School principals are viewed as critical mechanisms by which to improve student outcomes, but there remain important methodological questions about how to measure principals' effects. We propose a framework for measuring principals' contributions to student outcomes and apply it empirically using data from Tennessee, New York City, and Oregon. We find that using contemporaneous student outcomes to assess principal performance is flawed. Value-added models misattribute to principals changes in student performance caused by factors that principals minimally control. Further, little to none of the variation in average student test scores or attendance is explained by persistent effectiveness differences between principals.
A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.