Financial Transfers from Living Parents to Adult Children: Who Is Helped and Why?
Bibliographic record
Abstract
Abstract. To what extent can young adult children rely on their parents for financial support? This question will take on added importance if the commitments of the Social Security system put greater strain on the children of retirees. Despite the critical role that parents have in supporting their children, why they help some and not others remains unclear. Findings using two waves of data from the Health and Retirement Study that control for the needs of children and the resources of parents suggest that parents give more inter vivos financial assistance to their disadvantaged children rather than focusing on children most able to give financial help in return. Other measures of child well‐being besides income, including home ownership, education, parental status, and marital status, also suggest that parents help needier children more. Children who live nearby also receive more, a finding consistent with exchange motives or simply the ability of these children to more stridently demand support. Neither altruism nor exchange theories explain why stepchildren receive substantially less support than naturally born or adopted children. The diversity of effects suggests that giving is based on heterogeneous motives—parents may temper their altruism for children by the degree to which they feel responsible and by the stridency of some children in seeking support. Findings are robust upon allowing for unobserved differences across families by estimating fixed effect models.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".