Treatment evaluation in the presence of sample selection
AbstractSample 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.
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Bibliographic InfoPaper 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.
Length: 36 pages
Date of creation: Apr 2009
Date of revision:
treatment effects; sample selection; inverse probability weighting; propensity score matching.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-02 (All new papers)
- NEP-ECM-2009-05-02 (Econometrics)
- NEP-EDU-2009-05-02 (Education)
- NEP-LAB-2009-05-02 (Labour Economics)
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- Gerry H. Makepeace & Michael J. Peel, 2013. "Combining information from Heckman and matching estimators: testing and controlling for hidden bias," Economics Bulletin, AccessEcon, vol. 33(3), pages 2422-2436.
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