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Aging in China: An International and Domestic Comparative Study

Author

Listed:
  • Jie Feng

    (Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI 53705, USA)

  • Ganlin Hong

    (Department of Agricultural Economics and Management, School of Public Affairs, China Academy for Rural Development (CARD), Zhejiang University, Hangzhou 310000, China)

  • Wenrong Qian

    (Department of Agricultural Economics and Management, School of Public Affairs, China Academy for Rural Development (CARD), Zhejiang University, Hangzhou 310000, China)

  • Ruifa Hu

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100811, China)

  • Guanming Shi

    (Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI 53705, USA)

Abstract

This study investigates the age structure and aging process in China over the last two decades. Comparing internationally, we find that China’s aging status is currently moderate. However, its aging process is accelerating at a rate faster than that of developed countries and the other BRICS countries, but slower than other East Asian countries except for North Korea and Mongolia. Domestically, we find increasing divergence and spatial variations in the aging process across regions and between rural and urban sectors by applying spatial statistic comparisons using data from the China Statistical Yearbook. Results from the spatial econometrics model suggest that factors such as urbanization and regional GDP, but not population density, could deepen the urban–rural aging gap. The transition of the aging process over time, across regions, and between sectors could influence social and economic activity. The results can guide future research on aging in China.

Suggested Citation

  • Jie Feng & Ganlin Hong & Wenrong Qian & Ruifa Hu & Guanming Shi, 2020. "Aging in China: An International and Domestic Comparative Study," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5086-:d:374866
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    References listed on IDEAS

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    1. Robert W. Hartman & Thomas J. Espenshade, 1994. "Can immigration slow U.S. population aging?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 13(4), pages 759-768.
    2. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    3. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    4. Haberkorn, Gerald, 2002. "Aging in Rural and Regional Australia," Rural America/ Rural Development Perspectives, United States Department of Agriculture, Economic Research Service, vol. 17(3), September.
    5. Mao, Rui & Xu, Jianwei & Zou, Jingxian, 2018. "The labor force age structure and employment structure of the modern sector," China Economic Review, Elsevier, vol. 52(C), pages 1-15.
    6. Zou, Baoling & Mishra, Ashok K. & Luo, Biliang, 2018. "Aging population, farm succession, and farmland usage: Evidence from rural China," Land Use Policy, Elsevier, vol. 77(C), pages 437-445.
    7. Getis, Arthur, 2007. "Reflections on spatial autocorrelation," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 491-496, July.
    8. Zhong, Hai, 2011. "The impact of population aging on income inequality in developing countries: Evidence from rural China," China Economic Review, Elsevier, vol. 22(1), pages 98-107, March.
    9. 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.
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    4. Xinxin Wang & Jingjing Hong & Pengpeng Fan & Shidan Xu & Zhixian Chai & Yubo Zhuo, 2021. "Is China’s urban–rural difference in population aging rational? An international comparison with key indicators," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1866-1891, September.
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