The demand for AI skills in the labor market

Alekseeva, Liudmila; Azar, José; Giné, Mireia; Samila, Sampsa; Taska, Bledi

doi:10.1016/j.labeco.2021.102002

Cited by 112 publications

(98 citation statements)

References 12 publications

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“…More specifically, our results may serve as a nexus to two seemingly parallel strands of the literature. First, a number of studies shows that having experience in certain tasks has different returns in the market: interpersonal and use of ICT have been found to have high market returns, especially in recent years (Stinebrickner et al, 2019;Deming, 2017;Alekseeva et al, 2021). Second, there is evidence that experience in large firms also has higher return in the market (Arellano-Bover, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, differences in the use of ICT between workers in larger and smaller firms explains an additional 5.3% of the raw LFWP. These results reflect not only the disproportionately higher intensity that workers perform interpersonal tasks and use ICT in larger firms, but also the growing importance of interpersonal (Deming, 2017) and computer skills (Alekseeva et al, 2021) in the labor market. Combined, these two task components explain a total of 10.6% of the raw LFWP, a magnitude comparable to that explained jointly by education, age, and sex.…”

Section: Sources Of the Large Firm Wage Premiummentioning

confidence: 91%

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Firm Size and the Task Content of Jobs: Evidence from 47 Countries

Vera

Garcia-Brazales

2021

SSRN Journal

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Using a mix of household-and employer-based survey data from 47 countries, we provide novel evidence that workers in larger firms perform more non-routine analytical and interpersonal tasks, even within narrowly defined occupations. Moreover, workers in larger firms rely more on the use of information and communications technologies (ICT) to perform these tasks. We also document a 17% wage premium that workers in larger firms enjoy relative to their counterparts in smaller firms. We find evidence that the firm size gradient in the task content of jobs accounts for around 10% of the large firm wage premium.

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Section: Discussionmentioning

confidence: 99%

Section: Sources Of the Large Firm Wage Premiummentioning

confidence: 91%

Firm Size and the Task Content of Jobs: Evidence from 47 Countries

Vera

Garcia-Brazales

2021

SSRN Journal

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“…Probabilistic models quantify these uncertainties. 22 The different outcomes are associated with different levels of, for instance, performance measures like level of confidence, robustness or risk that can be optimised with different inferencing techniques. 23  AI systems can combine both deterministic and probabilistic models.…”

Section: Deterministic and Probabilistic Models {Where Objective And ...mentioning

confidence: 99%

OECD Framework for the Classification of AI systems

2022

OECD Digital Economy Papers

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This report was approved and declassified by written procedure by the Committee on Digital Economy Policy (CDEP) on 13 January 2022 and prepared for publication by the OECD Secretariat. For more information, please visit www.oecd.ai.This publication contributes to the OECD's Artificial Intelligence in Work, Innovation, Productivity and Skills (AI-WIPS) programme, which provides policymakers with new evidence and analysis to keep abreast of the fast-evolving changes in AI capabilities and diffusion and their implications for the world of work. For more information, please visit www.oecd.ai/wips. The programme aims to help ensure that adoption of AI in the world of work is effective, beneficial to all, people-centred and accepted by the population at large. AI-WIPS is supported by the German Federal Ministry of Labour and Social Affairs (BMAS) and will complement the work of the German AI Observatory in the Ministry's Policy Lab Digital, Work & Society. For more information, visit https://oecd.ai/work-innovation-productivity-skills and https://denkfabrikbmas.de/.

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“…Further implementation of technologies based on deep learning, machine learning, and other types of artificial intelligence will not necessarily eliminate existing jobs in their entirety. While some of the simpler jobs that do not require significant intellectual effort, empathy, or intuition may disappear, and new jobs related to the emerging technologies will eventually appear [20][21][22], the main tendency of the following years will be the redesign of existing jobs to accommodate technological advances.…”

Section: Micro-organizational Variables Job Variety and Job Richnessmentioning

confidence: 99%

“…Although several attempts were made to understand the nature of the interrelationship between AI-related technologies and the structural characteristics of an organization [20][21][22]30,39,40,46], to our knowledge, this study represents the first in its kind addressing the issue systemically across multiple dimensions of the ideal-type of a typical organizational structure. Indeed, the previous studies were mainly concerned with micro job-related issues, both vertically [20][21][22][23]25,26] and horizontally [29,31,32]. The important mesolevel variables-mainly related to the span of control, number of hierarchical levels, centralization, standardization, formalization, and incentives-have been consistently neglected by scholars.…”

Section: Main Findingsmentioning

confidence: 99%

Organizational Structure and Artificial Intelligence. Modeling the Intraorganizational Response to the AI Contingency

Rudko

Bonab

Bellini

2021

JTAER

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Different and profound are the consequences of the further development of artificial intelligence (AI) on society. One of the manifestations of the upcoming changes is the plethora of novel ways in which companies may organize labor and capital. So far, the influence of AI on organizational structure has been mainly studied from either a technological or a broader decision-making perspective. Our paper provides a unique take on the topic, emphasizing the distinctive role of human agency and its function in the upcoming AI-driven organizational changes. Relying on the existing academic literature, we theorized a set of hypotheses concerning best fits to the AI contingency on both macro and meso-organizational levels. To test whether the hypothesized changes might encounter organizational inertia or resistance from jobholders, multiple correspondence analysis (MCA) was applied to the online survey results. Four types of current and potential jobholders were distinguished according to their explicit attitudes towards hypothesized organizational changes: skeptics, doubtful skeptics, optimists, and doubtful optimists, the latter consisting primarily of emerging adults. Finally, we developed a model of intraorganizational response to the AI contingency based on four theoretical groups of individuals, as determined by the analysis. Our findings showed doubtful optimists to be the most important group, able to set organizational trends and positively influence skeptics and doubtful skeptics. Accordingly, promoters of AI-driven organizational changes are advised to design their communication efforts around emerging adults. As the survey was conducted amid the COVID-19 pandemic, its sociological and managerial implications are relevant to the looming reality of the postpandemic world.

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The demand for AI skills in the labor market

Cited by 112 publications

References 12 publications

Firm Size and the Task Content of Jobs: Evidence from 47 Countries

Firm Size and the Task Content of Jobs: Evidence from 47 Countries

OECD Framework for the Classification of AI systems

Organizational Structure and Artificial Intelligence. Modeling the Intraorganizational Response to the AI Contingency

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