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This study develops a methodology for rapidly obtaining approximate estimates of the economic consequences from numerous natural, man-made and technological threats. This software tool is intended for use by various decision makers and analysts to obtain estimates rapidly. 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 U.S. economy. E-CAT is intended to be a major step toward advancing the current state of economic consequence analysis (ECA) and also contributing to and developing interest in further research into complex but rapid 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. Functionality has been built into E-CAT so that its users can switch various consequence categories on and off in order to create customized profiles of economic consequences of numerous risk events. E-CAT incorporates uncertainty on both the input and output side in the course of the analysis.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Policymakers and analysts in disaster risk management need consistent and rapid estimates of the economic consequences of multiple threat types, including terrorism events, natural disasters, and technological accidents. Consistency is important to be able to compare the many threats for the purpose of allocating resources among them to reduce overall risk as efficiently as possible. To date, research on the economic consequences of disasters is generally conducted on a threat-by-threat basis, but comparing results from these studies is problematic because the analyses use different models, employ unique sets of assumptions and parameters, and present results in terms of different economic indicators. Rapid turnaround is important for facilitating analyses across many threats, but even more so for allocating post-disaster assistance. However, models that can quickly provide reasonably accurate estimates of economic consequence of most threats are lacking. This volume overcomes these limitations.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 2. Enumeration of Categories of Economic Consequences

Abstract
The purposes of this chapter are to identify a broad range of categories of economic consequences of major threats and to develop a checklist tool that provides a framework for their examination in subsequent chapters in this report. The Enumeration approach described below intends to improve the accuracy of economic consequence estimation. Many studies delve deeply into the estimation of a narrow set of economic consequence types but compromise accuracy by the exclusion of others. The Enumeration approach is the opposite—it provides approximate estimates for a comprehensive set of consequence categories. We contend that for many threats, this breadth can achieve more accurate overall estimation than the in-depth estimation of a limited number of consequence categories.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 3. Threat Scenarios and Direct Impacts

Abstract
The full set of U.S. Homeland Security National Risk Characterization (HSNRC) Threats is presented in Table 3.1. This chapter presents examples of scenarios and direct economic impacts for two example threats: earthquakes and human pandemic. Each section consists of a summary description of the scenario, conversion of concepts to drivers that can be used in our CGE model, and the filling in of both qualitative and quantitative entries in the Enumeration Tables discussed in the previous chapter.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 4. Computable General Equilibrium Modeling and Its Application

Abstract
This chapter details Step 4 of the research framework: CGE modeling. CGE model simulations are run for each of the multiple random draws for each different hazard scenario. Relevant Direct Impact values are input into the USCGE model of the US economy, which captures the combined and interactive effects of these impacts through price changes and substitution effects across multiple economic institutions – 58 sectors, 9 household groups, government institutions, and international traders. GDP and employment impacts are generated for each of these multiple scenarios, and where relevant the Economic Structure of the impacted region is also factored in by scaling the national average results across three different example regional economy structures to render four times the number of original unique GDP and employment combination results.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 5. User Interface Variables

Abstract
This chapter details Step 4 of the research framework. This Step plays a key role in linking CGE analysis results to the reduced-form model. The CGE output and employment results serve as the set of independent variables against which the independent variables are regressed. We refer to the independent variables as User Interface Variables. The resulting regression coefficients for each User Interface Variable in the reduced-form model are plugged into the E-CAT User Interface. Step 4 is presented in three parts. First is the identification of unique sets of User Interface Variables for each threat. Second is the randomized draw of 100 or more combinations of these variables using Latin Hypercube Sampling. Third is the 100 plus random draws, which are then converted to CGE inputs via a series of linkages. Detailed explanation of the reduced-from regression analysis is presented in the next chapter.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 6. Estimation of the Reduced Form Coefficients for the E-CAT User Interface

Abstract
This chapter illustrates the modeling procedures for estimating the reduced form coefficients for the E-CAT user interface tool. The process includes the following steps: a random sampling procedure, a CGE simulation with an automatic looping function, and an econometric analysis including both ordinary least squares estimation (OLS) and quantile regression. The key purpose is to establish the linkages between the threat characteristics identified in the user interface (type of threat, magnitude of threat, time of day, location, sectors impacted, etc.) and the CGE “driver” inputs (capital stock, labor, medical expenditures, tourism, etc.). For illustration purposes, the Human Pandemic scenario is used in the discussion below.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 7. Uncertainty Analysis

Abstract
Economic consequences of natural, intentional, and accidental hazards include uncertainties associated with hazardous events and the economic structure of regions affected by these events. These uncertainties may arise due to variability in an event’s magnitude, timing, duration, and location, as well as differing economic structures in various regions of interest. Quantification and propagation of these uncertainties result in probability distributions associated with various economic consequences. In this study, uncertainties associated with economic consequences are based on variability in stochastic regressors (predictor variables) within least squares and quantile regression models. Addressing uncertainties associated with regression model form (using linear predictor functions) was beyond the scope of this study. Variability in stochastic regressors may arise due to inherent randomness (aleatory uncertainty) or incomplete knowledge (epistemic uncertainty) about underlying phenomena. Epistemic uncertainty may be reduced to aleatory uncertainty with more information, whereas aleatory uncertainty is not reducible. These consequence distributions, presented within a user-friendly and readily deployable tool, may be valuable for homeland security policy-makers conducting national risk assessments and for emergency management decision-making.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 8. Validation of Computable General Equilibrium Based Models

Abstract
Model validation in economics is more difficult than in other disciplines, especially at the macroeconomic level. It is not subject to controlled experiments because it involves independent individual decision-makers and their interactions in the context of background conditions, such as changes business cycles and technological change, many of which are random or otherwise difficult to predict. Economics is more of an “observational” discipline like meteorology, astronomy, or sociology, and must therefore use approaches such as statistical analysis of data or simulation approaches.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Chapter 9. E-CAT User Interface Tool

Abstract
This chapter introduces the design of the E-CAT user interface tool. The tool is based on Excel and Visual Basic for Application (VBA). Three different economic consequence options are developed for each type of threat, including a point estimate (Option 1), interval estimate (Option 2) and uncertainty distribution (Option 3). Step-by-step instructions are presented in the User’s Guide in Appendix A.
Adam Rose, Fynnwin Prager, Zhenhua Chen, Samrat Chatterjee, Dan Wei, Nathaniel Heatwole, Eric Warren

Backmatter

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