La mauvaise perception des risques de longévité et de dépendance ne suffit pas à expliquer la faiblesse du marché de l'assurance dépendance (au Canada)
Bibliographic record
Abstract
This article studies the some of the reasons underlying the under-provision of LTC insurance in Québec and Ontario. Using 2016 survey data, we demonstrate that misperception biases regarding demographic risks (of mortality and of dependency) cannot alone explain the low demand for this insurance product. Even if individual perceptions of these risks are heterogenous, individuals tend on average to over-estimate their survival probability and the probability of entering a LTC home, which should lead to over-insurance rather than to under-insurance for LTC. We show instead that the most probable reason for the under-provision of LTC insurance is that individuals do not know this financial product. Hence, if policy makers were to foster the purchase of LTC insurance, they should run advertising campaigns to inform the public about these products. Another interesting policy could be to develop bundled insurance products.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".