Abstract: Business interruption after disasters is an important metric for community resilience planning because has both economic and social consequences. Each additional day that a business is nonoperational further compounds lost revenue, wages, and lack of access to goods and services needed for recovery. Therefore, the use of surveys has grown in the literature as a way to capture the diverse information needed for modeling business disaster outcomes. However, variable inclusion and measurement can vary widely across studies, and there is a lack of guidance on how to structure surveys most effectively to facilitate this effort. This study fills these gaps through an analysis of variable choice, variable measurement, and measurement timing using data from an interdisciplinary field study in Lumberton, North Carolina after 2016 Hurricane Matthew. We found that empirical business interruption models can be improved significantly by using a comprehensive set of utility and damage variables; integrating damage information based on damage states for building, contents, and machinery; and capturing recovery-time dynamics by using business downtime and utility outage durations, rather than binary measurements. The results suggest that making these relatively small changes to survey design in future studies can yield large returns in empirical business models for community resilience research.
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