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Free to Move? A Network Analytic Approach for Learning the Limits to Job Mobility

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  • Schmutte, Ian M.
Abstract
Job mobility has many overlapping determinants that are hard to characterize solely on the basis of industry or occupation transitions. Workers may match with, and move to, particular jobs on the basis of match quality, preferences, human capital, andmobility costs. This paper implements a novel method based on complex network analysis to describe how workers move from job to job. Using data from the Panel Study of Income Dynamics (PSID), I find first that the labor market is composed of four distinct segments between which job mobility is relatively unlikely. Second, these segments are not well-described on the basis of industry, occupation, demographic characteristics, or education. Third, mobility segments are associated with earnings heterogeneity, and there is evidence of positive assortative matching across segments. Fourth, the boundaries to job mobility are counter-cyclical: workers move more freely when unemployment is low.

Suggested Citation

  • Schmutte, Ian M., 2014. "Free to Move? A Network Analytic Approach for Learning the Limits to Job Mobility," Labour Economics, Elsevier, vol. 29(C), pages 49-61.
    • Handle: RePEc:eee:labeco:v:29:y:2014:i:c:p:49-61
    DOI: 10.1016/j.labeco.2014.05.003
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    References listed on IDEAS

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    Cited by:

    1. Nimczik, Jan Sebastian, 2017. "Job Mobility Networks and Endogenous Labor Markets," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168147, Verein für Socialpolitik / German Economic Association.
    2. David Berger & Kyle Herkenhoff & Andreas R. Kostøl & Simon Mongey, 2024. "An Anatomy of Monopsony: Search Frictions, Amenities, and Bargaining in Concentrated Markets," NBER Macroeconomics Annual, University of Chicago Press, vol. 38(1), pages 1-47.
    3. Axtell, Robert L. & Guerrero, Omar A. & López, Eduardo, 2016. "The Network Composition of Aggregate Unemployment," MPRA Paper 68962, University Library of Munich, Germany.
    4. Monica Costa Dias & Ella Johnson-Watts & Robert Joyce & Fabien Postel-Vinay & Peter Spittal & Xiaowei Xu, 2021. "Worker mobility and labour market opportunities," IFS Working Papers W21/29, Institute for Fiscal Studies.
    5. Agnes Norris Keiller, 2020. "Detecting labour submarkets from worker-mobility networks: a preliminary study," IFS Working Papers W20/30, Institute for Fiscal Studies.
    6. John M. Abowd & Kevin McKinney & Ian M. Schmutte, 2015. "Modeling Endogenous Mobility in Wage Determiniation," Working Papers 15-18, Center for Economic Studies, U.S. Census Bureau.
    7. Axtell, Robert L. & Guerrero, Omar A. & López, Eduardo, 2019. "Frictional unemployment on labor flow networks," Journal of Economic Behavior & Organization, Elsevier, vol. 160(C), pages 184-201.
    8. Lu, Lan & Yin, Shuiying & Wen, Fuying & Xu, Qingqing, 2023. "The spatial structure of labour force employment in China’s industries: Measurement and extraction," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 472-486.
    9. Yuanyang Liu & Gautam Pant & Olivia R. L. Sheng, 2020. "Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills," Information Systems Research, INFORMS, vol. 31(4), pages 1443-1466, December.
    10. Laurie A Schintler & Rajendra Kulkarni & Kingsley Haynes & Roger Stough, 2014. "Sensing ‘socio-spatio’ interaction and accessibility from location-sharing services data," Chapters, in: Ana Condeço-Melhorado & Aura Reggiani & Javier Gutiérrez (ed.), Accessibility and Spatial Interaction, chapter 5, pages 92-110, Edward Elgar Publishing.
    11. Guerrero, Omar A. & López, Eduardo, 2015. "Firm-to-firm labor flows and the aggregate matching function: A network-based test using employer–employee matched records," Economics Letters, Elsevier, vol. 136(C), pages 9-12.
    12. Kathyrn R. Fair & Omar A. Guerrero, 2023. "Endogenous Labour Flow Networks," Papers 2301.07979, arXiv.org, revised Mar 2023.
    13. Eric Auerbach, 2019. "Identification and Estimation of a Partially Linear Regression Model using Network Data," Papers 1903.09679, arXiv.org, revised Jun 2021.
    14. Christenko, Aleksandr, 2022. "Automation and occupational mobility: A task and knowledge-based approach," Technology in Society, Elsevier, vol. 70(C).
    15. Chiara Mussida & Luca Zanin, 2020. "I found a better job opportunity! Voluntary job mobility of employees and temporary contracts before and after the great recession in France, Italy and Spain," Empirical Economics, Springer, vol. 59(1), pages 47-98, July.

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