What is the degree of social disability risk in China under the background of the aging population? Social disability risk measurement index system design and evaluation research based on China.

OBJECTIVE

The impact of the aging population in China varies between regions. It is because regions with different resource endowments, such as those related to economy, population, and medical care, have different degrees of disability risk in the face of the increases in the disabled and semi-disabled older population caused by the overall aging of the population. This study aimed to construct an evaluation system to monitor and measure the degree of social disability risk in different regions in China and to evaluate and compare the degree of social disability risk in different regions using empirical data.

METHOD

This study used the Delphi method to construct a social disability risk measurement index system with macro, meso, and micro dimensions. At the same time, based on the data of CHARLS2018, an AHP-entropy method was used to calculate the index's total weight, and the standard deviation classification method was used to classify the total and criterion-level measurement scores of 28 provinces.

RESULTS

The regional degree of social disability risk was analyzed in subdimensions. Our research indicates that China's social disability risk situation is not promising, with a general medium to high-risk level. The score of degree of social disability risk among provinces is consistent with the regional economic development level to a large extent. The risk of social disability varies significantly among the eastern and central, and western regions of China and the provinces within the three regions.

DISCUSSION

Currently, the situation facing the degree of social disability risk in China is that the overall risk level of the country is higher, and the difference between regions is significant. It is necessary to take measures to meet better the needs of the aging population and the disabled and semi-disabled older populations in a large-range, large-scale, multilevel way.