United States US: Government Expenditure on Primary Education: % of Government Expenditure on Education data was reported at 30.965 % in 2014. This records a decrease from the previous number of 31.109 % for 2013. United States US: Government Expenditure on Primary Education: % of Government Expenditure on Education data is updated yearly, averaging 31.109 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 32.422 % in 2010 and a record low of 30.963 % in 2012. United States US: Government Expenditure on Primary Education: % of Government Expenditure on Education data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Expenditure on primary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

United States US: Government Expenditure on Tertiary Education: % of Government Expenditure on Education data was reported at 27.502 % in 2014. This records an increase from the previous number of 27.227 % for 2013. United States US: Government Expenditure on Tertiary Education: % of Government Expenditure on Education data is updated yearly, averaging 27.227 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 28.463 % in 2012 and a record low of 25.653 % in 2010. United States US: Government Expenditure on Tertiary Education: % of Government Expenditure on Education data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Expenditure on tertiary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

United States US: Government Expenditure on Education: Total: % of Government Expenditure data was reported at 13.452 % in 2014. This records an increase from the previous number of 13.277 % for 2013. United States US: Government Expenditure on Education: Total: % of Government Expenditure data is updated yearly, averaging 13.277 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 13.452 % in 2014 and a record low of 12.933 % in 2011. United States US: Government Expenditure on Education: Total: % of Government Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Education Statistics. General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

Data on annual expenditure by educational institutions per student, in Canadian and American dollars, reference year 2021/2022. At the primary/secondary level, the amount spent on educational core services and ancillary services is also presented.

Teachers' Use of Educational Technology in U.S. Public Schools, 2009 (FRSS 95), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 95 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology among teachers in public elementary and secondary schools during 2009. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted using surveys via the web or by mail. Telephone follow-up for survey non-response and data clarification was also used. Questionnaires and cover letters for the teacher survey were mailed to sampled teachers at their schools. Public schools and teachers within those schools were sampled. The weighted response rate for schools providing lists of teachers for sampling was 81 percent, and the weighted response rate for sampled teachers completing questionnaires was 79 percent. Key statistics produced from FRSS 95 were information on the use of computers and internet access in the classroom; availability and use of computing devices, software, and school or district networks (including remote access) by teachers; students' use of educational technology; teachers' preparation to use educational technology for instruction; and technology-related professional development activities.

United States US: Government Expenditure per Student: Secondary: % of(GDP) Gross Domestic Productper Capita data was reported at 22.500 % in 2014. This records a decrease from the previous number of 22.657 % for 2013. United States US: Government Expenditure per Student: Secondary: % of(GDP) Gross Domestic Productper Capita data is updated yearly, averaging 22.810 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 24.341 % in 2010 and a record low of 22.500 % in 2014. United States US: Government Expenditure per Student: Secondary: % of(GDP) Gross Domestic Productper Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

United States US: Current Education Expenditure: Tertiary: % of Total Expenditure in Tertiary Public Institutions data was reported at 89.136 % in 2014. This records a decrease from the previous number of 89.635 % for 2013. United States US: Current Education Expenditure: Tertiary: % of Total Expenditure in Tertiary Public Institutions data is updated yearly, averaging 89.050 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 89.635 % in 2013 and a record low of 85.426 % in 2012. United States US: Current Education Expenditure: Tertiary: % of Total Expenditure in Tertiary Public Institutions data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

The FY 2017 System-Wide Report is the report of final DOE expenses. FY 2017 began on July 1, 2016 and ended on June 30, 2017. The financial data used in the FY 2017 System-Wide Report represents the DOE’s 2017 year-end audited spending condition. In addition to using the audited school registers as of October 31, 2016 for pupil counts, pupil enrollment data has been refined to count students with disabilities with Individual Education Programs (IEPs) for specialized classroom instruction based on their program recommendations as of December 31, 2016.

Higher Education Institutions in the United States of America Dataset

This repository contains a dataset of higher education institutions in the United States of America. This dataset was compiled in response to a cybersecurity research of American higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

Data

The data includes the following fields for each institution:

Id: A unique identifier assigned to each institution.

Region: The federal state in which the institution is located.

Name: The full name of the institution.

Category: Indicates whether the institution is public or private.

Url: The website of the institution.

Methodology

The dataset was obtained from the Higher Education Integrated Data System (IPEDS) website [3], which is administered by the National Center for Education Statistics (NCES). NCES serves as the primary federal entity for collecting and analyzing education-related data in the United States. The data was collected on February 2, 2023.

The initial list of institutions was derived from the IPEDS database using the following criteria: (1) US institutions only, (2) degree-granting institutions, primarily bachelor's or higher, and (3) industry classification, which includes: public 4 - year or above, private not-for-profit 4 years or more, private for-profit 4 years or more, public 2 years, private not-for-profit 2 years, private for-profit 2 years, public less than 2 years, private not-for-profit for-profit less than 2 years and private for-profit less than 2 years.

The following variables have been added to the list of institutions: Control of the institution, state abbreviation, degree-granting status, Status of the institution, and Institution's internet website address. This resulted in a report with 1,979 institutions.

The institution's status was labeled with the following values: A (Active), N (New), R (Restored), M (Closed in the current year), C (Combined with another institution), D (Deleted out of business), I (Inactive due to hurricane-related issues), O (Outside IPEDS scope), P (Potential new/add institution), Q (Potential institution reestablishment), W (Potential addition outside IPEDS scope), X ( Potential restoration outside the scope of IPEDS) and G (Perfect Children's Campus).

A filter was applied to the report to retain only institutions with an A, N, or R status, resulting in 1,978 institutions. Finally, a data cleaning process was applied, which involved removing the whitespace at the beginning and end of cell content and duplicate whitespace. The final data were compiled into the dataset included in this repository.

Usage

This data is available under the Creative Commons Zero (CC0) license and can be used for any purpose, including academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.7614862

Contribution

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

Acknowledgment

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Instituto Politécnico de Viana do Castelo, Portugal.

References

Pending.

S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]

Integrated Postsecondary Education Data System, "Compare Institutions", Fev 2023. [online]. Available: https://nces.ed.gov/ipeds/use-the-data

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to develop demographic estimates and to support educational research and program administration. The NCES Common Core of Data (CCD) program is an annual collection of basic administrative characteristics for all public schools, school districts, and state education agencies in the United States. These characteristics are reported by state education officials and include directory information, number of students, number of teachers, grade span, and other conditions. The administrative attributes in this layer were developed from the most current CCD collection available. For more information about NCES school district boundaries, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries. For more information about CCD school district attributes, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

United States US: GERD: % of GDP data was reported at 3.586 % in 2022. This records an increase from the previous number of 3.483 % for 2021. United States US: GERD: % of GDP data is updated yearly, averaging 2.612 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 3.586 % in 2022 and a record low of 2.268 % in 1981. United States US: GERD: % of GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Gross Domestic Expenditure on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 18 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.Collections are available for the following years: School Neighborhood Poverty Estimates, 2020-2021School Neighborhood Poverty Estimates, 2019-2020 School Neighborhood Poverty Estimates, 2018-2019 School Neighborhood Poverty Estimates, 2017-2018 School Neighborhood Poverty Estimates, 2016-2017 School Neighborhood Poverty Estimates, 2015-2016 All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

United States US: Government Expenditure per Student: Primary: % of(GDP) Gross Domestic Productper Capita data was reported at 19.909 % in 2014. This records an increase from the previous number of 19.777 % for 2013. United States US: Government Expenditure per Student: Primary: % of(GDP) Gross Domestic Productper Capita data is updated yearly, averaging 20.561 % from Dec 2010 (Median) to 2014, with 5 observations. The data reached an all-time high of 22.100 % in 2010 and a record low of 19.777 % in 2013. United States US: Government Expenditure per Student: Primary: % of(GDP) Gross Domestic Productper Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median;

Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to provide a biennial count of the total number of private schools, teachers, and students. The PSS school location and associated geographic area assignments are derived from reported information about the physical location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the most current PSS school collection, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries

Previous collections are available for the following years:

2021-22 2019-20 2017-18 2015-16

All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

School District Finance Survey, 2007-08 (F-33 2007-08), is a study that is part of the Common Core of Data (CCD) program; program data available since 1990 at https://nces.ed.gov/ccd/f33agency.asp. F-33 2007-08 (https://nces.ed.gov/ccd/f33ageninfo.asp) is a universe survey that is designed to provide finance data for all local education agencies (LEAs) that provide free public elementary and secondary education in the United States. The data file for F-33 2007-08 contains 16,453 records representing the public elementary and secondary education agencies in the 50 United States and the District of Columbia. Key statistics produced from F-33 2007-08 are expenditures by object and function, indebtedness, and revenues by source. The F-33 is collaboration by the National Center for Education Statistics (NCES) and the Census Bureau. Census is the primary collection agent. Census refers to the collection as the Annual Survey of Local Government Finances: School Systems and releases its own version of the data file and publication based on that file. The NCES and Census files differ in their inclusion of independent charter school districts, the classification of some revenue items, and the inclusion of some expenditure items.

This web map provides and in-depth look at school districts within the United States. Clicking on a school district in the map will reveal different statistics about each district in the pop-up. The statistics presented in this map are approximations based on summarizing American Community Survey(ACS) data using tract centroids. They may differ from published statistics by school districts found on data.census.gov. A few things you will learn from this map:How many public and private schools fall within a district?Socioeconomic factors about the Census Tracts which fall within the district:School enrollment for grades Kindergarten through 12thDisconnected children in the districtChildren living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of population under the age of 19 in the districtFor more information about the data sources:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases estimates, so values in the map always reflect the newest data available.Current School Districts Layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Public Schools Layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.Private Schools Layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Web Map originally owned by Summers Cleary

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Our Demographics package in the USA offers data pertaining to the education of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Highest Educational Attainment

• High-school Diploma
• College - No Degree
• GED
• Associates Degree
• Bachelor's Degree
• Master's Degree
• Doctorate Degree
• Professional School Degree

Schooling In Child Population

• No Schooling
• Nursery School
• Kindergarten
• Primary School

Bachelor's Degree Attained

• Arts/Humanities/Other
• Business
• Education
• Science/Engineering

• • Highest Educational Attainment: Marketers can use this data to tailor their campaigns based on the level of education achieved by their target audience. For instance, educational institutions may focus on promoting advanced degree programs to individuals with a bachelor's degree or higher, while brands offering products related to skill development might target those with a high-school diploma or college degree.
  • Schooling In Child Population: This data provides insights into the educational stages of the child population, allowing marketers to customize their strategies. For example, companies specializing in children's products can create targeted promotions for specific age groups, such as educational toys for those in nursery or kindergarten.
  • Bachelor's Degree Attained: Marketers can leverage this information to tailor campaigns based on the specific field of study for individuals with a bachelor's degree. For instance, businesses offering services aligned with certain disciplines, like technology or the arts, can refine their messaging to appeal to the interests and preferences associated with each academic field.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing