A hybrid approach to targeting social assistance

Proxy means testing (PMT) and community-based targeting (CBT) are two of the leading methods for targeting social assistance in developing countries. 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, but we instead propose calibrating the weights to preference rankings from community targeting exercises. 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, which shows that our method achieves error rates lower than what PMT achieves when targeting household expenditures.