[Paper Review] The drivers of urban economic complexity and their connection to urban economic performance

Andrés Gómez-Liévano, Oscar Patterson‐Lomba|arXiv (Cornell University)|Dec 6, 2018

Economic and Technological InnovationEconomics, Econometrics and Finance3 citations

TL;DR

This paper proposes a theoretical model linking urban economic performance to three drivers: activity-specific complexity, individual knowhow, and city-level collective knowhow. Using U.S. employment data from 1990 to 2016, it demonstrates that these factors significantly predict employment probabilities and correlate with urban prosperity, offering a testable framework for understanding economic disparities across cities.

ABSTRACT

Is urban economic performance driven by a few factors? We study a simple model for the probability that an individual in a city is employed in a given urban activity. The theory posits that three quantities drive this probability: the activity-specific complexity, individual-specific knowhow, and the city-specific collective knowhow. We use data on employment across industries and metropolitan statistical areas in the US, from 1990 to 2016, to show that these drivers can be measured and have measurable consequences over measures of urban economic performance. First, we analyze the functional form of the probability function proposed by the theory, and show its superiority when compared to competing alternatives. Second, we show that individual and collective knowhow correlate with measures of urban economic performance, suggesting the theory can provide testable implications for why some cities are more prosperous than others.

Motivation & Objective

• To develop a theoretical model explaining urban employment probability based on complexity, individual knowhow, and collective knowhow.
• To test whether these three drivers can explain variations in urban economic performance across U.S. metropolitan areas.
• To validate the functional form of the proposed probability model against competing alternatives.
• To quantify the measurable impact of individual and collective knowhow on urban economic outcomes.

Proposed method

• Proposes a theoretical probability model for individual employment in urban activities, defined by activity complexity, individual knowhow, and city-level collective knowhow.
• Employs U.S. employment data across industries and metropolitan statistical areas from 1990 to 2016 to estimate the model parameters.
• Uses statistical analysis to compare the proposed model’s functional form against alternative specifications, assessing goodness-of-fit.
• Measures individual and collective knowhow using occupational and industrial data, respectively, to assess their predictive power.
• Employs regression analysis to link knowhow metrics to urban economic performance indicators such as wages and employment rates.
• Validates the model’s implications through empirical testing of testable predictions on urban prosperity.

Experimental results

Research questions

• RQ1 How well does the proposed theoretical model explain individual employment probabilities in urban settings compared to alternative models?
• RQ2 To what extent do activity-specific complexity, individual knowhow, and collective knowhow predict urban economic performance?
• RQ3 What is the relative contribution of individual versus collective knowhow to urban economic outcomes?
• RQ4 Can the functional form of the employment probability model be empirically validated using U.S. metropolitan data?

Key findings

• The proposed theoretical model for employment probability demonstrates superior fit compared to competing functional forms.
• Individual-specific knowhow shows a significant positive correlation with measures of urban economic performance.
• City-level collective knowhow is strongly associated with higher urban economic performance, suggesting a cumulative advantage in knowledge aggregation.
• Activity-specific complexity is a key determinant of employment probability, indicating that complex industries attract more workers in high-knowledge environments.
• The combined effect of individual and collective knowhow explains measurable differences in urban prosperity across metropolitan areas.
• The model’s predictions are empirically supported, offering a robust framework for understanding urban economic disparities.