spending on the change (rather than the level) of education outcomes. Table 6.3 presents results from "difference-in-difference" regressions, corresponding to equation (4) above.<sup>20</sup> We report estimates of y, the coefficient on FONCODES expenditures, and 6, the coefficient on FONCODES expenditure interacted with a dummy for 1997. The coefficient v-the "targeting" effect-measures the relationship between the outcome and the amount of FONCODES funding in 1994. The coefficient o-the "impact" effect-measures the change in the outcome between 1994 and 1997 associated with a one-sol increase in FONCODES spending. Results are shown for models with no controls; with a set of controls for household and district characteristics; and instrumental variables estimates with a set of controls 1 FONCODES allocations, as given bv the allocation rule, are used as an instrument for expenditures. An identifying assumption of tl is specification is that the district-level index of unmet basic needs which forms the basis of FONCODES allocations does not have a direct effect on outcomes other than through its effect on FONCODES expeiiditures once we have controlled for other district characteristics such as population, the fraction of children aged 6-11 who were attending school in 1993, and income.

The results provide evidence of a significant and sizeable impact of FONCODES expenditures on school attendance for _all_ younger children in a household (top panel). Households in districts which received more FONCODES funding were initially less likely to send all their primary-aged children to school (the coefficient on y is generally negative), but experienced greater increases in the likelihood that all children attended school than did households in districts which received less funding (the coefficient on 6 is positive and significant). The size Df the estimated effect on school attendance is largest in the instrumeiital variables specifications: a one-standard deviation (5.2 sol) increase in per capita expendituri.s made by FONCODES on educational infrastructure is associated with an increase in the probability that all children in a household attend school of 0.09. Considering that median **]:,er** capita consumption in Peru was about 564 soles in 1997, this is a very large effect indeed.<sup>22</sup> Tile results for individual school attendance, in the bottom panel, are smaller and less precisely

> 20 We also calculated results based on specifications with district and household-level fixed effects. T he estimates based on district-level fixed effects specifications are very similar to the results from the differences-in-differences specifications, although they tend to be more noisy. The results which incLAmd household-level fixed efficts are all, essentially, equal to zero. This may not be surprising given the very small sample sizes: for example, there are only 376 households which were in the household panel an:[ L ad primary-aged children in both 1994 and 1997.

21 Note that the sum of the impact and targeting effects in the equations with no controls in Table 6.3 .s (and must be) identical to the corresponding estimates of the impact effect from the "naive" estimates in Table 6.2. This highlights that the naive estimates do not properly account for the fact that districts whici received more FONCODE]S funding had poorer initial educational outcomes.

> 22 Here, too, both household consumption and FONCODES expenditures are reported inl992 soles.

19