Policy change and private health insurance: did the cheapest policy do the trick?
Bibliographic record
Abstract
From the introduction of Australia's national health insurance scheme (Medicare) in 1984 until recently, the proportion of the population covered by private health insurance declined steadily. Following an Industry Commission inquiry into the private health insurance industry in 1997, a number of policy changes were effected in an attempt to reverse this trend. The main policy changes were of two types: "carrots and sticks" financial incentives that provided subsidies for purchasing, or tax penalties for not purchasing, private health insurance; and lifetime community rating, which aimed to revise the community rating regulations governing private health insurance in Australia. This paper argues that the membership uptake that has occurred recently is largely attributable to the introduction of lifetime community rating which goes some way towards addressing the adverse selection associated with the previous community rating regulations. This policy change had virtually no cost to government. However, it was introduced after subsidies for private health insurance were already in place. The chronological sequencing of these policies has resulted in substantial increases in government expenditure on private health insurance subsidies, with such increases not being a cause but rather an effect of increased demand for private health insurance. The paper also considers whether the decline in membership that has occurred since the implementation of lifetime community rating presages the re-emergence of an adverse selection problem in private health insurance. Much of the decline to date may be attributable to failure on the part of some members to honour premium payments when they first fell due. However, the changing age composition of the insured pool since September 2000, resulting in an increasing average age of those insured, suggests the possible reappearance of an adverse selection dynamic. Thus the 'trick' delivered by lifetime community ratings may not be maintained in the longer term.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".