White Paper

This guide is a part of the European Training Foundation (ETF), ILO and the European Centre for the Development of Vocational Training (Cedefop) series of guides on skills anticipation and matching. The main goal of this guide is to provide guidance through labour market monitoring and analyses of skills supply and demand. It is an introductory tool for everyone who wants to understand how labour market information (LMI) can be used for better anticipation and matching of skills demand and supply. It provides advice and recommendations for policy- and decision-makers on how to respond to market signals and how to react to early warning messages driven by LMI. Technical analysts and professionals can use this guide as a source of inspiration on how LMI systems can be further developed and used for policy analyses and interventions.

I build a model in which structural change creates a mismatch between the skill requirements in the available jobs and workers’ current skills. When the mismatch is severe, labor markets go through a prolonged adjustment process wherein job creation is low, and unemployment is high. Due to matching frictions, firms find it harder to locate workers with the requisite skills for novel jobs and they respond by creating fewer jobs. The paucity of novel jobs increases unemployment for all workers—including those who already hold the requisite skills—and discourages skill acquisition by workers. Moreover, structural change interacts with the business cycle, causing a large and long-lasting increase in unemployment that concentrates in recessions. I demonstrate that the decline in routine-cognitive jobs outside manufacturing—a pervasive structural change that has affected U.S. labor markets since the late 90s—created a skill mismatch that contributed to the long-lasting increase in unemployment observed during the Great Recession. My evidence suggests that the amplification effects introduced by matching frictions are important. Moreover, I find that the skill mismatch has a larger effect during recessions and in labor markets where the demand for goods and services is depressed.

Workforce agility is defined as the ability of employees and organizations to remain steadfast and maintain productivity in the face of change. When we refer to a flexible workforce, we’re talking about a staffing model that features non-traditional workers*. Such a model allows an organization to hire on an as-needed/on-demand, temporary basis to fulfill short- or long-term contracts that fill skills gaps. These are not the ‘temps’ of the past; instead, they range from blue collar, light industrial to highly skilled IT, engineering, accounting and HR professionals. The specifics of how organizations apply the definition depends on their industry. As a result, there are many ways to be a non-permanent employee.

This paper documents that job polarization – the simultaneous expansion of high and low-wage occupations at the expense of middle-wage occupations – is pervasive in the non-agricultural workforce of advanced and emerging countries over the period from 1999 to 2007. To investigate what is driving these labour market developments, we develop a task-based model of production in global supply chains and propose a decomposition of changes in occupational labour demand within these chains. Using a new harmonized cross-country occupations database combined with world input-output tables, we find technological change drives job polarization in almost all countries. Cross-border task relocation contributes towards polarizing labour markets in advanced countries, while the opposite pattern is observed in offshore destination countries.

Trends in workforce size and composition and in the pace of technological change and economic globalization will have implications for the future of work. Employees will work in more decentralized, specialized firms; slower labor growth will encourage employers to recruit groups with relatively low labor force participation; greater emphasis will be placed on retraining and lifelong learning; and future productivity growth will support higher wages and may affect the wage distribution. Given this, some policies may need to be re-examined.

Technology is bringing structural changes to our economies that may leave many citizens behind. We must make people’s well-being a policy priority.

As robots, automation and artificial intelligence perform more tasks and there is massive disruption of jobs, experts say a wider array of education and skills-building programs will be created to meet new demands. There are two uncertainties: Will well-prepared workers be able to keep up in the race with AI tools? And will market capitalism survive?

This report provides new and more extensive evidence of job displacement and its consequences for 14 countries. Job displacement, i.e. involuntary job loss due to economic factors such as economic downturns or structural change, is highly cyclical but has not exhibited any upwards trend over the past decade. Differences in available data sources and definitions make cross-country comparisons difficult, but it appears that displacement affects around 2-7% of employees every year in the countries for which data are available.

The purpose of the current study was to determine what strategies Ontario's postsecondary institutions are implementing to mitigate or remove those barriers. The study employed an online survey of faculty and staff at postsecondary institutions who are involved in making WIL opportunities available to students. A total of 1,008 potential respondents were identified through a search of 44 Ontario postsecondary institutions' websites. An email invitation was sent from HEQCO to all 1,008. Respondents were not randomly selected. Participation was voluntary. A total of 176 respondents completed the survey, which was available from May 29 until June 16, 2017. The final data set includes surveys from 43 of the 44 institutions surveyed (i.e., all but one publicly funded Ontario postsecondary institution). In-depth telephone interviews were then conducted with 45 key informants. The purpose of these interviews was to further clarify the efficacy of the identified strategies and discuss the challenges of implementing them for specific types of WIL.