Abstract
Using panel data and a “true” fixed effect stochastic frontier model, we estimate persistent and transient technical inefficiency in mathematics (Math) and English Language Arts (ELA) test score gains in New York City public middle schools from 2014 to 2016. We compare several measures of transient technical inefficiency and show that around 58% of New York City middle schools are efficient in Math gains, while 16% are efficient in ELA gains. Multivariate inference techniques are used to determine subsets of efficient schools, providing actionable decision rules to help policymakers target resources and incentives.
Highlights
While improving public school education has been an empirical concern of parents, teachers, researchers, and policymakers for decades, a challenge has been the debate over the balance between increasing financial resources or pressing schools to improve efficiency
We believe the true fixed effect stochastic frontier model can address some of the major controversies that surround the use of value-added models or previous stochastic frontier techniques used for education policymaking, in part because the model is intended to identify inefficiency rather than quality, and in part because it separates persistent from transient inefficiencies, which allows for better targeting of policy levers towards each form of inefficiency
This study provides summaries of persistent and transient technical efficiency estimates for each of 425 New York City (NYC) middle schools using recent advancements in stochastic frontier modeling
Summary
While improving public school education has been an empirical concern of parents, teachers, researchers, and policymakers for decades, a challenge has been the debate over the balance between increasing financial resources or pressing schools to improve efficiency. We believe the true fixed effect stochastic frontier model can address some of the major controversies that surround the use of value-added models or previous stochastic frontier techniques used for education policymaking, in part because the model is intended to identify inefficiency rather than quality, and in part because it separates persistent from transient inefficiencies, which allows for better targeting of policy levers towards each form of inefficiency. We offer policymakers a pair of actionable decision rules that are methodologically rigorous and reflect true performance of schools, both derived from the true fixed effects model, including application of the conditional mode estimator to identify when schools operate efficiently or the more rigorous Horrace (2005) probabilities to identify a subset of the best.
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