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Treatment evaluation in the presence of sample selection

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Author Info
Martin Huber ()

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Abstract

Sample selection is inherent to a range of treatment evaluation problems as the estimation of the returns to schooling or of the effect of school vouchers on test scores of college admissions tests, when some students abstain from the test in a non-random manner. Parametric and semiparametric estimators tackling selectivity typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper proposes nonparametric weighting and matching estimators of average and quantile treatment effects that are consistent under more general forms of sample selection and incorporate effect heterogeneity with respect to observed characteristics. These estimators control for the double selection problem (i) into the observed population (e.g., working or taking the test) and (ii) into treatment by conditioning on nested propensity scores characterizing either selection probability. Weighting estimators based on parametric propensity score models are shown to be root-n-consistent and asymptotically normal. Simulations suggest that the proposed methods yield decent results in scenarios when parametric estimators are inconsistent.

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File URL: http://www.vwa.unisg.ch/RePEc/usg/dp2009/DP-07-Hu.pdf
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Publisher Info
Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2009 with number 2009-07.

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Length: 36 pages
Date of creation: Apr 2009
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Handle: RePEc:usg:dp2009:2009-07

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Related research
Keywords: treatment effects; sample selection; inverse probability weighting; propensity score matching.;

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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