Markets, Human Capital and Inequality: Evidence from Rural China
Bibliographic record
Abstract
Market reforms are generally credited with the rapid growth enjoyed by China's rural sector. This growth has not been without some cost, however, as inequality has also increased. Estimates suggest that the Gini rose from less than 0.20 to over 0.40 during this period. In this paper we go behind these numbers to explore the nature and causes of this inequality. To begin, we find that a considerable share of rural inequality is driven by local differences in household incomes, as opposed to regional income differences, that have been the focus of the previous literature. We then examine inter-household income differentials at the village level, exploring the links between education, market development, non-agricultural employment, and household income. To address these questions, we draw on a recently collected data set from Northeast China, that was collected by two of the authors in collaboration with Chinese colleagues in Hebei and Liaoning provinces in 1995. For purposes of comparison, we also draw on the Chinese Health and Nutrition Survey. We find that indeed, increasing rates of return to education and unevenly developed non-agricultural business opportunities contribute to the high levels of inequality in the countryside. Of most interest, however, is the implication that simultaneous improvements in educational attainment and off-farm market-development would allow more households to share in the rapid growth in rural China.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".