THE URBAN–RURAL INCOME GAP AND INEQUALITY IN CHINA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using new household survey data for 1995 and 2002, we investigate the size of China's urban–rural income gap, the gap's contribution to overall inequality in China, and the factors underlying the gap. Our analysis improves on past estimates by using a fuller measure of income, adjusting for spatial price differences and including migrants. Our methods include inequality decomposition by population subgroup and the Oaxaca–Blinder decomposition. Several key findings emerge. First, the adjustments substantially reduce China's urban–rural income gap and its contribution to inequality. Nevertheless, the gap remains large and has increased somewhat over time. Second, after controlling for household characteristics, location of residence remains the most important factor underlying the urban–rural income gap. The only household characteristic that contributes substantially to the gap is education. Differences in the endowments of, and returns to, other household characteristics such as family size and composition, landholdings, and Communist Party membership are relatively unimportant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it