In the 2022/23 financial year, various measures of inequality in the United Kingdom decreased when compared with 2021/22. The S80/20 ratio fell from *** to ***, the P90/10 ratio from *** to ***, and the Palma ratio between *** and ***.

At the turn of the twentieth century, the wealthiest one percent of people in the United Kingdom controlled 71 percent of net personal wealth, while the top ten percent controlled 93 percent. The share of wealth controlled by the rich in the United Kingdom fell throughout the twentieth century, and by 1990 the richest one percent controlled 16 percent of wealth, and the richest ten percent just over half of it.

The overall wealth of households in the United Kingdom was **** trillion British pounds in the period between 2020 and 2022. Of this overall wealth, the top ten percent of households had over *** trillion pounds of wealth, compared with **** billion owned by the lowest wealth decile.

In 2023, the United Kingdom's Gini coefficient score was 33.1, a slight decrease when compared with the previous year. The Gini coefficient is a measurement of inequality within economies, a lower score indicates more equality while a higher score implies more inequality.

75% of households from the Bangladeshi ethnic group were in the 2 lowest income quintiles (after housing costs were deducted) between April 2021 and March 2024.

The attached file includes data and code used to analyse population scaling and house size in the ancient Near East.

Examines how taxes and benefits redistribute income between various groups of households in the UK. The study shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: household income

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

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

Individual-level estimates of total wealth (July 2010 to March 2020) and regression estimates for the latest survey period.

Dataset of long-run data on wealth inequality drawn from existing sources and compiled into a single country-year dataset.

Percentage of total wealth owned by households in each decile for London and Great Britain. Data extracted from the ONS Wealth and Assets Survey (WAS) microdata.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

Replication package for "The regional dispersion of income inequality in nineteenth-century Norway", to be published in Explorations in Economic History (accepted September 2017). The files contain micro data foundations for estimates of Norwegian income inequality in 1868.The file "data_compact.dta" (Stata format) contains "pseudo-invididual" observations of all men age 25 or more in Norway in 1868, estimated as described in the paper. Note that any one individual data point cannot stand by itself; analysis must be conducted at the municipality and/or occupation level. This is further explained in the paper.The file "municipalityfile.dta" (Stata format) contains municipality-level Gini coefficients and covariates.The file "replicate.zip" contains the necessary files (Stata and Matlab) to replicate the analysis. See "DataReadMe.pdf" for instructions.Abstract for the paper: This paper documents, for the first time, municipality- and occupation-level estimates of income inequality between individuals in a European country in the nineteenth century, using a combination of several detailed data sets for Norway in the late 1860s. Urban incomes were on average 4.5 times as high as rural incomes, and the average city Gini coefficient was twice the average rural municipality Gini. All high- or medium-income occupation groups exhibited substantial within-occupation income inequality. Across municipalities, income inequality is higher in high-income municipalities, and lower in muncipalities with high levels of fisheries and pastoral agriculture. While manufacturing activity is positively correlated with income inequality, the association is not apparent when other economic factors such as the mode of food production is accounted for. The income Gini for Norway as a whole is found to have been 0.546, slightly higher than estimates for the UK and US in the same period.

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 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 to 2017
• Household 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 information

For 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.

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods. The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored. To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth. Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides. The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom. Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature. The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in rural China. By relying on field evidences, the project team will develop policy tools and checklists for policy makers to help rural households make sound financial decisions. Two types of tools will be developed for policy makers, namely, "push" tools that aim to achieve short-term policy compliance among rural households so that they can break out of the persistent poverty cycle and "pull" tools that can reduce fraud, error, and debt among rural households to prevent them from falling back into poverty. Finally, the project team will also use the research activities and findings as vehicles to engage and educate rural residents, local governments, regulators, and financial institutions. Standard and good practice will be proposed to interested parties for the designs of good behavioural interventions; ethical guidelines will be provided to encourage good practice. This important step ensures that the findings of this project will benefit academia and practice, with long-lasting, positive impacts. The findings will benefit researchers in behavioural finance and economics, rural economics, development economics, political sciences, and psychology. The findings of and the engagement in this project will help policy makers to develop cost-effective behavioural change policies. Rural households will benefit by being nudged into sound financial decisions and healthy financial habits. The project will provide insights on how to leverage behavioural insights to overcome persistent poverty in the developing world. Therefore, the research will be of interest to communities in China and internationally. The data were retrived from the British Household Panel Survey (BHPS) between 1997 and 2008, when both residential satisfaction scores and home valuations are available.

Three datasets, all carried out by YouGov and the WEALTHPOL team, are included. The first was conducted in Summer 2021, the second in Summer 2022, and the third in October 2022. CSVs, codebooks, and data construction files are included (note the latter include references to other files but show the coding).