Who’s Going Broke? Comparing Growth in Healthcare Costs in Ten OECD Countries
Bibliographic record
Abstract
Government healthcare expenditures have been growing much more rapidly than GDP in OECD countries. For example, between 1970 and 2002 these expenditures grew 2.3 times faster than GDP in the U.S., 2.0 times faster than GDP in Germany, and 1.4 times faster than GDP in Japan. How much of government healthcare expenditure growth is due to demographic change and how much is due to increases in benefit levels; i.e., in healthcare expenditures per beneficiary at a given age? This paper answers this question for ten OECD countries -- Australia, Austria, Canada, Germany, Japan, Norway, Spain, Sweden, the UK, and the U.S. Specifically, the paper decomposes the 1970-2002 growth in each countrys healthcare expenditures into growth in benefit levels and changes in demographics.Although healthcare spending is growing at unsustainable rates in most, if not all, OECD countries, the U.S. appears least able to control its benefit growth due to the nature of its fee-forservice healthcare payment system. Consequently, the U.S. may well be in the worst long-term fiscal shape of any OECD country even though it is now and will remain very young compared to the majority of its fellow OECD members.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".