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Earthquake insurance is a critical risk management strategy that contributes to improving recovery and thus greater resilience of individuals. Insurance companies construct premiums without taking into account spatial correlations between insured assets. This leads to potentially underestimating the risk, and therefore the exceedance probability curve. We here propose a mixed-effects model to estimate losses per ward that is able to account for heteroskedasticity and spatial correlation between insured losses. Given the significant impact of earthquakes in New Zealand due to its particular geographical and demographic characteristics, the government has established a public insurance company that collects information about the insured buildings and any claims lodged. We thus develop a two-level variance component model that is based on earthquake losses observed in New Zealand between 2000 and 2021. The proposed model aims at capturing the variability at both the ward and territorial authority levels and includes independent variables, such as seismic hazard indicators, the number of usual residents, and the average dwelling value in the ward. Our model is able to detect spatial correlation in the losses at the ward level thus increasing its predictive power and making it possible to assess the effect of spatially correlated claims that may be considerable on the tail of loss distribution.

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Extreme weather events lead to significant adverse societal costs. Extreme Event Attribution (EEA), a methodology that examines how anthropogenic greenhouse gas emissions had changed the occurrence of specific extreme weather events, allows us to quantify the climate change-induced component of these costs. We collect data from all available EEA studies, combine these with data on the socio-economic costs of these events and extrapolate for missing data to arrive at an estimate of the global costs of extreme weather attributable to climate change in the last twenty years. We find that US[Formula: see text] 143 billion per year of the costs of extreme events is attributable to climatic change. The majority (63%), of this is due to human loss of life. Our results suggest that the frequently cited estimates of the economic costs of climate change arrived at by using Integrated Assessment Models may be substantially underestimated.

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Both climate scientists and non-scientists (laypeople) attribute extreme weather events to various influences. Laypeople's attributions for these events are important as these attributions likely influence their views and actions about climate change and extreme events. Research has examined laypeople's attribution scepticism about climate change in general; however, few climate scientists are familiar with the processes underpinning laypeople's attributions for individual extreme events. Understanding these lay attributions is important for scientists to communicate their findings to the public. Following a brief summary of the way climate scientists calculate attributions for extreme weather events, we focus on cognitive and motivational processes that underlie laypeople's attributions for specific events. These include a tendency to prefer single-cause rather than multiple-cause explanations, a discounting of whether possible causes covary with extreme events, a preference for sufficient causes over probabilities, applying prevailing causal narratives, and the influence of motivational factors. For climate scientists and communicators who wish to inform the public about the role of climate change in extreme weather events, these patterns suggest several strategies to explain scientists' attributions for these events and enhance public engagement with climate change. These strategies include showing more explicitly that extreme weather events reflect multiple causal influences, that climate change is a mechanism that covaries with these events and increases the probability and intensity of many of these events, that human emissions contributing to climate change are controllable, and that misleading communications about weather attributions reflect motivated interests rather than good evidence.

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Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA's 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

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This paper compares economic recovery in the COVID-19 pandemic with other types of disasters, at the scale of businesses. As countries around the world struggle to emerge from the pandemic, studies of business impact and recovery have proliferated; however, pandemic research is often undertaken without the benefit of insights from long-standing research on past large-scale disruptive events, such as floods, storms, and earthquakes. This paper builds synergies between established knowledge on business recovery in disasters and emerging insights from the COVID-19 pandemic. It first proposes a disaster event taxonomy that allows the pandemic to be compared with natural hazard events from the perspective of economic disruption. The paper then identifies five key lessons on business recovery from disasters and compares them to empirical findings from the COVID-19 pandemic. For synthesis, a conceptual framework on business recovery is developed to support policy-makers to anticipate business recovery needs in economically disruptive events, including disasters. Findings from the pandemic largely resonate with those from disasters. Recovery tends to be more difficult for small businesses, those vulnerable to supply chain problems, those facing disrupted markets, and locally-oriented businesses in heavily impacted neighborhoods. Disaster assistance that is fast and less restrictive provides more effective support for business recovery. Some differences emerge, however: substantial business disruption in the pandemic derived from changes in demand due to regulatory measures as well as consumer behaviour; businesses in high-income neighborhoods and central business districts were especially affected; and traditional forms of financial assistance may need to be reconsidered.

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Aotearoa New Zealand is highly vulnerable to extratropical cyclones because of its unique location in the midlatitude south pacific region. This study empirically investigates the impact of the extratropical cyclones on individual income, combining the data from Statistics New Zealand's Integrated Data Infrastructure (IDI) and the weather-related insurance claims data from the Earthquake Commission. Our sample covers the administrative longitudinal panel data of all the IRD registered individual taxpayers between 2010 and 2019. We estimate a set of panel regressions with individual and time-fixed effects to assess the impact of extratropical cyclones on the affected individual's annual income. We find that income from salaries and wages is negatively affected by the cyclones across various specifications. Extratropical cyclones also negatively affect the total individual income from wages and salaries, benefit and compensation, and sole tradership. However, we have limited success in identifying individual characteristics influencing the affected people's income level in our study.

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If economists have largely failed to predict or prevent the Global Financial Crisis in 2008, and the more disastrous economic collapse associated with the pandemic of 2020, what else is the profession missing? This is the question that motivates this survey. Specifically, we want to highlight four catastrophic risks - i.e., risks that can potentially result in global catastrophes of a much larger magnitude than either of the 2008 or 2020 events. The four risks we examine here are: Space weather and solar flares, super-volcanic eruptions, high-mortality pandemics, and misaligned artificial intelligence. All four have a non-trivial probability of occurring and all four can lead to a catastrophe, possibly not very different from human extinction. Inevitably, and fortunately, these catastrophic events have not yet occurred, so the literature investigating them is by necessity more speculative and less grounded in empirical observations. Nevertheless, that does not make these risks any less real. This survey is motivated by the belief that economists can and should be thinking about these risks more systematically, so that we can devise the appropriate ways to prevent them or ameliorate their potential impacts.

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We evaluate the 1968 H3N2 Flu pandemic's economic cost in a cross-section of 52 countries. Using excess mortality rates as a proxy for the country-specific severity of the pandemic, we find that the average mortality rate (0.0062% per pandemic wave) was associated with a decline in output of 2.4% over the two pandemic waves. Our estimates also suggest the losses in consumption (-1.9%), investment (-1.2%), and productivity (-1.9%) over the two pandemic waves. The results are robust across regressions using alternative measures of mortality and output loss. The study adds to the current literature new empirical evidence on the economic consequences of the past pandemics in light of the potential impacts of the Covid-19 pandemic on productivity.

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Typically, disaster damages are measured separately in four dimensions: fatalities, injuries, dislocations, and the financial damage that they wreak. Noy (2016) developed a lifeyears index of disaster damage which aggregates these disparate measures. Here, we use this lifeyears index to assess the costs of the COVID-19 pandemic across countries and compare these costs to the average annual costs of all other disasters that have occurred in all countries in the past 20 years. We find that the costs of the pandemic, measured for 2020, far outweigh the annual costs associated with other disasters in the past two decades. It is the economic loss that dominates this impact. The human and social implications of this economic loss are plausibly much greater than the direct toll in mortality and morbidity in almost all countries. Finally, it is small countries like the Maldives and Guyana that have experienced the most dramatic and painful crisis, largely under the radar of the world's attention. Our conclusion from these findings is not that governments' policy reactions were unwarranted. If anything, we find that the loss of lifeyears is correlated positively across the three dimensions we examine. Countries that experienced a deeper health crisis also experienced a deeper economic one.

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This study describes the application of multi-criteria decision analysis (MCDA) to prioritise the seismic risk mitigation of existing buildings in earthquake-prone Wellington, New Zealand. Through retrofitting or demolition, this is an important requirement in many cities around the Pacific Rim and in other high-level seismic hazard locations. The prioritisation strategy proposed here, based on MCDA methods, can provide decision-makers with a fast and reliable support tool for identifying the optimal sequencing of their retrofitting programmes. The premise of the MCDA analysis presented in this paper is that there are multiple criteria that determine societal prioritisation preferences; these are limited not just to life safety (often the explicit/exclusive priority of governments) and commercial value (the main concern of many building owners). The study demonstrates how different measures, within four general criteria-life safety and geo-spatial, economic, and socio-cultural roles-can be operationalised as a viable framework for establishing intervention policy priorities.

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New Zealand's public insurer for natural hazards, the Earthquake Commission (EQC), provides residential insurance for some weather-related damage. Climate change and the expected increase in intensity and frequency of extreme weather-related events are likely to translate into higher damages and thus an additional financial liability for the EQC. We project future insured damages from extreme precipitation events associated with future projected climatic change. We first estimate the empirical relationship between extreme precipitation events and the EQC's weather-related insurance claims based on a complete dataset of all claims from 2000 to 2017. We then use this estimated relationship, together with climate projections based on future greenhouse gases concentration scenarios from six different dynamically downscaled Regional Climate Models, to predict the impact of future extreme precipitation events on EQC liabilities for different time horizons up to the year 2100. Our results show predicted adverse impacts that vary over time and space. The percent change between projected and past damages-the climate change signal-ranges between an increase of 7%-8% in liabilities for the period 2020 to 2040, and between 9% and 25% higher for the period 2080 to 2100. We also provide detail caveats as towhy these quantities might be mis-estimated. The projected increase in the public insurer's liabilities could also be used to inform private insurers, regulators, and policymakers who are assessing the future performance of both the public and private insurers that cover weatherrelated.

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New Zealand introduced a seismic retrofitting policy in the wake of the catastrophic Canterbury earthquakes of 2010-11. The aim was to enforce seismic strengthening of earthquake-prone commercial buildings throughout the country. This study focuses on regional urban centres and the economic obstacles to strengthening their aging building stock. In investigating one town, Whanganui, we describe conditions, analyse cases, and identify incentives that apply equally to many other towns in New Zealand. We argue that incentives that suit high-growth, high-value major urban centres are a poor fit for the periphery. Around the world, many places need to upgrade their privately-owned building stock to protect it from disasters, while governments face similar challenges as they struggle to initiate the strengthening of commercial buildings. We analyse the current incentive schemes that aim to support the achievement of policy goals and suggest alternative incentive schemes that can be implemented to improve strengthening outcomes.

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Earthquakes are insured in high-risk high-income countries only if the public sector is involved. Prototypical examples are the insurance schemes in California (United States), Japan, and New Zealand, but each is structured differently. This paper examines these variations using a concrete case study: the sequence of earthquakes in Christchurch, New Zealand, in 2010-11-the most heavily insured seismic event in history. It assesses what would have been the outcome had the Christchurch insurance system been different, focusing on the California Earthquake Authority (CEA) programme and Japan Earthquake Reinsurance (JER). Overall, the aggregate cost of the earthquake to the New Zealand public insurer (Earthquake Commission) was USD 6.2 billion. If a similar-sized disaster had occurred in Japan and California, homeowners would have received around USD 1.6 billion and USD 0.7 billion, respectively. This paper describes the distributive and spatial patterns of these scenarios and discusses some key policy questions that emerge from this comparison.

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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.

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We quantify the 'permanent' socio-economic impacts of the Great Hanshin-Awaji (Kobe) earthquake in 1995 by employing a large-scale panel dataset of 1,719 cities, towns, and wards from Japan over three decades. In order to estimate the counterfactual--i.e., the Kobe economy without the earthquake--we use the synthetic control method. Three important empirical patterns emerge: First, the population size and especially the average income level in Kobe have been lower than the counterfactual level without the earthquake for over fifteen years, indicating a permanent negative effect of the earthquake. Such a negative impact can be found especially in the central areas which are closer to the epicenter. Second, the surrounding areas experienced some positive permanent impacts in spite of short-run negative effects of the earthquake. Much of this is associated with movement of people to East Kobe, and consequent movement of jobs to the metropolitan center of Osaka, that is located immediately to the East of Kobe. Third, the furthest areas in the vicinity of Kobe seem to have been insulated from the large direct and indirect impacts of the earthquake.

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