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on Innovation |
| By: | James Driver |
| Abstract: | This project benefitted the U.S. Census Bureau by the creation of novel, dynamic and data-driven innovation-industry classification systems (i.e., IICSs). The goal of these classification systems is to track a firm’s endogenous choice of innovation and be able to assign it to one, or more, innovation-industries through time. To capture and measure a firm’s innovative output, it is proposed to categorize a firm by its portfolio of patent abstracts from granted, USPTO utility patents where the firm is the original assignee. The idea is to capture the firm responsible for the innovation and to observe the area(s) in which it is innovating. A benefit of utilizing granted patents is that it allows one to compare public and private firms, and their roles in innovation. |
| Keywords: | BERD, LBD, CMF, USPTO |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:tnotes:26-29 |
| By: | Michele Liberatore; Massimo Riccaboni |
| Abstract: | We study how licensing affects the allocation of innovation in pharmaceutical R&D. We develop a model in which projects differ in both quality and innovation regime, distinguishing between incremental and novel innovations. Information precision is higher for incremental projects and lower for novel ones, generating different equilibrium dynamics in the market for technology. The model predicts that licensing sustains positive selection and competitive return equalization for incremental innovation, while novel projects may exhibit weaker screening consistent with lemons-type frictions. Using product-level data and Double Machine Learning methods, we test these predictions across success probabilities and monetary returns. We find that licensing increases success probability overall, but return equalization holds primarily for incremental projects. For novel innovation, licensing does not exhibit the same equilibrium adjustment, suggesting residual market imperfections. Instrumenting for licensing using exogenous pipeline shocks confirms this pattern causally: the competitive risk-return trade-off is preserved for incremental 'rushed' licenses, but it breaks down for novel ones. Our results reconcile evidence on both competitive efficiency and information frictions in markets for technologies, showing that market performance depends systematically on the type of innovation being transacted. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.20365 |
| By: | C. Castaldi; F. Castellacci; A. Fronzetti Colladon; L. Segneri; F. Venturini |
| Abstract: | Researchers, managers and policymakers are exploring different approaches and data sources to map the development and the diffusion of Artificial Intelligence (AI). In this research note, we illustrate the opportunities offered by trademark data. We argue that AI trademarks can complement AI patents to capture different dimensions of AI innovation. AI trademarks can reveal the extent and ways in which companies exploit AI technologies to develop new goods and services. Importantly, trademark data offer a timely and globally available data source that covers all economic sectors. We present insights from using AI trademarks in an empirical exploration of Italian firms. In our discussion, we reflect on how AI trademarks can be used at different levels of analysis to tackle emerging questions about the development and diffusion of AI. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.18795 |
| By: | Barry Eichengreen; George Cui; Asmaa A. El-Ganainy; Yevgeniya Koriyenko; Elyad Shojaei; Li Zeng; Shihangyin Zhang |
| Abstract: | We link two global trends—AI and geoeconomic fragmentation—asking how fragmentation affects the international diffusion of AI, the magnitude of gains, and their distribution across economies. We ask these questions in general but also with a focus on the MENAP economies. While the effects of AI are potentially far-reaching, the benefits are neither guaranteed nor even. Frontier AI innovation is concentrated in a small number of economies, while countries benefiting through supply-chain participation or AI adoption—with outcomes shaped by their position in global trade and production networks and their AI preparedness. Geoeconomic fragmentation slows AI diffusion and reshapes its distribution by raising trade costs, restricting technology and data flows, fragmenting digital services, and reducing cross-border investment and collaboration. Yet proactive policy choices can turn this dynamic: economies that position themselves as connectors—maintaining trade and technology links across multiple partners—can potentially capture diverted flows and outperform even the no-fragmentation benchmark. For the MENAP economies, diversified links with all major technology hubs can cushion the effects of fragmentation and provide a structural foundation to emerge as net beneficiaries of AI diffusion, but realizing that potential requires reducing AI-related trade costs, improving AI preparedness, and building local AI-related capacity. |
| JEL: | F14 F17 F47 O33 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35597 |
| By: | Milad Abbasiharofteh; Hamid Bekamiri |
| Abstract: | This paper argues that generative artificial intelligence (GenAI) represents a methodological turning point in economic geography and regional studies and provides a proof of concept for this transition. Building on successive waves of data collection methods, from surveys and standardized secondary data to web-based data, we show how GenAI can complement existing methods by approximating firms’ product portfolios from unstructured information and linking them to standardized classifications. Moving beyond product mapping, we conclude by discussing a research agenda that addresses broader data and methodological challenges and outlines how GenAI can contribute to a new methodological frontier. |
| Keywords: | economic geography, regional studies; generative artificial intelligence; geography of products; web data |
| JEL: | C55 O33 R12 L26 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2616 |
| By: | Lundgren, Magnus; Tallberg, Jonas |
| Abstract: | Artificial intelligence (AI) is rapidly transforming economies, societies, and polities, raising fundamental questions about how it should be regulated. Policymakers face choices over whether to prioritize innovation or safety, rely on public oversight or private self-regulation, and govern nationally or internationally. Yet little is known about how citizens evaluate these competing priorities. Here we report a conjoint survey experiment conducted in seven countries with diverse political and economic profiles. We find that citizens strongly support regulating AI and generally prioritize safety over innovation, public governance over private self-regulation, and international over national approaches. The preference for safety is strongest among those who perceive AI as risky, unpredictable, and personally consequential. These findings reveal a systematic misalignment between dominant regulatory approaches and citizen preferences. |
| Date: | 2026–07–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:b5re6_v1 |