Natural disasters and economic growth: The role of banking market structure

Following a natural disaster, the rate of economic growth recovers faster in less competitive banking markets. A 10% reduction in competition increases the rate of economic growth by 0.3%. In less competitive markets, banks respond to a disaster by increasing the supply of real estate credit by refinancing mortgage loans, but do not lend more to businesses or consumers. Instead, government agencies provide disaster loans to affected businesses and households. Smaller, profitable and well-capitalized institutions that rely more on traditional retail banking originate most mortgage credit.     

A contingent plan for disaster response

Natural and man-made disasters imply a great deal of uncertainty in terms of potential damage, though it is certain that there would be a huge spike in the demand for relief supplies causing shortages and/or delays in providing aid. Ruptures in the infrastructure (roads, utility, and communication lines) cause additional delays due to repairs. Therefore, the relief providers need to work in collaboration with retailers, and infrastructure service providers for improving responsiveness. The relief providers (government and non government) rely on acquiring and delivering supplies in real time because such actions accompany little risk of resource underutilization, though the cost of real time acquisitions can be high. In contrast, a proactive response, while minimizing acquisition cost, can be very ineffective if demand surges are high. We study a hybrid of reactive and proactive approaches, where the reactive response is contingent upon the disaster intensity exceeding a certain threshold. We show how the threshold value may impact capacity acquisitions and prices and establish the optimality of contingent response. Further, we establish how an infrastructure contract may help reducing the social cost of disaster.     

Well-being effects of extreme weather events in the United States

The increase in weather and climate disasters in recent years has prompted an interest in analyzing their consequences and the mitigation and adaptation measures that can help minimize their potentially large impacts on individuals’ welfare. We match thirty-one billion-dollar disasters with individual survey data from the Behavioral Risk Factor Surveillance System to estimate the effect of extreme weather events on the subjective well-being of U.S. residents. Our results indicate that natural disasters have a negative and robust impact on subjective well-being in the affected communities, and that, on average, this impact peaks 6 months after the event, and then decays over time. We then investigate the attenuating impact of health care access, flood insurance, and governmental assistance programs and find a partial compensating role for risk-transfer and relief measures. We also find that stronger emotional and social support mitigates the negative impact of natural disasters.     

Forecasting in humanitarian operations: Literature review and research needs

Forecasting research in the humanitarian context is scarce. In this literature review, our goal is not only to show why forecasting research is important for the humanitarian sector, but also to identify what has been done so far, and where are the needs for further research. We conducted a structured literature search in Scopus, Web of Science, ABI Inform, and Google Scholar resulted in only 38 papers published between 1990 and 2018. Based on our findings we highlight three case studies as exemplary research in forecasting within the humanitarian context and list seven future research streams with specific research needs identified in each stream.     

Archetyping relationships with companion animals to understand disaster risk-taking propensity

Pets factor into the daily decision-making of many people. Importantly, various characteristics of these human–animal relationships are known to strongly influence pet owners’ risk behaviour and, consequently, their animals’ welfare during disasters. Yet, few studies have examined a range of such characteristics concurrently in order to describe risk propensity differences in these relationships. In this study, 437 Australian companion-animal (pet) owners reported human–animal relational, personality and attitudinal characteristics, to examine differences in stated tendency to act to secure their pet’s welfare whilst risking potential harm in a hypothetical disaster dilemma. Cluster analysis identified five archetypal profiles differing in relational, personality, attitude and risk-propensity characteristics, as well as in stated willingness to risk personal safety for the well-being of a pet. Results suggest that relational archetypes are an effective means of examining pet–owner risk propensity, to better understand owners’ risk-taking to protect their animals from harm during a disaster.     

Public-private collaboration for disaster risk management: A case study of hotels in Matsushima,Japan

This research paper focuses on public-private collaboration for disaster risk management in coastal destinations, particularly between the hotel industry and local government. To guide its research, this paper applies collaborative planning theory in developing a research framework to discover gaps between stakeholders attitudes towards collaboration, its outcomes, and obstacles preventing the adoption of specific hotel-based disaster management actions. A case study is used on Matsushima, Japan, a popular coastal destination in the Tohoku Region, which was devastated by the 2011 tsunami. Through surveys and interviews, this study found that hotels can play a key role in working together with the local government towards disaster risk management of coastal destinations. However collaboration gaps between the stakeholders limited the extent of the adoption of hotel-based disaster risk management initiatives.     

Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking

Weather forecasts, climate change projections, and epidemiological predictions all represent domains that are using forecast data to take early action for risk management. However, the methods and applications of the modeling efforts in each of these three fields have been developed and applied with little cross-fertilization. This perspective identifies best practices in each domain that can be adopted by the others, which can be used to inform each field separately as well as to facilitate a more effective combined use for the management of compound and evolving risks. In light of increased attention to predictive modeling during the COVID-19 pandemic, we identify three major areas that all three of these modeling fields should prioritize for future investment and improvement: (1) decision support, (2) conveying uncertainty, and (3) capturing vulnerability.     

公民有效参与灾后恢复重建:以汶川震后恢复重建为例——兼及托马斯"有效决策模型"的应用

灾后恢复重建工作法治化、民主化是《汶川地震灾后恢复重建条例》的一个亮点,也是中国自然灾害后恢复重建模式彻底转变的标志。公民参与有利于提高灾后恢复重建效率,有利于提升公众的满意度,有利于对政府工作的监督,还有利于灾后社会秩序的恢复与发展。必须立足现实,探索有效的公民参与机制,避免非制度化无序的公民参与所带来的消极影响,建构一个政府与社会良性互动的合作机制,调动一切力量实现灾后恢复重建的目标。     

The politics of natural disasters in protracted conflict: the 2014 flood in Kashmir

This paper explores the politics of the 2014 floods in the contentious and conflict-prone Indian state of Jammu and Kashmir. The September 2014 floods were the most serious natural disaster in the state in the past 60 years, and affected some two million people in the Kashmir valley. Drawing on qualitative interview evidence from 50 flood victims in south, central and north Kashmir, the paper examines the extent to which the disaster transformed existing political narratives. In doing so, it examines the role of the state and central governments, the army, local volunteers, and the media. The paper engages with the politics of disaster literature, exploring how disasters can serve as a lens rather than as a catalyst, and stressing the relevance of understanding the social construction of disaster narratives.     

The price of variance risk

Between 1996 and 2014, it was costless on average to hedge news about future variance at horizons ranging from 1 quarter to 14 years. Only unexpected, transitory realized variance was significantly priced. These results present a challenge to many structural models of the variance risk premium, such as the intertemporal CAPM and recent models with Epstein–Zin preferences and long-run risks. The results are also difficult to reconcile with macro models in which volatility affects investment decisions. At the same time, the data allows us to distinguish between different disaster models; a model in which the stock market has a time-varying exposure to disasters and investors have power utility fits the major features of the variance term structure.