Fabian Stephany is a Non-Resident Fellow at Bruegel and a member of the Future of Work and Inclusive Growth project which analyses the impact of technology on the nature, quantity and quality of work, welfare systems and inclusive growth. He is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute (OII), University of Oxford, and a Research Affiliate at the Humboldt Institute for Internet and Society in Berlin.
With the Skill Scale Project, Fabian investigates how we can create sustainable jobs via data-driven reskilling in times of technological disruption. He is a co-creator of the Online Labour Observatory – a digital data hub, hosted by the OII and the International Labour Organisation (ILO), for researchers, policy makers, journalists, and the public interested in online platform work.
Fabian holds a PhD and degrees in Economics and Social Sciences from different European institutions, including Universitá Bocconi Milan and University of Cambridge. As an Economist and Senior Data Scientist, Fabian has been facilitating Digital Policy Entrepreneurship with partners in the international policy landscape, such as the United Nations Development Programme, the World Bank, the ILO, or the OECD in Paris.
Future of Work and Inclusive Growth Annual Conference 2023
Annual Conference of the Future of Work and Inclusive Growth project
The ‘anywhere’ jobs are not everywhere – they’re in cities
Given new remote working arrangements, online gigs can be completed in the lowest-cost locations; they’re mainly done by workers in large cities.
Using online data to glimpse into the future of work
Labour-market data from online sources can identify emerging occupations and skill demand, helping policymakers prepare better for future needs.
Future of Work and Inclusive Growth Annual Conference 2022
First Annual Conference of the Future of Work and Inclusive Growth project
Is the workforce ready for the jobs of the future? Data-informed skills and training foresight
For many newly emerging jobs, labour-market mismatches prevail as workers and firms are unable to apply precise occupation taxonomies and training lag