Named after Italian statistician , 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 .

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.

Additional Resources

The following tutorials explain how to calculate a Gini coefficient using different statistical software: