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.

中文翻译：

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向同事学习

我们在工作场所调查学习情况。为此，我们使用德国的行政数据，其中包含有关场所样本中整个员工队伍的信息。我们记录到，拥有更高薪水的同事与未来的工资增长密切相关，特别是如果这些工人的收入更高。基于这一事实，我们提出了竞争性劳动力市场的动态理论，在这种竞争中，企业使用相互学习的异类工人团队进行生产。我们开发了一种方法，可以使用德国雇主与雇员匹配数据的全部丰富度来结构化地估计知识流。该方法是建立在竞争性劳动力市场使同事学习价格上涨的观察之上的。我们的定量方法对公司的生产职能施加了最小的限制，可以在非常短的面板上实施，并允许潜在丰富和灵活的同事学习功能。与我们的简化结果相一致，向同事学习非常重要，尤其是向知识渊博的同事学习。我们表明，总薪酬的4％至9％是学习的形式，总薪酬的不平等显着低于工资的不平等。