The Effect of Health Expenditure on Life Expectancy
Bibliographic record
Abstract
With this paper, we aim to analyse the effect of health expenditures and funding on the national life expectancy of OECD countries. We considered the influence of exogenous factors such as health expenditure, GDP per capita and productivity, population, infant mortality rates, potential years of life lost, deaths from cancer and the suicide rate. We used secondary data gathered between 2005 to 2018 from the annual reports of the OECD, the IMF and the World Bank. To derive the empirical results, econometric models such as linear regression, random effect, fixed effect, Hausman - Taylor Regression, GMM Model - Arellano Bond Estimation, Generalized Estimating Equations (GEE Model) and linear trend analysis through the historical and comparative method were used. Results show that health expenditures positively affect the national life expectancy of OECD countries, showing the impact and causality of national longevity in OECD countries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".