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Defying the LATE? Identification of local treatment effects when the instrument violates monotonicity

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  • de Chaisemartin, Clement

    (Department of Economics, University of Warwick)

Abstract

The instrumental variable method relies on strong "no-defiers" condition, which requires that the instrument affect every subject's treatment decision in the same direction. This paper shows that "no-defiers" can be replaced by a weaker "compliers-defiers" condition, which requires that a subgroup of compliers have the same size and the same distribution of potential outcomes as defiers. This condition is necessary and sufficient for IV to capture causal effects for the reamining part of compliers. In many applications, "compliers-defiers" is a very weak condition. For instance, in Angrist & Evans (1998), 94% of DGPs compatible with the data satisfy "compliers-defiers", while 0% satisfy "no-defiers". JEL classification: Instrumental variable ; heterongeneous effects ; defiers ; single index model JEL codes: C21; C26

Suggested Citation

  • 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.
  • Handle: RePEc:wrk:warwec:1020
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    File URL: https://warwick.ac.uk/fac/soc/economics/research/workingpapers/2013/twerp_1020_chaisemartin.pdf
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    References listed on IDEAS

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    1. 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.
    2. Rashmi Barua & Kevin Lang, 2009. "School Entry, Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE," NBER Working Papers 15236, National Bureau of Economic Research, Inc.
    3. DiNardo, John & Lee, David S., 2011. "Program Evaluation and Research Designs," Handbook of Labor Economics, Elsevier.
    4. Luc Behaghel & Bruno Crépon & Marc Gurgand & Thomas Le Barbanchon, 2015. "Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models," The Review of Economics and Statistics, MIT Press, vol. 97(5), pages 1070-1080, December.
    5. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    6. Klein, Tobias J., 2010. "Heterogeneous treatment effects: Instrumental variables without monotonicity?," Journal of Econometrics, Elsevier, vol. 155(2), pages 99-116, April.
    7. 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.
    8. Jeffrey R. Kling, 2006. "Incarceration Length, Employment, and Earnings," American Economic Review, American Economic Association, vol. 96(3), pages 863-876, June.
    9. 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.
    10. Kelly Bedard & Elizabeth Dhuey, 2006. "The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects," The Quarterly Journal of Economics, Oxford University Press, vol. 121(4), pages 1437-1472.
    11. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 555-574.
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    Cited by:

    1. repec:tpr:restat:v:99:y:2017:i:2:p:305-313 is not listed on IDEAS
    2. Ismael Mourifié & Yuanyuan Wan, 2017. "Testing Local Average Treatment Effect Assumptions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 305-313, May.

    More about this item

    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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