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In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.
In 2023, the gross domestic product per capita in London was 63,618 British pounds, compared with 37,135 pounds per capita for the United Kingdom as a whole. Apart from London, the only other region of the UK that had a greater GDP per capita than the UK average was South East England, at 38,004 pounds per capita. By contrast, North East England had the lowest GDP per capita among UK regions, at 26,347 pounds. Regional imbalance in the UK economy? London's overall GDP in 2022 was over 508 billion British pounds, which accounted for almost a quarter of the overall GDP of the United Kingdom. South East England had the second-largest regional economy in the country, with a GDP of almost 341.7 billion British pounds. Furthermore, these two regions were the only ones that had higher levels of productivity (as measured by output per hour worked) than the UK average. While recent governments have recognized regional inequality as a major challenge facing the country, it may take several years for any initiatives to bear fruit. The creation of regional metro mayors across England is one of the earliest attempts at giving regions and cities in particular more power over spending in their regions than they currently have. UK economy growth slow in late 2024 After ending 2023 with two quarters of negative growth, the UK economy grew at the reasonable rate of 0.8 percent and 0.4 percent in the first and second quarters of the year. This was, however, followed by zero growth in the third quarter, and by just 0.1 percent in the last quarter of the year. Other economic indicators, such as the inflation rate, fell within the expected range in 2024, but have started to rise again, with a rate of three percent recorded in January 2025. While unemployment has witnessed a slight uptick since 2022, it is still at quite low levels compared with previous years.
These statistics update the English indices of deprivation 2015.
The English indices of deprivation measure relative deprivation in small areas in England called lower-layer super output areas. The index of multiple deprivation is the most widely used of these indices.
The statistical release and FAQ document (above) explain how the Indices of Deprivation 2019 (IoD2019) and the Index of Multiple Deprivation (IMD2019) can be used and expand on the headline points in the infographic. Both documents also help users navigate the various data files and guidance documents available.
The first data file contains the IMD2019 ranks and deciles and is usually sufficient for the purposes of most users.
Mapping resources and links to the IoD2019 explorer and Open Data Communities platform can be found on our IoD2019 mapping resource page.
Further detail is available in the research report, which gives detailed guidance on how to interpret the data and presents some further findings, and the technical report, which describes the methodology and quality assurance processes underpinning the indices.
We have also published supplementary outputs covering England and Wales.
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Attribution-NoDerivs 4.0 (CC BY-ND 4.0)https://creativecommons.org/licenses/by-nd/4.0/
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OVERVIEW This dataset contains data from a survey of low income households in four cities across south India. This fileset includes a guidance document on how the data was collected and how to interpret and use the data. The survey data was collected between April-June 2019. A team of 11 survey enumerators and researchers were involved in the data collection which was collected through a collaboration between the University of Cambridge and the Indian Institute for Human Settlements. Data collection for this project received ethical approval from both the Department of Engineering, University of Cambridge and Indian Institute for Human Settlements. This anonymised dataset is being released to allow full use by others.
DATASET CONTENTS This dataset contains the following files: - Indian_Low_Income_Household_Energy_Survey_Codebook.pdf - south_indian_household_energy_survey_19.csv - south_indian_household_energy_survey_19.Rda - README.txt Data contained in the csv files is the same as data contained in the Rda file.
HOW TO USE All csv files can be opened using any appropriate software. Rdata script files must be opened and run using R. We recommend using RStudio and R version 3.5.1 (“Feather Spray”) or later.
This survey followed the same methodology and as an earlier survey of low-income households in Bangalore, India. The dataset from this earlier survey can be found at: https://doi.org/10.17863/CAM.59870
This dataset was used as external validation dataset for a microsimulation of cooking fuel use in India cities. Code for the microsimulation model can be found in the following GitHub repository: github.com/anetobradley/urban_energy_microsimulation_india
This study investigates the alarming rise of urban poverty in China; in particular the patterns of urban poverty and the institutional causes are examined. The researchers look for evidence of institutional innovations that have emerged as individuals and organisations seek to negotiate more secure access to vital civic goods and services. A case study approach was used due to the complexity of the issue and the size of the Chinese urban population. Six cities were chosen and four neighbourhoods in each city were investigated. These cities were distributed in the costal, central and western region respectively, including Guangzhou, Nanjing, Harbin, Wuhan, Kumin, and Xi’an.
Further information is available from the ESRC Award webpage.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
(:unav)...........................................
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
(:unav)...........................................
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
(:unav)...........................................
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.