Commitment and Automobile Insurance Regulation in France, Quebec and Japan
Bibliographic record
Abstract
Information problems have a large role to play in insurance markets and the regulations governing these markets were in part designed to take such problems into account. Classification variables are usually the tools used to reduce adverse selection, whereas bonus-malus (or merit-rating) schemes are introduced because risk categories lack homogeneity or fairness and because such categories do not really take moral hazard into account. Recent research findings also highlight the role commitment plays in bolstering incentives when faced with moral hazard in a bonus-malus scheme. In this paper, we shall give a detailed analysis of two different automobile insurance markets: those of the province of Quebec, in Canada and France. We will also document the Japanese regulatory system and that of other countries in Europe. In France and Quebec, adverse selection does not seem to require tight regulation, but regulated commitment does seem to help control moral hazard in both insurance regimes. Finally, we will show that the deregulation of insurance was a major concern for Japanese government over the recent years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".