Kırılgan Beşli Ülkelerinde Gelir Dağılımının Dinamikleri: Ampirik Bir Uygulama
Oğuz Öztunç, Seda Bayrakdarİktisat teorisinin önemli konularından birisi gelir dağılımı eşitsizliği tartışmalarıdır. İster gelişmiş ister gelişmekte olsun neredeyse her ülkelerde gelir dağılımı eşitsizliklerinin iyileştirilmesinde devletin ve sosyal kurumların rolü yadsınamaz. Bu nedenle gelir dağılımını etkileyen dinamiklerin tespit edilmesi, ülkelerde uygulanacak iktisat politikalarının doğru belirlenebilmesi açısından önem kazanmaktadır. Çalışmada son dönemde sıklıkla incelenen Kırılgan Beşli (Brezilya, Hindistan, Endonezya, Guney Afrika ve Turkiye) adı verilen ülkelerde gelir dağılımının dinamikleri panel veri analizi ile 1994-2017 yıllarını kapsayan dönem için CCE tahmincisi kullanılarak tespit edilmeye çalışılacaktır. Modelde bağımlı değişken olarak gelir dağılımını temsil etmesi açısından GINI Katsayısı kullanılmıştır. Analiz sonucunda spesifik bir politika önerisi yapılabilmesi için bağımsız değişkenler ise para politikası değişkenleri (politika faizi, para arzı) ve maliye politikası değişkenleri (kamu harcamaları, vergi gelirleri) olarak ikiye ayrılmıştır. Ek olarak literatür uyarınca kişi başına düşen milli gelir büyümesi, enflasyon oranı, ticari açıklık, finansal açıklık, finansal gelişmişlik ve beşeri sermaye verileri kontrol değişkenler olarak modele dahil edilmiştir. Kırılgan Beşli Ülkelerinde para ve maliye politikaları uyarınca böyle bir analize rastlanmaması nedeniyle bu çalışmanın literatüre katkı yapacağı düşünülmektedir. Ampirik sonuçlar neticesinde, Kırılgan Beşli olarak adlandırılan ülkelerde geniş para arzında ve beseri sermayede meydana gelen artışların gelir eşitsizliğini artırdığı, bunun aksine finansal gelişmişlikte meydana gelen artışların ise gelir eşitsizliğini azalttığı sonucuna ulaşılmıştır.
Dynamics Of Income Distribution in the Five Fragile Countries: An Empirical Application
Oğuz Öztunç, Seda BayrakdarOne of the important topics of economic theory is the discussion of income distribution inequality. The role of the state and social institutions in improving income distribution inequalities in almost every country, whether developed or developing, is undeniable. Therefore, determining the dynamics affecting income distribution is important in terms of correctly determining the economic policies to be implemented in the countries. In this study, the dynamics of income distribution in the countries called the Fragile Five (Brazil, India, Indonesia, South Africa and Türkiye), which have been frequently examined recently, will be determined using the CCE estimator between 1994 and 2017 with panel data analysis. The GINI Coefficient was used as the dependent variable in the model to represent the income distribution. To make a specific policy recommendation as a result of the analysis, the independent variables were divided into two as monetary policy variables (policy rate, money supply) and fiscal policy variables (public expenditures, tax revenues). In addition, according to the literature, per capita national income growth, inflation rate, trade openness, financial openness, financial development and human capital data were included in the model as control variables. It is thought that this study will contribute to the literature since there is no such analysis in terms of monetary and fiscal policies in the Fragile Five Countries. According to the empirical results, it is concluded that increases in the broad money supply and human capital in the so-called Fragile Five countries increase income inequality, while increases in financial development reduce income inequality.
One of the important matters in economic theory is the debate on income inequality. The role of the state and institutions in improving income inequality in developed and especially developing countries is gaining importance. In this context, it is necessary to determine the dynamics affecting income distribution to determine and create economic policies.
The study analyzes the years between 1994 and 2017. The selected group of countries is known as the Fragile Five (Brazil, India, Indonesia, South Africa and Türkiye). In these countries, income distribution dynamics will be determined by using panel data analysis - CCE estimator. CCE is an estimator that derives coefficients on the basis of panel overall as well as cross-sectional units. Therefore, this estimator allows the analysis results to be interpreted in the terms of the panel overall and cross-sectional unit.
The analysis of this study was executed by using eleven different variables. The GINI coefficient represents the income distribution as dependent variables. In addition, variables that are thought to affect income distribution inequality are policy interest, money supply, public expenditures, tax revenues, per capita national income growth, inflation rate, trade openness, financial openness, financial development and human capital data as independent variables.
When the coefficients were appraised in terms of the panel as a whole, it was concluded that the variables x2 (broad money supply), x9 (financial development) and x10 (human capital) have statistically significant effects on y (GINI coefficient) used to represent the income distribution in the Fragile Five countries. Although these statistically significant effects in the terms of variables x2 and x10 are positive, the opposite x9 is founded negative. Accordingly, these findings in terms of panel overall mean that augmenting in broad money supply and human capital increases income inequality, while increasing in financial development decreases income inequality.
The increase in human capital may not be sudden and total, and although there is an average increase in the country’s human capital, it is thought that the development of human capital and the increase in the number of qualified personnel lead to a widening of the income gap between a certain group of unqualified personnel and the existing one.
For this reason, it is assumed that the increase in human capital will be reflected in the GINI figures after a certain level. In addition, the broadly defined money supply could change the saving and borrowing capacity in favour of high-income groups. The lower income group will be able to benefit less from monetary expansion than the upper income group, so the income distribution may deteriorate further.
Financial development could impact income inequality through various channels. Improved access to financial services and opportunities may empower low-income individuals, thereby reducing inequality. However, if financial development benefits the wealthy or exacerbates financial market distortions, it can broaden income gaps, perpetuating inequality.
When the coefficients are evaluated in terms of the cross-sectional units:
In Brazil, it was concluded that variables x1 (policy rate), x3 (public expenditures), x6 (inflation rate), x9 (financial development) and x10 (human capital) have statistically significant effects on y (GINI coefficient) used to represent income distribution. Although these statistically significant effects in the terms of variables x1, x3, x6, and x9 are negative, the opposite x10 is founded positive. Accordingly, this finding, specific to Brazil, means that increasing in policy rate, public expenditures, and financial development decreases income inequality, while escalating in the human capital increases income inequality.
In India, only variable x2 (broad money supply) has a statistically significant impact on y (GINI coefficient) used to represent income distribution. This statistically significant effect of x2 on y is found to be positive. Accordingly, this finding, specific to India, means that augmenting the broad money supply increases income inequality
Similar to India, also Türkiye, only one variable, x10 (human capital), has statistically significant effects on y (GINI coefficient) used to represent income distribution. That statistically significant effect of x10 on y is founded positive. Accordingly, this finding, specific to Türkiye, means that an increase in human capital escalates income inequality.
In addition to those findings, for both Indonesia and South Africa, it was concluded that the whole explanatory variables, which are thought to affect income distribution inequality, do not have statistically significant effects on y (GINI coefficient).
