A Three-Decade Decline in the Homeownership Gender Gap: What Drove the Change, and Where Do We Go from Here?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past 30 years, women have made tremendous gains in closing both the income and the education gaps between them and men, and growth in their homeownership rates has become an important manifestation of these trends. In 1990, there were 15.7 million female-headed homeowner households. By 2019, that number had reached 39.2 million. In contrast, the number of male-headed homeowner households decreased from 44.4 million to 43.1 million.In this report, we examine homeownership by the gender of the household head and how it has changed over the past three decades. Our analysis explores factors narrowing the homeownership gender gap, overall and by race and ethnicity. This narrowing can be attributed mostly to gains in household income, followed closely by the fact that more married women are head of household.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it