The cost of social vulnerability: an integrative conceptual framework and model for assessing financial risks in natural disaster management

Groups that are unable to prepare for disasters, or to recover from damage on their own, have a high dependency on government services, which inevitably leads to more government spending. Given this, governments can better project the entire cost of disasters and, in turn, effectively manage their finances, by proactively identifying high-vulnerable populations in anticipating financial costs of disasters. However, little attention has been paid to social vulnerability in assessing financial risks in the natural hazards or public finance studies. Thus, this article fills this gap by bringing the concept of social vulnerability from three different fields of study to propose a conceptual framework and corresponding applicable model for estimating disaster costs to inform governmental financial management: the sociological literature on disaster management, economics literature on risk management, and environmental literature of disasters. We review 134 articles on vulnerability from 1990 to 2021, assessing the different conceptualizations of social vulnerability, and the factors affecting vulnerable populations, in each literature. This study contributes to the natural hazards literature on financial and emergency management by integrating the existing literature on social vulnerability into a conceptual framework for measuring social vulnerability and relating it to efforts to assess the financial impact of disasters. Furthermore, based on this conceptual framework, we develop an applicable model for estimating the financial costs of disasters that researchers or governments may apply to assess and develop effective strategies for managing the financial risks associated with disasters. Specifically, the model, which we call the cost of social vulnerability to disasters model (CSVDM), suggests specific indicators from the literature to measure the costs of social vulnerability to more accurately predict the financial impact of disasters.