Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state‐of‐the‐art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.     

Modeling the Role of Inventories and Heterogeneity in the Assessment of the Economic Costs of Natural Disasters

Estimates of the cost of potential disasters, including indirect economic consequences, are an important input in the design of risk management strategies. The adaptive regional input‐output (ARIO) inventory model is a tool to assess indirect disaster losses and to analyze their drivers. It is based on an input‐output structure, but it also (i) explicitly represents production bottlenecks and input scarcity and (ii) introduces inventories as an additional flexibility in the production system. This modeling strategy distinguishes between (i) essential supplies that cannot be stocked (e.g., electricity, water) and whose scarcity can paralyze all economic activity; (ii) essential supplies that can be stocked at least temporarily (e.g., steel, chemicals), whose scarcity creates problems only over the medium term; and (iii) supplies that are not essential in the production process, whose scarcity is problematic only over the long run and are therefore easy to replace with imports. The model is applied to the landfall of Hurricane Katrina in Louisiana and identifies two periods in the disaster aftermath: (1) the first year, during which production bottlenecks are responsible for large output losses; (2) the rest of the reconstruction period, during which bottlenecks are inexistent and output losses lower. This analysis also suggests important research questions and policy options to mitigate disaster‐related output losses.     

How to Integrate Labor Disruption into an Economic Impact Evaluation Model for Postdisaster Recovery Periods

Evaluating the economic impacts caused by capital destruction is an effective method for disaster management and prevention, but the magnitude of the economic impact of labor disruption on an economic system remains unclear. This article emphasizes the importance of considering labor disruption when evaluating the economic impact of natural disasters. Based on the principle of disasters and resilience theory, our model integrates nonlinear recovery of labor losses and the demand of labor from outside the disaster area into the dynamic evaluation of the economic impact in the postdisaster recovery period. We exemplify this through a case study: the flood disaster that occurred in Wuhan city, China, on July 6, 2016 (the “7.6 Wuhan flood disaster”). The results indicate that (i) the indirect economic impacts of the “7.6 Wuhan flood disaster” will underestimate 15.12% if we do not consider labor disruption; (ii) the economic impact in secondary industry caused by insufficient labor forces accounts for 42.27% of its total impact, while that in the tertiary industry is 36.29%, which can cause enormous losses if both industries suffer shocks; and (iii) the agricultural sector of Wuhan city experiences an increase in output demand of 0.07% that is created by the introduction of 50,000 short‐term laborers from outside the disaster area to meet the postdisaster reconstruction need. These results provide evidence for the important role of labor disruption and prove that it is a nonnegligible component of postdisaster economic recovery and postdisaster reduction.     

A CGE Framework for Modeling the Economics of Flooding and Recovery in a Major Urban Area

Coastal cities around the world have experienced large costs from major flooding events in recent years. Climate change is predicted to bring an increased likelihood of flooding due to sea level rise and more frequent severe storms. In order to plan future development and adaptation, cities must know the magnitude of losses associated with these events, and how they can be reduced. Often losses are calculated from insurance claims or surveying flood victims. However, this largely neglects the loss due to the disruption of economic activity. We use a forward‐looking dynamic computable general equilibrium model to study how a local economy responds to a flood, focusing on the subsequent recovery/reconstruction. Initial damage is modeled as a shock to the capital stock and recovery requires rebuilding that stock. We apply the model to Vancouver, British Columbia by considering a flood scenario causing total capital damage of $14.6 billion spread across five municipalities. GDP loss relative to a no‐flood scenario is relatively long‐lasting. It is 2.0% ($2.2 billion) in the first year after the flood, 1.7% ($1.9 billion) in the second year, and 1.2% ($1.4 billion) in the fifth year.     

Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model^{( 1 , 2 )} and the dynamic inoperability input-output model (DIIM).^{( 3 )} These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.     

Flood Footprint Assessment: A Multiregional Case of 2009 Central European Floods

Hydrometeorological phenomena have increased in intensity and frequency in last decades, with Europe as one of the most affected areas. This accounts for considerable economic losses in the region. Regional adaptation strategies for costs minimization require a comprehensive assessment of the disasters’ economic impacts at a multiple-region scale. This article adapts the flood footprint method for multiple-region assessment of total economic impact and applies it to the 2009 Central European Floods event. The flood footprint is an impact accounting framework based on the input–output methodology to economically assess the physical damage (direct) and production shortfalls (indirect) within a region and wider economic networks, caused by a climate disaster. Here, the model is extended through the capital matrix, to enable diverse recovery strategies. According to the results, indirect losses represent a considerable proportion of the total costs of a natural disaster, and most of them occur in nonhighly directly impacted industries. For the 2009 Central European Floods, the indirect losses represent 65% out of total, and 70% of it comes from four industries: business services, manufacture general, construction, and commerce. Additionally, results show that more industrialized economies would suffer more indirect losses than less-industrialized ones, in spite of being less vulnerable to direct shocks. This may link to their specific economic structures of high capital-intensity and strong interindustrial linkages.     

Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries

Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low‐ and middle‐income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model‐based cost‐benefit analyses for DRR in developing countries.     

Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors

Outbreaks of contagious diseases underscore the ever‐looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic‐induced workforce absenteeism. In particular, we develop a dynamic input‐output model capable of generating sector‐disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.     

Multiobjective Prioritization Methodology and Decision Support System for Evaluating Inventory Enhancement Strategies for Disrupted Interdependent Sectors

Disruptions in the production of commodities and services resulting from disasters influence the vital functions of infrastructure and economic sectors within a region. The interdependencies inherent among these sectors trigger the faster propagation of disaster consequences that are often associated with a wider range of inoperability and amplified losses. This article evaluates the impact of inventory‐enhanced policies for disrupted interdependent sectors to improve the disaster preparedness capability of dynamic inoperability input‐output models (DIIM). In this article, we develop the dynamic cross‐prioritization plot (DCPP)—a prioritization methodology capable of identifying and dynamically updating the critical sectors based on preference assignments to different objectives. The DCPP integrates the risk assessment metrics (e.g., economic loss and inoperability), which are independently analyzed in the DIIM. We develop a computer‐based DCPP tool to determine the priority for inventory enhancement with user preference and resource availability as new dimensions. A baseline inventory case for the state of Virginia revealed a high concentration of (i) manufacturing sectors under the inoperability objective and (ii) service sectors under the economic loss objective. Simulation of enhanced inventory policies for selected critical manufacturing sectors has reduced the recovery period by approximately four days and the expected total economic loss by $33 million. Although the article focuses on enhancing inventory levels in manufacturing sectors, complementary analysis is recommended to manage the resilience of the service sectors. The flexibility of the proposed DCPP as a decision support tool can also be extended to accommodate analysis in other regions and disaster scenarios.     

Systems Resilience for Multihazard Environments: Definition,Metrics, and Valuation for Decision Making

The United Nations Office for Disaster Risk Reduction reported that the 2011 natural disasters, including the earthquake and tsunami that struck Japan, resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide in 2011, with earthquakes producing the greatest number of fatalities. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to massive savings through risk reduction and expeditious recovery. The rational management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics. In this article, a resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk. Those metrics also meet logically consistent requirements drawn from measure theory, and provide a sound basis for the development of effective decision‐making tools for multihazard environments. Improving the resiliency of a system to meet target levels requires the examination of system enhancement alternatives in economic terms, within a decision‐making framework. Relevant decision analysis methods would typically require the examination of resilience based on its valuation by society at large. The article provides methods for valuation and benefit‐cost analysis based on concepts from risk analysis and management.     

The Economic Impacts of a Terrorist Attack on the U.S. Commercial Aviation System

We use data on air passenger travel expenditures per passenger as well as statistical analysis of the air traffic lost for the two-year aftermath of the September 11, 2001, attacks to estimate direct demand losses for air transportation services. These are used along with a national input-output model to assess the full costs of these losses. Depending on assumptions made, the full losses to the U.S. economy were between $214.3 and $420.5 billion. These estimates are similar to those from other studies of such an event, and suggest that the high costs of effective countermeasures may be justified.     

Supply Chain Vulnerability Analysis Using Scenario‐Based Input‐Output Modeling: Application to Port Operations

Disruptive events such as natural disasters, loss or reduction of resources, work stoppages, and emergent conditions have potential to propagate economic losses across trade networks. In particular, disruptions to the operation of container port activity can be detrimental for international trade and commerce. Risk assessment should anticipate the impact of port operation disruptions with consideration of how priorities change due to uncertain scenarios and guide investments that are effective and feasible for implementation. Priorities for protective measures and continuity of operations planning must consider the economic impact of such disruptions across a variety of scenarios. This article introduces new performance metrics to characterize resiliency in interdependency modeling and also integrates scenario‐based methods to measure economic sensitivity to sudden‐onset disruptions. The methods will be demonstrated on a U.S. port responsible for handling $36.1 billion of cargo annually. The methods will be useful to port management, private industry supply chain planning, and transportation infrastructure management.     

Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

In this article, we propose an integrated direct and indirect flood risk model for small‐ and large‐scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb‐Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input‐output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high‐ and low‐probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low‐probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high‐probability events are qualitatively different from low‐probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high‐probability and low‐probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.     

Disaster Risk Management Policies and the Measurement of Resilience for Philippine Regions

How can a government prioritize disaster risk management policies across regions and types of interventions? Using an economic model to assess welfare risk and resilience to disasters, this article systematically tackles the questions: (1) How much asset and welfare risks does each region in the Philippines face from riverine flood disasters? (2) How resilient is each region to riverine flood disasters? (3) What are, per region, the possible interventions to strengthen resilience to riverine flood disasters and what will be their measured benefit? We study the regions of the Philippines to demonstrate the channels through which macroeconomic asset and output losses from disasters translate to consumption and welfare losses at the micro-economic level. Apart from the regional prioritizations, we identify a menu of policy options ranked according to their level of effectiveness in increasing resilience and reducing welfare risk from riverine floods. The ranking of priorities varies for different regions when their level of expected value at risk is different. This suggests that there are region-specific conditions and drivers that need to be integrated into considerations and policy decisions, so that these are effectively addressed.     

Risk Management Solutions for Climate Change–Induced Disasters

In honor of the 40th anniversary of Risk Analysis, this article suggests ways of linking risk assessment and risk perception in developing risk management strategies that have a good chance of being implemented, focusing on the problem of reducing losses from natural hazards in the face of climate change. Following a checklist for developing an implementable risk management strategy, Section 2 highlights the impact that exponential growth of CO₂ emissions is likely to have on future disaster losses as assessed by climate and social scientists. Section 3 then discusses how people perceive the risks of low-probability adverse events and the cognitive biases that lead them to underprepare for future losses. Based on this empirical evidence, Section 4 proposes a risk management strategy for reducing future losses using the principles of choice architecture to communicate the likelihood and consequences of disasters, coupled with economic incentives and well-enforced regulations.     

Dynamic Economic Resilience and Economic Recovery from Disasters: A Quantitative Assessment

This article analyzes the role of dynamic economic resilience in relation to recovery from disasters in general and illustrates its potential to reduce disaster losses in a case study of the Wenchuan earthquake of 2008. We first offer operational definitions of the concept linked to policies to promote increased levels and speed of investment in repair and reconstruction to implement this resilience. We then develop a dynamic computable general equilibrium (CGE) model that incorporates major features of investment and traces the time‐path of the economy as it recovers with and without dynamic economic resilience. The results indicate that resilience strategies could have significantly reduced GDP losses from the Wenchuan earthquake by 47.4% during 2008–2011 by accelerating the pace of recovery and could have further reduced losses slightly by shortening the recovery by one year. The results can be generalized to conclude that shortening the recovery period is not nearly as effective as increasing reconstruction investment levels and steepening the time‐path of recovery. This is an important distinction that should be made in the typically vague and singular reference to increasing the speed of recovery in many definitions of dynamic resilience.     

Disasters as Learning Experiences or Disasters as Policy Opportunities? Examining Flood Insurance Purchases after Hurricanes

Flood insurance is a critical risk management strategy, contributing to greater resilience of individuals and communities. The occurrence of disasters has been observed to alter risk management choices, including the decision to insure. This has previously been explained by learning and behavioral biases. When it comes to flood insurance, however, federal disaster aid policy could also play a role since recipients of aid are required to maintain insurance. Using a database of flood insurance policies for all states on the Atlantic and Gulf coasts of the United States between 2001 and 2010, this article uses fixed effects models to examine how take‐up rates respond to the occurrence of hurricanes and tropical storms, as well as disaster declarations and aid requirements. Being hit by at least one hurricane in the previous year increases net flood insurance purchases by 7.2%. This effect dies out by three years after the storm. A presidential disaster declaration for floods increases take‐up rates by 6.7%. When disaster aid grants are made available to households, take‐up rates increase by 5%; this accounts for the majority of the increase in policies after occurrence of a hurricane. When the models are estimated taking into account which policies are required by disaster aid, hurricanes are estimated to lead to only a 1.5% increase in voluntary purchases. This overlooked federal policy that disaster aid recipients insure is responsible for a majority of insurance purchases postdisaster.     

Economics of Disaster Risk,Social Vulnerability,and Mental Health Resilience

We investigate the relationship between exposure to Hurricanes Katrina and/or Rita and mental health resilience by vulnerability status, with particular focus on the mental health outcomes of single mothers versus the general public. We advance a measurable notion of mental health resilience to disaster events. We also calculate the economic costs of poor mental health days added by natural disaster exposure. Negative binomial analyses show that hurricane exposure increases the expected count of poor mental health days for all persons by 18.7% (95% confidence interval [CI], 7.44–31.14%), and by 71.88% (95% CI, 39.48–211.82%) for single females with children. Monthly time‐series show that single mothers have lower event resilience, experiencing higher added mental stress. Results also show that the count of poor mental health days is sensitive to hurricane intensity, increasing by a factor of 1.06 (95% CI, 1.02–1.10) for every billion (U.S.$) dollars of damage added for all exposed persons, and by a factor of 1.08 (95% CI, 1.03–1.14) for single mothers. We estimate that single mothers, as a group, suffered over $130 million in productivity loss from added postdisaster stress and disability. Results illustrate the measurability of mental health resilience as a two‐dimensional concept of resistance capacity and recovery time. Overall, we show that natural disasters regressively tax disadvantaged population strata.     

How Do Disaster Characteristics Influence Risk Perception?

The main purpose of this study is to examine how risk perception is influenced by the type of disaster (flood or landslide) and victim characteristics. The data reported here are based on the National Risk Perception Survey (NRPS) that was administered for the victims and the general public in Taiwan in 2004. In that year, many towns in Taiwan were seriously affected by floods and landslides, resulting in huge economic losses and fatalities. The primary findings are: (1) the victims and the general public are concerned about the different potential hazards that might affect their residential area, (2) the negative associations between the sense of controllability and the perceived impact is high for landslide victims, but not for flood victims, and (3) disaster type, gender, and previously experienced disasters are good predictors of victims' attitudes toward natural disasters.     

Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input‐output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as‐planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health‐care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.