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Quantitative empirical trends in technical performance

Author

Listed:
  • Magee, C.L.
  • Basnet, S.
  • Funk, J.L.
  • Benson, C.L.

Abstract

Technological improvement trends such as Moore's law and experience curves have been widely used to understand how technologies change over time and to forecast the future through extrapolation. Such studies can also potentially provide a deeper understanding of R&D management and strategic issues associated with technical change. However, such uses of technical performance trends require further consideration of the relationships among possible independent variables — in particular between time and possible effort variables such as cumulative production, R&D spending, and patent production. The paper addresses this issue by analyzing performance trends and patent output over time for 28 technological domains. In addition to patent output, production and revenue data are analyzed for the integrated circuits domain. The key findings are:1.Sahal's equation is verified for additional effort variables (for patents and revenue in addition to cumulative production where it was first developed).2.Sahal's equation is quite accurate when all three relationships — (a) an exponential between performance and time, (b) an exponential between effort and time, (c) a power law between performance and the effort variable — have good data fits (r2>0.7).3.The power law and effort exponents determined are dependent upon the choice of effort variable but the time dependent exponent is not.4.All 28 domains have high quality fits (r2>0.7) between the log of performance and time whereas 9 domains have very low quality (r2<0.5) for power law fits with patents as the effort variable.5.Even with the highest quality fits (r2>0.9), the exponential relationship is not perfect and it is thus best to consider these relationships as the foundation upon which more complex (but nearly exponential) relationships are based.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:104:y:2016:i:c:p:237-246
    DOI: 10.1016/j.techfore.2015.12.011
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    References listed on IDEAS

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    1. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    2. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2001. "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools," NBER Working Papers 8498, National Bureau of Economic Research, Inc.
    3. 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.
    4. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    5. Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013. "Statistical Basis for Predicting Technological Progress," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    6. 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.
    7. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    8. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    9. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    10. William D. Nordhaus, 2009. "The Perils of the Learning Model For Modeling Endogenous Technological Change," NBER Working Papers 14638, National Bureau of Economic Research, Inc.
    11. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    12. Bresnahan, Timothy F, 1986. "Measuring the Spillovers from Technical Advance: Mainframe Computers inFinancial Services," American Economic Review, American Economic Association, vol. 76(4), pages 742-755, September.
    13. Arthur, W. Brian, 2007. "The structure of invention," Research Policy, Elsevier, vol. 36(2), pages 274-287, March.
    14. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    15. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
    16. David C. Mowery, 2009. "Plus ca change," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 18(1), pages 1-50, February.
    17. Willig, Robert D., 1978. "Incremental consumer's surplus and hedonic price adjustment," Journal of Economic Theory, Elsevier, vol. 17(2), pages 227-253, April.
    18. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.
    19. Benson, Christopher L. & Magee, Christopher L., 2014. "On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries," Renewable Energy, Elsevier, vol. 68(C), pages 745-751.
    20. Schilling, Melissa A. & Esmundo, Melissa, 2009. "Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government," Energy Policy, Elsevier, vol. 37(5), pages 1767-1781, May.
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