The Evolution of Income Inequality in Rural China
Bibliographic record
Abstract
We document the evolution of the income distribution in rural China, from 1987 through 1999, with an emphasis on investigating increases in inequality associated with transition and economic development. With a backdrop of perceived improvements in average living standards, we ask whether increases of inequality may have offset, or even threaten welfare gains associated with economic reforms. The centerpiece of the paper is an empirical analysis based on a set of household surveys conducted by the China’s Research Center for Rural Economy (RCRE) in Beijing. These surveys permit us to construct a set of comparable estimates of household income and consumption from a panel of over 100 villages from nine Chinese provinces. We provide a variety of summary statistics, including Gini coefficients, as well as more nonparametric summaries of the income distribution (i.e., Lorenz curves). In addition, we decompose the sources of inequality, exploring the contributions of spatial inequality to overall inequality, and the role of non-agricultural incomes in explaining rising dispersion of incomes. We find that the distribution of income improved by most measures during the early part of the period, as average incomes rose substantially with only a modest increase in inequality. However, the distribution has worsened significantly since 1995, with rising inequality, and falling absolute incomes, especially at the bottom end of the income distribution. We attribute most of the recent decline in welfare to collapsing agricultural incomes, probably brought about by lower farm prices. At the same time, increasing non-farm incomes have widened the gaps between those with and without access to nonagricultural opportunities. Based on explorations with different data sets, our RCRE-based results probably understate the divergence due to non-agricultural income growth and the increase in inequality over time. Our results highlight the need for further evaluation of the role of farming as a source of income in the countryside, and also underline the limitations of a land-based (and essentially grain-based) income support and redistribution mechanisms.
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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 | 0.002 | 0.003 |
| 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".