By Judy L. Baker
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Extra resources for Measuring the effects of geographic targeting on poverty reduction
The algorithm, designed by Ravallion and Chao (1989) minimizes poverty as characterized by the FGT a=2 measure, subject to a budget constraint, by calculating the optimal transfer to mutually exclusive sub-groups. The budget constraint can be set so that all transfers are positive, or so that some are negative implying that some groups will be taxed. In this simulation, which we have called the poverty minimization technique, regions have been used as the sub-groups and the transfer constraint set so that all transfers are positive.
Geographic targeting reduces poverty more than a uniform transfer, but much less than the ideal of "perfect" targeting. With the uniform transfer, the fixed budget allows a transfer of only Bs 50 per person. 0. The same budget would extend to give each poor person Bs 167 under perfect targeting. 26, a decrease of 54 percent from its initial level. Significance tests for the FGT a=0,1,2 measures following each transfer scheme indicate that all methods are found to be significantly different than no targeting, and perfect targeting.
This loss reflects the fact that the composite index does not measure the depth of poverty. As we saw in Table 5, a uniform transfer actually reduces poverty more than geographic targeting based on the composite index ranking when measured by the FGT a=2 index. e. residents in all participating states got the same level of benefit. This is not strictly necessary program benefits can vary by state. In order to see how much difference that makes, the simulation has been performed with the same budget as the other simulations, but allowing for different transfer levels in each state.