Panel Data Analysis of Determinants of Gini Coefficient

The researcher uses panel data analysis to examine determinants of the Gini coefficient. The researcher uses an unbalanced panel with data from 1988 to 2018. The panel data is from 103 countries. The data has panels based on income-level and region to discern differences among groups of countries. The study\u27s main determinants include the progressiveness of the tax system, regime type, political stability, corruption, ethnic fractionalization, and religion. The econometric model uses Driscoll and Kraay (1998) standard errors for nonparametric heteroscedasticity autocorrelation. The econometric model uses fixed effects for both country and time. The model uses Newey-West corrections to cross-sectional averages in the moment condition while adjusting the standard error estimates to guarantee covariance matrix estimators are consistent and independent of the cross-sectional dimension. The study’s findings are consistent with the literature. Among the study determinants, tax progressiveness is easiest for countries to change and has higher levels of certainty in reducing income inequality. The researcher finds an indeterminate and uncertain relationship between the Gini coefficient and regime type, political stability, ethnic fractionalization, and religion type. The relationship between determinants and the Gini coefficient depends on the characteristics of the different groups of countries. Thus, generalizations related to determinants are often limited to specific groups, not broadly. Institutional quality is more statistically significant than polity, corruption, ethnic fractionalization, and government stability. Religious tension is often more statistically significant than ethnic fractionalization and ethnic tension. Lower levels of religious tension correlate with lower income inequality. All major religions except Buddhism have a statically significant relationship with income inequality. Christianity and Hinduism correlate with higher income inequality, while Islam and Judaism correlate with lower income inequality. Countries with more years of schooling, higher tertiary education levels, and higher returns to education correlate to lower income inequality. Countries with a larger percentage of the economy in manufacturing correlate with lower income inequality. Higher unemployment and inflation levels correlate with higher income inequality