The spatial distribution of people living on minimum subsistence allowance in China and its determinants
【Abstract】Based on data of the people living on minimum subsistence allowance, the paper analyzes the spatial distribution and its determinants using spatial statistical analysis method. The study finds the following results. (1) The people living on minimum subsistence allowance are distributed more in the eastern regions than in the western regions. Those in southeast regions account for 83.5% in total. Yet, the proportion of population receiving the allowance is higher in the western regions and lower in the eastern regions. The proportion is 10.9% in the northwest of Hu Line and 3.9% in the southeast. (2) The size and relative share of the allowance receivers decrease in general, and the total population size decreases from west to east. The proportion in the western regions tends to increase, which makes the center of gravity of the subsistence allowance move to the northwest. (3) There is a strong positive spatial correlation in the relative share of allowance receivers, and the trend of spatial agglomeration is weakening. The hot-spot areas of the rate coincide highly with concentrated contiguous poverty-stricken areas and most of them are located in the northwest of the Hu Line. The cold-spot areas are concentrated in the eastern coastal areas from Tangshan to Leizhou Peninsula on the north coast of the Bohai Bay, where local economies are more developed. (4) The rate of allowance receivers is higher in areas with higher altitude, and GDP per capita and education per capita have a significantly negative impact on the allowance rate.
【Keywords】 people living on minimum subsistence allowance; spatial distribution; spatial-temporal change; influencing factors;
. ① Fourteen concentrated contiguous poverty-stricken areas are included in National Program for Rural Poverty Alleviation (2011–2020) published by Office of Poverty Alleviation and Development of the State Council. Because county-level data in some regions of Tibet and three prefectures in southern Xinjiang are missing, this study only counts the other 12 regions except these two regions. [^Back]
1. Gao, X. 大城市人口分布变动与郊区化研究:以上海为例. Shanghai: Fudan University Press, (2003).
2. Gu, J. et al. Journal of Natural Science of Hunan Normal University (湖南师范大学自然科学学报), (2) (2018).
3. Liang, H. & Fang, C. Economic Geography (经济地理), (10) (2011).
4. Meng, B. et al. Scientia Geographica Sinica (地理科学), (4) (2005).
5. Sun, S. & Qi, C. The Chinese Journal of American Studies (美国研究), (4) (2010).
6. Yuan, Y. et al. Progress in Geography (地理科学进展), (2) (2016).
7. Zhang, L. & Wang, Z. Chinese Journal of Population Science (中国人口科学), (6) (2017).
8. Bramley G., Lancaster S., Gordon G. (2000), Benefit Take-up and the Geography of Poverty in Scotland. Regional Studies. 34(6): 507–519.
9. Debarsy N., Ertur C. (2010), Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model. Regional Science and Urban Economics. 40(6): 453–470.
10. Gatrell A. C. (1979), Autocorrelation in Spaces. Environment and Planning A: Economy and Space. 11(5): 507–516.
11. Liu Y., Wu F. (2006), Urban Poverty Neighborhoods: Typology and Spatial Concentration under China’s Market Transition, A Case Study of Nanjing. Geoforum. 37(4): 610–626.
12. Neumark D., Powers E. T. (2006), Supplemental Security Income, Labor Supply, and Migration. Journal of Population Economics. 19(3): 447–479.
13. Vega S. H., Elhorst J. P. (2015), The Slx Model. Journal of Regional Science. 55(3): 339–363.
14. Wong S. (2016), Geographies of Medicalized Welfare: Spatial Analysis of Supplemental Security Income in the U.S., 2000–2010. Social Science & Medicine. 160: 9–19.