In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Polling data routinely indicates broad support for the concept of diverse schools, but integration initiatives—both racial and socioeconomic—regularly encounter significant opposition. We leverage a nationally-representative survey experiment to provide novel evidence on public support for integration initiatives. Specifically, we present respondents with a hypothetical referendum where we provide information on two policy options for assigning students to schools: 1) A residence-based assignment option and 2) An option designed to achieve stated racial/ethnic or socioeconomic diversity targets, with respondents randomly assigned to the racial/ethnic or socioeconomic diversity option. After calculating public support and average willingness-to-pay, our results demonstrate a clear plurality of the public preferring residence-based assignment to the racial diversity initiative, but a near-even split in support for residence-based assignment and the socioeconomic integration initiative. Moreover, we find that the decline in support for race-based integration, relative to the socioeconomic diversity initiative, is entirely attributable to white and Republican respondents.

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

This dataset contains measures of socioeconomic and demographic characteristics by US census tract 1990-2010. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.

IntroductionBoth Black juveniles and low-socioeconomic status (SES) juveniles are disproportionately represented in the U.S. legal system. Yet minimal experimental work has teased apart how a juvenile's race and SES interact when affecting judgments about guilt, blame, and punishment.MethodsTwo vignette experiments (N= 1074) varied a juvenile defendant's race (Black or White) and SES (low or high) in two types of crimes (stereotypically Black or stereotypically White).ResultsRace and SES interacted: across crime type, high-SES White juveniles were assigned more guilt and blame whereas high-SES Black juveniles were assigned less guilt and blame than their low-SES counterparts. Low-SES Black juveniles were also judged relatively harshly when their guilt was certain or when excluding participants who guessed the study was about race or SES. Moreover, stereotype-related judgments such as likelihood of recidivism and character mediated these effects.DiscussionThese surprising results highlight the need to investigate the intersection between race and SES. Potential explanations including aversive racism, social ecology, and changing stereotypes are considered.

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

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.

A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers need pre-tested names with data on perceptions of these attributes. Such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.

Abstract: The occupational specialization of social groups is closely tied to gender, racial, and class identities, segmenting the labor market into perceived White/Black and male/female roles and skills sets. Using data from 100 million formal workers in Brazil (2003–2019), we examine patterns of occupational segmentation across 426 occupations, identifying distinct skill demands and socioeconomic statuses linked to race/skin color and gender. Classifications of “male” or “female” occupations are shaped by required skills, while distinctions between “White” and “Black” occupations reflect socioeconomic status and historical inequalities. Women and men are segmented by gender-associated skill sets, such as engineering versus caregiving skills. Within these skill sets, strong hierarchical segregation persists, with Black individuals disproportionately concentrated in lower socioeconomic status positions. Despite recent socioeconomic changes, occupational specialization patterns have remained stable. Our findings highlight that the strong association between race and lower-status occupations must be addressed for a more inclusive societyIn case of any questions related with the content of this repository, please contact:Ben-Hur Cardoso (benhur.phys@gmail.com)Laís Fernanda S. Souza (lais.fssouza@gmail.com)Flavio L. Pinheiro (fpinheiro@novaims.unl.pt)Dominik Hartmann (dominik.hartmann@ufsc.br)ContentsThis repository contains the following contents:In the Regressions folder, we share the original regression tables supporting the robustness results shown in the Supplementary Material.The Dataset folder contains the minimum data necessary to reproduce all the results in the main manuscript and supplementary information.The Code folder contains two documents with the necessary code to reproduce all the results and visualizations in the main manuscript and supplementary informationDataset Folder DescriptionThe core datasets used in this study are:- CENSUS_data_by_occupation_socialgroup_year.csv: The Relative Specialization (RS) of each ISCO-08 occupation code in relation to its social group in each year, using Brazilian Census Data.- RAIS_data_by_occupation_socialgroup_year.csv: The Relative Specialization (RS) of each ISCO-08 occupation code within social group each year, using RAIS Data.- RAIS_data_by_region_college_age_occupation_socialgroup_year.csv: The Relative Specialization (RS) of each ISCO-08 occupation code with social group in each year, region, college, and age group, using RAIS Data.- RAIS_data_by_age_occupation_socialgroup_year.csv: The Relative Specialization (RS) of each ISCO-08 occupation code within social group in each year and age group, using RAIS Data.- data_by_occupation.csv: for each ISCO-08 occupation code we have-- isco08_label_en: english label of occupation-- phi_SX: the intensity of skill X-- theta_SX: the specialization of skill X-- isei: The ISEI of occupation-- ISEIa: The regressed Adjusted ISEISkills X correspond to a single-digit (from 1 to 8) encoding that refers tocommunication, collaboration, and creativityinformation skillsassisting and caringmanagement skillsworking with computershandling and movingconstructingworking with machinery and specialized equipmentAdditional data files include:- isco08_data.csv: extends the data_by_occupations.csv dataset with the RS by gender/race of each occupation- isco08_skill_similarity.csv, netskill.csv, and node_meta.csv provide information on the skill similarity structure between occupations and meta information at the node level (occupation), compiled from the other datasets mentioned above.- Network_layout.gdf encodes the network layout used to draw the networks.Code Folder DescriptionThis folder is composed of two primary documents:A Jupyter Notebook that contains all the code to generate the main visualizations of the manuscript and regression analysis.A Wolfram Mathematica notebook in which we perform the PCA analysis and generate the Graph/Network visualizations.These two notebooks read and process the shared datasets.

Here are Yost indexes for census tracts and block groups in the United States for various years from 1990-2019. The Yost index is a composite index of socioeconomic status that consists of a percentile score from 1 (highest SES) to 100 (lowest SES). Data for 1990 and 2000 include the 50 US states plus the District of Columbia. For years after 2000, the data additionally include Puerto Rico. To rescale the index to geographic units smaller than the US, the score column may be used, where scores range from about -1.8 for the highest SES to 1.8 for the lowest SES.More about the Yost index can be found here: Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. ​Cancer Causes and Control​ 2001; 12(8): 703–711.

Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. ​Cancer Causes and Control​. 2014; 25(1): 81-92.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

Purpose: This study explores whether communicative function (CF: reasons for communicating) use differs by socioeconomic status (SES), race/ethnicity, or gender among preschoolers and their mothers.Method: Mother–preschooler dyads (N = 95) from the National Center for Early Development and Learning’s (2005) study of family and social environments were observed during 1 structured learning and free-play interaction. CFs were coded by trained independent raters.Results: Children used all CFs at similar rates, but those from low SES homes produced fewer utterances and less reasoning, whereas boys used less self-maintaining and more predicting. African American mothers produced more directing and less responding than European American and Latino American mothers, and Latino American mothers produced more utterances than European American mothers. Mothers from low SES homes did more directing and less responding.Conclusions: Mothers exhibited more sociocultural differences in CFs than children; this suggests that maternal demographic characteristics may influence CF production more than child demographics at school entry. Children from low SES homes talking less and boys producing less self-maintaining coincided with patterns previously detected in pragmatic literature. Overall, preschoolers from racial/ethnic minority and low SES homes were not less deft with CF usage, which may inform how their pragmatic skills are described.Supplemental Material S1. Descriptives of proportion of child communicative functions by race/ethnicity, poverty, and gender.Supplemental Material S2. Descriptives of proportion of mother communicative functions by race/ethnicity and poverty.Kasambira Fannin, D., Barbarin, O. A., & Crais, E. R. (2018). Communicative function use of preschoolers and mothers from differing racial and socioeconomic groups. Language, Speech, and Hearing Services in Schools. Advance online publication. https://doi.org/10.1044/2017_LSHSS-17-0004

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

The study examines relationships between racial identities, socioeconomic status (SES),and access to health care, investigating to what degree racial minorities and lower socioeconomicgroups in the U.S. encounter more healthcare barriers and experience worse health outcomes.There is substantial evidence from previous studies that people of color, especially Blacks,Hispanics, and Native Americans, confront disproportionate obstacles when trying to get thehealthcare that they need (Buchmueller 2020; Cogburn 2019; Lee 2021; Phelan 2015; Weissman2018). These obstacles include greater rates of uninsurance, trouble locating doctors, andencounters with discrimination. This research used secondary data from the 2018 NationalSurvey of Health Attitudes (NSHA) conducted by the Robert Wood Johnson Foundation (RWJF)and Research and Development Corporation (RAND). A total of 7,187 individuals participatedin this survey. Consistent with Critical Race Theory and the Minority Stress Model, the results ofthe study indicate that those from lower socioeconomic backgrounds and non-whites face greaterobstacles when it comes to healthcare access, coverage, and health status compared to whiteAmericans.

This study sought to examine the interactive relations of socioeconomic status and race to corticolimbic regions that may play a key role in translating stress to the poor health outcomes overrepresented among those of lower socioeconomic status and African American race. Participants were 200 community-dwelling, self-identified African American and White adults from the Healthy Aging in Neighborhoods of Diversity across the Life Span SCAN study. Brain volumes were derived using T1-weighted MP-RAGE images. Socioeconomic status by race interactions were observed for right medial prefrontal cortex (B = .26, p = .014), left medial prefrontal cortex (B = .26, p = .017), left orbital prefrontal cortex (B = .22, p = .037), and left anterior cingulate cortex (B = .27, p = .018), wherein higher socioeconomic status Whites had greater volumes than all other groups. Additionally, higher versus lower socioeconomic status persons had greater right and left hippocampal (B = -.15, p = .030; B = -.19, p = .004, respectively) and amygdalar (B = -.17, p = .015; B = -.21; p = .002, respectively) volumes. Whites had greater right and left hippocampal (B = -.17, p = .012; B = -.20, p = .003, respectively), right orbital prefrontal cortex (B = -.34, p < 0.001), and right anterior cingulate cortex (B = -.18, p = 0.011) volumes than African Americans. Among many factors, the higher levels of lifetime chronic stress associated with lower socioeconomic status and African American race may adversely affect corticolimbic circuitry. These relations may help explain race- and socioeconomic status-related disparities in adverse health outcomes.

The WORLD Policy Analysis Center (WORLD) is committed to improving the quantity and quality of globally comparative data available to policymakers, citizens, civil society, and researchers on laws and policies that work to support human rights, including economic opportunity, social and civic engagement, human health, development, well-being, and equity. The WORLD Constitutions 2022 dataset was created to assess progress on constitutional rights that matter to equal opportunities through a systematic review of national constitutions across all 193 UN countries as of January 2022. The dataset covers equality and non-discrimination across race and/or ethnicity, gender and sex, migrants and refugees, religion and belief, disability status, socioeconomic status, sexual orientation, and gender identity, as well as the right to education, health, decent working conditions and non-discrimination in employment, and social protection.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity