# 1. **Introduction**

Lack of information is a serious constraint<sup>to targeting social programs effectively,</sup> especially in Less Developed Countries (LDCs)<sup>(Besley 1993; Ravallion 1993). Targeting social</sup> programs involves making distinctions between<sup>"deserving" (poor) and "undeserving" (non-poor)</sup> applicants. But this is no simple matter in countries<sup>where household characteristics such as</sup> income are rarely known. In such circumstances, policy-makers<sup>intent on targeting are forced to</sup> choose among imperfect solutions. They can rely<sup>on observable household characteristics, such</sup> as land ownership, the ratio of working age-adults to<sup>dependents, or ownership of durable goods</sup> that seem likely to separate poor from non-poor households.<sup>They can "self-target" programs by</sup> designing them so that they appeal mainly<sup>to the poor--perhaps by offering employment at below-</sup> market wages, or subsidizing foodstuffs consumed<sup>primarily by the poor. Or they can use</sup> "geographic targeting" to direct resources to<sup>areas in which, on average, poverty appears to be</sup> greatest (Akerlof 1978; Besley and Kanbur 1990;<sup>Grosh 1992).</sup> Geographic targeting is appealing because<sup>it is comparatively simple to administer.</sup> Different parts of a country--regions, provinces, districts,<sup>even city blocks--are ranked by some</sup> measure of deprivation. This measure could be income-based<sup>poverty or, more commonly, an</sup> indicator of health, educational or nutritional<sup>status, or access to basic services, such as electricity</sup> or running water. Resources are then allocated in<sup>inverse proportion to average welfare, so that</sup> poor regions receive higher per capita transfers than<sup>rich ones. Alternatively, rich areas can be</sup> excluded from the program altogether. The simplicity<sup>of geographic targeting is an important</sup> advantage when lack of information or administrative<sup>capacity is a serious concern.</sup> This paper compares a number of geographic<sup>targeting indicators that have been discussed</sup> by policy-makers in Peru. These include the infant<sup>mortality rate, which is used to target the</sup> Municipal Compensation Fund, the main block<sup>grant from the central government to local</sup> governments in Peru; a composite "poverty" index<sup>developed by the Peruvian Social Fund</sup> (FONCODES), which FONCODES uses to target its<sup>projects; and an estimate of "imputed"</sup> poverty which combines census and survey data in<sup>an attempt to approximate money-based</sup> measures of welfare.

To test the potential impact on poverty of targeting<sup>with alternative indicators, I conduct a</sup> series of simulations which combine information<sup>on household expenditures from the 1994 and</sup> 1997 Peru Living Standards Measurement<sup>Surveys (LSMS) with district-level averages of various</sup> welfare measures. I use the results of the simulations<sup>to compare leakage rates, trace out</sup> concentration curves, estimate the impact on various<sup>poverty measures of the Foster-Greer-</sup> Thorbecke (FGT) family, and graph non-parametric<sup>(kernel) density estimates of the log of per</sup> capita expenditures (PCE) when transfers are made<sup>on the basis of alternative indicators.</sup> 2