Research on regional differences of medical insurance funds for urban workers in China based on the GM(1,1) model

Inhalt:
In recent years, with the continuous improvement of China's social and economic level, the basic medical insurance for urban workers in China has entered a stage of steady development after realizing the systematic comprehensive coverage of all regions in our country. However, there is no denying that due to the low level of overall planning of medical insurance for urban workers, which is mostly limited to provincial and municipal levels, there are still outstanding differences in the actual medical security level of workers in different regions, which to some degree damage the equal right of urban workers to enjoy various medical services. This paper takes the basic medical insurance fund for urban workers in China as an object, and selects the income and expenditure data of medical insurance fund for urban workers in northeast, eastern, central and western of China from 2011 to 2020 to establishes GM (1,1) model to predict the income, expenditure and balance of medical insurance from 2021 to 2030. According to the results, the author finds that there are great differences among regions. The eastern region has the largest scale of fund balance, followed by the central and northeast regions, and the western region with weaker economic level has relatively less available fund balance. However, risks remain in the east, where the balance is relatively larger. It is hoped that the research of this paper can arouse relevant departments' attention to the regional differences of medical insurance for urban workers in China, and can provide evidence for further reform of medical insurance system and a higher level coordination of the medical insurance for urban workers. The government and relevant departments need to pay more attention to the efficiency and fairness of fund operation, so as to promote the comprehensive, stable and sustainable development of medical insurance for urban workers in China.