Why Does the Relationship between Economic Status and Child Health Strengthen for Older Children in the U.S.? Evidence from the Medical Expenditures Panel Survey and the Panel Study of Income Dynamics
Bibliographic record
Abstract
Case, Lubotsky, and Paxson (2002), using cross-sectional data, found a positive relationship between health and income and that the income relationship becomes more protective of children from higher income families as children age. Currie and Stabile (2003) point out that panel data allow the researcher to differentiate between the mechanisms underlying this relationship. Using a panel of Canadian children, they find that low-SES children respond to health shocks in the same way as high-SES children but that low-SES children, compared to high-SES children, are subject to more shocks as they age. To our knowledge we are unaware of any studies of the gradient that use panels of U.S. children. Our study utilizes the Medical Expenditures Panel Survey and the Child Development Supplements of the PSID. Our results for U.S. children are contrary to those found for children in Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".