****** DRAFT: Please do not cite ****** Willingness-to-Pay for Parallel Private Health Insurance: Canadian Evidence from a Laboratory Experiment
Bibliographic record
Abstract
Debate over the effects of public versus private health care financing has been, and continues to be, active in both academic outlets and policy circles. Theoretical literature on parallel health care financing is often built on untested behavioural assumptions and the empirical evidence generally depends upon the institutional details of the specific health care systems under analysis. This paper contributes to the literature on parallel health care finance by developing and executing a revealed preference laboratory experiment based on the theoretical model of parallel health care finance in Cuff et al. (2008). The theoretical model involves individuals with varying severities of illness who demand health care from a limited supply of health care resources. Health care resources are purchased by the public sector and rationed free of charge to individuals, or purchased by individuals through a private insurance market. The general theoretical model is converted into a discrete experimental representation of a large-scale economy where individuals are price takers, the probability of receiving public health care is exogenous and the willingness-to-pay (WTP) for private health insurance is elicited from subjects. The experimental design includes two within-subject factors based on the theoretical model: the public sector rationing rule (rationing based on need or severity versus rationing based on a random allocation) and the probability of being publicly treated (high versus low). The experimental design also includes two between-subjects treatments based on the frame of the experiment (neutral frame versus health frame) and on the distribution of private health insurance prices (high prices versus low prices). The results show the public insurer’s allocation rule and the probability of receiving health care from the public insurer both significantly affect an individual’s WTP for private health insurance in the predicted direction, although the WTP values tend to be above the actual theoretical predictions. When the public insurer allocates health care based on need, the average WTP is lower than under random allocation. A higher probability of receiving health care from the public insurer elicits a lower WTP regardless of how the public insurer allocates health care. When the public insurer allocates health care based on need, the WTPs are significantly higher under a neutral frame than a health frame, and the average WTP is significantly higher when the distribution of private health insurance prices is higher.
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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 | 0.026 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.009 |
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".