NCES Blog

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.

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.

June is LGBTQ+ Pride Month, and NCES is proud to share some of the work we have undertaken to collect data on the characteristics and well-being of sexual and gender minority (SGM) people. Inclusion of questions about sexual orientation and gender identity on federal surveys allows for a better understanding of SGM people relative to the general population. These questions generate data to inform the development of resources and interventions to better serve the SGM community. Giving respondents the opportunity to describe themselves and bring their “whole self” to a questionnaire also helps them to be more fully seen and heard by researchers and policymakers.

Sometimes, we get asked why questions like this appear on education surveys. They can be sensitive questions for some people, after all. We ask these questions so we can better understand educational equity and outcomes for SGM people, just as we do for other demographic groups, such as those defined by race, ethnicity, household income, and region of the country. Just as is the case for other demographic groups, it is possible that SGM people have unique experiences compared with students and educators from other demographic groups.

Over the past 10 years, NCES has researched how to best ask respondents about their sexual orientation and gender identity, how respondents react to these questions, and what the quality of the data is that NCES has collected in questionnaires and datasets that include sexual orientation and gender identity information.

Several NCES studies include background questions for adults about their sexual orientation and gender identity, including the High School Longitudinal Study of 2009 (HSLS:09) Second Follow-up in 2016, the Baccalaureate and Beyond Longitudinal Study (B&B) 08/18 and 16/21 collections, the National Postsecondary Student Aid Study (NPSAS) in 2020, the Beginning Postsecondary Students Longitudinal Study (BPS) 20/22 and 20/25 collections, and the 2023–24 National Teacher and Principal Survey. In addition, the School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS), conducted by the Bureau of Justice Statistics and sponsored by NCES, asks students several questions pertinent to SGM experiences. For example, the SCS asks students whether they were bullied due to their gender or sexual orientation and whether they experienced hate speech related to their gender or sexual orientation. As participants in the NCVS, students ages 16 and older who respond to the SCS also report their gender identity and sexual orientation. Collectively, these data allow NCES to describe the experiences of students who identify as sexual and gender minorities.

• As of 2021, 2009 ninth-graders who were bisexual and questioning left postsecondary education without degrees or credentials at higher rates than other groups of students who were in ninth grade in 2009, and they earned bachelor’s or higher degrees at lower rates than other students.¹
   
• In 2020, some 9 percent of students who identified as genderqueer, gender nonconforming, or a different identity had difficulty finding safe and stable housing, which is the three times the rate of students who identified as male or female (3 percent each).²
   
• In 2018, about 10 years after completing a 2007–08 bachelor’s degree, graduates who were gender minorities³ described their financial situations. Graduates who were gender minorities were less likely to own a home (31 percent) or hold a retirement account (74 percent) than graduates who were not gender minorities (63 percent and 87 percent, respectively).⁴
   
• Among 2008 bachelor’s degree graduates with a full-time job in 2018, those who were straight people reported higher average salaries than those who were either lesbian/gay or bisexual.    
   
• In the 2017–18 school year, 18 percent of public schools had a recognized student group that promoted the acceptance of students’ sexual orientation and gender identity, such as a Gay-Straight Alliance (GSA). This was an increase from the 2015–16 school year, in which 12 percent of schools reported having a GSA.^5|
   
• Among all students ages 12–18 in grades 6–12 who reported being bullied (19 percent), the percentage who reported being bullied due to their sexual orientation more than doubled from 2017 (4 percent) to 2022 (9 percent).⁶ That change was primarily driven by female students, for whom the percentage tripled from 2017 to 2022 (from 4 to 13 percent), while the percentage of bullied males who reported being bullied for their sexual orientation was not statistically significantly different across the period (3 percent in 2017 and 4 percent in 2022).

Figure 1. Among students ages 12–18 enrolled in grades 6–12 who reported being bullied, percentage who reported that they thought the bullying was related to their sexual orientation: 2017, 2019, and 2022

! Standard error for this estimate is 30 to 50 percent of the estimate’s value.

* Statistically significantly different (p < .05) from 2022. 

As we highlighted in our March blog post, NCES is excited to be in the field for the base-year data collection of our newest national early childhood study, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024). Although the study collects much needed data from a variety of adult respondents (i.e., parents/guardians, teachers, and school administrators), the heart of the ECLS-K:2024 is our data collection with the participating children.

With the permission of their parent/guardian, the children take part in the ECLS-K:2024 child session activities, answering engaging, age-appropriate reading and math questions during one-on-one sessions with trained ECLS-K:2024 team members (watch an example of children participating in the child activities). These ECLS-K:2024 child sessions are planned for every currently expected round of data collection, starting with the fall and spring of the current school year (2023–24) when the children are in kindergarten.

Although the child sessions look pretty similar every year, there are some changes to the activities as the children age. For example, in kindergarten and first grade, we include memory-related items in the sessions; we then swap out these items for child surveys in the later rounds, when children are in higher grades. In prior ECLS kindergarten cohort studies, child surveys included items on topics such as children’s sense of school belonging; worry or stress about school; media usage; and peer relationships. Explore the items we asked in the child surveys in the ECLS-K:2024’s sister studies, the ECLS-K and ECLS-K:2011. Many of these items will likely be asked again of the children participating in ECLS-K:2024. Also, in past studies, children had their height and weight measured to provide information about early physical development; this study component returns to the ECLS-K:2024’s spring data collection in some schools.

Child-provided data from the earlier ECLS program studies have been used extensively. A recent analysis conducted by the ECLS program team found that more than 1,000 studies and reports published between 2000 and 2021 have analyzed the ECLS academic skills and school performance data, with more than 80 percent of those utilizing the child assessment data. For example, NCES published a report on reading achievement over children’s early elementary school years using the ECLS-K reading assessment data. Use NCES’s Bibliography Search Tool to explore these reports (select “ECLS” from the Data Source drop-down menu).

If you’re instead interested in exploring trend data, research has been conducted on the differences between children who were in kindergarten during the 1998–99 and 2010–11 school years (use the Bibliography Search Tool to find reports on this topic). Additional research comparing kindergartners in the 1998–99 and 2010–11 school years with kindergartners in the 2023–24 school year is expected after this year’s ECLS-K:2024 data collection. Once the ECLS-K:2024 collection concludes, NCES will produce data files—made available to the public with deidentified data—that will allow for direct comparisons between these three groups of children. Our understanding of how the abilities of today’s kindergartners vary from those of kindergartners in the late 1990s and early 2010s relies on the participation of children in the ECLS-K:2024.  

Of course, it’s not just the children’s reading and mathematics data that will provide answers to key questions about education and child development. All the data the ECLS-K:2024 children are providing now in the study’s base year—as well as the data they will provide as they advance through their elementary years—will help inform our understanding of what today’s children know and can do.

On behalf of the thousands of researchers, policymakers, educators, and parents who rely on the important data provided by the ECLS-K:2024’s youngest contributors—thank you to our ECLS-K:2024 children!

Want to learn more?

• Check out this brief video about the ECLS program.
• Read up on the ECLS-K:2024 on NCES’s ECLS website or our ECLS website for study participants.
• Find resources for parents, teachers, and school administrators that use ECLS and other NCES data in our ECLS newsletters.
• Find other ECLS posts on the NCES Blog.
• Sign up for the NCES Newsflash.
• Follow NCES on LinkedIn, X, and Facebook.

Next up in this blog series celebrating the ECLS-K:2024, we’ll highlight the study’s parents and families. Keep an eye out this summer!

By Jill McCarroll and Korrie Johnson, NCES