As of 2022,/23 approximately 11.4 million people were living in relative poverty in the United Kingdom, with that number increasing to over 14.3 million when housing costs are considered.

In 2022/23, 25 percent of children in the United Kingdom were defined as living in absolute poverty, compared with 17.1 percent of working-age adults, and 12.1 percent of pensioners.

Approximately 21.4 percent of individuals in the United Kingdom were defined as living with relative income in 2022/23, after housing costs were considered, with 17.1 percent of people considered as being low-income before housing costs.

As of 2022/23 approximately 9.5 million people were living in absolute poverty in the United Kingdom, with that number increasing to 12 million when housing costs are considered.

FOCUSON**LONDON**2011:**POVERTY**:THE**HIDDEN**CITY

One of the defining features of London is that it is a city of contrasts. Although it is considered one of the richest cities in the world, over a million Londoners are living in relative poverty, even before the additional costs of living in the capital are considered.

This edition of Focus on London, authored by Rachel Leeser, presents a detailed analysis of poverty in London that reveals the scale and distribution of poverty in the capital.

REPORT:

Read the full report as a PDF.

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PRESENTATION:

What do we mean by living in poverty, and how does the model affect different types of families? This interactive presentation provides some clarity on a complex concept.

CHARTS:

The motion chart shows the relationship between child poverty and worklessness at borough level, and shows how these two measures have changed since 2006. It reveals a significant reduction in workless households in Hackney (down 12 per cent), and to a lesser extent in Brent (down 7 per cent).

The bar chart shows child poverty rates and the change in child poverty since 2006. It reveals that while Tower Hamlets has the highest rate of child poverty, it also has one of the fastest falling rates (down 12 per cent), though Haringey had the biggest fall (15 per cent).

Charts

DATA:

All the data contained within the Poverty: The Hidden City report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

FACTS:

Some interesting facts from the data…

● Highest proportion of children in workless households, by borough, 2010

1. Westminster – 35.6%
2. Barking and Dagenham – 33.6%
3. Lewisham – 33.1%
4. Newham – 31.4%
5. Islington – 30.6%

-31. Barnet – 9.1%

-32. Richmond upon Thames – 7.0%

● Changes in proportions of workless households, 2006-09, by borough

1. Hackney – down 12.3%
2. Brent – down 7.3%
3. Tower Hamlets – down 4.8%
4. Lambeth – down 4.2%
5. Hillingdon – down 4.1%

-31. Enfield – up 5.8%

-32. Bexley – up 7.3%

● Highest reduction in rates of child poverty 2006-09, by borough:

1. Haringey – down 15.0%
2. Newham – down 12.9%
3. Hackney – down 12.8%
4. Tower Hamlets – down 12.1%
5. Southwark – down 11.5%

-31. Bexley – up 6.0%

-32. Havering – up 10.3%

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.

This table presents data on all people living in households that have a household income below 60 per cent of the UK median household income after housing costs are paid.

This dataset is a Scottish Fuel Poverty Index created in the summer of 2023 by EDINA@University of Edinburgh as part of their student internship programme. The user guide provides descriptions of each data variable used in creating the index. The basic rationale was to replicate for Scotland work that had been conducted previously but only in respect to England and Wales. The two indices are not strictly directly comparable due to data availability and spatial granularity but provide standalone snapshots of relative fuel poverty across Great Britain. The Scottish Index is fully open source and for purposes of transparency and repeatability this guide provides an open methodology and is accompanied by the underlying data. Data are provided in good faith ’as is’ and is the sole product of student effort as part of mentoring activities conducted by EDINA at the University. Each variable that was used in the Index was normalised relative to the individual values for that variable - which means the values presented in the underlying FPI data table do not represent the actual numbers for each local authority - merely the percentage relative to the other local authorities in Scotland. A separate file ”Fuel-poverty-index-raw-data-with-calc.csv” is available which contains the raw percentages used for the index along with a table containing the calculations used to obtain the final score and the main FPI data table.

Fuel Poverty Index Excel: This file contains each Scottish local authority's ability to pay score, demand score and final score which were all obtained from the several different variables. The raw data for these variables can be found in the Raw Data file and an explanation for each variable can be found in the User Guide document. The scores are between 1 to 100 and are normalised relative to each other. This means the final scores do not represent the actual physical values for each area.

Fuel Poverty Index csv: This file contains the normalised processed data that makes up the Scottish fuel poverty index with variables being in range of 1 to 100. Some variables have been weighted depending on how important they are to the index. The final scores rating each Scottish local authority from 1 to 100 are also included.

Raw data: This file contains the raw unprocessed data that the index was created from for all Scottish local authorities.

User Guide: This file contains the documentation of the process to create the index as well as descriptions of what each column in the Fuel Poverty Index csv file contain. This file also provides some examples of the visualisation created from the index

Fuel Poverty Index Shapefile: This folder contains the .shp shape file comprising all the data from Fuel Poverty Index csv, in addition to also having the geospatial polygons associated with each local authority boundary. For the best viewing, the British National Grid EPSG 27700 coordinate system should be used.

The English Indices of Deprivation 2010 provide a relative measure of deprivation at small area level across England. Areas are ranked from least deprived to most deprived on seven different dimensions of deprivation and an overall composite measure of multiple deprivation. Most of the data underlying the 2010 Indices are for the year 2008. The domains used in the Indices of Deprivation 2010 are: income deprivation; employment deprivation; health deprivation and disability; education deprivation; crime deprivation; barriers to housing and services deprivation; and living environment deprivation. Each of these domains has its own scores and ranks, allowing users to focus on specific aspects of deprivation. In addition, two supplementary indices measure income deprivation amongst children - the Income Deprivation Affecting Children Index (IDACI) - and older people - the Income Deprivation Affecting Older People Index (IDAOPI). This data is available for Birmingham specific and for the UK as a whole. Licence: http://reference.data.gov.uk/id/open-government-licence Birmingham 2010 Overall.excel - https://data.birmingham.gov.uk/dataset/87394513-1745-45cb-b766-1ff106a6481a/resource/82d657eb-23e6-402a-9398-eebd049e5756/download/birminghamimdoverallscore2010.xlsx

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

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...

Among the OECD countries, Costa Rica had the highest share of children living in poverty, reaching 28.5 percent in 2022. Türkiye followed with a share of 22 percent of children living in poverty, while 20.5 percent of children in Spain, Chile, and the United States did the same. On the other hand, only three percent of children in Finland were living in poverty.

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS). The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data. The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further two years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK. The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and 2003. The LSMS constitutes Wave 1 of the panel survey so there are three years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/ Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/ Winter 2003 The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

This dataset is published as Open Data, is the most recent, and replaces any previously published dataset.The Scottish Index of Multiple Deprivation 2020 is the Scottish Government’s official tool for identifying those places in Scotland suffering from deprivation. It incorporates several different aspects of deprivation (employment, income, health, education, skills and training, geographic access, crime and housing), combining them into a single index.The 2020 Index provides a relative ranking for small areas in Scotland, defined by the Scottish Neighbourhood Statistics (SNS) Data Zone 2011 geography, from 1 (most deprived) to 6,976 (least deprived). By identifying small areas where there are concentrations of multiple deprivation, the SIMD can be used to target policies and resources at the places with greatest need. The SIMD also provides a rank for each data zone within each of the seven domains, and therefore it is possible to look at individual aspects of deprivation for each area, as well as the overall level of deprivation.The dataset can be viewed by Ward, Intermediate Zone (IZ) and Scottish Parliamentary Constituency (SPC).Details of the methodology used to determine the income, employment, education, health, access (to services), crime and housing domains can be opened from this link. Depending on the browser used to access this dataset, view the document from the options appearing on the screen.The SIMD dataset has been sourced from: SpatialData.gov.scotThis dataset is also used in the associated SIMD and Child Poverty map and application.