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on Technology and Industrial Dynamics |
| By: | Andres Rodriguez-Pose; Zhuoying You; Peter Teirlinck |
| Abstract: | This This paper explores the relationship between support for extreme political parties and research and innovation across regions in the European Union (EU). Extreme parties often exhibit deep scepticism towards expertise and science, with extreme right-wing parties, in particular, challenging the legitimacy of climate change; an attitude that may weaken green research and innovation. We draw on data from 1, 137 EU regions —including scientific publication and patent records— and apply Tobit regression models to find that stronger support for extreme parties is associated with lower levels of scientific research and technological innovation, both overall and in their green forms. While this pattern is visible across the political spectrum, important differences emerge. Support for extreme right-wing parties is consistently tied to reduced research output and innovation performance, particularly in green technological sectors. By contrast, the relationship with extreme left-wing support is more variable, depending on the degree of radicalism, and shows no consistent negative connection with green innovation. |
| Keywords: | research, innovation, climate change, extreme parties, regions, Europe |
| JEL: | D72 D74 O32 O33 R10 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2525 |
| By: | Simone Daniotti; Matte Hartog; Frank Neffke |
| Abstract: | Diversified economies are critical for cities to sustain their growth and development, but they are also costly because diversification often requires expanding a city’s capability base. We analyze how cities manage this trade-off by measuring the coherence of the economic activities they support, defined as the technological distance between randomly sampled productive units in a city. We use this framework to study how the US urban system developed over almost two centuries, from 1850 to today. To do so, we rely on historical census data, covering over 600M individual records to describe the economic activities of cities between 1850 and 1940, as well as 8 million patent records and detailed occupational and industrial profiles of cities for more recent decades. Despite massive shifts in the economic geography of the U.S. over this 170-year period, average coherence in its urban system remains unchanged. Moreover, across different time periods, datasets and relatedness measures, coherence falls with city size at the exact same rate, pointing to constraints to diversification that are governed by a city’s size in universal ways. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2522 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper investigates how executive demographics particularly age and gender influence artificial intelligence (AI) investment decisions and subsequent firm productivity using comprehensive data from over 500 Japanese enterprises spanning from 2018 to 2023. Our central research question addresses the role of executive characteristics in technology adoption, finding that CEO age and technical background significantly predict AI investment propensity. Employing these demographic characteristics as instrumental variables to address endogeneity concerns, we identify a statistically significant 2.4% increase in total factor productivity attributable to AI investment adoption. Our novel mechanism decomposition framework reveals that productivity gains operate through three distinct channels: cost reduction (40% of total effect), revenue enhancement (35%), and innovation acceleration (25%). The results demonstrate that younger executives (below 50 years) are 23% more likely to adopt AI technologies, while firm size significantly moderates this relationship. Aggregate projections suggest potential GDP impacts of 1.15 trillion JPY from widespread AI adoption across the Japanese economy. These findings provide crucial empirical guidance for understanding the human factors driving digital transformation and inform both corporate governance and public policy regarding AI investment incentives. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.03757 |
| By: | Noda, Hideo; Fang, Fengqi |
| Abstract: | In a knowledge-based economy, innovation plays a significant role in determining the level of economic growth and social welfare. Meanwhile, patent protection is a pivotal factor for research and development (R&D) incentives, and innovation performance depends on the degree of patent protection. Therefore, elucidating the mechanism for impact of patent protection on innovation; and hence economic growth is a crucial issue from the perspective of macroeconomic policy. Our research questions are twofold. (1) What conditions are necessary for patent protection to effectively promote innovation and economic growth? (2) Can strengthening patent protection enhance social welfare? This study addresses these problems using an expanding variety model of R&D-based endogenous growth. Our major findings are summarized as follows: If an economy satisfies conditions that the productivity in the final goods sector and labor force population are relatively large, while the patent duration elasticity of patent fee is relatively small, extending the patent duration fosters on the rate of innovation, the growth rate of gross domestic product (GDP) per capita, and the growth rate of livelihood-based public infrastructure. Moreover, strengthening patent protection by extending the duration of the patent right does not necessarily enhance social welfare. Furthermore, the patent duration that maximizes social welfare may be shorter than the patent duration that maximizes the growth rate of GDP per capita, the rate of innovation, or the growth rate of livelihood-based public infrastructure. |
| Keywords: | Economic growth, Innovation, Patent duration, Patent fee, R\&D, Social welfare |
| JEL: | E6 O3 O4 |
| Date: | 2025–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125646 |
| By: | Kiran Tomlinson; Sonia Jaffe; Will Wang; Scott Counts; Siddharth Suri |
| Abstract: | Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system. We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising. Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation. We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information. Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07935 |
| By: | De Sanctis, Alessandro; Kapp, Daniel; Vinci, Francesca; Wojciechowski, Robert |
| Abstract: | This study evaluates the effectiveness of EU Cohesion Policy as an investment programme, employing a novel dataset that links firm-level data from Orbis with project-level information from the Kohesio database. It focuses on two key questions: (1) Which firms receive EU funding? (2) How does receiving EU funding affect firm performance? By applying a logit model and a local projection difference-in-differences approach, we provide new insights into the allocation mechanisms of EU Cohesion Policy funds and their firm-level impact. Our findings show that funding tends to be allocated to firms that already perform relatively well, and that firms receiving EU funding experience a persistent productivity increase of approximately 3% after 4 years, with smaller and more financially constrained firms experiencing relatively greater improvements. Moreover, funding targeting “SME investment” tends to enhance firm performance disproportionately more than other categories, whereas projects directed the “green transition” appear comparatively less beneficial. JEL Classification: E22, D24, H54, O38, O52 |
| Keywords: | corporate investment, European Structural and Investment Funds, fiscal policy, place-based policy, productivity |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253099 |
| By: | Kuusi, Tero |
| Abstract: | Abstract This paper analyzes the macroeconomic impact of Generative Artificial Intelligence (GenAI) on the Finnish economy, integrating recent literature and empirical evidence into a quantitative multi-sector general equilibrium model. The results indicate that, over a ten-year horizon, GenAI adoption increases annual economic growth by less than 0.5 percentage points in the baseline scenarios, with the potential for larger impacts—exceeding 1 percentage point—under scenarios involving greater automation and shifts in labor and ICT factor shares. The model’s input-output structure reveals significant multiplier effects, as productivity gains in one sector propagate to others. The service sector emerges as a pivotal driver of adjustment, with its adaptability helping to offset slower growth in sectors less amenable to automation. The study acknowledges uncertainties regarding the broader impacts of artificial general intelligence, emphasizing the limitations of current forecasts, adaptation frictions, and the importance of anticipatory behavior in financial markets. Overall, the findings underscore the transformative potential of GenAI, contingent upon proactive policy measures to foster economic growth. |
| Keywords: | Artificial Intelligence, Productivity, Technology adoption |
| JEL: | C6 E1 O3 O4 O5 |
| Date: | 2025–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:131 |
| By: | Jörg Papenkordt (Paderborn University); Johannes Dahlke (University of Twente); Nicolas Neef (University of Hohenheim); Sarah Zabel (University of Hohenheim) |
| Abstract: | The integration of artificial intelligence technology in contemporary work environments raises questions about how human team members collaborate when being assisted by AI. We propose that the reductionist properties of AI technology could affect the logics by which teams operate. This experimental research project aims to identify possible changes in collaboration dynamics within teams when employing AI support in a creative task domain. We explore conversational changes in collaboration by analyzing problem-focused, procedural, action-oriented, and socio-emotional sentiments expressed by team members, as well as structural changes by examining the properties of the communication network resulting from team discussions. Based on the observed co-occurrence of contentual and structural changes, our research points toward emerging patterns of AI-augmented collaboration, indicating that the temporary duration of AI collaboration influences team dynamics differently. |
| Keywords: | Team-AI collaboration, Team dynamics, AI team member, Generative AI, Creativity |
| JEL: | C92 D83 C88 O31 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:146 |