Author(s): Kunofiwa Tsaurai
The paper’s main objective was to explore the determinants of income inequality in transitional economies using panel data analysis [(fixed effects, random effects, pooled ordinary least squares (OLS), dynamic generalized methods of moments (GMM)] with data ranging from 2003 to 2016. The study observed that human capital development is not a significant determinant of income inequality under all the econometric estimation methods used. The dynamic GMM noted that the lag of income inequality had a significant positive impact on income inequality. All the econometric estimation methods produced results which show that economic growth had a significant deleterious effect on income inequality, in line with most theoretical predictions. Transitional economies are therefore urged to implement economic growth spurring policies to reduce income inequality. On the other hand, the dynamic GMM method shows that unemployment reduced income inequality, a finding which contradicts theory. Fixed effects, random effects and pooled OLS approaches noted that the interaction between economic growth and unemployment had a significant positive effect on unemployment, a finding which shows that if economic growth does not enhance employment, income inequality grows. Transitional economies are therefore urged to development and implement concurrent economic growth and employment enhancement policies to reduce income inequality. Other variables which were found to have a significant positive impact on income inequality include information and communication technology (fixed effects, random effects), financial development (fixed effects, random effects, pooled OLS), foreign direct investment (random effects, pooled OLS, dynamic OLS), infrastructural development (fixed effects) and trade openness (fixed effects, random effects, pooled OLS, dynamic GMM).