(i.e., six years after our last contact with study participants). As shown in Table 2, there are no systematic differences in attrition by treatment group. Column (5) of the Table 2’s “merit scholarship” and “poverty scholarship” panels presents the differencein-difference among attritors and non-attritors, across respondents in the treatment and control groups (computed by OLS regression and including stratification fixed Our analysis combines data from five main sources. First, we collect outcome data through in-person interviews at the respondents’ residence, using handheld tablets. Second, to construct a variable reflecting intention-to-treat, we use the official government declaration (“Prakas”) of scholarship recipients. Third, we match each respondent to baseline data—application forms and baseline tests—as collected in December 2008 and January 2009. We can thus control for baseline test scores, and for students’ initial household characteristics. Fourth, we construct a vector of control variables through administrative data on baseline school characteristics, as provided by the country’s Educational Management Information System (EMIS).<sup>16</sup> Fifth, we take advantage of the fact that Cambodia’s 2008 census was conducted just before the scholarship program started. Using geographic coordinates, we match each school to its closest village and include this village’s demographic characteristics as additional controls.<sup>17</sup>

> 16 We include a binary indicator of whether a school had access to drinking water, a binary indicator of whether the school had a toilet facility, the number of primary school classrooms, the number of newly enrolled fourth-graders, the number of teaching staff, and the school’s income.

> 17Village-level data as published by the Cambodian National Institute of Statistics at the Ministry of Planning (2010). We control for the share of villagers who are literate in Khmer, the share

effects). Only two out of 16 indicators in the merit subsample and only three indicators in the poverty subsample show a statistically significant or marginally significant difference-in-differences; this result is not surprising given multiple comparisons. We also test for the individual coefficients being jointly equal to zero, using seemingly unrelated estimation (SUR); the resulting Chi-square statistics (and corresponding p-values) suggests that we should not reject that the two sub-samples are balanced. **5 Data and Measurement** Our analysis combines data from five main sources. First, we collect outcome data through in-person interviews at the respondents’ residence, using handheld tablets. Second, to construct a variable reflecting intention-to-treat, we use the official government declaration (“Prakas”) of scholarship recipients. Third, we match each respondent to baseline data—application forms and baseline tests—as collected in December 2008 and January 2009. We can thus control for baseline test scores, and for students’ initial household characteristics. Fourth, we construct a vector of control variables through administrative data on baseline school characteristics, as provided by the country’s Educational Management Information System (EMIS).<sup>16</sup> Fifth, we take advantage of the fact that Cambodia’s 2008 census was conducted just before the scholarship program started. Using geographic coordinates, we match each school to its closest village and include this village’s demographic characteristics as additional controls.<sup>17</sup> 13