Women in the One Percent: Gender Dynamics in Top Income Positions
Bibliographic record
Abstract
A growing body of research documents the importance of studying households in the top one percent of U.S. income distribution because they control enormous resources. However, little is known about whose income—men’s or women’s—is primarily responsible for pushing households into the one percent and whether women have individual pathways to earning one percent status based on their income. Using the 1995 to 2016 Surveys of Consumer Finances, we analyze gender income patterns in the one percent. Results show that women’s income is sufficient for one percent status in only 1 in 20 of all elite households. Although self-employment and higher education increase the likelihood that women will personally earn sufficient income for one percent status, marrying a man with good income prospects is a woman’s main route to the one percent. In contrast, men’s one percent status is most closely associated with their own characteristics (self-employment and higher education). Importantly, the gender gap in personally earning one percent income has not narrowed since the mid- to late-1990s, indicating another area in which gender progress has stalled. This research suggests that men retain most of the primary breadwinning positions in top income households and that a financial glass ceiling remains firmly intact at the one percent level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".