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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. 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.
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    3. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
    4. 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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    8. 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.
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    Cited by:

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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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