This appendix presents evidence that the US size and growth data sets analyzed in the main body of the paper are consistent with the stylized facts of (i) Zipf's law in city size distribution tails; (ii) Lognormality of city size distribution bodies; (iii) Gibrat's law (approximately) for the...

I develop an explanation of Zipf's law that is consistent with the observed marked heterogeneity in the growth of US cities. The explanation is that heterogeneous growth results in heterogeneous size distributions across cities, with the heaviest tailed distributions being Zipf and dominating...

We establish that the debate between Eeckhout (2004; 2009) and Levy (2009) has still not resolved the key issue of whether the distribution of large US urban places in 2000 is consistent with a lognormal for the entire size range. We resolve this by introducing a new distribution function which...

This note contests a widely cited, used and derived ‘result’ according to which Gibrat’s law implies Zipf’s law. I show that this result is only true under strict, unrecognized and ad hoc additional conditions some of which are even inconsistent with the economic models that have been...

We establish that the debate between Eeckhout (2004; 2009) and Levy (2009) has still not resolved the key issue of whether the distribution of large US urban places in 2000 is consistent with a lognormal for the intire size range. We resolve this by introducing a new distribution function which...

We establish empirically using three different definitions of US cities that the upper tail obeys a Pareto law and not a lognormal distribution. We emphasize estimation of a switching point between the body of the city size distribution (which includes most cities) and its upper tail (which...