Over 2000 teachers in the state of Washington received reduction-in-force (RIF) notices in the past two years. The authors link data on these RIF notices to a unique dataset that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF notice. They find a teacher's seniority is the greatest predictor, but (all else equal) teachers with a master's degree and teachers credentialed in the "high-needs areas" of math, science, and special education were less likely to receive a RIF notice. Value-added measures of teacher effectiveness can be calculated for a subset of the teachers and these show no relationship between effectiveness and the likelihood of receiving a RIF notice. Finally, simulations suggest that a very different group of teachers would be targeted for layoffs under an effectiveness-based layoff scenario than under the seniority-driven system that exists today. (Contains 3 figures, 7 tables and 68 footnotes.)