Octavidya, Ayuningtyas Puri
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Income Inequality in the Expanded BRICS: A Panel Analysis with Indonesia as a New Entrant Octavidya, Ayuningtyas Puri; Fevriera, Sotya
Journal of Enterprise and Development (JED) Vol. 7 No. 2 (2025)
Publisher : Faculty of Islamic Economics and Business of Universitas Islam Negeri Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/jed.v7i2.13607

Abstract

Purpose: This paper investigates the effect of GDP per capita, foreign direct investment (FDI), government expenditure, and inflation on income inequality in BRICS countries.Method: This research utilized a random effects model (REM) estimated using generalized least squares (GLS) with an autoregressive (AR(1)) disturbance to analyze panel data from six BRICS member countries from 1992 to 2017.Results: The findings indicate that GDP per capita has a significant negative influence on income inequality, while government expenditure has a significant positive impact on income inequality. However, FDI and inflation do not significantly affect income inequality.Practical Implications for Economic Growth and Development: The results suggest that the governments of Brazil, Russia, India, Indonesia, China, and South Africa should maintain their policies on micro, small, and medium enterprises (MSMEs) and develop strategies for the informal sector. This can be achieved by employing programs from the BRICS Bank that support sustainable development with a focus on inclusive economic growth. Encouraging non-governmental organizations (NGOs) that work in poverty alleviation, education, health, and the environment to secure funding from the New Development Bank could optimize expenditure directed towards sectors that enhance the income of impoverished populations, particularly in education and health.Originality/Value: This study contributes to the existing literature on the dominant BRICS countries, including Indonesia, by employing a new indicator for per capita income based on purchasing power parity and applying a GLS estimation specifically for addressing first-order autoregressive issues.