We investigated the potential impact of automation on work. Our analyses revealed that robots have reduced employment in the manufacturing sector in many EU countries and that job losses seem often offset by employment gains in other sectors. To understand plausible future developments, scenario studies suggest that the future labour market impact of automation depends on the speed of innovation and market diffusion, the capacity of technology to augment or substitute humans in job tasks, and government policies. By adjusting labour market forecasting models, we modelled how automation will affect European labour markets in various future scenarios, and show that in the best case scenario, automation will destroy about 5% of all jobs, but in the worst case, about 44% of all European jobs will be replaced. Impacts differ between countries.
How will this affect inequalities? A paper analysing how technological change affects workers' opportunities to use diplomas and skills to find matching jobs shows that automation-related mismatch problems differ markedly across social groups and classes. A second paper shows that one likely consequence of automation (a growing share of jobs with high skill requirements and workers with high education) hurts marginal workers’ employment, hitting youth and immigrants. A third report analyses how automation affects the role cognitive and non-cognitive skills, social class, and educational credentials play in the school-to-work transition. Results show large cross-national differences in these effects, which suggests that the way in which automation changes inequalities is strongly context dependent.
Our work package on educational responses delves into ways in which countries prepare today's and tomorrow's workers for the future of work. We provide various empirical analyses of cross-national skills and education surveys, and study how education systems and schools help children to learn digital literacy, computer skills, and problem solving. Our analyses underline that good schools are vital. We also explore if VET systems should teach general skills and show that general skills are as important for vocationally educated as they are for generally educated.
We also published an international data set on adult education systems and performed various empirical analyses that explored how education can prepare adults for the future of work. One report assesses unequal access to adult education, focussing specifically on inequalities between workers at different risk of automation and exploring differences between educational groups and genders. It also assesses the consequences of training participation for future learning and future careers. This report sheds light on favourable conditions for lifelong learning, and policies that increase the inclusiveness of adult education. Another empirical analysis suggests that there may be a small causal effect of adult education on digital skills, but that most of the differences are driven by selection.
We also assessed potential alternative social welfare strategies (e.g. participation income) that may be a potential response to automation. Our field experiments found no convincing evidence that alternative welfare regimes reduce employment effects. In some municipalities we find small positive effects on parttime and fulltime employment and on people’s self-efficacy and social trust. No effects were found on health and wellbeing. A systematic review suggests that European alternative welfare programmes generally do not affect labour supplies but increase subjective wellbeing, (mental) health and trust. We have also modelled macroeconomic impacts of such welfare regimes; these analyses suggest that the increase in disposable income from welfare may mitigate the negative impact of automation.
Technological change will also affect public finances. Our study of local and national governments shows that the impact of automation on fiscal revenues varies considerably across countries and regions and countries, suggesting that the degree in which regional economies adapt to technological change is crucial in understanding how automation impacts on public finances.
