An ‘Algorithmic Links with Probabilities’ Concordance for Trademarks with an Application Towards Bilateral IP Flows

Abstract

Trademarks (TMs) shape the competitive landscape of markets for goods and services in all countries. As a key element of branding, they can inform consumers about the quality and content of goods and services. Yet, researchers are largely unable to conduct rigorous empirical analysis of TMs in the global economy because TM data and economic data are organised differently and cannot be analysed jointly at the industry or sector level. We propose an ‘algorithmic links with probabilities’ (ALP) approach to match TM data to economic data and enable joint analysis with these data. 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 analyse differences in TM usage across both economic and TM sectors. We apply this ALP concordance for TMs to characterise patterns in TM registrations across countries and industries and to investigate some key determinants of international technology flows by comparing bilateral TM registrations and bilateral patent grants. We find that international patenting and TM flows are jointly determined by trade‐related influences with significant differences in intellectual property usage across industry sectors and income levels.