For a long period of time, city scales that are defined by population, economic scale, and other indexes have been regarded as an important foundation for judging the functions and positions of cities in a region (Debord, 2008; Xu et al., 2009). However, along with the emergence of new generations of transportation and communication technologies, the connection among cities has been remarkably enhanced; thus completely depending on the Central Place Theory to clarify urban hierarchy can no longer reflect the new characteristics of regional development (Taylor, Hoyler, and Verbruggen, 2010). Researchers come to aware that the study on separated properties inside a city can not fully demonstrate the essential nature of the city (Taylor, 2001). Therefore, as an extension of the “Space of Flows” theory (Castells, 2011), the urban system study is now shifting from the static hierarchy to the dynamic connected network (Taylor et al., 2013; Tang, Li, and Li, 2017).
In the empirical study on urban network, infrastructure and enterprise locations are the two types of data that are most widely applied (Liu and Derudder, 2013; Li et al., 2017; Zhang and Tang, 2018). Among them, the enterprise organization, compared with the infrastructure, finds a more theoretical basis to present the characteristics of an urban network and has been regarded as the mainstream data in urban network studies (Derudder, 2006; Wall, 2009), since the urban network theory originates from the International Labor Divisions theory (Meyer, 1986; Taylor, 1997) and the interpretation to location of multinational corporations (Rozenblat and Pumain, 2007). Both the “headquarters-branch method” put forward by Alderson et al., (2004) based on the data from Forbes Global, and the “interlocking model method” created by Taylor (1997) based on data of enterprises of the Advanced Producer Services (APS) has carried out effective empirical studies on urban network through skillful application of limited data base. They have been widely recognized by scholars all over the world, and been verified operable at the global, national, and regional level (Tang and Zhao, 2010; Derudder et al., 2013; Cheng, Zhang, and Zhao, 2016).
Nevertheless, as these two methods focus only on the connection among sections inside sample enterprises, or the intra-firm linkages, the urban network spatial structure resulting from such kind of methods is questioned by some researchers for lacking an overall picture of the region (Niu et al., 2018). The academia is calling for more supplementary analyses on urban networks from multiple perspectives in order to understand the urban network from a comprehensive angle (Neal, 2012; Li and Wu, 2018; Zhang, Li, and Yuan, 2018). In fact, the shortage of ideal relational data has been the primary reason for the questioning on urban network studies, which is also called the “dirty little secret” (Short et al., 1996) or the Achilles’ Heel of urban network studies (Beaverstock et al., 2000). However, compared to western countries, Chinese users are more inclined to share their data① and the close monitoring on national socio-economic operations by the Chinese government makes it possible to access some authentic operation data of certain enterprises and organizations. Therefore, with the authentic relational data on urban networks, Chinese researchers have an advantage in related studies.
There are three fundamental types of firm-based linkages: linkages between different firms, linkages within one firm, and linkages between firms and non-firm organizations (Yeung, 2005). In contrast to the large number of studies on the intra-firm linkages, the exploration on the inter-firm linkages is lacking, which endows it more value in supplementing the study on urban networks (Pan et al., 2017). In this regard, this paper takes the Yangtze River Delta (YRD), where the achievements in urban network study are the most abundant in China, as the study subject, and uses patent consulting corporations as the APS sample enterprises to analyze the interaction data between the consulting corporations and their clients, thus establishing an urban network from an inter-firm linkages’ perspective. We hope to provide new insights for the study on urban networks in the YRD and supplement the existing study framework.
1. Study framework
1.1 Data on inter-firm linkages
In empirical studies, the APS enterprises have been extensively used to measure the urban network at different spatial scales (Zhao et al., 2015). The existence and gathering of APS enterprises are regarded as an important sign showing a city’s competitiveness in the globalization era (Sassen, 2001; Lai, 2012). Besides, when providing localized services for multinational corporations, the APS enterprises have set up a worldwide service network, which is regarded as an external proof reflecting how multinational corporations promoted globalization. Therefore, APS enterprises have been applied in a series of GaWC (Globalization and World Cities Study Group and Network) guided research, and are believed to be ideal sample enterprises for the measurement of connections among global cities (Taylor and Derudder, 2015).
Considering the essential role of the advanced producer service industry in the modern economy, the data of business interaction between APS enterprises and their clients can reflect the business interactions among enterprises in the economic activities, and the consulting service provided by patent consulting corporations to patent applicants can be regarded as such a typical representation.② Along with the arrival of the knowledge economy era, patents have been regarded as a central asset by more and more enterprises, and patent consulting enterprises start playing an increasingly important role in the economic activities centering on patents (Li, Wang, and Zhou, 2007; Lü, Liang, and Huang, 2015). In fact, in patent application and maintenance, the consulting agency corporations provide not only professional consulting services to the applicants, but also certain legal adviser services in the patent rights and interest maintenance, so they are considered as an essential part supporting the innovation (Lin and Zhu, 2011). Therefore, to a certain extent, the patent consulting corporations provide compound services listed in the consulting and legal categories identified by GaWC, and can be regarded as typical in demonstrating the linkage between APS and other enterprises.
Thank to the open data on patents in Wanfang Database, we can get access to the information about city localities of both the patent applicant and the consulting agency of each patent, which can be transferred into the business interaction data between a city with patent applicants and a city with patent consulting agencies. Meanwhile, in order to compare the characteristics of urban networks formed by inter-firm linkages and intra-firm linkages, this study focuses on the YRD region where related studies on urban networks are the most concentrated in China (Tang and Zhao, 2010; Zhu, Wang, and Luo, 2014; Cheng, Zhang, and Zhao, 2016; Wang, Zhang, and Cheng, 2018; Tang, Li, and Zhang, 2019). Based on data from 2015 to 2017, the paper analyzes the characteristics of linkage among 41 cities at and above prefecture level in the YRD. By December 2018, there are 1,505.9 thousand pieces of open data on patent applications between 2015 and 2017 in the YRD, among which 299,000 are inter-city patent consulting aplications, which form the data base for this study.
Learning from the “headquarters-branch” method for urban network study (Tang, Li, and Li, 2017), this study analyzes the data of patent consulting business linkage among cities in the YRD by calculating the sum of the linkage degree, the sums of outward linkage and the outward linkage degree, and the index of the linkage direction.
Under the condition of i≠j, nij refers to the quantity of patent application authorization with the main application address located in city i and the agency in city j; and nji refers to the quantity of patent application authorizations with the main application address located in city j and the agency in city i. The linkage degree between random city i and j is indicated by Nij or Nji = nij + nji. For the convenience of comparison, the maximum value of the city linkage degree is set as 100, and the linkage values of other cities are indicated with the percentages of the maximum value.
The general linkage degree of each city, i.e., the i, is defined as the sum of its linkage degrees to all the other cities in the study area (j=1...n), that is, Ni = (i≠j). The sum of the outward linkage of city i indicates the total quantity of patent consulting services provided by patent agencies in city i to clients in other cities, that is, ni outward = (i≠j). The sum of the inward linkage of city i indicates the total quantity of patent consulting services provided by patent agencies in other cities to clients in city i, that is, ni inward=(i≠j). And the direction coefficient of each city i is defined as the linkage direction index of the city, that is, Di=(ni outward -ni inward)/(ni outward+ni inward).
If the linkage direction index Di of city i tends to be 0, the sum of outward linkage is basically equal to the sum of inward linkage, which means that the quantity of consulting services purchased by city i from other cities is generally balanced with the quantity of consulting services purchased by other cities from city i; if Di tends to be 1, the sum of outward linkage is significantly higher than the sum of inward linkage, which means that city i provides more consulting services to other cities in the region; and if Di tends to be -1, the sum of inward linkage is significantly higher than the sum of outward linkage, which means city i relies more on other cities in the region for consulting service.
2. The urban network in the YRD from the perspective of inter-firm linkages
Generally speaking, through analyzing the properties about hierarchy, direction, and layout of an urban network, we can get a basic identification and understanding of primary characteristics of an urban network, and these properties are also dimensions for comparison between inter-firm linkages network and intra-firm linkages.
2.1 Hierarchic property
First of all, there are the hierarchic characteristics of the general linkage degree of each city. We set the value of Nanjing, which is the city with the highest general linkage degree, as 100 and use it to conduct data standardization. The result is further divided into five levels by using SPSS software. As it is shown in Table 1, Nanjing and Hangzhou are the only two cities at level one and two respectively, and Suzhou and Hefei are at level 3. Shanghai, which is regarded as a conventional central city in the YRD, only ranks at level 4. The total connectivity degrees of other cities rank in a descending order, but the gap between level 4 and 5 is not so obvious. Compared with the existing study results concerning the YRD that applied traditional methods (Tang and Zhao, 2010; Zhu, Wang, and Luo, 2014; Cheng, Zhang, and Zhao, 2016), it can be observed that apart from the significant hierarchic characteristics of city total connectivity and the front running of cities like Nanjing, Hangzhou, Suzhou, and Hefei as before, the rank of Shanghai drops down to a significant extent. Its total connectivity degree only ranks No. 5 and at the level 4, showing an apparent gap to provincial central cities like Nanjing and Hangzhou.
Table 1 The total connectivity degree and the hierarchy of cities in the YRD
Similarly, the total connectivity degree of city-dyads can also be divided into five levels (see Table 2). It can be seen that the connectivity of cities inside the Jiangsu, Zhejiang, and Anhui Provinces is quite prominent, and the city connectivity degree inside Jiangsu Province is apparently higher than that of Zhejiang Province, which is in turn significantly higher than that of Anhui Province. What’s more, the primary or secondary connected cities of these three provinces are either their provincial capital cities or other central cities, reflecting obvious provincial economic characteristics. In contrast to the existing study results using the other methods, the position of Shanghai is obviously lower than the provincial capital cities in the urban network of the YRD, and its role as the network center is not fully shown. Besides, compared to the linkage of other cities, the total connectivity degree between Nanjing and Suzhou is extraordinarily high, indicating that a clear labor division in terms of a patent consulting service has been formed between the two cities and their collaboration is fairly close.
Table 2 The total connectivity degree of city-dyads in the YRD
2.2 Direction and layout property
When further comparing the network linkage direction index of each city (see Table 3) and the proportions of outward and inward connectivity degrees (see Figure 1), it can be seen that the direction index of cities in the YRD is apparently polarized. The direction index of cities including Nanjing, Hangzhou, Hefei, and Shanghai are all above 0.7 and approaching 1, which means that the cities provide patent consulting services to other cities in the YRD; and the index of most other cities are lower than 0 and approaching -1, indicating that these cities rely on the other cities for patent consulting services.
Table 3 The index of linkage direction of each city in the YRD
Figure 1 Proportion of total outward and inward linkage degree of cities in the YRD (the graph on the right is a partial enlarged view of the graph on the left)
On the basis of the analysis on the hierarchy and linkage direction of the urban network, the linkage network of 41 cities can be shown through Figure 2. Based on whether the city linkage crosses the provincial administrative border, the linkages can be divided into two types (see Figure 3). The first type is the “center-radiating” linkage centered at the provincial capitals inside Jiangsu, Zhejiang, and Anhui provinces, which makes up most of the medium and above level of linkage; the second type is the “cross-province” linkage, which is mainly composed of the linkage between Shanghai and other cities and between Nanjing and its neighboring cities in Anhui Province. The cross-province linkage degree is apparently lower than the city linkage degree within a province, and the latter forms the dominant part in city linkage.
Figure 2 Overall pattern of urban network in the YRD
Figure 3 Pattern of intra-provincial (left) and inter-provincial (right) city linkages
3. A comparative analysis on the inter-firm and intra-firm linkage networks
In comparison to the existing study results of urban networks in the YRD from other perspectives (Tang and Li, 2014; Cheng, Zhang, and Zhao, 2016; Luo, He, and Geng, 2011; Niu et al., 2018), the study on the inter-firm linkage network in the YRD from the perspective of the patent consulting business has shown apparent different results. Shanghai, as a provincial-level city and without other direct administrative hinterland, only shows relatively high linkage degree to its neighboring cities such as Suzhou, Jiaxing, Ningbo, and Hangzhou, and its network position is obviously weaker than the position result based on the conventional urban network study. As the provincial capital cities, Nanjing, Hangzhou, and Hefei lead the other cities inside their respective provinces, and become the center for the inter-firm business linkage. Particularly inside Jiangsu Province, Suzhou, a city concentrating a large number of high-tech enterprises, tremendously relies on Nanjing, the provincial capital of Jiangsu, for patent consulting services, so a close collaboration relationship in term of patent business has been formed between Nanjing and Suzhou, which is shown through an apparently higher degree of linkage between the two cities than the linkage between other main cities in Zhejiang and Anhui provinces.
In the patent consulting business network, the cross-province city linkage degree is obviously lower than the city linkage degree inside a province, which is the most highlighted urban network characteristics from the new study perspective. For example, in the field of patent consulting, the innovation activities happening in Suzhou are more inclined to buying patent consulting services from Nanjing (occupying 63.82% in total outside purchased services), which is far higher than the percentage of patent consulting services that Suzhou buys from Shanghai; the percentage of patent consulting services which Jiaxing buys from Hangzhou (71.35%) is also significantly higher than the percentage it buys from Shanghai (23.44%); and Ma’anshan, a city in Anhui Province, buys more patent consulting services from Hefei, its provincial capital (55.95%), than the percentage of services which it buys from Nanjing (36.29%), all of which shape a distinctive contrast to the high city linkage degrees between Suzhou, Jiaxing, and Shanghai, and between Ma’anshan and Hefei from other studies. In fact, the key of understanding the centralized position of provincial-level administrative areas in patent consulting service provision is the series of stimulating policies on patent that are issued by the provincial government. The increase in patent in China highly relies on the support and stimulation of the government (Shan et al., 2018). The inventors attach great importance to their patent not only because they need to protect their intellectual property, but also because of the special policy benefits (such as a taxation reduction to patent holding enterprises by the government, a funding subsidy to colleges and research institutions, and a talent recognition and material reward to individuals), which even leads to the phenomenon of “patent foam” (Zhang, Gao, and Xia, 2016). According to the Law of Patent of the People’s Republic of China, the administrative department of the provincial government, the government of autonomous region, and the government of municipality directly under the Central Government, are responsible for managing the patent work inside their respective administrative areas, and most of the patent stimulation policies are implemented within the provincial administrative areas to enhance the patent innovation activities in the region (Yin, 2011; Zhou, 2014). Take Jiangsu Province as an example, the series of patent stimulating policies issued by the Intellectual Property Bureau of Jiangsu Province are only applicable to patent applications within Jiangsu Province,⑤ which results in the market division of patent consulting services among different areas in reality (Yue and Wang, 2019). Therefore, the tremendous influences of administrative power at the provincial level over the choosing of patent innovation consulting service providers should be regarded as the primary factor leading to the obvious provincial divisions in the patent consulting agency network.
On the basis of analyzing the causes for the formation of provincial divisions, a clarification of the industrial structural disparities among different cities will help further understand the city linkage characteristics that are centered at provincial capital cities inside Jiangsu, Zhejiang, and Anhui provinces. According to data released by the State Administration of Intellectual Property, the distribution of patent data in different industries is extremely unbalanced, with the number of patents produced by the manufacturing industry occupying over 96% of the total invention patents③. Therefore, the scale of the manufacturing industry can be used to represent the basic scale of the patent application authorization in a city; correspondingly, the industrial scale of the science research and technology service industry can also be used to represent the concentration degree of the patent consulting agency industry. Taking the 13 cities in Jiangsu Province as samples, the relation between the gross output of large-scale manufacturing, the total value of scientific research and the technological service industry, the patent authorization amount and patent commission amount is analyzed, and the result is shown in Table 4. It can be seen that the relativity coefficient indicating the relation between the manufacturing scale and the patent authorization amount is very high, as is the relativity coefficient indicating the relation between scientific research and the technological service industry and the patent commission amount. In addition, the distribution of patent application authorizations is relatively scattered in all cities in Jiangsu Province, but the patent commissions are obviously concentrated in Nanjing, the provincial capital of Jiangsu. The disparity in city industrial structures has been the decisive factor determining such connectivity characteristics of the province.
Table 4 The industrial structure and patent authorization and commission amount in cities of Jiangsu Province (2015 – 2017)④
Source: Sorted based on the statistics yearbooks of Jiangsu Province
4. Conclusions and discussions
Although there is a large number of studies on urban networks, by using the intra-firm linkage data, it shows that the application value of the inter-firm linkage perspective in urban network studies is not yet fully explored. Regarding the increasingly important role of APS enterprises in modern economic activities, this study chooses the business linkage data between patent consulting service providers and their clients to measure urban networks in the YRD, which supplements existing urban network studies to a certain degree.
The results show that, different from conventional study perspectives such as the intra-firm linkages (Tang and Li, 2014; Cheng, Zhang, and Zhao, 2016), the commuting perspective (Luo, He, and Geng, 2011; Niu et al., 2018), and the information perspective (Wang, Zhang, and Cheng, 2018), all of which generate optimistic judgement on the integrated development in the YRD across the administrative boundary, the study on urban networks from inter-firm linkages reveals both obvious provincial divisions and market divisions among different areas in the YRD urban network (Yue and Wang, 2019), showing that the cross-province linkages are weaker and the position of Shanghai as a traditional central city in fact declines.⑥ We believe that the result demonstrates the different functions of inter-firm linkages and intra-firm linkages to the formation of regional structures, and also forecasts the major challenges for the regional integration in the YRD in the future, that is, how to realize the integration by breaking down the administrative restriction at provincial level.⑦
Besides, it needs to be particularly pointed out that the patent consulting business data chosen by this study is only one type of APS enterprises, and there is certain bias when it is used to represent the inter-firm linkages, which is a disadvantage of this study. However, considering that local government, represented by the provincial government, put great efforts into regulating the APS enterprises in the financial and legal industries, and related industry associations own power on examining qualifications of practitioners like accountants, lawyers and patent agents, it can be seen that the administrative barrier at the provincial level revealed in this study is a common phenomenon in business linkages among APS enterprises, and the study data can be regarded as effective in showing the business linkage between APS enterprises and their clients.
In conclusion, the fundamental nature of an urban network is the economic interaction relation between cities (Taylor, 2001). Although the great number of studies on the linkage between cities from the perspective of intra-firm linkages accomplished abundant achievements, they may neglect the actual economic linkage among cities because the nature of these studies is to infer the characteristics of “space of flows” by using “place space” data; therefore, they are always challenged for “contradicting to the theoretical foundation of ‘space of flows’” (Nordlund, 2004). The data of patent consulting business linkage used in this study originates from the authentic economic business interaction between enterprises and their clients, which not only reflects the characteristics of the urban economic linkage network, but also shows the general rule of enterprise operations, thus with advantages in the study on urban networks. By using new basic data, this study introduces a new study perspective for urban network studies and makes a breakthrough to the existing theoretical framework of related studies. What’s more, realizing theoretical and practical innovations by applying advantages in basic data should also be a key direction for future studies on urban network in China (Zhang, Liu, and Tang, 2019).
① In 2018, Wall Street securities analyst Mary Meeker, known as the “Queen of the Internet,” released her 2018 Internet Trends Report at the Code Conference in the United States, stating that “Chinese users are more willing to share data for profit than other countries.” The 2018 East Tech West Forum also said that “the availability of massive amounts of data is also a prominent advantage for China’s technology competition.”
② Patent consulting service enterprises are also called patent agencies, whose main business is to undertake the entrustment of the client and handle patent applications or other patent affairs in the name of the client within the scope of entrustment authority. Such enterprises need to have the knowledge of the discipline or field to which the patent belongs and master the relevant laws and regulations of patent application, which is a typical intellectual-intensive productive service industry.
③ According to the Patent Statistical Bulletin (General Issue No. 188) issued by the China National Intellectual Property Administration, the proportion of manufacturing industries in China’s invention patents from 2010 to 2014 reached 96.24%. This study classifies the patent data from 2015 to 2017 according to the International Patent Classification and National Economic Classification Reference Table (2018), and it shows that the proportion of manufacturing industries is 96.50%.
④ The yearbooks of other cities in Zhejiang and Anhui provinces did not publish the total manufacturing output value or the total scientific research and technical service output value above the scale, so only 13 cities in Jiangsu Province are selected for comparison in this study.
⑤ The Patent Inventor Reward Measures of Jiangsu Province, the Patent Promotion Regulations of Jiangsu Province, the Management Measures for Intellectual Property Special Funds of Jiangsu Province, the Notice on Handling Patent Priority Examination Applications of Jiangsu Province Intellectual Property Office, and other patent support and reward policies all clearly stipulate that they are only applicable to patent applications in Jiangsu Province.
⑥ In this study, despite the obvious decline of Shanghai’s network position, Shanghai is 2.46 times more than Nanjing, 6.84 times more than Hangzhou, and 26.40 times more than Hefei in terms of inter-provincial linkages, indicating that Shanghai is still the core city in the Yangtze River Delta region for promoting inter-provincial linkages at this stage.
⑦ In November 2018, the Opinions on the Establishment of a More Effective New Mechanism for Coordinated Regional Development was officially released by the central government, which clearly proposed to “eliminate regional market barriers, break administrative monopolies ... strengthen exchanges and cooperation between cities in inter-provincial border areas, establish and improve the system of joint meetings between municipal governments in cross-provincial cities, and improve the mechanism of inter-provincial meetings.”
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Cai Tong, Master, College of Architecture and Urban Planning, Tongji University, Shanghai, P. R. China.
Zhang Ze (corresponding author), PhD, College of Architecture and Urban Planning, Tongji University, Shanghai, P. R. China.