Work is Changing: In the past, building a successful career required young people to learn core technical skills for an occupation, and gradually broaden their skills and experience over time. This is what it meant to be ‘work smart’. Today, automation and globalization have led to a loud and compelling narrative about the future of work, and career paths appear more complicated. Media reports warn, almost daily, that “robots are coming to take your jobs”.

This paper shows that the introduction of the "gig-economy" changes the way employees respond to job loss. Using a comprehensive set of Uber product launch dates and employee-level data on job separations, we show that laid-off employees with access to Uber are less likely to apply for UI benefits, rely less on household debt, and experience fewer delinquencies. Our empirical strategy is based on a triple difference-in-difference empirical model, comparing the difference in outcome variables 1) pre- and post-layoff, 2) before and after Uber enters a market, and 3) between workers with and without the ability to participate on the ride-sharing platform (car-owners inferred from auto credit histories). In support of our identification strategy, we find no apparent pre-existing difference in outcomes in the months leading up to Uber's entry into a market. Moreover, the effects are severely attenuated for workers with an auto lease, for whom the viability of participating on the ride-sharing platform is significantly reduced. Overall, our findings show that the introduction Uber had a profound effect on labor markets.

The use of occupational licensing as a mechanism for increasing the demand for, and supply of, skills was considered – alongside other measures such as training levies – as part of the UK Commission‘s recent Review of Employer Collective Measures (Stanfield et al., 2009). However, that Review acknowledged that the general topic of occupational regulation remains severely under researched in the UK. It went on to recommend that a further, more detailed investigation of the issue should be carried out. This report presents the findings from that investigation. The overall aims of the research were to: · map the current pattern of occupational regulation in the UK; · review the theory regarding the operation and impact of occupational regulation; · examine the existing evidence on the impacts of occupational regulation in the UK and abroad; · provide initial estimates of the impact of occupational regulation on labour market outcomes such as skill levels, wages and employment in the UK.

This is the second part of the Report on the Future by Prime Minister Juha Sipilä’s Government. The two-part Report on the Future serves as an opener for discussions for the coming years. The rationale underlying reports on the future is to identify and highlight issues that are important for decision making and will require attention in the future. The aim of this second part of the Report on the Future is to identify desirable futures, decision-making needs and societal issues related to work. These issues and their alternative solutions will also be considered and resolved by future governments. The report is part of a long-term decision-making process, where the debate on the future and transformation of work continues. The goal is that solutions to this phenomenon affecting the whole of society can be implemented over a long-time span exceeding the electoral term. The main observations of this publication crystallise as follows: the forms of working and employment relationships will become more varied, which will require changes, for instance, in legislation and social security. Work will be less anchored to certain times and locations, but the change is not equally strong or synchronous in all sectors. Education highlights continuous learning, and Finland needs a high-quality model for lifelong learning for all population groups. Flexibility is increasing on the labour market and in working life, and issues pertaining to income are emphasised. Social dialogue is needed on the different and changing instrumental values of work, in addition to economic values. Responsibility for launching this dialogue rests with decision-makers.

Rapid advances in technology, including artificial intelligence and automation, are transforming industries and reshaping the skills necessary to secure and keep a job. In response, U.S. workers will need to acquire skills that are not easily automated or that complement ever-changing technology. Yet, data suggest an alarming trend: there is a steady decline in the amount employers are investing in their workforce.1 Federal and state policymakers should consider using tax incentives to encourage additional workforce training investments. To accomplish this, in May 2017 the Future of Work Initiative proposed the Worker Training Tax Credit. Modeled on the popular Research and Development (R&D) Tax Credit, this new tax credit could be used by small and large businesses to invest in training for their low- and middle-income workers. Based on economic modeling recently conducted by District Economics Group, the Worker Training Tax Credit would cost roughly $146.5 million over ten years, and lead businesses to increase training investments by 8.5 percent. Because the tax credit excludes training provided to high-income workers, these induced training investments would provide greater opportunity to low- and middle-income workers.

With increasing access to postsecondary education and a more diverse student body, delivering advising, and any student‐success initiative for that matter, in a cost‐effective and scalable manner is a priority. One way to reach more students is through group advising, an approach that has seen some positive results in American institutions.3 Furthermore, some colleges and universities in Canada use new programs, including mentoring by upper‐ class students, financial incentives, and self‐authoring workshops, to test and identify feasible and efficient ways to further improve student retention.4 This report evaluates a new experiment that focuses on proactive advising undertaken at Mohawk College. All first‐year students entering Mohawk in Fall 2015 were randomly assigned to two treatment groups and a control group. All students were contacted before the start of the semester via email and informed about the advising services that were available to them. The email reminders were more frequent for the two treatment groups while the control group received only one informational email. The two treatment groups were offered an opportunity to participate in an advising session six weeks prior to start of the semester, but one treatment group was invited to group advising sessions while the other treatment group was invited to one‐to‐one sessions

The overall study has two corresponding phases of investigation. Phase 1, which is the subject of this report, represents the work that EPRI undertook to further develop the predictive model of student retention at Mohawk College; to test the predictive model and the predictions it generates; to use this model to generate predictions of the probability that any given incoming student would leave Mohawk early; to use these predictions to divide students into three different risk groups (high, medium, low); to analyze the distribution of students across these different risk groups according to a range of individual, program and other characteristics; and to then calculate participation rates in existing student advising programs across the three risk groups. Phase 2 (and the second report) will consist of an analysis of the specific advising interventions offered to students of the 2015 entry cohort, including how the effects of these interventions vary across student risk groups as identified by the predictive model. In addition, this paper not only goes through the development and application of the predictive model and related analysis, but also provides a primer on the use of predictive modelling within the context of predictive analytics and student retention. The motivation for such a primer comes from the wide use of the term “predictive modelling” as pertaining to student retention, and the potential usefulness of identifying the relevant concepts.

This study aims to know the skills needed for the future development and growth of Québec's financial sector. Aimed at educational institutions and financial organizations in the world, it aims to equip them to ensure the adequacy between these skills and training provision. To do this, we analyzed from a qualitative perspective transformations to come and identified future skills needed in the main business of the three major sub-sectors (banking, insurance, asset management) finance. We sought to understand how these trades are doomed to change and how these changes will be different depending on the role and function of business within organizations. [googletranslate_en]

Since 2007 (prior to the economic downturn of 2008/2009) the overall labour force participation of Canadians declined by about two percentage points. The first part of the study investigates the extent to which aging affected changes in labour market participation rates since 2007, based on data from the Labour Force Survey (LFS). In the second part, the reasons behind the increase in the participation rates of Canadians aged 55 and over, which have been trending upwards since 1996, are explored. In 2016, individuals aged 55 and over accounted for 36% of the working-age population, the highest proportion on record (since the compilation of comparable statistics in 1976). By 2026, that proportion could reach 40%. Because labour market participation starts to decline after age 55, population aging was the main factor behind the decline in the overall participation rate in the years following the recession. At the same time, the labour market participation of older Canadians increased. From 1996 to 2016, the labour force participation rate of individuals aged 55 and over increased from 24% to 38%, reaching a record high in 2016. The participation rate of individuals aged 55 and over increased for all levels of education. For instance, the participation rate of those with a high school diploma or less increased from 19% in 1996 to 29% in 2016. Changes in age, family structure and educational factors explained 44% of the increase in the labour market participation of older Canadians from 1996 to 2016.