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Your very private job agency: Job referrals based on residential location networks

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  • Franziska Hawranek
  • Norbert Schanne

Abstract

Your very private job agency: Job referrals based on residential location networks This paper analyzes job referral effects that are based on residential location. We use georeferenced record data for the entire working population (liable to social security) and the corresponding establishments in the Rhine-Ruhr metropolitan area, which is Germany's largest (and EU's second largest) metropolitan area. We estimate the propensity of two persons to work at the same place when residing in the same neighborhood (reported with an accuracy of 500m×500m grid cells), and compare the effect to people living in adjacent neighborhoods. We find a significant increase in the probability of working together when living in the same neighborhood, which is stable across various specifications. Additionally, we look at how referral effects differ for various groups like age, skill, ethnic groups and industry sectors. We find that especially low skilled workers make use of residential networks for job search, as well as some groups of immigrants. Especially migrants from the new EU countries as well as Italians and people from former Yugoslavia have a highly increased probability of working together when they share the same neighborhood. This is clear sign for network effects especially for some immigrant groups in the German labor market. Job Further, we are able to investigate a number of issues in order to deepen the insight on actual job referrals: distinguishing between the effects on working in the same neighborhood and working in the same establishment ? probably the more accurate measure for job referrals ? shows that the latter yield overall smaller effects. Further, we find that clusters in employment although having a significant positive effect play only a minor role for the magnitude of the referral effect, which makes us confident that what we find is actually related to a true referral effect and not some spurious correlation. When we exclude short distance commuters, we find the same probabilities of working together, which reinforces our interpretation of this probability as a network effect. The paper investigates the effect of living together on the probability of working together. We find strong evidence for a positive and highly significant relationship, which is robust across several specifications and robustness tests, addressing common issues on the identification of neighborhood effects. JEL Classification: J20, J46, R23

Suggested Citation

  • Franziska Hawranek & Norbert Schanne, 2014. "Your very private job agency: Job referrals based on residential location networks," ERSA conference papers ersa14p49, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa14p49
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    References listed on IDEAS

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    Cited by:

    1. Peter Haller & Daniel F. Heuermann, 2020. "Opportunities and competition in thick labor markets: Evidence from plant closures," Journal of Regional Science, Wiley Blackwell, vol. 60(2), pages 273-295, March.
    2. Jahn, Elke & Neugart, Michael, 2020. "Do neighbors help finding a job? Social networks and labor market outcomes after plant closures," Labour Economics, Elsevier, vol. 65(C).
    3. Saygin, Perihan & Weber, Andrea & Weynandt, Michèle A., 2014. "Coworkers, Networks, and Job Search Outcomes," CEPR Discussion Papers 10003, C.E.P.R. Discussion Papers.
    4. Topa, Giorgio & Zenou, Yves, 2015. "Neighborhood and Network Effects," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 561-624, Elsevier.
    5. Perihan Ozge Saygin & Andrea Weber & Michèle A. Weynandt, 2021. "Coworkers, Networks, and Job-Search Outcomes among Displaced Workers," ILR Review, Cornell University, ILR School, vol. 74(1), pages 95-130, January.
    6. Elisabeth Bügelmayer & Daniel D. Schnitzlein, 2018. "Is it the family or the neighborhood? Evidence from sibling and neighbor correlations in youth education and health," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(3), pages 369-388, September.
    7. Jahn, Elke Jutta & Neugart, Michael, 2016. "Do neighbors help finding a job? Social networks and labor market," VfS Annual Conference 2016 (Augsburg): Demographic Change 145476, Verein für Socialpolitik / German Economic Association.

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    More about this item

    JEL classification:

    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J46 - Labor and Demographic Economics - - Particular Labor Markets - - - Informal Labor Market
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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