Average UK household incomes taxes and benefits by household type, tenure status, household characteristics and long-term trends in income inequality.

This is the proportion of all households that are in receipt of housing benefit. Statistical disclosure control has been applied to this data which guards against the identification of an individual claimant. 0 or 0.00 denotes a nil or negligible number of claimants or award amount, based on 4 or fewer claimants. Household: a single person, or a group of people living at the same address who have the address as their only or main residence and either share one main meal a day or share living accommodation (or both).Figures are based on the claimant's place of residence. Claimants registered as living abroad or where the location is unknown are excluded. Housing Benefit claimant statistics are derived from the Single Housing Benefit Extract (SHBE), as at the second Thursday of each month. SHBE is compiled from monthly returns of housing benefit and council tax benefit claimants from each individual local authority. Recording and clerical errors can occur within SHBE - for this reason, no reliance should be placed on very small numbers obtained through Stat-Xplore.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Data on the effects of taxes and benefits on household income publication from 2001, including average incomes, taxes and benefits and household characteristics of all, retired and non-retired individuals and households in the UK by quintile and decile groups.

In April 2013, the government introduced a cap on the total amount of benefit that working-age people can get, so that households on out-of work benefits will no longer get more in welfare payments than the average weekly wage for working households. These statistics cover:

• cumulative and point-in-time statistics on the number of households capped at GB, regional and local authority level by household type, number of children and amount of cap

• GB and regional level off-flow statistics from the benefit cap by reason for off-flow.

Official statistics publications are released quarterly on Gov.uk and on Stat-Xplore.

https://www.gov.uk/government/collections/benefit-cap-statistics

This data has been taken from LGInform at http://lginform.local.gov.uk/ data reference ID 5470 The figures show the numbers of people claiming unemployment benefits aged between 25-49 and living in Plymouth. The data is monthly and shows data ranging from Jan 2013 to May 2017. Number of people claiming unemployment related benefits, aged 25-49 - This is the total number of people aged 24-49 claiming unemployment related benefits (Claimant Count). The Claimant Count is a measure of the number of people claiming benefits principally for the reason of being unemployed, based on administrative data from the benefits system. From April 2015, the Claimant Count includes all Universal Credit claimants who are required to seek work and be available for work, as well as all Jobseeker's Allowance (JSA) claimants, between May 2013 and March 2015, the Claimant Count includes all out of work Universal Credit claimants as well as all JSA claimants prior to this the Claimant Count is a count of the number of people claiming JSA. The Claimant Count includes people who claim unemployment related benefits but who do not receive payment. For example some claimants will have had their benefits stopped for a limited period of time by Jobcentre Plus. Some people claim JSA in order to receive National Insurance Credits. The Claimant Count does not attempt to measure unemployment, which is a concept defined by the International Labour Organisation (ILO) as all those who are out of work, actively seeking work and available to start work. However, since the people claiming benefits are generally a particular subset of the unemployed, the Claimant Count can provide a useful indication of how unemployment is likely to vary between areas and over time. The Claimant Count estimates provide the best available estimates of the number of people claiming unemployment related benefits in the UK. Source name: Nomis Collection name: Claimant county by sex and age Polarity: No polarity Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

Abstract copyright UK Data Service and data collection copyright owner. This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are: Family Expenditure Survey (FES) from 1977-2001Expenditure and Food Survey (EFS) from 2001-2007Living Costs and Food Survey (LCF) from 2008 to 2017Household Finances Survey (HFS) from 2018 onwards Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including: United Kingdom National Accounts (ONS Blue Book)HM Revenue and Customs (HMRC)Department for Transport (DfT)Department of Health (DH)Department for Education and Employment (DfEE)Department for Communities and Local Government (DCLG) For further information, see the ONS Effects of taxes and benefits on household income webpage.Users should note that this combined ETB household (1977-2021) and person (2018-2021) datasets replace all previous individual year files, which have been withdrawn from use at the depositor's request. Latest edition informationFor the second edition (September 2022), revised data for 2019/20 and new cases for 2020/21 were added to the household and person files. Method of Data Collection The ETB has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK. Since 2018, the estimates in this analysis are based on data derived from the HFS Survey (the HCF is not currently held by the UK Data Service). The HFS is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people's homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design. The HFS data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First, the data are weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to the Labour Force Survey (LFS) employment totals was also applied. There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children). In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB Quality and Methodology Information webpage and the Effects of Taxes and Benefits on Household Income Technical Report. Data Sources The Household Finances Survey (HFS) is the source of the microdata on households from 2018 onwards. Previously, the Living Costs and Food Survey (LCF) was the data source. Derived variables are created using information from HFS and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government. Secure Access version A Secure Access version of the ETB is available from the UK Data Archive under SN 8253, subject to stringent access conditions. The Secure Access version includes variables that are not included in the standard End User Licence (EUL) version, including case number, age and economic position of chief economic supporter, and government office region. Users are strongly advised to check whether the EUL version is sufficient for their needs before considering an application for the Secure Access version. Main Topics: The analysis considers the distribution of household income using five income measures in order to assess the effect of government intervention. The five income measures are: original income: includes income from employment, self-employment, investment income, occupational pensions and annuitiesgross income: original income plus cash benefitsdisposable income: gross income minus direct taxes (income tax, employees' National Insurance contributions and local taxes)post-tax income: disposable income minus indirect taxesfinal income: post-tax income plus benefits in kind (from education, the national health system, travel and housing subsidies). Standard measures: Historically, the equivalence scale used in this analysis was the McClements scale (before housing costs are deducted). To allow for better comparability with other sources, this analysis adopted the modified-OECD scale for the 2009/10 article. The modified-OECD scale usually assigns a weight of 1.0 for the first adult in a household, 0.5 for each additional adult and a weight of 0.3 for each child (defined as those under 14 years old). However, in this analysis the modified-OECD scale has been rescaled so that a two adult household equivalence value is 1.0. This makes it easier to compare with data that uses the McClements equivalence scale without making any difference to the overall results. The values for each household member are added together to give the total equivalence number for that household. This number is then used to divide disposable income for that household to give equivalised disposable income.

Percentage of people claiming key benefits as a proportion of the working age population. The information is derived from count data which are already held on the Neighbourhood Statistics website: Working Age Client Group (August 2004) provided by the Department for Work and Pensions (DWP) and Mid-2004 small area population estimates data provided by the Office for National Statistics (ONS). The data includes breakdowns by statistical group, gender and 3 bands of age (16-24, 25-49 and 50 and over). The small area population estimates used to derived the percentages are also included in the dataset.

Source: Department for Work and Pensions (DWP)

Publisher: Neighbourhood Statistics

Geographies: Lower Layer Super Output Area (LSOA), Local Authority District (LAD), Government Office Region (GOR), National

Geographic coverage: England and Wales

Time coverage: 2001 to 2007

Type of data: Administrative data

This data deposit includes qualitative and quantitative data that help answer the question: Which people do the public think should be classified as 'incapacitated'? How should this be assessed? And should they be threatened with benefit sanctions if they don't do what Jobcentres ask them to do? More people claim out-of-work incapacity benefits than unemployment benefits in the UK, and this has been true throughout the recent recession - but we know little about what the public think about incapacity benefits. The data collection consists of: (1) Quantitative data: includes a comparative YouGov study of the UK and Norway, giving each respondent three pen-portraits ('vignettes') of different sorts of disabled and non-disabled benefit claimant to see which factors influence the public's responses. It also includes a follow-up study in the UK using the NatCen online panel. (2) Qualitative data include the results from six focus groups with the general public in the England in 2016, which also used vignettes but allowed a deeper investigation of how the public debated the situation of each one.Over a million older people claim incapacity benefits in Britain, on the grounds that their health or disability stops them from working - four times as many as those claiming unemployment benefits, despite the downturn. But what does it actually mean to say that someone is 'incapacitated'? Take two people with identical impairments: a London-based graduate and an unskilled person in Merthyr Tydfil. The graduate may have better working conditions, an employer who is more willing to change the job to fit them, or be able to find another job that their health permits them to do. The unskilled worker may have none of these options, particularly if they are older and therefore more likely to have lower qualifications, to be biologically 'slowing down', and to face age-related discrimination. It is these ‘non-medical factors’ that are the focus of this project. The research firstly involves a statistical analysis of working conditions, adjustments and the availability of work in the UK and Europe. It then looks at whether the public and elites think that non-medical factors should be taken into account in assessing incapacity, using both a new survey and a series of workshops with different groups. This data deposit includes three data sources: 1. YouGov UK/Norway survey, sampled from YouGov's opt-in panel 2. NatCen UK survey, sampled from the NatCen online follow-up of the representative British Social Attitudes survey 3. Focus groups from the UK

This indicator measures the percentage of the working age population who are claiming out of work benefits. Working age benefits include the main out-of-work client group categories (unemployed people on Jobseekers Allowance, Lone Parents on Income Support, Incapacity Benefits customers, and others on income-related benefits with the exception of carers who are not subject to activation policies in the same way as other groups). The working age population is defined as the sum of females aged 16-59 plus males aged 16-64. Data are presented as a rolling average of 4 quarters to account for seasonal variation.

The Labour Market Indicators spreadsheet for boroughs and regions will no longer be updated from March 2015. The final version from March 2015 will still be available to download at the bottom of this page. Most of the data is available within datasets elsewhere on the Datastore.

Workforce Jobs
Unemployment
Model based Unemployment for Boroughs
Claimant Count rates for Boroughs and Wards
Employment Rate Trends
Employment rates by Gender, Age and Disability
Number of Self Employed, Full and Part Time Employed
Employment by Occupation
Employment by Industry
Employment, Unemployment, Economic Activity and Inactivity Rates by Disability
Employment by Ethnicity
Economic Inactivity by Gender and Reason
Qualifications of Economically Active, Employed and Unemployed
Qualification levels of working-age population
Apprenticeship Starts and Achievements
Young People Not in Employment, Education or Training (NEET), Borough
19 year olds Qualified to NVQ Level 3
GCE A level examination results of 16-18 year olds
GCSE Results by Pupil Characteristics
People Claiming Out-of-Work Benefits
People Claiming Incapacity Benefit
Children Living in Workless Households
Gross Value Added, and Gross Disposable Household Income
Earnings by place of residence
Earnings by place of work
Business Demographics
Employment projections by sector
Jobs Density
Population Estimates
Population Migration

Core Indicators

Number of London residents of working age in employment
Employment rate
Number of male London residents of working age in employment
Male employment rate
Number of female London residents of working age in employment
Female employment rate
Workforce jobs
Jobs density
Number of London residents of working age who are economically inactive
Economic inactivity rate
Number of London residents aged 16+ who are unemployed (model based)
Proportion of London residents aged 16+ who are unemployed (model based)
Claimant unemployment
Claimant Count as a proportion of the working age population
Incidence of skill gaps (Numbers and rates)
GCSE (5+ A*–C) attainment including English and Maths
Number of working age people in London with no qualifications
Proportion of working age people in London with no qualifications
Number of working age people in London with Level 4+ qualifications
Proportion of working age people in London with Level 4+ qualifications
Number of people of working age claiming out of work benefits
Proportion of the working age population who claim out of work benefits
Number of young people aged 16-18 who are not in

Anonymised dataset of a two-wave survey of 1,843 people who claimed Incapacity Benefit in 2006/7. The survey looked at factors leading people to claim Incapacity Benefit, including their health, experiences at work, social characteristics, and local labour market.

The number of Jobseeker Allowance (JSA) claimants by data zone for Glasgow. Jobseeker's Allowance is a benefit payable to unemployed people. In general, to be entitled to claim a person must be available for work, be actively seeking work, and have entered into a Jobseeker's Agreement with Jobcentre Plus. The figures are broken down by geographical area. The geographical areas used are the codes of National Records of Scotland. More info. The links below download the data from the Office of National Statistics website (via an API). They return either a .csv or a .json file. The datasets below cover the last two years but if you need additional historical data, contact us: data@glasgow.gov.uk Licence: None jsa.json - https://dataservices.open.glasgow.gov.uk/Download/Organisation/41792b4a-74db-407b-a8b5-cc6695cb7151/Dataset/aa53dc0b-8c5f-4822-85a7-837ca1c45f56/File/e7327bb0-2152-4de3-8d14-a1abb308830c/Version/89623373-dd75-40bd-a2e1-b0eeb04167e2

The information in this dataset refers to numbers of Pensionable Age Benefit Claimants and is derived from a 100% data source; the Work and Pensions Longitudinal Study (WPLS). The dataset provides counts of benefit claimants categorised by their statistical group (their main reason for interacting with the benefit system), gender and age.

Source: Department for Work and Pensions (DWP)

Publisher: Department for Work and Pensions (DWP)

Geographies: Local Authority District (LAD), Government Office Region (GOR), National

Geographic coverage: Great Britain

Time coverage: 1999 to 2008

Type of data: Administrative data

Notes: The main advantage of this dataset is that the double counting of claimants of multiple benefits has been removed so that users will get a more accurate picture of benefit claiming among people of pensionable age

This dataset contains series for the Claimant Count (which measures the number of people claiming unemployment-related benefits) and vacancies.

Income support is state benefit for people who are on a low income. A person with savings over PS16,000 cannot get Income Support, and savings over PS6,000 affect how much Income Support can be received. Claimants must be between 16 and state pension age work fewer than 16 hours a week, and have a reason why they are not actively seeking work The data represented here is an extract of data relating to Income Support and based on the 694 geographic data zones in Glasgow. Dataset covers years 1999 - 2013. Full UK datasets are available from DWP. ((c) Crown copyright 2009). All counts have been adjusted using a variant of controlled rounding to avoid the disclosure of any personal information. All cells have been rounded to base 5 and the total equals the sum of the disaggregation shown. Please note that any counts that are shown as zero may not be a 'real' zero. . Although all small area totals are within 5 of the true value, aggregating them to form Local Authority totals is not recommended due to the fact that it is the sum of rounded figures. Local Authority totals should therefore be obtained from the DWP Tabulation Tool. More information about the DWP their tabulation tool and collections can be located here. Further qualifications or limitations to the data can be examined here.. Data is correct at time of upload 2014:04:10T16:40:00 Licence: None

Claimant Count by sex for local and unitary authorities, counties and regions in the UK, published monthly. These are official statistics in development.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Latest edition information

For the fourth edition (September 2023), the variables NSECM20, NSECMJ20, SC2010M, SC20SMJ, SC20SMN and SOC20M have been replaced with new versions. Further information on the SOC revisions can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Supporting dataset using data from Census, Pay As You Earn (PAYE) and National Benefits Database. Tables contain data on earnings progression and geographic mobility from tax year ending 2012 to tax year ending 2016, broken down by characteristics such as age, sex, ethnicity, qualification level and local authority. The dataset also includes regression model output tables.