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on Technology and Industrial Dynamics |
| By: | Riku Watanabe (Department of Economics, Kagawa University); Ken Tabata (School of Economics, Kwansei Gakuin University) |
| Abstract: | This study introduces endogenous resource-substituting innovation into a variety-expanding endogenous growth model with polluting non-renewable resources. The resulting task-based framework highlights the trade-off between resource-using variety-expanding innovation and resource-substituting innovation. We examine how subsidies for these two types of innovation affect economic growth and welfare, and how environmental policies that slow resource extraction and mitigate pollution shape these effects. Subsidies for new-variety R&D promote growth through faster variety expansion but reduce it through greater reliance on resource extraction, reflected in a lower share of firms adopting resource-substituting production methods and higher resource intensity in aggregate production. Subsidies for resource-substituting innovation have the opposite effects. Calibrating the model, we find that raising subsidies for new-variety R&D is more likely to improve welfare in resource-rich economies, whereas raising subsidies for resource-substituting innovation is more likely to improve welfare in resource-poor economies. We also find that more stringent environmental policies that conserve resources and mitigate pollution strengthen the welfare gains from subsidies for resource-substituting innovation, while weakening those from new-variety R&D subsidies. |
| Keywords: | Non-Renewable Resources, Resource-Substituting Innovation, R&D-based Growth |
| JEL: | O31 O44 Q55 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:kgu:wpaper:314 |
| By: | Horbach, Jens; Rammer, Christian |
| Abstract: | Eco-innovations are highly important for mitigating climate change and transitioning towards a circular economy. There is extensive literature on the determinants of eco-innovation. The analysis of the role of government subsidies, however, is still under-developed. Subsidies are controversially discussed because they may distort competition and lead to less efficient solutions. At the same time, eco-innovations may produce positive externalities both at the innovation stage (knowledge spillovers) and the diffusion stage (reducing environmental damage). Subsidies may hence be important to avoid private under-investment in such innovations. The paper investigates which types of subsidies (e.g. general vs. research oriented, regional vs. national vs. supranational) are used by firms that engage in different fields of eco-innovation. The analysis is based on panel-econometric estimations using data from the German part of the Community Innovation Survey (CIS), covering the years 2020 to 2024. The results show that regional subsidies and general industry support are important for the diffusion of eco-innovations, such as the substitution of fossil fuels by renewables, because they help to overcome financial constraints. Specific R&D subsidies are important for innovations leading to less CO2 emissions in production processes. |
| Keywords: | Green subsidies, eco-innovation, random effects panel probit models |
| JEL: | C23 C25 Q55 Q58 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:343046 |
| By: | Fazliddin Shermatov; Stephane Robin; Aldo Geuna |
| Abstract: | Whether artificial intelligence pays off for the firms that build it into their products is hard to establish, because AI innovation is itself hard to observe. The medical technology sector is a rare exception: an AI-enabled device must obtain clearance from a national health authority before it can reach a patient, leaving a dated, firm-attributable record of AI innovation output that can be observed directly rather than proxied. We exploit this setting with a three-stage recursive model estimated on a novel firm-level dataset linking FDA premarket clearances, USPTO patents, Scopus publications, and Orbis financials, tracing the full innovation chain from external collaboration through AI device introduction to firm performance. We find that external AI research collaboration is a robust driver of AI device introduction across firm sizes and estimators, with a larger effect for small firms, consistent with external knowledge ties substituting for limited internal R&D capacity. Decomposing by partner type, the effect is largest for industry and clinical collaborations and smallest for academic ties, consistent with the former being closer to the regulatory and commercialisation process. Firms that bring AI devices to market display higher labour productivity, an effect robust for small firms and the full sample that holds under both sequential and joint maximum-likelihood estimation and accumulates across successive device introductions. Effects on profit margins are present but weaker and do not survive all specifications, a pattern consistent with competitive entry eroding pricing power as AI devices diffuse through the sector. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.08485 |
| By: | Chang Liu; Kohei Takeda |
| Abstract: | Structural transformation from manufacturing to services varies sharply across US regions. We document that regions with universities, especially highly ranked research institutions, experience faster growth in high-skill services, larger increases in the college wage premium, and a greater concentration of new work. We develop a task-based theory in which universities affect local economies by supplying college labor and adding new tasks. Our quantitative analysis shows that new tasks concentrated in top university regions account for a substantial part of regional differences in high-skill-service growth and the college wage premium increase between 1980 and 2015. |
| Keywords: | structural transformation, skill premium, new task, economic geography |
| Date: | 2026–08–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2211 |
| By: | Piergiuseppe Fortunato |
| Abstract: | Artificial intelligence is increasingly analysed as a general-purpose technology, a source of productivity growth, and an object of geopolitical competition. Much less is known about the cross-country distribution of control over the infrastructures on which frontier AI depends. This paper introduces the Foundational AI Infrastructure Database (FAID), a cross-country data architecture that maps six complementary layers of the AI production stack: advanced logic fabrication; semiconductor manufacturing equipment and electronic design automation; frontier compute; cloud infrastructure; foundation models; and frontier high-performance computing. We use FAID to construct the AI Infrastructure Control Index (AICI), a composite measure of documented national control over these foundational assets. The index is deliberately structural rather than predictive: it measures position in the infrastructure stack, not AI adoption, innovation performance, digital readiness, or policy effort. We document three facts. First, control over foundational AI infrastructure is highly concentrated, substantially more so than conventional measures of international production and trade. Second, the geography of infrastructural control differs sharply from the geography implied by merchandise exports and aggregate economic size: several economies occupy positions in the AI stack that are disproportionate to their conventional market weight, while others with large markets remain weakly positioned at critical technological nodes. Third, control is functionally differentiated across layers, so aggregate rankings conceal distinct forms of dependence and leverage. An exploratory comparison with economy-wide industrial-policy support finds little contemporaneous cross-country association, consistent with the view that infrastructural position reflects cumulative capabilities and technology-specific bottlenecks rather than current policy intensity alone. FAID and AICI provide a measurement framework for research on structural power, technological sovereignty, industrial policy, geoeconomic dependence, and development in an increasingly infrastructure-centred world economy. |
| Keywords: | artificial intelligence, infrastructural power, structural power, semiconductors, compute, cloud, industrial policy, geoeconomics |
| JEL: | F50 F52 L52 O25 O33 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:irn:wpaper:26-09 |
| By: | Elise Petit; Nadin WN Woergoetter; Peeyush SP Sahu; Nicolas van Zeebroeck |
| Abstract: | When a patent application is pending, it is uncertain whether a patent will be granted and, if so, what protection it will confer. If the validity of the patent is then challenged, uncertainty continues until a final decision in opposition is obtained. This uncertainty extends when multiple applications surround a single innovation, a phenomenon that is common in life sciences and can therefore have a great impact on healthcare systems and patients. This article examines whether examination duration, and therefore the period of uncertainty, at the European Patent Office (EPO) varies systematically with parent and divisional applications, secondary patenting, and the economic value of the associated drug. Using bulk EPO Register data and IQVIA Ark5/MIDAS®6 data, 15, 737 patent applications filed at the EPO between 1978 and 2026 Q1 are linked to a drug. Cox proportional hazards models show that parent applications and secondary patents are associated with significantly longer pendency than single applications, particularly for high-value drugs. Additional indicators trace where and how these delays arise. The findings suggest that delays are connected to the combined use of several procedural rights, both across a patent family and within individual applications, and are not limited to multi-generation families. |
| Keywords: | life sciences; pharmaceutical patent;; divisional application;; secondary patent; patent family; European patent office; survival analysis |
| JEL: | O34 O38 L65 I18 K11 C41 |
| Date: | 2026–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/413551 |
| By: | Aydan Dogan; Ozgen Ozturk |
| Abstract: | We study how the financing of innovation shapes the transmission of monetary policy to productivity. Using US firm balance-sheet data matched to loan contracts, we show that contractionary monetary policy shocks reduce cash flow similarly across firms but lower R&D more among those without access to cash flow-based borrowing, where credit is extended against earnings rather than collateral. In a New Keynesian endogenous growth model with heterogeneous access to external finance, we show that a 25 basis point tightening lowers output persistently by 0.12%. Extending access to all firms reduces this loss by one third. The loss falls disproportionately on firms without access, which are younger and produce more and higher-quality patents. |
| JEL: | E22 E32 E44 E52 G32 |
| Date: | 2026–09–04 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:023581 |
| By: | Luca Bargna (Department of Economics, Insubria University, Varese, Italy); Davide La Torre (SKEMA Business School, Université Côte d'Azur, France); Benjamin Montmartin (SKEMA Business School, France; Université Côte d'Azur, CNRS, GREDEG, France); Lionel Nesta (Université Côte d'Azur, CNRS, GREDEG, France; OFCE, Sciences Po, France; SKEMA Business School, France) |
| Abstract: | We develop a spatial optimal-control model in which air pollution and climate-mitigation innovation evolve jointly through a coupled reaction–diffusion system. We characterize stability and optimal policy, derive closed-form controls under spatial homogeneity, and obtain bounds for nonlinear dynamics. Using data from 1, 181 European NUTS 3 regions over 2005–2020, we estimate the reaction–diffusion dynamics and simulate the optimal policy mix under alternative welfare valuations of pollution-generating activities. Pollution exhibits strong spatial diffusion, whereas innovation diffusion is limited. Innovation is associated with lower subsequent pollution growth, while higher pollution is followed by stronger innovation growth. The optimal policy mix combines environmental regulation and innovation support, but their timing and persistence differ. Abatement may be immediate or delayed depending on the net welfare contribution of pollution-generating activities, while sustained innovation support depends on the value assigned to the terminal innovation stock. |
| Keywords: | Optimal control; Reaction–diffusion systems; Pollution; Innovation; Spatial econometrics |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2026-21 |
| By: | J. David Brown; Matthew Denes; Ran Duchin; John Hackney |
| Abstract: | We study the effects of vast increases in U.S. small business program eligibility standards, which expanded larger firms' access to support for small businesses. Exploiting quasi-random variation in the timing of these expansions and using administrative Census data, we show that revenues decline for the smallest firms, particularly those that are younger, more productive, and financially constrained. Government procurement contracts also are reallocated to larger firms. Consequently, firm exits increase, wages decline, and patenting falls. These findings highlight the economic consequences of expanding eligibility: by crowding out the smallest firms, resources shift away from high-potential firms, reducing dynamism and innovation. |
| JEL: | E24 G38 H25 H57 L25 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35703 |
| By: | Samuel M. Hartzmark; Kelly Shue |
| Abstract: | We develop a new measure of impact elasticity: the change in a firm's environmental impact due to a change in its cost of capital. We find that reducing green firms' financing costs leads to minimal impact changes, while increasing brown firms' financing costs causes significant negative impact changes. Thus, sustainable investing strategies that shift capital from brown to green firms contain a counterproductive channel that makes brown firms more brown without making green firms more green. A mistaken focus on percentage reductions in emissions rewards already-green firms for trivial reductions in emissions and gives brown firms weak incentives to improve. |
| JEL: | G1 G3 G4 Q5 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35519 |