The Personal Cost and Affordability of Auto Insurance in Canada: 2011 Edition
Bibliographic record
Abstract
This study compares the average cost and affordability of personal passenger automobile insurance premiums in each of the 10 Canadian provinces from 2007 to 2009. Four provinces have government-owned monopolies that sell insurance coverage to drivers. The other six rely on a regulated competitive private sector to provide auto insurance.Comparisons across all 10 provinces in the years from 2007 to 2009 show that the average price for auto insurance premiums was highest in British Columbia, Ontario, Manitoba, and Saskatchewan. Of those provinces, three have government-run auto insurance monopolies. The least expensive average premium in 2008 was in Prince Edward Island where auto insurance is delivered in a regulated, competitive, private-sector insurance market. The least expensive premium in 2007 and 2009 was in Quebec, which has a government-run auto insurance monopoly but only for bodily injury.The study examines why Ontario has relatively high average premiums and why Quebec’s average premiums are relatively low. Ontario has relatively severe regulations, and is experiencing a significant problem with insurance fraud. Quebec has less onerous rate regulations and less generous prescribed benefits.The findings are generally consistent with previous editions of this study and other previous research comparing auto insurance in international jurisdictions including all 10 Canadian provinces. All studies show that auto insurance does not tend to be less costly in jurisdictions that have government auto insurance monopolies, despite claims to the contrary.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".