In 2024, 4.3 percent of U.S. Google leadership employees were of Latinx ethnicity. The majority of leadership employees, about six in ten, were white. Asian Google employees accounted for the second-largest group of employees in leadership positions.

This map shows a comparison of diversity and median household income in the US by tract. Esri's Diversity Index measures the likelihood that two persons, chosen at random from the same area, belong to different races or ethnic groups. In theory, the index ranges from 0 (no diversity) to 100 (complete diversity). If an area's entire population is divided evenly into two race groups and one ethnic group, then the diversity index equals 50. As more race groups are evenly represented in the population, the diversity index increases. Minorities accounted for 30.9 percent of the population in 2000 and are expected to make up 42.3 percent of the population by 2023. Vintage of data: 2023Areas in a darker orange are less diverse than light blue areas with higher diversity. Median household income is symbolized by size. The national median household income is $58,100 and any household below the national value has the smallest symbol size. The largest size has a median household income over $100,000 per year. Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years.Esri Updated Demographics DocumentationMethodologyUnderstanding Esri’s Updated Demographics portfolioEssential Esri Demographics vocabularyThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data.

In fiscal year 2023, more than half of the professional workforce at Deloitte in the United States were white. Percentage-wise, this is a decrease in the number of white employees from 2022. Asian employees made up the next largest demographic in between 2022 and 2023. Representation of all non-white demographics increased between 2021 and 2023.

Diversity index information by neighborhoods in Johns Creek, GA.Neighborhood boundaries are created and maintained by Johns Creek, GA.Demographics data is from Esri GeoEnrichment Services.

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.

Bank of America's workforce has undergone a significant shift in racial diversity over the past six years. The share of white employees decreased from 53.2 percent in 2019 to 47.2 percent in 2024, marking a notable change in the company's demographic composition. Meanwhile, the representation of Hispanic, Asian, and Black racial groups grew steadily. The second-largest racial group in the observed period was Hispanic, whose share increased from 17.9 to 19.2 percent.

As of January 2024, the majority of Google employees worldwide, almost 66 percent, were male. The distribution of male and female employees at Google hasn’t seen a big change over the recent years. In 2014 the share of female employees at Google was 30.6 percent. In 2021 this number has increased by only 3 percent. Considering that the total number of Google employees increased greatly between the years 2007 and 2020, the female quota among the employees had seen rather a small increase. Google as a company Google is a diverse internet company that provides a wide range of digital products and services. In 2022, the company’s global revenue was over 279 billion U.S. dollars. Most of its revenue, around 305 billion U.S. dollars, was from advertising. Among its services, the most popular ones are YouTube and Google Play. Male and female employees at tech companies Google is not the only tech company with a lower number of female employees. This pattern can be seen in other big tech companies too. In 2019, in a ranking of 20 leading tech companies worldwide, only 23andMe had more than a 50 percent share of female employees. The majority of tech companies in the ranking have far more male than female employees.

description: 2015-16 Demographic Data - Diversity Efforts; abstract: 2015-16 Demographic Data - Diversity Efforts

About this dataset

Context

The dataset presents the median household income across different racial categories in Sevier County. 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 Sevier County population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 92.54% of the total residents in Sevier County. Notably, the median household income for White households is $59,425. Interestingly, despite the White population being the most populous, it is worth noting that Black or African American households actually reports the highest median household income, with a median income of $111,137. This reveals that, while Whites may be the most numerous in Sevier County, Black or African American households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/sevier-county-tn-median-household-income-by-race.jpeg" alt="Sevier County 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 Sevier County.
• 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 Sevier County median household income by race. You can refer the same here

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.

This feature layer provides Esri 2018 demographic estimates for popular variables including: 2018 Total Population, 2018 Household Population, 2018 Median Age, 2018 Median Household Income, 2018 Per Capita Income, 2018 Diversity Index and many more. Data is available for block group centroids. For more information about Esri Demographics, visit the Updated Demographics documentation. For a full list of the service variables, click the Data tab. For more information about publishing hosted scene layers, visit Publish Hosted Scene Layers.

Uncovering what predicts genetic diversity (GD) within species can help us access the status of populations and their evolutionary potential. Traits related to effective population size show a proportional association to GD, but evidence supports life-history strategies and habitat as the drivers of GD variation. Instead of investigating highly divergent taxa, focusing on one group could help to elucidate the factors influencing the GD. Additionally, most empirical data is based on vertebrate taxa; therefore, we might be missing novel patterns of GD found in neglected invertebrate groups. Here, we investigated the predictors of the GD in crabs (Brachyura) by compiling the most comprehensive cytochrome c oxidase subunit I (COI) available. Eight predictor variables were analyzed across 150 species (16,992 sequences) using linear models (multiple linear regression) and comparative methods (PGLS). Our results indicate that population size fluctuation represents the most critical trait predi...

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.

This layer is no longer being actively maintained. Please see the Esri Updated Demographics Variables 2023 layer for more recent data and additional variables.This feature layer provides Esri 2018 demographic estimates for popular variables including: 2018 Total Population, 2018 Household Population, 2018 Median Age, 2018 Median Household Income, 2018 Per Capita Income, 2018 Diversity Index and many more. Data is available from country, state, county, ZIP Code, tract, and block group level with adjustable scale visibility. It is intended as a sample feature service to demonstrate smart mapping capabilities with Esri's Demographic data. Example feature views and web maps built from this layer include:Predominant Generations in the United StatesUnemployment in the United StatesMedian Home Value and IncomePopulation Growth or Decline?For more information, visit the Updated Demographics documentation. For a full list of variables, click the Data tab. Note: This layer is not being continuously updated or maintained.

Feed the Future (FTF) initiative in Ghana is a collaborative effort that supports country-owned processes and plans for improving food security and nutrition, particularly in the northern part of the country. These datasets cover the interim survey that took place in 2015 and was designed as a follow-up to the baseline survey that happened from 2012 to 2013. The survey covered a range of indicators organized around four groups: (1) economic well-being; (2) women and children anthropometry; (3) hunger and diet diversity; and (4) women's empowerment. The survey design involved two stages in which enumeration areas were selected followed by households. Data was collected in a face-to-face fashion using well-designed questionnaires and other study materials.

Climate change is rapidly transforming forests over much of the globe in ways that are not anticipated by current science. Large-scale forest diebacks, apparently linked to interactions involving drought, warm winters, and other species, are becoming alarmingly frequent. Models of biodiversity and climate have not provided guidance on if/where/when such responses will occur. Instead models often predict potential numbers of extinctions, but these forecasts not are linked in any mechanistic way to the processes that could cause them. Both modeling and field studies rely on aggregate metrics of species presence/absence or relative abundance at regional scales, but climate affects individuals. Aggregation of individual data to the species level, hides or even qualitatively changes climate effects. By sampling and analysis at the individual scale across continental variation in climate, this study can link the individual scale processes to regional responses. This study will exploit existing research sites and the new NEON platform of sites for synthesis of models and data to determine when and where predicting climate impacts on biodiversity is a plausible goal, understand where surprises are likely to occur, and attribute those predictions back to individual tree health and vulnerability to climate risk factors. The study will provide climate vulnerability forecasts for forest biodiversity that are directly linked to the process scale. Our goal is provide probabilistic forecasts for the joint distribution of forest responses to climate change, including growth, reproduction, and mortality risk. For scientists, US Forest Service researchers, and policy makers predictions will anticipate combined risks of increasing drought and longer growing seasons. Methods developed under this project will be disseminated through training workshops for postdoctoral associates at other universities and resource managers.

In 2023, about 44 percent of Citigroup's U.S.-based employees identified as white, making them the largest demographic group at the American banking giant. Hispanic or Latino employees accounted for around 19 percent, while employees of Asian origin represented just over 20 percent. Approximately 11.5 percent of the workforce identified as Black or African American. Between 2019 and 2023, the share of white employees gradually declined, while the representation of non-white employees steadily increased.

Enrollment counts are based on the October 31st Audited Register for 2018. Data on students with disabilities, English language learners and students poverty status are as of March 12 2019. Due to missing demographic information in rare cases, demographic categories do not always add up to citywide totals. In order to view all data there is an excel file attached which you would select to open.

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 posi..., 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..., Google Sheets or Excel is required to open Lafferty et al. Data_File.xlsx Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) used to create the Sankey diagram Figure 2 produced in R

, Reference Information

Provenance for this README

• File name: README_Dataset-Academic-JEDI.txt
• Authors: Erin A. McKenney
• Other contributors: Diana J. R. Lafferty, Tru Hubbard, Sarah Trujillo, DeAnna Beasley
• Date created: 2023-06-08
• Date modified: 2023-10-18

Dataset Attribution and Usage

• Dataset Title: Data for the article “A path forward: creating an academic culture of justice, equity, diversity and inclusionâ€
• Persistent identifier: DOI:10.5061/dryad.cfxpnvxbb
• License: Use of these data is covered by the following license:
  • Title: CC0 1.0 Universal (CC0 1.0)
  • Specification: https://creativecommons.org/publicdomain/zero/1.0/; the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed.

Methodological Information

• All data were collected by the authors.
• Methods of data collection/generation: see manuscript and Supplemental Materials f...

About this dataset

Context

The dataset presents the median household income across different racial categories in Winter town. 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 Winter town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.88% of the total residents in Winter town. Notably, the median household income for White households is $59,450. Interestingly, despite the White population being the most populous, it is worth noting that American Indian and Alaska Native households actually reports the highest median household income, with a median income of $73,806. This reveals that, while Whites may be the most numerous in Winter town, American Indian and Alaska Native households experience greater economic prosperity in terms of median household income.

https://i.neilsberg.com/ch/winter-town-wi-median-household-income-by-race.jpeg" alt="Winter town 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 Winter town.
• 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 Winter town median household income by race. You can refer the same here