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

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.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Between 2018 and 2022, Americans who identified as Black and Americans who identified as American Indian or Alaska Native were most likely to be living in poverty across all generations in the United States. Within the provided time period, ** percent of Gen Alpha who were Black lived in families with incomes below the federal poverty line in the United States, followed by ** percent who were American Indian or Alaska Native.

In 2023 the poverty rate in the United States was highest among people between 18 and 24, with a rate of 16 percent for male Americans and a rate of 21 percent for female Americans. The lowest poverty rate for both men and women was for those aged between 45 and 54. What is the poverty line? The poverty line is a metric used by the U.S. Census Bureau to define poverty in the United States. It is a specific income level that is considered to be the bare minimum a person or family needs to meet their basic needs. If a family’s annual pre-tax income is below this income level, then they are considered impoverished. The poverty guideline for a family of four in 2021 was 26,500 U.S. dollars. Living below the poverty line According to the most recent data, almost one-fifth of African Americans in the United States live below the poverty line; the most out of any ethnic group. Additionally, over 7.42 million families in the U.S. live in poverty – a figure that has held mostly steady since 1990, outside the 2008 financial crisis which threw 9.52 million families into poverty by 2012. The poverty gender gap Wage inequality has been an ongoing discussion in U.S. discourse for many years now. The poverty gap for women is most pronounced during their child-bearing years, shrinks, and then grows again in old age. While progress has been made on the gender pay gap over the last 30 years, there are still significant disparities, even in occupations that predominantly employ men. Additionally, women are often having to spend more time attending to child and household duties than men.

Black and poor people are more frequently convicted of committing crimes. However, the specific role played by skin color and social class in convicting a person has yet to be clarified. This article aims to elucidate this issue by proposing that belonging to a lower social class facilitates the conviction of black targets and that this phenomenon is because information about social class dissimulates racial bias. Study 1 (N = 160) demonstrated that information about belonging to the lower classes increases agreement with a criminal suspect being sentenced to prison only when described as being black. Furthermore, Studies 2 (N = 170) and 3 (N = 174) show that the anti-prejudice norm inhibits discrimination against the black target when participants were asked to express individual racial prejudice, but not when they expressed cultural racial prejudice. Finally, Study 4 (N = 134) demonstrated that lower-class black targets were discriminated against to a greater degree when participants expressed either individual or cultural prejudice and showed that this occurs when racial and class anti-prejudice norms are salient. The results suggest that social class negatively affects judgments of black targets because judgment based on lower class mitigates the racist motivation of discrimination.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Poverty threatens the overall well-being of individuals and families, limiting access to stable housing, healthy foods, health care, and educational and employment opportunities, among other basic needs.Poverty is associated with a higher risk of adverse health outcomes, including chronic physical and mental illness, lower life expectancy, developmental delays, and others.

Racist policies and practices have contributed to racial economic inequities. Nationally, Black, Indigenous, and people of color experience poverty at higher rates than white Americans, on average.

The District's Response

Boosting assistance programs that provide temporary cash and health benefits to help low-income residents meet their basic needs, including Medicaid, TANF For District Families, SNAP, etc.

Housing assistance and employment and career training programs to support resident’s housing and employment security. These include the Emergency Rental Assistance Program, Permanent Supportive Housing vouchers, Career MAP, the DC Infrastructure Academy, among other programs and services.

Creation of the DC Commission on Poverty to study poverty issues, evaluate poverty reduction initiatives, and make recommendations to the Mayor and the Council.

THE POLL

As part of Cards Against Humanity Saves America, this poll is funded for one year of monthly public opinion polls. Cards Against Humanity is asking the American people about their social and political views, what they think of the president, and their pee-pee habits.

To conduct their polls in a scientifically rigorous manner, they partnered with Survey Sampling International — a professional research firm — to contact a nationally representative sample of the American public. For the first three polls, they interrupted people’s dinners on both their cell phones and landlines, and a total of about 3,000 adults didn’t hang up immediately. They examined the data for statistically significant correlations which can be found here: [https://thepulseofthenation.com/][1]

Content

• Polls are released each month (they are still polling so this will be updated each month)
  
• Row one is the header and contains the questions
  
• Each row is one respondent's answers
  

Questions in the Sep 2017 poll:

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• What is your marital status?
• What would you say is the likelihood that your current job will be entirely performed by robots or computers within the next decade?
• Do you believe that climate change is real and caused by people, real but not caused by people, or not real at all?"
• How many Transformers movies have you seen?
• Do you agree or disagree with the following statement: scientists are generally honest and are serving the public good.
• Do you agree or disagree with the following statement: vaccines are safe and protect children from disease.
• "How many books, if any have you read in the past year?"
• Do you believe in ghosts?
• What percentage of the federal budget would you estimate is spent on scientific research?
• "Is federal funding of scientific research too high too low or about right?"
• True or false: the earth is always farther away from the sun in the winter than in the summer.
• "If you had to choose: would you rather be smart and sad or dumb and happy?"
• Do you think it is acceptable or unacceptable to urinate in the shower?

Questions from Oct 2017 poll

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• From what you have heard or seen do you mostly agree or mostly disagree with the beliefs of White Nationalists?
• If you had to guess what percentage of Republicans would say that they mostly agree with the beliefs of White Nationalists?
• Would you say that you love America?
• If you had to guess, what percentage of Democrats would say that they love America?
• Do you think that government policies should help those who are poor and struggling in America?
• If you had to guess, what percentage of Republicans would say yes to that question?
• Do you think that most white people in America are racist?
• If you had to guess, what percentage of Democrats would say yes to that question?
• Have you lost any friendships or other relationships as a result of the 2016 presidential election?
• Do you think it is likely or unlikely that there will be a Civil War in the United States within the next decade?
• Have you ever gone hunting?
• Have you ever eaten a kale salad?
• If Dwayne "The Rock" Johnson ran for president as a candidate for your political party, would you vote for him?
• Who would you prefer as president of the United States, Darth Vader or Donald Trump?

Questions from Nov 2017 poll

• Income
• Gender
• Age
• Age Range
• In politics today, do you consider yourself a Democrat, a Republican or Independent?
• Would you say you are liberal, conservative, or moderate?
• What is your highest level of education? (High school or less, Some college, College degree, Graduate degree)
• What is your race? (white, black, latino, asian, other)
• Do you live in a city, suburb, or small town?
• Do you approve, disapprove, or neither approve nor disapprove of how Donald Trump is handling his job as president?
• Do you think federal funding for welfare programs in America should be increased, decreased, or kept the same?
• Do you think poor black people are more likely to benefit from welfare programs than poor white people?
• Do you think poor people in cities are more likely to benefit from welfare programs than poor people in small towns?
• If you had to choose, would you rather live in a more equal society or a more unequal society?

Acknowledgements

These polls are from Cards Against Humanity Saves America and the raw data can be found here: [https://thepulse...

Over 38 million Americans experienced food insecurity in 2020 and a disproportionate number of those people, over 21 percent, were Black Americans (USDA, 2021). While Black people across the country experienced food insecurity at disproportionately high rates, the Deep South’s prevalence of food insecurity continues to outpace much of the rest of America with three of the top five food insecure states (Mississippi, Louisiana, and Arkansas) comprising the Mississippi Delta (Henchy & Jacobs, 2020). There is a paradox at play in the Mississippi Delta region regarding its role as one of the top agricultural producers in the country but simultaneously home to some of the food insecure communities as well. Food insecurity is associated with a number of poor health outcomes including, but not limited to, decreased cognitive performance in children, increased anxiety, and depression in non-senior adults, as well as higher rates of diabetes, hypertension, and general increased rates of poor health (Gundersen, 2015). Black households in the Mississippi Delta experience a series of social determinants that contribute to the high prevalence of food insecurity in the region including poverty, racial residential segregation, social isolation, and lack of access to nutritious foods. Food Insecurity and its complexity of confounding factors leave researchers with a significant task to find leverage points at which community leaders, policy makers and other actors in the socioecological framework might reduce food insecurity in places with high food insecurity like the Mississippi Delta. This report recommends addressing food insecurity in the Delta through improving the local structure of information flows by offering education programs to boost enrollment in social welfare programs underutilized in the region.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

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.

This product has been archived in accordance with Federal Grant Compliance and is no longer actively updated. The site remains accessible for historical reference purposes.Disclaimer: This application is a DRAFT and is still under development. A look at the Equity Atlas Poverty indicator in Dallas using the methodology described below. Poverty (S1701)

Each scored category represents 20% of the total population of the City of Dallas.

A score of 5 represents that the percentage of people in poverty is between 23.4% - 80.4%..

A score of 4 represents the percentage of people in poverty is between 16.4% - 23.4%.

A score of 3 represents that the percentage of people in poverty is between 9.9% - 16.3%.

A score of 2 represents that the percentage of people in poverty is between 5.1% - 9.8%.

A score of 1 represents the percentage of people in poverty is between 0.4% - 5%.

Parameter

Data Field

Data Source

American Community Survey 5-Year Estimate 2018-2022

POVERTY STATUS IN THE PAST 12 MONTHS

U.S. Census Bureau, Table: S1701

All people that are living in poverty

Estimated percent of all people that are living in poverty as of 2018-2022

U.S. Census Bureau, Table: S1701

White people who lived in poverty

Estimated percent of all White people who lived in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

Black or African American people who lived in poverty

Estimated percent of all Black or African American people who lived in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

Asian people who lived in poverty

Estimated percentage of all Asian people who lived in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

American Indian and Alaskan Native people who lived in poverty

Estimated percent of all American Indian and Alaskan Native people who lived in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

Native Hawaiian and Other Pacific Islander people who were living in poverty

Estimated percent of all Native Hawaiian and Other Pacific Islander people who were living in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

people of Some Other Race living in poverty

Estimated percent of all people of "Some Other Race" living in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

people of two or more races living below the poverty level

Estimated percent of all people of "two or more races" living below the poverty level between 2018-2022

U.S. Census Bureau, Table: S1701

Hispanic or Latino people who were living in poverty

Estimated percentage of all Hispanic or Latino people who were living in poverty between 2018-2022

U.S. Census Bureau, Table: S1701

Context

COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is a popular commercial algorithm used by judges and parole officers for scoring criminal defendant’s likelihood of reoffending (recidivism). It has been shown that the algorithm is biased in favor of white defendants, and against black inmates, based on a 2 year follow up study (i.e who actually committed crimes or violent crimes after 2 years). The pattern of mistakes, as measured by precision/sensitivity is notable.

Quoting from ProPublica: "

Black defendants were often predicted to be at a higher risk of recidivism than they actually were. Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent). White defendants were often predicted to be less risky than they were. Our analysis found that white defendants who re-offended within the next two years were mistakenly labeled low risk almost twice as often as black re-offenders (48 percent vs. 28 percent). The analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 45 percent more likely to be assigned higher risk scores than white defendants.

• Black defendants were also twice as likely as white defendants to be misclassified as being a higher risk of violent recidivism. And white violent recidivists were 63 percent more likely to have been misclassified as a low risk of violent recidivism, compared with black violent recidivists.
• The violent recidivism analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 77 percent more likely to be assigned higher risk scores than white defendants. "

Content

Data contains variables used by the COMPAS algorithm in scoring defendants, along with their outcomes within 2 years of the decision, for over 10,000 criminal defendants in Broward County, Florida. 3 subsets of the data are provided, including a subset of only violent recividism (as opposed to, e.g. being reincarcerated for non violent offenses such as vagrancy or Marijuana).

Indepth analysis by ProPublica can be found in their data methodology article.

Acknowledgements

Data & original analysis gathered by ProPublica. Original Data methodology article: https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

Original Article: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Original data from ProPublica: https://github.com/propublica/compas-analysis

Additional "simple" subset provided by FairML, based on the proPublica data:

http://blog.fastforwardlabs.com/2017/03/09/fairml-auditing-black-box-predictive-models.html

Inspiration

Ideas:

• Feature importance when predicting the COMPASS score itself, or recividism/crime risks.
• Reweighting data to compensate for bias, e.g. subsetting for the violent offenders, or adjusting better for base risk.
• Feature selection based on "legal usage"/fairness (E.g. exclude race and see how well your model works. It worked for me).

Between 2018 and 2022, Americans who identified as Black, American Indian or Alaska Native, and Hispanic or Latino were most likely to be living in low-income households across all generations in the United States. Within the provided time period, ** percent of Generation Alpha who were Black lived in families with incomes below the federal poverty line in the United States, followed by ** percent who were American Indian or Alaska Native, and ** percent who were Hispanic or Latino.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Low Moor. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Low Moor, the median household income for the households where the householder is White decreased by $13,943(21.96%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $63,497 in 2013 and $49,554 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in Low Moor, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Low Moor.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Low Moor median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Low Moor. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Low Moor, the median household income for the households where the householder is White decreased by $3,761(5.83%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $64,562 in 2011 and $60,801 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in Low Moor, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/low-moor-ia-median-household-income-by-race-trends.jpeg" alt="Low Moor, IA median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Low Moor.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Low Moor median household income by race. You can refer the same here

Many have argued that discrimination against pit bulls is rooted in the breed’s association with Black owners and culture. We theoretically and empirically interrogate that argument in a variety of ways and uncover striking similarities between the racialization of pit bulls and other racialized issues (e.g., poverty and crime) in public opinion and policy implementation. After detailing the reasons to expect pit bulls to be racialized as Black despite dog ownership in the U.S. generally being raced as white, the article shows: (1) Most Americans associate pit bulls with Black people. (2) Anti-Black attitudes, in general, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them; (3) stereotypes of Black men as violent, in particular, are significant, independent, predictors of both anti-pit views and of preferring other breeds over them. (4) Implicit racialization through a national survey experiment further eroded support for legalizing pits, with the treatment effect significantly conditioned by respondent’s race. And (5) state-level racial prejudice is a significant negative predictor of enacting legislation to preempt breed-specific bans. We conclude with our findings’ broader insights into the nature of U.S. racial politics. Michael Tesler, mtesler@uci.edu, corresponding author, is Professor of Political Science at UC Irvine; Mary McThomas, mary.mcthomas@uci.edu, is Associate Professor of Political Science at UC Irvine. An earlier version of this paper was presented at the American Political Science Association’s annual meeting. We thank Maneesh Arora, Rachel Bernhard, Nathan Chan, Louis Pickett, David Sears, DeSipio, Adam Duberstein, Jane Junn, Claire Kim, Jessica Manforti, J. Scott Matthews, Justin.

About this dataset

Context

The dataset tabulates the population of Show Low by race. It includes the population of Show Low across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Show Low across relevant racial categories.

Key observations

The percent distribution of Show Low population by race (across all racial categories recognized by the U.S. Census Bureau): 84.97% are white, 0.20% are Black or African American, 3.05% are American Indian and Alaska Native, 0.80% are Asian, 5.74% are some other race and 5.25% are multiracial.

https://i.neilsberg.com/ch/show-low-az-population-by-race.jpeg" alt="Show Low population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Show Low
• Population: The population of the racial category (excluding ethnicity) in the Show Low is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Show Low total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Show Low Population by Race & Ethnicity. You can refer the same here

BackgroundStreet-based heroin injectors represent an especially vulnerable population group subject to negative health outcomes and social stigma. Effective clinical treatment and public health intervention for this population requires an understanding of their cultural environment and experiences. Social science theory and methods offer tools to understand the reasons for economic and ethnic disparities that cause individual suffering and stress at the institutional level. Methods and FindingsWe used a cross-methodological approach that incorporated quantitative, clinical, and ethnographic data collected by two contemporaneous long-term San Francisco studies, one epidemiological and one ethnographic, to explore the impact of ethnicity on street-based heroin-injecting men 45 years of age or older who were self-identified as either African American or white. We triangulated our ethnographic findings by statistically examining 14 relevant epidemiological variables stratified by median age and ethnicity. We observed significant differences in social practices between self-identified African Americans and whites in our ethnographic social network sample with respect to patterns of (1) drug consumption; (2) income generation; (3) social and institutional relationships; and (4) personal health and hygiene. African Americans and whites tended to experience different structural relationships to their shared condition of addiction and poverty. Specifically, this generation of San Francisco injectors grew up as the children of poor rural to urban immigrants in an era (the late 1960s through 1970s) when industrial jobs disappeared and heroin became fashionable. This was also when violent segregated inner city youth gangs proliferated and the federal government initiated its “War on Drugs.” African Americans had earlier and more negative contact with law enforcement but maintained long-term ties with their extended families. Most of the whites were expelled from their families when they began engaging in drug-related crime. These historical-structural conditions generated distinct presentations of self. Whites styled themselves as outcasts, defeated by addiction. They professed to be injecting heroin to stave off “dopesickness” rather than to seek pleasure. African Americans, in contrast, cast their physical addiction as an oppositional pursuit of autonomy and pleasure. They considered themselves to be professional outlaws and rejected any appearance of abjection. Many, but not all, of these ethnographic findings were corroborated by our epidemiological data, highlighting the variability of behaviors within ethnic categories. ConclusionsBringing quantitative and qualitative methodologies and perspectives into a collaborative dialog among cross-disciplinary researchers highlights the fact that clinical practice must go beyond simple racial or cultural categories. A clinical social science approach provides insights into how sociocultural processes are mediated by historically rooted and institutionally enforced power relations. Recognizing the logical underpinnings of ethnically specific behavioral patterns of street-based injectors is the foundation for cultural competence and for successful clinical relationships. It reduces the risk of suboptimal medical care for an exceptionally vulnerable and challenging patient population. Social science approaches can also help explain larger-scale patterns of health disparities; inform new approaches to structural and institutional-level public health initiatives; and enable clinicians to take more leadership in changing public policies that have negative health consequences.

Urban Institute racial and economic inclusion indexes for 2016; extracted from source: https://apps.urban.org/features/inclusion/?topic=map.

The racial inclusion index is made up of five measures: racial segregation (white/person of color dissimilarity index), homeownership gap, educational attainment gap, poverty rate gap, and share of people of color. All racial gap measures calculate the disparity between white non-Hispanic residents and residents of color. For this analysis, we define people of color as any person identifying in US Census Bureau records as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, other race, two or more races, or Hispanic or Latino white. We recognize the issues that arise with placing all these groups under one umbrella—both in defining identity in comparison with whiteness and in papering over differences in how different groups experience state-sanctioned, institutionalized, systemic, and individual forms of racism. This broad racial disparity measure allows us to compare cities with differing demographic patterns while limiting the size of sampling error for groups within cities that have small populations.

The economic inclusion index is made up of four measures: income segregation (rank-order information theory index), rent burden, share of 16- to 19-year-olds who are not in school and have not graduated, and working poor. The overall inclusion index is the composite of the racial and economic inclusion indices. The economic health index is made up of four indicators: percentage change in employed people period over period, median family income, unemployment rate, and housing vacancy rate.