Zitatform

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): 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))

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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)

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Licht, Thomas & Klaus Wohlrabe (2024): AI Adoption Among German Firms. (CESifo working paper 11459), München, 21 S.

Abstract

"This paper examines the adoption of Artificial Intelligence (AI) among German firms, leveraging firm-level data from the ifo Business Survey. We analyze the diffusion of AI across sectors and firm sizes, showing a significant increase in AI usage from 2023 to 2024, particularly in manufacturing and services. The survey data allows us to explore not only sectoral patterns of adoption but also the drivers and barriers that firms face, including firm-specific characteristics and industry dynamics. Additionally, we investigate the role of managerial traits, such as risk tolerance and patience, in shaping AI adoption decisions. Finally, we assess the potential pro-ductivity impacts of AI at the firm level, with a focus on the expected long-term benefits of AI for different sectors of the German economy. Our findings contribute to the growing body of research on AI adoption by providing new evidence from a non-US context, offering valuable insights for both academia and politics." (Author's abstract, IAB-Doku) ((en))

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Liu, Yan (2024): Generative AI. Catalyst for Growth or Harbinger of Premature De-Professionalization? (Policy research working paper 10915), Washington, DC, 60 S.

Abstract

"This paper presents a multi-sector growth model to elucidate the general equilibrium effects of generative artificial intelligence on economic growth, structural transformation, and international production specialization. Using parameters from the literature, the paper employs simulations to quantify the impacts of artificial intelligence across various scenarios. The paper introduces a crucial distinction between high-skill, highly digitalized, tradable services and low-skill, less digitalized, less-tradable services. The model’s key propositions align with empirical evidence, and the simulations yield novel and sobering predictions. Unless artificial intelligence achieves widespread cross-sector adoption and catalyzes paradigm-shifting innovations that fundamentally reshape consumer preferences, its growth benefits may be limited. Conversely, its disruptive impact on labor markets could be profound. This paper highlights the risk of “premature de-professionalization”, where artificial intelligence likely shrinks the space for countries to generate well-paid jobs in high-skill services. The analysis portends that developing countries failing to adopt artificial intelligence swiftly risk entrapment as commodity exporters, potentially facing massive youth underemployment, diminishing social mobility, and stagnating or even declining living standards. The paper also discusses artificial intelligence ’s broader implications on inequality, exploring multiple channels through which it may exacerbate or mitigate economic disparities." (Author's abstract, IAB-Doku) ((en))

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Liu, Jin, Kaizhe Chen & Wenjing Lyu (2024): Embracing artificial intelligence in the labour market: the case of statistics. In: Humanities and Social Sciences Communications, Jg. 11. DOI:10.1057/s41599-024-03557-6

Abstract

"In an era marked by rapid advancements in artificial intelligence (AI), the dynamics of the labor market are undergoing significant transformation. A common concern amidst these changes is the potential obsolescence of traditional disciplines due to AI-driven productivity enhancements. This study delves into the evolving role and resilience of these disciplines within the AI-influenced labor market. Focusing on statistics as a representative field, we investigate its integration with AI and its interplay with other disciplines. Analyzing 279.87 million online job postings in the United States from 2010 to 2022, we observed a remarkable 31-fold increase in the demand for AI-specialized statistical talent, diversifying into 932 distinct AI-related job roles. Additionally, our research identified four major interdisciplinary clusters, encompassing 190 disciplines with a statistical focus. The findings also highlight a growing emphasis on specific hard skills within these AI roles and the differences in demand for AI talent in statistics across economic sectors and regions. Contrary to the pessimistic view of traditional disciplines’ survival in the AI age, our study suggests a more optimistic outlook. We recommend that professionals and organizations proactively adapt to AI advancements. Governments and academic institutions should collaborate to foster interdisciplinary skill development and evaluation for AI talents, thereby enhancing the employability of individuals from traditional disciplines and contributing to broader economic growth." (Author's abstract, IAB-Doku) ((en))

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Livanos, Ilias & Panagiotis Ravanos (2024): Job loss and Covid-19: an analysis on the impacts of remote work and automation. In: Applied Economics Letters, Jg. 31, H. 8, S. 712-723. DOI:10.1080/13504851.2022.2146641

Abstract

"Using a unique dataset from a dedicated Cedefop Skills Forecast scenario on the impacts of COVID-19, this paper explores two possible determinants of expected job loss in the European Union (EU) due to the pandemic, namely the potential of work from home and the impacts of automation. Our findings suggest that less remote work and more automation are both related to future job losses across countries and occupations. These links are stronger in 2020–2021 at the country level, while becoming significant at the occupation level after 2022 when several protective measures taken by EU governments are expected to have been lifted." (Author's abstract, IAB-Doku) ((en))

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Lühr, Thomas & Tobias Kämpf (2024): KI und der Wandel von Angestelltenarbeit. Zum „blinden Fleck“ der aktuellen Automatisierungsdebatte. In: WSI-Mitteilungen, Jg. 77, H. 2, S. 98-106. DOI:10.5771/0342-300X-2024-2-98

Abstract

"Der Beitrag analysiert den Wandel von Angestelltenarbeit vor dem Hintergrund der digitalen Transformation. Ausgangspunkt ist ein Automatisierungsschub, der durch erweiterte Möglichkeiten der Nutzung Künstlicher Intelligenz (KI) geprägt ist. Auf der Grundlage empirischer Befunde werden die qualitativen Veränderungstendenzen von Arbeit in den Blick genommen, und zwar sowohl aus der Anwenderperspektive der Sachbearbeiter*innen als auch aus der Sicht der hochqualifizierten Entwickler*innen und Implementoren neuer KI-Lösungen. Insgesamt wird ein Strukturwandel von Angestelltenarbeit konstatiert, der nicht nur das Risiko von Jobverlusten, sondern auch Potenziale für eine Aufwertung und Höherqualifizierung hervorbringt und sich im Angestelltenbewusstsein manifestiert. In arbeitspolitischer Perspektive eröffnen sich Anknüpfungspunkte für eine Vorwärtsstrategie im Sinne eines nachhaltigen Umbaus von Beschäftigung." (Autorenreferat, IAB-Doku)

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Lükemann, Laura (2024): Flexibility potentials of digital work communication – mothers’ labour market involvement in comparative perspective. In: Community, work & family, Jg. 27, H. 5, S. 627-648. DOI:10.1080/13668803.2024.2418560

Abstract

"Digital work communication offers increased flexibility in timing and location, helping employees, particularly mothers, balance work and private life. This flexibility can enhance mothers' ability to increase their contracted hours in paid employment, especially given the impact of childbirth on their careers. Drawing on boundary management theory, this study posits that digital communication with supervisors enables mothers to better manage work tasks alongside personal obligations, potentially allowing them to work longer hours. However, the actual use of this flexibility is influenced by a country's level of de-familialisation, which refers to family policies that reduce care dependency. Analyzing data from the European Social Survey involving 3,179 mothers across 25 countries, the study found that mothers generally worked longer hours when they utilized digital communication more frequently. Comparisons across countries showed that this relationship was stronger in nations with robust de-familialisation policies. The findings suggest that the potential of digital work communication to enhance work involvement is particularly leveraged in countries that promote de-familialisation, allowing mothers to navigate their professional and personal responsibilities more effectively." (Author's abstract, IAB-Doku) ((en))

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Madoń, Karol (2024): The relationship between Artificial Intelligence (AI) exposure and return to education. (IBS working paper / Instytut Badan Strukturalnych 2024,05), Warszawa, 17 S.

Abstract

"This paper studies the relationship between exposure to artificial intelligence (AI) and workers’ wages across European countries. Overall, a positive relationship between exposure to AI and workers’ wages is found, however it differs considerably between workers and countries. High-skilled workers experience far higher wage premiums related to AI-related skills than middle- and low-skilled workers. Positive associations are concentrated among occupations moderately and highly exposed to AI (between the 6th and 9th decile of the exposure), and are weaker among the least exposed occupations. Returns of AI-related skills among high-skilled workers are even higher in Eastern European Countries compared to Western European countries. The heterogeneity likely originates from the difference in overall labour costs between country groups. The results presented in this study were obtained from the estimation of Mincerian wage regressions on the 2018 release of the EU Structure of Earning Survey." (Author's abstract, IAB-Doku) ((en))

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Madoń, Karol (2024): The effects of automation on workers’ wages. (IBS working paper / Instytut Badan Strukturalnych 2024,06), Warszawa, 35 S.

Abstract

"This study examines the impact of automation on workers’ wages across 20 European countries between 2010–2018. Overall, it identifies a net positive effect of robot adoption on average wages at the sectoral level, especially pronounced among routine manual and nonroutine manual occupations. Importantly, these effects differ between countries- workers in Eastern European countries benefit twice as much from automation as their Western European counterparts. In Western European countries, higher average wages are associated with a decreasing share of routine workers. Results are robust to the exclusion of different capital measures, a battery of fixed effects, a change of instrument and an alternative measure of wages." (Author's abstract, IAB-Doku) ((en))

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Matthes, Britta (2024): Wie KI und andere digitale Technologien die Arbeit von Ingenieuren in der Elektro- und Informationstechnik verändern könnten. In: VDE Verband der Elektrotechnik, Elektronik und Informationstechnik (Hrsg.) (2024): Rolle der Künstlichen Intelligenz in der Elektro- und Informationstechnik, S. 21-27, 2024-10-14.

Abstract

"Die Liste der Technologien, die für die Arbeit von Ingenieuren in der Elektro- und Informationstechnik in den nächsten Jahren relevant sind, ist sehr lang. Dabei spielt KI in vielen technologischen Bereichen eine wichtige Rolle. Dennoch: Auch wenn die Automatisierungspotenziale bei diesen Berufen durch Einsatz von KI und anderen digitalen Technologien hoch sind, steht überhaupt nicht zur Debatte, dass es in Zukunft diese Berufe nicht mehr geben wird! Das hat verschiedene Gründe: Der wichtigste ist, dass KI erst dann produktiv zum Einsatz gebracht werden kann, wenn sie mit fachspezifischen Kenntnissen zusammentrifft. Es geht aber auch darum, dass sich die heute bereits bestehende Fachkräfteknappheit in diesen Berufen in den nächsten Jahren eher weiter verschärfen als abschwächen wird. Nicht nur, dass in den Ingenieurberufen der Elektro- und Informationstechnik in den kommenden Jahren überproportional viele der sozialversicherungspflichtig Beschäftigten in den Ruhestand gehen. Mit großer Wahrscheinlichkeit führt auch die Nutzung der Potenziale, die sich durch den Einsatz von KI in Wirtschaft und Gesellschaft ergeben, zu einer deutlichen Nachfragesteigerung in diesen Berufen. Hinzu kommt, dass die Elektro- und Informationstechnik zu denjenigen Gebieten gehört, die eine besondere Rolle bei der parallel zur digitalen Transformation stattfindenden ökologischen Transformation spielt. Gut ausgebildete Ingenieure der Elektro- und Informationstechnik werden zusätzlich gebraucht, um ihr spezifisches Wissen bei der Erzeugung und Speicherung von Wind- und Solarenergie, aber auch beim Umstieg vom Verbrenner zum Elektroantrieb einzusetzen. Eine der wichtigsten Fragen, die sich aus diesen Überlegungen ergibt, ist deshalb weniger, ob es auch in Zukunft noch Ingenieure der Elektro- und Informationstechnik geben wird. Vielmehr stellt sich die Frage, was lässt sich an der Arbeit, wie sie heute noch von diesen Ingenieuren erledigt wird, durch den Einsatz von KI und anderen modernen Technologien effizienter machen oder sogar automatisieren, um zu verhindern, dass die Fachkräftelücke noch größer wird. Dabei geht es nicht nur um die technologischen Potenziale, sondern auch darum, unter welchen Bedingungen tatsächlich automatisiert werden kann. Denn Automatisierung ist äußerst voraussetzungsvoll: Es muss investiert werden, nicht nur in Maschinen und Anlagen, sondern auch in Dateninfrastruktur und in die Fähigkeiten und Motivation derjenigen, die damit zukünftig arbeiten sollen. Häufig müssen erst die Datengrundlagen geschaffen, Prozesse neu etabliert, eine Aufbruchstimmung erzeugt werden; es gibt rechtliche Hürden wie zum Beispiel datenschutz- oder urheberrechtliche Vorgaben; die Wahrung von Geschäftsgeheimnissen steht einer Automatisierung entgegen; oder es bestehen ethische Bedenken." (Textauszug, IAB-Doku)

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Minniti, Antonio, Klaus Prettner & Francesco Venturini (2024): Unslicing the pie: AI innovation and the labor share in European regions. (Department of Economics working paper / Vienna University of Economics and Business 369), Wien, 46 S. DOI:10.57938/42db2bf4-07a0-4cea-8aab-ea40f1f71b29

Abstract

"We study how the development of Artificial Intelligence (AI) influences the distribution of income between capital and labor and how this, in turn, exacerbates geographic income inequality. To investigate this issue, we first build a theoretical framework and then analyze data from European regions dating back to 2000. We find that for every doubling of regional AI innovation, there is a 0.7% to 1.6% decline in the labor share, which may have decreased by between 0.20 and 0.46 percentage points from a mean of 52% due solely to AI. This new technology is particularly detrimental to high-skill and medium-skill labor. The impact on income distribution is driven by worsening wage and employment conditions for high-skill labor, and by wage compression for medium- and low-skill labor. The effect of AI is not driven by other factors affecting regional development in Europe, nor by the concentration process in the AI market." (Author's abstract, IAB-Doku) ((en))

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Montobbio, Fabio, Jacopo Staccioli, Maria Enrica Virgillito & Marco Vivarelli (2024): The empirics of technology, employment and occupations: Lessons learned and challenges ahead. In: Journal of Economic Surveys, Jg. 38, H. 5, S. 1622-1655. DOI:10.1111/joes.12601

Abstract

"This paper is a critical review of the empirical literature resulting from recent years of debate and analysis regarding technology and employment and the future of work as threatened by technology, outlining both lessons learned and challenges ahead. We distinguish three waves of studies and relate their heterogeneous findings to the choice of technological proxies, the level of aggregation, the adopted research methodology and to the relative focus on robots, automation and AI. The challenges ahead include the need for awareness of possible ex‐ante biases associated with the adopted proxiesfor innovation; the recognition of the trade‐off between microeconometric precision and a more holistic macroeconomic approach; the need for granular analysis of the reallocation and transformation of occupations and tasks brought about by different types of new technologies; the call for a closer focus on impacts on labor quality, in terms of types of jobs and working conditions." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))

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Nikolova, Milena, Femke Cnossen & Boris Nikolaev (2024): Robots, meaning, and self-determination. In: Research Policy, Jg. 53, H. 5. DOI:10.1016/j.respol.2024.104987

Abstract

"This paper is the first to examine the impact of robotization on work meaningfulness, autonomy, competence, and relatedness, which are essential to motivation and well-being at work. Using surveys of workers and robotization data for 14 industries in 20 European countries spanning 2005–2021, we find a consistent negative impact of robotization on perceived work meaningfulness and autonomy. Using instrumental variables, we find that doubling robotization leads to a 0.9 % decrease in work meaningfulness and a 1 % decline in autonomy. To put this in perspective, if the robotization levels of the top 5 industry were to match those of the leading industry in terms of robot adoption in 2020 (equivalent to a 7.5-fold increase), it would result in a decline of 6.8 % in work meaningfulness and 7.5 % in autonomy. The link between robotization, competence, and relatedness is also negative but less robust. We also examine how tasks, skills, and socio-demographic characteristics moderate the main relationships. We find that workers with routine tasks experience an even greater negative effect of robotization in terms of declines in their autonomy, competence, and relatedness. However, we also discover that utilizing computers as tools for independent work can help workers maintain a sense of autonomy, competence, and relatedness in industries and job roles that adopt robots. Our results highlight that by deteriorating work meaningfulness and self-determination, robotization can impact work life above and beyond its consequences for employment and wages." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))

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Nitsch, Verena, Vera Rick, Annette Kluge & Uta Wilkens (2024): Human-centered approaches to AI-assisted work: the future of work? In: Zeitschrift für Arbeitswissenschaft, Jg. 78, H. 3, S. 261-267. DOI:10.1007/s41449-024-00437-2

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Nixdorf, Christian Philipp (2024): Künstliche Intelligenz am Arbeitsmarkt auch imSozialwesen? Plädoyer für eine achtsame Unaufgeregtheit. In: Soziale Sicherheit, Jg. 73, H. 8-9, S. 10-16.

Abstract

"Manche Menschen sind „Künstlicher Intelligenz“ (KI) gegenüber offen eingestellt. Andere reagieren auf diese angesprochen mit Ängsten, zum Beispiel vor Arbeitsplatzverlust und hinsichtlich des Datenschutzes. Die Nutzung von KI weitet sich aus, was auch vor dem Sozialwesen nicht Halt macht.KI gewinnt hier nur langsam, aber doch stetig an Bedeutung. Was aber bedeutet das für die dortigen Fachkräfte? Das wird im Text reflektiert." (Textauszug, IAB-Doku)

Zitatform

Nurski, Laura & Nina Ruer (2024): Exposure to generative artificial intelligence in the European labour market. (Working paper / Bruegel 2024,06), Brüssel, 33 S.

Abstract

"We apply two sets of generative artificial intelligence (GenAI) occupational exposure scores – one task-based, one ability-based – to the European Labour Force Survey. While using different methodologies, our findings reveal consistent demographic patterns across the two approaches: jobs held by women, highly educated and younger workers are more exposed to GenAI technology in Europe. We also review the literature on the recent productivity impact of GenAI. Within the same occupations, less-experienced or less-skilled workers consistently get the largest productivity gains from GenAI support. We argue that a task-based analysis is more fruitful than an ability-based one, both for guiding GenAI adoption in organisations and their workplaces, and for assessing the employment and job quality impact on workers. Finally, we provide policy recommendations that can help workers (ie the labor supply) adjust to technological disruption, such as providing training and social safety nets. But we go further by also suggesting policy interventions that could redirect future labor demand towards better jobs, by promoting job redesign and organisational agility. Monitoring GenAI’s employment effects and researching the ‘jagged technological frontier’ is necessary to further build our understanding of the employment impact of this transformational technology." (Author's abstract, IAB-Doku) ((en))

Zitatform

Parmentier, Lucas (2024): Robotization and returns to tasks. In: Economics Bulletin, Jg. 44, H. 4, S. 1545-1551.

Abstract

"I provide new evidence of the impacts of robotization on the returns to tasks in US labor markets between 1990 and 2007. I find that the adoption of one robot per thousand workers increases the changes in the returns to abstract and routine tasks by 0.049 and 0.066 percentage points, respectively, relative to manual tasks. These magnitudes imply that the adoption of one robot per thousand workers has substantial effects on wages since it increases wages by 1.70% due to the positive impact of robotization on the returns to abstract tasks, and by 3.76% due to the positive effects on the returns to routine tasks. The results are robust to various specifications." (Author's abstract, IAB-Doku) ((en))

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Peede, Lennert & Michael Stops (2024): Artificial intelligence technologies, skills demand and employment: evidence from linked job ads data. (IAB-Discussion Paper 15/2024), Nürnberg, 62 S. DOI:10.48720/IAB.DP.2415

Abstract

"Wir untersuchen, wie künstliche Intelligenz (KI) die Arbeitsnachfrage auf der Betriebsebene beeinflusst. Um die Aktivitäten in der Entwicklung, Implementierung oder Nutzung von KI-Technologien zu messen, verwenden wir den Anteil derjenigen Stellenausschreibungen, die einen Bezug zu KI haben. Niedrige KI-Stellenanteile insgesamt zeigen, dass wir eine frühe Phase der KI-Einführung untersuchen. Auf der Betriebsebene hängt der KI-Stellenanteil mit einem relativ geringen Rückgang derjenigen Kompetenzanforderungen zusammen, die nicht mit KI-Technologien in Verbindung stehen. Darüber hinaus finden wir keine Auswirkungen auf die Gesamtbeschäftigung in den Betrieben, aber ein leicht höheres Beschäftigungswachstum in Jobs mit hoch komplexen Tätigkeiten." (Autorenreferat, IAB-Doku)