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- setting 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.
We are especially grateful to Matias Cattaneo for suggestions that significantly improved the chapter. We also thank Gary Solon, Helle Bunzel, Valentin Verdier, Thomas Fujiwara, Maggie Jones, and participants at the 2014 North American Summer Meetings of the Econometric Society, 2014 Midwest Econometrics Group, 2015 Econometric Society World Congress, Advances in Econometrics RD Conference, and U.S. Census Bureau for helpful comments. We also are grateful to Matthew Freedman and Emily Owens for providing us with data and code to compute QCT eligibility for all census tracts. The views expressed within are those of the authors and not necessarily those of the U.S. Census Bureau. Any errors or omissions are our own.
Bartalotti, O. and Brummet, Q. (2017), "Regression Discontinuity Designs with Clustered Data", Regression Discontinuity Designs (Advances in Econometrics, Vol. 38), Emerald Publishing Limited, Leeds, pp. 383-420. https://doi.org/10.1108/S0731-905320170000038017
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