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on Technology and Industrial Dynamics |
| By: | Anastasiia Pustovalova; Priit Vahter |
| Abstract: | This paper explores the complementarity of automation with social and problem-solving skills, focusing on the wage effects. The results based on detailed firm- and individual-level data from Estonia show that in manufacturing firms which recently adopted automation tools, there is additional wage premium for employees’ social skills. This effect is even more pronounced for the low-skilled workers, emphasizing both the importance of soft skills on low-wage jobs and how innovation at firms can have significant positive effects on some sub-groups of the low-skilled. The role of skills is different depending on how persistent the automation investments are at the firm. First-time automating firms start valuing the social skills first, while persistently automating firms reward the problem-solving skills instead. |
| Keywords: | automation, technological change, social skills, problem-solving skills, wage differentials |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:mtk:febawb:146 |
| By: | Riccardo Priore (Patlib Centre - Area Science Park, Trieste (Italy)); Marco Compagnoni (University of Milano-Bicocca, Milan (Italy)); Marinella Favot (Area Science Park, Trieste (Italy)) |
| Abstract: | The rare earth elements (REE) are currently essential enablers of the digital and decarbonization transitions. Nonetheless, their supply chain is highly concentrated and their extraction has high environmental impacts. Circular economy solutions could provide a twofold benefit, reducing the supply risk for import-dependent countries and mitigating REE mining impacts. This article focuses on REE recycling, providing a comprehensive, global overview of innovation dynamics in that sector by means of patent data. We propose a two-steps patent search methodology for the identification of REE recycling patents, based on OECD ENV-TECH classification for green technologies and keywords occurrence. Hence, we develop a series of quantitative and qualitative metrics to explore innovation dynamics at the country, applicant and technology type level. China clearly emerges as the most attractive market for REE recycling patents and Chinese universities as the most active applicants globally. Conversely, patent applications in all other countries registered stagnating trends over the last decade. In Europe, in particular, a lower number of patents are both filed and developed with respect to the US and Japan. However, patent quality indicators present a quite different picture, with US and Japanese applicants that seem to be at the technological forefront, receiving more citations and being more oriented to protect their inventions internationally. Therefore, our analysis underlines the importance of considering both quantitative and qualitative patent metrics when exploring innovation trends in REE recycling. We discuss the determinants of these observed phenomena and provide policy implications, particularly for countries dependent on REE imports. |
| Keywords: | innovation, patents, critical raw materials, rare earths, recycling, circular economy |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0424 |
| By: | Bachmann, Ronald (RWI, Heinrich-Heine-Universität Düsseldorf, IZA); Janser, Markus (Institute for Employment Research (IAB), Nuremberg, Germany); Lehmer, Florian (Institute for Employment Research (IAB), Nuremberg, Germany); Vonnahme, Christina (RWI) |
| Abstract: | "Using a text-mining approach applied to task descriptions of occupations together with worker-level administrative data, we explore the growth in the greenness of employment in Germany between 2012 and 2022. We first demonstrate that the greening of the labour market occurs both through an increase of green tasks and a decrease of brown tasks. Furthermore, the greening of occupations over time (“within-effect”) is at least as important for the overall greening of employment as shifting occupational employment shares (“between-effect”). Second, we show which occupations and which task types (brown or green) contribute most to the within-effect, and which worker flows are mainly responsible for the between-effect. Third, we investigate individual-level consequences of the greening of employment. We find that the employment prospects of foreign and of low-skilled workers are most at risk from the green transition, which may therefore increase existing labour-market inequalities." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | IAB-Beschäftigtenhistorik |
| JEL: | J23 J24 O33 Q55 R23 |
| Date: | 2024–09–17 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202412 |
| By: | Bernardo Caldarola; Luca Fontanelli |
| Abstract: | Recent empirical evidence finds positive associations between digitalisation and industry concentration. However, ICT may not be all alike. We investigate the effect of the purchase of cloud services on the long run size growth rate of French firms. Our findings suggest that cloud services positively impact firm growth rates, with smaller firms experiencing more significant benefits compared to larger firms. This evidence suggests that the diffusion of cloud technologies may help mitigate concentration in the era of the digital transition by favouring the digitalisation and growth of smaller firms, especially when the cloud services provided are more advanced. |
| Keywords: | cloud, ICT, concentration, firm growth rate, firm performance |
| Date: | 2024–10–02 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/25 |
| By: | Vardanyan, David (Warwick University) |
| Abstract: | This paper explores the effects of automation and the creation of new tasks on labour market outcomes by incorporating the task-based production of Acemoglu and Restrepo (2018a) into a modified Diamond-Mortensen-Pissarides (DMP) search and matching framework. While the effect on wages aligns with existing literature, the introduction of search frictions offers new insights regarding effects on unemployment. Automation is found to have a dual impact: it displaces workers from routine tasks but simultaneously generates productivity gains which can offset its negative effects. The net impact on unemployment and wages depends on the relative magnitude of these displacement and productivity effects which are analytically derived in the research. In contrast, the creation of new tasks has a more uniformly positive impact, as it both enhances the productivity and reinstates displaced workers, leading to lower unemployment and higher wages. The findings suggest that policies should ensure not to promote excessive automation, where it negatively affect the labour market. In contrast, fostering innovation and task creation can be effective ways to benefiting from technological advancements. |
| Keywords: | Automation ; labour market frictions ; productivity ; technology ; unemployment JEL classifications: E22 ; E24 ; J23 ; J24 ; O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:wrkesp:79 |
| By: | Bastani, Spencer (Institute for Evaluation of Labour Market and Education Policy (IFAU), Uppsala); Waldenström, Daniel (Research Institute of Industrial Economics (IFN)) |
| Abstract: | This chapter examines the implications of Artificial Intelligence (AI) and automation for the taxation of labor and capital in advanced economies. It synthesizes empirical evidence on worker displacement, productivity, and income inequality, as well as theoretical frameworks for optimal taxation. Implications for tax policy are discussed, focusing on the level of capital taxes and the progressivity of labor taxes. While there may be a need to adjust the level of capital taxes and the structure of labor income taxation, there are potential drawbacks of overly progressive taxation and universal basic income schemes that could undermine work incentives, economic growth, and long-term household welfare. |
| Keywords: | AI; Automation; Inequality; Labor Share; Optimal Taxation; Tax Progressivity |
| JEL: | H20 H21 |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:iuiwop:1501 |
| By: | Timothy DeStefano; Nick Johnstone; Richard Kneller; Jonathan Timmis |
| Abstract: | The arrival of cloud computing provides firms a new way to access digital technologies as digital services. Yet, capital incentive policies present in every OECD country are still targeted towards investments in information technology (IT) capital. If cloud services are partial substitutes for IT investments, the presence of capital incentive policies may unintentionally discourage the adoption of cloud and technologies that rely on the cloud, such as artificial intelligence (AI) and big data analytics. This paper exploits a tax incentive in the UK for capital investment as a quasi-natural experiment to examine the impact on firm adoption of cloud computing, big data analytics and AI. The empirical results find that the policy increased investment in IT capital as would be expected; but it slowed firm adoption of cloud, big data and AI. Matched employer-employee data shows that the policy also led firms to reduce their demand for workers that perform data analytics, but not other types of workers. |
| Keywords: | Capital incentives, Firms, Cloud computing, Artificial Intelligence |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:not:notgep:2024-04 |
| By: | Musina, Sofiya (Warwick University) |
| Abstract: | Large Language Models (LLMs), such as ChatGPT, demonstrate an unprecedented applicability in a variety of domains, and, unlike previous waves of innovation, are capable of nonroutine cognitive tasks - leaving educated, white-collar workers most exposed. However, few studies address the relevant labour market implications outside controlled experimental environments. This project investigates the effects of LLMs on knowledge worker competency requirements using a difference-in-difference model based on a sample of 105, 912 online job advertisements (Luxembourg, 2020-2024). The findings contain weak evidence that LLMs cause a reduction in demand for experience, education, cognitive skills and creativity, while leaving soft skills unaffected. |
| Keywords: | Employment ; Skills Demand ; Technology ; AI JEL classifications: J01 ; J23 ; J24 ; O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:wrkesp:75 |
| By: | Känzig, Diego R.; Williamson, Charles |
| Abstract: | We explore the increasing divergence between economic growth and energy consumption through energy-saving technical progress. Proposing a new measure of energy-saving technology, we study the underlying drivers in a semi-structural model of the U.S. economy. Our analysis shows that energy price shocks reduce consumption and stimulate energy-saving innovation, but also cause economic downturns and crowd out other innovations. Only energy-saving technology shocks can explain the negative co-movement between output and energy use. These sudden efficiency gains emerge as the primary driver of energy-saving technical change. Our findings highlight the importance of fostering energy-saving innovations in transitioning to a low-carbon economy. JEL Classification: E0, O30, Q32, Q43, Q55 |
| Keywords: | directed technical change, energy-saving, energy prices, innovation |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242984 |
| By: | Corentin Lobet; Patrick Llerena; André Lorentz |
| Abstract: | We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model incorporates productivity dynamics (learning and depreciation) and is extended with time-saving process innovations. Specifically, we assume workers possess inherent creativity that flourishes during idle periods. The firm, rather than laying off idle workers, is assumed to harness this potential by involving them in the innovation process. Results show that a firm's organizational and managerial decisions, the temporal structure of the production system, the degree of workers' learning and forgetting, and the pace of innovation are critical factors influencing production efficiency in both the short and long term. The co-evolution of production and innovation processes emerges in our model through the two-sided effects of idleness: the loss of skills through forgetting and the deflection of time from the production of goods to the production of ideas giving birth to idleness-driven innovations. In doing so, it allows us to question the status of labour as an adjustment variable in a productive organisation. The paper concludes by discussing potential solutions to this issue and suggesting avenues for future research. |
| Keywords: | Production Theory; Firm Theory; Agent-based model; Idleness; Innovation; Fund-flow |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/27 |
| By: | Naomi-Rose Alexander; Mauro Cazzaniga; Ms. Stefania Fabrizio; Ms. Florence Jaumotte; Longji Li; Dr. Jorge Mondragon; Sahar Priano; Ms. Marina Mendes Tavares |
| Abstract: | The transition to a sustainable and green economy requires workers to move out of carbon-intensive jobs and workers to move into green jobs. The pace and effectiveness of the transition hinge not only on climate policies but also on the skills and adaptability of workers. Evidence suggests that economies with a robust supply of STEM-educated workers and a more equal treatment of women are better placed to transition faster and at a lower cost to a green economy, even after controlling for other country characteristics, because these economies generate more green innovation and face lower bottlenecks in expanding the green workforce. Altogether, climate policies, particularly energy taxes, in these economies are associated with emission reductions that are 2 to 4 percentage points larger than in economies with a less inclusive and educated workforce. While green jobs have been growing worldwide, men currently hold close to two-thirds of these positions and women only one-third. Green jobs are associated with a 7 percent premium for men and an even higher premium of 12 percent for women, suggesting that men’s and women’s labor supply may not meet demand. These findings highlight the critical need for educational and labor policies that promote skill enhancement and gender inclusivity, to ensure a sufficient supply of workers for the green economy and that all workers can benefit from the green transition. Finally, AI could be beneficial for workers in green jobs. |
| Keywords: | Labor Market Transition; Climate Change; Employment; Gender Equality; gender green employment gap decomposition; employment share; green wage premium; worker occupation; women employment; green jobs workers' characteristic; Gender inequality; Women; Labor markets; Global |
| Date: | 2024–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfsdn:2024/003 |
| By: | Manish Jha; Jialin Qian; Michael Weber; Baozhong Yang |
| Abstract: | We use generative AI to extract managerial expectations about their economic outlook from over 120, 000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.03897 |
| By: | Mario W. Dávila-Dávila; Marwil J. Dávila-Fernández |
| Abstract: | Public perceptions of the urgency of fighting climate change differ between countries and have fluctuated over time. Heterogeneity in ecological thinking poses a problem because limiting global warming requires cohesion and coordination among the socioeconomic system’s leading players in developed and developing countries. Most studies in the field have wrongly treated advanced and emerging economies as similar systems in different positions of a linear development path. Developing economies are structurally different as they are populated by a large informal sector that accounts for up to half of economic activity. The role of the informal sector in economic development remains controversial, let alone the implications of its existence to a successful green transition. We present a macrodynamic model to study the interplay between informality and heterogeneity in ecological thinking. The model explains the endogenous emergence of four stable equilibria. Two have minor informality but significant differences in green attitudes. We refer to them as the US vs Europe cases in the Global North. In the other two, informality prevails, while we observe sharp differences in general support for mitigation policies, resembling an Asia vs Latin America scenario. Studying the basins of attraction allows us to provide policymakers with additional insights into the political economy of climate change in the Global South |
| Keywords: | Climate change; Informality; Green attitudes, Global South; Development. |
| JEL: | Q01 Q56 O11 O44 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:usi:wpaper:914 |
| By: | David Dorn; Florian Schoner; Moritz Seebacher; Lisa Simon; Ludger Woessmann |
| Abstract: | We measure human capital using the self-reported skill sets of 8.75 million U.S. college graduates from professional profiles on the online platform LinkedIn. We establish that these skills are systematically related to human capital investments such as different types of schooling and work experience. The average profile of the number of reported skills by age looks remarkably similar to the well-established concave age-earnings profiles. More experienced workers and those with higher educational degrees have larger shares of occupation-specific skills, consistent with their acquisition through professional-degree programs and on-the-job experience. Workers who report more, and particularly more specific and managerial, skills are more likely to hold highly paid jobs. Skill differences across workers can account for more earnings variation than detailed vectors of education and experience. We also document a substantial gender gap in reported skills, which starts to manifest when young women reach typical ages of first motherhood. Gender differences in skill profiles can rationalize a substantial proportion of the gender gap in the propensity to work in highly paid jobs. Overall, the results are consistent with an important role of multidimensional skills in accounting for several well-known basic labor-market patterns. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18638 |
| By: | Pablo Guerron-Quintana (Boston College); Tomoaki Mikami (Boston College); Jaromir Nosal (Boston College) |
| Abstract: | We study the potential impact of the generative artificial intelligence (Gen-AI) revolution on the US economy through the lens of a multi-sector model in which we explicitly model the role of Gen-AI services in customer base management. In our model with carefully calibrated input-output linkages and the size of the Gen-AI sector, we find large spillovers of the Gen-AI productivity gains into the overall economy. A 10% increase in productivity in the Gen-AI sector over a 10 year horizon implies a 6% increase in aggregate GDP, despite the AI sector representing only 14% of the overall economy. That shock also implies a significant reallocation of labor away from the AI sector and into non-AI sectors. We decompose these effects into parts coming from the input-output structure and customer base management and find that they each contribute equally to the rise in GDP. In the absence of either channels, real GDP essentially does not respond to the increase in productivity in the AI sector. |
| Keywords: | artificial intelligence, AI, productivity |
| Date: | 2024–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1080 |