The continuing importance of children in relieving elder poverty: evidence from Korea
Bibliographic record
Abstract
ABSTRACT The population of South Korea is ageing rapidly and government provision for older people is meagre. Hence the erosion of traditional family support for older people is of much concern. Yet relatively little is known about the actual financial status of elderly Koreans or the amount of economic support they receive from children. This paper addresses these issues using data from the 2006 Korean Longitudinal Study of Ageing. We find that almost 70 per cent of Koreans aged 65 or more years received financial transfers from children and that the transfers accounted for about a quarter of an average elder's income. While over 60 per cent of elders would be poor without private transfers, children's transfers substantially mitigate elder poverty, filling about one-quarter of the poverty gap. Furthermore, children's transfers tend to be proportionally larger to low-income parents, so elder income inequality is reduced by the transfers. Over 40 per cent of elders lived with a child and co-residence helps reduce elder poverty. By showing that Korean children still play a crucial role in providing financial old-age security, we demonstrate how important it is for the Korean government to design old-age policies that preserve the incentives for private assistance. This snapshot of today's Korea also has implications for other rapidly changing Asian countries that are following a similar trajectory.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".