Unlocking intergenerational support: The impact of long-term care insurance on elderly family dynamics

Y Yi Qu Y Yumeng Li A Anning Yin J Jia Li

Abstract

As China faces an aging population and changing family dynamics, including smaller nuclear families and increased mobility, it is crucial to understand how long-term care insurance policies affect intergenerational support for older adults. This study investigates the impact of these policies on economic, caregiving, and emotional support for individuals aged 60 and above. Using multi-period difference-in-differences models and unbalanced panel data from the CHARLS database (2011–2020), the analysis focuses on 66 Chinese pilot cities implementing long-term care insurance policies, emphasizing urban households and healthier elderly groups. The findings show that long-term care insurance policies significantly reduce children’s financial and caregiving burdens while strengthening emotional bonds between older parents and their children. These effects are stronger in urban areas and among healthier elderly populations, indicating that household context and health status influence policy benefits. The results highlight the role of these policies in easing intergenerational pressures amid demographic shifts and provide evidence to improve policy design. By revealing differences in policy effectiveness across family types, this study offers theoretical insights for developing targeted eldercare strategies. It supports government efforts to tackle aging-related challenges and promote sustainable family support systems. The research underscores the need for flexible policies that respond to evolving societal needs and ensure equitable care for China’s elderly.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 11
Published November 21, 2025
Pages e0337073
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (4)

Y

Yi Qu

Y

Yumeng Li

A

Anning Yin

J

Jia Li