Beginning in 2008, public school districts across the country were forced to implement widespread teacher layoffs in response to large budget cuts caused by the Great Recession.  The way these layoffs were implemented had important consequences for the teacher labor market. In practice, the vast majority of school districts prioritized seniority over any other criteria when selecting teachers for layoffs. The results of several simulation analyses suggest that layoff policies that prioritize teacher effectiveness, as measured by valued-added scores, would have resulted in the selection of less-effective teachers (Goldhaber and Theobald 2013; Boyd et al. 2011). However, measures of effectiveness derived from student achievement are controversial, only available for a minority of teachers, and, with high-stakes attached, can lead to gaming behaviors such as teaching to the test (Jacob 2005) or even outright cheating (Jacob and Levitt 2003).

Subjective performance evaluations, such as ratings by principals, provide an alternative measure of effectiveness that consider multiple criteria, are widely available, and are capable of distinguishing among the very best and worst teachers (Jacob and Lefgren 2008). However, administrators are often reluctant to give employees poor evaluations (MacLeod 2003). Subjective ratings are also susceptible to rater biases (Goldin and Rouse 2000) and contextual influences that can lower the reliability of scores (Hill, Charalambous, and Kraft 2012). Thus, open questions remain about the efficacy of performance-based layoffs.

In this study, I analyze the implementation and consequences of discretionary layoffs in the 18^th largest public school district in the nation to provide some of the first empirical evidence on performance-based layoffs in education. In total, Charlotte Mecklenburg Schools (CMS) eliminated almost 2,000 employees, including over 1,000 teaching positions in the two years following the onset of the Great Recession. I estimate the differential effects of layoffs based on teacher seniority and effectiveness, by exploiting quasi-experimental variation where some rising cohorts of students in a school entered grades in which a teacher was laid off, while others did not.

I find that a variety of RIF criteria predict the probability of being laid off in CMS including tenure status, job performance as measured by principal evaluation scores, licensure status and licensure type. Layoffs were particularly concentrated among high school teachers as well as foreign language and arts teachers. I find weak evidence that, on average, layoffs had negative grade-specific effects on student achievement. However, these average estimates mask wide variability in the impact of laying off individual teachers. Mathematics achievement in grades that lost an effective teacher (at the 75^th percentile), as measured by subjective or objective metrics, decreased by between 0.05 and 0.011 standard deviations more than in grades that lost an ineffective teacher (at the 25^th percentile). In contrast, I find that the marginal difference between laying off a senior versus early-career teacher is near zero and statistically insignificant.  Simulation analyses demonstrate how inverse-layoff policies based on a single performance measure are sub-optimal compared to a policy that considers both objective and subjective measures.