3 lu ming core-periphery model of urban economic growth-final
TRANSCRIPT
Core-Periphery Model of Urban Economic Growth:
Empirical Evidence from Chinese City-Level Data (1990-2006)
Zhao Chen, Ming Lu and Zheng Xu∗
Abstract: The geographic nature and openness of China since 1990s make it a feasible
application of the Core-Periphery (CP) model, which has hardly explained urban
systems in existing studies. Using Chinese city-level data from 1990 to 2006, this
paper estimates the impact of spatial interactions in China’s urban system on urban
economic growth. Our results verify the non-linearity of the CP Model of urban
system, present agglomeration shadow in Chinese urban economies. We also find
“border effect” of administrative boundaries among Chinese provinces, which
prevents urban economic activities from being absorbed by big cities in other provinces,
while limiting inter-city agglomeration.
Key Words: Core-Periphery (CP) Model, border effect, urban system, agglomeration
shadow, China
JEL Classification: O18, R11, R12
∗ Zhao Chen: China Center for Economic Studies, and Center for Industry Development Studies, Fudan University, Shanghai, 200433, Email: [email protected]; Ming Lu: School of Economics, Fudan University and Zhejiang University, Email: [email protected]. Zheng Xu: Corresponding author, China Center for Economic Studies, Fudan University, Shanghai, 200433, E-mail address: [email protected]. Financial support from Peking University-Lincoln Institute Center, the National Social Science Foundation (08BJL008), the Shanghai Leading Academic Discipline Project (B101) and “985” project of Fudan University are greatly appreciated. We thank Jacques-François Thisse, Thierry Mayer, Dao-Zhi Zeng, Chun Chung Au and seminar/conference participants at Bank of Finland, Fudan University and Peking University for their useful comments. The content of this article is the sole responsibility of the authors.
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I. Introduction
Krugman (1993) suggests that the presence of a particular site settled may skew
further development in vicinity in its favor, via agglomeration, which has been proved
by Core-Periphery (CP) Model of the New Economic Geography (NEG) theory in
many theoretical literatures (e.g. Fujita, Krugman and Venables, 1999). Although CP
model has been widely used in explaining urban, regional and international
development, and even as the backbone of the NEG literature (Krugman, 1991), few
empirical studies investigate its success in explaining current urban systems (Partridge
et al., forthcoming).
Our paper will show how spatial interactions among cities affect China’s urban
economic growth, within the CP structure. As China has been rapidly opened to the
world after 1994, we expect the geographic nature and openness make China a perfect
application of the CP model. The fast opening process is a natural experiment to see
whether the greater access to the global market will make the distance to major ports
matter in city growth and shift the system of cities. Thus, we contribute new evidence
to the theory of Core-Periphery (CP) Model of urban system.
Our empirical study assumes: there are two hierarchical urban systems in China--
one is the national urban system, cores of which are major ports, like Shanghai and
Hong Kong; the other is the regional urban system, cores of which are big cities in
China, like Guangzhou, Chongqing, etc. Furthermore, the spatial interactions within
each urban system show the Core-Periphery Structure, which will be tested by our
empirical results.
In the next section, we briefly review the theoretical background and empirical
studies on spatial interactions among cities; Section III introduces China’s urban
system and explains why China is a feasible application of the theory. Section IV
discusses the data and our econometric approach. Section V and VI present the results
and robustness checks. Concluding remarks are provided in the last section.
II. Literature Review
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The NEG theory emphasizes the interplay of agglomeration and dispersion
forces in determining urban system (Overman and Ioannides, 2001). Krugman and
Elizondo (1996) even said that any interesting model of economic geography involves
a tension between the "centripetal" forces that tend to pull population and production
into agglomerations and the "centrifugal" forces that tend to break such agglomerations
up. Here, centripetal forces can include both pure external economies and a variety of
market scale effects, such as the forward and backward linkages, knowledge spillovers
(Krugman, 1991), while centrifugal forces include pure external diseconomies such as
congestion and pollution, urban land rents, transportation costs, and the interests of
moving away from highly competitive urban locations to less competitive rural ones
(Tabuchi, 1998).
The CP model formalizes the role of agglomeration and dispersion in the
dynamic formation of an urban system, of which a prominent feature is the emergence
of a hierarchy of cities based on regional market potential, featuring a symbiotic
relationship among cities (Partridge et al., forthcoming). But CP model is
astoundingly difficult to manipulate analytically and indeed most results in the
literature are derived via numerical simulation, which as simulated by Fujita and Mori
(1997), Fujita, Krugman and Mori (1999), is a ∽-shaped curve between distance to
regional center and local market potential in a single-core urban system. This curve
shows that with the distance to a central city increasing, the market potential declines
first, and later rises, then declines again.
In the model of Fujita and Mori (1997), close proximity to suppliers of
intermediate inputs and customers lowers firm’s transportation costs (Venables, 1996),
in which scale economies may exist in the production of non-traded intermediate
inputs (Fujita, 1988). However, increased competition associated with close proximity
of economic activity acts as a dispersal force (Krugman, 1991; Combes, 2000),
limiting agglomeration. Firms in the areas closest to agglomeration centers find
themselves in what is referred to as “Krugman’s agglomeration shadow” (Dobkins
and Ioannides, 2001, Ioannides and Overman, 2004), in which they are able to
produce only the most basic goods and services for which there is less competition. 3 / 36
Small cities may thus serve only a local population, while larger cities serve wider
geographic markets that include small cities (Krugman, 1996), leading to an urban
hierarchy consistent with CP structure (Fujita and Thisse, 1996).
Due to the specific implications of the CP theory such as agglomeration shadow,
Dobkins & Ioannides (2001) noted that non-linearity (such as the “∽” shape) might
show up in the spatial interactions in urban system. So to directly examine the spatial
interactions among cities relating to their geographic distance, urban economies, or
their place in the urban hierarchy has generally become one of the streams of
empirical studies not only on agglomeration (Hanson, 2001), but also on spatial
distribution of cities (Partridge et al., forthcoming).
“Buttressing the approach with empirical work” is one of the most important
directions for the new economic geography future researches as Fujita and Krugman
(2004) suggests. However, “due to the highly nonlinear nature of geographical
phenomena”, it’s not easy “to make the models consistent with the data” (Fujita and
Krugman, 2004). Empirical studies on spatial interactions have partial testified the CP
model and agglomeration force in urban systems, and find that, more or less, closer is
better, but the “non-linearity” of CP model and “agglomeration shadow” remains to be
unverified.
Brülhart and Koenig (2006) analyze the internal spatial wage structures of the
Czech Republic, Hungary, Poland, Slovakia and Slovenia, using regional data for
1996–2000. They find that real wage falls with distance from national capitals and
from Brussels, but none of these estimates are statistically significant.
Related empirical evidences based on U.S. urban data are more complex and
puzzling. Black and Henderson (1999) examine the correlation between population
growth in U.S. metropolitan areas and initial conditions over the period 1950-1990.
Population growth is faster in cities with closer proximity to a coast and to cities with
larger initial populations, with this effect weakening as neighboring population
masses become larger. Dobkins and Ioannides (2000, 2001), Ioannides and Overman
(2004),using the U.S. metropolitan data 1900-1990, find that distance from the
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nearest higher-tier city is not always a significant determinant of size and growth and
no evidence of persistent non-linear effects of either size or distance on urban growth.
Partridge et al. (forthcoming) explore whether proximity to higher-tiered urban
centers affected 1990-2000 U.S. county population growth. Rather than
agglomeration shadow, their results suggest that larger urban centers promote growth
for more proximate places of less than 250,000 people. They think NEG theory (CP
model) only partially explains current U.S. urban growth, suggesting a need for a
broader framework.
Induced by the empirical studies above, we think it’s the critical time to clarify
whether the CP model is suitable for explaining the current urban system changes. We
believe our empirical study might give strong evidence to the CP model of the NEG
theory, because compared with the U.S. and other countries, China has several unique
advantages in its city-level data, which will be explained in the next session. And
instead of only being derived via numerical simulation (Baldwin et al., 2003), the
non-linearity of the Core-Periphery model captured by China’s data would partially
explain some of the puzzles in the studies using U.S. data, such as agglomeration
shadow.
III. China’s Urban System Evolution during Economic Opening
China may be an interesting application of the CP model, because, with the force
of spatial agglomeration, the quick development of both international and domestic
trade has reconstructed China’s regional and urban economies, especially since
China’s rapid openness to the world in 1994. The nature of Chinese urban system and
its evolution makes it an interesting case to testify the CP model of urban system. The
opening process is a natural experiment to see the role of the distance to main ports
and international markets in shifting the urban system.
3.1 China’s Urban System
In China, “city” is defined as a local administrative and jurisdictional entity.
There are three different administrative levels of cities in China’s urban system: 5 / 36
municipalities, prefecture-level cities and county-level cities. Small settlements with
townships or lower administrative levels are not treated as “cities”. The major
administrative criteria distinguishing cities, towns and rural places is the scale of
urban population, in particular there is a lower population boundary for cities. The
economic and political importance of an urban agglomeration is also one of the
government’s considerations in defining cities. However, the definition of cities has
generally been consistent since 1949, when the People’s Republic of China was
established (Anderson and Ge, 2005).
Information of both prefecture-level and county-level cities is reported by
China’s National Bureau of Statistics (NBS), but only prefecture-level and above
cities data are used in our research, for the county-level city boundaries and numbers
have fluctuated greatly. (See Table 1) The number of prefecture-level cites has also
increased a lot since 1990, so the prefecture-level cites boundaries might also have
changed. However, for cities of the prefecture level and above, NBS reports
information of both “Diqu” (urban area plus rural counties within the same
administration) and “Shiqu” (only urban area). “Shiqu” is a good definition of the
metropolitan area by international standards (Fujita et al., 2004), and the entities of
new cities usually happen outside “Shiqu” (urban area), so “Shiqu” information is
used in our study.
Table 1: Number of China's Cities
year total municipalities prefecture-level cities
county-level cities
1990 467 3 182 279 1995 640 3 207 427 2000 663 4 255 400 2005 657 4 282 370
Source: NBS, Chinese Urban Statistical Yearbooks (1991–2006).
Note:Our database does not include information of Hong Kong,Macao, Taiwan and Tibet.
The definition of China’s urban hierarchy is another problem. As we mentioned
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above, three administrative levels of cities are included in China’s urban system:
municipalities (or province-level cities), prefecture- and county-level cities, and
provincial capitals also conduct a special administrative level in prefecture-level cities.
But according to the CP model, our study focuses on the spatial agglomeration in
urban system, for which urban size (e.g. population) and market potential are much
more important than policy. Though the effects of policy and agglomeration may
correlate with each other, we try to distinguish them by different definitions as shown
by Table 2.
Table 2: China’s Urban Hierarchy
Year Prefecture- level and
above cities
Provincial
capitals
Non- agricultural
population>2 million
Non- agricultural
population>1.5 million
Non- agricultural population>
1 million 1990 188 29 9 14 31 1995 213 29 10 16 32 2000 263 30 13 18 38 2005 290 30 22 31 54
Source: NBS, Chinese Urban Statistical Yearbooks (1991–2006).
Note:Provincial capitals include both municipalities and other provincial capitals.
3.2 China’s Reform and Openness to the World
Hanson (1998) finds in Mexico, after trade reform and openness to the world,
especially the U. S., firms locate in regions with good access to foreign markets.
International trade changes the reference market for firms in a country, shifts
resources to locations with low-cost access to foreign markets such as border regions
and port cities, during which agglomeration matters. Fujita and Mori (1996) propose
an evolutionary model of spatial economic development in which agglomeration
economies and the hub-effect of transport nodes interplay. Their simulation of market
potential in a spatial system also shows somehow the non-linearity of core-periphery
pattern.
In fact, as for China, the economic reform has gone through three stages (Ho and 7 / 36
Li, 2008): 1978-84, reform of “household responsibility system” that linked
remuneration to output for agriculture; 1985-91, reform of state-owned enterprises
(SOEs); and 1992-present, a widespread opening up of markets and a commitment to
a market economy. The increasing importance of international trade in China is shown
in Figure 2, especially after 1992. 1994 was the year of unifying the official exchange
rate and black market exchange rate of RMB. Since then China has formed an
export-led growth pattern and has gained trade surplus up to now.
Figure 1: International Trade in China during Post–reform Era (1978-2008)
Source: NBS, China Statistical Abstract 2009.
Ever since the mid-1990s, China has become increasingly open to the
international market. International trade is also more and more important for China’s
national and urban economic growth. Wei (1995) finds that in China cities with large
export sectors grow fast. Furthermore, international trade strengthens the
agglomeration force of major ports in China and dynamically rebuilds China’s
national spatial system as well as the urban economic activities. Wei (1995) provides
some evidence that for obvious reasons, many emergence cities locate in coastal areas
near Hong Kong, the Pearl River Delta. More and more empirical studies find that
agglomeration changes the distribution of China’s regional and urban economies, so
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coastal area and big cities have higher economic growth rates (Chen et al., 2008; Bao
et al., 2002; Ho and Li, 2008).
China’s open-door policy is an exogenous shock to city growth and the urban
system. Different from the 1980s, when China selected several coastal cities to open,
in the 1990s, open-door was a national policy. Especially after China depreciated the
exchange rate of RMB in 1994, and the Chinese economy followed an export-led
growth pattern, preferential policies have not been the major force for cities to enjoy
the benefits of international trade and FDI inflow. The geographic difference between
cities, mainly their different distance to the ports that determines international trade
costs plays an essential role in city growth. Therefore, the opening process in the
1990s is a natural experiment for economists to see whether and how the urban
system evolves following a core-periphery model.
3.3 China’s Geography and Growth
Partridge et al. (2007) believe agglomeration economies may have a greater
geographic scope than usually assumed by economists. From this view, economic
geography, in particular the role of nonlinear spatial agglomeration may require a wide
range of space, long time of accumulation, while national boundaries, geographical
boundaries, wars and other factors often limit the free flow of resources, so it is difficult
to use the empirical econometric model and real-world data to portray how
agglomeration works. Therefore, it is studies on stably-developing large countries,
such as U.S. and China after 1978, which seems particularly important for the
empirical researches of the NEG theory.
Researches using China data may contribute to the empirical studies of the NEG,
mainly based on the following reasons: 1) China has a vast territory, as well as a large
population. The vast territory provides not only space required by agglomeration, but
also plenty of samples for empirical studies. Meanwhile, a large population provides
sufficient market potentials. 2) Compared with the United States, China has a larger
interregional geographical diversity and more obvious geographical heterogeneity,
because of the concentration of the ports distribution along the eastern coast, which 9 / 36
leads to a bigger variance within cross-section samples. In the United States, which has
big ports on both the western and eastern sides, interregional geographic differences
are reduced. 3) China has a rapid economic development in the last thirty years,
especially after 1992, with observably temporal changes of the spatial distribution of
economic activities, which allow us to observe the impact of spatial agglomeration on
urban economy in a longer term with data of recent years.
Besides, local statistical bureaus in China have for years collected data on all
enterprises in their local area; and report GDP figures at the level of the appropriately
defined metropolitan area (Fujita et al., 2004; Au and Henderson, 2006a). Though
doubts may be expressed to the quality of statistical data in China, these GDP figures
are of high quality as discussed by Au and Henderson (2006a), especially when
econometric models find highly significant results consistent with economic theories.
IV. Model Specification and Data
4.1 Model Specification
The reduced-form estimation is widely used in the former empirical studies on
spatial interactions among cities (Brülhart and Koenig, 2006; Dobkins and Ioannides,
2000). We use cross-section OLS regression as our basic reduced-form CP model of
urban system, based on the economic growth model of Barro (2000). Since the urban
system is evolving during the opening process, the shift of urban system will be
reflected in different growth rates of cities in different locations. Since all the
explanatory variables are either exogenous geographic factors or initial values of
those control variables, the potential endogeneity problem of OLS estimation is not a
major concern. The model specification is as follows:
0 0 0 0 0 0 0 0(ln , , , , , ; ; ...)it i i i i i i i iDgdp f gdp inve lab edu gov fdi con geo=
Here, per capita GDP growth is the dependent variable. Urban per capita GDP
growth, a measure of the urban economic activities is also used in existing literatures
(Glaeser et al., 1995; Dobkins and Ioannides, 2000; etc.). The reasons why we do not
use real wage, income, population or per capita GDP level are: First, wage and
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income data are not reported at the China’s city level. Second, in early years, urban
population in China did not include migrants without local household registration
identity (hukou) (Au and Henderson, 2006b), so it does not well represent urban
economy or size. Third, as we want to show the dynamics of urban economies after
China’s openness to the world, per capita GDP growth, instead of its level, would be a
better measurement.
In our studies, we highlight the roles of openness and international trade in
determining the distribution of China’s urban economic activities, so we conduct the
dataset for years 1990-2006. As discussed above, the third period of reform started in
1992, but most information about China’s cities in 1992 and 1993 are missing in
Chinese Cities Statistical Yearbook (National Bureau of Statistics, 1991-2007). We
will also use the data of 1994-2004 to check the robustness of our results after 1994,
the year of drastic openness.
Hanson (2001) pointed out three issues may trouble for identifying
agglomeration effects in empirical studies: unobserved regional characteristics,
simultaneity in regional data, and multiple sources of externalities, of which the
former two are particularly crucial when it comes to the spatial interaction of cities.
As to “unobserved regional characteristics”, we will try to control as many
variables as the data allow according to the literatures of economic growth and
empirical studies of China. “Simultaneity in regional data” may be a minor issue in
our study, since the essential explanatory variables in our model are geography and
will not change in our observations. However, we will use the initial state of other
explanatory variable in 1990 to alleviate simultaneity bias of the model, so the
estimated results would represent the long-term impact of the explanatory variables
on urban economic growth.
Initial advantage is a challenging concept for modelling. Where did initial
advantage come from in our story? History (Krugman, 1991) or geography (Fujita and
Mori, 1996)? This question is out of the scope of this paper and won’t be answered
directly by our empirical results, though some related evidences would be presented.
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4.2 Data
In this paper, we used China’s urban area (Shiqu) data (1990-2006) complied
from Chinese Cities Statistical Yearbook (National Bureau of Statistics, 1991-2007),
including 286 prefecture-level and above cities① from 30 provinces of mainland
China. The information of Hong Kong, Macao, Taiwan and Tibet is not included.
Other data sources and constructions of variables are as follows.
Urban Economic Growth
The dependent variable is the the average annual growth rate of real per
capita GDP from 1990-2006 deflated by provincial urban CPIs, respectively, for city i
year t. The theoretical assumptions of the NEG theory emphasize the agglomeration
of manufacture and service (Krugman 1991), so we removed the agricultural output
out of the GDP indicator and the agricultural population out of the population
indicator.
itDgdp
Spatial Interactions among Cities
Hanson (1998, 2005) approximates the access of each considered region to its
principal markets by geographic distance. Therefore, geographic distance as well as
driving distance or road/railway distance is used to approximate spatial interaction
among cities (Dobkins and Ioannides, 2000; Brülhart and Koenig, 2006); Partridge et
al., forthcoming). In particular, we use straight geographic distance as our
measurement of spatial interaction, because it’s exogenous as second nature, which
avoids the potential endogenous bias brought by traffic distance.
Our hypothesis is that there are two hierarchical urban systems in China. One is
the national urban system, cores of which are major ports, like Shanghai and Hong
Kong. The distance to these major ports measures a city’s remoteness to the global
market. The other is the regional urban systems, cores of which are big cities in China,
like Guangzhou, Chongqing, etc. Furthermore, the spatial interaction within each
① We have information of 286 cities in 2006, while 211 cities in 1990.
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urban system shows the Core-Periphery Structure, which will be testified by our
empirical results. So we use distances to the nearest big city (distbig) to measure the
interaction in regional urban systems, and distance to the nearer major port (disport)
to measure the interaction among national urban systems.
As to exploit how agglomeration of major ports affects urban economic activities
in China’s spatial system, we need to define “major ports” in China.
Our information about ports cargo throughput is complied from China Statistical
Abstract 2009 (National Bureau of Statistics, 2009). Though it contains information of
ports of mainland China and Hong Kong in 1990, they are measured in different ways
and hard to compare. So we have to use the recent year (e.g. 2008) cargo throughput
of ports which could be transferred in the same measurement as “ton”. According to
China Statistical Abstract 2009, in 2008, as to major ports in China: Hong Kong
accounted for 10.8% of China’s overall cargo throughput, Shanghai 10.6%, Tianjin
7.4%②; as to ports of different regions, the Bohai Sea ports (Tianjin, Qingdao,
Qinhuangdao, Dalian) totally accounted for 24%; the Pearl River Delta ports (Hong
Kong, Guangzhou, Shenzhen) 22.4%, the Yangtze River Delta ports (Shanghai,
Ningbo-Zhoushan) 21.4%. The data above clearly show that cargo throughput in
China mainly concentrates in those three areas, but the distribution of the Bohai Sea
ports is comparatively disperse, therefore, the agglomeration of Tianjin might be not
as strong as Shanghai and Hong Kong. So we define Shanghai and Tianjin as “major
ports” of China in our study, and also take Tianjin as major ports when testing the
robustness.
In Table 2, we try to find a proper definition of high-level cities by their
non-agricultural population. To construct time-consistent data, we will only use the
information of 1990 to conduct such definition for “big cities”, and information after
1990 is just to show how quickly China’s urban sizes evolve. Finally, we choose the
1.5 million-non-agricultural-population as the lower boundary of high-level cities in
China’s urban hierarchy. Reasons for such criteria is that: first, in 1990, there were ② Some may argue that the rank of Shanghai port is higher than Hong Kong in 2008 according to some international index, which is due to the different measurements of cargo throughput. It’s not a big issue since both are major ports in our study.
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nine cities with a non-agricultural population greater than 2 million, eight of which
located in coastal area, so this lower boundary (if so), would not well represent the
urban hierarchy in inland area; second, in 1990, there were thirty-one cities with a
non-agricultural population larger than 1.5 million, most of which are municipalities
and provincial capitals, therefore high-level cities according to this definition may
highly correlate with municipalities and provincial capitals in China. If we use 1
million non-agricultural population as the lower boundary, there will be too many
“big cities”.
Taking above reasons into consideration, we define “big cities” in China’s urban
hierarchy as those with a non-agricultural population more than 1.5 million in 1990.
Our assumption is that: via agglomeration, those big cities had or would generally
become the regional centers of domestic and regional market and thus, play a central
role in China’s urban system of each region within the CP structure. We will testify
such assumption, and also use “capitals” which as our definition of both
municipalities and provincial capitals to check the robustness of our empirical results.
The distribution of big cities in China is in Figure 2③ below.
As the CP model shows the non-linear nature as discussed before, we also add
the square and cube of distance in some of our regressions to capture such
non-linearity. The geographic distance is measured in China Map 2008, developed by
Beijing Turing Software Technology Co., Ltd, China Transport Electronic &
Audio-Video Publishing House.
For the defined fourteen big cities, their distances to the nearest big city are
defined as zero. Their economic growth may benefit from their city size, so we use a
dummy bigcity to control it. This would not be a big problem to major ports, for Hong
Kong is not in our sample and Shanghai would have already been controlled by
bigcity. Besides, GDP level of the nearest big city in the initial year will also be
controlled by gdpofbig, which is ln(GDP) in 1990, exclusive of agricultural output.
Border effect
③ They are Beijing, Tianjin, Shenyang, Wuhan, Guangzhou, Ha’erbin, Chongqing, Xi’an, Nanjing, Dalian, Chengdu, Changchun and Taiyuan.
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Policy is an important factor impacting spatial agglomeration in the NEG theory.
When it comes to studies on Chinese urban system, an important policy variable
which affects regional economic development cannot be overlooked, that is provincial
boundary. Studies have suggested that among Chinese provinces there is serious
market segmentation (Young, 2000; Ponect, 2005; Chen, et al., 2007). This division is
likely to add the actual distance between cities in different provinces, and limit the
spatial interaction among cities. Our studies will use a dummy variable to find out
whether the administrative boundary would limit spatial interaction within the urban
system. Denoted as samepro, the dummy variable represents whether a city is in the
same province where its nearest big city locates. If the “border effects” of China’s
province does not affect the spatial interaction among cities, and thus urban economic
growth, this variable would be insignificant; otherwise, significantly positive or
negative.
Other Geography Variables
Actually, spatial interaction is only the second nature of cities. For studies on
China’s urban economy, there are some other geographic factors of first nature should
be controlled for potential initial advantages (Krugman, 1993).
Mainland China usually is divided into three regions: East, Center and West.
Eastern region has eight provinces and three municipalities, Central China has seven
provinces while West has eleven provinces and one municipality. Due to the
concentration of the ports distribution, there are significant inter-regional differences
in climate, access to water, topography, and other features of the environment (Ho and
Li, 2008), as well as economic development. So we use a dummy “west” or “center”
to capture inter-regional differences of geography④. Actually, the regional division of
mainland China does not strictly follow the geographic location. For example,
Guangxi Province locates in the southeast coastal area but was divided into the West ④ West includes cities of 12 provinces and municipality: Chongqing, Sichuan, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Inner Mongolia, Guangxi, Guizhou; Center includes cities of 7 provinces: Hebei, Anhui, Jiangxi, Henan, Hubei, Hunan and Shanxi. Cities of other 12 provinces and municipalities belong in the East. They are Heilongjiang, Jilin, Liaoning, Tianjin, Beijing, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong and Hainan.
15 / 36
by the Strategy of Western Region Development launched by China government in
1999, for its economic performance.
The distribution of China’s provinces is presented in the Figure 2.
Figure 2: Distribution of Big Cities, Major Ports and Provinces in Mainland China
Note: (1) Deltas and squares, respectively, are big cities and major ports in our definition.
(2) Provinces in square brackets locate in the west, while those in parentheses in the center,
and others in the east.
Besides, cities with ports in coastal area or with river ports along big rivers might
benefit from access to international or domestic markets, so we also use dummy
seaport or riverport to control such initial geographic advantage. The list of cities
with sea ports or river ports is from “The First China Port City Mayors (International)
Summit Forum 2006” held by the Development & Research Center, the Ministry of
Communications, the Municipal Government of Tianjin, and the China
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Communications & Transportation Association.⑤
Initial State of Economic Growth Factors
Following the traditional economic growth literatures (Barro, 2000) and studies
on China’s economic growth, we will also measure some other factors that may
contribute to China’s urban economic growth, such as initial economic performance,
investment, labor, education, etc.
0ln igdp is the logarism of per capita output of manufacturing and service in
1990, which is controlled to observe whether Chinese economy experiences
conditional convergence at city level. is the ratio of investment to GDP, where
GDP is the total output of manufacturing and service in 1990.
0iinve
We control the ratio of employee to total population ( ) in 1990 to proxy
labor. China’s urban labor market reform was drastically pushed ahead in the
mid-1990s, so in 1990 urban employees contain lots of hidden unemployment in
state-own enterprise. Therefore, higher labor ratio may not be significantly positive
for urban economic growth. We use the ratio of teachers to students in primary and
junior schools ( ) in 1990 to control the effect of education, which may be not a
good measurement but the best we could find.
0ilab
0iedu
And the government expenditure-to-GDP ratio and FDI-to-GDP ratio in 1990, as
usually controlled by economic growth literatures, are denoted as and0igov 0ifdi in
this paper, where GDP is the total output of three sectors for we cannot distinguish the
ultimate flows of government expenditure or foreign direct investment among sectors.
The effects of government expenditure and foreign direct investment on economic
growth are difficult to predict (Barro,2000; Clarke, 1995; Partridge, 1997).
⑤There are thirty-two cities with sea ports: Qingdao, Yantai, Weihai, Rizhao, Haikou, Sanya, Tianjin, Tangshan, Qinhuangdao, Cangzhou, Dalian, Jinzhou, Yinkou, Lianyungang, Fuzhou, Xiamen, Quanzhou, Zhangzhou, Guangzhou, Shenzhen, Zhuhai, Shantou, Zhanjiang, Zhongshan, Shanghai, Ningbo, Wenzhou, Zhoushan, Taizhou, Beihai, Fangchenggang, Qinzhou; while twenty-two with river ports: Ha’erbin, Jiamusi, Wuhu, Ma’anshan, Tonglin, Anqinq, Yueyang, Nanjing, Wuxi, Suzhou, Nantong, Yangzhou, Zhenjiang, Foshan, Dongguan, Luzhou, Wuhan, Yichang, Nanchang, Jiujiang, Nanning, Wuzhou, Chongqing.
17 / 36
0icon represents some other control variables related to Chinese urban economic
growth, including: the ratio of the non-agricultural population to the total population
(urb) accounting for the level of urbanization; the population density (density) and its
square (den_2) accounting for internal population agglomeration in urban areas; the
ratio of service GDP to manufacturing for the industrial structure of urban sectors
(SM).
Policy
Policy can never be ignored in empirical studies on agglomeration or urban
economies. Besides the administrative boundary mentioned above, there might be
some other policy advantages affecting China’s urban economic growth as controlled
by literatures (Bao et al., 2002; Ho and Li, 2008)
As introduced before, in our database, there are still different administrative
levels of cities: municipalities and prefecture-level cities which also include
provincial capitals and other cities. Because municipalities and provincial capitals
may enjoy special policy from the central and provincial governments, we use dummy
capital to capture such policy benefits. Chongqing became a municipality in 1997
from an ordinary prefecture-level city. Take the special history of Chongqing into the
consideration⑥, we define it as a capital in 1990.
In the process of China’s reform and openness, four Special Economic Zones
(SEZs) were established since 1980 in China’s coastal area, and later 14 cities were
chosen as Open Coastal Cities in 1994⑦. Those Special Economic Zones and Open
Coastal Cities were established to attract foreign direct investment and access to
international market and have special policy advantages. We use the dummies SEZ
and open to measure the effects of the preferential policy. Since they may highly
correlate with FDI, dummy seaport or some others, we don’t expect significant
⑥ Chongqing once was the capital of Republic of China during the World War II, and is an important big city in western China. ⑦ Four Special Economic Zones established in 1980 are: Shenzhen, Zhuhai, Xiamen, Shantou; fourteen Open Coastal Cities in 1994 are: Tianjin, Shanghai, Dalian, Qinhuangdao, Yantai, Qingdao, Lianyungang, Nantong, Ningbo, Wenzhou, Fuzhou, Guangzhou, Zhanjiang, Beihai.
18 / 36
coefficients of them.
Descriptive statistics of all variables are in Table 3⑧.
Table 3: Descriptive statistics
Variable Obs Mean Std. Dev. Min Max
dgdp(%) 208 8.36 2.82 -.37 17.10
distbig(km) 286 291.33 251.35 0 2351.8
disport(km) 286 896.75 542.90 0 3526.4
bigcity 286 0.07 0.26 0 1
gdpofbig(100
million yuan)
286 114.93 80.74 37.36 344.47
samepro 286 0.41 0.49 0 1
seaport 286 0.11 0.32 0 1
riverport 286 0.08 0.28 0 1
center 286 0.35 0.48 0 1
west 286 0.29 0.46 0 1
gdp per
capita(yuan)
210 3581.295 2120.255 382.23 19820.83
inve(%) 210 25.91 16.67 1.54 127.88
labor(%) 209 57.60 7.79 30.01 93.79
edu(%) 207 4.68 1.26 3.02 18.58
gov(%) 199 10.26618 3.821074 1.04 29.16
fdi(%) 145 3.28 9.78 0.01 100.99
urb(%) 211 59.40 23.62 8.05 96.53
density (per
km2) 210 11849.77 4180.96 3631 32920
SM(%) 210 59.53 31.95 7.14 188.51
⑧ We drop the value of labor of Baicheng, Jilin Province and Shenzhen, Guangdong Province, and drop the value of gov of Siping, Jilin Province, because they are abnormal. In fact, our results are robust whether or not with those observations included.
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capital 286 0.11 0.31 0 1
open 286 0.05 0.21 0 1
SEZ 286 0.02 0.15 0 1
V.Estimation Results
Estimated results of the model are in Table 4. In addition to the traditional
economic growth factors, we add the geographical variables we concern in equation
(1)-(3). In equation (1) we only add the linear item of distance to big city and major
port, while we add the squares and cubes of distances in equation (2) and drop the
insignificant cube of distance to big city in equation (3).
Table 4: Distance and Urban Economic Growth
(Dependent variable is the compound average growth between 1990-2006.)
(1) (2) (3) dgdp dgdp dgdp distbig -0.00169 -0.00499 -0.00802** (0.00149) (0.00735) (0.00352) distbig_2 6.76e-06 1.32e-05** (1.48e-05) (5.40e-06) distbig_3 3.57e-09 (7.59e-09) disport -0.000215 -0.0129** -0.0112** (0.000716) (0.00555) (0.00429) disport_2 1.50e-05** 1.28e-05*** (6.79e-06) (4.82e-06) disport_3 -4.98e-09** -4.17e-09** (2.36e-09) (1.60e-09) bigcity 0.754 0.883 0.724 (0.918) (0.988) (0.925) gdpofbig0 -0.549 -0.742 -0.763 (0.625) (0.637) (0.633) samepro -1.947*** -2.165*** -2.211*** (0.666) (0.707) (0.697) seaport 1.978** 2.022** 1.962** (0.838) (0.843) (0.831) riverport 0.752 0.730 0.614 (0.649) (0.693) (0.645)
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center -1.391* -0.989 -1.025 (0.797) (0.812) (0.806) west -0.980 -1.349 -1.330 (0.878) (0.878) (0.874) lngdp -0.845 -1.249* -1.201 (0.726) (0.745) (0.735) inve -0.0345* -0.0278 -0.0274 (0.0201) (0.0201) (0.0200) labor 0.0427 0.0416 0.0411 (0.0336) (0.0344) (0.0343) edu 0.552*** 0.590*** 0.584*** (0.172) (0.171) (0.170) gov 0.0439 0.0269 0.0307 (0.0743) (0.0753) (0.0746) fdi 0.0305 0.0146 0.0132 (0.0256) (0.0262) (0.0259) urb 0.00701 0.00351 0.00460 (0.0135) (0.0136) (0.0133) density 0.000215 0.000270 0.000280 (0.000207) (0.000208) (0.000206) den_2 -5.07e-09 -7.77e-09 -7.99e-09 (6.00e-09) (6.05e-09) (6.01e-09) SM -0.00887 -0.00516 -0.00475 (0.0106) (0.0107) (0.0106) capital 1.051 0.598 0.548 (0.775) (0.792) (0.782) open -0.348 -0.287 -0.282 (1.043) (1.031) (1.027) SEZ -1.798 -1.976 -2.005 (1.436) (1.416) (1.410) Constant 13.06* 19.72** 19.39** (7.626) (8.290) (8.230) Observations 132 132 132 R2 0.387 0.430 0.428
Notes: (1) Standard errors in parentheses; (2) * p < 0.10, ** p < 0.05, *** p < 0.01.
Non-linearity of the Core-Periphery Structure in China’s Urban System
In equation (1), both of the distances are insignificantly negative, which means
urban economic growth decreases away from big city and major port. As we
discussed before, the non-linearity of the CP model may be shown in China’s urban
system. If so, as Fujita and Mori (1997), Fujita, Krugman and Mori (1999) showed, 21 / 36
the “∽”-shaped correlation between distance and economic activities might present in
our estimated results. So the squares and cubes of distances are added in equation (2)
Results of equation (2) show that distance to major port and its square and cube
are all significant, which partially proves the non-linearity in China’s urban system.
However, neither of the three terms of the distance to big city is significant. We guess
a “∽” shaped curve could only occur when distance to big cities is long enough,
otherwise only a U-shape can be seen in the real data. So we remove the cube of
distance to big city from equation (3). Obviously, in equation (3), all distance
variables are significant: distance to big city is negative, its square item positive;
distance to major port is negative, while the square item positive and the cubic term
negative.
Based on estimated results, we simulate the correlation between distances to the
major ports or big cities and urban economic growth rate, respectively, in Figure IV.
The horizontal axis represents the distance (kilometers) away from major ports, and
the vertical axis means the urban economic growth rate (%). We do find a
core-periphery pattern of urban system evolution.
Urban Economic Growth (%)
Shenzhen Hefei Wuhan Zhengzhou Chongqing Chengdu Xi’ning Suzhou Nanjing Changsha Taiyuan Xi’an Urumchi
Distance (km) Shanghai Hong Kong
Figure 3: Distance to Major port and Urban Economic Growth.
22 / 36
Figure 3 suggests that the impact of distance to major port on urban economic
growth has basically the same shape with the market potential curve of CP model in
urban system (Fujita and Mori, 1997; Fujita, Krugman and Mori, 1999). While a city
is located away from major ports within about 600 kms, the closer it is to the major
port and international market, the larger market potential and the higher economic
growth rate it has. While the distance is farther away, international market access is
no longer that important. Therefore, a location far away from ports might promote the
accumulation of regional and domestic market potential, as well as the the
development of local economies. When distance is long enough, farther than 1500
kms, cities far way from both domestic and international market would suffer from
low market potential and economic growth rate. Our result gives convincing evidence
of the CP model of city structure in the case of China.
In Figure 3, we mark major cities in China in accordance with their distance to
major port. To make those geographic distances more clear, we mark the two turning
points of economic growth curve on the map of mainland China in Figure 4. 600 kms
away from Hong Kong and Shanghai, denoted by a dashed curve, is the place with the
lowest average growth, while 1500 kms away, marked by a solid curve, is the place
with second highest growth rate.
23 / 36
Figure 4: Geographic Distance and Urban Economic Growth.
In Figure 5, we simulate the correlation between distance to big city and urban
economic growth based on our regression.
Urban Economic Growth (%)
Distance (km)
Figure 5: Distance to big city and Urban Economic Growth
24 / 36
When it’s close to big cities, scale effects and other external economies related
with spatial agglomeration promote big cities to absorb economic resources from
surroundings, which is the significant centripetal force. So the closer to the central
cities, the faster a city grows. But when it’s far away from big cities, instead of the
centripetal force, the centrifugal force plays a major role. So the farther the distance,
the faster a city grows. Our estimated result shows the turning point is about 300
kilometers, which means within a scope of 300 kilometers, interplay among cities
shows a strong centripetal force, which is similar with Hanson (2005). The difference
is that we also find that when the distance is greater than 300 kilometers, because of
transportation cost and other external diseconomies, the spatial interaction among
cities performs is dominated by the centrifugal force.
Comparing Figure IV and Figure VI, we find that: First, the impact of major
ports is stronger than big cities on urban economies, because within a certain distance,
the economic growth decreases much faster as distance from major ports increases
than from big city. Second, in Figure 4, the curve is U-shaped rather than “�”-shaped,
which is not surprising. In Figure 3, the complete “∽”-shaped curve requires at least
1,400-km distance, while the real distance to big city is not long enough. In fact,
China’s urban system today is the result of a long-term evolution, thus new big cities
might have emerged wherever there was large market potential due to the spatial
agglomeration of old big cities, as the numerical simulation of Fujita and Mori (1997),
and Fujita, Krugman and Mori (1999). So in Figure 5, we only can see the left part of
the “∽”-shaped curve instead of the whole.
From above, we may conclude that the national urban system in China has
presented a complete Core-Periphery Structure, due to the adjustment of urban
economies to the international market; and the importance of big cities and regional
urban systems is less significant but also verified.
Agglomeration Shadow
Something about agglomeration shadow (Krugman, 1993) has also been seen in
Figure 3-5. In Figure 3, cities located 400-600 kms away from major ports suffer from 25 / 36
the absorptive effect of major ports. So compared with cities located even farther,
their economic growth rates are even slower. Figure VI also presents some similar
results.
Partridge et al. (forthcoming) think current economic realities generally predict a
positive relationship between economic activities and close proximity to the largest
agglomeration centers, which is in contrast to Krugman’s NEG agglomeration shadow.
But our results may give evidence to such argument that agglomeration shadow exists
in different geographic scope. With distance to agglomeration centers increased to
farther enough, agglomeration shadow would appear.
As the results in Table III shows, initial economic scale of big cities (gdpofbig) is
insignificantly negative, which is similar with the finding in Partridge et al.
(forthcoming). This suggests the possibility that the larger economic scale a cities has,
the stronger its absorption to its vicinities. But this needs further evidence.
Border effect
The same-province dummy (samepro) is always significantly negative, which
seems the spatial agglomeration of big cities differs whether or not smaller cities are
in the same province with its nearest big city. If a city is in the same province with the
nearest big city, the absorptive effects from big cities will be larger, and consequently,
the city grows slowlier. On the contrary, this means that the "border effect" similar to
the findings in Parsley and Wei (2001) and Poncet (2005) exists among Chinese
provinces, which is likely to increase the actual distances between cities in different
provinces. Based on our estimates, China's inter-provincial "border effect" is
equivalent to as much as 260 kms⑨ for two neighboring cities. Poncet (2005) finds
that the inter-provincial border in China is just like international border in Europe, so
our estimate of the inter-provincial border effect is acceptable.
The "border effect" in this paper presents as the distortion of the spatial
concentration. We think it’s relevant with Chinese province-level market
26 / 36
⑨ We estimate this “border effect” by dividing the coefficient of the same-province dummy by that of the distance to the nearest big city in Equation (3).
segmentation (Young, 2000; Ponect, 2005) and migration restrictions (Au and
Henderson, 2006a, 2006b). Chen et al. (2007) argue that the provincial governments
have incentives to restrict the agglomeration effects from big cities in other provinces
by administrative forces, so as to protect their own economic development. This
argument is also consistent with the finding in this paper. Although for the cities of
the same province, such segmentation can prevent them from the absorptive effects of
large cities in other provinces, but market segmentation always brings loss of resource
allocative efficiency, thus a lower growth rate of the whole economy, resulting in
underdeveloped city scale (Au and Henderson, 2006a), and smaller scale inequality
among Chinese cities (Fujita et al., 2004).
Geography and Policy
We also notice that in equation (1)-(3), the center-west dummy or the capital
dummy is not significant, which obviously differs from previous researches, such as
Bao et al. (2002), Ho and Li(2008). This is because with the spatial interaction among
cities controlled in our paper, no obvious growth disadvantages exist in western or
central regions, and no other obvious advantages in provincial capitals or
municipalities. In other words, spatial agglomeration in China’s urban system
contribute most to the interregional economic disparities of China
Though correlated with the dummy variables of open and SEZ, the dummy
seaport is significantly positive, while open and SEZ insignificant This implies the
possibility that geography is more important than policy for China’s urban economies.
But this need further study. Besides, the dummy, riverport, is insignificantly positive.
Other Economic Growth Factors
In our regressions, we also control other urban economic growth factors, the
impacts of which don’t vary significantly among equation (1)-(3), and most of factors
are insignificant except for the education. Education (edu) is significantly positive.
This is because education investment in the initial stage promotes the economic
growth in the long run. 27 / 36
Our regression results also imply that, the impact of investment (inve) is
insignificantly negative which may be because China cities with high investment have
no obvious economic advantages in long the long term, because, as a whole, Chinese
economy suffers from low efficiency spawned by over-investment (Zhang, 2003).
Some other factors, like labor (lab), government expenditure (gov), FDI (fdi),
urbanization (urb), population density (density, den_2), industrial structure (SM) all
have insignificantly impacts on urban economic growth in the long run in our
regression. There are three possible explanations: First, those factors have no
long-term impacts on urban economic growth; Second, the distributions of those
factors may correlate with geographic distances controlled by our model. Third, the
measurement errors of the variables may have reduced the significance level. Actually,
the t-statistics for the significance test of coefficients of labor and population density
are greater than 1.
Finally, the initial level of per capita GDP has an insignificant negative impact
on China’s urban economic growth, showing no significant trend of conditional
convergence.
VI. Robustness Checks
Several models based on equation (3) are estimated to test the robustness of our
key findings in Table 4. In equation (4)-(5), we change the definition of big cities to
test the hypothesis about the regional urban systems. We redefine big cities as the
provincial capitals and municipalities, thus, distance to the nearest big city is replaced
by distance to its provincial capital (distcap), therefore, municipalities do not have
any spatial interactions in this definition. The geographic distance is also measured
using China Map 2008 (Beijing Turing Software Technology Co., Ltd, China
Transport Electronic & Audio-Video Publishing House, 2008). We put only the linear
term of distance to its provincial capital in equation (4) and add the square term into
equation (5). To be consist with the variable we control, we drop the dummy samepro
and bigcity, and replace the variable gdpofbig by gdpofcap -- the measurement of the
economic scale of capitals, that is ln(GDP) in 1990 exclusive of agricultural output. 28 / 36
In equation (6), we add Tianjin into our definition of major ports and replace
distance to the nearer major port between Shanghai and Hong Kong (disport) by
distance to the nearest major port among Shanghai, Hong Kong and Tianjin
(disport*).
Time span is also an important issue for our study, since we highlight the role of
China’s openness and international trade in the shift of urban system. But lots of
information about cities in 1992 and 1993 are missing in Chinese Urban Statistical
Yearbooks (National Bureau of Statistics, 1993–1994). Therefore, we use the urban
data of 1994 and replace the dependent variable by the average annual growth rate of
real per capita GDP from 1994 to 2006 (dgdp*) deflated by provincial urban CPIs,
where agricultural output and population are also excluded. Another benefit from this
robustness check is the greater number of observations from 133 in equation (3) to
193 in equation (7). Results of robustness checks are in Table IV, and only variables
about spatial interaction are reported to save space.
Table 4: Robustness: Distance and Urban Economic Growth
(3) (4) (5) (6) (7) dgdp dgdp dgdp dgdp dgdp* distbig -0.00802** -0.00953** -0.00925* (0.00352) (0.00430) (0.00540) distbig_2 1.32e-05** 1.47e-05** 1.07e-05 (5.40e-06) (5.68e-06) (8.04e-06) distcap -0.00748*** -0.0104 (0.00250) (0.00853) distcap_2 7.05e-06 (1.98e-05) gdpofcap -2.011*** -2.036*** (0.423) (0.430) disport -0.0112** -0.00373 -0.00384 -0.0139*** (0.00429) (0.00250) (0.00253) (0.00527) disport_2 1.28e-05*** 3.59e-06* 3.70e-06* 1.46e-05** (4.82e-06) (2.10e-06) (2.13e-06) (6.19e-06) disport_3 -4.17e-09** -8.54e-10* -8.76e-10* -4.28e-09** (1.60e-09) (4.63e-10) (4.69e-10) (2.07e-09) disport* -0.00881 (0.00554)
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disport_2* 1.44e-05* (7.55e-06) disport_3* -7.42e-09** (3.30e-09) Other controls Y Y Y Y Y Observations 132 132 132 132 192 R2 0.428 0.492 0.493 0.434 0.373 Notes: (1) Standard errors in parentheses;
(2) * p < 0.10, ** p < 0.05, *** p < 0.01;
As shown in Table IV, in equation (4), we only add distance to the provincial
capital, which is significantly negative, suggesting the provincial capitals have strong
absorption effects on the surrounding cities. In equation (5), we add both the distance
to the provincial capital and its square, both of which are not significant. So provincial
capitals have only significant centripetal forces, which is not conducive to the
economic growth of remote cities. We think this centripetal force might be due to the
administrative factors by provincial government to promote economic agglomeration
of provincial economies. Besides, the larger initial economic scale of the provincial
capital the slower its urban economy grows, as gdpofcap is significantly negative.
This result shows a stronger absorptive effect for bigger central cities. Variables of
distance to major port are always significant.
In equation (6) with Tianjin as a major port of China’s urban system, distance
variables to major port are still significant, but the significance levels decrease, which
proves that the economic agglomeration of Tianjin is not as strong as Shanghai and
Hong Kong. And distance variables to big city are insignificant with the same sign as
in equation (3), which suggests that the importance and robustness of big cites and
regional urban systems are much less than major ports and the national urban system.
In equation (7) with the dependent variable changed, the significance of distance
variables to major port are almost the same as in equation (3), while the significance
of the distance to big cities decreased a lot. The shorter time span or fewer
observations could be the explanation. However, it’s also possible that ever since
China’s rapid openness to the international market in 1994, the agglomeration patterns
30 / 36
of China’s urban system change to meet the needs of developing international trade,
so that distance to major ports are increasingly important. However, to confirm this
possibility, we need further studies.
VII. Conclusions
This paper is an early attempt to verify the spatial pattern of China’s urban
system following the Core-Periphery Model. The nature of China’s geography and
openness as nature experiment makes China a feasible application and an experiment
field for testing the New Economic Geography theory of urban system.
The most important finding of this paper is to verify the “∽”-shaped non-linear
correlation between the geographical distance to major ports and urban economic
growth, which is consistent with the Core-Periphery Model of urban system in the
New Economic Geography theory. We find that China’s urban economies adjust to the
increasingly important international trade according to their access to global market,
especially after China’s drastic opening up since 1992.
Agglomeration shadow modeled by Krugman (1993) is also found in China’s
urban system, which means that being closer to the agglomeration centers is not
always good news for local economy. This finding adds a new evidence to solve the
paradox between the positive closeness-growth relationship in real data and the
theoretical hypothesis of “agglomeration shadow” (Partridge et al., forthcoming).
The "border effect" due to Chinese inter-provincial segmentation is equivalent to
increasing actual distance between cities, as well as limiting the spatial interaction
among China’s cities. Such administrative boundaries protect the economic growth of
small cities from the absorption of big cities in other provinces. Nevertheless, it also
leads to efficiency losses of the interregional agglomeration and scale economy.
As the first paper to study China’s urban system within the structure of
Core-Periphery Model, our exercises also direct some further empirical researches
based on China’s urban data.
In the modern era, agglomeration patterns can change with lower transportation
costs, improved communication technology, shifts in trade patterns, and industry 31 / 36
structural change (Partridge et al., forthcoming). Our paper emphasizes the role of
shifts in trade patterns, and our robustness check also suggests some evidence of
agglomeration pattern changes after 1994. However, without variables of
transportation costs and communication technology controlled, it’s hard to identify the
impact of international trade on agglomeration patterns, so further researches are
needed to convince the effect of international trade on spatial interaction patterns
among China’s cities.
32 / 36
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