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Inferring welfare maximizing treatment assignment under budget constraints

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  • Bhattacharya, Debopam
  • Dupas, Pascaline

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.

Suggested Citation

  • Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
  • Handle: RePEc:eee:econom:v:167:y:2012:i:1:p:168-196 DOI: 10.1016/j.jeconom.2011.11.007
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    Citations

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    Cited by:

    1. Jonas Hjort, 2013. "Ethnic Divisions and Production in Firms," CESifo Working Paper Series 4449, CESifo Group Munich.
    2. Carneiro, Pedro & Lee, Sokbae & Wilhelm, Daniel, 2016. "Optimal Data Collection for Randomized Control Trials," IZA Discussion Papers 9908, Institute for the Study of Labor (IZA).
    3. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927RR, Cowles Foundation for Research in Economics, Yale University, revised Apr 2015.
    4. Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP10/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. List, John A. & Rasul, Imran, 2011. "Field Experiments in Labor Economics," Handbook of Labor Economics, Elsevier.
    6. Marc FERRACCI & Grégory JOLIVET & Gerard J van den Berg, 2009. "Treatment Evaluation in the Case of Interaction Within Markets," Working Papers 2009-22, Center for Research in Economics and Statistics.
    7. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2014.
    8. Susan Athey & Stefan Wager, 2017. "Efficient Policy Learning," Papers 1702.02896, arXiv.org, revised Oct 2017.
    9. Bhattacharya, Debopam, 2013. "Evaluating treatment protocols using data combination," Journal of Econometrics, Elsevier, vol. 173(2), pages 160-174.
    10. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2017. "Can Variation in Subgroups' Average Treatment Effects Explain Treatment Effect Heterogeneity? Evidence from a Social Experiment," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 683-697, July.
    11. Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The sorted effects method: discovering heterogeneous effects beyond their averages," CeMMAP working papers CWP74/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2017. "Optimal data collection for randomized control trials," CeMMAP working papers CWP45/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2017. "Are University Admissions Academically Fair?," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 449-464, July.
    14. Jonathan M.V. Davis & Sara B. Heller, 2017. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," NBER Working Papers 23443, National Bureau of Economic Research, Inc.
    15. Debopam Bhattacharya & Pascaline Dupas & Shin Kanaya, 2013. "Estimating the Impact of Means-tested Subsidies under Treatment Externalities with Application to Anti-Malarial Bednets," CREATES Research Papers 2013-06, Department of Economics and Business Economics, Aarhus University.
    16. Anders Bredahl Kock & Martin Thyrsgaard, 2017. "Optimal sequential treatment allocation," Papers 1705.09952, arXiv.org, revised Jan 2018.
    17. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927, Cowles Foundation for Research in Economics, Yale University.

    More about this item

    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

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