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on Technology and Industrial Dynamics |
| By: | Andrea Bastianin (Department of Economics, Management, and Quantitative Methods, University of Milan and Fondazione Eni Enrico Mattei); Paolo Castelnovo (Department of Economics, University of Insubria and Fondazione Eni Enrico Mattei); Federico Fabio Frattini (Fondazione Eni Enrico Mattei); Francesco Vona (Department of Environmental Science and Policy, University of Milan and Fondazione Eni Enrico Mattei) |
| Abstract: | This paper develops a novel text-based approach to identify CRM-saving innovation using patent data and studies how mineral price signals shape the direction of technological change. Using patent data from 1978–2020, we distinguish technologies that rely on CRMs from those that explicitly aim to reduce their use through efficiency improvements, substitution, or recycling. We provide evidence consistent with the induced-innovation hypothesis: higher mineral prices reallocate inventive effort toward CRM-saving technologies, while having little effect on CRM-reliant innovation. The response strengthens over time and is especially pronounced for battery minerals and rare earth elements. These findings are robust to alternative specifications and are reinforced by complementary identification strategies, including a falsification test and the use of plausibly exogenous supply-side price variation. |
| Keywords: | Energy Transition, Critical Raw materials, Patents |
| JEL: | C55 O31 O33 Q55 L72 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2026.05 |
| By: | Garbers, Julio (LISER); Gregory, Terry (LISER) |
| Abstract: | We develop a novel firm-level indicator of Artificial Intelligence adoption in Europe (MAP-AI) by extracting information from more than three million firm websites in Belgium, France, Germany, and Luxembourg between 2016 and 2024 using a Large Language Model. The indicator captures realized AI use as publicly signaled by firms, rather than potential exposure, and distinguishes firms by their role in the AI ecosystem and the type of AI technologies employed. Validation against human-coded benchmarks and external data confirms high accuracy. We show that the share of AI-active firms increased from 1% in 2016 to 12% in 2024, with a marked acceleration after 2022. This growth reflects a structural shift toward widespread adoption and more integrated AI use, including generative AI. AI adoption is concentrated among larger, younger, knowledge-intensive firms in urban regions, with workforce skills emerging as a key driver. Foundational data skills are necessary for adoption, while specialized AI skills—such as machine learning and natural language processing—act as strong complements, highlighting the central role of human capital in AI diffusion. |
| Keywords: | Artificial Intelligence, firm-level data, Large Language Models, AI diffusion, human capital, skills |
| JEL: | O33 C81 L25 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18434 |
| By: | Erik Brynjolfsson; J. Frank Li; Javier Miranda; Robert Seamans; Andrew J. Wang |
| Abstract: | This paper studies how minimum wage policy affects firms’ adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992–2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean. |
| JEL: | J38 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34895 |
| By: | Grenz, Sabrina (Utrecht University); Gregory, Terry (LISER); Lehmer, Florian (IAB Nueremberg) |
| Abstract: | The rapid evolution of technology is reshaping labor markets by altering skill demands and job profiles. This paper introduces a novel skill-based measure of occupational technology intensity -- the Occupational Technology Skill Share (OTSS) -- that distinguishes between manual, digital, and frontier technologies. Using natural language processing, generative AI, and supervised machine learning, we develop an AI-powered skill classification that enriches occupation-linked skill labels with standardized GenAI-generated descriptions and structured indicators of technological content, enabling transparent classification by technology intensity. We compute OTSS for all occupations in the German labor market. For the average worker in 2023, manual technologies account for the largest share of skill content (42\%), followed by digital (38\%) and frontier technologies (20\%). Frontier technologies remain concentrated in specialized occupations, while digital technologies are widespread. Linking these measures to administrative data from 2012–2023 shows a broad shift from manual and digital toward frontier skills across occupations, and reveals a U-shaped relationship between changes in frontier skill intensity and employment growth. |
| Keywords: | artificial intelligence, digitalization, skills, employment growth |
| JEL: | J21 J24 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18415 |
| By: | Ignacio Banares-Sanchez; Robin Burgess; Dávid László; Pol Simpson; John Van Reenen; Yifan Wang |
| Abstract: | Do industrial policies that promote clean energy offer a “ray of hope”, increasing a country’s growth and welfare, whilst simultaneously reducing carbon emissions? We study the impact of Chinese solar subsidies whose implementation by city-regions went alongside massive expansion of the sector and a dramatic fall in global solar prices. We construct new city and firm panel data on solar policies, patenting and output. Using synthetic-difference-in-differences 2004-2020, we find production and innovation subsidies were more effective than demand-side (installation) subsidies in generating large and persistent increases in local innovation, net entry, production and exports. Demand policies did, however, reduce local pollution. To examine aggregate effects, we build and structurally estimate a quantitative spatial model with endogenous innovation and heterogeneous productivity across firms and cities, which accounts for business stealing and knowledge spillovers. Counterfactual analysis shows that: (i) local effects remain substantial at the macro level explaining 40%-50% of the aggregate changes in solar innovation, prices and revenues; (ii) social benefits to Chinese citizens exceed subsidy costs by 65% (and double this when environmental benefits are included); and (iii) although all subsidy types increase welfare, innovation subsidies are the most cost-effective. |
| JEL: | H25 L25 L5 L52 N5 O31 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34893 |
| By: | Luca Repetto; Davide Cipullo; Edward Pinchbeck; Jan Bietenbeck |
| Abstract: | This paper studies how World War I mortality shocks to British communities affected long-run innovation. Linking parish-level military deaths to universal patent data (1895–1979) and inventor records, we compare high- and low-mortality areas. A 10 percent increase in deaths reduces the probability that a parish produces any patent by 0.09–0.12 percentage points and the probability that a parish produces a breakthrough patent by three times as much. Mortality depresses both the entry of new inventors and the productivity of established ones, particularly in frontier and technologically complex fields. Mobility, collaboration, and stronger local innovation ecosystems mitigate these effects, albeit only partially. |
| Keywords: | World War, innovation, human capital, patents, lost generation |
| JEL: | D74 O15 O31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12529 |
| By: | Martinez Cillero Maria (European Commission - JRC); Napolitano Lorenzo (European Commission - JRC); Rentocchini Francesco (European Commission - JRC); Seri Cecilia; Zaurino Elena (European Commission - JRC) |
| Abstract: | HIGHLIGHTS ‣ Technological mergers and acquisitions (M&As) increase investors' market power by around 2% beyond standard M&As, with stronger effects concentrated among top R&D investors, US-based investors, and high-tech manufacturing investors. ‣ The increase in market power seems primarily driven by the consolidation of control over existing patents, limiting knowledge diffusion and making it harder for competitors to catch up. ‣ These findings support ongoing policy discussions on updating merger review regulations, as traditional concentration metrics may not fully capture competition risks posed by large technology firms. ‣ Technological assets and innovations are often embedded and masked within larger M&A deals. Separating the technology component of patents would allow regulators to assess competition concerns related to innovation while still allowing the acquisition to proceed. ‣ The analysis draws on a newly constructed firm-level dataset to provide a more systematic picture of technological M&As and market power. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc145729 |
| By: | Nicola Cortinovis; Joric Donnet |
| Abstract: | This paper explores the impact of 3D printing (3DP), a technology popularly described as being able to “produce (almost) anything from anywhere†, on the spatial organization of production. Although some scholars have theorized that 3DP may affect the location of manufacturing, empirical evidence on its implications for the spatial footprint of production activities remains limited. This study investigates how 3DP adoption, together with pre- existing local capabilities, is associated with the export performance of countries in terms of 3D-printable products. Using trade data and exploiting a recent change in the Harmonized System classification, we identify 3DP adopting countries and analyze the relationship between 3DP adoption, pre-existing specializations and export outcomes. Our findings suggest that countries not previously specialized in a product but that adopted 3DP technologies tend to catch up with, and in some cases overtake, previously specialized countries, a result compatible with the idea of a shifting geography of production. We further examine the heterogeneity across product types and levels of complexity. This paper contributes to the literature by conceptually framing the spatial implications of 3DP, leveraging a novel empirical approach to capture 3DP adoption, and providing new empirical insights on the relation between 3DP and export performance. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2602 |
| By: | Dugoua, Eugenie; Noailly, Joelle |
| Abstract: | This paper examines the patterns and mechanisms of global clean technology diffusion over the last two decades. We document four stylized facts: uneven sectoral progress favoring power and light transport; China’s dominance in innovation and manufacturing; the role of modularity in driving cost declines; and limited adoption in developing economies. Through case studies of solar, electric vehicles, and hydrogen, we analyze how policy and infrastructure enable scale. Finally, we assess emerging challenges for the next phase of diffusion, including critical mineral constraints, artificial intelligence, and geopolitical fragmentation. |
| Keywords: | clean technology diffusion; climate change mitigation; renewable energy; industrial policy; solar photovoltaics; electric vehicles; hydrogen |
| JEL: | O33 Q55 O20 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137498 |
| By: | Steve Gray; Thomas Kemeny; Max Nathan; Ceren Ozgen; Guido Pialli; Jon Reades; Anna Rosso; Mateo Sere; Anna Valero |
| Abstract: | Linked employer-employee datasets are a hugely powerful tool for researchers and policymakers across the social sciences. However, access to such data is typically highly restricted and the quality of data varies substantially across countries. This paper presents GRAPH-EE, a large-scale employer-employee platform for larger companies in the UK. Leveraging a vast knowledge graph of the global public internet linked to UK company microdata and global patents data, GRAPHEE is a rich complement to administrative data sources. This version of the platform covers over 10, 500 companies and 800, 000 workers active in the UK during 2007-2023. We show the potential to extend our approach to multiple other countries. |
| Keywords: | data science, employer-employee data, productivity, innovation, skills |
| Date: | 2026–03–06 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2156 |
| By: | Binelli, Chiara; Luca, Teresa; Vergolini, Loris (University of Bologna); Marconi, Gabriele |
| Abstract: | We study how the introduction of generative AI (GPT-3) has impacted the demand for AI-related skills in the European labour market. Using a novel large-scale collection of online job advertisements in in twenty two European Union countries and the United Kingdom, we develop a detailed classification of AI skills and tasks, and we exploit the release of GPT-3 in November 2022 as a natural experiment and apply a difference-in-differences estimation to assess the shifts in the demand for AI skills for occupations that are exposed to ChatGPT relative to not-exposed occupations. We find that GPT-3 had a negative and statistically significant impact on the share of AI job ads for occupations in the treated group relative to the control group, so that the availability of generative AI decreased the demand for occupations whose most frequent core task is automatable through ChatGPT. To the best of our knowledge, this is the first paper that proposes a detailed classification framework and robust identification strategy to study the impact of generative AI on labour demand. |
| Date: | 2026–03–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:cy2s3_v1 |
| By: | Harashima, Taiji |
| Abstract: | Many empirical studies support the necessity of public funding of science, but endogenous growth models do not necessarily do so. In this paper, I distinguish between investments in research and development (R&D) for “discovery” and “invention” in a framework of an endogenous growth model and show that there is the optimal ratio of discovery to invention in the sense that the highest productivity of producing knowledge is achieved. Because discovery generally does not generate profit, investments in R&D for discovery have to be publicly financed. Therefore, a government has the responsibility to maintain an optimal ratio of discovery to invention to keep the highest rate of endogenous economic growth. |
| Keywords: | Endogenous growth; Discovery; Production of knowledge; Public funding of science; R&D |
| JEL: | H41 O32 O33 O38 O40 |
| Date: | 2026–01–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127672 |