Ten-Year Impacts of Burkina Faso’s BRIGHT Program

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IV: FINDINGS MATHEMATICA POLICY RESEARCH Figure IV.1. Ten-year impacts of the BRIGHT on self-reported enrollment Probability of self-reported enrollment 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 -250 -200 -150 -100 -50 0 50 100 150 200 250 Relative score C. Impact on test scores Students in villages selected for the BRIGHT program scored 0.19 standard deviation points higher than students in unselected villages (Table IV.3). We estimate this impact on a combined measure of math and French test scores, but this positive impact is consistent across the math and French sections of the exam (see Appendix C, Tables C.3 and C.4). 30 In addition to estimating impact on test scores for 6- to 22 year-olds, we estimate the effects for 6- to 17-yearolds and 6- to 12-year-olds using similar methodology. We find that for 6- to 17-year-olds, the test score effect declined from 0.29 in 2012 to 0.19 in 2015, and for 6- to 12-year-olds, the test score effect declined from 0.41 in 2008 and 0.23 in 2012 to 0.13 in 2015 (Table IV.3), all of which are statistically significant. We also restricted the current sample to children ages 13 to 19 to follow over time those who were ages 6 to 12 at the time of the 2008 survey. We found an impact of 0.31 for this group, a roughly 25 percent significant decrease from the impacts observed in 2008 (0.41) and 2012 (0.43) (Table IV.3). Thus, as was shown with the enrollment results, an impact of the BRIGHT program on test scores is still observed for children in the 10- year survey. The BRIGHT program has also continued to have a positive effect on children who have entered school since the program ended, but the magnitude of the impact is now somewhat smaller. 30 Note that this estimate includes the 16 villages whose receipt of the BRIGHT program did not follow the outcome of the assignment algorithm and does not statistically account for this noncompliance. However, because the number of such villages is so small (16 out of 291 villages), even accounting for the noncompliance yields a similar estimate of 0.23 percentage points. This estimate is based on a LATE estimator in which we estimate equation A.1 in Appendix A using an indicator variable for whether or not a village received a BRIGHT school as the dependent variable in place of the indicator variable for treatment assignment. We estimate the coefficient on the BRIGHT school indicator using treatment assignment as an instrumental variable via 2SLS. 38
IV: FINDINGS MATHEMATICA POLICY RESEARCH Table IV.3. Ten-year impacts of the BRIGHT program on test scores Selected villages Unselected villages Estimated impacts Sample size Ten-year impacts (2015 survey) Full sample (6- to 22-year-olds) 0.11 -0.09 0.19*** 30,474 Restricted sample (6- to 17-year-olds) 0.10 -0.09 0.19*** 26,348 Restricted sample (6- to 12-year-olds) 0.06 -0.06 0.13*** 18,665 Restricted sample (13- to 19-year-olds) 0.16 -0.15 0.31*** 9,585 Seven-year impacts (2012 survey) Full sample (6- to 17-year-olds) 0.13 -0.16 0.29*** 23,464 Restricted sample (6- to 12-year-olds) -0.03 -0.26 0.23*** 17,498 Restricted sample (10- to 16-year-olds) 0.59 0.16 0.43*** 12,490 Three-year impacts (2008 survey) Full sample (6- to 12-year-olds) 0.37 -0.04 0.41*** 17,970 Sources: Mathematica household survey (2015), Mathematica school survey (2015), Kazianga et al. (2016), and Kazianga et al. (2013). Notes: Test scores are measured in standard deviations of student achievement. ***Coefficient statistically significant at the 1% significance level. Figure IV.2 presents the estimated 10-year impact of BRIGHT on the total test score. The solid lines represent estimates of the relationship between the relative score and the test scores of students to the left and to the right of the cutoff point. The distance between the two solid lines at the cutoff point represents the impact of the BRIGHT program on test scores presented in Table IV.3. Figure IV.2. Ten-year impacts of the BRIGHT program on total test score Test score (normalized) -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -250 -200 -150 -100 -50 0 50 100 150 200 250 Relative score 39
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Ten-Year Impacts of Burkina Faso’
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ACKNOWLEDGEMENTS This report is the
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CONTENTS ACRONYMS .................
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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 31 and 32: I: INTRODUCTION MATHEMATICA POLICY
Page 39 and 40: II. EVALUATION DESIGN AND DATA COLL
Page 51 and 52: III. IMPLEMENTATION SUMMARY Under t
Page 53 and 54: III. IMPLEMENTATION SUMMARY MATHEMA
Page 55 and 56: IV. FINDINGS In this chapter, we pr
Page 57 and 58: IV: FINDINGS MATHEMATICA POLICY RES
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Page 73 and 74: V. COST-EFFECTIVENESS AND COST-BENE
Page 89 and 90: VI. SUMMARY OF FINDINGS AND DISCUSS
Page 95 and 96: REFERENCES Abeberese, Ama Baafra, T
Page 97 and 98: REFERENCES MATHEMATICA POLICY RESEA
Page 103 and 104: APPENDIX A STATISTICAL MODEL FOR IM
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Page 107 and 108: APPENDIX B VALIDATION OF REGRESSION
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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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