The objective of the research study is to test whether targeting cash transfers on the basis of deprivation leads to the highest per-dollar impact, as well as to find an optimal policy function based on commonly observed household characteristics. Using generalized random forests, we study the heterogeneity of the treatment effects of a randomized unconditional cash transfer program by the NGO GiveDirectly (GD) in Kenya. This program provided one-time cash transfers of about USD 1,000 to eligible rural households across 653 randomized villages. Using anonymized survey data from almost 5,000 households, we train random forests to predict 4 pre-specified endline outcomes (consumption, food security index, income and assets) using household observables commonly available to policymakers. For each outcome, households are then classified as most deprived if their predicted endline value is below the median. Similarly, using causal forests we predict household-level treatment effects and classify households as most impacted if they are above the median household. Having identified these subgroups and individual predictions, we analyze optimal policies and estimate the parameters of a social welfare function that would rationalize targeting the deprived.
We plan to use the requested resources to continue computing the Randomization Inference and Bootstrap components of the project for inference on robustness checks and alternative specifications.