Micro-to-Macro Simulation: A Primer With a Labor Market Example

Author:

Bergmann Barbara R1

Affiliation:

1. Distinguished Professor of Economics, American University, Washington, D.C..

Abstract

Simulation models, now widely used in the physical sciences, can also help economists in depicting the actions and interactions of individuals and firms through time. This article provides an introduction to microsimulation: how it works, how to do it, its potential, and its drawbacks. It then allows readers, even those with no experience in computer programming, to work through the details of a simple microsimulation model. Readers can put this model on their PCs, watch it run through its paces, and experiment with their own modifications. With this model as an example, it should be fairly easy to create programs for new models on other subjects for use in theoretical exploration, empirical research, or classroom demonstrations. The demonstration model I present depicts the experience of individual workers during recession and recovery in the labor market. As the model runs, its internal” Census Bureau” performs surveys on the microlevel, and sums up to macrolevel variables. The model is simple, but it has some interesting applications. After explaining how to set it up, I use it to explore the effect of unemployment insurance on the level of unemployment and to point up a common fallacy in current labor market literature.

Publisher

American Economic Association

Subject

Economics and Econometrics,Economics and Econometrics

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Evolution of Workers’ Behaviour in Dual Labor Markets;Communications in Computer and Information Science;2019

3. Interpreting the Beveridge curve. An agent-based approach;Journal of Economic Behavior & Organization;2019-01

4. Endogenous Firm Dynamics and Labor Flows via Heterogeneous Agents ✶ ✶Support from the John D. and Catherine T. MacArthur Foundation, the National Science Foundation (0738606), the Small Business Administration (SBAHQ-05-Q-0018), and the Mercatus Center at George Mason is gratefully acknowledged. I have no relevant or material financial interests that relate to the research described in this paper or the associated model. Earlier versions of this work were presented at research institutions (Aix-en-Provence, Arizona State, Brookings, Carnegie Mellon, Emory, Esalen, Essex, George Mason, Georgia, Georgia Tech, James Madison, Leicester, Leiden, Limerick, Nanyang Technological University, New School for Social Research, Office of Financial Research, Oxford, Queen Mary and Westfield, Sant' Anna (Pisa), Santa Fe Institute, Turino) and conferences (Eastern Economic Association, INFORMS, Society for Computational Economics, Southern Economic Association) where comments from attendees yielded significant improvements. For helpful feedback on the manuscript I am grateful to Zoltan Acs, Luis Amaral, Brian Arthur, David Audretsch, Bob Axelrod, Bob Ayres, Eric Beinhocker, Margaret Blair, Pete Boettke, David Canning, Kathleen Carley, John Chisholm, Alex Coad, Herbert Dawid, Art DeVany, Bill Dickens, Kathy Eisenhardt, Joshua Epstein, Doyne Farmer, Rich Florida, Duncan Foley, Xavier Gabaix, Chris Georges, Herb Gintis, Joe Harrington, John Holland, Stu Kauffman, Steve Kimbrough, Paul Kleindorfer, Blake LeBaron, Axel Leijonhufvud, Bob Litan, Francesco Luna, Jim March, Michael Maouboussin, Greg McRae, Benoit Morel, Scott Moss, Paul Omerod, J. Barkley Rosser Jr., Martin Shubik, Gene Stanley, Dan Teitelbaum, Leigh Tesfatsion, Sid Winter and several people who are no longer with us: Per Bak, Michael Cohen, Ben Harrison, Steve Klepper, Sam Kotz, and Benoit Mandelbrot. The late Herb Simon inspired and encouraged the work. Anna Nelson and Omar Guerrero each advanced the work through their Ph.D. dissertations. Thanks are due Miles Parker and Gabriel Balan for implementing the model in Java, first in Ascape and then in Mason. Errors are my own.;Handbook of Computational Economics;2018

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