The Distribution of Household Income in China: Inequality, Poverty and Policies
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.
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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 | 0.003 | 0.006 |
| 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) | 0.001 | 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".