Regression Discontinuity Designs with a Continuous Treatment

61 Pages Posted: 8 May 2018 Last revised: 29 Oct 2019

See all articles by Yingying Dong

Yingying Dong

UC Irvine

Ying-Ying Lee

University of California, Irvine - Department of Economics

Michael Gou

University of California, Irvine

Date Written: April 1, 2019

Abstract

Many empirical applications of regression discontinuity (RD) designs involve a continuous treatment. This paper establishes identification and bias-corrected robust inference for such RD designs. Causal identification is achieved by utilizing any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). Applying the proposed approach, we estimate the impacts of capital holdings on bank failure in the pre-Great Depression era. Our RD design takes advantage of the minimum capital requirements which change discontinuously with town size. We find that increased capital has no impacts on banks’ long-run failure rates.

Keywords: Distributional change, Treatment Quantile, Rank invariance, Rank similarity, Capital regulation

JEL Classification: C21, C25, I23

Suggested Citation

Dong, Yingying and Lee, Ying-Ying and Gou, Michael, Regression Discontinuity Designs with a Continuous Treatment (April 1, 2019). Available at SSRN: https://ssrn.com/abstract=3167541 or http://dx.doi.org/10.2139/ssrn.3167541

Yingying Dong (Contact Author)

UC Irvine ( email )

3151 Social Science Plaza
Irvine, CA 92617
United States

Ying-Ying Lee

University of California, Irvine - Department of Economics ( email )

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

Michael Gou

University of California, Irvine ( email )

P.O. Box 19556
Science Library Serials
Irvine, CA 62697-3125
United States

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