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Tracking the technological composition of industries with algorithmic patent concordances

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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 linking patents to measures of economic activity, which has proven to be difficult. We construct probabilistic linkages between the U.S. Patent Classification (USPC) system and Cooperative Patent Classification (CPC) system and industry and product classifications including the North American Industrial Classification System (NAICS), International Standard Industrial Classification (ISIC), Harmonized System (HS) and Standard International Trade Classification (SITC). We use these concordances to evaluate the persistence of technology-industry relationships over time by generating linkages over different years of patent data. We find strong persistence in technology usage within industries and, until recently, relatively little change in the technology composition of industries over time. As the technology composition of industries becomes more stable, we find evidence of increased specialization. Finally, we show that industries that exhibit changing technology composition also show shifting occupational composition.

Suggested Citation

  • Nathan Goldschlag & Travis J. Lybbert & Nikolas J. Zolas, 2020. "Tracking the technological composition of industries with algorithmic patent concordances," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 29(6), pages 582-602, August.
    • Handle: RePEc:taf:ecinnt:v:29:y:2020:i:6:p:582-602
    DOI: 10.1080/10438599.2019.1648014
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    Cited by:

    1. Jos'e Alejandro Mendoza & Faustino Prieto & Jos'e Mar'ia Sarabia, 2021. "Inequality in the use frequency of patent technology codes," Papers 2111.11211, arXiv.org.
    2. Robin Kaiji Gong & Yao Amber Li & Kalina Manova & Stephen Teng Sun, 2023. "Tickets to the global market: First US patent awards and Chinese firm exports," CEP Discussion Papers dp1962, Centre for Economic Performance, LSE.
    3. Ayerst, Stephen & Ibrahim, Faisal & MacKenzie, Gaelan & Rachapalli, Swapnika, 2023. "Trade and diffusion of embodied technology: an empirical analysis," Journal of Monetary Economics, Elsevier, vol. 137(C), pages 128-145.
    4. Mauro Caselli & Edwin Fourrier-Nicolai & Andrea Fracasso & Sergio Scicchitano, 2024. "Digital Technologies and Firms’ Employment and Training," CESifo Working Paper Series 11056, CESifo.
    5. Hötte, Kerstin, 2023. "Demand-pull, technology-push, and the direction of technological change," Research Policy, Elsevier, vol. 52(5).
    6. Sung Jin Kang & Sun Lee & Seon Ju Lee, 2021. "Impacts of Environmental Agreements on Bilateral Trade of Climate Industry," Energies, MDPI, vol. 14(21), pages 1-17, November.
    7. Steijn, Mathieu P.A. & Koster, Hans R.A. & Van Oort, Frank G., 2022. "The dynamics of industry agglomeration: Evidence from 44 years of coagglomeration patterns," Journal of Urban Economics, Elsevier, vol. 130(C).

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