NCES Blog

The National Center for Education Statistics (NCES) offers a variety of tools to support hurricane planning, response, and recovery efforts. These resources provide crucial data on educational institutions and infrastructure, helping decision-makers during this critical time. Below is an overview of the key NCES resources available to assist with hurricane response activities.

Key NCES Resources

Interactive Web Maps and APIs

Figure 1: School Weather Watch - Hurricane Milton

NCES provides interactive maps with detailed information on educational institutions across the United States, including the School Weather Watch - Hurricane Milton.

These maps are accessible via application programming interface (APIs), allowing users to easily integrate these data into their own applications. Available resources identify:

• Private School Locations
• Public School Locations
• Public School District Boundaries
• Postsecondary Institution Locations

Public and Private School Search Tools

• The Elementary/Secondary Information System (ELSI): A web application that enables users to view data and create reports on public and private schools across various metrics.
• Private School Search Tool: Search for private schools by state county, or ZIP Code to access detailed information, including addresses, enrollment numbers, and other key data.
• Public School Search Tool: Find public schools by state, county, or ZIP Code to access detailed information, including addresses, enrollment numbers, and other key data.

Postsecondary and Public School District Lookup Tools

School district boundaries and postsecondary institutions are critical for planning resource allocation and understanding affected regions.

• College Navigator: Search for postsecondary institutions by location, programs offered, and other characteristics.
• Public School District Lookup: Explore district boundaries and access information about schools within those districts.

School District Demographic Information

NCES also provides demographic data for school districts, derived from the American Community Survey. This information helps users understand the populations served by each district and is available through the NCES's School District Demographic Dashboard. Additionally, these data can be accessed through NCES’s ACS-ED Maps.

How These Resources Can Assist

By offering comprehensive, real-time access to school system data, NCES helps emergency planners, local authorities, and relief organizations make informed decisions. Whether assessing potential school closures or identifying facilities for emergency shelters, these tools ensure that educational considerations are integrated into broader response and recovery efforts.

Additional Resources

• U.S. Department of Education's Natural Disaster Resources
• Federal Emergency Management Agency (FEMA)
• Ready.gov
• NCES's Education Demographic and Geographic Estimates (EDGE) program

By Josue DeLaRosa and Douglas Geverdt, NCES

Note: This post was updated October 12th, 2024, with current link to the School Weather Watch resource.

Recently, the Early Childhood Longitudinal Study, Kindergarten Class of 2023–24 (ECLS-K:2024) wrapped up our first school year of data collection! Among the data collected was information provided by the study children’s parents. Without parents’ participation in the ECLS-K:2024, we wouldn’t have a detailed understanding of America’s children, their families, and their lives outside of school. Parents’ participation allows us to explore how different factors—at home and at school—relate to children’s development and learning over time.  

For ECLS-K:2024, much of the information we are collecting from parents has been collected from kindergartners’ parents in earlier ECLS program studies. Having data about different group of kindergartners across time will allow NCES and researchers to examine changes over the past decades. One thing that’s new for the ECLS-K:2024 are questions on the impact of the COVID-19 pandemic, such as how the pandemic affected parents and their family, including the study child. This information will help the public and state, local, and federal policymakers understand how to better support this generation of children.

For past ECLS kindergarten cohorts, we have collected a wealth of demographic data about participating children’s families that can be analyzed in conjunction with other data collected directly from the children themselves and their schools and teachers to shed light on influences on children’s school experiences and development. For instance, The Condition of Education’s “Characteristics of Children’s Families” uses data from the ECLS program studies along with other National Center for Education Statistics (NCES) study data to help us understand trends in children’s family composition over the years, such as the percentage of children living with a single male parent, with a single female parent, and in two-parent households. The ECLS-K:2024 data being collected now will allow us to add to these analyses, for example by examining data on same sex parent households and multigenerational households.   

Information on many parental and family characteristics is provided by parents in the ECLS-K:2024. The ECLS-K:2024 data extends our view, at the national level, of children’s family backgrounds and how they have changed over the years on a variety of dimensions beyond type of parent or parents in household. For example, from an analysis of the ECLS-K:2011 data, we know that 84 percent of first-time kindergartners in 2010-11 came from a household with English as the primary home language, 15 percent from a household with a language other than English as the primary home language, and 1 percent from a household with multiple home languages (no primary language identified). Has this pattern in family home language of our nation’s kindergartners changed since 2010-11? The ECLS-K:2024 data parents provided last school year will let us know.

The ECLS study team, as well as thousands of researchers, policymakers, educators, and parents, are excited to see what the ECLS-K:2024 parent-provided data tell us about today’s kindergartners and families, as well as any changes we see for today’s kindergartners as compared to those from 1998-99 and 2010-11.  Thank you to our ECLS-K:2024 parents for contributing to the study and helping us learn more about America’s children and families!

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.

Plus, be on the lookout early this fall for the next ECLS blog post celebrating the ECLS-K:2024, which will highlight schools, teachers, and principals. Stay tuned!

By Korrie Johnson and Jill Carlivati McCarroll, NCES

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.