Habit-Formation and the nfl



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CHAPTER III

THEORY

The purpose of this chapter is to discuss the theories that pertain to NFL attendance, especially rational addiction theory. This chapter will begin by discussing NFL football games as a habit-forming good, which will provide the motivation for using rational addiction theory to explain NFL attendance. Then, the theory of rational addiction will be outlined by setting up the rational consumer’s utility maximization problem for a bundle of goods that includes NFL spectatorship as a potentially habit-forming good. Next, a generalized rational addiction demand equation will be derived. Finally, the other theoretical factors that may affect NFL attendance will be discussed. The model that will be constructed in this chapter will be empirically tested in Chapter Four.



Habit-Formation and the NFL

Testing the habit-forming nature of NFL spectatorship will be an important and interesting contribution to the existing literature on the demand for professional football. Most rational addiction work to date has been tested using cigarette consumption data, which has an obviously addictive element: nicotine. Thinking of NFL fans as addicts may not be as obvious as thinking of smokers as addicts, but the theory behind the problem is really quite similar. It involves deriving the demand for a good, be it cigarettes or professional football, and modeling demand with a function that includes both last period’s consumption and next period’s expected consumption. This process will be outlined in the following subsection. However, it is important to discuss why such a model would be appropriate when considering NFL demand. Is football a habit-forming good?

Gerdy (2002) describes sports as the “all-American addiction,” and discusses how people have become so attracted to professional sports.1 However, most of his evidence is anecdotal:

“I know the addiction well. I have spent countless hours at games, watching them on television, or reading about them in newspapers or magazines. I have to willfully resist snapping on the television to check out that night’s big game, or to catch a quick sports news update from ESPN. I am drawn to it like a moth to a flame…We have become addicted to sport; it is our society’s opiate.”2

The rational addiction model can be adapted to professional sports to lend support to such claims of habit-formation. The key to the rational addiction model lies in what is known as the addictive stock, which can be a physical or psychological dependency to a good. The addictive stock of a good can be thought of as the habit-forming properties of the good in question.

For cigarettes, it is the addictive substance nicotine that causes them to be habit-forming. According to a 1988 Surgeon General’s Report, nicotine accumulates in the brain after inhalation and it is this accumulation that produces the pleasurable sensations that cause people to continue smoking.3 When smokers allow the level of nicotine to which their bodies are accustomed to drop, they experience the negative effects of withdrawal.4 As a smoker becomes increasingly experienced with the substance, the benefits from smoking each additional cigarette increase. This is known as reinforcement.5 Nicotine and its pleasure enhancing capabilities and subsequent physical and psychological dependence provide the addictive stock for cigarettes.

For NFL football, a substance like nicotine cannot explain the addictive stock. However, this study will test the notion that there is similar evidence of habit-formation, including pleasure, withdrawal, and reinforcement, among fans of professional football. Merriam-Webster’s dictionary defines fan, which is short for fanatic, as being “marked by excessive enthusiasm and often intense uncritical devotion.”6 Fans are created over time. Kahle, Kambara, and Rose (1996) found that collegiate football fans were motivated by overall attachment to and love for the team, which is created over time.7 It is the history of following the team year after year that creates the attachment that is characteristic of being a fan and this tradition that provides the addictive stock for sports teams, including NFL teams.

Being a fan often becomes an integral part of a person’s life, which further supports the argument for habit-formation for sports fans. In a recent study on the Minnesota Vikings football team, Fenn and Crooker (2003) report the results of a survey conducted during a Vikings home game outside the Metrodome in 1999. On-site interviews generated a sample of 209 respondents. The median number of games that the average respondent of the statewide survey watched was 10, which more than half of the total number of games that the Vikings play.8 Nearly 60% of the respondents reported that they discuss the Vikings’ trials and triumphs on a daily or weekly basis with friends, family, or co-workers.9 Over one-fifth of the respondents describe themselves as die-hard fans that “live and die with the Vikings.”10 These responses suggest that for the many Vikings fans, being a fan is part of their daily life. While Vikings fans may not be completely representative of all NFL fans, this survey study certainly provides some anecdotal evidence to support the idea that football fans may be addicts. Just as a person that is a smoker today is likely to be a smoker tomorrow due to the addictive properties of nicotine, a fan today is likely to be fan tomorrow due to the fan tradition that has become part of the person’s nature.




The Rational Utility Maximization Problem

Rational addiction, as noted before, implies that people make choices according to their consistent utility maximization plan. Becker and Murphy (1988) state that most addictions can be thought of as being rational due to the fact that they are the result of forward-looking utility maximization with unchanging preferences.11

Following the Becker-Murphy model,12 tested by Becker, Grossman, and Murphy (1994),13 and Chaloupka (1991),14 it will be assumed that utility at any time will depend on Ct, the amount of the addictive good consumed in the current time period t, Yt, a composite numeraire non-addictive good consumed in time period t, At, the accumulated addictive stock of the addictive good, and et, all the other unobservable events in time period t that affect utility. Note that for non-addictive goods, denoted by Yt, there is no addictive stock element, At. The utility function for time period t is expressed in equation 3.1.
(3.1)
It is through the addictive stock that past consumption and estimated future consumption are tied into present consumption. The addictive stock in any given period is equal to the last period’s consumption plus the portion of last period’s addictive stock that has not depreciated. It is in this manner that the addictive stock accumulates over time. This accumulation of addictive stock is represented by the addictive stock constraint in equation 3.2, which is consistent with Fenn (1998).15

(3.2)
In equation 3.2, Ct-1 is last period’s consumption of the addictive good, is the decay rate of the addictive stock, and At-1 is the addictive stock from the last period. In this manner, the current period’s consumption has two effects on utility. First of all, it has the immediate impact on current utility due to its inclusion in the utility function for any given period according to equation 3.1. Second, it has an impact on the future period’s utility through the accumulation of the addictive stock that is influenced by past consumption demonstrated by equation 3.2.

In the utility function shown in equation 3.1, it is assumed that the utility of Yt is positive, as is the utility of Ct, while the utility of At is negative. This can be expressed in terms of the partial derivatives of the utility function with respect to Yt, Ct, and At as follows:



This implies that as consumption of the addictive good, C, increases, marginal utility increases and as the consumption of all other goods, Y, increases, marginal utility also increases. However, as the addictive stock, A, increases, the marginal utility actually decreases. For cigarettes, alcohol, and similarly addictive goods, this can be thought of as the model explaining the effect of tolerance (), withdrawal (), and reinforcement ().16 When a consumer builds up tolerance, they achieve a lower level of response, or utility, from consuming a given amount of the addictive good as their consumption during previous time periods increases. It requires an increasing amount of consumption of the addictive good to achieve the same level of satisfaction as the effects of tolerance sets in. The notion of withdrawal can be explained in economic terms as a reduction in overall utility when consumption of the addictive good declines. Reinforcement refers to the idea that as a person’s experience with the addictive good increases, so does the utility he or she achieves from consuming that good. These three phenomena contribute to the build up of the addictive stock for a habit-forming good.

For professional sports in general and the NFL in particular, the addictive stock is built up over time. It is formed through the time investment that a person makes to be a fan of a particular sport or team. This time investment is evidenced by the number of games that a fan attends or watches on television, the amount of time a fan spends reading about his or her favorite team or sport, and also the amount of time that the fan devotes to discussing the trials and triumphs of the various teams. All of these activities are forms of consumption of professional sports. As fans become more invested in the team, they may build up a tolerance, just as cigarette smokers build up a tolerance for nicotine.

NFL teams play the vast majority of their games on Sundays. There is just one Monday night game per week and games on Saturdays only during the last month of the season, which means that the ability for an NFL fan to get his or her dose of football from watching games is limited to those days on which the NFL plays. During the week when a fan cannot watch an NFL game, the aforementioned effects of withdrawal may set in. Fans may turn to reading about or discussing their teams or the sport in general as a means of achieving the utility that comes from consumption of the habit-forming good, which is NFL football in this case. This has been shown to be true in for Vikings in Minnesota, where a statewide survey suggested that almost half of all respondents read about the Vikings every day and nearly 60% of the state based random sample discussed the team at least once a week during the season.17

This fortifies the idea of reinforcement that was previously discussed by increasing the fans’ experience with the good in question. The time that a fan spends reading about or talking about the NFL reinforces his or her habit. It is in these ways that the addictive stock for NFL fans accumulates.

Recall the formula for the addictive stock constraint from equation 3.2. In keeping with Fenn (1998), it will be assumed that for a non-addictive good, the decay rate of the addictive stock is one hundred percent.18 This implies that the in equation 3.2 always assumes a value of one. Therefore, both of the terms in the addictive stock constraint go to zero and no addictive stock is accrued. This allows the use of the same theoretical model in deriving demand curves for both addictive and non-addictive goods. Becker, Grossman, and Murphy (1994) assume that the stock constraint can be simplified to only include the past period’s consumption rather than the sum of the past period’s consumption and the past period’s addictive stock.19 This same assumption will be made for the case of professional football. Due to the lengthy off-season and the way in which teams can change from year to year, it can be assumed that the addictive stock fully depreciates during the course of a season. With this assumption, the addictive stock can now be expressed as simply being the previous period’s consumption. The previous period’s consumption, Ct, can then be substituted into the utility function for the addictive stock, At, which entirely eliminates At from the function. This is represented in equations 3.3 and 3.4.


(3.3)

(3.4)
Becker, Grossman, and Murphy (1994) also assume that consumers live forever, maximizing their total lifetime utility.20 The present study, following Fenn (1998), will assume that consumers live for T years.21 However, this model differs from the cigarette models in that T is not necessarily endogenous for fans of the NFL. Cigarettes have known effects on life expectancy due to the fact that they have been shown to cause cancer, among other maladies.22 However, this is not the case with the NFL, since there is not clear evidence that the watching NFL games is related to life expectancy. Therefore, this model does not address the possibility of T being endogenous.

The lifetime utility function is simply the sum of all of the utility functions at any given time, seen in equation 3.4, discounted at the market rate of interest, r. Therefore, the lifetime utility function is given by equation 3.5, where U represents lifetime utility and represents the discount factor .


(3.5)

As is the case in all utility maximization problems, the consumer is subject to a budget constraint. In accordance with Becker and Murphy (1988),23 Chaloupka (1991),24 and Becker, Grossman, and Murphy (1994),25 it is assumed that capital markets are perfect, implying that the total income available to the consumer at any given time is the present value of lifetime earnings, discounted at the market rate of interest, r. It is further assumed that the composite good Yt is what is called the numeraire good, meaning that the price, Py, is set to one with all other prices being measured relative to Py.26 The lifetime budget constraint can be seen as the sum of the all of the budget constraints at any given time, discounted at the market rate of interest, r. The lifetime budget constraint is depicted in equation 3.6, where W represents the present value of lifetime wealth, Yt is the numeraire good, Pt is the price of the addictive good in time t and Ct is the quantity consumed of the addictive good in time t and is again the consumer’s discount factor .


(3.6)
Individual rational consumers are thought to maximize their lifetime utility, given by equation 3.5, which incorporates the stock constraint, subject to their budget constraint, given by equation 3.6. Per Fenn (1998), solving for the first order conditions of this maximization problem allows a demand equation to be derived.27 This solution for the first order conditions is presented in the following subsection.

Solving for the First Order Conditions


Recall the objective function, which is the lifetime utility function equation 3.5, and the constraint, which is the lifetime budget constraint from equation 3.6. Both are reproduced below for convenience.

(3.5)
(3.6)

The resulting Lagrangian setup for this problem is seen here as equation 3.7:




(3.7)
Now, taking a partial derivative of equation 3.7 with respect to Yt and setting it equal
to zero yields the following:

(3.8)

Upon simplification, the following first order condition is obtained.



(3.9)

The next step involves taking the partial derivative of equation 3.7 with respect to Ct and setting it equal to zero. However, the Lagrangian problem from equation 3.7 must be expanded due to the fact that the variable of interest, Ct, is present in the summation not only at time t, but also at time t + 1. This makes it necessary to have four separate terms in the Lagrangian equation for four distinct scenarios. A term is needed for time 1 to t – 1, plus a term for time t, another term for time t +1, and a final term for time t + 2 to T in order to explain the four possibilities and isolate the scenarios at time t and time t + 1, which will be the times that Ct remains in the equation. The resulting Lagrangian is presented in equation 3.10.



(3.10)

The partial derivative with respect to Ct can now be taken, producing the following


equation, which has been set equal to zero:

(3.11)

Some simplification yields the following, which is the second of the first order


conditions:
(3.12)
According to Becker, Grossman, and Murphy (1994),28 the first order condition with respect to Yt obtained in equations 3.9 implies that the marginal utility from consuming the composite good is equal to the marginal utility of wealth, assuming that wealth is held constant. This is an important assumption to make and it is made possible according to the assumption of perfect foresight, per Becker, Grossman, and Murphy (1994).29 If it is assumed that agents operate under perfect foresight, then the marginal utility of wealth, , can be held constant. Similarly, the first order condition with respect to Ct from equation 3.12 can be interpreted as stating that the marginal utility from current consumption of the addictive good, which also impacts future consumption via the addictive stock, is equal to the product of the current price of the addictive good, and the marginal utility of wealth, which is again held constant. The key to the rational addiction model can be seen through these first order conditions in the fact that the rational utility maximizing addict considers the impact of current consumption not only on the current period’s utility but also the impact that the current consumption has on future utility due to the way in which it determines next period’s addictive stock. However, these first order conditions are useful not only for explaining the demand for addictive goods, but they can also be used to derive demand equations for any good. For non-addictive goods, it simply means that is equal to one, which causes the past values of consumption to drop out of the equation. This makes it possible to model the demand for all goods, addictive or not, with one utility maximization problem instead of using separate utility functions for the two classes of goods.30 Now that the necessary first order conditions have been solved for, they can be used to derive a demand curve for the good in question, which for the present study will be attendance at NFL football games. This derivation is outlined in the following subsection.
Deriving the Demand Equation

In keeping with Becker et al. (1994)31 and Fenn (1998),32 the utility function is assumed to be quadratic in Ct, the current period’s consumption of the addictive good, Yt, the composite good, At, the addictive stock, and et, the other unobservable life-cycle events that impact utility. Just as before, a substitution is made for At, according to the addictive stock constraint from equation 3.3. The result is the following utility function, first outlined in Becker and Murphy (1988):33


(3.13)

To avoid confusion, it is important to note that n equation 3.13, U1 and U2 are simply coefficients that precede Ct and not partial derivatives. Recalling the first order condition from equation 3.9, reproduced here as equation 3.14, the explicit utility function given in equation 3.13 can be used to determine the explicit form for the first order conditions. Taking the partial derivative of the exact utility function in equation 3.13 with respect to Yt and setting it equal to produces the exact form of the first order condition, given in equation 3.15.


(3.14)




(3.15)


Now, equation 3.15 can be solved for Yt.



(3.16)

Similarly, the first order condition in general form from equation 3.12, reproduced here as equation 3.17, can be made exact by substituting the exact utility function from equation 3.13, taking the partial derivative with respect to Ct, and setting it equal to.



(3.17)

(3.18)




Next, equation 3.16 will be used to substitute for Yt and Yt+1 in equation 3.18 in order to get the marginal utility function for the addictive good completely in terms of Ct and exogenous variables.




(3.19)

Now, the function can be solved for Ct. The solution is outlined below.



(3.20)

In the left-hand side of the equation, Ct can be factored out. Let the remaining pieces be equal to Both sides of the equation will then be divided by , leaving Ct alone on the left-hand side. The remaining terms on the right-hand side can now be reduced and terms can be collected to find coefficients for the intercept, Ct-1, Ct+1, Pt, et, and et+1. The intermediate steps have been relegated to Appendix A, showing only the solution here as equation 3.21, which is the demand equation for the addictive good. This is consistent with Fenn (1998).34



(3.21)

The coefficients and obtained from the algebraic simplification outlined in Appendix A are as follows:


(3.22)

(3.23)

(3.24)

(3.25)

(3.26)

(3.27)

(3.28)

The demand equation 3.21 generated through the utility maximization problem in this section illustrates how past and expected future consumption of an addictive good play a role in the present demand for that addictive good, evident by the presence of Ct-1 and Ct+1 in the demand equation. However, notice that when  is equal to one, the  coefficients for Ct-1 and Ct+1 will equal zero, removing them from the demand equation. This produces the demand equation for a non-addictive good, making this utility maximization solution useful for generating a demand curve for any good. Now that it has been shown that past and future consumption can theoretically affect demand for a potentially addictive good, the following section will discuss the other factors that may play a part in the demand for attendance at NFL games.



Theoretical Determinants of NFL Attendance

This section will discuss the various factors that are thought to affect attendance at National Football League games. Following several previous studies on attendance at professional sporting events, it is expected that various economic, demographic, team-specific variables such as winning percentage, and league-specific variables such as competitive balance have an impact on the number of people that attend NFL games. FIGURE 3.1 provides a visual overview of these factors, each of which will be discussed in detail in the following subsections.

FIGURE 3.1

Determinants of NFL Attendance

Ticket Price


Price of Parking, etc

Performance




Competitive Balance


NFL Attendance



Number of Pro Bowl Players

Age of Stadium

Income of Metropolitan Area

Number of Pro Sports Teams in City

Ticket Price


According to basic microeconomic theory, the quantity demanded for any normal good is negatively affected by the price for that good.35 In this study that examines the demand for professional football games in the NFL, ticket price is expected to be an important part of the demand function. Depending upon the elasticity of demand for the good, demand may be more or less sensitive to changes in price, but price is always expected to have a negative impact on NFL attendance.

Price of Complementary Goods

When a person decides to attend an NFL football game, they likely incur more costs than just the price of the game ticket. Other costs associated with attending an NFL game include the price for parking at the stadium and the cost of concessions at the game. While these costs vary from fan to fan and are not usually obligatory, the average fan usually incurs some of these costs, even if it is only the cost of parking, every time they decide to go to an NFL game. These costs should have some impact on the demand for professional football since they can be seen as being part of the price of attending a game. An increase in the price that the consumers must pay for parking or for a hot dog and soda at the game increases their real cost of attending the game. Microeconomic theory again tells us what happens to the demand for a good when the price of complementary goods changes.36 As expected, the price of complementary goods is also negatively related to the demand for a good. As the price of parking and concessions rises at NFL games, the demand for attendance at these games should theoretically decrease. Again, the sensitivity to these changes in price will depend upon the elasticity of demand for attendance.



Metropolitan Area Income

Once again calling on the theories of basic microeconomics, income is expected to play a role in the demand for any good. For normal goods, theory suggests that as income increases, quantity demanded increases as well.37 That would mean that as per capita income in the metropolitan area increases, the demand for attendance at NFL games would also increase, assuming that NFL games are indeed normal goods. However, in several previous studies on the demand for the NFL, it has been seen that income is actually negatively related to demand. For example, Welki and Zlatoper (1994) find that income is significant and negatively related to the demand for NFL game-day attendance in 1991.38 Noll (1974) found similar results regarding the relationship between income and demand in his study of Major League Baseball attendance.39 This suggests that professional football may in fact be an inferior good, meaning that as income increases, people actually consume less of the good. Depending on whether income is positively or negatively related to demand, it can be determined whether NFL games are normal or inferior goods.



Team Performance

The majority of past studies on attendance at professional sporting events have acknowledged that the performance of the team has something to do with the demand for attendance at their games. For example, Welki and Zlatoper (1994) use both the home team and visiting team records in their study on game day attendance in the NFL. They find that the home team record is a significant factor in determining game day attendance, while the record of the visiting team is found to be insignificant.40 The present study will focus on annual attendance for each team in the NFL rather than weekly games, making it impossible to have home and visiting team records included in the study. An argument can also be made for the fact that the previous season’s winning percentage may have an impact on the current season’s attendance. In college football, DeSchriver and Jensen (2002) found that the winning percentages from both the current and previous season positively impacted attendance.41 Also, playoff appearances are an alternative measure of team performance that may impact the demand for attendance. Rivers and DeSchriver (2002) find that attendance is positively related to recent playoff appearances.42 Current winning percentages, historical winning percentages, and post-season appearances are all measures of team performance that have been used to explain attendance.

Following all of the previous research that is reviewed in Chapter Two, it is expected that the performance of the team will have a significant and positive effect on attendance in the NFL. It seems that people prefer to watch winning teams and are more likely to attend a game if their team is performing well. Winning teams create greater interest in most cases. Therefore, it is expected that the higher the team’s winning percentage is in any given season, the greater attendance the team will have in that season. There also may be an impact in the following year. For example, if a team performs well one season, fans may expect the team to perform equally well the next season, causing them to want to watch more games. Also, a winning team often attracts interest from new fans that may not have previously followed the team, causing an increase in demand from a new group of spectators. This would mean that attendance might be higher in the year following a season of exceptional performance, making past performance an important factor in present attendance. In any case, performance is expected to be a deciding factor in the demand for attendance.

Competitive Balance

As important as team performance may be in determining attendance, there could be a limit to the positive effects that performance has on attendance. When team performance is consistently so good or so bad that game outcomes become predictable and boring, the competitive balance of the league is harmed, which may have implications on attendance at league games. Competitive balance in a sports league can be thought of as the distribution of wins in the league. If the distribution of wins in the league is relatively even among all teams, then there is said to be a relatively good competitive balance. There have been studies that suggest that having a poor competitive balance has a negative impact on attendance. In a study by El-Hodiri and Quirk (1971), it is found that predictable outcomes depress attendance.43 According to work by Quirk and Fort (1992), the NFL’s Cleveland Browns actually saw attendance figures fall during the seasons in which they dominated the whole league.44 Evidence suggests that people may be less likely to attend games when the outcome seems to be a foregone conclusion. There is also some recent empirical evidence that competitive balance in the NFL has been improving over time, which could be one of the reasons that attendance has steadily increased since the early days of the NFL. Larsen, Fenn, and Spenner (2003) find, using a variety of methods, that there is a trend of increasing competitive balance in the NFL. The standard deviation of winning percentages and the Herfindahl-Hirschman Index, an index used as a measure of competition in many industries, have both trended downward over the 1970 to 2002 period examined in the study.45 This can be seen as an indication that competitive balance in the NFL is improving, which may be directly related to attendance. An attempt will be made to include a measure of competitive balance in this study since it has been found to be an important factor in determining attendance at professional sporting events. Models will be estimated using both the Herfindahl-Hirschman Index46 and the standard deviation of win percentage. Following previous research in this area, it is hypothesized that increased competitive balance, meaning a lower standard deviation of wins and a lower Herfindahl-Hirschman Index value, would cause an increase in attendance.



Age of Stadium

The age of the facility in which a team plays may be another factor that affects attendance. There seems to be a novelty effect for stadiums that causes an increase in attendance in the years after a new stadium has been built. However, there has been much debate over just how long this novelty effect lasts. Some analysts have suggested that this period of the novelty effect could last for as many as eight to eleven years.47 Empirical evidence suggests that although the novelty effect loses much of its power after the first year or two with attendances often falling in subsequent years, the overall impact is still seen for many years afterward since attendances rarely drop below the figures seen before the stadium was built.48 Depken (2003) also finds that new stadiums for Major League Baseball teams have a positive and significant impact on attendance.49 These findings suggest that part of the increase in attendance that the NFL has enjoyed could be due to the onslaught of new and renovated stadiums built in recent years. Between 1992 and 2003, the Atlanta Falcons, Baltimore Ravens, Carolina Panthers, St. Louis Rams, Tampa Bay Buccaneers, Chicago Bears, Philadelphia Eagles, Tennessee Titans, Washington Redskins, Jacksonville Jaguars, Denver Broncos, Detroit Lions, Seattle Seahawks, New England Patriots, Houston Texans, Pittsburgh Steelers, and Cincinnati Bengals have all opened new stadiums.50 One of the reasons that franchises rationalize the construction of new stadiums is by increase in attendance that can be attributed to the presence of a new playing facility. It is expected that the novelty effect created by opening these new stadiums will increase attendance, but it is unknown exactly how great these effects will be or how long the novelty effects will last.



Number of Professional Sports Teams in the City

It is necessary to discuss the expected impact of complements and substitutes in the market for professional sports. In this case, two distinct arguments can be made about the impact that other professional sports teams in a city will have on NFL attendance based on whether they are viewed as complements or substitutes for each other.

On one hand, the other professional sports teams in the same area may be considered substitutes, albeit imperfect substitutes, for NFL teams. The other major professional sports leagues that share cities with NFL teams are often competing for the same fans’ entertainment dollar. Every dollar that a fan spends on attending an NFL game is one less dollar that they can use to attend an NHL game, and vice versa. According to basic microeconomic theory, competition from substitutes decreases the quantity demanded of a good.51 Therefore, it might be expected that the more professional sports teams that a city has to compete with an NFL team, the less the demand for attendance at NFL games in that city will be. Noll (1974) found that multiple sports teams in a city did indeed depress the demand for attendance at Major League Baseball games.52

However, on the other hand, there is the argument that other sports teams can actually be viewed as complementary goods for the NFL. If a city has many sports teams, it may be a particularly “sporty” city that has a greater than average interest in the various professional sports. In that case, a team of any league in that city would do well because of the interest generated by professional sports in general. This suggests that sports leagues are actually complements rather than substitutes for one another. Microeconomic theory suggests that the if the other leagues are in fact complements for the NFL, the presence of more professional teams in a city can actually bolster the demand for the NFL rather than depress it.53

There is yet another side to the argument that predicts that the number of professional teams may have a positive impact on attendance at any professional sporting event. This is due to the fact that the number of teams in the city can be seen as a proxy for the size of the market in which a team plays. Larger cities tend to house more sports teams and just the sheer size of these cities’ populations makes it possible for them to support all the teams. For example, the New York City metropolitan area houses nine major professional sports teams, but they do so because there are enough people in the area to support all of them. If the number of teams contained in a city is somehow related to its size, which seems plausible, then a greater number of teams could actually predict greater levels of attendance.

Number of Pro Bowl Players

Another factor that is a possible determinant of attendance at NFL games is the number of all-star players that a team has on its roster. In the NFL, there is a game at the end of each season for the best players in the league, known as the Pro Bowl. Pro Bowl players are voted to play in this all-star game by fans and coaches of the league. These players are often the big stars on their respective teams and it is stars like these that may draw fans to a game. In the NBA, superstar Michael Jordan single-handedly revived the Chicago Bulls franchise, causing attendance at Bulls games to go from league low figures to sell-out crowds in just three short years, despite the fact that they were still struggling with poor team performance at that time.54 However, this may be an exception to the rule. Rivers and DeSchriver (2002) found that star players only seemed to increase attendance at Major League Baseball games when their presence on the team was accompanied by an improvement in team performance.55 However, star players often do contribute to the performance of a team and in some cases, illustrated by Michael Jordan’s example, their mere presence on the court of field can draw in crowds. For this reason, it is expected that the number of all-star players on a team will be positively related to the attendance at that team’s games. NFL teams with rosters that boast more Pro Bowl players should enjoy higher attendance figures.



Strikes

The incidence of strikes in the league is also expected to have an impact on attendance. Strikes are expected to negatively affect demand, especially since they often shorten the season, reducing the total potential attendance for every team. Berri (2002) found that strikes do indeed reduce attendance at Major League Baseball games.56



Conclusion

This chapter has provided the theory behind the model that will be tested in the chapters to come. Among the hypotheses to be tested is the notion that there may be evidence of habit-formation among NFL spectators. If habit-formation exists, then past and expected future consumption should affect current consumption positively. Furthermore, traditional determinants of attendance at sporting events will be tested for the NFL, including team performance, star players, stadium age, and competitive balance. Team performance, star players, and metropolitan area income are expected to have a positive impact on attendance, while ticket price, stadium age, competitive balance, and strikes are expected to impact attendance negatively. The empirical model that will be outlined in the following chapter will test the various hypotheses that have been discussed in this chapter regarding the variables that may impact attendance at NFL games.



This concludes the discussion of the theory behind the determinants of NFL attendance. The following chapter will give a detailed description of the data set that will be used for empirical analysis of the theory that has been presented, including the sources from which the set was obtained. It will also give an overview of the econometric method of two-stage least squares regression that will be used to estimate the demand model that has now been outlined.


1 John R. Gerdy, Sports: The All-American Addiction, Jackson, Mississippi: University Press of Mississippi, 2002.


2 Ibid: 21-22.


3 U.S. Department of Health and Human Services, “The Health Consequences of Smoking: Nicotine Addiction: A Report Of the Surgeon General,” Washington, D.C.: Public Health Services, Office of Smoking and Health, 1988.

4


 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998: 43.


5 Ibid: 46.


6 Merriam-Webster’s Online Dictionary, available from http://www.merriam-webster.com/, accessed October 12, 2003.


7 L.R. Kahle, K.M. Kambara, and G.M. Rose, “A functional model of fan attendance motivations for college football,” Sport Marketing Quarterly, Volume 5, number 4, 1996: 51-60.


8 Aju Fenn and Skip Crooker, “The Willingness to Pay for a New Vikings Stadium under Threat of Relocation or Sale,” under review at Contemporary Economic Policy, 2003: 5.

9


 Ibid: 6.


10 Ibid.

11 Gary S. Becker and Kevin M. Murphy, “A Theory of Rational Addiction,” Journal Of Political Economy, Volume 6, Number 4, 1988: 675-700.


12 Ibid.

13


 Gary Becker, Michael Grossman, and Kevin Murphy, “An Empirical Analysis of Cigarette Addiction,” The American Economic Review, Volume 84, Number 3, 1994: 396-418.

14


 Frank Chaloupka, “Rational Addictive Behavior and Cigarette Smoking,” Journal of Political Economy, Volume 99, Number 4, 1991: 722-742.


15 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998: 43.



16 Ibid: 45.


17 Aju Fenn and Skip Crooker, “The Willingness to Pay for a New Vikings Stadium under Threat of Relocation or Sale,” under review at Contemporary Economic Policy, 2003: 6.


18 Ibid: 46.

19 Gary Becker, Michael Grossman, and Kevin Murphy, “An Empirical Analysis of Cigarette Addiction,” The American Economic Review, Volume 84, Number 3, 1994: 396-418.



20 Ibid.


21 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998.

22 National Cancer Institute, “Cigarette Smoking and Cancer,” available from http://cis.nci.nih.gov/fact/3_14.htm, accessed March 8, 2004.


23 Gary S. Becker and Kevin M. Murphy, “A Theory of Rational Addiction,” Journal Of Political Economy, Volume 6, Number 4, 1988: 675-700.


24 Frank Chaloupka, “Rational Addictive Behavior and Cigarette Smoking,” Journal of Political Economy, Volume 99, Number 4, 1991: 722-742.


25 Gary Becker, Michael Grossman, and Kevin Murphy, “An Empirical Analysis of Cigarette Addiction,” The American Economic Review, Volume 84, Number 3, 1994: 396-418.


26 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998.


27 Ibid: 47.

28 Gary Becker, Michael Grossman, and Kevin Murphy, “An Empirical Analysis of Cigarette Addiction,” The American Economic Review, Volume 84, Number 3, 1994: 396-418.

29 Ibid.


30 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998: 48.


31 Gary Becker, Michael Grossman, and Kevin Murphy, “An Empirical Analysis of Cigarette Addiction,” The American Economic Review, Volume 84, Number 3, 1994: 396-418.

32


 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998.



33 Gary S. Becker and Kevin M. Murphy, “A Theory of Rational Addiction,” Journal Of Political Economy, Volume 6, Number 4, 1988: 675-700.



34 Aju Fenn, “The impact of addiction information on cigarette consumption,” Ph.D. Dissertation, Iowa State University, 1998: 49-50.

35 Edgar Browning and Mark Zupan, Micoroeconomics: Theory and Applications, Seventh Edition, New York: John Wiley and Sons, Inc, 2002: 16.

36 John Robert Stinespring, Microeconomic Theory and Applications Using Calculus and Excel, Sixth Edition, Academedia, 2003: 20.


37 Ibid: 20.


38 Andrew Welki and Thomas Zlatoper, “US Professional Football: The Demand for Game-Day Attendance in 1991,” Managerial and Decision Economics, Volume 15, 1994: 489-495.

39


 Roger Noll, “Attendance and Price Setting,” In Government and the Sports Business, Washington, D.C.: The Brookings Institution, 1974: 115-157.


40 Andrew Welki and Thomas Zlatoper, “US Professional Football: The Demand for Game-Day Attendance in 1991,” Managerial and Decision Economics, Volume 15, 1994: 489-495.

41 Timothy DeSchriver and Paul Jensen, “Determinants of Spectator Attendance at NCAA Division II Football Contests,” Journal of Sport Management, Volume 16, 2002: 311-330.


42 Dominic Rivers and Timothy DeSchriver, “Star Players, Payroll Distribution, and Major League Baseball Attendance,” Sport Marketing Quarterly, Volume 11, Number 3, 2002: 164-173.


43 El-Hodiri and Quirk, “The economic theory of a professional sports league,” Journal of Political Economy, Volume 79, Number 6, 1971: 1302-1319.


44 James Quirk and Rodney Fort, Pay Dirt: The Business of Professional Team Sports, Princeton, New Jersey: Princeton University Press, 1992.

45 Andrew Larsen, Aju Fenn, and Erin Spenner, “The Determinants of Competitive Balance in the National Football League,” Working Paper, 2003.

46


 This will be discussed in greater detail in the following chapter.



47 Dennis Howard and John Crompton, “An Empirical Review of the Stadium Novelty Effect,” Sport Marketing Quarterly, Volume 12, Number 2, 2003: 111-116.

48 Ibid: 111.


49 Craig A. Depken II, “The Value of Property Rights: The Case of Major League Baseball Stadiums,” working paper # 03-010, Department of Economics, University of Texas at Arlington, 2003.



50 “New NFL Stadiums,” available from http://www.stadiumsofnfl.com/new, accessed October 12, 2003.


51 John Robert Stinespring, Microeconomic Theory and Applications Using Calculus and Excel, Sixth Edition, Academedia, 2003: 20.


52 Roger Noll, “Attendance and Price Setting,” In Government and the Sports Business, Washington, D.C.: The Brookings Institution, 1974: 115-157.

53 John Robert Stinespring, Microeconomic Theory and Applications Using Calculus and Excel, Sixth Edition, Academedia, 2003: 20.



54 Mark Vancil, “Michael Jordan: Phenomenon,” Hoop Magazine, December 1991.

55

 Dominic Rivers and Timothy DeSchriver, “Star Players, Payroll Distribution, and Major League Baseball Attendance,” Sport Marketing Quarterly, Volume 11, Number 3, 2002: 164-173.


56 David Berri, “The impact of the 1981 and 1994–1995 strikes on Major League Baseball attendance: a time-series analysis,” Applied Economics, Volume 34, Number 4, 2002: 471-479.

24


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