Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy

TL;DR

This paper constructs a global labor flow network from LinkedIn data to uncover hierarchical geo-industrial clusters, revealing their link to economic performance and productivity growth, offering new insights into economic dynamics.

Contribution

It introduces a systematic, data-driven method to identify hierarchical geo-industrial clusters at the global scale using labor flow networks.

Findings

Geo-industrial clusters show a strong link between educated-worker influx and financial performance.

Labor flow of educated-workers correlates with productivity growth.

Clusters defined by labor flow provide better economic insights than traditional units.

Abstract

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the global scale are rare. In this work, we use LinkedIn's employment histories of more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world and apply a recursive network community detection algorithm to reveal the hierarchical structure of geo-industrial clusters. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance, compared to existing aggregation units. Furthermore, our additional analysis of the skill sets of educated-workers supplements the relationship between the labor flow of educated-workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide better insights into the growth and the decline of the economy than other common economic units.