In the 2023/24 financial year, various measures of inequality in the United Kingdom are higher than in the late 1970s. The S80/20 ratio increased from ****to ***, the P90/10 ratio from ****to ***, and the Palma ratio from *** to ***.

In 2024, the United Kingdom's Gini coefficient score was 34.1, an increase when compared with the previous year. The Gini coefficient is a measurement of inequality within economies, a lower score indicates more equality while a higher score implies more inequality.

Ratio of household equivalised income of the top 10 per cent of households to the income of the bottom 10 per cent of households. Ratio calculated using weekly household income adjusted to take account of differences in numbers and ages of residents. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more. This dataset is one of the Greater London Authority's measures of Economic Development strategy. Click here to find out more.

Percentage of total wealth owned by households in each decile for London and Great Britain. Data extracted from the ONS Wealth and Assets Survey (WAS) microdata. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

What does the data show?

The data shows the S80/S20 income quintile ratio from the UK Climate Resilience Programme UK-SSPs project. The data is available for each ONS NUTS3 shape simplified to a 10m resolution.

The S80/S20 ratio is a measure of the inequality of income distribution. The ratio is the total income received by the 20% of the population with the highest income (the top quintile) against the total income received by the 20% of the population with the lowest income (the bottom quintile).

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Inequality

Metric

S80/S20 income quintile ratio

Unit

Ratio [unitless]

Spatial Resolution

NUTS 3

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

OECD 2011

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

The gini index in the United Kingdom was forecast to remain on a similar level in 2029 as compared to 2024 with **** points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Supplementary files for article Long-term relatedness and income distribution: understanding the deep roots of inequalityThis article explores the role of long-term relatedness between countries, captured by an index of genetic distance, in driving worldwide differences in income inequality. The main hypothesis is that genetic distance gives rise to barriers to the international diffusion of redistributive policies and measures, and institutions, leading to greater income disparities. Using cross-country data, I consistently find that countries that are genetically distant to Denmark—the world frontier of egalitarian income distribution—tend to suffer from higher inequality, ceteris paribus. I also demonstrate that genetic distance is associated with greater bilateral differences in income inequality between countries. Employing data from the European Social Survey, I document that second-generation Europeans descending from countries with greater genetic distance to Denmark are less likely to exhibit positive attitudes towards equality. Further evidence suggests that effective fiscal redistribution is a key mechanism through which genetic distance to Denmark transmits to greater income inequality.

PISA is the OECD's Programme for International Student Assessment.

The Mayor has a role to play in leading, shaping and responding to changes in London through the work of the GLA group. Inclusive London: the Mayor's equality, diversity and inclusion strategy sets out how he will help address the inequalities, barriers and discrimination experienced by groups protected by the Equality Act 2010, as well as wider issues. These include poverty and socio-economic inequality, and the challenges and disadvantage facing groups like young people in care, care leavers, single parents, migrants and refugees. This report, the equality, diversity and inclusion evidence base for London, informs the strategy. It presents evidence on London's diverse population, as well as the inequalities experienced by Londoners in areas such as housing, education, employment, transport, crime, health, social integration, culture and sport.

Unpaid work in the sciences is advocated as an entry route into scientific careers. We compared the success of UK science graduates who took paid or unpaid work six-months after graduation in obtaining a high salary or working in a STEM (Science, Technology Engineering and Mathematics) field 3.5 years later. Initially taking unpaid work was associated with lower earnings and lower persistence in STEM compared with paid work, but those using personal connections to obtain unpaid positions were as likely to persist in STEM as paid workers. Obtaining a position in STEM six months after graduation was associated with higher rates of persistence in STEM compared with a position outside STEM for both paid and unpaid workers, but the difference is considerably smaller for unpaid workers. Socio-economic inequality in the likelihood of obtaining entry in STEM by taking an unpaid position is a well-founded concern for scientific workforce diversity.

Sampson's article, `Technology Gaps, Trade, and Income,' examines the impact of innovation efficiency gaps on income, wages, and trade dynamics. Our replication, which involves utilizing additional patent metrics, broadening the country selection, extending the time frame, widening the range of the trade elasticity, and excluding outliers, reinforces the significant role of technology gaps in shaping economic inequality. However, our findings indicate that the strength of this effect varies depending on country heterogeneity and the measures of innovation used.

There is ongoing debate about whether the relationship between income and pro-social behaviour depends on economic inequality. Studies investigating this question differ in their conclusions but are consistent in measuring inequality at aggregated geographic levels (i.e. at the state, region, or country-level). I hypothesise that local, more immediate manifestations of inequality are important for driving pro-social behaviour, and test the interaction between income and inequality at a much finer geographical resolution than previous studies. I first analyse the charitable giving of US households using ZIP-code level measures of inequality and data on tax deductible charitable donations reported to the IRS. I then examine whether the results generalise using a large-scale UK household survey and neighbourhood-level inequality measures. In both samples I find robust evidence of a significant interaction effect, albeit in the opposite direction as that which has been previously postulated–higher income individuals behave more pro-socially rather than less when local inequality is high.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Multivariate regression of whether six-month position was found through personal connections or was in STEM, on demographic and job characteristics.

This is a mixed method data collection. The study is part of the Rural Economy and Land Use (RELU) programme. The data result from two RELU projects carried out by the same research team:
• Social and environmental conditions in rural areas (SECRA), 01/10/2004 - 30/09/2005
• Social and environmental inequalities in rural areas (SEIRA), 01/08/2007 -31/07/2009

Both SECRA and SEIRA consist of a series of social and environmental variables for the same 6,027 rural Lower Super Output Areas in England. SECRA is the base dataset produced during the pilot project. The SEIRA dataset contains additional variables. In addition, SEIRA also contains interviews with rural residents on perceptions of inequality and inequity. Interview results revealed that people recognise that rural areas offer limited opportunities for recreation and local services, and a lack of affordable housing.

SECRA: The dataset on social and environmental conditions in rural areas was intended to encourage and enable researchers and policy makers to include both social and environmental perspectives in their consideration of rural problems.

The original objectives of the one-year scoping study to produce the dataset were:
1. to compile a rural sustainability dataset incorporating both socio-economic and
environmental characteristics of rural census output areas in England;
2. to highlight and address the methodological difficulties in working with spatial and
survey data from sources in the social and environmental science domains;
3. to identify the limitations of currently available data for rural areas;
4. to pilot the use of the rural sustainability dataset for classifying rural areas according to socio-economic and environmental conditions and hence allowing the construction of typologies to provide sampling frames for further research and to inform policies for sustainable rural development;
5. to explore the possibilities of extending dataset coverage to Scotland and Northern
Ireland given differences in census data infrastructures and output design processes.

The SECRA dataset has been compiled at the level of the new Super Output Areas (SOAs) for England. The rural extent has been identified from the new Office of the Deputy Prime Minister (ODPM) definition of urban and rural areas which relies primarily on the morphology and context of settlements.

Further information and documentation for this study may be found through the ESRC Research Catalogue: Developing spatial data for the classification of rural areas.

SEIRA: This research project has investigated the nature and extent of social and environmental inequalities and injustice in rural England addressing the questions:
1. How can we measure rural spatial inequalities in (a) socio-economic and (b) environmental-ecological characteristics of small-scale areas of England?
2. How can inequality measures inform our understanding of the distributions of social and environmental deprivation in rural England?
3. How do rural residents experience the kinds of inequality identified by the research, and what types of inequalities do they perceive as inequitable?
4. Are there identifiable areas of rural England where the potential for environmental and social inequity suggests a need for policy intervention?

Inequality in social, economic and environmental conditions has important implications for individuals or groups of people experiencing its negative effects, but also for society as a whole. In urban areas, poor environments are associated frequently with deprivation and social exclusion. Where the unequal distribution of social and environmental goods is considered unfair, it constitutes social or environmental injustice. This project has quantified inequalities in social and environmental conditions throughout rural England and identified those areas where inequalities are greatest. It has also enhanced understanding of perceptions of inequality and injustice in rural areas. The work shows how rural policy can be refined and targeted to tackle these multi-faceted problems in the most appropriate way for the benefit of society.

Further information for this study may be found through the ESRC Research Catalogue webpage: Social and environmental inequalities in rural areas.

Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 3.800 % in 2015. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 3.800 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 3.800 % in 2015 and a record low of 3.800 % in 2015. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;