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on Technology and Industrial Dynamics |
| By: | D’Alessandro, Francesco; Santarelli, Enrico; Vivarelli, Marco |
| Abstract: | This study examines how regional technological relatedness and local AI knowledge influence regional innovative activity, as measured by patenting activity. Using a novel three-way longitudinal dataset (670 four-digit CPC classes × 302 NUTS-2 regions × nine four-year periods, 1986–2021) and leveraging a deep learning-based identification of AI patents, we show that two broad mechanisms operate in parallel. First, in accordance with the extant literature, technologies that are cognitively close to a region’s existing patent portfolio enjoy higher patenting activity, confirming that relatedness remains a strong and persistent predictor of innovative output. Second, local AI endowments are positively associated with patenting across technological fields, even after conditioning on relatedness, indicating that AI plays an enabling and cross-cutting role in a given regional innovation system. Moreover, the interaction between relatedness and AI turns out to be negative and statistically significant, implying that AI attenuates the extent to which local innovative efforts depend on the technology’s proximity to the regional portfolio. In sum, AI appears to enhance overall local innovative activity while reducing its reliance on pre-existing regional knowledge structures. |
| Keywords: | Artificial intelligence, AI, regional innovation, relatedness, Technological change |
| JEL: | O31 R11 |
| Date: | 2026–08–20 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2026012 |
| By: | Colin C. Caines; Florian Hoffmann; Gueorgui Kambourov |
| Abstract: | We document a strong, positive relationship between occupational problem complexity, measured from US data on problem-solving requirements, and occupational wage growth since 1980. In contrast, employment shifts toward more complex occupations have been modest, suggesting a race between the demand for and supply of complex skills. We rationalize these findings by formulating and structurally estimating a quantitative general equilibrium model on the granular occupational level. In our model, workers have heterogeneous comparative advantages in solving complex problems and physical capital admits capital-skill complementarity in occupation space. The equilibrium features Positive Assortative Matching of worker skills to occupational problem complexity, and the model quantitatively explains the evolution of the occupational wage- and employment structure over the last four decades. The model estimates uncover two distinct periods of technological change. Until around 2000, rising complexity premia were driven by capital-skill complementarity and declining equipment capital prices. Post-2000 patterns reflect supply-side technological change whereby occupations became more efficient in utilizing worker skills for complex problem-solving. Our framework helps unify distinct approaches to studying task automation and task augmentation on the one hand and skill-biased technological change on the other. |
| Keywords: | occupational task content; complex tasks; wage polarization; skills |
| JEL: | E24 J21 J23 J24 J31 |
| Date: | 2026–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:103645 |
| By: | Nathan Lane |
| Abstract: | Major economies are rearming, and rearmament is itself industrial policy. I ask whether defense promotion—especially innovation policy—can drive industrial development beyond the defense sector. I synthesize evidence on supply-side instruments (including R&D tax credits and grants) and demand-side procurement, tracing each from its effects on firms' own R&D investment through innovation and productivity outcomes to spillovers beyond the recipient firm. The evidence is conditionally positive: public support more often crowds private effort in than out, but returns concentrate among particular firms, technologies, and designs, and weaken as one moves from investment to outputs and from civilian to defense settings. Defense spillovers, in particular, cannot be assumed. I draw five lessons for designing defense industrial policy that delivers broader economic returns, with the greatest weight on Europe's rearmament. The developmental promise of defense promotion is real but conditional: whether rearmament also strengthens the wider industrial economy turns on how it is designed. |
| Keywords: | defense economics, industrial policy, innovation policy, R&D, procurement, EU, defence |
| Date: | 2026–08–12 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2208 |
| By: | Adi Weidenfeld; Tom Broekel; Nick Clifton |
| Abstract: | The Regional Innovation Systems (RIS) framework explains regional innovation performance primarily through structural conditions and external knowledge flows. While this perspective has generated influential typologies and policy models, it offers limited insight into how RISs emerge and transform from within. This paper advances a dynamic perspective that conceptualises RIS development as a continuum from fragmented interaction to systemic integration. We argue that generative network mechanisms and systemic dimensions co-evolve recursively, enabling or constraining the formation of integrated RISs. By shifting attention from static structures to self-reinforcing dynamics, the framework provides a foundation for understanding RIS formation, transformation, and reversibility, and suggests a more adaptive, mechanism-oriented approach to policy. |
| Keywords: | regional knowledge networks, regional innovation systems, network evolution, evolutionary economic geography, institutions |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2614 |
| By: | Timo Boppart; Peter J. Klenow; Reiko Laski; Huiyu Li |
| Abstract: | Which firms drive aggregate productivity growth? We document that firms with high price-earnings ratios tend to see increases in their subsequent earnings relative to sales, which we interpret as rents from ideas (innovation). We construct an endogenous growth model with shocks to firm innovation step-sizes and R&D efficiency and calibrate it to match patterns in the data. The model implies that growth would be much lower, even with the same innovative effort, if firms had the same step sizes. The model can be used to infer expected growth contributions of individual firms (such as members of the Magnificent Seven). We find that the share of growth coming from smaller listed firms substantially exceeds their sales share, whereas the largest listed firms account for less than their sales share. |
| JEL: | L11 O31 O41 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35594 |
| By: | Zanna Iscenko; Scott Strand; Yiyuan Chen; Guillaume Aimard; Mihai Codreanu; Vivek Sampathkumar; Alex Imas; Julian Jacobs; Evalyne Muiruri; Juan Mateos-Garcia; Jia Jen Ng; Samirah Javed; Josh Martin; Omar Ajmeri; Denis Calin; Andrew Kim; Fabien Curto Millet; James Manyika |
| Abstract: | This paper introduces the AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study), an ongoing economic research initiative using Google AI usage data. The first iteration of ATLAS is built on 15 million de-identified interactions across the Gemini App, Google AI Mode, and Gemini API. Using privacy-preserving algorithms as well as established and bespoke classification methods, we map AI usage to over 800 occupations, 4000 tasks, 300 household activities, 150 countries, and 140 languages. We then make a number of observations on what the data reveals about AI's diffusion, and its usage at work and in day-to-day life. In the workplace, we show that while AI adoption spans occupations covering just above 88% of US employment, penetration remains shallow and overwhelmingly collaborative in nature, with end-to-end task automation limited in scope. Outside of work, AI spans activities making up about 98% of Americans' non-sleep time, with disproportionately high use in high-friction tasks such as engaging with government and professional service providers, likely delivering economic value that standard national accounts may miss. Globally, adoption scales with national wealth and has broad linguistic distribution, with English queries representing only around a third of volume. As we build upon ATLAS and expand its scope and capabilities, we will continue to provide large-scale empirical evidence to inform the public, policy and academic questions about the ongoing AI transformation. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.00038 |
| By: | Grega Smrkolj (Newcastle University); Florian Wagener (University of Amsterdam) |
| Abstract: | We study a continuous-time duopoly model of process innovation with R&D spillovers, comparing noncooperative R&D with cooperative research regimes. We extend the standard constant-spillover framework by allowing knowledge transmission to decay with technological distance and to favor followers over leaders in asymmetric specifications. In a global Markov-perfect model, firms may invest before production is viable, enter or exit production as costs evolve, and converge to no-market, monopoly, or duopoly outcomes. State-dependent spillovers change R&D incentives, catch-up dynamics, long-run market structure, and the welfare effects of research cooperation. In the computed equilibria, more follower-favoring spillovers weaken the leader's private incentive to invest but accelerate catch-up, shorten monopoly phases, and make eventual duopoly more likely. When spillovers are weak, cooperation mainly softens dynamic rivalry; when information sharing is substantial, cooperation expands market formation, lowers long-run costs, and can raise both consumer and total surplus, especially under the research-joint-venture regime. The value of R&D cooperation depends on the direction and persistence of knowledge flows, not only on their average intensity. |
| JEL: | C73 D43 O31 |
| Date: | 2026–06–29 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260041 |
| By: | Zheng Tian; Luyi Han; Timothy Wojan; Stephan J. Goetz |
| Abstract: | Place-based policy is premised on the idea that productive investments that address endowment deficiencies may release latent comparative advantages, stimulating growth while reducing regional disparities. Extending place-based policy to the domain of innovation is conceptually challenging given conventional wisdom that settlement size is the principal determinant of agglomeration economies that fuels innovation. However, growing concern that more places are being left behind by the innovation economy has prompted greater interest in ways to expand the geography of innovation. Using confidential firm-level data from the 2018 Annual Business Survey, this study examines differences in self-reported innovation activity between firms in rural and urban counties to identify possible endowment deficiencies. We do this by employing the Oaxaca-Blinder decomposition method to quantify the urban-rural innovation gap to better understand the relative contributions of firm characteristics, owner characteristics, and county-level factors. |
| Keywords: | Place-based innovation policy, Rural–urban innovation gap, Oaxaca–Blinder decomposition, Annual Business Survey |
| JEL: | O31 R11 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-48 |
| By: | Juan David Munoz Henao; Nicholas Sly |
| Abstract: | We show that AI technologies are oriented toward jobs and workers that typically exhibit greater volatility in labor market outcomes over the business cycle. Occupations currently most exposed to AI are those that have historically exhibited (i) greater volatility in employment levels over business cycles, (ii) higher job-switching rates by workers, (iii) higher job-finding rates, and (iv) a lower likelihood for workers to exit the labor force following a job loss. The sectors of the U.S. economy that produce AI technologies have also historically exhibited high volatility in productivity. We then quantify how technology-led structural changes in economic activities contribute to aggregate business cycle volatility. Growth in the production of AI-based technologies in recent years increased the volatility of U.S. output by 2.8 percent, roughly 3.5 times what resulted from the late 1990s’ IT boom. If the orientation of AI toward occupations that exhibit higher variability in labor market outcomes results in a higher aggregate labor supply elasticity, the effects on aggregate volatility are even greater. |
| Keywords: | technological change; volatility; artificial intelligence |
| JEL: | E32 E37 J62 J63 O33 |
| Date: | 2026–08–13 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:103640 |
| By: | Bachmann, Ronald; Fischer, David; Gausing, Sibylle; Klauser, Roman; Rammert, Timo |
| Abstract: | This paper examines the extent and characteristics of labor shortages in the context of the green transition of the German labor market and discusses potential strategies to mitigate them. Using online job vacancy data, a firm survey, administrative employment and apprenticeship data, and measures of occupational greenness and labor shortages from the German Federal Employment Agency, we provide a comprehensive picture of green labor demand, supply, and shortages. We show that green labor demand has increased along both the extensive and intensive margins and identify the occupations and skills most relevant to the green transition. While green occupations are represented among both shortage and non-shortage occupations, firms increasingly expect the green transition to exacerbate skill and labor shortages. At the same time, the German apprenticeship system appears to play an important role in alleviating these shortages: although green occupations face shortages of apprentices, they remain comparatively attractive to applicants. Finally, firms predominantly rely on internal adjustment mechanisms - particularly training and increased technology use - rather than external recruitment strategies, such as hiring from abroad, to address changing labor demand. |
| Abstract: | Dieses Papier untersucht das Ausmaß und verschiedene Merkmale des Fachkräftemangels im Kontext der ökologischen Transformation des deutschen Arbeitsmarktes und erörtert mögliche Strategien zur Abmilderung der Konsequenzen. Die empirische Analyse basiert auf Daten aus Online-Stellenanzeigen, einer Unternehmensbefragungen sowie administrativen Beschäftigungs- und Ausbildungsdaten. Ergänzend werden berufsspezifische Indikatoren der Bundesagentur für Arbeit zur "Grünheit" von Berufen und zum Arbeitskräftemangel herangezogen. Dadurch lassen sich grüne Arbeitsnachfrage, das entsprechende Arbeitsangebot und bestehende Fachkräfteengpässe umfassend abbilden. Wir zeigen, dass die Nachfrage nach grünen Arbeitskräften sowohl entlang der extensiven als auch an der intensiven Marge gestiegen ist und identifizieren die für den grünen Wandel relevantesten Berufe und Qualifikationen. Grüne Berufe sind sowohl unter den Berufen mit ausgeprägtem Fachkräftemangel als auch unter den Berufen, die keinen Fachkräftemangel aufweisen, vertreten. Dennoch erwarten Unternehmen, dass die ökologische Transformation den Fachkräftemangel verschärfen wird. Eine zentrale Rolle bei der Abmilderung dieser Engpässe kommt dem deutschen Ausbildungssystem zu. Obwohl auch grüne Berufe einen Mangel an Auszubildenden verzeichnen, bleiben diese Berufe für Bewerberinnen und Bewerber vergleichsweise attraktiv. Unternehmen nutzen zur Bewältigung der sich wandelnden Arbeitsnachfrage überwiegend interne Anpassungsmechanismen, insbesondere Weiterbildung und verstärkten Technologieeinsatz, statt externe Rekrutierungsstrategien, wie die Anwerbung von Arbeitskräften aus dem Ausland. |
| Keywords: | green transition, labor demand, firm adjustment, green skills, labor shortages |
| JEL: | J23 J24 Q52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:342544 |
| By: | Suqin Ge; Naijia Guo; Zibin Huang; Junsen Zhang; Li Zhang |
| Abstract: | This paper examines how innovation shapes migration across skill groups. Using Chinese microdata from 2005-2015, we find that cities with faster patent growth attract more low-skilled than high-skilled migrants, opposite to patterns in developed countries. These cities see similar wage growth for both groups but limited amenity gains. We develop and estimate a spatial equilibrium model showing that low-skilled workers prioritize wages, while high-skilled workers value amenities, which rise with the share of skilled workers. Patent shocks draw in more low-skilled workers, reducing amenities and deterring high-skilled migration. Overall, technological growth raised wages and welfare without increasing spatial inequality. |
| Keywords: | Patent, Migration, Spatial equilibrium, Wage, Amenity |
| JEL: | J24 J61 R23 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26176 |
| By: | Hamid Bekamiri (Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark); Jan Auernhammer (Center for Design Research, ME Design Group, Stanford University, USA); Milad Abbasiharofteh (Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark); Jesper Lindgaard Christensen (Aalborg University Business School, The IKE Research Group, Aalborg University, Denmark) |
| Abstract: | Green-patent indicators based on Cooperative Patent Classification Y02 tags increasingly inform research, industrial policy, and climate-oriented investment, yet their construct validity has not been evaluated at corpus scale. We ask whether Y02 classification errors are random measurement noise or systematic, direction-specific bias. We introduce an Error-as-Signal framework in which disagreement between an administrative label and an independent model is treated as evidence of potential measurement error. Screening 9, 075, 421 USPTO granted patents from 1962-2024 with a fine-tuned domain model identifies 517, 772 disagreements. Two independent open-weight large language models then assess whether each flagged invention has a direct climate-mitigation or adaptation function. Cross-model consensus identifies 180, 384 administrative Type I errors (False Green) and 29, 465 Type II errors (Silent Green). Correcting these errors reduces the measured green-patent population by 25.5%, from 592, 387 to 441, 468 patents. Misclassification is systematic rather than random. Atypicality predicts Silent Green in an inverted-U pattern, while reflection complexity independently increases under-recognition: controlling for atypicality and filing year, a one-standard-deviation increase is associated with 1.61 times the odds of Silent Green. Structural complexity has the opposite association. Among consensus-attributed errors, the same increase in reflection complexity is associated with 2.45 times the odds that an error is Silent Green rather than False Green. Event tests show no discrete rise in misclassification when green classification became more salient and only limited evidence of increased explicit green framing after the 2013 CPC launch. The evidence is more consistent with bounded classification capacity than with applicant gaming. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23420 |
| By: | Amrit Amirapu; Malavika Thirumalai Ananthakrishnan; Alex Klein |
| Abstract: | This paper examines the role of agglomeration economies in shaping patterns of industrial coagglomeration in post-liberalisation India. Using granular, establishment-level data from a 2005 census of all non-farm establishments, we estimate the relative importance of two Marshallian channels of agglomeration economies -- input-output linkages and labour market pooling -- in determining which industries locate together. To address concerns regarding endogeneity and measurement error we employ instrumental variables based on U.S. industrial data. We find large effects for both Marshallian channels -- substantially larger than those the previous literature has found for advanced economies. By contrast, natural advantages appear less influential, though this may reflect measurement challenges. We also find that the two channels operate at distinct geographic scales: inputoutput linkages are similarly influential at both the district level and at the level of individual towns and villages, while labour market pooling effects appear more localized, as they are strongest at the town/village level. This pattern holds both in the full sample and within manufacturing alone. Our study is among the first detailed empirical assessments of coagglomeration patterns in a developing country, thus offering insights into the economic forces that shape spatial inequalities in such contexts. |
| Keywords: | coagglomeration; agglomeration economies; Marshallian externalities; input-output linkages; labour market pooling; India |
| JEL: | R12 R32 R23 O14 O18 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ukc:ukcedp:2604 |
| By: | Zoltan Elekes; Sandor Juhasz; Gergely Magyar; Balazs Lengyel; Gergo Toth |
| Abstract: | Regional industry clusters enhance firm performance, yet the geography of firm-to-firm transactions underlying this advantage remains unclear. Using nationwide supplier-buyer and labour-flow networks constructed from Hungarian administrative data, we examine how the spatial reach of cluster and non-cluster firms’ supplier, customer, and labour connections relates to firm performance. We find that greater geographic reach in both networks is associated with better firm performance. Among cluster firms, better-performing firms reach more distant customers while drawing from more geographically proximate labour markets. Our findings reveal that the spatial structure of inter-firm networks is a key source of the cluster premium. |
| Keywords: | supply chains, production networks, labour flows, regional clusters, firm performance |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2613 |
| By: | Gustavo de Souza; Andrew J. Fieldhouse; Karel Mertens; Ishan Nath; Valerie A. Ramey |
| Abstract: | This paper provides the first causal estimates of the global social returns to U.S. public R&D. We use a narrative identification strategy to quantify the effects of exogenous shocks to U.S. R&D appropriations on the dynamic TFP response of 69 foreign economies from 1980–2019. A U.S. R&D appropriations shock equal to 1 percent of the federal R&D capital stock raises foreign TFP by approximately 1 percent after 12 years. This response is driven primarily by nondefense rather than defense R&D and is concentrated in non-OECD economies. These patterns are most consistent with diffusion occurring through openly accessible scientific content, capital-embodied trade and technological leapfrogging by economies further from the global frontier. A back-of-the-envelope calculation suggests that the global social returns to U.S. public nondefense R&D are roughly twice as large as the domestic returns, meaning the U.S. captures about half of these productivity benefits. |
| Keywords: | Public R&D; International R&D Spillovers; Social Returns to R&D; Productivity; Technological Diffusion |
| JEL: | E62 F62 O33 O38 O47 |
| Date: | 2026–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:103620 |