## 2 Methods
### Data Collection and Preparation
Among these, GDP per capita was considered, as it reflects the economic capacity of individuals within a country, which can directly affect their preparedness and response to disasters. Data for this indicator were sourced from reputable institutions, including the World Bank ([[https://data.worldbank.org/indicator/NY.GDP.PCAP.CD](https://data.worldbank.org/indicator/NY.GDP.PCAP.CD)]([https://data.worldbank.org/indicator/NY.GDP.PCAP.CD](https://data.worldbank.org/indicator/NY.GDP.PCAP.CD)), accessed on 1 September 2024) and the International Monetary Fund (IMF) ([[https://www.imf.org/en/Data](https://www.imf.org/en/Data)]([https://www.imf.org/en/Data](https://www.imf.org/en/Data)), accessed on 1 September 2024). Another crucial indicator was governance quality, measuring how efficiently and effectively a government manages resources and responds to crises. These data were obtained from the World Governance Indicators (WGIs) provided by the World Bank ([[https://info.worldbank.org/governance/wgi/](https://info.worldbank.org/governance/wgi/)]([https://info.worldbank.org/governance/wgi/](https://info.worldbank.org/governance/wgi/)), accessed on 1 September 2024). On the other hand, population density, defined as the number of people per square kilometer, was also included to gauge the potential vulnerability of regions with high population concentrations. Data on population density were gathered from sources such as Worldometer([[https://www.worldometers.info/world-population/population-by-country/](https://www.worldometers.info/world-population/population-by-country/)]([https://www.worldometers.info/world-population/population-by-country/](https://www.worldometers.info/world-population/population-by-country/)), accessed on 5 September 2024) and the United Nations ([[https://population.un.org/wpp/](https://population.un.org/wpp/)]([https://population.un.org/wpp/](https://population.un.org/wpp/)), accessed on 1 September 2024). Lastly, the urbanization rate, representing the proportion of the population residing in urban areas, was analyzed because higher urbanization can enhance the disaster response efficiency but also heighten risks due to population and infrastructure concentration. Data for this indicator were collected from platforms such as Statista ([[https://www.statista.com/statistics/270860/urbanization-by-country/](https://www.statista.com/statistics/270860/urbanization-by-country/)]([https://www.statista.com/statistics/270860/urbanization-by-country/](https://www.statista.com/statistics/270860/urbanization-by-country/)), accessed on 6 September 2024) and the World Bank ([[https://data.worldbank.org/indicator/sp.urb.toll.in.zs](https://data.worldbank.org/indicator/sp.urb.toll.in.zs)]([https://data.worldbank.org/indicator/sp.urb.toll.in.zs](https://data.worldbank.org/indicator/sp.urb.toll.in.zs)), accessed on 6 September 2024). Following data collection, these indicators were compiled into an Excel file, with each country in the dataset assigned its corresponding socio-economic indicators. The Excel file was structured to include all relevant variables, such as total damage, number of deaths, injuries, and natural and man-made (technological) disasters. The data underwent a thorough cleaning process to ensure accuracy and completeness. This involved checking for missing values, identifying and managing outliers, and ensuring consistency across all variables. Missing data were addressed using mean imputation where applicable, while outliers identified through z-scores were carefully reviewed and processed to prevent any distortion of the results.