Ten-Year Impacts of Burkina Faso’s BRIGHT Program

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I: INTRODUCTION MATHEMATICA POLICY RESEARCH evaluation design is the same design previously used to assess the 3-year and 7-year impacts of BRIGHT. b. Overview of the 7-year impact findings Using survey data collected in 2012, we conducted a 7-year impact evaluation of the BRIGHT program examining impacts for children between ages 6 and 17. The evaluation sought to measure the impacts of BRIGHT on school enrollment, test scores, health, and child labor. We summarize the findings below. We found that 7 years after the start of program implementation, BRIGHT continued to have large positive impacts on school enrollment. Self-reported enrollment of children in the villages selected for BRIGHT was 15.4 percentage points higher compared to the children of villages not selected (Table I.3). This impact was quite large, considering that 85.5 percent of the unselected villages also had a school. The impact on school enrollment was accompanied by continued positive impacts on test scores. Students in villages selected for BRIGHT scored 0.29 standard deviations higher than students in villages not selected (Table I.3). This positive impact was consistent across the math and French sections of the exam. However, we found that BRIGHT did not have any impact on child health in terms of arm circumference (Table I.3) and four other anthropometric measures (height-for-age z-index, weight-for-age z-index, weight-for-height z-index, and body mass index). BRIGHT schools gave lunches to students through the canteens and gave girls with 90 percent attendance grain to take home but participation in the feeding programs was low. In addition, elementary-school-age children are less likely to respond to these programs compared to younger children in their very early years of life (Ainsworth and Ambel 2010). Finally, we found that BRIGHT modestly reduced the number of children engaged in six common household activities by 2.1 to 5.2 percentage points. This represents a reduction in 0.13 standard deviations when the outcomes are compiled into a standardized composite labor index (Table I.3). Table I.3. Seven-year impacts of the BRIGHT program on enrollment and test scores Selected villages Unselected villages Estimated impacts Sample size Self-reported enrollment 47.7% 32.3% 15.4 pp*** 26,430 Test scores 0.13 -0.16 0.29*** 23,464 Arm circumference (mm) 162.59 161.86 0.74 25,982 Overall labor index (standard deviation) -0.08 0.04 -0.13*** 25,081 Sources: Mathematica household survey (2012) and Mathematica school survey (2012). Notes: Test scores are measured in standard deviations of student achievement. pp = Percentage points. ***Coefficient statistically significant at the 1% significance level. 10
I: INTRODUCTION MATHEMATICA POLICY RESEARCH The evaluation also found that BRIGHT had larger positive impacts on girls compared to boys in terms of enrollment and test scores. Girls’ enrollment increased by 11.4 percentage points more than boys’ did, and their test scores increased by 0.21 standard deviations more. There was no differential impact for girls in terms of health outcomes, but the program was successful in having a greater reduction in the child labor index for girls by 0.07 standard deviations than boys. c. Overview of data collection for the 10-year impact analysis For the current evaluation of BRIGHT, we collected data in 2015 from the same set of 293 villages as in the two previous evaluations, including villages in both the participant and comparison groups. 13 In each village, we randomly selected 40 households to interview. These households constitute a new sample of households from the same villages for the current evaluation that is different from the samples used in 2008 and 2012 for the 3-year and 7-year impact evaluations of BRIGHT. 14 We used two survey instruments for data collection: a household survey and a school survey. The household survey collected information on households’ demographic characteristics and assets; children’s educational, health, and child labor outcomes; parents’ perceptions of education; and young adult outcomes. Also, all children and young adults 6 to 22 years old in these households were given math and French tests. The school survey collected information about schools’ characteristics and children’s enrollment and attendance. D. Link to economic rate of return (ERR) and beneficiary analysis Positive impacts from the BRIGHT program are likely to benefit for the rest of their lives the cohorts of children who had the opportunity to enroll in the schools. Continued enrollment in school is likely to result in future increased earnings for these children and their families. To assess whether investments in a school construction program like BRIGHT are sustainable, it is important to compare the cost of the intervention with the potential benefits. The ERR of an intervention gives a summary statistic of the economic merit of a public investment by comparing the cost and the benefits of the program. We conduct an ERR analysis as part of a larger cost-benefit analysis. However, we conduct this analysis within the constraints imposed by the research design. Because the treatment effect estimates reflect the impact of being selected for a BRIGHT school relative to the educational opportunities that exist in the unselected villages, we can estimate only the ERR of costs incurred in villages selected for BRIGHT relative to the expenditures on schools in unselected villages. In other words, we assess the ERR of only the additional costs that were expended in the selected villages due to the much higher rates of BRIGHT school construction necessitated by the stricter quality requirements. As a result, this analysis differs from the type of ERR analysis typically 13 A total of 293 villages applied to the BRIGHT programs; we collected data from all of them. 14 The plan was to conduct a longitudinal survey in 2012 of the households who participated in the 2008 follow-up survey. However, the data collection firm had limited success in tracking these households during the pilot and we decided to conduct a cross-sectional survey in both 2012 and 2015. Not conducting longitudinal surveys prevented us from estimating the changes in outcomes among individuals over time but allowed us to estimate the difference in outcomes between villages selected for the program and those that were not. 11
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
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Page 39 and 40: II. EVALUATION DESIGN AND DATA COLL
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Page 55 and 56: IV. FINDINGS In this chapter, we pr
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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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