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on Innovation |
| By: | Ganglmair, Bernhard; Robinson, W. Keith; Seeligson, Michael |
| Abstract: | We document the occurrence of process claims in granted U.S. patents over the last century. Using novel data on the type of independent patent claims, we show an increase in the annual share of process claims of about 25 percentage points (from below 10% in 1920). This rise in process intensity is not limited to a few patent classes but can be observed across a broad spectrum of technologies. Process intensity varies by applicant type: companies file more process-intense patents than individuals, and U.S. applicants file more process-intense patents than foreign applicants. We further show that patents with higher process intensity are more valuable but are not necessarily cited more often. Last, process claims are on average shorter than product claims; but this gap has narrowed since the 1970s. These patterns suggest that the patent breadth and scope of process-intense patents are overestimated when claim types are not accounted for. We conclude by describing in detail the code used to construct the claim-type data, showing results from a data-validation exercise (using close to 10,000 manually classified patent claims), and providing guidance for researchers on how to alter the classification outcome to adapt to researchers' needs. |
| Keywords: | innovation,patent claims,patents,patent breadth,patent scope,process claims,process intensity,R&D,text analysis |
| JEL: | C81 O31 O34 Y10 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:22011&r= |
| By: | Jean-Alain Héraud; Nathalie Popiolek |
| Abstract: | Economic innovations are not systematically triggered by scientific discoveries or technological inventions. They can benefit from a new scientific idea without really depending on it as a key element. For instance, incremental innovations almost by definition do not exploit a new techno-scientific paradigm. Moreover, some very creative ideas happen to arise in other fields than science or technology, like the domain of usage. Nevertheless, scientific discoveries and breakthrough innovations, during the 20th and 21th centuries, were often linked. We wish to check here the existence of cross-fertilization mechanisms between academic and industrial researches in specific cases of high creativity level, and try to describe the simultaneous discovery-innovation process taking place at such occasions. We base our study on historical examples and a series of interviews of actors from public research organizations as well as industrial R&D departments. We learnt a lot about the various dimensions of the knowledge co-creation, but also about the difficulties to overcome in such cooperative schemes: differences in individual and institutional motivations, in the perception of science (its raison d’être, its ownership), of risk, and of time (unsynchronized clocks). |
| Keywords: | Discovery, Radical innovation, Academy - industry partnerships, Models of innovation. |
| JEL: | O31 O32 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ulp:sbbeta:2022-12&r= |
| By: | Barbara Biasi; Song Ma |
| Abstract: | This paper documents differences across higher-education courses in the coverage of frontier knowledge. Comparing the text of 1.7M syllabi and 20M academic articles, we construct the “education-innovation gap,” a syllabus’s relative proximity to old and new knowledge. We show that courses differ greatly in the extent to which they cover frontier knowledge. More selective and better funded schools, and those enrolling socio-economically advantaged students, teach more frontier knowledge. Instructors play a big role in shaping course content; research-active instructors teach more frontier knowledge. Students from schools teaching more frontier knowledge are more likely to complete a PhD, produce more patents, and earn more after graduation. |
| Keywords: | education, innovation, syllabi, instructors, text analysis, inequality |
| JEL: | I23 I24 I26 J24 O33 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9653&r= |
| By: | Horbach, Jens; Rammer, Christian |
| Abstract: | Eco-innovations are crucial for the mitigation of climate change effects. It is therefore important to know if the existing climate change regulations and carbon pricing are appropriate and sufficient to trigger such innovations. Besides government measures, the demand for carbon neutral products or the impacts of climate change such as extreme weather conditions leading to higher costs for the affected firms may also promote eco-innovation activities. For the first time, the new wave of the Community Innovation Survey 2020 in Germany allows an analysis of the effects of climate change policy and costs, demand for climate friendly goods and extreme weather conditions on (eco-)innovation. The results of probit and treatment effect models show that innovative firms seem to be significantly more affected by climate change measures and consequences compared to other firms. All climate change indicators are positively correlated to eco-innovations. Interestingly, other innovation activities also profit from the extent to which a firm is affected by climate change albeit the marginal effects are lower compared to eco-innovations. Demand for climate neutral products is significantly important for all eco-product-innovations. |
| Keywords: | Climate change,eco-innovation,Community Innovation Survey,probit regression,treatment effect models |
| JEL: | C25 C21 O31 Q54 Q55 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:22008&r= |
| By: | Harashima, Taiji |
| Abstract: | In innovation-based endogenous (Schumpeterian) growth theory, the production of innovations is constrained basically by the finite nature of the labor supply. In this paper, I show that innovations are constrained because (1) the amount of fluid intelligence of researchers in an economy is limited and (2) the returns on investments in technologies and in capital are kept equal through arbitrage in markets. With these constraints, equilibrium values of the number of researchers and their average productivity in an economy exist, and the equilibrium value of average productivity determines the amount of innovation production in each period. Distributions of fluid intelligence among researchers are most likely heterogeneous across economies, but if economies are open to each other, an economy with a smaller number of researchers with a high level of fluid intelligence can grow at the same rate as an economy with more of them. |
| Keywords: | Endogenous growth; Fluid intelligence; Innovation; Production of innovation; Researchers |
| JEL: | O31 O40 |
| Date: | 2022–04–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:112813&r= |
| By: | Andrea Borsato; Andre Lorentz |
| Abstract: | This paper contributes to the understanding of the relationship between the nature of data and the Artificial Intelligence (AI) technological trajectories. We develop an agentbased model in which firms are data producers that compete on the markets for data and AI. The model is enriched by a public sector that fuels the purchase of data and trains the scientists that will populate firms as workforce. Through several simulation experiments we analyze the determinants of each market structure, the corresponding relationships with innovation attainments, the pattern followed by labour and data productivity, and the quality of data traded in the economy. More precisely, we question the established view in the literature on industrial organization according to which technological imperatives are enough to experience divergent industrial dynamics on both the markets for data and AI blueprints. Although technical change behooves if any industry pattern is to emerge, the actual unfolding is not the outcome of a specific technological trajectory, but the result of the interplay between technology-related factors and the availability of data-complementary inputs such as labour and AI capital, the market size, preferences and public policies. |
| Keywords: | Artificial Intelligence, Data Markets, Industrial Dynamics, Agent-based Models. |
| JEL: | L10 L60 O33 O38 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ulp:sbbeta:2022-09&r= |
| By: | Matthias van den Heuvel; David Popp |
| Abstract: | After a boom and bust cycle in the early 2010s, venture capital (VC) investments are, once again, flowing towards green businesses. In this paper, we use Crunchbase data on 150,000 US startups founded between 2000 and 2020 to better understand why VC initially did not prove successful in funding new clean energy technologies. Both lackluster demand and a lower potential for outsized returns make clean energy firms less attractive to VC than startups in ICT or biotech. However, we find no clear evidence that characteristics such as high-capital intensity or long development timeframe are behind the lack of success of VC in clean energy. In addition, our results show that while public sector investments can help attract VC investment, the ultimate success rate of firms receiving public funding remains small. Thus, stimulating demand will have a greater impact on clean energy innovation than investing in startups that will then struggle through the “valley of death”. Only with demand-side policies in place should governments try to plug funding gaps by targeting clean energy startups with low potential for outsized returns that will continue to find it hard to attract private capital. |
| Keywords: | venture capital, renewable energy, start-up firms |
| JEL: | G24 Q40 Q48 Q55 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9684&r= |