The Impact of Tort Reform on Employer-Sponsored Health Insurance Premiums
Bibliographic record
Abstract
We evaluate the effect of tort reform on employer-sponsored health insurance premiums by exploiting state-level variation in the timing of reforms. Using a dataset of health plans representing over 10 million Americans annually between 1998 and 2006, we find that the most common set of tort reforms during this period reduces premiums of employer-sponsored self-insured health plans by 2.1%. Of the four individual reforms comprising this set, caps on noneconomic damages and collateral source reforms have the greatest impact. We do not find reductions in premiums for fully insured plans, which in our sample are almost entirely Health Maintenance Organizations (HMOs). Further analysis reveals that self-insured HMOs are also unresponsive to reforms. Taken together, these findings suggest that HMOs reduce "defensive medicine, " even absent reform. The results are the first direct evidence that tort reform reduces healthcare costs in aggregate; prior research has largely focused on particular medical conditions. (JEL I1, K3, K13, K20) The Author 2010. Published by Oxford University Press on behalf of Yale University. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".