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Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers

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  • Qingbin Guo

    (School of Economics, Hainan University, Haikou 570228, China)

  • Kang Luo

    (School of Business, Hubei University, Wuhan 430062, China)

Abstract

This paper estimated and evaluated the spatial–temporal evolution of the concentration of healthcare resources (HCRs), in 31 provinces in China between 2004 and 2017, by using the entropy method. The spatial Durbin model (SDM) was used to further analyze the mechanisms behind the spatial driving forces at the national and regional levels. The findings revealed that: (i) The concentration of HCRs differed significantly among eastern, central, and western regions. The eastern, followed by the central region, had the highest concentration. Going east to west, the concentration of HCRs in the first echelon decreased, while it increased in the second and third echelons; (ii) places with higher concentrations clustered, while those with lower concentrations agglomerated; and (iii) economic development, population size, and urbanization promoted concentration. Education facilitated HCR concentration in the eastern and central regions, income stimulated HCR concentration in the eastern and western regions, and fiscal expenditure on healthcare promoted HCR concentration in the eastern region. Economic development inhibited HCR concentration in neighboring regions, population size restrained HCR concentration in neighboring areas in the western region, urbanization and income curbed HCR concentration in neighboring areas in the eastern and western regions, and fiscal expenditure on healthcare hindered HCR concentration in neighboring areas in the eastern region. Policy recommendations were proposed toward optimizing allocation of healthcare resources, increasing support for healthcare and education, and accelerating urbanization.

Suggested Citation

  • Qingbin Guo & Kang Luo, 2019. "Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers," IJERPH, MDPI, vol. 16(23), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4606-:d:289024
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    References listed on IDEAS

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    1. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    2. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    3. Teresa Romanillos & Roser Maneja & Diego Varga & Llorenç Badiella & Martí Boada, 2018. "Protected Natural Areas: In Sickness and in Health," IJERPH, MDPI, vol. 15(10), pages 1-19, October.
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    Cited by:

    1. Chao Song & Yaode Wang & Xiu Yang & Yili Yang & Zhangying Tang & Xiuli Wang & Jay Pan, 2020. "Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest Ch," IJERPH, MDPI, vol. 17(16), pages 1-23, August.
    2. Qingbin Guo & Kang Luo & Ruodi Hu, 2020. "The Spatial Correlations of Health Resource Agglomeration Capacities and Their Influencing Factors: Evidence from China," IJERPH, MDPI, vol. 17(22), pages 1-18, November.

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