Marketplace Plans With Narrow Physician Networks Feature Lower Monthly Premiums Than Plans With Larger Networks
Bibliographic record
Abstract
The introduction of health insurance Marketplaces under the Affordable Care Act has been associated with growth of restricted provider networks. The value of this plan design strategy, including its association with lower premiums, is uncertain. We used data from all silver plans offered in the 2014 health insurance exchanges in the fifty states and the District of Columbia to estimate the association between the breadth of a provider network and plan premiums. We found that within a market, for plans of otherwise equivalent design and controlling for issuer-specific pricing strategy, a plan with an extra-small network had a monthly premium that was 6.7 percent less expensive than that of a plan with a large network. Because narrow networks remain an important strategy available to insurance companies to offer lower-cost plans on health insurance Marketplaces, the success of health insurance coverage expansions may be tied to the successful implementation of narrow networks.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".