Intergenerational Persistence of Health in the U.S.: Do Immigrants Get Healthier as they Assimilate?
Bibliographic record
Abstract
It is well known that a substantial part of income and education is passed on from parents to children, generating substantial persistence in socio-economic status across generations.In this paper, we examine whether another form of human capital, health, is also largely transmitted from generation to generation, contributing to limited socio-economic mobility.Using data from the NLSY, we first present new evidence on intergenerational transmission of health outcomes in the U.S., including weight, height, the body mass index (BMI), asthma and depression for both natives and immigrants.We show that both native and immigrant children inherit a prominent fraction of their health status from their parents, and that, on average, immigrants experience higher persistence than natives in weight and BMI.We also find that mothers' education decreases children's weight and BMI for natives, while single motherhood increases weight and BMI for both native and immigrant children.Finally, we find that the longer immigrants remain in the U.S., the less intergenerational persistence there is and the more immigrants look like native children.Unfortunately, the more generations immigrant families remain in the U.S., the more children of immigrants resemble natives' higher weights, higher BMI and increased propensity to suffer from asthma.
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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 | 0.001 | 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) | 0.003 | 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".