The median annual earnings in the United Kingdom was 37,430 British pounds per year in 2024. Annual earnings varied significantly by region, ranging from 47,455 pounds in London to 32,960 pounds in the North East. Along with London, two other areas of the UK had median annual earnings above the UK average; South East England, and Scotland, at 39,038 pounds and 38,315 pounds respectively. Regional Inequality in the UK Various other indicators highlight the degree of regional inequality in the UK, especially between London and the rest of the country. Productivity in London, as measured by output per hour, was 33.2 percent higher than the UK average. By comparison, every other UK region, except the South East, fell below the UK average for productivity. In gross domestic product per head, London was also an outlier. The average GDP per head in the UK was 31,947 pounds in 2021, but for London it was 56,431 pounds. Again, the South East's GDP per head was slightly above the UK average, with every other region below it. Within London itself, there is also a great degree of inequality. In 2021, for example, the average earnings in the historic City of London borough were 1,138 pounds per week, compared with 588 pounds in Redbridge, a borough in the North East of London. Wages finally catch up with inflation in 2023 After the initial economic disruption caused by the COVID-19 pandemic subsided, wages began to steadily grow in the UK. This reached a peak in June 2021, when weekly wages for regular pay were growing at 7.3 percent, or 5.2 percent when adjusted for inflation. By that November, however, prices began to rise faster than wage growth, with inflation surging throughout 2022. In October 2022, for example, while regular pay was growing by 6.1 percent, the inflation rate had surged to 11.1 percent, Although inflation peaked in that month, it wasn't until June 2023 that wages started to outpace inflation. By this point, the damage caused by high energy and food inflation has precipitated the worst Cost of Living Crisis in the UK for a generation.

Abstract copyright UK Data Service and data collection copyright owner.

This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are:

• Family Expenditure Survey (FES) from 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 onwards

Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including:

• United Kingdom National Accounts (ONS Blue Book)
• HM Revenue and Customs (HMRC)
• Department for Transport (DfT)
• Department of Health (DH)
• Department for Education and Employment (DfEE)
• Department for Communities and Local Government (DCLG)

The Effects of Taxes and Benefits on Household Income (ETB) has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK. The estimates in this analysis are based mainly on data derived from the LCF Survey, which replaced the Family Expenditure Survey (FES) from 2001/02, and was known as the EFS until 2008. The LCF is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people’s homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design.

The LCF data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First the data is weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to Labour Force Survey (LFS) employment totals was also applied.

There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children).

In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB Quality and methodology information webpage and the Effects of taxes and benefits on household income technical report.

The Living Costs and Food Survey (LCF) is the source of the microdata on households from 2008-09 onwards. Previously, the Expenditure and Food Survey (EFS) was the data source. Derived variables are created using information from LCF and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government.

For further information, see the ONS Effects of taxes and benefits on household income webpage.

Variables available in the Secure Access version
The Secure Access version of the ETB datasets include additional variables not included in...

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.

Denmark, the Netherlands, and Norway were among the European countries with most indebted households in 2023 and 2024. The debt of Dutch households amounted to 200 percent their disposable income in , as they had a ratio of over 180 percent in the second quarter of 2024. Meanwhile, Norwegian households' debt represented 233 percent of their income. However, households in most countries were less indebted, with that ratio amounting to 97 percent in the Euro area. Less indebtedness in Western and Northern Europe There were several European countries where household's debts outweighed their disposable income. Most of those countries were North or West European. However, the indebtedness ratio in Denmark has been decreasing during the past decade. As the debt of Danish households represented nearly 273 percent in the last quarter of 2014, which has fallen very significantly by 2024. Other countries with indebted households have been following similar trends. The households' debt-to-income ratio in the Netherlands has also fallen from over 275 percent in 2013 to 200 percent in 2024. Debt per adult in Europe In Europe, the value of debt per adult varies considerably from an average of around 10,000 U.S. dollars in Europe to a much higher level in certain countries such as Switzerland. Debts can be formed in a number of ways. The most common forms of debt include credit cards, medical debt, student loans, overdrafts, mortgages, automobile financing and personal loans.

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.

This data file includes the Inequality and Poverty Key Figures (as of March 2022), constructed for all Luxembourg Income Study (LIS) Study datasets in all waves. It includes multiple national-level measures: • on inequality measures: Gini, Atkinson coefficients, and percentile ratios • on relative poverty rates for various demographic groups • median and mean of disposable household income

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.

United Kingdom UK: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 0.700 % in 2015. This records an increase from the previous number of 0.500 % for 2014. United Kingdom UK: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 0.700 % from Dec 2004 (Median) to 2015, with 12 observations. The data reached an all-time high of 1.200 % in 2004 and a record low of 0.400 % in 2012. United Kingdom UK: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population 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: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

United Kingdom UK: Poverty Gap at $5.50 a Day: 2011 PPP: % data was reported at 0.300 % in 2015. This stayed constant from the previous number of 0.300 % for 2014. United Kingdom UK: Poverty Gap at $5.50 a Day: 2011 PPP: % data is updated yearly, averaging 0.300 % from Dec 2004 (Median) to 2015, with 12 observations. The data reached an all-time high of 0.600 % in 2005 and a record low of 0.200 % in 2013. United Kingdom UK: Poverty Gap at $5.50 a Day: 2011 PPP: % 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: Poverty. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract copyright UK Data Service and data collection copyright owner. The Millennium Survey of Poverty and Social Exclusion (PSE) was designed to update the Breadline Britain Surveys which were conducted by Mori in 1983 and 1990 (see also Gordon et al, Breadline Britain in the 1990s). Firstly, a representative sample of the population of Great Britain was asked for their views on what constitutes the necessities of life in present day Britain. This was done in June 1999 using the Office for National Statistics (ONS) Omnibus Survey. Secondly, a specially selected sample was drawn from respondents to the 1998/99 General Household Survey, and interviewed in detail about their circumstances and their views on a range of issues associated with poverty and social exclusion. This dataset is associated with the second aspect of the survey; the follow-up to the GHS, referred to as PSE. The aims of the PSE survey were: To update the Breadline Britain Surveys; To estimate the size of groups of households in different circumstances; To explore movement in and out of poverty; To look at age and gender difference in experiences of and responses to poverty. It is planned that a similar survey will be carried out in other countries. Main Topics: The main topics covered include housing, health, time poverty, social networks and support, necessities, finance and debts, intra-household poverty, poverty over time, absolute and overall poverty, area deprivation, local services, crime, child's school, perceptions of poverty, activism as well as some demographics and information on income. A Kish Grid was used (see documentation for further information) Face-to-face interview Compilation or synthesis of existing material The face-to-face interviewing was done using CAPI and the interview included a card-sorting exercise. A Computer Assisted Self Interviewing (CASI) module was used to collect answers to sensitive questions, such as those on crime. Where the respondent was reluctant or unable to complete the self-completion section on the laptop the interviewer asked the respondent's permission to ask these questions. Some data were also obtained for this study from the General Household Survey. 1999 ADOPTED CHILDREN ADULTS AGE ALCOHOL USE ALCOHOLIC DRINKS APARTMENTS APPRENTICESHIP ATTITUDES BANK ACCOUNTS BASIC NEEDS BATHROOMS BEDROOMS BONUS PAYMENTS BUILDING MAINTENANCE BUILDING SOCIETY AC... BUSINESSES CARE OF DEPENDANTS CARE OF THE DISABLED CARE OF THE ELDERLY CENTRAL HEATING CEREMONIES CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILDREN CHIROPODY CHRONIC ILLNESS CLEANING CLOTHING COHABITATION COLOUR TELEVISION R... COMMERCIAL BUILDINGS COMPACT DISC PLAYERS COMPANY CARS COMPUTERS CONSUMER GOODS CONTRACEPTIVE DEVICES COOKING COSTS CRIME AND SECURITY DEBTS DENTISTS DEPRESSION DISABILITIES DISABLED PERSONS DISTANCE LEARNING DIVORCE DOMESTIC APPLIANCES DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL INSTITU... ELDERLY ELECTRIC POWER SUPPLY EMPLOYEES EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY ETHNIC GROUPS EXAMINATIONS EXPECTATION Equality FAMILIES FAMILY MEMBERS FATHER S PLACE OF B... FERTILITY FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FOOD AND NUTRITION FOSSIL FUELS FOSTER CHILDREN FRIENDS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GAS SUPPLY GENDER GENERAL PRACTITIONERS Great Britain HEADS OF HOUSEHOLD HEALTH HEALTH CONSULTATIONS HEALTH SERVICES HEALTH VISITORS HEARING HEARING AIDS HEARING IMPAIRMENTS HEATING SYSTEMS HIGHER EDUCATION HOBBIES HOLIDAYS HOME BUYING HOME HELP HOME OWNERSHIP HOME SHARING HOME VISITS HOSPITAL OUTPATIENT... HOSPITAL SERVICES HOSPITALIZATION HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLDS HOUSES HOUSEWORK HOUSING HOUSING AGE HOUSING FACILITIES HOUSING TENURE INCOME INSURANCE INTEREST FINANCE INTERNET INVESTMENT JOB HUNTING KITCHENS LANDLORDS LEISURE TIME ACTIVI... LOANS MANAGERS MARITAL HISTORY MARITAL STATUS MARRIAGE MARRIAGE DISSOLUTION MATERNITY PATIENTS MEALS ON WHEELS MEDICAL PRESCRIPTIONS MOBILE HOMES MORTGAGES MOTHER S PLACE OF B... MOTOR VEHICLES NEIGHBOURS NURSES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONAL TRAINING OCCUPATIONS ONE PARENT FAMILIES OPTICIANS OVERTIME PARENTS PART TIME COURSES PART TIME EMPLOYMENT PATIENTS PENSIONS PERSONAL DEBT REPAY... PERSONAL HYGIENE PHYSICIANS PLACE OF BIRTH POLICE SERVICES POVERTY PREGNANCY PRIVATE PERSONAL PE... PRIVATE SECTOR PROFESSIONAL CONSUL... PUBLIC HOUSES PUBLIC TRANSPORT QUALIFICATIONS REDUNDANCY PAY RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RETIREMENT ROOM SHARING ROOMS SANDWICH COURSES SATELLITE RECEIVERS SAVINGS SCHOOL LEAVING AGE SELF EMPLOYED SHARED HOME OWNERSHIP SHELTERED HOUSING SHOPPING SIBLINGS SICK PERSONS SMOKING SMOKING CESSATION SOCIAL DISADVANTAGE SOCIAL EXCLUSION SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SUPPORT SOCIAL WORKERS SOCIO ECONOMIC STATUS SPORT SPOUSES STANDARD OF LIVING STATE RETIREMENT PE... STEPCHILDREN STERILIZATION MEDICAL STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS Social behaviour an... Social conditions a... Specific social ser... TELEPHONES TELEVISION CHANNELS TELEVISION RECEIVERS TIED HOUSING TOBACCO TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNFURNISHED ACCOMMO... UNWAGED WORKERS VACANT HOUSING VIDEO RECORDERS VISION IMPAIRMENTS VISITS PERSONAL VOCATIONAL EDUCATIO... WAGES WALKING WATER SUPPLY WIDOWED inequality and soci...

In 2023, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately 709,600 U.S. dollars per person. Luxembourg was ranked second with an average wealth of around 607,500 U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the old adage goes “money can’t buy you happiness”, yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the more wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing in the list of top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that the larger proportion of the population has access to a high income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.

GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.

Average annual incomes, taxes and benefits, and household characteristics of retired and non-retired households in the UK. Data for financial years, by quintile and decile groups, country and region and tenure type.

The European Quality of Life survey (EQLS) examines both the objective circumstances of European citizens' lives, and how they feel about those circumstances, and their lives in general. It looks at a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, how satisfied they are with their lives, and how they perceive the quality of their societies.

The survey is carried out every four years.The European Foundation for the Improvement of Living and Working Conditions (Eurofound) commissioned GfK EU3C to carry out the survey.

The survey was carried in the 27 European Member States (EU27), and the survey was also implemented in seven non-EU countries. The survey covers residents aged 18 and over.

A selection of key findings from the 2010/11 data released in July 2013 are presented in this briefing: The socio-economic position of Londoners in Europe: An analysis of the 2011 European Quality of Life Survey.

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For the purposes of the rankings in this report, London is treated as a 35th European country.The themes covered in the analysis below are: volunteering, community relations, trust in society, public services ratings, well-being, health, wealth and poverty, housing, and skills and employment.

The tables following the analysis on page 4 show figures and rankings for:

- London,

- rest of the UK,

- Europe average,

- the highest ranked country, and

- the lowest ranked country.

Internet use data for all European NUTS1 areas included in spreadsheet. Note figures based on low sample sizes marked in pink.

Abstract copyright UK Data Service and data collection copyright owner.

This inquiry into the views of the year 2000 held by the younger generation took place under the auspices of the European Coordination Centre for Research and Documentation in the Social Sciences, established at Vienna, which was founded by UNESCO and which is a division of the International Social Science Council at Paris. The technical coordination was in the hands of the International Peace Research Institute, Oslo, under the direction of Johan Galtung.
The objective of the inquiry was to examine attitudes of people in the age group 15 - 40 years towards various aspects of the future, with particular reference to war, peace and disarmament. The great attractiveness of such an inquiry lies in comparing the results of countries with very different political and philosophical backgrounds. Eleven countries are covered by this study.
Main Topics:
Attitudinal/Behavioural Questions
Respondent's future-consciousness is assessed in terms of his thinking about the future of the world and of his country, his perception of the year 2000 as the near or distant future, his talking, seeing, hearing and reading about the future. Respondent predictions: what he considers will be the main differences between life today and life in the year 2000 (particularly what he feels would be the best and worst things that could happen). Employing a 9-point scale (i.e. 'best' - 'worst' possible life) the respondent is requested to indicate where he would place himself: a) at the present time, b) five years ago, c) five years from now, d) in the year 2000. Using the same procedure he is asked to assess future trends of his country and of the world.
More specifically, the respondent is asked to predict social trends in his country covering topics such as: happiness and work satisfaction, leisure, unemployment, religion, kinship and marriage, material wealth, spiritual contentment, sexual freedom, mental illness, use of narcotics and drugs, crime, social differentiation, the role of women, the role of young people, city dwelling and manual work. It is recorded whether, in most cases, the respondent's hopes coincide with his predictions.
Respondent predictions of the possibilities of science in the year 2000 are ascertained. Namely, whether it will be possible: to predetermine the sex and major personality feature of one's child, to cure dangerous diseases (e.g. cancer), to predetermine the weather, to travel to other planets. The respondent is again asked to state whether his hopes coincide with his predictions.
War, armament and disarmament: respondent assessments of world trends in this area are recorded. In addition, he is asked to assess the probable effects of a third world war on his native country, and to state his opinion on how such a war is most likely to break out (i.e. by accident, by extension of a limited conflict or by one big power attacking another big power). Any value, goal or ideal the respondent believes could justify a war with nuclear weapons/without nuclear weapons is noted. A list of 25 ideas on how world peace might be obtained is included and respondents are asked to state whether they agree or disagree with each statement (e.g. 'to obtain peace, hunger and poverty must be abolished all over the world', 'to obtain peace, we must have general and complete disarmament as soon as possible', etc.). Information also includes whether the respondent thinks that peace can be realised by the year 2000 and whether he believes he can contribute anything himself to the realisation of this proposal; what he believes is most likely to happen in the relations between capitalist and socialist countries, between rich and poor countries and between different races. Finally, respondent's knowledge of the membership of NATO and the Warsaw Pact is tested.
Opinion is ascertained on a number of items tapping the personality of the respondent (e.g. dogmatism). Social satisfaction of the respondent is measured in regard to income, job, influence on public affairs, living in his country, whether the respondent believes he has control over his future and, if so, how he feels he should direct this future. He is also asked to comparatively evaluate certain activities and views of the younger and older generations.
Background Variables
Age, sex, marital status, education, occupational details, work satisfaction (ideal occupation is noted), personal monthly income quartile, satisfaction with income received, occupation of head of household (where different), total monthly income quartile of household, household composition, area of residence (i.e. density of population, geographical region - where available), whether respondent practises religion or considers himself to be a `believer', parental household composition, father's occupational details, whether mother worked outside the home, area of childhood residence...

Wellbeing in Developing Countries is a series of studies which aim to develop a conceptual and methodological approach to understanding the social and cultural construction of wellbeing in developing countries. The Wellbeing in Developing Countries Research Group (WeD), based at the University of Bath, drew on knowledge and expertise from three different departments (Economics and International Development, Social and Policy Sciences and Psychology) as well as a network of overseas contacts. The international, interdisciplinary team formed a major programme of comparative research, focused on six communities in each of four countries: Ethiopia, Thailand, Peru and Bangladesh. All sites within the countries have been given anonymous site names, with the exception of Ethiopia where the team chose to follow an alternative locally agreed procedure on anonymisation. Data can be matched across studies using the HOUSEKEY (Site code and household number).

The research raises fundamental questions both for the academic study of development, and for the policy community. The WeD arrived at the following definition of wellbeing through their research: "Wellbeing is a state of being with others, where human needs are met, where one can act meaningfully to pursue one's goals, and where one enjoys a satisfactory quality of life".

Further information about the project can be found on the WeD website and the ESRC Award webpage.
Wellbeing in Developing Countries: Income and Expenditure, 2005-2006 comprises the Income and Expenditure Survey (carried out in Bangladesh and Peru) and Diary (carried out in Ethiopia and Thailand). The different instruments have been devised to fit with other aspects of data collection undertaken in the countries and hence also offer an opportunity for comparative evaluation of the methods involved. For each country there are data files at the visit level (ranging from 3 to 12 visits per household). There are several files for data at the lower level (i.e. data within a visit to a household). There are also files at the individual/respondent level.

Examines the extent to which the circumstances children grow up in affect their future life chances. The analysis investigates the relationship between childhood factors, such as parents’ education level and employment status, and educational attainment; as well as the extent to which these factors predict income poverty and material deprivation in adulthood. Analysis is presented both for the UK and a number of other EU countries.

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Intergenerational transmission of disadvantage in the UK & EU

Abstract copyright UK Data Service and data collection copyright owner.

The Organisation for Economic Co-operation and Development (OECD) Social and Welfare Statistics (previously Social Expenditure Database) available via the UK Data Service includes the following databases:

The OECD Social Expenditure Database (SOCX) has been developed in order to serve a growing need for indicators of social policy. It includes reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at programme level. SOCX provides a unique tool for monitoring trends in aggregate social expenditure and analysing changes in its composition. The main social policy areas are as follows: old age, survivors, incapacity-related benefits, health, family, active labour market programmes, unemployment, housing, and other social policy areas.

The Income Distribution database contains comparable data on the distribution of household income, providing both a point of reference for judging the performance of any country and an opportunity to assess the role of common drivers as well as drivers that are country-specific. They also allow governments to draw on the experience of different countries in order to learn "what works best" in narrowing income disparities and poverty. But achieving comparability in this field is also difficult, as national practices differ widely in terms of concepts, measures, and statistical sources.

The Child Wellbeing dataset compare 21 policy-focussed measures of child well-being in six areas, chosen to cover the major aspects of children’s lives: material well being; housing and environment; education; health and safety; risk behaviours; and quality of school life.

The Better Life Index: There is more to life than the cold numbers of GDP and economic statistics. This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

The Social Expenditure data were first provided by the UK Data Service in March 2004.

Main Topics:

Topics covered by the database include:

• old-age cash benefits
• disability cash benefits
• occupational injury and disease
• sickness benefits
• services for the elderly and disabled people
• survivors
• family cash benefits
• family services
• active labour market programmes
• unemployment
• health
• housing benefits
• other contingencies