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The ˆNames Game˜: Harnessing Inventors Patent Data for Economic Research

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  • Trajtenberg, Manuel
  • Shiff, Gil
  • Melamed, Ran

Abstract

The goal of this paper is to lay out a methodology and corresponding computer algorithms, that allow us to extract the detailed data on inventors contained in patents, and harness it for economic research. Patent data has long been used in empirical research in economics, and yet the information on the identity (i.e. the names and location) of the patents’ inventors has seldom been deployed in a large scale, primarily because of the “who is who” problem: the name of a given inventor may be spelled differently across her/his patents, and the exact same name may correspond to different inventors (i.e. the “John Smith” problem). Given that there are over 2 million patents with 2 inventors per patent on average, the “who is who” problem applies to over 4 million “records”, which is obviously too large to tackle manually. We have thus developed an elaborate methodology and computerized procedure to address this problem in a comprehensive way. The end result is a list of 1.6 million unique inventors from all over the world, with detailed data on their patenting histories, their employers, co-inventors, etc. Forty percent of them have more than one patent, and 70,000 have more than 10 patents. We can trace those multiple inventors across time and space, and thus study the causes and consequences of their mobility across countries, regions, and employers. Given the increasing availability of large computerized data sets on individuals, there may be plenty of opportunities to deploy this methodology to other areas of economic research as well.

Suggested Citation

  • Trajtenberg, Manuel & Shiff, Gil & Melamed, Ran, 2006. "The ˆNames Game˜: Harnessing Inventors Patent Data for Economic Research," Foerder Institute for Economic Research Working Papers 275702, Tel-Aviv University > Foerder Institute for Economic Research.
  • Handle: RePEc:ags:isfiwp:275702
    DOI: 10.22004/ag.econ.275702
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    4. Jain, Amit, 2023. "How knowledge loss and network-structure jointly determine R&D productivity in the biotechnology industry," Technovation, Elsevier, vol. 119(C).
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    6. Varshney, Mayank & Jain, Amit, 2023. "Understanding “reverse” knowledge flows following inventor exit in the semiconductor industry," Technovation, Elsevier, vol. 121(C).
    7. Benjamin Balsmeier & Mohamad Assaf & Tyler Chesebro & Gabe Fierro & Kevin Johnson & Scott Johnson & Guan‐Cheng Li & Sonja Lück & Doug O'Reagan & Bill Yeh & Guangzheng Zang & Lee Fleming, 2018. "Machine learning and natural language processing on the patent corpus: Data, tools, and new measures," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(3), pages 535-553, September.
    8. Hohberger, Jan & Wilden, Ralf, 2022. "Geographic diversity of knowledge inputs: The importance of aligning locations of knowledge inputs and inventors," Journal of Business Research, Elsevier, vol. 145(C), pages 705-719.
    9. Liu, Meijun & Hu, Xiao, 2022. "Movers’ advantages: The effect of mobility on scientists’ productivity and collaboration," Journal of Informetrics, Elsevier, vol. 16(3).
    10. Hosein Fallah, M. & Choudhury, Piyasi & Daim, Tugrul U., 2012. "Does movement of inventors between companies affect their productivity?," Technology in Society, Elsevier, vol. 34(3), pages 196-206.
    11. Amit Jain & Will Mitchell, 2022. "Specialization as a double‐edged sword: The relationship of scientist specialization with R&D productivity and impact following collaborator change," Strategic Management Journal, Wiley Blackwell, vol. 43(5), pages 986-1024, May.
    12. Favaro, Donata & Ninka, Eniel, 2019. "Inventors’ working relationships and knowledge creation: a study on patented innovation," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 45, pages 55-76.

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