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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters
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 scarc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6671949/ https://www.ncbi.nlm.nih.gov/pubmed/31371716 http://dx.doi.org/10.1038/s41467-019-11380-w |
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author | Park, Jaehyuk Wood, Ian B. Jing, Elise Nematzadeh, Azadeh Ghosh, Souvik Conover, Michael D. Ahn, Yong-Yeol |
author_facet | Park, Jaehyuk Wood, Ian B. Jing, Elise Nematzadeh, Azadeh Ghosh, Souvik Conover, Michael D. Ahn, Yong-Yeol |
author_sort | Park, Jaehyuk |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6671949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66719492019-08-02 Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters Park, Jaehyuk Wood, Ian B. Jing, Elise Nematzadeh, Azadeh Ghosh, Souvik Conover, Michael D. Ahn, Yong-Yeol Nat Commun Article 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. Nature Publishing Group UK 2019-08-01 /pmc/articles/PMC6671949/ /pubmed/31371716 http://dx.doi.org/10.1038/s41467-019-11380-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Park, Jaehyuk Wood, Ian B. Jing, Elise Nematzadeh, Azadeh Ghosh, Souvik Conover, Michael D. Ahn, Yong-Yeol Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title | Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title_full | Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title_fullStr | Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title_full_unstemmed | Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title_short | Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
title_sort | global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6671949/ https://www.ncbi.nlm.nih.gov/pubmed/31371716 http://dx.doi.org/10.1038/s41467-019-11380-w |
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