long-run GPA (Appendix Table A4.2). This is an interesting finding in itself, pointing towards either an inconsistent use of official records in the country, true dropout behavior, migration, or a combination of these. To further look into how attrition affects our findings, we re-estimate Table 5 using a balanced sample, including only students who are observable in official records during the entire period from school year 2015/2016 to school year 2016/2017 (Appendix Table A4.3). We find that our results (especially those for Roma students) remain robust using this specification, suggesting that attrition and resulting changes in sample composition, although a real phenomenon, are unlikely to drive the materializing impacts on GPAs over time.

In sum, for Roma students, we find a consistent pattern: large impacts on socio-emotional skills are mirrored by very small impacts on GPAs immediately after the intervention, which turn into medium impacts half a year later and medium to large impacts one year after. Such a pattern of emerging impacts over time might point towards sustained behavior change and that students reap the benefits of such behavior change over time only, at least when it comes to academic achievement as reflected in school GPAs.

Which Roma students benefited the most? To answer this question exploratorily (noting that sample sizes are small at times), we dig deeper into our finding on Roma students and extend our analysis on GPAs over time by looking at heterogeneous treatment effects (Appendix 3 Table A3.1). There is some evidence that, while both male and female Roma students benefited, female students benefited more in the long-run. The difference in GPAs between male and female students, however, cannot be rejected at a conventional level of statistical significance. When it comes heterogeneous treatment effects by pre-treatment GPAs, we find that students in higher terciles clearly benefited more; for students in the upper tercile, the intervention had a positive effect on GPAs at the 1% level, increasing GPAs by about 8% SD in the short-run, 21% in the medium-run, and 46% in the long-run in the teacher-delivered treatment (effects are also significant, although lower, in the student self-learning treatment). Finally, when estimating seemingly unrelated regressions for all results on Roma students (on average, by gender, and by student achievement), we can reject the null that the coefficients of the student-teacher treatment are jointly equal to zero across models at the 1% level.<sup>27</sup>

# **5.3 Robustness**

# _Attrition_

So far, we have a different number of students in our analysis of socio-emotional skills and our analysis of GPAs, the difference arising from attrition in our survey data, mainly due to some surveys not being returned, being unreadable, or being only partly filled out. Our analysis of socio-emotional skills, therefore, includes all students with complete surveys, whereas our analysis of GPAs includes all students who are present in official, administrative records by the Ministry of Education in North Macedonia (and should have participated in the intervention according to the randomization routine). This approach is agnostic as it simply takes all observations that are available. It is also conservative, in the sense that our estimates on GPAs are again likely to be downward biased, as some, potentially non-treated, students may be allocated to either of the two treatment groups.

> 27 A more complex issue arises in case that the intervention increased motivation and effort on the side of teachers such that teachers in any of the two treatment groups (and potentially differently by treatment group) recorded GPAs more swiftly post-treatment. Graphical evidence, however, suggests that the temporal distribution of when GPAs are recorded is similar between any of the two treatment groups and the control group.

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