Life-Cycle Demand for Major League Baseball Seung C. Ahn (480) 965-6574 [email protected]Arizona State University Tempe, AZ 85827 U.S.A. and Young H. Lee (822) 760-4066 [email protected]Hansung University Seoul, 136-792 Korea June 20, 2003 Draft prepared for the Western Economics Association Conference, July 11-15, 2003. 1
32
Embed
Life-Cycle Demand for Major League Baseballminiahn/archive/baseball.pdfLife-Cycle Demand for Major League Baseball 1. ... [Bird (1982), Fort and Roseman ... We will apply life-cycle
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
We include into xt, WPCT (winning percentage), GB (games back), and one dummy
variable. DUMNEW is a variable that allows a new or a renovated stadium to have a
linearly decreasing impact on attendance. DUMNEW is set to be 4-year trend since new
stadium or renovated stadium. DUMNEW is equal to 4 in the first year of a new
stadium, 3 in the second year, 2 in the third year, and 1 in the fourth year. We do not
include strike dummies since time effects will be considered.
13
We add individual effects as well as time effects to the demand function (7) and
we assume these effects are fixed since our sample are close to population in aspect of
teams. The equality of individual effects can not be rejected since the chi-squared
statistic is 12.05 and its p-value is 0.99. Therefore, the equality of individual effects is
not rejected at 1% significance level. The estimates of time and individual effects are
presented in Appendix Table A1, A2 and A3.
We test the quality of the instruments as shown in Table 2. The overall
significant test statistics are 919.34 in the estimation time effects only and 968.23 in the
estimation with time effects and individual effects, and both p-values are near zero.
According to the EHS exogeniety test developed by Eichenbaum, Hansen and Singleton
test (1981), ∆lnATTt turns out to be endogenous since the p-value is near zero as shown
Table 3. This is consistent with the model specification.
We estimate the demand function (7) by the Generalized Method of Moments
(GMM) controlling autocorrelations by the Newey-West method (setting bandwidth=3).
The choice of the bandwidth is reasonable if the et are not autocorrelated..
Table 3 and 4 present the GMM results with time effects and individual effects and
with time effects only, respectively, when the dependant variable is growth of
attendance. The first four columns of Table 3 contain GMM estimates by the two-step
14
estimation, while the last two columns contain an OLS estimate. The first four
columns of Table 4 contain GMM estimates, while the last column contains an OLS
estimate. The first two columns show the results of the two-step GMM estimation and
the next two columns show those of the continuous-updating GMM estimation.
The point estimates of γ1 are more than 0.5 and are significant at 1%. This
implies that there is strong habit-formation in the demand for attending MLB games.
The estimates of ∆INCt+1 are not significant at all in GMM estimation. Therefore, the
result is consistent with the notion that the MLB demand is habitual but rational. The
estimated price effects are negative, but not significant. Our explanation is that current
attendance may not responsive to current price because of strong habit-formation.
According to our brief application of the BGM model, we also get the empirical
result of habit formation. The estimated effect of past attendance on current attendance
is significantly positive as shown in Table 7 & 8. The estimate of 0.410-0.451 is more
or less equal to the coefficient (0.373-0.481) of past cigarette consumption estimated by
BGM. It implies the strength of habit in the consumption of MLB games may be
similar to those in the consumption of cigarette.
6. Conclusion
15
It has been a puzzle that previous literature analyzing the attendance demand
empirically finds inelastic ticket pricing consistently. This contradicts microeconomic
theory of profit maximization. In the inelastic range of demand, a monopoly firm can
raise its profit simply by reducing its output since marginal revenue is negative.
In this paper, we develop a theoretical model in which inelastic pricing can happen
as a result of profit maximization. We also broaden the discussions of attendance
demand to dynamic consumption models by taking account into the habitual aspects of
consumption. We apply the rational expectation life-cycle models to the demand for
attending MLB games.
Because of a habitual behavior, owners keep their ticket prices in the inelastic
portion of demand in order to increase current attendance. There might be some loss
of revenue in current period, but the increased attendance will encourage greater future
attendance enough to raise their life-cycle profits.
According to our empirical results, there is significant addiction or habit-
formation in the consumption of MLB games. This strong habit may cause demand
for attending MLB games to be insensitive to current ticket price change. Therefore,
short-run price elasticity is estimated to be less than unitary and/or to be insignificant
and sometimes to be positive in the previous empirical studies.
16
References
BaseballStats.net, 2003. [online], available: http://www16.brinkster.com/bbstats. Becker, G., M. Grossman, and K. Murphy, 1994. “An Empirical Analysis of Cigarette
Addiction,” The American Economic Review 84(June):396-418. Cairns, J., N. Jennet, and P.J. Sloane, 1985. “The Economics of Professional Team
Sports: A Survey of Theory and Evidence,” Journal of Economic Studies 13(3):179-186.
Coffin, D.A. 1996. “If You Build It, Will They Come? Attendance and New Stadium
Construction,” In E. Gustafson and L. Hadley (eds.) Baseball Economics, (Westport, CT: Praeger).
Dynam, K.E. 2000. “Habit Formation in Consumer Preerences: Evidence from Panel
Data,” The American Economic Review 90(June):391-406. Demmert, H.G. 1973. The economics of Professional Team Sports, (Lexington, Mass:
Lexington Books). Eichenbaum, M.S, L.P. Hansen, and K.J. Singleton, 1981. “A Time Series Analysis of
Representative Agent Models of consumption and Leisure Choice under Uncertainty,” The Quarterly Journal of Economics 103(1):51-78.
Fort, R. 2003a. Sports Economics (Upper Saddle River, NJ: Prentice Hall). Fort, R. 2003b. “Inelastic Sports Pricing,” Managerial and Decision Economics. Fort, R., and Quirk, J. 1995. “Cross-Subsidization, Incentives, and Outcomes in
Professional Team Sports Leagues.” Journal of Economic Literature XXXIII(September):1265-1299.
Fort, R. and R. Rosenman, 1999a. “Streak Management,” In J. Fizel, E. Gustafson, and
L. Hadley (eds.) Sports Economics: Curent Research (Westport, CT: Praeger Publishers).
Fort, R. and R. Rosenman, 1999b. “Winning and Managing for Streaks,” Proceedings of the joint Statistical Meetings of 1998, Section on Sports Statistics (Alexandrea, VA: American Statistical Association).
Hansen, L.P. 1982. “Large Sample Properties of Generalized Methods of Moments
Estimators,” Econometrica 50:1029-1055. Jennett, N. 1984. “Attendances, Uncertainty of Outcome and Policy in Scottish League
Football,” Scottish Journal of Political Economy 31(June):176-198. Lee, Y.H. 2002. “Decline of Attendance in Korean Baseball League: Economic Crisis
or Competitive Imbalance,” Unpublished Manuscript, Department of Economics, Hansung University, Seoul, Korea.
Newey, W. and K. West, 1987. “A Simple Positive Semi-Definite, Heteroskedasticity
and Autocorrelation Consistent Covariance Matrix,” Econometrica 55:703-708. Noll, R.G.. 1974. “Attendance and Price-Setting,” In R. Noll (eds.), Government and
the Sports Business, (Washington, D.C.: The Brookings Institution). Quirk, J., and Fort, R.D. 1992. Pay Dirt: The Business of Professional Team Sports
(Princeton, NJ: Princeton University Press). Scully, G.W. 1989. The Business of Major League Baseball (Chicago, IL: University
of Chicago Press). Siegfried, J.J., and J.D. Eisenberg, 1980. “The Demand for Minor League Baseball,”
Atlantic Economic Journal 8: 56-69. Welki, A.M. and T. J. Zlatoper, 1994. “US Professional Football: the Demand for
Game-Day Attendance in 1991,” Managerial and Decision Economics 15(5):489-495.
18
TABLE 1 Descriptive Statistics for Sample Data
Variable Mean Standard
DeviationMaximum Minimum
ATT: Attendance(mils.) 1.722 0.764 4.483 0.307
P: Ticket Price 5.023 3.205 20.618 1.457
INC: Income(thousands) 11.820 6.987 42.250 2.969
WPCT: Winning Percentage 0.502 0.069 0.704 0.321
GB: Games Back 14.132 11.548 52.000 0.000
19
TABLE 2
Testing Quality of Instrument Variables by First Stage OLS (∆lnATTt-1)
Dependent Variable Model 1 Model 2 Variable estimate t-value estimate t-value constant -0.184 -0.28
The numbers in [] are p-values. 1 is a correlation coefficient. Model 1 is with time effects and individual effects. Model 2 is with time effects only.
20
TABLE 3
GMM Estimation with Time Effects and Individual Effects
The numbers in () are t-values. The numbers in [] are p-values. 1 is a correlation coefficient. (i) & (ii) is the two-step GMM. (iii) & (iv) is the continuous-updating GMM.
22
TABLE 5 Testing Quality of Instrument Variables by First Stage OLS in the BGM Model
2000 -0.069(-1.50) -0.062(-1.31) -0.059(-1.24) -0.053(-1.07) -0.048(-1.08) The numbers in () are t-values. (i) & (ii) is the two-step GMM. (iii) & (iv) is the continuous-updating GMM.
28
TABLE A3
Individual Effects Estimation of the Model with Time Effects and Individual Effects
GMM(i) GMM(ii) OLS
Team estimate t-value estimate t-value estimate t-value