In 2023, 17.9 percent of Black people living in the United States were living below the poverty line, compared to 7.7 percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was 11.1 percent. Poverty in the United States Single people in the United States making less than 12,880 U.S. dollars a year and families of four making less than 26,500 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.

In the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.

In the U.S., the share of the population living in poverty fluctuated significantly throughout the six decades between 1987 and 2023. In 2023, the poverty level across all races and ethnicities was 11.1 percent. Black Americans have been the ethnic group with the highest share of their population living in poverty almost every year since 1974. In 1979 alone, Black poverty was well over double the national average, and over four times the poverty rate in white communities; in 1982, almost 48 percent of the Black population lived in poverty. Although poverty rates have been trending downward across all ethnic groups, 17.8 percent of Black Americans and 18.9 percent of American Indian and Alaskan Natives still lived below the poverty line in 2022.

In 2023, the gross median household income for Asian households in the United States stood at 112,800 U.S. dollars. Median household income in the United States, of all racial and ethnic groups, came out to 80,610 U.S. dollars in 2023. Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, Black, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. with 8.6 percent unemployed, according to the Bureau of Labor Statistics in 2021. Hispanic individuals (of any race) were most likely to go without health insurance as of 2021, with 22.8 percent uninsured.

In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

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.

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

WFRC Community Focus Areas (2023)Geographic Representation Units WFRC’s Community Focus Areas (CFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. CFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” CFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Community Focus Area Criteria UpdateFor the 2023 RTP planning cycle, WFRC will use two factors in designating geography-based CFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC CFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC CFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from CFAsSome census block groups that meet one or both of the CFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an CFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Community Focus Area Update FrequencyThe geography for WFRC CFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC CFAs used ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two CFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

This study's purpose was twofold: to investigate the nature of police patrol work in a community policing context and to field-test data collection instruments designed for systematic social observation. The project, conducted in Richmond, Virginia, where its police department was in the third year of a five-year plan to implement community policing, was designed as a case study of one police department's experience with community policing, focusing on officers in the patrol division. A team of eight researchers conducted observations with the police officers in the spring and summer of 1992. A total of 120 officers were observed during 125 observation sessions. Observers accompanied officers throughout their regular work shifts, taking brief field notes on officers' activities and encounters with the public. All of an observed officer's time during the shift was accounted for by either encounters or activities. Within 15 hours of the completion of the ridealong, the observer prepared a detailed narrative account of events that occurred during the ridealong and coded key items associated with these events. The study generated five nested quantitative datasets that can be linked by common variables. Part 1, Ridealong Data, provides information pertinent to the 125 observation sessions or "rides." Part 2, Activity Data, focuses on 5,576 activities conducted by officers when not engaged in encounters. Data in Part 3, Encounter Data, describe 1,098 encounters with citizens during the ridealongs. An encounter was defined as a communication between officers and citizens that took over one minute, involved more than three verbal exchanges between an officer and a citizen, or involved significant physical contact between the officer and citizen. Part 4, Citizen Data, provides data relevant to each of the 1,630 citizens engaged by police in the encounters. Some encounters involved more than one citizen. Part 5, Arrest Data, was constructed by merging Parts 1, 3, and 4, and provides information on 451 encounters that occurred during the ridealongs in which the citizen was suspected of some criminal mischief. All identification variables in this collection were created by the researchers for this project. Variables from Part 1 include date, start time, end time, unit, and beat assignment of the observation session, and the primary officer's and secondary officer's sex, race/ethnicity, years as an officer, months assigned to precinct and beat, hours of community policing training, and general orientation to community policing. Variables in Part 2 specify the time the activity began and ended, who initiated the activity, type, location, and visibility of the activity, involvement of the officer's supervisor during the activity, and if the activity involved problem-solving, or meeting with citizens or other community organizations. Part 3 variables include time encounter began and ended, who initiated the encounter, primary and secondary officer's energy level and mood before the encounter, problem as radioed by dispatcher, and problem as it appeared at the beginning of the encounter and at the end of the encounter. Information on the location of the encounter includes percent of time at initial location, visibility, officer's prior knowledge of the initial location, and if the officer anticipated violence at the scene. Additional variables focus on the presence of a supervisor, other police officers, service personnel, bystanders, and participants, if the officer filed or intended to file a report, if the officer engaged in problem-solving, and factors that influenced the officer's actions. Citizen information in Part 4 includes sex, age, and race/ethnicity of the citizen, role in the encounter, if the citizen appeared to be of low income, under the use of alcohol or drugs, or appeared to have a mental disorder or physical injury or illness, if the citizen was representing an establishment, if the citizen lived, worked, or owned property in the police beat, and if the citizen had a weapon. Also presented are various aspects of the police-citizen interaction, such as evidence considered by the officer, requests and responses to each other, and changes in actions during the encounter. Variables in Part 5 record the officer's orientation toward community policing, if the suspect was arrested or cited, if the offense was serious or drug-related, amount of evidence, if the victim requested that the suspect be arrested, if the victim was white, Black, and of low income, and if the suspect represented an organization. Information on the suspect includes gender, race, sobriety level, if of low income, if 19 years old or less, if actively resistant, if the officer knew the suspect adversarially, and if the suspect demonstrated conflict with others. Some items were recoded for the particular analyses for which the Arrest Data were constructed.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Surveys related to the family budget are considered one of the most important surveys types carried out by the Department Of Statistics, since it provides data on household expenditure and income and their relationship with different indicators. Therefore, most of the countries undertake periodic surveys on household income and expenditures. The Department Of Statistics, since established, conducted a series of Expenditure and Income Surveys during the years 1966, 1980, 1986/1987, 1992, 1997, 2002/2003, 2006/2007, and 2008/2009 and because of continuous changes in spending patterns, income levels and prices, as well as in the population internal and external migration, it was necessary to update data for household income and expenditure over time. Hence, the need to implement the Household Expenditure and Income Survey for the year 2010 arises. The survey was then conducted to achieve the following objectives: 1. Provide data on income and expenditure to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. 2. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 3. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 4. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty. 5. Identify consumer spending patterns prevailing in the society, and the impact of demographic, social and economic variables on those patterns. 6. Calculate the average annual income of the household and the individual, and identify the relationship between income and different socio-economic factors, such as profession and educational level of the head of the household and other indicators. 7. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the Kingdom. Where the Kingdom is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Household Expenditure and Income survey sample, for the year 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map. It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

To reach the survey objectives, 3 forms have been developed. Those forms were finalized after being tested and reviewed by specialists taking into account making the data entry, and validation, process on the computer as simple as possible.

(1) General Form/Questionnaire This form includes: - Housing characteristics such as geographic location variables, household area, building material predominant for external walls, type of tenure, monthly rent or lease, main source of water, lighting, heating and fuel cooking, sanitation type and water cycle, the number of rooms in the dwelling, in addition to providing ownership status of some home appliances and car. - Characteristics of household members: This form focused on the social characteristics of the family members such as relation to the head of the family, gender, age and educational status and marital status. It also included economic characteristics such as economic activity, and the main occupation, employment status, and the labor sector. to the additions of questions about individual continued to stay with the family, in order to update the information at the beginning of the second, third and fourth rounds. - Income section which included three parts · Family ownership of assets · Productive activities for the family · Current income sources

(2) Expenditure on food commodities form/Questionnaire This form indicates expenditure data on 17 consumption groups. Each group includes a number of food commodities, with the exception of the latter group, which was confined to some of the non-food goods and services because of their frequent spending pattern on daily basis like food commodities. For the purposes of the efficient use of results, expenditure data of the latter group was moved with the non-food commodities expenditure. The form also includes estimated amounts of own-produced food items and those received as gifts or in an in-kind form, as well as servants living with the family spending on themselves from their own wages to buy food.

(3) Expenditure on non-food commodities form/Questionnaire This form indicates expenditure data on 11 groups of non-food items, and 5 sets of spending on services, in addition to a group of consumption expenditure. It also includes an estimate of self-consumption, and non-food gifts or other items in an in-kind form received or sent by the household, as well as servants living with the family spending on themselves from their own wages to buy non-food items.

Cleaning operations

Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Organizing forms/questionnaires A compatible archive system, with the nature of the subsequent operations, was used to classify the forms according to different round throughout the year. This is to effectively enable extracting the forms when required for processing. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms are back to the archive system. 2- Data office checking This phase is achieved concurrently with the data collection phase in the field, where questionnaires completed in the fieldwork are immediately sent to data office checking phase. 3- Data coding A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were use, while for the rest of the questions, all coding were predefined during

Baltimore, Maryland Poverty Rate Statistics for 2023. Analyze over 60 metrics of the Baltimore, Maryland poverty database including by age, education, race, gender, work experience and more. In Baltimore, Maryland, an estimated 110,509 of 564,634 people live in poverty, which is 19.6%. Compared to the national average of 12.6%, the poverty rate in Baltimore is 55.56% higher.

In 1990, 48.1 percent of all Black families with a single mother in the United States lived below the poverty level. In 2023, that figure had decreased to 25.9 percent. This is significantly higher than white households with a single mother. 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.

Equity Atlas Data Description

Geographies Background:

Census Tract populations range from 1,200 to 8,000, have an average population of 4,000, and are intended to be relatively homogeneous units with respect to the resident population’s characteristics, economic status, and housing conditions. There are 375 Census Tracts completely within Bexar County. Census Tracts do not follow the CoSA boundary. Both Decennial Census and ACS Tract level data are available for Bexar County.

Blocks are the smallest subdivisions of Tracts. They are typically bounded by visible features like roads and boundaries like city limits. They can have populations that vary from zero to several hundred, such as when an apartment complex occupies the entire area. Blocks are the smallest geographic unit used by the Census Bureau for tabulation of 100-percent data (Data collected from all houses such as in the Decennial Census). There are 23,698 Blocks in Bexar County, 18,629 of which had a population of at least one and as much as 5,052 in the 2020 Decennial Census.

Demographic Data Background:

The U.S. Census Bureau’s Decennial Census is conducted once every ten years. During the Decennial Census, the Census Bureau strives to count every single person and every single residence using what was, prior to 2010, known as the “Short Form.” Decennial Census data are released down to the Census Block level. The data provided in the Decennial Census is much more accurate than the data available from the American Community Survey (ACS), which replaces what was known as the Decennial Census “Long Form.” However, since the Decennial Census is only conducted once every 10-years, the data are not as up to date as that provided by the ACS (Except for the year of Decennial Census data release).

The U.S. Census Bureau’s ACS sends out approximately 3.5-million surveys to nationwide households annually, approximately 135 households per Tract, nationwide, over a 5-year period. The ACS has a final approximate response rate of 67%, or 2.3-million surveys. This means that approximately 13,300 or 1.85% of 717,124 Total Households (Per 2021 ACS 5-Year estimates) in Bexar County respond to an ACS survey in a single year.

ACS 5-year estimates include survey results from 5-years, such as from 2017 to 2021 for the 2021 ACS 5-year estimates. The approximate 66,502 or 9.27% of Total Households within Bexar County responding to the ACS survey over a 5-years period, are the basis for numbers released that represent all households in the county. While the ACS data are more up-to date then Decennial Census data, they are less accurate due to the small sample size and Margin of Error.

Several 2021 ACS 5-Year Estimates tables were used to create the EquityScore GIS data layer attribute table, and the Equity Atlas companion data tables, EquityScoreAdditionalVariables and EquityScoreSpecialVariables. Those ACS tables are:

1. DP02 SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES

2. DP04 SELECTED HOUSING CHARACTERISTICS

3. DP05 ACS DEMOGRAPHIC AND HOUSING ESTIMATES

4. S1701 POVERTY STATUS IN THE PAST 12 MONTHS

5. S1903 MEDIAN INCOME IN THE PAST 12 MONTHS (IN 2017 INFLATION-ADJUSTED DOLLARS)

Split Tracts and Data Allocation:

A couple of issue arise with using the more up to data annually released ACS Census Tract estimates. These issues involve splitting Tracts and allocating demographic values between the split portions of Tracts.

First, Census Tract boundaries do not align with the CoSA boundary, and some Tracts are thus split by the CoSA boundary. To address this, when the portion of a Tract intersecting the CoSA was reduced to a very small area (e.g., Less than 10 Acres) or the intersecting portion is very long and exceedingly narrow sliver, those areas were merged with adjacent Tracts within the CoSA to avoid map clutter. The demographic data of the merged small area/sliver (Typically small counts, if any) do not convey to the Tract with which it was merged since it is important that the demographic values allocated to the portions of split Tracts add up to the original Tract’s values for quality assurance procedures. Instead, that value was added to the majority area portion of the original Tract that is outside the CoSA.

Second, the count values (e.g., Total Population, Race/Ethnicity, High School Education…) of a split by the CoSA boundary Tract need to be divided between the sub-portions of the Tract in a way that acknowledges the fact that population is often not evenly distributed within Tract areas. To address this, two allocation methods were used. The Dasymetric Allocation method divided the 2021 ACS 5-year Tract estimates values within its source Track, based on the 2020 Decennial Census total population values of sub-Tracts area Blocks. For instance, if Tract 1 had 10% of its 2020 Decennial Census Total Population within its Block A, then Block A would be assigned 10% of that Tract’s 2021 ACS Total Population. This methodology approximates population densities within a Tract. For variables with averages rather than counts (e.g., Median Household Incomes), portions of split Tracts retain the original values.

Blocks can also be split by the CoSA boundary. To address this, the Areal Allocation method divided split sub-Tract Block areas based on the percentage of the total area within or without the CoSA boundary. For instance, if a Block had a Dasymetric Allocation assigned Total Population value of 200, and that Block was split so that 75% of its area was in the CoSA, then that portion of the Block intersecting the CoSA was assigned a Total Population value of 150.

Equity Score Assignment:

Following the Split Tract Data Allocation, the CoSA Total Population was calculated as being 1,440,704. This value must be used rather than the Census Bureau’s ACS 5-Year estimate Total Population for the CoSA, 1,434,540, since the allocated values for all the Tracts must add up to the Total Population value. Discrepancies between the allocated from Tracts with the CoSA Boundary value and the Census Bureau CoSA value are minor (+6,164) and at least partly attributable to CoSA boundary changes in recent years (Census Bureau does not update their boundaries as frequently). For the People of Color, Median Household Income, Education and Language Equity Scores, the goal is to have approximately 20-percent of the Tract allocated CoSA Total Population, 288,141, in each of the 5 Equity scores (1-5) for a particular variable.

People of Color Score:

Since Hispanics (An ethnicity) are usually treated as a race, it is important to note that the Hispanic or Latino by Race table is used. This table includes a Hispanic and Latino value, as well as Not Hispanic and Latino race values (e.g., Not Hispanic or Latino White, Black or African American, Asian…). The use of this table, rather than the Hispanic and Latino value and the regular Race table, is necessary in order to sum the Race/Ethnicity populations and come up with the correct Total Population value.

The values for the People of Color variable are the sum of all Race/Ethnicity categories, except for the White Alone, Not Hispanic or Latino variable. The Percent People of Color (People of Color / Total Population) variable is symbolized in five classes with each class representing as near as possible, 20% of the above referenced total population. This was accomplished by sorting the Percent People of Color values from high to low and then, starting from the top, selecting records until the target summed Total Population, 288,141, was as close as possible for those selected records. Scores of five were given to the highest values of Percent People of Color. Scores decreased, in turn, to one for the lowest values of Percent People of Color.

Income Score:

The Median Household Income variable was sorted from low to high. Then, starting from the top, records were selected until the target summed Total Population, 288,141, was as close as possible for those selected records. Scores of five were given to the lowest values of Median Household Income. Scores decreased, in turn, to one for the highest values of Median Household Income.

Overall Score:

The Overall Equity score was calculated by adding the People of Color and Median Household Income scores. This results in nine Overall Equity scores (2-10).

Education Score:

The Percent Less than High School Graduate or Equivalent (Less than High School Graduate or Equivalent / Educational Attainment Age 25-Years and Up Total Population) variable values were sorted from high to low. Then, starting from the top, records were selected until the target summed Total Population, 288,141, was as close as possible for those selected records. Scores of five were given to the highest values of Percent Less than High School Graduate or Equivalent. Scores decreased, in turn, to one for the lowest values of Percent Less than High School Graduate or Equivalent.

Language Score:

The Percent Speaks English Less than "Very Well” (Speaks English Less than "Very Well" / Language Spoken at Home Age 5-years and Up Total Population) variable values were sorted from high to low. Then, starting from the top, records were selected until the target summed Total Population, 288,141, was as close as possible for those selected records. Scores of five were given to the highest values of Percent Speaks English Less than "Very Well." Scores decreased, in turn, to one for the lowest values of Percent Speaks English Less than "Very Well."

Data Used in Equity Score Determination:

The EquityScore GIS data layer attribute table only contains the actual Equity Scores or Ranks. The variables used to determine those scores (TOTAL_POP, PCT_PEOPLE_OF_COLOR, MED_HSHLD_INCOME_TOTAL_HSHLDS, PCT_EDU_LESS_THAN_HS_GRAD, and PCT_SPEAK_NOT_ENGLISH) are in the EquityScoreAdditionalVariables companion table. That companion table can be joined to the

Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group C data was reported at 949.900 AUD in 2019. This records an increase from the previous number of 922.200 AUD for 2018. Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group C data is updated yearly, averaging 832.050 AUD from Jun 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 949.900 AUD in 2019 and a record low of 730.900 AUD in 2010. Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group C data remains active status in CEIC and is reported by Fair Work Commission. The data is categorized under Global Database’s Australia – Table AU.G029: Minimum Weekly Rate of Pay: Mining Industry. GROUP C engaged in one of the following classifications: Computer Operator, Senior Clerk, Senior Stores Clerk, Laboratory Technician, Assistant Surveyor, and Assistant Safety Officer.

This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvantaged families into mixed-income communities are likely to reduce the persistence of poverty across generations. The opinions expressed in this paper are...

In 2023, 6.5 percent of Black married-couple 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.

Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group I data was reported at 1,074.600 AUD in 2019. This records an increase from the previous number of 1,043.300 AUD for 2018. Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group I data is updated yearly, averaging 941.300 AUD from Jun 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 1,074.600 AUD in 2019 and a record low of 826.800 AUD in 2010. Australia Minimum Weekly Rate of Pay: Black Coal Mining Industry: Group I data remains active status in CEIC and is reported by Fair Work Commission. The data is categorized under Global Database’s Australia – Table AU.G029: Minimum Weekly Rate of Pay: Mining Industry. GROUP I engaged in one of the following classifications: Foreperson, Senior Analyst and/or Programmer, Coal Preparation Plant Foreperson (Tasmania), Commercial Officer, Assistant Accountant, Chief Clerk, Purchasing and Store Control Officer, Mine Surveyor, Planning Officer, Occupational Health Nurse, Mines Rescue Training Officer Level 2, and Mines Rescue Technical Officer Level 2.

For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This dataset includes 18 tables which draw upon data from the 2016 Census of Canada. The tables are a custom order and contains data pertaining to core housing need and characteristics of households. 17 of the tables each cover a different geography in Canada: one for Canada as a whole, one for all Canadian census divisions (CD), and 15 for all census subdivisions (CSD) across Canada. The last table contains the median income for all geographies. Statistics Canada used these median incomes as the "area median household income (AMHI)," from which they derived some of the data fields within the Shelter Costs/Household Income dimension. Included alongside the data tables is a guide to HART's housing need assessment methodology. This guide is intended to support independent use of HART's custom data both to allow for transparent verification of our analysis, as well as supporting efforts to utilize the data for analysis beyond what HART did. There are many data fields in the data order that we did not use that may be of value for others. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada, all CDs & Country as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia), all CSDs & each Province as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), all CSDs & each Territory as a whole The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. Universe: Full Universe: Private Households in Non-farm Non-band Off-reserve Occupied Private Dwellings with Income Greater than zero. Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Note: Certain data fields from the original .ivt files were not included in the .csv extracts. Those data fields have been marked with an asterisk () below. Housing indicators in Core Housing Universe (12) 1. Total - Private Households by core housing need status 2. Households examined for core housing need 3. Households in core housing need 4. Below one standard only* 5. Below affordability standard only* 6. Below adequacy standard only* 7. Below suitability standard only* 8. Below 2 or more standards* 9. Below affordability and suitability* 10. Below affordability and adequacy* 11. Below suitability and adequacy* 12. Below affordability, suitability, and adequacy* Tenure Including Presence of Mortgage and Subsidized Housing; Household size (13) 1. Total - Private households by tenure including presence of mortgage payments and subsidized housing* 2. Owner* 3. With mortgage* 4. Without mortgage* 5. Renter* 6. Subsidized housing* 7. Not subsidized housing* 8. Total - Household size 9. 1 person 10. 2 persons 11. 3 persons 12. 4 persons 13. 5 or more persons household Shelter costs groups/statistics (20) 1. Total – Private households by household income proportion to AMHI_1 2. Households with income 20% or under of area median household income (AMHI) 3. Households with income 21% to 50% of AMHI 4. Households with income 51% to 80% of AMHI 5. Households with income 81% to 120% of AMHI 6. Households with income 121% or more of AMHI 7. Total – Private households by household income proportion to AMHI_2* 8. Households with income 30% and under of AMHI* 9. Households with income 31% to 60% of AMHI* 10. Households with income 61% or more of AMHI* 11. Total – Private households by shelter cost proportion to AMHI_1* 12. Households with shelter cost 0.5% and under of AMHI* 13. Households with shelter cost 0.6% to 1.25% of AMHI* 14. Households with shelter cost 1.26% to 2% of AMHI* 15. Households with shelter cost 2.1% to 3% of AMHI* 16. Households with shelter cost 3.1% or more of AMHI* 17. Total – Private households by shelter cost proportion to AMHI_2* 18. Households with shelter cost 0.75% or under of AMHI* 19. Households with shelter cost 0.76% to 1.5% of AMHI* 20. Households with shelter cost 1.6% or more of AMHI* Selected characteristics of the households (61) 1. Total - Private households by presence of at least one or of the combined activity limitations (Q11a, Q11b, Q11c or Q11f or combined) 2. Household has at least one person who had at least one or of combined activity limitations reported for Q11a, Q11b, Q11c or Q11f 3. Total – Private households by presence of at least one or of the combined activity limitations (Q11d or Q11e or combined) 4. Household has at least one person with activity limitations reported for Q11d and Q11e or combined Q11d and Q11e health issues 5.Total - Private households by household type including census family structure* 6. Census family households* 7. One-census-family households without additional persons* 8. One couple census family without other persons in the household* 9. Without children* 10. With children* 11. One lone-parent census family without other persons in the household* 12. One-census-family households with additional persons* 13. One couple census family with other persons in the household* 14. Without children* 15. With children* 16. One lone-parent census family with other persons in the household* 17. Multiple-family households* 18. Non-census-family households* 19. Non-family households: One person only* 20. Two-or-more person non-census-family household* 21. Total - Private households by Aboriginal household status* 22. Aboriginal household status* 23. Total - Private households by visible minority households 24. Visible Minority households 25.Total - Sex of the primary household maintainer (PHM) 26. PHM is male* 27. PHM is female 28. Total - Private households by Aboriginal identity of the primary household maintainer (PHM) 29. PHM is Aboriginal 30. Total - Visible minority status of the primary household maintainer (PHM) 31. PHM is a visible minority* 32. PHM is Chinese* 33. PHM is South Asian* 34. PHM is Black 35. PHM is a Filipino* 36. PHM is Latin American* 37. PHM is Southeast Asian* 38. PHM is an Arab* 39. PHM is West Asian* 40. PHM is Korean* 41. PHM is Japanese* 42. PHM is a visible minority*, n.i.e. The abbreviation 'n.i.e.' means 'not included elsewhere.' Includes persons with a write-in response such as 'Guyanese,' 'West Indian,' 'Tibetan,' 'Polynesian,' 'Pacific Islander,' etc. 43. PHM is multiple visible minorities* 44. PHM is not a visible minority* 45. Total - Private households by immigrant status and period of immigration of the primary household maintainer (PHM) 46. PHM is an immigrant* 47. PHM is a recent immigrant (immigrated between 2011 and 2016) 48. Total – Immigrant status and admission category of the primary household maintainer (PHM) 49. PHM immigrated between 1980 and 2016* 50. PHM immigrated with a refugee status 51. Total – Private households by admission category and recent immigration status of the primary household maintainer (PHM)* 52. PHM is admitted with a refugee status between 2011 and 2016 period of immigration* 53. Total - Private households by household family type of the primary household maintainer (PHM) 54. PHM is a female lone-parent 55. Total – Private households by age group of the primary household maintainer (PHM) 56. PHM is under 24 years 57. PHM is between 15 to 19 years* 58. PHM is between 20 to 24 years* 59. PHM is between 65 years and over 60. PHM is between 65 to 84 years* 61. PHM is between 85 years and over Median income (2) 1. Number of households 2. Median income of household ($) The household median income in the custom tabulation were estimates from a 25% sample-based data that have undergone weighting. These weights were applied to the sample data to produce estimates from the census long-form sample. The incomes used were drawn from the previous tax year, and therefore represent 2015 dollars. File list (38 total): Original data files (18): 1. ORD-05563-K2P1Y6_CT.1 (Canada)_revised.ivt 2. ORD-05563-K2P1Y6_CT.2 (CDs)_revised.ivt 3. ORD-05563-K2P1Y6_CT.3 (BC_CSDs)_revised.ivt 4. ORD-05563-K2P1Y6_CT.4 (Ontario_CSDs_2016)_revised.ivt 5. ORD-05563-K2P1Y6_CT.9 (AB_CSDs) 6. ORD-05563-K2P1Y6_CT.10 (Manitoba & CSDs) 7. ORD-05563-K2P1Y6_CT.11 (Sask_CSDs)_2016_revised 8. ORD-05563-K2P1Y6_CT.12.1 (Quebec & CSDs (2401023 to 2434135) 9. ORD-05563-K2P1Y6_CT.12.2 (Quebec & CSDs (2435005-2466107) 10. ORD-05563-K2P1Y6_CT.12.3 (Quebec & CSDs (2466112 to 2499140) 11. ORD-05563-K2P1Y6_CT.13 (NFL_CSDs) 12. ORD-05563-K2P1Y6_CT.14 (PEI_CSDs) 13. ORD-05563-K2P1Y6_CT.15 (NS_CSDs) 14. ORD-05563-K2P1Y6_CT.16 (NB_CSDs) 15. ORD-05563-K2P1Y6_CT.17 (YT_CSDs) 16. ORD-05563-K2P1Y6_CT.18 (NWT_CSDs) 17. ORD-05563-K2P1Y6_CT.19 (NU_CSDs) 18. 2016 Median income_all geographies.ivt Excel

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

U.S. Census Bureau QuickFacts statistics for Nevada. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.