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This annual publication provides details of taxes paid on wages in OECD countries. This year’s edition focuses on fiscal incentives for second earners in the OECD and how tax policy might contribute to gender gaps in labour market outcomes. For the year 2023, the report also examines personal income taxes and social security contributions paid by employees, social security contributions and payroll taxes paid by employers, and cash benefits received by workers. It illustrates how these taxes and benefits are calculated in each member country and examines how they impact household incomes. The results also enable quantitative cross-country comparisons of labour cost levels and the overall tax and benefit position of single persons and families on different levels of earnings. The publication shows average and marginal effective tax rates on labour costs for eight different household types, which vary by income level and household composition (single persons, single parents, one or two earner couples with or without children). The average tax rates measure the part of gross wage earnings or labour costs taken in tax and social security contributions, both before and after cash benefits, and the marginal tax rates the part of a small increase of gross earnings or labour costs that is paid in these levies.

People’s ability to access essential services is key to their labour market and social inclusion. An important dimension of accessibility is physical accessibility, but little cross-country evidence exists on how close people live to the services facilities they need. This paper helps to address this gap, focusing on three types of essential services: Public Employment Services, primary schools and Early Childhood Education and Care. It collects and maps data on the location of these services for a selection of OECD countries and links them with data on population and transport infrastructure. This allows to compute travel times to the nearest service facility and to quantify disparities in accessibility at the regional level. The results highlight substantial inequalities in accessibility of essential services across and within countries. Although large parts of the population can easily reach these services in most countries, some people are relatively underserved. This is particularly the case in non-metropolitan and low-income regions. At the same time, accessibility seems to be associated with the potential demand for these services once accounting for other regional economic and demographic characteristics.

This paper explores the economics of Artificial Intelligence (AI), focusing on its potential as a new General-Purpose Technology that can significantly influence economic productivity and societal wellbeing. It examines AI's unique capacity for autonomy and self-improvement, which could accelerate innovation and potentially revive sluggish productivity growth across various industries, while also acknowledging the uncertainties surrounding AI's long-term productivity impacts. The paper discusses the concentration of AI development in big tech firms, uneven adoption rates, and broader societal challenges such as inequality, discrimination, and security risks. It calls for a comprehensive policy approach to ensure AI's beneficial development and diffusion, including measures to promote competition, enhance accessibility, and address job displacement and inequality.

Social impact measurement and management is a particularly helpful practice for social economy entities to understand their contribution to society and potentially improve the achievement of their mission. Impact areas that are particularly important for the social economy, such as economic prosperity and employment, social inclusion and well-being and community, are often the hardest to translate into quantitative metrics. Current social impact measurement and management practices are largely shaped by funders and for-profits with limited focus on the social economy. This guide offers a simple, straightforward approach for social economy entities to measure, manage and ultimately maximise their impact, and to prioritise the use of findings for strategic organisational learning and improvement.

Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.). Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for the effect of artificial intelligence on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management and business skills. These include skills in general project management, finance, administration and clerical tasks. The results also show that there have been increases over time in the demand for these skills in occupations highly exposed to AI. For example, the share of vacancies in these occupations that demand at least one emotional, cognitive or digital skill has increased by 8 percentage points. However, using a panel of establishments (which induces plausibly exogenous variation in AI exposure), the report finds evidence that the demand for these skills is beginning to fall.

This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate.

Generative artificial intelligence (AI) presents myriad opportunities for integrity actors—anti-corruption agencies, supreme audit institutions, internal audit bodies and others—to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop.

Open unemployment and joblessness in Switzerland are low compared to OECD standards. Yet a comparatively high proportion of working-age individuals remain weakly attached to the labour market, with unstable jobs, or with limited working hours. As an initial step towards a possible in-depth project, this Faces of Joblessness feasibility study provides insight into the nature and incidence of the structural barriers that are likely to prevent individuals from fully engaging in employment and speculates on their possible links with underutilized employment potential. It shows that lack of recent work experience and substantial non-labour or partner income are two key employment barriers in Switzerland. Partner income can be a barrier for women in particular and might be one of the reasons why many women leave stable employment at childbearing age, alongside low supply and high cost of early childhood education and care programs. Workers over 60 also represent a significant underutilized employment potential, as many have taken early retirement. Non-EU migrant are particularly exposed to potential labour market difficulties at younger age, and many of them have low levels of education, poor professional skills or limited work experience. This study also suggests that many jobless are confronted with complex and inter-related employment obstacles.

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.