An Alternative Approach to Decomposing the Redistributive Effect of Health Financing Between and Within Groups Using the Gini Index: The Case of Out-of-Pocket Payments in Nigeria
Bibliographic record
Abstract
Equity in health financing remains significant in the universal health coverage discourse. The way a health system is financed, apart from determining whether people have access to needed health services, also has implications for income inequality in a country. Traditionally, the impact of health financing on income inequality or the redistributive effect of health financing is assessed by looking at whether income inequality reduces because of health financing. This is also decomposed into a vertical component (the extent of progressivity), a horizontal component (the extent to which households with similar incomes are treated equally when financing health services) and a reranking component (whether households change their relative socio-economic ranking after financing health services). Such an approach to decomposition is mainly essential to assess the equal treatment of equals and unequal treatment of unequals in the entire population. This paper argues that in decomposing the redistributive effect of health financing, the impact of health financing on changes in income inequality between and within population groups should be investigated as they are relevant for policy dialogues in many countries. It develops a framework for such analysis and applies this to data from Nigeria. Decomposing the Gini index of income inequality using the Shapley value approach, the results show that changes in inequality associated with out-of-pocket payments for health services within the geopolitical zones in Nigeria dominate the changes in income inequality between the geopolitical zones. Although not all the results in the application in this paper are statistically significant, this framework is still useful for policies in countries that aim to use health financing to reduce, among other things, income disparities between and within defined population groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".