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Geographical Distribution of Economic Impact: Sporting Events in Small CitiesJesyca Salgado-Barandela1, Angel Barajas2, and Patricio Sanchez-Fernandez21 Organización de Empresas y Comercialización, Universidade de Santiago de Compostela, Spain

2 Economía Financiera y Contabilidad, Universidade de Vigo, Spain

Jesyca Salgado-Barandela is a professor in the department of Business Organization and Marketing at Universidade de San-tiago de Compostela. Her research interests include the economy of sport, sport tourism, and the economic impact of events. Angel Barajas is a professor in the department of Financial Economics and Accounting at Universidade de Vigo. His research interests include the valuation of companies and intangibles, the economy of sport, and the economic impact of events.Patricio Sanchez-Fernandez is a professor in the department of Financial Economics and Accounting at Universidade de Vigo. His research interests include economic development, the economy of sport, and the economic impact of events.

AbstractThere are limitations in determining the economic impact of sporting events that need to be considered. One of these is represented by first-round leakages. This work focuses on explaining first-round leakages in the economic impact of sporting events on small cities. Seeking to identify this type of leakage, we estimated the spatial distribution of the economic impact of two small-sized events organized in a town with a population of 24,248 inhabitants. The results showed a first-round leakage exceeding €300,000 and identified higher average attendee expenditure in a more developed city adjacent to the host city. Moreover, an exploratory analysis concerning the influence of leakage in final spending was performed. Finally, the elements that would increase the probability of leakage were studied. Overall, the current case study highlighted the importance of considering the existence of leakage.Keywords: spatial distribution, economic impact, first-round leakages, medium-size events, small events, sport eventsDOI:

IntroductionMedium and small-sized events are a phenomenon that attracts the attention of sporting event organizers and the scien-tific community alike. Some researchers believe these events have a greater economic impact because they do not require large investments (Malchrowicz-Mośko & Póczta, 2018; Ziakas & Costa, 2011) and are more sustainable (Gibson et al., 2012; Malchrowicz-Mośko & Póczta, 2018). These types of events also have the ability to participate in the economic development of cities (Gibson et al., 2012; Taks et al., 2013; Veltri et al., 2009) and develop tourism (Csobán & Serra, 2014; Chalip & McGuirty, 2004).

Matheson (2006a) explained the advantages of small events over mega-events, focusing on four main aspects. The first is that small events have a lower possibility of producing tourism displacement. Secondly, they involve lower organization and security costs. A third aspect is their lower chance of causing deviations in the normal patterns of the local economy; this makes the estimation of the indirect impact through multipliers more precise. Fourth, Matheson (2006a) highlighted lower media pressure on these events. Hence, the incentive to show inflated data on economic impact is lower too.

Studies are increasingly focusing their analysis on medium and small-sized events. Works estimating the economic impact of these types of events include those developed by Taks et al. (2011) for the 2005 Pan-American Junior Athletic Championships and that by Sánchez et al. (2016) for the Spanish Winter Open Master Swimming Championship 2015, identifying, in both cases, a positive economic impact. Mondello and Rishe (2004) analyzed the determinants of econom-ic impact for different amateur events. Taks et al. (2013) and Gibson et al. (2003) studied the spending behavior of those attending medium and small-sized events. Salgado, Sánchez, Pérez, and Barajas (2018) applied the contingent valuation method to identify the willingness of attendees to pay for attending the Spanish Spring Open Absolute Swimming Cham-pionship 2017, finding that spectators were willing to pay for a ticket to attend the event.

International Journal of Sport Finance, 2023, 18, 45–53, © 2023, West Virginia University

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Medium and small-sized events represent an opportunity for cities to develop a brand image strategy that boosts local tourism and economy (Malchrowicz-Mośko & Póczta, 2018) in addition to achieving these objectives in a more sustain-able way (Csobán & Serra, 2014; Duglio & Beltramo, 2017). Getz and Page (2016) explained the need for greater respon-sibility, transparency, and integrity in strategic policies and investments linked to the organization of sporting events. When attempting to measure economic impact, it is important to consider possible limitations in small cities, which may result from leaks or tourism displacement. As Agha and Rascher (2016) and Siegfried and Zimbalist (2002) explained, the level of leakage in smaller territories is higher given the lower presence of goods and services at a local level.

Measuring the leakage effect requires knowledge of the geographical distribution of spending. Then, it would be pos-sible to determine which part of the direct economic repercussion generated by sports tourists stays in the host city of the event and which goes to the surrounding cities. The geographical distribution of spending has not been addressed in depth in economic impact studies. The main aim of this study was to provide insights on the geographical distribution of the economic impact of sporting events to identify the existence of first-round leakages into cities surrounding the host city. To contribute to this debate, this work analyzes two events organized in Marin (Spain) in 2018. Marin is a town of 24,248 inhabitants that proposed a strategy to attract sporting events and ultimately bring tourism into the area. The events under analysis were the Spanish Kickboxing Championship Tatami Sports (held from May 11–13, 2018) and the Spanish Basketball Championship for Boys Clubs (held from June 10–16, 2018). The empirical analysis was made up of two parts. First, the first-round leakage was estimated for the two proposed events. Second, an exploratory analysis was carried out identifying the sociodemographic variables that explained the existence of leakage. The main findings of the paper are as follows: 1) the most developed urban center may benefit a higher economic impact than the town hosting the event; 2) attendees who produced money leakage spent around 30% more than those who spent only in the city hosting the event; and 3) the type of event, the origin of the attendees, the length of stay, and age influenced the generation of a greater or lesser first-round leakage.

The article is organized as follows. First, the literature review is oriented to identify the literature that has analyzed the geographical distribution of tourist spending at sporting events. Second, the database and the methodology used in the article are presented. Then, the results are explained and discussed. Finally, the conclusions are provided.

Theoretical BackgroundThe correct estimation of the economic impact of an event implies considering a set of aspects studied in detail by Crompton (1995, 2006). He mentioned five aspects, which include using sales instead of household-income multipliers, the misrepresentation of employment multipliers, using incremental instead of normal multiplier coefficients, the use of “fudged” multiplier coefficients, and confusion of turnover and multiplier. Matheson (2006b) identified three factors that must be considered to avoid inaccurate estimates of the economic impact of an event: substitution effect, crowding-out effect, and leakage effect. The substitution effect occurs when the expenditure made in the territory is not solely motivated by the event (Crompton, 1995; Matheson, 2006b). Preuss et al. (2010) developed a careful analysis of the identification of attendees having an impact to correctly measure the substitution effect. To measure the substitution effect, it is necessary to identify the existence of attendees whose spending is not motivated exclusively by the event—in other words, attendees whose expense would have been generated even if the event had not occurred.

The crowding-out effect refers to the potential expenditure displaced by the event, which occurs when the organization of the event discourages the attendance of regular tourists from the area (Barclay, 2009; Crompton, 1995, 2006; Matheson 2006b). For their part, Coates and Depken (2009) identified other possible displacement effects of the event: the hun-ker-down and the skedaddle effects. The former occurs when residents stay at home because of the event, and the latter is generated when residents go to other territories to escape the negative effects of the event. Economic impact studies neglect the measurement of these types of effects. In this sense, Scott and Turco (2009) developed a model of consumer evaluation that allows for the estimation of the crowding-out effect of an event. Applying the model to the 2009 US Wom-en’s Open Golf Championship, these authors identified a slight displacement of tourism.

The leakage effect occurs when the creation of direct income in the territory has no effect on the local economy in the form of indirect income (Matheson, 2006b). As Barclay (2009) and Agha and Rasher (2016) explained, money can leave

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the economy for two reasons. It may simply be “natural” for this to happen because no local economy has everything it needs, and some products and services must be imported from other territories. Something similar happens with the em-ployment of non-local personnel; part of the money inevitably goes to the points of origin of these workers. Another type of leakage generated by aspects linked to event management occurs when the organization of the event generates expenses outside the territory to either acquire products or hire services (Agha & Rasher, 2016; Barclay, 2009).

Davies et al. (2013) explained that “In estimating an event’s direct expenditure on a host economy, researchers need to consider immediate (direct) leakages according to the ultimate location of expenditures connected with an event” (p. 37). This is what the authors define as first-round leakages.

An example that may be more frequent in small cities is the leakage generated by limited lodging capacity. Tourism displacement occurs if the city cannot absorb the demand for accommodation generated by the event. This in turn results in the leakage of direct money into other territories.

Knowing the spatial distribution of the economic impact generated by an event allows measurement of this type of leakage. Getz and Page (2008, 2016) stressed the importance of studying space and time in sport tourism. However, the literature dealing with the geographical distribution of economic impact is scarce.

Collins et al. (2018) and Lee et al. (2017) explained that this issue is scarcely studied in the scientific literature as opposed to the more usual aggregate estimate of the economic impact. Current studies on spatial distribution develop two research lines. Connell and Page (2006) and Daniels (2007) studied the spatial effect in the economic impact of events having more than one host location. They identified a two-fold impact of events held in the most populated counties with more devel-oped urban centers as compared to the impact of events taking place in smaller counties. On the other hand, Herrero et al. (2006) and Lee et al. (2017) studied the leakages of money to other cities during the celebration of an event. Lee et al. (2017) showed that the host region received 80% of the impact while neighboring regions received only 20%. A previous study by Herrero et al. (2006) found that the economic impact of Salamanca 2002, as the European Capital of Culture, benefitted the host region with a 70% economic impact while 20% went to the rest of country and 10% went abroad.

Another phenomenon yet to be quantitatively analyzed, which is becoming increasingly common, is the case of small cities developing a tourism strategy through the organization of sport events. In this context, the level of leakage is higher. In this sense, the results found by Herrero et al. (2006) and Lee et al. (2017) may be challenged.

Data and MethodologyTo develop the empirical part, different methodological tools were applied. First, the data regarding spending made by the athletes was collected to estimate the initial injection of money. Buning et al. (2016) and Frechtling (2006) defined the initial injection into the economy as the new money generated by visitors in the territory hosting the event. Based on the estimate of the initial injection of money, distinguishing according to geographical areas, the geographical distribution of spending can be measured, and it can be identified if there is a first-round leak.

Second, an exploratory analysis was carried out. On the one hand, the influence of leaks on final expenditure of attend-ees was analyzed using ordinary least squares (OLS) regression. On the other hand, a logistic regression was applied to determine which variables increased the probability of the generation of leaks. The variables used coincided with the vari-ables commonly analyzed in studies with similar characteristics (e.g., Salgado et al., 2018; Salgado et al., 2020). Obtaining the necessary data for the application of the methodology required the design of a survey. The survey was previously vali-dated by experts in the field in addition to being applied in previous studies on the economic impact of events. The survey included questions that allowed the necessary data to determine the spatial distribution of the impact to be obtained.

The survey contained four sections and 24 questions. The first part of the questionnaire identified whether the person under survey was a resident in the host city. Resident attendees were accounted for, but they were not surveyed. Non-res-ident attendees answered two questions to identify the presence of casuals and time-switchers. Matheson (2006) defined casuals as those who were already visiting the host territory and attended the event instead of carrying out another activity in the area, and time-switchers as those who had planned to visit the territory anyway and who changed the date to coincide with the celebration of the event. In this way, the study only considered non-resident attendees whose main motivation was to attend the event. The premise of economic impact establishes that they are the only attendees that inject

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new money into the territory (Preuss et al., 2010). To make a correct estimate of the direct economic impact of the event, only the expense of non-resident attendees whose main motivation for traveling to the host territory is to attend the event should be considered.

The second part of the survey asked questions about the place of accommodation, days spent in the city, length of stay, and activities carried out in addition to attending the event. However, the most important aspect of this section was the identification of the expense made by the attendees. Specifically, it asked about the amount of money the respondent intended to spend. Three types of distinct questions were proposed to handle the data as rigorously as possible. First, the respondent was requested to classify the expenditure (accommodation, maintenance, tourism, transport, shopping, others). If unable to distribute the expense, the respondent must state an amount. If no response was given to this second option, the respondent was provided with spending intervals to indicate the one in which the expense was located. Finally, attendees who were staying in surrounding cities were asked if they had any expenses in the host city.

The third part focused on knowing the touristic side of the respondents. Although this was not quantifiable, it served as a guide for a potential future impact on tourism and consequent economic performance. The survey inquired whether respondents had ever visited the city before and asked them to assess how satisfied they were with the image of the host city, the event, and the organization of the event.

The fourth part included sociodemographic aspects such as age, sex, profession, education level, and annual individual income. This section also requested the number of people accompanying the respondent.

A total of 537 people were surveyed (282 at the kickboxing championship and 255 at the basketball championship). This implies a 5% margin of error at a 95% confidence level. To guarantee survey randomness, the interviewers address one out of every three people in each area at different times throughout the day.

Out of the total surveys conducted at both events, 95.53% of the attendees were non-residents, and the remaining 4.47% were residents. At the kickboxing championship, casuals and time-switchers represented 0.74% of the sample. In the case of the basketball championship, no attendee of this type was identified.

ResultsThe attendances at the basketball and kickboxing championships were an estimated 845 and 668 spectators, respectively. In the first case, the attendees were counted at each match of the first qualifying round (this was possible because at each match the number of attendees did not exceed 60 persons, and they did not repeat their attendance until they began the following rounds). In the case of the kickboxing championship, the total number of attendees was provided by the orga-nizers of the event, considering ticket sales. The resident spectators whose expenses cannot be considered were deducted from these data. In addition, in the case of kickboxing, the percentage of casuals and time-switchers (0.74%) was also deducted. The final result was a total of 809 spectators with an economic impact for the basketball championship and 632 for the kickboxing championship.

Regarding expenses, the average expenditure made by attendees was determined for each event. Given the three alter-natives provided as an answer, a joint response rate of 83.3% was obtained for both events. Both the average expenditure data and the number of attendees were estimated for three distinct cases (Table 1). The first was the number of attendees staying in the host city (Marin). Second were attendees who did not spend the night on occasion of the event. These were attendees who returned to their place of residence on the same day and made their expenditure in the host city. Third were the attendees who stayed in neighboring cities.

The data collected in the survey showed that attendees not staying in the host city practically performed no spending in it. This circumstance responds to what Daniels (2007) explained using the central place theory. This theory explains that those attending an event turn to the most developed city to carry out tourism, shopping, or catering activities even if it is not the venue of the event. This situation is much more pressing when the lodging capacity of the host city is greatly limited.

As shown in Table 1, the basketball championship generated a total money injection of over €221,000. However, only €39,000 remained in the host city. The kickboxing championship reflected a similar situation in which only €17,000 of the total injection of €94,000 remained in the host city.

Most of the money injection went to the city of Pontevedra. This city is a developed urban area closest to the host town (about 9 km) with a population of 84,830 inhabitants (INE, 2021). For both championships, the average expenditure

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generated by spectators staying in the host city was also identified as being lower than the average expenditure generated in the surrounding areas.

The entities organizing the events provided data on participant expenditure. As was the case with spectator spending, the expenses incurred by resident teams or athletes was discarded. In the case of the kickboxing championship, a total of

Table 1. Estimated Number of Attendees with Impact and Average Expense

Kickboxing Championship Basketball ChampionshipNo. of

spectatorsAverageexpense/ person

Initial injection of


No. of spectators

Averageexpense/ person

Initial injection of


Stop-over in host city 110 131.82 14,500.2 119 282 33,558

No stop-over* 59 49.36 2,912.24 163 38.2 6,226.6

Stop-over in other areas 463 167.54 77,571.02 527 343.9 181,235.3

Total 632 94,983.46 809 221,019.9

Source: Authoŕ s estimates

664 non-resident participants (athletes, coaches, officials) attended. Marin hosted 178 participants who stayed overnight in Marin and generated a direct income of €20,730. Meanwhile, the remaining 486 participants generated an economic impact of €56,599 in the surrounding areas. In the case of participants, the expenses data were provided by the organiz-ers. As the participants were minors, the management of their expenses (travel, food, and accommodation) was carried out directly by the federations. In the case of the basketball championship, the attendance of 31 non-resident teams represented a total of 480 participants including athletes, coaches, and officials. The economic impact of six out of the 31 non-resident teams hosted in Marin was €7,500. The rest of the teams, housed outside of Marin, generated an economic impact of €45,000.

The spatial distribution of the economic impact shows that 18% of the expenses of the spectators (of each event) had a direct impact on the host city (see Table 2), which means that the remaining 82% was leaked. On the other hand, a respective 14% and 27% of the expenses generated by the participants in the basketball and kickboxing championships affected the town.

Table 2. Geographic Distribution of the Economic Repercussion (%)

Basketball Championship Kickboxing ChampionshipHost city Other areas Host city Other areas

Spectators 18.0% 82.0% 18.3% 81.7%

Participants 14.3% 85.7% 26.8% 73.2%

Source: Authoŕ s elaboration

We also identified differences in the distribution of expenditure among the different concepts. This situation mainly occurred in the basketball championship, where attendees spent an average of 4.5 days in the town for the event and were allowed more time to perform other activities given the configuration of the event. Thus, spectators staying in Marin dedicated 71% of their expenditure to accommodation and food. By contrast, those spending the night in the rest of the areas only dedicated 56% of their expenditure to accommodation and food. This allowed them to increase their spending on shopping, leisure, or tourism.

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The host town assumed the cost of the events in addition to some other expenses such as required medical services. This represented a total local money outflow of €25,438. The final result of the direct impact was positive, representing an economic repercussion for Marin (between the two events) of over €80,000 and a leak of €300,000.

Once the leak of money from the host city was identi-fied and estimated, the results of the exploratory analysis were determined. First, the results of the linear regression model are explained (see Table 3). We present three mod-els explaining the final expenditure of the attendees. We were interested in analyzing if the fact that the attendee will produce leakage will increase the expenditure. We controlled for different factors such as the event, if the attendees were from the region, the sex, the age, whether the attendees were repeating visits to the town, the days of stay, how often the attendees were in the town, whether they undertook other activities, and alternatively, in mod-el one, the level of education, in model two, the level of income, and in model three, both.

Analyzing the total expenditure of attendees at both events and regarding the variable of interest in this study, we observed that those attendees who created leakage in-creased the final expenditure by slightly over 30%. In oth-er words, those that produced leakage of money expended around 30% more than those that expended only in the city. This is reasonable as it could be expected that those that stayed in more than one place—at least for the event in one city and to overnight in another—would spend more.

Commenting on some of the other control variables, we can say that attendees from outside the region (Galicia) expended around 80% more than those from the home re-gion. This is consistent in the three models and seems to be reasonable as visitors from other regions usually spend more time and plan other activities.

Surprisingly, we found a marginal effect of gender where women spent nearly 12% less than men. However, this effect was not consistent in all models. Regarding age, there was a clear inverted u-shape relationship with a turning point around 48 years. This means that those who were younger and older than that age spent less.

The level of education results were not significant. Meanwhile, considering the income level, there was no significant difference in the final expenditure for those that reported an income less than €10,000. However, those with an income between €20,001 and €30,000 spent 23.7% more than the reference group (€10,000‒20,000), and those with an income higher than €30,000 spent 23.1% more if we included the level of education in the model.

One day more in the city increased the final expendi-ture of the attendee by around 24%. If the visitor did not

Table 3. Influence of Leakage in Final Expenditure




Leakage .317*** (.084)

.331*** (.091)

.347*** (.092)

Base event: Basket

Kickboxing -.076(.092)



From Galicia -.815***(.112)



Gender (Male) -.111*(.065)



Age .08*** (.026)

.058** (.029)

.068** (.03)

Age2 -.001*** (0)

-.001** (0)

-.001** (0)

Base education: elementary

High school -.075(.156)


Vocational training -.162(.154)


Higher education -.129(.151)

-.267 (.186)

Previous years .084(.068)



Days city .239***(.029)



Times in the city .017(.026)


.023 (.027)

No other activities -.296*(.161)



Complementary act .098(.081)



Family activities -.036(.15)



Gastronomic activities .275***(.09)



Touristic activities .008(.08)



Other activities -.022(.181)



Base income: (€10,000‒20,000)

less than €10,000 -.009(.134)


€20,001 and €30,000 .19** (.09)

.237** (.095)

more than €30,000 .178** (.09)

.231** (.096)

_cons 2.073***(.658)



Observations 434 377 376

R-squared .567 .588 .593

Notes: Standard errors are in parentheses. *** p < .01, ** p < .05, * p < .1.

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plan other activities, the final expenditure decreased close to 30%. This is something that could be expected. Finally, those who planned to enjoy the gastronomy of the town increased the final spending by around 29%.

The exploratory analysis continues showing the re-sults of the logistic regression model presented in Table 4. Considering the probability of having a leak, it seems clear that some events that can produce higher leaks than others. In the case analyzed, the kickboxing champion-ship had a probability 1.6 times higher than the basketball championship. While this was only a case study, it brings a clear idea that the kind of event should be considered when analyzing leakages. Considering that in kickboxing the competition is individual and that basketball is a team competition, maybe the differences between events could be due to the individual or team nature of the competition. This could be explored in future studies.

The second point to remark on is that those attendees who were closer to the area where the event was hosted would have less probability of producing leaks. That was the case of those attendees from Galicia, who had close to 1.8 times less probability of creating leaks than those from outside the region.

If the visitors planned to expend more days in the city, the probability of generating leaks increased by around 40%. This sounds reasonable as these types of visitors may look for alternative activities that are easier to find in some places around the area hosting the event.

The final expenditure results were significant in the first model, which considers age having a linear relation-ship. However, the second model shows that age presents a quadratic relationship, with an inverted u-shape, with a turning point close to 50 years. This means that it can be expected that younger and older visitors would have less probability of generating a leak. Considering the final ex-penditure of those aged between 40–60 years (see Figure 1), it seems logical that in the first model the final expen-diture results were significant but that by introducing the quadratic relationship, the significance disappeared.

Discussion and ConclusionsThis paper analyzed the geographical distribution of

spending by attendees at two sporting events. Although we presented the results of a couple of events, without the intention of generalizing, some lessons can be drawn on aspects to consider in other events with similar characteristics. Those attending the sporting events organized in Marin were forced to stay in the areas surrounding the town. This occurred because of the town’s limited accommodation capac-ity and originated what Daniels (2007) called false excursionists. False excursionists may be identified as sports tourists

Table 4. Factors that Increase the Probability of Leakage



Base event: Basket

Kickboxing 1.618*** (.33)

1.663*** (.339)

From Galicia -1.707***(.343)


Gender (Male) .001(.242)


Base education: elementary

High school -.082(.556)


Vocational training -.111(.545)


Higher education .348(.528)


Prev. years .186(.258)


Days city .401*** (.125)

.448*** (.126)

Final expenditure .002**(.001)


Times in the city -.072(.081)


Age -.013(.016)


Age2 -.003*** (.001)

_cons -.764(1.12)


Observations 504 504

Pseudo R2 .252 .277

Notes: Standard errors are in parentheses. *** p < .01, ** p < .05, * p < .1.

Figure 1. Distribution of expenditure by age


Notes: Standard errors are in parentheses. *** p < .01, ** p < .05, * p < .1.

Figure 1 Distribution of expenditure by age

0 0 0 0 ge




0 5






20 40 60 80AGE







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who stay in a neighboring city and visit the host city to attend the event. When estimating the economic repercussions of the event, those attendees are considered excursionists because they spend part of the day at the venue to attend the event but stay in another city.

This situation produced a leak of direct economic impact of over €300,000 in the studied case. The local administration assumes all the costs and local preparations for the event. Meanwhile, nearby cities receive an injection of money for which they have made no investment.

This research yields three main findings. First, that the most developed urban center obtains a higher level of impact despite not hosting the event. Additionally, the level of impact in the host city was below 20%. As shown in the work of Lee et al. (2017), these results are very different to those obtained when the host is a developed urban area. Second, it was identified that attendees who produced money leakage spent around 30% more than those who spent only in the city. Third, it was determined that the type of event, the origin of the attendees, the length of stay, and age were variables that influenced the generation of a greater or lesser first-round leakage.

Despite the leakage of money, the event benefitted Marin. However, it makes no sense to propose a strategy to attract sporting events in this situation. The present case would require developing a joint strategy with nearby towns of a similar size. This would enable them to develop a single brand and generate an agenda of sporting events throughout the year to share costs and investments and, ultimately, attract tourists and make an economic impact for the entire area. Daniels (2007) and Lee et al. (2017) already proposed cooperation between regions or cities for mega-events and developed urban centers. In the case of small cities—given their limited lodging capacity—this is not just an option but rather a decision of vital importance.

Our work contributes new evidence concerning the economic impact of sporting events on small cities. We were faced with a key aspect for estimating economic impact. Not only should economic impact studies assess aggregate results, but they should also identify geographical distribution. Ignoring this aspect will lead to overestimation of the economic repercussions of an event. This paper also points out the usefulness of this type of information for the development of city marketing strategies based on the attraction of events. In general, it identifies the location of a city and its development (supply of goods and services) as key elements for retaining potential income from sports’ tourism. As a main limitation, it should be noted that, with the available data, it was not possible to estimate the leakage generated per person. In future works, it would be interesting to explore in more detail the leakage per person in addition to analyzing how the particular characteristics of the events can influence first-round leaks.

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Volume 18 • Number 1 • 2023 • IJSF 53

Gibson, H. J., Kaplanidou, K., & Kang, S. J. (2012). Small-scale event sport tourism: A case study in sustainable tourism. Sport Man-agement Review, 15(2), 160‒170.

Gibson, H. J., Willming, C., & Holdnak, A. (2003). Small-scale event sport tourism: Fans as tourists. Tourism Management, 24(2), 181‒190.

Herrero, L. C., Sanz, J. Á., Devesa, M., Bedate, A., & Del Barrio, M. J. (2006). The economic impact of cultural events: A case-study of Salamanca 2002, European Capital of Culture. European Urban and Regional Studies, 13(1), 41‒57.

Lee, C. K., Mjelde, J. W., & Kwon, Y. J. (2017). Estimating the economic impact of a mega-event on host and neighbouring regions. Leisure Studies, 36(1), 138‒152.

Davies, L., Coleman, R., & Ramchandani, G. (2013). Evaluating event economic impact: Rigour versus reality? International Journal of Event and Festival Management, 4(1), 31–42.

Malchrowicz-Mośko, E., & Poczta, J. (2018). A small-scale event and a big impact: Is this relationship possible in the world of sport? The meaning of heritage sporting events for sustainable development of tourism: Experiences from Poland. Sustainability, 10(11): 4289.

Matheson, V. A. (2006a). Is smaller better? A comment on “comparative economic impact analyses” by Michael Mondello and Pat-rick Rishe. Economic Development Quarterly, 20(2), 192‒195.

Matheson, V. A. (2006b). Mega-events: The effect of the world’s biggest sporting events on local, regional, and national economies College of the Holy Cross, Department of Economics Faculty, 7 Research Series, Paper No. 06-10.

Mondello, M. J., & Rishe, P. (2004). Comparative economic impact analyses: Differences across cities, events, and demographics. Economic Development Quarterly, 18(4), 331‒342.

Porter, P. K., & Fletcher, D. (2008). The economic impact of the Olympic Games: Ex ante predictions and ex post reality. Journal of Sport Management, 22(4), 470‒486.

Preuss, H., Könecke, T., & Schütte, N. (2010). Calculating the primary economic impact of a sports club’s regular season competi-tion: A first model. Journal of Sporting Science and Physical Education, 60, 17–22.

Salgado, J., Barajas, A., & Sánchez, P. (2018). Determinants of the spending of sporting tourists: The case of attendees at professional basketball. European Research on Management and Business Economics, 24(3), 168‒176.

Salgado, J., Sánchez, P., & Barajas, A. (2020). Differences in the behavior of tourist expenditure according to the sports category of the event: Evidence for a master and absolute championship. In Á. Rocha, A. Abreu, J. Vidal de Carvalho, D. Liberato, E. A. González, & P. Liberato (Eds.), Advances in tourism, technology and smart systems (pp. 357‒367). Springer Singapore.

Salgado, J., Sánchez, P., Pérez, M., & Barajas, A. (2018). Economic valuation of a medium-sized sporting event: Impact of the Spanish Swimming Championship. Journal of Physical Education and Sport, 18, 1349‒1355.

Sánchez, P., Salgado, J., Rodríguez, A., & Barajas, A. (2016). Impacto económico del XXI Campeonato de España “Open” de Invierno Master de Natación de Pontevedra 2015. SPORT TK-Revista EuroAmericana de Ciencias del Deporte, 5, 169‒180.

Scott, A. K., & Turco, D. M. (2009). Distinguishing event spectator spending profiles: Projected impacts of the 2009 US Open Golf Championship. Choregia, 5(1).

Siegfried, J., & Zimbalist, A. (2000). The economics of sports facilities and their communities. The Journal of Economic Perspectives, 14(3), 95‒114.

Taks, M., Christine Green, B., Chalip, L., Kesenne, S., & Martyn, S. (2013). Visitor composition and event-related spending. Interna-tional Journal of Culture, Tourism and Hospitality Research, 7(2), 132‒147.

Taks, M., Chalip, L., & Green, C. (2015). Impacts and strategic outcomes from non-mega sport events for local communities. Europe-an Sport Management Quarterly, 15(1), 1‒6.

Taks, M., Kesenne, S., Chalip, L., & Green, C. B. (2011). Economic impact analysis versus cost benefit analysis: The case of a medi-um-sized sport event. International Journal of Sport Finance, 6(3), 187‒203.

Veltri, F. R., Miller, J. J., & Harris, A. (2009). Club sport national tournament: Economic impact of a small event on a mid-size com-munity. Recreational Sports Journal, 33(2), 119‒128.

Ziakas, V., & Costa, C. (2011). The use of an event portfolio in regional community and tourism development: Creating synergy between sport and cultural events. Journal of Sport & Tourism, 16(2), 149‒175.

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