Economic Consequences of Terrorism

Principal Investigator: Adam Rose

Other Researchers: Peter Dixon, James Giesecke, Dan Wei, Nathaniel Heatwole, Fynn Prager, Michael Orosz


The purpose of this project is to develop and transition a methodology for rapidly obtaining approximate estimates of the economic consequences from the nearly 40 threats listed in the Homeland Security National Risk Characterization (HSNRC) Risk Register.  The tool is intended for use by various DHS components and offices to obtain estimates almost instantly.  It is programmed in Excel and Visual Basic for Applications (VBA) to facilitate its use. This tool is called E-CAT (Economic Consequence Analysis Tool) and accounts for the cumulative direct and indirect impacts (including resilience and behavioral factors that significantly affect base estimates) on the national economy from terrorism, natural disasters, and technological accidents. E-CAT is intended to be a major step toward advancing the current state of economic consequence analysis (ECA) across DHS, and also contributing to and developing interest in further research into fast turnaround approaches.   The essence of the methodology involves running numerous simulations in a computable general equilibrium (CGE) model for each threat, yielding synthetic data for the estimation of a single regression equation based on the identification of key explanatory variables (threat characteristics and background conditions).  This transforms the results of a complex model, which is beyond the reach of most users, into a “reduced form” model that is readily comprehensible.  We have built functionality into E-CAT so that its users can switch various consequence categories on and off in order to create customized profiles of the economic consequences of numerous risk events.  E-CAT incorporates uncertainty on both the input and output side in the course of the analysis.  A premium has been placed on making E-CAT user friendly and transparent.    This project is a major milestone in CREATE’s 10-year progression of research on ECA and leverages its recent research for the Office of Health Administration National Biosurveillance Integration Center (OHA/NBIC) and the Defense Nuclear Detection Office (DNDO). It builds upon recent research for DHS on developing a reduced form modeling for selected threats and developing a user-friendly spreadsheet program to facilitate the performance of ECA.  It also incorporates insights from the completion of nearly 2 dozen ECA case studies, including definitive estimate of the economic impacts of the September 11 World Trade Center attack and simulation studies of a dirty bomb attack on the Los Angeles financial district, the shutdown of a major port complex in Texas, and a catastrophic Southern California earthquake, among others. In the course of developing E-CAT, the research team made several innovations, some of which overlapped with related studies to be discussed below.  We developed an “Enumeration Table” of various types of economic impacts that are potentially associated with each threat.  This provides a checklist that helps ensure that each ECA’s comprehensive.  We also updated and refined the CREATE US CGE Model for the purpose of the analysis. We developed an approach and the computer code for running hundreds of Monte Carlo simulations of the economic consequences of individual threats. We incorporated uncertainty into the analysis in relation to both inputs and results. We are also in the process of completing a sophisticated validation analysis using several techniques. Finally, we developed a user-friendly interface in Excel/VBA that will facilitate widespread use.    We have currently incorporated 10 major threats (including, nuclear attack, earthquakes, pandemic influenza, floods, and transportation system disruptions among others) into E-CAT.  We plan to develop another 20 threats once we have completed our assessment of feedback from potential users.  In addition to developing the software, our analyses have provided new insights into the importance of various explanatory factors, especially resilience and behavioral linkages, in the bottom-line consequences of individual threats.   E-CAT was originally intended for primary use by the DHS Policy Office.  However, it is also an ideal tool for more widespread use by those who need a quick turn-round capability to compare the economic consequences of many threats for decisions relating to resource allocation for mitigation and resilience and the disbursement of post-disaster assistance.  Interest in the tool has been expressed by FEMA, US Coast Guard, and Los Angeles Mayor’s Office.  CREATE will offer a short course for DHS staff, as well as other interested parties, and help advance the process of harmonization of modeling of economic impacts for various risk assessments.  The Science and Technology (S&T) Office of University Programs (OUP) will help tailor and shape the course materials for offering it to a broad set of risk and consequence analyst communities.    E-CAT is ideal for use risk assessments and risk management decisions.  It is intended to be easy to use, quick, reasonably accurate, and transparent. It also incorporates functionality such that end users can create tailor-made profiles of economic consequences of a broad range of threats, with associated measures of uncertainty. Recently, E-CAT received CREATE’s Transition Product of the Year Award.   Research on E-CAT has overlapped with several other current and recent projects. These include DHS OHA/NBIC on broadening the range of impacts it considers (Rose et al., 2015) and the Defense Nuclear Detection Office (DNDO) on analyzing the duration and time-path of radiological/nuclear events (Heatwole et al., 2014).  It builds upon prior work on developing a reduced form model to predict the economic consequences of earthquakes (Heatwole and Rose, 2013) and reduced form modeling for DNDO.  It also builds on the CREATE Urban Commerce and Security (UCASS) Project as well, where CREATE developed a user-friendly spreadsheet program to facilitate the performance of ECA (Rose et al., 2014).