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The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing

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  • Peter Thompson

Abstract

The concept of a learning curve for individuals has been around since the beginning of the twentieth century. The idea that an analogous phenomenon might also apply at the level of the organization took longer to emerge, but it had begun to figure prominently in military procurement and scheduling at least a decade before Wright's (1936) classic paper providing evidence that the cost of producing an airframe declined as cumulative output increased. Wright (1936) was careful not to describe his empirical results as a learning curve. Of his three proposed explanations for the relationships he observed between cost and cumulative quantity produced, only one is unambiguously a source of organizational learning; the others are consistent with organizational learning but also with standard static economies of scale. It quickly became apparent that the notion of organizational learning as a by-product of accumulated experience has important consequences for firm strategy. The Boston Consulting Group (BCG) built its consulting business around the concept of what it branded the experience curve, asserting that cost reductions associated with cumulative output applied to all costs, were "consistently around 20-30% each time accumulated production is doubled, [and] this decline goes on in time without limit" (Henderson 1968). Today, the negative relationship between unit production costs and cumulative output is one of the best-documented empirical regularities in economics. Nonetheless, the thesis of this paper is that the conceptual transformation of the relationship between cost and cumulative production into an organizational learning curve with profound strategic implications has not been sufficiently supported with direct empirical evidence.

Suggested Citation

  • Peter Thompson, 2012. "The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 203-224, Summer.
  • Handle: RePEc:aea:jecper:v:26:y:2012:i:3:p:203-24
    Note: DOI: 10.1257/jep.26.3.203
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    Citations

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    Cited by:

    1. Francine Lafontaine & Kathryn Shaw, 2016. "Serial Entrepreneurship: Learning by Doing?," Journal of Labor Economics, University of Chicago Press, vol. 34(S2), pages 217-254.
    2. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    3. Rupert Way & Franc{c}ois Lafond & J. Doyne Farmer & Fabrizio Lillo & Valentyn Panchenko, 2017. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Papers 1705.03423, arXiv.org.
    4. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    5. Nemet, Gregory F. & O’Shaughnessy, Eric & Wiser, Ryan & Darghouth, Naïm & Barbose, Galen & Gillingham, Ken & Rai, Varun, 2017. "Characteristics of low-priced solar PV systems in the U.S," Applied Energy, Elsevier, vol. 187(C), pages 501-513.
    6. Avdic, Daniel & Lundborg, Petter & Vikström, Johan, 2014. "Learning-by-Doing in a Highly Skilled Profession when Stakes are High: Evidence from Advanced Cancer Surgery," Working Paper Series, Center for Labor Studies 2014:9, Uppsala University, Department of Economics.
    7. repec:eee:tefoso:v:128:y:2018:i:c:p:104-117 is not listed on IDEAS
    8. Wei, Max & Smith, Sarah J. & Sohn, Michael D., 2017. "Experience curve development and cost reduction disaggregation for fuel cell markets in Japan and the US," Applied Energy, Elsevier, vol. 191(C), pages 346-357.
    9. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    10. Kathryn L. Shaw & Anders Sørensen, 2017. "The Productivity Advantage of Serial Entrepreneurs," NBER Working Papers 23320, National Bureau of Economic Research, Inc.
    11. Kareem Haggag & Brian McManus & Giovanni Paci, 2017. "Learning by Driving: Productivity Improvements by New York City Taxi Drivers," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 70-95, January.
    12. Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
    13. Lee, You-Na & Walsh, John P., 2016. "Inventing while you work: Knowledge, non-R&D learning and innovation," Research Policy, Elsevier, vol. 45(1), pages 345-359.

    More about this item

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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