Is post-communist health spending unusual? A comparison with established market economies
Bibliographic record
Abstract
What factors determine a country’s spending on health? And what factors determine the share of spending financed by the public sector? Taking these factors into account, is post-communist health spending unusual? For the OECD economies, we find that per capita health spending is strongly related to per capita income, with an elasticity of about 1.5. The elasticity for developing economies is close to one. Spending is also positively related to the elderly dependency rate, but the relationship is weaker than a static comparison of spending by the elderly and non-elderly would suggest. Even though health spending as a share of GDP in the post-communist countries of eastern and central Europe is below the OECD average, there is evidence of above normal health spending in most countries when we control for income and demographics. For Hungary, the ‘excess’ spending reached over three percentage points of GDP in 1994. For the OECD sample, four development indicators account for half the variation in the public sector share of total health spending. Political variables help explain the remainder. If the post-communist countries converge to the market economy pattern, the share of public financing will fall, yet still remain well above half.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".