Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints

64 Pages Posted: 3 Nov 2008 Last revised: 24 Jun 2010

See all articles by Debopam Bhattacharya

Debopam Bhattacharya

Dartmouth College

Pascaline Dupas

Dartmouth College - Department of Economics

Date Written: October 2008

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.

Suggested Citation

Bhattacharya, Debopam and Dupas, Pascaline, Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (October 2008). NBER Working Paper No. w14447, Available at SSRN: https://ssrn.com/abstract=1294122

Debopam Bhattacharya (Contact Author)

Dartmouth College ( email )

Hanover, NH 03755
United States

Pascaline Dupas

Dartmouth College - Department of Economics ( email )

Hanover, NH 03755
United States

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