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All Journal Jurnal Ekonomi & Studi Pembangunan Emerging Science Journal Journal of Applied Data Sciences
Oanh, Dao Le Kieu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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.
The Effect of Globalization on Income Inequality in Developing Countries: A Bayesian Approach Vy, Phan Dien; Lan, Dang Thi Ngoc; Oanh, Dao Le Kieu
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.948

Abstract

The rapid advancement of economic globalization over the last several decades has sparked fierce disagreements about its impact on income inequality on a global and domestic scale. Whether globalization improves (neoclassical theory) or worsens (dependence theory) income inequality is a matter of debate at the theoretical level. The results of empirical studies have been contradictory as well. This study examines the effects of three lenses of globalization (financial openness, trade openness, and social globalization) on income inequality in developing countries. Using Bayesian estimation with Markov Chain Monte Carlo, we analyze a balanced panel of 36 developing countries from 2010 to 2022. The Bayesian method is particularly well-suited for social science research because of its capacity to effectively manage complex relationships and integrate prior information, resulting in more contextually relevant and robust results. The findings reveal significant nonlinear relationships between different dimensions of globalization and income inequality. Specifically, the impact of trade openness on income inequality is U-shaped, with a threshold of 83.35% of GDP, whereas the impact of direct foreign investment and migration is in an inverted-U shape, with respective thresholds of 13.4% of GDP and 1.276% of the total population. Importantly, all sampled countries remain below the identified thresholds for direct foreign investment and migration, indicating that these channels currently exacerbate inequality. Consequently, policy measures designed for “post-threshold” conditions should be viewed as forward-looking. This study contributes by clarifying how globalization can alternately worsen or reduce inequality depending on a country’s stage of integration. From a policy perspective, developing countries should strengthen absorptive capacity and institutional readiness so that higher direct foreign investment inflows and migration eventually yield more equitable outcomes once thresholds are surpassed. Meanwhile, countries already beyond the trade openness threshold should proceed cautiously, prioritizing export diversification, vocational training, and inclusive trade policies to mitigate inequality risks.
Co-Authors Dang, Nguyen Mau Ba Gam, To Thi Hong Lan, Dang Thi Ngoc Trang, Do Doan Tu, Pham Thuy Vy, Phan Dien