Did higher inequality impede growth in rural China?
Bibliographic record
Abstract
This paper estimates the relationship between initial village inequality and subsequent household income growth for a large sample of households in rural China. Using a rich longitudinal survey spanning the years 1987-2002, and controlling for an array of household and village characteristics, the paper finds that households located in higher inequality villages experienced significantly lower income growth through the 1990s. However, local inequality s predictive power and effects are significantly diminished by the end of the sample. The paper exploits several advantages of the household-level data to explore hypotheses that shed light on the channels by which inequality affects growth. Biases due to aggregation and heterogeneity of returns to own-resources, previously suggested as candidate explanations for the relationship, are both ruled out. Instead, the evidence points to unobserved village institutions at the time of economic reforms that were associated with household access to higher income activities as the source of the link between inequality and growth. The empirical analysis addresses a number of pertinent econometric issues including measurement error and attrition, but underscores others that are likely to be intractable for all investigations of the inequality-growth relationship.
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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 | 0.001 |
| 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".