a statistically significant overall effect for rural districts; see bottom of table. The absence of test score declines for boys is arguably even more striking than for girls, given the more pronounced pattern of negative selection according to age found for boys in table 5.<sup>20</sup> A potential explanation for our results is that students may have reduced their effort on other exam subjects in order to focus on the required English and math sections. However, we find no decrease in the total number of exam subjects taken by students in response to the program, for either girls or boys. Nor do we find changes in the share of “easy” subjects taken in response to the program, where “easy” corresponds to a subject with a passing rate above the median in the pretreatment data. These results, reported in table S5 of the supplemental appendix, reveal no evidence of decreased student effort.

# _Spillover Effects on Boys_

Among the results presented thus far, the evidence of increased test taking for boys, with no corresponding decrease in achievement, in response to a program that targeted girls is perhaps the most intriguing. What explains this spillover effect? One possibility is that the scholarship alters constraints on human capital investment within a household. The opportunity to send a girl to school without incurring fees frees resources to send boys to school. Alternately, parental preferences for equivalent treatment of children, or a desire for boys to accompany their sisters, could induce a similar response.

In this subsection, we look for evidence of such intra-household spillovers in response to the program. We distinguish whether a secondary school-aged boy lives with younger or older girls, as we expect that the causes of spillovers might differ in the two cases. Spillovers from older girls to younger boys are more likely to reflect financial considerations, as liquidity-constrained households with older girls would gain relief earlier. We expect spillovers for nonfinancial reasons to be stronger when girls are younger, as parents would prefer older boys to accompany younger girls to school.

Unfortunately, the exam records do not identify which students belong to the same household, preventing us from testing these hypotheses using the same outcomes already considered. However, the Gambian Integrated Household Survey data allow us to explore the enrollment response using a richer set of individual and household characteristics. We use the 1992, 1998, 2003, and 2010 waves of the survey, limiting the sample to secondary school-aged boys (13–18) in regions 2–6. The data record all household members, allowing us to observe if boys live in the same household as scholarshipeligible girls (though we cannot distinguish siblings from other types of connections).

We run a series of linear probability models in which the outcome is an indicator for enrollment. Note that this outcome differs from others previously considered, because the sample is all boys in the age group, whereas previously our sample was Grade 12 boys taking the exit exam. In table 7, column (1), we find no statistically significant effect of the program on boys’ enrollment. The finding is consistent with Gajigo (2014), who found no increase in the boys’ enrollment rate in response to the program.

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