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AbstractStudies have often found that recent immigrants have better mental health than natives, whereas established immigrants have no such advantage. This could be interpreted as evidence for immigrants' mental health deteriorating with residence duration—the "unhealthy assimilation hypothesis." However, the methods used in the literature are unfit to assess whether the mental health differences between recent and established immigrants are due to individual‐level deterioration in mental health, compositional differences between immigration cohorts, or selective remigration. This is because previous studies mostly rely on cross‐sectional data, incur in overcontrol bias, and/or fail to disentangle variation with time since arrival from variation with age or between cohorts. In this article, I propose a novel analytical strategy to test the unhealthy assimilation hypothesis. Using fixed‐ and random‐effect regressions stratified by immigrants' age at arrival and data from waves 1–11 of the UK household longitudinal study, I find no evidence that immigrants' mental health deteriorates with time since arrival: immigrants' mental health trajectories are in line with natives' trajectories with age, and the cross‐sectional finding of more established immigrants having worse mental health is driven by differences between individuals who migrated at different times.

Purpose: Ongoing 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/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. Findings: The 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/value: While 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.

The EU Horizon 2020 CICERONE project (2018-2021) is developing a platform for EU programme owners (POs, i.e. public funders of programmes related to circular economy) to coordinate and collaborate on priority research and innovation actions. Part of this process was the development of the Circular Economy Strategic Research and Innovation Agenda (SRIA). This SRIA is as a strategic guidance document that specifically focuses on the topic of CE in the European Union. The SRIA targets national, regional and local programme owners to identify joint priorities in research and innovation, co-implement joint programmes and guide further cooperation. Collaboration will be based on the SRIA's joint programmes, which provide a framework for a more systemic innovation aiming to create holistic, cross-cutting and systemic programmes with greater impacts.