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Despite the significant attention paid to Canada's loss of manufacturing jobs at a broader level, little is known about how individual Canadian regions have fared since the manufacturing employment decline of 2003-2009, caused by a combination of increased overseas competition, a rising Canadian dollar, and the 2008-2009 Great Recession. Employment in manufacturing fell by over 500,000 positions between 2003 and 2009, with over 300,000 of the net decline occurring before the Great Recession of 2008-2009. Since then, there has been almost no employment growth in the sector. This research examines some straightforward questions: who was affected by this job loss, what happened to them, and what was the effect on the communities in which they had been employed? Were manufacturing jobs replaced with comparable jobs, in terms of skill level and pay, in the communities in which they were lost?

The key messages from the research are that: (1) Canadian manufacturing jobs lost since 2003 were only partially replaced with comparable ones in the construction, wholesaling, and trucking industries in the major metropolitan centres of Toronto, Montreal, and Vancouver, as well as in communities within commuting distance of these three cities: (2) because they had more manufacturing employment than the Canadian average, communities such as Miramichi, Oshawa, St. Catharines-Niagara, Prince Albert, Kitchener-Cambridge-Waterloo, and Windsor have experienced disproportionately high levels of employment disruption from the decline in manufacturing employment; and (3) although the number of manufacturing jobs in Canada has changed little since 2009, there has been a shift to higher-skilled occupations within the industry; and skills training is crucial to ensure workers are qualified for those jobs in construction and manufacturing.

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The Adult and Dislocated Worker programs are two of the largest public workforce investment programs in the United States, so it is important to determine the effectiveness of the services they offer. To rigorously assess the impact of these programs on job seekers' employment and earnings, the Employment and Training Administration of the U.S. Department of Labor contracted with Mathematica Policy Research and our partners at Social Policy Research Associates, MDRC, and the Corporation for a Skilled Workforce to conduct a national experimental evaluation. This report provides 30-month impact findings from this evaluation of 28 randomly selected local workforce investment areas.

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The process of technological progress is one of creative destruction, where some occupations, skills, products and firms become obsolete and are replaced by technologically superior alternatives. To determine the impact of automation on Canada's labour markets, the author uses the likelihood that each occupation could be automated in combination with labour market information to determine the jobs and industries most susceptible to disruption. The paper concludes that Canada's provinces vary significantly in industrial and labour market structure. These differences highlight priority areas that provincial governments should consider in developing education and employment policy responses to adapt to current and future technological change.

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This Insights article examines the degree to which workers who lost their job in 2009 started a business, changed regions, went back to school or began a registered apprenticeship in 2010, the year following job loss. The analysis combines the 2001 Census of Population with Statistics Canada's Longitudinal Worker File and Registered Apprenticeship Information System. The study shows that about one in six displaced workers used at least one of the aforementioned adjustment strategies in 2010. Displaced workers who did not find a new paid job in 2010 were more likely to do so than their counterparts who found a new paid job that year. Yet three-quarters of the displaced workers who had no paid job in 2010 did not use any of the four strategies defined above.

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This article examines how workers displaced from coal mining in recent years have fared after job loss. The analysis uses Statistics Canada's Longitudinal Worker File. The study shows that for many coal miners and workers in other industries, job loss leads to substantial and persistent earnings declines. However, three years or five years after being displaced, a significant fraction of workers displaced from coal mining or other industries earn more than they did in the year prior to job loss. Taken together, these findings do not support the view that all workers displaced from traditional energy-producing sectors experience substantial earnings declines after job loss. Nor do they support the view that all workers displaced from these sectors make a smooth transition to new employment after job loss.

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[Artificial intelligence] AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers' skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (2018) for the US and the World Bank's [Skills Towards Employability and Productivity] STEP survey for Lao [People's Democratic Republic] PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in Lao PDR are less likely than in Viet Nam to be in the 'machine terrain', where workers will have to adapt to occupational transformations due to AI and are at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.

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This brief report from the ILO highlights [Coronavirus Disease 2019] COVID-19 impacts on tourism employment in Asia and the Pacific that have left the sector reeling from job losses, deterioration in work quality and shifts towards increased informality. Evidence from five countries - Brunei Darussalam, Mongolia, Philippines, Thailand and Viet Nam - shows that job losses in the tourism-related sectors in 2020 were four times greater than in non-tourism sectors. Nearly one-third of the total jobs lost across the five countries was related to the tourism sector. Even where decreases in employment-related jobs were seemingly small - in Thailand and Viet Nam, for instance - there has been a shift of jobs to lower paid tourism sub-sectors and hence a decrease in the quality of available jobs. The reduction in average working hours in the tourism-related sectors in 2020 ranged from 4 per cent in Viet Nam to 38 per cent in the Philippines. As formal jobs in the tourism sector declined, workers moved into the informal sector. The vulnerability of the tourism sector to external shocks has become especially obvious in the current crisis and has thus invited a 're-think' of medium- and long-term tourism strategies within the context of broader growth objectives.

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Automation is changing the landscape of work, accelerated by the [Coronavirus Disease 2019] COVID-19 pandemic. As economies look to recover, millions of workers across Europe will need to retrain in order to transition into new roles. Focusing on occupations at a lower risk of automation will be a better investment for individuals and economies more widely. To date, most automation research has focused on identifying which occupations are most at risk of automation. This report goes one step further and provides a 'map of the labour market' that captures the similarities between over 1,600 occupations, based on the skills and tasks that make up each role. The map can be used to identify a set of jobs that require similar skills and activities to a worker's most recent role, demonstrating the transferability of their skillset. The algorithm behind the map can also pinpoint the skills that a worker may need in order to move into a new role. In addition, it can isolate the most effective skills for expanding a worker's job options, both for an individual worker or for a group of workers within a particular sector.

This report focuses on the impact of automation and shows how the map and underlying algorithm might help workers (in the UK, France and Italy) transition into jobs that are less exposed to this risk. The key finding from this research is that automation risk can be hard to escape. This is because occupations that are at high risk of automation tend to require similar skills (these occupations sit close to each other on the map). As a consequence, at-risk workers, who predominantly reside in sales, customer service and clerical roles, will require more retraining to find an occupation that is at a lower risk. The insights uncovered through the Mapping Career Causeways methodology have the potential to help policymakers, providers of training and careers services, employers and individuals to prepare for changes to jobs and skills resulting from automation. In a new system for supporting job transitions, the career transition recommendation algorithm has three functions: (1) broadening job search horizons; (2) providing tailored advice on upskilling; and (3) incorporating the risk of shocks, such as automation and COVID-19.

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The Fourth Industrial Revolution has transformed production industries with breathtaking speed and scale. This white paper notes that a combination of powerful forces - both technological and macro trends - are altering where and how goods are made and shifting labour demand. Jobs will be displaced by automation across global production value chains, but there will also be pockets of job creation within the wider manufacturing ecosystem. However, widespread mismatches are likely between where new jobs are and where there are workers with the required skills, which creates a new employment challenge that could be particularly difficult for developing economies. This paper is based on a study using the fundamental building blocks of all production industries - global value chains - as the starting point to understand how the global production workforce looks today; and how it could look tomorrow. It aims to bring a new lens to the future of production employment by focusing on the shifts in the production value chain and the impact of key trends on different value chain segments. Through modelling, insights were gained about who will be impacted the most across global production value chains, where, and at what magnitude.

The study estimates that 16 per cent of jobs are susceptible across five production industries - automotive, apparel and textiles, consumer electronics, chemicals, and industrial equipment. Although a smaller number than other studies have predicted, it does represent nearly 50 million people across the globe. This paper aims to support a focused discussion on the future of employment and skills in production industries, enabling multi-stakeholder collaboration on targeted actions across different geographies and segments of the value chain to shape the production workforce of tomorrow.

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This article examines how workers displaced from the oil and gas industry in recent years have fared after job loss. The analysis uses Statistics Canada's Longitudinal Worker File. The study shows that for many workers displaced from the oil and gas industry, job loss leads to substantial and persistent earnings declines. However, three years or five years after being displaced, a significant fraction of workers displaced from this sector earn more than they did in the year prior to job loss. Taken together, these findings do not support the view that all workers displaced from energy-producing sectors experience substantial earnings declines after job loss. Nor do they support the view that all workers displaced from these sectors make a smooth transition to new employment after job loss.

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