Final Draft

An Empirical Analysis on the Impact of a Professional Sports Team and Stadium on its Host Metropolitan Statistical Area

By:

Alexander Stephens

College of St Benedict and St. John’s University

April 2016

Abstract:

Professional sports in the United States have grown significantly in the past fifty years and with the growth has come an influx of new stadiums and renovations to existing stadiums. This paper uses a regression analysis to determine the impact of stadiums and professional sports teams on the aggregate income of the metropolitan statistical area (MSA). Ten MSA’s across the Midwest Region of the United States are analyzed and the results show that the construction or renovation of stadiums as well as the presence of a National Football League franchise have a statistically significant negative impact on personal aggregate income.

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Introduction

Professional sport in the United States has grown dramatically in the past 25 years and to support

the growth so has the amount of stadiums that have housed the teams. According to John Siegfried and

Andrew Zimbalist (2000), there were 46 stadiums either constructed or renovated between 1990 and 1998

for the top four United States sports leagues, and 49 more planned as of 1999. They also estimate that of

the stadiums built or planned $21.7 billion will be spent to cover the cost of construction, and of that,

close to two thirds will be paid for with public funds (Siegfried & Zimbalist, 2000). Leagues, and the

teams within them, argue the latest state-of-the-art facilities are needed to best serve the fans who attend

and the athletes who perform. The issue that arises is whether the money provided by the public will lead

to a return on their investment. As can be seen in Figure 1, in the appendix below, not all the stadiums

paid for by the city, county, or state will be owned by them. So the questions can be raised, what type of

impact does the public funding of stadiums for professional sports teams have on the metropolitan areas

that host them?

Government officials and league proponents for stadium construction and renovation argue that

hosting professional sports within a city, “project a world-class image”, as Robert Baade and Richard Dye

describe (1990), and modern facilities are just a necessary part of that. This claim is often supported with

values of economic benefit provided by boosters attempting to sway voters and attain funding. For

example, the St. Louis Regional Chamber and Growth Association claimed, in 2000, that the St. Louis

Cardinals have added an annual economic benefit of $301 million to the area (Baade, Baumann, &

Matheson, 2008). To put it in perspective, that is quite a small number when compared to the total real

gross domestic product (GDP) of the St. Louis metropolitan statistical area, according to the Bureau of

Economic Analysis (BEA) that value is just 0.29 percent of the $102.382 billion generated in 2001.

In this research project I will focus on analyzing the income, spending, and development of

metropolitan statistical areas (MSA) across the Midwest Region of the United States which host Major

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League Baseball and National Football League teams that have new or renovated stadiums. The goal of

this analysis is to further understand the impacts of professional sports teams and their facilities, and to

update the study done by Robert Baade and Richard Dye (1990) to reflect the recent construction of

stadiums in the National Football League (NFL) and Major League Baseball (MLB).

The final regression results parallel the results of the study by Baade and Dye in 1990, but

contain a few key differences. In the replication of the study the same signs were reached for the variables

when all MSA’s are included except for the stadium dummy variable in equation one and the football

dummy variable in equation two. It was also similar in terms of very little statistical significance within

the regressions. The final results of the study from 1984-2014 gave much stronger results in terms of

statistical significance. It showed that across all Midwest region MSA’s stadiums and professional

football teams have a statistically significant negative effect while professional baseball teams have a

small statistically significant positive effect.

The next sections of the paper will include a review of the literature, an overview of the

conceptual model, the empirical model, a description of the data and the sources, and the results and

conclusions of the empirical analysis.

Literature Review

A review of the economics literature pertaining to the impact of stadiums and professional sports

teams on a host city have shown consistent results supporting that there is a negative impact on the local

economy. Despite the consistent results, boosters from professional leagues and teams around the

country continue to push for support of new construction and expansion by touting huge economic

benefits as were described above. The studies provided by the boosters, a committee hired by the team to

illustrate the benefits of constructing new stadiums, show the direct and indirect impacts the team or

stadium will have on the area. Baade, Baumann, and Matheson (2008) describe the direct impact to rely

on three variables the attendance that an event draws, the number of days that each person stays in the

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metro area, and the money that each person will spend per day. In order to find the indirect effect a

multiplier is then applied to show the capital flowing through the economy multiple times (Baade,

Baumann & Matheson, 2008). However, the data has not supported these arguments over the years.

Baade (1990) states that while the revenues of the new stadiums have increased so too have the cost of

constructing the facilities which leads to a neutral return on the subsidies. Other problems are also found

when analyzing the benefits claimed by boosters. The substitution effect, crowding out, and leakages are

all issues that need to be accounted for when predicting the benefits of a stadium, team, or event (Baade,

Baumann & Matheson, 2008). Baade (1996) also brings to light that new stadiums have increased the

goods and services that they provide and can take away from the local bars and restaurants that promoters

claim to benefit.

A common theme in the literature is the disagreement of whether a sports franchise and stadium

adds value to the metropolitan economy or simply redistributes the revenues that would have been earned

elsewhere in the city. Dennis Coates (2007) argues that when stadiums and teams are based in the

downtown district of a city the economic activity is simply relocated from the leisure activities that are in

the suburbs to the downtown event. The presence of a stadium can be used to redevelop an area within a

city. Coates (2007), claims that values of property in areas surrounding the stadium will increase when

there is one newly constructed. This claims links in with the belief that a city who hosts a sports team

will project a world-class city status, and will attract businesses and gain a higher profile due to a larger

media presence. These benefits are difficult to quantify, but can be a source of further research. This idea

is described throughout the research including Coates (2007) and Baade & Dye (1990). These benefits

along with non-economic benefits such as the pride of having a professional sports team may have some

part in explaining why stadiums continue to be constructed and leagues continue to expand despite the

evidence supporting zero to negative economic impacts on the surrounding MSAs. Although the

construction of stadiums are typically justified because of large economic benefit projections, but

governments should be cautious knowing that the impacts may not meet the initial expectations.

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Conceptual Framework

Through the literature the conceptual framework behind the impact of sports teams and stadiums

on host cities can be constructed. It is clear that the sports organizations are just like every other firm in

the United States market their goal is to maximize their profits. One of the ways they can accomplish this

is to minimize cost while maximizing revenues. An organization will attempt to minimize cost by asking

the public to fund the infrastructure used to host their events. They support this request with many figures

and claims both economic and social. As previously discussed there are direct and indirect benefits to

hosting a professional sports team and the goal of this study is to show whether or not they reach a level

where they are providing a positive economic benefit to the city. The direct benefits are rather simple to

record they rely on the basic idea of whether or not the entertainment of professional sport is demanded,

and if that has a stronger benefit than the foregone alternative which could include any number of other

options. The indirect benefits are much more difficult to quantify and are where most economic

arguments begin. It begins with the multiplier effect which is the idea that the money used to purchase

tickets, merchandise, and etc. will flow through the city multiple times and otherwise would not be there

without the organizations (Baade, 1996). This so far has been observed to be untrue. The leisure time and

money spent at a major sporting event would have been typically used somewhere else within the

metropolitan area if it was no longer a viable option (Leroy, 2005). The redistribution of the revenues

essentially becomes a substitution effect; one service is purchased instead of the other (Baade & Dye,

1990). Then the argument can be furthered, if a professional sports team is earning the revenue rather than

another local business most of the revenues will no longer be distributed within the city because very few

owners and players reside within the MSA in which they are related. This is known as leakages which

means the money earned will flow out of the MSA rather than within it and at the same time the local

residents will continue to pay taxes which support the stadium subsidies (Baade, Baumann & Matheson,

2008). These three effects can be very difficult to quantify, but are the driving forces behind the economic

effects of professional sports teams and their stadiums. In this analysis aggregate personal income will be

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used as the variable to represent how well off the people of a particular MSA are over a period of time.

Other variables could be used to measure the well-being of a metropolitan area such as the taxable sales

studied by Baade, Baumann, and Matheson in 2008 or even a gross happiness index.

Empirical Model

In this paper I use the regression model developed by Robert Baade and Richard Dye (1990).

The model is designed to estimate the impact of professional stadiums and teams on the metropolitan

statistical areas (MSA). The MSA income statistics are regressed on the population, three dummy

variables which will be explained further, and a trend variable for each year used. This regression is

intended to evaluate the impact on income while accounting for the atmosphere of the host’s economy

before and after the stadiums and teams are introduced. Baade and Dye use two equations to test the

impacts:

Equation1 :ln (Y ¿)=b0+b1 ln (POP¿¿¿)+b2 STAD¿+b3 FOOT ¿+b4 BASE¿+b5TRENDt +e¿¿

Equation2 :Y ¿ /YR¿=b0+b1(POP¿

POP R ¿)+b2 STAD¿+b3 FOOT ¿+b4 BASE¿+b5 TRENDt+e¿

i=Metropolitan Statistical Area

t=1984 ¿2014

Y ¿ is the MSA’s real aggregate personal income, POP¿ is the MSA’s population, STAD¿ is a

dummy variable where zero represents before a stadium is built or renovated and one represents

afterward. FOOT ¿ is another dummy variable where the value of zero represents that an MSA does not

have a National Football League team and a value of one represents that it does. The next variable,

BASE¿, is also a dummy variable where a value of zero represents that an MSA does not have a Major

League Baseball team and a value of one represents that it does. The TRENDt variable assigns a value of

1 for 1984 and goes up to 31 for 2014 this variable is intended to account for systematic changes in the

MSA over time, and e i is the error term. From the second equation the Y ¿ /YR ¿ is the fraction of real

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aggregate personal income in the multi-state region, and POP¿

POP R ¿ is the fraction of regional population

represented by the MSA (Baade & Dye, 1990).

As Baade and Dye explain the dependent variable Y ¿ will be regressed on the independent

variables that control for the make-up of the metropolitan area (1990). POP¿ is expected to have a

positive effect on the dependent variable because an increase in population should increase the real

aggregate personal income. The next independent variable STAD¿ would intuitively provide a positive

impact on the real aggregate income, but according to the prior literature it should end up with a negative

impact. The effect is similar when analyzing the effect of FOOT ¿ andB ASE¿, an intuitive approach

would lead most to believe a boost in income would occur, but the literature again shows this is not the

case and they will have a negative coefficient. The TRENDtvariable should have a positive coefficient

assuming the cities are growing.

I have first replicated the study by Baade and Dye (1990) using the nine metropolitan statistical

areas they have chosen. The time frame I used is from 1969 to 1983 because the data is readily available

for the metropolitan statistical areas. Baade and Dye use 1965 to 1983 and used standard metropolitan

statistical areas. The MSA’s include Cincinnati, Denver, Detroit, Kansas City, New Orleans, Pittsburgh,

San Diego, Seattle, and Tampa Bay. I then updated the study by extending the years used to include 1984

to 2014, but I will focus only on the Midwest region of the United States. I have decided to restrict the

research to one region in order to have a relatively standardized culture of people, and because I have

personal interest in the Midwest region. I have modified the econometric model by using the logarithmic

form. The MSA’s included will differ based on the professional sport and are listed in the appendix in

Table 2.

Data Sources

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The data used to replicate the Baade and Dye (1990) study was found in the datasets created by

the Bureau of Economic Analysis. I extracted data containing aggregate personal income and population

figures for the nine MSA’s listed in table 1 in the appendix. I adjusted the base year to be 1983 using a

consumer price index specific to each region from the U.S. Bureau of Labor Statistics. I was limited to

data from 1969 to 1983 rather than as far back as 1965 because the definitions for metropolitan statistical

areas had changed since the initial study had been performed. I also retrieved the regional data for each of

the regions represented in the metropolitan statistical area from the Bureau of Economic Analysis.

Identical manipulations were made to the data set to change from current dollars to a base of 1983 using

the same consumer price index. All dummy variables were collected manually and compiled using the

official websites of both the Major League Baseball teams and National Football League teams.

In the updated version of the study I have used the annual data from Local Area Personal Income

and Employment for the real aggregate personal income, population, fraction of real aggregate income in

the Midwest Region of the United States, and the fraction of regional population variables. The income

data retrieved was in current dollars and was adjusted to real dollars with a base year of 2014. In order to

convert from nominal dollars to real dollars for the updated data I used a Consumer Price Index (CPI)

average for the Midwest Region of the United States for the years 1984 to 2014. All population and

income statistics have been found at the Bureau of Economic Analysis. The CPI data has been retrieved

through the Bureau of Labor Statistics. The dummy variable data sets have been created manually using

the dates when teams constructed stadiums, hosted NFL, or MLB teams. This information was found

using the official website of each individual MLB or NFL team within the metropolitan statistical area.

Results

The results of the study paralleled the results of much of the economics literature written on the

subject. This is evident in the regression results computed through the use of Stata. First it is important to

understand how each variable is defined this is described above under the section titled empirical model

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as well as in the two tables listed in the appendix titled Replication of Study by Baade & Dye 1990 and

Study by Baade and Dye Updated to the Years 1984-2014. The most important differences between the

two sets of variables are the time frame and the metropolitan statistical areas that are being observed. The

replication observes a period from 1969 to 1983 and the updated study observes a period of 1984-2014.

All metropolitan statistical areas that were observed are listed in Table 1 and Table 2, broken down by the

professional sport hosted within each MSA.

The two datasets used throughout the study are unique because of the time period and locations.

The data set used to replicate was a built up as a panel data set. Nine MSA’s were observed across a 15

year period to give an observation count of 135 for both the aggregate personal income and the population

this is illustrated in Table 3. The short time period limited the replication study to a regression run on just

the MSA’s as a total group. Table 3 also illustrates the descriptive statistics for the 1969 to 1983 data set.

It is important to note that all personal income statistics are measured in thousands of dollars and were

adjusted to real 1983 dollars. As can be observed in the table, the range of aggregate personal income is

very large containing a gap of about $53.88 billion. This wide range could be problematic when running a

regression, but in order to resolve this problem I used the logarithmic form of the aggregate personal

income. The issue becomes even more evident when observing the mean and median of the data set. The

mean is over $8.22 billion and the median is over $5.73 billion. The large disparity between the mean and

median is a sign that there may be outliers on the higher end of the data set which is to be expected when

working with income statistics. The population variable also shown in Table 3 shows a very similar

pattern. The maximum population of the MSA’s observed was about 4.46 million people while the

minimum was just over 1.08 million.

The variables of interest within the replication study were the dummy variables measuring the

effects of an introduction of a stadium, professional football team, or baseball team. The variation of the

independent variables is illustrated in Figure 2, Figure 3, and Figure 4. A limitation found throughout the

study was a lack of variation leading to multi-collinearity within the MSA’s. This was remedied partly by

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using the aggregate of the MSA’s rather than observing each individually across a time series data set. In

each of the figures the values given to each MSA is illustrated. The stadium variable had the largest

amount of variation which was to be expected due to the significant amount of stadiums constructed

across the country over the time period. The football and baseball dummy variables have very little

variation which is due in part to the stability of major professional sports across the United States.

The results of the replication had significant differences from the original study (1990), but did

not have overwhelming evidence to discount any of the original results. As previously mentioned, the

replication regressions used the logarithmic form of the aggregate personal income and the population in

order to account for the magnitude of the values. It is also important to note that the robust standard errors

were used across all regressions in the analysis because of the presence of heteroscedasticity detected

through the Breusch-Pagan test. The results of the regression on equation one is shown in Table 5. The

variables of interest are the three dummy variables (STAD, FOOT, and BASE) which display my

expected result. The STAD coefficient states that when a new stadium is created or renovated the

personal aggregate income decreases by 10.83 percent, ceteris paribus. The FOOT variable shows the

introduction of an NFL franchise decreases the personal aggregate income by 1.97 percent, ceteris

paribus. In regards to the BASE variable, the introduction of an MLB franchise to an MSA increases the

personal aggregate income by 7.12 percent, ceteris paribus. Each of the variables of interest produce

figures of economic significance, but fail to be statistically significant which leads them to be unreliable.

The R-squared value produced by the regression is 0.6267 which means that 62.67 percent of the

variation in personal aggregate income is described by the independent variables. The independent

variables ln(POP) and TREND act as control variables with in this regression and yield statistically

significant results both being positive which is to be expected for most major MSA’s across the United

States. The major difference between the replication and the original study (1990) is the sign on the

STAD variable this could be the result of the difference in how MSA’s are defined or by the four year

difference in the time period.

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The replication regression of aggregate personal income relative to the region produced similar

results in Table 6 to those in Table 5 in terms of sign. The variables of interest had much smaller

magnitudes however, and the STAD variable actually became statistically significant. The STAD

coefficient states that the introduction or renovation of a new stadium decreases the aggregate personal

income relative to the region by 0.23 percent, ceteris paribus. In contrast to Table 5 Table 6 contains an

unusually high R-squared of 0.9795which may be a sign of problems within the data set.

The next section of the analysis is the updated study using MSA’s from across the Midwest

Region of the United States these MSA’s are illustrated and broken down by sport in Table 2 of the

appendix. Again it is important observe the subtle differences in the variables for the updated study they

are described in the table labeled Study by Baade and Dye Updated to the years 1984-2014.The major

difference is the years observed are now 1984-2014 and the MSA’s are across the Midwest as mentioned

above.

The data being utilized for the 1984-2014 study will be panel data, and include ten MSA’s from

the Midwest Region and use a time period of 31 years which results in 310 observations as illustrated in

Table 4. In this data set the limitation of variability resulted in the omission of regressions on each MSA

individually. Table 4 shows the descriptive statistics for two of the variables aggregate personal income

and population. As was the case in the replication there is a very large range of about $481 billion dollars.

This demonstrates the vast differences between the MSA’s over time and across the Midwest. The mean

is again much higher than the median $120 billion and $88 billion respectively. This indicates there are

most likely outliers pulling the mean to be much greater than the median. The population variable also

has a very large range with the highest of 9.55 million and the lowest of 230,950. The large disparity

between the mean and median again show that there are most likely some outliers within the data set.

As they were in the replication the variables of interest are the three dummy variables STAD,

FOOT, and BASE. The most significant limitation for the data set was the lack of variation between these

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variables over the short period of time. A possible solution could have been to extend the time period

which was observed. The variation of the three variables is displayed in Figure 5, Figure 6, and Figure 7.

STAD again has the greatest variability because of the seemingly continuous construction and renovation

of stadiums. However, FOOT and BASE both have little to no variation in each of the Midwest Region’s

MSA’s. This continues to be the case because as the time period nears the present the stability of the

MLB and NFL has grown across the United States.

The results of the regression are illustrated in Table 7 for the total of the MSA’s across the

Midwest and also for Cleveland which was the lone MSA with enough variability to include in the

results. It is important to note that I will be using the logarithmic form of the aggregate personal income

and population for the years 1984 to 2014. I also used the robust standard errors in the regressions to

correct for the heteroscedasticity present after running a Breusch-Pagan test. I found the results were most

significant when using all MSA’s as a panel data set. Each of the variables of interest in the regression

displayed in Table 7 were statistically significant, but were not of great magnitude. The STAD coefficient

is interpreted as the introduction or renovation of a stadium will lead to a decrease in the aggregate

personal income of 2.40 percent, ceteris paribus. The FOOT coefficient gave the exact same result and the

BASE coefficient differed slightly stating, the introduction of an MLB team will lead to a 1.74 percent

increase in the personal aggregate income, ceteris paribus. The control variable of ln(POP) and TREND

were also statistically significant as they were in the previous regressions. Coefficient’s magnitudes and

signs change slightly when looking at Cleveland specifically. The sign on the STAD variable becomes

positive when observing just Cleveland which means that the introduction of a stadium has a positive

impact on the aggregate personal income, however it is not statistically significant. It is also important to

note that the BASE variable is omitted because the dummy variable had a constant value of one

throughout the time period.

Table 8 expresses the regression analysis of the aggregate personal income relative to the regional

aggregate personal income between 1984 and 2014. In terms of the all MSA results the coefficients are

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the exact same sign for STAD and FOOT but have a much smaller magnitudes. This shows that the

independent variables STAD and FOOT do set the MSA’s apart from other MSA’s across the region. The

BASE variable changes signs and then becomes statistically insignificant. The Cleveland results, found in

Table 8, show statistical significance in just one variable of interest, FOOT. The BASE variable is again

omitted due to a lack of variation.

The final two regressions that were run include two changes to equation one. The first is shown in

Table 9 which has NumStad meaning the number of stadiums in an MSA each year. This added variable

revealed that each additional stadium within an MSA led to a decrease in the personal aggregate income

by 1.16 percent, but was not statistically significant. The second change was to the dependent variable. It

was replaced by the per capita personal income. This change had an expected result it only changed the

coefficient of the population variable leaving all other values the same.

Conclusions

The conclusion of the regression results is that local governments should be very cautious before

investing hundreds of millions of dollars into a brand new stadium. They do not provide the economic

returns promised by many and in some case actually hurt the economy and become a hindrance to its

growth and development. This conclusion is very relevant to our world today because of the dramatic

increase in popularity of professional sports. It has a direct effect on the lives of each and every person

who pays taxes. It is important to be mindful of the results and understand that the introduction of a

stadium or brand new professional may be important for civic pride, but will not lead to monumental

economic benefits. I think there are many routes for future research. The study could be expanded to other

regions of the United States and internationally. It could also be furthered by extending the time period

studied this may even solve the limitation of variability. Another direction could be the difference

between a stadium’s impact and an arena’s impact. The research will continue to be significant as long

professional sports still have a major spotlight in the American culture.

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References

Baade, R. A. (1996). Professional Sports as Catalysts for Metropolitan Economic Development.

Journal of Urban Affairs J Urban Affairs, 18(1), 1-17. doi:10.1111/j.1467-

9906.1996.tb00361.x

Baade, R. A., Baumann, R., & Matheson, V. A.. (2008). Selling the Game: Estimating the

Economic Impact of Professional Sports through Taxable Sales. Southern Economic

Journal, 74(3), 794–810. http://doi.org/10.2307/20111996

Baade, R. A., & Dye, R. F. (1990). The Impact of Stadium and Professional Sports on

Metropolitan Area Development. Growth and Change, 21(2), 1-14.

Coates, D. (2007). Stadiums And Arenas: Economic Development Or Economic Redistribution?

Contemporary Economic Policy, 25(4), 565-577. doi:10.1111/j.1465-7287.2007.00073.x

LeRoy, Greg. "Chapter 7: Loot, Loot, Loot for the Home Team." The Great American Jobs

Scam: Corporate Tax Dodging and the Myth of Job Creation. San Francisco: Berrett-

Koehler, 2005. 157-67. Print.

Siegfried, J., & Zimbalist, A. (2000). The Economics of Sports Facilities and Their

Communities. Journal of Economic Perspectives, 14(3), 95-114. doi:10.1257/jep.14.3.95

U.S. Bureau of Economic Analysis. Personal Income, Population, Per Capita Income. 19 Nov.

2015. Raw data. U.S. Department of Commerce, Washington D.C.

U.S. Bureau of Labor Statistics. Consumer Price Index. 12 Feb. 2014. Raw data. Division of

Consumer Prices and Price Indexes, Washington D.C.

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Appendix

Figure 1

City County State Team/Private Total 6 10 3 7 26

Stadium Ownership within Midwest Region MSA's

Table 1

National Football League Major League BaseballCincinnati CincinnatiDenver-Aurora-Lakewood Detroit-Warren-DearbornDetroit-Warren-Dearborn Kansas CityKansas City PittsburghNew Orleans-Metairie San Diego-CarlsbadPittsburgh Seattle-Tacoma-BellevueSan Diego-CarlsbadSeattle-Tacoma-BellevueTampa-St. Petersburg-Clearwater

Metropolitan Statistical Area (Baade & Dye 1990)

Table 2

National Football League Major League BaseballChicago-Naperville-Elgin Chicago-Naperville-ElginCincinnati CincinnatiCleveland-Elyria Cleveland-ElyriaDetroit-Warren-Dearborn Detroit-Warren-DearbornGreen Bay Kansas CityIndianapolis-Carmel-Anderson Milwaukee-Waukesha-West AllisKansas City Minneapolis-St. Paul-BloomingtonMinneapolis-St. Paul-Bloomington St. LouisSt. Louis

Metropolitan Statistical Area

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Replication of Study by Baade & Dye

Variable DescriptionY ¿ The MSA’s real aggregate income (in 1983 dollars,

and measured in thousands of dollars)POP¿ The MSA’s populationSTAD¿ A dummy variable which has a value of 0 before

renovation or construction of a stadium and a value of 1 after

FOOT ¿ A dummy variable which has a value of 0 when a National Football League team is not present in the MSA and a value of 1 otherwise

BASE¿ A dummy variable which has a value of 0 when a Major League Baseball team is not present in the MSA and a value of 1 otherwise

TRENDt A variable assigned a value of 1 for 1969 and going up to 15 for 1983

Y ¿ /YR ¿ The fraction of real aggregate personal income in the MSA’s respective region (region defined by Bureau of Labor Statistics United States Census)

POP¿

POP R ¿

The fraction of regional population represented by the MSA (region defined by Bureau of Labor Statistics United States Census)

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Study by Baade and Dye Updated to the years 1984-2014

Variable

Description

Y ¿ The MSA’s real aggregate income (in 2014 dollars, and measured in thousands of dollars)

POP¿ The MSA’s populationSTAD¿ A dummy variable which has a value of 0 before

renovation or construction of a stadium within the MSA and a value of 1 after renovation or construction

FOOT ¿ A dummy variable which has a value of 0 when a National Football League team is not present in the MSA and a value of 1 otherwise

BASE¿ A dummy variable which has a value of 0 when a Major League Baseball team is not present in the MSA and a value of 1 otherwise

TRENDt A variable assigned a value of 1 for 1984 and going up to 31 for 2014

Y ¿ /YR ¿ The fraction of real aggregate personal income when compared to the Midwest Region of the United States (region defined by Bureau of Labor Statistics United States Census)

POP¿

POP R ¿

The fraction of regional population represented by the MSA (region defined by Bureau of Labor Statistics United States Census)

Table 3

Mean Median Minimum Maximum CountPersonal Income* 8,218,202.90$ 5,729,997.29$ 1,819,483.93$ 55,695,569.00$ 135Population 1,983,448 1,683,357 1,082,821 4,459,793 135

Metropolitan Statistical Areas (Baade & Dye 1990)

*Measured in Thousands of Dollars and Real 1983 Dollars

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Table 4

Mean Median Minimum Maximum CountPersonal Income* $120,000,683.41 $88,004,948.35 $6,521,571.36 $487,776,824.16 310Population 2,814,148 2,088,353 230,950 9,554,598 310

*Measured in Thousands of Dollars and Real 2014 Dollars

All Midwest Region MSA's

Figure 2

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Figure 3

20

Figure 4

21

Figure 5

22

Figure 6

23

Figure 7

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Table 5

The Impact of Stadiums, NFL, and MLB Teams on the Level of MSA Aggregate Personal

Income 1969-1983

MSA ln(POP) STAD FOOT BASE TREND R-squared

ALL 0.6267Coefficients 0.9552 -0.1083 -0.0197 0.0712 0.0688

Robust Standard Error 0.1251 0.0977 0.1283 0.1085 0.0121P value 0.0000 0.2700 0.8780 0.5130 0.0000

Data used measured in real dollars with a base year of 1983

Table 6


Income Relative to Regional Personal Income 1969-1983

MSA POP/POPR STAD FOOT BASE TREND R-squared



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Table 7


Income 1984-2014




CLE 0.9344Coefficients 1.4409 0.0131 -0.0048 - 0.0108

Robust Standard Error 0.5701 0.0148 0.0143 - 0.0013P value 0.0180 0.3840 0.7400 - 0.0000


Table 8


Income Relative to Regional Personal Income 1984-2014

MSA POP/POPR STAD FOOT BASE TREND R-squared

ALL 0.9527Coefficients 0.8792 -0.0017 -0.0012 -0.0014 0.0010


0.9867CLE Coefficients 1.6353 -0.0001 -0.0004 - 0.0008

Robust Standard Error 0.8044 0.0004 0.0002 - 0.0002P value 0.0520 0.8150 0.0640 - 0.0000


BASE variable omitted due to multi-collinearity

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Table 9

The Impact of the Number of Stadiums on the Personal Aggregate Income

MSA ln(POP) STAD FOOT BASE TREND NumStad R-squared

ALL 0.9966Coefficients 1.0501 -0.0175 -0.0168 0.0198 0.0134 -0.0116

Robust Standard Error 0.0057 0.0080 0.0101 0.0111 0.0005 0.0075P value 0.0000 0.0290 0.0960 0.0730 0.0000 0.1240

Table 10

The Impact of Stadiums, NFL, and MLB Teams on the Level of MSA Per Capita Personal

Income 1984-2014




27

Final Draft

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