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.

If test-takers do not engage with the assessment, the reliability of test scores and the validity of inferences about their proficiency may suffer. Test-taker disengagement is particularly likely in low-stakes assessments and, according to prior research, for certain types of students. But levels of engagement may also be related to aspects that test developers can manipulate, such as item characteristics. This paper investigates which item characteristics are associated with two indicators of test-taker disengagement, rapid guessing and breakoffs, in an international assessment of reading. Analyses of data from almost 500 000 students from 67 countries and economies that took part in the 2018 Programme for International Student Assessment (PISA) show that rapid guessing was observed mainly on simple multiple-choice questions. Breakoffs were more likely in the presence of idiosyncratic selected-response formats, such as hot spot or matching tasks. Both rapid guessing and breakoffs were more frequent on tasks involving long and complex texts.

Students are much more than their grades. Beyond performing well in school, students must learn to manage their relationships with others, confront stress, find purpose in what they do, and deal with a series of factors oftentimes beyond their control – all of this, during a particularly sensitive period of their lives. How they do across all these dimensions of life shapes their well-being, which in turn affects their school performance and their life outcomes beyond school. In 2015, PISA broke new ground by including indicators of student well-being alongside traditional measures of academic performance. However, the data on student well-being often remain overshadowed by country and economy scores in mathematics, science, and reading - traditionally considered the primary outputs of PISA. This paper presents a proposal to increase the visibility and policy impact of PISA indicators on well-being, by organising them in thematic areas and presenting them through data visualisations that respond to the needs of different kinds of users. The proposed PISA dashboard on students’ well-being has the potential to offer policy makers, educators, parents, and other stakeholders a comparative perspective on how well schools are fostering the essential foundations for students to lead fulfilling lives.

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.

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.

Engaging with research, and ensuring research evidence is used well, is key to professionalising education policy making processes, and ultimately to improving educational outcomes. But the systematic use of evidence in policy making faces many challenges. This policy brief draws on evidence from the OECD Strengthening the Impact of Education Research project’s country learning seminars, as well as the project’s policy survey that collected responses from ministries of education in 37 education systems from 29 countries. The project is based in the OECD’s Centre for Educational Research and Innovation (CERI). This brief presents a set of case studies on two questions: • What human resource strategies can build individual and collective civil service professionalism? • What stable structures and mechanisms can contribute to the systematic and thoughtful use of evidence in policy processes?

This paper reviews a number of previous studies that have investigated how measure of non-cognitive skills predict important life outcomes such as educational attainment, employment, earnings, and self-reported health and life satisfaction. All reviewed studies analyse data from large-scale surveys from multiple countries and rely on the Big-Five framework to assess non-cognitive skills. The paper finds that measures of non-cognitive skills are robustly and consistently associated to indicators of life success in youth and adulthood, and have incremental predictive power over traditional measures of cognitive ability.

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.

Young people from low socio-economic status (SES) backgrounds face additional barriers as they seek to convert their qualifications and experience into successful employment. They encounter particular challenges in seeking to enter high status jobs. The barriers they face can be productively conceptualised in terms of economic, human, social and cultural capital accumulation. Schools can help to build these resources through programmes of career guidance, but to be successful they must actively respond to predictable barriers relating to access to trusted information and useful experiences. PISA shows a need for socially focused interventions. Career uncertainty and confusion is shaped by SES. Low SES students are also less likely to engage in most commonplace career development activities. Equitable guidance systems will target greater provision at low SES students and aim ultimately to provide personalise provision to all students, encouraging and enabling understanding of and progression towards careers promising greatest personal fulfilment. Insights from longitudinal data provide new opportunities for more scientific and strategic approaches to delivering effective provision.

This paper explores enhancements to international large-scale assessments (ILSAs). It advocates for diversification, targeting specific groups or individuals for more precise diagnoses, and flexibilisation, refining the item bank for assessments' relevance and adaptability. The paper also introduces prototypes for new assessment tools, representing a significant evolution in ILSAs' design and application, aiming for broader impact and increased adaptability in ILSAs.