Economic inequality is defined as the difference of several measures of economic welfare among individuals in a population. Inequality tends to have several negative effects on a society such as the loss of social cohesion, rising crime rates, and reduced economic growth. So, how do we solve it?
In order to solve a problem we first have to analyze it, and the most used measurement tool for inequality is the Gini coefficient, created in 1912 by the Italian statistician Corrado Gini. Let’s see how it works.
To learn how the Gini coefficient works first we have to familiarize ourselves with the Lorenz curve, which is this one:
Well, there’s the curve, what now?
The horizontal axis (X) represents the percentage of the poorest population, e.g 0.2 is 20% of the lowest income population, 0.8 is 80% and 1 is the entire population.
The vertical axis (Y) represents the percentage of the total income of the population, e.g 0.1 is 10% of the total income.
The blue line represents a situation of perfect equality where the entire population has exactly the same income (the poorest 10% has 10% of the wealth, the poorest 50% has 50% and so on), the green angle represents a situation of perfect inequality where a single person has all the income and the rest of the population has nothing. The red curve is how you would see the inequality of any country in reality.
In simple terms, a country is more equitable when its Lorenz curve resembles a straight line, and less when it resembles a 90 ° angle.
Purple: Low inequality / Red: High inequality
The Gini coefficient is calculated using the area of the Lorenz curve and it’s basically the area between the line of equality and the Lorenz curve divided by the total area below the line of equality.
In mathematical terms:
G = A / (A + B)
Now that we know what we are measuring, let’s look at the data:
The country with the highest Gini coefficient is Comoros with 64.30, followed closely by Namibia with 63.90 and South Africa in third place with 63.90.
On the other hand, we have the countries with less inequality, led by Ukraine with a Gini of 24.09, Iceland with 24.4 and Sweden with 25.
Something interesting to note is how upper-middle income countries like those in Latin America have a much higher Gini than those in sub-Saharan Africa, which have the lowest incomes in the world.
One explanation for this phenomenon was given by Simon Kuznets in the 1950s when he proposed what is now known as the Kuznets curve. It says that as countries develop their inequality grows and once they reach a certain level of wealth it begins to decline.
To make it easier, let’s divide the population of a nation into two, one rural and one urban.
Stage of underdevelopment: Poor rural population; Poor urban population = Low Gini
Development stage: Poor rural population; Rich urban population = High Gini
Developed stage: Rich rural population; Rich urban population = Low Gini
Although the Kuznets curve has its merits, valid criticisms have been made that it does not take into account historical and political factors, these criticisms are especially noteworthy if one takes into account the historical development of some East Asian countries such as Taiwan, Singapore, Hong Kong and South Korea. Which experienced a rapid transition from being underdeveloped to developed countries without going through a period of high inequality.
Knowing the distribution of wealth is important to understand the social problems that affect a nation. However, this is not an extremely useful number in itself. Only when we analyze it together with other factors such as income we can have a better idea of the conditions of life in a certain nation.
Between two nations with similar incomes, it’s clearly preferable the one with less inequality. When we have countries with different incomes it is common that those with more income are preferable even when they have greater inequality.
There are other factors that may make an inequitable nation preferable over a more egalitarian one, the Gini coefficient measures the income of individuals, but does not take into account factors such as access to public services or state programs that improve the quality of life of the population without directly affecting income.