Spending on Health Care in the Netherlands: Not Going So Dutch
Bibliographic record
Abstract
Abstract The Netherlands is among the top spenders on health in the OECD. We document the life‐cycle profile, concentration and persistence of this expenditure using claims data covering both curative and long‐term care expenses for the full Dutch population. Spending on health care is strongly concentrated: the 1 per cent of individuals with the highest levels of expenditure account for one‐quarter of the aggregate in any one year. Averaged over three years, the top 1 per cent still account for more than a fifth of the total, indicating a very high degree of persistence in the largest expenses. Spending on long‐term care, which amounts to one‐third of all expenditure on health care, is even more concentrated: the top 1 per cent account for more than half of total spending on this type of care. Average expenditure rises steeply with age and even more so with proximity to death. Spending on individuals in their last year of life absorbs one‐tenth of aggregate health care expenditure. In a given year, spending on health care is highly skewed toward individuals with lower incomes. Average expenditure on the poorest fifth is more than three times that on the richest fifth.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".