Working Paper: NBER ID: w19767
Authors: Martin Gervais; Nir Jaimovich; Henry E. Siu; Yaniv Yedidlevi
Abstract: 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."
Keywords: Technological Learning; Labor Market Dynamics; Business Cycles; Search-and-Matching Model
JEL Codes: E24; E32; J64
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| shocks to the speed of technological learning (O33) | unemployment (J64) |
| shocks to the speed of technological learning (O33) | productivity (O49) |
| positive shocks to the learning rate (C53) | job creation (J68) |
| positive shocks to the learning rate (C53) | unemployment (J64) |
| negative shocks to the learning rate (C51) | job creation (J68) |
| negative shocks to the learning rate (C51) | unemployment (J64) |
| learning-by-doing processes (J24) | job creation (J68) |
| learning-by-doing processes (J24) | unemployment (J64) |
| model generates a much greater volatility of unemployment relative to productivity (J69) | volatility of productivity (O49) |
| shocks to learning rates (D89) | productivity fluctuations (O49) |