nep-ino New Economics Papers
on Innovation
Issue of 2022‒05‒09
four papers chosen by
Uwe Cantner
University of Jena

  1. Patent disputes as emerging barriers to technology entry? Empirical evidence from patent opposition By Arianna Martinelli; Julia Mazzei; Daniele Moschella
  2. Technology and jobs: A systematic literature review By Kerstin H\"otte; Melline Somers; Angelos Theodorakopoulos
  3. Exploring Artificial Intelligence as a General Purpose Technology with Patent Data -- A Systematic Comparison of Four Classification Approaches By Kerstin H\"otte; Taheya Tarannum; Vilhelm Verendel; Lauren Bennett
  4. Investments in R&D and Production Capacity with Uncertain Breakthrough Time : Private versus Social Incentives By Ketelaars, Martijn; Kort, Peter

  1. By: Arianna Martinelli; Julia Mazzei; Daniele Moschella
    Abstract: The recent surge of patent disputes plays an important role in discouraging firms from entering new technology domains (TDs). Using a large-scale dataset combining data from the EPO-PATSTAT database and ORBIS-IP and containing patents applied at EPO between 2000 and 2015, we construct a new measure of litigiousness using patent opposition data. We find that the degree of litigiousness and the density of patent thickets negatively affect the likelihood of firms entering new TDs. Across technologies, the frequency of oppositions discourages firms mostly in high-tech industries. Across firms, the risk of opposition falls disproportionately on small rather than large firms. Finally, for large firms, we observe a sort of learning-by-being-opposed effect. This evidence suggests that litigiousness and hold-up potential discourage firms from entering new TDs, shaping Schumpeterian patterns of innovation characterized by a stable number of large-established firms and a lower degree of turbulence.
    Keywords: Patent opposition; Technological entry; Innovation Strategies.
    Date: 2022–05–02
  2. By: Kerstin H\"otte; Melline Somers; Angelos Theodorakopoulos
    Abstract: Does technological change destroy or create jobs? New technologies may replace human workers, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. Furthermore, technology-driven productivity growth may increase disposable income, stimulating a demand-induced expansion of employment. To synthesize the existing knowledge on this question, we systematically review the empirical literature on the past four decades of technological change and its impact on employment, distinguishing between five broad technology categories (ICT, Robots, Innovation, TFP-style, Other). Overall, we find across studies that the labor-displacing effect of technology appears to be more than offset by compensating mechanisms that create or reinstate labor. This holds for most types of technology, suggesting that previous anxieties over widespread technology-driven unemployment lack an empirical base, at least so far. Nevertheless, low-skill, production, and manufacturing workers have been adversely affected by technological change, and effective up- and reskilling strategies should remain at the forefront of policy making along with targeted social support systems.
    Date: 2022–04
  3. By: Kerstin H\"otte; Taheya Tarannum; Vilhelm Verendel; Lauren Bennett
    Abstract: Artificial Intelligence (AI) is often defined as the next general purpose technology (GPT) with profound economic and societal consequences. We examine how strongly four patent AI classification methods reproduce the GPT-like features of (1) intrinsic growth, (2) generality, and (3) innovation complementarities. Studying US patents from 1990-2019, we find that the four methods (keywords, scientific citations, WIPO, and USPTO approach) vary in classifying between 3-17% of all patents as AI. The keyword-based approach demonstrates the strongest intrinsic growth and generality despite identifying the smallest set of AI patents. The WIPO and science approaches generate each GPT characteristic less strikingly, whilst the USPTO set with the largest number of patents produces the weakest features. The lack of overlap and heterogeneity between all four approaches emphasises that the evaluation of AI innovation policies may be sensitive to the choice of classification method.
    Date: 2022–04
  4. By: Ketelaars, Martijn (Tilburg University, Center For Economic Research); Kort, Peter (Tilburg University, Center For Economic Research)
    Keywords: research and development; welfare; Innovation; Subsidies; monopolist; government
    Date: 2022

This nep-ino issue is ©2022 by Uwe Cantner. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.