Omar A. Guerrero (The Alan Turing Institute): Labour Flow Networks: Past, Present, and Future
Abstract | Labor flow networks (LFN) is the study of employment and unemployment dynamics through a rugged economic landscape shaped by labor market frictions. Because of the complex configurations of such frictions, networks are the natural object to represent them in a reduced form. This approach is radically different from the dominant view in labor economics, in which workers and vacancies are matched in a well-mixed world through an aggregate matching function. In a similar way in which network science revolutionized epidemiological modeling by highlighting the limitations of well-mixed regimes, LFNs are rapidly changing the way we understand labor dynamics, whether these take place at the level of individual jobs, firms, industries, occupations, or regions. In this talk, I will present some of the early works in this field, which focus on the analysis of large employer-employee matched datasets; the past. Then, I will elaborate on current work on modeling dynamics on LFNs via stochastic processes; the present. Finally, I will discuss the need to move beyond the statistical mechanics approach in order to construct socially relevant mechanisms through fundamental economic behavior; the future.
Bio | Omar Guerrero is the Head of Computational Social Science Research and leads the Policy Modelling Theme at the Turing's public policy programme. He is an economist by training, and has a PhD in Computational Social Science (CSS) from George Mason University. Previously, he worked at University College London and at the University of Oxford.