The Impact of Income Inequality on Health of Chinese Residents — Decomposition Based on Individual Effect and Macro Effect
Bibliographic record
Abstract
This paper reviews the mechanism of income inequality affecting residents' health, and proposes a new measurement method to decompose the micro mechanism and macro effect of income inequality affecting residents' health. Based on the provincial data onto 1990, 2000 and 2010, an empirical analysis using the multi-period mixed cross-sectional data (Pool Data) model shows that income inequality has a significant negative impact on health in China. The method constructed in this paper is used to decompose the contribution rate of macro effect and individual effect. The results show that the negative impact of macro effect accounts for 27.7%, while the impact of micro effect accounts for 72.3%. With the continuous improvement on GDP per capital in China, the impact of macro effect of income gaps between life expectancy is getting smaller and smaller. The macro effect contribution rate decreases year by year. Therefore, on the one hand, it is necessary to reduce income inequality, but also to take targeted measures to reduce the negative impact of income inequality on individual health.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".