Zipf’s Law for Chinese Cities: Rolling Sample Regressions

We study the validity of Zipf’s Law in a data set of Chinese city sizes. Previous investigations are restricted to log-log rank-size regression for a fixed sample. In contrast, we use rolling sample regression methods in which the sample is changing with the truncation point. The intuition is that if the distribution is Pareto with a coefficient one (Zipf’s law holds), rolling sample regressions should yield a constant coefficient regardless of what the sample is. We find that the Pareto exponent is almost monotonically decreasing in the truncation point; the mean estimated coefficient is 0.84 for the full dataset, which is not so far from 1.