Early Family Formation, Selective Migration, and Childhood Conditions in Rural America☆
Bibliographic record
Abstract
Abstract Thirty years ago, rural Americans got married and had children at significantly younger ages than urban Americans. More recent data indicate that these differences persist today, but our understanding of what drives these differences remains limited. To address this gap, we (1) generate Kaplan–Meier estimates of the ages of the first marriage, first union, and first birth among those who lived in rural and urban areas in 2019, (2) evaluate the extent to which rural–urban differences in the timing of family formation reflect selective migration, (3) assess whether rural–urban differences in childhood SES and demographic characteristics further explain differences in timing, and (4) explore rural–urban differences by gender. We find substantial 4.3, 3.8, and 5.1‐year gaps in the ages at which rural and urban women marry, start unions, and become parents, respectively. These gaps largely do not reflect selective migration. Differences in women's age of first birth are attributable to differences in childhood conditions, yet differences in marital and union timing remain unexplained. Rural–urban gaps in the timing of family formation are much larger among women than among men. These patterns of early family formation in rural America have critical implications for families' and children's well‐being as well as rural depopulation.
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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".