This article uses data from the Current Population Survey to examine the state of the U.S. labor market 10 years after the start of the Great Recession of 2007–09. By December 2017, unemployment rates had returned to prerecession lows for people of all ages, genders, major race and ethnicity groups, and levels of educational attainment. However, the long-term decline in labor force participation continued during this recovery, while long-term unemployment and involuntary part-time employment remained elevated.
Source: Gerald Young, Center for State and Local Government Excellence, International Public Management Association for Human Resources, and the National Association of State Personnel Executives, May 2018
From the summary:
Since 2009, the Center for State and Local Government Excellence has partnered with the International Public Management Association for Human Resources and the National Association of State Personnel Executives to conduct a study on state and local workforce issues. This year’s report contains both 2018 data on emerging issues like the gig economy and flexible work practices and longitudinal data on recruiting challenges, retirement plan or health benefit changes, hiring, and separations from service.
Some places are losing more lawyers and accountants than factory workers.
My title is “Capitalism in the age of robots” and my aim is to consider the possible long-term impact of rapid technological progress – and in particular of work automation and artificial intelligence. And I will sometimes use the word “robots” as shorthand for any sort of machine – any combination of hardware and software – that can perform any sort of work, rather than specifically meaning something which looks like a human, with legs, arms and a smiley face.
I will argue that the rapid, unstoppable, and limitless progress of automation potential will have profound implications for the nature of and need for work, and for the distribution of income and wealth. But also profound implications for the very meaning of some concepts and measures which play a fundamental role in economic analysis – in particular productivity growth and GDP per capita. At the limit indeed, one can question whether the very concept of “an economy” or of “economics” – if defined as the study of production and consumption choices amid conditions of inherent scarcity – have any meaning in a world where, eventually, all human work activities can be automated.
And while the world of near total automation which I describe is still many years away – at least 50 and maybe 100– I will argue that gradual progress towards that future state is already having and will increasingly have, profound, paradoxical and potentially harmful, as well as potentially beneficial, consequences. In particular I will suggest that:
• The faster the underlying pace of technological advance, the lower the measured productivity growth rate will be
• Automation threatens income from activities essential to human welfare – but not income gained from zero-sum competition
• The more rapidly information and communication technology progresses, the more that wealth and income derive from inherently physical and subjective assets, such as land, brands, or beauty.
• In already rich developed countries increasing productivity growth should not be a major long term public policy priority
• Better skills cannot solve the problem of rising inequality : but excellent education must enable people to live fulfilled lives as engaged citizens, and is needed to prevent a radical decline in social mobility…..
From the abstract:
This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results. The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.
A study finds nearly half of jobs are vulnerable to automation
Source: The Economist, April 24, 2018
A WAVE of automation anxiety has hit the West. Just try typing “Will machines…” into Google. An algorithm offers to complete the sentence with differing degrees of disquiet: “…take my job?”; “…take all jobs?”; “…replace humans?”; “…take over the world?”
Job-grabbing robots are no longer science fiction. In 2013 Carl Benedikt Frey and Michael Osborne of Oxford University used—what else?—a machine-learning algorithm to assess how easily 702 different kinds of job in America could be automated. They concluded that fully 47% could be done by machines “over the next decade or two”.
A new working paper by the OECD, a club of mostly rich countries, employs a similar approach, looking at other developed economies. Its technique differs from Mr Frey and Mr Osborne’s study by assessing the automatability of each task within a given job, based on a survey of skills in 2015. Overall, the study finds that 14% of jobs across 32 countries are highly vulnerable, defined as having at least a 70% chance of automation. A further 32% were slightly less imperilled, with a probability between 50% and 70%. At current employment rates, that puts 210m jobs at risk across the 32 countries in the study.
Apprenticeship is a workforce development strategy that trains a worker for a specific occupation using a structured combination of paid on-the-job training and related instruction. Increased costs for higher education and possible mismatches between worker skills and employer needs have led to interest in alternative workforce development strategies such as apprenticeship. ….
…. To register an apprenticeship, a sponsor (an employer, union, industry group, or other eligible entity) submits an application to the applicable registration agency (either DOL or the appropriate SAA). The application must include a work process schedule that describes the competencies that the apprentice will learn and how on-the-job training and related instruction will teach those competencies. The application must also include a schedule of wage increases for the apprentice, a description of safety measures, and various assurances related to program administration and recordkeeping. ….
….. In recent years, the federal government has supplemented its typical registration activities with competitive grants to support the expansion of registered apprenticeship. These grants have gone predominantly to states and other intermediaries to support apprenticeship expansion through partnerships with apprenticeship sponsors.
While registered apprenticeship sponsors do not necessarily qualify for federal funding, several education and workforce programs have identified apprenticeship as an eligible use of funds. For example, some veterans may qualify to receive GI Bill benefits while participating in a registered apprenticeship and registered apprenticeships are eligible for federal workforce development funds through the Workforce Innovation and Opportunity Act (WIOA). …..
…. This report discusses federal efforts related to apprenticeship. It begins by describing the long-established federal role in certifying apprenticeships programs through the registered apprenticeship system. It then discusses more recent federal efforts to support apprenticeship expansion. The appendix of the report discusses federal funding streams that focus on other human capital development strategies but can support apprenticeship in certain circumstances. …..
Congress has held hearings and some Members have introduced legislation addressing the interrelated topics of the quality of mental health care, access to mental health care, and the cost of mental health care. The mental health workforce is a key component of each of these topics. The quality of mental health care depends partially on the skills of the people providing the care. Access to mental health care relies on, among other things, the number of appropriately skilled providers available to provide care. The cost of mental health care depends in part on the wages of the people providing care. Thus an understanding of the mental health workforce may be helpful in crafting policy and conducting oversight. This report aims to provide such an understanding as a foundation for further discussion of mental health policy.
No consensus exists on which provider types make up the mental health workforce. This report focuses on the five provider types identified by the Health Resources and Services Administration (HRSA) within the Department of Health and Human Services (HHS) as mental health providers: clinical social workers, clinical psychologists, marriage and family therapists, psychiatrists, and advanced practice psychiatric nurses. The HRSA definition of the mental health workforce is limited to highly trained (e.g., graduate degree) professionals; however, this workforce may be defined more broadly elsewhere. For example, the Substance Abuse and Mental Health Services Administration (SAMHSA) definition of the mental health workforce includes mental health counselors and paraprofessionals (e.g., case managers).
An understanding of typical licensure requirements and scopes of practice may help policymakers determine how to focus policy initiatives aimed at increasing the quality of the mental health workforce. Most of the regulation of the mental health workforce occurs at the state level because states are responsible for licensing providers and defining their scope of practice. Although state licensure requirements vary widely across provider types, the scopes of practice converge into provider types that generally can prescribe medication (psychiatrists and advanced practice psychiatric nurses) and provider types that generally cannot prescribe medication (clinical psychologists, clinical social workers, and marriage and family therapists). The mental health provider types can all provide psychosocial interventions (e.g., talk therapy). Administration and interpretation of psychological tests is generally the province of clinical psychologists. …..
…. What has happened to Black workers in the 50 years since the Memphis strike, after the upsurge of Black worker activism, the assault on labor by economic elites, the rise of income inequality, and the surge of right-wing authoritarianism? ….
It’s not just men working factory jobs in the Rust Belt—and it never really was….
…..There are problems with this image of the U.S. factory worker as he—and it is generally he—is depicted. First, in American factories, the workforce is far more diverse than the Rust Belt narrative would have it. The Carrier plant, site of Trump’s triumphant deal that, in fact, resulted in hundreds of workers still being laid off, had at least as many African American workers as white, and there were plenty of women laboring there, too. More important, those industrial workers who supposedly put Trump in office (a dubious assumption) have never made up the entirety of the working class or even its majority. These days, only around 11 percent of the working class are white men in industrial jobs.
From the summary:
Occupational licensing—the legal requirement that a credential be obtained in order to practice a profession—is a common labor market regulation that ostensibly exists to protect public health and safety. However, by limiting access to many occupations, licensing imposes substantial costs: consumers pay higher prices, economic opportunity is reduced for unlicensed workers, and even those who successfully obtain licenses must pay upfront costs and face limited geographic mobility. In addition, licensing often prescribes and constrains the ways in which work is structured, limiting innovation and economic growth…..