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Home Run or Wild Pitch?: Assessing the Economic Impact of Major League Baseball's All-Star Game

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Abstract

Major League Baseball has rewarded cities that build new baseball stadiums with the chance to host the All-Star Game. Although the league asserts a significant boost to metropolitan economies due to the game, are these economic impact estimates published by the league credible? In two separate economic impact models, the authors find that All-Star Games since 1973 are actually associated with worse than expected economic performance in host cities.
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Home Run or Wild Pitch?
Assessing the Economic Impact of Major
League Baseball’s All-Star Game
Robert Baade and Victor Matheson
Kerry Tan
May 24, 2011
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Motivation
Major League Baseball (MLB) estimated an economic
impact of $62 million from the 1999 All-Star Game on the
Boston economy.
MLB projected that the 2002 All-Star Game will generate
an economic impact in excess of $70 million for the city of
Milwaukee.
Is it possible that a sporting event can have that much of a
short-term impact?
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Motivation
MLB uses the promise of a future All-Star Game as an
enticement for cities to build new baseball stadiums.
"The National League has decided the All-Star Game
should be played in new facilities, except in special
circumstances." -Len Colemen, National League President
MLB believes that stadiums factor prominently into
consumer decisions relating to leisure spending.
MLB implies that public financial support for a new stadium
is a good investment for a city.
A single All-Star Game generates enough economic activity
within the metropolitan area to compensate for a substantial
portion of the cost of building a new stadium.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Purpose of the Paper
Baade and Matheson estimate the economic impact of
All-Star Games from 1973 through 1997.
Basic result: the economic impact of the All-Star Game on
the host city could be negative and is much lower, on
average, than the magnitude of the MLB estimate.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
The All-Star Game
The All-Star Game began in 1933 as an event to showcase
the most talented and most popular players in the league.
MLB selects the host city several years in advance of the
game.
Allows potential visitors to plan for their attendance
Allows the host city to make extensive preparations
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
The All-Star Game
The Super Bowl can only take place in a few select cities
due to weather considerations.
The All-Star Game takes place during a break in the
middle of the baseball season.
Nearly every city with a MLB team has hosted an All-Star
Game.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
The All-Star Game
The game has expanded to a 5-day event that includes a
home-run hitting contest.
The All-Star FanFest was set up as a type of baseball
convention.
sporting good vendors
baseball memorabilia
virtual reality games
autograph sessions
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Estimating the Economic Impact
Boston reported that about 110,000 people visited the
FanFest and more than 225,000 attended some portion of
the activities during the 5-day All-Star celebration.
A reported 14,000 hotel rooms were used in Boston for the
event.
How did they calculate the economic impact of all these
fans?
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Estimating the Economic Impact
The numbers quoted by MLB and city officials are
generated using a standard expenditure approach to
estimating the direct economic impact of the event.
The numbers are derived by estimating the number of
visitor days as a result of the game and multiplying that
statistic by the average estimated per diem expenditures
per visitor.
The total economic impact is estimated by applying a
multiplier.
In order to get credible economic impact estimates, you
need accurate estimates on direct expenditures.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Difficulty with Estimating the Economic Impact:
Reallocation of Resources
Spending in conjunction with the event would have
occurred in the absence of it.
Consideration would have to be given to the fact that
spending on the event may well merely substitute for
spending that would occur on something else in the local
economy in the absence of the event.
An event like the All-Star Game may simply yield a
reallocation of leisure spending while leaving total
spending unchanged.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Difficulty with Estimating the Economic Impact:
Reallocation of Resources
Spending at the All-Star Game is more likely to be
categorized as export spending because it is thought to be
undertaken by people from outside the community.
5% to 20% of fans at a regular season game are visitors
from outside the local metropolitan area.
The percentage of visitors at an event like an All-Star
Game is thought to be much higher.
So the question is how much of the spending was made by
locals and how much was made by visitors?
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Difficulty with Estimating the Economic Impact:
Leakage
Leakages from the circular flow of spending can lead to
biased predictions.
If the host economy is at or very near full employment or if
the work requires specialized skills, labor might have to be
imported from outside the city.
Indirect spending that constitutes the multiplier effect must
be adjusted to reflect this leakage of income and
subsequent spending.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Background Information
Economic Impact of the All-Star Game
Difficulty with Estimating the Economic Impact:
Leakage
Labor is not the only factor of production that might lead to
leakages.
Even if hotels experience higher than normal occupancy
rates during the All-Star Game, then you have to worry
about whether that revenue goes back into the local hotel
or if it gets redistributed to the national chain.
Are the profits being made staying within the community or
going back to the nationally owned chain?
Need to adjust the multiplier effect to take this into
consideration.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Model 1: Changes in Employment
The economic activity generated by the All-Star Game is
likely to be small relative to the overall economy.
Need to isolate the event’s impact.
Baade and Matheson establish what employment would
have been in the absence of the All-Star Game.
Compare this estimate to actual employment levels to
assess the contribution of the event.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Method: Difference-in-Difference Approach
Create a statistic for a city in a particular year.
Find "comparable" cities to the host city
Compute the average employment growth for those cities
Compute the employment growth for the host city
Look at the difference between the growth rates
The key thing to do is to make sure you control for
variables that affect all cities in similar ways.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Method: Difference-in-Difference Approach
Suppose that a particular city’s growth in employment is
10%.
If cities in general are growing at 5%, then you can say that
the city deviates from the norm by 5%.
Take a look at how much that the city deviation can be
attributed to the All-Star Game.
If the city experiences a growth in employment that is 5%
above the average before and after the All-Star Game, then
the All-Star Game has no effect on employment.
If there is an increase or decrease in employment before
and after the event, then the All-Star Game had an effect.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Regression Model
Baade and Matheson run a regression model using
percent change in employment in the MSA as the
dependent variable.
MSA stands for metropolitan statistical area
The regression model is used to predict the growth path for
employment.
This predicted value is compared to the actual growth in
employment.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Regression Model
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
More on MSAs
MSA is broader than city.
The actual city of Los Angeles might not actually be that
big.
A lot of people live in the suburbs outside of the actual city
limits.
These residents still impact the city’s economy.
Economists typically use data on MSA over city to get a
more complete perspective.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Data
Baade and Matheson examined the economic impact of 23
All-Star Games between 1973 and 1997.
The All-Star Games of 1982 and 1991 were excluded from
the analysis.
They were held in Canadian cities (Montreal and Toronto).
No data were available.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
The Effect of the All Star Game on Cleveland
Cleveland hosted the All-Star Game in 1981 and 1997.
The results show that, generally speaking, employment in
Cleveland has grown more slowly than in 57 comparable
cities.
Surprisingly, employment in Cleveland grew more slowly
than expected in 1981 and 1997.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
The Effect of the All Star Game on Cleveland
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
The Effect of the All Star Game on Cleveland
The key statistic to Baade and Matheson’s model is the
difference between the actual and predicted growth in jobs
for the city hosting the All-Star Game.
According to the Economic Report of the President in
1997, the U.S. economy produced roughly one job for
every $60,000 in economic activity.
MLB predicted that the economic impact for the Cleveland
All-Star Game was $60 million.
The All-Star Game should produce roughly 1,000 new jobs
in Cleveland due to the All-Star Game.
Baade and Matheson computed that 783 jobs were created.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
General Results
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
General Results
On average, the model predicted an increase in
employment in host cities by 2.11% during All-Star Game
years while the observed gains in employment averaged
just 1.73%
Baade and Matheson reject the idea that the All-Star
Game contributed to a net gain of at least 1,000 jobs.
The authors conclude that the net impact of $60 million is
likely to be very generous.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Changes in Taxable Sales
Another thing to look at is taxable sales.
If the All-Star Game significantly increases economic
activity in the host city, then the host city’s taxable sales as
a percentage of taxable sales in the rest of the state should
increase.
By comparing the city/rest-of-state ratio in an All-Star
Game time period to other time periods, an increase in
taxable sales can be inferred.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Changes in Taxable Sales
Baade and Matheson calculate the ratio of a host city’s
taxable sales to the taxable sales of the rest of the state in
which the city resides.
The key thing to do is isolate which factors are common to
the economy as a whole and which factors are specific to
the city being studied.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Things to Be Careful About
Lagged growth rates
If the economy of a city is growing at a faster rate than the
state’s economy, then the taxable sales ratio will grow over
time.
Seasonal variation
Taxable sales in warm weather, vacation destination cities
tend to increase as compared to other cities during the
winter.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Regression Model
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Effect of the All-Star Game on Taxable Sales
There are three possible explanations for the effect of the
All-Star Game on taxable sales:
1
No effect: the congestion caused by the All-Star Game
displaces local residents and other tourists who would
otherwise visit the city
The All-Star Game visitors supplant rather than supplement
tourism in the host cities.
2
Decrease taxable sales: More local residents or regular
visitors attempt to avoid the congestion than there are
visitors to the All-Star Game
3
Increase taxable sales: The All-Star Game stimulates an
increase in tourism that supplements the normal level of
tourism to the host city.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Data
County-by-county quarterly taxable sales data form
California
Examine three separate games in three different cities:
1992 All-Star Game: San Diego (San Diego County)
1989 All-Star Game: Anaheim (Orange County)
1987 All-Star Game: Oakland (Alameda County)
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Why California???
California is home to several MLB teams
This data set can be used to examine the economic impact
of several All-Star Games in the recent past.
Sales tax data are readily available from 1986 to 2000
This is a longer time period than what’s available form other
states.
California provides quarterly tax data in addition to annual
data.
Quarterly data is more useful in this setting in order to more
accurately pinpoint the impact of the All-Star Game.
Annual data would obscure the impact of the All-Star Game
with other events.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
General Results
In each of the three cities, the All-Star Game emerged as
having a negative (but statistically insignificant) impact on
taxable sales in the county in which the All-Star Game was
held.
On average, the presence of the All-Star Game correlated
to a 0.048% drop in the percentage of California’s taxable
sales accounted for by the host city.
Taxable sales dropped between $28.5 million to $29 million
in Oakland.
Taxable sales dropped between $22.5 million to $38.2
million in Anaheim.
Taxable sales dropped between $21.5 million to $29.9
million in San Diego.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
General Results
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
Implications of Model 2
Taxable sales dropped for all three host cities.
One can argue that spending by All-Star visitors simply
crowds out other spending that would have taken place in
the absence of the game.
What’s the bottom line?
At best, the All-Star Games lead to little or no net economic
benefit to the host city.
At worst, hosting the All-Star Game may lead to lower
economic activity than a city would normally expect during
the summer.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Model
Results
MLB’s Rebuttal
MLB would likely argue that other economic factors caused
the drop in taxable sales.
The drop would have been worse if not for the presence of
the All-Star Game.
Economic activity has increased but is simply not reflected
in taxable sales.
Hotel rooms are subject to a special hotel tax, which is not
included in sales tax.
Perhaps a better measure would be to look at how revenue
from the hotel tax changed before and after the All-Star
Game.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Conclusions
MLB has used the promise of hosting an All-Star Game as
an incentive for cities to construct new stadiums at
considerable public expense.
Recent MLB studies have estimated that All-Star Games
increase economic activity by about $60 million in host
cities.
Regression analysis reveals that All-Star Game cities had
employment growth below that which would have been
expected.
An examination of taxable sales data from California
reveals that taxable sales in host cities have on average
fallen nearly $30 million below what would have been
normally expected.
Economics 583: Economics of Sports Home Run or Wild Pitch?
Introduction
Model 1: Employment
Model 2: Taxable Sales
Conclusions and Policy Implications
Policy Implications
Cities would be wise to view the All-Star Game economic
impact estimates by the MLB with caution.
MLB economic impact estimates overstate the actual
economic effect.
Instead of being an economic "home run," hosting the
All-Star Game is an economic "wild pitch."
Economics 583: Economics of Sports Home Run or Wild Pitch?
... It is difficult to estimate the financial impact of a sporting event on a community, because of the inability to separate tourist visitors from local participants, since visitor costs are much higher than those of residents (Baade & Matheson, 2001). ...
Chapter
Despite claims, primarily from Republican lawmakers, that the removal of the 2021 Major League Baseball All-Star Game cost local businesses in the Atlanta area 100millionindamages,anexaminationofhoteloccupancyduringthe2014AllStarGameinMinneapolisandthe2019AllStarGameinClevelandsuggeststhattheseeventsgeneratedatmost10,000additionalroomnightsand100 million in damages, an examination of hotel occupancy during the 2014 All-Star Game in Minneapolis and the 2019 All-Star Game in Cleveland suggests that these events generated at most 10,000 additional room nights and 4.5 million in additional hotel revenues for the host cities. These figures suggest that the All-Star Game generates a total direct marginal increase in tourism spending of only 3.9to3.9 to 9.4 million. Claiming that Georgia lost $100 million from the removal of the game is pure fiction with no basis in economic data.
Chapter
The “Baade consensus,” i.e., the non-significance of staging major sporting or of the construction of new sports stadiums for employment, income, or tax revenues, was valid for some two or three decades. Some recent publications question the consensus. This contribution finds that these newer studies suffer from problematic modeling of the development trends that would have arisen without the event, from potential sample selection biases, and from potential variable selection biases. Furthermore, it is notable that most of the booster publications use highly aggregated data such as national GDP or national exports (often on a yearly basis), whereas the previous work from the Baade consensus used increasingly disaggregated datasets. Also, booster estimates imply multipliers which seem much too large compared to empirical results in other areas. The contribution also indicates that the studies on the effects of mega sports events may be a case of “retire statistical significance” as the power of the tests may be too low to uncover or exclude significant and economically plausible effects with a reasonable probability.
Chapter
Robert Baade was the first economist to estimate the effects of sports stadiums and teams on local economies. His work served as the foundation for the subsequent large and vibrant economics literature that continues to the present. His initial finding that stadiums have limited economic impacts on host localities has endured and shaped the consensus of the economics discipline that stadiums are poor public investments. This chapter reviews Dr. Baade’s early studies to demonstrate their proper context as pioneering research in the field.
Chapter
A 1999 study by Dennis Coates and Brad R. Humphreys found the presence of major sports franchises to have no significant impact on the growth rate of per capita personal income and to be negatively correlated with the level of per capita personal income for a sample of all cities that had been home to at least one franchise in any of three professional sports—baseball, basketball, and football—at some time between 1969 and 1994. This paper returns to the questions Coates and Humphreys asked using an additional 17 years of data and a number of new stadiums, arenas, and franchises. The data cover 1969–2011 and add hockey and soccer franchises to the mix while also including all standard metropolitan statistical areas rather than just those that housed franchises in the major professional leagues. The analysis also adds two new dependent variables: wage and salary disbursements and wages per job. The results here are generally similar to those of Coates and Humphreys; the array of sports variables, including the presence of franchises, arrival and departure of clubs in a metropolitan area, and stadium and arena construction, is statistically significant. However, individual coefficients frequently indicate harmful effects of sports on per capita income, wage and salary disbursements, and wages per job.
Chapter
A large body of evidence finds no tangible new economic impact of professional sports on the economy in host cities. Displacement spending, the idea that spending in and around sports facilities on game day would have been spent somewhere else in the city, represents one explanation for this lack of economic impact. I empirically analyze the departure of an NFL team from a large US city in 2015 using a difference-in-differences approach to develop evidence that displacement spending occurred after the team departure using data on establishments in the hospitality industry from County Business Patterns. Results show increases in the number of establishments, employment, and payroll in restaurants throughout the metropolitan area following the departure, supporting the presence of displacement spending in this setting. Little impact occurred in the sector containing bars. Evidence supporting the presence of displacement spending strengthens the existing evidence finding no economic impact of professional sports by providing a plausible mechanism explaining this lack of impact.
Chapter
We measure both the economic impact of a micro-event on the local economy and the consumer surplus benefits to participants using stated preference methods. We focus on a local participatory bike race called the “Beech Mountain Metric” (BMM), an amateur road bicycle event. We find that the economic impacts of the BMM declined from 301,000in2014to301,000 in 2014 to 185,000 in 2016 as the event lost popularity. The consumer surplus to participants fell from 11,000to11,000 to 6000. The consumer surplus benefits are most likely relatively low in magnitude because there are many bike races in the region to choose from including Blood Sweat and Gears and the Blue Ridge Brutal, both more popular races. Considering the stated preference model, we replicate Whitehead and Wicker (Int J Tour Res 21:180–186, 2019) using the willingness-to-travel approach. Using an intensity of preference correction can mitigate for hypothetical bias, but using only individuals who are “definitely sure” about return visitation will overcorrect the problem. This result suggests that the definitely yes and the sum of the probably and definitely yes probabilities provide a useful estimate of the range of return visitation that could be used in micro-event planning.
Article
This paper examines the growing trend of NFL players to forego participation in the league´s yearly All-Star exhibition game, the Pro Bowl. Viewership of the Pro Bowl has been substantially lower than the average game day in recent years, causing controversial discussions about the viability of the game and its future. As a consequence, the league revised the Pro Bowl´s concept entirely in 2022.Since the major determinant of viewership demand is the participation of (superstar) players, this paper analyses the individual athletes’ economic incentives in the decision to participate. To this end, it models the athlete’s decision as a rational evaluation of cost-benefit under incentives of monetary reward and punishment. It uses unbalanced panel data on Pro Bowl players from the Super Bowl era (1971-2019), alongside viewership data and official league data. It applies a range of econometric methods (Pearson-correlations, graphical examination) to evaluate hypotheses about the players’ decision-making process. It concludes that the incentives to participate in the Pro Bowl for the majority of players—esp. viewership-driving superstar players—were weak. The monetary incentives in their previous form were not an efficient way of positively manipulating the percentage of superstars in the game. If the goal was higher demand from players, the incentive structure had to be changed. Such changes are inter alia, the reduction of costs for participation in the form of minimizing the risk of injuries. Furthermore, possible changes to the design of the incentive structure are proposed that contain general learnings for the design of such events.
Article
Since the 1950s, taxpayers have been the primary investors in stadia built for the use of privately-owned professional sports teams. Team owners have argued that sports facilities boost local economic activity; however, economic reasoning and empirical evidence suggest the opposite. Public support for stadia is also driven by demand for community image, and owners of sports teams supply a scarce input into image enhancement--participation in the major league--for which they have been able to extract monopoly rents from dispersed taxpayers. We suggest reforms to dissipate the monopoly sports leagues exercise when negotiating with host communities for their teams.