Traditional Medicare Versus Private Insurance: How Spending, Volume, And Price Change At Age Sixty-Five
Bibliographic record
Abstract
To slow the growth of Medicare spending, some policy makers have advocated raising the Medicare eligibility age from the current sixty-five years to sixty-seven years. For the majority of affected adults, this would delay entry into Medicare and increase the time they are covered by private insurance. Despite its policy importance, little is known about how such a change would affect national health care spending, which is the sum of health care spending for all consumers and payers-including governments. We examined how spending differed between Medicare and private insurance using longitudinal data on imaging and procedures for a national cohort of individuals who switched from private insurance to Medicare at age sixty-five. Using a regression discontinuity design, we found that spending fell by $38.56 per beneficiary per quarter-or 32.4 percent-upon entry into Medicare at age sixty-five. In contrast, we found no changes in the volume of services at age sixty-five. For the previously insured, entry into Medicare led to a large drop in spending driven by lower provider prices, which may reflect Medicare's purchasing power as a large insurer. These findings imply that increasing the Medicare eligibility age may raise national health care spending by replacing Medicare coverage with private insurance, which pays higher provider prices than Medicare does.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".