We argue and show that patented inventions have greater future impact if they are classified in higher contrast patent classes. To identify the effect of patent class contrast on patent impact, we carry out two main tests. First, we estimate a patent fixed-effects model to estimate how a patent’s citation impact changes as a function of over-time variation in patent class contrast. Second, we analyze so-called “twin patents,” i.e., cases where an invention patented in the USPTO is also patented in the European Patent Office. Matching pairs of identical inventions that were concurrently assigned to distinct classification schemes eliminates unobserved heterogeneity across patents and enables us to isolate the effect of patent class contrast on patent impact. Both tests provide support for our hypothesis.
Balazs Kovacs is an Assistant Professor of Management at Yale University. He studies various topics in organization theory and strategy, including social networks, entrepreneurship, learning, diffusion, organizational identity, and status. He has investigated these issues in a variety of settings, including restaurants, movies, book publishing, innovation and patenting, and banks. His current work investigates the effects of category spanning and innovation in technological domains. He typically uses large-scale, “big data” approaches to study these questions, analyzing online reviews and social networks. His research appears in journals such as Administrative Science Quarterly, American Sociological Review, Organization Science, Management Science, Nature Biotechnology, Research Policy, Social Networks, and Strategic Management Journal.
