This report explores how automation impacts the economic security and opportunity of the American worker.

This report outlines a program to address automation’s challenges and opportunities. The paper calls for an all-of-the-above approach, from targeted interventions to those with systems-level impact; from place-based policies to national-level reforms to social safety net programs. The paper identifies 22 solutions to address four overarching objectives

The first section of the paper reviews changes in the labor market since UI was created. Although most workers still have traditional full-time jobs, the prevalence of non-traditional work has increased. Non-traditional work includes self-employed, independent contractors and freelancers who operate outside formal employer-employee relationships. Other types of non-traditional arrangements include temp-agency workers, on-call workers, contract company workers, multiple jobholders, and part-time workers. These arrangements can be structured as W-2 employment, yet do not provide traditional, stable full-time work. While non-traditional work can provide increased flexibility, it can also heighten job and financial insecurity, and can leave workers without access to critical employment-based benefits and protections. The second section of the paper explains why workers in non-traditional jobs often lack UI coverage. Independent contractors and freelancers are excluded from UI because they do not work for a traditional employer that would make contributions on their behalf. Other non-traditional workers, such as part-time workers, can qualify for UI as a benefit of W-2 employment, but are disadvantaged relative to traditional workers in how the program is structured. The third section of the paper discusses the opportunity to use non-traditional work to help unemployed Americans return to the labor market. Most states generally require UI recipients to seek traditional full-time work in order to continue receiving benefits, even if their original jobs were part-time. Very few states offer Self-Employment Assistance (SEA) to help UI recipients become entrepreneurs. Further, there are structural incentives that discourage regional workforce offices from helping workers find and prepare for non-traditional work. The final section of the paper reviews proposals to modernize the UI program to account for the growth in non-traditional work. The proposals are grouped according to three goals: first, protecting independent contractors — who currently lack UI coverage — from job and income loss; second, providing better coverage to non-traditional workers in W-2 arrangements; and third, supporting entrepreneurship and voluntary transitions from unemployment into non-traditional work. The paper does not include proposals for improving the solvency of state UI trust funds, as it focuses on reforms to broaden eligibility for workers in non-traditional work arrangements. However, the authors strongly support improving solvency as an important goal of any UI reform effort

This report reviews the available evidence on the economic value of intermediate vocational qualifications, looking at the private wage returns (and briefly the improved likelihood of being in employment) associated with such qualifications. The question that the review would like to answer is: ‘what is the economic value of acquiring intermediate vocational qualifications to a particular individual, on average? That is, by how much will his or her wages increase following the acquisition of such a qualification?’ This value could differ, according to whether the new qualification acquired is the individual’s new highest qualification and so takes them to a higher level of attainment than they had previously reached, or whether the individual already held qualifications at the same level or even at a higher level than the new qualification acquired. The review will therefore distinguish between such situations. This is particularly important when considering vocational qualifications, since they can often be acquired at a level at or below individuals’ existing highest academic qualification.

This paper was prepared as a background note for the International Conference on Jobs and Skills Mismatch to be held in Geneva on May 11-12, 2017. The term skill mismatch is very broad and can relate to many forms of labour market friction, including vertical mismatch, skill gaps, skill shortages, field of study (horizontal) mismatch and skill obsolescence. In this paper the authors provide a clear overview of each concept and discuss the measurement and inter-relatedness of different forms of mismatch. They present a comprehensive analysis of the current position of the literature on skills mismatch and highlight areas which are relatively underdeveloped and may warrant further research. Using data from the European Skills and Jobs Survey, they examine in detail the incidence of various combinations of skills mismatch across the EU and review the European Commission’s country specific recommendations and find that skills mismatch, when referring to underutilised human capital in the form of overeducation and skills underutilisation, receives little policy attention. They argue that in cases where skills mismatch it is specifically addressed by policy recommendations, the policy advice is either vague or addresses the areas of mismatch for which there is the least available evidence.

Over the last 20 years, important achievements have been made with regards to gender equality in the world of work. At least 50% of the world’s women are in paid wage and salary employment – an increase of 10% since the 1990s. In 2014, women held 24% of the world’s senior management positions, compared to 19% ten years ago. What these achievements do not tell us is who is providing the caregiving that is helping these women make inroads into the labour market. Possibly these women are working double shifts, one at the workplace and another at home. There is also evidence of a redistribution of tasks between men and women in households. However, chances are that there are other women helping out. Women of all ages are the principal providers of care for children, the elderly, the disabled, and for whole families and communities, at home and in private and public institutions. In the context of ageing societies, public health care deficits and cut-backs in public services, there is growing demand for care work in private households and an increased pressure on women’s time as care givers. To a large extent, it is also thanks to the labour of, predominantly female, domestic workers that other women have succeeded in occupying the paid labour market in increasingly larger proportions and in breaking the glass ceiling. When women engage in paid work outside the home, they either have to reduce their rest and leisure time, enlist the help of partners or other family members or – if the family or woman could afford it - pay someone else to do the unpaid care work that they would normally do.

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.