Abstract
Estimating the capabilities, or inputs of production, that drive and constrain the economic development of urban areas has remained a challenging goal. We posit that capabilities are instantiated in the complexity and sophistication of urban activities, the know-how of individual workers, and the city-wide collective know-how. We derive a model that indicates how the value of these three quantities can be inferred from the probability that an individual in a city is employed in a given urban activity. We illustrate how to estimate empirically these variables using data on employment across industries and metropolitan statistical areas in the USA. We then show how the functional form of the probability function derived from our theory is statistically superior when compared with competing alternative models, and that it explains well-known results in the urban scaling and economic complexity literature. Finally, we show how the quantities are associated with metrics of economic performance, suggesting our theory can provide testable implications for why some cities are more prosperous than others.
Highlights
Fine-grained representations of the economy of countries, regions and cities in terms of what they produce, and the industries they have, have revealed that the location of economic activities across places is not random [1,2,3,4,5,6]
Human capital, institutions or amenities, determine the choices of workers and firms for where to locate 2 and what to produce [15], there is less research on more agnostic models that do not specify a priori what those inputs are. The latter has been a big part of the research programme in the interdisciplinary field of economic complexity [6], and several dimensionality reduction algorithms have been applied to data to extract ‘metrics of complexity’ which quantify the availability and sophistication of inputs present in an economy [2,6,16,17,18,19,20,21,22]
Work in economic complexity and evolutionary economics has proposed that economic development is the process of accumulating an ever-increasing variety of capabilities [2,6,12,56,93], as opposed to a process of increasing the intensity of a few factors of production
Summary
Fine-grained representations of the economy of countries, regions and cities in terms of what they produce, and the industries they have, have revealed that the location of economic activities across places is not random [1,2,3,4,5,6]. Economic complexity metrics were recently applied to US regions and cities [24], prefectures in Japan [25], provinces in China [26], Indian states [27], Mexican states [28] and Colombian cities [29], and shown to explain different aspects of their economic growth (see [6,30] for a review of the literature) These dimensionality reduction algorithms have been difficult to connect with theoretical models of how economies work (see [31,32]). We present how a simple mathematical model can characterize the way inputs combine in cities to generate output (without making strong assumptions about what these inputs may be), how complexity variables emerge and a method to estimate them, and how they correlate with measures of urban economic performance
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