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.

In recent years, more than 1 million people a year have immigrated to the U.S., a level not seen since before the Great Depression. This boom is most apparent in the urban areas where immigrants tend to cluster. Given their numbers, these newly arrived residents must have some effect on local labor markets. Yet economists have been puzzled by the evidence that immigration has little impact on the wages and employment of native-born workers. So how great is immigration’s impact on local labor markets? Is it limited to markets where immigrants settle, or is it spread across the country? Ethan Lewis sifts through the theory and evidence to answer these questions.

In this report the authors make a case that the hollowing out of middle-class jobs in America has as much to do with the technology revolution and computerization of tasks as with global pressures like China. In so doing, they predict what the future of work will be in America and what it will take for the middle class to succeed. The collapse of the once substantial middle-class job picture has begun a robust debate among those who argue that it has its roots in policy versus those who argue that it has its roots in structural changes in the economy. The authors delve deeply into structural economic changes brought about by technology. The paper describes the exact kind of work tasks that are now, or will be, automated. The authors argue that the future human labor market will center on three kinds of work: solving unstructured problems; working with new information; and carrying out non-routine manual tasks. The bulk of the rest of the work will be done by computers with some work reserved for low wage workers abroad. The policy challenge goes well beyond calls for more years of education or better access to education. The authors argue, that in order to prepare young people to do the jobs computers cannot do, a re-focus on an education system around one objective is required: giving students the foundational skills in problem-solving and communication that computers don't have.

The retirement of baby boomers will create a shortage of skilled workers in mature economies worldwide, leading to higher wages and lower profits for the next 15 years. Is your company at risk of labor shortages? It depends on occupations, geographies, automation, and immigration, among other factors. The Conference Board has developed an index that helps companies forecast their risk of labor shortages in hundreds of occupations and industries in the US and Europe. This report discusses implications for companies and mitigating actions they can take.