Over 2,000 teachers in the state of Washington received reduction-in-force (RIF) notices across the 2008-09 and 2009-10 school years. We link data on these RIF notices to an administrative dataset that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF notice. Not surprisingly, we find that a teacher's seniority is the strongest predictor, but we also find (all else equal) that teachers with master's degrees and those credentialed in the high-need areas of math, science, and special education were less likely to receive a RIF notice. Value-added measures of teacher effectiveness, which can be calculated for a subset of the teachers, were not correlated with the probability 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 65 footnotes.)