Beyond the Gini index: a poverty-normalized spatial Bayesian model for assessing territorial inequality

Area of Science:

• Economics
• Spatial Analysis
• Public Policy

Background:

• The Gini index is a standard measure of income inequality but suffers from instability in small populations and limitations in cross-area comparisons.
• Existing methods struggle to provide reliable inequality estimates for areas with diverse socio-economic contexts and varying poverty levels.

Purpose of the Study:

• To introduce a modified Gini index that accounts for local poverty rates, improving precision and comparability of inequality measures.
• To explore the spatial distribution of income inequality conditional on poverty levels across Spanish municipalities.

Main Methods:

• A novel Gini index modification incorporating poverty rate as an offset.
• Application of Bayesian spatial modeling to enhance small-area estimation accuracy.
• Analysis of income data from 8,043 Spanish municipalities (2022) comparing original and normalized Gini indices.

Main Results:

• The normalized Gini index identified distinct spatial inequality patterns not evident with the original index.
• Specific regions showed higher or lower inequality than expected based on poverty levels, highlighting localized disparities.
• The normalized index revealed higher relative inequality in rural, low-density areas, contrasting with the original index's trend with urbanization.

Conclusions:

• The proposed normalized Gini index provides a more robust measure of income inequality, adjusted for local poverty.
• Its simplicity and adaptability make it valuable for informing local redistributive policies and understanding territorial inequalities.
• The method enhances the reliability of inequality comparisons across diverse socio-economic and geographic areas.