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Network constraints on worker mobility

Nature Cities volume 1, pages 94–104 (2024)Cite this article

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Abstract

How do skills shape career mobility and access to cities’ labor markets? Here we model career pathways as an occupation network constructed from the similarity of occupations’ skill requirements within each US city. Using a nationally representative survey and three resume datasets, skill similarity predicts transition rates between occupations and predictions improve with increasingly granular skill data. Thus, a measure for skill specialization based on a workers’ position in their city’s occupation network may predict future career dynamics. Job changes that decrease workers’ network embeddedness also increased wages, and workers tend to decrease their embeddedness over their careers. Further, city pairs with dissimilar job embeddedness have greater census migration and increased flows of enplaned passengers according to the US Bureau of Transportation Statistics. This study directly connects workplace skills to workers’ career mobility and spatial mobility, thus offering insights into skill specialization, career mobility and urbanization.

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Fig. 1: Occupations’ skill similarity predicts job transition rates.
Fig. 2: Workers decrease their employment-weighted embeddedness, ec,i, throughout their careers, and career moves that decrease ec,i correspond to higher wages according to resume data.
Fig. 3: Average combined embeddedness moderates the relationship between city size and intercity mobility.

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Data availability

This study uses several publicly available datasets from federal government sources. The O*NET database https://www.dol.gov/agencies/eta/onet and Occupation Employment and Wage Statistics https://www.bls.gov/oes/ are available for download from the US BLS website. The Current Population Survey and intercity migration data are available for download from the US Census Bureau website https://www.census.gov/topics/population/migration/data/tables/cps.html. Domestic Enplaned Passengers data are available for download through the US Bureau of Transportation Statistics https://www.bts.gov/browse-statistical-products-and-data/bts-publications. This study also uses three proprietary third-party resume datasets provided by Burning Glass Technologies Inc., FutureFit AI and Revelio Labs. We cannot make this proprietary data immediately available because of risks to individuals’ privacy. Access to these data is available through the third-party data vendors.

Code availability

Code used to produce figures and tables in this article is available at https://bit.ly/3BiZoOT.

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Acknowledgements

This research is supported by the University of Pittsburgh Pitt Momentum Fund and the Center for Research Computing. E.M. acknowledges support by Ministerio de Ciencia e Innovación, Agencia Española de Investigación (MCIN/AEI/10.13039/501100011033) grant PID2019-106811GB-C32 and the National Science Foundation grant no. 2218748. The authors thank E. Brynjolfsson for his insightful comments.

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Authors and Affiliations

  1. Department of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, PA, USA

    Morgan R. Frank

  2. Digital Economy Lab, Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA

    Morgan R. Frank

  3. Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Morgan R. Frank, Esteban Moro, Tobin South & Alex Pentland

  4. Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganes, Spain

    Esteban Moro

  5. Network Science Institute, Northeastern University, Boston, MA, USA

    Esteban Moro

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

    Alex Rutherford & Iyad Rahwan

  7. Burning Glass Technologies, Boston, MA, USA

    Bledi Taska

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Contributions

M.R.F. performed calculations and produced figures. M.R.F. and E.M. secured funding for this project. T.S. prepared the resume data from FutureFit AI. B.T. secured resume data from Burning Glass Technologies. All authors designed the research and wrote the manuscript.

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Correspondence to Morgan R. Frank.

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Nature Cities thanks Teresa Farinha, Marian-Andrei Rizoiu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Material Sections 1–11, Figs. 1–17 and Tables 1–30.

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Frank, M.R., Moro, E., South, T. et al. Network constraints on worker mobility. Nat Cities 1, 94–104 (2024). https://doi.org/10.1038/s44284-023-00009-1

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