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Record W1606699118

Public-Private Mix of Health Expenditure: A Political Economy Approach and A Quantitative Exercise

2012· preprint· en· W1606699118 on OpenAlexaboutno aff
Shuyun May Li, Solmaz Moslehi, Siew Ling Yew

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPublic economicsVotingEconomicsPublic healthHealth carePublic expenditureAggregate expenditureSample (material)Construct (python library)Public financeBusinessPoliticsPolitical scienceEconomic growthMacroeconomicsMedicine
DOInot available

Abstract

fetched live from OpenAlex

This paper constructs a simple overlapping generations model to examine how the choice of public and private health expenditure is affected by preferences and economic factors under majority voting. In the model, agents with heterogeneous income decide how much to consume, save, and invest in private health care, and vote for the income tax to be used to finance public health. Agents.survival probabilities are endogenously determined by a CES composite of public and private health expenditure. For the two special cases that public and private health are complements or perfect substitutes, we show that the voting equilibrium is unique and locally stable. For the general case, we calibrate the model to Canadian data to conduct a quantitative analysis. Our results suggest that the public-private mix of health expenditure is quite sensitive to the degree of substitutability between private and public health and the relative effectiveness of public and private health. Using a sample of advanced democratic countries, we further infer these two parameters and construct the shares of public health in total health expenditure for each country, and find that the predicted values match the data quite well.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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 head0.201
GPT teacher head0.477
Teacher spread0.276 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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