EPJ Data Sci. (2020) 9: 33
https://doi.org/10.1140/epjds/s13688-020-00251-w

Regular article

A network theory of inter-firm labor flows

¹ Department of Computational and Data Sciences, George Mason University, 4400 University Drive, MS 6A2, Fairfax, VA, USA
² Green Templeton College, University of Oxford, 43 Woodstock Rd, OX2 6HG, Oxford, UK
³ The Alan Turing Institute, 96 Euston Rd, Kings Cross, NW1 2DB, London, UK
⁴ Department of Economics, University College London, Drayton House, 30 Gordon St, Kings Cross, WC1H 0AX, London, UK

^* e-mail: elopez22@gmu.edu

Received: 21 February 2018
Accepted: 12 October 2020
Published online: 2 November 2020

Abstract

Using detailed administrative microdata for two countries, we build a modeling framework that yields new explanations for the origin of firm sizes, the firm contributions to unemployment, and the job-to-job mobility of workers between firms. Firms are organized as nodes in networks where connections represent low mobility barriers for workers. These labor flow networks are determined empirically, and serve as the substrate in which workers transition between jobs. We show that highly skewed firm size distributions are predicted from the connectivity of firms. Further, our model permits the reconceptualization of unemployment as a local network phenomenon related to both a notion of firm-specific unemployment and the network vicinity of each firm. We find that firm-specific unemployment has a highly skewed distribution. In coupling the study of job mobility and firm dynamics the model provides a new analytical tool for industrial organization and makes it possible to synthesize more targeted policies managing job mobility.