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Should we drop covariate cells with attrition problems?

Listed author(s):
  • Ferman, Bruno
  • Ponczek, Vladimir

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.

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File URL: https://mpra.ub.uni-muenchen.de/80686/1/MPRA_paper_80686.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 80686.

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Date of creation: 07 Aug 2017
Handle: RePEc:pra:mprapa:80686
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  1. repec:mpr:mprres:6097 is not listed on IDEAS
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  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.
  9. Garret S. Christensen & Edward Miguel, 2016. "Transparency, Reproducibility, and the Credibility of Economics Research," NBER Working Papers 22989, National Bureau of Economic Research, Inc.
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