Publications & Documents

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.

This report analyses the sequence of labour market support that individuals receive and evaluates two large public works programmes. It uses rich administrative data and finds positive labour market impacts of the Community Employment and Tús employment programmes. Building on the results of the analyses, the report makes recommendations on how Ireland can further adapt its active labour market policies (ALMPs) to better support its current and future jobseekers. This report on Ireland is the thirteenth country study published in a series of reports on policies to connect people with jobs, and is part of a joint project with the European Commission to strengthen countries’ capacity to evaluate ALMPs. The report is written jointly by the OECD, the Department of Social Protection of Ireland and the Joint Research Centre of the European Commission.