Who Benefits from Public Healthcare Subsidies in Egypt?
Bibliographic record
Abstract
Direct subsidization of healthcare services has been widely used in many countries to improve health outcomes. It is commonly believed that the poor are the main beneficiaries from these subsidies. We test this hypothesis in Egypt by empirically analyzing the distribution of public healthcare subsidies using data from Egypt Demographic and Health Survey and Egypt National Health Accounts. To determine the distribution of public health care subsidies, we conducted a Benefit Incidence Analysis. As a robustness check, both concentration and Kakwani indices for outpatient, inpatient, and total healthcare were also calculated. Results show some degree of inequality in the benefits from public healthcare services, which varied by the type of healthcare provided. In particular, subsidies associated with University hospitals are pro-rich and have inequality increasing effect, while subsidies associated with outpatient and inpatient care provided by the Ministry of Health and Population have not been pro-poor but have inequality reducing effect (weakly progressive). Results were robust to the different analytical methods. While it is widely perceived that the poor benefit the most from health subsidies, the findings of this study refute this hypothesis in the case of Egypt. Poverty reduction measures and healthcare reforms in Egypt should not only focus on expanding the coverage of healthcare benefits, but also on improving the equity of its distribution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".