Almost half of the population of the world lives in rural regions and mostly in a state of poverty. Such inequalities in human development have been one of the primary reasons for unrest and, in some parts of the world, even violence.
– Dr. A P J Abdul Kalam
Recently, World Happiness Report for the year of 2022 was released and it was very shocking for many Indians that out of 146 countries surveyed, India stood at the position of 136 in terms of the happiest countries in the world. The countries like Myanmar, Sri Lanka, Pakistan, Yemen have still secured better ranks in terms of the happiness index despite having completely opposite socioeconomic imagery in the world. This was the moment when I understood the importance of the key performance parameters of any country.
India didn’t perform well on the list of happiest countries (actually 11th from bottom). When a child secures lower marks in the class then he/she tries to find the bad things that the class topper shows to explain and convince his/her mind and especially parents. In the same naïve sense, I looked towards the neighboring relatively upper ranking countries which have performed better than India. They are far behind India in terms of economy, social environment, quality of life, per capita income, population of youth. Then I realized the parameters on which this was being ranked. The key factors to decide the happiness were GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity and perception of corruption.
The overall purpose of such socioeconomic surveys and the gap between conclusions drawn from them and the reality people perceive are always good topics for debates, discussions. The World Happiness Report itself is a good example for this. We will see one such simple concept which might give you some insight into the such indicators in the economics.
One of the key factors in deciding the happiness of the nations was the income inequality in the respective countries. Many times, the income inequality has also been linked to the social stability by some psychologist. Jordan Peterson- for example discusses that it is not the countries with less income where the crime rates, riots thereby social instability is high, rather the social instability is high where countries have larger gap between the incomes of poorest of poor and richest of rich. This income inequality and not ‘per capita income’ is strongly linked to the social stability thereby roughly speaking ‘the happiness of people’.
Gini coefficient has explored his domain in a very simple yet effective way, there are some advantages and some disadvantages to it, but it is an interesting concept to understand. When any news articles say that the valley between rich and poor is increasing, they are actually pointing towards the increasing Gini coefficient. You can find the Gini Coefficient for almost all countries on the website of OECD (Organisation for Economic Co-operation and Development).
But, first let us understand Gini Coefficient and Lorenz Curve.
Lorenz Curve
Lorenz curve (Figure 1) is used to graphically represent the distribution of the income within the population. First of all, each member of population is arranged in to the increasing order of income, cumulative income and cumulative member count is considered for the Lorenz curve. The X-axis indicates the cumulative population and Y-axis indicates the cumulative income; it can be taken as percentage also. Lorenz cure will now indicate the fraction of income earned for the respective fraction of population.
What comes after establishing Lorenz Curve is interesting. When we take sum of all the income of nation and divide it by the population earning it, we get average income. But soon it was found that average income is not sufficient enough to compare the economic well-being of any nation.
Let us take an example:
Here we have considered five countries with population of 25 people! (bear with the example for the sake of understanding)
You can see that the average population of each country is 25. By looking at average income of each group one might say that the economic condition of each country is same – that is 4 unit per person, but that might not be the real case. One has to look closely at the data points in each country where importance of Lorenz Curve and Gini Coefficient gets highlighted.
If you compare the incomes in each groups there is specific pattern in the incomes of people. Here, Gini coefficient helps in a better way.
We need to plot Lorenz curve for each group and segregate the area A and area B as shown in figure 3.
The Gini Coefficient (GC) is defined as follows using Lorenz Curve, here A and B both are areas highlighted in the figure 3:
The value of Gini coefficient always lies between 0 to 1. Higher the Gini Coefficient higher is the income inequality.
The lorenz curve and Gini coefficient for our example of Group A, B, C, D, E, F is as given in figure 4.
GC=0, Perfect income equality
Now, if one looks at Group A- the income of each member is 4 units thus the Gini coefficient here is 0 indicating the ideal condition of equal income- perfect equality. In reality perfect inequality is not possible as income/wealth is not evenly and exactly distributed all over the nation.
GC=1, Complete income inequality
If one looks at Group F- the average income of the group is still 4 units but the complete income of the group is concentrated to only one person of the group which is the ideal inequality, here the Gini Coefficient is 1 indicating complete income inequality.
These are the ideal condition to compare with the real conditions.
Now let us look at the Groups B, C, D, E. Here, the average income in each group is same as 4 units, but if you start plotting them in the form of Lorenz curve, you will notice the difference in the income distribution throughout each group. The income distribution in each group is not same even though the average income is the same.
If we find the Gini Coefficient for each group, the values are given as GCB=0.16, GCC=0.42, GCD=0.53, GCE=0.82.
In short, the Gini Coefficient gives much more important information than per capita income.
Poor Countries have Gini Coefficient values scattered all over the range on as low as 0.25 to as high as 0.71. Generally, it is observed from the historical data that the countries with Gini coefficient higher than 0.40 indicate highly socioeconomic instability.
The main advantage of Gini coefficient is that it highlights how much wealth is owned by the fraction of people for a given country.
It is also interesting to notice that the country with rising GDP and rising Gini Coefficient indicate increasing poverty in the country.
Gini coefficient does not consider the size of the economy because all the income and all the population are compressed to the scale of 100% thus any two economies can be easily compared on some common parameters using respective Gini Coefficients.
The main power of Gini Coefficient is that just a simple number can give you the idea of overall income distribution in the country.
There are some ‘lost in the calculation’ details in Gini Coefficient that one needs to understand before commenting on any nation’s economy just by looking at its Gini Coefficient.
It is observed that the countries with relatively larger population and cultural diversity will yield higher Gini Coefficient than its each individual state and relatively smaller coparing countries.
Gini coefficient does not consider the dependence of basic necessities with the income. Some countries have systems like food stamps or food ration which may not be counted as monetary incomes, thus it becomes important to understand the culture and lifestyle, availability of basic facilities while comparing countries based on Gini Coefficients.
The Gini Coefficient will yield different values for the same country if base data varies as income of individual or income of household. As we know, all of the population of the country is not earning population; there are some children, young adults, elders, even young men and women who are not earning population. Hence, it becomes more practical to consider the Gini Coefficient based on the household income and considering the weightage of each family member behind that income. This will give more practical Gini Coefficients. There are three famous ways to calculate Gini based on this idea called as Per Household Member Scale, Modified OECD (Organization for Economic Co-operation and Development) Scale and Square Root Scale.
Per household scale distributes the income of the family equally irrespective of their earning potential and age. Modified OECD Scale distributes the income in the family members as per their earning potential and age- the main earning person will have more weightage that the children dependent on him/her in this scale. Square root scale simply divides the income of family into the square root of family members count. These all scale will yield different Gini Coefficients and are used to derive specific meanings for the economy.
During many studies it is also found that the Gini Coefficients of given group are less sensitive to top 10% and bottom 10% population. Gini coefficients are more sensitive to the middle fraction of populations and wealth associated to it.
Though Gini coefficient has its limitations, but it is still the most simple and effective way to visualize and compare the income/ wealth inequality of any nation.
Economists have attempted to eliminate these limitations by incorporating various other indicators or techniques like Atkins Index, Coefficient of variation, Decile Ratios, Generalized Entropy Index, Kakwani progressivity index, Robin Hood index, Sen poverty measure, proportion of the total income earned.
Featured Image credit- billy cedeno – pixabay.com
Further readings:
- OECD Income inequality database
- Income inequality measures– Fernando G De Maio
- A simple method for measuring inequality– Nature – Thitithep Sitthiyot & Kanyarat Holasut
- World Happiness Report 2022
Parts Per Billion 