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Alternative Assumptions to Identify LATE in Fuzzy Regression Discontinuity Designs

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  • Yingying Dong

Abstract

There exist two alternative assumptions to identify local average treatment effects (LATE) in fuzzy regression discontinuity (RD) designs: local independence (LI) and local smoothness (LS). Together with the usual LATE assumptions requiring existence of a first‐stage and treatment monotonicity, either of these two assumptions is sufficient to identify RD LATE. I discuss the practical (and testable) implications of these alternative assumptions, and show that weakening LI by LS might be empirically relevant. However, when LI does hold, there are some practical implications one may explore. Numerical and empirical examples are briefly presented.

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  • 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.
    • Handle: RePEc:bla:obuest:v:80:y:2018:i:5:p:1020-1027
    DOI: 10.1111/obes.12249
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    Cited by:

    1. Colubi, Ana & Ramos-Guajardo, Ana Belén, 2023. "Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics," Econometrics and Statistics, Elsevier, vol. 26(C), pages 84-98.
    2. Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Working Papers 2021-022, Human Capital and Economic Opportunity Working Group.
    3. Yoichi Arai & Yu‐Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2022. "Testing identifying assumptions in fuzzy regression discontinuity designs," Quantitative Economics, Econometric Society, vol. 13(1), pages 1-28, January.
    4. Louise Grogan & Fraser Summerfield, 2019. "Government Transfers, Work, and Wellbeing: Evidence from the Russian Old-Age Pension," Journal of Population Economics, Springer;European Society for Population Economics, vol. 32(4), pages 1247-1292, October.
    5. 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.
    6. 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.
    7. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    8. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
    9. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2023. "A Guide to Regression Discontinuity Designs in Medical Applications," Papers 2302.07413, arXiv.org, revised May 2023.
    10. François Gerard & Miikka Rokkanen & Christoph Rothe, 2020. "Bounds on treatment effects in regression discontinuity designs with a manipulated running variable," Quantitative Economics, Econometric Society, vol. 11(3), pages 839-870, July.
    11. Alexander Krei{ss} & Christoph Rothe, 2021. "Inference in Regression Discontinuity Designs with High-Dimensional Covariates," Papers 2110.13725, arXiv.org, revised May 2022.
    12. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).
    13. 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.
    14. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    15. Bertanha, Marinho, 2020. "Regression discontinuity design with many thresholds," Journal of Econometrics, Elsevier, vol. 218(1), pages 216-241.
    16. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    17. Hsu, Yu-Chin & Shiu, Ji-Liang & Wan, Yuanyuan, 2024. "Testing identification conditions of LATE in fuzzy regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 241(1).
    18. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion Paper Series 730, Institute of Economic Research, Hitotsubashi University.
    19. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.
    20. Haitian Xie, 2022. "Nonlinear and Nonseparable Structural Functions in Fuzzy Regression Discontinuity Designs," Papers 2204.08168, arXiv.org, revised Jul 2022.

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