Inference in Regression Discontinuity Designs with Clustered Data

Music City Center 

Clustered sampling is prevalent in empirical regression discontinuity (RD) designs, but it did not gain much attention in the theoretical literature. In this paper, we introduce a general model-based framework for such settings and derive high-level conditions under which the standard local linear RD estimator is asymptotically normal. We show that clustered standard errors that are currently used in practice can be overly conservative in finite samples. To address these issues, we propose a new nearest-neighbor-type variance estimator. We verify that our high-level assumptions hold across a wide range of empirical designs, including settings of growing cluster sizes, and demonstrate the estimator's finite-sample performance via simulations and a diverse set of applications.

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