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Albinowski, Maciej & Piotr Lewandowski (2024): The impact of ICT and robots on labour market outcomes of demographic groups in Europe. In: Labour Economics, Jg. 87. DOI:10.1016/j.labeco.2023.102481

Abstract

"We study the age- and gender-specific labour market effects of two key modern technologies, Information and Communication Technologies (ICT) and robots. Our sample includes 14 European countries between 2010 and 2018. We use the variation in technology adoption between industries and apply the instrumental variables strategy proposed by Acemoglu and Restrepo (2020) to identify the causal effects of technology adoption. We find that exposure to ICT and robots increased the shares of young and prime-aged women in employment and in the wage bills of particular sectors. However, it reduced the shares of older women and prime-aged men. We do not detect significant effects of technology adoption on the relative wages of most demographic groups. Between 2010 and 2018, the growth in ICT capital played a larger role than robot adoption in the changes in the withinsector labor market outcomes of demographic groups." (Author's abstract, IAB-Doku, ©2024 Elsevier) ((en))

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

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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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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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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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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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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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Pfeiffer, Sabine (2024): KI im Unternehmen – Herausforderungen an die betriebliche Gestaltung moderner Arbeit. In: Faktor Arbeitsschutz H. 11, S. 34-39.

Abstract

"Eine Befragung von Beschäftigten zu ihrer Einstellung gegenüber der Nutzung von KI bei der Arbeit ergab – entgegen immer wieder behaupteter Angstzuschreibung – ein abgewogenes „Es kommt darauf an“. Wichtig ist es, Beschäftigte von Anfang an bei der Installation von KI im Betrieb zu beteiligen." (Autorenreferat, IAB-Doku)

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Saam, Marianne (2024): The Impact of Artificial Intelligence on Productivity and Employment – How Can We Assess It and What Can We Observe? In: Intereconomics, Jg. 59, H. 1, S. 22-27. DOI:10.2478/ie-2024-0006

Abstract

"Technological optimists have been predicting the artificial intelligence (AI) revolution since the beginning of the past decade. This expectation contrasts with low productivity growth in many countries. The commercial release of ChatGPT in late 2022 has lead to rising expectations about a dramatic shift at least equivalent to the one associated with the commercial introduction of the Internet. But what is AI from an economic point of view? How can we observe the diffusion of AI in the economy and assess its effects in order to the draw conclusions for economic policy?" (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Schludi, Martin, Anna Wallheinke, Laura Deckbar, Reint Gropp, Marcus König, Andrea Heilmaier, Bernd Fitzenberger, Annekatrin Niebuhr, Monika Schnitzer & Markus Lötzsch; Reint Gropp, Marcus König, Andrea Heilmaier, Bernd Fitzenberger, Annekatrin Niebuhr, Monika Schnitzer & Markus Lötzsch (sonst. bet. Pers.) (2024): Nürnberger Gespräche: Wie bewältigen Regionen die digitale und ökologische Transformation? (Podium). In: IAB-Forum H. 06.05.2024 Nürnberg. DOI:10.48720/IAB.FOO.20240506.02

Abstract

"Energiewende, Mobilitätswende, Digitalisierung, Künstliche Intelligenz: Die aktuellen Herausforderungen sind gewaltig. Die digitale und die ökologische Transformation setzen Staat und Unternehmen unter massiven Anpassungsdruck. Zugleich ist jede Region in unterschiedlicher Weise mit den Chancen und Risiken der Transformation konfrontiert. Resultieren daraus auch unterschiedliche regionale Anpassungsstrategien? Wie effektiv ist regionale Wirtschaftsförderung? Und führen die Transformationsprozesse zu einer Reorganisation von Wirtschaftsräumen? Diese und weitere Fragen diskutierten Expertinnen und Experten aus Wirtschaft, Wissenschaft und Regionalpolitik am 13. März dieses Jahres bei den „Nürnberger Gesprächen“." (Autorenreferat, IAB-Doku)

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Zhao, Yong, Da Yin, Lili Wang & Yihua Yu (2024): The rise of artificial intelligence, the fall of human wellbeing? In: International Journal of Social Welfare, Jg. 33, H. 1, S. 75-105. DOI:10.1111/ijsw.12586

Abstract

"Concerns exist regarding the impact on our lives of the rise of artificial intelligence (AI). Using a large dataset of 137 countries over the period 2005–2018 from multiple sources, we estimate the causal effect of AI on individual-level subjective wellbeing. Our identification strategy is inferred from the gravity framework and uses merely the variation in exogenous drivers of a country's AI development. We find a significant negative effect of AI on an individual's wellbeing, in terms of current levels or expectations of future wellbeing. The results are robust to alternative measures of AI, identification strategies, and sampling. Moreover, we find evidence of significant heterogeneity in the impact of AI on individual wellbeing. Further, this dampening effect on individual wellbeing resulting from the use of AI is more prominent among young people, men, high-income groups, high-skilled groups, and manufacturing workers." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))

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Acemoglu, Daron & Simon Johnson (2023): Rebalancing AI. In: Finance and development, S. 26-29.

Abstract

"Optimistic forecasts regarding the growth implications of AI abound. AI adoption could boost productivity growth by 1.5 percentage points per year over a 10-year period and raise global GDP by 7 percent ($7 trillion in additional output), according to Goldman Sachs. Industry insiders offer even more excited estimates, including a supposed 10 percent chance of an “explosive growth” scenario, with global output rising more than 30 percent a year. All this techno-optimism draws on the “productivity bandwagon”: a deep-rooted belief that technological change— including automation—drives higher productivity, which raises net wages and generates shared prosperity. Such optimism is at odds with the historical record and seems particularly inappropriate for the current path of “just let AI happen,” which focuses primarily on automation (replacing people). We must recognize that there is no singular, inevitable path of development for new technology. And, assuming that the goal is to sustainably improve economic outcomes for more people, what policies would put AI development on the right path, with greater focus on enhancing what all workers can do?" (Text excerpt, IAB-Doku) ((en))