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Heterogeneous Treatment Effects : Instrumental Variables Without Monotonicity?

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  • Klein, T.J.

    (Tilburg University, Center For Economic Research)

Abstract

A fundamental identification problem in program evaluation arises when idiosyncratic gains from participation and the treatment decision depend on each other. Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a local average treatment effect parameter using instrumental variables. More recently, Heckman and Vytlacil (1999) suggested estimation of a variety of treatment effect parameters using a local version of their approach. However, identification hinges on the same monotonicity assumption that is fundamentally untestable. We investigate the sensitivity of respective estimates to reasonable departures from monotonicity that are likely to be encountered in practice. Approximations to respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study.

Suggested Citation

  • Klein, T.J., 2008. "Heterogeneous Treatment Effects : Instrumental Variables Without Monotonicity?," Discussion Paper 2008-45, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:e015611a-6b97-4cd5-8dc8-7818c6c5e39e
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    Citations

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    Cited by:

    1. Chaisemartin, Clément de, 2014. "Tolerating defiance? Local average treatment effects without monotonicity," CAGE Online Working Paper Series 197, Competitive Advantage in the Global Economy (CAGE).
    2. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.
    3. Tobias Klein, 2013. "College education and wages in the U.K.: estimating conditional average structural functions in nonadditive models with binary endogenous variables," Empirical Economics, Springer, vol. 44(1), pages 135-161, February.
    4. Bernal Lobato, N., 2014. "Essays in applied microeconomics," Other publications TiSEM 9b638b3d-2f83-452a-b2c8-c, Tilburg University, School of Economics and Management.
    5. Ben Edwards & Mario Fiorini & Katrien Stevens & Matthew Taylor, 2013. "Is Monotonicity in an IV and RD Design Testable? No, But You Can Still Check on it," Working Paper Series 7, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    6. Ivan Zilic, 2018. "Effect of forced displacement on health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 889-906, June.
    7. Will Dobbie & Jae Song, 2015. "Debt Relief and Debtor Outcomes: Measuring the Effects of Consumer Bankruptcy Protection," American Economic Review, American Economic Association, vol. 105(3), pages 1272-1311, March.
    8. Lechner, Michael, 2013. "Treatment effects and panel data," Economics Working Paper Series 1314, University of St. Gallen, School of Economics and Political Science.
    9. Clément de Chaisemartin, 2012. "Late again, whithout Monotonicity," Working Papers 2012-12, Center for Research in Economics and Statistics.
    10. Dahl, Christian M. & Huber, Martin & Mellace, Giovanni, 2017. "It's never too LATE: A new look at local average treatment effects with or without defiers," Discussion Papers of Business and Economics 2/2017, University of Southern Denmark, Department of Business and Economics.
    11. Gautier, Eric & Hoderlein, Stefan, 2011. "A triangular treatment effect model with random coefficients in the selection equation," TSE Working Papers 15-598, Toulouse School of Economics (TSE), revised 25 Aug 2015.
    12. repec:eee:pubeco:v:154:y:2017:i:c:p:122-136 is not listed on IDEAS
    13. repec:bpj:jecome:v:8:y:2019:i:1:p:27:n:6 is not listed on IDEAS
    14. 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.
    15. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2012. "Late Again with Defiers," PSE Working Papers halshs-00699646, HAL.
    16. Bernal, Noelia & Carpio, Miguel A. & Klein, Tobias J., 2017. "The effects of access to health insurance: Evidence from a regression discontinuity design in Peru," Journal of Public Economics, Elsevier, vol. 154(C), pages 122-136.
    17. de Chaisemartin, Clement, 2013. "Defying the LATE? Identification of local treatment effects when the instrument violates monotonicity," The Warwick Economics Research Paper Series (TWERPS) 1020, University of Warwick, Department of Economics.
    18. Fiorini, Mario & Katrien Stevens, 2014. "Assessing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2014-13, University of Sydney, School of Economics.
    19. repec:jku:cdlwps:2015_08 is not listed on IDEAS
    20. Bernal, Noelia & Carpio, Miguel A. & Klein, Tobias J., 2017. "The effects of access to health insurance: Evidence from a regression discontinuity design in Peru," Journal of Public Economics, Elsevier, vol. 154(C), pages 122-136.

    More about this item

    Keywords

    Program evaluation; heterogeneity; identification; dummy endogenous variable; selection on unobservables; instrumental variables; monotonicity; nonseparable index selection model;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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