Adverse selection in a voluntary Rural Mutual Health Care health insurance scheme in China

This study examines adverse selection in a subsidized voluntary health insurance scheme, the Rural Mutual Health Care (RMHC) scheme, in a poor rural area of China. The study was made possible by a unique longitudinal data set: the total sample includes 3492 rural residents from 1020 households. Logistic regression was employed for the data analysis. The results show that although this subsidized scheme achieved a considerable high enrollment rate of 71% of rural residents, adverse selection still exists. In general, individuals with worse health status are more likely to enroll in RMHC than individuals with better health status. Although the household is set as the enrollment unit for the RMHC for the purpose of reducing adverse selection, nearly 1/3 of enrolled households are actually only partially enrolled. Furthermore, we found that adverse selection mainly occurs in partially enrolled households. The non-enrolled individuals in partially enrolled households have the best health status, while the enrolled individuals in partially enrolled households have the worst health status. Pre-RMHC, medical expenditure for enrolled individuals in partially enrolled households was 206.6 yuan per capita per year, which is 1.7 times as much as the pre-RMHC medical expenditure for non-enrolled individuals in partially enrolled households. The study also reveals that the pre-enrolled medical expenditure per capita per year of enrolled individuals was 9.6% higher than the pre-enrolled medical expenditure of all residents, including both enrolled and non-enrolled individuals. In conclusion, although the subsidized RMHC scheme reached a very high enrollment rate and the household is set as the enrollment unit for the purpose of reducing adverse selection, adverse selection still exists, especially within partially enrolled households. Voluntary RMHC will not be financially sustainable if the adverse selection is not fully taken into account.