Why Are So Many U.S. Mothers Becoming Their Family's Primary Economic Support?
Bibliographic record
Abstract
Although the growing prevalence of primary-earning mothers is well established, this article uses 1996 and 2014 Survey of Income and Program Participation data to show U.S. mothers' rate of transition to primary-earner status increased by nearly 50% over the observed period. The rate of transition to primary earning predominantly increased among mothers with some college experience and mothers racialized as White, largely catching up to the rate among mothers identifying as Black. A decomposition analysis determined that relationship instability in marital and cohabiting relationships accounts for less than 20% of the increased transition rate, although somewhat more for Hispanic mothers. Roughly 75% of the growth in maternal primary-earning spells was attributed to situations in which the mother's earnings increased in isolation or, for mothers with a partner, often paired with a decrease in the partner's earnings. This latter circumstance was particularly the case for mothers identifying as Black or Asian. Findings show that most of the growth in mothers becoming primary earners from the mid-1990s to the mid-2010s occurred not because mothers experienced more household economic changes (frequency), but because household economic changes often increased mothers' relative financial contributions (impact). The impact component accounted for the entire increased transition rate across mothers' educational attainment and racial and ethnic identity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".