Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems

TLDR: In this paper, a decision analysis tool is utilized by integrating the economic loss and inoperability metrics to study the interdependent effects of various hurricane intensities on Virginia's workforce sectors.

Abstract: Natural disasters, like hurricanes, can damage properties and critical infrastructure systems, degrade economic productivity, and in extreme situations can cause injuries and mortalities. This paper focuses particularly on workforce disruptions in the aftermath of hurricanes. We extend the dynamic inoperability input–output model (DIIM) by formulating a workforce recovery model to identify critical industry sectors. A decision analysis tool is utilized by integrating the economic loss and inoperability metrics to study the interdependent effects of various hurricane intensities on Virginia’s workforce sectors. The extended DIIM and available workforce survey data are incorporated in the decision support tool to simulate various hurricane scenarios. For a low-intensity hurricane scenario, the simulated total economic loss to Virginia’s industry sectors due to workforce absenteeism is around $410 million. Examples of critical sectors that suffer the highest losses for this scenario include: (1) miscellaneous professional, scientific, and technical services; (2) federal general government; (3) state and local government enterprises; (4) construction; and (5) administrative and support services. This paper also explores the inoperability metric, which describes the proportion in which a sector capacity is disrupted. The inoperability metric reveals a different ranking of critical sectors, such as: (1) social assistance; (2) hospitals and nursing and residential care facilities; (3) educational services; (4) federal government enterprises; and (5) federal general government. Results of the study will help identify the critical workforce sectors and can ultimately provide insights into formulating preparedness decisions to expedite disaster recovery. The model was applied to the state of Virginia but can be generalized to other regions and other disaster scenarios.