How Significant Events and Economic Factors Influence Taiwan's Outbound Tourism to China
Bibliographic record
Abstract
It was not until 1987 that the relations between Taiwan and China began to thaw, opening the door for cross-strait civilian contacts. In recent years, with China's rapid economic development, ever-increasing numbers of Taiwanese citizens have been traveling to China, in spite of the fact that official cross-strait relations remain hostile, with political and even military crises often occurring. How do such factors affect Taiwanese people's willingness to travel to China? This study is intended to discuss how significant events (such as the "Special State-to-State Relation" proposition, Taiwan's presidential elections, and the SARS outbreak) and economic factors influence Taiwan's outbound tourism to China. Quarterly data used in this study cover the 41-quarter period from 2Q 1994 to 2Q 2004. A unit root test ensures that all of the estimated variables are stationary; then, a casual model is developed to estimate how economic variables and significant events influence Taiwan's outbound travels to China. Empirical results show that significant political events do not remarkably reduce the Taiwanese people's willingness to travel to China, while the SARS outbreak had a greater impact. On the other hand, income, price, and outbound travels of the previous period are significant independent variables, while variables such as exchange rate and the prices of alternative destination tours are less significant.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".