APPLIED MICRO: Targeting impact versus deprivation; Professor Paul Niehaus (University of California San Diego)

Abstract

A large literature has examined how best to target anti-poverty programs to those most deprived in some sense (e.g., consumption). We examine the potential tradeoff between this objective versus targeting those most impacted by such programs. We work in the context of an NGO cash transfer program in Kenya, utilizing recent advances in machine learning methods and dynamic outcome data to learn proxy means tests that target both objectives in turn. Targeting solely on the basis of deprivation is generally not attractive under standard social welfare criteria, even when the planner’s preferences are highly redistributive.

Click here to view paper.