Property in whose name? Intrahousehold bargaining over homeownership in China
Bibliographic record
Abstract
Previous research typically examined homeownership inequality across individuals or households, overlooking the intrahousehold allocation of homeownership. Using couple-level data of the 2016 China Family Panel Studies, our study addresses the gap by examining the bargaining over homeownership between husbands and wives in China. Descriptive results reveal a large gender gap in homeownership: only about one-quarter of couples listed the wife as an owner on the Housing Ownership Certificate, whereas about 92% listed the husband. The gender gap in ownership, however, has narrowed among couples married after 2000. Multivariate analyses show that economic autonomy, relative resources, housing purchase conditions, and modernization significantly increase wives’ homeownership, but with varying degrees among rural and urban wives. Women’s own socioeconomic status is more important for acquiring homeownership for urban wives, yet rural wives’ homeownership depends more on the resource exchange with their husbands. Given the stratifying effects of homeownership, our findings of the unequal distribution of homeownership between husbands and wives underscore how family dynamics reproduce gender inequality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".