Consumption benefits of national hockey league game trips estimated from revealed and stated preference demand data.
Whitehead, John C. ; Johnson, Bruce K. ; Mason, Daniel S. 等
I. INTRODUCTION
When the Baltimore Orioles moved from their 1950s-vintage home in
Memorial Stadium to Oriole Park at Camden Yards in 1992, a new era in
stadium and arena construction dawned in North American professional
sports. The greatly enhanced premium seating, luxury suites,
concessions, sight lines, and other amenities made possible by advances
in architectural design and construction technology led to large
increases in revenues for the Orioles. Teams in Major League Baseball (MLB), the National Football League (NFL), the National Basketball
Association (NBA), and the National Hockey League (NHL) joined the rush
to replace their now economically obsolete arenas and stadiums with
their own versions of Camden Yards. Of the 30 NHL teams, 25 have moved
into new arenas since 1993. Several other facilities are under
consideration.
Two teams that have not moved into new arenas are the Edmonton
Oilers and the Calgary Flames, Alberta's two teams in the NHL. The
Oilers continue to play in Rexall Place, located just outside downtown
Edmonton, which opened in 1974. The Flames play in the Scotiabank
Saddledome, dating from 1983. Management of both teams complain of a
lack of space for luxury suites and other premium seating, as well as
cramped facilities for concessions, placing them at a competitive
disadvantage with other teams in the league (Kom 2008; Mah 2007).
As a result, a need for new facilities has been expressed in both
cities, where discussion has focused on where the new arenas would be
sited and to what extent they would be publicly financed (Mason 2010).
This paper takes the opportunity presented by the possibility of new NHL
arenas in Alberta to examine the demand for hockey game trips among
residents of Edmonton, Calgary, and the rest of the province of Alberta.
Using data on both revealed and stated preferences from a telephone
survey conducted throughout Alberta, we estimate the effect of ticket
prices, team quality, arena amenities, and capacity on the latent demand
for NHL hockey. Unlike most attendance studies, we measure private
consumption benefits of new arenas, in the form of consumer surplus, to
current and future game attendees. Furthermore, unlike the one previous
study that econometrically estimated consumer surplus for game
attendance (Irani 1997), this paper includes data on travel costs in the
estimation of the demand function, allowing an estimate of the full
consumer surplus.
II. LITERATURE REVIEW
Economists have conducted many empirical analyses of the demand for
attendance at sporting contests. Borland and Macdonald (2003) discuss
the conceptual issues and provide a theoretical discussion of the demand
for sporting events. Two studies in particular are relevant to this
paper because they address the impact of new stadiums and arenas on
season attendance. Zygmont and Leadley (2005) use a panel data set of
season attendance spanning 1970-2000 in MLB to estimate the
"honeymoon effects" of a new stadium on attendance and ticket
prices, and find substantial positive effects on both that persist, with
only modest declines, for 8-10 years. They conclude that a baseball-only
stadium that replaces an older multipurpose stadium will generate an
additional $228 million in ticket revenue over 15 years.
Leadley and Zygmont (2006) test for honeymoon effects in the NHL
from 1970 to 2003 using Tobit analysis as the capacity constraint is
usually binding in the NHL, unlike in MLB. For instance, during the
2009-2010 season, 11 NHL teams achieved season attendance greater than
or equal to 100% of seating capacity with an additional 6 teams
operating at better than 97% capacity. In MLB in 2010, five total teams
exceeded 90%, with only three of those playing to full capacity for the
entire season. The honeymoon effect in the NHL from 1994 to 2003, a
period in which 21 new arenas opened, increased an NHL team's
attendance by 15%-20% and the honeymoon lasted eight years.
There have been many other attendance studies, most of them
focusing on game-by-game attendance rather than season attendance.
Baseball and soccer attendance have been studied more often than
attendance in other sports (Borland and Macdonald 2003). Among the many
baseball articles are Bruggink and Eaton (1996), Kahane and Shmanske
(1997), Butler (2002), Coates and Harrison (2005), and Donihue, Findlay,
and Newberry (2007). Soccer attendance in European leagues has been the
subject of studies by Garcia and Rodriguez (2002) and Czarnitzki and
Stadtmann (2002), among others. Welki and Zlatopper (1994, 1999)
estimated game-day attendance in the NFL, while Price and Sen (2003) did
so for Division I-A (now called the Football Bowl Subdivision) college
football. Attendance at NBA games has been estimated by Burdekin and
Idson (1991), and attendance at NHL games by Paul (2003).
Despite the differences in locations, time periods, and sports, the
attendance studies have found broadly similar results. Most studies have
found that ticket prices have a negative impact on attendance, though
not always, and that except for Bruggink and Eaton (1996), demand tends
to be inelastic with respect to price. The inelasticity of demand is
surprising considering that the local monopolies usually enjoyed by
professional sports teams should result in prices where demand is
price-elastic. Many studies have also found that income affects demand,
with sports more often than not being a normal good. However, in some
cases, as in baseball (Bruggink and Eaton 1996), a sport appears to be
an inferior good, even as the same sport appears to be a normal good in
other studies. Quality of competition consistently proves significant:
the better the home team and its opponent, the higher the attendance.
Irani (1997) estimates a demand function for MLB using actual 1985
season attendance, as opposed to the usual game-by-game attendance, and
ticket price data. Using his estimates to calculate the choke price for
MLB tickets and integrating the estimated attendance function over the
range of ticket prices, he calculates average annual consumer surplus of
about $35 million (2007 dollars) per team (Irani 1997). Alexander, Kern,
and Neill (2000) calculate consumer surplus by assuming a constant
elasticity demand function, and combine evidence of team revenues with
assumptions about different demand elasticities. Not surprisingly, the
amount of consumer surplus depends strongly on the value of the assumed
elasticity. Alexander, Kern, and Neill did not estimate any demand
functions or price elasticities with attendance data.
Methods developed by environmental economists to measure the value
of public and private benefits of consumption provide the means to
improve upon the estimates of sports consumer surplus developed by Irani
and Alexander, Kern, and Neill. In this paper, we use the travel cost
method, which is a revealed preference method most often used to
estimate recreation benefits (Phaneuf and Smith 2005), but it has also
been used to estimate the benefits of other goods such as health care
(Clarke 2002). The travel and time costs incurred to get to an activity
site can constitute a major cost of participating in the recreation
activity. As individuals reside at varying distances from stadiums, the
variation in distance and the number of trips taken can be used to
estimate a demand function and derive the benefits of attendance and
stadium characteristics. Revealed preference approaches are limited to
the variation in historical experience. Forecasts beyond are often
inaccurate or impossible.
Stated preference methods provide additional tools to estimate the
benefits of sporting events and amenities. Stated preference approaches
include the contingent valuation method (CVM) and the contingent
behavior method (CBM). The CVM uses willingness to pay responses to
hypothetical situations to estimate benefits. Over the past decade or
so, economists have extended the use of CVM to sports in order to
estimate the use and nonuse values of sports public goods. In the
seminal CVM sports article, Johnson and Whitehead (2000) survey
respondents in Lexington, Kentucky, who are asked whether and how much
they would be willing to pay for a new basketball arena for the
University of Kentucky and for a baseball stadium to attract a minor
league team. For both projects, the nonmarket benefits fall short of the
costs of constructing the new buildings.
Since Johnson and Whitehead (2000) there have been a number of CVM
studies of sports public goods. Civic unity, community pride, improved
racial relations, and topics of conversation are some of the public
goods addressed by sports CVM studies covering the NFL, NBA, MLB,
amateur participatory sports, and the Olympics (Johnson 2008). Although
the later CVM studies tend to find substantially larger values of sports
public goods than did Johnson and Whitehead, peer-reviewed sports CVM
articles tend to find that the value of sports public goods fall short
of the cost of constructing arenas, stadiums, and other venues, except
in the case of the Minnesota Vikings of the NFL (Fenn and Crooker 2009).
(1)
In contrast to the CVM, the CBM is a stated preference approach
that directly elicits hypothetical behavior information from survey
respondents. Respondents are asked about their behavioral responses to a
hypothetical change. One strength of the CBM approach is its
flexibility. Hypothetical choices may be the only way to gain
policy-relevant behavior information about situations where revealed
preference data are not available. Yet, the major weakness of the CBM
approach is its hypothetical nature. Respondents face unfamiliar
situations with incomplete information. To date, the CBM has not been
applied to sports attendance behavior.
The combination and joint estimation of revealed and stated
preference data exploit the contrasting strengths of the alternative
approaches while minimizing their weaknesses (Whitehead et al. 2008).
Stated preference data allow analysis of behavior beyond the range of
historical experience. In many cases, hypothetical choices may not
reflect budget, and other, constraints on behavior. For example, in a
contingent behavior survey, respondents may answer a hypothetical game
trip question with their good intentions of buying season tickets and
going to every game. Yet, when they must make real choices, they
confront unexpected constraints on time and income and make fewer game
trips. Combining revealed preference and stated preference data allows
mitigation of the bias associated with hypothetical choices present in
stated preference data. Grijalva et al. (2002) and Whitehead (2005) find
evidence that jointly estimated revealed and stated preference models
generate valid predictions of future behavior.
In contrast to previous research valuing sports-related goods and
services that rely solely on revealed preference data or the CVM, we
jointly estimate a demand model using revealed and stated preference
data. In the rest of the paper we summarize the survey data, describe
the empirical methods, and present the empirical results. Conclusions
follow.
III. SURVEY DATA
A telephone survey conducted during late 2007 and early 2008
collected revealed preference and stated preference data. A random
sample frame of telephone numbers for the Edmonton and Calgary
metropolitan areas and for the rest of Alberta was generated. The
initial screening questions selected either a male or female potential
respondent aged 18 years or older. Based on pre-established quotas, data
were collected from 937 people (Edmonton, n = 339; Calgary, n = 331;
nonmetropolitan Alberta, n = 267). In order to meet the quota
requirements, 6,764 telephone numbers were called, with 1,610 of these
numbers being excluded for technical reasons (e.g., not in service,
busy/no answer), and another 2,346 numbers being excluded for
noneligibility and other reasons (e.g., business fax, less than 18 years
of age, unable to speak English). Another 1,871 people refused to
participate, resulting in an overall response rate of 33% (i.e.,
937/[937 + 1,871]).
The study instrument consisted of a computer-assisted telephone
interviewing questionnaire that included a description of a new downtown
hockey arena with various features (2):
Suppose the Flames/Oilers decide to build a new,
state-of-the-art hockey arena [in a complex that
would also include affordable housing, arts and cultural
space including galleries, theaters' and museum
space, and a casino] in downtown Calgary/Edmonton
to replace the Pengrowth Saddledome/Rexall Place.
[Suppose environmentally friendly materials and
design will be used.] Some people say that building
the arena, [housing, cultural complex, and casino]
downtown would improve the quality of life in Calgary/Edmonton
more than building it in the suburbs.
Respondents in the Edmonton and Calgary surveys received various
combinations of arena characteristics ranging from a new arena with no
additional characteristics and an arena with four additional
characteristics (green design, affordable housing, cultural complex, and
a casino).
The revealed preference data are based on hockey game trips that
actually occurred during the 2006-2007 season. The stated preference
data are based on future hockey game trips under various hypothetical
conditions. The stated preference data are used to simulate a change in
demand resulting from changes in the quality of the hockey game
experience, increased seating availability, and changes in ticket prices
(see Appendix). Stated preference questions asked about future trips to
the current arena: (1) with an expected first place finish, (2) with an
expected last place finish, and (3) with an expected third place finish.
Questions were then asked about future trips to a new downtown arena with additional upper level seating and a third place finish: (1) with a
$15 ticket price decrease from the lowest walk-up ticket price, (2) with
a $25 ticket price decrease from the lowest walk-up ticket price, and
(3) with a $5 ticket price decrease from the lowest walk-up ticket
price. The combined revealed and stated preference pseudo-panel data
have seven observations for each respondent.
Respondents in the Edmonton and Calgary surveys were asked
questions about Oilers and Flames games, respectively. Respondents in
the nonmetropolitan Alberta survey were asked "About how often do
you visit Calgary/ Edmonton?" Respondents who visit Calgary more
often are considered in the Flames market and asked questions about
trips to Flames games. Respondents who visit Edmonton more often are
considered in the Oilers market and asked questions about trips to
Oilers games. Respondents were also asked "How long does it usually
take you to get to Calgary/Edmonton?" The average number of hours
traveled is 4 to Calgary and 3 to Edmonton. Seventy-two percent of
nonmetropolitan Alberta respondents visit the city with the lowest
travel time more often. Hours traveled is converted to distance traveled
assuming an average speed of 75 km/hr.
Of the 937 completed interviews, we discard those cases with no
response to the stated preference attendance and other key questions. We
employ a sample of 828 respondents. Table 1 presents a summary of the
game-trip-dependent variable. We present the data in terms of four
subsamples: (1) Flames market respondents who attended a game during the
2006-2007 season (n = 133); (2) Flames market respondents who had not
attended a game (n = 246); (3) Oilers market respondents who had
attended a game (n = 148); and (4) Oilers market respondents who had not
attended a game (n = 301). Those in the Flames market who had attended
at least one game attended an average of five games during the past
season (5.19 in Table 1; we report rounded values in the text). Those in
the Oilers market who had attended at least one game attended an average
of four games during the past season. (3,4)
In each subsample a similar pattern appears in each hypothetical
scenario. Under the current arena and price scenario, respondents state
they would attend about the same number of games regardless of the
team's expected finish. With a new arena and lower ticket prices,
respondents who had attended a game during the past season said that
their future attendance would roughly double. With a new arena and lower
ticket prices, respondents who had not attended a game during the past
season said that their future attendance would also increase
significantly.
Table 2 presents descriptive statistics for the independent
variables. (5) The self-reported ticket price paid by Flames and Oilers
game attendees is $82 and $78, respectively. (6) Several differences
across subsamples emerge. The average travel cost is lower for attendees
relative to nonattendees, suggesting that travel distance is a factor in
the game trip decision. (7) The average household income is higher for
attendees compared to nonattendees, suggesting that respondent ability
to pay may be a factor in the game trip decision. (8) In both markets
male respondents are more likely to attend games. Game attendees are
younger relative to nonattendees. Perhaps because Scotiabank Saddledome
is closer to Calgary's downtown than Rexall Place is to
Edmonton's downtown, a higher proportion of Flames attendees live
and work downtown. A greater number of nonattendees are from the
nonmetropolitan, rest of Alberta sample. This is further evidence that
travel costs may affect attendance decisions. Other variables show
little difference across subsamples.
IV. EMPIRICAL MODEL
The Poisson regression model is often used to study count data such
as number of recreation trips (Haab and McConnell 2002). Assume that
[x.sub.it] is the number of revealed and stated preference hockey game
trips taken by individual i in scenario t, which is drawn from a Poisson
distribution with mean [[lambda].sub.it]:
(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], ...
The natural log of the mean number of revealed and stated
preference game trips is assumed to be a linear function of prices,
income, and scenario dummy variables. To allow for variation across
hockey fans that cannot be explained by the independent variables, we
assume that the mean number of revealed and stated preference game trips
also depends on individual specific and random error. The random effects Poisson demand model is as follows:
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
where [p.sup.R] is the revealed preference ticket price ([p.sup.R]
= 0 if x = 0), [p.sup.S] is the stated preference ticket price, c is the
round-trip travel cost to the hockey arena, y is income, RP is a
revealed preference dummy variable; [beta], [delta], and [gamma] are
coefficients (vectors) to be estimated; [[mu].sub.i] is the random
effect for group (person) i and [e.sub.it] is a mean zero error term.
Individuals are indexed i = 1, ..., n, and t = 1, ..., 7 denotes
seasonal hockey game trip demand in the pseudo-panel data. Z is a vector
of scenario design variables (i.e., new arena, new arena
characteristics, and team finish) and D is a vector of demographic
characteristics. The RP dummy variable is included to test for bias in
hypothetical responses: RP = 1 for revealed preference trip data (t = 1)
and 0 for stated preference trip data (t > 1). Pooling the data
suggests that panel data methods be used to account for differences in
variance across sample individuals, i, and scenarios, t. The
distribution of trips is Poisson with conditional mean and variance,
[[lambda].sub.it]. If exp([[lambda].sub.it]) is assumed to follow a
gamma distribution, then the unconditional trips, [x.sub.it], follow a
negative binomial distribution (Hausman, Hall, and Griliches 1984). (9)
With the semi-log functional form price, travel cost and income
elasticities are [e.sub.p] = [[beta].sub.p][[bar.p].sup.S], [e.sub.c] =
[[beta].sub.c][bar.c], [e.sub.y] = [[beta].sub.y][bar.y] when evaluated
at the means of the variables. The full-price elasticity is the weighted
average of the price and travel cost elasticities:
(3) [e.sub.pc] = [e.sub.p][[theta].sub.p] +
[e.sub.c][[theta].sub.c],
where [[theta].sub.k] = k/[p+c] = p, c, is the ticket price and
travel cost share of the full price.
With the semi-log functional form the economic benefit per hockey
game for the representative hockey fan as measured by average consumer
surplus (CS) per game in the ticket market is (Bockstael and Strand
1987):
(4) [CS.sub.p]/x = - [1/[[beta].sub.p]].
The economic benefit per hockey game trip is a weighted average of
the ticket price and travel cost consumer surplus
(5) CS.sub.pc/x = - ([1/[[beta].sub.p]][[theta].sub.p] +
[1/[[beta].sub.c]][[theta].sub.c]).
The economic benefit of a change in the hockey experience per game
measured by the jth scenario design variable [DELTA][Z.sub.j] and
evaluated in the ticket market is:
(6) [DELTA][CS.sub.p]([DELTA]Z)/x =
[delta].sub.Z][DELTA]Z/[[beta].sub.p],
where [delta][z.sub.j] is the regression coefficient on the jth
scenario design variable. The economic benefit of a change in the hockey
experience per hockey game trip is a weighted average of the change in
ticket price and travel cost consumer surplus
(7) [DELTA][CS.sub.pc]([DELTA]Z)/x =
-([[[delta].sub.Z][DELTA]Z/[[beta].sub.p]][[theta].sub.p] +
[[[delta].sub.Z][DELTA]Z/[[beta].sub.c]][[theta].sub.c]).
Equations (5) and (7) diverge from Equations (4) and (6) with
statistically significant coefficients on the travel cost coefficients.
If the travel cost coefficient is statistically insignificant then that
term drops out of the consumer surplus equation.
V. EMPIRICAL RESULTS
Table 3 presents the random effects Poisson coefficient estimates
for the Calgary Flames and Edmonton Oilers markets. The models include
the revealed preference ticket price as an independent variable. Each
model contains a dummy variable for the new arena scenario and the new
arena dummy interacted with the nonmetropolitan sample (this is the only
variable that indicates different behaviors across metropolitan and
nonmetropolitan samples). Team finish and dummy variables for arena
features, green design and construction, affordable housing, a casino
and a cultural complex are included. Each of the arena features dummy
variables is coded as zero for the nonmetropolitan sample because those
respondents were not asked the arena scenario questions. In addition,
dummy variables for the revealed preference scenario, gender, and
marital status are included. Other variables included in the model are
continuous: household income, age, and years of schooling. Other
variables in Table 1 are omitted from the demand models because of
multicollinearity or statistical irrelevance.
We split each market into those who had revealed a preference for
hockey games during the 2006-2007 season (attendees) and those who had
not (nonattendees). The random effects Poisson coefficient estimates for
each market and attendees and nonattendees subsamples are presented in
Table 3. In each of the attendee models, the revealed preference
coefficient is negative and significant. There are two interpretations
of this result. First, it might indicate hypothetical bias. In the
hypothetical scenarios, respondents said that they would like to attend
more games than they are actually able to attend. The second
interpretation is that this result measures latent demand at the current
seating capacity and ticket prices. Respondents would like to attend
more games but game sell-outs constrain their behavior.
A. Prices and Income
The effect of revealed preference ticket price on the number of
games attended is ambiguous because current game trips are quantity
constrained, ticket price reflects quality, and ticket price is
partially endogenous as an increasing function of income in the
secondary market. In each model, the actual ticket price coefficient is
not significantly different from zero. The coefficient on the stated
preference price variable is negative and statistically significant, as
expected. Demand is inelastic across markets: [e.sub.p] = -0.57 for
Calgary and [e.sub.p] = -0.77 for Edmonton. (10) The coefficient on the
travel cost variable is negative and statistically significant in the
Calgary attendee model. The travel cost elasticity is [e.sub.c] = -0.10
for Calgary and the full-price elasticity is [e.sub.pc] = -0.45 in the
Calgary attendee model. (11) The coefficient on the income variable is
positive and statistically significant in the Edmonton attendee model.
The income elasticity is [e.sub.y] = 0.78. Other price and income
coefficients are not statistically significant. In each nonattendee
model, the coefficient on the stated preference price variable is
negative and statistically significant. Demand is elastic: [e.sub.p] =
-1.25 for Calgary and [e.sub.p] = - 1.78 for Edmonton. The coefficients
on the travel cost and income variables are statistically insignificant
in each nonattendee model.
B. Capacity
In each model, a new downtown arena with plenty of upper level
seats would increase attendance in three of the four models, especially
for the nonmetropolitan Alberta sample in three of the four models.
According to the marginal effects, (12) Flames attendees and
nonattendees would go to two more games each year. Fifty-six percent of
Oilers nonattendees would attend one more game each year. Those from
nonmetropolitan Alberta in the Flames market who already attended games
would attend an additional three games in addition to the two suggested
by the NEWARENA coefficient each year. Those from the rest of Alberta in
the Oilers market who already attended games would also attend an
additional three games each year in a new downtown arena.
C. Other Results
In each model, an improvement in the standings would increase
attendance. According to the marginal effects, 47% and 21% of Flames
attendees and nonattendees would go to one additional game with a
one-place improvement in standings, as would 26% and 30% of Oilers
attendees and nonattendees. Flames attendees would go to five fewer
games each year if the new arena also included a cultural complex. No
other arena design features affect attendance. In terms of demographic
variables, older respondents attend fewer games. For Oilers attendees,
males attend two more games, married respondents attend almost three
fewer games, and each two years of schooling reduces attendance by one
game. Oilers nonattendees would go to one additional game if the new
arena contained a casino.
VI. BENEFITS
The demand models in Table 3 provide the basis for consumer surplus
estimates in Table 4. All of the consumer surplus values are obtained
from the stated preference price coefficient. We use the demand slope
based on the variation in stated preference prices with a new arena,
which are lower than what most attendees paid for their tickets, to
extrapolate the demand curve to the choke price. To the extent that this
extrapolation is inaccurate, the consumer surplus results are
inaccurate.
A. Consumer Surplus per Game
The status quo consumer surplus per game is $50 for both the Flames
and Oilers attendees ($50.37 and $50.14 in Table 4 (13)) and $23 and $22
for Flames and Oilers nonattendees. That attendee consumer surplus
exceeds that for (revealed preference) nonattendees makes theoretical
sense as the willingness and ability of nonattendees to pay for a game
is lower according to the revealed preference of nonattendance.
Consumer surplus per game may be an underestimate of the total
value of the game trip for all consumers. The full value of the game
trip is the consumer surplus of a game trip considering both ticket
prices and travel costs. The only statistically significant coefficient
for travel cost is for game attendees in the Flames market. The
full-price consumer surplus per game trip, $103, is weighted by the
dollar shares of the different costs. However, interpretation of the
full-price consumer surplus per game trip is complicated by the
possibility of multipurpose trips. As the travel cost coefficient is
statistically insignificant in the other three models we do not further
discuss its effect on the value of game trips until the conclusions,
except to note that consumer surplus per game estimates do not include
these values.
Under the latent demand interpretation of the revealed preference
dummy variable--indicating that respondents would attend more games if
seats were available--the consumer surplus for attendees is $9 per game
and $11 for each additional Flames and Oilers game with the current
arena and ticket prices, assuming the respondent could obtain a ticket.
This result makes intuitive sense as additional games are subject to
diminishing returns and should be expected to generate lower value.
The change in consumer surplus for each additional game in a new
downtown arena is $12 and $45 for Flames attendees and nonattendees
residing in Calgary, and $0 and $16 for Oilers attendees and
nonattendees living in Edmonton. In nonmetropolitan Alberta the consumer
surplus of each additional game in a new downtown arena is $29 and $36
for Flames attendees and nonattendees, and $21 and $30 for Oilers
attendees and nonattendees. The quality factor can be interpreted either
as an increase in perceived value of games in a new arena with
additional amenities or, in the case of Edmonton, that the downtown
location is preferred because of more convenience or proximity to
additional downtown amenities.
The bandwagon effect is evident in that each additional place in
the standings is worth $3 and $6 in additional consumer surplus per game
to Flames attendees and nonattendees, and $2 and $8 to Oilers attendees
and nonattendees. Adding a casino to a new downtown arena would increase
consumer surplus by $30 per game for current Oilers nonattendees. But,
perhaps indicating that artistic culture and sport do not mix (Johnson
et al. 2007), adding a cultural complex to a new arena in Calgary would
reduce consumer surplus per game by $31 for Flames attendees.
B. Consumer Surplus per Season
Combining the marginal effects (trip) estimates with the consumer
surplus per game estimates provides estimates of the change in consumer
surplus per season. Considering the status quo without the inclusion of
travel costs or higher ticket prices, Flames fans who attend an average
of five games enjoy consumer surplus of $261 over the course of the
season. Oilers fans who attend an average of four games enjoy consumer
surplus of $193 per season. Flames and Oilers fans who attend games
would enjoy an additional $13 and $14 per season, if seats were always
available.
With a new arena, Flames attendees and nonattendees and Oilers
nonattendees in the metropolitan area would enjoy an additional $23,
$75, and $9 in consumer surplus over the course of the season.
Similarly, with a new arena, Flames attendees and nonattendees, and
Oilers attendees and nonattendees from nonmetropolitan Alberta would
enjoy an additional $136, $48, $56, and $32 in consumer surplus over the
course of the season. As the impact on consumer surplus of a rise in the
standings affects all games attended, not only the increase in games,
the seasonal effect on consumer surplus is the product of the change in
consumer surplus per trip and the sum of baseline trips and the marginal
effect of a change in the standings. Flames attendees and nonattendees
would enjoy $14 and $27 in additional consumer surplus during the
season, while Oilers attendees and nonattendees would get an additional
$11 and $41 for a one-unit rise in the standings. Assuming the value of
games attended with a new arena is also affected, the cost of a cultural
complex to Flames attendees is $53 in lost consumer surplus over the
course of the season. The benefit of a casino to current Oilers
nonattendees is $46 in additional consumer surplus over the course of
the season.
C. Policy Implications
Our results can be used to inform policy makers. Most major league
sports stadiums and arenas in North America have been built with
extensive public subsidies. Long (2005) calculates an average present
value of public subsidies, in 2001 dollars, of $175 million for each of
the 99 major league hockey, basketball, football, and baseball
facilities in use in 2001 in North America. An extensive literature
measuring the economic impact of stadiums and arenas on jobs, taxes, and
incomes concludes that the economic impacts are much too small to
warrant such subsidies on efficiency grounds. See, for example, Baade
(1996), Noll and Zimbalist (1997), or Coates and Humphreys (1999). See
Coates (2007) for an overview of the literature. Others have measured
the benefits from public goods, such as civic pride, produced by sports
teams. They reach the same conclusion as the analyses of the economic
impacts--such large subsidies cannot be justified on efficiency grounds.
See Johnson, Groothuis, and Whitehead (2001) and Johnson, Mondello, and
Whitehead (2007) for examples, or Johnson (2008) for an overview of the
literature.
Studies of economic impacts or public goods, however, do not
address the consumption benefits as does the current paper. Could
consumption benefits enjoyed by game attendees make public subsidies
efficient? There are a number of alternative benefit-cost analysis scenarios and we consider only one. We estimate the benefits of a new
arena by the sum of consumer surplus of additional seats and the
additional consumer surplus of improved seating of the existing number
of seats. We assume the new arena reaches the maximum NHL capacity of
21,273 seats and the additional seats are filled with (revealed
preference) nonattendees. The per game consumer surplus for additional
seats in a new arena is $45 and $16 for those metropolitan residents in
the Calgary and Edmonton markets who do not currently attend games.
Forty-one home sellout games would generate annual consumer surplus of
$3.6 million and $2.8 million in Calgary and Edmonton for the additional
seating capacity. The Calgary Flames would generate additional
consumption benefits with a new arena. Current metropolitan attendees in
the Calgary Flames market would enjoy an additional $12 in consumer
surplus per game in a new arena. At current arena capacity over 41 home
games, the annual benefit of additional quality for the existing number
of seats is $9.5 million. The total annual benefit of a new arena in
Calgary is $13 million. The annual benefit of additional arena quality
in the Edmonton Oilers market is zero, so the total annual benefit of a
new arena is $2.8 million.
We consider two alternative discount rates: 2% and 7%. With a 2%
discount rate and consumption benefits accruing for 30 years, the
present value of consumer surplus is $292 million in Calgary and $63
million in Edmonton. With a 7% discount rate, the present value of
consumer surplus is $162 million in Calgary and $35 million in Edmonton.
A new NHL arena costs between $275 million and $450 million. Therefore,
the consumption benefits of new seating for the Edmonton Oilers do not
appear to justify a new downtown arena when future benefits are
discounted at 2% and 7%. Considering the Calgary Flames, the consumption
benefits only justify a new arena in a best case scenario: at the lower
end of the range of arena costs and when future consumption benefits are
discounted at 2%.
While a benefit-cost analysis is beyond the scope of this paper, a
number of issues should be considered. A sensitivity analysis of the
benefit estimates should consider the full consumer surplus of Flames
game trips based on ticket prices and travel costs. As the status quo
consumer surplus is about twice as high when considering travel costs,
the economic benefits of a new arena would be much higher and could
justify new arena construction.
These figures suggest that in some cases, consumption benefits may
help justify on efficiency grounds the large public subsidies typically
offered for new stadiums and arenas. This is not to say that subsidies
are good public policy. There may be other projects that would produce
greater benefits for the amount spent. Teams may be able to extract much
of the consumer surplus through personal seat licenses or through price
discrimination, making subsidies unnecessary. Nevertheless, this paper,
the first to estimate consumer surplus from data on individual fan
preferences, finds that large gains in consumer surplus may render some
stadium subsidies much less inefficient than suggested by studies of the
economic impacts and public goods alone.
VII. CONCLUSIONS
This paper is the first to estimate game trip demand functions for
individual sports fans using microdata. Because capacity constraints are
often binding in the NHL, estimation of individual hockey demand
functions using revealed preference data alone would be difficult, even
if individual-level microdata were available. The results show that
hockey fans who already attend games would buy more tickets each year if
seats were available at lower prices. The results also show that fans
are sensitive to team quality, and that each rung a team climbs in its
divisional standings will increase game trip demand. We include travel
costs as an additional variable to explain demand behavior and find some
evidence that it is a significant determinant of game attendance. The
full-price consumer surplus per game trip is twice as great as the
consumer surplus per game.
Binding capacity constraints have a large impact on the number of
games attended by metropolitan fans of the Calgary Flames, imposing a
large deadweight loss. Capacity constraints also have a large effect on
nonmetropolitan Alberta residents in both markets. These results suggest
that the benefits of new arenas in Calgary and Edmonton would accrue to
a large extent to nonmetropolitan residents. That the capacity
constraints could impose such large welfare costs may seem surprising,
but other evidence is consistent with this conclusion. For instance, the
average ticket prices in Calgary and Edmonton are among the highest in
the NHL, according to Team Marketing Report, a firm that compiles ticket
price data for professional sports teams. The high ticket prices and
sold out seasons, along with the fact that Calgary and Edmonton are
among the smallest metropolitan areas in the NHL, are evidence of a high
per capita demand for hockey tickets, consistent with the survey
responses and estimated demand functions in this paper.
Stadium and arena complexes built or proposed in the past decade
have increasingly included features and amenities far beyond the actual
sports building. Petco Field, a baseball stadium in San Diego, and
Nationwide Arena, a hockey arena in Columbus, Ohio, are the anchors of
development districts including housing, entertainment, and other
features. Proponents of stadiums and arenas as tools for urban
revitalization often argue that sports venues alone cannot succeed in
transforming decaying urban districts. Other types of development,
together with a stadium or arena, they say, can bring round-the-clock
activity and life to a downtown or other depressed neighborhood
(Rosentraub 2008).
We find that features such as housing, arts and cultural space, and
casinos in arena development projects would have little effect on the
demand for hockey tickets in Calgary and Edmonton, with two exceptions.
Demand for hockey by Calgarians who already attend games would fall if a
new arena complex included arts and cultural space, while demand for
Oilers tickets by non-Edmontonians would rise substantially if a casino
were built alongside a new arena. That nonsports features mostly have no
effect on demand for hockey, and may even decrease the demand for
hockey, suggests that increased consumer surplus of hockey fans cannot
be considered a benefit of diversity in development projects. However,
these additional features may attract nonhockey fans to the development
complex.
The results of the status quo annual consumer surplus estimation in
this paper, based on individual demand functions estimated from both
revealed and stated preference data, are broadly consistent with the
estimates of annual consumer surplus from MLB developed by Irani (1997).
Using the full consumer surplus of a new arena for the Calgary market
would suggest that the efficient outcome is to build the arena. However,
as the benefits would accrue to the fans attending games rather than to
the public at large, and because the Flames could finance much of the
cost by extracting additional consumer surplus, it would be difficult to
use the increased consumer surplus to justify a public subsidy.
Future research should investigate more detailed hypothetical
scenarios to determine the sensitivity of our results to survey design.
New stadiums and arenas incorporate many advances in construction
technology and architectural design that result in higher demand for
tickets. Better sightlines, more restrooms, restaurants, and concession
stands made easily accessible by wide aisles and concourses make for
more comfortable environments for spectators. Such improvements mean
that, at least in the honeymoon period, fans are willing to buy more
tickets at higher prices. The hypothetical scenarios presented in the
surveys in Alberta did not mention such amenities. Perhaps respondents
assumed such amenities, common in new arenas, would be included. But to
the extent they did not, the potential gains to consumer surplus from a
new arena might be different than the gains estimated in this paper.
Further, our analysis is limited in a number of ways. A potentially
important independent variable for game attendees is the actual ticket
price paid. Our survey design put little emphasis on collection of this
variable. Future research should consider the relationship between
ticket prices and individual game attendance. We also are cavalier about
capacity constraints in the hypothetical new arenas. If actual demand
for seats in a new arena exceeds capacity then ticket prices will rise
and consumer surplus will be transferred to producers or ticket dealers
and our consumer surplus estimates will be biased upwards.
Two econometric issues related to revealed and stated preference
data are not addressed. First, revealed preference data collection
likely suffers from recall bias and digit bias which results in
measurement error. This may result in inflated standard errors for our
coefficients. Second, we employ the random effects Poisson model which
is the most stable of the count data models applied to panel data.
However, as our dependent variable contains a large number of zeros,
zero-inflated Poisson models may be preferred. Future research should
pursue a comparison of alternative count data models in the context of
combined revealed and stated preference data.
Previous nonmarket valuation methods, such as the CVM, applied to
sports public goods have been used in isolation from others. Future
research should use multi-methods and consider the convergent validity of consumer surplus estimates. For example, a comparison study between
the hedonic pricing method, CVM, and travel cost method could be
designed to estimate the benefits of sports teams to a city. If the
consumer surplus estimates converge, or diverge for theoretical or
methodological reasons, then confidence in the individual estimates is
enhanced.
ABBREVIATIONS
CBM: Contingent Behavior Method
CVM: Contingent Valuation Method
MLB: Major League Baseball
NBA: National Basketball Association
NFL: National Football League
NHL: National Hockey League
doi: 10.1111/j.1465-7295.2011.00397.x
APPENDIX: SURVEY QUESTIONS
This appendix provides the telephone survey questions used to
elicit revealed and stated preference data for the Calgary Flames games.
Similar questions were used for the Edmonton Oilers games. The full
survey can be found at http://econ.appstate.edu/AGRI or upon request
from the authors.
The next few questions are about your attendance at Calgary Flames
games.
C1. How many Calgary Flames games did you attend during the 2006-07
season?
--games--if zero, skip to C3.
C2. About how much do you usually pay for your Flames tickets?
--dollars
C3. Suppose the Flames are expected to finish first in their
division and challenge for the Stanley Cup this season. About how many
Flames games would you plan to attend in Calgary this season?
--games--if zero, go to C6
C4. Now suppose that the Flames are expected to finish last in
their division. About how many Flames games would you plan to attend in
Calgary this season'?
--games
C5. Now suppose that the Flames are expected to finish third in
their division and make the playoffs this season. About how many Flames
games would you plan to attend in Calgary this season?
--games
C6. Suppose that the Flames build a new professional hockey arena
[+ casino, affordable housing, cultural complex] in downtown Calgary.
The new arena will have additional upper level seating with unobstructed
views and luxury suites. For these questions, please assume that your
personal situation stays the same. For example, you have the same job,
income and family situation. Also assume that the Flames are expected to
finish third in their division and make the playoffs. OK?
C7. If other ticket prices don't change at the new arena and
there are plenty of additional upper level seats with unobstructed views
available at a price of $20 per game, how many Flames games would you
plan to attend in the new downtown Calgary arena during the season?
--games
C8. If the tickets for the additional upper level seats with
unobstructed views are available at a price of $10 per game, how many
Flames games would you plan to attend in the new downtown Calgary arena
during the season?
--games
C9. If the tickets for the additional upper level seats with
unobstructed views are available at a price of $30 per game, how many
Flames games would you plan to attend in the new downtown Calgary arena
during the season?
--games
REFERENCES
Alexander, D. L., W. Kern, and J. Neill. "Valuing the
Consumption Benefits from Professional Sports Franchises." Journal
of Urban Economics, 48, 2000, 321-37.
Baade, R.A. "Professional Sports as Catalysts for Metropolitan
Economic Development." Journal of Urban Affairs, 18(1), 1996, 1-17.
Bockstael, N. E., and I. E. Strand. "The Effect of Common
Sources of Regression Error on Benefit Estimates." Land Economics,
63, 1987, 11-20.
Borland, J., and R. Macdonald. "Demand for Sport." Oxford
Review of Economic Policy, 19(4), 2003, 478-502.
Bruggink, T. H., and J. W. Eaton. "Rebuilding Attendance in
Major League Baseball: The Demand for Individual Games," in
Baseball Economics: Current Research, edited by J. Fizel, E. Gustafson,
and L. Hadley. Westport, CT: Praeger, 1996, 9-31.
Burdekin, R. C. K., and T. L. Idson. "Customer Preferences,
Attendance and the Racial Structure of Professional Basketball
Teams." Applied Economics, 23, 1991, 179-86.
Butler, M. R. "Interleague Play and Baseball Attendance."
Journal of Sports Economics, 3(4), 2002, 320-34.
Carlino, G., and N. E. Coulson. "Compensating Differentials
and the Social Benefits of the NFL." Journal of Urban Economics,
56(1), 2004, 25-50.
--. "Compensating Differentials and the Social Benefits of the
NFL: Reply." Journal of Urban Economies, 60(1), 2006, 132-8.
Clarke, P. M. "Testing the Convergent Validity of the
Contingent Valuation and Travel Cost Methods in Valuing the Benefits of
Health Care." Health Economics, 11, 2002, 117-27.
Coates, D. "Stadiums and Arenas: Economic Development or
Economic Redistribution?" Contemporary Economic Policy, 25(4),
2007, 565-77.
Coates, D., and T. Harrison. "Baseball Strikes and the Demand
for Attendance." Journal of Sports Economics, 6(3), 2005, 282-302.
Coates, D., and B. R. Humphreys. "'The Growth Effects of
Sports Franchises, Stadia, and Arenas." Journal of Policy Analysis
and Management, 18(4), 1999, 601-24.
--. "Proximity Benefits and Voting on Stadium and Arena
Subsidies." Journal of Urban Economics, 59(2), 2006, 285-99.
Coates, D., B. R. Humphreys, and A. Zimbalist. "Compensating
Differentials and the Social Benefits of the NFL: A Comment."
Journal of Urban Economics, 60(1), 2006, 124-31.
Czarnitzki, D., and G. Stadtmann. "The Uncertainty of Outcome
Versus Reputation: Empirical Evidence for the First German Football
Division." Empirical Economics, 27(1), 2002, 101-12.
Dehring C. A., C. A. Depken II, and M. R. Ward. "A Direct Test
of the Homevoter Hypothesis." Journal of Urban Economics, 64(1),
2008, 155-70.
Donihue, M. R., D. W. Findlay, and P. W. Newberry. "An
Analysis of Attendance at Major League Baseball Spring Training
Games." Journal of Sports Economics, 8(1), 2007, 39-61.
Fenn, A. J., and J. R. Crooker. "Estimating Local Welfare
Generated by an NFL Team under Credible Threat of Relocation."
Southern Economic Journal, 76(1), 2009, 198-223.
Garcia, J., and P. Rodriguez. "The Determinants of Football
Match Attendance Revisited: Empirical Evidence from the Spanish Football
League." Journal of Sports Economics, 3(1), 2002, 18-38.
Grijalva, T. C., R.P. Berrens, A. K. Bohara, and W.D. Shaw.
"Testing the Validity of Contingent Behavior Responses."
American Journal of Agricultural Economics, 84(2), 2002, 401-14.
Haab, T. C., and K. E. McConnell. Valuing Environmental and Natural
Resources: The Econometrics of Nonmarket Valuation. Northampton, MA:
Edward Elgar, 2002
Hausman, J., B.H. Hall, and Z. Griliches. "Econometric Models
for Count Data with an Application to Patents--R&D
Relationship." Econometrica, 52, 1984, 909-38.
Hynes, S., N. Hanley, and C. O'Donoghue. "Alternative
Treatments of the Cost of Time in Recreational Demand Models: An
Application to Whitewater Kayaking in Ireland." Journal of
Environmental Management, 90(2), 2009, 1014-21.
Irani, D. "Public Subsidies to Stadiums: Do the Costs Outweigh the Benefits?" Public Finance Review, 25, 1997, 238-53.
Johnson, B. K. "The Valuation of Non-Market Benefits in
Sport," in The Business of Sports, Vol. 2, Chapter 9, edited by B.
R. Humphreys and D. R. Howard. Westport, CT: Praeger, 2008, 207-233.
Johnson, B. K., and J. C. Whitehead. "Value of Public Goods
from Sports Stadiums: The CVM Approach." Contemporary Economic
Policy, 18(1), 2000, 48-58.
Johnson, B. K., P. A. Groothuis, and J. C. Whitehead. "The
Value of Public Goods Generated by a Major League Team: The CVM
Approach." Journal of Sports Economics, 2(1), 2001, 6-21.
Johnson, B. K., M. J. Mondello, and J. C. Whitehead. "The
Value of Public Goods Generated by a National Football League
Team." Journal of Sport Management, 21(1), 2007, 123-36.
Johnson, B. K., J. C. Whitehead, D. S. Mason, and G. J. Walker.
"Competition for Government Funding in Arts and Sports: The Lace
Underwear Set vs. the Jockstrap Crowd." Paper presented at the
International Atlantic Economic Society Meetings, Savannah, GA, 2007.
Kahane, L., and S. Shmanske. "Team Roster Turnover and
Attendance in Major League Baseball." Applied Economics, 29, 1997,
425-31.
Kom, J. "Dome Dreams: As a Brash Kid, Our City's Arena
Welcomed the 1988 Olympics, but the Hockey Hotshot Is Now an Aging
Player in the Sports World." Calgary Herald, 2008, April 6, B1.
Landry, C.E., and H. Liu. "Econometric Models for Joint
Estimation of RP-SP Site Frequency Recreation Demand Models," in
Preference Data for Environmental Valuation: Combining Revealed and
Stated Approaches, edited by J. C. Whitehead, T. C. Haab, and J.-C.
Huang. London: Routledge, 2011.
Leadley, J. C., and Z. X. Zygmont. "When Is the Honeymoon
Over? National Hockey League Attendance." Canadian Public Policy,
32(2), 2006, 213-32.
Long, J. G. "Full Count: The Real Cost of Public Funding for
Major League Sports Facilities." Journal of Sports Economics, 6(2),
2005, 119-43.
Mah, B. "Plan for an Aging Rexall." Edmonton Journal,
2007, February 22, A3.
Mason, D.S. "The Stadium Game in an Uncertain Environment: A
Preliminary Look at Arena Discourse in Edmonton, Canada," in
Optimal Strategies in Sports Economics and Management, edited by S.
Butenko, J. Gil-Lafuente, and P. M. Pardalos. London: Springer, 2010,
97-124.
Noll, R., and A. Zimbalist, eds. Sports, Jobs, and Taxes: The
Economic Impact of Sports Teams and Stadiums. Washington, DC: Brookings
Institution, 1997.
Paul, R. "Variations in NHL Attendance: The Impact of
Violence, Scoring, and Regional Rivalries." American Journal of
Economics and Sociology, 62(2), 2003, 345-64.
Phaneuf, D. J., and V. K. Smith. "Recreation Demand
Models," in Handbook of Environmental Economics: Volume 2, Valuing
Environmental Changes, Chapter 15, edited by K.-G. Maler and J. R.
Vincent. Elsevier North-Holland, 2005.
Price, D., and K. Sen. "The Demand for Game-Day Attendance in
College Football: An Analysis of the 1997 Division I-A Season."
Managerial and Decision Economics, 24, 2003, 35-46.
Rosentraub, M. S. "Sports Facilities and Urban Redevelopment:
Private and Public Benefits and a Prescription for a Healthier
Future," in The Business of Sports, Volume 3: Bridging Research and
Practice, Chapter 3, edited by B. R. Humphreys and D. R. Howard.
Westport, CT: Praeger, 2008.
Sports Business Journal. "By the Numbers 2003," 30
December 2002, 5(36), 91, 111.
Tarrant, M.A., and M.J. Manfredo. "Digit Preference, Recall
Bias, and Nonresponse Bias in Self Reports of Angling
Participation.'" Leisure Science, 15(3), 1993, 231-38.
Welki, A. M., and T. J. Zlatoper. "US Professional Football:
The Demand for Game-Day Attendance in 1991." Managerial and
Decision Economics, 15, 1994, 489-95.
--. "US Professional Football Game-Day Attendance."
Atlantic Economic Journal, 27(3), 1999, 285-98.
Whitehead, J. C. "Environmental Risk and Averting Behavior:
Predictive Validity of Revealed and Stated Preference Data."
Environmental and Resource Economics, 32(2), 2005, 301-16.
Whitehead, J. C., S. K. Pattanayak, G. L. Van Houtven, and B. R.
Gelso. "Combining Revealed and Stated Preference Data to Estimate
the Nonmarket Value of Ecological Services: An Assessment of the State
of the Science." Journal of Economic Surveys, 22(5), 2008, 872-908.
Zygmont, Z. X., and J. C. Leadley. "When Is the Honeymoon
Over? Major League Baseball Attendance 1970-2000." Journal of Sport
Management, 19(3), 2005, 278-99.
(1.) Other methods that have been or could be used to value sports
amenities include the hedonic price method (Carlino and Coulson 2004,
2006; Coates, Humphreys, and Zimbalist 2006), public referenda (Coates
and Humphreys 2006), or a combination of the two (Dehring, Depken, and
Ward 2008).
(2.) Pengrowth Saddledome was renamed Scotiabank Saddledome in
2010.
(3.) These data exhibit digit bias which is one strategy that
respondents rely on when recall is difficult (Tarrant and Manfredo
1993). Measurement error in the dependent variable should not bias
regression coefficient efficient estimates but it could lead to inflated
standard errors.
(4.) NHL teams host 41 regular season games each season.
(5.) Our sample is slightly older and more affluent than the
population.
(6.) Ticket prices paid for Flames and Oilers games range from $11
to $300 and $20 to $179, suggesting that the reported price paid
includes secondary market purchases.
(7.) Travel cost is computed as c = [alpha]d + ([theta]y/h)(d/mph),
where [alpha] is the average cost per mile (including gas, oil, and
depreciation), d is round-trip distance, [theta] is the opportunity cost
of time, 0 < [theta] <1, y is household income, h is annual work
hours, and mph is average miles per hour. We use the following values:
[alpha] = 0.32, [theta] = 0.33, h = 2000, and mph = 75 k/h. These values
are not unusual in the literature (Phaneuf and Smith 2005); however, our
results are likely sensitive to alternative values (Hynes, Hanley, and
O'Donoghue 2009).
(8.) Sixteen percent of the income variables are imputed using an
income regression ([R.sup.2] = 0.27). In this model income increases
with education and age (at a diminishing rate). Income is higher for
males, married respondents, and metro respondents (relative to rural
Albertans).
(9.) The panel data random effects Poisson model relaxes the equal
mean and variance assumption of a cross-sectional Poisson model. It is
distributed negative binomial with overdispersion parameter [alpha]. It
is the standard when estimating demand models with revealed and stated
preference data combined in a pseudo-panel (Whitehead et al. 2008). The
main advantage of the random effects Poisson over the random effects
negative binomial is that it allows for cross-equation correlation
(Landry and Liu, 2011). As there are a large number of zeros in the
dependent variable, zero-inflated Poisson models were also attempted in
the context of random parameter and latent class Poisson models. Each of
these models fits the data well. However, results lead to larger
consumer surplus estimates than we present here. As, to our knowledge,
no study in the revealed and stated preference literature has
investigated these alternative models and that investigation is beyond
the scope of this paper we leave this exploration to future research.
(10.) Economic theory indicates that each dollar of cost should be
considered equivalently by consumers, suggesting that ticket price and
travel cost should be added together to form a single cost variable.
However, model performance is superior with these variables entered
separately. This suggests that consumers respond differently to the two
types of cost. Perhaps, the travel cost variable is capturing the cost
of multipurpose trips. In addition to a hockey game, respondents could
also enjoy shopping or dining in the area. In this case we would expect
the travel cost elasticity to be less than the ticket price elasticity.
(11.) The market price and travel cost weights are 0.75 and 0.25,
respectively.
(12.) Marginal effects are not reported in Table 3.
(13.) Consumer surplus numbers reported in the text are rounded.
JOHN C. WHITEHEAD, BRUCE K. JOHNSON, DANIEL S. MASON and GORDON J.
WALKER *
* Preparation of this manuscript was supported by a grant from the
Alberta Gaming Research Institute. The authors would like to thank the
University of Alberta Population Research Lab staff for their assistance
collecting the data and Glenn Blomquist, Jeff Borland, Philip Porter,
Stefan Szymanski, and two anonymous referees for a number of comments
and suggestions that have greatly improved this paper. Previous versions
of this paper were presented in seminars at the University of Kentucky
and Appalachian State University and in a session at the 2008 Southern
Economic Association meetings.
Whitehead: Department of Economics, Appalachian State University,
Boone, NC 28607. Phone 828-262-6121, Fax 828-262-6105, E-mail
whiteheadjc@appstate.edu
Johnson: Department of Economics, Centre College, Danville, KY
40422. Phone 859-238-5255, Fax 859-238-9610, E-mail
bruce.johnson@centre.edu
Mason: Faculty of Physical Education & Recreation, University
of Alberta, Edmonton, AB, T6G 2H9, Canada. Phone 780-492-6822, Fax
780-492-1008, E-mail dmason@ualberta.ca
Walker: Faculty of Physical Education & Recreation, University
of Alberta, Edmonton, AB, T6G 2H9, Canada. Phone 780-492-0581, Fax
780-492-2364, E-mail gordon.walker@ualberta.ca
TABLE 1
Dependent Variables--Games Attended in Various Scenarios
Calgary Flames
Scenario Attend = 1 Attend = 0
Price
(Flames, Standard Standard
Arena Oilers) Finish Mean Deviation Mean Deviation
Old RP (a) 3.5 5.19 7.85 0.00 0.00
Old 35.45 1 7.08 11.86 0.27 0.80
Old 35.45 5 5.62 10.53 0.13 0.66
Old 35.45 3 5.95 10.31 0.19 0.70
New 20.30 3 10.47 13.52 1.67 5.20
New 10.20 3 13.30 15.59 2.68 7.34
New 30.40 3 8.99 12.19 1.13 3.62
Cases 133 246
Edmonton Oilers
Attend = 1 Attend = 0
Standard Standard
Arena Mean Deviation Mean Deviation
Old 3.84 4.42 0.00 0.00
Old 5.76 9.10 0.57 2.85
Old 4.87 10.42 0.23 0.91
Old 4.97 7.86 0.32 0.97
New 7.85 12.77 1.44 4.12
New 9.48 13.54 2.10 5.57
New 6.35 8.28 0.81 1.65
Cases 148 301
Notes: The mean figures show the average number of games attended in
various scenarios. Those in Row 1 are the average games actually
attended, according to revealed preference responses. Those in Rows
2-7 are stated preference averages under scenarios that differ
according to whether the arena is old or new, ticket prices, and
divisional finish. The responses in each of the two markets are broken
down among those who attended games in 2006-2007 (Attend = 1) and
those who did not (Attend = 0).
(a) Revealed preference during 2006-2007 season.
TABLE 2
Independent Variables
Calgary Flames
Attend = 1 Attend = 0
Standard Standard
Variable Description Mean Deviation Mean Deviation
RP-PRICE Actual ticket price 82.08 35.87 -- --
TRAVCOST Travel cost to 26.36 67.45 43.75 65.76
downtown
INCOME Household income 110.86 35.23 77.30 36.99
($1,000s)
MALE 1 if male 0.63 0.48 0.43 0.50
MARRIED 1 if married 0.73 0.45 0.65 0.48
AGE Age of respondent 42.83 13.66 50.15 15.25
EDUC Years of schooling 14.50 2.10 14.11 2.22
LIVE 1 if lives in 0.08 0.26 0.06 0.24
downtown
WORK 1 if works in 0.25 0.43 0.11 0.31
downtown
PROPERTY 1 if owns property 0.69 0.46 0.50 0.50
in the area
TENURE Length of time at 19.95 16.09 17.18 17.40
residence
ALBERTA 1 if Alberta survey 0.14 0.34 0.30 0.46
Cases 133 246
Edmonton Oilers
Attend = 1 Attend = 0
Standard Standard
Variable Mean Deviation Mean Deviation
RP-PRICE 78.33 23.50 -- --
TRAVCOST 39.47 57.68 59.87 83.35
INCOME 93.78 38.09 70.57 39.30
MALE 0.59 0.49 0.39 0.49
MARRIED 0.72 0.45 0.71 0.45
AGE 43.28 14.49 51.68 16.46
EDUC 13.84 2.12 13.73 2.59
LIVE 0.02 0.14 0.05 0.22
WORK 0.13 0.34 0.11 0.31
PROPERTY 0.56 0.50 0.48 0.50
TENURE 20.31 17.91 21.24 21.57
ALBERTA 0.25 0.43 0.35 0.48
Cases 148 301
TABLE 3
Random Effects Panel Data Poisson Model: Hockey Game Attendance
Demand (Attendee vs. Nonattendee)
Calgary Flames
Attend = 1 Attend = 0
Coefficient t-ratio Coefficient t-ratio
Constant 4.153 4.38 0.990 0.46
RP -0.177 -6.06
RP-PRICE 0.002 0.66
SP-PRICE -0.020 -15.66 -0.044 -23.12
TRAVCOST -0.004 -2.52 -0.001 -0.20
NEWARENA 0.237 9.80 1.943 28.86
NEWARENA x ALBERTA 0.341 11.35 -0.387 -3.16
FINISH -0.058 -7.81 -0.241 -2.95
INCOME 0.001 0.26 0.006 0.89
MALE 0.051 0.26 0.069 0.14
MARRIED 0.230 0.82 0.415 0.73
AGE -0.019 -2.32 -0.034 -2.26
EDUC -0.083 -1.24 -0.001 -0.01
GREEN 0.271 1.16 0.261 0.37
CASINO 0.225 0.75 -0.082 -0.10
HOUSING 0.285 0.86 0.243 0.33
ARTS -0.616 -1.90 -0.607 -0.74
[alpha] 1.185 5.93 5.591 7.57
LL -2733 -1198
Cases 133 246
Periods 7 7
Edmonton Oilers
Attend = 1 Attend = 0
Coefficient t-ratio Coefficient t-ratio
Constant 3.335 3.69 3.363 1.79
RP -0.214 -5.47
RP-PRICE 0.005 1.03
SP-PRICE -0.020 -10.63 -0.046 -14.60
TRAVCOST -0.001 -0.61 0.002 0.51
NEWARENA 0.004 0.13 0.718 9.41
NEWARENA x ALBERTA 0.420 19.14 0.663 7.68
FINISH -0.043 -4.90 -0.386 -11.90
INCOME 0.007 2.26 -0.003 -0.62
MALE 0.334 1.63 0.404 1.09
MARRIED -0.421 -1.86 -0.031 -0.06
AGE -0.014 -1.81 -0.047 -3.23
EDUC -0.079 -1.74 0.003 0.03
GREEN -0.045 -0.17 -0.335 -0.71
CASINO -0.024 -0.08 1.362 2.00
HOUSING -0.172 -0.61 -0.075 -0.13
ARTS 0.347 1.17 0.876 1.29
[alpha] 0.914 7.89 4.731 7.88
LL -2703 -1508
Cases 148 301
Periods 7 7
TABLE 4
Consumer Surplus Estimates
Calgary Flames
Attend = 1
Consumer Surplus (CS) per Game (X) CS/X t-stat
Status quo 50.37 15.66
Change in CS per Game [DELTA] CS/X t-stat
With current arena 8.89 5.34
With new arena for metro residents 11.92 6.14
With new arena for Alberta residents 29.10 8.37
Additional place in standings 2.95 6.38
With new arena and green design 13.65 1.17
With new arena and casino 11.35 0.75
With new arena and affordable housing 14.37 0.85
With new arena and cultural complex -31.01 -1.87
Calgary Flames
Attend = 0
Consumer Surplus (CS) per Game (X) CS/X t-stat
Status quo 22.92 23.12
Change in CS per Game [DELTA] CS/X t-stat
With current arena
With new arena for metro residents 44.52 14.84
With new arena for Alberta residents 35.66 10.29
Additional place in standings 5.52 2.85
With new arena and green design 5.97 0.37
With new arena and casino -1.87 -0.10
With new arena and affordable housing 5.58 0.33
With new arena and cultural complex -13.91 -0.74
Edmonton Oilers
Attend = 1
Consumer Surplus (CS) per Game (X) CS/X t-stat
Status quo 50.14 10.63
Change in CS per Game [DELTA] CS/X t-stat
With current arena 10.75 4.30
With new arena for metro residents 0.22 0.13
With new arena for Alberta residents 21.30 6.07
Additional place in standings 2.14 5.18
With new arena and green design -2.26 -0.17
With new arena and casino -1.20 -0.08
With new arena and affordable housing -8.62 -0.62
With new arena and cultural complex 17.41 1.18
Edmonton Oilers
Attend = 0
Consumer Surplus (CS) per Game (X) CS/X t-stat
Status quo 21.71 14.60
Change in CS per Game [DELTA] CS/X t-stat
With current arena
With new arena for metro residents 15.59 5.84
With new arena for Alberta residents 29.98 7.58
Additional place in standings 8.37 8.89
With new arena and green design -7.26 -0.71
With new arena and casino 29.58 1.95
With new arena and affordable housing -1.63 -0.13
With new arena and cultural complex 19.02 1.29