Do we know what jobs are in high demand?

Curated on Posted on by Stefaan Verhulst

Emma Rindlisbacher at Work Shift: “…Measuring which fields are in demand is harder than it sounds. Many of the available data sources, experts say, have significant flaws. And that causes problems for education providers who are trying to understand market demand and map their programs to it.

“If you are in higher education and trying to understand where the labor market is going, use BLS data as a general guide but do not rely too heavily on it when it comes to building programs and making investments,” said Jason Tyszko, the Vice President of the Center for Education and Workforce at the US Chamber of Commerce Foundation.

What’s In-Demand?

Why it matters: Colleges are turning to labor market data as they face increasing pressure from lawmakers and the public to demonstrate value and financial ROI. A number of states also have launched specialized grant and “free college” programs for residents pursuing education in high-demand fields. And many require state agencies to determine which fields are in high demand as part of workforce planning processes.

Virginia is one of those states. To comply with state law, the Board of Workforce Development has to regularly update a list of high demand occupations. Deciding how to do so can be challenging.

According to a presentation given at a September 2021 meeting, the board chose to determine which occupations are in high demand by using BLS data. The reason: the BLS data is publicly available.

“Although in some instances, proprietary data sources have different or additional nuances, in service of guiding principle #1 (transparency, replicability), our team has relied exclusively on publicly available data for this exercise,” the presentation said. (A representative from the board declined to comment, citing the still ongoing nature of constructing the high demand occupations list.)

The limits of the gold standard

For institutions looking to study job market trends, there are typically two main data sources available. The first, from BLS, are official government statistics primarily designed to track economic indicators such as the unemployment rate. The second, from proprietary companies such as Emsi Burning Glass, typically relies on postings to job board websites like LinkedIn. 

The details: The two sources have different strengths and weaknesses. The Emsi Burning Glass data can be considered “real time” data, because it identifies new job postings as they are released online. The BLS data, on the other hand, is updated less frequently but is comprehensive.

The BLS data is designed to compare economic trends across decades, and to map to state systems so that statistics like unemployment rates can be compared across states. For those reasons, the agency is reluctant to change the definitions underlying the data. That consistency, however, can make it difficult for education providers to use the data to determine which fields are in high demand.

BLS data is broken down according to the Standard Occupation Classification system, or SOC, a taxonomy used to classify different occupations. That taxonomy is designed to be public facing—the BLS website, for example, features a guide for job seekers that purports to tell them which occupation codes have the highest wages or the greatest potential for growth.

But the taxonomy was last updated in 2010, according to a BLS spokesperson…(More)”.

New York City passed a bill requiring ‘bias audits’ of AI hiring tech

Curated on Posted on by Stefaan Verhulst

Kate Kaye at Protocol: “Let the AI auditing vendor brigade begin. A year since it was introduced, New York City Council passed a bill earlier this week requiring companies that sell AI technologies for hiring to obtain audits assessing the potential of those products to discriminate against job candidates. The bill requiring “bias audits” passed with overwhelming support in a 38-4 vote.

The bill is intended to weed out the use of tools that enable already unlawful employment discrimination in New York City. If signed into law, it will require providers of automated employment decision tools to have those systems evaluated each year by an audit service and provide the results to companies using those systems.

AI for recruitment can include software that uses machine learning to sift through resumes and help make hiring decisions, systems that attempt to decipher the sentiments of a job candidate, or even tech involving games to pick up on subtle clues about someone’s hiring worthiness. The NYC bill attempts to encompass the full gamut of AI by covering everything from old-school decision trees to more complex systems operating through neural networks.

The legislation calls on companies using automated decision tools for recruitment not only to tell job candidates when they’re being used, but to tell them what information the technology used to evaluate their suitability for a job.

The bill, however, fails to go into detail on what constitutes a bias audit other than to define one as “an impartial evaluation” that involves testing. And it already has critics who say it was rushed into passage and doesn’t address discrimination related to disability or age…(More)”.

We Need a New Economic Category

Curated on Posted on by Stefaan Verhulst

Article by Anne-Marie Slaughter and Hilary Cottam: “Recognizing the true value and potential of care, socially as well as economically, depends on a different understanding of what care actually is: not a service but a relationship that depends on human connection. It is the essence of what Jamie Merisotis, the president of the nonprofit Lumina Foundation, calls “human work”: the “work only people can do.” This makes it all the more essential in an age when workers face the threat of being replaced by machines.

When we use the word in an economic sense, care is a bundle of services: feeding, dressing, bathing, toileting, and assisting. Robots could perform all of those functions; in countries such as Japan, sometimes they already do. But that work is best described as caretaking, comparable to what the caretaker of a property provides by watering a garden or fixing a gate.

What transforms those services into caregiving, the support we want for ourselves and for those we love, is the existence of a relationship between the person providing care and the person being cared for. Not just any relationship, but one that is affectionate, or at least considerate and respectful. Most human beings cannot thrive without connection to others, a point underlined by the depression and declining mental capacities of many seniors who have been isolated during the pandemic….

One of us, Hilary, has worked in Britain to expand caregiving networks. In 2007 she co-designed a program called Circle, which is part social club, part concierge service. Members pay a small monthly fee, and in return get access to fun activities and practical support from members and helpers in the community. More than 10,000 people have participated, and evaluations show that members feel less lonely and more capable. The program has also reduced the money spent on formal services; Circle members are less likely, for example, to be readmitted to the hospital.The mutual-aid societies that mushroomed into existence across the United States during the pandemic reflect the same philosophy. The core of a mutual-aid network is the principle of “solidarity not charity”: a group of community members coming together on an equal basis for the common good. These societies draw on a long tradition of “collective care” developed by African American, Indigenous, and immigrant groups as far back as the 18th century….Care jobs help humans flourish, and, properly understood and compensated, they can power a growing sector of the economy, strengthen our society, and increase our well-being. Goods are things that people buy and own; services are functions that people pay for. Relationships require two people and a connection between them. We don’t really have an economic category for that, but we should….(More)”.

A crowdsourced spreadsheet is the latest tool in Chinese tech worker organizing

Curated on Posted on by Stefaan Verhulst

Article by JS: “This week, thousands of Chinese tech workers are sharing information about their working schedules in an online spreadsheet. Their goal is to inform each other and new employees about overtime practices at different companies. 

This initiative for work-schedule transparency, titled Working Time, has gone viral. As of Friday—just three days after the project launched—the spreadsheet has already had millions of views and over 6000 entries. The creators also set up group chats on the Tencent-owned messaging platform, QQ, to invite discussion about the project—over 10000 people have joined as participants.

This initiative comes after the explosive 996.ICU campaign which took place in 2019 where hundreds of thousands of tech workers in the country participated in an online effort to demand the end of the 72-hour work week—9am to 9pm, 6 days a week.

This year, multiple tech companies—with encouragement from the government—have ended overtime work practices that forced employees to work on Saturdays (or in some cases, alternating Saturdays). This has effectively ended 996, which was illegal to begin with. While an improvement, the data collected from this online spreadsheet shows that most tech workers still work long hours, either “1095” or “11105” (10am to 9pm or 11am to 10pm, 5 days a week). The spreadsheet also shows a non-negligible number of workers still working 6 days week.

Like the 996.ICU campaign, the creators of this spreadsheet are using GitHub to circulate and share info about the project. The first commit was made on Tuesday, October 12th. Only a few days later, the repo has been starred over 9500 times….(More)”.

The Society of Algorithms

Curated on Posted on by Stefaan Verhulst

Paper by Jenna Burrell and Marion Fourcade: “The pairing of massive data sets with processes—or algorithms—written in computer code to sort through, organize, extract, or mine them has made inroads in almost every major social institution. This article proposes a reading of the scholarly literature concerned with the social implications of this transformation. First, we discuss the rise of a new occupational class, which we call the coding elite. This group has consolidated power through their technical control over the digital means of production and by extracting labor from a newly marginalized or unpaid workforce, the cybertariat. Second, we show that the implementation of techniques of mathematical optimization across domains as varied as education, medicine, credit and finance, and criminal justice has intensified the dominance of actuarial logics of decision-making, potentially transforming pathways to social reproduction and mobility but also generating a pushback by those so governed. Third, we explore how the same pervasive algorithmic intermediation in digital communication is transforming the way people interact, associate, and think. We conclude by cautioning against the wildest promises of artificial intelligence but acknowledging the increasingly tight coupling between algorithmic processes, social structures, and subjectivities….(More)”.

Making life richer, easier and healthier: Robots, their future and the roles for public policy

Curated on Posted on by Stefaan Verhulst

OECD Paper: “This paper addresses the current and emerging uses and impacts of robots, the mid-term future of robotics and the role of policy. Progress in robotics will help to make life easier, richer and healthier. Wider robot use will help raise labour productivity. As science and engineering progress, robots will become more central to crisis response, from helping combat infectious diseases to maintaining critical infrastructure. Governments can accelerate and orient the development and uptake of socially valuable robots, for instance by: supporting cross-disciplinary R&D, facilitating research commercialisation, helping small and medium-size enterprises (SMEs) understand the opportunities for investment in robots, supporting platforms that highlight robot solutions in healthcare and other sectors, embedding robotics engineering in high school curricula, tailoring training for workers with vocational-level mechanical skills, supporting data development useful to robotics, ensuring flexible regulation conducive to innovation, strengthening digital connectivity, and raising awareness of the importance of robotics….(More)“

The Diffusion of Disruptive Technologies

Curated on Posted on by Stefaan Verhulst

Paper by Nicholas Bloom, Tarek Alexander Hassan, Aakash Kalyani, Josh Lerner & Ahmed Tahoun: “We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread across space. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these technology hubs are more likely to arise in areas with universities and high skilled labor pools….(More)”

The ‘hidden data’ that could boost the UK’s productivity and job market

Curated on Posted on by Stefaan Verhulst

Report from Learning and Work Institute and Nesta (UK): “… highlights the complexities of labour market data used to support adults in their career planning…

The deficiencies in the UK’s labour market data are illustrated by the experiences of the winners of the CareerTech Challenge Prize, the team developing Bob UK, a tool designed to provide instant, online careers advice and job recommendations based on information about local vacancies and the jobseeker’s skills. The developers attempted to source UK data that directly replicated data sources used to develop the version of Bob which has helped over 250,000 jobseekers in France. However, it became apparent that equivalent sources of data rarely existed. The Bob UK team was able to work around this issue by carefully combining alternative sources of data from a number of UK and non-UK sources.

Many other innovators experienced similar barriers, finding that the publicly available data that could help people to make more informed decisions about their careers is often incomplete, difficult to use and poorly described. The impact of this is significant. A shocking insight from the report is that one solution enabled careers advisors to base course recommendations on labour market information for the first time. Prior to using this tool, such information was too time-consuming for careers advisors to uncover and analyse for it to be of use, and job seekers were given advice that was not based on employer demand for skills…To address this issue of hidden and missing data and unleash the productivity-raising potential of better skills matching, the report makes a series of recommendations, including:

  • The creation of a central labour market data repository that collates publicly available information about the labour market.
  • Public data providers should review the quality and accessibility of the data they hold, and make it easier for developers to use.

The development of better skills and labour market taxonomies to facilitate consistency between sources and enhance data matching…(More)”

Moving up: Promoting workers’ upward mobility using network analysis

Curated on Posted on by Stefaan Verhulst

Report by Marcela Escobari, Ian Seyal and Carlos Daboin Contreras: “The U.S. economy faces a mobility crisis. After decades of rising inequality, stagnating wages, and a shrinking middle class, many American workers find it harder and harder to get ahead. COVID-19 accentuated a stark divide, battering a two-tiered labor force with millions of low-wage workers lacking job security and benefits—as the long-term trends of globalization, digitalization, and automation continue to displace jobs and disrupt career paths.

To address this crisis and create an economy that works for everyone, policymakers and business leaders must act boldly and urgently. But the challenge of low mobility is complex and driven by many factors, with significant heterogeneity across regions, sectors, and demographic groups. When diagnostics fail to disentangle the complexity, our standard policy responses—centered on education, reskilling, and other reemployment services to help workers adapt—fall short.

This report offers a new approach to better understand the contours of mobility: Who is falling behind, where, and by how much. Using data on hundreds of thousands of real workers’ occupational transitions, we use network analysis to create a multidimensional map of the labor market, revealing a landscape riddled with mobility gaps and barriers. Workers in low-wage occupations face particular hurdles, and persistent racial and gender disparities hold some workers back more than others.

Even so, many workers travel on pathways to economic mobility. By showing where existing pathways can be expanded and where new ones are needed, this report helps policymakers, community organizations, higher education institutions, and business leaders better understand the challenge of mobility and see where and how to intervene, in order to help more workers move up faster….(More)”.

Tasks, Automation, and the Rise in US Wage Inequality

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu & Pascual Restrepo: “We document that between 50% and 70% of changes in the US wage structure over the last four decades are accounted for by the relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across a number of industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from employment opportunities for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences.

We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education differentials between 1980 and 2016. Our task displacement variable captures the effects of automation technologies (and to a lesser degree offshoring) rather than those of rising market power, markups or deunionization, which themselves do not appear to play a major role in US wage inequality. We also propose a methodology for evaluating the full general equilibrium effects of task displacement (which include induced changes in industry composition and ripple effects as tasks are reallocated across different groups). Our quantitative evaluation based on this methodology explains how major changes in wage inequality can go hand-in-hand with modest productivity gains….(More)”.