Military spending, armed conflict and economic growth in developing countries in the post-Cold War era
Notice bibliographique
Résumé
Purpose While the relationship between military expenditure and economic growth during the Cold War period is well-researched, relatively less is known on the issue for the post-Cold War era. Equally how the relationship varies with respect to exposure to conflict is also not fully examined. Therefore, the purpose of this paper is to investigate the causal impact of military expenditure on growth in the presence of internal and external threats for the period 1990-2013 using data from 70 developing countries. Design/methodology/approach The main estimates are based on the generalized method of moments (GMM) regression model. But for comparison purposes, the authors also report estimates using fixed and random effects as well as pooled cross-section regressions. The regression specification accounts for non-linear effect of military expenditure allowing for interaction with conflict variable (where distinction is made between external and internal conflict). Findings The analysis indicates that methods as well as model specification matter in studying the effect of military spending on growth. Full sample estimates based on GMM, fixed, and random effects models suggest a negative and statistically significant effect of military expenditure. However, fixed effects estimate becomes insignificant for low-income countries. The effect of military spending is also insignificant in the cross-sectional OLS model if conflict is not considered. When the regression model additionally controls for conflict, the effect of military spending conditional upon (internal) conflict exposure is significant and positive. No such effect is present conditional upon external threat. Research limitations/implications One important limitation of the analysis is the small sample size – the authors had to restrict analysis to 70 low and middle-income countries for which the authors could construct post-Cold War panel data on military expenditure along with information on armed conflict exposure (the later from the Uppsala Conflict Data Program, 2015). Originality/value To the best of the author’s knowledge, this is the first paper to examine the joint impact of military expenditure and conflict on economic growth in post-Cold War period in a sample of developing countries. Moreover, an attempt is made to review and revisit the large Cold War literature where studies vary considerably in terms findings. A key reason for this is the somewhat ad hoc choice of econometric methods – most rely on cross-section data and rarely conduct sensitivity analysis. The authors instead rely on panel data estimates but also report results based on naïve models for comparison purposes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».