Learning from Coworkers / Gregor Jarosch, Ezra Oberfield, Esteban Rossi-Hansberg.

By:

Jarosch, Gregor

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w25418.Publication details: Cambridge, Mass. National Bureau of Economic Research 2019.Description: 1 online resource: illustrations (black and white)Subject(s):

E24 - Employment • Unemployment • Wages • Intergenerational Income Distribution • Aggregate Human Capital • Aggregate Labor Productivity
J31 - Wage Level and Structure • Wage Differentials
O33 - Technological Change: Choices and Consequences • Diffusion Processes

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Abstract: 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.

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January 2019.

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.