Regression Discontinuity Designs with Clustered Data

Otavio Bartalotti and Quentin Brummet

A chapter in Regression Discontinuity Designs, 2017, vol. 38, pp 383-420 from Emerald Group Publishing Limited

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.

Keywords: Regression discontinuity designs; local polynomials; clustering; optimal bandwidth selection; C13; C14; C21 (search for similar items in EconPapers)
Date: 2017
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Working Paper: Regression Discontinuity Designs with Clustered Data (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320170000038017

DOI: 10.1108/S0731-905320170000038017

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