Hysteresis through learning by doing: Estimating endogenous productivity

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.

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Publication Details

Journal
Economics Letters
Published
2026-01-08
DOI
https://doi.org/10.1016/j.econlet.2026.112818
Primary Topic
Economic Growth and Productivity
Type
article
Field-Weighted Citation Impact
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article

Hysteresis through learning by doing: Estimating endogenous productivity

Juha Tervala, Timothy Watson
Economics Letters
Economic Growth and Productivity
article

Hysteresis through learning by doing: Estimating endogenous productivity

Juha Tervala, Timothy Watson
article en

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.

Economics LettersVol. 260
University of Helsinki (FI), Helsinki Institute of Physics (FI), Services Australia (AU)
Decent work and economic growth
Openalex Percentile: Top 96%
Economic Growth and Productivity
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