The Perils of the Learning Model for Modeling Endogenous Technological Change

20 Pages Posted: 15 Jan 2009 Last revised: 1 Jan 2023

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 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.

Suggested Citation

Nordhaus, William D., The Perils of the Learning Model for Modeling Endogenous Technological Change (January 2009). NBER Working Paper No. w14638, Available at SSRN: https://ssrn.com/abstract=1327259

William D. Nordhaus (Contact Author)

Yale University - Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States
203-432-3598 (Phone)
203-432-5779 (Fax)

Cowles Foundation, Yale University ( email )

Box 208281
New Haven, CT 06520-8281
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
58
Abstract Views
750
Rank
290,592
PlumX Metrics