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The Network Picture of Labor Flow

27 Pages Posted: 16 Jul 2015  

Eduardo Lopez

University of Oxford - Said Business School

Omar A. Guerrero

University of Oxford

Robert L. Axtell

George Mason University - Department of Computational Social Science; George Mason University - Department of Economics; Santa Fe Institute - Economics

Multiple version iconThere are 2 versions of this paper

Date Written: July 2, 2015

Abstract

We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing persistent behavior, and suggesting the construction of static graphs to act as the scaffolding for job mobility. Individuals in the job market (the workforce) are modeled by a discrete-time random walk on graphs, where each individual at a node can possess two states: employed or unemployed, and the rates of becoming unemployed and of finding a new job are node dependent parameters. We calculate the steady state solution of the model and compare it to extensive micro-datasets for Mexico and Finland, comprised of hundreds of thousands of firms and individuals. We find that our model possesses the correct behavior for the numbers of employed and unemployed individuals in these countries down to the level of individual firms. Our framework opens the door to a new approach to the analysis of labor mobility at high resolution, with the tantalizing potential for the development of full forecasting methods in the future.

Keywords: labor flow, networks, firm size, unemployment, matching, job search, micro-data, random walks, mobility, duration, firm-specific unemployment

JEL Classification: D4, D85, D62 C00, C50 C51, C54, C55, C59, C8, C6, J01, J2, J48, J6, J61, J63, J64, J68

Suggested Citation

Lopez, Eduardo and Guerrero, Omar A. and Axtell, Robert L., The Network Picture of Labor Flow (July 2, 2015). Available at SSRN: https://ssrn.com/abstract=2626179 or http://dx.doi.org/10.2139/ssrn.2626179

Eduardo Lopez (Contact Author)

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

Omar A. Guerrero

University of Oxford ( email )

Park End St.
Oxford, Oxfordshire OX1 1HP
United Kingdom

Robert L. Axtell

George Mason University - Department of Computational Social Science ( email )

4400 University Drive
375 Research Hall
Fairfax, VA 22030
United States

George Mason University - Department of Economics ( email )

4400 University Drive
Fairfax, VA 22030
United States

Santa Fe Institute - Economics ( email )

1399 Hyde Park Rd
Santa Fe, NM 87501
United States

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