Beyond the mean: Distributional differences in earnings and mental health in young adulthood by childhood health histories
Bibliographic record
Abstract
Research on the long-term effects of health in early life has predominantly relied on parametric methods to assess differences between groups of children. However, this approach leaves a wealth of distributional information untapped. The objective of this study was to assess distributional differences in earnings and mental health in young adulthood between individuals who suffered a chronic illness in childhood compared to those who did not using the non-parametric relative distributions framework. Using data from the Panel Study of Income Dynamics, we find that young adults who suffered a chronic illness in childhood fare worse in terms of earnings and mental health scores in adulthood, particularly for individuals reporting a childhood mental health/developmental disorder. Covariate decompositions suggest that chronic conditions in childhood may indirectly affect later outcomes through educational attainment: had the two groups had similar levels of educational attainment, the proportion of individuals with a report of a chronic condition in childhood in the lower decile of the relative earnings distribution would have been reduced by about 20 percentage points. Findings may inform policy aimed at mitigating longer run effects of health conditions in childhood and may generate hypotheses to be explored in parametric analyses.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".