Managing the Teacher Workforce in Austere Times: The Determinants and Implications of Teacher Layoffs

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 data set 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. © 2013 Association for Education Finance and Policy