Caregiving time costs and trade-offs: Gender differences in Sweden, the UK, and Canada
Bibliographic record
Abstract
Population ageing is putting pressure on pension systems and health care services, creating an imperative to extend working lives. At the same time, policy makers throughout Europe and North America are trying to expand the use of home care over institutional services. Thus, the number of people combining caregiving responsibilities with paid work is growing. We investigate the conflicts that arise from this by exploring the time costs of unpaid care and how caregiving time is traded off against time in paid work and leisure in three distinct policy contexts. We analyze how these tradeoffs differ for men and women (age 50-74), using time diary data from Sweden, the UK and Canada from 2000 to 2015. Results show that women provide more unpaid care in each country, but the impact of unpaid care on labor supply is similar for male and female caregivers. Caregivers in the UK and Canada, particularly those involved in intensive caregiving, reduce paid work in order to provide unpaid care. Caregivers in Sweden do not trade off time in paid work with time in caregiving, but they have less leisure time. Our findings support the idea that the more extensive social infrastructure for caring in Sweden may diminish the labor market effects of unpaid care, but highlight that throughout contexts, intensive caregivers make important labor and leisure tradeoffs. Respite care and financial support policies are important for caregivers who are decreasing labor and leisure time to provide unpaid care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".