Households below average income series: quality and methodology information report FYE 2023

Households below average income series: quality and methodology information report FYE 2023

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Introduction

The Households Below Average Income (HBAI) report presents information on living standards in the United Kingdom and is the foremost source for data and information about household income, and inequality in the UK. It provides annual estimates on the number and percentage of people living in low-income households.

HBAI statistics incorporate widely used, international standard measures of low income and inequality. They provide a range of measures of low income, income inequality, and material deprivation to capture different aspects of changes to living standards. The current series started in Financial Year Ending (FYE) 1995 and so allows for comparisons over time, as well as between different groups of the population.

The statistics are based on the Family Resources Survey (FRS), whose focus is capturing information on incomes, and as such captures more detail on different income sources compared to other household surveys. The FRS captures a lot of contextual information on the household and individual circumstances, such as employment, education level and disability. This is therefore a very comprehensive data source allowing for a lot of different analysis.

This report provides detailed information on key quality and methodological issues relating to HBAI data. Information on the FRS methodology is available in the FRS Background Information and Methodology.

Comparing official statistics across the UK

All official statistics from the HBAI for the UK and constituent countries in this publication are considered by the Department for Work and Pensions (DWP) as “Fully Comparable at level A*” of the UK Countries Comparability Scale (with the exception of measures estimated on a before housing cost (BHC) basis for Northern Ireland, due to differing treatment of water rates).

Accredited Official Statistics

These Accredited Official Statistics were independently reviewed by the Office for Statistics Regulation in November 2012. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’. Accredited official statistics are called National Statistics in the Statistics and Registration Service Act 2007.

OSR introduced the term ‘Accredited Official Statistics’ to describe National Statistics in September 2023. This was done following OSR’s review of the National Statistics designation and subsequent designation refresh project, which found the term ‘National Statistics’ was not well understood by users of statistics.

It is DWP’s responsibility to maintain compliance with the standards expected of Accredited Official Statistics. If DWP becomes concerned about whether these statistics are still meeting the appropriate standards, we will discuss any concerns with the Office for Statistics Regulation. Accredited Official Statistics status can be removed at any point when the highest standards are not maintained, and reinstated when standards are restored.

Acknowledgements

As in previous years, the DWP would like to thank the Institute for Fiscal Studies (IFS) for the substantial assistance that they have provided in checking and verifying the income data and grossing factors underlying the main results in this edition.

We are also grateful to HM Revenue and Customs (HMRC) for the provision of aggregated data from the Survey of Personal Incomes.

Users and uses

HBAI is a key source for data and information about household income and inequality and is used for the analysis of low income by researchers and the Government. Users include: policy and analytical teams within the DWP, the Devolved Administrations, other Government departments, local authorities, Parliament, academics, journalists, and the voluntary sector.

The Department for Work and Pensions’ responsibilities include understanding and dealing with the causes of poverty rather than its symptoms, encouraging people to work and making work pay, encouraging disabled people and those with ill health to work and be independent, and providing a decent income for people of pension age and promoting saving for retirement. Progress towards these responsibilities will affect these results.

The key uses of the published statistics and datasets are:

  • to provide detail on the overall household income distribution and low- income indicators for different groups in the population;

  • for international comparisons; and

  • for parliamentary, academic, voluntary sector and lobby group analysis. Examples include using the HBAI data to examine income inequality, the distributional impacts of fiscal policies and understanding the income profile of vulnerable groups.

The first three of the four income-related measures included under section 4 of the Welfare Reform and Work Act 2016.

The four measures cover the percentage of children in the United Kingdom:

a) who live in households whose equivalised net income for the relevant financial year is less than 60% of median equivalised net household income for that financial year;

b) who live in households whose equivalised net income for the relevant financial year is less than 70% of median equivalised net household income for that financial year, and who experience material deprivation;

c) who live in households whose equivalised net income for the relevant financial year is less than 60% of median equivalised net household income for the financial year beginning 1 April 2010, adjusted to take account of changes in the value of money since that financial year; and

d) who live in households whose equivalised net income has been less than 60% of median equivalised net household income in at least 3 of the last 4 survey periods.

Definitions for relevant key terms in the Act are consistent with those given in the Glossary, Income Definition, Equivalisation, and Combined Low income and Child Material Deprivation sections of this report.

Data for reporting against the fourth measure will be released via the Income Dynamics publication.

Further details of the uses of HBAI statistics are given in Annex 3.

What do you think?

We are constantly aiming to improve this report and its associated commentary. We would welcome any feedback you might have and would also be particularly interested in knowing how you make use of these data to inform your work. Please contact us via email: team.hbai@dwp.gov.uk.

New for this publication

Family Resources Survey (FRS) fieldwork during FYE 2023

In Great Britain, survey fieldwork operations used face-to-face interviewing as the preferred method of data collection for the duration of the year. Telephone interviewing was retained as an alternative based on household preference and (in the first few months) interviewer availability. In Northern Ireland, a return to prioritising face-to-face interviewing was rolled out fully by interviewers from July 2022. Across the UK, 72% of FRS households were interviewed face-to-face during FYE 2023.

This year, we have enhanced confidence in data quality due to the return of traditional fieldwork methods and the larger achieved sample size of 25,000 households, some 30% larger than was achieved in FYE 2020, and 50% higher than FYE 2022. As with other years, we have completed extensive quality assurance of all published estimates, including comparing changes with external data sources, and analysing subgroups in detail. The achieved sample compares well with FYE 2020, and representativeness has improved on what was observed during the pandemic.

We continue to advise users that changes in estimates over recent years should be interpreted being mindful of the differences in data collection approaches across the period and the effect this had on sample composition. Details of this can be found in the technical reports which were issued alongside the statistical releases covering the pandemic. In this report we continue to make assessments of observed changes in the data compared with both FYE 2022 and with pre-pandemic trends and estimates.

Annex 5 in this report provides more detail on the use of a mixed mode in the FRS in FYE 2023, how this affected the overall FRS sample, and the degree of impact this had on the HBAI statistics.

Cost of Living Support Schemes

During FYE 2023 the UK Government announced and implemented additional support to families with several cost of living support schemes, depending on people’s circumstances. These payments are included in the HBAI estimates of household income, and more information is available in the Income Definition section of this document.

Most of the schemes were introduced at pace, in a timeframe which made it difficult to adapt the FRS questionnaire to capture them. As each support scheme came with clear eligibility guidelines, receipt of the payments was imputed based on respondent characteristics. Further details on the methodology can be found in the FRS Background Information and Methodology document.

Educational Attainment

Development work to improve reporting on categories of level of education identified a separate issue with the FRS variable EDUCQUAL. This variable is used to present estimates of low income for working-age adults by their level of educational attainment. The estimates have been withdrawn from the FYE 2023 publication, affecting the following tables: 5.3db BHC, 5.3db AHC, 5.6db, and 5.9db. The breakdown has also been removed from Stat Xplore. We will provide an update on restoring the estimates when validation checks are complete.

For the survey period covering the coronavirus (COVID-19) pandemic, EDUCQUAL was used to adjust our weighting methodology to correct for the over-representation of degree educated working-age people in the FRS sample. Following the reintroduction of FRS face-to-face interviewing for FYE 2023, this additional weighting was no longer required, and the grossing has returned to the FYE 2020 position.

We are investigating how identified issues with EDUCQUAL may have affected previous releases and will update in due course.

Resumption of the material deprivation time series

The measurement of material deprivation was affected by restrictions introduced in response to the coronavirus (COVID-19) pandemic.

For FYE 2023, responses to the survey items asked as part of establishing the level of material deprivation are unaffected by the pandemic. We have resumed our material deprivation time series by comparing changes in the estimates directly with FYE 2020, when responses were last comparable.

We have chosen to display this in affected charts by presenting estimates for the pandemic period (FYE 2021 and FYE 2022) as individual data points. We advise users not to make a direct comparison of changes in material deprivation estimates with those published prior to the pandemic.

Other points of note

Following our decision to not publish breakdowns of the FYE 2021 HBAI estimates, all three-year rolling averages calculated and published for any period including FYE 2021 continue to be based on two data points only. Next year the calculation will revert to using three data points.

Using and Interpreting HBAI Results

Guide to published tables

A wide range of ODS supported tables are available alongside this release, breaking down the results presented in this report for different demographic characteristics. This includes breakdowns of the statistics by region, ethnic group, family type, and economic status. All tables can be downloaded via the HBAI homepage (see Directory of Tables link on this webpage to locate tables referenced in the following pages and to generally find the desired tables). Results are available for most series back to FYE 1995.

UK-level HBAI data is also available between FYE 1995 and FYE 2023 on the Stat-Xplore online tool. You can use Stat-Xplore to recreate measures in our static tables and also create your own bespoke HBAI analysis.

The source data behind these statistics is available for download and further analysis via the UK Data Service.

Note that unpublished FYE 2021 data is excluded from both the tables and Stat-Xplore. The HBAI dataset underpinning the headline estimates for FYE 2021 remains available for expert users and researchers in the UK Data Service, and we recommend consulting the FYE 2021 technical report for more guidance on use and interpretation of sub-national estimates.

Estimates of the change in the percentage and number that are significantly different from a previous year are shown with the notation [s]. Changes marked with an [s] are unlikely to have occurred as a result of chance. Changes that are not significant are shown with the notation [ns].

The series started in FYE 1995 and so allows for comparisons over time, as well as between different groups of the population.

What do we mean by average?

In HBAI, the term ‘average’ is used to describe the median. This divides the population of individuals, when ranked by income, into two equal-sized groups, and unlike the mean is not affected by extreme values.

HBAI measures

There are a range of measures of low income, income inequality, and material deprivation to capture different aspects of changes to living standards:

  • Relative low income measures the number and proportion of individuals who have household incomes below a certain proportion of the average in that year – and is used to look at how changes in income for the lowest income households compare to changes in incomes near the ‘average’. In the HBAI report we concentrate on those with household incomes below 60 per cent of the average. Information on those with household incomes below 50 and 70 per cent of the average is available in the detailed tables published on the HBAI homepage.

  • Absolute low income measures the proportion of individuals who have household incomes a certain proportion below the average in FYE 2011, adjusted for inflation. It is used to look at how changes in income for the lowest income households compare to changes in the cost of living. In the HBAI report we concentrate on those with household incomes below 60 per cent of the average FYE 2011 income. Information on those with household incomes below 50 and 70 per cent of the average is available in the detailed tables published on the HBAI homepage.

Rounding

Due to rounding, the estimates of change in percentages or numbers of may not equal the difference between the total percentage or number of individuals for any pair of years.

The publication and tables follow the following conventions:

[low] the estimate is less than 50,000 or the percentage is less than 0.5 per cent

[u] the estimate is not available due to small sample sizes (fewer than 100)

[x] the estimate is not available. In FYE 2021 this was due to sample quality concerns across different household sizes and compositions.

Population estimates are rounded to the nearest 0.1 million.

Percentages are rounded to the nearest 1 per cent.

Key terminology

Income

This is measured as total weekly household income from all sources after tax (including child income), national insurance and other deductions. An adjustment called ‘equivalisation’ is made to income to make it comparable across households of different size and composition.

Median

Median household income divides the population, when ranked by equivalised household income, into two equal-sized groups. The median is the value at the very middle of the distribution.

Deciles and Quintiles

These are income values which divide the whole population, when ranked by household income, into equal-sized groups. This helps to compare different groups of the population.

Decile and quintile are often used as a standard shorthand term for decile/quintile group.

Decile groups are ten equal-sized groups – the lowest decile describes individuals with incomes in the bottom 10 per cent of the income distribution.

Quintile groups are five equal-sized groups – the lowest quintile describes individuals with incomes in the bottom 20 per cent of the income distribution.

Income distribution

The spread of incomes across the population.

Equivalisation

Equivalisation adjusts incomes for household size and composition, taking an adult couple with no children as the reference point. For example, the process of equivalisation would adjust the income of a single person upwards, so their income can be compared directly to the standard of living for a couple.

Housing costs

Housing costs include rent, water rates, mortgage interest payments, buildings insurance payments and ground rent and service charges. A full list can be found in the glossary at the end of this report.

Benefit unit and households

HBAI presents information on an individual’s household income by various household and benefit unit (family) characteristics. There are important differences between households and benefit units.

Household The definition of a household used in the FRS is ‘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’. So, for example, a group of students with a shared living room would be counted as a single household even if they did not eat together, but a group of bed-sits at the same address would not be counted as a single household. A household may consist of one or more benefit units, which in turn will consist of one or more people (adults and children).

Family or Benefit Unit A family in the FRS is defined as ‘a single adult or couple living as married and any dependent children’. A dependent child is aged 16 or under, or is 16 to 19 years old, unmarried and in full-time non-advanced education. This is consistent with the DWP term “benefit unit”, which is a standard grouping used for assessing benefit entitlement.

So, for example, a husband and wife living with their young children and an elderly parent would be one household but two families or benefit units. The husband, wife and children would constitute one benefit unit and the elderly parent would constitute another.

Other terms

For more information on these and other terms used throughout the report, see the glossary at the end of this report, and the infographics explaining key terms.

Issues to consider

The following issues should be considered when using the HBAI:

  • Impact of the coronavirus (COVID-19) pandemic on FYE 2021 and FYE 2022 statistics: Fieldwork operations for the Family Resources Survey (FRS) were changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions in March 2020. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing from April 2020 for the whole of the 2020 to 2021 and 2021 to 2022 survey year. This change impacted on both the size and composition of the achieved samples for those years. The data published for FYE 2021 is limited to headline measures and not available in our supplementary tables or on our Stat-Xplore tool. It is, however, still deposited for download by users in the UK Data Service.
    We recommend caution is exercised when interpreting any data published for these survey years, particularly when making comparisons with years prior to the coronavirus (COVID-19) pandemic.
    This methodology report does not detail the effect the coronavirus (COVID-19) pandemic had on the sample data and estimates. For this information, users are advised to consult the technical reports which accompanied the FYE 2021 and FYE 2022 publications.
    Detail on the methodological changes made during the period to take account of new income sources (such as Coronavirus Job Retention Scheme) and temporary changes to the HBAI grossing regime to improve sample representativeness are provided later in this document.
  • Lowest incomes: Comparisons of household income and expenditure suggest that those households reporting the lowest incomes may not have the lowest living standards. The bottom 10 per cent of the income distribution should not, therefore, be interpreted as having the bottom 10 per cent of living standards. Results for the bottom 10 per cent are also particularly vulnerable to sampling errors and income measurement problems. For HBAI tables, this will have a relatively greater effect on results where incomes are compared against low thresholds of median income. For this reason, compositional and percentage tables using the 50 per cent of median thresholds have been italicised to highlight the greater uncertainty. We have also presented money value quintile medians in Table 2.3ts on three-year averages to reflect this uncertainty (any period including FYE 2021 is based on two data points).
  • Adjustment for inflation: As advised in a Statistical Notice published in May 2016, from FYE 2015 HBAI made a methodological change to use variants of CPI when adjusting for inflation. Prior to the FYE 2015 HBAI publication variants of RPI were used to adjust for inflation.
    This change followed advice from the UK National Statistician that use of RPI should be discontinued in statistical publications.
    Full details on the impact on this methodological change, together with estimates for trends in income and absolute low income under both the old and new methodologies, are presented in Annex 4 of the FYE 2015 HBAI Quality and Methodology Report.
  • Benefit receipt: Relative to administrative records, the FRS is known to under-report benefit receipt. However, the FRS is the best source for looking at benefit and tax credit receipt by characteristics not captured on administrative sources, and for looking at total benefit receipt on a benefit unit or household basis. It is often inappropriate to look at benefit receipt on an individual basis because means-tested benefits are paid on behalf of the benefit unit. DWP published research (Working Paper 115) which explores the reasons for benefit under-reporting with the aim of improving the benefits questions included within the FRS. Table M.6a of the FRS publication presents a comparison of receipt of state support between FRS and administrative data. Methodology Table M.6b compares the average weekly receipt of state support in the FRS with the average weekly receipt of state support from the administrative data sources. Some benefit types have not been included in this analysis because no directly comparable administrative data source is available.
  • Self-employed: All analyses in the HBAI publication include the self-employed. A proportion of this group are believed to report incomes that do not reflect their living standards and there are also recognised difficulties in obtaining timely and accurate income information from this group. This may lead to an understatement of total income for some groups for whom this is a major income component, although this is likely to be more important for those at the top of the income distribution. There is little difference in the overall picture of proportions in low-income households when analysis is performed either including or excluding the self-employed.
  • Savings and investments: The data relating to investments and savings should be treated with caution. Questions relating to investments are a sensitive section of the questionnaire and have a low response rate. A high proportion of respondents do not know the interest received on their investments. It is likely that there is some under-reporting of capital by respondents, in terms of both the actual values of the savings and the investment income. This may lead to an understatement of total income for some groups for whom this is a major income component, such as pensioners, although this is likely to be more important for those at the top of the income distribution.
  • Methodological change for FYE 2020 (FRS savings and investments variable used in HBAI): The level of savings and investments, for some families (benefit units) and households was estimated using a slightly different methodology from FYE 2020 than in previous years. The new method more accurately estimates savings in current accounts and basic bank accounts. It should be noted that savings and investments breakdowns from FYE 2020 are not directly comparable with those for previous years.
  • Comparisons with National Accounts: Table 1.2a shows comparisons between growth in Real Household Disposable Income and real growth in HBAI mean BHC unequivalised income. For some years, income growth in the HBAI-based series appears lower than the National Accounts estimates. The implication of this is that absolute real income growth could be understated in the HBAI series. Comparisons over a longer time period are believed to be more robust.
  • High incomes: Comparisons with His Majesty’s Revenue and Customs’ Survey of Personal Incomes (SPI), which is drawn from tax records, suggest that the FRS under-reports the number of individuals with very high incomes and understates the level of their incomes. There is also some volatility in the number of high-income households surveyed. Since any estimate of mean income is very sensitive to fluctuations in incomes at the top of the distribution, an adjustment to correct for this is made to ‘very rich’ households in FRS-based results using SPI data. The median-based low-income statistics are not affected.
  • Working status: DWP and ONS have jointly investigated the reasons for the FRS consistently giving higher estimates than the Labour Force Survey (LFS) of the percentage of children in workless households. A report on this investigation found that the main reasons for the divergence were:

    • FRS unweighted data identifying a higher proportion of children in lone parent families, who have a much higher worklessness rate, than does LFS;
    • FRS unweighted data showing a higher worklessness rate, in both lone parent and couple with-children families, than LFS;
    • LFS employing a grossing regime which substantially reduces the proportion of children in lone parent households, and thereby in workless households; whereas the FRS grossing regime has less of an effect in reducing these proportions;
    • The LFS grossing regime also reduces the worklessness rate in lone parent families; whereas the FRS grossing regime has less clear-cut effects.
  • Gender analysis: The HBAI assumes that both partners in a couple benefit equally from the household’s income and will therefore appear at the same position in the income distribution. Research has suggested that, particularly in low-income households, the assumption with regard to income sharing is not always valid as men sometimes benefit at the expense of women from shared household income. This means that it is possible that HBAI results broken down by gender could understate differences between the two groups. See, for instance “Purse or Wallet? By Gender Inequalities” by Goode, J., Callender, C. and Lister, R. (1998) and the Distribution of Income in Families on Benefits by JRF/Policy Studies Institute.
  • Students: Information for students should be treated with some caution because they are often dependent on irregular flows of income. Only student loans are counted as income in HBAI (with both the maintenance and tuition parts of the loan included), any other loans taken out are not. The figures are also not necessarily representative of all students because HBAI only covers private households and this excludes halls of residence.
  • Elderly: The effect of the exclusion of the elderly who live in residential homes is likely to be small overall except for results specific to those aged 80 and above.
  • Ethnicity analysis: Smaller ethnic minority groups exhibit year-on-year variation which limits comparisons over time. For this reason, analysis by ethnicity is usually presented as three-year averages. Please note that following the decision to not publish breakdowns of the FYE 2021 estimates, all three-year averages calculated and published for any period including FYE 2021 are based on two data points only.
  • Disability analysis: No adjustment is made to disposable household income to take account of additional costs that may be incurred due to the illness or disability in question. This means that using income as a proxy for living standards for these groups, as shown here, may be somewhat upwardly biased. Analysis excluding Disability Living Allowance and Attendance Allowance from the calculation of income has been published as part of the suite of online HBAI ODS (not available for FYE 2021).
  • Regional analysis: Disaggregation by geographical regions is usually presented as three-year averages. This presentation has been used as single-year regional estimates are considered too volatile. This issue was discussed in Appendix 5 of the FYE 2005 HBAI publication, where regional time series using three-year averages were presented. Although the FRS sample is large enough to allow some analysis to be performed at a regional level, it should be noted that no adjustment has been made for regional cost of living differences. It is therefore assumed that there is no difference in the cost of living between regions, although the AHC measure will partly consider differences in housing costs. Analysis at geographies below the regional level is not available from this data. Please see the Children in Low-Income Families publication for local level geographies.
    Please note that following the decision to not publish breakdowns of the FYE 2021 estimates, all three-year averages calculated and published for any period including FYE 2021 are based on two data points only.
  • Household food security and food bank usage: The individual level statistics presented in our tables relate to the household’s food bank usage or household food security. The circumstances of the household are applied to all individuals within that household. The questions do not ask, for example, about the food bank usage of the individual or food bank usage needs of children. It should also be noted that the statistics presented exclude shared households, such as a house shared by a group of professionals.
  • Changes to deflators: Since the HBAI FYE 2018 publication, the Office for National Statistics (ONS) have made some very minor revisions to the bespoke Consumer Price Index (CPI) series we use to make real-terms income comparisons within and between survey years. However, because the effect of these revisions on low-income measures is negligible no revisions have been made to the deflators used in HBAI. See the following ONS update for more details.
  • Revision to FYE 1995 to FYE 2019 due to treatment of income from child maintenance: In HBAI FYE 2020 a minor methodological change was made to capture all income from child maintenance. This resulted in more income from child maintenance being included, in turn slightly increasing some household incomes and so tending to slightly reduce low-income rates for families with children. The full back series back to FYE 1995 was revised so that comparisons over time are on a consistent basis across the full time series. This means that figures for FYE 1995 to FYE 2019 may be slightly different to the equivalent figures in publications issued prior to FYE 2019. Please refer to HBAI Quality and Methodology Information Report for FYE 2020 for more information.
  • Income from dividends: From FYE 2022, income received from director’s dividends is included in the estimates following an addition to the Family Resources Survey. From FYE 2023 there has been an adjustment to the treatment of dividends for a small group of respondents: 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 income is treated as income from earnings. More information on the treatment of specific income sources can be found in FRS Background Information and Methodology.

Survey Data

The statistics in the HBAI report come from the Family Resources Survey (FRS), a representative survey of 25 thousand households in the United Kingdom in FYE 2023. This was meaningfully higher than the over 16,000 achieved in FYE 2022. The achieved sample for FYE 2023 was short of the aim of 45,000 households but was still 30% above levels seen in the years prior to the COVID-19 pandemic (typically 19,000-20,000). In general, this means that the degree of uncertainty around this year’s survey estimates is smaller than in the last two years.

The focus of the FRS is on capturing information on incomes and, as such, is the foremost source of income data and provides more detail on different income sources than other household surveys. It also captures a lot of contextual information on the household and individual circumstances, such as employment, education level and disability. This is therefore a very comprehensive data source allowing for a lot of different analysis.

Surveys gather information from a sample rather than from the whole population. The sample is designed carefully to allow for this, and to be as accurate as possible given practical limitations such as time and cost constraints. Results from sample surveys are always estimates, not precise figures. This means that they are subject to a margin of error which can affect how changes in the numbers should be interpreted, especially in the short-term. The latest estimates should be considered alongside medium and long-term patterns.

In addition to sampling errors, consideration should also be given to non-sampling errors. Non-sampling errors arise from the introduction of some systematic errors in the sample as compared to the population it is supposed to represent. As well as response bias, such errors include inappropriate definition of the population, misleading questions, data input errors or data handling problems – in fact any 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.

HBAI is based on data from a household survey and so subject to the nuances of using a survey, including:

  • Sampling error. Results from surveys are estimates and not precise figures. Confidence intervals help to interpret the certainty of these estimates, by showing the range of values around the estimate that the true result is likely to be within. In general terms the smaller the sample size, the larger the uncertainty. Statistical significance is an attempt to indicate whether a reported change within the population of interest is due to chance. It is important to bear in mind that confidence intervals are only a guide for the size of sampling error.
  • Non-response error. Prior to the coronavirus (COVID-19) pandemic, the FRS response rate each year was around 50 per cent. Following the change in mode because of pandemic, the FYE 2021 the response rate fell to 23% and in FYE 2022 it improved to 26%. In FYE 2023, the response rate was 25%. To correct for differential non-response, estimates are weighted using population totals.
  • Survey coverage. The FRS covers private households in the United Kingdom. Therefore, individuals in nursing or retirement homes, for example, will not be included. This means that figures relating to the most elderly individuals may not be representative of the United Kingdom population, as many of those at this age will have moved into homes where they can receive more frequent help.
  • Survey design. The FRS uses a clustered sample designed to produce robust estimates at former government office region (GOR) level. The FRS is therefore not suitable for analysis below this level.
  • Sample size. Although the FRS has a relatively large sample size for a household survey, small sample sizes for some more detailed analyses may require several years of data to be combined to generate reliable estimates. From April 2011, the target achieved GB sample size for the FRS was reduced by 5,000 households, resulting in an overall achieved sample size for the UK of around 20,000 households from FYE 2012 onwards. We previously published an assessment concluding that this still allows core outputs from the FRS to be produced, though with slightly wider confidence intervals or ranges.
    The circumstances surrounding the coronavirus (COVID-19) pandemic resulted in a smaller achieved FRS sample size than pre-pandemic, with over 16,000 households in the FYE 2022 sample. This was an improvement on FYE 2021 where the achieved sample size was around 10,000 households.
    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. The impact of this was that the level of field capacity required to manage the sample boost was not achieved. For FYE 2023 the achieved sample was 25,000 households.
    It remains the case that gaining household response has continued to be challenging in FYE 2024. The primary challenge remains the recruitment and retention of sufficient interviewers; versus the call on their time (and from all large social surveys, not just the FRS). Consequently, we expect a final achieved sample for the year of around 17,500 households, rather than the 20,000 originally intended.
    Please see the FRS release strategy for more information.
  • Measurement error. The FRS is known to under-report certain income streams, especially benefit receipt. More detail can be found in Table M.6a and M.6b of the FRS report.

Further methodological details relating to the FRS are given in the FRS Background Information and Methodology.

Reporting Uncertainty

As above, survey results are always estimates, not precise figures and so subject to a level of uncertainty. Two different random samples from one population, for example the UK, are unlikely to give the same survey results, which are likely to differ again from the results that would be obtained if the whole population was surveyed. The level of uncertainty around a survey estimate can be calculated and is commonly referred to as sampling error.

We can calculate the level of uncertainty around a survey estimate by exploring how that estimate would change if we were to draw many survey samples for the same time period instead of just one. This allows us to define a range around the estimate (known as a “confidence interval”) and to state how likely it is that the real value that the survey is trying to measure lies within that range. Confidence intervals are typically set up so that we can be 95% sure that the true value lies within the range – in which case this range is referred to as a “95% confidence interval”. Annex 4 of this report provides further details on the Bootstrapping methodology used to estimate confidence intervals in HBAI, alongside estimates of the sampling error.

Population

The analyses in the HBAI report are primarily based on the FRS. Households in Northern Ireland (NI) were surveyed for the first time in the FYE 2003 survey year. A detailed analysis of observed trends, together with results for NI and the UK for the first three years of NI data can be found in Appendix 3 of the FYE 2005 HBAI publication.

The FRS time series in this publication are presented with discontinuities in the years where there is a change from GB to UK. Prior to FYE 2015, for some tables, estimates for NI were imputed for the years FYE 1999 to FYE 2002. This allowed for changes since FYE 1999 to be measured at the UK level. For further details, see Appendix 4 of the FYE 2005 HBAI publication. This imputation is no longer carried out from the FYE 2015 publication.

The survey covers the private household sector. All the results therefore exclude people living in institutions, e.g. nursing homes, halls of residence, barracks or prisons, and homeless people living rough or in bed and breakfast accommodation. The area of Scotland north of the Caledonian Canal was included in the FRS for the first time in the FYE 2002 survey year and, from the FYE 2003 survey year, the FRS was extended to include a 100 per cent boost of the Scottish sample. This has increased the sample size available for analysis at the Scottish level.

A further adjustment is that households containing a married adult whose spouse is temporarily absent, whilst within the scope of the FRS, are excluded from HBAI. Similarly, prior to the FYE 1997 data, households containing a self-employed adult who had been full-time self-employed for less than two months were excluded. This exclusion is no longer made because of the improvements in the self-employment questions in the FRS.

Grossing

The published HBAI analysis presents tabulations where the percentages refer to sample estimates grossed-up to apply to the whole population.

Grossing-up is the term usually given to the process of applying factors to sample data so that they yield estimates for the overall population. The simplest grossing system would be a single factor, 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 HBAI mirrors that of FRS grossing with two differences described below.

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 households. 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 analyses in mind. The population estimates for these groups, obtained from official data sources, provide control variables. The grossing factors are then calculated by a process which ensures the FRS produces population estimates that are the same as the control variables.

As an example, the grossed number of men aged 35 to 39 would be consistent with the Office for National Statistics (ONS) estimate (see Table 1). Some adjustments are made to the original control total data sources so that definitions match those in the FRS, e.g. an adjustment is made to the demographic data to exclude people not resident in private households. It is also the case that some totals have to be adjusted to correspond to the FRS survey year.

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.

As stated above, the system used to calculate grossing factors for HBAI mirrors that of FRS grossing with two differences. The first difference with FRS grossing is that the sample of households is smaller for HBAI purposes because households with spouses living away from home are excluded (see Population section above). The second difference is that separate control totals are introduced for ‘very rich’ households, so that the top end of the income distribution is more accurately reflected, which is particularly important for estimates of mean income or inequality as measured by the Gini coefficient.

As with the FRS, the grossing regime for HBAI currently uses population and household estimates based on the results of the 2011 Census. Prior to FYE 2013, 2001 census-based estimates were used. In addition, a review of FRS grossing was carried out on behalf of DWP by the ONS Methodological Advisory Service. In implementing the review recommendations, a number of relatively minor methodological improvements were implemented from FYE 2013.

The main changes implemented were as follows:

  • improvements to the categorisation of tenure control totals;

  • a full breakdown of the total number of households into each of the English regions (in addition breakdowns for Scotland, Wales and Northern Ireland); and

  • a new adjustment to account for the different rates of sampling in England and Wales, Scotland, and Northern Ireland.

A back-series of grossing factors calculated using the new methodology was created for each year back to FYE 2003 and are used in the HBAI publication tables from FYE 2013 onwards. Further details and analysis of the impact of these methodological changes are published in the grossing methodology review.

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, have been 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.

There are some 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 as a control variable is not applicable.

  • Northern Ireland housing data are based largely on small sample surveys. It is not desirable to introduce the variance of 1 survey into another by using it to compute control totals, therefore, tenure type has not been used as a control variable.

Details of the grossing regime for Northern Ireland are shown in Table 2.

FYE 2023 Grossing Regime

For FYE 2023, population estimates used to weight HBAI are still primarily based on 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). Note: This series of population estimates do not take account of the 2021 and 2022 Censuses across the UK. They are bespoke estimates provided to DWP by ONS which was necessary due to information from the Scottish 2022 Census being unavailable for use.

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 usual resident population using data from the 2011 Censuses on the proportion of people usually resident, by local authority, age and sex who live in private households.

Table 1: HBAI grossing regime for Great Britain, FYE 2023

Control totals for Great Britain Groupings Original Source Adjustments made by DWP
Private household population by region, age and sex Regions: North East, North West, Yorkshire and the Humber, East Midland, West Midlands, East, London, South East, South West, Wales, Scotland. Sex and age: Males 0-19 dependants, 16-24 independents, 25-29, 30-34, 40-44, 45-49, 50-59, 60-64, 65-74, 75-79, 80+; Females 0-19 dependants, 16-24 independents, 25-29, 30-34, 40-44, 45-49, 50-59, 60-69, 70-74, 75-79, 80+ Mid-year population estimates, Office of National Statistics ONS total population figures are adjusted for private household estimates using data supplied by ONS directly to DWP. 16-19-year-old dependents and non-dependents are split using data supplied by HMRC directly to DWP
Benefit Units with children Region: England and Wales, Scotland Families in receipt of child benefit, HM Revenue and Customs  
Lone Parents Sex: Males, Females Lone parent estimates, Labour Force Survey Adjusted for FRS survey year (April-March)
Households by region Region: North East, North West, Yorkshire and the Humber, East Midlands, West Midlands, East of England, London, South East, South West, Wales, Scotland. Households by region, Office for National Statistics (England) / Welsh Government (Wales) / Scottish Government (Scotland) Adjusted for FRS survey year (April-March)
Households by tenure type Tenure (Social Renters, Private Renters, Owner Occupied) Dwellings by tenure type, Department for Levelling Up, Housing and Communities Household control totals are calculated using dwellings data published by DLUHC, Welsh Government, Scottish Government. Adjusted for FRS survey year (April-March)
Households by council tax band Council Tax Band (NVS and A, B, C and D, E to I) Dwellings by council tax band, Valuations Office Agency, Dwellings by council tax band, Scottish Government Household control totals are calculated using dwellings data published by VOA / Scottish Government, adjusted for FRS survey year (April-March). Estimates for properties not-valued-separately (NVS) based on FRS sample proportions
Households containing ‘Very Rich’ people Pensioners, Non-pensioners HMRC Survey of Personal Incomes (SPI)  

Table 2: HBAI grossing regime for Northern Ireland, FYE 2023

Control totals for Northern Ireland Groupings Original Source Adjustments made by DWP
Private household population by age and sex Sex and age: Males 0-19 dependants, 16-24 independents, 25-29, 30-34, 40-44, 45-49, 50-59, 60-64, 65-74, 75-79, 80+; Females 0-19 dependants, 16-24 independents, 25-29, 30-34, 40-44, 45-49, 50-59, 60-69, 70-74, 75-79, 80+ Private household estimates, Department for Social Development in Northern Ireland  
Households   Household estimates, Department for Social Development in Northern Ireland  
Lone Parents   Household estimates, Department for Social Development in Northern Ireland  
Households containing ‘Very Rich’ people Pensioners, Non-pensioners HMRC Survey of Personal Incomes (SPI)  

Adjustment for individuals with very high incomes

An adjustment is made to sample cases at the top of the income distribution to correct for volatility in the highest incomes captured in the survey. This adjustment uses data kindly supplied by HM Revenue and Customs’ statisticians from HM Revenue and Customs’ Survey of Personal Incomes (SPI) to control the numbers and income levels of the ‘very rich’ while retaining the FRS data on the characteristics of their households. The methodology defines a household as ‘very rich’ if it contains a ‘very rich’ individual and it adjusts pensioners and non-pensioners separately. Thresholds have been set at the level above which, for each group, the FRS data is volatile due to small numbers of cases.

From the FYE 2010 publication, the SPI adjustment methodology was changed to be based on adjusting a fixed fraction of the population rather than on adjusting the incomes of all those individuals with incomes above a fixed cash terms level. This is intended to prevent an increasing fraction of the dataset being adjusted. The adjustment fraction was set at the same level as the fraction adjusted in FYE 2009. There was also a movement to basing all SPI adjustment decisions on gross rather than a mixture of gross and net incomes. These changes only have a very small effect on the results as presented.

The numbers of ‘very rich’ pensioners and non-pensioners in survey estimates are matched to SPI estimates by the introduction of two extra control totals into the grossing regime. One is for the total number of pensioners above the pensioner threshold and the other for the number of non-pensioners above the non-pensioner threshold. The grossing factors for individual cases are only marginally changed because of this adjustment. In addition, each ‘very rich’ individual in the FRS is assigned an income level derived from the SPI, as the latter gives a more accurate indication of the level of high incomes than the FRS. Again, this adjustment is carried out separately for pensioners and non-pensioners.

The latest SPI data available when we carried out our analysis was the FYE 2020, which was projected forward to cover the FYE 2023. This was due to the FYE 2021 SPI outturn data being significantly impacted by coronavirus (COVID-19) pandemic, and therefore HMRC continued to project using the previous year’s SPI. For FYE 2023, pensioners in Great Britain are subject to the SPI adjustment if their gross income exceeded £99,500 per year (£79,400 in Northern Ireland). Working-age adults (including the working-age partners of pensioners) are subject to the SPI adjustment if their gross income exceeded £351,500 per year (£181,000 per year in Northern Ireland).

Changes to the grossing regimes in FYE 2021 and FYE 2022

Due to the impact of the coronavirus (COVID-19) pandemic, there was a need to add in extra grossing controls for:

  • Month of interview (FYE 2021): the number of households sampled varied between months. There was no need to make changes to the back-series – adding month of interview in previous years has minimal impact as each month had approximately the same number of sample cases and there was less in-year variation in incomes.

  • Working-age adults with degrees (FYE 2021 and FYE 2022): there was a clear bias in the samples toward better-educated adults, specifically those with degrees. This bias was confirmed when comparing to an external data source – the Annual Population Survey (APS). It was important to address this as households with at least one working-age adult with a degree have statistically significantly higher incomes than households with adults that have lower levels of education. After adding in a control total for working-age adults with degree level education, there was still a bias towards younger adults with degrees so the grossing control was split into two: working-age adults aged 16 to 45 with a degree and working-aged adults over 45 with a degree. The grossing control totals were based on education level splits reported in the FRS prior to the pandemic, projected forward using growth in the APS.

  • Biannual grossing control for number of households (FYE 2022): this was introduced to balance the number of households in Great Britain across the two halves of the survey year. This was necessary due to the introduction of the pre-planned boost to the FRS issued sample in England and Wales in October 2021. In Northern Ireland, changes to the approach of contacting respondents in July 2021 meant that the achieved sample increased markedly partway through the year. This is not normally a feature of the FRS achieved sample, with response normally spread relatively equally over each twelve-month run of fieldwork. We introduced a quarterly household grossing control to balance their sample across the year.

Following the resumption of face-to-face interviewing in the FRS in FYE 2023, the methodology reverted to the grossing system in place before the pandemic (detailed in tables 1 and 2).

Plans to use 2021 Census outputs for grossing

We expect to receive UK population and private household estimates based on the 2021 Census (2022 for Scotland) later in 2024. This will include a back series of grossing factors from FYE 2013 to re-base the HBAI estimates from that year onwards.

As with previous rebasing exercises, we will also review other inputs into the grossing including the SPI adjustment received from HMRC. We will provide transparency to users on the main areas of change when the statistical series is reissued.

Equivalisation

HBAI uses net disposable weekly household income, after adjusting for the household size and composition, as an assessment for material living standards – the level of consumption of goods and services that people could attain given the net income of the household in which they live. To allow comparisons of the living standards of different types of households, income is adjusted to take into account variations in the size and composition of the households in a process known as equivalisation. HBAI assumes that all individuals in the household benefit equally from the combined income of the household. Thus, all members of any one household will appear at the same point in the income distribution.

The unit of analysis is the individual, so the populations and percentages in the tables are numbers and percentages of individuals – both adults and children.

Equivalence scales conventionally take an adult couple without children as the reference point, with an equivalence value of one. The process then increases relatively the income of single person households (since their incomes are divided by a value of less than one) and reduces relatively the incomes of households with three or more persons, which have an equivalence value of greater than one. The infographic below illustrates the process of equivalisation, Before Housing Costs.

Figure 1

Consider a single person, a couple with no children, and a couple with two children aged twelve and ten, all having unadjusted weekly household incomes of £300 (BHC). The process of equivalisation, as conducted in HBAI, gives an equivalised income of £448 to the single person, £300 to the couple with no children, but only £214 to the couple with children.

The main equivalence scales now used in HBAI are the modified OECD scales, which take the values shown in Table 3. The equivalent values used by the McClements equivalence scales are also shown for comparison alongside modified OECD values. The McClements scales were used by HBAI to adjust income up to the FYE 2005 publication.

In the modified OECD and McClements versions, two separate scales are used, one for income BHC and one for income AHC. The construction of household equivalence values from these scales is quite straightforward. For example, the BHC equivalence value for a household containing a couple with a fourteen-year-old and a ten-year-old child together with one other adult would be 1.86 from the sum of the scale values:

0.67 + 0.33 + 0.33 + 0.33 + 0.20 = 1.86

This is made up of 0.67 for the first adult, 0.33 for their spouse, the other adult and the fourteen-year-old child and 0.20 for the ten-year-old child. The total income for the household would then be divided by 1.86 in order to arrive at the measure of equivalised household income used in HBAI analysis.

Table 3: Comparison of modified OECD and McClements equivalence scales

OECD rescaled to couple without Children=1 OECD ‘Companion’ Scale to equivalise AHC results McClements BHC McClements AHC
First Adult 0.67 0.58 0.61 0.55
Spouse 0.33 0.42 0.39 0.45
Other Second Adult 0.33 0.42 0.46 0.45
Third Adult 0.33 0.42 0.42 0.45
Subsequent Adults 0.33 0.42 0.36 0.40
Children aged under 14 years 0.20 0.20 0.20 0.20
Children aged 14 years and over 0.33 0.42 0.32 0.34

Notes:

  • All scales are presented to 2 decimal places.

  • For the McClements scale, the weight for ‘Other second adult’ is used in place of the weight for ‘Spouse’ when 2 adults living in a household are sharing accommodation, but are not living as a couple. ‘Third adult and ‘Subsequent adult’ weights are used for the remaining adults in the household as appropriate. In contrast to the McClements scales, apart from for the first adult, the OECD scales do not differentiate for subsequent adults.

  • The McClements scale varies by age for children, appropriate averages are shown in the table.

Income Definition

The income measure used in HBAI is weekly net (disposable) equivalised household income. This comprises total income from all sources of all household members including dependants.

Income is adjusted for household size and composition by means of equivalence scales, which reflect the extent to which households of different size and composition require a different level of income to achieve the same standard of living. This adjusted income is referred to as equivalised income.

In detail, income includes:

  • usual net earnings from employment;

  • profit or loss from self-employment (losses are treated as a negative income);

  • income received from dividends (from FYE 2022);

  • state support – all benefits and tax credits;

  • income from occupational and private pensions;

  • investment income;

  • maintenance payments;

  • income from educational grants and scholarships (including, for students, student loans and parental contributions); and

  • the cash value of certain forms of income in kind (free school meals, free school breakfast, free school milk, free school fruit and vegetables, Healthy Start vouchers and free TV licences for those aged 75 and over who receive Pension Credit.

Income is net of the following items:

  • income tax payments;

  • National Insurance contributions;

  • domestic rates/council tax;

  • contributions to occupational pension schemes (including all additional voluntary contributions (AVCs) to occupational pension schemes, and any contributions to stakeholder and personal pensions);

  • all maintenance and child support payments, which are deducted from the income of the person making the payment;

  • parental contributions to students living away from home; and

  • student loan repayments.

Income After Housing Costs (AHC) is derived by deducting a measure of housing costs from the above income measure.

Housing costs

These include the following:

  • rent (gross of housing benefit);

  • water rates, community water charges and council water charges;

  • mortgage interest payments;

  • structural insurance premiums (for owner occupiers); and

  • ground rent and service charges.

For Northern Ireland households, water provision is funded from taxation and there are no direct water charges. Therefore, it is already taken into account in the Before Housing Costs measure.

In the FYE 1996 and subsequent datasets, a refinement was made to the calculation of mortgage interest payments to disregard additional loans which had been taken out for purposes other than house purchase.

Negative incomes

Negative incomes BHC are reset to zero, but negative AHC incomes calculated from the adjusted BHC incomes are possible. Where incomes have been adjusted to zero BHC, income AHC is derived from the adjusted BHC income.

State support

The Government pays money to individuals in order to support them financially under various circumstances. Most of these benefits are administered by DWP. The exceptions are Housing Benefit and Council Tax Reduction, which are administered by local authorities. Tax Credits are not treated as benefits, but both Tax Credits and benefits are included in the term State Support. Further information on UK state support and specific benefits for devolved administrations is available under ‘Benefits’ in the Glossary section of the FRS Background Information and Methodology.

Earnings from employment

During FYE 2021 and FYE 2022 many households experienced variation in their earnings within the survey year due to changes in employment and hours worked and/or receipt of support grants through schemes such as the Coronavirus Job Retention Scheme (CJRS) or the Self-Employment Income Support Scheme (SEISS). From May 2020, the FRS questionnaire incorporated questions to specifically ask about receipt of CJRS and from June 2020, this was extended to SEISS. The CJRS and SEISS schemes were closed in September 2021 and questions about income from the schemes were removed from the FRS questionnaire in January 2022.

For employees, receipt of CJRS (‘furlough’) and any resulting effect on levels of pay were fully reflected in the HBAI estimates. Employees who were furloughed were classified as employed, but temporarily away from work. This meant that, all things being equal, furloughed workers did not reduce the number of people in employment (or the employment rate). The calculation of ‘income from employment’ used wages which were treated as income rather than state support, irrespective of any support payments from CJRS that the respondent’s employer was receiving in respect of their employment.

Earnings from self-employment

For the self-employed, it is difficult to calculate current-year income, and in line with international standards, the FRS questionnaire asks for profit data for a previous tax year and/or regular self-employment income over the past twelve months. While this is less of an issue when incomes are broadly stable, it became more of a challenge in FYE 2021 given the sharp changes in self-employed incomes over the course of the pandemic. Although from June 2020 the FRS specifically asked about receipt of SEISS grants and amounts, questions were not asked about receipt of income from continued trading which was permissible under the terms of the scheme. It was therefore not possible to adapt our methodology to estimate in-year income more accurately, taking account of both SEISS and non-SEISS sources.

This means that the HBAI estimates indirectly, rather than explicitly, included information on the amount of SEISS received. This is because we pull through information on previous trading profits, upon which the SEISS grants are based. In FYE 2021, while there was an option to ‘add in’ the SEISS amounts received for this group, there was a risk of double counting, as there was evidence that some respondents had already included income from SEISS in their responses. In FYE 2022, receipt of the first three SEISS grants was treated as taxable income when calculating profits in FYE 2021 tax returns. Therefore, money received from the scheme will have been automatically included in income estimates for self-employed people who reported their FYE 2021 profit data.

Sensitivity analysis completed internally showed that, as in other years, changes to self-employed incomes had only a marginal effect on the overall estimated proportions of the population in low income.

Further information is available in the FRS Background Information and Methodology.

Treatment of cost of living and wider support schemes in the HBAI income estimates for FYE 2023

During FYE 2023 the UK Government announced and implemented additional support to families with several cost of living support schemes, depending on peoples’ circumstances.

These were:

  • 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 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 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. Up to £300 was paid with eligible households’ normal payments from November 2022. This is in addition to any other Cost of Living Payment received.

  • An Energy Bills Support Scheme grant of £400 to help with rising energy costs. The payment 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 were not eligible for this scheme, but equivalent support of £600 per household was provided.

  • Households in receipt of the Guarantee Credit element of Pension Credit or were on a low income and have high energy costs also received a one-off discount on their energy bill under the Warm Home Discount scheme. The rebate increased from £140 to £150 and was discounted automatically from bills. A further £200 was available in Wales for those in receipt of qualifying benefits (through the Wales Fuel Support Scheme).

  • A £150 non-repayable rebate for households in England in council tax 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.

Income from these sources is primarily categorised as state support in the FYE 2023 estimates, except for the £400 Energy Support Scheme payments (£600 in Northern Ireland), the £150 Council Tax Rebate payments which were made to households in bands A to D, and the Warm Home Discount and Wales Fuel Support Scheme. These are classified as miscellaneous income.

Most of the schemes were introduced at pace, in a timeframe which made it difficult to adapt the FRS questionnaire to capture them. As each support scheme came with clear eligibility guidelines, receipt of the payments was imputed based on respondent characteristics. Further details on the methodology used to impute receipt are in the FRS Background Information and Methodology document.

Interpreting low-income measures

Relative low income sets the threshold as a proportion of the average income and moves each year as average income moves. It is used to measure the number and proportion of individuals who have incomes a certain proportion below the average.

The percentage of individuals in relative low income will increase if:

  • the average income stays the same, or rises, and individuals with the lowest incomes see their income fall, or rise less, than average income; or

  • the average income falls and individuals with the lowest incomes see their income fall more than the average income.

The percentage of individuals in relative low income will decrease if:

  • the average income stays the same, or rises, and individuals with the lowest incomes see their income rise more than average income; or

  • the average income falls and individuals with the lowest incomes see their income rise, or fall less, than average income, or see no change in their income.

Absolute low income sets the low income line in a given year, then adjusts it each year with inflation as measured by variants of the CPI. This measures the proportion of individuals who are below a certain standard of living in the UK (as measured by income).

  • The percentage of individuals in absolute low income will increase if individuals with the lowest incomes see their income fall or rise less than inflation.

  • The percentage of individuals in absolute low income will decrease if individuals with the lowest incomes see their incomes rise more than inflation.

Income inequality, measured by the Gini Coefficient, shows how incomes are distributed across all individuals, and provides an indicator of how high and low-income individuals compare to one another. It ranges from zero (when everybody has identical incomes) to 100 per cent (when all income goes to only 1 person). The 90:10 ratio is the average (median) income of the top 20 per cent (quintile 5) divided by the average income of the bottom 20 per cent (quintile 1). The higher the number, the greater the gap between those with the highest incomes and those with the lowest incomes.

Figure 2

Before Housing Costs (BHC) measures allow an assessment of the relative standard of living of those individuals who were actually benefiting from a better quality of housing by paying more for better accommodation, and income growth over time incorporates improvements in living standards where higher costs reflected improvements in the quality of housing.

After Housing Costs (AHC) measures allow an assessment of living standards of individuals whose housing costs are high relative to the quality of their accommodation. Income growth over time may also overstate improvements in living standards for low-income groups, as a rise in Housing Benefit to offset higher rents (for a given quality of accommodation) would be counted as an income rise.

Therefore, HBAI presents analyses of disposable income on both a BHC and AHC basis. This is principally to take into account variations in housing costs that themselves do not correspond to comparable variations in the quality of housing.

Combined low income and child material deprivation

Material deprivation is an additional way of measuring living standards and refers to the self-reported inability of individuals or households to afford goods and activities that are typical in society at a given point in time, irrespective of whether they would choose to have these items, even if they could afford them.

A suite of questions designed to capture the material deprivation experienced by families with children has been included in the FRS since FYE 2005. Respondents are asked whether they have 21 goods and services, including child, adult and household items. Together, these questions form the best discriminator between those families that are deprived and those that are not. If they do not have a good or service, they are asked whether this is because they do not want them or because they cannot afford them.

The original list of items was identified by independent academic analysis. See McKay, S. and Collard, S. (2004). Developing deprivation questions for the Family Resources Survey, Department for Work and Pensions Working Paper Number 13. The questions are kept under review and for the FYE 2011 Family Resources Survey, information on four additional material deprivation goods and services was collected and from FYE 2012 four questions from the original suite were removed.

The trends table 4.5tr available in the Data Tables on the HBAI homepage shows figures using the original suite of questions up to and including FYE 2011, and the new suite of questions from FYE 2011 onwards. FYE 2011 data is presented on both bases as figures from the old and new suite of questions are not comparable.

See Appendix 3 of the FYE 2011 HBAI publication for a discussion of the implications of changing the items.

A prevalence weighted approach has been used, in combination with a relative low-income or severe relative low-income threshold. Prevalence weighting is a technique of scoring deprivation in which more weight in the deprivation measure is given to families lacking those items that most families already have. This means a greater importance, when an item is lacked, is assigned to those items that are more commonly owned in the population.

For each question a score of 1 indicates where an item is lacked because it cannot be afforded. If the family has the item, the item is not needed or wanted, or the question does not apply then a score of 0 is given. This score is multiplied by the relevant prevalence weight. The scores on each item are summed and then divided by the total maximum score; this results in a continuous distribution of scores ranging from 0 to 1. The scores are multiplied by 100 to make them easier to interpret. The final scores, therefore, range from 0 to 100, with any families lacking all items which other families had access to scoring 100.

A child is in combined low income and child material deprivation if they live in a family that has a final material deprivation score of 25 or more and an equivalised household income below 50/60/70 per cent of relative/absolute median income.

From the FYE 2009 edition of the HBAI publication, we moved to using the prevalence weights relative to the survey year in question, rather than fixed FYE 2005 weights, which were used in previous publications. The prevalence weights are shown in Table 4 below.

Table 4: Material deprivation scores used for children in FYE 2023

Material deprivation questions Weights Final Scores
For children    
Outdoor space or facilities nearby to play safely 0.941 5.80
Enough bedrooms for every child of 10 or over of a different sex to have their own bedroom 0.824 5.08
Celebrations on special occasions such as birthdays, Christmas or other religious festivals 0.951 5.86
Leisure equipment such as sports equipment or a bicycle 0.859 5.29
A family holiday away from home for at least 1 week a year 0.651 4.01
A hobby or leisure activity 0.752 4.64
Friends around for tea or a snack once a fortnight 0.635 3.91
Go on school trips 0.855 5.27
Toddler group/nursery/playgroup at least once a week 0.698 4.31
Attends organised activity outside school each week 0.700 4.31
Fresh fruit and vegetables eaten by children every day 0.915 5.64
Warm winter coat for each child 0.980 6.04
For adults    
Enough money to keep home in a decent state of decoration 0.779 4.80
A holiday away from home for at least 1 week a year, whilst not staying with relatives at their home 0.578 3.56
Household contents insurance 0.653 4.02
Regular savings of £10 a month or more for rainy days or retirement 0.655 4.03
Replace any worn out furniture 0.608 3.75
Replace or repair major electrical goods such as a refrigerator or a washing machine, when broken 0.700 4.31
A small amount of money to spend each week on yourself, not on your family 0.710 4.38
In winter, able to keep accommodation warm enough 0.865 5.33
Keep up with bills and regular debt payments 0.917 5.65
Sum of all weights 16.224 100

Combined low income and working-age adult material deprivation

From FYE 2022, the HBAI publication included statistics on combined low income and working-age adult material deprivation, with a back series of the data available to FYE 2011. The measures follow a similar methodology as for children, with the nine questions for adults detailed in Table 4 forming the basis of the material deprivation measure for all working-age adults. Working-age adults without children are also asked these questions.

For each question a score of 1 indicates where an item is lacked because it cannot be afforded. If the working-age adult has the item, the item is not needed or wanted, or the question does not apply then a score of 0 is given. This score is multiplied by the relevant prevalence weight. The scores on each item are summed and then divided by the total maximum score; this results in a continuous distribution of scores ranging from 0 to 1. The scores are multiplied by 100 to make them easier to interpret. The final scores, therefore, range from 0 to 100, with any working-age adults lacking all items which other working-age adults had access to scoring 100.

A working-age adult is in combined low income and working-age adult material deprivation if they have a final working-age adult material deprivation score of 25 or more and a household income below the relevant threshold of median income.

Every year the FRS does not gather data on a small proportion of working-age adults (this proportion varies year-on-year but is typically between 5-10%). Missing data appears to be non-random and predominantly is missing from those living in multiple benefit unit households or working-age adults in a couple where the other member of that couple is of pension age.

Missing values are therefore imputed using a method called hot-decking. Hot-decking 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. The specific variables used for the hot-decking procedure are:

  • benefit unit income

  • economic status

  • number of dependent children

  • savings held by the benefit unit

  • age that the head of benefit unit left education

  • ethnic group of the head of the benefit unit

  • family type (couple / single)

  • disability in the benefit unit

  • government region

This method ensures that imputed solutions are realistic and allows a wide range of outcomes which maintain variability in the data. This approach is also used for missing data in the child material deprivation measure.

More information on the methodology for the working-age adults measures accompanied the initial release of the data as official statistics in development in March 2022.

Below are the material deprivation prevalence weights for working-age adults in FYE 2023. Please note these are different to the weights given for the same questions in Table 4, as these relate to the children measure.

Table 5: Material deprivation scores used for working-age adults in FYE 2023

Material deprivation questions Weights Final Scores
For working-age adults    
Enough money to keep home in a decent state of decoration 0.768 11.59
A holiday away from home for at least 1 week a year, whilst not staying with relatives at their home 0.630 9.51
Household contents insurance 0.652 9.84
Regular savings of £10 a month or more for rainy days or retirement 0.692 10.45
Replace any worn out furniture 0.607 9.16
Replace or repair major electrical goods such as a refrigerator or a washing machine, when broken 0.676 10.20
A small amount of money to spend each week on yourself, not on your family 0.808 12.19
In winter, able to keep accommodation warm enough 0.856 12.91
Keep up with bills and regular debt payments 0.937 14.15
Sum of all weights 6.626 100

Material deprivation for pensioners

A suite of questions designed to capture the material deprivation experienced by pensioner families has been included in the Family Resources Survey since May 2008. Respondents are asked whether they have access to 15 goods and services. The list of items was identified by independent academic analysis. See Legard, R., Gray, M. and Blake, M. (2008), Cognitive testing: older people and the FRS material deprivation questions, Department for Work and Pensions Working Paper Number 55 and McKay, S. (2008), Measuring material deprivation among older people: Methodological study to revise the Family Resources Survey questions, Department for Work and Pensions Working Paper Number 54. Together, these questions form the best discriminator between those pensioner families that are deprived and those that are not. Note that this measure is only available for pensioners aged 65 or over.

Where they do not have a good or service, they are asked whether this is because:

  • they do not have the money for this;

  • it is not a priority on their current income;

  • their health / disability prevents them;

  • it is too much trouble or tiring;

  • they have no one to do this with or help them;

  • it is not something they want, it is not relevant to them; or

  • other.

A pensioner is counted as being deprived of an item where they lack it for one of the following reasons:

  • they do not have the money for this;

  • it is not a priority on their current income;

  • their health / disability prevents them;

  • it is too much trouble or tiring;

  • they have no one to do this with or help them; or

  • other.

The exception to this is for the unexpected expense question, where the follow up question was asked to explore how those who responded ‘yes’ would pay. Options were:

  • use own income but cut back on essentials;

  • use own income but not need to cut back on essentials;

  • use savings;

  • use a form of credit;

  • get money from friends or family; or

  • other.

Pensioners are counted as materially deprived for this item if and only if they responded ‘no’ to the initial question.

The same prevalence weighted approach has been used to that for children, in determining a deprivation score. Prevalence weighting is a technique of scoring deprivation in which more weight in the deprivation measure is given to families lacking those items that most pensioner families already have. This means a greater importance, when an item is lacked, is assigned to those items that are more commonly owned in the pensioner population.

For each question a score of 1 indicates where an item is lacked because of the reasons outlined on the previous page. If the pensioner family has the item, the item is not needed or wanted, or the question does not apply then a score of 0 is given. This score is then multiplied by the relevant prevalence weight. The scores on each item are summed and divided by the total maximum score; this results in a continuous distribution of scores ranging from 0 to 1. The scores are multiplied by 100 to make them easier to interpret. The final scores, therefore, range from 0 to 100, with any families lacking all items which other families had access to scoring 100. The prevalence weights are shown in Table 6 below.

Table 6: Material deprivation scores used for pensioners in FYE 2023

Material deprivation questions Weights Final Scores
For pensioners aged 65 and over    
At least 1 filling meal a day 0.986 7.26
Go out socially at least once a month 0.758 5.58
See friends or family at least once a month 0.935 6.89
Take a holiday away from home 0.567 4.17
Able to replace cooker if it broke down 0.914 6.73
Home kept in a good state of repair 0.968 7.13
Heating, electrics, plumbing and drains working 0.985 7.25
Have a damp-free home 0.942 6.94
Home kept adequately warm 0.945 6.96
Able to pay regular bills 0.967 7.12
Have a telephone to use, whenever needed 0.922 6.79
Have access to a car or taxi, whenever needed 0.924 6.80
Have hair done or cut regularly 0.868 6.39
Have a warm waterproof coat 0.986 7.26
Able to pay an unexpected expense of £200 0.912 6.71
Sum of all weights 13.579 100

A pensioner is in material deprivation if they live in a family that has a final score of 20 or more. For children and working-age adults, material deprivation is presented as an indicator in combination with a low-income threshold. However, for pensioners, the concept of material deprivation is broad and very different from low income, therefore it is appropriate to present it as a separate measure.

A technical note giving a full explanation of the pensioner material deprivation measure is available.

Material deprivation weighting methodology

We currently recalculate the prevalence weights each year based on the question responses from that year. The maximum possible material deprivation score for each year is then rescaled to 100 for ease of interpretation, and children in a family with a score of at least 25, working-age adults with a score of at least 25 or pensioners with a score of 20 or more, are classed as being materially deprived. If over time more families can afford a certain item, then a family lacking such a good will see an increasing overall deprivation score and will be considered as becoming more materially deprived.

A concern which has been raised with the current method is that if there is a general increase in access to items, this should imply that a family lacking a particular number of items is now suffering from greater relative deprivation than before. However, because of the rescaling of scores to 100, each item lacked still counts the same amount towards the overall material deprivation score and a family is still required to lack the same number of items to reach a score of 25 and be declared materially deprived.

The HBAI Technical Advisory Group considered this issue. The Group agreed that this is a complex issue and recommended that any changes made should be implemented following a considered and evidence-based exploration of options. As a result, the Group agreed that the recommendation should be to continue to use the current methodology for material deprivation until such time as a thorough exploration of this issue can be conducted.

Consideration of the use of prevalence weighting was part of the 2022 published review by the London School of Economics on the UK material deprivation measures, and their recommendations will be considered as part of refreshing the measures for publication in March 2025.

Impact of the coronavirus (COVID-19) pandemic on the material deprivation measures

Material deprivation statistics released for FYE 2023 should not be directly compared with FYE 2022 and FYE 2021. In the FYE 2023 publication an assessment of change is made using FYE 2020 as the comparator year, and the timeseries is resumed from this point.

During the pandemic, several of the questions asked as part of the measure were affected by government restrictions introduced in response to the coronavirus (COVID-19) pandemic. This meant that it was not possible for those sampled to access several social opportunities or services during periods of lockdown, regardless of deprivation or financial constraint. These included opportunities such as school trips, socialising with friends or family, attending organised activities or pursuing hobbies, going on holiday, and getting a haircut. Some of those in the sample may have responded to these questions with their ordinary circumstances in mind. Others may have responded according to their actual (lockdown affected) circumstances.

The effect was most marked for FYE 2021. For FYE 2022, although the impact on survey responses was reduced, restrictions remained in place throughout the first quarter of the survey year and the effect the coronavirus (COVID-19) pandemic had on social interactions continued to unwind during the remainder of the survey year as restrictions were removed and society began returning to normal. Therefore, for both FYE 2021 and FYE 2022, all estimates of material deprivation, including those combined with low-income measures, are not strictly comparable with the pre-pandemic period.

Refresh of Material Deprivation measurement in March 2025

The Code of Practice for Statistics requires regular reviews of Accredited Official Statistics. Material Deprivation items in the FRS on working age, pensioners and children were last reviewed in 2004, 2008 and 2011, respectively.

The Office for Statistics Regulation also made several recommendations in relation to the official UK material deprivation measures in 2021. This included a recommendation to review the current set of questions which underpin material deprivation and determine a way to compare material deprivation across groups.

In December 2021, the Department for Work and Pensions (DWP) commissioned the Centre for Analysis of Social Exclusion (CASE) at the London School of Economics and Political Science to conduct the review. A report on the outcome of this review has now been published.

Specifically, the aims of the review were to explore:

  • which material deprivation items for families with children, families with working-age adults and families with pensioners should be included in the Family Resources Survey (FRS);
  • the advantages and disadvantages of different approaches for determining who is materially deprived;
  • the advantages and disadvantages of developing a core set of questions for the whole population alongside measures aimed at working-age adults, children and pensioners; and
  • whether the advantages of updating the material deprivation measures outweigh the disadvantages.

In early 2022, focus groups were held to help assess whether, and what, changes were required to the items and activities included in the UK material deprivation measures. Participants were drawn from across the UK, different age groups, income groups, ethnic groups, gender, household types and disability status. Following the focus groups, set criteria were used to select a short-list of items and activities.

The result was a recommendation to test a short-list of 35 items and activities. Test questions were included in the FRS during April, May and June 2022. Following the pilot, the Steering Group for the project agreed introduce new questions into the FRS in FYE 2024. To support the presentation of analysis in the transition from one suite of measures to a new one, it was agreed that for FYE 2024, 75% of the FRS sample would be asked the new questions and the remaining 25% the old questions. A comparison will be published alongside the revised measures in March 2025.

The review concluded that the benefits of revising the measures outweighed the disadvantages. The main advantages included updating the items and activities to better reflect what the UK public perceive as necessary for a minimum acceptable standard of living, and standardisation in data collection methodology.

The review also examined and made recommendations in relation to the advantages and disadvantages of different approaches for determining who is materially deprived. These were: 1) determining optimum deprivation score thresholds; 2) prevalence weighting; 3) combining material deprivation status with low-income; 4) using simple absence versus constrained lack; 5) developing a core set of questions for the whole population.

We cannot determine the impact the review will have on the material deprivation measures as the FYE 2024 Family Resources Survey data is not yet available. Once the data is available, we will undertake further analysis to finalise the new measures and provide details of the decisions made. This will be published alongside estimates using the new measures in March 2025.

Household food security

In FYE 2020 measures of combined low income and household food security were added to the publication. To measure household food security, questions 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, the focus is on the period of 30 days leading up to interview. The questions are comparable to those used by other public bodies in the UK, and also internationally. From the questions, a ten-point household score is generated, and the household is given a food security status:

  • 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 1 or more household members were disrupted and food intake reduced because the household lacked money and other resources for food.

Households with high or marginal food security are “food secure”. Food secure households are considered to have sufficient, varied food to facilitate an active and healthy lifestyle. Households with low or very low food security are “food insecure”. Food insecure households have a risk of, or lack of access to, sufficient, varied food.

Food bank usage

A new series of questions was added to the FRS for FYE 2022 on the topic of food bank usage. Food bank usage questions are asked of the person in the household who knows the most about food purchasing and preparation. This means that 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. 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: 12 months prior to interview, and in the 30 days prior to interview. This means that caution may be needed when making direct comparisons between the FRS results and other research on this subject.

For details on household food security measurement and food bank questions please see the FRS Background Information and Methodology

Ethnicity categories

The ethnicity questions used in the FRS adopt the UK harmonised standards for use in major Government social surveys; that is, they adopt the standard way of collecting information on the ways in which people describe their ethnic identity. The latest harmonised standards were published in August 2011 and cover the ethnic group question in England, Wales, Scotland and Northern Ireland. They also cover harmonised data presentation for ethnic group outputs. The standards were updated in February 2013 detailing how Gypsy, Traveller and Irish Traveller should be recorded in the outputs, due to differences across the UK.

The FRS adopted these latest harmonised standards for England, Wales and Northern Ireland for the FYE 2012 survey questionnaire, and the standards for Scotland were adopted for the FYE 2013 survey questionnaire. The FYE 2012 publication therefore adopted the latest harmonised output standards for ethnic groups for the UK. The most significant changes to previous publications are that the ‘Chinese’ category has moved from the ‘Chinese or other ethnic group’ section to the ‘Asian/Asian British’ section; and ‘Irish Traveller’ is included under ‘Other ethnic group’ for respondents in Northern Ireland and ‘Gypsy or Irish Traveller’ is included under the ‘White’ section for respondents in Great Britain, therefore UK figures have been allocated accordingly.

Disability definition

The means of identifying people with a disability has changed over time. Data are not available for FYE 1995. Up until FYE 2002 all those who reported having a long-standing limiting illness were identified as having a disability. From FYE 2003, statistics are based on responses to questions about difficulties across a number of areas of life. Figures for FYE 2003 and FYE 2004 are based on those reporting substantial difficulties across eight areas of life and figures from FYE 2005 to FYE 2012 are based on those reporting substantial difficulties across nine areas of life. From FYE 2013 the FRS disability questions were revised to reflect new harmonised standards. Disabled people are identified as those who report any physical or mental health condition(s) or illness(es) that last or are expected to last 12 months or more, and which limit their ability to carry out day-to-day activities a little, or a lot.

FRS questions FYE 2005 to FYE 2012

The FRS/HBAI definition for an adult with a disability is if they answered yes to the ‘Health’ question and yes to any of the difficulties listed in ‘DisDif’.

Health:

Do you have any long-standing illness, disability or infirmity? By ‘long-standing’ I mean anything that has troubled you over a period of at least 12 months or that is likely to affect you over a period of at least 12 months.

If ‘yes’ to Health.

Health Problem Limit Activities (HProb):

Does this physical or mental illness or disability (Do any of these physical or mental illnesses or disabilities) limit your activities in any way?

If ‘yes’ to Health.

Health Problems cause Difficulties (DisDif):

SHOW CARD E1

Does this/Do these health problem(s) or disability(ies) mean that you have substantial difficulties with any of these areas of your life? Please read out the numbers from the card next to the ones which apply to you.

PROBE: Which others?

  1. Mobility (moving about)

  2. Lifting, carrying or moving objects

  3. Manual dexterity (using your hands to carry out everyday tasks)

  4. Continence (bladder and bowel control)

  5. Communication (speech, hearing or eyesight)

  6. Memory or ability to concentrate, learn or understand

  7. Recognising when you are in physical danger

  8. Your physical co-ordination (e.g.: balance)

  9. Other health problem or disability

  10. None of these

FRS questions FYE 2013 onwards

The FRS/HBAI definition for an adult with a disability is if they answered yes to the ‘Health1’ and yes, a lot or yes, a little to the ‘Condition’ question.

If ‘yes’ to Health1

Health Problems cause Difficulties (Dis1)

SHOW CARD E1

Do any of these conditions or illnesses affect you in any of the following areas?

  1. Vision (for example blindness or partial sight)

  2. Hearing (for example deafness or partial hearing)

  3. Mobility (for example walking short distances or climbing stairs)

  4. Dexterity (for example lifting and carrying objects, using a keyboard)

  5. Learning or understanding or concentrating

  6. Memory

  7. Mental Health

  8. Stamina or breathing or fatigue

  9. Socially or behaviourally (for example associated with autism, attention deficit disorder or Asperger’s syndrome)

  10. Other

  11. Refusal (spontaneous)

If Health1=Yes

Limiting longstanding illness (Condition)

Does your condition or illness/do any of your conditions or illnesses reduce your ability to carry-out day-to-day activities?

  1. Yes, a lot

  2. Yes, a little

  3. Not at all

INTERVIEWER: Day to day activities include washing and dressing, household cleaning, cooking, shopping for essentials, using public or private transport, remembering to pay bills, lifting objects from the ground or lifting objects from a work surface in the kitchen.

Comparisons over time

Compared to FYE 2012 the number of individuals in disabled families went up by 0.2m in FYE 2013 (similar to those in non-disabled families).

However, while the number of pensioners in non-disabled families increased by 0.4m, the number in disabled families decreased by 0.3m.

The reverse was true for the number of children in disabled families, which increased by 0.3m, while those in non-disabled families fell by 0.2m.

These figures could be affected by the change in the disability questions. Individuals might have different interpretations of particular health conditions or question wording meaning that changes to the disability question may have had a different effect on certain groups. Therefore, comparisons over time should be made with caution, as they may be affected by the change in the definition of disability.

Comparison with EU low-income statistics

The UK’s cross-Europe-comparable low-income statistics have previously been derived from the ONS Survey of Living Conditions, a different survey source than the HBAI, meaning that there will be some differences due to the different data source. In addition to this, the figures will differ for several further reasons:

  • Time period: The figures are presented on different timescales. The HBAI figures are presented for the financial year, while the EU comparable figures are presented for the calendar year.

  • Population groups: The European low-income statistics are presented in different age groups than the HBAI figures:

    • Children: the EU figures relate to those under 18 – HBAI figures are based on individuals aged under 16, in addition 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, are living with parents, and are in full-time non-advanced education or in unwaged government training;

    • Pensioners: EU figures relate to the 65+ population. The data in this report were collected throughout FYE 2022, during which the State Pension age for both men and women was 66 years.

  • Preferred measures: The European low-income estimates are usually presented on a Before Housing Costs basis, while this is consistent with the most commonly used measure for working-age adults and children, we choose to look at pensioners’ incomes after deducting housing costs as this better reflects pensioner living standards compared to others and over time.

  • Income derivation: The definition of income in the European figures differs from the official UK figures:

    • Pension contributions are not deducted from income in the European comparable methodology;

    • The European definition of income includes the value of non-cash employee income from company cars as employee income, which will raise the average income of people in work.

  • High income adjustment: For the HBAI figures an adjustment is made to sample cases at the top of the income distribution to correct for volatility in the highest incomes captured in the survey. This adjustment is not applied to the European figures.

  • In year deflation: The HBAI estimates make an in year adjustment to individuals’ incomes to ensure that respondents income collected across the financial year are comparable. This adjustment is not applied to the European figures.

  • Sample cases: The HBAI figures exclude cases containing a married adult whose spouse is temporarily absent whereas these are included in the European figures, however this has a minimal effect on the figures.

  • Income tax and national insurance: The European income tax and national insurance figures are calculated using a model of taxation, whilst the HBAI estimates are mostly calculated on the amount of tax and national insurance reported as being paid.

After the UK’s exit from the EU in 2020, some EU-SILC outputs were still delivered by ONS to Eurostat during the transition period in 2020, but all planned deliveries from 2021 onwards ceased. Data for the UK up to and including the 2018 calendar year remain available on Eurostat’s EU-SILC database.

Glossary

Adult

All those individuals who are aged 16 and over, unless defined as a dependent child (see Child); all adults in the household are interviewed as part of the Family Resources Survey (FRS).

Benefit units or Family

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

Bills in arrears

The number of bills in arrears is presented at a benefit unit level. Bills considered are: electricity, gas, other fuel, Council Tax, insurance, telephone, television / video rental, hire purchase, water rates, rent, mortgage payments and other loans. From FYE 2013 onwards, the analysis of income by whether people are behind with household bills has been extended to include rent, mortgage payments and other loans, so the figures are not comparable with those presented in previous reports.

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/a responsible adult; and

  • in full-time non-advanced education or in unwaged government training.

Confidence interval

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

Contemporary median income

The average income for the period covered by the survey. Household incomes are adjusted from the date of interview to an average of survey-year prices.

Deciles and Quintiles

These are income values which divide the whole population, when ranked by household income, into equal-sized groups. This helps to compare different groups of the population.

Decile and quintile are often used as a standard shorthand term for decile/quintile group.

Deciles groups are ten equal-sized groups – the lowest decile describes individuals with incomes in the bottom 10 per cent of the income distribution.

Quintiles groups are five equal-sized groups – the lowest quintile describes individuals with incomes in the bottom 20 per cent of the income distribution.

Disability

From FYE 2013 onwards, the definition of disability used 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’ is meant by more than minor or trivial, and long-term is meant by 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 and 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 and are no longer limited in their daily lives are still covered by the Act.

Economic status of the family

The economic status of the family classification is in line with the International Labour Organisation economic status classification. This means that no economic status data is available for FYE 1995 and FYE 1996 as the relevant information was not collected in the Family Resources Survey for these years. This also means the economic status of the family and economic status of the household classifications are aligned.

The ‘Workless, other inactive’ group consists of families in which all adults are economically inactive (i.e. where no adult is in work or unemployed). This includes working-age adults in receipt of sickness and disability benefits, who may have living standards lower than those implied by the results presented because of additional costs associated with their disability (for which no adjustment has been made here).

Families are allocated to the first applicable category:

  • One or more full-time self-employed – Benefit units where at least one adult usually works as self-employed in their main job where the respondent regards themselves as working full-time. Those respondents not working in the last seven days but doing unpaid work in their own business are considered as full-time self-employed.

  • Single or couple, all in full-time work – Benefit units where all adults regard themselves as working full-time. Those respondents not working in the last seven days doing unpaid work in a business that a relative owns are considered as in full-time work, as are those in training.

  • Couple, one in full-time work, one in part-time work – Benefit units headed by a couple where one partner considers themselves to be working full-time and the other partner considers themselves to be working part-time. Those respondents not working in the last seven days but doing an odd job are considered as working part-time.

  • Couple, one in full-time work, one not working – Benefit units headed by a couple, where one partner considers themselves to be working full-time and the other partner does not work.

  • No-one in full-time work, one or more in part-time work – Benefit units where at least one adult works but considers themselves to be working part-time.

  • Workless, one or more aged 60 or over – Benefit units where at least one adult is aged 60 or over.

  • Workless, one or more unemployed – Benefit units where at least one adult is unemployed.

  • Workless, other inactive – Benefit units not classified above (this group includes the long-term sick, disabled people and non-working single parents).

Economic status groups for children

Estimates for dependent children use an amended economic status classification closely related to the definitions used above. Children are grouped according to family type and the economic status of their parent(s) as defined in the previous section. As with the main economic status groups, individuals are allocated to the first category that applies in the following order:

  • Lone parent – In full-time work (includes full-time self-employed);

  • Lone parent – In part-time work; and

  • Lone parent – Not working (unemployed or inactive);

  • Couple with children – One or more full-time self-employed;

  • Couple with children – Both in full-time work;

  • Couple with children – One in full-time work, one in part-time work;

  • Couple with children – One in full-time work, one not working;

  • Couple with children – Neither in full-time work, one or more in part-time work; and

  • Couple with children – Both workless (unemployed or inactive).

Economic status of household

For the analysis of working and workless households, households are classified according to whether they contain a working-age adult or pensioner who works, but the status of non-working pensioners is not considered, except in the case of those households where children live only with pensioners, in which case the status of all adults is included.

Individuals are assigned to one of three categories:

  • All adults in work – A household where all working-age adults are in employment or are self-employed, or if there are no working-age adults in the household, at least one working pensioner.

  • At least one, but not all adults in work – A household where at least one working-age adult is in employment or is self-employed, or where a pensioner is in work if none of the working-age adults in the household are in work.

  • Workless household – A household where no adult members are in employment or are self-employed. Within households, pensioners are excluded from the classifications if they are not working and are included if they are working. So for example, a household with a pensioner in work, but a working-age person not in work, would be in the ‘At least one adult in work, but not all’ category. A household with all working-age adults in work and a pensioner not in work would be categorised as ‘All adults in work’.

Educational attainment

This looks at the highest level of educational attainment for each working-age adult. Information for students should be treated with some caution because they are often dependent on irregular flows of income. Only student loans are counted as income in HBAI, any other loans taken out are not. The figures are also not necessarily representative of all students because HBAI only covers private households and this excludes halls of residence.

Comparisons between the numbers with no qualifications in the FRS, LFS and the Census indicate that the FRS figures have historically overstated the numbers of working-age adults with no qualifications. As a result of the FRS mode change in FYE 2021 and FYE 2022, the raw FRS sample contained a much higher proportion of working age adults than in the years prior to the COVID-19 pandemic, and much lower numbers with no qualifications. We therefore introduced additional grossing controls in FYE 2021 and FYE 2022 to weight the sample by level of educational attainment. This boosted numbers with education levels below degree level in younger age groups. We did this using historical proportions from the FRS and calibrated growth over the two years to growth in levels recorded in the Annual Population Survey (APS), derived from the LFS. This maintained the previous relationship between the two sources while ensuring that grossed FRS proportions were more in line with expectations. Following the reintroduction of FRS face-to-face interviewing for FYE 2023, this additional weighting was no longer required, and the grossing returned to the FYE 2020 position.

In FYE 2023, development work to improve reporting on categories of level of education identified a separate issue with the FRS variable EDUCQUAL. We are investigating how this issue may have affected previous HBAI releases. Preliminary analysis has indicated that the issue primarily affects the classification of those with qualifications below degree level. On this basis, our current assessment is that the large increase in the representation of the degree group during the pandemic was a sampling issue and the decision to introduce additional grossing controls for this period was necessary. We will provide a further update when validation checks are complete.

Further information on the issue is provided in the FRS Background Information and Methodology.

Equivalisation

Income measures used in HBAI take account of variations in the size and composition of the households in which people live. This process is called equivalisation.

Equivalisation reflects the fact that a family of several people need a higher income than a single individual to achieve a comparable standard of living.

Equivalence scales conventionally take a couple with no children as the reference point. Consider a single person, a couple with no children, and a couple with two children aged twelve and ten, all having unadjusted weekly household incomes of £300 (BHC). The process of equivalisation, as conducted in HBAI, gives an equivalised income of £448 to the single person, £300 to the couple with no children, but only £214 to the couple with children.

Ethnicity

Ethnicity in HBAI reflect the harmonised standards included from the FYE 2012 publication onwards. The harmonised standards for Scotland were adopted in the FYE 2013 FRS questionnaire; however, there has been no change to the HBAI outputs as the harmonised output standards were previously adopted.

Individuals have been classified according to the ethnic group of the household reference person (see Household reference person) which means that information about households of multiple ethnicities is lost.

Smaller ethnic minority groups exhibit year-on-year variation which limits comparisons over time. For this reason, analysis by ethnicity is usually presented as three-year averages. Please note that following the decision to not publish breakdowns of the FYE 2021 estimates, all three-year averages calculated and published for any period including FYE 2021 are be based on two data points only.

Families/family unit

The terms ‘families’ and ‘family units’ are used interchangeably with benefit units. See Benefit unit definition.

Family type

For some analyses, individuals are classified into family type or economic status groups. Individuals are classified according to the status of the benefit unit in which they live. All individuals in a benefit unit (adults and children) will therefore be given the same classification. The classifications are defined below:

  • Pensioner couple – a couple where 1 or more of the adults are State Pension age or over. However, in the HBAI tables relating specifically to pensioners results for individuals who are in pensioner couples do not count anyone who is not a pensioner.

  • Single male pensioner – single male adult of State Pension age or over.

  • Single female pensioner – single female adult of State Pension age or over.

  • Couple with children – a non-pensioner couple with dependent children.

  • Single with children – a non-pensioner single adult with dependent children.

  • Couple without children – a non-pensioner couple with no dependent children.

  • Single male without children – a non-pensioner single adult male with no dependent children.

  • Single female without children – a non-pensioner single adult female with no dependent children.

Full-time work

The respondent regards themselves as working full-time, either as an employee or self-employed.

Gender

In any analysis of gender, it must be remembered that HBAI attempts to measure the living standards of an individual as determined by household income. This assumes that both partners in a couple benefit equally from the household’s income and will therefore appear at the same position in the income distribution. Any difference in figures can only be driven by gender differences for single adults, which will themselves be diluted by the figures for couples. The lower level gender disaggregation in the family type classification is therefore likely to be more informative.

Research has suggested that, particularly in low-income households, the above assumption with regard to income sharing is not always valid as men sometimes benefit at the expense of women from shared household income. This means that it is possible that HBAI results broken down by gender could understate differences between the two groups. See, for instance, Goode, J., Callender, C. and Lister, R. (1998) Purse or Wallet? Gender Inequalities and the Distribution of Income in Families on Benefits. JRF/Policy Studies Institute.

Gini coefficient

A widely-used, international standard summary measure of inequality. It can take values from zero to 100, where a value of zero would indicate total equality, with each household having an equal share of income, while higher values indicate greater inequality.

Head of benefit unit

The head of the first benefit unit will be the same as the household reference person. For second and subsequent benefit units, the head will be the first adult to be interviewed.

High Income

Results for the top 10 per cent are particularly susceptible to sampling errors and income measurement problems.

Household

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 both income from adults and 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:

Only households that report using a food bank in the last 12 month 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.

The questions are put to the person in each household who is best placed to answer about food shopping and preparation. These respondents are 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 1 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 (used from FYE 2002 onwards)

The household reference person (HRP) is usually the highest Income householder. Note:

  • 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.

The Head of benefit unit will not necessarily be the HRP.

Housing costs

Housing costs are made up of: rent (gross of housing benefit); water rates, community water charges and council water charges; mortgage interest payments (net of tax relief); structural insurance premiums (for owner occupiers); and ground rent and service charges.

Income

The income measure used in HBAI is weekly net (disposable) equivalised household income. This comprises total income from all sources of all household members including dependants. For BHC, housing costs are not deducted from income, while for AHC they are.

Households receive income from a variety of sources. The main ones are earnings, self-employment, state support (i.e. benefits and tax credits), interest on investments and occupational pensions.

In detail, income includes:

  • usual net earnings from employment;

  • profit or loss from self-employment (losses are treated as a negative income);

  • income received from dividends (from FYE 2022);

  • state support – all benefits and tax credits;

  • income from occupational and private pensions;

  • investment income;

  • maintenance payments;

  • income from educational grants and scholarships (including, for students, student loans and parental contributions); and

  • the cash value of certain forms of income in kind (free school meals, free school breakfast, free school milk, free school fruit and vegetables, Healthy Start vouchers and free TV licence for people 75 and over who receive Pension Credit).

Income is net of the following items:

  • income tax payments;

  • National Insurance contributions;

  • domestic rates / council tax;

  • contributions to occupational pension schemes (including all additional voluntary contributions (AVCs) to occupational pension schemes, and any contributions to stakeholder and personal pensions);

  • all maintenance and child support payments, which are deducted from the income of the person making the payment;

  • parental contributions to students living away from home; and

  • student loan repayments.

Income distribution

The spread of incomes across the population.

Income growth in real terms

For some years, income growth in the HBAI-based series appears slightly lower than the National Accounts estimates. The implication of this is that absolute real income growth could be understated in the HBAI series. Comparisons over a longer time period are believed to be more robust.

Income inequality

The extent of disparity between high income and low-income households, commonly measured using either the Gini coefficient or 90:10 ratio. The Gini coefficient is a widely-used, international standard summary measure of inequality. It can take values from zero to 100, where a value of zero would indicate total equality, with each household having an equal share of income, while higher values indicate greater inequality. The 90:10 ratio is the average (median) income of the top 20 per cent (quintile 5), divided by the average income of the bottom 20 per cent (quintile 1). The higher the number, the greater the gap between those with the highest incomes and those with the lowest incomes.

Low income

‘Low income’ is defined using thresholds derived from percentages of median income for the whole population. Households reporting the lowest incomes may not have the lowest living standards. The bottom 10 per cent of the income distribution should not, therefore, be interpreted as having the bottom 10 per cent of living standards. Results for the bottom 10 per cent are also particularly vulnerable to sampling errors and income measurement problems.

  • Individuals are said to be in relative low income if they live in a household with an equivalised income below a percentage of contemporary median income BHC or AHC. Relative low-income statistics fall if income growth at the lower end of the income distribution is greater than overall income growth.

  • Individuals are said to be in absolute low income if they live in a household with an equivalised income below a threshold of median income (for example 60 per cent of median income) in a specific year adjusted for inflation BHC or AHC. The FYE 2011 median is used in this report, in order to measure absolute low income as referenced in the Welfare Reform and Work Act 2016, and to keep the absolute measure more in line with contemporary living standards. Absolute low-income statistics fall if low-income households are seeing their incomes rise faster than inflation.

Material deprivation for children

A suite of questions designed to capture the material deprivation experienced by families with children has been included in the FRS since FYE 2005. Respondents are asked whether they have 21 goods and services, including child, adult and household items. If they do not have them, they are asked whether this is because they do not want them or because they cannot afford them. These questions are used as an additional way of measuring living standards for children and their families. A prevalence weighted approach has been used in combination with relative or absolute low-income thresholds.

Combined low income and child material deprivation

A child is in combined low income and child material deprivation if they live in a family that has a final child material deprivation score of 25 or more and an equivalised household income below 50/60/70 per cent of relative/absolute median income BHC.

Material deprivation for working-age adults

Measures of combined low income and working-age adult material deprivation are available since FYE 2011 and were first published in HBAI in FYE 2022. Working-age adults are asked whether they have access to 9 goods and services. If they do not have them, they are asked whether this is because they do not want them or because they cannot afford them. A prevalence weighted approach has been used in combination with relative or absolute low-income thresholds.

Combined low income and working-age adult material deprivation

A working-age adult is in combined low income and working-age adult material deprivation if they have a final working-age adult material deprivation score of 25 or more and a household income below the relevant threshold of median income, before housing costs.

Material deprivation for pensioners

A suite of questions designed to capture the material deprivation experienced by pensioners aged 65 or over has been included in the Family Resources Survey since May 2008. These questions are used as an additional way of measuring living standards for pensioners. Respondents are asked whether they have access to 15 goods, services and experiences. Where a pensioner lacks one of the material deprivation items for one of the following reasons they are counted as being deprived for that item:

  • they do not have the money for this;

  • it is not a priority on their current income;

  • their health / disability prevents them;

  • it is too much trouble or tiring;

  • they have no one to do this with or help them; or

  • other.

The exception to this is for the unexpected expense question, where pensioners are counted as materially deprived for this item if and only if they responded ‘no’ to the initial question.

A prevalence weighted approach has been used.

Mean

Mean equivalised household income of individuals is found by adding up equivalised household incomes for each individual in a population and dividing the result by the number of people.

Median

Median household income divides the population, when ranked by equivalised household income, into two equal-sized groups. Contemporary median income refers to the median income in the survey year being considered.

Part-time work

The respondent regards themselves as working part-time, either as an employee or self-employed.

Pensioner

Pensioners are defined as all those adults at or above State Pension age (SPa).

For women born on or before 5th April 1950, SPa is 60. Since 6 April 2010, the State Pension age for women increased until it matched men’s SPa of 65 in November 2018. The State Pension age for men and women then increased together, reaching 66 by October 2020.

State pension age timetables are available.

Pensioner classifications

In HBAI tables relating to ‘all individuals’, the classification pensioner couple includes individuals in a family unit where one member is above State Pension age, and one is below. This differs from results in HBAI tables relating specifically to ‘pensioners’, where only individuals above State Pension age are included. Thus, if a pensioner above State Pension age has a working-age partner, they will both be included under results for pensioner couple in ‘all individuals’ tables, but in ‘pensioner’ tables the working-age partner will be excluded as they will appear in the ‘working-age population’ tables.

Prevalence weighting

Prevalence weighting is a technique of scoring deprivation, in which more weight in the deprivation measure is given to families lacking those items that most families already have. This means a greater importance, when an item is lacked, is assigned to those items that are more commonly owned in the population.

Region and country

Regional classifications are based on the standard statistical geography of the former Government Office Regions: nine in England, and a single region for each of Scotland, Wales and Northern Ireland. These regions are built up of complete counties or unitary authorities. Tables also include statistics for England as a whole, and detailed breakdown tables split London into Inner and Outer London to aid comparison with other Family Resources Survey-based publications. For more information see ONS’s webpage on UK Geographies.

Disaggregation by geographical regions is usually presented as three-year averages. This presentation has been used as single-year regional estimates are considered too volatile. Please note that following the decision to not publish breakdowns of the FYE 2021 estimates, all three-year averages calculated and published for any period including FYE 2021 are based on two data points only.

Estimates for the UK are shown as single-year estimates for the latest available year.

Although the FRS sample is large enough to allow some analysis to be performed at a regional level, it should be noted that no adjustment has been made for regional cost of living differences, as the necessary data are not available. In the analysis here it is therefore assumed that there is no difference in the cost of living between regions, although the AHC measure will partly take into take account of differences in housing costs.

Sampling error

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

Savings and investments

The total value of all liquid assets, including fixed term investments. Figures are taken from responses to questions on the value of assets or estimated from the interest on the savings when these questions are not asked. Note that banded savings do not include assets held by children in the benefit unit/household. The derivation of total savings used in the tables means that “no savings” specifically relates to cases where the respondent said that they had no accounts/investments, refused to answer, didn’t know, or some accounts/investments were recorded but none of them yielded any interest/dividends.

The data relating to investments and savings should be treated with caution. Questions relating to investments are a sensitive section of the questionnaire and have a low response rate. A high proportion of respondents do not know the interest received on their investments. It is likely that there is some under-reporting of capital by respondents, in terms of both the actual values of the savings and the investment income.

The level of savings and investments, for some families (benefit units) and households was estimated using a slightly different methodology from FYE 2020 than in previous years. The new method more accurately estimates savings in current accounts and basic bank accounts. It should be noted that savings and investments breakdowns from FYE 2020 are not directly comparable with those for previous years.

Skewness

Skewness measures the degree to which a statistical distribution is asymmetrical or lopsided. A perfectly symmetrical distribution is not skewed. A distribution with a long tail to the right, such as the UK income distribution, is positively skewed.

Sources of income

Households receive income from a variety of sources. The main ones are earnings, state support (i.e. benefits and tax credits), interest on investments and occupational pensions.

It should be noted that comparisons with National Accounts data would suggest that surveys such as the FRS understate investment income. It is also the case that the FRS underestimates receipt of most types of State Support.

State support

The Government pays money to individuals in order to support them financially under various circumstances. Most of these benefits are administered by DWP. The exceptions are Housing Benefit and Council Tax Reduction, which are administered by local authorities. Tax Credits are not treated as benefits, but both Tax Credits and benefits are included in the term State Support. Further information on UK state support and specific benefits for devolved administrations is available under ‘Benefits’ in the Glossary section of the FRS Background Information and Methodology.

Threshold

An equivalised income value used for comparing sections of an income distribution over time or for comparing proportions of groups over time, for example: fractions of FYE 2011 median income or fractions of contemporary medians. A relative threshold is relative to the contemporary median for each year’s survey. A fixed threshold uses the median from an ‘anchor’ year which is then uprated for inflation as appropriate. For example, the absolute threshold ‘60 per cent of the FYE 2011 median income’ in FYE 2011 is the same as the relative threshold, but the corresponding value in the latest survey year has been up-rated by inflation from the FYE 2011 level over the intervening period.

Working-age

Working-age adults are defined as all adults below State Pension age.

Annex 1: Benefit and tax reform in FYE 2023

This Annex summarises some of the major benefit and tax reforms which came into effect in FYE 2023. It is not intended to represent an exhaustive list.

Council Tax

The Department for Levelling Up, Housing and Communities estimated that the average Band D council tax set by local authorities in England for 2022 to 2023 increased by 3.5% from 2021 to 2022 levels.

In Wales, the average band D council tax for Wales for 2022-23 represented an increase of 2.7% from 2021 to 2022 levels.

In Scotland, the average band D council tax for Scotland for 2022-23 represented an increase of 3% from 2021 to 2022 levels.

In Northern Ireland, there were increases in rates (poundage) of no more than one per cent in some council areas, but in others the rates (poundage) remained as it was in 2022 to 2023.

Additionally, the government introduced a £150 non-repayable rebate for households in England in council tax 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.

Income Tax

The personal allowance and its related income limit remained the same as in the 2021 to 2022 year (£12,570, and £100,000 annually). Both the rates and the bands for Basic, Higher and Additional income tax were also 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 bands.

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 increases 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 in April 2022 was reversed partway through the survey year, from 6 November 2022. This rate was thereafter 12%.

For the self-employed, the 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 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

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 National Minimum Wage 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 National Minimum Wage 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.

Uprating

In April 2022:

  • inflation-linked benefits and tax credits rose by 3.1% in line with the Consumer Prices Index (CPI).

  • 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 the increase in earnings, price inflation as measured by the CPI or 2.5%. The increase by CPI inflation of 3.1% applies this time. The Basic State Pension increased from £137.60 per week in 2021/22 to £141.85 per week, a cash increase of £4.25 per week. The New State Pension increased from £179.60 in 2021/22 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 with an additional £500 million of funding. A further extension was announced on 26 May 2022, extending the fund to 31 March 2023, and providing a further £500 million of funding 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 were not eligible for this scheme, but equivalent support of £600 per household was provided.

Cost of Living Payments

Households on means tested benefits, including Universal Credit, Pension Credit and Tax Credits, received a payment of £650 this year. This was made automatically in two instalments, one in summer and another in the autumn, and is in addition to the £400 discount on energy bills. Eligible households could have received up to 3 different types of payment depending on their circumstances on specific dates or during a particular period.

  • 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 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 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. Up to £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 increased from £140 to £150 and was discounted automatically from bills. 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, or for households in Scotland, met their energy supplier’s criteria for the scheme.

The Warm Home Discount scheme was not available in Northern Ireland.

Cold weather payments

An extra £25 a week was available between 1 November 2022 to 31 March 2023, for households already in receipt of the eligible benefits.

Households in Scotland were not eligible for Cold Weather Payments. Instead, households may have been eligible for the Winter Heating Payment provided by the Scottish Government.

Wales Fuel Support Scheme

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

Annex 2: Other relevant statistics

The HBAI report and statistics are released alongside a number of other statistics focused on income and low-income statistics across Government.

In February 2015 the United Kingdom Statistics Authority (UKSA) published a report on the outcome of a monitoring review into the Coherence and Accessibility of Official Statistics on Income and Earnings. A progress report was published in January 2016, with a further update in December 2018.

This review considered the way in which official statistics about income and earnings across Government are presented and includes summary details of the official statistics within the Review’s scope; discussion of the conceptual issues faced by users and advice needed when attempting to analyse official statistics; and makes recommendations around potential solutions to concerns identified and for the longer-term development of income and earnings statistics.

The Office for Statistics Regulation (OSR) published a further review of income-based poverty statistics on 19 May 2021. This included background information on why the review was commissioned as well as the findings and recommendations for statistics producers. Recommendations focussed on key areas including accessibility and guidance, understanding poverty, data gaps, data quality, and trustworthiness. Several of the recommendations were taken account of in the FYE 2021 and FYE 2022 HBAI publications. For example, reporting of material deprivation measures was extended to include working age adults, and a section on the strengths and limitations of the HBAI was added to the main statistical report.

Below Average Resources: a new poverty measure

DWP are developing a new additional poverty measure named ‘Below Average Resources’ (BAR) based on the approach proposed by the Social Metrics Commission (SMC). The BAR approach provides a more expansive view of available resources (both savings and inescapable costs) than the income measurement adopted under HBAI, and includes some methodological changes proposed by the SMC.

The Office for Statistics Regulation (OSR) Review of Income-Based Poverty Statistics recommended that the 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 varying measures. Once fully developed, the BAR measure will add to the understanding of poverty in the UK alongside HBAI.

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

Integration of administrative data into the FRS and HBAI estimates

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 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.

A technical report on FRS transformation, with illustrative results for DWP benefits, was published in March 2024 alongside the FRS publication. Our development work is continuing so we plan to follow this up, with further results using HMRC PAYE and Self-Assessment data, and other administrative sources, during FYE 2025. Our intention is to include details on how the use of administrative data might affect HBAI low-income estimates.

Please see the DWP Statistical Work Programme for updates on the status of these projects and planned future releases.

Income and earnings statistics interactive tool

Work was taken forward by the Government Statistical Service (GSS) Coherence Team at the Office for National Statistics (ONS), who carried out a review of signposting across income and earnings statistics and made several recommendations for improvement. The ONS also developed a new interactive tool which can be used to identify sources of statistics on income and earnings, and their key features.

The statistics highlighted below represent several statistical releases which might be considered alongside results from HBAI to give a more complete picture. This is not intended to be an exhaustive list and should be considered alongside details from the reviews highlighted, as well as ONS guidance on sources of data on earnings and income, with additional details at on important questions also available.

Poverty and income inequality in Scotland

In-depth analysis of HBAI data for Scotland.

Poverty statistics for Wales

In-depth analysis of relative income poverty in Wales can be found on the relative income poverty page of the Welsh Government website, which has links to material deprivation and persistent poverty analysis.

Households Below Average Income Report for Northern Ireland

In-depth analysis of HBAI data for Northern Ireland.

EU comparisons

After the UK’s exit from the EU in 2020, some EU-SILC outputs were still delivered by ONS to Eurostat during the transition period in 2020, but all planned deliveries from 2021 onwards ceased. Data for the UK up to and including the 2018 calendar year remain available on Eurostat’s EU-SILC database.

Details of the differences between the EU and HBAI methodology are given in the main body of this report.

OECD international comparisons

The OECD income distribution database provides international comparisons on trends and levels in Gini coefficients before and after taxes and transfers, average household disposable incomes, relative poverty rates and poverty gaps, before and after taxes and transfers.

The effects of taxes and benefits on household income

The UK has two main, official data sources of household income statistics: the Family Resources Survey (FRS) run by the Department for Work and Pensions (DWP) and the Household Finances Survey (HFS) run by the Office for National Statistics (ONS).

The FRS estimates underpin DWP’s Households Below Average Income (HBAI) series, which is the UK’s primary source of poverty estimates. With a larger sample size, it is also the main source on household incomes. HFS data are used to produce ONS’s Household Disposable Income Inequality (HDII) and Effects of Taxes and Benefits (ETB) series, and are the main source for considering the overall financial well-being of households.

The two sources of data are complimentary but there are some important methodological differences between them which means that their income estimates can be different. For example, the FRS focuses on respondents’ weekly incomes at the time of interview, whereas HFS focuses more on annual income. The treatment of pension contributions also differs, with ONS’ estimate of Gross Household Income being calculated before pension contributions. Further details are available in the income and earnings statistics guide.

Pensioners’ Incomes

The Pensioners’ Income (PI) publication gives more a more detailed analysis of pensioners’ incomes.

Family Resources Survey

The Family Resources Survey (FRS) publication gives some further results of FRS data analysis.

Income Dynamics

Income Dynamics (ID) is a publication based on longitudinal data, containing analysis of income movements and the persistence of low income for various population groups.

It supersedes Low-Income Dynamics, which was last published in September 2010.

Personal Incomes statistics

The Personal Incomes Statistics publication gives summary information about UK taxpayers, their income and the Income Tax to which they are liable.

Wealth in Great Britain

The Wealth and Assets Survey (WAS) is a large-scale longitudinal survey with seven rounds currently published. Round 7 (2018 to 2020) had a sample of around 18,000 private households or 39,000 individuals in Great Britain. It is conducted by the Office for National Statistics (ONS). The WAS dataset holds information about the economic status of households and individuals including their physical and financial assets, debts, and pension provision. WAS data are also used to understand how wealth is distributed and the factors which may affect financial planning, as well as a respondents’ attitudes and behaviours towards saving. The Pension Wealth tables in WAS provides estimates of the types of private (non-state) pension wealth, split by a wide range of socio-demographic and economic breakdowns

Measuring National Well-being

The Measures of National Well-being Dashboard: Quality of Life in the UK brings together the latest national well-being data from the Office for National Statistics (ONS) and other sources to give an overview of how the UK is doing across the 10 areas of life that the UK public told us matter most.

Estimates of income and low-income levels for small areas

HBAI data cannot be broken down below the level of region, due to sample size and coverage issues. However, there are some data sources that present information at smaller geographies:

Children in Low-Income Families Local Area Statistics

Children in Low Income Families provides estimates of the number and proportion of children living in low-income families, Before Housing Costs (BHC), across the United Kingdom by local area.

Small area model-based income estimates for England and Wales

ONS produce model-based estimates of income at Middle Layer Super Output Area (MSOA) level for FYE 2020.

Admin-based income statistics, England and Wales

ONS also produce experimental estimates of gross and net income based on data from the Pay As You Earn and benefits systems.

English Indices of Deprivation

The English Indices of Deprivation, produced by the Ministry of Housing, Communities and Local Government is a measure of relative levels of deprivation in small areas of England called Lower Layer Super Output Areas.

Welsh Index of Multiple Deprivation

The Welsh Index of Multiple Deprivation (WIMD) is the official measure of deprivation in small areas in Wales. It is a relative measure of concentrations of deprivation at the small area level.

Scottish Index of Multiple Deprivation

The Scottish Index of Multiple Deprivation (SIMD) is the Scottish Government’s official tool for identifying those places in Scotland suffering from deprivation.

Northern Ireland Multiple Deprivation Measure

The Northern Ireland Multiple Deprivation Measure (NIMDM) is the official measure of spatial deprivation in Northern Ireland.

Annex 3: Uses and users of HBAI statistics

HBAI is a key source for data and information about household income. Users include: policy and analytical teams within the DWP, the Devolved Administrations and other government departments, local authorities, parliament, academics, journalists, and the voluntary sector.

Researchers and analysts outside government use the statistics and data to examine topics such as income inequality, the distributional impacts of fiscal policies and understanding the income profile of vulnerable groups. Examples of published reports using HBAI data include:

Within government the statistics and data are used:

  • to inform policy development and monitoring, and for international comparisons;

  • for three of the four income-related measures in the Welfare Reform and Work Act 2016 where the HBAI report presents data for the income-related measures related to relative low income, combined low income and child material deprivation, and absolute low income;

  • in the DWP’s Policy Simulation Model (PSM) used extensively by analysts in DWP and the Department for Communities in Northern Ireland, for policy evaluation and costing of policy options;

  • HM Treasury’s Inter-Governmental Tax Benefit Model (IGOTM) used to model possible tax and benefit changes before policy changes are decided and announced;

  • to provide further equality information in compliance with the specific duties under the Equality Act 2010, as well as to the Ethnicity Facts and Figures (formerly the Race Disparity Audit). The data is also referenced as a key source in the Equalities Data Audit, published by the Office for National Statistics; and;

  • as one of the financial indicator domain measures in the National Wellbeing Dashboard, published by the Office for National Statistics (ONS) to measure quality of life in the UK.

The Scottish Government uses the HBAI data:

The Welsh Government uses the HBAI data:

The Department for Communities in Northern Ireland uses HBAI data to produce the Northern Ireland Poverty and Income Inequality Report – the primary source for data and information about poverty and income inequality in Northern Ireland.

Annex 4: Communicating uncertainty

Introduction

The figures in this publication come from the Family Resources Survey. This is a survey of over 25 thousand households across the UK for FYE 2023. Like all surveys, it gathers information from a sample rather than from the whole population. The size of the sample and the way in which the sample is selected are both carefully designed to ensure that it is representative of the UK as whole, whilst bearing in mind practical considerations such as time and cost constraints. Survey results are always estimates, not precise figures. This means that they are subject to a level of uncertainty which can affect how changes, especially over the short term, should be interpreted.

Estimating and reporting uncertainty

Two different random samples from one population, for example the UK, are unlikely to give the same survey results and are likely to differ again from the results that would be obtained if the whole population was surveyed. The level of uncertainty around a survey estimate can be calculated and is commonly referred to as sampling error. In addition to sampling error the HBAI estimates can also be affected by non-sampling error such as non-response and a tendency to under-report benefit receipt.

We can calculate the level of uncertainty around a survey estimate by exploring how that estimate would change if we were to draw many survey samples for the same time period instead of just one. This allows us to define a range around the estimate (known as a “confidence interval”) and to state how likely it is that the real value that the survey is trying to measure lies within that range. Confidence intervals are typically set up so that we can be 95% sure that the true value lies within the range – in which case this range is referred to as a “95% confidence interval”.

Measuring the size of sampling error

Accuracy of the statistics: Confidence intervals are used as a guide to the size of sampling error. A confidence interval is a range around an estimate which states how likely it is that the real value the survey is trying to measure lies within that range. A wider confidence interval indicates a greater uncertainty around the estimate. Generally, a smaller sample size will lead to estimates that have a wider confidence interval than estimates from larger sample sizes. This is because a smaller sample is less likely than a larger sample to reflect the characteristics of the total population and therefore there will be more uncertainty around the estimate derived from the sample.

Statistical significance: Some changes in estimates from one year to the next will be the result of different samples being chosen, whilst other changes will reflect underlying changes in income across the population. Confidence intervals can be used to identify changes in the data that are statistically significant; that is, they are unlikely to have occurred by chance due to a particular sample being chosen.

Confidence intervals can give a range around the difference in a result from one year to the next. If the range does not include zero it indicates this change is unlikely to be the result of chance. The examples below give more detail on how confidence intervals can be interpreted.

In the commentary report, results that are statistically significant are shown with an asterisk. Any results not marked by an asterisk are likely to have occurred as a result of chance. The HBAI estimates that are presented are the best estimate of the real value that the survey is trying to measure.

In the summary tables presented in this report, estimates of the percentage in low income that are statistically significant from the previous year are shown with the notation [s], with further information in the Uncertainty and Commentary Tables pages. Estimates of the number in low income that are statistically significant from the previous year are also shown with the notation [s]. Changes marked by an asterisk are unlikely to have occurred as a result of chance. The HBAI estimates that are presented are the best estimate of the real value that the survey is trying to measure.

Non-sampling error: In addition to sampling error, non-sampling error is another area of uncertainty and is present in all surveys as well as in censuses. Non-sampling error encompasses all error other than sampling error. Types of non-sampling error include: coverage error, non-response error, measurement error and processing error. These errors are minimised in this survey by rigorous procedures; however, it is not possible to eliminate it completely and it cannot be quantified. It is important to bear in mind that confidence intervals are only a guide for the size of sampling error and cannot tell us anything about non-sampling error.

Working with uncertain estimates: Some changes between years will be small in relation to sampling variation and other sources of error and may not be statistically significant. This is relevant for particular sub-groups, as these will have smaller sample sizes than the overall survey sample size. For these sub-groups it is important to look at long-term trends.

Calculating uncertainty in the HBAI report

As the FRS is a sample from the UK population, any statistics derived from it are only estimates of the true numbers for the overall population. Prior to the FYE 2013 publication, confidence intervals for HBAI estimates were calculated using an estimating function approach. Since then, DWP has used bootstrapping techniques to measure how different a HBAI estimate might have looked if different FRS samples had been drawn.

The bootstrapping methodology used for the FYE 2013, FYE 2014 and FYE 2015 publications applied the original HBAI grossing factors to simple random resamples of the HBAI dataset to calculate confidence intervals for HBAI estimates.

The Institute for Fiscal Studies (IFS) were commissioned to develop the DWP methodology further to account as fully as possible for the specific features of the FRS sampling design for Great Britain (GB) and Northern Ireland (NI) and HBAI grossing process.

The new methodology, introduced from the FYE 2016 publication onwards, produces:

  • GB resamples simulating the FRS stratified, cluster sampling of GB households.

  • NI resamples simulating the FRS stratified sampling of NI households.

  • A unique set of grossing factors for each GB and NI resample, replicating the original HBAI grossing process, to produce lower and upper confidence intervals.

Accounting for:

  • Cluster sampling – this widens confidence intervals for most estimates, reflecting that this feature makes survey estimates less precise.

  • Post-sample grossing to population totals – this narrows confidence intervals for estimates sensitive to incomes towards the very top of the income distribution, as specific control totals are set for high income individuals.

Further details on methodological work undertaken by IFS, together with illustrative details of the impact of different aspects of the new methodology on key HBAI estimates for FYE 2014, are available in the published IFS report.

The following diagrams present:

  • Figure A.4a: Summary of the New Bootstrapping Methodology

  • Figure A.4b: GB FRS Sampling and Bootstrapping Resampling Process

  • Figure A.4c: NI FRS Sampling and Bootstrapping Resampling Process

  • Figure A.4d: HBAI Grossing and Bootstrapping Grossing Process

Further development work has been carried out on the derivation of the confidence intervals for HBAI estimates in the FYE 2017 publication, meaning results published in reports before this date may have changed slightly. The resample grossing factor datasets from FYE 1995 to the latest published year have been deposited at the UK Data Archive, along with user guidance on creating confidence intervals.

Figure A.4a: Summary of the New Bootstrapping Methodology

Figure A.4b: Great Britain FRS Sampling and Bootstrapping Resampling Process

Figure A.4c: Northern Ireland FRS Sampling and Bootstrapping Resampling Process

Figure A.4d: HBAI Grossing and Bootstrapping Grossing Process

95 per cent confidence intervals

Confidence intervals are typically set up so that we can be 95 per cent sure that the true value lies within a certain range – in which case this range is referred to as a “95 per cent confidence interval”.

Example 1: Interpreting confidence intervals

17 per cent of individuals are estimated to be living in relative low income BHC. This figure has a stated confidence interval of 16 to 18 per cent (Table 8b). This means that we can be 95 per cent sure that between 16 and 18 per cent of individuals are in relative low income. Our best estimate is 17 per cent of individuals.

As well as calculating confidence intervals around the results obtained from one year of the survey, confidence intervals can also be calculated for the changes in results across survey years.

Example 2: Statistical significance

The estimated change in the percentage of individuals living in relative low income BHC from FYE 2022 to FYE 2023 is an increase of 1 percentage points (Table 8b). The confidence interval around this figure is -1 to 2 percentage points. This means that we can be 95 per cent sure that the actual change in the percentage of people living in relative low income is between a decrease of -1 percentage points and an increase of 2 percentage points, with the best estimate being an increase of 1 percentage points. As the confidence interval includes zero this change is not statistically significant, which indicates that there is at least a 5 per cent probability that the change in the percentage of individuals in relative low income is the result of chance.

If the confidence interval did not include zero, we would conclude that the change is statistically significant i.e. the change is unlikely to be the result of chance.

Annex 5: Use of a mixed mode approach to data collection in FYE 2023: impact on the FRS sample and HBAI estimates

Introduction

The composition of the FRS sample for FYE 2021 and FYE 2022 was affected by the move to telephone interviewing in response to the coronavirus (COVID-19) pandemic. This change to the fieldwork introduced several new areas of bias and uncertainty, exacerbated by the smaller achieved sample sizes for those years, particularly for FYE 2021. For transparency, a full assessment of FRS data quality and impact on published HBAI estimates was issued alongside both sets of annual statistics.

For FYE 2023, fieldwork operations for the FRS returned to using face-to-face as the preferred method of interviewing with telephone interviewing retained as an alternative, ultimately used by 28% of all households.

This year, we have enhanced confidence in data quality due to the return of traditional fieldwork methods and the larger achieved sample size of 25,000 households, some 30% larger than was achieved in FYE 2020, and 50% higher than FYE 2022. As with other years, we have completed extensive quality assurance of all published estimates, including comparing changes with external data sources, and analysing subgroups in detail. The achieved sample compares well with FYE 2020, and representativeness has improved on what was observed during the pandemic.

We continue to advise users that changes in estimates over recent years should be interpreted being mindful of the differences in data collection approaches across the period and the effect this had on sample composition. Additional caution is advised when interpreting trends and larger changes in the data during and since the pandemic, and when comparing directly with pre-pandemic estimates.

The purpose of this annex is to provide information about the composition of the FRS sample for FYE 2023 and to assess how the return to face-to-face interviewing may have affected some of the FYE 2023 estimates. Some insight will also be provided on how the use of a mixed mode has helped improve the quality of the overall achieved sample by ensuring different routes were available to participate in the survey.

Changes in FRS sample composition during the Coronavirus (COVID-19) pandemic

Several dimensions of the achieved samples during the pandemic were found to have been affected by the move to telephone interviewing. Some of observed changes reflected genuine societal change but larger changes partially reflected the constraints of some groups to complete a telephone interview during the pandemic. While there was some recovery in the representativeness of the achieved sample in FYE 2022, in most circumstances it had still not returned to pre-pandemic levels.

Some of the key differences seen in FYE 2021 and FYE 2022 were:

Age

A higher proportion of respondents were pensioners, and they were more affluent than in previous periods, with higher levels of savings and a higher rate of receipt of occupational or private pensions. There were lower proportions of younger adults, particularly those aged 16 to 24 years.

Tenure

Higher proportions owned their accommodation outright, with the number of respondents who were renters being notably lower than pre-pandemic levels. In addition, a higher proportion of responding households were in higher Council Tax Bands, relative to previous years.

Educational Attainment

The achieved samples contained a disproportionate number of working-age respondents who had been educated to at least degree level.

Disability

We had concerns about the representativeness of respondents with a disability in terms of the types of impairments reported. For example, there were notable decreases in numbers reporting hearing, memory, or vision impairments compared with previous years. This was a consequence of the move to telephone interviewing rather than a real-world change in prevalence.

Ethnicity

Several non-white ethnic groups (notably Asian – Indian, Pakistani, and Chinese) were underrepresented with a smaller proportion of non-white households in the achieved sample.

Family type

During the pandemic, a smaller share of the working-age FRS respondents had children, and reductions were seen both in the proportion of single parents and couples with children. There was some recovery in FYE 2022 but percentages were still below FYE 2020. Larger households (containing 3 or more children) were especially affected and there was a marked fall in the share of families where the youngest child was below the age of five years.

Benefit receipt

In FYE 2021 there was an increase in the Universal Credit undercount compared to previous time periods, meaning the sample was missing a higher percentage of low-income households who received UC. However, in FYE 2022 representation was better than levels recorded pre-pandemic.

The age, tenure and family profiles were largely corrected for using the FRS grossing regime, which weights each person in the achieved sample using population (and tenure) control totals. Additional controls were also introduced into the FRS and HBAI to align recorded education levels to those in the general population, using other data sources.

However, it was not possible to control for all the observed bias in the sample. There is a limit to the number of grossing controls that can practically be introduced, particularly when working with smaller samples. And it is not possible to control for unobserved bias, for example, where the characteristics of those people sampled within each group are different to those sampled in previous years.

FRS data collection for FYE 2023

In Great Britain, survey fieldwork operations within ONS and NatCen for FRS 2022 to 2023 returned to face-to-face interviewing as the preferred method of data collection 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.

In Northern Ireland the sampling and fieldwork for the survey is carried out by the Central Survey Unit at the Northern Ireland Statistics and Research Agency (NISRA). Only telephone interviewing was used during the first quarter of the FYE 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.

Prevalence of each mode

Of the just over 25,000 households sampled in the FRS across the UK in FYE 2023, 72% had a face-to-face interview and 28% were interviewed by telephone. There were some differences in the prevalence of each option across regions and age groups.

Figure A.5a: Percentage of the FRS sample interviewed face-to-face or by telephone, by age group and family type, FYE 2023

A higher proportion of pensioner households responded to the FRS using face-to-face interviewing, compared with less than 70% of working-age households responding face-to-face. This may reflect differences in the availability of the different groups during the working day. Prevalence of telephone interviewing did not significantly vary depending on the type of working-age household, although a slightly higher proportion of single parent households were interviewed by telephone.

Figure A.5b: Percentage of FRS sample interviewed face-to-face or by telephone, by UK region, FYE 2023

There were small variations in the proportion of interviews conducted face-to-face across English regions – with London and the South East less likely to respond face-to-face. The proportion of London households responding face-to-face was 12 percentage points lower than the average of the other English regions.

Wales and Scotland had some of the highest rates of face-to-face participation, and Northern Ireland had the lowest, reflecting the slower transition back to face-to-face interviewing.

FRS Sample Composition for FYE 2023

Overall, the characteristics of those in the achieved FRS sample were much closer to FYE 2020 and more closely aligned with long-term trends and expectations.

There were some observed differences in sample composition between those who responded face-to-face compared with telephone, but in most cases these differences were marginal. Some of these differences are described below. While the analysis highlights some interesting findings, it should not be taken as providing definitive conclusions about the types of people likely to respond using each mode if it were offered in isolation. For example, if telephone interviewing were not offered at all, some households will still have responded face-to-face. In addition, the size of the telephone sample was much smaller than the face-to-face sample, so conclusions carry a higher degree of uncertainty.

Pensioners

In the 5 years prior to the pandemic, pensioner households typically made up between 34 and 35% of the achieved FRS sample. In FYE 2021 this increased to over 39% before falling back to 37.5% in FYE 2022. In FYE 2023 it was just under 37%.

The reintroduction of face-to-face interviewing meant that the achieved sample captured a larger proportion of pensioners aged over 85 years, with disabilities, and those who live in social rented sector housing. There was also a lower proportion who had income from occupational pensions, at a level now consistent with FYE 2020.

However, there were some marked differences in sample composition by mode of interview for pensioners. Those responding by telephone had on average higher incomes, across every income decile, and this gap increased towards the top end of the income distribution. This is a consequence of those pensioner households interviewed by telephone having higher average amounts of income from occupational pensions, and a higher proportion receiving and amount of employment income (which may reflect their younger age profile).

Figure A.5c: FRS weekly household median income from occupational pensions and employment income (where received), by mode of interview, FYE 2023

Working age and children

Improvements in the sample size in FYE 2023 has meant that the numbers of households with children sampled was higher than before the pandemic, but their share of total responses has declined as the percentage of pensioners have increased.

The percentage of both working-age couples and singles with children is similar to FYE 2022, as is the percentage of working age-couples without children. There is evidence of a shift from working-age adults having children to not having children compared to FYE 2020, but this partially reflects genuine demographic change.

Compared to pensioners, there were smaller differences in the sample composition by mode of interview for working-age adults and children. Telephone interviewing contained a slightly higher proportion of families with children, but working-age adults without children had a similar level of representation for each mode. A higher percentage of families with youngest child aged under 5 were seen in the face-to-face sample, and a higher percentage of larger families were in the telephone sample.

Smaller differences in reported average incomes were seen. Working age adults with children who were interviewed by telephone reported lower average incomes, the opposite direction to pensioners. Those without children reported higher average incomes by telephone. This reflects the slightly different characteristics of those sampled for each mode. These differences broadly offset so that reported incomes of working-age adults overall were similar for each mode.

Tenure

Although the proportion of those in the sample who own their property outright remained above pre-pandemic levels, there was a marked improvement in the proportion of both social and private renters. Once grossed, observed FYE 2023 proportions were very close to FYE 2020 for all groups, with slight variation in expected areas (e.g. continuation of the longer-term trend towards outright home ownership).

Overall, those responding face-to-face were more likely to be renting rather than having a mortgage or owning outright. This difference was most marked for families with children.

Educational Attainment

The proportion of respondents who reported being educated to degree level was in line with expectations taking account of the longer-term trends in the population, and other data sources such as the Annual Population Survey (APS) and the 2021 Census in England and Wales. Therefore, the weighting introduced during the pandemic to correct for the over-representation of this group since FYE 2020 was not necessary in FYE 2023.

There was no difference between the proportion of the face-to-face and telephone respondents who were educated to degree level. This may be a surprising finding given the changes we saw during the pandemic, but it is important to remember face-to-face interviewing was not offered during this period and offering telephone interviewing will have yielded different behavioural outcomes.

During FYE 2023 a change to the FRS variable EDUCQUAL was introduced to improve reporting on categories of level of education which identified a separate issue with historic values of the variable. While this does not affect recorded numbers with a degree for FYE 2023, we are investigating the extent to which it impacted the statistics during the pandemic.

Preliminary analysis has indicated that the issue primarily affects those with qualifications below degree level. On this basis, our current assessment is that the large increase in the representation of the degree group during the pandemic was a sampling issue and the decision to introduce additional grossing controls for this period was necessary. We will provide a further update when validation checks are complete.

Disability

It was reassuring to see that the FYE 2023 FRS sample included a much higher proportion of individuals reporting visual, hearing and memory impairments and that the overall profile of impairments is closer to what was measured in FYE 2020. Additionally, where there are areas of growth (for example, in numbers reporting mental health conditions), they are supported by external evidence of increased prevalence of these impairments in the population.

Figure A.5d: Proportion of individuals in the FRS sample reporting a disability by impairment type, FYE 2020 to FYE 2023

There were also marked differences in sample composition by mode of interview, confirming what was identified during the pandemic. Reintroducing face-to-face interviewing successfully brought underrepresented groups back into the sample, with the representation of other impairments, such as mental health, being unaffected by the mode. Compared to the face-to-face sample, a much higher proportion of those interviewed by telephone reported “other”, non-specified impairments.

Figure A.5e: Proportion of individuals in the FRS sample reporting a disability by impairment type and mode of interview, FYE 2023

Ethnicity

The achieved ethnicity sample for FYE 2023 is more diverse, with resumption of face-to-face interviewing successfully improving representation of all groups, bringing proportions closer to FYE 2020. However, the sample percentage share of several ethnic groups are still below pre-pandemic levels (although numbers sampled have increased).

Figure A.5f: Proportion of individuals in the FRS sample by ethnic group, FYE 2020 to FYE 2023

Benefit Receipt

The FYE 2023 UC undercount matched that recorded in FYE 2020. A higher proportion of those interviewed face-to-face were in receipt of Universal Credit, or receiving state benefits more generally.

Mode of interview and HBAI low-income rates

In our previous technical reports, we pointed to the need for additional care when presenting and interpreting data collected during the pandemic. Estimates published during this time may not be strictly comparable with the pre-pandemic period, but based on our extensive quality assurance we have confidence in the direction of change seen in the published statistics for those years.

We make a direct comparison to the FYE 2022 estimates when assessing the change in this year’s statistics. Although the FYE 2022 sample and estimates were subject to less variation compared with FYE 2021, caution is still advised when interpreting larger changes in the FYE 2023 data, and a comparison to FYE 2020 may be more appropriate for certain subgroups.

While for most groups a return to face-to-face interviewing is not the main driver of measured change, it will have been a contributory factor, given our assessment that the characteristics of those in the achieved sample this year is closer to FYE 2020 than FYE 2022.

To demonstrate this, we illustrate below the difference in the headline HBAI low-income rates by mode of interview, for our main groups.

Figure A.5g: Difference in HBAI low-income rates for those interviewed face-to-face compared to telephone, FYE 2023

In Figure A.5g, the bars show the difference in measured headline low-income estimates for those interviewed face-to-face compared to those interviewed by telephone. From this we can draw the following conclusions:

  • Generally, those interviewed face-to-face have higher rates of low income;

  • The mode difference is larger for pensioners, with those interviewed face-to-face having low-income rates typically 2 to 3 percentage points higher than those interviewed by telephone. This reflects the higher incomes reported by telephone respondents (around £50 per week); and

  • The impact is smaller (typically less than 1 percentage point) for children and working-age adult groups. Differences in reported incomes for these groups were £10 to £15 per week. However, the differences are larger for the AHC measures which reflects tenure profile composition for each group – for children, a higher proportion of those interviewed face-to-face were renters, with typically higher housing costs and therefore lower AHC incomes.

Conclusions

The main conclusions about how the HBAI was affected by the return of face-to-face interviewing and use of a mixed mode for FRS fieldwork are as follows:

  • the overall FRS sample (most of whom were interviewed face-to-face) is more representative in FYE 2023, with overall composition being much closer to FYE 2020 (pre-pandemic) levels;

  • use of a mixed mode 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). Likewise, it ensured that those who may have found telephone interviews less accessible due to individual circumstances (e.g. disability) were still able to respond to the survey. This will have improved the overall representativeness of the sample. However, we don’t know how many of those who responded by telephone would have responded face-to-face if that was the only available option;

  • illustrative analysis shows that there were some differences in the characteristics 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;

  • these differences were more pronounced for pensioners and led to larger differences in reported incomes. Pensioners who were sampled by telephone reported higher incomes on average;

  • the FYE 2023 pensioner low-income estimates are broadly flat or slightly increasing. Some of this change is real, reflecting below-inflation uprating of the State Pension in FYE 2023, and a reduction in the real value of occupational pensions over the period. However, the degree of change is also likely to have been influenced by changes in pensioner sample composition between the two survey years and the move from telephone back to face-to-face interviewing. This broader context should be borne in mind when interpreting the observed changes in pensioner low-income rates, and a comparison to FYE 2020 is recommended; and

  • more generally, we advise users to apply additional caution when interpreting trends and larger changes in the data during and since the pandemic, and when comparing directly with pre-pandemic estimates, and to be mindful of the changes to FRS data collection over the period.

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