Chinese Geographical Science 2007 17(2) 099–109 DOI: 10.1007/s11769-007-0099-5 www.springerlink.com
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000 1
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LIU Chen , Kuninori OTSUBO , WANG Qinxue , Toshiaki ICHINOSE , Sadao ISHIMURA2 (1. National Institute for Environment Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan; 2. Tsurumi University School of Dental Medicine, 2-1-3 Tsurumi, Tsurumi, Yokohama 230-8501, Japan) Abstract: By studying the county-level census data of 1990 and 2000, we analyzed the spatial and temporal changes in the floating population in China between 1990 and 2000. The results of the analysis revealed the following characteristics. First, the spatial distribution of the migrants (referred to as ‘floaters’ in this paper) became increasingly concentrated in the cities during the 1990s. Second, the number of floaters increased rapidly during this period, and the area in which the floaters settled expanded quickly into four population explosion belts: the coast, the Changjiang River Delta, the Beijing-Guangzhou Railway and national border belts. Third, the number of inter-province floaters increased rapidly and exceeded that of intra-province floaters in the 1990s. In addition, to obtain a quantitative relationship between the number of floaters and 10 socio-economic variables by using statistical methods and also to find the chiefly important pulling factors of the migration destination, the authors selected approximately 100 cities with the largest population of floaters. Consequently, we found that four factors—GDP, passenger trips per 10,000 persons, per capita GDP and foreign direct investment—could provide an explanation for 83.7% of the number of floaters in 2000. The GDP showed the highest correlation with the number of floaters, suggesting that a highly developed economy is the most important factor that attracts floaters. Furthermore, a fairly close relationship between the number of floaters and the GDP was also found in 2000 for all the counties. Keywords: floating population; county-level census; spatial and temporal changes; China
1 Introduction In China, rural-to-urban migration before 1978 was strictly restricted by the household registration (hukou in Chinese) system. However, since the onset of reforms in 1978, the economic disparity among regions has widened, and surplus rural labourers have begun to migrate to urban areas that are insufficient in labours. However, since they are temporary urban migrants, most of them are unable to obtain a local household registration. In China, this group of migrants is referred to as the ‘floating population’, and the migrants are referred to as ‘floaters’. The 1980s and 1990s witnessed a dramatic and rapid increase in China’s floating population. Although there were no official and consistent statistics pertaining to the size of this population, it was believed that there were around 20×106 floaters by 1984, over 60×106 by 1993, and over 80×106 by 1995 (Davin, 1999). Appro-
ximately 80% of the floaters were from rural areas (Wei et al., 2002). In 2000, the number of floaters in Guangdong Province reached 25.3×106, which constituted 29% of the permanent residents, and 4×106 in Shanghai, which comprised 23% of the permanent residents (Population Census Office and State Statistical Bureau, 2003b). In other words, the regional pattern of the population distribution in China in the last decade has been greatly influenced by the floating population. With gradual relaxation of the population mobility control and increasing openness, people actively sought to fulfil their individual desires and to take full advantage of their talents. Due to the extremely rapid increase in the floating population, many studies were conducted on this issue (Mallee, 1996; Li, 2002). Researchers, both in and outside China, have examined many aspects of the Chinese floating population, including the number of floaters, the
Received date: 2006-04-25; accepted date: 2006-12-23 Foundation item: Under the auspices of the Domestic Research Fellowship of the Japan Foundation of Public Communication on Science and Technology (No. H-3) Correspondent author: LIU Chen. E-mail:
[email protected]
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determinants of migration based on census or survey data, descriptions of the socio-economic characteristics and geographical patterns of the floating population. These studies have shed light on many aspects of Chinese migration and have provided useful pointers for further research. For example, Chan et al. (1999) mapped the 30 largest inter-province migrations in China during 1985–1990 based on a 1% sample of the 1990 census. The map reveals that the flow of the inter-province floating population was primarily from the interiors to the eastern and southern coasts, converging at major economic hubs like Guangdong and metropolises such as Shanghai and Beijing. A considerable number of floaters migrated from the densely populated areas of Sichuan and Henan to border provinces like Xinjiang and Inner Mongolia. Ishihara (2004) compared the 2000 census data with that of 1990 at the province level. According to his study, the floaters migrating to Guangdong increased very rapidly in the 1990s, and the inflow exceeded the outflow in Zhejiang, Tibet and Yunnan, while the outflow exceeded the inflow in Hubei. However, taking province as a unit is very rough for a precise study of migration. For instance, within a province, a large number of floaters are suspected to continue migrating from rural areas to cities, thereby causing a remarkable expansion of the urban areas in all provinces; however, this aspect was completely omitted in the analyses conducted at the province level. Many researchers used regression analysis to analyze the relationship between the floating phenomena and socio-economic factors such as distance, GDP, population size, per capita income, per capita consumption, agricultural share, migration stock, foreign direct investment (FDI) and the land-labour ratio (Wang, 1993; 1997; Chan et al., 1999; Ishikawa, 1994; Ishihara, 2004). Additionally, these studies were conducted at the province-level because information and data pertaining to the origin and destination of the floaters could be easily obtained at this level. County-level data, on the other hand, are very difficult to collect, which makes the analyses of the same parameters at the county-level very difficult at present. Zhu et al. (2001; 2002) initially attempted a county-level study on floaters using the statistical data that was made available in 1996 by the Public Security
Ministry. On discussing the spatial distribution of floaters and the associated factors influencing it, they concluded that spatial distribution is an evident urban-rural duality with three clusters and five zones. Although their work is interesting, data accumulated for only a period of one year (1996) cannot provide much information regarding the rapid spatio-temporal changes in the floating population that occurred due to the rapid socio-economic changes in the 1990s. This report addresses the impact of the rapid spatio-temporal changes in the floating population on the regional environmental conditions. In most countries, it has been observed that the labour force moves from the rural areas to the cities during periods of economic growth. This phenomenon reduces the surplus labour force in rural areas and contributes to the development of cities and the entire economy of the country. On the other hand, excessive migration to cities in China will result in severe social and environmental problems. For instance, an influx of a large percentage of redundant labour force to cities will result in insufficient accommodations for the migrants and give rise to urban public hazards such as serious water shortages, air and water pollution and the expansion of slums. In this study, floaters have been defined as people who do not possess a household registration for their current residence and who have not resided where they are registered for more than one year (1990 census) or six ① months (2000 census) . People who had either changed their household registration when they shifted into their current residence (including normal migrants) or undertaken a short-term residence (e.g. less than half a year) between the origin and their destination were not included in this study.
2 Methodology Bouvier et al. (1977) proposed a push-pull theory to explain international population movement by introducing two reasons for migration: one is the push factors at the origin (poor living conditions that cause an outflow) and the other is the pull factors at the destination (good living conditions that cause an inflow). The push factors include unfavourable climatic conditions, surplus
① The statistical criteria of migration timing were one year or longer in the Census 1990, and six months or longer in the Census 2000.
Most floaters are surplus agricultural labours who move to the city for work. If they found steady job, they usually stayed a long period. Six-month or one year seems not to have a big effect on the number of floaters. Therefore, the comparison research in this paper was done by using the Census data of 1990 and 2000.
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000
population and poor economy. Some of the pull factors include abundant jobs and a highly developed economy. Since the data on the outflow at the county-level are not yet publicly available, this paper focuses on the quantitative analyses of the number of floaters flowing in and their pull factors. We conducted the following analysis on the spatio-temporal changes in China’s floating population during the 1990s at the county-level. The results obtained will contribute to the construction of a dynamic, and geographically explicit model of the floating population, which can be applied to the whole of China at a 20-km grid resolution (approximately at the county-level). First, based on the 1% sample of the 1990 Census and the 2000 County Census Data published in 2003 (Population Census Office and State Statistical Bureau of
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China, 1991; 2003a), we constructed datasets for the distribution of the final destinations of the floaters at the county-level in 1990 and 2000 by dividing the data into two categories—inter- and intra-province migrations. Authors then discussed the spatio-temporal changes in the distribution of floaters and the main factors for the changes from the viewpoint of socio-economic changes in China during the 1990s. Second, authors chose the top 100 county-level cities having the most number of floaters in order to analyze the relationship between the number of floaters in cities and the 10 socio-economic variables mentioned below. These variables were chosen from the variables in the City Statistics Yearbook of China of 1990 and 2000 (National Bureau of Statistics of China, 1991; 2001). The ten variables, their definitions and notations used in this paper are listed in Table 1.
Table 1 Variables used in statistical analysis Variable
Notation
Definition (unit)
GDP
G
GDP of an urban area (10,000 yuan (RMB))
Per capita GDP
GP
GDP of the urban area/total population in an urban area (yuan/person)
Mean annual income
I
Mean annual income of labours in an urban area (yuan/person)
Employment rate
ER
Employed population (person)/total population in an urban area (person)
Employment rate in secondary industry
ER2
Population employed in secondary industry (person)/total employed population (person)
Employment rate in tertiary industry
ER3
Population employed in tertiary industry (person)/total employed population (person)
Fixed asset investment
FAI
Investment in fixed assets (10,000 yuan)
Foreign direct investment
FDI
Actual amount invested directly by foreign country (10,000 dollars)
Passenger trips per 10,000 persons
T
Passenger transport (person)/total population (10,000 persons)
Urbanization level
UL
Total urban population (person)/total population (person) (including floating population)
Note: All the variables refer to urban destinations
The multiple stepwise regression method was adopted to obtain the quantitative relationships between the number of floaters and the indices mentioned above for the years 1990 and 2000. For this analysis, authors chose 87 cities that had more than 50,000 floaters in 1990 and 98 cities that had over 100,000 floaters in 2000. The number of floaters in 87 cities accounted for approximately 56% of the total floaters in 1990 and that in 98 cities approximately 65% of the total floaters in 2000. Third, we attempted to obtain a quantitative relationship between the number of floaters and the socio-economic indices available for all the counties in China. We searched the available socio-economic variables that were relevant to floating population for all the counties
and found that only two variables—GDP① and per capita GDP—were available in 2004 for this study. Since these two variables were dependent on each other, authors conducted a single regression analysis between the number of floaters and these variables.
3 Results and Discussion 3.1 Spatio-temporal changes of distribution of floaters The spatial distribution of floaters at the county-level in 1990 and 2000 is shown in Fig. 1②. Both the sub-parts of the figure show the distribution of the entire country along with close-up maps of the Guangdong Province. Table 2 summarizes the result of the analyses on the
① 1︰4,000,000 Chinese Resource Environment Map Database. This is a little different from that in the Cities Statistic Yearbook 2000. ② The urban areas of metropolises such as Beijing, Tianjin and Shanghai are shown as a united administrative unit on this map. For
instance, Beijing includes one city (the east, west, Xuanwu and Chaoyang districts are included, abbr. Beijing) and five rural areas (Daxing, Pinggu, Huairou, Miyun and Yanqing). Totally, there are 649 urban areas and 1678 rural areas shown on this map.
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Fig. 1 Spatial distribution of floating population in China in 1990 (a) and 2000 (b)
floaters, including the number of floaters in China, the ratio of China’s floating population to the total population and the top 10 cities with large floating population. According to Fig. 1a and b and Table 2, the characteristics of the spatial distribution of floaters in China in 1990 and 2000 are summarized as follows: (1) An increasing number of floaters were concentrated in cities during the 1990s. Migration into urban areas accounted for 70%–80% of all the floaters for both the years, and the regions that had more than 50,000 floaters mainly comprised urban areas. The floaters migrating to rural areas were few. This may be attributed to the difference in the economic activity between the urban and rural areas. According to the data published by the National Bureau of Statistics of China, the average net annual income per farmer in 2002 was 2476 yuan; this figure did not constitute even one-third of the annual per ① capita disposable income in urban areas (7703 yuan) . Furthermore, 40% of the net income of farmers was the conversion price of farm products, and 20% was spent on
fertilizers and agricultural chemicals; therefore, the money left with the farmers for their daily expenses did not exceed 1000 yuan annually. (2) In the 1990s, the major areas that absorbed floaters were expanding. The Beijing-Tianjin area, the Changjiang River Delta with Shanghai at its centre, the Zhujiang River Delta with Guangzhou and Shenzhen at its centre and provincial capitals like Chengdu, Wuhan, Kunming and Nanjing have been the major absorbers of floaters. This fact is attributed to the Chinese central governmental policy, that is, these areas have been authorized politically as well as economically as regions of reform and free-market economy as the regional centres. (3) The number of floaters and their settlement areas increased rapidly in the 1990s. In China, the proportion of floaters in the total population increased from 2.13% in 1990 to 6.34% in 2000, and the number of counties having more than 500,000 floaters increased from 4 in 1990 to 23 in 2000. Moreover, in 1990, floaters were restricted to settling in large cities such as Beijing,
① National Bureau of Statistics of China. http://www.stats.gov.cn/tjsj/ndsj/
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000
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Table 2 Results of analysis of floating population in China
Number of floaters (persons)
1990
2000
25202800
78133232
Floating population ratio (%)*
2.13
6.34
Floating population in urban areas (%)**
76.58
83.81
1990
2000
Number of counties Number of float-
No data
ers (×103 persons)
Number of cities
Number of counties
Number of cities
83
22
7
2
0–10
1851
360
1496
127
10–20
163
98
327
133
20–50
128
82
273
189
50–100
52
44
114
99
100–200
30
25
50
42
200–500
14
14
35
34
4
4
23
23
≥500 1990
2000
Top 10 cities with large floating population
Shanghai (1167.5)
Shenzhen (5848.5)
(×103 persons)
Shenzhen (1137.6)
Dongguan (4922.6)
Tianjin (703.6)
Shanghai (4056.8)
Beijing (514.4)
Guangzhou (3079.7)
Guangzhou (451.9)
Beijing (2343.6)
Wuxi (384.4)
Nanhai (1093.3)
Daqing (357.8)
Zhongshan (1044.9)
Dongguan (355.2)
Chengdu (1014.4)
Chengdu (332.8)
Wuhan (984.6)
Jinan (259.8)
Kunming (955.1)
Source: 1% sample of the 1990 census and the 2000 county census Notes: *defined as the floating population/total population; **defined as the floating population migrating to urban areas/total floating population
Shanghai and Guangzhou but expanded to areas surrounding these cities in the 1990s because the economies of these areas developed remarkably during this period. The most remarkable expansion occurred in the Changjiang River and Zhujiang River deltas. (4) Four belts with a large number of floaters were formed along the eastern coast area, the Changjiang River valley, the Beijing-Guangzhou Railway corridor and the national border. Many big blocs with a large number of floaters have been developed around large cities such as Beijing, Tianjin, Shanghai, Guangzhou, Nanjing and Wuhan. Some of them later merged into bigger blocs and developed to form four economic belts in the 1990s: 1) the coastal belt comprising the Beijing-Tianjin area and cities including Shanghai, Wenzhou, Fuzhou, Xiamen, Shenzhen, Dongguan, Guangzhou, Zhongshan and Zhuhai; 2) the Changjiang River belt consisting of the Shanghai, Nanjing and Chongqing joint regions; 3) an important road belt consisting of the Beijing, Shijiazhuang, Zhengzhou, Wuhan, Changsha and
Guangzhou joint regions along the Beijing-Guangzhou Railway; and 4) the north-western, north-eastern and south-western border belt along the nation’s border. Li and Qiao (2000) analyzed the spatial distribution of the economic differences in China at a county-level using the GP of 1990 and 1998. They found that the areas with highly developed economies were located mostly along the eastern and southern coastal areas, the lower Changjiang River Delta, the Beijing-Guangzhou Railway and the north-eastern border, and these areas developed to form four highly developed economic belts. We found that the locations of the four economic belts coincided well with the four zones with the expanding floating populations. Therefore, it was concluded that the floating migration was closely related to economic development, that is, people tended to move to areas with more highly developed economies. This tendency appears to produce a positive feedback between economic growth and population concentration.
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3.2 Spatio-temporal changes in intra- and inter-province floaters In China, each province functions as an independent block in terms of both administration and culture. Therefore, until about 1990, there was a sentimental barrier against inter-province migration (Wang, 1993). To determine whether such a sentimental barrier continued to influence the behaviour of floaters in the 1990s, we analyzed the intra- and inter-province migration separately.
The four sub-parts of Fig. 2 show the spatial distribution of the intra-provincial floaters in 1990 (a) and 2000 (b) and those of the inter-province floaters in 1990 (c) and 2000 (d), respectively. The number of intra- and inter-province floaters in 1990 and 2000, their ratio to the total population, the top 10 cities with a sizeable number of floaters and that in each city are listed in Table 3. Based on Fig. 2 and Table 3, we found four clear features of the intra- and inter-province floating populations:
(a) Intra-province floaters in 1990; (b) Intra-province floaters in 2000; (c) Inter-province floaters in 1990; (d) Inter-province floaters in 2000
Fig. 2 Spatial distribution of intra- and inter-province floating population in China Table 3 Analytical results of intra- and inter-province floating population in China 1990 Floating population (persons) Proportion of total floating population (%) Top 10 cities with a large floating population(×103 persons)
Intra-province 16030606 63.6 Shenzhen (746) Chengdu (398) Guangzhou (352) Wuxi (287) Hefei (264) Hangzhou (251) Dongguan (248) Jinan (230) Leqing (217) Shijiazhuang (204)
Source: 1% sample of the 1990 census and the 2000 county census
2000 Inter-province 9172194 36.4 Shanghai (623) Tianjin (570) Beijing (519) Shenzhen (392) Dongguan (107) Daqing (106) Shenyang (100) Guangzhou (103) Wuxi (97) Hegang (90)
Intra-province 36045929 46.1 Shenzhen (1793) Shanghai (1174) Guangzhou (1121) Chengdu (869) Dongguan (786) Wuhan (735) Kunming (444) Zhengzhou (406) Nanjing (385) Changshan (375)
Inter-province 42087303 53.9 Dongguan (4137) Shenzhen (4056) Shanghai (2883) Beijing (2262) Guangzhou (1959) Zhongshan (861) Nanhai (780) Tianjin (659) Wenzhou (546) Kunming (512)
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000
(1) Intra-province migration was prevalent during the 1990s as well as before 1990 in the Zhujiang River Delta and large inland cities where ‘reform and opening-up’ were promoted by the central government. The distribution patterns of the intra-province floaters did not change considerably during the 1990s; however, the number of floaters increased and the area they settled in expanded remarkably during this period. The number of intra-province floaters in Shanghai increased explosively from less than 100×103 in 1990 to 1.17×106 in 2000—the second largest number of intra-province floaters in the entire country. (2) The distribution of inter-province floaters in 1990 and 2000 were considerably concentrated than those of intra-province floaters. Prior to 1990, most of the inter-province floaters moved to the central areas of Shanghai, Beijing, Tianjin and the Zhujiang River Delta. However, in the 1990s, many inter-province floaters settled down in the large inland capitals such as Kunming, Chongqing, Wuhan, Urumqi and Xi’an as well as the mega-city areas mentioned above. The inter-province floaters also increased to such a large extent in the south-eastern coastal region between the lower Changjiang River and Zhujiang River deltas that these highfloater areas merged to form a large belt in the 1990s. (3) The number of inter-province floaters increased explosively and exceeded the intra-province floaters during the 1990s. The number of intra-province floaters increased from 16.03×106 in 1990 to 36.04×106 in 2000. On the other hand, the number of inter-province floaters increased from 9.17×106 in 1990 to 42.10×106 in 2000. The ratio of the intra-province floaters to the total number of floaters was 0.636, while the ratio of the inter-province floaters to the total number was 0.364 in 1990. The former ratio had decreased to 0.461 and the latter had increased to 0.539 in 2000. The inter-province floaters exceeded the intra-province floaters during the 1990s in terms of both the number of floaters and the rate of increase. For instance, in the cities of Dongguan, Shenzhen, Guangzhou, Zhongshan and Nanhai, which are located in the Zhujiang River Delta, the number of inter-province floaters increased 10-fold during the 1990s. The sentimental barrier appeared to have decreased during the 1990s, and a significant number of floaters were willing to travel hundreds or thousands of kilometres to very distant, culturally and environmentally different provinces to pursue jobs or business opportunities.
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(4) In 1990 and 2000, Beijing and Tianjin had many inter-province floaters, but only a small number of intra-province floaters. The first three features may be attributed mainly to the development of a market economy and the loosening of the household registration system in China during the 1990s. With the development of the market economy, goods, labour forces and capital began to circulate rapidly in the entire country. On the other hand, it became easier for rural people to reside in cities in the late 1980s when a temporary dwelling card was easily issued on request, and from the beginning of the 1990s, when the systems of rationing staple foods and union distribution of the labour force were abolished. The fourth feature is explained by the fact that, since the rents and costs of residence in cities like Beijing and Tianjin were directly controlled by the central government, they were maintained at a high level, and it was extremely difficult for farmers to convert their agricultural registration to a non-agricultural one. Furthermore, the internal difference between the living standards in Beijing and Tianjin was not as large as that in other provinces; the distance between the rural fringes and the central districts of the two cities are not very far, and travelling to work in the central districts is not difficult for people living in these fringes. These conditions may have restricted the number of intra-province floaters in Beijing and Tianjin. 3.3 Factors pulling population inflow into cities Table 4 shows the results of the correlation coefficients between the number of floaters (M) and 10 variables of the 87 cities (in 1990) and 98 cities (in 2000), respectively. Based on the values of the Pearson correlation coefficient (R) between M and the values of the 10 variables, we judged that M was significantly correlated with the values of the GDP (G), per capita GDP (GP), mean annual income (I), employment rate (ER), fixed assets investment (FAI) and foreign direct investment (FDI) in both 1990 and 2000. In 2000, the correlation coefficients of the urbanization level (UL) and passenger trips per 10,000 people (T) with M were high at 0.54 and 0.61, respectively; however, they were insignificant in 1990. This implies that these two variables were not the key variables when explaining M in 1990. It is noted that FDI has the highest coefficient of correlation with M, and this could be attributed to the existence of labour-intensive industries financed by FDI. For instance, the Zhujiang River Delta—the region that over
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one-fifth of the total number of floaters migrated—is known as ‘the factory of the world’. There are many enterprises with FDIs focusing on export-oriented manufacturing. In particular, most Hong Kong enterprises have adopted unique systems such as ‘third-parts ① ② processing’ and ‘compensation trade’ . These are labour-intensive production systems that require numerous cheap labors. Table 4 Correlation coefficient between number of floaters and 10 variables Pearson correlation coefficient (R)
Table 5 Results of multiple regression analysis of floating population
G GP
Unstandardized partial
Standardized partial
regression coefficients
regression coefficients
1990*
2000*
1990*
0.137
0.0864
0.439
2000* 0.437
–
13.6
－
0.203
－
－
－
－
ER
8.98 ×105
－
0.413
－
ER2
－
－
－
－
ER3
－
－
－
－
FAI
－
－
－
－
FDI
4.38
3.42
0.402
0.189
I
1990
2000
G
0.743*
0.688*
T
－
1.85
－
0.429
GP
0.672*
0.595*
UL
－
－
－
－
I
0.484*
0.641*
Intercepts
–4.52× 105
–4.19× 105
0
0
ER
0.410*
0.463*
Rm
0.904
0.915
0.904
0.915
ER2
0.083
Rm2
0.818
0.837
0.818
0.837
–0.184
ER3
0.316*
0.055
FAI
0.753*
0.665*
FDI
0.767*
0.786*
T
0.091
0.611*
UL
0.071
0.543*
Notes: 1. The effective case numbers were 57 in 1990 and 72 in 2000; 2. *Significant at the 0.01 level
Table 5 shows the results of the final step of the stepwise multiple regression analysis between M and the 10 variables for both the years. The value of the coefficient of multiple regression, Rm, was determined by the multiple regression analysis using the stepwise method. Based on the Rm, we can infer that the multiple regression equations obtained for both the years are significant as a whole. For the year 1990, three variables, namely, G, ER and FDI, were selected as explanatory variables of M, and the value of the determination coefficient Rm2 was 0.818. For the year 2000, four variables—G, GP, FDI and T—were selected, and the value of Rm2 was 0.837. The multiple regression equations obtained for M are expressed as follows. For 1990, (1) M=0.137G+8.98×105×ER+4.38FDI-4.52×105 For 2000, M=0.0864G+13.6GP+3.42FDI+1.85T-4.19×105 (2) In equations (1) and (2), the units of M, G, GP, ER, FDI and T are person, 10000 yuan, yuan/person, person/
Notes: 1. Effective case numbers were 57 in 1990 and 72 in 2000; 2. *Significant at the 0.01 level; 3. －Excluded using stepwise regression analysis; 4. Criteria: Probability-of-F-to-enter ≥0.050, probability-of-F-to-remove ≥0.100; 5. Rm is multiple regression coefficient, Rm2 is determination coefficient
person, US$10000 and person/10000 persons, respectively. In terms of the results of the multiple regression analysis, the following observations were made. (1) The standard regression coefficients of G were the highest among all variables, and they remained constant at 0.439 and 0.437 in 1990 and 2000, respectively. (2) The GP was used in Equation (2) for 2000, but not in Equation (1) for 1990. On the other hand, ER was used in Equation (1), but not in Equation (2). (3) The number of FDI remained an explanatory index of M for both the years; however, the correlation coefficient between M and this variable was high in 1990 at 0.402, but low in 2000 at 0.189. (4) The number of T was chosen for 2000 because it had the second highest standard regression coefficient at 0.429 in that year, but not in 1990. (5) Other variables such as the FAI and I were excluded from the multiple regression analysis that was carried out using the stepwise method. (6) Industrial composition rates (ER2 and ER3) were not selected as significant variables for M for both the
① "Third party processing" is a method by which the foreign capital sector offers raw material, machines and samples, and the Chinese
sector produces and exports all products. ② "Compensation trade" is a method by which the foreign capital sector offers the machines, and the cost of using the machines is paid for
by earnings from export of the products.
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000
years. These results should be discussed while considering the multi-co-linearity among the variables. On checking the correlations, we found some very high correlations between the variables (Table 6). The values of the correlation coefficients between G and FAI were more than 0.9, and the correlations between FDI and FAI and between G and FDI were more than 0.7 in both 1990 and 2000. The values of the correlation coefficient between GP and I were approximately 0.7 and 0.8 in 1990 and 2000, respectively. The values of the correlation coefficient between GP and ER and between FDI and GP were more than 0.6 in 1990. The values between T and UL and between T and ER were approximately 0.6 and 0.5 in 2000, respectively.
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This implies that the state of the regional economy was closely related to the cities’ capacity to absorb floaters. The GP, ER and I tend to reflect the living standards of the residents. Therefore, two out of the three variables were likely to be excluded from equations (1) and (2) using the stepwise analysis. The GP and I were excluded from Equation (1) for 1990 because ER remained in the equation, while ER and I were excluded from Equation (2) for 2000 because the GP remained in that equation. The standard coefficient between M and FDI was lower in 2000 than in 1990, mainly because a correlation was observed between FDI and T, and between FDI and GP, which are included in Equation (2) for 2000. FAI was excluded from both equations (1) and (2), mainly because of the high correlation between FAI and G.
Table 6 Pearson correlation coefficients of variables
G
G
GP
–
0.377*
I
ER
ER2
ER3
FAI
FDI
T
UL
0.236
0.047
0.107
0.359*
0.945*
0.762*
–0.096
0.061
GP
0.353*
–
0.691*
0.655*
0.084
0.365*
0.507*
0.620*
0.217
–0.090
I
0.568*
0.795*
–
0.398*
–0.102
0.311*
0.331*
0.397*
0.165
–0.026
ER
0.256
0.451*
0.366*
–
0.071
0.137
0.089
0.229
–0.247
ER2
–0.196
0.161
0.029
–0.224
–
–0.034
0.094
0.198
–0.241
–0.165
ER3
0.192
–0.132
0.002
–0.080
–0.872*
–
0.350*
0.222
0.207
–0.138
FAI
0.971*
0.306*
0.540*
0.231
–0.178
0.178
–
0.755*
–0.067
0.046
FDI
0.784*
0.479*
0.686*
0.426*
–0.185
0.087
0.766*
–
0.046
0.200
T
0.080
0.342*
0.345*
0.486*
–0.251
0.008
0.049
0.371*
–
0.354*
UL
0.467*
0.256
0.349*
0.573*
–0.406*
0.201
0.429*
0.476*
0.569*
–
0.365*
Notes: 1. The lower left section comprises the data for 2000 and the upper right section comprises the data for 1990; 2. *Significant at the 0.01 level．
As for the reason that industrial composition rates (ER2 and ER3) were not selected as significant variables for M, one possible explanation is that most of the floaters were from rural areas and retained their registration as farmers. Further, they worked in cities as seasonal workers who were categorized neither as secondary industry workers nor as tertiary industry workers. While summarizing the above discussion, we concluded that high economic activity, high living standard and the existence of labour-intensive industries financed by FDI were the main factors in determining the floaters’ destinations in 1990 and 2000. The development of new transportation systems such as expressways in big cities appeared to accelerate the migration from rural areas in 2000. In the preceding sections, we have discussed the migrations into considerably large cities. The equation obtained for M can be adapted to project future migration
into urban areas. However, it cannot be applied to all the counties in China because sufficient statistics on the variables in equations (1) and (2) are not available. Therefore, another method that is applicable to all the counties should be developed. At present, among the explanatory variables in equations (1) and (2), only the GDP and per capita GDP in 2000 are available for all the counties; thus, we conducted a simple regression analysis between the number of floaters (Mc) and GDP (Gc) of 2000. The regression equation obtained is expressed by Equation (3) and by line I in Fig. 3. Mc=0.0118Gc-1.73 (3) Here, the unit of Mc is 10,000 persons and that of Gc is 109 yuan. The correlation coefficient between Mc and Gc was 0.77 at the 1% significance level, implying that the value of the determination coefficient was 0.59. In Fig. 3, Shanghai (1), Beijing (2) and Guangzhou (3) are plotted around line I, while Dongguan (4) and Shenzhen
LIU Chen, Kuninori OTSUBO, WANG Qinxue et al.
108
(5) of the Guangdong Province are plotted far above this line. This is mainly due to the existence of many labour-intensive foreign-owned industries in Dongguan and Shenzhen that are very attractive to floating labours. Line II is a regression line between Mc and Gc in which the five cities were excluded. The regression equation is expressed by Equation (4).
Line I: regression line of the number of floaters and GDP; Line II: regressiｏｎ line of the number of floaters and GDP excluding the five cities (No. 1–5) in which GDP was more than 20０×109 yuan or the number of floaters was more than 2×106 persons No. 1: Shanghai, No. 2: Beijing, No. 3: Guangzhou, No.4: Dongguan, No. 5: Shenzhen
Fig. 3 Correlation between the number of floaters and GDP for all counties
Mc=0.0796Gc－ 0.534 (4) In this case, the correlation coefficient increases to 0.80, and the determination coefficient increases to 0.64. On the other hand, the correlation coefficient between the number of floaters and per capita GDP for all counties in 2000 was quite low at 0.28 at the 1% significance level. Above all, for the purpose of future projections of the number of floaters in China at the county-level, we can adopt Equation (4) for both urban and rural areas or Equation (2) for urban areas if we are able to obtain statistical data for G, GP, FDI and T; Equation (4) can be adopted for rural areas in order to project the future floating population.
4 Conclusions We studied the spatio-temporal properties of floating populations from 1990–2000 in China by analyzing the census data of 1990 and 2000 and relevant socio-economic data at the county-level. The following characteristics were noted. Firstly, the spatial distribution of the migrants showed
that the main destinations of floaters were Beijing, Tianjin, the Zhujiang River and Changjiang River deltas and the capitals of each province. Secondly, between 1990 and 2000, the number of floaters increased rapidly, and the destinations of the settled floaters expanded quickly. Until 1990, the floaters were restricted to settling in several specific large cities, while in the 1990s, the areas developed to form several belts such as the coastal belt (along the nation’s coast line), the Changjiang River belt (along the Changjiang River from Chengdu to Shanghai), the Beijing-Guangzhou Railway corridor (along the Jing–Guang Railway from Beijing to Guangzhou) and the north-western, north-eastern and south-western national border belt, which coincided well with the economically developed regions authorized and supported by the central government. Thirdly, the number of inter-province floaters increased rapidly and exceeded that of the intra-province floaters during the 1990s due to the expansion of the free-market economy, the loosening of the household registration system and the development of the public transportation system. We selected approximately 100 cities that have many floaters in order to analyze the relationship between the number of floaters and 10 socio-economic variables. We found that the number of floaters in 1990 was explained by G, ER and FDI, with a determination coefficient of 0.818. On the other hand, the number of floaters in 2000 was explained by G, T, GP and FDI, with a determination coefficient of 0.837. This suggests that high economic activity, high living standards and the existence of labour-intensive industries financed by FDI are the main factors that controlled migration of the floating population in both 1990 and 2000. In addition, the development of new transportation systems in large cities appeared to accelerate the migration in 2000. Furthermore, in both the years, G showed the highest standard correlation with the number of floaters, suggesting that a highly developed economy was the most important factor that attracted floaters. For this study, only two indices— GDP and per capita GDP— were available for all the counties in order to quantify the floaters in the 1990s. We conducted a simple regression analysis and observed a fairly good correlation between the number of floaters and GDP. The next step is to determine a two-dimensional probability density function of the inflow, that is, to determine the number of floaters and their origins as a function of distance and certain socio-economic variables. If we can
Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000
determine this function as well as the size of the inflow for each grid cell, we can additionally estimate the size of the outflow for each grid cell and run our dynamic model to project the future population at the grid cell level. References Bouvier Leon F, Shryrock Henry S, Henderson Harry W, 1977. International migration: yesterday, today, tomorrow. Population Bulletin, 32(4): 26. Chan Kam-wing, Liu Ta, Yang Yun-yan, 1999. Hukou and non-hukou migration in China: comparisons and contrasts. International Journal of Population Geography, 5(6): 425–448. Davin Delia, 1999. Internal Migration in Contemporary China. New York: St Martin's Press. Ishimura Hiroshi, 2004. A review of migration in China by Chinese Census 2000. Geographical Journal of Nara University, 10: 1–20. (in Japanese) Ishikawa Yositaka, 1994. Measurement Geography of Migration. Tokyo: Koukin-shoin. (in Japanese) Li Ling, 2002. Internal population migration in China since the economic reforms: A review. Population and Family Planning, (2): 35–44. (in Chinese) Li Xiaojian, Qiao Jiajun, 2000. County-level economic disparities of China in the 1990s. Acta Geographica Sinica, 55(2): 136–145. (in Chinese) Mallee Hein, 1996. In defence of migration: recent Chinese studies on rural population mobility. China Information, 10(3/4):
109
108–140. Population Census Office and State Statistical Bureau, 1991. 10 percent sample of the 1990 census data. Beijing: China Statistics Press. (in Chinese) Population Census Office and State Statistical Bureau, 2003a. County-level data of population census 2000. Beijing: China Statistics Press. (in Chinese) Population Census Office and State Statistical Bureau, 2003b. Collections of national projects based on the fifth population census. Beijing: China Statistics Press. (in Chinese) National Bureau of Statistics of China, 1991, 2001. China City Statistical Yearbook. Beijing: China Statistics Press. (in Chinese) Wang Guixin, 1993. Relationship between inter-provincial migration and distance. Population and Economics, (2): 3–8. (in Chinese) Wang Guixin, 1997. Population Distribution and Regional Economic Development in China: A New Approach of Population Distribution Economics. Shanghai: East China Normal University Publishing House. (in Chinese) Wei Jinsheng, Sheng Lang, Tao Ying, 2002. Floating Population of China. Beijing: People’s Education Press. (in Chinese) Zhu Chuangeng, Gu Chaolin, Ma Ronghua, 2001. The influential factors and spatial distribution of floating population in China. Acta Geographica Sinica, 56(5): 549–560. (in Chinese) Zhu Chuangeng, Gu Chaolin, Zhang Wei, 2002. The quantitative analysis of influential factors of floating population in China. Population Journal, 2: 9–12. (in Chinese)
