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Record W2434031099

CAUSAL LINK BETWEEN MILITARY EXPENDITURE AND GDP-A STUDY OF SELECTED COUNTRIES

2015· article· en· W2434031099 on OpenAlexaboutno aff
Ramesh Chandra Das, Soumyananda Dinda, Kamal Ray

Bibliographic record

VenueInternational Journal of Development and Conflict · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsAggregate expenditureCausality (physics)Consumption (sociology)Investment (military)ChinaReal gross domestic productDevelopment economicsPoliticsMacroeconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

There has been a great debate for long among the economists and political scientists on the issue of whether there has been any link or causations between GDP and military expenditure of a country. Professor Keynes does not believe on spending on military activities as it is wastage of resources, particularly when there is deficiency of aggregate consumption and investment demands in the economy. The present paper tries to examine whether there are causal link between GDP and military expenditure of randomly selected 20 countries in the world for the period 1988-2013. Applying the concerned time series econometric tools the study reveals that GDP causes military expenditure for seven countries including France, Germany and Italy and military expenditure causes GDP for five countries including USA, Canada, China and India. Bidirectional causalities are observed for Italy and Australia with no way causality observed in six countries including UK and Japan.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.239
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.278
Teacher spread0.216 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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