Impact of mobile payment adoption on household expenditures and subjective well‐being

Abstract

This paper estimates the effects of mobile payment adoption on household expenditures and people's subjective well‐being. We consider four categories of household expenditures (that on clothes, durable goods, consumer goods, and cultural and leisure activities) and four indicators (life satisfaction, contentment, income satisfaction, and depression) of subjective well‐being. We use the Augmented Inverse Probability Weighting estimator to analyze the 2017 Chinese General Social Survey data while accounting for the selection bias inherent in mobile payment adoption. The empirical results show that people's decisions to adopt mobile payment are positively associated with their educational levels, car ownership, social interaction, internet penetration rate, and residential location. Mobile payment adoption significantly increases household expenditures on consumer goods and cultural and leisure activities but not on expenditures on clothes and durable goods. Moreover, mobile payment adoption significantly decreases people's contentment while increasing depression. We also find that mobile payment adoption significantly decreases the contentment of urban people but significantly increases the depression of rural people.

Poverty reduction and migration in rural Vietnam: Role of local firm clusters

“…L. Nguyen, Grote, et al, 2017). Similarly, the literature has well documented that welfare indicators and migration decisions are influenced by characteristics of the household head (e.g., gender, education, and age) and household (e.g., household size, land, and location) (Duong et al, 2021;He et al, 2024;Ma, Vatsa, et al, 2023).…”

“…The existing literature has employed a variety of objective or subjective indicators to measure well-being (Duong et al, 2021;He et al, 2024;Ma, Vatsa, et al, 2023;C. V. Nguyen, Phung, et al, 2017).…”

“…The PSM estimations are performed using the psmatch2 command in STATA (Leuven & Sianesi, 2003). While PSM using cross-sectional data can control for the observed covariates, it cannot control for unobserved characteristics that may affect the treatment and/or outcome variables (Becker & Ichino, 2002;He et al, 2024;Rosenbaum & Rubin, 1983;A. Zhang & Imai, 2023).…”

“…Second, for empirical analysis, we use propensity score matching (PSM) as this method can help mitigate endogeneity bias arising from observed factors (Austin, 2011;He et al, 2024;A. Zhang & Imai, 2023).…”