Leveraging Quarterly Workforce Indicators to Analyze K–12 Education Labor Market Dynamics: Inequitable Trends in Turnover

Abstract

K–12 education labor markets vary considerably across the country and can change quickly during recessions. We use data from the Quarterly Workforce Indicators (QWI) on staff and teachers in elementary and secondary schools from 2000–01 to 2023–24. We demonstrate how to transform the quarter-level data in the QWI to construct valid education labor market measures. The strengths of the QWI address the limitations of other sources of labor market data, including (1) nonstandardized definitions, (2) sampling that is useful for describing local and regional trends, (3) contemporaneous availability, and (4) lack of data for subgroups. We demonstrate how the QWI addresses each of these data gaps and describe how education labor markets vary across time, region, and demographic characteristics. Finally, we show how the QWI can be used by education leaders or policymakers to examine how policy influences K–12 education labor markets.

Keywords

faculty careers retention teacher characteristics descriptive analysis education context teacher context labor market

Recessions and pandemics have unpredictable effects on K–12 education labor markets, and the lack of up-to-date data prevents policymakers from making targeted policy decisions. For instance, the Great Recession, which began in December 2007, was triggered by a bubble in the housing market, leading to a sharp decline in property tax revenues, a critical source of school funds (Bowling et al., 2019; Kenyon & Reschovsky, 2014). The stimulus package that followed was broad and did not provide additional support to areas where housing values declined the most (Kabaker, 2012). Similarly, in response to the sudden changes induced by the COVID-19 pandemic, stimulus funds (i.e., Elementary and Secondary School Emergency Relief Fund) were provided to schools, but little to no effort was made to provide targeted support to communities that were hit hardest by the pandemic (Roza & Roza, 2022). One of the key factors limiting how policymakers can respond to recessions is the lack of available K–12 education labor market data. These data are often years out of date and most often describe national instead of local trends (Bleiberg & Kraft, 2023). These two main limitations severely reduce policymakers’ capacity at the local, state, and federal levels to make targeted decisions to help schools and communities that are most affected by adverse events. To address this issue, we propose a method to transform a new source of K–12 education labor market data from the Quarterly Workforce Indicators (QWI) created by the Census Bureau to ameliorate the information gap faced by policymakers.

While national and state-level data sources exist, they have important limitations. None of the commonly used datasets combines (1) standardized definitions, (2) local estimates, (3) contemporaneous availability, and (4) cross-tabulations by educator characteristics. These issues severely limit policymakers’ ability to respond to unpredictable events that necessitate rapid and targeted decisions. First, definitions of labor market conditions vary widely (NCTQ, 2021). For instance, there are no common definitions of teacher shortages at the state and national level since there are multiple ways to conceptualize shortages (i.e., vacancy, under-qualification, lack of high-quality applicants; see Nguyen et al. (2024)). Additionally, the cancelation of National Center for Education and Statistics (NCES) contracts threaten the availability of the most commonly used education labor market data (Klein, 2025).

Second, while state and national trends have value for broad policymaking, local estimates are much more useful for targeted policymaking, particularly to provide support for the most adversely affected communities. Edwards et al. (2024) show that teacher shortages are highly localized, with substantial variation between schools and subjects within districts. Additionally, data sources from various levels of governance (e.g., district, state) may highlight alternative solutions to teacher staffing problems (Engel & Cannata, 2015; Engel et al., 2014; Goldhaber et al., 2014).

Third, almost all available data on K–12 education labor markets are at least a few years out of date, when they exist at all. Almost a decade passed between the publication of teacher turnover data from the 2011–12 Schools and Staffing Survey (SASS) and the 2020–21 National Teacher and Principal Survey (NTPS). Because the NTPS is conducted every few years, the next wave of national turnover data will not be published for at least a few more years and will be years out of date. Given that the Department of Education has canceled or delayed contracts and severely reduced the staff at the National Center for Education Statistics (NCES), it is unclear how and when the next wave of NTPS will be available (Mehta & Turner, 2025; Mervosh & Bender, 2025). Moreover, national teacher shortage data, including vacancies and under-qualification, are not systematically collected by the federal government but are instead collected by researchers (Nguyen et al., 2024). Labor market data for nonteacher roles do not exist or are collected and outdated (White, 2023). Similarly, state-level data are not consistently available (Bleiberg & Kraft, 2023), and only a few states provide annual data for teachers (e.g., Texas). The cancelation of NCES contracts will, at a minimum, delay future administrations of the NTPS and potentially leave the QWI as the sole source of national K–12 education labor market data. Additionally, restricted data licenses, which are required for the use of the NTPS, have been canceled (Levine, 2025).

Fourth, K–12 education labor market data are typically not available by educator characteristics such as race/ethnicity, gender, and educational attainment. Prior work has shown that educator characteristics have implications for equity and student learning (Bettini et al., 2025; Redding, 2022), and yet, state-level data are rarely disaggregated by educator characteristics. For example, even as the number of racially minoritized students with disabilities grows from year to year, the number of racially minoritized teachers remains well below the numbers needed to adequately serve students of color (Bettini et al., 2025). Nguyen show how improbable it would be to produce enough racially minoritized special educators to serve racially minoritized students with disabilities. More specifically, they show that achieving a more equitable educator workforce would require 80% of new teachers to be Black or Brown and for those teachers to remain in classrooms for at least 5 years. Greater numbers of racially minoritized teachers will not only provide more equitable representation for the educational experience of students of color but will also improve students’ classroom experience and academic outcomes. Students of color taught by teachers sharing their race/ethnicity report more positive feelings about the future, a stronger work ethic, and an environment that is more conducive to learning (Egalite & Kisida, 2018; Rasheed et al., 2020). Moreover, the academic outcomes of both students of color and White students benefit from instruction by racially minoritized teachers (Gershenson et al., 2021; Redding, 2019). In short, a more culturally diverse teacher workforce would benefit all students. Therefore, it is critical to obtain more data on the labor market for teachers of color.

The QWI labor market data are also valuable for journalists, community members, parents, and activists. For instance, education journalists can use the QWI data to broadly describe trends and variations in the K–12 education labor market at the county, state, or national level and provide empirical data to substantiate stories about staffing shortages or turnover. Similarly, for parents and activists, the QWI data can be used to understand the stability of their children’s schooling and provide evidence to advocate for better pay and working conditions in the face of high attrition. In particular, attrition and job loss are vital indicators for understanding the quality of educational institutions. Teacher attrition is correlated with lower student achievement and instructional quality (Hanushek et al., 2016; Henry & Redding, 2020; Ronfeldt et al., 2013; Sorensen & Ladd, 2020). Vacancies caused by job loss are likely to be correlated with lower student achievement (Papay & Kraft, 2016). The extent of the harm caused by attrition and job loss varies widely across school districts (Edwards et al., 2024). Our analysis provides a roadmap for how a diverse group of stakeholders can use the QWI to better understand the implications of the labor market for school quality. For instance, district and state leaders, as well as researchers, can use the QWI to compare local and regional education labor markets. The QWI is uniquely useful for advocates and community organizations seeking to elucidate labor market inequities. We also show how education practitioners and leaders can use the QWI to investigate potential disruptions to labor markets caused by education reforms. Specifically, we illustrate how the QWI can be used to examine how state takeover and the four-day school week are associated with changes in K–12 education labor market conditions. Access to recent education labor market data is invaluable for a wide variety of stakeholders and particularly for policy decisions that have implications for equity.

In summary, all recent studies have consistently shown substantial gaps in data systems for the education labor market. Our proposed approach using quarterly QWI data addresses all of these gaps by producing yearly K–12 education labor market measures that are consistent, measured at the county and state levels, available within 12 months of the previous school year, and can be disaggregated by educator characteristics. In this work, we ask two main questions:

1) How can quarter-level QWI data be used to create valid school-year education labor market measures?

2) How do education labor markets vary across time, locale, and educator characteristics?

As a brief preview of our work and results, we first describe in detail how the QWI data are constructed and how they can be used in various ways. Then we show that our proposed measures of education labor markets are strongly correlated with available state data on turnover (R = .89) and shortages (R = .84). We illustrate how the QWI can be used to explore trends across time, by county, and by educator characteristics. We show that postpandemic median turnover (2021–22 to 2023–24) is 2.7 percentage points higher than the prepandemic average (2012–13 to 2018–19) and that approximately 250,000 education positions were lost during the first year of the pandemic. Next, we demonstrate that the variation in education labor market measures between counties is at least 61 percent larger than the variation within counties. We then explore labor trends by race/ethnicity and find that median turnover for non-White educators is 9.6 percentage points higher than that for White educators. Finally, we document significant differences in the pandemic’s influence on education labor markets across states. Overall, this study contributes to the literature by demonstrating how up-to-date data can be used for policymaking.

Overview of Education Labor Market Data

In this section, we provide an overview of elementary and secondary education labor market data. We start with economics-related labor market sources that include education-related information and then move to education-specific labor market data. We also discuss data collected by researchers and nongovernmental agencies.

Economics-Related Data Sources

From the economics-related labor market data, the federal government produces the Current Population Survey (CPS), American Community Survey (ACS), Quarterly Census of Employment and Wages (QCEW), and Job Openings and Labor Turnover Survey (JOLTS). The CPS is one of the most widely used sources of labor market participation at the regional and national levels, drawing on a survey of 60,000 households each month (Census Bureau, 2024b). The CPS provides recent, comprehensive, and detailed data by occupation and individual characteristics. However, each monthly sample includes only a few hundred educators spread across every state, rendering the data unusable for nuanced analyses of K–12 education labor markets. The ACS provides detailed census information, such as demographic, economic, and social characteristics, but does not include labor market measures such as turnover or shortages (Census Bureau, 2024c).

The QCEW data provide quarterly counts of employees by industry and are available at the county level but have two important limitations. First, the QCEW does not take into account the number of people leaving specific organizations, which limits the type of information needed to comprehensively describe the K–12 education labor market. Second, it does not include demographic information. The JOLTS does provide education labor market data, but unfortunately, it is designed for a national overview and not for regional and local analyses. In particular, JOLTS data are available for the two-digit North American Industry Classification System (NAICS) codes rather than the more detailed four-digit codes that describe elementary and secondary education school employees.

Education-Specific Data Sources

Similarly, education-specific data have their own limitations and tradeoffs. We specifically discuss some of the most relevant data sources, including the SASS/NTPS, School Pulse Panel (SPP), and Civil Rights Data Collection (CRDC). The SASS has been conducted by the National Center for Education Statistics (NCES) every few years since 1988, and its new iteration, the NTPS, was launched in 2015. As noted previously, while the SASS/NTPS provides national data on teacher turnover, it is routinely years out of date. Moreover, it has very limited information on other aspects of the labor market (e.g., supply and demand, vacancies). Another limitation is that granular data from the SASS/NTPS are not readily available to policymakers because their use is restricted by stringent privacy protections.

The SPP collects data on the impact of the pandemic and includes monthly surveys. Some of the SPP surveys are repeated over time, and some are unique to a specific month. The intention was to provide contemporaneous national data that could be used in a rapid response to the pandemic. Currently, the SPP has data from January to December 2022 and from August 2023 to October 2024. The data describe broad ranges of vacancies and more detailed questions about vacancies for specific subjects. If funding for the SPP continues, it could provide valuable national data, including the number of vacancies.

The CRDC collects teacher labor market data from all U.S. schools. Vacancy and turnover data were available in 2013–14 but have not been available since then. This mitigates the CRDC’s value as a tool for studying teacher labor markets. Additionally, the CRDC is conducted every few years and therefore provides snapshots over time rather than a true longitudinal dataset. More importantly, because it takes time to collect CRDC survey data from every school, policymakers do not have access to the data when it is most useful. For example, school-level data from 2020–2021 are still unavailable in 2025. CRDC data were last collected in 2023–24, but it may be years before those data are publicly available.

In summary, education-specific data sources are incomplete, and more importantly, they are often outdated by a few years. Together, these factors limit the usefulness of education-specific data for policymakers to make informed and targeted policy decisions. To partially address this, researchers and organizations have collected data that can be used for policymaking.

Researchers and Nongovernmental Data Sources

RAND’s American Educator Panels (AEP) is an annual survey of more than 25,000 teachers, 8,000 principals, and 1,000 school-district leaders. The survey describes labor market data for approximately 20 states and the nation overall. The AEP survey includes data on how educators feel about teacher shortages but not the number of teachers who turn over or switch from one school to another or the number of vacant positions. The AEP is most suitable for describing educators’ perceptions of some aspects of the teacher labor market, and the results are most appropriate for a national sample (Doan et al., 2023; Grant et al., 2023; Hamilton et al., 2020).

Individual researchers also collect labor market data to provide timely results for policymaking. LPI (Franco & Patrick, 2023) and Nguyen et al. (2024) collected annual data on teacher vacancies and under-qualification at the state level (Franco & Patrick, 2023; Nguyen et al., 2024). Goldhaber et al. (2025) provided a novel way of examining educator shortages by collecting job posting information for Washington state. White (2023) collected annual data on superintendent turnover. While each of these sources provides unique data on the K-12 education labor market, they require intensive resources, and some cannot provide local estimates.

Data and Methods

We explore K–12 education labor markets using the QWI. The QWI is virtually unused in education labor market research. The QWI includes quarter-level counts of labor market conditions (e.g., employment, separations, hiring) at the county, metropolitan area, state, and national level (Census Bureau, 2016). The QWI includes North American Industry Classification System (NAICS) industries from 1990 Q1 to 2024 Q2, including elementary and secondary schools (i.e., 6111). This industry group includes primarily traditional public school districts. But it also includes parochial schools, private schools, charter schools, military academies, and schools for disabled students (Census Bureau, 2022b). In this analysis, we include public and private schools. It is possible to disentangle the data and results by public and private-school status. While we keep both public and private schools in our analysis, the results below are substantively similar to auxiliary analyses that include only public schools. We use changes in quarter-to-quarter counts of labor market measures to construct county-level school-year measures for turnover and net-negative job flow for educators.

The QWI includes workforce measure estimates by combining state unemployment insurance records with other federal data. More specifically, the QWI is created by linking employer and employee records from the Longitudinal Employer-Household Dynamics (LEHD) program, which includes data for about 95% of jobs in the United States (Census Bureau, 2022a). The QWI data describe the count of employees for a specific labor market condition, in a quarter (i.e., Q1 = January through March), and geographic unit. The data used to create the QWI are at the firm level or, in the context of this paper, at the school-district level. However, the QWI itself includes the sum of firm-level measures in a specified geographic area (e.g., state, county). For example, the QWI includes the count of employees (i.e., educators) who work at the same school district for two consecutive quarters by county and state. The QWI is updated about every 3 months, and the data are at most 9 months out of date. For instance, in March 2025, the most recent QWI data are from the second quarter of 2024. Similar to other federal datasets, the QWI data from prior years are revised as new information becomes available.¹ To create the QWI, the Census Bureau supplements unemployment insurance records with QCEW data on firms (e.g., industry, worksite locations), employee-level demographic data (e.g., race, ethnicity, gender, educational attainment) from Social Security administrative records, individual tax returns, and census data (e.g., ACS). A strength of QWI’s labor market measures is that they are created using employee pay data from tax returns. Consequently, teachers or other educators who do not work in the summer may appear to be incorrectly separated from their positions. Part of the contribution of this work is to demonstrate how to adjust labor market measures to account for erroneous summer “separations.”

QWI data are publicly available through the Census Application Programming Interface (API) (Census Bureau, 2016). State and national-level QWI data are available through a dashboard (Census Bureau, 2025). The county-level data employed in this analysis are available only through the API. The Census Bureau provides detailed instructions on how to access the API using a web browser (Census Bureau, 2016). The first step to accessing the QWI data is to request a Census API key. Second, write a query that requests the type of data (e.g., sex, race, ethnicity), economic indicator, geographic area (e.g., state, county), time period (i.e., business quarter, year), and the API key. The final step is to enter the query as a website address in a web browser.

Defining Educator Employee Occupations

Most elementary and secondary school employees are teachers. Appendix Figure A1 describes the proportion of employees by Census Occupation Classification using the CPS (Economic Policy Institute, 2024). A total of 76 percent of elementary and secondary education employees teach as a core function of their jobs (i.e., primary and secondary school teachers, special education teachers, prekindergarten teachers). We note that we prefer the use of the word educator in describing our work with the QWI data because we consider all positions contained in the QWI as having key roles in students’ education. This includes positions such as paraprofessionals, janitorial staff, and bus drivers. As such, it could be argued that school staff is a more appropriate term for describing the positions in the QWI. However, we contend that educator would still be preferable because the vast majority of jobs in the QWI are classroom teachers.

Analytic Sample

We explore education labor market conditions for a near census of counties from 2000 Q1 to 2024 Q2. Appendix Figure A2 describes the number of states and counties observed each year. Labor market measures are observed for at least 48 states and about 92.6% of counties from 2003 to 2024. The number of states observed in the QWI rose from three in 1990 to 36 in 1999. Due to the paucity of data before 1999, we use data from 2000 to 2024.²

Defining Turnover

To estimate educator turnover, we used the count of new hires and the total count of employees. The QWI defines newly hired employees as the “estimated number of workers who started a new job. More specifically, total hires that, while they worked for an employer in the specified quarter, were not employed by that employer in any of the previous four quarters” (Census Bureau, 2026).³ The QWI defines the count of employees in a specific reference quarter as the “count of people employed in a firm at any time during the quarter.” This is not a count of jobs. This measure may also be referred to as “flow” employment (Census Bureau, 2026).⁴ To estimate the number of leavers, we follow the logic of Reichardt et al. (2020), who demonstrated that the number of people leaving a school district equals the change in total employment subtracted from the number of hires. To estimate turnover, we divided the number of leavers by lagged employment.

t u r n o v e r_{c s t} = \frac{(h i r e s_{c s q 3 t - 1} + h i r e s_{c s q 4 t - 1} + h i r e s_{c s q 1 t} + h i r e s_{c s q 2 t}) - (e m p_{c s q 2 t} - e m p_{c s q 4 t - 1})}{e m p_{c s q 4 t - 1}}

(1)

Turnover is the proportion of educators who left their school district in county c, school year s, and calendar year t. We subtract the difference in employment for q2 (quarter 2) and calendar year t from q4 (quarter 4) in year t−1 from the sum of hires in q1 year t, q2 year t, q3 year t−1, and q4 year t−1. A strength of our approach is that it translates calendar year quarters into school years. More specifically, lagged quarters 3 and 4 correspond to the fall component of the school year, and quarters 1 and 2 correspond to the spring component. Subtracting the difference of employment for q2 (quarter 2) and calendar year t from q4 (quarter 4) in year t−1 allows us to capture late hires and educators who leave their positions during the school year. Using lagged Q4 employment creates a measure that parallels state data that are typically collected in October or November.

The QWI includes a turnover measure, which is not appropriate for examining K–12 education labor markets for the following three reasons. First, the “stable” measures are not useful for measuring education labor market trends that follow seasonal patterns and follow a school-year calendar. Second, the QWI’s stable employment measure varies considerably across quarters. More specifically, stable employment is the number of employees at firm j in quarters q−1, q, and q + 1. Observed stable employment levels declined by 6.1% from early spring (Q1) to late summer/fall (Q3). This is likely due to the number of seasonal educators who do not work in the summer. Finally, conventional education turnover measures for the current school year are created by dividing the number of leavers in the next year by the number of employees in that year. QWI’s stable measure of employment averages across the number of quarters in the current school year. Because the number of educators increases on average over time, using stable employment from the current year slightly increases the number of employees, which in turn increases estimated turnover.

Our measure of turnover addresses each of the issues with the QWI’s stable turnover measure. We used quarter-to-quarter counts and lag these measures to estimate turnover by dividing the number of leavers by employment in the prior school year. We find that our measure of turnover for educators has a stronger correlation with state labor market measures than the QWI measure of turnover. The correlation between state education staff turnover (i.e., Colorado, Maryland, and Pennsylvania) and our measure of turnover is .8925, which is significantly larger than the correlation between state education staff turnover and the stable QWI measure (i.e., QWI stable turnover Q1 .152, QWI stable turnover Q2 .576, QWI stable turnover Q3 .235, QWI stable turnover Q4 .527). An additional challenge in using the QWI measure of turnover is that the implied number of educators who leave their positions in any specific quarter is either too low or too high. For example, QWI Q2 turnover is correlated with comparable state measures but underestimates the total number of leavers because the number of education jobs declines in the summer.

Defining Net-Negative Job Flow

Net-negative job flow is the estimated number of jobs lost at firms throughout the quarter.

N N J F_{c q t} = a b s (e m p_{c q + 1} - e m p_{c q t})

(2)

We use the quarter-level counts of net-negative job flow to create a school-year measure.

N N J F_{c s t} = N N J F_{c s q 1 t} + N N J F_{c s q 2 t} + N N J F_{c s q 3 t - 1} + N N J F_{c q s 4 t - 1}

(3)

Net-negative job flow in county c, school year s, and calendar year t equals the sum of net-negative job flow in q1 year t, q2 year t, q3 year t−1, and q4 year t−1. Net-negative job flow is related to the same construct as an unfilled position. State definitions of vacancies are highly inconsistent (Nguyen et al., 2024). Broadly speaking, a shortage occurs when a position is vacant or unfilled. Vacancies are a likely consequence of a reduction in the total number of employees. For example, if a school district has 100 employees in Q3 and 90 employees in Q4, it is reasonable to infer that 10 positions remain unfilled or vacant. We consider net-negative job flow a useful proxy for recent increases in the number of unfilled educator positions.

We further explore turnover and net-negative job flow by educator race/ethnicity because of the shortage of teachers of color (Carver-Thomas, 2018; Castro, 2022; Gershenson et al., 2021). Additionally, we examine trends by educational attainment because it allows us to explore labor market outcomes for teachers.

Results

We begin our analysis by validating our proposed education labor market measures. We tested the criterion-related validity of our QWI labor market measures using similar state data. It is necessary to demonstrate the validity of our approach because of the differences between the QWI and the more commonly used state/federal measures. More specifically, quarter-to-quarter counts may not provide useful information about school-year education labor market trends. Although net-negative job flow is conceptually related to vacancies, it is important to test how strongly the constructs are related to each other.

We then report graphical and descriptive analyses for the distribution of turnover and net-negative job flow and how they vary across time, county, and educator characteristics. Finally, we explored recent changes during the pandemic. The focus of the analysis is to describe novel information about labor market trends and demonstrate how others can use the QWI. Analyses using proportions or rates (e.g., turnover, net-negative job flow per 100 employees) are weighted by the inverse of the number of educators (i.e., full-time equivalents). This approach upweights large counties when estimating national trends. Data on the number of educators were merged at the county by year level from the Common Core of Data (U.S. Department of Education, 2024). The goal of our analysis is to demonstrate how a broad set of stakeholders who are invested in educational systems can use the QWI. Therefore, we do not include regression analysis results in the main text of the paper. When we describe changes or differences that are “significant,” we are referring to statistical significance. More specifically, in the context of this analysis, significant means that the likelihood that a difference in labor market outcomes is zero is less than one in a hundred (p < .01). To estimate these differences, we used ordinary least squares and quantile regressions.

Validating Measures

Our QWI-based turnover measure is strongly correlated with state data. Figure 1 shows scatter plots of QWI and state education labor market measures. Panels A, B, and C describe the criterion-related validity of the components of turnover (i.e., leavers and employees) and turnover. Data on educator turnover, leavers, and employees were collected from Colorado, Maryland, and Pennsylvania for 2021–22 and 2022–23 (CDE, 2024; MSDE, 2024; PDE, 2024). These states were selected because labor market data were available for staff, administrators, and teachers. Data from Colorado, Maryland, and Pennsylvania are strongly correlated with the QWI measures of leavers (R = .85) and employees (R = .91). The correlation between state and QWI measures of turnover is attenuated but remains strong (R = .89; see Appendix Table A1). The validation exercise also serves as evidence of the value of the QWI. Education labor market data are most commonly available at the state level (Bleiberg & Kraft, 2023; Nguyen et al., 2024), but most states do not make local education labor market data readily available. The evidence of validity shows that the QWI can help fill this gap. Moreover, given that the local education labor market data are often at least a year out of date, the QWI data are more up-to-date and can help policymakers make better-informed decisions about how to respond to changes in the education labor market.

Figure 1.

Validating education labor market data.

The QWI and state measures serve as proxies for the same construct, but the measures have important differences. The average “error” obtained from subtracting QWI turnover from state turnover measures is −.11.⁵ More specifically, QWI turnover is about 11 percentage points higher than state measures. State and QWI turnover data differ for several reasons. First, QWI and state measures likely include different groups of employees. The QWI determines an employee’s industry based on the organization that provides their salary. The QWI education labor market measures will include employees who work for a business contracted to work with a school. State labor market measures will likely exclude employees who work for third-party organizations. This likely explains why QWI estimates of turnover and job loss are higher than state measures. Second, QWI data include noise to ensure confidentiality (Abowd et al., 2009). Third, the QWI data include nonpublic schools (e.g., charter, private) that are not included in the state data. Finally, districts in Pennsylvania and Colorado are typically, but not exclusively, located within a single county. The relationship between state and QWI measures is attenuated to the extent that school districts cross county boundaries. This likely explains why the evidence of criterion-related validity is stronger for Colorado and Pennsylvania than for Maryland. The correlation between QWI and state educator leavers is .9987 for Colorado, .9760 for Pennsylvania, and .9026 for Maryland (see Appendix Table A1).

Our QWI measure of net-negative job flow is strongly correlated with state vacancy rates. Panel D visualizes the criterion-related validity of net-negative job flow and educator vacancies. We compare QWI net-negative job-flow data to Virginia staff and teacher vacancy data from 2021–22 and 2022–23 (Virginia Department of Education, n.d.). We used data from Virginia because it is the only state that makes teacher and staff vacancy data available for each district. Additionally, Virginia school districts and counties have the same borders. QWI net-negative job flow and Virginia teacher/staff vacancy data are strongly associated (R = .84) (see Appendix Table A1). Net-negative job flow represents one dimension of educator vacancies. Net-negative job flow occurs when there is a decrease in the number of employees from one quarter to the next. If the number of employees increases over time, then net-negative job flow equals zero. However, it is still possible for the number of vacancies to increase if net job flow is positive and the number of employees increases. Overall, the evidence of criterion-related validity suggests that QWI measures are useful tools for describing vacancies.

National Education Labor Market Conditions

Turnover varies considerably across time and counties. Figure 2 includes histograms and box plots that visualize the distribution of education labor market measures by county from the 2000–01 school year to the 2023–24 school year. Figure 2, Panel A, displays the proportion of educators leaving their school districts by county and year. On average, one in four educators (median = 25.1; mean = 26.1) leaves their school district each year (see Appendix Table A2). The distribution of turnover has a heavy left tail, and in a small number of districts (1 percent), half or more of educators leave each year. The interquartile range for turnover was 10.1 (25th percentile = 20.4, 75th percentile = 30.5). Educator turnover varies more between counties than within them. The variation in turnover between counties (SD = .066) is 62.5 percent larger than the variation within counties (SD = .041). This pattern emphasizes that the education labor market differs throughout the country. A strength of the QWI is its utility for comparing current education labor market conditions to prior years in the same county, which is challenging with state or federal survey data.

Figure 2.

Distribution of education labor market measures.

The distribution of net-negative job flow is skewed by a small number of counties with high levels of job loss. Figure 2, Panel B, displays the log of net-negative job flow. The labels on the x-axis describe net-negative job flow in a specific county and year. The median net-negative job flow is 43, and the average net-negative job flow is 73.3 (see Appendix Table A2). The interquartile range for jobs lost is 53 (i.e., 25th percentile = 24, 75th percentile = 77). The net-negative job flow rate is quite low, and the median number of jobs lost per 100 employees is 1.08. Similar to turnover, we find that net-negative job flow also varies more between than within counties. The variation in net-negative job flow is 75.5 percent larger between counties (SD = 7.89) than within counties (SD = 4.49).

Educator turnover peaked during the early stages of the pandemic and then remained above the historical average. Figure 3 describes education labor market conditions by county from the 2000–01 school year to the 2023–24 school year. Figure 3, Panel A, describes the turnover rate, and Panel B describes the total number of leavers throughout the country in a specific year. The black dots and bars represent the 25th, 50th, and 75th percentiles, while the dashed line represents the mean. Median turnover rose significantly during the first year of the pandemic recession (2019–2020) by 3.3 percentage points to 29.7 percent, then fell precipitously in 2020–21 to 20.7 percent (see Appendix Table A3 & A4). Postpandemic (2021–22 to 2023–24), median turnover remained significantly higher than before the start of the pandemic (i.e., 2012–13 to 2018–19). Median turnover in 2023–24 had decreased from its peak in 2019–20.

Figure 3.

Education labor market conditions over time.

Net-negative job flow increased at the beginning of the pandemic before dropping below its historical average. Figure 3, Panel C, describes the rate of net-negative job flow, and Panel D describes the total number of jobs lost throughout the country in a specific year. Following the Great Recession (2009–10) to the year before the pandemic (2018–19), median net-negative job flow remained between .43 and .45 positions lost per 100 employees (see Appendix Table 4). In the first year of the pandemic (2019–20), the net-negative job flow rate surged by about one position lost per 100 employees. The result was a net-negative job loss of 225,000 education jobs in 2019–20 (see Appendix Table A4). From 2019–21 to 2023–24, net-negative job flow declined significantly from its peak in 2020–21 and remains .15 positions per 100 lower than the prepandemic average (2012–13 to 2018–19) (see Appendix Table A3).

The QWI shows that education labor market conditions are worse for racial and ethnic minority groups. Figure 4 describes median educator turnover and net-negative job flow per 100 positions by race and ethnicity at the state level. Turnover and net-negative job flow for non-White educators are significantly higher than for White educators. From 2000–01 to 2023–24, median turnover for White educators is between 9.6 percentage points lower, and net-negative job flow per 100 positions is on average 5.1 jobs lower than non-White educators (see Appendix Table A5). Similarly, turnover and net-negative job flow are significantly worse for Hispanic educators. During the beginning of the pandemic (i.e., 2020–21 to 2021–22), the difference between median turnover for Black educators subtracted from White educators increased in North Dakota, South Dakota, and Montana more than in any other state. During that same period, the difference between median net-negative job flow per 100 positions for Black educators subtracted from White educators increased in Montana, Vermont, and Idaho more than in any other state. Turnover is 8.4 percentage points higher, and net-negative job flow per 100 positions is 1.88 employees higher for Hispanic educators than for non-Hispanic educators (see Appendix Table A5).

Figure 4.

Education labor market over time and race/ethnicity.

The QWI shows that educational attainment is negatively correlated with turnover and job loss. Figure 5, Panel A, describes median educator turnover by attainment and state. Median turnover for educators with a bachelor’s or advanced degree is 7.0 percentage points lower than that of educators at other observed levels of attainment (see Appendix Table A5). Average turnover level for educators with a bachelor’s or advanced degree is about 3.7 percentage points above the average teacher turnover reported by states (Bleiberg & Kraft, 2023) and 3 percentage points above the National Teacher Principal Survey (Taie et al., 2023). This is likely because the QWI considers a broader group of school-site employees as teachers than the states do. This implies that the QWI’s data on educators with a bachelor’s or advanced degree can provide useful information about teacher turnover specifically, rather than for educators overall.

Figure 5.

Education labor market over time and educational attainment.

Since the conclusion of the Great Recession in 2009, educators with college degrees are more likely to lose their positions than educators without college degrees. Figure 5, Panel B, shows median educator net-negative job flow by attainment and state. The trend in net-negative job flow by educational attainment differs from turnover. Pooling across each school year from 2000–01 to 2023–24, there are no significant differences in median net-negative job flow across levels of educational attainment (see Appendix Table A5). Prior to the pandemic of 2019–20, about 14.3 educators per 100 positions with less than a high school degree and 13.3 educators per 100 positions with a bachelor’s or higher degree lost their jobs. However, from 2019–20 to 2023–24, educators with a bachelor’s or advanced degree were more likely to have lost their position (about one more position lost per 100 positions) than educators with a less advanced degree. The QWI’s data on net-negative job flow by educational attainment are useful for providing unique insights into education labor market conditions.

Variation Across Time and Local Areas

Education labor market conditions were stronger in the Mid-Atlantic and Southeast during the pandemic. Appendix Figure A3 visualizes median turnover and net-negative job flow from 2019–20 to 2023–24 by state.⁶ Turnover varies considerably across states, but during the pandemic, turnover was lower in the South Atlantic, East South Central, and Mid-Atlantic. Educator net-negative job flow per 100 positions was similarly low in the same areas of the country (i.e., South Atlantic, East South Central, Mid-Atlantic). The patterns in labor market conditions during the pandemic invite further research. The descriptive findings also highlight the unique nature of the QWI data for observing recent trends.⁷

QWI Use Case for Education Leaders

The QWI is a useful tool for education leaders to quickly assess how policy may influence educator turnover and job loss. While we have substantial evidence on why teachers leave their school or the profession (Borman & Dowling, 2008; DeMatthews et al., 2022; Madigan & Kim, 2021), we know much less about how education reform may influence the education labor market as a whole. To this point, Figure 6 shows how the QWI can be used to explore the relationship between education reforms (i.e., state takeovers, 4-day work week) and education labor market outcomes. States throughout the country are considering taking over districts and implementing a 4-day work week. While research has explored the influence of these policies on some outcomes, data were not available in a timely manner and did not allow for comparison to local labor market conditions. Data on state takeover and a 4-day work week were collected from open-access journal articles (Schueler & Bleiberg, 2022) or state education agencies (CDE, 2011; MDE, 2025). The bars in Figure 6 describe the predicted results from a regression, which can be easily estimated using freely available analysis software (e.g., Excel). The results show that state takeover and a 4-day work week are both correlated with significant increases in turnover and job loss. It is possible that education leaders may be pursuing reforms for reasons unrelated to educator staffing (e.g., student achievement, lower costs). A strength of the QWI is that it allows education leaders to understand the potential tradeoffs associated with a policy.

Figure 6.

Predicted relationship between K–12 education labor markets and policy.

Discussion

We demonstrate that the QWI’s quarter-to-quarter counts can serve as proxies for county by school year measures of K–12 education labor markets. We find that our school year QWI measures are strongly correlated with state-reported data for turnover (R = .89) and vacancies (R = .84). We show that turnover and net-negative job loss vary more between counties than within counties. Median turnover has remained high from 2021–22 to 2023–24 (about 26.9 percent) compared to the prepandemic average (i.e., 2012–13 to 2018–19). Net-negative job flow was highest in the first two years of the pandemic (2019–20), when about 225,000 education jobs were lost. We also find that turnover and net-negative job flow are substantively lower for White educators than for non-White educators.

The QWI includes unique information on education labor market conditions, but also has important limitations. The level of noise injected into the QWI data for privacy protection is proportional to the size of firms (Abowd et al., 2009). The resulting measurement error is minimal for densely populated areas, but substantial in sparsely populated areas. This mitigates the usefulness of the QWI for exploring education labor market trends in rural communities. The QWI data aggregates workforce measures by the industry of their employer. As a result, the QWI does not include data by occupation (e.g., teachers, aids, bus drivers). However, the QWI does include workforce estimates by education attainment. Using Census Occupation Classification, we find that about 90% of elementary and secondary education employees are teachers; 6% are administrators and other education professionals (e.g., guidance counselors, nurses). Currently, the QWI divides race and ethnicity into two non–mutually exclusive categories. This approach is meant to avoid conflating common ancestry with culture (Viano & Baker, 2020). However, this distinction is misaligned with popular conceptions of race and ethnicity. The Office of Management and Budget plans to update its standards for “maintaining, collecting and presenting race/ethnicity data across federal agencies” (Marks et al., 2024). Future QWI releases could use available personnel records to create a combined race/ethnicity measure consistent with the updated standard. Finally, while the county-level data provide a detailed view of education labor markets, it would be valuable to include district-level estimates. District-level data could be estimated following the procedure used to create the Small Area Income Poverty Estimates (Census Bureau, 2024a).

The procedure we describe for transforming QWI data is useful for researchers and policymakers. The data are freely available via the API created by the Census Bureau (2016). The measures of turnover and net-negative job flow proposed in this study are particularly useful for making regional comparisons. Descriptively exploring county variation has numerous implications for reacting to changing labor markets. For example, school leaders from rural counties can benefit from straightforward comparisons with their neighbors. Local education leaders can use these data to recruit staff. Additionally, the QWI is a valuable tool for leaders and researchers seeking to promote a more equitable education workforce. Finally, the procedure we describe here could easily be adapted to create similar measures for the postsecondary education sector.

Recent work has indicated that most education labor market data are not readily available and are most often years out of date (Bleiberg & Kraft, 2023; Nguyen et al., 2024). Without timely data, the capacity of policymakers is severely restricted because years-old data can rapidly become irrelevant during moments of economic distress. This current work clearly suggests that the QWI data may be able to fill in the information gaps that would be beneficial to policymakers as well as researchers to craft and implement policy solutions in a timely manner.

After the pandemic, local education leaders relied on their experience during the Great Recession. Their response was based on the assumption that property tax revenues would decline, when in reality housing prices rose rapidly (Bleiberg & Kraft, 2023; St. Louis Fed, 2025). To a certain extent, the conditions that lead to a recession are, by definition, unexpected. The lack of targeted support assumes that identifying the communities hit hardest by a recession is not possible. Policymakers can now use the QWI to better shield schools from future economic hardships.

Supplemental Material

sj-docx-1-ero-10.1177_23328584261443298 – Supplemental material for Leveraging Quarterly Workforce Indicators to Analyze K–12 Education Labor Market Dynamics: Inequitable Trends in Turnover

Supplemental material, sj-docx-1-ero-10.1177_23328584261443298 for Leveraging Quarterly Workforce Indicators to Analyze K–12 Education Labor Market Dynamics: Inequitable Trends in Turnover by Joshua Bleiberg and Tuan D. Nguyen in AERA Open

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Joshua Bleiberg

Tuan D. Nguyen

Notes

Authors

JOSHUA BLEIBERG is an assistant professor of education policy at the University of Pittsburgh. He uses quantitative research methods to estimate the causal effects of state and federal education policies on students and to elucidate areas where students lack access to high-quality teaching.

TUAN D. NGUYEN is an associate professor in the Department of Educational Leadership and Policy Analysis at the University of Missouri. He applies rigorous quantitative methods (quasi-experimental designs and meta-analysis) to examine 1) the teacher labor markets, particularly looking at the factors that drive teacher attrition and retention, and 2) the effects and implications of teacher policies and education policies intended for social equity and school improvement.

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