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

Jaehyuk Park, Ian B. Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael D. Conover () and Yong-Yeol Ahn ()
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Jaehyuk Park: Indiana University
Ian B. Wood: Indiana University
Elise Jing: Indiana University
Azadeh Nematzadeh: Indiana University
Souvik Ghosh: LinkedIn
Michael D. Conover: LinkedIn
Yong-Yeol Ahn: Indiana University

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-11380-w

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