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, the city of Philadelphia in Pennsylvania had the highest family poverty rate of the 25 most populated cities in the United States. The city with the next highest poverty rate was Houston, Texas.

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.

The McAllen-Edinburg-Mission metropolitan area in Texas was ranked first with 27.2 percent of its population living below the poverty level in 2023. Eagle Pass, Texas had the second-highest poverty rate, at 24.4 percent.

In 2021, New York city had the highest number of families living below the poverty line, at an estimated 272,461 families. New York city is also the most heavily populated city in the United States.

The municipalities of Manapiare and Bolívar, located in the Venezuelan states of Amazonas and Falcón, respectively, registered the highest share of population living under the poverty line in the country in 2021. That year, almost the entire population of these municipalities was reported to be living in poverty. All the 25 Venezuelan cities listed in this statistic had at least 99.7 percent of their population living under the poverty line.

Although the U.S. poverty rate was the same in 2000 as it was in 1970, the geographic distribution of the poor has become more concentrated. A higher concentration of poor in poor neighborhoods is a concern because it may mean the poor are exposed to fewer opportunities that affect their outcomes in life, like employment and income. We show where and how poverty has become more concentrated in the United States, and who is most likely to be affected.

Poverty Status by Town reports the number and percentage of people and children living in poverty, by race/ethnicity and age range.

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

Abstract

As part of its greater portfolio in Northeast Brazil, IFAD supported the Brazilian government and State of Bahia to implement the Rural Communities Development Project in the Poorest Areas of the State of Bahia (PRODECAR), popularly referred to as Gente de Valor (GDV), between 2007 and 2013 .The purpose of GDV was to address the multitude of basic service gaps, empowerment deficit, and productive capacity needs experienced by residents of Brazil's Northeast region. Beneficiaries were drawn from the local population of sertanejos; a regional population named in reference to the dryland, sertão agro-climatic zone and among the poorest people in Brazil. As a CDD-style project, GDV's objective was to address their needs through a participatory process that would provide access to water-harvesting cisterns (primarily for household consumption), training on ecologically appropriate agricultural practices, technical assistance and technical inputs, as well as community capacitation to identify and address future development needs.

GDV was selected to be part of the IFAD10 Impact Assessment Agenda that consists of a broader set of impact assessments across the world. The aim is to generate evidence and provide lessons for better rural poverty reduction programs and to measure the impact of IFAD-supported programmes on enhancing rural people's economic mobility, increased agricultural productive capacity, improved market participation and increased resilience.

As almost six years having passed since the project closed, the analysis evaluates the sustainable impacts of GDV under the realm of access to infrastructure, agricultural productivity, poverty impacts, and empowerment of both women, youth and the community at large. Given the role that drought plays in affecting the economic opportunities of sertanejos, it is also relevant that this project evaluates outcomes following the recent multi-year drought. From the years 2010 to 2016, Bahia experienced a drought characterized as one of the worst of the century; affecting 33.4 million people and resulting in an estimated damage of approximately 30 billion USD (Marengo et al., 2017).

For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-gente-de-valor-rural-communities-development-project-in-the-poorest-areas-of-the-state-of-bahia.

Geographic coverage

Regional coverage.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The qualitative portion of the evaluation was conducted prior to the quantitative survey in order to collect information on project targeting and implementation in the targeted areas. Two primary methodologies were employed: Focus Group Discussions (FGD) and Key Informant Interviews (KII). Qualitative interviews took place across seven sub-territories and 17 communities. Communities chosen for the qualitative survey were identified based on the following economic activities: cassava, goats, and backyard gardens in combination with high intensity of water-based activities.

The quantitative data collection covered 2,019 households, and 3,615 individuals (counting 1,615 partners interviewed for the WEAI), in 228 communities. Given that the nature of the intervention expected both household and community impacts, the construction of a counterfactual was a multi-stage process stratified at the community, and then household level.

More details on the sampling procedure can be found in the IA plan and reports, attached in the documentations tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data were collected using a mixed-method approach in order to capture both expected and unexpected impacts of GDV. The data collection took place six years after the closing of GDV, offering time to identify longer-term outcomes that can lead to more realistic interpretations of impact rather than if the project had been assessed immediately after closure. The event of the multi-year drought, in tandem with continuing erratic rainfall and the loss of support from farmer-oriented public programs, further allows for assessment of the ability of the project to make beneficiaries resilient to drought and economic shocks.

The quantitative portion of the evaluation was primarily used for measurement of impact and consisted of two main instruments: a household-level questionnaire and a community-level questionnaire. These instruments covered a range of modules in order to estimate the multi-faceted aspects of welfare. In particular, the household questionnaire focused on agricultural production, agricultural sales, other income sources such as employment or government assistance, and consumption. Additionally, it included modules on assets, shocks, and migration in order to assess any wealth accumulation, exposure to shocks, and coping strategies. Given that the project placed emphasis on increasing women's leadership and decision-making, an abridged version of the Women's Empowerment in Agriculture Index (WEAI), known as the Project WEAI (Pro-WEAI) was fielded to collect data on indicators that comparatively assess agency and empowerment of male and female decision-makers in a household.

The community questionnaire focused on services that are available to the community and relevant institutions such local infrastructure, economic activities, and access to services. The community questionnaire identified levels of community agency and resilience by asking about recent shocks, coping strategies, and collective action to promote local development. Because the project baseline was incomplete, project baseline data was not used, and respondents were asked to recall levels of assets owned at a reference period pre-GDV in both the community and household questionnaires.

Note: some variables have missing labels. Please, refer to the questionnaire for more details.

Two out of every three persons in Chiapas lived under the poverty line in 2022, making it the federal entity with the largest share of poor population in Mexico. On average, about 36 percent of the Mexican population was living in poverty that year.

The qualitative research was conducted in order to illuminate older people’s quality of life from the perspective of older people themselves. The aim was to paint a picture of the lives of older people and to gain insight into how older people in the region have been affected by the massive societal changes of the last 15 years and how they are coping with the impacts of these changes.

The project involves a mixed method design, combining quantitative analysis of the living standards of older people of recently available household survey data, with qualitative research providing deep insight into the reality of life for older people today. Obtaining greater insight into how the lives of older people have been affected by the socio-economic transformations of the last 15 years, and relative role of the state and family in both providing support to and benefiting from the contribution of, older people will aid the formulation of poverty alleviation programmes. Tajikistan, Kyrgyzstan and Moldova were chosen as countries for qualitative research as these three countries are the poorest of the former Soviet states. In each country, data collection sites were selected to represent different geographical and social conditions. Data collection commenced in each country with the capital city. Data were also collected in a smaller town and a rural location as it was seen as important to investigate any differences in older people’s experiences which might be related to the places in which they live. With consideration for the above criteria, sites were then selected according to safety and accessibility issues and the availability of local contacts.

This project examines the living conditions and sources of finance and social support (both state and family) amongst older people living in the seven poorest countries of the former Soviet Union. The break-up of the Soviet Union and the subsequent transition to market-led economies has been accompanied by a decade of economic and social upheaval on an unprecedented scale. Older people face particular challenges. Having lived their entire working lives under a paternal and relatively generous welfare system, they now find themselves in later life facing a new world – politically, economically, socially, psychologically and physically.

In 2023, Wildwood-The Villages metropolitan area in Florida was ranked first, with 39.3 percent of its population aged under 18 years living below the poverty level. McAllen-Edinburg-Mission metro area in Texas had the second-highest rate of child poverty in the nation.

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 2024, New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of just under 0.52. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year. On the other hand, Utah had the lowest Gini score among U.S. states. Overall, income inequality has been rising in the country over recent decades.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

In Italy, the largest part of population who live below the poverty line is located in the South. As of 2021, in three Southern regions, Apulia, Campania, and Calabria over 20 percent of the population was living below the poverty line. An Italian household with four members is considered poor when it has an availability of less than about 1.7 thousand euros a month.

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

19 of the 20 countries with the lowest estimated GDP per capita in the world in 2024 are located in Sub-Saharan Africa. South Sudan is believed to have a GDP per capita of just 351.02 U.S. dollars - for reference, Luxembourg has the highest GDP per capita in the world, at almost 130,000 U.S. dollars, which is around 400 times larger than that of Burundi (U.S. GDP per capita is over 250 times higher than Burundi's). Poverty in Sub-Saharan Africa Many parts of Sub-Saharan Africa have been among the most impoverished in the world for over a century, due to lacking nutritional and sanitation infrastructures, persistent conflict, and political instability. These issues are also being exacerbated by climate change, where African nations are some of the most vulnerable in the world, as well as the population boom that will place over the 21st century. Of course, the entire population of Sub-Saharan Africa does not live in poverty, and countries in the southern part of the continent, as well as oil-producing states around the Gulf of Guinea, do have some pockets of significant wealth (especially in urban areas). However, while GDP per capita may be higher in these countries, wealth distribution is often very skewed, and GDP per capita figures are not representative of average living standards across the population. Outside of Africa Yemen is the only country outside of Africa to feature on the list, due to decades of civil war and instability. Yemen lags very far behind some of its neighboring Arab states, some of whom rank among the richest in the world due to their much larger energy sectors. Additionally, the IMF does not make estimates for Afghanistan, which would also likely feature on this list.