The Impact of Regulation on theAvailability and Profitability of AutoInsurance in Canada
Bibliographic record
Abstract
This article investigates the impact of automobile insurance regulation on the size of the involuntary insurance market as well as the level and volatility of auto insurance loss ratios in Canada. We find that rate reduction orders, product reform and a pricing “Grid” that establishes maximum premiums increase the size of the involuntary market, while prior approval does not have any significant effect. In addition, unlike U.S. studies, we find that prior approval does not significantly impact loss ratio volatility. Our models also incorporate the impact of macroeconomic variables that proxy for the underwriting cycle and investment returns. The results suggest that the insurance underwriting cycle and stock market returns appears to be as important in determining insurers’ usage of the involuntary market as regulation. Taken together, our results suggests that regulatory interventions aimed at addressing affordability issues may have the unintended consequence of aggravating availability issues, and underlying market conditions may exacerbate this effect.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".