Name
Title | ||
|---|---|---|
Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy. |
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 LinkedInu0027s 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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Social and Information Networks | Journal |
Volume | Citations | PageRank |
abs/1902.04613 | 1 | 0.37 |
References | Authors | |
4 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaehyuk Park | 1 | 1 | 0.71 |
| Ian B. Wood | 2 | 1 | 0.71 |
| Elise Jing | 3 | 1 | 0.71 |
| Azadeh Nematzadeh | 4 | 12 | 3.31 |
| Souvik Ghosh | 5 | 23 | 7.36 |
| Michael Conover | 6 | 783 | 42.50 |
| Yong-Yeol Ahn | 7 | 2124 | 138.24 |