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The effects of Vietnam-era military service on the long-term health of veterans: A bounds analysis

2025· article· en· W3121707581 on OpenAlexfundno aff
Xintong Wang, Carlos A. Flores, Alfonso Flores‐Lagunes

Bibliographic record

VenueJournal of Health Economics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersMaxwell School of Citizenship and Public Affairs, Syracuse UniversityMcGill UniversityUniversity of California, DavisNational Center for Health StatisticsUniversidad Autónoma de Nuevo LeónLouisiana State UniversitySyracuse University
KeywordsTerm (time)Military serviceVietnam WarService (business)GerontologyService memberMilitary personnelPsychologyPolitical scienceMedicineBusinessMarketing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.409
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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