Assessing School District Decision-Making: In-Person Schooling and COVID-19 Transmission

Abstract

Abstract Recent controversies have highlighted the importance of local school district governance, but little empirical evidence exists evaluating the quality of district policymakers or policies. In this paper, we take a novel approach to assessing school district decision-making. We posit a model of rational decision-making under uncertainty that emphasizes districts learning over time. We test the predictions from the model using data on a set of highly visible and consequential decisions facing school district leaders—the choice of learning mode during the 2020-21 school year. We find that district behavior is consistent with a Bayesian learning process in several key respects. Districts respond on the margin to health risks: all else equal, a marginal increase in new cases reduces the probability that a district offers in-person instruction the next week. This negative response is magnified when the district was in-person the prior week and attenuates in magnitude over the school year, suggesting districts learn from experience about the effect of in-person learning on disease transmission in schools. We also find evidence that districts are influenced by the learning mode decisions of peer districts, but not their peers' experiences with in-person instruction and disease transmission, which implies that some important frictions exist.