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

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 data
The Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:

• Wards
• Parliamentary Constituency Areas for Wave 1 only
• Census Output Areas
• Lower Layer Super Output Areas
• Local Authorities
  
• Local Education Authorities

Prospective 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 Information
For 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.

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

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...

Data on household wealth in Great Britain by ethnic group. Includes total, property, financial, physical and private pension wealth by age, region, household composition and housing tenure.

About this dataset

Context

The dataset tabulates the England household income by gender. The dataset can be utilized to understand the gender-based income distribution of England income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• England, AR annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• England, AR annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of England income distribution by gender. You can refer the same here

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

Quarterly data on democratically weighted and CPI-consistent indices, annual inflation rates, expenditure shares and contributions for UK household groups.

In this paper, we propose a model of income dynamics which takes account of mobility both within and between jobs. The model is a hybrid of the mover-stayer model of income dynamics and a geometric random walk. In any period, individuals face a discrete probability of moving, in which case their income is a random drawn from a stationary recurrent distribution. Otherwise, they stay and incomes follow a geometric random walk. The model is estimated on income transition data for the United Kingdom from the British Household Panel Survey (BHPS) and provides a good explanation of observed non-linearities in income dynamics. The steady-state distribution of the model provides a good fit for the observed, cross-sectional distribution of earnings. We also evaluate the impact of tertiary education on income transitions and on the long-run distribution of incomes.

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.

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

About this dataset

Context

The dataset tabulates the England median household income by race. The dataset can be utilized to understand the racial distribution of England income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• England, AR median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in England, AR (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of England median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in England, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 12,300, while the mean income for the highest quintile (20% of households with the highest income) is 199,499. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 370,208, which is 185.57% higher compared to the highest quintile, and 3009.82% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for England median household income. You can refer the same here

FOCUSON**LONDON**2010:**INCOME**AND**SPENDING**AT**HOME**

Household income in London far exceeds that of any other region in the UK. At £900 per week, London’s gross weekly household income is 15 per cent higher than the next highest region. Despite this, the costs to each household are also higher in the capital. Londoners pay a greater amount of their income in tax and national insurance than the UK average as well as footing a higher bill for housing and everyday necessities. All of which leaves London households less well off than the headline figures suggest.

This chapter, authored by Richard Walker in the GLA Intelligence Unit, begins with an analysis of income at both individual and household level, before discussing the distribution and sources of income. This is followed by a look at wealth and borrowing and finally, focuses on expenditure including an insight to the cost of housing in London, compared with other regions in the UK.

See other reports from this Focus on London series.

REPORT:

To view the report online click on the image below. Income and Spending Report PDF

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PRESENTATION:

This interactive presentation finds the answer to the question, who really is better off, an average London or UK household? This analysis takes into account available data from all types of income and expenditure. Click on the link to access.

PREZI

The Prezi in plain text version

RANKINGS: https://londondatastore-upload.s3.amazonaws.com/fol/fol10-income-tableau-chart-thumb.jpg" alt="Alt text">

This interactive chart shows some key borough level income and expenditure data. This chart helps show the relationships between five datasets. Users can rank each of the indicators in turn.

Borough rankings Tableau Chart

MAP:

These interactive borough maps help to geographically present a range of income and expenditure data within London.

Interactive Maps - Instant Atlas

DATA:

All the data contained within the Income and Spending at Home report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

Report data

FACTS:

Some interesting facts from the data…

● Five boroughs with the highest median gross weekly pay per person in 2009:

-1. Kensington & Chelsea - £809

-2. City of London - £767

-3. Westminster - £675

-4. Wandsworth - £636

-5. Richmond - £623

-32. Brent - £439

-33. Newham - £422

● Five boroughs with the highest median weekly rent for a 2 bedroom property in October 2010:

-1. Kensington & Chelsea - £550

-2. Westminster - £500

-3. City of London - £450

-4. Camden - £375

-5. Islington - £360

-32. Havering - £183

-33. Bexley - £173

● Five boroughs with the highest percentage of households that own their home outright in 2009:

-1. Bexley – 38 per cent

-2. Havering – 36 per cent

-3. Richmond – 32 per cent

-4. Bromley – 31 per cent

-5. Barnet – 28 per cent

-31. Tower Hamlets – 9 per cent

-32. Southwark – 9 per cent

Annual estimates of UK regional gross disposable household income (GDHI) at current prices for ITL1, ITL2 and ITL3 regions.

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

Abstract copyright UK Data Service and data collection copyright owner.

The European Union Statistics on Income and Living Conditions (EU-SILC) is an instrument aimed at collecting timely and comparable cross-sectional and longitudinal multidimensional microdata on income, poverty and social exclusion. It is the European Union (EU) reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the 'Programme of Community action to encourage cooperation between Member States to combat social exclusion' and for producing structural indicators on social cohesion for the annual spring report to the European Council.

The EU-SILC instrument aims to provide two types of data: cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions, and longitudinal data pertaining to individual-level changes over time, observed periodically over, typically, a four years period. Further information may be found on the EU-SILC webpage.

Users should note that only the cross-sectional data are currently available from the UK Data Archive, and these data only cover UK. The Great Britain component of the EU-SILC dataset was collected by the Office for National Statistics (ONS) as part of the General Lifestyle Survey (GLF) (held at the Archive under Special Licence access conditions - see GN 33403). Following the closure of the GLF in 2012 the cross-sectional data have been collected via the Family Resources Survey (FRS) (held at the Archive under GN 33283). The FRS also provides the first wave of the EU-SILC longitudinal element, also carried out by ONS. The Northern Ireland component is collected by the Northern Ireland Statistics and Research Agency (NISRA) as part of the Living Conditions Survey (LCS) (not currently held at the Archive). The EU-SILC dataset has been produced in accordance with EU regulations under guidance from Eurostat. In addition, every year a European Commission regulation describing the list of secondary target variables (annual modules) is published (see Main Topics section for details).

The accompanying documentation for EU-SILC comprises: a Guidelines document that describes the survey, the variables including the module and recommendations given to the EU member states for data collection; and a document detailing the differences between the data collected and that held in Eurostat's User Database (UDB) (as described in the guidelines) for all member states, including the already established issues or particularities for the UK.


Main Topics:
The data contains interview survey data for adults aged 16 years and over, plus basic demographic information for children in the register files. These variables cover topics such as:

• basic personal and household data
• child care
• dwelling type, tenure status and housing conditions
• housing costs and amenities
• housing and non-housing related arrears
• non-monetary household deprivation indicators
• physical and social environment
• household and personal level income
• education
• health and access to healthcare
• labour information

Additionally, each year there is an annual module. The modules for 2008 and 2009 are:

• 2008: over-indebtedness and financial exclusion
• 2009: material deprivation

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about 0.1 percent were worth more than one million dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about 1.4 billion people, it was the second most populous country in the world. Of that 1.4 billion, about 28.5 million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds 77 percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.