The economic effects of financial relief delays following a natural disaster

ABSTRACT

In the U.S. the economic damages of natural disasters have increased substantially over time. While private insurance payouts tend to arrive relatively quickly, federal recovery monies are often allocated unevenly, with some communities waiting years to receive previously designated funds. We examine the costliness of delay by linking an economic model of the Joplin, Missouri economy to a civil engineering model that replicates the damage from a tornado that devastated the community in 2011. Building damage estimates from the natural hazard and engineering models are translated into capital stock losses, which subsequently impact the local economy through lost output. We examine several different recovery paths, with a focus on differences in the timing of recovery assistance. Our results show that delaying financial assistance can have important, irretrievable adverse outcomes in the short run.

KEYWORDS:

• Disaster impact modeling
• resiliency modeling
• disaster planning

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The Washington Post reported that only 17 percent of homeowners were covered by flood insurance in the 8 counties most affected by Hurricane Harvey (Long, 2017).

2 See Smith and Sutter (2013) for an in-depth description of the recovery from the 2011 tornado.

3 This paper is a result of a project financed by the National Institute of Standards and Technology, a U.S. federal agency. A large team of civil engineers, social scientists and programmers have pooled their efforts to construct an open-source platform that integrates simulation models from various disciplines to improve natural disaster modeling in order to help communities develop and evaluate alternative mitigation and recovery strategies. While other modeling efforts have linked hazard and economic models (e.g. HAZUS (Kircher et al., 2006) and SAFRR (Wein et al., 2013)), this initiative provides additional flexibility across both hazards and communities, as well as improvements in spatial economic modeling of natural disasters.

4 For an example of such decisions after Hurricane Harvey, see Fernandez et al. (2017).

5 For an example of the firm location problem in the wake of disasters focusing on spatial mismatch along racial dimensions of this problem, see Ferguson and Snellman (2016).

6 In a different example of bureaucratic delay, Swenson (2010) maintains that in the aftermath of the 2008 Iowa floods, a large fraction of monies allocated for FEMA Public Assistance, CDBG, USDA and US DOT were not yet spent in 2010.

7 Cavallo and Noy (2011) provide a thorough review of the literature on the economic impacts of natural disasters.

8 The following description of the CGE model is similar to Cutler et al. (2016).

9 The inclusion of intermediate inputs provides yet another modeling device for damages occurring to, say, electricity or water: the lack of substitutability in intermediate inputs enables the modeler to completely shut down production for the time in which these inputs are not available due to infrastructure damage.

10 This does not apply to goods and services that are provided by the government sector: the amount of such services and goods is constrained by total government revenues (taxes).

11 See Schwarm and Cutler (2003) for a complete description of the SAM.

12 See Ellingwood et al. (2004) for fragility development details.

13 It is worth mentioning that for studying the resilience of communities, simulations of the other physical infrastructure sectors of the community such as the electric power network (e.g. Attary et al., 2019) as well as social parameters such as population dislocation, considering their interdependencies should also be considered, which will be discussed in forthcoming publications as part of this research effort.

14 Evaluating the amount of capital stock in Joplin can be determined by using county assessor’s data or using replacements costs of repairing the damaged buildings. The two techniques vary substantially in value but since the tornado damaged a percentage of the town, our analysis is not influenced by the choice. Because we were unable to obtain reliable information on physical content for these structures, we just damaged building capital.

15 Equations 1–3 are appendix Equations 19–21, while Equation 4 is appendix Equation 34.

16 The index k only refers to commercial and residential buildings.

17 Berck et al. (1997) inspected the literature on estimates of ETAIX and concluded it equals unity.

18 Partridge and Rickman (2010) survey alternative dynamic specifications in CGE models. Besides the common approach used in this paper, they also examine ‘reasonable’ paths used by Auerbach and Kotlikoff (1987) and recursive approach using inter-temporal feedbacks, Wendner (1999) and a forward looking-looking behavior used by Dixon and Rimmer (2002).

19 We have suppressed t in Equations 3 and 4 for simplicity.

20 It is noteworthy that the tornado’s aggregate employment impact is effectively unnoticeable when looking at annual county-level data trends from the BEA.

21 To replace completely the damaged capital stock, we would have needed to increase investment to about $1.8 billion. Although this would change the productive capabilities of the economy, it would not make significant differences in the impacts of waiting, which is our primary interest.

22 When looking at the transition path to the long-run equilibrium (not shown here), it is worth pointing out that it takes between 6 and 12 periods for employment, real household income and domestic supply to return to their pre-disaster levels.

23 Initially, the rate of return to capital increases between 15 and 18 percent; in the long-run it returns to near its original value.

Additional information

Funding

This work was supported by the NIST Center of Excellence and funded through a cooperative agreement between the National Institute of Standards and Technology and Colorado State University under [grant number 70NANB15H044].