abstract - erasmus university rotterdam€¦ · web viewcompetitive balance is crucial in...

2015

The peculiar economics of

football:An analysis of the effect of the Great

Recession on competitive

balance in the top division of Italian

football

Table of Contents

Title Page 1

Table of Contents 2

Abstract 3

Introduction 4-5

Theoretical Framework 6-7

Literature Review 8-15

Methodology 16-19

Data and Results 20-38

Interpretation and Discussion 39-41

Conclusion 42-44

Bibliography 45-46

Appendix: Summary Statistics 47-48

2

AbstractCompetitive balance is crucial in professional sports. If one team excessively dominates a

league, results become predictable and the league loses its appeal. On the other hand, few have

investigated the effects of the Great Recession of 2008 on the football industry because many

question to what extent the two are even connected. This paper investigates to what extent the

wider macroeconomic ecosystem is connected to the football industry by studying how the Great

Recession affected competitive balance in the Italian Serie A division. The results are not entirely

conclusive because the variables used to measure the Recession, namely the unemployment rate

and GDP per capita, did not always significantly affect competitive balance. Nevertheless, the

results suggest that the crisis affected clubs differently; the bigger clubs have maintained their

dominance while the smaller clubs have struggled to keep up with the leading pack.

3

Introduction“Ooh Lord make us good, but not that good” (Schmidt & Berri, 2001).

The words of Walter Neale, who studied competitive balance in Major League Baseball (MLB)

in the United States of America, perfectly encapsulate the curious dilemma sports teams across

the world face. Every team desires sustained success but realizes that too much of it will dull

competition in the league and possibly, as a consequence, interest in the league as a whole. Fans

of opposing teams find no excitement in the games anymore and lose interest as the league

becomes increasingly predictable. Successful teams may become the victims of their own

success as the difference between the have’s and have not’s increases; competitive balance

slowly disappears.

To discuss how profoundly the global financial crisis of 2008, often referred to as the Great

Recession1, affected every facet of the global economy seems almost trivial at this point. One

sees it on the news on an almost daily basis; all the lost jobs, the worrying increase of income

inequality, the austerity cuts, the decline in household income, and so much more. However, few

have considered how this crisis has affected the sports industry or, specifically, the European

football industry. Is it because people assume that the football industry operates in its own little

bubble, isolated from the perils of the global economy? Is this multi-million dollar industry so

unique that it can operate efficiently without experiencing cyclical fluctuations in economic

indicators whenever the global economy does?

Thus follows the research question:

How did the financial crisis of 2008 affect the competitive balance of the Italian Serie A?

1 The exact timing of the crisis varied by country. For purposes of this paper, it is only relevant to consider when the crisis began in Italy.

4

The Italian men’s top division of professional football, also known as the Serie A, is a fascinating

case to study. Italy possesses such a rich cultural history and fascinating obsession for football

that its alarming fall from grace over the last two decades must have an explanation. Is it

economics? Is it politics? This is a league that boasted the mightiest teams and players in the 80s

and 90s but is now plagued by declining stadium attendances and revenues, skyrocketing

expenses, and even the occasional case of a bankruptcy, as in the case of the once-great team

Parma FC (The Guardian, 2015).

This topic is very relevant scientifically when one considers the existing body of literature that

discusses the notion of a financial crisis in European football. Lago et al. examine whether such a

crisis indeed currently exists. They use two types of evidence to analyze this: an imbalance

between income and expenditures and the evidence of rising debt. They conclude that there does

seem to be a systemic crisis present and recommend policy measures to the Union of European

Football Associations (UEFA) to tackle this problem (Lago, Simmons, & Szymanski, 2006).

Di Betta and Amenta (2010) study competitive balance in the Italian top division Serie A from

the intermediate and top levels of competitive balance. That is, the uncertainty of a club’s end-

of-season ranking and the long-term rivalry between clubs for trophies respectively. They find

that in the Italian championship there is “an aristocracy of clubs composed of at least 4 and at

most, 10 clubs, always present in the championship” that emerged from “a self-reinforcing

mechanism among clubs which reasonably depends on exogenous reasons.” They do not state

whether this is necessarily negative, but their analysis certainly highlights the pressing scientific

relevance of studying this topic (Di Betta & Amenta, 2010).

The true social relevance that supports this research question is that it allows for an insight into

how closely the football industry is integrated with and connected to the wider global economy.

Can it operate in its own little isolated bubble? Is it unfazed by cyclical fluctuations in

macroeconomic indicators? If so, what does this mean for the credibility of the economics of

football and sports as a whole? Such unanswered questions only serve to highlight the important

social relevance of this research.

5

Theoretical Framework

Before one can analyze the research question, certain vague but key terms must be defined in

order to facilitate the analysis and make the process more concrete. For instance, what is meant

by ‘competitive balance’? In the context of this paper, it is defined as the average tension present

in and the predictability of each game. Thus, it is looked at on a per-game level for every team. It

is best presented in a series of questions: What is the difference between the probability that one

team wins and the other loses? How easily can results be predicted before each game? If every

game is very predictable and the difference between the probability that one team wins and the

other loses is great, competitive balance is considered to be low.

Since there is a strong focus on the notion of a financial crisis, this term, and all its relevant

derivatives, must be defined. A financial crisis, in this context, is a recession which is defined as

a period of at least two consecutive quarters of decline in the Gross Domestic Product

(Investopedia LLC, 2015). The Gross Domestic Product (GDP) measures the total volume of

production within a country’s borders. It is considered a measure of a country’s wealth. The

growth rate of the GDP is its percentage change from one year to the next. Real GDP is GDP

adjusted for price changes, i.e. deflation and inflation. Furthermore, GDP per capita is the GDP

divided by the total population of a country (Krugman, Obstfeld, & Melitz, 2012). The

unemployment rate is the percentage of the total labor force that is willing and able to work but

is unable to find work. Unemployment is defined as the people who do not have a job but have

actively looked for one in the past four weeks, and are able to work (Borjas, 2013). Lastly,

investment spending is the portion of GDP used to increase a country’s stock of capital.

(Krugman, Obstfeld, & Melitz, 2012).

6

Thus, from this conceptualization of key terms follows the hypotheses that will be tested in this

paper:

Hypothesis 1: There is a negative relationship between the unemployment rate in Italy and

competitive balance in the Serie A.

The intuition behind this hypothesis is that a rise in the unemployment rate is a key characteristic

of a crisis and that a crisis affects competitive balance negatively. This is because the bigger

clubs, already entrenched in their solid domestic positions, are able to maintain their status with

their spending power while smaller clubs feel the drop in spending power disproportionately

stronger.

Hypothesis 2: There exists a positive relationship between real GDP per capita in Italy and

competitive balance in the Serie A.

GDP per capita is not only considered to be an important indicator of a crisis, but also one of the

average income and spending power of a country’s citizens2. Intuitively, bigger teams have less

room to grow financially and thus an increase in GDP per capita would grant smaller teams the

potential for greater financial growth than bigger teams. This in turn allows them to close the gap

between them and the leading pack and consequently increase competitive balance.

Literature Review2 Obviously, to measure the true spending power of a country’s population, one must account for inflation. This, however, is not relevant for the purpose of this analysis.

7

Mills and Fort (2014) examined the impact of the three main types of outcome uncertainty ―

game uncertainty (GU), play-off uncertainty (PU), and consecutive season uncertainty (CSU) ―

on league-level annual attendance in the National Basketball Association (NBA), National

Hockey League (NHL), and National Football League (NFL). Thus, they studied the validity of

Rotemberg’s ‘uncertainty of outcome’ hypothesis3. To capture GU, they used the measures

published in established literature, namely the Ratio of Standard Deviations and the Herfindahl

Index of Competitive Balance (HICB), which measure the breadth of the distribution of winning

percentages (Mills & Fort, 2014).

For PU, they decided to use the playoff-uncertainty measure devised by Krautmann, Lee, and

Quinn (2011). Additionally, they used the correlation in team performances across seasons as an

indicator of the CSU. Specifically, Mills and Fort tested the attendance series of each league for

stationarity using the Augmented Dickey-Fuller and Phillips-Perron tests. If the null of a unit

root could not be rejected, the series had to be further tested for stationarity with break points

using the Lagrange-Multiplier test. If the attendance series was stationary with one or more break

points, then the Bai-Perron method (BP) was used as a separate regression to study their

statistical significance and qualitative characteristics.. Using these methods, they estimated the

impact of GU, PU, and CSU on average attendances in the NBA, NHL, and NFL (Mills & Fort,

2014).

Mills and Fort concluded that there was very little evidence to suggest that all three types of

outcome uncertainty significantly impacted league-level annual attendance levels in the three

leagues. In the NFL, only PU had a significant impact. However, in the NBA, only GU had a

significant impact. That is, very exciting and tense games in the NBA increase attendance levels

while in the NFL only very exciting play-off series achieved the same result. Interestingly, if the

HICB was used instead of the Tail-likelihood measure, only CSU had a significant impact in the

NBA. This indicates that the results were not robust to changes in measures and that the

uncertainty of outcome hypothesis be deemed dubious at best. Lastly, they found that although

there was some statistical significance behind their results, there was minimal economic and

3 In 1956, Simon Rottenberg of the University of Chicago wrote a seminal sports economics paper where he hypothesized that games with uncertain outcomes are more likely to be viewed by fans (International Journal of Sport Finance Blog, 2011).

8

practical significance behind it. As they state: “Marginal alterations in outcome uncertainty can

improve league revenues only trivially in the NBA, NFL, and NHL.” They seemed to conclude

that the few instances where outcome uncertainty affected attendance levels, this effect did not

have any solid implications for policy-making (Mills & Fort, 2014).

In addition to the aforementioned empirical study, Lago, Simmons, and Szymanski studied

whether there was indeed a financial crisis in European football. They asked three crucial

questions: “Is there currently a financial crisis? What are the causes of the current financial

problems of football clubs? What are the solutions?” In order to answer these questions, they

noted that a systemic crisis shares two common characteristics. Firstly, there is a common

denominator of problems affecting all clubs negatively financially. Secondly, there is the

possibility of a contagion similar to that of a banking crisis. That is, if one club experiences a

crisis, it can threaten to spillover to other clubs. The second point is particularly prominent

because of the way clubs are connected― if one club were to fail, others would have less fixtures

to play and earn less revenue, both of which may undermine the credibility of the competition

(Lago, Simmons, & Szymanski, 2006).

The authors recognized that the current crisis cannot be one of income because of the dramatic

aggregate rise in clubs’ income, which was fueled by TV broadcasting rights, since the late

twentieth century. However, they recognized a curious contradiction in their analysis; football is

considered to be a normal good4 by economists yet here they spoke of the income of clubs rising

and the existence of a financial crisis in the same breath. Nevertheless, Lago et al. presented two

types of evidence to examine this claim. Firstly, they studied whether there was an imbalance

between income and expenditures and, secondly, whether there was consistent evidence of

unsustainably rising debt. Can operating losses be sustainably funded for the long-term? If not,

this might be a sign that clubs are struggling to find sources of finance. In this initial phase, they

found that, coincidentally, only in Italy is there a financial crisis for both the small and big clubs


What, then, were the causes of this crisis? The authors found that, paradoxically, it may be

exactly because income has risen so dramatically for football clubs that this crisis has occurred in

4 A normal good is one for which demand increases as income increases (Lago, Simmons, & Szymanski, 2006).

9

this manner. Clubs became carried away by these new riches and began to overspend and

accumulate debt in the expectation that the bubble would keep growing. Shareholders, who seek

to maximize returns on their investments, place excessive trust in club directors, who seek to

maximize success and prestige on the pitch. This principal-agent problem5 was exacerbated

because these shareholders scarcely monitored these directors. Furthermore, a lack of regulation

could be a cause of a crisis. For instance, the tight regulation in France has protected it from such

a situation. Meanwhile, in Germany, due to the ownership structure of clubs, clubs are very

restrained in their borrowing capacity and ability to attract exterior finance. Lastly, in countries

such as Spain and Greece, the authors found that there may be the case of clubs being ‘too big to

fail.’ Thus, their respective governments would be willing to intervene and bail out these clubs in

case of financial distress, in similar vein to when banks are bailed out during banking crises


Before concluding with policy recommendations, the authors warned of the problem of

credibility with any type of strict, draconian policies. Will UEFA be willing to strongly enforce

regulations on both massive European clubs as well as smaller, less prestigious ones?

Nevertheless, they offered a range of policy recommendations. Firstly, the national football

associations must adopt stricter financial regulations to ensure the long-term solvency of their

member clubs. Secondly, and most controversially, they suggested a restructuring of European

leagues to mirror that of American sports leagues. This includes, but is not limited to, the

imposition of salary caps, draft rules, and revenue-sharing policies (Lago, Simmons, &

Szymanski, 2006).

Di Betta and Amenta (2010) added to the existing literature by analyzing competitive balance in

the Italian top division from the year 1929 until 2009. They distinguish between three levels of

competitive balance: at the lowest level there is game uncertainty, at the intermediate level there

5 The principal-agent problem is one where the objectives of the principal, who hires the agent to perform a certain action that affects his payoffs, differ with that of the agent, who performs said actions. It is especially present when the actions of the agent are difficult or impossible to observe. In this specific case, it seems that moral hazard, i.e. when the actions of the agent are not in the best interests of the principal, is the problem (Besanko, Dranove, Shanley, & Schaefer, 2010).

10

is seasonal competitive balance, and at the highest level there exists the ‘excitement’ over the

long-term rivalry between clubs over trophies. They looked at the issue from two perspectives:

historical and seasonal competitive balance. The former is based on the cumulated rankings at

the end of each season while also accounting for a team’s position obtained in the preceding

season while the latter is based on points obtained and the points-gap between teams (Di Betta &

Amenta, 2010).

In order to measure the different levels of competitive balance, the authors used three measures:

the Herfindahl index6, a normalized version of this index, and the Gini coefficient7. Since the

Herfindahl index is commonly used to measure market power in an industry, Di Betta and

Amenta (2010) essentially equated high levels of market power to low levels of competitive

balance. They found that seasonal competitive balance does not depend on which version of the

Herfindahl index one uses; thus, it seems to be a robust measure. Furthermore, it seems that the

seasonal level of competitive balance is much more unbalanced than the long-term level.

This finding is reinforced, at least in a static sense, when using the Gini coefficient as a measure

of competitive balance (Di Betta & Amenta, 2010).

Since historical competitive balance seemed to be so irrelevant when compared to seasonal

competitive balance, Di Betta and Amenta (2010) isolated it in order to investigate the extent to

which a self-fulfilling prophecy might occur where a few clubs continue winning indefinitely

and the rest remain in mediocrity. They found that this did indeed occur, and to a worrying

extent too; there seems to be between four and ten clubs that have a firm grasp of power in the

league. Thus, it naturally followed that the problem had to be investigated from a different

perspective: what is the chance that promoted clubs could break the established order? Using

time-series analysis, they found that increasing the number of promoted8 clubs by one increases

the share of points that all promoted clubs gain by 4.5 percentage points. The authors suggested

6 The Herfindahl index (also known as the Herfindahl-Hirschman index) is an indicator of market concentration in

an industry. It equals the sum of the squared market shares of all firms in the industry i.e. ∑i

(Si)2 where Si is the

market share of firm i (Besanko, Dranove, Shanley, & Schaefer, 2010). 7 The Gini coefficient, along with the Lorenz curve, is used to measure inequality. The Lorenz curve graphically depicts the cumulative share of income that accrues to the various quintiles of households or, in this case, firms. An increase in the coefficient effectively illustrates an increase in (income) inequality. In the context of this paper, higher income inequality equates to lower competitive balance (Borjas, 2013).8 This is equivalent to entry and exit of clubs into the industry.

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two key policy measures in order to foster all forms of competitive balance in Italy. Firstly,

increasing the number of promoted teams into the first division in order to wrestle away some

power from the giants and, secondly, decreasing the total number of teams in the first division in

order to further foster seasonal competitive balance (Di Betta & Amenta, 2010).

Dejonghe and Van Opstal (2010) studied the issue of competitive balance in European football

from a different perspective. They examined competitive balance between national leagues after

the monumental Bosman ruling of 19959 using empirical and theoretical evidence. UEFA argued

that the pre-Bosman transfer system was just because the transfer fees were a form of revenue

sharing that maintained and even enhanced competitive balance. Sports economists, however,

argued that the pre-Bosman system would increase the concentration of player talent in the hands

of a few teams and decrease competitive balance. Therefore, they advocated for greater factor

mobility in the player-transfer market. They often used the invariance principle, which stated the

distribution of player talent would not be affected by the distribution of ownership rights of the

players, to support their argument10 (Dejonghe & Van Opstal, 2010).

One of the implications for competitive balance that resulted from the Bosman case was that

clubs lost their monopsony power and the (best) players now had monopoly power when

negotiating contracts. The opening of the labor market suddenly made the amount of player

talent hugely variable instead of relatively fixed. Due to this, European clubs became intensely

competitive in the acquisition of player talent. However, clubs located in bigger product markets,

such as England, Italy, or Spain, had a comparative advantage over counterparts in smaller

markets, such as The Netherlands, Belgium, or Portugal because of the disparaging differences in

turnover; the small clubs simply could not compete financially with the big clubs when acquiring

players. The authors suggested that the big clubs, due to their massive market power, became

market leaders that acquire the best players first and the small clubs settle with what remains.

9 The Bosman case occurred concluded on December 15th, 1995, when the European Court of Justice declared that the transfer system at the time in European football conflicted with Article 39 of the European Community treaty. Consequently, the court ruled that all players at the end of their contracts now became free agents allowed to sign for whichever club they so wish to sign for. Thus, a club wishing to sign a player that was out-of-contract need not pay a transfer fee to the player’s existing club (Dejonghe & Van Opstal, 2010).10 The invariance principle was inspired by Nobel Laureate Robert Coase’s famous Coase Theorem which stated that, given zero transaction costs and no market restrictions, bargaining will lead to optimal economic efficiency (Dejonghe & Van Opstal, 2010).

12

This means that clubs from the big markets solidify their dominant positions over time as player

talent becomes concentrated in these markets (Dejonghe & Van Opstal, 2010).

The authors examined the evolution of the UEFA National Association Coefficient11 by applying

a multiple regression analysis (Ordinary Least Squares). They found that, indeed, clubs from the

‘Big Five’ countries (i.e. England, Spain, Germany, Italy, and France) maintain a significantly

higher and increasing UEFA National Association Coefficient level than that of the smaller

countries. To solve this problem, the authors suggested two solutions. Firstly, they suggested

merging smaller markets, such as those in the Benelux region, in order to strengthen the financial

position of clubs in those markets. Secondly, they proposed legislation that would close the

market to a degree, such as through restrictions on foreign players and/or quotas on homegrown

players, i.e. players that were raised in a club’s own academy. The challenge with such

legislation is that it may contravene EU laws regarding free movement of labor (Dejonghe &

Van Opstal, 2010).

Lastly, Baroncelli and Lago contributed important work to the existing literature by analyzing

financial data from the Italian top division in order to discover the reasons for the evolving

financial crisis in Italian football and offer solutions to remedy it. They note the worrying

discrepancy between the increase in turnover (216%), average total costs (487%), and salaries

(453%) over the past decade. An analysis of the profit and loss accounts revealed that the Italian

football business was one that was close to bankruptcy (Baroncelli & Lago, 2006).

When exploring the possible reasons for the evolving crisis, Baroncelli and Lago (2006) found a

curious shift in the revenue composition of Italian clubs. In the 1990-1991 season, match-day

ticket sales accounted for 60% of total revenue but a decade later, it accounted for only 16% of

total revenue. Furthermore, almost all Italian stadia were, and still are, publicly-owned by the

local municipalities, a feature that seriously hampers the clubs’ ability to diversify revenue 11 This measures the performances of teams from a country in European Cup competitions during the last five seasons.

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streams. Additionally, the development of pay-TV and strong competition between broadcasting

companies for the rights to air Italian first and second division football led to a dramatic increase

in clubs’ revenue from this revenue source. Thus, it was no surprise to see that TV revenue

became such a pivotal source of income, constituting 54% of total income in the year 2001

(Baroncelli & Lago, 2006).

There are, however, two key events relating to TV revenue that strongly affected the income

statements of Italian clubs. Firstly, on January 30, 1999, Decree Law No. 15 was passed which

allowed clubs to negotiate TV rights individually with broadcasting companies. This was in

contrast to the situation beforehand where the Lega Calcio, representing all clubs, negotiated

these rights collectively12. Secondly, TV companies could not meet the growth expectations of

Italian clubs with respect to the money they were paying for the rights to broadcast matches.

Slowly, the value of these contracts decreased in value and the alarm bells began to ring. This

was the part of the pie that carried a 54% weight to it and now suddenly began to shrink in value

and content. Unfortunately, the small clubs suffered the most from this change in ruling13. The

biggest clubs have the biggest games that attract the biggest TV audience and thus small clubs

struggled to secure lucrative broadcasting contracts (Baroncelli & Lago, 2006).

So how then, the authors wondered, can this crisis be fixed? They suggested a variety of policy

measures to do so. Firstly, clubs must seriously reduce their wage bills. Secondly, they suggested

more inter-club business relationships, mainly between big and small clubs, similar to joint

ventures and partnerships in other industries. Thirdly, they must strongly enhance their

international brand images in order to capitalize on international customers that could strongly

boost revenues. Lastly, and most controversially, they suggested the creation of a European

Super League. This would entail having the biggest teams in Europe (e.g. Juventus, Real Madrid, 12 Nowadays, collective negotiation of TV rights is quite a common practice that aims to ensure that the income from this revenue stream is distributed relatively evenly and to enhance income equality between clubs.13 In 2010, this ruling was reversed again so that the Lega Calcio again negotiated these rights on behalf of the entire league (Wikipedia, 2015).

14

Barcelona, Chelsea etc.) breaking away from their domestic leagues and only playing against

each other in a league governed by its member clubs. The smaller clubs would suffer a severe

downsizing of the league but also experience less competition and a sharp reduction in the wage

bill (Baroncelli & Lago, 2006).

All in all, this overview of key literature illustrates the importance of competitive balance within

the industry, even though general opinion is sharply divided on the degree of its importance.

Furthermore, a majority of the work regarding financial crises in the industry (and specifically in

Italy) only studies the problem from the perspective of within the football industry rather than

from a dynamic, macroeconomic point-of-view. This paper adds to existing literature by

studying how this industry interacts with and is connected to the wider macroeconomic society.

Methodology14

In this analysis, a few variables measured the important concepts of competitive balance and a

financial crisis. Competitive balance is measured through betting odds data of all Serie A games

from the years 2000 through 2014. This data, after processing, shows the estimated probability of

14 Throughout the entire course of this paper, all statistical tests, carried out using the EViews program, were tested against an α of 5%.

15

victory, loss, and a draw for each team in each game which are important measures of

competitive balance. The absolute value of the difference between a team’s probability of victory

and its probability of loss is called game tension. This game tension further illustrates the

competitive balance in each game; higher absolute values of this variable signify predictable

games and thus a negative sense this concept. Furthermore, a financial crisis is measured through

data on unemployment rates and GDP per capita in Italy during the same period as mentioned

earlier. The betting odds data was obtained from the football-data.co.uk database15.

The betting odds data contained the returns one would receive if betting €y on a given result. For

example, if the returns are €2, €3, and €4 for a win, draw, loss respectively of a certain team, one

would receive (€3) multiplied by (€y) if she successfully bets for a draw. This data was

manipulated using the following formula in order to calculate the probability of each result

occurring.16 Suppose one wants to find the probability of a home team winning a specified game.

Then, the formula is as follows:Pw=1

Rw÷ ( 1

Rw+

1RD

+1RL ). The same process was done to find the

probability of a draw and loss for every game (and team) for the seasons 2000-2001 through

2014-2015.17 Also, the average probability of victory and loss for home and away games was

calculated.

Furthermore, the variable ‘game tension’ was formulated by taking the absolute value of the

difference between the probability of a team winning and a team losing a certain game. The

higher this value, the less tension there is in this game as the result seems relatively certain from

the start; either a certain loss or a certain victory for one of the teams. Since the only concern is

actually the degree of game tension in each game, and not its direction, the sign of this figure is

irrelevant and the absolute value should be used. This was again done for every game and team

15 The entire dataset is available upon demand by the reader.16 These probabilities are estimations made by the betting companies and, for the purposes of this paper, are thus a good indicator of the actual probabilities of each result occurring.17 It is important to note the term between brackets is greater than 1 because bookmakers add a profit margin to each bet. This margin is equal to this term minus 1.

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in the aforementioned period and separated for home and away games18. Lastly, the league-

average probabilities of home win and away win probabilities and home game tension and away

game tension were calculated for every season.

The following phase of the methodology concerns the processing of the data. The next step is to

test these league-average probabilities and game tension data series against the null of a unit root

using the Augmented Dickey-Fuller test (ADF). The lag length was determined using the

Schwarz Info Criterion with a maximum of three lags. The home win and away win probability

data series were tested for a unit root with an intercept and trend. The game tension for both

home and away games was tested for a unit root with only an intercept.

After testing the data series for stationarity, the subsequent procedure required to test it for

stationarity with break points using the Zivot-Andrews test19. This test checks if the data is

stationary with a break point in the trend. If this is the case, it means that the data series is

stationary except at the break point(s) where it deviates from the normal trend. This is a useful

preliminary tool to see if there are unusual data points and at when these deviations occur.

At this stage, the clubs were separated into two groups that will from hereon be referred to as

“big clubs” and “small clubs.” Based on the literature from Di Betta and Amenta (2010) that

concluded that in the long-run, “there is an aristocracy of clubs composed of at least 4 and at

most, 10 clubs, always present in the championship,” the “big clubs” group was composed of the

seven Italian clubs with the highest average revenue during the period 2000 through 201520 (Di

18 For some games, there were no quoted betting odds; these games were ignored. For other games, certain betting companies weren’t quoting odds while others were. For consistency reasons, one betting company was used as much as possible for the odds but sometimes this wasn’t possible.19 Thus, for example, a team like Juventus will have its average game tension when it plays its home games and a different figure for it when it plays its away games. This is similar for average win and loss probabilities for a team’s home games and away games.20 The lag length was specified for a maximum of four lags.

17

Betta & Amenta, 2010). Thus, the group “big clubs” consisted of SSC Napoli, SS Lazio Roma,

Juventus Torino, AS Roma, AC Milan, Internazionale Milano, and ACF Fiorentina.

The group “small clubs” consisted of Ancona, Ascoli, Atalanta, Bari, Bologna, Brescia,

Cagliari, Catania, Cesena, Chievo, Como, Empoli, Genoa, Lecce, Livorno, Messina, Modena,

Novara, Palermo, Parma, Perugia, Pescara, Piacenza, Reggina, Sampdoria, Sassuolo, Siena,

Torino, Treviso, Udinese, Venezia, Verona, and Vicenza. The same procedure for calculating the

average game tension and win probabilities done earlier in the analysis was repeated for these

two groups. Also, the same methodology for testing against the null of a unit root using the ADF

and with break points using the Zivot-Andrews test was conducted21.

Since the GDP and unemployment data series was used for regression analysis, they were also

tested for stationarity using the ADF. The lag length was determined using the Schwarz Info

Criterion with a maximum of ten lags. The unemployment and GDP growth series were tested

for a unit root with an intercept. The GDP per capita series was tested with an intercept and a

trend. The GDP and unemployment data was obtained from the OECD Stat Extracts database.

The hypotheses were now tested using breakpoint regression analysis that proceeded as follows.

For the first hypothesis, the Generalized Least Squares regression was run for every club that

was in the Serie A for more than eight seasons22. The win probability for all home games played

since the 2000-2001 season was used as the dependent variable and the second difference of the

log of GDP per capita23, a trend, and a dummy variable were used as the independent variables.

The dummy variable adopted the value 1 for every season from (and including) 2008-2009 and 0

for all other seasons24. This process was repeated with the away win probabilities as the

dependent variable. Also, these same regressions were run but with the dummy variable taking

the value 0 for every season before 2010-2011 and 1 for every other season. This was to account

for the potential effect that the change in the TV-deal in Italy may have had on competitive

21 Many thanks to Dr. Peeters for providing this data for the purposes of this paper.22 Due to a “near-singular matrix” error, the Zivot-Andrews test could not be conducted for the “big clubs” group.23 These clubs were Atalanta, Bologna, Cagliari, Chievo, Fiorentina, Inter, Juventus, Lazio, Milan, Napoli, Palermo, Parma, Roma, Sampdoria, Siena, and Udinese.24 This is to correct for non-stationarity of the log GDP per capita variable. The same occurs for the unemployment rate variable.

18

balance.25 Then, the Chow Break test was conducted for the groups ‘big clubs’ and ‘small clubs’

for the average win probabilities home and away and the game tension home and away. This

tested whether there was a structural break in the coefficient values of the regression after a

given year. This means that before a certain date, the coefficients were significantly different

than after that date, presumably due to a monumental event that occurred then, such as a

recession. The year 2008 was chosen as the breakpoint given that this was the year that Italy

entered the recession and suffered more than two consecutive quarters of negative real GDP

growth.26

For the second hypothesis, the Generalized Least Squares regression was also run using the win

probability for home games (and then away games) as the dependent variable and the same

independent variables as before, except that the second difference of the unemployment rate

variable replaced the second difference of the variable log GDP per capita. Furthermore, the

Chow Break test was conducted in the same way as before with the year 2008 chosen as the

breakpoint again.

Finally, a comment must be made about how the variables of the second difference of the

unemployment rate and log GDP per capita were manipulated in EViews. The sample size of

these variables were both only 13; there was one data point for each year minus the two adjusted

for the second difference. However, the sample size for the win probabilities had multiple points

for each year. Therefore, the sample size of the two aforementioned variables had to be adjusted

so that all variables had the same number of data points per year in order to avoid error readings

in EViews. This did not happen when conducting the Chow Breakpoint tests.

Data and Results27

Table 1: Unit Root Test for league averages and economic variables

Null Hypothesis: data series has a unit root, i.e. is non-stationary25If this dummy were statistically significant, it would show that the crisis had a significant effect on the dependent variable.26 The discussion in the literature review about the work done by Baroncelli and Lago (2006) explicitly explains this ruling.27 Specifically, it started in the second quarter of 2008.

19

Variable P-Value Intercept/Trend? ConclusionLeague Average Home Win

Probability0.0396** Intercept and Trend Reject the null

hypothesisLeague Average Away Win

Probability0.9988 Intercept and Trend Do not reject the null

hypothesisLeague Average Home Game

Tension0.1402 Intercept Do not reject the null

hypothesisLeague Average Away Game

Tension0.0105** Intercept Reject the null

hypothesisReal GDP Growth 0.0084** Intercept Reject the null

hypothesisUnemployment Rate 0.8455 Intercept Do not reject the null

hypothesis2nd difference of Unemployment

Rate0.0249** Intercept Reject the null

hypothesisGDP per Capita 0.8794 Intercept and Trend Do not reject the null

hypothesisLog GDP per Capita 0.8888 Intercept and Trend Do not reject the null

hypothesis2nd difference of Log GDP per

Capita0.0158** Intercept and Trend Reject the null

hypothesis

These results show that there are a few stationary variables and a few that are non-stationary.

The variables league average away win, league average home game tension, unemployment rate,

and GDP per capita are non-stationary. On the other hand, for the variables league average

home win, league average away game tension, and real GDP growth the null hypothesis is

rejected; they are stationary. After correcting for non-stationarity for the log GDP per capita and

unemployment rates variables, the second difference is stationary around an intercept and trend.

Table 2: Unit Root Test for Big Clubs

Null Hypothesis: data series has a unit root, i.e. is non-stationary

Variable P-Value Intercept/Trend? ConclusionBig Clubs Average Home Win


hypothesis

20

Big Clubs Average Away Win Probability

0.2418 Intercept and Trend Do not reject the null hypothesis

Big Clubs Average Home Game Tension

0.0263** Intercept Reject the null hypothesis

Big Clubs Average Away Game Tension

0.0470** Intercept Reject the null hypothesis

Moving on to the ADF test for the group “big clubs”, the results are quite conclusive. The only

variable that seems to be non-stationary is that of league average away win for this group.

Furthermore, the other variables, league average home win, league average home game tension,

and league average away game tension, seem to be stationary as the null hypothesis is rejected in

their cases.

Table 3: Unit Root Test for Small Clubs

Null Hypothesis: data series has a unit root, i.e. is non-stationary

Variable P-Value Intercept/Trend? ConclusionSmall Clubs Average Home Win


hypothesisSmall Clubs Average Away Win

Probability0.0004** Intercept Reject the null

hypothesisSmall Clubs Average Home

Game Tension0.1642 Intercept Do not reject the null

hypothesisSmall Clubs Average Away

Game Tension0.4833 Intercept Do not reject the null

hypothesis

Lastly, this table displays the results of the unit-root test for the group “small clubs.” Curiously,

both variables measuring game tension are non-stationary while the ones measuring win

probabilities are stationary.

Table 4: Zivot-Andrews Unit Root Test for league averages

Null Hypothesis: data series has a unit root with a structural break in the intercept

Variable P-Value Where is the structural break point?

Conclusion

21

League Average Home Win Probability

0.0448** 2008-2009 Reject the null hypothesis

League Average Away Win Probability

0.5211 2010-2011 Do not reject the null hypothesis

League Average Home Game Tension


League Average Away Game Tension


The results for the Zivot-Andrews unit root test must be observed slightly differently. If the null

hypothesis is rejected, which is the case only for the variable league average home win, it means

that the variable is stationary as usual but has a structural break in the intercept at the given break

point. If the null hypothesis cannot be rejected, then the variable is non-stationary also with a

structural break in the intercept at the given break point. The results for the first variable are

certainly the most interesting. The structural break occurs exactly when the crisis begins in

Italy.28 There exists, of course, the possibility some other economically-relevant event occurred

that at that point, but it is nevertheless a good initial sign that something is happening here. The

down-side is that the other variables of interest, namely the game-tension variables that also

measure competitive balance, do not show these same interesting results; the variables are non-

stationary and the break points are all different.

Table 5: Zivot-Andrews Unit Root Test for small clubs

Null Hypothesis: data series has a unit root with a structural break in the intercept

Variable P-Value Where is the structural break point?

Conclusion

28 The summary statistics are presented in the Appendix

22

Small Clubs Average Home Win Probability


Small Clubs Average Away Win Probability


Small Clubs Average Home Game Tension


Small Clubs Average Away Game Tension


For the group “small clubs,” all variables seem to be non-stationary with breakpoints at the

specified times. These results also don’t reveal too much conclusive or interesting except that the

break points for the away win and away tension coincide.

Table 6: Regression Results Coefficient values and Standard Errors (2008 Dummy)

Dependent Variable: Away Win Probability

23

Club Constant(St. Error)

Trend(St.

Error)

2nd Difference Log Gdp Per

Capita(St. Error)

Dummy(St. Error)

R2 Number of Observations

Atalanta 0.2091**(0.0183)

0.0000(0.0002)

-0.0876(0.1758)

0.0002(0.0246)

0.0017 186

Bologna 0.2190**(0.0163)

0.0002(0.0002)

-0.1047(0.1604)

-0.0748**(0.0340)

0.0785 176

Cagliari 0.1936**(0.0204)

0.0000(0.0002)

0.1966(0.1549)

0.0225(0.0208)

0.0199 197

Chievo 0.2553**(0.0163)

-0.0001(0.0002)

0.1265(0.1585)

-0.0296(0.0274)

0.0750 222

Fiorentina 0.2273**(0.0275)

0.0010**(0.0003)

0.1799(0.2269)

-0.0978**(0.0318)

0.0874 207

Inter 0.4722**(0.0214)

-0.0002(0.0002)

-0.1337(0.2058)

-0.0108(0.0308)

0.0250 241

Juventus 0.3759**(0.0225)

0.0011**(0.0002)

-0.3281(0.2238)

-0.1186**(0.0349)

0.0919 222

Lazio 0.3024**(0.0191)

0.0000(0.0002)

-0.0813(0.1835)

0.0163(0.0274)

0.0043 240

Milan 0.4526**(0.0219)

0.0002(0.0002)

0.2351(0.2101)

-0.0335(0.0314)

0.0139 240

Napoli -0.0254(0.0428)

0.0018**(0.0003)

0.3971(0.2360)

0.0133(0.0293)

0.4264 140

Palermo 0.3344**(0.0319)

-0.0005(0.0003)

0.1288(0.2188)

0.0183(0.0302)

0.0430 178

Parma 0.2699**(0.0183)

-0.0003(0.0002)

0.1180(0.1778)

0.0349(0.0281)

0.0138 222

Roma 0.3611**(0.0225)

-0.1292(0.2160)

-0.0426(0.0323)

-0.0426(0.0323)

0.0143 241

Sampdoria 0.2727**(0.0224)

-0.0002(0.0002)

0.1599(0.2050)

0.0101(0.0263)

0.0098 195

Siena 0.1652**(0.0211)

0.0002(0.0002)

-0.1034(0.1699)

-0.0263(0.0225)

0.0112 168

Udinese 0.2591**(0.0186)

0.0000(0.0002)

0.3333(0.1787)

0.0164(0.0267)

0.0233 241

Unfortunately, these regression results seem to suggest that the dummy variable barely

influences teams’ dominance away from home. It is only statistically significant for three teams:

Bologna, Fiorentina, and Juventus. It is no surprise then that the log GDP per capita variable is

24

also never significant; the dummy variable shows whether anything that occurred in the year

2008, i.e. the year that the crisis began, significantly impacted teams’ dominance while the

inclusion of the GDP variable measures how and if an economic downturn affected this

dominance. Furthermore, the regressions seem to have very little explanatory power given the

disappointingly low R2 readings29, except for the special case of Napoli. It is the only team that

does not have a significant constant but has, by far, the highest R2 of all teams. Unlike for the

other regressions, it seems that all the variables in the regression for Napoli seem to do a decent

job explaining the variation in the dependent variable ̶ the win probability away from home.

Lastly, it is curious to note that the coefficients of the constants are higher for the bigger teams

(e.g. Juventus, Milan, Inter, Roma etc.) than for the lower teams. This probably illustrates that

the bigger teams have greater win probabilities than the smaller teams.



Club Constant Trend 2nd Difference Dummy R2 Number of 29 Better yet, each season usually starts around early- to mid-August, which is approximately the third quarter of a financial year. The recession started in Italy in the second quarter of the year 2008.

25

(St. Error) (St. Error) Log Gdp Per Capita

(St. Error)

(St. Error) Observations

Atalanta 0.2169**(0.0190)

-0.0001(0.0002)

-0.0851(0.1671)

0.0161(0.0230)

0.0043 186

Bologna 0.2548**(0.0155)

-0.0005**(0.0001)

-0.0338(0.1537)

0.0458**(0.0209)

0.0784 176

Cagliari 0.1972**(0.0240)

0.0000(0.0002)

0.1230(0.1559)

0.0202(0.0213)

0.0186 197

Chievo 0.2890**(0.0160)

-0.0006**(0.0001)

0.1752(0.1457)

0.0529**(0.0211)

0.0963 222

Fiorentina 0.2149**(0.0285)

0.0009**(0.0002)

0.4265**(0.2150)

-0.1001**(0.0290)

0.0978 207

Inter 0.4176**(0.0203)

0.0005**(0.0002)

-0.0548(0.1892)

-0.1284**(0.0261)

0.1147 241

Juventus 0.4311**(0.0229)

0.0002(0.0002)

-0.1243(0.2201)

0.0267(0.0300)

0.0473 222

Lazio 0.3321**(0.0186)

-0.0004**(0.0002)

-0.1472(0.1732)

0.0790**(0.0239)

0.0469 240

Milan 0.4594**(0.0218)

0.0000(0.0002)

0.3026(0.2031)

-0.0154(0.0281)

0.0105 240

Napoli -0.0029(0.0613)

0.0017**(0.0004)

0.3733(0.2347)

0.0182(0.0316)

0.4269 140

Palermo 0.2731**(0.0352)

0.0001(0.0003)

0.2167(0.2208)

-0.0577(0.0297)

0.0614 178

Parma 0.2891**(0.0173)

-0.0005**(0.0002)

0.1577(0.1744)

0.0764**(0.0240)

0.0509 222

Roma 0.3609**(0.0223)

0.0004(0.0002)

-0.0344(0.2084)

-0.0391(0.0288)

0.0147 241

Sampdoria 0.2611**(0.0252)

-0.0001(0.0002)

0.1680(0.2114)

-0.0102(0.0277)

0.0098 195

Siena 0.2073**(0.0215)

-0.0002(0.0002)

-0.1019(0.1665)

0.0412(0.0223)

0.0233 168

Udinese 0.2698**(0.0184)

-0.0001(0.0002)

0.2862(0.1717)

0.0380(0.0237)

0.0323 241

In addition to the comments made on the previous table, the most interesting features of these

results are the following. Firstly, there are many more instances of statistically significant

26

dummy and trend variables. It seems like teams’ dominance away from home is more

significantly affected by events occurring in 2010 ̶ the year when the TV negotiating deal

changed ̶ than by those in 2008. Secondly, in the instances where there are significant trends, the

bigger teams (Fiorentina, Inter, Napoli) have positive trends while the smaller teams (Bologna,

Chievo, Parma) have negative trends. The exception here is Lazio, who are generally considered

to be a bigger team especially because they are located in the capital. This pattern also holds

when looking at the (non-significant) trends for the other clubs, but these cannot be validly

interpreted because they are not statistically significant.

The regression for Napoli again has a markedly high R2 value but now that of Inter has the

second-highest R2. Lastly, the regression for Fiorentina is the only one for which all the variables

are statistically significant. This is particularly interesting because it still has a very low R2 value

which suggests that there are many missing variables that could help explain the variation in the

dependent variable.


Dependent Variable: Home Win Probability

27


Trend(St. Error)


Capita(St. Error)

Dummy(St. Error)


Atalanta 0.4076**(0.0246)

-0.0002(0.0002)

0.0622(0.2360)

0.0157(0.0330)

0.0051 186

Bologna 0.3999**(0.0235)

0.0002(0.0003)

-0.5457**(0.2318)

-0.0981**(0.0491)

0.0100 176

Cagliari 0.4121**(0.0327)

-0.0003(0.0003)

-0.0628(0.2486)

0.0300(0.0334)

0.0065 197

Chievo 0.4250**(0.0229)

-0.0001(0.0003)

-0.2074(0.2216)

-0.0548(0.0384)

0.0815 221

Fiorentina 0.4494**(0.0324)

0.0006(0.0003)

-0.0803(0.2675)

-0.0524(0.0375)

0.0208 207

Inter 0.6470**(0.0238)

-0.0001(0.0002)

-0.0334(0.2290)

-0.0206(0.0342)

0.0194 241

Juventus 0.5850**(0.0223)

0.0008**(0.0002)

-0.2951(0.2215)

-0.1002**(0.0346)

0.0476 222

Lazio 0.5023**(0.0249)

-0.0002(0.0003)

0.2415(0.2390)

0.0210(0.0357)

0.0051 241

Milan 0.6526**(0.0235)

-0.0001(0.0002)

0.2062(0.2258)

-0.0102(0.0338)

0.0160 241

Napoli 0.1921**(0.0535)

0.0016**(0.0003)

-0.2883(0.2947)

0.0371(0.2947)

0.2751 140

Palermo 0.5195**(0.0382)

-0.0006(0.0003)

0.3171(0.2618)

0.0404(0.0361)

0.0262 178

Parma 0.4723**(0.0261)

-0.0005(0.0003)

-0.4903(0.2535)

0.0565(0.0401)

0.0379 222

Roma 0.5152**(0.0248)

0.0007**(0.0003)

-0.2301(0.2385)

-0.0825**(0.0357)

0.0324 241

Sampdoria 0.5286**(0.0274)

-0.0008**(0.0003)

0.2373(0.2511)

0.0594(0.0322)

0.0621 195

Siena 0.3797**(0.0356)

0.0000(0.0003)

-0.1086(0.2888)

-0.0254(0.0383)

0.0100 169

Udinese 0.4704**(0.0219)

-0.0002(0.0002)

0.1827(0.2111)

0.0432(0.0316)

0.0104 241

Interestingly, when the regressions are for home games instead of for away games, the constant

becomes significant for Napoli but its R2 value drops markedly. Also, the GDP variable is only

28

significant for Bologna and the value of the coefficient shows that Bologna’s home games are

strongly impacted by a change in GDP per capita. This relationship is very strange though; given

the sign of the coefficient, it suggests that as GDP per capita increases, Bologna’s dominance at

home strongly decreases (and vice-versa). Macroeconomic fluctuations affect the team’s home

dominance counter-cyclically. Lastly, the coefficients of the constants are much higher here than

for the regressions for away games, further confirming the belief that these constants are base

indicators of how much each team dominates games.



29


Trend(St. Error)


Capita(St. Error)

Dummy(St. Error)


Atalanta 0.4103**(0.0255)

-0.0002(0.0002)

0.0308(0.2245)

0.0185(0.0308)

0.0058 186

Bologna 0.4420**(0.0225)

-0.0006(0.0002)

-0.4418**(0.2231)

0.0465(0.0303)

0.0913 176

Cagliari 0.4184**(0.0385)

-0.0003(0.0003)

-0.1645(0.2500)

0.0288(0.0342)

0.0060 197

Chievo 0.4636**(0.0221)

-0.0007**(0.0002)

-0.1024(0.2061)

0.0429(0.0298)

0.0817 221

Fiorentina 0.4120**(0.0333)

0.0009**(0.0003)

0.0682(0.2505)

-0.1029**(0.0338)

0.0544 207

Inter 0.5919**(0.0227)

0.0005**(0.0002)

0.0675(0.2113)

-0.1383**(0.0292)

0.1029 241

Juventus 0.6358**(0.0225)

0.0000(0.0002)

-0.1282(0.2162)

0.0324(0.0295)

0.0164 222

Lazio 0.5410**(0.0242)

-0.0006**(0.0002)

0.1560(0.2256)

0.1031**(0.0312)

0.0477 241

Milan 0.6501**(0.0233)

-0.0001(0.0002)

0.2316(0.2178)

-0.0148(0.0301)

0.0166 241

Napoli 0.2416**(0.0765)

0.0014**(0.0005)

-0.3491(0.2932)

0.0412(0.0395)

0.2754 140

Palermo 0.4500**(0.0425)

0.0001(0.0003)

0.3613(0.2663)

-0.0499(0.0358)

0.0300 178

Parma 0.4747**(0.0251)

-0.0005(0.0002)

-0.4813(0.2530)

0.0596(0.0348)

0.0420 222

Roma 0.5391**(0.0249)

0.0003(0.0002)

-0.0708(0.2325)

-0.0227(0.0321)

0.0126 241

Sampdoria 0.4613**(0.0309)

0.0000(0.0003)

0.2794(0.2593)

-0.0577(0.0340)

0.0596 195

Siena 0.4290**(0.0363)

-0.0005(0.0003)

-0.1196(0.2831)

0.0538(0.0378)

0.0194 169

Udinese 0.4753**(0.0218)

-0.0002(0.0002)

0.0819(0.2031)

0.0497(0.0280)

0.0156 241

The results for Bologna, interestingly, show the same characteristics as earlier. There are again

many instances of significant trends and, again, it seems like the bigger teams are trending

upwards and the smaller teams trending downwards ̶ a worrying observation from the point of

view of competitive balance.



30


Trend(St. Error)

2nd Difference Unemployment

Rate(St. Error)

Dummy(St.

Error)


Atalanta 0.2029**(0.0188)

0.0001(0.0002)

0.0002(0.0071)

-0.0022(0.0243)

0.0036 168

Bologna 0.2214**(0.0168)

0.0002(0.0002)

0.0013(0.0066)

-0.0731**(0.0349)

0.0812 165

Cagliari 0.1919**(0.0232)

0.0000(0.0002)

-0.0067(0.0057)

0.0163(0.0215)

0.0193 180

Chievo 0.2559**(0.0174)

-0.0001(0.0002)

-0.0030(0.0068)

-0.0321(0.0274)

0.0760 204

Fiorentina 0.2195**(0.0297)

0.0011**(0.0003)

-0.0178**(0.0318)

-0.1070**(0.0318)

0.0959 189

Inter 0.4623**(0.0225)

-0.0001(0.0002)

-0.0039(0.0087)

-0.0168(0.0303)

0.0114 223

Juventus 0.3939**(0.0238)

0.0008(0.0003)

0.0193**(0.0093)

-0.0900(0.0348)

0.0675 204

Lazio 0.3226**(0.0204)

-0.0003(0.0002)

-0.0033(0.0078)

0.0351(0.0274)

0.0087 222

Milan 0.4306**(0.0233)

0.0004(0.0002)

-0.0026(0.0090)

-0.0570(0.0314)

0.0164 222

Napoli -0.0381(0.0496)

0.0019**(0.0003)

-0.0057(0.0081)

0.0059(0.0311)

0.3496 123

Palermo 0.3250**(0.0361)

-0.0004(0.0003)

-0.0066(0.0084)

0.0094(0.0318)

0.0336 171

Parma 0.2886**(0.0188)

-0.0006**(0.0002)

0.0112(0.0070)

0.0537(0.0275)

0.0530 204

Roma 0.3887**(0.0234)

0.0001(0.0002)

0.0036(0.0091)

-0.0169(0.0317)

0.0021 223

Sampdoria 0.2945**(0.0249)

-0.0004(0.0002)

0.0026(0.0087)

0.0226(0.0264)

0.0240 178

Siena 0.1654**(0.0212)

0.0002(0.0002)

0.0021(0.0064)

-0.0233(0.0220)

0.0096 168

Udinese 0.2327**(0.0198)

0.0003(0.0002)

0.0000(0.0076)

-0.0138(0.0268)

0.0264 223

These regression results now use the unemployment rate instead of the GDP per capita as one of

the variables. Napoli is again the point of interest, because it continues to display the pattern

found in earlier regression with away game dominance: the constant is not significant, but the

31

regression has the highest R2 of all clubs. The two most interesting observations are in the cases

of Fiorentina and Juventus. All variables are significant for the regressions of these two clubs

but, as noted earlier, the R2 value is extremely low. However, the more interesting factor is that

the unemployment rate is statistically significant for both cases but its effect is opposite for each

club. For Fiorentina, as the unemployment rate increases (decreases), its dominance in games

away from home decreases (increases). It is important to note that the unemployment rate is a

crucial economic indicator; the lower the rate, the better the state of the economy. However, for

Juventus, the higher the rate, the greater is its dominance in away games. Both are quite big clubs

in Italy, yet the difference in how the unemployment rate affects their probability of away

victories is notably different.



Club Constant Trend 2nd Difference Dummy R2 Number of

32

(St. Error) (St. Error) Unemployment Rate

(St. Error)

(St. Error)

Observations

Atalanta 0.2128**(0.0194)

0.0000(0.0002)

-0.0002(0.0071)

0.0188(0.0235)

0.0075 168

Bologna 0.2585**(0.0158)

-0.0005**(0.0001)

0.0002(0.0066)

0.0466**(0.0208)

0.0848 165

Cagliari 0.1972**(0.0248)

0.0000(0.0002)

-0.0060(0.0057)

0.0200(0.0207)

0.0212 180

Chievo 0.2938**(0.0160)

-0.0006**(0.0001)

-0.0048(0.0066)

0.0568**(0.0209)

0.1029 204

Fiorentina 0.2141**(0.0304)

0.0010**(0.0002)

-0.0209**(0.0089)

-0.1012**(0.0295)

0.0980 189

Inter 0.4104**(0.0207)

0.0006**(0.0002)

-0.0067**(0.0082)

-0.1288**(0.0258)

0.1109 223

Juventus 0.4422**(0.0235)

0.0001(0.0002)

0.0203**(0.0095)

0.0290(0.0298)

0.0409 204

Lazio 0.3428**(0.0194)

-0.0005**(0.0002)

-0.0013(0.0077)

0.0777**(0.0242)

0.0466 222

Milan 0.4514**(0.0229)

0.0001(0.0002)

-0.0034(0.0091)

-0.0104(0.0284)

0.0021 222

Napoli 0.0039(0.0777)

0.0016**(0.0005)

-0.0061(0.0081)

0.0253(0.0349)

0.3522 123

Palermo 0.2688**(0.0361)

0.0001(0.0003)

-0.0089(0.0084)

-0.0564(0.0292)

0.0543 171

Parma 0.2963**(0.0171)

-0.0006**(0.0002)

0.0096(0.0069)

0.0721**(0.0229)

0.0804 204

Roma 0.3767**(0.0227)

0.0002(0.0002)

0.0026(0.0090)

-0.0425(0.0283)

0.0110 223

Sampdoria 0.2807**(0.250)

-0.0002(0.0002)

0.0027(0.0091)

-0.0001(0.0267)

0.0199 178

Siena 0.2042**(0.0208)

-0.0002(0.0002)

0.0017(0.0064)

0.0391(0.0221)

0.0215 168

Udinese 0.2595**(0.0191)

0.0000(0.0002)

0.0007(0.0076)

0.0447(0.0239)

0.0406 223

The previously-observed pattern in the results for Fiorentina and Juventus occurs here again.

Furthermore, the big clubs often have negative coefficients for the dummy variable while the

small clubs have positive ones for it. This suggests that events in 2010 seem to have helped small

teams and hurt big teams in away games, although the dummy is not always significant.



33

Club Constant (St. Error)

Trend (St. Error)


Rate(St. Error)

Dummy(St.

Error)


Atalanta 0.4068**(0.0246)

-0.0002(0.0002)

-0.0021(0.0093)

0.0130(0.0318)

0.0041 168

Bologna 0.4095**(0.0247)

0.0001(0.0003)

0.0112(0.0098)

-0.0771(0.0513)

0.0748 165

Cagliari 0.3911**(0.0378)

-0.0001(0.0003)

0.0031(0.0092)

0.0182(0.0350)

0.0026 180

Chievo 0.4282**(0.0248)

-0.0002(0.0003)

0.0097(0.0097)

-0.0461(0.0389)

0.0800 203

Fiorentina 0.4310**(0.0344)

0.0081(0.0003)

-0.0059(0.0103)

-0.0670(0.0368)

0.0376 189

Inter 0.6483**(0.0257)

-0.0002(0.0003)

0.0095(0.0099)

-0.0189(0.0348)

0.0233 223

Juventus 0.6121**(0.0239)

0.0004(0.0003)

-0.0003(0.0094)

-0.0660(0.0350)

0.0174 204

Lazio 0.5072**(0.0261)

-0.0002(0.0003)

-0.0067(0.0101)

0.0220(0.0352)

0.0056 223

Milan 0.6302**(0.0243)

0.0001(0.0003)

-0.0084(0.0094)

-0.0344(0.0328)

0.0111 223

Napoli 0.1265**(0.0597)

0.0021**(0.0004)

0.0124(0.0098)

0.0229(0.0374)

0.3152 123

Palermo 0.5149**(0.0426)

-0.0005(0.0004)

-0.0263**(0.0100)

0.0310(0.0374)

0.0580 171

Parma 0.4997**(0.0279)

-0.0008**(0.0003)

0.0039(0.0104)

0.0842(0.0409)

0.0406 204

Roma 0.5324**(0.0267)

0.0005(0.0003)

-0.0011(0.0103)

-0.0631(0.0360)

0.0156 223

Sampdoria 0.5251**(0.0318)

-0.0008**(0.0003)

0.0088(0.0111)

0.0522(0.0337)

0.0454 178

Siena 0.3829**(0.0358)

-0.0001(0.0003)

0.0088(0.0109)

-0.0220(0.0373)

0.0131 169

Udinese 0.4704**(0.0219)

-0.0002(0.0002)

0.1827(0.2111)

0.0432(0.0316)

0.0104 241

There is not a single instance where the dummy variable is significant. The variable of the

unemployment rate is only significant for Palermo. In this case, it suggests that dominance in

home games is pro-cyclical; as the unemployment rate decreases (increases), the probability of a

victory increases (decreases).



34

Club Constant (St. Error)

Trend (St. Error)


Rate(St. Error)

Dummy(St.

Error)


Atalanta 0.4122**(0.0255)

-0.0002(0.0002)

-0.0023(0.0093)

0.0224(0.0308)

0.0063 168

Bologna 0.4457**(0.0233)

-0.0006(0.0002)

0.0100(0.0097)

0.0411(0.0306)

0.0722 165

Cagliari 0.3961**(0.0404)

-0.0001(0.0003)

0.0038(0.0093)

0.0211(0.0337)

0.0033 180

Chievo 0.4636**(0.0221)

-0.0007**(0.0002)

-0.1024(0.2061)

0.0429(0.0298)

0.0817 221

Fiorentina 0.4021**(0.0347)

0.0010**(0.0003)

-0.0087(0.0101)

-0.1026**(0.0337)

0.0672 189

Inter 0.5927**(0.0239)

0.0005**(0.0002)

0.0065(0.0095)

-0.1390**(0.0298)

0.1105 223

Juventus 0.6499**(0.0234)

-0.0001(0.0002)

0.0006(0.0095)

0.0269(0.0297)

0.0041 204

Lazio 0.5448**(0.0248)

-0.0007**(0.0002)

-0.0044(0.0098)

0.1030**(0.0309)

0.0519 223

Milan 0.6400**(0.0236)

0.0000(0.0002)

-0.0091(0.0094)

-0.0122(0.0295)

0.0069 223

Napoli 0.1177(0.0938)

0.0022**(0.0006)

0.0118(0.0098)

-0.0004(0.0422)

0.3130 123

Palermo 0.4473**(0.0429)

0.0002(0.0003)

-0.0288(0.0100)

-0.0500(0.0346)

0.0658 171

Parma 0.4906**(0.0259)

-0.0007(0.0002)

0.0018(0.0104)

0.0659(0.0347)

0.0376 204

Roma 0.5475**(0.0261)

0.0002(0.0002)

-0.0024(0.0104)

-0.0285(0.0325)

0.0053 223

Sampdoria 0.4675**(0.0319)

-0.0001(0.0003)

0.0051(0.0116)

-0.0428(0.0341)

0.0410 178

Siena 0.4275**(0.0351)

-0.0005(0.0003)

0.0082(0.0108)

0.0504(0.0375)

0.0217 169

Udinese 0.4727**(0.0225)

-0.0002(0.0002)

0.0045(0.0089)

0.0531(0.0281)

0.0193 223

The final regression results show nothing starkly different compared to all previous results.

Unfortunately, the regressions again seem to have very low explanatory power and many

instances of insignificant variables.

Table 14: Chow Breakpoint Test: Big Clubs

Independent variables: Constant, Trend, Second Difference LogGDP per capita

35

Null Hypothesis: No breakpoint present in the regression in the year 2008

Dependent Variable F-Statistic P-Value Conclusion?

Average Home Win Probability 9.0273 0.0121** Reject the Null hypothesis; there is a breakpoint in 2008

Average Away Win Probability 5.0601 0.0441** Reject the Null hypothesis; there is a breakpoint in 2008

Average Home Game Tension 9.6654 0.0103** Reject the Null hypothesis; there is a breakpoint in 2008

Average Away Game Tension 6.3413 0.0273** Reject the Null hypothesis; there is a breakpoint in 2008

The Chow Breakpoint test for the big clubs shows that there is strong reason to believe that

events in the year 2008 caused a change in the parameters of all the above regressions given that

the null hypothesis is rejected in all cases. It is most likely that changes in the GDP per capita in

that year were responsible for this breakpoint in the regressions. However, given the nature of

the Chow Break test, the direction of this change cannot be determined.

Table 15: Chow Breakpoint Test: Big Clubs

Independent variables: Constant, Trend, Second Difference Unemployment Rate


36


Average Home Win Probability 0.8319 0.5232 Do not reject the null hypothesis; there is no

breakpoint in 2008Average Away Win Probability 0.5971 0.6398 Do not reject the null

hypothesis; there is no breakpoint in 2008

Average Home Game Tension 1.2416 0.3744 Do not reject the null hypothesis; there is no

breakpoint in 2008Average Away Game Tension 6.3413 0.0273** Reject the Null hypothesis;

there is a breakpoint in 2008

The regression results where the average away game tension is the dependent variable are the

only ones where there is a breakpoint in the year 2008. This suggests that changes in the

unemployment rate did not influence competitive balance significantly after the Great Recession.

Table 16: Chow Breakpoint Test: Small Clubs

Independent variables: Constant, Trend, Second Difference LogGDP per capita



Home Win Probability 2.4208 0.1513 Do not reject the null hypothesis; there is no breakpoint in 2008

Away Win Probability 5.9910 0.0240** Reject the Null hypothesis; there is a breakpoint in 2008

Home Game Tension 0.5114 0.6871 Do not reject the null hypothesis; there is no breakpoint in 2008

Away Game Tension 0.9404 0.4707 Do not reject the null hypothesis; there is no breakpoint in 2008

Now the focus shifts from the big clubs to the small clubs. The results are not very revealing as

there is only a breakpoint in the year 2008 in one case: the regression that has the away win

probability as the dependent variable. This means that small clubs, as a group, experienced a

significant change in their probabilities of winning games away from home after the crisis.

37

Table 17: Chow Breakpoint Test: Small Clubs

Independent variables: Constant, Trend, Second Difference Unemployment Rate



Home Win Probability 1.0611 0.4326 Do not reject the null hypothesis; there is no breakpoint in 2008

Away Win Probability 1.7416 0.2577 Do not reject the null hypothesis; there is no breakpoint in 2008

Home Game Tension 0.6517 0.6103 Do not reject the null hypothesis; there is no breakpoint in 2008

Away Game Tension 0.4645 0.7175 Do not reject the null hypothesis; there is no breakpoint in 2008

The unemployment rate did not significantly change competitive balance for small clubs after

2008. Based on all the results of the Chow Breakpoint tests for small clubs, it certainly seems

that the Great Recession had mixed effects, if any, on competitive balance in the league.

Interpretation and DiscussionThe results from the analysis pose mixed views for interpretation and discussion. The

preliminary results from the unit-root tests seem to suggest that some of the measures of

competitive balance are stationary at league level and for both subgroups. If they indeed are

stationary around an intercept, it seems that competitive balance, in the long run, oscillates

38

around one level. This is the case for the league average away game tension, the average

probability of away victories for small clubs, and the game tension for big clubs in both home

and away games. The most interesting case to consider here is that of small clubs; is it the case

that small clubs cannot become more dominant in away games, given that this measure is

stationary around one level?

If, however, on the other hand, the aforementioned measures are stationary around a trend and an

intercept, the levels of competitive balance are slowly trending upwards. Thus, for instance, the

league average home win probability seems to be trending upwards which means that it is

steadily increasing over time. Teams playing at home are experiencing more predictable games

and have increased probabilities of winning. This finding, interestingly, is rebutted as it is also

the case with the probabilities of home victories for both big and small clubs. The interpretation

of this latter finding is more complete when coupled with the fact that the average probability of

away victories for small clubs seems to be stationary around an intercept, but their probability of

victory in home games is trending upwards. Small clubs are becoming more competitive at home

but cannot seem to repeat this feat away from home.

The most conflicting finding concerns the results for big clubs. The average probability of home

victories for big clubs seems to be trending upwards but, at the same time, the game tension for

these teams in home games is stationary around an intercept. It is a strangely contradicting

finding that will, perhaps, be clarified in the ensuing discussion.

It is very interesting to see that although the league-average win probability at home is not

stationary around an intercept, it is stationary with a structural break in the intercept, as Table 4

shows. It is most revealing to see that this break is in the 2008-2009 season, exactly when the

crisis first set in. This suggests that this measure oscillated around one level before the 2008-

2009 season but around a different level in the years following. This supports the belief that the

crisis had at least some effect on competitive balance.

39

Although the regression results are most useful in answering the hypotheses, and these must be

answered in the conclusion, it is still of utmost importance to interpret and discuss them now.

The most conclusive results are for the dummy variables in 2010 instead of 2008. It is important

to recall that the TV deal changed in 2010. When looking at the results from Table 7, it seems

that the events in 2010 positively influenced the win probabilities away from home for smaller

team like Bologna, Chievo, Lazio and Parma but negatively for bigger teams like Fiorentina and

Inter. This means that the change in the TV deal, and subsequent increase in revenue for small

teams relative to big teams, helped a group of small teams become more competitive away from

home. This effect was probably not profound enough for all small clubs given that the average

win probability away from home for this group was stationary around an intercept. A possible

explanation for this discrepancy is that the increase in revenue for small teams was not

significant enough to solidify their statuses in the league. However, changes in the

unemployment rate and GDP per capita, which are important measures of a recession, rarely

influenced measures of competitive balance for clubs that spent the most number of seasons in

the Serie A.

The one time that there was a significant coefficient for a big club for the GDP per capita was for

Fiorentina. In this case, it was for the away win probability with the 2010 dummy. Changes in

the GDP per capita significantly influenced their probability of winning away games. Positive

changes in this measure increased this probability while negative changes had the opposite

effect. Clearly, Fiorentina is strongly (and pro-cyclically) vulnerable to cyclical fluctuations in

the GDP per capita.

Finally, the Chow Breakpoint test was conducted to complement and verify the results from the

time-series regression analysis. There are very convincing results from this test for the big clubs

regarding all measures of competitive balance. There is a breakpoint in the parameters (i.e.

coefficient values) in the year 2008 for all four regressions for the big clubs. Therefore, the

strong shock in the GDP per capita after the crisis changed competitive balance for big clubs

significantly. However, given the regression results, it is difficult to determine the direction of

this change; it certainly warrants further investigation though. The unemployment rate only

40

caused a parameter break for the game tension in away games for big clubs. It seems that the

unemployment rate is not a good enough measure of a recession in the context of this analysis.

Finally, the small clubs in the Serie A did not experience much of a change in the status quo as a

result of the crisis, except when measured through the GDP per capita. Curiously, the only time

something significant occurred in this case was for the away win probability. This is an

interesting addition to the earlier discussion of the finding that small clubs were struggling to

become more competitive away from home. It seems that the crisis caused a shift in this strength

away from home, but it was not strong enough to enable them to become more competitive there.

All in all, the results from this analysis suggest that something indeed did happen after the crisis,

but that this is really only significant for the big clubs in Italy. Small clubs seem to be becoming

stronger at home, but not necessarily away from home. It is likely, however, that in both cases

there are various missing variables that could help explain the variation in competitive balance

better.

Conclusion

This paper analyzed the effect of the Great Recession on competitive balance in the Italian Serie

A division. This happened through time-series analysis that tested the stationarity of the average

home and away win probabilities and home and away game tension for big clubs, small clubs,

and the league as a whole. Furthermore, these data series were also tested for stationarity with

41

breakpoints using the Zivot-Andrews test to see whether there were points in time where the

general trend of these series significantly changed. To test the hypotheses, regressions were run

for clubs that were in the league for more than eight seasons using a constant, trend, and the

second difference of the log GDP per capita and of the unemployment rate as independent

variables. Finally, the Chow Breakpoint test was conducted to verify whether there was a

breakpoint in the year 2008 in the parameters values used in the regression analysis.

The first hypothesis cannot be answered conclusively because it seems like the unemployment

rate neither consistently nor significantly affected competitive balance. In the cases that it did, its

effect differed per team; Palermo and Fiorentina seemed to suffer from the crisis in their

dominance in home and away games respectively while Juventus seemed to ‘benefit’ from it in

away games. The most successful Italian team in terms of domestic titles, Juventus, became

stronger in their matches away from home after the crisis. The dramatic rise in unemployment

allowed Juventus to strengthen their dominance in away matches.

The second hypothesis, similarly, cannot be answered concretely because the GDP per capita had

almost no influence on competitive balance in the Serie A. Again, in the only cases that it did, its

effects were varied per club. For Bologna, for instance, the crisis positively affected their win

probability in home games. They are counter-cyclically affected by the fluctuations in the GDP

per capita as increases in this measure reduce their win probabilities and the opposite occurs

when it decreases. The win probabilities of Fiorentina, like in the conclusion from the first

hypothesis, are affected pro-cyclically by changes in the GDP per capita.

The conclusions of the two hypotheses can be used to definitively answer the research question

of this paper. The Great Recession of 2008 seems to have helped small clubs become more

competitive at home, but not necessarily away from home. The shock in GDP per capita levels

also significantly affected the competitive balance of big clubs. Although the direction of this

effect cannot be conclusively stated, the results seem to suggest that it helped solidify the

dominance of big clubs. It remains crucial to note that the Great Recession affected (different

42

sets of) clubs differently. This is mixed news with regards to policy-making. Ideally, small clubs

would become stronger and catch up to big clubs but the findings from this research suggest that

small clubs are not always doing so. Therefore, policymakers must focus on financially

supporting small clubs during these tough economic times and must carefully distinguish

between clubs that are affected pro-cyclically by recessions and those that are affected counter-

cyclically by them. Nevertheless, much work remains to be done to unravel the mysterious

connection between football and the wider macro-economy in order to understand which factors

must be manipulated in order to reach the desired level of competitive balance.

There were various limitations to the analysis in this paper. The use of betting odds data to

calculate win probabilities was perhaps neither a valid nor reliable tool to measure competitive

balance. Furthermore, using the averages instead of every data point for the unit-root and Chow

Breakpoint tests may have compromised the internal validity of the analysis. Using averages

significantly reduces the number of data points used in the time-series analysis. Also, the data

might need to be adjusted to account for how many seasons certain clubs spent in the Serie A.

There are clubs that spent one season in the division while there are others that were there every

season. Lastly, as mentioned earlier, given the lack of explanatory power of the regression

analyses, it seems that there were many missing variables that explain the variation in the various

measures of competitive balance better. GDP per capita and the unemployment rate simply do

not suffice.

Finally, there are several avenues for further research. It is well known that there is not only a

strong cultural, but also economic divide between the North and South of Italy. Therefore, it is

worth investigating if this pattern extends towards competitive balance and finances in football

between the teams in the North and those in the South of the country, especially since a majority

of the teams in the Serie A are from the Northern part of the nation. Thus, one could investigate

how the crisis affected Northern clubs compared to how it affected Southern clubs (The

43

Economist, 2015). Furthermore, one could investigate the effect of the crisis on financial

variables of Italian clubs plying their trade in the Serie A; that is, how did the crisis affect the

wages, turnover, revenues, and expenditures of Italian clubs? Also, although this research offered

various answers to the how questions, it offered little with regard to why questions. Further

research could tailor the investigation more specifically to offer answers to these why questions.

Lastly, given the interesting findings for Fiorentina, perhaps one should study in greater depth

how the crisis affected this particular team.

Though the overall results and conclusion from this analysis do not seem to suggest much

profound or revolutionary, as economists we must never vanquish the hunger to discover

something profound from the seemingly insignificant knowledge we currently possess. We must

never give up in the quest to explain the what’s, why’s, and how’s of economic phenomena,

especially in the peculiar realm that is the world of football.

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45

Appendix: Summary StatisticsTable 1

Statistics Home Win Probability (Away Win Probability between brackets)

League Big Clubs Small League Average Min Max

46

Average Average Clubs Average

Standard Deviation

2000-2001 0.4669(0.2568)

0.5589(0.3407)

0.4082(0.2034)

0.1035 0.3309(0.1441)

0.6395(0.4305)

2001-2002 0.4553(0.2683)

0.4539(0.2655)

0.4560(0.2697)

0.0152 0.4233(0.2306)

0.4836(0.3249)

2002-2003 0.4494(0.2708)

0.4610(0.2887)

0.4449(0.2638)

0.0276 0.4010(0.2193)

0.5106(0.3461)

2003-2004 0.4498(0.2680)

0.6044(0.4302)

0.3903(0.2057)

0.1137 0.2451(0.1097)

0.6420(0.4567)

2004-2005 0.4417(0.2642)

0.5268(0.3701)

0.3980(0.2189)

0.0941 0.3421(0.1727)

0.6518(0.4944)

2005-2006 0.4464(0.2692)

0.5821(0.4192)

0.3801(0.2049)

0.1228 0.2707(0.1332)

0.7006(0.5263)

2006-2007 0.4459(0.2617)

0.5886(0.4147)

0.3941(0.2107)

0.1081 0.2701(0.1210)

0.6822(0.5132)

2007-2008 0.4466(0.2659)

0.5522(0.3854)

0.3814(0.2016)

0.1141 0.3182(0.1613)

0.6870(0.5122)

2008-2009 0.4534(0.2634)

0.5515(0.3800)

0.3933(0.2006)

0.1030 0.3323(0.1526)

0.6681(0.4821)

2009-2010 0.4503(0.2695)

0.5475(0.3744)

0.3926(0.2130)

0.1050 0.2994(0.1512)

0.6792(0.5018)

2010-2011 0.4479(0.2687)

0.5496(0.3712)

0.3875(0.2135)

0.1017 0.3032(0.1572)

0.6350(0.4579)

2011-2012 0.4518(0.2751)

0.5537(0.3911)

0.3870(0.2126)

0.1092 0.2919(0.1455)

0.6656(0.5082)

2012-2013 0.4525(0.2743)

0.5579(0.3855)

0.3832(0.2143)

0.1107 0.2482(0.1206)

0.6885(0.5340)

2013-2014 0.4514(0.2889)

0.5970(0.4259)

0.3730(0.2151)

0.1239 0.3103(0.1567)

0.7325(0.5788)

2014-2015 0.4454(0.2902)

0.5628(0.4182)

0.3694(0.2212)

0.1186 0.2434(0.1316)

0.6856(0.5484)

Table 2

Statistics Home Game Tension (Away Game Tension between brackets)

League Average

Big Clubs Average

Small Clubs Average

League Average Standard

Min Max

47

Deviation2000-2001 0.2830

(0.2832)0.3929

(0.1971)0.2130

(0.3381)0.1035 0.1470

(0.1495)0.5049

(0.4727)2001-2002 0.2726

(0.2711)0.2434

(0.2583)0.2872

(0.2775)0.0152 0.1928

(0.2017)0.4232

(0.3294)2002-2003 0.2655

(0.2655)0.2743

(0.2286)0.2621

(0.2797)0.0276 0.1902

(0.1891)0.3197

(0.3705)2003-2004 0.2999

(0.2997)0.4556

(0.2167)0.2400

(0.3316)0.1137 0.2029

(0.1460)0.5093

(0.5685)2004-2005 0.2638

(0.2638)0.3828

(0.2306)0.2129

(0.2781)0.0941 0.1437

(0.1549)0.5307

(0.3997)2005-2006 0.3031

(0.3031)0.4630

(0.2492)0.2345

(0.3262)0.1228 0.1924

(0.1763)0.6003

(0.4960)2006-2007 0.2921

(0.2940)0.4642

(0.2527)0.2348

(0.3077)0.1081 0.1886

(0.1873)0.5749

(0.5240)2007-2008 0.2996

(0.2996)0.4252

(0.2625)0.2320

(0.3196)0.1141 0.1863

(0.1821)0.5831

(0.4128)2008-2009 0.2919

(0.2919)0.4067

(0.2232)0.2301

(0.3289)0.1030 0.1720

(0.1732)0.5519

(0.4375)

2009-2010 0.2770(0.2770)

0.3938(0.2101)

0.2141(0.3130)

0.1050 0.1585(0.1663)

0.5645(0.4564)

2010-2011 0.2777(0.2777)

0.3880(0.1930)

0.2184(0.3234)

0.1017 0.1684(0.1494)

0.5007(0.4390)

2011-2012 0.2829(0.2829)

0.4075(0.2184)

0.2159(0.3177)

0.1092 0.1663(0.1523)

0.5409(0.4749)

2012-2013 0.2790(0.2867)

0.4175(0.2290)

0.2044(0.3177)

0.1107 0.1568(0.1614)

0.5752(0.5515)

2013-2014 0.2965(0.2965)

0.4436(0.2447)

0.2173(0.3244)

0.1239 0.1690(0.1735)

0.6379(0.4536)

2014-2015 0.2846(0.2805)

0.4229(0.2225)

0.2101(0.3118)

0.1186 0.1657(0.1887)

0.5751(0.5175)

48

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