Effect of Terrorism and Travel Warning On Kenyan Tourism Demand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Security concerns especially from terrorism events and travel warnings against the country presents a challenge to the tourism industry in Kenya. This study applies the Arellano-Bond difference GMM model to analyze the effects of terrorism and travel warning on demand for tourism in Kenya. Quarterly arrivals data from the Kenya National Bureau of Statistics from 22 source countries covering the period 2010q1 to 2015q4 are used. The study focuses on travel warnings by the UK and US. The results obtained show that terrorism events, represented by fatalities, significantly reduce tourism demand. The adverse effect lasts at least through to the following quarter. Travel warnings also show a negative effect on tourist arrivals in the country. However, the evolution of its effect over time seems to depend on which country issued the warning. For a UK warning, the bulk of the impact comes in the following quarter, while for a US travel warning the negative effect is mainly in the same quarter. The sector appears to recover more quickly from a US travel warning relative to a UK warning.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it