MétaCan
Menu
Back to cohort
Record W3121850562 · doi:10.4054/mpidr-wp-2012-013

Happiness: before and after the kids

2012· preprint· en· W3121850562 on OpenAlexaff
Mikko Myrskylä, Rachel Margolis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsWestern University
Fundersnot available
KeywordsHappinessFertilitySocioeconomic statusPsychologyChild birthDemographyGermanTotal fertility rateDevelopmental psychologySocial psychologyGeographyPregnancySociologyPopulationFamily planningResearch methodology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.289
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicFamily Dynamics and RelationshipsFrench-language works237,207