Public-Private Mix of Health Expenditure: A Political Economy Approach and A Quantitative Exercise
Bibliographic record
Abstract
This paper constructs a simple overlapping generations model to examine how the choice of public and private health expenditure is affected by preferences and economic factors under majority voting. In the model, agents with heterogeneous income decide how much to consume, save, and invest in private health care, and vote for the income tax to be used to finance public health. Agents.survival probabilities are endogenously determined by a CES composite of public and private health expenditure. For the two special cases that public and private health are complements or perfect substitutes, we show that the voting equilibrium is unique and locally stable. For the general case, we calibrate the model to Canadian data to conduct a quantitative analysis. Our results suggest that the public-private mix of health expenditure is quite sensitive to the degree of substitutability between private and public health and the relative effectiveness of public and private health. Using a sample of advanced democratic countries, we further infer these two parameters and construct the shares of public health in total health expenditure for each country, and find that the predicted values match the data quite well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".