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Regression Discontinuity Designs with Clustered Data

In: Regression Discontinuity Designs

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  • Otávio Bartalotti
  • Quentin Brummet

Abstract

Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed-Gsetting that allows for cluster dependence in the error term. Simulation results demonstrate that accounting for clustering in the data while selecting bandwidths may lead to lower MSE while maintaining proper coverage. We then apply our cluster-robust procedure to an application examining the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.

Suggested Citation

  • Otávio Bartalotti & Quentin Brummet, 2017. "Regression Discontinuity Designs with Clustered Data," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 383-420, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320170000038017
    DOI: 10.1108/S0731-905320170000038017
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    Cited by:

    1. Bartalotti Otávio, 2019. "Regression Discontinuity and Heteroskedasticity Robust Standard Errors: Evidence from a Fixed-Bandwidth Approximation," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-26, January.
    2. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals [Using Maimonides’ rule to estimate the effect of class size on scholastic achievemen," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    3. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocío Titiunik, 2019. "Regression Discontinuity Designs Using Covariates," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 442-451, July.
    4. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    5. Ari Hyytinen & Jaakko Meriläinen & Tuukka Saarimaa & Otto Toivanen & Janne Tukiainen, 2018. "When does regression discontinuity design work? Evidence from random election outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 1019-1051, July.
    6. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2021. "Covariate Adjustment in Regression Discontinuity Designs," Papers 2110.08410, arXiv.org, revised Aug 2022.
    7. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    8. de Gendre, Alexandra & Lynch, John & Meunier, Aurélie & Pilkington, Rhiannon & Schurer, Stefanie, 2021. "Child Health and Parental Responses to an Unconditional Cash Transfer at Birth," IZA Discussion Papers 14693, Institute of Labor Economics (IZA).
    9. Sebastian Calonico & Matias D Cattaneo & Max H Farrell, 2020. "Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 192-210.
    10. Steven Dieterle & Otávio Bartalotti & Quentin Brummet, 2020. "Revisiting the Effects of Unemployment Insurance Extensions on Unemployment: A Measurement-Error-Corrected Regression Discontinuity Approach," American Economic Journal: Economic Policy, American Economic Association, vol. 12(2), pages 84-114, May.
    11. Nicholas A. Bowman & Nayoung Jang, 2022. "What is the Purpose of Academic Probation? Its Substantial Negative Effects on Four-Year Graduation," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(8), pages 1285-1311, December.
    12. Yang Lixiong, 2019. "Regression discontinuity designs with unknown state-dependent discontinuity points: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-18, April.
    13. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    14. Keita, Sekou & Mandon, Pierre, 2018. "Give a fish or teach fishing? Partisan affiliation of U.S. governors and the poverty status of immigrants," European Journal of Political Economy, Elsevier, vol. 55(C), pages 65-96.
    15. Samuel Lordemus, 2022. "Does Aid for Malaria Increase with Exposure to Malaria Risk? Evidence from Mining Sites in the D.R.Congo," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 719-748, August.

    More about this item

    Keywords

    Regression discontinuity designs; local polynomials; clustering; optimal bandwidth selection; C13; C14; C21;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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

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