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Estimation of the Gini coefficient based on two quantiles.
1School of Finance and Economics, Jimei University, Xiamen, Fujian, China.
Plos One
|February 11, 2025
Summary
This study introduces a practical method to estimate the Gini coefficient using income shares of the top 10% and bottom 40% of the population. The new approach demonstrates accuracy and robustness across various sample sizes and inequality levels.
Area of Science:
- Economics
- Econometrics
- Social Sciences
Background:
- Accurate measurement of income inequality is crucial for socioeconomic analysis.
- Traditional Gini coefficient estimation can be data-intensive.
- The Palma proposition highlights the relationship between top and bottom income shares.
Purpose of the Study:
- To develop and validate a practical method for estimating the sample Gini coefficient.
- To assess the accuracy and robustness of the proposed estimation technique.
- To utilize the Lorenz fitting curve and specific population quantiles for inequality measurement.
Main Methods:
- Estimation of the Gini coefficient based on the Palma proposition.
- Application of the Lorenz fitting curve.
- Utilizing the income share of the top 10% and bottom 40% of the population.
- Validation through empirical research and Monte Carlo simulation.
Main Results:
- The proposed method achieves an absolute error within a hundredth compared to the sample Gini coefficient.
- Monte Carlo simulations confirm good performance and robustness.
- The method is effective across different sample sizes and inequality levels.
Conclusions:
- Estimating the Gini coefficient using the income share of the top 10% and bottom 40% is a practical and accurate approach.
- The Lorenz fitting curve combined with these two quantiles provides a reliable estimation tool.
- This method offers a robust alternative for measuring income inequality.
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