We use rich longitudinally matched administrative data on students and teachers in North Carolina to examine the patterns of differential effectiveness by teachers’ years of experience. The paper contributes to the literature by focusing on middle school teachers and by extending the analysis to student outcomes beyond test scores. Once we control statistically for the quality of individual teachers using teacher fixed effects, we find large returns to experience for middle school teachers in the form both of higher test scores and improvements in student behavior, with the clearest behavioral effects emerging for reductions in student absenteeism. Moreover these returns extend well beyond the first few years of teaching. The paper contributes to policy debates by documenting that teachers can and do continue to learn on the job.

High teacher turnover imposes numerous burdens on the schools and districts from which teachers depart. Some of these burdens are explicit and take the form of recruiting, hiring, and training costs. Others are more hidden and take the form of changes to the composition and quality of the teaching staff. This study focuses on the latter. We ask how schools respond to spells of high teacher turnover and assess organizational and human capital effects. Our analysis uses two decades of administrative data on math and English language arts middle school teachers in North Carolina to determine school responses to turnover across different policy environments and macroeconomic climates. Based on models controlling for school contexts and trends, we find that turnover has marked, and lasting, negative consequences for the quality of the instructional staff and student achievement. Our results highlight the need for heightened policy attention to school-specific issues of teacher retention.

Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research Methods: Using longitudinal student records data from the North Carolina Department of Public Instruction, this article assesses modern prediction techniques, with a focus on tree-based classification methods and support vector machines. These methods incorporate 74 predictors measures from Grades 3 through 8, including academic achievement, behavioral indicators, and socioeconomic and demographic characteristics. Findings: Two of the assessed classification algorithms predict high school graduation and dropping out correctly for more than 90% of an out-of-sample student cohort. Findings reveal a shift toward lower dropout incidence in regions hit hardest by the economic recession of 2008, especially for male students. Implications for Research and Practice: Machine-learning procedures, as demonstrated in this study, offer promise for allowing administrators to reliably identify students at risk of dropping out of school so as to provide targeted, intensive programs at the lowest possible cost.

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