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Commuting, Wages and Bargaining Power

Author

Listed:
  • Peter Rupert
  • Elena Stancanelli
  • Etienne Wasmer

    (University of California, Santa Barbara
    CNRS, THEMA, University Cergy
    Sciences-Po, OFCE, IZA and CEPR)

Abstract

A search model of the labor market is augmented to include commuting time to work. The theory posits that wages are positively related to commute distance, by a factor itself depending negatively on the bargaining power of workers. Since not all combinations of distance and wages are accepted, there is non-random selection of accepted job offers. We build on these ingredients to explore in the data the relationship between wages and commute time . We find that neglecting to account for this selection will bias downward the wage impact of commuting, and marginally affect the coefficients on education, age and gender. The correlation between the residuals of the selectivity equation and the distance equation is -0.70, showing the large impact of commute time on job acceptance decisions. We also use the theory to calculate the bargaining power of workers which largely varies depending on demographic groups: it appears to be much larger for men than that for women and that the bargaining power of women with young children is essentially zero.

Suggested Citation

  • Peter Rupert & Elena Stancanelli & Etienne Wasmer, 2009. "Commuting, Wages and Bargaining Power," THEMA Working Papers 2009-02, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  • Handle: RePEc:ema:worpap:2009-02
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Eva Garcia-Moran & Zoe Kuehn, 2012. "With Strings Attached: Grandparent-Provided Child care, Fertility, and Female Labor Market Outcomes," CEPRA working paper 1202, USI Università della Svizzera italiana.
    2. Matthias Kehrig & Nicolas Vincent, 2013. "Disentangling Labor Supply and Demand Shifts Using Spatial Wage Dispersion: The Case of Oil Price Shocks," Working Papers 13-57, Center for Economic Studies, U.S. Census Bureau.
    3. Mishra, Khushbu & Sam, Abdoul G., 2016. "Does Women’s Land Ownership Promote Their Empowerment? Empirical Evidence from Nepal," World Development, Elsevier, vol. 78(C), pages 360-371.
    4. Gintare Morkute, 2014. "Growing surrounded by decline: do the growing sectors benefit from sharing a labour pool with declining sectors," ERSA conference papers ersa14p1584, European Regional Science Association.
    5. Eva Garcia-Moran & Zoe Kuehn, 2017. "With Strings Attached: Grandparent-Provided Child Care and Female Labor Market Outcomes," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 23, pages 80-98, January.
    6. Rupert, Peter & Wasmer, Etienne, 2012. "Housing and the labor market: Time to move and aggregate unemployment," Journal of Monetary Economics, Elsevier, vol. 59(1), pages 24-36.
    7. Heuermann, Daniel F. & Assmann, Franziska & vom Berge, Philipp & Freund, Florian, 2017. "The distributional effect of commuting subsidies - Evidence from geo-referenced data and a large-scale policy reform," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 11-24.
    8. García-Morán, Eva & Kuehn, Zoe, 2012. "With strings attached: Grandparent-provided child care, fertility, and female labor market outcomes," MPRA Paper 37001, University Library of Munich, Germany.
    9. Torfs, Wouter & Zhao, Liqiu, 2015. "Everybody needs good neighbors? Labor mobility costs, cities and matching," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 39-54.

    More about this item

    JEL classification:

    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis

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