National Statistic: yes
Survey name: Wealth and Assets Survey
How compiled: longitudinal survey
Geographic coverage: Great Britain, excluding addresses north of the Caledonian Canal, the Scottish Islands and the Isles of Scilly
Last revised: 25 February 2022
This Quality and Methodology Information (QMI) report contains information on the quality characteristics of the data (including the European Statistical System's five dimensions of quality) as well as the methods used to create it.
The information in this report will help you to:
understand the strengths and limitations of the data
learn about existing uses and users of the data
understand the methods used to create the data
decide suitable uses for the data
reduce the risk of misusing data
The latest release of Household total wealth in Great Britain April 2018 to March 2020 contains several estimates that have been adjusted for inflation, this is in line with the previous release, Total wealth in Great Britain April 2016 to March 2018; prior to this, all releases included estimates presented as current values (that is, the value at time of interview) that had not been adjusted for inflation.
All data presented in analysis tables supporting Household Wealth in Great Britain releases are presented as current values and consequently may not match the figures quoted within the latest (April 2018 to March 2020) and previous (April 2016 to March 2018) releases.
Alongside the household total wealth release additional analysis has been published providing estimates of wealth for individuals; the individual level release covers the distribution of individual total wealth by characteristics including more granular geographical estimates than previously published and estimates of linear regression to investigate associations between total individual wealth and various characteristics, such as age, disability and ethnic group.
All reasonable attempts have been made to ensure that the data are as accurate as possible; however, there are two potential sources of error that may affect the reliability of estimates and for which no adequate adjustments can be made, known as sampling and non-sampling errors.
The Wealth and Assets Survey (WAS) launched in 2006 is a biennial longitudinal survey conducted by the Office for National Statistics (ONS). This survey measures the well-being of households and individuals in terms of their assets, savings, debt and planning for retirement. The survey also examines attitudes and attributes related to these.
Data from this longitudinal survey will also provide users with the ability to measure changes of wealth in Great Britain over time. The survey is currently sponsored by a funding consortium, including the ONS; Department for Work and Pensions (DWP); HM Revenue and Customs (HMRC) and Scottish Government (SG).
Table 1 shows the response rates for the WAS from Wave 1 to the latest round 7. Wave 6 of the WAS was only in the field for 21 months, because of moving WAS to financial years (rounds); this is detailed in Recent improvements later in this section. The number of households sampled for Wave 6 was therefore approximately 16,000.
Round 7 of the WAS was the first fully round-based collection and was expected to be in the field for the full 24 months. Face-to-face interviews temporarily ended on 17 of March 2020 and re-started in the week commencing 23 March 2020 as telephone-only interviews. This was because of the national lockdown and the start of the coronavirus (COVID-19) pandemic meaning response rates for March 2020 were lower than usually seen.
In March 2020 the longitudinal cohort response dropped to 43%, typically this cohort had a response rate of around 67%. The response rate for the new cohort dropped from 37% to 27%. In total an achieved sample of approximately 17,500 households was delivered across round 7. The impact of the pandemic on the overall response rate was minimal.
|Period||Number of households sampled|
Download this table Table 1: Wealth and Assets Survey, overall sample sizes.xls .csv
As wealth is known to be unevenly distributed, addresses more likely to contain wealthier households were sampled at a higher rate to improve the efficiency of the sample. These addresses were identified using data from HMRC.
The datasets for Waves 1 to 5 and rounds 5 to 7 have been released to consortium members as well as the UK Data Service (UKDS) under an End User Licence (EUL) and to the Approved Researcher Scheme managed by the ONS. Given the need to maintain respondents' anonymity, certain variables have not been released to consortium members and the UKDS. To encourage the widest possible use of the data, including internationally, the latest EUL datasets were created and deposited with the UKDS in January 2022.
The WAS provides valuable data on topics that are not sufficiently covered elsewhere.
Uses and users
The results of the WAS are used by the DWP, HMRC, ONS and other government departments (including the Department for Business, Energy and Industrial Strategy (BEIS) and Department of Health (DH)) as well as academics. The data provide a greater understanding of the levels and distribution of wealth in terms of pensions, property, financial and physical assets, and indebtedness.
Strengths and limitations
Self-valuation tends to yield higher estimates of worth than most other property indicators may suggest.
As wealth is highly skewed towards the top, the survey was designed to pick up the very wealthy. However, this means that the sample now contains some very wealthy outliers. All such cases are thoroughly checked and, as a result, they are included in the survey results. Given the skewed nature of wealth data and the effect that outliers can have, the Household total wealth in Great Britain statistical bulletin and the associated background datasets do not generally report mean values. Instead, they use the median values to report central tendency (this is not possible for physical wealth estimates because of how physical wealth data are collected).
In April 2016 the survey period moved to a two-year, financial year-based periodicity (April to March), with this periodicity being referred to as a "round". This move to a two-year, financial-year basis allowed the WAS to be integrated with other household financial surveys as part of the ONS Data Collection Transformation Programme (DCTP). This programme sought to bring several surveys together to form the Household Finance Survey (HFS); more details are available in the Moving the Wealth and Assets Survey onto a financial years' basis methodology.
Round 7 of WAS commenced in April 2018 where a level of integration took place with the other HFS surveys with a common primary sample being drawn for all three surveys, and harmonisation of some income questions across the surveys.Nôl i'r tabl cynnwys
Several government departments initially joined the Wealth and Assets Survey (WAS) consortium because this survey was identified as being able to supply data on topics that were not sufficiently covered elsewhere. This survey fills a major information gap on wealth and indebtedness at a household and personal level. The pension wealth data in particular are unique owing to their detail. The longitudinal element provides a further dimension to this dataset, allowing users to analyse levels of change across all periods, from lower levels of wealth and indebtedness to households' or individuals' total wealth.
The survey has a large sample and almost complete coverage of Great Britain. The results of Wave 1 to round 7 have been and are likely to be used by the Department for Work and Pensions (DWP); HM Revenue and Customs (HMRC); other government departments; analysts within the Office for National Statistics (ONS); and academics to provide a greater understanding of the levels and distribution of wealth in terms of pensions, property, financial and physical assets, and indebtedness.
Accuracy and reliability
Multiple quality assurance methods ensure that the WAS data are as reliable as possible. These methods are applied during the interview and after collection through outlier detection and comparisons of the data between waves and rounds. All data that are identified as possible errors are investigated and, where appropriate, adjusted.
Revisiting respondents in subsequent waves or rounds provides the opportunity to confirm the current round's data against that which has been collected previously.
Coherence and comparability
Major government surveys now use harmonised questions on important topics to ensure comparability of results. Where appropriate, WAS questions are harmonised with other government surveys. Further information on the Government Statistical Service (GSS) Harmonisation Strategy can be found on the GSS website.
Separate datasets for each wave or round are issued after all checks have been completed. Although many of the variables are comparable between waves or rounds, some datasets have changed as have some of the categories of responses for particular variables. When this occurs, details are provided in the user guides and variable lists.
A glossary of the main terms used in the WAS is provided in Glossary: Wealth in Great Britain, 2006 to 2008.
There is limited comparable data from administrative sources or major surveys for some topics covered by the WAS. Nevertheless, using information that was available, the comparability of the results with results from other sources have been checked by the various contributors to the report as part of the validation process. This comparison will have included information from less extensive surveys, administrative data and the Financial Reporting Standards (FRS).
Accessibility and clarity
The UK Data Service (UKDS) at the University of Essex provides access to researchers under an End User Licence.
Documentation to guide the users of these datasets has been provided to the consortium members and is available to UKDS researchers.
Our recommended format for accessible content is a combination of HTML web pages for narrative, charts and graphs, with data being provided in downloadable formats such as CSV and Excel. We also offer users the option to download the narrative in PDF format. In some instances, other software may be used or may be available on request. Available formats for content published on our website but not produced by us, or referenced on our website but stored elsewhere, may vary. For further information, please email us at email@example.com.
Timeliness and punctuality
The survey has been in existence since 2006 and has a biennial interview wave pattern. The survey period moved to a two-year financial year-based periodicity (April to March) from April 2016, with this periodicity being referred to as a "round".
These data are available in the following main releases:
For more details on related releases, the Release calendar provides 12 months' advance notice of release dates. If there are any changes to the pre-announced release schedule, public attention will be drawn to the change and the reasons for the change will be explained fully at the same time, as set out in the Code of Practice for Statistics.
There is currently a delay of around 18 months between the end of data collection and the availability of full results for that particular wave or round. In order to better meet customer needs, provide timely metrics and added value before the main delivery of data, a series of releases that presents "early indicators", based on subjective questions, are published.
Early indicators are derived from simple frequency counts of variables included in the Wealth and Asset Survey questionnaire and are produced before any editing or imputation is carried out. Imputation is crucial to the estimation of wealth. The questions best suited to be used as early indicators are "opinion" questions or those relating to "ownership" of a particular asset. The set of indicators included in these releases are not fixed and varied over time with consideration and views and priorities of main users.
These data are available in the following recent Early Indicator releases:
Early indicator estimates from the Wealth and Assets Survey: attitudes towards saving for retirement, automatic enrolment into workplace pensions, credit commitments and debt burden, July 2016 to June 2017
Concepts and definitions (including list of changes to definitions)
The classifications used for the WAS are harmonised with other government surveys. These classifications are:
Household Outcome Code
UK Standard Industrial Classification (UK SIC)
UK Standard Occupational Classification (UK SOC)
country of birth
There is a trade-off between accuracy and timeliness of data dissemination. In theory, the WAS data could be disseminated immediately after fieldwork completion. However, the format of the data and level of item or unit non-response would significantly reduce the analytical value and usability of the data.
We have decided to undertake editing and imputation of WAS data prior to their dissemination. This significantly improves the quality and usability of data available for analysis, but it delays the dissemination of data. We are actively working to reduce the amount of time that the edit and imputation stages take, to retain accuracy while ensuring the data are disseminated as soon after data collection as possible.
The longitudinal nature of the survey allows us to validate the responses provided in the previous interview at the current interview. For example, on occasion, information is provided by proxy at one round and then in person at the subsequent round. Personal responses are considered to be more accurate and therefore we are able to take the opportunity to improve the quality of the previous round's responses. This can mean that higher quality data are available at a later date, which can lead to the revision of previously published estimates.
All reasonable attempts have been made to ensure that the data are as accurate as possible. However, there are two potential sources of error that may affect the accuracy of estimates and for which no adequate adjustments can be made: sampling and non-sampling errors.
Sampling error refers to the difference between the results obtained from the sample and the results that would be obtained if the entire population was fully enumerated. The survey estimates are therefore likely to differ from the figures that would have been produced if information had been collected for all households or individuals in Great Britain. The extent to which survey estimates vary from their population values can be estimated, to a given level of confidence, through the calculation of confidence intervals via the standard error of the estimate.
The standard error is a measure of sampling variability, which shows the extent to which the estimates are expected to vary over repeated random sampling. To estimate standard errors correctly, the complexity of the survey design needs to be accounted for.
Some estimates of standard errors for main variables are available in the supporting tables, Wealth in Great Britain round 7: Quality indicators. However, these standard error estimates do not account for imputation, which may affect variability.
Additional inaccuracies, which are not related to sampling variability, may occur for reasons such as errors in response and reporting. Inaccuracies of this kind are collectively referred to as non-sampling errors and may occur in a sample survey or a census. The main sources of non-sampling error are:
response errors resulting from misleading questions, interviewer bias or respondent misreporting
bias resulting from non-response, as the characteristics of non-responding persons may differ from responding persons
data input errors or systematic mistakes in processing the data
Non-sampling errors are difficult to quantify in any collection. However, every effort was made to minimise their effect through careful design and testing of the questionnaire, training of interviewers, and extensive editing and quality control procedures at all stages of data processing. Statistical imputation is another method used to improve accuracy resulting from missing observations in the dataset.
Response rates are reported on a monthly basis and are based on the number of fully and partially co-operating households as a proportion of the numbers of eligible households in the sample. A response rate of 55% was achieved for Wave 1, and 68% of the eligible households sampled responded in Wave 2. For Wave 3 onwards, the overall response rates and the breakdown for new and old cohorts are included in Table 2.
Regional response rates for WAS have not varied a great deal although London, in common with other social surveys, tends to exhibit markedly lower response rates.
|New cohort||Old cohort||Overall|
Download this table Table 2: Response rates, Wave 3 to round 7.xls .csv
How the output is created
This longitudinal Wealth and Assets Survey (WAS) measures the numbers and values of assets, debt and savings as well as attitudes to savings and indebtedness. Classificatory variables, including age, sex and employment status, are also covered. The first wave of the survey commenced with interviews carried out over two years from July 2006 to June 2008. For subsequent waves:
a second wave took place two years on from initial interviews, covering the period July 2008 to June 2010
a third wave began in July 2010, which was completed in June 2012
a fourth wave of WAS commenced in July 2012 and was completed in June 2014
a fifth wave of WAS commenced in July 2014 and was completed in June 2016
Wave 6 of WAS commenced in July 2016 but ran for only 21 months to March 2018; this was a result of moving WAS to a financial year and round basis
round 7 of WAS commenced in April 2018 and was completed in March 2020
Information on the creation of rounds 5 and 6 WAS data can be found in Moving the Wealth and Assets Survey onto a financial years' basis.
The WAS is a continuous survey with interviews spread evenly over the year, which helps to ensure that estimates are not biased by seasonal variations.
The survey samples private households in Great Britain, excluding north of the Caledonian Canal, the Scottish Islands and the Isles of Scilly.
The stratification of the sample for the first wave of WAS was based on regional and the 2001 Census variables and had two stages. At the first stage, a stratified sample of primary sampling units (PSUs) was drawn from a list of postcode sectors included in the small users' Postcode Address File (PAF). This list was sorted by geography (region by Metropolitan status), the proportion of households with the household reference person (HRP) in National Statistics Socio-economic Classification (NS-SEC) group 1 to 3, and the proportion of households without a car. This stratification is using judgement based on experience from optimising stratifiers for the Family Resources Survey (FRS) and Living Costs and Food Survey (LCF). The NS-SEC stratifier is the most powerful stratifier for economic social surveys. Car ownership was chosen over economic activity for the second stratifier because this is more correlated with wealth.
The second stage involved selecting 26 addresses per PSU being using systematic random sampling from the small users' PAF. The list of addresses in each PSU was sorted by postcode and street number. The sampling was carried out in such a way that the addresses flagged as expected to feature wealthier households had two and a half to three times the probability of being sampled as non-flagged addresses.
For the first two years of the first wave of the survey, 1,200 PSUs were drawn, giving a set sample of 31,200 addresses per year.
For the second and subsequent waves, all households that responded in the first wave and all households that could not be contacted in the previous wave were revisited. To ensure respondents' contact details are maintained between waves, a "keep in touch" phone call is administered approximately four months prior to the respondent's next interview. This exercise can document households that have split and those that have moved.
As the sample for each subsequent wave consists predominantly of the preceding wave's respondents, the size of the sample reduces with each wave. To mitigate the effect of attrition, a new cohort was introduced into the sample in Wave 3 (8,000 new addresses in year 1 and 4,000 in year 2). A further cohort of 8,000 addresses was introduced in Wave 4, 6,000 in Wave 5, 9,000 in round 6 and approximately 13,000 in round 7. The new cohort improves the size of the cross-sectional sample, which is required because attrition has reduced the sample since Wave 1. The new cohort then may help to reduce any bias introduced by attrition as the new cohort is selected from the current population and so helps in accounting for changes in the characteristics of population over time.
In round 7, PSUs for each of the two annual new cohorts were drawn from PSUs used in the previous year for Wave 1 of the Survey of Living Conditions (SLC) as well as the LCF, two other Office for National Statistics (ONS) household surveys. Altogether, 999 PSUs were selected, and in each PSU 13 addresses were sampled. Households likely to be in the top percentile of the Great Britain wealth distribution were oversampled by a factor of five, and households in the 2nd to 10th percentile by a factor of three.
An extensive range of validation checks and computer edits were applied to both the household and individual questionnaires during the computer-assisted personal interview (CAPI) and to the consolidated monthly data in the office.
Imputation is an adjustment process that is used to determine and assign replacement values to resolve problems of missing, invalid or inconsistent data.
The problem of missing data in the WAS is approached in two stages.
First, a deductive imputation method, followed by a statistical method. Deductive imputation was applied where a missing or inconsistent value could be deduced with certainty.
Secondly, statistical imputation was carried out using a nearest-neighbour imputation method where information from a donor record that had no errors or missing values was used to replace the missing values for a recipient record. In this approach, a donor is selected from a pool of potential donors with similar characteristics based on conditional probabilities.
For longitudinal households, where an observed value is present in one wave but the other wave is missing and therefore requires imputation, an imputed value is drawn from a donor with reference to the observed value or is calculated based on observed relationships or ratios between variables in the donor record. The imputation is conducted under edit constraints to ensure that outliers and implausible relationships are not introduced into the data through the imputation process.
As part of the data cleaning process, cross-sectional outliers were identified on all monetary variables used to compile derived variables. Large changes between waves were also identified as longitudinal outliers. Outlier thresholds were determined through analysis of the distribution of the data. Each variable was analysed dependent upon the nature of that variable and the spread of its data. A percentage of the highest and lowest values were identified as outliers. Not all variables had their lowest values labelled as outliers as low values can be acceptable for some variables, for example, zero values in financial accounts.
Outliers were checked for credibility through examination of other variables, including the previous waves' responses, in an attempt to find evidence to support or inform an edit to the outlier. This evidence includes the inspection of wealth, through income, assets and debts, and verification from linked variables (for example, comparisons of mortgage value with monthly mortgage payment and remaining term). There can be reasons to justify substantial longitudinal changes. For example, alterations to working status or household structure, in particular a split in partnership or a house move, can significantly affect the longitudinal change of many variables.
Amendments were only made to data where sufficient evidence to support an amendment existed. In Waves 1 and 2, approximately 5% of the data were investigated as outliers, of which a minority of these were amended. A more systematic approach was established for the identification of cross-sectional and longitudinal outliers from Wave 3 onwards.
A three-stage weighting procedure was implemented in the WAS.
First, a design weight, equal to the reciprocal of the address selection probability, was constructed.
Secondly, a non-response weight was created to reduce potential non-response bias. The non-response model currently includes region (GOR), a socio-economic indicator (OAC) and the HMRC-provided wealth index used to identify the wealthiest households. This applies to a new cohort. In older panels, an attrition adjustment was applied, and joiners were incorporated, before calibration.
The final stage of the weighting procedure calibrated the product of the design and non-response weights to known population totals taken from official population estimates present at the time of the fieldwork period. Different sets of weights have been created so that analysis can be performed both longitudinally (person-level) and cross-sectionally (household-level and person-level) on the data. Because of the timing of the outbreak of the coronavirus (COVID-19) pandemic very close to the end of round 7, no adjustments to the established weighting scheme have been applied.
Information on the creation of the round-based weights has been included in the articles Moving the Wealth and Assets Survey onto a financial years' basis and Changes to the Wealth and Assets Survey weighting system because of a shift in the reporting period.Nôl i'r tabl cynnwys
More information on the methods used to compile the output can be found in Chapter 10, Technical details, in the Wealth in Great Britain, Main Results from the Wealth and Assets Survey, 2006 to 2008 (PDF, 820KB) and Chapter 4 in the Wealth in Great Britain Wave 2, 2008 to 2010 (Part 1).
Statistical disclosure control methodology is applied to Wealth and Assets Survey (WAS) data. This ensures that information attributable to an individual or individual organisation is not identifiable in any published outputs. The Code of Practice for Statistics and specifically the Principle on Confidentiality set out practices for how we protect data from being disclosed. The Principle includes the statement that Office for National Statistics (ONS) outputs should "ensure that official statistics do not reveal the identity of an individual or organisation, or any private information relating to them, taking into account other relevant sources of information".
More information can be found in the National Statistician's guidance on the confidentiality of official statistics and on the disclosure control policy for social survey microdata page.Nôl i'r tabl cynnwys
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