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Sensitivity checks for the local average treatment effect

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  • Huber, Martin

Abstract

The nonparametric identification of the local average treatment effect (LATE) hinges on the satisfaction of three instrumental variable assumptions: (1) unconfounded assignment of the instrument, (2) no average direct effect of the instrument on the outcome within compliance types (exclusion restriction), and (3) weak monotonicity of the treatment in the instrument. While (1) often appears plausible in experiments when using randomization as instrument for actual participation, (2) and (3) may be controversial. For this reason, this paper proposes easily implementable sensitivity checks to assess the robustness of the LATE to deviations from either the exclusion restriction or monotonicity. An empirical illustration based on female labor supply data is also provided.

Suggested Citation

  • Huber, Martin, 2014. "Sensitivity checks for the local average treatment effect," Economics Letters, Elsevier, vol. 123(2), pages 220-223.
    • Handle: RePEc:eee:ecolet:v:123:y:2014:i:2:p:220-223
    DOI: 10.1016/j.econlet.2014.02.018
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    References listed on IDEAS

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    1. Kenneth I. Wolpin & Mark R. Rosenzweig, 2000. "Natural "Natural Experiments" in Economics," Journal of Economic Literature, American Economic Association, vol. 38(4), pages 827-874, December.
    2. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    3. Jungmin Lee, 2008. "Sibling size and investment in children’s education: an asian instrument," Journal of Population Economics, Springer;European Society for Population Economics, vol. 21(4), pages 855-875, October.
    4. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
    5. Jin‐young Choi & Myoung‐jae Lee, 2012. "Bounding endogenous regressor coefficients using moment inequalities and generalized instruments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(2), pages 161-182, May.
    6. Fabrizia Mealli & Barbara Pacini, 2013. "Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1120-1131, September.
    7. Carlos A. Flores & Alfonso Flores-Lagunes, 2013. "Partial Identification of Local Average Treatment Effects With an Invalid Instrument," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 534-545, October.
    8. Angrist, Joshua D & Evans, William N, 1998. "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size," American Economic Review, American Economic Association, vol. 88(3), pages 450-477, June.
    9. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
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    Citations

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    Cited by:

    1. Nicolas Apfel & Helmut Farbmacher & Rebecca Groh & Martin Huber & Henrika Langen, 2022. "Detecting Grouped Local Average Treatment Effects and Selecting True Instruments," Papers 2207.04481, arXiv.org, revised Oct 2023.
    2. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    3. 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.
    4. 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.
    5. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    6. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    7. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

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    More about this item

    Keywords

    Instrumental variable; Treatment effects; LATE; Sensitivity analysis;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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