Original Research

Evaluating the components of social and economic resilience: After two large earthquake disasters Rudbar 1990 and Bam 2003

Amir Bastaminia, Mohammad R. Rezaei, Mohammad H. Saraei
Jàmbá: Journal of Disaster Risk Studies | Vol 9, No 1 | a368 | DOI: https://doi.org/10.4102/jamba.v9i1.368 | © 2017 Amir Bastaminia, Mohammad R. Rezaei, Mohammad H. Saraei | This work is licensed under CC Attribution 4.0
Submitted: 22 August 2016 | Published: 25 April 2017

About the author(s)

Amir Bastaminia, Department of Geography, School of Humanities, Yazd University, Iran, Islamic Republic of
Mohammad R. Rezaei, Department of Geography, School of Humanities, Yazd University, Iran, Islamic Republic of
Mohammad H. Saraei, Department of Geography, School of Humanities, Yazd University, Iran, Islamic Republic of

Abstract

Extensive damages of natural disasters have made resilience a focus of disaster management plans in order to limit damages. The aim of this study was a comparative evaluation of social and economic resilience in Bam and Rudbar. This applied research attempted to quantify and compare different dimensions of social and economic resilience in Bam and Rudbar with a descriptive-analytical method. Cochran’s formula determined the sample size as 330 households from both cities (a total of 660 households). The indicators of social and economic resilience were identified from the literature, and then data were collected through a field study using questionnaires. Data were analysed using multiple linear regression and feedforward multilayer perceptron artificial neural network. Results denoted that several resilientrelated socio-economic features were significantly different for Bam and Rudbar cities, such as the number of earthquakes experienced, length of stay in current neighbourhood and mean individual and household income. Mean social and economic resilience scores were significantly higher for Rudbar (216.3 ± 33.4 and 30.6 ± 7.3) compared to Bam (193 ± 26.5 and 29.4 ± 7.07) ( p < 0.05). In addition, linear regression indicated that an increase in education level of the household head, length of stay in current neighbourhood and household income could result in an increase in social and economic resilience of the households under study. Neural network analysis revealed that social capital and employment recovery are the most and least effective factors, respectively, in both cities. In the population under study, social component, namely, social capital, was the most important determinant of resilience.

Keywords

resilience; social resilience; economic resilience; earthquake; Bam; Rudbar

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