The COVID-19 pandemic has caused a massive economic shock across the world due to business interruptions and shutdowns from social-distancing measures. To evaluate the socioeconomic impact of COVID-19 on individuals, a micro-economic model is developed to estimate the direct impact of distancing on household income, savings, consumption, and poverty. The model assumes two periods: a crisis period during which some individuals experience a drop in income and can use their savings to maintain consumption; and a recovery period, when households save to replenish their depleted savings to pre-crisis level. The San Francisco Bay Area is used as a case study, and the impacts of a lockdown are quantified, accounting for the effects of unemployment insurance (UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of three months, the poverty rate would temporarily increase from 17.1% to 25.9% in the Bay Area in the absence of social protection, and the lowest income earners would suffer the most in relative terms. If fully implemented, the combination of UI and CARES could keep the increase in poverty close to zero, and reduce the average recovery time, for individuals who suffer an income loss, from 11.8 to 6.7 months. However, the severity of the economic impact is spatially heterogeneous, and certain communities are more affected than the average and could take more than a year to recover. Overall, this model is a first step in quantifying the household-level impacts of COVID-19 at a regional scale. This study can be extended to explore the impact of indirect macroeconomic effects, the role of uncertainty in households' decision-making and the potential effect of simultaneous exogenous shocks (e.g., natural disasters).

Summary Regional seismic risk assessments and quantification of portfolio losses often require simulation of spatially distributed ground motions at multiple intensity measures. For a given earthquake, distributed ground motions are characterized by spatial correlation and correlation between different intensity measures, known as cross‐correlation. This study proposes a new spatial cross‐correlation model for within‐event spectral acceleration residuals that uses a combination of principal component analysis (PCA) and geostatistics. Records from 45 earthquakes are used to investigate earthquake‐to‐earthquake trends in application of PCA to spectral acceleration residuals. Based on the findings, PCA is used to determine coefficients that linearly transform cross‐correlated residuals to independent principal components. Nested semivariogram models are then fit to empirical semivariograms to quantify the spatial correlation of principal components. The resultant PCA spatial cross‐correlation model is shown to be accurate and computationally efficient. A step‐by‐step procedure and an example are presented to illustrate the use of the predictive model for rapid simulation of spatially cross‐correlated spectral accelerations at multiple periods.

Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households' well-being. However, for a given asset loss, a wealthy household might easily recover, while a poor household might suffer from major, long-lasting impacts. Ignoring such differential impacts can lead to inequitable interventions and exacerbate the impact of disasters on vulnerable populations. This research proposes a methodology for assessing socioeconomic effects of disasters that integrates the three pillars of sustainability: (1) environmental, i.e. environmental hazard and asset damage modeling; (2) economic, i.e. macro-economic modeling to quantify changes in sectors' production and employment; and (3) social, i.e. micro-simulations of disaster recovery at the household level. The model innovates by assessing the impact of disasters on people's consumption, considering asset losses and changes in income among other factors. We apply the model to quantify the effect of a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on poorer and richer households. The analysis reveals that poorer households suffer only 19% of the overall asset losses, but experience 41% of the well-being losses. The well-being losses extend over a larger region than that of severe asset losses, requiring design of policies to help people recover, in addition to reducing asset losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities, depending on their built environment and social and economic profiles. 1/20This is a non-peer reviewed preprint submitted to EarthArXiv and housing services, cost of reconstruction, and use of resources such as savings or insurance payouts in the process of recovery.Measuring disaster impacts with utility instead of consumption allows one to account for the differential impact of losing $1 in consumption, as a function of wealth. While richer individuals can reduce their consumption with limited impact on their well-being, poorer individuals cannot. At the extreme, the very poor have to reduce consumption of food, education, or health care. The immediate impact of such cuts on well-being can be large, and for children can have consequences that last a lifetime 16,17 .The well-being quantification methodology in this paper integrates the three aspects of sustainability: environmental (the impact of the hazard), economic (the cost of damages and implication for jobs and income), and social (the distributional impact of the shock and the role of socioeconomic factors). It builds on previous research 18,19 and uses a multi-stage simulation that explicitly quantifies damages to the built environment, post-disaster dynamics of economic sectors, and changes in household consumption across socioeconomic groups, while propagating uncertainties associated with disasters modeling. While previous approaches for evaluating disaster management policies typically focu...

Post earthquake decisions on whether to repair or to demolish and rebuild a damaged commercial building can be influenced by factors other than repair costs. These factors include the property's ability to generate income and the conditions of the real estate market—factors not currently considered in seismic performance estimation models. This paper introduces a framework that unifies performance-based earthquake engineering and real estate investment analysis to model cases in which repair of damaged buildings is feasible, but redevelopment or leaving the building unrepaired and vacant might offer greater economic value. A three-stage approach for quantifying the likelihood of repair, redevelopment, or leaving the property vacant is proposed. First, building seismic performance analysis is conducted using FEMA P-58 and Resilience-based Earthquake Design Initiative (REDi) methodologies; then, given repair and redevelopment costs and times, the net present value decision rule is used to evaluate alternative outcomes; and finally, the results from the two stages are integrated to quantify the probability of different decisions. An illustrative case study of four reinforced concrete buildings highlights the insights provided by the proposed framework.

Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households’ well-being. However, for a given asset loss, a wealthy household might easily recover, while a poor household might suffer from major, long-lasting impacts. Ignoring such differential impacts can lead to inequitable interventions and exacerbate the impact of disasters on vulnerable populations. This research proposes a methodology for assessing socioeconomic effects of disasters that integrates the three pillars of sustainability: (1) environmental, i.e. environmental hazard and asset damage modeling; (2) economic, i.e. macro-economic modeling to quantify changes in sectors’ production and employment; and (3) social, i.e. micro-simulations of disaster recovery at the household level. The model innovates by assessing the impact of disasters on people’s consumption, considering asset losses and changes in income among other factors. We apply the model to quantify the effect of a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on poorer and richer households. The analysis reveals that poorer households suffer only 19% of the overall asset losses, but experience 41% of the well-being losses. The well-being losses extend over a larger region than that of severe asset losses, requiring design of policies to help people recover, in addition to reducing asset losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities, depending on their built environment and social and economic profiles.