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

Abstract

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedIn’s employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide useful insights into the growth of the economy.

Abstract

There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance compared to existing aggregation units.