Top wealth households in China and the United States: Wealth portfolios and determinants of top wealth
Bibliographic record
Abstract
Top wealth households are crucial to understanding inequality. This study contrasts the characteristics of top wealth households in China and the United States, highlighting the salient differences between the two countries. A comprehensive review of extant literature was conducted to explain how divergent social and economic contexts in each country have led to the differences in top wealth ownership. The study further presents and empirically evaluates hypotheses regarding the differences in top wealth ownership between the two countries. Utilizing data from the Survey of Consumer Finances (SCF) and the China Household Finance Survey (CHFS), the study uncovers three significant differences. First, the wealth distribution in the United States is more unevenly distributed and concentrated at the top of the wealth distribution compared to China. Second, the top wealth households in the United States tend to have more diversified asset portfolios than their counterparts in China. Third, the demographic characteristics of the top wealth households differ significantly between the two countries. A notable finding is the marked difference in the age of the heads of top wealth households between the two countries, with the Chinese heads being much younger than their U.S. counterparts. The study concludes with the implication of these findings that social and economic contexts play a pivotal role in shaping the creation of top wealth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".