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
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- Besondere Personengruppen
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Literaturhinweis
Automation and Polarization (2025)
Zitatform
Acemoglu, Daron & Jonas Löbbing (2025): Automation and Polarization. In: Journal of Political Economy. 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
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
Winners and losers when firms robotize: wage effects across occupations and education (2025)
Zitatform
Barth, Erling, Marianne Røed, Pål Schøne & Janis Umblijs (2025): Winners and losers when firms robotize: wage effects across occupations and education. In: The Scandinavian Journal of Economics, S. 1-30. 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
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
The dynamics of automation adoption: Firm-level heterogeneity and aggregate employment effects (2025)
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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
Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy (2025)
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Bonoli, Giuliano, Patrick Emmenegger & Alina Felder-Stindt (2025): Re‐Skilling in the Age of Skill Shortage: Adult Education Rather Than Active Labor Market Policy. In: Regulation and governance, S. 1-13. 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
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
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
Technological innovations and workers’ job insecurity: the moderating role of human resource strategies (2025)
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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
How welfare states influence online platform work in Europe (2025)
Zitatform
Chueri, Juliana & Petter Törnberg (2025): How welfare states influence online platform work in Europe. In: Journal of European Social Policy, S. 1-17. 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
Equalising the effects of automation? The role of task overlap for job finding (2025)
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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)
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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)
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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
Cross-country skills-technology policy debates through large language models (2025)
Einhoff, Jan; López Trejos, Isabella; Paunov, Caroline;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
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
Contextualizing inequalities in the gig economy: evidence from online cleaning platforms in five European cities (2025)
Zitatform
Giuliani, Giovanni Amerigo & Rebecca Paraciani (2025): Contextualizing inequalities in the gig economy: evidence from online cleaning platforms in five European cities. In: The international journal of sociology and social policy, S. 1-20. 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
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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Literaturhinweis
Diverging paths: AI exposure and employment across European regions (2025)
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Guarascio, Dario, Jelena Reljic & Roman Stöllinger (2025): Diverging paths: AI exposure and employment across European regions. In: Structural Change and Economic Dynamics, Jg. 73, S. 11-24. DOI:10.1016/j.strueco.2024.12.010
Abstract
"This study explores exposure to artificial intelligence (AI) technologies and employment patterns in Europe. First, we provide a thorough mapping of European regions focusing on the structural factors—such as sectoral specialisation, R&D capacity, productivity and workforce skills—that may shape diffusion as well as economic and employment effects of AI. To capture these differences, we conduct a cluster analysis which group EU regions in four distinct clusters: high-tech service and capital centres, advanced manufacturing core, southern and eastern periphery. We then discuss potential employment implications of AI in these regions, arguing that while regions with strong innovation systems may experience employment gains as AI complements existing capabilities and production systems, others are likely to face structural barriers that could eventually exacerbate regional disparities in the EU, with peripheral areas losing further ground." (Author's abstract, IAB-Doku, © 2024 The Author(s). Published by Elsevier B.V.) ((en))
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Literaturhinweis
Robots vs. Workers: Evidence From a Meta‐Analysis (2025)
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Guarascio, Dario, Alessandro Piccirillo & Jelena Reljic (2025): Robots vs. Workers: Evidence From a Meta‐Analysis. In: Journal of Economic Surveys, Jg. 39, H. 5, S. 2254-2271. DOI:10.1111/joes.12699
Abstract
"This study conducts a meta-analysis to assess the effects of robotization on employment and wages, synthesizing the evidence from 33 studies (644 estimates) on employment and a subset of 19 studies (195 estimates) on wages. The results challenge the alarmist narrative about the risk of widespread technological unemployment, suggesting that the overall relationship between robotization and employment or wages is minimal. However, the effects are far from uniform, with adverse outcomes observed in specific contexts, such as the United States, manufacturing sectors, and middle-skilled occupations. The analysis also identifies a publication bias favoring negative wage effects, though correcting for this bias confirms the negligible impact of robotization." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Generative AI's Impact on Student Achievement and Implications for Worker Productivity (2025)
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Hausman, Naomi, Oren Rigbi & Sarit Weisburd (2025): Generative AI's Impact on Student Achievement and Implications for Worker Productivity. (CESifo working paper 11843), München, 39 S.
Abstract
"Student use of Artificial Intelligence (AI) in higher education is reshaping learning and redefining the skills of future workers. Using student-course data from a top Israeli university, we examine the impact of generative AI tools on academic performance. Comparisons across more and less AI-compatible courses before and after ChatGPT's introduction show that AI availability raises grades, especially for lower-performing students, and compresses the grade distribution, eroding the signal value of grades for employers. Evidence suggests gains in AI-specific human capital but possible losses in traditional human capital, highlighting benefits and costs AI may impose on future workforce productivity." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Large Language Models, Small Labor Market Effects (2025)
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Humlum, Anders & Emilie Vestergaard (2025): Large Language Models, Small Labor Market Effects. (BFI Working Papers / University of Chicago, Becker Friedman Institute for Research in Economics 2025,56), Chicago, 64 S. DOI:10.2139/ssrn.5219933
Abstract
"We examine the labor market effects of AI chatbots using two large-scale adoption surveys (late 2023 and 2024) covering 11 exposed occupations (25,000 workers, 7,000 workplaces), linked to matched employer-employee data in Denmark. AI chatbots are now widespread —most employers encourage their use, many deploy in-house models, andtraining initiatives are common. These firm-led investments boost adoption, narrow demographic gaps in take-up, enhance workplace utility, and create new job tasks. Yet, despite substantial investments, economic impacts remain minimal. Using difference-in-differences and employer policies as quasi-experimental variation, we estimate precise zeros: AI chatbots have had no significant impact on earnings or recorded hours in any occupation, with confidence intervals ruling out effects larger than 1%. Modest productivity gains (average time savings of 3%), combined with weak wage pass-through, help explain these limited labor market effects. Our findings challenge narratives of imminent labor market transformation due to Generative AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Robots & AI exposure and wage inequality: a within occupation approach (2025)
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Jaccoud, Florencia (2025): Robots & AI exposure and wage inequality: a within occupation approach. In: Eurasian business review. DOI:10.1007/s40821-025-00306-w
Abstract
"This paper examines the linkages between occupational exposure to recent automation technologies and inequality across 19 European countries. Using data from the European Union Structure of Earnings Survey (EU-SES), a fixed-effects model is employed to assess the association between occupational exposure to artificial intelligence (AI) and to industrial robots–two distinct forms of automation–and within-occupation wage inequality. The analysis reveals that occupations with higher exposure to robots tend to have lower wage inequality, particularly among workers in the lower half of the wage distribution. In contrast, occupations more exposed to AI exhibit greater wage dispersion, especially at the top of the wage distribution. We argue that this disparity arises from differences in how each technology complements individual worker abilities: robot-related tasks often complement routine physical activities, while AI-related tasks tend to amplify the productivity of high-skilled, cognitively intensive work." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Robots, AI, and unemployment (2025)
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Kudoh, Noritaka & Hiroaki Miyamoto (2025): Robots, AI, and unemployment. In: Journal of Economic Dynamics and Control, Jg. 174. DOI:10.1016/j.jedc.2025.105069
Abstract
"Do robots and artificial intelligence (AI) cause joblessness? We develop a dynamic general equilibrium model with search-matching frictions. In our model, robots substitute routine human tasks, and AI substitutes abstract human tasks. We find a cutoff level for the elasticity of substitution between routine labor input and robots, above which an increase in robot productivity leads to increased unemployment. We examine a scenario in which AI-driven automation of abstract tasks transforms high-skilled workers into unskilled ones. A substantial productivity gain through AI is required to offset the output loss associated with this labor displacement." (Author's abstract, IAB-Doku, © 2025 The Author(s). Published by Elsevier B.V.) ((en))
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Literaturhinweis
Upgrading jobs for all: How welfare states shape differences in life satisfaction between the winners and losers of structural change (2025)
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Küstermann, Leon (2025): Upgrading jobs for all: How welfare states shape differences in life satisfaction between the winners and losers of structural change. In: Socio-economic review, S. 1-27. DOI:10.1093/ser/mwaf029
Abstract
"Structural economic change transforms occupational structures in a way that has benefited college-educated knowledge economy workers while creating risks for workers in routine and interpersonal service jobs. However, looking beyond economic outcomes, it is striking that differences in life satisfaction between these occupational groups in some European countries are much smaller than in others. To explain this pattern, I analyze data from the European Social Survey and the European Working Conditions Survey for twenty-five countries. I show that these life satisfaction differences are smaller in countries where jobs in “losing” occupations are designed similarly to jobs in “winning” occupations. Further, I demonstrate that both social investment and social protection reduce this life satisfaction gap by equalizing job satisfaction and job design between occupational groups. Hence, my results support the argument that welfare states achieve inclusive outcomes in the context of structural economic change through their interactions with workplaces." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Good Jobs or Bad Jobs? Immigrant Workers in the Gig Economy (2025)
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Liu, Cathy Yang & Rory Renzy (2025): Good Jobs or Bad Jobs? Immigrant Workers in the Gig Economy. In: International migration review, S. 1-25. 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? (2025)
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Logchies, Thomas, Tom Coupé & W. Robert Reed (2025): Do robots decrease humans’ wages? In: Applied Economics Letters, S. 1-5. 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
Impact of robots and artificial intelligence on labor and skill demand: evidence from the UK (2025)
Zitatform
Lábaj, Martin, Tomáš Oleš & Gabriel Procházka (2025): Impact of robots and artificial intelligence on labor and skill demand: evidence from the UK. In: Eurasian business review, S. 1-49. DOI:10.1007/s40821-025-00314-w
Abstract
"Over the past four decades, automation technologies have replaced routine tasks performed by medium-skilled workers, and contributed to increased labor market polarization. With the advent of artificial intelligence, this dynamic may have shifted, extending task substitution to non-routine tasks performed by high-skilled workers. Using textual analysis and descriptions of technology found in patent texts, we construct novel occupational exposures to robot and artificial intelligence technologies. These occupational exposures are then used to analyze changes in labor and skill demand over the last decade in the United Kingdom. We find that the middle part of the income distribution is primarily exposed to robot technology, while exposure to artificial intelligence increases monotonically across income percentiles. Second, we find that exposure to robots is strongest among high school dropouts and declines monotonically with education, while artificial intelligence automation has a limited impact on the same workers, with a pronounced exposure among college graduates. Third, our findings suggest asymmetric effects of automation technologies across skill groups. Robot automation reduces demand for low-skilled workers, while AI technology shifts demand away from high-skilled workers, with the direct effects consistently negative despite the presence of several compensating mechanisms. Fourth, despite significant effects on wage bill, we find no robust relationship between automation exposure and changes in the employment-to-population ratio. Finally, a joint estimation of the effects of robot and AI automation shows that robot automation is positively associated with an increase in demand for skilled workers, while AI automation is weakly associated with a decrease in demand for skilled workers." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI innovation and the labor share in European regions (2025)
Zitatform
Minniti, Antonio, Klaus Prettner & Francesco Venturini (2025): AI innovation and the labor share in European regions. In: European Economic Review, Jg. 177. DOI:10.1016/j.euroecorev.2025.105043
Abstract
"This paper examines how the development of Artificial Intelligence (AI) affects the distribution of income between capital and labor, and how these shifts contribute to regional income inequality. To investigate this issue, we analyze data from European regions dating back to 2000. We find that for every doubling of regional AI innovation, the labor share declines by 0.5% to 1.6%, potentially reducing it by 0.09 to 0.31 percentage points from an average of 52%, solely due to AI. This new technology has a particularly negative impact on high- and medium-skill workers, primarily through wage compression, while for low-skill workers, employment expansion induced by AI mildly offsets the associated wage decline. The effect of AI is not driven by other factors influencing regional development in Europe or by the concentration of the AI market." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Just another cog in the machine? A worker‐level view of robotization and tasks (2025)
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Nikolova, Milena, Anthony Lepinteur & Femke Cnossen (2025): Just another cog in the machine? A worker‐level view of robotization and tasks. In: Economica, Jg. 92, H. 368, S. 1101-1148. DOI:10.1111/ecca.70006
Abstract
"Technological change has led to a decline in the share of routine and physical jobs, and a rise in the share of abstract and social ones at the economy level. However, much less is known about how these trends unfold at the individual level. Do workers' tasks become more or less routine and physical? Do workers shift towards more social and abstract activities? This paper is the first to explore these questions in the context of robotization. We use survey data from 20 European countries to develop worker-level indices of physical, routine, abstract and social tasks, which we link to industry-level robotization exposure. Using instrumental variable techniques, we find that robotization reduces physically demanding tasks but increases routine tasks, while also limiting opportunities for cognitively challenging work and human interaction. This study provides a worker-centric perspective on the relationship between technology and task composition, revealing insights that aggregate analyses miss." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Artificial intelligence, automation and employment dynamics: empirical evidence from G7 economies (2025)
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Okur, Fatih & Enes Özdemir (2025): Artificial intelligence, automation and employment dynamics: empirical evidence from G7 economies. In: Journal of Economic Studies, S. 1-17. DOI:10.1108/jes-06-2025-0414
Abstract
"Purpose: This study examines how the rapid adoption of artificial intelligence (AI) and automation affects employment dynamics across G7 economies. While previous research has often focused on either AI or robotics in isolation, their combined and long-term effects on employment remain poorly understood. Addressing this gap is crucial for policymakers seeking to balance technological progress with labor market stability. Design/methodology/approach: Using a balanced panel dataset covering 2010–2024 for the G7 countries, thestudy investigates the relationships between AI investment (proxied by information and communication technology (ICT) investment), robot density (ROBOT), wages, productivity (PRD) and education spending (EDU), and their impact on employment. The analysis employs panel unit root and cross-sectional dependence tests, a panel autoregressive distributed lag (ARDL) framework estimated via the pooled mean group (PMG) estimator, and robustness checks using Driscoll–Kraay fixed effects, common correlatedeffects (CCE) estimators, country-specific regressions and Dumitrescu –Hurlin panel causality tests. Findings: The results reveal that AI investment has a significant negative effect on employment in the long run, whereas ROBOT shows a positive but context-dependent relationship. Wage levels are negatively associated with employment, while PRD shows only a modest positive influence. Education expenditure exhibits mixed behavior – positive in the short run but negative in the long run – suggesting potential misalignment with evolving labor market needs. Causality tests confirm a unidirectional link from AI investment to employment, underscoring its structural role in labor market change. Research limitations/implications: This study is limited by data availability, particularly the lack of detailed sectoral or occupational breakdowns across countries. As a result, it cannot fully capture the distributional effects of AI and automation across different worker groups. The use of proxies, such as ICT investment for AI, may not reflect the full scope of AI deployment. Despite these limitations, the findings highlight important macro-level dynamics and suggest that technological investments significantly shape employment trends. Future research should utilize micro-level data to explore sector-specific impacts, wage effects and labor force transitions in response to digital transformation. Practical implications: The findings suggest that without targeted policy interventions, increased AI investment may displace workers in the long run. Policymakers should prioritize reskilling, adapt education systems to evolving technological needs, and differentiate strategies across sectors and worker skill levels. Social implications: This study highlights the potential for AI and automation to reshape labor markets, with implications for income distribution, job security and social cohesion. The displacement of routine jobs may disproportionately affect low-skilled and vulnerable workers, increasing the risk of inequality and social exclusion. To prevent deepening divides, social policies must focus on equitable access to education, digital literacy and lifelong learning. Supporting workforce adaptability through inclusive training programs and social safety nets is essential. The results underscore the urgent need for collaborative efforts between governments, educational institutions and industries to ensure a socially sustainable digital transformation. Originality/value: This study is among the first to jointly analyze AI and robotics within a dynamic panel framework, offering new cross-country evidence on their heterogeneous employment effects in advanced economies. By integrating multiple estimation strategies and country-specific perspectives, the paper contributes to a more nuanced understanding of how technological transformation reshapes labor markets and highlights the institutional conditions that mediate these effects." (Author's abstract, IAB-Doku, © EmeraldGroup) ((en))
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Literaturhinweis
Benign effects of technological change on the labour share: evidence from European regions (2025)
Zitatform
Pialli, Guido (2025): Benign effects of technological change on the labour share: evidence from European regions. In: Cambridge Journal of Economics, Jg. 49, H. 4, S. 795-824. DOI:10.1093/cje/beaf021
Abstract
"The labor share across European regions has shown significant variation since the late 1990s. This paper explores the role of technological change in explaining this regional variation. Specifically, this paper proposes and tests the hypothesis that the recent shift in technological change is labor-intensive, driven by a localized, bottom-up process that exploits the skills and learning processes of the workforce. The empirical analysis, using data from 171 European regions over the period 1999–2015, supports the theoretical framework, showing that technological change has a positive and economically significant impact on the labor share." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Occupational Autonomy and Wage Divergence: Evidence From European Survey Data (2025)
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Rabensteiner, Thomas & Alexander Guschanski (2025): Occupational Autonomy and Wage Divergence: Evidence From European Survey Data. In: BJIR, Jg. 63, H. 4, S. 696-713. DOI:10.1111/bjir.70003
Abstract
"Wages across occupations in Western Europe have diverged, resulting in increased wage inequality. However, existing theories such as routine-biased technological change (RBTC) or task offshoring fail to explain this trend. We propose a new explanation based on occupational autonomy. Autonomy measures workers' control and influence over their work process based on the tasks required in an occupation. Analysing individual-level data from the EU Survey of Income and Living Conditions, we reveal a rising autonomy wage premium, that is, higher wage growth for occupations with higher autonomy, which accurately predicts the observed occupational wage divergence. We also find that the autonomy premium increases more rapidly in countries and industries with greater employee monitoring and outsourcing, as well as in countries with declining minimum wages. These findings imply that low-autonomy occupations have been disadvantaged by recent socioeconomic trends that have altered power relations in the workplace. Notably, our analysis does not support previous explanations for occupational wage trends based on RBTC or task offshoring." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
European regional employment and exposure to labour-saving technical change: results from a direct text similarity measure (2025)
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Riccio, Federico, Jacopo Staccioli & Maria Enrica Virgillito (2025): European regional employment and exposure to labour-saving technical change: results from a direct text similarity measure. (LEM working paper series / Laboratory of Economics and Management 2025/19), Pisa, 34 S. DOI:10.57838/sssa/02jp-b197
Abstract
"Does labor-saving technological change pose a threat to European employment, and if so, to what extent? This study investigates the degree of employment exposure to labor-saving technological change across NUTS-2 regions in Europe. We construct a cross-walked metric between the SOC and ISCO classification systems to adapt the direct measure of occupational exposure developed by Montobbio et al. (2024) for the US economy and apply it to the European context. This methodology enables us to generate detailed insights into the exposure of European occupations by leveraging the similarity rankings between technological classifications in the USPTO (CPCs) and task descriptions. To evaluate the transmission from occupational exposure to employment outcomes, we utilise data from the European Structure of Earnings Survey (EU-SES), thereby constructing exposure indices at both sectoral and regional levels. Finally, we examine the industrial and geographical diffusion of labor-saving technological change in recent years and provide robust econometric evidence indicating that low-wage regions, as well as deindustrialising areas heavily integrated into global value chains, are disproportionately vulnerable to the threat of substitution." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Sociotechnical imaginaries of social inequality in the design and use of AI recruitment technology (2025)
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Sartori, Laura & Clementine Collett (2025): Sociotechnical imaginaries of social inequality in the design and use of AI recruitment technology. In: European Societies, Jg. 27, H. 3, S. 409-432. DOI:10.1162/euso_a_00035
Abstract
"Through interviewing 12 companies in Italy which either design (vendors) or use (clients) AI recruitment technology systems, we explore how these companies perceive their systems to interact with issues of social inequality and how these perceptions, in practice, carry societal impacts. Three sociotechnical imaginaries (Jasanoff and Kim, 2015) were consistently embedded within these companies’ visions of this intersection: the third eye, the river, and the car bonnet. Through critically analyzing these imaginaries, we find that they exhibit an overriding desire for productivity and talent capture from clients, and a consequential de-prioritization of addressing social inequality and scrutinizing the ways it could be reproduced from both vendors and clients. It demonstrates that the current ‘desired’ futures, shown by the sociotechnical imaginaries which vendors and clients share for AI-tec-tech are really leading us towards an ‘undesirable’ future of hiring which continues to perpetuate social inequality. This study contributes one of the first pieces of empirical work to simultaneously assess the perceptions of AI-rec-tech vendors ’ and clients’ surrounding social inequality, to shed light on the priorities for design and the motivations for usage, and to reflect upon how this impacts society. This is a significant and original contribution to the evolving body of literature on AI-rec-tech in sociology, critical data studies, and communications." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Artificial Intelligence and Technological Unemployment (2025)
Zitatform
Wang, Ping & Tsz-Nga Wong (2025): Artificial Intelligence and Technological Unemployment. (NBER working paper / National Bureau of Economic Research 33867), Cambridge, Mass, 53 S.
Abstract
"How large is the impact of artificial intelligence (AI) on labor productivity and unemployment? This paper introduces a labor-search model of technological unemployment, conceptualizing the generative aspect of AI as a learning-by-using technology. AI capability improves through machine learning from workers and in turn enhances their labor productivity, but eventually displaces workers if wage renegotiation fails. Three distinct equilibria emerge: no AI, some AI with higher unemployment, or unbounded AI with sustained endogenous growth and little impact on employment. By calibrating to the U.S. data, our model predicts more than threefold improvements in productivity in some-AI steady state, alongside a long-run employment loss of 23%, with half this loss occurring over the initial five-year transition. Plausible change in parameter values could lead to global and local indeterminacy. The mechanism highlights the considerable uncertainty of AI's impacts in the presence of labor-market frictions. In the unbounded-AI equilibrium, technological unemployment would not occur. We further show that equilibria are inefficient despite adherence to the Hosios condition. By improving job-finding rate and labor productivity, the optimal subsidy to jobs facing the replacement risk of AI can generate a welfare gain from 26.6% in the short run to over 50% in the long run." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Education pathways to mitigate automation anxiety: skill development as key for job satisfaction in the age of machines replacing human (2025)
Zitatform
Yuan, Bocong, Jiannan Li & Hairong Zhao (2025): Education pathways to mitigate automation anxiety: skill development as key for job satisfaction in the age of machines replacing human. In: International Journal of Manpower, Jg. 46, H. 9, S. 1676-1698. DOI:10.1108/ijm-02-2024-0093
Abstract
"Purpose: The application of intelligent machine in the workplace has led to increasing concern about technically induced unemployment. This study is to investigate the mechanism of how such risk affects the job satisfaction. Design/methodology/approach: We use the secondary data from SHARE (wave 8) and a longitudinal survey to examine the influence mechanism of how intelligent machine job substitution risk affects job satisfaction. Findings: Results show that intelligent machine job substitution risk has a negative impact on job satisfaction. Besides, skill development opportunity mediates the negative relation between intelligent machine job substitution risk and job satisfaction. Further, work support buffers the negative relation between intelligent machine job substitution risk and skill development opportunity, while enhancing the positive relation between skill development opportunity and job satisfaction. Originality/value: This study is the first to examine the mediation role of skill development opportunity in the relation between the intelligent machine job substitution risk and job satisfaction. Also, this study is the first to explore the role of work support in the above relation. This study enriches relevant research regarding the intelligent machine application in workplace and provides important insights for organization management." (Author's abstract, IAB-Doku, © Emerald Group) ((en))
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Literaturhinweis
The AI Redemption: How technology is rewriting the rules of cross-industry labor mobility (2025)
Zhang, Su; Wang, Xiaolin; Wang, Huijuan; Xia, Yan;Zitatform
Zhang, Su, Xiaolin Wang, Yan Xia & Huijuan Wang (2025): The AI Redemption: How technology is rewriting the rules of cross-industry labor mobility. In: International Review of Economics and Finance, Jg. 103. DOI:10.1016/j.iref.2025.104575
Abstract
"This study considers the evolution and iteration of digital technology, conducting both theoretical and empirical research on the effects of information technology and artificial intelligence technology on cross-industry labor mobility. Theoretically, we construct a general equilibrium model that includes labor and digital technology to analyze the intrinsic mechanisms by which digital technology affects cross-industry labor mobility. Empirically, using the probit model and the instrumental variable approach, we find robust evidence of a significant positive effect of digital technology on cross-industry labor mobility through the pooled four-wave data from the China Family Panel Studies (CFPS) from 2014 to 2020. The findings indicate that digital technology significantly promotes cross-industry labor mobility. Mechanism analysis reveals that information technology, represented by computers, drives low-skilled labor towards non-skill-intensive industries through substitution and productivity effects, while artificial intelligence technology promotes the flow of both low-skilled and high-skilled labor towards skill-intensive industries through “de-skilling” and “re-skilling”. The impact of digital technology on cross-industry labor mobility varies significantly across different genders, the type of hukou, age, and employment types. Further mechanism analysis suggests that digital technology facilitates higher wage gains by promoting cross-industry labor mobility." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier Inc.) ((en))
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Literaturhinweis
Effects of digital innovation on income inequality among different workforces: evidence from Chinese industries (2025)
Zhou, Yongguang; Xie, Weihong; Li, Qun; Li, Jingwu;Zitatform
Zhou, Yongguang, Weihong Xie, Jingwu Li & Qun Li (2025): Effects of digital innovation on income inequality among different workforces: evidence from Chinese industries. In: Applied Economics, Jg. 57, H. 22, S. 2809-2821. DOI:10.1080/00036846.2024.2331424
Abstract
"To understand the impact of digital innovation on the workforce and its role in achieving common prosperity, this paper uses data from Chinese A-share listed companies during 2006–2021 to investigate the effects of digital innovation on income inequality among different industry-level groups. We find that digital innovation significantly reduces income inequality among employees across industries, but it does not significantly impact income inequality within management groups. Through mechanistic analysis, we find that digital innovation decreases income inequality among ordinary employees whose incomes are closely linked to company performance and thereby for the entire workforce by narrowing the income gap across industries. However, as digital innovation does not significantly influence evaluation systems (e.g. educational degrees) for management income, it does not contribute to reducing income inequality among managerial levels. These findings provide valuable insights to develop policies for common prosperity." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Digital labour and welfare regimes: The impact of the institutional context on the prevalence of platform work (2025)
Zitatform
Zwysen, Wouter & Bianca Luna Fabris (2025): Digital labour and welfare regimes: The impact of the institutional context on the prevalence of platform work. In: Competition and Change, S. 1-21. DOI:10.1177/10245294251349484
Abstract
"Platform work is on the rise across Europe, but not similarly across countries as shown from the as yet limited cross-national research. This study sets out to analyze how structural differences in the organization of the economy and welfare state shape individual’s engagement with platform work and particularly (1) the take-up of platform work; and (2) the extent to which the more economically vulnerable are overrepresented. In a context where the labour market is more regulated, workers are more protected, and there is a more generous safety net, there is less need to engage in generally precarious platform work. This study makes use of two comparable cross-national datasets on engagement in platform work across Europe. We find indications that platform work is generally less likely in countries where there is greater social spending and redistribution, higher passive labour market policy spending, and lower labour market dualization. Such factors, namely, social security and the regulation of the labour market, particularly protect more vulnerable workers." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
New technology and workers’ perceived impact on job quality: Does labor organization matter? (2025)
Zitatform
ten Berge, Jannes & Fabian Dekker (2025): New technology and workers’ perceived impact on job quality: Does labor organization matter? In: Economic and Industrial Democracy, Jg. 46, H. 2, S. 619-654. DOI:10.1177/0143831x241265911
Abstract
"There is an emerging literature focusing on the impact of technological change on work quality. This study contributes to the literature by examining (1) workers’ expectations regarding the effect of technological change on perceived job insecurity, as well as physical and psychological job demands, and (2) how these expectations are shaped by the degree of labor organization within countries. The article uses cross-national data for 25 OECD countries. It is found that labor organization decreases perceived levels of job insecurity related to technological change, but also lowers workers’ expectations of technology improving the quality of their work. These findings may indicate that in environments where technological change is less strongly moderated by organized labor, workers put greater emphasis on technology as a driver of (short-term) work changes. Alternatively, these findings may signal a lack of ‘worker power’ of organized labor to enforce technologies that improve the quality of employment." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity (2024)
Zitatform
Acemoglu, Daron & Pascual Restrepo (2024): Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity. (NBER working paper / National Bureau of Economic Research 32536), Cambridge, Mass, 79 S. DOI:10.3386/w32536
Abstract
"This paper studies the effects of automation in economies with labor market distortions that generate worker rents—wages above opportunity cost—in some jobs. We show that automation targets high-rent tasks, dissipating rents and amplifying wage losses from automation. It also reduces within-group wage dispersion for exposed groups. Automation-driven rent dissipation is inefficient and reduces (and could even negate) the productivity gains from automation. Using data for the US from 1980 to 2016, we find evidence of sizable rent dissipation and reduced within-group wage dispersion due to automation. Using these estimates and accounting for equilibrium effects, we estimate that automation accounts for 52% of the increase in between-group inequality in the US since 1980, with rent dissipation being responsible for a fifth of this contribution. We also estimate that inefficient rent dissipation offset 60–90% of the productivity gains from automation since 1980." (Author'sabstract, IAB-Doku) ((en))
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Literaturhinweis
Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI (2024)
Zitatform
Acemoglu, Daron & Simon Johnson (2024): Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI. (NBER working paper / National Bureau of Economic Research 32416), Cambridge, Mass, 45 S. DOI:10.3386/w32416
Abstract
"David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo's time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
A Relational Work Perspective on the Gig Economy: Doing Creative Work on Digital Labour Platforms (2024)
Zitatform
Alacovska, Ana, Eliane Bucher & Christian Fieseler (2024): A Relational Work Perspective on the Gig Economy: Doing Creative Work on Digital Labour Platforms. In: Work, Employment and Society, Jg. 38, H. 1, S. 161-179. DOI:10.1177/09500170221103146
Abstract
"Based on interviews with 49 visual artists, graphic designers and illustrators working on two leading global digital labour platforms, this article examines how creative workers perform relational work as a means of attenuating labour commodification, precarity, and algorithmic normativity. The article argues that creative work on online labour platforms, rather than being entirely controlled by depersonalised, anonymised and algorithm-driven labour market forces, is also infused in relational infrastructures whose upkeep, solidity and durability depends on the emotional efforts undertaken by workers to match economic transactions and their media of exchange to meaningful client relations. By applying a relational work perspective from economic sociology to the study of platform-mediated gig work, the article elucidates the micro-foundations of creative work in the digital gig economy, including how labour inequalities are produced and reproduced within and around micro-level interpersonal interactions." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The impact of ICT and robots on labour market outcomes of demographic groups in Europe (2024)
Zitatform
Albinowski, Maciej & Piotr Lewandowski (2024): The impact of ICT and robots on labour market outcomes of demographic groups in Europe. In: Labour Economics, Jg. 87. DOI:10.1016/j.labeco.2023.102481
Abstract
"We study the age- and gender-specific labour market effects of two key modern technologies, Information and Communication Technologies (ICT) and robots. Our sample includes 14 European countries between 2010 and 2018. We use the variation in technology adoption between industries and apply the instrumental variables strategy proposed by Acemoglu and Restrepo (2020) to identify the causal effects of technology adoption. We find that exposure to ICT and robots increased the shares of young and prime-aged women in employment and in the wage bills of particular sectors. However, it reduced the shares of older women and prime-aged men. We do not detect significant effects of technology adoption on the relative wages of most demographic groups. Between 2010 and 2018, the growth in ICT capital played a larger role than robot adoption in the changes in the withinsector labor market outcomes of demographic groups." (Author's abstract, IAB-Doku, ©2024 Elsevier) ((en))
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Literaturhinweis
Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech (2024)
Zitatform
Avery, Mallory, Andreas Leibbrandt & Joseph Vecci (2024): Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech. (CESifo working paper 10996), München, 70 S.
Abstract
"The use of Artificial Intelligence (AI) in recruitment is rapidly increasing and drastically changing how people apply to jobs and how applications are reviewed. In this paper, we use two field experiments to study how AI recruitment tools can impact gender diversity in the male-dominated technology sector, both overall and separately for labor supply and demand. We find that the use of AI in recruitment changes the gender distribution of potential hires, in some cases more than doubling the fraction of top applicants that are women. This change is generated by better outcomes for women in both supply and demand. On the supply side, we observe that the use of AI reduces the gender gap in application completion rates. Complementary survey evidence suggests that anticipated bias is a driver of increased female application completion when assessed by AI instead of human evaluators. On the demand side, we find that providing evaluators with applicants' AI scores closes the gender gap in assessments that otherwise disadvantage female applicants. Finally, we show that the AI tool would have to be substantially biased against women to result in a lower level of gender diversity than found without AI." (Author's abstract, IAB-Doku) ((en))
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auch erschienen als: Monash Economics Working Papers, 2023-09
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