Poverty Targeting with Imperfect Information

A key challenge for targeted antipoverty programs in developing countries is that policymakers must rely on estimated rather than observed income, which leads to substantial targeting errors. The policy problem is not only to predict income, but to decide how noisy income estimates should be translated into feasible transfers. I formulate this as a statistical decision problem in which a policymaker chooses transfers to minimize a poverty-targeting loss subject to a fixed budget and a no-taxation constraint. I show that the standard plug-in rule, which treats estimated incomes as true, is inadmissible. I develop a nonparametric empirical Bayes targeting rule that assigns transfers using posterior distributions of true poverty gaps. Although the budget and no-taxation constraints make the targeting rule nonsmooth, Bayes regret is governed by the accuracy of the posterior functionals that determine the oracle allocation. In simulations using household survey data from nine African countries, the empirical Bayes rule reaches substantially more poor households and systematically improves poverty reduction relative to plug-in OLS and machine-learning benchmarks.