Key design considerations for flood risk pooling facilities at the sub-national level
Bibliographic record
Abstract
Disaster or catastrophe risk pooling refers to the sharing of risk by entities facing common risk exposure to an individual hazard or set of hazards over a geographical area. Risk pooling members can gain risk diversification benefits such as lower premium costs while facilities based on parametric insurance policies are able to provide timely post-disaster payouts to members. The topic of sub-national catastrophe risk pools is relatively unexplored. Sub-national risk pools are advantageous as they can overcome politicised issues of compromised sovereignty and joint decision-making while enhancing insurance access for smaller, rural sub-national authorities. This research represents a starting point on design considerations for developing a sub-national flood risk pool (SNFRP). The operation of an SNFRP may result in greater spatial correlation. This may affect the financial stability of SNFRPs or diminish the risk diversification benefits over time. The balancing of fully risk-based pricing and affordability is also likely to be a significant challenge for SNFRPs, especially those operating in emerging and developing economies (EMDE). Means-based subsidies can overcome this challenge; however, donor access may be limited. In addition to donor partnerships, SNFRPs require engagements with reinsurers and national government actors to assist with risk transfer and seed capitalisation, respectively. In EMDEs, an SNFRP focused on response and relief will likely be based on parametric insurance policies. Issues such as index selection, geographical basis risk, and data and modelling needs must be carefully considered during the design of flood parametric insurance policies. Geographic basis risk may be amplified in an SNFRP operating at smaller spatial scales as flood events are not restricted to the administrative boundaries of sub-national authorities. Other issues that could influence the implementation of a sub-national facility include gaining political buy-in; access to reinsurance markets; and risk reduction incentivisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".