The Effect of Community Socioeconomic Context on High School Attendance in China: A Generalized Propensity Score Approach

Abstract

Many developing countries have experienced increasing spatial inequality, but little is known about the effect of community disadvantages on educational attainment in these societies. Using data from the China Family Panel Studies (2010–2016), I examine the effect of community socioeconomic status (SES) on the transition into high school in urban and rural China, and I explore several mechanisms explaining the community effects. I adopt the generalized propensity score method to estimate the potential probability of high school entrance at different levels of community SES. Results show that community SES is positively associated with high school attendance in both urban and rural China, and the relationship is stronger in more disadvantaged communities in both contexts. In urban areas, the effect of community SES is partly attributable to collective socialization and children’s academic performance. In rural areas, spatial accessibility to high schools and children’s academic performance are the salient mechanisms.

Impact of participation in collective action on farmers’ decisions and waiting time to adopt soil and water conservation measures

“…The third step of GPSM is to estimate the “dose-response” function and the causal effect curve according to Formula (5) (Lei, 2022; Yu et al , 2023). We made the “dose-response” functions and the causal effect curves between the waiting time of SWC measures and participation degrees of collective action.…”

“…Second, GPSM can match continuous treatment variables, which effectively fills the gap of traditional PSM (the traditional PSM can only match binary treatment variables). With reference to the practices by Lei (2022) and Yu et al(2023), the analysis steps in this article are as follows:…”

“…Second, GPSM can match continuous treatment variables, which effectively fills the gap of traditional PSM (the traditional PSM can only match binary treatment variables). With reference to the practices by Lei (2022) and Yu et al (2023), the analysis steps in this article are as follows: Select matching variables, use the maximum likelihood method to estimate the conditional distribution function of treatment variables (degree of participation in collective action) and calculate the generalized propensity score (GPS) of sample farmers. Establish the model of outcome variables (waiting time) through continuous treatment variables (degree of participation in collective action) and GPS. Estimate the parameters by ordinary least squares: …”

“…In addition, the disparity in average waiting times for each measure may also contribute to the difference in average waiting time of each measure (average waiting time for terrace > average waiting time for water-saving irrigation > average waiting time for afforestation, see Table 1 for details). The third step of GPSM is to estimate the "dose-response" function and the causal effect curve according to Formula (5) (Lei, 2022;Yu et al, 2023). We made the "dose-response" functions and the causal effect curves between the waiting time of SWC measures and participation degrees of collective action.…”

Big data in education: Themes and trends

“…Wang (2013) analyzed 14,000 high school graduates in the ELS:02 to understand the influential factors of their entrance to STEM majors in colleges. Focusing on 1,6000 Chinese middle school students, Lei (2021) examined the effect of community socioeconomic status on high school attendance.…”