The Perils of the Learning Model For Modeling Endogenous Technological Change

William Nordhaus

No 1685, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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 (search for similar items in EconPapers)
JEL-codes: D83 O13 O3 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2009-01
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-knm
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Citations: View citations in EconPapers (41)

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Related works:
Journal Article: The Perils of the Learning Model for Modeling Endogenous Technological Change (2014)
Working Paper: The Perils of the Learning Model For Modeling Endogenous Technological Change (2009)
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