The first three columns of Table 5 present the intent-to-treat estimates of the impact of the program on total school enrollment, as well as enrollment by grade and by gender. We transformed enrollment data using the formula Loge(Enrrolment+1). We present the regression with a full set of covariates, which include schools characteristics at baseline as well as regional fixed effects. The results are stable to the inclusion of these baseline controls. Each column reports the results corresponding to information collected at Checks 1, 2 and 3.

We observe large and significant enrollment effects for lower grades and small and always insignificant impacts for higher grades that were not part of the program. At all Checks, we find an impact between 0.285 and 0.32 for grade 1, which translates to increments of between 33% and 38% in enrollment. At Check 3, in the second year of implementation, we observe effects in enrollment at both grades 1 and 2, of the order of 34% and 58% respectively. In the bottom panel of Table 5 we show that the increase in enrollment is roughly proportionate across genders. If anything, female beneficiaries account for slightly more than half of the students at checks 1 and 2. The last three columns of Table 5 show the IV estimates, which roughly scale up by a factor of 3 the ITT estimates. The same pattern as the ITT estimation is present in the IV model. The most conservative IV estimate (Check 3, point estimate 0.737) suggests an increment that doubles the size of the schools induced to participate in the program by the lottery.

Table 6 presents the impact of the program on test scores from NAPE in 2011 and 2012. We include both years of the test given that tested cohorts are variously exposed to the PPP program, with the 2011 cohort potentially exposed for 6 months while the 2012 cohort is exposed for 18 months. We report both on the overall test scores but also on the composition of students, a potential mechanism underlying the observed impacts that we discuss below. Panel A presents student characteristics reported on the day of the test. We tested if there are differences in these characteristics between treatment and control schools. Panel B presents test score results from the three NAPE tests and the independent Math test. Column (1) presents the mean and standard deviation for the control group. Column (2) presents the results of the regression with no controls. Column (3) presents the results of the regression with regional fixed effects, and baseline student characteristics (average at the school level) and school level characteristics. Column (4) presents the IV results, with the full set of controls.

Panel A shows that, in terms of characteristics of the individuals taking the exam, there are differences in age between treatment and control. However, once the full set of baseline controls and fixed effects are included, there are no differences in student characteristics. Nevertheless, results for the test scores are relatively stable to the inclusion of controls. In general, the test score impacts are positive. The ITT impact for the math independent test is 0.154 sd, and is marginally significant. The IV estimation shows, as expected, larger and highly significant positive effects, in the range of 0.3 and 0.4 sd.

NAPE 2012 results are very similar. Our preferred OLS specification, with full baseline controls and region fixed effects, shows positive effects on the test, but only the math independent test is statistically significant, with a point estimate of 0.127 sd. Again, these effects are quite stable to the inclusion of baseline controls. The results from the IV estimations range from 0.28 to 0.4 sd and are significant for all tests. The consistent and stable results for the independent math test are indicative of real learning impacts under the program, as opposed to impacts from teaching to the test etc. It is important to note that full controlled regressions had lower number of observations because of missing information for contemporaneous characteristics. In order to rule out biases due to the sample composition, we restricted all regressions to the sample of the full controlled equation and found similar results (not shown in the table).

- b. Mechanisms

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