Ali Asgary

Estimating rural households' willingness to pay for health insurance.

In many developing countries limited health budgets are a serious problem. Innovative ways to raise funds for the provision of health services, for example, through health care insurance, have a high priority. Health care insurance for rural households shields such patients from unexpected high costs of care. However, there are questions about whether, and how much, rural households are willing to pay to purchase such insurance, as well as the factors determining willingness to pay. In recent years the Iranian government has tried to improve health and medical services to rural areas through a health insurance program. This study was conducted to estimate rural households’ demand and willingness to pay for health insurance. A contingent valuation method (CVM) was applied using an iterative bidding game technique. Data has been collected from a sample of 2,139 households across the country.

Effects of the Bam earthquake on employment: A shift-share analysis

Disasters have potential short-term and long-term impacts on employment and employment structures in affected regions. While measuring the full economic impact of a disaster requires sophisticated econometrics and mathematical simulations, conventional regional economic models such as shift-share analysis can be used to assess some of these effects. This paper applies shift-share analysis to understand potential long-term impacts of disasters on employment using the December 2003 Bam earthquake as a case study. The results provide further evidence that disasters could have significant long-term effects on the employment structure of affected regions.

Estimating willingness to pay for a hypothetical earthquake early warning systems

Abstract The development of reliable, accessible, and transparent earthquake early warning systems (EEWSs) for disaster reduction have been given increased priority at local, national, and international levels. Accurately quantifying the social and economic benefits accrued to households and businesses from EEWSs are a challenging and difficult task. In this paper, the Contingent Valuation Method (CVM) is used to evaluate the benefits of a hypothetical EEWS to the citizens of Tehran Metropolitan. This study clarifies public willingness to pay (WTP) for EEWS in Tehran, and the dominant factors involved in WTP through a CVM analysis. The survey, completed by more than 504 households, showed that on average households are willing to pay 367,471 Rials (~38 US

Impacts of temporary housing on housing reconstruction after the Bam earthquake

) per month for the hypothetical EEWS. Those willing to pay the most for EEWS are households, which currently possess a fire alarm. Also the more educated the respondents and the more children the respondents have, the more willing they are to pay for EEWS. These results could be used by policy makers and technology firms in order to determine the optimal investments in early warning systems for earthquake disaster reduction.

MODELLING THE ADAPTATION OF BUSINESS CONTINUITY PLANNING BY BUSINESSES USING NEURAL NETWORKS

Purpose – This study aims to examine the impacts of temporary housing on housing reconstruction after disasters using data collected after the December 2003 Bam earthquake. Design/methodology/approach – A questionnaire was developed and completed by 281 disaster-impacted households living in small and large campsites and on site of damaged properties provided by government and non-government agencies. Cross tabulation analyses were carried out to analyze the data. Findings – Results of this study show that the type of temporary housing (campsite and on property site) that had been provided to the victims has impacted the speed, quality and the overall satisfaction of the housing reconstruction. Practical implications – Provision of on-site temporary housing to disaster impacted population where possible could speed up a higher quality and more satisfactory housing reconstruction. Originality/value – This paper provides original research evidences for the relationship between type of temporary housing and ...

Operator support for ageing nuclear critical infrastructure systems: integrating ecological interface design with prospect theory

SUMMARY Business continuity planning is an important element of business continuity management and is regarded as a fundamental step towards reducing the negative impacts of business disruptions caused by internal and external hazardous events. Many businesses are not prepared for such events, and very few studies have tried to examine and model the factors that contribute to business continuity management planning by various companies. In this paper we propose and develop a feed-forward neural network for modelling businesses continuity planning by businesses based on a dataset of 283 businesses operating in the Greater Toronto Area in Ontario, Canada. The fully connected neural network applied was trained on 65 % of the dataset records using different subsets of input variables. In order to preserve the generalization ability of the trained network, 15 % of the dataset records were used as a validation set for early stopping during the networks training process. Prediction capability of the trained networks was evaluated on 20 % and never-seen records of the dataset. The classification ability of the networks was then analysed using receiver operating characteristic and detection error trade-off curves, where the results obtained were promising. The equal error rate for the best models was 12 %, which reflects a very good accuracy of these models in predicting the existence of business continuity planning for a generic company. Copyright

A. ASGARY AND A. SADEGHI NAINI

Operator support is essential for making decisions involving ageing infrastructure. In particular, ageing plants may have deviated from their original condition into a new state with a less predictable set of possible actions and outcomes. Standard procedures and practices may be insufficient to handle the risks and vulnerabilities of ageing nuclear infrastructure. Operator support must be designed with an understanding of how operators make decisions under uncertainty and a view to supporting unexpected situations. Prospect theory (PT) and ecological interface design (EID) are proposed as two complementary approaches for guiding operator support. PT describes how people make decisions under uncertainty and EID is an interface design approach for aiding operators with the problem-solving process in unanticipated situations. We suggest that these two approaches can be integrated to improve complex decision making in ageing nuclear plants.

Fuzzy-JESS expert system for indexing business resiliency

SUMMARY Business continuity planning is an important element of business continuity management and is regarded as a fundamental step towards reducing the negative impacts of business disruptions caused by internal and external hazardous events. Many businesses are not prepared for such events, and very few studies have tried to examine and model the factors that contribute to business continuity management planning by various companies. In this paper we propose and develop a feed-forward neural network for modelling businesses continuity planning by businesses based on a dataset of 283 businesses operating in the Greater Toronto Area in Ontario, Canada. The fully connected neural network applied was trained on 65 % of the dataset records using different subsets of input variables. In order to preserve the generalization ability of the trained network, 15 % of the dataset records were used as a validation set for early stopping during the networks training process. Prediction capability of the trained networks was evaluated on 20 % and never-seen records of the dataset. The classification ability of the networks was then analysed using receiver operating characteristic and detection error trade-off curves, where the results obtained were promising. The equal error rate for the best models was 12 %, which reflects a very good accuracy of these models in predicting the existence of business continuity planning for a generic company. Copyright

Developing disaster mutual assistance decision criteria for electricity industry

A web-based Fuzzy-Jess expert system has been developed that measures business resiliency for businesses and organizations. Such system could help businesses to measures changes in their resiliency over time and benchmark it with other organizations.

Measuring small businesses disaster resiliency: case of small businesses impacted by the 2010 flood in Pakistan

Purpose Disaster mutual assistance (DMA) or mutual aid is a reciprocal arrangement between organizations that permits and prearranges one company to access resources from another company to recover from disaster impacts faster. As a practical tool to access response resources quickly, DMA can be an important element of an effective emergency management process, but the decision to provide (or not to provide) DMA is challenging and involves a number of factors. The purpose of this paper is to present the results of a study conducted to identify DMA decision criteria and their weights based on electricity companies operating in North America. Design/methodology/approach The authors employed a combination of Delphi and analytical hierarchy process (AHP) methods. Delphi method identified the decision criteria that should be considered before electricity utilities enact their DMA agreements. A standard AHP calculated the weights of identified DMA criteria. Findings In total, 11 criteria were identified and classified into three main groups: responding criteria, requesting criteria and disaster criteria. Of the 11, “Emergency Conditions” within the responding criteria group, “Extent of Damage” of the requesting criteria group, and “Size of Disaster”, associated with the disaster criteria group, had the highest weight. Three other factors (“Work Safety Practice”, “Natural Hazards” and “Availability of Resources”) carried a noticeable weight difference, while the remaining factors were weighted relatively lower. Practical implications At present, a decision to provide mutual assistance is highly subjective, based on “gut feel”, and dependent on interpersonal relationships between the requestor and the provider. However, mobilizing and dispatching electricity industry crews is a risky and costly operation for both requesting and responding companies and requires careful assessment for which a cost-benefit threshold has not been developed. This cost-benefit perspective is often frowned upon owing to the intended altruistic nature of DMA agreements and its influence on decision makers. The developed criteria in this study are intended to assist electricity companies in making a more informed and quantifiable decision when deliberating a request for mutual assistance. These criteria may also be used by assistance-requesting companies to better identify electricity companies that are more likely to provide assistance to them. Originality/value This study contributes to the literature by examining the current state of DMA in electricity utilities, identifying decision criteria and weighing such criteria to enable electricity companies in making more objective decisions, thereby, increasing the overall effectiveness of their disaster management process.