The nature of work is changing and so are the skills required to thrive in the workplace. With employment growth a continued focus in Victoria, it is critical that all Victorians have the opportunity to develop the literacy, numeracy, vocational and employability skills required to secure meaningful jobs, regardless of background or socio-economic status., In Victoria, there are 650,000 adults at the lowest levels of literacy, including 265,000 workers with low educational attainment in low skill jobs, and more than 17,000 15 to 19 year olds who are not engaged in employment, education or training. Responding to these challenges efficiently, effectively and at an appropriate scale will require a broad and multi-faceted strategy. The adult community education sector is the critical link between the needs of these Victorians and an opportunity for a better life, by providing them with education and training choices best suited to their individual needs., In April 2018, the Department of Education and Training commissioned the Future Opportunities for Adult Learners in Victoria (FOAL) project to ensure more at risk Victorians have access to high quality training. To achieve this, the FOAL Discussion Paper considers whether the adult community education sector is equipped with the system-level supports and capabilities it needs to support at-risk Victorians into social and economic participation., Edited excerpts from publication.

This paper addresses, both theoretically and empirically, the sectoral patterns of job creation and job destruction in order to distinguish the alternative effects of embodied vs disembodied technological change operating into a vertically connected economy. Disembodied technological change turns out to positively affect employment dynamics in the "upstream" sectors, while expansionary investment does so in the "downstream" industries. Conversely, the replacement of obsolete capital vintages tends to exert a negative impact on labour demand, although this effect turns out to be statistically less robust.

We examine the hypothesis that flexible work organization involves greater skill requirements and, hence, an increased likelihood of receiving employer provided training. Using unique linked employer-employee data from Germany, we confirm that employees are more likely to receive training when their jobs are characterized by greater decision-making autonomy and task variety, two essential elements of flexibility. Critically, the training associated with workplace flexibility does not simply reflect technology. Skill-biased organizational change plays its own role. Moreover, we show that the training associated with workplace flexibility is disproportionately oriented toward employees with a greater formal education. Our results also provide modest evidence of an age bias of workplace flexibility. However, the link between workplace flexibility and training does not appear to differ by gender.

Focusing on private sector workplaces in Britain, we investigate whether the employment of older workers has implications for workplace performance. We find no significant association between changes in the proportion of older workers employed and changes in workplace performance. We find some evidence that workplace labour productivity falls where the proportion of 'middle-aged' workers falls, either due to a rise in the proportion of older or younger workers, but this association does not carry through to financial performance. Overall, the findings suggest that any reluctance on the part of employers to employ greater numbers of older workers may be misplaced.

Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are “at risk of automation”, spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects – a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors.

This paper focuses on identifying determinants of 'automatability risk', namely the propensity of EU employees being in jobs with high risk of substitutability by machines, robots or other algorithmic processes, and uncovers its impact on labour market outcomes. Using relevant data on tasks and skill needs in jobs, collected by the European skills and jobs survey (ESJS), jobs are bundled according to their estimated risk of automation. The paper builds on the methodology of previous studies that estimate the latent relationship between 'true' automatability and job tasks (Frey and Osborne, 2013, 2017; Arntz et al., 2016; Nedelkoska and Quintini, 2018) but utilises highly disaggregated job descriptions provided by a subsample of the ESJS, as well as information on jobs' skill requirements. About 14 per cent of EU adult workers are found to face a very high risk of automation. The distribution of high automatability across industries and occupations is also found to be skewed towards routine jobs with low demand for transversal and social skills. The risk of job displacement by machines is higher among males and lower-skilled workers, with little evidence of polarisation. It is prevalent in private sector jobs that fail to provide remedial training to employees, accentuating the vulnerability of at-risk-workers and highlighting the need for stronger lifelong learning policies at EU level.

In this work, we propose a methodology for classifying occupations based on skill requirements provided in online job adverts. To develop the classification methodology, we apply semi-supervised machine learning techniques to a dataset of 37 million UK online job adverts collected by Burning Glass Technologies. The resulting occupational classification comprises four hierarchical layers: the first three layers relate to skill specialisation and group jobs that require similar types of skills. The fourth layer of the hierarchy is based on the offered salary and indicates skill level. The proposed classification will have the potential to enable measurement of an individual's career progression within the same skill domain, to recommend jobs to individuals based on their skills and to mitigate occupational misclassification issues. While we provide initial results and descriptions of occupational groups in the Burning Glass data, we believe that the main contribution of this work is the methodology for grouping jobs into occupations based on skills.

The National Skills Commission (Commission) will provide national leadership to the vocational education and training (VET) system. The Commission will oversee the Australian Government's investment in VET and drive long-term improvements to the VET sector. It will undertake research and analysis of future skills needs across industry to ensure government funding addresses national labour market priorities including those arising from developing technologies. The Commission's role and functions will be refined through the co-design process to ensure that the Commission complements and enhances the VET system, improves coordination, coherency and efficiency, and enables local and regional solutions., This discussion paper has been published to inform the conversation on what the final model may look like. Co-design for the Commission is focused on three broad questions: (1) Are the roles and responsibilities proposed for the National Skills Commission appropriate? (2) What organisational capabilities will the National Skills Commission need? (3) What governance is needed to give the National Skills Commission the legitimacy and impact it needs? Submissions, which close on 15 November 2019, will inform the best operating model of the Commission.

This paper investigates to what extent changes in the returns to occupational skill and declining occupational segregation have reduced wage inequality between men and women. As a first pass, I find that roughly 65% of the decline in the gender wage gap between 1985 and 2010 can be explained by a reduction in occupational segregation between the genders. The remaining 35% are explained by shifts in occupational wages which increased within occupations important for female employment, and declined in many occupations important for male employment such as producing occupations. Motivated by the central of Böhm et al. (2019) that average wages do not move as much as skill prices, I reestimate the part of the declining wage gap attributed to changes in (selection corrected) skill prices. The impact of movements in skill prices on the reduction in gender wage inequality was roughly 13 percentage points larger than the impact of changes in average wages alone. Similar findings hold when decomposing the rise in the proportion of women at higher percentiles of the wage distribution and vice versa for lower percentiles. This underscores the importance of accounting for selection effects in decompositions.