Learning and Labor Market Flows

I study labor market flows in an equilibrium model with a distinct role for firm- and worker- level uncertainty, and evaluate their contribution to the labor flows. Firms experience idiosyncratic productivity shocks to which the y react by adjusting the number of hired and separated workers. In addition, workers also switch jobs or leave their current firms for reasons related to the career development. Workers learn about their match quality while employed, build their careers through search for better job opportunities, and separate if they infer that their current job is not a good match. Firm-level productivity shocks impact the match quality of employed workers, which captures the idea that technology is partly embodied in workers and innovation can make some workers less suitable for the new technology. I use a large panel dataset of the labor market histories of individuals in Austria for the empirical investigation. I calibrate the model to match the aggregate labor market flows and show that the model generates dynamics which is consistent with the observed cross-sectional patterns for the job and worker flows. I use the calibrated model to evaluate the contribution of different mechanisms to the worker flows. The learning mechanism accounts for more than 50 percent of the flows which suggests that the uncertainty at the worker level plays an important role in explaining the large magnitude of the worker flows.