Foreign Bank Presence and Income Inequality in Africa: The Moderation role of Economic Freedom
Description
The paper contributes to the existing literature in four main ways: firstly, by investigating the direct impact of Foreign Bank Presence (FBP) on income inequality in Africa. Secondly, by exploring the threshold effect of FBP on income inequality. Thirdly, by analyzing the direct influence of economic freedom and its components on income inequality. Fourthly, by examining the combined impact of FBP and economic freedom on income inequality in Africa. The study utilizes macrodata spanning 19 years (1995 to 2013) for 33 African countries due to data availability. Income inequality data (Gini Index after tax) is sourced from the Standardized World Income Inequality Database, while foreign bank presence and economic freedom variables are from Claessens and van Horen (2015) and the Heritage Foundation, respectively. Control variables are sourced from the World Development Indicators. The Gini index ranges from 0 to 100, with lower values indicating greater equality (0) and higher values indicating greater inequality (100). Foreign bank presence is proxied by the percentage of foreign banks among total banks per country. Economic freedom is measured using the Economic Freedom Index (EFI), which ranges from 0 to 100 and comprises 12 individual indicators grouped into four components: rule of law, government size, regulatory efficiency, and open market. Control variables include population growth, trade openness, human capital, economic growth, private credit, and inflation.
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The paper gathered data from the aforementioned sources and selected countries with sufficient data for analysis, focusing particularly on data availability regarding foreign bank presence. Upon sorting the data obtained from various sources, the following procedures were employed to derive the results: Initially, the paper addressed missing values in inequality (Gini index after tax) and Economic Freedom variables through STATA interpolation. The command used was "ipolate old variable name Year, gen (new variable name) epolate by (id)". Note: "id" represents the country groups in this context. Subsequently, the individual 12 variables were grouped into four categories: rule of law (averaging property rights and government integrity), government size (averaging tax burden and government spending), regulatory efficiency (averaging business freedom, labor freedom, and monetary freedom), and open market (averaging trade freedom, investment freedom, and financial freedom). It's important to note that the rule of law and government size components only comprised two variables each, as there was no data available for judicial effectiveness and fiscal health, respectively, in Africa. Following this, descriptive statistics and a correlation matrix were estimated. Finally, regression analysis was conducted using the two-step system generalized method of moment (GMM).