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This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require 'people' engagement interacted with 'brains' are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with 'brains' and/or 'people'.

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Technology is disrupting labor markets. We analyze the demand and reward for skills at occupation and state level across two time periods using job postings. First, we use principal components analysis to derive nine skills groups: 'collaborative leader', 'interpersonal and organized', 'big data', 'cloud computing', 'programming', 'machine learning', 'research', 'math' and 'analytical'. Second, we comment on changes in the price and demand for skills over time. Third, we analyze non-linear returns to all skills groups and their interactions. We find that 'collaborative leader' skills become significant over time and that legacy data skills are replaced over time by innovative ones.

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This study posits a bioecological positivity to success (BEPS) model and examines how diverse bioecological factors predict graduates' career success. The BEPS model with an emphasis on hard (e.g. science, technology, engineering and mathematics [STEM]) and soft (e.g. interpersonal and critical thinking) skills generate a hypothetical model: positive aspects of person (mathematical/hard and affective/soft abilities), process (effective hard and soft competencies learning in higher education) and proximal contexts (original family income and present employment status) predict graduates' career success (job income and perceived extrinsic, intrinsic and autonomy satisfaction) in early adulthood. Gender, studying STEM, and study years are also included as predictors in the path analysis as control. Path analyses examine the model with cohort data from the Taiwan Education Panel Survey (TEPS) and its follow-up (TEPS-B), which are longitudinal studies of a group of young people (n = 2,700) since grade 7 till age 24-25 years old. Results reveal that soft skills and employment play the most significant roles in graduates' career success. Hard skills play a minor role. Findings support the BEPS model and provide implications for educational practices and policymaking to emphasise on soft skills learning, employability and entrepreneurship education.

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This study builds on Autor and Dorn's (2013) classification of automatable work at the three-digit occupation code level to identify additional jobs that will be automatable in the next decade by drawing on patent data. Based on this new classification the study provides estimates of the share of jobs that we expect to be automatable in the EU and across 25 individual countries. The study highlights that aspects of 47 per cent of jobs will be automatable over the next decade, with 35 per cent of all jobs being fully automatable. It also provides some evidence that 'thinking' and 'people' skills will become increasingly important for the fourth industrial revolution. The study puts emphasis on the fact that these estimates are based on static models. Assuming that some of the rents from labor technology will filter back into the economy it is expected that other occupations will expand in number as people consume more goods and services.

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