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

**Authors:** Jaehyuk Park, Ian Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael Conover, Yong-Yeol Ahn  
**arXiv:** [1902.04613](https://arxiv.org/abs/1902.04613)  
**Date:** 2019-02-12  
**Summary page:** https://www.summarizepaper.com/fr/paper/1902.04613v4/

## 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.