Socio-spatial relations observed in the global city network of firms.
One of the prevailing approaches to the study of the global economy is the analysis of global city networks based on the activities of multinational firms. Research in this vein generally conceptualises cities as nodes, and the intra-firm relations between them as ties, forming the building blocks f...
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oai:doaj.org-article:f28307f0ab5449e296621e6abdb872582021-12-02T20:18:01ZSocio-spatial relations observed in the global city network of firms.1932-620310.1371/journal.pone.0255461https://doaj.org/article/f28307f0ab5449e296621e6abdb872582021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255461https://doaj.org/toc/1932-6203One of the prevailing approaches to the study of the global economy is the analysis of global city networks based on the activities of multinational firms. Research in this vein generally conceptualises cities as nodes, and the intra-firm relations between them as ties, forming the building blocks for globally scaled interurban networks. While such an approach has provided a valuable heuristic for understanding how cities are globally connected, and how the global economy can be conceived of as a network of cities, there is a lack of understanding as to how and why cities are connected, and which factors contribute to the existence of ties between cities. Here, we explain how five distinct socio-spatial dimensions contribute to global city network structure through their diverse effects on interurban dyads. Based on data from 13,583 multinational firms with 163,821 international subsidiary locations drawn from 208 global securities exchanges, we hypothesise how regional, linguistic, industrial, developmental, and command & control relations may contribute to network structure. We then test these by applying an exponential random graph model (ERGM) to explain how each dimension may contribute to cities' embeddedness within the overall network. Though all are shown to shape interurban relations to some extent, we find that two cities sharing a common industrial base are more likely to be connected. The ERGM also reveals a strong core-periphery structure in that cities in middle- and low-income countries are more reliant on connectivity than those in high-income countries. Our findings indicate that, despite claims seeking to de-emphasise the top-heavy organisational structure of the global urban economic network, interurban relations are characterised by uneven global development in which socio-spatial embeddedness manifests through a combination of similarity (homophily) and difference (heterophily) as determined by heterogeneous power relationships underlying global systems of production, exchange and consumption.Thomas SiglerKirsten MartinusJulia LoginovaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255461 (2021) |
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Medicine R Science Q Thomas Sigler Kirsten Martinus Julia Loginova Socio-spatial relations observed in the global city network of firms. |
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One of the prevailing approaches to the study of the global economy is the analysis of global city networks based on the activities of multinational firms. Research in this vein generally conceptualises cities as nodes, and the intra-firm relations between them as ties, forming the building blocks for globally scaled interurban networks. While such an approach has provided a valuable heuristic for understanding how cities are globally connected, and how the global economy can be conceived of as a network of cities, there is a lack of understanding as to how and why cities are connected, and which factors contribute to the existence of ties between cities. Here, we explain how five distinct socio-spatial dimensions contribute to global city network structure through their diverse effects on interurban dyads. Based on data from 13,583 multinational firms with 163,821 international subsidiary locations drawn from 208 global securities exchanges, we hypothesise how regional, linguistic, industrial, developmental, and command & control relations may contribute to network structure. We then test these by applying an exponential random graph model (ERGM) to explain how each dimension may contribute to cities' embeddedness within the overall network. Though all are shown to shape interurban relations to some extent, we find that two cities sharing a common industrial base are more likely to be connected. The ERGM also reveals a strong core-periphery structure in that cities in middle- and low-income countries are more reliant on connectivity than those in high-income countries. Our findings indicate that, despite claims seeking to de-emphasise the top-heavy organisational structure of the global urban economic network, interurban relations are characterised by uneven global development in which socio-spatial embeddedness manifests through a combination of similarity (homophily) and difference (heterophily) as determined by heterogeneous power relationships underlying global systems of production, exchange and consumption. |
format |
article |
author |
Thomas Sigler Kirsten Martinus Julia Loginova |
author_facet |
Thomas Sigler Kirsten Martinus Julia Loginova |
author_sort |
Thomas Sigler |
title |
Socio-spatial relations observed in the global city network of firms. |
title_short |
Socio-spatial relations observed in the global city network of firms. |
title_full |
Socio-spatial relations observed in the global city network of firms. |
title_fullStr |
Socio-spatial relations observed in the global city network of firms. |
title_full_unstemmed |
Socio-spatial relations observed in the global city network of firms. |
title_sort |
socio-spatial relations observed in the global city network of firms. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doaj.org/article/f28307f0ab5449e296621e6abdb87258 |
work_keys_str_mv |
AT thomassigler sociospatialrelationsobservedintheglobalcitynetworkoffirms AT kirstenmartinus sociospatialrelationsobservedintheglobalcitynetworkoffirms AT julialoginova sociospatialrelationsobservedintheglobalcitynetworkoffirms |
_version_ |
1718374381552402432 |