# 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/en/paper/1902.04613v4/

## Key points

- Formation of geo-industrial clusters is a common strategy for groups of firms to achieve competitive advantage
- Hollywood and Silicon Valley are exemplary clusters that have been studied extensively
- Systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are rare
- Jaehyuk Park and colleagues used LinkedIn's employment histories of over 500 million users spanning 25 years to construct a labor flow network comprising more than four million firms worldwide
- Recursive network community detection algorithm was applied to reveal the hierarchical organization and dynamics of geo-industrial clusters
- Resulting clusters showed a stronger association between influx of educated workers and financial performance compared to existing aggregation units
- Analysis of skill sets of educated workers further strengthened relationship between labor flow and productivity growth
- Geo-industrial clusters defined by labor flow provide better insights into economic growth and decline than other common economic units
- Firms can leverage labor flows within these clusters to gain a competitive edge in today's global economy.

## Summary

The formation of geo-industrial clusters is a common strategy for groups of firms to achieve a competitive advantage. Exemplary clusters such as Hollywood and Silicon Valley have been studied extensively, but systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are rare. In this study, Jaehyuk Park and colleagues used LinkedIn's employment histories of over 500 million users spanning 25 years to construct a labor flow network comprising more than four million firms worldwide. They applied a recursive network community detection algorithm to reveal the hierarchical organization and dynamics of geo-industrial clusters. The resulting clusters showed a stronger association between the influx of educated workers and financial performance compared to existing aggregation units. Additionally, their analysis of the skill sets of educated workers further strengthened the relationship between labor flow and productivity growth. The authors argued that geo-industrial clusters defined by labor flow provide better insights into economic growth and decline than other common economic units. This study sheds light on how firms can leverage labor flows within these clusters to gain a competitive edge in today's global economy.