control. We reject the null hypothesis that jointly, the coefficients are equal to zero. As such, it is critical to control for baseline covariate differences in the estimation of effects below.<sup>13</sup> With respect to the household characteristics of S2 students (Panel B, Table 4), the average size of the household in the control group is 9.5; while the ratio of child to adult was over 1.5. 63% of students in the control group reported their parents being from the same district they currently live. On average, students in the control group were more likely to report that their parents were farmers than in the treatment group, with 63% of mothers and 47% of fathers, compared to 54% and 40% respectively in the treatment group. Students in the control group reported 61% of mothers and 84% of fathers as literate. The index of household services represents a simple additive composite of four variables: roof, toilet, drinking water and electricity. There was no significant difference in reports from the two groups, with an average of 2.17 on the index. Overall, two student attributes (mother and father education) were statistically different between treatment and control. We cannot reject the joint test that all these differences are equal to zero (Chi-square test of 14.033, with a probability of 0.172).

Despite the inability to reject the null hypothesis of these imbalances being equal to zero, the data show some differences in students and family characteristics between treatment and control schools at baseline. The key difference in baseline student characteristics appears to be that control schools have a higher proportion of repeating students than treatment schools – which creates an imbalance in average age of the student (and possibly gender). It is unclear the direction of the bias that these imbalances can induce for our estimators. On one hand, repeating and older students may perform better in test because either they already took the test or have learned the material before. On the other hand, these students already signal weaker academic performance. As a result, we control for these baseline characteristics in all of the analysis.

Another important test of the validity of the experiment pertains to differential attrition. Recall that, at check 3, the number of schools that we were able to visit was 94 (down from 101 schools included in the lottery). We use the school panel structure to test baseline characteristics differ from attritors and nonattritors and, more importantly, if there are differences between attritors across treatment status. Appendix 1 presents the analysis. The first three columns show means of key schools characteristics at baseline of non-attritors and attritors (at check 3) and the differences between treatment and control schools. Only one difference is statistically significant. Columns (4)-(6) present, for non-attritors, the differences between treatment and control status. Again, only one attribute difference is statistically significant. Finally, Columns (7) and (8) present the coefficient results from models that run each baseline characteristic against attrition status, treatment status and the interaction. We show only the coefficient of the attrition status and the interactions. None of the interactions are significant. This analysis suggests that it is unlikely that attrition at the school level can explain the results.

In summary, we see this analysis as a validation of the experiment design, despite lack of balance at the student level. The variables at the school level (the level of randomization) and teacher level seem similar across treatment and control group. In any case, we control all the specifications for a rich set of variables at the school and at the student level. Furthermore, attrition is balanced across treatment status.

# **4. Results**

- a. Enrollment and Test Schools

> 13 As a further test, we did the same analysis including only information collected in December 2010. This estimation has about 30% of the sample, and as such, it is noisier that estimations using all S2 students, both in December 2010 and April 2011. However, the results are very similar. The results are available upon request.

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