Public and Private Health Care Financing with Alternate Public Rationing Rules
Bibliographic record
Abstract
We develop a model to analyze alternative health care financing arrangements. Health care is demanded by individuals varying in income and severity of illness. There is a limited supply of health care resources used to treat individuals, causing some individuals to go untreated. We examine outcomes under full public finance, full private finance, and mixed, parallel public and private finance under two rationing rules for the public sector: needs-based rationing and random rationing. Insurers (both public and private) must bid to obtain the necessary health care resources to treat their beneficiaries. While public insurer's ability-to-pay is limited by its (fixed) budget; the private insurers willingness-to-pay reflects the individuals' willingness-to-pay for care. When permitted, the private sector supplies supplementary health care to those willing and able to pay. We find that the introduction of a private sector diverts treatment from relatively poor to relatively rich individuals. Moreover, if the public system allocates care according to need, then the average severity of the untreated is higher in a mixed system than in a pure public system. While we can unambiguously sign most comparative static effects for a general set of distribution functions, an analysis of the relationship between public sector rationing and the scope for a private health insurance market requires distributional assumptions. For a bivariate uniform distribution function we find that the private health insurance market is smaller when the public sector rations according to need as compared to random allocation of health care.
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| 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".