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Record W3021662929 · doi:10.1177/0020731420922689

Impacts of Absolute and Relative Income on Self-Rated Health in Urban and Rural China

2020· article· en· W3021662929 on OpenAlexaff
Jiaoli Cai, Audrey Laporte, Li Zhang, Yulin Zhao, Di Tang, Hongli Fan, Liqian Deng, Peter C. Coyte

Bibliographic record

VenueInternational Journal of Health Services · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGini coefficientIndex (typography)ChinaPopulationPanel dataDemographyInequalitySocioeconomicsGeographyDemographic economicsEconomicsEconomic inequalityEconometricsMathematicsSociology

Abstract

fetched live from OpenAlex

This study aims to assess the impacts of absolute and relative income on self-rated health (SRH) of residents in rural and urban China. Data were derived from the China Health and Nutrition Survey. Three distinct measures of relative income were considered (Gini coefficient, Yitzhaki index, and Deaton index) and computed for 3 geographic units (nation, province, and community). Nonlinear dynamic models for panel data were employed to test the absolute and relative income hypotheses. Absolute income was significantly associated with SRH among urban and rural populations. Relative income, as measured by the Gini coefficient, the Yitzhaki index, and the Deaton index, had statistically significant and negative impacts on SRH among the rural population, regardless of the reference group. For the urban population, the Gini coefficient was associated with SRH regardless of the reference group. In contrast, only the Yitzhaki index and the Deaton index at the provincial level were associated with SRH among the urban population. Our findings may provide a reference for policymakers to implement health policies designed to improve population health.

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 score0.245
Threshold uncertainty score0.975

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 studies0.0000.000
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 head0.015
GPT teacher head0.357
Teacher spread0.342 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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