Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
For a cluster-robust <i>t</i>-statistic under cluster heterogeneity we establish that the cluster-robust <i>t</i>-statistic has a gaussian asymptotic null distribution and develop the effective number of clusters, which scales down the actual number of clusters, as a guide to the behavior of the test statistic. The implications for hypothesis testing in applied work are that the number of clusters, rather than the number of observations, should be reported as the sample size, and the effective number of clusters should be reported to guide inference. If the effective number of clusters is large, testing based on critical values from a normal distribution is appropriate.