Technological Learning and Labor Market Dynamics

By Martin Gervais, Nir Jaimovich, Henry Siu and Yaniv Yedid-Levi

The search-and-matching model of the labor market fails to match two important business cycle facts: (i) a high volatility of unemployment relative to labor productivity, and (ii) a mild correlation between these two variables. We address these shortcomings by focusing on technological learning-by-doing: the notion that it takes workers time using a technology before reaching their full productive potential with it. We consider a novel source of business cycles, namely, fluctuations in the speed of technological learning and show that a search-and-matching model featuring such shocks can account for both facts. Moreover, our model provides a new interpretation of recently discussed “news shocks.”

Given the unusually large number of papers in the last NEP-DGE report, I am selecting a second paper for the blog. It is also a paper that mixes to good effect two literatures: news shocks and search-and-matching. In some sense, this argument has been made before at the aggregate level. Major technological advances require new investment to become usable, and this also adds a delay between the news of the new technology and the time it becomes effective. This could explain some asset price dynamics, for example. In the case of this paper, the burden is on the individual worker, and this helps explain labor market dynamics.

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