---
id: "lib-2026-07-07-employment-outlook-2026"
type: "library"
title: "Employment Outlook 2026"
lang: "EN"
org: "OECD"
source_type: "Report"
date: "2026-07-07"
week: "2026-W28"
url: "https://nosov.ai/library/lib-2026-07-07-employment-outlook-2026/"
original_url: "https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/07/oecd-employment-outlook-2026_a41e8b9f/7e710f54-en.pdf"
tier: "S"
tier_name: "Shapes the field"
tier_reason: "The OECD's annual flagship on labour markets, built on harmonised data across member countries and reviewed by the OECD Chief Economist, sets the reference figures governments and employers use."
summary_basis: "full-text"
original_minutes: "600"
tags: ["labor markets", "regional disparities", "ai exposure"]
---

# Employment Outlook 2026

Tier S (Shapes the field): The OECD's annual flagship on labour markets, built on harmonised data across member countries and reviewed by the OECD Chief Economist, sets the reference figures governments and employers use.

Summary based on the full text.

## In short

The OECD Employment Outlook 2026, edited by Andrea Bassanini and published in July 2026 by the Directorate for Employment, Labour and Social Affairs. It is the first edition centred on regional labour-market disparities, with further chapters on wages, returns to skills and training, non-compete clauses and employment protection. Labour markets remain strong but are weakening at the margin. On AI, the OECD concludes that large language models so far explain little of the rising unemployment gap for young graduates, which began before the pandemic.

## Key numbers

- 72.1%: average OECD employment rate in Q1 2026, a historic high
- 5.4% to 5.7%: median OECD unemployment rate, May 2025 to May 2026
- 30%: employees across 15 OECD countries bound by non-compete clauses
- 16% to 70%+: range of regional shares of workers in jobs exposed to generative AI

## What it reports

- Average OECD employment rate reached 72.1% in Q1 2026 and labour force participation 76.7%; the median unemployment rate rose from 5.4% in May 2025 to 5.7% in May 2026.
- Real wages were still below early-2021 levels in one-third of OECD countries, even before the 2026 energy price surge.
- The unemployment gap of young college graduates (ages 23-29) relative to the working-age population has widened since before the pandemic in all countries analysed; LinkedIn AI-hiring trends show no turning point when firms adopted LLMs from mid-2023 to early 2024.
- The OECD warns that studies attributing youth job losses to AI may be biased: exposed occupations cluster in sectors such as finance and professional services that cut hiring first when rates rise.
- In over half of OECD countries, employment rates across small regions differ by more than 20 percentage points; people in the poorest regions are nearly 40% more likely to stay in the lowest-income group over five years.
- Regional exposure to generative AI ranges from about 16% of workers to more than 70%, highest where ICT, finance and education concentrate.
- PIAAC 2022-2023: numeracy and formal education still predict wages but less than a decade ago; non-formal training focused on leadership, teamwork and project management shows a consistent positive wage link.
- One-third of workers in the countries analysed (30% across 15 countries in the infographic) are bound by non-compete clauses, now common well outside knowledge-intensive jobs.

## How they got it

OECD staff analysis of national labour force surveys, regional statistics, administrative income microdata, the Survey of Adult Skills (PIAAC) 2022-2023, Indeed and LinkedIn job data, and new surveys on post-employment clauses across 15 countries. The AI assessment combines a literature review with OECD comparisons of youth unemployment gaps and AI-hiring trends in Australia, Canada, the EU and the US.

## Limits

- The AI analysis is correlational and short; its job-posting evidence uses exposure indices, not observed firm adoption.
- Data mostly ends in early-to-mid 2026, before any later acceleration in agent use would register.
- AI is a secondary theme; regional disparities dominate the report.

## Why it matters for companies

The OECD's reading cautions against blaming weak graduate hiring mainly on AI; interest rates and sector cycles explain more so far, which matters when firms set graduate intake. The training chapter gives evidence that non-formal training in leadership, teamwork and project management pays off in wages, a useful benchmark for learning budgets. Rising scrutiny of non-compete clauses signals regulatory risk for contracts that apply them to staff without access to confidential information. Regional AI exposure differences affect where companies site roles and how they plan reskilling across locations.

Original: https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/07/oecd-employment-outlook-2026_a41e8b9f/7e710f54-en.pdf