Determinants of Spectator Attendance
311
Journal of Sport Management, 2002, 16, 311-330
© 2002 Human Kinetics Publishers, Inc.
Determinants of Spectator Attendance
at NCAA Division II Football Contests
Timothy D. DeSchriver
University of Massachusetts
Paul E. Jensen
Drexel University
The purpose of this study was to analyze the relationship between spectator
attendance at NCAA Division II football contests and selected determinants
by estimating multiple economic demand models. The two primary determinants analyzed were winning percentage and promotional activity. Demand
models were estimated using OLS and fixed-effect regression analysis. The
results suggested that both current and previous year winning percentages are
positively related to attendance. Furthermore, it is shown that the effect of
previous season winning on attendance diminishes while the effect of current
season winning increases as the season progresses. The results also indicated
that promotional activities, the number of enrolled students, and market competition significantly affected attendance. Overall, the demand models explained between 37 and 70 percent of the variation in spectator attendance.
The findings of this study may aid Division II athletic administrators who are
attempting to increase revenues by attracting additional spectators to smallcollege football contests.
Introduction and Purpose
Millions of spectators attend American intercollegiate sporting events each year.
In 2000, over 39 million people attended National Collegiate Athletic Association
(NCAA) football games across the four levels of competition (I-A, I-AA, II, and
III) (Campbell, 2001).1 Universities such as Penn State and the University of Tennessee attract over 95,000 spectators for each home game, and ticket revenues can
eclipse $3 million for a single contest. Even Division II institutions, such as North
Dakota State University, average over 12,000 spectators per home contest
(Campbell, 2001). While the dollar amount of revenues from football ticket sales
T.D. DeSchriver is with the Department of Sport Management, University of Massachusetts, Amherst, MA, and P.E. Jensen is with the Department of Economics and International Business, Drexel University, Philadelphia, PA.
311
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DeSchriver and Jensen
are drastically lower at NCAA Division II institutions they are, in many cases, an
equally important source of revenue. Unlike Division I-A institutions, Division II
schools generate very little, if any, revenue from sources such as media rights fees,
luxury seating, sponsorship, and advertising.
Sport consumers are most familiar with Division I-A football. Over 110 institutions compete in Division I-A football, the highest level of competition. Division II football differs significantly from Division I-A. First, the maximum allowable number of grants-in-aid is considerably smaller, 35 versus 85, and competition is focused on a regional level. Division I-A competition is considered to be
national in scope. Lastly, in 1999, 64 percent of Division I-A institutions reported
a net profit from their football operations while only 23 percent of Division II
programs reported a profit (Fulks, 2000).
Nine percent of all Division II athletic revenues, excluding institutional support and student fees, are generated by ticket sales to home contests (Fulks, 2000).
For these institutions, football is often the primary sport for the generation of ticket
sales revenue. As funding from traditional sources such as institutional support
and student fees become somewhat tenuous, there is a need for small college athletic administrators to maximize revenue from ticket sales. In addition, increased
football ticket sales also lead to increased revenue from sources such as concessions,
advertising, and booster donations (Coughlin & Erekson, 1985; Hilkemeyer, 1993).
While many Division II athletic administrators are aware of the financial
importance of ticket sales, they have very little information on the determinants of
spectator attendance at football contests. The purpose of this paper was to develop
economic demand models to empirically identify the determinants related to spectator attendance at NCAA Division II football games. The results will aid collegiate athletic administrators in developing marketing strategies to increase spectator
attendance and the corresponding revenue. Within this paper, the analysis of those
determinants that are controllable by athletic administrators will be emphasized.
Athletic administrators have substantial influence over variables such as onfield success, stadium age, and promotional activities. Perhaps the most widely
researched relationship has been between on-field success and spectator attendance. Within this paper, a new approach is taken to measure this relationship.
Similar to earlier studies, team-winning percentage is used as a proxy for on-field
success. Additionally, winning percentage is interacted with a time variable to
determine how the relationship between on-field performance and spectator attendance changes over the course of a season. It is logical to assume that the effect of
winning percentage will increase throughout the course of a season. A 10-0 team
should attract more spectators than a 1-0, holding all else constant. The following
research will add to the body of knowledge by determining the magnitude of this
effect, if it exists at all.
Another unique aspect of this paper is its definition of promotional activity.
In most demand models of professional sport attendance, promotional activity has
been represented through a single categorical variable that measures the existence
of promotions but does not differentiate between them (Marcum & Greenstein,
1985; Wall & Myers, 1989). In the research of McDonald and Rascher (2000) a
Determinants of Spectator Attendance
313
series of variables were used to represent promotional activity in Major League
Baseball. In general, the variables used by McDonald and Rascher (2000) were
related to the frequency and cost of the game-day promotional giveaways such as
Bat Day and Hat Day. While these definitions were successful for Major League
Baseball, they may be somewhat flawed for intercollegiate football due to the
presence of Homecoming. Most American colleges and universities celebrate Homecoming during the football season. This special promotion invites alumni and friends
of the university back to a campus for a day of fun and activities. While most
colleges have many other promotional activities such as Parent’s Day, Youth Day,
and Hall of Fame Day throughout the course of the season, few have the success of
Homecoming. To account for the uniqueness of Homecoming on most university
campuses, two categorical variables were used to analyze the relationship between
spectator attendance, Homecoming and other promotional activities.
There are several other determinants of spectator demand such as weather,
and student enrollment that are not controlled by collegiate athletic administrators
and in order to build an accurate demand model these determinants must also be
included. By incorporating these uncontrollable determinants a greater percentage
of the variation in spectator attendance may be explained and the model may be a
more accurate representation of the demand for Division II football contests.
Review of Literature
There has been a great deal of research devoted to the study of the factors influencing sport consumption, represented by spectator attendance. Researchers such as
Pease and Zhang (2001), Iso-Ahola and Hatfield (1986), Courneya and Carron
(1992), and Wann, McGeorge, Dolan, and Allison (1994) have focused on the
socio-motivational factors which relate to game attendance frequency by individual
consumers. Their research has focused on how areas such as spectator aggression,
audience facilitation, and team identification have been related to the decisions
made by individuals to attend sporting events. While this line of research is important, the focus of this paper is on the determinants that affect aggregate levels of
attendance at sporting events.
The use of economic demand models has been the most popular method for
the analysis of aggregate spectator attendance at collegiate football contests. The
majority of research using demand models has been directed toward professional
sports such as baseball (Kahane & Shmanske, 1997; McDonald & Rascher, 2000;
Noll, 1974; Scully, 1974), football (Hansen & Gauthier, 1989; Siegfried & Hinshaw,
1979), basketball (Burdekin & Idson, 1991; Whitney, 1988; Zhang et al., 1995),
soccer (Baimbridge, 1997; Dobson & Goddard, 1992; Peel & Thomas, 1988; Rivett,
1975), and ice hockey (Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al.,
1996). Overall, the previous research has found that a positive relationship exists
between spectator attendance and the two factors that are emphasized in this paper, promotional activity and on-field success.2
In a review of the literature, few articles were found related to the determinants of spectator attendance at American collegiate sporting events. Two studies
314
DeSchriver and Jensen
(Fizel & Bennett, 1989; Kaempfer & Pacey, 1986) focused on the relationship
between televised sport and collegiate football attendance at the NCAA Division
I-A level. Additionally, Wakefield and Sloan (1995) analyzed the effects of factors
such as team loyalty and facility characteristics on the decisions made by individuals to attend college football games.
Two additional studies (DeSchriver, 1999; Wells et al., 2000) analyzed the
determinants affecting spectator attendance for NCAA Division II football games.
However, DeSchriver (1999) analyzed only the individual relationships between
attendance and selected determinants through correlation analysis. A full economic
demand model was not constructed and, thus, there was no analysis of the effect of
the selected determinants as a group on attendance. Wells et al. (2000) constructed
a demand model but several important determinants, such as weather and the distance between the two competing institutions, were omitted from the model. In
addition, their use of winning percentage as a proxy for on-field success did not
attempt to incorporate the effect of how winning changes over the course of a
season.
Supply and Demand Model for Division II Football
In order to study this market and to highlight a somewhat unique feature of this
market we first develop a general model of supply and demand. The inverse demand for tickets is given by
PD = f(QD, X)
(1)
where PD is the price of the tickets, QD is the quantity demanded, and X represents
a vector of exogenous demand determinants. The marginal cost of production is
the cost of admitting another consumer into the event. Thus, the supply side of the
market may be
MC = g(Qs, Z)
(2)
where MC is the marginal cost of production, QS is the quantity supplied, and Z
represents a vector of exogenous cost variables. It is assumed that suppliers have
some market power and that they select quantity to maximize profit ( ). Thus the
first order conditions for profit maximization imply
(3)
Solving equation 3 for the optimal quantity yields
Q* = h(X,Z)
(4)
The optimal price is then obtained by substituting Q* into equation 1. Equation 4
clearly shows that the equilibrium values of price and quantity are functions of X
and Z. Therefore, for markets that are appropriately described by equations 1 and
Determinants of Spectator Attendance
315
2 we see that P* and Q* are determined simultaneously by the interaction of supply and demand conditions. In such cases one cannot simply estimate demand by
regressing observed equilibrium values of Q on P and X as this ignores the effect
that Z has on the equilibrium values. This is the classic simultaneous equations
problem.
When thinking about a model of supply and demand for Division II football
tickets, one important simplification can be made to the general model described
above. The major costs associated with the event (team expenses, equipment and
facility costs) are essentially independent of the number of people that attend the
event and thus are fixed costs. Therefore, for all practical purposes the marginal
cost of supplying tickets for a game is zero.3 Given this, equation 2 can be replaced
with the following
MC = 0
(2b)
Solving the firm’s profits maximization problem given equations 1 and 2b yields
(3b)
Solving this for Q yields
Q* = h(X)
(4b)
The critical point to make is that in this model P* and Q* are completely demand
determined. The intuition for this result is that with zero marginal costs, profit
maximization reduces to revenue maximization, which is independent of supply
conditions. Therefore, under these conditions there is no simultaneous equations
problem and one may estimate demand by regressing Q on P and X.
Econometric Model
An econometric demand model was constructed to analyze the effect of selected
determinants on game attendance. The determinants were selected based on a review of pertinent literature. The literature separated the determinants into four
general categories (Cairns et al., 1986). The following functional form represents
these groupings.
Attendance = f (economic determinants, demographic determinants, game
attractiveness determinants, residual preference determinants)
To accurately analyze these relationships, data were collected for three seasons of play (1994, 1996, and 1999). From the data, four demand models were
estimated. Individual models were estimated for each of the three seasons and a
fourth model was estimated for the three years of data pooled together. This allowed for the relationship between attendance and the determinants to be confirmed
by multiple demand models. In addition, the use of multiple years of data allowed
for the analysis of changes in spectator attendance across years, holding all else
316
DeSchriver and Jensen
constant. The following is a list of definitions for the dependent variable, spectator
attendance, and the 13 determinants. Given that some of these determinants were
measured through a series of explanatory variables, there were 21 explanatory
variables included in the single year models. With the inclusion of two categorical
variables for time, 23 explanatory variables were included in the multi-year model.
The following is a list of the variables including in the econometric model.
Complete descriptions of each variable are located in appendix A.
Dependent Variable
ATTEND – Game Attendance
Number of spectators who attended an individual football game.
Economic Determinants
GAP - General Admission Ticket Price
The price of a general admission ticket to a football contest.4
SAP – Student Admission Policy
1 if enrolled students are admitted free of charge, and 0 otherwise.
COMP – Number of Competitors in the Market Area
Number of competitors within 50 miles of institution.
Demographic Determinants
MILES – Miles Between Competing Institutions
Distance, in miles, between the cities in which the two competing teams were
located.
STUDENR – Student Enrollment
The number of full-time enrolled students.
CITYPOP – City Population
The number of residents living in the city where the home team was located.5
Game Attractiveness Determinants
WINPER – Home Team Winning Percentage
The interaction of winning percentage and the time period variables.
PREVWIN – Home Team Winning Percentage in Previous Season
The interaction of winning percentage in the previous season and the time period
variables.
HCPROM/OTHPROM - Promotional Activity
Two categorical variables were used to represent promotional activity. One variable accounted for the presence of Homecoming (HCPROM) and the second variable accounted for all other promotional activities (OTHPROM).
Residual Preference Determinants
WEAT – Weather
A categorical variable accounting for the presence of rain or snow.
Determinants of Spectator Attendance
317
STADAGE – Age of Stadium
The age of the stadium, in years.
TP – Time Period
The seasons were divided into four three-week time periods. Three categorical
variables where used to indicate the time period in which a game was played.
Year – Year of Season
Two categorical variables were used to indicate the year in which the game was
played (1994, 1996, or 1999).
Lastly, the issue of stadium capacity as it relates to attendance must be addressed. Some studies (Noll, 1974) have measured attendance as a percentage of
stadium capacity. This specification is accurate when measuring demand for individual contests where stadium capacity may limit attendance. This is true of sports
where sellouts occur such as the National Football League and the National Hockey
League. In these cases, the failure to account for capacity constraints can result in
the regression models being misspecified. However, empirical and anecdotal evidence strongly suggests that NCAA Division II football games had a very high
level of excess capacity. None of the survey respondents noted that their entire
supply of tickets were sold for any games. Therefore, similar to McDonald &
Rascher (2000), it was determined that spectator attendance was the most appropriate measure of demand given that stadium capacity was not a binding constraint.
Data Analysis
The primary method of data collection was the distribution of a survey to the sports
information/media relations offices at all NCAA Division II institutions that competed in football. The survey was distributed following the 1994, 1996, and 1999
seasons. In addition, secondary sources such as the US Higher Education Directory, US Census data, and TRIPMAKER computer software were used to obtain
data for student enrollment, city population, and miles between competing institutions. Table 1 provides a summary of the respondent’s information for each year,
Table 1
Respondent Summary Data
Year
Number of
institutions responding
Number of
total games played
1994
1996
1999
Total
96
82
82
260
476
411
415
1302
Note: In each of the three years, 147 teams competed in NCAA Division II football.
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Table 2a
DeSchriver and Jensen
Summary Statistics
Variable
Spectator attendance
Ticket price (GAP)
Student enrollment (STUDENR)
City population (CITYPOP)
Miles between institutions (MILES)
Home team winning %age
Home team winning %age in previous
season
Table 2b
Mean
SD
Minimum Maximum
3,796.5
$5.73
6,034.9
62,424.4
297.7
.496
.524
3,504.4
$1.82
4,589.5
129,208.0
255.9
.364
.245
200
$2
624
1,287
2
0.000
0.000
28,820
$21
35,319
999,999
2,947
1.000
1.000
Bivariate Correlation Matrix
ATT
Attendance
City
pop.
Stud
enr.
Ticket
price
HT Win
HT Win
%age
Miles %age (Prev. sea.)
1.00
City population
.13
1.00
Student
enrollment
.17
.29
1.00
Ticket price
.39
.15
–.02
1.00
Miles between
institutions
.06
.03
.14
–.00
1.00
Home team
winning %age
.14
–.04
.07
.01
–.02
1.00
HT winning %age
in prev. season
.21
–.09
.20
–.00
.06
.34
1.00
and Tables 2a and 2b show the summary statistics and bivariate correlations for the
key variables.
Ordinary least squares (OLS) regression analysis was initially used to estimate the model for each of the three years of data. The three years of data were
also combined and the model was estimated for the entire set of 1302 games played.
It should be noted that a White correction was incorporated into the OLS models
in order to correct for heteroscedasticity.
Determinants of Spectator Attendance
319
Additionally, the data were transformed into logarithmic functional forms
when appropriate. Determinants that took the form of categorical variables were
not transformed. The transformation allowed for the analysis of the relationship
between attendance and the determinants to be in the form of demand elasticities.
Given the definitions of the determinants and the logarithmic transformation, the
regression equation for each individual year took the following form:
log(ATTEND) = 0 + 1[log(GAP)] + 2[log(CITYPOP)] + 3[log(MILES)] +
[log(STUDENR)] + 5[log(STADAGE)] + 6[log(COMP)] + 7[log(SAP)] +
4
(WEAT) + 9(HCPROM) + 10(OTHPROM) + 11(TP1) + 12(TP2) + 13(TP3) +
8
[log( WINPER1)] + 15 [log( WINPER 2)] + 16 [log( WINPER3)] +
14
[log( WINPER4)] + 18 [log( PREVWIN1)] + 19 [log(PREVWIN 2)] +
17
[log(PREVWIN3)] + 21[log(PREVWIN4)] + ε
20
where ε is the model error term. The regression equation for the combined data set
took a similar form with the addition of two categorical variables representing the
year in which the game was played.
Findings and Discussion
Four demand equations were estimated using multiple regression analysis; one for
each year and one for the combined data set. Table 3 summarizes the statistical
results from the regression equations. Eleven of the 23 explanatory variables were
statistically significant at the .05 level in all of the regression equations. The range
of R2’s for the equations was from .368 to .483.
Several of the controllable determinants of spectator attendance were statistically significant. It was expected that current season-winning percentage would
have little, if any, significance in the early part of the season. But, as the season
progressed, winning percentage would increase in significance. For the most part,
the statistical results supported this hypothesis. Note that in the four equations that
were estimated, current season winning percentage is never significant in the first
three weeks of the season but is always significant in the last three weeks of the
season. Also note that in general, the magnitude of the coefficients increases as the
season progresses. For instance, in the combined case the coefficient increases from
.08 in the first time period to .95 in the last time period. From the statistical findings,
it appears that on-field success in the current year is positively related to attendance and that the magnitude of this relationship increases as a season progresses.
Obviously, all college athletic administrators want to have on-field success.
The findings suggest that on-field success has a direct relationship to attendance
and, therefore, also has an economic impact on the athletic program. Additional
spectators result in additional revenues from ticket sales, concessions, and merchandise. Administrators may wish to invest more resources into areas such as
grants-in-aid, recruiting, coaches’ salaries, and equipment if the result is more onfield success. However, it must be noted that this additional investment comes at a
cost. Administrators should only invest in these additional resources if they believe that the expected increase in revenue for the athletic department due to winning will offset the additional costs.
320
Table 3
DeSchriver and Jensen
Regression Results: Attendance at NCAA Division II Football Games
Regression coefficients
Variable
1994
1996
1999
Constant
2.712
4.168
5.549
GAP
.490**
(3.887)
Combined
4.309
1.438**
(11.870)
.814**
(5.426)
1.033**
(13.001)
CITYPOP
–.010
(.356)
.020
(.710)
.032
(1.212)
.009
(.563)
MILES
–.012
(.341)
–.184**
(4.022)
–.102*
(2.420)
–.101**
(4.216)
STUDENR
.220**
(4.593)
.156**
(3.215)
.164**
(3.885)
.183**
(6.637)
STADAGE
.004
(.124)
–.087**
(2.950)
–.046*
(2.333)
–.047**
(3.025)
COMP
–.133**
(2.996)
–.132*
(2.537)
–.361**
(7.821)
–.194**
(6.803)
SAP
.561**
(5.082)
.771**
(4.157)
–.386**
(3.388)
.330**
(3.970)
WEAT
.318**
(2.940)
.507**
(4.479)
.445**
(2.999)
.427**
(6.116)
HCPROM
.634**
(7.040)
.689**
(6.875)
.586**
(6.682)
.665**
(12.136)
OTHPROM
.290**
(3.889)
.284**
(3.459)
.278**
(3.899)
.296**
(6.711)
TP1
.337**
(2.827)
.769**
(6.242)
.257*
(2.236)
.441**
(6.640)
TP2
.310**
(3.655)
.650**
(5.500)
.226**
(2.777)
.349**
(6.468)
TP3
.153
(1.711)
.263*
(2.104)
.058
(.648)
.097
(1.678)
WINPER1
.056
(.326)
–.075
(.538)
.071
(.424)
.083
(.911)
WINPER2
.238
(1.699)
.418**
(2.583)
.268*
(1.495)
.314**
(3.349)
WINPER3
.835**
(3.555)
.364
(1.761)
–.061
(.487)
.218*
(1.114)
Determinants of Spectator Attendance
321
WINPER4
.954**
(3.188)
1.213**
(2.723)
1.018**
(4.277)
956**
(5.424)
PREVWIN1
.676*
(1.918)
1.160**
(4.433)
.234
(.574)
.583*
(1.912)
PREVWIN2
.292
(1.471)
.532*
(2.146)
–.158
(.611)
.264
(1.558)
PREVWIN3
.212
(.775)
.373
(1.353)
.828**
(3.455)
.577**
(3.284)
PREVWIN4
–.096
(.294)
.332
(.785)
–.030
(.160)
.063
(.421)
Year (1999)
–.132**
(2.732)
Year (1996)
.149
(1.728)
n (sample size)
R2
F–Statistic
476
.483
20.53**
411
.45
21.01**
415
.368
12.34**
1302
.386
39.54***
Note. Dependent Variable = Log(Attendance); *significant at 5% level, **significant at
1% level (absolute values of t–statistics are in parentheses)
The winning percentage of the home team in the previous season was also
included in the demand model. It was expected that this determinant would be
significant in the early portion of the season and that the relationship would weaken
as the season progressed. Early in the season, spectators may use the team’s performance in the previous season as a measure of on-field success because the team
has played few, if any, games, during the current season. Again, the regression
results were consistent with our expectations. In three of the four cases considered, previous year winning percentage was significant in the first period of the
season while in all four cases previous year winning percentage was insignificant
in the last period of the season. In addition, the magnitude of the coefficients tended
to decrease as the season progressed suggesting that the impact of the previous
season’s performance diminished. For example, based on the combined results, an
increase in previous-season winning percentage of 10 percent resulted in an expected increase in attendance of 5.8 percent for games played in the first time
period but only a .6 percent increase in the last period of the season.
Both of the explanatory variables representing promotional activities were
statistically significant and positively related to attendance. The Homecoming and
other promotion variables were significant at the .01 level for each of the four
regression equations. The presence of Homecoming increased attendance by
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DeSchriver and Jensen
approximately twice as much as the presence of other types of promotions. The
statistical findings support the claim that promotional activities are significant and
positively related to attendance. However, it must be noted that only 40.4 percent
of the non-Homecoming games had some type of promotional activity. Based on
these findings, it is recommended that Division II football marketers investigate
the initiation of special activities such as Parents’ Day, Hall of Fame Day, and
other types of promotions. But, these promotions must be analyzed from an economic viewpoint. The additional cost of these activities to the athletic department
should not be greater than the additional revenue that can be generated from the
increase in ticket sales, concessions, and merchandise sales.
Stadium age and the number of miles between the two competing teams
were also found to be significant for three of the four demand equations, 1994
being the exception in both instances. As expected, the age of the facility and the
distance between schools were both negatively related to attendance. The impact
of stadium age was relatively small as a 10 percent increase in age only reduced
attendance by .47 percent in the combined data set. The effect of distance traveled
was approximately twice as large as the impact of stadium age. For example, a 10
percent increase in distance traveled, decreased attendance by approximately 1
percent. This finding may be important with respect to the development of game
schedules and/or the selection of conference affiliation. Efforts should be made to
schedule non-conference games against teams that are close in proximity. Also,
there may be a situation in which an institution must make a decision on conference affiliation. While many factors may go into this decision, the effect on attendance for football games would likely be one of the criteria. The findings here
suggest that a conference with teams in close geographic proximity will positively
influence football attendance.
Two additional determinants were significantly related to attendance in the
regression equations. Student enrollment and weather were both positively related
to spectator attendance. For every 10 percent increase in student enrollment, there
was a corresponding increase in attendance of approximately 2 percent. Thus, holding all else constant, the largest Division II institutions had the highest levels of
attendance. Also, given the definition of the weather variable, the presence of rain
or snow for a game had a negative relationship with attendance. Obviously, college athletic administrators have no control over enrollment size or weather but it
is important for them to recognize that these variables influence attendance. Two
institutions may have a similar profile for all of the model variables, but if they
have different enrollments, the administrators should expect a difference in football attendance.
Time was another determinant that was included in the demand model. This
determinant was measured in two ways. First, the time within a season was captured through three categorical variables to determine if attendance changed
throughout the course of a single season. Secondly, given that data was obtained
for three different seasons, two categorical variables were used in the combined
data set to determine if there were any changes in attendance across seasons. The
statistical findings show that the higher levels of attendance occurred in the first
Determinants of Spectator Attendance
323
two time periods, or six weeks, of the season. All of the regression coefficients for
the first two time periods were significant in the four demand equations suggesting that attendance tends to decrease as the season progresses, holding all else
constant. This finding may occur because as the season progresses, the weather
becomes considerably colder in many regions of the country. As a result, some
consumers may opt to stay at home and consume football via media outlets such as
radio and television.
One of the two season variables was statistically significant. The categorical
variable for 1999 was significant and negatively related to attendance. Thus, holding all else constant, the 1994 and 1996 seasons had higher levels of attendance
than 1999. College football administrators should be somewhat concerned with
this finding. However, it should be stressed that this data set is limited to three
years. While the findings suggest that attendance in 1999 was below the 1996 and
1994 levels, this may not signal a long-term trend. Additional years of data are
needed to investigate this possibility. With that said, there may be some changes in
the collegiate football market that have led to this finding. The late 1990’s have
seen increases in both sports television networks and the number of televised games.
These findings may suggest that consumers are watching televised collegiate games
instead of attending Division II games.
Ticket pricing policy was another determinant that was included in the demand model. As stated earlier, ticket policy was represented through two variables; a categorical variable indicating whether or not students are admitted free
and a second variable measuring the dollar amount charged for general admission.
As expected free admission for students had a positive and significant effect. Surprisingly, the general admission price variable was also found to be positive and
significant at the .001 level for all of the regression equations. At first glance this
positive relationship between price and attendance may seem strange, but in this
case it is actually quite reasonable. First, note that maximizing profits with zero
marginal costs is equivalent to revenue maximization. In order to maximize revenue, all schools would simply set ticket prices such that the equilibrium values of
price and quantity correspond to the mid point of the demand curve they face.
Second, also note that most of the variation in our data set is cross-sectional variation as the data set has a large cross section, about 140 schools, while the time
series dimension is relatively small with 3 years of data. Thus, the positive relationship between price and quantity does not suggest that each school faces an
upward sloping demand curve, but rather it is a result of the nature of the data set
and the behavior of the schools. For simplicity sake imagine that all the school’s
individual demand curves are parallel. Larger schools have demand curves that are
shifted out to the right and small schools have demand curves that are shifted in to
the left. If each school selects price at the mid point of their demand curve, the
cross section of equilibrium prices and quantities traces out a line with a positive
slope. Therefore, the positive relationship between price and quantity is not problematic, but it does suggest that differences across schools are important. The next
step in the analysis was to investigate the significance of these differences through
the use of a fixed effects model.
324
Table 4
DeSchriver and Jensen
Fixed Effects Regression Model
Robust
Coefficient
Standard Error
Constant
GAP
CITYPOP
MILES
STUDENR
STADAGE
COMP
SAP
WEAT
HCPROM
OTHPROM
TP1
TP2
TP3
WINPER1
WINPER2
WINPER3
WINPER4
PREVWIN1
PREVWIN2
PREVWIN3
PREVWIN4
Year (1999)
Year (1996)
9.011
.238
.022
–.124**
–.316
.015
–.013
–.124
.486**
.654**
.209**
.424**
.330**
.047
.011
.112
.426**
.824**
.348
.342*
.331
.225
–.133*
–.239
.151
.074
.028
.326
.658
.163
.142
.078
.055
.050
.072
.061
.063
.098
.097
.140
.184
.240
.140
.188
.135
.054
.133
n (sample size)
R2
F–Statistic
618
.70
30.79**
Variable
t–statistic
1.577
.301
4.368
.970
.511
.080
.875
6.216
11.971
4.167
5.860
5.406
.756
.116
1.154
3.036
4.466
1.454
2.438
1.763
1.658
2.451
–1.801
Note. In order to save space, we are not reporting the estimates for the 39 institution fixed
effects coefficients. It can be reported that the results of the F–test for the group of fixed
effect coefficients was highly significant. The F(39,555) = 19.45 and was significant at
the .01 level.
A fixed effects model is estimated because the previous results suggest that
there are important unobservable differences across schools. For instance, an
important factor in determining attendance may be tradition. Some schools have
rich traditions in football and thus draw large crowds. If these differences are accounted for, then it is expected that there should not be a significantly positive
relationship between price and attendance. To do this, the model was reestimated
Determinants of Spectator Attendance
325
with a categorical variable for each school. The fixed effects results are reported in
Table 4.
The first thing to note about the results is that the coefficient on price is
much smaller and now statistically insignificant. Secondly, it is noted that the fixed
effects results are otherwise qualitatively equivalent to the OLS results. From this
one can conclude the following. In the OLS case price was significant because it
was correlated with unobservable individual school characteristics that affect demand. Once these fixed effects are accounted for price is no longer significant. As
a result, there is not much we can say about the price elasticity of demand based on
our study. If one were interested in examining the effects of price changes on
attendance then a data set with a longer time series and more variation in price
over time would be required.
Conclusions and Recommendations
The purpose of this paper was to analyze the relationship between spectator attendance at NCAA Division II football games and a set of selected determinants such
as on-field success and promotional activity. The relationships were analyzed
through the development of economic demand models using log-linear multiple
regression analysis. The results form OLS and fixed effects models were qualitatively equivalent and the models explained between approximately 40 to 70 percent of the variation in attendance. Both on-field success, as measured by winning
percentage, and promotional activity were found to be positively related to attendance. One of the primary contributions of this paper is that it examined how the
effect of winning on attendance changes throughout the season. By interacting
winning percentage with time, it was shown that the effect of winning in the current season increases over the course of a season while the impact of the previous
season’s winning percentage on attendance decreased as the season progressed.
Attendance was also positively influenced by promotional activities such as Homecoming. Given this, new promotional activities may be an effective strategy for
administrators who are attempting to increase attendance.
In closing, this study has shown that it is possible to explain the variation in
collegiate sport spectatorship through the use of a tool that has been primarily used
for professional sport. The need for additional research concerning the determinants of spectator demand for collegiate sport is evident. Similar models may be
constructed for other collegiate sports, at all levels, depending upon the availability of data. It may be of interest to determine the relationship between spectator
attendance in other sports like basketball and hockey with determinants such as
winning percentage, promotional activity, and ticket price. Lastly, we note that
this study has focused on the effects that promotions and on-field success have on
attendance, and due to the nature of our data set, we were unable to study the price
elasticity of demand. Administrators might be interested in the implications of
various pricing policies and thus future research based on a data set with a longer
time series would help examine this issue.
326
DeSchriver and Jensen
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Notes
1
For the remainder of the paper, the term football will be used to describe American
football.
2
All earlier research that has included winning percentage as a determinant of spectator attendance has found a significantly positive relationship. Earlier work such as McDonald
and Rascher (2000), Marcum and Greenstein (1983), and Wall and Myers (1989) found a
significant positive relationship between attendance and promotional activity.
3
This is true only when the capacity constraint of the stadium is non-binding. This is
the case in the vast majority games in our sample.
4
Ticket prices were adjusted for inflation using the GDP deflator index.
5
1990 US Census data were utilized for this variable.
6
Whitney (1988) also had success with use of logarithmic transformations of spectator attendance data sets.
7
Note that for the fixed effects estimation we only included the schools that are in the
sample in all three years and thus there were 40 schools used in this portion of the analysis.
We also reestimated our GLS model with this smaller sample and the results are qualitatively equivalent to the results reported in Table 2. This suggests that the results are not
sensitive to changes in the sample of schools considered.
8
Ticket prices were adjusted for inflation using the GDP deflator index.
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DeSchriver and Jensen
9
1990 US Census data were utilized for this variable.
It must be noted that .49 was subtracted from each winning percentage prior to it
being multiplied by the time period categorical variables. This was done to differentiate
between a game that was not played in a specific time period, and would therefore have a
value of 0, and a game where the home team had a winning percentage of 0. Statistically,
this has no effect on the results because every winning percentage was reduced by the same
value, .49. An example may help in describing the formulation of this determinant. Suppose
a game was played in the first time period and the home team had a .500 winning percentage prior to the game. In this instance, the WINPER1 variable has a value of .01, and the
WINPER2, WINPER3, and WINPER4 variables have a value of 0.
10
Appendix A
Description of Variables
Dependent Variable
ATTEND – Game Attendance
The number of spectators who attended an individual football game, as reported
by the host institution.
Economic Determinants
GAP - General Admission Ticket Price
The price of a general admission ticket to a football contest.8 It was expected that
a positive relationship existed between price and attendance.
SAP – Student Admission Policy
Responding institutions had two distinct policies with respect to the admittance of
full-time students to football games. In some cases, students were admitted free of
charge, while in other instances students were required to purchase a ticket. A
categorical variable was used to represent this variable with 1 representing no
payment for admission, and 0 otherwise. It was expected that a positive relationship existed between student admission policy and attendance.
COMP – Number of Competitors in the Market Area
The number of NCAA Division I and II institutions that competed in football and
were located within 50 miles of the responding institution. It was expected that a
negative relationship existed between COMP and attendance.
Demographic Determinants
MILES – Miles Between Competing Institutions
The number of miles between the cities in which the two competing football teams
were located. It was expected that a negative relationship existed between MILES
and attendance.
STUDENR – Student Enrollment
The number of full-time enrolled students, undergraduate and graduate, who attended the host institution during the year in which the game was played. It was
expected that a positive relationship existed between student enrollment and attendance.
Determinants of Spectator Attendance
329
CITYPOP – City Population
The number of residents living in the city where the home team was located.9 The
relationship between city population and attendance was expected to be positive.
Game Attractiveness Determinants
WINPER – Home Team Winning Percentage
The winning percentage for the home team was captured through four variables.
This allowed for the analysis of the effect of winning percentage on attendance
over the course of a season. A series of four categorical variables were used to
represent the time period in the season when a specific game was played. Each of
the three seasons analyzed had games played over a span of 12 weeks. The weeks
were separated into four time periods, three weeks in each period. One categorical
variable was assigned for each time period. The categorical variables took a value
of 1 if the game in question was played during that time period, and 0 otherwise.
For each game, the winning percentage of the home team prior to the game was
interacted with the four time period categorical variables.10 By representing winning percentage through this method, both the size and change over time of the
relationship between winning percentage and game attendance was analyzed. It
was expected that a positive relationship existed between winning percentage and
attendance and that this relationship would strengthen during the course of a season.
PREVWIN – Home Team Winning Percentage in Previous Season
This determinant was constructed in a manner that was similar to WINPER. Again,
this was done to determine how the effect of the home team’s on-field success in
the previous season would effect attendance throughout the following season.
PREVWIN was represented through a series of four variables. It was expected that
a positive relationship would expect between winning percentage in the previous
season and attendance. In addition, this relationship was expected to weaken
throughout the course of a season.
Promotional Activity
This determinant was measured through the use of two categorical variables. The
first variable (HCPROM) measured the existence of homecoming and had a value
of 1 if the game was played as part of an institution’s homecoming celebration,
and 0 otherwise. A second categorical variable (OTHPROM) was used to capture
the effect of other types of promotions such as parents’ day, youth day, and senior
citizens’ day on game attendance. It has a value of 1 if any promotion other than
homecoming occurs, and 0 otherwise. A positive relationship was expected between both promotional variables and spectator attendance. The relationship was
expected to be stronger for the HCPROM variable.
Residual Preference Determinants
WEAT – Weather
The weather conditions were represented by a categorical variable. It took the
value of 0 if rain or snow fell during the time period consisting of two hours prior
to the start of the football game and/or during the football game, 1 otherwise. A
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DeSchriver and Jensen
positive relationship was expected between good weather conditions and attendance.
STADAGE – Age of Stadium
The age of the stadium, in years, where the game was played. The negative relationship was expected between the age of the stadium and attendance.
TP – Time Period
As mentioned above, each of the three seasons was separated into four time periods. Each time period consisted of three weeks. Three categorical variables where
used to analyze the relationship between time and game attendance. The time period categorical variable took the value of 1 for games played during that time
period, and 0 otherwise. All of the time period variables took the value of 0 for
games played during the fourth time period. The expected relationship between
the time of the season in which the game was played and attendance was unclear,
a priori.
Year – Year of Season
For the demand model including data from all three of the years (referred to as the
combined data set), two categorical variable were included to account for the year
of the season. These variables allowed for the analysis of the relationship between
the season in which the game was played (1994, 1996, or 1999) and spectator
attendance. For the year 1994, both of the categorical variables were 0. The expected relationship between the year of the game and attendance was unclear prior
to the statistical analysis.