Generation Z and the New Economic Reality: A Machine Learning Perspective on Financial Challenges
DOI:
https://doi.org/10.33736/jcsi.9011.2025Keywords:
Generation Z, machine learning, socioeconomic disparities, financial challenges, income inequalityAbstract
This study explores the socioeconomic disparities and financial challenges faced by different generational cohorts, with a focus on Generation Z. The research aims to identify patterns in socioeconomic features, such as income distribution and housing affordability, that distinguish generations and impact their financial outcomes. Machine learning models were used, with classification models that predicted generational membership and regression models that estimated the year of birth as a continuous variable. Using mutual information for feature selection, the Explainable Boosting Machine (EBM) achieved the highest classification accuracy of 74.62%, while regression analysis demonstrated moderate predictive power ( = 0.6005) with an average absolute error of eight years. The results highlight significant generational differences, with Generation Z experiencing the highest median rent-to-income burden (60.0%) and substantial barriers to homeownership. Despite higher participation in the workforce compared to previous generations at similar life stages, systemic economic challenges, such as rising housing costs and stagnant wages, disproportionately affect Generation Z. These findings underscore the utility of machine learning in identifying generational trends and socioeconomic disparities, offering a framework for further research to refine models and explore additional socioeconomic variables to enhance understanding of generational dynamics. Code and data to reproduce the results are available in GitHub, as detailed in the Dataset Overview subsection.
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