A kinked meta-regression model for publication bias correction

Publication bias distorts the available empirical evidence and misinforms policymaking. Evidence of publication bias is mounting in virtually all fields of empirical research. This paper proposes the endogenous kink (EK) meta-regression model as a novel method of publication bias correction. The EK method fits a piecewise linear meta-regression of the primary estimates on their standard errors, with a kink at the cutoff value of the standard error below which publication selection is unlikely. We provide a simple method of endogenously determining this cutoff value as a function of a first-stage estimate of the true effect and an assumed threshold of statistical significance. Our Monte Carlo simulations show that EK is less biased and more efficient than other related regression-based methods of publication bias correction in a variety of research conditions.