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地理学报(英文版)  2015, Vol. 25 Issue (2): 236-256    DOI: 10.1007/s11442-015-1165-z
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Spatial patterns, driving forces, and urbanization effects of China’s internal migration: County-level analysis based on the 2000 and 2010 censuses
Tao LIU1(),Yuanjing QI2,Guangzhong CAO3,Hui LIU4,*()
1. Department of Geography, The University of Hong Kong, HK 999077, China
2. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
3. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
4. Department of Geography and Resource Management, The Chinese University of Hong Kong, HK 999077, China
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Abstract

China has witnessed unprecedented urbanization over the past decades. The rapid expansion of urban population has been dominantly contributed by the floating population from rural areas, of which the spatiotemporal patterns, driving forces, and multidimensional effects are scrutinized and evaluated in this study by using the latest national censuses conducted in 2000 and 2010. Analysis based on the county-level data comes to conclusions as follows. The spatial pattern of floating population has remained stable over the first decade of the new century. The top 1% cities with the largest floating population received 45.5% of all migrants in China. As the rapid development of mega-city regions, the coastal concentration areas of floating population tended to geographically united as a whole, whereas the spatial distribution of migrants within each region varied significantly. The migrant concentration area in the Yangtze River Delta was the largest and its expansion was also the most salient. However, the floating population has growingly moved into provincial capitals and other big cities in the inland regions and its gravity center has moved northward for around 110 km during the study period. The spatial pattern of floating population has been formed jointly by the state and market forces in transitional China and the impacts of state forces have been surpassed by those of market forces in the country as a whole. The attractiveness of coastal cities and counties to the floating population comes mainly from the nonagricultural employment opportunities and public services, reflecting that long-distance and long-term migrants have moved coastward not only to gain employment but also to enjoy city life. By contrast, in the central and western regions, places with a higher economic development level and at a higher administrative level are more attractive to floating populations, demonstrating that the state remains to play an important role in allocating economic resources and promoting regional development in inland China. As the main body of new urban residents, the floating population has contributed substantially to the elevation of the urbanization levels of migrant-sending and -receiving places, by 20.0% and 49.5% respectively. Compared with extensively investigated interprovincial migrants, intra-provincial migrants have higher intention and ability to permanently live in cities and thus might become the main force of China’s urbanization in the coming decades. The internal migration has also reshaped China’s urban system in terms of its hierarchical organization and spatial structure.

Key wordsfloating population    migration    urbanization    urban system    megacity region    census    China
收稿日期: 2014-06-05      出版日期: 2015-06-24
作者简介: Liu Tao (1987-), PhD Candidate, specialized in urbanization, population migration, and urban land use in China. E-mail: liutaopku@gmail.com
引用本文:   
. [J]. 地理学报(英文版), 2015, 25(2): 236-256.
Tao LIU,Yuanjing QI,Guangzhong CAO,Hui LIU. Spatial patterns, driving forces, and urbanization effects of China’s internal migration: County-level analysis based on the 2000 and 2010 censuses. Journal of Geographical Sciences, 2015, 25(2): 236-256.
链接本文:  
http://www.geogsci.com/CN/10.1007/s11442-015-1165-z      或      http://www.geogsci.com/CN/Y2015/V25/I2/236
Floating population % Hukou population Floating/hukou ratio
2000 Top 10 27.0 34.1 50.1 53.8
Top 23 (1%) 35.9 45.5 93.9 38.3
Top 100 53.5 67.7 204.5 26.1
Nation 79.0 100.0 1234.3 6.4
2010 Top 10 54.9 32.2 77.4 70.9
Top 23 (1%) 77.9 45.6 118.4 65.7
Top 100 120.3 70.6 235.6 51.1
Nation 170.6 100.0 1345.5 12.7
Table 1  
Figure 1  
Figure 2  
Eastern Central Western Nation
Floating population (million) 2000 51.10 12.38 15.53 79.01
2010 109.87 26.62 34.07 170.56
Growth rate (%) 115.0 115.1 119.4 115.9
Share in the national total (%) 2000 64.7 15.7 19.7 100.0
2010 64.4 15.6 20.0 100.0
Change (%) -0.3 -0.1 0.3 0.0
Floating/hukou ratio (%) 2000 11.1 3.0 4.4 6.4
2010 22.1 5.8 8.8 12.7
Change (%) 11.0 2.8 4.4 6.3
Table 2  
Figure 3  
Figure 4  
Figure 5  
Intra-county Inter-county/
Intra-provincial
Interprovincial Total
2000 Floating population 65.6 36.4 42.6 144.6
% 45.4 25.2 29.5 100.0
2010 Floating population 90.9 84.7 85.8 261.5
% 34.8 32.4 32.8 100.0
2000-2010 Growth 25.3 48.3 43.2 116.9
Growth rate 38.6 132.8 101.4 80.8
% change -10.6 7.2 3.4 0.0
Table 3  
Intra-provincial Interprovincial Chi-square (p value)
Intend to settle in cities (%) 61.65 48.83 26.58(0.000)
Plan to buy houses in cities (%) 29.23 21.54 13.21(0.000)
Plan to construct houses in villages (%) 23.50 27.78 3.95(0.047)
Table 4  
Figure 6  
Variable Definition No. Mean Std. Dev. Min Max
lnfltpop Floating population (logarithm) 2211 9.46 1.47 5.14 16.20
lnwage Urban average wage (logarithm) 2211 10.23 0.24 8.98 11.18
lnempna Nonagricultural employment (logarithm) 2211 11.21 1.14 7.50 16.29
FAI Fixed assets investment (per capita) 2211 1.83 1.66 0.04 21.20
lnpcfinex Per capita fiscal expenditure (logarithm) 2211 8.23 0.53 5.35 10.56
capital Province-level city or provincial capital (dummy) 2211 0.01 0.12 0 1
prefecture Prefecture-level cities (dummy) 2211 0.12 0.32 0 1
city County-level cities (dummy) 2211 0.16 0.37 0 1
Table 5  
Full sample By region By level
Market State Full Eastern Central Western Prefecture^ County∆
lnwage 1.784*** 1.152*** 0.564*** 1.179*** 0.492*** 0.687*** 1.152***
(20.14) (13.25) (3.27) (8.77) (3.38) (5.10) (12.03)
lnempna 0.884*** 0.766*** 0.970*** 0.570*** 0.710*** 1.031*** 0.769***
(53.15) (32.48) (22.91) (8.43) (18.50) (26.96) (28.07)
FAI 0.133*** 0.038*** -0.038 0.064** 0.095*** 0.041** 0.036***
(9.65) (3.36) (-1.54) (2.15) (5.63) (2.18) (2.86)
lnpcfinex 0.146*** 0.491*** 0.890*** 0.516*** 0.131** 0.111 0.553***
(3.19) (9.94) (10.34) (3.73) (2.02) (1.31) (10.10)
capital 5.280*** 1.768*** 0.932*** 2.421*** 2.171*** 0.440***
(38.61) (15.35) (4.33) (7.91) (13.08) (4.81)
prefecture 2.717*** 1.042*** 0.617*** 1.365*** 1.023***
(38.08) (16.60) (5.53) (8.64) (11.62)
city 1.249*** 0.421*** 0.391*** 0.312*** 0.739*** 0.421***
(19.37) (8.41) (4.99) (4.03) (7.54) (8.20)
_cons -18.952*** 7.667*** -15.237*** -14.567*** -13.762*** -4.815*** -9.490*** -15.779***
(-20.79) (20.55) (-17.93) (-10.81) (-7.26) (-2.86) (-7.59) (-16.08)
N 2211 2211 2211 628 703 880 286 1925
adj. R2 0.688 0.539 0.760 0.842 0.729 0.715 0.865 0.550
F 1514.00 769.66 1729.06 700.72 430.93 946.36 592.47 414.67
Table 6  
Figure 7  
Figure 8  
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