Data collection for the baseline and three-year follow-up occurred from December 2008 to January 2009, and from May to September 2011, respectively. Data collection for our latest round of follow-up took from December 2016 to May 2017. We guaranteed data-quality by following standard monitoring procedures, as described by Glennerster (2017). First, during the first week of field work, we conducted 30% We also collected data on four measures of cognitive skills. First, we administered a computer-adaptive math-test, in which respondents answered ten questions from a larger pool of 23 items.<sup>18</sup> We used a three-parameter logistic (3PL) item response theory (IRT) model with a single guessing parameter (Birnbaum 1968; Samejima 1969) to analyze responses to math tests from an evaluation of a similar scholarship program in Cambodia that was targeted to secondary school students (Filmer and Schady 2008). Participants in this assessment had been tested in two rounds, with overlapping items, and we follow the common (Stocking and Lord 1983) methodology for IRT-based scale equating.<sup>19</sup> Our test begins with the item of median difficulty.

of villagers with no schooling, the percentage of villagers engaged in crop or animal farming, the village’s population size, and a continuous measure of villagers’ household assets.

> 18To our best knowledge, this assessment constitutes the first computer-adaptive ability test as conducted during a household survey, in a developing country.

> 19We removed one item with low discrimination.

of re-surveys (“back-checks”, usually within three days) and then reduced this number, for an overall back-check rate of 15.7%. Second, we spot-checked approximately 20% of interviews, provided immediate feedback, and offered repeat-trainings to enumerators. These spot-checks were not only conducted by field supervisors but also through additional, independent field-monitoring. Third, we ran daily analytics on newly collected data to spot irregularities, and to identify training needs. Finally, we employed 15% of staff as dedicated quality-control officers, such that steps to improve data quality could be taken immediately, as part of the regular data flow. The following discusses our newly collected outcome measures in greater detail. As education outcomes, we measure educational attainment (highest grade completed), formal and informal training that lasted for at least one week (a binary variable), and whether the respondent received any formal education since the early three-year follow-up (a binary variable). We also collected data on four measures of cognitive skills. First, we administered a computer-adaptive math-test, in which respondents answered ten questions from a larger pool of 23 items.<sup>18</sup> We used a three-parameter logistic (3PL) item response theory (IRT) model with a single guessing parameter (Birnbaum 1968; Samejima 1969) to analyze responses to math tests from an evaluation of a similar scholarship program in Cambodia that was targeted to secondary school students (Filmer and Schady 2008). Participants in this assessment had been tested in two rounds, with overlapping items, and we follow the common (Stocking and Lord 1983) methodology for IRT-based scale equating.<sup>19</sup> Our test begins with the item of median difficulty.

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