Work Disability Among Native-born and Foreign-born Americans: On Origins, Health, and Social Safety Nets
Bibliographic record
Abstract
Public debates about both immigration policy and social safety net programs are increasingly contentious. However, little research has explored differences in health within America's diverse population of foreign-born workers, and the effect of these workers on public benefit programs is not well understood. We investigate differences in work disability by nativity and origins and describe the mix of health problems associated with receiving Social Security Disability Insurance benefits. Our analysis draws on two large national data sources-the American Community Survey and comprehensive administrative records from the Social Security Administration-to determine the prevalence and incidence of work disability between 2001 and 2010. In sharp contrast to prior research, we find that foreign-born adults are substantially less likely than native-born Americans to report work disability, to be insured for work disability benefits, and to apply for those benefits. Overall and across origins, the foreign-born also have a lower incidence of disability benefit award. Persons from Africa, Northern Europe, Canada, and parts of Asia have the lowest work disability benefit prevalence rates among the foreign-born; persons from Southern Europe, Western Europe, the former Soviet Union, and the Caribbean have the highest rates.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".