Abstract
This paper analyzes the net impacts of college football games on the sales tax revenues and taxable sales of four mid-sized cities in Texas. The paper addresses the question in the title, but also asks whether state policy-makers might be justified in encouraging schools in their state to play one another based on the local economic impact those games will have. In general, our evidence suggests the answer to that question is no.
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Notes
It should be noted that the absence of a net increase in economic activity associated with the event would not imply the net benefits of the event are negative. Indeed, the considerable consumer surplus generated by college football games makes it unlikely that the net benefits are negative.
The eight institutions included the University of Texas (Austin), Texas Tech University (Lubbock), Baylor University (Waco), Texas A&M (College Station), the University of Houston, Rice University (Houston), Southern Methodist University (Dallas), and Texas Christian University (TCU) (Fort Worth). After 1995, the four teams we investigate herein joined with the Big Eight Conference to form the Big XII Conference. The other four schools were left to find their own conference affiliations. For example, TCU played in the Western Athletic Conference from 1996–2001, then joined Conference USA from 2001 to 2005, and joined the Mountain West Conference starting in 2005. The conference affiliations of the other teams include Rice (Western Athletic Conference, 1996–2005; Conference USA, 2005-present), Houston (Conference USA, 1996-present), SMU (Western Athletic Conference, 1996–2005; Conference USA, 2005-present).
Other examples include the Governor of West Virginia using his 2005 State of the State speech to call for a reinstatement of the Marshall-West Virginia University rivalry, and a 1995 bill submitted to the North Carolina state legislature (but not passed) requiring the University of North Carolina and North Carolina State University to schedule football games against East Carolina University. These two schools now regularly include East Carolina University on their schedules, about which North Carolina state Senator Marc Basnight said, “There are no negatives to it. It benefits the economy of Eastern North Carolina and benefits Raleigh. It fills up the stadiums. All I’ve heard in the legislative building this week is ‘Big game, big game.’ Why play some out-of-state team when you can create this much interest?” (Raleigh News and Observer, 2007).
Examples of this work are Baade et al. [2007], which focuses explicitly on college football's effects, finding little impact on host communities, and Lentz and Laband [2008] who examine athletic department budgets and employment in accommodations and restaurants, finding a positive relationship.
Numerous papers focus on attendance to college football games. Kaempfer and Pacey [1986] and Fizel and Bennett [1989] investigate the impact of television broadcasts on game attendance with conflicting results. Leonard [2005] investigates attendance in the context of a “gravity model” and finds that geographic proximity of the two schools whose teams are playing enhances game attendance. Price and Sen [2003] investigate game day attendance to FBS football games during the 1997 season and find that team quality, conference membership, enrollment, and the percentage of students living on campus all enhance attendance whereas proximity to a professional football team attendance.
We gathered the dates and opponents of football games held in Austin, College Station, Lubbock, and Waco from James Howell's historical scores archive (available at www.jhowell.net/cf/cfindex.htm, last accessed May 2008).
Specifically, we used the CPI — All Urban Consumers (available at www.bls.gov, last accessed April 2008).
Local jurisdictions can also place taxes on hotel rooms, rental cars, liquor sales, and other specific transactions; however, these alternative revenue sources are not included in our data.
During the sample period there were six different local sales tax rates in Austin, College Station, and Waco, and seven different local sales tax rates in Lubbock. For the first year after a local sales tax rate change, the average year-to-year change in real sales tax revenues was positive ($170,000 in Austin, $90,000 in College Station, $182,000 in Lubbock, and $100,000 in Waco).
It is common practice for Division I FBS teams to schedule lower-tiered opponents or exotic out-of-state or out-of-conference teams for early home games (an example of the latter would be the annual Colorado–Colorado State game, which is typically the first game of the year for each team).
Traditionally, neither Baylor nor Texas Tech has been a football power and therefore might be scheduled by Texas or Texas A&M relatively early in the conference schedule as preliminary “warm-up” games before the more important games on the schedule, for example the annual “Red River Shootout” between UT and the University of Oklahoma, played in Dallas in October.
We attempted to control for the boom/bust cycle related to the oil industry in Texas using the monthly index of petroleum and natural gas production from the Federal Reserve Statistics. This variable was never statistically significant.
This does require an adjustment to the standard errors. Specifically, the fixed effects estimator will calculate the standard errors based on NT−N−k degrees of freedom whereas the correct degrees of freedom are actually NT−2N−k.
For example, population of a tax jurisdiction likely influences the sales tax revenue collected in that jurisdiction. However, population is only available on an annual basis and it is not clear how to interpolate monthly population levels from these annual observations. A similar problem arises when contemplating other potential explanatory variables such as business start-ups, unemployment levels, or disposable income.
City-specific heteroscedasticity might arise because of the different sizes of the four cities in the sample. Austin is the largest city, in terms of population, and is also the state capital. To the extent that city size might influence the volatility of sales tax revenues, heteroscedasticity might be expected. Moreover, we might expect to see first-order autocorrelation in the 12-month differenced data if spending patterns in, say, August of one year influence spending patterns in August of the next year. This might occur if new shopping, dining, or recreational opportunities were introduced to the city's economy that influenced spending during a particular month, for example, a new amusement park, a new minor league baseball team, or a new shopping mall. We employ the xtgls command in Stata 9.2 to estimate our models.
The data investigated herein only measure sales tax and thus do not include any additional excise or user taxes, such as hotel, car-rental, airport, or liquor taxes charged by the host city but collected by a different agency. In many cases, these excise or user taxes are already earmarked for specific projects, for example, to service debt on various public projects, and do not flow into the general funds of the host city. To the extent that the host city incurs marginal costs for an additional game, for example, extra police or medical personnel, only sales tax revenues to the city's discretionary spending can be used to offset these marginal costs. Moreover, we do not attempt to measure non-pecuniary benefits arising from football games such as consumer surplus, civic and school pride.
The table does not include the coefficients for total games, in-state games, or conference games. Coefficients for these two variables are, respectively, −26.38 and 26.18 and both coefficients have p-values less than 0.05.
Inclusion of the month dummies with all the game specific dummies generated perfect collinearity among the regressors.
A similar issue might arise between TCU, located in Fort Worth, and Southern Methodist University, located in Dallas (Texas), as the two institutions are approximately 35 miles apart. Duke University (located in Durham) and the University of North Carolina (located in Chapel Hill, North Carolina) are within 10 miles of each other. Among Division I FBS teams, other proximate dyads include Houston and Rice (6 miles apart), Washington State and Idaho (8 miles apart), Stanford and California — Berkeley (45 miles apart), Southern California and UCLA (14 miles apart), Georgia and Georgia Tech (70 miles apart), Vanderbilt and Middle Tennessee State University (32 miles apart), Miami (FL) and Florida International (7 miles apart), and the University of Central Florida and the University of South Florida (100 miles apart).
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Coates, D., Depken, C. The Impact of College Football Games on Local Sales Tax Revenue: Evidence from Four Cities in Texas. Eastern Econ J 35, 531–547 (2009). https://doi.org/10.1057/eej.2009.29
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DOI: https://doi.org/10.1057/eej.2009.29