Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

Part 4 out of 4 For more information, see: http://www.endchildpoverty.org.uk/poverty-in-your-area-2016/ Estimated rates of child poverty from 2016 and 2018 on the level of child poverty in each constituency, local authority and ward in the UK before and after housing costs. Data is split across 26 xlsx files. For more information, visit http://www.endchildpoverty.org.uk/poverty-in-your-area-2016/ and https://mss.carto.com/viz/064da52a-2edc-4b7b-a709-f3697a5928b0/public_map Visualisations on % children living in poverty can be found here: https://mss.carto.com/viz/064da52a-2edc-4b7b-a709-f3697a5928b0/public_map Estimated rates of child poverty from 2016 and 2018 on the level of child poverty in each constituency, local authority and ward in the UK before and after housing costs. Data is split across 26 xlsx files. For more information, visit http://www.endchildpoverty.org.uk/poverty-in-your-area-2016/ and https://mss.carto.com/viz/064da52a-2edc-4b7b-a709-f3697a5928b0/public_map

3 year based data, 2007-10 refers to 2007-2010. In 2020-21, there was a reduced sample size due to the COVID-19 pandemic. The data used for both the 2018-22 and 2019-23 figures actually covers 4 years, but the 2020-21 data is omitted.

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. End User Licence 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 End User Licence version first to see whether it meeds their needs, before making an application for the Special Licence version. Fuel Poverty Dataset The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. Full information on the EHS survey is available at the MHCLG EHS website and further information on Fuel Poverty and the EHS can be sought from FuelPoverty@beis.gov.uk and ehs@communities.gov.uk respectively. Guidance on use of EHS data provided by MHCLG should also be applied to the fuel poverty dataset. Further information may be found in the Annual Fuel Poverty Statistics Report: 2020 (2018 Data) on the gov.uk website.Latest edition informationFor the second edition (June 2021) the data file was replaced with a new version, with some errors corrected in the labelling of numeric values. Main Topics: The data cover modelled household fuel costs and consumption. See documentation for further details. Compilation/Synthesis

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.

End User Licence 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 End User Licence version first to see whether it meeds their needs, before making an application for the Special Licence version.

Fuel Poverty Dataset

The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. Full information on the EHS survey is available at the https://www.gov.uk/government/collections/english-housing-survey">MHCLG EHS website and further information on Fuel Poverty and the EHS can be sought from FuelPoverty@beis.gov.uk and ehs@communities.gov.uk respectively. Guidance on use of EHS data provided by MHCLG should also be applied to the fuel poverty dataset.

Further information may be found in the https://www.gov.uk/government/collections/fuel-poverty-statistics"> Annual Fuel Poverty Statistics Report: 2020 (2018 Data) on the gov.uk website.

Latest edition information

For the second edition (June 2021) the data file was replaced with a new version, with some errors corrected in the labelling of numeric values.

Between April 2008 and March 2024, households from the Pakistani and Bangladeshi ethnic groups were the most likely to live in low income out of all ethnic groups, before and after housing costs.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

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 containing results of the 2018 Leicester Health and Wellbeing Survey for questions related to Food and Fuel Poverty. Wards with 5 or fewer responses have been supressed to maintain anonymity. It shows which areas are most affected by Fuel/Food poverty.

Housing Options (PREVENT1) Statistics in Scotland

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 46 wave of the British Cohort Study (BCS70) into monthly panel format. There are separate do files to do this for each of the three aspects.This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process. The BCS70 study covers all children in England, Scotland and Wales born in one week in 1970. The archived materials are do files that alter the format of existing BCS70 datasets to create derived datasets. Original data can be accessed via Related Resources.

Youth unemployment rose sharply as a result of the Covid-19 pandemic and subsequent sector lockdowns in the UK and across the world with 18.5% of young people aged 15-24, unemployed across EU, 40% in Spain (European Parliament Study, 2021), and 14.9% in the UK (House of Commons Library, 2023). Although, the employment rates are showing some recovery, research shows that youth unemployment has delayed long-term negative impacts on future well-being, health and job satisfaction of individuals. It increases young people’s chances of being unemployed in later years and carry a wage penalty (Bell and Blanchflower, 2011). Young people (15-24 year olds) are also more likely to work part time, often not out of choice (Pay Rise Campaign 2015), are at higher risk of ‘in-work poverty’ (Hick and Lanau 2018), more likely to be employed in low-paid and insecure jobs (across OECD countries). In the UK, labour market disadvantage is coupled with the rising cost of higher education and crucially the tightening of social security conditionality through Welfare Reform (since 2012) which could be linked to a drop in eligible young people claiming welfare support (Wells 2018). A vast body of literature has emerged in the West on youth policies and the nature of welfare state (Esping-Andersen 1990; Taylor-Gooby 2004; Wallace and Bendit 2009; Pierson 2011). It, however, remains silent on the crucial question of devolution. This ESRC funded research examines the impact of devolution on welfare provision and the sub-state welfare regimes in the UK in the focused context of youth unemployment. The project is progressing in three phases (Wave 1: 2020-21 / Wave 2: 2022-23). Wave1 identified, categorised and compared scales and types of civil society involvement in youth unemployment policy between the three devolved nations of the UK: England, Scotland and Wales. In doing so examined the implications of these differences for both youth unemployment provision and devolved policy arrangements. It has provided an internationally salient analysis located in the global phenomenon of state reconfiguration, the emergence of sub-state welfare regimes and the adoption of welfare pluralism. The research found that devolved social policy in Scotland and, to a lesser extent, Wales goes some way to mitigating the work first policy approach emanating from Westminster. Crucial to this are the key points of convergence and contention between devolved (education) and non-devolved (welfare) areas of youth employment policy on the ground (Pearce and Lagana 2023). The way in which these key points of policy tension play-out in key institutional areas like Jobcentre Plus, is the focus of the second phase of project. Wave 2 focused on ground level sites of service delivery (2022-2023). Research shows that the policy structures and the perceptions of frontline staff about the policy provisions and people claiming them, shape the nature, attitudes and processes of service delivery, and have implications for service claimants and unemployment addressal (Cagliesi and Hawkes 2015; Fletcher 2011; Fletcher and Redman 2022; Rosenthal and Peccei 2006). This phase of project was a more in-depth, critical and comparative examination of the way policy plays out on the ground through a systematic investigation of the perspectives of frontline staff interacting with the young people, in the specific context of devolution. We interviewed frontline staff in England, Scotland and Wales to study how policy is perceived and translated on ground level at the sites of service delivery in these three devolved nations from the following five categories: 1). Work Coaches (Jobcentre Plus- All ages) 2). Youth Employability Coaches (Jobcentre Plus- Young People) 3). Additional Work Coaches (Youth Hubs) 4). Careers Wales / Fair Start / National Careers Service Advisers 5). Civil Society job advisers (CWVYS/Skills Development Scotland /Youth Employment UK) This research will continue to take advantage of the UK’s unique, asymmetrical devolved arrangements to address the identified gap in research examining youth (un)employment under devolved systems of governance. The broader aim is to critique the notion of 'one UK welfare state' and, in doing so, progress our understanding of the impact of decentralisation, devolution and territorial rescaling on welfare state formation across Western Europe.

This needs assessment assessed the level and impact of food poverty across Camden and Islington, to understand local service provision and requirements, and to inform the development of an action plan in each borough to tackle food poverty.

United Kingdom UK: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data was reported at 0.000 USD mn in Jun 2018. This stayed constant from the previous number of 0.000 USD mn for Mar 2018. United Kingdom UK: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data is updated quarterly, averaging 0.000 USD mn from Mar 1945 (Median) to Jun 2018, with 294 observations. United Kingdom UK: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s UK – Table UK.IMF.IFS: IMF Account: Fund Position: Quarterly.

Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 2.100 % in 2018. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 2.100 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 2.100 % in 2018 and a record low of 2.100 % in 2018. 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 Ecuador – Table EC.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/);;

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

In 2024/25, approximately 2.9 million emergency food parcels were distributed from Trussell Trust food banks in the United Kingdom, compared with 3.1 million in 2023/24. There has been a steep rise in food bank usage in the UK, with a threefold increase in the number of parcels distributed in 2023/24, compared with 2014/15. As of the most recent year, there were over 1,700 Trussell Trust food bank distribution centers in the UK, compared with 1,500 in 2018/19. Cost of Living crisis continues Since late 2021, UK households have had to grapple with a steep rise in the cost of living. This crisis appeared to have peaked in 2022, when around 90 percent of households were reporting monthly increases to their living costs, and inflation reached a 40-year high of 11.1 percent in October 2022. Although inflation subsequently came down and wages began to outpace inflation from 2023 onward, prices remain far higher than before the crisis began. Furthermore, the first half of 2025 has seen an uptick in inflation, which, although expected to subside towards the end of the year, has piled further misery on struggling UK households. Growing discontent with political mainstream After one year in power, the current Labour government is almost as unpopular as the Conservative government they replaced, which suffered one of their worst results in their history at the last election. To deal with the UK's precarious public finances without significant tax rises, Labour have attempted to make reforms to welfare, such as cutting the winter fuel allowances for all but the poorest pensioners. This cut in particular was so unpopular that Labour reinstated it for most pensioners, with further attempts at welfare reform also hitting a roadblock. These events, along with a stuttering economy, have seen Labour fall significantly at the polls, especially at the expense of the right-wing Reform Party, who have generally led the polls since the start of the year.

Jamaica Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 2.800 % in 2018. Jamaica Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 2.800 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 2.800 % in 2018 and a record low of 2.800 % in 2018. Jamaica 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 Jamaica – Table JM.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/);;

Georgia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 0.300 % in 2018. Georgia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 0.300 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 0.300 % in 2018 and a record low of 0.300 % in 2018. Georgia 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 Georgia – Table GE.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/);;