Assessing the Social Vulnerability to Floods in India: An Application of Superefficiency Data Envelopment Analysis and Spatial Autocorrelation to Analyze Bihar Floods

Abstract

Abstract The objective of this study is to assess and map the relative social vulnerability to floods in administratively declared flood-prone districts of Bihar for the period 2007–16. We employed the superefficiency data envelopment analysis (DEA) model to rank the efficient decision-making units (DMUs) as the standard DEA model estimated multiple efficient DMUs. The spatial characteristics of the vulnerability ranks are assessed using the global and local spatial autocorrelation tests. The results show that districts north of the river Ganges are more vulnerable than the districts in the south; the very high and high vulnerability scores correspond to the districts located in north Bihar with recurrent flood incidences in the locations with the high–high category. Districts constituting the low–low group have experienced very less number of flood damage in the considered study period. The spatial outliers are from the group of districts with a low level of vulnerability surrounded by the districts with either very high or high level of vulnerability, respectively.