Gender differences in routine housework among one-person households: A cross-national analysis
Bibliographic record
Abstract
BACKGROUND: Housework has long been associated with gender roles, with women traditionally burdened as the primary caregivers in many societies. However, most studies focus on the general population or on couples. This study explores the often overlooked domain of housework in one-person households, where the negotiation of tasks and roles is absent and individuals are solely responsible for their domestic chores. OBJECTIVE: Drawing from data collected across 19 countries, the research examines the time allocated to housework by men and women, shedding light on gender disparities in one-person living situations. METHODS: Data come from time-use surveys available in several harmonized databases. The analysis focuses on routine housework tasks, encompassing cleaning, food preparation, cooking, and laundry, which tend to show pronounced gender differences. The study is based on descriptive estimates by country and type of living arrangement, and multivariate regression to predicted gender gaps in routine housework in one-person households by country. RESULTS: Results indicate the existence of gender disparities in routine housework among individuals living in one-person households. Despite variations across countries, the United States and Canada display a lower gender gap, while Italy and Serbia maintain higher inequality. Greece, South Africa, and South Korea show moderate gender differences, similar to Nordic countries, which are usually considered the most egalitarian. CONCLUSIONS: The persistence of gender disparities in routine housework remains evident in one-person households, although it is less pronounced than in other living arrangements. CONTRIBUTION: The study underscores the enduring gender gap in housework, highlighting distinct standards and cross-national variations among one-person households.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".