IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v128y2018icp104-117.html
   My bibliography  Save this article

How well do experience curves predict technological progress? A method for making distributional forecasts

Author

Listed:
  • Lafond, François
  • Bailey, Aimee Gotway
  • Bakker, Jan David
  • Rebois, Dylan
  • Zadourian, Rubina
  • McSharry, Patrick
  • Farmer, J. Doyne

Abstract

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:128:y:2018:i:c:p:104-117
    DOI: 10.1016/j.techfore.2017.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517303736
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2017.11.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    3. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.
    4. Bosetti, Valentina & Catenacci, Michela & Fiorese, Giulia & Verdolini, Elena, 2012. "The future prospect of PV and CSP solar technologies: An expert elicitation survey," Energy Policy, Elsevier, vol. 49(C), pages 308-317.
    5. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    6. Way, Rupert & Lafond, François & Lillo, Fabrizio & Panchenko, Valentyn & Farmer, J. Doyne, 2019. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 211-238.
    7. 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.
    8. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    9. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    10. Michael P. Clements & David F.Hendry, 2001. "Forecasting with difference-stationary and trend-stationary models," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-19.
    11. Gavin Sinclair & Steven Klepper & Wesley Cohen, 2000. "What's Experience Got to Do With It? Sources of Cost Reduction in a Large Specialty Chemicals Producer," Management Science, INFORMS, vol. 46(1), pages 28-45, January.
    12. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    13. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    14. McDonald, John, 1987. "A New Model for Learning Curves, DARM: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 338-338, July.
    15. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    16. 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.
    17. Womer, N Keith & Patterson, J Wayne, 1983. "Estimation and Testing of Learning Curves," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 265-272, October.
    18. McDonald, John, 1987. "A New Model for Learning Curves, DARM," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 329-335, July.
    19. 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.
    20. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    21. Linda Argote & Sara L. Beckman & Dennis Epple, 1990. "The Persistence and Transfer of Learning in Industrial Settings," Management Science, INFORMS, vol. 36(2), pages 140-154, February.
    22. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    23. 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.
    24. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    25. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    26. Hutchby, James A., 2014. "A “Moore's Law”-like approach to roadmapping photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 883-890.
    27. Vigil, Dimas P. & Sarper, Huseyin, 1994. "Estimating the effects of parameter variability on learning curve model predictions," International Journal of Production Economics, Elsevier, vol. 34(2), pages 187-200, March.
    28. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    29. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    30. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    31. 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.
    32. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    33. Zheng, Cheng & Kammen, Daniel M., 2014. "An innovation-focused roadmap for a sustainable global photovoltaic industry," Energy Policy, Elsevier, vol. 67(C), pages 159-169.
    34. de La Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2013. "Predicting the costs of photovoltaic solar modules in 2020 using experience curve models," Energy, Elsevier, vol. 62(C), pages 341-348.
    35. Valentina Bosetti & Michela Catenacci & Giulia Fiorese & Elena Verdolini, 2012. "The Future Prospects of PV and CSP Solar Technologies," Review of Environment, Energy and Economics - Re3, Fondazione Eni Enrico Mattei, January.
    36. van der Zwaan, Bob & Rabl, Ari, 2004. "The learning potential of photovoltaics: implications for energy policy," Energy Policy, Elsevier, vol. 32(13), pages 1545-1554, September.
    37. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    38. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    39. Harvey, A C, 1980. "On Comparing Regression Models in Levels and First Differences," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(3), pages 707-720, October.
    40. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    41. Sampson, Michael, 1991. "The Effect of Parameter Uncertainty on Forecast Variances and Confidence Intervals for Unit Root and Trend Stationary Time-Series Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(1), pages 67-76, Jan.-Marc.
    42. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lafond, François & Greenwald, Diana & Farmer, J. Doyne, 2022. "Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment," The Journal of Economic History, Cambridge University Press, vol. 82(3), pages 727-764, September.
    2. Elizabeth Baldwin & Yongyang Cai & Karlygash Kuralbayeva, 2018. "To Build or Not to Build? Capital Stocks and Climate Policy," CESifo Working Paper Series 6884, CESifo.
    3. Way, Rupert & Lafond, François & Lillo, Fabrizio & Panchenko, Valentyn & Farmer, J. Doyne, 2019. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 211-238.
    4. Edoardo Ruffino & Bruno Piga & Alessandro Casasso & Rajandrea Sethi, 2022. "Heat Pumps, Wood Biomass and Fossil Fuel Solutions in the Renovation of Buildings: A Techno-Economic Analysis Applied to Piedmont Region (NW Italy)," Energies, MDPI, vol. 15(7), pages 1-25, March.
    5. Mitrašinović, Aleksandar M., 2021. "Photovoltaics advancements for transition from renewable to clean energy," Energy, Elsevier, vol. 237(C).
    6. Baldwin, Elizabeth & Cai, Yongyang & Kuralbayeva, Karlygash, 2020. "To build or not to build? Capital stocks and climate policy∗," Journal of Environmental Economics and Management, Elsevier, vol. 100(C).
    7. Thomas Hale, 2020. "Catalytic Cooperation," Global Environmental Politics, MIT Press, vol. 20(4), pages 73-98, Autumn.
    8. Zadourian, Rubina & Klümper, Andreas, 2018. "Exact probability distribution function for the volatility of cumulative production," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 59-66.
    9. Hann-Earl Kim & Yu-Sang Chang & Hee-Jin Kim, 2021. "Dynamic Electricity Intensity Trends in 91 Countries," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    10. Gary, Robert F. & Fink, Matthias & Belousova, Olga & Marinakis, Yorgos & Tierney, Robert & Walsh, Steven T., 2020. "An introduction to the field of abundant economic thought," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    11. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    2. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    3. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    4. Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    5. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    6. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    7. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    8. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    9. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    11. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, 2016. "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
    12. 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.
    13. Lafond, François & Greenwald, Diana & Farmer, J. Doyne, 2022. "Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment," The Journal of Economic History, Cambridge University Press, vol. 82(3), pages 727-764, September.
    14. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    15. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
    16. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    17. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    18. Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
    19. Harashima, Taiji, 2011. "A Model of Total Factor Productivity Built on Hayek’s View of Knowledge: What Really Went Wrong with Socialist Planned Economies?," MPRA Paper 29107, University Library of Munich, Germany.
    20. Anuraag Singh & Giorgio Triulzi & Christopher L. Magee, 2020. "Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description," Papers 2004.13919, arXiv.org.

    More about this item

    Keywords

    Forecasting; Technological progress; Experience curves;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:128:y:2018:i:c:p:104-117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.