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Learning-by-Doing and the Choice of Technology: The Role of Patience

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  • Karp, Larry S.
  • Lee, In Ho

Abstract

If agents learn-by-doing and are myopic, less advanced firms might adopt new technologies while more advanced firms stick with the old technology. This kind of overtaking can also occur if agents are forward looking and have high discount rates. However, overtaking never occurs if agents are sufficiently patient. A finite discount rate increases the set of states at which agents adopt new technologies, so more patient agents tend to upgrade their technology more frequently.

Suggested Citation

  • Karp, Larry S. & Lee, In Ho, 2000. "Learning-by-Doing and the Choice of Technology: The Role of Patience," CUDARE Working Papers 25108, University of California, Berkeley, Department of Agricultural and Resource Economics.
    • Handle: RePEc:ags:ucbecw:25108
    DOI: 10.22004/ag.econ.25108
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    References listed on IDEAS

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    Cited by:

    1. Rob Hart, 2009. "Bad Eggs, Learning-by-doing, and the Choice of Technology," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(4), pages 429-450, April.
    2. Marisa Cenci & Margherita Scarlato, 2002. "Istituzioni e mercato del lavoro nel Mezzogiorno d’Italia: un’analisi dinamica," Rivista di Politica Economica, SIPI Spa, vol. 92(3), pages 281-320, May-June.
    3. Rivas, Javier, 2010. "The effects of the market structure on the adoption of evolving technologies," Journal of Economic Dynamics and Control, Elsevier, vol. 34(12), pages 2485-2493, December.
    4. Ben Klemens, 2021. "Attributing Value to Patents and Trademarks in Complex Production Chains," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 842-875, June.
    5. Mateos-Planas, Xavier, 2004. "Technology adoption with finite horizons," Journal of Economic Dynamics and Control, Elsevier, vol. 28(11), pages 2129-2154, October.
    6. Hill, William W. & Beatty, Sharon E., 2011. "A model of adolescents' online consumer self-efficacy (OCSE)," Journal of Business Research, Elsevier, vol. 64(10), pages 1025-1033, October.
    7. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2014. "Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty," Computational Optimization and Applications, Springer, vol. 58(1), pages 31-85, May.
    8. James G. Mulligan & Nilotpal Das, 2005. "Persistent Adoption of Time-Saving Process Innovations," Working Papers 05-03, University of Delaware, Department of Economics.
    9. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2013. "Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 380-416, February.

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