Title

Learning from Coworkers

Author(s)

Gregor Jarosch Gregor Jarosch (Princeton University)

Ezra Oberfield Ezra Oberfield (Princeton University)

Esteban Rossi-Hansberg Esteban Rossi-Hansberg (Princeton University)

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.

Creation Date

2020-02

Section URL ID

Paper Number

2020-63

URL

https://drive.google.com/file/d/1R7-ydVJjECEE_Y6m4ZcTN-5wN87I-lpz/view

File Function

Jel

E24, J31, O33, D83

Keyword(s)

knowledge diffusion, production in teams, growth, income distribution, peer- effects

Suppress

false

Series

13