Learning From Coworkers

Gregor Jarosch, Ezra Oberfield, Esteban Rossi-Hansberg

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)647-676
Number of pages30
Issue number2
StatePublished - Mar 2021

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Knowledge diffusion
  • growth
  • income distribution
  • peer effects
  • production in teams


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