Human capital inequality, income inequality and convergence in developed and developing countries - Mahmood, Suraya (2015)
The investment in human capital measured through the average years of education is one of the most important instruments, especially in the 21st century. But the equal distribution of human capital in the country is also important in analysing the country’s economic performance, as well as reducing income inequality. There are two sides of driving forces in the determinants of human capital inequality that influence human capital inequality. One is the demand of education and the other is the supply of education. These determinants are important to be analysed towards reducing the human capital inequality in the world. In addition, the persistent and increasing income inequalities in most developed and developing countries since the 1980s have had a negative effect on the economies. Theoretically, the human capital inequality and income inequality are positively correlated. This study also examines the effect of human capital inequality on income inequality in the developed and developing countries using the Gini coefficient as a consistent measurement. This study adds several control variables, such as the Globalization Index, the GDP per capita, the GDP per capita squared and trade. The issue of inequality convergence in human capital inequality is also investigated, to see whether the distribution of inequality in human capital will achieve equalization or polarization in the future. For first and second objective, this study uses the dynamic panel data and the Generalized Method of Moment (GMM) two step method for the first and second objectives. Data from 92 countries over the period 1970 to 2010 with 5-year intervals (1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, and 2010) are applied in this study. For the third objective, this study uses the Ordinary Least Squared (OLS) and Generalized Method of Moments (GMM) methods to analyse data from 92 countries over the period 1965 to 2010 with 10-year intervals (1965, 1970, 1975, 1980, 1985,1990, 1995, 2000, 2005, and 2010). The empirical results show that the average years of education and lagged one year human capital inequality are significant in influencing human capital inequality in the world, developing and developed countries. For emigration rates by skill (low skill), only found low skill emigration in developed and high skill and medium skill in developing countries to be significant. However, total emigration rates, life expectancy and fertility rates are found to be not significant in influencing the human capital inequality in the developed and developing countries. For public expenditure, this study only finds significant impact on human capital inequality in developed countries. For the second objective, this study finds that human capital inequality, one-year lagged income inequality and initial income inequality are significant in influencing income inequality in the developed and developing countries. However, the GDP per capita and GDP per capita squared are consistently found to be negatively affecting income inequality in the developing countries. Generally, other control variables are found to be statistically insignificant. The last result of this study finds that the gap of international inequality of human capital (average years of education) from 1965 to 2010 in the developing and developed countries as a measured by the Gini Coefficient has consistently declined,despite the increasing trend of the gap in the average years of education in the developing and developed countries. Using the Generalized Method of Moments (GMM) and Ordinary Least Squared (OLS) methods, this study also finds absolute and conditional convergence in the developed and developing countries, but the speed of convergence in the developed countries is higher than the developing countries. The GMM results show that the developed countries tend to converge to a steady state growth rate of human capital Gini with the speed of convergence between 28.0 per cent and 29.0 per cent, compared to only 7.0 per cent and 25.0 per cent in the developing countries. The OLS results also show that the speed of absolute convergence and conditional convergence in the developed countries is high between 7.7 per cent and 8.8 per cent, while the speed of convergence in the developing countries are between 1.5 per cent and 3.2 per cent, respectively. Using Ordinary least Squared (OLS), this study also finds the emigration rate by low skill workers promote the convergence process in Developing Countries. By using Generalized Method of moment (GMM), this study finds emigration rate of low- skill workers, medium skill workers and trade promote convergence in developing countries. Additionally, the effect of emigration by lowskilled workers is found to enhance the convergence process in the developed countries. The results of this study provide an important understanding on the main determinant of human capital inequality across countries. Based on this finding, policy makers can formulate appropriate policies to reduce human capital inequality and indirectly reduce income inequality. The governments, policy makers and politicians in the developing and developed countries need to invest in human capital and improve the distribution of human capital by increasing the average year of education, as it has the potential effect in reducing income inequality. In addition, identifying the factors of human capital inequality is also important in understanding the gap between countries. In the past, most policy makers did not consider education as their top priorities. The human capital and education policies are very important because education could enhance both personal and national advancement. The result of this study further highlights the importance of reducing human capital inequality in reducing income inequality in the developed and developing countries. The positive relationship between human capital inequality and income inequality provide a clear-cut supporting evidence on using a consistent measurement for both human capital inequality and income inequality in future research. The data set of human capital Gini that has been computed in the developed and developing countries for the period 1960 to 2010 can be used by future researchers to investigate the relationship between human capital inequalities with other variables. Finally, this study offers an additional conclusion on the human capital inequality convergence, by using the GMM and OLS estimators on comprehensive panel data from the developed and developing countries. This study finds human capital inequality tend to converge across countries in the future.