Working Paper: NBER ID: w25418
Authors: Gregor Jarosch; Ezra Oberfield; Esteban Rossi-Hansberg
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.
Keywords: coworker learning; wage growth; knowledge transfer
JEL Codes: E24; J31; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| more highly paid coworkers (J31) | future wage growth (J39) |
| knowledgeable coworkers (Y80) | future wage growth (J39) |
| peers higher up in the wage distribution (J31) | future wage growth (J39) |
| learning from coworkers (C92) | wage growth (J31) |
| distribution of knowledge among coworkers (O36) | wage dynamics (J31) |