Does Segregation Lead to Lower Birth Weight?
Bibliographic record
Abstract
BACKGROUND: Racial residential segregation in the United States has been linked to racial differences in birth outcomes, with studies reporting associations between segregation and birth weight. However, this relationship is likely confounded, and many individual and neighborhood-level covariates included in previous models are likely mediators, potentially obscuring any causal impact of segregation on birth weight. METHODS: We compiled a record of non-Hispanic black and white singleton births to US-born/resident mothers in 2000, linked to segregation indices at the metropolitan statistical area (MSA) level in the non-Southern US. Segregation was measured via the dissimilarity index. The outcomes were individual-level birth weight and the metropolitan statistical area-level black/white gap in birth weight. We instrumented for segregation using the railroad division index. We compared race-stratified ordinary least squares models to two-stage least squares models, with cluster robust standard errors. RESULTS: We estimated a 1.2 g decrease in black birth weight for every one-percentage point increase in segregation (95% confidence interval [CI]: -1.9, -0.50) via ordinary least squares but a 2.8 g decrease (95% CI: -6.0, 0.48) using two-stage least squares. For white infants, our ordinary least squares estimate was 0.53 (95% CI: -0.23, 1.3), and our two-stage least squares estimate was in the opposite direction (-0.68, 95% CI: -3.5, 2.1). CONCLUSIONS: Ordinary least squares estimates may understate the effect of segregation on birth weight in blacks. Evidence from instrumental variable models was consistent with a causal impact of segregation on black birth outcomes, but estimates were imprecise and may be affected by weak instrument bias.
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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 | 0.002 | 0.008 |
| 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) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".