The Perils of the Learning Model for Modeling Endogenous Technological Change

Abstract

Modeling of technological change has been a major empirical and analytical obstacle for many years. One approach to modeling technology is learning or experience curves, which originated in techniques used to estimate cost functions in manufacturing. These have recently been introduced in policy models of energy and climate-change economics to make the process of technological change endogenous—that is, allow technologies to vary with economic conditions. It is not widely appreciated that using learning in modeling raises major potential problems. 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.

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