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TITLE: Executive Interview

SOURCE: International Journal of Sport Finance 3 no2 79-83 My 2008

COPYRIGHT: The magazine publisher is the copyright holder of this article and it is reproduced with permission. Further reproduction of this article in violation of the copyright is prohibited. To contact the publisher: http://www.fitinfotech.com/IJSF/IJSF.tpl

An Interview with Greg Beadles, CFO, Atlanta Falcons

Conducted by Norm O'Reilly, Director, School of Sports Administration, Laurentian University


Greg Beadles oversees all financial operations for the Atlanta Falcons organization, as well as being involved in business negotiations, strategic planning, and development for the organization. He serves as one of the team's primary liaisons with the stadium authority and oversaw the Dome renovation project.

Beadles's first experience with the Falcons was a 1995 internship in the finance department. In his 13 years in the organization, he has served as controller of the Falcon Inn & Conference Center, senior accountant, director of finance, vice president of finance, and is currently holding the title of CFO.

Beadles has also enjoyed his involvement in a number of other game day operations over the years as a member of the stats crew, video department assistant, and tracking player participation. He was a member of the team that launched the AtlantaFalcons.com website and started the club's successful online store.

Beadles earned a bachelor of science in accounting from Louisiana State University in 1991 and a master of science degree in sports administration from Georgia State University in 1995. He also holds a CPA license from the state of Georgia.


Q: The theme of this interview is "a day in the life of an NFL CFO." Given that, can you outline how you ended up in this position with the Falcons?

A: I often get the question from students or others on what is the best path to working in sports or the NFL specifically. There truly is no formula that I have seen among those I work with at the Falcons or colleagues at other teams.

No different than many sports enthusiasts, I played a number of sports growing up, including a year in small college basketball. I like to say that year helped me to understand that any future in sports for me would be outside the lines. However, I was fortunate to grow up with some behind-the-scenes experience. My father has been a college basketball coach for most of his 40+ professional years, so I grew up making road trips and recruiting visits and suffered the ups and downs that people in the sports industry all share. I think this insight aided me through the years.

I have always been interested in how businesses operate and decided with the help of others that accounting would be the most well-rounded business degree I could pursue. After graduating from Louisiana State University, I worked in the oil and gas industry in Houston, Texas for two years. That was a great experience because I learned early on that I did not want to do something for the rest of my life that I did not have a passion for. Some folks may get a charge out of depreciating barges and drilling derricks, but I didn't!

I decided to go back to school and obtain a graduate degree in sports administration at Georgia State in Atlanta. The classes I took were beneficial, but the best part was the contacts I made and the real world sports experience I was able to attain. I encourage students to use the opportunities they have during school as much as possible. Internships are the best ways to get real jobs. I worked at the Georgia Dome, IBM's Sports and Entertainment division, a professional golf tournament, college basketball tournaments, and in the athletic department at Georgia State. I now have working relationships 13 years later with some of the same people I worked for at those enterprises.

I ended my graduate work with a 10-week internship with the Falcons in the finance department that has now turned into 13 seasons of service.

Q: What is a typical day in the life of Greg Beadles?

A: The great thing about my job is that there is no typical day. That has evolved over the years as I have moved into more of a strategic role, but everyone in our organization does different things at different times of the year, particularly during the season. Sports organizations are typically pretty lean and we all wear multiple hats, which I enjoy.

Since I have been CFO the last several years, I have been able to head up a number of design and construction projects, including our current $30M renovation to the Georgia Dome. We've purchased an Arena Football League team, explored purchasing an MLB team, started ancillary businesses (retail, physical therapy), negotiated multiple amendments to our NFL stadium lease, and negotiated two arena leases, as some examples. I have been fortunate to be involved in minor ways in analyzing the NFL financial inner workings in the recent CBA extension and related topics either for our senior management or on NFL committees. Our organization is very proactive, which makes for exciting work. This all comes from the top and our owner, Arthur Blank.

Beyond the typical accounting and finance duties you would expect--budgeting, financial reporting, payroll, etc.--our facilities and travel and logistics departments report to me. I therefore get to be involved in some of the decision making on transporting our team to games and things of that nature.

Q: What are the necessary skills and experiences to excel in your position?

A: I would say it is most important to be able to translate the financial numbers to business success and vice versa. If you are excellent in putting projections together but can't communicate or understand what it takes to make them reality, it will be tough to succeed. I need constant work to understand what is going through the heads of our revenue generators, for example. I try to stay up to speed with their challenges and ideas so that I can find ways to help facilitate their creativity so it can be effectively applied to our situation. Understand the business you are in from as many different vantage points as possible. It is not enough, in my estimation, to know only "your" aspect of the business.

Q: You recently attended the NFL-Stanford NFL Managers Training Program at Stanford University with fellow executives from the other NFL teams. Why does the NFL invest in such a program? Did you find it beneficial?

As a CFO, were there specific learning(s) that you took home from the experience?

A: The NFL invests in the program really for the development of our younger executives to give them an overview of what is going on in the NFL from a business and a football standpoint. Like any job, you focus on your job and you have your blinders on and you don't get exposed to other parts of the business. So, this gives you a chance to interact with other parts of the business and then become overall leaders in the future. Personally, I found it extremely beneficial.

Q: Can anything knock the NFL from the #1 sport property position in North America?

A: I am currently reading Michael MacCambridge's book America's Game. I was fortunate to meet him recently when he gave a presentation on the book. It is a wonderful history of the National Football League and its rise to prominence. Most intriguing to me in the book is his description of how quickly Major League Baseball lost its crown as "America's Game" to the NFL over the course of a decade or so in the '50s and '60s.

What impresses me the most about the NFL ownership group is their vigilance in staying progressive and avoiding complacency. That is necessary to stay #1 in sports, or else we could lose that position. I think their approval of international expansion is an example of the vision and courage they have in this regard.

Q: From a financial point of view, can you discuss NFL international expansion?

A: I am really very excited about this, as I believe that there is a lot of financial opportunity attached to building our brand and connecting to folks who may have some interest in what we do but who do not have a way to connect to it on an ongoing basis. Sure, they may have TV, but they are not able to go to a game live or see our stars in person. So it is not the same kind of expansion as we are used to. This is England, Germany, Mexico, and Canada.

As you know, we have tried NFL Europe and World League and invested significantly to make it work, but it became clear that international sport fans know the real thing, and they want our best product.

Most importantly, the NFL's ownership group is not stagnant; they are not afraid to go forward or to go after things. We are #1 right now, so to take big risks takes a lot of courage. Some of our more traditional owners--the Rooneys and the Wilsons--are really stepping out to play regular season games overseas. The way that they are willing to do the things to push the NFL ahead is just amazing and exciting to me. This is going to be the greatest buzz in the NFL over the next four or five years.

Q: How do you define sport finance?

A: Interesting question, I don't really think of it as sport finance, although there are investment bankers who do focus on sports. There are also consultants who focus on sport finance and building financial performance for sport. However, I think it is really like any other industry. I have a friend who is a CFO in a non-sport organization in Dallas; he--like me--has to understand all the basics of accounting, of finance, of fund-raising, and of Wall Street investment banking. To be able to do that, you have to understand the inner-workings of your own industry and, for me, that is sport. And industry by industry, the way that works differs. So you have to know yours. In the NFL, for example, aspects like revenue-sharing, national contracts, non-shared stadium revenues, etc. differ from many other industries.

Q: What are your thoughts on pubiic financing of professional sport facilities?

A: We are definitely seeing a shift away from 100% public financing, which differs from municipality to municipality. In New York, for example, it is private--estimated at US$1.7 billon--whereas in Dallas it will be a 50/50 public/private partnership. So it differs city by city. And where is it going? I'd say to public/private partnerships that makes more sense. There are many studies out there that show why taxpayers should not invest, so it is not easy. It also differs by sport and usage of the stadiums. Teams, I think, know now that they can't come in and be demanding. They have to work as partners with the municipalities, and partnerships are not easy between sport franchises and municipalities. But, it can work. There are benefits that apply to both. Contrary to public thought, there are not huge cash flow numbers left over at the end of the day in most professional sport organizations. The NFL is fortunate to be the league that does the best in that regard, but the amount of stadium costs that NFL teams take on is growing every day, so we have to be willing to shift our own costs internally.

Q: What are the trends in NFL sport finance today?

A: In 1992, when the Georgia Dome first opened, we did our stadium deal with the State of Georgia. Since then, there have been 24 new or renovated stadiums in the NFL. So there have been a few changes, and the mix of total revenues for most of the teams in the league has changed. Essentially, teams have taken on more risk by financing their stadium privately, so they can also reap more rewards out of the stadium revenues. In summary, 15 years ago, most teams were tenants who paid rent and got ticket revenue and if lucky some share of other revenues. That is basically our deal and still is. Today, although the owners now take more of the risk and responsibility for construction costs, they are also able to share more in the revenues such as concessions, parking, boxes, premium seating, naming rights, and advertising.

Q: Can you comment on the diversification of revenue sources in the NFL (sponsorship, merchandising, TV, online, radio, satellite radio, ticket sales, etc.) and how you think that will change over the next 10 years?

A: We, the NFL, are looking really hard at all the options out there whether they are on the Internet, via satellite, or otherwise. Even locally, the Internet revenues are really coming back. We had the Internet boom and bust, and now folks are starting to figure out the real business model for the Internet, and the NFL teams are doing that as well. So we're investing a pile of money in our Internet sites, and I think it shows. At the Falcons, we launched our web portal this summer and already have considerable sponsorship interest in it. We are also looking at other potential revenue sources such iPods and cell phones.

Q: The Atlanta Falcons are your employer. As most know, you've recently been hit with a negative issue surrounding your star quarterback, Michael Vick. For the CFO, how does--if at all--negative media like this affect you?

A: It affects all of us because this is what we do and, in sports in general, we're so strongly associated with the brand, we're entertainment. Anytime you have any negative press, it has the potential to affect your revenues. We've had to monitor this situation closely. Fortunately, since Arther Blank bought the team, he has focused on giving back to the fans and the community. He calls these efforts "deposits in the fan bank"--and right now we clearly have to make some withdrawals from that bank.

So I think that all these deposits are allowing us to do OK through this situation from a financial standpoint, as folks are willing to stick with us and give us a chance. It has worked out so far.

Q: Describe the finance operations at the Falcons (number of staff, size of budget, role with other departments, etc.).

A: Well, most sporting organizations are pretty lean and mean, and we're no exception. On the finance end, we have seven people, and we handle all the financial operations for the Falcons, the Georgia Force arena football club, and a separate retail organization.

Q: Financially, how important to the Atlanta Falcons is (1) making the play-offs, (2) winning a play-off game, (3) going to the NFC championship, and (4) going to the Super Bowl?

A: Blank bought the team in 2002 and, that year, we upset the Packers at Green Bay in the wildcard game. This was the first time that Green Bay had ever lost a playoff game at home! We then went to Philly and lost. So, after that experience of two road playoff games, we started going through the playoff financial, and there were a lot of red numbers on the page. Arthur was perplexed as we had two playoff games but lost money. This is common in the NFL, where the League controls most of the playoff revenues, and it really isn't possible to make money unless you go the Super Bowl. The costs of traveling to the games and paying the players, staff, coaches, and stadium expenses are very high. In the early part of the playoffs, these costs are more than what you get from the League. The key is to have home games, as you can generate additional revenues. All of the playoff ticket revenues go to the League and is used to help offset postseason expenses.

One important point is that going to the playoffs does have a significant effect on revenues in the following season, and that is really where you see the benefit. You are going to have higher retention of season tickets holders, more interest in club seats, and increased sponsor interest, as you are a winner. So the impact doesn't come to bear for a year.

Q: The Atlanta Falcons recently signed Japanese wide receiver Noriaki Kinoshita to a contract. Can you, in general terms, outline the process that took place to complete the financial aspects of the deal?

A: Not much different than signing a North American player; however, the NFL has now allocated spots on practice rosters for international players to give them real opportunities to develop into top players. In the case of Noriaki, the coaches like him, but he likely won't make the top 53 this year, although he can be on the practice squad and develop and eventually make the team. In the past, he may not have made the practice squad, which would have made his long-term development more difficult. Q: In recent decades, Atlanta has been known to be a baseball town. How does this--if at all--affect the profitability of your team?

A: It is hard to say. There is a limited amount of entertainment dollars out there and we're one option in that pie. With our current ownership group, we've sold out 51 straight games and we anticipate all sell-outs again this year. I think the arena where we bump up against the Braves--like in any city with 2+ strong sport teams--is in the corporate arena. In some cases, like the Coca-Colas of the world, they will sponsor every good sports organization in town, but in most cases potential sponsors must select one property to primarily associate with. We've done some research that shows that we have been able to eat into the percentage of fans in Atlanta who rank the Braves #1, but they are still the most popular team in town from a fan avidity standpoint.

Q: How does the stadium financing work for the Falcons? A: The Georgia Dome was 100% publicly financed with bonds that are backed by a hotel/motel tax and stadium revenues.

Q: How do the Falcons determine their ticket prices? How would this work for a playoff game in 2009, should one occur?

A: We take a look at a number of different things. Before 2002, we used to have the same ticket price for every seat in the Georgia Dome. In 2002, we went in and scaled the building and set new prices for different areas. The low levels are now one price, and price goes down as you move up away from the field. We analyze where we rank among all the NFL teams each year and use that as input in pricing.

In 2001 we were 40% below the League average in pricing and total ticket revenue. Within six years we wanted to be at the average in pricing. Our larger stadium gives us the opportunity to generate more overall ticket revenue. After five years, we are at the NFL average ticket price and are the 10th in overall ticket revenue, which matches the fact that we're the #10 market in the League.

Q: Can you describe, generally, the team-player relationship for compensation: how are they paid, when, pensions, deductions, etc.?

A: It is different from how most people get paid. There are many different classes of players (draft, undrafted, free agent, etc.), but let's look at an average player. When a player signs, and say he has a $1 million signing bonus, once he has executed the contract and the NFL has approved it, he gets his bonus and we send him a million dollar check. He then wouldn't get any more money until the season starts. The players are paid their salary over the 17 weeks of the season. If the contract has reporting conditions or bonuses, they may be paid after the season, but this is rare. The vast majority of players do not have guaranteed contracts. So, in the case of $1.25 million over three years, the player may not see that money if he doesn't make the team.

Q: How much pressure are you under to run a balanced budget for the Falcons?

A: We are under a lot of pressure to improve our finances. The NFL gives us anonymous information in quartiles and, in recent few years, we were in the bottom of the 4th quartile. In the past seasons, we've been able to put a good product on the field, improve our revenues sources, and move up the list. The NFL is supporting these efforts. I'm on an NFL best-practices committee seeking to improve the sharing of best practices among similar markets. For example, we may look at Dallas, which is a comparable market.

Q: You have achieved one of the most sought after positions in sport finance. From this viewpoint, can you (1) describe where the practice of sport finance is in its development versus finance in other fields, (2) discuss what needs to change in the field, and (3) outline what universities can do to support the field?

A: What we've been talking about throughout this interview shows a very progressive change and development in finance in the NFL in recent years. Teams are now involved in stadium construction and have large debts. Thus, they now have to meet with Wall Street bankers.

We have to get involved in what I call "high finance," not just "management finance." We've had to expand our horizons and talk to bankers, bond insurers, and so on. This is new, and from that standpoint it has become much more complex. We have likely been behind finance in other fields; however, we are quickly catching up to them because of the more complicated nature.

I have a master's degree in sports administration, and I stay involved with academe. Right now, what you see is such a wide variety of ways by which students are prepared for a career in sports. For young people out there, it is a little confusing to know which way to go. You see some schools that have some type of sport business program that is a physical education degree, then there are others that are an MBA or JD that may even be overblown. I don't know if there is any way to get some commonality among these programs. And it may be due to the huge growth that there just aren't enough people out there who can help us more accurately express what is going on in sport education.

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Research has demonstrated that a Super Bowl victory increases the personal income of the individuals in the metropolitan area from which the winning teams come. We argue that the economic benefits should extend beyond just the championship team's city to the cities of teams that experience seasonal success, and thus, the winning percentages of National Football League teams were included in our model. When controlling for sources of bias, winning percentage of the local professional football team had a significant positive effect on real per capita personal income. Explanations for these conclusions are offered from a psychological perspective. (JEL L83, R19)

"It was the best of times and it was the worst of times." This classic phrase could be used to describe the period of 1990 through 1993 for fans of the Buffalo Bills. The Bills performed well enough to win the American Football Conference Championship four consecutive years, but each year the team's season ended with a Super Bowl defeat. The purpose of this study is to determine if fans of successful, but not world champion, sport teams (like the Buffalo Bills) experience economic benefits in conjunction with their team's successes.

Coates and Humphreys (2002) examined whether a sports team winning a championship had a positive effect on the real per capita personal income of the local metropolitan area. Despite examining various measures of success across several different sports,1 Coates and Humphreys found that the local National Football League (NFL) team winning the

*The authors would like to thank Brad Humphreys and Dennis Coates as well as Victor Matheson for providing us with their data. The authors would also like to thank two anonymous reviewers for the comments and suggestions.

Davis: Assistant Professor, Department of Economics, Missouri University of Science and Technology, Rolla, MO 65409. Phone 573-308-3031, Fax 573-341-4866, E-mail davismc@mst.edu

End: Assistant Professor, Department of Psychology, Xavier University, Cincinnati, OH 45207. Phone 513-745-3249, Fax 513-745-3327, E-mail end@xavier.edu

1. The variables that Coates and Humphreys included for the NFL were making the play-offs, winning the conference championship, and winning the Super Bowl. The sports included were the NBA, the NFL, and MLB.

Super Bowl was the only variable that had a significant positive effect on income. Although Matheson (2006) shows evidence contradicting the findings, Coates and Humphreys' results are interesting when considered in the context of other similar studies that fail to find a positive effect from the presence of the teams in the city (Coates and Humphreys 1999,2003), the building of stadia for the teams (Coates and Humphreys 1999), or the presence of major events like the Super Bowl or World Cup (Baade and Matheson 2000, 2004; Matheson and Baade 2006) on local income. In this paper, we use a psychological framework to provide a rationale for the increased economic well-being associated with a Super Bowl victory.

Additionally, we rely on the psychological literature and argue that the economic benefits of a winning team should extend beyond just the championship team to the cities of teams that experience seasonal success. To examine whether a winning effect can be extended to all teams in the league and is not limited to just the Super Bowl champion, we include the winning percentage of the local NFL team. Although lacking a formal model,


GMM: Generalized Method of Moments

MLB: Major League Baseball

NBA: National Basketball Association

NFL: National Football League

Economic Inquiry

> (ISSN 0095-2583)

> Vol. 48, No. 1, January 2010, 39-50

doi: 10.111 l/j.l465-7295.2008.00124.x

> Online Early publication April 8, 2008

> © 2008 Western Economic Association International

the psychological literature suggests multiple individual-level processes that may account for the economic impact of winning percentage. To test whether the effect is based on increased consumption or increased productivity, we estimate our models on the real wage income per capita as well as personal income.

Additionally, because the econometric model is a dynamic panel series model, a model that can exhibit substantial bias in the coefficients (Judson and Owen 1999), we use the method of Arellano and Bond (1991) to correct for bias. This method also provides insight in regard to the directionality of the winning percentage and personal income relationship, specifically that winning percentage drives changes in personal income as opposed to changes in personal income impacting winning percentage. In the Arellano-Bond estimations, winning percentage is treated as endogenous, meaning within the system, while the remaining variables are treated as being exogenous. As an additional further check, we reestimate the model including team salary. If the direction of causation flows from income to winning, it would be indicated by increases in the coefficient on pay-roll for the team. The results show that even after including team salaries in the model, winning percentage still positively impacts income.


Research has consistently demonstrated that people go to great lengths to publicly identify with winning sport teams (Cialdini et al. 1976; Cialdini and Richardson 1980; End 2001; Joinson 2000; Wann and Bran-scombe 1990). This tendency to bask in the reflected glory (Cialdini et al. 1976) is related to event-specific success (a team's victory) and global success (winning percentage, qualifying for play-offs, etc.). Specifically, End et al. (2002) found that when sport fans were asked to identify their favorite teams, the teams with which they identified had an average winning percentage significantly greater than 50%. Additionally, End et al. (2002) found a positive relationship between the fan preference and their team's winning percentage and between fan preference and team identification. These findings suggest that an individual's preference for a team and one's psychological identification with a sports team are influenced by the team's global (seasonal) performance.

The positive relationship between team performance and identification has a multitude of consequences for sport fans. In comparison to those with low team identification, those fans who have a strong identification with a team or those whose identification with a sports team is strengthened as a result of the team's successes experience stronger emotional reactions in response to their team's victories and defeats (Branscombe and Wann 1992; Wann et al. 1994). Additionally, Wann et al. (1999) reported finding a positive relationship between team identification and psychological health. Individuals who highly identified with a local team reported a healthier mood profile than individuals who reported low levels of identification. Finally, Schwarz et al. (1987) found that citizens of Germany reported higher levels of life satisfaction following a national soccer team's victory than they did prior to the game.

The impact of team performance on the sport fan is not limited to mood. Hirt et al. (1992) found that sport fans' judgments of their personal capabilities are influenced by the performance of the team with which they identify. Specifically, high-identifying fans who witnessed a victory reported higher personal competencies on mental, social, and motor skill tasks than fans who witnessed their sport team being defeated. Highly identified fans also report a decrease in self-esteem following their team's defeat (Bizman and Yinon 2002; Hirt et al. 1992).

If a sport team's performance influences judgments of personal competencies, mood, self-esteem, and so on, one could argue that it is possible that the outcome of a sporting event may influence one's performance at work. Judge and Watanabe (1993) theorize that positive mood experienced in one context (life satisfaction) can "spill over" to other contexts, including one's work environment. Judge and Watanabe argue and provide empirical evidence that this reciprocal spillover effect can account for the strong positive correlation between life satisfaction and job satisfaction (Tait, Padgett, and Baldwin 1989). Because meta-analytical research has demonstrated a positive relationship between job satisfaction and job performance (Iaffaldano and Muchin-sky 1985; Judge etal. 2001), the joy experienced by fans of successful teams may spill over and positively influence job satisfaction as well as their performance at work.

One might also argue that post-victory increases in fans' self-esteem and personal competencies indirectly account for improved job performance. As mentioned earlier, Hirt et al. (1992) found that fans who witnessed a victory reported higher personal competency on a variety of tasks. Because the increase in perceived competency was not limited to sports-related tasks, sport fans may experience a spillover and experience increased perceived competency at work as a result of the team's successes. Judge and Bono (2001) conducted a meta-analysis of the research examining the relationship between self-esteem and job performance. The authors found a positive relationship between job performance and self-esteem, which, as mentioned earlier, is also related to a sport team's success. Thus, the spillover of happiness, increased self-esteem, and self-competency may account for Lever's (1969) report that the outcome of soccer matches influenced workplace productivity in Brazil. Lever reported that victories were accompanied by increased production, while defeats resulted in an increase in workplace accidents.

Team success can also impact the economy via increased consumption, spending. Isen (1989) demonstrated that positive mood, similar to the mood experienced by fans of successful sport teams, positively impacts the economy via increased consumption. Evidence from the sport fan literature suggests that team success might influence spending. Specifically, research has demonstrated that spontaneous charitable contributions increase following a sport team's successes (Platow et al. 1999).

Although team success might bolster spending, the time of year when each of the leagues' seasons occur may strengthen other seasonal effects on consumption. Whereas the Major League Baseball (MLB) season has ended and the National Basketball Association's (NBA) season is still more than 5 mo from the start of its play-offs, December is the peak of the NFL season (the end of the season and play-offs). Large seasonal effects in output and income are often attributed in part to increased consumer demand as people purchase their holiday gifts and other seasonal items. These seasonality effects can influence business cycles greatly (Beaulieu, MacKie-Mason, and Miron 1992; Cecchetti, Kashyap, and Wilcox 1997; Wen 2002). Therefore, increased consumer spending due to the success of the football team, coupled with the holiday season, could lead to greater economic activity, which is evident in annual data.

The performance of sport teams predicts the extent to which fans identify with the teams. Team performance affects personal reactions and, thus, may have real consequences for the economy. For the reasons stated above, we hypothesize that team's winning performance predicts personal economic well-being, specifically demonstrated by increases in real per capita income and real wage income per capita. Because the NFL is the most popular league in the United States and thus the team success would impact the greatest number of fans, we hypothesize that the predicted relationship between winning percentage and economic well-being would be strongest among fans of the NFL.


We estimate the following dynamic panel model:



where xit is a series of explanatory variables that are included in the model and yit is the real per capita income for each city i in year t. ni is a fixed effect. The cities examined are metropolitan statistical areas as defined by the Bureau of Economic Analysis. The per capita personal income is deflated from nominal to real by using the national consumer price index. Judson and Owen (1999) explain that a fixed-effects model is typically desirable for macroeconomic analysis when the sample includes almost all the entities of interest. The first set of analyses is done on the Coates and Humphreys' (2002) data set. In this study, we are including every American city that had an NBA, MLB, or NFL team in the sample (38 cities) over the time span of 1969-1998. Included in the explanatory variables in the xit vector are the population growth rate, a time trend for each city, and a dummy variable for each year. Also included in the regression are variables reflecting the sports environment: the stadium size, the presence of professional sports teams, as well as the entrance of new teams into the market or the departure of old teams from the market, and years in which the city hosted a Super Bowl. Last, we include Coates and Humphreys' (2002) "success" variables, i.e., dummy variables for winning championships and making play-offs. All the variables mentioned were included in Coates and Humphreys' (2002) initial analysis. In order to test our hypotheses, the winning percentages of the local sports teams are added to the model. These variables are intended to test further the finding of Coates and Humphreys that a Super Bowl victory has a positive effect on the economic environment, specifically personal income. The winning percentages of the NFL franchises allow us to test whether the effect extends to teams that were successful during the regular season but that were unable to win the Super Bowl. In addition to the Coates and Humphreys' data set, we analyze Matheson's (2005) data set as a robustness check. The Matheson data set includes a larger sample of cities, 73 of the largest cities, and also three additional years of data (1999-2001). Consistent with Matheson's approach of including dummy variables for other major events that impacted local economies, we include dummy variables for the occurrence of Hurricane Andrew, the oil boom and busts in Texas and Louisiana, and the tech boom and bust in San Jose and San Francisco.

Equation (1) can also be estimated using the same explanatory variables as listed above but with the dependent variable (yit) being the real wage income per capita for each city as opposed to the real per capita personal income. Personal income measures income from all sources, including labor and capital. Wage income only includes wages and other forms of monetary compensation to employees. Evidence of an increase in the real wage income per capita could shed light on the way in which sports team success affects personal income. If productivity increases, at least some of the increased business income should flow to the workers in the form of increased wages. Therefore, if we fail to see an increase in the real wage income per capita, it suggests the possibility that workers have not increased their productivity.

The potential problem with relying solely on the above equation is that the coefficients on the explanatory variables are subject to bias due to the presence of the lagged dependent variable. In order to correct for this, we will also estimate the dynamic panel model of Arellano and Bond (1991). This model is a generalized method of moments (GMM) model, which uses the lagged values of the endogenous explanatory variables as instruments. The endogenous variables are the factors that have the potential to be affected by changes in income, as opposed to affecting income. In our model, the endogenous variables are the football winning percentage and football winning percentage squared variables. The model that is estimated is the first-differenced version of Equation (1) above:


In addition to differencing the equation, which eliminates the bias, the explanatory variables are separated into two groups, x represents the exogenous variables and w represents the endogenous variables. The first thing the differencing accomplishes is to remove the fixed effect from the model (n) but at the same time cause the error term to become correlated with the lagged dependent variable, which can bias the estimate.

In order to solve this problem, an instrumental variable approach is applied. These instruments include the lagged levels of the endogenous variable y, the lagged levels of the endogenous variables w, and the lagged and current values of the exogenous variables x. To address concerns over the endogeneity of the football winning percentage variables, these variables are declared to be endogenous. The remaining explanatory variables are assumed to be exogenous.

Judson and Owen (1999) present various methods that reduce the bias in the estimates and argue that the Arellano-Bond method reduces the bias significantly.2


The results of Equation (1), which are presented in Column 1 of Table 1, show that winning percentage of the local professional football team has a positive effect on real per capita income.3 The coefficient for the square of winning percentage is negative;

2. Although Judson and Owen claim that a method that they derive from the work of Kiviet (1995) is slightly superior to the Arellano and Bond method, we used the Arellano and Bond method because of its practicality.

3. The time trend and year dummy variables as well as the sports environment variables for baseball and basketball are suppressed in the tables but included in the regressions.

however, the overall effect of the winning percentage when both variables are included is positive. The overall effect of having a team in a city is unclear because the football franchise indicator variable is negative and significant. Specifically, Table 2 shows the gain in real per capita personal income per win (based on a 16-wk season). There appears to be a nonlinear relationship between winning and income. It is important to note that adding the winning percentage variable does not eliminate the significance of the Super Bowl coefficient originally observed by Coates and Humphreys (2002). Although there are positive economic effects of sharing residency with a team that has been successful over the course of the season (winning percentage), the results suggest that winning the Super Bowl accentuates the effect and delivers a "January bonus." Table 2 also indicates that the positive effect of winning is stronger for the first few wins. We can suggest three explanations for this finding. The first is that the economic benefit may be due to loss avoidance. Alternatively, the real economic benefit may be from having a hometown team in the play-offs, or at least play-off contention (which would be those teams that have managed to win eight or more games). Last, the nonlinearity results may be influenced more strongly by extreme values, of which there are a limited number of observations (e.g., there have been very few teams that have won 1 or fewer or 15 or more games in an NFL season). Also the MLB and NBA variables are not significant, confirming Coates and Humphreys' finding that only the NFL has any effect.


Effect of Winning and Football Variables on Income and Wage (Ordinary Least Squares Estimation)

Explanatory Variables


> Real Per

> Capita Income


> Real Wage

> Income Per Capita


> Real Per

> Capita Income


> Growth Rate of

> Real Per Capita Income

Real per capita income (-1)

0.823** (0.017)

Real wage ( — 1)

0.840** (0.015)

Football franchise

-3.518** (0.955)

-0.232** (0.079)

-3.667* (1.752)

-0.023** (0.007)

Football win %

5.193* (1.998)

0.334* (0.165)

2.442 (3.666)

0.037* (0.015)

Football win % squared


-0.238 (0.179)

-3.322 (3.987)

-0.028 (0.016)

Football stadium capacity

0.015* (0.023)

0.002 (0.002)

0.106* (0.042)

0.000 (0.000)

Football stadium capacity squared

-0.000 (0.000)

-0.000 (0.000)

-0.001** (0.000)

0.000 (0.000)

Football stadium construction

-0.042 (0.298)

0.002 (0.024)

-1.212* (0.545)

0.002 (0.002)

Multipurpose stadium construction

-0.448 (1.535)


7.603** (2.800)


Football team entry

0.947* (0.399)

0.050 (0.033)

1.876* (0.732)

0.003 (0.003)

Football team departure

-0.960 (0.493)


0.282 (0.904)

-0.008* (0.004)

Football team makes play-offs

-0.263 (0.251)


-0.246 (0.460)

-0.002 (0.003)

Football conference championship

0.055 (0.437)

-0.006 (0.036)

0.268 (0.803)

-0.001 (0.004)

Super Bowl champions

1.391* (0.589)

0.089 (0.049)

1.791 (1.081)

0.010* (0.003)

Host of Super Bowl

-0.131 (0.414)

-0.015 (0.034)

0.062 (0.761)

-0.001 (0.004)

Baseball franchise

3.296* (1.360)


7.912** (2.490)


Baseball win %

-0.761 (1.715)



-0.002 (0.013)

Basketball franchise


0.019 (0.041)

0.352 (0.914)

0.000 (0.004)

Basketball win %

0.990 (0.858)

0.072 (0.071)

1.092 (1.575)

0.008 (0.006)

Population growth

0.508** (0.092)

0.066** (0.007)

1.908** (0.159)

0.001 (0.001)



1.063** (0.121)

100.968** (1.226)

0.006 (0.005)

Note: Standard errors in parentheses.

*Significant at the 5% level; **significant at the 1% level.

We conduct additional analyses to provide insight into the economic process, specifically increased consumer spending and increased productivity, accounting for the observed effect of success on income. Whereas an increase in real per capita personal income may be the result of increased consumer spending, an increase in real per capita wage income may imply an increase of productivity. To examine this alternative source of economic impact, the identical regression analysis presented earlier is conducted including real wage income per capita instead of the real per capita personal income. As shown in Column 2 of Table 1, we find that winning percentage has a significant positive impact on real wage income per capita. This finding supports, albeit indirectly, the idea that the increase in income may be partially due to increased productivity. Interestingly, the Super Bowl championship variable does not show the same significant impact on real per capita wage income. Despite having a positive effect (.081), the effect is not significant (p = .094).


Value of Each Win to Personal Income

Additional Win during Season

Marginal Increase in Per Capita Personal Income ($)

































Notes: The table indicates the increase in per capita personal income of adding one more win by the NFL franchise during the season. For instance, a team winning their seventh game would add an additional $11.72 over the team only winning six games.

Inclusion of the lagged dependent variable might bias the coefficients. Typically, this bias issue is resolved as the time dimension of the panel moves toward infinity. Although the time frame of our data set is fairly long (30 yr of data), Judson and Owen (1999) suggest that a data set of this length may still be susceptible to bias. This potential bias can be addressed in a variety of ways.

One way of addressing this potential bias is to simply remove the lagged dependent variable from the regression analysis. This method was employed by Coates and Humphreys (2002). To minimize the bias in this investigation, the regression was rerun without the lagged dependent variable. As presented in Column 3 of Table 1, the coefficient associated with football winning percentage is now negative and not significant. A shortcoming with analyzing the data in this manner is that a dynamic aspect to the data is not incorporated into the model when the lagged dependent variable is excluded. Coates and Humphreys (2003) argue that the inclusion of the lagged dependent variable in the model is preferable because it captures other extraneous permanent effects to a city that are not included as explanatory variables. If excluded, these effects could lead to omitted variable bias. Such extraneous events could include public building projects such as transit systems or a convention center, as well as the entry of major private enterprises into the city.

Another solution to the problem of bias is to regress the growth rate of real per capita income on the above variables. Because the growth rate (percentage change) includes information on last year's income, estimating this model does not require the inclusion of the lagged dependent variable. As shown in Column 4 of Table 1, the football winning percentage clearly has a positive effect on the growth rate of real per capita personal income. A finding of a positive effect on the growth rate is not a derivative of the same finding on the level of real per capita personal income. However, since the two results show an increase in income due to an increase in winning percentage, they complement each other and strengthen the argument in favor of successful football teams having a positive effect on the local economy. To further elaborate on the difference between the two analyses, Coates and Humphreys (1999) find that the presence of sports teams has no effect on the growth rate of personal income but did find a negative effect on the level of personal income.

Last, we estimate the model using the Arellano and Bond (1991) GMM procedure. Jud-son and Owen (1999) show that this method greatly reduces the bias relative to the simple ordinary least squares method of estimation. These results are presented in Table 3, and the coefficients on winning percentage and winning percentage squared are similar in magnitude to their values in Table 1 and still significant. The coefficient on the Super Bowl victory variable also exhibits a similar result to the result found in Table 1.


Effect of Winning and Football Variables on Income and Wage (Arellano-Bond Estimation)

Explanatory Variables


> Real Per

> Capita Income


> Real Wage Incomer

> Per Capita

Real per capita income (-1)

0.804** (0.016)

Real wage income (— 1)

0.826** (0.013)

Football franchise

-3.827** (0.852)

-0.248** (0.064)

Football win %

6.130** (1.823)

0.408** (0.136)

Football win % squared

-5.221** (1.975)

-0.326* (0.148)

Football stadium capacity

0.011 (0.021)

0.002 (0.002)

Football stadium capacity squared

0.000 (0.000)

0.000 (0.000)

Football stadium construction

0.033 (0.275)

0.011 (0.021)

Multipurpose stadium construction



Football team entry

0.871* (0.366)

0.045 (0.028)

Football team departure

-1.130* (0.440)

-0.034 (0.033)

Football team makes play-offs

-0.243 (0.221)


Football conference championship

-0.140 (0.382)

0.004 (0.029)

Super Bowl champions

1.262* (0.515)

0.078* (0.039)

Host of Super Bowl


-0.015 (0.027)

Baseball franchise

3.083* (1.253)

0.184* (0.094)

Baseball win %

-1.177 (1.525)


Basketball franchise

0.198 (0.452)

0.009 (0.034)

Basketball win %

1.041 (0.767)

0.088 (0.057)

Population growth

0.546** (0.083)

0.066** (0.006)


0.858 (0.078)

0.038** (0.004)

Statistical test for

p Value for test of null hypothesis of no autocovariance

> in residuals of order 1



p Value for test of null hypothesis of no autocovariance

> in residuals of order 2



Note: Standard errors in parentheses.

*Significant at the 5% level; **significant at the 1% level.

In order for the estimates to be considered consistent, the presence of second-order serial correlation must be ruled out. Presented in Column 1 of Table 3 is the p value of the Arellano-Bond test for second-order serial correlation. The test statistic is miniscule (-.49), and therefore, we conclude that there is no second-order serial correlation in the residuals.

In Column 2 of Table 3, the results of the Arellano-Bond estimation regressing the real wage income per capita instead of the real per capita personal income are presented. Again, the coefficient on the football winning percentage is positive and significant. However, this estimation may not be valid because the assumption of no second-order autocorrelation is rejected.

These results demonstrate that the effect of higher winning percentages for the local NFL team on per capita personal income is quite robust. We are unable to discern whether the observed effect is related to a consumption effect or increased productivity. Our attempts to refute the productivity argument were thwarted when we found that the real wage income per capita also increases in response to increases in winning percentage. In support of the consumption hypothesis, the coefficients on basketball and baseball winning percentages are not significant in any of the estimations. As noted earlier, these two sports are not as popular as the NFL, and their seasons do not intersect with Christmas as directly as football, producing less of an effect under the consumption hypothesis.


Supplemental Data

Column 1 of Table 4 presents the results of Equation (1) using Matheson's (2005) data which include more cities (73) than Coates and Humphreys' data set and three additional years of data (1999-2001). The results parallel those generated from the Coates and Humphreys' data set.

We employ a hybrid of both Coates and Humphreys' (2002) and Matheson's (2006) methodologies. Consistent with Matheson's (2005) critique of Coates and Humphreys' methodology, we include a variable for each team's winning percentage separately. However, unlike Matheson, we do not estimate separate regressions for each city and instead estimate a fixed-effects model across all cities. Our approach does not correct for all of Math-eson's criticism (i.e., fixed-effects models being subject to heteroskedasticity); however, it does loosen the requirement that the success of each team be the same across all cities. Although this approach does not eliminate the possibility that one of the multitude of variables would be deemed significant spuriously, the inclusion of each winning percentage variable provides an additional opportunity to critically examine the hypothesized effects. Specifically, if only one winning percentage variable is significant, we can ignore the winning percentage effect. If many winning percentage variables are significant, it suggests that the effect is important across cities. Last, this methodology allows an easy comparison of the effects on income of all the city winning percentages through an F test.

Table 4 presents this regression in Column 2. Although the size of the coefficients varies greatly, four of the coefficients (all positive) are significant at the 5% level. The four cities are Houston, Minneapolis, Oakland, and Orange County, so they are quite diverse cities, and unlikely to be affected by the same unaccounted-for effect. Additionally, the majority of the insignificant coefficients are positive as well. The F test suggests that all the football winning percentage parameters together would be significant at the 10% level (F = 1.34, p = .095). Overall, the effect of the winning percentage variables seems to contribute positively toward the income of the area.


One concern with both the results found here and those reported by Coates and Humphreys (2002) is the direction of causation. We have concluded that a successful sports team strengthens an economy. An alternative explanation is that a successful sports team is a product of increased economic activity.

One argument in favor of causation running from team success to economic output is that the NFL winning percentage is significant, while the MLB one is not. Einolf (2004) showed that payroll was more strongly correlated with team success in MLB than in the NFL and that there seems to be little correlation between market size and payroll in the NFL. Unlike MLB, the NFL has a salary cap. Additionally, the NFL has a greater degree of revenue sharing, an attempt to keep teams equal regardless of their economic situations, than MLB.

Empirical support for the "income affects team success" argument would need to be consistent with the following causal path: higher income creates a greater demand for sports, which results in greater spending by the team, which cumulates in greater team success. Contrary to the income affects success predictions, the league that shows the stronger relationship between success and spending (baseball) does not show the stronger relationship between success and personal income (football).

Attempts were made to statistically test for the endogeneity of the football winning percentage. Specifically, in the Arellano-Bond results in Table 3, the winning percentage variables were included endogenously. The coefficients on the winning percentages were significant in these estimations.

The second statistical method we employ to test for the endogeneity is to include an additional variable in the model to incorporate the effect of income on the success of the team. Table 5 presents the results of the earlier regressions, including a variable for football team salary. Our assumption is that if the income of the city leads to a greater investment in the team, this relationship should be accounted for by the salary variable. If the winning percentage remains significant after the inclusion of the salary variable, it can be interpreted as additional support for the direction of causation originating from winning and thus impacting income. One limitation of this approach of testing endogeneity is that there are a limited number of years of data available (1981-1998).


Results Using Matheson Data Set



> Real Per Capita

> Income, NFL Win % Variable


> Real Per Capita Income,

> Individual NFL Win % Variables

Lagged real PCPI

0.843** (0.011)

0.836** (0.011)

Population growth



Football franchise

-42.121 (40.861)

-110.056* (53.180)

Football play-offs


-2.403 (26.263)



143.097 (248.642)

Oil boom

270.686** (44.120)

267.551** (44.533)

Oil bust

-160.886* (70.740)

-162.670* (71.686)

Hurricane Andrew

-1,307.835** (238.639)

-1,311.152** (239.625)

Tech boom 1999

1,982.275** (179.010)

2,069.523** (188.146)

Tech boom 2000

4,465.379** (181.975)

4,550.926** (188.053)

Tech bust

-1,773.961** (199.346)

-1,702.283** (200.414)

FB win %

120.978* (60.519)


-2.254 (260.674)






83.859 (205.877)






-70.250 (235.895)


-56.401 (228.433)


292.460 (250.364)


-241.577 (260.184)




425.571* (173.961)




160.495 (237.407)

Kansas City

-81.205 (259.550)

Los Angeles

59.305 (189.162)


220.749 (343.338)


519.919* (260.367)


81.345 (238.800)

New Orleans


New York

2.293 (302.115)


586.909** (161.083)

Orange County

484.604** (183.241)


90.738 (265.683)




384.843 (285.172)

San Diego

-385.905 (245.719)

San Francisco

368.358 (213.666)


-97.842 (240.993)

St. Louis



293.011 (278.918)

Washington, DC



3,135.14** (233.578)

3,255.587** (237.557)

Note: Standard errors in parentheses. PCPI, per capita personal income.

*Significant at the 5% level; **significant at the 1% level.


Results Including Football Salary Variable

Explanatory Variables


> Real Per

> Capita Income


> Real Per

> Capita Income


> Growth Rate

> of Real Per

> Capita Income


> Real Per

> Capita Income

Real per capita income (-1)

0.747** (0.025)

0.748** (0.025)

0.695** (0.023)

Football franchise

-3.468* (1.463)

-2.912* (1.293)

-0.011 (0.010)


Football win %

3.830 (2.567)

3.844 (2.567)

0.033 (0.018)

1.073 (0.684)

Football win % squared

-2.928 (2.797)

-2.889 (2.797)

-0.026 (0.020)

Football salary

0.000 (0.000)

-0.000 (0.000)

0.000 (0.000)

Football stadium capacity

-0.031 (0.040)

-0.036 (0.039)

-0.000 (0.000)

-0.067 (0.038)

Football stadium capacity squared

0.001 (0.000)

0.001 (0.000)

0.000 (0.000)

0.001* (0.000)

Football stadium construction

-0.533 (0.479)

-0.490 (0.476)

0.001 (0.003)

-0.643 (0.480)

Multipurpose stadium construction

-2.977 (2.287)

-2.745 (2.269)

-0.020 (0.016)

-1.825 (2.271)

Football team entry

1.985** (0.743)

1.926** (0.739)

0.011* (0.005)

2.221** (0.757)

Football team departure



-0.006 (0.005)

-1.873* (0.731)

Football team makes play-offs

-0.675* (0.300)

-0.679* (0.300)

-0.004* (0.002)

-0.770** (0.271)

Football conference championship

-0.069 (0.554)

-0.068 (0.554)

-0.001 (0.004)

-0.305 (0.526)

Super Bowl champions


0.922 (0.780)

0.007 (0.006)

0.720 (0.740)

Host of Super Bowl

-1.180* (0.518)

-1.166* (0.518)

-0.008* (0.004)

-0.747 (0.480)

Baseball franchise



-0.017 (0.017)


Baseball win %



-0.007 (0.015)


Basketball franchise

-0.236 (0.000)

-0.183 (0.941)

-0.003 (0.007)

-0.235 (0.875)

Basketball win %



-0.005 (0.009)

-1.665 (1.139)

Population growth

0.898** (0.134)

0.899** (0.134)

0.002* (0.001)

0.967** (0.125)


21.699** (3.012)

20.772 (2.787)

-0.031** (0.011)

1.159** (0.144)

Notes: Standard errors in parentheses. Columns 1-3 present results of standard regression. Column 4 presents the Arellano-Bond results.

*Significant at the 5% level; **significant at the 1% level.

Column 1 of Table 5 re-creates Column 1 of Table 1 but now includes the football salary variable. The dependent variable is the level of personal income. The salary variable appears to contribute very little to explaining the variation in income. The football winning percentage variables are not as significant and are smaller in magnitude, but that could be expected as the results are based on fewer observations (which reduces statistical power). Column 2 of Table 5 presents the results of the same regression analysis except that, this time, the football salary variable is excluded. The coefficients on football winning percentage and football winning percentage squared are essentially the same regardless of whether the football salaries are included or not. Therefore, we can conclude that winning percentage is affecting income separate from salary.

Presented in Column 3 of Table 5 are the results adjusting the estimation in Column 4 of Table 1 to include the salary of the teams. The impacts of the winning percentage variables, though no longer significant at the 5% level, maintain essentially the same magnitude as they did in Table 1. Also, the coefficients on winning percentage are unaffected by the inclusion of the salary variable.

Column 4 presents the results using the Arellano-Bond methodology, which is a rees-timation of Column 1 of Table 3. The winning percentage squared is removed from the equation because it has a very low p value in these estimations. Because we are now explicitly accounting for potential endogeneity of the winning percentage in the model, we assume that the variables are not endogenous. As in the simple regression results of Column 1 of Table 5, the results on winning percentage are weakened when estimated over the complete sample (1969-1998), but again the salary variable appears to be completely unimportant. The results with football salary excluded over the 1980-1998 time period are not included in the table, but the coefficients on winning percentage in each of these estimations is essentially the same whether salary is included or not.

Overall, the football salary variable has very little influence on the football winning percentage variable. The variable, included to control for more revenues influencing the success of the team, is unable to fully remove the importance of winning on income, which implies that the direction of causation runs from winning to personal income and not vice versa.


Our results extend the work of Coates and Humphreys (2002) by showing that an increase in the winning percentage of the local NFL franchise increases the real per capita personal income of the city. Consistent with this finding, the data suggest that the winning percentage increases the growth rate of real per capita personal income as well. One possible explanation for this relationship is that workplace productivity increases as a function of the team success. The observed increase in the real wage income per capita as a function of team winning percentage, as well as the reviewed literature that demonstrates the psychological impact of team successes, supports this enhanced productivity explanation. The findings seem to be quite robust with regard to estimation methodology, although the regression on real wage income per capita is not as convincing as the regression on per capita personal income.

The nonlinear aspect of the winning percentage results suggests that the gain to personal income from winning is strongest when the team has few wins. There even seems to be a decline in personal income from winning additional games above 11. These results suggest that competitive balance, where the teams perform at a fairly equal level, would benefit the cities. The parity that currently exists in the NFL, and sometimes condemned as mediocrity, is actually good for the economics of the cities that host NFL franchises. These findings suggest that cities should encourage the NFL to incorporate policies to maintain competitive balance.

One recommendation of a concrete policy proposal that can be derived from these results is that cities might want to consider making the contribution toward stadium financing dependent upon the success of the team. Because the benefits that the city derives from the team are higher with a more successful team, the city might want to require that the team makes all efforts to provide a successful team in order to allow the citizens to fully obtain the funding benefits. However, our findings do not show that the success of teams justifies spending money on a stadium in general, supporting the extensive literature that states that the gains from stadium financing to cities are minimal (Baade and Matheson 2004; Baade and Sanderson 1997; Coates and Humphreys 1999, 2003; Noll and Zimbal-ist, 1997a, 1997b; for an alternative view, see Carlino and Coulson 2004).

Because the nature of the data does not allow for definitive conclusions in regard to the factors that account for the increase in income, economists and psychologists should collaborate to establish a formal model to determine if the increases in real per capita personal income are a result of increases in productivity, consumption, or both factors. The establishment of a formal psychological model may also provide insight into the duration of the observed effects, as well as identify other individual-level factors that may be affected by team performance.


Arellano, M., and S. Bond. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and Application to Employment Equations." Review of Economic Studies, 58, 1991, 277-97.

Baade, R. A., and V. A. Matheson. "An Assessment of the Economic Impact of the American Football Championship, the Super Bowl, on Host Communities." Reflets et Perspectives, 39, 2000, 35-46.

—. "Quest for the Cup: Assessing the Economic Impact of the World Cup." Regional Science, 38, 2004, 341-52.

Baade, R. A., and A. R. Sanderson. "The Employment Effect of Teams and Sports Facilities," in Sports, Jobs and Taxes: The Economic Impact of Sports Teams and Stadiums, edited by R. G. Noll and A. Zimbalist. Washington, DC: Brookings Institution, 1997, 92-118.

Beaulieu, J. J., J. K. MacKie-Mason, and J. A. Miron. "Why Do Countries and Industries with Large Seasonal Cycles Also Have Large Business Cycles?" Quarterly Journal of Economics, 107, 1992, 621-56.

Bizman, A., and Y. Yinon. "Engaging in Distancing Tactics among Sport Fans: Effects on Self-Esteem and Emotional Responses." Journal of Social Psychology, 142, 2002, 381-92.

Branscombe, N. R., and D. L. Wann. "Role of Identification with a Group, Arousal, Categorization Processes, and Self-Esteem in Sports Spectator Aggression." Human Relations, 45, 1992, 1013-33.

Carlino, G., and N. E. Coulson. "Compensating Differentials and the Social Value of NFL Franchises." Journal of Urban Economics, 56, 2004, 25-50.

Cecchetti, S. G., A. K. Kashyap, and D. W. Wilcox. "Interactions between the Seasonal and Business Cycles in Production and Inventories." American Economic Review, 87, 1997, 884-92.

Cialdini, R. B., R. J. Borden, A. Thorne, M. R. Walker, S. Freeman, and L. R. Sloan. "Basking in Reflected Glory: Three (Football) Studies." Journal of Personality and Social Psychology, 34, 1976, 366-75.

Cialdini, R. B., and K. D. Richardson. "Two Indirect Tactics of Image Management: Basking and Blasting." Journal of Personality and Social Psychology, 39, 1980, 406-15.

Coates, D., and B. R. Humphreys. "The Growth Effects of Sport Franchises, Stadia, and Arenas." Journal of Policy Analysis and Management, 18, 1999, 601-24.

—. "The Economic Impact of Postseason Play in Professional Sports." Journal of Sports Economics, 3, 2002, 291-99.

—. "The Effect of Professional Sports on Earnings and Employment in the Services and Retail Sectors in US Cities." Regional Science and Urban Economics, 33, 2003, 175-98.

Einolf, K. W. "Is Winning Everything? A Data Envelopment Analysis of Major League Baseball and the National Football League." Journal of Sports Economics, 5, 2004, 127-51.

End, C. M. "An Examination of NFL Fans' Computer Mediated BIRGing." Journal of Sport Behavior, 24, 2001, 162-81.

End, C. M., B. Dietz-Uhler, E. A. Harrick, and L. Jacque-motte. "Identifying with Winners: A Reexamination of Sport Fans' Tendency to BIRG." Journal of Applied Social Psychology, 32, 2002, 1017-30.

Hirt, E. R., D. Zillman, G. A. Erickson, and C. Kennedy. "Costs and Benefits of Allegiance: Changes in Fans' Self-Ascribed Competencies after Team Victory versus Team Defeat." Journal of Personality and Social Psychology, 63, 1992, 724-38.

Iaffaldano, M. T., and P. M. Muchinsky. "Job Satisfaction and Job Performance: A Meta Analysis." Psychological Bulletin, 97, 1985, 251-73.

Isen, A. M. "Some Ways in Which Affect Influences Cognitive Processes: Implications for Advertising and Consumer Behavior," in Cognitive and Affective Responses to Advertising, edited by P. Cafferata and A. M. Tybout. Chicago, IL: Young and Rubi-cam, Inc., 1989, 91-117.

Joinson, A. N. "Information Seeking on the Internet: A Study of Soccer Fans on the WWW." CyberPsychol-ogy and Behavior, 3, 2000, 185-91.

Judge, T. A., and J. E. Bono. "Relationship of Core Self-Evaluations Traits—Self-Esteem, Generalized Self-Efficacy, Locus of Control, Emotional Stability—with Job Satisfaction and Job Performance: A Meta-Analysis." Journal of Applied Psychology, 86, 2001, 80-92.

Judge, T. A., C. J. Thoresen, J. E. Bobo, and G. K. Patton. "The Job Satisfaction-Job Performance Relationship: A Qualitative and Quantitative Review." Psychological Bulletin, 127, 2001, 376-407.

Judge, T. A., and S. Watanabe. "Another Look at the Job Satisfaction-Life Satisfaction Relationship." Journal of Applied Psychology, 78, 1993, 939-48.

Judson, R. A., and A. L. Owen. "Estimating Dynamic Panel Data Models: A Guide for Macroeconomists." Economics Letters, 65, 1999, 9-15.

Kiviet, J. F. "On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models." Journal of Econometrics, 68, 1995, 53-78.

Lever, J. "Soccer: Opium of the Brazilian People." Trans-Action, 7(2), 1969, 36-43.

Matheson, V. A. "Contrary Evidence on the Impact of the Super Bowl on the Victorious City." Journal of Sports Economics, 6, 2006, 420-28.

Matheson, V. A., and R. A. Baade. "Padding Required: Assessing the Economic Impact of the Super Bowl." European Sports Management Quarterly, 6, 2006, 353-74.

Noll, R. G., and A. Zimbalist. "Build the Stadium, Create the Jobs," in Sports, Jobs and Taxes: The Economic Impact of Sports Teams and Stadiums, edited by R. G. Noll and A. Zimbalist. Washington, DC: Brookings Institution, 1997a, 1-54.

—. Sports, Jobs and Taxes: The Economic Impact of Sports Teams and Stadiums. Washington, DC: Brookings Institution, 1997b.

Platow, M. J., M. Durante, N. Williams, M. Garrett, J. Walshe, S. Cincotta, G. Lianos, and A. Brautchu. "The Contributions of Sport Fan Social Identity to the Production of Prosocial Behavior." Group Dynamics: Theory, Research, and Practice, 3, 1999, 161-69.

Schwarz, N., F. Starck, D. Kommer, and D. Wagner. "Soccer, Rooms, and the Quality of Life: Mood Effects on Judgments of Satisfaction with Life in General and with Specific Domains." European Journal of Social Psychology, 17, 1987, 69-79.

Tait, M., M. Y. Padgett, and T. T. Baldwin. "Job and Life Satisfaction: A Reevaluation of the Strength of the Relationship and Gender Effects as a Function of the Date of the Study." Journal of Applied Psychology, 78, 1989, 939-48.

Wann, D. L., and N. R. Branscombe. "Die-Hard and Fair-Weather Fans: Effects of Identification on BIRGing and CORFing Tendencies." Journal of Sport and Social Issues, 14, 1990, 103-17.

Wann, D. L., T. J. Dolan, K. K. McGeorge, and J. A. Allison. "Relationship between Spectator Identification and Spectators' Perceptions of Influence, Spectators' Emotions, and Competition Outcome." Journal of Sport and Exercise Psychology, 16, 1994, 347-64.

Wann, D. L., S. Inman, C. L. Ensor, R. D. Gates, and D. S. Caldwell. "Assessing the Psychological Well-Being of Sport Fans Using the Profile of Mood States: The Importance of Team Identification." International Sports Journal, Winter 1999, 81-90.

Wen, Y. "The Business Cycle Effects of Christmas." Journal of Monetary Economics, 49, 2002, 1289-314.


> Title: A Winning Proposition: The Economic Impact of Successful National Football League Franchises

Source: Econ Inq 48 no1 Ja 2010 p. 39-50

ISSN: 0095-2583

DOI: 10.1111/j.1465-7295.2008.00124.x

Publisher: Blackwell Publishing Ltd.

9600 Garsington Road, Oxford OX4 2DQ, United Kingdom

The magazine publisher is the copyright holder of this article and it is reproduced with permission. Further reproduction of this article in violation of the copyright is prohibited. To contact the publisher: http://www.weainternational.org/

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

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Los Angeles Times

NFL owners are in "overwhelming support" for expanding the regular season to 18 games, Commissioner Roger Goodell told reporters at the owners meeting Wednesday in Atlanta.

But instead of voting on the enhanced season now, Goodell said the owners are working on making it part of the ongoing negotiations for a new collective bargaining agreement.

"From our standpoint, we think we've moved this concept along," Goodell said. "There's a tremendous amount of momentum for it."

But, he added, "right now, we have to get more specific as far as what we're talking about, how it can be successful and bring that to the players."

Goodell said a lack of progress in CBA discussions will likely prevent the expanded schedule from taking place in 2011.

"The reality is we're focused more on 2012," Goodell said.

Goodell said the expanded schedule would be good for fans who want "less preseason and more regular season." It would also "offer owners an opportunity for added revenue," he said.

"I look at this as an opportunity to create an agreement that will be good for players, good for the league and take our league to the next level," Goodell added.

-- Chuck Schilken

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Just doing a research paper. One of my choosing, so obviously I went with something football related. I'm writing about whether or not the NFl should extend the season by two games.




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What? I can't write an essay on whether it's a good idea why the NFL should or should not expand the regular season by two games? I think it's an important matter that deserves some outside investigation.

If I'm pursuing journalism I think it's something worth writing about and worth getting credit for.

I need record and books that deal with these issues which is harder then it seems.

I also interviews my boss and took a survey about the matter.

My boss was a strength and condition coach for the Yankees and also was a that same title and DB coach for Notre Dame back in the 90s. He has some credibility in the topic, so I should be able to get away with using him as one of my citations. I need 14.

I really need to interview him again. Also, I'm not sure how to go along with the interview. I guess I need to bring a pencil and paper and write down his every words or something?

He said no, which is what I am currently agreeing with. Saying that the extention would just soften up players even more for their post-season run. Teams in the playoffs should be playing at their highest level, but adding two more games would severely reduce the chances of that and would ultimately lead to more injuries throughout the season. He also mentioned that if they try to reduce the pre-season to compensate for the addition, that wouldn't work well either. It wouldn't work because the pre-season, despite how much I think it's bland and boring is nessessary. You can't remove two of those games and expect QBs or the entire team for that matter to play up to their peak level in a game that matters. It just won't work. Those games are to get people used to that system they are in. Some things stay the same, but it's always a new mold and everything from the year before even if similiar has to be tweaked to make work for that upcoming season. He also said that most coaches don't use the current pre-season games correctly either, stating that letting players sit during pre-season and expecting them to understand the tempo, speed, and longevity of a regular season game all the while lasting an entire game is extremely hard for those individuals who only played one or two quarters leading up to those games. granted there are exceptions to the rule, like some of the more elite players in the NFL, but that goes with just about anything involving routine. He also said this is more for the owners and their pocket books because it means at least one extra game for their stadiums to collect that insane amount of revenue. He also mentioned that was also the case back in 1978 when the NFL deciding to go forward with a 16 game season instead of the 14 game seasons they used to have prior. The owners wanted more games, and added more wild cards which ultimately meant the option (should yer team get the chance) to host even more games in your stadium, which of course meant tons of money for those teams who managed to make it to the playoffs. His thoughts were basically that this proposition is soley for the owners benefits and it devaules the players health and longevity in the long run.

Or something to that effect.

Need a shit ton of more work cited pages before next Wednesday.

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  • 4 weeks later...

Other than the New Orleans Saints getting thumped by the Cleveland Browns, the most disturbing thing I saw Sunday was the numbers on the “crowd’’ at Tampa Bay’s Raymond James Stadium.

We knew in advance the game with St. Louis wasn’t going to be sold out and would be blacked out on local television. But the actual number was shockingly low. The Bucs said they sold 42,020 tickets. According to the Tampa Sports Authority that runs the stadium, the actual number of people who came through the turnstiles was 36,008.

Bash the Bucs for a dismal 2009 season all you want. But they’re doing their part now. They’re winning and they’re playing some exciting games. Their staff also ran an aggressive marketing campaign to sell tickets in the offseason.

But that has added up to hugely disappointing numbers. After selling out every game since the stadium’s opening in 1998, the Bucs haven’t sold out a home game this season.

I know the economy is rough, Florida is full of transplants that still follow the teams in their old hometowns and the St. Louis Rams aren’t exactly a big draw. But, when a team is 4-2 and playing in one of the best stadiums in the league, shouldn’t it be drawing more than 36,000 people?

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