Long-Term Care Insurance: Knowledge Barriers, Risk Perception and Adverse Selection
Bibliographic record
Abstract
We conduct a stated-choice experiment where respondents are asked to rate various insurance products aimed to protect against financial risks associated with long-term care needs. Using exogenous variation in prices from the survey design, and objective risks computed from a dynamic microsimulation model, these stated-choice probabilities are used to predict market equilibrium for long-term care insurance using the framework developed by We investigate in turn causes for the low observed take-up of long-term care insurance in Canada despite substantial residual out-of-pocket financial risk. We first find that awareness and knowledge of the product is low in the population: 44% of respondents who do not have longterm care insurance were never offered this type of insurance while overall 31%report no knowledge of the product. Although we find evidence of adverse selection, results suggest it plays a minimal role in limiting take-up. On the demand side, once respondents have been made aware of the risks, we find that demand remains low, in part because of misperceptions of risk, lack of bequest motive and home ownership which may act as a substitute.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".