  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.
