Various empirical tests for the “obvious”

 Financial inequality among European football clubs has increased significantly over the last 50-year period. The main drivers behind this development have been the introduction of new European top leagues, UEFA Champions League as well as Europa League who have increased the revenues of the best clubs.

Furthermore, improved TV-deals have had the tendency to increase revenue for the best clubs more than the less good clubs. An indication of this development is shown in Lago, Simmons, and Szymanski (2006), where a more than 200% increase in Premier League revenues is reported between 1996 and 2003.

Finally, tremendous growth in sales of sport licensed products has shown a similar relative tendency – the rich clubs get richer, while the poor clubs get poorer – revenue-wise. See for instance Miller (2016) who reports 1,75 million Manchester United shirts (averaged per year over the 2011-2016 seasons) sold, while Tottenham Hotspurs in comparison sold only 268,000 shirts.

The path from the financial inequality trend mentioned above to an explanation of decreased UO, as demonstrated in Figure 1, seems short. After all, if the best clubs get richer and the poor clubs get poorer (relatively), one might expect a growing distance between these two sets of clubs – performance-wise.

It is expressed quite clearly by Szymanski (2001) in (i): (quote)

Thus, the received opinion contains two logical steps: (i) increasing income inequality tends to reduce competitive balance and (ii) competitive imbalance tends to reduce fan interest.”

An interesting theoretical contribution by Sass (2012) predicts the same, directly linking UO and financial inequality in a dynamic league equilibrium model.

However, increased financial inequality in favor of the big clubs (revenue-wise), does not necessarily mean the same profit-wise. As shown by Hamil and Walters (2010); in spite of the dramatic increase in revenues, as well as unequal distribution of these revenues, profits for all clubs show no sign of improvement.

That is, costs must have been increasing, in a pattern quite similar to the changed pattern of revenues. As such, it is actually hard to claim that the big clubs like Manchester United or Barcelona have been able to convert the increased revenues into improved bottom line results. Could it even be that the increase in revenues only are converted into new players, perhaps not necessarily better than the players already present? Could it be that this revenue increaser is “eaten up” by the best clubs being locked into a set of “Prisoner’s dilemma-like” games, forced to overspend this revenue on the same players they bought relatively cheap previously?

An interesting contribution arguing like this can be found in Haugen and Solberg (2010).

That is, it may be argued either that financial inequality leads to decreased UO, or actually no impact on UO at all. As a consequence, it seems relevant to try to test this potential association empirically. After all, if financial inequality has little or no impact on UO, it is one thing less to worry about for sport managers.

So, I decided to conduct some empirical tests aiming to establish whether financial inequality does affect UO. The first part of these tests is performed in a cross-sectional study, including estimation of UO for a reasonably high amount of European top football leagues, and testing through simple regression whether various financial proxies indicate association. All relevant data are given in Appendix A, subsection A.1.

Table 1: Regression analyses with UO (ρL) as independent and various alternatives as dependent variable (DV ∈ {FIFA-rank, CLM, WAGE2014}). That is, ρiL = β0 + β1 · DVi + εi. All estimation and tests performed with the R-package, see R Core Team (2013)


Table 1 contains the results of these regressions. The first regression, where the FIFA- rank is dependent variable, assumes that the FIFA-rank might be a proxy for financial inequality. This variable was chosen out of convenience, but it seems reasonable to assume that if a country is high in the FIFA rank, some notion of financial superiority within the national leagues should be expected.

At least should a high FIFA-rank indicate high performance in national championships which has direct positive (and typically not evenly distributed) financial effects for the clubs in the country. Still, this variable is clearly not the sharpest proxy for analyzing the underlying hypothesis.

Anyway, as table 1 indicates, no significant association between UO and FIFA- rank is present in these data.

In order to refine the proxy, a more direct financial variable was picked for the next regression. CLM, meaning direct money transfers from Champions League (as well as Europa League), was tested. Obviously, financial inequality is driven by more components than money transfers from UEFA[4], but as many authors have argued, see for instance Hennig (2011); Scarf and Shi (2008); Szymanski (2010), participation in these tournaments have become increasingly important, financially, for many European top clubs. Furthermore, as opposed to the FIFA- rank, we know that the best and biggest clubs receive this money directly.

Again, as table 1 shows, no significant association.

In the final regression, named WAGE2014, player salaries for each country are used as the dependent variable. Given the observations of Hamil and Walters (2010), of insignificant profits in most clubs, it seems reasonable to assume that player wages might be a good proxy for financial inequality between countries. Still, not even here, any significant association is present.

All the above regressions are cross-sectional studies. In reality, it should be better to perform empirics within a league, instead of between leagues or countries. As a consequence, a new longitudinal regression was performed again with UO as independent variable, but with the Gini index of wages, refer for instance to Atkinson (1970), as dependent variable.

Based on wage data collected for various years in Premier League, corresponding Gini indexes are calculated. Refer to Appendix A, subsection A.2 for the data. Figure 2 shows a plot of the calculated Gini index for the time period of available data.


Figure 2: Development of Gini index in Premier League – 2000/01 to 2013/14 seasons


Figure 2 indicates that the Gini index is increasing over the given time period. It turns out that the linear trend shown in the figure is significant as well (at the 99% level). That is, financial inequality (measured by the Gini index) in Premier League was increasing (significantly) between 2000/01 and 2013/14 seasons.

Results from a regression where UO is independent and Gini index is dependent variable are shown in table 2.


Table 2: Regression analyses with UO (ρL) as independent and the Gini index as the dependent variable.


 Even though the direction of the potential association is correct (β1is negative), the estimate is far from significant. As a consequence, none of the regressions indicate any association between UO and financial inequality.