Advantageous selection without moral hazard
Bibliographic record
Abstract
Advantageous (or propitious) selection occurs when an increase in the premium of an insurance contract induces high-cost agents to quit, thereby reducing the average cost among remaining buyers. Hemenway (1990) and many subsequent contributions motivate its advent by differences in risk-aversion among agents, implying different prevention efforts. We argue that it may also appear in the absence of moral hazard, when agents only differ in riskiness and not in (risk) preferences. We first show that profit-maximization implies that advantageous selection is more likely when markup rates and the elasticity of insurance demand are high. We then move to standard settings satisfying the single-crossing property and show that advantageous selection may occur when several contracts are offered, when agents also face a non-insurable background risk, or when agents face two mutually exclusive risks that are bundled together in a single insurance contract. We exemplify this last case with life care annuities, a product which bundles long-term care insurance and annuities, and we use Canadian survey data to provide an example of a contract facing advantageous selection.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".