Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 434

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

MODELS OF MILITARY EXPENDITURE AND GROWTH: A CRITICAL REVIEW

Open access
Dunne, J. Paul, Smith, Ron P., Willenbockel, Dirk·2005·Defence and Peace Economics

Abstract

This paper reviews some of the theoretical and econometric issues involved in estimating growth models that include military spending. While the mainstream growth literature has not found military expenditure to be a significant determinant of growth, much of the defence economics literature has found significant effects. The paper argues that this is largely the product of the particular specification, the Feder-Ram model, that has been used in the defence economics literature but not in the mainstream literature. The paper critically evaluates this model, detailing its problems and limitations and suggests that it should be avoided. It also critically evaluates two alternative theoretical approaches, the Augmented Solow and the Barro models, suggesting that they provide a more promising avenue for future research. It concludes with some general comments about modelling the links between military expenditure and growth.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/10242690500167791

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 14 references

Cited by

See all 434

Metadata

Authors
Dunne, J. P., Smith, R. P., & Willenbockel, D.
Venue
Defence and Peace Economics
Published
2005-01
Volume
16
Issue
6
Pages
449-461
ISSN
  • 1024-2694 (ISSN-L)
  • 1476-8267
OpenAlex
W1989789607
Cited by
434
References
14
FWCI
28.27
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: MODELS OF MILITARY EXPENDITURE AND GROWTH: A CRITICAL REVIEW. https://app.cochat.ai/papers/10.1080/10242690500167791
The paper
Dunne, J. P., Smith, R. P., & Willenbockel, D. (2005). MODELS OF MILITARY EXPENDITURE AND GROWTH: A CRITICAL REVIEW. Defence and Peace Economics. https://doi.org/10.1080/10242690500167791

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.