Working Paper: NBER ID: w14447
Authors: Debopam Bhattacharya; Pascaline Dupas
Abstract: This paper concerns the problem of allocating a binary treatment among a target population based on observed covariates. The goal is to (i) maximize the mean social welfare arising from an eventual outcome distribution, when a budget constraint limits what fraction of the population can be treated and (ii) to infer the dual value, i.e. the minimum resources needed to attain a specific level of mean welfare via efficient treatment assignment. We consider a treatment allocation procedure based on sample data from randomized treatment assignment and derive asymptotic frequentist confidence interval for the welfare generated from it. We propose choosing the conditioning covariates through cross-validation. The methodology is applied to the efficient provision of anti-malaria bed net subsidies, using data from a randomized experiment conducted in Western Kenya. We find that subsidy allocation based on wealth, presence of children and possession of bank account can lead to a rise in subsidy use by about 9 percentage points compared to allocation based on wealth only, and by 17 percentage points compared to a purely random allocation.
Keywords: welfare maximization; treatment assignment; budget constraints; antimalaria bed nets; randomized experiments
JEL Codes: C01; C14; I38
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| presence of children (J13) | treatment effect (C22) |
| ownership of a bank account (D14) | treatment effect (C22) |
| treatment allocation based on multiple covariates (C32) | subsidy use (H20) |
| treatment allocation based on covariates (C32) | welfare outcomes (I38) |