Ten-Year Impacts of Burkina Faso’s BRIGHT Program

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I. INTRODUCTION MATHEMATICA POLICY RESEARCH Figure I.3. (continued) Intervention Model women’s program Incentives for female teachers Association de Mères Educatrices (AMEs) Engaged Group directly affected Female community members Teachers AMEs Outcomes Short term Medium term Long term • Females identified and given support to act as positive female role models within the community • Teachers provided training and support • Female teachers given excellence awards to motivate and improve performance • AMEs given support to carry out mentoring and tutoring of female students Literacy campaign Mothers • Mothers given literacy training, with associated training in managing micro-projects Bisongos Girls and mothers • Bisongos constructed • Bisongos provided equipment, supplies, and food for students • Volunteer teachers trained in early childhood curricula (including hygiene and nutrition) • Positive, educated female role models for girls to emulate • Positive, educated female role models • Increased number and participation of female teachers • Positive, educated female role models • Increased number and participation of female teachers • Positive, educated female role models • Increased number and participation of female teachers • Positive, educated female role models • Increased number and participation of female teachers • Higher employment levels, increased income • Maintain school enrollment rates for girls; increase girls’ primary school completion rate • Improved educational outcomes 8
I: INTRODUCTION MATHEMATICA POLICY RESEARCH 2. The 7- and 10-year impact evaluation of BRIGHT As the BRIGHT program was extended, MCC contracted with Mathematica to conduct additional analysis of the impacts of BRIGHT using two more rounds of survey data: one collected 7 years and another 10 years after program implementation started. Below, we provide a brief overview of the impact evaluation design, findings from the 7-year study, and the data collection efforts for the 10-year study. a. Overview of evaluation design An impact evaluation estimates the impacts of a program by comparing outcomes among the beneficiaries of the program relative to what would have happened to the beneficiaries in the absence of the program. To estimate the impacts of BRIGHT, we assess how children in villages selected to receive the BRIGHT program fared relative to how they would have fared had their village not been selected. Because we could not directly observe the latter scenario (known as the counterfactual), we selected a group of children in a set of villages that were not selected to receive BRIGHT to estimate this “counterfactual” state of the world. We then estimate the differences in outcomes for these two groups using a research design called regression discontinuity (RD). The MEBA received applications for a BRIGHT school from 293 villages located in 49 departments. MEBA staff scored each of these villages based on pre-set criteria to identify communities that could benefit most from the schools. MEBA then ranked the villages within each department and selected the top half of villages for BRIGHT implementation. Our research design relies on the fact that the villages with scores placing them just below the top half of villages are, on average, very similar to the villages with scores just high enough to be selected for BRIGHT. As a result, the children living in these two sets of villages are similar in all respects, except for the fact that those living in selected villages are more likely to receive the BRIGHT program, allowing us to attribute any differences in the children’s outcomes solely to the program. Technically, children in villages with scores narrowly placing them in the bottom half allow us to estimate the counterfactual condition for those with scores just high enough to be in the top half. We describe the statistical techniques used to produce the RD estimates in more detail in Chapter II (Section C) and Appendix A. The intuition for the approach, however, is that we use the data from children in all of the villages considered for the BRIGHT program to construct a mathematical model of the relationship between each outcome of interest and the score assigned to each village during the selection process. Within each department, the scores of the lowestscoring selected villages and the highest-scoring unselected villages can be used to define a “cutoff” point for village scores such that villages scoring more than this value would be selected for the BRIGHT program and those scoring less would not. We then use the mathematical model to calculate the differences in outcomes for children in villages just above and below the cutoff score. 12 This difference is the estimated effect of being selected into the BRIGHT program. The 12 The purpose of the model is to allow us to estimate the average outcomes for hypothetical villages that have scores that place them as close to the cutoff as is possible while still being either selected or not selected for the program. (Formally, we estimate the right- and left-hand limits of the function at the point of the discontinuity.) These estimates are based on the actual outcomes observed in villages in our data set, but they are closer to the cutoff than any of those villages and, as a result, have more similar characteristics. 9
Page 1 and 2: Ten-Year Impacts of Burkina Faso’
Page 3 and 4: ACKNOWLEDGEMENTS This report is the
Page 5 and 6: CONTENTS ACRONYMS .................
Page 7 and 8: TABLES ES.1 ES.2 ES.3 ES.4 ES.5 ES.
Page 9 and 10: FIGURES I.1 Gross enrollment ratios
Page 11 and 12: ACRONYMS AMEs - Association de Mèr
Page 13 and 14: EXECUTIVE SUMMARY The BRIGHT progra
Page 15 and 16: EXECUTIVE SUMMARY MATHEMATICA POLIC
Page 23 and 24: I. INTRODUCTION From 2005 through 2
Page 25 and 26: I. INTRODUCTION MATHEMATICA POLICY
Page 27 and 28: I. INTRODUCTION MATHEMATICA POLICY
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Page 39 and 40: II. EVALUATION DESIGN AND DATA COLL
Page 51 and 52: III. IMPLEMENTATION SUMMARY Under t
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Page 55 and 56: IV. FINDINGS In this chapter, we pr
Page 57 and 58: IV: FINDINGS MATHEMATICA POLICY RES
Page 73 and 74: V. COST-EFFECTIVENESS AND COST-BENE
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V. COST-EFFECTIVENESS AND COST-BENE
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VI. SUMMARY OF FINDINGS AND DISCUSS
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REFERENCES Abeberese, Ama Baafra, T
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REFERENCES MATHEMATICA POLICY RESEA
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APPENDIX A STATISTICAL MODEL FOR IM
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APPENDIX A MATHEMATICA POLICY RESEA
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APPENDIX B VALIDATION OF REGRESSION
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APPENDIX B MATHEMATICA POLICY RESEA
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APPENDIX B MATHEMATICA POLICY RESEA
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APPENDIX C ROBUSTNESS OF IMPACT EST
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APPENDIX C MATHEMATICA POLICY RESEA
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APPENDIX D DETAILS ON COST-BENEFIT
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APPENDIX D MATHEMATICA POLICY RESEA
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APPENDIX E SURVEY FORMS
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APPENDIX E MATHEMATICA POLICY RESEA
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APPENDIX E BURKINA FASO MATHEMATICA
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APPENDIX E MATHEMATICA POLICY RESEA
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HOUSEHOLD LISTING FORM Village ID:
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ENROLLMENT MODULE VILLAGE ID: ___ _
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CHILD LABOR MODULE VILLAGE ID: ___
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YOUNG ADULT MODULE VILLAGE ID: ___
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ASSESSMENT CONSENT LANGUAGE: PLEASE
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MATH ASSESSMENT VILLAGE ID: ___ ___
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MATH ASSESSMENT VILLAGE ID: ___ ___
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FRENCH ASSESSMENT VILLAGE ID: ___ _
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FRENCH ASSESSMENT VILLAGE ID: ___ _
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BURKINA FASO SCHOOL INFORMATION PAN
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A: SCHOOL CHARACTERISTICS SC SC9. D
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C: SCHOOL PHYSICAL STRUCTURE SS SS5
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APPENDIX F MATHEMATICA POLICY RESEA
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Ten-Year Impacts of Burkina Faso’s BRIGHT Program

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