Children, Race/Ethnicity, and Marital Wealth Accumulation in Black and Hispanic Households
Bibliographic record
Abstract
As wealth inequality in the United States continues to grow, family characteristics have become increasingly important to researchers’ understanding of changes in wealth inequality over time. One aspect of adulthood is having children and transitioning to parenthood, which can affect numerous outcomes, including wealth trajectories. Due to widely-recognized structural constraints, black and Hispanic households generally have fewer financial resources to draw upon when they begin to have children. Therefore, existing racial/ethnic wealth inequality may increase when minority families have children. W e use growth curve modeling techniques to analyze a sample of continuously married couples from the National Longitudinal Survey of Youth, 1979 cohort. Results suggest that children affect family financial resources in different ways and that this effect varies by race and ethnicity. These findings improve our understanding of how a similar family event-having children-within families contributes to divergent financial outcomes between families.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".