Official Statistics

Get Britain Working: Labour Market Insights April 2026 – Background Information and Methodology

Published 30 April 2026

1. Introduction

This background report accompanies the Get Britain Working: Labour Market Insights April 2026 publication. The purpose of this report is to provide further contextual information to aid understanding of how the statistics presented in the main report and data tables were developed and quality assured.

A comprehensive set of data tables complementing the results presented is available alongside the publication. This document, the statistics release and data tables can be found via the collections page.

2. Status of the statistics

Official statistics in development

These statistics are official statistics in development. Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality, and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

The Code of Practice for Statistics is built around 3 main concepts, or pillars: 

• trustworthiness – is about having confidence in the people and organisations that publish statistics

• quality – is about using data and methods that produce statistics

• value – is about publishing statistics that support society’s needs

The following explains how we have applied the pillars of the Code in a proportionate way. 

HM Government analysts work to a professional competency framework and Civil Service core values of integrity, honesty, objectivity, and impartiality. The data and analysis in this release have been scrutinised and quality assured in line with the AQuA Book and received sign off by the subject expert lead Senior Civil Service analyst.

3. Contact

Press enquiries should be directed to DWP Press Office via newsdesk@dwp.gov.uk

Alternatively, you can contact The Office for Statistics Regulation (OSR) by emailing: regulation@statistics.gov.uk or via the Office for Statistics Regulation website.

4. Quality of the statistics

The analysis presented in this publication is based on a range of administrative and survey data sources.

More information on the quality assurance checks that take place on Universal Credit (UC) administrative data can be found in the Quality statement DWP benefits.

Analysis is quality assured (QA) by DWP analysts before it is released to ensure the findings are robust. This includes peer review of the approach and implementation of the analysis as well as spot checks and comparisons to other data sources where available. Although extensive QA of the underlying data takes place, it is possible that errors still exist that may impact these statistics. If identified these will be rectified and the analysis will be updated in a future publication.

Some data are also subject to retrospection, where additional data becomes available later that affects historic figures. Retrospection usually has the largest impact on the most recent figures, with a negligible impact on figures at least 6 months old.

5. Background

Accessibility

DWP has published an accessibility statement regarding dissemination of statistics. We have reviewed our publication tables and supporting guidance to ensure accessibility to users. For compliance with The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018, some formatting in the accompanying ODS data tables such as merged cells, has been avoided. Please see the Accessibility statement for www.gov.uk - GOV.UK for information on what to do if you need the information in this publication provided in a different format.

For ease of understanding, figures in the data tables have been rounded to an appropriate degree. When a figure rounds to 0 but is not equal to 0 it is labelled as [low]. In the case of geographies, these areas are shown as blank in the maps due to the small value.

Frequency

This publication is the third in a quarterly statistical publication series of labour market insights. The next edition is planned for July 2026. The publication contains some core statistics which will be updated periodically, while other statistics will be published as a one-off or updated less frequently. Every other edition will include a contextual chapter offering deeper analysis on a specific topic, helping readers build understanding without requiring full updates to all content. 

6. Source of the statistics

Into-work, sustained employment and worklessness rates

The analysis in 3a. The into-work rate, 3b. Into-work rate by local authority and Jobcentre Plus district, 3c. Into-work rate by duration on Universal Credit, 3d. Into-work rate by age of Universal Credit customer, 4. Sustained employment of Universal Credit Customers and 5. Measure of worklessness of Universal Credit ‘Searching for work’ customers in Get Britain Working: Labour Market Insights April 2026 explores into-work, sustained employment and worklessness rates which are produced using a combination of Universal Credit (UC) administrative data and HM Revenue and Customs’ (HMRC) Pay As You Earn (PAYE) Real Time Information (RTI) data.

The UC administrative data is collected from the UC data systems. The data is processed and released internally, often with a lag time of some months. To protect the confidentiality of customers, National Insurance numbers and other claim identifiers required for statistical processing are encrypted to prevent identification. A wide range of information is collected including information relating to the characteristics of UC customers and information about their claim and earnings, and how these change over time.

These HMRC PAYE data provides information on a customer’s earnings, beyond that collected by DWP, which enables more accurate identification of a movement into work, and the level of earnings received. It also provides functionality to observe earnings data, and by proxy ‘employment status’, of a previous customer following the closure of their UC claim.

Additional information is matched on from the DWP Customer Information System (CIS). CIS holds basic identifying information about all our customers, including their date of birth. Postcodes are taken from the CIS data and combined with geographical details obtained from the Office for National Statistics Postcode Look-up (NSPL). This process aggregates postcodes into local authorities. Jobcentre Plus geography is based on the Jobcentre Plus office which administers the UC claim. Jobcentre Plus offices build up to Jobcentre Plus districts. It is possible for the UC customer to reside in a different area (e.g. local authority) to the area administering their benefit claim.

Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions

The analysis in 6. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions in Get Britain Working: Labour Market Insights April 2026 uses Labour Force Survey (LFS) micro-data. The LFS - conducted by the Office for National Statistics (ONS) - is a large, representative household study and provides detailed information related to the UK population’s activity in the labour market. The LFS is the basis of the headline labour market statistics produced by ONS; and a valuable data source to conduct analysis into more detailed aspects of the labour market.

Get Britain Working outcome metrics

The analysis in 7a. Employment rate of people aged 18 to 66 years, 7d. Economic inactivity due to long-term sickness for people aged 18 to 66 years, 7e. Disability employment rate gap for people aged 18 to 66 years and 7j. Employment rate of women aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 uses LFS micro-data (more detail on this data above).

The analysis in 7b. Real earnings at the bottom half of income distribution (5th decile and below) in Get Britain Working: Labour Market Insights April 2026 uses DWP’s Households Below Average Income (HBAI) data.

The analysis in 7c. Local authorities employment rate gap between bottom decile and median for people aged 18 to 66 years and variation in local authority employment rate for people aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 uses Annual Population Survey (APS) data. The APS is a continuous household survey, covering the UK comprising 12 months of survey data. The APS is not a stand-alone survey; it uses data combined from 2 waves of the main LFS, collected on a local sample boost. Due to boosted sample sizes, the APS allows more robust analysis of subgroups such as by characteristics or lower-level geography.

The analysis in 7f. Proportion of young people, aged 18 to 24 years not in education, employment or training (NEET) comes from Office for National Statistics (ONS) publication Young people not in education, employment or training (NEET), UK - Office for National Statistics. Further requests for information can be directed to labour.market@ons.gov.uk.

The analysis in 7g. Proportion of people aged 16 to 21 years in education or training in England in Get Britain Working: Labour Market Insights April 2026 comes from the Department for Education’s publication NEET age 16 to 24. Further requests for information should be directed to DfE contact - 16 to 24 stats and data.

The analysis in 7h. employment rate gap between lone parents and parents in a couple for people aged 18 to 66 years and in 7i. Percentage of coupled households where at least one parent is out of work for people aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 uses household LFS micro-data. The household LFS enables users to conduct analysis by family type, the age of youngest or oldest child in a family unit, the number of children in the family unit and the economic activity status of a household or family unit, including how many people in a family unit are in work, unemployed or inactive. These are metrics that cannot be determined using the individual level LFS microdata.

Highest level of educational qualification statistics

The analysis in 8a. Highest level of educational qualification for Universal Credit customers compared to the 2021 England Census cohort, 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England and 8c. Into-work rate for Universal Credit customers in the ‘Searching for work’ conditionality regime by highest level of educational qualification for England in Get Britain Working: Labour Market Insights April 2026 uses a DWP-derived dataset based on education and training data received from DfE. DWP receives: the National Pupil Database (NPD) which covers school level data in England, Higher Education Statistics Agency (HESA) University Records which covers UK higher education data for pupils who went to school in England and the Individualised Learner Record (ILR) which covers further education and workplace learning in England.

These three data sources are combined by DWP to create a derived summary education dataset for all individuals who appear in at least one of the sources listed above. The summary dataset is structured with one row per encrypted national insurance number and for each individual, it includes their highest qualification identified from the three data sources.

UC administrative data is used to identify those in receipt of UC, their conditionality regime, their date of birth and their address at the date of interest.

The analysis in 8a. Highest level of educational qualification for Universal Credit customers compared to the 2021 England Census cohort, also uses data from the 2021 Census which is available via the ONS website. You can create a custom dataset from the Census 2021 data (Create a custom dataset - Office for National Statistics). See the methodology section for details on how to use the ONS data to produce a dataset on highest level of qualification for the England Census cohort.

The analysis in 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England, also uses the Employment Characteristic Database (ECD). See the section below for a full description of the ECD.

The analysis in 8c. Into-work rate for Universal Credit customers in the ‘Searching for work’ conditionality regime by highest level of educational qualification for England, also uses the into-work rate which is produced using a combination of UC administrative data and HMRC PAYE RTI data. See the section above for a full description of the data sources used to produce the into-work rate.

Employment by industrial sector statistics

The analysis in 9a. Flows between industrial sectors for Universal Credit customers and the wider UK population, 9b. Time since last employment by previous sector of employment for Universal Credit customers and 9c. Time since last employment by new sector of employment for Universal Credit customers in Get Britain Working: Labour Market Insights April 2026 uses the Employment Characteristics Database (ECD), a DWP-derived dataset based on HMRC PAYE data, alongside other data sources described below. This is not a HMRC dataset. It reflects a combination of P14 and P45 employment records rather than the monthly RTI submissions used in operational systems. 

The ECD provides spell-based employment and pension histories from the 2004 to 2005 tax year onwards, including start and end dates, durations, and cumulative earnings. Employer characteristics information is sourced via linkage to the Inter-Departmental Business Register (IDBR), enabling sectoral classification using Standard Industrial Classification (SIC) codes.

For these statistics the ECD data is used to identify current sector of work and when they started this work and the sector they previously worked in and when that work ended.

UC administrative data is used to identify those in receipt of UC.

7. Methodology

Into-work rate

Producing the into-work rate analysis in 3a. The into-work rate and 3b. Into-work rate by local authority and Jobcentre Plus district, 3c. Into-work rate by duration on Universal Credit and 3d. Into-work rate by age of Universal Credit customer in Get Britain Working: Labour Market Insights April 2026 involves the identification of whether a customer has moved into work. To calculate the into-work rate for a specific month we identify customers who are in the ‘Searching for work’ regime without earnings, where their assessment period end date falls in the preceding month. This is the base month, or the into-work denominator. The same customers are looked at in the following assessment period and any customers with earnings are included in the reporting month, or into-work numerator. The into-work rate is calculated by dividing the counts of these 2 groups. As the rate is based purely on the presence of earnings within assessment periods, the rate could miss some movements out of, and back into, work which happen within the time of 2 assessment periods if earnings are present in both.

The caseload characteristics analysis is based on those customers in the base month, or into-work denominator. Figures will not match the ‘People on Universal Credit with employment indicator by conditionality regime’ official statistics due to differences in methodology, most notably the treatment of self-employment, which is excluded from the official statistics.

Sustained employment rate

The focus of the sustainment rate of employment is on those who have started to earn and who have managed to immediately sustain earnings.

Producing the sustained employment rate analysis in 4. Sustained employment of Universal Credit Customers in Get Britain Working: Labour Market Insights April 2026 involves the identification of whether a customer has moved into work, as per above. To calculate the sustained employment rate for a specific month we identify customers without earnings who are in the ‘Searching for work’, ‘Working - with requirements’ and ‘Working - no requirements’ regimes, where their assessment period end date falls in the preceding month. The same customers are looked at in the following assessment period and any customers with earnings are included in the sustained employment denominator, as this identifies that they have moved into work. The same customers from the denominator are then looked at in the following 2 (or 5) assessment periods and any customers with earnings in all of these assessment periods are included in the 3-month (or 6-month) sustained employment rate numerator. The sustained employment rate is calculated by dividing the counts of the numerator by the denominator.

Measurement of worklessness

The analysis in 5. Measure of worklessness of Universal Credit ‘Searching for work’ customers in Get Britain Working: Labour Market Insights April 2026 looks at customers in the ‘Searching for work’ conditionality regime who have at least 6 consecutive months of no earnings.

We have updated the methodology used to calculate the measure of worklessness since the last publication in January 2026. These changes are designed to make the statistic clearer and easier to interpret.

Previously, the measure included customers who either moved from the ‘Searching for work’ conditionality regime into other regimes, or who left Universal Credit (UC) during the 6‑month tracking period. Under the revised methodology, the measure now focuses solely on individuals who remain in the ‘Searching for work’ conditionality regime with no earnings for the entire 6‑month tracking period.

Producing the worklessness rate involves the identification of whether a customer has been in the ‘Searching for work’ regime and out of work for at least 6 consecutive months. To calculate the worklessness rate for a specific month we identify customers without earnings who are in the ‘Searching for work’ conditionality regime. This is the worklessness rate denominator. The same customers from the denominator are then looked at to see if they have been in the ‘Searching for work’ conditionality regime for at least 6 consecutive months and received no earnings for at least 6 consecutive months. This is the worklessness rate numerator. The worklessness rate is calculated by dividing the counts of the numerator by the denominator. Individuals can count towards the indicator in multiple assessment periods, if worklessness continues or reappears. Each assessment period without earnings is a base month that the individual can be included in the indicator, if they are in the ‘Searching for work’ conditionality regime.

Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions

The analysis in 6. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions in Get Britain Working: Labour Market Insights April 2026 uses Office for National Statistics (ONS) Labour Force Survey (LFS) micro-data to make an estimate of those young people who are not in education, employment or training. The methodology behind constructing these estimates has been altered meaning that, although the change is minor and the figures are similar, the figures published in this publication cannot be compared with similar figures in the Get Britain Working: Labour Market Insights January 2026 and the Get Britain Working White Paper Analytical Annex. This methodology change is to increase alignment with the ONS methodology which is explained below.^{[footnote 1]}

Estimates of young people who are NEET are calculated by first deriving a variable to distinguish those in education or training from those not in education or training. Then, by cross tabulating these variables by labour market status (in employment, unemployed or economically inactive as defined by the ILO framework), a NEET estimate for each region can be calculated. The data is initially disaggregated by single-age and gender and then combined to the 16 to 24 years age category for each region after the figures have been adjusted for missing responses.

For these latest regional NEET estimates, we use the same apportioning method for missing responses that the ONS use. Missing data is a particular problem with respondents who turn 16 years old during their household’s inclusion in the LFS sample, and who are then not interviewed as a 16-year-old. Their data is imputed from the previous wave due to non-response in the current wave of the survey and these cases are then treated as economically inactive. To account for the fact that the majority of these cases are likely still in education, the “missings” are split between being in education or training or not being in education or training, based on the proportions of cases without missing data. This is done regionally within each category defined by age, sex and economic activity because educational and employment characteristics are related to age and gender. If this apportioning method was carried out in aggregate (for all people aged 16 to 24 years in the region), different numbers would be produced than if carried out for females ages 16, males aged 17 and so on. These NEET numbers are then combined to create the overall NEET figure for people aged 16 to 24 years in each region.

The NEET rates are calculated by dividing the total number of NEET individuals in each region by the overall 16 to 24 population in each region (and multiplying by 100). The England figures are calculated by summing the total number of young people and young people who are NEET in all of England’s regions. Because this age group represents a relatively small population subgroup, estimated from sample survey data, NEET estimates are particularly volatile. Our data is not seasonally adjusted, which may add to variability over time. To mitigate this inherent variability, NEET levels (available in the accompanying data tables) and rates were calculated using a rolling four-quarter average (for the year ending October to December 2025), providing a more stable estimate over time. For consistency, this smoothing approach is also applied when calculating the 95% confidence intervals. The standard errors for each estimate are derived from a four-quarter average of the raw sample sizes, ensuring that the confidence intervals appropriately represent the uncertainty in the aggregated estimates.

Get Britain Working outcome metrics – Individual LFS micro-data

The analysis in 7a. Employment rate of people aged 18 to 66 years, 7d. Economic inactivity due to long-term sickness for people aged 18 to 66 years, 7e. Disability employment rate gap for people aged 18 to 66 years, and 7j. Employment rate of women aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 all use individual-level LFS micro-data. All metrics apply to people aged 18 to 66 years, unless where otherwise stated. The rationale for this is that education or training should continue until age 18 in England and State Pension age will be 67 by March 2028. This age range, therefore, best reflects the ‘working age population’.

Library references are defined for the LFS individual and regional datasets, organised by calendar quarter. Quarters are grouped into standard three‑month periods: January to March, April to June, July to September, October to December.

Each quarter is linked to a specific directory, allowing analysis of LFS microdata across multiple years and enabling time‑series comparisons.

Quarterly LFS data from January to March 2014 to October to December 2025 period is combined into a single time‑series dataset. Data for each quarter is extracted using a standardised macro to ensure the same variables are used throughout. The quarterly datasets are then joined together in date order and given a time indicator so changes over time can be analysed. Where variable names have changed across years, including survey weights, these are aligned to a single consistent definition to maintain comparability.

Estimates are produced from this combined dataset for people aged 18–66 years. Weighted counts are used to calculate UK employment rates and economic activity outcomes. Outputs include overall employment rates, breakdowns by sex and disability status, and reasons for economic inactivity, including long‑term sickness. Survey weights are applied consistently to ensure results are representative of the UK population, and standard quarter labels are used to support clear time‑series reporting.

Real earnings of working households with a focus on the bottom half of the income distribution (5th decile and below)

The analysis in 7b. Real earnings at the bottom half of income distribution (5th decile and below) in Get Britain Working: Labour Market Insights April 2026 uses DWP’s annual Households Below Average Income (HBAI) data.

For income deciles, equivalised Household Income (on a Before Housing Cost basis) has been worked out for all individuals aged below State Pension age. Individuals are then “lined up” from lowest to highest equivalised household income and decile boundaries drawn.

The average Household Earnings figures for each decile are calculated by averaging the unequivalised household earnings for each adult in the income decile.

Figures are deflated to 2024/25 prices using standard HBAI Before Housing Cost CPI-based deflation factors.

Get Britain Working outcome metrics – Annual Population Survey

The analysis in 7c. Local authorities employment rate gap between bottom decile and median for people aged 18 to 66 years and variation in local authority employment rate for people aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 uses Annual Population Survey (APS) data.

The programme defines library references for the APS to support analysis using rolling 12‑month periods. APS datasets are available in rolling formats to meet different analytical needs, including January to December, April to March, July to June, October to September

Each rolling period is available across multiple years, allowing robust annual and sub‑annual comparisons.

APS data are used to produce time‑series estimates of economic activity for people aged 18 to 66. A standardised macro is applied to each APS dataset to extract the required variables in a consistent way, while allowing for changes in survey weights and local authority coding over time.

APS datasets covering rolling 12‑month periods from 2014 to the most recent available year are combined into a single dataset. These are joined together in chronological order and assigned a time identifier to support analysis of trends over time. Where survey weights differ between periods, they are aligned to maintain comparability across the time series.

Weighted tabulations are produced to show economic activity by county and local authority area. Different local authority formats are applied before and after 2019 to reflect changes in APS geography coding.

Further analysis uses a labour market typology, which groups local authorities into broader labour market types. Results are split where necessary to reflect the introduction of local authority codes based on the 2021 Census.

Survey weights are applied throughout to ensure estimates are representative of the population. Consistent time and geography labels are used to support clear comparison of economic activity levels across years and areas.

Proportion of young people not in education, employment or training (NEET) for people aged 18 to 24 years 

The analysis in 7f. Proportion of young people not in education, employment or training (NEET) for people aged 18 to 24 years in Get Britain Working: Labour Market Insights April 2026 uses LFS data [see above] and is part of published official statistics by the ONS. According to the ONS, estimates of those not in education, employment or training (NEET) are calculated by first deriving a variable to distinguish those in education or training (ET) from those not in education or training (NET). Then, by cross tabulating the derived ET or NET variable by economic status (in employment, unemployed or economically inactive), a NEET estimate can be calculated. For more details, please refer to Young people not in education, employment or training (NEET), UK, methodology: May 2022.

Proportion of people aged 16 to 21 years in education or training in England  

The analysis in 7g. Proportion of people aged 16 to 21 years in education or training in England in Get Britain Working: Labour Market Insights April 2026 is from the Department for Education’s Explore our statistics and data - Explore education statistics - GOV.UK where users can create their own tables. It is selected from the destination of pupils and students’ theme, and the NEET age 16 to 24 publication. Then the NEET and NET estimates from the LFS, focusing particularly on Table 2: NET by age. Then filter the age brackets for 16 to 21 years and percentage of the population this represents. By creating this table, you can calculate the proportion that are in education or training for England. For more information contact DfE through DfE contact - 16 to 24 stats and data.

Get Britain Working outcome metrics – Household LFS micro-data

The analysis in 7h. Parental employment rate gap between lone parents and parents in a couple for people aged 18 to 66 years and in 7i. Percentage of coupled households where at least one parent is out of work for people aged 18 to 66 years in Get Britain Working: Labour Market Insights April 2026 uses household LFS micro-data.

Additional library references are created for household‑level LFS datasets, which include imputed household variables. These datasets follow the same quarterly structure as the individual LFS data, allowing consistent linkage and analysis of individual and household‑level information over time.

Household LFS data are used to analyse the employment status of parents and couples with dependent children. A standardised macro is applied to each quarterly household dataset to ensure consistent processing and to accommodate changes in survey weighting variables across time.

For each quarter, households containing couples with dependent children are identified. Information for both adults within each family unit is combined to determine employment status, distinguishing between full‑time work, part‑time work, unemployment, and economic inactivity. Families are then grouped into broad employment categories, such as both parents in work, one parent in work, or neither parent in work. Same‑sex couples are identified separately.

Weighted tabulations are produced for families where at least one adult is aged 18 to 66. Household survey weights are applied to ensure the estimates are representative of the UK population.

A derived family type variable is also created to classify individuals into groups including lone parents, married or cohabiting parents, parents without children in the household, same‑sex couples, and single adults. These classifications are used to analyse the economic activity status of parents aged 18 to 66, including employment, unemployment, and inactivity.

The macro is run consecutively for each quarter from 2014 to the latest available data, ensuring consistent definitions and outputs over time and supporting quarterly time‑series analysis of parental employment outcomes.

Highest level of educational qualification statistics

The analysis in 8a. Highest level of educational qualification for Universal Credit customers compared to the 2021 England Census cohort, 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England and 8c. Into-work rate for Universal Credit customers in the ‘Searching for work’ conditionality regime by highest level of educational qualification for England in Get Britain Working: Labour Market Insights April 2026 uses educational qualification data to explore UC customers’ highest level of educational qualification and how individuals’ educational qualifications change their movement into employment.

To get the highest level of educational qualification for UC customers, we match UC administrative data to the DWP-derived summary education dataset (see more information on this in 6. Source of the statistics) using encrypted national insurance numbers. All of the analysis in section 8 is restricted to only consider individuals who are included in the DWP-derived summary education dataset, with an address in England at the date of interest, and a date of birth from 1988 onwards. The reasons for these restrictions are discussed in full detail in the limitations section below.

To get the highest qualification level of the England Census cohort, we use data from the 2021 Census. You can create a custom dataset from the Census 2021 data (Create a custom dataset - Office for National Statistics). First, select the population type “All usual residents”. Second, change the area type to “England and Wales”. Third, choose the option to add a variable to the dataset and select “Highest level of qualification” and “Age”. Ensure you have selected the 8 categories breakdown of “Highest qualification level” and the 101 categories breakdown of “Age”. Download the data and filter the data so you only include people in England and aged 16-33. The Census data is from 21st March 2021 so the age filter is necessary to apply the same restriction as used for the UC data (people born from 1988 onwards).

The methodology to produce the analysis in 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England, is outlined in the section below. The only difference is the way that employment spells were derived. For this analysis, we wanted to identify gaps between continuous employment spells. In most cases, individuals have one job at a time, so employment spells do not overlap. However, there are some cases where individuals have multiple jobs at the same time leading to cases where individuals had overlapping or nested employment spells. In these cases, the earliest employment start date, and the latest employment end date were used to identify one continuous employment spell. As outlined above; to get individuals’ highest level of educational qualification, we matched individuals who started an employment spell in the 2023/2024 financial year to the DWP-derived summary education dataset using encrypted national insurance numbers. In addition to the restrictions listed above, data is also restricted to not include individuals who were starting their first employment spell and therefore had no gap since their previous employment.

The methodology to produce the into-work rate in 8c. Into-work rate for Universal Credit customers in the ‘Searching for work’ conditionality regime by highest level of educational qualification for England, is outlined in the section above.

Employment by industrial sector statistics

The analysis in 9a. Flows between industrial sectors for Universal Credit customers and the wider UK population, 9b. Time since last employment by previous sector of employment for Universal Credit customers and 9c. Time since last employment by new sector of employment for Universal Credit customers in Get Britain Working: Labour Market Insights April 2026 explores the movement between sectors of employment for those in receipt of UC compared to the wider population. Focusing on the distribution of sectors people flow into, how this compares to someone’s previous sector and the length of time between employment.

Someone is classed as employed if they have an active or multiple active employment spells in the Employment Characteristics Database (ECD), supported by earnings and employer data. Employment spells are derived from the ECD, which combines P14 (annual earnings and tax) and P45 (start/end dates) records. Where both sources are available, P45 dates are prioritised for accuracy. Start dates and end dates are supplied by employers and are dates of contractual employment. An individual can be contractually employed but not receiving pay.

Sector classification is based on SIC 2007 codes, assigned to PAYE (Pay As You Earn) schemes via Inter-Departmental Business Register (IDBR) linkage. Where employers operate in multiple sectors, the primary Standard Industrial Classification (SIC) code is used, with caution applied to potential misclassification. A person’s sector of employment is the primary industry of the employer, as determined by the SIC code associated with the PAYE scheme in the ECD.

An employment flow relates to the contractual start date of employment.

An individuals’ previous sector is taken from the previous employment spell which is most recent, if multiple spells exist with the same end date then the spell with the same sector is taken, if that does not exist then the longest spell is chosen. Individuals may have multiple spells of employment at one time; in these cases when trying to identify someone’s previous sector if there is a spell with the same sector then that is chosen, otherwise the sector of the longest existing employment spell is taken.

Days since employment is defined as the number of days from the previous employment end date to the start of the current employment. As this is based on contractual end dates, it will capture those moving between employers naturally without any time looking for work in between. As such it will be seen that the time between employment for those not in receipt of UC is lower on average as due to the short time between employment, they do not go on to receive UC.

Data is restricted to those residing within England, Scotland and Wales only.

In terms of whether someone is classed as a UC customer, they are defined as such where they are in receipt of UC at the point of starting employment or within 90 days of last receiving UC.

8. Limitations of the statistics

Limitations of all statistics derived from the Labour Force Survey and Annual Population Survey

The Labour Force Survey (LFS) - conducted by the Office for National Statistics (ONS) - is a large, representative household study and provides detailed information related to the UK population’s activity in the labour market. The LFS is the basis of the headline labour market statistics produced by ONS.

The Annual Population Survey (APS) is a continuous household survey covering the UK, with the aim of providing estimates of main social and labour market variables between censuses, down to a local-area level. The APS is not a standalone survey; it uses combined data collected from two waves of the main LFS and data collected on local sample boost.

Sample surveys like the LFS and APS provide estimates of population characteristics, rather than exact measures. In principle, many random samples could be drawn, and each would give different results, because each sample is made up of different people who give different answers to the questions asked. The spread of these results is the sampling variability, which generally reduces with increasing sample size, but is present in all iterations of the survey data.

The micro-data estimates calculated use recent LFS and APS data which is subject to heightened volatility due to ongoing data quality challenges which the ONS are working on. The ongoing challenges with response rates, weighting approach and other aspects of the survey mean the LFS and APS-based labour market statistics are currently considered ‘official statistics in development’ until further review. Estimates of change should be treated with additional caution because of increased sample volatility of LFS and APS estimates and potential for elevated bias. This volatility is heightened when analysing smaller groups – e.g. the 16 to 24 age group is a smaller population group and therefore estimates tend to be more volatile.

The LFS microdata used is also not seasonally adjusted, meaning changes from quarter to quarter may reflect typical changes in the labour market over the course of a year (e.g. due to term times) as well as changes in the underlying strength of the labour market. 

Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions

The analysis in 6. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions in Get Britain Working: Labour Market Insights January 2026 and the corresponding NEET levels in table 8 of the accompanying data tables [link this to publication page] is calculated using LFS micro-data of young people aged 16 to 24 years who are NEET across England’s regions. The limitations of this are explained above. To counteract these issues the data is calculated as a 4-quarter average (for the year ending October to December 2025). The NEET rates and levels are only given for England’s regions and not the other UK countries, i.e. Scotland, Wales and Northern Ireland.

As the ONS’s method for missing response apportioning has been implemented into these statistics, assumptions are made surrounding how these “missings” are allocated. Although these assumptions are based on actual responses for the same age and gender category in each region, these cannot be measured directly. Additionally, the overall figures for England have been derived by totalling the number of young people and young people who are NEET in all of England’s regions. This means that, had the apportioning been carried out at country level and not per region, a different slightly result would have been produced.

Real earnings of working households with a focus on the bottom half of the income distribution (5th decile and below)

The analysis in 7b. Real earnings at the bottom half of income distribution (5th decile and below) in Get Britain Working: Labour Market Insights April 2026 uses DWP’s annual Households Below Average Income (HBAI) data. As the figures are estimates based on a household survey, they are subject to some measurement error so any changes from one year to the next should be treated with caution.

Highest level of educational qualification statistics

There are some limitations to the analysis in 8a. Highest level of educational qualification for Universal Credit customers compared to the 2021 England Census cohort, 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England and 8c. Into-work rate for Universal Credit customers in the ‘Searching for work’ conditionality regime by highest level of educational qualification for England in Get Britain Working: Labour Market Insights April 2026.

The most recent qualifications data available covers up to the 2022/2023 academic year. For analysis which covers beyond the 2022/2023 academic year, it is possible that individuals’ highest qualifications have not been fully captured as they may have achieved higher qualifications since the 2022/2023 academic year.

All three datasets cover education and training in England only. Some exceptions are possible e.g. university records are available for pupils who attended school in England and university in one of the other UK countries, and international students receiving education in England may not be included.

The data sources include qualification records for those aged 14 and over at the start of the academic year, regardless of year group. National Pupil Database (NPD) and Higher Education Statistics Agency (HESA) cover individuals born from 1988 onwards. Individualised Learner Record (ILR) further education data is available for all ages but will only give a partial picture for older individuals if it is used without the other sources.

In some cases, individuals may be identified in one of the DfE datasets (this shows that they were in the education system), but they do not have a record in any of the qualification datasets. When the DWP-derived summary education dataset is produced, there is no definitive way to tell if these individuals have qualifications which have not been captured or whether they have no qualifications. This could be because they moved, either to other parts of the UK, or abroad before taking exams, or left school before sitting any exams.

Given these coverage constraints, all of the analysis in section 8 has been restricted to only consider individuals with an address in England at the date of interest, and a date of birth from 1988 onwards. Individuals who do not have education and training data available are excluded.

A further limitation to the analysis in 8a. Highest level of educational qualification for Universal Credit customers compared to the 2021 England Census cohort, is that the 2021 England Census cohort is not necessarily directly comparable to the Universal Credit (UC) customers. While the Census includes highest qualifications for people educated up to the age of 18 in other parts of the UK and in other countries, the data DWP receives from DfE does not include pre-18 qualifications achieved outside of England. The comparison between UC customers and the 2021 England Census cohort is provided as an example and we would caution against making a direct comparison.

Please see the section below for a discussion of the limitations to the analysis in 8b. Time since last employment by highest level of educational qualification for Universal Credit customers for England, which also uses the Employment Characteristic Database (ECD).

Employment by industrial sector statistics

There are some limitations to the analysis in 9a. Flows between industrial sectors for Universal Credit customers and the wider UK population, 9b. Time since last employment by previous sector of employment for Universal Credit customers and 9c. Time since last employment by new sector of employment for Universal Credit customers in Get Britain Working: Labour Market Insights April 2026.

The ECD data used for this analysis does not include overseas employment or self-employment, as such the gap between spells may include such types of employment. Further to this, employment spells within the ECD may include periods of time where an individual may still be employed but is not in receipt of earnings.

Additionally, sectoral classification may be affected by:

• reduced coverage of Standard Industrial Classification (SIC) information for newly created businesses in the Inter-Departmental Business Register (IDBR)

• large employers reporting under a single enterprise-level SIC despite operating in multiple sectors

• the SIC 2007 framework may not fully capture hybrid or emerging industries

• employer’s Pay As You Earn (PAYE) schemes are sometimes restructured or reclassified, meaning their sectoral grouping may change year on year

Due to the mean of the measure of days since employment being skewed by large values a median is presented instead. This is particularly due to very large gaps in employment which may be due to overseas employment or self-employment.

9. Glossary 

This glossary gives a brief explanation for each of the key terms used in the publication.   

Assessment Period 

The amount of Universal Credit (UC) someone is eligible for is calculated based on their circumstances each month. These are called ‘assessment periods’. A customer’s UC payment is based on their circumstances in the previous assessment period, and their first assessment period starts on the day they make a claim. Assessment periods are monthly and begin on the same day each month. 

Customer (also claimant) 

A person making a claim for a benefit.  

Economically Inactive 

Economically inactive people are not in employment but do not meet the internationally accepted definition of unemployment. This is because they have not been seeking work within the last 4 weeks and/or they are unable to start work in the next 2 weeks. The economic inactivity rate is the proportion of people aged between 16 and 64 years who are not in the labour force.

Education and Training

People are considered to be in education or training if they are enrolled on an education course and are still attending or waiting for term to start or restart; are doing an apprenticeship; are on a government-supported employment or training programme; are working or studying towards a qualification; have had job-related training or education in the last 4 weeks.

Young people not in education, employment or training (NEET)

Any 16 to 24-year-old who is not in any of the forms of education or training listed above and not in employment is considered to be NEET. As a result, a person identified as NEET will always be either unemployed or economically inactive.^{[footnote 2]}

Into-work Rate

The into-work rate is defined as the proportion of customers in ‘Searching for work’ who have earnings in one assessment period who did not have earnings in the preceding assessment period.

Jobcentre Plus (including Jobcentre Plus district)

Jobcentre Plus (JCP) is a core part of support provided by the DWP for jobseekers in receipt of unemployment benefits and UC. It provides employment advice and uses knowledge of local labour markets to match unemployed customers to suitable job vacancies. It is also responsible for applying conditionality to the receipt of benefits.

Legacy Benefits

Universal Credit is replacing 6 benefits, commonly referred to as the legacy benefits:

• Income-based Jobseeker’s Allowance

• Income-related Employment and Support Allowance

• Income Support

• Working Tax Credit

• Child Tax Credit

• Housing Benefit

Level of qualification

There are 9 qualification levels.^{[footnote 4]}

• Entry level qualifications are: entry level award, entry level certificate (ELC), entry level diploma, entry level English for speakers of other languages (ESOL), entry level essential skills, entry level functional skills, Skills for Life.

• Level 1 qualifications are: first certificate, GCSE – grades 3, 2, 1 or grades D, E, F, G, level 1 award, level 1 certificate, level 1 diploma, level 1 ESOL, level 1 essential skills, level 1 functional skills, level 1 national vocational qualification (NVQ), music grades 1, 2 and 3.

• Level 2 qualifications are: CSE – grade 1, GCSE – grades 9, 8, 7, 6, 5, 4 or grades A*, A, B, C, intermediate apprenticeship, level 2 award, level 2 certificate, level 2 diploma, level 2 ESOL, level 2 essential skills, level 2 functional skills, level 2 national certificate, level 2 national diploma, level 2 NVQ, music grades 4 and 5, O level – grade A, B or C.

• Level 3 qualifications are: A level, access to higher education diploma, advanced apprenticeship, applied general, AS level, international Baccalaureate diploma, level 3 award, level 3 certificate, level 3 diploma, level 3 ESOL, level 3 national certificate, level 3 national diploma, level 3 NVQ, music grades 6, 7 and 8, T level, tech level.

• Level 4 qualifications are: certificate of higher education (CertHE), higher apprenticeship, higher national certificate (HNC), level 4 award, level 4 certificate, level 4 diploma, level 4 NVQ.

• Level 5 qualifications are: diploma of higher education (DipHE), foundation degree, higher national diploma (HND), level 5 award, level 5 certificate, level 5 diploma, level 5 NVQ.

• Level 6 qualifications are: degree apprenticeship, degree with honours – for example bachelor of the arts (BA) hons, Bachelor of Science (BSc) hons, graduate diploma, level 6 award, level 6 certificate, level 6 diploma, level 6 NVQ, ordinary degree without honours.

• Level 7 qualifications are: integrated master’s degree, for example Master of Engineering (Meng), level 7 award, level 7 certificate, level 7 diploma, level 7 NVQ, master’s degree, for example Master of Arts (MA), Master of Science (MSc), postgraduate certificate, postgraduate certificate in education (PGCE), postgraduate diploma.

• Level 8 qualifications are: doctorate, for example Doctor of Philosophy (PhD or DPhil), level 8 award, level 8 certificate, level 8 diploma.

Limited capability for work and limited capability for work-related activity

Customers can report a health condition that restricts their ability to work either when they first claim UC or later as a change of circumstances. A customer can self-certify for up to 7 days, after which medical evidence is required. Once medical evidence, usually a fit note, is accepted by DWP, the customer joins the UC Health Journey and is referred for a Work Capability Assessment (WCA) if the health condition continues for more than 4 weeks. Prior to a WCA outcome, the customer remains in the ‘Searching for work’ regime but with tailored conditionality. Following a WCA, if not capable of work, a customer is found to have either limited capability for work (LCW) (and placed in ‘Preparing for work’) or limited capability for work and work-related activity (LCWRA) (and placed in ‘No work requirements’).

Move to Universal Credit

The Move to UC programme invited customers on legacy benefits to make a claim to UC to continue to receive financial support.

Out of work

When someone is out of work they can be classified as unemployed or economically inactive.

Pre-COVID-19

The pre-COVID-19 reference period differs by data set and the methodology used.

State Pension age

The current State Pension age is 66 years old for both men and women and is currently set to rise to age 67 between 2026 and 2028, and to age 68 between 2044 and 2046.

Sustained Employment Rate

The sustained employment rate is defined as the proportion of customers in ‘Searching for work’, ‘Working - with requirements’ and ‘Working - no requirements’ regimes who moved into-work and sustained earnings for 3 (or 6) months.

Unemployed

Unemployed people are without a job and have been actively seeking work within the last 4 weeks and are available to start work within the next 2 weeks. The unemployment rate is not the proportion of the total population who are unemployed. Rather, it is the proportion of the economically active population (people in work and those seeking and available to work i.e. employed + unemployed) who are unemployed. The ONS measure the unemployment level and rate for people aged 16 and over. The ONS unemployment rate follows internationally agreed guidelines set out by the International Labour Organisation.

Universal Credit

A single, usually monthly payment, administered by DWP. UC is now the primary working-age benefit. UC replaces all the following state support: income-based Jobseeker’s Allowance, income-related Employment and Support Allowance, Income Support, Working Tax Credit, Child Tax Credit and Housing Benefit.

Most customers will be of working-age, though customers can be over State Pension age if their partner is still of working-age. UC supports those on low incomes with their housing and living costs, as well as child and childcare support where appropriate. It is not just for those who are out of work; it is also for those who are working, but whose earnings are low enough to qualify. Customers must have capital of less than a set limit to be eligible.

UC completed its roll-out for new claims in Great Britain at the end of 2018 and is available for new claims throughout the UK. Legacy benefit customers will continue to transfer to UC over several years.

People on UC are assigned to one of 6 conditionality regimes^{[footnote 3]}:

• Searching for work: not working, or with very low earnings. A customer is required to take action to secure work - or more or better paid work. The Work Coach supports them to plan their work search and preparation activity. Typical examples of people in this regime include jobseekers and self-employed in start-up period. Customers are only in this regime if they do not fit into one of the other regimes.

• Working – with requirements: in work, but could earn more, or not working but has a partner with low earnings.

• No work requirements: not expected to work at present. Health or caring responsibility prevents the customer from working or preparing for work. Examples of people on this include those in full time education, over state pension age, has a child under one and those with no prospect for work.

• Working – no requirements: individual or household earnings over the level at which conditionality applies. Required to inform DWP of changes or circumstances, particularly at risk of earnings decreasing or job loss.

• Planning for work: expected to work in the future/ lead parent or lead carer of child aged 1 (aged 1 to 2, prior to April 2017). The customer is required to attend periodic interviews to plan for their return to work.

• Preparing for work: expected to start work in the future even with limited capability to work at the present time or a child aged 2 (aged 3 to 4, prior to April 2017). The customer is expected to take reasonable steps to prepare for working including Work Focused Interview.

Universal Credit Health Journey

Customers can report a health condition that restricts their ability to work either when they first claim UC or later as a change of circumstances. A customer can self-certify for up to 7 days, after which medical evidence is required. Once medical evidence, usually a fit note, is accepted by DWP, the customer joins the UC Health Journey and is referred for a WCA if the health condition continues for more than 4 weeks. Prior to a WCA outcome, the customer remains in the ‘Searching for work’ regime but with tailored conditionality. Following a WCA, if not capable to work, a customer is found to have either LCW (and placed in ‘Preparing for work’) or LCWRA (and placed in ‘No work requirements’).

Worklessness Rate

The worklessness rate is defined as the proportion of customers in the ‘Searching for work’ conditionality regime with no earnings and who continue to have no earnings and remain in the ‘Searching for work’ conditionality regime for 6 consecutive months.

1. Young people not in education, employment or training (NEET), UK: February 2026 - Office for National Statistics (www.ons.gov.uk), published 26th February 2026. Available at: Young people not in education, employment or training (NEET), UK - Office for National Statistics ↩

2. Young people not in education, employment or training (NEET), UK: February 2026 - Office for National Statistics (www.ons.gov.uk), published 26th February 2026. Available at: Young people not in education, employment or training (NEET), UK - Office for National Statistics ↩

3. What qualification levels mean: England, Wales and Northern Ireland - GOV.UK ↩

4. Users should note that Universal Credit statistics uses the term ‘conditionality regime’ in place of conditionality groups and labour market regime. Available at: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology. ↩