Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
type="main" xml:id="ecin12225-abs-0001"> <p xml:id="ecin12225-para-0001">This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics may not be well suited to identifying this type of problem, we provide alternatives, and then discuss the usefulness of different approaches to addressing the bias. We then consider these issues in multiple non-simulated environments. (JEL C21, C14, I12)