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Zeyer-Gliozzo, Birgit (2023): Digitalisierung und die Rolle von Weiterbildung. Teilnahme und Erträge von Beschäftigten mit hohem Automatisierungsrisiko. Wiesbaden: Springer VS, XVIII, 352 S.

Abstract

"Zahlreiche Studien zu den Folgen der Digitalisierung für Arbeitsmärkte weisen auf einen damit einhergehenden Tätigkeitswandel hin, der sich in einem Rückgang substituierbarer Routinetätigkeiten und einer Zunahme analytischer und interaktiver Nicht-Routinetätigkeiten äußert. Fortschritte u.a. in künstlicher Intelligenz erweitern die Automatisierungsmöglichkeiten. Um mit diesen Veränderungen Schritt halten zu können, wird Weiterbildung große Bedeutung beigemessen. Besonders wichtig erscheint dies für Beschäftigte mit vielen automatisierbaren Tätigkeiten. In diesem Buch wird untersucht, inwieweit diese Personen an Weiterbildung teilnehmen und ob die Bildungsmaßnahmen einen entsprechenden Nutzen bringen. Analysen auf Basis des Nationalen Bildungspanels zeigen, dass Beschäftigte mit hohem Automatisierungsrisiko eine tendenziell geringere Weiterbildungswahrscheinlichkeit aufweisen, während Weiterbildungserträge durchaus existieren. Je nach Weiterbildungs- bzw. Ertragsform und unter Berücksichtigung der Heterogenität der Beschäftigten ergeben sich z.T. deutliche Unterschiede, die die Relevanz einer differenzierten Betrachtung, auch für die Ableitung politischer Implikationen, verdeutlichen." (Autorenreferat, IAB-Doku, © Springer)

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World Economic Forum (2023): Jobs of Tomorrow: Large Language Models and Jobs. (White paper / World Economic Forum), Cologny, Geneva, 33 S.

Abstract

"In the latest white paper of the Jobs of Tomorrow series, the World Economic Forum, in collaboration with Accenture, presents an examination of the potential impact of large language models (LLMs) on jobs. The integration of LLMs in various industries presents a paradigm shift in how we interact with information and, by extension, how we work." (Author's abstract, IAB-Doku) ((en))

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Bundesministerium für Arbeit und Soziales (2023): Auswirkungen von künstlicher Intelligenz auf den deutschen Arbeitsmarkt und Sozialstaat. Antwort der Bundesregierung auf die Kleine Anfrage der Fraktion der CDU/CSU (Drucksache 20/6008). (Verhandlungen des Deutschen Bundestages. Drucksachen 20/63736 (12.04.2023)), 13 S.

Abstract

Die Fragen und Antworten betreffen die Herausforderungen, Chancen und Risiken beim Einsatz von Künstlicher Intelligenz (KI) auf dem Arbeitsmarkt - Auswirkungen auf Lohnentwicklung, Produktivität und Erwerbsbeteiligung -, auf die soziale und wirtschaftliche Ungleichheit, auf den Arbeits- und Fachkräftemangel in Deutschland und im Bildungsbereich. Weitere Fragen gelten den Maßnahmen der Bundesregierung, einem möglichen Anstieg der Arbeitslosigkeit durch künstliche Intelligenz entgegenzuwirken, den Auswirkungen auf die Rolle der Betriebsratsarbeit und die betriebliche Mitbestimmung, die Chancen für behinderte Menschen auf soziale und ökonomische Teilhabe, Hilfe bei Erkrankungen und die Beteiligung der Betroffenen bei Entscheidungen über Fördermaßnahmen. Gefragt wird nach dem Einsatz von KI in der öffentlichen Verwaltung, nach dem Projekt 'Observatorium Künstliche Intelligenz in Arbeit und Gesellschaft', der Bewertung der Bundesregierung des Gesetzgebungsverfahrens der EU zur KI-Verordnung, nach dem Datenschutz als möglichen Standortnachteil, nach dem Netzwerk 'Künstliche Intelligenz in der Arbeits- und Sozialverwaltung' und der Nutzung der KI im Bereich der Träger der sozialen Sicherungssysteme, bei der Bundesagentur für Arbeit, und einem möglichen Einsatz von KI zur Bekämpfung von Sozialleistungs- und Steuerbetrug. (IAB)

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World Economic Forum (2023): Future of Jobs Report 2023. Insight Report. (The future of jobs report), Cologny/Geneva, 295 S.

Abstract

"The Future of Jobs Report 2023 explores how jobs and skills will evolve over the next five years. This fourth edition of the series continues the analysis of employer expectations to provide new insights on how socio-economic and technology trends will shape the workplace of the future." (Author's abstract, IAB-Doku) ((en))

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OECD (2023): OECD Employment Outlook 2023. Artificial Intelligence and the Labour Market. (OECD employment outlook), Paris, 264 S. DOI:10.1787/08785bba-en

Abstract

"The 2023 edition of the OECD Employment Outlook examines the latest labour market developments in OECD countries. It focuses, in particular, on the evolution of labour demand and widespread shortages, as well as on wage developments in times of high inflation and related policies. It also takes stock of the current evidence on the impact of artificial intelligence (AI) on the labour market. Progress in AI has been such that, in many areas, its outputs have become almost indistinguishable from that of humans, and the landscape continues to change quickly, as recent developments in large language models have shown. This, combined with the falling costs of developing and adopting AI systems, suggests that OECD countries may be on the verge of a technological revolution that could fundamentally change the workplace. While there are many potential benefits from AI, there are also significant risks that need to be urgently addressed, despite the uncertainty about the short- to medium-term evolution of AI. This edition investigates how to get the balance right in addressing the possible negative effects of AI on labour market outcomes while not stifling its benefits." (Author's abstract, IAB-Doku) ((en))

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OECD. Directorate for Employment, Labour and Social Affairs (2023): Artificial intelligence and labour market matching. (OECD social, employment and migration working papers 284), Paris, 86 S. DOI:10.1787/2b440821-en

Abstract

"While still in its infancy, Artificial Intelligence (AI) is increasingly used in labour market matching, whether by private recruiters, public and private employment services, or online jobs boards and platforms. Applications range from writing job descriptions, applicant sourcing, analysing CVs, chat bots, interview schedulers, shortlisting tools, all the way to facial and voice analysis during interviews. While many tools promise to bring efficiencies and cost savings, they could also improve the quality of matching and jobseeker experience, and even identify and mitigate human bias. There are nonetheless some barriers to a greater adoption of these tools. Some barriers relate to organisation and people readiness, while others reflect concerns about the technology and how it is used, including: robustness, bias, privacy, transparency and explainability. The present paper reviews the literature and some recent policy developments in this field, while bringing new evidence from interviews held with key stakeholders." (Author's abstract, IAB-Doku) ((en))

Abstract

"This study is made of two parts: part A and part B. Part A of the study analyses - through 27 country fiches - the extent to which each EU Member State is prepared for ensuring a socially fair digital transformation in the coming years, based on both its current situation and future prospects. In this analysis, key areas of focus include the labor market, digital skills of the population, social protection as well as cross-cutting dimensions, such as the digitalization level of businesses and the quality of digital infrastructures. Part B of the study reviews - through 30 case studies - some of the main actual and potential uses of digital technologies (including AI) by a country’s public sector for improving the design and the delivery of social benefits and active labor market policies, as well as for complementing the monitoring of poverty and income inequality (the case studies analysed are mainly in Member States but also in a few third countries)." (Author's abstract, IAB-Doku) ((en))

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Acemoglu, Daron, David Autor, Jonathon Hazell & Pascual Restrepo (2022): Artificial Intelligence and Jobs: Evidence from Online Vacancies. In: Journal of labor economics, Jg. 40, H. S1, S. S293-S340. DOI:10.1086/718327

Abstract

"We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable." (Author's abstract, IAB-Doku) ((en))

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Adhikary, Sudipta & Kaushik Banerjee (2022): Digitalisation and AI: what does the Future Hold for Labour Union? In: Glocalism: Journal of Culture, Politics and Innovation H. 1, S. 1-29. DOI:10.12893/gjcpi.2022.1.5

Abstract

"The Covid-19 problem has hastened a pace of significant digitalization in economic production and services that had already begun. For the first time, AI and robotics are becoming autonomous and self-learning, with human-like capabilities. The need to examine digitalization and the future of work has grown even more urgent. Until recently, labour unions were the most powerful institutions representing workers. However, the increasing prospect of intelligent robots replacing humans calls into doubt the viability of labour union policy. This development jeopardises their conventional power bases, which rely on the participation of large numbers of salaried workers and their ability to halt production. This paper tries to analyse the issues that unions face in capitalist democracies in this setting. The premise that the digital revolution will eventually generate new, better jobs has been endorsed by the majority of research work on labour relations. We propose that we investigate an alternate scenario, namely, a digital revolution that results in mass human worker replacement and structural, technological unemployment, which could broaden our perspective, particularly in terms of public policy design. We believe that labour unions now play two critical roles. The first is to protect workers' rights and interests as the economy shifts from paid labour to automated-autonomous production; and the second is to change their primary mission from representing employees to representing the social rights of all citizens, particularly the material interests of laypeople." (Author's abstract, IAB-Doku) ((en))

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Angrist, Joshua & Brigham Frandsen (2022): Machine Labor. In: Journal of labor economics, Jg. 40, H. S1, S. S97-S140. DOI:10.1086/717933

Abstract

"The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics? wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics." (Author's abstract, IAB-Doku) ((en))

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Bachmann, Ronald, Myrielle Gonschor, Piotr Lewandowski & Karol Madoń (2022): The impact of robots on labour market transitions in Europe. (Ruhr economic papers 933), Essen, 53 S.

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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Baiocco, Sara, Enrique Fernández-Macías, Uma Rani & Annarosa Pesole (2022): The Algorithmic Management of Work and its Implications in Different Contexts. (JRC working papers series on labour, education and technology 2022,02), Sevilla, 39 S.

Abstract

"This paper provides a conceptual framework for the emerging phenomenon of algorithmic management and outlines some of the implications for work, from work organisation to working conditions (job quality). The paper defines algorithmic management as the use of computer-programmed procedures for the coordination of labour input in an organisation and puts it into context to discuss its usage in both digital labour platforms and 'regular' workplaces and companies, exploring its implications and providing a few policy suggestions. The paper argues that while algorithmic management should be understood as the digital evolution of certain pre-existing trends that have long characterised the organisation of economic activity, it is potentially disruptive. This is because it increases considerably the organisational ability of controlling complex economic and work processes, as it benefits from the massive capacity to collect, store and process information of digital technologies. In algorithmic management, these technological developments are combined and used for re-organising control and re-shaping power balances in the workplace. This paper contributes to the growing academic and policy literature on algorithmic management, proposing a conceptual framework for empirical investigations and a basic compass for policy making in this area." (Author's abstract, IAB-Doku) ((en))

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Bazylik, Sergei & Michael Gibbs (2022): How is new technology changing job design? Machines' ability to perform cognitive, physical, and social tasks is accelerating, dramatically changing jobs and labor markets. (IZA world of labor 344), Bonn, 11 S. DOI:10.15185/izawol.344.v2

Abstract

"Der Fortschritt der Informations- und Kommunikationstechnologien hat unsere Arbeitswelt verändert. Viele Routinetätigkeiten wurden von Menschen- in Maschinenhand übergeben. Gleichzeitig hat der Technologieeinsatz Raum für kreative, kognitive und soziale Tätigkeiten geschaffen, deren Produktivität gestärkt und neue Jobs geschaffen. Das hat zu einer Polarisierung der Arbeitsmärkte und wachsender Ungleichheit geführt: Geringqualifizierte Beschäftigung stagniert, im mittleren Qualifikationssegment geht sie zurück und wird schlechter bezahlt, während Hochqualifizierte Einkommensgewinne erzielen. Die Zunahme künstlicher Intelligenz lässt Befürchtungen aufkommen, auch viele hochqualifizierte Jobs könnten automatisiert werden." (Autorenreferat, IAB-Doku)

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Büchel, Jan & Armin Mertens (2022): Die großen Nachfrager nach KI-Experten in Deutschland. (IW-Kurzberichte / Institut der Deutschen Wirtschaft Köln 2022,101), Köln, 3 S.

Abstract

"In Deutschland ist die Nachfrage der Unternehmen nach Experten mit KI-Kompetenzen groß. Bei genauer Betrachtung zeigt sich, dass der Großteil der KI-Stellenanzeigen jedoch lediglich von einigen wenigen Nachfragern ausgeschrieben wird." (Autorenreferat, IAB-Doku)

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(2022): KI-Bedarfe in Deutschland: Regionale Analyse und Entwicklung der Anforderungsprofile in KI-Stellenanzeigen. Gutachten im Projekt "Entwicklung und Messung der Digitalisierung der Wirtschaft am Standort Deutschland". Berlin, 40 S.

Abstract

"Im ersten Quartal 2021 schrieben Unternehmen und Forschungseinrichtungen deutschlandweit 11.537 KI-Stellenanzeigen aus. Das sind etwas mehr als in den Jahren 2019 (10.363) und 2020 (10.940). Allerdings sind die KI-Bedarfe in Deutschland auf einzelne Nachfrager konzentriert: Werden nur die KI-Stellenanzeigen ohne Vermittler betrachtet, entfallen auf einen Nachfrager durchschnittlich 3,9 KI-Stellenanzeigen im Jahr 2021. 291 Nachfrager, die mindestens fünf KI-Stellenanzeigen ausgeschrieben haben, sind für 66 Prozent der KI-Stellenanzeigen ohne Vermittler verantwortlich. Insgesamt werden 36 Prozent der KI-Stellenanzeigen im Jahr 2021 über Vermittler ausgeschrieben." (Autorenreferat, IAB-Doku)

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Chen, Ni, Zhi Li & Bo Tang (2022): Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations. In: PLoS ONE, Jg. 17, H. 11. DOI:10.1371/journal.pone.0277280

Abstract

"To identify the role of digital skill in the skill-biased technological changes caused by artificial intelligence, this study estimates the impacts of displacement risk on occupational wage and employment and examines the moderation effects of digital skill through the occupational data from the U.S. Bureau of Labor Statistics through the methods of fixed-effects modeling, heterogeneity analyzing and moderation effect testing. The results highlight three main points that (1) the displacement risk by artificial intelligence has significantly negative effects on occupational wage and employment, (2) the heterogeneous effects across occupational characteristics are significant, and (3) the digital skill exerts a significant moderation effect to protect against displacement risk. The core policy implication is suggested to emphasize digital skill in education and training across occupations to accommodate job requirements in the future." (Author's abstract, IAB-Doku) ((en))

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Dargnies, Marie-Pierre, Rustamdjan Hakimov & Dorothea Kübler (2022): Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. (CESifo working paper 9968), München, 36 S.

Abstract

"We run an online experiment to study the origins of algorithm aversion. Participants are either in the role of workers or of managers. Workers perform three real-effort tasks: task 1, task 2, and the job task which is a combination of tasks 1 and 2. They choose whether the hiring decision between themselves and another worker is made either by a participant in the role of a manager or by an algorithm. In a second set of experiments, managers choose whether they want to delegate their hiring decisions to the algorithm. In the baseline treatments, we observe that workers choose the manager more often than the algorithm, and managers also prefer to make the hiring decisions themselves rather than delegate them to the algorithm. When the algorithm does not use workers’ gender to predict their job task performance and workers know this, they choose the algorithm more often. Providing details on how the algorithm works does not increase the preference for the algorithm, neither for workers nor for managers. Providing feedback to managers about their performance in hiring the best workers increases their preference for the algorithm, as managers are, on average, overconfident." (Author's abstract, IAB-Doku) ((en))

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Del Giudice, Manlio, Veronica Scuotto, Luca Vincenzo Ballestra & Marco Pironti (2022): Humanoid robot adoption and labour productivity: a perspective on ambidextrous product innovation routines. In: The International Journal of Human Resource Management, Jg. 33, H. 6, S. 1098-1124. DOI:10.1080/09585192.2021.1897643

Abstract

"The increasing presence of humanoid robot adoption has generated a change in explorative and exploitative routines. If the explorative routines provoke creativity and critical thinking which are delivered by humans, exploitative routines induce repetitive actions and mimic activities which are executed by humanoids. This has raised the need for a better balance between both routines involving an ambidextrous dynamic process. Here, product innovations play a relevant role in enhancing such balance and labour productivity. If, from the conceptual standpoint, this phenomenon has already been explored, there is still the need to empirically analyse it. We thus offer a meso-analysis of twenty-four countries located in Europe through the lens of the Service Robot Deployment (SRD) Model and the conceptual lens of organizational ambidexterity. By a regression methodology, the results show that humanoid robot adoption is still not affecting labour productivity which, by contrast, is positively and significantly connected with both radically new and marginally modified/unchanged production of innovative routines. Our original contribution, which falls in the field of Human Resources Management and Artificial Intelligence, is that humanoids are not directly impacting labour productivity but indirectly through the generation of both new and marginally modified (or unchanged) routines. This situation persuades senior leaders to achieve a balance between exploitative and explorative product innovation routines." (Author's abstract, IAB-Doku) ((en))

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Diallo, Boubacar (2022): Machine learning approaches to testing institutional hypotheses: the case of Acemoglu, Johnson, and Robinson (2001). In: Empirical economics, Jg. 62, H. 5, S. 2587-2600. DOI:10.1007/s00181-021-02110-7

Abstract

"In their seminal 2001 work, Acemoglu, Johnson, and Robinson (AJR) argued that institutions influence economic development, using the logarithm of settler mortality as an instrument to establish a causal effect. A number of economists and other social scientists have challenged this work in terms of both data and identification strategy. Some of those criticisms concerned the IV estimated coefficients and standard errors, which were nearly twice as large as the OLS coefficients and standard errors. The research uses machine learning to test the robustness of AJR's findings. Using the AJR dataset, which I randomly divide into training data and testing data, I am able to predict the average protection against expropriation risk from settler mortality. These predicted values of property rights protection are then regressed on per capita GDP growth. The results indicate a strong and positive effect of property rights protection on growth, consistent with AJR's earlier results. Moreover, the use of machine learning to obtain institutional values yields estimates close to the OLS estimates, unlike AJR. Removing African countries and Neo-European countries, such as Canada, Australia, USA, and New Zealand, does not alter the sign and significance of the coefficient of interest. These results suggest that machine learning can be helpful to economists facing data issues." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Duch Brown, Nestor, Estrella Gomez-Herrera, Frank Müller-Langer & Songul Tolan (2022): Market Power and Artificial Intelligence Work on Online Labour Markets. (JRC digital economy working paper 2021-10), Seville, 40 S.

Abstract

"We investigate three alternative but complementary indicators of market power on one of the largest online labour markets (OLMs) in Europe: (1) the elasticity of labour demand, (2) the elasticity of labour supply, and (3) the concentration of market shares. We explore how these indicators relate to an exogenous change in platform policy. In the middle of the observation period, the platform made it mandatory for employers to signal the rates they were willing to pay as given by the level of experience required to perform a project, i.e., entry, intermediate or expert level. We find a positive labour supply elasticity ranging between 0.06 and 0.15, which is higher for expert-level projects. We also find that the labour demand elasticity increased while the labour supply elasticity decreased after the policy change. Based on this, we argue that market-designing platform providers can influence the labour demand and supply elasticities on OLMs with the terms and conditions they set for the platform. We also explore the demand for and supply of AI-related labour on the OLM under study. We provide evidence for a significantly higher demand for AI-related labour (ranging from +1.4% to +4.1%) and a significantly lower supply of AI-related labour (ranging from -6.8% to -1.6%) than for other types of labour. We also find that workers on AI projects receive 3.0%-3.2% higher wages than workers on non-AI projects." (Author's abstract, IAB-Doku) ((en))