Regression Discontinuity Designs with a Continuous Treatment
61 Pages Posted: 8 May 2018 Last revised: 29 Oct 2019
Date Written: April 1, 2019
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: Suggested Citation