Does rising income inequality affect mortality rates in advanced economies?
Bibliographic record
Abstract
Abstract What effect does rising income inequality have on longevity in advanced developed economies? This paper focuses on the effect of income inequality on mortality rates for men and women in a subset of OECD countries over nearly six decades from 1950–2008. Using adult mortality rates at aged sixty-five as the outcome measure of mortality, the latest available data on inverted Pareto-Lorenz coefficient as a measure of income inequality, the authors conduct a range of analysis to investigate the relationship. The findings show that income inequality has a negative effect on mortality rates for both men and women, that is, an increase in income inequality at the top of the distribution does not appear to have a detrimental effect on adult mortality rates in the population of advanced developed countries. For every one unit increase in income inequality, female mortality rates decreased by 0.024 percentage points (p<=0.001) and male mortality rates decreased by 0.052 percentage points (p<=0.001). Dynamic OLS results show that for every one unit increase in income inequality, female mortality rates decreased by 0.032 percentage points (p<=0.01) and male mortality rates decreased by 0.067 percentage points (p<=0.001). The findings remain robust to changes in methodology and the inclusion of control variables including GDP, population and the health capital index.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".