Deciphering Small Business Community Disaster Support using Machine Learning

Abstract

Small businesses that have demonstrated high levels of pre-disaster local involvement are more likely to take an active role in community resilience during a disaster, regardless of their own financial security. Our investigation of small business survey responses about COVID-19 impacts finds that they are conduits of national support to their local communities. In addition, businesses with natural hazard experience before or during COVID-19 gave to more community groups than hazard inexperienced businesses. While community resilience models often characterize small businesses as passive actors using variables such as employment or financial security, this research suggests that small businesses take an active role in community resilience by providing critical local support. The pandemic presented an opportunity to consider small business’ role in community resilience nationally, which was utilized here to identify the multi-dimensional factors that predict small business operators’ Community Disaster Support. This study improves upon previous research by studying the small business-community resilience interface at both regional (n=197) and national (n=6,121) scales. We predict small business’ active involvement in community resilience using random forest machine learning, and find that adding social capital predictors greatly increases model performance (F-score of 0.88, MCC of 0.67).

Keywords: COVID-19, small business, community disaster support, natural hazards, machine learning

JEL Classification: C38, C83, D22