Adventures in Machine Learning

Measuring Income Inequality with Python’s Gini Coefficient Calculation

Income inequality has long been a topic of discussion and research worldwide. It is a social issue that affects almost everyone, directly or indirectly.

Addressing income inequality requires understanding the complexities and intricacies of the system that causes it in the first place. One such tool used for better understanding income inequality is the Gini coefficient.

This article will provide an in-depth look at the Gini coefficient and how to use Python to calculate it.

The Gini Coefficient

What is the Gini Coefficient? The Gini coefficient is a statistical measure used to evaluate income distribution.

Corrado Gini, an Italian statistician, developed it in 1912. The Gini coefficient is a value between 0 and 1, with 0 indicating perfect income equality and 1 indicating perfect income inequality.

When calculating the Gini coefficient, income is measured on one axis, and the population is measured on the other. A Gini coefficient of 0 would mean that every person in the population has the exact same income, while a score of 1 would mean that all the income is concentrated in the hands of one individual, with everyone else having no income at all.

Range of the Gini Coefficient

When it comes to the Gini coefficient, there is a range of scores ranging from perfect income equality to perfect income inequality. A score of 0.20 or less indicates a fair degree of equality in income distribution.

A score between 0.20 and 0.30 indicates a relatively moderate degree of inequality. A score between 0.30 and 0.40 indicates a high degree of inequality, while a score between 0.40 and 0.50 indicates a very high degree of inequality.

A score above 0.50 indicates extreme inequality.

Calculating the Gini Coefficient in Python

Defining a function to calculate the Gini Coefficient

Python is a popular programming language used in data analysis and statistics. It offers a variety of libraries that make statistical calculations easy and efficient.

One such library is NumPy, which provides tools for working with arrays and matrices. To calculate the Gini coefficient in Python, we can define a function that takes an array of income values and population as input.

The first step is to sort the income values in ascending order. Then, we calculate the area between the Lorenz curve and the line of perfect equality.

Finally, we divide the area between the Lorenz curve and the line of perfect equality by the total area below the line of perfect equality to get the Gini coefficient.

Example of calculating the Gini Coefficient

Let’s say we have an array of income values and population:


import numpy as np

income = np.array([10000, 20000, 30000, 50000, 70000, 100000])

population = np.array([50, 70, 100, 150, 200, 300])


To calculate the Gini coefficient in Python, we can define a function that takes these two arrays as inputs:


def gini_coefficient(income, population):

income = np.sort(income)

cum_population = np.cumsum(np.sort(population))

cum_income = np.cumsum(income*population)

total_income = cum_income[-1]

total_population = cum_population[-1]

perfect_income_distribution = np.linspace(0, total_income, total_population)

area_below_curve = np.trapz(perfect_income_distribution) – np.trapz(cum_income)

area_total = np.trapz(perfect_income_distribution)

gini_coefficient = area_below_curve/area_total

return gini_coefficient


Using this function with the `income` and `population` arrays we provided earlier, we would get the following result:


gini_coefficient(income, population)

>> 0.32658563437298356


This result tells us that the income distribution in this example is relatively moderate, with a Gini coefficient score of 0.326. Conclusion:

The Gini coefficient and its range help us to measure and understand income inequality in a population.

Calculating the Gini coefficient in Python is easy and efficient with the use of the NumPy library. These tools allow individuals and organizations to better understand the impact of income inequality and take action to create a fairer distribution of wealth.

By using the information provided in this article, you can start your own calculations to better understand and fight against income inequality. In conclusion, the Gini coefficient is a valuable tool for evaluating income distribution, and Python’s NumPy library offers a simple and efficient way to calculate it.

The coefficient’s range provides insight into levels of income inequality, from mild to extreme, while the Python function helps individuals and organizations to take affirmative steps for creating a fairer distribution of income. Income inequality is a pressing issue that demands attention and action for the betterment of society.

Understanding and using the Gini coefficient can lead to recognition of the problem’s extent and could help to address it deliberately.

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