Periodic Reporting for period 2 - TECHNEQUALITY (Technological inequality – understanding the relation between recent technological innovations and social inequalities) Reporting period: 2020-01-01 to 2021-12-31 Summary of the context and overall objectives of the project Technological innovations (in robotics and AI) have the potential to greatly boost productivity and general welfare in Europe, but may also change the nature of social inequalities. Whether technological innovations will be a blessing or a burden to European societies depends on governance. However, it is unclear how automation impacts inequalities and which policies are most likely successful. TECHNEQUALITY's main objective was to understand how technological innovations will affect work, educations and inequality in the near future, and draw clear conclusions for policies. We answer six crucial questions.:1. To what extent will technological innovations affect employment and employability of workers in Europe?2. To what extent and how will technological innovations affect social inequalities in European countries?3.To what extent can different stages of education help develop the skills that are required for the future labour market?4. What are (un)intended consequenes of social welfare regimes that may be aimed at curbing technological unemployment?5. What are consequences of further automation for public finances at a local and regional level?6. How does the impact of the current technological innovations differ from that of previous technological revolutions within Europe?Between 2019 and 2021, researchers from Maastricht University, University of Oxford, Cambridge Econometrics, WZB Berlin Social Science Centre, Tilburg University, Stockholm University, Tallinn University and the European University Institute joined forces to conduct fundamental research and answer these questions. TECHNEQUALITY scholars also actively engaged with policy makers from local, national and international governments, experts from the automation industry, and other relevant stakeholders to cocreate policies that work. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far 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. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) TECHNEQUALITY improves our understanding of the relation between robotisation, automation, and digitisation and social inequalities in European countries. TECHNEQUALITY makes six crucial contributions beyond the state of the art:1.We improve forecasting models and use them to predict the consequences of technological innovations for European labour markets, given plausible scenario’s. 2.We empirically study how technological advancements change the relation between skills and income and, skills and labour market inclusion, and show which social groups and European countries are likely to be most affected. 3. We determine the role of education and educational policies for preparing young people and workers for tomorrow’s labour market and exacerbating or reducing inequalities. This is important as the role of education will likely change as a result of automation. We empirically study how various types of education over the life course can prepare today’s children and adult workers for tomorrow’s labour market. 4.We empirically study how alternative forms of social welfare play out in reality, and affect labour market inequality, access to or inclusion on the labour market, and productivity.5. We show what the consequences of automation are for public finances. 'Michelangelo' Picture used for communication activities
