This statistic shows the public opinion on the racial diversity of selected music genres in the United States as of May 2018, by age. During the survey, 25 percent of respondents stated that they considered rap/hip-hop to be the most racially diverse music genre.

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

Racial diversity is measured by a diversity index that is calculated using United States Census racial and ethnic population characteristics from the PL-94 data file. The diversity index is a quantitative measure of the distribution of the proportion of five major ethnic populations (non-Hispanic White, non-Hispanic Black, Asian and Pacific Islander, Hispanic, and Two or more races). The index ranges from 0 (low diversity meaning only one group is present) to 1 (meaning an equal proportion of all five groups is present). The diversity score for the United States in 2010 is 0.60. The diversity score for the San Francisco Bay Region is 0.84. Within the region, Solano (0.89) and Alameda (0.90) Counties are the most diverse and the remaining North Bay (0.55 - 0.64) Counties are the least diverse.

Ethnic diversity is generally associated with less social capital and lower levels of trust. However, most empirical evidence for this relationship is focused on generalized trust, rather than more theoretically appropriate measures of group-based trust. This paper evaluates the relationship between ethnic diversity – at national, regional, and local levels – and the degree to which coethnics are trusted more than non-coethnics, a value I call the “coethnic trust premium.” Using public opinion data from sixteen African countries, I find that citizens of ethnically diverse states express, on average, more ethnocentric trust. However, within countries, regional ethnic diversity is actually associated with less ethnocentric trust. This same negative pattern between diversity and ethnocentric trust appears across districts and enumeration areas within Malawi. I then show, consistent with these patterns, that diversity is only detrimental to intergroup trust at the national level in the presence of ethnic group segregation. These results highlight the importance of the spatial distribution of ethnic groups on intergroup relations, and question the utility of micro-level studies of interethnic interactions for understanding macro-level group dynamics.

How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.

Ethnic minorities were more likely to be in favor of racially diversifying adverts in the United States, a survey from June 2020 found. The African American demographic was most in favor of change, with 65 percent of respondents in saying they would like to see more racial diversity in ads. The same was true for 49 percent of Hispanics in the country.

Past research indicates that diversity at the level of larger geographic units (e.g., counties) is linked to white racial hostility. However, research has not addressed whether diverse local contexts may strengthen or weaken the relationship between racial stereotypes and policy attitudes. In a state-wide opinion survey, we find that black-white racial diversity at the zipcode level strengthens the connection between racial stereotypes and race-related policy attitudes among whites. Moreover, this effect is most pronounced among low self-monitors, individuals who are relatively immune to the effects of egalitarian social norms likely to develop within a racially diverse local area. We find that this racializing effect is most evident for stereotypes (e.g., African Americans are “violent”) that are “relevant” to a given policy (e.g., capital punishment). Our findings lend nuance to research on the political effects of racial attitudes and confirm the racializing political effects of diverse residential settings on white Americans.

This national, tract-level experienced racial segregation dataset uses data for over 66 million anonymized and opted-in devices in Cuebiq’s Spectus Clean Room data to estimate 15 minute time overlaps of device stays in 38.2m x 19.1m grids across the United States in 2022. We infer a probability distribution of racial backgrounds for each device given their home Census block groups at the time of data collection, and calculate the probability of a diverse social contact during that space and time. These measures are then aggregated to the Census tract and across the whole time period in order to preserve privacy and develop a generalizable measure of the diversity of a place. We propose that this dataset is a better measurement of the segregation and diversity as it is experienced, which we show diverges from standard measurements of segregation. The data can be used by researchers to better understand the determinants of experienced segregation; beyond research, we suggest this data can be used by policy makers to understand the impacts of policies designed to encourage social mixing and access to opportunities such as affordable housing and mixed-income housing, and more.

For the purposes of enhanced privacy, home census block groups were pre-calculated by the data provider, and all calculations are done at the Census tract, with tracts that have more than 20 unique devices over the period of analysis.

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

In 2021, the share of white employees at State Street Corporation was higher than ** percent in every position. It was the highest among the executive and senior-level officials and managers, where more than ** percent of the employees self-identified as white. The share of white employees was the lowest among the administrative support workers, where approximately ** percent of the employees were white.

In the United States, adult day center users were disproportionately more ethnically and racially diverse than the general population over 65 years in 2020. There was also a disproportionately high share of non-Hispanic Black patients in long-term care (LTC) hospitals. Meanwhile, White, non-Hispanic residents dominated assisted living communities, more so than other LTC services.

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

This survey was conducted among residents of the South (another sample of Non Southern states is also included) on many topics including race relations, opportunities for minorities, local communities, racial diversity, and inter-racial marriages and adoption. Demographic data include education, religious affiliation, marital status, employment status, income, race, household composition, party affiliation, political ideology,

BackgroundCancer health disparities research depends on access to biospecimens from diverse racial/ethnic populations. This multimethodological study, using mixed methods for quantitative and qualitative analysis of survey results, assessed barriers, concerns, and practices for sharing biospecimens/data among researchers working with biospecimens from minority populations in a 5 state region of the United States (Arizona, Colorado, New Mexico, Oklahoma, and Texas). The ultimate goals of this research were to understand data sharing barriers among biomedical researchers; guide strategies to increase participation in biospecimen research; and strengthen collaborative opportunities among researchers.Methods and PopulationEmail invitations to anonymous participants (n = 605 individuals identified by the NIH RePORT database), resulted in 112 responses. The survey assessed demographics, specimen collection data, and attitudes about virtual biorepositories. Respondents were primarily principal investigators at PhD granting institutions (91.1%) conducting basic (62.3%) research; most were non-Hispanic White (63.4%) and men (60.6%). The low response rate limited the statistical power of the analyses, further the number of respondents for each survey question was variable.ResultsFindings from this study identified barriers to biospecimen research, including lack of access to sufficient biospecimens, and limited availability of diverse tissue samples. Many of these barriers can be attributed to poor annotation of biospecimens, and researchers’ unwillingness to share existing collections. Addressing these barriers to accessing biospecimens is essential to combating cancer in general and cancer health disparities in particular. This study confirmed researchers’ willingness to participate in a virtual biorepository (n = 50 respondents agreed). However, researchers in this region listed clear specifications for establishing and using such a biorepository: specifications related to standardized procedures, funding, and protections of human subjects and intellectual property. The results help guide strategies to increase data sharing behaviors and to increase participation of researchers with multiethnic biospecimen collections in collaborative research endeavorsConclusionsData sharing by researchers is essential to leveraging knowledge and resources needed for the advancement of research on cancer health disparities. Although U.S. funding entities have guidelines for data and resource sharing, future efforts should address researcher preferences in order to promote collaboration to address cancer health disparities.

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 63.44% of the total residents in United States. Notably, the median household income for White households is $83,784. 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 $113,106. 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.

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 United States.
• 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 United States median household income by race. You can refer the same here

Institutions of higher education (IHE) throughout the United States have a long history of acting out various levels of commitment to diversity advancement, equity, and inclusion (DEI). Despite decades of DEI “efforts,” the academy is fraught with legacies of racism that uphold white supremacy and prevent marginalized populations from full participation. Furthermore, politicians have not only weaponized education but passed legislation to actively ban DEI programs and censor general education curricula (https://tinyurl.com/antiDEI). Ironically, systems of oppression are particularly apparent in the fields of Ecology, Evolution, and Conservation Biology (EECB)–which recognize biological diversity as essential for ecological integrity and resilience. Yet, amongst EECB faculty, people who do not identify as cis-heterosexual, non-disabled, affluent white males are poorly represented. Furthermore, IHE lack metrics to quantify DEI as a priority. Here we show that only 30.3% of US-faculty positions advertised in EECB from Jan 2019-May 2020 required a diversity statement; diversity statement requirements did not correspond with state-level diversity metrics. Though many announcements “encourage women and minorities to apply,” empirical evidence demonstrates that hiring committees at most institutions did not prioritize an applicant’s DEI advancement potential. We suggest a model for change and call on administrators and faculty to implement SMART (i.e., Specific, Measurable, Achievable, Realistic, and Timely) strategies for DEI advancement across IHE throughout the United States. We anticipate our quantification of diversity statement requirements relative to other application materials will motivate institutional change in both policy and practice when evaluating a candidate’s potential “fit”. IHE must embrace a leadership role to not only shift the academic culture to one that upholds DEI, but to educate and include people who represent the full diversity of our society. In the current context of political censure of education including book banning and backlash aimed at Critical Race Theory, which further reinforce systemic white supremacy, academic integrity and justice are more critical than ever. Methods Here we investigated the (lack of) process in faculty searches at IHE for evaluating candidates’ ability to advance DEI objectives. We quantified the prevalence of required diversity statements relative to research and/or teaching statements for all faculty positions posted to the Eco-Evo Jobs Board (http://ecoevojobs.net) from January 2019 - May 2020 as a proxy for institutional DEI prioritization (Supplement). We also mapped the job posts that required diversity statements geographically to gauge whether and where diversity is valued in higher education across the US. Data analysis We pulled all faculty jobs posted on Eco-Evo jobs board (http://ecoevojobs.net) from Jan 1, 2019, to May 31, 2020. For each position, we recorded the Location (i.e., state), Subject Area, Closing Date, Rank, whether or not the position is Tenure Track, and individual application materials (i.e., Research statement, Teaching statement, combined Teaching and Research statement, Diversity statement, Mentorship statement). Of the 543 faculty positions posted during this time, we eliminated 299 posts because the web links were broken or application information was no longer available (i.e., “NA”), leaving 244 faculty job posts. For each of the retained posts, we coded the requirement of teaching, research, diversity, and/or mentorship statements as follows:

"Yes” = statement required “No” = statement not required “Other” = application materials did not explicitly require a Diversity Statement (i.e., option or suggested that applicants include a statement on diversity and inclusion as a component of their teaching and/or research statement or in their cover letter)

Data visualization We created a Sankey diagram using Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) to compare diversity and representation from the general population, through (Science, Technology, Engineering, and Mathematics) STEM academia (a career hierarchy often referred to as the “leaky pipeline”). We procured population data from the US Census Bureau (US Department of Commerce: https://www.census.gov/quickfacts/fact/table/US/PST045219) and quantified the diversity/representation in Conservation Biology (https://datausa.io/profile/cip/ecology-evolution-systematics-population-biology#demographics) and Ecology (https://datausa.io/profile/cip/conservation-biology) using Data USA (developed by Deloitte Touche Tohmatsu Limited and Datawheel). We used the 2015 Diversity Index (produced by PolicyLink and the USC Program for Environmental and Regional Equity: https://nationalequityatlas.org/indicators/Diversity_index/Ranking:33271/United_States/false/Year(s):2015/) to quantify relative ethnic diversity per state, and graphed Figure 2B using the tidyverse, rgdal, broom, and rgeos packages in R (see Base code used to produce Figure 2 in R, below). The Diversity index measures the representation of White, Black, Latino, Asian/Pacific Islander, Native American, and Mixed/other race in a given population. A maximum possible diversity score (1.79) would indicate even representation of all ethnic/racial groups. We checked all figures using the Color Blindness Simulator (ColBlindor: https://www.color-blindness.com/coblis-color-blindness-simulator/) to maintain inclusivity.

Millennials were the largest generation group in the United States in 2024, with an estimated population of 74.19 million. Born between 1981 and 1996, Millennials recently surpassed Baby Boomers as the biggest group, and they will continue to be a major part of the population for many years. The rise of Generation Alpha Generation Alpha is the most recent to have been named, and many group members will not be able to remember a time before smartphones and social media. As of 2024, the oldest Generation Alpha members were still only aging into adolescents. However, the group already makes up around 13.85 percent of the U.S. population, and they are said to be the most racially and ethnically diverse of all the generation groups. Boomers vs. Millennials The number of Baby Boomers, whose generation was defined by the boom in births following the Second World War, has fallen by around seven million since 2010. However, they remain the second-largest generation group, and aging Boomers are contributing to steady increases in the median age of the population. Meanwhile, the Millennial generation continues to grow, and one reason for this is the increasing number of young immigrants arriving in the United States.

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).

This map layer shows the prevalent generations that make up the population of the United States using multiple scales. As of 2018, the most predominant generations in the U.S. are Baby Boomers (born 1946-1964), Millennials (born 1981-1998), and Generation Z (born 1999-2016). Currently, Millennials are the most predominant population in the U.S.A generation represents a group of people who are born around the same time and experience world events and trends during the same stage of life through similar mediums (for example, online, television, print, or radio). Because of this, people born in the same generation are expected to have been exposed to similar values and developmental experiences, which may cause them to exhibit similar traits or behaviors over their lifetimes. Generations provide scientists and government officials the opportunity to measure public attitudes on important issues by people’s current position in life and document those differences across demographic groups and geographic regions. Generational cohorts also give researchers the ability to understand how different developmental experiences, such as technological, political, economic, and social changes, influence people’s opinions and personalities. Studying people in generational groups is significant because an individual’s age is a conventional predictor for understanding cultural and political gaps within the U.S. population.Though there is no exact equation to determine generational cutoff points, it is understood that we designate generational spans based on a 15- to 20-year gap. The only generational period officially designated by the U.S. Census Bureau is based on the surge of births after World War II in 1946 and a significant decline in birth rates after 1964 (Baby Boomers). From that point, generational gaps have been determined by significant political, economic, and social changes that define one’s formative years (for example, Generation Z is considered to be marked by children who were directly affected by the al Qaeda attacks of September 11, 2001).In this map layer, we visualize six active generations in the U.S., each marked by significant changes in American history:The Greatest Generation (born 1901-1924): Tom Brokaw’s 1998 book, The Greatest Generation, coined the term ‘the Greatest Generation” to describe Americans who lived through the Great Depression and later fought in WWII. This generation had significant job and education opportunities as the war ended and the postwar economic booms impacted America.The Silent Generation (born 1925-1945): The title “Silent Generation” originated from a 1951 essay published in Time magazine that proposed the idea that people born during this period were more cautious than their parents. Conflict from the Cold War and the potential for nuclear war led to widespread levels of discomfort and uncertainty throughout the generation.Baby Boomers (born 1946-1964): Baby Boomers were named after a significant increase in births after World War II. During this 20-year span, life was dramatically different for those born at the beginning of the generation than those born at the tail end of the generation. The first 10 years of Baby Boomers (Baby Boomers I) grew up in an era defined by the civil rights movement and the Vietnam War, in which a lot of this generation either fought in or protested against the war. Baby Boomers I tended to have great economic opportunities and were optimistic about the future of America. In contrast, the last 10 years of Baby Boomers (Baby Boomers II) had fewer job opportunities and available housing than their Boomer I counterparts. The effects of the Vietnam War and the Watergate scandal led a lot of second-wave boomers to lose trust in the American government. Generation X (born 1965-1980): The label “Generation X” comes from Douglas Coupland’s 1991 book, Generation X: Tales for An Accelerated Culture. This generation was notoriously exposed to more hands-off parenting, out-of-home childcare, and higher rates of divorce than other generations. As a result, many Gen X parents today are concerned about avoiding broken homes with their own kids.Millennials (born 1981-1998): During the adolescence of Millennials, America underwent a technological revolution with the emergence of the internet. Because of this, Millennials are generally characterized by older generations to be technologically savvy.Generation Z (born 1999-2016): Generation Z or “Zoomers” represent a generation raised on the internet and social media. Gen Z makes up the most ethnically diverse and largest generation in American history. Like Millennials, Gen Z is recognized by older generations to be very familiar with and/or addicted to technology.Questions to ask when you look at this mapDo you notice any trends with the predominant generations located in big cities? Suburbs? Rural areas?Where do you see big clusters of the same generation living in the same area?Which areas do you see the most diversity in generations?Look on the map for where you, your parents, aunts, uncles, and grandparents live. Do they live in areas where their generation is the most predominant?

Why does ethnic violence occur in some places but not others? This paper argues that the local ethnic configuration below the national level is an important determinant of how likely conflict is in any particular place. Existing studies of ethnicity and conflict focus on national-level fractionalization or dominance, but much of the politics surrounding ethnic groups’ grievances and disputes takes place at a more local level. We argue that the existence of multiple ethnic groups competing for resources and power at the level of sub-national administrative regions creates a significant constraint on the ability of states to mitigate ethnic groups’ grievances. This in turn increases the likelihood of conflict between ethnic groups and the state. In particular, we argue that diverse administrative regions dominated by one group should be most prone for conflict. Using new data on conflict and ethnic group composition at the region level, we test the theory and find that units with one demographically dominant ethnic group among multiple groups are most prone to conflict.