A Study on How an Aging Population Affects the Effectiveness of Fiscal Policy Using an ARDL Model
Bibliographic record
Abstract
Purpose - This paper examines the impact of an aging population on fiscal policy efficiency. Two significant issues arise from Korea’s aging population: old age poverty and the sustainability of government debts. As Korea’s demographic trajectory is similar to that of Japan, it is vital to study fiscal soundness and efficiency from various points of view. Design/Methodology/Approach - The Autoregressive Distributed Lags (ARDL) - EC model was adopted. ARDL shows statistically robust results when the data are a mixture of stationary at the level or the first difference. The data was downloaded from 1994 to 2019 of five OECD countries; Canada, Japan, Spain, Sweden, and Korea. The country selection followed the regime classification of OECD (2012) based on the labor market structure and redistribution effect after tax. Findings - First, the dependency ratio negatively affects the efficiency of fiscal policy. The marginal propensity was suppressed due to the consumption variance in old age cohorts as the dependency ratio increases. Economic agents react to save for future uncertainty when government spending is funded by issuing debt. Second, the long-term elasticities differed among countries, with Korea having the highest at 0.56, followed by Canada at 0.55, Spain at 0.39, and Japan at 0.36. Research Implications - Efficiency of fiscal policy is dependent upon the reactions of economic agents. The priority of the government should focus on managing the future expectations of economic agents by implementing structural reforms in the labor market and pension system.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".