Reports

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.

Latvia’s public employment service, the State Employment Agency (SEA), is undertaking a modernisation of its service delivery. This review discusses the SEA’s digitalisation needs and strategy to guide its modernisation efforts. It provides a detailed assessment of the SEA’s digital infrastructure and key recommendations concerning its IT system, analytical capacity and digital tools for jobseekers and employers. This report on Latvia is the fourteenth country study published in this series.

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.

While the second half of the 20th century was characterised by a growing integration of the global economy, in recent years there have been growing calls for protectionism and reshoring. At the same time, COVID-19 resulted in higher levels of remote working, which showed that many jobs could be done from anywhere and could, in theory, be offshored. The future of offshoring and reshoring is therefore highly uncertain. This document summarises some of the key issues and trends with regards to offshoring and reshoring. It then sets out a research agenda which would result in a better understanding the future of offshoring and reshoring and their impact on domestic labour markets, which would help policy makers in OECD countries plan for the changes that lie ahead.

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.

People’s ability to access essential services is key to their labour market and social inclusion. An important dimension of accessibility is physical accessibility, but little cross-country evidence exists on how close people live to the services facilities they need. This paper helps to address this gap, focusing on three types of essential services: Public Employment Services, primary schools and Early Childhood Education and Care. It collects and maps data on the location of these services for a selection of OECD countries and links them with data on population and transport infrastructure. This allows to compute travel times to the nearest service facility and to quantify disparities in accessibility at the regional level. The results highlight substantial inequalities in accessibility of essential services across and within countries. Although large parts of the population can easily reach these services in most countries, some people are relatively underserved. This is particularly the case in non-metropolitan and low-income regions. At the same time, accessibility seems to be associated with the potential demand for these services once accounting for other regional economic and demographic characteristics.

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.