Widening inequalities of income and wealth in our modern society have started to attract the attention of the media and policy makers thanks to the excellent research done by many academics. In this article, in an attempt to familiarize the reader with one of the key concepts in inequality research — the Gini Coefficient, I will present the results of a side project of mine: Footballer value inequality in the English Premier League.

The most common measure to evaluate the extent of income (or wealth) inequality in a country is the Gini coefficient. Developed by Corrado Gini in 1962, this coefficient measures how incomes are distributed. Imagine a village of 100 people, where together they produce and sell goods, worth of $100,000. If each villager receives an equal amount of income each month, $1000, the Gini coefficient of this distribution would be 0, since incomes are distributed equally. On the other hand, if only 1 person in the village received the entire sales revenue, $100,000; while rest of the villagers receives nothing, the Gini coefficient would rise to its maximum of 1. Any other possible distribution of the $100,000 among these 100 villagers would get a Gini coefficient between 0 and 1.

In our modern world, countries with the lowest level of income inequality tend to be ex-Soviet or Scandinavian countries, with Gini coefficients, typically below 0.30 (or 30%). At the other end of the spectrum, we find South Africa, where the Gini coefficient is above 0.60. African and Latin American have high income inequality, while inequality in European countries are relatively lower compared to other parts of the world. Of course, it is worth noting that inequality data do not exist for many oil rich Middle-Eastern countries, where inequality levels might be the highest in the world.

Well, since we now know how the Gini works, we can use it to measure the distribution of other economic concepts. As you might have heard, the winter transfer window in the European football leagues closed recently. English clubs were big spenders with a total of £430 million, and they have actually broken their transfer deadline-day record. This number is huge, but when you think about it, most of this money was spent to transfer a handful of players, with the most expensive transfer of this window being £75 million, more than 1/6th of the entire amount.

What happens to the balance and equality within a team when a club spends the daily GDP (Gross Domestic Product) of Serbia to transfer just one player?

Let’s look at how unequal the teams in the English Premier League really are. First of all, in an ideal study on this subject, we would have perfect information on the incomes of each player. Unfortunately, as fas as I know, no such source of information exists. However, thanks to the Transfermarkt website, we know the estimated market value of the players. Of course, these are subjective values, and they do not necessarily project the actual market value, however, they can be a good proxy measure for incomes of the players, if we assume that players who are more valuable in the transfer market earn more than those who are less valuable.

Once we have the estimated market values of each player, we can calculate the Gini coefficients that gauge the distribution of footballer value in the Premier League teams. You can find the Gini coefficient for each team in the Premier League since 2005 in the figure above. We see that the average inequality has fallen from around 0.48 to 0.44 between 2005 and 2013, yet started to rise again since then. The Arsenal squad in 2005 is the most unequal one in our sample, with a Gini over 0.60, while Stoke City in 2013 has the lowest Gini value with 0.27.

As it stands, there is a strong an positive correlation between value inequality and success in the Premier League. Teams with more unequal squads have won more points in our sample. Of course, correlation does not mean causation. It could be that unequal teams are also the teams with the best (or more expensive) players, and the graph above is just picking up on that. In fact, when we look at the Big-Five — Arsenal, Chelsea, Liverpool, Manchester City and Manchester United, we see that their inequality levels are consistently higher than the Premier League average in the majority of the seasons, even though there is a downward trend in inequality (Figure below).

Evidently, when we check how total market value and success are linked, we see clear positive, but this time quadratic relationship (Figure below). The big-spenders are also more successful than the rest of the Premier League teams, but that isn’t really a surprise.

If you want to examine how inequality changed over the years for each team in the English Premier League, I also created an informative and easy to use Tableau dashboard — pictured below. Unfortunately, these dashboards cannot be embedded to Medium, so if you want to check it out, feel free to visit www.baymul.com/en/data-viz-epl.html and play with the data yourself.