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

Jaehyuk Park, Ian Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael Conover and Yong-Yeol Ahn

Papers from arXiv.org

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.

Date: 2019-02, Revised 2019-03
New Economics Papers: this item is included in nep-geo, nep-hme and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Published in Nature Communicationsvolume 10, Article number: 3449 (2019)

Downloads: (external link)
http://arxiv.org/pdf/1902.04613 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1902.04613

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().