<scp>First‐Party Versus Third‐Party Compensation for Automobile Accidents: Evidence From Canada</scp>
Bibliographic record
Abstract
Abstract Insurance regimes for compensating losses arising from automobile accidents vary by jurisdiction, ranging from a pure tort system to a pure no‐fault system, with both systems having well‐documented benefits and costs. The majority of published research focuses on the benefits and costs associated with the compensation for bodily injury. This article extends the existing literature by examining the differences between first‐party and third‐party recovery for both physical damage and bodily injury losses in Canada. Our comparison of auto insurance costs per insured vehicle suggests that government‐run, pure no‐fault provinces have lower average costs than provinces with private tort and modified no‐fault. Lower costs arise from the elimination of tort costs associated with noneconomic damages, lower claims settlement costs due to first‐party compensation, and scales of economy arising from monopoly power. The second goal of the article is to examine the impact of first‐ versus third‐party compensation on the settlement of property damage claims. We analyze the claim files of a large insurer that operates within both a traditional tort (third‐party) environment and a first‐party recovery environment for property damage. We find that in a first‐party recovery regime claims are settled sooner, settlement costs are lower, and not‐at‐fault drivers are compensated at a higher rate than in the traditional tort environment.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".