The Distribution of Household Income in China: Inequality, Poverty and Policies
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
Abstract This article examines recent trends in inequality and poverty and the effects of distributional policies in China. After a discussion of data and measurement issues, we present evidence on national, as well as rural and urban, inequality and poverty. We critically examine a selection of policies pursued during the Hu–Wen decade that had explicit distributional objectives: the individual income tax, the elimination of agricultural taxes and fees, minimum wage policies, the relaxation of restrictions on rural–urban migration, the minimum living standard guarantee programme, the “open up the west” development strategy, and the development-oriented rural poverty reduction programme. Despite these policies, income inequality in China increased substantially from the mid-1990s through to 2008. Although inequality stabilized after 2008, the level of inequality remained moderately high by international standards. The ongoing urban–rural income gap and rapid growth in income from private assets and wealth have contributed to these trends in inequality. Policies relaxing restrictions on rural–urban migration have moderated inequality. Our review of selected distributional policies suggests that not all policy measures have been equally effective in ameliorating inequality and poverty.
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 | 0.004 | 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 | 0.001 | 0.001 |
| 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