Getting patents and economic data to speak to each other: An 'Algorithmic Links with Probabilities' approach for joint analyses of patenting and economic activity

摘要

International technological diffusion is a key determinant of cross-country differences in economic performance. While patents can be a useful proxy for innovation and technological change and diffusion, fully exploiting patent data for such economic analyses requires patents to be tied to measures of economic activity. In this paper, we describe and explore a new algorithmic approach to constructing concordances between the International Patent Classification (IPC) system that organizes patents by technical features and industry classification systems that organize economic data, such as the Standard International Trade Classification (SITC) and the International Standard Industrial Classification (IS1C). This 'Algorithmic Links with Probabilities' (ALP) approach mines patent data using keywords extracted from industry descriptions and processes the resulting matches using a probabilistic framework. We compare the results of this ALP concordance to existing technology concordances. Based on these comparisons, we discuss advantages of this approach relative to conventional approaches. ALP concordances provide a meso-level mapping to industries that complements existing macro- and firm-level mappings - and open new possibilities for empirical patent analysis.