International Models of Mixed Public-Private Health Insurance: Lessons for China?
Bibliographic record
Abstract
Reform of China’s health care system has been one of the most controversial elements in the gradual transformation of China from a centrally planned economy to one based to a greater extent on competitive markets. Although there is agreement that the health care system that has emerged following the dismantling of the pre-1980s system has major flaws, there are widely diverging views on the way forward. While some proposals entail at least a partial return to the earlier system of publicly funded and managed health services system (the “command and control” model), others are based on a social insurance model that relies on competition among hospitals for patients and a strengthened role of the social insurance plans as purchasers of care. The debate partly reflects tensions between different central ministries and levels of government, and is intertwined with the debate about the decentralization of taxation authority and expenditure responsibilities. While a number of key directives for the future were published in 2009, many issues remain unresolved. In both the English and Chinese-language literature, there has been extensive debate about the organization and funding of the three different social insurance plans that now exist in urban and rural areas (see, for example, the special issues of Health Economics [Vol. 18, Issue S2, July 2009] and China Economic Review [Vol. 20, Issue 4, December 2009] and references there). However, less attention has been paid to the future role of private health insurance. In this paper, we first survey current trends in the emerging role of private insurance in China’s health financing system, both in urban areas (where private insurance is commonly used to complement the public plans), and in rural areas (where private insurers have a role in managing the publicly organized and subsidized rural cooperative medical schemes in some counties). We then discuss policy measures that can be used to create or strengthen the role of private insurance as a potential competitor for the publicly managed insurance plans, and how this can be done without sacrificing the long-term goal of universal coverage and equitable sharing of the burden of health financing. The analysis draws on the experience with mixed insurance systems in countries such as the U.S., France, Holland, and South Africa. Particular attention is paid to the difference between the role of private insurance as a supplement or complement to a public plan (along the lines of the U.S. Medigap plans, or the private plans in France), and its potential role as a substitute for a publicly funded plan (as the Medicare Advantage plans do in the U.S.), as well as to the Dutch model in which public and private insurance plans compete on an equal basis within a publicly funded risk-rated subsidy system. The case for a set of rules that permit a role for substitute private insurance, especially employment-related group insurance, is explored in the paper.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".