The impact of population ageing on economic growth in developing countries
Bibliographic record
Abstract
The study aims to analyse the economic effects of population ageing in Japan, Spain, Italy, the United States, South Korea, Germany, France, the United Kingdom, and Canada using the panel ARDL (Autoregressive Distributed Lag) model. GDP per capita was adopted as the dependent variable, while the independent variables included the proportion of the population over 65, health expenditure, the dependency rate, and the level of investment as a proportion of GDP. The results showed varying effects of ageing on economic growth. It had a positive impact in some of the countries under study (the USA, South Korea, and Germany) and a negative impact in others (Spain, Italy, and Japan). Health spending was also found to be an economic burden, while fixed investment was shown to be critical in supporting economic growth. The study recommends improving education and training to increase productivity, embracing innovation and technology, enhancing health systems through preventive care, and implementing flexible policies, such as encouraging women's participation in the labour market and promoting migration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".