How do Older Adults Spend Their Time? Gender Gaps and Educational Gradients in Time Use in East Asian and Western Countries
Bibliographic record
Abstract
Abstract This study is the first to document how older adults in East Asian and Western societies spend their time, across four key dimensions of daily life, by respondent’s gender and education level. To do this, we undertook a pioneering effort and harmonized cross-sectional time-use data from East Asian countries (China, Japan, South Korea, Taiwan) with data from the Multinational Time Use Study (Canada, Denmark, Finland, France, Italy, The Netherlands, Norway, Spain, United Kingdom, United States; to which we refer as Western countries), collected between 2000 and 2015. Findings from bivariate and multivariate models suggest that daily time budgets of East Asian older adults are different from their counterparts in most Western countries. Specifically, gender gaps in domestic work, leisure, and sleep time were larger in East Asian contexts, than in Western countries. Gender gaps in paid work were larger in China compared to all other regions. Higher levels of educational attainment were associated with less paid work, more leisure, and less sleep time in East Asian countries, while in Western countries they were associated with more paid work, less domestic work, and less sleep. Interestingly, Italy and Spain, two Southern European welfare regimes, shared more similarities with East Asian countries than with other Western countries. We interpret and discuss the implications of these findings for population aging research, and welfare policies.
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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 | 0.001 |
| 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".