Income distribution, public services expenditures, and all cause mortality in US states
Bibliographic record
Abstract
INTRODUCTION: The objective of this paper is to investigate the relation between state and local government expenditures on public services and all cause mortality in 48 US states in 1987, and determine if the relation between income inequality and mortality is conditioned on levels of public services available in these jurisdictions. METHODS: Per capita public expenditures and a needs adjusted index of public services were examined for their association with age and sex specific mortality rates. OLS regression models estimated the contribution of public services to mortality, controlling for median income and income inequality. RESULTS: Total per capita expenditures on public services were significantly associated with all mortality measures, as were expenditures for primary and secondary education, higher education, and environment and housing. A hypothetical increase of 100 US dollars per capita spent on higher education, for example, was associated with 65.6 fewer deaths per 100,000 for working age men (p<0.01). The positive relation between income inequality and mortality was partly attenuated by controls for public services. DISCUSSION: Public service expenditures by state and local governments (especially for education) are strongly related to all cause mortality. Only part of the relation between income inequality and mortality may be attributable to public service levels.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".