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Treatment evaluation in the presence of sample selection

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  • Martin Huber

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.

Suggested Citation

  • Martin Huber, 2009. "Treatment evaluation in the presence of sample selection," University of St. Gallen Department of Economics working paper series 2009 2009-07, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2009:2009-07
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    Cited by:

    1. Hans Fricke & Markus Frölich & Martin Huber & Michael Lechner, 2020. "Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 481-504, August.
    2. Philippe Van Kerm, 2013. "Generalized measures of wage differentials," Empirical Economics, Springer, vol. 45(1), pages 465-482, August.
    3. James J. Heckman & Ganesh Karapakula, 2019. "Intergenerational and Intragenerational Externalities of the Perry Preschool Project," NBER Working Papers 25889, National Bureau of Economic Research, Inc.
    4. Bargain, Olivier B. & Doorley, Karina & Van Kerm, Philippe, 2018. "Minimum Wages and the Gender Gap in Pay: New Evidence from the UK and Ireland," IZA Discussion Papers 11502, Institute of Labor Economics (IZA).
    5. Olivier Bargain & Karina Doorley & Philippe Van Kerm, 2019. "Minimum Wages and the Gender GAP in Pay: New Evidence from the United Kingdom and Ireland," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(3), pages 514-539, September.
    6. Akanksha Negi, 2020. "Doubly weighted M-estimation for nonrandom assignment and missing outcomes," Papers 2011.11485, arXiv.org.
    7. 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.
    8. Gayani Rathnayake & Akanksha Negi & Otavio Bartalotti & Xueyan Zhao, 2024. "Difference-in-Differences with Sample Selection," Papers 2411.09221, arXiv.org.
    9. Martin Huber & Lukáš Lafférs, 2022. "Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1141-1163, November.
    10. Takeshima, Hiroyuki & Hatzenbuehler, Patrick L. & Edeh, Hyacinth O., 2020. "Effects of agricultural mechanization on economies of scope in crop production in Nigeria," Agricultural Systems, Elsevier, vol. 177(C).
    11. Xiaolin Sun & Xueyan Zhao & D. S. Poskitt, 2024. "Partially Identified Heterogeneous Treatment Effect with Selection: An Application to Gender Gaps," Papers 2410.01159, arXiv.org, revised Oct 2024.
    12. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    13. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    14. Heng Chen & Geoffrey Dunbar & Q. Rallye Shen, 2020. "The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 341-357, Emerald Group Publishing Limited.
    15. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    16. Rahul Singh, 2021. "Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection," Papers 2111.05277, arXiv.org.
    17. Martin Huber, 2010. "Identification of average treatment effects in social experiments under different forms of attrition," University of St. Gallen Department of Economics working paper series 2010 2010-22, Department of Economics, University of St. Gallen.
    18. Jenna Nobles & Amar Hamoudi, 2019. "Detecting the Effects of Early-Life Exposures: Why Fecundity Matters," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 38(6), pages 783-809, December.

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    More about this item

    Keywords

    treatment effects; sample selection; inverse probability weighting; propensity score matching.;
    All these keywords.

    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

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