Bibliographic record
Abstract
Understanding how having children influences parents' subjective well-being ("happiness") has great potential to explain fertility behavior. We study parental happiness trajectories before and after the birth of a child, using large British and German longitudinal data sets. We account for unobserved parental characteristics using fixed-effects models and study how sociodemographic factors modify the parental happiness trajectories. Consistent with existing work, we find that happiness increases in the years around the birth of a first child and then decreases to before-child levels. Moreover, happiness increases before birth, suggesting that the trajectories may capture not only the effect of the birth but also the broader process of childbearing, which may include partnership formation and quality. Sociodemographic factors strongly modify this pattern. Those who have children at older ages or who have more education have a particularly positive happiness response to a first birth; and although having the first two children increases happiness, having a third child does not. The results, which are similar in Britain and Germany, suggest that having up to two children increases happiness, and mostly for those who have postponed childbearing. This pattern is consistent with the fertility behavior that emerged during the second demographic transition and provides new insights into low and late fertility.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".