Agglomeration density and tourism development in China: An empirical research based on dynamic panel data model

Tourism Management 33 (2012) 1347e1359

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Agglomeration density and tourism development in China: An empirical research based on dynamic panel data model Yong Yang* School of Business, East China Normal University, No. 500 Dongchuan Road, Shanghai 200241, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 May 2011 Accepted 17 December 2011

To explain the significant disparity of tourism development across Chinese provinces, this paper conducts a dynamic panel data analysis of tourism development in China using statistical data of Chinese provincial tourism industry for the 2000e2009 periods. The estimated results provide empirical evidence on the relationship between the agglomeration and development of Chinese provincial tourism in firm level. The econometric analysis shows that the tourism density in agglomeration has a positive influence on local tourism development. It appears that the variance of tourism development across Chinese provinces can be explained by the differences in the density of tourism economic activities. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Agglomeration density Tourism development Dynamic panel data model China

1. Introduction China has attracted an increasing attention because of its world’s largest population and the tremendous rate of economic growth in recent years. Since the implementation of the policy of “China’s Reform and Opening up to Outside World”, Chinese tourism industry has evolved and modernized considerably, agglomeration has become a significant driving force in tourism development (Shi, Zhang, Shen, & Zhong, 2005; Yang, 2011). Some researchers even regard the agglomeration as a key factor in promoting tourism development through externalities, which encourage enterprises’ competition and cooperation, especially when tourism enterprises gather together in close geographic proximity and establish relationships with each other in order to better perform certain tourism economic activities (Zhang, 2005; Zhang, Qu, & Yang, 2006). Generally speaking, agglomeration economies emphasize three kinds of externalities, which are localization economies, urbanization economies and Jacobs’ externalities (Frenken, Van Oort, & Verburg, 2007). Localization economies usually take the form of what are called, in a dynamic context, MarshalleArroweRomer (MAR) externalities (Arrow, 1962; Marshall, 1920; Romer, 1990), whereby agglomeration externalities operate within an industry and arise primarily from local concentration of that industry. Urbanization economies arise from urban size and density. And the so-called Jacobs (1969) externalities are external economies stemming from a local variety of producers.

* Tel.: þ86 13512146864; fax: þ86 21 54344955. E-mail address: [email protected]. 0261-5177/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2011.12.018

According to Porter’s (1998) definition, tourism agglomeration can be defined as “geographic concentrations of interconnected tourism enterprises, firms in related industries, and associated institutions in related fields that cooperate but also compete”. In tourism agglomeration, such tourism entities as enterprises, institutions and agencies are geographically bounded together with active channels for business transactions, dialog, communications, common opportunities and threats, and construct a sophisticated network system which enhances the tourism industry development. Besides externalities effect of agglomeration, several authors have recently stressed the importance of social network and tourism supply chain in tourism agglomeration (Michael, 2003), and tourism enterprises can benefit from network and agglomeration building, as they create their own tourism function and provide increased economic and social benefits for the local community involved. In consideration of heterogeneous tourism product, endowments of tourism resource, network system of tourism participants, institutional support to tourism industry, the mechanisms of tourism agglomeration in China’s different provinces are very complicated. Without regards to unobserved heterogeneity and possible endogeneity of tourism industry development, the empirical research will yield biased results. Taking both the heterogeneity and endogeneity into account, the Dynamic Panel Data (DPD) Model is used in this paper to analyze the difference of Chinese provincial tourism industry development, and seeks to contribute to this body of work by developing the argument that both spatial dimension of externalities and the dynamic mechanism contribute to Chinese provincial tourism development. The remainder of this paper is organized as

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follows: Section 2 provides an overview of the literature in tourism agglomeration effect, analyzing tourism agglomeration effect based on multi-dimensions of density in a dynamic context; Section 3 describes the evolution and disparity of provincial tourism industry development in China; Section 4 presents the key variables of the scale of tourism industry, surveys the important methodological issues associated with the measurement of density and constructs the variable of density of tourism industry; Section 5 introduces the approach adopted in this study to deal with such problems as heterogeneity and endogeneity in Chinese provincial tourism development; Section 6 describes the dataset used in this paper and interprets the empirical results; Finally, Section 7 concludes with some final observations and further research directions. 2. Literature review The geographical aspect of economic activities has long been of interest to many economists, geographers and planners. These disciplines have provided us with a considerable amount of literature which gives a valuable insight into why tourism industry in some provinces performs better than others and how interactions between the agglomeration and Chinese provincial tourism industry have developed. 2.1. Effects of agglomeration Over the past decades, several attempts have been made using clustering and agglomeration theories to explain the development of tourism. The research attention is mainly focused on the generalization of the industrial model as an analytical framework for measuring the performance of tourism industry and on the role of tourism firm agglomeration for their innovation and contribution to tourism development (Go & Williams, 1993; Jackson & Murphy, 2002, 2006; Novelli, Schmitz & Spencer, 2006; Saxena, 2005). Although there are some different definitions about cluster or agglomeration (Porte, 1998), the agglomeration has been regard as an effective force for Chinese tourism industry development (Zhang, 2005; Zhang et al., 2006). As the empirical results of Yang (2010, 2011) show, Chinese tourism has formed industrial agglomeration phenomena, which has a positive effect on the tourism development through the way of productivity improvement of tourism production factors. Corresponding to the analysis of MAR externalities and Jacobs’ externalities, an industry requires specialized or diversified industrial environment to develop faster. The specialization and diversity of Chinese tourism industry are just superficial phenomena of tourism development, several published papers have studied the determinants of Chinese tourism industry development (Crouch & Ritchie, 1999; Hassan, 2000; Ritchie & Crouch, 2003), and following intrinsic mechanism should be analyzed thoroughly. Firstly, knowledge externalities. The fact is spatial agglomeration plays an important role in knowledge spillover and diffusion of innovations (Krugman, 1991; Martin & Sunley, 1996). The knowledge and information are flowing much more easily between the firms in an agglomeration region. It is expected that the spillovers of tacit knowledge will be especially more common within a tourism cluster as a result of spatial proximity facilitating stronger social and economic network. When the knowledge diffuses quickly, it enhances the innovations of tourism (Jackson, 2006; Jackson & Murphy, 2006; Sørensen, 2007), which contributes to tourism development. This has been recognized in research in fields of hospitality (Hallin & Marnburg, 2008; Orfila-Sintes, CrespiCladera, & Martinezl-Ros, 2005; Siguaw, Enz, & Namasivayam,

2000) and other tourism sectors (Yang, 2007b), although detailed empirical studies remain relatively limited. Secondly, complementary firms. In today’s buyer’s market, tourism industry faces the challenges of providing sufficient product variety to meet diverse tourism customer requirements and responding quickly to dynamic tourists’ needs, and the provision of tourism products and services involved a wide range of interrelated tourism suppliers (Page, 2003). In other words, tourism agglomeration is the result of the co-location of complementary firms (Novelli et al., 2006). These mutually complementary enterprises can collectively deliver a bundle of attributes to make up a specialized regional product (Michael, 2003), and provide the tourists with much more splendid or abundant tourism experiences, which improve tourists’ satisfaction and enhance tourism industry’s competitiveness and development. Finally, business or social network. In the view of SEEDA (2003), an agglomeration is a progressive form of business network. Besides economic mechanism in tourism agglomeration, social network plays a critical role in tourism development, a few recent studies (Canina, Enz, & Harrison, 2005; Hall, 2005; Michael, 2003) address in more depth the implications of network and agglomeration in some tourism sectors. Specifically, the benefits of the network to tourism enterprises can be summed as: (i) Decreasing transaction costs and increasing exploitation of the economies of scale and scope in various activities (Tremblay, 2000) through spreading risk and enabling access to complementary resources (Kumar & Van Dissel, 1996); (ii) Avoiding the costs rising from the resolution of conflicts among the stakeholders in the long run (Roome, 2001); (iii) Improving the coordination of policies and related actions, promoting the consideration of the economic, environmental and social impacts of tourism in development strategies (Lane, 1994), which is particularly important for tourism firms to pursue sustainable development. 2.2. Density of agglomeration Much of the conclusion about positive spatial correlations between the indicators of clustering or agglomeration on one hand and the productivity, tourism development or innovation on the other, is based on the geographical proximity of related firms and related entities, which plays a key role in developing strong levels of trust and effective knowledge sharing among these enterprises (Shaw & Williams, 2009). When tourism enterprises in tourism agglomeration are localized in a close proximity within a particular geographic region, the intensity of tourism activities relative to the physical space is on the rise. It is believed that the density, measured by the amount of tourism activities per square kilometers, has included much more information than the geographical proximity. Then, why is the tourism density so important? Firstly, tourism firms can compete globally by co-operating locally (Cravens & Piercy, 1994). As tourism enterprises and social network have developed in cluster’s immediate area and not in a different location, it is easy for tourism firms to get specialized labor that is highly skilled for specific needs of tourism industry, sharing of valuable marketing information, innovation, opportunity to enter other networks, and so on (Saxena, 2005), which will increase tourism productivity and stimulate new tourism products to meet changing demands of tourists. Secondly, from a macro-scale point of view, tourism density enhances faster communication through local channels, constructs time-saving tourism experiences for tourists, provides multiple and instant product supply for tourists’ diverse demands. The result of this phenomenon shows that the satisfaction leads to a visitor’s repurchasing behavior (Petrick & Backman, 2002; Pritchard &

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Howar, 1997). While repeating visitation infers the loyalty, repurchasing behavior of tourists is more profitable than first time visit (Opperman, 2000), that will generate a self-reinforcing process to which other related tourism businesses are attracted because a growing agglomeration density signals good opportunity for sensitive entrepreneurs. Finally, tourism industry development is embedded in external environment, where such institutions as tourism industry association, government departments and non-profit organizations give great support and assistance to local tourism industry development (Piga, 2003; Taylor, 1998). Such particular basic infrastructure as the tourism information center, the tourism service center and so on have emerging in many Chinese provinces or cities, which provide the tourists with services of communications, tickets, and suggestions of choosing travel lines, etc. Furthermore, when tourism economic activities become more and more active, tourism related institution thickness goes up, the emerging of new organizations and collective network among these organizations, which involve a wide range of organizations from the public, private and nonprofit sectors (Tang & Tang, 2006), plays an important role in sustaining tourism development (Schout & Jordan, 2005). Overall, tourism agglomeration can be considered as a platform of local collaboration for tourism firms. The denser the tourism agglomeration, the more benefits tourism firms can get from the local collaborations, satisfying the tourists’ demands and institutional advantages, which efficiently form a mechanism in firmexternal economies. These are key elements of tourism productivity and development. There are no such studies that have examined the spatial density directly. Although the idea that denser tourism economic activities have advantages from tourism agglomeration is implicit in many of the earlier literatures, there isn’t any earlier work in which the density is an explicit element to be considered, nor has there been empirical work based on the effect of density. 2.3. Nature of tourism cluster Traditionally, in manufacturing cluster, an agglomeration is created through both technological and pecuniary externalities. Marshallian externalities are the engine of its development. More generally, the “Marshallian externalities” arise from (i) mass production, (ii) the formation of a highly specialized labor force based on the accumulation of human capital and face-to-face communications, (iii) the availability of specialized input services, and (iv) the existence of modern infrastructures (Fujita & Thisse, 1996). In contrast to manufacturing industry, tourism products are often viewed by the tourists as value-added chains of different service components that form service networks (Zhang, Song, & Huang, 2009). As a part of the service sector of macroeconomic system, tourism possesses such characteristics that distinguish it from the manufacturing sectors as follows: (i) The tourism is a coordination-intensive industry in which different products/ services (transportation, accommodation, and so on) are bundled together to form a final product; (ii) The product is intangible and perishable; (iii) The production and consumption are inseparable; (iv) The demands faces higher uncertainty and fluctuates significantly (McCole, 2002; Zhang et al., 2009). Bearing in mind its specific nature and characteristics, the agglomeration of tourism industry places a strong focus on understanding tourist needs and wants (Gibson & Yiannakis, 2002; Kim, Guo, & Agrusa, 2005), and the customer satisfaction becomes a dynamic process to promote tourism industry development over a certain period of time. On the other hand, when tourism product involves multiple suppliers and distributors, the coordination

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between these entities becomes critical to its success. In such a closely interlinked supply and demand diagonal system, any behavior of any party or consumer can evoke chain reactions that will directly influence the success of tourism industry in a destination. In essence, contrary to manufacturing industries and many service industries, the development of tourism agglomeration, with high degree of geographic concentration of tourism firms, should not only be seen as a spontaneous process due to the nature of businesses involved, but also as a positive feedback mechanism between tourists’ demands and tourism product system. Firstly, tourism agglomeration is an effective force for tourism enterprises to make optimal location decision. The essence of Krugman’s (1991, 1998) “new economic geography” with emphases on the “spatial dimension” of firms’ economic interactions has focused on the economic process of firm behavior from a geographic (space) perspective. This involves firms’ physical location related to institutional characteristics such as frictional factor (distance effect), mechanism of networking, organizations, degrees of information and knowledge sharing, etc. This conceptualization of ‘space’ suggests the persistent effects of ‘‘space’’ on firms’ identities and behavior, their interaction patterns, and individual and collective performance, which imply different driving force of agglomeration and their sectoral specificities. Furthermore, scale economies is considered, according to the “new economic geography”, as the incentives of agglomeration as well as the force to sustain concentration (Dixit & Stigitz, 1977; Fujita & Thisse, 1996). The externalities in the form of agglomeration economies are taken as the major elements in enterprises’ location decision (Fujita & Thisse, 1996). Based on value chain theory, a typical tourism system consists of following four components (Kaukal, Höpken, & Werthner, 2000): the tourism supplier, tour operator, travel agent and customer, which are in a single linked chain as an end-to-end seamless entity (Yilmaz & Bititci, 2006). From the point of view of location decision, the main objective of any profitmaximizing tourism firm in choosing favorable investment sites is whether the location is capable of generating positive net benefits. Such net benefits include: (i) geographical benefits or benefits derived from the process in forming tourism agglomeration and (ii) agglomeration benefits or benefits derived from other tourism firms in the same location. These agglomeration benefits may be iterated as agglomeration economies, localized tourism source of inputs and reduction of local tourism information searching costs, tourism market power, and tourism firm clustering behavior generated by a set of relevant location tourism factors which include various types of economic and institutional tourism effects. Secondly, in consideration of such mechanism as tourists’ satisfaction, tourism cluster shows a continuously self-enhanced process in specific region. While the integration of tourists’ experiences produce a wider base for tourism development, in the tourism context, the agglomeration in tourism can take place diagonally (‘diagonal integration’ in Poon (1994)), forming what Michael (2003, p. 138) calls ‘diagonal clustering’, where the colocation of directly and indirectly tourism enterprises adds value not only to the clustering firms’ diverse products but also to the tourists’ experiences. While tourism products are something tourists perceive and define where a product starts and where it ends (Framke, 2002), visitors have the ability to exert powerful influence upon tourism firms. On the other hand, it is well known that visitors tend to adjust their buying behavior based on past or other tourists’ experiences. As a kind of intangible goods, (Lewis & Chambers, 2000), it is difficult for visitors to evaluate its quality prior to consumption, interpersonal influence and word-of-mouth (WOM) are ranked the most important information source when a consumer is making a purchasing decision (Litvin, Goldsmith, &

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Pan, 2008). Loyal visitors are more likely to repurchase and spread word-of-mouth advertising, which can reduce tourists’ demand elasticity and tourism firms’ operating risk associated with their profitability and evoke a chain reaction that will directly influence the performance of tourism industry. Thirdly, tourism agglomeration is a dynamic system that evolves over time, especially when the demands of tourists changes dramatically. In the case of tourism agglomeration, tourism density can develop into internal economies of scale in terms of specific tourism trading links and customer-supplier relationships, which are strictly dependent upon the close geographic proximity of tourism firms localized within a special tourism agglomeration. Conversely, tourism firms are heavily dependent on the local tourism industry network system or linkages to support their novel products and services for tourists. As tourism system is about the spatial interaction between demand and supply, tourism firms in cluster can cooperate, create alliances and actively collaborate in order to deliver the diverse experiences that tourists seek (Michael, 2003) by promoting existing agglomeration and implementing potential or newly established ones. Hence, tourism destination can be understood as both a physical and intangible complex sociocultural entity (Seaton & Bennett, 1996), or as amalgam and unique entity of tourism products offering an integrated experiences to tourists (Buhalis, 2000), it exists not only physically but also mentally, in the minds of its current and potential tourists. Finally, the development of tourism agglomeration should not only be seen as a very complex process linked to strong stakeholder collaboration, but also as a spontaneous process due to the nature of the tourism social network involved. Geographical proximity conduces to strong social network, such as the trust and shared values, at the destination scale (Sørensen, 2007), which are critical for effective knowledge sharing (Shaw & Williams, 2009), hence, tourism agglomeration mechanisms can play a key role in knowledge transfer in terms of learning from product similar/dissimilar attractions within agglomeration. Despite the empirical evidences about the linkages between spatially bounded advantages and regional tourism development, the relationship should actually and most profoundly hold at the tourism firm-level. In essence, the tourism firm producing a complementary product or service is not a competitor, because its activities add much more values to the product and tourists’ experiences. Furthermore, the cooperation creates tourism alliances and social network system, makes better use of skills and resources and encourages tourism innovative business activities which improve local tourism development. The dynamic nature of tourism makes it very challenging to be analyzed. This present paper wishes to fill in this gap with a thorough analysis on the Chinese provincial tourism industry.

Table 1 Coefficient of variation of Chinese tourism income, 1978e2009. Year

Total income

Inbound tourism income

Domestic tourism income

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

0.2179 0.2083 0.2176 0.2201 0.2169 0.2145 0.2186 0.2279 0.2409 0.2428 0.2410 0.2297 0.2622 0.2803 0.2999 0.3175 0.3549 0.4894 0.5909 0.6732 0.7208 0.8287 1.0705 1.0401 1.0262 0.9884 0.9721 0.9437 0.8978 0.8830 0.8547 0.8271

0.5038 0.4562 0.5080 0.3663 0.3785 0.3968 0.4169 0.4342 0.4400 0.4395 0.4441 0.4492 0.4362 0.4566 0.4696 0.4810 0.4582 0.4535 0.5039 0.5267 0.5222 0.5461 1.8976 1.8395 1.7936 1.8853 1.6330 1.6894 1.6467 1.5608 1.5396 1.5291

0.7185 0.7181 0.7177 0.7188 0.7172 0.7159 0.5942 0.5156 0.4366 0.4345 0.3987 0.3759 0.3482 0.3254 0.3410 0.3538 0.3969 0.6490 0.8399 0.8930 0.9556 1.0197 0.9659 0.9525 0.9253 0.9458 0.9100 0.8777 0.8494 0.8389 0.8179 0.7950

Source: Chinese Tourism Statistical Yearbook and its Supplement over the years from 2001 to 2010.

disparities in the tourism development that actually occurred over this time period.

3.1. Evolution of china tourism industry inequality The degree of disparity of Chinese tourism industry in terms of tourism income can be measured by the Coefficient of Variation (CV). Denoting the tourism industry income in province i as Xi, the CV of tourism income can be addressed by calculating an index as

CV ¼ s=x

Where 3. Background In China, the tourism industry has been regarded as the means of promoting regional economic development and ameliorating regional inequalities. Recent initiatives, particularly the ‘Western Development’ policy, are designed to address these inequalities, with tourism industry playing a leading role in regional development. As Gao and Ge (2000, p. 240) comment “Tourism should take the lead [in regional development] to stimulate local employment and relevant businesses, promoting development of the third industry”. However, in China, there exist significant inequalities in the tourism distribution between eastern coastal gateways and western and inland provinces (see Table 2). From 1978 on, the Chinese tourism industry has experienced a rapid growth; however, these aggregating figures obscure large provincial

(1)

x ¼

P31

i¼1

Xi =31,

and

s ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 ð1=30Þ 31 i ¼ 1 ðXi  xÞ

ði ¼ 1; 2; 3; /; 31Þ. And Table 1 summarizes the evolution of the index of CV for Chinese tourism industry in the whole nation. The index of CV shows a general increasing trend in the disparity of the provincial tourism industry development in China. The current inequality in tourism income distribution in China can be attributed in part to the process by which China’s progress to a market economy has taken place. As the Table 1 shows, the disparity trend of Chinese tourism industry can be divided into two stages. From 1978 to 1994, the inequality of Chinese tourism industry income increased slowly, while this pattern of inequality has been reinforced by the market power after 1994 (See Fig. 1). Even as the CV has kept fluctuating from 1978 to 2009, Fig. 1 still shows a general increasing trend and depicts the evolution of inequality of the Chinese provincial tourism industry income distribution from “China’s Reform and Opening up to Outside World” in 1978. All of CVs about tourism total income, inbound

Y. Yang / Tourism Management 33 (2012) 1347e1359 Table 2 Provincial tourism income percentage in 2009 and 2000 (%). Province

Jiangsu* Guangdong* Zhejiang* Shandong* Beijing* Liaoning* Shanghai* Henan Sichuan Fujian Hunan Tianjin* Anhui Shanxi Hubei Guizhou Yunnan Shaanxi Hebei* Guangxi Chongqing Jiangxi Heilongjiang Neimenggu Jilin Hainan Gansu Xinjiang Qinghai Xizang Ningxia Total for top 5 (%)

% of total income

% of inbound income

% of domestic income

2009

2000

2009

2000

2009

2000

10.32 8.65 7.25 6.97 6.69 6.14 6.14 5.58 4.18 3.07 3.07 2.79 2.51 2.48 2.48 2.26 2.23 2.15 1.98 1.95 1.95 1.90 1.81 1.70 1.62 0.59 0.53 0.53 0.17 0.16 0.15 39.89

8.06 14.88 5.83 5.09 9.05 3.22 11.29 4.47 3.22 3.72 1.86 3.97 1.98 1.01 3.47 0.78 2.60 1.86 2.60 2.11 1.86 1.61 1.74 0.53 0.70 0.97 0.29 0.87 0.13 0.09 0.12 49.12

9.50 23.66 7.63 4.18 10.31 4.39 11.35 1.02 0.68 6.15 1.59 2.80 1.34 0.89 1.21 0.26 2.37 1.82 0.73 1.52 1.27 0.69 1.51 1.32 0.57 0.65 0.03 0.32 0.04 0.19 0.01 62.44

5.06 28.77 3.60 2.20 19.36 2.68 11.28 0.87 0.85 6.25 1.55 1.62 0.79 0.35 1.02 0.43 2.37 1.96 0.91 1.83 0.97 0.44 1.32 0.88 0.41 0.76 0.38 0.66 0.06 0.37 0.00 70.73

10.27 7.25 7.25 6.95 6.35 6.35 5.74 6.04 4.53 2.90 3.32 2.87 2.60 2.63 2.60 2.42 2.21 2.18 2.09 1.99 2.02 1.99 1.84 1.72 1.69 0.57 0.57 0.54 0.18 0.15 0.16 38.07

8.62 11.84 6.28 5.70 7.31 3.22 11.40 5.12 3.65 3.36 1.90 4.38 2.19 1.13 3.95 0.85 2.63 1.90 2.92 2.19 2.05 1.90 1.75 0.47 0.76 1.02 0.27 0.92 0.14 0.04 0.14 45.46

Notes: (1) *East provinces. (2) Source: Chinese Tourism Statistical Yearbook and its Supplement.

income and domestic income across nationwide show significant rising trend, indicating the deterioration of Chinese provincial tourism industry spatial dispersion. In Fig. 1, the CVs about tourism industry total income, inbound income and domestic income have changed from 0.2179, 0.5308 and 0.7185 in 1978 to 0.8271, 1.5291 and 0.7950 in 2009, which are 3.8, 3.0 and 1.1 times the number of 1978 respectively, implying a rising long run trend of Chinese tourism industry spatial inequality.

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disparity is caused not only by historical and geographical factors but also by political strategies that follow from the high involvement of the government in tourism economic activities. The policy of “China’s Reform and Opening up to Outside World” that, amongst other things, enabled inbound tourism and relevant earnings of foreign income, was introduced in December 1978, but was not applied to all provinces at the same rate (Démurger, 2000). The “Deng Xiaoping’s Talk in South China” in 1992 has been followed by a further economic development in China and the rhyme of Chinese economy transformation to the “Socialist Market System” has been enhanced, the tourism industry becomes an area in which the movement to a “Socialist Market System” has been developed most rapidly and completely. Currently, in tourism industry, west China lacks competitiveness compared with east China as shown in the earlier research of Yang (2007a). As the case with broader economic development in China, both domestic and international tourists’ activities are heavily concentrated on the coastal gateways, which cause a giant disparity of tourism industry development between coastal and inner provinces. Table 2 shows the data on inequality of tourism industry by province in the year of 2000 and 2009, and as can be seen from this table, there has been an enormous variation among provinces in tourism income. In 2000, the top five tourism provinces, as measured by total tourism income, inbound tourism income and domestic tourism income, accounted for 49.12%, 70.73% and 45.46% of total nationwide tourism income respectively, while in 2009, these numbers changed to 39.89%, 62.44% and 38.07% correspondingly (see Table 2). All of these top five provinces dominated by such eastern coastal provinces as Guangdong, Beijing, Jiangsu, Zhejiang, Shandong, while the percentage of tourism income in western provinces remained in a small range. In 2000, Xizang only accounted for 0.09% of the nationwide total tourism income, while in 2009, it became a little higher, to 0.16%. As Guangxi and Ningxia, the middle or western provinces in China, had the similar development trend from 2000 to 2009, while Xinjiang became even worse. In tourism agglomeration, tourism firms and their activities are positioned relative to each other in tourism production system. The coordination occurs between such different tourism service providers as the hotels and airlines, tour operators and travel agencies, within the same echelon and/or among different echelons, which enhances the tourism development through the performance of tourism firms.

4. Data and variables

There is a great gap of tourism industry development between coastal and interior provinces in China. This provincial tourism

Due to the shortage of statistical data, related empirical studies of tourism industry have not quantitatively evaluated the effect of

1.5 1 .5

Total Income Inbound Income Domestic Income

0

Coefficient of Variation

2

3.2. Disparity of Chinese tourism industry

1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Fig. 1. Trend of coefficient of variation of tourism income in China, 1978e2009.

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regional tourism agglomeration. As tourism industry boundary is ambiguous in nature (Smith, 2004; Smith & Zhao, 2003), and according to Chinese tourism statistical status, it is impossible to get the specific and detailed data about tourism development. Although Tourism Satellite Accounting (TSA) can give us some help, only a few provinces, such as Jiangsu and Yunnan, have tried to construct their own TSA system, and the researchers cannot rely on TSA to get nationwide data. Fortunately, the data of tourism enterprises is available, and this can give this research a good start point to study the effect of tourism agglomeration in firm level. Actually, tourism enterprises play an important role in tourism activities, they provide all kinds of services to meet the demands of tourists, and thus become critical intermediary between the supply and demand of tourism products and services. 4.1. Scale of tourism industry As a type of dynamic recreational behavior, tourists’ purposes have become more and more diverse, and the tourism industry has gradually expanded to cover more and more industry fields to meet tourists’ diverse demands. The tourism involves all aspects of tourists’ demands, and this relates to a multiple industries’ content. It is very important for tourism industry to combine or cooperate with other industries, which make tourism industry to become a comprehensive industry. But, it is difficult to distinguish tourism factors from other industries. It is difficult to describe tourism industry scale in the framework of Chinese System of National Account (SNA), and the main reason lies in that, on statistical perspectives, industry is measured and classified based on the data of production on the supply side, not on the data of its demand. As for tourism industry, it is principally defined from the tourists’ demands, and there is no such specific single and clear industry in SNA system corresponding to the tourists’ demands. The components of tourism industry actually hide in Chinese SNA system, or other different economic departments. On the perspective of the statistics, the tourism industry has been reflected and measured by its demand, and the tourism revenues come from the tourists’ total expenditure in specific region. Tourists’ expenditure includes the food, hotel, transportation, purchasing and entertainment etc., the corresponding supply is distributed among different sectors of Chinese national economy. Using the method of TSA, which is initiated by World Tourism Organization (WTO), the components related to tourism demand can be separated. Unfortunately, only a few provinces have begun the exploration to develop their own TSA, the nationwide qualified data cannot be acquired from TSA in China. The tourism is a sector with fragmented components and particularly characterized by the presence of a large number of participants in tourism production or service network which are not involved in the same economic sectors. Nonetheless, tourism consumption and its production factors can be distinguished and Chinese tourism statistical institution and system give great help with basic research data. Starting from 1993, National Tourism Administration of China (CNTA) authorized Urban Social and Economic Survey Team (USEST) of National Bureau of Statistics of China (CNBS) to conduct sample survey of urban residents’ domestic tourism. From 1997 on, CNTA cooperated with Rural Social and Economic Survey Team (RSEST) of CNBS to execute sample survey of rural residents’ domestic tourism. The main contents of these surveys include travel-person-times, dwelling time, expenditure, etc. and furthermore, calculated the national wide travelperson-times and total tourism revenues based on these data. In the year of 1999, CNTA and CNBS jointly promulgated the Tourism Statistical and Survey Institution, which acquired lessons from

previous tourism statistic practice in some provinces, and then promote Sampling and Survey Plan of Local Reception of Domestic Tourists to all of Chinese provinces or autonomous regions. These actions and institutions not only regulated our tourism statistic system, but also constructed solid foundation for improvement of tourism statistic system. According to the scope of tourism activities, there are three types of tourism, which are inbound tourism, domestic tourism and outbound tourism. The former two can create tourism revenues for a country, while the latter is a kind of expenditure leaking out of that country. As for inbound tourism, China’s total number of inbound tourists comes from the Customs Statistics, and their expenditure data comes from The Form of Inbound Tourist Spending Survey in China, which includes more than 10 survey questions such as inbound tourists’ demographic characteristics, evaluation of service quality, interest to Chinese tourism products, etc. Because the statistical definition of tourism is mainly based on the demand side of tourism industry, China’s tourism total tourism industry scale can be measured by or calculated from the tourists’ expenditure. According to the survey implemented by USEST and RSEST, the travel ratio and expenditure per person of the Chinese residents’ domestic travel can be calculated, and the total expenditure of Chinese residents’ domestic tourism, which formulates the Chinese domestic tourism revenues, can be obtained. On the other hand, multiplying the entry number, which is got from Bureau of Exit & Entry Administration Ministry of Republic Security of China (MPS), and the average cost of inbound tourists, which comes from inbound tourists sample survey, the total expenditure of inbound tourists, which formulates Chinese inbound tourism revenues, can be obtained. 4.2. Density of tourism industry When tourism firms locate near to each other, they benefit from externalities that are external to these firms but internal to the tourism industry. In consideration of the difficulties in the field survey about network system across all of Chinese provincial tourism agglomeration, the density of Chinese tourism industry can be used as a substitute variable to measure Chinese tourism social network, which acts as “common glue” that binds tourism firms together in clusters, and leads to tourism industry competitive advantages, which strictly stems from a high degree of geographic concentration of tourism firms and thus distant rivals cannot compete with. Tourism firms are shown to have close links and positive feedback effects with the agglomeration economies, especially with various non-economic exchange linkages between tourism firms, embeddedness, institutional thickness, and the creation of social structure for new populations of tourism firms. Following Glaeser, Kallal, Scheinkman, and Shleifer (1992), the MAR externalities, including the accumulation of knowledge, the development of an information network and the promotion of the innovations among firms, within the same sector are facilitated. In tourism agglomeration, these interactions affect local firms’ productivity and might favor the tourism industry growth. Many empirical papers have discussed the way to measure specialization related to MAR externalities. Such indexes as Hoover Coefficient (Hoover, 1936), gj Coefficient (Ellison & Glaeser, 1997) and Regional Specialization Coefficient (Fan, 2007) have been applied in empirical research of different industries in many countries. According to Henderson, Kuncoro, and Turner (1995), specialization index ðSs;p ¼ ðss;p =sp Þ=ðss;N =sN ÞÞ is considered the ratio of the share of sector (s) in region (p) to its average share across whole country (N), It measures the specialization level of sector (s) in region (p) in contrast to a national level. The specialization theory

Y. Yang / Tourism Management 33 (2012) 1347e1359

suggests that increased concentration of a particular sector within a specific geographic region facilitates knowledge diffusion among firms within the same sector, and enhances innovative activities. When some tourism enterprises concentrate in specific geographic region, the degree of spatial density of tourism enterprises and their activities will absolutely rise, the index of tourism specialization can be used to measure tourism industry density in Chinese different provinces. Furthermore, considering the ambiguity of tourism industry’s boundary, it is difficult to distinguish components in other industries related to tourism. The present paper just takes consideration of single tourism industry and use the ratio of provincial tourism enterprise revenues to the Chinese tourism enterprise total revenues as the measurement of provincial tourism relative specialization. And, considering discrepancy of space area to different provinces, province area (areai) is used to balance this index. That is,

Deni;t ¼

P SOTIi;t = i;t SOTIi;t areai;t

(2)

Where, SOTI stands for “Scale of Tourism Industry”. Note that the index Deni,t not only denotes the scale of tourism industry but also measures the specialized density of tourism industry. It indicates the relative scale and specialized density of tourism industry in province i to nationwide level, and underscores relative degree of tourism agglomeration. The rise of tourism industry scale and density will contribute to fast adoption of tourism knowledge, sharing common tourism labor and demand market among related enterprises, which can finally affect the development of tourism through endogenousscale effect in firm level. 4.3. Tourism enterprise According to China Tourism Statistical Yearbook and its Supplement, tourism enterprises have been classified into Star-rated Hotel, Travel Agency, Scenic Spots, Transport and other (HASTs). In 1999, Chinese government had implemented a sample survey of the hotel and hospitality facilities for the first time, including the foreign hotel, social hotel and individual hotel etc. In 2000, CNTA, CNBS, State Administration for Industry & Commerce (SAIC) and MPS had jointly conducted following-up investigation for these hotel and hospitality facilities by the means of comprehensive survey and sample survey. Starting from 2000, new Tourism Statistical and Survey System has been carried out by tourism enterprises, and tourism statistic range has been expanded further. Besides hotels and travel agencies, the scenic spot enterprises also have been included, Chinese tourism statistic system has covered such factors as the transportation, sightseeing, hotel, food, purchasing and entertainment. These enterprises of HASTs have become the most important components of tourism statistical system. According to the statistical investigation of tourism enterprises and tourists, large amounts of financial data of the star-rated hotel, travel agency, scenic spots and transportation enterprises has been fetched, and these data describes these tourism firms’ operation performance by reception number, occupancy rate, etc. These enterprises of HASTs in investigation constitute the core components of China tourism industry, but on the other hand, the investigation is implemented only in the scope of tourism administration, the factors outside of tourism administration have been ignored, and tourism related industries’ data cannot be obtained by this way. So, this paper uses the firm level data of HASTs to analyze the effect of the tourism agglomeration. The density of tourism cluster can improve the development level of Chinese provincial tourism industry in firm-level. The variable of density can represent the heterogeneity of Chinese

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provincial tourism industry not only in economic dimension but also in the dimension of network system. Both the dispersion of knowledge and technology and the communication of business information can bring out dynamic agglomeration (Bathelt, Malmberg, & Maskell, 2004), boost collective learning ability and creative ability among related enterprises. Hence, the appropriate analyzing tools should be introduced to discuss tourism agglomeration phenomenon. 5. Methodology and model specification Tourism agglomeration characterizes by an integrated supply of cultural and environmental resources and entertainments, and its operation is based on a complex cooperation network between a wide presence of cooperating tourism or other enterprises. Much of related researches have analyzed the different relationship between the airlines and travel agencies (Alamdari, 2002), the hotels and travel agencies (Bastakis, Buhalis, & Butler, 2004), the hotels and tourists (Cobanoglu, Corbaci, Moreo, & Ekinci, 2003), the tour operator and tourists (Campo & Yagüe, 2007), the hotels, coach companies, restaurants and tour operators (March, 2000), the suppliers of travel products and tour operators (Pearce, 2007) and the relationship of entire tourism supply chain (Yilmaz & Bititci, 2006). Due to the shortage of official data, the inherent complexity of tourism cooperation network and the difficulty in establishing quantifiable standards to measure cooperation network in tourism agglomeration, the emerging literature has concentrated largely on conceptual research (Bimonte & Punzo, 2007), whereas quantitative studies are very limited, even though in the existing studies of other fields quantitative methods are those most widely used. Even though some literatures have been dominated by the investigations and applying quantitative (Baum & Mudambi, 1994; García & Tugores, 2006; Wie, 2005) and empirical (Campo & Yagüe, 2007; Tsaur, Yung, & Lin, 2006) methods effectively to tourism agglomeration, they mainly focus on case studies in the specific region (Pearce, Tan, & Schott, 2007). In consideration of the inherent heterogeneity of different region, this paper puts a few more regions together, takes into account various developing background and network, and gets common rule about relationship of related variables in tourism development. Although rigorous research of tourism agglomeration development is underway, it is evident that the analysis in more depth is required and further examination of the issues about the relationship between the density and tourism development, which is critical to tourism agglomeration and development, is necessary. In this section, this paper proposes possible directions for future tourism agglomeration research, and it is worth mentioning one quantitative method, panel data model (PD), which is able to deal with the complexity of heterogeneity of tourism clusters, has been widely used to estimate dynamic economic models. Its advantage over tourism cross-section data is obvious: (i) It is impossible to estimate the tourism dynamic model from observations at a single province in one time, which is the principal method used in a case study or a field survey and it is impossible for a single cross-section survey to provide sufficient information about earlier time periods for the tourism dynamic mechanisms to be researched; (ii) The scope that panel data offers to investigate the heterogeneity in dynamics mechanisms between different provincial tourism can reduce the aggregation biases in this research. 5.1. CobbeDouglas function The aim of this paper is to determine how the development of a tourism industry in a given province is affected by the tourism

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agglomeration and its density. The “production function” essentially relates the various input factors in the production to the output from it. Traditionally, standard production models consider capital and labor as the fundamental factors of production. According to CobbeDouglas production function, this paper uses K, L to represent conventional capital and labor input factors in quantity respectively; efficiency factor A captures productivity which can be interpreted to measure such qualities of these two production factors as workforce skills. Chinese provincial tourism development can be summarized in a simple production model specification:

revit ¼ AKita Lbit

(3)

where revit represents total tourism enterprises revenues in province i (i ¼ 1; 2; /31) between 2000 and 2009; K, L and a, b are the capital and labor input with their relative contribution to the tourism production respectively, while A represents a multiplicative factor in the aggregate tourism production function that augments the productivity of capital and labor and is affected by tourism agglomeration density (Denit). By using the CobbeDouglas production function as equation (3), the efficiency factor can be decomposed into a set of agglomeration indices (e.g. tourism industry density as proxy indicators of tourism economic activity and social network), which can improve the tourism enterprise efficiency remarkably over time in different areas in China. So, the tourism industry development in Chinese provinces differs in its production factors such as the labor and capital input in quantity, and a variety of such qualitative factors as agglomeration externalities and social network. Besides the labor and capital, the agglomeration is hypothesized to affect the level of output in tourism industry production. Furthermore, the aim of this paper is to determine how tourism industry development in a Chinese given province is affected by local tourism agglomeration, and the density can be summarized in a simple growth model specification:

lnðrevit Þ ¼ a þ blnðDenit Þ þ glnðKit Þ þ dlnðLit Þ þ 3 it

(4)

Where, 3 it is the error term. Besides K, L, this equation indicates that tourism industry development is affected by the density, which measures different development dimension of provincial tourism agglomeration.

5.2. Dynamic panel data model Tourism agglomeration is geographically bounded concentration of interdependent tourism enterprises with active channels for tourism business transactions and information communications. In tourism agglomeration, different tourism enterprises or firms can collectively share common opportunities and threats; they compete with other tourism enterprises or firms in other province on a regional base, which enhances the competitive advantages of the regional tourism industry. The main challenge of this research, however, is to find a valid estimation method. The provincial tourism heterogeneity caused by the economic or social factors may induce biased estimation unless a special estimation technique is used. In this paper, the panel data model of Chinese provincial tourism industry is used to estimate a basic production function specification. The historical performance data are continuously recorded for tourism enterprises in each province. Reviewed as a whole, the data signal both cross-sectional (different provincial tourism industry) and longitudinal (different years) characteristics. This type of data can be treated as panel data, which is a specific term to describe

a set of data consisting of multiple sites, and periodically observed over a certain time frame. In these years, panel data models have been primarily used in areas of economics and social studies, and the techniques of estimating this type of model have been continuously developed by econometrics. One of the advantages of using the panel data model is that it allows cross-sectional heterogeneity, which is common in provincial tourism industry. Chinese provincial tourism industry development is serially correlated. The failure to correct these problems of the heterogeneity, which includes the persistence of tourism industry development and the endogeneity, which is dynamic mechanism of tourism development and caused by the reverse causality from tourism industry to its agglomeration, may lead to inconsistent coefficients. In order to deal with these problems, the dynamic panel data (DPD) models are adopted in this paper. By inclusion of a lagged dependent variable, the panel data models can be allowed for dynamic effects, which is a common approach in researches of firm performance in the field of industrial organization (Geroski & Machin, 1997). To account for the persistence of tourism industry development, this paper introduces lagged dependent variables in Eq. (4), extends panel data model easily to models with predetermined or endogenous explanatory variables or instruments. Specifically, dynamic panel model relies on first-differencing or related transformations to eliminate unobserved individual-specific effects, uses lagged values of endogenous or predetermined variables as instrument variables for subsequent first-differences, and can be expected to perform well in situations where the series are close to be autoregressive. 5.3. Data description and estimation method The data used in this paper is panel data for 2000e2009 on Chinese provincial tourism industry as a whole. All data is available or calculated for all 31 provinces for the 10-year period from 2000 to 2009, which is mainly from China Tourism Statistical Yearbook and its Supplement directly or indirectly. Descriptive statistics on the variables used in this analysis for the final sample of 310 observations are shown in Table 3. Several strategies have been proposed to generate a consistent estimator of dynamic panel data model. This paper uses the method described in Bond (2002), and focuses on the method of Generalized Method of Moments (GMM) which is widely used in this type of dynamic panel data model. Because this method uses more than one instrument variable, it is generally thought to be more efficient (Arellano & Bond, 1991). The Arellano and Bond (1991) GMM estimator has several characteristics: (i) It employs the lagged values of the endogenous regressors as instrument variables to solve the problem of endogeneity; (ii) It uses firstdifferences to remove the fixed effects; (iii) The lagged dependent variable is instrumented with its past values to solve the problem of autocorrelation; (iv) The Arellano-Bond estimator is designed for small-T large-N panels. Specifically, the GMM estimator is used in this paper for at least three reasons: (i) Inertia is most likely to be presented in the annual tourism data, and it seems proper to use a dynamic specification to

Table 3 Descriptive statistics. Variable

Obs

Mean

Std. Dev.

Min

Max

rev (billion yuan) SOTI (billion yuan) K (million yuan) L (ten thousand person) Den

310 310 310 310 310

9.9890 60.1882 18.3463 7.9541 0.8948

12.9946 65.2943 20.6662 8.5840 2.7164

0.0773 0.6910 0.4449 0.2088 0.0006

72.0541 372.3834 147.4410 61.4441 18.0421

Y. Yang / Tourism Management 33 (2012) 1347e1359

allow for it; (ii) Some of the tourism explanatory variables (such as Den) are likely to be jointly determined with tourism agglomeration and development, and it suggests feasible to accommodate the potential joint endogeneity of the explanatory variables; (iii) There is a possibility of unobserved province-specific effects correlated with the regressors, and it appears reasonable to take into account such effects. This paper will provide the estimation result of different method as for reference and comparison. This paper conducts a dynamic panel analysis of the effect of the tourism agglomeration using panel data of Chinese provincial tourism industry for the 2000e2009 periods. The focus of this paper will be on the estimation of single equation, autoregressivedistributed lag models from panels with a large number of crosssection units, each observed for a small number of time periods. 6. Empirical results 6.1. Dynamic procession of tourism enterprise revenues A dynamic panel data model is a specific form of panel data model that includes lagged dependent variables as predictors. As explained in previous sections, the Chinese provincial tourism development is a persistent and dynamic process and related to its previous condition. Tourism product innovations in tourism agglomeration thus lead to new products on the market which stimulates new tourism demand and Word-of-Mouth effect. This will increase demand and allow tourism firms to have giant development space. From the direct effect of product innovations on tourism development, it is expected that there is a positive relationship and cycle accumulation process in the tourism industry development. The inclusion of historical tourism industry development (lagged dependent variables) information allows the model to capture the dynamic trend of its performance and further to improve model accuracy. At first, this paper focuses on estimation methods for the simple AR(1) model

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revi;t ¼ arevi;t1 þðhi þ vit Þ; jaj < 1; i ¼ 1; 2; 3/N; t ¼ 1; 2; 3/T (5) Where revi,t is tourism enterprises revenues on series for province i in time t, revi,t1 is tourism enterprises revenues on the same series for the same province in the previous period, hi is an unobserved provincial-specific time-invariant effect which allows for the heterogeneity in the revi,t series across provinces, and vi,t is a disturbance term. A key assumption throughout this paper is that the disturbance vit is independent across provinces. The number of provinces for which data is available (N) is assumed to be large whilst the number of time periods for which data is available (T) is assumed to be small, and asymptotic properties are considered as N becomes large with T fixed. The individual effect (hi) is treated as being stochastic, which here implies that it is correlated with the lagged dependent variable revi,t1 unless the distribution of hi is degenerate. Initially, this paper further assumes that the disturbance (vit) is serially uncorrelated. These jointly imply that the Ordinary Least Squares (OLS) estimator of a in level equation (5) is inconsistent, since the explanatory variable revi,t1 is positively correlated with the error term (hi þ vit) due to the presence of the individual effects, and this correlation does not vanish as the number of individuals in the sample gets larger. In consideration of the problem of this model, this paper uses the method illustrated in Bond (2002) to estimate equation (5), and following Roodman’s (2006) suggestion, time dummies are included in this model. Table 4 shows the estimation results. Different estimation techniques for dynamic panel data models are tried in this paper. Table 4 reports the estimated results and some associated statistics to check the validity of the model. The lagged dependent variable is significant for all of five estimation models in Table 4. This paper uses dummy variables of year 2000year 2009 to capture the time trend of tourism development. Additionally, this paper reports a diagnostic test for first and

Table 4 Alternative Estimates of the AR(1) specification for tourism revenues. I

II

III

IV

V

OLS

WITHIN GROUP

GMM DIF

GMM DIF(I)

GMM DIF(II)

Intercept revt-1 Year 2001 Year 2002 Year 2003 Year 2004 Year 2005 Year 2006 Year 2007 Year 2008 Year 2009

0.2997*** 0.9277*** 0.0820 0.0407 0.1715 0.0256** 0.0007 0.0453 0.0594 0.0961 e

1.6846*** 0.2624*** 0.8659*** 0.7231*** 0.7324*** 0.5095*** 0.3807*** 0.2083*** 0.1651*** 0.1343** e

0.0146 0.3910*** e 0.1085 0.0473 0.2033** 0.0843 0.1334 e 0.0032 0.1123

1.6140** 0.2963** 0.8259*** 0.6883*** 0.7038*** 0.4822*** 0.3613*** 0.1954*** 0.1597*** 0.1323** e

1.8676*** 0.1495*** 0.9989*** 0.8484*** 0.8539*** 0.6100*** 0.4711*** 0.2741*** 0.1847*** 0.1396*** e

Instruments

e

e Revt-2

Revt-2 Revt-3

Revt-2 Revt-3 .. Rev1

e 0.0000 e e 0.0021 0.7969 248 e

e 0.0000 0.928 0.459 0.002 0.900 248 31

e 0.0000 0.024 0.876 0.002 0.902 248 31

Dependent variable: revit

R2-adjusted F(p-value) Sargan (p-value) Hansen test (p-value) AR(1) (p-value) AR(2) (p-value) Number of observations Number of groups

0.9307 0.0000 e e 0.0054 0.5408 279 e

0.7158 0.0000 e e e e 279 31

Notes: (1)*,**,*** denote significant at the 10%, 5%, and 1% level respectively. (2)“e”data cannot be obtained. (3) Sargan and Hansen is a test of the over-identifying-restriction for the GMM estimators, asymptotically c2. P-value is reported. This test uses the minimized value of the corresponding two-step GMM estimators. (4) AR(1) and AR(2) tests are Arellano-Bond test for that average autocovariance in residuals of order 1 or 2. (5) All estimations are made by using STATA11.

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second-order serial correlation, as well as the Sargan test of instrumental validity. In column IV of Table 4, all instruments pass commonly necessary used tests: the Sargan test (Sargan, 1958), Hansen test, the AR(1) and AR(2) test. The empirical result of column IV is analyzed in the following paragraph. According to the test of hypotheses, which are proposed by Arellano and Bond (1991) and are necessary condition for a valid instrumentation, there is no second-order serial correlation in the errors of the first differenced equation. If it is rejected, the GMM estimator is inconsistent. As shown in Table 4, the model passes this test (P-value ¼ 0.900). A test for the hypothesis that there is no firstorder serial correlation is also reported: the rejection of the null hypothesis (i.e., the presence of first order serial correlation) indicates the inconsistency of the estimator by the OLS method. The model also passes the Sargan test and Hansen Test for OverIdentifying-Restriction (OIR) (P-value ¼ 0.928 and 0.459), which is required for the GMM to be valid. Therefore, the model developed is statistically sound, the GMM method based on the ArellanoBond’s procedure seems more reasonable, and the development of tourism enterprise is also affected by their previous condition positively (a > 0), indicating there is an inertia and persistence mechanism in the Chinese tourism development process. 6.2. Estimation of dynamic panel data model The GMM estimators for autoregressive models outlined in the previous section extend in a natural way to autoregressivedistributed lag models of the formula (4). As the estimation of GMM DIF shows, the lagged dependent variable of revi,t1 has a significant effect on the variable of revi,t1. By adding lagged dependent variable in Eq. (4), the panel data model of Chinese tourism development takes the form as:

lnðrevit Þ ¼ a þ llnðrevit1 Þ þ blnðDenit Þ þ glnðKit Þ þ dlnðLit Þ þ 3 it (6) Where revi,t1 is observed tourism development of the last period and l is its regression coefficient, while all the other variables and parameters remain the same. This paper applies the system GMM estimation procedure, which reduces the potential biases and imprecision associated with the usual difference estimator. Using balanced panel data from 2000 to 2009 in China, this paper compares estimated results from such different estimation approaches as OLS, WITHIN GROUP and GMM DIF (difference GMM). The estimated results and some associated statistics to check the validity of the model (6) are shown in Table 5. In order to analyze individual effect of explanatory variables, this paper introduces the variable of the density gradually into formula (6) and uses different estimation techniques for comparison. Also, this paper includes time dummies in Eq. (6) according to Roodman’s (2006) suggestion. Table 5 presents empirical results from different approaches, and only statistically significant findings will be discussed in this paper. All explanatory variables are regarded as being weakly exogenous, and lagged values thereof are included as “internal instruments,” with Bond’s (2002) method being used to select instruments. As Table 5 shows, all of the instruments pass tests of the autocorrelation and OIR. On one hand, in regard of the problem of the autocorrelation, the standard Arellano-Bond test, which is proposed by Arellano and Bond (1991) with the hypothesis that there is no second order serial correlation in the errors of the first differenced equation, is used to check for autocorrelation in errors, that is a crucial point with respect to the validity of instruments. As presented in Table 5, the results of the AR(2) test except column IV do not reject the null

hypothesis of no second-order serial correlation, indicating that there is no serious problem of second-order serial correlation in these models. On the other hand, this paper tests the validity of the additional instruments in the GMM system model compared to the GMM difference model as proposed in Blundell and Bond (1998). In the models III to VI and VIII to XII, the Sargan Test does not reject instruments used in this research, and these models pass the Sargan test for OIR (Sargan, 1958), which is based in the two-step estimator and required for the GMM to be valid. It indicates there is no correlation between the instruments and the error term of the first differenced equation. Therefore, these models used in this paper are statistically sound. Additionally, in regard to the problem of endogeneity, the Hansen test is performed to test the validity of instruments. The null hypothesis is that these instruments are not correlated with the residuals. As shown in Table 5, for models except VI, the results of the Hansen test are all statistically insignificant, indicating that the exogeneity of instruments cannot be rejected, i.e., the instruments set in this research can be considered valid. These coefficients for K and L are significantly positive and very stable in all estimations in Table 5, indicating similar effects of the capital and labor input in different models. Nevertheless, the empirical results also indicate that, without taking density into account, pooled OLS can over-estimate the effects of lagged dependent variable on tourism development. In column XII of Table 5, the preferred model of this paper, there is a negative significant effect of revi,t1, indicating strong negative inertia or persistence for Chinese tourism development. This suggests that the Word-of-Mouth effect and consumer persistence have not played an active role in Chinese tourism development. The reason for this empirical result lies in following three aspects: (i) The repeating tourism behavior is not common and popular; (ii) In present stage, the visitors’ satisfaction of Chinese tourism service is distinctly underwhelming, which weakens tourists’ desire to revisit; (iii) The Chinese tourism products in different provinces sometimes are quite same to each other, and compete at same quality level. The major implication of this finding is that provision of high quality services is crucial for attracting new and repeat tourists for Chinese tourism development. As is shown in Table 5, the coefficients of Den from column VII to XII are always positive and highly significant. For models from VIII to XII, as expected, the coefficients of Den are positive and statistically significant. In model XII, the coefficient of Den is 0.4998 with statistical significance of 1% level, indicating that provinces with denser tourism industry have higher levels of tourism development. Specifically, the tourism density affects tourism development in several ways. Firstly, if capital and human resources have constant returns on themselves, the transportation of different part of tourism product from one firm to others involves time and other costs that rise with distance, the tourism agglomeration density can reduce these costs and thus increase production efficiency and tourists’ satisfaction within a particular geographical area. Secondly, if there are economic and social externalities associated with the physical proximity of tourism production, the agglomeration density will contribute to forming social network in specific region, which will enhance knowledge transfer between different tourism firms and therefore encourage such tourism innovations as product innovation, management innovation and institution innovation. Also, the information sharing can improve interactions among enterprises in tourism agglomeration and thus facilitate successful coordination among tourism agglomeration participants. Additionally, the development of institutions, which contributes to the enhancement of local tourism competitiveness,

Table 5 Estimation results based on different methods. I

II

III

IV

V

VI

VII

VIII

IX

X

XI

XII

OLS

WITHIN GROUP

GMM DIF (t2)

GMM DIF (t3)

GMM SYS (t2)

GMM SYS (t3)

OLS

WITHIN GROUP

GMM DIF (t2)

GMM DIF (t3)

GMM SYS (t2)

GMM SYS (t3)

Intercept revt-1 Kt Kt-1 Lt Lt-1 Dent Dent-1

0.2464** 0.5262*** 0.2678*** 0.0211 0.2180*** 0.0259

0.5976** 0.0020 0.2541*** 0.0943 0.2186*** 0.1235**

e 0.0328 0.1419 0.1551** 0.3009** 0.0788

e 0.1760* 0.2161* 0.2155** 0.2415 0.0801

0.2282 0.0564 0.2188 0.1736* 0.5618*** 0.2225***

0.8161 0.1553 0.4236*** 0.3387*** 0.4506*** 0.1914*

0.0004 0.3308*** 0.2722*** 0.0274 0.2391*** 0.1026** 0.2977 0.2094

0.5986 0.0068 0.2732*** 0.0806 0.1915** 0.1310** 0.3351** 0.3334**

0.0386 0.2103** 0.1054 0.2355** 0.0651 0.4202*** 0.2917**

0.1873** 0.2120** 0.1350** 0.1919 0.0654 0.5093*** 0.2179*

0.9477** 0.1530* 0.2433** 0.1333** 0.4064*** 0.1555*** 0.4792*** 0.2240*

1.1731** 0.3318** 0.2743** 0.1790* 0.3984** 0.2052** 0.4998*** 0.1744

Year Year Year Year Year Year Year Year Year

0.0090 e 0.1065 0.0240 0.0450 0.0528 0.0089 0.0068 0.0876

0.5812*** 0.5208*** 0.5693*** 0.4171*** 0.2974*** 0.1970*** 0.1760*** 0.1179** e

0.6738*** 0.5909*** 0.6359*** 0.4800*** 0.3403*** 0.2180*** 0.1885*** 0.1354***

0.7279*** 0.6307*** 0.6639*** 0.5106*** 0.3567*** 0.2303*** 0.1964*** 0.1358***

0.3486*** 0.3303*** 0.4255*** 0.3509*** 0.2542*** 0.1873*** 0.1917*** 0.1255**

0.1404 0.1460 0.2652* 0.2051* 0.1331 0.1148 0.1632** 0.1093**

0.0101 e 0.0896 0.0340 0.0882 0.1216 0.0881 0.1322 0.2135**

0.5721*** 0.5097*** 0.5541*** 0.4093*** 0.2887*** 0.1944*** 0.1729*** 0.1001** e

0.6793*** 0.5943*** 0.6217*** 0.4707*** 0.3282*** 0.2181*** 0.1815*** 0.1076***

0.8246*** 0.7120*** 0.7164*** 0.5585*** 0.3893*** 0.2538*** 0.1981*** 0.1152***

0.5681*** 0.5204*** 0.5645*** 0.4548*** 0.3234*** 0.2258*** 0.1960*** 0.1098***

0.6572*** 0.5952*** 0.6406*** 0.5319*** 0.3777*** 0.2637*** 0.2145*** 0.1203***

0.9462 0.0000 e e 0.0022 0.1482 277 e

0.7839 e e e e e 277 31

e e 0.582 1.000 0.001 0.208 245 31

e e 0.213 1.000 0.002 0.098 245 31

e e 0.956 1.000 0.000 0.106 277 31

e e 0.737 1.000 0.003 0.120 277 31

0.9537 0.0000 e e 0.0736 0.1442 277 e

0.7906 0.0000 e e e e 277 31

e e 0.754 1.000 0.003 0.240 245 31

e e 0.536 1.000 0.005 0.178 245 31

e e 0.877 1.000 0.001 0.116 277 31

e e 1.000 1.000 0.011 0.118 277 31

2001 2002 2003 2004 2005 2006 2007 2008 2009

R2-adjusted F(p-value) Sargan test (p-value) Hansen test (p-value) AR(1) (p-value) AR(2) (p-value) Number of observations Number of groups

Y. Yang / Tourism Management 33 (2012) 1347e1359

Dependent variable: revit

Notes: (1)*,**,*** denote significant at the 10%, 5%, and 1% level respectively. (2)“e”data cannot be obtained. (3) Sargan and Hansen is a test of the over identifying restriction for the GMM estimators, asymptotically c2. P-value is reported. This test uses the minimized value of the corresponding two-step GMM estimators. (4) AR(1) and AR(2) tests are Arellano-Bond test for that average autocovariance in residuals of order 1 or 2. (5) All estimations are made by using STATA11.

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Y. Yang / Tourism Management 33 (2012) 1347e1359

will increase with tourism agglomeration density. For example, publicly funded institutions such as education providers have developed programs specifically to support the research and practice of tourism cluster. So, given that tourism is a dynamic industry characterized by changing tourists’ demands, tourism enterprises have far greater power to provide diverse products and satisfy tourists’ demands through tourism agglomeration than their counterparts in other regions. Finally, the third source of density effects is the higher degree of beneficial feedback mechanism in areas with denser tourism activities. Taking the tourism industry as an entity, the density of tourism firms increase the opportunity for them to coordinate, and each actor within a tourism agglomeration performs its task by considering the impacts of its actions on other players. So, in tourism agglomeration, the coordination is a pattern of decision making and communication among a set of interrelated tourism players who perform tasks to achieve goals such as maximizing their utilities, satisfying diverse demands or the overall profit of the tourism agglomeration.

7. Conclusion The aim of this study is to investigate the relationship between tourism agglomeration and tourism development. The results and discussions in this paper confirm previous discussion about the effects of tourism agglomeration. This paper conducts a dynamic panel data analysis using data on Chinese provincial tourism industry for 2000e2009 periods, and investigates the dynamic effect associated with tourism agglomeration density. It is concluded that the dynamic panel data models better fit research questions that combine provincial tourism and spatial characteristics simultaneously, especially because they allow province-specific characteristics to be differently linked to their regional contexts. Using panel data for tourism industry in 31 Chinese provinces, the estimation of a tourism production equation sheds light on several issues. Briefly, this paper finds the evidences of the dynamic mechanism in the tourism industry development, and the econometric results lend support to hypothesis that there is a positive impact of provincial tourism agglomeration density and its development in this research. The main advantage of a dynamic panel data model is that it can capture the historical conditions and make adjustments accordingly, which generally results in a smaller estimation error compared to models without using existing conditions. But, turning finally to the directions for further research, there are a number of factors that are not considered in this analysis due to data limitations, such as the province characteristic, the heterogeneity of tourism resource, the local government support, and different development history, these factors should thus be incorporated in future research. Furthermore, it is not an easy task to deduce policy implications of these results. Nevertheless, the provincial tourism industry and other industries’ structure seem to exert a real influence on the development of the tourism industry. A better understanding of these phenomena is required for the formulation of policies to create incentives for the provincial tourism density that may result in rather long-term effects.

Acknowledgments I would like to thank anonymous reviewers for their very constructive and helpful comments on an earlier draft of this article. This work was supported by grant from the Social Science Fund Project of Ministry of Education of China (No. 09YJC790091).

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