Can Foreign Aid Dampen the Threat of Terrorism to International Trade? Evidence from 78 Developing Countries
Bibliographic record
Abstract
The study investigates whether development assistance can be used to crowd-out the negative effect of terrorism on international trade. The empirical evidence is based on a panel of 78 developing countries for the period 1984–2008 and quantile regressions. The following main findings are established. First, bilateral aid significantly reduces the negative effect of transnational terrorism on trade in the top quantiles of trade distribution. Second, multilateral aid also significantly mitigates the negative effect of terrorism dynamics on trade in the top quantiles of trade distributions. It follows that it is primarily in countries with above-median levels of international trade that development assistance can be used as an effective policy tool for dampening the adverse effects of terrorism on trade. Practical implications are discussed. Moreover, steps or strategies that can be adopted by managers of corporations involved in international trade are provided, inter alia: (a) the improvement in physical security in high risky places, (b) the reduction of uncertainty linked with politically risky investment environments, (c) the reduction of costs associated with investments in locations that are very likely to be impacted by terrorism, (d) the role of security consultants and (e) the enhancement of security in networks. JEL: F40, F23, F35, Q34, O40
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".