Jinglu Song, Bo Huang, and Rishikesh Pandey
Many scholars have advocated that disaster resilience of communities could contextualize how community members sustain themselves and recover from the destruction of the disaster. To substitute this statement, numerous empirical studies have been conducted to explore the determinants of disaster resilience that contribute to post-disaster recovery process. However, most existing works evaluated the post-disaster performances of communities based on the indirect recovery measurements instead of the direct assessment from affected members themselves. The primary purpose of this article is to increase evidence base for identifying a set of metrics that could contextualize and predict a resilient response. For this purpose, a case study shortly after the 2015 Nepal earthquake was conducted based on monthly survey data of community perceptions from August to December. Several hierarchical regression models have been constructed against monthly surveyed relief scores to validate indicators as proxy of resilience in social, economic, infrastructural and environmental domains. Regression results show that a set of derived indicators at the sub-district levels of geography correlate favorably as expected against survey data, which involves indicators related to household structure, industrial diversity, community capital, accessibility and emergency service, and earthquake experiences. Furthermore, the contributions of several validated indicators prove temporal variations during the post-disaster recovery process. These findings could not only justify the selection of resilience indicators that could predict response and recovery process of community members, and more importantly could provide guidance for policy makers and agency leaders on a time-dependent priority resilience enhancement.