Public and Private Hospitals, Congestion, and Redistribution
Bibliographic record
Abstract
Abstract This paper studies how congestion in the public health sector can be used as both an in‐kind and in‐cash redistributive tool. In our model, agents differ in productivity and they can obtain a health service either from a congested public hospital or from a noncongested private one at a higher price. With pure in‐kind redistribution, agents fail to internalize their impact on congestion, and the demand for the public hospital is higher than optimal. When productivities are not observable but the social planner can assign agents across hospitals, the optimal congestion is higher than in the full information case in order to relax incentive constraints and foster income redistribution. Finally, if agents can freely choose across hospitals, the optimal subsidy on the private hospital price may be negative or positive depending on the relative importance of redistribution and efficiency concerns. In this case, redistribution is limited if the quality of the public facility depends on the number of users.
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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 | 0.005 | 0.001 |
| 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 | 0.001 |
| 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".