A poor means test? Econometric targeting in Africa

Caitlin Brown,Dominique Van De Walle,Martin Ravallion

doi:10.1016/j.jdeveco.2018.05.004

Caitlin Brown, Dominique Van De Walle + Show 1 more

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https://doi.org/10.1016/j.jdeveco.2018.05.004

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Abstract

Proxy-means tests (PMTs) are popular for poverty-targeting with imperfect information. In a widely-used version, a regression for log consumption calibrates a PMT score based on covariates, which is then implemented for targeting out-of-sample. The performance of various PMT methods is assessed using data for nine African countries. Standard PMTs help filter out the non-poor, but exclude many poor people, thus diminishing the impact on poverty. Poverty-focused econometric methods such as using quantile regression generally do better. We also characterize the optimal informationally-feasible solution for poverty targeting and compare it to econometric methods. Even with a budget sufficient to eliminate poverty with full information, none of the targeting methods studied bring the poverty rate below about three-quarters of its initial value. The prevailing methods are particularly deficient in reaching the poorest. A basic-income scheme or transfers using a simple demographic scorecard often do as well, or even better, in reducing poverty.

Highlights

While universal social programs—whereby everyone is covered—are excellent at reaching the poorest, the beneficiaries can include many people who do not need this form of public help
Variables used include the type of toilet a household has; floor, wall and roofing material; type of fuel used for cooking; certain characteristics of the head, including gender, education and occupation; the household’s religion and demographic size and composition
The simple average R2 is 0.53, with a range from 0.32 to 0.64 (Burkina Faso); Table 3 provides summary statistics for the Basic proxy means testing (PMT). This explanatory power is typical of past studies

Summary

Introduction

While universal social programs—whereby everyone is covered—are excellent at reaching the poorest, the beneficiaries can include many people who do not need this form of public help. Governments have tried many ways of assuring better “targeting,” with the explicit aim of concentrating the benefits of a social policy on poor people. The means used vary in their data requirements, methodological sophistication and costs (both administrative and broader social costs). Efficiency considerations point to the need for indicators that are not manipulated by actual or potential beneficiaries. Proxy variables, such as gender and education, family size and housing conditions, have been common.. A score based on these variables is used in validating other targeting methods, such as those based on community-level subjective assessments of who is “poor.” The scores are entering many social-protection registries—national data bases that are used in various ways including to flag ineligible households in future schemes Proxy variables, such as gender and education, family size and housing conditions, have been common. A score based on these variables is used in validating other targeting methods, such as those based on community-level subjective assessments of who is “poor.” The scores are entering many social-protection registries—national data bases that are used in various ways including to flag ineligible households in future schemes

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