Bibliographic record
Abstract
We use a new method to estimate China’s income distributions based on publicly available interval summary statistics from China’s largest national household survey. We examine rural, urban, and overall income distributions for each year from 1985-2001. By estimating the entire distributions, we can show how the distributions change directly as well as examine trends in traditional welfare indices such as the Gini. We find that inequality has increased substantially in both rural and urban areas. Using an inter-temporal decomposition of aggregate inequality, we determine that increases in inequality within the rural and urban sectors and the growing gap in rural and urban incomes have been equally responsible for the growth in overall inequality over the last two decades. However, the rural-urban income gap has played an increasingly important role in recent years. In contrast, only the growth of inequality within rural and urban areas is responsible for the increase in inequality in the United States, where the overall inequality is close to that of China. As a robustness check, we show that consumption inequality (which may be a proxy for permanent income inequality) in urban areas also rose considerably
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".