The Effect of the UEFA Champions League Financial Payout System on Competitive Balance in European Soccer Leagues

Nathan A. Vestrich-Shade Advised by: Timothy Lambie-Hanson

Senior Thesis in Economics Haverford College

April 28, 2016

Abstract

The paper examines the effect of UEFA Champions League payouts on competitive balance across European leagues. A league-level specification identifies the magnitude of the effect of the UEFA payouts on three measures of competitive balance. The results confirm that the UEFA payouts have a statistically significant effect at the league-level depending on which competitive balance measure and league sample is used. However, the UEFA payouts had no statistically significant effect on individual clubs’ average annual payroll or the clubs’ qualification for the Champions League in the following season.

Nathan Vestrich-Shade

Table of Contents

Section I: Introduction…………………………………………………...... 3

Section II: Literature Review……………………………………………………………………...4

Section III: Data…………………………………………………………………………………...9

Section IV: Methodology………………………………………………………………………...12

Section V: Results………………………………………………………………………………..15

Section VI: Discussion…………………………………………………………………………...19

Section VII: Conclusion………………………………………………………………………….23

Appendix…………………………………………………………………………………………24

References………………………………………………………………………………………..40

Nathan Vestrich-Shade

Section I: Introduction

Over the years, select clubs in European domestic soccer leagues tend to “control” the

spots at the top of the end-of-season table. Champions of the domestic league qualify for the

UEFA Champions League,1 a pan-European competition that pits the best European soccer clubs against each other. Teams that qualify for the Champions League receive financial payouts from

UEFA based upon their performance in the competition2. For the 2014/15 season, the eventual

champion, FC Barcelona, received over 61 million Euros3.

The UEFA Champions League provides a unique lens with which to analyze competitive

balance across European domestic leagues because only teams that qualify are capable of

receiving the financial payout. To that end, this paper attempts to measure the effect of the

UEFA Champions League financial payout system on competitive balance across European

domestic leagues.

One plausible theory is that UEFA Champions League teams receive the financial payout

and put it toward the total payroll of the club. As a result, these clubs can spend more in the

transfer market and buy higher quality players. The higher quality players increase the quality of

the club and consequently, increase the probability the club will qualify for the Champions

League in the following season. Greater concentration of player talent reduces the quality of

matches played in the domestic league through a competitive balance effect.

The research seeks to test the theories empirically at the league-level from the 2003/04 to

2014/15 seasons and club-level from the 2008/09 to 2014/15 seasons for a range of European

leagues. With panel data on leagues, I estimate the effect of Champions League payout revenue

1 Some domestic leagues have more than one club qualify for the UEFA Champions League. The number of clubs that qualify is determined by a UEFA coefficient. 2 In addition, there is a “market pool” component for tv revenue. 3 See: http://www.uefa.com/MultimediaFiles/Download/competitions/General/02/29/45/25/2294525_DOWNLOAD.pdf. 3

Nathan Vestrich-Shade on competitive balance across domestic leagues. At the club-level, the data will measure whether club payroll is the mechanism through which the UEFA payouts operate. The payouts increase the total revenue of Champions League clubs, which can be spent on higher quality players. Furthermore, the panel data for clubs is used to test for a self-reinforcing effect over time: Do payouts to one club from qualifying in a given year affect that team’s probability of qualifying in the following year? A significantly positive effect would “crowd out” other teams in the domestic league from qualification, leading to lower competitive balance.

A league with low competitive balance is less exciting for consumers. If there is a significant effect in which the UEFA payouts reduce the level of competitive balance, there may be a stronger platform for introducing regulation into European soccer. The exact type of regulation used is not within the scope of this research. However, one policy may concern cutting the UEFA payouts. The issue considered is timely as UEFA has recently raised the size of the financial payouts.4 Therefore, if a significant effect exists, it may become more pronounced, increasing the cost to teams of missing qualification and to fans of less exciting contests.

Section II discusses the relevant competitive balance literature. Section III and IV introduce the data and methodology used, Section V presents the results, Section VI discusses the results and suggests policy reform, and Section VII concludes.

Section II: Literature Review

The foundation of this paper rests upon the notion that Champions League payouts

reduce the competitive balance of the league. Successful performance in the Champions League

enhances a club’s concentration of player talent. Under conventional monopoly theory, the

4 For more information, see: http://www.uefa.org/stakeholders/clubs/news/newsid=2229945.html. 4

Nathan Vestrich-Shade monopolistic firm charges a higher price to consumers and reduces quantity produced. While the clubs lack a monopoly on player talent, the greater concentration of player talent leads to a decline in competitive balance. The lower competitive balance does not reduce the quantity of matches played, but it does reduce the quality of matches.

Monopoly control of player talent is most observable in Major League Baseball during the reserve clause era. Prior to the removal of the reserve clause, the club had total control over the player: He could not move teams once a contract expired. Therefore, wealthy teams could collect player talent and remove it from the talent pool available to competing teams. After the introduction of free agency, empirical research shows competitive balance has increased.

However, the mechanism that led to the improvement has been debated. Butler (1995) provides empirical results that attempt to synthesize the existing explanations in the competitive balance baseball literature. He finds that the effect of various mechanisms on competitive balance is contingent upon the measure. For free agency in particular, it has no significant effect for one measure of competitive balance and increases competitive balance under the other measure. The only variable that has consistent effects across both measures is the amateur draft, which improves competitive balance.

Competitive balance has been studied in many sport contexts. Szymanski (2003) identifies three types of competitive balance at the match, seasonal, and championship level. A subtle distinction is between seasonal and championship competitive balance. As Szymanski explains, “Seasonal uncertainty means a close championship race within a season, while championship uncertainty means there is a variety of champions over a period of years, rather than domination by one or two teams” (1155-1156). This research will focus on the effect of

Champions League payouts on the competitive balance at the seasonal level. If the payout

Nathan Vestrich-Shade system has a large effect, competitive balance will be low in each league. These effects should hold at the seasonal level and become more pronounced over time as a few select clubs accumulate payouts.

Zimbalist (2002) examines competitive balance trends in baseball, ice hockey, basketball, football, and English soccer. He attributes the unique relegation system in soccer as a source of incentive for teams in the league to maintain a high level of competitive balance, differentiating it from American sports leagues. However, he does not include a robust empirical section to measure this effect.

European soccer lacks some of the competitive balance “stabilizing” systems found in US professional sports. Salary caps and drafts tend to be effective ways to control competitive balance. For instance, Larsen, Fenn, and Spenner (2006) show that “free agency and salary cap restrictions tend to promote competitive balance” in the National Football League (374).

Similarly, Dietl, et. al. (2012) use a theoretical model to illustrate a “percentage-of-revenue cap” increases competitive balance in European soccer (307). Therefore, a similar salary cap system in European soccer may provide a means through which to control for higher levels of competitive balance. As noted earlier, Butler (1995) concludes that the draft in MLB increases competitive balance. Without such regulation, European soccer leagues may be more susceptible to greater concentration of player talent.

Revenue sharing systems in sports leagues are viewed as a source to control levels of competitive balance. Results in this realm vary. For example, Kesenne (2000) determines that revenue sharing has positive effects in profit-maximizing and utility-maximizing settings (56).

However, Maxcy’s (2009) research on the MLB shows that when revenue disparities among teams run high, redistributive revenue sharing models lead to a decline in competitive balance.

Nathan Vestrich-Shade

In particular, the weakest teams in the league had “incentives to divest talent” (275). The author references several examples. For instance, The Tampa Bay Devil Rays “nearly halved their payroll from $35 million to $19 million to start the 2003 season” (278). Divestiture is used to invest in young, cheap talent (296). Maxcy’s result carries potentially important ties to a recent change in policy in European soccer. Although unrelated to revenue sharing, UEFA’s new regulation, Financial Fair Play, seeks to control clubs’ bottom line and prevent the rising debt problem across European soccer. However, some believe this regulation will only cause the top clubs to become more dominant as they are the only clubs that can offset large expenditures with large inflows. In connection with Maxcy’s research, low revenue clubs may be encouraged to sell their top talent in order to meet bottom line standards. In their research on Financial Fair

Play, Peeters and Szymanski (2014) support such a theory. Under such a system, competitive balance will suffer and weaker clubs will be unable to ascend to the top of the league. Over the years, conclusions surrounding competitive balance and Financial Fair Play should become clearer.

Research on the effect of the UEFA Champions league on competitive balance is limited, but the results are convincing. Pawlowski, Breuer, and Hovemann (2010) use a natural experiment to analyze competitive balance in England, Spain, France, Germany, and Italy after

UEFA substantially increased financial payouts in the 1999/00 season. By dividing time periods into a control setting from the 1992/93 to 1999/00 seasons and experimental setting from the

2000/01 to 2007/08 seasons, the authors show “a significant decrease in competitive balance” after the spike in payouts (186). The authors utilize several measures of competitive balance, many of which are standard among the literature. These measures include the Herfindahl

Hirshman Index, concentration ratio, and standard deviation of points (190-191). In addition, the

Nathan Vestrich-Shade authors use Humphreys’ (2002) competitive balance ratio, which compares the average of the team’s standard deviation of points to the average of the league’s standard deviation of points.

Outside of Pawlowski, et. al, some authors have constructed Gini coefficients as a measure for competitive balance (Schmidt and Berri, 2001).

Similarly to Pawlowski, et. al., Peeters (2009) finds a significant negative effect of the

Champions League coefficient on competitive balance at the league level. While Peeters’ publication focuses on the effect of broadcasting rights on competitive balance, he introduces a control variable to the regression to account for “total amount of prize money that the teams of a league have received relative to the total amount that was distributed in the Champions League, starting from the group phase” (9). The negative competitive balance effect indicated by the coefficient provides additional support that the Champions League financial system impairs competitive balance in European soccer leagues.

My research will be nestled amongst the existing literature. While Pawlowski, Breuer, and Hovemann (2010) find a decrease in competitive balance, they do not utilize any regression analysis. Although Peeters (2009) uses regression specifications, his work focuses on broadcasting rights. Alternatively, I will utilize regression to solely examine the effect of the payout system on competitive balance, which will show the magnitude to which it impacts competitive balance at the league-level. I will use a large number of European leagues, similar to Peeters, whereas Pawlowski, et. al. only examine the largest five European leagues. At the club-level, I want to determine whether club payroll is the mechanism through which owners use

UEFA Champions League payouts to decrease competitive balance. Furthermore, I am interested in identifying whether or not there is some cyclical effect where teams that qualify once for the Champions League begin to qualify season after season.

Nathan Vestrich-Shade

Section III: Data

The paper uses two panel data sets. In one, the league-level specification, the level of

observation is league, year. In the other, the club-level specification, the observation is club,

year. The primary sources of data for this paper are tables from domestic leagues and historical

UEFA Champions League payouts for European leagues from the 2003/04 season to the 2014/15

season. A league table provides end-of-season results for a domestic league. Useful statistics

include end-of-season place, goal differential, and points accumulated for each team. I use the

points from historical tables from worldfootball.net and statto.com to construct measures of

competitive balance. These websites include extensive data on European soccer competitions.

My hypothesis is current season competitive balance depends on the previous season’s UEFA

payout. For example, for the 2014/15 domestic league season, it is the 2013/14 UEFA payout

that should have any impact.

The league-level specifications analyze two unique samples. The first sample includes

15 top flight leagues for the 2003/04 to 2014/15 season5 from the 15 countries with the highest

UEFA coefficient rankings: Spain, Germany, England, Italy, France, Portugal, Russia, Ukraine,

Belgium, Netherlands, Turkey, Switzerland, Czech Republic, Greece, and Romania. The UEFA

coefficient determines the number of clubs from each domestic league that qualify for UEFA

competitions6. While these 15 leagues may be considered the most “successful” in terms of

results in UEFA competitions, the cutoff for including only 15 leagues is arbitrary. I begin to

account for payouts starting in the Round of 32 of the Champions League. Although I do not

include every league that has qualified for the Round of 32 in the time span, my sample should

5 Since the UEFA payout of interest is the one from the prior season, the time span for UEFA payouts is 2003/04 to 2013/14 and the time span for competitive balance measures is 2004/05 to 2014/15. The same will hold for data in the club-level analysis. 6 To see exactly how the UEFA coefficient is calculated, visit: http://www.uefa.com/memberassociations/uefarankings/country/. 9

Nathan Vestrich-Shade provide enough diversity and observations to have strong statistical power. Among these 15 leagues, I predict the size of the effect of payouts on competitive balance will vary. For instance,

England’s Premier League frequently has four teams in the Champions League, whereas the

Swiss Super League often only has one team. The increased number of teams capable of receiving payouts in England may mitigate the payouts’ effect on competitive balance. Since payouts are exclusive to one team in the Swiss Super League, the effect of payouts on competitive balance may be larger. The second sample consists of only the “Big Five” leagues:

England, France, Germany, Italy, and Spain. The “Big Five” leagues are considered the most prominent soccer leagues and earn the most revenue.

Following Pawlowski, Breuer, and Hovemann (2010), I construct several measures of competitive balance for the league-level specification. The three measures include a variation of the Herfindahl Hirshman Index, a concentration ratio, and the standard deviation of league points. Higher levels of the measures indicate lower levels of competitive balance. Therefore, to support the alternative hypothesis, I predict that the UEFA payout variable has a significant and positive effect, indicating lower competitive balance. For the club-level specification, two different dependent variables are used. The first is club payroll and the second is the probability of the club qualifying for the Champions League in the next season. To support the alternative hypothesis, I predict the UEFA payout variable has a significant and positive effect for both of the dependent variables.

The independent variable of interest is the total Champions League payout in a given year for both specifications. I collect data on payouts from the annual UEFA financial report. It includes a section dedicated to the Champions League. In the section, UEFA reports the total payouts awarded to each team for each round of the tournament. At the club-level, the

Nathan Vestrich-Shade independent variable is the total payout given to the club for each Champions League year. To get the Champions League payout for the league-level specification, I aggregate each league’s club-level payouts for each year of the Champions League.

I account for several control variables. The main control at the league-level is league revenue. In line with Peeters and Szymanski (2014), I include a dummy variable for Financial

Fair Play (FFP). Revenue for the “Big Five” leagues is collected from Statista. The FFP dummy variable takes a one in all years since 2011 when FFP first took effect. Since revenue data was limited for all 15 leagues, this variable is only included in the “Big Five” sample.

At the club-level, the sample differs slightly based on availability of payroll and revenue data. The sample consists of four clubs from four leagues (England, Germany, Italy, and Spain) from the 2008/09 to 2014/15 season7. I select the clubs from each league based on how frequently they finished in the top four spots of the domestic league over the 2009/10 to 2014/15 season. Since these leagues often send four teams to the Champions League based on the spot earned in the domestic league (this is not always the case depending on UEFA coefficients for a given season), it standardizes the sample and prevents overrepresentation by clubs from a particular league by including four clubs from each league. In the event of a tie, I select the club that finished higher in the top four more frequently. The 16 clubs include Arsenal, Chelsea,

Manchester City, Manchester United, Bayer Leverkusen, Bayern Munich, Borussia Dortmund,

Schalke 04, AC Milan, AS Roma, Fiorentina, Juventus, Atletico Madrid, Barcelona, Real

Madrid, and Valencia.

I include relevant determinants of the club’s payroll and performance as controls. When payroll is the dependent variable, I control for average age of the team and the club’s revenue.

Age data is from ESPN and revenue data is from Deloitte. When probability of UEFA

7 Again, this is UEFA payments from 2008/09 to 2013/14 while all other club-level data is from 2009/10-2014/15. 11

Nathan Vestrich-Shade qualification is the dependent variable, I control for the same variables and control for club payroll. The website SportingIntelligence publishes a “Global Sports Salaries Survey.” The payroll statistic in these publications is average annual payroll. Therefore, my payroll variable refers to average annual club payroll. Additionally, a dummy variable for FFP is included in the club-level specifications.

Figures 1-4 show scatter plots of the competitive balance measures against the log of league-level payouts for the 15 league sample. There are two clusters of the data: Leagues with loguefaleague observations less than 4 and leagues with loguefaleague observations greater than

4. The “Big Five” leagues tend to have higher observations of loguefaleague. In Figure 4, these patterns are depicted more closely for each league against the HICB measure. Summary statistics are presented for the league-level data in tables 1 and 2 and the club-level data in table

Section IV: Methodology

I develop two regression specifications to assess the effect of Champions League payouts

on competitive balance. The league-level specification analyzes competitive balance at the

league, year level while the club-level specification analyzes effects on payroll and Champions

League qualification at the club, year level. In the league-level model, I regress various

measures of competitive balance on UEFA payouts to the league. The main competitive balance

measure is a variation of the Herfindahl Hirshman Index, a common index used to calculate

market power, which Pawlowski, et. al. (2010) call the HICB index (190). The calculation can

be seen below, where s2 represents team i’s quadratic share of total league points in a given season:

Nathan Vestrich-Shade

! �! �� = !!! ! ×100 1/�

Furthermore, I utilize two different competitive balance measures to test for robustness. The first

is the concentration ratio for the top five teams in the league, where s represents team i’s share of

league points:

! � �5�� = !!! ! × 100 5/�

The second measure is the standard deviation of league points, where TPt,i represents team i’s

points in a given season and TP bar represents the average of league points:

! ! �� − �� = !!! !,! ! !,! �

The league-level specification includes the log of a contemporaneous UEFA payout variable to see if Champions League competitions have a significant effect on domestic league competitive balance. A significant result indicates that the competitive balance effect of

Champions League payouts is not confined to one season. I break the league-level analysis into two samples based on the availability of revenue data. The 15 league sample does not include a control variable for revenue, while the “Big Five” sample incudes a revenue control. Both samples include a dummy variable to control for FFP. Although the 15 league sample lacks the

Nathan Vestrich-Shade revenue control, the specification still provides valuable baseline estimates for the effect of

UEFA payouts on competitive balance, especially since the number of observations is much larger than the “Big Five” sample. The coefficient on ß2, logsduefa, is a measure of payout

dispersion. For a given Champions League season, the data set collects UEFA payouts for all

club participants from a given league. For league members that do not qualify, they receive a

zero for that season’s payout, which are aggregated when calculating the total UEFA payouts to

the league. Therefore, ß2 will measure the effect of a one percent increase in standard deviation

of payouts on competitive balance. For leagues with low competitive balance, I anticipate the

coefficient on ß2 to have a larger effect. The 15 league sample and “Big Five” sample include

random effects, denoted by ui.

15 league sample:

hicbit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3ffpit + ui + eit

crit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3ffpit + ui + eit

sdlpit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3ffpit + ui + eit

“Big Five” sample:

hicbit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3logrevenueit + ß4ffpit + ui + eit

crit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3logrevenueit + ß4ffpit + ui + eit

sdlpit = ß0 + ß1loguefaleagueit + ß2logsduefait + ß3logrevenueit + ß4ffpit + ui + eit

In the club-level model, the dependent variable switches to club payroll. At the club-

level, the Champions League payout to the club may not have a direct effect on competitive

Nathan Vestrich-Shade balance. However, if the payout has a significant effect on club payroll, this supports the notion that teams receiving Champions League payouts spend more on quality players. The payroll variable presents a viable mechanism through which negative competitive balance effects may exist. I term this the payroll effect. Therefore, I regress the log of club average annual payroll on the log of UEFA payouts at the club, year level, while controlling for log of club revenue,

FFP, and the average age of players. The model includes club fixed effects, denoted by αi.

logpayrollit = ß0 + ß1loguefait + ß2logrevenueit + ß3ffpit + ß4currentageit + αi + eit

The second effect I examine at the club-level is the probability of qualifying for the

Champions League next season based on the Champions League payouts received in the current season, or what I term the qualification effect. I use a logit and probit model to regress the probability of Champions League qualification for team i in season t+1 on the logged UEFA variable. I control for the same variables in the payroll effect model and add a control for the log of average annual club payroll. The model includes club fixed effects.

Pr(qualifyi(t+1)) = ß0 + ß1loguefait + ß2logpayrollit + ß3logrevenueit + ß4ffpit +

ß5currentageit + αi + eit

Section V: Results

At the league-level, the effect of UEFA Champions League payouts on competitive

balance depends on the dependent variable and sample used. While the positive coefficient on

the payout variable is in line with the alternative hypothesis, the statistical significance of the

Nathan Vestrich-Shade coefficient varies. Table 4 includes results for the three competitive balance measures for the 15 league sample. The loguefaleague coefficient is positive and statistically significant only when measured against the concentration ratio. As payouts to the league increase by 1%, the concentration ratio increases by approximately .065 units. When hicb or sdlp are regressed on loguefleague, the magnitude of the coefficient is less than 1 and statistically insignificant.

The “Big Five” league-level sample shows stronger support for the negative impact of

Champions League payouts (see Table 5). For two of the three competitive balance measures, the concentration ratio and standard deviation of league points, there is a positive and statistically significant coefficient on the loguefaleague variable. The coefficient is positive and statistically insignificant when looking at the hicb measure. For the “Big Five” leagues, a 1% increase in payouts increases the cr by approximately .145 units. The same change in payouts results in close to a .04 unit change in the sdlp measure. However, based on the Hausman test, the estimates for sdlp are not consistent when compared to the same model using fixed effects.

Therefore, for both the 15 league sample and the “Big Five” sample, the UEFA payouts only have consistent estimates with a positive and statistically significant effect on the concentration ratio.

While the results demonstrate some support for the alternative hypothesis that an increase in Champions League payouts has an adverse effect on league-level competitive balance, I fail to reject the null hypothesis in the club-level models and identify the mechanisms through which the competitive balance effect operates. When examining the payroll effect in Table 6, the coefficient on loguefa, the club-level Champions League payouts, is .049, which suggests that a

1% change in UEFA payouts increases average payroll by almost 0.5%. However, the loguefa coefficient is statistically insignificant. This result holds for the use of fixed effects and random

Nathan Vestrich-Shade effects although the Hausman test indicates that the random effects regressors are not consistent.

I choose to include these estimates for the sake of comparison and to better support the findings from the fixed effects. Therefore, I am unable to conclude that Champions League payouts increase the club’s average annual payroll. The only coefficient that is statistically significant under fixed and random effects is currentage, suggesting that a team with an older average age has a higher payroll, an intuitive result. The statistical significance of logrevenue depends on whether fixed or random effects are used. The coefficient is positive and statistically significant under random effects, but these estimates are inconsistent. Under fixed effects, it is positive and statistically insignificant for the 58 observations. However, by cutting off the data at all observations with logrevenue greater than 5, I lose 4 observations, but the logrevenue coefficient becomes statistically significant and positive under the fixed effects model, which is a much more sensible result as club revenue will play a large role in determining club payroll. The correlation between logpayroll and logrevenue is about 0.84.

Looking at the qualification effect in Table 7, when the qualify dummy variable is regressed on loguefa and other relevant controls, there are no statistically significant results.

Still, the direction of loguefa is at least positive. Therefore, I am unable to conclude that

Champions League payouts decrease competitive balance by increasing the probability of a club’s Champions League qualification. One way to improve upon assessing the qualification effect would have been to continue the Markov probability analysis from Pawlowski et. al.

(2010).

One of the most interesting results concerns the coefficient on the ffp dummy variable.

The statistical significance of this variable depends on the sample used. Under the 15 league sample (see Table 4), ffp is positive and statistically significant for hicb and cr, suggesting that

Nathan Vestrich-Shade the introduction of Financial Fair Play has actually led to decreased competitive balance across

European leagues. The results confirm the work of Peeters and Szymanski (2014). However, when switching to the “Big Five” sample, the ffp coefficient is positive but no longer statistically significant for all three competitive balance measures (see Table 5). The result is consistent with the findings of Peeters (2009) who concludes that leagues in a larger domestic market have stronger competitive balance. If leagues are in a wealthier domestic market, the clubs can insulate themselves more from any adverse effects from the Financial Fair Play regulations. In this way, the results are more pronounced for the 15 league sample that includes leagues from smaller domestic markets.

In the club-level models, I find no statistically significant effect for the ffp variable in the payroll effect regression (see Table 6). However, when I introduce an interaction term between ffp and logrevenue, I find a statistically significant and positive coefficient on ffp and a negative and insignificant coefficient on the interaction variable, ffprevenue. However, I am not inclined to accept the result when including the interaction term because when I drop four observations with logrevenue less than 5, the ffp term changes sign and becomes statistically insignificant.

Since the introduction of Financial Fair Play in 2011, club average annual payroll has remained unaffected.

Similarly, I find no significant effect for ffp in the qualification effect model (see Table

7). I am unable to conclude that Financial Fair Play had a substantial impact on a club’s probability of qualifying for the Champions League.

Nathan Vestrich-Shade

Section VI: Discussion

The results build upon the work of Pawloski, et. al. (2010) and Peeters (2009). I find that

Peeters’ (2009) result that the Champions League has an adverse effect on competitive balance is

robust to the concentration ratio competitive balance measure from Pawlowski, et. al (2010).

Furthermore, I separate this league-level analysis into two sub-sets: A “wealthy” subset

including only “Big Five” leagues and a broader subset including the “Big Five” and an array of

other notable European leagues. To that end, I find that the impact of Champions League

payouts on competitive balance is robust to the concentration ratio for the 15 league sample and

the “Big Five” sample.

With six total league-level regressions and only two yielding a positive and statistically

significant result, the effect of UEFA Champions League payouts on competitive balance is

inconclusive. Clearly, it depends on the measure used. Among the three measures of

competitive balance, it is interesting that only the concentration ratio has consistent, positive, and

statistically significant results under random effects. Of the three, it is difficult to determine

which measure “best” represents competitive balance. For the purposes of looking at the effect

of the UEFA Champions League payouts, the concentration ratio has some appealing properties,

namely that it relies only on calculating the share of points won only for the top five teams of a

league. While the leagues do not send five clubs to the Champions League, it is the top clubs

that will be in contention for these spots based on their domestic league performance. If CR rises

because the top five teams won a greater share of team points relative to the total league points

won, then competitive balance declines. Furthermore, the results show that CR rises and

competitive balance declines in part when Champions League payouts increase. Therefore, the

CR may be the best measure for capturing the effects of Champions League payouts because it

Nathan Vestrich-Shade relies only on the club points won by the top five teams, whose points in the current season should be most affected by the Champions League payouts from the prior season.

In addition, I try to identify the mechanisms through which competitive balance changes by looking at the payouts’ effect on clubs’ payroll and probability of qualification. Although I ultimately fail to reject the null hypothesis in these two instances, properly detailing the ways in which the UEFA payouts impact competitive balance is important because it allows regulators to tailor policy to specifically address the needs of European soccer leagues. Furthermore, I include a dummy variable for Financial Fair Play to examine the effect of this regulation on competitive balance. I find it has a detrimental impact on competitive balance in the 15 league sample but has no statistically significant impact for the “Big Five” leagues. The result follows findings from Peeters (2009), suggesting that leagues in large domestic markets are more balanced. The biggest leagues may be better situated to resist the competitive balance-destabilizing effects from

Financial Fair Play.

What is quite concerning about the league-level results is that the UEFA payouts constitute a minor portion of league revenue. For instance, see the summary statistics for the share variable, which shows the fraction of total league revenue represented by UEFA payouts

(see table 8). With a mean of 6% and a max of 12%, it is surprising that such a small source of revenue can still have a significant effect on league-level competitive balance. Although I do not find a significant effect of Champions League payouts in the club-level models, a similar trend holds: UEFA payouts represent a small fraction of a club’s total revenue. The mean for clubs is about 9% and the max is 28% (see table 9). While the mean is understated by the clubs receiving

0 for UEFA payouts in a given year, it is still clear that Champions League revenue is a less substantial source of a club’s revenue. It would be particularly helpful for future research to

Nathan Vestrich-Shade collect club-level revenue data for clubs in smaller leagues because the UEFA payouts will likely represent a larger share of the clubs’ revenue.

There are some obvious concerns with the results, namely the fact that the number of observations is relatively small in all of the regressions, particularly in the case of the “Big Five” league-level models. With only 55 observations, the results can only give a baseline estimate. A more robust sample is clearly needed. A similar problem exists for the club-level models, which only have 58 observations. In the 15 league sample, data constraints prevent me from including revenue as a league-level control. Similarly, in the club-level models there are missing payroll and revenue observations from limited data. Future research should try to include more years and include a more robust data set, especially in regards to payroll and revenue data.

Some attention should be given to my use of random effects in the league-level model rather than the more common use of fixed effects. The random effects model assumes that there are no other explanatory variables that affect the competitive balance measures. If this is the case, these variables would be captured in the error term and correlated with the regressors in my current models, making the estimates inconsistent. I emphasize that the Hausman tests provide strong support that I have consistent estimates for my regressors under random effects (see tables

10-12). The probability>chi squared is much larger than .05 for all three of the competitive balance measures for the 15 league sample (see table 10) and much larger than .05 for two of the three competitive balance measures in the “Big Five” sample (see table 11). I opt for fixed effects in my models unless the Hausman test supports the use of random effects as consistent estimators. I make note of any use of inconsistent random effects estimators in the results (for instance, in column 3 of table 5 or column 2 of table 6). Furthermore, the use of random effects

Nathan Vestrich-Shade in Peeters (2009) provides additional support for their inclusion in my work as a valid econometric method.

Since UEFA payouts have a significant effect on decreasing competitive balance of the league, it is imperative that regulators find a way to minimize the payouts’ impact. One of the most obvious policy changes would be to reduce the payouts dramatically or remove them from the competition entirely. However, such policy may provide disincentive for teams to play well in the tournament in the first place. A more subtle change to this policy would be to remove the

“market pool” portion of Champions League payouts, which distributes revenue based on the clubs’ domestic TV broadcasting market. In the past, market pool revenues have accounted for nearly half of total Champions League revenue received for many “Big Five” clubs. By eliminating this source of revenue that is linked closely to TV broadcasting, UEFA may see leagues become more balanced. While other policies could enhance competitive balance in

European leagues and make Champions League qualification more varied amongst the teams in the league, such a discussion is beyond the scope of this research. Altering the payout system could have a substantial impact on the league’s competitive balance without dramatically reducing qualifying clubs’ total revenue.

In terms of the findings surrounding Financial Fair Play, the program is still in its early stages. Reassessing its impact in several years is important to see if the findings of my research are sustained over a longer time sample. Reducing revenue from the Champions League brings revenue from the wealthiest clubs closer to the least wealthy clubs, if only marginally.

Nathan Vestrich-Shade

Section VII: Conclusion

The results presented support the hypothesis that UEFA Champions League payouts have

a negative impact on the league’s competitive balance, but the statistical significance of the

effect depends on which measure is used. The Champions League payouts only have a positive

and statistically significant effect on the concentration ratio, and this result applies to both the 15

league sample and the “Big Five” sample. However, I find that the payouts have no effect on the

club’s payroll or the club’s ability to qualify for the Champions League in the following season.

Based on this research, there is room to advocate for reform in the European soccer system by

altering the payout system. Still, the issue remains open for future research. In particular, access

to a larger data set that includes more years will provide stronger results and a clearer picture of

the story than the results captured in this research. Furthermore, research on the Champions

League should seek to better address the mechanisms that lead to a decline in competitive

balance.

Nathan Vestrich-Shade

Appendix

Figure 1: HICB vs. UEFA payouts (15 league sample) 125 120 115 HICB 110 105

2 3 4 5 loguefaleague

Figure 2: CR vs. UEFA payouts (15 league sample) 160 150 140 CR 130 120

2 3 4 5 loguefaleague

Nathan Vestrich-Shade

Figure 3: SDLP vs. UEFA payouts (15 league sample) 20 18 16 SDLP 14 12 10

2 3 4 5 loguefaleague

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Figure 4: HICB vs. UEFA payouts by league (15 league sample)

1 2 3 4 125 120 115 110 105

5 6 7 8 125 120 115 110 105

9 10 11 12 125 HICB 120 115 110 105 2 3 4 5

13 14 15 125 120 115 110 105 2 3 4 5 2 3 4 5 2 3 4 5 loguefaleague Graphs by League

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Table 1: League-level Summary Statistics (15 League Sample)

Table 2: League-level Summary Statistics (“Big Five” Sample)

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Table 3: Club-level Summary Statistics

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Table 4: 15 League Sample

(1) (2) (3) hicb cr sdlp loguefaleague 0.524 6.510* 0.964 (0.41) (2.47) (1.11)

logsduefa -0.667 -5.960 -0.508 (-0.40) (-1.71) (-0.44)

ffp 1.178* 2.348* 0.586 (2.16) (2.08) (1.55)

_cons 109.5*** 127.3*** 12.07*** (48.97) (27.92) (8.30) N 147 147 147 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

Nathan Vestrich-Shade

Table 5: “Big Five” Sample

(1) (2) (3) hicb cr sdlp loguefaleague 1.449 14.53** 3.982* (0.62) (2.63) (2.01)

logsduefa 0.212 -10.18 -2.139 (0.08) (-1.55) (-0.91)

logrevenue 2.521* 6.860* 2.010* (2.06) (2.40) (1.96)

ffp 0.815 2.502 0.760 (1.15) (1.41) (1.19)

_cons 83.17*** 48.31** -12.85* (11.62) (3.11) (-2.31) N 55 55 55 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

Random effects estimates in column 3 are not consistent according to the Hausman test.

Nathan Vestrich-Shade

Table 6: Club-level Payroll Effect

(1) (2) (3) (4) (5) logpayroll logpayroll logpayroll logpayroll logpayroll loguefa 0.0494 -0.0104 0.0693 0.0325 0.0780 (0.46) (-0.10) (0.83) (0.32) (0.92)

logrevenue 0.130 0.694*** 0.333* 0.410 0.259 (0.64) (8.70) (2.06) (1.78) (1.35)

ffp 0.149 0.0739 0.0397 1.986* -0.601 (1.85) (1.07) (0.60) (2.40) (-0.68)

currentage 0.0709* 0.0495* 0.0720** 0.0646* 0.0743** (2.53) (2.07) (3.31) (2.41) (3.36)

ffprevenue -0.320* 0.109 (-2.23) (0.73)

_cons -1.111 -3.561*** -2.258* -2.493 -1.918 (-0.95) (-4.86) (-2.40) (-1.96) (-1.81) N 58 58 54 58 54 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

Column 2 gives estimates using random effects. Although the Hausman test indicates these estimates are inconsistent, I include them in this table for comparison to the fixed effects in columns 1 and 3. Column 3 includes observations of logrevenue that are greater than 5. Columns 4 and 5 include the ffprevenue interaction term. Column 5 includes observations of logrevenue that are greater than 5.

Nathan Vestrich-Shade

Table 7: Club-level Qualification Effect

(1) (2) qualify qualify qualify loguefa 0.970 1.606 (1.02) (0.93)

logpayroll 0.853 1.164 (0.87) (0.68)

logrevenue 0.790 1.699 (0.88) (0.99)

ffp -0.936 -1.482 (-1.34) (-1.22)

currentage 0.0855 0.192 (0.40) (0.51)

_cons -9.612 -18.96 (-1.34) (-1.43) N 58 58 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

Column 1 is estimated using a probit model, whereas column 2 is estimated using a logit model.

Nathan Vestrich-Shade

Table 8: Proportion of “Big Five” Leagues’ Total Revenue Represented by UEFA Payouts

Table 9: Proportion of Clubs’ Total Revenue Represented by UEFA Payouts

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Table 10: Hausman Tests for 15 League Sample

RE vs. FE (HICB) RE vs. FE (CR) RE vs. FE (SDLP)

Chi-squared (3) 3.37 4.06 1.13

Prob>Chi squared 0.3382 0.2551 0.7698

Table 11: Hausman Tests for “Big Five” Sample

RE vs. FE (HICB) RE vs. FE (CR) RE vs. FE (SDLP)

Chi-squared (4) 2.35 3.14 12.31

Prob>Chi squared 0.6712 0.5347 0.0152

Nathan Vestrich-Shade

Table 12: Hausman Test for Payroll Effect

RE vs. FE

Chi-squared (4) 31.72

Prob>Chi squared 0.0000

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Table 13: League-level Variable List

Variable Description

league Ranges from 1-15 (full list of leagues corresponding to the number is available)

year The year when competitive balance is measured (the UEFA payout is from the prior year) hicb H-Index of Competitive Balance from Pawlowski et. al. (2010)

cr Concentration ratio from Pawlowski et. al. (2010)

sdlp Standard deviation of league points from Pawlowski et. al. (2010)

uefaleague The total Champions League payout to the respective league in millions of Euros

loguefaleague The log of total Champions League payouts to the league

sduefa Standard deviation of Champions League payouts to the league

logsduefa The log of the standard deviation of Champions League payouts

revenue Total revenue of the league in a given year in millions of Euros

logrevenue The log of total revenue of the league

ffp Dummy variable equal to one starting in 2011 to indicate Financial Fair Play

share Total Champions League payouts to the league divided by the league’s total revenue

Nathan Vestrich-Shade

Table 14: Club-level Variable List

Variable Description

club Ranges from 1-16 (full list of clubs corresponding to the number is available)

year The year indicating the club’s average annual payroll (the UEFA payout is from the prior year) currentage The average current age of the club in a given season

ffprevenue Interaction term between ffp and logrevenue variables

qualify A dummy variable equal to 1 if the club qualifies for the Champions League in the following season uefa The total Champions League payout to the respective clubs in millions of Euros

loguefa The log of total Champions League payouts to the club

payroll The average annual payroll of the club in USD

logpayroll The log of the club’s average annual payroll

revenue Total revenue of the club in a given year in millions of Euros

logrevenue The log of total revenue of the club

ffp Dummy variable equal to one starting in 2011 to indicate Financial Fair Play

share Total Champions League payouts to the club divided by the club’s total revenue

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Table 15: Number Identifier for Leagues

Number League

1 Belgium

2 Czech Republic

3 England

4 France

5 Germany

6 Greece

7 Italy

8 Netherlands

9 Portugal

10 Romania

11 Russia

12 Spain

13 Switzerland

14 Turkey

15 Ukraine

To account for string variables, I use numbers for each league instead of the country name of the league.

Nathan Vestrich-Shade

Table 16: Number Identifier for Clubs

Number Club (League)

1 Arsenal (England)

2 Chelsea (England)

3 Manchester City (England)

4 Manchester United (England)

5 Bayer Leverkusen (Germany)

6 Bayern Munich (Germany)

7 Borussia Dortmund (Germany)

8 Schalke 04 (Germany)

9 AC Milan (Italy)

10 AS Roma (Italy)

11 Fiorentina (Italy)

12 Juventus (Italy)

13 Atletico Madrid (Spain)

14 Barcelona (Spain)

15 Real Madrid (Spain)

16 Valencia (Spain)

To account for string variables, I use numbers for each club instead of the name of the club. 39

Nathan Vestrich-Shade

References

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Data:

Deloitte. “European soccer club revenues: 2009-2014.” http://www2.deloitte.com/uk/en/pages/sports-business-group/articles/deloitte-football-money- league.html.

Nathan Vestrich-Shade

ESPN FC. “Player age for clubs in England, Germany, Italy, and Spain: 2009-2014.” http://www.espnfc.us/.

SportingIntelligence. “Global sports salary survey: 2009-2014.” http://www.sportingintelligence.com/finance-biz/salaries-report/.

Statista. “Revenue of the biggest (Big Five*) European soccer leagues from 1996/97 to 2013/14 (in million euros).” http://www.statista.com/statistics/261218/big-five-european-soccer-leagues- revenue/.

Statto. “Tables for 15 European leagues: 2004-2014.” http://www.statto.com/.

UEFA. “UEFA financial reports: 2003-2015.” http://www.uefa.org/documentlibrary/aboutuefa/financialreports/index.html.

Worldfootball.net. “Tables for 15 European leagues: 2004-2014.” http://www.worldfootball.net/.