In 2023, 15.4 percent of Black families were living below the poverty line in the United States. Poverty is the state of one who lacks a certain amount of material possessions or money. Absolute poverty or destitution is inability to afford basic human needs, which commonly includes clean and fresh water, nutrition, health care, education, clothing, and shelter.

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2016 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP16: 2016 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2016) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP16CSA: 2010 census tract with 2016 city FIPs for incorporated cities, unincorporated areas and LA neighborhoods. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP16_AGE_0_4: 2016 population 0 to 4 years oldPOP16_AGE_5_9: 2016 population 5 to 9 years old POP16_AGE_10_14: 2016 population 10 to 14 years old POP16_AGE_15_17: 2016 population 15 to 17 years old POP16_AGE_18_19: 2016 population 18 to 19 years old POP16_AGE_20_44: 2016 population 20 to 24 years old POP16_AGE_25_29: 2016 population 25 to 29 years old POP16_AGE_30_34: 2016 population 30 to 34 years old POP16_AGE_35_44: 2016 population 35 to 44 years old POP16_AGE_45_54: 2016 population 45 to 54 years old POP16_AGE_55_64: 2016 population 55 to 64 years old POP16_AGE_65_74: 2016 population 65 to 74 years old POP16_AGE_75_84: 2016 population 75 to 84 years old POP16_AGE_85_100: 2016 population 85 years and older POP16_WHITE: 2016 Non-Hispanic White POP16_BLACK: 2016 Non-Hispanic African AmericanPOP16_AIAN: 2016 Non-Hispanic American Indian or Alaska NativePOP16_ASIAN: 2016 Non-Hispanic Asian POP16_HNPI: 2016 Non-Hispanic Hawaiian Native or Pacific IslanderPOP16_HISPANIC: 2016 HispanicPOP16_MALE: 2016 Male POP16_FEMALE: 2016 Female POV16_WHITE: 2016 Non-Hispanic White below 100% Federal Poverty Level POV16_BLACK: 2016 Non-Hispanic African American below 100% Federal Poverty Level POV16_AIAN: 2016 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV16_ASIAN: 2016 Non-Hispanic Asian below 100% Federal Poverty Level POV16_HNPI: 2016 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV16_HISPANIC: 2016 Hispanic below 100% Federal Poverty Level POV16_TOTAL: 2016 Total population below 100% Federal Poverty Level POP16_TOTAL: 2016 Total PopulationAREA_SQMIL: Area in square milePOP16_DENSITY: Population per square mile.POV16_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2016. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

The median income in 2023 was at 56,490 U.S. dollars for Black households. In 1990, the median income among Black households was 38,360 U.S. dollars (In 2023 U.S. dollars).

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2016 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2016 American Community Survey 1-Year Estimates

In 2025, around ***** million people in Africa were living in extreme poverty, with the poverty threshold at **** U.S. dollars a day. The number of poor people on the continent dropped slightly compared to the previous year. Poverty in Africa is expected to decline slightly in the coming years, even in the face of a growing population. The number of inhabitants living below the extreme poverty line would decrease to around *** million by 2030.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, the unemployment rate of African Americans in the United States stood at 5.5 percent. This was over the national average of 3.6 percent.

The high rate of unemployment

There are many reasons why the unemployment rate among minorities is different than the national average. When it comes to African Americans, a large part of this is due to historical events, such as slavery and the struggle for civil rights, as well as the number of Black families living below the poverty level. Additionally, in 2019, for every 100,000 of the population, there were 2,203 Black men in prison. This high rate of imprisonment can contribute to the unemployment rate for African Americans, since having been in prison can reduce one’s chances of finding a job once released.

Earning differences

African Americans also make less money than other ethnicities in the United States. In 2020, the median weekly earnings of African Americans were 794 U.S. dollars, compared to Asians, who made 1,310 U.S. dollars per week, and whites, who made 1,003 U.S. dollars per week. While the African American unemployment rate may be low, it is clear that much has to change in order to achieve full equality.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The categories for relationship to householder were revised in 2019. For more information see Revisions to the Relationship to Household item..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Title SNAP Households by Household Types and Demographics 2016-2020 ACS - SNAP_HH_2020

Summary SNAP Households by type and demographics from 2016-2020 5-year period in NM Census tracts

Notes

Source US CENSUS TABLE FOOD STAMPS/SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM (SNAP) S2201 2020 ACS 5-YEAR ESTIMATE

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=8c3e62b5050f4bcc8853ecf0130f976d

Alias Definition

ID id

GeoName Geographic Area Name

ETH_1 Estimate Total Households

ETH_2 Estimate Total Households With one or more people in the household 60 years and over

ETH_3 Estimate Total Households No people in the household 60 years and over

ETH_4 Estimate Total Households Married-couple family

ETH_5 Estimate Total Households Other family:

ETH_6 Estimate Total Households Other family: Male householder, no spouse present

ETH_7 Estimate Total Households Other family: Female householder, no spouse present

ETH_8 Estimate Total Households Nonfamily households

ETH_9 Estimate Total Households With children under 18 years

ETH_10 Estimate Total Households With children under 18 years Married-couple family

ETH_11 Estimate Total Households With children under 18 years Other family:

ETH_12 Estimate Total Households With children under 18 years Other family: Male householder, no spouse present

ETH_13 Estimate Total Households With children under 18 years Other family: Female householder, no spouse present

ETH_14 Estimate Total Households With children under 18 years Nonfamily households

ETH_15 Estimate Total Households No children under 18 years

ETH_16 Estimate Total Households No children under 18 years Married-couple family

ETH_17 Estimate Total Households No children under 18 years Other family:

ETH_18 Estimate Total Households No children under 18 years Other family: Male householder, no spouse present

ETH_19 Estimate Total Households No children under 18 years Other family: Female householder, no spouse present

ETH_20 Estimate Total Households No children under 18 years Nonfamily households

ETH_POV_1 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

ETH_POV_2 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

ETH_DIS_1 Estimate Total Households DISABILITY STATUS With one or more people with a disability

ETH_DIS_2 Estimate Total Households DISABILITY STATUS With no persons with a disability

ETH_RHO_1 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

ETH_RHO_2 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

ETH_RHO_3 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

ETH_RHO_4 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

ETH_RHO_5 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

ETH_RHO_6 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

ETH_RHO_7 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

ETH_RHO_8 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

ETH_RHO_9 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

ETH_INC_1 Estimate Total Households HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2020 INFLATION-ADJUSTED DOLLARS) Median income (dollars)

ETH_WS_1 Estimate Total WORK STATUS Families

ETH_WS_2 Estimate Total WORK STATUS Families No workers in past 12 months

ETH_WS_3 Estimate Total WORK STATUS Families 1 worker in past 12 months

ETH_WS_4 Estimate Total WORK STATUS Families 2 or more workers in past 12 months

EPH_2 Estimate Percent Households With one or more people in the household 60 years and over

EPH_3 Estimate Percent Households No people in the household 60 years and over

EPH_4 Estimate Percent Households Married-couple family

EPH_5 Estimate Percent Households Other family:

EPH_6 Estimate Percent Households Other family: Male householder, no spouse present

EPH_7 Estimate Percent Households Other family: Female householder, no spouse present

EPH_8 Estimate Percent Households Nonfamily households

EPH_9 Estimate Percent Households With children under 18 years

EPH_10 Estimate Percent Households With children under 18 years Married-couple family

EPH_11 Estimate Percent Households With children under 18 years Other family:

EPH_12 Estimate Percent Households With children under 18 years Other family: Male householder, no spouse present

EPH_13 Estimate Percent Households With children under 18 years Other family: Female householder, no spouse present

EPH_14 Estimate Percent Households With children under 18 years Nonfamily households

EPH_15 Estimate Percent Households No children under 18 years

EPH_16 Estimate Percent Households No children under 18 years Married-couple family

EPH_17 Estimate Percent Households No children under 18 years Other family:

EPH_18 Estimate Percent Households No children under 18 years Other family: Male householder, no spouse present

EPH_19 Estimate Percent Households No children under 18 years Other family: Female householder, no spouse present

EPH_20 Estimate Percent Households No children under 18 years Nonfamily households

EPH_POV_1 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

EPH_POV_2 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

EPH_DIS_1 Estimate Percent Households DISABILITY STATUS With one or more people with a disability

EPH_DIS_2 Estimate Percent Households DISABILITY STATUS With no persons with a disability

EPH_RHO_1 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

EPH_RHO_2 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

EPH_RHO_3 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

EPH_RHO_4 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

EPH_RHO_5 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

EPH_RHO_6 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

EPH_RHO_7 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

EPH_RHO_8 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

EPH_RHO_9 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

EPH_WS_2 Estimate Percent WORK STATUS Families No workers in past 12 months

EPH_WS_3 Estimate Percent WORK STATUS Families 1 worker in past 12 months

EPH_WS_4 Estimate Percent WORK STATUS Families 2 or more workers in past 12 months

SNAP_1 Estimate Households receiving food stamps/SNAP Households

SNAP_2 Estimate Households receiving food stamps/SNAP Households With one or more people in the household 60 years and over

SNAP_3 Estimate Households receiving food stamps/SNAP Households No people in the household 60 years and over

SNAP_4 Estimate Households receiving food stamps/SNAP Households Married-couple family

SNAP_5 Estimate Households receiving food stamps/SNAP Households Other family:

SNAP_6 Estimate Households receiving food stamps/SNAP Households Other family: Male householder, no spouse present

SNAP_7 Estimate Households receiving food stamps/SNAP Households Other family: Female householder, no spouse present

SNAP_8 Estimate Households receiving food stamps/SNAP Households Nonfamily households

SNAP_9 Estimate Households receiving food stamps/SNAP Households With children under 18 years

SNAP_10 Estimate Households receiving food stamps/SNAP Households With children under 18 years Married-couple family

SNAP_11 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family:

SNAP_12 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Male householder, no spouse present

SNAP_13 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Female householder, no spouse present

SNAP_14 Estimate Households receiving food stamps/SNAP Households With children under 18 years Nonfamily households

SNAP_15 Estimate Households receiving food stamps/SNAP Households No children under 18 years

SNAP_16 Estimate Households receiving food stamps/SNAP Households No children under 18 years Married-couple family

SNAP_17 Estimate Households receiving food stamps/SNAP Households No children under 18 years Other family:

SNAP_18 Estimate Households

As of 2024, around **** million people in South Africa are living in extreme poverty, with the poverty threshold at **** U.S. dollars daily. This means that ******* more people were pushed into poverty compared to 2023. Moreover, the headcount was forecast to increase in the coming years. By 2030, over **** million South Africans will live on a maximum of **** U.S. dollars per day. Who is considered poor domestically? Poverty is measured using several matrices. For example, local authorities tend to rely on the national poverty line, assessed based on consumer price indices (CPI) of a basket of goods of food and non-food components. In 2023, the domestic poverty line in South Africa stood at ***** South African rand per month (around ***** U.S. dollars per month). According to a survey, social inequality and poverty worried a significant share of the South African respondents. As of September 2024, some ** percent of the respondents reported that they were worried about the state of poverty and unequal income distribution in the country.   Eastern Cape residents received more grants South Africa’s labor market has struggled to absorb the country’s population. In 2023, almost a third of the economically active population was unemployed. Local authorities employ relief assistance and social grants in an attempt to reduce poverty and assist poor individuals. In 2023, almost ** percent of South African households received state support, with the majority share benefiting in the Eastern Cape.

The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 18 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.

Collections are available for the following years:

• School Neighborhood Poverty Estimates, 2020-2021
  
• School Neighborhood Poverty Estimates, 2019-2020
• School Neighborhood Poverty Estimates, 2018-2019
• School Neighborhood Poverty Estimates, 2017-2018
• School Neighborhood Poverty Estimates, 2016-2017
• School Neighborhood Poverty Estimates, 2015-2016

All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The Los Angeles County Climate Vulnerability Assessment identified and incorporated 29 social vulnerability indicators. These indicators are listed below alongside their description and data source. Full report: https://ceo.lacounty.gov/cva-report/Note: All indicators are at the census tract level. Census tracts with no population (data) are omitted from this layer. Indicator Description Source Countywide Average

Asian Percent identifying as non-Hispanic Asian US Census Bureau, American Community Survey 2018 5-Year Estimates 14.4%

Asthma Age-adjusted rate of emergency department visits for asthma California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 52.2

Black Percent identifying as non-Hispanic black or African American US Census Bureau, American Community Survey 2018 5-Year Estimates 7.9%

Cardiovascular Age-adjusted rate of emergency department visits for heart attacks per 10,000 California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 8.4

Children Percent of people 18 and under US Census Bureau, American Community Survey 2018 5-Year Estimates 24.9%

Disability Percent of persons with either mental or physical disability US Census Bureau, American Community Survey 2018 5-Year Estimates 9.9%

Female Percent female US Census Bureau, American Community Survey 2018 5-Year Estimates 50.7%

Female householder Percent of households that have a female householder with no spouse present US Census Bureau, American Community Survey 2018 5-Year Estimates 16.2%

Foreign born Percent of the total population who was not born in the United States or Puerto Rico US Census Bureau, American Community Survey 2018 5-Year Estimates 35.2%

Hispanic Latinx Percent identifying as Hispanic or Latino US Census Bureau, American Community Survey 2018 5-Year Estimates 48.5%

Households without vehicle access Percent of households without access to a personal vehicle US Census Bureau, American Community Survey 2018 5-Year Estimates 8.8%

Library access Each tract's average block distance to nearest library LA County Internal Services Department 1.14 miles

Limited English Percent limited English speaking households US Census Bureau, American Community Survey 2018 5-Year Estimates 13.6%

Living in group quarters Percent of persons living in (either institutionalized or uninstitiutionalized) group quarters US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

Median income Median household income of census tract US Census Bureau, American Community Survey 2018 5-Year Estimates $69,623

Mobile homes Percent of occupied housing units that are mobile homes US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

No health insurance Percent of persons without health insurance US Census Bureau, American Community Survey 2018 5-Year Estimates 0.2%

No high school diploma Percent of persons 25 and older without a high school diploma US Census Bureau, American Community Survey 2018 5-Year Estimates 10.8%

No internet subscription Percent of the population without an internet subscription US Census Bureau, American Community Survey 2018 5-Year Estimates 22.6%

Older adults Percent of people 65 and older US Census Bureau, American Community Survey 2018 5-Year Estimates 18.4%

Older adults living alone Percent of households in which the householder is 65 and over who and living alone US Census Bureau, American Community Survey 2018 5-Year Estimates 12.9%

Outdoor workers Percentage of outdoor workers - agriculture, fishing, mining, extractive, construction occupations US Census Bureau, American Community Survey 2018 5-Year Estimates 8.0%

Poverty Percent of the population living in a family earning below 100% of the federal poverty threshold US Census Bureau, American Community Survey 2018 5-Year Estimates 5.4%

Rent burden Percent of renters paying more than 30 percent of their monthly income on rent and utilities US Census Bureau, American Community Survey 2018 5-Year Estimates 16.1%

Renters Percentage of renters per census tract US Census Bureau, American Community Survey 2018 5-Year Estimates 54.3%

Transit access Percent of population residing within a ½ mile of a major transit stop Healthy Places Index, SCAG 52.8%

Tribal and Indigenous Percent identifying as non-Hispanic American Indian and Alaska native US Census Bureau, American Community Survey 2018 5-Year Estimates 54.9%

Unemployed Percent of the population over the age of 16 that is unemployed and eligible for the labor force US Census Bureau, American Community Survey 2018 5-Year Estimates 6.9%

Voter turnout rate Percentage of registered voters voting in the 2016 general election CA Statewide General Elections Database 2016 63.8%

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

IntroductionEndometrial cancer type 2 (EC2) carries a worse prognosis compared to EC type 1. EC2 disproportionately affects Black women among whom incidence is higher and survival is poorer compared to Whites. Here we assessed EC2 incidence and survival patterns among US Black ethnic groups: US-born Blacks (UBB), Caribbean-born Blacks (CBB), and Black Hispanics (BH).MethodsWe analyzed population-based data (n=24,387) for the entire states of Florida and New York (2005–2016). Hysterectomy-corrected EC2 incidence rates were computed by racial-ethnic group, and survival disparities were examined using Cox regression adjusting for tumor characteristics, poverty level, and insurance status.ResultsEC2 incidence rates were highest among UBB (24.4 per 100,000), followed by CBB (18.2), Whites (11.1), and Hispanics of all races (10.1). Compared to Whites, the age-adjusted cause-specific survival was worse for non-Hispanic Blacks (aHR: 1.61; 95%CI 1.52–1.71) and Hispanics of all races (aHR:1.09; 95% CI:1.01–1.18). In relation to Whites, survival was worse for non-Hispanic Blacks: UBB (aHR:1.62; 95%CI 1.52–1.74) and CBB (aHR:1.59; 95% CI:1.44–1.76) than for BH (aHR:1.30; 95% CI:1.05–1.61). Surgical resection was associated with a lower risk of death, while carcinosarcoma subtype and advanced stage at diagnosis were associated with a greater risk.ConclusionsAlthough higher EC2 incidence and lower survival are observed among all African-descent groups, there are significant intra-racial differences among UBB, CBB, and BH. This heterogeneity in EC2 patterns among Black populations suggests an interplay between genetic and socioenvironmental factors.

In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

In 2022, around **** percent of the world population in extreme poverty lived in Tanzania, considering that the poverty threshold was at **** U.S. dollars a day. The share fluctuated in recent years, following a generally increasing trend. As of 2016, poor Tanzanians accounted for *** percent of the world population living on a maximum of **** U.S. dollars daily. Moreover, Tanzania ranked top among the African countries with the highest share of global population in extreme poverty.

In 2023, nearly ** percent of the world population in extreme poverty lived in Nigeria, considering the poverty threshold at **** U.S. dollars a day. Within the studied timeframe, the share mainly rose. Overall, the number of people living in extreme poverty in Africa was estimated to reach *** million in 2025.

As of 2024, some 6.9 million people in Ghana lived in extreme poverty, with the poverty threshold at 2.15 U.S. dollars per day. This stood as an increase from the previous year when roughly 6.8 million people lived in the said state of poverty. In 2026, around 6.7 million Ghanaians are expected to live on a maximum of 2.15 U.S. dollars daily.

Poverty in the country is segregated

Indeed, poverty figures do not considerably vary when considering men and women apart. In 2024, around 3.5 million men lived in extreme poverty in Ghana, while the count reached roughly 3.3 million for women. On the other hand, in distinguishing the state of extreme poverty among rural and urban dwellers, the difference is striking, even when based on the previously set poverty line of 1.90 U.S. dollars per day. Overall, 1.1 percent of the world's population in extreme poverty lived in Ghana as of 2024.

Ghana's Private Wealth Position in Africa

Ghana is one of the African countries with the highest private wealth concentration, ranking 6th after Kenya as of 2021. That year, the country's total private wealth amounted to 59 billion U.S. dollars, corresponding to around 1,900 U.S. dollars per capita. Between 2011 and 2021, the total wealth held by individuals in Ghana increased, representing a higher growth in comparison to other African countries save five. Overall, the nation ranks 9th in Africa in terms of countries with high net-worth individuals.

According to the Global Hunger Index 2024, hunger worldwide decreased since 2000, but the pace of the reduction has slowed since 2016. In the Middle East and North Africa, for instance, the hunger index value was the same in 2024 as in 2016, and it had even increased marginally in Latin America and the Caribbean. In 2024, Somalia had the highest index score worldwide, meaning it was the country where hunger was most prevalent. The World Hunger Index combines four indicators: undernourishment, child stunting, child wasting, and child mortality.