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on Technology and Industrial Dynamics |
| By: | Nelli, Linnea (Università Cattolica del Sacro Cuore); Virgillito, Maria Enrica (Università Cattolica del Sacro Cuore); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | The aim of this paper is to understand whether what has been labelled as “twin transition”, at first as a policy flagship, endogenously emerges as a new technological trajectory stemming by the convergence of the green and digital technologies. Embracing an evolutionary approach to technology, we first identify the set of relevant technologies defined as “green”, analyse their evolution in terms of dominant blocks within the green technologies and concurrences with digital technologies, drawing on 560, 720 granted patents by the US Patent Office from 1976 to 2024. Three dominant blocks emerge as relevant in defining the direction of innovative efforts, namely energy, transport and production processes. We assess the technological concentration of the dominant blocks and construct counterfactual scenarios. We hardly find evidence of patterns of actual endogenous convergence of green and digital technologies in the period under analysis. On the whole, for the time being, the “twin transition” appears to be just a policy flagship, rather than an actual endogenous technological trajectory driving structural change. |
| Keywords: | technological trajectories, policy flagship, twin transition |
| JEL: | O33 Q55 Q58 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17779 |
| By: | McGuinness, Seamus (Economic and Social Research Institute, Dublin); Redmond, Paul (ESRI, Dublin); Pouliakas, Konstantinos (European Centre for the Development of Vocational Training (Cedefop)); Kelly, Lorcan (Economic and Social Research Institute, Dublin); Brosnan, Luke (Economic and Social Research Institute, Dublin) |
| Abstract: | Using the second wave of the European Skills and Jobs survey, this paper measures the relationship between technological change that automates or augments workers’ job tasks and their participation in work-related training. We find that 58 per cent of European employees experienced no change in the need to learn new technologies in their jobs during the 2020-21 period. Of those exposed to new digital technology, 14 per cent did not experience any change in job tasks, 10 per cent reported that new tasks had been created while 5 per cent only saw some of their tasks being displaced by new technology. The remaining 13 per cent simultaneously experienced both task displacement and task creation. Our analysis shows that employees in jobs impacted by new digital technologies are more likely to have to react to unpredictable situations, thus demonstrating a positive link between technologically driven task disruption and job complexity. We show a strong linear relationship between technologically driven job task disruption and the need for job-related training, with training requirements increasing the greater the impact of new technologies on task content. |
| Keywords: | upskilling, technological change, digitalisation, tasks, automation, training, complexity |
| JEL: | J24 O31 O33 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17753 |
| By: | Kumar Rishabh (University of Basel); Roxana Mihet (Swiss Finance Institute - HEC Lausanne); Julian Jang-Jaccard (Swiss Federal Office for Defence Procurement) |
| Abstract: | Does AI make firms vulnerable or resilient to cyber risk? To answer this, we develop a novel measure identifying AI-intensive U.S. public firms using publicly available patents and business-description data. While cyber threats typically suppress innovation, AI-intensive firms neutralize this effect. This protective effect strengthens with greater AI experience. Moreover, firms combining AI innovation and implementation exhibit a stronger buffer protecting their innovation and financial outcomes under cyber stress, whereas firms merely implementing AI without internal innovation gain no such resilience. Our results emphasize internal AI innovation as fundamental in enabling firms to effectively withstand cyber threats. |
| Keywords: | Cyberrisk, artificial intelligence, innovation, resilience, economics of AI, economics of cybercrime |
| JEL: | D8 O3 O4 G3 L1 L2 M1 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2539 |
| By: | Matano, Alessia (University of Barcelona); Naticchioni, Paolo (Roma Tre University) |
| Abstract: | This paper investigates the relationship between China’s import competition and the innovation strategies of domestic firms. Using firm level data from Italy spanning 2005-2010 and employing IV fixed effects estimation techniques, we find that the impact of China’s import competition on innovation varies depending on the type of goods imported (intermediate vs. final). Specifically, imports of final goods boost both product and process innovation, while imports of intermediate goods reduce both. Additionally, we extend the analysis to consider the role of unions in moderating these responses. We find that, in unionized firms, imports' impact on innovation is mitigated, specifically to protect workers' employment prospects. |
| Keywords: | unions, product and process innovation, final and intermediate goods, China’s import competition, IV fixed effects estimations |
| JEL: | C33 L25 F14 F60 O30 J50 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17764 |
| By: | Böhm, Michael Johannes (TU Dortmund); Etheridge, Ben (University of Essex); Irastorza-Fadrique, Aitor (Institute for Fiscal Studies, London) |
| Abstract: | As technological advances accelerate and labour demands shift, the ability of workers to reallocate across occupations will be crucial for shaping labour market dynamics, inequality, and effective policy design. In this paper, we develop a tractable equilibrium model of the labour market that incorporates heterogeneous labour supply elasticities to different occupations and across different occupation pairs. Using worker flows from German administrative data, we estimate these elasticities and validate them through external measures such as occupational licensing and task distance. Our model quantifies the heterogeneous impacts of recent labour demand shifts on occupational wages and employment, highlighting the role of cross-occupation effects in shaping market responses to shocks. Finally, we leverage this framework to project employment flows and wage adjustments under future occupational demand shifts that are implied by the latest automation technologies. |
| Keywords: | labour demand shocks, occupational substitutability, future projections, heterogeneous labour supply elasticities, automation technologies, german panel data, job flows, occupational employment and wages, automation technologies, future projections |
| JEL: | J21 J23 J24 J31 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17851 |
| By: | Joshua S. Gans |
| Abstract: | This paper examines how the introduction of artificial intelligence (AI), particularly generative and large language models capable of interpolating precisely between known data points, reshapes scientists' incentives for pursuing novel versus incremental research. Extending the theoretical framework of Carnehl and Schneider (2025), we analyse how decision-makers leverage AI to improve precision within well-defined knowledge domains. We identify conditions under which the availability of AI tools encourages scientists to choose more socially valuable, highly novel research projects, contrasting sharply with traditional patterns of incremental knowledge growth. Our model demonstrates a critical complementarity: scientists strategically align their research novelty choices to maximise the domain where AI can reliably inform decision-making. This dynamic fundamentally transforms the evolution of scientific knowledge, leading either to systematic “stepping stone” expansions or endogenous research cycles of strategic knowledge deepening. We discuss the broader implications for science policy, highlighting how sufficiently capable AI tools could mitigate traditional inefficiencies in scientific innovation, aligning private research incentives closely with the social optimum. |
| JEL: | D82 O30 O34 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33566 |
| By: | Jin, Zhangfeng; Prettner, Klaus |
| Abstract: | This paper examines the impact of technology transfers on long-term innovation. We propose an extended Schumpeterian growth framework to characterize the channels by which technology transfers impact on innovation. Exploiting variations in the adoption of Soviet-aided industrialization programs across Chinese cities, we find that firms located in cities affected by 156 major industrial projects of the Soviet Union witness fewer Investments in research and development on average after nearly half a century. The effect is particularly pronounced for non-state-owned firms. The decline in innovation inputs is further supported by a lower probability of patenting in these localities. A likely underlying mechanism is the low adoption of performance-based reward systems that influence labor reallocation within firms, rather than inadequate capital and skilled workers. Despite prior successes during the planned economy era, the adoption of such foreign aid tends to impede innovation as China transitions towards a more market-oriented economy. |
| Keywords: | Foreign Aid; Technology Transfers; Innovation Inputs; Pay for Performance; China |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wus005:73284957 |
| By: | Egana-delSol, Pablo (Universidad Adolfo Ibañez); Bravo-Ortega, Claudio (Universidad Adolfo Ibañez) |
| Abstract: | This study examines the implications of artificial intelligence (AI) on employment, wages, and inequality in Latin America and the Caribbean (LAC). The paper identifies tasks and occupations most exposed to AI using comprehensive individual-level data alongside AI exposure indices. Unlike traditional automation, AI exposure correlates positively with higher education levels, ICT, and STEM skills. Notably, younger workers and women with high-level ICT and managerial skills face increased AI exposure, underscoring unique opportunities. A comparison of LAC with the OECD countries reveals greater impacts of AI in the former, with physical and customer-facing tasks showing divergent correlations to AI exposure. The findings indicate that while AI contributes to employment growth at the top and bottom of wage quintiles, its wage impact strongly depends on the movement of workers from the middle class to below the wage mean of the high-level quintile of wages, hence decreasing the average income of the top quintile. |
| Keywords: | artificial intelligence, automation, labor market, developing economies, AI exposure, inequality, non cognitive skills, cognitive skills |
| JEL: | J23 J24 J31 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17746 |
| By: | Ashish Arora; Sharique Hasan; William D. Miles |
| Abstract: | Solving complex problems- in medicine, engineering, and other technological domains- often requires exploring multiple approaches, particularly when significant uncertainty exists about which one will lead to success. Conventional wisdom assumes that having many experimenters independently decide which approaches to pursue increases diversity and, thus, also the chances of finding a solution. However, if experimenters herd toward the most promising approach, this convergence may reduce diversity and thus the likelihood of solving the problem. In this paper, we develop a simple model to show that, holding the total number of experiments constant, markets dominated by a few large-scale experimenters- firms conducting multiple experiments- explore more diverse approaches than markets with many single-shot experimenters. Single-shot experimenters tend to converge on the most promising approach, while multi-experimenters are more likely to diversify to avoid the correlation inherent in pursuing multiple experiments within the same approach. We test our model's predictions using data from pharmaceutical R&D. Our analysis shows that increasing the average number of experiments per firm by one unit raises target diversity by over three standard deviations. In turn, a one-standard deviation increase from the mean in target diversity boosts the likelihood of at least one experiment reaching Phase 1 clinical trials by 25.9 percentage points. Our findings inform policies for the optimal allocation of experiments across firms to maximize approach diversity and market-level success. |
| JEL: | L1 L2 O31 O32 O33 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33682 |
| By: | Roxana Mihet (Swiss Finance Institute - HEC Lausanne); Kumar Rishabh (University of Lausanne - Faculty of Business and Economics (HEC Lausanne); University of Basel, Faculty of Business and Economics); Orlando Gomes (Lisbon Polytechnic Institute - Lisbon Accounting and Business School) |
| Abstract: | Artificial intelligence (AI) is transforming productivity and market structure, yet the roots of firm dominance in the modern economy remain unclear. Is market power driven by AI capabilities, access to data, or the interaction between them? We develop a dynamic model in which firms learn from data using AI, but face informational entropy: without sufficient AI, raw data has diminishing or even negative returns. The model predicts two key dynamics: (1) improvements in AI disproportionately benefit data-rich firms, reinforcing concentration; and (2) access to processed data substitutes for compute, allowing low-AI firms to compete and reducing concentration. We test these predictions using novel data from 2000–2023 and two exogenous shocks—the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. The results confirm both mechanisms: compute access enhances the advantage of data-intensive firms, while access to processed data closes the performance gap between AI leaders and laggards. Our findings suggest that regulating data usability—not just AI models—is essential to preserving competition in the modern economy. |
| JEL: | L13 L41 O33 D83 E22 L86 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2537 |
| By: | Jakub Growiec (SGH Warsaw School of Economics); Klaus Prettner (Department of Economics, Vienna University of Economics and Business) |
| Abstract: | Recent advances in artificial intelligence (AI) have led to a diverse set of predictions about its long-term impact on humanity. A central Focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from human extinction after a misaligned TAI takes over ("AI doom") to unprecedented economic growth and abundance ("post-scarcity"). However, the probabilities and implications of these scenarios remain highly uncertain. Here, we organize the various scenarios and evaluate their associated existential risks and economic outcomes in terms of aggregate welfare. Our analysis shows that even low-probability catastrophic outcomes justify large investments in AI safety and alignment research. We find that the optimizing representative individual would rationally allocate substantial resources to mitigate extinction risk; in some cases, she would prefer not to develop TAI at all. This result highlights that current global efforts in AI safety and alignment research are vastly insufficient relative to the scale and urgency of existential risks posed by TAI. Our findings therefore underscore the need for stronger safeguards to balance the potential economic benefits of TAI with the prevention of irreversible harm. Addressing these risks is crucial for steering technological progress toward sustainable human prosperity. |
| Keywords: | Transformative Artificial Intelligence (TAI), Economic Growth, Technological Singularity, Growth Explosion, AI Takeover, AI Alignment, AI Doom |
| JEL: | I30 O11 O33 Q01 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp378 |
| By: | Gordon H. Hanson; Enrico Moretti |
| Abstract: | We examine changes in the spatial distribution of good jobs across US commuting zones over 1980-2000 and 2000-2021. We define good jobs as those in industries in which full-time workers attain high wages, accounting for individual and regional characteristics. The share of good jobs in manufacturing has plummeted; for college graduates, good jobs have shifted to (mostly tradable) business, professional, and IT services, while for those without a BA they have shifted to (non-tradable) construction. There is strong persistence in where good jobs are located. Over the last four decades, places with larger concentrations of good job industries have tended to hold onto them, consistent with a model of proportional growth. Turning to regional specialization in good job industries, we find evidence of mean reversion. Commuting zones with larger initial concentrations of good jobs have thus seen even faster growth in lower-wage (and mostly non-tradable) services. Changing regional employment patterns are most pronounced among racial minorities and the foreign-born, who are relatively concentrated in fast growing cities of the South and West. Therefore, good job regions today look vastly different than in 1980: they are more centered around human-capital-intensive tradable services, are surrounded by larger concentrations of low-wage, non-tradable industries, and are more demographically diverse. |
| JEL: | J01 R0 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33631 |