|
on Technology and Industrial Dynamics |
| By: | Giovanna Ciaffi; Matteo Deleidi; Antonino LOfaro |
| Abstract: | This paper evaluates the impact of Mission–Oriented Innovation Policies (MOIPs) and public R&D investment by quantifying the responses of GDP, private investment, hours worked, labour productivity, and the real hourly wage. We combine a Bartik–type identification strategy with the Local Projections method on a novel dataset with a sectoral–regional dimension, covering 333 European NUTS–2 regions over 1995–2019. Results show that R&D government spending exerts robust and persistent expansionary effects, crowding in private investment, raising employment, and boosting productivity. Sectoral heterogeneity emerges, with high multiplicative effects in construction and finance, while employment effects are concentrated in construction and market services. |
| Keywords: | Fiscal policy; Mission-Oriented Innovation Policies; R&D government spending; Sectoral heterogeneity; Regional economics; Local Projections; European regions. Jel Classification: R11; E62; H50; O38 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:usi:wpaper:934 |
| By: | Filippo Boeri; Riccardo Crescenzi; Davide Rigo |
| Abstract: | Using administrative firm-level data covering the universe of remote workers in Italy, and leveraging exogenous pre-pandemic variation in firm-specific access to fibre broadband as an instrument, this paper investigates the impact of post-pandemic adoption of work from home (WFH) on firm productivity. We find that WFH had a large negative impact on productivity during the pandemic. However, larger firms and those with prior ICT investments mitigated these losses. In the longer term, the impact of WFH is no longer significant. Yet, we find suggestive evidence that firms employing highly qualified workers experienced productivity gains. |
| Keywords: | work from home, firms, productivity |
| JEL: | D22 J21 J24 L25 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12253 |
| By: | Lars Hornuf; Daniel Vrankar |
| Abstract: | Those seeking to drum up public support for the space industry frequently cite its potential to generate valuable spillovers to other industries. However, existing research on spillover effects overlooks differences in business models among commercial actors and focuses only on individual projects or specific space agencies. We analyze how evolving business models influence spillovers by comparing the dynamic capabilities of traditional aerospace conglomerates to those of new space firms, using a unique dataset of 35, 696 space-related patent applications. We find that, in addition to industries directly related to space, such as aeronautics, sectors like manufacturing and communication technology in particular benefit from space activities. At the firm level, we observe that new space business models present greater spillover potential and generate more spillovers than traditional aerospace conglomerates. However, traditional conglomerates such as Airbus or Boeing induce spillovers into digital systems and clean tech, while new space firms cannot translate their digital business models into digital spillovers and occupy more peripheral positions in the innovation network of space. Additionally, based on two different innovation metrics and more than 1.6 million additional patent applications, we find no evidence that the business models of the space industry have generally led to more spillovers than other high-tech industries. |
| Keywords: | new space business models, new space economy, innovation, dynamic capabilities, spillover, patent data |
| JEL: | D62 H57 L20 L21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12236 |
| By: | Roth, Felix; Rammer, Christian |
| Abstract: | Intangible assets have increasingly been identified as a main source of productivity gains. Since the pioneering work by Corrado, Hulten, and Sichel (2005), empirical research has largely focused on macro and industry-level studies, while firm-level studies have often been confined to a limited set of intangible assets, especially Research and Development (R&D). This paper employs a unique firm-level panel database that contains information on four types of intangible assets: R&D, software & databases (S&D), firm-specific human capital (HC), and brand value (BV). For R&D, we find much lower productivity returns than for S&D and HC. R&D even loses significance once controlling for other intangibles, except for high-tech manufacturing. In contrast to R&D, we find that S&D and HC tend to be the primary drivers of productivity gains, particularly in services. Our findings have implications for research policy, suggesting a stronger focus on supporting investment in non-R&D intangibles, including S&D and HC. |
| Keywords: | Non-R&D intangibles, Productivity, R&D, Digitalisation, Firm-specific human capital, Brand value, Firm-level panel data |
| JEL: | E22 O33 O38 D24 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:uhhhdp:20 |
| By: | Bekkers, Eddy; Humphreys, Lee; Kalachyhin, Hryhorii; Wilczynska, Karolina; Zhao, Danchen |
| Abstract: | This paper studies the macroeconomic impacts of artificial intelligence (AI) using a quantitative trade model with multiple sectors, multiple factors of production, and intermediate linkages. The reallocation of tasks from labour to AI services will generate productivity gains in the model, and AI will reduce operational trade costs. We build four scenarios that differ in how far less-prepared economies catch up. The simulations yield three main findings. First, AI adoption is projected to substantially boost global trade flows and eco-nomic growth: in the most favourable scenario, the diffusion of AI raises global GDP by an additional 13.2% over the next 15 years compared to the baseline. Global trade volumes are projected to be 35% larger than without AI. Second, low- and middle-income economies can capture more of these gains if they improve their digital infrastructure and ensure adequate AI deployment across the economy. Third, AI is projected to change the withincountry income distribution. While all factors gain in real terms, returns shift toward capital and the skill premium declines. The magnitude of these distributional effects depends on the long-run growth rate of AI and the degree of complementarity between production factors. |
| Keywords: | Artificial Intelligence, Computational general equilibrium, Productivity, Technology adoption, Trade Cost |
| JEL: | C68 E13 O33 O41 F17 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:wtowps:330670 |
| By: | Alexander Bertermann; Wolfgang Dauth; Jens Suedekum; Ludger Woessmann |
| Abstract: | How do firms and workers adjust to trade and technology shocks? We analyze two mechanisms that have received little attention: training that upgrades skills and early retirement that shifts adjustment costs to public pension systems. We combine novel data on training participation and early retirement in German local labor markets with established measures of exposure to trade competition and robot adoption. Results indicate that negative trade shocks reduce training — particularly in manufacturing — while robot exposure increases training — particularly in indirectly affected services. Both shocks raise early retirement among manufacturing workers. Structural change thus induces both productivity-enhancing and productivity-reducing responses, challenging simple narratives of labor market adaptation and highlighting the scope for policy to promote adjustment mechanisms conducive to aggregate productivity. |
| Keywords: | training, retirement, trade, technological change, automation, robots, firms, workers, labor market |
| JEL: | J24 J26 O33 F16 R11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12240 |
| By: | Salomons, Anna (Tilburg University); vom Baur, Cäcilia (ifo Institute, University of Munich); Zierahn-Weilage, Ulrich (Utrecht School of Economics) |
| Abstract: | Does educational content respond to technological advances, enabling workers to acquire new expertise? We study how digital technology transforms skill acquisition and impacts workers' careers. We construct a novel database of legally binding vocational training curricula in Germany over 5 decades, and link curriculum updates to breakthrough technologies using Natural Language Processing. Technological change spurs curriculum updates, shifting training content toward digital and social skills while reducing routine-intensive task content, predominantly through new skill emergence. Curriculum updates account for two-thirds of deroutinization in vocational skill supply over this period. Using administrative employer-employee data and a stacked DiD design, we show curriculum updates help workers adapt: new-skilled workers earn higher wages, with increases up to 5.5\% for technology-exposed occupations. In contrast, older incumbents experience wage declines, indicating skill obsolescence. Firms increase capital investments when exposed to workers with updated skills, consistent with capital-skill complementarity. These findings highlight within-occupation skill supply adjustments' central role in meeting evolving labor market demands. |
| Keywords: | vocational training, skill obsolescence, skill updating, technological change, educational content |
| JEL: | J23 J24 J31 O33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18248 |
| By: | Viet Nguyen-Tien |
| Abstract: | To what extent has the rise of clean energy technologies created new vulnerabilities in global supply chains? In this paper, I study the role of a new type of 'input uncertainty' associated with critical minerals that underpin the deployment of clean energy technologies. I combine firm-level performance data for publicly listed companies worldwide with textual information from quarterly earnings conference calls to construct text-based measures of technological involvement and mineral exposure. As a first result, my methodology is validated by the strong co-occurrence of clean energy technologies and critical mineral usage across transcripts. In a second result, I model the impact of critical mineral-related input uncertainty on firm performance which shows clear impacts for lithium and copper-related risks across different regions. Finally, I produce text-based evidence on how firms are mitigating supply chain risk, distinguishing between long-term process innovation and short-run operational measures. Overall, I find that new supply chain risks related to critical minerals are limited, most likely to the early stage of development of the sector. |
| Keywords: | critical minerals, energy transition, risk, exposure, sentiment, circular economy, material substitution, Green Growth |
| Date: | 2025–11–03 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2133 |
| By: | F. Atzori; L. Corazzini; A. Guarnieri |
| Abstract: | Artificial Intelligence (AI) has emerged as a transformative technology, capable of reshaping production processes, professions, and domains traditionally regarded as uniquely human—such as art and creativity. This study examines the relationship between AI, creativity, and personality traits, with the goal of understanding how individual differences influence perceptions, attitudes, and the utilization of AI. A total of 260 participants completed a comprehensive questionnaire assessing AI usage and perception, personality traits, and multiple creativity tasks, including the Divergent Association Task, the Alternative Uses Task, and a constrained narrative task. Our results reveal that creativity increases with reflective, moderate engagement with AI, while both minimal and excessive reliance reduce creative performance. Cluster analysis identifies four distinct attitudinal profiles toward AI—Enthusiasts, Alarmed, Critics, and Cautious—differing in trust, perceived risks, and frequency of use. Openness to Experience and Agreeableness emerge as key traits that predict these profiles - openness is positively associated with the creative and balanced use of AI, whereas high agreeableness correlates with more cautious or risk-averse perceptions. Overall, creativity thrives when curiosity and critical reflection coexist, suggesting that human originality benefits most from mindful, selective interaction with AI rather than from full automation. |
| Keywords: | creativity;Perception of AI;Personality traits |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202516 |
| By: | Fabio Franceschini |
| Abstract: | This paper provides robust empirical evidence that shocks to aggregate Research and Development (R&D) have persistent effects on macroeconomic dynamics and represent a significant risk for investors, as predicted by the 'long-run risk' literature. The analysis focuses on a single variable, 'effective R&D', which captures the entire contribution of R&D to productivity growth, flexibly accounting for knowledge spillovers and product proliferation effects. Deviations of effective R&D from its equilibrium level can be empirically identified leveraging the error correction term in the cointegration relationship among R&D, total factor productivity, and the labor force. In US data, structural effective R&D shocks affect productivity and consumption growth rates beyond business cycle horizons and are associated with a significant risk premium in a cross section of stock and bond portfolios (around 2% annually), with cash-flow sensitivities proving a key determinant. |
| JEL: | E32 E44 G12 O30 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bol:bodewp:wp1215 |
| By: | Ann-Kristin Becker (University of Cologne); Erik Hornung (University of Cologne) |
| Abstract: | Industrialization boosts aggregate incomes, but its distributional effects remain debated. We study the impact of coal-driven industrialization on unskilled labor incomes using novel panel data on wages from 667 Prussian localities (1800-1879), extended with county-level data through 1914. Exploiting spatial variation in coal proximity in difference-in-differences and event-study designs, we find that wage gains in coal-rich regions emerged once industrialization accelerated in the 1850s and continued to grow until WWI. Evidence from 3, 000 household accounts shows that coal proximity raised labor incomes primarily for low-skilled workers, with weaker effects for high-skilled and mechanical occupations. This pattern suggests that industrialization reduced wage inequality by compressing the local skill premium. Mediation analysis indicates that wage gains for unskilled workers were primarily driven by technology adoption and the increasing demand for low-skilled labor, rather than by sectoral change or the spread of the factory system. |
| Keywords: | Industrialization, Labor income, Energy transition, Structural change, Technological change, Deskilling ´ |
| JEL: | C23 J31 N33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:378 |
| By: | Rafael Guntin (UNIVERSITY OF ROCHESTER); Federico Kochen (BANCO DE ESPAÑA AND CEMFI) |
| Abstract: | What are the origins of top firms? What features characterize their life cycle trajectories on the way to the top? Using longitudinal firm-level data, we document novel facts about the first twenty years of the firms that reach the top 1 percent of the size distribution. Compared to the firms in the bottom 99 percent, top firms are eight times larger at entry and grow six times more during their first two decades. In terms of inputs, they start with high capital investments, yet their capital-output ratio and labor share decline as they age. As a result, their profit share is much more backloaded towards the second decade of their life cycle. We show that a firm dynamics model with ex-ante heterogeneity, non-homothetic input costs, and forward-looking financing can explain these empirical patterns. Our quantitative results showcase the importance of accounting for top and bottom firm dynamics for the aggregate implications of financial frictions, recent macroeconomic trends, and corporate taxation. |
| Keywords: | top 1 percent, firm size distribution, firm dynamics, financial frictions |
| JEL: | E44 O47 G30 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2541 |
| By: | Nils H. Lehr; Pascual Restrepo |
| Abstract: | Leading AI firms claim to prioritize social welfare. How should firms with a social mandate price and deploy AI? We derive pricing formulas that depart from profit maximization by incorporating incentives to improve welfare and reduce labor disruptions. Using US data, we evaluate several scenarios. A welfarist firm that values both profit and welfare should price closer to marginal cost, as efficiency gains outweigh distributional concerns. A conservative firm focused on labor-market stability should price above the profit-maximizing level in the short run, especially when its AI may displace low-income workers. Overall, socially minded firms face a trade-off between expanding access to AI and the resulting loss in profits and labor market risks. |
| Keywords: | Artificial intelligence; automation; corporate social responsibility |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/234 |
| By: | Fourné, Marius |
| Abstract: | Climate policies do not operate in isolation but propagate through global production networks, affecting industries beyond national borders. This paper combines international input-output data with a granular instrumental variable approach to capture how foreign regulations transmit through upstream and downstream linkages. Distinguishing between market-based policies, non-market regulations, and technology support, the analysis shows that foreign climate policies can enhance domestic productivity, with effects shaped by industry characteristics and operating through technological adjustment along supply chains. The results underscore the importance of accounting for international spillovers when evaluating the economic impact of environmental regulation. |
| Keywords: | climate policy, environmental regulations, global value chains, green innovation, international trade, productivity |
| JEL: | F18 L16 O44 Q37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:330918 |