Examining the impact of racial residential segregation on birth weight: an instrumental variable approach
Bibliographic record
Abstract
Background: Racial residential segregation is a persistent phenomenon in the Unites States and has been linked to racial differences in birth outcomes, with several studies reporting associations between segregation and birth weight. However, segregation is likely endogenous: unobserved factors driving segregation and birth outcomes at the individual and neighborhood levels are unaccounted for in standard regression models, leading to biased estimates and prompting calls for novel methods in order to adequately control for confounding. In addition, many of the individual and neighborhood-level covariates often included in prior models are likely mediators, further obscuring any impact of segregation on birth weight. I attempted to address these concerns by 1) using the Railroad Division Index (RDI) as an instrument for segregation, and 2) reassessing the role of covariates, and thus the conceptual causal model, based on existing research. Methods: Four data sources were merged to create a cross-sectional record of all non-Hispanic black and white singleton births to US-born/resident mothers in 2000, which were linked to segregation indices at the metropolitan statistical area (MSA) level. The main exposure was black-white residential segregation, measured via the dissimilarity index. The two outcomes of interest were birth weight, measured in grams, and the MSA-level black/white gap in birth weight, both modeled as continuous variables. Race-stratified standard linear regression (OLS) models were compared to two-stage least squares (2SLS) models, with cluster-robust standard errors. I performed several validity checks to assess RDI's suitability as an instrument. Results: The analytical sample contained 574,747 birth records across 93 MSAs. The magnitude of effect estimates yielded by OLS and 2SLS varied considerably. For black infants, OLS estimated a 1.17 gram decrease in individual birth weight for every one-percentage point increase in segregation (95% CI: -1.85, -.50), whereas 2SLS estimated a 2.76 gram decrease (95% CI: -6.01, .48). For white infants, OLS yielded an estimate of .53 (95% CI: -.23, 1.29), while the 2SLS estimate was in the opposite direction (-.68, 95% CI: -3.48, 2.11). Falsification checks revealed that the effect of RDI on birth weight was essentially the same for both races in locations where demand for segregation was low, further suggesting that the effect of segregation is differential by race. Conclusions: Evidence from instrumental variable models were consistent with a causal impact of segregation on birth outcomes, but 2SLS estimates were imprecise and the proposed causal mechanism was likely more plausible for blacks than for whites. OLS may underestimate the effect of segregation on birth weight in blacks. Future research should prioritize similar analytic methods using longitudinal data sources and nationally representative samples.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".