The effects of Vietnam-era military service on the long-term health of veterans: A bounds analysis
Bibliographic record
Abstract
We analyze the short- and long-term effects of the U.S. Vietnam-era military service on veterans' health outcomes using a restricted version of the National Health Interview Survey 1974-2013 and employing the draft lotteries as an instrumental variable. We conduct inference on the health effects of military service for individuals who comply with the draft-lotteries assignment (the "compliers"), as well as for those who volunteer for enlistment (the "always takers"). The causal analysis for volunteers, who represent over 70% of veterans, is novel in this literature that typically focuses on the compliers. Since the effect for volunteers is not point-identified, we employ sharp nonparametric bounds that rely on a mild mean weak monotonicity assumption. We examine a large array of health outcomes and behaviors, including mortality, up to 38 years after the end of the Vietnam War. We do not find consistent statistical evidence of detrimental health effects on compliers, in line with prior literature. For volunteers, however, we document that their estimated bounds show statistically significant detrimental health effects that appear around 10 years after the end of the conflict. As a group, veterans experience similar statistically significant detrimental health effects from military service. These findings have implications for policies regarding compensation and health care of veterans after service.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".