Separate Spheres: The Gender Division of Labor in the Financial Elite
Bibliographic record
Abstract
Abstract In recent decades, the financial elite have seen their economic resources grow significantly, while the income and wealth of other households have stagnated. The financial elite includes couples who are super-rich (top one percent), rich (the 90th–99th percentile), and upper-middle class (the 80th–89th percentile). Gendered work–family arrangements in top economic groups may contribute to inequality—particularly to wealth accumulation among the elite—but relatively little is known about how these couples divide paid and unpaid work or the extent to which their arrangements differ from other couples. In this study, we uncover novel work and family patterns and trends in the most economically powerful families in the United States. We use the Survey of Consumer Finances (1989–2019) to compare the household division of labor across income and wealth groups and over time, with a focus on financial elites. We find stark contrasts between super-rich couples and other couples in the division of labor. Specifically, super-rich couples are much more likely than all other couples, including rich and upper-middle class couples, to have a traditional male breadwinner–female homemaker/caregiver arrangement. Importantly, the striking patterns of traditional arrangements in the top one percent have not changed in 30 years and, as we uncover, appear to be driven by a couple’s wealth rather than income. These findings suggest that work–family arrangements may be an integral component of economic and gender inequality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".