Informal caregivers play an essential role in supporting older people, individuals with chronic illnesses, and people with disabilities, but providing this support can affect their own health. Using data from the European Health Interview Survey (EHIS), this study examines the situation of informal caregivers across European countries and investigates the relationship between caregiving intensity and health. France is among the countries with the highest share of informal caregivers, but they spend relatively less time providing care. In half of European countries, including France, informal caregiving for at least 20 hours per week is associated with poorer mental health, even when compared to non-caregivers with similar sociodemographic characteristics. In one third of countries they also report worse physical health. These differences are especially pronounced in Northern and Western Europe. France occupies an intermediate position, with differences in physical and mental health that remain relatively moderate but are nevertheless unfavourable to the most heavily involved caregivers.

While means-tested benefits such as minimum income benefits (MIB) and unemployment assistance (UA) are an essential safety net for low-income people and the unemployed, incomplete take-up is the rule rather than the exception. Building on desk research, open-ended surveys and semi-structured interviews, this paper investigates the opportunities and risks of using artificial intelligence (AI) for managing these means-tested benefits. This ranges from providing information to individuals, through determining eligibility based on pre-determined statutory criteria and identifying undue payments, to notifying individuals about their eligibility status. One of the key opportunities of using AI for these purposes is that this may improve the timeliness and take-up of MIB and UA. However, it may also lead to systematically biased eligibility assessments or increase inequalities, amongst others. Finally, the paper explores potential policy directions to help countries seize AI’s opportunities while addressing its risks, when using it for MIB or UA management.

In 2022, 7.1 million people in metropolitan France provided regular support to a relative or friend living at home with a disability or loss of autonomy. Informal caregivers assist with daily living activities, provide emotional support, or offer financial and material help. In three out of ten cases, care is provided without support from other caregivers, relatives, professionals, or volunteers. Caregiving situations are diverse: one third of caregivers combine emotional, practical, and financial support, while the time devoted to assistance ranges from less than one hour per day to 35 hours or more per week. Informal caregivers are predominantly women (58%) and average 53 years of age. Adult children caring for a parent form the largest group, while spouses and parents of children with disabilities are more likely to provide intensive care. Many caregivers must balance caregiving with other responsibilities: six in ten are employed, seeking work, or studying. Among those of working age, nearly half also have a dependent child.

AI can bring significant benefits to the workplace. In the OECD AI surveys of employers and workers, four in five workers say that AI improved their performance at work and three in five say that it increased their enjoyment of work. But the benefits of AI depend on addressing the associated risks. Taking the effect of AI into account, occupations at highest risk of automation account for about 27% of employment in OECD countries. Workers also express concerns around increased work intensity, the collection and use of data, and increasing inequality. To support the adoption of trustworthy AI in the workplace, this policy paper identifies the main risks that need to be addressed when using AI in the workplace. It identifies the main policy gaps and offers possible policy avenues specific to labour markets.

Despite the increasingly important role of informal caregivers in supporting people with disabilities or loss of autonomy, their numbers have declined in metropolitan France. Between 2008 and 2022, the number of people aged 16 and over providing regular assistance with daily living activities fell by 340,000, from 10% to 9% of the population. This decline reflects changes in family structures and employment patterns rather than a reduction in care needs, as the number of people reporting a need for help increased over the same period. At the same time, the caregiver population has aged, with an average age of 55 years in 2022. Caregiving is increasingly concentrated within the immediate family, and women remain the majority among caregivers. The intensity of care has also increased: caregivers perform a wider range of tasks and devote more time to helping relatives with growing needs. Men are becoming more involved in caregiving, and caregiving tasks are less strongly divided along gender lines. More than half of caregivers continue to combine caregiving with employment, job seeking, or education.

For many workers, the effects of artificial intelligence (AI) will be visible not in terms of lost employment but through changes in the tasks they perform at work and changes in job quality. This chapter reviews the current empirical evidence of the effect of AI on job quality and inclusiveness. For workers with the skills to complement AI, task changes should be accompanied by rising wages, but wages could decline for workers who find themselves squeezed into a diminished share of tasks due to automation. AI may affect job quality through other mechanisms as well. For example, it can reduce tedious or dangerous tasks, but it may also leave workers with a higher-paced work environment. The chapter further shows that using AI to support managers’ tasks affects the job quality of their subordinates. Finally, the chapter shows that using AI affects workplace inclusiveness and fairness, with implications for job quality.

This chapter provides an overview of countries’ policy action affecting the development and use of artificial intelligence (AI) in the workplace. It looks at public policies to protect workers’ fundamental rights, ensure transparency and explainability of AI systems, and clarify accountability across the AI value chain. It explores how existing non-AI-specific laws – such as those pertaining anti-discrimination and data protection – can serve as a foundation for the governance of AI used in workplace settings. While in some countries, courts have successfully applied these laws to AI-related cases in the workplace, there may be a need for AI- and workplace‑specific policies. To date, most countries primarily rely on soft law for AI-specific matters, but a number of countries are developing new AI-specific legislative proposals applicable to AI in the workplace.

This working paper shows that using Artificial Intelligence (AI) for training has the potential to improve the targeting of and access to adult learning systems. In order to ensure that AI yields benefits for all, it will be necessary to address potential drawbacks in terms of more demanding skills requirements, greater inequalities in access to data, technology and infrastructure, and important ethical issues.

See also this post on the OECD Skills and Work blog.

The COVID-19 pandemic further exacerbated the long-standing skills shortages in the health workforce across countries. Equipping health workers with the right skills is essential to respond to future health crises, to prepare for increasing use of digital technologies, and to plan for demographic change. This joint report by the OECD and ILO aims to enable more resilient health workforces by helping countries to assess future demand in terms of both numbers of health workers and skills needs, and to prepare appropriate policy responses. The report provides a comparative overview of practices in 16 countries to anticipate future skill needs in the health workforce, and of how such information is used by policy makers and social partners to foster a better alignment with labour market needs. Analysis is based on interviews with institutions that are responsible for anticipating skill needs in the health workforce, a virtual peer-learning workshop and desk research.

Policy-makers have long recognised that participation in adult learning is key to unlock benefits of a changing world of work. While much has been written about the need for progress in this area, many good initiatives struggle to translate into real change on the ground, as they get stuck in the difficulties of policy implementation. Drawing on the experience of six countries that have significantly increased participation over the past decades, this report identifies the main factors behind successful policies and programmes. Based on an analysis of 17 reforms deemed the most important for the observed increases in participation, five key lessons emerge on the design, implementation and evaluation of adult learning reforms.

See also this post on the OECD Skills and Work blog.

Improving the alignment between the skills required by employers and those of workers is increasingly challenging. The OECD Skills for Jobs database is designed for policymakers, practitioners and the general public to understand where gaps are emerging between skill supply and demand. The third update out now covers 43 countries, leverages a new methodology for calculating the importance of skills in occupations based on online job vacancies, and includes information on more detailed digital skills.

See also this post on the OECD Skills and Work blog.

This booklet highlights seven action points on how to involve social partners to make adult learning work for the future. It provides practical insights for stakeholders who are directly involved in the design, implementation and monitoring of adult learning policies, including policymakers and social partners. Each action point draws on research evidence and provides insights on how to translate it into practice by highlighting promising policies in OECD and emerging countries. 

See also this post on the OECD Skills and Work blog. 

Skills imbalances arise when the demand for and supply of skills are not well-aligned. Understanding where those imbalances are is a first step towards developing skills policies and programmes that are aligned with the needs of the labour market. The OECD Skills for Jobs indicators measure skills shortages, surpluses and mismatches across labour markets around the world. This report describes the methodology behind the indicators and the results for South Africa. It provides detailed insights into the occupations, sectors and skills that are facing shortages and surpluses, as well as into the match between individuals’ education and their jobs.

This working paper shows that using Artificial Intelligence (AI) for training has the potential to improve the targeting of and access to adult learning systems. In order to ensure that AI yields benefits for all, it will be necessary to address potential drawbacks in terms of more demanding skills requirements, greater inequalities in access to data, technology and infrastructure, and important ethical issues.

See also this post on the OECD Skills and Work blog.

This working paper looks at the labour market outcomes of individuals who hold a medium-level VET qualification (defined as upper or post-secondary non-tertiary education with vocational orientation) today, as well as how they have changed in the past 10 to 15 years and what can be expected in the medium-term. Based on the findings, the report discusses key policy directions to improve VET graduates’ access to high-quality jobs. 

See also this chapter in the OECD Employment Outlook 2020, and this post on the OECD Skills and Work blog.

This brochure presents empirical evidence drawn from the OECD Skills for Jobs (S4J) database developed in cooperation and with the financial support of the JPMorgan Chase Foundation. Data confirm that the shift towards a knowledge-based digital economy is already under way and that governments, workers and firms altogether face many challenges ahead. Results show that skill demands have gradually shifted towards a more intensive use of cognitive and interpersonal skills and that high-skilled occupations are in strong demand across most OECD countries. 

See also this post on the OECD Skills and Work blog.