A re‐examination of the US insurance market's capacity to pay catastrophe losses
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Cummins, Doherty, and Lo (2002) present a theoretical and empirical analysis of the capacity of the property liability insurance industry in the US to finance catastrophic losses. In their theoretical analysis, they show that a sufficient condition for capacity maximization is for all insurers to hold a net of reinsurance underwriting portfolio that is perfectly correlated with aggregate industry losses. Estimating capacity from insurers' financial statement data, they find that the US insurance industry could adequately fund a $100 billion event in 1997. As a matter of comparison, Hurricane Katrina in 2005 cost the insurance industry $40 to $65 billion (2005 dollars). Our main objective is to update the study of Cummins et al. (2002) with new data available up to the end of 2020. We verify how the insurance market's capacity has evolved over recent years. We show that the US insurance industry's capacity to pay catastrophe losses is higher in 2020 than it was in 1997. Insurers could pay 98% of a $200 billion loss in 2020, compared to 81% in 1997.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it