Reflections of Military Expenditures on Development in OECD Countries
Bibliographic record
Abstract
It is argued that the military expenditures have negative effects on economic development, most probably, due to the diversion of the country’s resources to the unproductive sectors and its adverse impact on civilian production and welfare level. Nevertheless, it is also recognized that the military expenditures, when coupled with openness to innovation and its evolution into civilian sectors, can contribute to sustainable development. This study investigates the functioning of the mechanism of transforming military expenditures into R&D, R&D into patents and patents into income for the period 1995-2020 for 32 OECD member countries with data available, through the Phillips and Sul (2007, 2009) club convergence analysis. The findings reveal that the countries in the first group with a high military expenditure share within GDP such as Australia, Canada, South Korea, and the United States, engage in military expenditures within the context of development and innovation hypotheses. On the other hand, findings show that the countries also in the first group such as Chile, Colombia, Greece, Portugal, and Türkiye, make their military expenditures with the aim of coping with external threats and deterrence, and this situation, by leading to an inefficiency of the resource utilization, results in these countries’ staying within the undeveloped group. Based on the findings of the study, it is concluded that, in countries exceeding a certain level of development, military expenditures can contribute to the development of the strategic sectors by stimulating civil sectors and foster the sustainable development of the countries through the direction of this to the high-technology fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".