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向同事学习

Learning From Coworkers
Econometrica · 2021 · Gregor Jarosch、Ezra Oberfield、Esteban Rossi‐Hansberg

中文摘要

我们考察工作场所中的学习。为此,我们使用德国行政数据,其中包含样本企业全体员工的信息。我们发现,拥有薪酬更高的同事与未来的工资增长高度相关,特别是当这些同事收入更高时。受这一事实启发,我们提出了一个竞争性劳动力市场的动态理论,其中企业使用由异质性工人组成的团队进行生产,工人之间相互学习。我们开发了一种方法,利用德国雇主-雇员匹配数据的全部丰富信息来对知识流动进行结构估计。该方法建立在竞争性劳动力市场对同事学习进行定价的观察之上。我们的定量方法对企业生产函数施加的约束极小,可以在非常短的面板上实施,并允许潜在丰富且灵活的同事学习函数。与我们的简约式结果一致,向同事学习是显著的,尤其是向知识更丰富的同事学习。我们表明,工人总报酬中有4%至9%以学习的形式存在,且总报酬的不平等显著低于工资的不平等。

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.
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