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Aging Population Spatial Distribution Discrepancy and Impacting Factor

Author

Listed:
  • Ke Zhang

    (Urban Mobility Institute, Tongji University, Shanghai 200090, China)

  • Hao Sun

    (Urban Mobility Institute, Tongji University, Shanghai 200090, China)

  • Xiangyu Li

    (Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

Abstract

The phenomenon of population aging is gradually spreading around the world. Consequently, it is leading to unsustainable economic development due to the decline of the labor force. Therefore, many people identify the aging population from national and intercontinental levels, as it would not be possible to recognize specific population spatial distribution characteristics and impacting factors in a province or state because of the spatial and temporal differences. In this paper, Jiangsu Province was selected as the study area to represent its aging population’s spatial characteristics and to identify the spatial heterogeneity with impacting factor by Geographically Weighted Regression (GWR), as well as to determine the impacting situation by marginal effect. The results show the following: (1) The impact factor’s spatial heterogeneity from the cities in Jiangsu Province is small but occurs in the city groups, while the impacting situation is the same in the north, central and south city groups, showing a disparity among them. (2) There is a significant change in the impact factor’s influence from 2010 to 2020. (3) The social–economic factor negatively relates to the aging population in 2020, with an interval value of [−1.0585, −1.0632]. This finding indicates that the spatial heterogeneity of the aging population at the province level is not the same as that at the national level. Therefore, we need to consider the local situation more. These findings further provide an empirical basis for the province-level study of the aging population, which differs from the national level.

Suggested Citation

  • Ke Zhang & Hao Sun & Xiangyu Li, 2022. "Aging Population Spatial Distribution Discrepancy and Impacting Factor," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9528-:d:879351
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    1. Dziauddin, Mohd Faris, 2019. "Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR)," Research in Transportation Economics, Elsevier, vol. 74(C), pages 10-20.
    2. Gustafsson,Björn A. & Shi,Li & Sicular,Terry (ed.), 2008. "Inequality and Public Policy in China," Cambridge Books, Cambridge University Press, number 9780521870450.
    3. Ruiwei Li & Gobi Krishna Sinniah & Xiangyu Li, 2022. "The Factors Influencing Resident’s Intentions on E-Bike Sharing Usage in China," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    4. Kim, Hyun Kyung & Lee, Sang-Hyop, 2021. "The effects of population aging on South Korea’s economy: The National Transfer Accounts approach," The Journal of the Economics of Ageing, Elsevier, vol. 20(C).
    5. Wu, Feifei & Yang, Hongna & Gao, Bo & Gu, Yan, 2021. "Old, not yet rich? The impact of population aging on export upgrading in developing countries," China Economic Review, Elsevier, vol. 70(C).
    6. Otsu, Keisuke & Shibayama, Katsuyuki, 2022. "Population aging, government policy and the postwar Japanese economy," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    7. Yuanyuan Wu & Yuxiang Song & Tingting Yu, 2019. "Spatial Differences in China’s Population Aging and Influencing Factors: The Perspectives of Spatial Dependence and Spatial Heterogeneity," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    8. Tan, Youchao & Liu, Xiumei & Sun, Hanwen & Zeng, Cheng(Colin), 2022. "Population ageing, labour market rigidity and corporate innovation: Evidence from China," Research Policy, Elsevier, vol. 51(2).
    9. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    10. Hwang, Seokchae & Choe, Chung & Choi, Koangsung, 2021. "Population ageing and income inequality," The Journal of the Economics of Ageing, Elsevier, vol. 20(C).
    11. Costantini, Mauro & Paradiso, Antonio, 2018. "What do panel data say on inequality and GDP? New evidence at US state-level," Economics Letters, Elsevier, vol. 168(C), pages 115-117.
    12. Park, Jun-ki & Ryu, Deockhyun & Lee, Keun, 2019. "What determines the economic size of a nation in the world: Determinants of a nation’s share in world GDP vs. per capita GDP," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 203-214.
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    Cited by:

    1. Fan Li & Jie Zhou & Wei Wei & Li Yin, 2023. "Spatial Distribution Pattern and Evolution Characteristics of Elderly Population in Wuhan Based on Census Data," Land, MDPI, vol. 12(7), pages 1-16, July.

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