title: Employment growth through labor flow networks.
abstract: It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.
authors: Department of Computational Social Science and Center for Social Complexity, George Mason University, Fairfax, Virginia, United States of America. oguerrer@gmail.com
authors: Omar A
authors: OA
authors: Guerrero
authors: Robert L
authors: RL
authors: Axtell
mesh_terms: D000367:Age Factors; D004651:Employment / Q000191:economics; D006801:Humans; D008962:Models, Theoretical; D060756:Social Networking
references: Science. 2002 Nov 15;298(5597):1428-32
references: 12434061
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Sep;74(3 Pt 2):036104
references: 17025705
references: Science. 2002 May 3;296(5569):910-3
references: 11988575
references: Science. 2009 Jul 24;325(5939):422-5
references: 19628858
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Sep;72(3 Pt 2):036133
references: 16241541
references: Science. 2005 Aug 12;309(5737):1083-7
references: 16079251
references: Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Feb;65(2 Pt 2):026107
references: 11863587
references: Proc Natl Acad Sci U S A. 2009 Jun 30;106(26):10570-5
references: 19549871
references: Phys Rev Lett. 2002 Nov 11;89(20):208701
references: 12443515
references: Science. 2002 Aug 30;297(5586):1551-5
references: 12202830
references: Proc Natl Acad Sci U S A. 2011 Mar 29;108(13):5199-202
references: 21402924
references: Science. 1999 Oct 15;286(5439):509-12
references: 10521342