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

By: Alex Stephens

College of Saint Benedict & Saint John’s University

April 23, 2016

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Introduction

•Professional sports have grown dramatically in the past 25 years▫ 46 Stadiums constructed or renovated between 1990

and 1998, and 49 more planned as of 2000 stated by John Siegfried and Andrew Zimbalist

▫ Estimated cost of $21.7 billion ▫ Close to two-thirds will be paid by public funds

Stadium Ownership within Midwest Region MSA's

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

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Question

•What impact does a professional sports team and stadium have on its host metropolitan statistical area (MSA)?▫Impact measured by change in real

aggregate personal income▫Results: Across all Midwest region MSA’s

stadiums and professional football teams have a statistically significant negative effect

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Preview

•In the upcoming slides:▫Review of Literature▫Conceptual Model▫Empirical Model▫Data Sources ▫Statistics and Results▫Limitations and Conclusions

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Literature Review

•Professional sports boosters vs. economics literature

•Baade, Baumann, and Matheson (2008) explain the issue of crowding out

•Baade (1996), describes the increased goods and services provided by stadiums

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Literature Review cont.•Coates (2007) argues that new stadiums

and professional sports teams redistribute economic activity

•Coates supports the claim that stadiums

can be used as a tool to redevelop areas because of increased property values.

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Theory/Conceptual Model

• Indirect benefits come into question when studying economic benefit of stadiums and teams

▫ Multiplier effect: Team revenues are expected to flow through the metropolitan area

▫ Leakages: Revenues flow out of the MSA’s

▫ Substitution effect: Leisure time and money would be spent

• These effects can not be directly measured, but are the driving force behind the impacts of stadiums and professional sports teams

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Empirical Model

Empirical model based on Baade & Dye “The Impact of Stadiums and Professional Sports on Metropolitan Area Development” (1990)

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Variable DescriptionThe MSA’s real aggregate income (in 2014 dollars, and measured in thousands of dollars)The MSA’s population

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

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

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

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

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)

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

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Data Sources

• Income and Population Data collected from the Bureau of Economic Analysis (Personal income, population, per capita income)▫MSA level from 1984-2014▫Income changed to 2014 dollars using Consumer

Price Index from the Bureau of Labor Statistics

• Dummy variables collected manually through respective professional team’s website

• Panel data was formed from All MSAs

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Data Sources: Midwest MSA’s1) Chicago-Naperville-

Elgin

2) Cincinnati

3) Cleveland-Elyria

4) Detroit-Warren-Dearborn

5) Green Bay

6) Indianapolis-

Carmel-Anderson

7) Kansas City

8) Milwaukee-Waukesha-West Allis

9) Minneapolis-St. Paul-Bloomington

10) St. Louis

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Descriptive Statistics

All Midwest Region MSA's

Mean Median Minimum MaximumCount

Personal Income*

$120,000,683.41

$88,004,948.35

$6,521,571.36

$487,776,824.16 310

Population 2,814,148 2,088,353 230,950 9,554,598 310

*Measured in Thousands of Dollars and Real 2014 Dollars

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Descriptive Statistics

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Descriptive Statistics

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Descriptive Statistics

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Estimation ResultsThe Impact of Stadiums, NFL, and MLB Teams on

the Level of MSA Personal Income 1984-2014MSA ln(POP) STAD FOOT BASE TREND R-squared

ALL 0.9965Coefficients 1.0452 -0.0240 -0.0240 0.0174 0.0136

Robust Standard Error 0.0045 0.0093 0.0080 0.0104 0.0005P value 0.0000 0.0100 0.0030 0.0950 0.0000

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

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Estimation Results The Impact of Stadiums, NFL, and MLB Teams on

the Level of MSA Personal 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

Robust Standard Error 0.0307 0.0009 0.0007 0.0011 0.0001P value 0.0000 0.0500 0.0790 0.1830 0.0000

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

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Limitations

•The lack of variability of the dummy variables representing professional football and baseball teams

•Future Research▫Arenas vs Stadiums▫Other U.S. Regions and Internationally

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Conclusions

•Newly constructed or renovated stadiums and National Football League teams have a small statistically significant negative impact on their host MSA

•Local governments should be cautious when investing public funds

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Questions/Comments/Suggestions?