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1. Risks to job quality from digital technologies: are industrial relations in Europe ready for the challenge?

Author:
Janine Berg, Francis Green, Laura Nurski, and David Spencer
Publication Date:
09-2022
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
We examine the job quality effects of new digital technologies in Europe, using the framework of seven job quality ‘domains’: pay, working time quality, prospects, skills and discretion, work intensity, social environment and physical environment. The theoretical effects from new technology are ambivalent for all domains. Data on robot shocks matched to the European Working Conditions Surveys for 2010 and 2015 is used to generate empirical estimates, which show significant aggregate negative effects in three domains, and a positive effect in one. Some negative effects are enhanced where there is below-median collective bargaining. In light of these analyses, and in order to think through the challenge of regulating the development and implementation of all forms of digital technologies, we review regulations in several European countries. Drawing on the principles of human-centred design, we advance the general hypothesis that worker participation is important for securing good job quality outcomes, at both the innovation and adoption stages. We also consider the application to the regulation of job quality of national and supra-national data protection legislation. In these ways, the paper extends the debate about the future of work beyond employment and pay, to a consideration of job quality more broadly.
Topic:
Industrial Policy, Science and Technology, Employment, and Work
Political Geography:
Europe

2. The impact of artificial intelligence on the nature and quality of jobs

Author:
Laura Nurski and Mia Hoffmann
Publication Date:
07-2022
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
Artificial intelligence (AI), like any workplace technology, changes the division of labour in an organisation and the resulting design of jobs. When used as an automation technology, AI changes the bundle of tasks that make up an occupation. In this case, implications for job quality depend on the (re)composition of those tasks. When AI automates management tasks, known as algorithmic management, the consequences extend into workers’ control over their work, with impacts on their autonomy, skill use and workload. We identify four use cases of algorithmic management that impact the design and quality of jobs: algorithmic work-method instructions; algorithmic scheduling of shifts and tasks; algorithmic surveillance, evaluation and discipline; and algorithmic coordination across tasks. Reviewing the existing empirical evidence on automation and algorithmic management shows significant impact on job quality across a wide range of jobs and employment settings. While each AI use case has its own particular effects on job demands and resources, the effects tend to be more negative for the more prescriptive (as opposed to supportive) use cases. These changes in job design demonstrably affect the social and physical environment of work and put pressure on contractual employment conditions as well. As technology development is a product of power in organisations, it replicates existing power dynamics in society. Consequently, disadvantaged groups suffer more of the negative consequences of AI, risking further job-quality polarisation across socioeconomic groups. Meaningful worker participation in the adoption of workplace AI is critical to mitigate the potentially negative effects of AI adoption on workers, and can help achieve fair and transparent AI systems with human oversight. Policymakers should strengthen the role of social partners in the adoption of AI technology to protect workers’ bargaining power.
Topic:
Science and Technology, Economy, Artificial Intelligence, Inclusion, and Work
Political Geography:
Global Focus

3. What is holding back artificial intelligence adoption in Europe?

Author:
Mia Hoffmann and Laura Nurski
Publication Date:
11-2021
Content Type:
Policy Brief
Institution:
Bruegel
Abstract:
Artificial intelligence (AI) is considered a key driver of future economic development, expected to increase labour productivity and economic growth worldwide. To realise these gains, AI technologies need to be adopted by companies and integrated into their operations. However, it is unclear what the current level of AI adoption by European firms actually is. Estimates vary widely because of uneven data collection and lack of a standard definition and taxonomy of AI. What is clear is that AI adoption in Europe is low and likely running behind other parts of the world. Discussions on the barriers to AI advancement often mix up different stages of innovation – research, development and adoption. Each stage is constrained by the availability of skills, data and financing in the European market, but there are nuances in how these barriers arise in each of the three stages. This policy contribution focuses on the final stage, AI adoption. We discuss theoretical and empirical evidence of the drivers of AI adoption. We outline the relevant barriers to adoption for European firms in terms of human capital, data availability and funding, and make international comparisons where possible. To accelerate the roll-out of AI technology across the European Union, policymakers should alleviate constraints to adoption faced by firms, both in the environmental context – labour market, financial market and regulation – and in the technological context – data availability, basic digitisation of businesses and technological uncertainty.
Topic:
Science and Technology, European Union, Economic Development, and Artificial Intelligence
Political Geography:
Europe