Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Despite the availability of more sophisticated methods, a popular way to estimate a Pareto exponent is still to run an OLS regression: log(Rank) = <italic>a</italic> - <italic>b</italic> log(Size), and take <italic>b</italic> as an estimate of the Pareto exponent. The reason for this popularity is arguably the simplicity and robustness of this method. Unfortunately, this procedure is strongly biased in small samples. We provide a simple practical remedy for this bias, and propose that, if one wants to use an OLS regression, one should use the Rank - 1 / 2, and run log(Rank - 1 / 2) = <italic>a</italic> - <italic>b</italic> log(Size). The shift of 1 / 2 is optimal, and reduces the bias to a leading order. The standard error on the Pareto exponent <italic>ζ</italic> is not the OLS standard error, but is asymptotically (2 / <italic>n</italic>)-super-1 / 2 <italic>ζ</italic>. Numerical results demonstrate the advantage of the proposed approach over the standard OLS estimation procedures and indicate that it performs well under dependent heavy-tailed processes exhibiting deviations from power laws. The estimation procedures considered are illustrated using an empirical application to Zipf's law for the United States city size distribution.