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An Inquiry into Wage Discrimination Based on Nationality: The Case of the Korean Baseball Organization.

Introduction

Salary structure is an important economic research topic. Salary is an important compensation system that can incentivize valuable employees and is considered one of the best ways to retain talent and reduce turnover. Employers must design appropriate salary structures to incentivize employee performance. Employees who exhibit better work performance should be paid a higher salary, or else they may lose the inducement to exert themselves.

Considering that the salary structure affects employee performance, wage discrimination is a perplexing question. Salary discrimination occurs in an organization when persons A and B exhibit the same performance, but person A's salary is lower than person B's because of discrimination based on factors unrelated to performance. In such a case, person A has less inducement to work hard, which may result in a making less of an effort. Since Gary Becker published The Economics of Discrimination in 1971, economists have paid close attention to wage discrimination as a topic of discussion.

Professional sports are part of the market economy, and the salary structure of athletes is a target of labor economics research. In professional baseball, the difference in salaries among players is obvious. If wage discrimination exists in professional sports, will certain baseball players therefore have lower performance inducement? Will it lead to poor performance by the team overall? These questions demonstrate that wage discrimination is a topic worthy of more in-depth study.

There are very few studies related to wage discrimination in professional baseball in Asia. The only leagues that have been the subject of related research include the Nippon Professional Baseball League and Taiwan's Chinese Professional Baseball League. Studies have found evidence that wage discrimination occurs among players on the basis of race and nationality in professional baseball (Jane, 2012, 2013; Tsai, Lei, & Hsieh, 2012). While there is no wage discrimination based on race in professional sports in Japan and Korea, wage discrimination based on race does indeed occur in the sport of professional baseball in Taiwan (Jane, 2012). With respect to nationality, Taiwan's Chinese Professional Baseball League (CPBL), Nippon Professional Baseball (NPB), and the Korean Baseball Organization (KBO) all accept players from foreign countries. Each team has a certain proportion of foreign players. In the KBO, the international position players mainly came from Major League Baseball (MLB) in the US (40%) and the NPB in Japan (37%). The international pitchers mainly came from the NPB (39%) and the MLB (33%). The distribution is presented as a pie chart in Figure 1. These main international players' average salaries in the KBO are shown in Figure 2.
Figure 1. Distribution of professional baseball position players and
pitchers.

        Position Players  Pitchers

MLB            40%          33%
NPB            20%          24%
CPBL            3%           4%
Others         37%          39%

Note: Table made from pie chart.

Figure 2. Average salary of position players and pitchers by league.

                  Avg. Salary
        Position Players  Pitchers

MLB         24444.84      25274.41
NPB         23650.87      25621.04
CPBL        13378.1       24474.23
Others      20747.37      20064.1

Note: Table made from bar graph.


At present, only Japan and Taiwan have corroborated literature addressing wage discrimination based on nationality. However, there has been no study on the KBO. The purpose of this paper is to determine whether wage discrimination based on nationality occurs in the KBO to better understand the entire picture of professional baseball in Asia with regards to wage discrimination.

When the phenomenon of wage discrimination is observed, further research is important to develop an understanding of the causes and effects of discrimination. Becker (1971) proposed that the salary gap may arise from three types of labor market discrimination: employer discrimination, employee discrimination (peer discrimination), and consumer and government discrimination.

Many studies have demonstrated cases in which baseball players exhibited the same performance, but their race or nationality produced significant differences in salary. The cause of many such differences in salary was employer discrimination (Brown, Spiro, & Keenan, 1991; Christiano, 1988; Gwartney & Haworth, 1974; Hamilton, 1997; Hill & Spellman, 1984; Jane, 2012; Jane, Chen, & Kuo, 2013; Kahn & Sherer, 1988; Kanazawa & Funk, 2001). In the U.S. professional basketball league, the National Basketball Association (NBA), there have been basketball club owners who have made racist comments to the media, leading to player backlash.

Because there are few empirical studies on the phenomenon of salary differences caused by peer discrimination in the general labor market, there are also no empirical studies on employee discrimination in professional sports, which is perhaps due to the difficulty of obtaining information from qualitative interviews and the fact that objective data cannot easily be found to be used as evidence. If a coach or peer discriminates against a player, such discrimination may affect the player's amount of playing time and performance achievement data. Furthermore, discrimination generates psychological stress in the player. Consumer discrimination in professional sports is mainly shown by spectator behavior. Empirical studies demonstrate that a player's race or nationality will affect television broadcast ratings and the number of on-site spectators (Kahn & Sherer, 1988; Kanazawa & Funk, 2001; Pedace, 2008; Tainsky & Winfree, 2010).

In summary, the purpose of this study is to investigate whether wage discrimination on the basis of nationality--including both employer and consumer discrimination--occurs in the KBO.

Literature Review

Wage Discrimination in Professional Sports

In the field of North American professional sports, wage discrimination against minority athletes has been a major topic of discussion in recent decades. There is a substantial amount of literature derived from the empirical study of wage discrimination in professional sports, including baseball (Bodvarsson & Pettman, 2002; Christiano, 1986, 1988; Gwartney & Haworth, 1974; Hill & Spellman, 1984; Holmes, 2011; Raimondo, 1983; Scully, 1973), basketball (Brown, Spiro, & Keenan, 1991; Hamilton, 1997; Kahn & Sherer, 1988; Kanazawa & Funk, 2001), football (Preston & Szymanski, 2000; Szymanski, 2000; Wilson & Ying, 2003), hockey (Jones & Walsh, 1988; Longley, 1995), etc. Previous studies have found that baseball players may be paid a lower salary due to race or nationality factors.

Race, nationality, gender, and educational level, among others (Ahemd & McGillivray, 2015; Bertocchi & Dimico, 2014; Grosso & Smith, 2007; Mussida & Picchio, 2014), are the factors commonly considered in wage discrimination studies. Since professional sports began to flourish, scholars have paid considerable attention to the topic of wage discrimination based on race or nationality in professional sports. For instance, in North America, Pascal and Rapping (1970) analyzed MLB player variables such as performance, experience, race, and replacement earning and salary to investigate the link between race and wage and concluded that race did not significantly affect salary. However, Scully (1973) found that black athletes who performed exceptionally well did not receive an equivalent salary, showing that the phenomenon of wage discrimination indeed exists in the professional sports world. Gwartney and Haworth (1974) analyzed literature and found that teams that were willing to hire black baseball players were more competitive than other teams because the outstanding performance of the black baseball players could lead the team to more victories and gain more profit. Bodvarsson and Pettman (2002) further noted that greater racial diversity in baseball players within the league helped to reduce the phenomenon of wage discrimination. However, some studies showed that the effect of race on salary was not significant; instead, a baseball player's status as a free agent had a greater impact on salary (Christiano, 1986, 1988; Hill & Spellman, 1984).

Similar results were found in studies that evaluated the NBA. Using basketball players' salaries, performance statistics, and their team's home city as variables, Kahn and Sherer (1988) found that the salaries of black basketball players were 20% lower than those of white basketball players. Eschker, Perez, and Siegler (2004) compared data regarding race and wage from the periods 1996-97 and 2001-02. Using players' personal information, game records, and environmental information, such as the population of the cities where games were played, as variables, they concluded that the extent to which race affected salary was not significant. In contrast, a player's number of transfers, height, and position significantly affected the magnitude of his salary.

Compared with North America, there are very few studies on professional sports in Asia that directly correlate race or nationality with wage discrimination; however, studies have shown that a baseball player's salary level is not completely determined by performance. Jane (2012) studied whether there is wage discrimination based on race in the CPBL and found that the salaries of aboriginal baseball players were greater than those of comparable Taiwanese baseball players, indicating that reverse ethnic discrimination occurred in the CPBL. Tsai, Lei, and Hsieh (2012) compared the performance of foreign and native position players (defined as non-pitchers) in the CPBL. The results indicated that the proportion of efficient foreign and native position players in the CPBL was close to even, but the overall salary of native position players was only approximately 50% of the foreign position players' salary. In addition, Jane, Chen, and Kuo (2013) studied the phenomenon of wage discrimination between native and foreign athletes in NPB. Controlling for the baseball players' performance, ceteris paribus, the salary paid to international baseball players was still higher than that paid to domestic baseball players. Compared with Japanese baseball players, the international baseball players' bonuses outside of salary were, on average, from 54.7% to 57.3% higher.

To increase the competitiveness of teams, the level of game excitement, and the development of new markets, today's professional sports leagues have allowed foreign players to join. Strong foreign athletes are given considerably high salaries. This is one of the reasons North American professional sports have formidable strength. However, one also knows from past literature that foreign baseball players have created a new wage discrimination phenomenon. In Asia's professional baseball environment, in instances in which baseball players performed the same, nationality and race indeed had a significant effect. Therefore, in addition to focusing research on wage discrimination, there is also a need to further understand the causes of wage discrimination.

Causes of Wage Discrimination in Professional Sports

Becker (1971) proposed that a salary gap may arise from three types of labor market discrimination: employer discrimination, employee discrimination, and consumer and government discrimination. In current empirical research on professional sports, no study has confirmed that employee discrimination affects baseball players' salary; therefore, this study focuses on investigating employer and consumer discrimination in professional sports.

Employer discrimination is a scenario in which an employer hires a specific applicant or decides a specific employee's working conditions based on personal bias or preference regarding certain traits (Cheng, 2002). In addition, an employer typically pursues a maximum expected profit as a goal. Due to the high cost and long time needed to collect information on employees for the decision-making process, however, employers tend to adopt some general information as a basis for decision making rather than depending on actual data on the employee's personal ability or work performance (Blau & Ferber, 1992). In the professional sports realm, a considerable number of studies have found evidence that it is common for players who show the same performance to be paid significantly different salaries (Brown, Spiro, & Keenan, 1991; Christiano, 1988; Gwartney & Haworth, 1974; Hamilton, 1997; Hill & Spellman, 1984; Jane, 2012; Jane, Chen, & Kuo, 2013; Kahn & Sherer, 1988; Kanazawa & Funk, 2001; Pascal & Corporation, 1972). Such discrepancies may arise from a club owner's racial or nationality discrimination against players, or they may be due to admiration for a player's personal charm, etc. No matter the reason, a situation in which the performance is the same but the salary is different is unfair in terms of a normal labor market.

Consumers are another cause of discrimination. A consequence of consumer discrimination is an increase in price for consumers. If consumers continue to choose to pay a high price to enjoy the effects of discrimination, then consumer discrimination will generate a long-term and deep impact on the group that is being discriminated against. In the realm of professional sports, consumer discrimination is an important topic of discussion. In addition, the effects of consumer discrimination will be reflected in spectator behavior or related merchandise sales. Past studies have shown that television broadcast ratings might demonstrate consumers' racial discrimination against players. Controlling for other variables, the game broadcasts of teams with more whites have higher Nielsen viewing ratings. Foley and Smith (2007) found that if non-white players were added to teams located in cities with a mostly white population, the number of on-site spectators would decrease. Tainsky and Winfree (2010) showed that an MLB team in 2000 could generate higher profits and improve spectator attendance rates by adding a foreign baseball player to its roster.

Related studies show that consumer discrimination indeed occurs in professional sports, and the factors that affect the number of spectators or viewing rate include players' races and nationalities (Hamilton, 1997; Kahn & Sherer, 1988; Kanazawa & Funk, 2001; Nutting, 2012; Tainsky & Winfree, 2010) and the structure of the market (fans/population; Burdekin & Idson, 1991; Foley & Smith, 2007). In addition, other studies that focused on the impact of the number of spectators who entered the arena (but did not investigate the issue of consumer discrimination) noted that a baseball team's record would affect the spectators' level of willingness to enter the arena and watch the game (Chuang, Chen, & Yao, 2004; Einolf, 2004; Foley & Smith, 2007; Liao & Yang, 2010). Therefore, this study will also list the baseball teams' record (win rate) as a consumer discrimination factor for investigation.

Data and Methodology

Mincer-type equations (Jane, 2012; Mincer, 1974) are adopted in the setting of a basic model. The ordinary least squares (OLS) regression is listed in equation (1):

[mathematical expression not reproducible] (1)

where [[beta].sub.1] is the coefficient on nationality, P and C are vectors of coefficients on individual performance and player/team specific characteristics, respectively, and [[epsilon].sub.it] is the error term.

In the equation, Sal is a baseball player's salary, P is the baseball player's performance, C represents the characteristics of the player and team, and Nat is nationality. The vector of variable P includes a pitcher's Cy Young indicator, a position player's Silver Slugger index, and various other performance data, which is fully described in the following section. The vector of variable C includes free agency (FA), the tenure of the athletes (TEN), the tenure square term (SQTEN), age (AGE), age square term (SQAGE), height, height square term (SQ Height), weight, weight square term (SQWeight), body mass index (BMI), BMI square term (SQBMI), position (POS), and baseball team (TEAM). The Nat variable is a dummy variable. The native nationality is 0, and the foreign nationality is 1. If a baseball team adopted wage discrimination toward foreign baseball players, then [[beta].sub.1]<0; if a baseball team adopted reverse wage discrimination toward foreign baseball players, then [[beta].sub.1]>0.

One may argue that it is simply too unclear how past performance is being measured. Are foreign players that enter the KBO being valued based on their performance abroad or strictly based on their performance after they start playing in Korea? Either way there is a potential problem. Performance statistics from other leagues would not be comparable, owing to different quality levels and other factors. On the other hand, ignoring such past data would be misleading insofar as salaries offered would surely take into account past performance abroad. Therefore, in order to solve the potential problems, we collect data on where the foreign players are coming from and how much past experience they had abroad before entering the KBO. (1) The dummy of a foreign player with performance outside KBO (FP), past experience they had before entering the KBO (Seniority), and the interaction term (FP* Seniority) are included in the model.

To further understand if consumer discrimination exists in the KBO, we adopt the basic model of Jane, Kuo, Wu, and Chen (2010), which studied what factors affected attendance. The model can be specified as follows:

ATT = C + X[gamma] + u-- (2)

where ATT is each team's annual attendance numbers and X is a set of control variables. The control variables include the number of foreign baseball players, foreign pitchers, foreign position players, the team record, and the home city population. Furthermore, competitive balance within the league affected attendance over time (Joo & Oh, 2015). We use two variables (CB_sd and GINI) to proxy the degree of competitive balance added in the regressions. CB_sd is measured by the standard deviation of win percentages in a league at game t, and GINI is calculated as equation (1) in Joo & Oh (2015). We also add lagged ATT (lagatt) in our regression to capture the autoregressive property of attendance numbers.

Data Description

This study selected data regarding 775 first-team baseball players in the KBO, including 397 pitchers and 378 position players. From 2001 to 2010, there were a total of 10 baseball seasons. Panel data on eight baseball teams from each season was collected. In addition, information on annual attendance, team record, and home city population on the eight teams within the 10-year period was collected. The analysis data came from the official website and the 2001-10 official yearbooks of the KBO. The home city population data came from the official websites of Busan, Seoul, and Gwangju. In the process of reviewing profile completeness and correctness, it was found that the performance data on some players had an extremely large gap compared with other players, such as 0 appearances, playing only once, etc. Factors such as injury and being benched due to poor performance might have caused these players to have poor official pitching and hitting records, so this study deleted extreme values after collating the information. In terms of baseball players' salary data, the salary data collected had two units--the South Korean won (KRW) and the U.S. dollar (USD). First, the currency unit was unified according to the calendar year's KRW and USD exchange rate and all units were converted to "ten thousand KRW" (1 USD equals about 1,170 KRW). Each year's consumer price index (CPI) was different, which would also affect real wages. Therefore, South Korea's CPI value in 2010 was used as the standard for computing the players' real wages.

Pitchers

With regard to the data collected on KBO athletes in this study, of the 397 pitchers, a total of 1,470 observations of data were screened. There were 104 foreign pitchers and 1,366 observations on South Korean pitchers. Foreigners made up 7.07% of KBO pitchers. The mean annual salary for international pitchers was 215,520.7 thousand KRW, which was 2.55 times greater than that of local players. Pitchers' performances included games played (GPP), wins (Win), loses (Lose), complete games (CG), shutouts (SHO), innings pitched (inning), home runs allowed (HRA), bases on balls (BBP), and strikeouts (SOP). The averages of the pitchers' personal variables and performance data are shown in Table 2.

Position players

With regard to the data on the KBO position players, of the 378 position players, a total of 1,319 observations of data were screened. There were 49 foreign position players and 1,270 observations on South Korean position players. Foreigners made up 3.71% of KBO position players. The mean annual salary for international position players was 249,814.8 thousand KRW, which was 2.37 times greater than that of local players. Position players' performance included at bats (AB), runs (R), safety hits (SH), two-base hits (DB), home runs (HR), total bases (TB), runs batted in (RBI), stolen bases (SB), caught stealing (CS), and strike outs (SOB). The averages of the position players' personal variables and performance data are shown in Table 3.

Data on Attendance

Variable descriptions and corresponding statistics for the analysis of attendance are listed in Table 4. The average number of annul attendees was 476,037.3, and it ranged from 118,582 to 1,380,018.

Results and Discussion

Analysis of the KBO's Wage Discrimination Based on the Nationality of Pitchers and Position Players

In full sample Model 1 of Table 5, a baseball player's salary is used as the dependent variable. The explanatory variables were nationality and the characteristics of the team and the individual. The results showed that, after controlling for unobservable individual effects in the random effects (RE) model and past experience before the KBO (Seniority), nationality had a significant and positive effect on salary; the salary of foreign pitchers was higher than that of South Korean pitchers by 67.95 million KRW. Model 2's explanatory variables were nationality and all of a baseball player's past performance and achievements. The results showed that, after controlling for unobservable individual effects, nationality had a significant and positive effect on salary; the salary of foreign pitchers was higher than that of South Korean pitchers by 142.02 million KRW. Model 3's explanatory variables included nationality, the player's past performance, and team and individual characteristics. The results showed that nationality had a significant and positive effect on salary; the salary of foreign pitchers was higher than that of South Korean pitchers by 62.99 million KRW. Evidence of wage discrimination toward local pitchers was found.

Further employing the average salary of KBO pitchers as a boundary of sample salary level, the average pitcher salary was 94 million KRW, the minimum salary was 21 million KRW, and the maximum salary was 883 million KRW. Model 1 used a baseball player's salary as the dependent variable. The explanatory variables included nationality and the characteristics of the team and the individual. For the sample in which salary was higher than average, nationality had no significant effect on salary. Model 2's explanatory variables were nationality and all of a baseball player's performance. The results showed that nationality had a significant positive effect on salary. The results showed that a foreign pitcher with a high salary was paid 36.67 million KRW more than that of South Korean pitchers. The evidence of wage discrimination toward local pitchers with a higher salary was found. Moreover, compared with the results of the full sample, the effect of discrimination was smaller. This indicated that local pitchers with a high salary were less discriminated against, and it may be due to the competition in the high-salary market.

All coefficients of free agency (FA) were significantly positive. This indicated that, on average in Model 3, the salary of a free agency pitcher was higher than other pitchers by 8.76 million KRW. As to other control variables, the results showed that higher, less-weighted, senior, foreign pitchers were paid more.

In addition, all coefficients of FP were positively related to salary. It indicated that a foreign pitcher with professional experience outside the KBO received an additional 15.07 to 51.03 million KRW.

In Model 1 of Table 6, a position player's salary is used as the dependent variable. The explanatory variables included nationality and the characteristics of the team and the individual. The results showed that, after controlling for unobservable individual effects in the RE model and past experience before the KBO (Seniority), nationality had a significant and positive effect on salary in the results from Models 1 to 3; taking the full model (Model 3) as an example, the salary of foreign position players was higher than that of South Korean position players by 65.03 million KRW. The evidence of wage discrimination toward local position players was found.

Further employing the average salary of KBO position players as a boundary of sample salary level, the average salary was 111 million KRW, the minimum salary was 15 million KRW, and the maximum salary was 871 million KRW. Model 1 used a player's salary as the dependent variable. The explanatory variables were nationality and the characteristics of the team and the individual. The results showed that in the sample in which salary was higher than average, nationality had no significant effect on salary. Model 2's explanatory variables were nationality and a player's past performance. The results showed that in the sample in which salary was higher than average, nationality had a significant effect on salary. The salary of foreign position players was higher than that of South Korean position players by 43.64 million KRW. The evidence of wage discrimination toward local position players with a higher salary was found. Moreover, compared with the results of the full sample, the effect of discrimination was smaller. This indicated that local position players with a high salary were less discriminated against, and it may be due to the competition in the high-salary market.

All coefficients of FA were significantly positive in Table 5. Taking Model 3 as an example, on average the salary of a free agent position player was higher than other position players by 34.82 million KRW. As to the other control variables, the results showed that shorter, less weighted, senior, and foreign position pitchers were paid more. However, for a position player with a higher salary, less weighted, junior, and foreign position pitchers were paid more.

In addition, the coefficients of FP were positively related to salary. It indicated that on average a foreign pitcher with professional experience outside the KBO received an additional 15.27 to 62.38 million KRW.

In fact, estimates of Height were not consistent between Tables 5 and 6. The estimates of Height and SQHeight were significantly positive and negative in pitchers' regression of Table 5, but they are negative and positive in position players of Table 6. Combined with the negative significance of Weight, an interesting finding is that higher and less-weighted pitchers received more pay, but higher and less-weighted position players received less pay in the KBO.

After controlling for a baseball player's performance and his team and individual characteristics, the phenomenon of wage discrimination based on nationality can indeed be observed to occur in the KBO. The results from the current study are the same as those from the study by Jane, Chen, and Kuo (2013). Why would the salary of South Korean baseball players be lower than that of foreign baseball players? This study proposes several points of explanation for this question.

First, the KBO has not set a lower limit on the salary of local baseball players. Major League Baseball sets a lower limit on the annual salary of its players, and this lower limit increases every year. In contrast, employers control the salaries of the KBO's players, thus compressing the growth of the baseball players' overall salaries.

Second, the reason that the salaries of South Korean baseball players are lower than those of foreign baseball players may be related to a restricted free agent system. Past studies have found evidence that the implementation of a free agent system in professional baseball led to sharp increases in players' salaries and in the number of multi-year contracts (Hill & Spellman, 1984; Hsien, 2008; Sommers & Quintion, 1982). However, if the system or the contract has too many restrictions or specifications, it can also damage the interests of professional players (Hsien, 2008; Lai, 2007; Lin, 2005; Wang, 2007; Yeh, 2009). In 1999, the KBO implemented a free agent system. A baseball player with nine years of experience (without a four-year college or university degree) or eight years of experience (graduated from college or university) can apply for free agent status. The baseball team that absorbs the free agent must pay three times that baseball player's annual salary as a transfer fee or pay two times the annual salary as a transfer fee in addition to releasing a baseball club protected player (a player outside the list of 18) to another team. Greater year restrictions reduce player mobility. The baseball clubs restrict their players' control of their salaries and opportunities for salary growth.

Third, salary arbitration can improve the salary of a baseball player who proposed the arbitration and, at the same time, improve the salary level of all baseball players (Hu, 2010). From 1974 to 2011, MLB implemented salary arbitration. The employers won 285 times and the labor side was victorious 210 times. From 1984 to 2012, however, the KBO implemented its own system of salary arbitration. The employers won 19 out of the 20 instances of arbitration, and the labor size was victorious only once. An overly low win rate may affect the willingness of baseball players to propose salary arbitration.

Finally, both foreign and South Korean position players and pitchers have considerable strength and receive high salaries, indicating that baseball clubs may have misjudged the strength of foreign baseball players and given them salary levels that were not proportionate to their strength. The reason for the salary disparity may also derive from baseball clubs' expectations of better performance by foreign athletes, prompting them to pay foreign players higher salaries. Alternatively, perhaps professional baseball club owners had a limited grasp of information pertaining to foreign baseball players' abilities (Jane, Chen, & Kuo, 2013). After a basketball team's management had considerable experience, it devoted more time and effort to evaluating players' talents (Eschker, Perez, & Siegler, 2004).

Analysis of the KBO's Consumer Discrimination

To analyze consumer discrimination in connection with the KBO, this study used each team's annual attendance numbers as the dependent variable. The number of foreign baseball players, foreign pitchers, foreign position players, the team record, and the home city population were the explanatory variables and the supporting information for the causes of wage discrimination based on nationality. Furthermore, competitive balance within the league affected attendance over time (Joo & Oh, 2015). Therefore, two variables (CB_sd and GINI) to proxy the degree of competitive balance are added in the regressions.

Table 7 shows the result of the attendance regressions. Model 1 used number of foreign players and CB_sd as the explanatory variables, while Model 2 used GINI as a variable. In Model 1, the number of foreign players does not affect attendance. But in Model 2, after controlling for the unobservable individual effects in the FE model, the number of foreign baseball players still had a significant effect on the total number of annual attendees. When the team had one more foreign baseball player, the number of spectators would increase by 42,328 people. The evidence indicated that fans of the KBO who would enter the arena to watch a game preferred foreign players.

Models 3 and 4 further investigated whether discrimination by fans came from a preference for watching the performance of foreign pitchers or position players. Model 3 used each team's total number of annual attendees as the dependent variable, and the number of foreign pitchers and position players, the team record, and the home city population as the explanatory variables to analyze whether the number of foreign baseball pitchers had a significant effect on the number of attendees. The results showed that, after controlling for a team's win rate and home city population, the number of foreign pitchers had a significant effect on the total number of annual attendees. When the baseball team had one more foreign pitcher, the number of attendees would increase by 46,805 people.

Model 4 showed that under the same control conditions, the number of foreign position players did not have a significant effect on the number of annual attendees. In addition, Model 4 used the number of foreign pitchers and position players, the team record, and the home city population as the explanatory variables for conducting analysis. The results showed that the number of foreign pitchers had a significant effect on the total number of annual attendees. When a baseball team had one more foreign pitcher, the number of attendees increased by 49,015 people.

The results of this study show that the number of foreign baseball players on a team's roster had a significant effect on a team's number of annual attendees, implying that the phenomenon of consumer discrimination occurs among spectators. The consumers exhibited reverse discrimination in favor of foreign baseball players (i.e., fans preferred foreign baseball players). Past studies on the English Football League and MLB also found that consumers preferred to watch foreign players play the game (Pedace, 2008; Tainsky & Winfree, 2010; Wilson & Ying, 2003), indicating that the phenomenon of reverse wage discrimination in favor of foreign players is a result of a ball club's pursuit of profits. This study posits that the effect of foreign players on fans' game watching behavior may be due to the generally high regard in which foreign players are held with respect to technique and capability; therefore, a strong foreign presence would make a positive contribution to a team's record while also raising the level of excitement of its contests. When foreign players play in a game, they may attract more spectators to enter the arena and watch. When the number of attendees increases, the ball club might believe that foreign players are an important factor in attracting fans, and would thus be willing to increase salaries for foreign players. However, Burdekin and Idson (1991) thought that although white players playing in a game would have a significant effect on the attendance of fans, it did not mean that the research findings would support letting more white players take the place of black players on the field. This study endorses this argument. Although fans might like to watch foreign players play the game, this study shows that the performance and achievements of foreign players were not significantly better than the performance of Korean players. Therefore, ball clubs should make efforts to cultivate local star players to attract fans.

Conclusions

This study analyzes wage discrimination in the Korean Baseball Organization and finds that both foreign pitchers and position players were paid significantly higher salaries than South Korean players with similar performance records. Similar results were found in Nippon Professional Baseball and the Chinese Professional Baseball League. The phenomenon ubiquitously exists in the Asian Professional Baseball League.

This paper also finds wage discrimination of the KBO, and that fans prefer to see foreign players compete. The audience attendance rate will increase when foreign players compete. In other words, consumer discrimination exists in the KBO. This result is similar to MLB, the British Football League, and the international football market.

This research has implications for the baseball clubs, players, and the KBO in general. Baseball clubs in the KBO should recognize that nationality discrimination prevents labor resources and funds from being fully used, which results in idleness and waste, thus detracting from the team's competitiveness. Introducing international baseball players is an international trend, but the foreign baseball players' past performance data and present physical conditions should be carefully analyzed to recruit baseball players who can provide substantial help to a team's performance. In addition, baseball clubs should have an in-depth understanding of the main factors that influence fans' entry into an arena to watch a game; furthermore, they should establish a set of analytical evaluation mechanisms to avoid affecting their players' salaries and interests due to subjective club owner perception.

To some extent, local players can be regarded as cheap labor with high performance. When wage discrimination occurs in professional sports, it certainly affects market development and local baseball player cultivation. The time limit is long and the restrictions are many for obtaining free agent status in the KBO, which can increase the level of difficulty for baseball teams to get outstanding players. When the talent mobility rate is low, salaries are consequently suppressed. Therefore, it is suggested that the KBO review its free agent system and give local baseball players a greater degree of freedom, which will help enhance baseball players' overall salaries.

This study has some limitations that can guide future research. First, this study did not include in its model some possible influences on productivity whose indicators cannot be quantified, such as personal motivation, talents, creativity, etc. Second, factors that influence spectator entry into the arena may be multi-faceted. This study used official information on team win rate and home city population as variables of the measurement model because a team's win rate represents its strength. A baseball team with formidable strength may have a higher level of fan support. The home city population was the basis for measuring the actual and potential number of fans. A large population may have more fans. Other difficult-to-quantify indices, such as star baseball players, the baseball team's marketing strategy, etc., were not included in this study's model. Finally, subsequent studies can use qualitative or quantitative research approaches to target the employers or employees for study to understand the operations of a baseball club's dispensation of wages.

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Endnotes

(1) We thank one of the anonymous referees for the valuable suggestion.

Authors' Note

The authors thank the editor and three anonymous reviewers for comments that helped improve the manuscript. Funding for this research was provided by the Mississippi Agricultural and Forestry Experiment Station.

Jye-Shyan Wang (1), Pei-Hsin Fang (2), and Tsong-Min Wu (2)

(1) National Taiwan Normal University

(2) National Taiwan University

Jye-Shyan Wang, PhD, is an associate professor in the Department of Physical Education. His research interests include sport economics and leisure economics.

Pei-Hsin Fang is a PhD student in the Institute of Health Policy and Management. Her research interests include sport management and sport policy.

Tsong-Min Wu, PhD, is a professor in the Department of Economics. His research interests include sport economics and long-term growth of Taiwan.
Table 1. Exchange Rate of KRW to USD and Consumer Price Index of Korea

      Exchange rate
Year  (KRW to USD)    CPI   Ration

2000    1,130.96      73.1  0.731
2001    1,290.99      76.1  0.761
2002    1,251.09      78.2  0.782
2003    1,191.61      80.9  0.809
2004    1,145.32      83.8  0.838
2005    1,024.12      86.1  0.861
2006      954.79      88.1  0.881
2007      929.26      90.3  0.903
2008    1,102.05      94.5  0.945
2009    1,276.93      97.1  0.971
2010    1,156.06     100    1

From: "Official exchange rate (LCU per US$, period average)," the World
Bank, 2014, retrieved from
http://data.worldbank.org/indicator/PA.NUS.FCRF?display=default
"World Bank Consumer price index (2010 = 100)," the World Bank, 2014,
retrieved from http://data.worldbank.org/indicator/FP.CPI.TOTL

Table 2. KBO Pitchers' Characteristics and Performance Data

                          Full samples        Foreign pitcher
Item
                       Mean          SD       Mean         SD

AGE                    27.35        5.05      31.86       3.43
Tenure                  5.40        3.69       4.76       3.49
Height                183.70        4.36     187.17       4.38
Weight                 86.64        7.95      89.37       8.98
BMI                    25.66        1.95      25.48       2.15
wage (ten
thousand            9,363.87   10,613.30  21,552.07  10,704.81
KRW)
GPP                    24.56       18.78      21.38      14.69
Win                     3.04        3.90       5.59       5.15
Lose                    2.98        3.32       5.22       3.53
CG                      0.19        0.77       0.43       1.07
SHO                     0.09        0.42       0.12       0.53
INNING                 55.28       50.71      87.62      61.73
HRA                     5.49        5.52       7.41       5.65
BBP                     3.59        4.51       5.67       5.61
SOP                    38.30       37.87      59.36      42.91
Past Experience
FP (*) Seniority        0.43        1.69       2.68       3.47
SQFP (*) Seniority      3.05       17.98      19.19      41.62
FP                      0.096       0.29       0.60       0.49

                        South Korean
Item                      pitchers       Ratio
                       Mean         SD

AGE                    27.01       4.98  1.18
Tenure                  5.44       3.70  0.87
Height                183.44       4.24  1.02
Weight                 86.43       7.83  1.03
BMI                    25.67       1.94  0.99
wage (ten
thousand            8,439.98  10,022.72  2.55
KRW)
GPP                    24.80      19.03  0.86
Win                     2.84       3.73  1.96
Lose                    2.81       3.24  1.86
CG                      0.17       0.74  2.54
SHO                     0.09       0.42  1.33
INNING                 52.83      48.94  1.66
HRA                     5.34       5.49  1.39
BBP                     3.44       4.38  1.65
SOP                    36.70      36.99  1.62
Past Experience
FP (*) Seniority
SQFP (*) Seniority
FP

Table 3. Position Players' Characteristics and Performance Data

                       Full samples          Foreign pitcher
Item
                     Mean          SD       Mean        SD

AGE                  28.12        4.50      34.18      3.12
Tenure                6.91        4.06       4.55      3.58
Height              181.65        4.26     184.02      3.93
Weight               85.65        9.13      89.53      8.11
BMI                  25.91        2.13      26.43      2.04
wage (ten
thousand         11,062.66   13,269.95  24,981.48  6,671.51
KRW)
AB                  208.10      158.20     321.57    139.61
R                    29.29       26.01      49.73     27.34
SH                   56.15       47.55      91.43     44.16
DB                    9.78        8.88      16.63      8.87
HR                    5.77        7.66      16.10     10.20
TB                   84.95       75.96     157.88     80.19
RBI                  27.89       27.32      57.41     31.47
SB                    5.20        8.76       4.61      6.02
CS                    2.39        3.05       2.49      2.57
FB                    3.50        4.02       4.45      4.87
SOB                  38.07       28.02      62.20     30.36
Past Experience
FP (*)                0.43        1.69       2.68      3.47
Seniority
SQFP (*)              3.05       17.98      19.19     41.62
Seniority
FP                    0.096       0.29       0.60      0.49

                       South Korean
Item                     pitchers      Ratio
                     Mean         SD

AGE                  27.89       4.38  1.23
Tenure                7.00       4.06  0.65
Height              181.56       4.24  1.01
Weight               85.50       9.14  1.05
BMI                  25.89       2.13  1.02
wage (ten
thousand         10,526.48  13,169.77  2.37
KRW)
AB                  203.73     157.29  1.58
R                    28.50      25.64  1.74
SH                   54.79      47.17  1.67
DB                    9.52       8.78  1.75
HR                    5.37       7.27  3.00
TB                   82.14      74.41  1.92
RBI                  26.76      26.51  2.15
SB                    5.22       8.85  0.88
CS                    2.39       3.07  1.04
FB                    3.47       3.98  1.28
SOB                  37.14      27.52  1.67
Past Experience
FP (*)
Seniority
SQFP (*)
Seniority
FP

Table 4. Descriptive Statistics of Attendance Analysis

                   Obs         Mean              SD

Attendance         80     476,037.3         308,769.5
Number of Foreign  80           1.925             1.270652
Players
Number of Foreign  80           1.3               1.246768
Pitchers
Number of Foreign  80           0.625             0.752633
Fielders
Team' Win Rate     80           0.498163          0.081578
Home City          80   5,182,566         3,815,006
Population
CB_SD Wins         80          10.85643           2.251473
GINI               80          21                 4.213701
Lag att            72     446,588.3         292,369.1

                         Min              Max

Attendance           118,582         1,380,018
Number of Foreign          0                 5
Players
Number of Foreign          0                 4
Pitchers
Number of Foreign          0                 3
Fielders
Team' Win Rate             0.265             0.659
Home City          1,352,797          1.06E+07
Population
CB_SD Wins                 7.837638         14.32219
GINI                      13.66667          26
Lag att              118,582         1,380,018

Table 5. Regression Results of KBO Pitcher Players in the Panel
Regression Analysis with Clustered Standard Errors

                                RE Model (full sample)
                    Model 1          Model 2           Model 3

Nationality       6,795 (***)     14,202 (***)        6,299 (***)
                   (283)            (179)              (355)
FA                1,013 (***)      4,625 (***)          876 (***)
                   (109)            (275)               (51.6)
AGE               2,135 (***)                         1,637 (***)
                   (454)                               (525)
SQAGE               -23.1 (**)                          -14.0
                    (10.1)                              (11.4)
Height            3,568 (*)                           3,882 (*)
                 (2,113)                             (2,240)
SQ Height            -8.74 (*)                           -9.51 (*)
                     (5.24)                              (5.58)
Weight             -960 (***)                        -1,105 (***)
                    (77.8)                             (131)
SQ Weight             3.50 (*)                            4.13 (*)
                     (2.05)                              (2.39)
BMI                 -72.6                                66.7
                    (61.5)                              (77.6)
SQBMI                25.1                                24.8
                    (16.1)                              (16.0)
Tenure              212 (***)                           270 (***)
                    (23.5)                              (30.9)
SQ Tenure             9.75                               12.5
                     (7.84)                              (8.88)
Transition       -1,297 (***)                        -1,251 (***)
                   (347)                               (244)
Pitchers Role     6,116 (***)                         4,820 (***)
                   (112)                               (418)
FP (*)             -681 (***)        175 (***)         -607 (***)
Seniority
                   (166)              (4.85)           (206)
SQFP (*)             12.6            -10.1 (***)         -0.67
Seniority
                    (10.5)            (0.34)            (11.8)
FP                1,507 (***)      3,644 (***)        5,103 (***)
                   (225)            (278)            (1,116)
W(Wins)                               33.0              -37.7
                                     (84.2)             (99.9)
L(Losses)                            277 (***)          184 (***)
                                     (10.8)              (0.41)
SHO                                 -207               -432
                                    (483)              (561)
INNING                               -24.9              -44.5 (***)
                                     (19.3)              (7.35)
SOP                                    4.34 (***)        30.6 (***)
                                      (0.95)             (6.84)
GPP                                   10.8                0.90
                                      (6.56)             (8.51)
CG                                   -28.3              122
                                    (241)              (293)
HA                                    44.9 (*)           39.7 (**)
                                     (23.7)             (16.7)
HRA                                 -118 (**)           -25.1
Foreign                               YES                 YES
Performance
YEAR                YES                                   YES
TEAM                YES                                   YES
                                     (46.2)             (55.3)
Constant       -348,605 (*)        5,284 (***)     -371,981 (*)
               (209,128)             (47.0)        (222,382)
Observation       1,625            1,624              1,624
Number of no        388              388                388
R2                    0.1287           0.0557             0.1289
LM test(2)         1287.85 (***)    1456.90 (***)       926.94 (***)

                       RE Model (high salary sample)
                   Model 1          Model 2          Model 3

Nationality        -510            3,667 (***)         -953
                 (1,249)            (205)            (1,400)
FA                3,803 (***)      5,125 (*)          3,831 (**)
                 (1,348)          (2,955)            (1,872)
AGE               6,900 (***)                         6,122 (***)
                 (1,656)                             (1,552)
SQAGE              -106 (***)                           -93.6 (***)
                    (26.5)                              (25.2)
Height            7,108 (***)                         7,752 (***)
                    (23.2)                             (243)
SQ Height           -18.2 (***)                         -20.0 (***)
                     (0.75)                              (0.047)
Weight               90.1                              -290
                 (1,944)                             (1,943)
SQ Weight            -3.36                               -1.20
                    (13.7)                              (13.6)
BMI                 199                                 289
                   (395)                               (445)
SQBMI                20.0 (**)                           17.9 (**)
                     (9.80)                              (7.73)
Tenure           -1,050                              -1,315
                   (694)                               (928)
SQ Tenure            46.1 (**)                           58.1 (*)
                    (21.3)                              (31.6)
Transition       -2,655 (*)                          -2,394
                 (1,551)                             (1,636)
Pitchers Role     4,264 (***)                         3,281 (***)
                   (421)                                (74.2)
FP (*)           -1,280 (***)         29.1           -1,075 (***)
Seniority
                   (453)             (20.1)            (240)
SQFP (*)             43.5 (**)        -2.13 (***)        24.1 (***)
Seniority
                    (17.4)            (0.26)             (7.27)
FP                4,155 (*)        2,420 (***)        7,439 (**)
                 (2,349)            (833)            (3,277)
W(Wins)                             -296 (***)         -315 (***)
                                     (64.3)             (21.2)
L(Losses)                            366 (***)          342 (***)
                                     (39.0)            (120)
SHO                                 -369               -129
                                  (1,465)            (2,407)
INNING                                59.7 (*)           21.7
                                     (30.5)             (23.9)
SOP                                  -58.7 (***)         -8.06
                                      (5.92)             (6.74)
GPP                                   -5.33               1.33
                                     (15.7)             (16.7)
CG                                  -698               -478
                                    (880)            (1,019)
HA                                    17.2                9.66
                                     (45.8)             (17.2)
HRA                                 -198 (***)          -25.8
Foreign                               YES               YES
Performance
YEAR                YES                                 YES
TEAM                YES                                 YES
                                     (72.1)             (16.5)
Constant       -787,705 (***)     18,388 (***)     -821,408 (***)
               (132,979)          (1,125)          (154,087)
Observation         543              542                542
Number of no        143              143                143
R2                    0.2106           0.1093             0.2364
LM test(2)          191.45 (***)     394.21 (***)       145.68 (***)

(***) p < .001, (**) p < .01, (*) p < .05

Table 6. Regression Results of KBO Position Players in the Panel
Regression Analysis with Clustered Standard Errors

                           RE Model (full sample)
                 Model 1         Model 2           Model 3

Nationality    10,491 (***)     13,200 (***)       6,503 (***)
                  (42.0)          (199)           (1,564)
FA              3,716 (***)      8,559 (***)       3,482 (***)
                 (196)            (272)             (475)
AGE             1,410 (***)                         -859
                 (509)                            (1,051)
SQAGE              -8.88                              28.3
                   (8.75)                            (20.3)
Height         -6,560 (**)                        -6,957 (***)
               (3,078)                              (472)
SQ Height          23.1 (**)                          24.2 (***)
                   (9.45)                             (0.051)
Weight         -2,382 (***)                       -2,104 (***)
                 (458)                              (424)
SQ Weight           2.37 (***)                         0.51
                   (0.16)                             (0.66)
BMI               413                                717
                 (826)                              (551)
SQBMI             130 (***)                          124 (***)
                  (45.1)                             (45.2)
Tenure            126                                386 (**)
                 (232)                              (151)
SQ Tenure          18.0 (*)                           45.4 (***)
                   (9.33)                            (15.3)
Transition       -632                               -396
                 (934)                              (875)
FP (*)          1,107 (***)      1,135 (***)       1,985 (***)
Seniority
                  (55.8)            (3.64)           (47.8)
SQFP (*)         -161 (***)        -91.4 (***)      -192 (***)
Seniority
                   (1.43)           (0.26)            (8.18)
FP              1,527 (***)      6,238 (***)       2,557 (***)
                 (434)             (51.6)           (107)
R                                   69.0 (***)       123 (***)
                                    (4.72)            (0.21)
HR                               1,476 (***)       1,112 (***)
                                   (98.3)            (37.5)
TB                                -555 (***)        -426 (***)
                                   (29.0)            (11.9)
BA                                 623             1,732 (***)
                                (1,652)             (390)
AB                                  44.6 (***)        25.2 (***)
                                    (3.31)            (3.72)
SH                                 469 (***)         359 (***)
                                   (58.4)            (33.2)
DB                                 416 (***)         291 (***)
                                   (22.9)            (44.2)
RBI                                191 (***)         175 (***)
                                    (9.75)           (14.0)
SB                                 -19.2 (***)        -5.14 (***)
                                    (1.59)            (1.60)
CS                                -162 (***)         -42.1
                                   (10.3)            (28.0)
SOB                               -101 (***)         -80.9 (***)
                                   (13.6)             (4.21)
Foreign                             YES               YES
Performance
Position           YES                                YES
YEAR               YES                                YES
TEAM               YES                                YES
Constant      506,175 (*)        2,924 (***)     560,038 (***)
             (284,282)            (278)          (43,680)
Observation     1,414            1,415             1,414
Number of         302              303               302
no
R2                  0.2751           0.1687            0.2283
LM test(2)       2052.10 (***)    1271.84 (***)      892.15 (***)

                          RE Model (high salary sample)
                  Model 1           Model 2           Model 3

Nationality       -428            4,364 (***)        -1,492
                (3,393)          (1,215)             (3,155)
FA               5,480 (***)     12,942 (***)         6,190 (***)
                  (569)          (1,672)             (1,144)
AGE              8,205 (***)                          6,574 (***)
                (1,178)                                (416)
SQAGE             -119 (***)                            -95.3 (***)
                   (26.5)                               (13.6)
Height          -5,191                              -10,369
               (22,235)                             (16,226)
SQ Height           27.0                                 41.6
                   (66.0)                               (50.7)
Weight          -5,136 (***)                         -5,364 (***)
                  (881)                              (1,610)
SQ Weight            3.85                                 3.15
                    (5.68)                               (4.66)
BMI              6,498 (***)                          5,709 (***)
                (1,438)                                (986)
SQBMI              175 (*)                              208
                   (96.4)                              (135)
Tenure            -598 (**)                          -1,071 (***)
                  (273)                                (223)
SQ Tenure           65.9 (***)                           99.1 (***)
                   (20.8)                               (16.4)
Transition       2,710                                2,733
                (1,927)                              (1,993)
FP (*)           1,793 (***)        833 (***)         2,713 (***)
Seniority
                   (56.5)           (20.0)             (259)
SQFP (*)          -250 (***)        -77.4 (***)        -278 (***)
Seniority
                   (17.3)            (2.14)             (16.0)
FP               7,001 (*)       10,004 (***)        16,086
                (3,662)          (1,217)            (15,105)
R                                    77.8 (***)         200 (***)
                                    (30.0)              (75.9)
HR                                1,858 (***)         1,737 (***)
                                   (372)               (225)
TB                                 -676 (***)          -617 (***)
                                   (149)                (45.0)
BA                               51,676 (***)        65,766 (*)
                                 (2,067)            (35,385)
AB                                   48.3 (***)          38.9
                                     (7.81)             (32.9)
SH                                  408 (**)            315 (***)
                                   (199)                (56.0)
DB                                  645 (***)           592 (***)
                                    (70.1)             (123)
RBI                                 140 (***)           175 (***)
                                    (13.4)              (15.4)
SB                                   82.6 (***)          84.4 (*)
                                    (27.9)              (44.1)
CS                                 -365 (***)          -127
                                    (65.3)             (154)
SOB                                 -41.8               -56.4 (***)
                                    (28.8)               (8.89)
Foreign                               YES                 YES
Performance
Position             YES                                  YES
YEAR                 YES                                  YES
TEAM                 YES                                  YES
Constant        72,534            9,167 (***)       569,834
             (1.86e+06)             (34.1)        (1.34e+06)
Observation        438              438                 438
Number of           91               91                  91
no
R2                   0.4082           0.3028              0.3209
LM test(2)          61.82 (***)     115.75 (***)         29.35 (***)

(***) p < .001, (**) p < .01, (*) p < .05

Table 7. KBO Attendance OLS Regression Analysis

                                        FE Model
                                Model 1            Model 2

Number of Foreign Players     39,034             42,328 (*)
                             (21,856)           (22,128)
Number of Foreign Pitchers

Number of Foreign Fielders

Team' win rate               872,574 (*)        889,010 (*)
                            (439,213)          (447,512)
Home city population        (439,213)          (447,512)
                                   0.11 (**)          0.093 (*)
CB_sd                             (0.044)            (0.047)
                                -521
GINI                                              3,110
                                                 (2,629)
lagatt                             0.70 (***)         0.68 (***)
                                  (0.050)            (0.054)
Constant                    -929,665 (***)     -898,201 (***)
                            (248,501)          (232,735)
Observation                       72                 72
R2                                 0.7292             0.731
Number of teamid                   8                  8
LM test(2)                         0.00               0.00
Hausman test                      16.47 (***)        17.72 (***)

                                     FE Model
                               Model 3           Model 1

Number of Foreign Players

Number of Foreign Pitchers    46,805 (*)         49,015 (*)
                             (24,326)           (24,403)
Number of Foreign Fielders    12,881             19,759
                             (28,303)           (29,036)
Team' win rate               913,019 (*)        921,851 (*)
                            (461,444)          (468,643)
Home city population        (461,444)          (468,643)
                                   0.12 (**)          0.10 (*)
CB_sd                             (0.047)            (0.051)
                              -2,996
GINI                                              1,543
                                                 (2,096)
lagatt                             0.69 (***)         0.67 (***)
                                  (0.049)            (0.053)
Constant                    -951,712 (***)     -923,965 (***)
                            (225,728)          (225,284)
Observation                       72                 72
R2                                 0.737              0.736
Number of teamid                   8                  8
LM test(2)                         0.00               0.00
Hausman test                      13.58 (***)        14.64 (***)

(***) p < .001, (**) p < .01, (*) p < .05
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Author:Wang, Jye-Shyan; Fang, Pei-Hsin; Wu, Tsong-Min
Publication:International Journal of Sport Finance
Date:Nov 1, 2017
Words:9997
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