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Growth in a cross-section of cities: location, increasing returns or random growth?

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This article analyzes empirically the main existing theories on income and population city growth: increasing returns to scale, locational fundamentals and random growth. To do this we implement a threshold nonlinearity test that extends standard linear growth regression models to a dataset on urban, climatological and macroeconòmic variables on 1,175 U.S. cities. Our analysis reveals the existence of increasing returns when per-capita income levels are beyond $19; 264. Despite this, income growth is mostly explained by social and locational fundamentals. Population growth also exhibits two distinct equilibria determined by a threshold value of 116,300 inhabitants beyond which city population grows at a higher rate. Income and population growth do not go hand in hand, implying an optimal level of population beyond which income growth stagnates or deteriorates.

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cc-by-nc-nd, (c) González-Val et al., 2011

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IEB (Institut d’Economia de Barcelona) – Working Papers

# metadata.dc.title[].value
Growth in a cross-section of cities: location, increasing returns or random growth?