In the past one and a half decades, Chinese cities have witnessed an influx of college-educated rural migrants. Until now, there has been little systematic research on the working and living conditions of this growing population. The objective of this paper is to empirically examine similarities and differences in living conditions between college-educated rural migrants, non-college-educated rural migrants, and local urban workers. The data are from the 2010 National Health and Family Planning Commission Survey of China’s migrant population. Our study has produced several findings. First, the monthly income of college-educated rural migrants is significantly higher than that of non-college-educated rural migrants and is similar to that of college-educated local urban workers. However, less-educated rural migrants earn more than local urban workers with similar levels of education. Second, while college-educated rural migrants work fewer hours than non-college-educated rural migrants, they work longer hours than their local urban counterparts. Third, local urban workers receive greater social benefits than college-educated rural migrants, whose benefits are better than those of non-college-educated rural migrants. Overall, while college-educated rural migrants enjoy more favourable working and living conditions than non-college-educated rural migrants, they remain disadvantaged when compared to urban workers with a similar level of education. These findings highlight the insurmountable institutionalised exclusion and discrimination that imposes significant barriers on rural-origin people to reap the benefits of higher education.

Purpose: This paper employs nationwide, large-scale field survey data to provide the first empirical evidence on the impact of human educational capital on the acquisition of health entitlement among Chinese migrant workers. The findings of this study hold significant practical implications for the formulation of policies aimed at improving the health protection of migrant workers, as well as for socioeconomic policies during China's transitional period.Design/methodology/approach: Using the IVProbit model, this research examines how human educational capital influences the attainment of health entitlement among migrant workers in China by analyzing the impact and mechanism of education on health entitlement. The study is based on the China Migrants Dynamic Survey (CMDS) data from 2018, which include 100,177 observations.Findings: For migrant workers in China, higher levels of education have a significant positive effect on the acquisition of health entitlements, including medical insurance, health records and health education. The positive impact of human educational capital on health entitlements is more significant for non-provincial cities and young-generation migrant workers. The results also show that human educational capital can influence the acquisition of health entitlements through mediators such as financial status, social integration and health status.Originality/value: This study represents the first empirical attempt to evaluate the influence of human educational capital on the access of migrant workers in China to health rights and interests. Additionally, the study develops a theoretical framework to examine how the impact of human educational capital varies across migrant workers with different characteristics and their access to health entitlements.

China has witnessed remarkable ongoing digitalization with the rapid spread and adoption of the Internet. However, this remarkable development remains uneven between urban and rural populations, and hence result in different impact on their income. Employing data China General Social Survey 2018, this study explores how internet usage affects income gap between the urban and rural China. Relying on the instrumental variables approach to regression analysis, we prove that internet usage contributes to higher increase in annual income for the urban employed compared to their rural counterparts. The RIF decomposition regression results then reveal the effects of differential urban-rural internet usage ratios, explaining the widened income gap between the urban and rural employed in various income levels. The difference in the returns to urban and rural internet usage narrowed the urban-rural income gap for low - and high-income employed, but further contributed to the urban-rural income gap for the middle-income employed.