Abstract
PurposeOngoing automation processes may render a fair share of the existing jobs redundant or change their nature. This begs the question to what extent employees affected invest in training in order to strengthen their labour market position in times of uncertainty. Given the different national labour market regimes and institutions, there may be an important geographical dimension to the opportunities to cope with the challenges set by automation. The purpose of this study is to address both issues.Design/methodology/approachUsing data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker's job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries.FindingsThe results show that employees in jobs vulnerable to automation invest relatively little in training. Also, there are significant differences across Europe in both the provision of training in general and the effect of automation on training provision.Originality/valueWhile there is quite a lot of research on the structural labour market effects of automation, relatively little is known about the actions that employees take to deal with the uncertainty they are faced with. This article aims to contribute to our understanding of such mechanisms underlying the structural macro-level labour-market dynamics.
Highlights
Ongoing automation processes may render a fair share of the existing jobs redundant or change their nature
We argue that one mechanism driving the asymmetric labour market effects of automation are differences in access to training – defined as non-formal education [1] – between workers with higher and lower risks of losing their jobs to automation
3.4 Principal variables The analysis aims at estimating the association between automation risk and training attendance
Summary
Ongoing automation processes may render a fair share of the existing jobs redundant or change their nature. Design/methodology/approach – Using data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker’s job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries. Introduction: automation and training Automation, fuelled by developments in artificial intelligence, adaptive automated learning and robotics, is a prime driver of current labour market dynamics It leads to the loss and restructuring of current jobs, the exact magnitude of the effect is heavily debated. Many thanks to Lianne Hans and Luuk Bos for their contributions to the data preparation and initial empirical analyses
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