Will Tort Reform Bend the Cost Curve? Evidence from Texas
Bibliographic record
Abstract
Will tort reform “bend the cost curve?” Health‐care providers and tort reform advocates insist the answer is “yes.” They claim that defensive medicine is responsible for hundreds of billions of dollars in health‐care spending every year. If providers and reform advocates are right, once damages are capped and lawsuits are otherwise restricted, defensive medicine, and thus overall health‐care spending, will fall substantially. We study how Medicare spending changed after Texas adopted comprehensive tort reform in 2003, including a strict damages cap. We compare Medicare spending in Texas counties with high claim rates (high risk) to spending in Texas counties with low claim rates (low risk), since tort reform should have a greater impact on physician incentives in high‐risk counties. Pre‐reform, Medicare spending levels and trends were similar in high‐ and low‐risk counties. Post‐reform, we find no evidence that spending levels or trends in high‐risk counties declined relative to low‐risk counties and some evidence of increased physician spending in high‐risk counties. We also compare spending trends in Texas to national trends, and find no evidence of reduced spending in Texas post‐reform, and some evidence that physician spending rose in Texas relative to control states. In sum, we find no evidence that Texas's tort reforms bent the cost curve downward.
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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 | 0.005 | 0.031 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".