The Economic Gap Among Women in Time Spent on Housework in Former West Germany and Sweden
Bibliographic record
Abstract
The quantitative scholarship on domestic labor has documented the existence of a gender gap in its performance in all countries for which data are available. Only recently have researchers begun to analyze economic disparities among women in their time spent doing housework, and their studies have been largely limited to the U.S. We extend this line of inquiry using data from two European countries, the former West Germany and Sweden. We estimate the “economic gap” in women’s housework time, which we define as the difference between the time spent by women at the lowest and highest deciles of their own earnings. We expect this gap to be smaller in Sweden given its celebrated success at reducing both gender and income inequality. Though Swedish women do spend less time on domestic labor, however, and though there is indeed less earnings inequality among them, the economic gap in their housework is only a little smaller than among women in the former West Germany. In both places, a significant negative association between women’s individual earnings and their housework time translates into economic gaps of more than 2.5 hours per week. Moreover, in both countries, women at the highest earnings decile experience a gender gap in housework that is smaller by about 4 hours per week compared to their counterparts at the lowest decile.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".