Do Financial Incentives for Supplementary Private Health Insurance Reduce Pressure on the Public System? Evidence from Australia
Bibliographic record
Abstract
In many developed countries, budgetary pressures have made government turn to private insurance as a means of reducing pressure on their public health system. Between 1997 and 2000 the Australian government implemented a series of financial incentives for supplementary private health insurance, intending to increase insurance enrollment and reduce public health care costs. Using the Australian Bureau of Statistics 2001 National Health Survey, we examine the impact of increased private insurance coverage on use of both public and private hospital systems. In particular, we investigate how supplementary private insurance affects public and private admissions and lengths of stay for new enrollees and for those insured prior to the reforms. We use Propensity Score Matching to control for selection in the insurance decision and we estimate a two-part model for hospital admission and length of stay. Our results indicate that there is selection associated with insurance choice. We also find that hospital use measured by unconditional public and private lengths of stay, differ significantly depending on insurance duration. Those who enrolled in response to the incentives behave more like the uninsured than the long-term insured. Their use of the public hospital system is slightly lower than that of the uninsured, but this reduction is outweighed their higher use of the private system. While the insurance incentives substantially increased the proportion of the population with supplementary private insurance, the impact on the use of the public system appears to be quite modest. The cost of the subsidy to insurance premiums was over $AUD2 billion in 2001/02. Our results suggest that using financial incentives to take up private insurance is not a cost-effective way of reducing pressure on public hospital systems.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".