National Center for Education Statistics

Leveraging Economic Data to Understand the Education Workforce

The Digest of Education Statistics recently debuted 13 new tables on K–12 employment and wages from a data source that is new to the Digest—the Occupational Employment Wage Statistics (OEWS) program of the Bureau of Labor Statistics (BLS). NCES’s Annual Reports and Information Staff conducted an extensive review of existing and emerging data sources and found that BLS’s OEWS program provides high-quality, detailed, and timely data that are suitable to inform policymaking in education and workforce development.1 In this blog post, we share why we added this new data source, how we evaluated and prepared these data, and our future plans to expand on these efforts.


Need for Education Workforce Data

NCES recognized that education stakeholders need more granular and timely data on the condition of the education workforce to inform decisionmaking. In the wake of the coronavirus pandemic, school districts are looking to address critical staffing needs. According to NCES’s School Pulse Panel, entering the 2023–24 school year (SY), just under half of U.S. public schools reported feeling understaffed and had a need for special education teachers, transportation staff, and mental health professionals.

Since staffing needs and labor markets vary from district to district and state to state, it is important that we create national- and state-level tabulations for specific occupations, including those of special interest since the pandemic, like bus drivers, social workers, and special education teachers. Similarly, we want to be able to provide annual data updates so stakeholders can make the most up-to-date decisions possible.

Annual Digest table updates, coupled with detailed occupational and state-level data, will provide relevant and timely information on employment and wage trends that will be valuable in current and future efforts to address teacher and staff retention and recruitment. See below for a list of the new Digest tables.

  • National-level employment and annual wages
  • Selected teaching occupations (211.70)
  • Selected noninstructional occupations (213.70)
  • State-level employment and annual wages
  • Preschool teachers (211.70a)
  • Kindergarten teachers (211.70b)
  • Elementary school teachers (211.70c)
  • Middle school teachers (211.70d)
  • Secondary school teachers (211.70e)
  • Kindergarten and elementary special education teachers (211.70f)
  • Middle school special education teachers (211.70g)
  • Secondary school special education teachers (211.70h)
  • Substitute teachers (211.70i)
  • Teaching assistants (211.70j)
  • All occupations in the Elementary and Secondary Education industry (213.75)


Strengths of OEWS

OEWS and the Digest tables are aligned with the Federal Committee on Statistical Methodology’s Data Quality Framework, specifically the principles of objectivity (standardization), utility (granularity and timeliness), and integrity (data quality).


OEWS produces employment and wage estimates using standardized industry and occupational classifications. Using the North American Industry Classification System, establishments are grouped into categories—called industries—based on their primary business activities. Like industries, occupations are organized into groups or categories based on common job duties (using the Standard Occupational Classification). Occupations that are common to K–12 schools can also be found in other industries, and the OEWS provides both cross-industry estimates and industry-specific estimates for just Elementary and Secondary Education industry. To provide the most relevant and comparable data for education stakeholders, NCES chose to focus on distinct occupational estimates for the Elementary and Secondary Education industry, since all establishments (e.g., school boards, school districts) provide the same services: instruction or coursework for basic preparatory education (typically K–12).2     

Another advantage of the OEWS data is the ability to examine specific detailed occupations, like elementary school teachers, secondary school teachers, and education administrators. Digest tables include estimates for specific instructional and noninstructional occupations, which allows users to make comparisons among teachers and staff with similar job responsibilities, providing opportunities for more targeted decisionmaking.


In addition to data on detailed occupations, OEWS data provide data at national and state and levels, allowing for comparisons across geographies. National-level Digest tables include estimates for public and private education employers.3 Publicly funded charter schools run by private establishments are included in private ownership estimates, as they can be managed by parents, community groups, or private organizations. Public ownership is limited to establishments that are run by federal, state, or local governments. State-level Digest tables provide more localized information covering labor markets for the 50 states, the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands.

Timeliness and Data Quality

OEWS data are updated annually from a sample of about 1.1 million establishments’ data collected over a 3-year period. The OEWS sample is drawn from an administrative list of public and private companies and organizations that is estimated to cover about 95 percent of jobs.4 When employers respond to OEWS, they report from payroll data that are maintained as a part of regular business operations and typically do not require any additional collections or calculations. Payroll data reflect wages paid by employers for a job, which has a commonly accepted definition across employers or industries. This allows for more accurate comparisons of annual wages for a particular job. In contrast, when wages are self-reported by a respondent in person-level or household surveys, the reported data may be difficult to accurately code to a specific industry or detailed occupation, and there is greater chance of recall error by the respondent. Additionally, OEWS provides specialized respondent instructions for elementary and secondary schools and postsecondary institutions that accommodate the uniqueness of what educators do and how they are paid. These instructions enable precise coding of the occupations commonly found in these industries and a more precise and consistent reporting of wages of workers with a variety of schedules (e.g., school year vs. annual, part time vs. full time).   

OEWS uses strict quality control and confidentiality measures and strong sampling and estimation methodologies.5 BLS also partners with state workforce agencies to facilitate the collection, coding, and quality review of OEWS data. States’ highly trained staff contribute local knowledge, establish strong respondent relationships, and provide detailed coding expertise to further ensure the quality of the data. 

After assessing the strengths of the OEWS data, the Digest team focused on the comparability of the data over time to ensure that the data would be best suited for stakeholder needs and have the most utility. First, we checked for changes to the industrial and occupational classifications. Although there were no industrial changes, the occupational classifications of some staff occupations—like librarians, school bus drivers, and school psychologists—did change. In those cases, we only included comparable estimates in the tables.

Second, all new Digest tables include nonoverlapping data years to account for the 3-year collection period. While users cannot compare wages in 2020 with 2021 and 2022, they can explore data from 2016, 2019, and 2022. Third, the Digest tables present estimates for earlier data years to ensure the same estimation method was used to produce estimates over time.6 Finally, we did not identify any geographical, scope, reference period, or wage estimation methodology changes that would impact the information presented in tables. These checks ensured we presented the most reliable and accurate data comparisons.


Next Steps  

The use of OEWS data in the Digest is a first step in harnessing the strength of BLS data to provide more relevant and timely data, leading to a more comprehensive understanding of the education workforce. NCES is investigating ways we can partner with BLS to further expand these granular and timely economic data, meeting a National Academies of Science, Engineering, and Medicine recommendation to collaborate with other federal agencies and incorporate data from new sources to provide policy-relevant information. We plan to explore the relationship between BLS data and NCES data, such as the Common Core of Data, and increase opportunities for more detailed workforce analyses.

NCES is committed to exploring new data sources that can fill important knowledge gaps and expand the breadth of quality information available to education stakeholders. As we integrate new data sources and develop new tabulations, we will be transparent about our evaluation processes and the advantages and limitations of sources. We will provide specific examples of how information can be used to support evidence-based policymaking. Additionally, NCES will continue to investigate new data sources that inform economic issues related to education. For example, we plan to explore Post-Secondary Employment Outcomes to better understand education-to-employment pathways. We are investigating sources for building and land use data to assess the condition and utilization of school facilities. We are also looking for opportunities to integrate diverse data sources to expand to new areas of the education landscape and to support timelier and more locally informed decisionmaking.

How will you use the new Digest tables? Do you have suggestions for new data sources? Let us know at


By Josue DeLaRosa, Kristi Donaldson, and Marie Marcum, NCES

[1] See these frequently asked questions for a description of current uses, including economic development planning and to project future labor market needs.

[2] Although most of the K–12 instructional occupations are in the Elementary and Secondary Education industry, both instructional and noninstructional occupations can be found in others (e.g., Colleges, Universities, and Professional Schools; Child Care Services). See Educational Instruction and Library Occupations for more details. For example, preschool teachers differ from some of the other occupations presented in the Digest tables, where most of the employment is in the Child Care Services industry. Preschool teachers included in Digest tables reflect the employment and average annual wage of those who are employed in the Elementary and Secondary Education industry, not all preschool teachers.

[3] Note that estimates do not consider differences that might exist between public and private employers, such as age and experience of workers, work schedules, or cost of living.

[4] This includes a database of businesses reporting to state unemployment insurance (UI) programs. For more information, see Quarterly Census of Employment and Wages.

[5] See Occupational Employment and Wage Statistics for more details on specific methods.

[6] Research estimates are used for years prior to 2021, and Digest tables will not present estimates prior to 2015, the first year of revised research estimates. See OEWS Research Estimates by State and Industry for more information.

Knock, Knock! Who’s There? Understanding Who’s Counted in IPEDS

The Integrated Postsecondary Education Data System (IPEDS) is a comprehensive federal data source that collects information on key features of higher education in the United States, including characteristics of postsecondary institutions, college student enrollment and academic outcomes, and institutions’ employees and finances, among other topics.

The National Center for Education Statistics (NCES) has created a new resource page, Student Cohorts and Subgroups in IPEDS, that provides data reporters and users an overview of how IPEDS collects information related to postsecondary students and staff. This blog post highlights key takeaways from the resource page.

IPEDS survey components collect counts of key student and staff subgroups of interest to the higher education community.

Data users—including researchers, policy analysts, and prospective college students—may be interested in particular demographic groups within U.S. higher education. IPEDS captures data on a range of student and staff subgroups, including race/ethnicity, gender, age categories, Federal Pell Grant recipient status, transfer-in status, and part-time enrollment status.

The Outcome Measures (OM) survey component stands out as an example of how IPEDS collects student subgroups that are of interest to the higher education community. Within this survey component, all entering degree/certificate-seeking undergraduates are divided into one of eight subgroups by entering status (i.e., first-time or non-first-time), attendance status (i.e., full-time or part-time), and Pell Grant recipient status.

Although IPEDS is not a student-level data system, many of its survey components collect counts of students and staff by subgroup.

Many IPEDS survey components—such as Admissions, Fall Enrollment, and Human Resources—collect data as counts of individuals (i.e., students or staff) by subgroup (e.g., race/ethnicity, gender) (exhibit 1). Other IPEDS survey components—such as Graduation Rates, Graduation Rates 200%, and Outcome Measures—also include selected student subgroups but monitor cohorts of entering degree/certificate-seeking students over time to document their long-term completion and enrollment outcomes. A cohort is a specific group of students established for tracking purposes. The cohort year is based on the year that a cohort of students begins attending college.

Exhibit 1. IPEDS survey components that collect counts of individuals by subgroup

Table showing IPEDS survey components that collect counts of individuals by subgroup; column one shows the unit of information (student counts vs. staff counts); column two shows the survey component

IPEDS collects student and staff counts by combinations of interacting subgroups.

For survey components that collect student or staff counts, individuals are often reported in disaggregated demographic groups, which allows for more detailed understanding of specific subpopulations. For example, the Fall Enrollment (EF) and 12-month Enrollment (E12) survey components collect total undergraduate enrollment counts disaggregated by all possible combinations of students’ full- or part-time status, gender, degree/certificate-seeking status, and race/ethnicity. Exhibit 2 provides an excerpt of the EF survey component’s primary data collection screen (Part A), in which data reporters provide counts of students who fall within each demographic group indicated by the blank cells.

Exhibit 2. Excerpt of IPEDS Fall Enrollment (EF) survey component data collection screen for full-time undergraduate men: 2022­–23

[click image to enlarge]

Image of IPEDS Fall Enrollment survey component data collection screen for full-time undergraduate men in 2022–23

NOTE: This exhibit reflects the primary data collection screen (Part A) for the 2022–23 Fall Enrollment (EF) survey component for full-time undergraduate men. This screen is duplicated three more times for undergraduate students, once each for part-time men, full-time women, and part-time women. For survey materials for all 12 IPEDS survey components, including complete data collection forms and detailed reporting instructions, visit the IPEDS Survey Materials website.

As IPEDS does not collect data at the individual student level, these combinations of interacting subgroups are the smallest unit of information available in IPEDS. However, data users may wish to aggregate these smaller subgroups to arrive at larger groups that reflect broader populations of interest.

For example, using the information presented in exhibit 2, a data user could sum all the values highlighted in the green column to arrive at the total enrollment count of full-time, first-time men. As another example, a data user could sum all the values highlighted in the blue row to determine the total enrollment count of full-time Hispanic/Latino men. Note, however, that many IPEDS data products provide precalculated aggregated values (e.g., total undergraduate enrollment), but data are collected at these smaller units of information (i.e., disaggregated subgroup categories).

Student enrollment counts and cohorts align across IPEDS survey components.

There are several instances when student enrollment or cohort counts reported in one survey component should match or very closely mirror those same counts reported in another survey component. For example, the number of first-time degree/certificate-seeking undergraduate students in a particular fall term should be consistently reported in the Admissions (ADM) and Fall Enrollment (EF) survey components within the same data collection year (see letter A in exhibit 3).

Exhibit 3. Alignment of enrollment counts and cohorts across IPEDS survey components

Infographic showing the alignment of enrollment counts and cohorts across IPEDS survey components

For a full explanation of the alignment of student counts and cohorts across IPEDS survey components (letters A to H in exhibit 3), visit the Student Cohorts and Subgroups in IPEDS resource page.

Be sure to follow NCES on TwitterFacebookLinkedIn, and YouTube, follow IPEDS on Twitter, and subscribe to the NCES News Flash to stay up-to-date on IPEDS data releases and resources.


By Katie Hyland and Roman Ruiz, AIR