school year when the baseline took place), so that we only have 18,796 students who reported their age in August 2010.

We drop any students who are not uniquely identified after matching by name and age, yielding 15,940 students for whom the algorithm found a CPF in the SRF data. Of these, 3,657 had reported a CPF during the short-run impact evaluation. Comparing these CPFs with the ones found by the algorithm shows that they are almost always the same (only 2.68 percent are different), suggesting that the algorithm does an accurate job of identifying CPFs.<sup>5</sup>

# **_3.c. Attrition and Balance of Baseline Characteristics_**

The percentage of students in the starting sample for whom the algorithm found a CPF in the SRF data is 44.27 percent in the treatment group and 46.02 percent in the control group. The difference in the percentage of students with a CPF across the treatment and control group is thus small, although it is statistically significant at the 10 percent level.

We follow Bruhn et al. (2016) in examining the balance of student and school characteristics across the treatment and control groups, using the baseline data from the short-term impact evaluation (Table 1). As in Bruhn et al. (2016), we find that the percentage of female students is statistically significantly higher in the treatment group than in the control group (56 percent vs 54 percent) and hence we control for gender in our regressions. The only other variable that shows a statistically significant difference across the treatment and control groups is the one indicating that the student “Receives income,” but this difference is only significant at the 10 percent level.

In Table A1, we check if the identified sample shows similar short-run effects of the intervention to the ones in the original sample. The effect of treatment on financial proficiency scores is positive and statistically significant in both samples. The point estimates are slightly higher in the longterm impact evaluation sample, but just like in the short-term sample they decrease moderately between follow-up 1 and 2 and as more controls are included in the regressions.

# **_3.d. Data Sources_**

To capture the effects of the financial education program on students’ financial life and employment outcomes, we combine four individually identified<sup>6</sup> administrative data sets housed at the BCB. This section briefly describes the information available in each of these data sets. A caveat is that most of the administrative data cover only formal financial and employment

> 5 For students with more than one age-compatible homonym, we could further try to identify the correct CPF in the SRF data using geographic information. However, the SFR contains only the most recent location and not location of birth, so that using this information could lead to false matches if students have moved. The location in the SRF registry is updated when individuals file their tax reports or when the individual requests an update. Imposing that the student location from the short-term impact evaluation matches the one in the SFR gives 2,130 additional unique matches. Out of these, 440 had reported a CPF in the short-term impact evaluation and 6.14 percent of these CPFs are not the same as those identified by the algorithm. Given that this mismatch rate is higher than in the sample that does not use location to identify matches, we use only the 15,940 students uniquely identified through name and age matching in the analysis.

> 6 All individually identified information was handled exclusively by the BCB staff.

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