choice of welfare indicator which is used to distribute resources _across_ districts. Under this assumption, which seems quite reasonable, the actual estimates of changes in poverty under different targeting regimes will be biased (up, if there is positive intra-district targeting, down if the converse is true), but the preferred rank-order of the indicators used to assign resources _across_ districts should be unaffected.

The simulations assume that benefits from program investments in a district accrue entirely to the residents of that district. This might not hold, say, if beneficiaries of a food distribution program implemented in one district are residents of a different district. But intra-district spillovers are unlikely to be systematic--that is, they should not consistently favor residents of one kind of district over residents of another. Intra-district spill-overs should therefore not affect the rank order of indicators either.

Some additional assumptions have to be made about the impact of transfers on various poverty measures. Poverty in Peru has generally been defined as an individual's inability to meet a specified level of expenditures-the poverty line-when individual expenditures are approximated by total household expenditures divided by the number of eligible household members.<sup>6</sup> We must therefore translate expenditures by social programs into household expenditures--by first translating program expenditures into changes in household income, and then estimating the proportion of additional disposable income that is spent. As a matter of convenience, I have assumed that all program expenditures translate into additional household income and that all of this additional income is spent7 Finally, the simulations assume that the cost of administering programs is constant across regions, and ignore the effects of transfers on behavior such as migration towards districts which 6 The 1995 and 1996 INEI household surveys, which used income to measure poverty, are exceptions. 7Two points are worth noting here. First, many social programs in Peru are involved with the construction of small-scale infrastructure. The wages paid to laborers in these projects are only a fraction of the total cost, and other benefits--say, of having an additional classroom--are unlikely to have a short-term impact on household income. We can easily relax the assumption of a one-for-one equivalence between changes in program expenditures and changes in household income, however, if we simply model a smaller budget. For example, if only 50% of the expenditures on poverty alleviation programs translate into short-term increases in income, the "relevant" budget would be only half the actual budget. Second, households typically do not spend all of an increase in income. Assuming that all households spend a fixed fraction of additional income is not entirely satisfactory either, because the marginal propensity to save is likely to differ _systematically_ across households. For example, if the fraction of income that is saved is higher in rich districts than in poor districts, simulations based on a constant marginal propensity to save might under-estimate the short-term impact of program investments in poor districts relative to rich districts, and under-estimate the relative performance of targeting indicators which assign a higher share of their resources to the poorest districts. One potential solution would be to estimate marginal propensities to save for households from the LSMS itself. The simplest way to do this would be to convert measures of income and expenditures into current prices, and then take the difference between them as a measure of savings (see especially Paxson 1992). By this measure, however, almost two-thirds (61%) of households in the 1994 LSMS _dissaved._ This seems unreasonable and suggests that income in these surveys is seriously underestimated vis-a-vis expenditures.

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