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Relaxing conditions for local average treatment effect in fuzzy regression discontinuity

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  • Choi, Jin-young
  • Lee, Myoung-jae

Abstract

In fuzzy regression discontinuity with a running/forcing variable S and a cutoff c, the identified treatment effect is the ‘effect on compliers at S=c’. This well-known ‘local average treatment effect (LATE)’ interpretation requires (i) a monotonicity condition and (ii) the independence of the potential treatment and potential response variables from S. These assumptions can be violated, however, particularly (ii) when S affects potential variables, which can easily happen in practice. In this paper, we weaken both assumptions so that LATE in fuzzy regression discontinuity has a better chance to hold in the real world, and practitioners can claim their findings in fuzzy regression discontinuity to be LATE.

Suggested Citation

  • Choi, Jin-young & Lee, Myoung-jae, 2018. "Relaxing conditions for local average treatment effect in fuzzy regression discontinuity," Economics Letters, Elsevier, vol. 173(C), pages 47-50.
    • Handle: RePEc:eee:ecolet:v:173:y:2018:i:c:p:47-50
    DOI: 10.1016/j.econlet.2018.09.010
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    References listed on IDEAS

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    1. Frandsen, Brigham R. & Frölich, Markus & Melly, Blaise, 2012. "Quantile treatment effects in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 168(2), pages 382-395.
    2. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
    3. Yingying Dong, 2018. "Alternative Assumptions to Identify LATE in Fuzzy Regression Discontinuity Designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(5), pages 1020-1027, October.
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    5. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    6. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    7. Lee, Myoung-jae, 2016. "Matching, Regression Discontinuity, Difference in Differences, and Beyond," OUP Catalogue, Oxford University Press, number 9780190258740.
    8. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    9. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
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    Cited by:

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    2. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    3. Dervisevic,Ervin & Perova,Elizaveta & Sahay,Abhilasha, 2022. "Conditional Cash Transfers and Gender-Based Violence—Does the Type of Violence Matter ?," Policy Research Working Paper Series 10122, The World Bank.
    4. Lee, M-j.; & Park, S-s.; & Shim, H-c.;, 2019. "Regression Discontinuity with Integer Running Variable and Non-Integer Cutoff: Dental Care Program Effect on Expenditure," Health, Econometrics and Data Group (HEDG) Working Papers 19/16, HEDG, c/o Department of Economics, University of York.
    5. Jin‐young Choi & Myoung‐jae Lee, 2023. "Score manipulation, density continuity and intent‐to‐treat effect for regression discontinuity," Pacific Economic Review, Wiley Blackwell, vol. 28(4), pages 552-569, October.
    6. Choi, Jin-young & Lee, Myoung-jae, 2023. "Complier and monotonicity for Fuzzy Multi-score Regression Discontinuity with partial effects," Economics Letters, Elsevier, vol. 228(C).
    7. Goeun Lee & Myoung-jae Lee, 2023. "Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment," Evaluation Review, , vol. 47(2), pages 182-208, April.

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    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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