Skill 045

Economic Inequality Analysis

This skill provides insights into techniques and methodologies for analyzing economic inequality, a critical issue in socio-economic research.

Overview

Economic inequality refers to disparities in wealth, income, and access to resources among individuals or groups. Understanding these inequalities is essential for:

• Informing policy decisions
• Understanding social dynamics
• Promoting economic equity

Measuring Economic Inequality

Several metrics can be used to quantify economic inequality, including:

• Gini Coefficient
• Lorenz Curve
• Theil Index

Gini Coefficient

The Gini coefficient measures income inequality within a population, ranging from 0 (perfect equality) to 1 (maximum inequality).

Python Implementation

import numpy as np
import pandas as pd
def gini_coefficient(data):
    n = len(data)
    if n == 0:
        return 0
    index = np.arange(1, n + 1)
    return (2 * np.sum(index * np.sort(data)) / np.sum(data) - (n + 1)) / n

# Load your income data
# income_data = pd.read_csv('income_data.csv')

# Calculate Gini Coefficient
gini = gini_coefficient(income_data['income'])
print(f'Gini Coefficient: {gini}')

Lorenz Curve

The Lorenz curve is a graphical representation of income distribution, showing the proportion of total income earned by cumulative percentages of the population. It helps visualize inequality.

Python Implementation

import matplotlib.pyplot as plt

# Calculate cumulative income shares
income_data = income_data.sort_values('income')
income_data['cumulative_income'] = income_data['income'].cumsum() / income_data['income'].sum()

# Plot Lorenz Curve
plt.plot(income_data['cumulative_income'], label='Lorenz Curve')
plt.plot([0, 1], [0, 1], linestyle='--', color='red')  # Line of equality
plt.title('Lorenz Curve')
plt.xlabel('Cumulative share of population')
plt.ylabel('Cumulative share of income')
plt.legend()
plt.show()

Conclusion

Analyzing economic inequality provides valuable insights into the distribution of wealth and resources. Employing various measurement techniques allows researchers and policymakers to address disparities effectively.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: legendtkl
• Source: legendtkl/agentic-skill-router
• License: MIT
• Homepage: https://legendtkl.github.io/agentic-skill-router/

Install and usage instructions live in the source repository linked above.