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on Innovation |
| By: | Charlotte Guillard; Ralf Martin; Pierre Mohnen; Catherine Thomas; Dennis Verhoeven |
| Abstract: | Research and development is underprovided whenever it creates knowledge spillovers that drive a wedge between its total and private economic returns. Heterogeneity in the intensity of this market failure across technological areas provides an argument to vertically target public support for R&D. This paper examines potential welfare gains of such vertical industrial policy for innovation. It develops measures of private and spillover value of patented innovations using global data on patents and their citations. Our new method identifies a large number 'Hidden Giants' - i.e. innovations scoring higher on our new spillover measure than on the traditional forward citation count measure - which are shown to be particularly prevalent among patents applied for by universities. The estimated distributions of private values by technology area are then used to parameterize a structural model of innovation. The model permits estimation of the marginal returns to technology-area-specific subsidies that reduce innovators' R&D costs. Marginal returns are high when knowledge spillovers in the technology area are valuable, when private innovation costs are low, and when private values in a technology sector are densely distributed around the private cost. The results show large variation in the marginal returns to subsidy and suggest that targeted industrial policy would have helped mitigate underprovision of R&D over the time period studied. Variation in the extent to which knowledge spillovers are internalized within countries also makes a compelling case for supranational policy coordination, especially among smaller countries. |
| Keywords: | research and development, patented innovations, decoupling, targeted industrial policy |
| Date: | 2021–11–04 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1813&r= |
| By: | Nicholas Bloom; Tarek Alexander Hassan; Aakash Kalyani; Josh Lerner; Ahmed Tahoun |
| Abstract: | We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread across space. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these technology hubs are more likely to arise in areas with universities and high skilled labor pools. |
| Keywords: | disruptive technologies, technological change, firms, labor markets , |
| Date: | 2021–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1798&r= |
| By: | Felix Bracht; Dennis Verhoeven |
| Abstract: | Existing estimates of the economic costs of air pollution do not account for its effect on inventive output. Using two weather phenomena as instruments, we estimate this effect in a sample of 1,288 European regions. A decrease in exposure to small particulate matter of 0.17µg/m3 - the average yearly reduction in Europe - leads to 1.7% more patented inventions. After ruling out reallocation of human capital, inventor mortality and R&D expenditures as drivers of the effect, we conclude that air pollution's harm to economic output increases by at least 10% when accounting for innovation. |
| Keywords: | air pollution, air quality, innovation, patent, productivity |
| Date: | 2021–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1817&r= |
| By: | Daimer, Stephanie; Havas, Attila; Cuhls, Kerstin; Yorulmaz, Merve; Vrgovic, Petar |
| Abstract: | : We contribute to the Responsible Research and Innovation (RRI) literature in two ways: (i) we consider how societal aspects are taken into account in research and innovation (R&I) activities in four fundamentally different scenarios, as opposed to analysing current practices; and (ii) put the emphasis on the political conditions of the interactions among the actors, as opposed to focussing on RRI principles and instruments. In the Kingdom of RRI citizens participate directly in decision-making processes; Fortress Europe depicts a libertarian system; Failed Democracy is a populist regime; while Benevolent Green Eurocrats describes a technocratically coordinated strong state. The scenarios offer novel insights into the nature and repercussions of possible policy problems, that is, efficacy; efficiency; legitimacy of R&I activities; societal involvement; equity; and freedom of research. Meaningful interactions between lay people and professional actors in an innovation system can be safeguarded even in the harshest ideological and political framework. |
| Keywords: | Responsible Research and Innovation (RRI); Innovation systems; Ideological stances on linkages between society, research, and innovation; Scenarios; Futures of research, innovation, and society |
| JEL: | F50 F52 H10 H11 O20 O25 O3 O30 O31 O32 O33 O38 P11 Q54 Q55 Q56 |
| Date: | 2021–10–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113098&r= |
| By: | Bratanova, Alexandra; Pham, Hien; Mason, Claire; Hajkowicz, Stefan; Naughtin, Claire; Schleiger, Emma; Sanderson, Conrad; Chen, Caron; Karimi, Sarvnaz |
| Abstract: | We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions with comparative advantage in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers can use the results of this study to facilitate and monitor the growth of AI capability to boost economic recovery. Investors may find these results helpful to learn about the current landscape of AI business and innovation activities in Australia. |
| Keywords: | Artificial intelligence, cluster, revealed technology advantage, regional innovation, Australia |
| JEL: | O31 O33 O38 R12 |
| Date: | 2022–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113237&r= |
| By: | Philippe Aghion |
| Abstract: | It is widely feared that private sector innovation is constrained by 'red tape'. Philippe Aghion and colleagues test that view on data from France, where firms of more than 50 employees face far heavier regulatory obligations. The results suggest that regulations do hamper innovation - but the negative effects are only for incremental innovations, not radical innovations. |
| Keywords: | innovation, policy, France, regulation, growth |
| Date: | 2021–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepcnp:607&r= |
| By: | Rachel Griffith; John Van Reenen |
| Abstract: | We examine the economic analysis of the relationship between innovation and product market competition. First, we give a brief tour of the intellectual history of the area. Second, we examine how the Aghion-Howitt framework has influenced the development of the literature theoretically and (especially) empirically, with an emphasis on the "inverted U": the idea that innovation rises and then eventually falls as the intensity of competition increases. Thirdly, we look at recent applications and development of the framework in the areas of competition policy, international trade and structural Industrial Organization. |
| Keywords: | competition, innovation, creative destruction |
| Date: | 2021–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1818&r= |
| By: | Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio; Matarrese, Marco Maria |
| Abstract: | The following article investigates the determinants that lead innovative SMEs to collaborate. Data from 36 European countries is analyzed using Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel models. The analysis shows that the ability of innovative SMEs to collaborate is positively associated with the following variables: "Linkages", "Share High and Medium high-tech manufacturing", "Finance and Support", "Broadband Penetration", "Non-R&D Innovation Expenditure" and negatively to the following variables: "New Doctorate graduates", "Venture Capital", "Foreign Controlled Enterprises Share of Value Added", "Public-Private Co-Publications", "Population Size", "Private co-funding of Public R&D expenditures". A clustering with k-Means algorithm optimized by the Silhouette coefficient was then performed and four clusters were found. A network analysis was then carried out and the result shows the presence of three composite structures of links between some European countries. Furthermore, a comparison was made between eight different predictive machine learning algorithms and the result shows that the Random Forest Regression algorithm performs better and predicts a reduction in the ability of innovative SMEs to collaborate equal to an average of 4.4%. Later a further comparison is made with augmented data. The results confirm that the best predictive algorithm is Random Forest Regression, the statistical errors of the prediction decrease on average by 73.5%, and the ability of innovative SMEs to collaborate is predicted to growth by 9.2%. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation |
| JEL: | O30 O31 O32 O33 O34 |
| Date: | 2022–05–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113008&r= |
| By: | Kim, Jinhwan (Stanford U); Valentine, Kristen (U of Georgia) |
| Abstract: | We examine the spillover effect of public firm innovation disclosures on the patent trading market. Relative to equity markets, the patent market is decentralized and rife with information frictions, yet it serves as an important mechanism through which innovations reallocate to the most productive users. Using data on patent transactions, we find that going from the 25th percentile to the 75th percentile in innovation-relevant public firm disclosures – proxied by the number of innovation-relevant sentences in 10-K filings – is linked to a 13.0% to 14.9% increase in future patent sales by other parties that likely consume these disclosures. These results are consistent with financial statement disclosures generating positive information externalities useful for trading patents. The positive link between innovation-relevant firm disclosures is stronger where information asymmetry is likely greatest (transactions between public and private firms) and where information uncertainty likely prevails (transactions between private firms) relative to transactions less likely to suffer from information frictions (transactions between public firms). We corroborate that the positive link between public firm disclosures and other parties’ patent sales is likely due to the resolution of information frictions through several cross-sectional tests, the use of proprietary patent broker data, and the plausibly exogenous implementation of Edgar by public firms. Our results speak to an important, but previously underexplored, externality of financial statement disclosures – their contribution to a well-functioning patent market. |
| JEL: | D23 M40 M41 O30 O31 O32 O34 O39 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:4013&r= |
| By: | John Van Reenen |
| Abstract: | In the second anniversary lecture marking 30 years since CEP began, former director John Van Reenen focused on productivity, an issue that has been at the heart of the Centre's work for three decades. He sets out how technological innovation and better management can bring about growth that is both inclusive and compatible with efforts to address the world's climate emergency. |
| Keywords: | Technological change, Productivity, environment, innovation, management, growth, climate |
| Date: | 2021–06–15 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepcnp:604&r= |
| By: | Nomaler, Önder (UNU-MERIT, Maastricht University); Verspagen, Bart (UNU-MERIT, Maastricht University) |
| Abstract: | In an earlier paper (Nomaler & Verspagen, 2022) we introduced a 'supervised learning' based alternative to the competing unsupervised learning algorithms (e.g., Hidalgo and Hausmann, 2009 vs. Tacchella et al, 2012) proposed in the so-called 'economic complexity' literature. Similar to the existing ones, our alternative, which we refer to as the "Canonical Correlation Complexity Method (CCCM)", also aims at reducing the high dimensionality in data on the empirical patterns of co-location (be it nations or regions) of specializations in products or technologies, while the ultimate objective is to understand the relationship between specialization, diversification, and economic development. In our alternative method which combines the toolkit of the Canonical Correlation Analysis with that of Principal Component Analysis, the data on trade or technology specializations and multiple dimensions of economic development are processed together from the very beginning in order to identify the patterns of mutual association. This way, we are able to identify the products or technologies that can be associated with the level or the growth rate of per capita GDP, and (un)employment. In this follow up paper, we use the CCCM to analyse the development patterns of European regions in relation to their respective technology specializations. Our findings provide insights for EU's industrial policies, especially those considered under the 'smart specialization' framework. |
| Keywords: | Economic complexity, economic development, supervised learning, Canonical Correlation Analysis, Principal Component Analysis, technological specialization, revealed technological advantage, European regional development, smart specialization |
| JEL: | F14 F63 O11 O33 R11 |
| Date: | 2022–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2022016&r= |
| By: | Patricia Canto-Farachala (Orkestra Basque Institute of Competitiveness); James R. Wilson (Orkestra Basque Institute of Competitiveness); Eskarne Arregui-Pabollet (European Commission - JRC) |
| Abstract: | This technical report presents the results of a cross-case analysis of the eleven case studies conducted under the Higher Education for Smart Specialisation project during the period 2016-2020. The analysis identifies key themes and innovative practice examples from across case studies, developing a structured typology of innovative practices for higher education engagement in innovation ecosystems in the context of the design and implementation of Smart Specialisation Strategies (S3). More concretely, it contributes to identify: (i) Innovative practices to their regional innovation ecosystems and the design and implementation of S3.(ii) The key features of these practices that have made possible the transformative role of higher education in their regional innovation system, with particular attention on how they integrate education, research and innovation. |
| Keywords: | Smart specialisation strategies, higher education institutions, universities, territorial development, research and innovation, innovative practices, Northern Netherlands, Centre-Val de Loire, Lower Austria, North-East Romania, North Central Bulgaria, Portugal, Lithuania, Puglia, Lubelskie, Navarre, Eastern Macedonia and Thrace |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc128679&r= |
| By: | Jan Fagerberg (Centre for Technology, Innovation and Culture, University of Oslo); Håkon Endresen Normann (The Nordic Institute for Studies of Innovation, Research and Education (NIFU)) |
| Abstract: | This paper addresses the role of innovation policy, including regulation, in the transition to a society characterized by net zero emissions of climate gasses. A broad range of policy-actors, notably the European Union, have already publicly embraced this goal. Nevertheless, transforming the society to a state consistent with the net-zero objective is a very demanding task, and to succeed in this endeavour extensive change – including a lot of innovation - in the way energy is provided, distributed and used across all parts of society will be needed. A crucial question, therefore, is how policy – and particularly innovation policy – can contribute to mobilize innovation for this purpose. This paper critically examines the extant literature on the subject, and discusses examples of transformational change from policy practice, including onshore wind and solar in Denmark and Germany; offshore wind in the UK, Denmark and Norway; and the emerging quest for zero-emission ships. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:tik:inowpp:20220531&r= |