SUMINISTRO DE AGUA 1 CÁLCULO DEL CAUDAL MEDIO

The data set we used includes information on the four seasons prior to the lockout, from 2000-01 to 2003-04, and the four seasons immediately following the lockout, from 2005-06 to 2008-09. At the beginning of the 2000-01 season, the NHL expanded from 28 to 30 teams as the Minnesota Wild and the Columbus Blue Jackets joined the league. Because we took the previous season into account, the resulting (unbalanced) panel data set contained 238 observations on all variables included in the estimates.

Frontier models require identifying inputs and outputs. In order to determine how efficiently the franchises operated, it was essential for us to use a financial ratio as the output. Forbes magazine reports data annually on sport franchise´s team values, as well as revenues for all major leagues. It breaks down franchise valuation into four categories: sport, market, stadium, and brand management. Team value has been previously applied as a dependent variable to analyze determinants of franchise values (Alexander & Kern 2004; Humphreys & Mondello, 2008). Therefore, as franchise values are not equally distributed, this study applied the natural logarithm of team values as output. Furthermore, to ensure the robustness of our results, we used the natural logarithm of revenues for each franchise as a second output. This data is also published by Forbes magazine on a yearly basis.

The input variables represented the various factors that were most likely to determine a team´s franchise value. Therefore, we included the natural logarithm of the population of each team´s metropolitan area in order to account for market-size effects on franchise values. In metropolitan areas with more than one NHL franchise (e.g., Los Angeles and New York), each franchise was credited with the entire population in the metropolitan area—this is because the market cannot be unambiguously separated between each franchise. Data were obtained from the U.S. Bureau of Economic Analysis´ Regional Economic Accounts and Statistics, Canada. Since franchises share larger pools of potential fans, we expected a positive relationship between teams located in larger markets and franchise values as well as revenues. It should be noted that, unlike in European soccer, only very few fans join their favorite teams for road games. This is due in part to a greater number of games and a lengthier distance between competing teams.

The team´s stadium is another important input factor for multiple reasons. A franchise with a new stadium can expect higher revenues, and hence higher team values due to e.g. state-of-the-art luxury boxes, for example. Alexander and Kern (2004) and Miller (2007) identified that new sport stadiums experience a honeymoon effect, where attendees visit the stadium for the stadium and not necessarily to watch the team, which lasts between 6 to 10 years after inauguration. Hence, we included stadium age, as well as stadium age in quadratic form, in our analysis and expected a negative impact of arena age and an increase in marginal returns on both dependent variables. Data on arena age was collected from Munsey and Suppes´ website (http://www.ballparks.com). In addition, the natural logarithm of attendees per game was included. We assumed that, since each attendee generates revenues for the franchise, the higher the number of

attendees, the greater the team value. To measure this revenue stream, we used the team marketing annual reports from the Fan Cost Index (FCI), which are constructed for each franchise and year.9 The FCI tracks the cost of attending a sporting event for a family of four.10 The more a franchise is able to charge for their tickets and other amenities, the more revenues it generates. Thus, we presumed that the coefficient for the FCI would also be positively related to the team value. To analyze how franchise history affects team value, we included the duration of a team in the league and the squared duration of a team in the league. We expect that teams with a longer franchise history also report a higher team value.11

We also control for the athletic achievements of a team. Since NHL standings are based on points and not wins, the rank is not expressed in terms of winning percentages; this is because teams receive a point for an overtime loss. We estimated athletic achievement by dividing the team´s total in the previous season by the average points of all teams in the previous season. Following the approach by Miller (2007), points achieved in the previous season are considered to be an important component in determining ticket prices, season ticket sales, media revenues, and advertising prices. We expected a positive coefficient, suggesting that a better athletic achievement in the previous season leads to higher revenues and, therefore, a higher franchise value. One of the most important input factors in professional sports is team expenses. We measure these by including the natural logarithm of the team payroll in our analysis. Data were

drawn from USA Today

9 _{Information is available at Rodney Fort’s website at http://www.rodneyfort.com.}

10 _{The FCI comprises the prices of four average-price tickets, two small draft beers, four small soft drinks,} four regular-size hot dogs, parking for one car, two game programs and two least-expensive, adult-size adjustable caps.

11 _{We did not include the natural logarithm of the input variables Duration and Age Arena since the value} of 0 is not defined.

(http://content.usatoday.com/sports/hockey/nhl/salaries/default.aspx). We assumed that a team with high payroll expenses will offer a superior team quality and, therefore, provides a superior utility to fans. Due to this assumption, we anticipated that higher team expenses would positively influence the team´s value. All monetary magnitudes in this analysis (e.g., team value, FCI, payroll) were deflated by the Consumer Price Index (CPI), which was taken from U.S. Bureau of Labor Statistics, and expressed at prices for the year 2000. Descriptive statistics for all variables introduced above are shown in Table 3-1.

Table 3-1: Descriptive Statistics of indicators influencing team values

Variable Operationalization Mean Min. Max.

Log Value Natural log of the team value in Dollar 18.88 18.18 19.78 Log Population Natural log of metropolitan area population 15.13 13.64 16.76

Age Arena Tenure of the team in the arena 12.38 0 47

Age Arena² Squared tenure of the team in the arena 270.0 0 2209 Duration Duration of the team in the league 34.45 0 99 Duration² Squared duration of the team in the league 1,964 0 9,801 Relative Points Achieved points in previous season/average

points 1 0.45 1.37

Log Attendance Natural log of attendance 9.72 9.18 9.97

Log FCI Natural log of fan cost index 5.46 4.98 5.98

Log Pay Natural log of team payroll 17.40 16.28 18.11