Top-Down versus Bottom-Up
Agglomeration effects among Chinese domestic firms
Author: Johanna Gómez Ramírez
Candidates of Master of Science in International Economics with Focus on China
Department of Economics, Lund University
Supervisor: Professor Sonja Opper
Submission date: 15th October 2011
Abstract
Industrial Parks in China have played a crucial role in attracting foreign investment and in
enhancing regional development. However, there is an open question: do industrial parks
develop comparable effects as natural grown clusters? Following agglomeration theory that
highlighted the economic advantages from proximity between firms, and making use of a
rich dataset from the Chinese domestic firms, this paper estimates the relationship between
agglomeration effects and the performance of firms. The results show that firms located
within industrial parks are more innovative and present better performance; however there
is not evidence that linkages between firms have the same positive relation what contradicts
the theory.
Keywords: Industrial parks, natural grown clusters, agglomeration effects.
Table of Contents
Chapter 1 Introduction ............................................................................................................ 1
1.1 Research Question ............................................................................................................ 1
1.2 Method .............................................................................................................................. 3
1.3 Limitations ........................................................................................................................ 3
1.4 Structure ........................................................................................................................... 4
Chapter 2 Agglomeration Theory and Hypothesis ................................................................. 4
2.1 Defining Agglomeration Effects ...................................................................................... 4
Chapter 3 Industrial Parks Vs Natural Clusters .................................................................... 12
3.1 Overview of Industrial Parks and Cluster Development ................................................ 12
3.2 Features of Industrial Parks in China ............................................................................. 14
3.2.1 Advantages offered ...................................................................................................... 15
3.2.2 Development constrains .............................................................................................. 17
3.3 Features of natural clusters in China .............................................................................. 19
3.3.1 Gains from clustering .................................................................................................. 20
3.4 Top down versus Bottom-up: The China case................................................................ 22
Chapter 4 Empirical Analysis...............................................................................................23
4.1 Research Design: Modelling Agglomeration Effects.....................................................23
4.2 Data.................................................................................................................................25
4.3 Variables.........................................................................................................................25
4.4 Methodology and Results................................................................................................29
4.4.1Results………………………………………………………………………………...29
5. Conclusions…………………………………………..……………………….................33
References………………………………………………………………………………….35
Appendix 1 Pairwise Correlations ………………………………………………………..39
Chapter 1 Introduction
1.1 Research Question
Industrial zones are playing a crucial role in the Chinese economy and research related to
this topic is still limited. This study is an effort to understand the effectiveness of industrial
zone policy by determining whether firms located within industrial parks enjoy the
advantages of agglomeration effects that are found in naturally grown clusters. In China’s
regional development, local governments rely on the creation of industrial agglomeration
advantages by promoting the construction of industrial parks in order to generate growth
and attract investment. However, it is important to analyze whether successful industrial
zone experiences can be duplicated; and if these initiatives actually create positive effects
on firm's performance, in the same way that natural clustering develops agglomeration
advantages over time.
Around the world, governments are creating industrial areas with the idea to offer services
and special conditions for firms, which aims to transfer and generate growth in lagging
regions or potential ones. In recent years, the creation of industrial areas worldwide has
increased dramatically; in the USA there are currently around 8800 industrial parks, 1200
in Canada, 200 in the UK and 300 in Germany (Geng, 2009). However, it is important to
recognize that some of the most successful industrial parks in developed countries are the
result of interactions between pre-existing institutions and firms. Over time, these
interactions create clusters that develop agglomeration effects, which at some point are
named industrial parks and become priority for regional economic growth. In other words,
IP´s in developed countries are the result of an evolutionary process. Silicon Valley in USA
is a recognize example; in 1950’s start-up companies found advantages from being located
around Stanford University, and dynamics that involve the region, firms and institutions,
evolved to make of this area an example of exceptional development..
The success of Silicon Valley has inspired governments around the world to rely on
government sponsored industrial parks to try and imitate similar agglomeration advantages
and spur economic development. However, it is an open question whether the top-down
approaches used to design parks have comparable effects as naturally grown firm clusters.
1
In China, industrial zones are identified as the cornerstone of industrial development (Geng,
2009). The interest from local governments in attracting capital using industrial parks that
offer special benefits for firms is considered crucial for regional economic development
(Chen, 2005). However, there are also successful cases of clustering in China where local
firms and the dynamics from competition have created the conditions for industry growth.
In clusters unlike industrial parks, local governments have played a supporting role by
providing public goods and promoting clustering. In those cases, first initiatives were
caused by local dynamics such as entrepreneurship, history, institutions and location.
In this sense, the purpose of this analysis is to look at the effectiveness of the establishment
of industrial parks as a government policy that incentivizes firm's growth and innovation;
the reasoning behind this is to determine if the government should create and support new
industrial areas, rather than simply reinforce and build on established and emerging
clusters. The theory of agglomeration covers both, industrial zones and clusters
development, highlighting the benefits of localization, economies of scale and spillover
effects. The importance of industrial agglomeration, from a broad perspective, is to reduce
costs and increase returns not only for the firm but also for the geographical space where
the industrial park or the cluster is situated (Krugman, 1991).
More specifically, this research will analyze if being located in an industrial park is
significant for Chinese domestic firms; and if the actual impact of agglomeration
economies reflected on domestic firm´s growth and innovation is due to the localization
within clusters and industrial parks. My hypothesis follows the theory of agglomeration; I
argue however, that despite the great contribution of industrial zones in attracting foreign
capital and upgrading industrial techniques in China (Chen, 2005), these areas are still in
the construction phase, due to the fact that the local government’s objective is to attract
capital at any cost, which has created intense competition and has lead to an over
development of industrial zones. The research is, therefore, an analysis of the industrial
park policy in its purpose of creating agglomeration advantages for firms.
2
1.2 Method
This study was conducted in two phases. In the initial phase the goal was to build up an
understanding of industrial parks in China; this phase involved two face to face interviews
with park project developers. These interviews were semi-structured and conversational
using open-ended questions that allowed for the development of areas of interest. Despite
the limitation in the number of interviews, the sites are very different from each other,
which gives a better picture of their current situation and development.
With the information from these interviews and a literature review about agglomeration
theory, a case study was built using quantitative data from the 2003 World Bank
Investment Climate Survey. This survey provides information on 2400 domestic Chinese
firms, from which 600 are located within industrial parks. The questionnaire covers
different aspects of firm’s activities and its business environment, which made possible the
elaboration of measures of agglomeration with the aim of understanding the effect of
proximity in firm’s performance.
1.3 Limitations
The main limitations identified during the investigation were primarily related to the
database. First of all, the survey was done between 1999 and 2003, which may show a
different picture from what is the actual situation within domestic firms in China today, so
the interpretation of the results should be done carefully.
Furthermore, not all regions were considered in the survey and specifically inland regions
show a small participation overall (Dollar, 2004). However, as industrial parks are mainly
located within medium and big cities, the survey is a good approach to analyze IPs
effectiveness. Additionally, there is a high proportion of state owned enterprises compared
with other legal ownership forms which could also bias the results.
The survey also offers different questions that try to approach the same subject; as a result,
the variables used as a proxy for agglomeration can be determined from different
perspectives. In this study, I used the ones considered relevant based on a subjective
exercise that took into account the theory, but I recognize that the research outcome can
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vary if instead other questions are used as a proxy. Finally, I should emphasis that the
survey offers a good picture of both, firms located within industrial parks and those outside,
which fits perfectly with the aim of the study.
1.4 Structure
The structure of the paper is as follows. In the first section I placed the features of industrial
zones within agglomeration theory, where the main contributions and recent approaches are
explained. In this part, I present the research hypothesis which will be explored in the rest
of the paper.
The second part focuses on industrial parks and cluster development in China; first, by
explaining the advantages that are offered to firms and then by identifying its main
development constrains. The next section describes the empirical results from the
multivariate regression and explains the relation found between agglomeration effects and
firm´s performance and innovation. In this section, the variables used to measure clustering
are disclosed.
The final section considers possible implications of the empirical evidence on policy
initiatives, making a critical analysis of the government’s role in the construction of IP´s
and the promotion of clusters in China.
Chapter 2 Agglomeration Theory and Hypothesis
2.1 Defining Agglomeration Effects
There are two central theoretical fields that explain firm localization and its relation with
productivity and development; one is agglomeration theory and the other one endowmentdriven localization. The latter states that firms will be located in regions with favourable
factor endowments (Deavtz, 2008), for example, an area with abundant input supply or
natural resources. However, as firms concentrate and compete for the use of endowments,
prices increase, which reduces the benefits from localization. For this reason, in order to
justify long term benefits for firms, industry localization has to be determined not only by
endowment factors but by other aspects highlighted within agglomeration theory.
4
Agglomeration is considered in economic geography literature as high spatial density of
economic activity. The notion of agglomeration could be used to explore different
situations like the development of cities, industrial clusters, commercial districts and the
imbalance between regions (Masahisa, 2002); in general, this theory is used to analyze
economic concentration, due to proximity between economic agents. In this sense,
agglomeration can occur at different geographic scales and examples can be taken from
almost every location; from the dense shopping streets in Central London, the urban
corridor from Liverpool to Milan, or the concentration of economic activity in the city of
Shanghai (Lindquist, 2009, p:13).
Agglomeration theory analyzes the effect of spatial distance on firm’s performance;
distance refers to the space between firms and between firms and consumers. Research in
this field has been focused on understanding ´what factors determine and sustain the
advantages of proximity within firms´. Theorists in this field have identified economic
benefits and general advantages due to the reduced distance between firms, however
empirical studies supporting this theory present conflicting results.
One of the advantages from agglomeration is external economies of scale that are
considered the essence of this theory. Marshall (1920) identified three different external
economies within what he called industrial districts. These industrial districts were areas of
high spatial concentration of firms within the same sector. External economies are an
explanation for benefits that firms find outside their own production process, for example:
• Access to a market of workers with specialized skills,
• Facility to develop specialized inputs and services, which would otherwise be impossible
to obtain if a single firm was the sole producer.
• Benefits from technological spillovers, available through the transfer of skills and the
sharing of ideas between colleagues and competitors, which leads to the creation of
inventions.
These three aspects show the benefits from localization arising from firms sharing
production factors. In other words, proximity reduces costs because it avoids the difficulties
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of finding a sufficient market for inputs and labor. The main idea behind economies of
scale is the shared access to goods and services (Deutz, 2008).
In addition to the externalities found by Marshall, it is considered that agglomeration
effects are enhanced by the dynamics of competition. Porter (2007) supports this idea by
arguing that within clusters, transactions are more efficient, there is more flexibility in
operations, it is easier to start a new business, and it is faster to implement and perceive
innovations. Rather than being concerned with a single industry, agglomeration of firms is
based on the link between different industries and entities that are considered important to
competition.
The causes of agglomeration of certain industries within a determined area are also a
subject of study. Within these causes, transport cost has been identified as the most
important. Krugman (1991) concludes that the nature of the macroeconomic adjustment
processes within regions and the character of external economies are due mainly to the
reduction in transport costs. He argues that manufacturing firms have an incentive to
concentrate near markets and suppliers, which produces “a centripetal tendency to
agglomeration”. For Krugman, the combination of scale economies and transport costs are
positive externalities that encourage agglomeration. Models of monopolistic competition
support the idea that as transport costs decrease there is an increased tendency towards
agglomeration. Reducing transport costs increases competition; firms tend to differentiate
their products and locate close to a big pool of consumers. It has been also determined that
firms locate close to each other to exploit internal economies of production. Multinational
companies, for example, would rather produce in one site, and ship their production to
other countries, gaining from internal economies of scale (Masahisa, 2002).
There are different arguments behind the causes of agglomeration and its relation with
firm´s performance, but in general the core theory identifies a positive relation between
both. The evolution of this theory has included new analyses that also look at aspects
within the business environment at industrial parks and clusters: co-operation between
firms, the importance of personal contact, network connections to support innovation
within firms and business relationships with institutions, are some of the features studied by
the agglomeration theorists in resent years. To understand the contributions of this new
6
approach, Deutz and Gibbs (2008) identify four common features present in agglomeration
theory research:
1. External economies of scale. These are benefits outside a firm’s operation, which
reduce the average unit cost and enhance productivity. A common example is
industry growth. In the presence of proximity effects, firms share access to factors
of production and supporting institutions, which is crucial to economic
performance; for instance, aspects like: experienced labor, financial and legal
services, and public services among others, are usually better established in an
industrial location. Firms benefit from sharing services as it gives them access to
factors that they cannot produce internally or for what they may not constitute a
sufficient market to attract suppliers.
2. Networking. Agglomeration brings benefits to the firm by facilitating face-to-face
contact with managers from different organizations. The possibility of establishing
close business relations reduces transaction costs, increase the willingness to do
business with other firms, and also creates the opportunity for alliances and
organizations that enhance competitiveness are more likely to arise. Proximity
increases the likelihood of personal contact which allows for exchanging tacit
knowledge or knowledge that cannot be written down, for example information
from past business experiences. Economic relations are embedded in social
networks that depend on the characteristics of the society. In the Chinese case, and
in East Asia in general, interpersonal relations are highly important. Networks are
based on mutuality, trust and cooperation. In this sense, face to face contact
enhances the development of assets like reputational capital, sharing of information
and reciprocity; aspects that determine transaction cost and enhance competiveness
(Fan, 2003).
3. Policy. One of the main interests for policy makers is to generate economic growth.
Two aspects have led policy initiatives to promote proximity between firms. First of
all, most successful regional experiences around the world have been based on
agglomeration advantages (a well known example is Silicon Valley, USA);
additionally, academic literature has recognized the importance of agglomeration
formation as a policy initiative that can promote investment in lagging regions or
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enhance regional capabilities. Policy makers have taken the role of providing
opportunities and supporting external economies and network connections between
firms; for example when building industrial parks or promoting clustering, the aim
is to enhance contact between firms, infrastructure and business services. In the last
30 years, the economic transformation of China from a socialist system towards a
social economy market has pushed industrial policy towards the creation of areas
that offer certain benefits to the firms with the idea of accelerating investment,
innovation and growth.
Hypothesis 1. Proximity between firms generates agglomeration effects that benefit firm’s
performance.
4. Knowledge spillover effects. Spillovers refer to valuable information that appears to
flow within firms. The belief is that proximity enhances the flow of knowledge and
makes casual communication inexpensive (Deutz, 2008). Due to the dependence of
external economies on the proximity effect, benefits increase as more firms are
agglomerated in one location. These externalities generate benefits for business
depending on their absorptive capacity; more specifically, technological spillovers
rely on firm’s ability to use information and new technologies.
Firm proximity increases the possibility of innovation and the emergence of new techniques
when enterprises share knowledge, technologies and resources. The activity of exchanging
information enables companies to be more flexible and innovative (Walcott, 2003 p.1). So,
in general, the idea is that a space with a large number of firms generates dynamism. This is
because when firms are close to each other, there is a big chance to contact early adaptors
of new technologies, and information flows occur more efficiently.
Spillover effects are considered an external factor; however, because of its importance in
the literature, I have separated it from the general concept of external economies. To
analyze the importance of spillover effects in firm’s performance, there have been used two
hypotheses, the first one is called the Marshall-Arrow-Romer proposition, which specifies
that information and knowledge are easily exchanged between firms within the same
industry, which could be referred to the cluster cases. However, there is evidence that
8
innovation increases when firms interact with actors from other industries (Johansson,
2004); the last argument supports the development of industrial areas that captures firms
within different industries. Nevertheless, these arguments have not been proved by
empirical studies, and research in this field is still limited (Johansson, 2004), for instance,
there may be cases where the value of innovation is reduced due to the rapid flow of
information between firms, what produces disadvantages over firm’s performance instead.
Within agglomeration theory, one of the areas that captures the most interest is the relation
of agglomeration effects and innovation. The reason is that spatial concentration of firms is
considered a desirable phenomenon, because reducing the distance between firms increases
the likelihood of the transmission of information, which in turn increases the occurrence of
innovative activities and decreases the learning curve (Walcott, 2003 p.35).
Hypothesis 2. Due to proximity, information flow is more efficient within industrial areas
and knowledge spillovers are most likely to occur. Therefore, the innovative capacity for a
firm within an industrial park or cluster will be higher compared to outside firms.
The literature states that firms benefit by locating in particular areas or districts due to the
agglomeration advantages. Being in an area with improved infrastructure, a skilled labor
market, financial and legal services, information access and contact with firms with similar
needs generates positive effects on performance (Walcott, 2003 p.4), network creation is
also improved. In this sense, research in agglomeration theory has identified positive effects
for firms due to their localization.
9
Figure 1 Firm´s benefits from Agglomeration Effects
Industrial Parks and
Clusters
External Economies
of Scale
Share of factors,
institutions,markets
Networking
Reduction in
transaction costs
Policy incentives
Tax exceptions,
export licenses,
acces to credit
Knowledge spillover
effect s
Increases the
possiblity of
innovation
Agglomeration
Effects
Agglomeration effects have been evaluated with different models and methods, all of them
with the purpose of understanding the benefits of proximity within firms. However, there is
an on-going debate about the positive effects of agglomeration on firm performance since
empirical studies resulted in contradictory findings. Malmberg and others (2000), studied a
sample of Swedish export firms and found that the importance of localization economies
(links between firms) in firm performance is not as crucial as theory predicts. The effects of
urbanization economies and traditional scale economies are more important. This study
recognized the need to reinforce the empirical evidence within economic localization
literature, specifically in how and to what extend localization strengthens firms
performance.
However, looking at how proximity improves innovation, Audretsch (1998) explains why
and how geography matters, arguing that globalization has increased the importance of
knowledge based economic activity. His argument is that as knowledge is generated and
transmitted efficiently by local proximity, economic activities based on knowledge will
tend to agglomerate. His study is based on the idea that knowledge is vague and difficult to
codify, and that the marginal cost of transferring knowledge increases with distance.
10
In the case of China, there are no studies about the relation between agglomeration and
firm´s operation; however, Fan and Scott (2003) found positives effects in region´s
economic performance. There are interesting examples of positive cases within certain
industries, specifically industries that are essential for the economic transformation process,
like consumer electronics for example. When looking at the causes of agglomeration in
China, Chen and Lu (2005), identify a positive relation between economic opening and
industrial agglomeration.
On the other hand, it is considered that there is an over excitement about industrial zone
implementation that is not supported by enough empirical evidence. Potter and Watts
(2010) argue that Marshall’s agglomeration economies decrease the economic performance
of the firms in the manufacturing sector; the research shows that agglomeration economies
are positive within the first stages of the industry life cycle. Their analyses are based on
evolutionary theories; accordingly, effects from agglomeration economies depend on time.
Similar findings are presented in Kukalis (2010), he finds that there is no relationship
between agglomeration externalities and a firm’s performance in the semiconductor and
pharmaceutical industries. He compares firms within a cluster with outside ones,
determining that outside firms can even have higher benefits at the later stages of the
industry cycle. He emphasizes that the enthusiasm of politicians and scholars in this field.
In this scenery, my research is an effort to understand the effects of proximity and
agglomeration in firm´s performance and innovation, evaluating whether localisation within
clusters and industrial parks have an actual relation with firm´s operations. Considering
that there is no strong empirical evidence within this field and bearing in mind that there is
an over excitement in China to use industrial zones as a policy tool to encourage growth
and innovation, my empirical model determines to what extend the enthusiasm in this
policy shall be revaluated.
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Chapter 3 Industrial Parks Vs Natural Clusters
3.1 Overview of Industrial Parks and Cluster Development
Agglomeration effects occur when firms concentrate within a specific area, sharing benefits
from being near each other; proximity within firms is product of a variety of factors like:
history, localization, natural resources or government initiative. In recent years,
governments have promoted proximity through the construction of industrial parks with the
idea of attracting investment and economic growth. On the other hand, governments have
supported cluster´s formation as a policy tool to enhance development, due to the
advantages from dense networks of firms, powerful externalities and information spillovers
(Porter, 2007). Within this context, this chapter analyse the development process of
industrial parks and clusters in China, as policies that seek to generate local economic
growth, underlying the importance of regional institutional development, when taking
advantages of these areas.
Around the world, two types of industrial agglomeration can be found. The first one
emerges deliberately by government policies to attract foreign investors and certain type of
industries within a limited space. An example is the construction of industrial parks, where
the main characteristic is the presence of large firms with low inter-firm connections (Long,
2009).
The second type is the spontaneously grown clusters; in this case, natural
agglomeration forces create conditions that incentivise economic agents to follow
commercial initiatives (Yoshino, 2010 p.43). In China, many clusters overlap between
spontaneously formed clusters and policy-driven initiatives, and in some cases, it is
difficult to make distinctions between them. However, when looking to natural grown
clusters, I refer to clusters conformed by small and medium firms, with little or non
government support. It is considered that this type of clusters emerges in developing
countries to encounter difficult business environment, with weak institutions and high
transaction costs (Yoshino, 2010 p. 44). Specifically, proximity and strong linkages
between firms increase the likelihood of relying on trust relations instead of contracts and
formal market institutions that usually are not efficient in developing countries. For
instance, in terms of access to credit and financial resources, Ruan and Zhang (2009)
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provide empirical evidence from the cashmere sweater cluster in China, showing that
regular interaction between buyers and suppliers builds strong relationships, what helps
small business to face financial constrains through trade credit among firms.
In general, industrial agglomeration in China has increased dramatically with the economic
opening process that takes place in the last 30 years (Chen, 2005). In 1978, the Chinese
government launched the economic reform that has lead to an unprecedented development
in the country. The main characteristics of this reform are its gradualism, experimental
approach and most importantly, the government commitment; The Special Economic Zones
are an example; everything began when the central government opened the doors of the
country to foreign investment and offered special benefits to international companies within
delimitated areas. This areas are characterized by having better conditions in terms of
infrastructure, labour and institutions, and by a more liberal and safe environment to make
business. During the first years of the reform, four special economic zones were
established, with the idea of testing a market orientated economy and with this attract
foreign investment, technologies, promote exports and generate employment. After the
success of the zones, the central government have promoted its development all over the
country by giving more autonomy to local governments to capture investment.
During this process, different types of economic zones have been created and usually,
within these areas industrial parks are located. Different aspects of the Chinese economy
have been improved due to the economic zones; contributions in growth of GDP,
employment, technology upgrading, exports promotion, attraction of investment, among
others, are some of the benefits attributed. In 2008, the GDP of the 54 state level Economic
and Technological Development Zones (ETDZs) accounted by 5.1% of China´s GDP (US$
220.3 billion); the High-tech Industrial Development Zones (HIDZs) generated 9.1% of
China total value-added industrial output. And in terms of FDI, the ETDZs attracted 21.1%
of China´s total (US$19.5 billion) (China Knowledge Online, 2011). It has been recognized
that despite the differences in performance between these zones, their general effect in
economic development has been positive.
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On the other hand, clusters have benefit from the opening up process through knowledge
spillovers from foreign companies and new market niches; for example, it is considered that
companies like IBM, Acer and Compaq located in the costal areas, have provided initial
technologies and skills needed to develop software clusters. In China, clusters have played
an important role in promoting local economic growth, enhancing competiveness of
industrial sectors within domestic and foreign markers, and in the technological upgrading
process. However, it is difficult to measure the contributions of clusters to the economy;
Douglas (2008) find that when looking at ten representative clusters in China, the average
number of firms is 3.427 per cluster with 160.000 employees and 157 million yuan annual
output; the sample includes toys, men´s clothes, children´s clothes, lamps, furniture, among
other clusters.
3.2 Features of Industrial Parks in China1
Around the world, governments are using industrial areas with the idea of offering services
and special conditions for firms, aiming to transfer and generate growth in lagging regions
or potential ones. In resent years, the creation of industrial areas worldwide has increased
dramatically. In developing countries, governments have the role to create and support
economic growth, in some cases resources are invested to attract certain kind of capital
through constructing sites that offer to investors, good deals in terms of transport facilities,
infrastructure, low taxes, and good business environment, among others.
Industrial parks (IP) in China are considered the cornerstone to attract investment. IPs
represent groups of enterprises in the field of production activities and services
concentrated within a specified territory, and which share the same infrastructure, roads,
transport and public utilities; the function of these structures is to reduce costs and generate
economies of scale (Zekovic, 2009). In other words, an industrial park, or area, is a
collective location or limited space that belongs to a number of firms from the same or
different industrial sectors. In China, there are 11 different types of industrial areas (Table
1), which present attractive opportunities for firms.
1
This section is based on personal interviews to managers of two industrial zones in China and the paper by Geng, 2009;
there were limited sources of information to construct this section, industrial park´s websites were also used.
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Table 1 Industrial zones in China
Industrial Parks
Industrial Area
Industrial Development Zone
Economic Development Zone
Free Trade Zone
Scientific-Industrial Park
Software Park
Economic & Technological Development Zone
Export Processing Zone
High-tech Industrial Development Zone
Development Area
Source: http://industrial-zone.fengpu.com/
For the purpose of this research, the name of industrial parks is given to all the different
industrial zones in China. In 2007, there were 1568 industrial parks. It has been recognized
there is a boom of industrial parks construction, due mainly to the autonomy of local
governments in attracting private investments which has generated competition between
regions; IPs are considered the major policy tool to meet targets of local growth.
A typical industrial park in China has some areas that are common for firms; the industrial
production area, scientific research area, residential area, recreational area and business and
services area (Geng, 2009), however these features varies depending on each site, since
industrial parks have been designed to cater firm´s needs; to do that, there are different
types of parks: technology parks, export zones, software parks, trade zones, among others.
In general, industrial parks have better infrastructure and conditions, compared with the
terms offered to firms located elsewhere.
Within this context, it is important to understand the advantages offered by industrial parks
and its development constraints, as these areas are considered an important engines for
China´s remarkable growth and its experience could be an example of the positive benefits
of agglomeration economies.
3.2.1 Advantages offered
Industrial parks offer a bunch of good deals to private investors, within them, the most
attractive are the preferential policies; for instance, inexpensive land, tax breaks, custom
clearance, duty free import of raw materials for export products and export tax exemptions,
are the most common ones (China knowledge, 2011).
15
On the other hand, these sites are considered to have better infrastructure and investment
conditions, due to a better planning and support (Geng, 2009); advantages like transport
facilities, logistics, real state, infrastructure and qualified labor force, are better established
within these areas. In terms of infrastructure, industrial zones offer better quality in public
services like electricity, roads, water, telecommunication, drain system, gas, water disposals
and in some cases, steam for manufactures; prices for these services are the same for all
companies in the area. When looking at websites, there is a common pattern in the
information offered; government support and preferential policies are the principal aspects
that shows up, after that, location and transport facilities are presented in clear detail, and
finally, information about other advantages is presented, for example specific features of
the park, the surrounding universities, R&D investments, linkages with other parks or the
characteristics of the residential areas.
The business environment is also improved in the parks. A common example is the services
offer to set up SME and the constant assistance in business operations; the Nordic
Industrial Park (NIP) presents a pool of specialist to assist its investors: lawyers,
accountants, human resources specialists, IT engineers, among others (NIP website). Due
to the characteristics of the economic and bureaucratic system in China, specialized
services are offered, especially for foreign investors; among them, the most common one is
advisory services for foreign and domestic companies in terms of bureaucratic procedures;
the administration office of the park is committed to help dealing with registrations,
licenses, tax process, and certifications. For instance, dealing with the High Technological
Certification is a common service; in recent years this process has become very important,
not only for firms, but also for the industrial zone recognition.
Looking at these services and conditions, it can be concluded that industrial parks reduce
infrastructure construction costs for single firms, they also offer better access to markets
due to transport facilities, and finally, regulation is easier to follow; for example, in
environmental issues, the parks as a whole has to deal with all government requirements,
and the firms has to comply and follow the environmental friendly system already
established by the park, what is less complicated than dealing with these issues
individually.
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3.2.2 Development constrains
Despite the attractive benefits that parks offer to firms and its success in attracting capital
and new technologies, there are challenges that the central government has to face. The
over-development of industrial parks in China is related to the incentives from local
governments to attract new investments at any cost, due to the fact that political careers of
local bureaucrats are linked to economic growth. Therefore, financial risk and the
environmental consequences are not always taken into account when industrial areas are
planned. Some of the areas have been created without any thought and are not interesting
for investors; therefore, in some cases the real capabilities and potential of the parks are in
doubt (Geng, 2009).
One of the main limitations for industrial park development is the scarcity of land. First of
all, project developers have to face complicated bureaucratic processes to acquire land. The
central government determines a land quota every year, which is given to industrial zones
at the municipal and county-town level. In 2008, industrial parks accounted by 0.004% of
the country's land that attracted 10% of foreign investment (Geng, 2009). In terms of land
administration, the government has a bind between their motivation to attract capitals and
the importance of land for food production. In some areas, usually far from the coastal
region, industrial zones developers break the law in terms of land acquisition. However,
land scarcity has helped in controlling industrial park´s construction that has been a
difficult issue in the implementation of this policy. In 2002, there were 6.866 industrial
parks (state-level, provincial, municipal and county- level), after the program to normalize
land usage launched by the central government, the number of parks has been reduced
dramatically to 1,568 by the end of 2007 (China Knowledge Online 2011).
On the other hand, during first stages of industrial park's development, the initial
investments on infrastructure rely on bank loans that are approved by the municipal and
central government. The importance on careful planning is crucial to use public resources
in a proper way. To deal with the investment goals imposed by different levels of
administration, during the first years, parks concentrate their efforts in attracting
investment, in most cases, foreign capital and well-known companies that could be signal
of credibility for other firms; the process is done through sellers who hunt investment,
17
offering a variety of facilities and good deals. The competition between parks and the
urgency to meet the goals has caused the waste of public resources; firms have been located
within certain areas without planning future operations or without taking into account the
importance of linkages with other firms. It is considered the desirability to concentrate the
same or similar industry sectors in specific areas, instead of placing firms randomly what
decreases the gains from agglomeration and comparative advantages (Douglas, 2011).
Moreover, it is well recognized that institutional features are crucial for firm’s development
and growth. In this context, despite the great support and proactive participation from the
Chinese government in the promotion of IPs, aspects like trust, planning and pro-business
environment are engine for the success of industrial parks and more specifically crucial for
attracting investment and generating growth. These aspects are not well developed in all
regions in China; Douglas (2011) gives a good example:
While Shenzhen was quick in identifying its industrial position and to build a good
enabling environment, Zhuhai and Shantou seemed a step behind…Zhuhai actually
overbuilt its infrastructure beyond sustainable demand … Its over- sized airport
exhausted its initial capital ... Shantou has reached average rates of economic
growth, but at various times that growth has been stalled by scandals traced to
corruption, customs irregularities, smuggling, and the like. It also suffers from poor
social credit and trust. In addition, the urban and zone management is not well
planned, and there have been some institutional conflicts…Shenzhen seems to be
more innovative in designing many pro-business policies and institutions, perhaps
because of its immigrant culture, where investors feel more accepted and have a
sense of ownership. In comparison, Zhuhai and Shantou are historic cities with
strong local customs and culture, as well as their own languages. Such an
environment might sometimes deter foreign investors and innovative approaches.
(p.23)
In this context, not all parks are successful; some of them offer poor services and
exaggerate their capabilities on websites and advertising. With the boom of industrial zones
in China, policy makers have left behind concerns about difficulties in duplicating
successful experiences that arise due to differences between regions or districts, in terms of
18
local policies, historic contingencies and quality of institutions (Lovering, 1999). The
government's interest in attracting firms in certain areas has been dominating, and policy
makers have promoted setting up considerable number of industrial parks. Nevertheless, the
region or district sometimes does not have the necessary condition to transfer the
agglomeration advantages to firms; as a result, the actual impact of the industrial zones
policy is in doubt, despite of the general argument, supported by the economic literature,
about agglomeration advantages in firm’s performance and local growth (Hengxin, 2009).
In this context, Cluster´s support could play a better role in regions where the necessary
conditions for attracting investment are not sufficient; since within time, clusters improve
institutions and the business environment (Porter, 2007).
3.3 Features of natural clusters in China
Clusters are the concentration of firms, suppliers, support services, specialized
infrastructure and specialized institutions located in a specific area; usually within a cluster
there is a mix of manufacturing and services companies that support similar industries
(Porter, 2007). The whole idea and main contributions in this field were done by Porter
1990, in his book The Competitive Advantage of Nations. The cluster idea is based in the
agglomeration theory, where efforts and resources from nearby firms, institutions and
governments, are shared to improve productivity and business development.
In this section, I will focus my attention on natural grown clusters in China, clusters that
emerge spontaneously and that develop agglomeration advantages over the time. An
example is the eyewear cluster in Danyang City located in Jiangsu Province; the industry
started in the 1930s and 1940s when peasants moved to Shanghai and Suzhou to work in
eyewear factories. When they went back to their home town and the economic reform
process started, the production of different products like lenses, frames and screws
expanded within the area. In 1985 there were 23 townships factories and, by 2004, there
were 50,000 workers and more than 1,000 factories and firms related to the sector (Ding,
2007).
19
In China, there are certain general characteristics that are common among natural clusters.
The first one is the specialization of rural towns in a specific manufactured product, who
frequently label themselves as the world’s “sweater city”, “socks city”, “kid’s clothing
city”, “footwear capital” and so on (Yeung, 2009), formed by small and medium firms with
strong linkages between them often due to family and social relationships in a specific
town. The most common features of Chinese clusters are: the high division of labour, low
capital barriers to entry, low reliance on external financing and fierce competition (Long,
2010). The benefits from clusters are expanding over the time due to historical, locational,
and institutional factors. In general, it is well recognized that clusters improve industrial
competitiveness through different channels as product specialization, strong relation
between firms that creates business value chains and the stimulation of productivity and
innovation (Douglas, 2011).
3.3.1 Gains from clustering
It is considered that China’s manufacturing clusters compete basically with low price,
cheap materials, labour flexibility and low-cost labour. Criticism about unfair conditions
has been extensively done by different institutions around the world, what has pushed the
central government to support cluster´s development, and its upgrading process (Wang,
2009). However, most clusters in China are still labour intensive with a low use of capital
when comparing to the not clustered firms. These features are in line with the country’s
comparative advantage, low capital-labour ratio and high population density, what explains
the fast development of some clusters (Long, 2010). Different conditions, due to the
characteristics of the Chinese economy, have helped the growth and development of
industrial clusters and have brought benefits for firms and industry competitiveness.
First of all, the efficient division of labour, within value chains and the constitution of
strong production networks, are considered advantages for clusters competitiveness in
China. Small and Medium Enterprises (SME) focus their operation in a specific phase of
production, in some cases production tasks are developed in family workshops. At first
stages of the cluster formation, there are low capital and technological barriers, what
facilitates the entry of new firms and also increases the competition (Yeung, 2010). The
20
children´s garment cluster in Zhili Township, located in Zhejiang Province is a good
example where the whole town is involved in the production of these goods with more than
6,000 enterprises, about 200,000 workers and account for one-third of the national market
(Fleisher, 2009).
The reduction of credit constraints is another positive effect from clustering in China;
proximity reduces the temptation of dishonesty and makes trade credit possible (Long
2010). Also, when clusters are developed within towns and villages, long term relationships
between family and neighbours become institutional substitutes for court enforcement,
between lenders and borrowers and between buyers and suppliers (Fleisher, 2009).
Moreover, the importance of proximity between firms within the same industrial sector is
crucial to compete and capture domestic and foreign markets, since clusters create a
collective image for firms that facilitate marketing capabilities and the positioning of
products, what become a competitive advantage and a public good for all firms within the
cluster (Kang, 2007).
In addition, the development of clusters has brought the government support. Infrastructure,
institutions and a good business environment have been placed within successful clusters.
The construction of specialized markets, logistic business centres, industrial parks, public
technology innovation centres, and also preferential legislation like tax reduction, are some
of the strategies used. An example is the implementation of regulation that keeps
production standards and has become very common among the different clusters, for
instance, the Quality Control and Inspection System in the Cashmere Marketplace in
Puyuan, Tongxiang. However, this support comes when the cluster has shown its
potentiality; usually, at first stages, SMEs base their production process in cheap labour,
family capital and old techniques.
There are different constraints that affect the long term development of clusters. The main
constraint is the difficulties on implementing technological improvements. It is considered
that intense price competition, common within Chinese natural grown clusters, leads to the
deterioration of quality (Kang, 2007) in this cases competition does not generate efficiency
21
or innovation. Also, difficulties arise from shortages of skilled labor, weak management
capabilities and lack of training programs, what limits a step forward on the value chain
(Yeung, 2010). Despite those problems, studies have found that firms within natural
clusters have a better performance in terms of sales; also, their ability to reach markets is
superior than for outsider firms (Mytelka, 2000). It has been recognized that government
support, especially in the upgrading process, is crucial for the survival of clusters; because
of these the local governments in China have developed different policies to forecast
clusters capabilities. There are still problems to overcome in terms of quality, rising wage
costs and institutions that enforce market relations; however, there are positive cases where
clusters have brought the attention of local governments, and public goods and institutions
needed have been placed. In general, the government commitment and its awareness about
the importance of clusters are positive signals of future cluster development.
3.4 Top down versus Bottom-up: The China case.
In Table 2, there is a detail description of clusters and industrial parks development, which
summarizes their advantages and constraints what explained before. From the table, a main
difference between bottom-up and top-down approach can be identified. Successful natural
grown clusters are based on linkages between firms and strong collective action that allow
to face market failures and generate a collective image that helps in capturing markets; on
the other hand, policy-driven initiatives forget, in some cases, the importance of linkages
between firms, focusing on capturing capital and investment to support the infrastructure
and future development of the sites. There are successful cases of industrial parks and
clusters; however, there are also concerns for their long term development.
Moreover, clusters are established over time, where market forces and natural conditions
evolve and create linkages between economics agents, in this cases, the government role is
to speed up the process, for example through industrial parks promotion that brings the
infrastructure needed to enhance the clusters operations. However, the characteristics of
regions and market's needs should be taken into account where industrial parks are
constructed, and a careful planning about investors and sector specification must be done.
It is considered that a combination of bottom-up and top-down approach is an ideal policy,
22
specially for developing economies, where it is crucial to enhance innovation and
accelerate the developing process (Douglas, 2011).
Table 2 Top-down vs. Bottom up approach in China.
Main Features
Origen
Industrial Parks- Top-down
Clusters – Bottom-up
Government initiatives to attract investment in
specific areas.
Different factors evolve spontaneously and create the
necessary linkages between firms, the market and
institutions that support and enlarge the development of
markets.
Difficult to develop, big capital investment, need of
government commitment
Emerge spontaneously, over time
It is composed by SEO, foreign and private, medium
It is composed by domestic small and medium firms
and large firms
Localization
In its construction has to be considered the
capacities and the quality of institutions of the
region.
Long history of production and business activities in a
particular sector, which allows for tacit knowledge and
skills that are transmitted by generations.
Mainly in Costal Areas
Usually located where there are abound human and natural
resources, mostly in towns or major cities.
There is proximity between firms, however not from Firms emerge to support a common market and they are
the same or related sectors
specialized in related products within the same sector.
Characteristics
Operate within more technology and capital
intensive sector
Operate in low technology and labor intensive sectors
Relies on government support
Relies on face to face contact and strong social relations
developed between firms; for example, to lower financial
constraints. The government's support is received after the
cluster shows its potential.
Better infrastructure and transport facilities
Low entry barriers and capital investment
More foreign investment
There are successful cases, where the products compete in
foreign markets
Strong links with the foreign market
Source: Douglas (2011); Long (2010)
Chapter 4 Empirical Analysis
4.1 Research Design: Modelling Agglomeration Effects
This paper asks if there is a positive effect between agglomeration economies and firm´s
performance and innovation. Most of the studies have approached the subject using
aggregate data and it is considered that there is little evidence to support the importance of
agglomeration on the performance of firms (Burger et al., 2008). This study wants to
contribute in this field using a micro-level approach.
A number of researchers have analyzed this phenomenon using different methods;
Andersson and Lööf (2009) study the relation between agglomeration effects and labour
23
productivity; they find a positive effect using the size of regions as a proxy for
agglomeration, their main conclusion is that firms located in larger regions become more
productive. Malmberg et al. (2000) focus on the relation between scales economies and
urbanization economies on firm´s export performance, its results expose a positive relation
between them; however, linkages between firms did not appear to have a significant
impact. When looking at firm’s survival and employment growth, Bruger et al. (2008)
conclude that agglomeration economies have a limited effect that depends on the size of the
firm. On the other hand, Konoben (2001) find that a firm’s characteristics determine the
relation between agglomeration advantages and firm´s performance, where firm´s size and
face to face communication are found to be crucial factors that moderate this relation.
When looking at agglomeration effects, advantages can be found from the use of a firmlevel approach. To begin with, the theory is based on the relationship between the external
local environment and individual firms; when controlling a firm´s attributes, the effect of
these external forces can be estimated. Moreover, a micro analysis allows reducing
heterogeneity, when controlling for firm´s features the internal attributes which could affect
the performance of firms are controlled (Andersson, 2009). And, by using firm level
method, I avoid the common critiques to economic geography studies, in terms of the use
of highly aggregated industry units (Malmberg, 2000).
The empirical model develop in this study is similar to Malmberg (2009), who looks at the
relation between export performance and agglomeration economies, where different
measures are constructed to determine the importance of urbanisation, localisation and
scale economies. However, in this study, I apply two different proxies for agglomeration:
industrial parks and linkages with other firms within a city. The proxies are chosen for two
reasons; first, industrial park´s effect is assumed to be external to the firm, where urban and
external economies are available for all firms within a limited area. Second, the relation
with other firms evaluates the effect of linkages and proximity between firms with similar
conditions and technology. Different studies have used firm’s linkages as a proxy for
agglomeration (Amiti 2007), and in my analysis, it is also used to describe cluster’s effect.
As in other studies, which have used firm-level data, I control by a set of firm´s features
that can affect firm´s performance and innovation.
24
4.2 Data
The data for this study is from the 2003, World Bank Investment Climate Survey. The
dataset contains information from 2400 domestic Chinese firms of which 604 are located
within an industrial park. In the survey, there are relevant questions that could help us to
determine the relationship between agglomeration effects, firm’s performance and
innovation. In general, the survey covers aspects such as labour relations, constrains for the
establishment, finance, the firm's performance, infrastructure and services, conflict
resolution, government-firms relations, legal environment, crime, capacity and innovation
(Dollar, 2004).
The survey includes domestic firms which have been randomly chosen in terms of sector
and location; more specifically, the firms have been selected from different regions of the
country. However, there are some limitations in the database that could affect the results in
the econometric analysis. Among these limitations are that firms located within inland
regions show a small participation (Dollar, 2004), the survey also covers medium and big
firms, giving a limited picture of the whole business environment; in addition there are a
high proportion of state owned enterprises in comparison with other forms of legal
ownership. However, the dataset is still a good core to analyze the effectiveness of IPs and
clusters, as it contains information about both, firms located within and outside industrial
parks (For a better understanding of the survey, see Dollar et al. 2004).
As previously stated, agglomeration theory highlights the positive benefits from proximity
and economies of scale. To analyse these benefits and its relation with firm´s performance,
I have estimated a regression model that considers the theoretical background exposed in
chapter 2.
4.3 Variables
Dependent Variables
I study agglomeration advantages on firm’s performance in two domains: sales growth and
innovation. In the first model, the firm’s economic performance is determined by the firm’s
sales growth in 2002, in order to use the absolute value of sales in other years as a control
variable, this method has been applied in previous firm level studies (Opper, 2010) when
25
using the same dataset. Sales growth is considered a measure of firm´s performance, since
it captures the firm's survival capacity and its ability to enter new markets. Economic
theory determines that the drivers of firm's growth are the firm’s internal factors and the
market environment (Bahadir, 2005); therefore in the model I have controlled by firm´s
features that makes possible to observe the effect of the market environment.
In the second model, innovation is considered as a share of new products introduced by a
firm since 1999. The term innovation covers different activities that vary between firms,
like the introduction of new products, new processes and patents, among others. It is also
considered that the use of yes or not question to measure innovation, allows to categorise
firms into innovative or not (Roger, 1998). From the survey, 40 % of the firms have
introduced at least one product to the market between 1999 and 2003.
Independent Variables
In many agglomeration studies it is believed that disentangling concepts such as clusters
and industrial districts are fruitful and necessary to study agglomeration effects (Knoben,
2011). In this study I use two proxies to capture both concepts.
Industrial Park. A dummy variable is created where I give the value of 1 if a firm is located
within an industrial park or the value of zero for a firm which is located outside. The survey
asks if the firm is located in an industrial park, a science parks or an export processing
zone; 30% of the firms are located within these areas. The variable intends to capture the
benefits of urbanization economies and external economies available to all firms placed
within a specific location, irrespective of the sector they belong (Burger, 2005). This
pattern is the most common within IPs in China, where firms are placed irrespective of their
sector of origin, and through the sharing of infrastructure, labour markets and institutions
may develop certain advantages. With this variable, I try to evaluate IPs as a policy
instrument that tries to enhance firm’s development.
Suppliers and Competitors within a City: Two variables were created to measure cluster
effect, firm’s relation with suppliers and firm´s relation with competitors. The question
used as a proxy is the percentage of competitors and suppliers located within the same city.
The variable is considered as a good proxy due to its capacity to capture firm’s links with
26
other firms, since clusters are considered as dense networks of interrelated firms that take
place in a specific place or region (Porter, 2007). Most specifically, the variables capture
localisation economies that are considered the benefits for firms within the same or similar
sector which arise from being situated close to each other. Different studies have used
external links with other firms as a measure for agglomeration (Burger, 2005; Amiti, 2007).
Amiti (2007), uses industry data to estimate linkages with potential suppliers within the
same industry and district. However, the dataset used in this study allows me to look at the
actual level of suppliers within the same location, which could be considered as a better
approximation. When using competitors, two different aspects are captured: first, the
proximity between firms that share related technology, which is considered desirable to
generate spillover effects (Amiti, 2007). Second, when city is used as a determinant for
localisation, it is made certain that the distance between firms allows to face-to-face contact
which in turn cultivates the exchange of knowledge (Burger, 2005).
Control Variables
The additional economic variables used to control firm’s features have been selected based
on firm-level studies:
Firm Size: It is measured by the natural logarithm of total assets in 2003. This variable
represents the internal economies of scale, since the unit cost of production for firms is a
decreasing function of output (Burger, 2005). I also include this variable, to capture highly
heterogeneous firms within the dataset. Also, since larger firms are usually more attractive
to locate within industrial parks and clusters are characterized by the presence of small and
medium firms, it is important to control by this feature.
Ownership: I divided the database between private and government owned firms, with the
idea of differentiating between agglomeration effects and the form of ownership. In 22% of
the cases, the government owns more than 50% of the firm. This control variable has been
used by Amiti et al. (2007) to control by firm heterogeneity.
Industry: Eight dummy variables have been constructed for industry with the idea of
capturing the effect of differences in fixed costs and industry mark-ups (Amiti, 2007). This
variable has been included in various agglomeration studies (Andersson, 2011; Amiti,
2007; Knoben, 2001), the reason being that traditional sectors are considered to be more
27
aggregated, therefore when controlling this aspect, it is possible to look at specific effects
determined by a firm´s location. Also, industry policies in China are sector specific and can
affect directly firm´s performance.
Firm's age: The likelihood of being located within an industrial area can be affected by the
age of the firm. Also, previous studies have determined that older firms have advantages in
innovation as they are more likely to access loans, assets, among others; and, that newer
firms are more flexible and fast in this aspect (Kanoben, 2008). The natural logarithm is
taken for this variable.
City: I have controlled by city level to separate its effect, from the agglomeration
advantages. A city can offer better infrastructure and business environment that should be
differentiating from the features of industrial parks and clusters.
Export license: It is considered that firms that are able to compete abroad have more
capabilities since they must meet foreign standards and its innovative capabilities may be
affected due to the contact with other markets (Malmberg, 2000). A dummy variable is
used, which capture whether the firms has an export license.
Manager's education: The performance of the firm is related to the manager’s capacities;
managers are in charge of the utilization of internal assets and the operational efficiency of
the firm (Bahadir, 2005). From the survey, a dummy variable is created where I give the
value of 1 when the manager has completed undergraduate education or higher, and 0
otherwise.
Table 3 reports summary statistics of the data used. I have applied logarithm to all
continuous variables, as they were skewed to the left since the transformation helps to
reduce the impact of outliers. Among the most important features, the size of the firm
determined by the firm´s assets shows that the survey represents mainly medium and large
firms, whit 25 % with more than 1 million yuan on fix assets. Moreover, the correlation
matrix (Appendix 1) shows that the independent variables are not heavily related to each
other, with pairwise correlation coefficients from -0.21 to 0.30. Also, there is a weak
positive correlation between competitors and suppliers within the same city (0.31) however,
to analyse agglomeration effect it is important to look at the relations of firms within a
limited space, and those variables may capture this relation. When looking at the
relationship between industrial parks and firm´s age, there is a negative correlation (-0.23)
28
that indicates that older firms are slightly less likely to be located within IPs. On the other
hand , the matrix shows that an agglomeration´s proxies are independent from each other;
industrial parks, and suppliers and competitors within the same city correlates negatively,
however, the correlation is rather low (-0.12,-0.11). In terms of innovation, there is a
positive correlation with the industrial park variable with a low value (0.20), which is not
surprising as firms within these sites have larger investment in R&D, also in some cases
these sites are specialised in high-tech sectors. The variance inflation factors also confirmed
the low correlation between variables with values around 1, so in this model there is no
evidence of multicollinearity.
Table 3. Summary statistics
Variable
Observations
Mean
SD
Min
Max
Sales Growth 2002
2354
22.4
93.3
-100
897.56
Innovation
2335
0.4
0.49
0
1
Industrial Park
2353
0.25
0.43
0
1
% of Suppliers within a city
2212
26.3
32.2
0
100
% of Competitors within a city
2274
23.3
31.7
0
100
Firm age (Log)
2399
2.42
0.79
1.09
3.97
Value of sales 2001 (Log)
2372
9.08
2.25
0.19
18.86
Total fix assets 2002 (Log)
2395
8.93
2.43
0.18
17.7
Export activity
2277
0.23
0.42
0
1
Government
2399
0.21
0.41
0
1
Dependent Variables
Independent Variables
Agglomeration
Firm´s features
Source: Investment Climate Survey 2003
4.4 Methodology and Results
The general model that I use for the empirical analysis is expressed as:
Yi = βo + β1 Ai + β2 Xi + ci + si + u
In the equation, Y is the vector that captures the performance of the firm in terms of
innovation and growth. A is defined as the vector for agglomeration effects; X reflects the
29
vector for the control variables at a firm level; ci and si denotes the control variables for city
and sector.
4.6.1 Results
I run separate regressions to look at sales growth and innovation performance, the results
are presented in table 4. The first column gives the estimates for sales growth using OLS;
and the second column presents the results when looking at the relation between innovation
and agglomeration effects, the estimation is done using the probit method.
For sales growth (Model 1), the main finding is that firms located within an industrial park
have a positive advantage over outside firms; this means that firms located in industrial
parks have higher sales growth, all else equal. In terms of cluster relations, none of the
variables seems to have a significant effect in sales performance. The results suggest that
the effect of proximity is consistent to hypothesis 1, meaning that agglomeration effects
contribute to firm´s performance. From the model, the industrial parks variable has a
coefficient 0.20 and it is significant at 5%; if the firm is located within an IP its sales will
grow faster than for an outside firm. In the case of China, aspects related with urbanisation
economies that are common for all firms, like infrastructure and better transport facilities,
have a positive effect on the performance of the firm. However, linkages with other firms
do not have the same effect; relations with competitors has a positive sign what is expected
from the theory however the relation with suppliers has a negative sign, both measures are
not statistically significant. Having a higher percentage of firms related to the same
business line in the local environment does not have an effect on sales growth. One of the
interviewers highlighted that transport facilities are good enough within the area and that it
makes no big difference to having suppliers within short distances or not.
30
Table 4. Agglomeration effects and firm´s performance
Sales Growth
Innovation
Coefficient
Coefficient
(t-ratio)
(z-ratio)
Agglomeration Effects
0.20**
0.29***
Industrial Parks
(2.03)
(3.93)
-0.00
-0.00**
Suppliers city
(-0.09)
(-1.98)
0.00
-0.003**
Competitors city
(0.09)
(-3.05)
Firm's features
-0.16**
0.00
Log firm age
(-2.37)
(0.22)
-0.32 ***
0.18***
Log Sales 2001
(-8.05)
(6.83)
0.05 ***
-0.003
Log Total assets 2002
(4.67)
(-0.11)
-0.176
-0.035
Government
(4.67)
(-0.42)
0.23 **
0.12
Exports
(2.09)
(1.44)
0.06
0.25**
Education
(0.54)
(2.84)
Industry
Yes
0.89**
(2.38)
0.36 **
(2.52)
0.38**
(2.01)
0.38 **
(2.19)
0.51 *
(1.83)
-0.15
(-0.26)
0.90 **
(2.51)
0.89 **
(2.25)
1. Garment & Leather
2. Auto parts
3. Electronics
4. Information Technology
5. Chemical Products and
Medicine B.P
6. Food Processing
7. Metallurgical Products
8. Business services
City
Yes *(1)
3.41
(7.22)***
OLS
0.11
1194
Yes
0.242*
(1.80)
0.252 **
(2.03)
0.37 ***
(2.65)
0.36 ***
(2.58)
0.510
(1.41)
0.799 *
(1.91)
0.305 **
(1.98)
1.08 **
(2.10)
No (2)
-1.25
(-2.65)**
Probit
0.13
1950
Constant
Method
R^2
N
*p < 0.10 **p <0.05 ***p<0.01
(1) Changsha, Kunming, Nanning, Wenzhou, Wuhan: Non
significative
(2) Chongqing, Haerbin *
Firm's age and size have significant coefficients and present the expected signs, which can
support the consistency of the model with the theoretical analysis. Among the control
31
variables, export has the strongest effect on firm´s performance, which supports the general
hypothesis that firms which are able to compete in foreign markets have some advantages
over firms focus on domestic markets (Malmberg, 2000). Also, industry sectors and city
variables are significant and positive, which signals that sectoral composition and
localisation are crucial in determining firm’s performance.
A probit model (Model 2) has been conducted to look at the relation between firm’s
innovation and agglomeration; the results are presented in column 2. The model evaluates
whether firm´s innovation status is determined by agglomeration effects and firm´s
features. The main conclusion is that being located within an industrial park increases the
probability for firms to engage on innovative activities. Relationships with suppliers and
competitors within the same city are also significant; however, there is a negative relation
but the coefficient is very small in magnitude. The hypothesis 2 is supported with the
results, proximity enhances the innovation capabilities of the firms when looking at
industrial park's effect, the coefficient has positive sign as expected and is significant at
0.01%.
However, it is not clear whether spillover effects, due to face-to-face contact with other
firms have a positive significant relation. There is the possibility that within industrial
parks, a firm develops strong relations with other firms, but in the model, the importance of
the actual relationship with suppliers and competitors is not significant. Among the control
variables, the education of the manager has a significant and positive effect on innovation,
what is expected from the theory. Moreover, sector composition is relevant to increase the
probability of firm’s innovation activities; although city variables do not have the same
effect.
The above models have an acceptable but not very high R-square and pseudo R-square
(0.11 and 0.13 respectively), implying that some of the variation of sales growth and
innovation can be explained by the variation of the explanatory variables included in the
model. To determine the consistency of the findings, I determine whether industrial parks
present advantages for the firms due to firm’s performance. Measures are applied to avoid
32
reverse causality, i.e. whether a firm's performance determines its presence within an
industrial park. I applied lagged measures for firm size and the substantial findings remain
the same. Also, I observed whether the results are sensitive to the spatial scale; for this,
district level variables are used instead of city level as control variable, and interaction with
suppliers and competitors is taken at district level; as before, all main findings were
confirmed.
5. Conclusions
This study presents a simple test for agglomeration economies at firm level. Localisation
within industrial parks and the relations between the firm, its suppliers and competitors, are
assumed to reflect the effects of agglomeration economies. This paper investigates the
relationship between agglomeration and firm’s performance, and provides evidence that
being located in industrial parks brings benefits for the firm; however, when looking at the
effect of linkages between firms, there is not evidence of a relation with the performance,
and a negative effect on innovation.
Three questions were studied; first, does proximity create agglomeration effects that benefit
the performance of firms? Second, due to proximity, is innovative capacity higher in an
industrial park or cluster? Third, do industrial parks, as a top-down approach, generate
comparable effects as natural-grown clusters? From the empirical exercise, three aspects
are indentified. First of all, agglomeration theory highlights advantages from proximity; the
dataset used, gives evidence of a positive relation between firm´s performance and
proximity, when looking at industrial park's features. In China, industrial parks are
characterised by their superior conditions in terms of infrastructure and facilities, where in
most of the cases, firms are located irrespective of the sector they belong. The results can
be generalized by saying that proximity through urban economies produces positive effects
on the performance of firms.
Second, industrial parks generate positive effects on firm’s innovative capacity, while
linkages between firms do not have any significant impact. Proximity between firms which
belong to similar sectors has negative effect on the innovative capacity, which goes against
agglomeration theory. As previously explained, small firms rely on local institutional
33
arrangements and cooperation, however the dataset used in this study includes medium and
larger size firms which can give biased results. Finally, as a top down approach, industrial
parks offer infrastructure and conditions that benefit firm’s performance and innovation;
the positive effects of natural grown clusters are not clear from the model, however, its
importance for the Chinese economy is substantially significant for industrial development,
meaning that a combination of both methods could enhance the future development of
clusters and the competitiveness of the Chinese domestic firms.
The analysis in this paper can be extended in future studies. For example, the relation
between city features and agglomeration economies could help to determine how the
characteristics of the environment shape the effect of the agglomeration economies. The
importance of continued research on this field is due to the increase interest of local and
industrial policies, in supporting industry agglomeration and proximity between firms.
34
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Appendix 1. Pairwise Correlations
Variable
1. Sales Growth
2. Innovation
1
2
3
4
5
6
7
1.000
0.065
0.171
1.000
4. Suppliers C
-0.031
-0.109
-0.147
1.000
5. Competitors C
-0.025
-0.105
-0.110
0.311
1.000
7. Log total assets.
-0.057
0.253
0.116
-0.100
-0.181
1.000
6. Log firm age
-0.053
0.001
-0.215
0.033
-0.050
0.253
1.000
0.032
0.183
0.240
-0.134
-0.171
0.377
-0.044
-0.047
-0.014
-0.188
0.044
0.038
0.244
0.004
0.182
0.164
-0.139
-0.112
0.264
8. Export activities
9. Government
10. Education
9
10
1.000
-0.017
3. Industrial Park
8
39
1.000
0.416 -0.085
-0.062
0.156
1.000
0.066
1.000