Women’s Work Characteristics and Fertility Expectations
Bibliographic record
Abstract
Previous research has shown that employment is an important social context affecting fertility, yet relatively little is known about the extent to which work characteristics affect fertility expectations. Using over 25 years of data from the 1979 National Longitudinal Survey of Youth, we analyzed the associations between part-time work and characteristics associated with autonomy over working time, specifically self-employment and managerial/professional occupation, and childbearing expectations among women ages 18-45 (N=4,415). Logistic regression models for longitudinal data reveal that work characteristics are significantly associated with fertility expectations, but that the specific nature of the relationship varies by parity. Among women with one child, those working part-time had predicted probabilities of expecting to have additional children that were 2% higher than those working full-time. In contrast, among women without any children, those working part-time had predicted probabilities that were 2% lower than those working full-time. Similar contrasting relationships by parity were found when comparing self-employed women to employees and managers/professionals to those in other occupations. Findings were consistent across racial and ethnic groups. These results suggest that different mechanisms link work characteristics to fertility plans for mothers and non-mothers, specifically that role incompatibility and work-family conflict are more salient for mothers but that financial strain is so for non-mothers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".