The aim of the project described in this report was to test the practicality of using a detailed CGE model as the link between driving factors in TRA scenarios and economic implication variables.
The theoretical advantages of CGE relative to I-O (the previous linking tool) are well known: short-run and long-run perspective; increased variable coverage; and better recognition of resource constraints, price effects, and debt accumulation. But the practicality of using CGE had not been established. To do this we needed to overcome two related problems: (1) computation; and (2) security.
Through our elasticity approach, implemented in GRAD-ECAT, we have provided a solution to both problems. We have shown that CGE can be adapted to the needs of the TRAs and deliver insights well beyond those available from I-O. The main insights arising from the GRAD-ECAT analysis of the sample scenarios presented in section 7 are as follows:
(a) In ranking terrorism events in terms of economic damage, the use of welfare as a metric rather than GDP is likely to lead to quite different conclusions.
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(b) With life valued at $9.6 million, scenarios with a significant loss of life are likely to generate much bigger welfare losses than those in which the main costs are property losses, visitor discouragement and clean-up expenses.
(c) For scenarios with the same array of $ shocks and deaths, the target region is unimportant in determining outcomes for national variables.
(d) By contrast, short-run regional outcomes depend crucially on the target region. (e) The only shock with significant long-run implications for GDP and national
employment is loss of life.
(f) The only shock with long-run implications at the regional level that are significantly different from those at the national level is aversion.
(g) Long-run regional implications for employment can differ sharply from short-run implications.
(h) The state of the economy (recessed or non-recessed) can have a significant bearing on the short-run implications for GDP and employment of a given scenario at both the national and regional levels.
(i) By contrast, the state of the economy in the year of the incident has almost no bearing on the long-run implications for GDP and employment but it does have noticeable implications for welfare.
(j) Varying the discount rate for welfare within the range that is usually recommended for cost-benefit analyses is unlikely to have a major impact on the damage ranking of terrorism events.
The CGE model underlying GRAD-ECAT is USAGE-TERM. This is a new variant of USAGE, with a greatly enhanced regional dimension. The estimation of elasticities, E(s,d,v), for GRAD-ECAT was the first major application of USAGE-TERM. In the course of
applying USAGE-TERM for this project we learnt several technical lessons about the model. These lead to improvements in: (1) computation through a better treatment of zero data points; (2) estimation of interregional trade flows through more realistic gravity formulas; and (3) delineation of regions. With regard to this last point, our initial plan was to set up 4- region versions of USAGE-TERM in which the regions were: Target congressional district; Rest of city; Rest of state; and Rest of USA. This did not prove adequate for coping with joint cities such as NewYork and Newark or for cities on state borders such as Kansas City. Although we retained the original nomenclature, we defined the regions in the 4-region versions of USAGE-TERM by reference to distances from the centre of the target congressional district.
8.1. Directions for future research
There are many ways in which GRAD-ECAT can be improved and developed further. Here we discuss five.
First, we could improve the estimation of the elasticities, EA(s,d,v). Detailed examination of
the tables in appendix 1containing the proportionality coefficients, C(s,v), reveals that for some of the driving factors, s, and some of the implication variables v, especially regional variables, there is considerable variation across our estimates. This means that the relevant variables, RV(s,d,v), do not fully encapsulate all of the factors in USAGE-TERM that explain differences across target regions d in the reaction of implication variable v to shocks of type s. Further research on the RV(s,d,v)s would allow us to improve the estimation of elasticities by bringing the estimates of the proportionality coefficients C(s,v) more closely into line.
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There are also possibilities for improving the consistency of the estimates of the C(s,v)s by making improvements in the specification of USAGE-TERM. For example, in appendix 1 we pinpointed a problem with the treatment of indirect taxes that led to inconsistencies in the C(s,v) estimates for s equals food loss in the U.S. and v equals year-1 regional variable. Second, we could reduce doubt about the legitimacy of the EA(s,d,v) estimates by basing
them on more than four models. Initially we made estimates of the EA(s,d,v)s based on three
4-region models. In these models the target regions were FL24, AZ07 and WA09.
Subsequently we added a fourth model with the target region being NY14. The addition of the fourth model lead to noticeable modifications in some of the elasticity estimates. On this basis it seems worthwhile to make further increases in the number of 4-region models
underlying the elasticity estimation.
Third, we could improve the equations for estimating the effects of scenarios on implication