Bibliographic record
Abstract
Given recent controversies about the existence of a gender wellbeing gap we revisit the issue estimating gender differences across 55 subjective well-being metrics -37 positive affect and 18 negative affect -contained in 8 cross-country surveys from 167 countries across the world, two US surveys covering multiple years and a survey for Canada.We find women score more highly than men on all negative affect measures and lower than men on all but three positive affect metrics, confirming a gender wellbeing gap.The gap is apparent across countries and time and is robust to the inclusion of exogenous covariates (age, age squared, time and location fixed effects).It is also robust to conditioning on a wider set of potentially endogenous variables.However, when one examines the three 'global' wellbeing metricshappiness, life satisfaction and Cantril's Ladder -women are either similar to or 'happier' than men.This finding is insensitive to which controls are included and varies little over time.The difference does not seem to arise from measurement or seasonality as the variables are taken from the same surveys and frequently measured in the same way.The concern here though is that this is inconsistent with objective data where men have lower life expectancy and are more likely to die from suicide, drug overdoses and other diseases.This is the true paradoxmorbidity doesn't match mortality by gender.Women say they are less cheerful and calm, more depressed, and lonely, but happier and more satisfied with their lives, than men.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".