Digitale Arbeitswelt – Chancen und Herausforderungen für Beschäftigte und Arbeitsmarkt
Der digitale Wandel der Arbeitswelt gilt als eine der großen Herausforderungen für Wirtschaft und Gesellschaft. Wie arbeiten wir in Zukunft? Welche Auswirkungen hat die Digitalisierung und die Nutzung Künstlicher Intelligenz auf Beschäftigung und Arbeitsmarkt? Welche Qualifikationen werden künftig benötigt? Wie verändern sich Tätigkeiten und Berufe? Welche arbeits- und sozialrechtlichen Konsequenzen ergeben sich daraus?
Dieses Themendossier dokumentiert Forschungsergebnisse zum Thema in den verschiedenen Wirtschaftsbereichen und Regionen.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
- Gesamtbetrachtungen/Positionen
- Arbeitsformen, Arbeitszeit und Gesundheit
- Qualifikationsanforderungen und Berufe
- Arbeitsplatz- und Beschäftigungseffekte
- Wirtschaftsbereiche
- Arbeits- und sozialrechtliche Aspekte / digitale soziale Sicherung
- Deutschland
- Andere Länder/ internationaler Vergleich
- Besondere Personengruppen
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Literaturhinweis
Automation and Polarization (2026)
Zitatform
Acemoglu, Daron & Jonas Löbbing (2026): Automation and Polarization. In: Journal of Political Economy, Jg. 134, H. 3, S. 1017-1072. DOI:10.1086/739330
Abstract
"We develop an assignment model of automation. Each of a continuum of tasks of variable complexity is assigned to either capital or one of a continuum of labor skills. We characterize conditions for interiorautomation, whereby tasks of intermediate complexity are performed by capital. Interior automation arises when the most skilled workers have a comparative advantage in the most complex tasks relative to capital, and when the wages of the least skilled workers are sufficiently low relative to both their own productivity and the effective cost of capital in low-complexity tasks. Minimum wages and other sourcesof higher wages at the bottom make interior automation less likely. Starting with interior automation, a reduction in the cost of capital (or an increase in capital productivity) causes employment and wage polarization. Specifically, further automation pushes workers into tasks at the lower and upper ends ofthe task distribution. It also monotonically increases the skill premium above a threshold and reduces the skill premium below this threshold. Moreover, automation tends to reduce the real wage of Workers with comparative advantage profiles close to that of capital. We show that large enough increases in capital productivity ultimately induce a transition to low-skill automation and qualitatively alter the effects of automation—thereafter inducing monotone increases in skill premia rather than wage polarization." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation, Learning, and Career Dynamics (2026)
Zitatform
Afrouzi, Hassan, Andres Blanco, Andrés Drenik & Erik Hurst (2026): Automation, Learning, and Career Dynamics. (NBER working paper / National Bureau of Economic Research 35157), Cambridge, Mass, 58 S.
Abstract
"We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The end of work feels near. How do people perceive the impact of digital technologies and automation? (2026)
Zitatform
Arntz, Melanie, Sebastian Blesse & Philipp Doerrenberg (2026): The end of work feels near. How do people perceive the impact of digital technologies and automation? In: Labour Economics, Jg. 102, 2026-05-11. DOI:10.1016/j.labeco.2026.102897
Abstract
"Anxieties about technological change in the context of the labor market are a recurring historical phenomenon. Using customized survey data collected in 2019 in the US and Germany, prior to the recent wave of generative AI applications, we study how respondents perceive the impact of the digital (automation) technologies available at the time of the survey on the labor market. We document that a majority views digital technologies and automation as a major threat to overall employment and as a cause of rising inequality, while a quarter is concerned about their own labor market prospects. Providing scientific information on the likely labor market implications of digital technologies in a randomized experiment reduces these concerns. Yet, treatment responses depend on prior beliefs about the future of work, resulting in heterogeneous and opposing treatment effects on policy demand." (Author's abstract, IAB-Doku, © 2026 Elsevier) ((en))
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Literaturhinweis
Winners and losers when firms robotize: wage effects across occupations and education (2026)
Zitatform
Barth, Erling, Marianne Røed, Pål Schøne & Janis Umblijs (2026): Winners and losers when firms robotize: wage effects across occupations and education. In: The Scandinavian Journal of Economics, Jg. 128, H. 1, S. 3-32. DOI:10.1111/sjoe.12593
Abstract
"This paper analyses the impact of robots on workers' wages in the manufacturing sector, with a particular focus on relative wages for workers with different levels of education and in different occupations. Using high-quality matched employer–employee register data with firm-level information on the introduction of industrial robots, we identify the effects of robotization on relative wages within firms. Skilled blue-collar workers with a vocational degree experience a decline in wages when firms introduce robots, while there are only small effects for the other groups of workers. These results suggest that robots are substitutes for tasks undertaken by skilled blue-collar workers in manufacturing, and furthermore that the adoption of robots contributes to a polarization of the labor market and a hollowing out of the wage distribution, rather than to skill-biased technical change." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation Experiments and Inequality (2026)
Zitatform
Benzell, Seth Gordon & Kyle R. Myers (2026): Automation Experiments and Inequality. (NBER working paper / National Bureau of Economic Research 34668), Cambridge, Mass, 26 S., App. DOI:10.3386/w34668
Abstract
"Many experiments study the productivity effects of automation technologies such as generative algorithms. A key test in these experiments relates to inequality: does the technology increase output more for high- or low-skill workers? However, the theoretical content of this empirical test has been unclear. Here, we formalize a theory that describes the experimental effect of automation technologies on worker-level output and, therefore, inequality. Worker-level output depends on a task-level production function, and workers are heterogeneous in their task-level skills. Workers perform a task themselves or delegate it to the automation technology. The inequality effect of improved automation depends on the interaction of two factors: (i) the correlation in task-level skills across workers, and (ii) workers' skills relative to the technology's effective skill. In many cases we study, the inequality effect is non-monotonic --- as technologies improve, inequality decreases then increases. The model and descriptive statistics of skill correlations generally suggest that the diversity of automation technologies will play an important role in the evolution of inequality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Digital divide and income inequality: causal evidence from Italian provinces (2026)
Zitatform
Bergantino, Angela Stefania, Giulio Fusco, Mario Intini & Gianluca Monturano (2026): Digital divide and income inequality: causal evidence from Italian provinces. In: The Annals of Regional Science, Jg. 75, H. 1. DOI:10.1007/s00168-025-01440-z
Abstract
"The digital economy can function either as a catalyst to stimulate economic growth or else as a driver of socioeconomic inequality when its benefits are unevenly distributed. This study investigates the effect of rural digital connectivity on income inequality in Italy. Utilizing NUTS 3 panel data spanning 2014–2022, we conduct a counterfactual Difference-in-Differences approach with continuous treatment intensity to estimate the impact of introducing rural broadband coverage at speeds of 30 and 100 Mbps on multiple measures of income distribution, including the Gini, Theil, and Atkinson indices. The empirical framework incorporates a comprehensive set of socioeconomic controls, as well as provincial and time fixed effects, to account for unobserved heterogeneity and regional path dependencies. Our findings indicate that broadband expansion is significantly associated with increasing inequality, suggesting that access alone does not guarantee inclusive outcomes, particularly in localities characterized by structural fragility and limited human capital. Additional heterogeneity and spatial analyses demonstrate that these inequality effects are more evident in southern provinces and localities with a higher concentration of inner areas, where the digital divide remains more pronounced. These findings accentuate the dual role of digitalization and highlight the necessity of coordinated policy interventions that combine infrastructure investment with digital skills development, institutional capacity-building, and spatially integrated governance strategies." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy: Special Issue: Bringing the Ecological and the Social Together in the Green Transition: A Multilevel Analysis (2026)
Zitatform
Bonoli, Giuliano, Patrick Emmenegger & Alina Felder-Stindt (2026): Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy. Special Issue: Bringing the Ecological and the Social Together in the Green Transition: A Multilevel Analysis. In: Regulation and governance, Jg. 20, H. 2, S. 482-494. DOI:10.1111/rego.70065
Abstract
"European economies face the task of providing the necessary skills for the “twin transition ” in a period of skill shortage. As a result, we may expect countries to reorient their labor market policy towards re-skilling. We look for evidence of a reorientation in two relevant policy fields: active labor market policy (ALMP) and adult education (AE). We explore general trends in both fields based on quantitative indicators and compare recent policy developments in four countries with strong ALMP and AE sectors: Denmark, France, Germany, and Sweden. We do not observe clear evidence of a general movement away from activation and towards re-skilling in ALMP. However, in AE, we identify several re-skilling initiatives that address skill shortages. Relying on insights from queuing theories of hiring and training, we argue that due to changes in the population targeted by ALMP, the locus of re-skilling policy is increasingly moving towards AE." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Explaining women's skepticism toward artificial intelligence: The role of risk orientation and risk exposure (2026)
Borwein, Sophie ; Bonikowski, Bart ; Ognyanova, Katherine; Alvarez, R. Michael; Loewen, Peter J.; Magistro, Beatrice ;Zitatform
Borwein, Sophie, Beatrice Magistro, R. Michael Alvarez, Bart Bonikowski & Peter J. Loewen (2026): Explaining women's skepticism toward artificial intelligence: The role of risk orientation and risk exposure. In: PNAS nexus, Jg. 5, H. 1. DOI:10.1093/pnasnexus/pgaf399
Abstract
"This article examines the gender gap in attitudes toward the adoption of AI in the workplace, with a focus on how gender differences in risk orientation and risk exposure drive skepticism toward AI’s economic benefits. Using original surveydata from ∼3,000 respondents across Canada and the United States, we find that women consistently perceive AI to be riskier than men. We identify two key drivers behind this gender gap: women’s higher general risk aversion and their greater exposure to AI-related risks. To establish a causal relationship between risk and AI attitudes, we show experimentally that as the probability of net positive employment effects decreases, women’s support for companies adopting AI falls more sharply than men’s. Finally, structural topic modeling of open-ended responses confirms that women express greater uncertainty about AI’s benefits and more frequently anticipate little to no benefits. Given AI’s potential to exacerbate existing gender inequalities, our study highlights the critical importance of incorporating women’s perspectives into AI policy-making. Policies that do not address gender-specific risks may not only reinforce existing inequalities in employment and income but could also generate political backlash against AI adoption." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Comment on “Artificial Intelligence and Technological Unemployment” by Wang and Wong (2026)
Braxton, J. Carter;Zitatform
Braxton, J. Carter (2026): Comment on “Artificial Intelligence and Technological Unemployment” by Wang and Wong. In: Journal of monetary economics, Jg. 159. DOI:10.1016/j.jmoneco.2026.103924
Abstract
"Since the launch of ChatGPT in November 2022 there has been a surge in the uptake of generative artificial intelligence (AI). The recent advances in AI have also been met with sweeping statements about the potential future employment effects. For example, the CEO of Anthropic said in May 2025 that “AI could wipe out half of all entry-level white-collar jobs - and spike unemployment to 10%–20% in the next one to five years.”1 In their paper, Wang and Wong (2025) develop and quantify an equilibrium model of the labor market to evaluate the potential employment effects of the spread of AI. Their model builds on labor search models with technological change (e.g., Mortensen and Pissarides (1998), and Postel-Vinay (2002)), augmenting the framework to capture the specifics of AI and calibrating it to recent empirical evidence on the impact of AI. The authors arrive at a striking result that the spread of AI will increase productivity by a factor of three but decrease employment by 23%, with approximately half of the increase occurring over the next 5-years. In this comment, I first discuss the central model ingredients of Wang and Wong (2025), which implies that there are both a job creation and job destruction channel of AI. I then discuss how we can learn about the relative strength of the job creation and destruction channels from the spread of computers between the 1980s and early 2000s. Finally, I conclude with avenues for future research." (Author's abstract, IAB-Doku, © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
Revisiting the occupational impact of AI in the generative AI era (2026)
Casas, P.; González-Vázquez, I.; Salotti, S.; Martínez-Plumed, F.; Gómez, E.; Fernández-Macías, E.;Zitatform
Casas, P., E. Fernández-Macías, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti (2026): Revisiting the occupational impact of AI in the generative AI era. (JRC working papers series on labour, education and technology 2026,02), Sevilla, 71 S.
Abstract
"Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evolution of AI exposure in the European labour market from 2008 to 2024 by linking 352 AI benchmarks to 14 cognitive abilities, 108 work tasks and 127 ISCO-3 occupations, weighting benchmarks by their research intensity in the AI literature and thus deriving AI exposure by cognitive ability. Bundling work tasks into occupations based on intensity indicators, we explore occupational exposure to AI. We find that the cognitive abilities most exposed to the recent surge of AI research are ideas-related, such as attention and search, comprehension and expression and logical reasoning. Because the associated information processing and problem-solving tasks are the most transversal across occupations, we find an exponential increase in AI exposure across all occupational categories of workers, even though comparatively high-skilled occupations are more exposed than elementary occupations. This points at a substantial and transversal labour market impact of AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
How welfare states influence online platform work in Europe (2026)
Zitatform
Chueri, Juliana & Petter Törnberg (2026): How welfare states influence online platform work in Europe. In: Journal of European Social Policy, Jg. 36, H. 2, S. 119-135. DOI:10.1177/09589287251357463
Abstract
"Digital labor platforms are reshaping global labor markets by enabling the transnational contracting of service workers. While the dominant perspective emphasizes market forces, predicting that lower-wage countries will dominate the supply side, this view overlooks the institutional context in which platform labor emerges. This paper advances the argument that national welfare institutions are key to shaping participation in the platform economy. We provide the first large-scale cross-national comparative analysis of platform labor, combining micro-level data from one of the world’s largest remote work platforms with country-level indicators from 26 European countries. In line with market expectations, we find that lower-wage countries supply most low-skilled labor, while higher-wage countries show a more balanced distribution between low- and high-skilled workers. Crucially, however, our analysis reveals that greater welfare state generosity is associated with lower levels of platform participation, especially in low-skilled occupations. We argue that platform labor cannot be understood solely as a function of technological change or wage differentials. It is also an expression of structural constraints: where social protections are weak, people are more likely to turn to precarious forms of online work." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Retirement decisions in the age of COVID-19 pandemic: are older employees in digital occupations working longer? (2026)
Zitatform
Gallo, Giovanni & Amparo Nagore García (2026): Retirement decisions in the age of COVID-19 pandemic: are older employees in digital occupations working longer? In: Review of Economics of the Household, S. 1-34. DOI:10.1007/s11150-025-09827-9
Abstract
"This paper investigates the retirement response to the pandemic and to the resulting acceleration in the adoption of new technologies. Using the European Union Statistics of Income and Living Conditions datasets and leveraging the natural experiment of many workers being forced to work from home in Europe during the lockdown, we compare the retirement response of older workers in digital occupations (i.e. more exposed to the accelerated adoption of new technologies) versus non-digital occupations to detect any differences in retirement behaviour, which we interpret as digitalization effects. In addition, we analyze changes in retirement decisions by gender and geographic area. We find that retirement rates increased during COVID-19 in Europe, especially in Mediterranean countries and among women. This trend may be linked to gender occupational segregation. In Mediterranean countries, digitalization increases female retirement, likely due to challenges in balancing digital work and family responsibilities while working from home. In Eastern countries, and to a lesser extent in Northern countries, digitalization leads to postponing retirement among women, likely due to greater gender equality in unpaid work. In contrast, the retirement age for men is less affected by the pandemic with no significant differences between digital and non-digital occupations. This may exacerbate the existing gender gap in labor force participation and pension outcomes." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))
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Literaturhinweis
Technology adoption and migration dynamics: A gravity model analysis of bilateral flows (2026)
Zitatform
Ghodsi, Mahdi, Michael Landesmann & Antea Barišić (2026): Technology adoption and migration dynamics: A gravity model analysis of bilateral flows. In: Technology in Society, Jg. 86. DOI:10.1016/j.techsoc.2026.103298
Abstract
"This paper explores the complex interplay between technology adoption, specifically robotisation and digitalisation, and international migration within the EU and other advanced economies, including Australia, the UK, Japan, Norway and the US, over the period 2001-2019. Utilising a gravity model approach grounded in neoclassical migration theory, the study analyses how technological advancements influence migration flows. The research uniquely integrates these technological factors into migration analysis, considering both push and pull effects and reveals two main results: (1) In migration origin countries, higher adoption of both digitalisation and robotisation is associated with lower emigration, suggesting ‘complementarity’ between technologies and workers, i.e. less incentive to emigrate when the level of technology adoption was higher. This indicates that technology adoption at origin is associated with better opportunities in the labour market and weaker incentives to emigrate. (2) In migration destination countries, the results indicate a more differentiated picture in that we find some evidence for a ‘substitution’ effect with regard to higher levels of digital assets adoption but a ‘complementarity’ effect with respect to higher levels of robotisation. Additionally,this study accounts for various other migration determinants including macroeconomic conditions, demography and policy factors. The findings reveal insights about the relationships between technological progress, labour market conditions and migration patterns, thereby contributing significantly to current literature and suggests implications for migration policies and accelerated technology adoption." (Author's abstract, IAB-Doku, © 2026 Elsevier Ltd. All rights are reserved, including those fortext and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
Contextualizing inequalities in the gig economy: evidence from online cleaning platforms in five European cities (2026)
Zitatform
Giuliani, Giovanni Amerigo & Rebecca Paraciani (2026): Contextualizing inequalities in the gig economy: evidence from online cleaning platforms in five European cities. In: The international journal of sociology and social policy, Jg. 46, H. 5-6, S. 736-755. DOI:10.1108/ijssp-12-2024-0619
Abstract
"Purpose: This paper explores the impact of national contexts on the profile of workers in the gig economy, with a specific focus on online cleaning platforms. The study aims to understand how national contexts influence the gender and ethnic composition of workers on domestic cleaning platforms, examining the intersectional effects of gender and ethnicity in platform-based work. Design/methodology/approach: Focusing on the case of the Yoopies platform operating in five Western European cities – Berlin, Copenhagen, Paris, Rome and Stockholm – this exploratory research is based on an original dataset that combines platform-based data directly collected from Yoopies with national-level data provided by Eurostat. Hypotheses were tested using simple correlation analysis to assess cross-country differences. Findings: The study shows that national contexts play an important role in shaping the gender and ethnic composition of workers on online cleaning platforms. Specifically, it identifies how structural features of the offline labor market influence the gendering and racialization of these platforms, highlighting variations across countries. The research also finds evidence of intersectional effects, where gender and ethnicity intersect to shape the profile of platform workers. Originality/value: This paper contributes to the growing literature on domestic work in the digital platform economy by providing a comparative perspective on cross-country differences in the composition of the platform workforce. It highlights the importance of national offline labor market characteristics in contributing to shaping platform-mediated work and provides new insights into the intersectionality of gender, ethnicity, and work in the gig economy. The findings contribute to both platform economy research and labor market studies, offering implications for policy and future research on the dynamics of digital work." (Author's abstract, IAB-Doku, © Emerald Group) ((en))
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Literaturhinweis
The Self-Other Gap in Perceived Automation Risk: Evidence from the United States and Canada (2026)
Zitatform
Glavin, Paul, Scott Schieman & Alexander Wilson (2026): The Self-Other Gap in Perceived Automation Risk: Evidence from the United States and Canada. In: Socius, Jg. 12, S. 1-3. DOI:10.1177/23780231261453968
Abstract
"This visualization shows a systematic misperception in how workers judge automation risk. Drawing on the 2026 Measuring Employment Sentiments and Social Inequality study, the authors compare paired measures of perceived automation likelihood for self and most others, using nationally representative samples of American and Canadian workers. Approximately three quarters of study participants in both countries rated their own jobs as at low risk of automation in the next few years, yet more than 70 percent believed that most other workers face at least some likelihood of automation. The pattern aligns with pluralistic ignorance: most workers hold one view of their own automation risk while assuming that most others hold a different one. The self-other gap is invariant across occupational categories and across two distinct national contexts, consistent with an informational asymmetry in which beliefs about others’ risk reflect prevailing public narratives about artificial intelligence rather than workers’ direct experience." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2026)
Zitatform
Humlum, Anders & Emilie Vestergaard (2026): Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI. (RF Berlin - CReAM Discussion Paper Series 2026,78), Berlin, 81 S.
Abstract
"We study the early labor market impacts of AI chatbots by linking large-scale adoption surveys to administrative labor market records in Denmark. We document rapid currents: most employers in exposed occupations have adopted chatbot initiatives, workers report productivity benefits, and new AI-related tasks are widespread. Yet these currents have not broken the surface: using difference-in-differences, we estimate precise null effects on earnings and recorded hours at both the worker and workplace levels, ruling out effects larger than 2% two years after the launch of ChatGPT. What moves is the structure of work: employers absorb AI through task reorganization-including new tasks in content generation, AI oversight, and AI integration-and adopters transition into higher-paying occupations where AI chatbots are more relevant, though still too few to move average earnings. Technological change reshapes work well before it surfaces in earnings or hours." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation and the risk of labor market exclusion across Europe (2026)
Zitatform
Lamperti, Fabio & Davide Castellani (2026): Automation and the risk of labor market exclusion across Europe. In: Structural Change and Economic Dynamics, Jg. 77, S. 62-76. DOI:10.1016/j.strueco.2025.12.014
Abstract
"Labor market exclusion represents a major concern in several European economies, particularly affecting highly exposed demographic groups. This paper examines the potential effect of automation technologies on the risk of being locked into protracted unemployment or inactivity, using Labour Force Survey data for the European Union 27 countries and the United Kingdom, between 2009 and 2019. Our study employs repeated cross-sections of individual-level data to compute probabilities of exclusion outcomes due to automation adoption, controlling for several individual, macroeconomic, and region-specific characteristics, and for potential selection mechanisms. Findings highlight that, on average, the adoption of new automation technologies is associated with a higher probability of being inactive. This is consistent with the view that automation may exacerbate job insecurity, psychological discouragement, and detachment from job-seeking. This relationship is heterogeneous across demographic groups, with younger individuals being relatively more affected." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Good Jobs or Bad Jobs? Immigrant Workers in the Gig Economy (2026)
Zitatform
Liu, Cathy Yang & Rory Renzy (2026): Good Jobs or Bad Jobs? Immigrant Workers in the Gig Economy. In: International migration review, Jg. 60, H. 1, S. 114-138. DOI:10.1177/01979183241309585
Abstract
"New work arrangements enabled by online platforms, or gig work, saw substantive growth during the COVID-19 pandemic. Various estimates have suggested the wide participation of workers in the gig economy, with minority and immigrant workers well represented. The quality of work is a multi-dimensional concept that goes beyond earnings. One framework of good jobs and bad jobs centers on control over work schedule, content and duration, stability, safety, benefits and insurance, as well as career advancement opportunities. Using a newly released national survey focused on entrepreneurs and workers in the United States, we find that about 18.5 percent immigrant workers and 21.1 percent native-born workers participated in the gig economy as their primary or secondary job. In terms of job quality, immigrant gig workers work shorter hours and have significantly less fringe benefits than non-gig workers as well as U.S.-born gig workers, reflecting a double disadvantage. However, they tend to have higher entrepreneurial aspirations, suggesting the transient nature of gig arrangements and potential for career advancements. This paper provides a comprehensive analysis of the characteristics and implication of immigrants’ engagement with the gig economy and offers policy and theoretical discussions." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Do robots decrease humans’ wages? (2026)
Zitatform
Logchies, Thomas, Tom Coupé & W. Robert Reed (2026): Do robots decrease humans’ wages? In: Applied Economics Letters, Jg. 33, H. 13, S. 2194-2198. DOI:10.1080/13504851.2025.2466748
Abstract
"While there are studies that show a positive or negative impact of robots on wages, a meta-analysis of 2,586 estimates from 52 studies in this paper finds that when one looks at the literature as a whole, there is no clear evidence of a sizable impact of robots on wages." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The Sum of All (Workplace) Fears: How Managers Mediate the Fear of AI Job Displacement (2026)
Makridis, Christos;Zitatform
Makridis, Christos (2026): The Sum of All (Workplace) Fears: How Managers Mediate the Fear of AI Job Displacement. (CESifo working paper 12678), München, 55 S.
Abstract
"AI is transforming work, but workers' responses to these technologies depend not only on exposure to AI, but also on how organizations, especially managers, oversee the transition. Using longitudinal data from the Gallup Workforce Panel from 2023-2026, I examine whether managers and workplace practices shape employees' fears that AI will eliminate their jobs. Across survey waves, roughly 3-4 percent of workers say their job is very likely to be eliminated within five years because of new technology, automation, robots, or artificial intelligence, while about 14-19 percent say it is somewhat or very likely. Concern is substantially higher among frequent AI users. Stronger workplace practices are associated with lower displacement fear: a one-standard-deviation increase in workplace quality is associated with 13-24 percent lower odds of reporting greater displacement risk, and workers reporting the highest level of organizational wellbeing support are 6-6.8 percentage points less likely to say their job is somewhat or very likely to be displaced in cross-sectional specifications. Frequent AI use is positively associated with perceived displacement risk, with estimates ranging from about 3-12.9 percentage points across the main specifications and reaching 6 percentage points in the most saturated respect model. However, this association is weaker in higher-quality workplace environments: among workers reporting the highest level of organizational wellbeing support, the frequent-AI-use premium is reduced by up to 9.0 percentage points. In short, managers play a central role in shaping how workers interpret AI adoption." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Improving the effects of industrial robot adoption on employment, total factor productivity, and real wages in 52 world economies and OECD members (2026)
Zitatform
Matsuki, Takashi (2026): Improving the effects of industrial robot adoption on employment, total factor productivity, and real wages in 52 world economies and OECD members. In: Review of world economics, Jg. 162, H. 2, S. 417-448. DOI:10.1007/s10290-025-00626-z
Abstract
"This study investigates the effects of industrial robot adoption in the production process on unemployment rate, employment ratio in manufacturing, and total factor productivity (TFP) growth in 52 countries, and real wage growth in 31 and 20 OECD member countries for 2007–2019. The operating stock of robots per employee significantly impacts these variables; robot adoption lowers the unemployment rate and raises TFP and real wage growth. However, it reduces the employment ratio in manufacturing. In addition, the slight but significant positive contribution of robot adoption is observed only in the 90-percentile (top 10-percentile) of the real wage distribution. Interestingly, workers in the bottom and top tails (10- and 90-percentiles) of the wage distribution asymmetrically benefit from robotization. The industry ratio of value-added improves the labor market by reducing the unemployment rate and raising the employment ratio in manufacturing, TFP growth, and real wage growth. The information and communication technology (ICT) development also positively contributes to the employment ratio in Asia’s manufacturing, TFP growth, and real wage growth." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
How local labour market skill relatedness and size moderate the impacts of automation (2026)
Zitatform
Njekwa Ryberg, Peter (2026): How local labour market skill relatedness and size moderate the impacts of automation. In: Regional Studies, Jg. 60, H. 1. DOI:10.1080/00343404.2025.2598031
Abstract
"This paper examines how local labour market skill relatedness and size moderate the impacts of automation on occupations across Swedish local labour markets. Using administrative data and a spatially explicit risk of automation measure that accounts for regional differences in occupational task contents, it finds a negative association between automation and employment growth and wage income growth for non-metropolitan occupations between 2011 and 2021. Skill relatedness and labour market size mitigate these negative relationships. In contrast, no negative associations are found for metropolitan occupations. Due to their higher shares of non-automatable tasks, they are more resilient to adverse automation effects." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Human-centred digital transitions and skill mismatches in European workplaces (2026)
Zitatform
Pouliakas, Konstantinos & Giulia Santangelo (2026): Human-centred digital transitions and skill mismatches in European workplaces. (CEDEFOP working paper series / European Centre for the Development of Vocational Training 2026,01), Luxembourg, 163 S. DOI:10.2801/9894877
Abstract
"New digital and artificial intelligence technologies are fast reshaping skill requirements in the EU labour market, fostering skill mismatches. There are marked concerns about the potentially adverse consequences of automation and AI on employment, as well as the lagging competitiveness of EU economies as individuals’ upskilling or reskilling is failing to adapt. To deepen understanding of how digitalisation is affecting the nature of work and skill mismatches in EU labour markets, Cedefop carried out the second wave of the European skills and jobs survey in 2021. In this special edition of Cedefop’s working paper series, ten original, short contributions have been drafted in which researchers explore in depth, for the first time, the ESJS2 microdata. The publication presents a wealth of focused and robust empirical analyses, covering a wide range of different issues on how the digital transition is affecting jobs, skills and training in Europe." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Where Have All the (Boomer) Routine Workers Gone? (2026)
Scotese, Carol A.;Zitatform
Scotese, Carol A. (2026): Where Have All the (Boomer) Routine Workers Gone? In: The B.E. Journal of Economic Analysis and Policy, Jg. 26, H. 2, S. 403-446. DOI:10.1515/bejeap-2024-0396
Abstract
"This paper examines the employment outcomes of a cohort of non-college educated individuals who exit employment from occupations most exposed to automation risk. The analysis employs a novel set of granular task measures estimated from the detailed job attributes in the Occupational Information Network (O*NET). The granularity enables a rich characterization of non-routine work and task mobility choices for those without a college degree. The data yield multiple types of interpersonal, decision-making, cognitive, and technical tasks. Employing the granular tasks to analyze the employment outcomes for non-college educated workers who transition out of routine work, this study finds (1) the granular measures detect abstract tasks performed intensively in a range of skill contexts, (2) when exiting routine intensive work, non-college propensity to enter abstract work is just under 65 %, and (3) approximately one-quarter of those entries are into tasks yielding average wage gains for those making that transition." (Author's abstract, IAB-Doku, © De Gruyter) ((en))
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Literaturhinweis
Click, Code, Earn: The Returns to Digital Skills (2026)
Zitatform
Soares Martins Neto, Antonio, Yan Liu, Saloni Khunara & Juan Manuel Porras Lopez (2026): Click, Code, Earn: The Returns to Digital Skills. (Policy research working paper / The World Bank 11313), Washington, DC, 55 S.
Abstract
"This paper provides the first comprehensive, cross-country evidence on the wage returns to digital skills, using more than 67 million job postings from 29 countries between 2021 and 2024. The paper develops a harmonized digital skills taxonomy and examines returns across extensive (any digital skill required), intensive (number of digital skills), and qualitative (type of digital skill) margins. Digital skills command substantial wage premiums globally, with particularly pronounced returns in low- and middle-income countries where such competencies remain scarce. Requiring at least one digital skill raises advertised wages by 1.6 percent on average, with returns of 1.3 percent in high-income countries and 7.5 percent in low- and middle-income countries. Each additional digital skill increases wages by 0.5 percent in high-income countries and 2.6 percent in low- and middle-income countries. Intermediate and advanced skills yield even higher premiums of 0.8 percent in high-income countries and 3 percent in low- and middle-income countries. Each traditional artificial intelligence skill offers returns of 2.9 percent across all countries. Most remarkably, generative artificial intelligence skills demonstrate the highest premiums: wage increases of 7 to 9 percent in technical occupations, and sizable premiums of 25 to 36 percent for generative artificial intelligence literacy skills in nontechnical roles, reflecting both their productivity potential and current scarcity. Returns are consistently higher in digitally-intensive industries and occupations and are amplified by workers' education and experience, suggesting strong complementarities between digital competencies and traditional human capital. These findings highlight the critical importance of digital skills for individual earnings and economic development, particularly in low- and middle-income countries." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment (2026)
Zitatform
Stephany, Fabian, Ole Teutloff & Angelo Leone (2026): AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment. (arXiv papers), 46 S. DOI:10.48550/arXiv.2601.13286
Abstract
"The growing adoption of artificial intelligence (AI) technologies has heightened interest in the labour market value of AI-related skills, yet causal evidence on their role in hiring decisions remains scarce. This study examines whether AI skills serve as a positive hiring signal and whether they can offset conventional disadvantages such as older age or lower formal education. We conduct an experimental survey with 1,700 recruiters from the United Kingdom and the United States. Using a paired conjoint design, recruiters evaluated hypothetical candidates represented by synthetically designed résumés. Across three occupations – graphic designer, officeassistant, and software engineer –, AI skills significantly increase interview invitation probabilities by approximately 8 to 15 percentage points. AI skills also partially or fully offset disadvantages related to age and lower education, with effects strongest for office assistants, where formal AI certification plays an additional compensatory role. Effects are weaker for graphic designers, consistent with more skeptical recruiter attitudes toward AI in creative work. Finally, recruiters’ own background and AI usage significantly moderate these effects. Overall, the findings demonstrate that AI skills function as a powerful hiring signal and can mitigate traditional labour market disadvantages, with implications for workers’ skill acquisition strategies and firms’ recruitment practices." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Artificial intelligence and technological unemployment (2026)
Zitatform
Wang, Ping & Tsz-Nga Wong (2026): Artificial intelligence and technological unemployment. In: Journal of monetary economics, Jg. 158. DOI:10.1016/j.jmoneco.2026.103905
Abstract
"How large are the effects of artificial intelligence (AI) on labor productivity and unemployment? We develop a labor-search model of technological unemployment where AI learns from workers, raises productivity, and displaces them if renegotiation fails. The model admits three steady states: no AI; some AI with limited capability, more job creation but higher unemployment; unbounded AI with endogenous growth and employment gains. Calibrated to U.S. data, the model implies a threefold productivity gain in the long run for workers exposed to AI but a 23% employment loss, half within five years. Plausible parameters give rise to global and local indeterminacy with endogenous cycles in productivity and unemployment, underscoring the uncertainty of AI’s impacts in line with a wide range of empirical findings. Equilibria are inefficient despite the Hosios condition; subsidizing jobs at risk of AI displacement is constrained optimal." (Author's abstract, IAB-Doku, © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
The Impact of Aging and AI on Japan's Labor Market: Challenges and Opportunities (2025)
Asao, Kohei; Seitani, Haruki; Stepanyan, Ara; Xu, TengTeng;Zitatform
Asao, Kohei, Haruki Seitani, Ara Stepanyan & TengTeng Xu (2025): The Impact of Aging and AI on Japan's Labor Market: Challenges and Opportunities. (IMF working papers / International Monetary Fund 2025,184), Washington, DC, 17 S.
Abstract
"This paper explores the complex roles of demographic changes and technological innovation in shaping Japan's labor market. We use regression analysis to assess the impact of population aging on labor productivity and shortages. Our findings indicate that the aging workforce contributes to labor shortages and potentially weighs on labor productivity. We also investigate occupational level data to identify the complementarity and substitutability of AI in occupational tasks as well as skill transferability. Our research reveals that Japanese workers face lower exposure to AI compared to their counterparts in other advanced economies, thereby constraining AI's potential to mitigate labor shortages. Furthermore, the disparities in skill requirements across occupations with different AI exposures highlight the importance of facilitating labor mobility from displaced jobs to those in demand." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
On automation, labor reallocation and welfare (2025)
Zitatform
Auray, Stéphane & Aurélien Eyquem (2025): On automation, labor reallocation and welfare. In: Journal of Economic Dynamics and Control, Jg. 177. DOI:10.1016/j.jedc.2025.105129
Abstract
"We develop an open-economy model of endogenous automation with heterogeneous firms and labor-market reallocation to quantify the contribution of various trends to the adoption of robots in the U.S. economy. The decline in the relative price of robots is the major trend leading to automation, but interacts with other trends that either hinder (rising entry costs, rising markups) or slightly foster (rising labor productivity, declining trade costs) the adoption of robots. Taken alone, the decline in the relative price of robots produces moderate welfare gains in the long run, but less than labor productivity growth. We then exploit our model to show that a decline in the relative price of robots (i) generates small positive cross-country automation spillovers and (ii) produces inefficient labor-market reallocation since a small subsidy on robots combined with a training subsidy can generate small welfare gains. Our main conclusion is that automation can not be simply modeled as an exogenous decline in the price of robots, and must be analyzed in a broader framework taking into account trends affecting firms, such as the decline in business dynamism and the rise in markups." (Author's abstract, IAB-Doku, © 2025 The Author(s). Published by Elsevier B.V.) ((en))
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Literaturhinweis
Expertise (2025)
Autor, David; Thompson, Neil;Zitatform
Autor, David & Neil Thompson (2025): Expertise. In: Journal of the European Economic Association, Jg. 23, H. 4, S. 1203-1271. DOI:10.1093/jeea/jvaf023
Abstract
"When job tasks are automated, does this augment or diminish the value of labor in the tasks that remain? We argue the answer depends on whether removing tasks raises or reduces the expertise required for remaining non-automated tasks. Since the same task may be relatively expert in one occupation and inexpert in another, automation can simultaneously replace experts in some occupations while augmenting expertise in others. We propose a conceptual model of occupational task bundling that predicts that changing occupational expertise requirements have countervailing wage and employment effects: automation that decreases expertise requirements reduces wages but permits the entry of less expert workers; automation that raises requirements raises wages but reduces the set of qualified workers. We develop a novel, content-agnostic method for measuring job task expertise, and we use it to quantify changes in occupational expertise demands over four decades attributable to job task removal and addition. We document that automation has raised wages and reduced employment in occupations where it eliminated inexpert tasks, but lowered wages and increased employment in occupations where it eliminated expert tasks. These effects are distinct from—and in the case of employment,opposite to—the effects of changing task quantities. The expertise framework resolves the puzzle of why routine task automation has lowered employment but often raised wages in routine task-intensive occupations. It provides a general tool for analyzing how task automation and new task creation reshape the scarcity value of human expertise within and across occupations." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Intersecting Shocks: The Combined Labor Market Impacts of Automation and Immigration (2025)
Bennett, Patrick; Johnsen, Julian Vedeler;Zitatform
Bennett, Patrick & Julian Vedeler Johnsen (2025): Intersecting Shocks: The Combined Labor Market Impacts of Automation and Immigration. (CESifo working paper 12217), München, 41 S.
Abstract
"We study how the labor market shocks of automation and immigration interact to shape workers' outcomes. Using matched employer –employee data from Norwegian administrative registers, we combine animmigration shock triggered by the European Union's 2004 enlargement with an automation shock based on the adoption of industrial robots across Europe. Although these shocks largely occur in separate industries, we show that automation reduces earnings not only in manufacturing but also in construction, where tasks overlap with robot-exposed sectors. Importantly, workers jointly exposed to automation and immigration suffer earnings losses greater than those facing either shock in isolation. These losses are driven by downward occupational mobility into low-wage services and re-sorting into lower-premium firms. Even within the Norwegian welfare system, the ability of social insurance to offset these long-run earnings declines is limited. Our findings underscore the importance of analyzing labor market shocks jointly, rather than in isolation, to fully understand their distributional consequences." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Remote work, skill upgrading, and wage inequality post-COVID (2025)
Zitatform
Bennett, Jeremy (2025): Remote work, skill upgrading, and wage inequality post-COVID. In: Economics of Innovation and New Technology, S. 1-24. DOI:10.1080/10438599.2025.2602133
Abstract
"This paper examines how the widespread shift to remote work during the COVID-19 pandemic reshaped skill development and wage inequality across occupations in the United States. Using a difference-in-differences framework and data from the Current Population Survey (CPS), American Time Use Survey (ATUS), and O*NET, we compare outcomes for remote-capable and non-remote occupations before and after the pandemic. Results show that remote-capable jobs experienced significantly higher wage growth – approximately 4–5 percent – relative to non-remote jobs, even after accounting for worker and occupational characteristics. These occupations also displayed greater gains in educational attainment and digital skill engagement, while non-remote occupations faced disruptions in access to training. The findings align with human-capital and task-based theories, suggesting that remote work intensified skill-biased technological inequality. Policy implications include the need for targeted workforce training, equitable digital infrastructure investment, and institutional support for workers in less adaptable roles. The study contributes to understanding how technological and organizational change reshape human capital formation and wage structures in the post-pandemic labor market." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The dynamics of automation adoption: Firm-level heterogeneity and aggregate employment effects (2025)
Zitatform
Bisio, Laura, Angelo Cuzzola, Marco Grazzi & Daniele Moschella (2025): The dynamics of automation adoption: Firm-level heterogeneity and aggregate employment effects. In: European Economic Review, Jg. 173. DOI:10.1016/j.euroecorev.2024.104943
Abstract
"We investigate the impact of investment in automation-related goods on adopting and non-adopting firms in the Italian economy during 2011–2019. We integrate datasets on trade activities, firms’, and workers’ characteristics for the population of Italian importing firms and estimate the effects on adopters ’ outcomes within a difference-in-differences design exploiting import lumpiness in product categories linked to automation technologies (including robots). We find a positive average adoption effect on the adopters’ employment: firms are, on average, around 3% larger in terms of employment after an automation spike. Crucially, the employment effect is heterogeneous across firms: a positive effect is predominant among small firms, which are around 5% larger five years after the spike; on the contrary, a negative displacement effect is predominant among medium and large firms, with an employment contraction at five years of around -4%. This result can shed light on one potential reason behind the mixed results in the literature, i.e. different size distribution of the samples used. We complete the framework with a 5-digit sector-level analysis showing that adopting automation technologies has an overall weak negative effect on aggregate employment, and with an analysis of the competition effects of automation, showing that non-adopters suffer a loss in sales and employment." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Automation and segmentation: Downgrading employment quality among the former “insiders” of Western European labour markets (2025)
Zitatform
Buzzelli, Gregorio (2025): Automation and segmentation: Downgrading employment quality among the former “insiders” of Western European labour markets. In: International Journal of Social Welfare, Jg. 34, H. 2. DOI:10.1111/ijsw.70011
Abstract
"The literature on labor market segmentation traditionally looks at servitisation as the main structural driver behind the rise of employment precariousness, overlooking another crucial engine of the knowledge-economy transition: the Information and Communication Technologies (ICT) revolution. This paper proposes a task-based approach to complement the skill-biased framework usually applied to labor market segmentation, investigating the correlation between occupational exposure to the risk of automation and low-quality employment. The empirical analysis, based on 14 countries sampled from ESS (2002–2018), shows a strong correlation between technological replaceability and low income across all of Western Europe, especially after the Great Recession, while its association with atypical employment is mainly driven by fixed-term contracts in Central and Southern Europe and by part-time arrangements in Anglo-Saxon and Scandinavian countries. Overall, a “recalibrated” dualisation emerges in Western European labor markets, characterized by the diffusion of low labor earnings and atypical contracts among mid-skill routine workers, besides the low-skill service precariat." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Technological innovations and workers’ job insecurity: the moderating role of human resource strategies (2025)
Zitatform
Caselli, Mauro, Andrea Fracasso, Arianna Marcolin & Sergio Scicchitano (2025): Technological innovations and workers’ job insecurity: the moderating role of human resource strategies. In: Journal of industrial and business economics, Jg. 52, H. 1, S. 153-176. DOI:10.1007/s40812-024-00329-w
Abstract
"In this paper, we empirically assess the impact of firms’ technological innovations on the workers’ perceived probability of job loss. We take advantage of a unique dataset based on a large and representative cross-sectional survey covering several characteristics of Italian workers and their firms. We find that a firm ’s technological adoption reduces job insecurity among its surviving workers, and the effect is stronger when the innovation makes tasks simpler and their execution more precise. We also find that the relationship between technological innovation and job insecurity is moderated by human resource strategies, such as training programs, labor-saving automation and dismissal plans adopted after the introduction of the innovation. Thus, workers’ perceptions of job insecurity vary significantly across innovative firms, and firms’ human resource strategies act as arelevant moderating factors." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment (2025)
Zitatform
Caselli, Mauro, Andrea Fracasso, Sergio Scicchitano, Silvio Traverso & Enrico Tundis (2025): What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment. In: Research Policy, Jg. 54, H. 1. DOI:10.1016/j.respol.2024.105135
Abstract
"This work investigates the impact that changes in the local exposure to robots had on changes in Italian employment over the period 2011–2018. It contributes to the debate by providing novel and granular evidence on the impact of robot adoption on new activity-based groups of occupations and by focusing on the overlap between the functional similarities of robot applications and occupations. This framework, consistently centered on workers ’ and robots’ activities, reveals highly heterogeneous effects of robotization, ranging from positive to negative across different groups of occupations, thereby supporting a nuanced and granular reading of this debated phenomenon. In particular, the local share of robot operators increases where the increase in robot adoption is larger, while the local share of workers using intensively their torso decreases." (Author's abstract, IAB-Doku, © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
AI and the global productivity divide: Fuel for the fast or a lift for the laggards? (2025)
Zitatform
Chaar, Tania, Francesco Filippucci, Cecilia Jona-Lasinio & Giuseppe Nicoletti (2025): AI and the global productivity divide. Fuel for the fast or a lift for the laggards? (OECD Artificial Intelligence Papers 51), Paris, 42 S. DOI:10.1787/c315ea90-en
Abstract
"Artificial Intelligence (AI) has the potential to be an important driver of productivity growth over the next decade, even if with significant cross-country heterogeneity. This paper examines the potential of AI to foster productivity growth in Low-Income Countries (LICs) and Lower-Middle-Income Countries (LMICs). LICs and LMICs risk benefiting less from AI due to low incidence of knowledge-intensive services, where gains from AI mostly occur. Additionally, barriers to AI adoption include inadequate digital infrastructure, low levels of education and skills in the workforce, limited access to financing for high AI adoption costs, and underdeveloped regulatory frameworks. At the same time, LICs and LMICs may benefit from factors such as a young workforce and international spillovers through knowledge transfers. Overall, structural weaknesses in LICs and LMICs risk outweighing these potential advantages. This underscores the need for policies that enhance capabilities for AI adoption in LICs and LMICs and help seizing long-run opportunities from the global AI economy." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The Iceberg Index: Measuring Workforce Exposure Across the AI Economy (2025)
Chopra, Ayush; Bhattacharya, Santanu; Schwarze, Alice C.; Ahmad, Feroz; Balaprakash, Prasanna; Garg, Aditi; Salvador, DeAndrea; Wright, Teddy; Raskar, Ramesh; Paul, Ayan;Zitatform
Chopra, Ayush, Santanu Bhattacharya, DeAndrea Salvador, Ayan Paul, Teddy Wright, Aditi Garg, Feroz Ahmad, Alice C. Schwarze, Ramesh Raskar & Prasanna Balaprakash (2025): The Iceberg Index: Measuring Workforce Exposure Across the AI Economy. (arXiv papers), 21 S. DOI:10.48550/arXiv.2510.25137
Abstract
"Artificial Intelligence is reshaping America’s over $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI automates quality control in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human–AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills across 3,000 counties and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approximately $211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approximately $1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how capabilities may spread under alternative scenarios, Project Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation. Iceberg is built with the AgentTorch framework." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Equalising the effects of automation? The role of task overlap for job finding (2025)
Zitatform
Dabed, Diego, Sabrina Genz & Emilie Rademakers (2025): Equalising the effects of automation? The role of task overlap for job finding. In: Labour Economics, Jg. 96. DOI:10.1016/j.labeco.2025.102766
Abstract
"This paper investigates whether task overlap can equalise the distributional effects of automation for unemployed job seekers displaced from routine jobs. Using a language model, we establish a novel job-to-job task similarity measure. Exploiting the resulting job network to define job markets flexibly, we find that only the most similar jobs affect job finding. Since automation-exposed jobs overlap with other highly exposed jobs, task-based reallocation provides little relief for affected job seekers. We show that this is not true for more recent software exposure, for which task overlap lowers the inequality in job finding." (Author's abstract, IAB-Doku, © 2025 The Authors. Published byElsevier B.V.) ((en))
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Literaturhinweis
Macroeconomic and Labor Market Drivers of AI Adoption in Europe: A Machine Learning and Panel Data Approach (2025)
Zitatform
Drago, Carlo, Alberto Costantiello, Marco Savorgnan & Angelo Leogrande (2025): Macroeconomic and Labor Market Drivers of AI Adoption in Europe: A Machine Learning and Panel Data Approach. In: Economies, Jg. 13, H. 8. DOI:10.3390/economies13080226
Abstract
"This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Narrowing the digital divide: Economic and social convergence in Europe’s digital transformation (2025)
Duff, Cían; Soldi, Rossella; Hyland, Marie; Cavallini, Simona; Peruffo, Eleonora; Krieg, Marielena;Zitatform
Duff, Cían, Marie Hyland, Marielena Krieg, Eleonora Peruffo, Simona Cavallini & Rossella Soldi (2025): Narrowing the digital divide. Economic and social convergence in Europe’s digital transformation. (Eurofound research report / European Foundation for the Improvement of Living and Working Conditions), Dublin, 822 S. DOI:10.2806/1764165
Abstract
"Digitalization has been on the EU policy agenda since 2000. While great strides have been made in this area over the past two decades, the digital transformation is not yet complete. This report seeks to deepen our understanding of the evolution towards a digital Europe. By applying the lens of convergence, the report assesses the progress of Member States towards the EU ’s policy targets, where Member States are growing together and wheredigital gaps are expanding. It also considers the gaps in the progress of digitalization between socioeconomic groups and regions. According to almost all indicators analysed, historically lower-performing Member States have been catching up with the digital leaders. However, at a more granular level, digitalization of businesses has been uneven and significant inequalities persist between regions and socioeconomic groups. The report shines a light on the role of digitalization in the EU’s economic convergence and considers the progress in and benefits of digitalisation for the private sector. The findings show that access is still an issue for vulnerable groups, in particular low-income households, older individuals and those with lower levels of education. Importantly, these are the groups that are more reliant on public services, and they may struggle to access e-government. While progress is being made, some groups remain at risk of being left behind in the digital transition. Considering this, the report highlights a range of policy approaches being deployed across Europe that aim to narrow the digital divide." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Industrial robots and employment change in manufacturing: A decomposition analysis (2025)
Zitatform
Eder, Andreas, Wolfgang Koller & Bernhard Mahlberg (2025): Industrial robots and employment change in manufacturing: A decomposition analysis. In: Structural Change and Economic Dynamics, Jg. 74, S. 591-602. DOI:10.1016/j.strueco.2025.05.014
Abstract
"This paper examines the contribution of industrial robots to employment change in manufacturing in a sample of 17 European countries and the USA over the period 2004 to 2019. We combine index decomposition analysis (IDA) and production-theoretical decomposition analysis (PDA). First, we use IDA to decompose employment change in the manufacturing industry into changes in (aggregate) manufacturing output, changes in the sectoral structure of the manufacturing industry, and changes in labor intensity (the inverse of labor productivity) which is a composite index of labour intensity change within each of the nine sub-sectors of total manufacturing. Second, we use PDA to further decompose labor intensity change to isolate the contribution of technical efficiency change, technological change, human capital change, change in non-robot capital intensity and change in robot capital intensity to employment change. In almost all of the countries considered, labour intensity is falling in entire manufacturing, exerting a dampening effect on employment. Robotization contributes to this development by reducing labor intensities and employment in all countries and sub-sectors, though to varying degrees. Manufacturing output, in turn, grows in all countries except Greece, Spain and Italy, which increases employment and counteracts or in some countries even more than offsets the dampening effect of declining labor intensities. The structural change within manufacturing has an almost neutral effect in many countries." (Author's abstract, IAB-Doku, © 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.) ((en))
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Literaturhinweis
SME digitalisation in the EU: Trends, policies and impacts (2025)
Eiffe, Franz Ferdinand; Biaggi, Elena; Riso, Sara; Miliadis, Grigorios; Loo, Jasper van;Zitatform
Eiffe, Franz Ferdinand, Sara Riso, Elena Biaggi, Jasper van Loo & Grigorios Miliadis (2025): SME digitalisation in the EU: Trends, policies and impacts. (Eurofound research report / European Foundation for the Improvement of Living and Working Conditions), Luxembourg, 78 S. DOI:10.2806/8684886
Abstract
"This report discusses the digital transformation of small and medium-sized enterprises (SMEs) in the European Union, highlighting its importance for their competitiveness and the EU’s economy. The report explores the degreeof digitalisation in SMEs in the EU, including the adoption of digital technologies, e-commerce and e-business practices. It also examines the impact of the COVID-19 pandemic on SMEs’ digitalisation and identifieskey challenges, including lack of infrastructure, financing and digital skills. In addition, the report reviews policy frameworks and support measures related to digitalisation and the development of digital skills in SMEs. Furthermore, it presents an empirical analysis of how digital technology use is related to job quality at the workplace level." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Cross-country skills-technology policy debates through large language models (2025)
Zitatform
Einhoff, Jan, Isabella López Trejos & Caroline Paunov (2025): Cross-country skills-technology policy debates through large language models. (OECD science, technology and industry working papers 2025,20), Paris, 43 S. DOI:10.1787/d5f669be-en
Abstract
"Language models, this paper conducts a cross-country comparative innovation policy analysis of skills-technology policy debates across seven OECD member countries (Austria, Canada, Finland, Germany, Korea, Sweden, and the United Kingdom). Results highlight the dominance of STEM (science, technology, engineering and mathematics) and digital skills in these policy debates, the relative neglect of green skills, and the emphasis on soft skills across all technology fields. The analysis also identifies common policy instruments, which include collaborative platforms and direct financial support. Overall, the paper shows how large language models can help policy analysts identify patterns and gaps in extensive policy texts that nonetheless critically demands expert oversight and careful interpretation." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Predictive AI and productivity growth dynamics: Evidence from French firms (2025)
Zitatform
Fontanelli, Luca, Mattia Guerini, Raffaele Miniaci & Angelo Secchi (2025): Predictive AI and productivity growth dynamics: Evidence from French firms. In: Journal of Economic Behavior & Organization, Jg. 240. DOI:10.1016/j.jebo.2025.107336
Abstract
"While artificial intelligence (AI) adoption holds the potential to enhance business operations through improved forecasting and automation, its relation with average productivity growth remain highly heterogeneous across firms. This paper shifts the focus and investigates the impact of predictive AI on the volatility of firms’ productivity growth rates. Using firm-level data from the 2019 French ICT survey, we provide robust evidence that AI use is associated with increased volatility. This relationship persists across multiple robustness checks, including analyses balancing AI users and other firms based on key observables. To propose a possible mechanisms underlying this relation, we compare firms that purchase AI from external providers (“AI buyers”) and those that develop AI in-house (“AI developers”). Our results show that heightened volatility is concentrated among AI buyers, whereas firms that develop AI internally experience no such association. Finally, we find that the AI-volatility link among “AI buyers” is mitigated in firms with a higher share of ICT engineers and technicians, suggesting that AI’s successful integration requires complementary human capital." (Author's abstract, IAB-Doku, © 2025 The Author(s). Published by Elsevier B.V.) ((en))
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Literaturhinweis
The digital skill premium: Evidence from job vacancy data (2025)
Garcia-Lazaro, Aida ; Mendez-Astudillo, Jorge ; Newnes, Linda ; Larkin, Charles ; Lattanzio, Susan ;Zitatform
Garcia-Lazaro, Aida, Jorge Mendez-Astudillo, Susan Lattanzio, Charles Larkin & Linda Newnes (2025): The digital skill premium: Evidence from job vacancy data. In: Economics Letters, Jg. 250. DOI:10.1016/j.econlet.2025.112294
Abstract
"This paper examines the relationship between digital skills demand and posted wages in the UK using novel vacancy data. Digital skills — classified into basic, intermediate, and advanced using an XGBoost model — are linked to significant wage premiums. Within occupations, they are associated with 5.8% higher wages, with advanced and intermediate skills increasing wages by up to 8.9% when listed in job postings. Each additional digital skill increases wages by 1%, rising to 1.6% for advanced and intermediate skills. Artificial intelligence (AI) and cybersecurity skills yield particularly high returns, increasing wages by 8.6%–9.7% when listed and by 4.8%–5.4% per additional skill." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
A technological construction of society: Comparing GPT-4 and human respondents for occupational evaluation in the UK (2025)
Zitatform
Gmyrek, Pawel, Christoph Lutz & Gemma Newlands (2025): A technological construction of society: Comparing GPT-4 and human respondents for occupational evaluation in the UK. In: BJIR, Jg. 63, H. 1, S. 180-208. DOI:10.1111/bjir.12840
Abstract
"Despite initial research about the biases and perceptions of large language models (LLMs), we lack evidence on how LLMs evaluate occupations, especially in comparison to human evaluators. In this paper, we present a systematic comparison of occupational evaluations by GPT-4 with those from an in-depth, high-quality and recent human respondents survey in the UK. Covering the full ISCO-08 occupational landscape, with 580 occupations and two distinct metrics (prestige and social value), our findings indicate that GPT-4 and human scores are highly correlated across all ISCO-08 major groups. At the same time, GPT-4 substantially under- or overestimates the occupational prestige and social value of many occupations, particularly for emerging digital and stigmatized or illicit occupations. Our analyses show both the potential and risk of using LLM-generated data for sociological and occupational research. We also discuss the policy implications of our findings for the integration of LLM tools into the world of work." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))
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Literaturhinweis
Generative AI and jobs: a refined global index of occupational exposure (2025)
Gmyrek, Pawel ; Troszyński, Marek; Berg, Janine ; Kamiński, Karol; Nafradi, Balint ; Konopczyński, Filip; Rosłaniec, Konrad; Ładna, Agnieszka;Zitatform
Gmyrek, Pawel, Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Balint Nafradi, Konrad Rosłaniec & Marek Troszyński (2025): Generative AI and jobs. A refined global index of occupational exposure. (ILO working paper / International Labour Organization 140), Geneva, 72 S. DOI:10.54394/hetp0387
Abstract
"This study updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI), incorporating recent advances in the technology and increasing user familiarity with GenAI tools. Using a representative sample from the 29,753 tasks in the Polish occupational classification system and a survey of 1,640 people employed in each 1-digit ISCO-08 groups, we collect 52,558 data points regarding perceive potential of automation for 2,861 tasks. We then compare this input with a survey and several rounds of Delphi-style discussions among a smaller group of international experts. Based on this process, we create a repository of knowledge about task automation that goes beyond national specificities and use it to develop an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08. Our 2025 scores are presented in a revised framework of four progressively increasing exposure gradients, with a new set of global estimates of employment shares exposed to GenAI. Clerical occupations continue to have the highest exposure levels. Additionally, some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles. Globally, one in four workers are in an occupation with some GenAI exposure. 3.3% of global employment falls into the highest exposure category, albeit with significant differences between female (4.7%) and male employment (2.4%). These differences increase with countries’ income (9.6% female vs 3.5% male in Gradient 4in HICs), and so does the overall exposure (11% of total employment in LICs vs 34% in HICs). As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI. Linking our refined index with national micro data enables precise projections of such transformations, offering a foundation for social dialogue and targeted policy responses to manage the transition." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Governing the Digital Transition: The Moderating Effect of Unemployment Benefits on Technology‐Induced Employment Outcomes (2025)
Zitatform
Golboyz, Mark (2025): Governing the Digital Transition: The Moderating Effect of Unemployment Benefits on Technology‐Induced Employment Outcomes. In: Social Inclusion, Jg. 13. DOI:10.17645/si.10114
Abstract
"The digital transition shapes work in numerous ways. For instance, by affecting employment structures. To ensure that the digital transition results in better employment opportunities in terms of socio-economic status, labor markets have to be guided appropriately. The European Pillar of Social Rights can be the political framework to foster access to employment and tackle inequalities that result from the digital transition. Current research primarily examines scenarios of occupational upgrading and employment polarisation. In the empirical literature, there is no consensus on which of these developments prevail. Findings vary between countries and across different study periods. Accordingly, this article provides a theoretical explanation for the conditions under which occupational upgrading and employment polarization become more likely. Further, this article examines how the use of information and communication technology (ICT) capital in the production of goods and services affects the socio-economic status of individuals and, more importantly, whether unemployment benefits moderate this effect. Methodologically, the article uses multilevel maximum likelihood regression models with an empirical focus on 12 European countries and 19 industries. The analysis is based on data from the European Labour Force Survey (EU-LFS), the European Union Level Analysis of Capital, Labour, Energy, Materials, and Service Inputs (EU-KLEMS) research project, and the Comparative Welfare Entitlements Project (CWEP). The results of the article indicate that generous unemployment benefits are associated with occupational upgrading. This implies that educational and vocational labor market policies need to be developed to prevent the under-skilled from being left behind and to enable these groups to benefit from the digital transition. Consequently, it is not only the extent to which work involves routine tasks or the skills of workers that determine how technological change affects employment, but also social rights shape employment through unemployment benefits." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Does the Technological Transformation of Firms Go Along With More Employee Control Over Working Time? Empirical Findings From an EU-Wide Combined Dataset (2025)
Zitatform
Greenan, Nathalie & Silvia Napolitano (2025): Does the Technological Transformation of Firms Go Along With More Employee Control Over Working Time? Empirical Findings From an EU-Wide Combined Dataset. In: Review of Political Economy, Jg. 37, H. 2, S. 500-522. DOI:10.1080/09538259.2024.2445096
Abstract
"We investigate the links between the technological transformation of firms and employee control over working time. We conduct EU-wide analysis at the meso-level by relating information from the European Company Survey 2019 (Eurofound and Cedefop) with the Labour Force Survey ad hoc module 2019 (Eurostat). This dataset allows analysing the technological transformation of firms as a relationship between three types of investments (in R&D, digital technologies and learning capacity of the organisation) that spur innovation outputs. We then study the consequences of the technological transformation on the spread of unfavourable working time arrangements, distinguishing between individual and organisation-oriented arrangements. Our model considers the direct effects of investments in Digital technologies adoption and use and Learning capacity of the organisation and the mediating role of firms' innovation strategies. Results indicate that the Learning capacity of the organisation is directly associated with more individual-oriented working time flexibility, but entails higher organisation-oriented working time flexibility. The effect of Digital technologies adoption and use depends instead on firms' innovation strategy: product innovation leads to more employee control over working time, while marketing innovation has the opposite outcome. Process and organisational innovations yield mixed consequences buffering employees from organisation-oriented working time flexibility in more time-constrained work environments." (Author's abstract, IAB-Doku) ((en))
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