Does unpaid caregiving erode working hours among middle-aged Chinese adults?
Bibliographic record
Abstract
Abstract Middle-aged adults are commonly confronted with the burden of paid work and multiple caregiving roles. This paper examines the relationship between weekly hours of unpaid caregiving and hours of work using data from the baseline survey of the China Health and Retirement Longitudinal Study. The analysis was conducted on a nationally representative sample of 3,645 working-age Chinese adults aged 45-60 years who were not farming and had a young grandchild and/or a parent/parent-in-law. For women and men separately, we combined the use of a Heckman selection procedure and instrumental variables to estimate the relationship between weekly caregiving hours and hours of work. A caregiving threshold was also identified for women and men separately to allow for the testing of a kink and/or a discontinuity in this relationship. We found that for women, their working hours were initially unrelated to hours of caregiving before the threshold of 72 caregiving hours per week; then, their working hours experienced an almost two-fold increase at the caregiving threshold before falling by 2.02 percent for each additional hour of caregiving beyond the threshold. For men, their hours of work fell by 2.74 percent for each hourly increment in caregiving. Although a caregiving threshold of 112 hours was identified for men, there was insufficient evidence for a statistically significant kink or discontinuity in this relationship. These findings provide support for a range of fiscal and human resource policies that target employed family caregivers in order to advance their well-being while also maintaining their work productivity.JEL Classification: D13, J21, J22
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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 | 0.002 |
| 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 | 0.001 | 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".