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.

Link to this report's codebookUnfulfilled Promise of Racial EqualityUS states unequally distribute resources, services, and opportunities by raceThe US is failing to deliver on its promise of racial equality. While the US founding documents assert that ‘all men are created equal,’ this value is not demonstrated in outcomes across areas as diverse and varied as education, justice, health, gender, and pollution. On average, white communities receive resources and services at a rate approximately three times higher, than the least-served racial community (data on Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities, were used as available). Evidence shows that unequal treatment impacts each of these communities, however, it is most often Black and Indigenous communities that are left the furthest behind. When states are scored on how well they deliver the United Nations Sustainable Development Goals (SDGs) to the racial group least served, no state is even halfway to achieving the SDGs by 2030 (see Figure 1). To learn more about the Sustainable Development Goals, see the section “SDGs & Accountability.”One example of this inequality is in life expectancy. In Figure 2, the scatter plot on the left demonstrates a pattern in which Black and Indigenous communities, represented by orange and green dots closest to the bottom of the graph, are consistently the communities with least access to years of life. In the graph on the right, each box represents a racial population in a specific state, the boxes are organized from left to right, lowest to highest, according to the life expectancy for that group and state. The graph shows how large the gap is in life expectancy across racial communities and states, with green and orange boxes, representing Indigenous and Black communities respectively, clustered to the left of the graph.Patterns like this one, demonstrating both deep and wide racial inequalities, occur across the 51 indicators this analysis includes, covering 12 of 17 SDGs. In a similar example (Figure 3), a pattern emerges where white students are least likely to attend a school where 75 percent or more of its students receive free or reduced cost lunch when compared to all other racial groups. In the most unequal state, North Dakota, Indigenous students attend high poverty schools at a rate 42 times higher than white students. As Figure 3 shows, although the percentage of students from the least served racial group attending high poverty schools ranges from 2 percent in Vermont to 73 percent in Mississippi, the group least served, represented by the dots closest to the top of the graph, are most often Hispanic and Indigenous communities.Lack of Racial DataMore, and better, racially and ethnically disaggregated data are needed to assess delivery of racial equalityA significant barrier to evaluating progress is the unavailability of racial data across all areas of measurement. For too many important topic areas, such as food insecurity, maternal mortality and lead in drinking water, there is no racial data available at the state level. Even in the areas where there is some racial data, it is often not available for all groups (see Figure 4). Particularly missing, were measures of environmental justice; in Goals focusing on Water, Clean Energy, and Life on Land (Goals 6, 7, and 15), racial data was not found for any indicators, despite the fact that there is research indicating that clean water, for example, is unequally distributed across racial groups. The reasons for these gaps vary. For some indicators, data is not tracked through a nationally organized database, for other indicators, the data is old and out of date, and in many cases, surveys are not large enough to disaggregate by race. As was made clear with the disparate impacts of COVID-19 (for example, see CDC 2020), understanding to whom resources are being distributed has real life implications and is an important part of holding democratic institutions accountable to promises of equality.People are often left behind due to a combination of intersecting identities and factors; they remain hidden in averages. Evaluating the Leave No One Behind Agenda through the lens of gender, ability, class and other identities are undoubtedly important and urgent. Disaggregating data along two axes such as race and location—is revealing. But an even more refined analysis using multilevel disaggregation, such as looking at women and race in urban settings, would likely reveal even starker inequalities. Those are not included here and are important areas for future work. Other areas for further exploration include the use of longitudinal data to understand how these inequalities are changing over time.Though the full extent of this unequal treatment is unknown, this analysis sheds some light on the clouded story told by state averages. Whole group averages leave out important information, particularly about inequality. Racially disaggregated data is essential for holding governments accountable to the promise of racial equity. Without it, it is too easy to hide who is being excluded and left behind.SDGs and AccountabilitySDGs and AccountabilityThe SDGs can be an accountability tool to address racial inequality. This would not be the first time UN frameworks have been used to call attention to racial inequality in the US. In 1951, the Civil Rights Congress (CRC) led by William L. Patterson and Paul Robeson put a petition to the UN, named: “We Charge Genocide,” which charged that the United States government was in violation of the Charter of the United Nations and the Convention on the Prevention and Punishment of the Crime of Genocide (Figure 5). While this attempt did not succeed in charging the US government with genocide, it is a central example of how international instruments can be used to apply localized pressure to advance civil rights.All 193 member countries of the UN, including the United States, signed on to the Sustainable Development Goals in 2015, to be achieved by 2030. The Goals cover 17 wide-ranging topics, with 169 specific targets for action (Figure 6). The first agenda of the SDGs, the Leave No One Behind Agenda (LNOB), requires that those left furthest behind by governments must have the SDGs delivered to them first. The results of this project demonstrate that in a US-context, those left furthest behind would undoubtedly include Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities. The SDGs can offer a template for US states attempting to deliver on their promise of racial equality. The broad topic areas covered by the SDGs, in combination with the Leave No One Behind agenda, can be a tool to hold states accountable for addressing racial inequalities when and through developing solutions for clean water, quality education, ending hunger, delivering justice and more. This highlights an important implication of the Leave No One Behind Agenda, it is not meant to pit communities against each other, but rather to remind us how much everyone has to gain by building and advocating for sustainable communities that serve us all.Explore ResultsExplore the data from the In the Red: the US failure to deliver on a promise of racial equality in our interactive dashboards.These maps display how US states are delivering sustainability across different racial and ethnic groups. As part of the Leave No One Behind Agenda, which maintains that those who have been least served by development progress must be those first addressed through the SDGs, progress toward the goals in each state is displayed based on the racial group with the least access to resources, programs, and services in that state. In other words, the “Overall scores’’ map shows the score for the racial group least served in each state. Click on a state to toggle through the state’s performance by different SDGs, and click on an indicator to view how a state performs on a given indicator. At the indicator level, horizontal bar charts show the racial disparity in the selected indicator and state, when data is available.AboutIn the Red: the US Failure to Deliver on a Promise of Racial EqualityIn the Red: the US Failure to Deliver on a Promise of Racial Equality project highlights measurable gaps in how states deliver sustainability to different racial groups. The full report can be read here. It extends an earlier report, Never More Urgent, looking at policies and practices that have led to the inequalities described in this project. It was prepared by a group of independent experts at SDSN and Howard University.UN Sustainable Development Solutions Network (SDSN)The UN Sustainable Development Solutions Network (SDSN) mobilizes scientific and technical expertise from academia, civil society, and the private sector to support practical problem solving for sustainable development at local, national, and global scales. The SDSN has been operating since 2012 under the auspices of the UN Secretary-General Antonio Guterres. The SDSN is building national and regional networks of knowledge institutions, solution-focused thematic networks, and the SDG Academy, an online university for sustainable development.SDSN USASDSN USA is a network of 150+ research institutions across the United States and unincorporated territories. The network builds pathways toward achievement of the UN Sustainable Development Goals (SDGs) in the United States by mobilizing research, outreach, collective action, and global cooperation. SDSN USA is one of more than 40 national and regional SDSN networks globally. It is hosted by the UN Sustainable Development Solutions Network (SDSN) in New York City, and is chaired by Professors Jeffrey Sachs (Columbia University), Helen Bond (Howard University), Dan Esty (Yale University), and Gordon McCord (UC San Diego).

Concerns about systemic racism at academic and research institutions have increased over the past decade. Here, we investigate data from the National Science Foundation (NSF), a major funder of research in the United States, and find evidence for pervasive racial disparities. In particular, white principal investigators (PIs) are consistently funded at higher rates than most non-white PIs. Funding rates for white PIs have also been increasing relative to annual overall rates with time. Moreover, disparities occur across all disciplinary directorates within the NSF and are greater for research proposals. The distributions of average external review scores also exhibit systematic offsets based on PI race. Similar patterns have been described in other research funding bodies, suggesting that racial disparities are widespread. The prevalence and persistence of these racial disparities in funding have cascading impacts that perpetuate a cumulative advantage to white PIs across all of science, technology, engineering and mathematics.

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.

This project examined economic differences in the neighborhoods where whites, blacks, Hispanics, and Asians live in the U.S. Although it is commonly believed that blacks and Hispanics generally live in neighborhoods where poverty rates are higher than they are in the neighborhoods where whites and Asians live, very little research has tracked the change in racial disparities in neighborhood conditions over time. In prior research, this project's investigators found that racial differences in neighborhood economic conditions have diminished in the U.S. Since 1980 the decline in racial neighborhood inequality has been much faster than the decline in racial residential segregation. Because prior research on neighborhoods has focused on change in the residential segregation of different racial and ethnic groups, the trend in racial neighborhood inequality has been largely overlooked, and its causes are unknown. The objective of this project is to account for the decline in racial neighborhood inequality by investigating why it has declined faster in some metropolitan areas than in others.

About this dataset

Context

The dataset presents the median household income across different racial categories in State Center. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of State Center population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 90.70% of the total residents in State Center. Notably, the median household income for White households is $72,500. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $72,500.

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 State Center.
• Median household income: Median household income, adjusting for inflation, 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 State Center median household income by race. You can refer the same here

IntroductionLyme disease (LD) incidence in the United States is highly regional, with most cases occurring in 16 high-incidence jurisdictions. LD incidence and severity of disease have been found to vary by race. This study describes racial differences in knowledge, attitudes toward vaccination, and risk practices related to LD.MethodsFour web-based surveys were conducted with adults and caregivers of children in high-incidence jurisdictions and 10 states neighboring them. Respondents were recruited via an established online panel to represent the general population. Self-reported race was pooled into 3 categories: ‘White’, ‘Black or African American’, and ‘Other’ for analysis. Analyses were conducted separately for each jurisdiction (high-incidence vs. neighboring) and respondent type (adult vs. caregiver).ResultsThe final sample across all surveys included 2,249 respondents who identified as White, 493 respondents who identified as Black or African American, and 674 respondents of other races. White respondents were older, had higher incomes, and were likelier to live in small cities and rural areas. Though attitudes toward vaccination in general were similar between racial categories, when differences were present, Black respondents were more likely to have concerns about vaccines than White respondents. In all surveys, White respondents engaged in more outdoor activities than Black respondents and performed these activities more often. However, both White adults and caregivers in high-incidence jurisdictions were significantly less likely to have occupations with primarily outdoor work than corresponding respondents in other racial groups. Black respondents also had lower knowledge about LD than White respondents across all surveys. This difference was significant after adjusting for state incidence level and urbanicity.ConclusionThere are some racial differences in knowledge, attitudes, and practices around LD, with White respondents reported having higher knowledge of LD, less concerns about vaccines, and higher frequency of risk practices. These differences might contribute to racial disparities in LD outcomes.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

In this secondary analysis, the research team used publicly available federal sentencing data from the United States Sentencing Commission (USSC) to measure racial disparities for multiple race groups and stages of sentencing across time (fiscal years 1999-2021). They sought to answer the following research questions: Do racial disparities vary across 3 stages of federal sentencing and over time? If so, how? During which years do the measured racial disparities have a statistically significant decrease? Which policies likely impacted these decreases the most? What are the commonalities between them? To answer the research questions, the research team measured racial disparities between matched cases across three stages of federal sentencing, represented by two elements each; identified at which points in time the disparities changed significantly using time series plots and structured break analyses; and used this information to systematically review federal policies to identify which might have contributed to significant decreases in racial disparities. This collection contains 1 analytic dataset (n = 1,281,732) containing 27 key variables for all fiscal years and the code/syntax used to complete the secondary analysis: 5 files to compile and clean the original data and produce matched datasets (3 R, 1 SAS, 1 Stata) 6 files to analyze sentences by race (all R) 4 files to analyze sentences by federal sentencing guideline (all R) 11 files to analyze sentences by circuit court (all R) Please refer to the Data Sources metadata field and accompanying documentation for details on obtaining the original data.

Across the United States there are racial disparities in pollution exposure, with Black and Latino Americans far more likely to be exposed to environmental risks than White Americans. Compared to White Americans, Black and Latino Americans are at a far higher risk of proximity to hazardous facilities, such as TSDF's (treatment, storage, and disposal facilities), with Latino's most at risk.

Although Latino Americans have a higher risk of exposure to traffic than Black Americans, they were less exposed to diesel particulate matter, PM.25 exposure, and air toxic cancer risk. However, both were significantly more exposed than the average White American.

About this dataset

Context

The dataset presents the median household income across different racial categories in United States. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of United States population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 68.17% of the total residents in United States. Notably, the median household income for White households is $79,933. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $106,954. This reveals that, while Whites may be the most numerous in United States, Asian households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/united-states-median-household-income-by-race.jpeg" alt="United States median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-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 United States.
• Median household income: Median household income, adjusting for inflation, 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 United States median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in State Line City. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of State Line City population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 89.80% of the total residents in State Line City. Notably, the median household income for White households is $64,167. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $64,167.

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 State Line City.
• Median household income: Median household income, adjusting for inflation, 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 State Line City median household income by race. You can refer the same here

b: Logistic regression results for racial disparities in mortality among hospitalized COVID-19 patients from neighborhoods with very high vulnerability.

Do Black households make as much as the typical household in the US? This map shows that this doesn't seem to be the case. This map compares the median household income of households with Black householders compared to the 2020 US median household income: $67,340. If the Black households in a county make as much as a "typical" household, the county is shown in turquoise. If Black households in a county make less than the US median income, it is shown in orange. The size of the symbol highlights where there are the highest counts of Black population in the US.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.The data were obtained from one state prison system that was characterized by a diverse and rising prison population. This prison system housed more than 30,000 inmates across 15 institutions (14 men's facilities; 1 women's facility). The data contain information on inmates' placements into different housing units across all 15 state prison complexes, including designated maximum security, restrictive housing units. Inmates placed in restrictive housing were in lockdown the majority of the day, had limited work opportunities, and were closely monitored. These inmates were also escorted in full restraints within the institution. They experienced little recreational time, visitation and phone privileges, and few interactions with other inmates. The data contain information on inmates' housing placements, institutional misconduct, risk factors, demographic characteristics, criminal history, and offense information. These data provide information on every housing placement for each inmate, including the time spent in each placement, and the reasons documented by correctional staff for placing inmates in each housing unit. Demographic information includes inmate sex, race/ethnicity, and age. The collection contains 1 Stata data file "Inmate-Housing-Placements-Data.dta" with 16 variables and 124,942 cases.

This map compares homeownership rates between households with a non-Hispanic White householder and households with a Black or African American householder. This map shows us where there is a disparity in home ownership based on race/ethnicity. The pattern is shown at the state, county, and tract levels. Zoom or pan around the map to explore the map. You can also search for your city and explore the pattern in your local area. If you zoom out, you can see the nationwide pattern. The data comes from the most current release of American Community Survey (ACS) estimates from the U.S. Census Bureau. The layer being used in this map can be found here, and also within ArcGIS Living Atlas of the World. Click here to find more ACS layers within Living Atlas. Note: areas where there are no Black or White householders, no symbol is shown. This map compares areas where there are both White and Black householders.

In the field of American state politics, the tension between majoritarian institutions and equality has largely been ignored. Do state institutions that empower majority preferences exacerbate disparities in social outcomes? Under what conditions do majoritarian institutions exacerbate inequalities in the American states? Our argument is that equality is most likely to be threatened under majoritarian institutions when (1) there are systemic participatory biases and/or (2) there are widespread prejudices about particular groups in society. We find that more majoritarian institutions are associated with larger disparities between white and black life expectancy and poverty rates across the American states, but not differences in educational attainment. We also find that this effect is moderated by racial context, with majoritarian institutions being associated with greater disparities for states with diverse racial contexts and smaller disparities in more homogenous states. These findings suggest that majoritarian institutions operate to the benefit of the white majority, while coming at the cost of minority population outcomes when a racial threat is perceived, and presumably, public opinion is biased.

Objectives: To quantify the Black/Hispanic disparity in COVID-19 mortality in the United States (US).Methods: COVID-19 deaths in all US counties nationwide were analyzed to estimate COVID-19 mortality rate ratios by county-level proportions of Black/Hispanic residents, using mixed-effects Poisson regression. Excess COVID-19 mortality counts, relative to predicted under a counterfactual scenario of no racial/ethnic disparity gradient, were estimated.Results: County-level COVID-19 mortality rates increased monotonically with county-level proportions of Black and Hispanic residents, up to 5.4-fold (≥43% Black) and 11.6-fold (≥55% Hispanic) higher compared to counties with

The graph illustrates the number of victims of race-based hate crimes in the United States in 2023. The x-axis lists various ethnic groups, while the y-axis represents the corresponding number of victims. The data reveals that Anti-Black hate crimes were the most prevalent, with 3224 victims, followed by Anti-Hispanic and Anti-Asian crimes with 861and 430 victims respectively. Other categories include Anti-Other Race (418), Anti-American Indian (112), Anti-Arab (154), and Anti-Native Pacific (15). The data indicates a significant disparity in the number of victims across different ethnic groups, with Anti-Black hate crimes being the most prominent.

About this dataset

Context

The dataset presents the median household income across different racial categories in State College. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of State College population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 80.12% of the total residents in State College. Notably, the median household income for White households is $50,296. Interestingly, despite the White population being the most populous, it is worth noting that Some Other Race households actually reports the highest median household income, with a median income of $60,333. This reveals that, while Whites may be the most numerous in State College, Some Other Race households experience greater economic prosperity in terms of median household income.

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 State College.
• Median household income: Median household income, adjusting for inflation, 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 State College median household income by race. You can refer the same here