The Impact of Tort Reform on Employer-Sponsored Health Insurance Premiums
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it