Ten-Year Impacts of Burkina Faso’s BRIGHT Program

APPENDIX C
MATHEMATICA POLICY RESEARCH
Table C.6. Explanation of impacts of employment and marriage
Females: Employment Males: Employment Females: Marriage
(1) (2) (3) (4) (5) (6)
Selected for BRIGHT -0.06*** -0.01 -0.06*** -0.02 -0.06*** -0.04***
(0.02) (0.02) (0.03) (0.03) (0.01) (0.01)
Relative score -0.04 -0.02 -0.04 0.00 0.07*** 0.08***
(0.04) (0.04) (0.08) (0.06) (0.03) (0.02)
Relative score^2 0.00 0.00 0.00 -0.01 0.00 0.00
(0.01) (0.01) (0.02) (0.02) (0.01) (0.01)
Currently enrolled -0.49*** -0.677*** -0.19***
(0.02) (0.01) (0.01)
Constant -0.38** -0.09 0.557*** 0.72*** -0.79*** -0.66***
(0.15) (0.13) (0.31) (0.02) (0.11) (0.10)
Observations 6,906 6,861 7,281 7,250 6,927 6,882
R-squared 0.230 0.373 0.005 0.367 0.524 0.548
Demographic controls Yes Yes Yes Yes Yes Yes
Department fixed effects Yes Yes Yes Yes Yes Yes
Note:
This table presents estimates of the estimated discontinuity in the relationship between current employment or current
marriage and the child's village being selected for the BRIGHT program. Columns 1, 3, and 5 show estimates of the
model using the indicated specification for equation (A.1). Columns 2, 4, and 6 show estimates of the model using the
indicated specification for equation (A.1) and including current enrollment as a control in the regression. Relative score is
measured in units of 1,000 points because of the small magnitude of the coefficients.
**/***Coefficient statistically significant at the 5%/1% significance level.
Table C.7 includes the estimated differences in reasons parents provided for why their child
is not enrolled in school, separated by gender and age. The results show that despite the focus of
the program on improving enrollment for girls, the importance of access to schools in BRIGHT
villages is shared by parents of both girls and boys. The increase in access was also consistently
valued by parents of children who are primary and secondary school age (6–12 years old and 13–
19 years old, respectively), which suggests that access continued to play an important role in
non-enrollment beyond primary school.
Table C.7. Heterogeneity in probability that the indicated reason is provided
as a reason for not enrolling child in school
Dependent variables
Girls:
Estimated
differences
Boys:
Estimated
differences
Ages 6–12
estimated
differences
Ages 13–19
estimated
differences
Access (no school or school too far away) -7.4 pp*** -10.0 pp*** -8.2 pp*** -10.0 pp***
School fees -0.1 pp 0.2 pp 0.0 pp 0.8 pp
Child too young 0.0 pp 0.0 pp 0.0 pp 0.0 pp
Household work -0.2 pp 0.2 pp 0.1 pp -0.2 pp
Child too old 0.0 pp -0.1 pp* 0.0 pp -1.8 pp*
Other -0.2 pp*** 0.0 pp 0.0 pp -1.5 pp***
Sources: Mathematica household survey (2015) and Mathematica school survey (2015).
Notes: Sample size varies between 10,095 and 17,810.
pp = percentage points
*/***Coefficient statistically significant at the 10%/1% significance level.
C.12