interpret scores relative to the national norm. It also explains why mean _z_ -scores tend to be negative in our estimation sample.

Although our primary interest is the population of schools and students eligible for the program, nonrandom sorting of students into public and private schools in response to the scholarship may bias results. Such sorting is also an interesting potential outcome to investigate. In 2004, when program rollout was complete, only two of 43 districts had both a public and private high school (grades 10–12). By 2012, the last year for which we have data, this figure had grown to nine. We will therefore assess whether the growth in private school enrollment was related to the scholarship program.

However, all private schools are located in the urban districts of region 2, near the capital.<sup>9</sup> Students in most areas are therefore constrained to attend their local public school. We later check robustness of results to various definitions of the estimation sample, such as excluding region 2 or including private schools.

We also use data from the 1998 wave of the Integrated Household Survey (IHS) to explore heterogeneity in results by baseline characteristics. This survey, which is conducted by the Gambia Bureau of Statistics, is nationally representative and collects information on assets, demographics, and socioeconomic characteristics. In the 1998 survey, slightly over 1,900 households were covered including approximately 4,500 school-aged children. A third and final data set we use in the analysis is the annual school census conducted by the Ministry of Education, which spans the same years as the exam data. The Ministry reports enrollment by grade and gender for each school, and the number and gender of teachers.

Unfortunately, we are unable to link individuals across these three data sets, preventing us from connecting individual enrollment decisions or household characteristics with test results. We therefore use district and school characteristics to analyze treatment effect heterogeneity. At the district level, we construct three measures using the 1998 IHS. We define “low enrollment” districts as those that fell below the national median enrollment rate for secondary school-aged children (ages 13–18).<sup>10</sup> “Rural” districts are above the median percentage of population living in a rural area. “Wealthy” districts are above the median level of average household assets, with assets measured as the first principal component of ownership dummies for bicycle, car, refrigerator, motorcycle, sewing iron, television, radio, and VCR. At the school level, “distant” schools are located beyond the median distance from a main road. Importantly, all of these characteristics are predetermined with respect to the policy. All are also dummy variables, allowing for easy interpretation of interactions with the treatment indicator.

The nature of the scholarship program poses a potential challenge regarding data quality. Because the Ministry of Education remitted fees to schools for each girl enrolled, schools have an incentive to over-report enrollment in order to attract more resources (Sandefur and Glassman 2015).<sup>11</sup> Our reliance on exam records alleviates this concern, because the examination body WAEC is independent of the Ministry and requires separate student registration rather than automatically enrolling those on school rosters. The Ministry 9