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Partial Identification of Local Average Treatment Effects with an Invalid Instrument

Author

Listed:
  • Carlos A. Flores

    () (Department of Economics, University of Miami)

  • Alfonso Flores-Lagunes

    (Department of Economics, University of Florida)

Abstract

We derive nonparametric bounds for local average treatment effects without requiring the exclusion restriction assumption to hold or an outcome with a bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. We illustrate the identifying power of the bounds by analyzing the effect of attaining a GED, high school, or vocational degree on subsequent employment and weekly earnings using randomization into a training program as an invalid instrument.

Suggested Citation

  • Carlos A. Flores & Alfonso Flores-Lagunes, 2010. "Partial Identification of Local Average Treatment Effects with an Invalid Instrument," Working Papers 2010-24, University of Miami, Department of Economics.
  • Handle: RePEc:mia:wpaper:2010-24
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    References listed on IDEAS

    as
    1. Alfonso Flores-Lagunes & Arturo Gonzalez & Todd Neumann, 2010. "Learning But Not Earning? The Impact Of Job Corps Training On Hispanic Youth," Economic Inquiry, Western Economic Association International, vol. 48(3), pages 651-667, July.
    2. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    3. Peter Z. Schochet & John Burghardt & Steven Glazerman, 2001. "National Job Corps Study: The Impacts of Job Corps on Participants' Employment and Related Outcomes," Mathematica Policy Research Reports db6c4204b8e1408bb0c6289ec, Mathematica Policy Research.
    4. Alfonso Flores-Lagunes & Audrey Light, 2010. "Interpreting Degree Effects in the Returns to Education," Journal of Human Resources, University of Wisconsin Press, vol. 45(2).
    5. repec:mpr:mprres:2951 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Das, Tirthatanmoy & Polachek, Solomon, 2017. "Micro Foundations of Earnings Differences," IZA Discussion Papers 10922, Institute for the Study of Labor (IZA).
    2. Hans (J.L.W.) van Kippersluis & Niels (C.A.) Rietveld, 2017. "Beyond Plausibly Exogenous," Tinbergen Institute Discussion Papers 17-096/V, Tinbergen Institute.
    3. Huber, Martin, 2014. "Sensitivity checks for the local average treatment effect," Economics Letters, Elsevier, vol. 123(2), pages 220-223.
    4. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.
    5. Huber, Martin, 2012. "Statistical verification of a natural "natural experiment": Tests and sensitivity checks for the sibling sex ratio instrument," Economics Working Paper Series 1219, University of St. Gallen, School of Economics and Political Science.
    6. Anderson, D. Mark & Lochner, Lance, 2016. "Introduction to the Special Issue on Education and Crime," Economics of Education Review, Elsevier, vol. 54(C), pages 211-213.
    7. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    8. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, Institute for the Study of Labor (IZA).
    9. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    10. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    11. 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.

    More about this item

    Keywords

    causal inference; instrumental variables; treatment effects; nonparametric bounds; principal stratification;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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