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The Department for Work and Pensions (DWP) launched the Restart Scheme in June 2021, as part of the government's 'Plan for Jobs' [available in VOCEDplus at TD/TNC 156.245], in response to the expected increase in long-term unemployment resulting from the [Coronavirus Disease 2019] COVID-19 pandemic. This employment support programme provides up to 12 months of support for long-term unemployed people. Referrals to the scheme will continue to June 2026. Learning and Work Institute (L&W) led the evaluation of the Restart Scheme in partnership with Ipsos UK. The research comprised: a longitudinal cohort study, a survey of Restart providers and case study research of 12 geographic areas.

The evaluation found that the Restart Scheme was generally effective in achieving its objectives to: move people into or towards sustainable employment; have a positive effect on participants' wellbeing and life satisfaction; and move people into different sectors. The Restart Scheme was less able to support people with health conditions as well as the more highly skilled or those with specialist qualifications. This is particularly significant in the context of rising levels of economic inactivity which means that future employment support programmes will need to be equipped to support high numbers of people with health conditions.

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Skills Bootcamps were introduced in England in 2020 to support the Government's ambition to deliver sector-specific skills to support employers. These courses aim to help individuals upskill or reskill so that they can transition from working in declining sectors and occupations that offer limited progression to growth areas where there are skills shortages and progression opportunities. The Skills Bootcamps are available to people who are unemployed or inactive, as well as those in employment. For people not currently in work, they aim to build the base level skills necessary for job entry that then enable acceleration once in work; for those already in employment, the courses can help employers and employees build the skills to be resilient to occupational change.

IES was commissioned by the Gatsby Charitable Foundation to undertake research to build a better understanding of what the Skills Bootcamps deliver and how far this articulates with provision elsewhere in the English skills system. A sample of Skills Bootcamps to examine and extract from was selected from the Department for Education (DfE) dataset of the 1800 Skills Bootcamps funded from March 2023. From the sample of Skills Bootcamps, information was extracted on their characteristics to help understand the extent of any trends, variation and gaps in delivery, skills development and groups targeted. It also aimed to understand where the Skills Bootcamps fit within the technical education system through identifying where they could be mapped to occupational standards. This also identified Skills Bootcamps that do not appear to be linked to existing occupational standards.

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Greening the economy entails jobs contracting in 'high-polluting' economic activities and expanding in environment-friendly activities. Minimizing the corresponding transition costs is crucial to accelerate decarbonisation and reduce displacement costs for affected workers. Using individual-level labour force data for a large sample of European countries, this paper finds that the shares of green and high-polluting jobs remained approximately stable between 2009 and 2019, hinting at a slow or yet-to-come green transition in labour markets.

Green and high-polluting jobs are unequally distributed across socioeconomic groups: women are under-represented in both green and high-polluting jobs, while green jobs are associated with higher educational attainment, and high-polluting jobs with lower educational attainment. Equally important from a policy perspective, the results show that high-polluting jobs are concentrated in rural areas. These results are confirmed by analyzing labour market transitions: for instance, while women are more likely to transition from study to job, they are significantly less likely to get a green job. Overall, the results suggest that well designed and targeted policies are needed to support efficient and inclusive labour market transitions in the greening economy: to minimize scarring effects for displaced workers, help individuals' upskilling and reskilling, and support the matching between workers and jobs in higher demand.

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The transition to a net zero economy is both a necessity and an opportunity for the UK. This report focuses on the challenges workers face in the transition away from gas, undertaking a detailed analysis of the transferability of skills and future career options for approximately 115,000 workers in the gas sector. The report argues that delivering a fair and well managed transition must include developing an understanding of future career options for these workers well in advance of those industries decarbonising, as well as a strategy for supporting them in the transition.

Key recommendations from the report are: (1) a green industrial strategy: set out a comprehensive green industrial strategy that, inter alia, ends licensing of new oil and gas fields, clarifies the role of gas capacity in the future energy mix, introduces strict capture rate standards for carbon capture and storage (CCS), and sets out a plan for decommissioning gas grid including heat zones that set out the future shape of the gas grid and the few locations where hydrogen may be appropriate for home heating; (2) a fair transition approach involving workers and their unions: set up Fair Transition Bodies comprised of national and local government representatives, industry representatives, trade unions, representatives of non-union workers and local community stakeholders. These bodies should commit to delivering a workforce plan that negotiates with stakeholders over a range of fair transition commitments such as a funded right to retrain, a right to interview and travel assistance if new jobs are located far away; and (3) a reformed skills system: reform and expand the current skills system by introducing an annual Green Training Fund to provide free training to workers in gas sectors (and other industries that are decarbonising) that may need to change occupation.

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What if? papers are designed to compare, increase, and re-evaluate understandings of popular topics relating to vocational education and training (VET). Starting with an overview of the topic, these papers then ask the question 'What if...?' to create a space for readers to revisit these topics with a fresh perspective and consider contrasting ideas and alternative possibilities.

Displacement felt throughout the workforce for many years has been caused by a number of factors including automation, digital transformation, globalisation, demographic trends, the push for greener economies, and the influence of artificial intelligence (AI) now becoming apparent as well. Automation particularly has had long-term significant influence. Whilst the recent [Coronavirus Disease 2019] COVID-19 pandemic propelled displacement into the forefront as restrictions and lockdowns caused immediate impact on everyday lives. Currently we are seeing even more fall-out from the pandemic, with businesses closing due to slowing economies, lost revenue, and work from home options causing changes to spending.

This is a fast-moving work environment and, in response, careers have become less defined. Career guidance has had to evolve quickly, and continue to do so, to keep up. In this paper, we look at career guidance through the lens of recent impacts from COVID-19 with consideration to longer term redundancies caused by automation. What was once a reasonably straightforward task, though still complex, has become increasingly ambiguous and poses the question, what if A plus B no longer equals Career guidance?

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How quickly do men and women find work after mass lay-offs due to workplace closures? This research reveals that women face a greater risk of unemployment than men and lose a larger share of their earnings. More of this gender gap is due to the unequal division of childcare responsibilities than to differences in education or seniority at work.

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Climate change mitigation policies affect the allocation of workers on the labor market: jobs in high-polluting industries will contract, while jobs in the 'green' sector will grow. A just transition in the labour market requires policies to improve the allocation of workers and their deployability, for instance towards performing green tasks; as well as to manage and minimise scarring effects associated with job losses in polluting industries. Using an econometric analysis, this paper investigates the role of structural and policy factors in shaping a number of relevant labour market transitions, uncovering heterogeneity across different groups of workers.

Education is the most important individual-level driver of transitions from non-employment to green jobs, with a particularly strong effect from graduating in scientific fields for young people entering the labour market. Women are significantly less likely than men to move into green jobs out of non-employment. Workers employed in high-polluting occupations face higher displacement risks than other workers, but this does not translate into higher long-term unemployment risks. In terms of policies, the paper finds that the labour market implications of the greening economy can be addressed by general structural policies favouring labour market efficiency in terms of workers' reallocation, labour market inclusiveness in terms of promoting equality of opportunities and minimising long-term scars. Results also suggest that place-based policies are needed to mitigate scarring effects for displaced workers.

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Starting from the premise that technological change is a good thing but unmanaged technological change comes with risks and disruptions, this report examines the role of policy in the future of artificial intelligence (AI). With another technological wave driven by generative AI on the horizon, these past experiences show that policymakers should explore risks and benefits before deployment becomes widespread. Generative AI can be economically disruptive through its impact on wage inequality, wealth inequality and potential job displacement. In other words, there will be winners and losers. The key message is that the world of knowledge work will be transformed by generative AI and that preparation for this needs to start now.

Back office jobs, which are more likely to be occupied by women, are most exposed in the first phase of generative AI deployment, roughly since 2023, with two-thirds of UK jobs likely to be transformed in phase two. The second phase is one where generative AI becomes more deeply integrated with existing organisational processes, and in which a large number of jobs could 'feel' its impact, increasingly high paying jobs too. The report argues for a 'job-centric' industrial strategy for AI in order to realise the benefits of generative AI and avoid unintended costs, and presents three scenarios, discussing the ramifications of each.

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The transition to net‑zero emissions by 2050 will have profound impacts on the labour market and the jobs of millions of workers. Aggregate effects on employment are estimated to be limited. But many jobs will be lost in the shrinking high‑emission industries, while many others will be created in the expanding low‑emission activities. This edition of the OECD Employment Outlook examines the characteristics of the jobs that are likely to thrive because of the transition ('green‑driven jobs'), including their attractiveness in terms of job quality, and compares them to jobs in high‑emission industries that tend to shrink. The cost of job displacement in these latter industries is assessed along with the trajectories of workers out of them towards new opportunities, and the labour market policies that can facilitate job reallocation.

Particular attention is devoted to upskilling and reskilling strategies to facilitate workers' transition into fast‑growing, green‑driven occupations. The distributive impacts of climate‑change mitigation policies are also examined, with a focus on carbon pricing and options to redistribute its tax revenue to those most impacted. As usual, the first chapter of the Outlook assesses recent labour market developments (including wage trends), but also provides an update of the OECD Job Quality indicators.

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Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills.

To inform the discussion on the potential impact of AI on the future of work and which policies countries should enact in response, this note aims to answer six questions: (1) Which countries are more exposed to AI adoption? Which countries are likely to benefit most? (2) How differently will AI affect workers within countries? Which segments of workers are likely to thrive and which face more risks? (3) Historically, how frequently did workers shift between roles now facing varying AI exposure? What insights do these shifts reveal about labor adaptability? (4) In what ways could AI reshape income and wealth inequality? (5) What is the potential impact for growth and productivity? (6) Which countries appear better prepared for the AI transition? How can policies maximize gains and mitigate likely AI-related challenges?

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