Live Labour Market Statistics Dashboard:
Automated Official Data Streams
Real-time indicators mapped from the UK Office for National Statistics (ONS) and Eurostat APIs. This dashboard demonstrates end-to-end pipeline automation, processing raw SDMX and JSON data into production-ready analytical assets.
Latest UK Labour Market Figures
UK Labour Market Over Time
Seasonally adjusted — sourced live from ONS API. Auto-updates on each new release.
Employment Rate (16–64)
%, seasonally adjusted, monthly
Data source: ONS UK API via dataset LF24
The ONS defines the employment rate as the proportion of people aged 16 to 64 years who are in employment, based on the Labour Force Survey. A person counts as employed under the ILO definition if they did any paid work in the reference week, including those temporarily away from a job (such as on leave) who expect to return to it.
Unemployment Rate (16+)
%, seasonally adjusted, monthly
Data source: ONS UK API via dataset MGSX
The ONS, following the International Labour Organisation (ILO) definition, classes a person as unemployed if they are without a job, have actively sought work in the last four weeks, and are available to start within two weeks — or have found a job and are waiting to start it. The unemployment rate is this group expressed as a share of the economically active population (those in work plus those unemployed), not of the whole population.
Youth Unemployment Rate (16–24)
%, seasonally adjusted, monthly
Data source: ONS UK API via dataset MGWY
This applies the same ILO unemployment criteria — without work, available within two weeks, actively seeking in the past four weeks — to the 16–24 age group specifically, as a share of that age group's own labour force. Because many 16–24 year-olds are full-time students and therefore outside the labour force altogether, this rate is typically higher than the overall unemployment rate and should not be read as the share of all young people who are unemployed.
Avg Weekly Earnings Growth
Annual % change, total pay, seasonally adjusted
Data source: ONS UK API via dataset KAC3
Average Weekly Earnings (AWE) is the ONS's lead monthly measure of pay, calculated from the Monthly Wages and Salaries Survey by dividing total employer pay spend by total employee numbers. This series tracks total pay, which includes bonus payments as well as regular pay and overtime; growth is shown in nominal terms, not adjusted for inflation.
Economic Inactivity Rate (16–64)
%, seasonally adjusted, monthly
Data source: ONS UK API via dataset LF2S
The ONS defines economically inactive people as those not in employment who also don't meet the internationally accepted definition of unemployed — typically because they haven't been seeking work in the last four weeks, or aren't able to start within two weeks (for example, students, the long-term sick, or retirees). The economic inactivity rate is this group as a proportion of the 16–64 population.
Claimant Count Rate
% of 16-64 population, seasonally adjusted
Data source: ONS UK API via dataset BCJE
The Claimant Count measures the number of people claiming benefits principally because they are unemployed — Jobseeker's Allowance plus Universal Credit claimants required to seek and be available for work — drawn from DWP administrative records rather than survey responses. The ONS is explicit that it does not measure unemployment under the ILO definition: it's a narrower administrative count, currently classified as official statistics in development, that's nonetheless a useful indicator of how claims are moving over time.
Source: Office for National Statistics — Open Government Licence v3.0
Latest Eurostat Labour Market Figures — European Union (27)
EU Labour Market Over Time
Sourced live from Eurostat API. Updated twice daily.
Monthly Indicators
une_rt_m
Unemployment Rate (15+)
%, seasonally adjusted, monthly
Data source: Eurostat API via dataset une_rt_m
According to the EU Labour Force Survey definition used by Eurostat, an unemployed person is someone aged 15–74 who was without work during the reference week, was available to start work within two weeks, and had actively sought employment in the preceding four weeks. The unemployment rate expresses this group as a share of the labour force (employed plus unemployed). The monthly series shown here is seasonally adjusted, harmonised across EU member states, and produced using statistical estimation methods distinct from the quarterly LFS series, to allow timely month-to-month comparison.
Youth Unemployment Rate (<25)
%, seasonally adjusted, monthly
Data source: Eurostat API via dataset une_rt_m
The youth unemployment rate applies the same Eurostat unemployment definition — without work, available within two weeks, actively seeking in the past four weeks — to the population aged under 25. It is expressed as a share of the youth labour force, not of the total youth population; because a large share of under-25s are still in education and therefore outside the labour force, this rate is typically markedly higher than the overall unemployment rate and should not be confused with the proportion of all young people who are unemployed.
Unemployment by sex — by Sex
Source: Eurostat API via dataset teilm010
May 2026
| January 2026 | 13,207K persons |
| February 2026 | 13,387K persons |
| March 2026 | 13,351K persons |
| April 2026 | 13,203K persons |
| May 2026 | 13,163K persons |
Unemployment by sex - age group 15-24 — by Sex
Source: Eurostat API via dataset teilm011
May 2026
| January 2026 | 2,958K persons |
| February 2026 | 2,983K persons |
| March 2026 | 2,993K persons |
| April 2026 | 2,900K persons |
| May 2026 | 2,918K persons |
Unemployment by sex - age group 25-74 — by Sex
Source: Eurostat API via dataset teilm012
May 2026
| January 2026 | 10,248K persons |
| February 2026 | 10,404K persons |
| March 2026 | 10,358K persons |
| April 2026 | 10,303K persons |
| May 2026 | 10,245K persons |
Unemployment rate by sex — by Sex
Source: Eurostat API via dataset teilm020
May 2026
| January 2026 | 6% |
| February 2026 | 6% |
| March 2026 | 6% |
| April 2026 | 5.9% |
| May 2026 | 5.9% |
Unemployment rate by sex - age group 15-24 — by Sex
Source: Eurostat API via dataset teilm021
May 2026
| January 2026 | 15.3% |
| February 2026 | 15.5% |
| March 2026 | 15.5% |
| April 2026 | 15.1% |
| May 2026 | 15.2% |
Unemployment rate by sex - age group 25-74 — by Sex
Source: Eurostat API via dataset teilm022
May 2026
| January 2026 | 5.1% |
| February 2026 | 5.1% |
| March 2026 | 5.1% |
| April 2026 | 5.1% |
| May 2026 | 5.1% |
Quarterly Indicators
lfsi_emp_q · une_rt_q · lfsi_sla_q
Employment Rate (20–64)
%, seasonally adjusted, quarterly
Data source: Eurostat API via dataset lfsi_emp_q
The employment rate is the share of the 20–64 population in employment — anyone who, during the reference week, worked for pay or profit for at least one hour, or had a job they were temporarily away from (e.g. leave or illness). This age band (20–64) is the EU's main employment-rate target population, used to track progress against the EU 2030 headline employment target.
Employment Rate (15–64)
% of working-age population · by sex · lfsi_emp_q
Data source: Eurostat API via dataset lfsi_emp_q
This view applies the employment rate to the broader 15–64 working-age population, split by sex. A person counts as employed if they worked at least one hour for pay or profit in the reference week, or held a job they were temporarily absent from. Comparing the male and female rates over time is a standard way to track the gender employment gap.
Combined Labour Market Indicators (15+)
Employment, unemployment, inactivity, participation · age 15+ · une_rt_q · lfsi_emp_q
Data source: Eurostat API via dataset une_rt_q, lfsi_emp_q
These four series follow the EU Labour Force Survey's standard three-way split of the population aged 15+: employed (worked at least one hour for pay or profit), unemployed (without work, available within two weeks, actively searching in the past four weeks), and inactive (neither of the above — e.g. students, retirees, or people not seeking work). The participation rate is employed plus unemployed as a share of the population; inactivity is its complement.
Unemployment Rate — by Sex & Age
Seasonally adjusted · EU + member states · une_rt_q
Data source: Eurostat API via dataset une_rt_q
Breaking the unemployment rate down by sex and by age (15+ vs the youth band 15–24) follows the same Eurostat definition throughout — without work, available within two weeks, actively seeking in the past four weeks — applied separately to each subgroup's own labour force, not the population as a whole. This lets the chart isolate whether changes in overall unemployment are concentrated among men, women, or younger workers.
Labour Market Slack
% extended labour force, seasonally adjusted, quarterly
Data source: Eurostat API via dataset lfsi_sla_q
Labour market slack is Eurostat's broader measure of unmet demand for work, going beyond the standard unemployment rate. It sums four groups as a share of the extended labour force: the unemployed; people available to work but not actively searching; people seeking work but not immediately available; and part-time workers who want, and are available for, more hours. It captures spare labour capacity that the headline unemployment rate alone misses.
Labour Force Participation Rate
LF as % of working-age population · by sex & age group · lfsi_emp_q
Data source: Eurostat API via dataset lfsi_emp_q
The labour force participation rate measures the share of the working-age population that is economically active — employed or unemployed — as opposed to inactive. Unlike the employment rate, it doesn't distinguish whether active people currently have a job; it simply asks whether they are in, or seeking, the labour market at all.
Job vacancy statistics by NACE Rev. 2.1 activity, occupation and NUTS 2 region - quarterly data — by NACE Activity
Source: Eurostat API via dataset jvs_q_isco_r21
Q1 2026
| Q1 2025 | 2.2 |
| Q2 2025 | 2.1 |
| Q3 2025 | 2 |
| Q4 2025 | 2.1 |
| Q1 2026 | 2 |
Job vacancy statistics by NACE Rev. 2.1 activity - quarterly data (from 2001 onwards) — by NACE Activity
Source: Eurostat API via dataset jvs_q_r21
Q1 2026
| Q1 2025 | 2.2 |
| Q2 2025 | 2.1 |
| Q3 2025 | 2 |
| Q4 2025 | 2.1 |
| Q1 2026 | 2 |
Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data — by NACE Activity
Source: Eurostat API via dataset lc_lci_r2_q
Q1 2026
| Q1 2025 | 120.6 (index) |
| Q2 2025 | 121.9 (index) |
| Q3 2025 | 122.9 (index) |
| Q4 2025 | 123.8 (index) |
| Q1 2026 | 124.9 (index) |
Labour cost index by NACE Rev. 2 — by NACE Activity
Source: Eurostat API via dataset teilm100
Q1 2026
| Q1 2025 | 4.1% |
| Q2 2025 | 4.4% |
| Q3 2025 | 3.8% |
| Q4 2025 | 3.7% |
| Q1 2026 | 3.6% |
Labour cost index by NACE Rev. 2 - percentage change Q/Q-1 — by NACE Activity
Source: Eurostat API via dataset teilm120
Q1 2026
| Q1 2025 | 1.2% |
| Q2 2025 | 1.1% |
| Q3 2025 | 0.8% |
| Q4 2025 | 0.8% |
| Q1 2026 | 0.8% |
Labour cost index by NACE Rev. 2 - percentage change Q/Q-4 — by NACE Activity
Source: Eurostat API via dataset teilm130
Q1 2026
| Q1 2025 | 4.1% |
| Q2 2025 | 4.4% |
| Q3 2025 | 3.8% |
| Q4 2025 | 3.7% |
| Q1 2026 | 3.6% |
Labour cost index by NACE Rev. 2 - Index (2020=100) — by NACE Activity
Source: Eurostat API via dataset teilm140
Q1 2026
| Q1 2025 | 120.6 (index) |
| Q2 2025 | 121.9 (index) |
| Q3 2025 | 122.9 (index) |
| Q4 2025 | 123.8 (index) |
| Q1 2026 | 124.9 (index) |
Gross domestic product (GDP) and main components (output, expenditure and income) - quarterly data
Source: Eurostat API via dataset namq_10_gdp
Q1 2026
| Q1 2025 | 113.8 (index) |
| Q2 2025 | 114 (index) |
| Q3 2025 | 114.5 (index) |
| Q4 2025 | 114.7 (index) |
| Q1 2026 | 114.7 (index) |
Annual Indicators
edat_lfse_20 · une_ltu_a
NEET Rate (15–29)
% not in employment, education or training, annual
Data source: Eurostat API via dataset edat_lfse_20
NEET — "not in employment, education or training" — measures the share of 15–29 year-olds who are neither working nor enrolled in any formal or non-formal education or training in the four weeks before the survey. Because it captures young people disengaged from both the labour market and the education system, NEET is tracked separately from youth unemployment, which only covers those actively job-seeking.
Long-term Unemployment Rate
% of active population, 1+ year unemployed, annual
Data source: Eurostat API via dataset une_ltu_a
Long-term unemployment counts people who meet the standard unemployment definition and have been continuously without work for 12 months or more, expressed as a share of the active population. It is widely used as an indicator of structural, rather than short-term cyclical, labour market difficulty, since long unemployment spells are associated with skill erosion and greater difficulty returning to work.
Real GDP per capita
Source: Eurostat API via dataset sdg_08_10
2025
| 2021 | €32,470 |
| 2022 | €33,420 |
| 2023 | €33,390 |
| 2024 | €33,700 |
| 2025 | €34,100 |
Overall employment growth
Source: Eurostat API via dataset tesem040
2025
| 2021 | 1.6% |
| 2022 | 2.2% |
| 2023 | 1.3% |
| 2024 | 0.8% |
| 2025 | 0.5% |
Nominal unit labour cost growth
Source: Eurostat API via dataset tesem170
2025
| 2021 | -0.2% |
| 2022 | 3.4% |
| 2023 | 6.8% |
| 2024 | 4.8% |
| 2025 | 3.4% |
At-risk of poverty rate of unemployed persons — by Sex
Source: Eurostat API via dataset tesem210
2025
| 2021 | 45.1% |
| 2022 | 46.1% |
| 2023 | 46.9% |
| 2024 | 48.8% |
| 2025 | 49.3% |
Job vacancy rate by NACE Rev. 2.1 activity - annual data — by NACE Activity
Source: Eurostat API via dataset jvs_a_r21
2025
| 2021 | 2.4 |
| 2022 | 3 |
| 2023 | 2.8 |
| 2024 | 2.4 |
| 2025 | 2.1 |
Labour cost index by NACE Rev. 2 activity - item weights — by NACE Activity
Source: Eurostat API via dataset lc_lci_r2_itw
2024
| 2020 | 1,000 |
| 2021 | 1,000 |
| 2022 | 1,000 |
| 2023 | 1,000 |
| 2024 | 1,000 |
Technical Architecture & Statistical Datasets
This automated dashboard serves as a functional reference framework for National Statistical Offices (NSOs) shifting towards real-time labour market data dissemination. By eliminating manual file processing, the infrastructure demonstrates end-to-end pipeline automation aligned with GSBPM standards.
Data streams are established via direct API integrations. The United Kingdom indicators ingest seasonal and non-seasonal series from the Office for National Statistics (ONS) database, monitoring key metrics such as the headline employment rate (series LF24), unemployment (series MGSX), youth labour statistics (series MGWY), and average weekly earnings growth (series KAC3).
The European Union data pipeline connects directly to the Eurostat dissemination architecture, executing daily data ingestion protocols. The charts and indicators render quarterly and monthly structural metrics dynamically by querying Eurostat core datasets.
• Core Labour Market Slack Framework: Dataset lfsi_sla_q (quarterly indicators) • Main Employment Pipeline: Dataset lfsi_emp_q (20-64 rate, working-age breakdown) • Unemployment & Youth Labour Statistics: Dataset une_rt_m (monthly rate, youth under 25) • Annual Structural Vulnerability: Datasets edat_lfse_20 (NEET rate) & une_ltu_a (long-term unemployment)
Labour Market Statistics & NSO Modernisation
The UK's Office for National Statistics (ONS) and Eurostat represent the gold standard in modern official statistics production — timely, high-quality, and fully aligned with international methodological frameworks. However, keeping pace with shifting economic models requires more than traditional pipelines.
At EnStatX, we leverage this live data setup to demonstrate the power of automated data processing. By replacing manual interventions with resilient scripts, National Statistical Offices (NSOs) can cut dissemination latency down significantly. This architectural foundation is what allows us to securely deploy AI and Machine Learning frameworks for downstream operations, such as advanced predictive forecasting and automated text classification of occupations under ISCO and NACE standards.
We help statistical agencies worldwide implement these exact systems. From building GSBPM-compliant pipelines to redesigning complex Labour Force Surveys (LFS) and establishing register-based census systems, we ensure your infrastructure is fast, modern, and compliant with UNECE guidelines.