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Record W3148199486

Who's Going Broke? Comparing Growth in Healthcare Costs in Ten OECD Countries

2005· article· en· W3148199486 on OpenAlexaboutno aff
Christian Hagist, Laurence J. Kotlikoff

Bibliographic record

VenueNational Bureau of Economic Research · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careProductivityBeneficiaryEconomicsDemographic economicsDemographicsBaby boomGovernment (linguistics)Agricultural economicsBusinessEconomic growthPopulationDemographyFinance
DOInot available

Abstract

fetched live from OpenAlex

Government healthcare expenditures have been growing much more rapidly than GDP in OECD countries. For example, between 1970 and 2002 these expenditures grew 2.3 times faster than GDP in the U.S., 2.0 times faster than GDP in Germany, and 1.4 times faster than GDP in Japan. How much of government healthcare expenditure growth is due to demographic change and how much is due to increases in benefit levels; i.e., in healthcare expenditures per beneficiary at a given age? This paper answers this question for ten OECD countries -- Australia, Austria, Canada, Germany, Japan, Norway, Spain, Sweden, the UK, and the U.S. Specifically, the paper decomposes the 1970-2002 growth in each country’s healthcare expenditures into growth in benefit levels and changes in demographics. Growth in real benefit levels has been remarkably high and explains the lions share — 89 percent — of overall healthcare spending growth in the ten countries. Norway, Spain, and the U.S. recorded the highest annual benefit growth rates. Norway’s rate averaged 5.04 percent per year. Spain and the U.S. were close behind with rates of 4.63 percent and 4.61 percent, respectively. Allowing benefit levels to continue to grow at historic rates is fraught with danger given the impending retirement of the baby boom generation. In Japan, for example, maintaining its 1970-2002 benefit growth rate of 3.57 percent for the next 40 years and letting benefits grow thereafter only with labor productivity entails present value healthcare expenditures close to 12 percent of the present value of GDP. By comparison, Japan’s government is now spending only 6.7 percent of Japans current output on healthcare. In the U.S., government healthcare spending now totals 6.6 percent of GDP. But if the U.S. lets benefits grow for the next four decades at past rates, it will end up spending almost 18 percent of its future GDP on healthcare. The difference between the Japanese 12 percent and U.S. 18 percent figures is remarkable given that Japan is already much older than the U.S. and will age more rapidly in the coming decades. Although healthcare spending is growing at unsustainable rates in most, if not all, OECD countries, the U.S. appears least able to control its benefit growth due to the nature of its fee-for-service healthcare payment system. Consequently, the U.S. may well be in the worst long-term fiscal shape of any OECD country even though it is now and will remain very young compared to the majority of its fellow OECD members.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.337
GPT teacher head0.608
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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