Who’s Doing the Housework and Childcare in America Now? Differential Convergence in Twenty-First-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, the authors argue that examining which domestic tasks undergo gender convergence, whose changes narrow the gap, and why changes happen is critical for understanding gender inequalities in unpaid labor time. Using data from the 2003-2023 American Time Use Survey, the authors examine trends in total housework (including core and occasional housework), shopping, and childcare time. Results for married individuals indicate that the historically large gender gap in total housework time narrowed further this century, from a women-to-men ratio of 1.8:1 in 2003-2005 to 1.6:1 in 2022-2023. This shrinking of the gender gap was concentrated in traditionally feminine core housework (decreasing by 40 percent, from 4.2:1 to 2.5:1), particularly housecleaning and laundry. The gender difference in shopping time also narrowed, nearing parity. For childcare time, the gender gap shrunk from 2:1 to 1.8:1, though this change was not statistically significant. Decomposition analyses indicate that women's reduced housework time was explained mainly by population compositional shifts, whereas men's increased core housework time likely reflected behavioral or normative changes. With men taking on more female-typed domestic activities, the gendered norms associated with different forms of unpaid 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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".