Data and Methods
We use longitudinal administrative data from the North Carolina Education Research Data Center. With this information, we can track individual teachers matched to specific classrooms and schools for a time period of 22 years—from the 1994–1995 school year to the 2015–2016 school year. This data set contains a number of files at the student, teacher, classroom, and school levels, from which we extract relevant measures to create a final merged data set.

We restrict the sample to teachers of math and ELA in the middle school Grades 6 through 8. Within a school, the teachers of math (or ELA) in these grades are likely to teach similar types of material, may work together to offer a coherent curriculum, and, to some extent, may be interchangeable. Importantly, the departure of one of them is likely to affect the others. Turnover of teachers within these clearly defined subject groups, which conveniently also correspond to student-tested subjects, should allow clear interpretation of the effects of turnover. We further restrict the sample to teachers of only yearlong courses that do not combine multiple subjects and to cohorts with at least three teachers teaching that subject in the school that year.2

The data set begins with a single observation for every math or ELA classroom for each year, which generates approximately 600,000 total observations, or around 300,000 per subject. Each classroom is matched to its primary instructor. We merge in instructor-specific information on their licensure area code, type of teaching certification, teacher licensure exam scores (Praxis), and years of experience. We also merge in information on students, including total number of students in the classroom and proportion by race and gender. We collect information specific to each school, including the geographic location and the proportion of students eligible for free/reduced-price lunch.

We specify five outcome measures designed to capture the following three categories of school response to teacher turnover: (1) changing the average qualifications of teachers through hiring or replacement; (2) shifting teachers within the school to subject areas that are not their primary area; and/or (3) combining class sections and increasing class size. Corresponding to the first category, we observe the proportion of teachers with three or fewer years of experience; the proportion of teachers with lateral or provisional licenses; and the average teacher licensure exam score measured in standard deviations (SD). Corresponding to the second category, we observe the proportion of teachers who are not certified in the subject they are teaching. And corresponding to the third type of response, we observe average class size. All outcome measures are calculated at the subject, school, and year levels. For example, to calculate the proportion of teachers who are novice, we divide the number of teachers in school j, subject s with three or fewer years of experience at time t by the total number of teachers in school j, subject s at time t. This means that, in contrast to earlier studies on the topic, we are not examining the characteristics of teachers leaving, or of teachers coming in, but rather the aggregate net effects of turnover on the full group of math and ELA teachers at the school.

The teacher turnover rate is our primary independent variable of interest. Because we are exploring the impacts of teacher departure on teachers of related subjects at the same school, we calculate teacher turnover at the school, subject, and year levels. This contrasts with Ronfeldt et al. (2013), who define both teacher attrition and teacher entry at the grade level. The use of school- and subject-level measures makes sense in the context of middle school math and ELA courses because teachers often teach across multiple grade levels and/or switch back and forth across grades.3 At school j in subject s, turnover is calculated as the number of teachers who left between school year �−1 and school year � divided by the total number of teachers teaching in that subject and school at year �−1:Turnover���=Teachersleaving��,�−1Teachers��,�−1.

This variable incorporates no information on why a teacher leaves the school and makes no distinction between a teacher leaving the profession or simply moving to a different school. As noted by Papay et al. (2017), counting teachers who leave a school temporarily and return in a later year in the turnover measure leads to misleadingly high turnover rates. This type of departure could represent personal leave or lapses in administrative records and is likely to be less disruptive to schools than teachers leaving for good. Therefore, we only count a teacher under Teachersleaving��,�−1 if they do not return to the same school.4

Recent research emphasizes the importance of measuring the long-term instability of schools with longitudinal turnover data for understanding the cumulative effects of turnover on schools (Holme et al., 2018).5 Although prior research typically used an annual turnover rate, we hypothesize that school administrators are more likely to respond to sustained periods of high turnover. Accordingly, we calculate a 3-year running average of teacher turnover for each subject within each school:Averageturnover���=13∑�=�−2�Teachersleaving��,�−1Teachers��,�−1.

We also examine alternative dynamic specifications of turnover by incorporating multiple lagged annual turnover rates (see Appendix Figure A1), and we test the sensitivity of our results to different moving averages (Appendix Table A1) and to the exclusion of outlier turnover years that could possibly skew the moving average (Appendix Table A2). All of our turnover measures include departure events both at the end of the school year and during the school year, from which we would expect particularly detrimental impacts on student learning (Henry & Redding, 2018).

Since both the independent and the dependent variables of interest vary at the school-subject rather than the classroom level, we collapse the student- and classroom-level data set to one observation for all math classrooms and one observation for all ELA classrooms for each year within each school. For most of the analyses, we also exclude the 1995, 1996, and 1997 school years since average turnover from the prior 3 years can only be calculated from the 1998 school year forward. This exclusion still allows a 19-year panel of data and results in a new, collapsed sample size of 15,640 observations, or 7,820 for each subject.6

Table 1 provides the summary statistics for the resulting analytical data set. One can note that, on average across math and ELA middle school classrooms, 21% of teachers have three or fewer years of experience, 12% have lateral or provisional licenses, and 29% are teaching outside their subject of certification. Licensure exam scores of middle school math and ELA teachers are on average 0.13 SD below the mean for all teachers.7 The average class size for this sample is 19.9 students. Table 1 Summary Statistics of Analytical Sample of Middle School Math and ELA Teachers.

Read more: https://academicscores.com/2024/05/14/a-research-on-the-cost-implications-of-teacher-turnover/