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

There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo-industrial clu...

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Autores principales:	Jaehyuk Park, Ian B. Wood, Elise Jing, Azadeh Nematzadeh, Souvik Ghosh, Michael D. Conover, Yong-Yeol Ahn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/19ac37b569f44c51953b1343772035bc
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spelling	oai:doaj.org-article:19ac37b569f44c51953b1343772035bc2021-12-02T17:31:22ZGlobal labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters10.1038/s41467-019-11380-w2041-1723https://doaj.org/article/19ac37b569f44c51953b1343772035bc2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-11380-whttps://doaj.org/toc/2041-1723There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance compared to existing aggregation units.Jaehyuk ParkIan B. WoodElise JingAzadeh NematzadehSouvik GhoshMichael D. ConoverYong-Yeol AhnNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jaehyuk Park Ian B. Wood Elise Jing Azadeh Nematzadeh Souvik Ghosh Michael D. Conover Yong-Yeol Ahn Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters
description	There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance compared to existing aggregation units.
format	article
author	Jaehyuk Park Ian B. Wood Elise Jing Azadeh Nematzadeh Souvik Ghosh Michael D. Conover Yong-Yeol Ahn
author_facet	Jaehyuk Park Ian B. Wood Elise Jing Azadeh Nematzadeh Souvik Ghosh Michael D. Conover Yong-Yeol Ahn
author_sort	Jaehyuk Park
title	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_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_sort	global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/19ac37b569f44c51953b1343772035bc
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Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters

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