COMPUTATIONAL SOCIAL SCIENCE

Guerrero Doctoral Dissertation Defense

Wednesday, July 24, 1:00 p.m.
92 Research Hall

Doctoral Dissertation Defense: Coupled Dynamics of Labor and Firms through Complex Networks

Omar Guerrero,PhD Candidate
Department of Computational Social Science
George Mason University

Abstract: This dissertation bridges the gap between labor and firm dynamics through the study of complex networks in labor markets. With extensive use of large-scale employer-employee matched micro-data and agent-based modeling, we tap into the effects that networked structures (between individuals or between firms) exert in labor outcomes and employment dynamics. Some of the contributions of this work are: (i) the first characterization of a network of firms for an entire economy (connected through labor flows, i.e. labor flow networks); (ii) the study of the relationship between labor flow networks and employment dynamics; (iii) agent-based models that generate rich stylized facts about labor, firm, and social dynamics from microeconomic behavior; (iv) providing the microeconomic foundations of the formation process of labor flow networks by coupling job search models with models about the formation of complex networks. We show that the study of labor dynamics can be enriched by coupling firm dynamics. Using agent-based modeling is a natural way to deal with the heterogeneous experiences of workers and firms while maintaining a simple representation of the labor market. Despite their simplicity these models are grounded on empirical evidence obtained from large-scale micro-data and are capable of generating numerous stylized facts simultaneously. This approach has great potential for the design and evaluation of labor policies. Therefore, governments, regulators, and policy-makers would be greatly benefited from collecting large-scale labor micro-data, analyzing labor flow networks, and developing agent-based models of labor markets.