|
on Technology and Industrial Dynamics |
| By: | Koski, Heli; Wang, Maria |
| Abstract: | Abstract This study evaluates the impacts of public subsidies on firms in energy-intensive industries, focusing on R&D subsidies and compensation subsidies. Using firm-level data from Finnish energy-intensive industries between 2010 and 2022, it examines how these subsidies influence firm competitiveness and innovation outcomes. Compensation subsidies, designed to alleviate the additional electricity costs imposed by the EU Emissions Trading Scheme (ETS) on firms operating in certain energy-intensive industries, and to enhance their international competitiveness show no significant effects on employment, value added, or labor productivity. R&D subsidies, instead, demonstrate a substantial positive impact on innovation. Specifically, R&D subsidies significantly increase the citation stocks of climate change mitigation technology patents filed with the United States Patent and Trademark Office (USPTO). Total patent citation stocks associated with the European Patent Office (EPO) and USPTO also show statistically significant growth. |
| Keywords: | Firm subsidy, R&D subsidies, EU ETS, Competitiveness, Green innovation, Patents |
| JEL: | D22 H23 L52 O3 Q58 |
| Date: | 2025–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:125 |
| By: | Aidan Toner-Rodgers |
| Abstract: | This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1, 018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.17866 |
| By: | Ariell Reshef (UP1 - Université Paris 1 Panthéon-Sorbonne, PSE - Paris School of Economics, CESifo - CESifo - Munich); Farid Toubal (CEPII - Centre d'Etudes Prospectives et d'Informations Internationales - Centre d'analyse stratégique, CEPR - Center for Economic Policy Research, LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | While job polarization was a salient feature in European economies in the decade up to 2010, this phenomenon has all but disappeared, except in a handful of Southern-European economies. The decade following 2010 is characterized by occupational upgrading, where low-paid jobs shrink and high paid jobs expand. We show that this is associated with automation: employment shares in low paid, highly automatable jobs shrinks, while employment shares of better paid jobs that are unlikely to be automated expands. Techies (engineers and technicians with strong STEM skills) help explain cross country variation in occupational upgrading: economies that are abundant in techies or exhibit high growth of techies see strong skill upgrading; in contrast, polarization is observed in economies with few techies. Robotization is associated with skill upgrading in manufacturing. We discuss the additional roles of globalization, structural change and labor market institutions in driving these phenomena. Hitherto, artificial intelligence (AI) seems to have similar impacts as other automation technologies. However, there is uncertainty about what new AI technologies harbor. |
| Keywords: | automation, robots, techies, tasks, STEM, occupations, employment, polarization |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04837769 |
| By: | Simon Alexander Wiese; Johannes Lehmann; Michael Beckmann |
| Abstract: | Using novel establishment-level observational data from Switzerland, we empirically examine whether the usage of key technologies of Industry 4.0 distinguishes across firms with different types of organizational culture. Based on the Technology-Organization-Environment and the Competing Values framework, we hypothesize that the developmental culture has the greatest potential to promote the usage of Industry 4.0 technologies. We also hypothesize that companies with a hierarchical or rational culture are especially likely to make use of automation technologies, such as AI and robotics. By means of descriptive statistics and multiple regression analysis, we find empirical support for our first hypothesis, while we cannot con-firm our second hypothesis. Our empirical results provide important implications for managerial decision-makers. Specifically, the link between organizational culture and the implementation of Industry 4.0 technologies is relevant for managers, as this knowledge helps them to cope with digital transformation in turbulent times and keep their businesses competitive. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.12752 |
| By: | Zenne Hellinga; Julia Bachtrögler-Unger; Pierre-Alexandre Balland; Ron Boschma |
| Abstract: | The Smart Specialization Strategy (S3) is a cornerstone of the EU’s Cohesion Policy, with over €61 billion allocated for Research & Innovation from 2014 to 2020. This paper explores the prioritization of technological domains within regional S3 strategies and their influence on funding allocation of the European Regional Development Fund. Our findings indicate that while regions select a broad range of S3 priorities, they tend to prioritize those more related to their existing technological capabilities. This is particularly true for less developed andtransition regions. The lack of selectivity in S3 strategies appears to be mitigated when these priorities are converted into funding allocations. There we observe that funding allocation appears to align more closely with regional capabilities than initial S3 priorities. We also find that, although the complexity of technologies is somewhat considered in selecting S3 priorities, it seems to gain importance when regions dedicate their funding to specific R&I projects. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2502 |
| By: | Elisabeth Nindl (European Commission - JRC); Lorenzo Napolitano (European Commission - JRC); Hugo Confraria (European Commission - JRC); Francesco Rentocchini (European Commission - JRC); Peter Fako (European Commission - JRC); James Gavigan (European Commission - JRC); Alexander Tuebke (European Commission - JRC) |
| Abstract: | The 2024 edition of “The EU Industrial Research & Development (R&D) Investment Scoreboard” continues in the 21st year to monitor and analyse industrial R&D investment trends in the context of the EU’s 3% of GDP R&D investment policy target, which is a key performance indicator of the EU’s long-term competitiveness. As emphasised in the recent ‘Draghi’ report, it is crucial for the EU to substantially increase private R&D investments in order to tackle our historic productivity gaps with respect to main global competitors. The 2024 Scoreboard’s monitors the world's top 2 000 R&D investors, responsible for over three quarters of R&D performed by the business sector globally, based on the financial information in the firms’ latest published audited accounts. Chapter 2 analyses the main global trends and benchmarks the EU’s top R&D investing companies against global competitors. Chapter 3 provides details per sector, and chapter 4 deep-dives on a subsample of the EU’s top 800 R&D investing firms. Chapter 5 analyses the R&D productivity from a long-term perspective, and combines the sample with data on Mergers & Acquisitions (M&A) to delve into corporate innovation strategies. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc140129 |
| By: | Enrico Maria Fenoaltea; Dario Mazzilli; Aurelio Patelli; Angelica Sbardella; Andrea Tacchella; Andrea Zaccaria; Marco Trombetti; Luciano Pietronero |
| Abstract: | The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.04924 |
| By: | Milene Tessarin; Ron Boschma; Deyu Li; Sergio Petralia |
| Abstract: | This paper presents an evolutionary perspective on regional development traps that centers around the structural inability of regions to develop new and complex occupations. Using European Labor Force Survey data, we follow occupational trajectories of 237 European regions and provide evidence on which regions are trapped, what kinds of traps they have fallen into, and which regions have managed to escape such traps. We find a clear-cut divide in Europe: almost all non-trapped regions are in Northern and Western Europe, while trapped regions are found primarily in South and Eastern Europe. However, this geographical divide does not apply to all types of regional traps. Our results also show that regional development traps are persistent: regions often remain in the same trap, but not always. Our study suggests a feasible pathway for low-complexity regions to overcome a development trap is by building capabilities in related occupations and then diversify into complex occupations. Once complexity levels are high, regions tend not to lose their complexity. |
| Keywords: | regional development traps, evolutionary traps, occupations, relatedness, complexity, low complexity trap, structural trap |
| JEL: | J24 J82 R11 O15 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2501 |
| By: | Meiling Huang; Ming Jin; Ning Li |
| Abstract: | Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications. This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks. Through two random controlled experiments in flash fiction writing and song composition, we uncover a paradox: while AI democratizes access to creative tools, it simultaneously amplifies cognitive inequalities. Our findings reveal that AI enhances general human capital (cognitive abilities and education) by facilitating adaptability and idea integration but diminishes the value of domain-specific expertise. We introduce a novel theoretical framework that merges human capital theory with the automation-augmentation perspective, offering a nuanced understanding of human-AI collaboration. This framework elucidates how AI shifts the locus of creative advantage from specialized expertise to broader cognitive adaptability. Contrary to the notion of AI as a universal equalizer, our work highlights its potential to exacerbate disparities in skill valuation, reshaping workplace hierarchies and redefining the nature of creativity in the AI era. These insights advance theories of human capital and automation while providing actionable guidance for organizations navigating AI integration amidst workforce inequalities. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.03963 |
| By: | Tina Highfill; David Wasshausen; Gregory Prunchak |
| Abstract: | Much of the current literature on the economic impact of Artificial Intelligence (AI) focuses on the uses of AI, but little is known about the production of AI and its contribution to economic growth. In this paper, we discuss basic concepts and challenges related to measuring the production of AI within a standard national accounting framework. We first present a variety of examples that illustrate how both the production and use of AI software are currently reflected in macroeconomic statistics like Gross Domestic Product and the Supply and Use Tables. We then discuss a broader approach to measurement using a thematic satellite account framework that highlights production of AI across foundational areas, including manufacturing, software publishing, computer and data services, and research & development. The challenges of identifying and quantifying AI production in the national accounts using existing data sources are discussed and some possible solutions for the future are offered. |
| JEL: | E01 O30 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bea:papers:0134 |
| By: | Samuel Arts; Nicola Melluso; Reinhilde Veugelers |
| Abstract: | New scientific ideas drive progress, yet measuring scientific novelty remains challenging. We use natural language processing to detect the origin and impact of new ideas in scientific publications. To validate our methods, we analyze Nobel Prize-winning papers, which likely pioneered impactful new ideas, and literature review papers, which typically consolidate existing knowledge. We also show that novel papers have more intellectual neighbors published after them, indicating they are ahead of their intellectual peers. Finally, papers introducing new ideas, particularly those with greater follow-on reuse, attract more citations. |
| Keywords: | natural language processing, science, novelty, impact, breakthrough, Nobel, OpenAlex |
| Date: | 2025–01–13 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:757417 |
| By: | Joep Konings; Aaron Putseys |
| Abstract: | We assess how subsidies for on-the-job training affect firm performance. Using a difference-in- differences research design, we find that these subsidies positively influence firm size, wages, and productivity. Over four years, employment increases by 3.55%, value added by 5.68%, and labor costs by 3.60%. Average wages and labor productivity grow by 1.95% and 2.12%, respec- tively. In the first year of treatment, a notable discrepancy exists between the wage (1.21%) and productivity (2.18%) effects, indicating incomplete rent-sharing. These positive effects are primarily seen in smaller firms, which significantly increase training expenditures and hours in the year they receive subsidies, resulting in more trained and skilled workers. Larger firms do not show similar effects, highlighting the possibility that these firms relabel existing training ac- tivities to take advantage of the training subsidy program. Additionally, we find that subsidies focused on training in human resource management, logistics, and business skills drive these positive outcomes for firm size at the firm level. |
| Keywords: | Productivity, Programme evaluation, SMEs growth, Training subsidies |
| Date: | 2025–01–10 |
| URL: | https://d.repec.org/n?u=RePEc:ete:ceswps:757420 |