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Simple Tests for Selection Bias: Learning More from Instrumental Variables

Author

Listed:
  • Black, Dan A.

    (University of Chicago)

  • Joo, Joonhwi

    (University of Chicago)

  • LaLonde, Robert J.

    (Harris School, University of Chicago)

  • Smith, Jeffrey A.

    (University of Wisconsin-Madison)

  • Taylor, Evan J.

    (University of Arizona)

Abstract

We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects. The tests allow researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. The tests are quite simple; undergraduates after an introductory econometrics class should be able to implement these tests. We illustrate our tests with two empirical applications: the impact of children on female labor supply from Angrist and Evans (1998) and the training of adult women from the Job Training Partnership Act (JTPA) experiment.

Suggested Citation

  • Black, Dan A. & Joo, Joonhwi & LaLonde, Robert J. & Smith, Jeffrey A. & Taylor, Evan J., 2015. "Simple Tests for Selection Bias: Learning More from Instrumental Variables," IZA Discussion Papers 9346, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp9346
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    References listed on IDEAS

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    6. Curtis Eberwein & John C. Ham & Robert J. Lalonde, 1997. "The Impact of Being Offered and Receiving Classroom Training on the Employment Histories of Disadvantaged Women: Evidence from Experimental Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 655-682.
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    21. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2005. "Evaluating the effect of education on earnings: models, methods and results from the National Child Development Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 473-512, July.
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    23. Dan A. Black & Seth G. Sanders & Evan J. Taylor & Lowell J. Taylor, 2015. "The Impact of the Great Migration on Mortality of African Americans: Evidence from the Deep South," American Economic Review, American Economic Association, vol. 105(2), pages 477-503, February.
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    Cited by:

    1. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    2. Martini, Alberto & Rettore, Enrico & Barbetta, Gianpaolo, 2017. "The Impact of Traineeships on the Employment of the Mentally Ill: The Role of Partial Compliance," IZA Discussion Papers 10582, Institute of Labor Economics (IZA).
    3. Steven J. Haider & Melvin Stephens Jr., 2020. "Correcting for Misclassified Binary Regressors Using Instrumental Variables," NBER Working Papers 27797, National Bureau of Economic Research, Inc.
    4. Amanda E Kowalski, 2023. "Behaviour within a Clinical Trial and Implications for Mammography Guidelines," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(1), pages 432-462.
    5. Johansen, Eva Rye, 2021. "Relative age for grade and adolescent risky health behavior," Journal of Health Economics, Elsevier, vol. 76(C).
    6. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    7. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    8. Anne Sofie Tegner Anker & Jennifer L. Doleac & Rasmus Landersø, 2021. "The Effects of DNA Databases on the Deterrence and Detection of Offenders," American Economic Journal: Applied Economics, American Economic Association, vol. 13(4), pages 194-225, October.
    9. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
    10. Brunello, Giorgio & Rocco, Lorenzo, 2016. "Is Childcare Bad for the Mental Health of Grandparents? Evidence from SHARE," IZA Discussion Papers 10022, Institute of Labor Economics (IZA).
    11. Nicolas Apfel & Julia Hatamyar & Martin Huber & Jannis Kueck, 2024. "Learning control variables and instruments for causal analysis in observational data," Papers 2407.04448, arXiv.org.
    12. Giorgio Brunello & Lorenzo Rocco, 2019. "Grandparents in the blues. The effect of childcare on grandparents’ depression," Review of Economics of the Household, Springer, vol. 17(2), pages 587-613, June.
    13. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 871-905, August.
    14. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.

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    More about this item

    Keywords

    selection; local average treatment effects; instrumental variables;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies

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