A Statistical Test of City Growth: Location, Increasing Returns and Random Growth

This article analyzes the main existing theories on income and population city growth: the existence of increasing returns to scale, the importance of locational fundamentals, and random growth. To do this we develop a nonlinearity test that is implemented to a dataset on urban, climatological and macroeconomic variables on 1,175 U.S. cities. The conclusions of our analysis are that there are increasing returns to scale on city income growth; nevertheless, the most important variables to explain income growth are locational fundamentals. Both sets of variables need to be jointly considered to avoid inconsistent model parameter estimates. We also observe increasing returns to scale on population growth; larger cities grow at a faster pace than smaller cities. These cities are not, however, within the group of wealthiest cities implying the existence of a threshold on population beyond which per-capita income growth stagnates or even deteriorates.