Appendix 2 Tables A2.1 to A2.3 re-estimate our previous tables by including only those students for whom we have completed surveys and who we know for sure that they did participate in the intervention. The results are quite similar to our previous findings, and the estimates obtained from using a balanced sample are indeed often larger in size than those obtained from using an unbalanced sample, in line with a lowerbound interpretation of our previous findings. Note that, for Roma students, impacts on GPAs become twice as large but significance drops from 1% to 10% only, most likely because the sample size drops substantially and the standard error becomes much larger.

Appendix 4 Table A4.1 shows the results of an attrition test for our survey data in which we regress the availability of the different survey instruments (i.e. having a baseline or endline survey, or both) on dummies for the two treatment groups, pre-treatment GPAs, and socio-demographic characteristics. Appendix 4 Table A4.2 conducts the same attrition test for our administrative data. The results show, for the survey data, that having better GPAs is associated with a higher likelihood to have completed the different survey instruments. However, the effects are rather small: increasing GPAs by 1% SD increases the likelihood to have complete either or both survey instruments by between 1% and 2%. There are notable differences in survey attrition between our first treatment group (student self-learning) and our second treatment group (teacher delivery), as well as between the first and the control group. In fact, the first group is less likely to have returned the endline survey by about 15%. This is most likely related to a printing error: the print work of the surveys for all sixth and seventh-grade students in the country was shared among three survey firms, and one of these firms printed the endline survey twice (instead of both baseline and endline survey) for about one third of the first group. This is also the reason why a large share of socio-demographic controls is missing (cf. Table 1), as these were captured in the baseline survey. While this is likely to introduce noise only due to the reduced sample size, impacts on the first group should nevertheless be interpreted with this point in mind.

With regards to the attrition test for the GPAs, things look different. Since schooling is mandatory, there should be medium-run and long-run GPAs available for each and every student. However, we can see that some students do drop out: a higher GPA reduces the likelihood to drop out by about 1%, and being in any treatment group by between 4% and 5%, respectively. This suggests that being exposed to treatment may actually _reduce_ the likelihood to drop out of formal schooling relative to the control group. As described above, when looking only at students who are observable during the entire observation period using a balanced sample, our results remain robust.

# _Observer Effects_

It could be the case that some, if not all, of the positive impacts of our intervention come about as observer (Hawthorne) effects. Teachers and students, both of whom are not blind to the experiment, may change their behavior as a result of being part of an experiment, rather than due to the actual contents being taught in the experiment. In case of students, observer effects may pertain both to self-reporting behavior (e.g. social desirability to satisfy the expectations of the experimenters) or to genuine learning behavior, motivated by feelings of being cared about and taken seriously. In the case of teachers, observer effects may pertain mostly to teachers treating students differently or to changing grading behavior. In both cases, impacts come about as artifacts of the experiment rather than genuine impacts of the contents learned.

Although we cannot exclude observer effects for either teachers or students with certainty, we argue that they are unlikely to be the main driver behind our findings. When it comes to students, note that the intervention was embedded into an existing “Life Skills” curriculum. The students were familiar with this curriculum being taught during Monday morning class hours (yet without expectation of upcoming content) and we made sure that our intervention resembled its basic structure and appeal (in fact, our local psychologist designed the “Life Skills” curriculum some years ago). Moreover, our curriculum was rather light, in the sense that it consisted only of five content sessions. Finally, baseline and endline data were collected before and after these sessions, with a timely spacing, respectively. There were never experimenters or intervention 23