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on Technology and Industrial Dynamics |
| By: | Sipeng Zeng; Xiaoning Wang; Tianshu Sun |
| Abstract: | Although Artificial Intelligence (AI) holds great promise for enhancing innovation and productivity, many firms struggle to realize its benefits. We investigate why some firms and industries succeed with AI while others do not, focusing on the degree to which an industrial domain is technologically integrated with AI, which we term "domain AI readiness". Using panel data on Chinese listed firms from 2016 to 2022, we examine how the interaction between firm-level AI capabilities and domain AI readiness affects firm performance. We create novel constructs from patent data and measure the domain AI readiness of a specific domain by analyzing the co-occurrence of four-digit International Patent Classification (IPC4) codes related to AI with the specific domain across all patents in that domain. Our findings reveal a strong complementarity: AI capabilities yield greater productivity and innovation gains when deployed in domains with higher AI readiness, whereas benefits are limited in domains that are technologically unprepared or already obsolete. These results remain robust when using local AI policy initiatives as instrumental variables. Further analysis shows that this complementarity is driven by external advances in domain-AI integration, rather than firms' own strategic pivots. Time-series analysis of IPC4 co-occurrence patterns further suggests that improvements in domain AI readiness stem primarily from the academic advancements of AI in specific domains. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09634 |
| By: | Serguey Braguinsky; Joonkyu Choi; Yuheng Ding; Karam Jo; Seula Kim |
| Abstract: | We provide evidence that mega firms have played an increasingly important role in shaping new technological trajectories in recent years. While the share of novel patents---defined as patents introducing new combinations of technological components---produced by mega firms declined until around 2000, it has rebounded sharply since then. Furthermore, we find that the technological impact and knowledge diffusion of novel patents by mega firms have grown relative to those by non-mega firms after 2001. We also explore potential drivers of this trend, presenting evidence that the rise in novel patenting by mega firms is tied to their disproportionate increase in cash holdings and the expansion of their technological scope. Our findings highlight an overlooked positive role of mega firms in the economywide innovation process. |
| Keywords: | Mega Firms; Innovation; Novel Patents; Knowledge Diffusion |
| JEL: | O31 O33 O34 L11 L25 |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-60 |
| By: | Luisa Corrado; Stefano Grassi; Aldo Paolillo (Cambridge Judge Business School, University of Cambridge) |
| Abstract: | Recent studies suggest that space activities generate significant economic benefits. This paper attempts to quantify these effects by modelling both business cycle and long-run effects driven by space sector activities. We develop a model in which technologies are shaped by both a dedicated R&D sector and spillovers from space-sector innovations. Using U.S. data from the 1960s to the present day, we analyse patent grants to distinguish between space and core sector technologies. By leveraging the network of patent citations, we further examine the evolving dependence between space and core technologies over time. Our findings highlight the positive impact of the aerospace sector on technological innovation and economic growth, particularly during the 1960s and 1970s. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:jbs:wpaper:202502 |
| By: | Lapo Santarlasci; Armando Rungi; Antonio Zinilli |
| Abstract: | This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02287 |
| By: | Ribeiro, Marcos J; Prettner, Klaus |
| Abstract: | How do new technologies affect economic growth and the skill premium? To answer this question, we analyze the impact of industrial robots and artificial intelligence (AI) on the wage differential between low-skill and high-skill workers across 52 countries using counterfactual simulations. In so doing, we extend the nested CES production function framework of Bloom et al. (2025) to account for cross-country income heterogeneity. Confirming prior findings, we Show that the use of industrial robots tends to increase wage inequality, while the use of AI tends to reduce it. Our contribution lies in documenting substantial heterogeneity across income groups: the inequality- increasing effect of robots and the inequality-reducing effects of AI are particularly strong in high-income countries, while they are less pronounced among middle- and lower-middle income countries. In addition, we show that both technologies boost economic growth. In terms of policy recommendations, our findings suggest that investments in education and skill-upgrading can simultaneously raise average incomes and mitigate the negative effects of automation on wage inequality. |
| Keywords: | Skill Premium; Automation; Industrial Robots; Artificial intelligence |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wus005:76354821 |
| By: | Cossu, Fenicia |
| Abstract: | We examine how exposure to artificial intelligence (AI) and robotics reshapes the skill composition of occupations. Using O*NET data from 2006 to 2019, we construct indicators tracking the importance of seven broad skill categories within each occupation over time. We link these indicators to task-based measures of technological exposure at the occupational level. We then focus on the effect of AI and robotics in altering the skill composition of high-, middle- and low-skilled groups of occupations. We find that AI primarily affects high-skill occupations by increasing the importance of Technical and Resource Management skills and decreasing that of Systems and Social skills. Robotics instead boosts Technical skills in middle and low-skill occupations and reduces Process skills in low-skilled ones. Notably, neither AI nor robots affect the importance of Complex Problem Solving skills. |
| Keywords: | Skills, Artificial Intelligence, Robots, Skill Composition, Occupational change |
| JEL: | J23 J24 O3 O33 |
| Date: | 2025–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125404 |
| By: | Enrique Ide |
| Abstract: | Recent advances in Artificial Intelligence (AI) have fueled predictions of unprecedented productivity growth. Yet, by enabling senior workers to perform more tasks on their own, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential skills. In this paper, I develop a model to examine how advanced automation affects the intergenerational transmission of knowledge. The analysis reveals that automating entry-level tasks yields immediate productivity gains but can undermine long-run growth by eroding the skills of subsequent generations. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce U.S. long-term annual growth by approximately 0.05 to 0.35 percentage points, depending on its scale. I also demonstrate that AI co-pilots - systems that democratize access to expertise previously acquired only through hands-on experience - can partially mitigate these negative effects. However, their introduction is not always beneficial: by providing expert insights, co-pilots may inadvertently diminish younger workers' incentives to invest in hands-on learning. These findings cast doubt on the optimistic view that AI will automatically lead to sustained productivity growth, unless it either generates new entry-level roles or significantly boosts the economy's underlying innovation rate. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16078 |
| By: | Jun Cui |
| Abstract: | This study examines the relationship between human-AI technology integration transformation and green Environmental, Social, and Governance (ESG) performance in Chinese retail enterprises, with green technology innovation serving as a mediating mechanism. Using panel data comprising 5, 400 firm-year observations from 2019 to 2023, sourced from CNRDS and CSMAR databases, we employ fixed-effects regression models to investigate this relationship. Our findings reveal that human-AI technology integration significantly enhances green ESG performance, with green technology innovation serving as a crucial mediating pathway. The results demonstrate that a one standard-deviation increase in human-AI integration leads to a 12.7% improvement in green ESG scores. The mediation analysis confirms that approximately 35% of this effect operates through enhanced green technology innovation capabilities. Heterogeneity analysis reveals stronger effects among larger firms, state-owned enterprises, and companies in developed regions. These findings contribute to the growing literature on digital transformation and sustainability by providing empirical evidence of the mechanisms through which AI integration drives environmental performance improvements in emerging markets. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.03057 |
| By: | De Santis, Roberto A.; Vinci, Francesca |
| Abstract: | We employ a novel regional dataset on European private investment and business R&D spanning the years 2000 to 2021, along with comprehensive historical data on European Union Structural and Investment (ESI) funds, to estimate whether ESIfunds have crowding-in or crowding-out effects on private investment and business R&D. Our analysis, leveraging regional variation and a fiscal instrument immune to region-specific shocks, reveals a significant crowding-in effect, with 1 euro in ESI funds increasing private investment by 1.1 euros and business R&D by 0.1 euros after two years. The effect is stronger in developed regions for private investment and in less developed regions for R&D. Additionally, crowding-in effects are stronger in regions where corporate private debt is relatively higher. Among the different ESI funds, the Cohesion Fund (CF) shows the largest estimated impact, while the European Regional Development Fund (ERDF) yields somewhat smaller but statistically more robust results. JEL Classification: E22, H54, O38, O52, R11, R58 |
| Keywords: | EU, fiscal instruments, private Investment, R&D, structural and investment funds |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253098 |