Population Aging and Public Health Expenditure as Fiscal Drivers of Economic Growth in Asia 1975–2024
Bibliographic record
Abstract
This study examines the impact of population aging on economic growth through the fiscal channel of government health expenditure throughout the dataset from World Bank Indicators.It focuses on 12 Asian economies over the period from 1975 to 2024.Utilizing a unbalanced panel dataset characterized by cross-sectional dependence, non-stationarity, and weak evidence of cointegration, the analysis applies the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model to estimate short-run effects and the role of lagged adjustment terms.The findings reveal that population aging does not have a statistically significant direct effect on economic growth.In contrast, government health spending emerges as a critical transmission mechanism that links demographic transitions to output performance.In the short run, health expenditure positively and significantly influences GDP per capita.reflecting its productivity-enhancing and demand-stimulating effects.However, the lagged health spending term exhibits a negative and significant coefficient.This suggests that there are fiscal trade-offs and adjustment costs in subsequent periods.These results align with endogenous growth theory and highlight that public expenditure can have both productive and counterproductive effects depending on timing and efficiency.Policy makers should emphasize the need for balanced health financing strategies, preventive healthcare investments, and coordinated regional fiscal responses to demographic change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".