programs in Peru, spent 2.2 billion soles on programs which could be targeted in 1995.10 For the sake of parsimony, I present graphs only for the poverty gap measure. Results for the headcount index and the P2 measure, which are available from the author upon request, are very similar." Once again, because targeting outcomes with different indicators are very similar to each other but are clearly superior to the no targeting regime, I graph the _differences_ in poverty gaps. One way to understand the graphs is therefore as a double-difference: the first difference is the change in the poverty gap when a given budget is transferred-separately, for the no targeting scenario, and for geographic targeting using the FONCODES index, the infant mortality rate, and the measure of imputed poverty. The second difference is the difference between the change in the poverty gap under the no targeting scenario, on the one hand, and the changes in the poverty gap when there is targeting by a given indicator. The value of the y-axis at any given budget and for any given curve is therefore the additional increase in the poverty gap which could be attained from switching from no targeting to targeting with the indicator in question.

Impact on povet: Like the concentration curve analysis, Figures 2.1 and 2.2 show that targeting with any one of the indicators in question is clearly preferable to no geographic targeting: all of the curves are above zero throughout. Figures 2.1 and 2.2 also show that none of the indicators clearly outperforms the others, although transfers based on imputed poverty appear to result in smaller decreases in poverty than the corresponding transfers made on the basis of infant mortality or the FONCODES index.<sup>12</sup> Budget savings: Table 3 presents the same information from a different angle: it considers the impact on the headcount index, poverty gap, and P2 measures of spending the 2.2 billion sol reference budget without geographic targeting, and estimates the budget that would be necessary to achieve this same reduction in poverty when geographic targeting is conducted by the infant mortality rate, imputed poverty, or FONCODES index, respectively. The results show that the '° These expenditures included programs in housing (Banco de Materiales, ENACE), potable water, sanitation, and electricity (UTE-FONAVI), educational infrastructure (INFES), food and nutrition (PRONAA), social services (INABIF), and numerous multi-sectoral programs (FONCODES, COOPOP, INADE) (World Bank 1996).

**'** The curves for the headcount index tend to be much more choppy. The reason for this is that transfers will not reduce the headcount index unless at least one person in the country (or, in this case, one person in the survey) is bumped over the poverty line. This need not happen, for example, if resources are spent on those who are well below the poverty line. An increase in expenditures on the poor will always reduce poverty, however, if poverty is measured by the poverty gap or P2 measures.

12 Once again, bootstrapped standard errors suggest that the differences between, on the one hand, the no targeting case and, on the other hand, the measures of imputed poverty, infant mortality, and FONCODES index are statistically significant at the 1% level or better. The differences between the infant mortality rate, FONCODES index and imputed poverty are not significant (estimates available from the author upon request).

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