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on Innovation |
| By: | Antonin Bergeaud; Cyril Verluise |
| Abstract: | Innovation is an important driver of potential growth but quantitative evidence on the dynamics of innovative activities in the long-run are hardly documented due to the lack of data, especially in Europe. In this paper, we introduce PatentCity, a novel dataset on the location and nature of patentees from the 19th century using information derived from an automated extraction of relevant information from patent documents published by the German, French, British and US Intellectual Property offices. This dataset has been constructed with the view of facilitating the exploration of the geography of innovation and includes additional information on citizenship and occupation of inventors. |
| Keywords: | history of innovation, patent, text as data |
| Date: | 2022–12 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp1850&r= |
| By: | Fletcher, Joshua (RTI International); Howard, Eric (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); O'Connor, Alan (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | This paper explores the impact that external sources of information have on the effectiveness of R&D in small, entrepreneurial firms. The effectiveness of R&D is measured in terms of two probabilities; the probability that a firm that received and completed a Phase I SBIR-funded research project is invited to submit a proposal for a Phase II award, and given such an invitation, the probability that a firm receives the Phase II award. Information from competitors is an important, in a statistical sense, covariate with the probability of being asked to submit a Phase II proposal whereas information from suppliers and customers in an important covariate with the probability of receiving a Phase II award. |
| Keywords: | Small Business Innovation Research (SBIR) program; small firms; entrepreneurial firms; R&D; knowledge sources; program evaluation; |
| JEL: | H43 L26 O31 O32 O38 |
| Date: | 2022–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:ris:uncgec:2022_002&r= |
| By: | Giacomo Vaccario; Luca Verginer; Antonios Garas; Mario V. Tomasello; Frank Schweitzer |
| Abstract: | Firms' innovation potential depends on their position in the R&D network. But details on this relation remain unclear because measures to quantify network embeddedness have been controversially discussed. We propose and validate a new measure, coreness, obtained from the weighted k-core decomposition of the R&D network. Using data on R&D alliances, we analyse the change of coreness for 14,000 firms over 25 years and patenting activity. A regression analysis demonstrates that coreness explains firms' R&D output by predicting future patenting. |
| Date: | 2022–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2205.07677&r= |
| By: | Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | We describe public support for AI research in small firms using data from U.S. Department of Defense-funded SBIR projects. Ours is the first collection of firm-level project information on publicly funded R&D investments in AI. We find that the likelihood of an SBIR funded research project being focused on AI is greater the larger the amount of the SBIR award. AI-focused research projects are associated with a 7.6 percent increase in average award amounts. We also find suggestive evidence that the likelihood of an SBIR project being AI-focused is greater in smaller-sized firms. Finally, we find that SBIR-funded AI research is more likely to occur in states with complementary university research resources. |
| Keywords: | Artificial intelligence; machine learning; Department of Defense; Small Business Innovation Research program; agglomeration; |
| JEL: | O31 O38 |
| Date: | 2022–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:ris:uncgec:2022_003&r= |
| By: | Chowdhury, Farhat (University of North Carolina at Greensboro, Department of Economics); Link, Albert (University of North Carolina at Greensboro, Department of Economics); van Hasselt, Martijn (University of North Carolina at Greensboro, Department of Economics) |
| Abstract: | A spatial distributional analysis of the population of Phase II research projects funded by the U.S. SBIR program in FY 2020 shows differences across states in projects focused on Artificial Intelligence (AI). AI is a relatively new research field, and this paper contributes to a better understanding of government support for such research. We find that AI projects are concentrated in states with complementary AI research resources available from universities nationally ranked in terms of their own AI research. To achieve a more diverse spatial distribution of AI-related technology development, the availability of complementary AI research resources must be expanded. We suggest that the National Science Foundation’s National AI Research Institutes represents an important step in this direction. |
| Keywords: | Artificial intelligence (AI); Public sector program management; Small Business Innovation Research (SBIR); Agglomeration; University research; |
| JEL: | H54 O31 O38 R11 |
| Date: | 2022–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:ris:uncgec:2022_004&r= |
| By: | Nicholas Stern; Anna Valero |
| Abstract: | The climate crisis and the global economic impact of the Covid-19 crisis occur against a background of slowing growth and widening inequalities, which together imply an urgent need for a new environmentally sustainable and inclusive approach to growth. Investments in "clean" innovation and its diffusion are key to shaping this, accompanied by investments in complementary assets including sustainable infrastructure, and human, natural and social capital which will not only help achieve net-zero greenhouse gas emissions, but will also improve productivity, living standards and the prospects of individuals. In this article, we draw on the theoretical and empirical evidence on the opportunities, drivers and policies for innovation-led sustainable growth. We highlight the importance of a coordinated set of long-term policies and institutions that can enable and foster private sector investments in clean innovation and assets quickly and at scale. In doing so, we draw inspiration from Chris Freeman's work on the system-wide drivers of innovation, and his early vision of achieving environmental sustainability by reorienting growth. |
| Keywords: | innovation, sustainable growth, net-zero transition, clean technology |
| Date: | 2021–06–01 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp1773&r= |
| By: | Colin Davis (The Institute for the Liberal Arts, Doshisha University, JAPAN); Laixun Zhao (Research Institute for Economics & Business Administration (RIEB), Kobe University, JAPAN) |
| Abstract: | We study how tax policy affects economic growth through entrepreneurs' choice of commercialization mode. Introducing both heterogeneous quality jumps and a leapfrog versus sell choice into the quality-ladders model, we show that entrepreneurs use high-quality innovations to leapfrog incumbent firms and become new market leaders, but sell low quality innovations to incumbents. Tax incentives that promote leapfrogging slow the rate of innovation. A numerical analysis concludes surprisingly that corporate taxes, capital gains taxes, and subsidies to market entry all harm welfare. |
| Keywords: | Innovation based growth; Heterogenous quality improvements; Innovation sales; Corporate tax; Capital gains tax; Market entry subsidy |
| JEL: | O31 O33 O43 |
| Date: | 2022–06 |
| URL: | https://d.repec.org/n?u=RePEc:kob:dpaper:dp2022-28&r= |
| By: | Raffaele Danna; Martina Iori; Andrea Mina |
| Abstract: | The accumulation of knowledge and its application to a variety of human needs is a discontinuous process that involves innovation and change. While much has been written on major discontinuities associated, for instance, with the rise of new technologies during industrial revolutions, other phases of economic development are less well understood, even though they might bring into even sharper focus the mechanisms through which growth is generated by the systematic application of human knowledge to practical problems. In this paper, we investigate the transmission of new mathematical knowledge from the 13th to the end of the 16th century in Europe. Using an original dataset of over 1050 manuals of practical arithmetic, we produce new descriptive and quasi-experimental evidence on the economic importance of the European transition from Roman to Hindu-Arabic numerals (0, 1, 2, 3, 4, 5, 6, 7, 8, 9). This numerical revolution laid the foundations for the commercial revolution of the 13th century, and the diffusion of knowledge through organised learning had positive and significant effects on the growth of pre-modern European economies. |
| Keywords: | Human capital; knowledge diffusion; learning; economic growth. |
| Date: | 2022–06–22 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2022/18&r= |