Nathaniel Heatwole

A reduced-form rapid economic consequence estimating model: Application to property damage from U.S. earthquakes

Modeling the economic consequences of disasters has reached a high level of maturity and accuracy in recent years. Methods for providing reasonably accurate rapid estimates of economic losses, however, are still limited. This article presents the case for “reduced-form” models for rapid economic consequence estimation for disasters, and specifies and statistically estimates a regression equation for property damage from significant U.S. earthquakes. Explanatory variables are of two categories: (1) hazard-related variables pertaining to earthquake characteristics; and (2) exposure-related variables pertaining to socioeconomic conditions. Comparisons to other available earthquake damage estimates indicate that our Reduced-Form Model yields reasonably good results, including several statistically significant variables that are consistent with a priori hypotheses. The article concludes with a discussion of how the research can be enhanced through the collection of data on additional variables, and of the potential for the extension of the reduced-form modeling approach to other hazard types.

E-CAT User Interface Tool

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.

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

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.

Computable General Equilibrium Modeling and Its Application

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.

User Interface Variables

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.

Enumeration of Categories of Economic Consequences

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.

Threat Scenarios and Direct Impacts

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.

Validation of Computable General Equilibrium Based Models

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.