A regression discontinuity design for ordinal running variables: Evaluating central bank purchases of corporate bonds

Regression discontinuity (RD) is a widely used quasi-experimental design for causal inference. In the standard RD the assignment to treatment is determined by a continuous pretreatment variable (i.e., running variable) falling above or below a prefixed threshold. Recent applications increasingly feature ordered categorical or ordinal running variables which pose chal-lenges to RD estimation due to the lack of a meaningful measure of distance. This paper proposes an RD approach for ordinal running variables under the local randomization framework. The proposal first estimates an ordered pro-bit model for the ordinal running variable. The estimated probability of being assigned to treatment is then adopted as a latent continuous running variable and used to identify a covariate-balanced subsample around the threshold. Assuming local unconfoundedness of the treatment in the subsample, an estimate of the effect of the program is obtained by employing a weighted es-timator of the average treatment effect. Two weighting estimators—overlap weights and ATT weights—as well as their augmented versions are consid-ered. We apply the method to evaluate the causal effects of the corporate sector purchase programme (CSPP) of the European Central Bank which in-volves large-scale purchases of securities issued by corporations in the euro area. We find a statistically significant and negative effect of the CSPP on corporate bond spreads at issuance.

Duke Scholars

Author Fan Li Statistical Science