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Concordance for Trademarks: Algorithmic Links with Probabilities (ALP) Industry Level-to-Trademark (NICE Level) Crosswalk

Author

Listed:
  • Travis J. Lybbert

    (Agricultural & Resource Economics, UC Davis)

  • Nikolas J. Zolas

    (Center for Economic Studies, United States Census Bureau)

  • Prantik Bhattacharyya

    (Software Engineer, Klout)

Programming Language Abstract
An "Algorithmic Links with Probabilities" Concordance for Trademarks For Disaggregated Analysis of Trademark and Economic Data. We propose an ‘Algorithmic Links with Probabilities’ (ALP) approach to match TM data to economic data and enable these data to speak to each other. Specifically, we construct a NICE Class Level concordance that maps TM data into trade and industry categories forward and backward. This concordance allows researchers to analyze differences in TM usage across both economic and TM sectors.

Suggested Citation

  • Travis J. Lybbert & Nikolas J. Zolas & Prantik Bhattacharyya, 2014. "Concordance for Trademarks: Algorithmic Links with Probabilities (ALP) Industry Level-to-Trademark (NICE Level) Crosswalk," WIPO Economics & Statistics Related Resources 5, World Intellectual Property Organization - Economics and Statistics Division, revised May 2014.
    • Handle: RePEc:wip:eccode:5
    Note: The authors (Nikolas Zolas and Travis Lybbert) continue to update and provide additional industry classifications to the crosswalk and they encourage feedback from users. If you would like to see an industry classification currently not provided for in future editions of the crosswalk, please contact the authors with this request. When using the concordance, please cite the following paper: Zolas, Nikolas J. and Travis J. Lybbert and Prantik 2013. “An ‘Algorithmic Links with Probabilities’ Concordance for Trademarks: For Disaggregated Analysis of Trademark & Economic Data.” CES Working Paper 13-49 and WIPO Economic Research Working Paper 14.
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