Abstract:
We investigate learning at the workplace. To do so, we use German administrative data that contain information on the entire workforce of the sample establishments. We document that having more highly paid coworkers is strongly associated with future wage growth. We then show how wage growth is affected by other characteristics of the distribution of coworkers. We then develop a novel way to structurally estimate a coworker learning function within a benchmark model of idea flows in a competitive labor market. 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. We demonstrate the approach using several different specifications and find that, in line with our reduced form work, learning from more knowledgeable coworkers is significant and increases with knowledge gaps. In contrast, learning is less sensitive to less knowledgeable coworkers. Our estimated model can be used to examine the relationship between changes in the organization of work brought about by technological change and both earnings inequality and aggregate growth.
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Host: Manudeep Bhuller