Hysteresis through learning by doing: Estimating endogenous productivity

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Authors: Timothy Watson, Juha Tervala
Cited by: 1
References: 16

Abstract

Learning by doing (LBD), a mechanism whereby productivity depends on past employment, is increasingly used in macroeconomic models to capture hysteresis and endogenous productivity. A key challenge has been calibrating its structural parameters, primarily due to the limited availability of empirical estimates. We estimate a productivity persistence of 0.91 and an elasticity of productivity with respect to lagged employment ranging from 0.13 to 0.30, with seven statistically significant estimates averaging 0.22. These results provide empirically grounded parameters and demonstrate the value of LBD as a tractable approach to capturing endogenous and procyclical productivity dynamics in macroeconomic models. • Learning by doing is a mechanism whereby productivity depends on past employment. • We estimate key parameters for learning-by-doing models using macroeconomic data. • Productivity persistence is estimated at 0.91. • The elasticity of productivity with respect to lagged employment averages 0.22. • Learning by doing effectively models endogenous, procyclical productivity.

Fields of Study & Subjects

Economic Growth and Productivity Monetary Policy and Economic Impact Firm Innovation and Growth

Publication details

Journal / source
Economics Letters
Publisher
Elsevier BV
Volume
260
Pages
112818–112818
Publication date
8 January 2026
Language
EN