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An �Algorithmic Links With Probabilities� Concordance For Trademarks: For Disaggregated Analysis Of Trademark & Economic Data

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  • Nikolas Zolas
  • Travis J. Lybbert
  • Prantik Bhattacharyya

Abstract

Trademarks (TMs) shape the competitive landscape of markets for goods and services in all countries through branding and conveying information and quality inherent in products. Yet, researchers are largely unable to conduct rigorous empirical analysis of TMs in the modern economy because TM data and economic activity data are organized differently and cannot be analyzed jointly at the industry or sectoral level. 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. In this paper, we apply this ALP concordance for TMs to characterize patterns in TM applications across countries, industries, income levels and more. We also use the concordance to investigate some of the key determinants of international technology transfer by comparing bilateral TM applications and bilateral patent applications. We conclude with a discussion of possible extensions of this work, including deeper indicator-level concordances and further analyses that are possible once TM data are linked with economic activity data.

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  • Nikolas Zolas & Travis J. Lybbert & Prantik Bhattacharyya, 2013. "An �Algorithmic Links With Probabilities� Concordance For Trademarks: For Disaggregated Analysis Of Trademark & Economic Data," Working Papers 13-49, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:13-49
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    References listed on IDEAS

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