Working Paper Series, Paper No. 16-01
The Impact of Price Discrimination on Major League Baseball
Team’s Revenue
Brian P. Soebbing1, Nicholas M. Watanabe2, and Chad S. Seifried3
August 2016
Abstract
The empirical evidence supporting the impact that second degree price discrimination has
on a firm’s revenue has received little attention. The present research examines ticket data from
Major League Baseball from 1990 through 2010. Estimating a two-staged least squares model,
we find neither price discrimination variable has an impact on team revenues. However, we find
that both of these price discrimination variables impact revenues when examining different
facility types. We discuss the impact on the role that price behavior and venues have on price
dispersion.
JEL Classification Codes: D42, L12, L83
Keywords: Price Discrimination, Price Dispersion, Information, Revenue, Facilities, Tickets
Acknowledgements: 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.
We would also like to thank Drs. Brad Humphreys, Neil Longely, and Andy Weinbach, along
with participants at the 2013 Southern Economic Association Conference and the 2014 Western
Economic Association International Conference for their comments regarding earlier versions of
this manuscript.
1
University of Alberta, Faculty of Physical Education and Recreation, Alberta, Canada,
E-mail: [email protected]
2
University of Mississippi, Department of Health, Exercise Science, and Recreation
Management, University, MS, Phone: E-mail: [email protected]
3
Corresponding author: Louisiana State University, School of Kinesiology, 112 Huey
P. Long Field House, Baton Rouge, LA, 70803, Tel.: 225-578-4632, E-mail: [email protected]
1. Introduction
First mentioned by Pigou (1912; 1920), price discrimination is a popular topic to examine
within economics. Stigler (1987) defined the presence of price discrimination when two or more
similar or same goods are sold at different prices accounting for different ratios to marginal
costs. Ticket prices for sports and entertainment events is a popular empirical setting to study
price discrimination (Courty, 2000; Courty & Pagliero, 2012). Leslie and Sorensen (2014)
recently commented the pricing schemes in ticket markets for sports and entertainment events
are very simple, particularly with opportunities to engage in price discrimination. Much of the
previous literature considering ticket prices for sport and entertainment events examines the
pricing behavior in the context of second degree price discrimination (Courty, 2000). Second
degree price discrimination occurs when a seller offers to all consumers a range of options for
their consumption. Each of these options is a different price, forcing the consumer to choose
his/her preferred category. Courty (2000) summarized early empirical work looking at price
discrimination and ticket prices. Some topics analyzed in previous research included scaling the
house, choice of seat quality, the pricing of multiple events, advance sales and demand
uncertainty.
Price dispersion is categorized within second degree price discrimination (Courty &
Pagliero, 2012), and has been empirically examined in the context of sport and entertainment
event pricing (Courty, 2000; Rascher & Schwartz, 2012). Pan et al. (2002) defined price
dispersion as “the distribution of prices of an item with the same measured characteristics across
sellers” (p. 434). Early research by Stigler (1961) stated price dispersion surfaces from the
buyers’ willingness to pursue the best deal or secure the best price possible for their investment.
He proposed such activity (i.e., price dispersion) may develop as a result of the seller’s
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uncertainty about the demand for a specific product or service. Borenstein and Rose (1994) later
stated price dispersion arises due to three conditions: prices set by rivals, a lack of knowledge,
and an unstable demand or supply of a product. Additionally, the lack of information regarding
consumer behavior known to the organization is what makes the organization practice price
dispersion in its venues or events, and thus can be categorized as second degree price
discrimination (Courty, 2000).
More recently, empirical research has set to analyze not only the causes of price
dispersion and price discrimination but also the effects of these behaviors (e.g., Courty &
Pagliero; Eckard & Smith, 2012; Humphreys & Soebbing, 2012; Soebbing & Watanabe, 2014).
One of these effects is the revenue generation for organizations who engage in price
discrimination. As Courty and Pagliero (2012) expressed, little empirical research accounts for
the impact of price discrimination or price dispersion on revenues. Their study of concert
revenues associated with the top 100 grossing musical artists from 1992 to 2005 found that price
discrimination, defined as offering more than one ticket price for a concert, led to a significant
increase in revenues. However, they observed these concerts only offered one or two price
levels. While their results shed additional information regarding the impact of price
discrimination, they are limited by the small variation in the number of prices offered to
consumers in the primary ticket market.
The purpose of the present research is to extend the literature examining the impact of
second degree price discrimination/price dispersion on team revenues. The empirical setting
used is Major League Baseball (MLB) from 1990 through 2010. During the sample period, MLB
teams set their ticket prices prior to the start of each season for the 81 home games they will host
will little opportunity to adjust these prices (Shapiro & Drayer, 2014). This action leads to
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uncertainty regarding how to accurately price tickets for each of these games. Thus, it is
expected that MLB teams would attempt to price discriminate in order to capture additional
revenue based on discernable seat quality and consumer’s willingness to pay.
In addition to examining the impact price discrimination has on revenues, the present
research looks at the impact of price discrimination across facilities of various ages (i.e., facility
eras). During the last 20 years, most MLB teams moved into new “fully loaded” stadiums.
These new stadiums replaced existing facilities that were not physically obsolete, rather, they
were commercially obsolete by failing to provide the necessary accommodations for fans and
revenue sources for teams (Seifried, 2010). Moreover, baseball only facilities appear to provide
additional information regarding the impact that price discrimination can have on revenues,
something that previous research has not examined.
Using this ticket price data along with data collected on MLB revenues and other
controlling factors, we estimate a two-staged least squares (2sls) estimation. Results from the
2sls model indicate that neither the number of price levels offered nor the Gini coefficient of
these prices impact team revenues. Specifically examining how price discrimination impacts
facility age, we find both price discrimination measures have a positive impact on revenues in
new facilities. However, price discrimination within older facilities does not impact team
revenue. These results provide further information regarding the role of price discrimination
over time throughout an industry and the impact newer facilities have in maximizing revenues.
2. Literature Review
Entertainment ticket price theory traces back to Hedonic theory (Rosen 1974), in which
ticket prices are assigned to seats based on the quality of the seat characteristics. Assuming a
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competitive environment, pricing seats in such a manner is similar to second degree price
discrimination (Courty, 2000). From this, little research attempted to analyze how organizations
should sort their seating categories, as well as the optimal prices for these seating categories.
The primary empirical examination in this area found organizations that did not scale prices for
their events saw reduced revenues compared to ones who offered multiple priced tickets
(Huntington, 1993).
Courty and Pagliero (2012) noted the dispersion of ticket prices for events in venues can
be traced to the practice of second degree price discrimination. Dana Jr. (2001) posited when
demand is unknown to the producer, it is in the producer’s best interest to price discriminate.
Even though the sources and determinants of price discrimination have been analyzed, few
studies considers the impact the practice has on an organization’s revenues. Theoretically, price
discrimination compared to just offering one price would allow firms to increase their revenues
(Leslie, 2004). Empirical evidence by Huntington (1993), examining the relationship between
price dispersion and box office revenue in Great Britain, found organizations unable or unwilling
to practice multiple pricing strategies saw reduced revenues compared to organizations offering
multiple priced tickets. Leslie (2004) similarly examined pricing behavior across one Broadway
show for the year. Within, Leslie (2004) found price discrimination increased a firm’s profit by
5% and questioned why the firm did not offer additional price levels.
Recent empirical work in the music industry led to additional information regarding the
impact of price discrimination and revenues. Eckard and Smith (2012) studied multi-tiered
pricing, charging different prices for seats in different sections of venues, for 45 concerts. They
found that multi-tiered pricing led to an increase of just under $1 million. Thus multi-tiered
pricing impacts not only the bottom line, but also it can assist in the segmentation of consumers
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by income level prior to the purchasing decision. Finally, Courty and Pagliero (2012) undertook
a systematic study of the entertainment industry looking at 21,000 concerts put on by the top 100
grossing artists over a 13-year period. Controlling for artist, market, and venue, they looked to
see the impact of an artist offering more than one price on revenue. Results found the action of
price discrimination led to an increase in revenue by about 5%.
Further considering the conception of price dispersion being a practice of price
discrimination, it is notable that economic literature has examined the difference in price for
similar products as being related to marginal cost variation (Shepard, 1991; Clerides, 2004).
From the perspective of sport, the practice of offering multiple ticket prices for an event has been
discussed in the frame of price dispersion (Soebbing & Watanabe, 2014; Watanabe et al., 2013),
and thus is not often linked directly to price discrimination. Second degree price discrimination,
the one which is noted as being the cause of price dispersion (Courty, 2000), is when the
organization, because of imperfect information, will sell different amounts of tickets at different
prices. In this, there is a curious question as to which practices are being conducted by
professional sport organizations in the setting of their ticket prices. The literature to date
indicates that the practice of managers is a form of second degree price discrimination, that is,
tickets are sold with relatively little information with the belief that the dispersion in prices will
help capture additional revenues
A distinction is made between different types of price discrimination and why firms price
discrimination. For example, a review of the literature notes that the economic focus on pricing
in the entertainment industry has been rather scarce (Courty, 2000). Next, because tickets are not
always a truly homogeneous product, that is every seat is different, Courty (2000) argued there
was a need to consider second degree price discrimination as an “insightful” way to investigate
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entertainment ticket markets. From this, price discrimination and ticket pricing theory are
important in attempting to understand various ticketing practices, and the financial and economic
benefits which they can bring to organizations (Gailey, Dixit, Whipple, & Javalgi, 2012). As
research has shown that increases in price dispersion can be traced to price discrimination
(Borenstein & Rose, 1994), it becomes even more important to understand the relations between
price discrimination and price dispersion.
In summary, limited research found a positive impact on revenues for firms in the
entertainment industry who offered different prices for the same good. While this result is not
surprising, it does raise interesting questions related to two specific areas. First, previous
research noted on the limited amount of price discriminated practiced by the music industry and
wondered what impact more price levels would have on revenue. Second, the facilities in which
the entertainment events are performed received very limited examination in previous research.
MLB provides a good empirical setting to examine these two questions.
3. Major League Baseball
In the case of Major League Baseball (MLB), the league produces contests available for
consumption at its ballparks and stadiums. Yet MLB, despite being one of the largest and
traditionally most successful sport entities in North America, has not been able to maximize
revenues from ticket sales for separate events (Rascher et al., 2007; Salant, 1992). Coates and
Humphreys (2007) and Fort (2004) positioned the demand for tickets from an inelastic strategy
where set ticket price ranges do not significantly affect demand. Additional research on
professional sport franchises show ticket pricing strategies may be part of a complex pricing
package involving other goods and services offered at sporting events (Heilman & Wendling,
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1976; Krautmann & Berri, 2007). In this approach, the inelastic pricing method involving tickets
is positioned as a tool to help prompt spending on other consumer opportunities (e.g., retail
merchandise and concessions) at the individual event (Marburger, 1997).
Specifically considering price dispersion, Humphreys and Soebbing (2012) empirically
tested Dana Jr.’s (2001) theoretical model examining the determinants of price dispersion in
MLB. Using MLB ticket data from 1975 to 2008, Humphreys and Soebbing (2012) found teams
producing a higher variability in their seasonal winning percentage over a five-year period
experienced a decrease in price dispersion. They posited this outcome resulted from “teams with
larger variation in demand are able to learn more about their customers from this variation, and
can offer tickets at fewer price points to exploit differences in demand elasticity” (p. 306).
Watanabe et al. (2013) built off Humphreys and Soebbing’s (2012) research to look at the impact
that MLB’s agreement with the secondary ticket market company Stub Hub and how it altered
the pricing policies of teams. They found teams not only increased the number of price levels,
but also the disparity between price levels. Finally, Soebbing and Watanabe (2014) analyzed the
impact price dispersion had on regular season MLB attendance. They found increased price
dispersion had a negative effect on regular season attendance and postulated the negative
relationship between price dispersion and attendance meant teams were utilizing multiple
different price levels to increase revenue. This rationale is consistent with Dana Jr’s (2001)
research but not tested in their research.
3.1. MLB Facilities
When investigating ticket pricing behavior in MLB, it is important to understand the role
facilities may play in the pricing behavior as well as overall team revenues. To support the
possibility of price dispersion and the opportunities franchises took to improve revenues as part
7
of a complex pricing package involving other goods and services offered at sporting events, this
work recognizes several other settled points related to facility age and the pursuit of revenues.
Ritzer and Stillman (2001) argued MLB sought to reestablish the spectator intimacy it lost when
the multi-purpose cookie cutter era (i.e., 1953-1992) structures emerged. Seifried (2010) defined
cookie cutter venues as municipally-built facilities that were “gigantic, symmetrical, sterile
buildings” generally surrounded by vast parking areas within suburban locations that hosted both
MLB and National Football League (NFL) franchises simultaneously (p. 65). Next, Seifried
(2010) characterized these major league venues as regularly failing to maximize revenue from
the growing number and variety of revenue sources like gate receipts, media broadcasting rights,
and in-stadium opportunities (advertising, parking, concession, luxury seating, and merchandise
sales) prior to the modern era of facility construction (i.e., 1992 to present). Thus, the search to
maximize revenues acted as the main driver for the replacement of cookie cutter facilities.
This position emerged despite a lack of anything being physically wrong with cookie
cutter era sport facilities (Seifried, 2010). However, like Seifried (2010) argued and Euchner
(1994) and Ritzer and Stillman (2001) suggested, cookie cutter structures ultimately became
failures because their existing shapes and forms prevented inexpensive renovation efforts
capable of providing the necessary luxury accommodations and entertainment options demanded
by fans, participants, and business partners of MLB and the NFL at various cost points. Further
driving this agenda was the growing recognition that luxury accommodations and their
associated profits did not have to be shared with other members of the league like their gate
receipts (Fort, 1997; Hamilton & Kahn, 1997; Zimmerman, 1997).
The change from Memorial Stadium to the creation of Oriole Park at Camden Yards
(Baltimore), the recognized first post-modern ballpark (e.g., Chapin, 2004; Seifried, 2010),
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illustrates this point well. Memorial Stadium, the predecessor to Oriole Park, emerged as a
horseshoe 50,000-seat multi-purpose facility for football and baseball in 1954 following a
$7,500,000 renovation to welcome the new Orioles (MLB) franchise (via St. Louis) to the
Baltimore area (Richmond, 1993). Yet, by the end of the 1980s, the Orioles initiated
conversations with the City of Baltimore to help them build a new ballpark. Specifically, the
Orioles believed they could not field a competitive team without the additional revenue a new
stadium’s variable resources would provide them (Hamilton & Kahn, 1997; Richmond, 1993).
Hamilton and Kahn (1997) also acknowledged the Baltimore Orioles sought to incorporate large
premium and luxury seating arrangements within their new and smaller sport facility (e.g.,
48,262) to help them earn additional money. From Oriole Park, Baltimore immediately became
one of the most lucrative teams by 1994 because the facility provided them with an additional
$19.7 million in revenue (Ozanian, 1995). Since this point, many other ballparks followed the
Oriole Park model (Chapin, 2004; Seifried, 2010). Moreover, as Greenberg (2004) posited
“[n]ow that the owners and facility districts have determined that venues are the new Taj Mahal's
packed with cash flow potential, facility districts and the owners will continue to figure out how
to tap into lucrative revenue streams that increase the bottom line” (p. 107). This movement to
newer, baseball only facilities provides an opportunity to examine the impact that price
discrimination has on revenues in both old and new facilities and to understand the influence of
luxury seating options as potentially important price points.
4. Data and Empirical Specification
The present research looks at the impact that price discrimination/price dispersion has on
MLB total team revenue from all MLB teams located in the United States (US). The sample
period is from 1990 until 2010 where there are 572 U.S. team-season observations. The revenue
9
data were obtained from Forbes magazine and Rod Fort’s sport business pages1 and converted to
2012 dollars using the Consumer Price Index (CPI) for all urban consumers through the Bureau
of Labor Statistics. Consistent with previous research examining revenues in both sport (e.g.,
Gustafson & Hadley, 2007) and non-sport settings (e.g., Courty & Pagliero, 2012), total team
revenues are transformed to its natural log (LogRevenues).
To evaluate the natural log of real team revenues, explanatory variables are partitioned
into five groups: ticket pricing, facility characteristics, team characteristics, market
characteristics, and league characteristics. The main variables of interest are two ticket price
variables, two stadium variables, and their interaction terms. Ticket prices were obtained from
the MLB annual media guide publications. These publications, called the Red Book for the
American League and the Green Book for the National League, have been published since the
1960s and contain a variety of data including player statistics, team hotel locations, and
attendance. After 2008, MLB moved from a print format of its media guides to electronic format.
With this move to electronic format, MLB also required a MLB granted password to gain access
to the media guides for these seasons. The 2009 guides did not provide any ticket prices. As a
result, prices for 2010 were collected from each individual team’s website prior to the start of the
regular season. After 2011, many MLB teams moved to a dynamic ticket pricing system. Thus,
no set ticket price levels or ticket prices could be obtained.
For the present research, the ticket prices are at the levels for which the teams offer the
most amount of games. If the Tampa Bay Rays offered three types of prices for its home games
throughout a season (e.g., regular, prime, premier), for example, the prices listed as the regular
prices when identified were collected. In the case where teams did not identify a price level as a
1
https://sites.google.com/site/rodswebpages/codes.
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regular price but did identify the total number of home games that each price level was offered,
we collected the prices where the price level was offered the most times during the regular
season. Finally, if neither of those conditions were present, we collected either the middle level
(if 3 levels were presented) or the cheapest price level (if only 2 levels were presented) to avoid
price dispersion that is not due to price discrimination as outlined by Courty (2000).
The first price discrimination variable is the number of non-luxury suites price levels
(PL#) a team offers during the observed season. The second measure of price discrimination is
the Gini coefficient of the 2012 real dollar value for each of the collected price levels (Gini TP).
The Gini coefficient, calculated using the “inequal” command in STATA, “measure the degree
of inequality of a variable in the distribution of its elements” (Gailey et al., 2012, p. 97). The two
stadium variables revolve around the age of the stadium. The first variable indicates if a stadium
is less than or equal to 10 years old (NewStad). Previous literature indicated a detectable novelty
effect associated with the opening of a new stadium (e.g., Coates & Humphreys, 2005). Ten
years is consistent with the research by Coates and Humphreys (2003) who examined the
economic impact of professional sports in U.S. cities.
The second stadium variable revolves around classic stadiums defined as stadiums whose
age is greater than 48 years. Research by McEvoy, Nagel, DeSchriver, and Brown (2005)
examining MLB stadiums found the age of the stadium had a negative impact on attendance until
age 48. After age 48, a positive relationship occurred between stadium age and attendance. As a
result, the present research includes a variable equal to 1 for these stadiums (ClassicStad), 0 for
stadiums less than 48 years old. For both stadium age variables, the ages are based upon the
years the facility has been in operation regardless of whether a team occupied that stadium for all
the years the facility has been in operation. In order to examine the combined impact that
11
stadium type, as defined by age, and price discrimination have on team total revenues, the price
dispersion variables are interacted with the stadium variables (i.e., PL*NewStad, Gini*NewStad,
PL*Classic, and Gini*Classic).
In addition to the stadium age variables, we employ five other stadium variables. The
first is a dummy variable indicating the observed team played in a “single use” stadium
(SingleUse), meaning a stadium is built for one single sport (i.e., baseball). In all other cases, a
team played in a multipurpose stadium. It is anticipated a single use facility will generate higher
team revenues due to improved sightlines for baseball where teams can charge higher prices for
all seats and gain additional revenue. The second variable is the total number of luxury suites in
the team’s facility in the observed season (LuxurySuites). The third variable is the total number
of club suites in the team’s facility in the observed season (ClubSeats). For both variables, the
squared terms are also included (LuxurySuites2, ClubSeats2).
Both the number of luxury suites and club seats were obtained from archival research
looking at stadium designs, structural plans, and renovations over time. These variables are
important as teams can have direct control over how many luxury suites and club seats they can
provide within the stadium each year. In addition, the prices of these options are not included in
the price discrimination variables. It is anticipated the number of luxury suites and club seats
will have a positive impact on team revenue in the observed season.
The quality of the team, which can be observed by its winning percentage (Winpct), is an
important team factor. In addition to the winning percentage in the observed season, the variable
Perf3 is included which examines the sustained organizational performance. This variable is
operationalized as the standard deviation of the observed team’s final regular season winning
percentage over the past three seasons. The number of All Star players a team had on its roster in
12
the previous season (AllStar(t-1)) is used to control for the preference fans may have to watch well
recognized players (Gustafson & Hadley, 2007).
Two market characteristics are included. The first variable looks at the number of years
the observed team has been in the Metropolitan Statistical Area (MSA; TeamAge), reflecting
both the novelty effect of a new team in the MSA either by relocation or expansion (Coates &
Harrison, 2005) and the reflection of the sports team as a highly visible symbol for the MSA
(Crompton, 2004). The second variable controls for the market size of the team, measured by the
market’s real per capita personal income of the observed team’s MSA (PCPI). These figures
were retrieved from the Bureau of Economic Analysis website that is available through the U.S.
Department of Commerce and converted to 2012 real dollars.
Finally, the model includes three league variables. The first variable controls for the
seasons following the creation of the Major League Baseball Advanced Media platform
(MLBAM), created in 2000 to serve as the internet provider for MLB games and other programs
(Watanabe et al., 2013). The second variable is an indicator variable to control for the seasons
following the adoption of Major League Baseball’s agreement with StubHub in 2007 which
provided MLB with revenues from tickets sold through Stub Hub (Drayer, 2011; Watanabe et
al., 2013).
The final variable in the league category is the competitive balance of the league (CB).
In the present study, competitive balance in operationalized by the Noll-Scully Ratio (NSR) and
its squared term. The NSR measures the dispersion of winning percentage amongst all league
members by calculating the actual standard deviation of winning percentage and dividing it by
the ideal standard deviation of winning percentage in the league. In the present research, two
Noll-Scully Ratios are calculated for each season, one each for the American and National
13
leagues. The interpretation of the NSR is the lower the NSR, the higher the competitive balance
in the league. As for the squared term of the NSR variable, Coates, Humphreys, and Zhou
(2014) developed a theoretical model looking at the impact of loss aversion on live game
attendance in MLB. Their results showed a decreasing support for uncertainty of outcome
hypothesis. In order words, fans wanted to see the home team win. Thus, a positive and
significant coefficient of the squared term would support their theory. Finally, a trend variable
(Trend) is included taking the value of 1 for the first year of the sample (i.e., 1990), 2 for the
second year of the sample, and continues until 2010.
4.1. Model
Equation 1 presents the full model described above.
LogRevenuesit = θi + β1PL#it + β2GiniTPit + β3NewStadit + β4ClassicStadit + β5PL*NewStadit +
β6Gini*NewStadit + β7PL*Classicit + β8Gini*Classicit + β9SingleUseit + β10LuxurySuitesit +
β11LuxurySuites2it + β12ClubSeatsit + β13ClubSeats2it + β14Winpctit + β15Perf3it +
β16AllStarsi(t-1) + β17TeamAgeit + β18PCPIit +β19MLBAMt+ β20StubHubt + β21CBit + β22CB2it +
β23Trendit + µit
(1)
In Equation 1, i indexes teams, t indexes seasons, θi are team fixed effects, and µit is the equation
error term. Team fixed effects are important to the present research to control for unobserved
demand heterogeneity similar to how Courty and Pagliero (2012) used facility fixed effects in
their study of price discrimination and revenue in the concert industry.
4.2. Estimation Issues
The biggest estimation issue is the correlation between the team winning percentage in
the observed season and the equation error term, µit. As Gustafson and Hadley (2007) stated,
inconsistent results can emerge when estimating a model similar to Equation 1 using Ordinary
14
Least Squares. As a result, a two-staged least squares (2sls) model is estimated controlling for
the endogeneity associated with the winning percentage variable and the equation error term.
When estimating a 2sls model, finding the proper instrument addressing the endogeneity
problem but not be correlated with µit in Equation 1 can be a difficult task. In the present
research, we adopt two instruments from Gustafson and Hadley’s (2007) study. These two
instruments along with the explanatory variables in Equation 1 are used in the first-stage
regression. The first instrument is the real team payroll (RealPayroll) at the beginning of the
observed season. Payroll numbers were obtained from USA Today and Rod Fort’s Sports
Business Pages, and converted into 2012 Dollars using the CPI for all urban consumers through
the Bureau of Labor Statistics. The second instrument is the number of All Star players on the
team in the observed season (All Star), which is a sign of team quality to help explain winning
percentage.
When estimating a 2sls model, it is imperative the variables in the first-step regression
are valid, meaning they are not correlated with the equation error term. There are a number of
tests to examine the validity of the first-step equation. Staiger and Stock (1997) provided a
diagnostic test revolving around the F-statistic of the first-step regression. They stated an Fstatistic above 10 for the first-stage regression indicates a strong instrument, which occurred in
the present model. The second diagnostic test determines if the first-stage model is
underidentified, with the null hypothesis as the model is underidentified. The results of this test,
using both the Cragg-Donald Wald statistic and the Anderson statistic rejects the null hypothesis.
The final test is the Sargan test for overidentification. With this test, the null hypothesis is the
instruments are valid. The alternative hypothesis is the model is overidentified and the regressors
are not valid. The null hypothesis cannot be rejected, meaning the instruments are valid. Overall,
15
the variables included in the first-step regression are suitable for controlling for the endogeneity
issue present between win percent variable and the equation error term in Equation 1. The firststage regression results are presented in Appendix 1.
5. Results
Table 1 presents the summary statistics for the non-interacted variables in Equation 1 and
the two instruments in the first-stage regression. Recall from above, 2009 is omitted from the
study due to a lack of ticket price data. In addition, the Washington Nationals’ observation for
2005 was removed because no ticket price data was published. Finally, the first season for each
of the expansion teams (i.e., Arizona Diamondbacks, Florida Marlins, Colorado Rockies, and
Tampa Bay Rays) during the sample time period were removed due to not having any previous
winning percentage for the Perf3 variable.2 As a result, the final sample contains 538 teamseason observations for MLB teams located in the United States from 1990 until 2010.
The average total revenue for each team in the sample was $141,513,000. Teams offered
8 different price levels on average with the number of different price levels ranging from 3 to 23.
The average dispersion of those ticket prices was 0.266 with a range from 0.100 (low dispersion)
to 0.582 (high dispersion). In the sample, 29 percent of the team-season observations had
stadiums that were 10 years old or less while 13 percent of observations played their games in a
classic stadium. Approximately 64 percent of the sample observations were played in a single
use stadium. The average number of luxury suites was 72 while the number of club seats was
3,146. Teams averaged approximated $71 million in overall team payroll with two All Stars on
each team. Fifty-two percent of the observations occurred after the creation of MLBAM.
2
For the second and third seasons, we include the previous season and the previous two seasons as the measure of
organization performance for the Perf3 variable as to not lose any additional observations.
16
Finally, the average Noll-Scully Ratio was 1.774 with a range of 1.24 (high competitive balance)
to 2.686 (low competitive balance).
*******************
**Insert Table 1 Here**
********************
Table 2 presents the results from Equation 1. The variables in the present research
explain 85 percent of the observed variation in the dependent variable, LogRevenues. The results
indicate the increase in the number of price levels does not impact overall team revenues. The
dispersion of the ticket prices measures by the Gini coefficient is also not significant. Playing
home games in a stadium 10 years old or less does not impact total revenues. However, playing
home games in a classic stadium does have a significant decrease in team revenues. Interacting
the ticket price variables with the stadium variables, there is a negative and significant decrease
in regards to the ticket price levels in a new stadium. Examining the disparity of ticket prices in
a new stadium, we find that an increase in ticket price disparity increases team revenue. Neither
ticket price dispersion variable is significant when interacting the variable with a classic stadium.
Playing in a single use stadium does have a positive impact on team revenue. Even
though the number of luxury suites does not impact team revenue, the number of club seats does
impact team revenue. On-field performance in the observed season and over the past three
seasons is positive and significant with regards to total revenue. There is a positive and
significant increase in revenue due to the team’s market size and following the creation of
MLBAM. Competitive balance did not have any significant impact with a team’s total revenue.
17
*******************
**Insert Table 2 Here**
********************
6. Discussion
The present research sought to examine the impact that price dispersion, as a form of
second degree price discrimination, has on total revenues of MLB teams. The results from the
2sls model found an increase in the number of price levels (PL#) and the disparity of these ticket
prices using the Gini coefficient (GiniTP) did not impact total team revenue. These results are
not consistent with Salop and Stiglitz’s (1982) early research and more recent research by
Courty and Pagliero (2012) who found price discrimination, measured by an indicator variable
noting if more than one ticket price level is offered for a concert, increased revenue. The result
in Table 2 does extend this research by examining the actual number of levels, not whether an
organization offered multiple prices or a single price.
The effect on stadium age on total team revenue presents differing results. We find a
new stadium, classified as a stadium 10 years old or less, does not increase revenues. Contrary to
new stadiums, classic stadiums have a negative impact on team total revenues. In models such as
Equation 1 where the dependent variable has been log-transformed and the predictors have not,
the format for interpretation is the dependent variable changes by ((exp(β)-1)*100) percent for a
one unit increase in the independent variable while all other variables in the model are held
constant. Thus, playing in a classic reduces the observed team’s total revenues by 20.3%
[(0.7969-1)*100]. Even though previous research found classic stadiums positively influence
attendance (e.g., McEvoy et al., 2005), there is a negative impact on total team revenue. This
18
finding could be due to the lack of modern-day amenities of new ballparks that a classic stadium
cannot support. However, there may be intangible benefits (e.g., nostalgia) that represents values
for the team.
Specifically examining the impact that price dispersion and facility age has on team total
revenue, interesting results are found looking at new versus classic stadiums. Both price
dispersion variables, when interacted with the variable indicating that the stadium is a new
stadium, are statistically significant. However, the impact on total revenue differs. For the
number of price levels (PL*NewStad), total revenues decline. Considering the actual percentage
impact, it is calculated that team revenues decline by approximately 0.8%. In contrast, the
higher the ticket price dispersion in a new stadium (Gini*NewStad), the higher the total revenue.
It is estimated that a one-unit increase would increase team revenues by approximately 101%
([(0.7969-1)*100]-[(1-exp(β)-1)*100)]).
Taken together, these results provide interesting insights regarding the effects of price
dispersion and price discrimination. One could argue that as facilities become more sport
specific, teams are able to differentiate between seat quality and the different amenities that the
stadium offers. The ability to differentiate presents an opportunity to more accurately price
tickets compared to just offering a number of ticket price levels. Thus, it speaks to the efficiency
in ticket pricing.
The other interpretation is that perceived fairness, measured by the disparity in the ticket
price levels, is valued in terms of an organization being able to increase revenue rather than just
offering a number of different price levels in the stadium in the hopes of capturing additional
revenue. Courty (2003) noted an organization sets ticket prices in a way to maintain the
perception of fairness in the minds of consumers regarding their ticket prices. Thus, the positive
19
sign may be an indication of consumers responding to perceived fairness amongst price levels,
which has been found in recent studies examining dynamic ticket pricing markets (e.g., Shapiro,
Drayer, & Dwyer, 2016).
In addition, these results allude to previous research by Salop (1977) who stated firms
practice price dispersion in order to price discriminate. If one would operationalize price
dispersion and price discrimination as the number of price levels and the Gini coefficient
respectively, then the findings in Table 2 are consistent with Salop’s (1977) predictions.
Examining specific facility factors, single use stadium is found to have a significant
impact on team revenues, impacting total team revenues by 13.2%. This finding is consistent
with previous research as a variety of scholars recognized the changing shape of stadiums from
the mid-20th century to present occurred through an emphasis to encourage and maximize
spending by those in attendance before, during, and after the event (Noll & Zimbalist, 1997;
Ritzer & Stillman, 2001; Rosentraub, 1997). Seifried (2010), for example, recognized the most
recent era of major league sport stadiums and ballparks produced venues that were engineered to
become entertainment palaces/destinations for spectators in both live and remote attendance.
Focused within Seifried’s (2010) historical analysis of the changing ballpark was the pursuit of
revenue through the creation of fan amenities and technological advances which occurred as
owners “pushed for fresh facilities because the multipurpose sporting venue [i.e., cookie cutter
from 1953 to 1992] generated an inadequate amount of revenue to meet expenses and maximize
profit expectations or investment returns” (Seifried, 2010, p. 68). The positive impact of single
use facilities supports these findings.
The insignificant impact of number of luxury suites has on a team’s total revenue is
surprising. Examining Table 2, the number of club seats does have a positive impact on a team’s
20
total revenue. The impact of one additional club seat, however, is small (less than 1%). Similar
to the luxury suites, this finding is surprising as one would anticipate that both club and luxury
suites would have a much greater impact in regards to total team revenues.
The results in the present research also provide additional insight on the impact that team,
market, and league factors have on revenues. Team performance, both in the current season and
over the previous three seasons, positively impacts a team’s total revenue. This impact for both
variables is small (less than 1% for a 1-unit increase). Furthermore, the number of All Stars in
the previous season positively impacts team revenues in the observed season by approximately
two percent and is consistent with previous research by Gustafson and Hadley (2007). A team’s
total revenue increases by about 12.8% following the creation of MLBAM, which has been a
success for the league and its individual clubs. Revenues generated from the internet platform
are shared equally amongst all league members (Watanabe et al., 2013). Neither the NSR nor its
squared term has any impact on total team revenues. This result is not too surprising based upon
the theoretical model by Coates et al. (2014). Finally, a positive increase over time is found as
denoted by the Trend variable. The impact of a one unit increase is approximately 3 percent in
revenues and it is reflective of the increasing revenues throughout the sample period.
7. Conclusion
This research study considers the importance of price discrimination and price dispersion
in regards to revenues for sport organizations. The work examines how tickets being sold at
different numbers of price levels and the disparity between those ticket price levels in MLB can
influence the revenues for these franchises. This research places itself within an important line
of price discrimination and price dispersion research both within (Humphreys & Soebbing, 2012;
21
Watanabe et al., 2013) and outside (Courty & Pagliero, 2012; Eckard & Smith, 2012;
Huntington, 1993; Salop & Stiglitz, 1982) the realm of sport. This study is unique as it looks at
the impact on price discrimination/price dispersion based on facility age. It was found price
discrimination, by itself, did not impact total team revenues. However, price discrimination in a
newer facility did have an impact on team revenues. The findings also help to further the
argument for employing price dispersion at sport and entertainment events, as they support
Huntington’s (1993) early finding of theaters boosting revenue through price dispersion. The
results from the present research also illustrates Courty’s (2000) comment regarding price
discrimination where consumers privately are aware of their willingness to pay, thus, the
organization offers a number of ticket price options to allow consumers to choose their ticket
price.
From a theoretical standpoint, the results and implications drawn from this research help
to further the understanding of price discrimination. While recent research found a positive
increase regarding price discrimination and revenue, they are limited due to the fact that very few
ticket prices are offered for their events. In the case of the present research, MLB is an industry
where every organization practices some form of second degree price discrimination. In
addition, these firms offer up to 18 different price levels providing a large amount of variation
not seen in previous research. The second contribution of this research looks at the variation of
these price levels. The variation of the face value of these ticket price levels matter in terms of
total revenue. Thus, the research here presents additional insights that Courty and Pagliero
(2012) as well as Eckard and Smith (2012) could not examine. Finally, the present research
looks at the impact of facilities and how they impact the price discrimination and revenue
relationship. New facilities (defined as less than 10 years old) matter in terms of positively
22
impacting total revenue. Again, the variation of facilities and the amount of new facilities built
during this time period leads us to be able to examine facility age and how age interacted with
price discrimination impacts revenue. The results suggest price discrimination in newer facilities
is a legitimate way for teams to look at consumer’s willingness to pay and is able to capture
additional revenue by offering multiple prices.
23
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29
Table 1
Summary Statistics (n=538)
Variable
RealRev (in 000)
PL#
GiniTP
NewStad
ClassicStad
Singleuse
LuxurySuites
ClubSeats
Perf3
AllStari(t-1)
TeamAge
PCPI
MLBAM
CB
Trend
WinPct (*100)
RealPayroll (in 000)
AllStar
n=538
Mean
141,513
7.913
0.266
0.294
0.251
0.639
72.429
3,146
49.918
2.082
60.151
44.736
0.528
1.774
10.916
50.083
71,384
2.076
Std. Dev.
Min
Max
62,678
16,528
449,594
3.298
3
23
0.094
0.100
0.582
0.456
0
1
0.434
0
1
0.481
0
1
34.733
0
158
2,811
0
10,000
5.565
33.800
63.767
1.316
1
8
39.941
2
135
6.940
33.610
68.665
0.500
0
1
0.352
1.238
2.686
5.819
1
21
6.990
26.500
71.600
35,507
14,207
244,885
1.329
1
8
30
Table 2
Regression Results: Dependent Variable is LogRevenues
Variable
PL#
GiniTP
NewStad
ClassicStad
PL*NewStad
Gini*NewStad
PL*ClassicStad
Gini*ClassicStad
Singleuse
LuxurySuites
LuxurySuites2
ClubSeats
ClubSeats2
WinPct (*100)
Perf3
AllStari(t-1)
TeamAge
PCPI
MLBAM
CB
CB2
Trend
R2
Angrist-Pischke F Stat
Anderson canon corr. LM Stat
Hansen J Stat
Coef
Stnd Error p-value
Impact (in%)
0.011
0.007
0.119
-0.012
0.216
0.957
0.043
0.087
0.620
-0.227
0.108
0.036
-20.306
-0.019
0.009
0.031
-0.761
0.707
0.308
0.022
101.596
0.007
0.012
0.571
-0.210
0.466
0.653
0.125
0.059
0.036
13.275
-0.003
0.002
0.121
<0.001
<0.001
0.138
<0.001
<0.001
0.040
0.005
<0.001
<0.001
0.031
0.010
0.003
<0.001
0.972
0.009
0.002
<0.001
0.913
0.020
0.008
0.009
1.995
-0.001
0.003
0.766
0.016
0.004
<0.001
1.607
0.120
0.034
<0.001
12.797
-0.173
0.221
0.433
0.057
0.058
0.331
0.031
0.005
<0.001
3.146
0.85
94.44
<0.001
150.35
<0.001
2.51
0.113
31
Appendix 1
First-Stage Results for Equation 1 [Dependent Variable is WinPct (*100)]
Variable
PL#
GiniTP
NewStad
ClassicStad
PL*NewStad
Gini*NewStad
PL*ClassicStad
Gini*ClassicStad
Singleuse
LuxurySuites
LuxurySuites2
ClubSeats
ClubSeats2
Perf3
AllStari(t-1)
TeamAge
PCPI
MLBAM
CB
CB2
Trend
RealPayroll
AllStar
Coef
0.004
2.418
3.829
1.879
-0.630
0.024
-0.317
-0.010
3.412
-0.039
<0.001
-0.001
<0.001
-0.040
0.225
-0.187
0.115
-1.961
-0.796
0.153
0.090
<0.001
2.376
Stnd Error
p-value
0.190
0.983
5.232
0.644
2.084
0.067
2.823
0.506
0.227
0.006
0.037
0.511
0.434
0.465
0.051
0.838
1.680
0.043
0.049
0.422
<0.001
0.584
0.001
0.143
<0.001
0.064
0.065
0.534
0.213
0.292
0.090
0.039
0.119
0.334
0.994
0.049
6.324
0.900
1.672
0.927
0.146
0.539
<0.001
<0.001
0.198
<0.001
32