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The Perils of the Learning Model For Modeling Endogenous Technological Change

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  • 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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 14638.

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Date of creation: Jan 2009
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Handle: RePEc:nbr:nberwo:14638

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Cited by:
  1. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
  2. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
  3. Enrica Cian & Valentina Bosetti & Massimo Tavoni, 2012. "Technology innovation and diffusion in “less than ideal” climate policies: An assessment with the WITCH model," Climatic Change, Springer, vol. 114(1), pages 121-143, September.
  4. Bela Nagy & J. Doyne Farmer & Quan M. Bui & Jessika E. Trancik, 2012. "Statistical Basis for Predicting Technological Progress," Papers 1207.1463, arXiv.org.
  5. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages S12-S23.
  6. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
  7. Aguilera, Roberto F., 2014. "Production costs of global conventional and unconventional petroleum," Energy Policy, Elsevier, vol. 64(C), pages 134-140.
  8. Lion Hirth, 2013. "The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects their Welfare-optimal Deployment," Working Papers 2013.90, Fondazione Eni Enrico Mattei.
  9. Amavilah, Voxi Heinrich, 2011. "The Full Value of the Nobel Prize - Part 1: Mining “Data Without Theory”," MPRA Paper 33483, University Library of Munich, Germany.
  10. Yuichiro Kamada & Fuhito Kojima, 2013. "Voter Preferences, Polarization, and Electoral Policies," Discussion Papers 12-021, Stanford Institute for Economic Policy Research.

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