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Presented By: Causal Inference in Education Research Seminar (CIERS)

Causal Inference in Education Research Seminar (CIERS)

The Causes and Consequences of Teacher Turnover: New Evidence from Michigan presented by Daniel Hubbard, University of Michigan

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Abstract:
While few measurable characteristics are correlated with a teacher's effectiveness at improving students' exam scores, teachers do often gain in effectiveness as they accumulate experience, particularly in their first few years of teaching. The distribution of teacher quality is already stacked against students of color and economically disadvantaged students; if these students are also exposed to more teacher turnover, this will only perpetuate inequities in the educational system. Using administrative data on teachers hired in Michigan public schools between 2004 and 2009, we seek to determine what characteristics of schools, students, and teachers might predict whether a teacher will leave his or her first teaching job and for what type of new position, if applicable. We begin with a multinomial logit framework in which we compare the probabilities of several different turnover outcomes against the base case of a teacher staying in his or her first job for five years; we continue by using a survival analysis framework to look more closely at year-by-year transitions. In line with other studies of teacher turnover such as Boyd et al. (2002) and Clotfelter et al. (2006), we find that teachers are more likely to leave schools with more black students and more low-performing students; we also find that teachers at charter schools are more likely to leave than their counterparts at traditional public schools.
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