In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.

In 2022 the top one percent of earners in the United Kingdom accounted for around 10.2 percent of the overall national income of the UK. The share of national income earned by the top one percent increased from 6.8 percent in 1980 to a peak of 14.8 percent in 2007.

In 2024, the average annual full-time earnings for the top ten percent of earners in the United Kingdom was 72,150 British pounds, compared with 22,763 for the bottom ten percent of earners. As of this year, the average annual earnings for all full-time employees was 37,430 pounds, up from 34,963 pounds in the previous year. Strong wage growth continues in 2025 As of February 2025, wages in the UK were growing by approximately 5.9 percent compared with the previous year, with this falling to 5.6 percent if bonus pay is included. When adjusted for inflation, regular pay without bonuses grew by 2.1 percent, with overall pay including bonus pay rising by 1.9 percent. While UK wages have now outpaced inflation for almost two years, there was a long period between 2021 and 2023 when high inflation in the UK was rising faster than wages, one of the leading reasons behind a severe cost of living crisis at the time. UK's gender pay gap falls in 2024 For several years, the difference between average hourly earnings for men and women has been falling, with the UK's gender pay gap dropping to 13.1 percent in 2024, down from 27.5 percent in 1997. When examined by specific industry sectors, however, the discrepancy between male and female earnings can be much starker. In the financial services sector, for example, the gender pay gap was almost 30 percent, with professional, scientific and technical professions also having a relatively high gender pay gap rate of 20 percent.

In 2022/23 approximately *****million taxpayers in the United Kingdom earned between 20,000 and 29,999 British pounds in this tax year, the most of any income level, while approximately *******taxpayers in the UK earned over one million pounds.

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.

In the 3 years to March 2021, black households were most likely out of all ethnic groups to have a weekly income of under £600.

Households in the bottom decile in the United Kingdom earned, on average, ****** British pounds per year in 2023/24, compared with the top decile which earned around ******* pounds per year.

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.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time.

In 2023, the number of individuals living in the United Kingdom (UK) with a net worth of over *** million U.S. dollars excluding the value of their primary residence was roughly over ***** million people. This number has been forecasted to decrease to *** million people in 2028.

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

The number of high net worth individuals (HNWIs) in Europe has grown steadily over the past decade. This trend reflects broader wealth accumulation patterns across the continent, with the richest segments of society gaining an increasingly larger share of total wealth. Despite this concentration at the top, recent years have seen some positive signs in terms of overall income inequality reduction in Europe. Wealth concentration at the top From 1995 to 2021, the wealthiest one percent in Europe increased their share of wealth from 22 percent to over 26 percent. During this same period, the bottom 90 percent saw their collective share shrink. This concentration of wealth at the top aligns with the growth in HNWIs observed in countries in Europe. The top 10 percent of wealthy Europeans now own more than the remaining 90 percent combined, highlighting the significant wealth disparity that persists despite the overall increase in HNWIs.

Signs of improving income equality While wealth concentration has increased, there are indications that income inequality in the European Union has been improving in recent years. The Gini coefficient, a measure of income inequality, has been declining in both the EU and Eurozone since 2014, reaching new lows of 29.6 and 29.8 respectively in 2023. Additionally, the income ratio between the top 20 percent and bottom 20 percent of earners in the EU has fallen from 5.22 in 2015 to 4.74 in 2022. These trends suggest that despite the growth in HNWIs, efforts to address income disparities may be having some positive effects across the broader population.

Advani, Hughson and Tarrant (2021) model the revenue that could be raised from an annual and a one-off wealth tax of the design recommended by Advani, Chamberlain and Summers in the Wealth Tax Commission’s Final Report (2020). This deposit contains the code required to replicate the revenue modelling and distributional analysis. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.Around the world, the unprecedented public spending required to tackle COVID-19 will inevitably be followed by a debate about how to rebuild public finances. At the same time, politicians in many countries are already facing far-reaching questions from their electorates about the widening cracks in the social fabric that this pandemic has exposed, as prior inequalities become amplified and public services are stretched to their limits. These simultaneous shocks to national politics inevitably encourage people to 'think big' on tax policy. Even before the current crisis there were widespread calls for reforms to the taxation of wealth in the UK. These proposals have so far focused on reforming existing taxes. However, other countries have begun to raise the idea of introducing a 'wealth tax'-a new tax on ownership of wealth (net of debt). COVID-19 has rapidly pushed this idea higher up political agendas around the world, but existing studies fall a long way short of providing policymakers with a comprehensive blueprint for whether and how to introduce a wealth tax. Critics point to a number of legitimate issues that would need to be addressed. Would it be fair, and would the public support it? Is this type of tax justified from an economic perspective? How would you stop the wealthiest from hiding their assets? Will they all simply leave? How can you value some assets? What happens to people who own lots of wealth, but have little income with which to pay a wealth tax? And if wealth taxes are such a good idea, why have many countries abandoned them? These are important questions, without straightforward answers. The UK government last considered a wealth tax in the mid-1970s. This was also the last time that academics and policymakers in the UK thought seriously about how such a tax could be implemented. Over the past half century, much has changed in the mobility of people, the structure of our tax system, the availability of data, and the scope for digital solutions and coordination between tax authorities. Old plans therefore cannot be pulled 'off the shelf'. This project will evaluate whether a wealth tax for the UK would be desirable and deliverable. We will address the following three main research questions: (1) Is a wealth tax justified in principle, on economic or other grounds? (2) How should a wealth tax be designed, including definition of the tax base and solutions to administrative challenges such as valuation and liquidity? (3) What would be the revenue and distributional effects of a wealth tax in the UK, for a variety of design options and at specified rates/thresholds? To answer these questions, we will draw on a network of world-leading exports on tax policy from across academia, policy spheres, and legal practice. We will examine international experience, synthesising a large body of existing research originating in countries that already have (or have had) a wealth tax. We will add to these resources through novel research that draws on adjacent fields and disciplines to craft new solutions to the practical problems faced in delivering a wealth tax. We will also review common objections to a wealth tax. These new insights will be published in a series of 'evidence papers' made available directly to the public and policymakers. We will also publish a final report that states key recommendations for government and (if appropriate) delivers a 'ready to legislate' design for a wealth tax. We will not recommend specific rates or thresholds for the tax. Instead, we will create an online 'tax simulator' so that policymakers and members of the public can model the revenue and distributional effects of different options. We will also work with international partners to inform debates about wealth taxes in other countries. The modelling draws on data from the Wealth and Assets Survey, supplemented with the Sunday Times Rich List, which we use to implement a Pareto correction for the under-coverage of wealth at the top.

Average weekly earnings at sector level headline estimates, Great Britain, monthly, seasonally adjusted. Monthly Wages and Salaries Survey.

This is the proportion of children aged under 16 (0-15) living in families in absolute low income during the year. The figures are based on the count of children aged under 16 (0-15) living in the area derived from ONS mid-year population estimates. The count of children refers to the age of the child at 30 June of each year.

Low income is a family whose equivalised income is below 60 per cent of median household incomes. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support, and pensions. 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.

Absolute low income is income Before Housing Costs (BHC) in the reference year in comparison with incomes in 2010/11 adjusted for inflation. A family must have claimed one or more of Universal Credit, Tax Credits, or Housing Benefit at any point in the year to be classed as low income in these statistics. Children are dependent individuals aged under 16; or aged 16 to 19 in full-time non-advanced education. The count of children refers to the age of the child at 31 March of each year.

Data are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI. For further information and methodology on the construction of these statistics, visit this link. Totals may not sum due to rounding.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included).The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:"The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates."Survey Periodicity - "Waves" to "Rounds"Due to the survey periodicity moving from "Waves" (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Users should note that issues with linking have been reported and the WAS team are currently investigating.Secure Access WAS dataThe Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:WardsParliamentary Constituency Areas for Wave 1 onlyCensus Output AreasLower Layer Super Output AreasLocal AuthoritiesLocal Education AuthoritiesProspective users of the Secure Access version of the WAS will need to fulfil additional requirements, including completion of face-to-face training, and agreement to the Secure Access User Agreement and Licence Compliance Policy, in order to obtain permission to use that version (see 'Access' section below). Users are therefore strongly encouraged to download the EUL version (SN 7215) to see if it contains sufficient detail for their needs, before considering making an application for the Secure Access version.Latest Edition InformationFor the ninth edition (October 2022), the Round 7 person and household data have been updated. The Round 7 Wave 1 Variable Catalogue Excel file has also been updated. Main Topics: The WAS questionnaire was divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This was completed by one person in the household (usually the head of household or their partner) and predominantly collected household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This was given to each adult in the household and asked questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ALIMONY ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BEDROOMS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS EXPENDITURE FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FREQUENCY OF PAY FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCOME TAX INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INTEREST FINANCE INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LAND VALUE LANDLORDS LIFE INSURANCE LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT RETIREMENT AGE ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS SURVIVORS BENEFITS TAX RELIEF TAXATION TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WAR VETERANS BENEFITS WEALTH WILLS WINNINGS WORKPLACE property and invest...

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 46 wave of the British Cohort Study (BCS70) into monthly panel format. There are separate do files to do this for each of the three aspects.This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process. The BCS70 study covers all children in England, Scotland and Wales born in one week in 1970. The archived materials are do files that alter the format of existing BCS70 datasets to create derived datasets. Original data can be accessed via Related Resources.