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%

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%

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...

In 2024/25, there were ******* food bank parcels distributed in London, the region with the highest number of food parcels distributed in that period.

The menstrual needs of girls and women are important to health, education, and well-being. Unmet need and harm from poor menstrual health in low-and- middle-income countries have been documented, with little empirical research undertaken in high income countries. Continuing austerity in the UK suggests menstruators are likely more vulnerable to ‘period poverty’ than previously, with the COVID-19 pandemic assumed to exacerbate the situation. Aim To explore the menstrual experiences and perceptions of women in the UK who are living under circumstances of deprivation, alongside views of staff working in organisations supporting these women, to understand whether women’s menstrual needs are met. Methods A qualitative study was conducted in an inner-city in NW England. Three focus group discussions and 14 in-depth interviews were conducted across three study sites supporting impoverished women. Data was analysed thematically. Results Themes were: reflections on menstruation; affordability of products; access to public facilities; organisational support; potential solutions. Many women perceived menstruation as a burden in three aspects: physical discomfort and pain; psychological anxiety; and shame and stigma. Managing menstruation was difficult due to cost relative to low incomes, with food, heating and lighting prioritised, leaving women improvising with materials or wearing products for longer than desired. Most suggested that products should be free, often remarking if men required similar items this would happen. Most women were unaware supporting organisations provided free products. Staff felt the small range of products offered did not meet client needs and were ill prepared to have conversations on products and clients’ menstrual needs. Conclusion: Impoverished women lack the necessary resources to manage their menses well, which negatively impacts their health and brings stress, embarrassment, and shame. Support, including access to free products, is needed at both local and national level to help impoverished women manage their menstrual hygiene.

The study aimed to explore the menstrual experiences and needs of women living in circumstances of deprivation together with staff from organisations supporting such women. A qualitative study was undertaken using focus group discussions (FGD) and in-depth interviews (IDIs) among staff and clients at 3 sites was undertaken. Site 1 - Supported accommodation; Site 2 - Approved Premises; Site 3 - Foodbanks. Thirty seven women and staff participated; 23 contributed to three separate FGDs and 14 to IDIs, with recruitment curtailed due to COVID-19 restrictions. All participants provided written consent. IDIs and FGDs were recorded, transcribed and the resulting data were analysed by thematic analysis