Informing Ex Ante Event Studies with Macro‐Econometric Evidence on the Structural and Policy Impacts of Terrorism

Abstract

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event-specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE-relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al. yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis.

Behavioral Economic Consequences of Disasters: A Basis for Inclusion in Benefit–Cost Analysis

“…Various forms of macro-level analysis are able to subsume direct and indirect behavioral responses, but not to be able to isolate them from other causal factors. A bottom-approach, exemplified by the CGE analysis of Nassios and Giesecke ( 2018 ) has demonstrated the worthiness of this latter approach, in part by showing the results are consistent with those derived from the econometric timeseries modeling, such as that undertaken by Blomberg and Hess ( 2009 ).…”

“…The reader is referred to Giesecke et al ( 2012 ) for a detailed account. Note also that this approach has been formally validated by Nassios and Giesecke ( 2018 ) in comparing their methodology to a macroeconometric analysis of the economic consequences of the September 11 World Trade Center attacks by Blomberg and Hess ( 2009 ).…”

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

“…Using counterfactuals as a method of intellectual inquiry is not new, as it has been applied by philosophers and historians for at least two millennia (Tetlock & Belkin, ). Although there has been some focus on developing counterfactuals for risk analysis purposes (Bachand, Sulsky, & Curtin, ; Holford & Clark, ; Jeon et al., ; Nassios & Giesecke, ), they have not been used to assess vulnerability, or ideally to develop resilience analytics for cyber‐physical risk—an area where there is significant potential for application due to data limitations. Indeed, there are numerous ways that counterfactual analysis could usefully be applied to the science of emerging risks, including modeling of past events using stochastic forensics or scenario event trees (Woo et al., ).…”

Estimating the Socioeconomic Impacts of Flooding on Regional Economies With a Computable General Equilibrium Model

“…CGE models address IO shortcomings by incorporating economic principles that firms maximize profits and households maximize consumption utility driven by budget constraints and an implicit price mechanism 2 . Going back to pioneering work by Leif Johansen (1960), today CGE models for disaster impact analysis 3 can be classified into static single‐region models (Chen & Rose, 2018; Prager et al., 2018; Rose et al., 2007), dynamic single‐region models (Dixon et al., 2011; Gertz et al., 2019; Nassios & Giesecke, 2015), static multiregional and spatial models (Carrera et al., 2015; Davlasheridze et al., 2021; Haung & Hosoe, 2016; Okiyama & Tokunaga, 2017), and dynamic spatial models (Tokunaga & Okiyama, 2017; Withey et al., 2016; Wittwer, 2012).…”