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Capstone Presentation
- 1. 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 Johnβs University April 23, 2016 1
- 2. Introduction β’ Professional sports have grown dramatically in the past 25 years β« 46 Stadiums constructed or renovated between 1990 and 1998, and 49 more planned as of 2000 stated by John Siegfried and Andrew Zimbalist β« Estimated cost of $21.7 billion β« Close to two-thirds will be paid by public funds 2 Stadium Ownership within Midwest Region MSA's City County State Team/Private Total 6 10 3 7 26
- 3. Question β’ What impact does a professional sports team and stadium have on its host metropolitan statistical area (MSA)? β« Impact measured by change in real aggregate personal income β« Results: Across all Midwest region MSAβs stadiums and professional football teams have a statistically significant negative effect 3
- 4. Preview β’ In the upcoming slides: β« Review of Literature β« Conceptual Model β« Empirical Model β« Data Sources β« Statistics and Results β« Limitations and Conclusions 4
- 5. Literature Review β’ Professional sports boosters vs. economics literature β’ Baade, Baumann, and Matheson (2008) explain the issue of crowding out β’ Baade (1996), describes the increased goods and services provided by stadiums 5
- 6. Literature Review cont. β’ Coates (2007) argues that new stadiums and professional sports teams redistribute economic activity β’ Coates supports the claim that stadiums can be used as a tool to redevelop areas because of increased property values. 6
- 7. Theory/Conceptual Model β’ Indirect benefits come into question when studying economic benefit of stadiums and teams β« Multiplier effect: Team revenues are expected to flow through the metropolitan area β« Leakages: Revenues flow out of the MSAβs β« Substitution effect: Leisure time and money would be spent β’ These effects can not be directly measured, but are the driving force behind the impacts of stadiums and professional sports teams 7
- 8. Empirical Model 8 πΈππ’ππ‘πππ 1 βΆ ππ(πππ‘) = π0 + π1 ππ(πππππ‘) + π2 πππ΄π·ππ‘ + π3 πΉπππππ‘ + π4 π΅π΄ππΈππ‘ + π5 ππ πΈππ·π‘ + πππ‘ πΈππ’ππ‘πππ 2: πππ‘/ππ ππ‘ = π0 + π1( πππππ‘ ππππ ππ‘ ) + π2 πππ΄π·ππ‘ + π3 πΉπππππ‘ + π4 π΅π΄ππΈππ‘ + π5 ππ πΈππ·π‘ + πππ‘ π = πππ‘πππππππ‘ππ ππ‘ππ‘ππ π‘ππππ π΄πππ π‘ = 1984 π‘π 2014 Empirical model based on Baade & Dye βThe Impact of Stadiums and Professional Sports on Metropolitan Area Developmentβ (1990)
- 9. 9 Variable Description πππ‘ The MSAβs real aggregate income (in 2014 dollars, and measured in thousands of dollars) πππππ‘ The MSAβs population πππ΄π·ππ‘ 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 πΉπππππ‘ 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 π΅π΄ππΈππ‘ 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 ππ πΈππ·π‘ A variable assigned a value of 1 for 1984 and going up to 31 for 2014 πππ‘/ππ ππ‘ 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) πππππ‘ ππππ ππ‘ The fraction of regional population represented by the MSA (region defined by Bureau of Labor Statistics United States Census)
- 10. Data Sources β’ Income and Population Data collected from the Bureau of Economic Analysis (Personal income, population, per capita income) β« MSA level from 1984-2014 β« Income changed to 2014 dollars using Consumer Price Index from the Bureau of Labor Statistics β’ Dummy variables collected manually through respective professional teamβs website β’ Panel data was formed from All MSAs 10
- 11. Data Sources: Midwest MSAβs 1) 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 11
- 12. Descriptive Statistics 12 All Midwest Region MSA's Mean Median Minimum Maximum Count 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
- 16. Estimation Results 16 The Impact of Stadiums, NFL, and MLB Teams on the Level of MSA Personal Income 1984-2014 MSA ln(POP) STAD FOOT BASE TREND R-squared ALL 0.9965 Coefficients 1.0452 -0.0240 -0.0240 0.0174 0.0136 Robust Standard Error 0.0045 0.0093 0.0080 0.0104 0.0005 P value 0.0000 0.0100 0.0030 0.0950 0.0000 CLE 0.9344 Coefficients 1.4409 0.0131 -0.0048 - 0.0108 Robust Standard Error 0.5701 0.0148 0.0143 - 0.0013 P value 0.0180 0.3840 0.7400 - 0.0000
- 17. Estimation Results 17 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 0.9527 Coefficients 0.8792 -0.0017 -0.0012 -0.0014 0.0010 Robust Standard Error 0.0307 0.0009 0.0007 0.0011 0.0001 P value 0.0000 0.0500 0.0790 0.1830 0.0000 0.9867 CLE Coefficients 1.6353 -0.0001 -0.0004 - 0.0008 Robust Standard Error 0.8044 0.0004 0.0002 - 0.0002 P value 0.0520 0.8150 0.0640 - 0.0000
- 18. Limitations β’ The lack of variability of the dummy variables representing professional football and baseball teams β’ Future Research β« Arenas vs Stadiums β« Other U.S. Regions and Internationally 18
- 19. Conclusions β’ Newly constructed or renovated stadiums and National Football League teams have a small statistically significant negative impact on their host MSA β’ Local governments should be cautious when investing public funds 19