Economic and social determinants of life expectancy in China
Bibliographic record
Abstract
Life expectancy (LEX) is closely linked to SDG 3: Good Health and Wellbeing, which aims to ensure healthy lives and promote wellbeing for all ages. Improving LEX reflects progress in reducing preventable diseases, enhancing healthcare access, and addressing social determinants of health. By focusing on economic growth, education, and healthcare, countries can achieve sustainable improvements in life expectancy, contributing to the broader goals of SDG 3. Previous studies take economic or social determinants of LEX individually. Thus, this study fills the gap to empirically examines the impact of Economic and social determinants of LEX in China from 2000 to 2023. This study utilizes the Autoregressive Distributed Lag (ARDL), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) estimators. The finding shows that health expenditure (HE), birth rate (BR), and education (EDU) have a positive effect on LEX. On the other hand, inflation (INF), population growth (PG), and mortality rate (MR) negatively affect LEX. This study has several policy recommendations based on findings to enhance the LEX in China.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".