Analysis of cross countries income inequality panel data: Using random effect regression trees

Reducing income inequality is one of the major steps toward economic development. When the level of inequality in the distribution of income and wealth is high in the society, many economic, social and even political problems might happen. So, many studies in the economic literature tried to find the determinants of income inequality and propose some policies to decline it. In this paper, we will address the analysis of income inequality panel data across different countries through ۲۰۱۱ to ۲۰۱۵. One of the commonly used methodologies to analyze panel data is the linear mixed effects model. Since the linearity assumption might be violated, recently, the idea of mixed effect models are combined with the flexibility of tree-based estimation methods which allows for potential higher order interactions as well. In this paper, we apply the resulting estimation method, called the RE-EM tree, to the income inequality panel data. The results show that the RE-EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to simple regression trees without random effects. This is due to the fact that each country applies its own specific poverty reduction measures handled via country-specific random coefficients of RE-EM tree.

Keywords:

Income inequality , Gini coefficient , Mixed effects model , Mixed effect Regression trees , Panel data