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on Innovation |
| By: | Arenas Díaz, Guillermo; Piva, Mariacristina; Vivarelli, Marco |
| Abstract: | This study investigates the relationship between Artificial Intelligence (AI) and innovation inputs in Spanish manufacturing firms. While AI is increasingly recognized as a driver of productivity and economic growth, its role in shaping firms’ innovation strategies remains underexplored. Using firm-level data, our analysis focuses on whether AI complements innovation inputs - specifically R&D and Embodied Technological Change (ETC) - and whether AI can be considered as a Method of Invention, able to trigger subsequent innovation investments. Results show a positive association between AI adoption and both internal R&D and ETC, in a static and a dynamic framework. Furthermore, empirical evidence also highlights heterogeneity, with important peculiarities affecting large vs small firms and high-tech vs low-tech companies. These findings suggest that AI may act as both a complement and a catalyst, depending on firm characteristics. |
| JEL: | O31 O32 |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025022 |
| By: | Sara Amoroso (DIW Berlin); Simone Vannuccini (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | The participation of top R&D investors in publicly funded research collaborations is a common, yet largely unexplored phenomenon. It creates opportunities for knowledge spillovers and may increase the chance for a project to be funded. At the same time, the unbalanced nature of such partnerships could exacerbate power asymmetries and hinder the overall performance of such collaborations. In this paper, we examine whether cooperating with top R&D companies affects the innovative performance of publicly funded research consortia. We build a fit-for-purpose dataset that matches information from the European Union's Seventh Framework Programme (FP7) on R&D collaborative projects and proposals with data on the world's top 2, 500 companies with the highest R&D investment (R&D Scoreboard). Accounting for both sample selection and endogeneity in the participation of top R&D investors in a two-part count model framework, we find that teaming up with leading R&D companies increases the probability of obtaining funds. However, this comes at the cost of hindering the innovative performance of the funded projects, both in terms of patents and publications. In light of this evidence, the tradeoffs of mobilizing top R&D players should be carefully leveraged in the evaluation and design of innovation policies aimed at R&D collaboration and technology diffusion. |
| Keywords: | Research collaboration, Public funding, Innovation performance, Appropriability, Top R&D investors |
| JEL: | L24 L25 O33 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-41 |
| By: | Jianwei DANG; Kazuyuki MOTOHASHI; Quihan ZHAO |
| Abstract: | We examine how financial markets value patented innovation across disclosure stages in the pharmaceutical sector. Using U.S. patents from publicly listed firms, we construct text-based measures of technological novelty and early diffusion impact. Event-study regressions based on KPSS returns reveal that market reactions are already sizable at the stage of scientific publication for patent–paper pairs (PPPs) and are nearly as large at pre-grant publication as at the patent granting stage—challenging the grant-centric view of patent valuation. Stage-specific regressions show a marked shift in valuation logic: novelty is priced early, especially when peer-reviewed science certifies the invention, while impact becomes salient only once technical content and downstream reuse become observable at the patent publication or granting stage. In PPPs, novelty is strongly rewarded in early stages, but impact is not, suggesting that science-linked inventions follow a distinct valuation channel. These patterns are robust to stricter PPP-matching thresholds and alternative impact metrics. Our findings highlight that both when and what gets disclosed jointly shape the financial value of innovation. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25097 |
| By: | Boeing, Philipp; Brandt, Loren; Dai, Ruochen; Lim, Kevin; Peters, Bettina |
| Abstract: | China's patenting activity has surged over the past two decades, yet questions remain about the quality and sources of innovation. We develop a new method to measure the importance of a patent for innovation, based on the use of a Large Language Model to process patent text data and a new theory of the innovation process. We apply this method to study the evolution of patenting in China from 1985 until recently, and also classify patent ownership using a comprehensive business registry. Our method and data yield several novel facts about Chinese patenting. Among these are that the patents which are important for innovation have become less important on average; that knowledge within China has become more important than knowledge outside of China for directing innovation in China; and that knowledge produced by Chinese entities has been more important than knowledge produced by foreign entities in China. These findings have implications for China's growth trajectory and reflect both global trends in the decline of innovativeness and potential effects of domestic policy. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewpbs:328016 |
| By: | Fausto Cavalli; Alessandra Mainini; Enrico Moretto; Ahmad Naimzada |
| Abstract: | We propose an evolutionary model to study the transition toward green technology under the influence of innovation. Clean and dirty technologies are selected according to their profitability under an environmental tax, which depends on the overall pollution level. Pollution itself evolves dynamically: it results from the emissions of the two types of producers, naturally decays, and is reduced through the implementation of the current abatement technology. The regulator collects tax revenues and allocates them between the implementation of the existing abatement technology and its innovation, which increases the stock of knowledge and thereby enhances abatement effectiveness. From a static perspective, we show the existence of steady states, both with homogeneous populations of clean or dirty producers and with heterogeneous populations where both technologies coexist. We discuss the mechanisms through which these steady states emerge and how they may evolve into one another. From a dynamical perspective, we characterize the resulting scenarios, showing how innovation can foster a green transition if coupled with a suitable level of taxation. At the same time, we investigate how improper environmental policies may also produce unintended outcomes, such as environmental deterioration, reversion to dirty technology, or economic unsustainability. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25272 |
| By: | Gustavo de Souza; Gabriel Garber |
| Abstract: | We study the effect of an innovation subsidy on the long-run growth of firms in a developing country. Using administrative microdata from Brazil and a quasi-experimental design that compares near-winners to near-losers of R&D subsidy applications, we find that the program had a persistent effect on firm size: fourteen years after receiving the subsidy, subsidized firms were 59% larger. The effect is strongest among small and young firms facing high borrowing costs, which is consistent with the subsidy alleviating financial constraints. This growth, however, did not come from firms developing frontier innovations. Instead, firms used the subsidy to expand their product lines into high-tariff markets, producing local versions of foreign goods. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:631 |
| By: | Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb |
| Abstract: | This paper examines how the emergence of transformative AI systems providing ``genius on demand" would affect knowledge worker allocation and labour market outcomes. We develop a simple model distinguishing between routine knowledge workers, who can only apply existing knowledge with some uncertainty, and genius workers, who create new knowledge at a cost increasing with distance from a known point. When genius capacity is scarce, we find it should be allocated primarily to questions at domain boundaries rather than at midpoints between known answers. The introduction of AI geniuses fundamentally transforms this allocation. In the short run, human geniuses specialise in questions that are furthest from existing knowledge, where their comparative advantage over AI is greatest. In the long run, routine workers may be completely displaced if AI efficiency approaches human genius efficiency. |
| JEL: | D24 J24 O33 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34316 |
| By: | Benjamin Jones |
| Abstract: | How much can AI accelerate progress in different research fields? This paper shows that three features—the share of research tasks AI performs, the productivity of AI at those tasks, and the strength of bottlenecks—are key determinants of AI’s implications in any area, from cancer therapeutics to software design. The model maps changes in AI capabilities to research outcomes, quantifies the “marginal returns to intelligence, ” and shows how AI can shift returns to R&D investment. Concepts like superintelligence, Powerful AI, and Transformative AI are further engaged and disciplined. Finally, the framework sets a measurement agenda linking AI benchmarks to field-specific opportunities for accelerating progress. |
| JEL: | O3 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34312 |
| By: | R. Maria del Rio-Chanona; Ekkehard Ernst; Rossana Merola; Daniel Samaan; Ole Teutloff |
| Abstract: | Generative AI is altering work processes, task composition, and organizational design, yet its effects on employment and the macroeconomy remain unresolved. In this review, we synthesize theory and empirical evidence at three levels. First, we trace the evolution from aggregate production frameworks to task- and expertise-based models. Second, we quantitatively review and compare (ex-ante) AI exposure measures of occupations from multiple studies and find convergence towards high-wage jobs. Third, we assemble ex-post evidence of AI's impact on employment from randomized controlled trials (RCTs), field experiments, and digital trace data (e.g., online labor platforms, software repositories), complemented by partial coverage of surveys. Across the reviewed studies, productivity gains are sizable but context-dependent: on the order of 20 to 60 percent in controlled RCTs, and 15 to 30 percent in field experiments. Novice workers tend to benefit more from LLMs in simple tasks. Across complex tasks, evidence is mixed on whether low or high-skilled workers benefit more. Digital trace data show substitution between humans and machines in writing and translation alongside rising demand for AI, with mild evidence of declining demand for novice workers. A more substantial decrease in demand for novice jobs across AI complementary work emerges from recent studies using surveys, platform payment records, or administrative data. Research gaps include the focus on simple tasks in experiments, the limited diversity of LLMs studied, and technology-centric AI exposure measures that overlook adoption dynamics and whether exposure translates into substitution, productivity gains, erode or increase expertise. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.15265 |