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on Technology and Industrial Dynamics |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, and AIAS, Aarhus University); Schroede, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Örebro University School of Business) |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures. |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2026_003 |
| By: | Emanuele Bazzichi; Massimo Riccaboni; Fulvio Castellacci |
| Abstract: | We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02189 |
| By: | Ravish Gupta; Saket Kumar |
| Abstract: | This paper extends the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end workflows involving multi-step reasoning, tool invocation, and autonomous decision-making, substantially expanding occupational displacement risk beyond what existing task-level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters--not a regression estimate--incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information-intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43-0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.00186 |
| By: | Anton Korinek; Joseph E. Stiglitz |
| Abstract: | Rapid progress in new technologies such as AI has led to widespread anxiety about adverse labor market impacts. This paper asks how to guide innovative efforts so as to increase labor demand and create better-paying jobs while also evaluating the limitations of such an approach. We develop a theoretical framework to identify the properties that make an innovation desirable from the perspective of workers, including its technological complementarity to labor, the relative income of the affected workers, and the factor share of labor in producing the goods involved. Applications include robot taxation, factor-augmenting progress, and task automation. In our framework, the welfare benefits of steering technology are greater the less efficient social safety nets are. As technological progress devalues labor, the welfare benefits of steering are at first increased but, but beyond a critical threshold, decline and optimal policy shifts toward greater redistribution. Moreover, as labor's economic value diminishes, steering progress focuses increasingly on enhancing human well-being rather than labor productivity. |
| JEL: | D63 E64 O3 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34994 |
| By: | Drydakis, Nick (Anglia Ruskin University) |
| Abstract: | 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. |
| Keywords: | artificial intelligence, artificial intelligence capital, business innovation, innovation, SMEs |
| JEL: | O31 O33 O32 L26 L25 M15 D83 J24 O14 O39 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18476 |
| By: | Alexander Bick; Adam Blandin; David Deming; Nicola Fuchs-Schündeln; Jonas Jessen |
| Abstract: | This paper combines international evidence from worker and firm surveys conducted in 2025 and2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI. |
| Keywords: | generative artificial intelligence (AI); technology adoption; labor productivity |
| JEL: | J24 M16 O14 O33 |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:102950 |
| By: | Wensu Li; Atin Aboutorabi; Harry Lyu; Kaizhi Qian; Martin Fleming; Brian C. Goehring; Neil Thompson |
| Abstract: | This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3, 778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.29121 |
| By: | Qayoom Khachoo (Indian Institute of Foreign Trade); Ridwan Ah Sheikh (Indira Gandhi Institute of Development Research); Pritam Banerjee (Indian Institute of Foreign Trade) |
| Abstract: | This study leverages India's Patents (Amendment) Act, 2002, as a quasi-natural experiment within a difference-in-differences framework to examine how domestic reforms related to patents may affect firms' export behavior and their integration to the global value chains. Exploiting a detailed firm-level database covering the universe of Indian manufacturing firms, we find that heightened patent protection is associated with approximately a 18 increase in exports and a 12 increase in total imports among high-tech firms relative to low-tech firms, even including firm, year, and industry-by-year fixed effects. We further show that stronger enforcement of intellectual property rights (IPRs) has a positive impact on firms' imports of intermediate inputs. Specifically, high-tech firms experienced 20 increase in raw-material imports relative to their low-tech counterparts. In contrast, the reform was associated with a significant reduction in imports of spares and stores. While the average treatment effects on capital and final goods imports remain insignificant, event-study estimates suggest positive and statistically significant effects, albeit with a delay. This study provides policy-relevant evidence that stronger IPRs in emerging market economies such as India enhance firms' trade performance by stimulating innovation, promoting technology transfer and adoption, and enabling access to advanced global inputs. |
| Keywords: | IPRs, Exports, Imports, Global value chains, Difference-in-Differences |
| JEL: | F13 F14 O30 O33 O34 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ind:igiwpp:2026-001 |
| By: | Alejandro Bello-Pintado (Universidad Pública de Navarra); Carlos Bianchi (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Sofía Maio (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | This study examines how innovation modes, STI (Science and Technology-based Innovation), DUI (Innovation based on learning-by-Doing, learning-by-Using, learning by-Interacting) and their combination, shape firms’ use of formal and informal intellectual property protection mechanisms (IPPM) and influence product innovation performance. Using panel data from the National Innovation Activities Survey (2010 2021) of Uruguay, results show that STI drives formal IPPM and enhances innovation likelihood and novelty, while DUI fosters informal IPPM with limited impact on innovation outcomes. However, combined STI-DUI strategies generate coordination tensions, constraining innovation performance |
| Keywords: | innovation modes, knowledge appropriability strategies, firm organization |
| JEL: | O31 O32 O54 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-02-26 |
| By: | Nigar Hashimzade; Haoran Sun |
| Abstract: | Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.29542 |
| By: | Christian Rutzer; Dragan Filimonovic; Jeffrey T. Macher; Rolf Weder |
| Abstract: | The CD index is a widely used measure of disruptive inventions. Most studies compute it using USPTO data. This creates a puzzle because the US appears less disruptive than European and Asian countries. We show that this largely stems from missing international citations. Using a global citation network, we quantify and correct this bias. The disruptiveness advantage of non-US inventors drops by 64% to 148% of the US baseline mean. The US emerges as a disruption leader over Europe, with Asia's advantage substantially reduced. Globally integrated citation data are essential for credible measurement of disruptive innovation in international contexts. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.17881 |
| By: | David H. Autor; Caroline Chin; Anna 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. |
| Keywords: | new work, technological change, occupations, tasks |
| JEL: | E24 J11 J23 J24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12577 |
| By: | Arpan Chakraborty |
| Abstract: | This paper analyzes the macroeconomic consequences of military spending and militarization within a dynamic growth framework. Building on a Keynesian goods-market model, we examine how the allocation of government expenditure between civilian and military sectors affects capital accumulation and technological progress. Military spending generates opposing effects: it stimulates aggregate demand and may support innovation through defense-related research, but it also crowds out civilian investment and creates structural rigidities. We formalize these mechanisms in a stylized endogenous-growth model in which productivity depends on the degree of militarization, producing a non-linear relationship between the military burden and long-run growth. Calibrated simulations show that moderate levels of military spending can temporarily support growth, whereas excessive militarization reduces long-run development. We further illustrate the asymmetric growth costs of conflict using a simple two-country war simulation between an advanced economy and a sanctioned middle-income economy. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.23980 |