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Patent Citations and the Size of the Inventive Step - Evidence from Hybrid Corn

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  • Petra Moser
  • Joerg Ohmstedt
  • Paul W. Rhode

Abstract

Patents are the main source of data on innovation, but there are persistent concerns that patents may be a noisy and biased measure. An important challenge arises from unobservable variation in the size of the inventive step that is covered by a patent. The count of later patents that cite a patent as relevant prior art – so called forward citations – have become the standard measure to control for such variation. Citations may, however, also be a noisy and biased measure for the size of the inventive step. To address this issue, this paper examines field trial data for patented improvements in hybrid corn. Field trials report objective measures for improvements in hybrid corn, which we use to quantify the size of the inventive step. These data show a robust correlation between citations and improvements in yields, as the bottom line measure for improvements in hybrid corn. This correlation is robust to alternative measures for improvements in hybrid corn, and a broad range of other tests.We also investigate the process, by which patents generate citations. This analysis reveals that hybrids that serve as an input for genetically-related follow-on inventions are more likely to receive self-citations (by the same firm), which suggests that self-citations are a good predictor for follow-on invention.

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  • Petra Moser & Joerg Ohmstedt & Paul W. Rhode, 2015. "Patent Citations and the Size of the Inventive Step - Evidence from Hybrid Corn," NBER Working Papers 21443, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:21443
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    Cited by:

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    2. Whalen, Ryan, 2018. "Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system," Research Policy, Elsevier, vol. 47(7), pages 1334-1343.
    3. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    4. Cheng, Yuanyuan & Du, Kerui & Yao, Xin, 2023. "Stringent environmental regulation and inconsistent green innovation behavior: Evidence from air pollution prevention and control action plan in China," Energy Economics, Elsevier, vol. 120(C).
    5. Jiri Schwarz & Martin Stepanek, 2016. "Patents: A Means to Innovation or Strategic Ends?," Working Papers IES 2016/08, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2016.
    6. Mariam Barry & Giorgio Triulzi & Christopher L. Magee, 2017. "Food Productivity Trends from Hybrid Corn: Statistical Analysis of Patents and Field-test data," Papers 1706.05911, arXiv.org.
    7. Fackler, Thomas A. & Giesing, Yvonne & Laurentsyeva, Nadzeya, 2020. "Knowledge remittances: Does emigration foster innovation?," Research Policy, Elsevier, vol. 49(9).

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    More about this item

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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