2022 Conference Presentation
Background: With the acceleration of aging and aging, the care demand of the disabled increases significantly, and the prediction of demand for Long-Term Care Insurance creates good conditions for the future development of the Long-Term Care Insurance system. To some extent, we can measure the number of disabled people and the cost of Long-Term Care Insurance in the future, thus, laying a foundation for the choice of the Long-Term Care Insurance system model.
Methods: The Markov disability transfer model and Analogical Actuary were used to predict the demand for Long-Term Care Insurance in China from 2020 to 2050. Calculations of the disability state transfer matrix of Long-Term Care Insurance were in line with the characteristics of the elderly group in China.The data from the China Health and Retirement Longitudinal Study(CHARLS), three waves: 2011,2013,2015, followed samples. At the same time, Using the bottom-up method, Demanders of Long-Term Care Insurance were calculated according to the proportion of domestic disabled persons and the predicted population in the future. Through relevant indicators, the average annual nursing cost of disabled people in China is calculated and the total nursing cost of Long-Term Care Insurance will be clear. The demand for Long-Term Care Insurance is measured according to the predicted number of disabled persons and the total cost of Long-Term Care Insurance in the future.
Results: The demand for Long-Term Care Insurance in China is large, and the the number of people increased, and showed a state of rapid growth during 2020-2050. The growth will slow down from 2030-2040, but the overall demand will still be large. The increase in nursing expenses has also caused great pressure on the national financial expenditure.
Conclusion: In order to alleviate the above pressure, the reform of Long-Term Care Insurance system can be carried out from many aspects, such as fund-raising mechanisms, financing level, nursing staff training and disability risk prevention.