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The Trojan-horse mechanism: How networks reduce gender segregation

The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse...

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Detalles Bibliográficos
Autores principales: Arvidsson, M., Collet, F., Hedström, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8051882/
https://www.ncbi.nlm.nih.gov/pubmed/33863731
http://dx.doi.org/10.1126/sciadv.abf6730
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author Arvidsson, M.
Collet, F.
Hedström, P.
author_facet Arvidsson, M.
Collet, F.
Hedström, P.
author_sort Arvidsson, M.
collection PubMed
description The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse mechanism, which, in contradiction to the main tenet of previous research, suggests that network-based recruitment reduce rather than increase segregation levels. We identify the conditions under which networks are desegregating, and using unique data on all individuals and all workplaces located in the Stockholm region during the years 2000–2017, we find strong empirical evidence for the Trojan-horse mechanism and its role in the gender segregation of labor markets.
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spelling pubmed-80518822021-04-26 The Trojan-horse mechanism: How networks reduce gender segregation Arvidsson, M. Collet, F. Hedström, P. Sci Adv Research Articles The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse mechanism, which, in contradiction to the main tenet of previous research, suggests that network-based recruitment reduce rather than increase segregation levels. We identify the conditions under which networks are desegregating, and using unique data on all individuals and all workplaces located in the Stockholm region during the years 2000–2017, we find strong empirical evidence for the Trojan-horse mechanism and its role in the gender segregation of labor markets. American Association for the Advancement of Science 2021-04-16 /pmc/articles/PMC8051882/ /pubmed/33863731 http://dx.doi.org/10.1126/sciadv.abf6730 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Arvidsson, M.
Collet, F.
Hedström, P.
The Trojan-horse mechanism: How networks reduce gender segregation
title The Trojan-horse mechanism: How networks reduce gender segregation
title_full The Trojan-horse mechanism: How networks reduce gender segregation
title_fullStr The Trojan-horse mechanism: How networks reduce gender segregation
title_full_unstemmed The Trojan-horse mechanism: How networks reduce gender segregation
title_short The Trojan-horse mechanism: How networks reduce gender segregation
title_sort trojan-horse mechanism: how networks reduce gender segregation
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8051882/
https://www.ncbi.nlm.nih.gov/pubmed/33863731
http://dx.doi.org/10.1126/sciadv.abf6730
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