Endogenous Matching Functions: an Agent-Based Computational Approach

The matching function has become a popular tool in labor economics. It relates job creation – a flow variable – to two stock variables: vacancies and job searchers. In most studies the matching function is exogenous and assumed to fulfill certain properties. This study looks at the properties of an endogenous matching function. For that purpose we program an agent-based-computational labor market model with endogenous job creation and endogenous job search behavior. Our simulations suggest that job creation is increasing in the number of job searchers and vacancies. The endogenous matching technology is subject to decreasing returns to scale. The Beveridge ∗The paper is prepared for the Wild@Ace Workshop on Industry and Labor Dynamics to be held on October 3rd and 4th in Turin. I would like to thank Richard Freeman for encouraging discussions that substantially motivated me to do research with agentbased computational models. There is no agent-based modelling without knowledge of an appropriate programming language. While I was on leave from the Social Science Center Berlin (WZB) at Harvard University I had the chance to participate in Lars-Eric Cederman’s course ‘Introduction to computational modelling for social scientists’ to learn RePast. My thanks go also to him. None of the two takes any responsibility for the content of the paper. †Chemnitz University of Technology, Department of Economics, Reichenhainer Strasse 39, 09107 Chemnitz, Germany, e-mail: [email protected]