How Have Health Insurers Performed Financially Under the ACA' Market Rules?
Bibliographic record
Abstract
Issue: The Affordable Care Act (ACA) transformed the market for individual health insurance, so it is not surprising that insurers' transition was not entirely smooth. Insurers, with no previous experience under these market conditions, were uncertain how to price their products. As a result, they incurred significant losses. Based on this experience, some insurers have decided to leave the ACA’s subsidized market, although others appear to be thriving. Goals: Examine the financial performance of health insurers selling through the ACA's marketplace exchanges in 2015--the market’s most difficult year to date. Method: Analysis of financial data for 2015 reported by insurers from 48 states and D.C. to the Centers for Medicare and Medicaid Services. Findings and Conclusions: Although health insurers were profitable across all lines of business, they suffered a 10 percent loss in 2015 on their health plans sold through the ACA's exchanges. The top quarter of the ACA exchange market was comfortably profitable, while the bottom quarter did much worse than the ACA market average. This indicates that some insurers were able to adapt to the ACA's new market rules much better than others, suggesting the ACA's new market structure is sustainable, if supported properly by administrative policy.
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.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".