Aliza N. Husain

Despite increasing recognition of the importance of high-quality school leadership, we know remarkably little about principal skill development. Using administrative data from Tennessee, Oregon, and New York City, we estimate the returns to principal experience as measured by student outcomes, teacher hiring and retention patterns, and teacher and supervisor ratings of principals. The typical principal leads a school for only 3–5 years and leaves the principalship after 6–7 years. We find little evidence that school performance improves as principals gain experience, despite substantial improvement in supervisor ratings. Our results suggest that strategies intended to increase principal retention are unlikely to improve school outcomes absent more comprehensive efforts to strengthen the link between principal skill development and student and school outcomes.

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School principals are viewed as critical actors 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. As commonly implemented, value-added models misattribute to principals changes in student performance caused by unobserved time-varying factors over which principals exert minimal control, leading to biased estimates of individual principals’ effectiveness and an overstatement of the magnitude of principal effects. Based on our framework, which better accounts for bias from time-varying factors, we find that little of the variation in student test scores or attendance is explained by persistent effectiveness differences between principals. Across contexts, the estimated standard deviation of principal value-added is roughly 0.03 student-level standard deviations in math achievement and 0.01 standard deviations in reading.

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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.

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