An econometric analysis of the demand surge effect

Döhrmann, David; Gürtler, Marc; Hibbeln, Martin

doi:10.1007/s12297-013-0239-1

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…Clean-up costs can also be influenced by market forces, but the relationship is not straight-forward. Demand surge has been observed following large scale disasters where costs rapidly inflate by 10-50% comparatively with predisaster costs (Döhrmann et al 2013;Hallegatte and Ghil 2008;Olsen and Porter, 2011). However, costs can also decrease due to increased competition from a large number of contractors descending on an affected area (Brown et al 2016).…”

Section: Uncertainties Relating To Clean-up Costmentioning

confidence: 99%

A model to assess tephra clean-up requirements in urban environments

et al. 2017

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Tephra falls can cause a range of impacts to communities by disrupting, contaminating and damaging buildings and infrastructure systems, as well as posing a potential health hazard. Coordinated clean-up operations minimise the impacts of tephra on social and economic activities. However, global experience suggests clean-up operations are one of the most challenging aspects of responding to and recovering from tephra falls in urban environments. Here, we present a method for modelling coordinated municipal-led (town/district level authorities) tephra clean-up operations to support pre-event response and recovery planning. The model estimates the volume of tephra to be removed, clean-up duration, and direct costs. The underpinning component of the model is a scalable clean-up response framework, which identifies and progressively includes more urban surfaces (e.g., roofs, and roads) requiring clean-up with increasing tephra thickness. To demonstrate model applicability, we present four clean-up scenarios for the city of Auckland, New Zealand: 1 mm and 10 mm distal tephra fall across the city, along with two local 'wet' eruption scenarios (low and high volume tephra deposition) from within the Auckland Volcanic Field. Depending on the modelled scenario, outputs suggest that coordinated clean-up operations in Auckland could require the removal of tens of thousands to millions of cubic metres of tephra. The cost of these operations are estimated to be NZ$0.6-1.1 million (US$0.4-0.7 million) for the 1 mm distal tephra scenario and NZ$13.4-25.6 million (US$9-17 million) for the 10 mm distal tephra scenario. Estimated clean-up costs of local eruptions range from tens of millions to hundreds of millions of dollars. All eruption scenarios indicate clean-up operations lasting weeks to months, but clean-up in some areas impacted by local eruptions could last for years. The model outputs are consistent with documented historic tephra clean-up operations. Although we use Auckland as a proof-of-concept example, the method may be adapted for any city exposed to a tephra hazard.

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Section: Uncertainties Relating To Clean-up Costmentioning

confidence: 99%

A model to assess tephra clean-up requirements in urban environments

et al. 2017

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“…Most studies in the demand surge area focus on the general economic losses following natural hazards (Hallegatte, 2008; Belasen and Polachek, 2009; Döhrmann et al , 2013). Few studies investigated how labor markets react to the natural hazards.…”

Section: Research Backgroundmentioning

confidence: 99%

“…They found that higher wind speeds in the cities with respect to the distance between the wind center and the cities could result in higher labor cost increases in the Residential Building sub-sector. Döhrmann et al (2013) used retail labor price index data from 2002 to 2009 in the United States to compare changes in retail labor price indices in the affected regions with that of the United States. They calculated the cumulative relative percent changes in a two-year horizon following large-scale natural hazards.…”

Section: Research Backgroundmentioning

confidence: 99%

Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the U.S. construction sector

Esfahani

Shahandashti

2020

JFMPC

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Purpose The primary objectives of this study are to (1) highlight subsectors and industry groups of the construction sector that are most vulnerable to weather-related disasters (with highest labor cost escalation) and (2) analyze how immediate this labor wage escalation happens in different subsector of the construction sector. Design/methodology/approach The research methodology consists of three steps: (i) integrating various data sources to enable measurement of the county-level labor wage changes following large-scale weather-related disasters; (ii) measuring postdisaster labor wage changes at the county level; and (iii) comparing amount and timing of postdisaster labor wage changes among all sub-sectors (and industry groups) of the construction sector. Findings The results show that among the three construction subsectors (Heavy and Civil Engineering Construction subsector, Construction of Buildings subsector, and Specialty Trade Contractors sub-sector), Heavy and Civil Engineering Construction subsector is the most vulnerable to weather-related disasters. The industry groups under the Heavy and Civil Engineering Construction subsector showed the same vulnerability level; however, under the Construction of Buildings subsector, Industrial Building Construction industry group showed to be the most vulnerable; and under the Specialty Trade Contractors subsector, the Building Foundation and Exterior Contractors industry group is the most vulnerable. The results also showed that in approximately 75% of the damaged counties, there were increases in wages of all construction labors, over the following three quarter after the disasters. In average, labor wages in Construction of Buildings subsector and the Specialty Trade Contractors subsector decreased by 0.6% and 0.8%, respectively, in the quarter of disaster and gradually increased by 4.4% and 4.6%, respectively, in the following three quarters. On the other hand, Heavy and Civil Engineering Construction’s labor wages did not experience this decrease right after the disasters; wages increased immediately after disasters hit the counties and continually increased by 8.6% in three quarters after the disasters. It is expected that the results of this study will help policy makers, cost estimators and insurers to have a better understanding of the post-disaster construction labor wage fluctuations. Originality/value This study is unique in the way it used construction labor wage data. All data are location quotient, which makes the comparison among the affected counties (with different construction size) feasible.

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“…Disregarding this effect might lead to inaccurate comparisons between observed and estimated impacts. In this context, several studies have investigated how PLA should be defined, what should be included, its possible causes, how to measure it, and how to model it (Do¨hrmann et al, 2013;General Insurance Practice Committee, 2015;Olsen and Porter, 2010, 2011a, 2011b. Olsen and Porter (2010) developed a remarkable basis on this topic, describing the main definitions that have been used in the insurance, academic, and governmental sectors since 1970.…”

Section: Introductionmentioning

confidence: 99%

“…The unaccounted increase in losses due to catastrophic events has led to solvency issues in the insurance and reinsurance sector in the past (e.g. Hurricane Katrina 2005; Northridge Earthquake 1994), as well as to budgeting shortfalls in local and national governments during the reconstruction and recovery stages (Döhrmann et al, 2013; Olsen and Porter, 2011b). This phenomenon known as post-loss amplification (PLA) is common in large disasters and can be defined as the additional or increased cost in the aftermath of the event.…”

Section: Introductionmentioning

confidence: 99%

A database and empirical model for earthquake post-loss amplification

Hoyos,

Silva

2023

Earthquake Spectra

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The impact of destructive earthquakes might exceed the local capacity to cope with disasters and lead to an increase in the reconstruction costs. This phenomenon is commonly termed as post-loss amplification, and its main causes include the increase in the cost of construction materials and labor due to the sudden demand, the need to reconstruct following higher standards, or other unexpected costs. We reviewed 70 past earthquakes to identify events where post-loss amplification was observed, and collected a set of seismogenic, socio-economic, geographical, and impact variables for those events. Using this database, we developed two models to predict post-loss amplification, using a composite indicator that reflects the level of destruction in the region, or a parameter that characterizes the frequency of the event. This study indicates increased costs (>10%) for events where the economic losses exceed 1% of the regional gross domestic product, or for events with an estimated return period of at least 10 years. These models can be applied directly in the amplification of economic losses in earthquake scenarios or in probabilistic seismic risk assessment.

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An econometric analysis of the demand surge effect

Cited by 13 publications

References 11 publications

A model to assess tephra clean-up requirements in urban environments

A model to assess tephra clean-up requirements in urban environments

Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the U.S. construction sector

A database and empirical model for earthquake post-loss amplification

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