Learning from Coworkers
We investigate learning at the workplace. To do so, we use German administrative data that contain information on the entire workforce of a sample of establishments. We document that having more highly paid coworkers is strongly associated with future wage growth, particularly if those workers earn more. Motivated by this fact, we propose a dynamic theory of a competitive labor market where firms produce using teams of heterogeneous workers that learn from each other. We develop a methodology to structurally estimate knowledge flows using the full-richness of the German employer-employee matched data. The methodology builds on the observation that a competitive labor market prices coworker learning. Our quantitative approach imposes minimal restrictions on firms' production functions, can be implemented on a very short panel, and allows for potentially rich and flexible coworker learning functions. In line with our reduced form results, learning from coworkers is significant, particularly from more knowledgeable coworkers. We show that between 4 and 9% of total worker compensation is in the form of learning and that inequality in total compensation is significantly lower than inequality in wages.
We thank the IR Section at Princeton University for providing a secured facility to access the German data and the IES at Princeton University for financial support. We thank our discussant Hugo Hopenhayn and participants at various seminars and conferences for useful comments. We also thank Mingduo Zhao for research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Rossi-Hansberg was a long-term consultant at the Richmond Fed while writing parts of this paper. This relationship did not affect the research or conclusions presented in the paper.