Economic resilience, in its static form, refers to utilizing remaining resources efficiently to maintain functionality of a household, business, industry, or entire economy after a disaster strikes, and, in its dynamic form, to effectively investing in repair and reconstruction to promote accelerated recovery. As such, economic resilience is oriented to implementing various post-disaster actions (tactics) to reduce business interruption (BI), in contrast to pre-disaster actions such as mitigation that are primarily oriented to preventing property damage. A number of static resilience tactics have been shown to be effective (e.g., conserving scarce inputs, finding substitutes from within and from outside the region, using inventories, and relocating activity to branch plants/offices or other sites). Efforts to measure the effectiveness of the various tactics are relatively new and aim to translate these estimates into dollar benefits, which can be juxtaposed to estimates of dollar costs of implementing the tactics. A comprehensive benefit-cost analysis can assist public- and private sector decision makers in determining the best set of resilience tactics to form an overall resilience strategy.
Joanne R. Stevenson, Ilan Noy, Garry McDonald, Erica Seville, and John Vargo
The economics of disasters is a relatively new and emerging branch of economics. Advances made in analysis, including modeling the spatial economic impacts of disasters, is increasing our ability to project disaster outcomes and explore how to reduce their negative impacts. This work is supported by a growing body of case studies on the organizational and economic impacts of disasters, such as Chang’s in-depth analysis of the Port of Kobe’s decline following the 1995 Great Hanshin earthquake, and the evolving studies of the workforce trends during the ongoing recovery of Christchurch, New Zealand, following a series of earthquakes in 2010 and 2011. The typical view of post-disaster economies depicts a pattern of destruction, renewal, and improvement. Evidence shows, however, that this pattern does not occur in all cases. The degree of economic disruption and the time it takes for different economies to recover varies significantly depending on characteristics such as literacy rates, institutional competency, per capita income, and government spending. If the impacts are large relative to the national economy, a disaster can negatively affect the country or sub-national region’s fiscal position. Similarly, disasters may have significant implications for the national trade balance. If, for example, productive capacity is reduced by disaster damage, exports decrease, the trade balance may weaken, and localized inflation may increase. Studies of individual, household, industry, and business responses to disasters (i.e., microeconomic analyses) cover a broad range of topics relevant to the choices actors make and their interactions with markets. Both household consumption and labor markets face expansion and contraction in areas affected by disasters, with increased consumption and employment often happening in reconstruction related industries. Additionally, the ability of businesses to absorb, respond, and recover in the face of disasters varies widely. Characteristics such as size, number of locations, and pre-disaster financial health are positively correlated with successful business recovery. Businesses can minimize productivity disruptions and recapture lost productivity by conserving scarce inputs, utilizing inventories, and rescheduling production. Assessing the progress of economic recovery and predicting future outcomes are important and complex challenges. Researchers use various methodologies to evaluate the effects of natural disasters at different scales of the economy. Surveys, microeconomic models, econometric models, input-output models, and computable general equilibrium models each offer different insights into the effect of disasters on economies. The study of disaster economics still faces issues with consistency, comprehensiveness, and comparability. Yet, as the science continues to advance there is a growing cross-disciplinary accumulation of knowledge with real implications for policy and the private sector.
Randrianalijaona Mahefasoa, Razanakoto Thierry, Salava Julien, Randriamanampisoa Holimalala, and Lazamanana Pierre
Economics is the science of wealth, the main objective of which is to satisfy human needs within the constraint of limited available resources. The production and consumption patterns of economic agents are examined in order to identify the most efficient and optimal ways of meeting needs. At the same time, redistribution problems have an important place in economic science, and they lead to questions of development and economic growth. Since the 1990s, officially declared by the United Nations as the International Decade for Natural Disaster Reduction (IDNDR), the relatively frequent occurrence of devastating disasters, induced mainly by natural hazards but also by human activities, development efforts and economic growth have been seriously threatened. Poverty alleviation efforts undertaken by nations in the Global South and supported by international donors, as well as development outcomes worldwide, are suffering from disasters. The international community has become more and more aware of the need to systematically mainstream disaster risk reduction in development policy and strategy. Therefore, disaster risk reduction economics is becoming a priority and part of economics as a science. For more than three decades, based on risk assessment, risk prevention and mitigation strategies, including structural and nonstructural measures, such as but not limited to, risk retention and transfer, preparedness as well as ex-post activities such as response, recovery and reconstruction are using economic variables and tools since mid-2000s to become more efficient. Furthermore, protecting economic growth and development benefits is possible only if enough attention is given to risk science. From this perspective, risk science is becoming part of economics, as evidenced by the new branch called risk reduction economics, which is essential to the attainment of sustainable development goals and resilient societies.
Natural disasters cause massive social disruptions and can lead to tremendous economic and human losses. Given their uncertain and destructive nature, disasters invariably induce significant governmental responses and typically pose severe financial challenges for jurisdictions across all levels of government. From a public finance perspective, disasters cause governments to incur additional spending on various emergency management activities, and by disrupting normal business activities they also affect tax base robustness and cause revenue losses. The question is: How significant are these fiscal effects and how do they affect hazards governance more generally? Understanding the fiscal implications of natural disasters is essential to evaluating the size of the economic costs of disasters as well as forecasting governments’ financial exposure to future shocks. Furthermore, how disaster costs are shared among different levels of government is another important question concerning the intergovernmental dynamics of disaster management. In the US federal system, the direct fiscal costs of natural disasters (i.e., increased government expenditures due to disaster shocks) are largely borne by the federal government. It is estimated that Hurricane Katrina cost the federal government approximately $120 billion while Hurricane Sandy cost $60 billion. Even in the years without large-scale disaster events, federal disaster spending is between $2 billion and $6 billion annually. Under the Stafford Act, the federal government plays a critical role in funding disaster-related programs (e.g., direct relief, mitigation grants, and subsidized insurance programs) and redistributing the actual costs of natural hazards, meaning that a considerable portion of the local disaster burden is shifted to all US taxpayers. This raises a set of issues concerning the equity and efficiency of the US disaster policy framework. Managing disasters involves multiphased activities to mitigate, prepare for, respond to, and recover from disaster shocks. There is a common belief that the federal government inappropriately spends far more on ex post disaster response, relief, and recovery than what it spends on ex ante mitigation and preparedness, often driven by political motivations (e.g., meeting voters’ preferences for postdisaster aid) and the current budget rules. As pointed out by many others, federal disaster relief and assistance distort the subnational incentive to invest in local disaster prevention and mitigation efforts. Furthermore, given the mounting evidence on the cost-effectiveness of disaster mitigation programs in reducing future disaster damages, the current practice of focusing resources on postdisaster assistance means substantial public welfare losses. In recent years there has been a call for the federal government to shift its disaster policy emphasis toward mitigation and preparedness and also to facilitate local efforts on mitigation. To achieve the goal requires a comprehensive reform in government budgeting for emergency management.
Stephanie E. Chang
Infrastructure systems—sometimes referred to as critical infrastructure or lifelines—provide services such as energy, water, sanitation, transportation, and communications that are essential for social and economic activities. Moreover, these systems typically serve large populations and comprise geographically extensive networks. They are also highly interdependent, so outages in one system such as electric power or telecommunications often affect other systems. As a consequence, when infrastructure systems are damaged in disasters, the ensuing losses are often substantial and disproportionately large. Collapse of a single major bridge, for example, can disrupt traffic flows over a broad region and impede emergency response, evacuation, commuting, freight movement, and economic recovery. Power outages in storms and other hazard events can affect millions of people, shut down businesses, and even cause fatalities. Infrastructure outages typically last from hours to weeks but can extend for months or even years. Minimizing disruptions to infrastructure services is thus key to enhancing communities’ disaster resilience. Research on infrastructure systems in natural hazards has been growing, especially as major disasters provide new data, insights, and urgency to the problem. Engineering advances have been made in understanding how hazard stresses may damage the physical components of infrastructure systems such as pipes and bridges, as well as how these elements can be designed to better withstand hazards. Modeling studies have assessed how physical damage disrupts the provision of services—for example, by indicating which neighborhoods in an urban area may be without potable water—and how disruption can be reduced through engineering and planning. The topic of infrastructure interdependencies has commanded substantial research interest. Alongside these developments, social science and interdisciplinary research has also been growing on the important topic of how infrastructure disruption in disasters has affected populations and economies. Insights into these impacts derive from a variety of information sources, including surveys, field observations, analysis of secondary data, and computational models. Such research has established the criticality of electric power and water services, for example, and the heightened vulnerability of certain population groups to infrastructure disruption. Omitting the socioeconomic impacts of infrastructure disruptions can lead to underinvestment in disaster mitigation. While the importance of understanding and reducing infrastructure disruption impacts is well-established, many important research gaps remain.
Edward J. Oughton
Space weather is a collective term for different solar or space phenomena that can detrimentally affect technology. However, current understanding of space weather hazards is still relatively embryonic in comparison to terrestrial natural hazards such as hurricanes, earthquakes, or tsunamis. Indeed, certain types of space weather such as large Coronal Mass Ejections (CMEs) are an archetypal example of a low-probability, high-severity hazard. Few major events, short time-series data, and the lack of consensus regarding the potential impacts on critical infrastructure have hampered the economic impact assessment of space weather. Yet, space weather has the potential to disrupt a wide range of Critical National Infrastructure (CNI) systems including electricity transmission, satellite communications and positioning, aviation, and rail transportation. In the early 21st century, there has been growing interest in these potential economic and societal impacts. Estimates range from millions of dollars of equipment damage from the Quebec 1989 event, to some analysts asserting that losses will be in the billions of dollars in the wider economy from potential future disaster scenarios. Hence, the origin and development of the socioeconomic evaluation of space weather is tracked, from 1989 to 2017, and future research directions for the field are articulated. Since 1989, many economic analyzes of space weather hazards have often completely overlooked the physical impacts on infrastructure assets and the topology of different infrastructure networks. Moreover, too many studies have relied on qualitative assumptions about the vulnerability of CNI. By modeling both the vulnerability of critical infrastructure and the socioeconomic impacts of failure, the total potential impacts of space weather can be estimated, providing vital information for decision makers in government and industry. Efforts on this subject have historically been relatively piecemeal, which has led to little exploration of model sensitivities, particularly in relation to different assumption sets about infrastructure failure and restoration. Improvements may be expedited in this research area by open-sourcing model code, increasing the existing level of data sharing, and improving multidisciplinary research collaborations between scientists, engineers, and economists.