Learning after the storm: Characterizing and understanding prolonged unplanned school closures after hurricanes

Schools are critical hubs for both education and social services. However, disasters can force them to close unexpectedly and for prolonged periods, leading to learning loss and precarity in recovery. Between the 2011-12 and 2018-19 school years, over 13.4 million U.S. students experienced school closures lasting at least one week. Despite prior research highlighting how extended school closures can challenge regional recovery efforts, the factors that influence the duration of hurricane-related prolonged closures remain insufficiently explored at scale. In this study, we use statistical learning models to characterize both the likelihood and duration of unplanned prolonged school closures following hurricanes across all school districts in the Atlantic basin of the United States from 2011 to 2019. We find that indicators of hazard intensity, including wind speed and precipitation are among the important factors in explaining both the likelihood and duration of prolonged closures. Additionally, regional resources play a significant role. Higher per capita spending on students and district-wide per capita income makes districts more likely to experience prolonged closures, suggesting regional differences in risk tolerances or that low-resource schools understand their unique role in facilitating recovery and act quickly to reopen. However, the relationship between district expenditures and closure duration is more complex when prolonged closures occur; increasing per capita student spending is associated with shorter closures up until the 70 th percentile of expenditures, at which point, increasing expenditures is associated with moderately longer closures. Finally, political leaning is important, with more conservative-leaning districts experiencing longer-duration closures. Our findings offer concrete evidence for policymakers, school administrators, and emergency managers on contributors of prolonged school closures and for building community resilience in the face of future hurricanes. • Built machine learning models to predict school closures after hurricanes • Wind gusts and heavy rainfall are key predictors of school closure duration • Coastal districts are more likely to experience school closures after hurricanes • School resources and local wealth play a significant role in school closures • Political leanings influence the length of school closures in affected districts