Search

Motivating property owners to mitigate seismic risks for existing buildings is a major challenge for many earthquake-prone regions. This article identifies primary factors that may affect the adoption of seismic retrofit by owners of commercial and residential buildings, assesses the influence of economic, social, regulatory, and individual factors on retrofit implementation in three California cities, and discusses potential approaches to promoting seismic retrofits. Data for three retrofit programs are utilized to create predictive models for retrofit probability. The results suggest that retrofit probability for multifamily residential buildings may increase with building height, median housing value, educational attainment, and population density in the neighborhood, but may decrease with building age, building size, land value, and housing vacancy rate in the neighborhood. The retrofit decision for commercial buildings is strongly correlated with the number of stories and rooms, land value, vacancy rate, and population density, while the retrofit decision for residential buildings is highly associated with building age, number of rooms, land value, median housing value, median contract rent, and educational attainment. Overall, promoting seismic retrofits requires careful consideration of different motivators and impediments to owner’s retrofit actions for commercial and residential buildings, and for older, taller, larger buildings, which tend to be more vulnerable but are associated with higher retrofit costs. In addition, neighborhood characteristics including median housing value and vacancy rate may be strong indicators of the retrofit probability among building clusters.

This article presents the current state-of-practice with respect to quantifying the total cost to retrofit an existing building. In particular, we combine quantitative, qualitative, and heuristic data to provide a taxonomy for understanding the direct and indirect costs associated with seismic risk mitigation. Much of the literature to date has focused on estimating structural retrofit costs, the costs of retrofitting the structural elements of a building. In contrast, there is very little research or data on the remaining cost components of the total cost. We propose using structural cost as the foundation for approximating the remaining cost components and the total cost itself. To validate our findings, we compare the proposed approximations with actual cost estimates developed by engineering professionals.

This article reviews the current state of practice in seismic risk mitigation, focusing on policies in ten of the most earthquake-prone countries around the world. In particular, the review compare...

We introduce a version of the Cumulative Offer Algorithm of Hatfield and Milgrom (Am Econ Rev 95(4):913–935, 2005) modified for the supply chain networks setting of Ostrovsky (Am Econ Rev 98(3):897–923, 2008). The algorithm provides an alternative proof for existence of a chain stable network. Moreover, we show that when choice functions satisfy same-side substitutes and cross-side complements, and the Law of Aggregate Demand and Supply, then contracts satisfy Irrelevance of Rejected Contracts condition, a condition implicitly assumed in Ostrovsky (2008).

Cedar Rapids, IA, offers a unique case study in planning for increased resilience. In 2008, Cedar Rapids experienced severe flooding. Rather than simply rebuilding, the city of Cedar Rapids began to invest in a resilient flood control system and in the revitalization of its Downtown neighborhood. This paper develops a Computable General Equilibrium (CGE) model for the regional economy of Cedar Rapids to quantify 'resilience dividends': net co-benefits of investing in increased resilience. A resilience dividend includes benefits to the community even if another disaster does not occur. We build a CGE model of Cedar Rapids at two different time periods: one in 2007, before the flooding, and one in 2015, after the flooding and initial investment in resilience. We show that a positive economic shock to the economy results in larger co-benefits for key economic indicators in 2015 than in 2007. Our approach illustrates how co-benefits are distributed throughout the economy.

This article presents a methodology for estimating seismic retrofit costs from historical data. In particular, historical retrofit-cost data from Federal Emergency Management Agency (FEMA) 156 is used to build a generalized linear model (GLM) to predict retrofit costs as a function of building characteristics. While not as accurate as an engineering professional’s estimate, this methodology is easy to apply to generate quick estimates and is especially useful for decision makers with large building portfolios. Moreover, the predictive modeling approach provides a measure of uncertainty in terms of prediction error. The article uses prediction error to compare different modeling choices, including the choice of distribution for costs. Finally, the proposed retrofit-cost model is implemented to estimate the cost to retrofit a portfolio of federal buildings. The application illustrates how the choice of distribution affects cost estimates.

The goal of this report is to provide small- and medium-sized enterprise (SME) stakeholders (e.g., owners and managers) who took part in the NIST Business Survey: COVID-19 Impact and Recovery survey1 between July 8 and August 8, 2020 with a summary of self-reported data. This survey was the initial phase of a longitudinal research effort on SME Complex Event Resilience (CER) undertaken by the Applied Economics Office (AEO) at the National Institute of Standards and Technology (NIST) and the National Oceanic and Atmospheric Administration (NOAA).

In 2017, U.S. damages from natural hazard events exceeded $300B, suggesting that current targets for building performance do not sufficiently mitigate loss. The significant costs borne by individuals, insurers, and government do not include impacts from social disruption, displacement, and subsequent economic and livelihood effects. In 2016, Congress mandated the National Institute of Standards and Technology (NIST) develop a report (NIST SP 1224) describing the research needs, implementation activities, and engineering principles necessary to improve the performance of residential and commercial buildings subjected to natural hazards. An Immediate Occupancy Performance Objective (IOPO) could help preserve building and social functions post event, minimizing physical, social, and economic disaster. The stakeholder-informed NIST report sets forth items needed for multi-hazard building design that can support enhanced resilience decision-making. This paper highlights the social and economic considerations that require additional research, particularly with regard to feasibility and potential impacts from an IOPO. These topics must be considered prior to and throughout the IOPO technical development and community implementation processes to ensure better outcomes after natural hazard events.

In early October 2016, Hurricane Matthew crossed North Carolina as a Category 1 storm, with some areas receiving 0.38–0.46 m (15–18 in.) of rainfall on already saturated soil. The NIST-fund...

In 2017, United States damages from natural hazard events exceeded $300 billion, suggesting that current targets for building performance do not sufficiently mitigate loss. The significant ...

In the wake of the COVID-19 pandemic small businesses made headlines as hard hit by customer losses, revenue declines, and business closures. Yet, the impacts have been felt disproportionately by small businesses that suffered interruption due to pre-existing socioeconomic stressors and/or concurrent natural hazards experienced during the pandemic. To illuminate those compound impacts, we conducted a survey of over 1350 U S.-based small businesses. Our findings indicate that those businesses that experienced concurrent natural hazards during the pandemic were associated with relatively greater negative impacts. But importantly, enterprises that are historically underrepresented group operated (HUGO)-minority, women, and veteran-operated businesses- saw largely amplified negative impacts from COVID-19. In terms of the magnitude of COVID-19 impacts, the effect size of belonging to HUGO was more than twice as large as the effect size of experiencing a concurrent natural hazard during the pandemic. These results provide evidence for the disproportionate impacts that HUGOs face due to the pandemic, which are exacerbated when compounded by natural hazards. Given these results, there is evidence that the opportunity gap between HUGO and non-HUGO businesses is significant ahead of additional stressors or shocks. This opportunity gap is further accelerated when compounded with other events, here the compounding of natural hazards and COVID-19. Additional interventions need to be offered to HUGO businesses in areas with high likelihood of overlapping incidents. Further work is required to address social inequity and economic fragility of HUGO businesses, especially those that face the complexity of additional shocks, such as natural hazards.

A matching problem exists when two sets of agents want to find each other. Platform markets can provide a solution to this problem. However, existing models of platform markets restrict agent heterogeneity and thus the matching problem is trivial. We introduce agent heterogeneity by modeling platform markets as a matching problem. This raises three important questions. The first question is how to model matching in platform markets. We propose a model in which platforms are objects of choice, with agents matching directly with each other. Second, we adopt stability as our equilibrium concept since it eliminates the possibility of an individual or group of agents switching in equilibrium. Third, we consider properties of equilibrium. In particular, we show that opposition of interests at stable matchings implies a form of the “seesaw principle:” in equilibrium, one side gets more favorable terms at the expense of the other side. Moreover, we provide conditions under which the market may tip toward a dominant platform in equilibrium. Finally, we show that coordination failure occurs if agents from the same side are required to join a platform under the same terms. In this environment, we obtain stability if agents have lexicographic preferences. Our matching model provides a new approach to studying platform markets.

We introduce a version of the Cumulative Offer Algorithm of Hatfield and Milgrom (2005) modified for the supply chain networks setting of Ostrovsky (2008). The algorithm provides an alternative proof for existence of a chain stable network. When sellers propose downstream, the algorithm produces the allocation that is optimal for suppliers of basic inputs. Moreover, the proof illustrates the importance of Aygun and Sonmez (2012b)'s Irrelevance of Rejected Contracts (IRC) condition for establishing existence in the supply chain networks setting.