The Social Impacts of Infrastructure Failure: Estimation of the Value of a Statistical Life for Community Resilience Analysis in Iran

This study aims to provide a model for estimating the direct social loss incurred by communities in Iran due to the loss of life resulting from the earthquake-induced damage to infrastructure systems. Once estimated, the direct social losses caused by an earthquake can be used to quantify and analyze community resilience. Numerous past studies have focused on estimating the direct and indirect economic consequences of earthquake-induced infrastructure failure. However, a review of the relevant literature demonstrates a lack of appropriate models for calculating social costs associated with such events considering the characteristics of the subject community. Research is needed to develop suitable analytical models that quantify the social costs of earthquakes. Previous research studies have proposed using concepts like the value of a statistical life to quantify the social losses caused by hazards such as accidents, environmental pollution, and Terroristic Attacks and Fatal Diseases. Accordingly, past studies on the quantification of community seismic resilience used the value of a statistical life for countries such as the United States in conjunction with the exchange rate to arrive at costs incurred by a community due to earthquake-induced casualties. Since Iran is a country with high seismicity, underestimating the damage caused by the death of individuals and as a result, incorrect estimation of the amount of social loss incurred by the community may misguide the vulnerability reduction efforts. This will have adverse economic, political, and social consequences for the community. Several methods have been proposed to calculate the value of a statistical life, the most important of which are "Revealed Preference" and "Stated Preference." This study uses the revealed preference method to calculate the value of a statistical. This manuscript reports the methodology adopted for collecting the relevant data and the development of the model that estimates the value of a statistical life.