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152

JOURNAL OF SPORTS ECONOMICS / May 2005

TABLE 4:

Team Revenues From Home-Game Attendance

Coefficient

Coefficient

(Robust Std. Error)

(Robust Std. Error)

Explanatory Variables

Dependent Variable: Model (1)

Home Game Revenues Model (2)

WINPER: Team winning percentage COMPETITORS: Number of competing

0.906*** (0.090)

0.879*** (0.087)

professional sports franchises in the city STADIUMCAP: Stadium capacity (ln) INCOME (ln) POP: Total SMSA population (ln) TIME: Time trend (1990 = 0) TWHITE: Percentage of team White TWHITE*TIME

–0.040** (0.020) 0.919*** (0.108) 0.274* (0.141) 0.140*** (0.043) 0.040** (0.019) –0.416 (0.286) 0.125** (0.062)

–0.044** (0.020) 0.912*** (0.108) 0.314** (0.150) 0.166*** (0.043) 0.039** (0.018) –1.370 (1.173)

POPWHITE: Percentage White of SMSA population TWHITE*POPWHITE

–0.144 (0.381) 1.278 (1.468)

TWHITE*POPWHITE*TIME

0.158** (0.075)

–1.351

–1.938

.65

.66

CONSTANT

Adj. R2

NOTE: The models show the natural log of home-game revenue ($000), estimated as average ticket price times average attendance (TICKET PRICE * ATTENDANCE) as a function of explanatory variables, including the variables describing the racial composition of the team. The results are based on 251 obser- vations over nine NBA seasons (1990-1999). Ticket price is average ticket price for home games during the regular season (on the Web site of Team Marketing Report, see www.teammarketing.com); atten- dance is from the Official NBA Register (Sporting News, various years) and the NBA Web site (see www.nba.com); winning percentage is from the Official NBA Register (Sporting News, various years); competitors is number of professional sports franchises (MLB, NBA, NFL, and NHL) in the SMSA area and is available from Web sites for the pro sports leagues (see www.mlb.com, www.nba.com, www.nfl.com, and www.nhl.com); income and population data are from the U.S. Bureau of the Census and County and City Extra: Annual Metro City and County Data Book (various years), and for Canadian cities, Statistics Canada (see www.statcan.ca) and the Canadian Ministry of Finance (see www.bcstats.gov.bc.ca). *t statistic significant at .10 level; **t statistic significant at .05 level; ***t statistic significant at .01 level.

The first regression is the most parsimonious and includes the percentage of team members who are White (TWHITE) as an independent variable along with TWHITE interacted with the time trend (TWHITE*TIME). The coefficient on TWHITE is insignificant, but the interaction with time is positive and significant at the 95% confidence level. The time interaction allows us to evaluate how revenue varies with changes in time in team composition. According to this first set of results, and ignoring statistical significance, TWHITE had a negative total effect on revenue (the implied coefficient is –0.416) during the 1990-1991 season. This sug- gests an excess supply of White players relative to demand but is also consistent with the hypothesis that White players, on the margin, were of lower quality than

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