When Is the Honeymoon Over? Major League Baseball Attendance

Transcription

1 278 Zygmont and Leadley Journal of Sport Management, 2005, 19, Human Kinetics, Inc. When Is the Honeymoon Over? Major League Baseball Attendance Zenon X. Zygmont and John C. Leadley Western Oregon University This study tests for the presence of a honeymoon effect in Major League Baseball by using a set of panel data for the period 1970 to It expands on the existing attendance demand literature by incorporating a theoretical model of attendance and price, imposing a more flexible form for the honeymoon effect, and distinguishing multipurpose stadiums from vintage and current baseball-only parks. The honeymoon effect for attendance and ticket price is substantial, and it continues with only a modest decline over the first eight to ten years. We conclude that a new baseball-only park that replaces an older multipurpose stadium will generate an additional $228 million in ticket revenue over 15 years. Although this is less than the cost of constructing a new facility, additional revenue sources might be sufficient to eliminate the need for public subsidies. A professional sports franchise often experiences a surge in attendance after a new stadium or arena opens. With time the novelty of the facility wears off, and a decline in attendance begins. This relationship between attendance and the age of the sports facility is known as the honeymoon effect. Hamilton and Kahn (1997, p. 253) suggested that for Major League Baseball stadiums, the honeymoon begins to fade after three years and... the steady state is achieved in approximately eight years. (Other discussions of the honeymoon effect include: Austrian & Rosentraub, 1997, p. 380; Baade & Sanderson, 1997a, pp. 96, 104; Baade & Sanderson, 1997b, p. 461; Noll & Zimbalist, 1997, pp ) The magnitude and duration of the honeymoon effect has important public policy implications because professional sports facilities are often subsidized. A recent article in this journal (Rosentraub & Swindell, 2002) indicated that since 1991, 40 professional sports facilities in the United States received approximately $7.1 billion in construction subsidies. More than half of those facilities were built since Team owners and other interest groups continue to claim that public contributions for the construction of new stadiums and arenas are justified because The authors are with is with the Division of Business and Economics, Western Oregon University, Monmouth, Oregon. 278

2 Major League Baseball Attendance 279 of the positive impact an increase in attendance has on the local economy (cf. Noll & Zimbalist, 1997; Siegfried & Zimbalist, 2000). Sports researchers recognize that the economic impact reports used to justify subsidies are often based on questionable assumptions. Consider a recent economic impact study for a new stadium for the Boston Red Sox (C. H. Johnson Consulting, 1999). That report projected average attendance per game at 90% of stadium capacity. The projections included in the Red Sox report were criticized in a study by Baade (2001) and seemed to fly in the face of mounting evidence that new baseball stadiums are not an economic panacea for cities (e.g., Robinson, 2002). Walker (1999, pg. 8) mentioned, The trend of strong attendance at new ballparks in urban settings is continuing in Through May 31, Coors Field in Denver has the highest pergame attendance (42,305) in Major League Baseball. Cleveland s Jacobs Field has the second highest per-game attendance (42,237)... [and] Camden Yards has the third highest per-game attendance (40,909). By 2002, however, per-game attendance at Coors Field, Jacobs Field, and Camden Yards had fallen to 33,800, 32,307, and 33,116, respectively. Recent attendance trends for the Detroit Tigers and the Atlanta Braves are also instructive. The Braves moved to Turner Field, a privately funded baseball-only facility, in Despite winning the N.L. East Division each subsequent season, attendance has steadily declined (from an average of 42,765 in 1997 to 34,858 in 2002). The Tigers, a team of dubious quality, have experienced an even more rapid decline. When their subsidized stadium opened in 2000, the team attracted an average attendance of 30,106 per game. In 2002 attendance averaged only 18,563. Public policy considerations aside, the honeymoon effect also applies to baseball stadiums built solely with private funds. For example, after they were unable to secure public subsides for the construction of Pacific Bell Park, the owners of the San Francisco Giants turned to private financing. If the honeymoon effect is ignored or underestimated, the Giants revenues will fall short of projections and the long-run financial success of Pacific Bell Park will be threatened. Thus, the presence and duration of a honeymoon effect is an important factor in ownership and management decisions concerning construction of all new Major League Baseball stadiums, regardless of the source of financing. This study tests for the presence of a honeymoon effect in Major League Baseball (MLB) from 1970 to We chose this period because it includes substantial growth in spectator attendance, a proliferation of stadium construction in North America, and an evolution in stadium design from multipurpose cookie cutter facilities to baseball-only retrospective-style stadiums. We also made a methodological contribution to the literature on attendance demand by using a model of team profit maximization in order to determine the relationship between attendance and ticket price simultaneously, rather than treating price as an exogenous variable in a demand equation. We estimated reduced-form equations for attendance and ticket price using a set of independent variables that affect ticket

3 280 Zygmont and Leadley demand, including stadium age and type, city-level economic conditions, team performance, and fixed-effects dummies for location and year. Our results indicate that a new stadium is an important factor in determining attendance and ticket price, increasing both by more than 25% in the first year. Unlike earlier studies that conclude that the honeymoon effect declines gradually, reaching zero by Year 8 to 10, we estimate that the decline is much slower over the first eight years, with a large drop off in Years 9 and 10. We estimate that the present value of the increase in ticket revenue over the first 15 years is $116 million. If an older multipurpose stadium is replaced by a baseball-only park, the increase in revenue is $228 million. This weakens the argument that public subsidies are required for teams to finance new stadiums. (We acknowledge that the monopoly nature of MLB franchises allows owners to hold host cities hostage and extract subsidies regardless of need.) Literature Review Demmert (1973) and Noll (1974) are the pioneers in estimating models of MLB attendance demand. Demmert studied attendance over the period , and Noll examined the 1970 and 1971 seasons. Demmert and Noll incorporated several explanatory variables in their models, which are now commonly used in the literature; these included ticket price, income, market size (population), age of the stadium, team performance, and the presence of substitutes (the number of other professional sports franchises in each team s geographic area). A summary of the empirical results for these and subsequent papers is presented in Tables 1 and 2. Because stadium age is the focus of our study, we will not discuss attendance demand studies that do not include a new stadium variable, but they are noted in the tables. Demmert and Noll reached opposite conclusions regarding stadium age. Noll suggested that attendance declines with the age of the stadium (the honeymoon effect), but Demmert found no such relationship. Baade and Tiehen (1990) extended Noll s model and applied it to a longer time series ( ). They found that stadium age was not significant and called into question whether newness of a park... impart[s] any discernable advantage in attracting fans (Baade & Tiehen, p. 26). Coffin (1996) examined MLB attendance from and added new explanatory variables, including a new team dummy. Coffin estimated that for stadiums built after 1976, the honeymoon effect added about 1 million additional fans in the first year of operation, and the new team effect added between 780,000 and 2,548,000 fans during the first four years. These results suggested two honeymoons are present; one is between the fans and the team, and the other is between the fans and the stadium. Kahane and Shmanske s (1997) investigation of team roster turnover and attendance during the 1990, 1991, and 1992 MLB seasons found a new stadium to be a significant stimulus to attendance, but they did not attempt to estimate the rate at which attendance declined after the facility was opened. Rivers and DeSchriver

4 Major League Baseball Attendance 281 Table 1 Literaure Summary: Attendance per Season Baade & Kahane & Rivers & Demmert Noll Whitney Tiehen Coffin Shmanske DeSchriver (1973) (1997) (1988) (1990) (1996) (1997) (2002) Years , Observations 282 a 46 b Independent variables: ticket price * + c * income * + * + population * + * stadium size * * new stadium * + + * d new team other teams in city * MLB time trend * + + star players * + * close pennant race * * * team performance games behind * winning percentage + + games ahead + prior-year performance games behind * winning percent + + pennant winner * + + * Note. The following attendance studies did not include a new stadium variable: Greenstein & Marcum (1981); Pan et al. (1999); Porter (1992); Scully (1989). a The dependent variable is season attendance divided by city population. b All independent variables are multiplied by city population. c Ticket price is measured by average realized price rather than average constructed price. d Separate effects for first year and Years 2 through 5. *Variable included, but the estimated coefficient was not statistically significant.

5 282 Zygmont and Leadley Table 2 Literature Summary: Attendance per Game Bruggink & McDonald & Rascher Hill, Madura & Eaton (1996) Rascher (2000) (1999) Zuber (1982) Year Observations 1068 (AL) 1040 (NL) 1,500 2,267 2,103 Independent variables: new stadium + + a + a + b classic stadium c * + stadium size * + ticket price + fan cost index + * income in city unemployment in city + population in city Black% in city + * Latino% in city 2nd MLB team in city + other recreation in city + * Note. The following attendance studies did not include a new stadium variable: Butler (2002); Hansen & Gauthier (1989); Knowles, Sherony, & Haupert (1992); and Marcum & Greenstein (1983). a Dummy = 1 if the stadium was built within the previous 10 years. b Dummy = 1 if the stadium was recently built or renovated. c Dummy = 1 for Fenway Park and Wrigley Field. *Variable included but the estimated coefficient was not statistically significant.

6 Major League Baseball Attendance 283 (2002) found that a significant honeymoon effect occurred in the first year, but it declined rapidly in Years 2 through 5. They did not test for the presence of an effect beyond the fifth year. Finally, Burger and Walters (2003) used data from 1995 to 1999; their findings indicated that the honeymoon effect increased team revenue by roughly $41 million in the first season of operation with revenue declining about $2.3 million in each subsequent season. Several studies attempted to explain attendance on a game-by-game basis rather than by seasons. Bruggink and Eaton (1996) studied the 1993 season and determined that, for American League ballparks, attendance declines % for each 10 years of a stadium s age (p. 22). For the National League, age and attendance are positively related, an outcome the authors attribute to an absence of recently constructed National League stadiums. Rascher (1999) found a pronounced honeymoon effect; a stadium built within the past decade added an estimated 16,000 fans per game, and classic stadiums (Wrigley Field and Fenway Park) contributed 10,544 more spectators. Rascher did not distinguish between modern baseballonly parks and multipurpose stadiums. Also, by assuming that the effect would be the same for all newer stadiums, he was unable to estimate the length of the honeymoon effect. Using game-by-game information for the 1996 MLB season, McDonald and Rascher (2000) found a stadium built within the last 10 years added 8,121 fans per game. The Model Previous studies regressed attendance on a set of variables, such as ticket price, stadium capacity, team performance, and city characteristics, that are likely to influence the quantity of tickets demanded. Although this approach results in a high R 2, it might not actually estimate the demand function. The role of stadium capacity in determining demand is questionable, and price and attendance are at least partially determined simultaneously. Ticket prices are set before the beginning of the season, so that the error in the attendance equation will be independent of the price variable, but those prices are based on the same expected market conditions that also affect the level of attendance. Our model is a variation of one used by Jones and Ferguson (1988) to estimate attendance in the National Hockey League. This model assumes that attendance demand is a linear function of ticket price, or Q = a bp. The resulting formula for price elasticity of demand is e = (a Q)/Q. Marginal cost is assumed to be zero, so that expected ticket revenue and therefore profit are maximized at the point where elasticity of demand is equal to one. We consider possible deviations from unit elastic pricing after presenting the entire model. Setting elasticity equal to one yields Q* = a/2 and P* = a/2b as the optimal quantity and price. Taking the natural logs of both sides of these equations separates the demand intercept and slope terms, resulting in: (1) ln Q* = ln 1/2 + ln a, (2) ln P* = ln 1/2 + ln a ln b.

7 284 Zygmont and Leadley The model also assumes that there are nonlinear relationships between the intercept (a) and slope (b) of the demand curve and a vector of team and city characteristics (x). These equations, of the form a = Ax α and b = Bx β, are linearized by taking the natural logs of both sides of the equations, so that (3) ln a = α 0 + α 1 ln x, (4) ln b = β 0 + β 1 ln x. Combining these assumptions gives: (5) ln P* = ln 1/2 + α 0 + α 1 ln x β 0 β 1 ln x = ln 1/2 + (α 0 β 0 ) + (α 1 β 1 ) ln x, 6) ln Q* = ln 1/2 + α 0 + α 1 ln x. Because ticket prices are announced before the start of the season, teams must rely on a forecast of demand rather than actual demand. Although some demand-side variables for the upcoming season are known (z), such as age of the stadium, others (w), such as team winning percentage, are unknown. We will assume that for the latter teams use the naïve forecast that next season will be like the one just completed, so that the price equation should be estimated using the values from the previous season for these variables (w 1 represents the value of w 1 year prior). Of course, teams often make changes in personnel during the off-season that are expected to improve team performance, and these changes will be known when ticket prices are set, but it is not clear how this could be measured. The resulting ticket price equation is: (7) ln P* = ln 1/2 + (α 0 β 0 ) + (α 1 β 1 ) ln z + (α 2 β 2 ) ln w 1. The results of uncertain demand and the team s inability to adjust price during the season are more complicated for the attendance equation. Demand might be higher or lower than expected based on the actual values of the w variables, but ticket prices are fixed in advance and cannot be adjusted to move attendance to the new revenue maximizing point (a/2). If the intercept of the demand curve increases from a to a, attendance does not increase to a /2, but rather to a/2 + (a a). We assume that the intercept of the demand curve changes by a multiplicative factor based on the ratio of the actual value of w to its expected value, or w/w 1, so: (8) ln a = α 0 + α 1 ln z + α 2 ln w 1 + γ 1 ln (w/w 1 ). The resulting equation for actual attendance is: (9) ln Q = ln 1/2 + α 0 + α 1 ln z + α 2 ln w 1 + γ 1 ln (w/w 1 ). Teams might not set ticket price at P* if the corresponding level of attendance exceeds stadium capacity. Whereas we recognize that this might be true for some individual games, it is unlikely to occur for an entire season. Until very recently, teams charged the same prices for all games during the season, whether it was a weekend game against the Yankees or a weekday game with the division

8 Major League Baseball Attendance 285 doormat. To maximize revenue for the season, those prices would be set based on demand for an average game, for which capacity is much less likely to be a constraint. The average of season attendance per game divided by stadium capacity for our sample is less than 45%, with only 4% of the teams having capacity utilization for a season exceeding 90%. As noted above, this model is based on the assumption of unit elastic pricing. There is considerable empirical evidence that teams maximize profits by pricing in the inelastic region of the demand curve for tickets (Fort, 2004), although the estimated values range from close to 1 to as low as A common explanation for this is that lower ticket prices increase the revenue for concessions and parking from the additional fans by more than the decrease in ticket revenue. For our model, the effect of a deviation from unit elastic pricing is relatively benign. If the profits are maximized where the elasticity of demand for tickets equals e, then the optimal attendance and ticket price are Q* = a/1 + e and P* = ea/(1 + e)b. This will change only the constant terms in equations (5) and (6). This is because a shift in demand, represented by a change in the intercept a, will still change actual attendance by a fraction of that shift, whether it is 1/2 if elasticity equals 1 or 2/3 if elasticity is 0.5. Although the coefficients for the independent variables in equation (6) will measure the effect on actual attendance irrespective of the value of e, the implied shift in demand will vary with e. If teams price where elasticity is equal to one, then the shift in demand is twice as large as the change in Q*. If elasticity is set equal to 0.5, then the shift in demand is 1.5 times larger than the change in Q*. Lacking any consistent alternative value in the literature, we will continue assuming that price elasticity is equal to one but also recognize that shifts in demand resulting from changes in Q* will probably be smaller than those implied by our estimated coefficients. Our sample includes all MLB teams that played from 1970 to 2000: a total of 787 observations. Table 3 summarizes the variables and the data sources, and descriptive statistics are reported in Table 4. We estimate a fixed-effects model for the reduced-form equations (7) and (9) using Zellner s method of seemingly unrelated regressions, which corrects for both contemporaneous correlation and heteroskedasticity in the error terms. The dependent variables are average attendance per home game and average ticket price. We use attendance per game rather than total season attendance because our sample included 2 years with significantly fewer games because of labor disputes. Year dummies are included as independent variables to capture any change in attendance per game played caused by adverse fan reactions for those and subsequent seasons. Defining a ticket price variable is more complex because it can be measured either by the price of a standardized ticket, such as a box seat behind home plate, or a weighted average of all tickets. We chose to use an average, with the weights based on number of seats available at each price level (constructed average price) rather than the number of tickets actually sold (realized average price). Although the latter is much simpler to calculate (total revenue divided by total tickets sold),

9 286 Zygmont and Leadley Table 3 Summary of Variables Variable Definition Sources Attendance Attendance per game Thorn, Palmer, & Gershman (2001) Ticket price Real ticket price Nominal ticket prices for and provided by Doug Pappas, SABR, Business of Baseball Committee ( baseball/data.htm). Nominal ticket prices for collected by the authors from various team media guides. New stadium n Dummy for a new or renovated Munsey & Suppes ( stadium in its nth year of operation for n = 1 to 15 New team n Dummy for a new team (expansion Munsey & Suppes ( or relocation) in its nth year of operation for n = 1 to 15 Multipurpose Dummy for a multipurpose stadium Munsey & Suppes ( Unemployment City unemployment rate U.S. Bureau of Labor Statistics, Geographic Profile of Employment and Unemployment (various years). U.S. Census Bureau, County and City Data Book, State and Metropolitan Area Data Book, Statistical Abstract of the United States (various years) Winning percentage Team winning percentage at the end Baseballstats.net (www16.brinkster.com/bbstats/statistics/standings.html) of the season Games behind Games behind at the end of the season Baseballstats.net (www16.brinkster.com/bbstats/statistics/standings.html) Note. Nominal ticket prices are adjusted using the CPI-U (consumer price index, all urban consumers, annual average, ), rather than a CPI by SMSA. Source: U.S. Bureau of Labor Statistics (ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt). The method used to calculate average ticket price changed in Before that time club seats were not included, resulting in a slightly lower value. For much of our sample period, the effect is essentially zero because this type of club seat did not exist.

10 Major League Baseball Attendance 287 Table 4 Descriptive Statistics Mean SD Minimum Maximum Attendance 22,798 9,473 3,787 57,570 Real ticket price ($) Unemployment Winning percent Games behind Note. Number of new stadiums = 30, number of new teams = 8. sales revenue data are not reported by most teams. Further, Coffin (1996) argued that using the realized price could create a spurious correlation between attendance and average price. He also noted that [constructed prices] reflect the announced ticket pricing policy of the teams, and are, therefore, the price structures to which ticket buyers respond (p. 36). We used Doug Pappas web site ( data.htm) for the nominal ticket-price data from and ; the 1991 to 2000 average ticket prices are from the Team Marketing Report ( We calculated the constructed average for the period 1985 to 1990 using individual team media-guide data on the number of seats available at each price. If a range of prices was given for a specific type of seating, the midpoint of the range was used. The data were available for all 5 years for 16 teams and for all but 1 year for 6 of the remaining teams. The standard analysis of panel data can account for differences in the intercept or slope coefficients across individuals using fixed or random effects. A fixedeffects approach is appropriate because our sample includes all MLB teams. With a large number of independent variables and no a priori reason that a given coefficient will vary across teams, we chose to model the team differences in terms of the intercept only. It is also common with panel data to allow the intercept to vary over time, with the change in the intercept the same for all cross-section observations. This will account for changes that affect all teams equally. Indeed, it is readily apparent that MLB gained popularity beginning in the 1970s and suffered some setbacks in the 1980s and 1990s because of labor disputes. The alternative to year dummies is to impose a functional form to the secular change. Coffin (1996) and Demmert (1973) included linear trends (which Coffin assumed to be zero for the first 14 years of his sample, ). The advantage of this approach is that the number of estimated coefficients is reduced (for our sample, from 31 to 1). We believe, however, that the loss in degrees of freedom is offset by the lack of restrictions on the nature of the changes over this extended time period.

11 288 Zygmont and Leadley Prior attendance studies have included a selection of city-level variables assumed to influence demand, such as unemployment, real per-capita income, and population. We excluded the latter two variables after noting a high degree of collinearity with the team dummies. The host cities that had a high per-capita income at the beginning of the sample period also had a high income at the end. The same was generally true for population. Income was not statistically significant when it was included. The unemployment rate in each city exhibited much more variation over time than did income and thus avoided the collinearity problem. Unemployment is expected to have a negative effect on attendance. If unemployed fans use their free time to attend games, however, a positive effect is possible. A new stadium can affect attendance simply by its novelty or by improving the fan experience. As the honeymoon ends, attendance will decline. Demmert (1973) included a dummy variable only for a stadium s inaugural year, whereas Kahane and Shmanske used a dummy for stadiums built within the previous 5 years. Noll (1974), Baade, and Tiehen (1990) and Coffin (1996) assumed a linear decline in attendance after the first year. Coffin determined that the best fit occurred with a 4-year decline, whereas the other authors used a linear decrease over 10 years. A simple nonlinear relationship can be estimated by using stadium age and age squared. The actual relationship, however, is not a parabola for all stadiums, but only up to the age where the honeymoon ends. Beyond that point, the effect of stadium age on attendance is constant at zero. The problem is how to determine the correct cutoff point. Rather than simply assuming which year will mark the end of the honeymoon, a series of regressions can be used to find the value that yields the best fit to the data. The drawback to this approach is that it is fitting the model to the data rather than testing a model. Instead of assigning a structure to the decline, we included separate dummy variables for each of the first 15 years after a new stadium opens. If the honeymoon does not extend beyond the 10 years assumed by others, we would expect to find coefficients of zero for those latter years. This method does use more degrees of freedom than those with specific functional forms. Using the last year with a positive and significant coefficient as the cutoff, we also estimated the quadratic functional form; the results were generally similar to our dummy coefficients, with the exception that the vertex is between the third and fourth years, implying increasing attendance after the first year. A total of 20 new stadiums were built during the sample period. Stadiums that opened within the 15 years prior to 1970 are also included in our analysis for their remaining honeymoon years. Nine such facilities opened between 1965 and 1969, and one was completed in A total of 18 stadiums were 15 years old at some point during our sample period. During the 1960s and 1970s most new stadiums were designed for more than one sport, that is, they were not optimized for baseball. This pattern reversed in the 1990s, beginning with the construction of Camden Yards in Baltimore. Accordingly, we define a dummy variable for multipurpose stadiums, that is, those stadiums used for other professional sports. If attendance demand increases for

12 Major League Baseball Attendance 289 baseball-only parks, the coefficient on the multipurpose dummy will be negative. Of the 20 new stadiums built since 1970, 13 were baseball-only parks. Six of those replaced existing multipurpose stadiums, four replaced older baseball-only parks, and three were built in expansion cities. The last multipurpose stadium built was the Sky Dome in Toronto, which opened in In contrast, of the nine stadiums opened in the 15 years prior to 1970, seven were multipurpose facilities. Fans might be more likely to attend games for a team that has recently located in their city. For the period 1970 to 2000, there were five expansion teams (Arizona Diamondbacks, Colorado Rockies, Florida Marlins, Seattle Mariners, and Tampa Bay Devil Rays) and two relocated teams (Texas Rangers, formerly the Washington Senators, and Milwaukee Brewers, formerly the Seattle Pilots). In order to capture this effect, we defined dummy variables for the first 15 years of operation in a city. If the decline is nonlinear, this approach would be preferred to the ad hoc assumption used by Demmert (1973) and Coffin (1996) that the decline is linear over time. If the decline is linear, then the use of separate dummies would reduce efficiency. All of the dummies used as independent variables in equations (7) and (9) are zero or one, which corresponds to one and e, respectively, in the original nonlinear demand equations. If the estimated coefficient for a particular dummy is α, then the percentage change in the dependent variable is e α 1, not α. Although the difference is negligible for relatively small coefficients, if α = 0.25 the effect would be approximately 28.4%. A team s performance on the field can be measured absolutely or relative to other teams in its division. Our measure of absolute performance is the team s winning percentage for that season, whereas relative performance is measured by the number of games behind first place the team finishes in its division at the end of the season. Based on the specification of our model, both current and prior season performance are included, with the latter measuring expected performance for the current season. For expansion teams, prior season games behind and winning percentage are not available. To avoid losing these observations, we calculated the average games behind and winning percentage for all such teams in their first year and entered these values for prior performance. For an expansion team, it is reasonable for fans to expect their new team to be similar to other expansion teams in their first year. Results The adjusted R 2 for the price and attendance reduced-form equations are and 0.714, respectively. The estimated coefficients and robust (Newey West) t statistics are given in Table 5. Caution must be exercised when interpreting the estimated coefficients for winning percentage, games behind, and local unemployment. The values of these variables from the previous season (w 1 ) are included in both equations because we assume that teams use them to forecast demand for the upcoming season. Because ticket prices are set prior to the start of the season, they will not be affected

13 290 Zygmont and Leadley Table 5 Regression Results estimated Coefficients (Newey-West t Statistics) Attendance Price Previous season unemployment ( 2.72) (1.51) Previous seasons winning % (7.91) (4.53) Previous season games behind ( 3.46) (1.30) Current previous unemployment * ( 1.65) Current previous winning % (9.25) Current previous games behind * ( 1.77) Multipurpose * ( 1.81) ** ( 2.19) New stadium year (5.24) 2.72 (6.29) year (3.09) (5.37) year (3.52) (5.64) year (3.80) (6.43) year (3.63) (6.34) year (2.64) (6.22) year (3.10) (4.65) year (2.65) (4.65) year * (1.92) (3.16) year (0.65) (2.85) year (0.20) ** (2.41) year ( 0.21) (3.16) year ( 0.76) ** (2.31) year (0.03) (1.12) year (0.10) ** (2.29) New team year (4.41) ( 0.11) year (0.38) (0.62) year * ( 1.84) ( 0.86) year ( 2.69) ( 0.96) year ( 3.07) ** ( 2.21) year ( 1.29) ( 1.37) year ** ( 2.45) * ( 1.69) year * ( 1.79) ** ( 2.17) year ( 1.39) ( 0.85) year ( 1.50) ( 0.24) year ( 1.13) (0.49) year * ( 1.69) (0.57) year * ( 1.83) ( 0.01) year ( 0.23) (0.76) year ( 0.91) ( 0.19) R Durbin Watson * p <.10 (two tailed). ** p <.05 (two tailed). p <.01 (two tailed).

14 Major League Baseball Attendance 291 by the actual conditions that occur during that season. Attendance will depend on whether those expectations were fulfilled. Thus, the variables for the current season (w) appear in the attendance equation but not the price equation. The interpretation of the coefficients for the previous season variables in the price equation is relatively straightforward. The winning percentage in the previous season will affect the expected intercept and slope for the demand curve. The coefficient in the reduced-form price equation equals the difference between the effects on the intercept and the slope (α β). Note that it is possible for an increase in winning percentage to have no effect on ticket price if the expected increase in demand is offset by a decrease in elasticity of demand (flatter demand curve). To interpret the coefficients for the attendance equation, one must distinguish between an expected change in demand and the actual change. If team performance was good during the season just completed (that is, w 1 is high), we assume that management will expect the same for the following year and forecast high demand for tickets. If that expectation is fulfilled, so that w/w 1 equals one, then attendance will change with a coefficient of α from Equation (9). If performance in the following season does not improve as expected, then the effect on attendance of the change in w 1 is reduced to α γ. Given the specification of the model, there is a different impact on attendance for an expected change in performance that does not occur and a change in performance that is not expected. The effect of an unexpected change in current season performance (w), that is, holding prior season performance (w 1 ) constant, is measured by γ. In summary, α can be interpreted as the coefficient for changes that were expected, γ is for changes that were not expected, and α γ is for changes that were expected to occur but did not. For the attendance equation, winning percentage from the previous season has a positive and significant effect. This suggests that an increase in winning percentage in one season that occurs again the next season, as expected by management and fans, will increase attendance by a significant amount. The coefficient on the ratio of current to previous winning percentage, which measures the effect of an unexpected increase in winning percentage, is also positive and significant. Note that the estimated coefficient of 0.97 for an unexpected change in winning percentage is smaller than the 1.23 for an expected change. The difference between these coefficients, or 0.26, which measures the effect of a change in expected winning percentage that does not actually occur, is positive and also statistically significant. Apparently the increase in season ticket and early season sales in anticipation of another good season is large enough to measurably increase attendance for a season in which the team performs poorly. In the price equation, the effect of previous winning percentage is positive, as expected, and significant. The coefficient on the number of games behind from the previous season on attendance is negative and significant. An increase in games behind in a season, if it also happened the previous season and thus was expected to occur, will reduce attendance. If that drop in performance was not expected, the decline is still negative and significant at the 10% level, but not at 5%. The effect is also smaller in magnitude than for an expected change, which is not surprising given that the

15 292 Zygmont and Leadley number of games behind is largely a factor later in the season. An expected increase in games behind that does not actually occur also has a negative and significant effect on attendance. If fans purchase fewer season tickets because they expect a poor performance, even if the team finishes better than expected, the effect on season attendance will be negative. The effect of games behind for the previous season in the price equation is positive, but it is not significant. The coefficient for the unemployment rate in the previous year is negative and statistically significant in the attendance equation. This suggests that a high unemployment rate, which management correctly forecasted based on prior-year unemployment, will have a negative effect on demand for tickets. The estimated effects of an unexpected change in current unemployment and an expected change that does not occur are equal in magnitude and both significant. Previous-year unemployment is not significant in the price equation. A multipurpose stadium has a negative and statistically significant effect in each equation, resulting in lower attendance ( 15%) and ticket prices ( 12%). This empirical result is not unexpected given that teams began building baseballonly stadiums exclusively in recent years. The small difference in the coefficients implies that the demand curve becomes slightly steeper, partially offsetting the effect of the decrease in demand on price. The coefficients for the new stadium dummies are positive and significant in the attendance equation for each of the first 9 years, then drop abruptly in magnitude and are no longer statistically significant. The coefficients in the price equation decline more gradually, and are statistically significant in all but the 11th and 14th years. The estimated coefficients for both equations are shown in Figure 1. Figure 1 New Stadium Effects

16 Major League Baseball Attendance 293 Although the largest new stadium effect on attendance occurs in the first year, suggesting an increase of nearly 30%, it is only slightly lower for the next 7 years. The coefficient for Year 9 is smaller, and for the 10th year it is close to zero and statistically insignificant. For the coefficients in the price equation, the decline is more gradual, falling from 30% in the first year to 24% for the next 6 years, and then declining to an average of 7% for the last 6 years. The slower decline in the coefficient in the price equation suggests that the eventual decrease in demand is offset by a more permanent increase in the slope of the demand curve (less elastic demand). This might also occur because teams are reluctant to adjust prices each season when the expected decrease in demand is relatively small. They can simply allow real prices to slowly decline as inflation occurs. The estimated coefficients for the dummy variables for a new team are shown in Figure 2. For the attendance equation, there is a large positive effect in the first year followed immediately by a sharp decline that becomes negative and significant for most of the next 14 years. Starting in the fourth year, there is a slow upward trend towards zero, climbing from 0.24 to just This suggests that fan interest for a new team is very short lived, and that it quickly becomes apparent why a MLB team was not already located in that city. By the end of the 15 years, however, the city might finally offer the market conditions to support a team in a manner similar to cities with older franchises. Figure 2 New Team Effects

17 294 Zygmont and Leadley The new-team coefficients in the price equation have a similar pattern of decline and then slow recovery but without the large positive effect in the first year. Demand might be more elastic in the first year, or teams are more concerned with getting fans to games than charging higher prices that they suspect they will have to lower in the very near future. The estimated coefficients for the year effects for the two equations are shown in Figure 3. The effects are compared with the year 2000,which is assumed to have a value of zero. Although not restricted to any particular functional relationship, there is a very clear upward trend in the attendance equation. The effects of the 1981 and 1994 labor disputes can also be seen. The 1981 strike, which occurred during the season, led to a drop in attendance per game of approximately 8% compared with its value if the upward trend had continued. Attendance rebounded the following year, with no apparent lingering fan resentment. This cannot be said for Figure 3 Major League Baseball Attendance Effects

18 Major League Baseball Attendance 295 the 1994 strike, which caused the cancellation of the World Series and delayed the opening of the 1995 season. Attendance per game was down slightly for games played before the strike in For 1995, the negative effect was much larger, with a drop in attendance per game of more than 30% compared with 1993, the last complete year before the strike. As of the 2000 season, MLB had recovered only to 1986 levels, far from the peak of the early 1990s. Before examining the implications of the estimated coefficients, several possible econometric issues should be addressed. First, are there any outliers or observations that have undue influence? For the attendance equation there were just two observations that were more than 3 standard deviations from the fitted line. Oakland had significantly lower attendance than predicted in 1979, whereas Montreal had higher than predicted attendance in For the price equation, there were five outliers, all from the late 1990 s and Boston, Seattle, and the New York Mets had higher prices than predicted, whereas Minnesota and Montreal had lower prices. The only observations with centered leverage values that exceed 0.35 (based on 3 number of variables/number of observations) are for Arizona and Tampa Bay, suggesting that omission of any one observation would have a significant effect on an estimated coefficient. These expansion teams had only three observations each, making the coefficient for the team dummy very sensitive to the omission of any one observation. This is not a serious problem given that we do not attempt to interpret the team dummies and that excluding these teams led to no discernable changes in any of the other estimated coefficients. To test for possible multicollinearity, we examined the variance inflation factor (VIF) for each independent variable. Only one of the values exceeded 4, with the multipurpose stadium dummy having a VIF of 6.47, and even this value is less than the commonly used critical value of 10. A potentially serious problem is the existence of autocorrelated error terms. The Durbin Watson statistic is for the attendance equation and for the price equation, well within the conclusive region for positive first-order autocorrelation. Even a casual observation of the plotted residuals confirms significant inertia. We suspect that this is impure autocorrelation resulting from omitted variables that are serially correlated rather than an incorrect functional form. Most previous attendance studies have encountered evidence of autocorrelation, and some have responded by including the lagged value of the dependent variable as a regressor. Although this results in a Durbin Watson statistic closer to 2, that statistic is biased when the lagged dependent variable is used and the OLS estimates are biased and inconsistent (these studies do not mention using the recommended Durbin m test). Given the drawbacks associated with using lagged dependent variables, we used the Newey West method to obtain standard errors for the estimated coefficients that are corrected for autocorrelation. This method is valid in large samples and also corrects for heteroskedasticity, similar to the White method. Given that the standard errors are biased downwards in the presence of autocorrelation, the Newey West correction should decrease the t statistics, and some variables might no longer be statistically significant. Compared with the results from OLS, only

19 296 Zygmont and Leadley three instances occurred in which variables that were significant at the 5% level became significant only at the 10% level. No variables that were significant at some level failed to retain significance at the 10% level or lower. Discussion The increase in attendance and ticket price for a new stadium is important for two reasons. First, the resulting increase in revenue might cover a significant part of the cost of construction, reducing the need for public subsidies. Second, there might be a local economic impact from increased employment at the ballpark and in related businesses, such as restaurants and hotels. A large economic impact could be used to support the argument for public subsidies. Consumers, however, might substitute attendance at baseball games for other forms of local entertainment, in which case the increase in attendance will yield little net benefit. Without a basis for linking attendance and economic impact, we can only comment on the probable effect on team revenue. Holding team performance and other explanatory variables constant, we estimate that the increase in demand caused by a new stadium will increase attendance by approximately 6,000 fans per game in the 1st year, declining to just 1,100 additional fans in the 10th year, and 500 in the 15th year. The team will also raise ticket prices, with a substantial 30% increase in the average ticket price during the first year. Using an average MLB team for 2000 (the last year in our sample) with a stadium built more than 15 years ago as a benchmark, a new stadium will increase the present value of ticket revenue over the first 15 years by $116 million (in 2003 prices, using a real discount rate of 5%). If an older multipurpose stadium is replaced by a baseball-only park, the permanent increase in demand will cause attendance per game to increase by an additional 3,200 and ticket price will also increase. The resulting 15-year increase in ticket revenue is $228 million. Poitras and Hadley (2003) estimated that a new facility with 50 luxury boxes and 2,500 club seats will add $5 million per year, for a 15-year revenue stream of $53 million, bringing the total to $169 million for a new multipurpose stadium and $281 million for a baseball park. Additional revenue will be generated by concessions, parking fees, and broadcast and stadium naming rights. This is compared with an approximate cost of $250 million for constructing a new facility. The policy implication of a honeymoon effect with sufficient magnitude and duration to generate a substantial percentage of the construction cost of a new ballpark is that the argument for public subsidies is weakened. Further, much of the increase in revenue is the result of higher ticket prices, which, unlike an increase in attendance, will not have an external benefit to the local business community. Our analysis makes a number of contributions to the existing literature on baseball attendance. First, we use a model of profit maximization to derive reducedform equations for attendance and ticket price rather than regressing attendance on price and other variables without considering how prices and quantity are

20 Major League Baseball Attendance 297 determined. Second, our data set is the most comprehensive to date, covering all MLB teams for a 31-year period. This time period covers the era of the multipurpose stadium and the retro baseball-only parks from the 1990s. The latter have a significant positive estimated effect on attendance and ticket price. Third, we are able to document the honeymoon effect without imposing the assumption of a linear decline in attendance. Although our results agree with earlier studies that conclude that the honeymoon ends in approximately 8 to 10 years, we estimate that the decline is much slower over the first 8 years, with a large drop-off in Years 9 and 10. Fourth, we document the lingering effects of the 1994 labor dispute, which marked the end of the consistent growth in attendance during the 1970s and 1980s. Finally, we show that new franchises, though popular during the first season, quickly experience declining attendance. This has important implications for cities contemplating subsidies to attract a MLB team. There might be good economic reasons why they do not already have a franchise. Acknowledgments The authors wish to thank the following individuals and institutions for their assistance in preparing this article: the Amateur Athletic Foundation of Los Angeles Sports Library, Hamid Bahari-Kashani, Don Coffin, Larry Hadley, Dianna Hewett, Marc Poitras, and the staff of Hamersly Library at Western Oregon University. References Austrian, Z., & Rosentraub, M.S. (1997). Cleveland s gateway to the future. In R.G. Noll & A. Zimbalist (Eds.), Sports, jobs, and taxes: The economic impact of sports teams and stadiums (pp ). Washington, D.C.: The Brookings Institution. Baade, R.A. (2001). Some observations on a new Fenway Park: Is it necessary? Is it financially prudent? Prepared for the Sports Fan Project of the Center for Study of Responsive Law, Washington, D.C., and Save Fenway Park, Boston, MA. Baade, R.A., & Sanderson, A.R. (1997a). The employment effect of sports teams and facilities. In R.G. Noll & A. Zimbalist (Eds.), Sports, jobs, and taxes: The economic impact of sports teams and stadiums (pp ). Washington, D.C.: The Brookings Institution. Baade, R.A., & Sanderson, A.R. (1997b). Minor league teams and communities. In R.G. Noll and A. Zimbalist (Eds.), Sports, jobs, and taxes: The economic impact of sports teams and stadiums (pp ). Washington, D.C.: The Brookings Institution. Baade, R.A., & Tiehen, L.J. (1990) An analysis of major league attendance, Journal of Sport and Social Issues, 14, Bruggink, T.H., & Eaton, J.W. (1996). Rebuilding attendance in Major League Baseball: The demand for individual games. In J. Fizel, E. Gustafson, & L. Hadley (Eds.), Baseball economics: Current research (pp. 9-31). Westport, CN: Praeger. Burger, J.D., & Walters, S.J.K. (2003). Market size, pay, and performance: A general model and application to Major League Baseball. Journal of Sports Economics, 4, Butler, M.R. (2002). Interleague play and baseball attendance. Journal of Sports Economics, 3,