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It is never too LATE: a new look at local average treatment effects with or without defiers

Author

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  • Christian M Dahl
  • Martin Huber
  • Giovanni Mellace

Abstract

SummaryIn heterogeneous treatment effect models with endogeneity, identification of the local average treatment effect (LATE) typically relies on the availability of an exogenous instrument monotonically related to treatment participation. First, we demonstrate that a strictly weaker local monotonicity condition—invoked for specific potential outcome values rather than globally—identifies the LATEs on compliers and defiers. Second, we show that our identification results apply to subsets of compliers and defiers when imposing an even weaker local compliers-defiers assumption that allows for both types at any potential outcome value. We propose estimators that are potentially more efficient than two-stage least squares (2SLS) in finite samples, even in cases where 2SLS is consistent. Finally, we provide an empirical application to estimating returns to education using the quarter of birth instrument.

Suggested Citation

  • Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    • Handle: RePEc:oup:emjrnl:v:26:y:2023:i:3:p:378-404.
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    File URL: http://hdl.handle.net/10.1093/ectj/utad013
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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. 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.
    3. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Feb 2025.
    4. Camilo Garcia-Jimeno & Sahar Parsa, 2024. "Cultural Change Through Writing Style: Gendered Pronoun Use in the Economics Profession," Working Paper Series WP 2024-23, Federal Reserve Bank of Chicago.
    5. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 20 Jan 2025.
    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. Attar, Itay & Cohen-Zada, Danny & Elder, Todd E., 2024. "Measuring and Correcting Monotonicity Bias: The Case of School Entrance Age Effects," IZA Discussion Papers 17088, Institute of Labor Economics (IZA).
    8. 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.
    9. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    10. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.
    11. Yuta Ota & Takahiro Hoshino & Taisuke Otsu, 2024. "Causal Inference With Auxiliary Observations," Keio-IES Discussion Paper Series 2024-022, Institute for Economics Studies, Keio University.

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    Keywords

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    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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