Intergenerational financial transfers and physical health of old people in rural China: evidence from CHARLS data
Bibliographic record
Abstract
ABSTRACT: With the reduction of intergenerational temporal transfers, financial transfers from adult offspring to their elderly parents are prevailing in rural China. Although much has been done, little is known about the association between the expansion of intergenerational transfers and rural old people’s physical health in China. The purpose of this paper was to examine the effect of intergenerational financial transfers on the elders’ physical health in rural China. Using data collected from China Health and Retirement Longitudinal Study (CHARLS), panel data fixed effect model and threshold model are employed to estimate the impact of intergenerational financial transfers on the old people’s physical health in rural areas. Results showed that; although, the intergenerational financial transfers have a positive effect on the old people’s physical health, no linear relationship exists between them. Intergenerational financial transfers are clearly less effective for low-income old people’s physical health than those of middle-income, while the effect on high-income old people’s health is the most insignificant. Studies concerning the effect of intergenerational financial transfers on the elders’ health in developing countries remain limited. Findings of this paper provided great insights into how intergenerational transfers, such as intergenerational financial transfers, may affect the well-beings of old residents in rural areas. Additionally, this study can offer inspiration to policy makers regarding what measures they should take to enhance rural old residents’ well-beings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".