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It's never too LATE: A new look at local average treatment effects with or without defiers

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Abstract
In heterogeneous treatment effect models with endogeneity, identification of the LATE typically relies on the availability of an exogenous instrument monotonically related to treatment participation. We demonstrate that a strictly weaker local monotonicity condition identifies the LATEs on compliers and on defiers. We propose simple estimators that are potentially more efficient than 2SLS, even under circumstances where 2SLS is consistent. Additionally, when easing local monotonicity to local stochastic monotonicity, our identification results still apply to subsets of compliers and defiers. Finally, we provide an empirical application, rejoining the endeavor of estimating returns to education using the quarter of birth instrument.

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  • 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 on Economics 2/2017, University of Southern Denmark, Department of Economics.
    • Handle: RePEc:hhs:sdueko:2017_002
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    1. Kasey S. Buckles & Daniel M. Hungerman, 2013. "Season of Birth and Later Outcomes: Old Questions, New Answers," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 711-724, July.
    2. Klein, Tobias J., 2010. "Heterogeneous treatment effects: Instrumental variables without monotonicity?," Journal of Econometrics, Elsevier, vol. 155(2), pages 99-116, April.
    3. 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.
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    5. Mammen, Enno & Polonik, Wolfgang, 2013. "Confidence regions for level sets," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 202-214.
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    8. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
    9. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    10. Kelly Bedard & Elizabeth Dhuey, 2006. "The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(4), pages 1437-1472.
    11. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 979-1014.
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    Cited by:

    1. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    2. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    3. van ’t Hoff, Nadja & Lewbel, Arthur & Mellace, Giovanni, 2023. "Limited Monotonicity and the Combined Compliers LATE," Discussion Papers on Economics 2/2023, University of Southern Denmark, Department of Economics.
    4. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
    5. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Jan 2024.
    6. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
    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; local monotonicity;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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