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Spatiotemporal Evolution of High-Quality Population Development in China under the New Development Paradigm

Author(s)

Xiaoxia Gou*, Jingjie Mu, Xiaomin Zheng

Affiliation(s)

College of Economics, Northwest Normal University, Lanzhou, Gansu, China *Corresponding Author.

Abstract

This paper develops an evaluation index system to assess high-quality population development in China. Employing the entropy weight method to calculate composite scores, we examine the temporal evolution of high-quality population development from 2010 to 2022. Kernel density analysis is applied to determine score distributions across the nation and various regions, while the spatial Markov regime-switching model is used to analyze spatial evolution trends. The results reveal that (1) China’s high-quality population development score has shown a steady upward trend, mainly driven by increases in scores for population distribution and structure, while the score for population size has declined. There are also regional imbalances, with the eastern and central regions leading, and the western and northeastern regions lagging. (2) Kernel density analysis reveals an upward shift in scores nationally, with a higher density of high scores in the eastern and central regions, forming a secondary peak on the right side of the distribution. In the western region, there is also a rightward shift in the primary peak, with a thick left tail remaining. (3) Using the comprehensive scores, we apply a spatial Markov regime-switching model to analyze spatial evolution trends, showing a dominant “club convergence” effect nationwide. Low-level and high-level population regions remain relatively fixed in their categories, with low-level areas struggling to transition to higher levels through internal growth, while high-level areas have established a stable economic base that attracts skilled populations. This entrenched disparity may hinder high-quality population development at the national level.

Keywords

High-Quality Population Development; Entropy Weight Method; Kernel Density Estimation; Markov Regime-Switching Model; Spatial Markov Regime-Switching Model

References

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