Markets, Human Capital and Income Inequality in Rural China
Bibliographic record
Abstract
The introduction of the household responsibility system in the early 1980s and market liberalizing reforms are generally credited with the rapid growth enjoyed by China's rural sector. This growth has not been without some cost however. Over the last two decades, inequality appears to have increased appreciably in the countryside. Estimates suggest that the inequality as measured by the Gini has increased from less than 0.20 to 0.35 over this period. Much of the focus in the literature has been on the role of growing regional disparities in rural incomes caused by differential rates of growth in the TVE sector. Much less attention has been given to emerging differences within localities, and its contribution to overall inequality during transition. Recent calculations (Benjamin and Brandt, AEA Proceedings, 1999) suggest, however, that nearly three-quarters of the inequality in the rural sector are the product of intra- (as opposed to inter-) village differences in income. Our objective in this paper is to examine the emerging inter-household income differentials at the village level. In light of the growing market development in the countryside and the relaxation of restrictions on mobility, we want to analyze how emerging markets and institutions have interacted with household endowments and demographic processes to affect the distribution of income at this level. We are especially interested in the effect that human capital accumulation and differences in the rate of return to human capital across localities have had on intra-local inequality. We are also interested in the indirect effect that market development and growth has had on inequality through its effect on household organization. Over the last fifteen years, we observe a marked increase in the percentage of nuclear households, and a tendency for kids to live on their own once they become adults. To address these questions, we draw on two unique data sets. The first is a detailed household level data set that was collected by two of the authors in collaboration with Chinese colleagues in Hebei and Liaoning provinces in 1995. Altogether, 780 households in 30 villages in six counties in two provinces were surveyed. We also draw on for comparison household level data collected as part of the CHNS (Chinese Health and Nutrition Survey). These data extend to over 4000 households in 6 provinces, and have the added feature of being in the form of a panel (1989, 1991, 1993). This paper will contribute to our knowledge of inequality in rural China and to the impact of economic transition on inequality more generally.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".