A Methodological Framework for Studying Digital Financial Inclusion Disparities Using Public Data: Evidence from Cross-Country Analysis

As digital financial services expand globally, understanding demographic disparities in access becomes crucial for inclusive development. However, proprietary platform data requirements often limit research in this area, particularly in developing countries with restrictive data protection frameworks. This paper develops and validates a methodological framework for studying digital financial inclusion disparities using exclusively public data sources. We demonstrate the approach through comparative analysis of mobile money adoption patterns across 18 countries using World Bank Global Findex Database (2021), IMF Financial Access Survey data (2019-2023), and national financial inclusion surveys. The framework combines descriptive decomposition analysis, cross-country regression techniques, and policy natural experiments to identify systematic patterns. Across our sample, women exhibit 8-15 percentage point lower mobile money adoption rates, whilst rural populations show 12-23 percentage point gaps. Decomposition analysis suggests infrastructure and capability constraints explain 60-80% of observed disparities, with remaining gaps potentially reflecting access barriers or discrimination. The methodology enables rigorous disparity analysis without requiring proprietary partnerships, offering particular value for researchers in data-restrictive environments. We provide replication materials and detailed guidance for applying the framework across different contexts.