article · Economic Analysis Letters
Majority of existing studies have a country-specific, cross-country, and regional focus, but none of these studies have examined the determinants of global financial inclusion using world average data. This study examines the determinants of global financial inclusion from 2005 to 2023 using world average data obtained from the world development indicators. The study used multiple regression estimation methods namely the fully modified ordinary least squares (FOLS) regression method, the generalized linear model (GLM) regression method, and the quantile regression method. The findings reveal that global indebtedness has an adverse effect on global financial inclusion while adult literacy rate is beneficial for global financial inclusion. Inflation, unemployment, and economic growth do not have a significant effect on global financial inclusion.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.58567/eal05020003
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
