Increasing countries’ financial resilience through global catastrophe risk pooling

The devastating economic consequences of extreme weather events, such as tropical cyclones, floods, and heavy precipitation, are particularly pronounced in LMICs. These events can cause short-term declines in macroeconomic variables, including GDP growth, local income, tax revenue, and increased inflation. The resulting need for increased government spending often leads to debt increases, posing significant challenges for countries already struggling with debt sustainability. Recovery frequently depends on foreign aid, which is inherently slow, uncertain, and often insufficient to cover the full extent of the damage. Historically, only around 60% of humanitarian aid requests are met, and funding distribution has been uneven across emergencies. In contrast, ex-ante financial instruments like insurance provide faster, more predictable funding, allowing governments to manage costs over time. These instruments also complement non-financial disaster risk management strategies by encouraging investments in risk reduction and improved preparedness. International policy agendas, such as the Sendai Framework for Disaster Risk Reduction and the Paris Agreement, emphasize the importance of strengthening financial resilience through ex-ante mechanisms. The InsuResilience Global Partnership highlights sovereign catastrophe risk pools as a promising approach, especially for countries with limited geographical or temporal risk-spreading capacity. Existing regional pools, while beneficial, have limitations. Coverage may be insufficient for full recovery, requiring supplemental foreign aid. Members might underinsure to reduce premiums, and some still rely on foreign donors for premium payments. Crucially, regional limitations restrict risk diversification potential. This study addresses these issues by proposing a method to design optimal risk pools that maximize diversification, considering both regional and global scenarios.

The existing literature extensively documents the economic impact of extreme weather events on LMICs, highlighting the slow and unreliable nature of post-disaster foreign aid. Studies have quantified the losses in various macroeconomic indicators resulting from such events. The literature also examines the role of ex-ante financial instruments, such as insurance and risk pooling, in enhancing resilience and reducing the financial burden of disasters. Several works have analyzed the effectiveness of existing regional catastrophe risk pools, identifying their strengths and weaknesses. The existing research indicates a need for improved risk pooling strategies that go beyond regional boundaries to achieve greater risk diversification and financial stability for LMICs. This research builds upon these prior analyses by introducing a novel method for optimizing risk pool composition for maximizing risk diversification.

This study introduces a novel method for identifying optimal risk pools that maximize risk diversification. The method involves two main optimization steps. The first step focuses on finding the optimal regional pools for each of four regions prone to tropical cyclones: East Asia & Pacific (EAP), Latin America & Caribbean (LAC), South Asia (SA), and Sub-Saharan Africa (SSA). This step minimizes risk concentration (RC) within each region, using a multi-objective optimization approach to identify the optimal combination of countries for each pool. Risk concentration is calculated as 1 minus risk diversification (RD). Risk diversification is measured using the Expected Shortfall (ES), a coherent risk measure that considers the tail of the loss distribution. The ES is calculated for the 200-year event, implying an α of 0.995. The second optimization step builds upon the results from the first step, exploring the potential for further risk diversification by allowing the regional pools to incorporate countries from other regions. This step solves a multi-objective optimization problem, aiming to simultaneously maximize the risk diversification of all four regional pools. The optimization accounts for potential trade-offs between the diversification levels of different pools, finding Pareto optimal configurations. A 10,000-year time series of annual tropical cyclone losses was constructed using a statistical-dynamical downscaling method that generates synthetic tropical cyclone tracks and estimates damages using the CLIMADA impact model. CLIMADA integrates hazard (tropical cyclone wind fields), exposure (asset values disaggregated using the LitPop approach), and vulnerability data to assess economic damages. The optimization problems were solved using the Python Pymoo package, employing genetic algorithms to find optimal solutions. The Pearson correlation coefficient was used to assess the correlation structure of losses between countries.

The analysis revealed that the optimal regional pools primarily consist of countries with low bilateral correlations or low individual risk contributions to the pool's overall risk. The LAC region exhibited the highest diversification among the regional pools, followed by EAP, SSA, and SA. Global pooling significantly enhanced risk diversification for all four regions, resulting in a Pareto improvement. The increase in diversification was most significant for SA and SSA, doubling in the former and increasing by 40% in the latter. EAP and LAC, already showing high regional diversification, experienced more modest improvements of approximately 15% and 6.5%, respectively. The study further examined the existing PCRAFI and CCRIF pools. Optimal regional pooling increased diversification for PCRAFI by 35% and for CCRIF by 40%. However, CCRIF's diversification remained 11% below the maximum achievable regional diversification, suggesting room for improvement in its design. Global optimal pooling offered even greater potential, increasing diversification by 65% for PCRAFI and 60% for CCRIF. While trade-offs existed among Pareto optimal global pool configurations, these were minimal for CCRIF, simplifying the selection process. Global pooling tended to decrease individual countries' risk shares, redistributing risk across the globe. It also allowed some regions to include countries that were not part of the optimal regional pools due to the reduced individual risk shares in the global context. This was observed even for moderately correlated countries, highlighting the benefits of global diversification.

The findings demonstrate the significant potential for enhancing the financial resilience of LMICs through globally diversified catastrophe risk pools. The proposed optimization method effectively identifies optimal pool compositions by considering both correlation structure and individual risk contributions. Global pooling offers substantial risk diversification benefits compared to regional approaches, leading to lower capital requirements and increased affordability for member countries. While global pooling does introduce trade-offs among different pools, the impact of these trade-offs can be minimized through careful consideration of cooperation and political factors. The application of the method to existing pools, PCRAFI and CCRIF, revealed significant room for improvement in terms of risk diversification. The results emphasize the importance of considering global diversification when designing catastrophe risk pools. However, the study also highlights the limitations of solely focusing on risk diversification. The existing pools were primarily designed for immediate response, leaving the full recovery still dependent on foreign aid. Addressing this broader need requires fundamental redesign, going beyond mere pool composition.

This research demonstrates the substantial benefits of global catastrophe risk pooling for enhancing the financial resilience of LMICs. The introduced method effectively identifies optimal pool compositions that maximize diversification, highlighting the limitations of regional-only approaches. The analysis of existing pools showcases the potential for significant improvements through both regional and global optimization. However, it is crucial to acknowledge that enhanced risk pooling alone is insufficient for ensuring full post-disaster recovery. Future research should explore multi-hazard pooling, optimal (re-)insurance policy design, and the evolving needs of optimal pools under socioeconomic and climatic change.

This study focused specifically on tropical cyclone risk. The generalizability of the findings to other hazards requires further investigation. The model relies on synthetic data for long-term loss projections, which might not perfectly capture the complexities of real-world events. The optimization considers only risk diversification, not accounting for other factors that influence pool design such as political and economic considerations. The selection of the optimal global configuration among Pareto optimal solutions was made based on maximizing diversification of PCRAFI; further exploration of decision criteria for choosing among Pareto optimal configurations would be beneficial.