The international tourism effect of hosting the Olympic Games and the FIFA World Cup

Abstract

This study analyzes whether hosting mega-sporting events is a useful marketing platform for countries to promote international tourism on a longer term basis. Our model adopts the gravity equation of international trade to bilateral international tourist arrivals. We find a country-level tourism effect only for the Summer Olympic Games. Hosting increases international tourist arrivals significantly in the 8 years before, during, and in the 20 years after the event. In contrast, hosting the FIFA World Cup is overall ineffective in tourism promotion despite higher tourist arrivals in the event year. We attribute these differences in international tourism legacy to the level of strategic planning in promoting tourism, the impact of media on the broadcasting audience, and the participating countries.

Keywords

FIFA World Cup gravity equation international tourist arrivals mega-sporting events Olympic Games tourism effect

Introduction

Reasons to bid for hosting the Olympic Games and the FIFA World Cup are multifaceted. With “attendances in the millions and a media audience in the billions,”¹ putting the city and country on the global agenda and promoting tourism are often among the primary goals (Baade and Matheson, 2016; Billings and Holladay, 2012; Matheson and Baade, 2004; Shoval, 2002; Weed, 2015). In recent quantitative ex post studies, Fourie and Santana-Gallego (2011) and Song (2010) analyze the tourism effect of hosting the Olympic Games. Both articles present increased international tourism in the event year and 3–4 years before, but they disagree on the impact afterward. While the first authors finds no significant post-event impact on tourism, the second author shows a positive short-term, yet negative long-term tourism effect after the Olympics.

Next to diverging results, the studies mentioned above might suffer from a selection bias. They compare the world’s largest economies such as Australia, Canada, France, Germany, Japan, the United Kingdom, and the United States with less privileged countries such as Tanzania and Uganda. In similar mega-sporting event impact studies, avoiding this selection bias through a matching process leads to nonsignificant effects on exports and gross domestic product (GDP) (Brückner and Pappa, 2015; Langer et al., 2017; Maennig and Richter, 2012; Rose and Spiegel, 2011). Furthermore, an omitted-variable bias occurs in Song (2010) as the article focuses on the Summer Olympic Games and ignores other mega-sporting events such as the Winter Olympics and the FIFA World Cup. Finally, Fourie and Santana-Gallego (2011) pay no attention to long-term effects by testing only 3 years before and after the event while also mixing all event types. Moreover, Fourie and Santana-Gallego (2011) present tourism growth of 3.4% in the event year for failed bids of the Summer and Winter Olympics and the FIFA World Cup but again based on a combined event variable. They attribute this tourism growth in unsuccessful bidding countries to signaling as done by Rose and Spiegel (2011).

This empirical study contributes to the research on legacies and economic impacts of the world’s three largest mega-sporting events by studying their effect on international tourist arrivals on a more differentiated basis. We aim at specifying the direction, magnitude, and duration of the tourism effects induced by hosting and bidding for the Summer and Winter Olympic Games and the FIFA World Cup. We thereby follow the broad definition of Weed (2008) and focus on the generic, non-sports-related tourism motivated by the exposure “to the Olympic host destination through various […] media.” Consequently, we take on a longer term perspective with 8 years before and 20 years after the event. Our study analyzes country-level effects through the changes of the international tourist arrivals in the host and bidding countries. In contrast to previous studies, we prevent a selection bias through matching, avoid an omitted-variable bias, and consider long-term effects.

We find an Olympic tourism effect: Hosting and, to a lesser extent, bidding for the Summer Olympic Games increase international tourist arrivals significantly before, during, and after the event. In contrast, the FIFA World Cup is overall ineffective for promoting tourism. Host countries generate increased international tourism in the event year but decreased arrivals in the 4 years after the FIFA World Cup. Similarly, the Winter Olympic Games exhibit a small positive tourism legacy at the country level.

This article is structured as follows. The second section examines the broader literature on tourism impacts of mega-sporting events. The third section discusses our empirical strategy and variable selection. The fourth section presents the empirical results, which form the basis for the following discussion. The last section concludes the analysis.

Literature review

Tourism legacy regularly serves as a primary motivation to bid for hosting a mega-sporting event (Getz, 2008; Li and McCabe, 2013; Preuss, 2007). This legacy might materialize in three complementary ways: (1) increased tourism, (2) improved tourism infrastructure, and (3) enhanced destination image (Gaudette et al., 2017). While we focus our quantitative analysis on the tourism impact of hosting a mega-sporting event, research on the destination image helps to explain why some host cities and countries experience increased tourist arrivals before, during, and after the event (Chalip et al., 2003; Kaplanidou and Vogt, 2010).

Multiple academics conducted event case studies and longitudinal research on the central question if mega-sporting events increase tourism on country and city level. The following discussion focuses on the estimated tourism impacts, while we exclude the essential aspects of costs and investments associated with hosting these mega-sporting events and generating these impacts.

The Olympic Games

The Olympic Games are the largest of all sports events with about 10,500 athletes from over 200 countries, 6.2 million tickets sold, and a global television audience of 3.2 billion people (figures for Rio 2016) (International Olympic Committee, 2016a, 2016b; Müller, 2015b). As a result, plans and projections for a significant increase in tourism, with the Summer Olympics as a catalyst, are part of every bid campaign (Solberg and Preuss, 2007). All Summer Olympic Games post-WWII except Atlanta 1996 took place in one of the host country’s three largest cities, which also signals its relevance from a national perspective. Many hosts have, therefore, sought not only tourism growth in one region but also on country level (Weed, 2015).

The modern, commercial era of the Olympics, characterized by a growing scale and more focus on economic impacts and marketing, begins with Los Angeles 1984 (Preuss, 2004). However, this commercial focus did not include promotional plans to attract more tourists to California (Pyo et al., 1988). Despite 400,000 tourists to the Olympics, the tourism industry around Los Angeles suffered a short-term loss due to the crowding-out effect (Andranovich et al., 2001; Pyo et al., 1988). Nevertheless, Andranovich et al. (2001) claim a total tourism benefit of US$9.6 billion for Southern California.

For the 1988 Olympic Games, Seoul focused on promoting domestic tourism (Weed, 2008). As a result, Kang and Perdue (1994) report a positive tourism impact for Seoul, which diminishes over time and totals close to one million additional visitors in the 3 years following the event. Faulkner et al. (2000) conclude that direct tourism effects in both Los Angeles and Seoul were moderate and below expectations if diversion and time switching are considered, but that longer term advertising effects can be relevant.

Barcelona with the 1992 Olympics is a prominent role model for attracting tourists and reshaping the urban landscape (Baade and Matheson, 2016; Solberg and Preuss, 2007). From 1990 to 2000, tourism to Barcelona grew by 105%, far outpacing other European cities and also significantly increasing Spain’s tourist arrivals (Duran, 2005). Despite this tremendous growth, Moss et al. (2018) cannot identify a substantial increase in international airline passengers traveling to Barcelona in their empirical analysis. The tourism growth of Barcelona must not only be attributed to the advertising effect of the Olympics but also to the urban transformation and upgrading of tourism infrastructure (Duran, 2005; Preuss, 2007).

The 1996 Olympic Games in Atlanta reportedly generated US$5 billion in tourism growth (Andranovich et al., 2001). In terms of airline passengers, Moss et al. (2018) find a decline in domestic travelers and a sustained rise in international travelers to the busiest airport hub for North America, Atlanta. Preuss (2007) and Weed (2008) conclude that the 1996 Olympics did not generate the same tourism attractiveness as Barcelona and Atlanta “missed a golden opportunity”, possibly due to mixed reviews of Atlanta’s hosting performance (Andranovich et al., 2001; French and Disher, 1997).

In 2000, Sydney hosted the first Olympic Games with a strong focus on international tourism promotion for the whole country, creating dedicated strategies to spread tourism beyond Sydney and New South Wales (Morse, 2001; Weed, 2008). Ex ante studies predicted an increase of 1.3–2.3 million tourists to Australia over a period of 13–14 years (Kasimati, 2003). Analysis of airline passengers to Sydney airport show an increase during the Olympics, but reveal a sustained negative trend after 2 years, which, however, is influenced by 9/11 happening a year after the Olympics (Moss et al., 2018). Brown (2008) finds a recovery with increasing international tourist arrivals since 2003 but concludes it impossible to determine the Olympic effect on the growth.

The primary objective of the 2004 Olympic Games in Athens, which were modeled after Barcelona 1992, was urban regeneration and development. Again, ex ante predictions were optimistic with a tourism growth between 4.8 and 5.9 million for an 11- to 13-year period (Kasimati, 2003). However, international tourism promotion seems to have suffered from a lack of coordination between private organizations and government agencies as well as negative media coverage before the Olympics (Gibson et al., 2008; Ziakas and Boukas, 2014). While airline passengers increased during the Games, no sustained international arrivals growth was identified (Moss et al., 2018).

China’s central goal of hosting the 2008 Olympic Games in Beijing was to enhance its global image concerning political diplomacy, exports, investments, and tourism. (Chen, 2012; Gibson et al., 2008; Li and Kaplanidou, 2011). During the Olympic Games, Beijing recorded a 30% decrease in international tourists (Baade and Matheson, 2016). International tourism to Beijing was also slow in the year following the Olympics due to the global financial crisis but showed strong growth in the following years, even above the expectations of tourism professionals (Singh and Zhou, 2016). Multiple scholars also state the positive effect on the destination image of China, which impacts the intention to visit and thereby future tourism growth (Chen, 2012; Singh and Zhou, 2016).

Besides helping London to stay ahead in “the hierarchy of the world’s cities”, the UK government intended to leverage the Olympic Games 2012 to promote tourism across the country (Gold and Gold, 2016; Shoval, 2002; Weed, 2008). The United Kingdom’s Department for Culture, Media, and Sport estimated a potential impact of £2.1 billion for the period 2007–2017, which correlates with an additional 3.5 million international visitors (Smith and Stevenson, 2009).² In a meta-analysis of three models, Weed (2015) considers these figures overestimated and the maximum benefit for London and the United Kingdom combined could be a total of £1.6 billion (or 2.7 million tourists). As with the 2008 Olympics in Beijing, tourist arrivals to the United Kingdom were 6.0% lower during the 2012 Olympics (Baade and Matheson, 2016). Similarly, Moss et al. (2018) find no significant change in international tourist arrivals at Heathrow airport during and after the Olympic Games.

Brazil hosted the 2014 FIFA World Cup and the Rio 2016 Olympic Games with high expectations on the economic benefit, notably through international tourism (Meurer and Lins, 2017). As of 2015, Brazil was not even in the top 40 countries in international tourist arrivals (Meurer and Lins, 2017). On the supply side, Rio created 15,000 new hotel rooms for the Olympic Games (Baade and Matheson, 2016). During the Olympics, the city recorded a 28% increase in tourism receipts, which was, however, short lived as the 2 months after the event saw a similar decline (Meurer and Lins, 2017).

In their empirical study of the Olympic Games from 1996 (Atlanta) to 2004 (Athens), Fourie and Santana-Gallego (2011) find a significant positive impact of +19% on international tourist arrivals in the event year. They also find a net benefit of bidding for the event, which, nevertheless, is significantly smaller than the hosting effect. Similarly, Song (2010) reports a positive effect in the 4 years before, the Olympic year itself, and the 4 years after that for the Olympics from 1984 to 2008.

In conclusion, research studies vary in their scope and approach with regional versus country-level impact, short-term versus longer term impact, and different research methods. Most of the studies claim a positive Olympic tourism effect on a regional level during the event and afterward. At least four of the last nine Olympic hosts appear to be experiencing the growth in international tourist arrivals on country level, not just in the event year but also in subsequent years and the following years as well (Spain, Australia, China, the United Kingdom).

The Winter Olympic Games

Due to its specific climatic and geographic requirements as well as its specialized sports infrastructure, the Winter Olympic Games are regarded as an “atypical” mega-event (Essex and Chalkley, 2004; Gaudette et al., 2017). Regarding global popularity, the Winter Olympics are considerably smaller with 2,800 athletes from 88 countries, 1.0 million tickets sold, and 2.1 billion television viewers for Sochi 2014 (International Olympic Committee, 2014).

While major ski resorts with excellent winter sports infrastructure hosted the first Winter Olympics, the 1980s saw a more urban model emerging (Gaudette et al., 2017; Weed, 2008). Consequently, tourism promotion focused less on one ski resort than on the host region. At the regional level, Teigland (1999) and Spilling (1996) report positive short- and long-term tourism changes for Calgary 1988, Albertville 1992, and Lillehammer 1994, although not as high as ex ante predictions and not driven by the Olympic Games alone.

Since the new millennium, the hosts of Winter Olympics have redefined the host area as a mountain and an urban cluster, where the opening and closing ceremonies and indoor competitions take place (Gaudette et al., 2017). In their systematic review of tourism effects, Gaudette et al. (2017) analyze 16 case studies of the four Winter Olympics since 2002. They conclude that the findings are inconsistent with some authors reporting positive, some mixed, and some negative effects of the Winter Olympics.

Tourism promotion with the 2002 Salt Lake City Olympics focused exclusively on the North American market (Weed, 2008). In contrast to other hosts, Moss et al. (2014, 2018) find a sustainable, long-term increase in domestic tourism in the region, based on passengers arriving at Salt Lake City airport.³ Similarly, Baade and Matheson (2016) report a +20% tourism increase in Utah’s ski resorts over a 15-year period, while the base rate expectation with Colorado as a benchmark was 8% resulting in a potential 12% growth due to the Winter Olympics. Also, Andranovich and Burbank (2013) report an increase of +64% in hotel rooms from 1994 to 2002.

For the 2006 Winter Olympics in Torino, the city planned to promote itself as an alternative tourism spot in northern Italy, generating an estimated 100,000–150,000 additional tourist visits a year as the legacy (Weed, 2008). Ex post studies by Dansero and Puttilli (2010) and Bondonio and Guala (2011) highlight the positive tourism legacy of the Olympics for the region, yet it appears that growth is driven more by domestic and not by international visitors. In contrast, Moss et al. (2014, 2018) find no tourism impact of the 2006 Olympic Games when studying the passenger arrivals at nearby Milano airport.

For Vancouver 2010, tourism promotion was a key goal with an anticipated 1.1 million on-top visitors coming to British Columbia over 4 years (Kaplanidou and Karadakis, 2010; Weed, 2008). While Vancouver’s image improved compared to before the Games, studies show no more than a short-term peak during the Games (Gaudette et al., 2017; Moss et al., 2018).

The clear focus of Sochi 2014 was improving the image of Russia in the world (Gaudette et al., 2017). Tourism promotion acted as a secondary goal and focused mainly on providing a domestic holiday alternative for Russians, who regularly spent their holidays abroad in more affordable locations (Müller, 2015a). In this respect, the summer of 2014 marked success with +22% tourist arrivals in the region.

In their quantitative, longitudinal study on Nagano 1998, Salt Lake City 2002, and Torino 2006, Fourie and Santana-Gallego (2011) show a negative international tourism impact of the Winter Olympics Games in the year of the event at −7%.

In summary, some host cities/ski resorts have likely experienced an increase in domestic tourism. No research to date suggests that any host generated significant increases in international tourism particularly on a country level from the Winter Olympics.

The FIFA World Cup

Every 4 years in the same year as the Winter Olympics, the 32 qualifying national teams compete in the FIFA World Cup in up to 12 cities per host country (Baade and Matheson, 2004). Concerning tickets sold (3.4 million) and global broadcasting audience (3.2 billion people), the edition in Brazil 2014 is of similar dimension as the Summer Olympics (FIFA, 2014a, 2014b).

For the 1994 FIFA World Cup in the United States, ex ante studies claimed one million international visitors and an economic impact of US$4 billion (Baade and Matheson, 2004). Despite 3.5 million event tickets sold, Baade and Matheson (2004) show that reality is far from projections with a significant crowding-out effect and negative impact on the US economy very probable.

For the 1998 World Cup in France, Allmers and Maennig (2009) find no significant effect on overnight stays by foreigners or on receipts from international tourism during the event.

The FIFA World Cup 2002 is the only one co-hosted by two countries together, Japan and South Korea (FIFA, 2010). As with previous World Cups, anticipations were high with more than one million visitors to both countries (Horne and Manzenreiter, 2004). Survey results in South Korea indicate that 57.7% of all tourist arrivals during the event were related to the World Cup (Lee and Taylor, 2005). Despite this, South Korea did not experience any increase in international tourist arrivals in the event year, although more visitors came from Europe, offset by less usual tourists from Japan (Baade and Matheson, 2004; Horne and Manzenreiter, 2004; Kim et al., 2006). Likewise, Japan recorded only 30,000 more foreign tourists during the World Cup, an increase of +6.2% over the previous year (Horne and Manzenreiter, 2004).

Ex ante studies by Kurscheidt and Rahmann (1999) as well as the German Hotel and Catering Association forecasted between 340,000 and 3.3 million foreign tourists from the 2006 World Cup in Germany (Allmers and Maennig, 2009). Analog to earlier World Cups, Allmers and Maennig (2009) identify less than 100,000 additional international hotel tourists during the event. For France 1998 and Germany 2006, they conclude that the tourism effects in the year of hosting are “small and mostly negligible”. In the event month, hotel occupancy rate even dropped by 2.7% points compared to June 2005 (Du Plessis and Maennig, 2011). Preuss (2011) notes, however, that a significant proportion of visitors did not stay in a registered accommodation.

South Africa hosted the 2010 FIFA World Cup to improve its country’s image and to promote international tourism (Allmers and Maennig, 2009; Walker et al., 2013). For the World Cup, South Africa expected to welcome at least 480,000 international tourists, while regular tourism would only be crowded out at a rate of 20% (Allmers and Maennig, 2009). However, Du Plessis and Maennig (2011) only see a maximum net increase of 108,000 tourists during the World Cup due to a crowding-out effect. Peeters et al. (2014) present growing tourism due to South Africa hosting the World Cup outside of the primary tourism season, although still below expectations with an additional 294,804 arrivals. Based on a gravity model, Fourie and Santana-Gallego (2015) estimate the tourism growth of +26.7% during the event year, an increase of 1.7 million tourist arrivals due to the World Cup. While their estimated pre-event effect for 2007–2009 is insignificant, they find a post-event effect (2011–2013) of +12.2%, which equates to 3.0 million additional tourists.

As mentioned earlier, the 2014 FIFA World Cup in Brazil was part of a mega-event strategy in Brazil to promote international tourism (Meurer and Lins, 2017). For the event year, Baumann and Matheson (2018) show a net increase in one million international tourists, strongly influenced by many fans from Argentina. While international tourism receipts in the 2 months of the World Cup grew by 50% compared to the previous year, the effects seem short lived as the post-World Cup months are at the level of the previous year (Meurer and Lins, 2017).

In their longitudinal study discussed above, Fourie and Santana-Gallego (2011) find a significant positive effect of the FIFA World Cup on international tourist arrivals, although with +8% in the event year considerably smaller than for the Olympic Games.

To conclude, all host countries of the FIFA World Cup share high growth expectations for international tourist arrivals. However, most ex ante studies seem overly optimistic as most host countries experience limited tourism effects in the year of the World Cup. Recently, Fourie and Santana-Gallego (2015) present the first study indicating significant and higher than planned tourism impacts during and after hosting the 2010 FIFA World Cup for South Africa.

Empirical strategy

The empirical strategy follows Fourie and Santana-Gallego (2011) and Song (2010), who adjust the gravity equation of international trade to international tourist arrivals (Morley et al., 2014; Santeramo and Morelli, 2014). The gravity model was developed in the 1960s to explain the international trade flows between two countries as a function of their economic masses and their distance (Bergstrand, 1985; Tinbergen, 1962). With tourism being a particular form of trade in services, scholars started to apply the gravity model to international tourism flows in the 1970s and 1980s despite a lack of reliable theoretical foundation (Getz, 1986; Sheldon and Var, 1985; Uysal and Crompton, 1985). Over the past decade, the gravity model for tourism has regained popularity by an initial theoretical framework and successful applications in a variety of studies (Keum, 2010; Kimura and Lee, 2006; Morley et al., 2014). Besides forecasting future tourism, authors employ the gravity equation to study how specific factors affect tourism flows, for example, the transport infrastructure (Khadaroo and Seetanah, 2008), terror and war (Eryiğit et al., 2010), or climate change (Priego et al., 2015). With regard to tourism and sport, studies focus on the impact of mega-sporting events on tourism flows (Fourie and Santana-Gallego, 2011, 2015; Fourie and Spronk, 2011; Song, 2010).

We analyze the tourism effects of hosting and bidding for the Olympic Games from 1964 to 2020, the Winter Olympics from 1964 to 2022, and the FIFA World Cup from 1962 to 2022.⁴ Bilateral tourism data between 156 tourist destination countries and 169 tourist originating countries from 1982 to 2014 come from the World Tourism Organization (2016). We estimate the international tourism effects in the year of the mega-sporting event, the two quadrennial periods (“Olympiads”) preceding it (from −8 to −5 years, from −4 to −1 years), and the five quadrennial periods (from 1 to 4 years, from 5 to 8 years, from 9 to 12 years, from 13 to 16 years, from 17 to 20 years) succeeding it. The Olympic hosts and bids are based on GamesBids (2017), and the FIFA World Cup hosts and bids are based on FIFA (2010).

Parallel to Eilat and Einav (2004) and Fourie and Santana-Gallego (2011), we control for bilateral trade flows (International Monetary Fund, 2016), GDP per capita, and population (Feenstra et al., 2015). Also, we include the distance between the two countries, the purchasing power parities and factors such as common border, common language, colonial ties, and currency union. Parallel to Rose and Spiegel (2011) and Song (2010), we add regional trade agreements (Sousa, 2012), the number of islands in the pair, product of the land area, and common country. We index all nominal financial data to the year 2011 (Bureau of Labor Statistics, 2017) and take natural logarithms of all continuous variables in the data set.

We, therefore, estimate the following model:

\begin{matrix} L n {Tou}_{i j t} = β_{0} + β_{1} L n {Trade}_{i j t} + β_{2} L n GDP p c_{i t} + β_{3} L n GDP p c_{j t} + β_{4} L n {Pop}_{i t} \\ + β_{5} L n {Pop}_{j t} + β_{6} L n {Dist}_{i j} + β_{7} L n {PPP}_{i j t} + β_{8} {Border}_{i j} + β_{9} {Lang}_{i j} + β_{10} {Colony}_{i j} \\ + β_{11} {CU}_{i j t} + β_{12} {RTA}_{i j t} + β_{13} {Islands}_{i j} + β_{14} L n {Area}_{i j} + β_{15} {Country}_{i j} + γ_{1} {SOG}_{i t} \\ + γ_{2} {WOG}_{i t} + γ_{3} {FWC}_{i t} + γ_{4} {BSOG}_{i t} + γ_{5} {BWOG}_{i t} + γ_{6} {BFWC}_{i t} + η_{i} + δ_{j} + λ_{t} + ε_{i j t} \end{matrix}

where Ln denotes natural logarithms, i represents destination country, j origin country, and t the year. The dependent variable Tou _ijt is the number of international tourist arrivals to country i from country j in year t. Trade _ijt denotes the real bilateral trade-in-goods as the sum of exports and imports between countries i and j in year t; GDPpc _it and GDPpc _ij represent the real GDP per capita of countries i and j in year t; Pop _it and Pop _ij denote the population of countries i and j in year t; Dist _ij is the distance between the capitals of countries i and j; PPP _ijt is the purchasing power parity (PPP) between countries i and j in year t; Border _ij is a binary variable which equals one if countries i and j share a common land border and zero otherwise; Lang _ij is a binary variable which equals one if countries i and j share a common language; Colony _ij is a binary variable which equals one if a colonial relationship between countries i and j existed; CU _ijt is a binary variable which equals one if countries i and j in year t have a common currency; RTA _ijt is a binary variable which equals one if countries i and j in year t had a regional trade agreement; Islands _ij is the number of islands in the pair (0/1/2); Area _ij is the product of the areas of both countries; and Country _ij is a binary variable which equals one if countries i and j were or are the same country. SOG _it , WOG _it , and FWC _it are binary variables which are one if country i hosted the Summer Olympic Games, Winter Olympic Games, or FIFA World Cup, respectively, in year t. Also, BSOG _it , BWOG _it , and BFWC _it are binary variables which are one if country i bid for hosting the Summer Olympic Games, Winter Olympic Games, or FIFA World Cup, respectively, in year t. Finally, η_i refers to destination country-fixed effects, δ_j to origin country-fixed effects, and λ_t to year-fixed effects. ε_ijt is a well-behaved disturbance term.

All models employ a linear regression with the absorption of the large dummy variable set accounting for year-specific and country-level effects. To overcome the critical selection bias, we employ propensity score matching (Rosenbaum and Rubin, 1983) on the destination countries. The covariates estimating the propensity score should influence the outcome variable tourism and the treatment variable hosting and bidding for a major sporting event (Caliendo and Kopeinig, 2008). Based on Eilat and Einav (2004), Maennig and Vierhaus (2017), and Maennig and Vierhaus (2016), we employ trade, GDP per capita, and population as independent variables.

We create the following five models, which differ in the type of effects and the number of observations to assess the robustness of our results. Models (a) through (c) focus only on host effects, while (d) and (e) include host and bidding effects. The full models (a) and (d) consist of all available 163,653 data points.⁵ Model (b) covers the 25 host destination countries⁶ and the 18 destination countries⁷ identified with propensity score matching. Model (c) includes the 25 host destination countries and only adds the 22 bidding destination countries⁸ for one of the global sporting events. Model (e) consists of the 25 host destination countries, 22 bidding destination countries, and 28 destination countries⁹ matched with propensity score matching. For all models, we consider the total of international tourist arrivals from all origin countries.

Empirical results

As shown in Table 1, all models describe the international tourist arrivals in country _i from country _j very well with an adjusted R ² above 0.85. All control determinants, except currently in a colonial relationship, are significant in at least three of the models. Despite the differences in variable selection, we find the same directions and similar sizes of these variables to Fourie and Santana-Gallego (2011) and Song (2010). As expected, trade, GDP per capita, population, the product of land areas, and all dichotomous variables have a positive impact on tourist arrivals, while distance, PPP, and the number of islands in the pair negatively impact tourism.

Table 1.

Effects of mega-sporting events on international tourism arrivals.

Model	(a)	(b)	(c)	(d)	(e)
Host effects	x	x	x	x	x
Bid effects				x	x
Full model	x			x
Propensity score matching		x			x
Host and bid countries only			x
Year-specific fixed effects	x	x	x	x	x
Country-level fixed effects	x	x	x	x	x
Observations	163,563	53,800	66,758	163,653	94,903
Adjusted R ²	0.8501	0.8824	0.8865	0.8504	0.8739
Summer Olympic host −8 to −5 years	0.207***	0.137***	0.139***	0.174***	0.162***
Summer Olympic host −4 to −1 years	0.340***	0.292***	0.280***	0.309***	0.310***
Summer Olympic host event year	0.374***	0.341***	0.350***	0.326***	0.347***
Summer Olympic host +1 to +4 years	0.269***	0.217***	0.206***	0.272***	0.287***
Summer Olympic host +5 to +8 years	0.065	0.053	0.040	0.120*	0.142**
Summer Olympic host +9 to +12 years	0.134**	0.099*	0.125**	0.185***	0.228***
Summer Olympic host +13 to +16 years	0.189***	0.136**	0.186***	0.232***	0.267***
Summer Olympic host +17 to +20 years	0.096**	0.060	0.103**	0.150***	0.173***
Winter Olympic host −8 to −5 years	0.030	0.046	0.032	0.014	0.053
Winter Olympic host −4 to −1 years	0.001	0.001	−0.017	0.016	0.047
Winter Olympic host event year	−0.037	−0.050	−0.055	0.004	0.033
Winter Olympic host +1 to +4 years	−0.081	−0.064	−0.077	0.002	0.060
Winter Olympic host +5 to +8 years	0.088	0.085	0.057	0.139*	0.164**
Winter Olympic host +9 to +12 years	0.111*	0.097	0.076	0.047	0.049
Winter Olympic host +13 to +16 years	−0.042	0.032	−0.005	−0.073	−0.072
Winter Olympic host +17 to +20 years	−0.073	−0.002	−0.005	−0.070	−0.073
FIFA World Cup host −8 to −5 years	−0.114**	−0.087*	−0.085*	−0.025	−0.008
FIFA World Cup host −4 to −1 years	−0.118**	−0.132***	−0.134***	−0.008	−0.008
FIFA World Cup host event year	−0.048	−0.022	−0.043	0.092	0.119*
FIFA World Cup host +1 to +4 years	−0.240***	−0.203***	−0.226***	−0.172***	−0.163***
FIFA World Cup host +5 to +8 years	−0.072	−0.064	−0.086*	−0.044	−0.069
FIFA World Cup host +9 to +12 years	−0.060	−0.062	−0.063	0.037	0.023
FIFA World Cup host +13 to +16 years	−0.198***	−0.154***	−0.163***	−0.077	−0.046
FIFA World Cup host +17 to +20 years	−0.093	−0.052	−0.055	0.016	0.056
Summer Olympic bid −8 to −5 years				0.065***	0.079***
Summer Olympic bid −4 to −1 years				0.136***	0.155***
Summer Olympic bid event year				0.151***	0.168***
Summer Olympic bid +1 to +4 years				0.120***	0.127***
Summer Olympic bid +5 to +8 years				0.040	0.042*
Summer Olympic bid +9 to +12 years				0.066**	0.057**
Summer Olympic bid +13 to +16 years				0.086***	0.076**
Summer Olympic bid +17 to +20 years				0.116***	0.070*
Winter Olympic bid −8 to −5 years				0.036	0.042*
Winter Olympic bid −4 to −1 years				0.102***	0.091***
Winter Olympic bid event year				0.107***	0.092***
Winter Olympic bid +1 to +4 years				0.035	0.032
Winter Olympic bid +5 to +8 years				0.065*	0.038
Winter Olympic bid +9 to +12 years				−0.007	−0.044
Winter Olympic bid +13 to +16 years				0.070*	−0.017
Winter Olympic bid +17 to +20 years				0.060	−0.004
FIFA World Cup bid −8 to −5 years				0.068***	0.050**
FIFA World Cup bid −4 to −1 years				0.014	−0.023
FIFA World Cup bid event year				0.047	−0.019
FIFA World Cup bid +1 to +4 years				−0.280***	−0.348***
FIFA World Cup bid +5 to +8 years				−0.036	−0.061
FIFA World Cup bid +9 to +12 years				0.065*	0.059
FIFA World Cup bid +13 to +16 years				0.252***	0.254***
FIFA World Cup bid +17 to +20 years				0.055*	0.037
Ln Trade _ij	0.221***	0.343***	0.276***	0.221***	0.262***
Ln GDP per capita _i	0.370***	0.240***	0.344***	0.373***	0.367***
Ln GDP per capita _j	0.252***	0.193***	0.216***	0.252***	0.221***
Ln Population _i	0.146	0.286	−0.399*	0.206**	0.037
Ln Population _j	0.048	0.154*	0.099*	0.050	0.086*
Ln Distance _ij	−1.253***	−1.147***	−1.255***	−1.252***	−1.183***
Ln PPP _ij	−0.069**	−0.109**	−0.139***	−0.082***	−0.184***
Ln Product of land areas	0.114**	−0.013	0.792***	0.094*	0.194***
Common border	0.834***	0.408**	0.558***	0.835***	0.649***
Common language	0.722***	0.538***	0.51***	0.721***	0.569***
Ever in colonial relationship	0.980***	0.998***	0.976***	0.979***	0.973***
Common colonizer post 1945	0.626***	0.325**	1.132***	0.626***	0.741***
Currently in colonial relationship	−1.409	−0.011	0.091	−1.407	−1.539
Currency union	0.349	0.707*	0.557	0.346	0.655*
Regional trade agreement	0.486***	0.342***	0.387***	0.486***	0.438***
Islands in the pair	−0.763**	−0.920**	0.766	−1.186***	−1.033***
Same nation	2.417**	3.003**	4.747***	2.415**	2.712**
Constant	3.124**	0.466	−6.758	2.612*	0.939

Note: GDP: gross domestic product.

***p < 0.001; **p < 0.01; *p < 0.05.

The variables of interest yield comparable, yet different results across the models. Of considerable interest is the difference between the full models (which suffer from selection bias) and the models consisting of homogenous country groups based on propensity score matching. In fact, most variables are identical in significance, but coefficients differ significantly between the models. The full model (d) underestimates host effects and overestimates bid effects in comparison with the model (e), which consists of a homogenous country group based on matching. Consequently, we focus our discussion on the most sophisticated model (e). As the dependent event variable features natural logarithms, we take the exponential of the significant coefficients to display the event-induced annual changes in international tourist arrivals in Table 2.

Table 2.

Significant effects of mega-sporting events on tourism arrivals based on model (e).

Years	−8 to −5	−4 to −1	Event year	+1 to +4	+5 to +8	+9 to +12	+13 to +16	+17 to +20
Summer Olympic host (%)	17.5	36.3	41.4	33.2	15.2	25.7	30.6	18.9
Winter Olympic host (%)					17.8
FIFA World Cup host (%)			12.7	−15.0
Summer Olympic bid (%)	8.2	16.7	18.3	13.6	4.3	5.8	7.9	7.3
Winter Olympic bid (%)	4.2	9.5	9.7
FIFA World Cup bid (%)	5.1			−29.4			28.9

We find an Olympic tourism effect. Ceteris paribus, host countries of the Summer Olympic Games generate significantly higher international tourist arrivals not only in the Olympic year (+41.4%) but also in the 8 years before and the 20 years after the event (average effect +25.9%). For reference, Fourie and Santana-Gallego (2011) present a growth of +19.4% in their analysis of the event year only. Failed bids for the Summer Olympics, in comparison, show a weaker yet significant positive impact on tourism in the event year as well the years preceding and succeeding it (+9.4% on average). The different tourism impacts in the 20 years after the Olympics between the hosts (+29.4%) and the failed bids (+5.6%) illustrate the critical role of media coverage.

The Winter Olympics show a more irregular pattern. While host countries do not experience any positive effect in the event year, tourist arrivals increase 5–8 years after the event (average effect +2.5%). We consider this growth as an artifact because it is also only visible in two of the five models. Failed bids for the Winter Olympics show slowly increased tourism in the Olympic year and the 8 years prior (+2.2% on average).

Surprisingly, hosting the FIFA World Cup is ineffective in promoting tourism. Short-term, host countries generate on average +12.7% international tourist arrivals in the event year. In comparison, Fourie and Santana-Gallego (2011) find +7.9% of tourism growth in the event year. In the 4 years after, however, −15.0% visit the World Cup host country, which results in −1.6% tourist arrivals per year in the period under consideration. Countries with failed bids for the FIFA World Cup show on average +0.6% growth in tourism.

To illustrate these results, we adopt the average effects of the Summer Olympic hosts from Table 2 to the London 2012 Olympics. In 2012, the United Kingdom registered international tourist arrivals¹⁰ of 31.1 million, of whom 18.6 million were casual tourists, who would have visited even without any hosting and bidding for any mega-sporting event. The Olympic Games 2012 induced 7.7 million incremental tourists visiting the United Kingdom in 2012 (+41.4% above the base level). Statistically, the Olympic bids 1996, 2000 and the FIFA World Cup Bid 2018 attracted the remaining 4.8 million tourists.

In addition to incremental tourists in 2012, the London Olympic Games generated 49.7 million tourist arrivals (26% of the total additional visitors) in the 8 years leading up to the event (2004–2011) and a projected 130.8 million (69%) in the 20 years after the Olympics (2013–2032). In the 29-year estimation period considered, the London 2012 Olympic Games induced up to 188.2 million incremental international tourist arrivals, representing an annual average of 6.5 million people and a reasonable share of 19.2% of the total estimated 980.3 million tourists in this period. Consider that the United Kingdom is the sixth largest tourism country in the world and that all empirical growth attributed to the Olympics is relative, that is, smaller tourism countries will thus generate less impact.

These statistical estimations need perspective. First, VisitBritain (2013), the United Kingdom’s national tourism agency, reported that between July and September 2012, 871,000 international tourists visited mainly for the Olympics or attended a Games-related event. We conclude that of the 7.7 million incremental tourist arrivals in 2012, which were triggered by the Olympics, a fair share of 11.3% was directly related to the event. We note that our estimation of 7.7 million additional tourists in 2012 significantly exceeds the ex ante predictions of 2.7–3.5 million incremental tourists for the period 2007–2017.

Second, we compare the projected 130.8 million additional visits in the 20 years after the event (2013–2032) with the global media coverage of the Olympic and Paralympic Games. These estimated visits make up a fair share of 3.6% of the 3.6 billion media audience worldwide. Note that incremental visits contain a considerable proportion of unique tourists, who visit more than once. Therefore, the “true” percentage of media audience traveling to the United Kingdom could be closer to 1–2% in the 20 years.

Open remain the reasons why the event types yield different tourism impacts. The structural difference between the Olympic Games and the FIFA World Cup is initially not plausible, given almost identical media coverage. Nonetheless, most of the academic research described in the second section supports the general finding of this study that the Summer Olympic Games increase international tourist arrivals at the country level, whereas for the FIFA World Cup this is not the case. We also observe that despite comparable media coverage, not all Olympic and World Cup hosts enjoy the same tourism benefits. Therefore, we support the contention of several authors that not the mega-sporting event itself and its media coverage explain the difference in tourism benefits (Chalip et al., 2003; Faulkner et al., 2000; Gibson, 1998; Weed, 2008). Instead, we attribute the differences between the event types, between failed and successful bids, and the editions of the same type to three essential factors: the tourism promotion strategy and actions, the impact of media content on the broadcasting audience, and which origin countries participate in the event.

First, the goal of tourism promotion, especially at the international level, has a different priority for bids of the mega-sporting events. Recent Olympic bids with a stronger focus on promoting the city and also the country were Barcelona 1992, Sydney 2000, Athens 2004, Cape Town 2004, Beijing 2008, and London 2012 (Andranovich et al., 2001; Gibson et al., 2008; Hiller, 2000; Solberg and Preuss, 2007; Weed, 2008). For example, the Sydney 2000 Olympics were supported by multiple tourism promotion campaigns in 11 key markets around the world starting in 1992 until at least 2001 (Morse, 2001; Weed, 2008). The fact that host countries and countries bidding for the Olympic Games enjoy positive tourism benefits before, during, and after the Olympics confirms the importance of defined tourism promotion strategies, as failed Olympic bids receive only a fraction of the media attention.

Bids for the Winter Olympic Games also often prioritized tourism promotion, yet mainly at the regional level and partly with a focus on domestic tourism. Therefore, it is logical to find no significant international tourism effects at the country level for the Winter Olympics (Gaudette et al., 2017; Teigland, 1999; Weed, 2008). Also, note that promoting tourism through hosting the Winter Olympics often focuses on active winter sports, which are per se more regional, as the majority of winter tourists choose to limit travel to a nearby destination (Weed, 2008). Consequently, one might assume that the Winter Olympics as a tool for promoting tourism would facilitate the shift from one region/ski resort to another, which could limit the benefits for the host country if most tourists were to spend their winter holidays in the same country.

In comparison, recent FIFA World Cup hosts such as USA 1994, Japan/South Korea 2002, Germany 2006, Russia 2018, and Qatar 2022 aimed mostly at non-tourism economic benefits and the improvement of their nation image (Baade and Matheson, 2004; Kaplanidou and Karadakis, 2010; Kasimati and Dawson, 2009; Matheson and Baade, 2004). In recent years, only South Africa 2010 and Brazil 2014 claimed the goal of promoting international tourism, yet little is known about their tourism promotion strategies beyond hosting the FIFA World Cup to attract visitors in the event year and relying on the media effect.

FIFA World Cup host countries show a particular pattern of international tourism arrivals with a growth of +12.7% in the event year, which is offset by a decline of -15.0% in the four years after the event. We conclude that our results do not point toward a crowding-out effect but rather to a time-switching effect (Baade and Matheson, 2016; Solberg and Preuss, 2007). Tourists appear to prepone their trip to the host country, which otherwise would have been planned in the years after the event.

Second, the improvement in awareness and image perception through global media coverage is the central argument why mega-sporting events might promote international tourism (Essex and Chalkley, 1998; Faulkner et al., 2000; Getz, 2008; Gibson et al., 2008). With the same size of global media coverage over 3 billion people for the Olympics and the FIFA World Cup, we explain the differences in tourism effect through differences in broadcasting content and with varying attitudes and interests of the media audience. In general, the three mega-sporting events differ in the priority of the sport itself (Weed, 2008). For the Olympic Games, people watching are regularly not only interested in the sports competitions but, for example, also culturally in the host’s local interpretation of the global spectacle Olympic Games (Weed, 2008). In contrast, fans traveling to the FIFA World Cup are mainly focused on the atmosphere and the football itself, while the actual host and the destination attractiveness are almost irrelevant (Florek et al., 2008; Lee et al., 2005). Holtzhausen and Fullerton (2013) also point out that the FIFA World Cup has little impact on the host country’s reputation. Assuming similar attitudes and demands for the global broadcasting audience, the limited tourism effect of the World Cup through media coverage seems understandable. Similarly, ski and winter sports tourists value the significance of Olympic sites, yet the advertising effects are indeed limited to these sites (Weed, 2008).

The media coverage of the event is very much in line with these attitudes and interests of its global audience. To maximize the tourism impact through media coverage, many of the favorite Olympic events, such as the marathon, road cycling, and beach volleyball, are regularly found close to the core of the city with iconic tourism sites in the background—perfect for marketing the city to a worldwide audience (Weed, 2008). In contrast, the stadiums of the FIFA World Cup are often located in peripheral areas of the host cities. Similarly, the sites of the Winter Olympics are either indoor in the urban cluster or feature ski slopes in the mountains, which, apart from winter sports tourists, are less impactful for tourism promotion in the host region. Moreover, background reports during the Olympic Games focus on the host country, the region, and the city, while stories during the FIFA World Cup coverage are often related to the competing nations.

We also attribute the difference between failed and successful bids of the Summer Olympics to the differences in media coverage in the years leading up to the event as well as afterward. We thereby confirm the result of Fourie and Santana-Gallego (2011), who also find a statistical difference in tourism arrivals between losing and winning bid countries.

Third, the increase in tourism might come exclusively from countries actively participating in the event (Fourie and Santana-Gallego, 2011, 2015; Peeters et al., 2014). The FIFA currently limits its World Cup to 32 participating nations,¹¹ while all countries are invited to take part in the Olympic Games. For South Africa 2010, Fourie and Santana-Gallego (2015) quantify the effect of France qualifying in place of the Republic of Ireland on nearly 75,000 additional tourists for the event year and 3 years afterward.

Conclusion

Many countries bidding for the Olympic Games or the FIFA World Cup with the goal of promoting tourism and ex ante studies support these hopes. Ex post studies, however, show inconsistent results. This study answers the question of the direction, size, and length of a potential tourism effect from mega-sporting events. We employ a multi-country, multi-year, and multi-event setup using the bilateral trade gravity model adapted to international tourist arrivals and avoid a selection bias as well as an omitted-variable bias.

We find an Olympic tourism effect. Hosting the Summer Olympic Games increases the number of international tourist arrivals significantly in host countries in the 8 years before, during, and in the 20 years after the event. In contrast, the FIFA World Cup is ineffective in promoting tourism in the longer term. Although international tourism increases in the event year, this growth is more than offset by the decreased tourism from 1 to 4 years after the event. The Winter Olympic Games have a limited, almost irrelevant positive effect from 5 to 8 years after the event. Bidding for the Olympics has a smaller, yet still significant positive effect.

We attribute this international tourism legacy of the Olympic Games to three factors: first, the high priority for promotion tourism followed by a dedicated strategy and corresponding actions; second, the impact of the media content on the broadcasting audience; and third, the participating countries.

Countries with the goal of tourism promotion shall bid for hosting the Summer Olympic Games, choose the most attractive city for hosting, plan the venues close to major tourist attractions, and leverage the Games and its media coverage as a marketing platform for significant tourism promotion activities in their key target markets. This strategy could lead to substantial “Games-motivated tourism” in the years leading up to the event, the event year, and in the two decades afterward.

We note that this study focuses on identifying the effect of hosting and bidding for mega-sporting events on the country’s international tourist arrivals. We do not analyze the length of stay or the expenditure of international tourists. It is also limited in explaining city and regional effects and the reason for the structural difference between the Olympics and the FIFA World Cup, which goes beyond theoretical explanations. The difference in the quality of broadcasting content, which in our opinion acts as the primary advertising tool for tourism, requires further dedicated research.

Footnotes

Acknowledgements

The author thanks Wolfgang Maennig for valuable comments as well as Felix Richter and Wonho Song for the contribution of data and STATA code to this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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