Assessing the Determinants and Implications of Teacher Layoffs

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Assessing the Determinants and Implications of Teacher Layoffs


Over 2000 teachers in Washington state received reduction-in-force (RIF) notices in the past two years. Linking data on the 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. Results suggest a different group of teachers would be targeted for layoffs under an effectiveness-based vs. seniority-driven layoff system.

Teacher Layoffs: An Empirical Illustration of Seniority vs. Measures of Effectiveness

In the face of unavoidable teacher layoffs, policymakers must juggle a variety of issues in choosing the best criteria for laying off teachers. Analyzing data on 4th and 5th grade teachers in New York City public schools, researchers find substantial differences in which teachers get cut under a seniority-based layoff policy versus a policy based on teacher effectiveness (value-added). The authors model the two layoff scenarios to respond to a (fictional) budget shortfall equivalent. The bottom line: teacher layoffs based on teacher performance, preferably multiple performance measures, lead to a more effective workforce and improved student performance.