Treatment evaluation in the presence of sample selection

Martin Huber

University of St. Gallen Department of Economics working paper series 2009 from Department of Economics, University of St. Gallen

Abstract: Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem (i) into a selective subpopulation (e.g., working - selection on unobservables) and (ii) into a binary treatment (e.g., training - selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Root-n-consistent weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.

Keywords: treatment effects; sample selection; inverse probability weighting; propensity score matching. (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2009-04
New Economics Papers: this item is included in nep-ecm, nep-edu and nep-lab
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Citations: View citations in EconPapers (4)

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http://ux-tauri.unisg.ch/RePEc/usg/dp2009/DP-0907-Hu.pdf (application/pdf)

Related works:
Journal Article: Treatment Evaluation in the Presence of Sample Selection (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2009:2009-07

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