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Endogenous Stratification in Randomized Experiments

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  • Alberto Abadie
  • Matthew M. Chingos
  • Martin R. West

Abstract

Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an index of baseline characteristics that predicts the values that individual outcomes would take on in the absence of the treatment. This article shows that substantial biases may arise in practice if the index is estimated, as is often the case, by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We analyze the behavior of leave-one-out and repeated split sample estimators and show they behave well in realistic scenarios, correcting the large bias problem of the full sample estimator. We use data from the National JTPA Study and the Tennessee STAR experiment to demonstrate the performance of alternative estimators and the magnitude of their biases.

Suggested Citation

  • Alberto Abadie & Matthew M. Chingos & Martin R. West, 2013. "Endogenous Stratification in Randomized Experiments," NBER Working Papers 19742, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:19742
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    References listed on IDEAS

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    Cited by:

    1. Altmann, Steffen & Falk, Armin & Jäger, Simon & Zimmermann, Florian, 2018. "Learning about job search: A field experiment with job seekers in Germany," Journal of Public Economics, Elsevier, vol. 164(C), pages 33-49.
    2. Arash Nekoei & Andrea Weber, 2017. "Does Extending Unemployment Benefits Improve Job Quality?," American Economic Review, American Economic Association, vol. 107(2), pages 527-561, February.
    3. Onur Altindag, 2016. "Son Preference, Fertility Decline, and the Nonmissing Girls of Turkey," Demography, Springer;Population Association of America (PAA), vol. 53(2), pages 541-566, April.
    4. Abebe, Girum & Caria, Stefano & Fafchamps, Marcel & Falco, Paolo & Franklin, Simon & Quinn, Simon & Shilpi, Forhad, 2017. "Matching firms and workers in a field experiment in Ethiopia," LSE Research Online Documents on Economics 86572, London School of Economics and Political Science, LSE Library.
    5. Casalin, Fabrizio & Dia, Enzo, 2019. "Information and reputation mechanisms in auctions of remanufactured goods," International Journal of Industrial Organization, Elsevier, vol. 63(C), pages 185-212.
    6. Russell, Lauren, 2019. "Better outcomes without increased costs? Effects of Georgia’s University System consolidations," Economics of Education Review, Elsevier, vol. 68(C), pages 122-135.
    7. Cusolito, Ana Paula & Dautovic, Ernest & McKenzie, David J., 2018. "Can Government Intervention Make Firms More Investment-Ready? A Randomized Experiment in the Western Balkans," CEPR Discussion Papers 13098, C.E.P.R. Discussion Papers.
    8. Patterson, Richard W., 2018. "Can behavioral tools improve online student outcomes? Experimental evidence from a massive open online course," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 293-321.
    9. C de Chaisemartin & X D’HaultfŒuille, 2018. "Fuzzy Differences-in-Differences," Review of Economic Studies, Oxford University Press, vol. 85(2), pages 999-1028.
    10. Liang Jiang & Xiaobin Liu & Yichong Zhang, 2020. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," Papers 2005.11967, arXiv.org.
    11. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Mar 2019.
    12. Onur Altindag & Theodore J. Joyce & Julie A. Reeder, 2015. "Effects of Peer Counseling to Support Breastfeeding: Assessing the External Validity of a Randomized Field Experiment," NBER Working Papers 21013, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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