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The purpose of this paper is to develop an analytical framework for estimating the behavioral effects of disasters and their economic consequences. The reduction of these losses represents the benefits of pre-disaster mitigation and post-disaster recovery. We provide conceptualizations, definitions, classifications, and a formal welfare analysis of this category of economic consequences. We also examine methods used to measure behavioral reactions to fear for insight into improving their delineation. Because we are interested in a comprehensive assessment of behavioral effects, we also include resilience adjustments and extend our initial partial equilibrium analysis to the general equilibrium level. The analysis is intended to serve as the basis for the legitimate inclusion of behavioral consequences of disasters in benefit-cost analysis.

In recent years, there have been many high-profile attacks on large, relatively unprotected venues, including entertainment and shopping complexes in the U.S. and around the world. Public and private decision-makers can choose from a wide array of terrorism countermeasures. A question arises as to whether patrons’ complaints about delays, inconvenience and invasion of privacy actually translate into decisions to attend fewer such events. This paper presents the bottom-line economic impacts of terrorism countermeasures on business revenue at three public assembly venues and on their surrounding regional economic activity. These venues include an MLB Stadium, an NBA/NHL Arena, and a Convention Center. The analysis is based primarily on survey responses relating to changes in attendance that stem from public perception of the implementation of four major types of countermeasures. The surveys indicated that the majority of patrons were not affected either way by the presence of the countermeasures, but nearly all of the remainder felt more secure in the presence of the countermeasures to both terrorism and ordinary crime, which resulted in an increased likelihood of attendance. The economic impact estimates yield a small but notable positive impact on business revenues, though this outcome varies significantly across venue types.

This paper introduces the use of machine learning models in the characterization of bitmap fail patterns occurring on SRAM to identify FEOL/MEOL layers defectivity distribution. The results of bitmap patterns with test conditions are used for fault analysis post-processing and manufacturing yield improvement methodologies. Several machine learning models were built for prediction of the FEOL/MEOL layer defects based on hundreds of bitmap physical failure analysis results. A model utilizing a multilayer perceptron (MLP) architecture with backpropagation of error were optimized and it can be easily applied to volume products with millions of bitmap test results with >80% accuracy. It is the first time we are able to investigate the FEOL/MEOL defects density quantitatively through an automatic diagnosis tool.

We evaluate the impacts of the Customs and Border Protection (CBP) Center of Excellence and Expertise (CEE) Program on the U.S. trade community. CBP established ten CEEs in order to facilitate trade by removing non‐tariff barriers. CEEs process importers at “virtual” centres as opposed to individual ports of entry, and they are specialised according to particular sectors. CEEs were anticipated to use government resources more efficiently, reduce paperwork, improve information flows and contribute towards a single window for documenting trade. We use difference‐in‐difference (DID) analysis of CBP administrative data to identify the impacts of importer membership in a CEE on key outcomes, including import shipment examination rates and form filings. We also use survey‐generated data on importer administrative and business costs in order to monetise the value of impacts. These data reveal that the cost to importers of having a shipment examined at a port is roughly ten to twenty times higher than the cost of importers having to file a form with a CEE. Estimated impact results suggest that two of the ten CEEs‐‐Electronics and Automotive‐‐had a statistically significant negative impact on examination rates. We review the reasons for these results, which underscore the need when doing an impact analysis for careful understanding of the underlying data and processes involved in order to develop and explain impact results. For the two CEEs that had significant impacts on import examinations, we quantify the monetary value of these impacts and estimate that they saved their members roughly $1 million in trade examination costs in FY 2014. We also estimate that expanding membership to cover more importers could yield savings of between $7 million and $18 million annually. Finally, we find that CBP could redirect approximately 9,000 import examinations and 7,000 form filings from lower‐risk CEE importers to higher‐risk importers, possibly increasing trade‐enforcement effectiveness.

Models to estimate economic impacts of disasters have recently been augmented to include resilience. However, most research has incorporated only a limited set of resilience tactics and has not estimated their individual loss reduction effect. We present a comprehensive framework for estimating the relative effects of a broad set of post‐disaster resilience tactics. Our methodological innovation is illustrated by adapting the TERM multi‐regional CGE model for a seaport disruption, distinguishing inherent resilience working through the price system from adaptive resilience and other inherent tactics to cope with input shortages. We also overcome a path‐dependency problem in the modelling process. Los modelos para estimar los efectos economicos de los desastres se han mejorado recientemente para poder incluir la resiliencia. Sin embargo, la mayoria de las investigaciones han incorporado solo un conjunto limitado de tacticas de resiliencia y no han estimado su efecto de reduccion de perdidas individuales. Este articulo presenta un marco integral para estimar los efectos relativos de un amplio conjunto de tacticas de resiliencia posteriores a un desastre. Esta innovacion metodologica se ilustra mediante la adaptacion del modelo EGC multirregional TERM para una perturbacion de un puerto maritimo, distinguiendo la resiliencia inherente, que funciona a traves del sistema de precios, de la resiliencia adaptativa y otras tacticas inherentes para hacer frente a la escasez de insumos. Tambien se subsana el problema de dependencia de la trayectoria en el proceso de modelizacion. 災害による経済的影響を推計するモデルは、最近では、レジリエンスを含むように拡大されている。ところが、ほとんどの研究では、限定されたレジリエンス戦略しか組み込んでおらず、個々の損失低減効果の推定を行っていない。本稿では、広範な災害後のレジリエンス戦略相対的効果を推定する包括的フレームワークを提示する。我々の研究方法のイノベーションは、TERM多地域CGEモデルを港湾破壊に応用し、価格システムを通して機能する固有のレジリエンスを、適応レジリエンス及びインプット不足に対処する他の固有の戦術と区別することにより示される。また、モデリング過程における経路依存問題を克服している。

We analyze a major potential reform of the current FEMA Public Assistance Program that would establish a deductible against the coverage of losses and would offer credits for expenditure on risk reduction by states as an incentive to offset the deductible. While the current FEMA Program is targeted primarily towards repairing damaged property, there is a potential to formulate a Deductible/Credit System (DCS), so as to achieve a reduction in other worthy goals as well, such as reducing fatalities and accelerating recovery. We analyze the effect of the DCS on the achievement of these alternative goals and on state and federal expenditure under various assumptions about which types of disaster losses are eligible for credits. Although the effect of the DCS depends on how states respond to the credit incentive, it is unlikely to reduce total state expenditure on the combination of risk reduction and disaster losses in the short term.

Return migration is key to community recovery from many disasters. Japanese governments have conducted radiation decontamination efforts in the Exclusion Zone designated after the 2011 Fukushima nuclear disaster in order to encourage this outcome. Little is known, however, about the factors that influence post‐disaster migrants to return, and, if people are relatively unresponsive to decontamination, then the costs of promoting recovery may exceed the benefits. We exploit a unique survey of Fukushima evacuees to determine the factors that influence their decision to return after a disaster. Location‐specific capital characteristics, such as housing tenure and the extent of property damage, are estimated to be strong factors. The radiation dose rate of the home location is found to be a statistically significant factor for intent to return, but its effect is small. We also found that households with various other characteristics were non-committal about the return option and likely to defer their decisions, which implies that “return” and “not‐return” are asymmetric. Our simulation analysis found that the number of returnees encouraged by this decontamination was 12,882, or less than 8% of the total evacuees, while the decontamination cost per returnee was $3.36 million USD. This result implies that the government could have improved the well‐being of evacuees at a lower cost by policies other than decontamination., Kansai University Fund for the Promotion and Enhancement of Education and Research KAKEN Grant Number 17H02072 2017～2018年 関西大学教育研究高度化促進費

As a result of catastrophic events, firms and other organizations are faced with input shortages and price shocks. Firms can respond to these events using a variety of “resilience” actions, or tactics. Here we provide a microeconomic foundation for analyzing a comprehensive range of these tactics, incorporating both inherent and adaptive concepts of resilience. We classify these tactics and derive optimality conditions for production with the use of each class of resilience in the context of a nested Constant Elasticity of Substitution (CES) function consisting of aggregated Capital (K), Labor (L), Infrastructure (I), and Materials (M). The framework has broad applicability, including measurement and scoring of resilience, cost-effectiveness assessment of resilience tactics individually and as a group, calculation of resilience indices, and supply-chain management.

Background:. Preparation of nerve ends is an essential part of nerve repair surgery. Multiple instruments have been described for this purpose; however, no consensus exists regarding which is the least traumatic for tissue handling. We believe that various instruments used for nerve-end excision will lead to different surface roughness. Methods:. Median and ulnar nerves from fresh frozen cadavers were dissected, and 1–2 cm lengths were excised using a No. 11 blade, a razor blade, or a pair of scissors. Using electron microscopy, 3-dimensional surface analysis of roughness (Sa) for each specimen was performed using ZeeScan optical hardware and GetPhase software (PhaseView, Buisson, France). An ANOVA or Kruskal-Wallis test compared roughness measures among cutting techniques. Results:. Forty nerves were included. Of these, 13 (32.5%) were cut using scissors, 15 (37.5%) using a razor blade, and 12 (30%) using a No. 11 blade. An ANOVA test showed statistical differences in Sa among the cutting techniques (P = 0.002), with the lowest mean Sa noted in the scissors group (7.2 µM, 95% CI: 5.34–9.06), followed by No. 11 blade (7.29 µM, 95% CI: 5.22–9.35), and razor blade (11.03 µM, 95% CI: 9.43–12.62). Median Ra (surface profile roughness) was 4.58 (IQR: 2.62–5.46). A Kruskal-Wallis test demonstrated statistical difference in Ra among techniques (P = 0.003), with the lowest by No. 11 blade (3 µM, IQR: 1.87–4.38), followed by scissors (3.29 µM, IQR: 1.56–4.96), and razor (5.41 µM, IQR: 4.95–6.21). Conclusion:. This novel technique of 3-dimensional surface analysis found razor blade use demonstrated poor roughness, whereas a No. 11 blade or nerve-specific scissors led to equivocally smooth nerve ends.

We present a formal analysis of the macroeconomic impacts of the COVID-19 pandemic in the U.S., China and the rest of the world. Given the uncertainty regarding the severity and time-path of the infections and related conditions, we examine three scenarios, ranging from a relatively moderate event to a disaster. The study considers a comprehensive list of causal factors affecting the impacts, including: mandatory closures and the gradual re-opening process; decline in workforce due to morbidity, mortality and avoidance behavior; increased demand for health care; decreased demand for public transportation and other leisure activities; potential resilience through telework; increased demand for communication services; and increased pent-up demand. We apply a computable general equilibrium (CGE) model, a state-of-the-art economy-wide modeling technique. It traces the broader economic ramifications of individual responses of producers and consumers through supply chains both within and across countries. We project that the net U.S. GDP losses from COVID-19 would range from $3.2 trillion (14.8%) to $4.8 trillion (23.0%) in a 2-year period for the three scenarios. U.S. impacts are estimated to be higher than those for China and the ROW in percentage terms. The major factor affecting the results in all three scenarios is Mandatory Closures and the Partial Reopenings of businesses. These alone would have resulted in a 22.3% to 60.6% decrease in U.S. GDP across the scenarios. Pent-up Demand, generated from the inability to spend during the Closures/Reopenings, is the second most influential factor, significantly offsetting the negative impacts stemming from other causal factors.

Our research considers a radar which employs an aperture consisting of thousands of elements with multifunctional MIMO capabilities. The increased spatial degrees of freedom permits the radar to be multi-functional without significantly sacrificing desired beamwidth and angular resolution. In this paper, we consider a notional massive MIMO radar which performs multiple roles such as search, multi-target tracking, and full duplex communication to and from users of the channel and the radar, while simultaneously suppressing emissions where communications must be denied as well as sources of interference and jamming to the radar. Spatial processing algorithms are derived and we characterize the system performance using simulations in a u-v domain. Large aperture arrays have relatively smaller beamwidths which are not preferred for large volume surveillance. Therefore, we employ convex optimization algorithms to intentionally and selectively spoil certain transmit beams while leaving the rest unaltered while additionally suppressing emissions in other spatial sectors.

The Maritime Transportation System (MTS) accounts for more than 80% of global merchandise trade in volume and roughly a sixth of the Total Gross Output of the United States. Given that national and global economies depend upon efficient supply chains, port stakeholders must develop security plans to respond to all hazards, natural and manmade. Given recent cyber attacks affecting shipping ports, along with the multi-billion dollar cyber insurance gap, ports need to understand the tradeoffs between increased competitiveness and increased risk through investment in automation and advanced logistics technologies. This article addresses the need to understand the economic impact of cyber attacks that affect shipping port operations and thereby enable risk assessments that holistically evaluate interactions among port Information Technology (IT) and Operational Technology (OT) systems. We extend Boland et al's Dynamic Discretization Discovery (DDD) algorithm to include capacity constraints and delay arcs to accommodate commodities arriving late due to disruption. Using a Nearly-Orthogonal Latin Hypercube (NOLH) experimental design, we construct disruption profiles based on actual cyber attacks that specify the range of operational effects of IT/OT dependencies on stakeholder transportation assets. Economic loss functions for seven commodity categories based on the willingness to pay literature are used to compute delay costs so that stakeholders can estimate the range of economic and operational impacts within a disruption profile. Results based on data provided by Port Everglades, FL, illustrate impacts at $80,000 and $1.2M on average for cyberattacks on landlord port and terminal operator assets during the first week in October, 2017. The runtime performance of our DDD algorithm improves on the state of the art by an order of magnitude and on larger problem sizes based on real-world port networks.

Society is increasingly focused on resilience to catastrophic events. Firms generally observe two forms of economic disruption, property damage and business interruption (BI). In contrast to pre-event mitigation intended primarily to avoid property damage, firms can use a variety of resilience tactics once the disaster strikes to reduce BI losses by improving stability and continuity of operations. This paper makes a theoretical contribution by incorporating resilience into longstanding economic production theory and by identifying the key components for evaluating the cost and effectiveness of resilience. It also makes important empirical contributions by designing, administering and analyzing surveys of firms affected by two of the most devastating disasters in U.S. history. This paper is the first to econometrically analyze these production theory relationships, identifying the cost-effectiveness of a full range of explicit resilience tactics. Results show that BI losses exceeded property damage losses by over 900% and, on average, firms avoided $4.57 in BI for every dollar spent on resilience. Generalizable comparative static estimates are presented for the manufacturing sector to test formalized relationships, which identify tactics capable of improving factor productivity can significantly enhance the cost-effectiveness of resilience.

Large-scale electricity outages have the potential to result in substantial business interruption losses. These losses can be reduced through a number of tactics within the broader strategies of mitigation and resilience. This paper presents a methodology for analyzing the tradeoffs between mitigation and three categories of resilience. We derive optimality conditions for various combinations of strategies for a Cobb-Douglas damage function and then explore implications of a less restrictive Constant Elasticity of Substitution damage function. We also calibrate the model and perform Monte Carlo simulations to test the sensitivity of the results with respect to changes in major parameters. Simulation results highlight the possibility that substitution away from mitigation towards resilience may lower total expected costs from large-scale outages for a given level of risk reduction expenditure when the marginal benefit of resilience is high relative to the expected marginal benefit of mitigation.

Policymakers and analysts in homeland security and emergency management need standardized estimates of economic impacts to allocate resources to reduce losses across multiple threat types including terrorism events, natural disasters, and technological accidents. To date, mainly single-threat analyses have been conducted, using a wide variety of methods. This article proposes a multi-threat analytical framework based on computable general equilibrium (CGE) modeling. CGE-generated GDP impacts are calculated using Monte Carlo simulations of key drivers of disaster losses, creating synthetic data for regression analysis of causal factors influencing results. This approach improves the overall accuracy of modeling, facilitates multi-hazard comparison of economic impacts, and allows for decomposition of direct and general equilibrium impacts. The decomposed results across five threat types confirm the importance of behavioral and economic resilience factors, which are rarely modeled, differ substantially across various threat types, and are critical in risk management. Results from this approach allow policymakers to rapidly and easily evaluate the magnitude of numerous threats and efficiently allocate budgets across interdiction, mitigation and resilience options.

Background Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death among people with chronic epilepsy. People with intellectual disability (ID) are overrepresented in this population. The SUDEP and Seizure Safety Checklist (“Checklist”) is a tool to discuss risk factors influencing seizures and the risk of SUDEP. It includes questions about the availability of nocturnal monitoring. In Cornwall UK, people with epilepsy and ID and their relatives and carers are routinely advised to consider nocturnal surveillance to reduce harm from potential nocturnal seizures. We assessed the retention of advice provided on nocturnal monitoring and if there were differences between those in residential care and those living with their families. Methods A postal questionnaire was sent to carers of all people with epilepsy and ID in Cornwall followed by the adult specialist ID epilepsy service. All those who were contacted had received the same advice on SUDEP and nocturnal monitoring at least once in the past year. Each person was categorized into living in a residential setting or with their family group. Intergroup differences were compared using Fisher's exact test. Results Carers for 170 people were contacted and 121 responded (71%). The family group had statistically more nocturnal seizures than the residential group. While there was no difference in the awareness of SUDEP, the groups differed in their recollection of the person-centered discussion of risk with carers in residential setting being less aware. Where nocturnal monitoring advice was given, it was followed, and previously unknown seizures were identified in 75%. Conclusions Carers in residential settings are less likely to recall specific person-centered discussion of risks to the individual they support as compared with those living with families although general awareness of SUDEP and implementing advice such as nocturnal monitoring is present equally in both groups. In improving detection of nocturnal seizures, audio monitoring may be a useful strategy to reduce risk of harm for people with ID.

We analyze the economic impacts of the United States-South Korea Free Trade Agreement by applying the Global Trade Analysis Project (GTAP) computable general equilibrium model to highly disaggregat...

Despite increased domestic production, the U.S. is still importing more than one-third of its crude oil needs, the vast majority via ocean tankers. At the same time, there are increasing concerns about the vulnerability of ports and terminals to man-made and natural disasters. This paper advances a methodology for estimating the total economic consequences of and resilience to a disruption of crude oil and refined petroleum product trade at a major seaport. The methodology is able to estimate not only the direct impacts of such disruptions but also the supply-chain effects. It also estimates the effects of muting the impacts by various resilience tactics such as ship re-routing, drawing inventories from storage, accessing the Strategic Petroleum Reserve, geographic shifting of petroleum refining, and production rescheduling. We apply the methodology to a 90-day disruption of petroleum trade at the twin seaports of Beaumont and Port Arthur, Texas. The results indicate that port region and national economic activity could decline by billions of dollars, but that resilience can reduce these consequences significantly. We also conclude that factors associated with the recent surge in the extraction of shale and tight oil resources has significantly enhanced the potential effectiveness of some resilience tactics.

While post-disaster migration can move vulnerable populations from dangerous regions to relatively safe ones, little is known about decisions that migrants use to select new homes. We develop an econometric model of migrant flows to examine the characteristics of the destinations that attracted migrants leaving the New Orleans area following Hurricane Katrina in 2005 relative to migration behaviors in other years. We find an increased flow of migrants to large, nearby counties with a mixed effect of economic variables on migration. We find that counties that had experienced fewer disasters received a greater proportion of total migrants in 2005, but there was an overall increase in migration flow to disaster-prone regions as well.

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.

The coordination of international climate policy, such as linking systems of tradable greenhouse gas (GHG) emissions allowances, can greatly lower the cost to all participants of slowing cl...

Advances in theoretical and computable general equilibrium modeling brought their conceptual foundations more in line with standard microeconomic constructs. This reduced the theoretical gap between welfare measurements using a partial or a general equilibrium approach. However, the separation of the partial and general equilibrium literatures lingers in many applications that this manuscript seeks to bridge. The now shared conceptual foundations, the importance of functional specification, the role of common price movements and closure rules are discussed. The continuing stricture in U.S. Government guidelines against including secondary effects in welfare measures is questioned.

The Conference of the Parties to the UN Framework Convention on Climate Change (COP21), held in Paris in December 2015, resulted in voluntary greenhouse gas (GHG) reduction pledges, independent of each other, by 195 countries. The purpose of this paper is to analyze the equity implications of this “bottom-up” approach to climate change negotiations in two major ways. First we analyze the GHG reduction targets specified in the COP21 agreement prior to any emissions trading in terms of the Gini Coefficient equity metric. We also compare the pledges with specific equity principles proposed by developing countries for many years, such as the Egalitarian and Ability to Pay equity. Second, we analyze the equity outcomes after emissions trading takes place for a sample of countries/regions. We find that international policy coordination through a system of tradable GHG emissions allowances can greatly lower the cost to all participants of reducing atmospheric concentrations of greenhouse gases. An emissions trading system involving all countries and regions that made unconditional pledges at COP21 could reduce total GHG mitigation costs from an estimated $1.71 trillion to $0.4 trillion (in 2015 dollars), a savings of 77%. The ensuing allowance sales revenues would greatly enhance the capability of lower-income countries to meet their COP21 pledges. Moreover, the cost-savings in high-income countries would facilitate contributions to the promised fund of $100 billion per year to assist climate policy implementation in low-income countries.

We evaluate the potential regional macroeconomic impacts of a set of eighteen greenhouse gas (GHG) mitigation policy options intended to enable the Southern California Association of Governments (SCAG) to comply with the California's emission reduction targets related to transportation and land use. The Regional Economic Models, Inc. (REMI) Policy Insight Plus (PI+) and TranSight (TS) Models were applied in the analysis by carefully linking technical and microeconomic aspects of each mitigation option to the workings of the regional economy. We account for key considerations, such as the extent to which investment in mitigation options would be generated from new revenue sources or would displace ordinary private business investment. Our results indicate that the combined eighteen policies could generate an employment gain of almost 14 thousand jobs per year and result in an increase in GDP of $22 billion over the entire planning period from now to 2035. Sensitivity analyses of key assumptions and parameters for the transportation and land use policies indicate that the results are robust.

We estimate the macroeconomic impacts of mandatory business closures in the U.S. and many other countries in order to control the spread of the COVID-19. The analysis is based on the application of a modified version of the GTAP computable general equilibrium (CGE) model. We simulate mandatory closures in all countries or parts of countries that had imposed them as of April 7 for three-month and six-month cases. For the three-month scenario, we estimate a 20.3% decline of U.S. GDP on an annual basis, or $4.3 trillion. The employment decline of 22.4% in the U.S. for the three-month closure represents 35.2 million workers for that time period. Should the mandatory closures be extended to six months, these negative impacts slightly more than double. The employment impacts are slightly greater in percentage terms than the GDP impacts because most service sectors, which are generally more labor-intensive, are much more negatively impacted by the closures than are manufacturing sectors and other critical sectors. Our results should be considered upper-bound estimates given such assumptions as businesses laying off workers no longer paying them wages or salaries. Note also that the paper examines the mandatory closures alone and does not factor in any countervailing fiscal or monetary policies.

Dumping is the unfair trade practice of a foreign firm exporting its goods at a price below the sales price in its home market or below its production cost plus markup. Dumping displaces production of similar commodities in the importing country, because its domestic producers have difficulty competing with the lower import price. Antidumping (AD) is a major Priority Trade Issue of concern to U.S. Customs and Border Protection (CBP), and its enforcement is undertaken in parallel with that of countervailing duties (CVD), tariffs on imported goods that offset subsidies for these goods in the exporting country.

This Working Group focuses on the economic recovery of businesses and economies from the COVID-19 pandemic. With communities around the world experiencing severe economic impacts, we anticipate a proliferation of efforts to track, assess, explain, and improve economic recovery. We have discussed key concepts, resources, and priority research topics to enable comparable data collection and mutual learning in this pandemic, as summarized below.

This chapter presents, further clarifies, and extends the foundations of economic resilience, with an eye to concerns of measurement. We examine the potential usefulness of theoretical concepts and empirical analyses in related fields such as ecology, management, regional science, and planning. We contend that there exists no single best approach to defining or measuring resilience across disciplines and that research should evolve to fit specific applications, though adhering to sound principles within each discipline. We illustrate the usefulness of our conceptual foundations in the design of an econometric analysis of economic resilience and summarize its application to survey data from two major disasters. In relation to the themes of this volume, economic resilience can serve to reduce entropy by countering the disarray in an economy in the aftermath of a disaster. At the microeconomic level, this also intersects with complexity in terms of synergies within an individual firm, and at the macroeconomic level manifests itself most notably through supply-chain interactions and network effects. In terms of spatial dynamics, it relates to geographic shifts in the location of economic activity by actual physical relocation of businesses, shifts of activity to branch plants, and the rebalancing of trade across regions.