Official Statistics

Background information and methodology for financial year ending 2023

Published 10 October 2024

Applies to England, Scotland and Wales

Introduction

This background report accompanies the main income-related benefits: estimates of take-up report or financial year ending (FYE) 2023.

These statistics are designed to give an estimate of the take-up of benefits within the entitled pensioner population. They aim to provide an indication of whether, and to what extent, Pension Credit (PC) and Housing Benefit (HB) are taken up by the entitled pensioner population.

The purpose of this report is to provide further contextual information to aid understanding of the statistics presented in the main report and data tables. It outlines points to note as well as strengths and limitations of the information presented in each section of the main report, alternative data sources, as well as changes this year.

A detailed description of the income-related benefits: estimates of take-up methodology, data processing and quality assurance are presented within the relevant sections in this report. These descriptions are intended to help users with the use and interpretation of the FYE 2023 data.

This document, the statistics release and data tables, along with previous releases, can be found on the collections page.

Editorial Team

Publication Lead: Kate Martin

Take-up team: John Bilverstone, Sophie Rae, Yente Meijers, Owusu Appiah

Feedback

We welcome feedback.

If you have any comments or questions, please contact: [email protected]

1. Overview of the Statistics

1.1 History of the Statistic

These statistics are designed to give an estimate of the take-up of benefits within the entitled pensioner population. They aim to provide an indication of whether, and to what extent, Pension Credit (PC) and Housing Benefit (HB) are taken up by the entitled pensioner population. The current broad methodology to produce the take-up statistics dates back to FYE 2020, but take-up estimates using different methodologies have been produced for a number of years and the approach and publication have evolved over that time.

Further information regarding this can be found via the National Archives.

1.2 Status of the statistics

These statistics are official statistics.

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.

You are welcome to contact us directly with any comments about how we meet these standards by email to: [email protected].

Alternatively, you can contact OSR by emailing: [email protected] or via the Office for Statistics Regulation website.

1.3 Recent improvements to the statistics

Key updates within this release include an improved background information and methodology document.

2. Background Information

A comprehensive set of data tables complementing the results presented are available alongside the publication. These tables are referenced throughout the published report.

2.1 Relevance

These statistics can be used for:

• increasing awareness / take-up of benefits
• the take-up estimates, and further analysis tables can help increase awareness of benefits
• measuring the effect of take-up initiatives
• policy development
• answering ministerial briefings, ad-hoc enquiries, Freedom of Information (FOI) requests, and Parliamentary Questions (PQs) published on the UK Parliament website

There are both internal and external users of these statistics. It is a key source of information that is used to inform government thinking on relevant policies, as well as related programmes and projects. The Department for Work and Pensions (DWP) uses these statistics for policy analysis, decision making, and answering queries. Our external users include press, journalists, researchers, academics, voluntary organisations, and charities. These groups use the statistics for research purposes and awareness.

2.2 Accessibility and Clarity

Accessibility

We have further 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 ODS tables, such as merged cells, has been avoided. For more information, please see the Accessibility statement for DWP statistics.

Clarity

Within both the Quick guide to published tables and results for financial-year-ending-2023 and each published data table, there is additional guidance to help in interpreting the take-up estimates. There is also a glossary of key terms, found at the end of this document.

See Section 9 or more detail on coherence and comparability, and Section 10 for more information regarding the limitations of the statistics.

Contact email [email protected] for further information.

2.3 Timeliness and Punctuality

Take-up statistics are modelled estimates based on Family Resources Survey (FRS) data. Modelling work to produce the entitlement dataset can only start once the final FRS dataset is published.

Given the use of the FRS, extensive QA takes place before the FRS dataset is released, 12 months after fieldwork has been completed. See Section 7 for more information on the detail.

Due to the complexity of the various data sources, take-up processes and QA that is carried out, we now publish the take-up report approximately 6 to 7 months after the FRS publication.

In terms of punctuality, data has been released both as planned and as announced in the release calendar.

2.4 Confidentiality and security

Personal identifiers on the data provided from administrative systems are encrypted to prevent identification of individuals within the data.

Data is held securely. Access is only given to analysts who have completed internal data access and security training and who have the business need to access the data.

In accordance with the Code of Practice for Statistics T3.3 and T3.4, access to statistics before their public release is limited to those involved in the production of the statistics and the preparation of the release, and for quality assurance and operational purposes. Analysis of data, either in tabular or chart form is given to a group of subject matter experts for quality assurance purposes.

Accurate records of those who have access before the data are finalised are maintained. Several controls are applied to this access.

Anyone with access prior to the publication release is made clear about their role and the need to prevent any disclosure that is any indication of the statistics or messages they convey. Access is restricted to named individuals within teams, who are restricted from discussing the statistics with their colleagues during that access period.

3. Concepts and definitions

Take-up refers to the receipt of benefits that a benefit unit is entitled to. Such a benefit unit is referred to as an entitled recipient (ER). There are also families who are entitled to benefits, but not in receipt of them. Such a family is referred to as an entitled non-recipient (ENR).

Take-up is estimated in 2 ways – by caseload and by expenditure:

• caseload take-up compares the number of benefit recipients, averaged over the year, with the estimated number that would be receiving if everyone took up their entitlement for the full period of their entitlement
• expenditure take-up compares the total amount of benefit received, over a year, with the estimated total amount that would be received if everyone took up their entitlement for the full period of their entitlement

4. Context of the statistics

The benefits covered in the publication are:

• Pension Credit (PC)
• Housing Benefit (HB) for pensioners

Pension Credit (PC)

Overview

Pension Credit (PC) is an income-related benefit that was introduced on 6 October 2003 and replaced the Minimum Income Guarantee (MIG).

There are 2 parts to PC:

• Guarantee Credit (GC)
• Savings Credit (SC)

Claimants may qualify for either or both parts of PC.

Guarantee Credit (GC) tops up the claimant’s income to a guaranteed level (a higher guaranteed level applies for couples). The level may increase if the claimant is a carer, severely disabled, responsible for a child or young person, or has certain housing costs.

To qualify:

• the claimant must live in Great Britain (GB)
• the claimant (and their partner from 15 May 2019) must have reached PC qualifying age. Therefore, from FYE 2020 onwards both parties in a couple must be State Pension age (SPa) or above to be entitled to claim PC (unless the couple are in receipt of HB (for pensioners) where only one party in a couple needs to be above SPa)

Savings Credit (SC) is extra money given by the government to people who have made provision for their retirement above the SC threshold (such as savings or a second pension).

Most people who reached SPa on or after 6 April 2016 will not be eligible for SC.

However, if a single claimant has reached State Pension age before 6 April 2016, or a claimant is in a couple and one of them reached SPa before 6 April 2016, then they may be entitled to Savings Credit.

If a claimant who is a member of a couple in the circumstances above stops being allowed to claim SC for any reason, they will not be able to claim it again. As of 6 May 2020, DWP introduced an online claim option to support existing methods of applying for PC by telephone or post.

Please see GOV.UK for more information on current PC entitlement rules.

Up-rating

In April 2022:

• The ‘triple lock’ ensures that the basic State Pension and new State Pension increase every year by whichever is the highest of the following: earnings, prices as measured by the Consumer Prices Index (CPI), or 2.5%. In FYE 2023, both the Basic and New State Pension increased by 3.1%. The Basic State Pension increased from £137.60 per week to £141.85 per week, a cash increase of £4.25 per week. The New State Pension increased from £179.60 per week to £185.15 per week, a cash increase of £5.55 per week
• the Standard Minimum Guarantee (SMG) in PC increased by 3.1%. For those who were single, the SMG in PC increased from £177.10 per week to £182.60 per week, a cash increase of £5.50 per week. For couples, this increased from £270.30 per week to £278.70 per week, a cash increase of £8.40 per week

Benefit rates

The Standard Minimum Guarantee (SMG) is the minimum level of income that is guaranteed through GC. This is uprated each year by at least the level of the increase in earnings.

The Savings Credit Threshold (SCT) determines the level of income at which someone becomes eligible for SC, while the SC Maximum (SC max) is the maximum amount of SC that can be awarded. In recent years, the rate of the increase of the SCT has had the effect of reducing the SC max.

The rates below are taken from: Benefit and pension rates 2022 to 2023.

Singles	FYE 2023 rates per week
Standard Minimum Guarantee	£182.60
Savings Credit Threshold	£158.47
Savings Credit Maximum	£14.48

Couples	FYE 2023 rates per week
Standard Minimum Guarantee	£278.70
Savings Credit Threshold	£251.70
Savings Credit Maximum	£16.20

The qualifying age

Between April 2016 and November 2018, the PC qualifying age for males and females rose to 65 in line with the increase in SPa for females. The PC qualifying age increased in line with further increases in SPa for both males and females, from 65 to 66 between December 2018 and October 2020. For FYE 2023 the SPa for both males and females is 66. Thereafter, it will continue to increase in line with further increases in SPa for both males and females, so will rise to 67 by 2028. More information can be found in the following table:

Date	Qualifying Age for females	Qualifying Age for males
April 2009	60 years	65 years
April 2010	60 years	65 years
April 2011	Between 60 years 5 months and 60 years 7 months	65 years
April 2012	Between 60 years 11 months and 61 years 1 month	65 years
April 2013	Between 61 years 5 months and 61 years 7 months	65 years
April 2014	Between 61 years 11 months and 62 years 1 month	65 years
April 2015	Between 62 years 5 months and 62 years 7 months	65 years
April 2016	Between 62 years 11 months and 63 years 3 months	65 years
April 2017	Between 63 years 8 months and 64 years	65 years
April 2018	Between 64 years 5 months and 64 years 9 months	65 years
April 2019	65 years	65 years
April 2020	65 years	65 years
April 2021	66 years	66 years
April 2022	66 years	66 years
April 2023	66 years	66 years

Changes to Pension Credit (PC) rules

Capital disregards are amounts that the claimant has but are not taken into account when considering entitlement or calculating any assumed yield income. From November 2009, the level of the capital disregard increased to £10,000 (up from £6,000). Previously, only those who lived permanently in a care home had a £10,000 capital disregard.

Mixed age couples

From 15 May 2019, mixed age couples were no longer able to choose whether they claimed Universal Credit (UC) or PC or pension age HB. Both parties of a couple will have to reach the PC qualifying age before they can be entitled to either PC or pension age HB or both. From 15 May 2019, if a claimant who receives HB or PC’s circumstances change, or they take a break from claiming, they may have to claim UC instead. Some changes that affect claims include:

• a change of address to a different local council area
• going abroad for more than 4 weeks
• a change in the amount of capital a claimant has
• stopping claiming a benefit that helps a claimant qualify for PC or HB
• separating from a partner and then getting back together

Pensioner TV licences

From 1 August 2020, anyone who was aged 75 years or over and received PC was entitled to a free TV licence.

Interaction with other income-related benefits

Until November 2018, males aged between SPa for females and 65 were able to claim one of PC, Employment and Support Allowance (ESA income-related (IR)), Jobseeker’s Allowance (income-based (IB)) or, in certain areas, Universal Credit (UC). From November 2018 males could only claim PC when PC qualifying age for males was reached. This choice also existed for mixed age couples until May 2019 where one member was aged above the PC qualifying age, and the other was aged below.

For the purposes of estimating take-up, we modelled those males aged over the PC qualifying age, but under 65, would have claimed PC rather than ESA (IR). This can be explained as follows. It is assumed that a working-age person can only claim a working-age benefit, such as ESA (IR). Males aged over the PC qualifying age but under 65 have a choice over what they can claim – PC or a working-age benefit. Most males would opt for PC because the amount of benefit received would normally be greater than their working-age benefits. Additionally, with PC there are no conditionality requirements (the working-age benefits need a justification as to why the claimant is not working). Similarly, mixed age couples were assumed to claim PC.

From FYE 2020 onwards mixed age couples can only claim UC.

PC can be paid in conjunction with HB.

Cost Of Living Payments (COLP)

PC Claimants were eligible for the first Cost of Living Payment of £326 if they were entitled to a payment (or later found to be entitled to a payment) of Pension Credit for any day in the period 26 April 2022 to 25 May 2022.

PC Claimants were eligible for the second Cost of Living Payment of £324 if they were entitled to a payment (or later found to be entitled to a payment) of Pension Credit for any day in the period 26 August 2022 to 25 September 2022.

Read further information relating to the Cost Of Living Payment.

Pension Credit government campaign

The Department for Work and Pensions (DWP) launched an awareness campaign to promote Pension Credit which started in April 2022. It consisted of TV adverts, radio adverts, press, social media and information in GP surgeries and post offices, encouraging people to check their eligibility for the benefit.

Read the Pension Credit Official Statistics.

Housing Benefit (HB)

Overview

Housing Benefit (HB) helps tenants who are on a low income to pay their rent. HB is administered by local authorities who decide whether a claimant is eligible for HB, and how much they qualify for. Tenants can apply for HB irrespective of whether they are in work, or out-of-work. It may be paid alongside other income-related benefits, non-income-related benefits, or on its own. Since December 2018, due to the rollout of UC, new claims for HB can only be made in certain circumstances.

Subject to having an eligible rent liability, the vast majority of those on the Guarantee Credit (GC) part of PC, Income Support (IS), or ESA (IR) are automatically eligible for maximum HB.

Claimants not in receipt of one of the benefits listed above may have their HB reduced if they have capital or income more than certain thresholds. If their capital or income is too high, they may not qualify for HB at all.

The amount awarded can also vary based on the numbers, age, and disabilities of members of the household. For many claimants in the private rented sector, the level of their eligible rent is also limited by the Local Housing Allowance (LHA) rates which apply to where they live. The size and composition of the claimant’s household determines the size of the accommodation which they might need. In turn, the specific LHA rate is used to assess entitlement to HB.

There are some exceptions. Those that do not qualify for HB include:

• those who live in the home of a close relative
• any full-time students - unless they are disabled or have children
• asylum seekers or those sponsored to be in the United Kingdom (UK)

As of December 2018, new claims for HB can only be made if:

• a claimant (and their partner from 15 May 2019) has reached SPa. Therefore, from FYE 2020 onwards both parties in a couple must be SPa or above before being entitled to claim HB (unless the couple are in receipt of PC where only one party in a couple needs to be above SPa).
• a claimant lives in supported, sheltered or temporary housing

See more information on current HB entitlement rules.

Benefit rates

There is no set rate of HB that a claimant will receive, but the thresholds used for the means-tested side of the HB assessment are aligned to rates of other benefits. Claimants in the private rented sector are usually restricted to the LHA rate for their family size and postcode.

Changes to Housing Benefit (HB) rules

Since 2010 there have been several changes to the structure of Housing Benefit (HB).

Local Housing Allowance (LHA) changes

Phased in from April 2011:

• LHA rates are calculated based on the 30th percentile of rents in the area. Previously, they were based on the 50th percentile (or median of rents)
• claimants are no longer entitled to keep any excess between their rent and the LHA rate (previously, this was up to £15 a week)
• LHA rates are capped to overall maximum weekly levels (affecting the most expensive areas such as inner London)
• claimants can no longer get the 5-bedroom rate

Phased in from January 2012:

• the age threshold for the shared accommodation rate (SAR) (previously under 25) was extended to include single people aged under 35

LHA rates were frozen for four years from April 2016. In April 2020 they were raised to the 30th percentile rent levels but were subsequently frozen in cash terms.

Find more information on Local Housing Allowance (LHA) and LHA reforms (House of Commons Library (parliament.uk)).

Introduction of the National Living Wage

Phased in from April 2016, the National Living Wage was introduced, and HB family premium began to be phased out. This tended to reduce HB entitlement and move people, with very small entitlements under the FYE 2016 system, off benefit altogether.

Removal of the Spare Room Subsidy

The removal of the spare room subsidy was introduced nationally on 1 April 2013. It applies the size criteria test that pre-existed in the LHA to determine the number of bedrooms needed by working-age HB claimants living in the social rented sector. Where claimants are found to be under-occupying, their eligible rent is reduced by 14% if they have one extra bedroom and by 25% if they have 2 or more. Pensioners are exempt from the policy, with easements for certain groups (for example claimants with overnight carers).

Interaction with other income-related benefits

HB can be paid alongside PC. HB is calculated based on an ‘applicable amount’ intended to cover basic weekly living expenses. The amount of benefit is calculated by comparing a person’s income with their applicable amount which is intended to cover day-to-day living expenses, taking account of the size and make-up of the household.

If the net income is equal to or is less than the applicable amount or they are in receipt of an income-related benefit, they will receive 100% of the rent for which benefit can be paid less any non-dependent deductions. This would be subject to any deductions for non-dependents who live with them and help with rent would not exceed the LHA rate which applies to their household. If the net income is more than the applicable amount, they will receive reduced HB. For each pound of extra income over the applicable amount, after disregards, 65 pence will be deducted.

Read the Housing Benefit official statistics.

Universal Credit (UC)

Effect on take-up Statistics

There is a specific challenge in creating a Universal Credit (UC) take-up measure and interpreting and communicating its results within the scope of the Code of Practice for Statistics.

From December 2018 there can be no new claims for any of the working-age legacy benefits. Therefore, from FYE 2020 onwards this publication focusses on take-up for pensioners only.

We began development work on a take-up measure for UC but are unable to complete this work during the managed migration of claimants onto UC as part of the UC roll-out programme. Whilst there were still large numbers of people in receipt of legacy benefits & credits in FYE 2023, a UC take-up rate would not provide the full picture of what was happening for the entitled working-age population.

To develop a methodology that takes account of both UC and legacy benefits & credits there are several complex conceptual and methodology issues we are having to work through, for example in defining and calculating the estimate of Entitled Non-Recipients and their entitled amounts, as well as developing our understanding of new datasets for this analysis.

As stated in DWP’s statistical work programme, this measure to assess UC and income related legacy benefit take-up for the working-age population is currently under development.

The Universal Credit (UC) Official statistics provide the primary source of information about people and households on UC.

5. Source of the statistics

Figures in this analysis are based on DWP administrative data, Local Authority administrative data, and data from the Family Resources Survey (FRS). The survey data from the FRS is modelled using a static micro simulation model known as the Policy Simulation Model (PSM). A more detailed explanation can be found in the Data Sources, Compilation and Methodology section below.

6. Data Sources, Compilation and Methodology

6.1. Data Sources

Introduction

The following information within this section is a broad outline and there are improvements, changes and differing data sources made to this approach almost every year and in recent years.

Use the background information and methodology document as a source of information for details relevant to that year.

Some key changes in recent years are outlined in the Improvements and Refinements section of this document.

Overview

To produce the take-up estimates, information can be taken from DWP administrative data sources to present the average number of those in receipt of the benefit along with the average amount claimed. However, because administrative benefit entitlement datasets do not exist, survey-based estimates of the population and unclaimed amounts of those who are entitled but not receiving are needed. These survey-based estimates come from the adjusted Policy Simulation Model (PSM) entitlement dataset.

The take-up estimates are derived using information taken directly from several data sources and a small number of evidence-based adjustments are introduced where necessary.

ENR estimates are for private households only. In the production of the adjusted PSM entitlement dataset, benefit units are linked to their administrative records on WPLS, SHBE and other datasets so actual receipts and amounts paid are used rather than modelled entitled amounts.

The data linking allows for more accurate classification of benefit units as ENRs and improves the estimates obtained.

Main data sources for receipts and ENR estimates

a) Work and Pensions Longitudinal Study (WPLS)

The WPLS links DWP benefit and programme data on claimants with their employment records from HMRC.

The WPLS is used to produce receipt estimates for PC as it provides 100% DWP administrative records for all those receiving the benefits. The datasets are available quarterly.

The WPLS is also used to remove benefit units that are in non-private households in the receipt estimates for PC. This removal ensures the estimate of the average numbers receiving each benefit is consistent with the estimate of the average number of ENRs, which are for private households only. The WPLS is also used to data link with FRS matched cases for the creation of ENR estimates.

Read more information on Work and Pensions Longitudinal Study.

b) Single Housing Benefit Extract (SHBE)

SHBE is a monthly electronic scan taken directly from local authority computer systems and provided to DWP for analysis. Each local authority monthly scan taken, contains 100% administrative records of all those receiving HB in that local authority during that month. The data are used as they provide 100% administrative records for all those receiving HB.

Some local authorities are unable to provide some monthly scans to the DWP, but this is a very small number of all HB administrative records that the DWP receives overall. No adjustment is made for this deficit of records.

The Single Housing Benefit Extract (SHBE) is the data source used for Housing Benefit (HB) receipt. As predominantly only private households can receive HB, no adjustment is made to remove non-private households for the receipt estimates.

SHBE is also used to data link with FRS matched cases for the creation of the ENR estimates. This also helps to improve the quality of the ENR estimates.

Read more Housing Benefit information.

c) Registration and Population Interactions Dataset (RAPID)

RAPID data provides an annualised view of all the interactions a person has with DWP and HMRC throughout the tax year. It also generates an annualised view of all the income derived from those activities (in particularly Tax Credits and Child Benefit details). It is also used to data link with FRS matched cases for the creation of the ENR estimates.

d) Family Resources Survey (FRS)

The FRS is an annual survey, collecting information on private households in the UK, although only GB information is used within this analysis. Its primary function is to collect information on household income received from all sources, including wages and salaries, state benefits, payable Tax Credits, private (occupational and personal) pension schemes, and investments. The information allows analysis at an individual level, benefit unit level and household level. The survey is sponsored by the DWP and for FYE 2023, this was a survey of around 25,000 private households.

As well as being the main data source for the Policy Simulation Model (PSM), information is taken directly from the FRS to identify reported receipt of the income-related benefits for a small number of cases where data linking was not possible.

Read more information about the Family Resources Survey.

e) Policy Simulation Model (PSM)

The DWP’s PSM is a static micro-simulation model of the UK tax and benefit system. It takes reported information from the FRS on benefit units and then simulates (or models) what the benefit unit might be entitled to, based on the tax and benefit rules in the FRS year. The model estimates what a similar benefit unit, with those same characteristics, might be entitled to for future financial years, accounting for the tax and benefit rules in that future year. The PSM is used extensively by DWP analysts for policy evaluation and costing of policy options.

A specific adjusted PSM entitlement dataset is used in the derivation of the ENR estimates, as it provides a modelled entitlement dataset, for the survey year for the income-related benefits presented in this report. Some important adjustments are made to the main PSM dataset to provide more precise estimates of ENRs and their average weekly amounts unclaimed.

6.2 Data compilation and methodology

Introduction

To produce the take-up estimates, information can be taken from DWP administrative data sources to present the average number of those in receipt of the benefit along with the average amount claimed. However, because administrative benefit entitlement datasets do not exist, survey-based estimates of the population and unclaimed amounts of those who are entitled but not receiving are needed. These survey-based estimates come from the adjusted Policy Simulation Model (PSM) entitlement dataset.

The methodology used, aims to provide the best estimate based on the evidence available, using the FRS. At the same time, there are limitations with the methodology as there are known data issues in the FRS. More information can be found in the FRS background information and methodology.

This report presents the methodology which is strongly focused on using what evidence is available from administrative sources and the FRS, to produce the take-up estimates.

Flow chart of how take-up estimates are calculated

6.3 Estimating Receipt

Overview

Receipt estimates of PC are taken from their data source and adjusted to represent private households only. Since HB is predominantly only available to private households, HB receipt is taken directly from the data source. Estimates of average amounts claimed are not adjusted as private households account for around 95% of all households receiving PC. It is assumed that the average amounts claimed remain representative of private households.

Pension Credit (PC)

The WPLS is used to estimate the average number of those receiving PC and average amounts claimed for the financial year, using data taken at the end of each quarter. Estimates are produced for the type of PC claimed (whether GC and/or SC) and by family type.

The WPLS is also used to remove benefit units that are in non-private households in the receipt estimates for PC. This removal ensures the estimate of the average numbers receiving each benefit is consistent with the estimate of the average number of ENRs, which are for private households only. As the take-up receipt estimates for PC focus on private households only, these are lower than published DWP estimates, which include all households.

Housing Benefit (HB)

SHBE datasets from each month of the financial year, are combined and a 12-month average is estimated for the number of those receiving HB and average amounts of HB claimed. HB is predominantly only available to people living in private households.

For HB, the take-up receipt estimates for FYE 2013 onwards are similar to published DWP estimates. However, for FYE 2010, the take-up receipt estimates are slightly lower. For FYE 2010, bespoke SHBE datasets were produced for months April 2009 to November 2009, to capture the necessary information for breakdowns by family type, tenure type and employment status. These bespoke datasets were combined with the December 2009 to March 2010 datasets, and a 12-month average number of those receiving HB and average amounts of HB claimed, were estimated.

6.4 Estimating Entitled Non-Recipients (ENRs)

Overview

The DWP administrative data sources do not record information on those entitled to a benefit but not receiving it (ENRs). Therefore, a combination of an adjusted PSM entitlement dataset, which includes some linking to WPLS, SHBE, RAPID, and UC administrative data, and reported receipt data from the linked FRS dataset, are used to produce the average number of ENRs and average amounts unclaimed.

To produce the ENR estimates, some adjustments are made to the main PSM dataset to create a specific adjusted PSM entitlement dataset.

FRS matched cases are families where it was possible from the data provided to accurately match to administrative data. In FYE 2023 this was over 95% of all families.

For benefit units that were not able to be data linked, modelled PSM entitlement for the benefit is used. Due to improvements in recent years this has only been required for a small percentage of cases.

Each benefit unit is categorised into one of the following groups:

• entitled non-recipients (ENRs)
• entitled recipients (ERs)
• non-entitled, non-recipients (NENRs)

From this, the average numbers of ENRs, and the average amounts unclaimed are estimated, along with 95% confidence intervals to reflect uncertainty. These estimates are combined with the receipt estimates to produce the lower bound, central, and upper bound take-up estimates. It should be noted that, if existing, any ‘non-entitled recipients’ are assumed to be entitled to the benefits they get.

The PSM dataset

For each financial year, a PSM dataset is produced for DWP use. Characteristics are taken from the FRS, and the PSM models future entitlement to each of the income-related benefits by applying the tax and benefit system rules to each benefit unit. The dataset retains reported FRS characteristics such as family type, age, marital status, tenure type and employment status.

Where a benefit unit has one member above SPa and one member below (a mixed age couple), the PSM models entitlement to UC only. This is due to a policy change meaning that mixed age couples can only make new claims for UC from FYE 2020. If the mixed age couple is in receipt of PC, then they are modelled as getting PC.

The take-up estimates are only provided for GB. Therefore, the UK-based PSM dataset is adjusted to remove any Northern Ireland benefit units, so that the average number of ENRs and average amounts unclaimed are on the same basis as the receipt estimates.

Adjusting the PSM dataset to create the adjusted PSM entitlement dataset for take-up statistics

The adjustments made to the PSM dataset are:

a) Data linking to DWP administrative receipt records for FRS matched cases

Each year DWP spends substantial resources in-house editing the raw Family Resources Survey (FRS) data. Particular attention is given to ensure that benefit and income information is as accurate as possible (see the FRS Background Information and Methodology for further details). If we can link respondents’ administrative data to their survey data, we can improve data quality, in particular, for the production of the take-up estimates.

FRS respondents were first asked for consent to link their survey responses to administrative records starting in 2007. The approach to obtaining consent was designed to meet the requirements of the Data Protection Act 1998. On average, each year, around two thirds of respondents consented.

The Family Resources Survey (FRS) tested a new approach to obtaining agreement to link respondents’ data to administrative data from January 2017 to April 2017. The new approach replaces an explicit Yes and No question in the FRS questionnaire with an up-front statement that DWP will link respondents’ information to administrative records held by the department. A randomised control trial ran from January 2017 to April 2017, with the FRS sample split 50:50 between those receiving the new enhanced fair processing statement and the existing explicit data linking question. The trial was a success, and the new method was implemented from May 2017 onwards. This change means that the number of cases available for data linking has increased since FYE 2018.

Since 2018 all FRS personal data processing, including linking respondents to their administrative records, is undertaken on the basis that it is necessary for a “function of a government department”. This is allowed by section 8 of the Data Protection Act, and Article 6(1)(e) Public Task of the UK General Data Protection Regulation (UK GDPR).

So, due to developments over recent years, we are now able to try and link all respondents survey responses to admin data.

To use administrative data to determine an FRS respondent’s true benefit status, we need their National Insurance Number (NINO), which is the unique identifier that DWP uses to record a person’s information. The FRS does not directly record NINOs, so we must match the Personally Identifiable Information (PII) that is recorded on the FRS, to information held on the department’s Customer Information System (CIS).

A lookup file is created for each survey year consisting of anonymised identifiers for FRS respondents (household, benefit unit and person), together with their encrypted NINOs. The lookup file provides the link between FRS respondents’ survey data and their administrative records.

The analytical techniques, the exact variables used and the quality assurance process to produce the match has continued to evolve in recent years.

Further information can be found in integrating-administrative-data-into-the-FRS.

For more information on the methods used in previous years specifically in the context of take-up statistics, see the Income-related benefits: estimates of take-up FYE 2020 background information and methodology.

The take-up methodology uses linked survey-administrative data to improve the quality of the estimates by replacing survey responses, which are subject to reporting error, with accurate administrative data on benefit receipt. Therefore, improvements in the coverage and quality of data linking means that more administrative data can be used in the production of the estimates, improving their accuracy. An increased amount of linked data provides a better estimate of the number of entitled non-recipients (ENRs) for a benefit where benefit under-reporting has occurred in the FRS. This influences the mean and median amounts unclaimed. The actual effect on the take-up estimates will depend on the observed data for each survey year.

By attempting to link all FRS respondents to administrative records, we can improve the quality of the data used to produce the take-up estimates and improve the quality of the estimates by reducing the inaccuracies within the entitlement dataset. The data linking approach has continued to develop and in FYE 2023 we were able to link over 95% of FRS cases.

b) Grossing-up to Great Britain (GB) control totals

Grossing-up is the term usually given to the process of applying factors to sample data so that they yield estimates for the overall population. The simplest grossing system would be a single factor, for example the number of households in the population divided by the number in the achieved sample of a survey. However, surveys are normally grossed by a more complex set of grossing factors that attempt to correct for differential non-response at the same time as they scale up sample estimates. The bespoke grossing control variables, in addition to population grossing control variables, along with their breakdowns and data sources used to gross up the PSM dataset to the GB population are:

Grossing Control Variable	Breakdowns Used	Data Source
Household numbers	Country and region	Housing statistics – Office for National Statistics (ONS)
Housing tenure (household level)	None – GB level only	Housing statistics - DLUHC, Welsh Government and National Records of Scotland
Council Tax bands (household level)	None – GB level only	Council Tax statistics - Valuation Office and Scottish Government
Employment (individual level)	Age and gender	Employment caseloads – Office for Budget Responsibility (OBR) using Labour Force Survey data
Caseloads for non-income related benefits (individual or benefit unit level)	Age and gender	Administrative data for Attendance Allowance (AA), Disability Living Allowance (DLA) and Personal Independence Payment (PIP), IB and SDA and Contributory ESA – DWP

There are issues unique to using the PSM as a model to estimate entitlement that go beyond standard sample to grossing caveats. It is not possible to gross to entitlement figures because it is not known what these are. As noted elsewhere this is why we create a model to estimate them. It is known that benefit recipients are typically under-represented in the FRS. It is possible that ENRs are over or under-represented by large amounts and grossing cannot correct for this. This is an unknown, but the implicit assumption is that after grossing, ENRs are accurately represented. Grossing to a total for chosen variables can also distort sub-population variables that are not grossed. It is possible that ENR totals are subject to this kind of distortion.

c) Removal of National Minimum Wage (NMW) and National Living Wage (NLW) adjustment

The main PSM includes an adjustment for the NMW or NLW. This adjustment is not included in the PSM adjusted entitlement datasets used for the take-up statistics. No adjustment is made to the earnings data reported in the FRS.

d) Estimating ENRs

FRS matched cases who are shown to be receiving benefit according to the data linking to administrative data are automatically allocated to the ER group. FRS matched cases who are not receiving benefit according to the data linking to administrative data are classed as either ENRs or NENRs depending on whether they are modelled as entitled according to the PSM.

For benefit units that are unmatched (which is a very small percentage of cases due to improved data linking), modelled PSM entitlement to the benefit is compared to FRS reported receipt of the benefit to determine whether they are an ER, ENR or NENR.

Having created the adjusted PSM entitlement dataset the average numbers of ENRs and the average amounts unclaimed are estimated by specific types and breakdowns for each income-related benefit. Due to small sample sizes for certain characteristics, further detailed breakdowns are not possible.

6.5 Uncertainty in the receipt and ENR Estimates

The receipt and ENR estimates are fully reliant on the accuracy of the data used and any subsequent adjustments, evidence-based assumptions, and modelling. Any errors in the data or these processes will affect uncertainty around the estimates.

Uncertainty in the receipt estimates is assumed to be marginal due to the processes in place to produce accurate DWP administrative data for publication as statistics. Therefore, no estimate of confidence intervals or similar is undertaken.

For the ENR estimates, the level of uncertainty is higher as they are reliant on reported information from FRS sample data and the subsequent modelling of entitlement in the PSM. Therefore, the estimates are affected by known areas of uncertainty surrounding survey results and using a model to estimate ENRs. While processes are in place to limit this uncertainty, such as the data cleaning of FRS responses to produce the published FRS dataset and continuous development of the PSM methodology, confidence intervals are used to present the uncertainty in the final take-up estimates. Note that confidence intervals only estimate the sampling error, the difference between the sample and population values, and do not include other forms of uncertainty.

6.6 What are confidence intervals?

A confidence interval is a range of values that is likely to contain the true population parameter with a specified level of confidence. The level of confidence is typically expressed as a percentage, such as 95%. The larger the sample size, the narrower the confidence interval tends to be, and the more precise the estimate of the population parameter.

When the sample size is small, the confidence interval tends to be wider because there is more uncertainty in the estimate. This means that the range of values that could plausibly contain the true population parameter is larger.

As the sample size increases, the standard error of the estimate decreases, and the confidence interval becomes narrower. This means that the range of plausible values for the population parameter becomes smaller, and the estimate becomes more precise.

For FYE 2022, the FRS achieved a sample of sample of around 16,000 households (of which just under 5,000 were pensioners) which was then grossed up to reflect the UK population. For FYE 2023, the FRS achieved a sample of around 25,000 households (of which just under 8,000 were pensioners) which was grossed to reflect the UK population. The increase in the sample size for FYE 2023 used in producing the take-up estimates, has meant that the width of the confidence intervals seen within the publication for FYE 2023 has become narrower in comparison to previous financial years.

Info-graphic summarising the purpose of confidence intervals

6.7 Confidence intervals for the ENR Estimates

As the ENR estimates are heavily reliant on the FRS sample, the level of uncertainty can be estimated by exploring how that estimate would change if many survey samples were drawn for the same time period instead of just one. From this, a range around the estimate can be defined, known as a confidence interval. This indicates how likely it is that the real value that the survey is trying to measure lies within that range. Confidence intervals are typically set up to be 95% certain that the true value lies within the range. This is known as a 95% confidence interval.

This leads to uncertainty in the figures. Confidence intervals are then used to show how much the estimate may differ from the true value. Bigger confidence intervals show less precise estimates and smaller confidence intervals show more precise estimates.

Method

The ‘variance estimation based on sample design’ method was used to produce confidence intervals for the take-up estimates as:

• the sampling frame of the FRS is taken into consideration in this method
• it is appropriate when calculating the prevalence of a characteristic in a population, such as those entitled and not receiving

The ENR estimates are derived from the adjusted PSM entitlement dataset rather than the published FRS dataset. However, this should not affect the sampling error, as this occurs at the stage of the sampling, and therefore any modelling of FRS sample benefit units in the PSM should not affect the confidence intervals. The average numbers of ENRs, and the average amounts unclaimed are estimated, along with 95% confidence intervals to reflect uncertainty. These estimates are combined with the receipt estimates to produce the lower bound, central, and upper bound take-up estimates.

6.8 Estimating take-up rates

Overview

The take-up rates are based on the following definition:

(In Receipt) ÷ (In Receipt + Entitled Not in Receipt)

Where the take-up rate is defined as the proportion receiving the benefit of all those entitled (that is those receiving the benefit they are entitled to and those who are entitled but not receiving the benefit). A central estimate take-up rate is calculated using the central estimate of those entitled and not in receipt. The lower and upper take-up rate bounds are calculated using the upper and lower bound estimates of those entitled and not in receipt.

Caseload formulae

The formulae for lower bound, central estimate and upper bound are as follows.

	Formula
Lower Bound	Average number of benefit units receiving benefit ÷ (Average number of benefit units receiving benefit + Upper average number of ENR benefit units)
Central Estimate	Average number of benefit units receiving benefit ÷ (Average number of benefit units receiving benefit + Central average number of ENR benefit units)
Upper Bound	Average number of benefit units receiving benefit ÷ (Average number of benefit units receiving benefit + Lower average number of ENR benefit units)

Expenditure formulae

For the expenditure formulae, the average weekly amounts claimed (received) and unclaimed (not received) are converted to yearly amounts.

The expenditure take-up formulae are as follows.

	Formula
Lower Bound	Total amount of benefit received ÷ (Total amount of benefit received + Upper total amount of benefit not received)
Central Estimate	Total amount of benefit received ÷ (Total amount of benefit received + Central total amount of benefit not received)
Upper Bound	Total amount of benefit received ÷ (Total amount of benefit received + Lower total amount of benefit not received)

The formulae for the total amount of benefit received and not received are as follows.

	Total amount of benefit received formula	Total amount of benefit not received formula
Lower Bound	Average number of benefit units receiving benefit × Average amount of benefit received × Week to Year Factor	Lower total = Lower average number of benefit units ENRs × Lower average amount not received × Week to Year Factor
Central Estimate	Average number of benefit units receiving benefit × Average amount of benefit received × Week to Year Factor	Central total = Central average number of benefit unit ENRs × Central average amount not received × Week to Year Factor
Upper Bound	Average number of benefit units receiving benefit × Average amount of benefit received × Week to Year Factor	Upper total = Upper average number of benefit units ENRs × Upper average amount not received × Week to Year Factor

7. Quality Management

7.1 Initial data checks

The administrative data that feeds into the receipts process of take-up has:

• initial checks carried out by the data loading team to ensure the data is accurate and fit for purpose
• caseload counts taken, as well as checks on field format/sizes, as if the data does not match the original data specification it will not load correctly

When the data is loaded into SAS (analytical software package) DWP statisticians perform the following quality assurance checks:

• comparison of current quarters caseload with the caseload from previous quarters
• frequency counts conducted against all the fields contained in the data to check volumes against previous quarters
• checks to ensure no erroneous values/formats entered

If data irregularities are found, statisticians work with the DWP data loading team to provide context to any changes, or resolve issues, and subsequently repeat the quality assurance checks on any new and corrected data.

More information on the quality assurance checks that take place can be found in the Quality statement DWP benefits.

These are all checks that take place before the take-up team access the data to start processing it for the take-up statistics.

The original FRS survey data undergoes a rigorous QA process during development and processing, the details of which can be found in the FRS background methodology.

The survey data is then linked to administrative data, which is quality assured internally. This dataset is then provided to the PSM team who produce entitlement data. This data is then quality assured internally.

As such, the take-up estimates are based on several data sources, all of which have their own processes in place to ensure their quality. When producing the take-up estimates, all content has been independently quality assured by different members of the team to ensure the methodology is fit for purpose. All commentary in the publication is reviewed by the team and analysts from the relevant policy areas to ensure the information presented is accurate and meets user needs.

7.2 Standard Quality Assurance (QA)

Every SAS program:

• has its log checked to ensure no errors or warning messages are flagged when ran
• where updates to code are required before running, this is highlighted within the program with examples of how this should have been updated
• checks that code has been updated correctly, form part of the quality assurance of each section of the overall take-up process

7.3 Independent assessors

Quality Assurance (QA) is conducted by:

• members of the Surveys Branch team
• a specifically defined QA group, consisting of people within the Surveys team, and specific analysts from within DWP, who are experts on the benefits being measured within the final publication

8. Accuracy and Reliability

8.1 Use of administrative data

It is assumed that DWP administrative data are an accurate record of benefit receipt. We accept all benefit recipients as being entitled with any ‘non-entitled recipients’ ignored for the purpose of this publication. However, the data are subject to error at all stages of the process of data collection: from recipients giving inaccurate information, to input error, to data cleaning and analysis.

8.2 Use of survey data

The size of the FRS sample and the way in which the sample is selected are carefully designed to ensure that it is representative of the UK, whilst bearing in mind practical considerations like time and cost constraints. Survey results are always estimates, not precise figures. This means that they are subject to a level of uncertainty which can affect how changes, especially over the short term, should be interpreted.

The FRS is subject to the nuances of using a survey, including:

• the sampling error – two different random samples from one population are unlikely to give the same survey results, which are likely to differ again from the results if the whole population was surveyed – this level of sampling error varies to a greater or lesser extent depending on the level of breakdowns at which results are presented
• the non-response error – the overall response rate for the FRS, as a percentage of the issued sample who were eligible to take part, was 25%
• the survey coverage – the FRS covers private households in the UK, therefore, individuals in nursing or retirement homes, for example, will not be included - this means that figures relating to the most elderly individuals may not be representative of the UK population, as many of those at this age will have moved into homes where they can receive more frequent help
• the survey design – the FRS uses a clustered sample design to produce strong estimates at regional level - the FRS is therefore not suitable for analysis below this level
• the sample size – from April 2011, the target achieved UK sample size for the FRS was reduced by 5,000 households from 25,000 to 20,000 households a year. A published assessment concluded that this reduced sample still allowed the core outputs from the FRS to be produced. Due to the coronavirus (COVID-19) pandemic, there was a smaller achieved sample size in FYE 2021 and FYE 2022. Further details on the FRS sample can be found in the FRS background information and methodology

8.3 Under-reporting receipt and benefit confusion in the FRS

There is a known issue of benefit under-reporting in the FRS. Sometimes the FRS respondents confuse which benefit they are receiving. For example, there is often confusion over State Pension (SP) and PC payments. A benefit unit may not realise they are receiving PC and report the overall payment they receive as SP. Therefore, there is an over-reporting of the SP amount paid and under-reporting of the PC amount paid. Processes are in place for the FRS interviewer to check these responses against other questions if there is a discrepancy. Further data-cleaning is conducted once the survey is filled in to check whether responses from the benefit unit taken across the questionnaire are consistent. However, even with these processes in place, there remains scope for uncertainty and remaining under-reporting of benefit receipt.

To reduce under-reporting and benefit confusion, data linking allows recorded benefit amounts in DWP administrative data to replace reported FRS receipt or modelled PSM receipt in the PSM entitlement dataset. This was possible for over 95% of FRS respondents in FYE 2023.

For the remaining unlinked cases, it is possible that the PSM categorises a benefit unit into the wrong group. In the example above, the benefit unit could be modelled as an ENR, despite receiving PC as they did not report receipt or give an amount in the FRS interview. In other situations, the benefit unit might not report important information which is used to identify which group they should be allocated to, such as receiving Attendance Allowance for the estimation of PC and HB entitlement.

8.4 Unmatched cases

Where a benefit unit might have been modelled as an ENR using self-reported survey data but data linking indicated that they were receiving a payment, the benefit unit would be categorised as an ER and as a result the average numbers of ENRs would reduce. However, where benefit units are unmatched cases, it is possible for it to remain an ENR but still receive the benefit in reality.

The scale of this issue has reduced over time as we have been able increase the proportion of cases matched. Now this issue only affects a small number of cases due to the high match rate. In FYE 2023 it was possible to link data over 95% of all families.

8.5 Imperfect modelling of entitlement

Imperfect modelling of the tax and benefit system rules in the PSM could result in:

• over-statement of entitlement - this is where the benefit unit is incorrectly modelled to be entitled and or the modelled amount is too high
• under-statement of entitlement - this is where the benefit is incorrectly modelled to not be entitled and or the modelled amount is too low

If there is a linked amount in the administrative data then entitlement becomes the linked amount, not the modelled amount. Processes are in place to regularly review and develop the modelling to ensure the PSM accurately represents the tax and benefit system rules for each year. However, as it is heavily reliant on reported information in the FRS, the PSM inherits the areas of uncertainty of the FRS.

8.6 Inaccurate grossing-up to the GB population

The PSM grossing method uses several control variables to ensure that various characteristics of the benefit units are considered in deriving the grossing factors. But it is another potential source of uncertainty as it does assume that many of the characteristics of the ENR sample are representative of all GB benefit units.

9. Coherence and comparability

Given the adjustments and coverage of the take-up methodology, receipt and expenditure estimates here will not be the same as the Official Statistics published by DWP. DWP publishes a wide range of statistics on receipt of benefits and benefit expenditure. Read about DWP Official Statistics

Read the Benefit expenditure and caseload tables.

Stat-Xplore is a DWP tool that provides users with access to data. Users can download and analyse statistics on a range of different benefits, programmes, and other information collected and stored by the department.

Read the Cost of Living management information.

HMRC published estimates of the take-up of Tax Credits. The FYE 2018 publication of Tax Credit take-up rates was the last in the series of publications due to UC-related data issues from FYE 2019 onwards.

DWP have released a new publication to go alongside the annual Fraud and Error in the Benefit system publication. This is called “Unfulfilled eligibility in the benefit system Financial Year Ending (FYE) 2024”.

This new publication does not cover the take-up of benefits and so complements the Income-related benefits: estimates of take-up publication which focuses on entitled non-recipients.

This new publication estimates how much extra money benefit claimants could be getting if they told DWP accurately about their circumstances. These people are already getting some money on a certain benefit but may not be getting all the money they could be eligible for on this benefit – this is called unfulfilled eligibility. These unfulfilled eligibility estimates are based on information that was previously included in the Fraud and Error in the Benefit system statistics as Claimant Error underpayments. They were removed and reclassified and are now published separately following a planned review of the fraud and error statistics.

For further information please contact Fraud and Error Measurement Analysis (FEMA) at [email protected].

There are other data sources that can provide information on areas of interest like those in the take-up publication. These are listed below:

• Explaining income and earnings
• GSS income and earnings statistics interactive database – (civilservice.gov.uk)
• Income and earnings coherence workplan for 2022 – Government Analysis Function – (civilservice.gov.uk)
• Admin-based income statistics, England, and Wales – Office for National Statistics (ons.gov.uk)

10. Limitations of the statistics

There is general uncertainty in our estimates, especially in those of entitled non-recipients (ENRs) of benefits, given the complexity of estimation. The FRS, used in the estimation of ENRs, is not designed specifically to measure entitlement criteria to the same extent as an application for a specific benefit. It is a multi-purpose household survey.

The estimates of ENRs are also based on PSM data, which is modelled FRS data, and are therefore subject to sampling variation and other forms of error associated with a sample survey. These include reporting errors, under-reporting, systematic bias, and random sampling error. For more details, please see the FRS background information and methodology.

Due to restrictions in modelling and available data, certain populations are excluded from our analysis. As such the results do not include:

• those living in non-private households
• estimates for Northern Ireland (Only figures for GB are reported) due to the differences in benefit systems

Take-up statistics are not available at a lower level of geography than GB. This is because the survey sample sizes for ENRs are too small to calculate take-up rates in smaller geographical areas. Similarly, HB (for pensioners) take-up statistics are only available as totals. This is also because the survey sample sizes for entitled non-recipients are too small to be able to calculate take-up rates for sub-groups.

With the abolition of SC for new pensioners from April 2016, and the reducing number of SC only cases in the eligible population, we are no longer able to estimate SC breakdowns with a sufficient level of confidence. Therefore, from FYE 2022 onwards, SC breakdowns are not included in the data tables. Historic SC breakdowns can be found in previous releases.

Trying to explain the reasons for non-take-up is difficult and we do not have the data in our modelling to do this. But to put the results into context, it is useful to outline some of the broad factors that have been found to have an effect. Take-up may be affected by factors such as the size or other attractiveness of the benefit, lack of awareness of the benefit or application procedure, lack of awareness of entitlement, the perceived stigma of receiving a benefit, or other factors (Eurofound, 2015). Read the full report Access to social benefits: Reducing non-take-up, Eurofound – (europa.eu).

The income-related benefits: estimates of take-up Official Statistics were suspended for FYE 2021. Estimates were not published due to data issues following the coronavirus (COVID-19) pandemic.

The income-related benefits: estimates of take-up Official Statistics were re-instated for FYE 2022. This was due to the improved quality of data collected by the Family Resources Survey (FRS) for FYE 2022.

There was increased uncertainty in our modelling for younger entitled non-recipients (ENRs) in FYE 2022 as we did not have linked administrative-based data to use and relied on self-reported survey data.

See the Family Resources Survey financial year 2022 to 2023 background information-and methodology for further information.

11. Improvements and Refinements

The take-up estimates use a complex methodology that uses numerous data sources. Each year the team checks all the contributing codes and processes for any potential improvements that could be made to improve the accuracy of the take-up estimates produced. The department’s policy statement describes how DWP will handle revisions.

11.1 Methodological refinements applied from FYE 2017 onwards

There were four methodological changes applied from FYE 2017 onwards. Comparisons to earlier years should be treated with caution.

The changes are:

• improvement to the modelling of State Pension receipt, by making use of more information from respondents, this prevents any assumption of no State Pension being made by the model
• PC calculations now consider mixed age couples (where one member is aged above PC qualifying age, and the other is aged below PC qualifying age) working-age benefits (IS, ESA, and JSA), this involves removing the calculated IS, ESA, and JSA amount from PC (until FYE 2020)
• additional savings information is now considered in capital calculations
• Support for Mortgage Interest (SMI) entitlement is removed from estimated IS and PC entitlement amounts, SMI used to be paid within PC, SMI is now paid out separately as a loan and SMI has therefore been removed from PC calculations

For more information regarding this see Support for Mortgage Interest (SMI).

These changes provide an improved approach to estimating eligibility of caseload and expenditure take-up which:

• reduce the estimated number of entitled non-recipients (ENRs)
• reduce the estimated unclaimed amount

For example, for FYE 2018, it is estimated that these changes affect the estimated PC take-up rate by approximately 2 percentage points (increase) for caseload and 4 percentage points (increase) for expenditure. Changes to HB are more minimal (increase is less than 1 percentage point) for both caseload and expenditure. The exact scale of the effect could vary between years.

11.2 Data linking to additional benefits

From FYE 2020 onwards we can link to UC, Disability Living Allowance (DLA) (mobility component), Personal Independence Payment (PIP), Tax Credits, Carers Allowance and Child Benefit as well as benefits that we have been linking to in previous years.

11.3 Removal of pipeline variables from the Family Resources Survey (FRS)

In previous years, it was assumed that if a benefit unit reports they are awaiting an outcome of a claim, and the Policy Simulation Model (PSM) models them as entitled, it is likely that they would have gone on to receive the benefit and were categorised as entitled recipients. From FYE 2022, the FRS no longer collects information on those awaiting an outcome of a claim due to the small number of respondents. Therefore, the take-up estimates from FYE 2022 onwards no longer include those awaiting an outcome of a claim. This has no effect on the overall results.

11.4 Pension Credit (PC) receipts data is now based on a single data source

The Quarterly Statistical Enquiry (QSE) dataset was previously used to adjust the receipts data for PC to exclude any non-private households. The Work and Pensions Longitudinal Study (WPLS) now includes variables that allow for this exclusion to be done exclusively on the WPLS dataset, and so the QSE dataset is no longer used for the receipts data from FYE 2022 onwards. This change has a minimal impact on the receipts data and allows for a more accurate approach to calculate receipts data.

11.5 Removal of Savings Credit (SC) breakdowns

With the abolition of SC for new pensioners from April 2016, and the reducing number of SC only cases in the eligible population, we are no longer able to estimate SC breakdowns with a sufficient level of confidence. Therefore, from FYE 2022 onwards, SC breakdowns will not be included in the data tables. Historic SC breakdowns can be found in previous releases.

11.6 Historic Errors

A few small errors were identified in the FYE 2020 and FYE 2022 data tables. These have been corrected within this FYE 2023 release and therefore previous data tables should not be used.

12. Glossary

This glossary gives a brief explanation for each of the key terms used in the take-up Publication. Further details on these definitions, are available on request from the DWP Take-Up Team at: [email protected]

See the income and earnings Glossary produced by the ONS.

Average

See Mean.

Benefits

Financial support from the government. Most of these benefits are administered by DWP. The major exceptions are Housing Benefit and Council Tax Reduction, which are administered by local authorities and certain Scottish benefits administered by Social Security Scotland.

Benefit unit or family

There are various types of benefit unit:

A single adult or married or cohabiting couple, and any dependent children.

Examples of Benefit units:

• pensioner couple: Benefit units where both partners are over SPa
• single male pensioner: Benefit units headed by a single male adult over SPa
• single female pensioner: Benefit units headed by a single female adult over SPa
• couple with children: Benefit units containing two adults, headed by a non-pensioner, with dependent children
• couple without children: Benefit units containing two adults, headed by a non-pensioner, with no dependent children
• single with children: Benefit units containing a single adult (male or female), headed by a non-pensioner, with dependent children
• single without children: Benefit units containing a single adult (male or female), headed by a non-pensioner, with no dependent children

Claimant or Customer

A person making a claim for a benefit.

Confidence interval

A measure of sampling error. A confidence interval is a range around an estimate which states how likely it is that the real value that the survey is trying to measure lies within that range. A wider confidence interval indicates a greater uncertainty around the estimate. Generally, a smaller sample size will lead to estimates that have a wider confidence interval than estimates from larger sample sizes. This is because a smaller sample is less likely to reflect the characteristics of the total population and therefore there will be more uncertainty around the estimate derived from the sample. Note that a confidence interval ignores any systematic errors that may be present in the survey and analysis processes.

Cost of Living Support Schemes

During 2022 to 2023 the government announced and implemented additional support to families with several cost-of-living support schemes, depending on peoples’ circumstances. Read more information about Cost Of Living support.

Entitled

A family is said to be entitled to receive a benefit if they satisfy the qualifying conditions for that benefit.

Entitlement

Entitlement is the amount an entitled family is estimated to be paid in benefit, according to the linked amount or modelling.

Entitled non-recipient (ENR)

A family that is modelled to be entitled to a benefit but is not receiving it is said to be an ENR.

Entitled recipient (ER)

A family that is modelled to be entitled to a benefit and is receiving it is said to be an ER.

Non-entitled non-recipient (NENR)

A family that is modelled to be not entitled to a benefit and is not receiving it is said to be an NENR.

Family

See Benefit unit.

Family Resources Survey (FRS)

The FRS is one of the largest cross-sectional household surveys in the country. Prior to FYE 2003 the survey covered GB; from FYE 2003 the survey was extended to cover the UK. From April 2011, the target achieved UK sample size for the FRS was reduced by 5,000 households from 25,000 to 20,000 households a year. A published assessment concluded that this reduced sample still allowed the core outputs from the FRS, to be produced. Due to the coronavirus (COVID-19) pandemic, there was a smaller achieved sample size in FYE 2021 and FYE 2022. In FYE 2023, the achieved sample size was 25,000 (30% larger than pre-COVID levels).

Read the latest FRS information and publication.

Household

A household consists of one person living alone or a group of people (not necessarily related) living at the same address, who share cooking facilities and share a living room or sitting room or dining area. A household will consist of one or more benefit units.

Housing Benefit

Housing Benefit (HB) helps tenants who are on a low income to pay their rent. HB is administered by local authorities who decide whether a claimant is eligible for HB, and how much they qualify for. Tenants can apply for HB irrespective of whether they are in work, or out-of-work. It may be paid alongside other income-related benefits, non-income-related benefits, or on its own. Since December 2018, due to the rollout of UC, new claims for HB can only be made in certain circumstances.

Income-related benefit

Benefits where a potential claimant’s income is considered when deciding whether they are entitled to the benefit.

Jobseeker’s Allowance

A working-age benefit that is paid to help people while they look for work.

Local Housing Allowance

Used to calculate Housing Benefit for tenants in the private rented sector.

Mean

The mean weekly amount claimed or unclaimed, is the average, found by adding up the amount for each benefit unit in a population and dividing the result by the total number of benefit units.

Means-tested benefits

See Income-related benefit.

Median

The median weekly unclaimed amount is the value that divides the population of entitled non-recipients, when ranked by their modelled entitlements, into two equal-sized groups. In other words, the median is the exact middle point where half the entitled non-recipients have larger unclaimed amounts and half have smaller unclaimed amounts.

Minimum Income Guarantee

A system of social welfare provision that guarantees that all citizens or families have an income sufficient to live on, provided they meet certain conditions.

Mixed age couple

A couple where one member is above SPa and the other is below.

National Living Wage

The minimum wage for workers aged 25 and over.

National Minimum Wage

The minimum wage for workers ages 16 to 24.

Non-dependant deductions

A reduction in Housing Benefit when an adult friend or family member lives with the claimant.

Non-means tested benefits

Benefits where a potential claimant’s income is not considered when deciding whether they are entitled to the benefit.

Pension Credit

The primary income-related benefit for those of State Pension age and above.

Private Rented Sector

Properties being rented which are owned by a landlord.

Sampling error

The uncertainty in the estimates which arises from taking a random sample of the household population. The likely size of this error for a particular statistic can be identified and expressed as a confidence interval.

Social Rented Sector

Properties being rented which are owned by the local council or a housing association (not-for-profit organisations that own, let, and manage rented housing).

Spare Room Subsidy

A reduction in Housing Benefit when the recipient lives in a property in the social rented sector that is deemed to have at least one spare bedroom.

State Pension

State Pension is a payment made to qualifying individuals who have reached SPa. A new single-tier State Pension launched on 6 April 2016 for people who reach SPa on or after April 2016, to replace the previous system. This consolidated the basic State Pension and Additional State Pension into one single amount.

People who reached SPa before 6 April 2016 continue to receive the basic State Pension and Additional State Pension if eligible. Read more information on the State Pension.

State Pension age (SPa)

SPa is the earliest age you can start receiving your State Pension. For FYE 2023 data, individuals are defined to be of SPa based on their date of birth and the date of interview. Since 6 April 2010, the SPa has been gradually increasing. The FRS data contained in this report were collected throughout the FYE 2023, during which the SPa for pensioners remained at 66 years, the same as FYE 2022. Proposed increases to the SPa are expected to be phased in between 2026 and 2028, bringing the SPa up to 67 years.

A breakdown of the increases can be seen in the SPa timetables and details of planned changes to SPa can be found in proposed new timetable for State Pension age increases. Read further guidance on calculating State Pension eligibility age.

Statistical significance

This is a technical concept that says whether a reported change is likely to have arisen only by chance due to variations in the sampling.

Support for Mortgage Interest

A loan to provide help on paying interest on mortgages or interest on other loans for home purchase, repair, and improvements. To qualify for a Support for Mortgage Interest loan one of the following benefits must be received:

• Income Support (IS)
• Income-based Jobseeker’s Allowance (JSA)
• Income-related Employment and Support Allowance (ESA)
• Universal Credit (UC)
• Pension Credit (PC)

Tax Credits

Working Tax Credits (WTC) and Child Tax Credits (CTC) are paid by HMRC.

Tax credits are ending on 5 April 2025 and are being replaced by Universal Credit (UC). Read more information on Tax Credits,

Tenant

A person who rents a property in the social rented sector or private rented sector.

Triple lock

The basic State Pension and the new State Pension increases every year by whichever is the highest of the following:

• earnings - the average percentage growth in wages (in GB)
• prices - the percentage growth in prices in the UK as measured by the Consumer Prices Index (CPI)
• 2.5%

In FYE 2023, the increase was 3.1%, in line with CPI inflation.

Universal Credit (UC)

A single, usually monthly payment, administered by DWP. Universal Credit (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.

Working-age benefit

A benefit paid to a working-age person.

Working-age person

A person aged between 16 years and SPa.