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Should we drop covariate cells with attrition problems?

Author

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  • Ferman, Bruno
  • Ponczek, Vladimir

Abstract

It is well known that sample attrition can lead to inconsistent treatment effect estimators even in randomized control trials. Standard solutions to attrition problems either rely on strong assumptions on the attrition mechanisms or consider the estimation of bounds, which may be uninformative if attrition problems are severe. In this paper, we analyze strategies of focusing the analysis on subsets of the data with less observed attrition problems. We show that these strategies are asymptotically valid when the number of observations in each covariate cell goes to infinity. However, they can lead to important distortions when the number of observations per covariate cell is finite.

Suggested Citation

  • Ferman, Bruno & Ponczek, Vladimir, 2017. "Should we drop covariate cells with attrition problems?," MPRA Paper 80686, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:80686
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    File URL: https://mpra.ub.uni-muenchen.de/80686/1/MPRA_paper_80686.pdf
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    References listed on IDEAS

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    4. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, Oxford University Press, vol. 76(3), pages 1071-1102.
    5. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    7. Donald W. K. Andrews & Marcia M. A. Schafgans, 1998. "Semiparametric Estimation of the Intercept of a Sample Selection Model," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 497-517.
    8. Peter Z. Schochet & John Burghardt & Sheena McConnell, 2008. "Does Job Corps Work? Impact Findings from the National Job Corps Study," American Economic Review, American Economic Association, vol. 98(5), pages 1864-1886, December.
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    More about this item

    Keywords

    impact evaluation; attrition; partial identification;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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