Ten-Year Impacts of Burkina Faso’s BRIGHT Program

APPENDIX D
MATHEMATICA POLICY RESEARCH
Our estimates of returns to schooling are comparable to other studies that have estimated
returns to schooling for Burkina Faso or countries in sub-Saharan Africa. Psacharopoulos (1994)
and Psacharopoulos and Patrinos (2004) compiled rates of returns to schooling for all countries
where estimates are available and reported a 9.6 percent rate of returns in Burkina Faso.
Kazianga (2004) reported a 9.9 percent rate of return to primary-level schooling in Burkina Faso
using the 1994 and the 1998 Burkina Faso household surveys that we also use. However, our
estimates for these two periods are higher because they are average returns across all levels of
schooling from primary to tertiary, and returns are higher at the secondary and tertiary levels. 71
Estimates of return to schooling in the literature for sub-Saharan Africa are also comparable
to our estimates. Psacharopoulos (1994) and Psacharopoulos and Patrinos (2004) reported rates
of returns of 13.4 percent and 11.7 percent, respectively, for the region. Banerjee and Duflo
(2005) updated the Psachropoulos and Patrinos (2004) data with additional studies and found
similar estimates. However, these estimates from the compilation of studies could be limited
because they use different sample coverage and methodologies. To address this issue,
Montenegro and Patrinos (2013) estimated returns to schooling using 545 comparable household
surveys from 131 countries between 1970 and 2011. They reported a 12.8 percent return to
schooling in sub-Saharan Africa, which is exactly the same estimate we have when we pool all
four rounds of surveys.
It is important, however, to use these values cautiously. As we had noted, the assumptions
needed to monetize the benefits of the BRIGHT program are strong. The estimation of the
returns to schooling requires the strong assumption that the relationship between earnings and
educational attainment is not affected by other factors that might be correlated with both. For
example, highly motivated children are likely to progress far in school. When compared to lessmotivated
children with similar socio-demographic characteristics, they are also more likely to
be productive and to earn more. The result is that what we interpret as a return to schooling could
also reflect the relationship between earnings and education due to these other confounding
factors. Unfortunately, we have no way to control for such factors in the estimates presented in
Table D.10.
3. Estimating benefits of the BRIGHT programs
Using the estimates of returns to schooling above, we use several steps to estimate monetary
benefits of the BRIGHT programs for all cohorts of children exposed to the intervention. First,
we calculate the number of years these cohorts are exposed to the intervention. For example, the
1994 cohort was 12 years old in 2006 and was exposed to the intervention for one year before
entering the labor market in 2009. Each subsequent cohort after that experienced one additional
year of exposure to the intervention, with the cohorts from 1999 to 2034 experiencing the full six
years of the intervention. The 2035 cohort experiences five years of the intervention before the
schools stop operating in 2045. Similarly, each subsequent cohort after that is exposed to one
71
In fact, Kazianga (2004) reported rates of returns to schooling of 16.5 percent and 20.6 percent for secondary and
tertiary levels, respectively.
D.20