receive large per capita transfers, offsetting reductions in private intra-household transfers or employment, and the impact of taxes needed to finance poverty alleviation programs.

# _The data set_

For all of the estimations below, I combine information from two sources: district-level averages of the infant mortality rate, the FONCODES index, and the measure of imputed poverty, and household-level data on expenditures. District-level averages are available from INEI, and household-level data can be estimated from the 1994 and 1997 LSMS, both of which were executed by the Peruvian think-tank Cuanto.

District-level data can be used to estimate the proportion of total funds that would be allocated to every district under alternative targeting regimes. Further dividing this fraction by the total population of the district in question allows us to calculate the proportion of funds that would be allocated to every individual. Finally, multiplying this proportion by the total budget available for poverty alleviation programs, we can estimate per capita transfers.

The LSMS can be used to estimate the expenditures of the households in the sample (3,558 for 1994, and 3,840 for 1997) and, dividing total household expenditures by household size, for household members (18,362 for 1994 and 19,562 for 1997). These estimates can be combined with information on poverty lines to calculate the headcount index, poverty gap, and P2 measure at a national level before any transfers take place.8 The 1994 and 1997 Peru LSMS drew households from 364 and 397 clusters, respectively, and the accompanying literature lists the districts from which each one of these clusters was drawn. Observations in the LSMS can be coded manually with district identifiers which match those used by INEI, and district-level and household-level data can then be merged. Having done this, we keep only those observations for which there are matching codes for place of residence in both data sets--in effect, discarding the district-level information for all but the 199 and 238 districts which were sampled in the 1994 and 1997 LSMS, respectively.<sup>9</sup> The new, composite s Note that regional price deflators available from Cuanto and INEI are used throughou **t** he paper to deflate _both_ household expenditures and simulated transfers.

9 In theory, the first step in the FGT approach implemented in the simulations in this paper would divide a given budget amongst the 1812 districts in Peru. How much gets allocated to each district would then depend on the targeting indicator and the allocation formula in question. But the fact that households in the 1994 and 1997 LSMS were only drawn from 199 and 238 districts across the country, respectively, raises a potential problem: since every targeting indicator allocates a different amount to each district, the total budget for this sample of districts would _not_ be constant across indicators. For example, the proportion of the total budget allocated to the 199 districts in the 1994 LSMS would be smaller by the FONCODES index than by the measure of imputed poverty. As a result, the total amount transferred to the 3,558 households in the survey would be smaller when we use the FONCODES index than when we use the imputed poverty measure, even after each household in the survey is weighted by its expansion factor. At the heart of the problem is the fact that the LSMS draws a nationally-representative sample of households 9