Spatiotemporal evolution and influencing factors of the allocation of social care resources for the older adults in China

Int J Equity Health. 2023 Oct 18;22(1):222. doi: 10.1186/s12939-023-02007-0.

Abstract

Background: The reasonable allocation of social care resources for the older adults is a key measure to actively respond to population aging. This study aims to evaluate the evolutionary trend, spatial differences and influencing factors of the social elderly care resources (SECR) allocation in China.

Methods: This study constructed a comprehensive index system consisting of three dimensions: material resources, human resources and financial resources, to measure the level of SECR in mainland China. The Kernel density estimation was used to reveal the dynamic evolution trend, and Dagum Gini Coefficient and its decomposition method were used to investigate the equity of SECR allocation. Spatial panel regression models were used to analyze the influencing factors of the allocation of SECR.

Results: The level of SECR is rising from 0.197 in 2013 to 0.208 in 2019. The middle-high- and high-level areas of SECR were mainly distributed in the eastern and western China. The Gini coefficient of SECR decreased from 0.262 in 2013 to 0.249 in 2019. Per capita GDP, the proportion of social welfare expenditure in GDP and the proportion of the tertiary industry in GDP have significant positive effects on the allocation of SECR. Population aging and the development of service industry exhibit significant negative spatial spillover effects on the allocation of SECR.

Conclusions: The fairness of the allocation of SECR in China has been improved, while the spatial distribution is imbalanced. Economic development, fiscal input and the development of service industry have significant positive effects while population aging has significant negative effects on the SECR allocation.

Keywords: Dagum Gini Coefficient; Social elderly care resource; Spatial distribution; Spatial panel regression models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • China
  • Humans
  • Resource Allocation*
  • Workforce