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)”
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)”
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)”.
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)”.
Article by Lee Jong-Wha: “…Addressing such questions is essential to preparing for the post-pandemic era, when all countries will need to embrace new ways of working, producing, and consuming. Digitalization can make a huge contribution to public health, the environment, consumer welfare, and wealth creation across society, but only if the public and private sectors work together to ensure inclusiveness.
Most countries will need policies to narrow the gaps in digital skills and access, because a growing share of jobs will require more technological know-how. Education systems must do more to equip students with the knowledge and skills they will need in a digital future. And job training must keep all workers up to date on the latest digital technologies.
Governments have a critical role to play on all of these fronts. It was state support and commitments that brought us revolutionary innovations like the internet, antibiotics, renewable energy, and the mRNA technology behind the development of the most effective COVID-19 vaccines. To fulfill their role as market makers, governments need to increase investments in physical infrastructure and human capital, and provide financial and tax incentives to ensure equitable access to critical technologies. They should also be exploring ways to provide more grants, subsidies, and technical support for small and medium enterprises and start-ups, so that the benefits of digital revolution do not remain limited to a few large companies….(More)”.
Paper by Katya Klinova and Anton Korinek: “Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity…(More)”.
GovTech article: “While New York is not the first state to propose data privacy legislation, it is the first to propose a data privacy bill that would implement a tax on big tech companies that benefit from the sale of New Yorkers’ consumer data.
Known as the Data Economy Labor Compensation and Accountability Act, the bill looks to enact a 2 percent tax on annual receipts earned off New York residents’ data. This tax and other rules and regulations aimed at safeguarding citizens’ data will be enforced by a newly created Office of Consumer Data Protection outlined in the bill.
The office would require all data controllers and processors to register annually in order to meet state compliance requirements. Failure to do so, the bill states, would result in fines.
As for the tax, all funds will be put toward improving education and closing the digital divide.
“The revenue from the tax will be put towards digital literacy, workforce redevelopment, STEAM education (science, technology, engineering, arts and mathematics), K-12 education, workforce reskilling and retraining,” said Sen. Andrew Gounardes, D-22.
As for why the bill is being proposed now, Gounardes said, “Every day, big tech companies like Amazon, Apple, Facebook and Google capitalize on the unpaid labor of billions of people to create their products and services through targeted advertising and artificial intelligence.”…(More)”
Paper by Mabel Choo and Mark Findlay: “Originally this short reflection was intended to explore the relationship between the under-regulated labour environment of gig workers and their appreciation of work-life quality. It was never intended as a comprehensive governance critique of what is variously known as independent, franchised, or autonomous service delivery transactions facilitated through platform providers. Rather it was to represent a suggestive snapshot of how workers in these contested employment contexts viewed the relevance of regulation (or its absence) and the impact that new forms of regulation might offer for work-life quality.
By exploring secondary source commentary on worker experiences and attitudes it became clear that profound information deficits regarding how their personal data was being marketed meant that expecting any detailed appreciation of regulatory need and potentials was unrealistic from such a disempowered workforce. In addition, the more apparent was the practice of the platforms re-using and marketising this data without the knowledge or informed consent of the data subjects (service providers and customers) the more necessary it seemed to factor in this commercialisation when regulatory possibilities are to be considered.
The platform providers have sheltered their clandestine use of worker data (whether it be from pervasive surveillance or transaction histories) behind dubious discourse about disruptive economies, non-employment responsibilities, and the distinction between business and private data. In what follows we endeavor to challenge these disempowering interpretations and assertions, while arguing the case that at the very least data subjects need to know what platforms do with the data they produce and have some say in its re-use. In proposing these basic pre-conditions for labour transactions, we hope that work-life experience can be enhanced. Many of the identified needs for regulation and suggestions as to the form it should take are at this point declaratory in the paper, and as such require more empirical modelling to evaluate their potential influences in bettering work-life quality….(More)”
Paper by Tommaso Ciarli et al: “In order to better understand the complex and dialectical relationships between digital technologies, innovation, and skills, it is necessary to improve our understanding of the coevolution between the trajectories of connected digital technologies, firm innovation routines, and skills formation. This is critical as organizations recombine and adapt digital technologies; they require new skills to innovate, learn, and adapt to evolving digital technologies, while digital technologies change the codification of knowledge for productive and innovative activities. The coevolution between digital technologies, innovation, and skills also requires, and is driven by, a reorganization of productive and innovation processes, both within and between firms. We observe this in all economic sectors, from agriculture to services. Based on evidence on past technologies in the innovation literature, we suggest that we might require a new set of stylized facts to better map the main future trajectories of digital technologies, their adoption, use, and recombination in organizations, to improve our understanding of their impact on productivity, employment and inequality. The papers in this special issue contribute to a better understanding of the interdependence between digital technologies, innovation, and skills….(More)”.
User Guide by Nesta: “This user guide shows how providers of careers information advice and guidance, policymakers and employers can use our innovative data tools to support workers and job seekers as they navigate the labour market.
Nesta’s Mapping Career Causeways project, supported by J.P. Morgan as part of their New Skills at Work initiative, applies state-of-the-art data science methods to create an algorithm that recommends job transitions and retraining to workers, with a focus on supporting those at high risk of automation. The algorithm works by measuring the similarity between over 1,600 jobs, displayed in our interactive ‘map of occupations’, based on the skills and tasks that make up each role.
Following the publication of the Mapping Career Causeways report, data visualisation and open-source algorithm and codebase, we have developed a short user guide that demonstrates how you can take the insights and learnings from the Mapping Career Causeways project and implement them directly into your work….
The user guide shows how the Mapping Career Causeways research can be used to address common challenges identified by the stakeholders, such as:
- Navigating the labour market can be overwhelming, and there is a need for a reliable source of insights (e.g. a tool) that helps to broaden a worker’s potential career opportunities whilst providing focused recommendations on the most valuable skills to invest in
- There is no standardised data or a common ‘skills language’ to support career advice and guidance
- There is a lack of understanding and clear data about which sectors are most at risk of automation, and which skills are most valuable for workers to invest in, in order to unlock lower-risk jobs
- Most recruitment and transition practices rely heavily on relevant domain/sector experience and a worker’s contacts (i.e. who you know), and most employers do not take a skills-based approach to hiring
- Fear, confidence and self esteem are significant barriers for workers to changing careers, in addition to barriers relating to time and finance
- Localised information on training options, support for job seekers and live job opportunities would further enrich the model
- Automation is just one of many trends that are changing the make-up and availability of jobs; other considerations such as digitalisation, the green transition, and regional factors must also be considered…(More)”.