MétaCan
Menu
Back to cohort
Record W3124679790

How's Life at Home? New Evidence on Marriage and the Set Point for Happiness

2014· article· en· W3124679790 on OpenAlexaff
Shawn Grover, John F. Helliwell

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British ColumbiaGovernment of Canada
Fundersnot available
KeywordsHappinessSet pointSet (abstract data type)Point (geometry)PsychologyDemographic economicsEconomicsSocial psychologyMathematicsComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Subjective well-being research has often found that marriage is positively correlated with well-being. Some have argued that this correlation may be result of happier people being more likely to marry. Others have presented evidence suggesting that the well-being benefits of marriage are short-lasting. Using data from the British Household Panel Survey, we control individual pre-marital well-being levels and find that the married are still more satisfied, suggesting a causal effect, even after full allowance is made for selection effects. Using new data from the United Kingdom's Annual Population Survey, we find that the married have a less deep U-shape in life satisfaction across age groups than do the unmarried, indicating that marriage may help ease the causes of the mid-life dip in life satisfaction and that the benefits of marriage are unlikely to be short-lived. We explore friendship as a mechanism which could help explain a causal relationship between marriage and life satisfaction, and find that well-being effects of marriage are about twice as large for those whose spouse is also their best friend. Finally, we use the Gallup World Poll to show that although the overall well-being effects of marriage appear to vary across cultural contexts, marriage eases the middle-age dip in life evaluations for all regions except Sub-Saharan Africa.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.271
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicGender, Labor, and Family DynamicsFrench-language works237,207