Learning from Coworkers

Gregor Jarosch, Ezra Oberfield and Esteban Rossi-Hansberg

No 25418, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: E24 J31 O33 (search for similar items in EconPapers)
Date: 2019-01
New Economics Papers: this item is included in nep-eur, nep-lma, nep-mac and nep-ure
Note: EFG
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Published as Gregor Jarosch & Ezra Oberfield & Esteban Rossi‐Hansberg, 2021. "Learning From Coworkers," Econometrica, Econometric Society, vol. 89(2), pages 647-676, March.

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Related works:
Journal Article: Learning From Coworkers (2021)
Working Paper: Learning from Coworkers (2020)
Working Paper: Learning from Coworkers (2018)
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