Settlement at Policy Limits and the Duty to Settle: Evidence from Texas
Bibliographic record
Abstract
All liability insurance policies have coverage limits, and insurers usually control whether a case is settled or tried. If the insurer rejects a within-limits settlement offer, the insured bears the risk of an above-limits verdict. In response, virtually every state has imposed a “duty to settle” on insurers, which creates incentives for plaintiffs to make at-limits offers and for insurers to accept those offers when expected damages exceed limits. We study the association between the duty to settle, settlement at limits, claim duration, and defense costs using detailed data from Texas for 1988–2005 on closed, commercially insured personal injury claims. We focus principally on medical malpractice suits against physicians, but find consistent evidence for other types of cases. We find strong evidence that the duty to settle affects settlement dynamics. Essentially, all physician-defendant cases that settle at limits are preceded by an at-limits demand. Roughly 20 percent of physician-defendant cases settle at 90–100 percent of policy limits (broad at-limits) and 13 percent settle exactly at limits (exact at-limits). Broad- and exact-at-limits cases close about five months faster than similar “below-limits” cases—a roughly 20 percent shorter time from suit to settlement, controlling for payout and type of harm. Broad- and exact-at-limits cases also have substantially lower defense costs, controlling for case duration and complexity. More broadly, as the payout/limits ratio approaches 1 from below, duration declines (controlling for payout) and defense costs decline (controlling for payout and duration). Payouts above limits are uncommon; when they occur, insurers are the primary payers. Policy limits alone cannot explain these results; most likely they reflect a combination of policy limits and the duty to settle.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".