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A Combinatorial Optimization Model for Transmission of Job Information through Contact Networks

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  • Scott A. Boorman
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    Abstract

    A combinatorial network model is constructed to describe the information structure of job finding in a homogeneous job market. A fundamental distinction is drawn between strong and weak contacts: strong contacts are assumed to have strict priority as recipients of information about the existence of job vacancies, while maintenance of a given strong contact requires more time than does maintenance of a weak contact. Facing a limited time-budget, each individual confronts a tradeoff between these two possible ways of investing time. In the model presently investigated, each individual is assumed to develop his contacts so as to maximize probability of getting some new job in the event that he loses his present job. Concepts of both stability and optimality are defined. It is found that if the probability of becoming jobless is low (u <<1), then a situation where all individuals choose only weak ties ("all-weak network") will be stable and will be a Pareto optimum under the maximizing behavior assumed. Also, all-weak networks are the only Pareto optima in this case. For u near 1, a different situation obtains: now networks containing only strong ties will be stable and all-weak networks will be unstable. In this second limiting case, however, stability and optimality do not coincide: all-strong networks are not Pareto optimal.

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    Bibliographic Info

    Article provided by The RAND Corporation in its journal Bell Journal of Economics.

    Volume (Year): 6 (1975)
    Issue (Month): 1 (Spring)
    Pages: 216-249

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    Handle: RePEc:rje:bellje:v:6:y:1975:i:spring:p:216-249

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    Cited by:
    1. Linda Datcher Loury, 2004. "Some Job Contacts are More Equal Than Others: Earnings and Job Information Networks," Discussion Papers Series, Department of Economics, Tufts University 0404, Department of Economics, Tufts University.
    2. Holzner, Christian & Gautier, Pieter, 2013. "Simultaneous search and network efficiency," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79740, Verein für Socialpolitik / German Economic Association.
    3. Yannis Ioannides & Adriaan Soetevent, 2006. "Wages and Employment in a Random Social Network with Arbitrary Degree Distribution," Discussion Papers Series, Department of Economics, Tufts University 0601, Department of Economics, Tufts University.
    4. Granovetter, Mark, 2000. "A Theoretical Agenda for Economic Sociology," Center for Culture, Organizations and Politics, Working Paper Series qt4mk4g08q, Center for Culture, Organizations and Politics of theInstitute for Research on Labor and Employment, UC Berkeley.
    5. Matt Jackson, 2003. "The Effects of Social Networks on Employment and Inequality," Theory workshop papers 658612000000000032, UCLA Department of Economics.
    6. DeCanio, Stephen J. & Watkins, William E., 1998. "Information processing and organizational structure," Journal of Economic Behavior & Organization, Elsevier, vol. 36(3), pages 275-294, August.
    7. Tomohiro Hayashida & Ichiro Nishizaki & Rika Kambara, 2014. "Simulation Analysis for Network Formulation," Computational Economics, Society for Computational Economics, vol. 43(3), pages 371-394, March.
    8. Daniel F. Heuermann, 2009. "Career Networks and Job Matching - Evidence on the Microeconomic Foundations of Human Capital Externalities," IAAEG Discussion Papers until 2011 200901, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).
    9. Olivier Godechot, 2010. "Getting a Job in Finance-The Strength of Collaboration Ties," Working Papers 2010-42, Centre de Recherche en Economie et Statistique.
    10. Hensvik, Lena & Nordström Skans, Oskar, 2013. "Networks and youth labor market entry," Working Paper Series 2013:23, IFAU - Institute for Evaluation of Labour Market and Education Policy.

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