In 2023, ** percent of surveyed Americans said that being Black hurts people's ability to get ahead in the United States while ** percent said that being Hispanic hurts people's ability to get ahead.

Racial disparities arise across many vital areas of American life, including employment, health, and interpersonal treatment. For example, 1 in 3 Black children live in poverty (vs. 1 in 9 White children) and on average, Black Americans live 4 fewer years than White Americans. Which disparity is more likely to spark reduction efforts? We find that highlighting disparities in health-related (vs. economic) outcomes spurs greater social media engagement and support for disparity-mitigating policy. Further, reading about racial health disparities elicits greater support for action (e.g., protesting) than economic or belonging-based disparities. This occurs, in part, because people view health disparities as violating morally-sacred values which enhances perceived injustice. This work elucidates which manifestations of racial inequality are most likely to prompt Americans to action., The data from Studies 1a, 1b, 3, 4a, and 4b were collected via online platfroms (i.e., Mturk.com, Prolific Academic, and NORC’s AmeriSpeak Panel). All analyses were run in R with the R code provided (title: Health_Disparities_Syntax.R)., , # Highlighting Health Consequences of Racial Disparities Sparks Support for Action

There are a total of 5 datasets available (Studies 1a, 1b, 3, 4a, 4b) each collected by the researchers from online survey platforms. All data files are .sav files. We recommed using SPSS or RStudio to work with the data. We provide our code using RStudio and a codebook with the name of all variables in each dataset.

Description of the data and file structure

Study 1a and Study 1b utilized a within-subjects experimental design (S1a: N=191; S1b, preregistered: N=337, 50% White participants, 50% Black participants) where samples of U.S. citizens recruited from MTurk.com and Prolific Academic read nine examples of racial disparities, three each from the domains of health, economics, and belonging. After each example, participants reported whether the disparity was unjust and fair (reverse-coded; 2-items averaged to create a perceived injustice scale). Participants also indicated their agreement (1=s...

According to a survey conducted in 2023, ** percent of Americans believed that the bigger problem of racial discrimination in the United States was people not seeing racial discrimination where it really does exist. In comparison, ** percent of Americans who were Black shared this belief.

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.

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.

Americans remain largely unaware of the magnitude of economic inequality in the nation and the degree to which it is patterned by race. In the present research we exposed a community sample of respondents to one of three interventions designed to promote a more realistic understanding of the Black-White wealth gap. The interventions were developed to conform to best practices in messaging about racial inequality drawn from the social sciences, yet differed in the extent to which they highlighted a single story versus data-based trends in Black-White wealth inequality or both. The interventions that highlighted data versus only a single story of racial inequality were most effective in both shifting how people talk about racial wealth inequality—eliciting less speech about personal achievement—and, critically, improving accuracy in perceptions of the Black-White wealth gap. These increases in accuracy persisted up to 18 months following the intervention, though accuracy did decline across time. The initial findings from this study highlight how data can be leveraged, along with current recommendations in the social sciences, to promote more accurate understandings of the magnitude of racial inequality in society, laying the necessary groundwork for messaging about equity-enhancing policy.

A striking negative correlation exists between an area's residential racial segregation and its population characteristics, but it is recognized that this relationship may not be causal. I present a novel test of causality from segregation to population characteristics by exploiting the arrangements of railroad tracks in the nineteenth century to isolate plausibly exogenous variation in areas' susceptibility to segregation. I show that this variation satisfies the requirements for a valid instrument. Instrumental variables estimates demonstrate that segregation increases metropolitan rates of black poverty and overall black-white income disparities, while decreasing rates of white poverty and inequality within the white population. (JEL I32, J15, N31, N32, N91, N92, R23)

This paper evaluates racial inequalities in healthcare affordability between high-deductible and conventional insurance. Using the 2011-2017 National Health Interview Survey, the study finds that Blacks in high-deductible plans are not disproportionately higher-income nor more engaged in other savings vehicles, unlike their White counterparts, indicating they may be income constrained rather than desiring to partially self-insure. Furthermore, conditional on income, wealth explained more of the racial disparity in healthcare access among high-deductible enrollees than conventional enrollees, consistent with the hypothesis that benefit designs relying on households’ cash reserves would yield greater disparities due to the magnitude of racial inequalities in assets.

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. Methods All data were collated from publicly available annual merit review reports published by the National Science Foundation, which can be accessed online at the following link: https://www.nsf.gov/nsb/publications/pubmeritreview.jsp

In 2023, the Gini index for Black households in the United States stood at ***, which was higher than the national index that year. The Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to one. A measure of one indicates perfect inequality, i.e., one household having all the income and the rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

What is the income gap between blacks and whites within the same metropolitan region? What variable puts individuals in greatest disadvantage: skin color or place of residence? Should mitigating policies against inequality be global or local? To answer these questions we compare the wages of blacks and whites living in the center and in the periphery of six Brazilian metropolitan regions. Results from the PNAD (2008) show that the impact of skin color on wages is larger than that of the geographic location within the city. We also show that there is substantial spatial heterogeneity in income differentials by race.

ABSTRACT Brazil carries in its history centuries of slavery and racist ideologies that are reflected in its current social inequalities. Research shows that black women experience the worst access and quality of health care, which would be a consequence of institutional racism. Based on those data, a literature review was applied using the systematic review methodology with the aim to survey the Brazilian scientific production regarding institutional racism and the health of black women, as well as to analyze how the theme has been treated by researchers. It became clear that the literature on the subject remains scarce, reinforcing the need to address the theme racism in further research. Although racial inequality is confirmed in all articles analyzed, their conclusions vary among them, and some authors interpreted data solely as a consequence of economic inequality. We concluded that the debate about racism is of pivotal importance in the fight against it and that the identification of racial inequality with economic condition is a consequence of the racial democracy myth that contributes to the institutional racism perpetuation. Raising awareness about racism is needed among professionals so that it becomes essential to consider the category ‘race’ for equal health.

Research on race in Brazil has for a long time recognized that racial categories are based on skin color distinctions along a black-white continuum. However, quantitative evidences about racial inequality are mostly based on the white versus non-white (brown and black) dichotomy or on the threefold categorization (white, brown, and black). This way of using the variable contributed to show the high levels of racial inequality. This finding, however, has often been questioned because of another aspect: the high ambiguity in racial classification and the possibility of “whitening” with money or with upward mobility. If this last feature is true, it is hard to make a reliable measure of racial inequality. In order to deal directly with this dilemma, I measure the “skin color continuum” combining answers to an open question (respondents free choice) and to a closed question (census categories) about skin color. I implement counterfactual simulations to access the possible effects of “whitening with money” on educational, occupational and economic attainments.

This Dataset contains year, date of incident, US State and location wise total number of adult and juvenile victims and offenders. The dataset also has data based on offender race, offender ethnicity, offense name, bias description and victim type level

A growing body of research documents the importance of wealth and the racial wealth gap in perpetuating inequality across generations. We add to this literature by examining the impact of wealth on child income. Our two stage least squares regressions reveal that grandparental and parental wealth have an important effect on the younger generation’s stock (first stage results), which in turn affects the younger generation’s household income (second stage results). We further explore the relationship between income and wealth by decomposing the child’s income by race. We find that the intergroup disparity in income is mainly attributable to differences in family background. These findings indicate that wealth is an important source of income inequality.

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).

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of income inequity which is measured using the index of concentration at the extremes (ICE). ICE is a measure of social polarization within a particular geographic unit. It shows whether people or households in a geographic unit are concentrated in privileged or deprived extremes. The privileged group in this study is the number of households with a householder identifying as White alone, not Hispanic or Latino, with an income equal to or greater than $100,000. The deprived group in this study is the number of households with a householder identifying as a different race/ethnic group (e.g., Black alone, Asian alone, Hispanic or Latino), with an income equal to or less than $25,000. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

The Multi-City Study of Urban Inequality was designed to broaden the understanding of how changing labor market dynamics, racial attitudes and stereotypes, and racial residential segregation act singly and in concert to foster contemporary urban inequality. This data collection comprises data for two surveys: a survey of households and a survey of employers. Multistage area probability sampling of adult residents took place in four metropolitan areas: Atlanta (April 1992-September 1992), Boston (May 1993-November 1994), Detroit (April-September 1992), and Los Angeles (September 1993-August 1994). The combined four-city data file in Part 1 contains data on survey questions that were asked in households in at least two of the four survey cities. Questions on labor market dynamics included industry, hours worked per week, length of time on job, earnings before taxes, size of employer, benefits provided, instances of harassment and discrimination, and searching for work within particular areas of the metropolis in which the respondent resided. Questions covering racial attitudes and attitudes about inequality centered on the attitudes and beliefs that whites, Blacks, Latinos, and Asians hold about one another, including amount of discrimination, perceptions about wealth and intelligence, ability to be self-supporting, ability to speak English, involvement with drugs and gangs, the fairness of job training and educational assistance policies, and the fairness of hiring and promotion preferences. Residential segregation issues were studied through measures of neighborhood quality and satisfaction, and preferences regarding the racial/ethnic mix of neighborhoods. Other topics included residence and housing, neighborhood characteristics, family income structure, networks and social functioning, and interviewer observations. Demographic information on household respondents was also elicited, including length of residence, education, housing status, monthly rent or mortgage payment, marital status, gender, age, race, household composition, citizenship status, language spoken in the home, ability to read and speak English, political affiliation, and religion. The data in Part 2 represent a telephone survey of current business establishments in Atlanta, Boston, Detroit, and Los Angeles carried out between spring 1992 and spring 1995 to learn about hiring and vacancies, particularly for jobs requiring just a high school education. An employer size-weighted, stratified, probability sample (approximately two-thirds of the cases) was drawn from regional employment directories, and a probability sample (the other third of the cases) was drawn from the current or most recent employer reported by respondents to the household survey in Part 1. Employers were queried about characteristics of their firms, including composition of the firm's labor force, vacant positions, the person most recently hired and his or her salary, hours worked per week, educational qualifications, promotions, the firm's recruiting and hiring methods, and demographic information for the respondent, job applicants, the firm's customers, and the firm's labor force, including age, education, race, and gender.

We analyzed satisfaction with care, out-of-pocket costs, and specialist access among community-dwelling Medicare Current Beneficiary Survey respondents, 2015–2019, in the 50 states and Washington, DC. For each measure, we constructed a binary indicator indicating very satisfied (vs. very dissatisfied to satisfied).;We used logistic regression to model outcomes as a function of Medicare Advantage - MA (vs. Traditional Medicare - TM) enrollment, respondent-reported race/ethnicity, and interactions of MA with race/ethnicity. Race/ethnicity was categorized as non-Hispanic Black, Hispanic, and non-Hispanic White. We adjusted for age, sex, education, income, tobacco use, chronic conditions, functional limitations, disability, and geographic factors. Racial/ethnic disparities reflect effects of structural factors that systematically disadvantage members of minoritized racial/ethnic groups. Because structural racism contributes to disparities in socioeconomic status (including income and education), we verified that our estimates did not change appreciably when we did not adjust for socioeconomic factors. ;Analyses were weighted by a composite of survey weights and propensity score weights to balance MA and TM populations within racial/ethnic groups. Separate analyses were conducted for beneficiaries with vs. without dual eligibility for full Medicaid.

We used SAS to process the data.