Relationships Between Defence Expenditures and Economic Growth in G7 Countries: Panel Bootstrap Causality Analysis
Bibliographic record
Abstract
It is important to reveal the relationship between defence expenditures and economic growth for both developed countries and developing countries. Dynamic political and economic developments affect defence expenditures. Many macroeconomic variables of countries are affected by the change in defence expenditures. Economic growth comes first among these macroeconomic variables. This study aims to determine the relationships between the economic growth and defense expenditures of the G7 countries for the period 1988-2018. For this purpose, the relationships between variables were examined using bootstrap panel causality analysis developed by Kónya (2006). According to the analysis results, it was found that there is a unidirectional causality relationship from defense expenditures to economic growth in the USA, Germany, Japan, England, and Canada. The sign of causality relationships is negative in the USA, UK, and Canada, and positive in Germany and Japan. On the other hand, in the findings of the study, an insignificant causality relationship was found between variables in France and Italy. Besides, for the G7 countries, an insignificant causality relationship has been determined from economic growth to defense expenditures. Economic and political inferences were made based on the findings obtained at the end of the study.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".