Population Aging and the Three Demographic Dividends in Asia
Bibliographic record
Abstract
The present study first examines the trends in age structural shifts in selected Asian economies over the period 1950–2050 and analyzes their impact on economic growth in terms of the first and second demographic dividends computed from the system of National Transfer Accounts. Then, using the National Transfer Accounts, we analyze the effect of the age structural shifts on the pattern of intergenerational transfers in Japan; the Republic of Korea; and Taipei,China. A brief comparison of the results reveals that, in the next few decades, the latter two are likely to follow in Japan's footsteps by increasing public transfers and asset reallocations, and by reducing familial transfers, particularly among older persons. Next, we consider a newly defined demographic dividend, which is generated through the use of the untapped work capacity of healthy older persons and to which we refer as “the silver” or “the third” demographic dividend. By drawing upon microlevel datasets obtained from Japan and Malaysia, we calculate the magnitude of the impact of that dividend on macroeconomic growth in each of the two economies, concluding that while in Japan the expected effect is substantial, in Malaysia it will take several decades before the country can enjoy comparable benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".