Equity in public health spending in Ethiopia: a benefit incidence analysis
Bibliographic record
Abstract
Inequality in access and utilization of health services because of socioeconomic status is unfair, and it should be monitored and corrected with appropriate remedial action. Therefore, this study aimed to estimate the distribution of benefits from public spending on health care across socioeconomic groups in Ethiopia using a benefit incidence analysis. We employed health service utilization data from the Living Standard Measurement Survey, recurrent government expenditure data from the Ministry of Finance and health services delivery data from the Ministry of Health's Health Management Information System. We calculated unit subsidy as the ratio of recurrent government health expenditure on a particular service type to the corresponding number of health services visits. The concentration index (CI) was applied to measure inequality in health care utilization and the distribution of the subsidy across socioeconomic groups. We conducted a disaggregated analysis comparing health delivery levels and service types. Furthermore, we used decomposition analysis to measure the percentage contribution of various factors to the overall inequalities. We found that 61% of recurrent government spending on health goes to health centres (HCs), and 74% was spent on outpatient services. Besides, we found a slightly pro-poor public spending on health, with a CI of -0.039, yet the picture was more nuanced when disaggregated by health delivery levels and service types. The subsidy at the hospital level and for inpatient services benefited the wealthier quintiles most. However, at the HC level and for outpatient services, the subsidies were slightly pro-poor. Therefore, an effort is needed in making inpatient and hospital services more equitable by improving the health service utilization of those in the lower quintiles and those in rural areas. Besides, policymakers in Ethiopia should use this evidence to monitor inequity in government spending on health, thereby improving government resources allocation to target the disadvantaged better.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".