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In the quest to achieve SDG 1 on eradicating poverty, improving savings rates in East Africa is crucial, as it lays the foundation for reducing financial vulnerability and achieving economic resilience. This paper investigated the determinants of individual savings in selected East African countries. Specifically, internet access, financial inclusion, and borrowing were the covariates while individual savings in East Africa were the predicted. This study utilized data from the Global Findex 2021 World Bank database. This dataset was selected because it is one of the most comprehensive databases on financial inclusion, providing a comprehensive view of adults' savings, borrowing, payments, and risk management across global and regional contexts. This study used a basic framework to explore the factors influencing individual savings by examining production, income distribution, and accumulation. A panel data logistic regression model was utilized, and Instrumental Variables (2SLS) regression was employed to test endogeneity. The Sargan and Basmann tests assessed overidentification restrictions to validate the instruments. Shea’s partial R-squared measured the unique contribution of each instrument in explaining the variation in endogenous variables, accounting for the influence of other instruments and endogenous variables. The Hausman test which model was suitable for panel data modeling. The 2SLS regression showed no evidence of endogeneity, as internet access was insignificant with a high p-value (> 0.05). Overidentification tests confirmed instrument validity with p-values of 0.1018 and 0.1016. However, Shea’s partial R-squared revealed weak instruments, especially for borrowing, with an adjusted value of 0.0328. Results revealed that borrowing is strongly and positively associated with saving. The higher the borrowing, the more the savings. In East Africa, most borrowers are savers. These use the borrowed funds to create an injection of income flow through capital accumulation. Internet access significantly predicts savings in East Africa today. In this digital era, most businesses are run online. Therefore, it is almost impossible to save with no internet access. Similarly, those who use digital payment systems are most likely to enhance their savings. Age and gender do not influence the saving of an individual in East Africa. Likely, owning a financial account does not translate to more savings. Most East Africans have non-functional bank accounts. Education levels significantly influence the savings of individuals in East Africa. Highly educated people utilize financial literacy skills to save more. Being out of the workforce reduces individual savings since most of these are out of the working age. 

In the quest to achieve SDG 1 on eradicating poverty, improving savings rates in East Africa is crucial, as it lays the foundation for reducing financial vulnerability and achieving economic resilience. This paper investigated the determinants of individual savings in selected East African countries. Specifically, internet access, financial inclusion, and borrowing were the covariates while individual savings in East Africa were the predicted. This study utilized data from the Global Findex 2021 World Bank database. This dataset was selected because it is one of the most comprehensive databases on financial inclusion, providing a comprehensive view of adults' savings, borrowing, payments, and risk management across global and regional contexts. This study used a basic framework to explore the factors influencing individual savings by examining production, income distribution, and accumulation. A panel data logistic regression model was utilized, and Instrumental Variables (2SLS) regression was employed to test endogeneity. The Sargan and Basmann tests assessed overidentification restrictions to validate the instruments. Shea’s partial R-squared measured the unique contribution of each instrument in explaining the variation in endogenous variables, accounting for the influence of other instruments and endogenous variables. The Hausman test which model was suitable for panel data modeling. The 2SLS regression showed no evidence of endogeneity, as internet access was insignificant with a high p-value (> 0.05). Overidentification tests confirmed instrument validity with p-values of 0.1018 and 0.1016. However, Shea’s partial R-squared revealed weak instruments, especially for borrowing, with an adjusted value of 0.0328. Results revealed that borrowing is strongly and positively associated with saving. The higher the borrowing, the more the savings. In East Africa, most borrowers are savers. These use the borrowed funds to create an injection of income flow through capital accumulation. Internet access significantly predicts savings in East Africa today. In this digital era, most businesses are run online. Therefore, it is almost impossible to save with no internet access. Similarly, those who use digital payment systems are most likely to enhance their savings. Age and gender do not influence the saving of an individual in East Africa. Likely, owning a financial account does not translate to more savings. Most East Africans have non-functional bank accounts. Education levels significantly influence the savings of individuals in East Africa. Highly educated people utilize financial literacy skills to save more. Being out of the workforce reduces individual savings since most of these are out of the working age.