Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi

As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies.

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Publication Details

Journal
International Journal of Disaster Risk Reduction
Published
2025-03-28
DOI
https://doi.org/10.1016/j.ijdrr.2025.105433
Citations
3
Primary Topic
Agricultural risk and resilience
Type
article
Field-Weighted Citation Impact
3.56
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article

Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi

Norman Kerle, Frank Osei, Marc J. C. Van den Homberg, Carolina Ferman Carral et al.
3 citations
International Journal of Disaster Risk Reduction
Agricultural risk and resilience
3.56
article

Spatial econometric modeling of socioeconomic vulnerability and flood impact: Towards a risk-layering approach in southern Malawi

Norman Kerle, Frank Osei, Marc J. C. Van den Homberg, Carolina Ferman Carral, Marija Bočkarjova
article en
3 citations

Abstract

As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified Beneficiary Register, Malawi Census, and Rapid Damage Assessments from the 2020 and 2022 floods. Moran's I analysis emphasized the need to account for those spatial spillover effects. The spatial econometric framework, represented by a spatial lag variable in the Spatial Generalized Linear Model, captured these dependencies effectively. Expected associations emerged between flood impacts and socioeconomic indicators such as wealth, education, and household savings, suggesting that economically secure households are less vulnerable. However, unexpected correlations also appeared: food security was positively associated with flood impact, while disability occurrence showed a negative association. These findings challenge resilience assumptions and raise concerns about data accuracy and aftershock dynamics. Insights highlight the need for DRM strategies that incorporate exposure and socioeconomic indicators, along with improved data collection to ensure vulnerable groups are adequately represented. Despite challenges from data scarcity and spatial granularity, this study demonstrates the potential of Spatial Econometric Models to identify spatially interconnected vulnerabilities. The approach can be transferred to other contexts, but further research is needed to refine spatial resolutions and ensure actionable vulnerability characterizations. Integrating spatial dependencies into risk assessments highlights the need for spatially explicit policy interventions to strengthen resilience and advance risk-layering strategies.

International Journal of Disaster Risk ReductionVol. 121
Red Cross Hospital (GR), Vrije Universiteit Amsterdam (NL), University of Twente (NL)
Climate action
Openalex Percentile: Top 9%
Agricultural risk and resilience
3.56
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