Examining the impact of the StubHub agreement on price dispersion in Major League Baseball.

Watanabe, Nicholas M. ; Soebbing, Brian P. ; Wicker, Pamela 等

Introduction

The second-hand ticket market has increased in recent years (Drayer, 2011). While the history of the secondhand ticket market can be traced back to the first sporting events which tickets were sold for, the growth in technology has truly evolved this market. Twenty years ago, consumers participating in the second-hand market would need to visit a ticket broker or find a scalper in person from which to purchase tickets. The rapid growth of technology has made purchasing of tickets, both from the organization as well as from second-hand sources, convenient and easy. The rise of the internet quickly alleviated the need for individuals to spend time physically searching for tickets, as they were able to find tickets being resold through various websites. Several prominent websites quickly took advantage of the existence of the second-hand ticket market, including EBay, StubHub, and Craigslist.

The secondary ticket market has been noted for its ability to offer new opportunities for those groups which are part of a primary market (Drayer, Stotlar, & Irwin, 2008). For example, Major League Baseball (MLB) signed a revenue sharing agreement with the ticket sales website StubHub.com in 2007 (just several months after StubHub was acquired by Ebay Inc.; Lee & Mohl, 2007). Part of this agreement between StubHub and MLB provided a platform through which consumers could legitimately sell tickets (Drayer, 2011), since the secondary market was plagued with issues regarding counterfeit tickets and fraud (Drayer et al., 2008). Currently, most professional sport leagues in North America have an official website for ticket reselling (Drayer, 2011).

The empirical setting for the present research is MLB. Prior to the agreement in 2007 with StubHub, MLB franchises were concerned about the rise of the secondary ticket market. For example, the New York Yankees made the public claim that much of the decline in their ticket sales can be directly attributed to ticket reselling on StubHub (Sandomir, 2006). Since the Yankees and other MLB teams have to price their tickets before the start of the regular season, the price of the ticket on the secondary market can reflect the current information regarding team and game quality along with consumer preferences for attending that game. With the agreement with StubHub, MLB and its member teams were able to gain important information about the real value of the ticket sales, which also provided a better understanding of how organizations could maximize their ticket revenue (Drayer et al., 2008).

The purpose of the present research is to examine how the MLB agreement with StubHub has changed the ticket price dispersion of MLB teams. Price dispersion is the distribution of prices for similar or the same goods (Pan, Ratchford, & Shankar, 2002). The present research uses ticket prices from the MLB media guides (called the Red and Green Books) and individual websites to construct a unique dataset of MLB ticket prices from 1975 until 2010 season (except for the 2009 seasons). Estimating a fixed effect Ordinary Least Squares (OLS) regression to control for team, league, and market factors, we find that the StubHub agreement increases price dispersion amongst MLB teams. The findings have implications for the marketing of professional sport franchises. Teams can be encouraged to use the second-hand ticket market to their advantage and benefit from the opportunity to receive information to better price tickets prior to the season.

Theoretical Framework and Literature Review

Pricing in Sport

Pricing is an important component of the marketing mix because pricing is the element of marketing where managers have expectations of customers paying their money to the organization (Kopalle et al., 2009). Compared with other elements of the marketing mix, pricing has the advantage of a high level of flexibility. The flexibility is due to the fact that prices tend to change continually (Smith, 2008). This flexibility can be considered an advantage over other components of the marketing mix. However, this advantage is less prevalent in some sports leagues because of regulations, often placed by sport governing bodies, which contain modulations and restrictions in regards to prices that are allowed to be set (Smith, 2008).

The opportunity of quick price changes can also have disadvantages, if they are not chosen deliberately. For example, customers stop purchasing the product when prices are set too high and they do not come back when prices are lowered afterwards. Yet, due to the high involvement and emotional attachment of sports fans to their teams (Mullin, Hardy, & Sutton, 2007); this effect should be less pronounced in professional team sports. Taken together, prices can be easily changed in many cases. However, there are organizations like MLB which employ rules and regulations that make changing prices difficult (Smith, 2008). In MLB, teams have to set prices prior to the regular season. A glance at the literature reveals a considerable number of papers which examined the effect of pricing from multiple perspectives (e.g., Coates & Humphreys, 2007; Drayer & Shapiro, 2011; Drayer, Rascher, & McEvoy, 2012; McDonald, Milne, & Hong, 2002; Rishe & Mondello, 2004).

Price Dispersion

While ticket prices received attention in a variety of sport disciplines (e.g., Drayer et al., 2012; Fort, 2004; Rascher, McEvoy, Nagle, & Brown, 2007), few articles have focused on the concept of price dispersion within sport (Humphreys & Soebbing, 2012). Price dispersion is "the distribution of prices of an item with the same measured characteristics across sellers" (Pan et al., 2002, p. 434). The seminal work of Stigler (1961), who observed the phenomenon in the different prices for cars in the Chicago car market, began the research in this area.

Price dispersion has received attention in other industries, whereas the sport literature has largely neglected this topic with a few exceptions (Humphreys & Soebbing, 2012). Over the past decade, a considerable amount of research examined this pricing strategy within traditional, online retailers, and multi-channel (mixture of online and traditional) businesses (Pan et al., 2002). The present research is particularly focused on the literature examining the impact of online merchants on the price dispersion offered by traditional retailers. Brown and Goolsbee (2002) found that the introduction of online retailers within the life insurance market led to an initial increase in price dispersion. As consumers began to increasingly use the web to search life insurance rates and gather additional information, the dispersion of prices decreased. Research by Kung, Monore, and Cox (2002) found that the internet and internet based companies may assist traditional brick and mortar companies in developing better pricing strategies.

In the literature, there are reasons for why price dispersion occurs. Traditionally, it was due to lack of information and uncertainty for both sellers and consumers (Carlson & McAfee, 1983; Che & Gale, 2002; Dana Jr., 1999, 2001; Stigler, 1961). Dana Jr.'s (1999) development of a theoretical model put forth the idea that all firms face some demand uncertainty. He noted that a good empirical setting to test price dispersion under demand uncertainty would be to examine MLB since prices have to be set well in advance of the actual event occurring. Humphreys and Soebbing (2012) examined the phenomenon in MLB from 1975 until 2008. They found that uncertainty, measured by the standard deviation of a team's winning percentage over the last five seasons, resulted in less dispersion. However, they did not examine the potential impact that the Stub Hub agreement had on a team's ticket pricing.

Price Dispersion and the Secondary Ticket Market

The fact that not all tickets are sold well in advance before an event or that some tickets are sold again presents opportunities for the secondary ticket market. Within this sophisticated market, brokers and scalpers have established several ways to acquire tickets for the best seats and sell them at prices between face and market value (Courty, 2000). Importantly, the level of price dispersion is associated with differences in consumer preferences (Courty, 2003). Ideally, there are two types of consumers: early buyers and late deciders. Promoters of events have to make tickets available early to meet the needs of the early buyers who value planning and want to secure the event consumption well ahead of the event. The late deciding consumers, however, prefer buying tickets close to the event date (typically from brokers). This leads to potential conflicts between promoters and brokers. Given the high prices on the secondary market (Shapiro & Drayer, 2012), promoters are interested in securing the profits made by brokers. Yet, Courty (2003) showed that the promoter is neither able to prevent the broker from entering the market nor capture the broker's profits. Therefore, some event promoters and leagues have put regulations in place that restrict brokers.

For MLB, a lack of certainty regarding each game's expected demand has contributed to the growth of the secondary ticket markets. In 2007, MLB and StubHub entered into a five-year revenue sharing agreement (Stone & Richtel, 2007). As part of this agreement, MLB can directly capture some of those ticket revenues that previously went to StubHub and the secondary ticket market sellers. Moreover, the agreement also calls for sharing of information between the two organizations (Newman, 2007). Thus, it provides an opportunity to better understand consumer preferences. Within increased information on consumer preferences, MLB teams can specifically learn about the characteristics of late deciders who would typically buy their tickets on the secondary market and not from the team. As a result, more effective pricing strategies can be developed by individual teams with the goal of increasing revenue (Drayer et al., 2008). These strategies support the overall conclusion of Kung et al. (2005).

John Davis, Vice President for Ticket Sales for the Cincinnati Reds, articulated how StubHub assists MLB teams in their pricing strategy in the primary market. He stated, "'Teams are provided a clear picture as to nuances of the secondary buyer by pricing categories, proximity to the ballpark, and timing in regards to time of purchase and the actual game [...] All these factors are extremely helpful in understanding our fan base, how best to message to them, and how to properly price our tickets'" (Associated Press, 2012, n.p.).

In summary, MLB teams are able to offer more prices that are specifically targeted at the preferences of their various consumer groups. MLB benefits from this agreement since it can learn about consumer preferences, provide a ticket price that may more accurately reflect demand for games, and capture revenue that would previously be gained by secondary ticket market sellers. Thus, we hypothesize that:

H1: An increase in information available to MLB teams prior to setting regular season ticket prices with its agreement with StubHub is associated with increasing price dispersion.

Method

Data Collection

In order to examine the effect that the agreement between StubHub and MLB has on the ticket price dispersion of MLB teams, the present research examines the ticket prices set by MLB teams prior to the start of the season from 1975 to 2011. The ticket pricing data were gathered from two main sources. The first source was the media guides published by both the American and National Leagues. These publications contain information for all the member teams as well as the league. The media guides were purchased from 1975 until 2008. Starting in 2009, MLB moved to electronic publication requiring an MLB granted password for viewing. Repeated attempts by the authors for a password were denied by MLB. As a result, ticket pricing levels were gathered from each of the individual team's websites prior to the start of the 2010 seasons. The authors are still unable to find the ticket prices for the 2009 season. Thus, the complete sample period is from 1975 to 2008 and the 2010 seasons.

The sample size used in the present research was also decreased for two additional reasons. The first was the elimination of the observations for teams in Canadian markets. In the media guides, there are inconsistencies with the reporting of the ticket prices. Second, there were some instances where ticket prices were not published in the media guides. Ticket prices were not published for the Baltimore Orioles in 1979 and the Washington Nationals in 2005. Eliminating these two team-season observations and all observations from Canadian teams results in 899 team-season observations in the sample period.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

Measures and Variables

Previous literature measured price dispersion in many different ways (Ancarani & Shankar, 2004). In the present research, there are two different dependent variables (DVs) that measure price dispersion. The first is the number of price levels (PLN) a team offers in the observed season. The number of price levels indicates a basic level of dispersion, similar to using a range of prices. The second DV is the Gini Coefficient (Gini) of those ticket price levels. The Gini Coefficient is a widely used distribution measure in many disciplines (Powell, 2003). Figures 1 and 2 examine the dependent variables over the sample period. Notice in both figures that both the number of price levels and ticket price inequality is increasing over the sample period. This increase has been pronounced since the 1990s.

The present research measures the inequality amongst those ticket price levels. Two regression models are run to examine the potential impact that the StubHub agreement with MLB has on ticket pricing behavior of MLB teams to test the two hypotheses of this research. Model 1 uses the Gini Coefficient of the ticket price (Gini) as the DV. In Model 2, the number of price levels a team offers in one season (PLN) is used as the DV. Both models are estimated in the following general form using Ordinary Least Squares (OLS):

(1) [Ticket.sub.it] = [alpha] + [[beta].sub.1] [StubHub.sub.t] + [[beta].sub.2] [MLBAM.sub.t] + [[beta].sub.3] [Perf.sub.it] + [[beta].sub.4] [NewStad.sub.it] + [[beta].sub.5] [Classic.sub.it] + [[beta].sub.6] [StadCap.sub.it] + [[beta].sub.7] [Trend.sub.t] + [[epsilon.sub.it]

In Equation 1, i indexes teams, t indexes seasons, [alpha] is an MSA fixed effect controlling for metropolitan statistical area specific coefficients, and e is the equation error term reflecting unobserved variation. StubHub is a variable equal to 1 for each team in the seasons following the agreement between MLB and StubHub, 0 otherwise. MLBAM is equal to 1 for each team in the seasons following the creation of Major League Baseball Advanced Media (MLBAM). MLBAM was created in 2000 and serves as the platform to direct consumers to StubHub (Stone & Richtel, 2007). MLBAM also serves as the platform to other broadcasts such as the NCAA March Madness (Warzel, 2012). Overall, MLBAM has been a revenue producer for the league, which shares these revenues equally amongst all 30 teams. This additional revenue from MLBAM may provide additional information for teams regarding consumer preferences for MLB games. PERF is the standard deviation of a team's winning percentage over the past five seasons. This variable controls for the variance in team performance as outlined in Dana Jr. (2001) and empirically examined in Humphreys and Soebbing (2012). Dana Jr. (2001) did not predict how team performance would affect price dispersion, while Humphreys and Soebbing (2012) found that an increase in the standard deviation of team performance led to a decrease in the standard deviations of ticket price levels.

Three stadium variables are included in the model. NewStad is equal to 1 in the seasons where the stadium is less than or equal to 10 years old. This reflects the novelty effect of a new stadium, which previous research defined as a short-term increase in attendance attributed to the newness of the facility (Coates & Humphreys, 2005). Due to this increase, teams may increase the dispersion of their ticket prices in order to capture additional revenue from fans. Previous research has also indicated that older stadiums see an increase in attendance (McEvoy, Nagel, DeSchriver, & Brown, 2005). Thus, Classic is equal to 1 for a team-season observation if its stadium is greater than 47 years old, corresponding to the result of McEvoy et al. (2005). Stadium ages were calculated from the Ballparks.com website. StadCap is the stadium capacity in season t. Capacity information was obtained from the Red and Green books when available and then from ESPN.com for the seasons after 2008. Finally, a linear time trend variable is included (Trend) equal to 1 for the year 1975, 2 for 1976, etc. The linear time trend controls for both the increase in the number of ticket prices and for the increase in the Gini coefficient over the sample period as illustrated by Figures 1 and 2. Controlling for the general increase over time will further isolate the impact that the StubHub agreement has on a team's ticket pricing policy.

Table 1 presents the summary statistics and correlation coefficients for all the variables included in Equation 1. The final sample of observations is 889 due to the elimination of the first two seasons for each expansion team during the time period since there is no opportunity to calculate a standard deviation of winning percentage prior to year 3. During the sample period, MLB teams offered around six or seven price levels on average per season. Notice that there is a wide range of ticket prices. Some teams offered only two price levels in one season, while other teams offered as many as 23 different price levels. The average Gini Coefficient is 0.237 during the sample period with a range of 0.100 (more equality) and 0.582 (less equality). Six percent of team-season observations occur after the agreement between StubHub and MLB. Twenty-three percent of the observations play in a stadium that is 10 years old or less. Eighteen percent of team-season observations play in a stadium that is considered a classic stadium. Average stadium capacity during the sample period is approximately 50,000.

Estimation Issues

Two potential estimation issues of the model should be discussed. The first issue is multicollinearity. The presence of multicollinearity is assessed using both the correlation coefficients and the variance inflations factors. Notice in Table 1, all the correlation coefficients are under the suggested threshold of 0.9 (Tabachnick & Fidell, 2007). Examining the variance inflation factors (vif), all are less than or equal to 4 and thus under the acceptable threshold of 6 (Keith, 2007). As a result, multicollinearity should not be an issue with Equation 1.

The second issue is the standard error correction. There are certain facility effects that are unobserved due to the nature of different, seats, viewing angles, etc. Thus, the standard errors are clustered corrected by facility. Equation 1 was also estimated with population and per capita income as independent variables. This information was collected from the Bureau of Economic Analysis website that is through the U.S. Department of Commerce. Including these two independent variables does not change the results. They are excluded from the present model because the vif for many independent variables are greater than 6. Finally, there is concern that since Pln is a count variable, estimating an OLS regression model without transforming the dependent variable could present estimation issues. Thus, the logarithmic transformation of the Pln (logPln) is presented along with Pln.

Results

Table 2 presents the results of the fixed effect equation with facility clustered standard errors. The three models explain between 64 and 74 percent of the observed variation in the dependent variables (Gini, Pln, and logPln). In all three models, the StubHub variable is positive and significant, providing support for H1. The effect of StubHub on Gini indicates that the agreement with StubHub increased the dispersion in ticket prices that MLB teams offered prior to the start of the season.

Due to the additional information that MLB teams receive from the agreement with StubHub, they are able to increase their price dispersion to account for that information. By increasing price dispersion, teams may be able to capture additional consumer surplus consistent with the previous literature on the sources of price dispersion. Furthermore, the agreement with StubHub significantly (p<.001) increased the number of price levels (Pln and logPln), again supporting H1.

The introduction of MLBAM also increases both the inequality of ticket price levels that a team offers and the number of price levels offered for the season. The performance variable presents some interesting and conflicting results. Recall that performance is measured by the standard deviation of winning percentage for a team over the past five seasons. Table 2 shows that performance has a negative effect on ticket price inequality, a result that is similar to Humphreys and Soebbing (2012). This means that the higher the variation in team performance, the lower the price dispersion. However, variation in team performance does not statistically impact the number of price levels a team offers. Thus, price levels are not impacted by team performance rather the price difference within those levels.

The three stadium variables provide relatively consistent results across models. In all three models, playing in a new stadium increases both the number of price levels and ticket price inequality. A classic stadium also increases the inequality of prices; however, there are mixed results when examining the number of price levels. It does increase the number of price levels when not transforming Pln to logPln. Overall, these two results indicate that teams are attempting to take advantage of both the novelty effect of new stadiums and the nostalgia effect of old stadiums by increasing the number of price levels and the inequality within those price levels. The stadium capacity variable does not have any effect on the levels and the prices of MLB tickets, a finding that is consistent with previous research (Humphreys & Soebbing, 2012). Finally, the time trend reflects both the increasing inequality of ticket prices and the number of price levels of the sample period.

Discussion

The present research looked at the effect of the StubHub agreement on price dispersion in MLB. The major findings of this research (Table 3) have implications for marketing practice. As presented in Table 2, the results showed that the StubHub agreement had a significant and positive impact on price dispersion. Therefore, it can be recommended that sport leagues enter official agreements with second-hand ticket sellers to also generate the revenues from this market. Increased price dispersion can help increase these revenues, as previous research has shown that variable ticket pricing was associated with higher revenues for sport franchises (Rascher et al., 2011). Thus, the use of second-hand ticket market websites like StubHub seems to be beneficial, although some teams complain about these partnerships (Sandomir, 2006). From a policy perspective, the findings of this research indicate sport leagues and organizations which sell tickets to events should consider similar partnerships in the future, to better capture the second-hand ticket market. Moreover, policy makers should work on the legit imacy issues that second-hand ticket markets experience (Drayer & Martin, 2010).

This study provided evidence that teams have offered significantly more price levels and that the inequality amongst ticket price levels has significantly increased since the StubHub agreement. As there are still some teams that only offer few price levels, they can be encouraged to augment their number of price levels. Teams should use the second-hand online ticket market to their advantage and increase their variation in ticket prices. Given the positive relationship between variable ticket prices and revenues (Rascher et al., 2011), teams should benefit from the pricing flexibilities the second-hand ticket market provides (or gives back) to them, despite existing ticket price regulations set by the MLB. Taken together, it seems that the second-hand ticket market has increased the complexity of pricing behavior of teams in MLB.

The present research is not without limitations. It only looks at price inequality and the number of different price levels, but does not consider the actual prices and revenues that are associated with the different price levels. Another limitation is that this research only examines one sport league in one country. There may be other regulations in other leagues and countries which lead to different structures in pricing and effects on the secondary ticket market. Moreover, this study takes--like most of the current price dispersion literature--a seasonal approach to pricing of tickets to events. Thus, the current research does not examine the use of variable ticket pricing for sporting events from a game-to-game perspective.

The limitations of this study represent avenues for future research. Further studies should examine the impact on the prices in the secondary ticket markets to see if additional consumer surplus is indeed being captured by teams with their pricing prior to the start of the regular season. There is also a need to consider the importance of the StubHub deal as the newest deal between MLB since the company had three franchises (Chicago Cubs, New York Yankees, Los Angeles Dodgers) opt out of participating in future years (Matthews, 2012). The three franchises supposedly decided to remove themselves from the new five-year deal because they felt that StubHub was decreasing the value of their tickets. Results in this research hint that this may not be true, and there will be need for future research in this area.

Another avenue for future research is to look at price dispersion and the effects of the second-hand ticket market in other leagues and countries. A further investigation of the effects of price dispersion on consumption of tickets to sporting events for other professional and collegiate sport leagues would be beneficial to see if additional empirical evidence supports the findings of this study and previous research. Additionally in future research, a game-to-game approach should be used that considers variations in ticket prices during the season.

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Nicholas M. Watanabe, PhD, is an assistant professor of sport management in the Department of Parks, Recreation, and Tourism at the University of Missouri. His research interests include the demand for sport, pricing, competitive, policy, develop, and communications in sport.

Brian P. Soebbing, PhD, is an assistant professor of sport management in the School of Kinesiology at Louisiana State University. His research interests include the strategic behaviour of sports leagues and teams, as well as the social and economic impacts of gambling.

Pamela Wicker, PhD, is a senior lecturer at the Department of Sport Economics and Sport Management at the German Sport University Cologne. Her research interests include sport finance, economics of sport consumer behavior, and development of non-profit sport clubs.

Authors' Note

To the knowledge of the authors, there is no university library that holds collections of the Red and Green Books. Daniel Rascher made the initial purchase of media guides, and Brad Humphreys made a second purchase of the media guides several years ago. We thank Dan and Brad for allowing us access to the media guides to develop this unique dataset.

Table 1

Summary Statistics and Correlation Coefficients

Variable              Mean    Std. Dev.    Min      Max     1

# Name

1 Pln                6.570      3.100       2        23     1
2 Gini               0.237      0.083     0.100    0.582    0.70
3 StubHub            0.098      0.297       0        1      0.53
4 MLBAM              0.319      0.467       0        1      0.71
5 Perf               0.051      0.020     0.008    0.153    -0.01
6 NewStad            0.227      0.419       0        1      0.32
7 Classic            0.181      0.385       0        1      -0.15
8 StadCap ('000)     49.807     8.590     33.679   78.000   -0.31
9 Trend                19        10         1        36     0.72

Variable             2       3       4       5

# Name

1 Pln
2 Gini               1
3 StubHub            0.42    1
4 MLBAM              0.64    0.48    1
5 Perf               -0.03   -0.03   0.00    1
6 NewStad            0.29    0.10    0.28    0.00
7 Classic            -0.08   -0.07   -0.13   -0.05
8 StadCap ('000)     -0.21   -0.21   -0.34   0.03
9 Trend              0.57    0.50    0.79    0.08

Variable             6       7       8       9

# Name

1 Pln
2 Gini
3 StubHub
4 MLBAM
5 Perf
6 NewStad            1
7 Classic            -0.26   1
8 StadCap ('000)     -0.20   -0.10   1
9 Trend              0.21    -0.14   -0.28   1

n = 889

Table 2

Summary of Regression Results

DV          Gini                Pln                 logPln

             coeff.      p       coeff.      P       coeff.      P

StubHub      0.036     0.019     2.476     <0.001    0.182     <0.001
            (0.015)             (0.506)             (0.045)
MLBAM        0.066     <0.001    1.713     <0.001    0.205     <0.001
            (0.010)             (0.277)             (0.040)
Perf         -0.248    0.027     -5.683    0.086     -0.735    0.102
            (0.110)             (3.237)             (0.441)
NewStad      0.032     0.001     0.922     <0.001    0.092     0.009
            (0.009)             (0.233)             (0.033)
Classic      0.042     0.020     1.047     0.035     0.129     0.072
            (0.017)             (0.483)             (0.070)
StadCap      0.000     0.599     -0.025    0.191     -0.004    0.135
            (0.001)             (0.019)             (0.003)
Trend        0.001     0.035     0.109     <0.001    0.019     <0.001
            (0.000)             (0.013)             (0.002)
[R.sup.2]    0.644               0.732               0.739
Fixed         MSA                 MSA                 MSA
  Effects
SE          Facility            Facility            Facility
  Cluster

Note Displayed are the unstandardized coefficients, standard errors
in brackets. Dependent variable is the Gini Coefficient of a team.

Table 3

Summary of Major Findings and Implications

Major Point Resulting       Major Implication for Practice
From Research Inquiry       and/or Further Research

1. StubHub agreement has    It can be recommended that sport leagues
significant and positive    (and teams) enter official agreements
impact on price             with second-hand ticket sellers to
dispersion                  generate the revenues from the
                            second-hand market. This can be
                            beneficial since variable ticket pricing
                            was associated with higher revenues in
                            previous research.

2. Teams in MLB have        Teams with fewer price levels can be
offered significantly       encouraged to increase their number of
more price levels since     price levels.
the StubHub agreement.

3. The inequality amongst   It can be recommended that teams use the
ticket price levels has     second-hand online ticket market to
significantly increased     their advantage and increase their
since the StubHub           variation in ticket prices. Future
agreement.                  research could examine the impact on the
                            prices in the secondary ticket markets
                            to see if additional consumer surplus is
                            being captured by teams.

4. Second-hand ticket       Further research should look at the
market has increased        effects of the second-hand ticket market
the complexity of           in other leagues and countries.
pricing behavior of
MLB teams.