Abstract
The Department of Homeland Security (DHS) considers the effects of hypothetical terrorism scenarios distinguished by many dimensions including: perpetrator; target; location; weapon; and delivery method. For each scenario, DHS requires a computationally rapid, in-house (secure) tool for translating impact effects or “driving variables” (e.g. capital destruction, clean-up expenditures, etc.) into economic implication variables (e.g. GDP in the short and long run, regional output in the short and long run, and economic welfare). We use a detailed, dynamic, multi-regional CGE model to generate elasticities E(s,d,v) of 9 implication variables (v) with respect to 14 driving variables (s) occurring as a result of incidents in any of the US’s 436 congressional districts (d). Equipped with these elasticities, DHS can apply trivial calculations to estimate the national and regional economic implications of an enormous variety of scenarios. Rose et al. (Economic consequence analysis tool (E-CAT), Springer, Tokyo, 2017) also propose a CGE-based rapid calculation tool for translating terrorism-related driving variables into economic implication variables. They refer to this tool as E-CAT (Economic Consequence Analysis Tool). Compared with E-CAT, our tool has a more general coverage of economic variables (both driving and implication variables) and introduces regional and dynamic dimensions. In view of the similarities and differences between our approach and E-CAT, we title the tool created here as GRAD-E-CAT (Generalized, Regional And Dynamic Economic Consequence Analysis Tool).
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Notes
- 1.
This refers to capital being taken out of use temporarily during, for example, a decontamination period.
- 2.
An elasticity is the percentage effect on one variable of a 1% change in another variable.
- 3.
The theory underlying USAGE is based on Dixon and Rimmer (2002).
- 4.
- 5.
See Dixon et al. (2007).
- 6.
See Horridge et al. (2005).
- 7.
These are real discount rates, that is they are applied after correcting values of future variables for changes in the price level.
- 8.
- 9.
- 10.
We assume zero inflation or equivalently that next year’s dollar is adjusted for inflation.
- 11.
DHS is following the Department of Transportation, see https://www.transportation.gov/sites/dot.gov/files/docs/VSL%20Guidance%202016.pdf. For earlier estimates of the value of life see Partnoy (2012).
- 12.
This is an average of the numbers used by the Environmental Protection Agency, the Food and Drug Administration and the Department of Transformation in 2012, see Partnoy (2012).
- 13.
This is calculated as 170 models times 20 years times 14 shocks times 2 runs (baseline and perturbation) times 2 sets of assumptions (Keynesian and Neoclassical).
- 14.
EA(•,d,•) is the 14 by 10 matrix with components EA(s,d,v).
- 15.
After 1 year, public expenditure returns to its baseline path. We adopt this approach for all of the expenditure shocks (rows 3 to 6).
- 16.
Includes outputs of all agricultural and processed food products.
- 17.
FL24’s “state” doesn’t cover the whole of Florida. Nevertheless, for convenience we will refer to it as Florida.
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This is an abridged version of a report to the U.S. Department of Homeland Security that we prepared under a sub-award from the Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California. We received excellent administrative support from CREATE and valuable advice throughout the project from Scott Farrow (Coordinator for Economics at CREATE). Tony Cheesebrough from DHS Headquarters was extremely helpful in setting up the project. In working on the project we benefited from enthusiastic encouragement from the project managers Jessica Cox and Scott White. Throughout the project we enjoyed close collaboration with Battelle’s Dave Winkel. Adam Rose provided valuable comments on an earlier draft of this chapter. None of the people mentioned here or their organizations is responsible for the contents of the chapter.
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Dixon, P.B., Jerie, M., Rimmer, M.T., Wittwer, G. (2019). Rapid Assessments of the Economic Implications of Terrorism Events Using a Regional CGE Model: Creating GRAD-ECAT (Generalized, Regional and Dynamic Economic Consequence Analysis Tool). In: Okuyama, Y., Rose, A. (eds) Advances in Spatial and Economic Modeling of Disaster Impacts. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-030-16237-5_6
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