The impact of digital financial inclusion on income inequality amid economic complexity: a GMM and Bayesian regression approach

Purpose - This study aims to examine the relationship between Digital Financial Inclusion (DFI), Economic Complexity (EC) and their interaction with respect to income inequality (II). Design/methodology/approach - The data comprises a sample of 14 countries globally for the period 2005–2021. Two traditional methods, Generalized Method of Moments (GMM) and Bayesian probability, are utilized to explore the nuanced effects of these factors on II. Findings - The results show that both DFI and EC contribute to reducing II. However, the interaction between DFI and EC is found to unexpectedly exacerbate II, highlighting the complex dynamics between these factors. Originality/value - First, this study provides both a theoretical foundation and empirical evidence on how DFI impacts II within the context of EC, addressing a significant research gap. Second, the use of Bayesian regression addresses challenges such as small sample sizes, autocorrelation and endogeneity, thereby enhancing the robustness of the findings. Third, by combining traditional and probabilistic approaches, this study not only enhances the reliability of the results but also provides policymakers with actionable insights into how DFI, EC and their interaction influence II, supporting more effective policy interventions.