Gender differences in time use across age groups: A study of ten industrialized countries, 2005–2015
Bibliographic record
Abstract
This study uses largescale cross-national time-diary data from the Multinational Time Use Study (MTUS) (N = 201,972) covering the period from 2005 to 2015 to examine gender differences in time use by age groups. The study compares ten industrialized countries across Asia, Europe, and North America. In all ten countries, gender differences in time use are smaller in personal care, sleeping and meals, followed by leisure time (including screen-based leisure and active leisure), and largest in housework, care work and paid work activities. Gender disparities in time use are higher in South Korea, Hungary, and Italy, followed closely by Spain, with moderate gender gaps in Western European countries like France and Netherlands, and lowest differences in Finland and Anglo-Saxon countries, including Canada, US, and the UK. Gender differences in housework and caring time increase from adolescence (10-17 years) to early adulthood (18-29 years), showing strong gender gaps in early/middle adulthood (30-44 years), but narrow again during late adulthood (65 years or older). However, the age gradient in care work and housework is most pronounced in Italy and South Korea, being less prominent in Canada and Finland. Gender gaps in paid work are larger in early/middle adulthood (30-44) and middle/late adulthood (45-64), with strongest age gradients observed in the Netherlands and weaker gradients for the US. Gender differences in active leisure increase by age, especially in Southern European countries, while screen-based leisure shows more stable gender gaps by age groups across different countries. Overall, this study shows that age and gender intersect strongly in affecting time-use patterns, but also that the national context plays an important role in shaping gender-age interactions in time use allocation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".