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.

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

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%

Households in Fuel Poverty using the government Low Income Low Energy Efficiency (LILEE) method. The data shows numbers and percentages of households at County, District, and Lower Super Output Area (LSOA) geographies. The dataset is updated annually. Source: Experimental statistics published by the Department for Business, Energy and Industrial Strategy (DBEIS). See the source weblink for further guidance on the statistics and their uses and limitations. (For example, this data should only be used to look for particular areas of high fuel poverty, but not to analyse trends over time. Caution is advised regarding data for small areas such as LSOA, and other local data ideally should be used together with this data).

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%

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.

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 dataset is published as Open DataScottish Index of Multiple Deprivation, Small Area Population Estimates, and Child Poverty 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.National Records of Scotland Small Area Population Estimates (2021)Child Poverty by Datazone (2022/23)

Pan London financial capability data to support Local Authorities Child Poverty Needs Assessments, updated in April 2011 with 2010 data. This data is designed to help local authorities improve their understanding of the areas within their borough where low financial capability is most likely to exist. This could be useful to child poverty needs assessments, and subsequent work to develop and target support services for residents within their borough. Further Information For more information on the Money Advice Service (formerly the Consumer Financial Education Body): http://www.moneyadviceservice.org.uk For more information on Child Poverty Unit: http://www.education.gov.uk/childrenandyoungpeople/ families/childpoverty For details of the Experian model: http://webarchive.nationalarchives.gov.uk/ http://www.hm-treasury.gov.uk/thoresen_review_index.htm

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) 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 Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 4th of each month.

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?

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) 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 Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.

Data is Powered by LG Inform Plus and automatically checked for new data on the 4th of each month and shows MSOAs (Middle Layer Super Output Areas) at the 2021 Census Geography.

This app is published as Open Data, is the most recent, and replaces any previously published dataset.Scottish Index of Multiple Deprivation (2020), Small Area Population Estimates (2021), and Child Poverty (2022/23)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.Child Poverty by Datazone (2022/23)This app uses the following published resources:mapdataset

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) 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 Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Context

This dataset contains the locations found in the Kiva datasets included in an administrative or geographical region. You can also find poverty data about this region. This facilitates answering some of the tough questions about a region's poverty.

Content

In the interest of preserving the original names and spelling for the locations/countries/regions all the data is in Excel format and has no preview (I think only the Kaggle recommended file types have preview - if anyone can show me how to do this for an xlsx file, it will be greatly appreciated)

The Tables datasets contain the most recent analysis of the MPI on countries and regions. These datasets are updated regularly. In unique regions_names_from_google_api you will find 3 levels of inclusion for every geocode provided in Kiva datasets. (village/town, administrative region, sub-national region - which can be administrative or geographical). These are the results from the Google API Geocoding process.

Files:

• all_kiva_loans.csv

Dropped multiple columns, kept all the rows from loans.csv with names, tags, descriptions and got a csv file of 390MB instead of 2.13 GB. Basically is a simplified version of loans.csv (originally included in the analysis by beluga)

• country_stats.csv

1. population source: https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)
2. population_below_poverty_line: Percentage
3. hdi: Human Development Index
4. life_expectancy: Life expectancy at birth
5. expected_years_of_schooling: Expected years of schooling
6. mean_years_of_schooling: Mean years of schooling
7. gni: Gross national income (GNI) per capita This dataset was originally created by [beluga][1].

• all_loan_theme_merged_with_geo_mpi_regions.xlsx

This is the loan_themes_by_region left joined with Tables_5.3_Contribution_of_Deprivations. (all the original entries from loan_themes and only the entries that match from Tables_5; for the regions that lack MPI data, you will find Nan)

These are the columns in the database:

1. Partner ID
2. Field Partner
3. Name
4. sector
5. Loan Theme ID
6. Loan Theme Type
7. Country
8. forkiva
9. number
10. amount
11. geo
12. rural_pct
13. City
14. Administrative region
15. Sub-national region
16. ISO
17. World region
18. Population Share of the Region (%)
19. region MPI
20. Education (%)
21. Health (%)
22. Living standards (%)
23. Schooling (%)
24. Child school attendance (%)
25. Child Mortality (%)
26. Nutrition (%)
27. Electricity (%)
28. Improved sanitation (%)
29. Drinking water (%)
30. Floor (%)
31. Cooking fuel (%)
32. Asset ownership (%)

• mpi_on_regions.xlsx

Matched the loans in loan_themes_by_region with the regions that have info regarding MPI. This dataset brings together the amount invested in a region and the biggest problems the said region has to deal with. It is a join between the loan_themes_by_region provided by Kiva and Tables 5.3 Contribution_of_Deprivations.

It is a subset of the all_loan_theme_merged_with_geo_mpi_regions.xlsx, which contains only the entries that I could match with poverty decomposition data. It has the same columns.

• Tables_5_SubNational_Decomposition_MPI_2017-18.xlsx

Multidimensional poverty index decomposition for over 1000 regions part of 79 countries.

Table 5.3: Contribution of deprivations to the MPI, by sub-national regions
This table shows which dimensions and indicators contribute most to a region's MPI, which is useful for understanding the major source(s) of deprivation in a sub-national region.

Source: http://ophi.org.uk/multidimensional-poverty-index/global-mpi-2016/

• Tables_7_MPI_estimations_country_levels.xlsx

MPI decomposition for 120 countries.

Table 7 All Published MPI Results since 2010
The table presents an archive of all MPI estimations published over the past 5 years, together with MPI, H, A and censored headcount ratios. For comparisons over time please use Table 6, which is strictly harmonised. The full set of data tables for each year published (Column A), is found on the 'data tables' page under 'Archive'.

The data in this file is shown in interactive plots on Oxford Poverty and Human Development Initiative website. http://www.dataforall.org/dashboard/ophi/index.php/

• unique_regions_from_kiva_loan_themes.xlsx

These are all the regions corresponding to the geocodes found in Kiva's loan_themes_by_region. There are 718 unique entries, that you can join with any database from Kiva that has either a coordinates or region column.
Columns:

• geo: pair of Lat, Lon (from loan_themes_by_region)

• City: name of the city (has the most NaN's)

• Administrative region: first level of administrative inclusion for the city/location; (the equivalent of county for US)

• Sub-national region: second lev...

The Painting Pictures of Place series includes Topic Profiles which help provide an understanding of a topic of interest for different places in England and Wales using the most relevant data. These profiles also serve as a signpost to help others access these official statistics.

The first profile to be released provides a picture of Worklessness in England and Wales. Child poverty and Enterprise topic profiles are due to be released at a later date.

Source agency: Office for National Statistics

Designation: Supporting material

Language: English

Alternative title: Painting Pictures of Place Series

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale. The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage. Climate Just Map Tool includes maps on: Flooding (river/coastal and surface water) Heat Fuel poverty. The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data. Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls Indicators include: Climate Just-Flood disadvantage_2011_Dec2014.xlsx Fluvial flood disadvantage indexPluvial flood disadvantage index (1 in 30 years)Pluvial flood disadvantage index (1 in 100 years)Pluvial flood disadvantage index (1 in 1000 years) Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx Percentage of area at moderate and significant risk of fluvial floodingPercentage of area at risk of surface water flooding (1 in 30 years)Percentage of area at risk of surface water flooding (1 in 100 years)Percentage of area at risk of surface water flooding (1 in 1000 years) Climate Just-SSVI_indices_2011_Dec2014.xlsx Sensitivity - flood and heatAbility to prepare - floodAbility to respond - floodAbility to recover - floodEnhanced exposure - floodAbility to prepare - heatAbility to respond - heatAbility to recover - heatEnhanced exposure - heatSocio-spatial vulnerability index - floodSocio-spatial vulnerability index - heat Climate Just-SSVI_indicators_2011_Dec2014.xlsx % children < 5 years old% people > 75 years old% people with long term ill-health/disability (activities limited a little or a lot)% households with at least one person with long term ill-health/disability (activities limited a little or a lot)% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% households rented from social landlords% households rented from private landlords% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% people with % unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (equivalised after housing costs) (Pounds)% all pensioner households% born outside UK and IrelandFlood experience (% area associated with past events)Insurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carCrime score (IMD)% area not roadDensity of retail units (count /km2)% change in number of local VAT-based units% people with % not home workers% unemployed% in low income occupations (routine & semi-routine)% long term unemployed / never worked% households with no adults in employment and dependent childrenAverage weekly household net income estimate (Pounds)% all pensioner households% born outside UK and IrelandInsurance availability (% area with 1 in 75 chance of flooding)% single pensioner households% lone parent household with dependent children% people who do not provide unpaid care% disabled (activities limited a lot)% households with no carTravel time to nearest GP by walk/public transport (mins - representative time)% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP Number of GPs within 15 minutes by walk/public transport Number of GPs within 15 minutes by car Travel time to nearest hospital by walk/public transport (mins - representative time)Travel time to nearest hospital by car (mins - representative time)% of at risk population outside of 30 minutes by walk/PT to nearest hospitalNumber of hospitals within 30 minutes by walk/public transport Number of hospitals within 30 minutes by car % people with % not home workersChange in median house price 2004-09 (Pounds)% area not green space Area of domestic buildings per area of domestic gardens (m2 per m2)% area not blue spaceDistance to coast (m)Elevation (m)% households with the lowest floor level: Basement or semi-basement% households with the lowest floor level: ground floor% households with the lowest floor level: fifth floor or higher

Rate of claimants / beneficiaries of incapacity benefit / severe disablement allowance with mental or behavioural disorders, per 1,000 working age population, for all people, for the years 2005 to 2007. This indicator is a proxy measure of levels of severe mental illness in the community and a direct measure of socio-economic disadvantage in those ‘not in work’ because of mental illness. Severe mental illness severely restricts the capacity to fully participate in society and in particular the employment market. Unemployment rates are high amongst people with severe mental illness. In the UK, unemployment rates of 60-100% have been reported. These high rates not only reflect the disability caused by severe mental illness, but also reflect discrimination (unemployment rates are higher than in other disabled groups) and the low priority given to employment by psychiatric services. People with long-term psychiatric disabilities are even less likely to be in employment than those with long term physical disabilities. Despite high unemployment rates amongst the severely mentally ill, surveys have consistently shown that most want to work. These low rates of employment should be considered against the facts that at least 30-40% of people who are significantly disabled by enduring mental illness are capable of holding down a job. More than 900,000 adults in England claim sickness & disability benefits for mental health conditions. This group is now larger than the total number of unemployed people claiming Jobseeker’s allowance in England. Increasing the proportion of socially excluded adults in settled accommodation and employment, education or training is currently a priority action, as set out in the PSA Delivery Agreement 16 in the HM Treasury Group Strategic Objectives 2008–2011. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01045

This dataset contains detailed Global Multidimensional Poverty Index (MPI) data at the sub-national level for 71 countries.The Global MPI reflects the combined simultaneous disadvantages poor people experience across different areas of their lives, including education, health and living standards. If people are deprived in at least one-third of ten weighted indicators, they are identified as multi-dimensionally poor. For further information on the MPI visit: http://www.ophi.org.uk/multidimensional-poverty-index/

The dataset is an appendix to OPHI's Methodological Note – Winter 2014/2015 (http://www.ophi.org.uk/multidimensional-poverty-index/mpi-2014-2015/mpi-methodology/)

Please cite the data as: Alkire, S., Conconi, A., Robles, G. and Seth, S. (2015). “Multidimensional Poverty Index, Winter 2014/2015: Brief Methodological Note and Results.” OPHI Briefing 27, University of Oxford, January.

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.