IES Blog

In 2018, NCER funded a research network to build the evidence base for career and technical education (CTE). As with other research networks, the CTE Research Network comprises a set of research teams and a lead team, which is responsible for collaboration and dissemination among the research teams. On March 20, 2024, the Network held a final convening to present its findings to the field. In this blog, Network Lead Member Tara Smith, Network Director Kathy Hughes, and NCER Program Officer Corinne Alfeld reflect on the success of the final convening and share thoughts about future directions.

Insights From the Convening

An audience of CTE Research Network members, joined by educators, administrators, policymakers and other CTE stakeholders, gathered for a one-day convening to hear about the Network’s findings. Several aspects of the meeting contributed to its significance.

• The presentations highlighted an important focus of the Network – making research accessible to and useable for practitioners. The agenda included presentations from four Network member researchers and their district or state partners from New York City and North Carolina. Each presentation highlighted positive impacts of CTE participation, but more importantly, they demonstrated the value of translating research findings into action. Translation involves collaboration between researchers and education agency staff to develop joint research questions and discuss the implications of findings for improving programs to better serve students or to take an innovative practice and scale it to other pathways and schools.
• Brand-new and much-anticipated research was released at the convening. The Network lead announced a systematic review of all of the rigorous causal research on secondary-level CTE from the last 20 years. This is an exciting advancement for building the evidence base for CTE, which was the purpose of the Network. The meta-analysis found that CTE has statistically significant positive impacts on several high school outcomes, such as academic achievement, high school completion, employability skills, and college readiness. The review found no statistically significant negative impacts of CTE participation. The evidence points to the power of CTE to transform lives, although more research is needed. To guide future research, the review provided a “gap analysis” of where causal research is lacking, such as any impacts of high school CTE participation on academic achievement in college or attainment of a postsecondary degree.
• National CTE leaders and experts put the research findings into a policy context and broadcasted its importance. These speakers commented on the value of research for CTE advocacy on Capitol Hill, in states, and in informing decisions about how to target resources. Luke Rhine, the deputy assistant secretary of the Office of Career, Technical, and Adult Education (OCTAE) said, “The best policy is informed by practice [...] and the best practice is informed by research.” Kate Kreamer, the executive director of Advance CTE, emphasized the importance of research in dispelling myths, saying that “if the data are not there, that allows people to fill the gaps with their assumptions.” However, she noted, as research increasingly shows the effectiveness of CTE, we must also guard against CTE programs becoming selective, and thus limiting equitable access.

New Directions

In addition to filling the critical gaps identified by the Network lead’s review, other future research questions suggested by researchers, practitioners, and policymakers at the convening include:

• How can we factor in the varied contexts of CTE programs and the wide range of experiences of CTE students to understand which components of CTE really matter?  What does it look like when those are done well?  What does it take to do them well? Where is it happening?
• How can we learn more about why students decide to participate in CTE generally and in their chosen pathway? What are the key components of useful supports that schools can provide to help them make these decisions?
• How do we engage employers more deeply and actively in CTE programs and implement high quality work-based learning to ensure that students are acquiring skills and credentials that are valued in the labor market?
• What are evidence-based practices for supporting special student populations, such as students with disabilities, or English language learners?
• How can we harness state longitudinal data systems that link education and employment data to examine the long-term labor market outcomes of individuals from various backgrounds who participated in different career clusters or who had access to multiple CTE experiences?

While IES alone will not be able to fund all the needed research, state agencies, school districts, and even individual CTE programs can partner with researchers to study what works in their context and identify where more innovation and investment is needed. The work of the CTE Research Network has provided a good evidence base with which to start, and a good model for additional research that improves practice and policy. Fortunately, the CTE research field will continue to grow via the support of a new CTE Research Network – stay tuned for more information!

This blog was co-written by CTE Network Lead Member Tara Smith of Job for the Future, CTE Network Director Kathy Hughes of AIR, and NCER Program Officer Corinne Alfeld.

Questions can be addressed to Corinne.Alfeld@ed.gov.

The National Center for Education Statistics (NCES) has released an updated data table (Excel) on local education agencies (LEAs)¹  that serve multiple counties. This new data table—which was updated with 2022–23 data—can help researchers examine LEA structures and break down enrollment by LEA and county. Read this blog post to learn more about the table and how it can be used to understand structural differences in school districts.

The data table—which compiles data from both the Common Core of Data (CCD) and Demographic and Geographic Estimates (EDGE)—provides county and student enrollment information on each LEA in the United States (i.e., in the 50 states and the District of Columbia) with a separate row for each county in which the agency has a school presence. The table includes all LEA types, such as regular school districts, independent charter school districts, supervisory union administrative centers, service agencies, state agencies, federal agencies, specialized public school districts, and other types of agencies.

LEA presence within a county is determined by whether it had at least one operating school in the county. School presence within a county is determined by whether there is at least one operating school in the county identified in the CCD school-level membership file. For example, an LEA that is coterminous with a county has one record (row) in the listing. A charter school LEA that serves a region of a state and has a presence in five counties has five records. LEA administrative units, which do not operate schools, are listed in the county in which the agency is located.

In the 2022–23_LEA_List tab, column D shows the “multicnty” (i.e., multicounty) variable. LEAs are assigned one of the following codes:

1 = School district (LEA) is in single county and has reported enrollment.

2 = School district (LEA) is in more than one county and has reported enrollment.

8 = School district (LEA) reports no schools and no enrollment, and the county reflects county location of the administrative unit. 

9 = School district (LEA) reports schools but no enrollment, and the county reflects county location of the schools.

In the Values tab, the “Distribution of local education agencies, by enrollment and school status: 2022–23” table shows the frequency of each of the codes (1, 2, 8, and 9) (i.e., the number of districts that are marked with each of the codes in the 2022–23_LEA_List tab):

• 17,042 LEAs had schools in only one county.
• 754 LEAs had schools located in more than one county and reported enrollment for these schools (note that in the file there are 1,936 records with this characteristic since each LEA is listed once for every county in which it has a presence).
• 1,008 LEAs had no schools of their own and were assigned to a single county based on the location of the LEA address. (Typically, supervisory union administrative centers are examples of these LEAs.)
• 262 LEAs had schools located in one county but did not report enrollment for these schools (note that in the file there are 384 records with this characteristic since each LEA is listed once for every county in which it has a presence).

This data table is part of our effort to meet emerging data user needs and provide new products in a timely manner. Be sure to follow NCES on X, Facebook, LinkedIn, and YouTube and subscribe to the NCES News Flash to stay informed when these new products are released.

By Tom Snyder, AIR

[1] Find the official definition of an LEA.

[2] See Number and enrollment of public elementary and secondary schools, by school level, type, and charter, magnet, and virtual status: Selected years, 1990–91 through 2018–19; Enrollment of public elementary and secondary schools, by school level, type, and charter, magnet, and virtual status: School years 2010–11 through 2021–22 (ed.gov); Number of public elementary and secondary education agencies, by type of agency and state or jurisdiction: 2004–05 and 2005–06; and Number of public elementary and secondary education agencies, by type of agency and state or jurisdiction: School years 2020–21 and 2021–22.

[3] See Education Governance for the Twenty-First Century: Overcoming Structural Barriers to School Reform.

[4] The annual School District Finance Survey (F-33) is collected by NCES from state education agencies and the District of Columbia. See Documentation for the NCES Common Core of Data School District Finance Survey (F-33) for more information.

In 2011, IES took a first step towards supporting what was then a burgeoning open science movement—publication and data sharing requirements for awardees. This growing movement found its first government-wide footing in 2013 with the release of a memo from the White House Office of Science and Technology Policy (OSTP) that provided guidance on the need for federally funded researchers to share publications and develop plans for sharing data.

Since that time, infrastructure and informational support for open science practices have continued to grow across federal funding agencies, and adherence to open science principles has evolved with them. In August 2022, OSTP released a new memo providing updated guidance on open science practices. The memo focused on equity, increasing public access to and discoverability of research, and establishing new data and metadata standards for shared materials.

In this blog post, Dr. Laura Namy, associate commissioner of the Teaching and Learning Division at NCER, and Erin Pollard, project officer for the Education Resources Information Center (ERIC) at NCEE, describe IES’s new Public Access Plan and address some important changes in requirements resulting from the new White House guidance for researchers receiving federal funding.

IES, in collaboration and consultation with other funding agencies, has been developing and implementing new policies and guidance to extend our commitment to open science principles. These new policies serve to support broader access among researchers, educators, and policymakers, as well as the general public whose tax dollars subsidize federally-funded research. The resulting changes will certainly require some adjustments and some learning, and IES will be offering guidance and support as these requirements are implemented.   

IES’s commitment to open science practices is already reflected in our Standards for Excellence in Education Research (SEER principles) and other expectations for awardees. These include—

• Pre-registering studies
• Uploading full text of published articles to ERIC
• Submitting (and adhering to) a data management plan
• Sharing published data
• Including the cost of article processing charges (APCs) in project budgets to support publishing open access (OA)

The new policies reflect dual priorities: increasing both immediacy and equity of access. For current grant and contract awards, the requirements in place at the time that awards were made will still apply for the duration of those current awards. For each future award, Requests for Applications/Proposals (RFAs and RFPs), Grant Award Notices (GANs), and contracts will indicate the relevant public access/sharing requirements to identify which requirements are in place for the specific award.

Below are some important changes and what they mean for our IES-funded research community.

All publications stemming from federally funded work will have a zero-day public access embargo.

This means that an open access version must be available in ERIC immediately upon publication for all articles proceeding from federal research funding. The current 12-month grace-period before articles become fully available will be gone. Although we’ve seen this change coming, publishers of journals that are not already open access will need to adapt to this new normal, as will universities and many researchers who do not already routinely publish OA. 

What does this mean for IES-funded researchers? 

IES-funded researchers are already required to upload the full text of all articles to ERIC immediately after acceptance. Until now, ERIC released the full text within 12 months of publication. However, for all NEW grants awarded in fiscal year 2025 (as of Oct 1, 2024) and beyond, this zero-day public access embargo requirement will be in effect. Note that the relevant public access requirement depends on the year that the award was made, not the publication date of the article (for example, articles published in 2025 and beyond based on data collected through grants awarded before 2025 will still be under the 12-month embargo). IES awardees will need to ensure (either through your publisher or your own efforts) that a full-text version of the accepted manuscript or published article is uploaded to ERIC for release as soon as it is available online. To facilitate the transition, we encourage all awardees to publish their work in OA journals where feasible, and to budget for APCs accordingly. IES will provide additional guidance to support researchers in complying with this new requirement.

Data sharing will be required at time of publication, or if unpublished, after a certain time interval, whichever comes first.

This means that data curation and identification of an appropriate data repository will need to occur in advance of publication so that data can be shared immediately after publication rather than as a follow-along activity after publication occurs. Although funding agencies will vary in their sharing timelines, IES anticipates requiring data to be shared at time of publication or (for unpublished work) no later than 5 years after award termination. 

What does this mean for IES-funded researchers? 

All awardees who publish findings based on data collected under a new award made in fiscal year 2025 and beyond will need to release the reported data into a data repository at the time of publication. This calls for a change in data curation practices for many researchers who have focused on preparing their data for sharing post-publication. As noted above, any data that remain unshared 5 years post-award will need to be shared, even if publications are still pending. One best practice approach is to set up the data filesharing templates and curation plans in anticipation of sharing prior to data collection so that data are ready for sharing by the time data collection is complete (see Sharing Study Data: A Guide for Education Researchers). When multiple publications stem from the same data set, we recommend planning to share a single master data set to which additional data may be added as publications are released. Researchers should budget for data curation in their applications to support this activity.

Applications for IES funding have shifted from including a data management plan (DMP) to a data sharing and management plan (DSMP) to foreground the shift in emphasis to routine data sharing. Specific plans for sharing data, documentation, and analytic codes in particular repositories will need to be included. In anticipation of new requirements, we encourage researchers to move away from hosting data sets on personal websites or making them available solely upon request. DSMPs should identify an appropriate publicly available data repository. There is now guidance on Desirable Characteristics of Data Repositories for Federally Funded Research that should be followed whenever feasible. IES will be providing additional guidance on repository selection in the coming year. Principal investigators (PIs) and Co-PIs must be in compliance with data sharing requirements from previous IES awards in order to receive new awards from IES.

Unique digital persistent identifiers (PIDs) will need to be established for all key personnel, publications, awards, and data sets.

Digital object identifiers (DOIs) for journal articles are PIDs that uniquely identify a single version of a single publication and can be used to identify and reference that specific publication. This same concept is now being extended to other aspects of the research enterprise including individual researchers, grant and contract awards, and data sets. Unique PIDs for individuals facilitate tracking of individual scholars across name changes, institution changes, and career-stage changes. Having universal conventions across federal funding agencies for individuals, awards, and data sets in addition to publications will not only facilitate discoverability but will help to link data sets to publications, investigators to grants, grants to publications, etc. This will help both researchers and funders to connect the dots among the different components of your important research activities.

What does this mean for IES-funded researchers? 

All key personnel on new IES-funded projects are now required to establish an individual digital PID (such as ORCID) prior to award. DOIs will continue to be the PID assigned by publishers for publications. Authors reporting on IES-funded data should be vigilant about acknowledging their IES funding in all publications stemming from their IES grant awards. Coming soon, IES-funded researchers should be prepared for new digital PIDs (in addition to the IES-specific award numbers) associated with their grants to ensure consistency of PID conventions across funding agencies. New guidance for PID conventions for awards and data linked to IES-funding is forthcoming. 

The Bottom Line

These changes constitute an important step forward in increasing equitable access to and transparency about IES-funded research activities, and other federal funding agencies are making similar changes. The immediate changes at IES (establishing an individual PID and preparing a DSMP) are not onerous, and the bigger changes still to come (immediate sharing of publications and supporting data, using PIDs to refer to awards and data sets) will be rolled out with guidance and support. 

Please don’t hesitate to reach out to us with questions or concerns at Laura.Namy@ed.gov or Erin.Pollard@ed.gov. Or to learn more, please view the presentation and discussion of Open Science at IES that took place at the 2023 PI Meeting.

The National Center for Education Statistics recognizes the need to provide expanded economic data on education, including data about the education labor market. In this blog we will be presenting public education sector data from the February 2024 release of the Job Openings and Labor Turnover Survey (JOLTS) data produced by the Bureau of Labor Statistics. JOLTS data provide national monthly estimates of job openings, hires, and separations. These data can be used to monitor current labor market demand in education and to assess the presence or extent of labor shortages¹.

JOLTS Design

JOLTS is a monthly survey of about 21,000 public and private employers across all nonagricultural industries in the 50 States and District of Columbia. JOLTS estimates are produced by industry sector, including education². Additionally, JOLTS provides separate estimates for public and private education. This enables our analysis to focus on the public state and local education industry (“state and local government education” as referred to by JOLTS)³, which includes all persons employed by public elementary and secondary school systems and public postsecondary institutions.

The JOLTS program does not produce estimates by Standard Occupational Classification⁴. When reviewing these findings, please note occupations⁵ within the public state and local education industry vary⁶ (e.g., teachers and instructional aides, administrators, cafeteria workers, and transportation workers).

Analysis

This analysis of JOLTS data highlights key statistics describing employment availability, hiring, and turnover in public local and state education. Table 1 includes estimates on the number of job openings, hires, and separations from February 2020 through February 2024. Table 2 includes estimates on the corresponding rates of job openings, hires, separations, fill and churn rate measures from February 2020 through February 2024. The job openings rate is computed by dividing the number of job openings by the sum of employment and job openings. Metric rates for hires, total separations, quits, layoffs and discharges, and other separations are defined by taking the number of each metric and dividing it by employment. Fill rate and churn rates are calculated economic measures that are not readily available from the JOLTS database. Fill rate is defined as the ratio of the number of hires to the number of job openings and the churn rate is defined as the sum of the rate of hires and the rate of total separations^7,8.

Table 1. Number of job openings, hires, separations, and net change in employment in public state and local education, in thousands: February 2020 through February 2024

*Significantly different from February 2024 (p < .05).

NOTE: Data are not seasonally adjusted. Detail may not sum to totals because of rounding.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS), 2020–2024, based on data downloaded April 2, 2024, from https://data.bls.gov/cgi-bin/dsrv?jt.

Table 2. Rate of job openings, hires, and separations in public state and local education and fill and churn rates: February 2020 through February 2024

*Significantly different from February 2024 (p < .05).

NOTE: Data are not seasonally adjusted. Detail may not sum to totals because of rounding.

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS), 2020–2024, based on data downloaded April 2, 2024, from https://data.bls.gov/cgi-bin/dsrv?jt.

Overview of February 2024 Estimates

The number of job openings in public state and local education was 226,000 on the last business day of February 2024, which was higher than in February 2021 (170,000) and lower than in February 2022 (322,000) and February 2023 (314,000) (Table 1). In percentage rate terms, 2.0 percent of jobs had openings in February 2024, which was lower than in February of the previous two years (3.0 percent in 2022 and 2.8 percent in 2023) (Table 2). The number and percentage of job openings in February 2024 were not measurably different from the number and percentage in February 2020. The number of hires in public state and local education was 107,000 for February 2024, which was not measurably different from in February of the previous three years, but was lower than February 2020 (141,000) (Table 1). The number of job openings at the end of February 2024 (226,000) was nearly double the number of staff hired that month (107,000). In addition, the fill rate for that month (0.5) was less than 1, which suggests a need for public state and local government education employees that was not being filled completely by February 2024.

The number of total separations in the state and local government education industry in February 2024 (80,000) was not measurably different from in February of the previous four years. In February 2024, the number of quits (56,000) was higher than the number of layoffs and discharges (17,000). Layoffs and discharges accounted for 21 percent of total separations in February 2024 (which as not measurably different from the percentage of layoffs and discharges out of total separations in February 2023, 2022, 2021, or 2020) while quits accounted for 70 percent of total separations (which was not measurably different from the percentage of quits out of total separations in February 2023, 2022, 2021, or 2010).

This blog is part of NCES’ effort to share more economic data from other federal statistical agencies that is relevant to education. We plan to provide regular updates to selected months from JOLTS to enable our data users to find and follow useful information about the education workforce.

By Josue DeLaRosa, NCES

¹ “Job Openings and Labor Turnover Survey Overview Page.” BLS.gov.  Last modified November 28, 2022. https://www.bls.gov/jlt/jltover.htm

In Education Across America, we explore the condition of education across four main geographic locales: cities, suburbs, towns, and rural areas. In this blog post, we use select findings from Education Across America to focus on the experiences of elementary and secondary school students in distant and remote rural areas (find the definitions of these locales and sublocales).

This blog post provides a snapshot of these students’ experiences and includes data—which were collected at various points during the 2019–20 school year—on family characteristics, characteristics of student populations, characteristics of schools, school choice, coursetaking, and educational outcomes.

Family Characteristics

The percentage of children ages 5 to 17 who were living in poverty in remote rural areas was higher than the national average. Similarly, a higher percentage of students in remote rural areas lived in homes without internet access compared with all other sublocales.

• In 2019, the percentage of related children¹ ages 5 to 17 who were living in poverty was 21 percent in remote rural areas, which was higher than the national average of 16 percent.
• In 2019, among the 43 states for which data were available, the percentages of children in remote rural areas living in poverty ranged from 6 percent in Vermont to 42 percent in Arizona. The states with the highest percentages of children in poverty in remote rural areas were concentrated in the West (e.g., Arizona, New Mexico) and the South (e.g., South Carolina, Georgia).
• In 2019, the percentage of students who lived in homes without internet access or with access only through dial-up was higher in remote rural areas (11 percent) than in all other sublocales (ranging from 3 percent in large suburban areas to 9 percent in distant rural areas).
• In 2019, the percentage of students who had fixed broadband internet access² was lower in remote rural areas (69 percent) than in in all other sublocales except distant rural areas (ranging from 77 percent in remote towns to 88 percent in large suburban areas).

Explore more data on Children in Rural Areas and Their Family Characteristics and Rural Students’ Access to the Internet.

Characteristics of Student Populations

Public schools in remote and distant rural areas had smaller populations of Black, Hispanic, and English learner students compared with those in other sublocales. However, public schools in remote rural areas had a larger populations of students with disabilities.

• In fall 2019, the percentage of public school students who were Black was lower in remote (6 percent) and distant (7 percent) rural areas than in all other sublocales (ranging from 7 percent in fringe towns to 24 percent each in large and midsize cities).³
• In fall 2019, the percentage of public school students who were Hispanic was lower in distant and remote rural areas (each 10 percent) than in all other locales (ranging from 19 percent in fringe rural areas to 43 percent in large cities).
• In fall 2019, the percentage of public school students identified as English learners (EL) was lower for school districts in distant and remote rural areas (3 and 4 percent, respectively) than for school districts in all other sublocales (ranging from 5 percent in fringe rural areas to 17 percent in large cities).
• In fall 2019, the percentage of public school students who were students with disabilities was higher for school districts in remote rural areas (16 percent) than for districts in all other sublocales, which ranged from 13 percent in midsized cities to 15 percent each in fringe and distant rural areas, all three town sublocales, and midsized suburban areas.

Explore more data on Children in Rural Areas and Their Family Characteristics and English Learners and Students with Disabilities in Rural Public Schools.

Characteristics of Schools

When compared with public schools in other sublocales, public schools in distant and remote rural areas had smaller school enrollment sizes and lower ratios of students to staff and teachers—meaning the average staff member or teacher was responsible for fewer students.

• In fall 2019, a lower percentage of public schools were located in remote rural areas than in other types of rural areas. Six percent of all public schools were located in remote rural areas, 10 percent were located in distant rural areas, and 11 percent were located in fringe rural areas. In comparison, 26 percent were located in large suburban areas and 15 percent were located in large cities.

• In fall 2019, average public school enrollment sizes in distant rural areas (285 students) and remote rural areas (165 students) were smaller than those of all other sublocales (ranging from 402 students in schools in remote towns to 671 students in schools in large suburban areas).
• In fall 2019, the average public school pupil/teacher ratios and pupil/staff ratios in distant rural areas and remote rural areas were lower than the ratios in all other sublocales.
  • For example, the average pupil/teacher ratios in distant rural areas (14.0) and remote rural areas (12.5) were lower than the ratios in all other sublocales (ranging from 15.4 to 16.9).

Explore more data on Enrollment and School Choice in Rural Areas and Staff in Rural Public Elementary and Secondary School Systems

School Choice

Enrollment in both charter schools and private schools was lower in remote rural areas than in larger towns and cities, reflecting limited access to alternative educational institutions in remote rural areas.

• In fall 2019, the percentage of public school students enrolled in charter schools was lower in remote rural areas (2 percent) than in all other sublocales, which ranged from 2 percent each in distant towns and distant rural areas to 17 percent in large cities.⁴
• In fall 2019, the percentage of students enrolled in private schools was lower in remote rural areas (3 percent) than in the other sublocales, which ranged from 5 percent in distant rural areas and fringe towns to 14 percent in large cities.

Explore more data on Enrollment and School Choice in Rural Areas.

High School Coursetaking

Compared with those from cities, a lower percentage of public and private high school graduates from remote rural areas had taken advanced math but a higher percentage had taken career and technical education (CTE) courses.

• In 2019, the percentage of graduates in remote rural areas who had earned any advanced mathematics credits was lower than the percentage in large cities (85 vs. 93 percent).
• In 2019, the percentage of graduates who had completed any CTE course was higher in remote rural areas (97 percent) than in most other sublocales (ranging from 75 percent in large cities to 92 percent in fringe towns).⁵
• In 2019, a higher percentage of graduates in remote rural areas than in most other sublocales had taken courses in the following six CTE subject areas: agriculture, food, and natural resources; architecture and construction; human services; information technology; manufacturing; and transportation, distribution, and logistics.
  • For example, 47 percent of graduates in remote rural areas had taken a course in agriculture, food, and natural resources, while this percentage ranged from 3 percent for graduates in large cities to 24 percent in distant towns.
• Conversely, the percentage of graduates who had taken a course in engineering and technology was lower for those in remote rural areas (5 percent) than for those in most other sublocales (ranging from 12 to 16 percent).

Explore more data on College Preparatory Coursework in Rural High Schools and Career and Technical Education Programs in Rural High Schools.

Educational Outcomes

Public high school graduation rates were higher in remote rural areas than in cities. Despite this relatively high graduation rate, the percentage of adults age 25 and over with at least a bachelor's degree in remote rural areas was lower than in all other sublocales.  

• In 2019–20, the adjusted cohort graduation rate (ACGR) in remote rural areas (88 percent) was higher than the ACGRs in cities (ranging from 79 percent in large cities to 86 percent in small cities) and in remote towns (85 percent) but lower than the ACGRs in large and midsized suburban areas (89 percent each) and in fringe and distant rural areas (91 and 90 percent, respectively).
• In 2019, the percentage of adults age 25 and over who had not completed high school in remote rural areas (13 percent) was higher than the percentages in 8 of the 11 other sublocales, not including large cities, distant towns, and remote towns.
• In 2019, the percentage of adults age 25 and over who had earned a bachelor’s or higher degree in remote rural areas (19 percent) was lower than the percentages in all other sublocales, which were as high as 38 percent in large cities and large suburban areas.

Explore more data on Public High School Graduation Rates in Rural Areas and Educational Attainment in Rural Areas.

Check out the Education Across America hub and the indicators linked throughout this blog post to learn more about how the landscape of education varies by locale/sublocale. Be sure to follow NCES on X, Facebook, LinkedIn, and YouTube and subscribe to the NCES NewsFlash to stay informed when new locale-focused resources are released.