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This research note reviews the literature on non-standard employment, and the definitions of it that have been adopted. Examines the extent of non-standard employment in the EU and the way it has changed over the recent past, especially over the crisis period. Considers social security systems in the different EU Member States as they apply to different types of non-standard employment, namely: self-employment, fixed term contracts, and part-time work. The aim is to identify features that disadvantage, or are likely to disadvantage, workers in these types of employment as compared with those in standard jobs – i.e. with permanent contracts of employment and full-time work. The main focus is on unemployment, sickness, and maternity benefits, though the relevant features of public pension schemes are also considered. Assesses the relative number of people in these types of employment in different EU Member States (based on EU Labour Force Survey data), and therefore at risk of not being entitled to social benefits in the event of becoming unemployed, falling ill or having a child.

This paper presents a short synthesis of recent findings on the changing skill structure of employment and on some of the sources of change emerging from Human Resources Development Canada’s research programme on the theme of “Skills Requirements in the Knowledge-Based Economy.” It addresses three main questions: How has the skill structure of employment evolved in Canada? What has been the role of technological change in the changing structure of employment? Do the skills of the Canadian workforce meet the requirements of the knowledge-based economy? The bulk of the paper examines the changing structure of employment using an occupational classification scheme that categorizes occupations on the basis of the tasks performed by workers. This approach was originally developed by Wolff and Baumol and adapted for Canada by Marie Lavoie and Richard Roy in the paper “Employment in the Knowledge-Based Economy: A Growth Accounting Exercise for Canada.”

Artificial Intelligence (AI) systems help organizations manage complexity: they reduce the cost of predictions and hold the promise of more, better and faster decisions that enhance productivity and innovation. However, their deployment increases complexity at all levels of the economy, and with it, the risk of undesirable outcomes. Organizationally, uncertainty about how to adopt fallible AI systems could create AI divides between sectors and organizations. Transactionally, pervasive information asymmetries in AI markets could lead to unsafe, abusive and mediocre applications. Societally, individuals might opt for extreme levels of AI deployment in other sectors in exchange for lower prices and more convenience, creating disruption and inequality. Temporally, scientific, technological and market inertias could lock society into AI trajectories that are found to be inferior to alternative paths. New Sciences (and Policies) of the Artificial are needed to understand and manage the new economic complexities that AI brings, acknowledging that AI technologies are not neutral and can be steered in societally beneficial directions guided by the principles of experimentation and evidence to discover where and how to apply AI, transparency and compliance to remove information asymmetries and increase safety in AI markets, social solidarity to share the benefits and costs of AI deployment, and diversity in the AI trajectories that are explored and pursued and the perspectives that guide this pro- cess. This will involve an explicit elucidation of human and social goals and values, a mirror of the Turing test where different societies learn about themselves through their responses to the opportunities and challenges that powerful AI technologies pose.

The aim of this study is to examine whether there is a relationship between the type of product strategy adopted by an employer and the extent of the skills they require, and the skills challenges they face. It uses data from the Employer Skills Surveys carried out in England in 2001 and 2009 to examine the nature of the relationships, and also investigate the extent to which patterns available in 2001 were still present in 2009. It builds on previous analysis carried out by Mason (2004) on the 2001 Employer Skills Survey. This report provides evidence that establishments pursuing high value-added product market strategies are more likely to have higher workforce skill levels than their counterparts with medium or lower value-added product market strategies. In addition, these establishments are more likely to be actively looking to update the skill levels of their staff and less likely to have skill gaps, perhaps as a result of their pre-emptive action to address problems before they arise. The relationships identified are interdependent. This reinforces the message in literature on resource and knowledge-based theories of the firm, which suggests that business strategies and firm-level resources tend to evolve together over time.

This study, using original survey data of 10,000 individuals, analyzes the possible impacts of artificial intelligence (AI) and robotics on employment. The first interest of this study is to ascertain, from the viewpoint of workers, what types of worker characteristics are associated with the perception of risk of jobs being replaced by the development of AI and robotics. The second interest is to identify, from the viewpoint of consumers, what types of services are likely to be replaced by AI and robotics. The results suggest that malleable/adaptable high skills acquired through higher education, particularly in science and engineering, are complementary with new technologies such as AI and robotics. At the same time, occupation-specific skills acquired by attending professional schools or holding occupational licenses, particularly those related to human-intensive services, are less likely to be replaced by AI and robotics.

The OECD Survey of Adult Skills is the jewel in the crown of its Programme for the International Assessment of Adult Competencies (PIAAC). This paper argues that the findings and policy lessons from the project to date justify the high hopes which were placed in PIAAC when detailed planning for the project began in 2003. First, it presents a brief recap of PIAAC and its two predecessor international skills surveys. Second, it outlines the main themes which have been investigated to date using data from PIAAC. Third, the main findings and policy lessons drawn from PIAAC are highlighted. Finally, looking forward to the second cycle of PIAAC, for which planning is now underway, the paper suggests some priority areas for improvements to the survey design in order to add to its analytical usefulness and enhance its utility to policy makers.

Digital capabilities, adoption, and usage are evolving at a supercharged pace. While most users scramble just to keep up with the relentless rate of innovation, the sectors, companies, and individuals on the digital frontier continue to push the boundaries of technology use—and to capture disproportionate gains as a result. The pronounced gap between the digital “haves” and “have-mores” is a major factor shaping competition at all levels of the economy. The companies leading the charge are winning the battle for market share and profit growth; some are reshaping entire industries to their own advantage. Workers with the most sophisticated digital skills are in such high demand that they command wages far above the national average. Meanwhile, there is a growing opportunity cost for the organizations and individuals that fall behind. Our new McKinsey Global Institute (MGI) report, Digital America: A tale of the haves and have-mores, represents the first major attempt to measure the ongoing digitization of the US economy at a sector level. It introduces the MGI Industry Digitization Index, which combines dozens of indicators to provide a comprehensive picture of where and how companies are building digital assets, expanding digital usage, and creating a more digital workforce. In addition to the information- and communication-technology sector, media, financial services, and professional services are surging ahead, while others have significant upside to capture.

Building on our January 2017 report on automation, McKinsey Global Institute’s latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation (PDF–5MB), assesses the number and types of jobs that might be created under different scenarios through 2030 and compares that to the jobs that could be lost to automation. The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. Our key finding is that while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging—matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.

In advanced and emerging economies alike, individuals are struggling to find work and build careers that make use of their skills and capabilities. The strains in global labor markets have been worsening for decades, and the challenges have been magnified in the aftermath of the global recession. In many countries, concerns about employment have been exacerbated by long-term trends of stagnant wage growth and automation. But at the same time, there has been a constant refrain from employers about the difficulties of finding talent with the right skills. This report notes that online talent platforms are increasingly connecting people to the right work opportunities and that this trend may begin to address these problems - and even to swing the pendulum slightly in favor of workers by empowering them with broader choices, more mobility, and more flexibility. These tools are fundamentally altering the way individuals go about searching for work and the way many employers approach hiring. This research aims to build a deeper understanding of how these platforms can affect labor markets, although it does not attempt to address the many broader issues affecting employment prospects, including wage stagnation, automation, and aggregate demand.