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Record W1975840439 · doi:10.1002/hec.1463

The growth of poor children in China 1991–2000: why food subsidies may matter

2009· article· en· W1975840439 on OpenAlexaff
Lars Osberg, Jiaping Shao, Kuan Xu

Bibliographic record

VenueHealth Economics · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPovertyCouponQuantile regressionSubsidyConsumption (sociology)EconomicsPer capitaChinaFood securityDemographyDemographic economicsEnvironmental healthGeographyMedicineEconomic growthAgriculturePopulationSociology

Abstract

fetched live from OpenAlex

How did rapid growth in per capita income and rising income inequality during 1991-2000 in China affect the health status of Chinese children, given that the disappearance in the 1990s of subsidized food coupons simultaneously increased the importance of money income in enabling consumption of basic foods by poor families? Using the China Health and Nutrition Survey data for 1991, 1993, 1997, and 2000 on 4400 households in nine provinces, we examine the height-for-age of Chinese children aged 2-13, with particular emphasis on the growth of children living in poor households. We use 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: (i) controlling for standard variables (e.g. parents' weight, height, and education) poverty is correlated with slower growth in height-for-age between 1997 and 2000 but not earlier; (ii) in 2000, poverty is negatively correlated with strong growth in height-for-age; and (iii) food coupon use in earlier periods correlates positively with growth in height-for-age. The general moral is the crucial social protection role that subsidized food programmes can potentially play in maintaining the health of poor children.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.266
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2009
Admission routes1
Has abstractyes

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