Brief Assessing the Determinants and Implications of Teacher Layoffs
Dan Goldhaber, Roddy Theobald
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Over 2000 teachers in Washington state received reduction-in-force (RIF) notices in the past two years. Linking data on these RIF notices to a unique dataset of student, teacher, school, and district variables the authors determine factors that predict the likelihood of a teacher receiving a RIF notice. A teacher's seniority is the greatest predictor, but (all else equal) master's degree teachers and credentialed teachers in the "high-needs areas" of math, science, and special education were less likely to receive a RIF notice. For a subset of the teachers there is no observed relationship between effectiveness and the likelihood of receiving a RIF notice. Results suggest a different group of teachers would be targeted for layoffs under an effectiveness-based vs. seniority-driven layoff system.
Research Areas Education
Tags K-12 education Teachers