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All Journal Jurnal Ekonomi & Studi Pembangunan Emerging Science Journal
Oanh, Dao Le Kieu
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Economics, Econometrics & Finance Environmental Science

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The impact of foreign direct investment on income inequality in developing countries: The Bayesian approach Gam, To Thi Hong; Oanh, Dao Le Kieu; Dang, Nguyen Mau Ba
Jurnal Ekonomi & Studi Pembangunan Vol 24, No 1: April 2023
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jesp.v24i1.18164

Abstract

Inequality in general and income inequality in particular have existed for a long time and tend to increase daily. Foreign direct investment (FDI) is expected to be an important factor contributing to mitigating that situation. However, the results of previous empirical studies on the impact of FDI on income inequality have not reached a consistent conclusion. Therefore, this study evaluated the impact of foreign direct investment on income inequality in developing economies. The study has provided evidence that the relationship is nonlinear through data from a sample of 36 developing countries between 2008 and 2020 and the Monte-Carlo algorithm according to the Bayesian approach. We document a U-shaped effect of FDI on income inequality. Besides, other factors, including trade openness and migration, obviously impact income inequality. Different results were found when FDI interacted with trade or migration, representing important channels through which inequality is affected. With these results, we suggest that policymakers in developing countries should develop appropriate policies on FDI attraction encourage trade openness and migration to reduce income inequality.
Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN Tu, Pham Thuy; Oanh, Dao Le Kieu; Trang, Do Doan
Emerging Science Journal Vol. 9 No. 3 (2025): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-03-04

Abstract

This study leverages machine learning and advanced variable selection techniques to enhance the prediction of the Bank Financial Stability Index (Z-score) in emerging ASEAN markets. Utilizing a comprehensive secondary dataset comprising macroeconomic and bank-specific indicators from 61 commercial banks across Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam (2010–2023), we systematically evaluate the predictive power of multiple machine learning models. A rigorous cross-validation framework is employed to optimize forecasting accuracy, integrating Linear Regression, Random Forest, K-Neighbors, Decision Tree, Gradient Boosting, AdaBoost, Support Vector Regression, and XGBoost with Lasso, Ridge, and Elastic Net regularization. Empirical results reveal that key drivers of financial stability include equity capital, financial leverage, return on equity, GDP growth, inflation, technological advancements, and systemic shocks like the COVID-19 pandemic. Notably, the Ridge-optimized XGBRegressor model achieves the highest predictive accuracy (~89%), demonstrating the efficacy of hybrid machine learning approaches in financial stability forecasting. These findings offer crucial insights for policymakers and regulators, facilitating data-driven strategies to strengthen banking resilience and mitigate systemic risks in volatile economic environments. Jel Classifier: C45, C52, C55, G21, G32.
Co-Authors Dang, Nguyen Mau Ba Gam, To Thi Hong Trang, Do Doan Tu, Pham Thuy