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 ***.

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.

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.

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).

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

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.

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

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.

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.

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.

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

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

Total wealth is the sum of the four components of wealth and is therefore net of all liabilities.

As of 2023, the countries in Europe with the greatest share of national wealth taken by the top one percent of wealthy people were Russia, Turkey, and Hungary, with over two-thirds of wealth in Russia being owned by the wealthiest decile. On the other hand, the Netherlands, Belgium, and Slovakia were the countries with the smallest share of national wealth going to the top one percent, with more than half of wealth in the Netherlands going to the bottom 90 percent.

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).