International Tourism and the Olympics: The Legacy Effect
Bibliographic record
Abstract
INTRODUCTION This study examines international tourism as a legacy of hosting the Olympics. In this research, international tourism is measured by the number of international air passengers enplaning and deplaning each month at the international airport(s) serving the Olympic host city. Hypothetically, increases in tourism to the host city is composed of four major components: (1) increased tourism at the time of the Olympics as a direct result of tourists coming to watch the Olympics, (2) Olympic visitors returning for an additional visit, (3) Olympic tourists encouraging friends back home to visit the host city, and (4) visitors who are generated by the media coverage of the Olympics and the Olympic host city. The most important of these is the extensive media coverage (Preuss, 2004). The US is exposed to more media coverage of the Olympics than any other country (Short, 2004). The US comprised 20.9 percent of the viewers watching the opening ceremonies of the 2006 Olympics (ETOA, 2006). AC Neilson estimated that 40.7 million people watched the opening ceremonies for the London 2012 Olympics, an all-time high number of viewers. The 1996 Atlanta Olympics held the record previously (Collins, 2012). An overall audience of 219.4 million viewers for the games made the London Olympics the most watched event in American history (IOC, 2013). Although the US is the largest source of international visitors for many of the host cities at any time, a recent study shows that exposure in the US to the games does not produce sustained increases in international tourism from the US to the Olympic host city (Gruben, 2012). The cost of hosting the Olympics has dramatically escalated since the 1984 games in Los Angeles (Malfas, 2004), yet cities wanting to host the event form long lines years in advance to put their names in the pool of those to be considered as a host city. Although short term profit may be a motivating factor, Los Angeles was the first city in modern times to generate a profit from hosting the games (Holloway, 2006; Yongjian, 2008). Few Olympic host cities have shown a profit since the 1984 Los Angeles games. London, host site of the 2012 Olympics, spent 2.38 billion[pounds sterling] over an eight-year period to hold the games, yet generated revenue of only 2.41 billion[pounds sterling] over the same period (Owen, 2013). Regardless of the profitability, hosting the games is considered a prestigious honor for the host city. This paper examines the changes in international tourism at Olympic venues during the games as well as the time just prior to and after the event. Increasing international tourism is the largest economic justification for hosting the Olympics. Measuring the change in international tourism has also been one of the more difficult research issues related to the Olympics. The time series methodology used in this research will improve upon the estimation process by controlling for existing trends in international tourism to the host city. The methodology and data used will also improve on prior studies by controlling for the displacement effect. Another improvement on prior studies is the use of international tourism (measured by international air passengers) from all originations versus domestic visitors or visitors from one country to the Olympic site. This paper is organized as follows. First, a review of the literature pertaining to Olympic tourism will be presented. A description of the data will come next. A discussion of the methodology will follow. Fourth, the results for six Olympic host cities (Atlanta, London, Salt Lake City, Sydney, Turin, and Vancouver) will be presented. These six cities were selected based on data availability for the primary international airports serving the region. Finally, concluding remarks and implications of the study are discussed. LITERATURE REVIEW The Olympic bidding process is expensive and time consuming. …
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How this classification was reachedexpand
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".