Named after Italian statistician Corrado Gini, the Gini coefficient is a way to measure the income distribution of a population.
The value for the Gini coefficient ranges from 0 to 1 where higher values represent greater income inequality and where:
- 0 represents perfect income equality (everyone has the same income)
- 1 represents perfect income inequality (one individual has all the income)
You can find a list of Gini coefficients by country here.
The following example shows how to calculate a Gini coefficient in Python.
Example: Calculate Gini Coefficient in Python
To calculate a Gini coefficient in Python, we’ll need to first define a simple function to calculate a Gini coefficient for a NumPy array of values:
import numpy as np #define function to calculate Gini coefficient def gini(x): total = 0 for i, xi in enumerate(x[:-1], 1): total += np.sum(np.abs(xi - x[i:])) return total / (len(x)**2 * np.mean(x))
Next, we’ll use this function to calculate a Gini coefficient for an array of income values.
For example, suppose we have the following list of annual incomes for 10 individuals:
Income: $50k, $50k, $70k, $70k, $70k, $90k, $150k, $150k, $150k, $150k
The following code shows how to use the gini() function we just created to calculate the Gini coefficient for this population:
#define NumPy array of income values incomes = np.array([50, 50, 70, 70, 70, 90, 150, 150, 150, 150]) #calculate Gini coefficient for array of incomes gini(incomes) 0.226
The Gini coefficient turns out to be 0.226.
Note: In a real-world scenario there would be hundreds of thousands of different incomes for individuals in a certain country, but in this example we used 10 individuals as a simple illustration.
The following tutorials explain how to calculate a Gini coefficient using different statistical software: