Is the boom in East Asian tourism happening at the expense of other destinations?: A cross-country analysis
Bibliographic record
Abstract
Over the years, tourism has emerged as the world’s largest peacetime industry employing approximately 221 million people worldwide. According to the UNWTO, the tourist traffic rose to a record 1.087 billion arrivals in 2013 and this surge is expected to continue through 2030, in annual increments of 3.8 percent. Much of this unprecedented boom may be attributed to newly-affluent Asian nations such as Taiwan, South Korea and Malaysia as well as the huge populations of China, India and Indonesia - the first, second and fourth most populated countries in the world. Many analysts worry that the Asia Pacific region - a collection of dissimilar states squeezed between the Indian Ocean and the Pacific - could potentially be gaining market shares at the expense of their older rivals. We disagree with such a notion and will argue instead that the boom in East Asian tourist traffic is attributable to local factors primarily. Our examination of tourism data from the World Development Indicators database comprising a total of 92 countries covering a 16-year period (1995-2011), provides strong empirical support for our claim that the regional variation in tourism growth does not imply that some destinations are gaining at the expense of the rest. We examine changes in tourist arrivals among all relevant bilateral country pairs to test for a link between changes in tourist arrivals in Southeast Asian nations with Europe and North America. We do find that growth in tourist arrivals to countries of Southeast Asia has been particularly strong, but at the same time, tourism growth to Eastern Europe and to the Middle East has also been strong. This suggests that tourism is driven at least partly by economic growth of a destination, and it may also drive that growth. In examining changes in tourist arrivals by bilateral country pairs we find that for the vast majority of cases there is no support for the hypothesis that growth of tourism in one location comes at the expense of tourist arrivals to other countries. The tourism industry is dynamic and growing, and the success of new destinations does not come at the expense of traditional destinations. Rather countries seem to be establishing niches for themselves in terms of tourist offerings and as such may well be more complementary to each other than competitive. All countries—both developed and developing—may be able to stake a claim in the ever expanding global tourist trade.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".