White Paper

Canada’s immigration policy needs to be updated and improved to address these two challenges. Four specific actions should be taken by the Government of Canada to boost economic growth and increase prosperity for all Canadians. 1. Increase annual permanent economic immigration from 300,000 to 450,000 over 5 years (translating to an increase of about 75K principal applicants and about 75K of their family members) to expand workforce growth and counter the drag from slowing population growth and aging 2. Facilitate entry for senior and specialized talent by streamlining permanent and temporary entry programs to be faster and less burdensome on employers to help give high-growth and innovative companies the managerial capacity and skills they need to scale and be globally competitive 3. Rethink Express Entry points allocations to qualify more international students studying in Canada for permanent residency so that firms can tap into an already-integrated pool of young, educated talent 4. Improve national accreditation standards to create the conditions for all immigrants to Canada to reach their economic potential, to the benefit of all Canadians These recommendations will address current drags on growth from an aging population and specialized talent shortages, but they are only part of the answer. For immigration to fully offset the impact of Canada’s impending demographic squeeze, annual permanent economic immigration would need to nearly double from the current level of about 300,000 per year – a much more dramatic increase than the 50 percent increase recommended here.3 Further, not all talent gaps can or should be addressed through immigration. Fast-growing firms may face no alternative given the immediacy of their talent requirements, but over the longer term both governments and employers should ensure that domestic training and education programs are responsive to emerging labour market needs.

The report analyses the ways in which unpaid care work is recognized and organized, the extent and quality of care jobs and their impact on the well-being of individuals and society. A key focus of this report is the persistent gender inequalities in households and the labour market, which are inextricably linked with care work. These gender inequalities must be overcome to make care work decent and to ensure a future of decent work for both women and men.

It was 1997-eons ago, in internet years and the Web was only beginning to take off. People used dial-up modems to get online, and Netscape Navigator was the browser of choice. Google was still a research project of two Stanford students, and Facebook-well, Mark Zuckerberg was a 13-year-old having his Star Wars-themed bar mitzvah. Flash forward to 2011. The Web has since reinvented itself time and again: when businesses embraced it in the late 1990s, when Google dominated search in the early 2000s, when user-generated content became prominent in the mid-2000s. Today the Web is going through another reinvention, morphing into a place where our social interactions are ever more important. And the main force behind this phenomenon is, of course, Facebook, led by Zuckerberg, now a 27-year-old billionaire.

We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change—corresponding to an increasing ratio of older to middle-aged workers—is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.

As robots and other computer-assisted technologies take over tasks previously performed by labor, there is increasing concern about the future of jobs and wages. We analyze the effect of the increase in industrial robot usage between 1990 and 2007 on US local labor markets. Using a model in which robots compete against human labor in the production of different tasks, we show that robots may reduce employment and wages, and that the local labor market effects of robots can be estimated by regressing the change in employment and wages on the exposure to robots in each local labor market—defined from the national penetration of robots into each industry and the local distribution of employment across industries. Using this approach, we estimate large and robust negative effects of robots on employment and wages across commuting zones. We bolster this evidence by showing that the commuting zones most exposed to robots in the post-1990 era do not exhibit any differential trends before 1990. The impact of robots is distinct from the impact of imports from China and Mexico, the decline of routine jobs, offshoring, other types of IT capital, and the total capital stock (in fact, exposure to robots is only weakly correlated with these other variables). According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 percentage points and wages by 0.25-0.5 percent.

We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation increases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.

A central organizing framework of the voluminous recent literature studying changes in the returns to skills and the evolution of earnings inequality is what we refer to as the canonical model, which elegantly and powerfully operationalizes the supply and demand for skills by assuming two distinct skill groups that perform two different and imperfectly substitutable tasks or produce two imperfectly substitutable goods. Technology is assumed to take a factor-augmenting form, which, by complementing either high or low skill workers, can generate skill biased demand shifts. In this paper, we argue that despite its notable successes, the canonical model is largely silent on a number of central empirical developments of the last three decades, including: (1) significant declines in real wages of low skill workers, particularly low skill males; (2) non-monotone changes in wages at different parts of the earnings distribution during different decades; (3) broad-based increases in employment in high skill and low skill occupations relative to middle skilled occupations (i.e., job 'polarization'); (4) rapid diffusion of new technologies that directly substitute capital for labor in tasks previously performed by moderately-skilled workers; and (5) expanding offshoring opportunities, enabled by technology, which allow foreign labor to substitute for domestic workers in specific tasks. Motivated by these patterns, we argue that it is valuable to consider a richer framework for analyzing how recent changes in the earnings and employment distribution in the United States and other advanced economies are shaped by the interactions among worker skills, job tasks, evolving technologies, and shifting trading opportunities. We propose a tractable task-based model in which the assignment of skills to tasks is endogenous and technical change may involve the substitution of machines for certain tasks previously performed by labor. We further consider how the evolution of technology in this task-based setting may be endogenized. We show how such a framework can be used to interpret several central recent trends, and we also suggest further directions for empirical exploration.

This report focuses on workers facing a double disadvantage—a higher risk of technological disruption from automation and fewer resources to embrace new career pathways. How can we support these workers as they make this transition?

This report was requested by the Higher Education Quality Council of Ontario (HEQCO) to support its sixth annual conference, Transitions: Learning across Borders, Sectors and Silos. It presents a customized analysis of findings from a survey undertaken in fall 2015 with Academica Group’s StudentVu Panel to gather the perspectives of current students and recent graduates on their experiences transitioning into, through and out of postsecondary education (PSE). The report is organized around these three transitions, presenting key findings, strengths in current processes and challenges barriers encountered by students for each.