Complex Economic Activities Concentrate in Large Cities

C. Jara-Figueroa, P.A. Balland, and C. Hidalgo 

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less—complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy.

The Figure below shows maps of the spatial concentration of A patents, B research papers, C GDP of industries, and D number of employees in U.S. Metropolitan Statistical Areas (MSAs). Scaling relations between the population of an MSA and E the total number of patents granted from 2000 to 2009, F the number of research papers published from 1996 to 2008, G the GDP of industries in 2015, and H the number of employees in 2015. Scaling relationships for pairs of economic activities with large differences in their scaling exponents: I patents in “computer, hardware, and software” and “pipes and joints," J Research papers in “Neuroscience” and “Arts and Humanities,” K economic output (GDP) of “professional and scientific activities” and “retail trade,” L employment in “computer and mathematical” occupations and in “installation, maintenance, and repair.” 

We find that urban concentration increases with complexity. The figure below shows that the urban concentration of economic activities, as captured by the scaling exponent, increases with (A) The average year when the patent sub-classes were introduced; (B) the average number of authors in a publication in a field; (C) the average years of education of the workers employed in an industry; and (D) the average years of education of workers within an occupational category. 

Over time, the historical scaling of patenting activity has been increasing for the most complex patents. (A) shows that the scaling exponent of the top 25% most complex technologies increases throughout the observation period, while that of the bottom 25% of technologies based on complexity, peaks in 1960 and then decreases. The scaling exponent for all patents increases from 1850 to 1930, and then remains relatively stable until 2000. (B) shows the scaling exponent of the main six patent categories between 1850 and 2000.