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on Technology and Industrial Dynamics |
| By: | Giovanni Guidetti; Riccardo Leoncini; Mariele Macaluso |
| Abstract: | This paper studies patenting trends in artificial intelligence (AI) and robotics from 1980 to 2019. We introduce a novel distinction between traditional robotics and robotics embedding AI functionalities. Using patent data and a time-series econometric approach, we examine whether these domains share common long-run dynamics and how their trajectories differ across major innovation systems. Three main findings emerge. First, patenting activity in core AI, traditional robots, and AI-enhanced robots follows distinct trajectories, with AI-enhanced robotics accelerating sharply from the early 2010s. Second, structural breaks occur predominantly after 2010, indicating an acceleration in the technological dynamics associated with AI diffusion. Third, long-run relationships between AI and robotics vary systematically across countries: China exhibits strong integration between core AI and AI-enhanced robots, alongside a substantial contribution from universities and the public sector, whereas the United States displays a more market-oriented patenting structure and weaker integration between AI and robots. Europe, Japan, and South Korea show intermediate patterns. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.05034 |
| By: | Ralf Martin; Arjun Shah; Anna Valero; Dennis Verhoeven |
| Abstract: | Quantifying spillovers from scientific knowledge to technology is important for understanding the social returns to science and for designing policy. A key challenge is how to credit scientific work with the value generated in downstream technologies when ideas diffuse through chains of follow-on research. We propose a new measure - Science Rank - that uses the combined patent and paper citation network to assign a share of the private value of patented inventions to the scientific papers they directly or indirectly rely on. Validated against various types of scientific awards, the measure substantially outperforms direct patent-to-paper citation counts in identifying influential science. We document large heterogeneity in spillovers across countries, disciplines, and institutions. The US emerges from our analysis as a powerhouse of science spillovers, benefiting both domestic and foreign technology development. We apply our methodology to examine how different countries and individual institutions contribute to innovation that addresses global challenges such as climate change or more equal economic development. We find that a relatively large share of the total value generated by research in Lower and Middle Income Country (LMIC) feeds into climate change related innovation. We also highlight countries and institutions that are making particular contributions to LMIC innovation. |
| Keywords: | Technological change, growth, patents, spillovers, climate change, economic development |
| Date: | 2026–03–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2165 |
| By: | Drydakis, Nick |
| Abstract: | Artificial intelligence (AI) is increasingly recognised as a key driver of business innovation, yet its adoption among small and medium-sized enterprises (SMEs) varies considerably. This study examines whether AI Capital, defined as AI-related knowledge, skills and capabilities, is associated with business innovation among SMEs in England. Using a two-wave longitudinal panel dataset comprising 504 observations from SMEs collected in 2024 and 2025, the study develops and validates a 45-item AI Capital of Business scale. Business innovation is measured across five dimensions: product and service innovation, process innovation, technology adoption, market and customer engagement, and organisational culture and strategy. Regression models, including pooled OLS, Random Effects, and Fixed Effects specifications, are employed. The findings reveal a robust positive association between AI Capital and business innovation across all model specifications. This association holds across all business innovation dimensions and remains consistent for SMEs with differing levels of financial performance, size, and operational maturity. Each component of AI Capital independently exhibits a positive association with business innovation outcomes. The results highlight the central role of AI Capital in enabling SMEs to translate AI adoption into tangible business innovation. From a policy perspective, the findings indicate the value of targeted interventions that prioritise AI upskilling, organisational capability development, and accessible support mechanisms to promote inclusive and sustainable AI-driven business innovation among SMEs. |
| Keywords: | Artificial Intelligence, Artificial Intelligence Capital, Business Innovation, Innovation, SMEs |
| JEL: | O31 O33 O32 L26 L25 M15 D83 J24 O14 O39 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1723 |
| By: | Sebastian Ritter (AQR-IREA Research group, Universitat de Barcelona, Spain.); Vicente Royuela (AQR-IREA Research group, Universitat de Barcelona, Spain.) |
| Abstract: | As the EU races to meet its 2030 emissions reduction target, regional disparities in transition progress threaten to leave some territories behind. We introduce the Regional Green Transition Performance Index (RGTP), a novel composite measure capturing progress across seven pillars (environmental; energy; circular economy and waste; sustainable development; just transition; innovation and policy; and transport and mobility) for 232 European NUTS2 regions over 14 years. Drawing on 31 indicators, we map spatial patterns and dynamic processes. Furthermore, we argue that the green transition acts as a structural force whose potential effects on regional development can be expressed along two axes: vulnerability and opportunity. We propose an alternative measure of Regional Green Transition Opportunity index (RGTO) which we combine with the existent Regional Green Transition Vulnerability index (RGTV) of Rodríguez-Pose & Bartalucci (2024) to construct a simple 2×2 typology of regions. We translate this evidence into a policy playbook: pair risk-mitigation with opportunity-creation and embed diffusion mechanisms so gains propagate beyond individual regions. The paper contributes an open dataset, a transparent methodology to separate performance, opportunities, and vulnerabilities which responds to the EU’s performance-based policy agenda by offering a region-level monitoring tool that complements cohesion instruments (ERDF/CF/JTF/ESF+) and flags where to reduce vulnerabilities while mobilizing opportunities in the green transition. |
| Keywords: | Green Transition; European Union; Regional Inequality; Green Transition Index. JEL classification: C43; Q56; R11; R12. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ira:wpaper:202602 |
| By: | Julia Müller; Thorsten Upmann |
| Abstract: | This paper develops a dynamic model in which the productivity of joint research governs strategic investment timing in innovation races. Departing from the standard assumption that discovery rates scale proportionally with the number of active firms, we allow research to exhibit decreasing or increasing returns, thereby endogenizing the aggressiveness of innovation competition. We show that returns to joint research determine whether innovation races exhibit preemption or coordination. When research efforts are substitutes, follower entry is unattractive, generating a first-mover advantage and a preemption equilibrium. When complementarities are sufficiently strong, the gains from early investment vanish and firms invest simultaneously. The model thus identifies a regime shift in innovation races: competition accelerates investment under decreasing returns but promotes coordinated entry under increasing returns. These findings highlight the research technology as a central determinant of market dynamics and provide a unified perspective on heterogeneous patterns of innovation. |
| Keywords: | innovation races, R&D competition, strategic investment timing, preemption and coordination, research complementarities |
| JEL: | O31 D81 C73 L13 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12552 |
| By: | Davide Antonioli; Elisa Chioatto; Giovanni Guidetti; Riccardo Leoncini; Mariele Macaluso |
| Abstract: | This paper analyses how firms' skill development strategies affect their propensity to introduce innovation. We develop an adjustment-cost framework that links human capital theory and institutionalist and evolutionary approaches, considering innovation as an activity that entails costs in labour adjustment arising either from the training activities of workers or the recruitment of skilled employees. Using a two-wave panel of Italian manufacturing firms observed in 2017-2018 and 2019-2020, we analyse firms' adoption of total, product, process, and circular innovation as a function of internal training practices and of external skills acquisition. Overall, the empirical analysis confirms the expected positive relationship between training and innovation, while also revealing important nuances in the workforce upskilling strategies required for different types of innovation. Moreover, while training activities and skills development are essential across all forms of innovation, our findings indicate that internal training is particularly effective in supporting the implementation of circular innovations. By contrast, external recruitment appears to be consistently necessary whenever innovations are introduced, regardless of their type. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.05153 |
| By: | Salomé Baslandze; Zachary Edwards; John Graham; Ty McClure; Brent H. Meyer; Michael Sparks; Sonya R. Waddell; Daniel Weitz |
| Abstract: | We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation-and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI. |
| JEL: | D22 D24 G0 J01 J24 M15 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34984 |
| By: | Panle Jia Barwick; Hongyuan Xia; Tianli Xia |
| Abstract: | This paper examines China’s transition from pharmaceutical “free rider” to global innovator over the last decade. In 2010, China accounted for less than 8% of global clinical trials; by 2020, it had surpassed the US in annual registered clinical trial volume. To study this transformation, we compile a comprehensive, synchronized database spanning the pharmaceutical drug development supply chain, covering scientific publications, clinical trials, drug development milestones for China, the U.S., and Europe, alongside drug sales and government policies over the same period. We provide strong evidence that China’s rise was primarily driven by the National Reimbursement Drug List (NRDL) reform, which dramatically expanded the effective market size for innovative drugs. We document a sharp rise in both the quantity (86% increase) and novelty of drug trials post reform, with growth concentrated in reform-exposed disease categories, first- or best-in-class drugs, and among domestic firms. A decomposition exercise reveals that the NRDL reform accounts for 43% of the growth in oncology trial activity, nearly doubling the combined contribution of upstream knowledge accumulation and talent flows (24%), while other government policies play a minor role. Finally, dynamic gains from induced innovation exceed the reform’s static gains in consumer access to innovative drugs by threefold, underscoring the importance of accounting for the reform’s long-run effects on innovation incentives in addition to near-term improvements in drug affordability. |
| JEL: | I18 L65 O31 O38 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34977 |
| By: | David Autor; Caroline Chin; Anna M. Salomons; Bryan Seegmiller |
| Abstract: | We study the role of expertise in new work–novel occupational roles that emerge as technological and economic conditions evolve–using newly available 1940 and 1950 Census Complete Count files and confidential American Community Survey data from 2011-2023. We show that new work is systematically distinct from simply more work in existing occupations in four respects. First, it attracts workers with distinct characteristics: new work is disproportionately performed by younger and more educated workers, even within detailed occupation-industry cells. Second, new work commands economically significant wage premiums that persist beyond workers' initial entry into new work, consistent with returns to scarce, specialized expertise rather than temporary market disequilibrium. Third, these premiums decline across vintages as expertise diffuses, with 'newer' new work commanding larger premiums than older new work. Fourth, the emergence of new work can be traced to specific demand shocks in particular locations and time periods, suggesting that expertise formation responds systematically to economic opportunities. These findings suggest that new work serves as a countervailing force to automation-driven job displacement not merely by creating additional employment, but also by generating new domains of human expertise that command market premiums. This expertise-based mechanism helps explain both the expanding variety of work activities across decades and the historical resilience of the labor share. |
| JEL: | E24 J11 J23 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34986 |