Background/Context: Students with disabilities (SWDs) spend most of their day in general education classrooms. Notably, there has been a growing trend towards the placement of students with disabilities in these general education settings (Williamson et al., 2020), where general educators are the primary instructors of educating students with disabilities. However, research suggests that general educators feel unprepared to meet the needs of students with disabilities and are more likely to leave their positions when faced with a higher number of students with disabilities in their classrooms (Gilmour & Wehby, 2020). The partnership between special educators and general educators is intended to provide support to both general educators and students with disabilities to foster meaningful inclusion (Brownell et al., 2010). However, the collaboration between special educators and general educators is threatened by high turnover rate among special educators. Special educator turnover consistently exceeds that of general educators by 3 to 4 percentage points (Theobald et al., 2023). This turnover issue raises a critical concern: when a special educator leaves, it represents the loss of a potential collaborative partnership for general educators and removes a crucial support for which they rely on to teach students with disabilities. In this paper, we investigate the consequences of special educator turnover, with a specific focus on examining potential spillover effects onto general educator turnover. Purpose/Objective/Research Question: We estimate the impact of special education teacher turnover on general education teacher turnover. We posit a connection between the two as GETs depend on SETs for supporting and instructing SWDs. Setting: We use Indiana Department of Education administrative data spanning academic years 2011/2012 through 2021/2022. Population/Participants/Subjects: This data includes information about school employment positions, including school, position type, and salary in specific positions, as well as demographic information about these employees. These demographic data will allow us to disaggregate teacher turnover rates by experience, gender, race, and ethnicity. In addition to teacher characteristics, the dataset also includes information about students, such as details regarding their race, ethnicity, gender, socioeconomic status, and disability type. We have [approximately]638,000 teachers in the sample with [approximately]20,000 schools. Research Design: We categorize turnover into three distinct types for general and special educators: school-level turnover (transferring from one school to another), district-level turnover (moving to a different district), and state turnover (leaving the state or workforce altogether). [equation omitted] Drawing on Ronfeldt et al. (2013), we examine turnover at the school-by-grade-by-year level. We leverage school by-year fixed effects and grade fixed effects to look at more accurate assessment of the relationship between SET turnover and GET turnover. We account for unobserved factors that may be specific to each school and vary over time, e.g., leadership or school policies. Grade fixed effects account for factors specific to each grade level within schools like different ways teachers interact with each other and students. We identify special educators and general educators who taught one grade per year and excluded teachers who taught multiple grades. We also conduct a temporal analysis. In the placebo test, we examine whether the current year's turnover among special education teachers affects last year's turnover among general education teachers. Ideally, we aim for these results to be nonsignificant, as this serves as a placebo test. We then look at the effect of the current year's SET turnover on GET turnover in the subsequent year. This analysis helps distinguish whether these relationships occur simultaneously or have a persistent effect over time. In practical terms, consider a scenario where a SET leaves. If the GET decides to remain in their position for another year, but the replacement SET does not foster a productive working relationship, the GET might then choose to leave. The persistence analysis allows us to investigate whether the initial turnover among special education teachers predicts subsequent turnover among general education teachers. In the third analysis, we examine whether the relationship between SET turnover and GET turnover is influenced by the share of students with disabilities assigned to a GET. For example, if the share of students with disabilities is high in a general education teacher's class, SET turnover might have a stronger effect on GET turnover due to increased demands and challenges associated with supporting these students. Findings/Results: All of the following models include school-by-year fixed effects and grade fixed effects including student and teacher controls. Table 1 illustrates the impact of SET turnover on GET turnover across different turnover categories. Results indicate if 100% of special educators leave a school, general educator turnover increases by 4 percentage points. The results are significant at the school level but not district or state level. Teachers in the same school often collaborate, share resources, and rely on each other for support. The non-sig effects at the district and state level are consistent with my conceptual framing; the importance of SETs for supporting GETs' instruction for SWDs. In Table 2, our temporal analysis reveals that SET turnover does not have an impact on last year's GET turnover, providing reassuring results. However, we observe that the current year's SET turnover does influence GET turnover in the subsequent year, with GET turnover increasing by an additional 0.03 percentage points. In Table 3, we conduct a moderator analysis to examine whether the proportion of SWDs assigned to GETs affects the relationship between SET turnover and GET turnover. Holding all variables constant, we find that GET turnover increases by an additional 0.07 percentage points when GETs teach more SWDs. Conclusions: SET turnover is related to increased GET turnover. GETs responsible for higher percentages of SWDs are more likely to leave their positions when there are increased rates of turnover among SETs. SET turnover has a persistent relationship; GETs are likely to leave their positions in the subsequent year following SET turnover. GETs rely on SETs and initiatives to address SET turnover can have positive downstream effects for GET turnover.