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on Technology and Industrial Dynamics |
| By: | Kriesch, Lukas (Justus Liebig University Giessen); Losacker, Sebastian (Justus Liebig University Giessen) |
| Abstract: | Many governments worldwide have proposed transitioning from a fossil-based economy to a bioeconomy to address climate change, resource depletion, and other environmental concerns. The bioeconomy utilizes renewable biological resources across all sectors and is strongly founded on scientific advances and technological progress. Given that the bioeconomy spans multiple sectors, industries, and technological fields, tracking it is challenging, and both policymakers and researchers lack a comprehensive understanding of the bioeconomy transition's progress. We aim to solve this problem by providing a dataset on patents, a commonly used indicator to study the development of novel knowledge and technological change, that identifies bioeconomy-related inventions. We leverage the advanced semantic understanding embedded in pre-trained transformer models to identify bioeconomy-related patents based on patent abstracts, and we use a topic modelling approach to identify several coherent technological fields within the corpus of bioeconomy patents. The dataset can be linked to other patent databases and therefore provides rich opportunities to study the technological knowledge base of the bioeconomy. |
| Keywords: | Patents; Bioeconomy; Natural Language Processing; Innovation |
| JEL: | O31 O34 Q16 Q55 |
| Date: | 2024–06–11 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_008&r= |
| By: | Koch, Michael (Aarhus University); Lodefalk, Magnus (Örebro University School of Business) |
| Abstract: | We use individual survey data providing detailed information on stress, technology adoption, and work, worker, and employer characteristics, in combination with recent measures of AI and robot exposure, to investigate how new technologies affect worker stress. We find a persistent negative relationship, suggesting that AI and robots could reduce the stress level of workers. We furthermore provide evidence on potential mechanisms to explain our findings. Overall, the results provide suggestive evidence of modern technologies changing the way we perform our work in a way that reduces stress and work pressure. |
| Keywords: | Artificial intelligence technologies; Automation; Task content; Skills; Stress |
| JEL: | I31 J24 J28 J44 N34 O33 |
| Date: | 2024–06–14 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2024_005&r= |
| By: | Dobbelaere, Sabien (Vrije Universiteit Amsterdam); König, Michael D. (Vrije Universiteit Amsterdam); Spescha, Andrin (ETH Zurich); Wörter, Martin (ETH Zurich) |
| Abstract: | The fraction of R&D active firms decreased in Switzerland but increased in the Netherlands from 2000-2016. This paper examines reasons for this divergence and its impact on productivity growth. Our micro-data reveal R&D concentration among high-productivity firms in Switzerland. Innovation support sustains firms' R&D activities in both countries. Our structural growth model identifies the impact of innovation, imitation and R&D costs on firms' R&D decisions. R&D costs gained importance in Switzerland but not in the Netherlands, explaining the diverging R&D trends. Yet, counterfactual analyses show that policies should prioritize enhancing innovation and imitation success over cost reduction to boost productivity growth. |
| Keywords: | R&D, innovation, imitation, R&D costs, policy, productivity growth, traveling wave |
| JEL: | E61 E65 D22 O31 O47 O52 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17026&r= |
| By: | Robin Cowan; Nicolas Jonard; Ruth Samson |
| Abstract: | Many scholars observed changes in the intellectual property rights systems in the 1980s and 1990s throughout the world. Patent systems in particular seemed to be expanding their scope, and the legal system seemed to be changing its attitudes towards intellectual property rights. At the same time, and probably in response, firms started to change their patenting behaviour — treating patents as tools of competition and bargaining rather than as a means to protect the fruits of intellectual labour. In this paper we present a simulation model that can be used to discuss that shift. Firms search for new technologies and patent what they find. But different firms have different strategies: one is to protect an invention; a second is to protect a technology space; the third is to attack others’ technology spaces. In the literature the latter two have been described as different types of blocking. We examine different IPR regimes, characterized by who is able to infringe whose patent rights. This is an extreme case of who is able to extract rents from a given configuration of patent rights. |
| Keywords: | Innovation, Patents, Knowledge network, Blocking strategies. |
| JEL: | O31 O34 C6 L5 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ulp:sbbeta:2024-20&r= |
| By: | Mauro Cazzaniga; Carlo Pizzinelli; Emma J Rockall; Ms. Marina Mendes Tavares |
| Abstract: | We document historical patterns of workers' transitions across occupations and over the life-cycle for different levels of exposure and complementarity to Artificial Intelligence (AI) in Brazil and the UK. In both countries, college-educated workers frequently move from high-exposure, low-complementarity occupations (those more likely to be negatively affected by AI) to high-exposure, high-complementarity ones (those more likely to be positively affected by AI). This transition is especially common for young college-educated workers and is associated with an increase in average salaries. Young highly educated workers thus represent the demographic group for which AI-driven structural change could most expand opportunities for career progression but also highly disrupt entry into the labor market by removing stepping-stone jobs. These patterns of “upward” labor market transitions for college-educated workers look broadly alike in the UK and Brazil, suggesting that the impact of AI adoption on the highly educated labor force could be similar across advanced economies and emerging markets. Meanwhile, non-college workers in Brazil face markedly higher chances of moving from better-paid high-exposure and low-complementarity occupations to low-exposure ones, suggesting a higher risk of income loss if AI were to reduce labor demand for the former type of jobs. |
| Keywords: | Artificial intelligence; Employment; Occupations; Emerging Markets |
| Date: | 2024–06–07 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/116&r= |
| By: | Eder, Andreas; Koller, Wolfgang; Mahlberg, Bernhard |
| Abstract: | This paper investigates the contribution of industrial robots to employment change in manufacturing in a sample of 17 European countries and the USA over the period 2004 to 2019. We combine index decomposition analysis (IDA) and production-theoretical decomposition analysis (PDA). First, we use IDA to decompose employment change in the manufacturing industry into changes in (aggregate) manufacturing output, changes in the sectoral structure of the manufacturing industry, and changes in labour intensity which is a composite index of labour intensity change within each of the nine sub-sectors of total manufacturing. Second, we use PDA to further decompose labour intensity change to isolate the contribution of technical efficiency change, technological change, human capital change, change in non-robot capital intensity and change in robot capital intensity to employment change. In almost all of the countries considered, the labour intensity is falling in entire manufacturing, which has a dampening effect on employment. Robotisation contributes to this development by reducing labour intensities and employment in all countries and sub-sectors, though to varying degrees. Manufacturing output, in turn, grows in all countries (except Greece, Spain and Italy), which increases employment and counteracts or in some countries even more than offsets the dampening effect of declining labour intensities. The structural change within manufacturing has an almost neutral effect in many countries. |
| Keywords: | automation; robotisation; decomposition; structural change; data envelopment analysis |
| JEL: | C43 J21 J24 O33 |
| Date: | 2024–06–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121128&r= |
| By: | Fossen, Frank M. (University of Nevada, Reno); McLemore, Trevor (University of Nevada, Reno); Sorgner, Alina (John Cabot University) |
| Abstract: | This survey reviews emerging but fast-growing literature on impacts of artificial intelligence (AI) on entrepreneurship, providing a resource for researchers in entrepreneurship and neighboring disciplines. We begin with a review of definitions of AI and show that ambiguity and broadness of definitions adopted in empirical studies may result in obscured evidence on impacts of AI on en-trepreneurship. Against this background, we present and discuss existing theory and evidence on how AI technologies affect entrepreneurial opportunities and decision-making under uncertainty, the adoption of AI technologies by startups, entry barriers, and the performance of entrepreneurial businesses. We add an original empirical analysis of survey data from the German Socio-economic Panel revealing that entrepreneurs, particularly those with employees, are aware of and use AI technologies significantly more frequently than paid employees. Next, we discuss how AI may affect entrepreneurship indirectly through impacting local and sectoral labor markets. The reviewed evidence suggests that AI technologies that are designed to automate jobs are likely to result in a higher level of necessity entrepreneurship in a region, whereas AI technologies that transform jobs without necessarily displacing human workers increase the level of opportunity entrepreneurship. More generally, AI impacts regional entrepreneurship ecosystems (EE) in multiple ways by altering the importance of existing EE elements and processes, creating new ones, and potentially reducing the role of geography for entrepreneurship. Lastly, we address the question of how regulation of AI may affect the entrepreneurship landscape by focusing on the case of the European Union that has pioneered data protection and AI legislation. We conclude our survey by discussing implications for entrepreneurship research and policy. |
| Keywords: | artificial intelligence, machine learning, entrepreneurship, AI startups, digital entrepreneurship, opportunity, innovation, entrepreneurship ecosystem, digital entrepreneurship ecosystem, AI regulation |
| JEL: | J24 L26 O30 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17055&r= |
| By: | Benatti, Nicola; Groiss, Martin; Kelly, Petra; Lopez-Garcia, Paloma |
| Abstract: | We examine the extent to which environmental regulation affects innovation and which policy types provide the strongest incentives to innovate. Using a local projection framework, we estimate the regulatory impact on patenting activity over a five-year horizon. As a proxy for environmental policy exposure, we estimate firm-level greenhouse gas emissions using a machine learning algorithm. At the country-level, policy tightening is largely associated with no statistically significant change in environmental technology innovation. At the firm-level, however, environmental policy tightening leads to higher innovation activity in technologies mitigating climate change, while the effect on innovation in other technologies is muted. This suggests that environmental regulation does not lead to a crowding-out of non-clean innovations. The policy type matters, as increasing the stringency of technology support policies and non-market based policies leads to increases in clean technology patenting, while we do not find a statistically significant impact of market-based policies. JEL Classification: O44, Q52, Q58 |
| Keywords: | emissions, environmental regulation, euro area, innovation, Porter hypothesis |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242946&r= |
| By: | Zhang, Min; Rodríguez-Pose, Andrés |
| Abstract: | Innovation is key for economic growth and well-being. The capacity for innovation, however, is profoundly influenced by the quality of local institutions. Although the impact of national institutions on innovation is well-documented, the effects of subnational institutional variations on innovation remain underexplored. This paper studies the impact of government agency reforms, designed to enhance local government effectiveness, on the innovation performance of city-regions in China. We examine the adoption of these reforms between 2009 and 2016 as an exogenous shock to regional institutions. Our analysis identifies a positive and significant relationship between improvements in institutional quality and the innovation performance of Chinese city-regions, particularly pronounced in regions with medium to high levels of innovation. The results are robust to a series of checks including placebo and endogeneity tests and potential confounding policies. This research highlights the critical role of government institutions in driving innovation across China, bringing the fore important regional variations in the adoption of government agency reforms that are defining the country’s innovation landscape. |
| Keywords: | institutions; government quality; institutional reform; regional innovation; China; REF fund |
| JEL: | R11 O11 |
| Date: | 2024–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122728&r= |
| By: | Ulrich Schetter; Dario Diodato; Eric Protzer; Frank Neffke; Ricardo Hausmann |
| Abstract: | Economic development is a path-dependent process in which countries accumulate capabilities that allow them to move into more complex products and industries. Inspired by a theory of capabilities that explains which countries produce which products, these diversification dynamics have been studied in great detail in the literature on economic complexity analysis. However, so far, these capabilities have remained latent and inference is drawn from product spaces that reflect economic outcomes: which products are often exported in tandem. Borrowing a metaphor from biology, such analysis remains phenotypic in nature. In this paper we develop a methodology that allows economic complexity analysis to use capabilities directly. To do so, we interpret the capability requirements of industries as a genetic code that shows how capabilities map onto products. We apply this framework to construct a genotypic product space and to infer countries' capability bases. These constructs can be used to determine which capabilities a country would still need to acquire if it were to diversify into a given industry. We show that this information is not just valuable in predicting future diversification paths and to advance our understanding of economic development, but also to design more concrete policy interventions that go beyond targeting products by identifying the underlying capability requirements. |
| Keywords: | product space , economic complexity , economic convergence , export diversification , industrial policy , poverty trap , structural change |
| JEL: | O11 O14 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2419&r= |