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(2025): How do people experience new technologies and generative AI? Insights from a few countries worldwide. (OECD Policy Insights on Well-being, Inclusion, Sustainability and Equal Opportunity 23), Paris, 12 S. DOI:10.1787/49b8d10e-en

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(2025): OECD Skills Outlook 2025. Building the Skills of the 21st Century for All. (OECD skills outlook), Paris, 261 S. DOI:10.1787/26163cd3-en

Abstract

"The OECD Skills Outlook 2025 examines how countries can build the 21st-century skills needed to sustain growth and social progress. It explores how differences in background, education and opportunity shape who develops, uses and benefits from key skills such as literacy, numeracy and adaptive problem solving. Differential access to skills development limits the potential of many, thereby constraining economic performance. Socio-economic background strongly influences who builds skills that are valued in the labour market, whereas differences between men and women appear mainly in how skills are used and rewarded. As skill demands evolve faster than policy cycles, investing in lifelong learning and using timely labour-market intelligence are crucial to help people adapt, strengthen productivity and ensure that no one is left behind in a changing world." (Author's abstract, IAB-Doku) ((en))

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(2025): D21-Digital-Index 2024/2025. Jährliches Lagebild zur Digitalen Gesellschaft. (D21-Digital-Index), Berlin, 41 S.

Abstract

"Der D21-Digital-Index ist Deutschlands wichtigstes Lagebild zur Digitalisierung der Gesellschaft. Er zeigt, wie tief die digitale Transformation verschiedene Lebensbereiche durchdringt und wie gut Bürger*innen mit den Anforderungen des Wandels umgehen können. Gleichzeitig offenbart er Spaltungen und Herausforderungen: Wer profitiert, wer droht abgehängt zu werden? Der D21-Digital-Index ist mehr als eine Analyse: Er ist Basis für wirkungsvolles Handeln von Politik, Wirtschaft und Gesellschaft, um die Resilienz im digitalen Wandel zu stärken und gemeinsam eine inklusive digitale Zukunft zu gestalten, in der alle von den Chancen der Digitalisierung profitieren. Im Fokus steht in diesem Jahr der digitale Wandel als Treiber für weitere Transformationsprozesse, etwa bei der Wertschöpfung, beim Informations- und Kommunikationsverhalten oder beim ökologischen Wandel. Denn unsere Gesellschaft befindet sich inmitten eines tiefgreifenden Wandels. Der D21-Digital-Index begleitet sie in dieser Transformation seit vielen Jahren. Ein weiterer Schwerpunkt der Studie liegt auf der digitalen Bildung und den Kompetenzen, die für eine souveräne und kritische Nutzung digitaler Technologien erforderlich sind. Zudem wird untersucht, wie die Bevölkerung den Einsatz Künstlicher Intelligenz wahrnimmt: Welche Erwartungen, Chancen und Bedenken gibt es? Welche Faktoren beeinflussen die Akzeptanz neuer KI-Technologien im Alltag? Wie steht es um das Bewusstsein für die Transformationskraft von KI, insbesondere für die Beschäftigungschancen?" (Autorenreferat, IAB-Doku)

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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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Afonso, Óscar & Rosa Forte (2024): Routine and non-routine sectors, tasks automation and wage polarization. In: Applied Economics, Jg. 56, H. 55, S. 7262-7285. DOI:10.1080/00036846.2023.2280461

Abstract

"Recent and detailed data point to a polarization of wages with regard to the distribution of skills, particularly in developed countries over the past three decades, requiring the literature to address modelling approaches focused on automating different types of tasks. In the DTC literature, the technological-knowledge bias leads to an increase in the wage of skilled workers relative to unskilled workers. Motivated by this literature, this paper considers three types of workers (skilled, medium-skilled and unskilled) but retain the economic mechanisms that produce the results. Thus, wage inequality continues to result from the technological-knowledge bias, which, in the face of automation dynamics, reveals that medium-skilled workers are the relatively most penalized, generating wage polarization. Furthermore, as in the directed technical change literature, the relative supply of skilled workers continues to affect the skill premium." (Author's abstract, IAB-Doku) ((en))

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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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Almyranti, Margarita, Eric Sutherland, Nachman Ash & Samuel Eiszele (2024): Artificial Intelligence and the health workforce. Perspectives from medical associations on AI in health. (OECD Artificial Intelligence Papers 28), Paris, 53 S. DOI:10.1787/9a31d8af-en

Abstract

"Healthcare has progressed through advancements in medicine, leading to improved global life expectancy. Nevertheless, the sector grapples with increasing challenges such as heightened demand, soaring costs, and an overburdened workforce. Factors contributing to health workforce strain include ageing populations, increasing burden from non-communicable and chronic diseases, healthcare providers' burnout, and evolving patient expectations. Artificial Intelligence (AI) could potentially transform healthcare by alleviating some of these pressures. But AI in health poses risks to health providers through potential workforce disruption – with changing roles requiring adapted skills with some functions subject to automation. Striking a balance between innovation and safeguards is imperative." (Author's abstract, IAB-Doku) ((en))

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André, Elisabeth, Angelika Bullinger-Hoffmann, Eva Bittner, Michael Heister, Norbert Huchler, Matthias Peissner, Pierre Prasuhn, Christoph M. Schmidt, Jochen Steil, Andrea Stich, Oliver Suchy & Uta Wilkens (2024): KI für die Fachkräftesicherung nutzen. Lösungsansätze für Automatisierung, Teilhabe und Wissenstransfer. München, 41 S. DOI:10.48669/pls_2024-2

Abstract

"Ob im Handwerk, Medizin oder der Verwaltung – in fast allen Branchen kommen Fachkräfteengpässe auf uns zu oder sind bereits spürbar. Zunehmend verstärkt durch demografische Entwicklungen wie den bevorstehenden Renteneintritt der Babyboomer. Um die Fachkräftebasis von morgen zu sichern, kann auch Künstliche Intelligenz (KI) als technologischer Baustein – vor allem in den Bereichen Automatisierung und KI-basierte Assistenz – eine bedeutende Rolle spielen. Das Whitepaper gibt einen Überblick, wie KI-Technologien gezielt zur Fachkräftesicherung beitragen können, um die Wettbewerbsfähigkeit zu stärken und Arbeitsplätze in Deutschland zu sichern." (Autorenreferat, IAB-Doku)

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Anger, Silke, Bernhard Christoph, Agata Galkiewicz, Shushanik Margaryan, Frauke Peter, Malte Sandner & Thomas Siedler (2024): A Library in the Palm of Your Hand? A Randomized Reading Intervention with Low-Income Children. (HCEO working paper / Human capital and economic opportunity global working group 2024,018), Chicago, 61 S.

Abstract

"Reading skills are crucial for academic success and long-term educational attainment. However, children from disadvantaged backgrounds read less than their more privileged peers. This study assesses the impact of a randomized reading intervention conducted in Germany targeting 11–12-year-olds from low-income households. The intervention involved distributing e-book readers, which provided free access to a large digital library of age-appropriate books, directly to the children's homes. Our results show that the intervention led to increased reading engagement among the children, which in turn improved their academic performance, particularly in reading comprehension and math. Additionally, we observe positive effects on their socio-emotional well-being." (Author's abstract, IAB-Doku) ((en))

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Anger, Silke, Bernhard Christoph, Agata Galkiewicz, Shushanik Margaryan, Frauke Peter, Malte Sandner & Thomas Siedler (2024): A Library in the Palm of Your Hand? A Randomized Reading Intervention with Low-Income Children. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 17322), Bonn, 61 S.

Abstract

"Reading skills are crucial for academic success and long-term educational attainment. However, children from disadvantaged backgrounds read less than their more privileged peers. This study assesses the impact of a randomized reading intervention conducted in Germany targeting 11–12-year-olds from low-income households. The intervention involved distributing e-book readers, which provided free access to a large digital library of age-appropriate books, directly to the children's homes. Our results show that the intervention led to increased reading engagement among the children, which in turn improved their academic performance, particularly in reading comprehension and math. Additionally, we observe positive effects on their socio-emotional well-being." (Author's abstract, IAB-Doku) ((en))

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Armutat, Sascha, Malte Wattenberg & Nina Mauritz (2024): Artificial Intelligence – Gender-Specific Differences in Perception, Understanding, and Training Interest. In: C.-P. Marti Ballester (ed.) (2024): Proceedings of the 7th International Conference on Gender Research, S. 36-43. DOI:10.34190/icgr.7.1.2163

Abstract

"In light of the growing importance of Artificial Intelligence (AI) in science, business, and society, broad acceptance is crucial. However, recent studies indicate a significant underrepresentation of women in the emerging AI-driven professions of the future job market. This hampers the innovation potential of technologies due to the lack of diverse perspectives in development. Gender-specific differences also manifest in the perception of AI: Men tend to view AI applications more positively, rate their own AI competencies higher, and have more trust in the technology compared to women. However, both genders agree on the critical importance of the comprehensibility of AI decisions and are equally willing to pursue further education in the field of AI. This study aimed to investigate gender-relevant aspects in the perception and understanding of AI, as well as the need for further education and opportunities for communication and exchange on the topic of AI. To achieve this, focus groups with female students were conducted in May 2023. The analysis of the conversation data and materials used was carried out using an inductive coding method. Overall, women perceive knowledge as the key to generating more interest in AI. However, they also identify obstacles such as discrimination, gender stereotypes, and a lack of gender equality. Additionally, they desire more practical examples, improved communication regarding the advantages and disadvantages of AI, as well as more democratic and transparent decision-making processes. The paper emphasizes that an inclusive educational environment requires awareness and education for women, along with measures against discriminatory barriers and stereotypes. Furthermore, it suggests the early involvement of women in the development of AI applications and the establishment of clear rules to ensure gender equality in the workplace. These study findings provide valuable support to companies in the gender-specific planning of awareness and training processes for introducing AI." (Author's abstract, IAB-Doku) ((en))

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Arntz, Melanie, Sabrina Genz, Terry Gregory, Florian Lehmer & Ulrich Zierahn-Weilage (2024): De-Routinization in the Fourth Industrial Revolution - Firm-Level Evidence. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 16740), Bonn, 65 S.

Abstract

"This paper examines the extent to which aggregate-level de-routinization can be attributed to firm-level technology adoption during the most recent technological expansion. We use administrative data and a novel firm survey to distinguish frontier technologies from older technologies. We find that adopters of frontier technologies contribute substantially to deroutinization. However, this is driven only by a subset of these firms: large adopters replace routine jobs and less routine-intensive adopters experience faster growth. These scale and composition effects reflect firms' readiness to adopt and implement frontier technologies. Our results suggest that an acceleration of technology adoption would be associated with faster de-routinization and an increase in between-firm heterogeneity." (Author's abstract, IAB-Doku) ((en))

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Arntz, Melanie, Michael J. Böhm, Georg Graetz, Terry Gregory, Florian Lehmer & Cäcilia Lipowski (2024): Firm-Level Technology Adoption in Times of Crisis. (ZEW discussion paper 24-057), Mannheim, 66 S.

Abstract

"This study investigates how crises affect firms’ adoption of frontier technologies using the Covid-19 pandemic as a case study. The analysis tracks the nature, timing, and pandemic-related motivations of investments among German firms, using longitudinal survey data linked with administrative worker–firm records. We find clear evidence for a shift toward remote work technologies that helped firms mitigate negative employment effects. Overall, however, the pandemic slowed down the diffusion of new technologies. This procyclical pattern of technology adoption is particularly striking since the pandemic created strong incentives to experiment with new technologies." (Author's abstract, IAB-Doku) ((en))

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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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Bachmann, Ronald, Myrielle Gonschor, Piotr Lewandowski & Karol Madoń (2024): The impact of Robots on Labour market transitions in Europe. In: Structural Change and Economic Dynamics, Jg. 70, S. 422-441. DOI:10.1016/j.strueco.2024.05.005

Abstract

"Dieses Papier untersucht die Auswirkungen von Robotern auf Arbeitsmarkttransitionen in 16 europäischen Ländern. Generell reduzieren Roboter Übergänge von der Beschäftigung in die Arbeitslosigkeit und erhöhen die Wahrscheinlichkeit, einen neuen Job zu finden. Arbeitskosten sind eine wichtige Erklärung für die beobachteten Unterschiede zwischen Ländern: In Ländern mit niedrigeren Arbeitskosten zeigt sich ein stärkerer Effekt auf Einstellungen und Trennungen. Diese Auswirkungen sind bei Arbeitskräften in Berufen mit manuellen oder kognitiven Routineaufgaben besonders ausgeprägt, bei Berufen mit nicht-routine kognitiven Aufgaben hingegen vernachlässigbar. Für junge und ältere Arbeitskräfte in Ländern mit niedrigeren Arbeitskosten wirken sich Roboter positiv auf Übergänge aus. Unsere Ergebnisse deuten darauf hin, dass die Einführung von Robotern in den meisten europäischen Ländern zu einem Anstieg der Beschäftigung und einem Rückgang der Arbeitslosigkeit geführt hat, vor allem durch einen Rückgang der Übergänge in die Arbeitslosigkeit." (Autorenreferat, IAB-Doku)

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Bankins, Sarah, Stefan Jooss, Simon Lloyd D. Restubog, Mauricio Marrone, Anna Carmella Ocampo & Mindy Shoss (2024): Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research. In: Journal of vocational behavior, Jg. 153. DOI:10.1016/j.jvb.2024.104011

Abstract

"As artificial intelligence (AI) use expands within organizations, its influence is increasingly permeating careers and vocational domains. However, there is a notable lack of structured insights regarding AI's role in shaping individual career paths across career stages. To address this gap, we undertook a systematic literature review of 104 empirical articles, aiming to synthesize the scholarship on AI in the context of careers. Drawing upon career stage theory, we examine the implications of AI on careers, identify key barriers and enablers of AI use in this area, and reveal how the utilization of AI impacts individuals' career competencies. In doing so, we illustrate how AI actively shapes individuals' career trajectories and we dissect these effects both within and across various career stages to situate AI within the broader context of careers research. Adopting a sustainable career lens, we conclude by outlining a future research agenda that advocates for the design and adoption of AI systems that promote sustainable and equitable careers." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Barišić, Antea, Mahdi Ghodsi & Robert Stehrer (2024): Which Migrant Jobs are Linked with the Adoption of Novel Technologies, Robotization, and Digitalization? (WIIW working paper 241), Wien, 66 S.

Abstract

"In recent decades, the development of novel technologies has intenzified due to globalization, prompting countries to enhance competitiveness through innovation. These technologies have significantly improved global welfare, particularly in sectors like healthcare, where they have facilitated tasks and boosted productivity, for example playing a crucial role in combating the COVID-19 pandemic. However, certain technologies, such as robots, can negatively impact employment by replacing workers and tasks. Additionally, the emergence of artificial intelligence as digital assets not only replaces specific tasks but also introduces complexities that may displace employees who are unable to adapt. While the existing literature extensively explores the heterogeneous effects of these technologies on labor markets, studies of their impact on migrant workers remain scarce. This paper presents pioneering evidence on the effects of various novel technologies on migrant employment in the European Union. The analysis covers 18 EU member states from 2005 to 2019 focusing on the impact of novel innovations, robot adoption, three types of digital assets, and total factor productivity, on migrant employment. The key findings reveal that innovations measured by the number of granted patents increase both the number and proportion of migrant workers relative to the overall workforce. While robots do replace jobs, their impact on native workers surpasses that of migrant workers, resulting in a higher share of migrant workers following robot adoption. Total factor productivity positively influences migrant workers, while the effects of digital assets are heterogeneous. Moreover, the impacts of these technologies on migrant workers vary significantly across different occupation types and educational levels." (Author's abstract, IAB-Doku) ((en))

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Barišić, Antea, Mahdi Ghodsi, Michael Landesmann, Alireza Sabouniha & Robert Stehrer (2024): New Technologies, Migration and Labour Market Adjustment: An Intra-European Perspective. (WIIW policy notes and reports 77), Wien, 26 S.

Abstract

"In this note, we study the relationship between the use of new technologies (e.g. robots and various ICT assets), labor demand and migration patterns. The adoption of new technologies might change the demand for labor in various ways, which in turn will have an impact on skill composition and wage levels of different types of workers. We report the main results from a study that first analyses the impact of robot adoption on wages by sector and skills. Second, we study the impact of robot adoption in manufacturing industries on the attraction of migrants while controlling for other factors in the labor demand function. This is followed by an analysis of push and pull factors of bilateral migration that focuses on the impact of relative automation gaps across countries. Finally, using the OeNB Euro Survey, we examine determinants of the intention to migrate and the role of income differentials between the countries of origin and destination." (Author's abstract, IAB-Doku) ((en))

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Bastani, Spencer & Daniel Waldenström (2024): AI, Automation and Taxation. (IZA policy paper / Forschungsinstitut zur Zukunft der Arbeit 212), Bonn, 19 S.

Abstract

"This paper examines the implications of Artificial Intelligence (AI) and automation for the taxation of labor and capital in advanced economies. It synthesizes empirical evidence on worker displacement, productivity, and income inequality, as well as theoretical frameworks for optimal taxation. Implications for tax policy are discussed, focusing on the level of capital taxes and the progressivity of labor taxes. While there may be a need to adjust the level of capital taxes and the structure of labor income taxation, there are potential drawbacks of overly progressive taxation and universal basic income schemes that could undermine work incentives, economic growth, and long-term household welfare. Some of the challenges posed by AI and automation may also be better addressed through regulatory measures rather than tax policy." (Author's abstract, IAB-Doku) ((en))

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Beneito, Pilar, Maria Garcia-Vega, Oscar Vicente-Chirivella & Guillaume Wilemme (2024): Robots and firms' labour search: The role of temporary work agencies. (Research paper / Nottingham Centre for Research on Globalisation and Economic Policy 2024,02), Nottingham, 55 S.

Abstract

"We study the impact of industrial robots on the use of labor intermediaries or temporary work agencies (TWAs) and firm productivity. We develop a theoretical framework where new technologies increase the need for quality match workers. TWAs help firms to search for workers who better match their technologies. The model predicts that using robots increases TWA use, which increases robots' productivity. We test the model implications with panel data of Spanish firms from 1997 to 2016 with information on robot adoption and TWA use. Using staggered difference-in-difference (DiD) estimations, we estimate the causal effects of robot adoption on TWAs. We find robot adopters increase the probability of TWA use compared to non-adopters. We also find that firms that combine robots with TWAs achieve higher productivity than those who adopt robots without TWAs." (Author's abstract, IAB-Doku) ((en))

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Benítez, Miguel & Eric Parrado (2024): Mirror, Mirror on the Wall: Which Jobs Will AI Replace After All?: A New Index of Occupational Exposure. (Working papers / Inter-American Development Bank 13696), Washington, DC, 33 S. DOI:10.1823

Abstract

"This paper introduces the AI Generated Index of Occupational Exposure (GENOE), a novel measure quantifying the potential impact of artificial intelligence on occupations and their associated tasks. Our methodology employs synthetic AI surveys, leveraging large language models to conduct expert-like assessments. This approach allows for a more comprehensive evaluation of job replacement likelihood, minimizing human bias and reducing assumptions about the mechanisms through which AI innovations could replace job tasks and skills. The index not only considers task automation, but also contextual factors such as social and ethical considerations and regulatory constraints that may affect the likelihood of replacement. Our findings indicate that the average likelihood of job replacement is estimated at 0.28 in the next year, increasing to 0.38 and 0.44 over the next five and ten years, respectively. To validate our methodology, we successfully replicate other measures of occupational exposure that rely on human expert assessments, substituting these with AI-based evaluations. The GENOE index provides valuable insights for policymakers, employers, and workers, offering a data-driven foundation for strategic workforce planning and adaptation in the face of rapid technological change." (Author's abstract, IAB-Doku) ((en))

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Bergholz, Christian, Lena Füner, Moritz Lubczyk, Rolf Sternberg & Johannes Bersch (2024): Infrastructure required, skill needed: Digital entrepreneurship in rural and urban areas. In: Journal of Business Venturing Insights, Jg. 22, 2024-07-31. DOI:10.1016/j.jbvi.2024.e00488

Abstract

"In this paper, we study the spatial implications of digital entrepreneurship. Leveraging detailed micro-data on the universe of new venture formations in Germany between 2011 and 2018, we illustrate regional determinants of digital entrepreneurship. Unlike conventional entrepreneurship, digital entrepreneurship demonstrates sustained growth rates throughout this time period, highlighting the policy importance of understanding the drivers of digital ventures’ location choices. The key insight of our study is that digital entrepreneurship requires both digital infrastructure and highly-skilled human capital. If both are present, digital entrepreneurship can flourish in rural areas, even if digital venture formations generally concentrate in urban centers." (Author's abstract, IAB-Doku, © 2024 The Authors. Published by Elsevier Inc.) ((en))

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Bick, Alexander, Adam Blandin & David J. Deming (2024): The Rapid Adoption of Generative AI. (NBER working paper / National Bureau of Economic Research 32966), Cambridge, Mass, 44 S. DOI:10.3386/w32966

Abstract

"Generative Artificial Intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from the first nationally representative U.S. survey of generative AI adoption at work and at home. In August 2024, 39 percent of the U.S. population age 18-64 used generative AI. More than 24 percent of workers used it at least once in the week prior to being surveyed, and nearly one in nine used it every workday. Historical data on usage and mass-market product launches suggest that U.S. adoption of generative AI has been faster than adoption of the personal computer and the internet. Generative AI is a general purpose technology, in the sense that it is used in a wide range of occupations and job tasks at work and at home." (Author's abstract, IAB-Doku) ((en))

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Bockelmann, Carsten, Damian Dudek, Andreas Haja, Alexander Hanuschkin, Cosima Klischat, Sebastian Lehnhoff, Mathias Magdowski, Britta Matthes, Gerhard Rigoll, Michael Schanz, Hoai My Van, Dirk Wübben & Niclas Zeller (2024): Rolle der Künstlichen Intelligenz in der Elektro- und Informationstechnik. VDE „Studium, Beruf und Gesellschaft“. 43 S.

Abstract

"Dieses Papier zeigt, wo bereits heute in den verschiedenen Fachgebieten der Elektro- und Informationstechnik die Künstliche Intelligenz eine wichtige und insbesondere selbstverständliche Rolle spielt. Dabei besteht eine wechselseitige Beziehung: KI ist nicht nur Mittel zum Zweck – mächtiges Werkzeug zum Lösen elektrotechnischer Aufgabenstellungen sowie Helferlein im Arbeitsalltag – sondern auch Gegenstand der elektrotechnischen Forschung bzw. wird durch elektrotechnische Verfahren z.B. in der Nachrichtentechnik unterstützt. An vielen Stellen kommt die KI (noch) an ihre Grenzen. Wir zeigen, wo diese liegen und geben Ausblicke. Einen weiteren Schwerpunkt bildet die Auseinandersetzung mit dem Thema „KI in der elektrotechnischen Lehre“ sowie die Nutzung von Large Language Models im Studium und beim wissenschaftlichen Arbeiten. Wir lernen außerdem den Unterschied zwischen Data Scientist und Elektroingenieur in der Nachrichtentechnik kennen. Auch die Frage „Wird die KI Elektroingenieurinnen und Elektroingenieure ersetzen?“ klären wir hier mit Hilfe einer einschlägigen Berufsforscherin auf." (Autorenreferat, IAB-Doku)

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Bonfiglioli, Alessandra, Rosario Crinò, Harald Fadinger & Gino Gancia (2024): Robot Imports and Firm-Level Outcomes. In: The Economic Journal, Jg. 134, H. 664, S. 3428-3444. DOI:10.1093/ej/ueae055

Abstract

"We use French data over the 1994-2013 period to study how imports of industrial robots affect firm-level outcomes. Guided by a simple model, we develop a novel empirical strategy to identify the causal effects of robot adoption. Our results suggest that, while demand shocks generate a positive correlation between robot imports and employment at the firm level, exogenous exposure to automation leads to job losses. We also find that robot exposure increases labor productivity and some evidence that it may raise the relative demand for high-skill professions." (Author's abstract, IAB-Doku) ((en))

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Bonney, Kathryn, Cory Breaux, Catherine Buffington, Emin Dinlersoz, Lucia Foster, Nathan Goldschlag, John Haltiwanger, Zachary Kroff & Keith Savage (2024): The impact of AI on the workforce: Tasks versus jobs? In: Economics Letters, Jg. 244. DOI:10.1016/j.econlet.2024.111971

Abstract

"Will the adoption of AI by businesses substitute for worker tasks or jobs? This is a core question for which relatively scarce evidence exists—especially in the wake of recent advances in generative AI. Using a new large-scale business survey by the U.S. Census Bureau, we find that AI use is having a much greater impact on worker tasks than on employment levels at the firm level. About 27% of firms using AI report replacing worker tasks, but only about 5% experience employment change due to AI use. These rates are expected to increase to nearly 35% and 12%, respectively, in the near future." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Borwein, Sophie, Beatrice Magistro, Peter Loewen, Bart Bonikowski & Blake Lee-Whiting (2024): The gender gap in attitudes toward workplace technological change. In: Socio-economic review, Jg. 22, H. 3, S. 993-1017. DOI:10.1093/ser/mwae004

Abstract

"We provide the first systematic analysis of how attitudes toward workplace automation and artificial intelligence (AI) vary by gender, using survey data from ten countries. Our analyses reveal a significant gender gap in the perceived fairness of automation and AI, similar in magnitude to that of job offshoring. Drawing on the literature on economic shocks, we examine four explanations based on gender differences in (a) economic self-interest, (b) technological knowledge, (c) sociotropic concerns and (d) social status perceptions. Including these variables in our models, however, narrows the observed gender gap by only 40%. To better understand the sources of attitudinal variation by gender, we rely on Kitagawa–Oaxaca–Blinder decomposition, which shows that distributional differences in group characteristics, specifically women’s lower levels of technological knowledge and self-reported social status, account for approximately one-third of the gap, while the other two-thirds are explained by differences in how specific variables differentially influence attitudes." (Author's abstract, IAB-Doku) ((en))

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Brzozowski, Michał & Joanna Siwińska-Gorzelak (2024): Did robots make wages less responsive to unemployment? In: Technological forecasting & social change, Jg. 209. DOI:10.1016/j.techfore.2024.123769

Abstract

"Over recent years, there has been a notable change in the relationship that ties wage dynamics and unemployment, bearing significant implications for the formulation and implementation of monetary policies. Previous studies have identified a range of factors that potentially underlie this phenomenon but neglected the impact of robotisation. This paper seeks to address this gap by using data for 33 advanced economies and presenting compelling and robust empirical evidence of the moderating effect of robotisation on the relationship between unemployment and wage inflation." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Burkert, Carola, Annette Röhrig & Daniel Jahn (2024): Digitalisierung der Arbeitswelt: Mögliche Auswirkungen auf den Arbeitsmarkt in Hessen – Aktualisierung 2022. (IAB-Regional. Berichte und Analysen aus dem Regionalen Forschungsnetz. IAB Hessen 02/2024), Nürnberg, 26 S. DOI:10.48720/IAB.REH.2402

Abstract

"Der Einsatz von neuen digitalen Technologien wird die Arbeitswelt verändern, und auch – oder gerade – Hochqualifizierte werden betroffen sein. Das Substituierbarkeitspotenzial gibt an, in welchem Ausmaß Berufe gegenwärtig potenziell durch den Einsatz von Computern oder computergesteuerten Maschinen ersetzbar sind. Es entspricht dem Anteil an Tätigkeiten in einem Beruf, die schon heute durch den Einsatz moderner Technologien ersetzt werden könnten. Die vorliegende Studie zeigt, wie sehr sich die Arbeitswelt bereits verändert hat. Allerdings ist zu betonen, dass die Studie das technisch Mögliche der Ersetzbarkeit des Menschen durch die Maschine untersucht. Ob dies am Ende wirklich so eintrifft, steht nicht fest. Sicher ist aber: Für Unternehmen und Beschäftigte wird vor allem die permanente Weiterqualifizierung bzw. lebenslanges Lernen noch mehr an Gewicht gewinnen. In diesem IAB-Regional präsentieren wir die neuen Werte des Substituierbarkeitspotenzials 2022 für Hessen anhand der Anforderungsniveaus und der Berufssegmente und stellen teilweise auch die Entwicklung von 2013 bis 2022 dar. Weiterhin betrachten wir die Betroffenheit von Auswirkungen des Einsatzes neuer Technologien in Hessen, indem wir die Anteile sozialversicherungspflichtig Beschäftigter in Berufen mit niedrigem, mittlerem und hohem Substituierbarkeitspotenzial analysieren und untersuchen den Zusammenhang zwischen Substituierbarkeitspotenzialen und Beschäftigungsentwicklung." (Autorenreferat, IAB-Doku)

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Burkert, Carola, Katharina Grienberger, Britta Matthes & Annette Röhrig (2024): Digitale und KI-Technologien verändern inzwischen verstärkt auch die Arbeitswelt von Frauen. In: IAB-Forum – Grafik aktuell H. 06.09.2024, 2024-08-28. DOI:10.48720/IAB.FOO.GA.20240906.01

Abstract

"Durch die Digitalisierung und den Einsatz von KI-Technologien können immer mehr berufliche Tätigkeiten automatisiert werden. Dieser Anteil wird als Substituierbarkeitspotenzial bezeichnet. Immer mehr Beschäftigte arbeiten in Berufen mit einem hohen Substituierbarkeitspotenzial. Dabei verändert sich die Arbeitswelt der Frauen mittlerweile stärker als die der Männer." (Autorenreferat, IAB-Doku)

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Burzyński, Michał (2024): Spatial and Occupational Mobility of Workers Due to Automation. (LISER working papers 2024-04), Esch-sur-Alzette, 52 S.

Abstract

"Automation of labor tasks is one of the most dynamic aspects of recent technological progress. This paper aims at improving our understanding of the way that automation affects labor markets, analyzing the example of European countries. The quantitative theoretical methodology proposed in this paper allows to focus on automation-induced migration of workers, occupation switching and income inequality. The key findings include that automation in the first two decades of the 21st century had a significant impact on job upgrading of native workers and generated gains in many local labor markets. Even though net migration of workers was attenuated due to convergence in incomes across European regions, mobility at occupation levels had a sizeable impact on transmitting welfare effects of automation." (Author's abstract, IAB-Doku) ((en))

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Büchel, Jan & Jan Felix Engler (2024): Generative KI in Deutschland. Künstliche Intelligenz in Gesellschaft und Unternehmen. (IW-Report / Institut der Deutschen Wirtschaft Köln 2024,23), Köln, 27 S.

Abstract

"Generative KI sorgte in jüngster Vergangenheit für großes Aufsehen. Grund ist, dass generative KI-Anwendungen selbstständig Inhalte wie Texte, Bilder, Programmiercodes oder Videos generieren können, die oft nur schwer von menschlich erstellten Inhalten zu unterscheiden sind. Sinnbildlich für generative KI steht dabei in der öffentlichen Wahrnehmung oft noch die spezifische Anwendung ChatGPT, wie eine Analyse der Internetsuchanfragen und Zeitungsartikel in Deutschland zeigt. Davon profitiert allerdings auch das generelle Interesse an KI nachhaltig. Eine Auswertung von Online-Stellenanzeigen zeigt dagegen, dass Unternehmen nicht nur Interesse an generativer KI zeigen, sondern immer häufiger konkrete Anwendungsfälle in ihren Unternehmen identifizieren und dafür entsprechende Kompetenzen suchen. Dabei nimmt ChatGPT in der ersten Jahreshälfte 2023 ebenfalls eine entscheidende Rolle ein und prägte das Kompetenzprofil vieler Stellenanzeigen. Allerdings lösen sich die Unternehmensbedarfe im zweiten Halbjahr zunehmend von diesem Fokus. Stattdessen nehmen Unternehmen weitere Anwendungen oder Einsatzbereiche sowie die zugrundeliegenden Modelle der generativen KI stärker in den Blick. Sie möchten eigene Anwendungen entwickeln, die auf die jeweiligen Bedarfe und Geschäftsmodelle der Unternehmen abgestimmt sind. Nichtsdestotrotz kommt ChatGPT eine innovationsreibende Wirkung zu, die diese Entwicklung begünstigt hat. Es zeigt sich etwa die Tendenz, dass gerade große Unternehmen personalisierte geschlossene Lösungen entwickeln möchten, in denen Daten das Unternehmensnetzwerk nicht verlassen. Es könnte ein Indiz dafür sein, dass sich Unternehmen in Deutschland verstärkt mit den Chancen und insbesondere auch den Kosten und Risiken beschäftigt haben, die mit generativer KI einhergehen. Denn der Einsatz von generativer KI wirft urheberrechtliche, haftungsrechtliche und datenschutzrechtliche Fragen auf, die teilweise noch nicht abschließend geklärt sind. Ebenso sind die Auswirkungen des kürzlich verabschiedeten AI Acts noch weitestgehend ungeklärt. Regional betrachtet, sind die Unternehmensbedarfe in den südwestlichen Städten Deutschlands sowie in Berlin und dem angrenzenden Umland besonders hoch. Ähnliche Clusterwirkungen sind in den Regionen um München, Stuttgart, Karlsruhe und Heidelberg sowie um Köln und Bonn erkennbar. Gerade Unternehmen aus dem Technologiebereich und Fahrzeugbau sowie Forschungseinrichtungen schreiben dort viele Stellenanzeigen zu generativer KI aus. In Berlin und München treiben ebenfalls viele KI-Start-ups die Bedarfe zu generativer KI." (Autorenreferat, IAB-Doku)

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Büchel, Jan & Roschan Monsef (2024): Künstliche Intelligenz - Bessere Entlohnung durch Produktivitätsbooster? - Institut der deutschen Wirtschaft (IW). In: IW-Trends, Jg. 51, H. 2, S. 42-63. DOI:10.2373/1864-810X.24-02-03

Abstract

"Künstliche Intelligenz (KI) wird bereits vielfältig von Unternehmen und in der Gesellschaft eingesetzt. Auswirkungen auf die Arbeitswelt werden dabei kontrovers diskutiert: Auf der einen Seite stehen mögliche Produktivitätszuwächse durch KI-Anwendungen, auf der anderen Seite die Sorgen der Beschäftigten, dass KI ihre Arbeitsplätze ersetzen könnte. Deshalb kann gerade die Perspektive der Beschäftigten mehr Klarheit dazu bringen, welche Effekte KI konkret auf ihre Arbeitssituation haben kann. Auswertungen des Sozio-oekonomischen Panels zeigen, dass 37 Prozent der Beschäftigten in Deutschland im Jahr 2020 mit KI-Anwendungen arbeiten. KI-Beschäftigte sind häufig männlich, im Alter von 18 bis 44 Jahren und haben oftmals einen Meister-, Fachhochschul- oder Universitätsabschluss. Sie äußern seltener Sorgen, mit dem technischen Fortschritt nicht mithalten zu können, und mehr Sorgen, dass ihre beruflichen Qualifikationen abgewertet werden könnten. Eine multivariate Analyse zeigt, dass die Bruttostundenlöhne von KI-Beschäftigten durchschnittlich um 4 Prozent höher sind als bei Beschäftigten ohne KI-Nutzung. Dies gilt unabhängig davon, ob Beschäftigte intensiv oder weniger intensiv mit KI-Anwendungen arbeiten. Im Gegensatz zu früheren technologischen Neuerungen, von denen besonders Hochqualifizierte profitieren konnten, zeigt sich der positive Zusammenhang zwischen Lohnhöhe und KI-Nutzung für alle Bildungsgruppen." (Textauszug, IAB-Doku)

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Carioli, Paolo, Dirk Czarnitzki & Gastón P. Fernández (2024): Evidence on the adoption of Artificial Intelligence: The role of skills shortage. (ZEW discussion paper 24-013), Mannheim, 37 S.

Abstract

"Artificial Intelligence (AI) is considered to be the next general-purpose technology, with the potential of performing tasks commonly requiring human capabilities. While it is commonly feared that AI replaces labor and disrupts jobs, we instead investigate the potential of AI for overcoming increasingly alarming skills shortages in firms. We exploit unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. We measure skills shortage by the number of job vacancies that could not be filled as planned by firms, distinguishing among different types of skills. To account for the potential endogeneity of skills shortage, we also implement instrumental variable estimators. Overall, we find a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, we find evidence that scarcity of labor with academic education relates to firms exploring and adopting AI." (Author's abstract, IAB-Doku) ((en))

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Carstensen, Tanja & Kathrin Ganz (2024): Künstliche Intelligenz und Gender - eine Frage diskursiver (Gegen-)Macht? In: WSI-Mitteilungen, Jg. 77, H. 1, S. 26-33. DOI:10.5771/0342-300X-2024-1-26

Abstract

"Mit der Digitalisierung von Arbeit ist häufig die Frage verbunden, ob sich Geschlechterungleichheiten verändern. Aktuell wird dies vor allem mit Blick auf Künstliche Intelligenz (KI ) kontrovers diskutiert. Im betrieblichen Alltag gewinnt KI zunehmend an Relevanz ; in politischen, medialen und wissenschaftlichen Diskursen wird bereits seit einigen Jahren thematisiert, inwiefern KI aus Geschlechterperspektiven relevant ist. Dieser Beitrag untersucht diese Diskurse und widmet sich der Frage, inwiefern sich durch die Anwendung von KI -Technologien geschlechtliche Machtungleichheiten in der Arbeitswelt verändern und ob sich mit KI Ansatzpunkte für die Entwicklung von Gegenmacht erkennen lassen." (Textauszug, IAB-Doku)

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Casas, Pablo & Concepción Román (2024): The impact of artificial intelligence in the early retirement decision. In: Empirica, Jg. 51, H. 3, S. 583-618. DOI:10.1007/s10663-024-09613-3

Abstract

"This paper examines the impact of Artificial Intelligence (AI) on early retirement (ER) decisions in Europe. For the analysis, we utilize microdata from the Survey of Health, Ageing and Retirement in Europe, along with occupation-level data on AI advances and AI exposure. Initially, we investigate the influence of AI advances and AI exposure separately, finding in both instances a significant reduction in ER likelihood, though this only applies to workers with higher education. Subsequently, we explore the interaction between AI advances and AI exposure concerning ER probability. This interaction proves critical in determining AI’s impact on ER transitions. Specifically, we observe a significant reduction in ER probabilities for workers whose occupations exhibit high levels of AI advances and high expectations for further implementation of this technology in the future. Finally, we jointly analyse the interaction between AI advances, AI exposure, and education level. This analysis highlights that workers’ ER probabilities may either increase or decrease in response to the AI revolution, depending on their education level and the characteristics of their occupations in terms of AI advances and AI exposure." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Caselli, Mauro, Edwin Fourrier-Nicolai, Andrea Fracasso & Sergio Scicchitano (2024): Digital Technologies and Firms' Employment and Training. (CESifo working paper 11056), München, 63 S.

Abstract

"This study examines the causal influence of digital technologies, specifically operational (ODT) and information digital technologies (IDT), on firms' employment structure using Italian firm-level data. It employs a unique empirical approach, constructing instrumental variables based on predetermined employment composition and global technological progress, proxied by patents. Findings indicate that IDT investment positively affects employment, favoring a skilled, IT-competent workforce, as supported by firms' training and recruitment plans. Conversely, ODT investment does not significantly alter total employment but skews the workforce towards temporary contracts. The study contributes methodologically by distinguishing between ODT and IDT and highlighting nuanced employment dynamics within firms." (Author's abstract, IAB-Doku) ((en))

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Casey, Gregory (2024): Unemployment and the direction of technical change. In: European Economic Review, Jg. 168. DOI:10.1016/j.euroecorev.2024.104802

Abstract

"I construct and analyze a growth model in which technical change can increase unemployment. I first analyze the forces that deliver a constant steady state unemployment rate in this setting. Labor-saving technical change increases unemployment, which lowers wages and creates incentives for future investment in labor-using technologies. In the long run, this interaction generates a balanced growth path that is observationally equivalent to that of the standard neoclassical growth model, except that it also incorporates a positive steady state level of unemployment and a falling relative price of investment. I also study the effects of a permanent increase in the ability of R&D to improve labor-saving technologies. In the long run, this change leads to faster growth in output per worker and wages, but it also yields higher unemployment and a lower labor share of income. In the short run, this change exacerbates existing inefficiencies and slows economic growth." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Cattaneo, Maria Alejandra, Christian Gschwendt & Stefan C. Wolter (2024): How Scary Is the Risk of Automation? Evidence from a Large Scale Survey Experiment. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 17097), Bonn, 36 S.

Abstract

"Advances in technology have always reshaped labor markets. Automating human labor has lead to job losses and creation but most of all, for an increasing demand for highly skilled workers. However, emerging AI innovations like ChatGPT may reduce labor demand in high skilled occupations previously considered "safe" from automation. While initial studies suggest that individuals adjust their educational and career choices to mitigate automation risk, it is unknown what people would be willing to pay for a reduced automation risk. This study quantifies this value by assessing individuals' preferences for occupations in a discrete-choice experiment with almost 6'000 participants. The results show that survey respondents are willing to accept a salary reduction equivalent to almost 20 percent of the median annual gross wage to work in an occupation with a 10 percentage point lower risk of automation. Although the preferences are quite homogeneous, there are still some significant differences in willingness to pay between groups, with men, younger people, those with higher levels of education, and those with a higher risk tolerance showing a lower willingness to pay for lower automation risk." (Author's abstract, IAB-Doku) ((en))

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Chen, Xi, Xiang Wen Cai, Xu Ding, Le Song & Cheng Chen (2024): Intellectualization and substitution elasticity of capital on the labour force in logistics enterprises: evidence from China and the United States. In: Applied Economics Letters, Jg. 31, H. 5, S. 395-400. DOI:10.1080/13504851.2022.2136615

Abstract

"This paper addresses the substitution elasticity of capital on the labor force in the context of the development of intellectualization. Given the substitution of capital for labor, China's benchmark listed logistics companies are compared with an American company to discuss the evolution of capital - labor substitution. A large-scale intellectualization process began in 2017, and based on a variable elasticity of substitution, this paper creates an econometric model of substitution elasticity between capital and labor and its evolution between 2017 and 2021. The American logistics company UPS maintains a relatively high level of substitution elasticity, and Chinese logistics companies are quickly catching up. The substitution elasticity of capital on labor in Chinese enterprises trends upward year after year. In 2021, the capital - labor substitution elasticity of logistics enterprises in both countries showed considerable growth. The calculation model of substitution elasticity presented in this paper can be extended to different regions and industries to measure intelligent development levels and the relationship between capital and the labor force." (Author's abstract, IAB-Doku) ((en))

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Cirillo, Valeria, Andrea Mina & Andrea Ricci (2024): Digital technologies, labor market flows and training: Evidence from Italian employer-employee data. In: Technological forecasting & social change, Jg. 209. DOI:10.1016/j.techfore.2024.123735

Abstract

"New technologies can shape the production process by affecting the way in which inputs are embedded in the organization, their quality, and their use. Using an original employer-employee dataset that merges firm-level data on digital technology adoption and other characteristics of production with employee-level data on worker entry and exit rates from the administrative archive of the Italian Ministry of Labor, this paper explores the effects of new digital technologies on labor flows in the Italian economy. Using a Difference-in-Difference approach, we show that digital technologies lead to an increase in the firm-level hiring rate – particularly for young workers - and reduce the firm-level separation rate. We also find that digital technologies are positively associated with workplace training, proxied by the share of trained employees and the amount of training costs per employee. Furthermore, we explore the heterogeneity of effects related to different technologies (robots, cybersecurity and IoT). Our results are confirmed through several robustness checks." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Damelang, Andreas & Michael Otto (2024): Who is Replaced by Robots? Robotization and the Risk of Unemployment for Different Types of Workers. In: Work and occupations, Jg. 51, H. 2, S. 181-206. DOI:10.1177/07308884231162953

Abstract

"We study the effects of robotization on unemployment risk for different types of workers. We examine the extent to which robotization increases inequality at the skill level and at the occupational level using two theoretical frameworks: skill-biased technological change and task-biased technological change. Empirically, we combine worker-level data with information on actual investments in industrial robots. Zooming in on the German manufacturing industry, our multivariate results show that robotization affects different types of workers differently. We do not observe an increase in unemployment risk for low- and medium-skilled, but we find a considerably lower unemployment risk among high-skilled workers. Moreover, the unemployment risk is significantly higher in occupations with highly substitutable tasks, but only in industries that invest largely in robots." (Author's abstract, IAB-Doku, © SAGE) ((en))

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Danzer, Alexander M., Carsten Feuerbaum & Fabian Gaessler (2024): Labor supply and automation innovation: Evidence from an allocation policy. In: Journal of Public Economics, Jg. 235. DOI:10.1016/j.jpubeco.2024.105136

Abstract

"Despite a longstanding interest in the potential substitution of labor and capital, limited empirical evidence exists regarding the causal relationship between labor supply and the development of labor-saving technologies. This study examines the impact of exogenous changes in regional labor supply on automation innovation by leveraging a German immigrant allocation policy during the 1990s and 2000s. The findings reveal that an increase in the low-skilled workforce reduces automation innovation, as measured by patents. This reduction is most pronounced for large firms within the manufacturing sector and primarily concerns process-related automation innovations. This suggests that the effect is channeled through changes in internal demand for automation innovation. Consistent with a labor scarcity mechanism, the effect is confined to tight labor markets." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Dauth, Wolfgang & Michael Moritz; Katharina Grienberger, Florian Lehmer, Steffen Müller, Bernd Fitzenberger, Verena Plümpe, Oliver Falck, Anja Bauer, Anja Sonnenburg, Markus Janser, Christian Schneemann, André Diegmann, Britta Matthes & Anna Solms (sonst. bet. Pers.) (2024): Wie bewältigen Regionen die digitale und ökologische Transformation von Wirtschaft und Arbeitsmarkt? (Podium). In: IAB-Forum H. 06.05.2024. DOI:10.48720/IAB.FOO.20240506.01

Abstract

"Was bedeuten die absehbaren Transformationsprozesse der kommenden Jahrzehnte auf regionaler Ebene und wie können sie gemeistert werden? Antworten auf diese Fragen gab der IWH/IAB-Workshop zur Arbeitsmarktpolitik, der in diesem Jahr erstmals am IAB in Nürnberg stattfand." (Autorenreferat, IAB-Doku)

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Dehnbostel, Peter (2024): Neugestaltung der betrieblichen Weiterbildung in der digitalen Transformation. In: Magazin Erwachsenenbildung.at H. 51, S. 64-73.

Abstract

"Die betriebliche Weiterbildung ist seit den 1980er Jahren stark gewachsen und hat sich zum größten Einzelbereich der beruflichen Weiterbildung entwickelt. Ihre aktuelle Entwicklung erfordert eine grundlegende Neugestaltung, die maßgeblich auf die digitale Transformation von Arbeit und Lernen zurückgeht und nach einem erweiterten Verständnis von beruflicher und betrieblicher Weiterbildung verlangt. Für die betriebliche Weiterbildung stehen nicht mehr die einschlägigen Angebote mit Kursen und Seminaren im Vordergrund, sondern der Kompetenzerwerb im Prozess der Arbeit selbst. Die digitale Transformation von Arbeit und Lernen zeigt sich insbesondere im arbeitsintegrierten Lernen, in neuen betrieblichen Lernkonzepten und Lernformen sowie in der Validierung von Kompetenzen, die Beschäftigte durch informelles und nicht-formales Lernen erwerben. Dies fördert die Durchlässigkeit beruflicher Bildungswege sowie die Gleichwertigkeit beruflicher und hochschulischer Bildung, was entscheidend zur Entwicklung eines eigenständigen und gleichwertigen Gesamtsystems der Berufs- und Weiterbildung beiträgt. Die noch am Anfang stehende Neugestaltung der betrieblichen Weiterbildung stärkt laut Beitrag den Bildungsanspruch im Sinne einer selbstbestimmten und humanen Persönlichkeitsentwicklung." (Autorenreferat, IAB-Doku)

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Demirci, Ozge, Jonas Hannane & Xinrong Zhu (2024): Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms. (CESifo working paper 11276), München, 22 S.

Abstract

"This paper studies the impact of Generative AI technologies on the demand for online freelancers using a large dataset from a leading global freelancing platform. We identify the types of jobs that are more affected by Generative AI and quantify the magnitude of the heterogeneous impact. Our findings indicate a 21% decrease in the number of job posts for automation-prone jobs related to writing and coding, compared to jobs requiring manual-intensive skills, within eight months after the introduction of ChatGPT. We show that the reduction in the number of job posts increases competition among freelancers while the remaining automation-prone jobs are of greater complexity and offer higher pay. We also find that the introduction of Image-generating AI technologies led to a 17% decrease in the number of job posts related to image creation. We use Google Trends to show that the more pronounced decline in the demand for freelancers within automation-prone jobs correlates with their higher public awareness of ChatGPT’s substitutability." (Author's abstract, IAB-Doku) ((en))

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Deng, Liuchun, Verena Plümpe & Jens Stegmaier (2024): Robot Adoption at German Plants. In: Jahrbücher für Nationalökonomie und Statistik, Jg. 244, H. 3, S. 201-235., 2023-10-14. DOI:10.1515/jbnst-2022-0073

Abstract

"Using a newly collected dataset at the plant level from 2014 to 2018, we provide the first microscopic portrait of robotization in Germany and study the correlates of robot adoption. Our descriptive analysis uncovers five stylized facts: (1) Robot use is relatively rare. (2) The distribution of robots is highly skewed. (3) New robot adopters contribute substantially to the recent robotization. (4) Robot users are exceptional. (5) Heterogeneity in robot types matters. Our regression results further suggest plant size, high-skilled labor share, exporter status, and labor shortage to be strongly associated with the future probability of robot adoption." (Author's abstract, IAB-Doku, © De Gruyter) ((en))

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Dieckvoẞ-Ploch, Miriam; Anne Otto (interviewte Person) (2024): Künstliche Intelligenz ändert einiges. „Arbeitsmarkt der Zukunft“: Interview mit der Wirtschaftsgeographin Dr. Anne Otto über den Arbeitsmarkt in der Westpfalz, Automatisierung, künstliche Intelligenz und vieles mehr. In: Die Rheinpfalz H. 27.09.2024, 2024-09-27.

Abstract

"Weltweit schreitet die Digitalisierung in enormem Tempo voran und verändert die Berufswelt. Auch in der Westpfalz, die sich im Spannungsfeld von traditionellem Handwerk und hochmoderner Technologie befindet. KI-Systeme und Roboter werden immer leistungsfähiger und sind zunehmend in der Lage, Aufgaben selbstständig zu übernehmen. Wirtschaftsgeographin Dr. Anne Otto ist als Wissenschaftliche Mitarbeiterin im Regionalen Forschungsnetz des Instituts für Arbeitsmarkt- und Berufsforschung (IAB) unter anderem im Bereich regionale Arbeitsmarktforschung tätig und gibt Einblicke in den „Arbeitsmarkt der Zukunft“." (Autorenreferat, IAB-Doku)

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Dijksman, Sander, Danique Eijkenboom, Didier Fouarge, Marie-Christine Fregin, Evie Graus, Simon Janssen, Mark Levels, Raymond Montizaan, Pelin Özgül, Nicholas Rounding, Sanne Steens & Michael Stops (2024): Künstliche Intelligenz in der Arbeitswelt: Wie KI-unterstütztes Training die Gespräche im Kundenservice verändert. In: IAB-Forum H. 08.10.2024, 2024-11-18. DOI:10.48720/IAB.FOO.20241008.01

Abstract

"Wie wirkt sich der Einsatz von Künstlicher Intelligenz (KI) in der Weiterbildung auf die Produktivität von Beschäftigten aus? Diese und andere Fragen wurden im Rahmen des Forschungsprojektes „ai:conomics“ anhand von Daten aus verschiedenen europäischen Großunternehmen untersucht. Die ersten Ergebnisse deuten darauf hin, dass sich KI positiv auf die Produktivität von Beschäftigten auswirken kann. Dies gilt vor allem, wenn diese noch nicht lange im Unternehmen tätig sind." (Autorenreferat, IAB-Doku)

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Drydakis, Nick (2024): Artificial intelligence capital and employment prospects. In: Oxford economic papers, Jg. 76, H. 4, S. 901-919. DOI:10.1093/oep/gpae005

Abstract

"There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term ‘AI capital’ as a vector of knowledge, skills, and capabilities related to AI technologies, which could boost individuals’ productivity, employment, and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms." (Author's abstract, IAB-Doku) ((en))

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Erdsiek, Daniel, Robin Sack, Irene Bertschek, Patrick Breithaupt, Thomas Niebel, Bettina Schuck & Eliza Stenzhorn (2024): Digitale Souveränität. Herausforderungen aus Sicht der Unternehmen. 51 S.

Abstract

"Für die vorliegende Schwerpunktstudie für das Jahr 2024 wurde im Juni und Juli 2024 eine repräsentative Befragung von rund 1.200 Unternehmen in der Informationswirtschaft und im Verarbeitenden Gewerbe durchgeführt. Durch den Vergleich der aktuellen Ergebnisse mit den Erkenntnissen aus der Vorgängerstudie (Seifried & Bertschek 2021) können Veränderungen in der Wahrnehmung der verschiedenen Dimensionen digitaler Souveränität über die Zeit hinweg aufgezeigt und analysiert werden. Darüber hinaus werden in der aktuellen Untersuchung weitere Aspekte der digitalen Souveränität beleuchtet, die noch nicht Teil der Schwerpunktstudie des Jahres 2021 waren. Auf Basis der Ergebnisse lassen sich Empfehlungen ableiten, welche zum Erhalt und zur Stärkung der digitalen Souveränität der deutschen und der europäischen Wirtschaft beitragen sollen." (Textauszug, IAB-Doku)

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Erinc Oztas, Miray (2024): Migrant Women in the UK’s Digital Economy: The Elimination of Labour Market Barriers in the Digital Labour Market. In: Social Sciences, Jg. 13, H. 9. DOI:10.3390/socsci13090494

Abstract

"This research paper delves into the complexities migrant women face within the UK labor market, with a specific focus on the digital economy’s role as both a barrier and a conduit for employment opportunities. Migrant women in the UK encounter dual barriers to labour market entry: systemic challenges rooted in migration and gender biases, and the digital divide that exacerbates access and inclusion issues within the burgeoning digital economy. Through an exploratory descriptive analysis, this study explores how digitalism—defined as the integration of digital technologies into economic and societal practices—circumvents traditional labor market entry barriers such as languages barriers. By setting out the grounds for a potential hypothesis and further research in the era of AI, this paper underlines how through the implementation of AI tools, traditional barriers such as language barriers are eliminated in the digital labour market." (Author's abstract, IAB-Doku) ((en))

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Falck, Oliver, Yuchen Guo, Christina Langer, Valentin Lindlacher & Simon Wiederhold (2024): Training, Automation, and Wages: International Worker-Level Evidence. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 17503), Bonn, 72 S.

Abstract

"Job training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers' automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages." (Author's abstract, IAB-Doku) ((en))

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Falck, Oliver, Anna Kerkhof & Anita Wölfl (2024): Künstliche Intelligenz – wie Unternehmen sie nutzen und was sie noch daran hindert. In: Ifo-Schnelldienst, Jg. 77, H. 9, S. 57-63.

Abstract

"Der Künstlichen Intelligenz (KI) wird ein großes Potenzial als Schlüsseltechnologie zugeschrieben. Dieses Potenzial wird in Deutschland und der EU allerdings noch nicht ausgeschöpft, wie die Auswertung der Eurostat-Daten zur KI-Nutzung in Unternehmen von 2023 zeigt. Die Anwendung von KI konzentriert sich noch auf wenige Branchen sowie auf große Unternehmen. Was die KI-Technologien angeht, so werden vor allem Text Mining und KI-basierte Prozessautomatisierung eingesetzt, und dies hauptsächlich in den Bereichen Marketing, Produktion und IT-Sicherheit. Die KI wird dabei meistens eingekauft; in nur wenigen Branchen entwickeln Unternehmen sie selbst. Unternehmen, die darüber nachdenken, KI zu nutzen, aber noch zögern, sehen hauptsächlich mangelnde Expertise sowie hohe Kosten als Hindernis. Daneben sind insbesondere für deutsche Unternehmen die Sorge bezüglich des Datenschutzes oder der Datenverfügbarkeit sowie die Unsicherheit hinsichtlich möglicher rechtlicher Konsequenzen bedeutende Hindernisse." (Autorenreferat, IAB-Doku)

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Fana, Marta, Fernanda Bárcia De Mattos, Valeria Esquivel, Guendalina Anzolin, David Kucera & Sheeba Tejani (2024): The effects of automation in the apparel and automotive sectors and their gender dimensions. (JRC science for policy report 136639), Brüssel, 66 S.

Abstract

"This report is the final output of a research project investigating the effects of automation on employment in the automotive, apparel and footwear industries in five countries, namely Germany, Indonesia, Mexico, Romania, and Spain. The main objective of this project has been to improve our understanding of how ongoing processes of technological upgrading, particularly automation, impact women's and men's employment and work in these industries. Our findings suggest that, in the short term, close to the introduction of new automation technology, the impact on employment takes the form of reassignment of workers directly involved in automated processes to other positions, tasks, and occupations. This study also explored the impact of automation in terms of work organization and working conditions. Across the case studies, it emerged that the adoption of automation technologies has reduced heavy and repetitive tasks and improved health and safety for workers directly concerned by automation. Another interesting and related common finding is the reduction of workers' autonomy who are now subject to more standardization of tasks together with an ongoing process of deskilling of operators. Finally, in the apparel and footwear sector, we did not find evidence of defeminisation at the establishment level as well as the automotive factories remains highly male-dominated. Cultural norms and stereotypes which influence not only the jobs women and men apply to and get hired for, but also which training and education they engage in, contribute to this gender segregation in both sectors." (Author's abstract, IAB-Doku) ((en))

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Findeisen, Sebastian, Wolfgang Dauth & Oliver Schlenker (2024): Organized Labor Versus Robots? Evidence from Micro Data. (Working Paper Series / Universität Konstanz, Cluster of Excellence 'The Politics of Inequality' 25), Konstanz, 31 S. DOI:10.48787/kops/352-2-pkkgn822nr6u9

Abstract

"New technologies drive productivity growth but the distribution of gains might be unequal and is mediated by labor market institutions. We study the role that organized labor plays in shielding incumbent workers from the potential negative consequences of automation. Combining German individual-level administrative records with information on plant-level robot adoption and the presence of works councils, a form of shop-floor worker representation, we find positive moderating effects of works councils on retention for incumbent workers during automation events. Separations for workers with replaceable task profiles are significantly reduced. When labor markets are tight and replacement costs are high for firms, incumbent workers become more valuable and the effects of works councils during automation events start to disappear. Older workers, who find it more challenging to reallocate to new employers, benefit the most from organized labor in terms of wages employment. Concerning mechanisms we find that robot-adopting plants with works councils employ not more but higher quality robots. They also provide more training during robot adoption and have higher productivity growth thereafter." (Author's abstract, IAB-Doku) ((en))

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Fleischer, Julia & Camilla Wanckel (2024): Job Satisfaction and the Digital Transformation of the Public Sector: The Mediating Role of Job Autonomy. In: Review of Public Personnel Administration, Jg. 44, H. 3, S. 431-452. DOI:10.1177/0734371X221148403

Abstract

"Worldwide, governments have introduced novel information and communication technologies (ICTs) for policy formulation and service delivery, radically changing the working environment of government employees. Following the debate on work stress and particularly on technostress, we argue that the use of ICTs triggers “digital overload” that decreases government employees’ job satisfaction via inhibiting their job autonomy. Contrary to prior research, we consider job autonomy as a consequence rather than a determinant of digital overload, because ICT-use accelerates work routines and interruptions and eventually diminishes employees’ freedom to decide how to work. Based on novel survey data from government employees in Germany, Italy, and Norway, our structural equation modeling (SEM) confirms a significant negative effect of digital overload on job autonomy. More importantly, job autonomy partially mediates the negative relationship between digital overload and job satisfaction, pointing to the importance of studying the micro-foundations of ICT-use in the public sector." (Author's abstract, IAB-Doku) ((en))

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Fregin, Marie-Christine, Theresa Koch, Verena Malfertheiner, Pelin Özgül & Michael Stops (2024): On the Automation of Job Tasks: Occupational exposure to Artificial Intelligence and Software. (ROA external reports / Researchcentrum voor Onderwijs en Arbeidsmarkt (Maastricht) 4 ai:conomics policybrief), Maastricht, 10 S.

Abstract

"While rapid advances in digital technologies transformed the occupational structures and workers‘ skill and task composition over the past decades, much less is known about how Artificial Intelligence technologies (AI) will shape future labour markets. As part of the “ai:conomics” project, we analyze the extent to which employees subject to social security contributions in Germany are potentially exposed to AI and software technology. Our results show that highly educated, high-income workers are most exposed to AI, while their exposure is lower to software. Overall, the findings suggest that given AI’s far-reaching potential to carry out different sets of tasks, these technologies are expected to impact workers across a wider skill and wage spectrum, which previous automation technologies had limited impact on." (Author's abstract, IAB-Doku) ((en))

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Freundl, Vera, Philipp Lergetporer, Katharina Wedel & Katharina Werner (2024): Informationsbereitstellung zur Automatisierbarkeit von Berufen erhöht Weiterbildungsbereitschaft. In: Ifo-Schnelldienst, Jg. 77, H. 3, S. 39-43.

Abstract

"Beschäftige in Deutschland unterschätzen die Automatisierbarkeit ihres Berufs. Dies gilt vor allem für Beschäftigte in Berufen mit hoher Automatisierbarkeit, wie eine neue Studie von Lergetporer et al. (2023) zeigt. Die randomisierte Bereitstellung von Informationen über die tatsächliche Automatisierbarkeit ihrer Berufe erhöht die Arbeitsmarktsorgen und die Einschätzung über Veränderungen des Arbeitsumfelds. Außerdem wird die Teilnahmebereitschaft an Weiterbildungs und Umschulungsmaßnahmen erhöht, insbesondere bei Befragten in Berufen mit hoher Automatisierbarkeit. Dadurch verringert sich der Unterschied in der Weiterbildungsbereitschaft zwischen Beschäftigten in Berufen mit hoher und niedriger Automatisierbarkeit um 95,5 %, die Lücke in der Umschulungsbereitschaft wird sogar vollständig geschlossen." (Textauszug, IAB-Doku)

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Gabriel, Stefan, Tommy Falkowski, Jannis Graunke, Roman Dumitrescu, Anike Murrenhoff, Veronika Kretzschmer & Michael ten Hompel (2024): Künstliche Intelligenz und industrielle Arbeit – Perspektiven und Gestaltungsoptionen. Expertise des Forschungsbeirats Industrie 4.0. München, 46 S. DOI:10.48669/fb40_2024-1

Abstract

"In der neuen Expertise „Künstliche Intelligenz und industrielle Arbeit“ des Forschungsbeirats Industrie 4.0 zeigen das Fraunhofer IEM und das Fraunhofer IML Gestaltungsoptionen und Handlungsfelder auf, wie KI in der deutschen Industrie erfolgreich eingesetzt werden kann. Ziel ist sowohl eine Steigerung der Wettbewerbsfähigkeit als auch eine Aufwertung von Arbeitsplätzen. Der KI-Einsatz beinhaltet Produktionsabläufe planen, Montagetätigkeiten übernehmen, Steuerungen programmieren oder Lager organisieren." (Autorenreferat, IAB-Doku)

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Garoscio, Lidia & Katharina Wiehe (2024): Einsatz von KI in Werkstätten für behinderteMenschen. In: Soziale Sicherheit, Jg. 73, H. 8-9, S. 23-27.

Abstract

"Werkstätten für behinderte Menschen haben bereits Erfahrungen mit dem Einsatz von KI-gestützten Assistenzsystemen gemacht. Diese können Menschen mit Behinderungen in unterschiedlichen Arbeitsbereichen unterstützen. Der Artikel zeigt die Potenziale des Einsatzes anhand einiger Praxisbeispiele auf, diskutiert aber auch dessen Hürden und geht auf die Rolle desProjekts „KI-Kompass Inklusiv“ ein." (Textauszug, IAB-Doku)

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Gathmann, Christina, Christian Kagerl, Laura Pohlan & Duncan Roth (2024): The pandemic push: Digital technologies and workforce adjustments. In: Labour Economics, 2024-04-05. DOI:10.1016/j.labeco.2024.102541

Abstract

"Using a novel firm survey matched to administrative employee records, we demonstrate that the COVID-19 pandemic was a push factor for the diffusion of digital technologies in Germany. Two out of three firms invested in digital technologies. Three quarters of those investing firms invested because of the pandemic, particularly in hardware and software to enable decentralized communication, management, and coordination. These investments also fostered additional firm-sponsored training, underscoring the complementarity between investments in digital technologies and training. We then show that the investments helped firms insure their workers against the economic downturn. Firms with additional digital investments retained more of their employees on regular working hours and relied less on short-time work. Low- and medium-skilled, as well as young workers, benefited the most from the insurance effect of digital investments." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Gathmann, Christina, Felix Grimm & Erwin Winkler (2024): AI, Task Changes in Jobs, and Worker Reallocation. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 17554), Bonn, 50 S.

Abstract

"How does Artificial Intelligence (AI) affect the task content of work, and how do workers adjust to the diffusion of AI in the economy? To answer these important questions, we combine novel patent-based measures of AI and robot exposure with individual survey data on tasks performed on the job and administrative data on worker careers. Like prior studies, we find that robots have reduced routine tasks. In sharp contrast, AI has reduced non-routine abstract tasks like information gathering and increased the demand for 'high-level' routine tasks like monitoring processes. These task shifts mainly occur within detailed occupations and become stronger over time. While displacement effects are small, workers have responded by switching jobs, often to less exposed industries. We also document that low-skilled workers suffer some wage losses, while high-skilled incumbent workers experience wage gains." (Author's abstract, IAB-Doku) ((en))

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Georgieff, Alexandre (2024): Artificial intelligence and wage inequality. (OECD artificial intelligence papers 13), Paris, 37 S. DOI:10.1787/bf98a45c-en

Abstract

"This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate." (Author's abstract, IAB-Doku) ((en))

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Gerlmaier, Anja & Alexander Bendel (2024): Wie kollegial ist Künstliche Intelligenz? Risikowahrnehmungen und Gestaltungsanforderungen aus Sicht von Beschäftigten. (IAQ-Report 2024-01), Duisburg ; Essen, 15 S. DOI:10.17185/duepublico/81427

Abstract

Zukünftig werden immer mehr Beschäftigte nicht nur in ihrem privaten Umfeld, sondern auch am Arbeitsplatz mit Systemen zusammenarbeiten, die auf Künstlicher Intelligenz (KI) basieren. Das IAQ untersuchte im Rahmen des "HUMAINE"-Projektes, wie Beschäftigte die Kooperation mit solchen KI-Systemen bewerten und welche Gestaltungsanforderungen sie an diese neue Form der hybriden Mensch-KI-Zusammenarbeit haben. Es zeigte sich, dass KI-Systeme je nach Interaktionsform unterschiedliche Potenziale und Risiken aufweisen. Um die KI-basierten Risiken zu verringern, sollten Nutzer*innen frühzeitig an der Konzeption und Implementierung beteiligt und dabei arbeitswissenschaftliche Gestaltungskriterien berücksichtigt werden. (Author's abstract, IAB-Doku)

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Ghodsi, Mahdi, Robert Stehrer & Antea Barišić (2024): Assessing the impact of new technologies on wages and labour income shares. In: Technological forecasting & social change, Jg. 209. DOI:10.1016/j.techfore.2024.123782

Abstract

"This paper advances the literature on the impacts of new technologies on labor markets, focusing on wage and labor income shares. Using a dataset from 32 countries and 38 industries, we analyze the effects of new technologies – proxied by patents, information and communication technology (ICT) capital usage, and robot intensity – on average wages and labour income shares over time. Our results indicate a positive correlation between patents and wage levels along with a minor negative impact on labor income shares, suggesting that technology rents are not fully passed on to labor. Robot intensity is positively associated with labor income shares, while ICT capital has an insignificant effect. These effects persist over time and are reinforced by global value chain (GVC) linkages. Our conclusions align with recent research indicating that new technologies have a generally limited impact on wages and labour income shares." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Green, Andrew (2024): Artificial intelligence and the changing demand for skills in the labour market. (OECD artificial intelligence papers 14), Paris, 55 S. DOI:10.1787/88684e36-en

Abstract

"Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.) to work with AI. Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for the effect of artificial intelligence (AI) on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management and business skills. These include skills in general project management, finance, administration and clerical tasks. The results also show that there have been increases over time in the demand for these skills in occupations highly exposed to AI. For example, the share of vacancies in these occupations that demand at least one emotional, cognitive or digital skill has increased by 8 percentage points. However, using a panel of establishments (which induces plausibly exogenous variation in AI exposure), the report finds evidence that the demand for these skills is beginning to fall in establishments most exposed to AI." (Author's abstract, IAB-Doku) ((en))

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Grienberger, Katharina, Britta Matthes & Wiebke Paulus (2024): Anteil der beruflichen Tätigkeiten, die automatisiert werden könnten, variiert regional erheblich. In: IAB-Forum – Grafik aktuell H. 12.03.2024.

Abstract

"Das Substituierbarkeitspotenzial gibt an, in welchem Ausmaß Berufe potenziell durch den Einsatz von digitalen Technologien und KI ersetzbar sind. Bei der Aktualisierung der Daten für das Jahr 2022 zeigt sich, dass in Deutschland durchschnittlich 38 Prozent der sozialversicherungspflichtig Beschäftigten in einem Beruf arbeiten, in dem das Substituierbarkeitspotenzial hoch ist, also bei über 70 % liegt (siehe auch IAB-Kurzbericht 5/2024). Dabei weisen nach wie vor das Saarland, Baden-Württemberg und Thüringen die höchsten Anteile an sozialversicherungspflichtig Beschäftigten in Berufen mit einem solch hohen Substituierbarkeitspotenzial auf. In Berlin, Mecklenburg-Vorpommern, Hamburg, Brandenburg, Schleswig-Holstein und Sachsen-Anhalt sind diese Anteile am niedrigsten." (Autorenreferat, IAB-Doku)

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Grienberger, Katharina, Britta Matthes & Wiebke Paulus (2024): Folgen des technologischen Wandels für den Arbeitsmarkt: Vor allem Hochqualifizierte bekommen die Digitalisierung verstärkt zu spüren. (IAB-Kurzbericht 05/2024), Nürnberg, 8 S. DOI:10.48720/IAB.KB.2405

Abstract

"Die Potenziale, dass berufliche Tätigkeiten durch Computer oder computergesteuerte Maschinen vollautomatisch erledigt werden könnten, ändern sich, wenn neue Technologien auf dem Markt verfügbar werden. Bei der Neuberechnung solcher Substituierbarkeitspotenziale wird neben dieser Entwicklung auch berücksichtigt, dass sich die Tätigkeitsprofile in den Berufen verändern, neue Berufe und Tätigkeiten entstehen und Beschäftigte ihren Beruf wechseln. Die Autorinnen zeigen für die technologischen Möglichkeiten im Jahr 2022, wie hoch das Substituierbarkeitspotenzial derzeit ist und wie es sich seit 2013 verändert hat." (Autorenreferat, IAB-Doku)

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Groß, Michael & Jörg Staff (Hrsg.) (2024): KI-Revolution der Arbeitswelt. Perspektiven für Management, Organisation und HR. Freiburg: Haufe Group, 322 S.

Abstract

"Künstliche Intelligenz (KI) gilt als zentrales Zukunftsthema in nahezu allen Bereichen der Wirtschaft. Schon heute sind die Veränderungen durch KI in unserer Arbeitswelt spürbar. Dieses Buch von Prof. Dr. Michael Groß und Jörg Staff bietet hochaktuelle Beiträge über die bereits heute möglichen Anwendungen von KI im Personalbereich und deren Auswirkungen. Sie lernen die wesentlichen Perspektiven für den Einsatz von KI kennen und gewinnen einen Überblick über Chancen und Risiken von KI in Arbeit, Führung und Organisation. Zudem erhalten Sie wichtige Impulse für den Einsatz von KI im Management. Mit konkreten Handlungsempfehlungen sowie Praxisbeispielen namhafter Unternehmen und Institutionen, z.B. REWE, Zeiss, SAP, Workday, Coach Hub sowie Fraunhofer IAO, Ethikrat HR Tech, DGFP, DFKI." (Autorenreferat, IAB-Doku, © Haufe)

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Guarascio, Dario, Alessandro Piccirillo & Jelena Reljic (2024): Will robot replace workers? Assessing the impact of robots on employment and wages with meta-analysis. (GLO discussion paper / Global Labor Organization 1395), Essen, 31 S.

Abstract

"This study conducts a meta-analysis to assess the effects of robotization on employment and wages, compiling data from 33 studies with 644 estimates on employment and a subset of 19 studies with 195 estimates on wages. We identify a publication bias towards negative outcomes, especially concerning wages. After correcting for this bias, the actual impact appears minimal. Thus, concerns about the disruptive effects of robots on employment and the risk of widespread technological unemployment may be exaggerated or not yet empirically supported. While this does not preclude that robots will be capable of gaining greater disruptive potential in the future or that they are not already disruptive in specific contexts, the evidence to date suggests their aggregate effect is negligible." (Author's abstract, IAB-Doku) ((en))

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Guarascio, Dario, Alessandro Piccirillo & Jelena Reljic (2024): Will robot replace workers? Assessing the impact of robots on employment and wages with meta-analysis. (LEM working paper series / Laboratory of Economics and Management 2024,03), Pisa, 31 S.

Abstract

"This study conducts a meta-analysis to assess the effects of robotization on employment and wages, compiling data from 33 studies with 644 estimates on employment and a subset of 19 studies with 195 estimates on wages. We identify a publication bias towards negative outcomes, especially concerning wages. After correcting for this bias, the actual impact appears minimal. Thus, concerns about the disruptive effects of robots on employment and the risk of widespread technological unemployment may be exaggerated or not yet empirically supported. While this does not preclude that robots will be capable of gaining greater disruptive potential in the future or that they are not already disruptive in specific contexts, the evidence to date suggests their aggregate effect is negligible." (Author's abstract, IAB-Doku) ((en))

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Guo, Yuchen Mo, Oliver Falck, Christina Langer, Valentin Lindlacher & Simon Wiederhold (2024): Training, Automation, and Wages: Worker-Level Evidence. In: Verein für Socialpolitik (Hrsg.) (2024): Upcoming Labor Market Challenges. Beiträge zur Jahrestagung des Vereins für Socialpolitik 2024.

Abstract

"This paper investigates the impact of job training on workers’ susceptibility to automation. Using rich individual-level data from the Programme for the International Assessment of Adult Competencies (PIAAC) across 37 industrialized countries, we construct a unique individual-level measure of automation risk based on the tasks performed at work. We uncover substantial variation in automation risk within detailed occupations, which would have been overlooked by previous occupation-level automation measures. To estimate the effect of training on workers’ automation risk, we include tested numeracy skills as a proxy for unobserved ability that are unique to our data, and apply entropy balancing to account for selection bias. We find that job training is an important factor in explaining workers’ susceptibility to automation, even within narrowly defined occupations. Our results show that workers who participate in job training witness a 4.7 percentage point reduction in their automation risk compared to observationally equivalent workers without training. Additionally, workers participating in training earn approximately 8 percent higher wages compared to their counterparts without training. While training is effective in reducing automation risk and increasing wages in all sample countries, there is a substantial heterogeneity in the magnitude of training effects. Moreover, training benefits both younger and older workers equally, and is more effective for women. Our findings thus underscore the crucial role of training in enabling the workforce to adapt and thrive amidst evolving technological changes." (Author's abstract, IAB-Doku) ((en))

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Güven, Barış (2024): Has labor-saving technology accelerated? Evidence from industry-level data. In: Structural Change and Economic Dynamics, Jg. 70, S. 442-456. DOI:10.1016/j.strueco.2024.05.008

Abstract

"What role has labor-saving technological change played in the recent past in charting out the trajectory of employment? Have we already transitioned into a new technological regime where production technologies are more invasive upon labor’s terrain? In this study, I provide empirical evidence to answer these questions. Using industry-level data from 12 advanced economies for 1970–2007, I show that capital goods did not become more effective in labor-saving after 1980 or 1990. Similarly, the strength of the relationship between employment and output did not decline after 1980 or 1990. While many recent econometric studies have estimated the number of workers displaced due to industrial robots with which the media and public are highly preoccupied, there is nothing new in the fact that production technologies are labor-saving and displace workers. The importance of demand side factors and structural change (mainly deindustrialization) in determining employment patterns is often neglected, leading to a misleading assessment of the impact of labor-saving technologies on employment." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Hanna, Alexis, Christopher D. Nye, Andrew Samo, Chu Chu, Kevin A. Hoff, James Rounds & Frederick L. Oswald (2024): Interests of the future: An integrative review and research agenda for an automated world of work. In: Journal of vocational behavior, Jg. 152. DOI:10.1016/j.jvb.2024.104012

Abstract

"Research on automation and the future of work is a major focus for both academics and practitioners due to technological changes disrupting the labor market and educational pathways. Although recent articles have published projections about the types of tasks and jobs most likely to be automated in the coming years, little attention has been devoted to how different types of vocational interests are susceptible to automation, as well as resulting changes to the match between people's interests and their jobs. In the present article, we provide an integrative review of vocational interests and automation projections within and across jobs. By standardizing and mapping projections to Holland's RIASEC interest model, we found that Investigative (scientific) and Conventional (detail-oriented) interests, including STEM interests, are most susceptible to automation, whereas Social (people-oriented) and Realistic (hands-on) interests are least susceptible. For Artistic and Enterprising interests, some creative work, decision-making, and leadership skills may be affected by automation across a range of jobs. We build on these projections to propose a future research agenda integrating interests, technology, and careers. Specifically, we identify five areas for future research, including using intentional work design to enhance interests, the role of interests in career decisions related to project-based work, changes in people's interests following automation, increased use of basic interests, and the systematic impacts of automation on different groups of people. Overall, this review highlights how vocational interests will remain an important topic with high relevance for career guidance, education, and organizations as the future of work evolves." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Hannane, Jonas, Ozge Demirci & Xinrong Zhu (2024): Generative Künstliche Intelligenz reduziert Nachfrage nach Freelance-Arbeit auf Online-Plattformen. In: DIW-Wochenbericht, Jg. 91, H. 35, S. 539-545. DOI:10.18723/diw_wb:2024-35-1

Abstract

"Einführung generativer KI birgt das Potenzial für höheres Wirtschaftswachstum, stellt. Arbeitnehmer*innen aber auch vor große Herausforderungen • Auftragszahlen auf Online-Arbeitsmärkten zeigen: Nachfrage nach leicht automatisierbaren Tätigkeiten bleibt deutlich hinter der nach anderen Tätigkeiten zurück. Auftragszahlen für leicht ersetzbare Arbeiten wie Schreib- oder Grafikarbeiten sind aufgrund der Einführung generativer KI bis zu 30 Prozent zurückgegangen. Bei verbleibenden Aufträgen steigen die Anforderungen, also die Komplexität der Aufträge – sie werden zugleich aber auch besser dotiert. Weiter- und Fortbildungsmaßnahmen unerlässlich – insbesondere für Frauen und Ältere, die KI deutlich weniger am Arbeitsplatz nutzen als Männer und Jüngere." (Textauszug, IAB-Doku)

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Heider-Willms, Angela (2024): KI: Skepsis im Arbeitsalltag. In: Personalwirtschaft, Jg. 50, H. 9, S. 74-75.

Abstract

"Wann ist der Einsatz von Künstlicher Intelligenz in der Personalarbeit sinnvoll? Und was für einen Einfluss hat diese auf die Arbeitskultur? Damit beschäftigen sich zwei aktuelle Umfragen." (Autorenreferat, IAB-Doku)

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Heluo, Yuxi & Oliver Fabel (2024): Job computerization, occupational employment and wages: A comparative study of the United States, Germany, and Japan. In: Technological forecasting & social change, Jg. 209. DOI:10.1016/j.techfore.2024.123772

Abstract

"This study adds to the growing literature on wage and employment responses to the risk of job computerization. Specifically, it revisits the original occupational perspective and inquires into the nature of the adjustments of occupational wages and employment, i.e., the potential benefits and costs associated with professional careers in such occupations. The investigation further aims at identifying whether these adjustment processes are universal - as suggested by the global availability of the respective technology - or reflect country-specific peculiarities. To this end, it conducts a comparative analysis with data from the United States, Germany, and Japan, three G7 lead countries which share the commitment to fostering technological progress, but which are also characterized by distinctly different labor market institutions and approaches to industrial policies. Generally consistent with the country-specific employment institutions and common corporate strategies, transmission channels - as reflected by the relationship between adjustments of occupational employment and wages - differ between countries. In all three countries, though, higher risks of computerization are associated with relative wage losses in occupations which require low levels of formal education or training." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Hemel, Ulrich, Edeltraud Leibrock, Christoph Metzler, Alexander Nüßgen, Margot Ruschitzka & Christian Rusche (2024): Künstliche Intelligenz als Co-Pilot - Warum Unternehmen im Fahrersitz bleiben müssen. (IW policy paper / Institut der Deutschen Wirtschaft Köln 2024,01), Köln, 27 S.

Abstract

"In today’s digital era, we are witnessing a revolution driven by the progressive development and integration of Artificial Intelligence (AI) into all aspects of life. This article sheds light on this transformation by highlighting the remarkable advancements and the growing significance of AI for society and economy. The article is divided into several chapters that illuminate various dimensions of AI integration in society and economy. Firstly, the article elaborates that AI enables not only productivity enhancements and efficiency gains but also serves as the foundation for innovations that can simplify our daily lives. The intelligent automation of routine tasks provides people with the freedom to engage in more creative and challenging activities, contributing to an improvement in quality of life and prosperity. Secondly, when examining the successful implementation of AI in companies, the article emphasiszes that a well-thought-out AI strategy is necessary to effectively utilize the technology: businesses must invest not only in the relevant tools but also in the training of their employees. Comprehensive AI competence within the workforce is crucial for developing innovative solutions and fully harnessing the potential of AI. Thirdly, the development of personnel and competencies represents another essential chapter. The world of work will transform due to AI, leading to new skill requirements for employees. Lifelong learning and continuous training in digital competencies are essential to keep pace with rapid technological advancements. Simultaneously, educational institutions must adapt their curricula to prepare the next generation for a future where AI plays a central role. Based on the insights from conducted analyses, actionable options regarding AI are derived. Regulatory frameworks and ethical guidelines ensure that the advancement and utilization of AI align with societal values and norms. Human-centricity remains paramount, with technology serving as a complement to human actions, enhancing and extending them, but not replacing them. AI presents a dual challenge: On one hand, it offers incredible opportunities for innovation and prosperity; on the other hand, it requires careful control and adaptation at individual, entrepreneurial, and societal levels. By developing critical AI competencies and practicing responsible use, we can fully harness the potential of AI while minimizing potential risks. Understanding AI as a co-pilot in this dynamic environment is the key to setting the course for a future where technology and humans collaborate harmoniously for mutual benefit." (Author's abstract, IAB-Doku) ((en))

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Henke, Justus (2024): Generative KI in der Hochschulkommunikation. Ergebnisse der 2. Welle. (HoF-Arbeitsberichte 126), Lutherstadt Wittenberg, 42 S.

Abstract

"Diese Studie untersucht erneut die Anwendung und Wahrnehmung generativer KI-Tools in der Hochschulkommunikation im Jahr 2024 und vergleicht die Ergebnisse mit 2023. Hochschulkommunikation umfasst die interne und externe organisationale Kommunikation der Hochschulen. Die Befragung unter deutschen Hochschulen fragte nach Nutzungsmustern, Herausforderungen und Potenzialen dieser Technologien. Die Ergebnisse zeigen, dass die Nutzung von Textgenerierungstools wie ChatGPT deutlich zugenommen hat, während Übersetzungstools wie DeepL weiterhin am häufigsten verwendet werden. Private Hochschulen integrieren generative KI-Tools häufiger und vielfältiger als öffentliche Einrichtungen. Die Zufriedenheit mit diesen Tools hat sich leicht verbessert, bleibt jedoch moderat. Im Vergleich zu 2023 zeigen sich spürbare Effizienzgewinne und eine erhöhte Anpassungsfähigkeit an verschiedene Kommunikationskanäle. Herausforderungen wie Faktentreue und Datenschutz bleiben zentrale Themen. Ein offener Dialog, die Etablierung hochschulspezifischer Umgangsweisen und Weiterbildung im Umgang mit generativen KI-Tools sind notwendig, um deren Potenziale und Risiken besser zu verstehen und produktiv für die Hochschulkommunikation zu nutzen. Die Studie betont die Notwendigkeit einer umfassenden Herangehensweise, die technologische Fähigkeiten, operative Bedürfnisse und das sozio-technische Umfeld berücksichtigt, um eine erfolgreiche Integration dieser Tools zu gewährleisten. Die Veränderungen zur Vorjahresstudie zeigen zunehmend positive Auswirkungen auf Arbeitsprozesse, während zentrale Herausforderungen weiterhin bestehen." (Autorenreferat, IAB-Doku)

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Hoffmann, Manuel, Sam Boysel, Frank Nagle, Sida Peng & Kevin Xu (2024): Generative AI and the Nature of Work. (CESifo working paper 11479), München, 69 S.

Abstract

"Recent advances in artificial intelligence (AI) technology demonstrate considerable potential to complement human capital intensive activities. While an emerging literature documents wide-ranging productivity effects of AI, relatively little attention has been paid to how AI might change the nature of work itself. How do individuals, especially those in the knowledge economy, adjust how they work when they start using AI? Using the setting of open source software, we study individual level effects that AI has on task allocation. We exploit a natural experiment arising from the deployment of GitHub Copilot, a generative AI code completion tool for software developers. Leveraging millions of work activities over a two year period, we use a program eligibility threshold to investigate the impact of AI technology on the task allocation of software developers within a quasi-experimental regression discontinuity design. We find that having access to Copilot induces such individuals to shift task allocation towards their core work of coding activities and away from non-core project management activities. We identify two underlying mechanisms driving this shift - an increase in autonomous rather than collaborative work, and an increase in exploration activities rather than exploitation. The main effects are greater for individuals with relatively lower ability. Overall, our estimates point towards a large potential for AI to transform work processes and to potentially flatten organizational hierarchies in the knowledge economy." (Author's abstract, IAB-Doku) ((en))

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Hu, Shengming, Kai Lin, Bei Liu & Hui Wang (2024): Does robotization improve the skill structure? The role of job displacement and structural transformation. In: Applied Economics, Jg. 56, H. 28, S. 3415-3430. DOI:10.1080/00036846.2023.2206623

Abstract

"The literature generally focuses on the impact of robots or artificial intelligence on the employment and wages, but ignores the effect of robotization on the skill structure and its underlying mechanisms and lacks empirical evidence from developing countries. We theoretically develop a task model by introducing the skill structure and empirically investigate the effect of robotization on the skill structure based on Chinese provincial panel data from 2006 to 2018. Results show that: (1) the development of robotization in China is conducive to improving the skill structure, and the baseline conclusion still holds even though adopting multiple indexes of skill structure and controlling the endogeneity bias. (2) Robotization generates not only job displacement effect by displacing unskilled workers with robots but also structural transformation effect by increasing the proportion of technology-intensive industries, which can improve the skill structure. (3) In coastal provinces with strong Internet foundation, information transmission capacity and labour protection intensity, high labour cost and ageing rate, robotization plays a stronger role in improving the skill structure. Moreover, robotization can induce the employment polarization. These conclusions can help avoid technical unemployment and promote the upgrading of the skill structure in China." (Author's abstract, IAB-Doku) ((en))

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Hölzle, Katharina, Oliver Riedel, Wilhelm Bauer & Thomas Renner (Hrsg.) Kaiser, Simone, Matthias Peissner, Udo-Ernst Haner, Matthias Engelbach, Lydia Uhler, Jan Mackensen, Pia Mozer, Bernd Dworschak, Jens Drawehn, Jessica Wulf, Bernd Bienzeisler, Thomas Renner, Wolfgang Beinhauer, Dennis Klau & Maximilien Kintz (sonst. bet. Pers.) (2024): Potenziale Generativer KI für den Mittelstand. Wie große KI-Modelle die Arbeitswelt verändern. Stuttgart, 72 S. DOI:10.24406/publica-2246

Abstract

"Seit der Veröffentlichung von ChatGPT im November 2022 haben die Entwicklungen im Bereich Generative KI deutlich an Fahrt aufgenommen. In kurzer Abfolge wurden - und werden immer noch - neue Modelle und Funktionen vorgestellt. Zunehmend zeichnen sich breite Einsatzmöglichkeiten in den Unternehmen ab, mit einem hohen zu erwartenden Nutzenpotenzial. Vor allem für mittelständische Unternehmen stellt es eine Herausforderung dar, die Bedeutung der Entwicklungen einzuschätzen und eine strukturierte Vorgehensweise zum Thema Generative KI zu definieren und umzusetzen. Das Ministerium für Wirtschaft, Arbeit und Tourismus Baden-Württemberg hat das Fraunhofer IAO beauftragt, mittels einer Studie eine Orientierungshilfe zu den aktuellen Entwicklungen zu bieten und konkrete Empfehlungen für den Umgang mit Generativer KI zu geben. Ein vielköpfiges Autorenteam des Fraunhofer IAO aus verschiedenen Forschungsbereichen hat, neben einer ausführlichen Literaturrecherche, 48 Expertinnen und Experten im Bereich Generativer KI zu ihren Einschätzungen befragt. Es wurden sowohl Forschungseinrichtungen, KI-Anbieter, Dienstleister als auch Anwenderunternehmen miteinbezogen. Das Ergebnis der Recherche und Befragung liegt in Form dieser Studie vor, die einen Beitrag zum bewussten und zielgerichteten Umgang mit Generativer KI in den Unternehmen leisten soll." (Autorenreferat, IAB-Doku)

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Jestl, Stefan (2024): Industrial robots, and information and communication technology: the employment effects in EU labour markets. In: Regional Studies, Jg. 58, H. 11, S. 1981-1998. DOI:10.1080/00343404.2023.2292259

Abstract

"This paper explores the effects of industrial robots and information and communication technology (ICT) on regional employment in European Union countries. The empirical analysis relies on a harmonized comprehensive regional dataset that combines business statistics and national and regional accounts data. This rich dataset enables us to provide detailed insights into the employment effects of automation and computerisation in EU regions for the period 2001–16. The results suggest relatively weak effects on regional total employment dynamics. However, industrial robots show negative employment effects in local manufacturing industries and positive employment effects in local non-manufacturing industries. While the negative effect is concentrated in particular local manufacturing industries, the positive effect has operated in local service industries. Information technology investments show positive employment effects in local manufacturing industries and some individual local service industries, while communication technology investments are shown to be irrelevant for employment dynamics. In contrast, software and database investments have had a predominantly negative association with local employment." (Author's abstract, IAB-Doku) ((en))

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Jurkat, Anne, Rainer Klump & Florian Schneider (2024): Wie Roboter die Welt (und das Wirtschaften) verändern: Ein Überblick über Daten, Forschungsergebnisse und wirtschaftspolitische Strategien. In: Perspektiven der Wirtschaftspolitik, Jg. 25, H. 2, S. 130-152. DOI:10.1515/pwp-2024-0007

Abstract

"Der industrielle Einsatz von Robotern und die damit verbundenen Veränderungen wirtschaftlicher und sozialer Beziehungen sind ein schnell wachsendes Forschungsfeld. In diesem Beitrag geben Anne Jurkat, Rainer Klump und Florian Schneider einen Überblick über Datenquellen und aktuelle Ergebnisse der empirischen Forschung zum Robotereinsatz. Nach einer Präsentation der thematischen Schwerpunkte der Forschung erörtern sie die unterschiedlichen Analyseebenen und die drei zentralen Wirkungseffekte des Robotereinsatzes (Produktivitäts-, Substitutions- und Wiedereinsetzungseffekt). Abschließend analysieren sie die aktuellen wirtschaftspolitischen Strategien zum Umgang mit Robotik in Deutschland, die auf die Sicherung von Wettbewerbsfähigkeit und technologischer Souveränität abzielen." (Autorenreferat, IAB-Doku)

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Keil, Mareike Victoria & Dominic Ketzer (2024): Neurodivergent Employees: AI's Role in New Work Challenges. Exploring Neurodiverse Team Dynamics in the Era of New Work: Leveraging AI for Inclusive Environments. Mannheim, 20 S.

Abstract

"Disruptive change has driven the digitalization and transformation of work structures in the wake of the Covid-19 pandemic, with new types of work models increasingly finding their way into familiar work structures with lasting impact. This has triggered a rapid development as part of the New Work megatrend, which, alongside challenges such as teleworking, has created great opportunities such as better integration of individuals and certain groups of people, e.g. people with disabilities, into the primary labor market. Neurodiverse teams face particular challenges due to the changing workplace, especially in terms of communication, self-organization and working practices. This paper addresses these challenges and proposes solutions based on artificial intelligence (AI) to ensure the competitiveness of companies in the implementation of New Work methods and models and to counteract the shortage of skilled workers." (Author's abstract, IAB-Doku) ((en))

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Keitel, Christiane; Michael Stops & Lennert Peede (interviewte Person) (2024): KI-Kompetenzen gefragt: Studie zeigt tendenziell steigende Nachfrage in Stellenanzeigen (Interview). In: IAB-Forum H. 27.11.2024. DOI:10.48720/IAB.FOO.20241127.01

Abstract

"In den letzten Jahren gab es eine Vielzahl von Innovationen im Bereich der künstlichen Intelligenz (KI). Die öffentliche Debatte schwankt zwischen der Befürchtung, dass viele Tätigkeiten künftig nicht mehr von Menschen erledigt werden und Arbeitsplätze wegfallen, und der Hoffnung, dass neue Tätigkeitsfelder und damit eine neue Qualität von Arbeit entstehen. In einer Studie untersuchen die IAB-Forscher Michael Stops und Lennert Peede unter anderem anhand einer Analyse von Stellenanzeigen aus den Jahren 2015 bis 2019, wie sich KI-Technologien in dieser frühen Phase bereits auf die Arbeitsnachfrage und die Beschäftigung auf Betriebsebene auswirkten." (Autorenreferat, IAB-Doku)

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Keppeler, Florian (2024): No Thanks, Dear AI! Understanding the Effects of Disclosure and Deployment of Artificial Intelligence in Public Sector Recruitment. In: Journal of Public Administration Research and Theory, Jg. 34, H. 1, S. 39-52. DOI:10.1093/jopart/muad009

Abstract

"Applications based on artificial intelligence (AI) play an increasing role in the public sector and invoke political discussions. Research gaps exist regarding the disclosure effects—reactions to disclosure of the use of AI applications—and the deploymenteffect—efficiency gains in data savvy tasks. This study analyzes disclosure effects and explores the deployment of an AI application in a preregistered field experiment (n = 2,000) co-designed with a public organization in the context of employer-driven recruitment. The linear regression results show that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates. The explorative analysis of the deployment of the AI application indicates that the person–job fit determined by the leaders can be predicted by the AIapplication. Based on the literature on algorithm aversion and digital discretion, this study provides a theoretical and empirical disentanglement of the disclosure effect and the deployment effect to inform future evaluations of AI applications in the public sector. It contributes to the understanding of how AI applications can shape public policy and management decisions, and discusses the potential benefits and downsides of disclosing and deploying AI applications in the public sector and in employer-driven recruitment." (Author's abstract, IAB-Doku) ((en))

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Kerkhof, Anna, Thomas Licht, Manuel Menkhoff & Klaus Wohlrabe (2024): Die Nutzung von Künstlicher Intelligenz in der deutschen Wirtschaft. In: Ifo-Schnelldienst, Jg. 77, H. 8, S. 39-43.

Abstract

"Künstliche Intelligenz (KI) hat sich zu einem zentralen Treiber der modernen Wirtschaft entwickelt. Insbesondere in Deutschland transformiert KI eine Vielzahl von Branchen – von der Automobilindustrie bis hin zur Finanzbranche – und beeinflusst maßgeblich strategische Entscheidungen und Kundeninteraktionen. Die Europäische Verordnung über Künstliche Intelligenz, die im August 2024 in Kraft getreten ist, verfolgt einen risikobasierten Ansatz zur Regulierung von KI-Systemen, um hohe Sicherheits- und Ethikstandards zu gewährleisten. Trotz der regulatorischen Herausforderungen hat die KI-Nutzung in deutschen Unternehmen im vergangenen Jahr stark zugenommen: Der Anteil der Unternehmen, die angeben, KI zu nutzen, stieg von 13,3 % im Juni 2023 auf 27 % im Folgejahr. Besonders im Verarbeitenden Gewerbe nutzen 31 % der Unternehmen KI. Die Mehrheit der Unternehmen erwartet positive Produktivitätseffekte durch KI, mit geschätzten Produktivitätssteigerungen von 8 % für das eigene Unternehmen und gesamtwirtschaftlich 12 % in den nächsten fünf Jahren. Insgesamt zeigt sich, dass KI in Deutschland zunehmend als Schlüsseltechnologie wahrgenommen wird, die wesentliche Wachstumspotenziale für die Zukunft bietet." (Textauszug, IAB-Doku)

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Kikuchi, Shinnosuke (2024): Automation and Offshoring on Wage Inequality in Japan. (RIETI discussion paper 24046), Tokyo, 24 S.

Abstract

"I examine the effect of task displacement from automation technology and offshoring on wage inequality using data for Japan since 1980. First, I do not find evidence that task displacement from automation increases wage inequality, which contrasts with the finding for the US. Second, I find that the rise in offshoring has distributional consequences and is progressive after the mid-1990s. The surge in offshoring is concentrated in industries where ex-ante low-wage workers work and disproportionally increases their wages. This increase in wages is due to the increases in monthly payroll, decreases in hours worked, decreases in employment rate, and decreases in the share of offshorable occupations." (Author's abstract, IAB-Doku) ((en))

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Kim, Hyejin (2024): The impact of robots on labor demand: evidence from job vacancy data in South Korea. In: Empirical economics, Jg. 67, H. 3, S. 1185-1209. DOI:10.1007/s00181-024-02585-0

Abstract

"The debate about the impact of robots on employment has been lively. In this paper, I examine the effect of robots on local labor demand in South Korea, one of the most technologically advanced countries in terms of robotics. Using the regional variation in robot exposure constructed from national industry-level robot adoption data and the initial distribution of industrial employment in cities, I find that robots did not reduce local labor demand. However, I estimate declines in labor demand in the manufacturing sector and routine jobs. An increase in one robot per 1000 workers in terms of exposure to robots is correlated with a decline in the job vacancy growth rate of 2.6%p in the manufacturing sector and of 2.5%p in routine jobs. No significant relationship is found between robot exposure and labor demand in the service sector or non-routine jobs." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Korinek, Anton (2024): Economic Policy Challenges for the Age of AI. (NBER working paper / National Bureau of Economic Research 32980), Cambridge, Mass, 27 S.

Abstract

"This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distribution, and the value of education and human capital. I explore what roles may remain for labor post-AGI, and which production factors will grow in importance. The paper then identifies eight key challenges for economic policy in the Age of AI: (1) inequality and income distribution, (2) education and skill development, (3) social and political stability, (4) macroeconomic policy, (5) antitrust and market regulation, (6) intellectual property, (7) environmental implications, and (8) global AI governance. It concludes by emphasizing how economists can contribute to a better understanding of these challenges." (Author's abstract, IAB-Doku) ((en))

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Kristal, Tali, Efrat Herzberg-Druker & Adena White (2024): Is the wage premium on using computers at work gender-specific? In: Research in Social Stratification and Mobility, Jg. 89. DOI:10.1016/j.rssm.2024.100890

Abstract

"Past research on the relationship between computers and wages has revealed two stylized facts. First, workers who use a computer at work earn higher wages than similar workers who do not (termed as ‘the computer wage premium’). Second, women are more likely to use a computer at work than are men. Given the recognized computer wage premium and women’s advantage in computer use at work, we ask: Is the wage premium on using computers at work gender- or non-gender-specific? Given gendered processes operating at both the occupational and within-occupation levels, we expect that returns to computer usage are gender-bias. This contrasts the skill-biased technological change (SBTC) theory assumption that the theorized pathways through which computers boost earnings are non-gender-specific productivity-enhancing mechanisms. Analyzing occupational data on computer use at work from O*NET attached to the 1979–2016 Current Population Surveys (CPS) and individual-level data from the 2012 Survey of Adult Skills (PIAAC), we find that the computer wage premium is biased in favor of men at the occupation level. We conclude by suggesting that computer-based technologies relate to reproducing old forms of gender pay inequality due to gendered processes that operate mainly at the structural level (i.e., occupations) rather than at the individual level." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Köhler, Thomas R. & Julia Finkeissen (2024): Business 5.0. Der Praxis-Guide für Künstliche Intelligenz in Unternehmen - Chancen und Risiken. Frankfurt;New York: Campus Verlag, 253 S.

Abstract

"Endlich ist er da, der Durchbruch für Künstliche Intelligenz (KI) bzw. Artificial Intelligence (AI). Doch Zweifel an der "Universalwaffe" ChatGPT und ähnlichen KI-Systemen sind erlaubt. Thomas R. Köhler und Julia Finkeissen liefern in ihrem neuen Buch eine Bestandsaufnahme der aktuellen Technologien und trennen dabei schonungslos Hype von Wirklichkeit. Sie liefern das Rüstzeug für jede Führungskraft, um KI aktiv im Unternehmen sinnvoll einzusetzen. Business 5.0 zeigt in sieben Schritten, wo und wie KI-Projekte im Unternehmen etabliert werden können, und liefert konkrete Beispiele für unterschiedliche Branchen und Querschnittsfunktionen. Ein nachhaltiger KI-Einsatz im Unternehmen steht dabei im Mittelpunkt." (Autorenreferat, IAB-Doku, © Campus)

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Körner, Theresa (2024): Generalisiertes Vertrauen in automatisierten Journalismus. Bedeutung und Einflussfaktoren auf das Vertrauen deutscher Leser*innen. Wiesbaden: Springer VS, 279 S. DOI:10.1007/978-3-658-42735-1

Abstract

"In dieser Arbeit geht es um die Frage, wie Leser:innen in Deutschland automatisiert generierte Nachrichten wahrnehmen und welche Bedeutung sie den Verfahren im Journalismus zuschreiben. Im Mittelpunkt steht die Frage, ob das Publikum dem automatisierten Journalismus vertraut und welche Einflussfaktoren bei dieser Entscheidung eine Rolle spielen. Ein Mindestmaß an Vertrauen der Bevölkerung in Journalismus ist wichtig für die Stabilität demokratischer Gesellschaften. In der Forschung ist bisher wenig thematisiert, ob Medieninnovationen wie die automatisierte Berichterstattung Einfluss auf das generalisierte Vertrauen der Lesenden haben. Zudem gibt es wenig Wissen über den Umgang mit dem, sowie über die Wahrnehmung und die Bewertung des automatisierten Journalismus durch verschiedene Publika. Basierend auf der Operationalisierung verschiedener Vertrauensbeziehungen und der Aufarbeitung des Forschungsstands zur bewerteten Glaubwürdigkeit computergenerierter Nachrichtentexte wurde ein Modell entwickelt, das mögliche Einflussfaktoren auf die Vertrauensbewertung des automatisierten Journalismus darstellt sowie Raum für die Exploration weiterer Faktoren lässt. Zur empirischen Überprüfung wurden Focus Groups mit gezielt rekrutierten Leser:innen eingesetzt: Neben einer heterogen gemischten Focus Group haben einmal Personen mit hoher Technikaffinität und Vorwissen zu Verfahren der Künstlichen Intelligenz sowie einmal Personen mit hoher Medienkompetenz teilgenommen. Die Studienergebnisse zeigen, dass es keine monokausalen Antworten auf die Frage nach dem Vertrauen der Lesenden in automatisierten Journalismus gibt. Grundsätzlich stehen sie dem Technologieeinsatz neutral und gleichzeitig neugierig sowie – vor allem mit Blick auf die Zukunft – skeptisch gegenüber. Die Teilnehmenden fordern einen transparenten Umgang der Medienorganisationen mit automatisierter Berichterstattung und wollen mehr Informationen zum Einsatz, zur Verbreitung und zur Technologie haben. Als Einflussfaktoren auf die Vertrauensbewertung wurden ausgewählte Personen- sowie Text- und Publikationsmerkmale und Eigenschaften des Untersuchungsgegenstands getestet. Hohe Relevanz haben erkennbar die Angst vor gezielter Manipulation, die individuellen Vorstellungen über Künstliche Intelligenzen sowie die Kontingenz von Texten. Die Bedeutung dieser Studie besteht darin, dass sie das theoretische Verständnis von Vertrauen in Journalismus erweitert sowie die Wahrnehmung des automatisierten Journalismus vertieft. Außerdem wird das empirische Verständnis der Bewertung und Einordnung des Publikums der automatisiert generierten Berichterstattung durch die Studienergebnisse exploriert." (Autorenreferat, IAB-Doku, © Springer)

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Lane, Marguerita (2024): Who will be the workers most affected by AI? A closer look at the impact of AI on women, low-skilled workers and other groups. (OECD Artificial Intelligence Papers 26), Paris, 60 S. DOI:10.1787/14dc6f89-en

Abstract

"This paper examines how different socio-demographic groups experience AI at work. As AI can automate non-routine, cognitive tasks, tertiary educated workers in “white-collar” occupations will likely face disruption, even if empirical analysis does not suggest that overall employment levels have fallen due to AI, even in “white-collar” occupations. The main risk for those without tertiary education, female and older workers is that they lose out due to lower access to AI-related employment opportunities and to productivity-enhancing AI tools in the workplace. By identifying the main risks and opportunities associated with different socio-demographic groups, the ultimate aim is to allow policy makers to target supports and to capture the benefits of AI (increased productivity and economic growth) without increasing inequalities and societal resistance to technological progress." (Author's abstract, IAB-Doku) ((en))

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Larsson, Stefan, James Merricks White & Claire Ingram Bogusz (2024): The Artificial Recruiter: Risks of Discrimination in Employers’ Use of AI and Automated Decision‐Making. In: Social Inclusion, Jg. 12. DOI:10.17645/si.7471

Abstract

"Extant literature points to how the risk of discrimination is intrinsic to AI systems owing to the dependence on training data and the difficulty of post hoc algorithmic auditing. Transparency and auditability limitations are problematic both for companies’ prevention efforts and for government oversight, both in terms of how artificial intelligence (AI) systems function and how large-scale digital platforms support recruitment processes. This article explores the risks and users’ understandings of discrimination when usingAI and automated decision-making (ADM) in worker recruitment. We rely on data in the form of 110 completed questionnaires with representatives from 10 of the 50 largest recruitment agencies in Sweden and representatives from 100 Swedish companies with more than 100 employees (“major employers”). In this study, we made use of an open definition of AI to accommodate differences in knowledge and opinion around how AI and ADM are understood by the respondents. The study shows a significant difference between direct and indirect AI and ADM use, which has implications for recruiters’ awareness of the potential for bias or discrimination in recruitment. All of those surveyed made use of large digital platforms like Facebook and LinkedIn for their recruitment, leading to concerns around transparency and accountability—not least because most respondents did not explicitly consider this to be AI or ADM use. We discuss the implications of direct and indirect use in recruitment in Sweden, primarily in terms of transparency and the allocation of accountability for bias and discrimination during recruitment processes." (Author's abstract, IAB-Doku) ((en))

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Leduc, Sylvain & Zheng Liu (2024): Automation, Bargaining Power, and Labor Market Fluctuations. In: American Economic Journal. Macroeconomics, Jg. 16, H. 4, S. 311-349. DOI:10.1257/mac.20220181

Abstract

"We argue that the threat of automation weakens workers’ bargaining power in wage negotiations, dampening wage adjustments and amplifying unemployment fluctuations. We make this argument based on a business cycle model with labor market search frictions, generalized to incorporate automation decisions. In the model, procyclical automation threats create endogenous real wage rigidity that amplifies labor market fluctuations. The automation mechanism is consistent with empirical evidence. It is also quantitatively important for explaining the large volatilities of unemployment and vacancies relative to that of real wages, a puzzling observation through the lens of standard business cycle models." (Author's abstract, IAB-Doku) ((en))

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Leduc, Sylvain & Zheng Liu (2024): Reshoring, Automation, and Labor Markets Under Trade Uncertainty. (Working papers series / Federal Reserve Bank of San Francisco 2024-16), San Francisco, Calif., 42 S. DOI:10.24148/wp2024-16

Abstract

"We study the implications of trade uncertainty for reshoring, automation, and U.S. labor markets. Rising trade uncertainty creates incentive for firms to reduce exposures to foreign suppliers by moving production and distribution processes to domestic producers. However, we argue that reshoring does not necessarily bring jobs back to the home country or boost domestic wages, especially when firms have access to labor-substituting technologies such as automation. Automation improves labor productivity and facilitates reshoring, but it can also displace jobs. Furthermore, automation poses a threat that weakens the bargaining power of low-skilled workers in wage negotiations, depressing their wages and raising the skill premium and wage inequality. The model predictions are in line with industry-level empirical evidence." (Author's abstract, IAB-Doku) ((en))

Zitatform

Lerch, Christian, Elna Schirrmeister, Angela Jäger, Daniel Berner & Philipp Köbe (2024): Bedarfsermittlung Zukunft Mittelstand-Digital Vergabenummer 331-2022-15 PT 6681119. Studie für den DLR Projektträger im Rahmen der Projektträgerschaft „Mittelstand-Digital“. Köln, 117 S.

Abstract

"Diese Studie im Auftrag des DLR-PT untersucht ergebnisoffen die Herausforderungen und Bedarfe des Mittelstands bei der Digitalisierung. Sie geht verschiedenen Fragestellungen nach: Wie ist der Stand der Digitalisierung im Mittelstand? Welche Unterschiede gibt es? Wie wird die Digitalisierung im Mittelstand vorangetrieben? Vor welchen zukünftigen, absehbaren Herausforderungen steht „der Mittelstand“ bis circa zum Jahr 2030? Welche digitalen Technologien sind für den Mittelstand relevant? Welchen Beitrag leistet die Digitalisierung zur Nachhaltigkeit? Welche Hemmnisse lassen sich bei der Digitalisierung im Mittelstand feststellen? Welche Förderbedarfe gibt es aus Sicht der Unternehmen in den nächsten Jahren? Welche Förderungen gibt es bereits? Gibt es vor diesem Hintergrund Lücken bei der Förderung der Digitalisierung im Mittelstand? Welche Ansätze gibt es zur zukünftigen Stärkung der Digitalisierung im Mittelstand?" (Textauszug, IAB-Doku)