Who’s Doing the Housework and Childcare Now? Differential Convergence in 21st-century Gender Gaps in Home Tasks
Bibliographic record
Abstract
Gender scholars have debated whether the recent movement toward a more equal division of domestic labor is stalling. Using a differential convergence perspective, we argue that examining which domestic tasks experience gender convergence, whose changes narrow the gap, and why changes happen is critical for understanding gender convergence in domestic labor time. Drawing on data from the 2003-22 American Time Use Survey, we examine trends in core housework, occasional housework, shopping, and childcare time. Results for married women and men indicate that the historically large gender gap in housework time narrowed further this century, from a women-to-men ratio of 1.8:1 in 2003-05 to 1.5:1 in 2022. The decrease in the gender gap was concentrated in traditionally “feminine” core housework (from 4.2:1 to 2.5:1), particularly in housecleaning and laundry. The gender gap in shopping time also narrowed significantly, reaching gender parity. However, the gender gap in childcare time remained at a mother-to-father ratio of 2:1. Decomposition analyses indicate that women’s reduced housework time could mainly be explained by population compositional changes, whereas men’s increased housework time reflected behavioral changes. With men taking on more “female-typed” domestic activities, the gendered norms associated with different types of domestic labor may be becoming redefined.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".