How's Life at Home? New Evidence on Marriage and the Set Point for Happiness
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".