100+ datasets found
  1. d

    2020 - 2021 Diversity Report

    • catalog.data.gov
    • data.cityofnewyork.us
    • +1more
    Updated Nov 29, 2024
    + more versions
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    data.cityofnewyork.us (2024). 2020 - 2021 Diversity Report [Dataset]. https://catalog.data.gov/dataset/2020-2021-diversity-report
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    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

  2. E

    Diversity in Tech Statistics 2024 – By Countries, Companies And Demographic...

    • enterpriseappstoday.com
    Updated Mar 1, 2024
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    EnterpriseAppsToday (2024). Diversity in Tech Statistics 2024 – By Countries, Companies And Demographic (Age, Gender, Race, Education) [Dataset]. https://www.enterpriseappstoday.com/stats/diversity-in-tech-statistics.html
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    Dataset updated
    Mar 1, 2024
    Dataset authored and provided by
    EnterpriseAppsToday
    License

    https://www.enterpriseappstoday.com/privacy-policyhttps://www.enterpriseappstoday.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    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

  3. w

    2015-2016 Demographic Data - Diversity Efforts

    • data.wu.ac.at
    • data.cityofnewyork.us
    • +2more
    application/excel +5
    Updated Aug 16, 2018
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    Vaughan Coleman (2018). 2015-2016 Demographic Data - Diversity Efforts [Dataset]. https://data.wu.ac.at/schema/data_cityofnewyork_us/dG5jYi1hZ3Y0
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    xml, application/excel, application/xml+rdf, csv, json, xlsxAvailable download formats
    Dataset updated
    Aug 16, 2018
    Dataset provided by
    Vaughan Coleman
    Description

    Demographic Data - Diversity Efforts

  4. m

    State Employee Diversity Dashboard

    • mass.gov
    Updated Jul 14, 2022
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    Office of Diversity and Equal Opportunity (2022). State Employee Diversity Dashboard [Dataset]. https://www.mass.gov/info-details/state-employee-diversity-dashboard
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    Dataset updated
    Jul 14, 2022
    Dataset provided by
    Office of Diversity and Equal Opportunity
    Human Resources
    Area covered
    Massachusetts
    Description

    Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

  5. Racial diversity in the workforce of Bank of America in the U. S. 2019-2024

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Racial diversity in the workforce of Bank of America in the U. S. 2019-2024 [Dataset]. https://www.statista.com/statistics/1319055/us-racial-diversity-bank-of-america/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    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 **** percent in 2019 to **** 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 **** to **** percent.

  6. N

    Median Household Income by Racial Categories in Wake Forest, NC (2021, in...

    • neilsberg.com
    csv, json
    Updated Jan 3, 2024
    + more versions
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    Neilsberg Research (2024). Median Household Income by Racial Categories in Wake Forest, NC (2021, in 2022 inflation-adjusted dollars) [Dataset]. https://www.neilsberg.com/research/datasets/369c3dba-8904-11ee-9302-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Jan 3, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    North Carolina, Wake Forest
    Variables measured
    Median Household Income for Asian Population, Median Household Income for Black Population, Median Household Income for White Population, Median Household Income for Some other race Population, Median Household Income for Two or more races Population, Median Household Income for American Indian and Alaska Native Population, Median Household Income for Native Hawaiian and Other Pacific Islander Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To portray the median household income within each racial category idetified by the US Census Bureau, we conducted an initial analysis and categorization of the data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). It is important to note that the median household income estimates exclusively represent the identified racial categories and do not incorporate any ethnicity classifications. Households are categorized, and median incomes are reported based on the self-identified race of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the median household income across different racial categories in Wake Forest. 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 Wake Forest population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 70.43% of the total residents in Wake Forest. Notably, the median household income for White households is $116,222. 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 $137,159. This reveals that, while Whites may be the most numerous in Wake Forest, Asian households experience greater economic prosperity in terms of median household income.

    https://i.neilsberg.com/ch/wake-forest-nc-median-household-income-by-race.jpeg" alt="Wake Forest 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 Wake Forest.
    • 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 Wake Forest median household income by race. You can refer the same here

  7. Population of the U.S. by race 2000-2023

    • statista.com
    Updated Aug 20, 2024
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    Statista (2024). Population of the U.S. by race 2000-2023 [Dataset]. https://www.statista.com/statistics/183489/population-of-the-us-by-ethnicity-since-2000/
    Explore at:
    Dataset updated
    Aug 20, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jul 2000 - Jul 2023
    Area covered
    United States
    Description

    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.

  8. O

    Community and City Employee Demographics: Ethnicity

    • data.mesaaz.gov
    • citydata.mesaaz.gov
    application/rdfxml +5
    Updated Jan 23, 2025
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    US Census (2025). Community and City Employee Demographics: Ethnicity [Dataset]. https://data.mesaaz.gov/Diversity/Community-and-City-Employee-Demographics-Ethnicity/szc2-bi5d
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    csv, json, tsv, xml, application/rdfxml, application/rssxmlAvailable download formats
    Dataset updated
    Jan 23, 2025
    Dataset authored and provided by
    US Census
    Description

    Comparing the percentage of city residents (community) ethnicity to the percentage of city employee ethnicity. Employee information comes from Employee Demographics: Ethnicity https://citydata.mesaaz.gov/Human-Resources/Employee-Demographics-Ethnicity/6kd3-uaks. Community information comes from Community Demographics: Ethnicity at https://citydata.mesaaz.gov/Census/Community-Demographics-Ethnicity/g34w-9rxw

  9. j

    Demographics (Diversity Index)

    • datahub.johnscreekga.gov
    • opendata.atlantaregional.com
    • +1more
    Updated Dec 8, 2015
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    City of Johns Creek, GA (2015). Demographics (Diversity Index) [Dataset]. https://datahub.johnscreekga.gov/datasets/demographics-diversity-index-1
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    Dataset updated
    Dec 8, 2015
    Dataset authored and provided by
    City of Johns Creek, GA
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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.

  10. A

    ‘2015-2016 Demographic Data - Diversity Efforts’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘2015-2016 Demographic Data - Diversity Efforts’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-2015-2016-demographic-data-diversity-efforts-fc58/6d681d36/?iid=000-731&v=presentation
    Explore at:
    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘2015-2016 Demographic Data - Diversity Efforts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ade5460e-c0fd-48ce-8245-913bfb2a5583 on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    Demographic Data - Diversity Efforts

    --- Original source retains full ownership of the source dataset ---

  11. d

    Demographic modeling data (including code) at various sites in the Great...

    • catalog.data.gov
    • data.usgs.gov
    • +1more
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Demographic modeling data (including code) at various sites in the Great Basin, USA [Dataset]. https://catalog.data.gov/dataset/demographic-modeling-data-including-code-at-various-sites-in-the-great-basin-usa
    Explore at:
    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Great Basin, United States
    Description

    These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

  12. Livelihood Diversity and Demographic data.sav

    • figshare.com
    bin
    Updated Apr 13, 2024
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    Desta Dereje; Temesgen Tilahun; Teshome Yirgu (2024). Livelihood Diversity and Demographic data.sav [Dataset]. http://doi.org/10.6084/m9.figshare.25598496.v1
    Explore at:
    binAvailable download formats
    Dataset updated
    Apr 13, 2024
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Desta Dereje; Temesgen Tilahun; Teshome Yirgu
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Livelihood Diversity and Demographic data.sav: this data file mainly encompasses different household resilience determining explanatory (demographic and socio-economic) variable.

  13. N

    Median Household Income by Racial Categories in Savannah, GA (2022)

    • neilsberg.com
    csv, json
    Updated Jan 3, 2024
    + more versions
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    Neilsberg Research (2024). Median Household Income by Racial Categories in Savannah, GA (2022) [Dataset]. https://www.neilsberg.com/research/datasets/3657fd19-8904-11ee-9302-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 3, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Savannah, Georgia
    Variables measured
    Median Household Income for Asian Population, Median Household Income for Black Population, Median Household Income for White Population, Median Household Income for Some other race Population, Median Household Income for Two or more races Population, Median Household Income for American Indian and Alaska Native Population, Median Household Income for Native Hawaiian and Other Pacific Islander Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates. To portray the median household income within each racial category idetified by the US Census Bureau, we conducted an initial analysis and categorization of the data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). It is important to note that the median household income estimates exclusively represent the identified racial categories and do not incorporate any ethnicity classifications. Households are categorized, and median incomes are reported based on the self-identified race of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the median household income across different racial categories in Savannah. 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 Savannah population by race & ethnicity, the population is predominantly Black or African American. This particular racial category constitutes the majority, accounting for 52.35% of the total residents in Savannah. Notably, the median household income for Black or African American households is $43,499. Interestingly, despite the Black or African American population being the most populous, it is worth noting that White households actually reports the highest median household income, with a median income of $71,602. This reveals that, while Black or African Americans may be the most numerous in Savannah, White households experience greater economic prosperity in terms of median household income.

    https://i.neilsberg.com/ch/savannah-ga-median-household-income-by-race.jpeg" alt="Savannah 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 Savannah.
    • 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 Savannah median household income by race. You can refer the same here

  14. T

    Austin MSA Racial and Ethnic Diversity Index

    • datahub.austintexas.gov
    • data.austintexas.gov
    • +1more
    application/rdfxml +5
    Updated Oct 30, 2024
    + more versions
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    City of Austin, Texas - data.austintexas.gov (2024). Austin MSA Racial and Ethnic Diversity Index [Dataset]. https://datahub.austintexas.gov/City-Government/Austin-MSA-Racial-and-Ethnic-Diversity-Index/izag-sk98
    Explore at:
    tsv, json, csv, application/rdfxml, application/rssxml, xmlAvailable download formats
    Dataset updated
    Oct 30, 2024
    Dataset authored and provided by
    City of Austin, Texas - data.austintexas.gov
    Area covered
    Austin Metropolitan Area
    Description

    These are the data used for the Racial and Ethnic Diversity for the Austin MSA story map. The story map was published July 2024 but displays data from 2000, 2010, and 2020.

    Decennial census data were used for all three years. 2000: DEC Summary File 1, P004 2010: DEC Redistricting Data (PL 94-171), P2 2020: DEC Redistricting Data (PL 94-171), P2

    Geographic crosswalks were used to harmonize 2000, 2010, and 2020 geographies.

    Racial and Ethnic Diversity Index for the Austin MSA Storymap: https://storymaps.arcgis.com/stories/88ee265f00934af7a750b57f7faebd2c

    City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

  15. r

    US Census - ACS and Decennial files **

    • redivis.com
    Updated Mar 11, 2024
    + more versions
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    (2024). US Census - ACS and Decennial files ** [Dataset]. https://redivis.com/workflows/sex8-4b053ws9z
    Explore at:
    Dataset updated
    Mar 11, 2024
    Description

    Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

    Census data plays a pivotal role in academic data research, particularly when exploring relationships between different demographic characteristics. The significance of this particular dataset lies in its ability to facilitate the merging of various datasets with basic census information, thereby streamlining the research process and eliminating the need for separate API calls.

    The American Community Survey is an ongoing survey conducted by the U.S. Census Bureau, which provides detailed social, economic, and demographic data about the United States population. The ACS collects data continuously throughout the decade, gathering information from a sample of households across the country, covering a wide range of topics

  16. N

    Median Household Income by Racial Categories in Sevier County, TN (2022)

    • neilsberg.com
    csv, json
    Updated Jan 3, 2024
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    Neilsberg Research (2024). Median Household Income by Racial Categories in Sevier County, TN (2022) [Dataset]. https://www.neilsberg.com/research/datasets/365e1be7-8904-11ee-9302-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Jan 3, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sevier County, Tennessee
    Variables measured
    Median Household Income for Asian Population, Median Household Income for Black Population, Median Household Income for White Population, Median Household Income for Some other race Population, Median Household Income for Two or more races Population, Median Household Income for American Indian and Alaska Native Population, Median Household Income for Native Hawaiian and Other Pacific Islander Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates. To portray the median household income within each racial category idetified by the US Census Bureau, we conducted an initial analysis and categorization of the data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). It is important to note that the median household income estimates exclusively represent the identified racial categories and do not incorporate any ethnicity classifications. Households are categorized, and median incomes are reported based on the self-identified race of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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

  17. d

    International Data Base

    • dknet.org
    • rrid.site
    • +2more
    Updated Jan 29, 2022
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    (2022). International Data Base [Dataset]. http://identifiers.org/RRID:SCR_013139
    Explore at:
    Dataset updated
    Jan 29, 2022
    Description

    A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

  18. N

    Median Household Income by Racial Categories in South Pasadena, CA (2021, in...

    • neilsberg.com
    csv, json
    Updated Jan 3, 2024
    + more versions
    Share
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    Neilsberg Research (2024). Median Household Income by Racial Categories in South Pasadena, CA (2021, in 2022 inflation-adjusted dollars) [Dataset]. https://www.neilsberg.com/research/datasets/366e3eb8-8904-11ee-9302-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jan 3, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California, South Pasadena
    Variables measured
    Median Household Income for Asian Population, Median Household Income for Black Population, Median Household Income for White Population, Median Household Income for Some other race Population, Median Household Income for Two or more races Population, Median Household Income for American Indian and Alaska Native Population, Median Household Income for Native Hawaiian and Other Pacific Islander Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. To portray the median household income within each racial category idetified by the US Census Bureau, we conducted an initial analysis and categorization of the data. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). It is important to note that the median household income estimates exclusively represent the identified racial categories and do not incorporate any ethnicity classifications. Households are categorized, and median incomes are reported based on the self-identified race of the head of the household. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents the median household income across different racial categories in South Pasadena. 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 South Pasadena population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 47.27% of the total residents in South Pasadena. Notably, the median household income for White households is $143,370. 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 $146,547. This reveals that, while Whites may be the most numerous in South Pasadena, Asian households experience greater economic prosperity in terms of median household income.

    https://i.neilsberg.com/ch/south-pasadena-ca-median-household-income-by-race.jpeg" alt="South Pasadena 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 South Pasadena.
    • 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 South Pasadena median household income by race. You can refer the same here

  19. Google: global corporate demography 2014-2024, by gender

    • statista.com
    Updated Oct 28, 2024
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    Statista (2024). Google: global corporate demography 2014-2024, by gender [Dataset]. https://www.statista.com/statistics/311800/google-employee-gender-global/
    Explore at:
    Dataset updated
    Oct 28, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  20. U.S. population by sex and age 2023

    • statista.com
    • ai-chatbox.pro
    Updated Aug 20, 2024
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    Statista (2024). U.S. population by sex and age 2023 [Dataset]. https://www.statista.com/statistics/241488/population-of-the-us-by-sex-and-age/
    Explore at:
    Dataset updated
    Aug 20, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The estimated population of the U.S. was approximately 334.9 million in 2023, and the largest age group was adults aged 30 to 34. There were 11.88 million males in this age category and around 11.64 million females. Which U.S. state has the largest population? The population of the United States continues to increase, and the country is the third most populous in the world behind China and India. The gender distribution has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the highest population in 2023. The U.S. population by race and ethnicity The United States is well known the world over for having a diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

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data.cityofnewyork.us (2024). 2020 - 2021 Diversity Report [Dataset]. https://catalog.data.gov/dataset/2020-2021-diversity-report

2020 - 2021 Diversity Report

Explore at:
Dataset updated
Nov 29, 2024
Dataset provided by
data.cityofnewyork.us
Description

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

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