<![CDATA[This study aims to analyze the direct and indirect effects of FinTech on poverty in Indonesia using panel data from 31 provinces for the 2020–2024 period. Poverty (Pov) is measured by the percentage of the population living below the poverty line, and the FinTech Index (IF) was constructed from electronic money data and P2P lending data. To address endogeneity and omitted variables, the "Generalized Method of Moments Instrumental Variable" (IV-GMM) method uses the mobile telephone (Mob) variable and its first and second lags as instruments. Research results show that FinTech has a direct negative effect on poverty, meaning its rise tends to lower the level of poverty. However, in general, FinTech's influence on poverty is positive through direct growth and development finance, indicating that benefits are not yet evenly distributed. Therefore, the government and authorities need to strengthen regulation, improve digital finance literacy, expand productive FinTech access for low-income households and MSMEs, and strengthen digital infrastructure to promote inclusive and sustainable FinTech growth.]]>
Copyrights © 2026
