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 for the years 1999–2004, when the numbers of cities remain almost constant after a rapid urbanization process during the period of the market-oriented economy and reform-open policy. 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.