Association for Public Policy Analysis & Management

Research, policy design, and implementation relating to resilience to disasters continues to accelerate. While much of the literature has focused on community and regional resilience (Norris, 2008; Tierney, 2014; Martin and Sunley, 2015), there is a notable absence of work in the important area of resilience at the level of the individual business, or firm. One of the most serious gaps is the measurement of key aspects of various resilience tactics and strategies, especially their benefits and costs. These are indispensable to effective risk management given the increasing frequency and magnitude of disasters and continued limited resources to cope with them.

This paper presents the development and application of an approach to the measurement of the costs and effectiveness of several economic resilience tactics. These estimates are based on two recent surveys to gather primary data from recent victims of Superstorm Sandy and Hurricane Harvey. Sandy was a severe storm that occurred in the Fall of 2012, striking the New Jersey and New York coastline and resulting in hundreds of fatalities and $75 billion in property damage. Firms that either suffered property damage and/or direct business interruption due to the disaster were included in the survey sample. Likewise, Harvey was a major hurricane affecting Texas and other parts of the Gulf Coast, causing $70 to $90 billion in property damage (RMS, 2017). We present an approach to the development of a survey instrument to measure the cost and effectiveness of various static resilience tactics. The survey questions are based on economic principles relating to production theory and benefit-cost analysis.

We translate our survey results into a set of marginal cost curves for individual economic resilience tactics that are intended to minimize the negative effects of critical input disruptions following the disaster (e.g., conserving on or finding substitutes for critical inputs in short supply, relocating economic activity to where these inputs are available). The curves relate the costs of implementing each tactic to the reduction in business interruption (BI) loss it prevents. The cost curve approach has been shown to be a facile and transparent way to optimize resource allocation in relation to such goals as energy conservation (Rose, 1985) and greenhouse gas mitigation (Rose and Stevens, 1993; Ellerman and Decaux, 1998; Wei and Rose, 2014). We then conduct econometric analyses using these metrics as inputs, that enable us to explain firm recovery, as well as resilience costs and effectiveness as a function of magnitude and extent of observed infrastructure disruptions.

The results are critically important to policymakers and industry alike, as they inform the most cost-effective use of actions to avoid business interruption, which in most cases, greatly exceeds property damage in dollar terms.