Technology Variation vs. R&D Uncertainty: What Matters Most for Energy Patent Success?
R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D--which could be important for capturing the range of costs associated with climate change mitigation policies--by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on returns to energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions - that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research efforts appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.
|Date of creation:||Jan 2012|
|Date of revision:|
|Publication status:||published as “Technology Variation vs. R&D Uncertainty: What Matters Most for Energy Patent Success?” Resources and Energy Economics, November 2013, 35(4), 505-533 (with Nidhi Santen, Karen Fisher-Vanden and Mort Webster).|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pakes, Ariel S, 1986.
"Patents as Options: Some Estimates of the Value of Holding European Patent Stocks,"
Econometric Society, vol. 54(4), pages 755-84, July.
- Ariel Pakes, 1984. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," NBER Working Papers 1340, National Bureau of Economic Research, Inc.
- Machado, Jose A.F. & Silva, J. M. C. Santos, 2005.
"Quantiles for Counts,"
Journal of the American Statistical Association,
American Statistical Association, vol. 100, pages 1226-1237, December.
- Otto, Vincent M. & Löschel, Andreas & Reilly, John, 2008. "Directed technical change and differentiation of climate policy," Energy Economics, Elsevier, vol. 30(6), pages 2855-2878, November.
- Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
- Griliches, Zvi, 1990.
"Patent Statistics as Economic Indicators: A Survey,"
Journal of Economic Literature,
American Economic Association, vol. 28(4), pages 1661-1707, December.
- Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
- Zvi Griliches, 1990. "Patent Statistics as Economic Indicators: A Survey," NBER Working Papers 3301, National Bureau of Economic Research, Inc.
- Goulder, Lawrence H. & Mathai, Koshy, 2000. "Optimal CO2 Abatement in the Presence of Induced Technological Change," Journal of Environmental Economics and Management, Elsevier, vol. 39(1), pages 1-38, January.
- Baker, Erin & Adu-Bonnah, Kwame, 2008. "Investment in risky R&D programs in the face of climate uncertainty," Energy Economics, Elsevier, vol. 30(2), pages 465-486, March.
- Kerr, Suzi & Newell, Richard, 2001.
"Policy-Induced Technology Adoption: Evidence from the U.S. Lead Phasedown,"
dp-01-14, Resources For the Future.
- Suzi Kerr & Richard G. Newell, 2003. "Policy-Induced Technology Adoption: Evidence from the U.S. Lead Phasedown," Journal of Industrial Economics, Wiley Blackwell, vol. 51(3), pages 317-343, 09.
- Jaffe, Adam B & Fogarty, Michael S & Banks, Bruce A, 1998.
"Evidence from Patents and Patent Citations on the Impact of NASA and Other Federal Labs on Commercial Innovation,"
Journal of Industrial Economics,
Wiley Blackwell, vol. 46(2), pages 183-205, June.
- Adam B. Jaffe & Michael S. Fogarty & Bruce A. Banks, 1997. "Evidence from Patents and Patent Citations on the Impact of NASA and Other Federal Labs on Commercial Innovation," NBER Working Papers 6044, National Bureau of Economic Research, Inc.
- Goulder, Lawrence H. & Schneider, Stephen H., 1999. "Induced technological change and the attractiveness of CO2 abatement policies," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 211-253, August.
- Bosetti, Valentina & Tavoni, Massimo, 2009.
"Uncertain R&D, backstop technology and GHGs stabilization,"
Elsevier, vol. 31(Supplemen), pages S18-S26.
- Valentina Bosetti & Massimo Tavoni, 2007. "Uncertain R&D, Backstop Technology and GHGs Stabilization," Working Papers 2007.6, Fondazione Eni Enrico Mattei.
- David Popp, 2002. "Induced Innovation and Energy Prices," American Economic Review, American Economic Association, vol. 92(1), pages 160-180, March.
- David Popp, 2006. "They Don'T Invent Them Like They Used To: An Examination Of Energy Patent Citations Over Time," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(8), pages 753-776.
- Koenker,Roger, 2005.
Cambridge University Press, number 9780521608275, june. pag.
- Malte Schwoon & Richard S.J. Tol, 2004.
"Optimal CO2-abatement with socio-economic inertia and induced technological change,"
FNU-37, Research unit Sustainability and Global Change, Hamburg University, revised Jan 2004.
- Malte Schwoon & Richard S.J. Tol, 2006. "Optimal CO2-abatement with Socio-economic Inertia and Induced Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 25-60.
- Bye, Brita & Jacobsen, Karl, 2011. "Restricted carbon emissions and directed R&D support; an applied general equilibrium analysis," Energy Economics, Elsevier, vol. 33(3), pages 543-555, May.
- repec:fth:harver:1473 is not listed on IDEAS
- Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, 04.
- Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
- Valentina Bosetti, Carlo Carraro, Marzio Galeotti, Emanuele Massetti, Massimo Tavoni, 2006. "A World induced Technical Change Hybrid Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 13-38.
- Alfonso Miranda, 2005.
"Planned Fertility and Family Background: A Quantile Regression for Counts Analysis,"
Keele Economics Research Papers
KERP 2005/07, Centre for Economic Research, Keele University.
- Alfonso Miranda, 2008. "Planned fertility and family background: a quantile regression for counts analysis," Journal of Population Economics, Springer;European Society for Population Economics, vol. 21(1), pages 67-81, January.
- Popp, David, 2006. "ENTICE-BR: The effects of backstop technology R&D on climate policy models," Energy Economics, Elsevier, vol. 28(2), pages 188-222, March.
- Alfonso Miranda, 2006. "QCOUNT: Stata program to fit quantile regression models for count data," Statistical Software Components S456714, Boston College Department of Economics, revised 08 Aug 2007.
- Blanford, Geoffrey J., 2009. "R&D investment strategy for climate change," Energy Economics, Elsevier, vol. 31(Supplemen), pages S27-S36.
When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:17792. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.