How Much Should We Trust Regression-Kink-Design Estimates?

Abstract

In a Regression Kink (RK) design with a finite sample, a confounding smooth nonlinear relationship between an assignment variable and an outcome variable around a threshold can be spuriously picked up as a kink and result in a biased estimate. In order to investigate how well RK designs handle such confounding nonlinearity, I firstly implement Monte Carlo simulations and then study the effect of fiscal equalization grants on local expenditure using a RK design. Results suggest that RK estimation with a confounding nonlinearity often suffers from bias or imprecision and estimates are credible only when relevant covariates are controlled for.