A Multilevel Analysis on the Determinants of Regional Health Care Expenditure
Bibliographic record
Abstract
We apply a multilevel hierarchical model to explore whether an aggregation fallacy exists in estimating the income elasticity of health expenditure by ignoring the regional composition of national health expenditure figures. We use data for 110 regions in eight OECD countries in 1997: Australia, Canada, France, Germany, Italy, Spain, Sweden and United Kingdom. In doing this we have tried to identify two sources of random variation: within countries and between-countries. Our results show that: 1- Variability between countries amounts to (SD) 0.5433, and just 13% of that can be attributed to income elasticity and the remaining 87% to autonomous health expenditure; 2- Within countries, variability amounts to (SD) 1.0249; and 3- The intra-class correlation is 0.5300. We conclude that we have to take into account the degree of fiscal decentralisation within countries in estimating income elasticity of health expenditure. Two reasons lie behind this: a) where there is decentralisation to the regions, policies aimed at emulating diversity tend to increase national health care expenditure; and b) without fiscal decentralisation, central monitoring of finance tends to reduce regional diversity and therefore decrease national health expenditure.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".