White Paper

Much of the research on automation, including our own, has focused on the potential for job displacement and has taken a national-level view. This report looks beneath the national numbers to examine the present and potential future of work for different people and places across America. Local economies across the country have been on diverging trajectories for years, and they are entering the automation age from different starting points. Our view incorporates the current state of local labor markets as well as the jobs that could be lost and gained in the decade ahead.

By assessing what students know and can do (i.e.. learning outcomes), postsecondary institutions can document the quality, relevance and value of academic programs, and make evidence-based decisions to improve student outcomes. To understand learning outcomes assessment practices currently used in certificate, diploma and undergraduate degree programs, HEQCO surveyed the provosts and vice-presidents academic of public colleges and universities across Canada in the fall of 2015. This report summarizes the findings.

This anthology identifies promising sectors and occupations for the next 15-20 years. It will help to understand which industries will develop, which new technologies, products, management practices and what new competencies will be needed for employers.

Predictive modelling — the analysis of large data sets to predict future outcomes — remains a small but growing practice among higher education institutions as a means of identifying students who are at risk and putting in place targeted interventions to improve student retention and success. A survey of Canadian universities and colleges found that 36% of respondents used predictive modelling as a means of improving student retention and almost 40% indicated that they were considering doing so, according to a new report, Opportunities and Challenges in Predictive Modelling for Student Retention, published by the Higher Education Quality Council of Ontario.

Cette commission indépendante de 27 membres regroupe des personnalités mondiales du monde des affaires, des syndicats, des groupes de réflexion, ainsi que des organisations gouvernementales et non gouvernementales, sous la houlette de ses deux coprésidents, le Président de la République d’Afrique du Sud, Cyril Ramaphosa, et le Premier ministre suédois, Stefan Löfven. La commission a été créée en 2017 dans le cadre de l'Initiative du centenaire sur l'avenir du travail de l'Organisation internationale du Travail. L'OIT célèbre son 100e anniversaire en 2019. Parmi les questions clés examinées par la commission figurent les nouvelles formes de travail, les ramifications institutionnelles de la nature changeante du travail, l'apprentissage tout au long de la vie, l'inclusion et l'égalité du genre, la mesure de l'emploi et du bien-être des êtres humains et le rôle de la protection sociale universelle dans un avenir de travail stable et équitable.

Industrial production was transformed by steam power in the nineteenth century, electricity in the early twentieth century, and automation in the 1970s. These waves of technological advancement did not reduce overall employment, however. Although the number of manufacturing jobs decreased, new jobs emerged and the demand for new skills grew. Today, another workforce transformation is on the horizon as manufacturing experiences a fourth wave of technological advancement: the rise of new digital industrial technologies that are collectively known as Industry 4.0. How will this next wave of industrial evolution play out? Will it create or destroy jobs? How will job profiles evolve? And what types of skills will be in demand? The answers to these questions are critical to business leaders and policy makers as they seek to take full advantage of the opportunities arising from Industry 4.0 by ensuring that an appropriately skilled workforce is in place to capture them. To understand how the industrial workforce will evolve with Industry 4.0, we looked at the effects that these new technologies will have on Germany’s manufacturing landscape, which is among the world’s most advanced.

Despite the increasing importance of digitization for economy and society, there is few structuring of the very heterogeneous kinds of digital work. Representatives from business, politics and science need a basis for the development of strategies to encounter the challenges that result from this digitization. We aim at delivering a contribution to that basis by systematically investigating what different types of digital work exist and by developing a taxonomy. As a first important step towards this goal, we investigate in this paper what digital work tools exist since such tools are a major constituent element of digital work. Using a hybrid approach including both a deductive conceptual-to-empirical and an inductive empirical-to-conceptual procedure, we create an artifact that gives business leaders an overview of existing digital work tools as a basis for strategic decisions and at the same time provides researchers with stimuli for future investigations in the dynamic domain of digital work.

Together, automation and artificial intelligence (AI) have the potential to fundamentally reshape economics and social life. How will these trends affect politics and public policy? Will they expand or lessen the appeal of populism? Will they make it easier or more difficult for governments to shape public policy? This report explores the potential for automation and AI to lead to widespread political and policy unrest and change in Canada. To examine this, we consider four related questions about automation and AI: how knowledgeable are citizens about automation and AI? What do they expect its effects to be for themselves, for employment and the economy, and for society? How worried are they about the potential effects of automation and AI? What kinds of politics and bundles of policy responses are citizens willing to support to confront the challenges (and opportunities) of automation and AI? To understand citizens’ views on automation and AI and their related policy preferences, we surveyed 1,995 Canadians in May and June 2019. Our survey sample was drawn from multiple panels with quotas for age, gender and region, providing a representative sample of the population. Our goal was to understand how people’s exposure to automation and AI and their own beliefs about them—which may not align—relate to their preferences for various policy responses to the challenges of automation and AI.