Offer-of-Judgment Rules and Civil Litigation: An Empirical Study of Automobile Insurance Litigation in the East
Bibliographic record
Abstract
Legal scholars have long debated the efficacy of offer-of-judgment rules in promoting the resolution of civil disputes. Unfortunately, measuring the rules' effect in actual litigation has proven difficult because federal and most state courts adopted a version that has long been in place and is generally regarded as toothless and inconsequential. This Article overcomes this measurement obstacle by studying the recent experience of New Jersey, which in 1994 allowed bilateral pre-trial offers with uncapped attorneys' fees as a cost-shifting measure. Using individual-level data from a large national insurer, we analyze insurance-based suits filed in New Jersey and neighboring states between 1992 and 1997. We find that in the aftermath of the rule revision damage awards did not significantly change, but litigants took systematically less time to resolve their disputes and markedly lowered their attorneys' fees for the insurer. These findings suggest that offer-of-judgment rules, if properly designed, can provide an effective and arguably social welfare-enhancing mechanism for resolving civil disputes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".