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

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.

Households that have liquidity problems and solvency problems only

This dataset is about book series. It has 1 row and is filtered where the books is The wealth of Britain, 1085-1966. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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

The Gross Domestic Product (GDP) in the United Kingdom was worth 3380.85 billion US dollars in 2023, according to official data from the World Bank. The GDP value of the United Kingdom represents 3.21 percent of the world economy. This dataset provides the latest reported value for - United Kingdom GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Standard error information for total mean, median and change of total wealth and its components.

UK: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 4.203 % in 2010. This records an increase from the previous number of 4.196 % for 2000. UK: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 4.203 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 4.206 % in 1990 and a record low of 4.196 % in 2000. UK: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

This dataset is about book subjects, has 1 rows. and is filtered where the books is The wealth of England : the medieval wool trade and its political importance 1100-1600. It features 10 columns including book subject, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

United Kingdom UK: Urban Land Area data was reported at 58,698.750 sq km in 2010. This stayed constant from the previous number of 58,698.750 sq km for 2000. United Kingdom UK: Urban Land Area data is updated yearly, averaging 58,698.750 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 58,698.750 sq km in 2010 and a record low of 58,698.750 sq km in 2010. United Kingdom UK: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters data was reported at 2,897.068 sq km in 2010. This stayed constant from the previous number of 2,897.068 sq km for 2000. United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 2,897.068 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2,897.068 sq km in 2010 and a record low of 2,897.068 sq km in 2010. United Kingdom UK: Urban Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This folder contains MATLAB and R software and data to accompany the paper "Sectoral slowdowns in the UK: Evidence from transmission probabilities and economic linkages" by E.F. Janssens and R.L. Lumsdaine published in the Journal of Applied Econometrics.

This version: Feb 2022. The following items are provided:

1. LICENSE AGREEMENT (CC BY-NC-SA 4.0): The software is distributed under a Creative Commons Attribution NonCommericial-ShareAlike (CC BY-NC-SA) 4.0 International Public License, available at https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode .
   The license agreement explains the terms and conditions under which you can use, share and adapt this Software.

2. DATA:

We have directly copied the original data into the Matlab workspace file 'workspace_2020_07_01.mat'. You don't have to do anything with this, the relevant scripts you run will load this data into Matlab for you. The original data sources are:

For the whom-to-whom matrices: https://www.ons.gov.uk/economy/nationalaccounts/uksectoraccounts/datasets/enhancedfinancialaccountsflowoffundstotalfinancialaccountsexperimentalstatistics Here we use what they label as 'the 2018 edition'.

For the total financial assets, liabilities and net worth https://www.ons.gov.uk/economy/nationalaccounts/uksectoraccounts/datasets/unitedkingdomeconomicaccountsflowoffunds/current Here we use the data released on the 31th of March 2020.

These data are made available under the Open Government License v3.0, so we are allowed to redistribute them.

We provide you these data in csv files, under the names 'enhanced_financialaccounts.csv' and 'financial_assets.csv', 'financial_liabilities.csv' and 'financial_networth.csv'. These are the same data as in 'workspace_2020_07_01.mat', which we elaborate on below:

'workspace_2020_07_01.mat' contains the following variables:

financialaccounts_matrix (as in 'enhanced_financialaccounts.csv'): this matrix is 144x90, which is because the initial data comes with 11 sectors (+unknown) and 90 time observations, and this matrix essentially stacks the financial flow matrices between all sectors. Later in our files we merge certain sectors and will end up with a total of 8 sectors (note it also lists NMMF which is a subsection of OFI so we get rid of that too, so there are actually only 10 sectors in this set already). These data are obtained from the enhanced financial accounts linked above. Goes from Jan 1997 to April 2019.

Flowoffundsbasedassets (as in 'financial_assets.csv'), Flowoffundsbasedliab (as in 'financial_liabilities.csv'), Flowoffundsbasednetworth (as in 'financial_networth.csv'): these matrices contain the total assets, total liabilities and total net worth obtained from the flow of funds data linked above. These have 10 different sectors and a longer time period (133 periods, note that the last values in the matrices are nan's which is why their length does not correspond to 133): goes from Jan 1987 to Jan 2020.

N: number of sectors in the financial accounts matrix = 12, but after merging and cleaning this will go to 8.

Sectors: string 1x12 containing all the sector names of the enhanced financial accounts data

T: number of time observations of the enhanced financial accounts matrix, equals 90

Time: months and year for each data observation in the enhanced financial accounts data (1x90 datetime datatype)

1. CODE:

This folder has three scripts you need to run to replicate the results in the paper. The code does not work if you deviate from this order. That is, you need to first run (i) before (ii) and (iii).

(i) NetworkAnalysis_new_fof.m:

This script generates the maximum likelihood estimates. Run this script.

Useful output: - p_i_mode_mode_alt_stdev: this matrix contains the mode and standard deviation of the MLE of p_i, corresponding to those in Table 1 of the paper. Use the second and third row of the matrix. Also in Table 3 and Table E1. - q_i_mode_mode_alt_stdev: this matrix contains the mode and standard deviation of the MLE of p_i, corresponding to those in Table 1 of the paper. Use the second and third row of the matrix. Also in Table 3 and Table E1. - R0c_mean_modealt_stdev: these statistics are reported in Table 1 - R0d_mean_modealt_stdev: these statistics are reported in Table 1 - pijs_mode_mle_alt: these are the values of the pijs reported in Table 1 - pijs_stdev_mle: these are the standard errors of the pijs reported in Table 1

Apart from these statistics, this script generates several figures given in the paper:

Figure C4 in the Online Appendix is figure 11 in the script. Figure C1 in the Online Appendix is made by figure 6 and 7 in the script. Figure C3 in the Online Appendix is made by figure 101 in the script.

Figure D1 in the Online Appendix is made by figure 23 in the script. Figure F3 in the Online Appendix is made by figure 12 and 13 in the script.

(ii) NetworkAnalysis_new_withpriors_fof.m:

This script generates the Bayesian estimates with scaling factor. Run this script.

Useful output: - pijs_mode_alt: reported in Table 3 - pijs_se: reported in Table 3 - prior_pijs_mean: reported in Table 2 - prior_pijs_se: reported in Table 2 - R0c_mean_mode_var: reported in Table 3 - R0d_mean_mode_var: reported in Table 3 - z_mean_mode_var: reported in Table 3 - n_mean_mode_var: reported in text

In addition, the script produces several figures.

Figure 3 of the main paper is figure 18 in the script. Figure F1 is figure 23 in the script.

(iii) NetworkAnalysis_new_withpriors_noscale_fof.m: This script generates the Bayesian estimates without the scaling factor (z=1). Run this script.

Useful output - pijs_mode_alt: reported in Table E1 - pijs_se: reported in Table E1 - R0c_mean_mode_var: reported in Table E1 - R0d_mean_mode_var: reported in Table E1

In addition, the script generates among others the following figure: Figure E1 is figure 23 in the script

% of residents who feel its important for them to feel part of their local community

*This indicator is discontinued

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. Data are revised on a wave by wave basis, as a result of backwards imputation from the current wave's data. 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.

A Secure Access version of the WAS, subject to more stringent access conditions, is available under SN 6709; it contains more detailed geographic variables than the EUL version. Users are advised to download the EUL version first (SN 7215) to see if it is suitable for their needs, before considering making an application for the Secure Access version.

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.

Occupation data for 2021 and 2022 data files

The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

The data dictionary for round 8 person file is not available.

Latest edition information

For the 19th edition (February 2025), the round 8 data files and documentation have been added to the study. Values labels for variable "ILnUse01W4" in the file "was_wave_4_person_eul_oct_2020" have also...

United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters data was reported at 7,375.000 sq km in 2010. This stayed constant from the previous number of 7,375.000 sq km for 2000. United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 7,375.000 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 7,375.000 sq km in 2010 and a record low of 7,375.000 sq km in 2010. United Kingdom UK: Rural Land Area Where Elevation is Below 5 Meters data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This dataset is about artists. It has 1 row and is filtered where the artworks is Study for ‘England: Richmond Hill, on the Prince Regent’s Birthday’. It features 9 columns including birth date, death date, country, and gender.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

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 Assets

This 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.

The data are from a large Internet-based survey designed and administered in collaboration with the British Broadcasting Corporation (BBC). Between November 2009 and April 2011, 588,014 individuals competed the “Big Personality Test”.

Volunteers were told that the survey was designed to assess personality and that by clicking on the link to proceed to the survey they were giving their consent to participate. Informed consent was not requested from the next of kin, caretakers, or guardians on behalf of minors or children because only individuals 18 and older were eligible to participate. Initiating the survey was used as a record of participant consent. To complete the survey, respondents clicked on a link on the BBC’s Lab UK website.

Before beginning the survey, respondents were asked to create a BBC ID if they did not already have one. This was used to invite participants to take part in future projects and to prevent individuals from repeat responding – the survey could not be completed more than once with the same ID. After completing the survey, participants received customized feedback about their personalities based on their responses to the survey items.

A primary aim of the data was to map the distribution of personality in Great Britain (GB), so of all the participants who completed the survey, only those who reported living in England, Wales, and Scotland were included. Participants who did not complete the personality measure were also excluded. These selection criteria resulted in a total sample of 386,375 respondents.

In the present dataset, the researchers are sharing select demographic variables and the personality data used for mapping personality variation across GB. The dataset includes postcode sector information, which allows for aggregating responses for 380 Local Authority Districts.

Further information can be found on the BBC Lab UK Big Personality Test webpage.

Some participants also took part in other studies hosted by the BBC’s Lab UK (with the same unique ID) so in principle matching is possible across data sets. A combined dataset containing matched respondents who also completed the BBC Big Money Test is available from the UK Data Archive under SN 8132.