Reports

Data portability enhances access to and sharing of data across digital services and platforms. It can empower users to play a more active role in the re-use of their data and can help stimulate competition and innovation by fostering interoperability while reducing switching costs and lock-in effects. However, the effectiveness of data portability in enhancing competition depends on the terms and conditions of data transfer and the extent to which competitors can make use of the data effectively. Additionally, there are potential downsides: data portability measures may unintentionally stifle competition in fast-evolving markets where interoperability requirements may disproportionately burden SMEs and start-ups. Data portability can also increase digital security and privacy risks by enabling data transfers to multiple destinations. This note presents the following five dimensions essential for designing and implementing data portability frameworks: sectoral scope; beneficiaries; type of data; legal obligations; and operational modality.

Earth's orbits are polluted by more than 100 million debris objects that pose a collision threat to satellites and other spacecraft. The risk of perturbing highly valuable space-based services critical to life on Earth, such as weather monitoring and disaster management, is making debris mitigation an urgent policy challenge. This book provides the latest findings from the OECD project on the economics of space sustainability, which aims to improve decision makers’ understanding of the societal value of space infrastructure and costs of space debris. It provides comprehensive evidence on the growth of space debris, presents methods to evaluate and quantify the value of the satellites at risk and discusses ways to ensure a more sustainable use of the orbital environment. It notably includes case studies from Italy, Japan and Korea on the socio-economic value of different types of space infrastructure and discusses the feasibility and optimal design of fiscal measures and voluntary environmental rating schemes to change operator behaviour. This work is informed by contributions from researchers worldwide involved in the OECD project.

Inclusive and sustainable economic growth in the six Western Balkan (WB6) economies depends on greater economic competitiveness. Although the gap is closing gradually, the standards of living in WB6 are well below those of the OECD and EU. Accelerating the rate of socio-economic convergence will require a holistic and growth oriented approach to policy making. This is the fourth study of the region (formerly under the title 'Competitiveness in South East Europe') and it comprehensively assesses policy reforms in the WB6 economies across 15 policy areas key to strengthening their competitiveness. It enables WB6 economies to compare economic performance against regional peers, as well as EU-OECD good practices and standards, and to design future policies based on rich evidence and actionable policy recommendations. The regional profile presents assessment findings across five policy clusters crucial to accelerating socio-economic convergence of the WB6 by fostering regional co-operation: business environment, skills, infrastructure and connectivity, digital transformation and greening. Economy-specific profiles complement the regional assessment, offering each WB6 economy an in-depth analysis of their policies supporting competitiveness. They also track the implementation of the previous 2021 study's recommendations and provide additional ones tailored to the economies’ evolving challenges. These recommendations aim to inform structural economic reforms and facilitate the region’s socio-economic convergence towards the standards of the EU and OECD.

Recent AI technological advances, particularly the rise of generative AI, have raised many data governance and privacy questions. However, AI and privacy policy communities often address these issues independently, with approaches that vary between jurisdictions and legal systems. These silos can generate misunderstandings, add complexities in regulatory compliance and enforcement, and prevent capitalising on commonalities between national frameworks. This report focuses on the privacy risks and opportunities stemming from recent AI developments. It maps the principles set in the OECD Privacy Guidelines to the OECD AI Principles, takes stock of national and regional initiatives, and suggests potential areas for collaboration. The report supports the implementation of the OECD Privacy Guidelines alongside the OECD AI Principles. By advocating for international co-operation, the report aims to guide the development of AI systems that respect and support privacy.

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.

OECD countries are increasingly investing in better understanding the potential value of using Artificial Intelligence (AI) to improve public governance. The use of AI by the public sector can increase productivity, responsiveness of public services, and strengthen the accountability of governments. However, governments must also mitigate potential risks, building an enabling environment for trustworthy AI. This policy paper outlines the key trends and policy challenges in the development, use, and deployment of AI in and by the public sector. First, it discusses the potential benefits and specific risks associated with AI use in the public sector. Second, it looks at how AI in the public sector can be used to improve productivity, responsiveness, and accountability. Third, it provides an overview of the key policy issues and presents examples of how countries are addressing them across the OECD.

As part of broader digitalisation efforts, half of public employment services (PES) in OECD countries are employing Artificial Intelligence (AI) to enhance their services. AI is being adopted across all key tasks of PES, including most commonly to match jobseekers with vacancies. While several PES have been using such tools for a decade, adoption of AI has been increasing in recent years as these become more accessible. New AI use cases have emerged to assist employers in designing vacancy postings and jobseekers in their career management and job-search strategies. AI initiatives have significant impact on PES clients, changing how they interact with the PES and receive support, and PES staff, altering their day-to-day work. As PES seek to maximise the opportunities brought by AI, proactive steps should be taken to mitigate associated risks. Key considerations for PES include prioritising transparency of AI algorithms and explainability of results, establishing governance frameworks, ensuring end-users (staff and clients) are included and supported in the development and adoption process, and committing to rigorous monitoring and evaluation to increase the positive and manage any negative impact of AI solutions.

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 Canada on 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, communication and digital skills. These include skills in budgeting, accounting, written communication, as well as competencies in basic word processing and spreadsheet software. The results also show that, in Canada, demand for social and language skills have increased the most over the past decade in occupations highly exposed to AI. Using a panel of establishments confirms the increasing demand for social and language skills, as well as rising demand for production and physical skills, which may be complementary to AI. However, the establishment panel also finds evidence of decreasing demand for business, management and digital skills in establishments more exposed to AI.

Employment has recovered strongly from the COVID-19 pandemic despite large structural changes in labour markets, such as the widespread adoption of digital business models and remote work. We analyse whether the pandemic has been associated with labour reallocation across occupations and triggered mismatches between occupational labour demand and supply using novel data on employers’ job postings and jobseekers’ clicks across 19 countries from the online job site Indeed. Findings indicate that, on average across countries, the pandemic triggered large and persistent reallocation of postings and clicks across occupations. Occupational mismatch initially increased but was back to pre-pandemic levels at the end of 2022 as employers and workers adjusted to structural changes. The adjustment was substantially slower in countries that resorted to short-time work schemes to preserve employment during the pandemic.

As AI use grows, so do its benefits and risks. These risks can lead to actual harms ('AI incidents') or potential dangers ('AI hazards'). Clear definitions are essential for managing and preventing these risks. This report proposes definitions for AI incidents and related terms. These definitions aim to foster international interoperability while providing flexibility for jurisdictions to determine the scope of AI incidents and hazards they wish to address.