Gender Patterns and Value of Unpaid Care Work: Findings From<scp>C</scp>hina's First Large‐Scale Time Use Survey
Bibliographic record
Abstract
Using data from the 2008 C hina Time Use Survey, this paper examines the gender patterns of time allocation over paid work, unpaid care work, and non‐work activity and estimates the monetary value of unpaid care work. A seemingly unrelated regression ( SUR ) technique is applied to explore the tradeoff between the three types of activity. The estimates show that, holding constant individual characteristics and regional effects, the total work time of women is higher than that of men by 7 hours per week in the rural sector and by 10.5 hours per week in the urban sector. The monetary value of unpaid care work is estimated by five methods. Depending on the method used, the value assigned to unpaid care work varies from 25 to 32 percent of C hina's GDP , from 52 to 66 percent of final consumption, and from 63 to 80 percent of the gross products of tertiary industry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".