Indirect Effect of Terrorism on Economic Growth
Bibliographic record
Abstract
Throughout the world, terrorism has become a major challenge to socio-economic development. Although there are studies that have examined the direct impact of terrorism on economic growth, this study complements the existing literature by investigating the moderating effect of terrorism on economic growth by introducing variables as moderations to analyze the indirect effect of terrorism on economic growth. To do this, a simultaneous equation model was applied to panel data for a sample of 31 countries (18 developing and 13 developed). The results of this study show the following: The indirect effect of FDI and trade openness on economic growth is significantly positive in the case of the entire sample; The moderating effect of unemployment, investment and public spending significantly and negatively affects economic growth in the case of the entire sample; For the case of developed countries, we note that the indirect effects of unemployment and public spending are not significant; In other words, the indirect impacts of trade opening, investment and FDI are significantly positive on economic growth in the case of developed countries, For the case of developing countries, the indirect effects of trade opening, public spending as well as public investment on economic growth are significantly negative as is the indirect effect of unemployment. It can be concluded that the direct or indirect effect of terrorism on economic growth is not remarkable in developed countries.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".