Experimental Evidence for Coverage Preferences in Flood Insurance
Bibliographic record
Abstract
Abstract We used a hypothetical choice experiment to estimate the effect of dwelling value and coverage limits on the probability of purchasing flood insurance while holding the probability of flooding and insurance price constant. The results indicate that demand for flood insurance is negatively associated with the amount of insurance coverage. For people assigned higher-valued dwellings, however, the opposite effect is observed. Since more coverage is generally preferred to less, all else being equal, differences in purchase probability dependent on dwelling value indicate an inconsistent approach to home protection. The higher probability of purchasing flood insurance from people in higher-valued dwellings may indicate an investment into the home as a financial asset, a strategy that is not observed to the same extent among people in lower-valued dwellings. This suggests that use of coverage limits may be differentially preferred based on dwelling value, such that low coverage insurance may have lower uptake for those in high-valued dwellings. As Canada evaluates a national flood insurance program, this research suggests that variable coverage maximums could be a way to increase accessibility and uptake of insurance. This research shows an inconsistent demand for flood insurance, dependent on dwelling value and independent of income.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".