In the 2021/22 academic year, about 4.1 million higher education degrees were earned in the United States. By the 2031/32 academic year, this figure is projected to increase to about 4.83 million degrees.

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

In 2021, about **** percent of all women who had completed high school or completed a GED in the United States during the calendar year enrolled in a 2-year college afterwards. This is compared to **** percent of all men who did the same.

The National Survey of College Graduates (NSCG) provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering workforce. It samples individuals who are living in the United States during the survey reference week, have at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation. The survey is sponsored by the National Center for Science and Engineering Statistics (NCSES) within the U.S. National Science Foundation.

This repository contains a dataset of higher education institutions in France. This includes 349 higher education institutions in France, including universities, universities of applied sciences and Higher Institutes as Higher Institute of Engineering, Higher Institute of biotechnologies and few others. This dataset was compiled in response to a cybersecurity investigation of France higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

The data includes the following fields for each institution:

• ETER_Id: A unique identifier assigned to each institution.
• Name: The full name of the institution.
• Category: Indicates whether the institution is public or private.
• Institution_Category_Standardized: Indicates whether the institution is University, University of applied sciences or other.
• Member_of_European_University_alliance: Indicates if the institution is member of European University Alliance (A kind of collaborative higher education institutions network in Europe).
• Url: The website of the institution.
• NUTS2: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS2 being the second level.
• NUTS2_Label_2016: Refers to the classification of regions at the NUTS2 level according to the 2016 criteria set by the European Union.
• NUTS2_Label_2021: Refers to the classification of regions at the NUTS2 level according to the 2021 criteria set by the European Union.
• NUTS3: Nomenclature of Territorial Units for Statistics (NUTS): A classification by the European Union to divide member states' territories into statistical units. The NUTS system has three hierarchical levels, with NUTS3 being the third level.
• NUTS3_Label_2016: Refers to the classification of regions at the NUTS3 level according to the 2016 criteria set by the European Union.
• NUTS3_Label_2021: Refers to the classification of regions at the NUTS3 level according to the 2021 criteria set by the European Union.

The methodology for creating the dataset involved obtaining data from two sources: The European Higher Education Sector Observatory (ETER)[3]. The data was collected on December 26, 2024, the Eurostat for NUTS - Nomenclature of territorial units for statistics 2013-16[4] and 2021[5].

This section outlines the methodology used to create the dataset for Higher Education Institutions (HEIs) in France. The dataset consolidates information from various sources, processes the data, and enriches it to provide accurate and reliable insights.

Data Sources

1. ETER Database: The primary dataset was sourced from the ETER database, containing detailed information about HEIs in Europe.
   • File: eter-export-2021-FR.xlsx
2. Eurostat NUTS Data: Two datasets from Eurostat were used for regional information:
   • NUTS 2013-2016 regions: NUTS2013-NUTS2016.xlsx
   • NUTS 2021 regions: NUTS2021.xlsx

Data Cleaning and Preprocessing Column Renaming Columns in the raw dataset were renamed for consistency and readability. Examples include:

• ETER ID → ETER_ID
• Institution Name → Name
• Legal status → Category

Value Replacement

1. HEI Categories: The Category column was cleaned, with government-dependent institutions classified as "public."
2. Standardized Institution Categories: Mapped numerical values to descriptive labels such as "University" and "University of applied sciences."
3. European University Alliance Membership: Replaced binary values with "Yes" or "No."

Handling Missing or Incorrect Data

1. Specific entries with missing or incorrect data were updated manually based on their ETER_ID. For instance:
   • Adjusted URLs for entries like FR0333 (updated to www.icam.fr)
   • Adjusted URLs for entries like FR0906 (updated to epss.fr)
   • Adjusted URLs for entries like FR0104 (updated to www.ensa-nancy.fr)
   • Adjusted URLs for entries like FR0466 (updated to www.clermont-auvergne-inp.fr)
   • Adjusted URLs for entries like FR0907 (updated to insp.gouv.fr) - This universety also changed your name for Institut national du service public
   • Removed entries such as FR0129 and FR0944 due to insufficient or invalid information.
   • Removed FR0513 Institut supérieur européen de gestion Lyon because it's the same url and school of Paris, so remains only the main campus in Paris
   • Remove FR0235 Institut supérieur de l'électronique et du numérique Toulon, because it's the same url of Institut supérieur de l'électronique et du numérique Lille, so remains only the main campus
   • Remove FR0106 and FR010 École spéciale militaire, because it's the url returns 403 forbiden
   • Remove FR0970 École nationale de la meteorologie beucase of invalid HTTPS

Regional Data Integration

1. Merged NUTS 2016 and NUTS 2021 data to enrich the dataset with regional labels.

Final Dataset The final dataset was saved as a CSV file: france-heis.csv, encoded in UTF-8 for compatibility. It includes detailed information about HEIs in France, their categories, regional affiliations, and membership in European alliances.

Summary This methodology ensures that the dataset is accurate, consistent, and enriched with valuable regional and institutional details. The final dataset is intended to serve as a reliable resource for analyzing French HEIs.

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

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.

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.

1. Pending
2. 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]
3. The European Higher Education Sector Observatory, Dec 2024. Available: ETER
4. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2013-2016
5. NUTS - Nomenclature of territorial units for statistics, Dec 2024. Available: NUTS-2021.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2020-2021 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.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.

In the 2021/22 academic year, private nonprofit bachelor's higher education institutions in the United States spent an average of 33,650 U.S. dollars per full-time equivalent (FTE) student on education and related expenses. This compares to 29,710 U.S. dollars for the 2011/12 academic year, when adjusted to 2021 dollars.

Snapshot img

The online higher education market share in the US is expected to increase by USD 33.35 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 19.82%.

This online higher education market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers online higher education market in the US segmentation by subjects (commerce and management, STEM, Arts, and others) and courses (non-degree courses and degree courses). The online higher education market in the US report also offers information on several market vendors, including American Public Education Inc., Adtalem Global Education Inc., Apollo Education Group Inc., Graham Holdings Co., Grand Canyon Education Inc., ITT Educational Services Inc., LINCOLN EDUCATIONAL SERVICES Corp., Perdoceo Education Corp., Strategic Education Inc., and Zovio Inc. among others.

What will the Online Higher Education Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Higher Education Market Size in the US for the Forecast Period and Other Important Statistics

Online Higher Education Market in the US: Key Drivers, Trends, and Challenges

The collaborations between enterprises and educational institutions is notably driving the online higher education market growth in the US, although factors such as designing e-learning courses may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online higher education industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Online Higher Education Market Driver in the US

The collaborations between enterprises and educational institutions is one of the key drivers supporting the online higher education market growth in the US. Although such collaborations can take numerous forms, the most common are training, partnerships, or R&D partnerships. For instance, IBM-Charlotte was designed by the University of North Carolina-Charlotte (UNC-Charlotte) and IBM with the aim of enhancing the university’s technical vitality, expanding its local personnel resource bank, and ultimately offering IBM's technical communicators a way of earning degrees in their field of work. For UNC-Charlotte, the relationship with IBM gave its developing Technical Communication program community support and visibility and simultaneously provided faculty with research opportunities and practical experience at the workplace. Such collaborations are driving the growth of the online higher education market in the US during the forecast period.

Key Online Higher Education Market Trend in the US

Increased use of wearable gadgets is another factor supporting the online higher education market growth in the US. Technologies such as augmented reality (AR) are also changing the learning experience of participants. 3D simulations and scenarios that are developed using wearable technology devices give users a chance to learn in different learning environments. Moreover, the theoretical explanation of various concepts and step-by-step training on operations in an organization, followed by familiarizing students with on-the-floor working environments, are time-consuming. Therefore, wearable technology devices can help universities or educational institutions to engage with students directly on the floor. This reduces the duration and makes students more comfortable with online learning. Thus, the affordable prices of wearable gadgets will foster their greater adoption, in turn fostering the growth of the online higher education market in the US

Key Online Higher Education Market Challenge in the US

Designing e-learning courses is one of the factors hindering the online higher education market growth in the US. A significant amount of time, money, and resources are needed for developing the content for online courses. On average, moderately interactive online content takes about 90-240 hours to develop and costs developers approximately $10,000 per produced hour for moderate-level content. Similarly, the cost keeps rising as the complexity of the content increases. The major factors impacting the cost incurred on creating online education content are the resources needed, the state of the source content, the elements embedded in the online content, and the interactivity and instructional complexities involved. Therefore, this is a challenging factor for the growth of the online higher education market in the US.

This online higher education market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market

United States Number of Researchers: Total data was reported at 1,889,780.000 Person in 2021. This records an increase from the previous number of 1,823,522.000 Person for 2020. United States Number of Researchers: Total data is updated yearly, averaging 1,856,651.000 Person from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 1,889,780.000 Person in 2021 and a record low of 1,823,522.000 Person in 2020. United States Number of Researchers: Total 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: Number of Researchers and Personnel 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.

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

In 2021, the state of California spent ***** billion U.S. dollars on higher education via state and local governments. In that same year, Texas' state and local governments spent ** billion U.S. dollars on higher education.

This dataset contains data on the number of students participating in designated Early College programs since school year 2020-21. Early College is a program that designates partnerships between high schools and colleges to support high school students to complete college courses. The list of designated partnerships is available here.

Students are counted in this dataset if they are marked as an Early College student by the district. The district also submits each student's affiliation with a single institution of higher education (IHE), though some Early College students take credits at more than one IHE. The period column allows you to filter for Fall or Spring, or to see the full-year deduplicated count of participants.

The dataset is updated after each semester, when the SIMS collection for that semester is certified.

The data here are the same as the participation data in the Early College Dashboard.

Data note: For the Fall 2021 collection, only 2 digits of the college code were stored, so college names could not be loaded. The incomplete 2-digit codes are shown in this dataset, but the college name field is blank for that collection.

This dataset provides information about Massachusetts public high school graduates enrolling into institutions of higher education by student group since 2004. It includes the count and percentage of students enrolled in any college or university, as well as a breakdown of enrollment in private vs. public, two-year vs. four-year, and Massachusetts vs. out-of-state institutions. It also includes the percentage of students enrolled in a Massachusetts community college, a Massachusetts state university, or the University of Massachusetts system.

The data provided in the report are based on point-in-time matching of graduates with higher education enrollment data from the National Student Clearinghouse (NSC). For more information about working with NSC data, including coverage and FERPA implications, please visit their Working with Our Data page.

Results are not reported for higher education enrollments of fewer than 15. Prior to the 2015 high school graduating class, the data refers to students attending college within 16 months of graduating high school. From 2015 on, the data is also provided by high school graduates attending college by the March following their high school graduation year. The percentages in the report are available by college attendee or high school graduate.

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset contains the same data that is also published on our DESE Profiles site: Graduates Attending Higher Ed

Graph and download economic data for Expenditures: Food Prepared on Out-of-Town Trips by Highest Education: Less Than College Graduate: Less Than High School Graduate (CXU190904LB1403M) from 2012 to 2021 about no college, secondary schooling, secondary, education, expenditures, food, and USA.

This dataset is accessed from https://www.kaggle.com/jessemostipak/college-tuition-diversity-and-pay and was downloaded on August 4, 2021. The following excerpt is from Kaggle regarding the sources of this dataset: The data this week comes from many different sources but originally came from the US Department of Education. Tuition and fees by college/university for 2018-2019, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Diversity by college/university for 2014, along with school type, degree length, state, in-state vs out-of-state from the Chronicle of Higher Education. Example diversity graphics from Priceonomics. Average net cost by income bracket from TuitionTracker.org. Example price trend and graduation rates from TuitionTracker.org Salary potential data comes from payscale.com. This dataset included the following files: 1. diversity_school.csv 2. historical_tuition.csv 3. salary_potential.csv 4. tuition_cost.csv 5. tuition_income.csv After data cleaning, the data in diversity_school.csv and tuition_cost.csv were merged and the data in salary_potential.csv and tuition_income.csv were merged. The combined datasets were then split based on the US Census Regions into West, Midwest, Northeast and South (https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf).

Graph and download economic data for Consumer Unit Characteristics: Number of Children Under 18 by Highest Education: Less Than College Graduate: Associate's Degree (CXU980050LB1406M) from 2012 to 2021 about no college, associate degree, consumer unit, child, education, and USA.

United States US: GERD Financed: Higher Education and PNP Sectors data was reported at 4.980 % in 2022. This records a decrease from the previous number of 5.339 % for 2021. United States US: GERD Financed: Higher Education and PNP Sectors data is updated yearly, averaging 4.998 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 7.119 % in 2015 and a record low of 2.784 % in 1981. United States US: GERD Financed: Higher Education and PNP Sectors 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.

In 2020, there were 3,931 higher education institutions across the United States. This was a decrease from 2015, when there were 4,583 higher education institutions across the nation.

Higher education in the U.S.

Higher education in the United States refers to colleges and universities in the country. The U.S. has some notable distinctions in regards to higher education when compared to the rest of the world, including NCAA sports, Greek life, and high attendance costs. However, a large majority of the world’s best universities are located in the United States. Some of these universities include the eight Ivy League schools, Massachusetts Institute of Technology, and Stanford University.

Higher education costs

The cost of university in the United States has increased significantly over the last few decades. As a result of these high tuition costs, it has caused students to take out exorbitantly high student loans. Both federal and state governments have decreased the amount of funding towards public schools, but mandatory outlays for higher education are expected to increase over the next several years. In 2021, California had the highest amount of higher education expenditures by state and local governments. California also has the most higher education institutions in the country.

The purpose of College and Beyond II (CBII) is to better understand how students' undergraduate experiences in and out of the classroom relate to their long-run development. The CBII Alumni Survey provides data collected from graduates ten years after earning their bachelor's degrees. It serves as the main source of information on students' long-run development for the study series. Domains covered by the CBII Alumni Survey include arts appreciation and engagement; health and well-being; labor market participation and wealth; civic engagement; beliefs about diversity and pluralism; and democratic participation. The survey also asks about respondents' recollections of their college experiences, as well as their current family structure, social identities, educational attainment, experiences with discrimination, and political and religious identification. The survey contains scales that are widely used in various disciplines such as education, economics, political science, and psychology. The CBII Alumni Survey data can be linked to other studies in the CBII series using the ID_PERSON anonymized student identifier. These CBII studies provide information about respondents' academic backgrounds, undergraduate course transcripts, descriptions of courses taken, and postsecondary enrollment and degree attainment. Contextual data about respondents' neighborhoods can be linked to the National Neighborhood Data Archive using current zip codes, and contextual data about the colleges respondents applied to can be linked to the Integrated Postsecondary Education Data System using U.S. Department of Education UNITID identifiers.