heterogeneity of program impacts using the data at hand. Second we rely on quantitative and qualitative data from a sub-sample of head teachers about recruitment of new students.

We explore the likely nature of the selection observed by testing whether the observed expansion in enrollment or student characteristics varies by attributes associated with a lower cost of school-driven selection. These attributes include (i) number of secondary schools within 10 km (number of competing schools in the market of the school) and (ii) age of the school in years. We argue that these attributes are likely to reduce the cost of selection either through proximity to higher quality students, higher demand for strong reputation signals and well established and arguably better schools (see Melitz (2002)).<sup>16</sup> We run the standard model:<sup>17</sup> where ��,� captures the heterogeneity attribution (competition or age); and ��,� represents enrollment (by grade) and, for check number 3, school average test results in the PLE. Table 11 shows the results of this exploration. The table only shows the coefficients �� and ��. The main effect of the attribute and other controls (region fixed effects) are not shown. Each panel corresponds to each survey round. In panel C, we include a measure of student quality- the school average score on the national primary leaving examination – as the dependent variable.

The presence of nearby secondary schools and age of the schools seems to negatively affect changes in student enrollment in treatment schools. All coefficients on the interaction between treatment and the attribution are negative, and all but one estimate is not statistically significant. In short, the higher the number of secondary schools in the vicinity, the lower the number of students that treatment schools can attract. Likewise, the older the treatment school, the lower the growth in student enrollment. These results also hold for the secondary school entrance exam scores measured at Check 3.

Next, we use a survey of 30 head teachers conducted a year after control schools enter the program. The survey elicited responses both on the composition of applicant pools and the degree of selectivity. The data show firstly that more than two-thirds of applications are publicly funded students. Second, the data show that both in program and non-program schools, the admission rate is very high at 93%. Finally, even though these responses are the most vulnerable to measurement bias, the distribution of quality between admitted students and applicants is very similar, suggesting that schools are not very selective at all.

Finally we use data from interviews with 11 head teachers and focus groups with 20 parents conducted by one of the authors in December 2014. In response to the question on whether they are selective in admission, 10 of the 11 schools indicated that they admit just about everyone who applies and qualifies for the PPP.

The preponderance of evidence suggests that the observed selection is driven by households and not schools. Since this program is effectively an expansion of school choice for budget constrained households, desirable attributes such as proximity to the school potentially underpin the results we obtain

> 16 We also tested heterogeneity by being founded by a private individual (as opposed to the local public goods that community founded schools may be). The results were very noisy and highly sensitive to the inclusion of controls, presumably due to the fact that the majority of the schools were founded by a private individual. As such, we report only heterogeneity by competition and age.

> 17 We run the models with and without controls; results are stable to the inclusion of baseline covariants.

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