Gender Differences in Multigenerational Caregiving Across the Life Course in Europe
Bibliographic record
Abstract
To better understand how caregiving varies across contexts and the impact of caregiving on social and economic outcomes, we need to understand how it varies across the entire adult life course and how sensitive caregiving estimates are to the inclusion of different directions and types of care. This is the first study to comprehensively describe multigenerational caregiving patterns by gender and age across European countries. We use the Generations and Gender Survey (GGS) Wave 1 (N=114,147) to consider multiple definitions of multigenerational caregiving. In addition to personal care and financial transfers, we also include emotional transfers which are rarely examined. We also examine multigenerational care that includes simultaneous care for any two generations rather than just to parents and children which is most often studied. Across our sample of 11 European countries, we find that women are significantly more likely to give care than men across the life course, and these gender gaps are especially large during critical periods like young adulthood and mid-life around retirement ages. Including emotional caregiving as well as horizontal care (to a spouse, sibling, or friend) are both substantively important in shaping the life course pattern of caregiving and the size of the gender gap. The gender gap in the life course pattern of caregiving have implications for aging, intergenerational inequality, and human capital accumulation across the life course.
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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 | 0.002 | 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 | 0.001 | 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) | 0.002 | 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".