My main and most interest finding in this paper, is the lack of association between various variables used as proxies for financial inequality and UO. This is contradictory to mainstream thinking and given that my (limited) empirical evidence indicates reality, it ought to be important.
This is because if UO is not driven by financial inequality, the necessity to regulate financial inequality is limited. There is no doubt that a growing concern related high, and unequally distributed, player transfers and wages has emerged the latter years, and several practitioners as well as experts have argued that more financial equality may be a cure. My empirical evidence disputes this.
If financial inequality has no or limited impact on UO, then various regulative means like for instance endless quarrels on the fairness of TV-money distribution may be a waste of time.
Not to speak about UEFA Fair play constraints who may have little or no importance what so ever. If the system forces the rich clubs to be trapped in games where their financial strength does not pay in the conversion to playing strength, then we could spend far less resources on discussing these topics.
The fact that I was able to show that corruption was significantly associated with UO may be important, although I personally am quite insecure on whether this variable actually is that relevant. Still, the empirical evidence is there, and should perhaps be investigated further.
In any case, the understanding of the development of UO, alone or as a consequence of other instruments is relevant. If the development illustrated in Figure 1 will continue, most would agree that the football business might experience severe demand problems in the future. After all, sporting contests with predictable results will in the long run fail to engage audiences.
Appendix A Data used in the empirical analyses
A.1 The cross-sectional study
Table 4 contains data used in the analyses leading up to the results in table 1.
Table 4: Data used in the (cross-sectional) empirical analyses.
All data except The FIFA rank, picked from May 2018 and wages (WAGE2014), picked from the 2013/2014 season, are picked from the 2016/2017 or 2017 seasons. All data, except ρL, are picked from open internet
sources like: www.altomfotball.no, www.fifa.com, www.uefa.com, www.transparency.org, and www.deadspin.com. ρLis calculated by
Where N is the number of matches played in the league, APiis the point score achieved by team i on the final table, LCPiis the point score achieved by team i if the league is maximally imbalanced, and MCP is the point score achieved by team i if the league is maximally balanced. Refer for instance to Haugen (2008) or Haugen and Heen (2018) for further information.
A.2 The longitudinal study
Table 5: Data used in the longitudinal empirical analysis.