A hybrid approach to targeting social assistance

TL;DR

This paper introduces a Bayesian hybrid targeting method combining community preferences with verifiable indicators for social assistance, demonstrated with data from Burkina Faso and Indonesia.

Contribution

It develops a novel Bayesian framework that integrates community-based preferences into proxy means testing for improved social assistance targeting.

Findings

Effective incorporation of community preferences into targeting

Flexible model extensions for multiple rankings and elite capture

Empirical validation with data from Burkina Faso and Indonesia

Abstract

Proxy means testing (PMT) and community-based targeting (CBT) are two of the leading methods for targeting social assistance in developing countries. In this paper, we present a hybrid targeting method that incorporates CBT's emphasis on local information and preferences with PMT's reliance on verifiable indicators. Specifically, we outline a Bayesian framework for targeting that resembles PMT in that beneficiary selection is based on a weighted sum of sociodemographic characteristics. We nevertheless propose calibrating the weights to preference rankings from community targeting exercises, implying that the weights used by our method reflect how potential beneficiaries themselves substitute sociodemographic features when making targeting decisions. We discuss several practical extensions to the model, including a generalization to multiple rankings per community, an adjustment for elite capture, a method for incorporating auxiliary information on potential beneficiaries, and a dynamic updating procedure. We further provide an empirical illustration using data from Burkina Faso and Indonesia.