Working Paper: NBER ID: w22919
Authors: Caitlin Brown; Martin Ravallion; Dominique Van De Walle
Abstract: Proxy-means testing is a popular method of poverty targeting with imperfect information. In a now widely-used version, a regression for log consumption calibrates a proxy-means test score based on chosen covariates, which is then implemented for targeting out-of-sample. In this paper, the performance of various proxy-means testing methods is assessed using data for nine African countries. Standard proxy-means testing helps filter out the nonpoor, but excludes many poor people, thus diminishing the impact on poverty. Some methodological changes perform better, with a poverty-quantile method dominating in most cases. Even so, either a basic-income scheme or transfers using a simple demographic scorecard are found to do as well, or almost as well, in reducing poverty. However, even with a budget sufficient to eliminate poverty with full information, none of these targeting methods brings the poverty rate below about three-quarters of its initial value. The prevailing methods are particularly deficient in reaching the poorest.
Keywords: Proxy means testing; Poverty targeting; Sub-Saharan Africa; Econometric targeting; Inclusion errors; Exclusion errors
JEL Codes: I32; I38; O15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Standard PMT (G19) | reduction in inclusion errors (C52) |
| Standard PMT (G19) | substantial exclusion errors (C52) |
| Poverty-quantile regression (C21) | reduction in exclusion errors (C52) |
| Poverty-quantile regression (C21) | increase in inclusion errors (C52) |
| Basic income schemes or demographic scorecards (J68) | comparable performance in reducing poverty impacts (I32) |
| Targeting methods (C90) | effectiveness in reaching the poorest (I32) |