Hiring Domestic Help and Family Well-Being in Hong Kong: A Propensity Score Matching Analysis
Bibliographic record
Abstract
The outsourcing of household tasks has become an important strategy for couples to avert the conflict between work and family roles. There is an increasing trend of domestic outsourcing, including the use of hired domestic help, in many societies where work-family conflict is prevalent. Despite the increasing popularity of outsourcing domestic tasks, the possible impact of hiring domestic help on family relations remains an important gap in the literature. The contributions of hiring domestic help to the employers' family, such as averting marital conflict and improving marital quality, are often assumed in the literature but are rarely examined using empirical data, especially in a non-western context. This paper investigates the effects of hiring domestic help on two indicators of the employers' family well-being: marital conflict and marital quality. Analyzing data from a territory-wide representative household survey in Hong Kong (N = 974) using the propensity score matching method, the current study finds only weak positive effects of hiring domestic help. None of the effects are statistically significant. Contrary to previous claims, the data from this study suggest that the effects of hiring domestic help on the employers' family wellbeing are far from substantial. These findings are consistent with previous research suggesting that hiring a paid domestic helper to share the housework does not substantially change the division of domestic labor. Previous studies which assumed that hiring domestic help enabled families with resources to enjoy a better family life may have been overly optimistic. Further investigations on the family life of these families after they had hired domestic help are called for.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".