So What If There Is Income Inequality? The Distributive Consequence of Nonfarm Employment in Rural China
Why this work is in the frame
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Bibliographic record
Abstract
It is interesting to note, that of the studies concerning income distribution in rural China, few have examined the effect of household employment in the nonfarm sector on levels of mean income or, more generally, standard of living. The most important question, from our perspective, is whether nonfarm employment and income opportunity contribute to rising farm household income while simultaneously widening the income gap among the farm households. If so, should we applaud or condemn such a development process? What, if any, is the role of education in facilitating access to nonfarm employment and in determining income? Or are these scarce opportunities allocated by means of a less universalistic criterion, such as personal connections or "social capital"? <sup>s</sup> Are these opportunities more or less equal in areas where collectives or TVEs assume a less predominant role? These are, we believe, important questions that need to be addressed in relation to the issue of rural income inequality. On the basis of a unique farm survey covering 400 rural households in four predominantly agricultural Chinese counties, this article sets out to answer these questions. The article is organized as follows. Section II describes our surveyed counties and provides an economic profile of 400 farm households there. In Section III, we estimate the income function of these households, and in Section IV we measure directly income inequality among them. In light of the possible disequalizing effect of nonfarm income, we examine its determinants in Section V, and in Section VI we draw some conclusions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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