(2) _Allocation, (Index, * Population,)_ The "FONCODES method" thus makes all districts in the country eligible for benefits, but weights the population of each one by its poverty index. For example, Coronel<sup>Castaffeda, the</sup> district with the highest value of the FONCODES index (36.38),<sup>and a population of 607</sup> inhabitants, would be allocated (36.38*607)/346,201,217 = .0064%<sup>of the total budget for that</sup> year. By contrast, Pacocha, the district with the lowest value<sup>of the FONCODES index (1.00),</sup> and a population of 6500, would be allocated (1*6,500)/346,201,217<sup>= .0019% of the total budget</sup> for that year. Per capita allocations to inhabitants of Coronel<sup>Castafieda Castaiieda would</sup> therefore be almost 37 times per capita allocations to inhabitants of Pacocha<sup>4Note that if the</sup> index is a poverty rate, allocations to district i simply correspond to the fraction<sup>of the poor who</sup> live in district i. I adapt the "FONCODES method" to other indicators by substituting<sup>the infant</sup> mortality rates and the imputed poverty measure for the FONCODES index<sup>in equation (2)</sup> above, and compute the corresponding district-level allocations.

The simulations in this paper make a number of assumptions. The most<sup>important</sup> assumption is that there is no targeting of program resources _within_<sup>a given district. This is</sup> clearly unrealistic: Paxson and Schady (1999) use non-parametric<sup>regressions to estimate the</sup> probability of benefiting from social programs as a function of the number<sup>of standard deviations</sup> a given household's income is above or below mean district income.<sup>Their results show that the</sup> within-district distribution of investments made by FONCODES<sup>and the food distribution</sup> program PRONAA is hump-shaped, peaking at about one-standard<sup>deviation above mean district</sup> income, while the within-district distribution of investments made by the school<sup>construction</sup> program INFES is regressive, rising with household income. A<sup>more plausible assumption for</sup> the simulations in this paper is that the degree of within-district<sup>targeting is independent of the</sup> 4 In actual fact, FONCODES' allocation mechanism is a little more complicated<sup>than this. FONCODES</sup> first allocates 60% of resources to rural areas and 40% to urban areas. The<sup>final allocation to each district</sup> is then the sum of the rural and urban allocations--standardized toadd up to 100%, Ad hoc adjustments are also made to privilege border areas, to coordinate investments with other<sup>public sector programs, and to</sup> ensure that each of FONCODES' regional offices (which correspond roughly<sup>to individual departments)</sup> has a minimum operating budget. I do not take these "refinements" into account<sup>in the simulations below.</sup> **5** Of course, this is only one of a potentially infinitely large number<sup>of fortnulas which could be used. For</sup> example, the Technical Team of the Ministry of the Presidency has<sup>proposed an allocation formula which</sup> makes a distinction between districts with a high _proportion_ of poor<sup>people (as measured by the</sup> FONCODES index) and districts with a large _number_ of poor people<sup>(as measured by the product of the</sup> FONCODES index and population). Proposed investments would then be<sup>directed to 262 districts with the</sup> highest proportion of the population in poverty and 232 districts with<sup>the highest number of people in</sup> poverty. The final count is 419 of Peru's 1812 districts, because<sup>some districts have a high value of the</sup> FONCODES index and a high value of the product of the index and population.

7