The Impact of Internet Use on Residents' Subjective Well-Being: An Empirical Analysis Based on National Data

Internet use has become an indispensable part of people's daily lives, and the relationship between Internet use and residents' subjective well-being is receiving increasing attention from researchers. However, the existing literature on the relationship between Internet use and residents' subjective well-being is still controversial and inconclusive, and there has been relatively little discussion of the mechanisms by which Internet use affects well-being. On the basis of data from the China General Social Survey 2015 (CGSS2015), this paper provides an empirical analysis of the impact of Internet usage on residents' subjective well-being. The study finds that while Internet use had no significant impact on the well-being of residents compared to non-use, frequency of Internet usage did significantly improve subjective well-being. On this basis, we tested the mechanisms by which Internet use affects residents' well-being through the three channels of the personal income effect, the human capital effect and the social capital effect. The findings partially verify the hypothesis of the mediating effect of social capital between Internet use and residents' subjective well- being, but do not verify the hypothesis of the moderating effect of personal income and human capital. Compared with previous studies, this paper is innovative in the following three ways: it uses the latest available representative national microdata, which is better able to reflect recent Internet usage and its impact on residents' subjective well-being; on the basis of existing research on the impact of Internet usage on residents' well-being, it analyzes the specific mechanisms through which Internet use affects well-being, using the moderating effect model and the mediating effect model to make up for the shortcomings of existing research on the impact mechanism; and it applies the Bioprobit model to deal with the potential endogeneity of key explanatory variables, using a variety of model estimation methods to obtain more robust estimation results.