State and Local Government Expenditures and Infant Mortality
Bibliographic record
Abstract
OBJECTIVES: A previous study reported that increased state and local government expenditures were associated with decreased infant mortality rates (IMRs). However, reported estimates of the association between expenditures and IMR represented the degree to which the association changed each year, not the main effect. We reproduced the original results, reporting this main effect and replicated the analysis using improved methodology and updated data. METHODS: For the reproduction analysis, we used methods and data identical to the original study: A publicly-posted, state-level data set of expenditures from 2000 to 2014 US Census Bureau survey data linked to 2-year lagged IMR data with a random intercept model including an interaction between time and expenditures. For the replication analysis, we added 5 years of data and adjusted for fixed state differences and inflation. RESULTS: In the reproduction, the main effects of total, environmental, and educational expenditures on IMR were much larger than the interaction effects previously reported as the main effects. For example, a 1-SD increase in per-capita total expenditures was associated with a reduction of 0.35 infant deaths per 1000 live births instead of 0.02 deaths per 1000 live births originally reported. In the updated replication, the main effects were generally even larger (eg, -0.51 deaths per 1000 per SD increase in total expenditures). Increased total expenditures were associated with absolute but not relative reductions in Black-white IMR gaps. CONCLUSIONS: State and local government expenditures are associated with greater reductions in IMR than previously reported, underscoring the importance of continued public investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".