Data and code for: Targeting Impact versus Deprivation

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 and targeting those most impacted by such programs. We work in the context of an NGO cash transfer program in Kenya, employing recent advances in machine learning methods and dynamic outcome data to learn proxy means tests that jointly target both objectives. Targeting solely on the basis of deprivation is not attractive in this setting under standard social welfare criteria unless the planner’s preferences are extremely redistributive.
This repository provides data and code accompanying the article.

The analysis uses household sampling weights Response Rates: See Egger et al. (2022) for full survey response rate details. Households were randomly sampled within study villages, stratified by cash transfer eligibility status. See Egger et al. (2022) for full details. Households in 653 villages in Siaya County, Kenya taking part in an NGO cash transfer experiment
. Smallest Geographic Unit: household computer-assisted personal interview (CAPI);