Agglomeration Economies and Diseconomies in the Context of Chinese Urbanization:
Roles of Population and Migration in Foreign Direct Investment Location
Abstract
With China’s rapid urbanization, urbanization agglomeration effects are playing
increasingly significant roles in foreign direct investment (FDI) location. However,
existing studies on FDI location in the context of Chinese urbanization fail to
distinguish urbanization agglomeration effects stemming from different urbanization
processes: the increase of urban-hukou population and the migration. This paper
employs two sets of population data from two systems for the collection and reporting
of statistical data, China City Statistical Yearbooks generated from the hukou
registration statistics and China Censuses including migrant workers, to distinguish
urbanization agglomeration effects resulting from two different urbanization
processes. The results indicate that the urbanization agglomeration stemming from
two different urbanization processes had opposite effects on FDI location: the
urbanization agglomeration economies resulting from the increase of the urban-hukou
population and the urbanization agglomeration diseconomies resulting from the
migration. The findings provide policy implications on developing periphery cities to
motivate and encourage migrant workers to move from core cities within the
economic zones and provincial capitals, especially cities in the eastern/coastal
regions, to the periphery cities.
Keywords: Agglomeration economies and diseconomies, Urbanization, Population
and migration, Foreign direct investment, Location decision, and China
1. Introduction
Agglomeration economies, a form of externalities and scale economies, have been
well known as one of fundamental explanations of industrial location (Marshall, 1920;
Henderson, 1974; Jacobs, 1969; Krugman, 1991). Localization economies and
urbanization economies are two types of agglomeration economies considered in the
empirical literature on foreign direct investment (FDI) location: localization
economies are related to the concentration of industries while urbanization economies
are related to a city size (Marshall, 1920; Jacobs, 1969; Henderson, 1974). The
identification and examination of microfoundations of agglomeration can help
understand the process and mechanisms behind the behavior of FDI location. Also,
the assessment of relative importance of localization and urbanization economies
provides significant policy implications on urban development and industrial
development.
China’s rapid urbanization has been considered as an important force shaping the
world development in the twenty-first century. Between 2000 and 2010, China’s
urbanization rate has raised from 36.2 percent to 49.7 percent (NBS, 2011). In 2012,
711.82 million urban dwellers accounted for 52.6 percent of the total population
(NBS, 2013) and thus the urbanization rate is more than 50 percent. More people live
in urban areas than in rural areas. Rapid urbanization has provided a large amount of
cheap labor and is widely considered to be the main driver of China’s economic
growth, especially in attracting FDI, but the impact of urbanization economies on FDI
has not been thoroughly identified in the context of Chinese urbanization, especially
in the 2000s.
The urbanization process in China performs different from that in the Western world
and displays unique Chinese characteristics. China’s urbanization is hukou-based
urbanization. The hukou system is a state institution separating urban and rural
residents in terms of social, economic, and political dimensions, which has resulted in
an institutionalized two-tier, rural-urban dual structure (Chan, 2014). Given the
hukou-based urbanization, urban population growth can be divided into two
components: one is the net migration of rural population (i.e. migrant workers) and
the other is the increase of the urban-hukou population (including natural increase and
residential reclassification) (Chan, 2012). Two components reflect different
urbanization processes. Therefore, impacts of urbanization economies stemming from
two components should not be expected to be same. However, existing studies
examining impacts of urbanization economies on FDI do not distinguish these two
components (Leung, 1990; Gong, 1995; He, 2002).
This paper identifies impacts of urbanization economies in 2011, examines
differences in impacts stemming from two components, and assesses the relative
importance of urbanization economies and localization economies in FDI location. I
employ two sets of population data distinguishing different components of
urbanization: China Census 2010 and China City Statistical Yearbooks. I believe that
by considering the difference in processes of urbanization, impacts of different
urbanization economies will be better understood and roles of population and
migration in FDI will be displayed. My findings suggest that urbanization economies
stemming from natural increase of the urban-hukou population and residential
reclassification have a positive impact on FDI location while urbanization economies
stemming from the migration have a negative impact.
In the next section, I review the factors that contribute to FDI location and elaborate
upon urbanization economies stemming from two components of China’s
urbanization. This is followed by an analysis of the spatial distribution of migrant
workers. I also investigate the difference in impacts of urbanization economies
stemming from two components. This is followed by sections presenting a discussion
and the paper’s conclusions.
2. Literature Review
Three schools of thought are relevant to patterns and location of FDI. The neoclassical
theory indicates the importance of comparative advantages including factor
endowments, such as labor and infrastructure (Kojima, 1982; Kojima and Ozawa,
1984). The institutional economics interpret the institutional impacts on FDI. The new
economic geography emphasizes the significance of market access (Markusen and
Venables, 1998) and agglomeration economies (Krugman, 1991; Venables, 1996).
The literature indicates that five major factors affect FDI location.
2.1. Comparative Advantages and Market Access
First, labor costs considered as one of factor endowments in neoclassical theories,
affect FDI location (Kojima, 1982; Kojima and Ozawa, 1984). Foreign firms break
the industrial chain to access to low labor costs through FDI. However, Japaneseaffiliated automotive-related manufacturing establishments in the United States were
located in areas that are characterized by higher wages because they are willing to pay
high wages for highly skilled labor (Smith and Florida. 1994). Similarly, Woodward
(1992) found that although wage rate was insignificant for Japanese manufacturers in
the U.S., higher productivity attracts more investment. In China, there were similar
phenomena. He (2003) found that wage rate adjusted by labour productivity in a city
has a negative relationship with FDI.
Second, transportation infrastructure is another factor endowment influencing FDI
location. Advanced transportation infrastructure is particularly significant attractive to
foreign firms due to its improvement in transport accessibility. In the U.S. whether
there is an interstate connection is considered as an important indicator for transport
accessibility (Woodward, 1992; Smith and Florida, 1994). In China, in addition to
road transportation, seaport, airport, and telecommunication infrastructures are
significant attractions to FDI (Leung, 1990; Gong, 1996; Head and Ries, 1996).
Third, new economic geography associates market access with FDI location. Market
access and competition generate mobility of capital, leading to FDI concentration
(Markusen and Venables, 1998). Foreign firms duplicate a subset of their activities to
access to foreign markets and participate in local competition directly. Consequently,
studies have found that markets play a dominant role in FDI location: in the United
States, for example, larger market size attracted more Japanese manufacturing startups (Woodward, 1992). In the European Union, a 10% increase in the market
potential raises the chance of a region being chosen by Japanese investors by 3% to
11% (Head and Mayer, 2004). Similarly, He (2003) found that American, Hong Kong
and Taiwanese manufacturers valued access to Chinese markets.
2.2. Institutional Factors
Institutional factors also affect FDI in the United States. Unionization and fiscal
policies are two major institutional factors influencing investment (Woodward, 1992;
Smith and Florida, 1994). Studies have found that labor union membership represents
a major deterrent to FDI: for example, it been found that a 1 percent increase in the
unionization rate leads to an 8.7 percent decrease in estimated locational probability
of Japanese manufacturing start-ups (Woodward, 1992). However, Smith and Florida
(1994) found that the presence of unions in a county deters new establishments of
Japanese manufacturers, but plays only a slight role for total Japanese manufacturing
establishments. In addition to unionization, existing studies emphasize the
significance of tax rate. The worldwide and domestic unitary taxes have led to FDI
reduction effect in California and Oregon during the 1980s (Woodward, 1992). In
terms of personal and property taxes, however, while taxes have an impact on the
location of Greenfield FDI, they play no role in acquisition FDI (Smith and Florida,
1994).
In the case of China, preferential policies and development zones are two main factors
influencing FDI (Huang and Wei, 2011). In the early 1980s, the central government
initiated China’s open policy through designating cities as special economic zones
(SEZs) and open coastal cities (OCCs) for FDI. These cities offered preferential
policies to motivate FDI. In the late 1980s, China expanded preferential policies to
open delta economic areas: the Yangtze River Delta (YRD), the Pearl River Delta
(PRD), and the Bohai Rim Region (BRR). In the 1990s, all provincial capital cities
provided preferential policies. In addition to preferential policies, development zones
play a significant role in attracting FDI by tax reduction, red elimination, and superior
infrastructure provision.
2.3.Agglomeration Economies
Another factor that explains FDI location is associated with agglomeration
economies. New economic geography models indicate that, due to increasing returns,
firms and workers tend to locate close to each other (Krugman, 1991; Venables,
1996). There are two types of agglomeration economies: localization agglomeration
economies arise from scale economies in industries while urbanization agglomeration
economies arise from economies of size of cities (Marshall, 1920; Jacobs, 1969;
Henderson, 1974).
China has been experienced a rapid urbanization since the economic reform of 1978.
During more than three decades, urban population has increased from 185 million in
1978 to 712 million in 2012, resulting in an increase of urbanization from 19 percent
to 53 percent (NBS, 2013). And according to China’s new urbanization plan, this
rapid growth is continuing. The plan proposed to grant urban household registration
status to 100 million people by 2020. This rapid growth in the size of cities becomes a
basis for urbanization agglomeration economies influencing FDI location. However,
the impact of urbanization economies on FDI has not been thoroughly identified in
the context of Chinese urbanization, especially in the 2000s, due to the unique
characteristics of China’s urbanization.
China’s urbanization is different from the urbanization process in the Western world
in that it is hukou-based urbanization. The Hukou system formalized in 1958 has
created differences in basic social welfare and economic and political opportunities
between urban and rural residents, leading to an institutionalized rural-urban divide
(Chan, 2009). The hukou divide is consistent with the geographical rural-urban
boundary before the economic reform of 1978. However, after 1978, along with
China’s increasing opening and participation in the global economy, the number of
rural hukou residents working and living in cities has been dramatically increased
(Chan, 2012). However, due to their rural hukou type, these migrant workers do not
have same urban social security benefits and public service opportunities as urban
residents. Thus, mismatch between the geographical rural-urban boundary and hukou
divide appeared and became significant along with the rapid urbanization.
This mismatch leads to two different components of urban population growth
reflecting different urbanization processes: one is the migration (i.e. migrant workers)
and the other is the increase of the urban-hukou population (including natural increase
and residential reclassification) (Chan, 2012). Impacts of urbanization economies
stemming from two different components should not be expected to be same.
Distinguishing urbanization economies resulting from these two different components
can identify and examine microfoundations of agglomeration, help understand the
process and mechanisms behind the behavior of FDI location, and thus provide
significant policy implications on urban development. However, the existing literature
examining impacts of urbanization economies on FDI do not distinguish these two
components (Leung, 1990; Gong, 1995; He, 2002). This paper employs two sets of
population data to distinguish two components and thus understand contributions of
different urbanization agglomeration economies to FDI location.
3. Data and Methods
3.1. Data
In this paper, I use two sets of population data distinguishing different components of
urban population growth: China Census 2010 and China City Statistical Yearbooks.
These two sets of population data are from two systems for the collection and
reporting of statistical data, China City Statistical Yearbooks generated from the
hukou registration statistics and China Censuses including rural migrants who had
lived in cities. Thus, population data from China City Statistical Yearbooks reflects
the component of the increase of the urban-hukou population while population data
from China Census 2010 reflects the total of two components including both the
migrant workers and the increase of the urban-hukou population. The population of
migrant workers is calculated by subtracting the increase of the urban-hukou
population from the total. Socioeconomic data for all prefecture-level cities, including
FDI, GDP, wage, and road area, are from China City Statistical Yearbooks. GIS
shapefiles are from the China Data Center website (http://chinadatacenter.org).
3.2. Spatial Statistics: Moran’s I and Getis-Ord G
This study analyzes spatial patterns and trajectories of migrant workers, applying
global and local spatial statistics. I use global statistics to analyze whether the spatial
pattern of migrant workers is clustered or dispersed, and local statistics to identify
local spatial clusters among cities.
Global Moran’s I is conducted to examine the degree to which the spatial distribution
of migrant workers deviates from the null hypothesis of spatial randomness. It is
calculated in ArcGIS to indicate whether there is a spatial autocorrelation of migrant
workers in China. Local Moran’s I, Local Indicators of Spatial Association (LISA)
statistics are calculated and mapped in ArcGIS to identify local clusters of spatial
autocorrelation of migrant workers. In addition, in order to detect the concentration of
immigrant workers and emigrant workers, local Getis-Ord G statistics are calculated
and mapped to identify hot or cold spots of migrant workers.
3.3. Locational Determinants: Regression Model
The study employs regression models to examine forces influencing FDI location,
especially different urbanization agglomeration economies. In order to test spatial
dependence, I calculated five Lagrange Multiplier test statistics. The t values for the
Lagrange Multiplier Lag statistic and the Lagrange Multiplier Error statistic are 0.59
and 0.34, so they are not significant for spatial dependence. Therefore, I use the
following ordinary least squares (OLS) regression model to investigate the locational
determinants of FDI in 2010:
Yi=β0+β1Xi+Regioni+ εi
As shown in Table 1, I consider FDI inflows as the dependent variable Yi. Region is a
fixed effect term controlling for unmeasured region-scale variables by estimating
region-level effects. These region-level effects are included as fixed instead of
random effects. China can be divided into eastern, central, and western regions in
terms of physical, socioeconomic, and institutional characteristics. So I use two
dummy variables to estimate regional-level effects.
INSERT TABLE ONE
Xi consists of three groups of factors influencing FDI location: comparative
advantage, agglomeration, and institutional policies. Three variables related to
comparative advantages are GDP, Salary, and road area per capita (RoadAreaPC).
GDP is used to measure the market demand of a city. Larger market size can attract
more FDI (Woodward, 1992). The salary is to measure the labor cost. Higher labor
costs deter FDI (Smith and Florida, 1994; He, 2003). In addition, the variable road
area per capita is to measure a city’s transportation capacity. Three variables are
related to agglomeration economies. The variable FDI stock (FDIStock) representing
the total amount of existing FDI measures localization agglomeration economies.
Two variables, the urban-hukou population (CSPop) and the population of migrant
workers (MWPop), measure the urbanization agglomeration economies stemming
from the natural increase of urban population and urban migration, respectively.
I use six dummy variables to measure the impact of institutional factors. The variable
NDZ is to indicate whether a city has a national development zone. The variable
SEZOCC is to indicate whether a city is a SEZ or OCC. The variable CAPITAL is to
indicate whether a city is a national or provincial capital. Another three variables,
PRD, YRD, and BRR, are used to indicate whether a city is located in the PRD, the
YRD, and BRR, respectively.
4. Results
4.1.Most Dynamic and Rapidly Growing Cities
China’s urbanization is spatially uneven: national and provincial capitals and
eastern/coastal cities had more immigrant population while central and western cities
had more emigrant population. In 2010, among 287 prefectural cities, 105 cities had
urban population growth while the rest 182 cities had negative net migration. Figure 1
shows the spatial distribution of net migration of urban population at the prefecturelevel cities in 2010. All national and provincial capitals except Nanning in Guangxi
Province had positive net migration of urban population. In addition to these capitals,
cities with positive net migration are mostly located in the eastern/coastal region.
Cities with negative net migration are mostly located in the central and western
regions. In the Central and Western China, Jiangxi, Hunan, Henan, and Shaanxi only
have provincial capitals that were cities with positive net migration and the rest had
negative net migration. In Anhui, in addition to the provincial capital, Hefei, only
Maanshan adjacent to the eastern/coastal area, had positive net migration. In Sichuan,
in addition to the provincial capital, Chengdu, only Panzhihua had positive net
migration. In Hubei, in addition to the provincial capital, Wuhan, Yichang and Ezhou
had positive net migration.
INSERT FIGURE ONE
The most dynamic and rapidly growing cities are concentrated in the eastern/coastal
region. Most of them are located in the Pearl River Delta (PRD) and the Yangtze
River Delta (YRD). Table 2 shows the top 20 cities with positive net migration. 17
out of 20 cities are located in the eastern/coastal region while 3 cities located in the
central and western regions are Chengdu, Wuhan, and Zhengzhou. Additionally, all
17 cities in the eastern/coastal region are located in four major economic areas in this
region, the Pearl River Delta (PRD), the Min River Economic Corridor, the Yangtze
River Delta (YRD), and the Bohai Rim Region (BRR). Most of them are located in
the PRD and the YRD, Xiamen and Quanzhou are in the Min River Economic
Corridor, and Beijing and Tianjin are in the BRR. These most dynamic and rapidly
growing cities have the highest level of urbanization, which may lead to the
urbanization agglomeration economies influencing FDI location.
INSERT TABLE TWO
4.2.Spatial patterns of migrant workers and FDI
The result of global Moran’s I indicates that migrant workers were clustered in 2010.
The global Moran’s Index is 0.11 and the z score for global Moran’s I is 8.27,
showing a statistically significant clustering pattern of the population of migrant
workers in 2010.
The local clusters of positive spatial autocorrelation with a high number of immigrant
workers were located in four economic zones within the eastern/coastal region. Table
3 and Figure 2 show results of LISA (local Moran’s I). In addition to Tianjin in the
BRR and Xiamen in Minjiang Delta, clusters of immigrant workers were located in
the YRD and the PRD, further confirming that the YRD and the PRD have the most
dynamic and rapidly growing cities in China. In addition, there were three clusters of
negative spatial autocorrelation. They are cities with a high number of immigrant
workers surrounded by cities with a high number of emigrant workers. These three
clusters were Zhengzhou, Wuhan, and Chengdu that are core cities for three economic
zones in the central and western regions, the Central Plains (or called Zhongyuan)
Economic Zone, the Wuhan Economic Zone, and Chengyu Economic Zone,
respectively.
INSERT TABLE THREE AND FIGURE TWO
All clusters of positive spatial autocorrelation with a high number of emigrant
workers were located in four provinces within the central and western regions. In
addition to Zunyi within Guizhou Province, clusters of emigrant workers were in
Central Plains Economic Zone within Provinces of Henan and Anhui and Chengyu
Economic Zone within Sichuan Province. This indicates the migration from the
periphery cities to core cities within the economic zone within the central and western
region.
Nine cities were hotspots of immigrant workers: only Chengdu in Sichuan Province
was located in the western region and others were located in the BRR, the YRD, and
the PRD in the eastern/coastal region. Table 4 and Figure 3 show results of local
Getis-Ord G. Three municipalities, Beijing, Tianjin, and Shanghai, were hotspots of
immigrant workers. Contrastively, there were four hotspots of emigrant workers:
Fuyang, Xinyang, and Zhoukou in the Zhongyuan Economic Zone within the central
region and Chongqing in Chengyu Economic Zone. This further confirms the intraregion migration from periphery cities to core cities. The interesting finding is that
Chongqing, as a municipality, was a hot spot of emigrant workers, which was distinct
from other municipalities that are hot spots of immigrant workers. The relative low
development level compared to other municipalities and a large amount of population
(the largest population among four municipalities) in Chongqing may explain why
Chongqing was the hot spot of emigrant workers.
INSERT TABLE FOUR AND FIGURE THREE
4.3.Locational Determinants: Urbanization Agglomeration Effects
The regression model is significant at the 1 percent level and the adjusted R2 value is
0.873, indicating that 87 percent of the dependent variable can be explained by the
independent variables (Table 5). The multicollineairty condition number is 27.9 that is
greater than 15, indicating potential multicollinearity. Collinearity diagnostics were
performed to examine multicollinearity. Table 6 shows the result of collinearity
diagnostics. The condition indices are less than 30, indicating no collinearity
problems.
INSERT TABLES FIVE AND SIX
First, the localization agglomeration had significant impacts on FDI location. The
variable representing localization agglomeration effects, FDI stock was statistically
significant positive at 1 percent level, showing the positive effects of localization
agglomeration in FDI location. This indicates the attraction ability of existing FDIs to
new ones in the same city. Also, according to the standardized coefficients, the
localization agglomeration effect is the most significant factor among all forces and
thus had the greatest effect on FDI location.
Second, the urbanization agglomeration stemming from different urbanization
processes had opposite effects on FDI location. The urbanization agglomeration
resulting from the increase of the urban-hukou population (i.e. natural increase and
residential reclassification) had statistically significant positive effects on FDI
location, according to the coefficient of the variable population whose data is derived
from China City Statistics Yearbooks and is based on the number of urban hukou
residents. However, the urbanization agglomeration resulting from the population of
migrant workers had statistically significant negative effects on FDI location,
according to the coefficient of the variable population of migrant workers. The
opposite impacts of different urbanization agglomerations shed light on the questions
of city size and migrant workers. On one hand, cities are still under-agglomerated and
have capabilities of providing agglomeration economies by continuous natural
increase of urban population. On the other hand, migrant workers population deters
FDI. The mismatch between skills of migrant workers and the labor skills that foreign
firms require may explain the deterrence: migrant workers usually receive limited
education and skills, and thus they are limited to low-paid factory and service jobs.
Third, the wage cost had a positive relationship with FDI. This result is opposite to
the expectation. The measurement of labor cost does not identify labor market
segments and distinguish between different levels of skilled labor, which might
explain the positive relationship between the wage cost and FDI. In addition, this
positive relationship implies the domination of the type of FDI demanding more
skilled labor than less skilled labor, suggesting that the cheap labor resource seeking
is not the main motivation of FDI in China anymore. Also, the positive relationship
between wage cost and FDI further shed light on the deterrence of migrant workers to
FDI, considering that migrant workers are low-skilled.
Last, national and provincial capitals are significant to FDI location. The variable
Capitals is positively, statistically significant, showing that foreign firms prefer to
invest in capitals than other cities. Contrastively, the special economic zones and open
coastal cities were not significant to FDI as they were before (Gong, 1995; Head and
Ries, 1996; He, 2002). In addition, the dummy variable the BRR is significant,
suggesting that the Bohai Rim Region is attractive to FDI. The significance of the
BRR implies that the Binhai New Area in Tianjin as a new national growth area is
playing an important role in FDI when foreign capital policy focus spread from the
PRD to the YRD, then to the BRR.
5. Conclusion
China’s urbanization agglomeration has been a subject of investigation in FDI
location because of its implications for urbanization development and globalization
integration. This paper differs from many of these studies by distinguishing
urbanization agglomeration effects stemming from different urbanization processes:
the increase of urban-hukou population and the migration. This study offers a more
nuanced picture of urbanization agglomeration effects, especially those related to
migration. China’s urbanization is accompanied by the population migration from the
central and western regions to eastern/coastal region, especially from the central
region to the eastern region. Consequently, the most dynamic and rapidly growing
cities are concentrated in the eastern/coastal region, especially the YRD and the PRD.
The analysis reveals that all clusters of positive spatial autocorrelation with a high
number of immigrant workers were in the PRD, the YRD, the BRR, and the Minjiang
Delta within the eastern/coastal region. Three clusters of negative spatial
autocorrelation and clusters of positive autocorrelation with a high number of
emigrant workers in central and western regions may be explained by the intra-region
migration from the periphery cities to core cities within the economic zones. In
addition, there is a striking contrast between Chongqing as a hot spot of emigrant
workers and other municipalities as hot spots of immigrant workers. Besides
development and population levels, further research is needed to explain this contrast
in order to help understand the regional difference in urbanization development.
The urbanization agglomerations stemming from the increase of urban-hukou
population and the migration had opposite effects on FDI location: the urban-hukou
population generated urbanization agglomeration economies while the population of
migrant workers generated urbanization agglomeration diseconomies. This finding
provides policy implications on developing periphery cities to motivate and encourage
migrant workers to move back from core cities within the economic zones and
provincial capitals to the periphery cities. The analysis in this paper has moved away
from more traditional notions of urbanization agglomeration effects on FDI. It
establishes the linkages between migration, urbanization agglomeration, and FDI.
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Table 1. Dependent and Independent Variables of Regression Models
Dependent Variables: FDI inflows
Types
Independent Definition
Variable
GDP
Gross domestic product
Comparative
Advantages
Institutional
Policies
Salary
ROADAPC
NDZ
PRD
YRD
BRR
SEZOCC
Capitals
Agglomeration FDIStock
CSPop
MWPop
Average salary per year
Road area per capita
Whether a city has national economic and technological
development zones or high-tech development zones
Whether a city is located in the Pearl River Delta
Whether a city is located in the Yangtze River Delta
Whether a city is located in the Bohai Rim Region
Whether a city is designated as a Special Economic Zone
or Open Coastal City
Whether a city is a provincial capital
The amount of existing FDI
The total urban-hukou population
The total population of migrant workers
Table 2. The Most Dynamic and Rapidly Growing Cities in 2010
Rank Region
Province
City
Number of Migrant Workers
1
Eastern
Shanghai
Shanghai
8954048
2
Eastern
Guangdong
Shenzhen
7828738
3
Eastern
Beijing
Beijing
7094268
4
Eastern
Guangdong
Dongguan
6417737
5
Eastern
Guangdong Guangzhou
4697000
6
Eastern
Jiangsu
Suzhou
4111294
7
Eastern
Guangdong
Foshan
3501711
8
Eastern
Tianjin
Tianjin
3114724
9
Western
Sichuan
Chengdu
2604125
10
Eastern
Zhejiang
Ningbo
1880200
11
Eastern
Zhejiang
Hangzhou
1837900
12
Eastern
Fujian
Xiamen
1745347
13
Eastern
Jiangsu
Wuxi
1711624
14
Eastern
Jiangsu
Nanjing
1693680
15
Eastern
Guangdong Zhongshan
1635684
16
Central
Hubei
Wuhan
1423992
17
Eastern
Fujian
Quanzhou
1297930
18
Eastern
Zhejiang
Wenzhou
1292500
19
Eastern
Guangdong
Huizhou
1288802
20
Central
Henan
Zhengzhou
1246505
Table 3. LISA results of the population of migrant workers in 2010
LISA Type
High-High
Region
Eastern
Economic Zone
Bohai Rim Region
Province
Tianjin
Shanghai
City
Tianjin
Shanghai
Yangtze River
Delta
Minjiang Delta
Pearl River Delta
High-Low
Low-Low
Central
Central Plains (or
Zhongyuan)
Greater Wuhan
Western Chengyu
Central Central Plains (or
Zhongyuan)
Economic Zone
Western Chengyu
Jiangsu
Jiangsu
Zhejiang
Zhejiang
Zhejiang
Fujian
Guangdong
Guangdong
Guangdong
Guangdong
Guangdong
Guangdong
Guangdong
Guangdong
Henan
Wuxi
Suzhou
Hangzhou
Ningbo
Jiaxing
Xiamen
Guangzhou
Shenzhen
Zhuhai
Foshan
Jiangmen
Huizhou
Dongguan
Zhongshan
Zhengzhou
Hubei
Sichuan
Anhui
Wuhan
Chengdu
Fuyang
Bozhou
Shangqiu
Zhoukou
Zhumadian
Nanyang
Xinyang
Zunyi
Chongqing
Luzhou
Yibin
Nanchong
Ziyang
Dazhou
Guangan
Bazhong
Henan
Guizhou
Sichuan
-
Table 4: Hotspots and coldspots of net migration of urban population in 2010
Spot Type
Region
Economic Zone
Province
City
Hotspots
Eastern
Bohai Rim Region
Beijing
Beijing
Tianjin
Tianjin
Yangtze River
Shanghai
Shanghai
Delta
Jiangsu
Suzhou
Pearl River Delta
Guangdong
Shenzhen
Guangdong
Dongguan
Guangdong
Guangzhou
Guangdong
Foshan
Sichuan
Chengdu
Central Plains (or
Anhui
Fuyang
Zhongyuan)
Henan
Xinyang
Economic Zone
Henan
Zhoukou
Chengyu
Sichuan
Chongqing
Western
Coldspots
Central
Western
Table 5. Results of regression models for FDI in 2010
Variable
Coefficient
CONSTANT -95467.56
GDP
-9.23E-4
Salary
1.85
RoadAreaPC -236.63
FDIStock
0.16
CSPop
72.28
MWPop
-0.03
NDZ
2993.05
SEZOCC
-27292.86
Capitals
46994.91
PRD
-35565.22
YRD
10594.64
BRR
28647.53
Adjusted R Square
Std.Error
27895.85
9.05E-4
0.88
622.50
9.30E-3
28.77
8.41E-3
9312.20
18187.04
15420.99
25973.60
17522.63
12709.11
0.873
Table 6. Collinearity diagnostics of regression model
Dimension Eigenvalue Condition
Index
t-Statistic
-3.42
-1.02
2.10
-0.38
16.81
2.51
-3.21
0.32
-1.50
3.05
-1.37
0.60
2.25
F-statistic
Probability
7.22E-4
0.31
0.04
0.70
0.00
0.01
1.48E-3
0.75
0.13
2.55E-3
0.17
0.55
0.03
133.922
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
6.598
2.287
1.086
1.029
0.970
0.729
0.665
0.484
0.402
0.298
0.211
0.124
0.086
0.023
0.008
1.000
1.699
2.465
2.533
2.608
3.009
3.149
3.693
4.053
4.703
5.599
7.298
8.761
17.040
28.756
Figure 1: Spatial distribution of population of migrant workers in 2010
Figure 2: LISA map of population of migrant workers in 2010
Figure 3: Getis-Ord G map of population of migrant workers in 2010