Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this paper, we examine the impact of increasing the size of a data set in detecting structural breaks. Based on an empirical application, supported by theoretical justification and a simulation experiment, we find that larger sample sizes may make it more rather than less difficult to determine the existence of a structural break.