Universal resilience patterns in labor markets.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
30 03 2021
Historique:
received: 16 03 2020
accepted: 22 02 2021
entrez: 31 3 2021
pubmed: 1 4 2021
medline: 1 4 2021
Statut: epublish

Résumé

Cities are the innovation centers of the US economy, but technological disruptions can exclude workers and inhibit a middle class. Therefore, urban policy must promote the jobs and skills that increase worker pay, create employment, and foster economic resilience. In this paper, we model labor market resilience with an ecologically-inspired job network constructed from the similarity of occupations' skill requirements. This framework reveals that the economic resilience of cities is universally and uniquely determined by the connectivity within a city's job network. US cities with greater job connectivity experienced lower unemployment during the Great Recession. Further, cities that increase their job connectivity see increasing wage bills, and workers of embedded occupations enjoy higher wages than their peers elsewhere. Finally, we show how job connectivity may clarify the augmenting and deleterious impact of automation in US cities. Policies that promote labor connectivity may grow labor markets and promote economic resilience.

Identifiants

pubmed: 33785734
doi: 10.1038/s41467-021-22086-3
pii: 10.1038/s41467-021-22086-3
pmc: PMC8009945
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1972

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Auteurs

Esteban Moro (E)

Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, Leganés, Spain. emoro@math.uc3m.es.
Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. emoro@math.uc3m.es.
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA. emoro@math.uc3m.es.
Sociotechnical Systems Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA. emoro@math.uc3m.es.

Morgan R Frank (MR)

Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sociotechnical Systems Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Informatics and Networked Systems, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
Digital Economy Lab, Institute for Human-Centered AI, Stanford University, Stanford, CA, USA.

Alex Pentland (A)

Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sociotechnical Systems Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.

Alex Rutherford (A)

Center for Humans & Machines, Max Planck Institute for Human Development, Berlin, Germany.

Manuel Cebrian (M)

Center for Humans & Machines, Max Planck Institute for Human Development, Berlin, Germany.

Iyad Rahwan (I)

Center for Humans & Machines, Max Planck Institute for Human Development, Berlin, Germany. rahwan@mpib-berlin.mpg.de.

Classifications MeSH