National statistics

Family Resources Survey: background information and methodology

Updated 26 March 2024

Introduction

This background report accompanies the main Family Resources Survey 2022 to 2023 publication.

The Family Resources Survey (FRS) publishes annual information on the incomes and circumstances of individuals living in a representative sample of private households in the United Kingdom.

The purpose of this report is to provide further contextual information to aid understanding of the statistics presented in the main publication. In summary it outlines:

• improvement and other changes in the collection of data
• statistical concepts and definitions used
• adherence to the Code of Practice for Statistics
• coherence and comparability of the data against other sources, as well as against previous years
• strengths, limitations, and trade-offs of the information presented

A detailed description of the FRS methodology, fieldwork operations, data processing and quality management are presented in turn within the relevant sections in this report. These descriptions are intended to help users in the use and interpretation of FRS 2022 to 2023 statistics and underlying data.

Editorial team

Alex Brandon-Bravo, Anna Britton, Claire Cameron, Annabel Connolly, Oliver Davies, Scott Fox, Jake Lipscombe, Sheridan Lomas, Elizabeth MacInnes, Cait Marlow, Justyna Owen, Clive Warhurst

Acknowledgements

We wish to give special acknowledgement to:

• the respondents in households across the United Kingdom who agreed, and made time, to be interviewed
• the interviewers from the Office for National Statistics (ONS), NatCen Social Research and the Northern Ireland Statistics and Research Agency who conducted and collated the interviews
• all those who have contributed towards the Family Resources Survey 2022 to 2023 publication, through providing quality assurance and feedback
• our web support team
• the UK Data Service (UKDS), who distribute our research data, as well as the ONS Secure Research Service

Feedback

Family Resources Survey statistics are published by the FRS Team, part of Surveys Branch, at the Department for Work and Pensions (DWP).

If you have any comments or questions about any aspect of the FRS, or are interested in receiving information on consultations, planned changes, and advance notice of future releases, please contact: team.frs@dwp.gov.uk

Lead Statistician: M A Vaughan

1. Overview of the Statistic

1.1. History of the Statistic

The FRS is a continuous survey. It collects information on the incomes and circumstances of individuals living in a representative sample of private households in the United Kingdom. The survey has been running in Great Britain since October 1992 and was extended to cover Northern Ireland in the survey year 2002 to 2003. Over this period, of more than 30 years, the publication has evolved significantly; it has adapted to changes in information technology, publication standards (including internet standards) and user needs in the field of household income research. Several routes are now available for users to access these statistics, which are described in later sections.

1.2. Status of the Statistics

These accredited official statistics were independently reviewed by the Office for Statistics Regulation in 2011 and were then confirmed as National Statistics by the Office for Statistics Regulation (OSR) in November 2012. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should now be labelled ‘accredited official statistics’.

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. Our statistical practice is regulated by the OSR.

You are welcome to contact us directly with any comments about how we meet these standards. Alternatively, you can contact OSR by emailing: regulation@statistics.gov.uk or via the OSR website.

The OSR published its Review of Income-based Poverty Statistics on 19 May 2021, which is a report aimed at ensuring that statistics on poverty provide a robust evidence base for national and local policy development and decision making. Several recommendations were implemented in the 2021 to 2022 publication, which have been subsequently maintained in this year’s publication.

1.3. Recent improvements to the FRS

Stat-Xplore can be used to create bespoke tables and statistics, across a very wide range of FRS variables. FRS statistics continue to be available via the Stat-Xplore online tool. Several years of data, going back to the 2002 to 2003 survey year, have been released; this year’s data has now been added.

New information on childcare is presented in table form for the first time, in this 2022 to 2023 publication. The underlying FRS dataset has contained data on childcare for over ten years. In the 2008 to 2009 survey year, in order to try to reduce disparities between estimates of childcare on different surveys, the FRS questionnaire was adapted to adopt the approach used on the Labour Force Survey (LFS). The content of the FRS publication continues to evolve in response to user needs, with the addition of new material for emerging areas of policy interest.

The “Cost of Living payments” made to households during the 2022 to 2023 financial year have been added to the FRS dataset. This is an important example of how new questions and variables are added, as necessary to reflect changes in policy. FRS published tables using variables that include the calculation of benefit unit and household income now reflect the Cost of Living payments that the person or their household would have received. Further detail on the methodology applied is given in section 3.3. Further information on policy changes in 2022 to 2023 is given in section 2.1.

An issue with the variable EDUCQUAL was identified during development work to improve reporting on categories of level of education. This variable is used to present estimates of household food security status and household food bank usage, by educational attainment of head of household. Two breakdowns included in tables 9.5 and 9.16 have not been published and the breakdowns have been temporarily removed from Stat Xplore. There will be an update on restoring these estimates once validation checks are completed.

Rural-Urban classification variables were used in the FRS publication for the first time in the 2021 to 2022 publication of the household food bank usage tables. It was later identified that the Rural-Urban classification on the FRS dataset, both for England and Wales and for Scotland, was still based on the Census 2001 classification. This publication presents tables using the newer Census 2011 classification, and for both 2021 to 2022 and 2022 to 2023 survey years. Further detail on the differences between these can be found on the Office for National Statistics’ Rural-Urban classifications page.

Auditing of processing methodology is a regular feature of survey-derived data. Subsequent changes to imputation methodology and the construction of derived variables have led to improvements in the quality of statistics. For example, the components of income-related variables were reviewed in 2021 to 2022, to reflect current income sources more accurately. As a result, the reported income bands were adjusted, to address the need for more granularity at both ends of the income distribution. The same approach has also been applied, for 2022 to 2023, to the bands used for levels of savings and investment.

Users have been informed in advance of changes to the FRS publication. Please see the DWP Statistical Work Programme and the FRS Release Strategy for more details.

1.4. Summary of the changes to fieldwork approach for 2022 to 2023 and effects on the sample

Whilst there were clear effects of the pandemic on the 2021 to 2022 survey year, that impact was much reduced for 2022 to 2023. During 2022 to 2023 the FRS sought to return to pre-pandemic fieldwork practices. Whilst fieldwork operations were not identical to pre-pandemic conditions, there was a gradual return to former practices. Overall, fieldwork was much more stable than in 2020 to 2021 or 2021 to 2022.

Interviewers from ONS and NatCen returned to using face-to-face interviewing as the preferred mode of data collection for the duration of the year, with telephone interviewing being used as and when needed. A return to prioritising face-to-face interviewing was rolled out fully by interviewers from NISRA from July 2022, having carried out a pilot during June of 2022. Overall, the method of data collection was split between face-to-face (72% of achieved sample) with 28% of achieved sample through telephone interviewing.

This change in approach affected the composition of the FRS achieved sample. Use of a mixed-mode approach to interviewing was successful in ensuring that those who may have been unable to complete a face-to-face interview were able to participate by telephone (e.g. larger families with children). This also will have improved the overall representativeness of the sample. It is however unknown how many of those who responded by telephone would have instead responded face-to-face if that was the only available option. Illustrative analysis shows that there were some differences in the types of people interviewed face-to-face or by telephone, in particular by age, disability, and ethnicity, and to a lesser extent family type and size.

More generally, and at just over 25,000 households, the achieved sample is meaningfully higher than that achieved in 2021 to 2022 (nearly 16,400) and 2020 to 2021 (just over 10,000). In statistical terms, this means that the uncertainty on this year’s survey estimates will be materially smaller than the last two years.

1.5. Questionnaire changes

Changes on specific topics are routinely made to the FRS questionnaire through an annual consultation process. This is to improve the understanding of respondents’ circumstances, improve data quality through better informed data processing decisions, and to collect information to support future policy analysis. The questionnaire was largely unchanged from the previous 2021 to 2022 survey year, with eight topics being represented in new variables on the FRS dataset. The main FRS publication and the set of FRS tables available from the Department’s Stat Xplore are not altered by these changes. However, an important change relating to the debt block of questions should be noted:

Personal debt

New questions on personal debt were originally planned to be included in the FRS from April 2020. Their inclusion was paused, given the coronavirus (COVID-19) pandemic and broader changes to the survey approach during that period. For the survey year 2021 to 2022, the questions were included but only for a small subset of households (4% of those interviewed).

For the survey year 2022 to 2023, and with the return to face-to-face interviewing at scale, the questions were included for face-to-face interviews (72% of those interviewed).
The questions relate to the month immediately before the interview. Respondents are asked about:

• types of debt (Credit card, Store card or Charge card; Hire purchase; Personal loans; and any catalogue or mail-order debts)
• number of debts
• repayment period and repayment amount
• for card repayments only: whether the card was repaid in full, partially, or the minimum; and how often repayments are due

These new questions supplement other debt-related questions which have been part of the FRS for several years. Respondents are elsewhere asked:

• whether they are up-to-date with utility bills, or the rent or their mortgage repayments
• whether they are in arrears on any payments
• whether they are overdrawn, in terms of their current account, or similar bank account

1.6. FRS transformation: integration of administrative data

We first started asking FRS respondents for consent to link their survey responses to their administrative records in 2007. This was via a question asked at the end of the interview. The approach was designed to meet the requirements of the Data Protection Act 1998. On average each year, around two thirds of respondents consented. We successfully matched around 80% of consenting respondents, giving us an overall match rate (for all respondents) of just over 50% on average.

The implementation of the GDPR in 2018 has provided an alternative to consent as the legal basis for linking. We can now link all respondents on the basis that the processing is necessary for the department to carry out its functions as a public body (GDPR Article 6(1)(e)).

This change, together with improvements in our linking methodology, means that we can now link at least 95% of FRS respondents to their administrative records.

As outlined in the DWP Statistical Work Programme – section 2.4, the department is committed to transforming its surveys through the integration of administrative data. This is in the wider context of the UK Statistics Authority’s Strategy for data linking Joining Up Data for Better Statistics – Office for Statistics Regulation and OSR recommendations in their 2021 review of income-based poverty statistics, that DWP should explore the feasibility and potential of social survey and administrative data integration Review of Income-based poverty statistics – Office for Statistics Regulation

A technical report on FRS Transformation work to date, with illustrative results for DWP benefits, and further details of development plans, is available at: Family Resources Survey Transformation: integrating administrative data into the FRS

2. Background Information

The primary objective of the FRS is to provide DWP with information that informs the development, monitoring and evaluation of social welfare policy. Detailed information is collected on respondents’ incomes, including:

• earnings from work including self employment
• private pensions
• state pensions and state benefits
• other state and local government support

Information is also collected on:

• caring needs and responsibilities
• health and disability
• pension participation
• housing tenure and expenditure
• education
• childcare, family circumstances and child maintenance
• household food security and food bank usage.

Each year the questionnaire and data processing are updated to reflect the latest changes in social welfare policy.

2.1. Policy Changes for 2022 to 2023

Council Tax and rates

For 2022 to 2023 the Department for Levelling Up, Housing and Communities estimated that the average Band D rate set by local authorities in England had increased by 3.5% from 2021 to 2022 levels. Additionally, in England the Government introduced a £150 non-repayable rebate for households in bands A to D, known as the “Council Tax Rebate”. This was in response to the rising cost of household bills in 2022 to 2023.

In Wales, the average band D rate for 2022 to 2023 represented an increase of 2.7% from 2021 to 2022 levels. In Scotland, the average band D rate for 2022 to 2023 represented an increase of 3% from 2021 to 2022 levels. In Northern Ireland, the rates (poundage) increased by no more than one per cent in some council areas, but in other areas the rates (poundage) remained as in 2022 to 2023.

Income Tax

The annual personal allowance (£12,570) and its related income limit (£100,000) remained the same as in the 2021 to 2022 year.

How much Income Tax a person pays in each tax year depends on how much of their income is above their Personal Allowance and how much of their income falls within each tax band. Both the rates and the bands for Basic, Higher and Additional income tax were held at their 2021 to 2022 levels in England, Wales and Northern Ireland. Rates in Scotland also remained unchanged. However, there were some changes to Scottish bands.

A person may get a dividend payment if they own shares in a company. They do not pay tax on any dividend income that falls within their Personal Allowance (the amount of income they can earn each year without paying tax). They also get a dividend allowance each year. Tax is only payable on any dividend income above the dividend allowance, dependent upon the person’s Income Tax band.

From 6 April 2022 the rates of Income Tax applicable to dividend income increased by 1.25%. The dividend ordinary rate was set at 8.75%, the dividend upper rate was set at 33.75% and the dividend additional rate was set at 39.35%. The dividend trust rate also increased to 39.35% to remain in line with the dividend additional rate. In addition, the dividend allowance remained at £2,000 annually, as it had been in 2021 to 2022.

National Insurance Contributions (NICs)

For employees, the 1.25% rise in class 1 NICs (based on earnings from PAYE income only and made up of a combination of employee salary deductions and employer payments) in April 2022 was reversed partway through the survey year, from 6 November 2022. The rate thereafter was 12%.

For the self-employed, the flat rate for class 2 NICs increased from £3.05 per week to £3.15 per week. However, this was offset by the introduction of the Lower Profits Threshold as the new floor for contributions, below which NICs were not payable. This was set at £11,908 per year (higher than the small profits threshold, which had been £6,515 per year in 2021 to 2022 and increased to £6,725 in 2022 to 2023).

Also, for the self-employed, the applicable rates for class 4 NICs (payable on profits above a set level) were increased from 9% to 9.73% and from 2% to 2.73% this survey year; however, this was offset by an increase in the lower profits limit, from £9,568 annually before the year to £11,908 throughout this year.

National Living Wage

Employers are legally required to provide a minimum amount that an employee earns per hour, based on their age. This is called the “National Minimum Wage” (NMW). However, there are some employers that provide their employees with a minimum wage that takes into consideration the cost of living, which is higher than the NMW. This is called the “National Living Wage”.

On 1 April 2022, the National Living Wage increased to £9.50 per hour for employees aged 23 years and above.

Employees aged under 23 years continued to receive the National Minimum Wage. On 1 April 2022, the NMW increased to £9.18 per hour for those aged 21 to 22 years inclusive, £6.83 per hour for those aged 18 to 20 years inclusive and £4.81 per hour for those aged below 18 years (but over compulsory school leaving age).

Additionally, the NMW rose to £4.81 per hour for apprentices, both those aged below 19 years and those aged 19 years and above, who were in the first year of their apprenticeship.

Up-rating of benefit amounts

In April 2022:

• inflation-linked benefits and tax credits rose by 3.1% in line with the Consumer Prices Index (CPI), at September 2021
• the Basic State Pension and New State Pension increased by 3.1% in line with the ‘triple lock’, which ensures that the Basic and New State Pension increases by the highest of (1) the increase in earnings, price inflation as measured by the CPI, or (2) 2.5%
  • the increase by CPI inflation of 3.1% applied this time
  • the Basic State Pension increased from £137.60 per week in 2021 to 2022 to £141.85 per week, a cash increase of £4.25 per week
  • the New State Pension increased from £179.60 in 2021 to 2022 to £185.15 per week, a cash increase of £5.55 per week
• the Standard Minimum Guarantee in Pension Credit increased by 3.1%
  • for those who were single, the Standard Minimum Guarantee in Pension Credit 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
• both the lower and higher Universal Credit Work Allowances rose broadly in line with CPI inflation

Household Support Fund

On 23 March 2022, the Household Support Fund was extended to 20 September 2022. A further extension was announced on 26 May 2022, extending the fund to 31 March 2023. Both extensions provided a further £500 million of funding (£1 billion in total), which will be used by local authorities to support vulnerable households. The aim was to ensure that the daily needs such as food, clothing, and utilities of those in vulnerable households were met.

Energy Bills Support Scheme

From October 2022, all domestic electricity customers in Great Britain began to receive a £400 government Energy Bills Support grant to help with rising energy costs. The £400 was received by customers between October 2022 and March 2023 either as a monthly credit on bills, applied directly to the meter, or paid as a voucher.

Households in Northern Ireland received a different support, of £600 per household.

Cost of Living Payments

Households on income-related benefits, including Universal Credit, Pension Credit and Tax Credits, received a payment of up to £650 this year. This was made automatically in two instalments, one in summer and another in the autumn, and was in addition to the Energy Bills Support Scheme. Eligible households could have received up to 3 different types of payment depending on their circumstances on specific dates or during a particular period, which are as follows:

• a Cost of Living Payment for households on a qualifying low-income benefit or tax credits. A payment of £650 was paid in 2 lump sums of £326 and £324, in summer and autumn respectively, to households already in receipt of the eligible benefits. This payment was made on top of any benefit payments received by the claimants.
• a Disability Cost of Living Payment for households on a qualifying disability benefit. A lump sum payment of £150 was paid in September or October, to those already in receipt of the eligible benefits. To be eligible for the payment, households must have received a payment (or later receive a payment) of one of these qualifying benefits before 25 May 2022.
• a Pensioner Cost of Living Payment for households entitled to a Winter Fuel Payment for winter 2022 to 2023. An extra £150 or £300 was paid with eligible households’ normal payments from November 2022. This is in addition to any other Cost of Living Payment received.

Warm Home Discount

Between October 2022 and March 2023, eligible households began to receive a one-off discount on their energy bill under the Warm Home Discount scheme. The rebate initially was set at £140 but was increased to £150. It was automatically being discounted from energy bills for houses in England and Wales where households were eligible if they were either in receipt of the Guarantee Credit element of Pension Credit or were on a low income and have high energy costs.

Alternatively, in Scotland, households were eligible if they were either in receipt of the Guarantee Credit element of Pension Credit or were on a low income met their energy supplier’s criteria for the scheme. The discount was not automatic; an application to the energy supplier was required.

The scheme was not available in Northern Ireland.

Wales Fuel Support Scheme

Eligible households were able to claim a payment from their local authority to help towards fuel bills. This was in addition to the GB-wide Energy Bills Support Scheme. This scheme ran between October 2022 and March 2023.

2.2. Relevance

DWP uses – the Policy Simulation Model and other policy analysis

The FRS is used extensively within DWP. It underpins the Policy Simulation Model (PSM) which is used for the development and costing of policy options. FRS responses are uprated to current prices, benefits and earnings levels and can be calibrated to the DWP Departmental Report forecasts of benefit caseload. Using FRS data has made it possible to model some aspects of the benefit system that could not be done previously, for example allowances for childcare costs. In addition to their use in formal modelling, FRS data play a vital role in the analysis of patterns of benefit receipt for policy monitoring and evaluation, and benefit forecasting.

The FRS remains the basis for several other accredited official statistics, and official statistics; these are outlined below.

Other DWP publications

Households Below Average Income (HBAI)

This publication uses household disposable incomes, adjusted for household size and composition, as a proxy for material living standards (or, more precisely, for the level of consumption of goods and services that people could attain given the disposable income of the household in which they live). HBAI

Pensioners’ Incomes Statistics

The FRS and HBAI datasets are used in the Pensioners’ Incomes Statistics, DWP’s analysis of trends in components and levels of pensioners’ incomes (PI)

Income-Related Benefits: Estimates of Take-Up

The FRS provides information about people’s circumstances, which is used to estimate numbers of people who are not claiming benefits to which they may be entitled. The statistics are based on a combination of survey and also administrative data. Take-up

Separated Families Statistics

Official statistics relating to separated families and their child maintenance arrangements. Separated Families

Below Average Resources

DWP are developing a new, and separate poverty measure named ‘Below Average Resources’ (BAR) based on the approach proposed by the Social Metrics Commission (SMC) and using FRS data.

The Office for Statistics Regulation (OSR) Review of Income-Based Poverty Statistics recommended that DWP assess how the SMC’s proposals can be implemented to enhance the public value of our statistics. The OSR recognised that a basket of main poverty measures is required to meet varying user needs, but that signposting and coherence between different statistics could be improved to help users navigate the different measures.

Once fully developed, the BAR measure will add value and sit alongside HBAI. The BAR approach provides a more expansive view of available resources (both savings and inescapable costs) than the income measurement adopted under HBAI, and also includes some methodological changes proposed by the SMC.

The first Official Statistics in Development publication in this series was published in January 2024.

Other government department uses

The FRS contains information used by other government departments, particularly for tax and benefit policy analysis by His Majesty’s Treasury and His Majesty’s Revenue and Customs. Other users include the Ministry of Justice, the Department for Education and the Department for Environment, Food and Rural Affairs.

The Office for National Statistics produces (model-based) small area income estimates. These are the official estimates of annual household income at the middle layer super output area (MSOA) level in England and Wales. The estimates are produced using a combination of survey data from the FRS and Census data, plus several administrative data sources.

The Race Disparity Unit has published a series of summaries of data on their Ethnicity Facts and Figures website since June 2019. Ethnicity Facts and Figures provides information about the different experiences of people from a variety of ethnic backgrounds. It gathers data collected by government in one place, making it available to the public, specialists and charities. The FRS contributes data on state support, that is, receipt by ethnicity and type of benefit.

The FRS Household Food Security data is used as a UK indicator of Sustainable Development Goals. This relates to Indicator 2.1.2 – ‘Prevalence of moderate or severe food insecurity in the population’, based on the Food Insecurity Experience Scale (FIES).

In Northern Ireland, the Department for Communities uses the FRS to produce similar reports to those from DWP, which are focused on Northern Ireland. For more information see: Northern Ireland Family Resources Survey and Department for Social Development Northern Ireland Family Resources Survey

In-depth analysis of FRS-based HBAI data for Scotland can be found in the Scottish Government report on Poverty and income inequality statistics in Scotland.

In-depth analysis of relative income poverty in Wales, based on HBAI data, can be found on the Relative income poverty page of the Welsh Government website, which also has links to material deprivation analysis.

The academic and research community

The FRS is used extensively by academics and research institutes. The FRS team within DWP engages with users in the following ways:

• data and Publication Quality Assurance Groups
• expert Advisory Group consultations
• the annual Family Finance Survey Users conference, run in association with ONS and the UKDS
• responses to Parliamentary Questions and Freedom of Information requests
• team email: team.frs@dwp.gov.uk for general enquiries

2.3 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 specific to DWP’s statistical releases: Accessibility statement for DWP statistics

Other measures to improve accessibility include the FRS report and ‘Background Information and Methodology’ now being released in HTML format. FRS Tables are released as both Excel and ODS tables.

Clarity

In both the technical advice provided alongside the publication, and in the supporting documentation, there has been clarity on the strengths and limitations of the data, clarity on the appropriate uses and quality of the statistics and data, and transparency in how this may differ from past years.

For example, a Technical User Guide for the FRS detailing the assessment of the impact of the coronavirus (COVID-19) pandemic on the FRS statistics, is available on the UKDS to support microdata users.

Within each published data table, a guidance page offers further help in interpreting the estimates. To aid interpretation a glossary of key terms can be found at the end of this document. For more detail on the coherence and comparability of the FRS themes see Section 9.

2.4. Documentation

See section 3 for how to access the FRS dataset. The documentation list available from the UK Data Service and ONS Secure Research service contains the following metadata:

• variable Listing
• variable Metadata
• derived Variable Summary
• fixed Rate Constants
• variable Rate Constants
• documentation of Changes to FRS Dataset
• benefits documentation
• questionnaire (GB and NI)
• guide to [questionnaire] changes (GB and NI)
• interviewer pocket guide
• [Questionnaire] Showcards (GB and NI)
• future [questionnaire] changes summary

2.5. Timeliness and Punctuality

In terms of timeliness, data relating to any given financial year are released at the end of the following financial year (March). FRS data is used for producing HBAI which is used for three of the four income-related measures in the Welfare Reform and Work Act 2016. The Act requires HBAI to be published by the end of the financial year. Prior to this the FRS was regularly published in June.

In terms of punctuality, data has been released both as planned and as announced in the release calendar; this has been in March each year since 2017. For more information on the trade-offs between timeliness and quality see the Trade-offs in Section 11 of this document.

2.6. Confidentiality, Security, Transparency

Treatment of respondent’s personal information

Respondents to the FRS questionnaire are provided with the FRS Privacy Notice and further guidance on how their data will be handled.

Prior to publication: Data handling and access control

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

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. Each person is required to submit a National Statistics declaration, prior to being granted access to the analysis and a record of these is maintained.

Anyone with access prior to publication is made clear about their role and the need to prevent any disclosure – 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.

Post-publication: Limitation of the risk of disclosure of identities of data subjects

There are two types of FRS dataset made available for download via the UKDS:

• End User Licence (1994-95 – Present)

The End User Licence (EUL) version is a non-disclosive dataset. Following advice from the Statistical Disclosure Control Unit at ONS, several variables are treated (rounded, top-coded or removed) in order to ensure that the risk of identification of individuals is minimised. The list of variables which have been treated has changed over time, in line with changes to the questionnaire, and following improvements in disclosure control best practice. Please note that the anonymisation process applied to the dataset may mean that EUL users will not be able exactly to replicate published estimates.

• Secure Access Files (2009-10 – Present)

The Secure Access File was introduced in 2012. This version contains several additional variables versus the EUL copy; and unlike EUL, no other treatment procedures are applied. The purpose of this file is to allow academics, analysts and researchers to carry out even more detailed analysis.

It can only be accessed using the UKDS ‘Safe Room’. Any user wishing to access this version of the dataset must register with UKDS as an accredited researcher, then submit an application for use.

The copy of the FRS deposited at ONS’ Secure Research Service is the same as the Secure Access File.

3. Statistical Presentation

3.1. Overview dissemination process

The FRS statistical release includes a report, which is divided into topic-specific chapters, accompanied by several excel and ODS detailed tables. These present a wide range of statistics from the FRS dataset. The tables are referenced throughout the report.

Users can generate their own statistics and tables, by using the department’s Stat-Xplore online tool. This includes a wide range of variables from more than twenty years of the FRS.

Researchers and analysts outside government can also access the FRS dataset via:

• the UK Data Service, and on application for the Secure Access File version of the dataset
• the ONS Secure Research Service, on application

3.2. Data Description

The FRS dataset is a relational database, with all tables using household (by the unique identifier of ‘sernum’). Data is organised within different tables, with different key variables as appropriate to the subject of the table. For example, family (benefit unit), individual (person), or household, plus others if appropriate. An explanation of this is provided to users in a full listing of Tables and Variables.

3.3. Statistical Concepts and Definitions

A guide to the different definitions of earnings and income, with information on alternative sources of data, is available.

There is also a guide to contrasting income and earnings statistics to help identify the most appropriate statistic, describing the source data, output data and availability, the concept being measured, its main strengths, limitations and uses.

Definitions of groups used for analysis and presentation

Results presented in the FRS publication, depending on the context, are presented at either household level, family (benefit unit) level, or individual level.

• Household level definition

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, sitting room or dining area. A household will consist of one or more families or benefit units.

• Family or benefit unit level definition

A single adult, or a couple living as married, and any dependent children.

Cost of Living Support Schemes 2022 to 2023: FRS methodology

During the 2022 to 2023 survey year, there were multiple schemes to support households with the increased cost of living. Most were introduced at pace, and in a timeframe which made it difficult to adapt the questionnaire in the field to capture them. Therefore, the FRS did not attempt to collect data by including direct questions to survey respondents. Instead, eligibility was modelled, and amounts applied post-interview to the dataset. Details of the methodology used is given below.

As the support schemes came with clear eligibility guidelines, it was possible to model eligibility based on the respondents’ characteristics. Policy experts were consulted on the treatment of Cost of Living Payments on the FRS to ensure that survey observations aligned with the real-world change to incomes. To determine the appropriate treatment for each support payment, multiple factors were considered, including eligibility for the payment, and timing of payment versus interview.

To align treatment of the Cost of Living Payments on the FRS with the rest of the survey methodology, interview dates were chosen as the basis for payment eligibility. This approach was applied to the Disability and Income-Related Cost of Living Payments. For the Pensioner Cost of Living Payment, eligibility was determined in the same way as the Winter Fuel Payments are treated on the survey.

The approach adopted on the FRS aims to treat these payments in line with how benefit receipt elsewhere is treated. The aim was to award the payments to households that would have this financial resource available at the time of interview; and not award payments to households that would have not benefitted from them.

On the FRS, it was assumed that each Cost of Living Payment was supposed to last until the next payment of the same kind was made to the recipient. It was treated as such when added to income-related variables. This aligned with FRS treatment of incomes received from other sources.

• Cost of Living Payment for households on a qualifying low-income benefit or tax credits

On the FRS, eligibility for this payment has been determined via interview dates and receipt of a qualifying benefit. Payments were assigned to respondents reporting receipt of a qualifying benefit at the time of interview and who were interviewed during the window when payments were made. Therefore respondents were assigned the payment that occurred just before the interview, so there were no respondents receiving both payments (£650) on the FRS.

• Disability Cost of Living Payment

On the FRS, this payment has been assigned to all respondents with a disability related benefit on the survey dataset, and who were interviewed from 20th September onwards.

• Pensioner Cost of Living Payment

On the FRS, Winter Fuel Payment eligibility was used to award this payment. Where a Winter Fuel Payment recipient met the criteria specified in the Pensioner Cost of Living Payment eligibility guide, a £150 or £300 Pensioner Cost of Living Payment has been assigned.

Other schemes to support households with the cost of living

The assignment of these other schemes to FRS households was made as follows:

• the Council Tax Rebate has been assigned to all eligible households interviewed in the survey year 2022 to 2023
• the Energy Bills Support Scheme payment has been assigned to all eligible households interviewed between October 2022 and March 2023
• Warm Home Discount has been assigned only to households interviewed between October 2022 and March 2023; and where they met the eligibility criteria relating to a person in receipt of the Guarantee Credit element of Pension Credit. It was not possible accurately to model other eligibility criteria relating to the energy cost score for individual households, due to the absence of data on this
• The Wales Fuel Support Scheme has been assigned to all eligible households in Wales interviewed between October 2022 and March 2023

For all Cost of Living Support schemes, amounts have been weeklyised as appropriate, dependent upon the timing of the payment date and assumptions of the duration for which the benefit was intended to cover. For further details on the methodology on how Cost of Living support payments have been applied to the FRS data, please contact the FRS Team Inbox: team.frs@dwp.gov.uk. A technical paper will also be made available from the UK Data Service or the ONS Secure Research Service.

The tables below sets out how the various Cost of Living Payments have been included, within the Excel and ODS data tables to this publication:

4. Source data

Survey Data

The FRS is designed to be representative of all private households in the United Kingdom. Individuals who live in communal settings are not included, for example, students in halls of residence or residents of nursing homes.

A consortium consisting of the Office for National Statistics (ONS) and NatCen Social Research conducts Great Britain fieldwork on behalf of DWP. Fieldwork in Northern Ireland is conducted by the Northern Ireland Statistics and Research Agency (NISRA).

The FRS Questionnaire

In 2022 to 2023 the FRS questionnaire was designed for Computer Assisted Personal Interviewing (CAPI) with an alternative version for Computer Assisted Telephone Interviewing (CATI) also available.

The questionnaire is divided into three parts:

• the first part is the household schedule which is addressed to one person in the household (usually the household reference person, although other members are encouraged to be present) and mainly asks household level information, such as relationships of individuals to each other, tenure and housing costs.
• next is the benefit unit schedule which is addressed to each adult in turn and asks questions about income from work and earnings, pensions, state benefits, investments, and any other income. Information on children in the household is collected by proxy from a responsible adult.
• a final section asks the value of investments (by type) for respondents with savings between a lower and an upper pound limit.

Each year, DWP runs a questionnaire consultation and draws up a list of possible questionnaire changes. Users are asked to identify individual questions or sections which were no longer of interest. The FRS questionnaire is lengthy and demanding and a key concern is, where possible, to reduce (or at least not increase) its length, so as not to overburden respondents or interviewers.

New questions are added with the expectation that they will produce useful data that can be delivered to users through additional variables and used to support future policy analysis. Some changes are made to improve the interview experience or to support improvements to data processing.

The new variables are released in the published dataset following quality assurance, but not all are added to the main FRS publication on GOV.UK; or to the set of FRS tables available from the Department’s Stat Xplore tool.

Administrative data

DWP makes use of its own administrative data on state benefits, to support the survey responses on state benefit receipt. Such data has been used for several years to correct or edit responses, where respondents have said they claim a state benefit. This includes use of data on Universal Credit for all such responses.

4.1. Survey Data – Sampling design

The sampling frame in Great Britain

The Great Britain FRS sample is drawn from the Royal Mail’s small users Postcode Address File (PAF). The small users PAF is limited to addresses which receive, on average, fewer than 50 items of post per day and which are not flagged with Royal Mail’s “organisation code”. The sampling frame therefore excludes people living in communal settings, e.g. nursing homes, halls of residence, barracks or prisons, and people living in temporary (bed and breakfast) accommodation.

An updated version of this list is obtained twice a year. By using only the small-user delivery points most large institutions and businesses are excluded from the sample. Small-user delivery points which are flagged as small business addresses are also excluded. However, some small businesses and other ineligible addresses remain on the sampling frame. If sampled, they are recorded as ineligible once the interviewer verifies that no private household lives there.

The sample design in Great Britain

The Great Britain FRS uses a stratified clustered probability sample design. The survey samples 3,407 postcode sectors, from around 9,200 in Great Britain, with a probability of selection that is proportional to size. Each postcode sector is known as a Primary Sampling Unit (PSU). The PSUs are stratified by 27 regions and three other variables, described below, derived from the 2011 Census of Population. Stratifying ensures that the proportions of the sample falling into each group reflect those of the population.

Within each region the postcode sectors are ranked and grouped into eight equal bands using the proportion of households where the household reference person (HRP) is in National Statistics Socio-Economic Classification (NS-SEC) 1 to 3.

Within each of these eight bands, the PSUs are ranked by the proportion of economically active adults aged 16-74 and formed into two further bands, resulting in sixteen bands for each region.

These are then ranked according to the proportion of economically active men aged 16-74 who are unemployed. This set, known as “stratifiers” is chosen to have maximum effectiveness on the accuracy of two key variables: household income and housing costs.

The information below summarises the stratification variables.

FRS sample stratification variables for Great Britain

Regions – 19 in England (inc. Metropolitan vs non-Metropolitan split, 4 in London), 2 in Wales, 6 in Scotland

The proportion of households where HRP is in NS-SEC 1 to 3 – 8 equal bands

The proportion of economically active adults aged 16-74 – 2 equal bands

The proportion of economically active men aged 16-74 who are unemployed – Sorted within above bands

Each year, half of the PSUs are retained from the previous year’s sample, but with new addresses chosen; for the other half of the sample, a fresh selection of PSUs is made (which in turn will be retained for the following year). This is to improve comparability between years. Within each PSU a sample of addresses was selected. In 2022 to 2023, 28 addresses were selected per PSU.

The total Great Britain set sample size in 2022 to 2023 was 95,396 addresses. This reflected the continuation of the sample boost introduced in 2021 to 2022. Each address therefore had approximately a 1-in-298 chance of being included in the survey. For England each address had approximately a 1-in-301 chance of inclusion in the survey. In Wales each address had approximately a 1-in-260 chance of inclusion in the survey. In Scotland each address had approximately a 1-in-294 chance of inclusion in the survey.

The sampling frame in Northern Ireland

The sampling frame employed on the Northern Ireland FRS is derived from the Northern Ireland Statistics and Research Agency (NISRA) Address Register (NAR). The NAR is developed within NISRA and is primarily based on the Land and Property Services (LPS) Pointer database, the most comprehensive and authoritative address database in Northern Ireland, with approximately 752,000 address records available for selection.

The sample design in Northern Ireland

A systematic random sample is selected for the Northern Ireland FRS from the NAR. Addresses are sorted by district council and ward, so the sample is effectively stratified geographically. Multi-households are not selected in Northern Ireland.

In Northern Ireland the sample size and sample approach was the same as in the previous survey year, FYE 2022. A systematic random sample of 4,080 addresses was selected for the 2022 to 2023 Northern Ireland FRS from the NISRA Address Register. Addresses are sorted by district council and ward, so the sample is effectively stratified geographically. Each address had approximately a 1-in-184 chance of being selected for the survey.

4.2. Data Collection

Data collection in Great Britain

Within Great Britain, each month the PSUs are systematically divided between ONS and NatCen and addresses are then assigned to interviewers.

As noted in earlier sections, fieldwork operations for FRS 2022 to 2023 returned to large-scale face-to-face interviewing as the preferred method of data collection, and for the duration of the year. Telephone interviewing was retained but used only as and when needed based on household preference and (in the first few months) interviewer availability.

Before interviewers visit the selected addresses, a letter is sent to the occupier explaining that they have been chosen for the survey and that an interviewer will call. The letter also explains that the survey relies on the voluntary co-operation of respondents and emphasises that information given in the interview will be treated in the strictest confidence and used only for research and statistical analysis purposes. As a token of appreciation and to encourage participation, a £10 voucher is included with the letter. If more than one household receives mail at an address then only one household is interviewed.

All ONS addresses receive two letters as standard: one sent out centrally and then another directly from their individual interviewer. All NatCen addresses receive one centrally despatched letter, sent in advance of the start of the fieldwork period.

The main face-to-face contact with respondents is via doorstep contact. If contact is not made on the first attempt, the interviewer is required to make additional calls to an address. These calls must be made at different times of the day and on different days of the week, including at least one weekend call.

Data collection in Northern Ireland

As set out above, in Northern Ireland the sampling and fieldwork for the survey are carried out by the Central Survey Unit at NISRA. The responsibilities for programming the survey questionnaire, making annual modifications, initial data processing and data delivery are retained within ONS and NatCen.

During the first quarter (April to June) of 2022 to 2023 survey year, NISRA interviewers continued with the ‘knock-to-nudge’ methodology whereby they were permitted to visit sampled addresses, as would be usual in face-to-face interviewing, and talk to the residents to encourage participation in the study. Only telephone interviewing was permitted during the first quarter of the 2022 to 2023 survey year. From July 2022, NISRA allowed face-to-face interviewing to resume on the FRS, and interviewers were encouraged to try, where possible, to secure an interview using this method. The option of a telephone interview continued to be offered to respondents throughout the remainder of the survey year.

As in Great Britain, before interviewers visit the selected addresses, a letter was sent to the occupier explaining that they had been chosen for the survey and that an interviewer would call. The letter also explained that the survey relies on the voluntary co-operation of respondents and emphasised that information given in the interview would be treated in the strictest confidence and used only for research and statistical analysis purposes. As a token of appreciation and to encourage participation, a £10 Post Office voucher was included with the letter.

As in Great Britain, the main face-to-face contact with respondents was via doorstep contact. If contact is not made on the first attempt, the interviewer is required to make additional calls to an address. These calls must be made at different times of the day and on different days of the week.

4.3. Response

A household is defined as fully co-operating when it meets the requirement of interviewing all adults aged 16 and over, except those aged 16 to 19 who are classed as dependent children and there are fewer than 13 ‘don’t know’ or ‘refusal’ answers to monetary amount questions in the benefit unit schedule (i.e. excluding the assets section of the questionnaire).

Proxy interviews are accepted when a household member is unavailable for interview. In 2022 to 2023, for those households classed as fully co-operating, proxy responses were obtained for 25% of adults. The fall from 29% in 2021 to 2022 is likely to be a result of the move away from telephone interviewing and a focus on increasing face-to-face interviews.

It should be noted that all data shown in the main body of this publication refer only to fully co-operating households.

Methodology Table M_1 summarises the household response.

The UK-wide sample chosen for 2022 to 2023 consisted of 99,476 households. In total 25,056 households fully co-operated. As every year, a few households were removed during DWP quality assurance processes, to arrive at the final useable dataset (25,050). This is the largest achieved sample in over a decade, since the 2010 to 2011 survey (25,356 households).

The overall response rate for the FRS, as a percentage of the issued sample who were eligible to take part, was 25%; this was slightly lower than in 2021 to 2022 (26%).

Response rates are calculated as follows:

The number of fully co-operating households, multiplied by 100 / the number of households contained in the sample.

A further 815 households partially co-operated (one per cent). A total of 37,404 households refused to proceed with the interview (38%), either refusing when contacting the office, refusing when contacted by the interviewer, or being away during the fieldwork period. The interviewer was unable to contact 36,201 households (36%), which is a decrease from 2021 to 2022 when it was 41%.

Methodology Table M_2 shows response rates broken down by region. Response rates in several regions remained the same between 2021 to 2022 and 2022 to 2023 (e.g. North West, Yorkshire and Humberside, South East and South West).

Whilst some regions experienced a decrease in response rates (e.g. East Midlands, West Midlands, London and the East of England), the North East experienced an increase in response rates. The North East had the highest response rate in England, where 30% of all households selected responded fully. London had the lowest, with 16% of the eligible households fully co-operating.

DWP had previously announced plans for a significant boost to the FRS sample size, with the aim to increase the achieved sample to 45,000 households annually, from April 2022. However, the primary challenge to achieving this stemmed from recruiting and retaining sufficient interviewers.

A number of factors conspired to cause existing interviewers to leave at a higher rate than previously, while at the same time it was hard to attract and retain new interviewers. Recruitment issues were experienced by all three collecting organisations, but caused greater challenges for NatCen and ONS because the sample boost was in GB only. The impact of this was that the level of field capacity required to manage the sample boost was not achieved.

Refusals and Non-response

Refusals include those residents of an address that make contact to refuse to participate and residents who refuse to participate in the survey when contacted by the interviewer, either by telephone or on the doorstep. Those who aren’t available to proceed with the interview for other reasons, such as being away throughout the fieldwork period, are not counted in “Total number of refusals” shown here.

When respondents in Great Britain refuse to participate in the FRS, interviewers record up to three reasons for refusal. The most common reasons for refusal in 2022 to 2023 are shown below:

Reasons for refusal to participate in the FRS, Great Britain, 2022 to 2023

Reason for refusal	Percentage of households
Couldn’t be bothered	28
Genuinely too busy	21
Don’t believe in surveys	15
Invasion of privacy	13
Disliked survey of income	9
Concerns about confidentiality	7
Personal problems	7
Anti-government	4
Temporarily too busy	4
Survey too long	3
About to go away	1
Total number who gave a reason for refusal	20,825
Total number of refusals	30,873

People can give up to three reasons for not responding, so percentages do not sum to 100%. The percentage shown is of the households where someone gave a reason for refusal.

The lower the response rate to a survey, the greater the likelihood that those who responded are significantly unlike those who did not, and so the greater the risk of systematic bias in the survey results. For a United Kingdom survey of the size and complexity of the FRS, a total non-response rate of around 50% is considered reasonable.

4.4. Length of interview

The length of each fully co-operating interview is recorded by the questionnaire program. In 2022 to 2023 the median interview length for Great Britain was 61 minutes, but the time varied according to the size of household and its circumstances. The increase from 53 minutes in 2021 to 2022 is likely to be due to the reintroduction of the Debt block of questions (only asked in face-to-face interviews); plus the use of an extra questionnaire block, for the first three months of the year, to trial a refreshed set of material deprivation questions.

The distribution of interview lengths in Great Britain is shown below, with full data in Methodology Table M_7. The timings exclude interviewer time spent preparing for and completing administration tasks after the interview. They are based on completed audit data from 22,867 fully productive ONS and NatCen interviews, excluding outliers with times outside of three standard deviations from the mean.

Distribution of FRS interview lengths, 2022 to 2023, Great Britain

4.5. Respondent burden

The Code of Practice for Statistics states that producers of statistics should consider the burden on survey respondents. The FRS can measure the burden placed on respondents by using measured interview times for 22,867 full interviews, in Great Britain. Respondent burden in Great Britain is calculated as follows:

Number of responses x median interview time

The median interview time for these 22,867 interviews was 60.7 minutes. Therefore, the respondent burden for the FRS in 2022 to 2023 was roughly 964 days.

In comparison, the median interview time for the 17,128 interviews for the FRS in 2019 to 2020 was 52.2 minutes. The respondent burden was therefore 621 days.

With respect to V5.5 of the Code of Practice for Statistics one of the aims of the Questionnaire Consultation process is to reduce the burden on those providing their information, and on those involved in collecting, recording and supplying data. The value gained from the questions asked is judged to be proportionate due to the benefits arising from the use of FRS statistics.

5. Data validation and processing

5.1. Interview stage validation

In addition to unit non-response, where a household does not participate, a problem inherent in all large surveys is item non-response. This occurs when a household agrees to give an interview, but either does not know the answer to certain questions or refuses to answer them. This does not prevent them being classified as fully co-operating households because there is enough known data to be of good use to the analyst (although see Section 4.3 for information about non-response to monetary questions).

The fact that the FRS allows missing values in the data collection can create problems for users, so missing values are imputed where appropriate. The policy is that for variables that are components of key derived variables, such as total household income and housing costs, and areas key to the work of DWP, such as benefit receipt, there should be no missing information in the final data.

In addition to imputation, prior to publication, FRS data is put through several stages of validation and editing. This ensures the final dataset presented to users is as accurate as possible. The stages in the validation, editing, conversion and imputation process are laid out below:

5.2 Data validation

State support validation

Information on benefit and tax credit receipt is one of the key areas of the FRS, and it is very important that this section is thoroughly validated and cleaned.

It is not appropriate to use imputation methods such as hot-decking, algorithms or bulk edits (see section 5.3) for benefits data so instead a separate procedure of validation and editing is used. The following types of validation were carried out for 2022 to 2023 FRS data:

• Missing values

For cases where a respondent had answered ‘yes’ to whether they are in receipt of a particular benefit, but did not give the amount received, we impute using linked data where possible, depending on the benefit. For benefits such as Universal Credit, where the rate could vary greatly depending on the circumstances of the respondent, we replace all reported amounts with linked amounts, because of the difficulty of making individual benefit assessments.

However, for benefits with no access to suitable administrative data, such as the Armed Forces Compensation Scheme, or where fewer rates apply, a more general method has been used and an imputation decision has been made, based upon all the available evidence available about that person’s circumstances.

• Near-zero amounts

It is not possible for interviewers to enter zero amounts where it is inappropriate to do so. For example, in response to a question on receipt of benefit, a zero amount will result in a warning message being displayed. Some interviewers try to avoid this message by recording near-zero amounts. As a result, all near-zero values are examined, and a decision taken as to whether the value is genuine or whether the value should be treated as missing.

• Multiple benefits

Any combined benefit amounts (for example where State Pension is paid with Attendance Allowance) are assessed on an individual basis and amended accordingly, depending on whether the data had errors or no errors. However, the reported total is preserved where possible.

• Validation reports

Computer programs are run to carry out a final check for benefit entitlement and to output any cases that look unreasonable. All cases detected because of this validation exercise are individually checked and edited where necessary.

Other pre-imputation cleaning

In preparation for imputing missing values, the dataset is made as clean as possible. This includes a number of edits and checks:

• Weekly amounts

In the FRS, most monetary amounts are converted to a weekly equivalent. To calculate this, respondents are usually asked the amount, then the length of time this amount covered. The latter is known as a “period code”. Period codes are used in conjunction with amounts to derive weekly figures for all receipts and payments. Some variables, such as interest on savings accounts, refer to the amount paid in the whole of the past year. These are also converted to a weekly amount.

Sometimes the period code relates to a lump sum or a one-off payment. In these cases, the corresponding value does not automatically convert to a weekly amount. For the data to be consistent across the survey, edits are applied to convert most lump sums and one-off payments to weekly amounts. In the same way, where period codes are recorded as ‘don’t know’ or ‘refused’, these are imputed so that the corresponding amount can be converted to a weekly value in the final dataset.

• Near-zero amounts

In the same way as benefit amounts recorded as near-zero are treated, any cases of near-zero amounts in other variables are examined individually, and an edit decision is made.

• Outliers

Statistical reports of the data are produced to show those cases where an amount was greater than four standard deviations from the mean. These relate to outliers, which is data that is beyond the expected value range of the variables being explored based on the other data in the set. Outliers are important to be transformed so that they can validly contribute toward the analysis or be omitted. Although if the outliers are omitted this could increase the risk that false conclusions are drawn.

For the seven largest values over four standard deviation from the mean, the individual record is examined and where necessary (but only if a value looks unrealistic), the case is edited. The outliers remaining in the dataset are verified by examining other relevant data for that household; to establish whether the amount is aligned to values reported for other questions. Compared with earlier FRS years, only a small number of these edits are now carried out, because of the many range checks in the computerised questionnaire.

• Credibility checks

Checks are carried out for the internal consistency of certain variables. For example, one check on mortgage payments ensures that payments to the mortgage from outside the household are not greater than the mortgage payment itself. Such cases are examined and edited where necessary.

5.3. Imputation

The responses to some questions are much more likely to have missing values than others. For example, it is very unlikely that a respondent will refuse to give or will not know their age or marital status; whereas it is much more likely that they will not be able to provide precise information on the amount of interest received from their investments.

Areas where missing values are a problem are typically income values, such as employee earnings, income from self employment and income from investments. This is because these values are required in the calculation of derived variables, used for reporting total Individual Income [INDINC], Benefit Unit Income [BUINC] and ultimately Household Income [HHINC], used in the Households Below Average Income (HBAI) publication.

Results in the tables provided in this publication include imputed values. Elsewhere however, values are left to remain as missing in some variables (such as hours of care).

Methodology Table M_4 illustrates the extent of missing values. Of the 16.1 million set values in the 2022 to 2023 FRS dataset, one per cent were originally recorded as either ‘don’t know’ or ‘refused’. Out of 224,867 missing values, approximately 91% were then imputed.

• Closing down routes

As with any questionnaire, a typical feature of the FRS is a gatekeeper question positioned at the top of a sequence of questions, at which a particular response will open the rest of the sequence. If the gatekeeper question is answered as ‘don’t know’ or ‘refused’ then the whole sequence (route) is skipped.

A missing gatekeeper variable could be imputed such that a further series of answers would be expected. However, these answers will not appear because a whole new sequence (or route) has been opened. For example, if the amount of rent is missing for a record and has since been imputed, any further questions about rent would not have been asked. From the post-imputed dataset, it will appear that these questions should have been asked because a value is present for rent.

For this reason, where the gatekeeper question has been skipped the onward routes should be closed. In most cases, gatekeeper variables are of the ‘yes or no’ type. If missing, these would be imputed to ‘no’, on the basis that if a respondent does not know whether an item is received or paid, then it is likely that it was not received or paid.

• Hot-decking

This process looks at characteristics within a record containing a missing value to be imputed and matches it up to another record with similar characteristics for which the variable is not missing. It then takes the known variable and copies it to the missing case. For example, when imputing the Council Tax Band of a household, the number of bedrooms, type of accommodation and region are used to search for a case with a similar record. This method ensures that imputed solutions are realistic and allows for a wide range of outcomes which maintain variability in the data.

• Algorithms

These are used to impute missing values for certain variables, for example variables relating to mortgages. The algorithms range from very simple calculations to more sophisticated models, based on observed relationships within the data and individual characteristics, such as age and gender.

• ‘Mop-up’ imputation

This is achieved by running a general validation report of all variables and looking at those cases where missing values are still present. At this stage, variables are examined on a case-by-case basis to decide what to impute. Credibility checks are re-run to identify any inconsistencies in the data caused by imputation, and further edits are applied where necessary.

All imputations, by each of the methods above, are applied to the un-imputed dataset by a transaction database. This ensures auditability in that it is always possible to reproduce the original data.

Points to note with imputed data

• whilst several processes are used to impute missing values, it should be remembered that they represent only a very small proportion (typically two per cent) of the dataset
• imputation will have a greater effect on the distribution of original data for variables that have a higher proportion of non-response, as proportions of imputed data will be higher
• as mentioned above, in certain situations, imputed values will be followed by ‘skipped’ values. It was decided in some cases that it was better to impute the top of a route only, and not large amounts of onward data
• for a small proportion of imputations it is not possible to close down a route. These cases are followed by ‘skipped’ responses (where a value might otherwise be expected)

6. Data compilation and methodology

6.1. Derived variables

Derived variables (DVs) are those which are not created by the original interview, but instead are made by combining information, both within the survey and from other sources.

They are created at the FRS user’s request. Their main purpose is to make it easier for users to carry out analysis and to ensure consistent definitions are used in all FRS analyses. For example, INDINC is a DV which sums all components of income to find an individual’s total income. This is possible because of the various sources collected by the survey. As new information is collected in the survey, the relevant DVs are updated as necessary.

6.2. Grossing

The FRS publication presents tabulations where the percentages refer to sample estimates grossed-up to apply to the whole population.

Grossing-up is the term 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 e.g. the number of households in the population divided by the number in the achieved sample. 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 system used to calculate grossing factors for the FRS divides the sample into different groups. The groups are designed to reflect differences in response rates among different types of household. The FRS stratified sample structure is designed to minimise differential non-response in the achieved sample. Grossing is then designed to account for residual differential non-response.

They have also been chosen with the aims of DWP analysis in mind. The population estimates for these groups, obtained from official data sources, provide control totals. The grossing factors are then calculated so that the FRS produces population estimates that are as close as possible to the control totals. As an example, a grossed FRS count of the number of men aged 35-39 would be consistent with the ONS population estimates of the same group.

In developing the grossing regime careful consideration has been given to the combination of control totals, and the way age ranges, Council Tax Bands and so on, are grouped together. The aim has been to strike a balance so that the grossing system will provide, where possible, accurate estimates in different dimensions without significantly increasing variances.

Some adjustments are made to the original control total sources, so that definitions match those in the FRS. For example, an adjustment is made to the demographic data to exclude people whose residence is not a private household. It is also the case that some control totals must be adjusted to correspond to the FRS survey year which runs from April to March.

A software package called CALMAR, provided by the French National Statistics Institute, is used to reconcile control variables at different levels and estimate their joint population. This software makes the final weighted sample distributions match the population distributions through a process known as calibration weighting. It should be noted that if a few cases are associated with very small or very large grossing factors, grossed estimates will have relatively wide confidence intervals.

A review of the FRS grossing methodology was carried out by the ONS Methodological Advisory Service in 2013.

A number of relatively minor methodological improvements were made as a result, with the grossing calculations updated to use 2011 Census data at that point. Further details on the methodological changes were published.

For the 2022 to 2023 publication year, changes had to be made to the source of the mid-year population estimates and the mid-year private household population estimates. Mid-year population estimates based on 2021 census data were not available in time for the FRS to use for grossing purposes.

For the 2022 to 2023 survey the population estimates used to weight the FRS are primarily based on the mid-year estimates rolled forward from the 2011 Census to mid-2019 and subnational population projections (2018-based) for mid-2020 and mid-2021. For England, Wales, and Northern Ireland the projection for mid-2021 was rolled forward to mid-2022 using official estimates of population change. For Scotland, the mid-2022 population estimates are taken from the subnational projections (2018-based). This series of population estimates does not take account of the 2021 and 2022 Censuses across the UK.

The mid-year estimates cover the usual resident population and were adjusted to reflect the population living in private households and covered by the FRS sample. This was achieved by deflating the usually resident population using data from the 2011 Census on the proportion of people usually resident, by local authority, age and sex who live in private households.

Both Great Britain and Northern Ireland data use the same CALMAR software to reconcile control variables at different levels and estimate their joint population. There are minor differences between the methods used to gross the Northern Ireland sample as compared with the Great Britain sample:

• local taxes in Northern Ireland are collected through the rates system, so Council Tax Band is not applicable as a control variable.
• Northern Ireland housing data is based largely on small-sample surveys. It is not desirable to introduce the variance of one survey into another by using it to compute control totals; therefore tenure type is not used as a control variable.

Details of the control variables used in the grossing methodology for Great Britain and Northern Ireland are shown below.

Grossing regime for Great Britain 2022 to 2023

Grossing regime for Northern Ireland, 2022 to 2023

Comparisons have been made between the achieved sample of FRS responses in Great Britain, and administrative data for 2022 to 2023 on the number of households within each Council Tax band.

Methodology Table M_3 shows that the achieved (ungrossed) FRS sample has a smaller proportion of households in the lower Council Tax bands (A and B) than the administrative data, and a higher proportion of households in higher Council Tax bands.

Table M_3 also shows the extent to which the FRS grossing regime controls for this bias in the achieved sample, effectively correcting it to be closer to the proportions seen on the administrative data.

7. Quality Management

7.1. Quality Assurance During Development

Questionnaire design and pre-fieldwork development

The interview

One of the benefits of interviewing using CAPI/CATI is that in-built checks can be made at the interview stage. This helps to check respondents’ responses and that interviewers do not make keying errors. There are checks to ensure that amounts are within a valid range and cross-checks which make sure that an answer does not contradict a previous response. However, it is not possible to check all potential inconsistencies, as this would slow down the interview to an unacceptable degree, and there are also capacity constraints on interviewer notes. FRS interviewers can override most checks if the answers are confirmed as accurate with respondents.

Post-interview checks

Once an interview has taken place, data is returned to ONS, NatCen, or NISRA. At this stage, editing takes place, based on any notes made by interviewers. Notes are made by the interviewer when a warning has been overridden, for example, where an amount is outside the expected range, but the respondent has documentation to prove it is correct. Office-based staff make editing decisions based on these notes. Other edits taking place at this stage are checking amounts of fixed-rate benefits and, where possible, separating multiple benefit payments into their constituent parts, such as separating Disability Living Allowance into the Care and Mobility components.

Data conversion

Before further validation, FRS data is converted from CAPI/CATI format into SAS-readable tables. Using DWP specifications, SAS-readable tables are created by ONS, with each table displaying information from different parts of the questionnaire. Both DWP and ONS then carry out validation checks on key input and output variables to ensure that the data have converted correctly to the new format. Checks include ensuring that the number of adults and children recorded is correct, and that records are internally consistent.

7.2. Quality Assurance during processing

FRS Interface

The old Interface for processing FRS data was developed in the 1990s, as a SAS AF/SCL based application. This technology is now at end-of-life in support terms. It has therefore been replaced with a new, HTML-led data management solution which has been built to modern standards.

The new Interface (FRESCO) recreates many of the old interface’s functions. However, it also offers improved code version control, as well as some new features, such as improved data viewing, on-screen editing and a part-automated anonymisation setup.

Routine Quality Assurance

The FRS dataset is checked at the 6-month and 12-month stages by a group of stakeholders, both within DWP and within other government departments and devolved administrations.

After, internal validation checks and cleaning of the data have been completed stakeholders are presented with a summary of changes to the data and any issues that the FRS team have identified. The test dataset is shared with these stakeholders.

Any issues raised at the 6-month stage are logged and responded to accordingly at the 12-month meeting. Any further issues are then dealt with by direct discussion with the stakeholder. There may be a further issue of a revised test dataset, before the data is declared to be final, and ready to use for publication of analysis.

DWP has an ongoing dialogue with expert users of the FRS-based statistics in relation to several data issues. DWP expects this to continue in the future as part of its long-term work programme.

External assessors

As part of the process of agreeing annual questionnaire changes, suggestions from users are also considered, as well as those arising from an evaluation of feedback from interviewers. Any changes to the questionnaire are checked for consistency with the harmonised standards for social surveys across government.

DWP has established an Expert Advisory Group on Survey-based Income Statistics to support its development work. The purpose of the Group is to provide advice to the Chief Statistician on (1) plans to implement the integration of administrative data into the FRS and related outputs; and (2) other technical issues as they arise.

Members of the Group include key users of the FRS and related outputs, including academic experts, users from third-sector organisations and methodology input from ONS. Statistical work programme - GOV.UK

8. Accuracy and reliability

8.1. Document consultation

Interviewers encourage respondents to consult documentation at all stages of the interview to ensure that the answers provided are as accurate as possible. For some items, whether certain documents are consulted or not is recorded on the questionnaire. This assists FRS users in assessing the accuracy of the data.

Given the mixed-mode approach to interviews this year, the consultation rates reported below may be less reliable than for a fully face-to-face interview survey year, as the interviewer cannot observe directly whether documents are being checked during a telephone interview (28% of interviews)

• employees have consulted their latest payslip for 39% of jobs they have reported. Of all employees, 96% reported having one job only and four per cent reported having more than one job
• employees did not have a payslip to consult for six per cent of jobs they reported; 24% could not consult a payslip because their payslips were only received electronically
• sixty-four per cent of all reported benefit and payable Tax Credit receipt involved consultation of documentation (that is, a letter from DWP or HM Revenue and Customs, or a bank statement)
• sixty-three per cent of households in Great Britain consulted a Council Tax bill or statement in answering questions on their Council Tax payments

8.2. Comparisons of survey and administrative data

Several comparisons of FRS and administrative data are available. See Methodology tables M_6a and M_6b and M_8, for a summary of how FRS benefit caseloads and amounts compare with DWP administrative data.

• Methodology Table M_6a This compares the grossed number of benefit recipients in the FRS 2022 to 2023 data, with the total caseload on benefit from administrative data sources. For all benefits (except Income Support and Disability Living Allowance), the FRS numbers in receipt are below those seen in administrative data. The difference varies by benefit.

• Methodology Table M_6b This compares the average weekly receipt of state support in the FRS 2022 to 2023 data, with average receipt from administrative data sources. Some benefit types have not been included in this analysis because no directly comparable administrative data source is available.

• Methodology Table M_8 This also compares FRS and administrative data, but linked at a record level, and shows receipt of DWP benefits for the 2022 to 2023 survey year across either or both of those sources. Percentages are on a grossed basis. Some benefits are better represented on the FRS than others: For example, 99% of adults in receipt of State Pension are represented on the FRS, while only 66% of those in receipt of Attendance Allowance are.

Percentage of adults shown in receipt of DWP benefits, FRS and administrative data, 2022 to 2023, Great Britain

8.3. Sampling Error

Results in this report are subject to a margin of error which can affect how changes should be interpreted, especially between groups and in the short term.

Results from surveys are estimates and not precise figures. In general terms, the greater the sample size, the smaller the uncertainty of the estimates. As noted above, this year’s larger achieved sample means that the uncertainty around 2022 to 2023 estimates is materially smaller than the previous two survey years.

All survey estimates have a sampling error attached to them, approximated from the variability of the observations in the sample. From this, a margin of error (confidence interval) is estimated, which indicate the likely range of results that would appear if the same survey was conducted with a different sample with the same characteristics as the current sample.

It is this confidence interval, rather than the estimate itself, that is used to make statements about the likely ‘true’ value in the population; specifically, to state the probability that the true value will be found between the upper and lower limits of the confidence interval. In general, a 95% confidence interval is calculated within which there is a 95% chance that the true value of the population is found. A small margin of error will result in a narrow interval, and a more precise estimate of where the true value lies.

The FRS sample in Great Britain, as described earlier, is selected using a stratified design, based on addresses clustered within postcode sectors. As a result, FRS sampling error is not just dependent on the variability between sample units (households, benefit units, individuals), but also on the variability between postcode sectors.

For example, if a sample characteristic is distributed differently by postcode sector (i.e. is clustered) the representativeness of a given sample is reduced, and the variability of repeated samples would be greater overall than would occur in a simple random sample of the same size. Therefore, the mathematical accounting for clustering causes the (actual) sampling error to be greater than a sampling error calculated under the assumption of simple random sampling.

Stratification attempts to account pre-emptively for some of the variation between clusters using information at a cluster level known prior to the survey, and so its effect is to reduce the sampling error, relative to what it would otherwise have been. Clustering is not used in Northern Ireland, but households are contacted using systematic random sampling, which has a similar effect to stratification.

Following the survey fieldwork, far more is known about those sampled. For certain characteristics, this information can be compared to known population totals and households weighted, to ensure proportionate representation of those otherwise disproportionately sampled (see the Grossing Information in Section 6.2 ). This can be thought of as increasing the representativeness of the sample, or reducing the variability that would be observed if making repeated samples. The effect is therefore similar to stratification and reduces the sampling error. For this reason, this process is also called post-stratification.

Standard errors

Standard errors and confidence intervals vary from survey estimate to survey estimate. Standard errors for a selection of survey estimates are set out below. A new standard error methodology was introduced from the 2021 to 2022 publication. Standard errors, design factors, and confidence intervals on estimates are now calculated using a bootstrap resampling method that accounts for the complex survey design and post-stratification weighting as fully as possible.

A perfect method for calculating variability in survey estimates would be to have performed the survey many times, independently, and measure the range of estimates observed from so doing. As the survey fieldwork has been performed only once, the real-world method of estimating uncertainty is as follows:

• uncertainty is approximated by treating the achieved sample as if it were the population and repeatedly drawing sub-samples at random from that achieved sample
• this process is referred to as ‘resampling’ and the result is a series of ‘resamples’. Resamples are drawn in a way to mimic the original sampling methodology and replicate its effects on the reliability of any results. This means that stratification and clustering information and systematic random sampling processes are used to replicate the original FRS household selection process
• households can be selected multiple times, and the unequal probability of selection of households is accounted for by using a grossing factor. Each FRS resample is smaller than the original sample size by about half. Once households are selected, each FRS household is assigned a grossing factor in a process identical to the full sample. Altogether, this produces a series of alternative samples from which to calculate a series of alternative estimates

The variability in these alternative estimates is used to quantify the uncertainty in the original estimate in two ways:

• the standard deviation of these alternative estimates is the approximate standard error of the original estimate
• the series of estimates produced are ranked by size, and the 2.5th and 97.5th percentiles extracted. These define the lower and upper confidence limits of the approximate 95% confidence interval around the original estimate

The size of the actual standard error relative to the standard error calculated under the assumption of simple random sampling is represented by the design factor, which is calculated as the ratio of the two. Where the standard errors are the same, the design factor equals one, implying that there is no loss of precision associated with the use of a complex sample design with post-stratification. Conversely a design factor of less than one implies the FRS estimate is more precise than would be obtained from a simple random sample. In many cases, the design factor will be greater than one, implying that FRS estimates are less precise than those of a simple random sample of the same size.

Excel and ODS files are published for Methodology and Standard Error Data Tables, alongside the main publication tables. Methodology Tables SE_1 to SE_9 provide standard errors, design factors and confidence limits for a selection of variables from this year’s survey. An example of how to interpret figures in this table follows:

Example: Uncertainty measures for household composition, table SE_1

Table SE_1 shows that 73% of households did not contain any children.

The standard error is estimated as 0.1 percentage points. This is the final estimate after rounding.

The design factor for this variable is 0.3. That is, the effect of using a complex survey design and post-stratification, rather than a simple random sample, is a reduction in uncertainty of 70%, when using standard error as the measure of uncertainty. In contrast, a design factor of 1.5 would have denoted an increase in such uncertainty of 50%. Among smaller groups, such larger design factors are not uncommon.

The 95% confidence interval is given as 72.4% to 72.7%. That is, if sampling error is the sole source of error, there is a 95% chance that the true percentage of households without children lies within this range.

See the methodology paper for information on estimating variance and confidence intervals in special circumstances e.g. where the occurrences of a response in the sample are very small.

8.4. Non-sampling error

Non-sampling errors are systematic inaccuracies in the sample when compared with the population. Non-sampling errors arise from the introduction of some systematic bias in the sample compared with the population it is supposed to represent.

As well as response bias, such biases include inappropriate definition of the population; misleading questions; data input errors; data handling problems; or any other factor that might lead to the survey results systematically misrepresenting the population. There is no simple control or measurement for such non-sampling errors, although the risk can be minimised through careful application of the appropriate survey techniques from the questionnaire and sample design stages through to analysis of results.

Non-sampling error is minimised in the FRS through effective and accurate sample and questionnaire design, active fieldwork management, the use of skilled and experienced interviewers and extensive quality assurance of the data.

Interviewers new to the FRS are briefed on the questionnaire and an annual re-briefing is given to all interviewers on changes to the questionnaire. Interviewers who have worked on the survey for some time also complete a written field report each year, describing their experiences with specific parts of the questionnaire and commenting on how changes were received in the field.

However, it is not possible to eliminate non-sampling error completely, nor can it be easily quantified.

9. Coherence and comparability

DWP considers that all Family Resources Survey statistics in this publication are “Fully Comparable (there is agreement between producers that the headline statistic is comparable between nations for all major known uses of it), at level A” of the UK Countries Comparability Scale across countries.

The Government Statistical Service (GSS) harmonised principles are guidance on how to make statistics more comparable by encouraging producers to use the same methods of data collection and presentation.

They are used across the FRS for a number of topics. The harmonised principles contain harmonised definitions, survey questions, standards for administrative data and standards for presentation. They have been developed by topic groups, after wide consultation with producers and customers across the GSS and beyond.

Further information on Harmonised Principles is available via the Government Statistical Service.

9.1. All chapters: Adjusting for inflation

Some figures in the main FRS report and the accompanying tables combine several years of income data. In these circumstances, uprating factors are used to adjust for inflation by bringing values from previous years into current price terms.

Since the 2014 to 2015 FRS, the Consumer Price Index (CPI) has been used to adjust for inflation. More information concerning this methodological change was published as a statistical notice

9.2. Income and state support

All income figures are presented gross of tax, national insurance and before other deductions from wages except where noted.

It is thought that household surveys underestimate income from both self employment and investment income. We rely on respondent recall of very detailed financial information across a comprehensive range of income sources. Some of these are hard for respondents to recall. The FRS interviewers ask respondents to check pay slips, tax returns and other financial paperwork at the time of the interview. This helps to improve the reliability of what respondents report they earn.

Income from dividends have, as last year, been included in the sources of income. For a small group of respondents there has been an adjustment to the treatment of dividends: in cases where respondents are all of (i) self-employed, and (ii) state they are directors, and (iii) where their calculated income rests on profits from annual accounts, as opposed to the other figures reported; then it is assumed that the profit figure is already inclusive of any dividend also reported.

The FRS captures detailed information on benefit receipt. In most cases this is analysed at a benefit unit (family) level because income-related benefits are paid to families rather than being separately assessed for each individual. Some respondents do not know or do not have the necessary information to answer specific questions about individual benefits which makes it difficult to collect accurate information: see State Benefits on the Family Resources Survey (WP115).

Relative to administrative records, the FRS under-reports numbers on benefit (caseload). See Methodology Tables M_6a and M_6b for a comparison of (i) numbers on benefit (caseload) and (ii) the average £ per week received, showing any differences between DWP administrative data and the numbers implied by the survey results. However, one of the strengths of the FRS is that it collects many personal and family characteristics which are not available from administrative sources. This means that the FRS can be used to analyse income and benefit receipt in ways which are not possible from administrative sources alone.

Alternative Data Sources

A Guide to Sources of Data on Earnings and Income

The Income and Earnings Interactive Tool where you can filter by government department and country of interest to find relevant statistics.

The Effects of Taxes and Benefits on Households

Family spending in the UK: April 2021 to March 2022

Cost of living insights tool

Cost of Living Payment 2022 to 2023 management information

Wealth and Assets Survey

Income, spending and wealth: how do you compare? – joined-up data from the Wealth and Assets Survey (WAS) and the Living Costs and Food Survey (LCF) providing insight into the financial vulnerability of different households.

Annual Survey of Hours and Earnings

Labour Force Survey

Stat-Xplore for statistics on benefits, Households Below Average Income, and Pensioners’ Income

Income Dynamics: Income movements and persistence of low incomes.

ONS: explanation of incomes and earnings

Changing trends and recent shortages in the labour market, UK: 2016 to 2021

9.3. Tenure

As presented in the FRS, the “social renting sector” is a combination of the categories “Rented from Council” and “Rented from a Housing Association”. These categories are combined because some housing association tenants may misreport that they are council tenants. For instance, where their home used to be owned by the council and although ownership has now transferred to a housing association, the tenant may still think that their landlord is the council (local authority).

FRS outcomes are similar in composition to the English Housing Survey (EHS).

Alternative Data Sources

Private Landlords Survey English Private Landlords Survey

Index of Private Housing Rental Prices

Housing affordability in England and Wales: 2022

Rent affordability: Literature and evidence review: 2019

Landing page of the GSS housing and planning statistics interactive tools

9.4. Disability

The FRS does not record information on individuals in nursing or retirement homes. This means that figures relating to people in these groups may not be representative of the UK population, as many elderly people may have moved into homes where they can receive more frequent help. Therefore, it is likely that disability figures and impairments among all older people are higher than estimated from the FRS.

The way in which disabled people have been identified in the FRS has changed over time. From FYE 2003 statistics were based on responses to questions about barriers across several areas of life; figures from FYE 2005 to FYE 2012 are based on those reporting barriers across nine areas of life.

From the 2012 to 2013 survey year, a person is considered to have a disability if they regard themselves as having a long-standing illness, disability or impairment which causes substantial difficulty with day-to-day activities. This updated definition is consistent with the core definition of disability under the Equality Act 2010, and complies with harmonised standards for social surveys published in August 2011

An impairment is different to a medical condition. It looks at the functions that a person either cannot perform or has difficulty performing because of their health condition. For example, glaucoma is a medical condition but being unable to see or being partially sighted is an impairment.

Some people classified as disabled and having rights under the Equality Act 2010 are not captured by this definition, such as people with a long-standing illness or disability, which is not currently affecting their day-to-day activities.

Figures in tables 4.1 to 4.6 are consistent with the Employment of Disabled People 2023 publication. Employment figures in table 4.7 are close to the Labour Force Survey, but the Labour Force Survey remains the preferred source of data on economic inactivity by reason.

Alternative Data Sources

Life Opportunities Survey

Outcomes for disabled people in the UK 2021

The employment of disabled people 2023

Labour market data for protected groups in Wales and the UK, April 2004 to March 2021

Labour Market Statistics for Scotland by Disability: January to December 2022

Employment Gap in Northern Ireland 2020

Revisions to medical condition ICD high level grouping codes for ESA and IB/SDA

Economic inactivity data from the labour force survey

9.5. Care

FRS respondents are asked if they receive care from anyone. This includes both professional help – paid-for care from the local authority, health professionals or domestic staff – but it also includes informal care. This is any care where their carer is not doing it as a paid job; it can be for many, or only a few hours a week, and can take several different forms. The survey is intentionally not prescriptive about what counts as care; it could, for example, include going shopping for someone, or helping them with paperwork.

The FRS does not record information on individuals in nursing or retirement homes. This means that figures relating to people in these groups may not be representative of the UK population, as many elderly people may have moved into homes where they can receive more frequent help. Therefore, it is likely that care provision and receipt amongst older people is higher than estimated from the FRS.

Where a care recipient is receiving care at least once a week, further questions are asked to determine the care provider and the length of time spent providing care by each carer.

FRS respondents are also asked if they provide care to someone else on an informal basis. That person could be living with them, in their household, or they could live somewhere else (outside the household).

Alternative Data Sources

Census 2021 (Unpaid Care) dataset: Provision of unpaid care - Office for National Statistics

Census 2021: Unpaid care by age, sex and deprivation, England and Wales

Health, Disability and Unpaid Care, Census 2021 in England and Wales

The geographic divide in general health, disability and unpaid care: Census 2021

Personal Social Services Adult Social Care Survey Series (England)

UK adult social care statistics

Health Survey for England Series

Scottish Health Survey Series

National Survey for Wales Series

Health Survey Northern Ireland Series

English Longitudinal Study of Ageing Wave 9, 2002-2019

ELSA Wave 10 (2021-23) has now been released and is available to use

Understanding Society data access

Sandwich Carers dataset

9.6. Pension participation

The FRS pension participation data tables present estimates for both ‘all adults’ and ‘working-age adults only’. Those over State Pension age are often excluded from analysis of pension participation in other publications, although they could continue to work and participate in pension schemes. The ‘all adults’ category allows data for this group to be represented and provides continuity across all chapters within the FRS.

The data presented does not separate by eligibility for automatic enrolment. Therefore, the categories shown (i.e. employees) will include both eligible and non-eligible individuals for automatic enrolment.

Individuals who are not eligible are often still automatically enrolled by their employers, and those earning between the LEL and the £10,000 trigger are still due employer contributions to their pensions if they choose to opt in themselves.

Employer-sponsored pensions comprise any company or occupational pension scheme run by an employer, including group personal pensions and group stakeholder pensions.

Individual pensions include individual stakeholder pensions and retirement annuity contracts as well as personal pensions.

Although the numbers are relatively small, self-employed people can contribute to an employer-sponsored pension scheme, for a variety of reasons. Doctors and dentists in private practice can be members of an occupational scheme. People who have recently become self-employed can continue to contribute to their previous employer scheme and people whose main job is self-employed, may work part-time as an employee and contribute to an employer scheme. These circumstances are captured within the FRS tables under the ‘Self-Employed – Other’ category.

Alternative Data Sources

Occupational Pension Schemes Survey

Note that the collection and publication of the annual Occupational Pension Schemes Survey (OPSS) has ceased. A quarterly publication has superseded this.

Employers’ Pension Provision Survey

The Pensions Regulator – DC Trust: a presentation of scheme return data

The Pensions Regulator

HMRC Pensions Tables

English Longitudinal Study of Ageing Wave 9: 2002-2019

ELSA Wave 10 (2021-23) has now been released and is available to use

Annual Survey of Hours and Earnings (pension tables)

9.7. Savings and investment

The FRS captures information on liquid financial assets, referred to in the survey as ‘savings and investments’. Estimates for savings and investments should be treated with caution, as they are likely to be under-estimates, since respondents often inaccurately report their account details.

New categories have been applied for total savings bands

Tables 7.9, 7.10, 7.11 and 7.12 have been published, as two variations. Those with suffix ‘a’ show the original savings bands, while those with suffix ‘b’ show the revised bands; to allow consistency for users.

TOTSAV	Estimated value of accounts and investments
1	Less than £100
2	From £100 up to £1,500
3	From £1,500 up to £3,000
4	From £3,000 up to £6,000
5	From £6,000 up to £16,000
6	From £16,000 up to £30,000
7	From £30,000 up to £50,000
8	From £50,000 up to £200,000
9	From £200,000 to £500,000
10	Over £500,000
11	Does not wish to say

The bandings prior to 2022 to 2023 were:

TOTSAV	Estimated value of accounts and investments
1	Less than £1,500
2	From 1,500 up to 3,000
3	From 3,000 up to 8,000
4	From 8,000 up to 20,000
5	From 20,000 up to 25,000
6	From 25,000 up to 30,000
7	From 30,000 up to 35,000
8	From 35,000 up to 40,000
9	Over 40,000
10	Does not wish to say

The process of gathering information on savings and investments maintained the methodology used since 2020 to 2021:

• respondents who have at least one account are asked, as a benefit unit, to say which of several £ bands their total level of savings and investments are in

• benefit units that report between £1,500 and less than £30,000) (35% of applicable benefit units) are then asked, for each of their accounts and assets, how much each is worth and how much interest they accrue. The total level of savings and investments is then calculated using this set of reported values

• benefit units with reported savings and investments outside those limits – below £1,500 or above £30,000 (65% of applicable benefit units) – are only asked how much interest each account and asset accrue. These respondents are also asked to estimate the value of all their current accounts and basic bank accounts combined.

Alternative Data Sources

Annual savings statistics 2023: HMRC statistics on Individual Savings Accounts, Child Trust Funds and Help to Save accounts

Wealth and Assets Survey

9.8. Self employment

The FRS asks a detailed set of questions to capture earnings from self employment, as described in the Glossary, at the end of this document.

The FRS does not fully capture information on all types of income-in-kind accurately – for example, benefits of vehicles, computers and mobile phones purchased by the business – that are also for personal use. These benefits are likely to be more important for the self-employed than for employees. Therefore, the FRS earnings measures are likely to underestimate the true monetary and other benefits of self employment. However, it is very difficult to quantify this.

Other benefits of self employment compared to employment are not captured, such as flexibility in working patterns, independence and flexibility in the way money is drawn from the business. The complexity of self employment circumstances, with irregular income and benefits-in-kind coming from a range of sources, could also contribute to inaccuracy of information capture.

For self-employed individuals, net income figures are presented after any deductions which include, but are not limited to tax, national insurance and pension contributions. Where gross income figures are presented, these include all these elements.

One of the significant advantages of the FRS is that it has captured self employment in a consistent way over time. Therefore, the trends in self employment compared to employment are likely to be reasonably accurate.

Alternative Data Sources

Trends in self employment in the UK

Understanding changes in self-employment in the UK

9.9. Household food security and food bank usage

Since the introduction of questions on household food security in the 2019 to 2020 survey year the FRS continues to provide evidence on this subject. From April 2021, the FRS asked additional questions on food bank usage. Food banks can provide support other than food, such as financial advice or mental health support, but the FRS records “usage” as visits to a food bank for the purpose of obtaining emergency food supplies only.

Questions covering household food security and food bank usage are asked of the person in the household who knows the most about buying and preparing food. In common with the rest of the FRS, household food security questions focus on the period immediately before the interview (30 days).

For household food bank usage, questions are asked about two separate time periods: a lead-in question asks about usage within the 12 months prior to interview; and then households that report using a food bank in the last 12 months are asked about usage within the 30 days prior to the interview. The questions do not directly ask about the food bank usage needs of children, and it cannot be determined which individual or individuals the food parcels are for.

Caution is needed when comparing household food security status with 12-month food bank usage. The effect of household food security upon food bank usage, cannot be fully deduced because the former only asks about the household’s circumstances in the last 30 days.

There is no standard approach to either household food security or food bank usage. The questions used by the FRS are like those used by other public bodies in the UK, and also internationally, but there are some differences in their application.

These statistics should be treated with caution when interpreting them:

• where a household is food insecure, information about the individual experiences of food insecurity within the household is not available. A young child’s experience in a food insecure household may be very different from their parent’s, for example

• household food security statistics do not directly measure hunger. They instead explore the financial situation of households and how that affects their access to food. Only households with very low food security would anticipate substantive disruption to their food intake

• the statistics presented exclude shared households, such as a house shared by a group of professionals

With both measures of household food bank usage captured in the FRS, caution should be taken when interpreting figures and comparing with other sources. Most other sources capture usage from a single period, so may only be comparable to one FRS measure, if at all. It should also be noted that some sources measure individual food bank usage rather than household.

For 2022 to 2023, an update was made to the Rural-Urban classifications used to produce table 9_21

The new classifications used are based on the Census 2011 data. Previously, classifications based on the Census 2001 data were used. This change affected the data for Scotland and, England and Wales.

To maintain continuity and allow comparison with the previous survey year, the new classifications were applied to the 2021 to 2022 FRS dataset and results have been presented alongside the current survey year at table 9_22. To increase harmonisation with other data sources, the categories used to present the data for England and Wales have also been changed.

For further information see the Glossary (Section 12) and the relevant publication tables.

Alternative Data Sources

Food Standards Agency

Food and You 2 – Wave 6

Food and You 2 – Wave 5

ONS Winter Survey, February 2023

ONS Winter Survey, January 2024

ONS Opinions and Lifestyle Survey

Public opinions and social trends, Great Britain

9.10. Childcare

Questions on childcare have been present and available on the underlying FRS dataset for over ten years. Due to the increased interest in the topic, data tables have been added to the main annual publication. A selection of childcare options have also been added to the FRS section of Stat-Xplore, enabling users to create their own tables of statistics.

The development of these tables has taken coherence into account, which is the approach to categorisation and naming in regard to other data sources which report on childcare. This includes the approach to banding childcare hours used and grouping of childrens’ ages. Furthermore, breakdowns by age include information for children aged from 0 to 16, because this is the range of ages covered by most policy areas relating to childcare costs, including eligibility on Universal Credit for childcare costs.

The scope of the data is all families with children, as the FRS questionnaire asks all if they use childcare. If they do, data on the type of childcare, its cost and number of hours is then captured. Then, within the FRS dataset, one record is created per child, per childcare provider used for that child. This means that a child can have more than one record on the FRS dataset, if (for example) the family uses a breakfast club and a childminder, then two detailed records will be created. Other types of childcare surveyed include after-school clubs and holiday clubs, and the FRS can also record whether a nanny or au pair is employed by the family. Overall, eighteen different types of childcare are listed; these have been categorised into two high-level categories (“formal” and “informal”), with further aggregated sub-groups.

As these are new tables of statistics, users are advised to familiarise themselves with all accompanying definitions in the Glossary (Section 12) and the Notes tab of published tables, to prevent any potential misinterpretation. The Notes tab included in the data tables also has some example interpretations for the breakdowns.

Alternative Data Sources

DWP Universal Credit statistics (childcare)

Department for Education statistics on childcare (early years):

Childcare and early years survey of parents

Childcare and early years provider survey

Coram Family and Childcare:

Coram Childcare Survey

10. Data Revision

Planned

Revisions or changes planned for the next or subsequent year are announced and described in the Release Strategy. Users are invited to consult with us regarding the likely impact of these changes. Please see the DWP Statistical Work Programme for more details.

Unplanned

Any unplanned changes are notified to users via our Collections page updates, when the date of the publication is announced on Gov.UK. If any further changes occur within this 4-week window these are detailed within an update to the Collections Page on the day of publication and further detail provided in this Background, Information and Methodology report.

11. Trade-offs between output quality components

The FRS is produced under the Code of Practice for Statistics . Compliance with the Code gives users confidence that published government statistics have public value, are high quality, and are produced by people and organisations that are trustworthy.

Producers have to strike a balance between delivering quality and public value. Quality means that statistics fit their intended uses, are based on appropriate data and methods, and are not materially misleading. Value means that the statistics and data are useful, easy to access, remain relevant, and support understanding of important issues.

The United Nations’ Canberra Group Handbook on Household Income Statistics states that income statistics are “inevitably some of the most complex statistics produced by national and international organisations.” This is reflected by the extensive production and validation procedures described above. These procedures have been developed for three main reasons:

• statistics derived from the FRS are amongst the most high-profile produced by DWP and across government. The Households Below Average Income (HBAI) publication, based on FRS data, meets DWP’s statutory obligation to publish a measure of relative and absolute low income, and combined low income and material deprivation for children under section 4 of the Welfare Reform and Work Act 2016. The FRS dataset is used extensively for policy development and costings across a range of departments and the data is also used for answering Parliamentary Questions and other queries.
• because of the complexity of the analysis and the range of income sources and other variables used in the calculations, issues have arisen in some previous publications. The 2005 to 2006 HBAI publication was re-issued due to problems with the grossing factors being applied to the dataset. The role that the Institute for Fiscal Studies now has in signing off the HBAI dataset and results originally arose in part due to quality concerns with results.
• the datasets are used for a wide range of analyses beyond the published tables, and small groups of cases could affect these results. This requires a thorough examination of case-specific information. For many users, it is the dataset that is more important than the statistical releases themselves.

Timeliness of the FRS has improved since 2017 with the move to a March publication window. Beforehand, the FRS was regularly published in June. This 12-month gap between the close of fieldwork and publication of results is comparable to or shorter than some other similar household surveys.

The time taken stems from the need to validate the whole dataset. Given the large number of variables used in the derivation of the HBAI and PI datasets, so it is not possible simply to isolate the key variables to release headline results earlier. The length of time invested in production is a key mitigation to the risk that the eventual statistics and dataset are inaccurate, in their portrayal of UK household incomes.

The use of the FRS dataset for policy modelling places a premium on accuracy, in that an inaccurate dataset could lead to policy costs or benefits being incorrectly assessed, and/or a suboptimal choice of policy option. It is therefore DWP’s view that the processes set out above are needed under the current survey model.

One of the potential future benefits of the FRS Administrative Data Transformation Project is an improvement in timeliness (see Technical Report for more detail on this development).

12. Glossary

This glossary provides a brief explanation for each of the key terms used in the FRS. Further details on these definitions, including full derivations of variables, are available on request from the FRS team at: team.frs@dwp.gov.uk

Adult

All individuals who are aged 16 and over are classified as an adult, unless the individual is defined as a dependent child. All adults in the household are interviewed as part of the FRS.

Age

Respondent’s age at last birthday (at the time of the interview).

Automatic enrolment

Automatic enrolment requires all employers to enrol their eligible workers into a workplace pension scheme if they are not already in one. This enrolment also commits the employer to make contributions into the employee’s pension. The staged timetable began in October 2012 for larger firms, with enrolment for all employers completed in 2019. To preserve individual responsibility for the decision to save, workers can opt out of the scheme. To be eligible for automatic enrolment, the jobholder must be at least 22 years old, under State Pension age, earn above the earnings threshold for automatic enrolment, and work or usually work in the UK.

However, those not eligible for automatic enrolment may be entitled to opt in. People currently defined as self-employed could have been a member of an employer scheme from past auto-enrolment, and are entitled to remain in their auto-enrolled scheme and make their own contributions. Likewise, someone who is now an employee, who was previously self-employed can have employer contributions to their previous scheme.

For more information see this pensions guide.

Benefit unit or family

A benefit unit may consist of: a single adult, or a married or cohabiting couple, plus any dependent children. Same-sex partners (civil partners and cohabitees) have been included in the same benefit unit since January 2006. Where a total for a benefit unit is presented (such as total benefit unit income) this includes income from adults plus any income from children.

There are various types of benefit unit:

• Pensioner couple: Benefit units headed by a couple where the head of the benefit unit is over State Pension age

Note that this differs from definitions used in the Households Below Average Income, Income Dynamics and Pensioners’ Incomes Statistics reports. These publications define a benefit unit as a pensioner couple if either the head of the benefit unit or their partner is over State Pension age.

• pensioner couple, married or civil partnered: Benefit units headed by a couple where the head of the benefit unit is over State Pension age and the couple are either married or in a civil partnership
• pensioner couple, cohabiting: Benefit units headed by a couple where the head of the benefit unit is over State Pension age, and the couple are neither married nor in a civil partnership
• single male pensioner: Benefit units headed by a single male adult over State Pension age
• single female pensioner: Benefit units headed by a single female adult over State Pension age
• couple with children: Benefit units containing two adults, headed by a non-pensioner, with dependent children
• couple with children, married or civil partnered: Benefit units containing two adults, headed by a non-pensioner, with dependent children and the couple are either married or in a civil partnership
• couple with children, cohabiting: Benefit units containing two adults, headed by a non-pensioner, with dependent children and the couple are neither married nor in a civil partnership
• couple without children: Benefit units containing two adults, headed by a non-pensioner, with no dependent children
• couple without children, married or civil partnered: Benefit units containing two adults, headed by a non-pensioner, with no dependent children and the couple are either married or in a civil partnership
• couple without children, cohabiting: Benefit units containing two adults, headed by a non-pensioner, with no dependent children and the couple are neither married nor in a civil partnership
• single with children: Benefit units containing a single adult, headed by a non-pensioner, with dependent children
• single male without children: Benefit units containing a single male adult, headed by a non-pensioner, with no dependent children
• single female without children: Benefit units containing a single female adult, headed by a non-pensioner, with no dependent children

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. For more information see Devolved Benefits below.

Child Benefit is administered by HM Revenue and Customs. HMRC also administer Tax Credits. These are not treated as benefits, but both Tax Credits and benefits are included in the term State Support. Tax Credits will ultimately be superseded by Universal Credit.

Benefits are often divided into income-related benefits and non-income-related benefits. In assessing entitlement to the former, the claimant’s income and savings will be checked against the rules of the benefit. In contrast, eligibility for non-income-related benefits is dependent on the claimant’s circumstances (a recent bereavement, for example), rather than their income and savings.

‘Disability-related benefits’ is the term used to describe all benefits paid on grounds of disability. These are: Personal Independence Payment, Disability Living Allowance, Severe Disablement Allowance, Attendance Allowance, the Armed Forces Compensation Scheme, Industrial Injuries Disablement Benefit, Northern Ireland Disability Rate Rebate, Adult Disability Payment and Child Disability Payment.

Some benefits have sample sizes which are too small to be presented separately in this publication. A list of the main state benefits shown in “state support” tables in this publication can be found in the table below:

Benefits

Income-related benefits	Non-income-related benefits
Council Tax Reduction	Attendance Allowance
Employment and Support Allowance (income-related element)	Carer’s Allowance
Housing Benefit	Child Benefit
Income Support	Disability Living Allowance (both mobility and care components)
Pension Credit	Employment and Support Allowance (contributory element)
Universal Credit	Personal Independence Payment (Daily Living and Mobility components)
–	State Pension

Child

A dependent child is defined as an individual aged under 16. A person will also be defined as a child if they are 16 to 19 years old and they are:

• not married nor in a civil partnership nor living with a partner; and
• living with parents (or a responsible adult); and
• in full-time non-advanced education or in unwaged government training.

Child Benefit

This is a non-income-related benefit in terms of eligibility (but remains taxable in households where one adult is earning more than £50,000 per year).

Childcare

‘Childcare’ is the care that is provided outside of educational hours for children under 16. In the FRS, questions are asked of people who have children in their benefit unit. Questions relate to the family’s use of childcare in the 7 days immediately before the interview. Respondents are asked about the circumstances of childcare use, for each child one at a time, collecting information about:

• the type of childcare provider used
• the number of hours of childcare (paid and unpaid)
• whether the childcare costs anything (if yes, how much?)
• how frequently the cost of childcare was paid
• whether the childcare is registered, approved, employer provided or not

Childcare provider types are not mutually exclusive, as one child can use multiple different types of childcare. There are several categories:

Formal

• playgroup or pre-school: nursery education often run by a community/voluntary group, parents themselves, or privately. Fees are usually charged, with sessions of up to 4 hours. Pre-school is used to describe a type of playgroup
• day nursery or crèche: nursery education running for the whole working day but may be closed for a few weeks in the summer (if at all). They may be run by employers as a workplace crèche, private companies, community/voluntary groups or the Local Authority, and can take children who are a few months to 5-years-old
• nursery school: a school in its own right, with most children aged 3 to 5. Sessions normally run for 2 to 3 hours in the morning and/or afternoon
• nursery class: a class attached to a primary or infants’ school but is often a separate unit within the school, with those in the nursery class aged 3 or 4. Sessions normally run for 2 to 3 hours in the morning and/or afternoon
• reception class (at Primary/Infants school): a class that children go into when they first start school, with those in the reception class aged 4 or 5
• breakfast club: a club providing breakfast and activities for 4 to 16-year-olds before the start of the school day. They are usually, but not always, run by and physically located in schools. Fees may be charged
• after school club/activities: After school clubs offer a variety of activities for 4 to 16-year-olds including arts and crafts, sports or games. May include homework clubs. The clubs are held in a variety of venues including schools and community centres or halls. They can serve several schools in the same area and are open from the end of the school day until 6pm. Fees are usually charged
• holiday scheme/club: These offer school-holiday childcare for 4 to 16-year-olds, via a variety of activities including arts and crafts, sports, games and outings. Meals may be provided and fees are usually charged
• special day school/nursery unit for children with special educational needs: a nursery, school or unit for children with special educational needs. This does not include regular school
• childminder: someone who uses their own home to look after others’ children
• nanny or au pair: a nanny is a paid employee and professional caregiver, who looks after a child in its own home. An au pair is usually on a form of exchange scheme (from another country), and helps with childcare on a casual basis, usually in exchange for food, accommodation and pocket money
• other formal: any other form of childcare provided by an official institution

Informal

• family members: a relative of the child being cared for who is not one of their primary caregivers. There are several sub-categories:
  • grandparents
  • non-resident parent/ex-spouse/ex-partner
  • child’s brother or sister
  • other relatives
• non-relatives: someone who is not related to the child:
  • friends or neighbours
  • other non-relatives (includes babysitters)

Council Tax

The tax is based on a set of bands that a property’s value falls into and is evaluated accordingly by each local or unitary authority. Its headline rate is based on two adults per household.

Devolved Benefits

The Scotland Act 2016 gave Scottish Parliament powers over a number of social security benefits, transferring ownership from DWP to the Scottish Government. In September 2018, Carer’s Allowance became the first of these benefits to have policy ownership transferred from DWP to Scottish Ministers. On 1 April 2020, executive competence transferred to the Scottish Government for all remaining disability and industrial injuries benefits due to be devolved. Further details of these are given below: Social Security Scotland - Benefits

Carer Benefits

• Carer’s Allowance Supplement – an automatic payment made twice a year to people who get Carer’s Allowance through DWP
• Young Carer Grant - an annual payment for 16, 17 or 18-year-olds caring for people who get a disability benefit for an average of 16 hours a week or more

Disability Benefits

• Adult Disability Payment - extra money to help people who have a long-term illness or a disability that affects their everyday life. It replaces Personal Independence Payment people in Scotland, as delivered by DWP
• Child Disability Payment - extra money to help with the costs of caring for a child with a disability or ill-health condition. From 2021 to 2022 it began to replace Disability Living Allowance for children in Scotland, as delivered by DWP. Many existing child DLA claims will continue until 2025

Family Payments

From October 2018, the Sure Start Maternity Grant was replaced in Scotland by the Best Start Grant, comprising:

• Best Start Grant Pregnancy and Baby Payment – one off payment from 24 weeks in pregnancy up until a baby turns 6 months for families who get certain benefits
• Best Start Grant Early Learning Payment – one off payment when a child is between two and three years and six months for families who get certain benefits
• Best Start Grant School Age Payment – one off payment when a child would normally start primary one for families who get certain benefits
• Best Start Foods – is broadly the Scottish equivalent of Healthy Start Vouchers: A pre-paid card from pregnancy up to when a child turns three for families on certain benefits to help buy healthy food
• Scottish Child Payment began in February 2021 for households with children under the age of six. A temporary bridging payment for households with school age children in receipt of free school meals, remained in place until the second phase was implemented in November 2022. This phase extended eligibility to under 16 year-olds from 14 November 2022. SCP is paid every four weeks to help towards the costs of looking after each child under 16 for families who get certain benefits

Heating Benefits

• Child Winter Heating Payment - a payment to help families of a child on the highest rate care component of DLA for Children to heat their homes
• Winter Heating Payment – an annual payment to help people on income-related benefits who might have extra heating needs during the winter

Other Benefits

• Funeral Support Payment – money towards funeral costs for people on certain benefits who are responsible for paying for it
• Job Start Payment – for 16 to 24-year-olds who have been on certain benefits for six months or more to help with the costs of starting a job

Disability

The definition of disability used in this publication is consistent with the core definition of disability under the Equality Act 2010. A person is considered to have a disability if they “have a physical or mental impairment that has a ‘substantial’ and ‘long-term’ negative effect on their ability to do normal daily activities”. Whereby ‘substantial’ means more than minor or trivial, and ‘long-term’ means 12 months or more. However, some individuals classified as disabled and having rights under the Equality Act 2010 are not captured by this definition:

• people with a long-standing illness or disability who would experience substantial difficulties without medication or treatment
• people who have been diagnosed with cancer, HIV infection or multiple sclerosis but who are not currently experiencing difficulties with their day-to-day activities
• people with progressive conditions, where the effect of the impairment does not yet impede their lives
• people who were disabled in the past but are no longer limited in their daily lives are still covered by the Equality Act 2010

This definition of disability differs from that used for Economic status.

Economic status

This classification follows the harmonised output category for economic status, based on respondents’ answers to the survey questions. All definitions conform to those of the International Labour Organization (ILO):

• employee: where respondents have an arrangement with an employer, whereby work is done in exchange for a wage or salary. This would include those doing unpaid work in a business that a relative owns.
• self-employed: where respondents report regular working activities, which over time are responsible only to themselves (and not an employer). Various groups are classified as self-employed, including farmers, doctors in private practice and some builders, as well as anyone whose job is habitually done on a freelance basis (e.g. journalists or musicians). The self-employed include anyone doing work for their own business, but which is currently unpaid.

Several respondents have more than one job. The FRS identifies which of these is their ‘main job’. This is the job which the respondent says is the dominant activity. Where they cannot decide, the number of hours worked will determine which is the main job. This process of categorisation also applies to respondents who are employees in one job but self-employed in another; whilst the survey will capture information on both jobs, only one can be their main job.

• unemployed: Adults who are under State Pension age and not working, but are available and have been actively seeking work in the last four weeks; includes those who were waiting to take up a job already obtained and were to start in the next two weeks.

• economically inactive: Individuals who are both out of work, and not seeking or not available to work. There are several sub-categories:

  • Retired: individuals who are over State Pension age, or say they are now retired
  • Student: individuals who have not completed their education
  • Looking after family or home: working-age individuals who are looking after their family or their home
  • Permanently sick or disabled: working-age individuals who have been sick, injured or disabled for longer than 28 weeks
  • Temporarily sick or disabled: working-age individuals who have been sick, injured or disabled for less than 28 weeks. Note that the sick or disabled definitions are different to that used for Disability, as they are based on different questions that are only asked of working-age adults who are not working
  • Other inactive: all respondents not already classified above

Employment status

This classification is equivalent to economic status but includes those in employment only.

Ethnic group

The ethnic group to which respondents consider that they belong. Ethnicity representation rates are now calculated from known declarations and exclude ‘choose not to declare’ and ‘unknown’.

Where respondents do volunteer their ethnicity, this is captured as one of several recognised groups. This is consistent with the harmonised principles for ethnicity, as set out by the Government Statistical Service, wherever social surveys are carried out.

For example, if the harmonised country specific ethnic group questions for England, Wales, Scotland and Northern Ireland are used to produce a UK output then the presentation of data should follow the list below.

• White
• Irish Traveller
• Mixed or Multiple ethnic groups
• Asian or Asian British
• Indian
• Pakistani
• Bangladeshi
• Chinese
• Arab
• Any other Asian background
• Black or African or Caribbean or Black British
• Other ethnic group

Variations within England, Wales, Scotland and Northern Ireland are taken account of. For example, sample sizes for ‘Gypsy, Traveller or Irish Traveller’ are small. In Northern Ireland, ‘Irish Traveller’ is included in ‘Other ethnic group’ whereas elsewhere ‘Gypsy or Irish Traveller’ is included in ‘White’.

Food bank usage

See Household food bank usage

Food security

See Household food security

Full-time education

Individuals registered as full-time at an educational establishment. Students on sandwich courses are coded as working, or studying, depending on their position at the time of interview.

Harmonised principles

The harmonised principles contain harmonised definitions, survey questions, standards for administrative data and standards for presentation. They have been developed by topic groups, after wide consultation with producers and customers across the GSS and beyond. Further information is available via the Government Statistical Service pages.

Head of benefit unit

If the household reference person does not belong to the benefit unit, then the head of benefit unit is simply the first person from that benefit unit, in the order they were named in the interview. If the household reference person does belong to the benefit unit, they are also the head of that benefit unit.

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. Where a total value for a household is presented, such as total household income, this includes income from adults plus any income from children.

Household food bank usage

Household food bank usage in the FRS refers only to visits to a food bank when emergency food supplies (food parcels) were obtained. This excludes visits to the food bank made only for other support (e.g. financial advice or mental health support). The FRS asks food bank usage questions relating to two time periods:

• usage within the 12 months prior to interview
• usage within the 30 days prior to interview

Only households that report using a food bank in the last 12 months are asked about 30-day usage.

Household food security

“Food security” as a concept is defined as “access by all people at all times to enough food for an active, healthy life”. Questions relate to the household’s experience in the 30 days immediately before the interview.

Each person in the household is asked who is best placed to answer about food shopping and preparation. That person is then asked the first three questions, on whether they are concerned about:

• food running out before they had enough money to buy more
• the food they had bought not lasting, and not having money to buy more
• not being able to afford balanced meals

The possible answers are ‘often, ‘sometimes’ or ‘never’ true. If respondents say that all three statements are never true they will not be asked further questions on food security. If respondents answer that any of these statements are sometimes or often true they will be asked further questions on the extent of their food security.

Taking the responses together, a household ‘score’ for food security is then derived. This is a measure of whether households have sufficient food to facilitate active and healthy lifestyles. This measure has four classifications:

• high food security (score=0): The household has no problem, or anxiety about, consistently accessing adequate food
• marginal food security (score= 1 or 2): The household had problems at times, or anxiety about, accessing adequate food, but the quality, variety, and quantity of their food intake were not substantially reduced
• low food security (score = 3 to 5): The household reduced the quality, variety, and desirability of their diets, but the quantity of food intake and normal eating patterns were not substantially disrupted
• very low food security (score = 6 to 10): At times during the last 30 days, eating patterns of one or more household members were disrupted and food intake reduced because the household lacked money and other resources for food

High and marginal food security households are considered to be “food secure”. Food secure households are considered to have sufficient, varied food to facilitate an active and healthy lifestyle. Conversely, low and very low food security households are considered to be “food insecure”. Food insecure households are where there is risk of, or lack of access to, sufficient, varied food.

The broad structure and sequence of the questions is the same as those used internationally. They are used within the UK (Food Standards Agency) and are also used by other countries, including the United States Department of Agriculture, enabling broad international comparability of the results.

Household reference person (HRP)

The highest income householder. This is defined by:

• in a single-adult household, the HRP is simply the sole householder (i.e. the person in whose name the accommodation is owned or rented)
• if there are two or more householders, the HRP is the householder with the highest personal income, taking all sources of income into account
• if there are two or more householders who have the same income, the HRP is the elder

Where we refer to ‘Head’ in tables relating to households, this is the HRP. The head of benefit unit will not necessarily be the HRP.

Individual

An adult or child. Where ‘people’ are presented, this is all adults and children.

Informal carers

Individuals who provide any regular service or help to someone. That person can be inside or outside of their household, and might be sick, disabled or elderly; this description excludes those who give this service or help as part of a formal job.

Marital status

This is the person’s de facto marital status:

• married or civil partnership: currently married or in a civil partnership, and not separated from spouse (excludes temporary absences)
• cohabiting: not married nor in a civil partnership, but living as a couple
• single: is not currently cohabiting and has never been married nor in a civil partnership
• widowed: widowed and not currently cohabiting
• separated: married or in a civil partnership, but separated from spouse and is not currently cohabiting
• divorced or civil partnership dissolved: marriage or civil partnership legally dissolved and is not currently cohabiting

Non-advanced education

Non-advanced education for benefits purposes includes:

• ‘A’ levels or similar qualifications (e.g. the International Baccalaureate and Pre-U)
• ‘T’ levels (introduced in September 2020)
• Scottish national qualifications at higher or advanced higher level
• NVQ at Level 3
• Study programme in England
• National diploma
• Ordinary national diploma
• National certificate of Edexcel

If the young person is studying for a course that is not classed as advanced education, the education is normally treated as non-advanced. Non-advanced education does not include university courses.

Pension

• employer-sponsored pension: schemes that are set up and run by the employer.
• occupational pension: an occupational pension scheme is an arrangement an employer makes to give their employees a pension when they retire. They are often referred to as ‘company pensions’. As of October 2017, the Occupational Pension Schemes regulations introduce restrictions on early exit charges for those aged 55 and over and who are eligible to access the pension freedoms.

There are two main types of occupational pension:

1. Defined-benefit (DB) schemes (also called salary-related pension or superannuation schemes). In a defined benefit scheme, the pension is based on the number of years you belong to the scheme and how much you earn. Your employer contributes to the scheme and trustees look after scheme members’ interests. Employees often have to pay contributions into the scheme on top of those made by the employer. Some schemes are ‘non-contributory’: The employee either makes no contributions, or makes a small contribution, typically one to two per cent of salary.

2. Defined-contribution (DC) schemes (also called Money purchase schemes). A defined contribution scheme can be a personal pension arranged by the individual or a workplace pension arranged by the employer (such as NEST). Money is paid in by the individual or the employer over time and is then invested by the pension provider. The size of the pension available to take out when the individual retires depends on how much was paid in and the level of growth from the investments. With a defined contribution pension the individual can also decide how to take their money out.

• group personal pension: some employers who do not offer an occupational pension scheme may arrange for a third-party pension provider to offer employees a pension instead. The employer may have negotiated special terms with the provider, which means that administration charges are lower than those for individual personal pensions. Although sometimes still referred to as ‘company pensions’, they are not run by employers and should not be confused with occupational pensions, which have different tax, benefit and contribution rules.
• group stakeholder pension: like Group Personal Pensions, an employer can make an arrangement with a pension provider and offer their employees a Group Stakeholder Pension (see Stakeholder Pension).
• personal pension: a pension provided through a contract between an individual and the pension provider. The pension which is produced will be based upon the level of contributions, investment returns and annuity rates; a personal pension can be either employer provided (see Group Personal Pension) or privately purchased (see Private pension).
• private pension: includes occupational pensions (also known as employer-sponsored pensions) and personal pensions (including stakeholder pensions). People can have several different private pensions at once.
• stakeholder pension: enables those without earnings, such as non-earning partners, carers, pensioners and students, to pay into a pension scheme. Almost anybody up to the age of 75 may take out a stakeholder pension and it is not necessary to make regular contributions.

For more information, see: GOV.UK pension guide.

Pension Credit

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

Poundage

The district rate set by local authorities, the eleven district councils in Northern Ireland. It is for services such as refuse collections and disposal, leisure, parks and street cleaning.

Region (also see Urban-Rural)

Regional classifications are based on the standard statistical geography of UK Regions: nine in England, and a single region for each of Wales, Scotland and Northern Ireland. Tables will also show statistics for the UK, Great Britain, and England as a whole. Some split London into Inner and Outer where there is sufficient data to provide meaningful comparisons.

• Inner London boroughs: Camden, Greenwich, Hackney, Hammersmith and Fulham, Islington, Kensington and Chelsea, Lambeth, Lewisham, Southwark, Tower Hamlets, Wandsworth, the City of Westminster; and the City of London
• Outer London boroughs: Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Croydon, Ealing, Enfield, Haringey, Harrow, Havering, Hillingdon, Hounslow, Kingston upon Thames, Merton, Newham, Redbridge, Richmond upon Thames, Sutton, Waltham Forest

Sandwich carer

A sandwich carer is defined in the FRS as somebody:

• who is aged 16 to 70; and
• cares for a child within their household and/or has a child dependent on them within their household; and
• also cares for an adult relative.

Savings

The total value of all liquid assets, including fixed-term investments. Pound amounts are informed by responses to questions on the value of assets or, in some cases, estimated from the interest on the savings. Note that banded savings do not include assets held by children in the benefit unit or household.

The FRS asks questions about all saving and investment products, including bank and building society accounts, and shares. These products go by many names. In this publication, the products are labelled as follows:

• basic bank account: This type of account is similar to a current account. Payments can be received from other sources and it can pay bills by direct debit, but unlike a current account there are no overdraft facilities. Withdrawals can be made from cash machines and, in some cases, over the counter of the bank or building society itself.
• Child Trust Funds (CTFs) have been replaced by Junior ISAs (JISAs) as the main tax-free savings account for children. See ISA.
• current account: This includes all accounts at both banks and building societies, which are used for day-to-day transactions; with a bank card. Overdraft facilities may be offered.
• company share schemes (profit sharing): Some companies provide extra rewards or bonuses to their employees depending on the profitability of the company. In publicly traded companies, this often takes the form of shares in the company. This label is given to any scheme which follows this general principle.
• credit union: A credit union is a financial co-operative similar in many respects to mainstream building societies. Its members both own and control the credit union, which is run solely for their benefit. All members of a specific credit union must share what is known as a “common bond” i.e. they must be connected in some way to the other members of that credit union. The members pool their savings into a single ‘pot’ from which loans can be made to members of the credit union. Members who have deposited money receive an annual dividend, while those to whom money is lent must pay interest on the loan.

• endowment policy (not linked): An Endowment Policy taken out to repay a mortgage but no longer used to do so. This is where the mortgage has either been paid off or, more usually, converted to a different method of repayment. The respondent has decided to retain the endowment as an investment, even though it is no longer intended to repay the mortgage.

• ISA: An Individual Savings Account (ISA) pays interest on a tax-free basis.

To be eligible for a Junior ISA, children must be under 18 and living in the UK. Junior ISAs are now included at the question ChSave. There is a limit on annual payments into JISAs.

As with Child Trust Funds, the Junior ISA is a long-term savings account which can only be accessed by the child on their 18th birthday. The Junior ISA is then transferred to an Adult ISA so that the child can access their money.

• Investment trust: See Unit trusts.
• National Savings & Investments (NS&I): All types of investments in this category are collected on the survey, including (Income) Bonds and Direct Saver.
• Premium bonds: Investments which do not earn interest but are entered in a monthly draw for tax-free cash prizes.
• Other bank or building society account: Accounts belonging to adults recorded under categories “savings account, investment account or bond, any other account with bank building society, etc.”
• Stocks and shares: This includes all shares, bonds, debentures, and other securities which are usually traded on the financial markets. A share is a single unit of ownership in a company. If respondents are members of a shares club they will be included with those owning stocks and shares. ‘Stocks’ is the general term for various types of security issued to raise financial support. Bonds issued by foreign governments, or local authorities would also be recorded here.
• Unit trusts: A collectively managed investment in the financial markets, where investors buy ‘units’ of a fund, which invests in shares, stocks, Gilts, etc. The data presented for unit trusts also includes investment trusts, since these two assets are combined in the FRS.
• Any other type of asset: This is a catch-all category for the small numbers who own other types of financial asset. This includes Gilts (HM Government bonds) which raise money for the UK Government by offering a secure investment, usually over a fixed term, and usually with a set rate of interest although some are index-linked. Interest is paid half-yearly.

The above products cover all types of savings and accounts. Some of them are grouped together in other ways in the tables, for example as a ‘direct payment account’ that can accept electronic payment of benefits via BACS (the Banker’s Automated Clearing System).

Sources of income

• wages and salaries: for a respondent currently working as an employee, income from wages and salaries is equal to: gross pay before any deductions, less any refunds of income tax, any motoring and mileage expenses, any refunds for items of household expenditure and any Statutory Sick Pay or Statutory Maternity Pay, plus bonuses received over the last 12 months (converted to a weekly amount) and any children’s earnings from part-time jobs.
• self-employed income: the total amount of income received from self employment gross of income tax and national insurance payments, based on profits (where the individual considers themselves as running a business) or on estimated drawings otherwise. Excludes any profits due to partners in the business. Any losses are recorded as such.
  • self-employed respondents are asked questions on their most recent business accounts as submitted to HMRC: dates of the accounts, profit or loss figures, and amounts paid in income tax and National Insurance Contributions.
  • they are then asked if they draw money from their business accounts for non-business purposes, such as for payments to themselves, personal spending, paying domestic bills etc. and how much this is per month on average. They are also asked if they receive other income from their business for personal use, e.g. cash in hand, and how much this is per month on average.
  • those who do not keep annual business accounts and do not draw money for non-business purposes are asked for their income after paying for items such as materials, equipment and goods, and whether they make income tax and National Insurance payments on this amount
• investments: Interest and dividends received on savings and investments. See Savings for details of investments covered by the FRS.
• Tax Credits: Income from Tax Credits
• State Pension plus any Pension Credit: for any adults who are over State Pension age, any State Pension plus any Pension Credit which is received; these benefits are shown together because of known issues with separating these amounts for pensioners.
• other pensions: payments received from pension schemes, including occupational, stakeholder or personal pension schemes; employee pensions for surviving spouses, annuity pensions, trusts and covenants.
• disability benefits: payments received from any of the benefits payable due to disability – see Benefits
• Universal Credit: See separate entry
• other benefits: payments received from any of the other Benefits
• other sources: payments from all other sources including, for example, landlord income and royalties, odd jobs, sub-tenants, baby-sitting, allowances from absent spouses including child maintenance and alimony, organisations, educational grants and Healthy Start Vouchers.

State Pension age

During the whole of the 2022 to 2023 survey year the State Pension age was 66.

Since 6 April 2010, the State Pension age had increased gradually for women and since December 2018 it increased for both men and women, reaching 66 by October 2020.

See details of further planned changes to State Pension age.

State support

An individual or family is in receipt of state support if they receive one or more benefits or are being paid Tax Credits.

Tax Credits

Working Tax Credits and Child Tax Credits are paid by HMRC. Tax Credits are being phased out, as they are replaced by Universal Credit.

Tenure

This is the basis on which the head of household is resident in their dwelling. Types of renting or ownership as classified as follows:

• social renting: includes all cases where the landlord is either the local authority, or a housing association
• private renting: all cases where the property is rented from a private landlord, including those on a rent-free basis

Rent-free accommodation is any provided free by an employer or by an organisation to a self-employed respondent, provided that the normal activities of the tenant are to further the cause of the organisation (e.g. Church of England clergy). Accommodation is not classed as rent-free if anyone, apart from an employer or organisation, is paying a rent or mortgage on a property on behalf of the respondent.

• buying with a mortgage: includes local authority and housing association part-own-and-part-rent, and shared ownership arrangements
• owned outright: households who pay neither rent, nor any mortgage or loan used to purchase the property. These households may have other loans secured on their property for which information is collected on the FRS. However, these payments are excluded from the costs of housing

Universal Credit

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 claimants will be of working-age, though claimants 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. Claimants 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 claimants will continue to transfer to UC over a number of years.

The Universal Credit Official Statistics provide the primary source of information about people and households on universal credit.

Urban-rural classifications

England and Wales

The 2011 Rural-Urban Classification for Output Areas in England and Wales

The classification defines areas as rural if they fall outside of settlements with more than a resident population of 10,000. The classification assigns them to one of 4 urban or 6 rural categories (compressed to 2 Urban and 3 Rural for use in FRS published tables):

Scotland

Scottish Government Urban Rural Classification 2020

Northern Ireland

Since the 2015 review of the ‘Statistical classification and delineation of settlements’, urban settlements in Northern Ireland are classified as those with a population greater than or equal to 5,000 people (Bands A-E).

Working

All respondents whose employment status was employed or self-employed, irrespective of full-time or part-time working patterns.

Working-age

Adults (see Adult and Child) under State Pension age.