Documents Nurski, Laura 12 results

"The study examines how key megatrends – including technological, environmental, geopolitical, and demographic changes – are likely to reshape labour markets and social inclusion in Europe over the next decade. The research explores projected shifts in labour and skills demand and supply, the structural drivers influencing equality of opportunity, and the challenges these trends pose for the sustainable development of inclusive societies. The analysis draws on a review of future-oriented literature, EU and national labour market forecasts, occupational automation risk scores, expert interviews, stakeholder consultations, and a systemic mapping of interdependencies. The study anticipates major sectoral and occupational transformations, with a declining working-age population and persistent mismatches and shortages likely to exacerbate labour market pressures. Technological change, especially AI advances, is expected to alter task composition within occupations, with uneven impacts by gender, region and skill level. Beyond employment, megatrends are likely to affect job quality, access to services, and the adequacy of social protection systems, creating further risks for inclusion and equality. The study also identifies three self-reinforcing vicious cycles linked to intergenerational poverty and social exclusion, low-quality employment, and skills underutilisation, stressing the need for targeted – yet systemic – interventions. The findings aim to inform future programming of the European Social Fund Plus (ESF+)."

"This report provides a comprehensive analysis of the impact of digital technologies on work and occupations in Europe, critically reassessing dominant narratives of mass unemployment and job polarisation. The report synthesises work done by the JRC Employment team over the last years. Drawing on a wide range of empirical research, the report introduces an analytical framework distinguishing three main vectors of change: automation, the replacement of labour by machines; digitisation, the increasing use of digital tools in work processes; and platformisation, the use of digital platforms for coordinating work. Contrary to widespread fears, our research finds that the impact of automation, such as industrial robots, on net employment levels in recent decades has been modest and often positive. While specific tasks are automated, this has primarily boosted productivity and led to a reallocation of labour rather than a net destruction of jobs. The most profound transformation stems from digitisation. This process, while enhancing efficiency, has fundamentally altered work organisation by enabling unprecedented levels of standardisation, monitoring, and managerial control. This creates a central paradox: while employment shifts away from routine occupations, work processes within many non-routine professional roles are becoming increasingly routinised and subject to digital control, impacting worker autonomy and job quality. Finally, the report identifies the rise of platformisation, not just in the gig economy, but as a logic of algorithmic management and surveillance extending into traditional workplaces. This trend is reshaping the nature of workplace control across the economy. Analysis of occupational structures reveals that job upgrading, rather than job polarisation, has been the most common pattern of change across the EU, driven largely by the growth of high-skilled service sector jobs. The report concludes that the primary impact of the digital era on work is a qualitative transformation in its nature, focusing on coordination, control, and job quality. The effects of technology are not deterministic; they are strongly mediated by institutional frameworks, with regulation and collective bargaining playing a crucial role in shaping outcomes for workers in the digital age."

"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."

"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."

"This case study illustrates the drivers of and barriers to artificial intelligence adoption by organisations, and acceptance of AI by workers in the public sector. Several factors were crucial in the successful adoption of a human-centred approach to AI, including a fast discovery phase that involved workers (or end users) in the development early on, and aligning human resources, information technology and business processes. Subsidy support mechanisms were also specifically targeted and acquired to support the adoption.

However, making AI support available to workers proved insufficient to ensure its widespread usage throughout the organisation. The slow adaptation of existing work processes and legacy IT systems was a barrier to the optimal usage of the technology. Moreover, the usefulness of the technology depended on both the task routineness and worker experience, thereby necessitating a rethinking of the work division between technology and workers, and between junior and senior workers.

Successful human-centred roll-out of AI in Europe will therefore depend on the availability of, or investments in, complementary intangible organisational capital. Very little is currently known about these investments."

"We examine job quality effects of new digital technologies, using the European frame of seven job quality domains: Pay, Working Time Quality, Prospects, Skills and Discretion, Work Intensity, Social Environment, and Physical Environment. Theoretical effects are ambivalent across all domains. The analysis of these effects confirms that digital technologies can both improve and harm job quality depending on how they are used. In light of this analysis and to think through the challenge of regulating digital technologies, we review emerging regulations across several European countries. Drawing on the principles of human-centred design, we argue that worker participation is important for securing good job quality outcomes, at both the innovation and adoption stages. We also consider the application of data protection legislation to the regulation of job quality. Overall, the paper extends debate about the future of work beyond employment and pay, on to a consideration of job quality more broadly."