Determinants of early retirement preferences in Europe: The role of grandparenthood
Bibliographic record
Abstract
Various family characteristics are acknowledged as important determinants of retirement preferences. Yet, the relevance of the third family generation – the grandchildren – has been largely overlooked. In this article we bring the association between grandparenthood and retirement preferences to the fore. We expect to find such a relationship for two main reasons: first, rising participation rates in the labor market, especially among mothers, increases the need for childcare which, in some countries, is only partially provided by the state. Second, for many people grandparenthood marks the transition to a new phase in the life-course, implying new role-identities. We thus expect grandparenthood to decrease anxieties associated with retirement and with the potential loss of one’s role-identity as a working person. We test the association between grandparenthood and retirement preferences using data from the Survey of Health, Aging, and Retirement in Europe (SHARE). The findings confirm that grandparenthood increases an individual’s chances of looking forward to retiring early, thus supporting the claim that individuals’ lives are linked to the lives of their family members. Contrary to expectations, the association of grandparenthood with retirement preferences is particularly strong in countries that provide extensive childcare support.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".