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An ‘Algorithmic Links with Probabilities’ Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition

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  • Nathan Goldschlag
  • Travis J. Lybbert
  • Nikolas J. Zolas

Abstract

Patents are a useful proxy for innovation, technological change, and diffusion. However, fully exploiting patent data for economic analyses requires patents be tied to measures of economic activity, which has proven to be difficult. Recently, Lybbert and Zolas (2014) have constructed an International Patent Classification (IPC) to industry classification crosswalk using an ‘Algorithmic Links with Probabilities’ approach. In this paper, we utilize a similar approach and apply it to new patent classification schemes, the U.S. Patent Classification (USPC) system and Cooperative Patent Classification (CPC) system. The resulting USPC-Industry and CPC-Industry concordances link both U.S. and global patents to multiple vintages of the North American Industrial Classification System (NAICS), International Standard Industrial Classification (ISIC), Harmonized System (HS) and Standard International Trade Classification (SITC). We then use the crosswalk to highlight changes to industrial technology composition over time. We find suggestive evidence of strong persistence in the association between technologies and industries over time.

Suggested Citation

  • Nathan Goldschlag & Travis J. Lybbert & Nikolas J. Zolas, 2016. "An ‘Algorithmic Links with Probabilities’ Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition," Working Papers 16-15, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:16-15
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    References listed on IDEAS

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    1. Mansfield, Edwin & Schwartz, Mark & Wagner, Samuel, 1981. "Imitation Costs and Patents: An Empirical Study," Economic Journal, Royal Economic Society, vol. 91(364), pages 907-918, December.
    2. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    3. 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.
    4. Wesley M. Cohen & Richard R. Nelson & John P. Walsh, 2000. "Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. Manufacturing Firms Patent (or Not)," NBER Working Papers 7552, National Bureau of Economic Research, Inc.
    5. Lybbert, Travis J. & Zolas, Nikolas J., 2014. "Getting patents and economic data to speak to each other: An ‘Algorithmic Links with Probabilities’ approach for joint analyses of patenting and economic activity," Research Policy, Elsevier, vol. 43(3), pages 530-542.
    6. Zvi Griliches, 1998. "R&D and Productivity: The Econometric Evidence," NBER Books, National Bureau of Economic Research, Inc, number gril98-1.
    7. Richard C. Levin & Alvin K. Klevorick & Richard R. Nelson & Sidney G. Winter, 1987. "Appropriating the Returns from Industrial Research and Development," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 18(3, Specia), pages 783-832.
    8. Griliches, Zvi, 1998. "R&D and Productivity," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226308869, October.
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    Cited by:

    1. Marco Grazzi & Chiara Piccardo & Cecilia Vergari, 2020. "Concordance and complementarity in IP instruments," Industry and Innovation, Taylor & Francis Journals, vol. 27(7), pages 756-788, August.
    2. M. Grazzi & C. Piccardo & C. Vergari, 2019. "Concordance and complementarity in Intellectual Property instruments," Working Papers wp1127, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Kukharskyy, Bohdan, 2020. "A tale of two property rights: Knowledge, physical assets, and multinational firm boundaries," Journal of International Economics, Elsevier, vol. 122(C).
    4. Diodato, Dario & Neffke, Frank & O’Clery, Neave, 2018. "Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time," Journal of Urban Economics, Elsevier, vol. 106(C), pages 1-26.
    5. Bryan Kelly & Dimitris Papanikolaou & Amit Seru & Matt Taddy, 2021. "Measuring Technological Innovation over the Long Run," American Economic Review: Insights, American Economic Association, vol. 3(3), pages 303-320, September.
    6. Giovanni Dosi & Marco Grazzi & Daniele Moschella, 2017. "What do firms know? What do they produce? A new look at the relationship between patenting profiles and patterns of product diversification," Small Business Economics, Springer, vol. 48(2), pages 413-429, February.

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