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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.

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  • 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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    Cited by:

    1. Nimczik, Jan Sebastian, 2017. "Job Mobility Networks and Endogenous Labor Markets," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168147, Verein für Socialpolitik / German Economic Association.
    2. Axtell, Robert L. & Guerrero, Omar A. & López, Eduardo, 2016. "The Network Composition of Aggregate Unemployment," MPRA Paper 68962, University Library of Munich, Germany.
    3. 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.
    4. Robert L. Axtell & Omar A. Guerrero & Eduardo L'opez, 2019. "Frictional Unemployment on Labor Flow Networks," Papers 1903.04954, arXiv.org.
    5. Laurie A Schintler & Rajendra Kulkarni & Kingsley Haynes & Roger Stough, 2014. "Sensing ‘socio-spatio’ interaction and accessibility from location-sharing services data," Chapters,in: Accessibility and Spatial Interaction, chapter 5, pages 92-110 Edward Elgar Publishing.
    6. 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.
    7. Eric Auerbach, 2019. "Identification and Estimation of a Partially Linear Regression Model using Network Data," Papers 1903.09679, arXiv.org.

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    Keywords

    Job Mobility; Complex Networks; Job Matching;

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