What Makes a Desirable Spouse in China? New Evidence From a National Survey Experiment
Bibliographic record
Abstract
ABSTRACT Objective This study examines unmarried Chinese people's preferred characteristics of a spouse and how the preferences vary by gender and across socioeconomic groups. Background Extensive research has attempted to uncover mate selection preferences, a crucial factor shaping who marries whom. Predominantly analyzing observational data, however, existing research provides only indirect inferences about individuals' mate preferences independent of structural opportunities in the marriage market. Method This study employs a novel survey experiment, fielded in the 2021 Chinese General Social Survey, to directly examine unmarried individuals' mate selection preferences in China. We estimate conditional logit models to assess the relative importance of six characteristics in shaping hypothetical marriage candidates' desirability: income, property ownership, education, rural/urban origin, age, and appearance. Results Both unmarried men and women prefer to marry a similarly‐aged urban‐origin property owner with a high income, good education, and attractive appearance, suggesting a gender convergence of mate preferences. Individuals' mate selection preferences also vary with their ascribed (rural/urban origin) and achieved (education) socioeconomic status, and mate selection standards are more relaxed among those over the mean marriage ages for their sex in China. Conclusion Our study highlights the importance of directly examining mate preferences in clarifying the mechanisms of marital sorting and the value of survey experiments in family research.
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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 | 0.008 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".