In 2021, Philadelphia, Pennsylvania was the city with the highest poverty rate of the United States' most populated cities. In this statistic, the cities are sorted by poverty rate, not population. The most populated city in 2021 according to the source was New York city - which had a poverty rate of 18 percent.

In 2021, New York city had the highest number of people living below the poverty line, with 1.4 million people living in poverty. This is significantly higher than any of the other most populated cities.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about child, poverty, percent, and USA.

In 2023, it was estimated that 2.7 percent of children and adolescents in the South of the United States aged 17 years and younger had fair or poor health. In comparison, the share of children and adolescents in the Northeast who had just fair or poor health was 1.4 percent at that time.

The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.

Overall, all the world regions saw a drop in the poverty gap over the past three decades. However, it was fluctuating more in some regions than in others. East Asia & The Pacific saw the most dramatic fall, from ** percent in 1990 to less than *** percent in 2022. Sub-Saharan Africa has the highest poverty gap in the world at ** percent as of 2019. The poverty gap measures how far the average poor family is below the poverty line, here set at 3.65 U.S. dollars a day in terms of 2017 Purchasing Power Parities. Hence, it not only measures general poverty but also the intensity of poverty.

Using a poverty metric of 2.15 U.S. dollars per day, 37 percent of the women in Sub-Saharan Africa were living in extreme poverty in 2023. This is expected to fall to one third by 2023. On the other hand, less than one percent of the population in Europe and North America as well as Australia and New Zealand were living in extreme poverty. Nevertheless, there are also many people in these regions struggling to make ends meet.

The poly shapefile is a subset of data that was derived from HRSA data that shows medically underserved ares within each county. Out of nearly 2,650 counties with such areas, there are only 96 counties that have areas that have incomes that are below the poverty level. The top five counties that are medically underserved and are poor are: 1. Radford, VA (52.7% pop below poverty level) 2. Autauga, AL (51.3% pop below poverty level) 3. Baldwin county, AL (50.2% pop below poverty level) 4. Washington D.C., (41.5% pop below poverty level) 5. Montgomery County, VA (40.4% pop below poverty level)

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

This map shows the percent of adults 18+ who report 14 or more days during the past 30 days during which their physical health was not good.As stated by the CDC in the methodology:Physical health is an important component of Health-related quality of life (HRQOL), a multi-dimensional concept that focuses on the impact of health status on quality of life.Who is included in this survey?Resident adults aged ≥18 years. Respondents aged ≥18 years who report or do not report the number of days during the past 30 days during which their physical health was not good (excluding those who refused to answer, had a missing answer, or answered “don’t know/not sure”).Data SourceCDC's 2017 500 Cities ProjectArcGIS Living Atlas of the World contains multiple years of 500 Cities CDC layers, which can be found here. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of 0.52 in 2023. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year.

The average for 2020 based on 10 countries was 30.53 percent. The highest value was in Mexico: 43.9 percent and the lowest value was in Chile: 10.8 percent. The indicator is available from 2000 to 2023. Below is a chart for all countries where data are available.

United States US: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 41.500 % in 2016. This records an increase from the previous number of 41.000 % for 2013. United States US: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 40.400 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 41.500 % in 2016 and a record low of 34.600 % in 1979. United States US: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The data collection consists of a comprehensive User Guide, 48 transcripts from individual interviews and Toolkits workshops and background documents. The interviews and workshops were conducted with diverse participants drawn from policy and practitioner fields in two geographical areas of the world: the Southern African Development Community(SADC) region in Southern Africa, and the Union of South American Nations(UNASUR) region in South America. Research participants included public officials (spanning policy formation, bio-statistical experts, monitoring and evaluation) from government ministries and regional secretariats, and representatives of health and poverty-focused civil society organisations (service and advocacy non-governmental organisations, and health practitioners).

The data is organised into two principal file strands: Interviews and Toolkits. The Interviews file strand is sub-divided into South America (UNASUR) and Southern Africa (SADC) components. Each of these strands contains qualitative data, while the Toolkits data also contains quantitative data (indicators). This data is already published in the Toolkits themselves and freely available, but for the convenience of the user of this dataset is included in this deposition. Transcripts of interviews and workshops relating to the UNASUR regional health governance component of the project are in the Spanish language, and this may limit access for some users; all other transcripts are in English. The User Guide contains further information about the organisation of the research within the project, the analytical methods framing the cross-regional comparative analysis and the Toolkits, a list of acronyms and abbreviations for the English and Spanish language elements of the project, and project documents (workshop schedules and sample project information sheets, consent forms and interview guides) in the English language.

The growing presence of multilateral regional organisations in public policy making is increasingly the subject of North-South and South-South development agendas. It is also generating development initiatives and research into the benefits of regional economic integration for public goods provision and for people living in poverty. Regional integration ambitions and initiatives extend beyond commercial trade and investment to embrace health and welfare policy. However, little is known about whether and how regional commitments on reducing poverty and enhancing social equity are being implemented in these domains, and what kinds of regional policy development processes are conducive to the emergence of embedded ‘pro-poor’ approaches in regional and national spheres.

This project examined Southern multilateral regional organisations' approaches to poverty reduction through regional health policy, in particular access to health and medicines. It was guided by the following questions: what regional institutional practices and methods of regional policy formation are conducive to the emergence of embedded ‘pro-poor’ health strategies, and what can national, regional and international partners do to promote such practices and methods? The project investigated regional agendas and ambitions, regional programmes of action and regional processes of policy development of the Southern African Development Community (SADC) and the Union of South American Nations (UNASUR). It undertook in-depth fieldwork in relation to SADC and UNASUR as regional organisations and in relation to low-income member states of SADC (Zambia, Swaziland) and UNASUR (Bolivia and Paraguay) which are confronted with serious socio-economic challenges, low levels of service delivery and immense public health challenges.

The project also developed regional indicators-based monitoring systems. Previous such systems developed systems for monitoring regional integration policies in the economic sphere but this project sought to give proper attention to the regional social sphere. To this end, in collaboration with stakeholders from policy and practitioner communities in SADC and UNASUR regions, the project developed indicator-based systems (‘Toolkits’) for monitoring regional health policy change and success which incorporated a focus on ‘pro-poor’ indicators.

The research design was informed by international comparative research methodology in relation to the study of SADC and UNASUR regional health governance, policy and programming, and by collaborative modes of Participatory Action Research in relation to the PRARI Monitoring Toolkits. Data was collected using available datasets and official documents plus individual interviews, workshops and consultations with sampled members of policy and practitioner communities within the SADC and UNASUR regions. These methods allowed the project to contribute evidence and analytical perspectives capable of informing processes of regionalisation as they relate to health policy, and in particular the health-poverty nexus.

Over the past three decades, the number of people living on less than 2.15 U.S. dollars a day in terms of 2017 Purchasing Power Parities either dropped or remained stable across all regions except for Sub-Saharan Africa. On the continent, the number of people living on less than 2.15 U.S. dollars a day increased from 282.2 million in 1990 to nearly 411.15 million in 2019. East Asia & The Pacific saw the most significant poverty reduction, where 20.28 million lived in poverty in 2022 compared to more than one billion in 1990. Even though the absolute number of people living in poverty in Sub-Saharan Africa increased, the share fell during the same period, indicating that there has been poverty reduction in the region as well.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

In 2022, an estimated population of ** million people in Nigeria lived in extreme poverty, the majority in rural areas. The count of people living on less than **** U.S. dollars a day in rural regions reached **** million, while around *** million extremely poor people were located in urban areas. Overall, throughout the period examined, the poverty incidence remained above ** million in rural communities.

Access to safely managed drinking water was high in most of the countries in Europe and North America. However, there is still room for improvement in parts of the Balkans and Caucus region, with around 70 percent having access to clean drinking water in Georgia, Albania, and Azerbaijan. Europe and North America had the highest clean drinking water coverage worldwide.

As part of the US government’s Feed the Future initiative that aims to address global hunger and food security issues in sub-Saharan Africa, the US Agency for International Development is supporting three multi-stakeholder agricultural research projects under Africa Research In Sustainable Intensification for the Next Generation (Africa RISING - AR) program. The overall aim of the program is to transform agricultural systems through sustainable intensification projects in Ghana, Ethiopia, Tanzania, Malawi, Mali, and (potentially) Zambia. In West Africa, IITA works with multi-disciplinary R4D partners in selected communities located in Northern Ghana and Southern Mali. More particularly, in Northern Ghana three regions were chosen for the study: the Northern, Upper-East and Upper-West regions. These areas cover both maize-based and rice-vegetables-based systems and therefore allow to address the production constraints characterizing both realities7. As IFPRI (2012) highlights, the northern regions of Ghana are characterized by small land holdings and low input - low output farming systems, which adversely impact food security. In particular, they are subject to a seasonal cycle of food insecurity of three to seven months for cereals (i.e., maize, millet and sorghum) and four to seven months for legumes (i.e., groundnuts, cowpeas, and soybeans). These crops in the savannahs are often produced in a continuous monoculture, steadily depleting soil natural resources and causing the yields per unit area to fall to very low levels. The poverty profile of Ghana identifies the three northern regions as the poorest and most hunger-stricken areas in the country. Gender inequalities are also apparent in these regions, since women have limited access to resources and therefore limited capacity to generate income on their own.

In Bahrain, Israel, Kuwait, and Qatar, almost all the population had access to safely managed drinking water in 2022. By comparison, less than half had the same in Lebanon, Moreover, numbers were unavailable for conflict-ridden countries such as Yemen, Syria, and Sudan.