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Modeling the Economics of Greenhouse Gas Mitigation Summary of a Workshop (2011) / Chapter Skim
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The Perils of the Learning Model For Modeling Endogenous Technological Change--William D. Nordhaus
Pages 69-75

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From page 69...
... 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. The Challenge of Endogenous Technological Change Most studies and models of environmental and climate-change policy -- indeed of virtually all aspects of economic policy -- have sidestepped the thorny issue of endogenous technological change or induced innovation.
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From page 70...
... First, the paper shows that there is a fundamental statistical identification problem in trying to separate learning from exogenous technological change. As a result of the identification problem, estimated learning coefficients will generally be biased upwards.
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From page 71...
... In particular, it is unclear whether the learning is embodied in individual workers and firms, whether there are interindustry or international spillovers, and whether the improvements lead to durable techno logical changes, and even whether the learning effects can be distinguished from other technological changes. In this section, we focus on the problem of identifying differences in productivity due to learning from exog enous changes.
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From page 72...
... The general conclusion is that because of the interaction of demand, output growth, exogenous technological change, and learning, behavioral learning curves will generally have an upward bias in estimated learning coeffi cients. The only general case in which the coefficient is unbiased is when exogenous (non-learning)
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From page 73...
... These are based on government data for 1929 to 2007 and extrapolate backwards from 1929 using an assumed constant growth rate of 3.9 percent per year. I then estimate bivariate learning functions and exogenous technological change rates.
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From page 74...
... The danger in using learning to model exogenous technological change arises when the models select tech nologies on the basis of their cost characteristics. Learning models have total marginal costs that are lower than current marginal costs because an additional unit of output lowers all future costs as producers move down the learning curve.
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From page 75...
... Suppose, for example, that the true learning parameter is 0.1 and because of the biased discussed above the estimated parameter is 0.3. With a 3 percent discount rate and a 10 percent growth rate, the learning discount is overestimated by a factor of two.
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Key Terms

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  • endogenous technological
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  • empirical learning
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  • learning parameters
  • learning curves
  • induced innovation
  • global warming
  • separate learning

  • true learning
  • learning parameter
  • william nordhaus
  • incorrectly specified
  • fundamental statistical

  • statistical identification
  • estimate learning
  • learning rates
  • learning curve
  • learning discount

  • using learning
  • induced technological
  • zero exogenous
  • current marginal
  • constant growth


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