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on Technology and Industrial Dynamics |
| By: | Damioli, Giacomo; Van Roy, Vincent; Vertesy, Daniel; Vivarelli, Marco |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be "enabling technologies", a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, Technological Paradigm, Structural Change, Patents |
| JEL: | O31 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1467 |
| By: | Xavier Giroud; Ernest Liu; Holger Mueller |
| Abstract: | The vast majority of U.S. inventors work for firms that also have inventors and plants in other tech clusters. Using merged USPTO–U.S. Census Bureau plant-level data, we show that larger tech clusters not only make local inventors more productive but also raise the productivity of inventors and plants in other clusters, which are connected to the focal cluster through their parent firms' networks of innovating plants. Cross-cluster innovation spillovers do not depend on the physical distance between clusters, and plants cite disproportionately more patents from other firms in connected clusters, across large physical distances. To rationalize these findings, and to inform policy, we develop a tractable model of spatial innovation that features both within- and cross-cluster innovation spillovers. Based on our model, we derive a sufficient statistic for the wedge between the social and private returns to innovation in a given location. Taking the model to the data, we rank all U.S. tech clusters according to this wedge. While larger tech clusters exhibit a greater social-private innovation wedge, this is not because of local knowledge spillovers, but because they are well-connected to other clusters through firms' networks of innovating plants. In counterfactual exercises, we show that an increase in the interconnectedness of U.S. tech clusters raises the social-private innovation wedge in (almost) all locations, but especially in tech clusters that are large and well-connected to other clusters. |
| JEL: | G30 O30 R30 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32677 |
| By: | Alexandre Georgieff |
| Abstract: | This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate. |
| Keywords: | Artificial intelligence, Employment, Skills |
| JEL: | J21 J23 J24 O33 |
| Date: | 2024–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:13-en |
| By: | Jaedo Choi; Younghun Shim |
| Abstract: | Should policymakers in developing countries prioritize foreign technology adoption over domestic innovation? How might this depend on development stages? Using historical technology transfer data from Korea, we find that greater productivity gaps with foreign firms correlate with faster productivity growth after adoption, despite lower fees. Furthermore, non-adopters increased patent citations to foreign sellers, suggesting knowledge spillovers. Motivated by these findings, we build a two-country growth model with innovation and adoption. As the gaps narrow, productivity gains and spillovers from adoption diminish and foreign sellers strategically raise fees due to intensified competition, which renders adoption subsidies less effective. Korea’s shift from adoption to innovation subsidies substantially contributed to growth and welfare. We also explore the optimal policy and its interaction with import tariffs. |
| Keywords: | Technology Adoption; Innovation; Industrial Policy; Strategic Interaction |
| Date: | 2024–07–19 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/154 |
| By: | Josef Taalbi; Mikhail Martynovich; ; ; |
| Abstract: | While recent studies have heralded large cities as “innovation machines†, the majority of regional studies of innovation are based on patent indicators. In this paper, we compare regional patent and innovation counts in Sweden (1970-2014) and document the presence of a sizeable urban bias in patent indicators, which is primarily explained by higher patent filing propensity in urban areas. We also show that using administrative spatial units which do not account for spatial organization of economic activity tends to exacerbate this bias. This poses a problem for academic studies that wish to understand regional innovation, or policy reports benchmarking regional performance. |
| Keywords: | Regional Innovation, Patents, Urban Scaling, Urban Bias of Patents |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2422 |
| By: | Maria Petrova; Gregor Schubert; Bledi Taska; Pinar Yildirim |
| Abstract: | Career opportunities and expectations shape people’s decisions and can diminish over time. In this paper, we study the career implications of automation and robotization using a novel data set of resumes from approximately 16 million individuals from the United States. We calculate the lifetime "career value" of various occupations, combining (1) the likelihood of future transitions to other occupations, and (2) the earning potential of these occupations. We first document a downward trend in the growth of career values in the U.S. between 2000 and 2016. While wage growth slows down over this time period, the decline in the average career value growth is mainly due to reduced upward occupational mobility. We find that robotization contributes to the decline of average local labor market career values. One additional robot per 1000 workers decreased the average local market career value by $3.9K between 2004 and 2008 and by $2.48K between 2008 and 2016, corresponding to 1.7% and 1.1% of the average career values from the year 2000. In commuting zones that have been more exposed to robots, the average career value has declined further between 2000 and 2016. This decline was more pronounced for low-skilled individuals, with a substantial part of the decline coming from their reduced upward mobility. We document that other sources of mobility mitigate the negative effects of automation on career values. We also show that the changes in career values are predictive of investment in long-term outcomes, such as investment into schooling and housing, and voting for a populist candidate, as proxied by the vote share of Trump in 2016. We also find further evidence that automation affected both the demand side and supply side of politics. |
| JEL: | J01 L6 M0 M20 M29 M55 O14 O3 P0 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32655 |
| By: | Joshua S. Gans |
| Abstract: | This paper examines the regulation of technological innovation direction under uncertainty about potential harms. We develop a model with two competing technological paths and analyze various regulatory interventions. Our findings show that market forces tend to inefficiently concentrate research on leading paths. We demonstrate that ex post regulatory instruments, particularly liability regimes, outperform ex ante restrictions in most scenarios. The optimal regulatory approach depends critically on the magnitude of potential harm relative to technological benefits. Our analysis reveals subtle insurance motives in resource allocation across research paths, challenging common intuitions about diversification. These insights have important implications for regulating emerging technologies like artificial intelligence, suggesting the need for flexible, adaptive regulatory frameworks. |
| JEL: | L51 O33 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32741 |
| By: | Phares Akari; Chirantan Chatterjee; Matthew J. Higgins |
| Abstract: | We explore how firms respond to downstream product shocks. We find that affected firms increase R&D and make additional safety-related investments in their existing assets-in-place. These investments vary with firm capabilities and across shock severity. Competitors appear to vicariously learn and also engage in similar upstream investments. We present evidence that these upstream investments have important performance implications. First, these investments are positively related to transition probabilities and approval rates for products that received them. Second, these investments are related to a decrease in the intensity and rate of future downstream product shocks. Surprisingly, however, these investments appear to have limited impact on mitigating the negative demand response caused by these shocks. |
| JEL: | L10 L51 L65 M21 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32703 |
| By: | Réka Juhász; Shogo Sakabe; David Weinstein |
| Abstract: | This paper studies technology absorption worldwide in the late nineteenth century. We construct several novel datasets to test the idea that the codification of technical knowledge in the vernacular was necessary for countries to absorb the technologies of the Industrial Revolution. We find that comparative advantage shifted to industries that could benefit from patents only in countries and colonies that had access to codified technical knowledge but not in other regions. Using the rapid and unprecedented codification of technical knowledge in Meiji Japan as a natural experiment, we show that this pattern appeared in Japan only after the Japanese government codified as much technical knowledge as what was available in Germany in 1870. Our findings shed new light on the frictions associated with technology diffusion and offer a novel take on why Meiji Japan was unique among non-Western countries in successfully industrializing during the first wave of globalization. |
| JEL: | F14 F63 N15 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32667 |
| By: | Simon Baumgartinger-Seiringer (Department of Geography and Regional Research, University of Vienna, Vienna, Austria); Balazs Pager (Department of Geography and Regional Research, University of Vienna, Vienna, Austria); Michaela Trippl (Department of Geography and Regional Research, University of Vienna, Vienna, Austria) |
| Abstract: | This article focuses on regions in industrial transitions (RITs) in the context of climate change mitigation and their varying paths towards sustainability, drawing on rich data from 11 regions in 9 countries in the Danube area in Europe. Inspired by recent work on green regional vulnerability, challenge-oriented regional innovation systems and transformative resilience, the article conceptualizes regional industrial transition pathways as the outcome of a complex interplay between distinct geographies of (1) vulnerability to, (2) preparedness for, and (3) responsiveness to transition pressures. Empirically, the article employs a mixed-method approach, combining quantitative analyses of regional structural conditions (focusing on vulnerability and preparedness) with qualitative investigations of agency of regional and non-regional actors (focusing on responsiveness). In doing so, the article unravels diverse pathways that regions adopt to navigate industrial transitions. We contend that these insights hold important implications for the design of tailor-made regional industrial transition strategies. |
| Keywords: | Regions in industrial transitions, vulnerability, preparedness, responsiveness, transformative resilience |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:aoe:wpaper:2403 |
| By: | Naomi-Rose Alexander; Longji Li; Dr. Jorge Mondragon; Sahar Priano; Ms. Marina Mendes Tavares |
| Abstract: | This study examines the green transition's effects on labor markets using a task-based framework to identify jobs with tasks that contribute, or with the potential to contribute, to the green transition. Analyzing data from Brazil, Colombia, South Africa, the United Kingdom, and the United States, we find that the proportion of workers in green jobs is similar across AEs and EMs, albeit with distinct occupational patterns: AE green job holders typically have higher education levels, whereas in EMs, they tend to have lower education levels. Despite these disparities, the distribution of green jobs across genders is similar across countries, with men occupying over two-thirds of these positions. Furthermore, green jobs are characterized by a wage premium and a narrower gender pay gap. Our research further studies the implications of AI for the expansion of green employment opportunities. This research advances our understanding of the interplay between green jobs, gender equity, and AI and provides valuable insights for promoting a more inclusive green transition. |
| Keywords: | Labor Market Transition; Climate Change; Employment |
| Date: | 2024–07–19 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/156 |
| By: | Gaétan de Rassenfosse; Adam B. Jaffe; Joel Waldfogel |
| Abstract: | The arrival of creative machines—software capable of producing human-like creative content—has triggered a series of legal challenges about intellectual property. The outcome of these legal challenges will shape the future of the creative industry in ways that could enhance or jeopardize welfare. Policymakers are already tasked with creating regulations for a post-generative AI creative industry. Economics may offer valuable insights, and this paper is our attempt to contribute to the discussion. We identify the main economic issues and propose a framework and some tools for thinking about them. |
| JEL: | O38 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32698 |
| By: | Severin Borenstein; Lucas W. Davis |
| Abstract: | Over the last two decades, U.S. households have received $47 billion in tax credits for buying heat pumps, solar panels, electric vehicles, and other “clean energy” technologies. Using information from tax returns, we show that these tax credits have gone predominantly to higher-income households. The bottom three income quintiles have received about 10% of all credits, while the top quintile has received about 60%. The most extreme is the tax credit for electric vehicles, for which the top quintile has received more than 80% of all credits. The concentration of tax credits among high-income filers is relatively constant over time, though we do find a slight broadening for the electric vehicle credit since 2018. The paper then turns to the related question of cost effectiveness, examining how clean energy technology adoption has changed over time and discussing some of the broader economic considerations for this type of tax credit. |
| JEL: | H23 Q42 Q58 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32688 |
| By: | Andrew Green |
| Abstract: | Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.). Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for Canada on the effect of artificial intelligence on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management, communication and digital skills. These include skills in budgeting, accounting, written communication, as well as competencies in basic word processing and spreadsheet software. The results also show that, in Canada, demand for social and language skills have increased the most over the past decade in occupations highly exposed to AI. Using a panel of establishments confirms the increasing demand for social and language skills, as well as rising demand for production and physical skills, which may be complementary to AI. However, the establishment panel also finds evidence of decreasing demand for business, management and digital skills in establishments more exposed to AI. |
| Keywords: | Artificial Intelligence, Canada, Skills |
| JEL: | J23 J24 J63 |
| Date: | 2024–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:17-en |