FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES A Decomposition of the Black-White Differential in Birth Outcomes
Bibliographic record
Abstract
Abstract: Substantial racial disparities continue to persist in the prevalence of preterm births and lowbirth-weight births. Health policy aimed at reducing these disparities could be better targeted if the differences in birth outcomes are better understood. This study decomposes these racial disparities in birth outcomes to determine the extent to which the disparities are driven by differences in measurable characteristics of black mothers and white mothers as well as the extent to which the gap results from differences in the impact of these characteristics. The analysis is focused on three adverse birth outcomes: preterm, early preterm birth (less than 32 weeks gestation), and low birth weight. The results suggest that differences in covariates accounted for approximately 25 percent of the gap in the incidence of preterm births. The specific characteristics that matter the most are marriage rates, father’s characteristics, and prenatal care. For gestation-adjusted birth weight, approximately 16 percent of the racial gap for first births is explained by covariates; for subsequent births this covariate explanation rises to 22 percent of the gap. Furthermore, differences in coefficients explain about another quarter of the gap in preterm birth outcomes but very little of the gap in birth weight. JEL classification: I1 Key words: racial disparities, preterm birth, low birth weight
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".