Determinants of Income Inequality: A Compositional Data Approach

The determinants and correlates of income distribution have received significant attention in economics and public policy literature over recent decades. Income distribution, representing the share of income received by each quintile or decile of a population expressed as a vector of nonnegative proportions that sum to one, is inherently compositional data. However, most research has traditionally used aggregate inequality measures, such as the Gini coefficient, as the dependent variable when modeling relationships with economic indicators. Unlike a compositional data analysis (CoDA) approach, this reliance on aggregate measures limits insights into the tradeoffs among income classes as inequality determinants change. To date, only one study has applied a logratio-based model to analyze the determinants of income inequality in the U.S., leaving substantial gaps in understanding the broader implications of CoDA in income studies. To address this, our study proposes a Dirichlet regression model for country-level income distribution, integrating relevant economic and development indicators. This model aims to identify key determinants of income inequality and assess their specific impacts on income shares across different income groups. The performance of our proposed model is compared against a traditional Gini-based model, highlighting its potential for more nuanced and comprehensive insights into income distribution dynamics.