The Earthquake Insurance Protection Gap: A Tale of Two Countries
Bibliographic record
Abstract
In this paper, we examine reasons why take-up rates for earthquake insurance are significantly higher in the Lower Mainland of British Columbia than in western Washington state even though earthquake risk is largely the same. Achieving and maintaining high insurance take-up rates for catastrophic events matters because this can play an important role in improving the resiliency of communities. After exploring several factors known to influence the supply and demand of insurance for high-severity but low-frequency events, we find only two key differences: 1) disaster assistance is more readily available in the U.S.; and 2) Canadians are more willing to purchase earthquake insurance when they are told they should. We conjecture that many policy options to increase insurance take-up rates, such as product redesign or cross subsidization, are not likely to be effective in Washington. Making insurance mandatory—either via legislation, making earthquake coverage a prerequisite for a mortgage or embedding it into property taxes—might be the only viable way to increase take-up rates, although these options may be politically difficult to enact.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".