Individual Versus Household Income and Life Satisfaction: The Moderating Effects of Gender and Education
Bibliographic record
Abstract
Previous research has extensively examined the association between income and subjective well-being, but few studies have made a distinction between individual income and household income. China offers an interesting case to study this topic due to its rapid economic and demographic changes. By analyzing data from 12,484 men and 13,828 women aged 15 to 64 based on the nationally representative China Family Panel Studies, the current study estimated the independent effects of individual and household income on self-reported level of life satisfaction. The study further examined whether the relationship differed across gender and education groups. The results suggested that overall, both individual income and household income contributed independently to life satisfaction, but household income appeared to be more influential than individual income. The study further revealed interesting differences between men and women in that individual income showed greater impact on life satisfaction among Chinese men than women. A further stratification by educational status showed that such difference was mainly among the less-educated men and women, but not among those who were well-educated. The findings suggest the greater importance of family factor than individual factor on Chinese people’s subjective well-being. The study also highlights the important role of education in reducing gender disparity in China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".