Born Poor? Racial Diversity, Inequality, and the American Pipeline
Bibliographic record
Abstract
The authors examine racial disparities in infants’ exposure to economic disadvantage at the family and local area levels. Using data from the 2008–2014 files of the American Community Survey, the authors provide an up-to-date empirical benchmark of newborns’ exposure to poverty. Large shares of Hispanic (36.5 percent) and black (43.2 percent) infants are born poor, though white infants are also overrepresented among the poor (17.7 percent). The authors then estimate regression models to identify risk factors and perform decompositions to identify compositional factors underlying between-race differences. Although more than half of the black-white poverty gap is explained by differences in family structure and employment, these factors account for less than one quarter of white-Hispanic differences. The results also highlight the unmet need for social protection among babies born to poor families lacking access to assistance programs and the safety net. Hispanic infants are particularly likely to be doubly disadvantaged in this manner. Moreover, large and disproportionate shares of today’s black (48.3 percent) and Hispanic (40.5 percent) babies are born into poor families and places with poverty rates above 20 percent. These results raise important questions about persistent and possibly growing racial inequality as America makes its way to a majority-minority society as early as 2043.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".