Development and validation of a stochastic disaster impact model
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Natural disasters result in economic losses as capital and industry are destroyed, as transportation and communication lines are severed, and as customers temporarily flee a region. In many cases, production and sales levels remain depressed as the region rebuilds. Furthermore, these direct losses account for only a portion of the total economic loss experienced. This paper presents a disaster impact model (DIM) to quantify the economic impact of natural disasters on regional output, GDP contribution, labor income, tax income, and employment, as well as the time to recovery. The model is developed to measure the impact of Hurricane Ike on eight Texas Gulf Coast counties and is then extended to a wildfire in central Texas. Actual county sales data are modeled for a period of years before the event, accounting for trends over time and between fiscal quarters, and are used to predict quarterly and annual sales by industry and agricultural commodity in post-event quarters. IMPLAN input-output multipliers are applied to the predicted sales, and stochastic estimates of total impacts are generated using Simetar. Actual postevent impacts are then compared to predicted impacts to estimate the quarterly and annual total economic losses attributable to each industry. Losses are reported at the county and state levels for individual industries as well as across the regional economy. Results show that Hurricane Ike (and a global recession) impacted the coastal economy negatively for at least three years after the event, and industries and locations exhibited different initial responses and recovery paths. Wildfire losses were more sector-specific and shorter in duration. 2013 WIT Press.
