Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 11, Issue 1


Published
on

March 31, 2025


Pages


DOI

Article

The Choice of Pension Model and Its Determinants for Only-child Parents in Urban Northeast China: An Empirical Analysis Based on Binary Logistic Regression Model

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Authors

Jialin Zhang Affiliation:
College of Humanities & Social Development, Northwest A & F University, Xianyang, Shaanxi, 712100, China


Abstract

To unpack a better understanding of the genuine elderly care demands of only-child parents in cities of the Northeast region and effectively cope with the elderly care crisis of urban only-child families in the context of the deepening degree of demographic aging in China, this paper conducts an empirical study on the choice of senior care frameworks and their determinants for only-child parents in urban areas of the Northeast region. Utilizing the binary logistic quantitative analysis model, it analyzes the choice intentions of only-child parents in urban areas of the Northeast region for four pension models: family pension model, institutional pension model, home-based community pension model, and sojourn pension model, as well as the influencing factors. The results indicate that only-child parents in cities of the Northeast region mostly exhibit a propensity to select the family endowment pattern. The basic characteristics, economic income level, intergenerational support level, as well as the elderly care cognition and demands of only-child parents will all exert varying degrees of influence on the selection of pension models for current only-child parents in cities of the Northeast region.


Keywords

Only-child parents, Choice of pension model, Influencing factors of pension model, Cities in Northeast China, Binary logistic regression model, 46N301


Citation

Zhang, J. (2026). The choice of pension model and its determinants for only-child parents in urban northeast china: An empirical analysis based on binary logistic regression model. Applied Mathematics and Nonlinear Sciences, 11(1). https://doi.org/10.2478/amns-2025-0846

Published by: Engineering Journals

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