Digital financial inclusion: index construction and panel data analysis in the context of E7 countries

The traditional way of using financial services has changed significantly over time This change, along with the widespread adoption of Internet services and technological advancements, has led to the digitalization of the financial sector. This development has contributed to the inclusion of individuals with no or limited access to financial services into the system, thereby increasing the use of formal financial services. However, despite all these advancements, the existence of a large segment that does not benefit from financial services has become an important issue on a global scale. This situation has highlighted the importance of financial inclusion and its continuation, digital financial inclusion. Therefore, the examination of the level of digital financial inclusion, which refers to the provision of financial services through digital tools such as the internet and mobile phones, and the factors affecting it, has formed the basis of this research. In this context, the index of the EAGLE countries (Brazil, China, India, Indonesia, Russia, Mexico, Turkey), which have become important for the future of the global economy, in the period covering the years 2004-2023, is calculated, and the factors affecting this index are investigated. In creating the index, commonly used index calculation methods in the literature, namely Principal Component Analysis and the Sarma Index, have been employed. The index values calculated by both methods give close results to each other. According to the index values, China has a higher level of digital financial inclusion compared to other countries. Afterwards, panel data analysis was used to determine the factors affecting these two indexes. As a result of the literature review, gross domestic product per capita, urban and rural population, and the unemployment rate are determined as explanatory variables and digital financial inclusion indices are determined as the explained variables. In the developed models, after detecting cross-sectional dependence and heterogeneity in the data related to the relevant variables, a second-generation unit root test is conducted. According to these test results, the Westerlund Panel Cointegration test, the Common Correlated Effects Mean Group Estimator (CCEMG) for long-term coefficient estimation, and the Dumitrescu-Hurlin causality tests were used. As a result of the analysis, a long-term cointegration relationship exists between the variables. According to the CCEMG test results applied for the estimation of cointegration coefficients, GDP, unemployment, and the urban population positively influenced both indexes, while the rural population had a negative effect. In addition, a causality relationship exists from GDP to digital financial inclusion and from urban and rural populations to digital financial inclusion. While no causality relationship is found between unemployment to digital financial inclusion in the first model, the existence of a causality relationship is determined in the second model. Based on the findings, the level of development of countries and the level of digital financial inclusion are largely parallel. In addition, the findings conclude that economic growth, unemployment, and population structure significantly affect digital financial inclusion. Therefore, this study emphasizes the necessity of increasing initiatives to develop strategies based on economic and demographic factors in order to increase digital financial inclusion. The steps towards the development of financial services and digital infrastructure, especially in rural areas, will reduce financial exclusion and encourage the use of formal financial services.