Abstract
Geographic targeting is perhaps the most popular mechanism used to direct social programs to the poor in Latin America. This paper empirically compares geographic targeting indicators available in Peru. To this effect, I combine household–level information from the 1997 Peru Living Standards Measurement Survey (LSMS) and district–level information from the 1993 Peru Population and Housing Census. I then conduct a series of simulations which estimate leakage rates, concentration curves, the impact of transfers on poverty as measured by the headcount index, poverty gap and P2 measures of the FGT family, and non–parametric (kernel) densities when transfers are based on alternative indicators. I conclude that there is substantial potential for geographic targeting in Peru. However, the differences in outcomes across geographic targeting indicators are small, and are not statistically significant. These results are in keeping with earlier work which suggests that (among reasonable alternatives) the choice of geographic targeting indicator does not have an important bearing on poverty outcomes, and are at odds with more recent research which stresses the advantage of poverty maps which “impute” consumption or income.
Highlights
Lack of information is a serious constraint to targeting social programs effectively, especially in Less Developed Countries (LDCs) (Besley 1993; Ravallion 1993)
Targeting social programs involves making distinctions between "deserving" and "undeserving" applicants. This is no simple matter in countries where household characteristics such as income are rarely known
Policy-makers intent on targeting are forced to choose among imperfect solutions. They can rely on observable household characteristics, such as land ownership, the ratio of working age-adults to dependents, or ownership of durable goods that seem likely to separate poor from non-poor households
Summary
He combines householdlevel information from the 1994 and 1997 Peru Living Standards Measurement Surveysand district-level information from the 1993 Peru Population and Housing Census He conducts a series of simulations that estimate leakage rates; concentration curves; the impact of transfers on poverty as measured by the headcount index, poverty gap, and PI measures of the Foster-GreerThorbecke family; and nonparametric (kernel) densities when transfers are based on alternative indicators. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank. They do not necessarily represent the view of the World Bank. its Executive Directors, or the comntries they represent
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