Joost R. Santos

A Risk-based Input–Output Methodology for Measuring the Effects of the August 2003 Northeast Blackout

The 2003 Northeast Blackout revealed vulnerabilities within the US electric power-grid system. With the economy so dependent on electric power for most aspects of life, a power-grid failure can have far-reaching higher-order effects and can impair the operability of other critical infrastructures. An inoperability of the power sector can result from different types of disasters (e.g., accidents, natural catastrophe, or willful attacks). This paper demonstrates the Inoperability Input-Output Model (IIM) to measure the financial and inoperability effects of the Northeast Blackout. The case study uses information from sources such as the US input–output tables and sector-specific reports to quantify losses for specific inoperability levels. The IIM estimated losses of the same magnitude as other published reports; however, with a detailed accounting of all affected economic sectors. Finally, a risk management framework is proposed to extend the IIMs capability for evaluating investment options in terms of their implementation costs and loss-reduction potentials.

DISASTER IMPACT AND INPUT–OUTPUT ANALYSIS

Macroeconomics models, such as the input–output model, the social accounting matrix, and the computable general equilibrium model, have been used for impact analysis of catastrophic disasters for some time. While the use of such models to disaster situation, which may quite differ from the ordinary economic setting, has been critiqued (for recent example, see Albala-Bertrand, 2013), there are still valuable reasons for the use of such models. In particular, such models can be used in order to quickly provide a ballpark estimate of the system-wide impact for recovery plan and finance and/or to evaluate disaster countermeasures in the pre-event period. This paper presents how these methodologies have evolved to incorporate with disaster-specific feature and discusses how far they still need to go from the current stage. This paper also serves as a preface to this special issue, which encompasses several papers devoted to the use of macroeconomic data and models to assess economic losses from disasters.

A Risk-Based Approach for Identifying Key Economic and Infrastructure Systems

This article introduces approaches for identifying key interdependent infrastructure sectors based on the inventory dynamic inoperability input-output model, which integrates an inventory model and a risk-based interdependency model. An identification of such key sectors narrows a policymakers focus on sectors providing most impact and receiving most impact from inventory-caused delays in inoperability resulting from disruptive events. A case study illustrates the practical insights of the key sector approaches derived from a value of workforce-centered production inoperability from Bureau of Economic Analysis data.

Sequential Decision-making in Interdependent Sectors with Multiobjective Inoperability Decision Trees: Application to Biofuel Subsidy Analysis

Abstract Decision-making involving large-scale systems often involves considerations for temporal changes, interdependencies in organizational structures, multiple competing objectives, and risk and uncertainty, among others. In this paper we develop a risk-based methodology, the Multiobjective Inoperability Decision Tree (MOIDT). It integrates several dimensions of decision-making processes associated with interconnected systems in terms of: (i) evaluation of sequential policies; (ii) analysis of interdependencies; (iii) treatment of multiple objectives and their tradeoffs; and (iv) characterization of uncertainties. To demonstrate the integration of these four components, we present a case study to analyze the impact of government policies towards mass-scale biofuel production. Using a multi-period decision framework, the analysis utilizes economic input–output data to model the probabilistic demand adjustments for sectors that will likely be affected by biofuel policies.

Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems

Natural disasters, like hurricanes, can damage properties and critical infrastructure systems, degrade economic productivity, and in extreme situations can cause injuries and mortalities. This paper focuses particularly on workforce disruptions in the aftermath of hurricanes. We extend the dynamic inoperability input–output model (DIIM) by formulating a workforce recovery model to identify critical industry sectors. A decision analysis tool is utilized by integrating the economic loss and inoperability metrics to study the interdependent effects of various hurricane intensities on Virginia’s workforce sectors. The extended DIIM and available workforce survey data are incorporated in the decision support tool to simulate various hurricane scenarios. For a low-intensity hurricane scenario, the simulated total economic loss to Virginia’s industry sectors due to workforce absenteeism is around

Estimating Workforce-Related Economic Impact of a Pandemic on the Commonwealth of Virginia

410 million. Examples of critical sectors that suffer the highest losses for this scenario include: (1) miscellaneous professional, scientific, and technical services; (2) federal general government; (3) state and local government enterprises; (4) construction; and (5) administrative and support services. This paper also explores the inoperability metric, which describes the proportion in which a sector capacity is disrupted. The inoperability metric reveals a different ranking of critical sectors, such as: (1) social assistance; (2) hospitals and nursing and residential care facilities; (3) educational services; (4) federal government enterprises; and (5) federal general government. Results of the study will help identify the critical workforce sectors and can ultimately provide insights into formulating preparedness decisions to expedite disaster recovery. The model was applied to the state of Virginia but can be generalized to other regions and other disaster scenarios.

Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model

A pandemic outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. The consequences of a pandemic can significantly affect and disrupt a large spectrum of workforce sectors in todays society. This paper, motivated by the impact of a pandemic, extends the formulation of the dynamic inoperability input-output model (DIIM) to account for economic perturbations resulting from such an event, which creates a time-varying and probabilistic inoperability to the workforce. A pandemic is a unique disaster, because the majority of its direct impacts are workforce related and it does not create significant direct impact to infrastructure. In light of this factor, this paper first develops a method of translating unavailable workforce into a measure of economic-sector inoperability. While previous formulations of the DIIM only allowed for the specification of an initial perturbation, this paper incorporates the fact that a pandemic can cause direct effects to the workforce over the recovery period. Given the uncertainty associated with the impact of a pandemic, this paper develops a simulation framework to account for the possible variations in realizations of the pandemic. The enhancements to the DIIM formulation are incorporated into a MatLab program and then applied to a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

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

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 models 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.

International Trade Inoperability Input-Output Model (IT-IIM): Theory and Application

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 NCRs 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.

A VULNERABILITY INDEX FOR POST-DISASTER KEY SECTOR PRIORITIZATION

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).