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National Center for Education Statistics

Public State and Local Education Job Openings, Hires, and Separations for February 2024

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 Classification4When reviewing these findings, please note occupationswithin 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 separations7,8.

 

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

Employment activity

February 2020

February 2021

February 2022

February 2023

February 2024

Job openings

259

170*

322*

314*

226

Hires

141*

95

135

129

107

Total separations

74

77

106

81

80

   Quits

49

54

75*

54

56

   Layoffs and discharges

16

12

18

18

17

   Other separations

9

12

13

9

7

Net change in employment

67*

18

29

48

27

*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

Employment activity

February 2020

February 2021

February 2022

February 2023

February 2024

Job openings

2.3

1.6*

3.0*

2.8*

2.0

Hires

1.3*

0.9

1.3*

1.2

1.0

Total separations

0.7

0.8

1.0*

0.7

0.7

   Quits

0.4

0.5

0.7*

0.5

0.5

   Layoffs and discharges

0.1

0.1

0.2

0.2

0.2

   Other separations

0.1

0.1

0.1

0.1

0.1

Fill Rate

0.5

0.6

0.4

0.4

0.5

Churn Rate

2.0

1.7

2.3*

1.9

1.7

*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

2  For more information about these estimates, please see https://www.bls.gov/news.release/jolts.tn.htm.

3 JOLTS refers to this industry as state and local government education, which is designated as ID 92.

4 For more information on the reliability of JOLTS estimates, please see https://www.bls.gov/jlt/jltreliability.htm.

5 North American Industry Classification System (NAICS) is a system for classifying establishments (individual business locations) by type of economic activity. The Standard Occupational Classification (SOC) classifies all occupations for which work is performed for pay or profit. To learn more on the differences between NAICS and SOC, please see https://www.census.gov/topics/employment/industry-occupation/about/faq.html.

6 JOLTS data are establishment-based and there is no distinction between occupations within an industry. If a teacher and a school nurse were hired by an establishment coded as state and local government education, both would fall under that industry. (Email communication from JOLTS staff, April 7, 2023)

7 Skopovi, S., Calhoun, P., and Akinyooye, L. “Job Openings and Labor Turnover Trends for States in 2020.” Beyond the Numbers: Employment & Unemployment, 10(14). Retrieved on March 28, 2023, from https://www.bls.gov/opub/btn/volume-10/jolts-2020-state-estimates.htm.

8 Standard error estimates for fill rates, churn rates, and net employment were calculated using error propagation. The formulas used in deriving the standard errors for these estimates can be found in Taylor, J.R. (2022) “Propagation of Uncertainties,” in An introduction to error analysis: The study of uncertainties in physical measurements. New York: University Science Books, pp. 45–91.

Education Across America: Exploring the Education Landscape in Distant and Remote Rural Areas

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 XFacebookLinkedIn, and YouTube and subscribe to the NCES NewsFlash to stay informed when new locale-focused resources are released.

 

[1] Related children include all children who live in a household and are related to the householder by birth, marriage, or adoption (except a child who is the spouse of the householder). The householder is the person (or one of the people) who owns or rents (maintains) the housing unit.

[2] Excludes mobile broadband, but includes all other non-dial-up internet services, such as DSL, cable modem, and fiber-optic cable.

[3] Although both round to 7 percent, the unrounded percentage of students who were Black in fringe towns was higher than the unrounded percentage of students who were Black in distant rural areas (6.9 vs. 6.8 percent).

[4] In fall 2019, the percentage of students in remote rural areas who were enrolled in public charter schools was 1.6 percent, compared with 1.9 percent for students in distant towns and 2.0 percent for students in distant rural areas.

[5] Ninety percent of graduates in distant towns, 93 percent in remote towns, and 95 percent in distant rural areas had taken at least one CTE course. These percentages were omitted from the discussion because they were not measurably different from the percentage for remote rural areas.