Bibliographic record
Abstract
Understanding how having children influences the parents' subjective well-being ("happiness") has great potential to explain fertility behavior.Most prior research on this topic is limited in that it uses cross-sectional data or has not considered modifying factors.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.Overall, we find that happiness increases in the years around the birth of the first child, then decreases to before-child levels.Sociodemographic factors strongly modify this pattern.Those who have children at older ages and those with higher socioeconomic resources have more positive and lasting happiness response to a first birth than younger or less educated parents.We also find that although the first two children increase happiness, the third does not.The results are similar in Britain and Germany and suggest that up to two, children increase happiness, and mostly among those who postpone childbearing.This pattern, which is consistent with the behavior emerging during the second demographic transition, provides new insights into the factors behind 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".