L e v i t t | 1

Analyzing the Effects of the Revenue-Sharing Program on Competitive Balance in Major League Baseball

Ethan LevittColgate University

Abstract

This paper seeks to add econometric analysis and insight to the discussion over the effects of the revenue-sharing program on competitive balance in Major League

Baseball. It finds that the assumptions of the program hold if applied to the league as a whole, but differ dramatically if the teams are grouped based on spending

habits. The relationships tested are between wins and revenue using attendance as an intermediate factor, and between payroll spending and winning. Using a

combination of pooled OLS, fixed effects, and 2SLS regressions, we ultimately find evidence of simultaneity and significant categorical differences. Only upon further team-specific analysis does this paper offer some meaningful conclusions and ideas about how to improve competitive balance in the upcoming collective bargaining

negotiations.

Introduction

The 1990s saw the emergence of major market teams, highlighted by the

dominance of the Atlanta Braves and New York Yankees, as perennial winners in

Major League Baseball. In response to a growing concern that fans would lose

interest in their team if it had no real chance at winning, the Commissioner of

Major League Baseball, Bud Selig, commissioned a Blue Ribbon Panel on Baseball

Economics to assess the problem and develop potential solutions to create

competitive balance. Their main finding was that:

“The goal of a well-designed league is to produce adequate competitive balance. By this standard, MLB is not now well-designed. In the context of baseball, proper competitive balance should be understood to exist when there are no clubs chronically weak because of MLB's structural features. Proper competitive balance will not exist until every well-run club has a regularly recurring reasonable hope of reaching postseason play.” (Levin, Mitchell, Volcker, and Will; 2000)

This conclusion was driven to a large degree by the perceived disparity in

ability between teams to generate revenue based predominantly on market size.

Larger cities had more potential fans that could lead to more interest in the team

and thus more fans in attendance and watching on TV. This higher demand allowed

major market teams to generate more revenue and thus provide them with more

L e v i t t | 2

money to spend on payroll. In order to address this, a revenue sharing system was

suggested and subsequently instituted.

The goal of this program is relatively simple: by redistributing revenue from

the wealthy, higher spending teams typically located in major metropolitan areas

to the poorer, lower spending teams in smaller markets, more teams would have

the ability to field a competitive team and, by winning, increase their respective

fan bases. This in turn would lead to greater interest in the League and generate

more revenue for Major League Baseball and the teams in it. Since its introduction

in 1998, the revenue-sharing system has evolved to penalize higher-spending

teams more and redistribute more revenue across more teams. The implicit

assumption behind such a system is clearly that there is a universally positive

relationship between spending, winning, and earning. Specifically, a team that

spends more wins more, and a team that wins more makes more money.

However, leaked financial statements from several Major League teams

suggest that some teams have been keeping the revenue-sharing money they

receive instead of spending it on team payroll, as was the intention of the program.

While the primary response to these leaked documents has been frustration and

anger from the fans of the respective teams, many have also concluded that the

money these teams collect from the revenue-sharing system exceeds the amount

they expect to generate from an improved team and thus creates a disincentive for

them to spend the money in an effort to win more games. Comparing this finding

with the fact that 23 of the 30 MLB teams have reached the playoffs in the past

decade and no team has won back to back World Series since the New York

Yankees in 1999 and 2000 has led many to argue that MLB is more competitively

balanced and that the revenue-sharing program has played a substantial role in

that change. However, there is no conclusive evidence to corroborate the latter

claim and thus a debate has arisen as to what the true effects of revenue sharing

are. With the current CBA set to expire at the end of 2011, leaving these programs

open to review, fans, owners, and analysts alike are challenging that the new CBA

must address revenue sharing and potentially reform it.

Theory

This paper seeks to assess the effects (if they exist) of the revenue-sharing

program on competitive balance in Major League Baseball. Thus the economic

L e v i t t | 3

theory focused on in this paper is the theory behind the revenue-sharing program;

namely that providing money to small market teams, or in a sense subsidizing

them, will allow them to be able to field competitive teams. This theory, and the

revenue-sharing program based on it, depends on two relationships: team revenue

as a positive function of team wins and team wins as a positive function of team

payroll. Furthermore, it seems to be an implicit assumption of the program that

these relationships create a perpetual feedback mechanism in that more revenue

provides teams with more money to spend on payroll which they thus use to win

more games and generate more revenue.

Literature Review

Major League Baseball fills an interesting societal and economic niche as

both America’s pastime and a rare monopoly with an antitrust exemption. This

combination has made it the focus of a great deal of sports-based economic

research. With regards to the subjects of revenue-sharing and competitive balance,

a few particular works have contributed valuable insight which helped inform the

construction of my econometric models.

J.C. Bradbury, in his book The Baseball Economist, studied the effects of city

population on the number of games a team wins and found that not only do big

market teams tend to win more games, but that “the difference in market size

explains about 40 percent of the difference in wins between the top and bottom

markets”(Bradbury, 2007). While Bradbury takes a relatively objective stance on

whether the big market advantage is fair, he does assert that there is a definite

and quantifiable advantage to being in a big city. He also found that the “average

team”, which I interpret as being defined by being based in a city with an average

population relative to other MLB cities, faces a potential “revenue loss trap”

caused by the costs of improving from a mediocre team to an average team being

outweighed by a minimal increase in fan interest and a lack of sufficient revenue

generation as a result.

A study by Gustafson and Hadley (2007) corroborates this result. Using data

from 1997-2001, Gustafson and Hadley used a four-equation simultaneous model of

win percent, team payroll, team total revenue, and team local revenue to examine

the impact of market size on competitive balance. Having accounted for

simultaneity bias using the two-stage least squares technique, they found that local

L e v i t t | 4

population has a statistically significant positive effect on local revenue, and that

this effect can roughly translate, through an increase in payroll, into an increase in

wins. They measure this effect to be about 1 additional win for every million people

in the population, which means up to a 10 win difference between the largest (New

York) and smallest (Milwaukee) markets.

Data

The data in this research spans the years 2003-2009, due to the fact that

team revenue numbers for 2010 have not yet been released. The decision to use 7

years worth of data was guided by the fact that the most recent Collective

Bargaining Agreement, which changed the terms of the revenue-sharing system to

benefit the poorer teams even more, went into effect in 2007, giving us 4 years of

data before and 3 years after. This does not allow for a comparison of teams from

the pre-revenue-sharing and during revenue-sharing periods, but may allow us to

get an adequate estimate of whether changing the terms of the revenue-sharing

program does or does not have an effect on a team. The data compiled from these

7 years consists of Opening Day team payroll measured in millions of dollars,

number of wins, average attendance per game measured as a percent of total

stadium capacity, and team revenue estimations calculated by Forbes. It is

important to note that the revenue numbers used in this paper are estimations and

that the actual revenue numbers are not publicly available information. Thus, the

coefficients from the regressions will not be entirely accurate, but it is accepted

that the estimation technique Forbes uses is consistent so the relationship of

revenue between teams should be fairly accurate (Zimbalist, 2003).

The second thing worth mentioning is the inclusion of attendance rate as a

way of gauging fan responsiveness or interest. Whereas the two research papers

discussed use local population to account for differences in fan base or fan

interest, that approach seems quite broad for the effects I am trying to measure.

Using attendance rate inherently controls for difference in stadium size and factors

directly into team revenue. More concisely, given that cities with larger

populations have an inherent advantage in the ability to generate revenue, I am

interested in focusing on how that translates to how that can directly affect a

team’s finances. Gustafson and Hadley (2007) use average ticket price (a disputed

measure) and age of stadium to help control for similar stadium effects.

L e v i t t | 5

Econometric Specification of the Model and Results

Given that the data is panel data, a primary way to detect if there are

significant team factors besides wins that affect revenue is through a fixed effects

model. Thus I used the following fixed effects model to test a) the validity of

Bradbury’s revenue loss trap assertion* and b) if team-specific effects affecting

revenue are correlated to wins:

(1) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + µ

*The expectation is that β1>0, β2<0, β3>0, with year included as a control for league-wide changes in revenue over time.

Following from the results of equation (1) and a Hausman test of fixed

effects vs. random effects (see Appendix), it follows that there are significant team-

specific factors that affect revenue besides wins, but that not are correlated to

wins. I used the following equation to test the hypothesis that attendance rate is

one of those factors:

(2) Revenue = δ0 + δ1attend + ε

(3) Attend = α0 + α1wins + ν

Given the results of equation (3) and (4), with the latter helping to clarify the

other half of the relationship between wins and revenue, I decided to add

attendance rate to equation (1):

(4) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + β5attend + µ

In order to test the second relationship of the revenue-sharing program, I

used the following model, with interaction terms designed to see if there is a

difference in the effectiveness of payroll spending across groups (the groups are

categorized in the Appendix using each team’s average payroll over the 7 year

period):

(5) Wins = φ0 + φ1payroll + φ2small + φ3major + φ4payroll*small + φ5payroll*major + ω

L e v i t t | 6

In order to test for the simultaneity bias that Gustafson and Hadley were

concerned about in their model, I used Equation (5) to instrument for the three win

terms in Equation (4):

(6) (1) Revenue = β0 + β1wins + β2wins2 + β3wins3 + β4year + β5attend + µ(2) Wins Wins2 Wins3 = φ0 + φ1payroll + φ2small + φ3major + φ4payroll*small +

φ5payroll*major + ω

Table 1Form of Estimation

Fixed Effects(1)

OLS(2)

OLS(3)

OLS(4)

OLS(5)

2SLS(6)

Dependent Variable

Revenue Revenue Attend Revenue Wins Revenue

Constant -23742.8**(976.3)

55.745**(9.81)

.0801(.081)

-22349.81**(1996.91)

85.139**(7.54)

-29528.94**(5541.89)

Wins 2.071(6.279)

.0074**(.001)

22.718**(11.544)

89.457(130.67)

Wins2 -3.663(8.322)

-35.254**(15.20)

-125.795(179.418)

Wins3 2.082(3.625)

17.653**(6.593)

61.01(75.401)

Year 11.899**(.484)

10.945**(.993)

13.696**(2.968)

Attend 165.256**(14.003)

129.942**(12.286)

-57.138(81.141)

Payroll -.1075(.1128)

Small -15.45*(8.63)

Major -11.65(8.328)

Payroll*Small

.2327*(.1381)

Payroll*Major

.2256*(.1173)

R2 value .3154 .401 .2093 .6506 .2222 N/A

F statistic 153.54 139.27 55.07 75.98 11.65 78.18

L e v i t t | 7

(model)39.08 (on fixed effect)

(wald chi-squared)

Sample Size 210 210 210 210 210 210

Notes: * and ** indicated statistical significance at the 10% and 5% significance levels, respectively. Values in parentheses are White-corrected robust standard errors and the significance levels reflect that adjustment.

Beginning with Equation (1), it is clear that revenue is affected by much

more than just how many games a team wins, and that the significant team-specific

effect (as determined by the F-statistic of 39.08 for the fixed effect) is not

correlated to wins. Previous literature posited that hometown population size was

that team-specific effect. Based off the assumption that attendance rate is not only

a reflection of population size, but also a more direct factor in team revenue

generation, Equations (2) and (3) were included to observe the effects of

attendance rate and test for correlation with both revenue and wins. While the

effect of attendance on revenue is both statistically and economically significant,

the effect of wins on attendance does not appear to be economically significant

despite its statistical significance (this will be discussed further in the Conclusion).

It is important to add that while RESET and LM tests suggested that the functional

form of equations (2) and (3) could be improved by adding higher-order terms of

the explanatory variables, doing so made the coefficients statistically insignificant

due to high variance inflation and distorted the basic positive relationship. Thus, I

chose to leave them as they are presented.

Equation (4) shows that when holding attendance rate constant, the

coefficients on all the win variables not only become statistically significant, but

also follow the pattern proposed by the revenue loss trap at a economically

significant level. Given the result of equation (3) indicating positive correlation

between wins and attendance, it follows that when that effect is “partialled out”,

the magnitude of the effect of wins will increase. While this result does indicate

that even during the revenue-sharing period there is a disincentive for small

market teams to try and win more games for fear of falling into the revenue loss

trap, it does not take into account the simultaneity bias that Gustafson and Hadley

found in their data.

L e v i t t | 8

The underlying assumption behind any analysis of the revenue loss trap is

that teams have the ability to spend or buy their way out of the bottom tier.

Equation (5) interestingly seems to indicate that the effect of increasing payroll for

small budget teams and large budget teams is positive and statistically significant,

but that mid-range budget teams are not as effective payroll spenders, which is

perhaps why they are in that loss trap to begin with. At the same time, the results

of this regression also indicate that on average, a one million dollar increase in

payroll spending for a small market team is associated with about a .12 increase in

the number of games won, holding other factors constant. However, spending 8 or

9 million dollars to win somewhere in between zero and two more games would not

be considered a solid investment by any MLB team. Despite their statistical

significance, the two coefficients do not indicate that, on average, MLB teams are

very good at buying more wins.

In Equation (6), we see that the simultaneity bias substantially altered the

coefficients and that the coefficients on the win variables are no longer statistically

significant. I maintain that this is due to incredibly high variance inflation arising

from collinearity of the explanatory variables (which is evidenced in the Appendix

under Equation (4)), and that ultimately, equations (4), (5), and (6) show that:

a) The revenue loss trap exists and is significant

b) Simultaneity is present the model (see the Hausman test results in the

Appendix) and thus teams should expect a positive feedback cycle for

spending money on team payroll

c) Since small market teams do not always increase payroll, there feedback

effect must be small or different aspects of the cycle are failing for

individual teams.

Conclusion

On the face of it, the results indicate that revenue-sharing should be an

effective program for creating competitive balance. The presence of simultaneity

indicates that the key variables we focused on in the regressions are

interdependent and for the most part, we found that these interdependent

relationships are positive. The group that seems to be the main cause of distorting

these simultaneously positive relationships is the middle-spending group. They do

L e v i t t | 9

not win a consistent (high or low) number of games annually and thus appear as

neither efficient spenders nor effective revenue generators. However, this

“problem” for our analysis can be viewed as a benefit to competitive balance. The

constant rotation of competitive teams may be preferable even if it means some

teams have to lose money once in a while.

While the issues facing middle-spending teams are worthy of inspection, the

revenue-sharing program, and thus the focus of this paper, is really targeted at

addressing the winning habits of small-spending teams. In order to get a better

idea of how the individual team relationships of payroll spending to winning,

winning to attendance, and attendance to revenue, play into the bigger picture of

categorical and league-wide differences; I ran single variable regressions for each

team in each relationship using each team’s 7 or 8 years of data, and compared

those team-specific coefficients. The following conclusions rely on both the

multiple regressions and the single regressions.

Payroll-Wins as an Indicator of Effective Spending

While this relationship is not necessarily the most important to a team’s

financial success, it is the most publicized and most heavily debated amongst

writers and sabermetricians. As mentioned before, the most commonly held

assumption (or at least expectation) is that this relationship is positive, i.e. that if a

team spends more money, it will win more games. However, results from the past

few seasons, particularly from the 2010 season, provide evidence to the contrary.

The correlation between winning and spending, as measured in Figure 2, has been

generally decreasing since the 1998 season, from .71 in 1998 to below .20 in 2010.

Figure 2 Figure 3

This diminution in a team’s ability to “buy” wins has coincidentally been

met with a leveling out of average payroll in the past three years, as seen in Figure

L e v i t t | 10

3. Many attribute this shift to the recent economic downturns forcing teams to be

more conservative in their spending habits. It has also been suggested that

improvements in scouting and developing younger players have allowed teams to

make smaller investments with greater upsides and hold on to these players for

many years before they can begin to demand expensive contracts or become free

agents. While the philosophy of building a team around a young core of home-

grown talent and then filling in the remaining pieces with established veterans has

been growing in popularity and been met with great success over the past several

seasons (see 2010 Rangers and Giants, 2008 Rays, and even to a certain degree

the 2009 Phillies), it is by no means the only philosophy employed. Other major

market teams like the Yankees, Red Sox, and Cubs have continued to sign players

to enormous contracts and have also been successful in their efforts. I believe this

suggests two factors are at play.

The first relies on the work of Harry Raymond, who studied the draft pick-

free agent compensation system. He found that the current system of rewarding

teams that lose free agents with premium draft picks was outdated. Raymond used

sabermetric statistics to quantify in a more definitive way the relationship between

the value of free agents and corresponding draft picks in terms of wins. His

findings suggested that the current system significantly overcompensates teams

that lose a player to free agency, which encourages teams to spend less money on

free agents and focus more on developing young players.

The second factor draws on the findings of Solow and Krautmann (2005).

Their article finds statistically and economically significant evidence that what they

call the “Marginal Revenue of a win” increases as market size increases. I attribute

this to a key distinction regarding the concept of payroll efficiency. I believe that

the concept of payroll efficiency has two interdependent components to it: 1)

spending the right amount on each player on the team, and 2) spending the right

amount on total payroll. In choosing the appropriate level of spending, a team must

consider the expected return on a player. Professor and author Vince Gennaro

offers a dual approach to player valuation, asserting that a player has both

performance and marquee/franchise value. This is meant to adjust for the fact that

signing a player like Stephen Strasburg or Manny Ramirez has bigger implications

than just what they do on the field since they are incredibly marketable players.

This is particularly true in major markets where advertising can have a much

L e v i t t | 11

greater impact. Thus, it can be argued that it is justified for major teams to spend

more, or specifically spend beyond performance value on a player since they will

generate revenue for the franchise beyond the revenue through wins. Since a key

clause of the report used to create revenue sharing was “well-run club”, I think it

is important to distinguish that well-run can mean different things in different

markets, in particular that major market teams have reason to spend money not

only on talent, but on fan appeal. This dichotomy in spending strategy combined

with improvements in scouting and player development may offer some insight as

to why major market teams have not dominated baseball in the past decade.

Attendance-Wins as an Indicator of Fan Interest

Of the many fundamental changes Major League Baseball has undergone

over the past decades, one of the most observable is in how it is viewed. The

advent, expansion and success of MLB Advanced Media (“MLBAM”), regional

sports networks, and new theme park-like stadiums, has dramatically altered the

baseball fan’s landscape. However, one this has not changed over time: fans prefer

winning teams to losing teams. While there is substantial evidence to corroborate

this claim, the recent indifference shown by the fans of the Tampa Bay Rays during

their AL East Division title illustrates that success does not guarantee attendance

in every market. Seeing as how the revenue sharing program was designed to

benefit those specific teams which suffer the most from a disinterested fan base,

the system would only be working if the “preference for winning” premise held

true universally.

Surprisingly, while the league average for the slope of the attendance per

wins relationship was always positive and significant over time, it was not positive

or significant for every team. This suggested external factors were influencing

attendance. While it is intuitive to create the two categories of fans (namely those

who are responsive to a winning team and those that aren’t), it is difficult to assess

which fans (or cities) belong in those categories. On the one hand are the teams

that have had substantial variation in performance and attendance over the past 8

seasons (this was the case for 12 of the teams in MLB). In these cases the

relationships were always positive and confirmed the long-standing assumption

that winning brings in more fans. However, three groups of outliers presented an

L e v i t t | 12

interesting issue for the model. First, and I suppose foremost, are the

uncompetitive teams that didn’t draw many fans. For a number of these teams,

there was no significant fluctuation in wins, so it was impossible to tell if an

improvement in their performance would have generated the attendance spike that

would be expected. Second was the category of teams with fans who were so loyal

they continued to attend in high numbers regardless of performance. Third is the

category of teams that performed well throughout all 8 years of the study and drew

a high number of fans because of it, so that any potential effects of a diminution in

performance did not have a chance to occur. I believe that any attempt to disallow

those teams would be subjective and inappropriate (one cannot assume that

because it didn’t happen, it couldn’t), so I elected to keep them in the data set

despite any clear method to control for the varying conditions. It is a goal of my

continued to research to develop a model that can accommodate differences

between markets more adequately with regards to attendance.

However, even solving this conundrum would not completely clarify the

issue of how attendance relates to winning. From an economic point view, a

baseball team has a monopoly over tickets in that they are the sole supplier of the

product and control the prices. However, if a team cannot identify what the

demand for their tickets is, then they cannot set the “right” prices, whatever their

conception of “right” (most likely profit-maximizing) is. This is only further

exacerbated by the fact that baseball teams charge multiple prices for various

ticket quantities and qualities (based on location in the stadium, giveaways,

opponent, etc.). If ticket prices are set too high, then they will prohibit fans from

positively responding to a winning team by attending more games.

It is important to note that not all winning teams are competitive teams, and

not all competitive teams necessarily win that many games. A team with a .500

winning percentage may be in the playoff hunt in September one year while a team

that ends up winning 90 games never really has a chance that same season. Given

that playoff contention is not based solely on the number of wins a team has but

rather the number of wins a team has relative to other teams in its division or in

Wild Card race, I think future analysis would benefit from studying whether fans

are interested in seeing a winning team or a competitive team. One good example

of this the 82 win 2005 NL West Champion San Diego Padres that drew a

proportionately high number of fans relative to their win total.

L e v i t t | 13

One interesting observation associated with this relationship is that

attendance has dropped the last three years (Figure 4).

Figure 4

This is likely due to a combination of multiple factors including the general

economic recession, smaller stadiums, and higher ticket prices. Regardless of the

explanation though, it is an important trend to account for in looking at recent data

since teams that maintained attendance levels actually increased relative to the

league. In addition, new stadiums always bring more fans to the ballpark and more

interest to the team, and that effect usually lasts a few years, though the effect can

vary. Stadiums have been developed and built with a greater frequency in the past

decade than ever before. However, the new stadiums were not considered when

looking at changes in attendance and a method for accounting for differences in

both size and age of a ballpark should be addressed in future research.

Revenue-Attendance as an Indicator of Front Office Operations

This relationship was originally meant to connect the final dots between

spending and earning, with the assumption that the relationship between

attendance and revenue would be similarly positive across all teams. In the

broader sense, the coefficient for this relationship is supposed to represent roughly

how much a team makes in additional total revenue from one fan per game. Using

the financial reports (see Figure 5 below) as a sample to determine how much

revenue from attendance comprises total revenue, I was able to see a clear and

distinct difference in revenue generation between teams depending on market size.

L e v i t t | 14

Figure 5

Thus there is evidence that different

markets rely on different factors to varying

degrees, and a program which seeks to neutralize those differences runs the risk of

poorly accounting for how those differences play out over time. Some teams are

more dependent on attendance and local based revenue and some on rely on

general broadcast revenue. The goal of revenue sharing was to redistribute local

revenues from rich teams to poor teams in order to mitigate the difference. An

interesting trend has developed over the past three years which may have a

substantial impact on how effective that method of revenue sharing is. While

revenues have continued to increase across MLB, attendance has dropped the past

three years. This has caused the coefficients for the Revenue-Attendance

relationship to increase (Figure 6). This means that each fan is technically

generating more revenue in each subsequent year than in previous years. With this

current trend, the incentive seems to be heading towards maximizing profit per fan

rather than maximizing the number of fans, a dangerous path for Major League

Baseball to be on.

Figure 6

Pittsburgh Pirates

2007 2008Home Game Receipts (tickets) 34.42 32.13% of total 25% 22%Broadcasting 40.326 39.01% of total 29% 27%Total Revenue 138.636

145.99

LA Angels of Anaheim

2008 2009

103.21 100.1243% 42%

42.97 45.99818% 19%

237.87 240.824

L e v i t t | 15

A corresponding decrease in the R-squared values of the year-by-year

regressions indicates that this may not explain the full story. Given that 8 of the 30

MLB teams have a negative coefficient for their attendance-revenue relationship in

our model, which suggests that for some teams higher average attendance has a

negative effect on revenue, our model does affirm that in general more fans leads

to more revenue. The examples of teams with decreasing attendance and

increasing revenue suffer from a variety of factors which alter their situation and

make them outliers. With the interaction of all these various factors at play in

different ways and to different degrees, we turn to the conclusions that can be

drawn.

Conclusion

Ultimately, having taken all these things into consideration, the fact remains

that 25 teams in Major League Baseball saw annual increases in revenue from

2003 to 2008 (including in 2008). Of these 25 teams, none of them, not one,

increased team payroll every year or increased in wins every year. While this was

never the assumption, it does provide sufficient evidence that there is more that

goes into generating revenue than spending and winning.

Given that as a foundation, this paper proposes three further conclusions

pertaining specifically to the revenue sharing program. First, it is rational for

major market teams to adopt different spending strategies than small market

teams which includes signing elite players to lucrative deals. These are riskier

investments for teams and thus a “well-run” team will still have to make them

efficiently. Second, the simultaneous stadium-building boom (which draws more

L e v i t t | 16

fans to the game) and media outlet expansion (which makes it easier for people to

watch games without coming to the ballpark) have stagnated the effects of the true

relationships between winning, attendance, and revenue. New stadiums will not

continue to be built at their recent rate and the effects of that as a revenue driver

will diminish. As access to baseball media becomes more widespread and

accessible, teams will have to develop new ways to generate local revenue, which

is something revenue sharing cannot directly fix.

However, with the current Collective Bargaining Agreement set to expire in

December 2011, there are two prescriptions I would recommend be considered in

the negotiations of the new agreement:

1) Restructure the draft pick- free agent compensation system.

Small market teams have been successful with growing

regularity in the past few seasons by drafting good players

and developing them, but competitive teams need experience

players. If small market teams are reduced to signing old

veterans to short contracts because they cannot compete with

the big spending teams financially, then can not create the

kind of long term performance consistency that is necessary

to win over fans.

2) Stay away from excessive reform and continue with the type

of oversight that we saw with the Florida Marlins. As many of

the facts mentioned in this paper have made clear, more

teams are competitive than there were in the years leading

up to revenue-sharing. In the instances where that is not the

case, the problem seems to lie not with the program, but with

the team.

Appendix

Equation 1 test -

L e v i t t | 17

Equation 4 tests -

Ramsey RESET test using powers of the fitted values of revenue Ho: model has no omitted variables F(3, 201) = 8.53 Prob > F = 0.0000

Variable | VIF 1/VIF -------------+---------------------- wins2 | 19432.69 0.000051 wins3 | 5471.62 0.000183 wins | 4411.68 0.000227 attend | 1.29 0.772630 year | 1.04 0.964681-------------+---------------------- Mean VIF | 5863.66

DM test

Equation 5 tests-

L e v i t t | 18

Ramsey RESET test using powers of the fitted values of wins Ho: model has no omitted variables F(3, 201) = 0.94 Prob > F = 0.4242

Equation 6 tests -

Hausman test on 2SLS

Single Regression Results -

Team

Wins per Million Spent on Payroll

Avg. number of fans in thousands per Win

Revenue in Millions per avg number of fans in thousands

Product of Three = Rate of Return

Philadelphia Phillies 0.177 1163.8 0.0055 1.1329593

Tampa Bay Rays 0.6258 245.1 0.0055 0.84360969

St. Louis Cardinals 0.758 158.06 0.00670.80272351

6

Milwaukee Brewers 0.2503 657.17 0.00420.69085653

4Los Angeles Angels of Anaheim 0.497 129.9 0.0086 0.55521858

Baltimore Orioles -0.1709 931.93 -0.00290.46187382

7 double neg

New York Yankees -0.131 -651.44 0.00420.35842228

8 double neg

Oakland A's -0.2245 361.6 -0.0044 0.35718848 double neg

Detroit Tigers 0.234 348.09 0.0029 0.23621387

L e v i t t | 19

4

Texas Rangers -0.62 173.45 -0.002 0.215078 double neg

Colorado Rockies 0.223 223.53 0.00380.18941932

2

Minnesota Twins 0.071 232.7 0.0093 0.15365181

Seattle Mariners -0.416 178.81 -0.00160.11901593

6 double neg

Toronto Blue Jays 0.1236 140.09 0.00680.11774284

3

Boston Red Sox -0.051 -99.6 0.0215 0.1092114 double neg

San Francisco Giants -0.0072 33.169 -0.00650.00155230

9 double neg

Cincinnati Reds 0.147 -78.84 -0.005 0.0579474

Washington Nationals -0.2711 -49.708 0.00380.05120818

7Arizona Diamondbacks -0.2977 44.216 -0.0024

0.031591448

Los Angeles Dodgers 0.196 14.063 0.01030.02839038

4

Pittsburgh Pirates 0.193 28.803 0.00140.00778257

1

Chicago White Sox -0.0596 -15.757 0.00450.00422602

7

Kansas City Royals 0.0116 74.83 0.00030.00026040

8

Atlanta Braves 0.0072 -89.014 0.0065

-0.00416585

5

Florida Marlins 0.12 51.979 -0.0013

-0.00810872

4

San Diego Padres -0.426 28.693 0.0017

-0.02077947

1

Chicago Cubs -0.03 50.931 0.0215

-0.03285049

5

Houston Astros -0.5242 147.8 0.0028

-0.21693492

8

New York Mets -0.121 580.85 0.0039

-0.27410311

5

Cleveland Indians -0.207 240 0.0056 -0.278208

Bibliography and References

L e v i t t | 20

Baseball Almanac, found at www.baseball-almanac.com.

Baseball Refereece, found at www.Baseball-reference.com.

Bradbury, J.C. The Baseball Economist: The Real Game Exposed. New York: Dutton, 2007.

Bradbury, J.C. “Revenue Sharing, Incentives, and Competitive Balance” www. Sabernomics.com. September 1, 2010.

Brown, Maury. “Will leaked MLB financials alter Revenue-Sharing?” Fangraphs. August 25, 2010. http://www.fangraphs.com/blogs/index.php/will-leaked-mlb-financials-kill-revenue-sharing/

Elanjian, Michael R. and Dessislava A. Pachamanova. “Is Revenue Sharing Working for Major League Baseball? A Historical Perspective”. The Sport Journal. Volume 12, No. 2. http://www.thesportjournal.org/article/revenue-sharing-working-major-league-baseball-historical-perspective

Futterman, Matthew. “The Year Money Didn’t Matter”. Wall Street Journal. September 16, 2010. http://online.wsj.com/article/SB10001424052748703743504575493942146685242.html

Gennaro, Vince. Diamond Dollars: The Economics of Winning in Baseball. Hingham, Ma.: Maple Street Press, 2007.

Gustafson, Elizabeth and Lawrence Hadley. “Revenue, Population, and Competitive Balance in Major League Baseball”. Contemporary Economic Policy. Volume 25, No. 2. April 2007.

Hakes, Jahn K., and Raymond D. Sauer 2006. "An Economic Evaluation of the Moneyball Hypothesis." Journal of Economic Perspectives, 20(3): 173–186.

Jacobson, David. “The Revenue Model: Why Baseball is Booming”. Bnet website. July 11, 2008. http://www.bnet.com/article/the-revenue-model-why-baseball-is-booming/210671

Levin, R. C., Mitchell, G. J., Volcker, P. A., & Will, G. F. (2000). The Report of the independent members of the commissioner's Blue Ribbon Panel on baseball economics. Retrieved December 10, 2010 from Major League Baseball Website: http://www.mlb.com/mlb/downloads/blue_ribbon.pdf

Solow, J., and A. C. Krautmann. “Leveling the Playing Field or Just Lowering Salaries?” Working Paper, University of Iowa, 2005.

Zimbalist, Andrew. May the Best Team Win: Baseball Economics and Public Policy. Washington D.C. : The Brookings Institution, 2003.