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.

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.

In 2023, half of Generation Z in the United States were white. In comparison, 48 percent of Gen Alpha were white in that year, making it the first generation that does not have a majority white population in the United States.

Despite comprising of a smaller share of the U.S. population than African Americans or Hispanics, the most represented non-white U.S. CEOs were of an Asian background. They made up 55 percent of CEO positions at Fortune 500 and S&P 500 companies in 2024. By comparison, 11 percent of CEOs at the time were African American. The rise of environmental, social, and corporate governance (ESG) Investments in ESG have risen dramatically over last few years. In November 2023 there were approximately 480 billion U.S. dollars in ESG ETF assets worldwide, compared to 16 billion U.S. dollars in 2015. ESG measures were put in place to encourage companies to act responsibly, with the leading reason for ESG investing stated to be brand and reputation according to managers and asset owners. Gender diversity With the general acceptance of ESG in larger companies, there has still been a significant employment gap of women working in senior positions. For example, the share of women working as a partner or principal at EY, one of the largest accounting firms in the world, was just only 28 percent in 2023.

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.

California is home to 12 percent of the nation's population yet accounts for more than 20 percent of the people living in the nation’s hardest-to-count areas, according to the United States Census Bureau (U.S. Census Bureau). California's unique diversity, large population distributed across both urban and rural areas, and sheer geographic size present significant barriers to achieving a complete and accurate count. The state’s population is more racially and ethnically diverse than ever before, with about 18 percent of Californians speaking English “less than very well,” according to U.S. Census Bureau estimates. Because the 2020 Census online form was offered in only twelve non-English languages, which did not correspond with the top spoken language in California, and a paper questionnaire only in English and Spanish, many Californians may not have been able to access a census questionnaire or written guidance in a language they could understand. In order to earn the confidence of California’s most vulnerable populations, it was critical during the 2020 Census that media and trusted messengers communicate with them in their primary language and in accessible formats. An accurate count of the California population in each decennial census is essential to receive its equitable share of federal funds and political representation, through reapportionment and redistricting. It plays a vital role in many areas of public life, including important investments in health, education, housing, social services, highways, and schools. Without a complete count in the 2020 Census, the State faced a potential loss of congressional seats and billions of dollars in muchneeded federal funding. An undercount of California in 1990 cost an estimated $2 billion in federal funding. The potential loss of representation and critically needed funding could have long-term impacts; only with a complete count does California receive the share of funding the State deserves with appropriate representation at the federal, state, and local government levels. The high stakes and formidable challenges made this California Complete Count Census 2020 Campaign (Campaign) the most important to date. The 2020 Census brought an unprecedented level of new challenges to all states, beyond the California-specific hurdles discussed above. For the first time, the U.S. Census Bureau sought to collect data from households through an online form. While the implementation of digital forms sought to reduce costs and increase participation, its immediate impact is still unknown as of this writing, and it may have substantially changed how many households responded to the census. In addition, conditions such as the novel Coronavirus (COVID-19) pandemic, a contentious political climate, ongoing mistrust and distrust of government, and rising concerns about privacy may have discouraged people to open their doors, or use computers, to participate. Federal immigration policy, as well as the months-long controversy over adding a citizenship question to the census, may have deterred households with mixed documentation status, recent immigrants, and undocumented immigrants from participating. In 2017, to prepare for the unique challenges of the 2020 Census, California leaders and advocates reflected on lessons learned from previous statewide census efforts and launched the development of a high-impact strategy to efficiently raise public awareness about the 2020 Census. Subsequently, the State established the California Complete Count – Census 2020 Office (Census Office) and invested a significant sum for the Campaign. The Campaign was designed to educate, motivate, and activate Californians to respond to the 2020 Census. It relied heavily on grassroots messaging and outreach to those least likely to fill out the census form. One element of the Campaign was the Language and Communication Access Plan (LACAP), which the Census Office developed to ensure that language and communication access was linguistically and culturally relevant and sensitive and provided equal and meaningful access for California’s vulnerable populations. The Census Office contracted with outreach partners, including community leaders and organizations, local government, and ethnic media, who all served as trusted messengers in their communities to deliver impactful words and offer safe places to share information and trusted messages. The State integrated consideration of hardest-to-count communities’ needs throughout the Campaign’s strategy at both the statewide and regional levels. The Campaign first educated, then motivated, and during the census response period, activated Californians to fill out their census form. The Census Office’s mission was to ensure that Californians get their fair share of resources and representation by encouraging the full participation of all Californians in the 2020 Census. This report focuses on the experience of the Census Office and partner organizations who worked to achieve the most complete count possible, presenting an evaluation of four outreach and communications strategies.

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 layer summarizes racial and ethnic diversity in the United States. The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity).The data shown is from Esri's 2020 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2020/2025) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

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.

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

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.

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.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

The COVID-19 pandemic, which began in China in late 2019, and subsequently spread across the world during the first several months of 2020, has had a dramatic impact on all facets of life. At the same time, it has not manifested in the same way in every nation. Some countries experienced a large initial spike in cases and deaths, followed by a rapid decline, whereas others had relatively low rates of both outcomes throughout the first half of 2020. The United States experienced a unique pattern of the virus, with a large initial spike, followed by a moderate decline in cases, followed by second and then third spikes. In addition, research has shown that in the United States the severity of the pandemic has been associated with poverty and access to health care services. This study was designed to examine whether the course of the pandemic has been uniform across America, and if not how it differed, particularly with respect to poverty. Results of a random intercept multilevel mixture model revealed that the pandemic followed four distinct paths in the country. The least ethnically diverse (85.1% white population) and most rural (82.8% rural residents) counties had the lowest death rates (0.06/1000) and the weakest link between deaths due to COVID-19 and poverty (b = 0.03). In contrast, counties with the highest proportion of urban residents (100%), greatest ethnic diversity (48.2% nonwhite), and highest population density (751.4 people per square mile) had the highest COVID-19 death rates (0.33/1000), and strongest relationship between the COVID-19 death rate and poverty (b = 46.21). Given these findings, American policy makers need to consider developing responses to future pandemics that account for local characteristics. These responses must take special account of pandemic responses among people of color, who suffered the highest death rates in the nation.

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.

About this dataset

Context

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

Key observations

Based on our analysis of the distribution of State Center population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 88.04% of the total residents in State Center. Notably, the median household income for White households is $79,138. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $79,138.

https://i.neilsberg.com/ch/state-center-ia-median-household-income-by-race.jpeg" alt="State Center 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) 2017-2021 5-Year Estimates.

Racial categories include:

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

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in State Center.
• 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 State Center median household income by race. You can refer the same here

This study investigates whether ethnic and other forms of social diversity affect militarization of society. Recent scholarship in economics finds that high diversity leads to lower provision of public goods. At the same time, many conflict studies find that highly diverse societies face a lower risk of civil war, as opposed to relatively more homogenous populations. The authors explore whether diversity prompts governments to militarize heavily in order to prevent armed conflict, which would then crowd out spending on other public goods in a ‘guns versus butter’ trade-off. Thus, ‘preventive militarization’ would explain both outcomes. Yet the authors find the opposite: higher levels of ethnic diversity predict lower levels of militarization. If high diversity lowers the hazard of civil war, as many find, then it does not happen via preventive militarization. If diverse societies spend less on public goods, then this is not because they are crowded out by security spending. The results support those who suggest that diversity may, in fact, pose a lower security threat to states, since it is highly likely that states facing potential social strife would prioritize state militarization.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

About this dataset

Context

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

Key observations

Based on our analysis of the distribution of State Line City population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95% of the total residents in State Line City. Notably, the median household income for White households is $64,404. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $64,404.

https://i.neilsberg.com/ch/state-line-city-in-median-household-income-by-race.jpeg" alt="State Line City 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) 2017-2021 5-Year Estimates.

Racial categories include:

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

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in State Line City.
• 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 State Line City median household income by race. You can refer the same here

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.