4.2. Proximity and Distance
Spatially, the proximity values among cities based on the similarity of automobile markets are related to the physical distances among them. After all, nearby cities tend to have similar cultural backgrounds or purchasing preferences and their markets are more likely to be penetrated by the same manufacturers around them. Figure 2
delineates the distribution of the 2802 links corresponding to the top 10 proximity values of each city. The frequencies of the links below 500 km (the average distance is 750.82 km) are relatively higher, with the largest number of links (over 400) falling within the distance between 100 km and 200 km. In general, the frequencies of links decrease with increased distances, excluding the distance interval between 0 and 100 km. It appears that the links or city pairs tend to have higher levels of market proximity as the distance between them decreases.
To further explore the relationship between the distance and the proximity values, the links were arranged into five groups based on proximity values, and a boxplot graph was produced. As Figure 3
shows, the distance varies dramatically even when the proximity values are more than 0.70 (considered high enough), but the average distance becomes smaller as the proximity increases. A panel of pie charts of lengths were made to show the proportions of different ranges of distance in each group (Figure 4
). It reveals that links with higher proximity values are more likely to be with shorter distances. For instance, when the proximities are higher than 0.8, the proportion of distances shorter than 250 km reaches 46.77%, and the proportion of links with distances under the average (788 km) is also fairly high. Conversely, the proportion of links involving distances greater than 1000 km is minimal.
In short, if two cities possess higher proximity values, the distance between them is more likely to be shorter. Although we cannot directly conclude that distance alone is the most important factor in determining the proximity value between two cities, the geographical closeness could imply specific regional characteristics and probably certain similar underlying determinants in automobile demand, leading to higher proximity values.
4.4. Heterogeneity among City-Clusters
The means and coefficients of variation (CV) of major variables for all prefecture-level cities and for each of the four clusters are shown in Table 1
, including the total car sales, population, GDP, per capita GDP, employed population, per capita disposable income of urban residents, as well as the proportion of foreign automakers, Sino-foreign joint ventures, and domestic automakers. The coefficient of variation (CV) was used to measure the degree of dispersion of each variable, independent of the variable’s measurement unit. Coefficients of variation of nearly all the variables are noticeably lower in each cluster than those for all the cities. The variables exhibit less variation within the cluster, indicating more homogeneity among cities in a cluster. Cities in the same cluster are likely to have similar consumer preference and purchasing capabilities, resulting in similar market structures measured by the index of proximity. The mean of each variable, except the proportion of car sales of domestic automakers, is the highest for the Southeast city-cluster. Both the average proportions of car sales of foreign automakers and Sino-foreign joint ventures are highest in the southeast cluster.
A high level of heterogeneity exists across four city-clusters in market structures. Table 1
also shows the statistics of the top 10 automakers that dominate city markets in each of the four clusters. The dominance of a manufacturer is measured by the percentage of cities that it gains revealed comparative advantage (RCA) out of all the cities in each city-cluster. Names of foreign automakers and domestic automakers are indicated by an asterisk (*) and a pound sign (#
), respectively; the others are Sino-foreign joint ventures. In the Southeast developed city-cluster, the top 10 dominant manufacturers are all foreign-owned, with the exception of BMW Brilliance and Beijing-Benz which are high-end Sino-foreign joint ventures. In the other three city-clusters, as the socioeconomic status decreases, the top 10 dominant manufacturers change dramatically. The Sino-foreign joint ventures and Chinese manufacturers become the dominant brands. Chinese automakers, such as Geely, Chery, Great Wall, BYD, and Changan, become dominant, especially in the cities in the Northeast and West China city-clusters. For instance, the Geely automobile company is dominant in all the cities in the West China city-cluster and its biggest manufacturing base is located in Lanzhou in the middle of the cluster.
A linear discriminant analysis was performed to account for the heterogeneity among four clusters, with three statistically significant discriminant functions (variates) being calibrated. Table 2
presents the standardized discriminant function coefficients and Table 3
shows the centroid values (i.e., mean function scores) of each city-cluster on all three functions. Overall, the discriminant functions correctly classified 91.8% of the cities into one of the four city-clusters.
Two variables are strongly loaded onto Function 1, including per capita disposable income of urban residents and the proportion of car sales from foreign automakers. This function thus reflects the underlying dimension of “personal income” and corresponding purchasing preferences in automobile demand. Function 1 primarily distinguishes the Southeast developed city-cluster, with its high levels of personal income and sales of foreign-owned cars, from the other three clusters, particularly the West China city-cluster. This indicates that the similarity among cities within this group are mainly due to their common preference for these high-end cars and higher purchasing capability, despite the higher prices of these vehicles. In China, cars produced by foreign automakers and Sino-foreign joint automakers usually possess better performance and higher quality, hence higher prices, while the cars of domestic manufactures usually have the advantage of lower prices. Therefore, as urban residents’ economic status changes in different clusters, the proportion of car sales of different types of automakers changes accordingly. The lower economic status a cluster has (represented by lower per capita disposable income and purchasing capability), the lower proportion of car sales of foreign automakers and higher proportion of car sales of domestic automakers in the cluster, particularly in relatively underdeveloped regions such as western China. The distribution of higher-end and lower-end automobile markets in China is apparently related to the regional disparity in urban residents’ income level and purchasing capability.
The most important variable for Function 2 is the level of employment, along with per capita GDP and geographical location variables. The North China city-cluster is distinguished mostly from the Northeast city-cluster and the West China city-cluster by this function. The strong negative mean score of Function 2 for cities in the North China city-cluster is directly associated with the overall high employment and fine GDP levels. Many cities in this cluster are important industrial centers involving heavy industries, mining and energy industries, with significant amount of employment including those employed by state-owned enterprises. In addition, Changan automobile company, a domestic automaker with dominance in cities across North China, has a large manufacturing base in Baoding (Hebei province) located in the middle of this cluster. Function 2 is thus considered representing the underlying dimension of “industrial development”. The robust performance of these cities in this cluster in automobile sales, especially domestic automakers, echoes the general trend and relationship between demand for automobiles and overall economic development.
Function 3 is largely dominated by the dummy variable corresponding to northeastern China. It mainly discriminates the Northeast city-cluster from the rest of the clusters. The personal income level and other economic indicators are basically average for cities in this cluster. However, geographical and historical background makes this cluster quite distinguishable. The Northeast was one of the earliest regions to industrialize in China, and continued to be a major industrial base after the founding of the People’s Republic of China in 1949. Recent years, however, have seen the stagnation of Northeast China’s heavy-industry-based economy, as China’s economy continues to liberalize and privatize. The central government has initialized the Revitalizing the Northeast campaign to counter this problem. From the perspective of the automobile market, the FAW-Volkswagen Company has dominance in the cluster and produces cars in Changchun and Shenyang, which are very close to the cluster’s cities.
Other factors which are not directly revealed by statistical analyses but deemed to have an impact on regional clusters’ market structures could be associated with the fact that their products can meet special regional demands. For example, more consumers in Western China prefer buying SUVs because of the rugged topography and the relative lack of public transportation. The manufacturers, such as Ceely, Hawtai, Great Wall, and GAC-Changfeng with SUVs as their major products can sell more automobiles in western China. It is notable that foreign-owned manufacturers, such as Toyota, Hyundai and Mitsubishi also obtain noticeable dominance in the West China city-cluster, probably because their SUVs are in high demand in western China. Because the prices of these imported automobiles are usually higher, the fully foreign-owned manufacturers obtain dominance in smaller numbers of cities than their Chinese counterparts. However, in the North China city-cluster, consumers prefer energy-saving and inexpensive domestic automobiles because of flatter land and high-quality roads spread in the region.
Different levels of government in China have also played an important role in shaping the automobile market in different cities, by giving specific preferences towards certain manufacturers. Although the data regarding tax structures and automobile fees (registration) are not available in any Chinese statistical books or other sources, the influence on the automobile market from the government is significant and should be considered. For example, in order to promote the development of the automobile industry, local governments may not only provide preferential tax and registration convenience for the automakers, but may also regulate the brands and the models of cars in the process of government procurement. Besides, the providers for taxis in a city are also usually confined to some specific automakers by the rules of administrative agencies. In Jilin province, for instance, the vast majority of taxi vehicles in all nine prefecture-level cities are the Jetta model from FAW-Volkswagen, whose headquarters are located in Changchun, the capital city of Jilin province. Because the local government vehicles and taxis make up a considerable percentage of the market of a city, government preferences to some specific automakers also impact the market structure. If city governments in a region in the same province have the same automobile purchasing preference, they would very likely help foster similar city market structures.