Bibliographic record
Abstract
Available data indicates a growing urban-rural income gap (the ratio of mean urban to rural incomes) with a significant increase from around 1.8 in the late 1980's to over 3 today.These estimates do not take into account the higher volatility of rural incomes in China.Current literature based on analyses of rural income volatility in China decomposes poverty into chronic and transient components using longitudinal survey data and assesses the fraction of the Foster, Greer and Thorbecke poverty gap attributable to mean income over time being below the poverty line.Resulting estimates of 40-50 % transient poverty point to the policy conclusion that poverty may be a less serious social problem than it appears in annual data due to rural income volatility.Here we use a direct method instead to adjust rural income for volatility using a certainty equivalent income measure and recompute summary statistics for the distribution of volatility corrected incomes, including the urban-rural income gap on which much of current poverty debate in China focuses.Since an uncertain income stream is worth less in utility terms than a certain income stream we argue that heightened rural volatility increases the effective urban-rural income gap and intensifies not weakens poverty concerns.Using Chinese longitudinal rural survey data for which current decompositions can be replicated, we make adjustments for certainty equivalence of rural household income streams which not only widen the urban-rural income gap in China but also increases other distributional summary statistics.Depending upon values used for the coefficient of relative risk aversion, the measured urban-rural income gap increases by 20-30% using a certainty equivalent measure to adjust rural incomes for volatility.We also conduct similar analyses using consumption data, for which slightly larger increases occur.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".