employment opportunities, and ethnicity. Many of these factors cannot be changed with public policies-at least not in the short run. But educational attainment is also likely to be a function of the number and quality of teachers, learning material, and classrooms which are available in a community. Poor districts and poor households may therefore need additional resources, including resources spent on school facilities, to catch up with their better-off counterparts. In this section, we use nonparametric regressions to assess the extent to which FONCODES has reached poor districts and poor households.<sup>7</sup> (i) Geographic targeting: Has FONCODES effectively reached poor districts? We consider three aspects of the geographic targeting of FONCODES investments in education: changes in targeting over time, the sectoral composition of FONCODES expenditures, and a comparison of district-level expenditures made by a number of programs in education, including FONCODES. In each case, the analysis is based on actual, rather than allocated expenditures 8 Breaking down FONCODES expenditures on education by year shows how FONCODES' geographic targeting has evolved over time. Similarly, breaking down FONCODES expenditures by sector (education, health, water and sanitation, etc.) allows us to describe the relationship between average district welfare and project choice. Finally, comparing district-level expenditures made by FONCODES, INFES, and the feeding program PRONAA (which is responsible for a large school breakfast program) places FONCODES' geographic targeting in the context of other government programs in education.

Figure 5.3 presents a nonparametric univariate density estimate of the population living in districts with different mean per capita incomes. Figure 5.3 shows that most Peruvians live in districts with a mean per capita income of between 150 and 500 soles, although there are some districts which are considerably better off?

The main results of the analysis on geographic targeting are summarized in Figures 5.4 through 5.6. Figure 5.4 presents nonparametric regressions of the predicted per capita FONCODES expenditures in a district given its mean per capita income in four "typical" years: 1992, 1994, 1996, and 1998. The regression lines show that FONCODES expenditures on education were highest in 1994, and that poorer districts generally received more in per capita

> 7 All nonparametric regressions are Fan regressions, with a quartic kernel (see Fan, 1992, and Deaton, 1997).

> a There are (at least) two reasons why allocated and actual expenditures are likely to differ in practice. First, allocations are an exact figure, while FONCODES projects are "lumpy" (for example, one classroom), so that it would be almost impossible for actual expenditures to correspond to allocations in any given district. Second, because of the demand-drive **n** ature of a program like FONCODES, some districts may be proactive and prepare a large number of fundable projects, while others may not prepare enough projects of an acceptable technical quality to meet their allocations.

> 9 These figures are taken directly from a INEI publication (lNEI, 1996), and are therefore in _1995_ soles.

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