Distribution of individual total wealth by characteristic in Great Britain: April 2018 to March 2020

Individual-level results from the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020.

This is the latest release. View previous releases

Cyswllt:
Email Hayley Bennett

Dyddiad y datganiad:
7 January 2022

Cyhoeddiad nesaf:
To be announced

2. Main points

  • Wealth is unevenly distributed between individuals in Great Britain, with the wealthiest 10% estimated to hold around half of all wealth, primarily in the form of private pensions and property.

  • Between April 2018 and March 2020, median individual wealth was £157,000 higher in the South East than the North East of England and this regional disparity has increased over time.

  • On average individual wealth increases with age, peaking in the 60-to-64 age group at a level nine times as high as the 30-to-34 age group, before falling in older age groups as people use their wealth to support life in retirement.

  • While age was the best predictor of individual wealth, wealth was also lower for women (negative £101,000 compared with men) and those with a longstanding illness or disability (negative £65,000 compared with those without), identifying as bisexual (negative £65,000 compared with heterosexual) or from several ethnic minority groups, after adjusting for other characteristic differences between groups.

  • An individual's education and the way they make a living are also important factors for their wealth prospects, with positive associations modelled between wealth and degree-level qualifications, higher socioeconomic class occupations and public sector employment.

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3. Distribution of wealth

Median total wealth for individuals in Great Britain was estimated to be £125,000 between April 2018 and March 2020.

The mean was higher at £305,000, reflecting the uneven distribution of wealth across the population. The wealthiest 10% of individuals were estimated to hold almost half of the wealth captured within the Wealth and Assets Survey (WAS). This has been the case since our first estimates of individual wealth in July 2010 to June 2012. However, the full picture of wealth is difficult to measure, which could mean that Britain’s richest individuals hold a larger share than our estimates suggest.

Figure 1: The wealthiest 10% of individuals hold almost half of all wealth

Percentage of total wealth held by individuals in each individual wealth decile, Great Britain, April 2018 to March 2020

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For individuals in the poorest decile, most of their wealth comes from physical items such as household possessions and vehicles. Median net financial wealth in this group was zero, meaning that less than half of the poorest decile have financial assets such as savings and investments that outweigh their financial liabilities, such as outstanding credit card balances and loans. Similarly, less than half of respondents in this decile own property with positive equity or have any form of private pension.

By contrast, most individuals in the highest wealth decile have positive bank balances (median net financial wealth £90,000), own property (median £310,000) and have a private pension pot to support their retirement (median £627,000). On average, respondents in the richest decile held 50% of their wealth in pensions and a further 32% in property.

To better understand the types of people that have more wealth than others, we estimated average total wealth among individuals responding to the WAS, grouped by personal and household characteristics.

Figure 2: Median and mean total individual wealth, by characteristic

Great Britain, April 2018 to March 2020

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Notes:
  1. Additional breakdowns can be found in the associated dataset.
  2. Error bars represent 95% confidence intervals.
  3. Estimates for the ethnic groups Mixed - White and Black African and Other Black background are based on fewer than 50 unweighted cases and such data should be treated with some caution.
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Understanding patterns in average wealth is complicated when there are also demographic differences between these groups, which can affect wealth. For example, women are more likely to fill part-time working positions than men, while people with a disability tend to be older and less likely to have a university degree. To estimate associations between wealth and each characteristic in isolation, we used linear regression to model differences in mean wealth for a hypothetical population who otherwise have the same demographic and socioeconomic profile.

Taking education as an example, Figure 2 shows that from the surveyed population mean wealth was estimated to be more than £300,000 higher among individuals with a degree-level qualification (mean £478,000) than those without qualifications (mean £157,000). However, Figure 3 shows a difference between these groups of less than £200,000 when other characteristics are controlled for. While a statistically significant association was estimated between wealth and education, a smaller modelled difference suggests that the difference in wealth between the two surveyed groups may be partially explained by other factors adjusted for in the analysis.

Figure 3: Difference in mean total individual wealth by characteristic compared with reference group, when controlling for other factors

Great Britain, April 2018 to March 2020

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Notes:
  1. Other factors included in this regression but not shown are listed in Measuring the data.
  2. The full regression results can be found in the associated dataset.
  3. Error bars represent 95% confidence intervals.
  4. Estimates for the ethnic groups Mixed - White and Black African and Other Black background are based on fewer than 50 unweighted cases and such data should be treated with some caution.
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.xlsx

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4. Regional inequalities

As shown in regional analysis of household wealth, there is significant disparity in wealth across Great Britain and the gap has widened.

Average wealth among individuals in the South East of England was higher than any other region of Great Britain. The difference in median individual wealth between the South East (£236,000) and North East (£79,000) regions of England was £157,000 in April 2018 to March 2020, almost twice as high as in July 2010 to June 2012 (£80,000 in nominal terms or £92,000 after adjusting for inflation).

For the average respondent in the South East of England, 33% of their wealth came from property compared with just 20% in the North East. Property ownership and property value explains a lot of the difference in average wealth between these regions. Despite the South East having the highest house prices outside London, those living there were among the most likely to own property (64%) and had median net property wealth of £111,000. By contrast, those in the North East were the most likely to live in rented accommodation, except for London, where people are younger on average and see the highest house prices of any region.

Breaking Britain into smaller areas (Figure 4) or grouping people by the type of area they live in, points to the inequality that exists within these broad geographical regions. When classifying areas of the country by socio-demographic characteristics, the majority of South East England is classed as Affluent England. This area classification supergroup had the highest median individual wealth. By contrast London comprises a mix of high and low wealth supergroups, from Affluent England (median £277,000) to Ethnically Diverse Metropolitan Living (£64,000) and London Cosmopolitan (£37,000). London had the highest mean to median ratio (4.1 compared with 2.4 for Great Britain) indicating high inequality in individual wealth of respondents living in this area.

Figure 4: South East England was the wealthiest region of Great Britain

Median and mean total individual wealth, by geographic region, Great Britain, April 2018 to March 2020

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Notes:
  1. Confidence intervals indicating uncertainty around these estimates are available in the associated dataset. These should be considered before comparing results for individual regions.
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5. The generation gap

Average wealth varied most by age, with median total wealth amongst respondents aged in their early 60s almost nine times as high as those aged in their early 30s.

Home ownership and preparations for retirement play significant roles in this divide, with median property wealth rising across all age groups and pension wealth peaking as individuals approach State Pension age. People typically accumulate wealth gradually over the course of their working life, building savings, paying into pension schemes and buying property, before using this wealth to support their life in retirement and often passing it on to younger generations. 

Today's younger generations are less likely to own their own home than their predecessors. This is noteworthy since living in a rented home was associated with lower wealth than mortgaged homes when controlling for age and other factors. Not only does a homeowner's wealth grow as long as house prices rise, but buying property with a mortgage acts as a form of obligatory saving. Renting households are the most likely to be in problem debt, and have been shown to have lower financial resilience in the form of savings that could act as a buffer against unexpected expenses or losses of income.

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6. Demographics and personal characteristics

While age was the best overall predictor of individual wealth, several other characteristics were associated with significant differences in wealth.

Who we are

When holding other factors constant, wealth was estimated to be:

  • £101,000 lower for women than men 

  • £65,000 lower for individuals with a longstanding illness or disability than those without

  • £65,000 lower for individuals identifying as bisexual compared with those identifying as heterosexual

  • lower for individuals from the Pakistani, Indian, Other Asian, White and Black African, Black African and Other ethnic groups, compared with the White British ethnic group

In addition to pay and increasing pension inequalities, household composition and the way wealth is divided when family dynamics change may contribute to the disparity seen between men and women. Though the gender pay gap has fallen over the last 20 years, women still earn around 15% less than men. Differences in the types of jobs undertaken by men and women and an uneven distribution of caring and unpaid work are among explanations offered for this gap, together with time spent away from the labour market associated with having children.

Reduced time in the workforce, because of part-time working or career breaks, also heavily impacts the growth of private pension wealth. The gender pay gap is present across occupations and income groups, but is particularly noticeable in wealthier regions of England, such as London and the South East. Disability and ethnicity pay gaps may also contribute to differences in the accumulation of individual wealth, both larger in London than other regions of the UK.

Where we come from

Average wealth was higher among UK-born respondents than those born outside the UK, but this association was not statistically significant when controlling for differences in other factors.

The Wealth and Assets Survey cannot give a complete picture of an individual's socioeconomic background and data describing respondents' teenage years were less complete than some other variables considered. However, there was evidence to suggest associations between parental education, employment and homeowner status, and a child's total wealth later in life. For example, wealth was £47,000 higher for individuals whose parents were buying or owned their teenage home than those whose parents rented, when controlling for other factors.

While the implications of a parent's education or employment can change with generational shifts in attitudes and behaviour, parental homeownership may represent opportunities for the direct transfer of wealth through families.

How we make a living

An individual's education and subsequent employment are also important factors for their wealth prospects. As noted earlier, average wealth was higher for those educated to degree-level, than those with lower or no qualifications. Despite high debts associated with university education, graduates earn higher wages and most can expect positive financial returns on their investment into university attendance (PDF, 1.01MB). 

Median total wealth was almost 12 times higher among respondents who worked in higher managerial and administrative occupations or were employers in large organisations than those working in routine occupations. After age, this socioeconomic classification of occupations was the most influential factor considered in the modelling. Both income and expenditure levels increase with socioeconomic class. However, while disposable income and spending levels are roughly equal in poorer households, those in the top income quintile only spend around half their disposable income giving them far greater opportunity to build wealth through saving or other means.

Public sector employees had significantly more wealth than those working in the private sector. Earnings in the public sector have previously been estimated to be 7% higher when accounting for employer contributions to pensions and controlling for job, industry and personal factors. A large proportion of total wealth among respondents employed in the public sector came from pension wealth (44% compared with 27% among private sector employees, before adjustment for age and other factors). This is likely to be explained by higher participation in workplace pensions, higher contribution rates and greater availability of defined benefit schemes for public sector workers.

Unlike pay, where part-time employees tend to be paid less per hour than those working full-time, average wealth was similar between respondents who worked full- or part-time in their current or most recent job. Adjusting for the imbalance in these working patterns between men and women, among other factors, led to a positive modelled relationship between part-time working and wealth. While our part-time category covers a wide range of working patterns, the reasons people work reduced hours are equally varied, from availability of work and commitments outside the workplace to affordability. For individuals able to choose their working pattern, this choice may be influenced by living expenses and other sources of income within their household, factors not captured within our analysis.

Demographic differences were also important when grouping workers by industry. Average wealth was lowest among respondents working in accommodation and food services, roles most commonly filled by the 16-to-24 age group. For young workers in this industry, lower wealth and in particular, lower financial assets relative to their income, put the economic well-being of many at risk when the coronavirus (COVID-19) pandemic impacted jobs in some industries more than others.

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7. Distribution of individual total wealth by characteristic data

Individual wealth: wealth in Great Britain
Dataset | Released 7 January 2022
Individual-level estimates of total wealth (July 2010 to March 2020) and regression estimates for the latest survey period.

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8. Glossary

Financial wealth (net)

The values of any financial assets held, both formal investments such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts (ISAs), endowments, stocks and shares, and informal savings (money under the bed or loaned to family or friends) and children's assets; less any financial liabilities such as outstanding balances on credit cards, arrears on household bills, loans (including student loans) from formal or informal sources.

Physical wealth

The (self-evaluated) value of household contents, possessions and valuables owned such as antiques, artworks, collections and any vehicles owned by individuals (including the value of any personalised number plates).

Private pension wealth

The value of any pension pots already accrued that are not state basic retirement or state earning-related. This includes occupational pensions, personal pensions, retained rights in previous pensions and pensions in payment. Estimating the value of some private pension pots is straightforward. For example, if a pension is a defined contribution type scheme (not in payment) the valuation is obtained from the respondents' latest statement from their pension administrator. This is an accurate estimation of each individual pension pot taking into account any relevant market influences (for example, investment returns).

Property wealth (net)

Respondents' self-valuation of any property owned, both their main residence plus any other land or property owned in the UK or abroad, less the outstanding value of any loans or mortgages secured on these properties. Self-valuation tends to yield higher estimates of worth than most other property indicators may suggest - however, when assessing individuals' opinions or behaviours, it is this perceived worth that will drive the individuals concerned.

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9. Measuring the data

Data sources

The Wealth and Assets Survey (WAS) is a two-yearly survey that measures the well-being of households and individuals in terms of their assets, debts and plans for retirement. More information on data collection, methods, strengths and limitations is available in the WAS Quality and Methodology Information and measuring wealth on an individual level.

This release reports analysis of data from 31,829 individuals, aged 16 years or over and not in full-time education, surveyed between April 2018 and March 2020. The associated dataset contains further results from earlier survey periods. Missing data describing country of birth, ethnic group or socioeconomic background were obtained from other survey periods where available.

Estimating average wealth

We use both mean and median as measures of average wealth. Mean wealth is the total wealth of all individuals divided by the number of individuals. Median wealth is that of the middle person, if everyone was sorted from poorest to richest. The median is generally a better measure of central tendency for wealth, because it is less affected by very wealthy outliers in the data.

We used linear regression to investigate associations between total individual wealth and various characteristics. The modelled estimates relate to differences in mean wealth because of the method of estimation used and control for differences in:

  • age

  • country of birth

  • disability

  • education

  • ethnic group

  • employment status, sector, industry, working pattern

  • household composition

  • housing tenure

  • socioeconomic group

  • socioeconomic background: family situation and parental employment, education and housing tenure as a teenager

  • sex

  • sexual orientation

This choice of factors was guided by published literature and domain knowledge. While they represent the most relevant available from the WAS, this list is not exhaustive and the model does not seek to explain all variation in wealth between individuals.

Other indicators of socioeconomic background, parental wealth and geography are among factors that may influence wealth that we could not include in the modelling because of data or methodological limitations. Geographical region was not controlled for in the modelling, because while there is an association between region and wealth, it is unclear whether region determines wealth or wealth determines region. In the case of the latter, controlling for region would distort the model estimates.

We use confidence intervals to represent uncertainty around estimates and assessed statistical significance at the 5% level. To do this, we used information on the survey's primary sampling units and applied geographical region as strata. More information is available on uncertainty and how we measure it and the WAS sampling strategy.

Components of wealth

Total wealth is the sum of net wealth in property, private pensions, financial and physical items. When estimating the contribution of each component to total wealth we consider both positive and negative values, because debt is an important part of an individual's wealth position. For example, for an individual with physical wealth worth £9,000, net financial wealth of negative £1,000 and no property or pension wealth, we would estimate that 90% of their total wealth came from physical wealth and 10% from net financial wealth.

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Manylion cyswllt ar gyfer y Bwletin ystadegol

Hayley Bennett
wealth.and.assets.survey@ons.gov.uk
Ffôn: +44 1633 582595