Modeling the Cost of Population Aging in Iran
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: The decline in fertility rates and the increase in life expectancy have changed the demographic structure of many countries substantially and entail long-term economic implications too. Population aging has adverse effects on countries’ economies, especially with respect to the social security system and the welfare structure. METHODS: Given the importance of the phenomenon of population aging and the increased longevity in recent decades, the present study was conducted to address the welfare implications of population aging in Iran during a span of 150 years using the Overlapping Generations (OLG) model. RESULTS: Examining the effect of reduced population growth or population aging on economic welfare, labor supply, capital assets and government expenditure during the span of 150 years suggested a decline in economic welfare in the early years; however, the rate of decline slowed down toward the end of the period; the same finding also applies to labor supply. Overall, population aging had the greatest impact on capital assets. CONCLUSION: Population aging can cause a drastic transition in consumption and saving behaviors. Labor markets can also undergo similar transitions in their labor supply and have implications for labor productivity. The combination of these changes affects economic growth and welfare. The results of the study suggest that supporting the workforce and employing the immigrant population in the labor market can help reduce the adverse consequences of the phenomenon of population aging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".