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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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 167 (2012)
Issue (Month): 1 ()
Pages: 168-196

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Handle: RePEc:eee:econom:v:167:y:2012:i:1:p:168-196

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Web page: http://www.elsevier.com/locate/jeconom

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Find related papers by JEL classification:

• C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
• C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

References

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1. Hahn, Jinyong & Hirano, Keisuke & Karlan, Dean, 2011. "Adaptive Experimental Design Using the Propensity Score," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 96-108.
   
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   • Hahn, Jinyong & Hirano, Keisuke & Karlan, Dean, 2008. "Adaptive Experimental Design Using the Propensity Score," MPRA Paper 8315, University Library of Munich, Germany.
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2. Hansen, Bruce E., 2008. "Uniform Convergence Rates For Kernel Estimation With Dependent Data," Econometric Theory, Cambridge University Press, vol. 24(03), pages 726-748, June.
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7. Cesar Martinelli & Susan W. Parker, 2006. "Deception and Misreporting in a Social Program," Working Papers 0602, Centro de Investigacion Economica, ITAM.
   
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8. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
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   • Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
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Cited by:

1. Debopam Bhattacharya, 2012. "Evaluating Treatment Protocols using Data Combination," Economics Series Working Papers 609, University of Oxford, Department of Economics.
2. List, John A. & Rasul, Imran, 2011. "Field Experiments in Labor Economics," Handbook of Labor Economics, Elsevier.
   
   • John List & Imran Rasul, 2010. "Field experiments in labor economics," Artefactual Field Experiments 00092, The Field Experiments Website.
   • John A. List & Imran Rasul, 2010. "Field Experiments in Labor Economics," NBER Working Papers 16062, National Bureau of Economic Research, Inc.
3. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2012. "Are University Admissions Academically Fair?," Economics Series Working Papers 608, University of Oxford, Department of Economics.

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