Official Statistics

Get Britain Working: Labour Market Insights October 2026 - Background Information and Methodology

Published 29 October 2026

1. Introduction

This background report accompanies the October 2026 publication of Get Britain Working: Labour Market Insights. 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.

The Get Britain Working: Labour Market Insights collection contains previous editions of this publication.

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 – having confidence in the people and organisations that publish statistics
• quality – using data and methods that produce reliable statistics
• value – 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. 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 DWP statistics 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 fifth in a quarterly statistical publication series of labour market insights. The next edition is planned for January 2027. The publication contains some core statistics which will be updated periodically, while other statistics will be published on a one-off basis or updated less frequently. Every other edition will include a contextual chapter offering deeper analysis of a specific topic, helping readers build understanding without requiring full updates to all content. In this publication, the contextual chapter focuses on geographic disparities in labour markets.

5. 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 on 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.

6. Source of the statistics

Into-work, sustained employment and worklessness rates

The analysis in sections 3, 4, 5, and 7b. in Get Britain Working: Labour Market Insights October 2026 explore 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 on characteristics of UC customers, their claim and earnings, and how these change over time.

These HMRC PAYE data provide 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 DWP 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 October 2026 publication uses Labour Force Survey (LFS) microdata. 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.

Local labour markets

The analysis in 7a. Local labour market response to economic shocks and 7b. Local context, claimant composition and into-work rates in Get Britain Working: Labour Market Insights October 2026 publication explores the experiences of local labour markets using DWP-developed local labour market types. The local labour market types were created using cluster analysis, based on 6 local labour market variables. These variables and the data sources used to create them are detailed in Table 1.

Table 1: Variables used to classify local authorities

Variable	Data source
A. Proportion of the population claiming UC Searching for work and Jobseeker’s Allowance in the local authority	ONS Claimant Count – includes UC Searching for Work and Jobseeker’s Allowance claims - 2023
B. Local authority employment rate	ONS Annual Population Survey - 2022 to 2023
C. Local authority working-age work-limiting disability rate	ONS Annual Population Survey - 2022 to 2023 average
D. Proportion of local authority working-age population with at least level 4 qualifications [footnote 1]	ONS Annual Population Survey - 2022 to 2023 average
E. Local authority musculoskeletal condition rate	OHID Local Authority Health Profiles - 2023
F. Proportion of local authority population with a mental health condition	OHID Local Authority Health Profiles - 2017

The cluster analysis uses the Annual Population Survey (APS) microdata provided to DWP by the ONS. The APS is a continuous household survey, covering the UK. The topics covered include employment and unemployment, as well as housing, ethnicity, religion, health and education. The purpose of the APS is to provide information on important social and socio-economic variables at local levels. The published statistics enable monitoring of estimates between censuses for a range of policy purposes and provide local area information for labour market estimates.

Local labour market response to economic shocks

The analysis in 7a. Local labour market response to economic shocks uses the APS. The observed data period covers 2007 to 2025.

Local context, claimant composition and into-work rates

For the analysis in 7b. Local context, claimant composition and into-work rates, the gender composition and age composition all use DWP Universal Credit administrative data.

Into-work rates produced use a combination of UC administrative data and HM Revenue and Customs’ (HMRC) Pay As You Earn (PAYE) Real Time Information (RTI) data. This additional data on a customer’s earnings, beyond that collected by DWP, 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. The observed data period is August 2025 to July 2026 for into-work rates and August 2026 for the compositional analysis.

Highest level of educational qualification statistics

The analysis in 7c. Highest level of educational qualification for Universal Credit customers across regions uses a DWP-derived dataset based on education and training data received from the Department for Education (DfE). DWP receives: the National Pupil Database (NPD) which covers school-level data in England, Higher Education Statistics Agency (HESA) University Records which cover 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 the UC caseload in March 2023, their conditionality regime, their date of birth and their address at the date of interest. Universal Credit caseload figures are point-in-time estimates rather than monthly totals. The March 2023 caseload represents the number of people claiming Universal Credit on the official statistics count date, which was 9 March 2023 (the second Thursday of the month).

Using customers’ addresses at the date of interest, DWP can extract the postcode and use ONS geography look-up tables to aggregate the postcodes into Census output areas, local authorities, regions and mayoral strategic authorities.

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, 3d. Into-work rate by age of Universal Credit customer and some analysis in 7b. Local context, claimant composition and into-work rates in Get Britain Working: Labour Market Insights October 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.

Sustained employment rate

The focus of the sustained employment rate 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 October 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 October 2026 looks at customers in the ‘Searching for work’ conditionality regime who have at least 6 consecutive months of no earnings.

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 October 2026 uses Office for National Statistics (ONS) Labour Force Survey (LFS) microdata to make an estimate of those young people who are not in education, employment or training. The methodology behind constructing these estimates was altered in Get Britain Working: Labour Market Insights April 2026 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 Get Britain Working: Labour Market Insights January 2026 or the Get Britain Working White Paper Analytical Annex. This methodology change was made to increase alignment with the ONS methodology which is explained below.

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 year of 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 the regional NEET estimates, we use the same apportioning method for missing responses that the ONS uses. 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 aged 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. The 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, providing a more stable estimate over time.

Local labour market response to economic shocks

The statistics are calculated using APS data on the unemployed population. This has been matched to local labour market types with a selection presented in the publication. A percentage point change is used to determine the change for each local labour market type during economic shocks.

Local context, claimant composition and into-work rates

The analysis in section 7b: Local context, claimant composition and into-work rates uses DWP administrative data. Gender composition measures the proportion of UC ‘Searching for work’ claimants by gender within each labour market type. Age composition of the ‘Searching for work’ caseload measures the proportion of ‘Searching for work’ claimants in each age group within each labour market type.

The average into-work rate is split by labour market type. The age breakdowns in the accompanying tables are grouped for those under 25 and 50 and over.

Highest level of educational qualification statistics

The analysis in 7c. Highest level of educational qualification for Universal Credit customers across regions uses educational qualification data to explore UC customers’ highest level of educational qualification and how this breakdown varies by geography.

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. The analysis in section 7c. 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.

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 through a 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 microdata 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 the potential for 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 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 October 2026 and the corresponding NEET levels in Table 14 of the accompanying data tables are calculated using LFS microdata 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 four-quarter average. 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 slightly different result would have been produced.

The confidence intervals are based on pooled observations across four quarters, whereas the central estimates use a simple four-quarter average. Consequently, there is some minor inconsistency between the estimation of central estimates and sampling variability. However, comparison of the results shows that the NEET rates and levels obtained from the pooled sample are very similar to those derived from the four-quarter average approach. The confidence intervals that have been calculated do not account for the complex survey design of the LFS.

Local labour markets data

There are some limitations to the Annual Population Survey (APS) data, as described above, that effect the cluster analysis used to determine the local labour market types used in 7a. Local labour market response to economic shocks and 7b. Local context, claimant composition and into-work rates.

While the Office for National Statistics (ONS) considers the quality of the APS to be robust for national and headline regional estimates, there are concerns with the quality of estimates for smaller segments of the population, such as local authority geographies. ONS outputs produced using the APS data, including those disseminated on the ONS’s NOMIS website, are now labelled as ‘Official Statistics in Development’ ^{[footnote 2]}. There is greater uncertainty associated with estimates at a local authority level compared to estimates at a regional or national level. Areas with smaller populations will, other things being equal, have smaller samples and thus wider margins of error.

There are some limitations with the cluster analysis used to determine the local labour market types. The groupings are only based on the 6 variables listed above. The choice of variables to construct the groups is, to an extent, subjective. The variables are intended to provide a good description of labour market strengths and weaknesses. Other variables were considered as part of the analysis, with the aim of identifying a set of variables which are related to the local labour market but not highly correlated with each other. This explains why there are some variables (e.g. wages or vacancy rates) that might seem relevant as labour market measures but which are not included in the analysis.

The groupings will vary depending on the variables selected and the time period to which the data pertains, so they should not be taken as rigid groupings that define the characteristics of group members. This analysis is a data-driven exercise to generate groupings that will be practically useful rather than intended to identify fixed qualities of local areas.

The groupings are of local authorities – these are groupings of administrative geographies rather than areas that might be recognised as spatially distinct labour markets (e.g. travel to work areas). The reason for this is that most sub-national data is readily available at the local authority level and hence it made sense to maximise the data available for this analytical exercise. In addition, policy devolution is at the local authority or the strategic authority level, so it makes sense to produce analysis that matches this.

Unemployment by local labour market type

The analysis in 7a. Local labour market response to economic shocks also uses the APS. Sample surveys like the 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.

Recent APS data is subject to heightened volatility due to ongoing data quality challenges as explained above. The ongoing challenges with response rates and other aspects of the survey mean ONS labour market statistics based on the APS are currently considered ‘Official Statistics in Development’ until further notice. Because of increased volatility of APS estimates, estimates of change should be treated with additional caution.

Unemployment rates for labour market types are calculated by aggregating local authority-level unemployment rates and are therefore based only on local authorities for which unemployment data are available.

Highest level of educational qualification statistics

There are some limitations to the analysis in 7c. Highest level of educational qualification for Universal Credit customers across regions.

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, the analysis in section 7d. 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.

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 3]}.

Into-work rate

The into-work rate is defined as the proportion of customers in UC ‘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

Limited capability for work (LCW) and limited capability for work-related activity (LCWRA)

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 dataset and the methodology used. Throughout section 3c. Into-work rate by duration on Universal Credit, references to pre-COVID-19 are comparing to before February 2020.  

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 4]}:

• 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, those responsible for a child aged 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 of 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 of 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’ regime with no earnings and who continue to have no earnings for 6 consecutive months.

1. What qualification levels mean - GOV.UK. ↩

2. Stephanie Howarth to Ed Humpherson: Request to suspend APS accreditation. Available at: Request to suspend APS accreditation - ONS Regulation. ↩

3. Young people not in education, employment or training (NEET), UK: May 2026 - Office for National Statistics (www.ons.gov.uk), published 28 May 2026. Available at: Young people not in education, employment or training (NEET), UK - Office for National Statistics ↩

4. Users should note that Universal Credit statistics uses the term ‘conditionality regime’ in place of conditionality groups and labour market regime. Available at: Universal Credit statistics: background information and methodology. ↩