Estimating the Main Economic Sectors-Income Inequality Connection for EAGLE Countries: Application of Seemingly Unrelated Regression
Abstract
The target of this research is to analyze the possible effects of growth in the main sectors of the economy, labor force participation, population growth and human development on income inequality in Emerging and Growth-Leading Economies (EAGLE) countries between the years 1990-2022. The methodology of panel time series analysis was utilized to determine the driving factors of income inequality. In this context, the Seemingly Unrelated Regression (SUR) model, an estimator that takes into account parameter heterogeneity and inter-unit correlation, was used with the slope homogeneity and cross-sectional dependency pre-tests conducted for panel data analysis. While the influence of the agricultural sector, one of the main economic sectors, on income inequality is statistically insignificant, the industrial and service sectors are significant. However, labor force participation rate, population growth and human development also appear to be the driving forces of income inequality. Although literature evidence shows the existence of a connection extending from economic growth to income inequality, the fact that no empirical research has been found that evaluates the impacts of sectoral growth on income inequality and whether labor force participation, population growth, and human development affect income distribution is attributed to the original value of this research.
Keywords
Income Inequality, Main Economic Sectors, Seemingly Unrelated Regression
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