Working Paper: NBER ID: w14638
Authors: William D. Nordhaus
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 curves; technological change; energy policy; global warming; marginal cost
JEL Codes: D83; O13; O3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| fundamental statistical identification problem (C46) | upward bias in estimated learning coefficient (C51) |
| exogenous factors not accounted for (F29) | estimated learning coefficient exceeds true learning coefficient (C51) |
| model specifications (C52) | learning parameters are not robust to alternative specifications (C51) |
| overestimate of learning coefficient (C51) | underestimate of total marginal cost of output (D24) |