Source: Own calculations based on PARE's database.

Table 6 shows a simple ordinary linear square model that captures only about 6% of the variance in the difference of scores (between 4th and 6th grades) amongst students in rural schools and 12% amongst students in native schools. No doubt this reflects an inadequate specification of the model be it in its functional form or inclusion of relevant explanatory factors. To the extent that the measured test scores fail to capture the true level of performance in the sample, much of the influence of variables such as parental background, the quality of teaching, etc., is lost in the model. The point to note, however, is that, even so, the explanatory variables behave as expected.<sup>4</sup> More importantly, the coefficient of the experimental variable is large and significant. The PARE program has a large positive impact on student achievement in this counterfactual experiment by all means in the scenarios and specifications.

The impact is larger for the native schools, a result that is consistent with the orientation of the program. As reported in Table 6, the marginal contribution of each explanatory variable is measured in terms of standard deviations of the dependent variable; i.e., of the percentage change in performance between 4th and 6th grades. This is in order to control for possible demand driven effects and hence simplify the analysis. For the average student at native schools, attendance at a school fully served by the program would, on average, increase the percentage change by 25%. The comparable percentage change for students attending rural schools is half as large. The variables- of school ".supply" (the performance of teachers, principals and supervisors) are partly an _outcome_ of the program. Thus, the program, at its _maximum_ effect estimated with the results of Table 6, could increase the performance of the average student by one-half of the standard deviation of the percentage change in test scores for the respective sub-ample.

It should be noted that the variables measuring the characteristics of students, parents, school personnel and facilities are all numerical indices constructed by C.E.E analysts. Some indices aggregate answers to as many as a dozen questions in the original survey. The model in Table 6 is a simple, parsimonious representation. In particular, it could be argued that if the characteristics of the demand (family and community background, parental attitude towards and involvement in schooling, academic history, self-esteem, etc.) were adequately measured, the additional effect of the PARE program would be smaller, even insignificant. Alternatively, if the characteristics of the supply 4 Three observations may be pertinent. First, for students attending native schools it seems that selfesteem, measured at 5th grade and residence in a community with greater access to public services is _negatively_ correlated with performance. One plausible explanation is due to the conflictual character of native education: Students that are positively self-selected may have a greater resentment in attending special schools. Second, and for the same group, while the performance of teachers does not seem to alter significantly the perfornance of students, the performance of principals and supervisors does. This result may be due to the generally poor quality of teaching in native schools. Finally, it is puzzling to note that, in the nrral sub-sample, parental involvement _diminishes_ students' performance. One possible reason for this is the possibility that parental involvement increases as the quality of the school diminishes. Parents act only when the problems are large and apparent.

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