The wider the gap between rich and poor the higher the flood mortality

Abstract

Economic inequality is rising within many countries globally, and this can significantly influence the social vulnerability to natural hazards. We analysed income inequality and flood disasters in 67 middle- and high-income countries between 1990 and 2018 and found that unequal countries tend to suffer more flood fatalities. This study integrates geocoded mortality records from 573 major flood disasters with population and economic data to perform generalized linear mixed regression modelling. Our results show that the significant association between income inequality and flood mortality persists after accounting for the per-capita real gross domestic product, population size in flood-affected regions and other potentially confounding variables. The protective effect of increasing gross domestic product disappeared when accounting for income inequality and population size in flood-affected regions. On the basis of our results, we argue that the increasingly uneven distribution of wealth deserves more attention within international disaster-risk research and policy arenas.

An Empirical Social Vulnerability Map for Flood Risk Assessment at Global Scale (“GlobE‐SoVI”)

“…Therefore, we could not estimate a first model configuration based on all variables. To establish the variables to include in the model, we tested each variable individually by adding it to the baseline model, retaining only those variables that were statistically significant ( p < 0.05) (Burton, 2015; Lindersson et al., 2023). If perfectly collinear variables still remained, we dropped those leading to a lower model fit. Stepwise selection: We ran a stepwise regression algorithm based on all preselected variables, using the “stats” package in R. The algorithm drops or adds variables to the model per step, searching for the best model fit based on the Akaike information criterion (AIC) (R Core Team, 2022).…”

“…To account for the fact that impacts are driven by the interaction of hazard, exposure and vulnerability, we estimated a multiple linear regression model to predict flood fatalities (Equation 1), similar to previous work (Bakkensen et al., 2017; Lindersson et al., 2023; Lloyd et al., 2022; Peduzzi et al., 2009). Being aware of the limitations of regression models (e.g., causality cannot be inferred) (Lindersson et al., 2023), we used this modeling approach for a first‐order analysis as interpretation of results was straight‐forward and the available data were too limited for a more data‐intensive modeling approach. We used flood fatalities (Fat) as reported for all events i included in the GFD as the dependent variable of the model.…”

“…Therefore, we could not estimate a first model configuration based on all variables. To establish the variables to include in the model, we tested each variable individually by adding it to the baseline model, retaining only those variables that were statistically significant ( p < 0.05) (Burton, 2015; Lindersson et al., 2023). If perfectly collinear variables still remained, we dropped those leading to a lower model fit.…”

“…Therefore, we could not estimate a first model configuration based on all variables. To establish the variables to include in the model, we tested each variable individually by adding it to the baseline model, retaining only those variables that were statistically significant (p < 0.05) (Burton, 2015;Lindersson et al, 2023) (James et al, 2013). If the remaining variables exceeded the p value defined during this process, we repeated steps 3 and 4 iteratively until all variables were statistically significant and had a VIF value < 5.…”

Integrating social vulnerability into global flood risk mapping

“…As a result, the places that appear to have the highest risk are either the most densely populated areas or those with a high concentration of assets. Yet it is the poorest households and communities that have the least coping capacity when confronted with a natural hazard event and suffer the greatest well-being losses 2, [14][15][16][17] . Failure to mitigate hazard risks for the most vulnerable contributes to the perpetuation of poverty 12 and can exacerbate social inequalities within and between countries [18][19][20] .…”