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Close to you? Bias and precision in patent-based measures of technological proximity

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  • Benner, Mary
  • Waldfogel, Joel

Abstract

Patent data have been widely used in research to characterize firms' locations in technological or knowledge space, as well as the proximities among firms. Researchers have measured firms' technological or knowledge proximities with a variety of measures based on patent data, including Euclidean distances (using the technological classifications listed on patents), and overlap in cited patents. Often research has employed only the first listed patent classification in measures of proximities. We explore the effects of using the first listed patent class as well as other methods to measure proximities. We point out that measures of proximity based on small numbers of patents are imprecisely measured random variables. Measures computed on samples with few patents or a single patent class generate both biased and imprecise measures of proximity. We discuss the implications of this for typical research questions employing measures of proximity, and explore the effects of larger sample sizes and coarser patent class breakdowns in mitigating these problems. Where possible, we suggest that researchers increase their sample sizes by aggregating years or using all of the listed patent classes on a patent, rather than just the first.

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Bibliographic Info

Article provided by Elsevier in its journal Research Policy.

Volume (Year): 37 (2008)
Issue (Month): 9 (October)
Pages: 1556-1567

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Handle: RePEc:eee:respol:v:37:y:2008:i:9:p:1556-1567

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  1. Fleming, Lee & Sorenson, Olav, 2001. "Technology as a complex adaptive system: evidence from patent data," Research Policy, Elsevier, vol. 30(7), pages 1019-1039, August.
  2. Hall, Bronwyn H & Jaffe, Adam B & Trajtenberg, Manuel, 2001. "The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools," CEPR Discussion Papers 3094, C.E.P.R. Discussion Papers.
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  5. Lucia Piscitello, 2004. "Corporate diversification, coherence and economic performance," Industrial and Corporate Change, Oxford University Press, vol. 13(5), pages 757-787, October.
  6. Manuel Trajtenberg & Adam B. Jaffe & Michael S. Fogarty, 2000. "Knowledge Spillovers and Patent Citations: Evidence from a Survey of Inventors," American Economic Review, American Economic Association, vol. 90(2), pages 215-218, May.
  7. Juan Alcácer & Michelle Gittelman, 2006. "Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 774-779, November.
  8. Jaeyong Song & Paul Almeida & Geraldine Wu, 2003. "Learning--by--Hiring: When Is Mobility More Likely to Facilitate Interfirm Knowledge Transfer?," Management Science, INFORMS, vol. 49(4), pages 351-365, April.
  9. Brian S. Silverman, 1999. "Technological Resources and the Direction of Corporate Diversification: Toward an Integration of the Resource-Based View and Transaction Cost Economics," Management Science, INFORMS, vol. 45(8), pages 1109-1124, August.
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Cited by:
  1. McNamee, Robert C., 2013. "Can’t see the forest for the leaves: Similarity and distance measures for hierarchical taxonomies with a patent classification example," Research Policy, Elsevier, vol. 42(4), pages 855-873.
  2. Nicholas, Tom, 2011. "The origins of Japanese technological modernization," Explorations in Economic History, Elsevier, vol. 48(2), pages 272-291, April.
  3. Nemet, Gregory F., 2012. "Inter-technology knowledge spillovers for energy technologies," Energy Economics, Elsevier, vol. 34(5), pages 1259-1270.
  4. Pierre Azoulay & Joshua S. Graff Zivin & Jialan Wang, 2010. "Superstar Extinction," The Quarterly Journal of Economics, MIT Press, vol. 125(2), pages 549-589, May.
  5. Alvaro Escribano & Szabolcs Blazsek, 2012. "Patents, secret innovations and firm's rate of return : differential effects of the innovation leader," Economics Working Papers we1202, Universidad Carlos III, Departamento de Economía.
  6. Marcela Miozzo & Lori DiVito & Panos Desyllas, 2011. "Cross-border acquisitions of science-based firms: Their effect on innovation in the acquired firm and the local science," DRUID Working Papers 11-17, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
  7. Bar, Talia & Leiponen, Aija, 2012. "A measure of technological distance," Economics Letters, Elsevier, vol. 116(3), pages 457-459.
  8. Davide Castellani & Giulio Giangaspero & Antonello Zanfei, 2013. "Heterogeneity and distance. Some propositions on how differences across regions, firms and functions affect the role of distance in FDI location decisions," Working Papers 1308, University of Urbino Carlo Bo, Department of Economics, Society & Politics - Scientific Committee - L. Stefanini & G. Travaglini, revised 2013.
  9. Antonio Messeni Petruzzelli & Daniele Rotolo & Vito Albino, 2014. "Determinants of Patent Citations in Biotechnology: An Analysis of Patent Influence Across the Industrial and Organizational Boundaries," SPRU Working Paper Series 2014-05, SPRU - Science and Technology Policy Research, University of Sussex.
  10. Nemet, Gregory F. & Johnson, Evan, 2012. "Do important inventions benefit from knowledge originating in other technological domains?," Research Policy, Elsevier, vol. 41(1), pages 190-200.
  11. Khoury, Theodore A. & Pleggenkuhle-Miles, Erin G., 2011. "Shared inventions and the evolution of capabilities: Examining the biotechnology industry," Research Policy, Elsevier, vol. 40(7), pages 943-956, September.
  12. Carayannopoulos, Sofy & Auster, Ellen R., 2010. "External knowledge sourcing in biotechnology through acquisition versus alliance: A KBV approach," Research Policy, Elsevier, vol. 39(2), pages 254-267, March.

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