Publications & Documents

This working paper provides an overview of a standardised Employee Well-being Survey implemented in four companies in Japan. This survey aligns with international measurement guidelines and practices, including the 2017 OECD Guidelines on Measuring the Quality of the Working Environment, it has been developed under the guidance of the Committee on Statistics and Statistical Policy, and it allows for the calculation at firm level of an equivalent of the Job Strain index, namely the third pillar of the OECD Job Quality framework. The objectives of the study were: i) to pilot the new Employee Well-being Survey at the firm level; ii) to demonstrate the potential of harmonised employee survey data as a source of information on business social performance, with associated benefits for companies, stakeholders, investors, governments and national statistical offices; and iii) to operationalise one element of a proposed framework on measuring non-financial performance of businesses.

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.

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.

The pandemic has stimulated growing interest in using advanced practice nurses such as Nurse Practitioners (NPs) to address growing primary care needs linked to population ageing and more people living with chronic conditions, although not all countries are moving at the same speed. This OECD Health Working paper reviews recent developments in advance practice nursing (APN) in primary care in OECD countries. It focusses on NPs in those countries that are recognising this category of nurses, but also describes the emergence of other categories of nurses taking on new roles such as family and community nurses in some European countries. In those countries that have achieved decisive breakthroughs in new forms of task sharing between primary care doctors (GPs) and nurses, increasing the number of APNs in primary care is seen as a real opportunity to respond to primary care needs and reduce pressures on GPs and hospitals.

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.

This paper provides two innovative measures of well-being for French communes, namely a well-being aggregate index and an index of multi-dimensional poverty. These measures provide an unprecedented view of well-being at the local level by using 7 of the 11 key dimensions of the OECD Better Life Initiative (income, unemployment, housing, education, civic engagement, health and environmental quality). The results show that joint deprivation in at least five dimensions of well-being is starkly concentrated among 316 communes, representing as many as 5.2 million inhabitants (7.7% of the French population).

Existing well-being measurement initiatives in the region, such as the Quality of Life Indicators in Korea, Bhutan’s Gross National Happiness Index and Quality of Life Index in the Philippines, shed some insight on dimensions that should be considered for measuring well-being beyond GDP in Asia. Dimensions of housing, health, education, environment and civic engagement recur across several Asian well-being measurement frameworks, as well as dimensions such as family and culture which are more characteristic of the region. Identifying vulnerable population groups and securing better evidence on social mobility are also necessary to better measure progress in the region. Going forward, it would be helpful for countries to exchange knowledge on how well-being data available can be used for policy making in a more concrete way, for example, by including it in national development plans or budgeting processes.

This paper first presents a meta-analysis of the causal impact of cultural participation on well-being. The meta-analysis classifies the literature according to the strength of the evidence available and various types of cultural activities. Secondly, this paper uses data from time use surveys from Canada, France, Italy, the United Kingdom, and the United States to study individuals’ emotional responses to a series of daily activities. This is then used as a basis for an empirical assessment of the drivers of time allocation across different activities, showing that expectations of future well-being are one of the reasons why individuals decide to engage in cultural activities. Furthermore, the model helps explain why cultural participation, in spite of being one of the most enjoyable human activities, is also the least undertaken. We show that heterogeneity of preferences results in a strong selection effect in available statistics.

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.