published in: Econometrica, 2023, 91 (3), 1119 - 1153
We build an analytically and computationally tractable stochastic equilibrium model of unemployment in heterogeneous labor markets. Facing search frictions within markets and reallocation frictions between markets, workers endogenously separate from employment and endogenously reallocate between markets, in response to changing aggregate and local conditions. Empirically, using the 1986-2008 SIPP panels, we document the occupational mobility patterns of the unemployed, finding notably that occupational change of unemployed workers is procyclical.
The heterogeneous-market model yields highly volatile countercyclical unemployment, and is simultaneously consistent with procyclical reallocation, countercyclical separations and a negatively-sloped Beveridge curve. Moreover, the model exhibits unemployment duration dependence, which (when calibrated to long-term averages) responds realistically to the business cycle, creating substantial longer-term unemployment in downturns. Finally, the model is also consistent with different employment and reallocation outcomes as workers gain experience in the labor market, on average and over the business cycle.
We use cookies to provide you with an optimal website experience. This includes cookies that are necessary for the operation of the site as well as cookies that are only used for anonymous statistical purposes, for comfort settings or to display personalized content. You can decide for yourself which categories you want to allow. Please note that based on your settings, you may not be able to use all of the site's functions.
Cookie settings
These necessary cookies are required to activate the core functionality of the website. An opt-out from these technologies is not available.
In order to further improve our offer and our website, we collect anonymous data for statistics and analyses. With the help of these cookies we can, for example, determine the number of visitors and the effect of certain pages on our website and optimize our content.