Behavioral Biases and Long-Term Care Insurance: A Political Economy Approach
Bibliographic record
Abstract
Abstract We develop a model where individuals all have the same probability of becoming dependent and vote over the social long-term care insurance contribution rate before buying additional private insurance and saving. We study three types of behavioral biases, all having in common that agents under-weight their dependency probability when taking private decisions. Sophisticated procrastinators anticipate their mistake when voting, while optimistic and myopic agents have preferences that are consistent across choices. Optimists under-estimate their own probability of becoming dependent but know the average probability, while myopics underestimate both. Sophisticated procrastinators attain the first-best allocation, while myopics and optimists insure too little and save too much. Myopics and optimists more (resp., less) biased than the median are worse off (resp., better off), at the majority-voting equilibrium, when private insurance is available than when it is not.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".