisation for Economic Co-operation and Development OREALC UNESCO Regional Bureau for Education in Latin America and the Caribbean PASEC Programme d’analyse des systèmes éducatifs de la CONFEMEN PEC Programa Escuelas de Calidad (Mexico) PETS Public Expenditure Tracking Survey PIRLS Progress in Reading Literacy Study PISA Programme for International Student Assessment (OECD) PPP Purchasing power parity PREAL Programa de Promoción de la Reforma Educativa de América Latina y el Caribe PROHECO Programa Hondureño de Educación Comunitaria (Honduras) PRSP Poverty reduction strategy paper PTA Parent-teacher association PTR Pupil/teacher ratio SACMEQ Southern and Eastern Africa Consortium for Monitoring Educational Quality SERCE Segundo Estudio Regional Comparativo y Explicativo SIMECAL Sistema de Medición y Evaluación de la Calidad de la Educación (Bolivia) SSA Sarva Shiksa Abhiyan (India) SWAp Sector-wide approach TIMSS Trends in International Mathematics and Science Study TNER Total primary net enrolment ratio TRC Teacher resource centre TVET Technical and vocational education and training UIL UNESCO Institute for Lifelong Learning UIS UNESCO Institute for Statistics UN United Nations UN-HABITAT United Nations Human Settlements Programme UNAIDS Joint United Nations Programme on HIV/AIDS UNDP United Nations Development Programme UNESCO United
 Nations Educational, Scientific and Cultural Organization UNEVOC International Centre for Technical and Vocational Training (UNESCO) UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund UNPD United Nations Population Division UPE Universal primary education USAID United States Agency for International Development WEI World Education Indicators WHO World Health Organization Index This index is in word-by-word order and covers chapters 1 to 5.
Page numbers in italicsindicate figures and tables; those in bold refer to material in boxes; bold italicsindicates a figure or table in a box.

The letter ‘n’ following a page number indicates information in a note at the side of the page; the letter ‘m’ indicates a map.

Page numbers in superscript, e.g.

1332, indicate the number of references to the topic on that page.

Definitions of terms can be found in the glossary, and additional information on countries can be found in the statistical annex.
2010 aid targets 205, 207, 221, 221 A Abecedarian Project 50 absenteeism pupils 81 teachers 120-1, 165, 166, 172, 179 academic achievement seeschool achievement access to education see alsoenrolment; poverty; universal primary education (UPE) access to qualified teachers 131 basic education 84, 214 boys 64 and corruption 139 disabled pupils 82-3 early childhood care and education, and equity 42 and education expenditure 132 effect of failures 26, 207-8 gender disparities 65, 98-9 girls 63-4, 65, 206 increasing 63, 231, 232 and location 116 policies improving 65 pre-school education 50-1, 53, 55 primary education 60-7, 63, 100, 231 and quality 77 responsibility of governments 153 rural areas 58, 58, 231 and school costs 62 secondary education 32, 84 tertiary education 90 women 28 achievement seeeducational attainment; school achievement acquired immune deficiency syndrome seeHIV/AIDS adolescents, youth literacy 94-5 adult education see alsolifelong learning; post-secondary education; teacher training; tertiary education monitoring 91-2 adult literacy (EFA goal) see alsoyouth literacy benefits and barriers 93 4 4 2 EDI indicator 122 gender disparity 95, 105-6 inequalities within countries 95-6 literacy rates 93-4, 93, 94-5, 94, 95 progress towards 94 projections 93 PRSP strategies 200-1 and success of information campaigns 141 women 28, 29, 93, 93, 95 Afghanistan adult literacy 94, 94n capacity 227 education aid 227, 228 education projects and governance 231, 232 enrolment 103 gender parity/disparity 99, 100, 101, 103 pre-primary education 52 primary education 99 PRSPs 194 secondary education 101 teachers 1172, 176, 177 tertiary education 103 Afghanistan Reconstruction Trust Fund 227 Africa Andorra primary education 119, 120 teachers 119, 120 Angola child labour 80 education expenditure 134, 135 malnutrition 46, 47 Anguilla primary education 71, 119 teachers 119 antenatal care 34, 34 antiretroviral drugs 82 Arab States see alsoindividual countries adult literacy 93, 94, 95, 95 EDI 122 education expenditure 133,
 1342, 137 gender parity/disparity 95, 97, 98, 99, 1002, 101, 101 learning assessments 110 malnutrition 47 out-of-school children 61, 64 pre-primary education 51, 51, 52 primary education 56, 56, 57, 572, 60, 98, 99, see alsoindividual countries; North Africa; 1002 Sub-Saharan Africa; West Africa secondary education 84, 85, 86, 86, 98, 101, gender parity/disparity 106 malnutrition 46 mathematics achievement 106 primary education 71 African Development Bank 63, 215, 215, 217, 101 teachers 107, 118, 119 tertiary education 89, 90, 98 TVET 85 youth literacy 94 217 Argentina aid see alsoeducation aid; ODA aid commitments, to education 208, 208, 209-10, 210, 211, 214, 217, 218 aid delivery 220 aid effectiveness aligning aid with government priorities 224-7 donor investment in governance 230 improving donor coordination 227-9 programmatic approach 220, 221-2 progress towards targets 221 aid flows 137, 206-7, 220, 226, 240 aid governance 205, 219-20 donor coordination 227-9 donor influence 129, 141, 220, 222, 229-30, 230, 233 and government systems 224-7 and national ownership 222-4 and programmatic approach 221-4 child labour 80 decentralization 148 EDI 124 education expenditure 135, 136 efficiency 138 gender parity/disparity 100, 103, 106n learning assessments 111 learning environment 1172 literacy 112 malnutrition 47 pre-primary education 50, 53 primary education 71, 100, 112 pupil achievement 112 reading literacy 106n science achievement 110 secondary education 85, 103 socio-economic background 116 teachers 121 aid practices, OECD-DAC survey 220, 221, 221 aid predictability (aid flows) 137, 206-7, 220, 226, armed conflict seeconflicts Armenia 240 AIDS seeHIV/AIDS Albania child labour 80 EDI 124 pre-primary education 53 primary education 61 Algeria adult literacy 94, 94 EDI 122 education aid 218 education expenditure 137 gender parity/disparity 99 malnutrition 47 pre-primary education 52 primary education 60, 71, 99, 119 teachers 119 gender parity/disparity 99, 106
 mathematics achievement 106 pre-primary education 52 primary education 68, 99, 113, 122n pupil achievement 113 secondary education 87, 87 Aruba EDI 124 primary education 71, 119 teachers 119 Asia see alsoCentral Asia; East Asia; individual countries; South and West Asia education expenditure 142 inequalities 142 Asian Development Bank 215, 215, 217, 217 assessment America seeindividual countries; Latin America; North America; United States anaemia seeiron deficiency; parasitic worms see alsomonitoring as measure of efficiency 138 of student learning seelearning assessments attainment seeeducational attainment; school achievement attendance seeschool attendance Australia education aid donor 214, 215, 215, 226 education expenditure 134, 136 inequalities 114n ODA 207 primary education 60, 113 pupil achievement 113 science achievement 110 tertiary education 73 Austria education aid donor 214, 215, 228 education expenditure 135, 136 inequalities 114n ODA 207 pre-primary education 53 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 Azerbaijan child labour 80 EDI 124 education expenditure 134, 137 gender parity/disparity 100, 103n pre-primary education 52 primary education 60, 100, 119 science achievement 110 secondary education 103n teachers 119 B background seedisadvantage; educational background; socio-economic background Bahamas gender parity/disparity 100 primary education 61, 71, 100, 119, 120 teachers 119, 119, 120 Bahrain EDI 124 gender parity/disparity 106 learning assessments 110n malnutrition 47 mathematics achievement 106 pre-primary education 52 primary education 60, 71 secondary education 84, 84n Balochistan Education Support Project (Pakistan) 231, 233 Bangladesh adult literacy 94, 94n, 942 antenatal care 34 child mortality rate 33, 43, 452 corruption safeguards 140 EDI 123, 124 education aid 206, 212, 212 education expenditure 133, 135, 137 education policies 191 education projects and governance 232, 233 EIIIG 125
 gender parity/disparity 100, 102, 103, 104, 191, 206 inequalities 28, 74, 74, 75, 76, 78, 79, 88, 88, 115 malnutrition 47 non-formal learning 91 nutrition policies 192 out-of-school children 61-2, 62, 65, 66, 76, 133, 212, 212 pre-primary education 52, 52, 54 primary education 60, 71, 71, 73, 74, 75, 78, 79, 100, 103, 104 secondary education 88, 88, 102, 104 stunted children 35 teachers 119, 119-20, 120, 177 vaccination 33 Barbados education expenditure 135 primary education 61, 71 barriers seeaccess to education Basic and Primary Education Programme (BPEP) (Nepal) 223 basic education see alsolower secondary education; pre-school education; primary education; universal primary education access 84, 214 additional funding 149 aid 209, 209, 211, 218, 218 commitments and disbursements 208, 209, 210, 211, 212, 214, 216, 222, 226 components 222 donors 214, 214-15, 214, 215, 215, 217, 218, 228, 240 low-income countries 210, 210, 211, 211, 215, 216, 217-18, 218, 218, 218 and out-of-school children 211-12, 212 and primary enrolment 213, 213 programmatic support and governance 230-1, 231-2 proportion of education aid 209 sector-wide aid 223-4 share of total aid 209 definition 84n enrolment 213, 213 see alsoenrolment, primary education; enrolment, secondary education Basic Education Project (Uzbekistan) 231 Basic Education Sub-sector Investment Programme (BESSIP) (Zambia) 223 basic skills seeliteracy; numeracy Belarus education expenditure 135, 136 primary education 122n teachers 119 Belgium education aid donor 214, 215 education expenditure 136 inequalities 114n ODA 207 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 Belize child labour 80 EDI 122, 124 education expenditure 135, 137 malnutrition 47 pre-primary education 53 primary education 61, 71, 119 teachers 119 Benin adult literacy 94n antenatal care 34 child mortality rate 452 education expenditure 134, 135, 137 EIIIG 123, 125, 1252 gender parity/disparity 99, 100, 101, 103, 104
 inequalities 74, 75, 76, 782, 88, 88, 89 malnutrition 47 I N D E X out-of-school children 62, 76 pre-primary education 52, 58 primary education 60, 67, 68, 68, 71, 72, 73, 74, 75, 782, 99, 100, 104, 119, 121, 140 secondary education 88, 882, 89, 101, 103, 104 stunted children 35 teachers 119, 121 Bermuda gender parity/disparity 99 primary education 71, 99, 119 teachers 119 BESSIP (Basic Education Sub-sector Investment Programme) (Zambia) 223 Better Education through Reformed Management and Universal Teacher Upgrading (Indonesia) 231 Bhutan adult literacy 94n gender parity/disparity 99, 100 malnutrition 47 pre-primary education 52 primary education 60, 71, 72, 99, 100, 119, 120 teachers 119, 120 bias 106-7, 107 Bihar Development Policy Loan/Credit (India) 232, 233 bilateral donors aid to education 214-15, 214, 215, 226 commitments and disbursements 206-7, 214, 215 non-DAC 216 block grants 140, 148, 150 Bolivarian Republic of Venezuela seeVenezuela, Bolivarian Republic of Bolivia antenatal care 34 child labour 80 child mortality rate 32, 33, 44, 452 disabled children 83 EDI 124 education aid 213 education expenditure 135, 137 EIIIG 125 enrolment 213 gender parity/disparity 104 health and nutrition 49 inequalities 74, 76, 79, 89 language difficulties 114 learning assessments 182-3 malnutrition 47 out-of-school children 76, 213 pre-primary education 53 primary education 61, 71, 79, 104 PRSPs 196 school-based management 154-5 secondary education 104 stunted children 35 teachers 120 vaccination 33 Bolsa Família (Brazil) 1952 Bono de Desarrollo Humano (Ecuador) 49, 195 books, access to 116, 117 Bosnia and Herzegovina child labour 80 pre-primary education 54 Botswana education expenditure 135, 136 gender parity/disparity 100 literacy 109 malnutrition 47 pre-primary education 52 primary education 60, 71, 73, 100 boys see also'gender' entries; men access to education 64
 out-of-school children 64, 65 performance 105-7 primary education 98 school attendance 103-4 secondary education 87, 97, 98 tertiary education 97, 98 brain development, effect of malnutrition 238 Brazil adult literacy 94 cash transfers 195 child labour 80 corruption 139 education aid 217 education expenditure 135, 1372, 142 gender parity/disparity 99, 106n inequalities 76, 115, 142 learning assessments 110, 111 literacy 29 malnutrition 47 mathematics achievement 106n out-of-school children 62, 64, 65, 66, 76 pre-primary education 512, 53 primary education 61, 68, 71, 99 reading literacy 106n redistributive finance 148 school grants 156-7, 176 science achievement 110, 110 socio-economic background 116 teachers 121, 176 tertiary education 90 British Virgin Islands gender parity/disparity 99 primary education 61, 99, 119, 120 teachers 119, 120 Brunei Darussalem pre-primary education 52 primary education 71, 119, 120 teachers 119, 120 Bulgaria EDI 124 education expenditure 136 pre-primary education 53 primary education 61, 113 pupil achievement 113 science achievement 110 Burkina Faso adult literacy 94n antenatal care 34 child mortality rate 33, 452 civil society participation 198 EDI 122 education aid 213, 213, 226 education expenditure 135, 137 EIIIG 123, 125, 125 enrolment 213, 213, 229 gender parity/disparity 64, 99, 100, 103, 104, 104, 105, 106n governance 226 inequalities 28, 74, 74, 75, 76, 88, 103, 104 malnutrition 47 mathematics achievement 106n non-formal learning 91 out-of-school children 61, 62, 64, 66, 75, 76, 213 pre-primary education 52 primary education 60, 68, 68, 69, 70, 71, 72, 74, 75, 99, 100, 104, 119 PRSPs 200-1 reading literacy 105 4 4 4 secondary education 84, 88, 104 stunted children 35 teachers 118, 119 vaccination 33 primary education 113 pupil achievement 113 science achievement 110, 110 tertiary education 71, 73 Burundi capacity abolition of school fees 199 adult literacy 94n child labour 80 disabled children 83 education expenditure 134,
 135, 137 education projects and governance 231, 232 gender parity/disparity 99, 99, 100 malnutrition 46, 47 out-of-school children 64 pre-primary education 52 primary education 68, 70, 71, 72, 73, 99, 100 C Caicos Islands seeTurks and Caicos Islands Cambodia adult literacy 94n antenatal care 34 child labour 80 child mortality rate 33 disabled children 83 EDI 123 education aid 205, 225, 227 education expenditure 134 EIIIG 125, 125 gender parity/disparity 99, 100, 103, 104 HIV/AIDS 192 inequalities 75, 76, 77, 88, 88, 89 literacy 109 malnutrition 47 out-of-school children 76, 76 parental participation 157 pre-primary education 50, 52 primary education 60, 67, 68, 68, 69, 70, 71, 72, 75, 77, 99, 100, 104, 119, 121, 205, 225 PRSPs 189, 200-1 school expansion 225 secondary education 85n, 88, 88, 89, 103, 104 teachers 119, 121, 173, 175-6, 176, 177 vaccination 33 Cameroon abolition of school fees 80 antenatal care 34 child labour 80 child mortality rate 33, 452 education expenditure 134, 135, 137 EIIIG 125 gender parity/disparity 99, 103n, 104 inequalities 75, 76, 88 malnutrition 47 out-of-school children 75, 76 pre-primary education 52 primary education 68, 68, 69, 69, 75, 99, 104, 119, 121 secondary education 88, 103n, 104 stunted children 35 teachers 119, 121, 174 vaccination 33 Canada adult literacy 96 education aid donor 214, 215, 217, 218, 2262 education expenditure 135 inequalities 114n ODA 207 pre-primary education 53 governments 148, 168, 215, 224, 225, 227, 229 management 226 redistributive 151 school 156, 156 teachers 155 capacity building education aid 224, 229 teachers 155 Cape Verde education expenditure 135, 137 gender parity/disparity 99, 100 pre-primary education 52 primary education 60, 71, 99, 100, 119, 120 teachers 119, 120 capitation grants 63, 141, 143-4, 144, 156-7 carers/caregivers seemothers Caribbean see alsoindividual countries; Latin America and the Caribbean adult literacy 93, 95 gender parity/disparity 103 out-of-school children 61, 64
 pre-primary education 51 primary education 56, 572 secondary education 85 teachers 118 tertiary education 90, 103 TVET 85, 85 youth literacy 94 Carrera Magisterial 178 cash transfers 49-50, 49, 77, 195, 196, 238 caste system and contract teachers 174 effect on girls 105 and low-fee private education 87, 167 effect on parental involvement 158 Catalytic Fund 215, 216, 218, 241 see alsoFast Track Initiative Cayman Islands primary education 119, 120 teachers 119, 120 Central African Republic adult literacy 94, 94n child labour 80 education expenditure 133, 135, 137 enrolment 103 gender parity/disparity 98, 99, 100, 100, 103 malnutrition 47 out-of-school children 64 pre-primary education 52 primary education 71, 99, 100 tertiary education 103 Central and Eastern Europe see alsoindividual countries adult literacy 93, 95 EDI 122 education expenditure 133, 134, 135, 137 gender parity/disparity 98, 99, 1002, 101 inequalities 113 out-of-school children 61, 64 pre-primary education 51, 53 primary education 56, 572, 61, 98, 99, 1002 secondary education 85, 86, 862, 98, 101 teachers 107, 118 tertiary education 90, 98 TVET 85, 85 Central Asia see alsoindividual countries adult literacy 93, 95 education expenditure 133, 1342, 137 gender parity/disparity 98, 99, 1002, 101 learning environment 117 out-of-school children 61, 64 pre-primary education 51, 52 primary education 56, 572, 60, 98, 99, 1002 school-based management 155 secondary education 85, 86, 86, 98, 101 teacher incentives 178 teachers 107, 118, 172 tertiary education 90, 98 TVET 85 science achievement 110, 111 socio-economic background 116 teacher incentives 178 teachers 120, 177 voucher programmes 160 China adult literacy 94, 94 decentralization 147 education aid donor 216 education expenditure 137, 142 inequalities 142 malnutrition 47 out-of-school children 66 pre-primary education 52 teachers 118, 173 Central Independent Monitoring Unit (CIMU) choice (Indonesia) 140 central transfer mechanisms 145-6, 147, 148, 151 Chad adult
 literacy 94n antenatal care 34 child labour 80 child mortality rate 452 disabled children 83 EDI 122, 123, 124 education expenditure 134, 135, 137 EIIIG 123, 125, 125 enrolment 103 gender parity/disparity 98, 99, 100, 1002, 1032, 104, 104 inequalities 74, 75, 76-7, 76, 79, 88, 103, 104 malnutrition 46, 47 out-of-school children 70, 76 primary education 75, 76-7, 79, 99, 100, 104 secondary education 88, 103, 104 stunted children 35 teachers 117, 118 tertiary education 103 vaccination 33 charter schools 160-1 child development effect of health interventions 49 effect of malnutrition 42, 45-6, 47m, 48, 238 child health and nutrition 42, 43, 44, 79-83 see alsohealth programmes; malnutrition effect of education 32-5 programmes 47-8, 49, 49 effect of social protection policies 195, 196 child labour 79-80, 80 in education provision 159-63, 239 and inequality 152 for parents 1522, 163, 165, 167 effect on school achievement 162 citizenship 35-6 civil society organizations role in equity 241 formulation of national education plans 198-9 climate change 36-7 coalitions (partnerships) 131, 159, 160, 168, 169, 239 cognitive development, effect of malnutrition 238 cognitive skills acquisition 109, 110 Colombia antenatal care 34 child labour 80 child mortality rate 33, 452 compensatory finance 148 decentralization 146 disabled children 83 education aid 217 education expenditure 135, 136 education projects and governance 231 EIIIG 125, 125 gender parity/disparity 100, 103n, 104, 106, 106n inequalities 75, 76, 79, 88, 88, 89, 103n learning assessments 111 malnutrition 47 mathematics achievement 106n out-of-school children 75, 76 pre-primary education 53 primary education 61, 68, 69, 71, 75, 79, 100, programmes reducing 50, 194-5, 195 104, 112, 120 child migrants 193 child mortality rate 32-3, 33, 43-5, 44, 45, 46, 48, 238 Child Support Grant (South Africa) 195 child survival 43 Chile child labour 80 education expenditure 135, 136 gender parity/disparity 99, 106n governance reform 130,
 162 inequalities 162 language difficulties 114 learning assessments 180, 183 learning environment 116, 117 literacy 111 malnutrition 47 mathematics achievement 106n numeracy 111 pre-primary education 53 primary education 99, 120 reading literacy 106n pupil achievement 112 science achievement 106, 110 secondary education 88, 88, 89, 103n, 104 stunted children 35 teachers 120 Common Work Plan (PCT) (Nicaragua) 223 community involvement, school management 157-8 Comoros child labour 80 gender parity/disparity 99, 100, 103n malnutrition 47 pre-primary education 52 primary education 99, 100 secondary education 103n compensatory funding 142, 148 see alsoschool grants competition in education provision 159-63, 239 in governance reform 131 and school management 152 I N D E X completion rates seeschool completion compulsory education seeuniversal primary education conditional cash transfer (CCT) programmes 195, 238 conditional grants 141, 146, 147 conflicts see alsofragile states effect on education planning 193-4 PRSP strategies 200-1 Congo antenatal care 34 child labour 80 child mortality rate 33 education expenditure 134, 135, 136, 137 gender parity/disparity 99 malnutrition 47 out-of-school children 64, 133 pre-primary education 52 primary education 99, 119, 121 stunted children 35 teachers 118, 119, 121 Congo, Democratic Republic seeDemocratic Republic of the Congo construction costs and corruption 139 school buildings 138 context for educational choice and competition 160-1 for school governance reform 152, 160 contract teachers 172-3, 173, 174, 239-40 Convention on the Rights of Persons with Disabilities (2006) 82, 192 Cook Islands gender parity/disparity 99 primary education 60, 99, 119 teachers 119 corruption 132, 138-41, 140, 141, 177 cost-of-living increases 46 Costa Rica education expenditure 135, 136 gender parity/disparity 106n literacy 111, 112 malnutrition 47 mathematics achievement 106n numeracy 111 pre-primary education 53 primary education 119, 120
 science achievement 111 secondary education 85n teachers 119, 120 Côte d’Ivoire adult literacy 94n, 95n child labour 80 EIIIG 125 gender parity/disparity 98, 98n, 99, 104 inequalities 74, 75, 75, 76, 88, 95n malnutrition 47 out-of-school children 76 pre-primary education 52, 52, 54 primary education 74, 75, 75, 99, 104, 121 secondary education 88, 104 teachers 121 Commission of the European Union see European Commission countries in transition seetransition countries crisis situations seeconflicts; fragile states Croatia EDI 124 education expenditure 136 pre-primary education 53 primary education 61, 120 science achievement 110, 111 teachers 120 cross-sectoral planning 186 Cuba EDI 124 education expenditure 135 gender parity/disparity 99, 106n literacy 111, 112 malnutrition 47 numeracy 111 primary education 61, 67, 71, 99, 119 reading literacy 106n science achievement 111 secondary education 85n teachers 119 cultural differences, inequality 78, 143 cultural practices, effect on girls 105 curriculum reform effect of changes on teachers 172 resulting from testing 182 Cyprus EDI 124 education expenditure 135 pre-primary education 53 primary education 61, 120 teachers 120 Czech Republic education expenditure 135, 136 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 Denmark disadvantage education aid donor 214, 215, 215, 228 education expenditure 135, 136 inequalities 114n learning assessments 110 ODA 207, 207 pre-primary education 54 primary education 61, 113 pupil achievement 113 science achievement 110 tertiary education 73 Department for International Development (DFID) (UK) 224 deprivation seedisadvantage; exclusion; household wealth; inequality; inequity; marginalization; poverty developed countries see alsoOECD countries adult literacy 93, 95, 96 attitudes to aid 205 Dakar Framework 204 education aid 219 education expenditure 134
 gender parity 101 health care 49 learning outcomes 112-13, 117 out-of-school children 61, 63 primary education 51, 56 private education 160 quality of education 28 secondary education 85, 86, 87 teachers 118 tertiary education 90 Czechoslovakia seeCzech Republic; Slovakia developing countries D DAC seebilateral donors; multilateral donors Dakar Framework for Action see alsoEFA goals and aid equity 210 commitment to equity 72-3 EFA goals 3 and4 91 governance principles 130 pledges 132, 157, 204, 208 progress towards 205, 217 relationship to MDGs 25 requirement to remove inequality 185 targets 41 data collection, improvements for planning 64 decentralization effect on education 132, 145-51, 147, 149, 150, 190 education tax effects 147 effects on poverty 189, 190 and equity 145-6, 147, 148, 150, 190, 239 and governance 129, 1302, 131 role of governments 147, 151, 159, 239 democracy, and education 36 Democratic People's Republic of Korea malnutrition 47 primary education 71 Democratic Republic of the Congo adult literacy 94 education projects and governance 231, 232 malnutrition 47 out-of-school children 63, 64, 66 primary education 68 PRSPs 194 demographic change, school-age populations 57, 57 see alsoleast developed countries; low-income countries; middle-income developing countries adult literacy levels 93, 93, 94, 95 blueprint governance reforms 130 child stunting 46 Dakar Framework 204 diseases, see alsoHIV/AIDS education aid 218, 219 education expenditure 134, 136 educational achievement 29 gender parity 101, 101 growth of private education 164-5 effect of high-stakes testing 181 learning outcomes 110-11, 112-13, 117 NER increases 58 ODA seeODA out-of-school children 61, 63 primary education 26, 51, 56, 57 quality of education 28 school-based management 153 secondary education 26, 85, 86, 87-9, 87, 88, 89 teacher salaries 172 teachers 118 tertiary education 90 Development Assistance Committee see bilateral donors; multilateral donors devolution of authority see
 alsodecentralization and equity 130, 145, 146-7, 147, 241 deworming programmes 81 diarrhoea 43, 44 disabilities funding for 143 inclusive education 82-3, 83, 192-3 and school attendance 82-3, 83 4 4 6 see alsoaccess to education; ethnic minority groups; exclusion; inclusive education; inequality; inequity; marginalization; poverty and costs of equity 142 and educational opportunity 27-8 failure of governance reforms 162 funding for 143 impact on gender disparity 104-5 and indigenous language 89, 114 and literacy 95-6 and parental involvement in schools 158 recruitment of teachers from disadvantaged groups 175 targeted programmes 140, 142, 194-5, 238 and UPE 75, 77, 78 disbursements of ODA 207, 2072, 220, 226 school capitation grants 156 discrimination 106-7, 107 diseases 43, 44, 79-82 see alsochild health and nutrition; health programmes; HIV/AIDS disparity seedisadvantage; gender parity/disparity; geographic disparity; inequality; inequity; within-country disparities District Primary Education Programme (DPEP) (India) 224, 224 Djibouti education aid 216 education expenditure 134 gender parity/disparity 99, 103n malnutrition 47 out-of-school children 64 pre-primary education 52 primary education 59, 60, 99, 119, 121 secondary education 84, 103n teachers 119, 120, 121 domestic disparities seewithin-country disparities domestic expenditure on education 132-7, 133, 1342, 136, 142 Dominica gender parity/disparity 99 pre-primary education 53 primary education 61, 71, 99, 119, 120 teachers 119, 120 Dominican Republic child labour 80 child mortality rate 33, 452 EDI 122, 124 education aid 213 education expenditure 137 EIIIG 125 enrolment 213 gender parity/disparity 99, 100, 104, 106n inequalities 75, 76, 79, 88 language difficulties 114n learning assessments 111 literacy 109 malnutrition 47 out-of-school children 76, 213 pre-primary education 53, 54 primary education 61, 67, 68, 71, 75, 79, 99, 100, 104, 119, 120 reading literacy 106n secondary education 88, 104 stunted
 children 35 teachers 119, 120 vaccination 33 donor coordination 221, 227-8 donors aid to education 214, 214-15, 214, 215, 215, 216-17, 217, 217, 218, 218, 218, 228, 240 and capacity building 224, 229 commitments and disbursements 205, 214, 215 failure 59, 204, 206-7, 219, 241 use of national management systems 226 coordination 221, 227-8 failure of aid effectiveness 219, 220 influence on governance 129, 141, 220, 222, 229-30, 230, 233 importance of PRSPs 187 need to increase aid 240 need for increased equity 240-1 pledges to Catalytic fund 216 relationship with governments seeaid governance support for SBM 153 and SWAps 222, 223, 224, 224, 229 influence on tertiary education 225 double shifts, to improve access 63 DPEP (District Primary Education Programme) (India) 224, 224 dropout see alsoout-of-school children; school completion; school participation from primary education 58, 62, 64, 68, 69, 70 and school-based management 154 from secondary education 86 Dubai Cares (United Arab Emirates) 216 E early childhood care and education (ECCE) (EFA goal) 42 see alsopre-school education aid requirements 204, 208 health and nutrition 42, 43 importance for equity 42-3, 114, 237 participation 50-5, 55 programmes, under-3s provision 49-50 progress towards 40-1 PRSP strategies 201 share of education aid 51 effect of social protection programmes 195 earnings, effect of education 30 East Africa, out-of-school children 194-5 East Asia and the Pacific see alsoindividual countries adult literacy 93, 93, 95 child mortality rate 44 decentralization 147 EDI 122 education expenditure 133, 1342, 137 gender parity/disparity 98, 99, 101 inequalities 86 learning environment 117 malnutrition 47 out-of-school children 61, 64, 64 pre-primary education 51, 52 primary education 56, 56, 57, 572, 60, 98, 99, 1002 secondary education 85, 86, 862, 98, 101 socio-economic background 113 teachers 107, 118, 118 tertiary education 90, 98 TVET 85 Eastern Europe seeCentral and Eastern Europe; individual
 countries EC seeEuropean Commission ECCE seeearly childhood care and education economic growth, and education 29, 30 Ecuador child labour 80 EDI 122, 124 health and nutrition 49 inequalities 48 language difficulties 114, 114n learning assessments 111 learning environment 116 literacy 109 malnutrition 47 pre-primary education 53 primary education 61, 71, 119, 120 teachers 119, 120 Educación con Participación de la Comunidad (EDUCO) (El Salvador) 153, 154, 154 education see alsoearly childhood care and education; pre-school education; primary education; ‘school’ entries; secondary education; tertiary education access seeaccess to education aid seeeducation aid benefits 24-5 expenditure seeeducation expenditure effect of opportunity on equity 24-5 quality seequality of education education aid 209, 209, 211, 218, 218 basic education seebasic education, aid commitments and disbursements 208, 209-10, 210, 211, 214, 217, 218 delivery 220 donors seedonors effect on enrolment 59 and governance 230-1, 231-2 growth 215 for low-income countries 210-13, 211, 212, 213, 216-17 programme-based share 221 sector-wide aid 223-4 share of total aid 208, 209, 210 education costs and corruption 139 effect of decentralization 147 grade repetition 68 low-income countries 208 policies reducing 205 effect on school participation 65, 206 education expenditure effect of decentralization 145-51 and enrolment 134-5 and equity targets 142-51, 237 and GNP 133-4, 133, 134-5, 147, 169, 206 improving efficiency 138-41, 141 effect of increases 59 per-student 136, 136-7, 136-7, 142, 143, 147, 147, 148, 149m, 237 public 132-7, 133, 1342, 136, 142 relationship to achievement 132, 133 salaries 135 share of public expenditure 133-4, 138-9 effect of underfunding 136 Education for All seeEFA Education for All Project (Haiti) 231 education goals, need for budgets 186 education governance 128-31 aim 37 decentralization 145-51 I N D E X education expenditure 132-7 efficiency and corruption 138-41 equity 142-5
 finance systems 129, 131 indicators 129 and learning outcomes 117 low-fee private schools 168 school reforms 130, 152-70, 162 teachers 117, 119, 129, 171-4 education infrastructure, deficiencies 116 Education Phase Two Sector Programme (Gambia) 231 education plans civil society organizations 198-9 and demographic change 57 endorsement of HIV/AIDS programmes 192 and equity 199 funding 186, 216, 238-9 governance themes 189-90 improvements 107-8 lack of national coordination 149, 238 need for context 186 for UPE 77 education policies, informed by learning assessments 182-3, 183 Education Quality, Governance and Institutional Strengthening (Honduras) 231 Education Quality Improvement Programme (Afghanistan) 231 Education Reconstruction Project (Burundi) 231 education sector, corruption 139 Education Sector Development Programmes (Ethiopia) 58 Education Sector Development Programmes (PISE) (Mali) 223 Education Sector Development Support Project (Bangladesh) 232 Education Sector Investment Programme Project (Mali) 231 education sector plans see alsosector-wide approaches (SWAps) integration with broader reforms 131 Education Sector Project (Democratic Republic of the Congo) 231 Education Sector Support Project (Kenya) 232 Education Strategic Investment Plan (Uganda) 225 Education Strategic Plan (Cambodia) 225-6 educational attainment see alsoschool achievement primary pupils 74, 108-12, 112-13 secondary education 26, 29, 86-7, 86, 112-13 and teacher recruitment 176 educational background effect of maternal 32, 34-5, 34, 35, 60 and parent participation 158 educational opportunity and economic growth 29-32 and health 32-5 inequalities 26-9, 27, 28, 71-2, 72-3, 79, 90 and social equality 24-5 educational outcomes seelearning outcomes educational reform 130, 152-70 EDUCO (Educación con Participación de la Comunidad) (El Salvador) 153, 154, 154 EFA goals seeEFA goals importance of aid 205, 219 progress 37, 122-5, 123, 124 EFA Development Index 124 indicators 122-3 4 4 7 9 0 0 2
Index 124 indicators 122- EFA goals goal 1, early childhood care and education 42 aid requirements 204, 208 health and nutrition 42, 43 importance for equity 42-3, 114 participation 50-5 programmes, under-3s provision 49-50 progress towards 40-1 PRSP strategies 201 share of education aid 51 effect of social protection programmes 195 goal 2, universal primary education 25, 56 see alsobasic education; primary education aid requirements 204, 208, 217, 225 barriers 78-9, 79-83, 131 EDI indicator 122 focus of PRSPs 188 Millennium Development Goal 204 progress towards 25, 40, 41, 56-79, 206, 213, 213, 217, 236 projects and programmes 231-2 successful achievement 77 teachers needed 118 trends 65-7, 66 goal 3, lifelong learning 91-2 and economic growth 32 projects and programmes 231-2 goal 4, adult literacy see alsoyouth literacy aid requirements 204, 208 benefits and barriers 93 EDI indicator 122 gender disparity 95, 105-6 inequalities within countries 95-6 literacy rates 93-4, 93, 94-5, 94, 95 progress towards 91, 94 projections 93 PRSP strategies 200-1 and success of information campaigns 141 women 28, 29, 93, 93, 95 goal 5, gender parity access to education 65 achievement of goal 103 adult literacy 95, 105-6 EDI indicator 122 education and earning power 30 and female employment 31, 191 use of female teachers 107, 107 out-of-school children 62 primary education 64-5, 97-100, 206 progress towards 41, 97-107 school performance 105-7, 114 secondary education 97, 98, 101, 102, 103, 104, 206 tertiary education 97, 98, 101, 106 within countries 102-5 goal 6, quality of education effect of contract teachers 173, 173, 174 deficits 62, 171 EDI indicator 122 and education expenditure efficiency 138 effect of high-stakes testing 180-2, 181 and effect on human capital 32 with improved access 63, 77 effect of inequalities 110-13 measurement 28, 108 see alsolearning assessments; monitoring 171, 183, 238 policy requirements 238
 pre-school 50 in private schools 166 progress towards 41 effect of school organization and environment 114-17 teachers 117-21 bias of PRSPs 188 and financing 189 progress towards 122-3, 123, 241 projects and programmes 231-2 EFA Inequality Index for Income Groups (EIIIG) 123, 125, 125 efficiency increases 57-8, 59, 59, 63, 123, 138, 213, 213, 2252 malnutrition affecting 48 projects 231 and school grants 144, 144 and survival to grade 9 70, 71, 72 private schools 162-3, 167 secondary education 49, 84-5, 84, 85, 86, 86, 101, 101, 102, 102, 103, 147 effect of social protection policies 195 tertiary education 73, 90 entrants to grade 1 primary 56, 56, 62, 64, 67, 68, 69, 80, 82 see alsogross intake rate environmental awareness, and science literacy in education expenditure 138-41 and school-based management 155 37 equality Egypt adult literacy 94, 94 antenatal care 34 child mortality rate 33 cognitive skills 109, 110 EDI 122 gender parity/disparity 99 learning assessments 110n malnutrition 47 pre-primary education 52, 53, 53, 53 primary education 60, 68, 69, 69, 71, 99 stunted children 35 unemployment 31 EIIIG (EFA Inequality Index for Income Groups) 123, 125, 125 El Salvador child labour 80 EDI 122 education expenditure 135, 137 gender parity/disparity 99, 100, 103, 106 inequalities 111 learning achievement 154 learning assessments 111 literacy 112 malnutrition 47 mathematics achievement 106n parental participation 175 pre-primary education 53 primary education 68, 71, 73, 99, 100, 119 school-based management 153, 154 science achievement 106 secondary education 103 teachers 119, 175 textbook provision 143 elementary education seeprimary education elite capture, in school management sharing 157 emergency contexts see alsoconflicts and social protection 194-5 effect on teaching time 115 employment 31, 191 empowerment of parents and communities, school management 157-8 enrolment and abolition of school fees 206 and education expenditure 134-5 effect of expansion 58, 225
 and gender parity 99-100 effect of maternal education 35 out-of-school children 63-4 pre-school education 49-50, 50-1, 51, 52-3, 53 see alsoequity; gender equality; inequality and equity 142 social 24-5 equalization grants 146 Equatorial Guinea gender parity/disparity 99 malnutrition 47 pre-primary education 52 primary education 99 equitable share transfers 146 equity see alsogender equality; gender parity/disparity; inequality; inequity and aid provision 210-14 and use of contract teachers 173, 174, 175 effect of corruption 139 Dakar commitment 72-3 effect of decentralization 145-6, 147, 148, 150, 190, 239 and devolution of authority 130 and ECCE 42-3, 114 and education choice 161-2 in education expenditure 142-51, 237 and equality 142 goals 151 government commitment 199 improving through projects 59, 231, 232 improving through SWAps 186 and income distribution 31 influence of education 24-5 and information from learning assessments 182 policies for 77 effect of private education 167 of education quality 108 responsibility of governments 153 and school-based management 153, 155 target-setting 236-7 Eritrea antenatal care 34 child mortality rate 33 education expenditure 134, 135, 137 enrolment 103 gender parity/disparity 99, 100, 101, 103, 103n malnutrition 47 out-of-school children 64, 133 pre-primary education 52 primary education 60, 71, 72, 99, 100, 119, 121 secondary education 101, 103n stunted children 35 teachers 117, 119, 121 tertiary education 103 learning outcomes primary education 57, 58, 58, 59, 60-1, 99 and aid 213, 213 4 4 8 Estonia EDI 124 education expenditure 135, 136 primary education 61, 120 science achievement 110 teachers 120 Ethiopia adult literacy 94, 94, 94n, 95-6 antenatal care 34 child mortality rate 33 decentralization 148, 150 EDI 122, 123, 124 education aid 205, 212, 212, 213, 213 education expenditure 133, 134, 135, 137 efficiency 138 EIIIG 123, 125, 1252 enrolment 213, 213, 229 equity programmes 150 gender parity/disparity 99, 99, 100,
 101, 104 inequalities 28, 74, 75, 76-7, 76, 79, 88 learning assessments 182 malnutrition 46, 47-8, 47 nutrition policies 192 out-of-school children 61-2, 62, 64, 66, 66, 67, 75, 76, 133, 206, 212, 212, 213 pre-primary education 52 primary education 58, 58, 60, 67, 68, 68, 692, 70, 71, 72, 74, 75, 76-7, 79, 99, 100, 104, 140, 140 PRSPs 189, 200-1 secondary education 85n, 88, 101, 104 stunted children 35 teachers 118 vaccination 33 ethnic minority groups and pre-school participation 52-3, 54, 55 PRSP strategy 193, 193, 200-1 and public education expenditure 143 teacher recruitment 176 EU seeEuropean Union Europe seeCentral and Eastern Europe; individual countries; North America and Western Europe European Commission see alsoEuropean Union education aid donor 214, 214, 215, 217, 218, 218, 219, 2232 governance promotion 230 sector programme support 224 European Union, code of conduct for aid distribution 228 evaluation seemonitoring evidence, importance in school reform 152, 160 exclusion see alsoaccess to education; disadvantage; ethnic minority groups; inclusive education; inequality; inequity; marginalization and literacy 96 I N D E X FUNDESCOLA (Fundo de Fortalecimento da Escola) (Brazil) 156-7 funding see alsoeducation aid; finance, for education education plans shortfall 216 formulas for 143-5, 146, 150 Fundo de Fortalecimento da Escola (FUNDESCOLA) programme (Brazil) 156-7 further education seelifelong learning; post-secondary education; tertiary education G G8 summits, donor pledges 204, 207, 208, 219 Gabon gender parity/disparity 98n malnutrition 47 Gambia adult literacy 95 child labour 80 education expenditure 134 education projects and governance 231, 232 enrolment 103 gender parity/disparity 95, 99, 1032 inequalities 95 malnutrition 47 out-of-school children 64 pre-primary education 52, 54 primary education 60, 99, 119 PRSPs 200-1 secondary education 103 teachers 119 tertiary education 103 Gates Foundation 216 gender bias 106-7, 107 gender differences, school
 performance 105-7, 114 gender equality, PRSP strategies 191, 200-1 gender parity/disparity (EFA goal) 103 access to education 65, 98-9 adult literacy 95, 105-6 EDI indicator 122 education and earning power 30 female employment 31, 191 female teachers 107, 107 out-of-school children 62 primary education 64-5, 97-100, 98, 99, 99, 100, 104, 167, 168, 206 progress towards 41, 97-107 school performance 105-7, 114 secondary education 97, 98, 101, 102, 103, 104, 206 tertiary education 97, 98, 101, 106 within countries 102-5 gender parity index primary education 97, 98, 99, 104, 168, 169 secondary education 98, 103, 104 tertiary education 98 gender stereotyping, attitudes of teachers 106-7, 107 general budget support 209, 209 general sector reform programmes 2322 geographic disparity out-of-school children 61, 61 pre-school education 52 primary education 79 private education 165 PRSP strategies 193, 200-1 Georgia education expenditure 134 gender parity/disparity 103 F family structure, effect on learning 113-14 Fast Track Initiative 192, 215-16, 215, 218, 227, 241 females seegirls; mothers; women Fiji EDI 124 education expenditure 134, 136 pre-primary education 52 primary education 60, 71 finance see alsocash transfers; education aid; funding; redistributive finance for education education plans 186, 216, 238-9 governance 129, 131 public expenditure 132-7 and equity 143, 149 for PRSP targets 189 financial incentives performance-related pay 177-9, 178, 240 to reduce child labour 194-5, 195 financial management systems 132, 140-1, 140, 141, 221, 225, 232 Finland education aid donor 215, 215, 228 education expenditure 135, 136 ODA 207 pre-primary education 53, 54 primary education 61 science achievement 110, 110 food prices 46 for-profit sector seeprivate education former Yugoslav Republic of Macedonia see the former Yugoslav Republic of Macedonia formula funding 143-5, 146, 148, 150 Foundation Assisted Schools programme (Pakistan) 169 fragile states see alsoconflicts
 education aid 213 emergency aid 213n effect of poor government capacity 227 projects involving governance 231 PRSPs in 193 teacher allocation 176, 177 fragmentation, in tackling poverty 186, 188 Framework for Action seeDakar Framework for Action France adult literacy 96 education aid donor 214, 2142, 215, 215, 217, 218, 218, 228 education expenditure 135, 136, 137 governance reform 230 inequalities 114n ODA 207 pre-primary education 54 primary education 72, 113 pupil achievement 113 science achievement 110 tertiary education 73, 90 Free Compulsory Universal Basic Education policy (Ghana) 144 free primary education (fee abolition) 59, 80, 144, 199, 205, 206 FTI (Fast Track Initiative) 192, 215-16, 215, 218, 227, 241 FUNDEF programme (Brazil) corruption 139 funding of teachers 176 redistribution of education funds 148 pre-primary education 52 primary education 60, 113, 122n pupil achievement 113 secondary education 103 GER seegross enrolment ratio Germany education aid donor 214, 2142, 215, 215, 217, 218, 218 education expenditure 135, 136, 137 inequalities 114n literacy 111 ODA 207 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 Ghana abolition of school fees 80 adult literacy 29, 94n child labour 80 child mortality rate 32, 33, 452 education aid 205, 213, 216, 227 education expenditure 134, 135, 137 EIIIG 125, 125 enrolment 213 gender parity/disparity 99, 103n, 104 inequalities 28, 74, 74, 75, 76, 88, 103n literacy 29, 111 malnutrition 47 non-formal learning 91 numeracy 111 out-of-school children 62, 62, 64, 66, 75, 76, education seeeducation governance and equity 77 importance for education 241 objectives 129 and quality improvements 62, 63 reform seegovernance reform effect of weaknesses 62-3, 128 World Bank strategies 230 governance reform 51, 189 evaluation 130 effect of political implications 199 in PRSPs 130-1, 189 schools
 152-70, 162, 241 government capacity 148, 168, 215, 224, 225, 227, 229 governments commitment to aid governance 205 role in decentralization 147, 151, 159, 239 early childhood provision 49-50, 55 education expenditure 132-7, 133, 1342, 136, 142 education policy-making 182 education programmes seeprogrammes financial management systems 132, 140-1, 221, 225, 232 fragmentation 186, 188 lack of nutrition policies 48 leadership in aid programmes 224, 224, 229 national ownership 199, 205, 219, 220, 221, 222-4 partnerships with non-state providers 131, 159, 160, 168, 169, 239 111, 213 policies pre-primary education 53 primary education 99 Grenadines seeSaint Vincent and the Grenadines gross enrolment ratio and gender parity 99, 169 pre-school education 50-1, 51, 52-3, 53 primary education 57, 58, 99 secondary education 85, 86, 86, 101, 101, 102, 103 tertiary education 90 gross intake rate, primary education 56, 56 gross national income (GNI) and aid disbursements 207, 207 aid percentage 207, 207 and education expenditure 133 gross national product (GNP) and education expenditure 133-4, 133, 134-5, 147, 169, 206 and teacher salaries 172 Guatemala child labour 80 EDI 122, 124 education expenditure 134, 137 enrolment 104 gender parity/disparity 99, 100, 104, 106n inequalities 28, 76, 104-5, 111 language difficulties 114, 114n learning assessments 111 learning environment 116 literacy 109 malnutrition 47 mathematics achievement 106n out-of-school children 75, 76 pre-primary education 53 primary education 61, 68, 70, 71, 71, 72, 73, 78, 99, 100, 120, 134 secondary education 85n teachers 120 textbook provision 143 pre-primary education 51, 52 primary education 60, 68, 68, 69, 74, 75, 99, 103n, 104, 119, 121, 205 private education 164, 166 PRSPs 188, 200-1 school grants 144 secondary education 87, 87, 88, 103n, 104 stunted children 35 teachers 118, 119, 120, 121, 121 girls see also‘gender’ entries; women access to education 63-4, 65, 206 effect of disadvantaged backgrounds 104-5
 gender parity programmes 206 out-of-school 63-4, 63, 105 performance 105-7 primary education 58, 98 school attendance 103-4 secondary education 87, 97, 98 survival rate to grade 5 100 tertiary education 97, 98 Gleneagles summit 204, 207, 208, 219 global financial crisis 26 Global Fund to Fight AIDS, Tuberculosis and Malaria 43 Global Monitoring Reports 208 global public education expenditure 136-7, 1372 global warming 36-7 GNI seegross national income (GNI) good governance agenda 50-1 governance effect on access to education 59, 63 of aid seeaid effectiveness; aid governance aid for 230 component in education projects and programmes 231-2 contributing to gender parity 103, 105 donor influence 129, 141, 220, 222, 229-30, 230, 233 4 5 0 objectives and coherence 237 effect on quality and equity 130 PRSP priorities 199 rationalization of aid 229-30 redistributive role 151 responsibility for access to education 153 responsibility for primary/basic education 170 Guinea share of total aid 210 support for school-based management 156 GPI seegender parity index grade 1, entrants 56, 56, 62, 64, 68, 68, 69, 80, 82 grade 5, survival rate 27, 27, 68, 77, 87, 89, 100, 100 grade 9, survival rate 28, 69, 69, 70, 71, 72, 87, 89 grade 12, survival rate 87, 87, 88-9, 89 grade repetition see alsoschool progression feedback from learning assessments 182 and gender disparity 99-100 and high-stakes testing 181 and malaria 81 and orphanhood 82 primary schools 68, 68, 69, 99-100 and school-based management 154 secondary schools 88-9 grants seeschool grants Greece education aid donor 214, 215, 215, 228 education expenditure 135, 136 ODA 207, 207 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 Grenada education expenditure 137 gender parity/disparity 99 adult literacy 94n antenatal care 34 child mortality rate 33 education aid 213 education expenditure 134 EIIIG 125 enrolment 103 gender parity/disparity 99, 100, 100, 1033,
 104, 104 inequalities 75, 76, 88, 103, 104 learning environment 116 malnutrition 47 out-of-school children 62, 64, 76, 213 pre-primary education 52 primary education 60, 68, 69, 71, 73, 75, 99, 100, 104, 119 PRSPs 200-1 secondary education 85n, 88, 103, 104 teachers 119, 173 tertiary education 103 Guinea-Bissau adult literacy 95n child labour 80 inequalities 95n malnutrition 47 pre-primary education 54 Guyana child labour 80 education expenditure 135, 137 malnutrition 47 I N D E X H Haiti antenatal care 34 child mortality rate 33 education projects and governance 231, 232 EIIIG 125, 125 gender parity/disparity 104 inequalities 75, 76, 88 malnutrition 47 out-of-school children 76 primary education 68, 69, 75, 104 secondary education 88, 104 stunted children 35 vaccination 33 Head Start Project 55 head teachers, leadership 155-6 health seeantenatal care; child health and nutrition; HIV/AIDS health programmes delivered through schools 81, 192 share of total aid 209, 210 social protection programmes 195 Heiligendamm summit 208 Herzegovina seeBosnia and Herzegovina Hewlett Foundation 216 high-income countries, education expenditure 133, 134 High-Level Group on Education for All 219 high-stakes assessments 180-2, 181 higher education seetertiary education HIV/AIDS effect of education 35 government response to 192, 192 improved access to drugs 43 PRSP strategies 200-1 effect on school participation 81-2 effect on teachers 121 Hokkaido summit 219 home environment, importance 42, 113 home language 78, 114, 114n, 143 home literacy 113 Honduras antenatal care 34 child labour 80 child mortality rate 33 education aid 227 education projects and governance 231, 233 gender parity/disparity 100 learning achievement 154 malnutrition 47 pre-primary education 53 primary education 68, 68, 69, 71, 100, 120 school-based management 154 social protection programmes 195 stunted children 35 teachers 120 Hong Kong, China inequalities 114n literacy 111 hours of instruction 115, 238 household
 costs, and increased enrolment 58 household wealth see alsodisadvantage; household costs; poverty and affordability of private education 166-7 and ECCE participation 51-2, 55 and educational attainment 74 and educational opportunity 27 EIIIG indicator 123, 125, 125 and gender parity 102-4, 104 and inequity in funding 142, 142, 143 and literacy 30, 95 and poor educational provision 132, 169 and school participation 62, 73-7, 74, 75, 76, primary education 68, 69, 692, 71, 74, 74, 75, 78, 99, 103, 104, 222-4, 224 private education 164, 165, 165, 166, 167, 79, 88-9, 88, 89 social protection policies 194 Human Development Report 55 human immunodeficiency virus seeHIV/AIDS Hungary EDI 124 education expenditure 135, 136 literacy 111 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 I IBRD seeWorld Bank Iceland education expenditure 136 gender parity/disparity 106 mathematics achievement 106 primary education 113 pupil achievement 113 science achievement 110 tertiary education 73 illiteracy seeadult literacy; literacy; youth literacy illness seedisease ILO Minimum Age Convention 79n immigrants see alsomigrants learning achievement 114 imputed student costs, as aid 218, 218 incentives to improve education access 53 to improve school attendance, health and employment 196 to reduce child labour 194-5, 195 teachers 148, 175, 177-9, 178, 179, 239, 240 inclusive education, disabilities 82-3, 83, 192-3 income inequalities 30-1 see alsohousehold wealth India adult literacy 94, 94n, 942 antenatal care 34 budget support 232 child mortality rate 33 disabled children 83 EDI 123 education aid 216, 223-4, 224 education expenditure 135, 137 EIIIG 125, 125 enrolment 229 gender parity/disparity 64, 99, 103, 104, 105, 167 governance 223-4, 224 health and nutrition 81 inequalities 28, 31, 74, 74, 75, 76, 78, 88, 88, 89, 103 learning environment 116, 117 literacy 29, 109, 109 malnutrition 47, 48 numeracy 109, 109 out-of-school
 children 62, 64, 64, 65, 66, 75, 76, 133 parental participation 158, 158 pre-primary education 50, 52 168 secondary education 88, 88, 89, 104 stunted children 35 teacher incentives 179, 179 teachers 107, 118, 1202, 121, 173, 174, 177 vaccination 33 indigenous language schools 75-6 indigenous populations see alsoethnic minority groups educational disparity 104-5 language difficulties 114, 114n literacy levels 96 indigenous schools 75-6, 153-4 Indonesia adult literacy 94, 94 antenatal care 34 child mortality rate 33, 452 corruption safeguards 140 curriculum 155 decentralization 147 disabled children 83 education projects and governance 231 EIIIG 125, 125 gender parity/disparity 99, 100, 104 inequalities 74, 75, 76, 79, 88 learning assessments 110 literacy 111, 111n non-formal learning 91 numeracy 111 out-of-school children 75, 76, 111 pre-primary education 52 primary education 71, 72, 74, 75, 79, 99, 100, 104, 112 pupil achievement 112 school-based management 155 science achievement 110, 110 secondary education 88, 104 teachers 118 vaccination 33 inequality see alsoequity; household wealth; inequity addressed through funding formulas 143, 148 barriers to EFA 131 change through use of low-fee private education 168 and child health 44 and choice/competition 152, 161-3 between countries 44, 132, 136-7 effect of decentralization 131, 147, 147 education expenditure 136-7, 142, 142 in educational opportunity 26-9 and high-stakes testing 180 and learning outcomes 112-17 lifelong learning 91 primary education 70-2, 73-8, 73, 75, 136-7 secondary education 86-7, 88, 88 tertiary education 90 and UPE 78-9 within countries seewithin-country disparities inequity see alsoaccess to education; disadvantage; equity; gender parity/disparity; geographic disparity; inequality; within-country disparities from decentralization 147, 148, 149 in educational opportunity 70-2, 73, 79, 90 PRSP strategies 186-94, 198-9, 200-1 in public education expenditure 142 4 5 1 9 0 0 2 t r o p e R i g n r o t n o M
142 infants 49-50 infectious diseases 43, 44, 79-82 informal education seelifelong learning informal payments, and corruption 140, 177 information campaigns, against corruption 140, 141, 141 instructional time for learning 115, 238 Integrated Child Development Services (ICDS) (India) 48 Integrated Project for Child Development (Bolivia) 49 Inter-American Development Bank (IDB) 217, 217 international aid seeeducation aid; ODA International Bank for Reconstruction and Development (IBRD) seeWorld Bank International Development Association (IDA) 214, 214, 215, 2152, 217, 218 international learning assessments 29, 110-11, 111, 112-13, 179-80, 181-2 intestinal helminths 81 investment projects 222 iodine deficiency 46, 48, 81 Iran seeIslamic Republic of Iran Iraq child labour 80 EDI 124 gender parity/disparity 99, 100, 100, 101 malnutrition 47 out-of-school children 62, 66 pre-primary education 52 primary education 60, 71, 72, 99, 100 secondary education 101 Ireland education aid donor 214, 215, 2282 education expenditure 135, 136 ODA 207, 207 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 iron deficiency 46, 48, 81, 191 Islamic Republic of Iran adult literacy 94, 94 education expenditure 135, 137 gender parity/disparity 99 literacy 111n malnutrition 47 out-of-school children 62 pre-primary education 52 primary education 60, 99, 112, 120 pupil achievement 112 secondary education 84 teachers 120 Israel education expenditure 135, 136 primary education 61, 113 pupil achievement 113 science achievement 110 Italy education expenditure 135, 136, 137 literacy 111 ODA 207, 207 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 Ivory Coast seeCote d’Ivoire J Jamaica child labour 80 disabled children 83 education expenditure 137 malnutrition 47 primary education 61 Japan education aid donor 214, 215, 215, 217, 218, 222, 2282 education expenditure
 134, 137 ODA 207, 207 pre-primary education 52 primary education 113 pupil achievement 113 science achievement 110 tertiary education 71, 73 Joint Assistance Strategies 228-9 joint missions, aid 220, 221, 227 Jomtien Conference 24, 40 Jordan antenatal care 34 child mortality rate 33 education expenditure 137 gender parity/disparity 106 learning assessments 110n malnutrition 47 mathematics achievement 106 pre-primary education 52 primary education 60, 71 science achievement 110 stunted children 35 K Kazakhstan education expenditure 134 pre-primary education 52, 54 Kenya abolition of school fees 80, 199, 206 antenatal care 34 child labour 80 child mortality rate 33, 452 civil society participation 198 EDI 122 education aid 205, 212, 216 education expenditure 134, 135, 137 education projects and governance 232, 233 EIIIG 125, 125 enrolment 206 gender parity/disparity 98n, 99, 103n, 104 health and nutrition 81 HIV/AIDS 822, 121 inequalities 75, 76, 88 learning assessments 181, 182 learning environment 116 literacy 109 malnutrition 47 mathematics achievement 106 non-formal learning 91 out-of-school children 62, 62, 64, 66, 75, 76, 133, 212, 212 pre-primary education 51, 52 primary education 59, 60, 68, 69, 71, 75, 99, 104, 205, 206 private education 164, 167, 167 PRSPs 193 school grants 144 secondary education 88, 103n, 104 stunted children 35 teacher incentives 179 teachers 117, 118, 121, 177 Kiribati gender parity/disparity 100 pre-primary education 52 primary education 100 Korea seeDemocratic People’s Republic of Korea Kuwait education expenditure 136 malnutrition 47 pre-primary education 52 primary education 60, 71, 73, 112, 119, 120 pupil achievement 112 secondary education 85 teachers 119, 120 Kyrgyzstan EDI 124 education expenditure 134 pre-primary education 52, 54 primary education 60, 119, 120 science achievement 110, 111 teachers 119, 120 L language achievement see alsoliteracy and school-based management 154 language disparities 78, 89, 114, 114n, 143 see
 alsoliteracy Lao People's Democratic Republic education expenditure 134, 137 enrolment 105 gender parity/disparity 99, 105 inequalities 105 malnutrition 47 pre-primary education 52 primary education 60, 67, 68, 71, 73, 99, 119, 121 teachers 119, 121, 175-6, 176 late enrolment, primary education 62, 64, 68, 68, 80, 82 Latin America and the Caribbean see alsoindividual countries adult literacy 93, 95 cash transfers 195 child labour 79 child mortality rate 44 decentralization 147-8 EDI 122 education expenditure 133, 134, 135, 137 education plans 190 gender parity/disparity 97, 98, 99, 1002, 101, 101, 105-6, 106 inequalities 90, 114 language difficulties 114 learning achievement 154 learning assessments 112 learning environment 116, 117 malnutrition 47 out-of-school children 61, 64, 64 pre-primary education 51, 53 primary education 56, 56, 572, 61, 682, 98, 99, 1002 reading literacy 105-6 redistributive finance 148 school attendance 28 school-based management 1542 school survival rates 27 science achievement 106 secondary education 85, 86, 88, 98, 101, 101 socio-economic background 113, 116 teachers 107, 1172, 118, 172 tertiary education 89, 90, 98 TVET 85 Latvia EDI 124 education expenditure 135 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 LDCs seeleast developed countries leadership see alsopolitical leadership schools 155-6 league tables, effect 180, 181 learning assessments 131 use for education policy-making 182-3, 182 effectiveness 179-82 improvement in test scores 138 international 29, 110-11, 111, 112-13, 179-80, 181-2 effect of performance-related pay 178 learning environment 115-17, 115, 238 learning and life skills seelifelong learning learning outcomes disparities 110-17, 110, 111 effect of environment 42, 113, 115-17, 115, 238 gender equality 105-6 improved by teacher support 183 international assessments 110-11 pupil/teacher ratios 117-20 and school-based management 154 and school
 choice 160-1, 162 effect of school systems 114-15 student background 113-14 effect of teacher absenteeism 120-1 and teacher pay 178-9 within-country disparities 112-13 least developed countries secondary education 88 stunted children 35 teachers 119, 121 vaccination 33 Liberia adult literacy 94n gender parity/disparity 99, 99, 100 out-of-school children 64 primary education 60, 99 libraries, access to 117 Libyan Arab Jamahiriya gender parity/disparity 99 malnutrition 47 pre-primary education 52 primary education 99 life skills seelifelong learning lifelong learning (EFA goal) 91-2 and economic growth 32 projects and programmes 231-2 literacy see alsoadult literacy; youth literacy aid requirements 204 gender differences 95, 105-6 and household wealth 30, 95 and marginalized groups 96 primary education 29, 109, 109, 112 literacy rates 94-5, 95 Lithuania EDI 124 education expenditure 136 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 local communities, involvement in schools 157-8, 158 local government seedecentralization location, and access for girls 105 low birth weight, and malnutrition 46, 47m low-fee private schools 164-7, 168, 169, 239 low-income countries see alsodeveloping countries; least see alsodeveloping countries; low-income developed countries countries education aid 210, 211, 2112, 218 Lebanon education expenditure 134, 137 gender parity/disparity 99, 100 learning assessments 110n malnutrition 47 pre-primary education 52 primary education 60, 71, 73, 99, 100, 119, 120 teachers 119, 119, 120 lenders seedonors Lesotho antenatal care 34 child labour 80 child mortality rate 33 EDI 124 education expenditure 134, 135, 137 gender parity/disparity 98n, 99, 100 inequalities 88 learning assessments 182 literacy 109 malnutrition 47 out-of-school children 64 pre-primary education 52 primary education 59, 60, 68, 69, 71, 99, 100, 119, 121 education aid 210, 210, 211, 211, 213-14, 215, 216,
 217-18, 218, 218, 218 programme-based aid 222 requirements for EFA goals 204, 207-8, 217 education expenditure 133, 133, 134 efficiency 138 pre-school participation 51 profile of group 211 secondary education 87-8, 87-9, 87, 88, 89 low-income groups, effect of poor provision 132 low-stake assessments 180, 182 lower secondary education 86, 86 see alsobasic education Luxembourg education aid donor 214, 215, 215 education expenditure 136, 136 gender parity/disparity 100 inequalities 114n ODA 207, 207 pre-primary education 53 primary education 61, 100, 113 pupil achievement 113 science achievement 110 I N D E X M Macao, China gender parity/disparity 99 inequalities 114n pre-primary education 52 primary education 60, 99, 119, 120 science achievement 110 secondary education 85n teachers 119, 120 Madagascar adult literacy 94n antenatal care 34 child mortality rate 33 EDI 123 education aid 216 education expenditure 134, 134, 135, 137 EIIIG 125 gender parity/disparity 99, 100, 103n, 104 inequalities 74, 75, 76, 88, 103n learning environment 116 literacy 188 malnutrition 47 nutrition policies 192 out-of-school children 62, 76 pre-primary education 52, 58 primary education 60, 67, 68, 68, 692, 702, 71, 72, 73, 74, 75, 99, 100, 103n, 104, 121, 140 PRSPs 200-1 secondary education 84, 88, 104 stunted children 35 teachers 121 malaria 43, 44, 81 Malawi child labour 80 child mortality rate 33 citizenship and democracy 36 EDI 123, 124 education aid 213, 213 education expenditure 134, 135, 137 EIIIG 125 enrolment 213, 213 gender parity/disparity 99, 103n, 104 inequalities 74, 75, 76, 78, 88, 103n learning assessments 182 learning environment 116 literacy 109 malnutrition 46, 47 out-of-school children 76, 213 primary education 60, 67-8, 67, 68, 69, 702, 71, 72, 73, 74, 75, 78, 99, 103n, 104 private education 166-7, 168 PRSPs 200-1 secondary education 85, 87, 87, 88, 104 stunted children 35 teachers 118, 172, 177 vaccination 33 Malaysia education expenditure 134, 136 inequalities 115
 learning environment 116, 117 primary education 71 secondary education 84n Maldives malnutrition 47 pre-primary education 52 primary education 119, 120 secondary education 85n teachers 119, 120 male seeboys; men Mali adult literacy 94n antenatal care 34 child labour 80 child mortality rate 452 civil society participation 198 EDI 122 education aid 213, 213, 223 education expenditure 134, 135, 137 education projects and governance 231, 232 EIIIG 123, 125, 125 enrolment 213, 213 gender parity/disparity 98, 99, 100, 100, 101, 103, 104, 104, 106n inequalities 27, 28, 75, 76-7, 76, 88, 88, 103, 104 malnutrition 47 mathematics achievement 106n out-of-school children 61, 62, 64, 66, 75, 76, 213 pre-primary education 52 primary education 27, 60, 67, 68, 69, 692, 71, 75, 76-7, 99, 100, 104 PRSPs 200-1 secondary education 87, 87, 88, 101, 104 stunted children 35 teachers 118, 177 malnutrition see alsochild health and nutrition effect on child development 42, 45-6, 47m, 48, 238 low priority in PRSPs 191-2 undernutrition 46, 116 Malta education expenditure 135, 136 primary education 61 management capacity 225-6 marginalization see alsoaccess to education; disadvantage; ethnic minority groups; exclusion; inclusive education; inequality; inequity and educational opportunity 72-3 and literacy 96 neglect in PRSPs 193 marriage, effect on girls 105 Marshall Islands education expenditure 134 pre-primary education 52 secondary education 85 mathematics achievement 29, 112-13, 154 see alsonumeracy charter schools 161 gender differences 106, 114 and improved efficiency 138 Mauritania EDI 124 education expenditure 134, 134, 137 gender parity/disparity 99, 100 pre-primary education 52 primary education 59, 60, 70, 71, 72, 73, 99, 100, 119, 121, 134 PRSPs 200-1 secondary education 84 teachers 119, 121 Mauritius EDI 124 education expenditure 134, 135, 136 gender parity/disparity 98n learning
 assessments 182 malnutrition 47 primary education 60, 71, 119, 120 secondary education 84, 84n teachers 119, 120 4 5 4 measles 43 men see alsoboys tertiary education 97, 98 Mexico adult literacy 94 cash transfers 196 child labour 80 corruption 139 education aid 217 education expenditure 135, 136 efficiency 138 gender parity/disparity 106n health and nutrition 49, 49 language difficulties 114 learning assessments 111 literacy 111 malnutrition 47 non-formal learning 91 numeracy 111 out-of-school children 111 primary education 71, 120 reading literacy 106n school-based management 154, 155 school grants 156, 156 science achievement 110 secondary education 85n social protection programmes 195 socio-economic background 116 teacher incentives 178 teachers 120 micronutrient deficiency 46, 48 Middle East secondary education 85n teacher incentives 178 monitoring see alsoassessment education aid 221 education expenditure 140-1, 141 of equity 189 importance 40 learning outcomes 179-84, 179n lifelong learning 91-2 quality of education 171, 183, 238 regulation of low-fee private education 168 of teachers by parents 175 Montenegro pre-primary education 54 science achievement 110 Montserrat primary education 119 teachers 119 Morocco adult literacy 94, 94n antenatal care 34 child mortality rate 33, 452 education aid 218 education expenditure 134, 137 gender parity/disparity 99, 100, 105 inequalities 79 learning assessments 110n literacy 29, 111, 112 malnutrition 47 out-of-school children 62 pre-primary education 52 primary education 59, 60, 71, 73, 79, 99, 100, see alsoArab States; individual countries; 112, 120 Islamic Republic of Iran; Israel child mortality rates 44 education expenditure 142 secondary education 86 middle-income developing countries see alsodeveloping countries education aid 210, 216-17, 222 education expenditure 133, 134 migrants see alsoimmigrants child 193 Millennium Challenge Corporation 226 Millennium Development Goals (MDGs) contracts 226 focus of PRSPs 187,
 191 malnutrition 46 missed targets 44, 48 relationship with EFA goals 25 and UPE 204 Millennium summit 25 minimum learning standards 182 Ministry of Education Strategic Plan (MoESP) (Zambia) 223 minorities seedisadvantage; ethnic minority groups; exclusion; marginalization mission-free periods 227 moderate stunting 46, 46n, 47m MoESP (Ministry of Education Strategic Plan) (Zambia) 223 Moldova seeRepublic of Moldova Mongolia child labour 80 disabled children 83 EDI 124 education expenditure 134, 137 gender parity/disparity 99 pre-primary education 52, 54 primary education 60, 99 pupil achievement 112 reading literacy 105 secondary education 85n stunted children 35 teachers 120 mortality rate children 32-3, 33, 43-5, 44, 45, 46, 48, 238 mothers 33 mother tongue seehome language mothers effects of educational background 32, 34-5, 34, 35, 60 mortality rate 33 motivation of teachers 121, 131, 154, 171, 173, 174, 175, 239 Mozambique adult literacy 94, 94n antenatal care 34 child mortality rate 33, 43, 452 disabled children 83 EDI 123, 124 education aid 205, 213, 224 education expenditure 134, 135, 137 EIIIG 123, 125, 125 enrolment 213 gender parity/disparity 99, 100, 103n, 104, 106 governance 224 HIV/AIDS 121 inequalities 27, 28, 75, 76, 88, 89 learning environment 116 literacy 109 malnutrition 47 mathematics achievement 106 out-of-school children 62, 64, 66, 66, 75, 76, 133, 213 adult literacy 96 education aid donor 214, 2142, 215, 2152, pre-primary education 52 primary education 60, 68, 69, 692, 71, 72, 73, primary education 27, 60, 68, 68, 69, 692, 70, out-of-school children 62, 62, 64, 75, 76, 71, 72, 72, 73, 75, 99, 100, 104, 119, 121, 205 PRSPs 200-1 secondary education 84, 88, 103n, 104 stunted children 35 teachers 117, 119, 119, 121, 121 vaccination 33 multilateral donors, education aid 214, 214, 215, 216-17, 217 Multiple Indicator Cluster Surveys (MICS2) 51 Myanmar EDI 122 inequalities 78 malnutrition 47 pre-primary education 52 primary education 60, 71, 78, 119
 teachers 119, 119, 119n N Namibia child labour 80 child mortality rate 45 EDI 124 education expenditure 134, 137 EIIIG 125 gender parity/disparity 98n, 100, 104 inequalities 75, 76, 88 learning assessments 182 literacy 109 malnutrition 47 out-of-school children 76 pre-primary education 52 primary education 60, 68, 69, 71, 73, 75, 100, 104 secondary education 88, 104 teachers 119, 175 national education plans 198-9 national EFA programme (Nepal) 223 national goals, and decentralization 151 national income seegross national income national learning assessments 111, 180, 181-2, 183 national ownership (government strategies) 199, 205, 219, 220, 221, 222-4 see alsopoverty reduction strategy papers National Program Support for Basic Education Project (Philippines) 231 National Survey on Corruption and Good Governance (Mexico) 139 206, 213 pre-primary education 51, 52 primary education 59, 60, 68, 68, 692, 702, 71, 72, 75, 79, 99, 100, 104 PRSPs 189, 196, 198 school grants 157 secondary education 88, 103, 104 stunted children 35 teachers 1192, 173 vaccination 33 NER seenet enrolment ratio net attendance rates primary education 73, 74, 79, 103, 104, 149, 149m secondary education 103, 104 net enrolment ratio effect of expenditure efficiency 138 primary education 57, 58, 59, 60-1 and aid 213 and grade survival 70, 71, 72 and grants 144 increases 59, 59, 63, 123 secondary education 84, 84 Netherlands 216, 217, 218, 2262, 2282 education expenditure 135, 136 governance reforms 230 inequalities 114n ODA 207, 207 primary education 113 pupil achievement 113 science achievement 110 tertiary education 73 Nevis seeSaint Kitts and Nevis New York summit 25, 26 New Zealand education aid donor 214, 215, 2152 education expenditure 134, 136 inequalities 114n ODA 207 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 NGOs see alsocivil society organizations; private education role in EFA 237, 241 private aid 216 National Teacher Performance Assessment
 Nicaragua System (Chile) 178 Nauru, pre-primary education 52 Nepal adult literacy 94, 96 antenatal care 34 child mortality rate 33, 43, 452 decentralization 190 EDI 123, 124 education aid 206, 213, 223 education expenditure 135, 137 EIIIG 125, 125 enrolment 213, 229 gender parity/disparity 99, 99, 100, 1032, 104, 105 governance 67 inequalities 75, 76, 79, 88, 103 malnutrition 47 non-formal learning 91 child labour 80 child mortality rate 442, 452 corruption 139 EDI 123, 124 education aid 216, 223, 227 education expenditure 135, 137 EIIIG 125, 125 gender parity/disparity 100, 103n, 104, 106n inequalities 28, 74, 75, 76, 79, 88, 88, 103n language difficulties 114n learning assessments 111 learning environment 116 malnutrition 47 mathematics achievement 106n out-of-school children 75, 76, 221 pre-primary education 53 primary education 61, 68, 68, 69, 69, 702, 71, 72, 73, 74, 75, 79, 100, 103n, 104, 119, 121 I N D E X PRSPs 188, 200-1 school-based management 154, 155-6 secondary education 87, 87, 88, 88, 103n, 104 social protection programmes 195 teachers 119, 121, 173 textbook provision 143 Niger adult literacy 94n antenatal care 34 child mortality rate 32, 33 EDI 122 education aid 212, 213, 213 education expenditure 135, 137 EIIIG 123, 125 enrolment 103 gender parity/disparity 98, 99, 100, 100, 101, 103, 104, 104, 106n inequalities 74, 74, 75, 76-7, 76, 88, 104 learning assessments 182 learning environment 116 malnutrition 47 mathematics achievement 106n out-of-school children 61, 62, 64, 66, 76, 212, 213 74, 75, 782, 99, 100, 104, 119, 121 secondary education 84, 88, 101, 104 stunted children 35 teachers 118, 119, 119, 121, 173 tertiary education 103 Nigeria adult literacy 94, 94, 94n antenatal care 34 child mortality rate 33, 44, 452 decentralization 146, 148, 149 education aid 212, 213 education expenditure 136 education projects and governance 231, 232 efficiency 138 EIIIG 125, 125 enrolment 213 gender parity/disparity 64, 99, 100, 100, 103n, 104 governance 62,
 67 inequalities 28, 74, 74, 75, 76, 78, 79, 88 learning assessments 182 learning environment 116 malnutrition 47 non-formal learning 91 out-of-school children 61, 62, 62-3, 64, 64, 65, 66, 67, 75, 76, 78, 212, 213 pre-primary education 52 primary education 47, 60, 67, 68, 69, 69, 71, 74, 74, 75, 78, 79, 99, 100, 104 private education 164-5, 168 secondary education 88, 103n, 104 stunted children 35 teachers 117, 118, 119 vaccination 33 Niue gender parity/disparity 99 primary education 99 No Child Left Behind Act (United States) 181-2, 181 non-consessional loans 216-17, 217 non-DAC bilateral donors 216 non-formal education 91-2, 91n non-government organizations seecivil society organizations; NGOs; private education non-state providers of education seecivil society organizations; NGOs; private education North Africa child mortality rate 44 education expenditure 142 inequalities 142 secondary education 86 North America and Western Europe see alsoindividual countries adult literacy 93, 94, 95 EDI 122 education expenditure 133, 134, 135, 136-7, 137 gender parity/disparity 97, 98, 99, 101 inequalities 113 literacy 111 out-of-school children 61, 64 pre-primary education 51, 53 primary education 56, 56, 572, 61, 98, 99, 1002 secondary education 85, 86, 86, 98, 101 socio-economic background 113 teachers 107, 118 tertiary education 89, 90, 98 TVET 85, 85 Norway education aid donor 214, 215, 215, 217, 218, 228 education expenditure 135, 136 inequalities 114n ODA 207, 207 pre-primary education 54 primary education 113 pupil achievement 113 science achievement 110 tertiary education 73 numeracy, primary education 109, 109 nutrition policies see alsochild health and nutrition and social protection programmes 195 O ODA 207, 2072, 208 see alsoeducation aid OECD countries see alsodeveloped countries educational attainment 26 learning outcomes 111 pre-school education 54-5 OECD-DAC see
 alsobilateral donors; multilateral donors aid practices survey 220, 221, 221 official development assistance seeODA Oman education expenditure 134 gender parity/disparity 99, 103n pre-primary education 52 primary education 60, 71, 99, 119, 120 secondary education 84n, 103n teachers 119, 120 OPEC (Organization of the Petroleum Exporting Countries) 206 Oportunidades programme (Mexico) 49, 49, 1952 Opportunity NYC programme (United States) 196 Organization of the Petroleum Exporting Countries (OPEC) 206 orphans/orphanhood, late entry and grade repetition 82 4 5 6 out-of-school children 59-67, 62, 63, 65 see alsodropout; school attendance and child labour 79-80, 194-5 and education aid 205, 2062, 212, 212, 214 and education expenditure 133 and EFA goals 25, 56, 62 gender disparity 62 geographic disparity 61, 61 and HIV/AIDS 82 and household wealth 74-6, 76 effect on international learning assessment figures 111 PRSP strategies 200-1 reductions/increases 58, 59-60, 62, 63 social and economic impact 60-1 trend projections 65-7, 66 over-age entry 68, 68, 69, 69, 70 P Pacific see alsoEast Asia and the Pacific; individual countries adult literacy 93, 942, 95 education expenditure 142 gender parity/disparity 103 inequalities 142 primary education 56, 572 secondary education 85 teachers 118 tertiary education 90, 103 TVET 85 Pakistan adult literacy 94, 94, 94n, 95 child labour 80 education expenditure 133, 135 education projects and governance 231, 232, 233 enrolment 105, 169 gender parity/disparity 31, 64, 99, 100, 105, 167, 168 governance 67 inequalities 95, 105, 168 literacy 29, 109 malnutrition 47 numeracy 109 out-of-school children 62, 64, 64, 66, 67 parental participation 157-8 pre-primary education 52 primary education 67, 71, 73, 99, 100, 119, 121 private education 164, 166, 167, 169 PRSPs 196, 197 secondary education 84 teachers 117, 119, 121, 166, 173, 175 Palau gender parity/disparity 99 pre-primary education 52 primary education 99 Palestinian Autonomous Territories
 learning assessments 110n malnutrition 47 pre-primary education 52 primary education 60, 71, 121 secondary education 84 teachers 121 Panama child labour 80 EDI 124 education expenditure 135 gender parity/disparity 99, 106n language difficulties 114n learning assessments 111 learning environment 116 malnutrition 47 pre-primary education 53 primary education 71, 99, 119, 120 reading literacy 106n teachers 119, 120 Papua New Guinea gender parity/disparity 99 primary education 99 Paraguay child labour 80 EDI 124 education expenditure 135, 137 gender parity/disparity 99, 100, 106n inequalities 115 language difficulties 114n learning assessments 111 learning environment 116, 1172 literacy 112 malnutrition 47 pre-primary education 53 primary education 71, 99, 100 reading literacy 106n teachers 107 parasitic worms 81 parent associations, lack of power 157 parental choice 1522, 163, 165, 167 parental voice, in school management 158 parents see alsomothers choice in education 1522, 163, 165, 167 involvement in schools 157-9, 158, 166, 175 and school accountability 129 Paris Declaration on Aid Effectiveness 219, 220, 224, 228 participation see alsoschool participation tertiary education 90 participatory poverty assessments 198 partnerships, between governments and non- state providers 131, 159, 160, 168, 169, 239 PASEC learning assessments 182 pastoral populations, PRSPs 193 pay performance-related 177-9, 178, 240 salaries 135, 148, 171-2, 172, 173, 177, 225 PCT (Common Work Plan) (Nicaragua) 223 PEC (Programa Escuelas de Calidad) (Mexico) 154, 155, 156, 156 pedagogy, and school-based management 154-5 PEDP (Primary Education Development Plan) (United Republic of Tanzania) 223 per-student education expenditure 136, 136-7, 136-7, 142, 143, 147, 147, 148, 149m, 237 performance-related pay, teachers 177-9, 178, 240 Perry Preschool programme 50 Peru child labour 80 corruption 141 education expenditure 135, 137 EIIIG 125 gender parity/disparity 104, 106 inequalities 28, 75, 76, 88,
 88, 111 language difficulties 114, 114n learning assessments 110, 111 learning environment 116, 117 literacy 29, 109 malnutrition 47 mathematics achievement 106n I N D E X out-of-school children 76 pre-primary education 53 primary education 61, 68, 69, 692, 71, 75, 104, 120 science achievement 106 secondary education 88, 88, 104 socio-economic background 116 teachers 107, 120, 1212 Philippines antenatal care 34 child labour 80 child mortality rate 32, 33, 44, 452 decentralization 147 EDI 122 education expenditure 134, 137 education projects and governance 231 EIIIG 125, 125 gender parity/disparity 100, 103-4, 103n, 104, 106 health and nutrition 49, 81, 116 inequalities 28, 31, 75, 76, 79, 88, 88, 103n, 115 learning environment 116, 1172 malnutrition 46, 47 mathematics achievement 106 non-formal learning 91 out-of-school children 62, 64, 66, 75, 76 pre-primary education 51, 52 primary education 60, 68, 69, 70, 71, 72, 73, 75, 79, 100, 104, 112, 121, 134 pupil achievement 112 secondary education 88, 88, 103n, 104 teachers 121, 121 vaccination 33 PISA (Programme for International Student Assessment) 110, 111 PISE (Education Sector Development Programmes) (Mali) 223 place of residence, and inequality 125 pneumonia 43, 44 Poland education expenditure 135, 136 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 policies seegovernments, policies political agendas, effect on reforms 199 political leadership effect of decisions on equity 143 importance for EFA goals 237 need to promote education 26 poor countries seedeveloping countries; least developed countries; low-income countries population programmes, share of total aid 210 Portugal child labour 80 education aid donor 214, 215, 215, 226 education expenditure 135, 136 gender parity/disparity 99 ODA 207, 207 pre-primary education 53 primary education 99, 113, 120 pupil achievement 113 science achievement 110 teachers 120 post-secondary education see alsolifelong
 learning; tertiary education aid 218 attendance 26, 27 poverty see alsodisadvantage; household wealth and child labour 80 effect on education 78 effects of corruption 139 failure of PRSPs to address causes 190-1 and gender disparity 103 effect on literacy 95 and low-fee private education 165, 165 and net attendance rates 149 and public education expenditure 142 in rural areas 105 and UPE 75-6, 78 poverty reduction and economic growth 29-30 and education 29 and governance reforms 130-1 integration of policies 185, 196, 238 social protection policies 194-6, 197 strategies 186-94, 196, 198-9, 200-1 Primary Education Development Plan (PEDP) (United Republic of Tanzania) 223 primary school, transition to 181 private education and achievement 115, 160-1, 162 donor support 233 enrolment 162-3, 167 geographic disparity 165 low-fee private schools 164-7, 168, 169, 239 primary schools, pupil/teacher ratios 120 providing choice 131, 152 regulation 190 service delivery 159 Swedish model 163 private tutoring 172 pro-poor programmes 187 see alsopoverty reduction; poverty reduction strategy papers PRODEC (Programme for the Development of Education) (Mali) 223 poverty reduction strategy papers (PRSPs) 131, Programa Escuelas de Calidad (PEC) 185, 187-94, 187 bias in EFA goals 187, 188, 191 civil society consultations 197-8 coordination problems 197 failure to address poverty 190-1 inequity strategies 200-1 neglected areas 188, 191-3 and power balances 198-9 social protection policies 194-6, 197 stakeholder consultation 196-7 target setting 188-9 Pratham (India) 216 pre-primary seeearly childhood care and education; pre-school; under-3s pre-school education see alsoearly childhood care and education access 50-1, 53, 55 attendance 53, 54 benefits 50 disparities 51-2, 54 enrolment 49-50, 50-1, 51, 52-3, 53 teachers 107 pregnancy antenatal care 34, 34 malnutrition during 46 primary education see alsobasic education; universal primary education access 60-7, 63, 100, 231 aid seebasic
 education, aid attainment 74, 108-12, 112-13 attendance 27, 73, 74-6, 74, 75, 76, 79, 103, 104, 149, 149m completion rates 63, 70, 82 dropout 58, 62, 64, 68, 69, 70 enrolment seeenrolment, primary education expenditure on 134-5, 135, 136-7, 142 gender parity 64-5, 97-100, 98, 99, 99, 100, 104, 167, 168, 206 geographic disparity 79 gross intake rates 56, 56 improving assessment scores 138 improving quality 63 inequalities 70-2, 73-8, 73, 75, 112-17, 136-7 progression 67-72, 67, 68 PRSP strategies 200-1 survival rates to grade 5 27, 27, 68, 77, 87, 89, 100, 100 (Quality Schools Programme) (Mexico) 154, 155, 156, 156 programmatic approach to aid seesector-wide approaches (SWAps) programme-based support 220, 221, 225, 230 see alsosector-wide approaches (SWAps) Programme d'analyse des systèmes éducatifs de la CONFEMEN (PASEC) (France) 106 Programme for the Development of Education (PRODEC) (Mali) 223 Programme for International Student Assessment (PISA) 110, 111 programmes see alsoeducation plans aid seeeducation aid cash transfers 49-50, 49, 77, 195, 196, 238 child labour 194-5 gender parity 103, 206 health 47-8, 49, 49, 50, 81, 192, 209, 210 lifelong learning 91 poverty reduction 187 pre-school 50, 55, 55 redistributive finance 142-5 school-based management 153-4 school choice 161-2 school grants seeschool grants sector-wide approaches 231-2, 233 social protection 185, 194-6 targeting for disadvantage 140, 142, 194-5, 238 teacher recruitment 176-7, 176 UPE 59 Progresa-Oportunidades (Mexico) 49, 49 Progress in International Reading Literacy Study (PIRLS) 105, 110-11, 111, 112, 182 progression seeschool progression project-based aid 222, 225, 230, 233 PRSPs seepoverty reduction strategy papers PTR seepupil/teacher ratio public expenditure 132-7, 133, 1342, 136, 142 Public Expenditure Tracking Surveys (PETS) 141, 141 public-private partnerships 131, 159, 160, 168, 169, 239 public schools (government schools) see alsocharter schools; education governance failure in
 provision 162-3, 164 pupil/teacher ratios 120 teachers 107, 107, 117, 118, 118, 119-21 public sector reform seegovernance reform Punjab, education projects and governance 231, 232, 233 Punjab Education Development Policy Credit Project (Pakistan) 232 Punjab Education Foundation 169 pupil/teacher ratio and contract teachers 173 disparities 117-18, 119-20, 120-1 and enrolment 58 trained staff 119 Q Qatar gender parity/disparity 99 malnutrition 47 pre-primary education 52 primary education 60, 71, 99, 119, 120 science achievement 110 secondary education 84 teachers 119, 120 qualifications see alsoteacher training teachers 119, 119, 175 quality of education (EFA goal) effect of contract teachers 173, 173, 174 deficits 62, 171 EDI indicator 122 and education expenditure efficiency 138 effect of high-stake testing 180-2, 181 and effect on human capital 32 with improved access 63, 77 effect of inequalities 110-13 measurement 28-9, 108 see alsolearning assessments; learning outcomes monitoring 171, 183, 238 policy requirements 238 pre-school 50 in private schools 166 progress towards 41 effect of school organization and environment 114-17 teachers 117-21 Quality Schools Programme (Mexico) 154, 155, 156, 156 quasi-market reform, school systems 152, 163 R Read India 216 reading literacy charter schools 161 gender differences 95, 105-6 primary education 109, 112 redistributive capacity 151 redistributive finance 142-5, 146, 148, 151 see alsocash transfers regional differences, literacy 95-6 religion and pre-school participation 52-3, 54 PRSP strategies 200-1 remote areas see alsorural areas use of contract teachers 175 increasing access to education 143, 144, 148 repetition seegrade repetition Republic of the Congo seeCongo Republic of Korea education expenditure 134, 136 4 5 8 primary education 60, 71, 113, 120 pupil achievement 113 science achievement 110 teachers 120 tertiary
 education 73 Republic of Moldova child labour 80 EDI 124 education expenditure 135 gender parity/disparity 103, 106 mathematics achievement 106 pre-primary education 53 primary education 61, 68, 69, 113 pupil achievement 113 secondary education 103 resource transfers, in decentralization 145-6, 147, 148, 151 resources seeaid; funding; school resources revenue-raising powers, local government 150 rich seehousehold wealth rich countries seedeveloped countries; European Union; individual countries; OECD; United States Romania child labour 80 disabled children 83 EDI 124 education expenditure 135, 137 learning assessments 182 pre-primary education 53 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 rural areas see alsoremote areas; urban areas access to education 58, 58, 231 and child mortality 44, 45 use of contract teachers 174 and education expenditure 148 EDUCO schools 153 effects of poverty 105 enrolment 169 effect on girls 105 inequalities 78, 79, 125 and literacy 95-6 out-of-school children 60 pre-school attendance 52, 53 private education 165, 167 progress towards EFA 125 PRSP strategies 200-1 quality of teaching 171 school attendance 79 school resources 116 teacher recruitment 175-6, 176 teacher shortages 120 gender parity/disparity 98n, 99, 100, 103n, 104 HIV/AIDS 82 inequalities 74, 75, 76, 88, 95n, 103n malnutrition 47 out-of-school children 76 primary education 68, 69, 71, 73, 74, 75, 99, 100, 103n, 104, 119, 121 PRSPs 200-1 secondary education 88, 103n, 104 stunted children 35 teachers 1172, 119, 119, 120, 121 S SACMEQ assessments 1822 Saint Kitts and Nevis education expenditure 135 gender parity/disparity 99 primary education 99 Saint Lucia EDI 124 education expenditure 135, 137 gender parity/disparity 99 pre-primary education 53 primary education 71, 99, 119, 120 secondary education 85n teachers 119, 120 Saint Vincent and the Grenadines education expenditure 135, 136 gender parity/disparity 99 pre-primary
 education 53 primary education 99 salaries, teachers 135, 148, 171-2, 172, 173, 177, 225 Samoa pre-primary education 52 primary education 60 sanitation 62, 65, 116, 209, 210 Sao Tome and Principe EDI 122 gender parity/disparity 98n, 100 malnutrition 47 pre-primary education 52 primary education 60, 71, 100 Sarva Shiksha Abhiyan (India) 158, 222-3, 223-4 Saudi Arabia education expenditure 134 learning assessments 110n malnutrition 47 pre-primary education 52 Save the Children, grant to 216 SBM (school-based management) 131, 152, 153-7, 154 Scandinavia seeDenmark; Finland; Iceland; Rural Education Project (Colombia) 231 Russian Federation Norway; Sweden school achievement inequalities 114n literacy 111 pre-primary education 53 primary education 113 pupil achievement 113 science achievement 110 Rwanda adult literacy 94n, 95n child labour 80 child mortality rate 33 education aid 225 education expenditure 135, 137 EIIIG 125 see alsoeducational attainment and education expenditure 132, 133 and education vouchers 161 girls 107 languages 154 mathematics 29, 106, 112-13, 114, 138, 154, 161 private education 115, 160-1, 162 science literacy 29, 37, 106, 109, 110, 112 and teacher incentives 178-9 school-age populations 57 school attendance see alsodropout; early childhood care and education (ECCE), participation; enrolment; out-of-school children; school participation boys 103-4 and child labour 79-80, 80 and disability 82-3, 83 disadvantage index 80 girls 103-4 primary school 27, 73, 74-6, 74, 75, 76, 79, 103, 104, 149, 149m in rural areas 53 secondary and post-secondary 27, 103-4, 104 targets 237 school autonomy 78, 115, 154-5 see alsoschool-based management school-based management (SBM) 131, 152, 153-7, 154 school buildings 58, 63, 116, 238 school capacity, and school-based management 156, 156 school completion increases 63 and malaria 82 and pre-school education 50 primary school 63, 70, 73, 82 school costs seeeducation costs school curriculum seecurriculum reform school
 environment (learning environment) 115-17, 115, 238 school fees, abolition 59, 80, 144, 199, 205, 206 school governance reforms 130, 152-70 school grants 148, 156-7 block grants 140, 148, 150 capitation grant 63, 141, 143-4, 144, 156-7 School Improvement Grant Programme (SIGP) (Indonesia) 140 school inspection 179, 183-4, 184 school leadership 155-6 school management councils 158 school meals 53, 81, 192 school participation see alsobasic education, aid attendance 88-9, 88, 89, 103-4, 104 benefits 84, 103 disparities 88-9, 88, 89 dropout 86 educational attainment 26, 29, 86-7, 86, 112-13 enrolment 84-5, 86, 86, 101, 102, 102, 103 expenditure on 135, 135, 142 gender parity 97, 98, 101, 102, 104, 206 importance for EFA goals 237 improving assessment scores 138 maths and science literacy 29, 112-13 neglect in PRSPs 188 participation 85, 86 PRSP strategies 200-1 teachers 107, 107, 117, 118, 118 transition to 84, 85, 86, 86 sector-wide approaches (SWAps) aid through 205, 221, 221, 222 alignment of aid 225 donor involvement 223 and national ownership 222 need for government capacity 229 out-of-school children 205 programmes and governance 231-2, 233 success 186, 186 Segundo Estudio Regional Comparativo y Explicativo (SERCE) 105-6 selective education 115, 162 Senegal abolition of school fees 143 adult literacy 94n, 95n child labour 80 child mortality rate 33 education aid 213, 213 education expenditure 134, 135, 137 efficiency 138 EIIIG 125 enrolment 213, 213 gender parity/disparity 98n, 99, 103, 103n, 104, 106n inequalities 74, 75, 76, 782, 88, 95n, 103, see alsoenrolment; out-of-school children; 103n school attendance effect of education costs 65, 206 and household wealth 62, 73-7, 74, 75, 76, 79, 88-9, 88, 89 pre-school 54 primary 123 see alsouniversal primary education secondary 85, 86 school progression see alsograde repetition and gender parity 99-100 school resources 62, 115-17, 132, 135, 182 school retention see alsoschool completion; survival rate and gender
 disparity 100 and pre-school enrolment 50 school supervision 179, 183-4, 184 school support committees 157 schools see alsoentries beginning'school'; primary education; secondary education relationship with government bodies 129 science gender differences 106 literacy in 29, 37, 109, 110, 112 secondary education see alsobasic education aid 218 learning assessments 182 malnutrition 47 mathematics achievement 106n non-formal learning 91 out-of-school children 62, 64, 66, 76, 133, 213 pre-primary education 52 primary education 60, 68, 692, 70, 71, 72, 73, 74, 75, 782, 99, 103n, 104 PRSPs 188, 196, 200-1 secondary education 88, 104 stunted children 35 teachers 118 Serbia pre-primary education 53, 53, 54 science achievement 110 secondary education 85 severe stunting 46, 46n, 47m Seychelles education expenditure 134, 135, 136 gender parity/disparity 98n, 106 mathematics achievement 106 secondary education 84 Sharva Shiksha Abhiyan (SSA) 158, 222-3, 223-4 shifts, to improve access 63 short-term achievements, versus capacity building 229 Sierra Leone I N D E X adult literacy 94n, 95n child labour 80 education expenditure 135 gender parity/disparity 99, 100 inequalities 95n malnutrition 47 pre-primary education 52, 54 primary education 99 SIMECAL evaluation system (Bolivia) 182-3 Sindh, education projects and governance 231, 232, 233 Sindh Education Sector Development Policy Credit Project (Pakistan) 232 Singapore literacy 111 malnutrition 47 primary education 113 pupil achievement 113 Sistema Nacional de Evaluación del Desempeño (Chile) 178 Sixth International Conference on Adult Education 92 Slovakia education expenditure 135, 136 literacy 111 primary education 113, 120 pupil achievement 113 science achievement 110 teachers 120 Slovenia education expenditure 136 literacy 111 pre-primary education 53 primary education 61, 113 pupil achievement 113 science achievement 110 slums inequalities 78, 193 low-fee private education 167-8, 167, 233 social class see
 alsosocio-economic background and use of private education 165 social equality 24-5 social protection programmes 185, 194-6 social sector, share of aid 209, 210 social welfare programmes, importance 77 socio-cultural inequalities 78, 105 socio-economic background see alsosocial class access to public education 167 effect on learning 113, 132 mix in schools 115 and school choice 162 and teacher quality 171 Solidario (Mexico) 195 Solomon Islands gender parity/disparity 99 primary education 99 Somalia capacity 227 malnutrition 47 South Africa child labour 80 decentralization 146 disabled children 83 EDI 124 education expenditure 134, 135, 136 gender parity/disparity 99, 100 governance reforms 130 HIV/AIDS 121 learning assessments 180, 182, 183 literacy 29, 109, 111, 111n, 112 malnutrition 47 parental participation 158 pre-primary education 52 primary education 60, 71, 99, 100, 112 pupil achievement 112 teachers 1212 South America seeindividual countries; Latin America South Asia child mortality rate 32, 432, 44, 44 EDI 122 gender parity/disparity 41 malnutrition 463, 47 stunted children 34 teachers 171, 172, 175 South and West Asia see alsoindividual countries; South Asia adult literacy 93, 93, 94, 95, 95 education expenditure 133, 1332, 134, 135, 137, 137 education plans 190 EFA goals 41 gender parity/disparity 41, 95, 97, 98, 98, 99, 99, 1002, 101, 101, 102, 103 inequalities 27-9 learning environment 117 out-of-school children 61, 61, 64, 64 pre-primary education 51, 52 primary education 56, 57, 572, 58, 60, 60, 68, 98, 99, 1002 school survival rates 27 secondary education 842, 85, 86, 98, 101, 101, 102 teachers 107, 1172, 118, 118, 119 tertiary education 90, 98, 103 TVET 85, 85 youth literacy 94 Southern and Eastern Africa Consortium for Monitoring Educational Quality 106 Spain education aid donor 214, 215, 216, 2282 education expenditure 135, 136 ODA 207, 207 primary
 education 113, 120 pupil achievement 113 science achievement 110 teachers 120 special needs see alsodisabilities funding for 143 Sri Lanka child labour 80 equity 186 health and nutrition 81, 116 learning assessments 182 learning environment 116, 117 malnutrition 47 teachers 173 staff seeteachers state seegovernments stipend programmes 103, 140, 197 streaming, affect on learning outcomes 114 street children 193 stunted children 34, 35, 45, 46, 46n, 47m, 195, 195n Sub-Saharan Africa see alsoindividual countries adult literacy 93, 93, 943, 95, 95 child labour 80 child mortality rate 32, 43, 44 decentralization 148 democratic attitudes 36 4 6 0 EDI 122 education aid 217 education expenditure 133, 1332, 134, 1342, 137, 137, 142 education plans 190 EFA goals 412 gender parity/disparity 41, 95, 97, 98, 982, 99, 99, 100, 1002, 101, 101, 102, 103, 105 health and nutrition 34, 81 HIV/AIDS 82 inequalities 27-8, 86, 142 learning assessments 182 learning environment 116 malnutrition 463, 47, 47 ODA 206 out-of-school children 59-60, 60, 61, 61, 64, 64, 133 post-secondary education 26, 27 pre-primary education 51, 512, 52 primary education 26, 27, 41, 56, 56, 572, 572, 58, 60, 60, 68, 70, 98, 99, 1002, 134 school attendance 26, 27-8, 27 school survival rates 27, 27 secondary education 26, 27, 843, 85, 86, 862, 98, 101, 101, 102 stunted children 34 teachers 107, 1172, 118, 118, 171, 172, 175 tertiary education 89, 90, 98, 103 TVET 85, 85 youth literacy 94 Sudan adult literacy 94, 95n education aid 216 gender parity/disparity 99 inequalities 95n malnutrition 47 out-of-school children 66 pre-primary education 52 primary education 99, 119 teachers 119 Suriname malnutrition 47 pre-primary education 53 primary education 120 teachers 120 survival rate to grade 5 27, 27, 68, 77, 87, 89, 100, 100 to grade 9 27-8, 69, 69, 70, 71, 72, 87, 89 to grade 12 87, 87, 88-9, 89 primary and secondary education 77-8, 77, 87, 89 Swaziland child labour 80 EDI 122, 124 education expenditure 134, 135, 137
 gender parity/disparity 99, 100 literacy 109 malnutrition 47 pre-primary education 52 primary education 60, 71, 99, 100 Sweden education aid donor 214, 215, 215, 217, 218 education expenditure 135, 136 governance reform 163 inequalities 114n ODA 207, 207 pre-primary education 54 primary education 61, 113, 120 private education 159 pupil achievement 113 science achievement 110 teachers 120 voucher programmes 159, 160 Swedish model, educational choice 161, 163, 239 Switzerland education aid donor 214, 215, 215 education expenditure 135, 136 inequalities 114n ODA 207 primary education 61, 113 pupil achievement 113 science achievement 110 Syrian Arab Republic education expenditure 137 gender parity/disparity 99 learning assessments 110n malnutrition 47 pre-primary education 51, 52, 54 primary education 99 secondary education 85n system assessments seelearning assessments T Tajikistan education expenditure 134, 137 gender parity/disparity 99, 103n pre-primary education 52 primary education 99, 119 secondary education 103n teachers 119 Tanzania seeUnited Republic of Tanzania target setting for equity 236-7 PRSPs 188-9 for successful UPE 77 targeting child labour programmes 194-5, 195 for disadvantage 140, 142, 194-5, 238 Tawana Pakistan Project 197 teacher absenteeism 120-1, 165, 166, 172, 179 teacher resource centres 177 teacher shortages 119 teacher training 119, 175, 231 teachers see alsopupil/teacher ratio; 'teacher' entries absenteeism 120-1, 165, 166, 172, 179 capacity building 155 contract teachers 172-3, 173, 174, 239-40 deployment 171-9, 177, 238, 239 dismissal 172-3 female 107, 107 gender biased attitudes 106-7, 107 and governance 117, 119, 129, 171, 173 incentives 148, 175, 177-9, 178, 179, 239, 240 morale and motivation 121, 131, 154, 171, 173, 174, 175, 239 pre-school 107 primary education 107, 107, 117, 118, 118, 119-21 qualified 119, 119 and quality of education 117-21, 118, 171, 175 recruitment 58, 63, 87n, 171-5, 175-7, 176, 232, 239, 240 salaries 135,
 148, 171-2, 172, 173, 177, 225 and school-based management 154-5 secondary education 107, 107, 117, 118, 118 tertiary education 107 training 119, 175, 231 unqualified 62, 120, 175 teaching conditions (learning environment) 115- Tonga 17, 115, 238 teaching hours 115, 238 teaching materials seelearning environment; school resources; textbooks teaching staff seeteachers technical cooperation 222 technical and vocational education and training (TVET) 85, 85, 201 education expenditure 134 gender parity/disparity 99 pre-primary education 52 primary education 60, 71, 99 secondary education 85 total primary net enrolment ratio 65, 66 tracking, for public expenditure 140, 140-1, 140, 141 teenagers, youth literacy 94-5 ten year memoranda of understanding (UK) 226 tertiary education training, see alsoteacher training transaction costs, aid delivery 221, 228 transfer of governmental resources 145-6, 147, see alsopost-secondary education aid 215, 217, 218, 218 enrolment 89-90, 90 expenditure on 142 gender parity 97, 98, 101, 106 influence of donors 225 teachers 185 transition to 73 textbooks access to 62, 116 equitable distribution 143 gender bias 107 provision through projects 197, 231 Thailand curriculum 155 education expenditure 134 malnutrition 47 pre-primary education 54 primary education 113 pupil achievement 113 school-based management 155 science achievement 110 the former Yugoslav Republic of Macedonia education expenditure 142, 143 inequalities 142 pre-primary education 53, 54 primary education 61, 112 pupil achievement 112 Third World seedeveloping countries; least developed countries; low-income countries; middle income developing countries time for learning 115, 238 timely promotion seegrade repetition Timor-Leste gender parity/disparity 99 malnutrition 47 out-of-school children 64 pre-primary education 52 primary education 99 TIMSS (Trends in International Mathematics and Science Study) 110, 112, 182 TNER 65, 66 Tobago seeTrinidad and Tobago toddlers 49-50 Togo
 adult literacy 94n, 95n child labour 80 gender parity/disparity 99, 100, 100, 103 inequalities 78, 95n malnutrition 47 pre-primary education 52 primary education 60, 71, 73, 78, 99, 100, 121 secondary education 103 teachers 121, 173, 173 Tokelau gender parity/disparity 99 primary education 99 secondary education 84n 148, 151 transition to primary school 181 to secondary education 84, 85, 86, 86 to tertiary education 73 to upper secondary 86 transition countries education expenditure 134 gender parity 101 learning outcomes 112-13, 117 literacy 93, 95 out-of-school children 61, 63 primary education 51, 56 secondary education 85, 85, 86, 86 teachers 118 tertiary education 90 Trends in International Mathematics and Science Study (TIMSS) 110, 112, 182 Trinidad and Tobago child labour 80 gender parity/disparity 100 literacy 111n, 112 malnutrition 47 pre-primary education 53 primary education 61, 71, 100, 112 pupil achievement 112 tuition fees, abolition 59, 80, 144, 199, 205, 206 Tunisia EDI 122 education aid 218 education expenditure 134, 136 gender parity/disparity 99, 103 health and nutrition 116 inequalities 115 learning assessments 110, 110n learning environment 116, 1172 malnutrition 47 primary education 60, 71, 99, 112, 120 pupil achievement 112 science achievement 110, 110 secondary education 103 teachers 107, 120 Turkey adult literacy 94, 94 child labour 80 education expenditure 135, 137 gender parity/disparity 99 inequalities 89 out-of-school children 64, 66 pre-primary education 53 primary education 99, 112 pupil achievement 112 science achievement 110 Turks and Caicos Islands gender parity/disparity 99 primary education 99 I N D E X U UBEC (Universal Basic Education Commission) intervention fund (Nigeria) 149 Uganda abolition of school fees 143 child mortality rate 33, 45 corruption 141 decentralization 146, 148 disabled children 83 education expenditure 135, 137 EIIIG 125 enrolment 229 gender parity/disparity 98n, 99, 103, 104 governance reforms 130
 inequalities 74, 75, 76, 88 learning assessments 182 learning environment 116 literacy 109 malnutrition 47 out-of-school children 76, 221 poverty reduction 187 pre-primary education 52 primary education 68, 69, 692, 72, 73, 74, 75, 99, 104 PRSPs 198, 200-1 school supervision 184 secondary education 84, 88, 103, 104 stunted children 35 teachers 118, 120, 121, 175, 177 Uganda National Institute of Special Education 83 Ukraine education expenditure 135 primary education 119 teachers 119 under-3s, ECCE provision 49-50 under-5s see also early childhood care and education; pre-school education mortality rate 33, 44, 44, 452, 238 see alsochild mortality rate under-age entry 68, 68, 69, 69 underdeveloped countries seedeveloping countries; least developed countries; low- income countries; middle income developing countries undernutrition 46, 116 see alsochild health and nutrition Understanding Child Work programme 79 UNICEF 206, 215, 216 unit cost approach 150 United Arab Emirates adult literacy 122n EDI 124 education expenditure 136 malnutrition 47 pre-primary education 52 primary education 60, 71, 73, 120 teachers 120 United Kingdom education aid donor 214, 2142, 215, 2152, 216, 217, 218, 226, 2282 education expenditure 135, 136, 137 governance reforms 230 ODA 207 pre-primary education 53 primary education 113 private education 159 pupil achievement 113 science achievement 110 tertiary education 72, 73 United Nations summits 25, 26 United Republic of Tanzania abolition of school fees 80, 143 adult literacy 94, 94n child labour 80 child mortality rate 33, 452 civil society participation 198 education aid 2052, 212, 212, 213, 213, 223, 228 EIIIG 125, 125 enrolment 213, 213, 229 gender parity/disparity 98n, 100, 104, 106 governance 67 health and nutrition 81, 81-2 HIV/AIDS 82 inequalities 28, 75, 76, 79, 88 learning assessments 182 learning environment 116 malnutrition 47, 47
 mathematics achievement 106 non-formal learning 91 out-of-school children 62, 62, 63, 65, 76, 205, 212, 212, 213 pre-primary education 52, 58 primary education 59, 60, 67, 68, 68, 69, 70, 71, 72, 75, 79, 100, 104, 119, 140, 205 PRSPs 187, 188, 193, 200-1 school grants 144 secondary education 87, 87, 88, 104 stunted children 35 teachers 117, 119, 121 vaccination 33 United States cash transfers 196 charter schools 159, 160-1 ECCE 55 education aid donor 214, 215, 2153, 218, 218, 222, 226, 2282 education expenditure 135, 137 governance reforms 230 inequalities 30, 114n, 163 learning assessments 181-2, 181 ODA 207, 207 pre-primary education 50, 53, 54-5 primary education 61, 113, 120 pupil achievement 113 science achievement 110 teachers 120 tertiary education 73, 90 voucher programmes 159, 161 Universal Basic Education Commission (UBEC) intervention fund (Nigeria) 149 Universal Elementary Education programme (India) 158 universal primary education (UPE) (EFA goal) see alsoprimary education aid requirements 204, 208, 217, 225 barriers 78-9, 79-83, 131 EDI indicator 122 EFA goal 25, 56 focus of PRSPs 188 Millennium Development Goal 204 progress towards 25, 40, 41, 56-79, 206, 213, 213, 217, 236 successful achievement 77 teachers needed 118 trends 65-7, 66 unqualified teachers 62, 120, 175 UPE seeuniversal primary education upper secondary education 86, 86 urban areas enrolment 169 inequalities 78, 125, 193 4 6 2 low fee private education 167-8, 167, 233 pre-school attendance 53 primary attendance 79 quality of teaching 171 urban bias, quality of teaching 175 Uruguay education expenditure 135 gender parity/disparity 100, 106n learning assessments 180, 183 learning environment 116, 117 literacy 111 malnutrition 47 numeracy 111 pre-primary education 50, 53 primary education 71, 100 reading literacy 106n science achievement 110, 111 teachers 121 USSR seeArmenia; Azerbaijan; Belarus; Estonia; Georgia; Kazakhstan; Kyrgyzstan; Latvia; Lithuania; Republic of Moldova; Russian
 Federation; Ukraine; Uzbekistan.
Uzbekistan child labour 80 education projects and governance 231, 233 pre-primary education 52, 54 primary education 119 teachers 119 V vaccination 33, 43, 48 Vanuatu education expenditure 134 pre-primary education 52 primary education 60 Venezuela, Bolivarian Republic of EDI 124 education aid 217 education expenditure 137 gender parity/disparity 100 malnutrition 47 pre-primary education 53 primary education 61, 71, 100, 112 pupil achievement 112 secondary education 85n Viet Nam adult literacy 96 antenatal care 34 child labour 80 child mortality rate 33, 45 decentralization 145-6 EIIIG 125 gender parity/disparity 104 inequalities 31, 74, 75, 75, 76, 88 learning assessments 182 learning environment 182 malnutrition 47 out-of-school children 76 pre-primary education 52, 54 primary education 75, 75, 104, 119 PRSPs 188, 200-1 secondary education 88, 104 teachers 119 vaccination 33 village education committees 158 Vitamin A deficiency 48, 191 vocational education, TVET 85, 85 voice, parental 158 voluntary participation, school management 155 vouchers, for education 161 W water provision, share of total aid 210 wealth seehousehold wealth West Africa, teachers 177 Western Europe seeindividual countries; North America and Western Europe within-country disparities 95-6, 123-5, 124, 125 educational opportunity 27 gender disparity 102-5 income distribution 31 inequitable financing 143, 149 learning outcomes 112-17 pre-school education 51-2, 54 primary education 73-8, 73 private education 169 secondary education 88-9, 88, 89 teachers 119-20 under-5 mortality 452 women see also‘gender’ entries; girls access to education 28 earnings 31 employment 191 and HIV/AIDS 35 literacy 28, 29, 93, 93, 95 as teachers 107, 107 tertiary education 97, 98, 106 woredaadministration (Ethiopia) 150, 150 World Bank education aid donor 153, 206, 209, 214, 224 education projects and programmes 231-2 governance strategies 230 grants 206 non-concessional loans 217, 217 promotion of private education 164 Public Expenditure Tracking Surveys 141, 141
 World Food Programme, education aid donor 206 worms 81 writing skills see alsoliteracy primary education 109 Y Yemen child labour 80 EDI 123, 124 education aid 213, 216 enrolment 213 gender parity/disparity 99, 100, 101, 103, 107 malnutrition 46, 47 out-of-school children 62, 64, 66, 66, 213 pre-primary education 52 primary education 59, 60, 71, 99, 100 secondary education 101, 103 young children seeearly childhood care and education; pre-school education young people seelifelong learning youth literacy 94-5 Yugoslavia seeBosnia and Herzegovina; Croatia; Montenegro; Serbia; Slovenia; the former Yugoslav Republic of Macedonia Z Zambia abolition of school fees 143 adult literacy 29, 94n child labour 80 child mortality rate 33, 44, 452 disabled children 83 EDI 122, 124 education aid 205, 212, 212, 213, 213, 223, 228 education expenditure 134, 135, 137 EIIIG 125 enrolment 213, 213, 229 gender parity/disparity 98n, 99, 100, 103, 104, 106 inequalities 28, 75, 76, 88, 88 learning assessments 182 learning environment 116 literacy 109 malnutrition 47 mathematics achievement 106 non-formal learning 91 out-of-school children 62, 75, 76, 212, 212, 213 primary education 58, 59, 60, 68, 69, 692, 71, 72, 75, 752, 99, 100, 104, 121, 135, 140, 205 PRSPs 200-1 secondary education 88, 88, 103, 104 stunted children 35 teacher salaries 135 teachers 118, 121 Zanzibar see alsoUnited Republic of Tanzania gender parity/disparity 106 learning assessments 182 mathematics achievement 106 Zimbabwe child labour 80 child mortality rate 33 EIIIG 125, 125 gender parity/disparity 98n, 104 inequalities 75, 76, 88 learning assessments 182 malnutrition 47 out-of-school children 76 primary education 60, 68, 692, 75, 104 secondary education 88, 104 vaccination 33 I N D E X 4 6 3 Overcoming inequality: why governance matters Education is a basic human right.
Yet across the world there are vast disparities in education based on wealth, gender, location, language and other markers for disadvantage.

These disparities threaten to undermine efforts to achieve the six Education for All (EFA) goals agreed by over 160 governments in 2000.
Failure to place strategies for greater equity at the centre of the EFA agenda will deny millions of children, youth and adults the education and learning opportunities they need to realize their potential, escape poverty and participate fully in society.

This is the seventh edition of the EFA Global Monitoring Report.

It documents progress towards the EFA goals.


Focusing on those being left behind, the Report explores current approaches to education governance reform and assesses whether these are helping to improve access, quality, participation and accountability.

It also examines aid governance.

Rich countries have pledged that no national education strategy will be allowed to fail for want of finance.

Yet this promise is not being honoured.


The Report includes statistical indicators on all levels of education in some 200 countries and territories, and serves as an authoritative reference guide.

www.unesco.org/publishing Cover photo Unlike many children his age from poor households, this young boy in Indonesia is learning to read and write at school.

© BODY PHILIPPE / HOA-QUI