Inferring welfare maximizing treatment assignment under budget constraints

Debopam Bhattacharya, Pascaline Dupas

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

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% points compared to allocation based on wealth only, and by 17% points compared to a purely random allocation.

Original languageEnglish (US)
Pages (from-to)168-196
Number of pages29
JournalJournal of Econometrics
Volume167
Issue number1
DOIs
StatePublished - Mar 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Economics and Econometrics

Keywords

  • C14
  • C21

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