Authors: J. Nassios and J.A. Giesecke
Government decision makers must make plans for a wide range of terrorism threat scenarios. In the formulation and evaluation of these plans, economic consequence analysis plays an important role in elucidating the benefits of successful deterrence, mitigation, and post-event management. However, planning in this regard is not easy, particularly when terrorism events have diverse characteristics defined along many dimensions, including the method, location, scale and frequency of attack(s). As discussed in previous work by [Giesecke et al. (2015)], CGE models are well-suited to the analysis of the economic consequences of a diverse range of threat scenarios; with a large number of exogenous variables, CGE models can be used to model shocks related to the many particular characteristics that can define a given individual terrorism event. This also makes them well suited to the analysis of the many hypothetical scenarios that must be investigated in contingency planning by defence and emergency management decision makers. In defining a terrorism event for input to a CGE model, two broad sets of shocks are typically recognised: (1) Physical impacts on observable economic variables, e.g., fatalities, asset damage, business interruption; and (2) Behavioural impacts on unobservable structural variables, e.g., the effects of fear and uncertainty on workers, investors, and consumers. Assembling shocks related to the physical characteristics of a terrorism event is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing the behavioural characteristics of terrorism events is difficult; with values for unobservable variables such as impacts on required rates of return, worker compensating wage requirements, and consumer willingness to pay having to either be inferred or estimated by indirect means. Typically, this has been achieved via reference to extraneous literature. But how confidant can planners be that the impact magnitudes reported in such ex-ante analyses are plausible? Ex-post econometric studies of terrorism, such as those by [Blomberg et al. (2004)] and [Blomberg and Hess (2006)], present models for the response of observable economic variables, e.g., real GDP, investment, government expenditure and trade, to terrorism and other forms of conflict. [Dixon and Rimmer (2002)] demonstrate that a CGE model can be used to infer outcomes for unobservable structural variables using observable economic variables. In a similar way, in this paper we use the findings by [Blomberg et al. (2004)] to determine point estimates for the relevant (unobservable) structural variables impacted by terrorism events using the USAGE 2.0 dynamic CGE model of the US economy ([Dixon and Rimmer (2002)]; [Dixon and Rimmer (2004)]). This allows us to: (i) Explore the relative contributions of implicit structural and policy shifts in the results for observable variables reported in [Blomberg et al. (2004)]; (ii) Extend Blomberg's analysis of results for macro variables into the sectoral dimension, thereby elucidating the consequences of terrorism on prospects for individual industries; and (iii) Compare implicit structural shocks in Blomberg with the assumed structural shocks in earlier CGE papers.
JEL classification: C68, F52
Keywords: Terrorism, Economic impact, Dynamic CGE modelling.
Please cite the later published version in:
Nassios, J. and J. Giesecke (2017), "Informing ex-ante event studies with macro-econometric evidence on the structural and policy impacts of terrorism", Risk Analysis 38 (4), pp. 804-825 https://doi.org/10.1111/risa.12874
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