Variable definitions.

This study utilizes provincial panel data from China spanning from 2013 to 2019 and considers indices such as residents’ income, employment, housing, education, and medical care to comprehensively measure the welfare index of each province using the coefficient of variation. It also examines the impact and mechanism of digital financial inclusion on welfare and evaluates its influence on welfare imbalance through panel data models and RIF regression. The estimation results of digital financial inclusion on welfare indicate that digital financial inclusion is an important driver elevating the income, employment, housing, education, medical treatment and other welfares of Chinese residents, and the conclusion remains robust after using instrumental variable estimation and replacing the measurement method of welfare. Regional differences reveal that digital financial inclusion has a greater contribution to the welfare in the Midwest. The effect mechanism test shows that digital financial inclusion improves the welfare by creating employment opportunities and plays a greater role in the Midwest, and will also improve the welfare by increasing the welfare expenditure. However, this mechanism will exacerbate the welfare imbalance between the Midwest and East. The estimation results of digital financial inclusion on the welfare imbalance show that digital financial inclusion reduces welfare imbalance between the central and western regions and the eastern regions, as well as within the central and western regions.