ABSTRACT
This study aims to analyze the economic ripple effect of a sport event on the city that hosts the event using the Simple Location Quotient method (SLQ) and an inter-regional-industry relation table. The SLQ is one of the most widely used economic base analysis methods, which compares the local economy indices to those of a reference site to identify specializations in the local economy. For this analysis, this paper investigates the expenditures of the participants on a youth soccer tournament hosted by the city of Gyeongju, South Korea using an interview method. The results of the analysis show that the sport event has contributed tens of millions of US dollars to the regional economy. In addition, the value added to the city consists of the wages of the employees, the business surplus, the consumption of fixed capital and the net production cost. Also this paper provides a few important implications on the method used to investigate the economic ripple effect including choices of survey subjects and economic matrices.
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DOI: 10.3923/jas.2013.546.554
URL: https://scialert.net/abstract/?doi=jas.2013.546.554
INTRODUCTION
Recently, sports have been classified as a high value-added industry, rather than as mere health promotion or a leisure activity. Additionally, sports have become a business that seeks profit maximization through investment. In other words, sports, which used to be only a means to provide a visual service, have taken an unparalleled position in business. According to Crompton (1995), sports teams and events are deemed business investments, which promote the regions that support them on an individual or group level. Among the many reasons for supporting sports, to enhance the economy is perhaps the foremost reason for investing in professional athletes, university facilities and sporting events. Regional sporting events encourage tourism that produces profits for the hosting regions. On the individual level, for an athlete or a business person, it may be easier to measure such a return on investment but on a larger scale, in terms of a region, the estimation of a sports investment return can be difficult in such a broad public environment. Hence, there is a need for a suitable analysis method.
Crompton (1995) described the reasons for regional investments in sports in a sequence. First, it provides taxes from the people of the hosting region in the form of a fund. Local councils can then use the fund in subsidizing sporting events or facility developments. These events draw people from other regions, encouraging them to spend inside and outside the facilities. Such money from other regions generates profits for the council and promotes employment. Thus, the cycle is completed; the local residents come to consider generating funds their own responsibility and in return, receive benefits in the form of profits or employment.
Turco and Kelsey (1992) defined the economic effect of sports investment as the net profit from the expenditures within sporting events and facilities and Crompton (1995) reported that the purpose of the analysis of the economic effect is to measure the total economic profit.
The economic ripple effects of such sporting events have drawn the attention of local authorities and corresponding research is actively ongoing (Ahlert, 2001; Hinch and Higham, 2001; Arthur and Andrew, 1996).
According to Hunter (1988), in practice, the analyses of these economic effects are politically motivated to justify the cost of public officials in sports organizations or the costs of the regional host councils. Also, Crompton (1995) suggests that public officials, under pressure to explain the benefit of the tax distribution, are interested in the economic analysis as a way to make a point about government subsidies to private sports enterprises and show the public benefit. He also suggests that the development of such research in sports organizations shows that these investments are valuable to public officials and taxpayers and are a financial asset to the host region.
Some research papers (Fimrite, 1992), advocate the subsidizing of sporting events by local authorities, mentioning the economic profit, while other researchers like Baade and Dye (1990) question the viability of the economic figures they support. Especially, Baade (1987) who spotted that regional public servants and beneficiaries of the subsidies purposefully perform their research with an aim to exaggerate economic figures in order to persuade voters to support these investments.
Lipsitz (1984) and Baade and Dye (1990) claim that the analysis is conducted freely, independent from such pressure, but Dunnavant (1989) points out that consultants are hired in order to tell the clients what they want to hear. Further, Crompton (1995) and Turco and Kelsey (1992) all indicate that the credibility and objectivity of research papers dealing with these economic effects are not reliable due to differences in estimation methods. Such problems arise when estimating the ripple effects through indirect measurement of the total consumption of all potential participants before the sporting events occur (Crompton 1995; Turco and Kelsey, 1992). Their research indicates that an objective and quantitative estimation is lacking for the ripple effect analysis, because the only analysis done for such effects has been for the Olympics, the World Cups and other so-called mega-sporting events.
According to Fletcher (1989), in terms of methods to analyze the effect, there are economic-based models, income-expenditure models and inter-industrial models. The income-expenditure model is used for small-size economy analysis, while the inter-industrial model is useful for nationwide analysis. It turns out that an inter-industrial model developed by Leontief (1966) not only analyzes the correlation between industries, but also the correlation of the economic effects of the total consumption of individuals to industries, including the measurement of profits from people from other regions.
The methods for producing an inter-regional-industry table are, in general, the survey method and the non-survey method. In the first case, the advantage is high accuracy and the drawbacks are time and money. The second method induces an inter-regional-industry table from a national inter-industry table combined with regional output data and offers an advantage in terms of time and money. For this reason, a non-survey method was used for this study. Of non-survey methods, there is a Location Quotient method (LQ), RAS method and Supply-demand Pool Method. Among these methods, LQ is widely used in Korea due to practicality and efficiency (Kang and Kim, 2010; Lee, 2008, 1997; Lee and Choi, 2003).
For an objective and quantitative estimation of the effects of nation-wide sporting events held by local authorities, actual participants, rather than potential participants, were used in the preparation of the list of input data and the analysis of the effect is based on a regional inter-industry table rather than a national one implementing LQ. The purpose of this research is to analyze the regional industrial structure via an inter-industry relation model and to examine the ripple effect of nationwide sporting events held by local authorities on regional economies (based on a regional inter-industry table) using SLQ(simple location quotient method).
MATERIALS AND METHODS
This research considers the delegations and visitors of the 2012 Hwa-Rang-Dae-Gi National Elementary School Youth Soccer Tournament, which was held in Gyeongju, South Korea, as the data population, 186 delegations of 455 teams of 186 schools and 450 visitors out of 636 are chosen to be the sample target. The self-administration survey method was conducted by teams of 20 professionals who, with two to a team, were sent to the Gyeongju public stadium, a soccer park and the Alcheon stadium to conduct one-on-one, face-to-face interviews. As for the response rate, 175 delegations (94.0%) and 415 visitors (92.7%) responded, a total of 590 people (92.7%). Of these, survey papers of 145 delegations (82.8%) and 378 visitors (91.0%) were selected as valid, equaling a sampling size of 532 (88.6%).
This study focuses on ensuring the objectivity of the expenditure estimation of the nationwide tournament. In order to achieve this, the data from Gyeongju-si, Korea Youth Football Association, soccer institutes of Gyeongju-si, are included to avoid any omission and to figure out the expenditure pattern of delegates and visitors, the cost of lodging, transportation, food and beverage, shopping and admission. Moreover, the regional industrial structure is analyzed through a regional inter-industry model; the ripple effect of the tournament is analyzed based on the 2005 regional inter-industry relation table published by the Bank of Korea in 2009 and the region of Gyeongju is analyzed in detail using SLQ.
Production estimations: The LQ of Gyeongju is classified by industries on the regional inter-industry relation table and input in GRDP standard is shown Table 1.
Input coefficient estimations: The Location Quotient (LQ) is a method to estimate the relative importance of a certain regional industry to the economy of the entire nation. It is common to produce input coefficients by using the national inter-industry relation table, but in this analysis of the tournament, the regional inter-industry relation table is used, so the equation for the LQ is as follows:
Where:
Xri | : | The number of employees of i industry in r region (the number of enterprises, etc; the rest the same) |
Xr | : | the number of employees in r region |
Xni | : | The number of employess of i industry in Gyeongbuk |
XN | : | The number of employees in Gyeongbuk |
Table 1: | Gross products of Gyeongbuk and Gyeongju (in% and US dollars) |
The regional gross products are priced in the standard of 2009, Gyeongsangbuk-do |
Table 2: | Number of employees of Gyeongbuk and Gyeongju regions (in US dollars) |
Manufacturing industry is listed in detail, Gyeongsangbuk-do |
As a result of the calculations, Table 2 shows that LQs of Gyeongju were high in the following industries: mining, lodging and foods, real-estate and leasing and business service. LQ computes the regional input coefficients by deducting the amount of transfer from Gyeongbuk to Gyeongju in order to discern the input structure among industries, assuming the regional input structure corresponds to that of Gyeongbuk.
Single-region I/O analysis model: Industries within a national economy buy goods and services as raw or supplementary materials, or get involved in activities such as consumption, investment and export. This model estimates such relationships between industries, quantitatively. The regional inter-industry relation table illustrates the trade details by industry by partitioning a nation into regions to reflect their trade patterns and different production technology structures.
Input system of a regional inter-industry relation table:
The input coefficient is intermediate input, such as the purchase of raw materials and fuel from other industries divided by the total input, which indicates the intermediary goods needed per one unit of output of each industry.
Producing the input coefficients: When (a11, a21, , an1) are the details of the first industry sector, (X11, X21, , Xn1) divided by the total input, X1, is the input coefficient that describes the size of industries needed for producing one unit of output in the first industry. The equation is as follows:
The measurement of the amount of induced production of each industry, directly and indirectly, from a unit increase of final demand, will be called the analysis for production inducement effects.
Producing the production inducement coefficient: From the supply-demand relationship of the outputs of each industry on the inter-industry relation table, it can be noted that when the sum of intermediate consumption and final consumption is subtracted by the imports, it coincides with the total production, so equations such as Eq. 1 as follows can be derived:
(1) |
Since the input coefficient is obtained from intermediate inputs divided by the total production, the Eq. 1 can be transformed as follows:
(2) |
There are three unknowns in Eq. 2, X1, X2 and X3. Since it is a linear simultaneous equation of three unknowns, it can be solved for the unknowns and it can be shown in matrix form as Eq. 3 as follows:
(3) |
Expressing Eq. 3 neatly is as Eq. 4 follows:
(4) |
where, A is the input matrix, X is the total production vector, Y is the final consumption vector and M is the imports vector.
Expanding Eq. 4 to solve for X is as follows:
(5) |
Here, the matrix, (I-A)-1 , is called a production inducement coefficient, where I is an identity matrix. With the production inducement coefficient set, X can be obtained easily and it directly or indirectly varies according to Y and M. The coefficient can be defined as, (I-A)-1 = R and by multiplying it the final consumption can be calculated. Also, by multiplying the value-added inducement coefficient and employment inducement coefficient to the production inducement coefficient, the effects of value-added inducement and employment inducement can be obtained. Thus, the ripple effect can be thoroughly analyzed by looking at the obtained value-added inducement coefficient and employment inducement coefficient.
ANALYSIS
Analysis of the ripple effects
Methodology and structure: The ripple effects to the regional economy are analyzed based on the 2005 regional inter-industry relation table from the Bank of Korea. As a methodology to classify the industries, a 78-sector table (the Korean Standard Industrial Classification Table) is used to select the input sectors. Production inducement cost is the sum of added value inducement cost and intermediary input cost, with this representing a multiplier effect.
The added value cost consists of the wage of employees, operating surplus, consumption of fixed capital (depreciation) and net production cost:
Calculating the inputs: The input is obtained by adding up the investment costs (event supporting cost and facility investment cost) and the total expenditure of participants (the delegations, cheering squad, parents and normal visitors). The operating cost of the tournament is estimated to be $1,004,436. Of this, $967,169 is from the event supporting costs of the Korean Youth Football Association and the Gyeongju football institute; $28,393 is from the wages of the Gyeongju health center and the Gyeongju volunteer center workers; $8,873 from the direct support of Gyeongju. Forty million won towards building the light shields and the wages for the maintenance is included in the event-supporting cost calculation minus one billion won for facility materials.
The expenditure of the participants is the sum of the expenses spent by parents, the cheering squad and the delegations and this, as shown in Table 3, is estimated to be $20,797,692. To begin, the expense of the delegations is calculated by multiplying an individual expense per day by the total number of delegations. To enhance accuracy, the list of participants was obtained directly from the Gyeongju Football Association for the calculation. In the list, the number of players in the U-12, U-11, U-10 categories were 3,813, 2,311 and 1,152, respectively. In total, the number of players was 7,276 (including the delegations). Excluding for the repeated players, the size of player group was 6,876; they stayed 10.3 days on average; therefore, the size of the accumulation is 60,983. The expenditure of visitors (including parents and cheering squads) is calculated by multiplying the individual expenditure by the total number of visitors as shown in Table 4. The data is obtained from Gyeongju, which frequently recorded the number of visits to practices, the opening ceremony and each stadium, with the average stay calculated as 5.9.
Deciding the total expenditure: The total expenditure (of input sectors) is estimated to be $20,797,692, which consists of the expenditure of the visitors, the cost of supporting the events and the investment in the facilities as shown in Table 5. Specifically, the total wages spent for the tournament is estimated at $299,733, including the extra pay-per-game for coaches and referees and the travel expenses for coaches, supervisors and referees. The wage rate is taken from the report, 2010 Total Expenditure of Households by Purpose (Bank of Korea) by extracting the component ratios of wages for each category. The component ratios are obtained by averaging the values of the regions of Daegu and Gyeongbuk:
where, Di* is the expenditure per category, W* is the total wage and Ri* is the ratio per category. Sixteen categories were selected to be input categories by appropriating the total expenditure per category.
Analyzing the economic ripple effects
The ripple effect on the regional economy
Production inducement cost: The ripple effect can be calculated using an impact factor (backward linkage effect) and sensitivity index (forward linkage effect) for input cost as presented in Table 6. The impact factor is a coefficient that represents an impact on all industries with a unit increase in production of an industry. That is to say, it is a coefficient that quantifies the capital goods purchase level of an industry and distinguishes that from other industries. It is called the backward linkage effect:
Table 3: | Individual expenditure of the player groups |
Table 4: | Visitors (parents and cheering squad) daily expenditure |
Table 5: | Setting up the input categories (in US dollars) |
Table 6: | The ripple effect to a regional economy (In US dollars; people) |
With the price standard of 2012 |
Table 7: | Structure of value added. (In US dollars; people) |
With the price standard of 2012 |
The index of the sensitivity of dispersion is a coefficient that quantifies the level of impact on a certain industry when the final consumption of production of the entire industry increases by one unit. It is called the forward linkage effect:
It is estimated that the production inducement is $29,812,777; value-added inducement is $12,034,605 and employment inducement is 722 people in the second row of Table 6.
Value-added inducement: The compensation of employees includes social security, annuity and insurance that employers pay for as well as wages in cash or in kind and is estimated at $5,241,348. The operating surplus, perceived as adding value as a reward for profit-pursuing enterprises participating in production activities, is estimated at $4,193,433. The depreciation of fixed capital is the reduction in value of fixed capital assets due to deterioration or daily damages and it is estimated at $1,016,858. The taxes on production represent those imposed on producers for production, purchase, or use of goods and services. In practice, producers include such taxes in the price of their products. Here, the tax estimation is $1,583,850. The details on the structure of value added are displayed in Table 7.
The ripple effects on each industry: Generally, industries with high input show a high ripple effect. This is seen in the industries related to tourism such as restaurant and lodging, water and air transportation, amusement services, track and field sports industries and wholesale and retail industries.
General ripple effect: In order to analyze the ripple effect of the tournament, 16 input sectors were selected among 78 sectors in a regional inter-industry table. The input consisted of event supporting expense ($1,128,660) and the visitors' expenditure ($20,797,692) amounting to $21,926,353. The economic ripple effect was composed of production induction ($30,049,689), value-added induction ($12,034,605) and employment induction (722 people). Of the value-added induction, $5,243,123 was from the compensation of employees; $4,193,433 was from the operating surplus; $1,016,858 was from the depreciation of fixed capital and $1,583,850 was from the net production tax. It is common to extract information from broadcast media, advertisements and other marketing campaigns when analyzing the economic ripple effects of a single event of culture, sports, or art, but the size of the tournament was negligible in terms of awareness so such information was excluded in the analysis. However, in the input sectors, there is a broadcasting sector, so any such backward/forward linkage effects are well reflected. Production inducement does not only represent the business profit or deficit from the event, but the backward/forward linkage effects of the input as a whole (Table 8).
Table 8: | The ripple effect on each industry (In US dollars; people) |
DISCUSSION
The results of the analysis are as follows: First, in an attempt to objectively estimate all the expenses, it was assessed that $21,399,290 was spent by the delegations and visitors (parents and cheering squad) and $1,137,533 was spent on the tournament operation. Second, the ripple effect of the tournament was estimated at a total of $30,049,689; the value-added inducement was $12,034,605 and the employment inducement was 772 people. Third, of the value-added inducement, the compensation of employees, the operating surplus, the depreciation of fixed capital and the net taxes on production was estimated at $5,927,240, $5,241,348, $1,016,858 and $1,583,850, respectively.
In addition, since the average stay was 10.3 days for the delegations and 5.9 days for the visitors, the tournament can be deemed successful in terms of the length of stay. As Crompton (1995) pointed out, this event is a successful example in that it lengthened the stay, which directly affects total consumption and it implemented an encouraging strategy of consumption for a large number of visitors, maximizing the economic ripple effect. Also, the fact that $1,137,533 of tournament operating cost created $20,797,692 expenditure, which is more than eighteen-fold, is remarkable for a small-size regional city.
Although, the data for the delegations is enough for the research objective, as it was done through direct surveys based on the register, the data for the visitors might have been overestimated as the number of visitors was provided by Gyeongju. This research was not utterly independent in that the visitor information was not estimated scientifically enough. For this reason, Burgan and Mules (1992) claimed that ordinary visitors who are event participants should be excluded in their report, "Economic Impact of a Sporting Event." Despite such efforts for objectivity, many reports that analyze ripple effects are suspected of over calculating the impact by overestimating the expenditure variables. Therefore, a stricter standard should be implemented in estimating input variables. The analysis done in this study implies that the expansion of the implementation of SLQ (which is an indirect method of tabulating the inter-regional-industry relations table) needs to be based on the corresponding regional inter-industry relations table not the national inter-industry relations table and in order to maintain an objective view of the expenses of the consumers input, industries should be selected considering the expense of actual participants rather than potential participants using a direct survey method, not indirect method.
CONCLUSION
This research has analyzed the industrial structure of a region, via a regional inter-industry relation model, to assess the ripple effects of a nationwide sports event organized by local authorities (based on the 2005 regional inter-industry relation table, Bank of Korea, using SLQ. To achieve the goal, the ripple effects of a youth soccer tournament, held in Gyeongju in 2012, were analyzed. The delegations and visitors were chosen to be the population of the research in an attempt to avoid any omission and the sampling size was 636 in total, consisting of 186 delegations of 186 schools and 450 visitors; of them, 175 delegations (94.0%) and 415 visitors (92.7%) were included. 590 people (88.6%) in total participated in the survey and invalid surveys or those carelessly written were excluded. The final sample consisted of 145 delegations (82.8%) and 378 visitors (91.0%) a total of 532 people (88.6%).
The estimation of the ripple effect is summarized that the tournament has contributed $30,049,689 to the regional economy; the value added amounts to $12,034,605 with induced employment of approximately 722. The value added consists of the wages of the employees, the business surplus, the consumption of fixed capital and the net production cost. The delegations stayed for an average of 10.3 days, while the families and cheering squads stayed for an average of 5.9 days. Hence, the tournament is regarded as a successful sporting event for lengthening the average stay of the visitors.
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