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.
Nordhaus, William D., "The Perils of the Learning Model For Modeling Endogenous Technological Change" (2009). Cowles Foundation Discussion Papers. 2002.