White-Hispanic differences in meeting lifetime fertility intentions in the U.S.
Bibliographic record
Abstract
BACKGROUND: Hispanics in the U.S. have higher fertility than non-Hispanic Whites but it is not clear why this difference exists nor whether fertility levels reflect the preferences of individuals in these groups. Understanding racial-ethnic differences in fertility is important for understanding American fertility more broadly since the majority of births in the U.S. are to non-White women. OBJECTIVE: This paper examines the correspondence between fertility intentions and outcomes for Hispanic and White women and men in the U.S. METHODS: Panel data from the National Longitudinal Survey of Youth are used to describe intended family size (recorded at age 22), completed family size (recorded at age 42 and above), and the likelihood that these numbers match, for Hispanic and White women and men. Regression analyses are used to understand why the correspondence between intentions and outcomes varies across groups. RESULTS: Although Hispanics come closer to achieving parity intentions in the aggregate (Hispanic women fall short by a quarter of a birth, compared to more than two-fifths for Whites), at the individual level they are not more likely to meet their intentions (33% of Hispanic women achieve their desired parity, compared with 38% of Whites). Hispanics have higher fertility than Whites both because they intend more children at the start of their reproductive lives and because they are more likely to exceed these intentions. CONCLUSIONS: Higher fertility among Hispanics compared with Whites in the U.S. is due to a combination of wanted and unwanted fertility. In addition, despite relatively high completed fertility, a large proportion of Hispanic women and men fall short of early life intentions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".