Official Statistics

Get Britain Working: Labour Market Insights January 2026 – background information and methodology

Published 29 January 2026

Applies to England, Scotland and Wales

1.  Introduction

This background report accompanies the Get Britain Working: Labour Market Insights January 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

You are welcome to contact us directly with any comments: labour.marketstatistics@dwp.gov.uk.

Alternatively, you can contact 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 Accessibility statement for www.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

The next edition is planned for April 2026.  

The publication contains some core statistics which will be updated with each release, while other statistics will be published on a one-off basis or updated less frequently. Each edition will include contextual analysis on a specific topic, helping readers build understanding on a different subject with each release. In this publication the focus is on young people statistics.

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, 4. Sustained employment of Universal Credit Customers and 5. Measure of worklessness of Universal Credit customer in Get Britain Working: Labour Market Insights January 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 (PDF, 325KB) (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 (for example local authority) to the area administering their benefit claim.

People on Universal Credit in employment 

The analysis in 6. People on Universal Credit in employment by age group in Get Britain Working: Labour Market Insights January 2026 uses the Employment Characteristics Database (ECD), a DWP-derived dataset based on HM Revenue and Customs (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 Real Time Information (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.

It is created using HMRC P14 and P45 records. It is used within these statistics for identifying those who are employed and what sector they work within. Universal Credit (UC) administrative data is used only to identify UC status on the reference date. Someone is classed as being on UC if they have completed the UC claim process and accepted their Claimant Commitment. This analysis uses UC administrative data (more detail on this data above). Additionally, proportions in this publication are not comparable with the monthly statistics on the number of people on UC. This is because they are drawn from the underlying administrative databases rather than the extracts that comprise the monthly Official Statistics (which would not facilitate this analysis). The metadata on Stat-Xplore for the People on UC Official Statistics  provides details of the methodology used for those statistics, which differs in various ways from the definitions in this publication.

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

The analysis in 7. 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 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.

Local labour markets

The local labour market analysis in Get Britain Working: Labour Market Insights January 2026 explores the experiences of local labour markets using DWP developed local labour market types. To create the local labour market types, we used 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. Number of UC ‘Searching for work’ and Jobseeker’s Allowance customers in the local authority	ONS Claimant Count – includes UC Searching for work and Jobseeker’s Allowance claims - 2023[footnote 1]
B. Local authority employment rate	ONS Annual Population Survey - 2022-23[footnote 2]
C. Local authority working-age work-limiting disability rate	ONS Annual Population Survey - 2022-23 average
D. Proportion of local authority working-age population with at least level 4 qualifications[footnote 3]	ONS Annual Population Survey - 2022-23 average
E. Local authority musculoskeletal condition rate	OHID Local Authority Health Profiles - 2023[footnote 4]
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), micro-data 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.

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

The analysis in 8. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026 uses Annual Population Survey micro-data to identify those who are not in education, employment, or training, by age (16 to 24 years) at the district local authority level, alongside the DWP local labour markets cluster analysis.

Risk factor of young people aged 16 to 24 years old not being in education, employment or training (NEET) across England’s local labour markets

The analysis in 9. Risk factor of young people aged 16 to 24 years not being in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026 uses analysis from the Youth Futures Foundation’s dashboard.^{[footnote 5]} This study was conducted using Next Steps, a national longitudinal cohort study which followed a sample of individuals aged 13 through to the age of 25. This study explores the extent and degree of overlap between different forms of economic disadvantage among young people in England, and how experiencing multiple types of marginalisation may increase the risk of young people not being in employment, education, or training (NEET). A logistic regression model was used to identify the risk factors associated with a young person’s likelihood of becoming NEET. Based on the findings of the logistic regression a risk index was developed and used to calculate a NEET risk factor score. This was used alongside the DWP local labour markets cluster analysis.

Apprenticeship participation of young people aged 16 to 24 years by local labour market type

The analysis in 10. Apprenticeship participation of young people aged 16 to 24 years by local labour market type in Get Britain Working: Labour Market Insights January 2026 uses Individualised Learner Record (ILR) administrative data for the 2024/25 academic year produced by the Department for Education.^{[footnote 6]} The ILR is an administrative data collection system designed primarily for operational use in order to fund training providers for learners in further education and on apprenticeship programmes. This was used alongside population estimates from the Office for National Statistics (ONS) and the DWP local labour markets cluster analysis.

The mid-year population estimates from the ONS contains the population estimates for each country and local authority of the UK rebased to the results of the 2021/2022 censuses across the UK. These estimates replace previously published estimates for 2011 to 2022.  The estimated resident population of an area includes all those people who usually live there, regardless of nationality. Arriving international migrants are included in the usually resident population if they remain in the UK for at least a year. Emigrants are excluded if they remain outside the UK for at least a year. This is consistent with the United Nations definition of a long-term migrant. Armed forces stationed outside of the UK are excluded. Students are taken to be usually resident at their term time address.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ conditionality regime, by local authority and local labour market type in Britain

The analysis in 11a. Percentage of young people aged 16 to 24 years on the Universal Credit ‘Searching for work’ conditionality regime by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 uses DWP Universal Credit administrative data from the People on Universal Credit (UC) dataset on Stat-Xplore and Office for National Statistics (ONS) mid-year population estimates (detailed above) to derive the proportion of those aged 16 to 24 years who are in the ‘Searching for work’ conditionality regime in each local authority within Britain.

The number of UC claimants includes those who have started UC (completed the UC claim process and accepted their Claimant Commitment) and have not had a closure of their claim recorded for this spell, up to the ‘count date’ (second Thursday in each month). A closure of their claim would be recorded either at the request of the individual or if their entitlement to UC ends, for example, if they no longer satisfy the financial conditions to receive UC as they have capital over £16,000.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘No work requirements’ conditionality regime with a health condition, by local authority in Britain

The analysis in 11b. Percentage of young people aged 16 to 24 years on the Universal Credit ‘No work requirements’ conditionality regime with a health condition by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 uses DWP Universal Credit (UC) administrative data from the “UC Health” dataset on Stat-Xplore and Office for National Statistics (ONS) mid-year population estimates (detailed above). To identify young people in the ‘No work requirements’ conditionality regime with a health condition, we use those aged 16 to 24 years who have a limited capability for work and work-related activity (LCWRA) outcome. LCWRA status is used as a proxy for this group, as only individuals with LCWRA are placed in the ‘No work requirements’ regime due to a health condition.

To calculate proportions for each local authority, the analysis combines this data with ONS mid-year population estimates to derive the proportion of those aged 16 to 24 years who are in the ‘No work requirements’ conditionality regime with a health condition, in each local authority.

When a customer makes a claim for UC they will be asked if they have a mental/physical health condition or a disability which prevents, or limits, their ability to work. When claimants declare they have a restricted ability to work due to their health condition and DWP receives medical evidence in support of the claim - the claimant is placed on UC Health. This will include cases where claimants are in work but report a health condition which limits the amount of work they can do.

The number of people on UC Health comprises those with a restricted ability to work supported by acceptable medical evidence (pre-WCA) or with an LCW/LCWRA (limited capability for work/limited capability for work and work-related activity) outcome. Figures show the number of people on UC Health on the ‘count date’ which is the second Thursday of each month.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regimes, by parliamentary constituency in Britain 

The analysis in table 15 of the accompanying data tables document, which presents the proportion of 16 to 24 years in the ‘Searching for work’ conditionality regime by Westminster parliamentary constituency across England and Wales (January 2019 to October 2025) and Scotland (January 2022 to December 2022), uses DWP Universal Credit administrative data from the People on Universal Credit dataset on Stat‑Xplore (detailed above). The proportions are derived using mid‑year population estimates from the Office for National Statistics (for Westminster parliamentary constituencies in England and Wales) and mid‑2022 population estimates from National Records of Scotland (for Westminster parliamentary constituencies in Scotland). 

The analysis in table 18 of the accompanying data tables document, which presents the proportion of 16 to 24 years in the ‘No work requirement’ conditionality regime with a health condition, by Westminster parliamentary constituency across England and Wales (April 2019 to September 2025) and Scotland (January 2022 to December 2022), uses DWP Universal Credit administrative data “UC Health” dataset on Stat-Xplore (detailed above). The proportions are derived using mid‑year population estimates from the Office for National Statistics (for Westminster parliamentary constituencies in England and Wales) and mid‑2022 population estimates from National Records of Scotland (for Westminster parliamentary constituencies in Scotland).  

The mid-year population estimates from the ONS contains the population estimates for each country and Westminster parliamentary constituency of England and Wales rebased to the results of the 2021/2022 censuses across the UK, the same as the population estimates for local authorities in the UK (detailed above). However, the ONS does not provide mid-year population estimates for Westminster parliamentary constituencies in Scotland. 

The mid-year estimates for Westminster parliamentary constituencies in Scotland are from the National Records of Scotland mid-2022 estimates. The National Records of Scotland mid-2022 population estimates are based on Scotland’s Census 2022; however, the data zones used are 2011 data zones as recent data zones are not yet available. As a result, mid-year estimates prior to 2022 are not available. Scottish Westminster parliamentary constituency population estimates are based on small area population estimates which are used to assign the 2011 Data Zone population estimates to a particular Scottish Westminster parliamentary constituency area. 2011 Data Zones do not fit exactly into Scottish Westminster parliamentary constituency boundaries and are allocated on a ‘best fit’ basis. Where a data zone crosses the boundary of two or more constituencies it is allocated to the one that contains the population-weighted centroid of the data zone.

The estimated population of a constituency includes all those usually resident there, whatever their nationality. Students are treated as being resident at their term-time address. Members of United Kingdom (UK) and non-UK armed forces stationed in Scotland are included; UK forces stationed outside Scotland are excluded. Short-term international migrants are excluded. 

The 2022 figures are the first set based on Scotland’s Census 2022 and are therefore not currently comparable with figures published for previous years. National Records of Scotland will conduct a rebasing exercise to revise the annual small area population estimates for 2011 to 2021 in light of the 2022 census results.

Young people aged 16 to 24 years on Universal Credit in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regimes, by local labour market type in Britain

The analysis in 11a. Percentage of young people aged 16 to 24 years on the Universal Credit ‘Searching for work’ conditionality regime by local authority and local labour market type and 11b. Percentage of young people aged 16 to 24 years on the Universal Credit ‘No work requirements’ conditionality regime with a health condition by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 matches local labour market types to Universal Credit administrative data and ONS population estimates to calculate the proportion of young people aged 16 to 24 years in each conditionality regime, by local labour market type in Britain.

Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type

The analysis in 12. Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type in Get Britain Working: Labour Market Insights January 2026 uses the Annual Population Survey (APS) micro-data to identify those who are economically inactive due to long term sickness or disability and are aged between 16 to 24 years. This was used alongside the DWP local labour markets cluster analysis.

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 in Get Britain Working: Labour Market Insights January 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 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 January 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. Individuals who are in the ‘Searching for work’, ‘Working - with requirements’ and ‘Working - no requirements’ conditionality regimes in the assessment period that they start work are in scope for this indicator.

Measurement of worklessness

The focus of the worklessness rate is on customers in the ‘Searching for work’ conditionality regime who have 6 consecutive months of no earnings.

Producing the worklessness rate analysis in 5. Measure of worklessness of Universal Credit customer in Get Britain Working: Labour Market Insights January 2026 involves the identification of whether a customer has been out of work for 6 consecutive months. To calculate the worklessness rate for a specific month we identify customers without earnings who are in the ‘Searching for work’ regime 6 months prior to that month. This is the worklessness rate denominator. The same customers from the denominator are then looked at in the following 5 assessment periods and any customers with no earnings in all of these assessment periods are included in 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 in the first month of the 6-month tracking period. Individuals may transition between conditionality groups as circumstances change.

People on Universal Credit in employment 

The analysis in 6. People on Universal Credit in employment by age group in Get Britain Working: Labour Market Insights January 2026  explores the sectors of employment for people on Universal Credit (UC), on 13 March 2025. The focus is on employment status and sectoral distribution, using robust administrative data sources. A breakdown by age is also available.

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 schemes via IDBR linkage. Where employers operate in multiple sectors, the primary 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.

The analysis uses a snapshot approach to identify individuals employed on 13 March 2025, that is having an employment spell with a start and end date covering this date, aligning with the data used for the People on Universal Credit statistics. For individuals with multiple concurrent employments, the most recently started spell is selected for sectoral analysis.

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

The analysis in 7. 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 data tables 7, 8 and 9 in the accompanying data tables document uses Office for National Statistics Labour Force Survey micro-data to make an estimate of those young people who are not in education, employment or training. 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 July to September 2025), providing a more stable estimate over time. For consistency, this smoothing approach is also applied when calculating 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.

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

The analysis in 8. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026 matches the Annual Population Survey micro-data of those who are not in education, employment or training, by age (16 to 24 years) at the local authority level to the DWP local labour market types. To account for the small sample sizes in the annual data a 3 year average (October 2022 to September 2025) of the individual local authority NEET rates by local labour market type has been used to determine the NEET rate for each local labour market type.

We apply a different methodology and use a different data source to allow NEET rates to be analysed at this more localised geographic level - compared to the regional NEET analysis in this publication.

Risk factor of young people aged 16 to 24 years not being in education, employment or training (NEET) across England’s local labour markets

The analysis in 9. Risk factor of young people aged 16 to 24 years not being in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026 matches the local authority level NEET risk factors from the Youth Futures Foundation dashboard to DWP local labour market types. An average of the individual local authorities’ rates in each local labour market type is used to produce the statistics in this section.

Apprenticeship participation of young people aged 16 to 24 years by local labour market type

The analysis in 10. Apprenticeship participation of young people aged 16 to 24 years by local labour market type in Get Britain Working: Labour Market Insights January 2026 uses the number of individuals aged 16 to 24 years enrolled on an apprenticeships by local authority. The local authority level apprenticeship participation rate is then calculated using the total apprenticeship participation divided by the aged 16 to 24 population estimates from the Office for National Statistics. This is presented by local labour market type by using an average of the local authority apprentice participation rates for each local labour market type.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ conditionality regime, by local authority in Britain

The analysis in 11a. Percentage of young people aged 16 to 24 years on the Universal Credit ‘Searching for work’ conditionality regime by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 uses DWP Universal Credit (UC) administrative data for the period January 2019 to October 2025 and ONS mid-year population estimates from 2019 to 2024. The DWP UC administrative data provides the monthly caseload figures for those aged 16 to 24 years who are in the ‘Searching for work’ conditionality regime, by local authority in Britain, with the ‘Isles of Scilly’ local authority omitted due to a small population size. For each month, the caseload is divided by the mid-year estimates for the corresponding year and local authority to produce a time series of proportions by local authority. For 2025, the 2024 population estimates were used.

The statistics are calculated by dividing the average caseload for each local authority over the last 12 months (November 2024 to October 2025) by the 2024 population estimates for the corresponding local authority. The average proportions are categorised into five bands to identify the highest and lowest rates of young people in the specified conditionality regime, by local authority in Britain.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ conditionality regime, by parliamentary constituency in Britain

The analysis in table 15 of the accompanying data tables document uses DWP Universal Credit (UC) administrative data for January 2019 to October 2025, ONS mid‑year population estimates for Westminster parliamentary constituencies in England and Wales for 2019 to 2024, and National Records of Scotland mid‑2022 population estimates for Westminster parliamentary constituencies in Scotland. The DWP UC administrative data provides monthly caseload figures for those aged 16 to 24 years who are in the ‘Searching for work’ conditionality regime, by Westminster parliamentary constituency in Britain. For each month, caseloads are divided by the population estimate for the corresponding year and Westminster parliamentary constituency to produce a time series of proportions.  

For Westminster parliamentary constituencies in England and Wales in 2025, the 2024 mid‑year population estimates were used. For Westminster parliamentary constituencies in Scotland, only mid‑2022 population estimates are available, and therefore proportions are calculated for 2022 only.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘No work requirements’ conditionality regime with a health condition, by local authority in Britain

The analysis in 11b. Percentage of young people aged 16 to 24 years on the Universal Credit ‘No work requirements’ conditionality regime with a health condition by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 is calculated using DWP Universal Credit (UC) administrative data, for the period April 2019 to September 2025. The DWP UC administrative data provides the monthly caseload figures for those aged 16 to 24 years who have a health condition in the ‘No work requirements’ conditionality regime, by local authority in Britain, with the ‘Isles of Scilly’ local authority omitted due to a small population size. For each month, the caseload is divided by the mid-year estimates for the corresponding year and local authority to produce a time series of proportions by local authority. For 2025, the 2024 population estimates were used.

The statistics are calculated by dividing the average caseload for each local authority over the last 12 months (October 2024 to September 2025) by the 2024 population estimates for the corresponding local authority. The average proportions are categorised into five bands to identify the highest and lowest rates of young people in the specified conditionality regime, by local authority in Britain.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘No work requirements’ conditionality regime with a health condition, by parliamentary constituency in Britain

The analysis in table 18 of the accompanying data tables document uses DWP Universal Credit (UC) administrative data for April 2019 to September 2025, ONS mid‑year population estimates for Westminster parliamentary constituencies in England and Wales for 2019 to 2024, and National Records of Scotland mid‑2022 population estimates for Westminster parliamentary constituencies in Scotland. The DWP UC administrative data provides monthly caseload figures for those aged 16 to 24 years who have a health condition in the ‘No work requirements’ conditionality regime, by Westminster parliamentary constituency in Britain. For each month, caseloads are divided by the population estimate for the corresponding year and Westminster parliamentary constituency to produce a time series of proportions.  

For Westminster parliamentary constituencies in England and Wales in 2025, the 2024 mid‑year population estimates were used. For Westminster parliamentary constituencies in Scotland, only mid‑2022 population estimates are available, and therefore proportions are calculated for 2022 only.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regime, by local labour market type in Britain

The analysis in 11a. Percentage of young people aged 16 to 24 years on the Universal Credit ‘Searching for work’ conditionality regime by local authority and local labour market type and 11b. Percentage of young people aged 16 to 24 years on the Universal Credit ‘No work requirements’ conditionality regime with a health condition by local authority and local labour market type in Get Britain Working: Labour Market Insights January 2026 is calculated separately for each conditionality regime. For both conditionality regimes, the average monthly caseload for each local authority over the last 12 months of the latest available data is divided by 2024 population estimates for that local authority. Each local authority is then matched to a local labour market type, and the average of these percentages is calculated for each local labour market type over the last 12 months. 

For each local labour market type, the local authorities with the lowest and highest percentages of young people on the specified conditionality regime were identified to showcase the range within each local labour market type, compared to the average percentage of young people on the specified conditionality across Britain.

Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type

The analysis in 12. Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type in Get Britain Working: Labour Market Insights January 2026 matches the Annual Population Survey micro-data of those aged between 16 to 24 years who are economically inactive due to long term sickness or disability to the DWP local labour market types. The rates for the local labour market types are calculated as the number of inactive people aged 16 to 24 year (due to long term sickness or disability) by local labour market type divided by the total number of people aged 16 to 24 years by local labour market type. A 3 year average across October 2022 to September 2025 is used to produce the statistics. This approach is consistent with local labour market type analysis of inactivity trends in previous publications. It does however differ from the approach used in this publication which calculate rates on the basis of the average of local authority rates by local labour market type.

8. Limitations of the statistics

People on Universal Credit in employment

There are some limitations to the analysis in 6. People on Universal Credit in employment by age group in Get Britain Working: Labour Market Insights January 2026. People who are self-employed are not included in this analysis – only people who work as employees. This means that the analysis undercounts the number of people on Universal Credit (UC) that are employed.

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, i.e. their sectoral grouping may change year on year

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

The analysis in 7. 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 is calculated using Labour Force Survey (LFS) micro-data of young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s regions. 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.

Sample surveys like the LFS 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 data which is subject to heightened volatility due to ongoing data quality problems. The ongoing challenges with response rates and other aspects of the survey mean the LFS is currently considered ‘official statistics in development’ until further notice. Because of increased volatility of LFS estimates, estimates of change should be treated with additional caution.

The 16 to 24 age group is a smaller population group and has wider margins of error/volatility than for other, larger groups of the population. Therefore, micro-data on the youth labour market is both volatile and uncertain (due to the relatively high level of sample variability in the data for this smaller population group and because the data is not seasonally adjusted); meaning estimates produced at different points in the year will vary significantly.

To counteract these issues the data is calculated as a 4-quarter average (for the year ending July to September 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. This is due to issues with comparability between the methodology used for each nation’s NEET statistics.

The total NEET level and rate may differ from the statistics published by the ONS due to the difference in the treatment of missing values in this analysis.

Local labour markets

There are some limitations to the Annual Population Survey (APS) data used for the cluster analysis to determine the local labour market types in Get Britain Working: Labour Market Insights January 2026. The APS is a sample survey that is derived from the Labour Force Survey in combination with additional data, so the same limitations should be considered.

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’ NOMIS website, are now labelled as ‘official statistics in development’.^{[footnote 7]} 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. We did however experiment with a number of different labour market variables and used decision rules to narrow down our choice to the 6 listed above. Ideally in cluster analysis you want dimensionality - i.e. variables that relate to the central construct of interest (in this case the local labour market) but are not highly correlated with each other. In this way you get cluster groups forming out of combinations of high and low values of the variables. This explains why there are some variables (for example wages or vacancy rates) that might seem relevant labour market measures that 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. Our 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 administrative groupings rather than areas that might be recognised as spatially distinct labour markets. 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 exercise. In addition, policy devolution is at the local authority or the combined authority level, so it makes sense to produce analysis that matches this.

‘Isles of Scilly’ and ‘City of London’ do not report data for the variables in the cluster analysis. As a result, these two local authorities do not have a local labour market type.

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

The analysis in 8. Young people aged 16 to 24 years who are not in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026 uses the Annual Population Survey (APS) and DWP local labour market analysis, the limitations of which are explained above.

In the APS 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. To resolve the issue of small samples in the lower level of geography used for this analysis a 3 year average between October 2022 to September 2025 is used.

Low sample sizes at the local authority level resulted in 10 local authorities not having any NEET counts due to the sample size being insufficient. These local authorities were excluded from the analysis by local labour market type.

North Northamptonshire and West Northamptonshire are two separate unitary councils formed in 2021 from the merger of the former county council and district/borough councils in Northamptonshire. The data in the years 2024 and 2025 used local authority codes from the former local authorities. As a result, this analysis uses NEET rates for the unitary local authorities, calculated by dividing the sum of the NEET counts by the sum of the ONS mid-year population estimates for 2024.

Risk factor of young people aged 16 to 24 years not being in education, employment or training (NEET) across England’s local labour market

Although the Youth Futures Foundation analysis, used to create the analysis in 9. Risk factor of young people aged 16 to 24 years not being in education, employment or training (NEET) across England’s local labour markets in Get Britain Working: Labour Market Insights January 2026, explores multiple indicators of economic disadvantage there are some forms of disadvantage of relevance to a young person’s likelihood of being NEET that were not considered. Additionally, in some cases, the measures that have been included can only give a partial measure of that risk factor. Despite these limitations, the Youth Futures Foundation felt the selected dataset was most valuable given its breadth of coverage and its longitudinal dimension, meaning this research can look across multiple domains of disadvantage to understand their overlaps, over a decade. For more information on the limitations of the data used please see the Youth Futures Foundation report (PDF, 3.96MB.

The risk factor associated with a local area may not be representative of the level or rate of NEETs in that local area. The figures on risk factors indicate the proportion of young people in the local area who experience the risk factor, not the NEET rate for the local area

This analysis also uses the DWP local labour market types, the limitations of which have been explained above.

In 2023, significant local government reorganisation (LGR) occurred in England, primarily in Cumbria, North Yorkshire, and Somerset, moving from two-tier systems (county/district) to single unitary authorities. The NEET risk factor data from the Youth Futures Foundation uses the local authorities prior to this change, rather than the unitary authorities. To resolve this matter a weighted average NEET risk factor was created for the new areas by using the original risk factors with the data available and population estimates from the ONS.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ conditionality regime, by local authority in Britain

The following limitations apply to the analysis in sections 11a. Percentage of young people aged 16 to 24 years on the Universal Credit ‘Searching for work’ conditionality regime by local authority in Get Britain Working: Labour Market Insights January 2026.

DWP administrative data: Universal Credit and in the ‘Searching for work’ conditionality regime

There are some limitations with the DWP administrative data using the ‘People on Universal Credit’ dataset surrounding the count of those on Universal Credit (UC) and geographical locations. For each given month, the number of people on UC includes all individuals who have an open claim on the count date for the month. Some people will have their claim terminated either at the request of the individual or if their entitlement to UC ends. If a termination is recorded, but the person is still receiving a payment, then the claim will still be classed as live at the end of each reporting month. A person’s conditionality is based on the individual’s circumstances on the count date.

It is possible for an individual to be working and placed in the ‘Searching for work’ conditionality regime if they are earning very low amounts. This includes:

• single claimants with earnings below the individual Administrative Earnings Threshold (AET)
• claimants with earnings below the AET and in a household with earnings below the couple AET
• lead carers, who are either not working or earning below the AET and whose youngest child or children are aged 3 or over
• claimants who are gainfully self-employed and in a start-up period (claimants who are gainfully self-employed, and the Minimum Income Floor (MIF) does not apply), regardless of earnings

Residence based geographies

Residence based geographies are used to determine the breakdowns by local authority. Residence based geographies have been derived from postcodes related to a claimant’s place of residence and not the Jobcentre Plus office administrating the claim.

ONS mid-year population estimates

The latest ONS mid-year population estimates for each local authority is for the period 2019 to 2024. To calculate the proportion of young people aged 16 to 24 years on Universal Credit in a given conditionality regime, the mid-year estimate for a given local authority in the corresponding year is used. For 2025, the 2024 population estimates were used.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘No work requirements’ conditionality regime with a health condition, by local authority in Britain

The following limitations apply to the analysis in 11b. Percentage of young people aged 16 to 24 years on the Universal Credit ‘No work requirements’ conditionality regime with a health condition by local authority in Get Britain Working: Labour Market Insights January 2026.

DWP administrative data: Universal Credit and in the ‘No work requirements’ with a health condition

There are some limitations with the DWP administrative data using the ‘Universal Credit Health Caseload’ dataset surrounding the count of those on Universal Credit (UC) and geographical locations.

For those on UC with a health condition, the ‘Universal Credit Health Caseload’ data is used where individuals have a limited capability for work and work-related activity (LCWRA) as a proxy for ‘No work requirements’ with a health condition. This proxy is used because individuals can be in the ‘UC Health Caseload’ dataset and have a health condition but are in either the pre-WCA phase, and in the ‘Searching for work’ regime with tailored conditionality, or 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’).

The count of people in the ‘No work requirements’ conditionality regime with a health condition includes all those on UC Health and have LCWRA reported as of the second Thursday of each month.

The geographical and ONS mid-year population estimates limitations are as outlined in the section above.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regime, by parliamentary constituency in Britain

The limitations of the DWP administrative data used for the analysis seen in table 15 and table 18 are as outlined above. 

There are some additional limitations on the geographical and mid-year population estimates used to calculate the caseload proportions by Westminster parliamentary constituency. 

Residence based geographies 

Residency based geographies for Westminster parliamentary constituencies are derived from address information as recorded on the Customer Information System (CIS). CIS is a more reliable source of addresses as it links to all of the DWP benefit systems and contains the most up to date address for each individual. 

These addresses are then put through a data cleansing procedure which makes sure postcodes are formatted correctly and the address fields are populated correctly. The Census Output Area 2021 (COA) are then assigned to claimants using the ONSPD (ONS Postcode Directory), starting with a direct postcode to COA lookup and then working through a logical allocation routine. These COAs are then used to merge on higher level geographies from the National Statistics Postcode Look-Up (NSPL). 

All higher level geographies (Lower Layer Super Output Area/Data Zone, Middle Layer Super Output Area/Intermediate Zone, Local Authority, Region, Country) are derived from COA. 

Mid-year population estimates 

In addition to the limitations as outlined above, the ONS mid-year population estimates for Westminster parliamentary constituencies are only available for England and Wales for the period 2019 to 2024 and do not provide estimates for Westminster parliamentary constituencies in Scotland. To align the methodology with the breakdowns by local authority, National Records of Scotland mid-2022 population estimates were used. 

The National Records of Scotland mid-2022 population estimates are based on Scotland’s Census 2022; however, the data zones used are 2011 data zones as recent data zones are not yet available. As a result, mid-year estimates from previous years are not available. Estimates for the special areas are built up using the 2011 Data Zone level Small Area Population Estimates. 2011 Data Zones do not always fit exactly into the boundaries of other geographical areas, so they are allocated on a best-fit basis. 

Only mid‑2022 population estimates are available for Westminster parliamentary constituencies in Scotland. Because population estimates for earlier years are not yet available or rebased, it is not possible to produce comparable trends over time for Scottish Westminster parliamentary constituencies. Population change can vary significantly across local areas, so relying on a single year of estimates does not provide a reliable basis for comparison with the multi‑year population series available for England and Wales. As a result, the 2022 proportions for Westminster parliamentary constituencies in Scotland should not be compared with proportions for other years for Westminster parliamentary constituencies in England and Wales.

Young people aged 16 to 24 years who are on Universal Credit and in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regime, by local labour market type in Britain

The limitations on the data used to derive the proportions of young people on Universal Credit and in the ‘Searching for work’ or ‘No work requirements’ (with a health condition) conditionality regime are as stated above. For limitations specific to local labour market types, see the section on local labour market types above.

Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type

The analysis in 12. Long-term sickness/disability inactivity rate of young people aged 16 to 24 years by local labour market type in Get Britain Working: Labour Market Insights January 2026 also uses the Annual Population Survey and DWP local labour market types, the limitations of which have been explained above. To resolve the issue of small samples in the lower level of geography used for this analysis a 3 year average between October 2022 to September 2025 is used.

9. Glossary

This glossary gives a brief explanation for each of the key terms used in the publication. Further details on these definitions are available on request from the DWP Labour Market Analysis team at: labour.marketstatistics@dwp.gov.uk.

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 8]}

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

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 invites 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. Throughout sections 3, 4 and 5c references to pre-COVID-19 are comparing to February 2020.

Section 5a uses January 2019 to January 2020 as the pre-COVID-19 reference period.

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 9]}:

• 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’ regime with no earnings and who continue to have no earnings for 6 consecutive months.

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1. Number of UC Searching for work and Jobseeker’s Allowance customers in the local authority. Available at: Claimant count and vacancies time series - Office for National Statistics ↩

2. Nomis - Official Census and Labour Market Statistics. Local authority employment rate. Available at: Dataset: annual population survey - Office for National Statistics ↩

3. What qualification levels mean. Available at: What qualification levels mean - GOV.UK. ↩

4. Department for Health and Social Care, Fingertips Public Health Profiles, Musculoskeletal health profile. Available at: Musculoskeletal health profile - Department of Health & Social Care ↩

5. Risk factors for being NEET among young people, National Centre for Social Research for Youth Futures Foundation. Available at: Risk factors for being NEET among young people - Youth Futures Foundation (PDF, 3.96MB) ↩

6. Apprenticeships, Department for Education. Available at: Apprenticeships - Academic year 2024/25. ↩

7. Stephanie Howarth to Ed Humpherson: Request to suspend APS accreditation – ONS Regulation. Available at: Stephanie Howarth to Ed Humpherson: Request to suspend APS accreditation - Office for Statistics Regulation. ↩

8. Young people not in education, employment or training (NEET), UK: November 2025 - Office for National Statistics (www.ons.gov.uk), published 20th November 2025. Available at: Young people not in education, employment or training (NEET), UK - Office for National Statistics ↩

9. 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 - GOV.UK. ↩