Gini Coefficient

Part of assessing the effects of inequality is understanding the different ways in which inequality is measured. I am going to give a brief introduction to the Gini coefficient, a commonly used metric for the amount of inequality in a particular population. I will then show a brief example of how the Gini coefficient can be used to put economic development in perspective in a way that facilitates better policy making.

            The Gini coefficient was first used by Italian statistician Corrado Gini in 1912. It is probably best understood by looking at the graph from which the measure comes from. In the above graph you can see the x-axis holds the cumulative share of people in the population from lowest to highest incomes. That means that 25% represents the poorest quarter of the population. The y-axis displays the cumulative share of total income earned a particular percentage of the population receives. The point (25%, 15%) represents the fact the poorest quarter of the population accounts for 15% of total income earned. In a society where wealth is perfectly equally distributed, the graph would be a 45 degree straight line where everyone earned the same amount of income. This is represented by the “Line of Equality” in the above image.

            

             In reality, income earned varies among the population, creating a curved line. The Gini coefficient is equal to the area between the line of equality and the curve that represents income distribution for a particular population, divided by the total area underneath the line of equality. In our picture this is represented by A divided by A + B. This number is really only useful when put into relative terms. Below are the values of the Gini coefficient for a number of countries to provide some intuition for what normal values are. Remember, the higher the number, the more inequality in that country.

South Africa: 63.1

Costa Rica: 50.7

China: 47.0

United States: 45.0

Qatar: 41.1

Japan: 38.1

United Kingdom: 34.0

Afghanistan: 27.8

Denmark: 24.0

           

            As we can see, the numbers vary quite a bit, and the Gini coefficient isn’t necessarily a good indicator of economic prosperity within a country. But it can be very useful for a number of practical purposes like looking into the role inequality plays when conducting policy planning for development.

            This graph pulled from Martin Ravallion’s 2005 paper Inequality is Bad for the Poor shows how reduction of poverty associated with changing mean incomes has a positive relationship with the amount of inequality in a country. Effectively, the less inequality in a region, the more poverty reduction occurs from raising the mean income. This finding is crucial for policy makers who are looking at the most important factors to address when aiming to reduce poverty in a country.

            

        I plan to bring the Gini coefficient back up throughout my blog so that readers can have a numerical grounding when reading about levels of inequality. Although it is a very simple measure and leaves room for more investigation, its simplicity allows for easy comparison and basic quantification of income distribution. Maybe by looking at how different levels of inequality affect prosperity and happiness in different countries we will be able to get a better understanding for whether or not inequality really is so bad.