Reports

Ensuring equality for LGBTI+ individuals is a human rights imperative, but it also makes a lot of economic sense. Inclusion enables LGBTI+ individuals to achieve their full employment and labour productivity potential, benefitting not only their economic and social well-being, but also society as a whole. Yet, robust evidence supporting the economic case for greater LGBTI+ equality is still scarce due to challenges in accurately measuring the size and life situation of the LGBTI+ population. This report bridges this gap by using a unique set of microdata from the United States. The report begins with an overview of the share of US adults identifying as LGBTI+, their geographic distribution and key demographics. It then evaluates the extent to which LGBTI+ Americans face discrimination, assessing how this population fares, including in the labour market. Finally, utilising the OECD long-term model, the report quantifies the potential increase in GDP resulting from closing the unexplained LGBTI+ gaps in employment and labour productivity. The findings highlight significant economic gains, although they capture only a portion of the potential benefits. Notably, the broader societal impacts, such as the advancement of women's empowerment through the disruption of heteronormative standards, are not quantified.

This paper applies Machine learning techniques to Google Trends data to provide real-time estimates of national average subjective well-being among 38 OECD countries since 2010. We make extensive usage of large custom micro databases to enhance the training of models on carefully pre-processed Google Trends data. We find that the best one-year-ahead prediction is obtained from a meta-learner that combines the predictions drawn from an Elastic Net with and without interactions, from a Gradient-Boosted Tree and from a Multi-layer Perceptron. As a result, across 38 countries over the 2010-2020 period, the out-of-sample prediction of average subjective well-being reaches an R2 of 0.830.

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.

This paper examines the impact of the COVID-19 crisis on women's economic empowerment in the Middle East and North Africa (MENA), in the context of elevated gender-based discrimination in social institutions – formal and informal laws, social norms, and practices. The analysis focuses on Egypt, Jordan, Morocco and Tunisia. Using 2023 data from the fifth edition of the Social Institutions and Gender Index (SIGI), the paper analyses how discriminatory laws and social norms hamper women's economic empowerment. The paper also explores how the COVID-19 pandemic has exacerbated this discrimination. Finally, it provides policy recommendations to tackle discriminatory social institutions and address the specific needs of women and girls, both in the face of public health crises and beyond, aiming to foster more inclusive and resilient societies in the MENA region.

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.

The frequency and severity of extreme wildfires are on the rise in Greece, causing unprecedented disruption and increasingly challenging the country’s capacity to contain losses and damages. These challenges are set to keep growing in the context of climate change, highlighting the need to scale up wildfire prevention and climate change adaptation. This paper provides an overview of Greece's wildfire policies and practices and assesses the extent to which wildfire management in the country is evolving to adapt to growing wildfire risk under climate change.

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.

Italy’s Universal Civil Service (UCS) engages young people in volunteering activities that enhance practical skill development for employability, active citizenship, and personal growth. Through a joint project between the OECD, the European Commission, and the Department for Youth Policies, Italy aims to improve the design and implementation of the UCS. As part of the project, this report analyses the current monitoring and evaluation framework of the UCS and provides guidance for the development of a robust results-based Monitoring and Evaluation system to improve the system’s ability to track progress and demonstrate impact.

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.

Despite European Union efforts to fight discrimination as part of its Union of Equality strategies, it is difficult to analyse discrimination in EU Member States given the scarcity of official data sources. This paper uses new survey data to examine discrimination against people from racialised communities, LGBTIQ+ people, persons with disability and religious minorities. It explores the role discrimination plays in driving well-being gaps between at-risk groups and the majority of the population. Discrimination, particularly when it occurs frequently, is associated with severe effects across many aspects of people’s lives – constraining income-earning opportunities, exacerbating housing and financial stress, subjecting people to violence, fear and low self-esteem, and contributing to mental ill health. These consequences come at a huge personal cost to the individuals directly affected and to society as a whole.