Who Needs Rules? The Impact of Deregulation in Traditional Public Schools

Kylie Anglin

Traditional public schools in the United States must comply with a variety of regulations on educational inputs like teacher certification, maximum class sizes, and restrictions on staff contracts. Absent regulations, policymakers fear that troubled districts would make inappropriate decisions, but it is also possible that strict regulations hinder schools from adapting inputs to their unique contexts and from optimizing their ability to educate students. In this dissertation, I test the salience of these two hypotheses within the context of a widespread deregulation effort in Texas. This policy change allows traditional public school districts to claim District of Innovation status and opt out of regulations not related to health, safety, and civil rights. Districts have responded enthusiastically; today, the majority of Texas school districts are Districts of Innovation and free to opt out of regulations. This widespread response provides an opportunity to explore heterogenous responses to regulatory freedom and the impact of regulatory freedom on school operations and student outcomes. To this end, I merge a scraped dataset of regulatory exemptions with administrative data and capitalize on variation in implementation dates by conducting a generalized difference-in-difference analysis. I estimate the impact of regulatory flexibility on teacher characteristics, class sizes, and student achievement. This is the first study to explore the impact of regulatory freedom on a near state-wide population of traditional public schools. Findings will have broad implications for the kinds of statutes legislatures should prioritize and whether education regulations may be better tailored to local needs.

About Kylie Anglin

Kylie L. Anglin is a Ph.D. student in Education Policy at the University of Virginia. Her research focuses on developing and using data science approaches to examine variations in policy and intervention implementation, as well as the impact of intervention heterogeneity on student outcomes. In an article published in the Journal of Research on Educational Effectiveness, she proposed a framework for using web scraping and natural language processing techniques to collect data on district responses to state policies from online policy documents. In that article, Kylie illustrates this framework in the context of statewide education deregulation under the Texas District of Innovation statute. In her dissertation, she builds on her descriptive work surrounding the District of Innovation statute to evaluate the impact of the policy, and heterogeneous district responses, on student outcomes. Kylie is also developing methods for using natural language processing techniques to assess treatment fidelity and replicability in intervention evaluations that take place in educational settings. She has published on methods related to causal inference, replication, and open science in Evaluation Review, Oxford Bibliography in Education, and Zeitschrift für Psychologie, and she is a regular presenter at APPAM, AEFP, and SREE. Prior to coming to University of Virginia, Kylie earned a B.A. in Political Science from Southwestern University, a Post-Baccalaureate in Mathematics from Northwestern University, and a Masters in Public Policy from the University of Virginia. Kylie has worked as a 7th grade English teacher and as an evaluator for an after-school program.