Estimating the drivers of urban economic complexity and their connection to economic performance

TL;DR

This paper develops a model to estimate urban capabilities from employment data, linking urban activity complexity to economic performance, and demonstrates its empirical validity and relevance to urban prosperity.

Contribution

It introduces a novel model connecting urban activity complexity, worker knowhow, and city-wide collective knowhow to economic development, validated with US employment data.

Findings

The proposed probability model outperforms alternatives in explaining employment patterns.

Estimated capabilities correlate with urban economic performance metrics.

The model aligns with established urban scaling and economic complexity results.

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 knowhow of individual workers, and the city-wide collective knowhow. 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 US. We then show how the functional form of the probability function derived from our theory is statistically superior when compared to 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.