China urban competitiveness in industrialization: Based on the panel data of 25 cities in China from 1990 to 2009

Abstract

There is a consensus in China that industrialization, urbanization, globalization and information technology will enhance China’s urban competitiveness. We have developed a methodology for the analysis of urban competitiveness that we have applied to China’s 25 principal cities during three periods from 1990 through 2009. Our model uses data for 12 variables, to which we apply appropriate statistical techniques. We are able to examine the competitiveness of inland cities and those on the coast, how this has changed during the two decades of the study, the competitiveness of Mega Cities and of administrative centres, and the importance of each variable in explaining urban competitiveness and its development over time. This analysis will be of benefit to Chinese planners as they seek to enhance the competitiveness of China and its major cities in the future.

Keywords

Chinese cities competitiveness industrialization

Introduction

Against the backdrop of globalization and rapid technological change, the economic performance of cities has emerged as the central element in assessing competitiveness. Economic performance objectives are being affected by demographic changes, technological advancement, development of urban assets, such as education and culture, public security, and transportation and communication infrastructure, as well as the effectiveness of government and governance. These aspects have undergone accelerated change in the 1990s, and have stimulated profound changes in the concept of space and time and in the process of decision-making in global activities. The development of the economy and technological change have not only strengthened the role of cities in global activities and local affairs, but have also intensified competition among individual cities, which are often located thousands of miles apart.

In China, the dominant entity in most policy has traditionally been the central government, and city administrations have largely implemented policy at the local level that has been set at a higher level. But China has been implementing decentralization reforms since the 1980s, which have granted municipal governments more authority and responsibility. Industrialization, information technology, marketization and globalization in China have brought not only significant opportunities for development of the local economy but also unprecedented challenges for Chinese cities. With each city trying to advance its relative competitive position, the competition among cities is increasingly intense. This promotion of the sustainable prosperity of cities works to the advantage of the nation as a whole by increasing competitiveness of both the city and the nation.

In recent years, scholars have devoted themselves to the study of urban competitiveness. First, regarding the formation of urban competitiveness, some scholars (Begg, 1999; Haughton and Sirin, 2003; Lever and Turok, 1999; Storper, 1997; Webster and Muller, 2000) emphasise the necessity of creating an attractive environment so as to enhance the quality of life for residents. However, this concept, open to multiple interpretations, can lead to confusion in policy formation. Second, the measurement of urban competitiveness involves several economic factors such as efficiency and growth, but existing indices of measurement of urban competitiveness (Lever and Turok, 1999; Organization for Economic Cooperation and Development (OECD), 2006; Porter, 1990) are unable to reflect the characteristics of city space. Third, the study of effective factors of urban competitiveness is also important. Sinkiene (2008) studied several perspectives that clarify competitiveness factors from different angles, i.e. the economic and strategic, the structural-dynamic, the internal and internal-external and the actor-condition.

All researchers have emphasised acquired high-end factors as being critical, because they have selected sample cities in the advanced stage of economic development. This analysis has less compelling explanatory value for China, which is still in its development stage. Thus, the theoretical frameworks need to be more discriminating in order to identify effective factors of urban competitiveness according to the stage of development.

This study builds directly on Ni (2001, 2003), who analysed urban competitiveness from the perspective of the Bowstring Model (explained below). Profitability of a city’s firms is the target, while the general business environment and the industrial system are related to the value system. Gross domestic product (GDP), GDP growth, GDP per capita and labour productivity are used to build a comprehensive index of urban competitiveness by the principal component analysis method with data for 24 Chinese cities. The soft and hard factors of urban competitiveness are divided into 12 elements. The results show that capital and culture are the most important factors (Ni, 2001). Ni (2003–2010) conducted a series of studies on the continuous improvement of the urban competitiveness index and the relative effective factors analysis. On the basis of these studies, Ni and Kresl (2006, 2008, 2010, 2012) studied global urban competitiveness, but did not conduct standard regression analysis or test effective factors of urban competitiveness in the index system, which is the goal of this paper. Ni’s index measuring urban competitiveness is also unable to reflect the characteristics of city space.

Based on the research of previous scholars, and especially that of Ni, we will develop a universal comprehensive index of urban competitiveness in this paper based on our understanding of urban competitiveness. We will then estimate a competitiveness index for 25 Chinese cities during the industrial acceleration stage (1990–2009) and identify historical trends. Then, we will develop and analyse a new conceptual framework and new hypotheses. Finally, we will offer our principle conclusions and their implications for public policy.

Building an urban competitiveness index and measurement analysis

China’s size and that of its cities make it possible for us to examine two interesting hypotheses. The first hypothesis has to do with Mega Cities, cities with populations of 10 million or more. Much has been written about them in laudatory tones¹– that they have a special place in the economy of today and tomorrow, that they are places to be admired and that they are the key to national competitiveness. No other country has as many Mega Cities as China, which is home to the Mega Cities of Shanghai, Beijing, Tianjin, Guangzhou and Shenzhen. Do they live up to this billing? Using natural logarithms for population and GDP per capita in purchasing power parity (PPP) of a sample of 78 OECD metro-regions (2002), the OECD found there to be a positive correlation between city population size and income for cities with fewer than 10 million inhabitants, but a negative correlation for cities with more than 10 million. The second hypothesis is that administrative cities may have an advantage over other cities. This same OECD study identified several advantages for capital cities and found them to have higher incomes than non-capital cities.² China has national and regional administrative centres, as well as cities that are economic in their function. This begs the question, which categories are the most interesting from the standpoint of urban competitiveness in China, rather than in Europe?

Building an urban competitiveness index

Without immersing ourselves in the rich literature devoted to defining urban competitiveness, we will simply take it to be the condition in which one city is relatively more efficient at producing wealth and at providing welfare for its citizens than other cities, regardless of city location, function and industry. The ability of an economy to generate more output from a given supply of inputs comes from a city’s competitive advantage. The best indicators to reveal urban competitiveness may be a city’s total productivity and employment rate (Kitson et al., 2006). These data cannot always be obtained and may be inaccurate; however, the combination of both is similar to a city’s economic output represented by GDP.

Therefore, the comprehensive index of urban competitiveness (IUC), which we will calculate, is composed of three targets: GDP, GDP/km² and the growth rate of GDP. The full methodology and results are too extensive for inclusion here, but are presented in the Appendix to this paper.

GDP is a well-understood concept and need not be discussed here. GDP/km² reflects all output produced by economic activities in a certain physical space. It encompasses all economic rents and economic profits. Second, it indicates the density of wealth creation and accumulation, both in scale and in efficiency. Third, it shows the quality and structure of urban economic activity in the city space. Fourth, it jointly reflects input and output rates, factor utilization and factor endowments. In short, it comprehensively reflects the spatial concentration of a city’s economic activity and is a necessary component of an index that indicates urban competitiveness.

The growth rate of GDP allows one to ascertain whether economic growth in a city, as well as the pace of improvement in residential quality of life, is fast or slow in relation to other cities.

In comparing and evaluating a group of samples through multiple indicators, one encounters a number of issues, such as multi-indicator synthesis and information overlap owing to correlation among some indicators. To handle these issues, one can adopt principal component analysis or factor analysis. Principal component analysis is an analytical method that is used to explain the variance-covariance structure of multiple variables with fewer principal components. As a mathematical procedure, it converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables (principal components), while keeping the information of the original variables and the total variance unchanged. The first principal component has the largest possible variance and the second principal component has the second largest variance. Factor analysis has a similar function to principal component analysis, but it can only explain part of the variation while principal component analysis can explain all the variation. In this paper, the three indicators of IUC have a certain correlation, so we adopt principal component analysis to compute the competitiveness index of the sample cities in China as an indication of the performance of urban economic prosperity (the explained variable), and use the following equation:

IUC = \sum_{i - 1}^{k} θ_{i} Q_{i}

(1)

Q_{i} = a_{i} x_{1} + b_{i} x_{2} + c_{i} x_{3}

(2)

where $Q$ is the principal component, $θ$ is the coefficient as Rotation Squared Loadings, $x_{1}, x_{2}, x_{3}$ , respectively, represent GDP, GDP/km² and growth rate of GDP, $a, b, c$ , respectively, represent the contribution of original indicators $x_{1}, x_{2}, x_{3}$ to principal components, k<3.

Today China has more than 660 cities and nearly 20,000 towns. As Chinese cities are at different stages of economic development, we will limit our study to 25 principal cities, including national and regional administrative and economic centres, each of which is in the stage of accelerated industrial growth and at a higher level of development compared with other cities in China. Based on their function and location, these cities are divided into five categories. The first category of cities is national centres of decision-making or Mega Cities on the east coast: Beijing, Shanghai, Guangzhou and Tianjin. The second is regional economic centres on the east coast: Shenzhen, Qingdao, Ningbo and Dalian. The third is regional administrative centres in coastal areas: Nanjing and Hangzhou. The fourth is inland regional administrative centres: Wuhan, Zhengzhou, Taiyuan, Chengdu, Xi’an, Shenyang, Harbin and Changchun. The last category includes newly industrializing cities (new manufacturing-aggregation cities) in coastal areas: Zhuhai, Dongguan, Zhongshan, Suzhou, Jiaxing, Wenzhou and Tangshan. Figure 1 shows the placement of all of these cities.

Figure 1.

The 25 Chinese cities of this study.

During the past 20 years, most of these cities were in the intermediate stage of industrialization. In 1990, 21 out of 25 cities had secondary industry accounting for in excess of 50% of their industrial mixes. Of the remaining four cities, the share of Guangzhou’s tertiary industry was higher than its secondary industry; the share of secondary industry was consistently lower than 50% in Zhuhai, Jiaxing and Dongguan, and the shares of tertiary industry in these cities are even lower than their secondary industry. In 2009, the share of secondary industry was higher than 50% in nine of the 25 cities, was between 40% and 50% in 12 cities, and was below 30% in only Shanghai, Beijing and Zhengzhou. Over the two decades, most of the 25 cities experienced population growth, with more dramatic increases in eastern coastal cities, especially Shenzhen and Dongguan. The IUC changes every year and some of the changes are quite dramatic. Thus, the position of urban competitiveness one year cannot correctly reflect the full performance of a city with regard to its urban competitiveness. In order to trace the evolution of the urban competitiveness of the 25 cities, panel data was selected for three periods: 1990–1996, 1997–2002 and 2003–2009. The data are obtained from China City Statistical Yearbook (SSB, 1990–2011).

Index of urban competitiveness and the positional changes of cities

Table 1 shows the computed index and rank (1990–2009) using the principal component analysis method.

Table 1.

Index of urban competitiveness (IUC) and positional changes of 25 Chinese cities from1990 to 2009.

Region	Category of city	City	IUC 1990–1996	Rank1	IUC 1997–2002	Rank 2	IUC 2003–2009	Rank 3	Rank 2-1	Rank 3-2	Rank 3-1
East coast inland	National centres	Shanghai	3.229	1	2.872	1	2.405	2	0	−1	−1
		Beijing	1.345	5	1.482	6	1.209	12	−1	−6	−7
		Tianjin	0.987	10	0.829	13	1.284	10	−3	3	0
		Guangzhou	1.775	3	2.153	2	1.927	3	1	−1	0
	Regional economic centres	Shenzhen	1.822	2	2.124	3	2.985	1	−1	2	1
		Qingdao	1.089	8	1.046	9	1.825	4	−1	5	4
		Ningbo	0.859	15	0.788	14	1.080	16	1	−2	−1
		Dalian	0.883	14	0.930	11	1.344	8	3	3	6
	Newly industrialized cities	Suzhou	1.214	7	1.617	5	1.699	5	2	0	2
		Jiaxing	0.427	25	0.341	24	0.754	24	1	0	1
		Wenzhou	1.057	9	0.949	10	0.872	21	−1	−11	−12
		Zhuhai	0.848	17	0.715	19	0.814	23	−2	−4	−6
		Dongguan	0.605	22	0.752	16	1.637	6	6	10	16
		Zhongshan	0.556	24	0.583	23	1.178	13	1	10	11
		Tangshan	0.709	19	0.613	21	1.173	14	−2	7	5
	Regional centres	Nanjing	1.255	6	1.200	7	1.113	15	−1	−8	−9
		Hangzhou	1.683	4	1.748	4	1.299	9	0	−5	−5
Inland		Wuhan	0.986	11	0.760	15	0.857	22	−4	−7	−11
		Zhengzhou	0.834	18	0.610	22	1.072	17	−4	5	1
		Taiyuan	0.571	23	0.257	25	0.892	20	−2	5	3
		Shenyang	0.935	12	0.747	17	1.407	7	−5	10	5
		Changchun	0.687	21	0.650	20	0.911	19	1	1	2
		Harbin	0.703	20	0.855	12	0.923	18	8	−6	2
		Chengdu	0.907	13	1.101	8	1.231	11	5	−3	2
		Xi’an	0.857	16	0.733	18	0.740	25	−2	−7	−9

Table 1 demonstrates that Shenzhen, Shanghai and Guangzhou are the most competitive cities and have performed well on all three indicators. The least competitive cities are Jiaxing and Taiyuan. In terms of location, the more competitive cities are located in the east with higher GDP/km² and GDP growth rates (with the exception of Jiaxing and Zhuhai). Less competitive cities are mostly inland cities, such as Taiyuan and Xi’an, this in spite of central government efforts to stimulate the development of cities and regions in central and western China. In terms of the categories of cities, the more competitive cities are national centre cities and regional economic centre cities, with the exception of Tianjin, for which GDP/km² is comparatively low. The less competitive cities are mostly regional centre cities and newly industrializing cities. Exceptions include Hangzhou, a competitive regional centre city, and Suzhou, a competitive, newly industrializing city. Some newly industrializing cities are even more competitive than regional centre cities. Competitive cities tend to be more populous while less competitive cities have smaller populations. More competitive cities exhibit higher levels of both industrialization and service activity while less competitive cities have achieved lower levels of both, with the exception of Beijing, Zhengzhou and Taiyuan.

As Table 1 shows, the positions of some cities have changed dramatically during different periods. The city with the fastest rise in position was Dongguan, which rose 16 places through the period, while the most rapid fall was that of Wenzhou, which fell 12 places.

It is apparent that the gap in urban competitiveness rankings between inland cities with falling positions and rising eastern, coastal cities has increased over time. However, there are also exceptions. Shenyang, an inland city, has improved 5 positions, while Wenzhou as an eastern coastal city has deteriorated the most among all sample cities.

There are significant differences in the urban competitiveness of newly industrialized cities. Suzhou has been among the more competitive cities, while Jiaxing has been weaker. In addition, the positions of Dongguan and Zhongshan have been rising dramatically, while those of Wenzhou and Zhuhai have fallen sharply.

Beijing, the national centre city, has suffered obvious decline in comparison with other cities. Among regional economic centre cities, Qingdao and Dalian have improved while Ningbo has fallen. Regional centres Nanjing, Wuhan and Xi’an have all fallen, whereas Zhengzhou, Taiyuan and Shenyang initially fell only to rise again. Harbin and Chengdu have risen, dropped, and then risen, while Changchun has stayed stagnant at a lower level.

These results indicate that the Mega Cities of Shanghai, Beijing, Tianjin, Guangzhou and Shenzhen, while ranking between 1 and 12 in competitiveness, have seen negative changes in their competitiveness and have lost their edge to many smaller cities. This conforms to the findings of Xu and Zhu (2009) that smaller cities were gaining in population versus the larger cities, and that this reduced the advantages of larger cities with regard to economies of agglomeration. We also found that administrative centres (national and regional) declined in competitiveness, while economic centres (regional economic centres and newly industrialized cities) increased their competitiveness. This too is in line with the consequences of additional market-based reforms.

Through this comparison, we can understand the urban competitiveness of different cities and their changes in competitiveness over the years, but the more interesting issues are the reasons why the 25 sample cities have differing levels of urban competitiveness and why they have experienced dramatically different changes during the period from 1990 to 2009. Classifying and comparing the changes in the urban competitiveness index and analysing each city with regard to its own characteristics seems to suggest some answers, but not entirely convincing explanations. To answer these two questions, we need to conduct further theoretical and empirical analysis of urban competitiveness.

Driving factors of urban competitiveness: A conceptual framework

Having constructed an index of urban competitiveness, we can now use it to measure the relative competitiveness of each of the 25 Chinese cities in our study. In addition, we can identify the principle factors affecting urban competitiveness and then investigate the extent to which each factor contributes to a city’s competitiveness. Finally, this information can be utilized to enhance an individual city’s competitiveness in relation to other more or less competitive cities.

Many schools of economic theory have interpreted urban competitiveness, including classical, neoclassical, new economic growth, urban growth, new economic geography, ‘new’ institutional economics and so on, by explaining a city’s natural and acquired endowments as its main source of absolute advantage, comparative advantage, competitive advantage and external economies. A city’s competitiveness is usually determined by a variety of factors, each of which connects with and overlays the others. Some cities are, of course, dominated by a very limited set of factors, or a single factor, that relate to a single industry.

We can classify them into two categories: hard factors and soft factors. Utilizing our earlier terminology, hard factors are referred to as the bow, while soft factors are referred to as the string, with each category consisting of a number of specific elements. Based on this, we have constructed the Bowstring Model to analyse the driving factors of urban prosperity.

The remainder of this section will consist of analysis of the hard and soft factors and their specific impacts on urban competitiveness. Following this, we will apply this understanding to the 25 Chinese sample cities.

Hard factors

We have identified nine elements as being components of the hard factors category:

Labour and human capital

Recent research (Abel and Gabe, 2011; Glaeser and Sacerdote, 1999; Glaeser et al., 1995, 2000; Simon, 1998) has empirically verified the role of human capital in urban regional growth. Florida (2002b) and Tuerck (2005) highlight the contribution of human resources to a city’s attractiveness and the generation of both business and innovative and knowledge-based industries. The effect of the labour force or talent on urban competitiveness is represented by the average wage of employees, i.e. the cost of human resources.

Local demand

Krugman (1991) demonstrates that local market demand is not only important to urban competitiveness, but is also likely to generate accumulative consequences. Porter (1990) holds that more critical local demand could force local enterprises to improve the quality and standards of products, so as to enhance both the cost effectiveness and competitiveness of what is produced. Advancement of local demand determines business innovation and access to above-normal profits, or rents. The effect of local market size on urban competitiveness is reflected in retail sales of consumer goods.

Financial and physical capital

Although capital is becoming increasingly mobile, the availability of affordable, local capital is still an important factor in competitiveness, particularly to small and medium-sized domestic enterprises (Webster and Muller, 2000). Moreover, the development of financial institutions and the availability of capital are key factors in the promotion of economic growth (Levine, 1997; Levine et al., 2000). It is clear that investment in fixed assets can reflect urban physical capital investment, while the relationship between resident savings and urban competitiveness can reflect the effectiveness of the financial system.

Technology and innovation

In a globalized economy, the key resources for regional and urban competitiveness depend on localised processes of knowledge creation, in which people and firms learn about new technology, learn to trust each other, and share and exchange information (Malecki, 2003). The presence of an innovative and creative class (knowledge/creative capital) serves to support and underpin an efficient productive base of the regional economy in the form of regional externalities (Kitson et al., 2006). The number and sophistication of universities and research facilities increases the effect of science and technology on urban competitiveness.

Outside connection and openness

Castells (1996) points out that a city ‘is not a place but a process’, indicating that cities exist in interrelated global networks. Markgren (2001), Lundequist (1998), Larsson and Lundmark (1991), Angel and Engstrom (1995) and Ni (2010) all hold the view that global connection is even more important than local connection, since it is an essential way to improve and upgrade a city’s capabilities by participating in the global value chain. Bathelt et al. (2004) conclude that innovation and new knowledge is best understood as a combination of local and global interactions. It is quite clear that a city’s openness to trade and FDI can reflect its connection and openness, both of which are closely correlated, but accurate data for trade are difficult to obtain for Chinese cities.

Infrastructure

Aschauer (1989) and Borensztein et al. (1998) demonstrate physical infrastructure’s ability to facilitate wealth creation with their empirical analysis. UN Habitat (2009) analysis of the 245 fastest growing cities in developing countries shows that investment in transport infrastructure is the most fundamental source of urban development, capable of explaining the growth of more than one-third of cities. The business volume of posts and telecommunication indicates the effect of infrastructure on urban competitiveness.

Agglomeration of industries and cities

Economic agents concentrate in places such as cities in order to capture the advantages of positive externalities and to benefit from non-traded inputs (Jacobs, 1969; Krugman, 1991; Marshall, 1921; Porter, 1996). Recent research has empirically verified the role of both density (Carlino et al., 2007; Ciccone, 2002; Gibbons and Silva, 2008) and diversity (Feldman and Massard, 2002; Florida, 2002a) in urban innovation and growth. A fundamental feature of non-agricultural industry is agglomeration. The proportion of non-agricultural output value to GDP is a good indicator to reflect industrial and urban agglomeration.

Natural geographic location

Despite the fact that improved communications and transport technologies have reduced the importance of natural location to certain degree, a favourable natural location can help attract population and capital, since a location near coastlines and navigable rivers can reduce transport costs to promote trade, a location near natural resources can help reduce production costs, and a location near major cities and urban agglomerations brings low-cost access to markets and factors of production (UN Habitat, 2009). The effect of city location on urban competitiveness is indicated by the proximity of a city to a harbour. If a city is close to a harbour, the dummy index is 1, otherwise the dummy index is 0.

Ecological environment

Meadows (1972) demonstrates that environmental pollution is an important factor affecting economic growth. Quality of life and urban amenities have been found to matter in the location decisions of high-human-capital households (Glaeser and Shapiro, 2001). An observation of cities promoting their prosperity with science and technological innovations shows that many also have the world’s best urban ecological environments and quality of life, e.g. San Jose, Vienna, Stockholm, Helsinki, Seattle, Singapore, etc. A good environment is a factor that enhances city attractiveness.

Soft factors

We have identified the following three variables as being components of the soft factors category:

Government regulation and service

Government plays an indispensable but challenging role in the promotion of urban prosperity. Among other things, it can help compensate for market failure. Well-administered and well-governed cities that are open to new ideas, cultures and technologies can act as a nation’s best catalyst of economic growth and human development (UN Habitat, 2010). We capture the effect of government on urban competitiveness by the variable of total fiscal revenue per capita. Other things being equal, the higher a city’s revenue per capita is, the greater the city government’s ability to provide public services.

Institutional capital

There is a growing consensus that the institutional milieu is one of the key factors in explaining the competitiveness of successful regions, particularly in more developed economies (Aron, 2000; Porter, 1990; Saxenian, 1994; Storper, 1997). Since it is difficult to quantify institutional factors, the effect of the institutions on urban competitiveness is represented by the ratio of general budget revenue to GDP. This represents the tax and fee payments of residents and enterprises in a city. The lower the index of the tax and fee burden is, the more conducive to competitiveness the regulatory regime.

Culture and social values

Culture directly influences individual behaviour through its impact on values and preferences (Rabin, 1993), is the dominant resource and decisive factor of production (Drucker, 1995) and can be translated as the social norms and the individual beliefs that sustain Nash equilibrium as focal points in repeated social interactions (e.g. Greif, 1994; Myerson, 1991; Schotter, 1981). The correlation coefficient for the basic talent index and cultural amenities is positive and significant (Florida, 2002a). The range and quality of cultural facilities and assets serve to support and underpin an efficient, productive base to the regional economy in the form of regional externalities (Kitson et al., 2006). Considering the availability of data, this paper only employs the ‘number of theatres’ as the indicator to reflect cultural facilities.

All of these factors are important at various stages of the development of urban competitiveness. Starting at a city’s most basic level of economic development, the primary hard factors of production, specifically, natural endowments, are critical as sources of absolute and comparative advantage. In the middle stages of economic development, the primary or hard factors, advanced hard factors and soft factors are critical. In the advanced stage of economic development, the acquired, high-end soft factors are critical. Based on this framework, we suggest two hypotheses. The first is that all of the aforementioned factors are important to China’s urban competitiveness. The second is that some low-end hard factors are the primary contributors to the competitiveness of China’s cities, because China has been in an economic take-off period.

Based on the foregoing discussion, the variables we will use in our examination of the impacts of hard and soft factors on urban competitiveness in the following section of this paper are as follows: wage = average wage of employee; savig = residents’ savings deposits at the end of the year; invet = investment in fixed assets; nagdb = ratio of non-agriculture value added to GDP; postal = volume of posts and telecommunication; dharbor = proximity to a harbour; ntea = number of theatres; plreven = ratio of general budget revenue to GDP; sale = retail sales; fdi = foreign direct investment; univer = number of universities; and reven = revenue per capita.

Empirical results and analysis

In this section we will discuss the model we have constructed on the basis of the material presented in the previous section, and then present the results of our empirical analysis of the urban competitiveness of 25 Chinese cities.

Building a measurement model

Our study of the main factors affecting urban competitiveness and the importance of each factor in contributing to urban competitiveness uses the following empirical model:

U C_{it} = ϕ_{j} \sum 1 n (X_{it}) + α_{t} + μ_{i} + ε_{it}

(3)

where $i$ is city, $t$ is time. Panel data in 25 cities from 1990 to 2009 is used in this empirical analysis. $α_{t}$ is the time effect, $μ_{i}$ is the individual effect, $ε_{it}$ is the error, $X_{it}$ is a set of explanatory variables representing effective urban competitiveness, and $ϕ_{j}$ is the explanatory variable coefficient. All data on explanatory variables are obtained from China City Statistical Yearbook (SSB, 1990–2011), China City Construction Statistical Yearbook (Ministry of Housing and Urban-Rural Development (MOHURD), 1990–2011).

In processing the panel data, several key aspects should receive primary attention. First, a unit root test is conducted to analyse the stability of the data. Second, we must carry out co-integration and model updating. Third, we must develop the panel data model and make the choice of regression.

Unit root test of panel data

There are a variety of unit root test methods for panel data, which apply to different scopes with their own advantages and disadvantages. To avoid deficiencies of a single method and to better fit the characteristics of the data samples, this paper adopts the LLC test, IPS test, ADF test and PP test for unit root testing of the variables. Results are shown in Table 2.

Table 2.

Panel data unit root test.

Variables	LLC	lPS	ADF	PP
IUC	−13.382***	−11.184***	215.841***	255.597***
ln(nagdp)	−18.723***	−16.656***	476.960***	301.327***
ln(fdi)	−8.331***	−4.803***	126.559***	217.247***
ln(saving)	−13.332***	−5.778***	123.934***	195.710***
ln(invest)	0.562**	5.764***	19.973***	32.148***
D(ln(sale))	−11.902***	−10.853**	205.354***	261.449***
ln(nthea)	−3.441**	−2.633**	71.816**	72.011**
D(ln(reven))	−23.712***	−20.023***	378.215***	883.908***
diharbor	−10.981***	−8.923**	221.311***	242.126**
ln(wage)	−8.560***	−1.772**	75.003**	79.464**
ln(plreven)	−5.293***	−6.084***	122.991***	122.178***
ln(postal)	−12.574***	−4.932***	107.411***	153.643***
D(ln(univer))	−12.422***	−10.801***	204.264***	240.601***

Notes: Hereafter, ***, ** and * indicate the significance level of critical value to test value at 1%, 5% and 10%, respectively.

Stationary results show that variables selected in this paper have all passed the significance test at least on the 10% level, i.e. they are stationary without unit root, so there is no need for a co-integration test of the panel data.

Model estimate and result analysis

This paper uses STATA 11.0 to conduct empirical analysis of the panel data. The first step is selection of the panel data model. Among the mixed regression model, the fixed effects model, and the random effects model, the Wald F Test (F = 18.46 > F0.05(24, 426)) suggests that the mixed model is not suitable; between random effects model and fixed effects model, the Hausman Test (H = 19.55 > χ20.05(6)) rejects the null hypothesis, suggesting that the fixed effects model should be used.

A generalised least squares (GLS) estimation method can avoid the problem of autocorrelation and heteroscedasticity. The seven models listed in Table 3 are all concluded from the panel data. Model 1 includes six explanatory variables: ‘nagdp’, ‘fdi’, ‘invest’, ‘sale’, ‘diharbor’ and ‘wage’. Based on model 1, six control variables are gradually added in model 2-7: ‘saving’, ‘reven’, ‘nthea’, ‘plreve’, ‘postal’ and ‘univer’. To make the estimated results more accurate, a GLS method has been used to conduct empirical analysis on the following seven fixed effects models. The regression results for each model are shown in Table 3.

Table 3.

The results of multiple regression analysis, with urban competitiveness as the explained variable.

Explanatory variables	Model 1	Model2	Model 3	Model 4	Model 5	Model 6	Model 7
ln(nagdp)	0.715**(0.509)	0.696***(0.508)	0.695**(0.513)	0.733***(0.513)	0.762***(0.519)	0.553***(0.524)	0.652***(0.513)
ln(wage)	−0.218***(0.096)	−0.295**(0.112)	−0.307***(0.108)	−0.288**(0.111)	−0.279**(0.109)	−0.373**(0.114)	−0.302**(0.113)
diharbor	0.320***(0.125)	0.369***(0.130)	0.369***(0.120)	0.367***(0.123)	0.384***(0.111)	0.367***(0.114)	0.324**(0.126)
ln(fdi)	0.044***(0.034)	0.035***(0.036)	0.042***(0.035)	0.038***(0.035)	0.046***(0.035)	0.040***(0.035)	0.023***(0.034)
ln(invest)	0.246***(0.053)	0.216***(0.057)	0.212***(0.057)	0.211***(0.058)	0.210*(0.057)	0.213***(0.058)	0.204**(0.054)
D(ln(sale))	0.129*(0.103)	0.138***(0.103)	0.145*(0.105)	0.140***(0.103)	0.142**(0.106)	0.142**(0.105)	0.143***(0.103)
ln(saving)		−0.011*(0.074)	−0.012*(0.073)	−0.011*(0.075)	−0.013**(0.073)	−0.019**(0.085)	−0.013*(0.069)
D(ln(reven))			0.001***(0.060)	0.006**(0.060)	0.008**(0.062)	0.008***(0.061)	0.004***(0.059)
ln(nthea)				0.018**(0.028)	0.027**(0.029)	0.029**(0.028)	0.025**(0.027)
ln(plreven)					−0.076*(0.059)	−0.075*(0.058)	−0.074**(0.060)
ln(postal)						0.140***(0.062)	0.128*(0.058)
D(ln(univer))							0.001***(0.035)
Constant	−1.004***(0.677)	−1.384***(0.727)	−1.449***(0.677)	−1.496***(0.690)	−2.167***(0.740)	−1.408***(0.756)	−1.508***(0.760)
Adj. R²	0.403	0.421	0.389	0.465	0.473	0.398	0.379
DW	1.886	1.894	1.903	1.895	1.924	1.887	1.932
Hausman	26.321	29.564	24.682	30.247	27.893	28.546	19.242
P	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Samples	500	500	500	500	500	500	500

Note: (1) values in parentheses are standard deviations; (2) Hausman Test’s null hypothesis is that there is no systematic difference between the estimate coefficients of the fixed effects model (FE) and random effects model (RE).

Explanation of model estimation results

The model estimation results prove the theoretical construct and demonstrate that the cities, in the stage of China’s accelerating industrialization, are quite different from the cities of developed countries in the determinants of urban competitiveness.

There is a negative correlation between average employee wage and urban competitiveness. An employee wage increase of 1% results in a 0.218–0.373% deterioration of the index of urban competitiveness. The negative correlation between wage cost and urban competitiveness shows that for Chinese cities, urban competitiveness depends on cheap labour rather than talent. This partially explains the phenomenon of newly industrializing cities and regional economic centre cities rising rapidly in their competitiveness, while regional centre cities are weaker in their competitiveness and slower in urban growth.

There is a positive correlation between investment in fixed assets and urban competitiveness, and its elasticity coefficient is in the range of 0.204–0.246. There is a positive correlation between market size and urban competitiveness. The first order differential coefficient of elasticity is in the range of 0.129–0.145, indicating that market size has a significantly positive effect on urban competitiveness.

Access to foreign direct investment, reflecting the degree of a city’s outside connection, makes a significant contribution to urban competitiveness enhancement. Its elasticity coefficient is in the range 0.023–0.046. This explains the phenomenon in China where regional economic centre cities and newly industrializing cities with higher degrees of outside connection and openness are more competitive than regional cities.

The variable ‘diharbo’, a dummy variable indicating proximity to a harbour, has a positive effect on urban competitiveness, suggesting that coastal cities tend to develop more rapidly than do interior cities, generally speaking. This is in conformity with the reality that cities in China’s eastern coastal areas are more competitive than inland cities.

A higher ratio of non-agriculture added value to GDP will improve urban competitiveness dramatically, with its elasticity coefficient being in the range 0.553–0.762, a figure that is far higher than any other elasticity coefficient. This suggests that cities with higher levels of industrialization tend to be more competitive.

There is a negative correlation between savings deposits and urban competitiveness. The greater savings deposits are, the lower is urban competitiveness. A low capital utility efficiency cannot improve urban competitiveness and highlights the failure of the financial system to play its proper roles.

The variable ‘postal’, the volume of posts and telecommunications business, has a positive effect on urban competitiveness. When this variable increases by 1%, the index of urban competitiveness rises 0.128 to 0.14%.

Fiscal revenue per capita, an index of government fiscal resources, has a positive effect on the enhancement of urban competitiveness. Obviously, strong city financial resources are not only the result of urban competitiveness but also one of its causes through the provision of adequate public services.

The variable ‘plreven’, the ratio of general budget revenue to GDP, passes the significance level test at 10%. Again, there is a negative correlation between this variable and urban competitiveness.

There is a positive correlation with the number of theatres (nthea), reflecting the linkage between culture activities and assets, and urban competitiveness. The coefficients have passed the test at the 10% significance level, but, with an elasticity coefficient of 0.018 to 0.0293, it is one of the weaker contributors to urban competitiveness. This explains why regional centre cities tend to be less competitive and slower in urban growth.

The variable ‘univer’, the number of universities, which is a proxy for science and technology, has a positive effect on urban competitiveness. Municipal authorities often encourage universities to cooperate with local enterprises to do research and to innovate with new products and technologies. This is an important way to improve urban competitiveness.

With Adj. R² between 0.379 and 0.473, the variables explain urban competitiveness and its enhancement during the process of industrialization in China.

As a final note, we must reiterate that adequate data on the ecological environments of cities are unavailable; hence, our regression models have not been able to evaluate the role of this variable in the enhancement of the competitiveness of China’s cities. In addition, the recent real estate bubble has imposed a significant impact on China’s urban competitiveness. The lack of sufficient time series data makes it difficult for us to incorporate this factor into our model. We will include these variables in a future study should these data become available.

Conclusions and policy suggestions

We began this paper with a definition of urban competitiveness, constructed an index of urban competitiveness and then applied this index to data for the 25 principal cities in China. The following results of this study are based on the measurement and the analysis of the factors that explain the relative urban competitiveness of these cities.

First, the competitiveness of eastern coastal, national centre and regional economic centre cities has generally been greater than that of inland, most regional centre and newly industrializing cities. Urban competitiveness has increased quickly for other cities with the recent development of the manufacturing sector.

Second, our results demonstrate that the much-discussed advantages of size are false. China’s five Mega Cities have weakened substantially during the period of this study, with smaller cities gaining relatively in competitiveness. We also demonstrate that economic-based urban economies have a competitive advantage over administration-based urban economies.

Third, the primary factors that have powerful effects on the competitiveness of Chinese cities have proven to be basic hard factors: the level of cheap labour, investment in fixed assets, the degree of city openness, the size of the local market, national location and agglomeration. Advanced hard factors of human capital, science and technology, and soft factors of culture, institutions and government are less important for the competitiveness of Chinese cities.

Fourth, Chinese urban competitiveness has been maintained in large part because of the active participation of central and local governments, the benefits of advantageous location and local markets, efforts to attract both foreign capital and cheap labour from the countryside, and the promotion of the agglomeration and development of low-end manufacturing through institutional reform and openness. This model may be good in the early stages of economic development but unsustainable in the long term.

In the future, in order to improve the competitiveness of China’s major cities, it will be necessary to effect a progressive transition from primary to advanced production activities. Government must accelerate market-oriented reforms and must promote the utilization of highly skilled labour and advanced technology. The government must also facilitate financial reforms both through improvements to the tax system, which by reducing the tax burden will expand consumption, and through the creation of a more effective mechanism for intermediation between savings and investment. Public investment in science and technology, and in research and education, must also be increased. City amenities and their residential environments should be improved so as to enhance the soft factor power of cities and to attract high-end factors. Urbanization (bringing economies of scale for cities), economic transition (driving local demand and innovation) and industrial upgrading (toward knowledge-based industries) should be accelerated.

Footnotes

Appendix

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

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