The Perils of the Learning Model for Modeling Endogenous Technological Change

20 Pages Posted: 4 Jan 2009

See all articles by William D. Nordhaus

William D. Nordhaus

Yale University - Department of Economics; Cowles Foundation, Yale University; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: January 2, 2009

Abstract

Learning or experience curves are widely used to estimate cost functions in manufacturing modeling. They have recently been introduced in policy models of energy and global warming economics to make the process of technological change endogenous. It is not widely appreciated that this is a dangerous modeling strategy. The present note has three points. First, it shows that there is a fundamental statistical identification problem in trying to separate learning from exogenous technological change and that the estimated learning coefficient will generally be biased upwards. Second, we present two empirical tests that illustrate the potential bias in practice and show that learning parameters are not robust to alternative specifications. Finally, we show that an overestimate of the learning coefficient will provide incorrect estimates of the total marginal cost of output and will therefore bias optimization models to tilt toward technologies that are incorrectly specified as having high learning coefficients.

Keywords: Learning by doing, Experience curves, Energy models, Technological change

JEL Classification: O3, O13, D83

Suggested Citation

Nordhaus, William D., The Perils of the Learning Model for Modeling Endogenous Technological Change (January 2, 2009). Cowles Foundation Discussion Paper No. 1685. Available at SSRN: https://ssrn.com/abstract=1322422

William D. Nordhaus (Contact Author)

Yale University - Department of Economics ( email )

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Cowles Foundation, Yale University ( email )

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National Bureau of Economic Research (NBER)

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