OECD labour markets have continued to show resilience over the past year, with employment and labour force participation remaining at historic highs. However, there have been new signs of weakening, with a slight rise in unemployment, a slowdown in the growth in employment and labour force participation, and a continued easing of labour market tightness – even though structural labour shortages persist. In addition, even before the recent surge in energy prices, the real wage recovery was slowing down, despite real wages remaining below the levels seen in early 2021 – just before the post-pandemic inflation surge – in one‑third of OECD countries. As real wage growth slows down, the contributions of profits and wages to domestic price pressures tend to stabilise to patterns similar to those prevailing before the COVID‑19 pandemic.
OECD Employment Outlook 2026
1. From resilience to risk: Employment and wages under pressure
Copy link to 1. From resilience to risk: Employment and wages under pressureAbstract
In Brief
Copy link to In BriefOECD labour markets have continued to show resilience, but new signs of weakening emerged over the past year, with rising unemployment, a slowdown in employment and labour force participation growth, a further easing of labour shortages, and a deceleration of the real wage recovery.
The latest labour market data available at the time of writing suggest that:
Employment and labour force participation rates remain at historic highs, while OECD unemployment remains low. The OECD unemployment rate was 4.9% in May 2026, the average employment rate in the OECD reached 72.1% in Q1 2026, while the average labour force participation rate rose to 76.7%.
However, there are further signs of weakening in labour markets, as unemployment is rising slightly, employment trends are flattening and growth in labour force participation is slowing down. In particular, the median unemployment rate in the OECD increased from 5.4% in May 2025 to 5.7% in May 2026.
Many young entrants to the labour market have become more likely to be unemployed. The unemployment gap between young college graduates and the working age population has been widening since before the pandemic in all countries analysed. Young entrants without a graduate degree have also recently seen their unemployment gap increase in a few countries. So far, the role of recent advances in large language models (LLM) in explaining the difficulties facing young people appears to be limited. Instead, young labour market entrants may have been particularly vulnerable to the recent weakening of labour markets, as well as long-term changes in technology and skill needs.
Labour shortages continued to ease, but structural shortages remain. While labour market tightness is now lower than at the onset of the COVID‑19 pandemic in many countries, it remains higher than during most of the pre‑pandemic decade and, in particular, than during comparable stages of the business cycle.
Labour productivity growth is slowing down. In the euro area, labour hoarding (among other factors) has supported employment in spite of weak economic growth, and this has mechanically reduced labour productivity growth. Nonetheless, sectors that play a central role in AI adoption have boosted productivity performance in the United States.
Even before the recent surge in energy prices, the real wage recovery was slowing down, while real wages remained below the levels seen in early 2021 (just before the post-pandemic inflation surge) in one‑third of OECD countries. Annual real wage growth was positive in virtually all OECD countries in Q1 2026, but it was lower than one year earlier in two‑thirds of them. It was 2.2% in Q1 2026 on average across countries, compared with 2.7% in Q1 2025.
Real statutory minimum wages remain above January 2021 levels and continue to rise. In April 2026, the real minimum wage was higher than a year earlier in a majority of the 30 OECD countries that have a national statutory minimum wage and was higher than in January 2021 in virtually all of them.
As a result, the wages of the lowest-paid workers have proved more resilient to inflation surge than median wages. Statutory minimum wages have risen more than median wages since 2021 (i.e. Kaitz indexes have increased), and real wages have been more resilient in low wage sectors in some countries, reflecting a compression of the wage distribution at the bottom.
As real wage growth slows down, the profit – wage “catch-up” phase may be shifting towards a steadier pattern similar to that seen before the pandemic. Unit labour costs continued to rise faster than unit profits between Q1 2025 and Q1 2026 in most OECD countries, but there are signs that the catching-up of real wages – following the disproportionate contribution of unit profits to the inflation surge in 2021‑2022 – could be slowing down, with their relative contribution to domestic price pressures stabilising around pre‑pandemic levels in some countries.
Looking ahead, labour market policies (e.g. unemployment insurance, active labour market policies, collective bargaining) will play a key role in addressing the labour market impacts of persistent economic uncertainty and elevated energy costs. In particular, in a context of stabilising wage pressure and rising inflation, wage setting policies, collective bargaining systems and social dialogue have a role to play in ensuring that the cost of inflation is shared fairly between workers and employers. Addressing structural labour shortages will also require co‑ordinated efforts across various areas of labour market policy.
Introduction
Copy link to IntroductionOECD economies suffered major shocks in the wake of the COVID‑19 crisis and Russia’s war against Ukraine, and OECD labour markets proved resilient to the negative consequences of these shocks, with high employment, low unemployment and substantial labour shortages in many sectors of the economy. Several factors may have contributed to these strong employment outcomes. In particular, surging inflation kept real wages relatively low, labour hoarding became widespread in many countries, and workers’ preferences shifted toward fewer working hours (OECD, 2025[1]; 2024[2]; 2023[3]; 2022[4]; ECB, 2024[5]).
However, this decoupling between economic activity and labour market performances showed some first signs of ending in 2023, with labour shortages declining, employment growth slowing down, and the downward trend in unemployment coming to halt (albeit with historically low unemployment rates) in many OECD countries (OECD, 2024[2]; 2025[1]).
This chapter reviews recent developments by presenting the latest trends observed in the labour market and wages across OECD countries. It is organised as follows: Section 1.1 reviews recent labour market developments; Section 1.2 reports on recent wage developments, including updated information on statutory minimum wages and negotiated wages; Section 1.3 highlights the particular difficulties faced by young labour market entrants and the potential role of Artificial Intelligence (AI) in explaining these difficulties; and Section 1.4 concludes.
1.1. Labour markets have remained resilient but there have been further signs of weakening
Copy link to 1.1. Labour markets have remained resilient but there have been further signs of weakeningIn Q1 2026, at the onset of the recent surge in energy prices, GDP growth in OECD countries remained resilient (Figure 1.1). However, the evolving conflict in the Middle East is testing the resilience of the global economy. The duration and extent of the conflict remain uncertain, though at the time of finalising this publication a negotiated settlement is in place. Even after the conflict ends, the economic effects are likely to be felt for some time given the months it will take to restore damaged infrastructure and transport routes and deliver products around the world (OECD, 2026[6]).
Figure 1.1. GDP growth remained resilient in the first quarter of 2026
Copy link to Figure 1.1. GDP growth remained resilient in the first quarter of 2026Real GDP indexed to 100 in Q1 2021, seasonally adjusted, selected OECD countries
Note: Euro area refers to the averages of 21 Eurozone countries.
Source: OECD calculations based on OECD Data Explorer, “Quarterly real GDP growth – OECD countries”, http://data-explorer.oecd.org/s/2ah (accessed on 5 June 2026).
1.1.1. Unemployment remains low but is on the rise
Unemployment remains low in many OECD countries but is rising in about two‑thirds of them (Figure 1.2, Panel A). In particular, the unemployment rate was more than 0.2 percentage points (p.p.) higher in Q1 2026 than a year earlier in many European countries1 and Chile (Panel A). While the median unemployment rate has increased from 5.4% in Q1 2025 to 5.7% in Q1 2026, the overall OECD unemployment rate has risen only slightly, from 4.9% to 5%. This recent increase in unemployment mainly affects young and prime age workers (Annex Figure 1.A.1). Young workers have faced specific labour market difficulties in recent years, which are discussed in detail in Section 1.3.
Still, unemployment has declined substantially in some countries over the past year. In Q1 2026, the annual decline in the unemployment rate was greater than 0.2 p.p. in two Baltic states (Estonia and Latvia), three Southern European countries (Italy, Portugal and Spain), Colombia, Costa Rica and Sweden.
Figure 1.2. Unemployment rates remain low but are on the rise
Copy link to Figure 1.2. Unemployment rates remain low but are on the riseUnemployment rate (percentage of labour force), seasonally adjusted
Note: The labour force comprises all persons aged 15 and over. Euro area (“EA21”) refers to the labour force‑weighted average unemployment rate across the 21 euro area countries, and European Union (“EU27”) to the labour force-weighted average across the 27 EU countries. “OECD” denotes the labour force‑weighted average across the 38 OECD countries. “OECD (median)” denotes the median unemployment rate across these countries. In Panel B, “OECD (median)” refers to the change in the OECD median unemployment rate (not the median of country-level changes). Data refer to Q4 2025 (Q4 2024 for the year-earlier comparison) for the United Kingdom.
Source: OECD Data Explorer, “Monthly unemployment rates”, http://data-explorer.oecd.org/s/2ai (accessed on 15 June 2026).
1.1.2. Growth in employment and labour force participation is slowing down
Employment and labour force participation rates remain at historic highs – 72.1% and 76.7% respectively in Q1 2026 on average in the OECD, but their growth is slowing down. On average in the OECD, employment trends have flattened. Labour force participation rates rose by 0.1 p.p. between Q1 2025 and Q1 2026, compared with 0.2 p.p. between Q1 2024 and Q1 2025 (Figure 1.3, Panel A). Employment rates decreased markedly over the past year in Iceland, Ireland and Luxembourg. By contrast, employment rate growth accelerated dramatically in Estonia, Latvia and Poland.
Over the past two years, employment rate growth has been strongest in some Latin American countries (Colombia and Costa Rica) and Southern European countries (Italy, Greece, Portugal and Spain), as well as in Japan and Lithuania. The growth in the labour force participation rate has also been relatively strong in all these countries, except for Costa Rica, Italy and Spain (Figure 1.3, Panel B).
In most OECD countries, the gender gap in employment and labour participation rates have narrowed over the past year (Annex Figure 1.A.2, Panels A and B), continuing a trend observed throughout the recovery from the COVID‑19 crisis (OECD, 2024[2]).
On average across OECD countries, approximately one‑third of the increase in the employment rate between Q1 2024 and Q1 2026 is due to the increase in the employment rate of prime age workers (25‑54), and two‑thirds to the increase in the employment rate of older workers (55‑64) (Annex Figure 1.A.1, Panel B). By contrast, the employment rate of younger workers (15‑24) fell over the period. The increase in the employment rate for older workers is part of a trend that has been ongoing for two decades (OECD, 2025[1]). Similarly, the increase in the labour force participation rate between Q1 2024 and Q1 2026 is solely due to prime age and older workers, in roughly equal shares on average across OECD countries (Panel C).
In some countries, the increase in employment and/or rising labour force participation rates has been largely driven by growing employment and/or labour force participation rates among migrants. This is the case in Austria, Belgium, Denmark, Germany, Greece, Latvia, Norway, the Netherlands and Sweden (Annex Figure 1.A.3). Nonetheless, the gaps between both the average employment and unemployment rates of migrants and native‑born people in the OECD widened in 2024, following a strong post-pandemic recovery in migrants’ labour market outcomes in 2022‑2023 – in most countries, migrants fare worse than native‑born people in terms of employment and unemployment, even though they tend to participate more in the labour market (OECD, 2025[7]).
Figure 1.3. Growth in employment and labour force participation rates is slowing down
Copy link to Figure 1.3. Growth in employment and labour force participation rates is slowing downp.p. change among the working age population (persons aged 15‑64), seasonally adjusted data
Note: OECD is the unweighted average of the 38 OECD countries shown in this Chart. Euro area refers to the 21 Eurozone countries. Statistics for Iceland and the United Kingdom refer to Q4 2023, Q4 2024, and Q4 2025. p.p: percentage point.
Source: OECD Data Explorer, “Employment rate”, http://data-explorer.oecd.org/s/2al (accessed on 16 June 2026), and OECD Data Explorer, “Labour force participation rate”, http://data-explorer.oecd.org/s/2am (accessed on 16 June 2026).
1.1.3. Labour markets continued to ease but structural shortages remain
Labour markets have eased further over the past year in most of the 27 OECD countries analysed in Figure 1.5 Panel A. Labour market tightness (measured by the number of vacancies per unemployed person)2 was higher in Q1 2026 than one year earlier in only five countries: Estonia, Poland, Portugal, Slovenia and Spain. With the exception of Slovenia, these countries are among the one‑third of OECD countries where unemployment has declined over the past year (Figure 1.2).
In addition, in Q1 2026, labour market tightness was close to or below pre‑COVID‑19 levels in most of the countries analysed. It was also well below the post-COVID‑19 peak in all countries where there was one, with the exception of Israel. In the EU, in Q1 2026, the share of firms citing lack of labour as one of the factors limiting production is also close or below the pre‑pandemic records of 2019: one in six firms in industry and one in five firms in services mentioned to be limited by lack of labour.3 In this context of decreasing recruitment difficulties, labour hoarding behaviours have continued to decline (see Box 1.1).
Box 1.1. Labour hoarding in the EU has been declining but remains above pre‑COVID‑19 levels
Copy link to Box 1.1. Labour hoarding in the EU has been declining but remains above pre‑COVID‑19 levelsSince the pandemic, employment has been supported by strong labour hoarding in many OECD countries, including in EU countries (ECB, 2024[5]), the United Kingdom1 and the United States2 – in other words, firms have preferred to adjust to negative shocks by retaining their staff while reducing working hours rather than cutting jobs. This has accentuated the decoupling between employment levels and economic activity.
Between the onset of the pandemic (Q1 2020) and early 2023, on average over the 20 OECD EU countries analysed, labour hoarding behaviour (measured by the share of firms expecting their output to decrease but their employment to increase or remain unchanged over the next 3 months) reached historic highs, overall and in most sectors (the construction sector being an exception, with persistently high labour hoarding) (Figure 1.4, Panel A). Labour hoarding was initially (2020-2021) encouraged by the widespread use of job retention schemes and other exceptional measures aimed at supporting employment during the pandemic. Then, in a second phase (2022-2023), high profit margins and persistent labour shortages increased the expected costs of rehiring. In this context, Europe’s high exposure to rising gas prices and the uncertainty surrounding Russia’s war against Ukraine led many firms to hoard labour while waiting for the economic situation to improve (ECB, 2025[8]; ECB, 2024[5]; OECD, 2021[9]). This rise in labour hoarding was particularly marked in the trade sector.
Since then, labour hoarding has declined (Figure 1.4, Panel A), as economies have adjusted to the war in Ukraine, and profits (see Section 1.2) and labour shortages (Figure 1.5) have moved closer to pre‑pandemic levels. In Q1 2026, labour hoarding was lower than one year earlier in two‑thirds of the countries analysed (Panel B).
Nevertheless, labour hoarding also remains above pre‑pandemic levels in two‑thirds of the countries analysed (Figure 1.4, Panel B), and by more than 10% in about half of them, as economic uncertainty remains high and real wages have not caught up with the post-pandemic inflation surge (see Section 1.2). In addition, shifts in worker preferences towards shorter hours may have increased rehiring costs, further fuelling labour hoarding (OECD, 2025[1]).3
In the future, labour hoarding could remain substantially higher than before the pandemic, as real wages could remain below pre‑pandemic levels in a number of countries. Elevated energy costs in particular may weaken labour markets and raise inflation, at least in the short run. Several other factors could increase the expected costs of rehiring: demographic ageing will impose workforce reductions (OECD, 2025[1]) – thereby increasing labour market tightness –, the digital transformation (AI) and the move toward carbon neutrality may accentuate skill mismatches (OECD, 2023[3]; OECD, 2024[2]), and worker preferences may continue to shift toward fewer working hours.1
Figure 1.4. Labour hoarding in the EU has been declining but remains above pre‑COVID‑19 levels
Copy link to Figure 1.4. Labour hoarding in the EU has been declining but remains above pre‑COVID‑19 levelsPercentage of firms which expect their output to decrease but their employment to increase or remain unchanged over the next three months, seasonally adjusted data
Note: The labour hoarding indicator measures the share of firms that do not expect to reduce their workforce (employment margin) despite a recent worsening in their business conditions (activity margin). Statistics refer to the non-agricultural business sector and include “Manufacturing” (Section C of the NACE Rev. 2.1), “Construction” (Section F), “Retail trade” (Section 47), and “Other services” (Sections H to N, and sections R to S). European Union (EU20) refers to the unweighted averages across the 20 EU countries shown in Panel B.
Source: OECD calculations based on the European Commission, Business and consumer surveys, https://economy-finance.ec.europa.eu/economic-forecast-and-surveys/business-and-consumer-surveys_en (accessed on 30 April 2026).
Figure 1.5. Labour market tightness is close to or below pre‑COVID‑19 levels in most countries but remains high
Copy link to Figure 1.5. Labour market tightness is close to or below pre‑COVID‑19 levels in most countries but remains highDifference in the vacancies-to‑unemployed ratio relative to its Q4 2019 level
Note: OECD is the unweighted average of vacancies per unemployed across the 27 OECD countries shown in Panel A (not including Chile, Colombia, Costa Rica, Czechia, Denmark, Iceland, Italy, Japan, Mexico, New Zealand and Türkiye). Europe (19) is the weighted average of vacancies per unemployed across the 19 EU countries shown in Panel A. Czechia is not shown due to a major break in the vacancy series in Q1 2025, which led to the automatic removal of older vacancies from the register and resulted in a structural discontinuity.
In Panel A, the peak refers to the maximum value of vacancies per unemployed reached between Q4 2019 and Q1 2026. For Estonia and Latvia, vacancies per unemployed remain below their Q4 2019 levels throughout the period considered; consequently, no peak is identified for these countries.
Statistics on job vacancies for Estonia and Switzerland in Panel A are not seasonally adjusted. Vacancy statistics for France exclude “Public administration and defence; compulsory social security” (NACE Rev. 2 section O), while public institutions are not fully covered in “Education” and “Human health and social work activities” (NACE Rev. 2 sections P and Q).
Vacancy statistics for Israel refer to enterprises with at least five employees and exclude “Public Administration and Defence; Compulsory Social Security”, primary and secondary schools, kindergartens and day-care centres, but include government-owned enterprises in the business sector and public hospitals (including government-owned).
Labour-market tightness is measured as the number of job vacancies divided by the number of unemployed (ILO definition). Definitions of job vacancies are not fully harmonised across countries, which limits international comparability. For Israel, job vacancies refer to posts for which employers are actively seeking a candidate from outside the enterprise following retirement, resignation or promotion. Coverage is limited to enterprises with at least five employees and excludes public administration and defence, compulsory social security, and most education services, while including government-owned enterprises in the business sector and public hospitals. For country-specific vacancy definitions for all other countries, see the note to Figure 1.7 in OECD (2025[1]). Statistics for Norway, Switzerland and the United Kingdom are available up to Q4 2025.
Source: Job Vacancies (Australian Bureau of Statistics, ABS) for Australia; Job vacancies, payroll employees, and job vacancy rate (Statistics Canada) for Canada; Job vacancies and labour turnover (Statistics Estonia) for Estonia, https://andmed.stat.ee/api/v1/en/stat/PAV011; Eurostat, Job vacancy statistics by NACE Rev.2 activity (Table jvs_q_nace2) for the European countries; Job Vacancy Survey (Central Bureau of Statistics, CBS) for Israel; Labor Force Survey at Establishments (Ministry Of Employment and Labor, MOEL) for Korea; Vacancy Survey (Office for National Statistics, ONS) for the United Kingdom; and Job Openings and Labor Turnover Survey (Bureau of Labor Statistics, BLS) for the United States; OECD Data Explorer, “Monthly unemployment rates”, http://data-explorer.oecd.org/s/2aq (accessed on 16 June 2026).
However, most OECD labour markets remain tight when viewed over a longer time horizon. While, on average across the countries analysed, labour market tightness was lower in Q1 2026 than at the onset of the pandemic, it was higher in Q1 2026 than during most of the pre‑pandemic decade and, in particular, than during comparable stages of the business cycle (Figure 1.5, Panels B and C). In fact, average labour market tightness had been on the rise for several years when the pandemic hit. A number of structural factors, which are still at play, contribute to explaining this trend, including population ageing, the digital transformation, the move toward carbon neutrality and poor job quality in certain sectors (Dorville, Filippucci and Marcolin, 2025[10]; Filippucci, Laengle and Marcolin, 2025[11]; OECD, 2025[1]; Knez and Arpaia, 2026[12]).
To capture labour market tightness at industry level, Figure 1.6 provides an overview of the industry-specific vacancy rates (i.e. the fraction of all jobs available in the industry that are unfilled and for which employers report that they are actively trying to recruit) in Australia, the euro area and the United States.
The loosening of the labour market over the past year has been particularly pronounced in some sectors, including business services (Australia and the euro area), transportation and storage (Australia and the euro area), information and communication (the euro area and the United States), real estate (Australia and the United States), and education (the United States) (Figure 1.6).
In Q1 2026, in both the euro area and the United States, labour market tightness was close to or below pre‑COVID‑19 levels in most sectors, including some where the peak in tightness had been particularly high, such as accommodation and food service activities or business services (Figure 1.6).4 The sectors where labour market tightness remained highest compared with pre‑crisis levels are (i) in the euro area: arts and entertainment, and manufacturing; (ii) in the United States: finance and insurance, and manufacturing. In Australia, however, labour market tightness remained substantially above pre‑COVID‑19 levels in most sectors, particularly those where peak tightness had been among the highest (Figure 1.6): accommodation and food service activities, manufacturing, and real estate.
In the three areas analysed, most sectors remain tight when looking at a longer time horizon (Figure 1.6 and Annex Figure 1.A.4). In Q1 2026, even in the euro area and the United States, labour market tightness remained higher than in Q1 2015 in most sectors, particularly in those where it had however returned to pre‑COVID‑19 levels, such as human health and social work activities; or, in the euro area, construction, and transportation and storage. Interestingly, in no country there appears to be a correlation between the typical level of pay of the sector and the difference between current tightness and Q1 2015 levels, suggesting that structural labour shortages occur both in high and low-skill jobs.
Figure 1.6. Labour market tightness is close to pre‑COVID 19 levels in most sectors in the euro area and the United States
Copy link to Figure 1.6. Labour market tightness is close to pre‑COVID 19 levels in most sectors in the euro area and the United Statesp.p. difference in job vacancy rates by industry relative to their Q4 2019 levels
Note: Industries are ranked by median wages in 2018 using the European Structure of Earnings Survey (SES). This ranking is broadly consistent when using 2019 median wage data from the United States Current Population Survey. The definition of vacancies is not harmonised across countries (see Figure 1.5 for further details).
For Australia, job vacancy rates by industry are OECD estimates based on vacancies from the Job Vacancy Survey and employees from the Labour Force Survey. Job vacancy rates by industry are seasonally adjusted for the United States. For Australia and the euro area, series are smoothed using a Hodrick-Prescott filter.
Industries refer to ANZSIC 2006 (1‑digit) for Australia, NACE Rev. 2.1. (1‑digit) for the euro area and NAICS for the United States and are therefore not fully comparable across countries. For the euro area (21), “Information and communication” is calculated as the unweighted average of the job vacancy rates for NACE Rev. 2.1 sections J (“Publishing, broadcasting, and content production and distribution activities”) and K (“Telecommunication, computer programming, consulting, computing infrastructure and other information service activities”). In the United States, “Business services” includes administrative services, “Real estate” includes rental and leasing, “Transportation and storage” includes warehousing and utilities, while “Human health and social work activities” and “Education” cover the private sector only. Euro area refers to the 20 euro area countries. p.p: percentage point.
Source: Job Vacancies (Australian Bureau of Statistics, ABS) for Australia; Job vacancy statistics by NACE Rev.2.1. activity for the euro area; and Job Openings and Labor Turnover Survey (Bureau of Labor Statistics, BLS) for the United States.
1.1.4. The reduction in annual hours worked per worker has slowed down
Over the past two years, the reduction in total hours worked per worker and per year has decelerated compared to its long-term trend (Figure 1.7). On average across the OECD countries for which data are available, the annualised reduction in hours worked was around 0.24% during the period 2023-2025, compared with 0.27% during the period 1995-2019 and 0.33% during the period 2019-2023. The strongest slowdown (or acceleration) in the decline (growth) in working hours between these two periods was observed in the Baltic states (Estonia, Latvia and Lithuania), five Central European countries (Austria, Czechia, Hungary, the Slovak Republic and Slovenia), Ireland and the United Kingdom. In fact, annualised growth in working hours turned positive in most of these countries.
Figure 1.7. .The reduction in annual hours worked per worker has slowed down
Copy link to Figure 1.7. .The reduction in annual hours worked per worker has slowed downPercentage change in average hours worked per worker and per year
Note: Average hours worked per worker are calculated as total actual hours worked divided by total employment. For Canada, the series refers to average annual hours worked per job. For Colombia, 2025 refers to 2024. The percentage change over 1995-2019 is not available for Korea, as the series starts in 2008. For Estonia, the percentage change over 1995-2019 refers to 2000-2019. OECD is the unweighted average of the 36 OECD countries shown in the chart, excluding Korea and Türkiye. Euro area (21) refers to the 21 euro area countries.
Source: OECD Data Explorer, “Average annual hours actually worked per worker”, https://data-explorer.oecd.org/s/4o5 (accessed on 4 June 2026), and OECD calculations based on Eurostat, Annual National Accounts (Employment by main industry (NACE Rev.2), Table nama_10_a10_e) for the euro area and the European Union.
The decline in hours worked can be explained by several factors, including productivity gains, changes in workers’ preferences towards fewer overtime hours or part-time jobs, changes in working time regulations or collective agreements (OECD, 2021[9]), but also compositional shifts towards jobs requiring fewer working hours. Evidence from Europe suggests that worker preferences are at play rather than regulatory changes or compositional effects (Astinova et al., 2024[13]; ECB, 2021[14]): while the long-term downward trend in hours worked reflects preferences for part-time employment, the recent decline observed since the end of 2021 appears to be due to a decrease in long working hours and overtime among full-time workers.5
The recent slowdown in the decline in annual working hours per worker has not been accompanied by a deceleration in the rise (or an acceleration in the decline) of part-time or temporary employment. Instead, on average across the countries analysed, the incidence of part-time and temporary employment increased more (or decreased less) between 2023 and 2025 than between 2019 and 2023 (Annex Figure 1.A.5, Panel B), possibly in part due to the loosening of labour markets – firms had less need to attract workers by offering them full-time open-ended contracts.
1.1.5. Labour productivity growth is slowing down
Since the onset of the COVID‑19 crisis, labour productivity growth, measured in terms of real gross domestic product (GDP) per hour worked, has been substantially below its pre‑COVID‑19 trend on average across OECD countries: its average annualised growth rate was 1% over the period 2019-2025, compared with 2% over the period 1995-2019 (Figure 1.8).
Figure 1.8. Labour productivity growth is slowing down
Copy link to Figure 1.8. Labour productivity growth is slowing downAnnualised percentage change in real GDP per hours worked
Note: “OECD (average)” and “OECD (median)” are the unweighted average and the median across the 36 OECD countries shown in this Chart (not including Chile and Türkiye), respectively. Euro area refers to the averages of 20 Eurozone countries and European Union to the averages of 27 EU countries. 1995-2019 refers to 1997-2019 for Canada and 1996-2019 for Estonia. 2023-2025 refers to 2023-2024 for Australia, Colombia, Costa Rica, Israel, Japan, Korea, Mexico, New Zealand and the United Kingdom.
Source: OECD Data Explorer, “Productivity database”, https://data-explorer.oecd.org/s/4rg (accessed on 15 June 2026).
The recent slowdown in productivity growth has been particularly marked in the euro area (Figure 1.8), where long-term productivity growth (1995-2019) was already relatively weak. As a result, annualised labour productivity growth in the euro area was far below the OECD median between 2019 and 2025 (Figure 1.8 and Annex Figure 1.A.6). This can be explained by the more pronounced effects of higher gas prices and uncertainty related to Russia’s war against Ukraine, as well as a tendency to hoard labour in European countries (ECB (2024[15]) and Box 1.1).
By contrast, labour productivity growth has been relatively high since the onset of the COVID‑19 crisis in two Latin American countries (Colombia and Mexico), two Central and Eastern European countries (Poland and the Slovak Republic), two Baltic states (Latvia and Lithuania), as well as in Korea and the United States (Figure 1.8 and Annex Figure 1.A.6). In all these countries, except for Mexico, productivity growth has been relatively high since at least the mid‑1990s. But productivity growth has even exceeded its long-term dynamic trend in Poland and the United States. In the United States, labour productivity growth registered a small uptick above its long-term trend of 1.7‑1.8% over 2023-2025 (Figure 1.8), driven by a number of sectors that play a central role in the adoption of AI technologies and the transition to online retail (e.g. computer systems, data processing, online retail)6 (Hobijn et al., 2025[16]). This suggests that AI may have played some role in the United States’ strong productivity performance over recent years.
1.1.6. Labour markets are highly uncertain
In the near future, economic activity and, as a result, labour markets, will be largely shaped by the evolution of the energy crisis, the time taken to achieve a lasting settlement to the conflict in the Middle East and the resulting policy responses (OECD, 2026[6]).
Assuming the disruptions from the conflict are sizeable but limited to a relatively short period of time, and an otherwise solid underlying momentum in the global economy – with output boosted by strong AI-related investment, production and trade, supportive financial and fiscal conditions, and lower US effective tariff rates on imports than originally announced – global growth is projected to soften considerably in the second quarter of 2026, before picking up gradually thereafter (OECD, 2026[6]). Consistent with these developments, employment growth is expected to be relatively subdued in 2026 before edging up in 2027, while the median unemployment rate across OECD economies is projected to rise further in 2026 before falling back around its Q4 2025 level at the end of 2027 (Figure 1.9).
These projections should nonetheless be treated with caution, given the exceptionally uncertain situation and the extent to which economic prospects depend on achieving a durable resolution to the conflict (OECD, 2026[6]). Failing to secure a peace agreement would lead to substantially worse economic and labour market outcomes and could have adverse impacts on a wider range of countries or industries.
Figure 1.9. OECD labour markets are expected to slow down temporarily in 2026 if the conflict in the Middle East remains of limited duration
Copy link to Figure 1.9. OECD labour markets are expected to slow down temporarily in 2026 if the conflict in the Middle East remains of limited duration
Note: (p) OECD projection. OECD (median) refers to the median unemployment rate across 35 OECD countries (not including Greece, Lavia and Slovenia). Euro Area refers to the 17 EU member states using the euro as their currency which are also OECD Member States. The 2000‑2019 trend refers to the average quarterly employment growth rate prevailing in Q1 2000 to Q4 2019. Projections are based on the assumption that the disruptions from the conflict in the Middle East are sizeable but limited to a relatively short period of time.
Source: OECD Data Explorer, “Economic Outlook 119”, https://data-explorer.oecd.org/s/4nt (accessed on 3 June 2026).
In response to practices by its trading partners that the United States considered to be unfair and discriminatory, the country made a historic turn in its import tariff policy, shifting from targeted protection on selected goods toward broader and higher tariff regimes affecting multiple products (e.g. cars, car parts, steel and aluminium products, but also energy and oil) and most trading partners.7 2025 marks the year of most significant tariff increases. In the absence of a causal analysis, the assessment of the impact of 2025 tariff rates must rely on structural modelling and empirical analyses of previous (smaller) tariff increases, which makes it somewhat speculative. Moreover, the 2025 tariff rate hike was partially reverted following a decision of the US Supreme Court in February 2026,8 and uncertainty remains about future tariff rates.
The overall impact of the 2025 tariff rates on employment in the United States is expected be slightly negative, once inflation, higher input costs and possible foreign retaliation are accounted for. Structural modelling points to moderate negative net employment effects in the United States alongside composition shifts (Ai et al., 2025[17]; OECD, 2025[18]): tariffs may increase manufacturing employment, but at the cost of employment declines in other sectors (e.g. service and agriculture), with negative effects overall (Rodríguez-Clare, Ulate and Vasquez, 2025[19]).9 Empirical analyses of 2018-2019 tariff implementations support these conclusions: they find that import protection gains were outweighed by rising input costs and retaliation (Autor et al., 2024[20]; Smarzynska Javorcik et al., 2023[21]), even within the (protected) manufacturing sector (Flaaen and Pierce, 2019[22]). Choi and Hong (2025[23]) complement these findings by examining the 2018 safeguard measures on washing machines. They show that the new production jobs created were not in US major domestic manufacturers, but rather in multinationals which relocated production.
Similarly, the detrimental employment effects of 2025 tariffs on OECD partner countries should remain moderate overall (OECD, 2025[18]). Nonetheless, in some countries (e.g. Canada or EU countries), substantial impacts can be expected in the manufacturing sector – which is particularly exposed to tariffs (Galgóczi, Watt and Mehtap, 2025[24]; Statistics Canada, 2025[25]). In addition, besides lowering exports and raising input costs (Salotti et al., 2019[26]), US tariffs may redirect tariff-hit Chinese exports towards the euro area, a phenomenon observed following the 2018 tariff wave (ECB, 2025[8]). This could intensify competition in vehicles and chemicals and put further pressure on manufacturing jobs in Europe (ECB, 2025[27]).
Uncertainty in US tariff policy remains high (OECD, 2025[28]), which can exert negative effects on employment independently of the actual tariff rates applied (Javorcik et al., 2019[29]). Ongoing legal challenges and negotiations, and the risk of new tariffs on currently exempt items might discourage investment, disrupt production planning, and delay hiring decisions, particularly in sectors integrated into global value chains. Over time, this policy volatility might weaken business confidence and dampen labour demand even in the case of moderate or declining tariff levels.
1.2. The real wage recovery was already slowing down before the recent surge in energy prices
Copy link to 1.2. The real wage recovery was already slowing down before the recent surge in energy pricesInflation (as measured based on the Consumer Price Index) was on a downward path until the recent surge in energy prices hit OECD economies. In Q1 2026, it was lower than one year earlier in a majority of OECD countries (Figure 1.10, Panel A), and headline inflation had decreased from 4.3% in Q1 2025 to 3.6% in Q1 2026. Energy inflation was also lower in Q1 2026 than one year earlier (Panel B); in other words, the effects of the conflict in the Middle East were still not reflected in prices in Q1 2026. The expected rise in energy inflation will weigh more heavily on low-income households, which tend to spend a larger share of their budget on these products (Caisl et al., 2023[30]).
As a result, headline inflation was a long way from its 2022 peak at the onset of the latest surge in energy prices. In Q1 2026, inflation in the OECD area (3.6%) was less than half its Q3 2022 level of 10.4% (OECD, 2025[1]), but still above 2%, which is the central bank target for many OECD countries (e.g. the euro area, the United Kingdom and the United States). Inflation remained above this target in 25 OECD countries – 31% in Türkiye and above 4% in three other OECD countries.
Figure 1.10. Inflation was on a downward path until the recent surge in energy prices
Copy link to Figure 1.10. Inflation was on a downward path until the recent surge in energy pricesInflation defined as annual percentage change in the consumer price index (CPI), Q1 2026
Note: “OECD (average)” and “OECD (median)” are the unweighted average and the median across the 38 OECD countries, respectively. Countries are ordered in descending order of the year-on-year percentage change in the consumer price index (all items) in Q4 2025 (Panel A). The latest quarter available refers to Q4 2025 for Poland. CPI: consumer price index.
Source: OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 15 June 2026).
1.2.1. Real wage growth was slowing down in early 2026, while there was still room for catching up in one‑third of OECD countries
According to the latest data available, annual real wage growth was positive in virtually all OECD countries in Q1 2026 – before the recent surge in energy prices –, but was slowing down (Figure 1.11, Panel A), in spite of decreasing inflation.10 The average annual growth in real wages across the 37 countries for which data are available was 2.2% in Q1 2026, compared with 2.7% in Q1 2025, and annual real wage growth in Q1 2026 was lower than a year earlier in two‑thirds of these countries. In addition, recent data on wages advertised in online vacancies for nine countries also indicate a further slowdown in real wage growth in recent months (Box 1.2), as the conflict in the Middle East began to affect prices.
Despite persistent annual growth, in Q1 2026, real wages remained below their Q1 202111 levels (pre‑dating the post-pandemic inflation surge) in about one‑third (13) of the 37 countries analysed (Figure 1.11, Panel C). Real wages were more than 2% below Q1 2021 levels in a quarter of countries: Australia, Czechia, Denmark,12 Italy, New Zealand and Sweden. It should be noted that wage recovery is slowing down in most of these countries, with annual real wage growth in Q1 2026 being lower than a year earlier – wage growth accelerated only in Czechia and Sweden (Panel A). Nevertheless, real wages have regained some of the lost ground in virtually all OECD countries – real wages are near the trough of the cost-of-living crisis only in New Zealand and Australia.
Several factors could explain the slowdown in real wage growth between Q1 2025 and Q1 2026. The slowdown in labour productivity and the easing of labour market tightness, as discussed in the previous section, may have moderated wage growth, particularly for new hires. On the institutional side, negotiated wage claims have been tempered in some countries (see Box 1.3 and European Commission (2025[31])), while geopolitical and trade tensions have maintained a climate of high economic uncertainty.13 In the future, geopolitical uncertainties and a time-limited increase in energy costs may significantly weaken labour markets while exerting further upward pressure on inflation, which likely will depress wages (Figure 1.12) (OECD, 2026[6]).
Figure 1.11. Real wage growth is slowing down while real wages remain below Q1 2021 levels in one‑third of OECD countries
Copy link to Figure 1.11. Real wage growth is slowing down while real wages remain below Q1 2021 levels in one‑third of OECD countries
Note: Unless otherwise indicated, nominal hourly wages correspond to the wages and salaries component of the Labour Cost Index, adjusted for a constant industry structure. *: The constant-industry-structure adjustment of average hourly earnings has been estimated and revised by the OECD using total wages and salaries by industry from the 2019 Annual National Accounts. †: Nominal hourly wages refer to actual wages, without adjustment for compositional shifts; comparisons with other countries should therefore be interpreted with caution. ‡: Nominal hourly wages control for additional compositional effects depending on the country, including regions, job and worker characteristics, sex, and occupations.
Nominal wage series are seasonally adjusted for all countries except Canada, Costa Rica, Israel, Mexico, New Zealand and Switzerland. For Japan, statistics refer to regular employees in establishments with five or more regular employees in all industries excluding agriculture, forestry, fisheries and government services. Regular employees are workers employed indefinitely or employed under a contract for a period of one month or longer. Statistics cover only the business sector (NACE Rev. 2 sections B to N) for the Republic of Türkiye. Statistics for Germany and Iceland refer to enterprises with 10 or more employees; for Ireland, to enterprises with three or more employees; for Greece, to enterprises with five or more employees; and for Poland, to entities or local units with 10 or more employed persons. For Finland, statistics cover the public sector and all private sector enterprises with at least 20 or 30 employees. For Mexico, nominal hourly wages are affected by a significant degree of unreported income.
Real hourly wages are calculated by deflating nominal hourly wages using the consumer price index (CPI, all items). For Poland and the Slovak Republic, real wages in Q1 2026 are estimated using the OECD price index (COICOP 1999), available up to Q4 2025, chained with the new national price indices (COICOP 2018), available from Q1 2026.
In Panel B, the trough corresponds to the quarter in which real hourly wages reached their lowest level since Q1 2021. For Switzerland, the latest observation and the trough is identified among fourth-quarter observations only and corresponds to the lowest real hourly wage recorded since Q4 2020.
Countries are ordered by descending order of the year-on-year percentage change in real hourly wages in Q1 2026 (Panel A). The latest observation available refers to Q2 2025 for Belgium and Q4 2025 for Switzerland. OECD (average) and OECD (median) refer respectively to the unweighted average and the median across the 37 OECD countries shown in the chart (not including Colombia). Euro area (21) refers to the 21 Eurozone countries.
Source: OECD calculations based on national wage indices. For a full description of country-specific data sources, see Figure 1.15 in OECD (2025[1]), OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?, https://doi.org/10.1787/194a947b-en. OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 17 June 2026).
Figure 1.12. Real wages are expected to weaken
Copy link to Figure 1.12. Real wages are expected to weakenPercentage change relative to Q1 2026 in projected real compensation rate
Note: The real compensation rate is defined as compensation of employees per employee deflated by the consumer price index. Euro area (17) refers to the 17 EU Member States that use the euro as their currency and are also OECD Member countries. Projections are based on the assumption that the disruptions from the conflict in the Middle East are sizeable but limited to a relatively short period of time.
Source: OECD calculations based on OECD Data Explorer, “Economic Outlook 119”, http://data-explorer.oecd.org/s/2an (accessed on 3 June 2026).
Box 1.2. Online vacancies from selected OECD countries point to a recent slowdown in the growth of real posted wages
Copy link to Box 1.2. Online vacancies from selected OECD countries point to a recent slowdown in the growth of real posted wagesData on advertised wages from job postings on the online platform Indeed1 indicate a slowdown in the growth of real posted wages between November 2025 and April 2026 in most countries for which data are available (Figure 1.13). In countries where real wage growth is decreasing (France, Germany, Ireland, Italy and the United States), this is due to higher inflation in April 2026, sometimes combined with declining nominal wage growth (France, Spain and the United States). The euro area as a whole has also experienced both declining nominal wage growth and higher inflation in April 2026.
In Canada, the Netherlands, Spain and the United Kingdom, the effects of the conflict in the Middle East had remained limited up to April 2026. In the Netherlands and the United Kingdom, nominal wage growth was following a fall in inflation between November 2025 and April 2026, so real wage growth remained virtually constant. In Canada and Spain, both inflation and nominal wage growth remained stable over the period.
Figure 1.13. Posted wages point to a recent slowdown in real wage growth
Copy link to Figure 1.13. Posted wages point to a recent slowdown in real wage growthYear-on-year percentage change, three‑month moving averages, from November 2025 to April 2026
Note: Posted wages refer to the average year-on-year percentage change in wages and salaries advertised in job postings on Indeed. Inflation refers to the Consumer Price Index (CPI, all items). As CPI data for the United States were not published for October 2025, the three‑month moving averages for November and December 2025 are based on two months only, excluding October 2025.
Source: Indeed Wage Tracker (https://github.com/hiring-lab/indeed-wage-tracker); OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 27 May 2026).
1. Indeed data are subject to a number of limitations, including the over-representation of high-skilled jobs and the under-representation of low skilled jobs, jobs posted by small firms and the agricultural sector. Nevertheless, there is evidence that job postings at the aggregate level tend to track well with job vacancies from government sources (Adrjan and Lydon, 2024[32]; Adrjan et al., 2021[33]; Bellatin and Galassi, 2022[34]; Ciminelli et al., 2024[35]).
Box 1.3. Real negotiated wage growth in selected OECD countries is slowing down, but there is still room for catching up
Copy link to Box 1.3. Real negotiated wage growth in selected OECD countries is slowing down, but there is still room for catching upReal negotiated wages (i.e. wages defined in collective agreements, as opposed to those actually paid to workers) continued to increase, albeit at a slower pace than a year earlier (Figure 1.14). Growth in real negotiated wages was positive between Q1 2025 and Q1 2026 in the 13 countries analysed as well as in the euro area as a whole, although generally lower than between Q1 2024 and Q1 2025 – only Canada, Finland and Sweden recorded a marked acceleration.
Nevertheless, in all countries except Denmark and the Netherlands, where they have fully recovered, and Mexico, where they did not fall with the inflation surge (OECD, 2025[1]), real negotiated wages remain below the levels observed just before the post-COVID‑19 inflation surge (Figure 1.14). Still, in Q1 2026, real negotiated wages were less than 2% below their Q1 2021 levels in about one‑third the countries analysed (Austria, Belgium, France and the United States).
The dynamics of real negotiated wages reflect a combination of factors, including the staggered and infrequent nature of collective bargaining, the time lag between the completion of negotiations and actual wage revisions, the infrequent use of automatic inflation indexation, and workers’ bargaining power (OECD, 2023[3]). Between the start of the cost-of-living crisis and Q1 2026, as bargaining rounds multiplied and affected a growing number of workers, negotiated wages regained more of the ground lost during the inflation surge.
Looking ahead, the European Central Bank’s (ECB) wage tracker forecasts annual nominal negotiated wage growth to stabilise at around 2.6% in Europe by the end of 2026, below the levels seen in 2025 and in the first quarter of 2026.1
Figure 1.14. Real negotiated wages continued to increase but remain below Q1 2021 levels
Copy link to Figure 1.14. Real negotiated wages continued to increase but remain below Q1 2021 levelsPercentage change in real negotiated wages (i.e. resulting from collective agreements) since Q1 2021
Note: LS: wages including lump sums and/or special payments. International comparability of data on negotiated wages is affected by differences in definitions and measurement. Statistics are representative of all employees covered by a collective wage agreement for Austria, Belgium, the euro area (20), Finland, France, Germany, Italy, the Netherlands, Sweden and the United States. In Canada, statistics refer to collective bargaining settlements of all bargaining units covering 500 or more employees (units of 100 or more employees for the Federal Jurisdiction). In Denmark, statistics refer to collective agreements negotiated by the main union and employer confederations, FH and DA, and do not cover public sector employees and the academic unions (AC). For Australia, Canada and Mexico, statistics refer only to employees affected by an increase of the negotiated wage at date. For Denmark, wage increases cover basic wages, employer pension contributions, “fritvalg” (flexible benefit) accounts, and other collectively agreed elements (e.g. wages in the event of sickness absence or maternity/paternity leave) from the five major sectoral collective agreements (the “breakthrough agreements”) that set the financial framework for the subsequent minimum wage agreements or standard wage agreements to be negotiated in the private sector. Calculations are based on the assumption of full relative pass-through of centrally negotiated wage rates to actual wage adjustments (i.e. a 1% increase in the minimum wage or standard wage rate leads to a 1% increase in locally negotiated wages higher up the wage distribution). In Mexico, statistics cover mainly employees in the private sector and certain public enterprises operating under the Federal Labour Law. For Sweden, statistics refer to wage increases resulting from central collective agreements. Wage increases in Austria, Belgium, the euro area (20), Finland, Germany, Italy, the Netherlands, Sweden and the United States refers to the average increase in negotiated wages weighted by the employment composition for a reference year (Laspeyres index). For Australia, Canada, France and Mexico wage increases refer to the average increase in negotiated wages weighted by the number of employees affected of the period considered. Private sector in Germany refers to all industries excluding agriculture, public administration, education, health, and other personal services (Sections B to N of the NACE rev. 2). Private sector in Finland refers to non-financial, financial and insurance corporations. Percentage changes for Australia, Canada, Denmark, the euro area, France, Mexico and Sweden are OECD estimates based on published year-on-year percentage changes and can therefore only be calculated between two similar quarters (Q1 in this chart). Statistics for Australia, the euro area and Mexico refer to the percentage change in Q4 2025 (Q4 2024 and Q4 2023) since Q4 2020.
Source: OECD calculations based on Trends in Federal Enterprise Bargaining (Department of Employment and Workplace Relations, DEWR) for Australia; index of agreed minimum wages (Statistics Austria) for Austria; index of agreed wages (Federal Public Service, FPS) for Belgium; Major wage settlements (Economic and Social Development Canada, ESDC) for Canada; Statistics Committee, Status Report, 3rd quarter 2025 (Ministry of Economic Affairs) for Denmark; negotiated wages, euro area 20, Quarterly (European Central Bank) for the euro area; the Index of Negotiated Wages and Salaries (Statistics Finland) for Finland; Negotiated wages France (Banque of France) for France; index of collectively-agreed hourly earnings (Destatis) for Germany; hourly index of wages according to collective labour agreements (Istat) for Italy; nominal contractual pay increases in federal and local jurisdictions (Secretaría del Trabajo y Previsión Social, STPS) for Mexico; CAO wages, contractual wage costs and working hours; index (Central Bureau of Statistics, CBS) for the Netherlands; short-term statistics, wages and salaries (Mediation Office) for Sweden; Employment Cost Index (BLS) for the United States; and OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 10 June 2026).
1.2.2. The wages of the lowest-paid workers have proved more resilient than median wages to the inflation surge
Real minimum wages remain above January 2021 levels but their growth is slowing down
Real minimum wages increased year-on-year but the rate of growth is slowing down. In April 2026, their annual growth rate was positive in nearly two‑thirds of the 30 OECD countries that have a national minimum wage in place (Figure 1.15, Panel A). However, real minimum wages increased less between April 2025 and April 2026 than between April 2024 and April 2025 in about two‑thirds of countries; and their median annual growth rate was 1.4% in April 2026, compared with 2.3% in April 2025. The real minimum wage decreased between April 2025 and April 2026 in 11 countries: Australia, Belgium, Canada, France, Greece, Luxembourg, New Zealand, Poland, Spain, Türkiye and the United States.14
The real statutory minimum wage was also higher in April 2026 than in April 2021 in virtually all OECD countries with a national statutory minimum wage (Figure 1.15, Panel B). The real minimum wage was 11% higher in April 2026 than in April 2021 on average across these 30 countries, and its median increase, unaffected by outliers (e.g. Mexico), was 9%. The real minimum wage was lower in April 2026 than in January 2021 in only two countries – Canada and the United States.
During the post-pandemic inflation surge and the ensuing cost of living crisis, national minimum wages were able to keep pace with inflation thanks to automatic or discretionary increases in the nominal minimum wage, introduced by countries to support the lowest earners (OECD, 2023[3]). By contrast, negotiated minimum wages have been less dynamic in countries without a national minimum wage (see Box 1.4). During 2022, the real gains resulting from automatic or discretionary increases in the nominal minimum wage quickly faded away on average across countries, as inflation continued to rise (Figure 1.15, Panel C). In 2023 and 2024, larger minimum wage adjustments and moderate inflation allowed the real minimum wage to catch up (in 2023) and exceed (in 2024) its January 2021 level on average across countries. During 2025 and early 2026 (before the surge in energy prices), still moderate inflation combined with minimum wage adjustments similar to those in 2024 on average led to a further increase in the average real minimum wage. In April 2026, the impact of the new surge in energy prices on real minimum wages has become visible (‑1.5% since the beginning of the year on average across countries), although rapid adjustment mechanisms in many countries can be expected to restore purchasing power in the future months. Nevertheless, minimum wage trends should be carefully monitored given the high level of geopolitical uncertainty.
Box 1.4. Minimum wages have grown by less in countries without a national minimum wage
Copy link to Box 1.4. Minimum wages have grown by less in countries without a national minimum wageIn countries without a national statutory minimum wage, wage floors are predominantly established through collective agreements. Eurofound has analysed minimum wages negotiated in ten representative low-paid occupations (such as cleaners, waiters, and cooks) in six European countries without a national minimum wage – i.e. Austria, Denmark, Finland, Italy, Norway and Sweden (Eurofound, 2025[36]).
Between January 2020 and January 2025, negotiated minimum wages decreased in real terms in most of the occupations analysed. This contrasts with the increases in the real minimum wage observed in virtually all OECD countries with a national statutory minimum wage during this period (OECD, 2025[37]; OECD, 2024[2]). There is some variation across countries: for example, real negotiated minimum wages in Italy have fallen increasingly behind those of the five other countries, while Austria recorded the strongest real growth.
Yet, several post-2022 collective agreements have incorporated specific mechanisms to protect low-wage workers. In Sweden, 2023 and 2024 collective agreements ensured that wages below a certain threshold received a higher percentage increase than wages above that threshold. Similarly, under 2024 collective agreements in Norway, workers in low-wage sectors often received wage increases above the general increase, and in several sectors (e.g. retail, and hotels and restaurants), lowest-paid employees received a wage increase above the sector’s general increase.
Figure 1.15. Real minimum wages remain above January 2021 levels and continue to rise
Copy link to Figure 1.15. Real minimum wages remain above January 2021 levels and continue to rise
Note: The real minimum wage for New Zealand in April 2026 is estimated assuming that the CPI for April 2026 remained unchanged from Q1 2026. For Poland and the Slovak Republic, the real minimum wage in April 2026 is estimated using the OECD price index (COICOP 1999), available up to December 2025, chained with the new national price indices (COICOP 2018), available from January 2026. “OECD (average)” is the unweighted average of the 30 OECD countries shown except the United States (weighted) and “OECD (Median) is the median values across the same countries. Canada (weighted) is a Laspeyres index based on minimum wage of provinces and territories (excluding the Federal Jurisdiction) weighted by the share of employees of provinces and territories in 2019. United States (weighted) is calculated as a Laspeyres index of applicable minimum wages across US states. Where states set a minimum wage above the federal level, the state rate is used; otherwise, the federal minimum wage applies. State minimum wages are weighted by each state’s share of non-farm private employment in 2019. US territories are excluded. For the Netherlands, the minimum wage is defined in hourly terms, while it was defined on a daily, weekly and monthly basis before 1 January 2024. The hourly minimum wage before 2024 is therefore estimated based on the weekly (or daily) minimum wage of an employee usually working 40 hours per week (or 8 hours per day). See Appendix Table 1.C.1 of the 2024 employment outlook (OECD, 2024[38]) for more details on the calculations.
Source: OECD calculations based on national data and OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 4 June 2026), Prices indices of Consumer Goods and Services (Statistics Poland) for Poland, and Consumer prices and prices of production statistics (Statistical Office of the SR) for the Slovak Republic.
Statutory minimum wages have increased more than median wages since 2021
In most OECD countries, minimum wages increased by more than median wages between 2021 and 2025, reflecting a compression of the wage distribution at the bottom. Figure 1.16 illustrates the changes in the minimum wage‑to-median wage ratio (or Kaitz index) during this period. There was an increase in 20 of the 30 countries analysed. On average, the Kaitz index rose from 54.9% in 2021 to 55.6% in 2025. Mexico exhibits by far the largest increase in the Kaitz index, reflecting the dramatic rise in the minimum wage illustrated in Figure 1.15, Panel B. In some countries, this wage compression at the bottom of the distribution led to a substantial increase in the share of employees earning the minimum wage, as the latter caught up with slightly higher wages (Box 1.5). Türkiye is an exception, with a substantial drop in the Kaitz index, in line with very strong real growth of average wages since 2021 (Figure 1.11).
This trend toward wage compression at the bottom of the distribution can be explained in part by the fact that wages negotiated in collective agreements generally take longer than the minimum wage to keep pace with inflation (OECD, 2023[3]), particularly in a context of low productivity growth (OECD, 2024[38]) and a long-term trend toward a reduction in the scope of collective agreements (OECD, 2019[39]). Further wage compression should be observed in 2026, as inflation fuelled by rising energy costs is expected to lead to substantial minimum wage increases while impacts on negotiated wages can be expected to take time.
Between 2021 and 2025, as minimum wages moved closer to median wages, minimum wage workers became less at risk of in-work poverty – i.e. their net disposable income increased relative to the poverty line (set at 50% of median net disposable income) (Figure 1.17).15 This is true for the majority of OECD countries analysed, as well as for the average across countries. The same remains valid when looking at the evolution of real income relative to an invariant “pre‑inflation” poverty line (anchored at its 2021 level, i.e. 50% of the 2021 median income), albeit to a slightly lesser extent, in line with the overall increase in real minimum wages over the period.
However, in a number of countries, particularly those where real wages remain close to or below pre‑pandemic levels, workers with middle and lower-middle earnings, who did not directly benefit from minimum wage increases, saw their economic security deteriorate (European Commission, 2025[31]). In addition, by narrowing pay differentials, wage compression can reduce early-career progression prospects, especially for workers with weaker bargaining power, by lowering the returns to incremental skill acquisition and job mobility within the low-wage segment. For the tax-and-benefit systems, compression may mechanically increase the caseload and budgetary cost of in-work supports and bottom-targeted transfers if more workers cluster near eligibility thresholds. A larger number of workers eligible for targeted transfers could also further weaken workers’ incentives to accept slightly higher-paid jobs or invest in short training spells. Indeed, in some countries the tax and benefit system is susceptible to generate low-wage traps mechanisms, i.e. gross wage gains at the bottom of the wage distribution translate into limited net income increases once taxes and social contributions are applied and means-tested benefits are withdrawn (as is the case, for example, in France, Germany or the United Kingdom, see Barreto et al. (2025[40])).
Figure 1.16. Statutory minimum wages have increased more than median wages
Copy link to Figure 1.16. Statutory minimum wages have increased more than median wagesGross minimum wage as a percentage of gross median wage of full-time workers
Note: The Gross minimum wage as a percentage of gross median wage of full-time workers for the United States is an estimate based on a Laspeyres index of applicable minimum wages across US states. Where states set a minimum wage above the federal level, the state rate is used; otherwise, the federal minimum wage applies. State minimum wages are weighted by each state’s share of non-farm private employment in 2019. US territories are excluded. The minimum wage used for Canada is an average of the provinces’ minimum wages (not including territories) weighted by employment shares. OECD is the unweighted average of the 30 OECD countries shown. The latest year available refers to 2024 for Chile and Ireland.
Source: OECD Data Explorer, “Minimum relative to average wages of full-time workers”, http://data-explorer.oecd.org/s/2ay.
Figure 1.17. In-work poverty risk has decreased for minimum wage workers
Copy link to Figure 1.17. In-work poverty risk has decreased for minimum wage workersActual and anchored poverty gaps for selected minimum-wage household types
Note: The actual (anchored) poverty gap is measured as the p.p. difference between (real) net equivalised disposable income expressed as a percentage of (2021) median equivalised disposable income and 50% (the poverty line is defined as 50% of median equivalised disposable income). OECD refers to the unweighted average of the 26 OECD countries shown. Results in Panel A refer to a single adult aged 40 without children, working full-time (40 hours per week) at the minimum wage. Results in Panel B refer to a one‑earner couple (both aged 40) with two children aged four and six; the earner works full-time at the minimum wage, while the non-working partner is not eligible for unemployment benefits. Other benefits, including social assistance, housing and family benefits, are available if eligibility and income conditions are met. Housing benefits are calculated assuming private market rent plus related charges amounting to 20% of the national average wage. Statistics refer to 2024 for the Netherlands.
Minimum wages used in the TaxBEN model correspond to their statutory levels in force on 1 January of each year. For the United States, the minimum wage refers to the statutory minimum wage in Michigan. For Canada, the minimum wage is calculated as an employment-weighted average of provincial minimum wages, as reported in the OECD minimum wages database. p.p: percentage point.
Source: OECD calculations based on OECD Tax-Benefit (output from the OECD tax-benefit model (Model version 2.8.0); and OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 18 May 2026).
Box 1.5. The share of minimum wage workers evolved along different trends across countries during the cost-of-living crisis
Copy link to Box 1.5. The share of minimum wage workers evolved along different trends across countries during the cost-of-living crisisMinimum wage hikes during the cost-of-living crisis could have resulted in an increase in the share of workers earning the minimum wage, if the minimum wage had caught up with wages higher up in the distribution. This phenomenon would be more pronounced in countries where wages close to the minimum wage (i) cover a large number of workers or (ii) benefit from only limited spillover effects from minimum wage increases.
However, among the 10 countries analysed, minimum wage increases between 2021 and 2024 were associated with a steady increase in the share of minimum wage workers in only two of them: France and Japan (Figure 1.18). By contrast, in six other countries (Ireland, the Netherlands, New Zealand, Spain, the United Kingdom and the United States), the share of minimum wage workers follows a U-shape curve over time (Figure 1.18, Panel A). It initially declined, mainly in 2022, possibly because many minimum wage workers were able to negotiate higher wages in response to soaring inflation, because of widespread labour shortages at the bottom of the wage distribution in the wake of the pandemic and during the cost-of-living crisis (Autor, Dube and McGrew, 2023[41]). The share of minimum wage earners then increased in 2023 – 2024, sometimes to levels above those of 2021 (e.g. in Ireland, Spain and the United Kingdom), as the minimum wage rose significantly, possibly regaining some of the ground lost to market wages. Finally, in Lithuania and Canada, the share of workers at the minimum wage did not increase. In Canada, this pattern likely occurs because market wages increased together with the minimum wage – median wages increased at the same pace as the minimum wage (see Figure 1.16 above). In Lithuania, persistent labour shortages in low-skill sectors, likely due to high outmigration of low-skilled workers (OECD, 2025[1]), may have conferred larger bargaining power to workers at the bottom of the distribution.
Figure 1.18. The share of minimum wage workers evolved along different trends across countries during the cost-of-living crisis
Copy link to Figure 1.18. The share of minimum wage workers evolved along different trends across countries during the cost-of-living crisis
Note: In Panel A, the proportion of minimum-wage earners cannot usually be established with certainty and can vary between data sources and studies. Counts of minimum-wage earners are commonly based on survey data or administrative records, which are affected by measurement error, both in earnings and in working hours. It is therefore common to include those with wages below the minimum and slightly above it. Data for Canada refer to employees earning less than 101% of the applicable provincial minimum wage (excluding territories) and do not take account of the federal minimum wage. Data for France refer to private sector employees (excluding salaried self-employed) aged 18‑64, in metropolitan France, earning less than four times the gross hourly minimum wage in their main job (i.e. highest paid in terms of gross wage); employees paid at minimum wage or below are those earning 105% or less of the statutory minimum wage, based on total remuneration, including bonuses and overtime. Data for Japan refer to enterprises with five or more employees (Basic Survey on Wage Structure) and use the impact rate, defined as the share of employees whose wage would fall below the revised minimum wage following the annual adjustment. For Lithuania and Spain, data refer to full-time employees only; for New Zealand, to employees aged 16‑64. For the Netherlands, the 2024 share of minimum-wage workers (around 5%) is not reported due to a change in the methodology used to identify minimum-wage jobs (https://www.cbs.nl/nl-nl/longread/diversen/2025/methodebeschrijving-minimumloonbanen--vanaf-2024--/3-berekeningswijze-minimumloonbanen). For the United States (federal and states), data refer to employees paid by the hour earning less than the applicable federal or state minimum wage, respectively.
In Panel B, the hourly minimum wage in the Netherlands (prior to 2024) and Spain is estimated from the statutory weekly and daily minimum wages, respectively, assuming a usual working time of 40 hours per week and 8 hours per day. Since the 1 January 2024, the minimum wage in the Netherlands is defined in hourly terms, while it was defined on a daily, weekly and monthly basis before. For Canada, the minimum wage is a Laspeyres index based on provincial and territorial minimum wages (excluding the Federal Jurisdiction), weighted by the share of employees in each province and territory in 2019. For the United States (weighted), statistics refer to a Laspeyres index of applicable minimum wages across US states. Where states set a minimum wage above the federal level, the state rate is used; otherwise, the federal minimum wage applies. State minimum wages are weighted by each state’s share of non-farm private employment in 2019. US territories are excluded.
Source: Panel A: Canada: OECD estimates based on the Canadian Labour Force Survey (LFS PUMFs); France: Bozio and Wasmer (2024[42]), and OECD estimates based on the Bases et Panels Tous Salariés; Ireland: Central Statistical Office (CSO), Labour Force Survey National Minimum Wage Estimates; Japan: Ministry of Health, Labor and Welfare, Minimum wage data and statistics (https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/toukei_00001.html); Lithuania: Statistics Lithuania, Number of employees by October the amount of the salary; Mexico: INEGI, Encuesta Nacional de Ocupación y Empleo (ENOE), Población ocupada según nivel de ingreso, nacional trimestral; Netherlands: Central Bureau of Statistics (CBS), Statistiek Werkgelegenheid en Lonen (SWL), minimumloners; New Zealand: Ministry of Business, Innovation and Employment (MBIE), Minimum Wage reviews (https://www.mbie.govt.nz/business-and-employment/employment-and-skills/employment-legislation-reviews/minimum-wage-reviews); Spain: Instituto Nacional de Estadística (INE), Encuesta Anual de Estructura Salarial, https://www.ine.es/jaxiT3/Tabla.htm?t=28182; United Kingdom: Low Pay Commission, estimates based on the Annual Survey of Hours and Earnings (ASHE); United States (federal): Bureau of Labor Statistics (BLS), Characteristics of minimum wage workers, 2024, https://www.bls.gov/opub/reports/minimum-wage/2024/; and the United States (states): OECD estimates based on the Current Population Survey Outgoing Rotation Group (CPS MORG). Panel B: OECD calculations based on national data.
Real wages have been more resilient to the post-pandemic inflation surge in low-wage sectors in some countries
In addition to the minimum wage, other factors may have influenced wage compression at the bottom of the distribution in recent years. In the United States, for example, it was the tightening of the labour market rather than minimum wage increases that enabled some low-wage workers to benefit from substantial wage increases (Autor, Dube and McGrew, 2023[41]).
In fact, since the onset of the post-pandemic inflation surge (i.e. since Q1 2021), there has been a clear trend towards wage compression between sectors in the United States, a phenomenon that has not been observed in Australia or the euro area (Figure 1.19). In the United States, accommodation and food service activities is the sector with the highest wage growth over the period Q1 2021 – Q1 2026, while the largest declines in real wages are all in sectors where wages are relatively high: finance, information and communication, education, business services, and construction. The situation is less clear-cut in some other OECD countries (Annex Figure 1.A.8): there are some trends toward wage compression between sectors in Chile, Mexico and Poland but, except in Chile, they are significantly less pronounced than in the United States.
Wage compression between sectors in the United States has ceased if we focus on the latest period Q1 2025 – Q1 2026 (Figure 1.19). The only two sectors where real wages decreased are low-wage sectors (accommodation and food service activities and administrative and support services), while finance exhibits the strongest wage growth.
Figure 1.19. There has been a clear trend towards wage compression between sectors since Q1 2021 in the United States but not in Australia or the euro area
Copy link to Figure 1.19. There has been a clear trend towards wage compression between sectors since Q1 2021 in the United States but not in Australia or the euro areaPercentage change in real hourly wages since Q1 2021
Note: Nominal hourly wages correspond to the wages and salaries component of the Labour Cost Index, adjusted for a constant industry structure for the euro area, and controls for additional occupational shifts for Australia and the United States. Real hourly wage is estimated by deflating the nominal hourly wage with the consumer price index (CPI-all items). The trough corresponds to the quarter in which real hourly wages reached their lowest level since Q1 2021. Industries are ranked by median wages in 2018 using the European Structure of Earnings Survey (SES). This ranking is broadly consistent when using 2019 median wage data from the United States Current Population Survey.
Industries are defined at the 1‑digit level using ANZSIC 2006 for Australia, NACE Rev. 2 for the euro area, and NAICS for the United States; as a result, industry classifications are not fully comparable across countries. For the United States, changes in real wages in Wholesale and retail trade correspond to employment-weighted averages of the wholesale trade and retail trade industries, based on non-farm payroll employment weights from Q4 2005. Similarly, changes in real wages in Arts and entertainment are derived from the Leisure and Hospitality and Accommodation and Food Services industries, using non-farm payroll employment weights from Q4 2005.
Source: OECD calculations based on the Wage Price Index (Australian Bureau of Statistics) for Australia; wages and salaries component of labour cost index by NACE Rev. 2 activity (Eurostat) for the euro area, the Employment Cost Index (U.S. Bureau of Labor Statistics) for the United States; and OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 17 June 2026).
1.2.3. The catching up of real wages relative to unit profits was already slowing down before the recent surge in energy prices
Unit labour costs16 (i.e. nominal compensation of employees divided by real GDP) rose more than unit profits (i.e. gross operating surplus divided by real GDP) between Q1 2025 and Q1 2026 in most OECD countries (Figure 1.20), continuing a trend that began around Q1 2023 (OECD, 2024[2]). While unit profits remained virtually unchanged in the majority of countries between Q1 2024 and Q1 2026 – the magnitude of the variation was greater than 5% in only one‑third of the 29 countries analysed –, unit labour costs increased significantly in many countries – by more than 5% in about three‑quarters of countries.
These developments, most of which pre‑date the recent surge in energy prices, still reflect the catching-up of purchasing power by wages and should not be interpreted as a warning sign of price‑wage spirals – i.e. a sustained acceleration in nominal wage growth passing through to domestic prices beyond its effects on productivity –, as (i) wages had not fully caught up with the inflation surge in half of OECD countries (see previous section), which suggests that, in these countries, unit profits continue to lose the ground gained during the inflation surge;17 and (ii) wage pressures are expected to stabilise at moderate levels for the remainder of 2026.18
Besides, there are signs that the catching-up of real wages might be slowing down. On average across countries, the increase in unit labour cost was slightly lower between Q1 2025 and Q1 2026 compared with the year before, while that of unit profits was slightly higher (see Figure 1.20, where the increase in the past year is represented by the difference between the top of the bar and the diamond). In addition, the relative contribution of unit labour costs to domestic price pressures has stabilised around pre‑pandemic levels in the euro area (Figure 1.21), while it is even decreasing below these levels in the United States.19,20
Figure 1.20. Profits continue to buffer rising labour costs
Copy link to Figure 1.20. Profits continue to buffer rising labour costsPercentage change since Q1 2024, seasonally adjusted data
Note: Unit labour costs and unit profits are calculated by dividing compensation of employees and gross operating surplus respectively, by real GDP. For Japan and Norway, gross operating surplus is approximated by deducting compensation of employees from nominal GDP – and hence also include unit net taxes. For Norway, the data are based on mainland Norway. The latest data available for the Netherlands refer to Q4 2025. “OECD” is the unweighted average of the 29 OECD countries shown in this Chart (not including Chile, Colombia, Costa Rica, Iceland, Israel, Korea, Mexico, New Zealand and Türkiye). “Euro area” refers to 21 Eurozone countries.
Source: OECD calculations based on OECD Data Explorer, “Quarterly GDP and components – income approach”, http://data-explorer.oecd.org/s/2az (accessed on 15 June 2026), Cabinet Office, Government of Japan, Economic and Social Research Institute (ESRI) Quarterly Estimates of GDP for Japan, and Statistics Norway, Quarterly National Accounts for Norway.
Figure 1.21. The contribution of unit profits to domestic price pressures has stabilised around pre‑pandemic levels in the euro area and is increasing in the United States
Copy link to Figure 1.21. The contribution of unit profits to domestic price pressures has stabilised around pre‑pandemic levels in the euro area and is increasing in the United StatesContribution to the GDP deflator, year-on-year percentage changes, seasonally adjusted data
Note: Euro area refers to 21 Eurozone countries. Unit labour costs, unit profits and unit taxes less subsidies are calculated by dividing compensation of employees, gross operating surplus and taxes less subsidies on productions and imports, respectively, by real GDP. Compensation of employees, gross operating surplus, taxes less subsidies on productions and imports, gross domestic products and deflators are denominated in local currencies. For the United States, changes in the GDP deflator are reported net of statistical discrepancies.
Source: OECD calculations based on OECD Data Explorer, “Quarterly GDP and components – income approach”, http://data-explorer.oecd.org/s/2az (accessed on 15 June 2026).
1.3. Young labour market entrants face particular labour market difficulties
Copy link to 1.3. Young labour market entrants face particular labour market difficultiesYoung labour market entrants have been increasingly exposed to unemployment compared to the rest of the working age population, following trends that vary according to their education level. While the unemployment rate of young college graduates has been rising relative to that of the overall population in all countries analysed since before the COVID‑19 pandemic, young entrants without a graduate degree have seen a significant increase in their unemployment gap relative to the overall population more recently in Canada and the United States,21 but not in Australia and the euro area (Figure 1.22).
Figure 1.22. Young labour market entrants have been increasingly exposed to unemployment compared to the rest of the working age population
Copy link to Figure 1.22. Young labour market entrants have been increasingly exposed to unemployment compared to the rest of the working age population
Note: The unemployment gap is measured as the p.p. difference between the unemployment rate of recent non-college or college graduates and the overall unemployment rate of non-enrolled persons aged 15‑64 (16‑64 for the United States). Panel A refers to recent college graduates, defined as persons not currently enrolled in education, aged 23‑29, holding a bachelor’s degree or higher. Panel B refers to recent non-college graduates, defined as persons not currently enrolled in education, aged 22‑25, holding a non-university degree. All series are smoothed using a four‑quarter moving average. European Union (27) refers to the weighted average of the 27 EU countries. p.p: percentage point.
Source: OECD estimates based on the Australian Labour Force Survey, the Canadian Labour Force Survey (PUMFs), the European Union Labour Force Survey (EU-LFS) for the European Union, and the Current Population Survey (CPS) for the United States.
1.3.1. So far, the role of LLMs in explaining the difficulties encountered by young people entering the labour market appears to be limited
Recent advances in generative AI and Large Language Models (LLMs) may contribute to explaining the particular difficulties faced by young labour market entrants if these technologies tend to replace human labour.22 To date, evidence on this subject is mixed and focusses mainly on the United States.23 Some studies find no significant employment effects of generative AI (Chen and Stratton, 2026[43]; Gimbel et al., 2025[44]; Humlum and Vestergaard, 2025[45]), while others suggest that generative AI may indeed have a disproportionate impact on the employment prospects of early-career workers (with no evidence that this impact is particularly concentrated among college graduates) (Azar, Gine and Sanz-Espín, 2025[46]; Brynjolfsson, Chandar and Chen, 2025[47]; Klein Teeselink, 2025[48]; Lichtinger and Hosseini Maasoum, 2025[49]; Liu, Wang and Yu, 2025[50]; Lodefalk et al., 2026[51]). Nevertheless, some of these results might be biased by the greater sensitivity of occupations exposed to generative AI to macroeconomic developments, as these occupations are overrepresented in certain sectors that are particularly sensitive to capital cost and general economic uncertainty (e.g. information, finance and insurance, or professional and technical services), so that hiring is quickly reduced in these sectors when macroeconomic conditions worsen. Thus, what may seem an effect of the diffusion of LLMs is in fact simply the result of worsening macroeconomic environment (Curto Millet and Iscenko, 2026[52]; Frank et al., 2026[53]).
Data from Australia, Canada, the European Union and the United States suggest that the role of LLMs in explaining the unemployment gap of young labour market entrants remains limited, both for those with and without a graduate degree. Indeed, the data indicate divergent trends between, on the one hand, AI hiring (used as a proxy for AI use by firms), and, on the other hand, (i) unemployment gaps of young labour market entrants or (ii) online job postings in occupations exposed to AI language modelling capabilities24 (which will be referred to as “high LLM-exposure occupations”).
With regard to college graduates, there has been an upward trend in the unemployment gap since before the pandemic (Figure 1.22, Panel A), and therefore well before firms began turning to LLMs. In addition, trends in AI hiring from LinkedIn25 (Figure 1.23, Panel A) suggest that firms began using intensively LLMs from mid-2023 to early 2024 (depending on the country),26 with a dramatic increase in LLM use since then.27 As no turning point in the unemployment gap trend for college graduates is visible around those dates (Figure 1.22, Panel A) in any of the countries analysed, it can be concluded that LLMs diffusion is unlikely to be the main factor behind the increase in the unemployment gap for college graduates.
Figure 1.23. There have been divergent trends between AI hiring, and online job postings in high LLM-exposure occupations relative to low-LLM exposure occupations
Copy link to Figure 1.23. There have been divergent trends between AI hiring, and online job postings in high LLM-exposure occupations relative to low-LLM exposure occupations
Note: In Panel A, the relative growth in AI talent hiring is defined as the difference between the year-on-year growth rate of the LinkedIn AI hiring rate and the year-on-year growth rate of the overall LinkedIn hiring rate in the same country. The LinkedIn hiring rate is measured as the share of LinkedIn members who added a new employer in a given period, divided by the total number of LinkedIn members in the corresponding location. The AI hiring rate is computed using the same methodology but restricted to members classified as AI talent. AI talent includes LinkedIn members who have explicitly listed at least two AI-related skills on their profile and/or who are currently employed or have previously been employed in an AI-related occupation. Values are shown as 12‑month moving averages. A positive value indicates that AI talent hiring is growing faster than overall hiring. European Union (median) refers to the median relative growth in AI-talent hiring across 24 EU countries (not including Bulgaria, Malta and the Slovak Republic). Panel B shows the ratio of the index of new online job postings in high language‑model-exposed occupations (top quintile of the Language Model Exposure score) to the index of new online job postings in low language‑model-exposed occupations (bottom quintile of the Language Model Exposure score), as developed by Felten, Raj and Seamans (2023[54]). Series are smoothed using a four‑quarter moving average and indexed to 100 in Q4 2019. New online job postings correspond to job advertisements first published in a given quarter. European Union (median) refers to the median relative index of new job postings in high versus low LLM-exposed occupations across 15 EU countries (Austria, Belgium, Czechia, Denmark, France, Germany, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Poland, Portugal, Spain and Sweden), data for each individual country are provided in Annex Figure 1.A.11. LLM: large language model.
Source: OECD.AI (2026), data from LinkedIn Economic Graph, last updated 2026‑2003‑06, accessed on 2026‑2005‑13, https://oecd.ai/ (Panel A); and OECD calculations based on Lightcast online job postings and Language Model Exposure score from Felten, Raj and Seamans (2023[54]) made available in ISCO classification by Nurski (2025[55]) (Panel B).
A similar argument can be made for young labour market entrants without a college degree (Figure 1.22, Panel B). While the unemployment gap for this group in the United States – which started increasing around early 2024 – could potentially have been caused by LLMs diffusion, this is not the case in Australia – where the unemployment gap started declining in mid-2024 – nor in Canada or the euro area – where it has remained virtually flat since at least early 2024.
In addition, in all countries analysed, trends in online job postings (from Lightcast data) in high-LLM exposure occupations relative to low-LLM exposure occupations exhibits no break at the end of 2023 – which, as discussed, marks the start of intensive use of LLMs (Figure 1.23, Panel B).28 By contrast, job postings in LLM-exposed occupations appear, as expected (see the discussion above), more sensitive to macroeconomic shocks: posting went down during the COVID‑19 pandemic, improved in the ensuing recovery, and contracted again during the subsequent inflation shock, logistic disruption and monetary tightening. These observations remain unchanged when focussing on junior-level job postings in the United-States (Box 1.6).
The conclusion that LLMs diffusion has not significantly impacted the employment prospects of young entrants remain preliminary, and LLMs may well have a stronger impact in the future, as technological advances and the use of LLMs by firms develops. These broad observations may also mask substantial impacts in specific occupations or sectors, and further monitoring of the effects of LLMs advances on the labour market and the resulting skill needs is necessary.
Box 1.6. Online job postings for junior positions in the United States have not declined disproportionately in LLM-exposed occupations when firms began using LLMs
Copy link to Box 1.6. Online job postings for junior positions in the United States have not declined disproportionately in LLM-exposed occupations when firms began using LLMsLightcast data for the United States include a job seniority indicator that classifies job postings based on explicit mentions in the job title or role description indicating that the position targets a junior or senior candidate (e.g. specific words such as “junior”, “senior”, “entry-level”, “lead”, “principal”, as well as structured phrases related to seniority).
As with job postings overall, the end of 2023 – which marks the start of LLM use according to job postings and hiring data (Figure 1.24, Panel A) – does not correspond to either the start or a sustained acceleration of a reduction in junior postings in high-LLM exposure occupations relative to low-LLM exposure occupations (Figure 1.24, Panel B). Junior job postings in LLM-exposed occupations have, however, been more sensitive to macroeconomic shocks, and to the pandemic in particular, because, as noted above, these occupations are also the most exposed to these shocks (e.g. information, finance and insurance, or professional and technical services) (Curto Millet and Iscenko, 2026[52]), and it has been shown that, during downturns, employers reduce especially hiring in entry-level jobs which require little experience – see e.g. Raaum and Røed (2006[56]) and Brunner and Kuhn (2013[57]).
Figure 1.24. There have been divergent trends between AI hirings, and junior job postings in high LLM-exposure occupations relative to low-LLM exposure occupations in the United States
Copy link to Figure 1.24. There have been divergent trends between AI hirings, and<em> junior</em> job postings in high LLM-exposure occupations relative to low-LLM exposure occupations in the United States
Note: In Panel A, the relative growth in AI talent hiring is defined as the difference between the year-on-year growth rate of the LinkedIn AI hiring rate and the year-on-year growth rate of the overall LinkedIn hiring rate in the same country. The LinkedIn hiring rate is measured as the share of LinkedIn members who added a new employer in a given period, divided by the total number of LinkedIn members in the corresponding location. The AI hiring rate is computed using the same methodology but restricted to members classified as AI talent. AI talent includes LinkedIn members who have explicitly listed at least two AI-related skills on their profile and/or who are currently employed or have previously been employed in an AI-related occupation. Values are shown as 12‑month moving averages. A positive value indicates that AI talent hiring is growing faster than overall hiring. AI-related postings are identified using keyword searches covering both AI occupations (e.g. “Artificial Intelligence”, “Machine Learning”, “Data Science”) and generative AI terms (e.g. “Generative AI”, “Large Language Models”, “ChatGPT”). The share of AI-related job postings is based on a seven‑day trailing average. A value of 1% indicates that 1% of all job postings contain AI-related keywords. Further methodological details are available at: https://github.com/hiring-lab/ai-tracker (LinkedIn Hiring Lab). In Panel B, statistics refer to ratios of the online job postings index for junior positions in language‑model-exposed occupations (Q2–Q5) relative to junior positions in low language‑model-exposed occupations (first quintile of the Language Model Exposure score). Exposure quintiles are based on the occupation-level Language Model Exposure score developed by Felten, Raj and Seamans (2023[54]). Junior positions are identified using Lightcast’s job seniority classifier, which flags postings as “Junior” based on job titles and text content; the classifier prioritises accuracy over recall, implying that not all junior postings are identified. New online job postings correspond to job advertisements first published in a given quarter. Series are shown as four‑quarter moving averages and indexed to 100 in Q4 2019. AI: artificial intelligence. LLM: large language model.
Source: OECD.AI (2026), data from LinkedIn Economic Graph, last updated 2026‑2003‑06, accessed on 2026‑2005‑13, https://oecd.ai/, Indeed AI Tracker, https://data.indeed.com/#/ai (accessed on 12 May 2026), and OECD calculations based on Lightcast online job postings and Language Model Exposure score from Felten, Raj and Seamans (2023[54]) made available in ISCO classification by Nurski (2025[55]) (Panel B).
1.3.2. Young labour market entrants may have been particularly vulnerable to the recent weakening of labour markets and long-term changes in technology and skill needs
Although resilient, OECD labour markets have been showing some signs of weakening since 2023 (OECD, 2024[2]; 2025[1]), which may have been felt more strongly by young labour market entrants (Curto Millet and Iscenko, 2026[52]). Young entrants are indeed more likely to be looking for a job or working on a fixed-term contract, which are the first to be terminated when the labour market deteriorates. In the United States, for example, hiring has fallen to its lowest level in a decade,29 preventing many young individuals from entering employment. Specific sectoral problems have added to these difficulties, such as job cuts in the high-tech, information, and business services sectors, following excessive hiring during the pandemic.30,31 Working conditions may also appear less favourable than expected – lengthening the time it takes to find a job that meets expectations – as, even before the recent surge in energy prices, real wages remained below their 2021 levels in many countries (Section 1.2), and some of the flexibility gained from the remote revolution has been rolled back.32 Country-specific factors may also have played a role, such as the record increase in non-permanent residents in Canada in 2023 that led to a disproportionate rise in the young working-age population.33
On the longer-run, structural changes have been particularly unfavourable to graduate entering the labour market, which may explain the long-term upward trends in unemployment for this group compared to the rest of the working age population. Several studies show that the rapid growth of digital technologies has been fuelling a wave of “digital offshoring” or “telemigration” (see Broecke (2024[58]) for a review) In response to rapidly evolving skill needs, hiring models are also increasingly relying on demonstrated skills rather than formal credentials (OECD, 2025[59]) – see also Chapter 4. From a demographic perspective, longer working lives may have reduced hiring in management occupations relative to other occupations by increasing the proportion of older workers, who tend to be over-represented in these occupations (Aspen Economic Strategy Group, 2026[60]). Some of these trends may have been fostered by the COVID‑19 pandemic – which has boosted online leaning and digital offshoring – and recent labour shortages – which may have encouraged employers to offshore and relax degree requirements.
1.4. Concluding remarks
Copy link to 1.4. Concluding remarksOECD labour markets have remained resilient. However, they have shown further signs of weakening, i.e. rising unemployment, further slowdowns in the growth in employment and labour force participation, a continuation of the decline in labour shortages, and a slowdown in real wage recovery. Young individuals entering the labour market may have been particularly vulnerable to this recent labour market slowdown in some countries.
Several factors – both cyclical and structural – could explain these developments. The catching up of real wages has brought real labour costs back to their pre‑pandemic levels in many – albeit not all – countries, the reduction in labour hoarding may have led to an increase in separations, the reduction in working hours has slowed down, and the use of generative AI by firms may have begun to automate certain jobs – although the data show little evidence of this to date. These factors may also be linked to the developments in labour productivity observed since the onset of the pandemic. In particular, labour hoarding has mechanically lowered labour productivity in EU countries, while certain sectors that play a central role in AI adoption account for the strong productivity performances of the United States over the past years.
Despite signs of weakening labour markets, structural labour shortages persist. While labour market tightness is now less pronounced than at the onset of the pandemic in many countries, it remains higher than during most of the pre‑pandemic decade and, in particular, than during comparable stages of the business cycle. A number of factors could contribute to this trend, including population ageing, the digital transformation, the energy transition, and poor job quality in some sectors.
Addressing these shortages requires co‑ordinated policy efforts across various areas of labour market policy (Filippucci, Laengle and Marcolin, 2025[11]), and regional variations in shortages in some countries require particular attention to local implementation. Encouraging the participation of underrepresented groups in the labour market – i.e. older people (e.g. by removing mandatory retirement, see Chapter 6), women, or migrants – could increase the supply of labour (OECD, 2025[1]). Streamlining occupational licenses and non-compete agreements (see Chapter 5) could promote labour mobility and matching efficiency. Education, reskilling and upskilling policies (Chapter 4) have a role to play in developing the skills needed for the digital transformation (OECD, 2023[3]) and the energy transition (OECD, 2024[2]), while ensuring that older workers continue to have meaningful employment in a context of demographic ageing (OECD, 2025[1]). Minimum wage and collective bargaining systems should also ensure that work offers attractive working conditions.
Labour market policies (e.g. unemployment benefits, active labour market policies, collective bargaining) will also remain essential in addressing the labour market impacts of persistent economic and geopolitical uncertainty and elevated energy costs. In particular, in a context of stabilising wage pressure and rising inflation, wage setting institutions – minimum wage and collective bargaining – have a role to play in ensuring that the cost of inflation is fairly distributed between workers and employers. Collective bargaining and social dialogue should also ensure that low-wage workers whose pay exceeds the minimum wage – and who therefore do not directly benefit from minimum wage increases – are protected against inflation. Collective bargaining can help identify solutions tailored to sectors and firms’ varying capacities to sustain further wage increases. In particular, the timing of wage negotiations matters, and could be adjusted through the frequency of bargaining rounds, early wage negotiations, bridge agreements and one‑off payments. Wage‑setting institutions could play an even bigger role than they did during the post-pandemic inflationary episode, when more dynamic labour markets were providing stronger support for market wages.
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Annex 1.A. Additional results
Copy link to Annex 1.A. Additional resultsAnnex Figure 1.A.1. Employment rates have increased among prime aged and older individuals, but not among youth
Copy link to Annex Figure 1.A.1. Employment rates have increased among prime aged and older individuals, but not among youthDecomposition of the overall change in the unemployment rate, employment rate and labour force participation rate (persons aged 15‑64) by age group between Q1 2024 and Q1 2026
Note: Statistics for Iceland and the United Kingdom refer to the change in Q4 2023-Q4 2025. OECD is the unweighted average of the 38 OECD countries shown in this chart. Euro area (21) refers to the 21 Eurozone countries. p.p: percentage point.
Source: OECD Data Explorer, “Infra-annual labour statistics”, https://data-explorer.oecd.org/s/4sf (accessed on 17 June 2026).
Annex Figure 1.A.2. Gender employment and participation gaps have narrowed
Copy link to Annex Figure 1.A.2. Gender employment and participation gaps have narrowedMale‑to-female differences in employment and labour-force participation rates (p.p.), persons aged 15‑64, seasonally adjusted
Note: The gender employment gap (participation gap) is defined as the male‑to-female p.p. difference in employment rate (labour force participation rate). Statistics for Iceland refer to the second quarter. OECD is the unweighted average of the 38 OECD countries shown in this Chart. Euro area refers to the 21 Eurozone countries. Statistics for Iceland and the United Kingdom refer to Q4 2023, Q4 2024, and Q4 2025. p.p: percentage point.
Source: OECD calculations based on OECD Data Explorer, “Employment rate”, http://data-explorer.oecd.org/s/2al (accessed on 16 June 2026), and OECD Data Explorer, “Labour force participation rate”, http://data-explorer.oecd.org/s/2am (accessed on 16 June 2026).
Annex Figure 1.A.3. The share of working-age migrants in employment or participating in the labour market has increased in some countries
Copy link to Annex Figure 1.A.3. The share of working-age migrants in employment or participating in the labour market has increased in some countriesDecomposition of the overall change in the employment rate and labour force participation rate (persons aged 15‑64) by country of birth between Q1 2024 and Q1 2026
Note: Changes in employment and labour force participation rates shown in this chart may differ from those reported in Figure 1.3 because the Eurostat data used here are not seasonally adjusted, whereas the OECD data used in Figure 1.3 are seasonally adjusted. Euro area refers to the 21 Eurozone countries, and European Union to the 27 EU member countries. Statistics for Iceland refer to Q4 2023 and Q4 2025. p.p: percentage point.
Source: OECD calculations based on Eurostat, Population by sex, age, country of birth and labour status (Table lfsq_pgacws), https://doi.org/10.2908/LFSQ_PGACWS (accessed on 17 June 2026).
Annex Figure 1.A.4. Labour market tightness is close to pre‑COVID 19 levels in most sectors in the euro area and the United States
Copy link to Annex Figure 1.A.4. Labour market tightness is close to pre‑COVID 19 levels in most sectors in the euro area and the United Statesp.p. difference in job vacancy rates by industry relative to their Q4 2019 levels
Note: Industries are ranked by median wages in 2018 using the European Structure of Earnings Survey (SES). This ranking is broadly consistent when using 2019 median wage data from the United States Current Population Survey. The definition of vacancies is not harmonised across countries (see Figure 1.5 for further details).
For Australia, job vacancy rates by industry are OECD estimates based on vacancies from the Job Vacancy Survey and employees from the Labour Force Survey. Job vacancy rates by industry are seasonally adjusted for the United States. For Australia and the euro area, series are smoothed using a Hodrick-Prescott filter.
Industries refer to ANZSIC 2006 (1‑digit) for Australia, NACE Rev. 2.1. (1‑digit) for the euro area and NAICS for the United States and are therefore not fully comparable across countries. For the euro area (21), “Information and communication” is calculated as the unweighted average of the job vacancy rates for NACE Rev. 2.1 sections J (“Publishing, broadcasting, and content production and distribution activities”) and K (“Telecommunication, computer programming, consulting, computing infrastructure and other information service activities”). In the United States, “Business services” includes administrative services, “Real estate” includes rental and leasing, “Transportation and storage” includes warehousing and utilities, while “Human health and social work activities” and “Education” cover the private sector only. Euro area (21) refers to the 21 euro area countries. p.p: percentage point.
Source: Job Vacancies (Australian Bureau of Statistics, ABS) for Australia; Job vacancy statistics by NACE Rev.2.1. activity for the euro area; and Job Openings and Labor Turnover Survey (Bureau of Labor Statistics, BLS) for the United States.
Annex Figure 1.A.5. Incidence of part-time workers and temporary workers
Copy link to Annex Figure 1.A.5. Incidence of part-time workers and temporary workersAnnualised percentage change, 2019-2023 and 2023-2025
Note: In Panel A, part-time work refers to employed persons usual working less than 30 hours per week. Statistics for Korea refer to actual hours worked, and to employees for the United States. OECD is the unweighted average of the 38 OECD countries. In Panel B, OECD is the unweighted average of 34 OECD countries shown (not including Australia, Israel, Mexico and the United States). European Union (27) is the weighted averages of the 27 EU countries.
Source: OECD Data Explorer, “Incidence of full-time and part-time employment based on OECD-harmonised definition”, https://data-explorer.oecd.org/s/4or (accessed on 8 June 2026), and OECD Data Explorer, “Employment by permanency of the job - Incidence”, https://data-explorer.oecd.org/s/4os (accessed on 8 June 2026).
Annex Figure 1.A.6. Labour productivity growth has been diverging between OECD countries
Copy link to Annex Figure 1.A.6. Labour productivity growth has been diverging between OECD countriesAnnualised percentage change in real GDP per hours worked, 1995-2019 and 2019‑2025
Note: “OECD (average)” and “OECD (median)” are the unweighted average and the median across the 36 OECD countries shown in this Chart (not including Chile and Türkiye), respectively. Euro area refers to the averages of 20 Eurozone countries and European Union to the averages of 27 EU countries. 1995-2019 refers to 1997-2019 for Canada and 1996-2019 for Estonia. 2019-2025 refers to 2019-2024 for Australia, Colombia, Costa Rica, Israel, Japan, Korea, Mexico, New Zealand and the United Kingdom.
Source: OECD Data Explorer, “Productivity database”, https://data-explorer.oecd.org/s/4rg (accessed on 15 June 2026).
Annex Figure 1.A.7. Nominal and real wage index by country since Q4 2019
Copy link to Annex Figure 1.A.7. Nominal and real wage index by country since Q4 2019Index base 100 in Q4 2019
Note: Unless otherwise indicated, nominal hourly wages correspond to the wages and salaries component of the Labour Cost Index, adjusted for a constant industry structure. The constant-industry-structure adjustment of average hourly earnings has been estimated and revised by the OECD using total wages and salaries by industry from the 2019 Annual National Accounts for Costa Rica and Mexico. For Japan, Korea and the United Kingdom, nominal hourly wages refer to actual wages, without adjustment for compositional shifts; comparisons with other countries should therefore be interpreted with caution. For Australia, Canada, Chile and New Zealand, nominal hourly wages control for additional compositional effects depending on the country, including regions, job and worker characteristics, sex, and occupations.
Nominal wage series are seasonally adjusted for all countries except Canada, Costa Rica, Israel, Mexico, New Zealand and Switzerland. Statistics cover only the private sector for Japan and Korea, and the business sector (NACE Rev. 2 sections B to N) for the Republic of Türkiye. For Mexico, nominal hourly wages are affected by a significant degree of unreported income. Series for Switzerland refer to an OECD estimate based on published year-on-year percentage change and can therefore only be calculated between two similar quarters (Q4 in this chart) with reference to Q4 2019. Due to high seasonality, series for Israel are shown for comparable quarters (Q1 in this chart) with reference to Q1 2019.
Real hourly wages are calculated by deflating nominal hourly wages using the consumer price index (CPI, all items). For Poland and the Slovak Republic, real wages in Q1 2026 are estimated using the OECD price index (COICOP 1999), available up to Q4 2025, chained with the new national price indices (COICOP 2018), available from Q1 2026.
In Slovenia, the sharp increase in wages in Q4 2025 mainly reflects the payment of a one‑off winter bonus in November and December recently introduced by the Act on the Right to the Winter Bonus and the Reform of Tax Base Assessment. The rise was also influenced by fewer hours worked and, to a lesser extent, by the reform of the public-sector wage system, which contributed to particularly strong increases in education and in public administration (Sections O and P of NACE Rev. 2).
“OECD (median)” refers to the median across the 37 OECD countries shown in the chart (not including Colombia). Euro area (21) refers to the 21 Eurozone countries.
Source: OECD calculations based on national wage indices. For a full description of country-specific data sources, see Figure 1.15 in OECD (2025[1]), OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?, https://doi.org/10.1787/194a947b-en. OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw, and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 17 June 2026).
Annex Figure 1.A.8. Change in real wage by industry in some non-euro area OECD countries
Copy link to Annex Figure 1.A.8. Change in real wage by industry in some non-euro area OECD countriesPercentage change in real hourly wages since Q1 2021
Note: Real wages are obtained by deflating nominal wages by consumer price inflation (all items). For Poland, real wages in Q1 2026 are estimated using the OECD price index (COICOP 1999), available up to Q4 2025, chained with the new national price indices (COICOP 2018), available from Q1 2026. Industries are ranked by the median wage in 2018 in the European Structure of Earnings Survey (SES). The ranking of industries is broadly consistent when 2019 data on median wages from the Current Population Survey of the United States are used.
Industries are ordered from low- to high-pay industries as follows: 1. Accommodation and food service, 2. Administrative and support service, 3. Arts, entertainment and recreation, 4. Wholesale and retail trade; 5. Transportation and storage, 6. Manufacturing, 7. Other service, 8. Real estate activities, 9. Construction; 10. Human health and social work, 11. Education, 12. Professional activities, 13. Information and communication, and 14. Finance and insurance.
Series are not seasonally adjusted for Canada, Chile, Japan, Korea, Mexico and the United Kingdom. Statistics for the accommodation and food service sector are not available for Denmark. For Japan, statistics refer to regular employees in establishments with five or more regular employees. Regular employees are workers employed indefinitely or employed under a contract for a period of one month or longer. For Türkiye, statistics exclude public administration and defence; compulsory social security; education; human health and social work activities; arts, entertainment and recreation; and other service activities. Nominal hourly wage presents a significant amount of unreported income for Mexico. The trough refers to the quarter where real hourly wages were at their lowest value for the indicated industry since Q1 2021.
Source: OECD calculations based on the Wage Price Index (Australian Bureau of Statistics) for Australia; Fixed weighted index of average hourly earnings for all employees (Statistics Canada) for Canada; the Labour cost index by NACE Rev. 2 activity (Eurostat) for Czechia, Denmark, Hungary, Iceland, Norway, Poland and Sweden; Monthly Labour Survey (Japanese Ministry of Health, Labour and Welfare) for Japan; Labour Force Survey at Establishments (Korean Ministry of Employment and Labour) for Korea; Nacional de Ocupación y Empleo, Encuesta Telefónica de Ocupación y Empleo, Encuesta Nacional de Ocupación y Empleo Nueva Edición (Instituto Nacional de Estadística y Geografía, Mexico) for Mexico; Labour Cost Index (Statistics New Zealand) for New Zealand; and Monthly Wages and Salaries Survey (UK Office for National Statistics) for the United Kingdom; OECD Data Explorer, “Consumer price indices (CPIs, HICPs), COICOP 1999”, http://data-explorer.oecd.org/s/2aw and “Consumer price indices (CPIs), COICOP 2018”, http://data-explorer.oecd.org/s/2ax (accessed on 17 June 2026).
Annex Figure 1.A.9. Unemployment rates among young labour market entrants with and without a college degree
Copy link to Annex Figure 1.A.9. Unemployment rates among young labour market entrants with and without a college degree
Note: In Panel A, recent non-college graduates are persons not currently enrolled in education, aged 22‑25, holding a non-university degree. In Panel B, recent college graduates are persons not currently enrolled in education, aged 23‑29, holding a bachelor’s degree or higher. European Union (27) refers to the weighted average of the 27 EU countries.
Source: OECD estimates based on the Australian Labour Force Survey, the Canadian Labour Force Survey (PUMFs), the European Union Labour Force Survey (EU-LFS) for the European Union, and the Current Population Survey (CPS) for the United States.
Annex Figure 1.A.10. Trends in AI online job postings
Copy link to Annex Figure 1.A.10. Trends in AI online job postingsShare of job postings on Indeed containing Artificial Intelligence (AI) terms in percentage
Note: AI-related postings are identified using keyword searches covering both AI occupations (e.g. “Artificial Intelligence”, “Machine Learning”, “Data Science”) and generative AI terms (e.g. “Generative AI”, “Large Language Models”, “ChatGPT”). The share of AI-related job postings is based on a seven‑day trailing average. A value of 1% indicates that 1% of all job postings contain AI-related keywords. Further methodological details are available at: https://github.com/hiring-lab/ai-tracker (LinkedIn Hiring Lab).
Source: Indeed AI Tracker, https://data.indeed.com/#/ai (accessed on 12 May 2026).
Annex Figure 1.A.11. Relative index of new job postings in high- versus low-LLM exposure occupations in European countries
Copy link to Annex Figure 1.A.11. Relative index of new job postings in high- versus low-LLM exposure occupations in European countriesRatio of the index of new online job postings in high LLM-exposed occupations to the index of new online job postings in low LLM-exposed occupations, four‑quarter moving averages, base 100 in Q4 2019, European countries
Note: Panels A and B show the ratio of the index of new online job postings in high language‑model-exposed occupations (top quintile of the Language Model Exposure score) to the index of new online job postings in low language‑model-exposed occupations (bottom quintile of the Language Model Exposure score), as developed by Felten, Raj and Seamans (2023[54]). Series are smoothed using a four‑quarter moving average and indexed to 100 in Q4 2019. New online job postings correspond to job advertisements first published in a given quarter. LLM: large language model. In Panels C and D, the relative growth in AI talent hiring is defined as the difference between the year-on-year growth rate of the LinkedIn AI hiring rate and the year-on-year growth rate of the overall LinkedIn hiring rate in the same country. The LinkedIn hiring rate is measured as the share of LinkedIn members who added a new employer in a given period, divided by the total number of LinkedIn members in the corresponding location. The AI hiring rate is computed using the same methodology but restricted to members classified as AI talent. AI talent includes LinkedIn members who have explicitly listed at least two AI-related skills on their profile and/or who are currently employed or have previously been employed in an AI-related occupation. Values are shown as 12‑month moving averages. A positive value indicates that AI talent hiring is growing faster than overall hiring.
Source: OECD calculations based on Lightcast online job postings and Language Model Exposure score from Felten, Raj and Seamans (2023[54]) made available in ISCO classification by Nurski (2025[55]) (Panels A and B); OECD.AI (2026), data from LinkedIn Economic Graph, last updated 2026‑2003‑06, accessed on 2026‑2005‑13, https://oecd.ai/ (Panels C and D).
Notes
Copy link to Notes← 1. This is the case in Austria, Czechia, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Lithuania, Luxembourg, Norway, the Slovak Republic, Switzerland and the United Kingdom.
← 2. It should be noted that vacancies might be overestimated, as employers tend to advertise job openings without intending to fill them, especially in specialised industries and in larger firms (“ghost jobs”) (Ng, 2024[62]).
← 3. https://ec.europa.eu/eurostat/databrowser/view/ei_bsse_q_r2__custom_16762528/default/table and https://ec.europa.eu/eurostat/databrowser/view/ei_bsin_q_r2__custom_16762125/default/table.
← 4. Nonetheless, the share of firms citing lack of labour as one of the factors limiting production are still close to peak (and much higher than before the pandemic) in the food service sectors in the EU (https://economy-finance.ec.europa.eu/economic-forecast-and-surveys/business-and-consumer-surveys/download-business-and-consumer-survey-data/subsector-data_en).
← 6. It should be noted that significant production of intangible assets in these sectors could distort productivity measures.
← 8. 24‑1 287 Learning Resources, Inc. v. Trump, 607 US _ (2026).
← 9. Recent evidence however highlights that the effective tariff rates are significantly lower than those announced, once exemptions and tariff evasion are taken into account (Gopinath and Neiman, 2026[61]).
← 10. Most of the data used in this section refer to the “wages and salaries” component of the Labour Cost Index (i.e. excluding employer’s social security contributions) produced by Eurostat – or similar measure for non-European countries (see notes to the figures for the details on the countries for which different wage measures have been used). In addition to separating wages from other labour cost components, these indicators have two main advantages relative to measures of compensation per hour worked derived from National Accounts. First, they are generally constructed to follow the evolution of hourly nominal wages for a constant industry structure, therefore minimising the potential impact of compositional changes on aggregate wage dynamics. Second, they are available at a more detailed sectoral breakdown than measures of compensation of employees from National Accounts, allowing the analysis on wage dynamics by industry of different pay levels of the next section.
← 11. Examining wage growth since Q4 2019 instead of Q1 2021 reveals positive wage changes in a greater number of countries. However, using Q1 2021 as the reference quarter is more relevant than pre‑COVID‑19 data (Q4 2019) for analysing the wage recovery from the inflation surge. Nominal wage index series since Q4 2019 show that the artificial increase in wages induced by composition effects during the pandemic had subsided by Q1 2021 in virtually all OECD countries (Annex Figure 1.A.7).
← 12. By contrast, the Danish standardised index of average earnings was only 1.3% above pre‑inflation levels in Q1 2026. This is because the labour cost index used in Figure 1.11 refers to a constant industry structure and covers all employees, while the standardised index of average earnings is adjusted for both the industrial and occupational structure and does not include employees in private‑sector firms with less than 10 employees.
← 13. Compositional shifts in the sectoral composition of the workforce are adjusted for in most countries in Figure 1.11.
← 14. In the United States the nominal federal minimum wage has remained unchanged since 2009, but state level minimum wages have risen frequently in recent times, so that the employment-weighted average of state level real minimum wages has been much been more dynamic than the federal real minimum wage.
← 15. The actual poverty gap (Figure 1.17) measures the p.p. difference between net equivalised disposable income expressed as a percentage of median equivalised disposable income, and 50% (the poverty line is defined as 50% of median equivalised disposable income). It is therefore a measure of relative poverty, reflecting the shape of the bottom of the income distribution in a given year. In contrast, by anchoring the poverty line at its 2021 level and focussing on real income, the anchored poverty line better captures developments in absolute poverty.
← 16. To enable a comparison of the dynamics between labour costs and profits, this section uses indicators from the National Accounts (see note to Figure 1.20). Using the income approach, nominal GDP can be decomposed as where is the GDP deflator, is real GDP, is nominal compensation of employees, is gross operating surplus, and is nominal taxes. Unit labour costs and profits are derived by dividing the two relevant GDP components by real GDP. Unit labour costs increase when growth in compensation exceeds growth in real GDP or, dividing these two components by hours worked, when growth in compensation per hour worked exceeds growth in labour productivity. This measure of unit labour costs differs in some important respects from the measure of hourly wages based on the “wages and salaries” component of the labour cost index used in the previous sections (see note 7). Most notably, unit labour costs include employer’s social security contributions and do not control for changes in the sectoral composition of the economy.
← 17. Unit profits made an unusually large contribution to domestic price pressures during the 2021‑2022 inflation surge (OECD, 2023[3]).
← 19. In Australia, however, the contribution of unit profits to domestic price pressures has remained at historically low levels since Q2 2023, after a sharp drop in that quarter.
← 20. It should be noted that this decomposition of domestic price pressures provides only a partial picture of real wage catch-up, as they exclude the distribution of productivity gains between labour and profits.
← 21. In absolute term, there has been an upward trend in the unemployment rate for both graduate and non-graduate young entrants in these three countries in recent years (Annex Figure 1.A.9).
← 22. For example, LLMs could replace simpler cognitive tasks, which are widespread within entry jobs, while more sophisticated expert tasks could be more difficult to automatise. Even if it does not automate more intensively entry-level jobs, generative AI could have a disproportionate impact on youth unemployment because any general negative employment shock is likely to have a stronger impact on young people by making it more difficult for them to enter the labour market.
← 23. Humlum and Vestergaard (2025[45]) focusses on Denmark and Klein Teeselink (2025[48]) on the United Kingdom.
← 24. Occupational exposure to language models is an index developed by Felten, Raj and Seamans (2023[54]) and made available in ISCO classification by Nurski (2025[55]). It measures the extent to which occupations are exposed to advances in AI language modelling capabilities.
← 25. A LinkedIn member is considered AI talent if they have explicitly added at least two AI skills to their profile and/or they are or have been employed in an AI job. LinkedIn categorises AI skills into 2 mutually exclusive groups: “AI Engineering” and “AI Literacy” skills, where, broadly, AI Engineering skills refer to the technical expertise and practical competencies required to design, develop, deploy, and maintain artificial intelligence systems, and AI Literacy skills refer to the knowledge, abilities, and critical thinking competencies needed to understand, evaluate, and effectively interact with AI technologies. The top skills that comprise the “AI engineering” skill grouping are machine learning, natural language processing, artificial intelligence, computer vision, image processing, deep learning, TensorFlow, and OpenCV, among others. The skills that comprise the “AI literacy” grouping are prompt engineering, GPT‑3, GPT‑4, stable diffusion, ChatGPT, Github copilot, generative art, Dall-E, Midjourney and Google Bard. An AI job is an occupation that requires AI skills to perform the job (https://oecd.ai/en/linkedin).
← 26. The time lag between the release of ChatGPT (November 2022) and its use by firms might be explained by the later release dates of the OpenAI API – a prerequisite for building custom applications – and ChatGPT Enterprise – offering data privacy and security assurances for corporations (Curto Millet and Iscenko, 2026[52]). The former was released in March 2023, and the latter in August 2023.
← 27. These trends are confirmed by AI job postings from the online platform Indeed (Annex Figure 1.A.10). AI-related postings in Indeed data are identified using keyword searches covering both AI occupations (e.g. “Artificial Intelligence”, “Machine Learning”, “Data Science”) and generative AI terms (e.g. “Generative AI”, “Large Language Models”, “ChatGPT”).
← 28. Annex Figure 1.A.11 shows that this finding holds for the vast majority of the 15 EU countries analysed taken separately.
← 29. The hiring rate fell from about 4.5% at its 2022 peak to less than 3.5% in 2025 (https://www.apricitas.io/p/the-no-hire-economy).
← 32. LinkedIn global data for 2022-2023 shows that the number of applications for remote positions was increasing, while the number of remote job postings was declining (https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/resources/pdfs/future-of-recruiting-2024.pdf).