Unexpected Changes in Rural Families: Fewer Married Parents, Lower Child Poverty
Bibliographic record
Abstract
ABSTRACT Objective To examine whether family change in rural America is widening the rural–urban child poverty gap and increasing inequalities between children raised in married parent families and those raised in other family types. Background Mounting evidence of falling marriage rates across rural areas has led to concerns that child poverty rates have increased. However, new methods of measuring poverty and recent increases in Child Tax Credits challenge these assumptions about rural child poverty trends and the poverty penalties associated with nonmarital families. Methods Current Population Survey data are used to estimate rural and urban trends in family structures and child poverty, using the Supplemental Poverty Measure, from 2000 to 2023. Logistic regressions test whether the poverty penalties associated with living in four kinds of nonmarital families (cohabiting, formerly married, never married, and kinship care) have changed for rural and urban children over this period. Results By 2023, significantly more rural (37.9%) than urban (32.5%) children lived in nonmarital families; simultaneously, rural poverty rates declined significantly. Further, although rural children living in nonmarital families faced greater poverty penalties than urban children in 2000–2003, by 2020–2023 these penalties had diminished substantially for both rural and urban children, and the elevated poverty penalties for rural children had disappeared. Conclusions Despite a sizable increase in rural children living with nonmarried parents, rural child poverty rates sharply declined.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".