An ‘Algorithmic Links with Probabilities’ Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition
Travis Lybbert () and
Working Papers from U.S. Census Bureau, Center for Economic Studies
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.
Pages: 22 pages
New Economics Papers: this item is included in nep-ino, nep-ipr and nep-pr~
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https://www2.census.gov/ces/wp/2016/CES-WP-16-15.pdf First version, 2016 (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:16-15
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