Socioeconomic Status and Health: Why is the Relationship Stronger for Older Children?
Bibliographic record
Abstract
Case, Lubotsky, and Paxson (2001) show that the well-known relationship between socioeconomic status (SES) and health exists in childhood and grows more pronounced with age.However, in cross-sectional data it is difficult to distinguish between two possible explanations.The first is that low-SES children are less able to respond to a given health shock.The second is that low SES children experience more shocks.We show, using panel data on Canadian children that: 1) the gradient we estimate in the cross section is very similar to that estimated previously using U.S. children; 2) both high and low-SES children recover from past health shocks to about the same degree; and 3) that the relationship between SES and health grows stronger over time mainly because low-SES children receive more negative health shocks.In addition, we examine the effect of health shocks on math and reading scores.We find that health shocks affect test scores and future health in very similar ways.Our results suggest that public policy aimed at reducing SES-related health differentials in children should focus on reducing the incidence of health shocks as well as on reducing disparities in access to palliative care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".