In 2023/24, 26.4 percent of children in the United Kingdom were defined as living in absolute poverty, compared with 16.9 percent of working-age adults, 13.2 percent of pensioners, and 20 percent of families where someone is disabled.

Approximately **** percent of individuals in the United Kingdom were defined as living with relative income in 2023/24, after housing costs were considered, with **** percent of people considered as being low-income before housing costs.

The Children in low-income families' local area statistics (CiLIF), provides information on the number and proportion of children living in Absolute low income by local area across the United Kingdom.The summary Statistical Release and tables which also show the proportions of children living in low income families are available here: Children in low income families: local area statistics - GOV.UK (www.gov.uk)Statistics on the number of children in low income families by financial year are published on Stat-Xplore. Figures are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI, for example by Local Authority, Westminster Parliamentary Constituency and Ward.Absolute low-income is defined as a family in low income Before Housing Costs (BHC) in the reference year in comparison with incomes in 2010/11. A family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits, or Housing Benefit) at any point in the year to be classed as low income in these statistics. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support and pensions.

Statistical disclosure control has been applied with Stat-Xplore, which guards against the identification of an individual claimant.

This is a quantitative data collection. This study aimed to collect comprehensive information on all forms of resources (including income and assets) and indicative information on deprivation and style of living in order to define and measure poverty among a representative sample of the population of the United Kingdom. This major study was the result of fifteen years research. In 1964 the Joseph Rowntree Memorial Trust agreed to finance pilot studies on fatherless families, large families and unemployed and disabled people which were then to be followed by a national survey of poverty. In 1967-68, following pilot work, interviews were completed with 2,052 households (6,045 people), in 630 parliamentary constituencies throughout the United Kingdom. Another 1,514 households (3,539 people), were later interviewed in a poor area of Ireland, Scotland, England and Wales to secure information about the populations of the poorest areas. There were mixed reactions to the book’s publication in 1979. The concept of relative deprivation provoked much discussion but the issue of multiple deprivation experienced by individuals and families was largely ignored. Comparatively little attention was paid to certain forms of deprivation - such as deprivation at work and environmental or locational deprivation - although the report gave data about multiple deprivation drawn from 60 indicators. Nearly 50 years later this study was reanalysed in a project funded by Economic and Social Research Council (ESRC). The ‘Advancing Paradata’ project looked at shifts and continuities in the social process of gathering household survey data about poverty. In part it does this through analysis of survey paradata from the 1968 Poverty in the UK survey. Paradata captures the gamut of by-products of the collection of survey data and is of interest in understanding and improving survey quality and costs. The main focus has been on automatically captured macro items, but this is now expanding to include interviewer-generated observations. For the ‘Advancing Paradata’ project, information available only on paper questionnaires at the UK Data Archive was converted into digitised form and related metadata was created. A sample of 100 survey booklets has been selected for this collection. These booklets were chosen because they have significant quantities of marginalia written on the booklets. These booklets are available via the UK Data Service QualiBank, an online tool for browsing, searching and citing the content of selected qualitative data collections held at the UK Data Service. Names of survey respondents have been removed to protect confidentiality.

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

Context

Most countries of the world define poverty as a lack of money. Yet poor people themselves consider their experience of poverty much more broadly. A person who is poor can suffer from multiple disadvantages at the same time – for example they may have poor health or malnutrition, a lack of clean water or electricity, poor quality of work or little schooling. Focusing on one factor alone, such as income, is not enough to capture the true reality of poverty.

Multidimensional poverty measures can be used to create a more comprehensive picture. They reveal who is poor and how they are poor – the range of different disadvantages they experience. As well as providing a headline measure of poverty, multidimensional measures can be broken down to reveal the poverty level in different areas of a country, and among different sub-groups of people.

Content

OPHI researchers apply the AF method and related multidimensional measures to a range of different countries and contexts. Their analyses span a number of different topics, such as changes in multidimensional poverty over time, comparisons in rural and urban poverty, and inequality among the poor. For more information on OPHI’s research, see our working paper series and research briefings.

OPHI also calculates the Global Multidimensional Poverty Index MPI, which has been published since 2010 in the United Nations Development Programme’s Human Development Report. The Global MPI is an internationally-comparable measure of acute poverty covering more than 100 developing countries. It is updated by OPHI twice a year and constructed using the AF method.

The Alkire Foster (AF) method is a way of measuring multidimensional poverty developed by OPHI’s Sabina Alkire and James Foster. Building on the Foster-Greer-Thorbecke poverty measures, it involves counting the different types of deprivation that individuals experience at the same time, such as a lack of education or employment, or poor health or living standards. These deprivation profiles are analysed to identify who is poor, and then used to construct a multidimensional index of poverty (MPI). For free online video guides on how to use the AF method, see OPHI’s online training portal.

To identify the poor, the AF method counts the overlapping or simultaneous deprivations that a person or household experiences in different indicators of poverty. The indicators may be equally weighted or take different weights. People are identified as multidimensionally poor if the weighted sum of their deprivations is greater than or equal to a poverty cut off – such as 20%, 30% or 50% of all deprivations.

It is a flexible approach which can be tailored to a variety of situations by selecting different dimensions (e.g. education), indicators of poverty within each dimension (e.g. how many years schooling a person has) and poverty cut offs (e.g. a person with fewer than five years of education is considered deprived).

The most common way of measuring poverty is to calculate the percentage of the population who are poor, known as the headcount ratio (H). Having identified who is poor, the AF method generates a unique class of poverty measures (Mα) that goes beyond the simple headcount ratio. Three measures in this class are of high importance:

Adjusted headcount ratio (M0), otherwise known as the MPI: This measure reflects both the incidence of poverty (the percentage of the population who are poor) and the intensity of poverty (the percentage of deprivations suffered by each person or household on average). M0 is calculated by multiplying the incidence (H) by the intensity (A). M0 = H x A.

Find out about other ways the AF method is used in research and policy.

Additional data here.

Acknowledgements

Alkire, S. and Robles, G. (2017). “Multidimensional Poverty Index Summer 2017: Brief methodological note and results.” OPHI Methodological Note 44, University of Oxford.

Alkire, S. and Santos, M. E. (2010). “Acute multidimensional poverty: A new index for developing countries.” OPHI Working Papers 38, University of Oxford.

Alkire, S. Jindra, C. Robles, G. and Vaz, A. (2017). ‘Multidimensional Poverty Index – Summer 2017: brief methodological note and results’. OPHI MPI Methodological Notes No. 44, Oxford Poverty and Human Development Initiative, University of Oxford.

Inspiration

• Which countries exhibit the largest subnational disparities in MPI?
• Which countries have high per-capita incomes yet still rank highly in MPI?

FOCUSONLONDON2011:POVERTY:THEHIDDENCITY 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. 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). 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 the spreadsheet. FACTS: Some interesting facts from the data… ● Highest proportion of children in workless households, by borough, 2010 Westminster – 35.6% Barking and Dagenham – 33.6% Lewisham – 33.1% Newham – 31.4% Islington – 30.6% -31. Barnet – 9.1% -32. Richmond upon Thames – 7.0% ● Changes in proportions of workless households, 2006-09, by borough Hackney – down 12.3% Brent – down 7.3% Tower Hamlets – down 4.8% Lambeth – down 4.2% 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: Haringey – down 15.0% Newham – down 12.9% Hackney – down 12.8% Tower Hamlets – down 12.1% Southwark – down 11.5% -31. Bexley – up 6.0% -32. Havering – up 10.3%

The figures presented here are from the End Child Poverty Coalition are based on tax credit data, used to estimate the percentage of children on low incomes in local authorities, parliamentary constituencies and wards across the UK. They also use national trends in worklessness to estimate recent changes in the number of children who are in poverty because their parents have lost their jobs, to update the local tax credit data which is more than two years old. This is not a direct measure of exactly how many children are in poverty on the official definition, but is based on the closest to an equivalent measure we have of local levels of child poverty. The data have been adjusted to produce figures compatible with the measures derived from the national survey of income, showing how many children live in households with below 60 per cent of median income. Specifically, the adjustments ensure that the total reported level of child poverty, before and after housing costs, is similar when adding up all the local figures as the official national totals. Thus, the local data gives an idea of the relative poverty levels in different areas, but are adjusted to estimate what these actual levels would be if they could be measured on the same basis as the national household income survey. The local data starts by classifying children in poverty if they live in families in receipt of out of work benefits or in receipt of in-work tax credits where their reported family income is less than 60 per cent of median income. This indicator, compiled officially as a local estimate of child poverty, has been reported for August 2011 by HMRC. However, on its own it is provides an inaccurate picture of actual child poverty, considerably overstating the numbers in out-of-work poverty and understating the numbers in working poverty. While these factors may balance out overall, they can seriously misrepresent the overall trend where working and non-working poverty change in different ways, as well as misrepresenting local differences where working poverty is relatively more important in some areas than others. Therefore, the figures include an upward adjustment in the in-work figure and a downward adjustment in the out-of-work figure. The adjustments are made separately to for AHC and BHC estimates, in each case according to how the total of the local estimates compare to the actual national measure. Figures are then updated, taking into account Labour Force Survey data on the number of children in non-working households for the final quarter of 2013. Additional metadata: - Licence: http://reference.data.gov.uk/id/open-government-licence

About the dataset This dataset uses information from the DWP benefit system to provide estimates of children living in poverty for wards in London. In order to be counted in this dataset, a family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits or Housing Benefit) during the year. The numbers are calibrated to the Households Below Average Income (HBAI) dataset used to provide the government's headline poverty statistics. The definition of relative low income is living in a household with equivalised* income before housing costs (BHC) below 60% of contemporary national median income. The income measure includes contributions from earnings, state support and pensions. Further detail on the estimates of dependent children living in relative low income, including alternative geographical breakdowns and additional variables, such as age of children, family type and work status are available from DWP's statistical tabulation tool Stat-Xplore. Minor adjustments to the data have been applied to guard against the identification of individual claimants. This dataset replaced the DWP children in out-of-work benefit households and HMRC children in low income families local measure releases. This dataset includes estimates for all wards in London of numbers of dependent children living in relative low income families for each financial year from 2014/15 to the latest available (2022/23). The figures for the latest year are provisional and are subject to minor revision when the next dataset is released by DWP. Headlines Number of children The number of dependent children living in relative low income across London, rose from below 310,000 in the financial year ending 2015 to over 420,000 in the financial year ending 2020, but has decreased since then to below 350,000, which is well below the number for financial year ending 2018. While many wards in London have followed a similar pattern, the numbers of children in low income families in some wards have fallen more sharply, while the numbers in other wards have continued to grow. Proportion of children in each London ward Ward population sizes vary across London, the age profile of that population also varies and both the size and make-up of the population can change over time, so in order to make more meaningful comparisons between wards or over time, DWP have also published rates, though see note below regarding caution when using these figures. A dependent child is anyone aged under 16; or aged 16 to 19 in full-time non-advanced education or in unwaged government training. Ward level estimates for the total number of dependent children are not available, so percentages cannot be derived. Ward level estimates for the percentage of children under 16 living in low income families are usually published by DWP but, in its latest release, ward-level population estimates were not available at the time, so no rates were published. To derive the rates in this dataset, the GLA has used the ONS's latest ward-level population estimates (official statistics in development). Percentages for 2021/22 are calculated using the 2021 mid year estimates, while percentages for 2022/23 are calculated using the 2022 mid year estimates. As these are official statistics in development, rates therefore need to be treated with some caution. Notes *equivalised income is adjusted for household size and composition in order to compare living standards between households of different types.

DATASET: Alpha version 2008 estimates of proportion of people per grid square living in poverty, as defined by the Multidimensional Poverty Index (http://www.ophi.org.uk/policy/multidimensional-poverty-index/), and associated uncertainty metrics. REGION: Africa SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 1km at the equator) PROJECTION: Geographic, WGS84 UNITS: Proportion of residents living in MPI-defined poverty (poverty dataset); 95% credible interval (uncertainty dataset) MAPPING APPROACH: Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates applied to GPS-located household survey data on poverty from the DHS and/or LSMS programs. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Examples - ken08povmpi.tif = Kenya (ken) MPI poverty map for 2008. ken08povmpi-uncert.tif = uncertainty dataset showing 95% credible intervals. DATE OF PRODUCTION: January 2013 CITATION: Tatem AJ, Gething PW, Bhatt S, Weiss D and Pezzulo C (2013) Pilot high resolution poverty maps, University of Southampton/Oxford.

UK: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 0.200 % in 2015. This stayed constant from the previous number of 0.200 % for 2014. UK: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 0.200 % from Dec 2004 (Median) to 2015, with 12 observations. The data reached an all-time high of 0.700 % in 2005 and a record low of 0.200 % in 2015. UK: Poverty Headcount Ratio at $3.20 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 $3.20 a day is the percentage of the population living on less than $3.20 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.

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

Safeguarded and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the Safeguarded Licence (previously known as End User Licence (EUL)) version first to see whether it meets their needs, before making an application for the Special Licence version.

The English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence is the outcome of analysis conducted to produce estimates of fuel poverty in England in 2022 undertaken by the Department for Energy Security and Net Zero (DESNZ).

Fuel poverty in England is measured using the Low Income Low Energy Efficiency (LILEE) indicator, which considers a household to be fuel poor if:

• it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) Methodology; and
• its disposable income (income after housing costs (AHC) and energy costs) would be below the poverty line. The poverty line (income poverty) is defined as an equivalised disposable income of less than 60 per cent of the national median in Section 2 of the ONS publication 'Persistent poverty in the UK and EU: 2017'.

The Low Income Low Energy Efficiency model is a dual indicator, which allows us to measure not only the extent of the problem (how many fuel poor households there are), but also the depth of the problem (how badly affected each fuel poor household is). The depth of fuel poverty is calculated using the fuel poverty gap. This is the reduction in fuel costs needed for a household to not be in fuel poverty. This is either the change in required fuel costs associated with increasing the energy efficiency of a fuel poor household to a Fuel Poverty Energy Efficiency Rating (FPEER) of band C or reducing the costs sufficiently to meet the income threshold.

The fuel poverty dataset is derived from the English Housing Survey, 2022 database created by the MHCLG. This database is constructed from fieldwork carried out between April 2021 and March 2023. The midpoint of this period is April 2022, which can be considered as the reference date for this dataset.

Main Topics:

A brief summary of each of the variables included in the English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence dataset is included in the study documentation. The variables can be grouped into the following categories:

• Low Income Low Energy Efficiency fuel poverty indicator variables
• income and fuel costs variables
• 10 per cent affordability indicator variables
• additional fuel poverty variables
• English Housing Survey variables
• policy eligibility flags
• income split variables
• energy cost variables
• energy use variables
• weights
• variables introduced in 2015 data release (first published 2017)

This study contains variables derived from the English Longitudinal Study of Ageing (ELSA). The main ELSA study is held at the UKDA under SN 5050.

The project consisted of a six-month ESRC User Fellowship (awarded to Malcolm Nicholls of the Department for Work and Pensions), examining factors leading to income poverty in old age, focusing on the influence of work histories and other life course factors. While much is known about the income position of current pensioners and the characteristics of those on low incomes, there is relatively little evidence about the life experiences which lead to these outcomes. The research that exists suggests that such events may have less of an impact on low income in later life than has generally been assumed. This may be because of the role of the state in protecting people against disrupted and interrupted work histories. The lack of research in this area in the UK reflects, in part, the limited availability of suitable longitudinal datasets. The life history data collected in the ELSA study was used to investigate how far low incomes in retirement are associated with people's work, partnership, parenting and health experiences once the role of the state is taken into account.

This study found that for the most part life-course events as measured here are not strongly associated with the chances of being on a low income in retirement. Low income risks, however, are typically best explained by a range of individual characteristics, many of which - such as social class and education - reflect circumstances or events earlier in a person's life. It appears that, while some aspects of an individual's life course do have a lingering impact on later life incomes, the direct effect of other life history variables for individuals is mitigated by the operation of state pensions and other benefits, and for women, by the role of marriage. The results of the study will help to inform the development of policy, by providing a better understanding of the causes of low income in old age.

Users are also advised to consult the main ELSA data and documentation (SN 5050). Further information may be found on the project's ESRC award and UPTAP web pages.

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. https://londondatastore-upload.s3.amazonaws.com/fol/fol11-poverty-cover-thumb.jpg" alt=""> 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%

Indicator : Fuel PovertyTheme : Net ZeroSource : Department for Energy Security & Net Zero Definition : Fuel poverty is measured by using the Low Income Low Energy Efficiency (LILEE) fuel poverty metric. The LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date [EPC]: Energy Performance Certificates Methodology ; and its disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line.Period : 2022Link : https://www.gov.uk/government/collections/fuel-poverty-statistics#2023-statistics

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.

The English Housing Survey (EHS) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

Safeguarded and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the Safeguarded Licence (previously known as End User Licence (EUL)) version first to see whether it meets their needs, before making an application for the Special Licence version.

The English Housing Survey: Fuel Poverty Dataset, 2012 is derived from the 2012 EHS database created by the DCLG. This database is constructed from fieldwork carried out between April 2011 and March 2013. The midpoint of this period is April 2012, which can be considered as the reference date for the fuel poverty dataset. Guidance on use of EHS data provided by DCLG should also be applied to the fuel poverty dataset. The dataset is the outcome of analysis conducted to produce estimates of the number of households living in fuel poverty in England in 2012. Previously, a household was defined as being fuel poor if they spent more than 10% of their income on fuel.

For the third edition (October 2015), revised data files and documentation were deposited.