The relationship between economic complexity index (ECI) and income inequality: An application on OECD countries

Income refers to the income obtained from the goods and services produced in an economy in a certain period. There are many factors in the economy that affect the distribution of earnings between societies and individuals, and today, facilitating the ways to reach needs is not effective in eliminating the problem of inequality in income distribution. Many factors and types of income distribution that affect income distribution have been the subject of study for schools of economic thought and economic thinkers since the beginning of economic history. In an economy, it is necessary to minimize the problem of income inequality in order for individuals to live together and earn profits and not to decrease the level of welfare in the economy. It is important for economies to gain a large volume in the world market. In order to gain a place in the world market, it is necessary to produce diversified goods that provide competitive advantage and include intensive technology and information. It is possible to measure the knowledge, technology and quality in the produced goods with the economic complexity index.Accordingly, in this study, the relationship between the economic complexity index (ECI) and income inequality was examined by considering the data between 2000 and 2020 in OECD countries. In the model, the research variable was chosen as the economic complexity index. As the dependent variable, the Gini coefficient of OECD countries; The independent variables were selected as gross domestic product, trade openness rate and the ratio of foreign direct investments in OECD countries to gross domestic product, analysis and tests were applied. The analyzes and tests used are respectively; cross-section dependency test, homogeneity test, panel unit root analysis, panel time series analysis (long-term homogeneity –Hausman- test), pooled mean group (PMG) estimator and Dumitrescu – Hurlin (2012) panel causality tests. According to the results of the cross-sectional dependence and homogeneity test, no dependency was found between the variables in the model, and it was concluded that the variables were stationary according to the panel unit root analysis. According to the panel time series analysis, every 1% increase in the long-term economic complexity index, trade openness rate and the share of foreign direct investments in gross domestic product within the framework of OECD countries is the gini coefficient, respectively; It was observed that it decreased by 0.029%, 0.043%, and 0.027%. As a result of the 1% increase in the gross domestic product in the long term, it was observed that the gini coefficient increased by 0.021%. According to panel time series analysis, it has a homogeneous structure in the long term and does not change from unit to unit. According to the results of the panel causality analysis, it was concluded that there is a two-way causality relationship between the economic complexity index, gross domestic product and trade openness ratio and the gini coefficient in OECD countries. It has been observed that there is no causal relationship between the share of foreign direct investments in gross domestic product and the gini coefficient.