The emerging, non-routine European skills of the future

Abstract

Purpose – This paper investigates the future of emerging skills and their impact on occupations in the European Union by setting up a powerful paradigm of leveraging the European Skills, Competences, Qualifications and Occupations (ESCO) Delta files to predict future skills in a repeatable fashion, allowing policymakers and educational institutes to easily gain insights into emerging skill needs. This study explores the implications of automation on skill development, addressing key questions regarding the emergence of non-automatable skills across all industries and skills contained in the ESCO dataset. Design/methodology/approach – The research design is exploratory, quantitative and cross-sectional. This study employed a big data analysis of the ESCO Delta datasets combined with the ALM-hypothesis framework to analyse the emergence of non-automatable (herein called non-routinisable) skills and what this means for future occupations. The proposed methodology provides a replicable framework for analysing future ESCO Delta file releases with regard to emerging, non-routinsable skills. Findings – Our empirical findings reveal that (1) there is a Self-Automating Effect, i.e. non-routinisable skills are growing more in non-technical industries and technical industries may be automating themselves out of jobs. (2) The Matthew Effect in Skills, i.e. high-skill jobs are requiring more skills (compared to low-skill jobs as measured by educational requirements) and so becoming more specialised, however, counterintuitively, AI may be reducing this gap. (3) The Red Queen Effect: in spite of the effect of AI in (2), we find that in jobs requiring higher educational degrees, the new skill requirements are increasing most (with a few notable exceptions), requiring employees to run just to stay in the same place. Originality/value – This study contributes to the existing literature in three ways: (1) it shows a practical and easy-to-replicate approach to leverage the high quality, well curated ESCO data set to carry out predictions of future skills (routinisable or not); (2) it answers questions around skill inequalities potentially arising due to technology advancements; (3) it helps governments and educational institutes understand which skills are emerging, “future-proof” skills that future graduates and current employees will likely need in order to remain competitive in the labour markets of the future.