The Growth of Poor Children in China 1991-2000: Why Food Subsidies May Matter
Bibliographic record
Abstract
Between 1991 and 2000, both average incomes and income inequality grew rapidly in China. Although the average measurable health status of Chinese children also improved dramatically, changes in average health status may mask differential impacts within the distribution of health status. Using the China Health and Nutrition Survey1 (CHNS) data for 1991, 1993, 1997 and 2000 on 4,400 households in 9 provinces, this paper examines the height-for-age of Chinese children aged 2 to 13, with particular emphasis on the growth of children living in poor households. It uses mean regression and quantile regression models to isolate the dynamic impact of poverty status and food coupon use on child height-for-age. Our principal findings are: (1) controlling for standard variables (e.g., parents’ weight, height and education) poverty is correlated with slower growth in height between 1997 and 2000 but not earlier; (2) in 2000, poverty primarily reduces the likelihood of strong growth in height- for-age; (3) food coupon use in earlier periods increases growth in height-for-age. The disappearance in the 1990s of subsidized food coupons in China has increased the importance of money income poverty in enabling consumption of basic foods by poor families. The general moral is the crucial social protection role that subsidized food programs can potentially play in maintaining the health of poor children.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".