The progressivity of health care revenue financing in 29 countries: A comparison
Bibliographic record
Abstract
BACKGROUND: This study assesses progressivity in public and private health care revenue collection among 29 high-income countries by combining the results of two previous articles comprising this special section of Health Policy. In those studies, we developed qualitatively based scores regarding revenue collection policies for three public revenue sources (income taxes, social insurance contributions, consumption taxes) and two private revenue sources (voluntary health insurance, out-of-pocket payments). OBJECTIVE: The current study sums these scores, weighted by the shares of each revenue source in each country, to calculate an overall progressivity score for each country. METHODS: We derived weights for each revenue source using publicly available OECD and Eurostat macrolevel data on the structure of health care financing and government revenues. RESULTS: France was the country that had the most progressive system, and Latvia, Hungary, and Bulgaria, the least progressive. CONCLUSIONS: Countries relying more on out-of-pocket payments tend to be more regressive overall, suggesting that, from an equity perspective, their role should remain limited. Tax-based systems do not inherently ensure progressivity, especially when relying heavily on regressive consumption taxes. While wealthier countries and those with less income inequality tend to be more progressive, in contrast, Switzerland and Germany both scored among the more regressive countries. Our study shows that policy matters in promoting progressivity in health system revenue collection. Both public and private sources can be regressive if nothing is done. Yet, there are policy instruments that can mitigate regressivity, and even private sources of funds can be made less regressive.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".