International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 6, 8 Dec. 2025
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Quantitative Analysis of Socio-Economic Determinants of Adult Income Using Machine Learning Techniques
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Author(s)
Sabrina Akter
1
Sadia Enam
1
Md. Moshiur Rahman
2
Fahmida Ahmed Antara
1,*
Index Terms
Socioeconomic, Income Prediction, Exploratory Data Analysis, Economic Inequality, Gender Disparities
Abstract
Income inequality is a persistent issue in both developed and developing economies, influenced by complex socio-economic factors such as education, occupation, and gender. This study addresses a critical gap by applying advanced machine learning techniques to analyze the socio-economic determinants of income in Bangladesh and global contexts. The primary objectives were to identify the most influential factors affecting income and assess the effectiveness of various machine learning models in predicting income levels. Using datasets from Bangladesh and global sources, this study employed Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machines to predict income and assess feature importance. Key findings showed that education, occupation, gender and hours worked per week were the most significant predictors of income. The Bangladeshi dataset highlighted limited access to higher education and pronounced gender disparities, while the global dataset reflected gender pay gaps and more equitable educational access. Random Forest Classifier appeared as the most effective model, achieving 100% accuracy in Bangladesh and 96% accuracy globally. These findings underscore the need for targeted policies to improve educational access, promote vocational training, and address gender inequality to reduce income disparities. Additionally, the study demonstrates the potential of machine learning to uncover non-linear relationships in socio-economic data, providing valuable insights for evidence-based policymaking. This research highlights the importance of integrating advanced data-driven methods to address the socio-economic drivers of income inequality and promote inclusive economic growth.
Cite This Paper
Sabrina Akter, Sadia Enam, Md. Moshiur Rahman, Fahmida Ahmed Antara, "Quantitative Analysis of Socio-Economic Determinants of Adult Income Using Machine Learning Techniques", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.6, pp. 1-17, 2025. DOI:10.5815/ijieeb.2025.06.01
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