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FDI spillovers in the Chinese hotel industry: The role of geographic regions, star-
rating classifications, ownership types, and foreign capital origins
Zhenxing Mao, Ph.D.
Associate Professor
Collins College of Hospitality Management
California State Polytechnic University, Pomona, California, USA
Email: zmao@csupomona.edu
Phone: (909) 869-4849
Fax: (909) 869-4805
Yang Yang, Ph.D.
Assistant Professor
School of Tourism and Hospitality Management
Temple University, Philadelphia, Pennsylvania, USA
Email: yangy@temple.edu
Phone: (215)204-8701
Fax: (215)204-8705
FDI spillovers in the Chinese hotel industry: The role of geographic regions, star-
rating classifications, ownership types, and foreign capital origins
Abstract: Although productivity spillovers have long been recognized to be a major benefit of
foreign direct investment (FDI), such spillovers have not yet been systematically studied in the
context of the hotel industry. In this paper, we investigate hotel-related FDI spillover effects as
well as moderating factors (geographic region, star rating classification, ownership type and
foreign capital origin) in China. Evidence from province-level panel data reveals the existence
and significance of intra-industry spillovers from foreign to domestic hotels in China, although
the nature and magnitude vary based on different moderating factors. Domestic hotels in eastern
and western China and those with alliance and limited liability ownership structures benefit
significantly from productivity spillovers. Foreign-invested three-star hotels transfer a significant
amount of positive productivity, whereas domestic three- and five-star hotels benefit the most
from productivity spillovers. Moreover, foreign-invested hotels from Hong Kong, Macau, and
Taiwan (HMT) and non-HMT countries generate positive spillovers of similar magnitudes.
Keywords: Foreign direct investment (FDI), productivity spillovers, Chinese hotel industry,
Hong Kong, Macau, and Taiwan (HMT) investment
1. Introduction
Foreign direct investment (FDI) is an important channel for diffusing advanced international
technology and transferring capital across national borders (Chen, Kokko, & Tingvall, 2011;
Görg & Strobl, 2001). The host nation not only directly benefits from additional capital,
employment, and new technology but is also expected to indirectly benefit from economic
externalities in the form of improved productivity (Blomström, Kokko, & Zejan, 2000). These
externalities are commonly known as productivity spillovers because foreign firms are not able
to fully capitalize on all related benefits.
Since 1978, China has emerged from a closed economy to become a top FDI recipient in the
world, surpassing the United States in 2003 (Ran, Voon, & Li, 2007). A plethora of studies on
FDI spillovers in China have been published over the past two decades because China offers an
unusually diverse, complex, and dynamic setting that enables in-depth examination of a wide
variety of issues related to FDI (Wang & Zhao, 2008). Concurrently, the hotel industry in China
has grown substantially, from just 137 hotels in 1978 to 11,367 hotels in 2012 (CNTA, 2013).
One of the main drivers of this rapid development of the hotel industry can be attributed to FDI
through the expansion of multinational hotel groups in China (Zhang, Guillet, & Gao, 2012).
China has become an FDI “heaven” for multinational hotel management groups due to the strong,
steady growth of tourism (Guillet, Zhang, & Gao, 2011). The Chinese hotel industry is
considered a symbol of reform and openness to foreign investment, and this success has
contributed greatly to social and economic development over the last two decades (Pine, Zhang,
& Qi, 2000).
Despite the rapid growth and importance of foreign investment in China’s hotel industry, FDI
has received limited attention from researchers. Among the few exceptions are Guillet, Zhang,
and Gao (2011) and Zhang, Guillet, and Gao (2012). To our knowledge, no researchers have
examined FDI spillover effects in the Chinese hotel industry, and this externality and its
moderating factors are not yet well understood. Because the Chinese hotel industry has both
industry-unique features such as the star-rating classification system and country-specific
characteristics such as regional economic disparity, it is of critical importance and interest to
understand the impacts of these factors (i.e., the moderating effects) relative to FDI spillovers.
The previous literature on the topic has suggested that spillovers from FDI are affected by
multiple factors such as the characteristics of host countries and the features of industry sectors
(Zhang, Guo, &Wang, 2014). Therefore, the objectives of the study are to analyze FDI spillovers
and explain the role of moderating effects in the Chinese hotel industry.
We make three significant contributions to the current knowledge of literature in this paper. First,
based on an econometric analysis of province-level panel data, we provide a general overview of
the effects of FDI spillovers on the Chinese hotel industry. Second, we provide empirical
evidence revealing the nature and magnitude of specific aspects of FDI (i.e., geographic region,
ownership, and foreign capital origin) on spillovers within the hotel context, making it a
comprehensive investigation of the effects of FDI in China. Third, we examine the impact of
star-rating classification—which is unique to the hotel industry in China—on FDI spillovers,
thereby enriching the FDI literature. Our study not only fills an important gap in the literature on
FDI spillovers in the Chinese hotel industry but also provides new insights into the optimal use
of hotel FDI in China.
2. Literature review
2.1. FDI spillovers
FDI spillovers refer to positive externalities that result in productivity increase among domestic
firms due to the entry of FDI (Kim, 2015). FDI spillovers occur when the productivity or
technology of domestic firms changes as a result of a foreign presence without any market
transactions that explicitly compensate or reward a foreign firm for the possible benefits accruing
to domestic firms (Chen, Kokko, & Tingvall, 2011). When the spillover effects occur within the
same industry, they are referred to as intra-industry or horizontal effects. If the spillovers affect
non-industry firms, they are referred to as inter-industry or vertical effects (Barthel, Busse, &
Osei, 2011). Productivity spillovers within an industry can occur through at least four major
channels or mechanisms: demonstration/imitation, labor mobility, exports, and competition
(Blomström & Kokko, 1998; Crespo & Fontoura, 2007).
First, through demonstration (by foreign presence)/imitation (by domestic firms), domestic firms
may imitate and adopt the similar technologies when foreign firms demonstrate advanced
technologies in the local market. Domestic firms could learn about products and technology via
reverse engineering and managerial innovations brought in by foreign firms. Doing so inspires
domestic firms to develop their own new products and processes based on foreign technology
(Meyer & Sinani, 2009).
Second, domestic firms can obtain technology and knowledge by employing skilled workers who
previously worked for foreign firms and thus acquire related knowledge and technology through
employee turnover (Sinani & Meyer, 2004). Labor mobility is particularly important in the
service industry, as knowledge and technical know-hows are often embedded in employees’
mind (Fernandes & Paunov, 2012). Thus, movement of skilled workers from foreign firms to
domestic firms will greatly facilitate advanced technology transfer in the service industry, such
as the hospitality industry.
Exports are a third channel by which domestic firms can benefit from the FDI presence
(Greenaway, Sousa, & Wakelin, 2004). Foreign-invested firms generally have well-developed
distribution systems, established transportation infrastructures, and knowledge of consumers’
tastes in foreign markets (Greenaway et al., 2004). By following the export processes of foreign
firms, domestic firms may reduce entry costs into foreign markets (Görg & Greenaway, 2004),
which may improve their productivity (Crespo & Fontoura, 2007).
Lastly, spillovers may result from increased competition triggered by the entry of foreign firms
into a market. The competition effect in the host nation is a two-edged sword. While competition
from foreign companies may serve as an incentive and a must for domestic firms to make
efficient use of available resources and even adopt new technology to remain competitive,
competition may drive domestic firms out of the market when foreign companies have dominant
assets, strong financing, and advanced technology (Crespo & Fontoura, 2007).
Recently, it has been shown that the existence, valence, and magnitude of FDI spillovers to
domestic firms hinge substantially upon a number of moderating factors related to a foreign
firm’s features as well as the contextual characteristics of host countries, industries, and
indigenous firms (Crespo & Fontoura, 2007). Among all characteristics (e.g., size, capital
intensity, market orientation), absorptive capacity, which is generally associated with the size of
the technology gap between foreign and domestic firms, is arguably the most crucial factor
affecting domestic firms’ ability to capitalize on indirect benefits from FDI (Girma, 2005; Meyer
& Sinani, 2009; Spencer, 2008). In general, it has been recognized that the greater the
technology gap between foreign and domestic firms, the lesser the absorptive capacity of the
latter (Girma, 2005; Meyer & Sinani, 2009).
2.2 Absorptive capacity and FDI spillovers
Cohen and Levinthal (1990) first conceptualized absorptive capacity as the ability of a firm to
recognize the value of new external information, assimilate it, and apply it commercially. They
argued that absorptive capacity reflects a firm’s capability to develop and improve products by
adopting technological externalities. Absorptive capacity also enables domestic firms to better
communicate, assess and transfer both internal and external knowledge. Absorptive capacity is a
function of accumulated technology and human capital; thus, investment in new technology and
skilled labor is expected to strengthen it (Glass & Saggi, 1998).
A number of firm-specific characteristics, e.g., previous knowledge base, human capital,
physical infrastructure, and organizational structure, may contribute greatly to a firm’s
absorptive capacity (Daghfous, 2004). These internal factors enable firms to accumulate
technological knowledge over time. Absorptive capacity is also associated with external features
of firms’ operating environments (Narula & Driffield, 2012). Institutional factors, such as
education, financial and market systems, cultural environment, and infrastructures for codifying
and disseminating knowledge, play significant roles in a firm’s learning ability (Meyer, 2004). In
addition, the social structure and inter-firm business networks to which a firm belongs shape, at
least to some extent, its ability to undertake the activities necessary to absorb knowledge
effectively (Eapen, 2012).
In the FDI literature, an absorptive capacity hypothesis asserts that domestic firms may benefit
from positive spillovers of foreign investment only when they can adequately absorb and adopt
the technology from their foreign-invested counterparts (Dimelis, 2005; Hamida & Gugler,
2009). The larger the gap between domestic and foreign-invested firms, the less likely domestic
firms are to benefit from the knowledge transferred by foreign firms; hence, a large technology
gap hinders the diffusion of productivity spillovers. When a large technology gap exists,
domestic firms may have no internal knowledge or resources that enable them to recognize the
value and content of a variety of knowledge elements possessed by foreign firms, thus making
spillovers unlikely. Scholars have also theorized that firms with an extremely low or extremely
high absorptive capacity may not benefit from spillovers because the former do not reach the
minimum threshold level of knowledge to absorb foreign technology, whereas the latter
presumably already operate near the technology frontier and thus have little to learn from foreign
firms (Haskel, Pereira, & Slaughter, 2002).
2.3. Moderating factors for FDI spillovers in China
A large body of empirical literature has been devoted to investigating FDI spillover effects in
China over the past two decades due to the continuing growth of FDI inflows to China (Hale &
Long, 2011; Qi & Li, 2008). While results based on Chinese data reflect findings from other
studies to a certain extent (i.e., mixed spillover effects but more positive than negative), the
Chinese context has three distinct features related to variations in the absorptive capacities of
indigenous firms, which, in turn, affect their ability to take advantage of potential spillovers from
the presence of foreign investment.
First, the country of origin of FDI appears to make a difference in China. Many researchers have
found distinct spillover effects associated with two categories of FDI sources (CNTA, 2013):
firms from Hong Kong, Macao, and Taiwan (HMT) and firms from foreign (non-HMT)
countries (e.g., Buckley, Clegg & Wang, 2002, 2004, 2007; Lin, Liu, & Zhang, 2009; Wang &
Zhao, 2008). Investment from HTM has competitive advantages over that from non-HTM in
cultural connection and resource endowments, whereas the advantages of non-HMT FDI lie in
technical efficiency and labor productivity (Lin et al., 2009). Therefore, it is theoretically unclear
which source generates more spillovers to domestic Chinese firms. Empirically, some have
found greater FDI spillover effects from HMT-invested firms; others have reached the opposite
conclusion, indicating that non-HMT-invested firms are superior spillover generators (Lin et al.,
2009; Wei & Liu, 2006).
Second, the geographic location of FDI within China also plays a vital role in spillover effects.
As a large country, China can be roughly divided into three geographic regions (i.e., eastern,
central and western) to control for area-specific effects (i.e., infrastructure) because of uneven
economic development, structural differences, and different government policies. The
geographic division aligns with both economic/management literature and practice in China (e.g.,
Davis, 2013; Ran et al., 2007; Wei & Liu, 2006). As the hotel industry is strongly linked to the
nation’s economy, this geographical division is appropriate for this study. FDI has a direct
impact on the local economy by creating jobs and enhancing capital formation (Xu & Sheng,
2012); hence, the pattern of FDI geographic distribution mirrors that of regional growth in China.
Lian (2013), Wei and Liu (2006), and Xu and Sheng (2012), reported FDI spillovers vary across
Chinese regions, with productivity highest in the eastern region and lowest in the western region.
The differences in spillovers are mainly assumed to reflect regional variations in absorptive
capacity and industry distribution.
Lastly, the ownership of domestic firms is another important determinant for FDI spillovers.
According to CNTA (2013), Chinese domestic firm ownerships are classified as state-owned;
collective; shareholding co-operative; alliances, limited liability; limited liability shares;
privately owned; and others, in a descending order of state control. On one hand, a key
motivation of China’s FDI policy after reform is to improve the technology and productivity of
the state-owned enterprises (SOEs) by learning from FDI (Long, 2005). SOEs are still the
dominant ownership type in China, enjoying soft-budget constraints and easy access to financial
capital (Jiang, Gretzel & Law, 2014). On the other hand, SOEs are constantly ranked as the least
efficient firms in terms of productivity in China (Girma & Gong, 2008). The identified reasons
may include but not be limited to a failure to separate ownership and management, bureaucratic
structure and control, unprofessional operation and management, unclear business objectives, a
shortage of skilled human resources, and a lack of knowledge of capital management and
operations (Chen, 2013; Heung, Zhang, & Jiang, 2008; Hsu, Liu & Huang, 2012; Hung, 2013,
Pine & Phillips, 2005). The domestic ownership structure (mainly SOE vs. non-SOE firms) has
been found to be associated with varying FDI spillover effects in terms of significance, intra- or
inter-industry effects, spillover channels, and direction (Blake, Deng, & Falvey, 2009; Li, Liu, &
Parker, 2001; Lin et al., 2009; Liu, Wang, & Wei, 2009).
2.4. FDI research in the hotel industry
To our knowledge, only a handful of researchers have investigated the effects of FDI in tourism
contexts (Endo, 2006), and even less is known about the role of FDI in the hotel industry (Pranić,
Ketkar, & Roehl, 2012). There are many theories explaining why firms are interested in FDI
activities. These theories include transaction cost theory, internalization theory, the Uppsala
model, and the eclectic paradigm (Boyen & Ogasavara, 2013). In particular, the eclectic
paradigm asserts that a firm’s FDI activities will be determined by its advantages related to
ownership, internationalization, and location (Dunning, 2000). Ownership advantages are the
competitive advantages that devolve from multinational enterprises. In the hotel industry,
ownership advantages are associated with service quality, reservation systems, and brand
(Dunning & McQueen, 1981). Internationalization advantages are associated with owning
production rather than other types of indirect cooperation such as licensing or joint ventures. In
the hotel industry, this type of advantages are created through both equity and contract control to
minimize costs and maximize returns (Dunning & McQueen, 1981). Location advantages are
created when value-adding activities for multinational enterprises are performed in other
countries. Specifically, in the hotel industry, elements of location advantages include the size and
rate of tourism growth, tourism infrastructure, availability and quality of hotel inputs, the host
government’s policy towards FDI, and political, social, and economic stability.
A few studies have obtained results that help explain the international expansion behavior of
global hotel firms (Pranić et al., 2012), with a focus on mode of entry and location choices.
Although the current literature examines FDI spillovers in the service industry (Ben Hamida,
2011; Doytch & Uctum, 2011), no research has specifically targeted the hotel industry.
Contractor and Kundu (1998) suggested that both environmental (country) and firm-specific
variables affect the choice of organizational entry mode of FDI (i.e., equity ownership, franchise,
or management contract) in the international hotel business. Rodriguez (2002) performed a
similar study to empirically examine the key factors affecting the entry mode choices associated
with international expansions by Spanish hotel companies. A few factors, such as cultural
differences, the development level of the destination, its risk level, and the existence of FDI in
the country, among others, can best explain the different choices associated with their entry into
foreign markets. Zhang, Guillet, and Gao (2012) studied the factors that determine the location
strategies of multinational hotel firms in China, and they indicate that market demand and market
size, the business environment, policies, and mega-events are significant factors affecting
international hotel firms’ locational choices for investments. The brief summary of hotel FDI
research demonstrates the scarcity of studies on spillovers and the critical need for the current
study.
In the hospitality research of China, the star rating system is a distinctive characteristic of the
hotel industry. Chinese hotel properties are rated by the government on a number of hotel
attributes (i.e., equipment, hotel products, and services). The system classifies properties into
different categories based on standards, with five stars designating luxury hotels, three stars
designating mid-scale hotels, and one star designating low-end hotels. Every year, the regional
tourism administrative unit evaluates the star-rated hotel properties to determine whether their
hardware (e.g., facility) and software (e.g., service) qualify; the star ratings serve as an effective
differentiation mechanism and influence guests’ perceptions of hotel quality (Ryan & Gu, 2007;
Pine & Phillips, 2005). In general, the higher the star rating of a hotel, the better it performs
(Jiang et al., 2014). Star ratings reflect the strategic groups to which hotels belong, based on
available resources and capabilities (Claver-Cortés, Molina-Azorín, & Pereira-Moliner, 2006),
which are decisive factors in absorptive capacity. Therefore, it is expected that the star-rating
classification would affect FDI spillovers for domestic hotels in China through absorptive
capacity.
3. Empirical model and data
We propose an empirical productivity spillover model based on previous literature (Abumustafa
& Mostafa, 2009; Görg & Strobl, 2001). In this study, productivity spillovers of foreign
investment are defined as the positive influences caused by the presence of foreign-invested
hotels on domestic hotels, which, in turn, improves the productivity levels of domestic hotels.
The effects of these productivity spillovers are examined by estimating the effect of foreign
investment within the hotel industry on productivity measures of domestic hotels after
controlling for other factors (Abumustafa & Mostafa, 2009). Hotel productivity can be measured
in several ways, e.g., revenue per available room (RevPAR) (Sainaghi, 2011), occupancy rate
(Okumus, Altinay, & Arasli, 2005), and labor productivity (Hu & Cai, 2004). A large number of
hotels in China have diversified their service offerings by expanding into the restaurant,
entertainment, and retail markets (Yang, Wong, & Wang, 2012). According to the Chinese
Tourism Statistical Yearbook, accommodation revenue represented only 41.54% of total revenue
for domestic hotels as of 2012. Therefore, we decided to use labor productivity as the dependent
variable in the empirical model because it is a more comprehensive measure of productivity
across hotel operations regardless of sector.
To measure the presence of foreign investment, scholars have utilized three different variables in
previous studies: the share of foreign-invested equity (Javorcik, 2004), the share of employment
in foreign-invested firms (Buckley et al., 2002), and the share of outputs from foreign-invested
firms (Sinani & Meyer, 2004). Because employee mobility is an important channel facilitating
productivity spillovers, and soft-skills and knowledge can be easily transferred through employee
turnover in the hospitality industry, we use the share of employment in foreign-invested hotels to
measure the intensity of foreign investment activities in the hotel industry.
We propose the following panel data model to scrutinize productivity spillovers and factors
facilitating/inhibiting these spillovers:
,
where i is the region and t is the year. A detailed definition of dependent and independent
variables can be found in Table 1. In the empirical model, we incorporate the moderating
variable z to represent various variables that may moderate the productivity spillover effect. A
set of control variables is considered in vector x. We also introduce an individual-specific effect,
, which is constant over the research period for region i, and a year-specific effect which is
constant across all regions for year t. The error term is assumed to be normally distributed
with a zero mean and finite variance.
(Please insert Table 1 about here)
As shown in Table 1, we use labor productivity to measure productivity, which is consistent with
the model specification of the conventional FDI spillover literature (Iršová & Havránek, 2013)
derived from the Cobb-Douglas production function (Görg & Greenaway, 2004). In the hotel
industry, labor costs generally represent the highest percentage of hotel operating expenses. For
this reason, it is recommended that productivity be measured in relationship to labor (Tsai, 2009).
Furthermore, we incorporated a set of control variables in the empirical model. The variable
lncapital(domestic) represents the capital intensity of domestic hotels, which has been widely
recognized as a vital factor influencing productivity measures in the traditional Cobb-Douglas
production function (Görg & Greenaway, 2004).
Other variables measuring domestic hotel characteristics, lnstar(domestic) and lnSOE(domestic),
capture the impacts of average star rating and SOE percentage, respectively, of domestic hotels
in a region. Hotels with different star ratings are associated with different scales, markets, quality
levels and managerial knowledge and skill sets (Gu, 2003). According to Tang, Xi, Chen, and
Wang (2006), state-owned hotels are usually owned by the government and tend to focus less on
economic performance. As a result, the share of SOE ownership tends to influence the average
level of domestic hotel productivity in a region.
Two additional control variables are incorporated to measure the lodging market environment.
To capture the influence of hotel supply, we use lnrooms_GDP because oversupply has been
identified as a critical factor undermining hotel performance and productivity in China (Yu & Gu,
2005). Another control variable, lntourism_GDP, helps to control for the level of tourism
specification in a region. The tourism industry is highly supported and advocated by local
governments and residents in many regions of China, and favorable government policies attract
substantial hotel investments from both domestic and foreign companies. In such regions, high
demand for lodging from tourists is likely to boost productivity (Luo, Yang, & Law, 2014).
To understand the complex pattern of productivity spillovers from foreign-invested hotels, we
introduce four general categories of moderating variables into the model: star rating
classification of foreign-invested hotels, star rating classification of domestic hotels, ownership
structure of domestic hotels, and capital origin of foreign-invested hotels. A very small number
of one-star hotels are included in the sample because many low-end hotels are reluctant to
disclose a one-star rating. Therefore, we merged the one-star hotels with the two-star hotels to
create a group representing the share of hotels with low star ratings. Star ratings capture domestic
hotels’ absorptive capacity because hotels in distinct star-rating classes are endowed with
different resources and managerial know-how necessary to leverage potential productivity
spillovers. For foreign-invested hotels, star ratings reflect their capability to generate and diffuse
productivity spillovers.
Our empirical analysis is based on aggregate hotel performance data for domestic and foreign-
invested hotels in each Chinese province. All Chinese star-rated hotels are required to report
their financial information every year to the local tourism administrative unit. Most of the data
used in this study can be found in the Chinese Tourism Statistical Yearbook and its supplement.
We obtained GDP data from the Chinese Statistical Yearbook. Our data set covers 31 provincial
regions in China from 2001 to 2012. Figure 1 presents a flowchart for the empirical analysis used
in this study.
(Please insert Figure 1 around here)
4. Results
4.1. Results of descriptive statistics
In Table 1, we present the descriptive statistics for the variables used in the empirical model.
Among the moderating variables (for which the data are not log transformed), three-star (36.5%)
and four-star (32.7%) hotels represent most of the foreign-invested hotels in the sample, whereas
one- and two-star (47.2%) and three-star (39.8%) hotels represent most of the domestic hotels in
the sample; more than half (50.2%) of domestic hotels in the sample are SOEs, and
approximately 56.1% of foreign-invested hotels are headquartered in the HMT region, while the
other 43.9% are headquartered in non-HMT countries.
In Table 2, we present the mean values of major variables incorporated in the empirical spillover
model from 2001 to 2012. In general, our dependent variable, lnproductivity (domestic),
increased steadily over the research period, along with foreign investment presence (lnFDI) and
average star rating of domestic hotels (lnstar (domestic)). The capital intensity of domestic hotels
(lncapital (domestic)) declined in 2003 and 2004 and then became relatively stable afterward.
Moreover, the results suggest that the percentage of SOEs decreased over time.
(Please insert Table 2 around here)
Figure 2 shows the spatial distribution of several variables of interest in 2012. The upper left
panel shows that domestic hotels in eastern provinces such as Guangdong, Zhejiang, and Jiangsu
have high levels of labor productivity. The pattern of foreign investment presence in the hotel
industry, shown in the upper right panel, is partly consistent with the pattern shown on the upper
left panel. This result highlights the potential association between domestic hotels' productivity
and foreign investment presence in the hotel industry. Furthermore, as shown in the bottom left
panel, a relatively large percentage of SOEs are located in western and central provinces. The
map in the bottom right panel highlights several provinces with a high share of HMT investment
in all foreign-invested hotels—Hunan, Guangdong, Guangxi, and Hainan—which are
geographically proximate to Hong Kong and Macau.
(Please insert Figure 2 around here)
4.2. Results of panel data model
We employed a fixed effects (FE) panel data model to estimate the proposed productivity
spillover model. Compared with its counterpart, the random effects (RE) model, the FE model
does not impose any restrictions on the independence of the independent variables from
individual-specific effects or vice versa. Because various time-invariant effects such as
geographical location, tourism resource endowments, and local entrepreneur cultures may be
embedded in the individual-specific effect to determine domestic hotels’ productivity, it is
reasonable to specify an FE rather than an RE model for estimation purposes. The panel data
model is tailored to efficiently leverage longitudinal information, which alleviates the problem of
multicollinearity if any such problem should emerge (Baltagi, 2013).
In Table 3, we present the estimation results of the empirical productivity spillover model with
different specifications. Model 1, which is served as the benchmark model, estimates the whole
sample without taking any moderating factors into consideration. The presence of foreign
investment (lnFDI) is estimated to be positive and statistically significant. Its estimated
coefficient, 0.0389, suggests that a 1 percent increase in the share of foreign hotel employment
would lead to a 0.0389 percent increase in the labor productivity of domestic hotels. Regarding
other variables in Model 1, as expected, capital intensity, lncapital(domestic), is positive and
significant with a large estimated coefficient. Moreover, the productivity of domestic hotels is
found to be negatively associated with average star rating but positively associated with SOE
presence. The low productivity level of hotels with high star ratings can be explained by the
oversupply of high-end hotels (Luo, Yang, & Law, 2014). The relatively higher productivity
level of SOE hotels may be a result of the declining percentage of SOEs in the hotel industry (see
Table 2) after the exit and privatization of a large number of state-owned hotels. The remaining
SOE hotels essentially have a monopoly owing to tight linkages to the government and
concomitant demand of business guests, resulting in higher average productivity.
(Please insert Table 3 around here)
To better understand the regional heterogeneity of productivity spillovers across China, we split
the provinces into three geographic regions - eastern, central, and western (Cheung & Lin, 2004),
and estimated separate productivity spillover models (Models 2 to 4). The estimated coefficients
of lnFDI reveal significant productivity spillovers from foreign-invested hotels in eastern and
western China. Based on the magnitude of the coefficient, spillovers are most substantial in the
eastern region, which is usually regarded as the most economically developed region in China.
Consistent with findings from Wei and Liu (2006), productivity gains from FDI are also highest
among hotels in eastern China. Furthermore, capital intensity plays a dominant role in all three
areas, regional tourism specialization and SOE presence significantly influence the productivity
of domestic hotels in eastern and western provinces, and average star rating level is negatively
associated with productivity in western provinces.
Models 5 and 6 incorporate extra interaction terms with the star ratings of foreign-invested and
domestic hotels, respectively. Of the four interaction terms in Model 5, only the interaction term
for the presence of three-star foreign-invested hotels is statistically significant, suggesting that
the domestic hotels tend to benefit greatly from productivity spillovers in an environment with a
high share of three-star foreign-invested hotels. This result can be explained by the fact that
three-star hotels dominate both foreign-invested and domestic hotel samples (see Table 1), and
similar market demand facilitates productivity spillovers. The same argument can be applied to
explain the significant interaction of foreign investment presence with three-star hotel presence
for domestic hotels in Model 6. Another significant interaction in Model 6, the presence of five-
star foreign-invested hotels, indicates the superior absorptive capability of five-star domestic
hotels in assimilating productive spillovers from foreign-invested hotels.
In Table 4, we also present the estimation results of the productivity spillover model with
ownership types and foreign capital origins as moderating factors. Models 7 to 14 incorporate the
moderating factor of each single domestic ownership type. As suggested by the statistically
significant and positive interaction terms, the results indicate higher absorptive capability among
hotels with collective, alliance, and limited liability ownership structures. When all eight
ownership-related interaction terms are included simultaneously in Model 15, the interaction
terms for the shares of alliance and limited liability shareholder ownership structures among
domestic hotels remain statistically significant and positive. Lastly, Model 16 examines the
moderating effect of foreign capital origin, and two interaction terms are introduced for two
different sources of foreign capital: HMT regions and non-HMT countries. Both interaction
terms are estimated to be positive and statistically significant, but HMT has a moderately larger
coefficient. However, a Wald test of the equality of the coefficients of the two interaction terms
suggests that the difference between the two does not differ significantly from zero.
(Please insert Table 4 around here)
5. Discussion and conclusions
5.1. Discussion of results
The hotel industry was the first to attract FDI in China after the economic reform in 1978 (Pine
et al., 2000). However, one major externality of FDI remains under-researched in academia:
productivity spillovers. In this paper, we investigated the existence of intra-industry FDI
spillovers from foreign-invested to domestic hotels in China using provincial panel data from
2001 to 2012. In addition, we examined the moderating effects of geographic region, star-rating
classification, domestic ownership type, and foreign capital origin on the transfer of spillover
benefits from foreign-invested to domestic hotels. Overall, our findings reveal positive spillovers
to Chinese hotels from FDI. The intra-industry FDI spillover hypothesis is confirmed by the
statistically significant association between the level of foreign investment in China’s hotel
industry and the resulting increased labor productivity in China’s domestic hotels. Moreover, we
found an uneven distribution of productivity spillovers across regions, domestic ownership types,
and star-rating classifications. In essence, the absorptive capacities of domestic hotels and
regions are preconditions for incorporating the benefits embedded in foreign investment
externalities.
As a transition economy, China is characterized by domestically heterogeneous institutional
arrangements that can be classified geographically into three regions (Zhang et al., 2012). Our
results reveal substantial spatial variation in hotel FDI spillovers, confirming previous findings
about regional effects of FDI spillovers in general (Crespo & Fontoura, 2007). Domestic hotels
in the eastern and western regions of China have benefited significantly from positive spillovers
related to foreign investment, whereas hotels in the central region have gained little. Regions
vary greatly with respect to factors such as human capital, physical infrastructure, tax treatments,
and policies that influence absorptive capacity. Hotels in eastern China, the most developed area
with abundant human capital, sufficient infrastructure, superior knowledge assimilation, and
sound external institutional environments, are better able to facilitate the absorption of foreign
technology. Additionally, as expected, Chinese hotels in the eastern region better capture
knowledge spillovers from foreign investment.
Contrary to our expectations, domestic hotels in western China also benefitted significantly from
positive spillovers, which is quite intriguing. Western China is the least developed region with
the lowest income per capita, a scarcity of knowledge and technology, fewer human resources,
and a weak infrastructure. Thus, productivity gains from FDI among hotels in this region deserve
further discussion. Various tourist destinations in western China have become more popular as a
result of their unique natural landscapes and richly endowed tourism resources (Zhang et al.,
2012). According to Fu (2008), FDI in western China mainly relates to labor, land or resource-
intensive products and activities. Foreign firms are able to take advantage of tax benefits
provided by local governments as well as low labor and land costs (Fu, 2008). The hotel industry,
which is inherently capital-, labor- and land-intensive, fits well with this motive for FDI.
In addition, location proximity is of crucial importance to technology/knowledge spillovers, and
the high degree of spatial concentration of hotel FDI in the western region supports strong
spillover effects (Cheung & Lin, 2004). Most foreign-invested hotels are located in major
metropolises and popular tourist destinations in western China. Moreover, technology orientation
in the hotel industry is relatively low, and technology innovations tend to be minor.
Backwardness in technology may generate positive and significant results for spillovers in a
relatively low-technology industry (Griffith, Redding, & Van Reenen, 2004). The location
pattern of FDI spillovers in this study shows a U-shaped curvilinear relationship between a
region’s level of development and spillovers, echoing the results of Meyer and Sinani’s (2009)
study.
Our results show that the domestic ownership type plays an important role for domestic hotels in
absorbing and internalizing externalities from foreign investment. The hotel sector in China was
among the earliest to open to foreign investment in the early 1980s and to embrace diverse
ownership structures (Qu, Ennew, & Sinclair, 2005). Our findings confirm Buckley, Clegg, and
Wang’s (2007) findings that the ownership structures of recipient firms significantly affect the
scope and scale of productivity spillovers. Among all eight domestic ownership types, hotels
owned by alliances and limited liability shareholders benefit substantially from spillovers related
to the presence of foreign firms on account of their distinct characteristics. These non-SOE
domestic hotels engage in more market-oriented business practices than SOE hotels (Kong &
Cheung, 2009; Pine, 2002). At the same time, they have more access to financial capital than
private hotel firms. As SOEs still represent the majority of the domestic hotel stock in China, our
results, by and large, reaffirm the importance of diversifying hotel ownership away from SOEs.
We infer that these three types of hotels may have the right absorptive capacity to benefit the
most from spillovers and improve their labor productivity from foreign investment.
The effectiveness of knowledge spillovers hinges largely upon the technical capabilities of both
foreign and local firms (Sinani & Meyer, 2004). Therefore, we also examined whether FDI from
different sources (i.e., HMT and non-HMT countries) has a different impact on Chinese hotels
because the technological capacities of firms from different countries vary. On one hand, FDI
from HMT mainly originates from Chinese citizens living overseas, who have the advantages of
sharing the same culture and language, geographical proximity and a better understanding of
Chinese business practices, which tend to facilitate FDI transfer (Sachs & Woo, 1994). On the
other hand, anecdotal evidence suggests that non-HMT firms are superior to HMT firms in terms
of labor productivity, advanced technology, global production capacity, and internationally
recognized brand names (Tong, 2005). This trade-off leads to the conclusion that any
comparison of FDI spillover effects between HMT and non-HMT countries would be far from
conclusive; thus, it becomes a matter of empirical evidence. In the context of the hotel industry,
HMT hotels apply international business standards and management techniques, are better
connected within China, and have a better understanding of the local environment (Pine &
Phillips, 2005). Additionally, the cultural and linguistic similarity of HMT hotels facilitates
negotiation and cooperation with domestic Chinese hotels and further fosters the diffusion of
knowledge and technology to local hotels (Abraham, Konings, & Slootmaekers, 2010).
A unique feature of the Chinese hotel industry is the star-rating system, which is managed by the
Chinese government. Most FDI in China’s hotels are high-star properties (see Table 1) and their
target markets are business travelers and inbound tourists (Zhang et al., 2012). Our results
indicate that FDI spillovers affect hotels with different star ratings in a different way. Because
foreign-invested three-star hotels generate significant productivity spillovers, perhaps this type is
the best fit for the domestic hotel industry because three-star hotels have optimal levels of
technology and knowledge that can be assimilated and absorbed by Chinese hotels. Additionally
because the most popular rating among domestic star-rated hotels is three stars, it would be
easier to benefit from spillovers generated by three-star foreign-invested hotels because similar
technologies and knowledge are used to serve similar market segments. Evidence of significant
spillover contributions from four- and five-star foreign-invested hotels is lacking in our data.
Overall, domestic three-star and five-star hotels appear to enjoy the most positive spillovers from
foreign investment. While domestic three-star hotels may easily benefit from spillovers, as
explained previously, a possible explanation for significant and positive spillovers among
domestic five-star hotels is that they may possess a superior absorptive capacity and thus have
the potential to assimilate the most knowledge from their foreign-invested counterparts.
5.2. Implications, limitations and future studies
Our findings convey several key implications for FDI in China’s hotel industry. First, the
government should encourage more foreign investment in hotels in general to maximize the
potential spillover benefits to domestic hotels. FDI is able to transfer not only technical know-
how but also brand and reputation effects, demonstration power and managerial skills (Ran et al.,
2007). While domestic hotels in both the eastern and western regions have significantly
increased their productivity as a result of the presence of foreign investment, particular attention
should be focused on the latter. Hotels in western regions have the most potential to use foreign
advanced technology and knowledge to improve their productivity. We highly recommend that
the government decentralize FDI administration and introduce policies favoring the western
region.
Second, the government and industry should promote hotel ownership reform. The ability of
hotels with certain non-SOE ownership structures such as alliances and limited liability
shareholders to capture more positive FDI spillovers offers additional justification for renewed
ownership diversification and decentralization efforts in China. Third, foreign-invested three-star
hotels, which are associated with the most significant contributions of positive productivity, tend
to be a nice fit. More guidance from the government is needed to facilitate the spillovers from
three-star foreign hotels to domestic properties. Finally, as absorptive capacity is the key to FDI
spillovers, governmental policies should be aimed at increasing the learning capabilities and
labor skills (the stock of human capital) of domestic workers. Educational enhancement policies
should be implemented to bridge the technology gap, which may be essential to increasing the
absorptive capacity of domestic hotels. Hotels could be incentivized to innovate and build their
own technological capabilities through organizational learning. This initiative requires effort
from the hotel industry as well as support from the government.
Some limitations of this study must be acknowledged and deserve further investigation. First, we
did not investigate inter-industry productivity spillovers or spillover channels due to data
unavailability. If data become available, separate intra- and inter-industry spillovers as well as
specific effects of each spillover channel (i.e., demonstration, competition, export, and labor
mobility) should be considered to generate a comprehensive understanding of FDI spillovers in
Chinese hotel industry. Second, we used province-level panel data with aggregate information in
this study. A better choice would be to employ firm-level data so that additional important firm
characteristics such as firm size and capital structure could be captured. Firm-level data would
enable us to reveal much more detailed information about FDI spillover effects and better
evaluate the effectiveness of FDI spillovers (Girma & Gong, 2008; Sinani & Meyer, 2004; Wang
& Zhao, 2008). Third, due to data unavailability, we were unable to construct a multi-faceted
measure of productivity. Fourth, our study can be viewed as an initial attempt to investigate the
effects of FDI productivity spillovers in the hotel industry. In the future, researchers may
consider using international samples such as data from Organization for Economic Co-operation
and Development (OECD) countries to verify and validate our findings, as our results are
insufficient to draw reliable conclusions about the global hotel industry.
References
Abraham, F., Konings, J., & Slootmaekers, V. (2010). FDI spillovers in the Chinese
manufacturing sector. Economics of Transition, 18(1), 143-182.
Abumustafa, N. I., & Mostafa, M. M. (2009). Do domestic firms benefit from multinational
enterprises? A meta-analysis of the empirical research. Journal of Transnational
Management, 14(1), 3-15.
Baltagi, B. (2013). Econometric Analysis of Panel Data (5th ed.). Chichester, UK: John Wiley &
Sons.
Barthel, F., Busse, M., & Osei, R. (2011). The characteristics and determinants of FDI in Ghana.
The European Journal of Development Research, 23(3), 389-408.
Ben Hamida, L. (2011). FDI and spillovers in the Swiss services/construction industry. Critical
Perspectives on International Business, 7(3), 224-249.
Blake, A., Deng, Z., & Falvey, R. (2009, February). How does the productivity of foreign direct
investment spill over to local firms in Chinese Manufacturing? The University of
Nottingham Research Paper Series China and the World Economy 2009/03. Retrieved
from http://www.nottingham.ac.uk/gep/documents/papers/2009/09-03.pdf.
Blomström, M., & Kokko, A. (1998). Multinational corporations and spillovers. Journal of
Economic Surveys, 12(3), 247-277.
Blomström, M., Kokko, A., & Zejan, M. (2000). Foreign Direct Investment: Firm and Host
Country Strategies. London: Macmillan.
Boyen, M.H., & Ogasavara, M.H. (2013). Internationalization patterns of multinational lodging
firms in Brazil. Tourism & Hospitality Research, 13(4), 181-200.
Buckley, P.J., Clegg, J., & Wang, C. (2002). The impact of inward FDI on the performance of
Chinese manufacturing firms. Journal of International Business Studies, 33(4), 637–655.
Buckley, P.J., Clegg, J., & Wang, C. (2004). The relationship between inward foreign direct
investment and the performance of domestically-owned Chinese manufacturing industry.
Multinational Business Review, 12(3), 23-40.
Buckley, P.J., Clegg, J., & Wang, C. (2007). Is the relationship between inward FDI and
spillover effects linear? An empirical examination of the case of China. Journal of
International Business Studies, 38(3), 447-459.
Chen, M-H. (2013). Risk determinants of China’s hotel industry. Tourism Economic, 19(1), 77-
99.
Chen, T., Kokko, A., & Tingvall, P.G. (2011). FDI and spillovers in China: non-linearity and
absorptive capacity. Journal of Chinese Economic and Business Studies, 9(1), 1-22.
Cheung, K-Y., & Lin, P. (2004). Spillover effects of FDI on innovation in China: Evidence from
the provincial data. China Economic Review, 15(1), 25-44.
Claver-Cortés, E., Molina-Azorín, J. F., Pereira-Moliner, J. (2006). Strategic groups in the
hospitality industry: Intergroup and intragroup performance differences in Alicante,
Spain. Tourism Management, 27(6), 1101-1116.
CNTA. (2013). Annual Statistics Report of Tourism, 2012: CNTA.
Cohen, W.M., & Levinthal, D.A. (1990). Absorptive capability: A new perspective on learning
and Innovation. Administrative Science Quarterly, 35(1), 128-152.
Contractor, F.J., & Kundu, S.K. (1998). Modal choice in a world of alliances: Analyzing
organizational forms in the international hotel sector. Journal of International Business
Studies, 29(2), 325-356.
Crespo, N., & Fontoura, M.P. (2007). Determinant factors of FDI spillovers-What do we really
know? World Development, 35(3), 410-425.
Daghfous, A. (2004). Absorptive capacity and the implementation of knowledge-incentive bests
practices. SAM Advanced Management Journal, 69(2), 21-27.
Davies, K. (2013). China investment policy: An update. OECD Working Papers on International
Investment, 2013/01, OECD Publishing. http://www.oecd-ilibrary.org/finance-and-
investment/china-investment-policy_5k469l1hmvbt-en
Dimelis, S. (2005). Spillovers from foreign direct investment and firm growth: Technological,
financial and market structure effects. International Journal of the Economics of Business,
12(1), 85-104.
Doytch, N., & Uctum, M. (2011). Does the worldwide shift of FDI from manufacturing to
services accelerate economic growth? A GMM estimation study. Journal of International
Money and Finance, 30(3), 410-427.
Dunning, J.H. (2000). The eclectic paradigm as an envelope for economic and business theories
of MNE activity. International Business Review, 9(2), 163-190.
Dunning, J.H., & McQueen, M. (1981). The eclectic theory of international production: A case
study of the international hotel industry. Managerial and Decision Economics, 2(4), 197-
210.
Eapen, A. (2012). Social structure and technology spillovers from foreign to domestic firms.
Journal of International Business Studies, 43(2), 244-263.
Endo, K. (2006). Foreign direct investment in tourism-flows and volumes. Tourism Management,
27(4), 600-614.
Fortanier, F. (2007). Foreign direct investment and host country economic growth: Does the
investor’s country of origin play a role? Transnational Corporations, 16(2): 41-76.
Fu, X. (2008). Foreign direct investment, absorptive capacity and regional innovation
capabilities: Evidence from China. Oxford Development Studies, 36(1), 89-110.
Girma, S. (2005). Absorptive capacity and productivity spillovers from FDI: A threshold
regression analysis. Oxford Bulletin of Economics and Statistics, 67(3), 281-306.
Girma, S. & Gong, Y. (2008). FDI, linkages and the efficiency of state-owned enterprises in
China. Journal of Development Studies, 44(5), 728-749.
Girma, S. & Görg, H. (2007). The role of efficiency gap for spillovers from FDI: Evidence from
the U.K. electronics and engineering sectors. Open Economies Review, 18(2), 215-232.
Glass, A.J., & Saggi, K. (1998). International technology transfer and the technology gap.
Journal of Development Economics, 55(2), 369-398.
Görg, H., & Greenaway, D. (2004). Much ado about nothing? Do domestic firms really benefit
from foreign direct investment? World Bank Research Observer, 19(2), 171-197.
Görg, H., & Strobl, E. (2001). Multinational companies and productivity spillovers: A meta-
analysis. Economic Journal, 111(475), 723-739.
Greenaway, D., Sousa, N., & Wakelin, K. (2004). Do domestic firms learn to export from
multinationals? European Journal of Political Economy, 20(4), 1027-1043.
Grriffith, R., Redding, S., & Simpson, H. (2002). Productivity convergence and foreign
ownership at the establishment level. IFS Working Paper WP02/22.
Griffith, R., Redding, S., &Van Reenen, J. (2004). Mapping the two faces of R&D: Productivity
growth in a panel of OECD industries. Review of Economics and Statistics, 86(4), 883-
895.
Gu, Z. (2003). The Chinese lodging industry: Problems and solutions. International Journal of
Contemporary Hospitality Management, 15(7), 386-392.
Guillet, B.D., Zhang, H.Q., & Gao, B.W. (2011). Interpreting the mind of multinational hotel
investors: Future trends and implications in China. International Journal of Hospitality
Management, 30(2), 222-232.
Hale, G., & Long, C. (2011). Are there productivity spillovers from foreign direct investment in
China? Pacific Economic Review, 16(2), 135-153.
Hamida, L.B., & Gugler, P. (2009). Are there demonstration-related spillovers from FDI?
Evidence from Switzerland. International Business Review, 18(5), 494-508.
Haskel, J.E., Pereira, S.C., & Slaughter, M.J. (2002). Does inward foreign direct investment
boost the productivity of domestic firm? NBER Working Paper 8724. Cambridge, MA:
National Bureau of Economic Research.
Heung, V.C.S., Zhang, H., & Jiang, C. (2008). International franchising: Opportunities for
China’s state-owned hotels? International Journal of Hospitality Management, 27(3),
368-380.
Hsu, C.H.C., Liu, Z., & Huang, S. (2012). Managerial ties in economy hotel chains in China:
Comparison of different ownership types during entrepreneurial processes. International
Journal of Contemporary Hospitality Management, 24(3), 477-495.
Hu, B.A., & Cai, L.A. (2004). Hotel labor productivity assessment. Journal of Travel & Tourism
Marketing, 16(2-3), 27-38.
Hung, K. (2013). Chinese hotels in the eyes of Chinese hoteliers: The most critical issues. Asia
Pacific Journal of Tourism Research, 18(4), 354-368.
Iršová, Z., & Havránek, T. (2013). Determinants of horizontal spillovers from FDI: Evidence
from a large meta-analysis. World Development, 42, 1-15.
Javorcik, B.S. (2004). Does foreign direct investment increase the productivity of domestic firms?
In search of spillovers through backward linkages. The American Economic Review,
94(3), 605-627.
Jiang, J., Gretzel, U., & Law, R. (2014). Influence of star rating and ownership structure on
brand image of mainland China hotels. Journal of China Tourism Research, 10(1), 69-94.
Kim, M. (2015). Productivity spillovers from FDI and the role of domestic firm’s absorptive
capacity in South Korean manufacturing industries. Empirical Economics, 48(2), 807-827.
Kong, H., & Cheung, C. (2009). Hotel development in China: A review of the English language
literature. International Journal of Contemporary Hospitality Management, 21(3), 341-
355.
Li, X., Liu, X., & Parker, D. (2001). Foreign direct investment and productivity spillovers in the
Chinese manufacturing sector. Economic Systems, 25(4), 305-321.
Lian, L. (2013). Research on the FDI inflows in western region of China-Exemplified by
Guangxi Zhuang Autonomous Region. International Journal of Financial Research, 4(2),
42-48.
Lin, P., Liu, Z., & Zhang, Y. (2009). Do Chinese domestic firms benefit from FDI inflow?
Evidence of horizontal and vertical spillovers. China Economic Review, 20(4), 677-691.
Liu, X., Wang, C., & Wei, Y. (2009). Do local manufacturing firms benefit from transactional
linkages with multinational enterprises in China? Journal of International Business
Studies, 40(7), 1113-1130.
Long, G. (2005). China's policies on FDI: Review and evaluation. In T. H. Moran, E. M. Graham,
& M. Blomstrom (Eds.), Does foreign direct investment promote development? (pp. 315-
336). Washington D. C.: Institute for International Economics, Center for Global
Development.
Luo, H., Yang, Y., & Law, R. (2014). How to achieve a high efficiency level of the hotel
industry? International Journal of Contemporary Hospitality Management, 26(8), 1140-
1161.
Meyer, K.E. (2004). Perspectives on multinational enterprises in emerging economies. Journal of
International Business Studies, 35(2), 259-276.
Meyer, K.E. & Sinani, E. (2009). When and where does foreign direct investment generate
positive spillovers? A meta-analysis. Journal of International Business Studies, 40(7),
1075-1094.
Narula, R., & Driffield, N.L. (2012). Does FDI cause development? The ambiguity of the
evidence and why it matters. The European Journal of Development Research, 24(1), 1-7.
Okumus, F., Altinay, M., & Arasli, H. (2005). The impact of Turkey's economic crisis of
February 2001 on the tourism industry in Northern Cyprus. Tourism Management, 26(1),
95-104.
Pine, R. (2002). China’s hotel industry: Serving a massive market. Cornell Hotel and Restaurant
Administration Quarterly, 43(2), 61-70.
Pine, R. & Phillips, P. (2005). Performance comparisons of hotels in China. International
Journal of Hospitality Management, 24(1), 57-73.
Pine, R., Zhang, H., & Qi, P. (2000). The challenges and opportunities of franchising in China’s
hotel industry. International Journal of Contemporary Hospitality Management, 12(5),
300-307.
Pranić, L., Ketkar, S., & Roehl, W.S. (2012). The impact of macroeconomic country-specific
factors on international expansion of US hotel chains. Tourismos: An International
Multidisciplinary Journal of Tourism, 7(1), 155-173.
Qi, J., & Li, H. (2008). Spillover effect of FDI on China’s knowledge creation. Chinese
Management Studies, 2(2), 86-96.
Qu, R., Ennew, C., & Sinclair, M.T. (2005). The impact of regulation and ownership structure on
market orientation in the tourism industry in China. Tourism Management, 26(6), 939-
950.
Ran, J., Voon, J.P., & Li, G. (2007). How does FDI affect China? Evidence from industries and
provinces. Journal of Comparative Economics, 35(4), 774-779.
Rodriguez, A.R. (2002). Determining factors in entry choice for international expansion: The
case of the Spanish hotel industry. Tourism Management, 23(6), 597-607.
Ryan, C., & Gu, H. (2007). Perceptions of Chinese hotels. Cornell Hospitality Quarterly, 48(4),
380-391.
Sachs, J., & Woo, W.T. (1994). Structural factors in the economic reforms of China, Eastern
Europe, and the former Soviet Union. Economic Policy, 9(18), 101-145.
Sainaghi, R. (2011). RevPAR determinants of individual hotels: Evidences from Milan.
International Journal of Contemporary Hospitality Management, 23(3), 297-311.
Schroath, W.F., Hu, Y.M., & Chen, H. (1992). Country-of-origin effects of foreign investments
in the People’s Republic of China? Journal of International Business Studies, 24(2), 277-
290.
Sinana, E. & Meyer, K.E. (2004). Spillovers from technology transfer: The case of Estonia.
Journal of Comparative Economics, 32(3), 445-466.
Spencer, J.W. (2008). The impact of multinational enterprise strategy on indigenous enterprises:
Horizontal spillovers and crowding out in developing countries. Academy of Management
Review, 33(2), 341-361.
Tan, D., & Meyer, K. (2011). Country of origin and industry FDI agglomeration of foreign
investors in an emerging economy. Journal of International Business Studies, 42(4), 504-
520.
Tang, F-F., Xi, Y., Chen, G., & Wang, R. (2006). Ownership, corporate governance, and
management in the state-owned hotels in the People’s Republic of China. Cornell Hotel
and Restaurant Administration Quarterly, 47(2), 182-191.
Tong, S.Y. (2005). Ethnic networks in FDI and the impact of institutional development. Review
of Development Economics, 9(4), 563-580.
Tsai, H. (2009). Star-rated hotel productivity in China: A provincial analysis using the DEA
cross-efficiency evaluation approach. Journal of China Tourism Research, 5(3), 243-258.
Wang, C., & Zhao, Z. (2008). Horizontal and vertical spillover effects of foreign direct
investment in Chinese manufacturing. Journal of Chinese Economic and Foreign Trade
Studies, 1(1), 8-20.
Wei, Y., & Liu, X. (2006). Productivity spillovers from R&D, exports and FDI in China’s
manufacturing sector. Journal of International Business Studies, 37(4), 544-557.
Xu, X., & Sheng, Y. (2012). Are FDI spillovers regional? Firm-level evidence from China.
Journal of Asian Economics, 23(3), 244-258.
Yang, Y., Wong, K. K. F., & Wang, T. (2012). How do hotels choose their location? Evidence
from hotels in Beijing. International Journal of Hospitality Management, 31(3), 675-685.
Yu, L., & Gu, H. (2005). Hotel reform in China: A SWOT analysis. Cornell Hotel and
Restaurant Administration Quarterly, 46(2), 153-169.
Zhang, C., Guo, B., & Wang, J. (2014). The different impacts of home countries characteristics
in FDI on Chinese spillover effects: Based on one-stage SFA. Economic Modeling, 38,
572-580.
Zhang, H.Q., Guillet, B.D., & Gao, W. (2012). What determines multinational hotel groups’
locational investment choice in China? International Journal of Hospitality Management,
31(2), 350-359.
Figure 1. The flowchart for the empirical analysis
Descriptive analysis and mapping of
variables
Benchmark model
(bae
Models of
different
geographic
regions
Models
with star
rating as
moderator
Models
with
ownership
type as
moderator
Discussion and conclusions
Econometric Analysis
Models of
HMT and
non-HMT
spillovers
Figure 2. Spatial pattern of variable distributions in 2012
Note: upper-left: labor productivity of domestic hotels; upper-right: foreign investment presence
(employees); bottom-left: percentage of SOEs among domestic hotels; bottom-right: percentage of HMT-
invested hotels among foreign-invested hotels.
~10
10~12
12~14
14~16
16~
~2%
2%~4%
4%~8%
8%~12%
12%~
~20%
20%~30%
30%~40%
40%~50%
50%~
~20%
20%~35%
35%~50%
50%~65%
65%~
Table 1. Descriptions and descriptive statistics of variables in the empirical model
Variable
Description
Source
Mean
Std. Dev
Dependent variable
lnproductivity(domestic)
Labor productivity (total added value per
employee) of domestic hotels in the region
(in 10,000 CNY per capita) (in log)
CTSY (s)
2.027
0.479
Independent variable of major interest
lnFDI
Number of employees in all foreign-invested
hotels relative to number of all hotel
employees in the region (in log)
CTSY (s)
-2.745
0.972
Control variables (x)
lncapital(domestic)
Per capita capital assets of domestic hotels in
the region (in 10,000 CNY per capita) (in log)
CTSY (s)
3.015
0.365
lnrooms_GDP
Number of hotel rooms relative to GDP in the
region (in room per 100 billion CNY) (in log)
CTSY,
CSY
4.059
0.656
lntourism_GDP
International tourism revenue relative to GDP
in the region (in 10,000 USD per 100 billion
CNY) (in log)
CTSY,
CSY
-0.447
1.236
lnstar(domestic)
Average star rating of domestic hotels in the
region (in log)
CTSY (s)
0.960
0.108
lnSOE(domestic)
Percentage of SOE hotels among all domestic
hotels in the region (in log)
CTSY (s)
-0.759
0.381
Moderating variables (z)
star1_2(foreign)
Percentage of 1-star and 2-star hotels among
all foreign-invested hotels in the region
CTSY (s)
0.126
0.144
star3(foreign)
Percentage of 3-star hotels among all foreign-
invested hotels in the region
CTSY (s)
0.365
0.231
star4(foreign)
Percentage of 4-star hotels among all foreign-
invested hotels in the region
CTSY (s)
0.327
0.234
star5(foreign)
Percentage of 5-star hotels among all foreign-
invested hotels in the region
CTSY (s)
0.182
0.197
star1_2(domestic)
Percentage of 1-star and 2-star hotels among
all domestic hotels in the region
CTSY (s)
0.472
0.157
star3(domestic)
Percentage of 3-star hotels among all
domestic hotels in the region
CTSY (s)
0.398
0.099
star4(domestic)
Percentage of 4-star hotels among all
domestic hotels in the region
CTSY (s)
0.110
0.064
star5(domestic)
Percentage of 5-star hotels among all
domestic hotels in the region
CTSY (s)
0.021
0.022
ownership1(domestic)
Percentage of state-owned hotels among all
domestic hotels in the region
CTSY (s)
0.502
0.187
ownership2(domestic)
Percentage of hotels with collective
ownership among all domestic hotels in the
region
CTSY (s)
0.072
0.057
ownership3(domestic)
Percentage of hotels owned by shareholding
cooperatives among all domestic hotels in
the region
CTSY (s)
0.022
0.017
ownership4(domestic)
Percentage of hotels owned by alliances
among all domestic hotels in the region
CTSY (s)
0.006
0.008
ownership5(domestic)
Percentage of hotels with limited liability
ownership among all domestic hotels in the
region
CTSY (s)
0.132
0.101
ownership6(domestic)
Percentage of hotels owned by limited
liability shareholders among all domestic
hotels in the region
CTSY (s)
0.044
0.024
ownership7(domestic)
Percentage of privately owned hotels among
all domestic hotels in the region
CTSY (s)
0.179
0.133
ownership8(domestic)
Percentage of hotels with other domestic
ownership structures among all domestic
hotels in the region
CTSY (s)
0.043
0.074
HMT(foreign)
Percentage of HMT-invested hotels among all
foreign hotels in the region
CTSY (s)
0.561
0.256
non-HMT(foreign)
Percentage of non-HMT-invested hotels
among all foreign hotels in the region
CTSY (s)
0.439
0.256
Notes: CTSY(s) stands for the Chinese Tourism Statistical Yearbook (supplement); CTSY stands for the
Chinese Tourism Statistical Yearbook; CSY stands for the Chinese Statistical Yearbook.
Table 2. Mean values of variables over time
Year
lnproductivity
(domestic)
lnFDI
lncapital
(domestic)
lnrooms_GDP
lntourism
_GDP
lnstar
(domestic)
lnSOE
(domestic)
2001
1.620
2.745
-2.154
4.334
-0.244
0.827
-0.416
2002
1.641
2.838
-2.188
4.400
-0.226
0.860
-0.455
2003
1.540
2.808
-2.418
4.356
-0.858
0.871
-0.457
2004
1.737
2.867
-2.822
4.396
-0.487
0.918
-0.587
2005
1.918
2.941
-2.694
4.316
-0.417
0.930
-0.667
2006
1.982
2.983
-2.804
4.260
-0.368
0.945
-0.707
2007
2.049
3.051
-2.875
4.154
-0.301
0.965
-0.781
2008
2.161
3.107
-3.042
3.997
-0.550
0.990
-0.843
2009
2.253
3.139
-2.911
3.970
-0.508
1.005
-0.918
2010
2.373
3.209
-2.885
3.653
-0.482
1.053
-0.981
2011
2.507
3.243
-3.007
3.476
-0.463
1.074
-1.108
2012
2.546
3.249
-2.993
3.393
-0.457
1.088
-1.186
Table 3. Estimation results of the empirical productivity spillover model (I)
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
All
Eastern
Central
Western
All
All
lnFDI
0.0389**
0.0651*
-0.0396
0.0498**
(0.016)
(0.035)
(0.044)
(0.022)
lncapital(domestic)
0.601***
0.527***
0.704***
0.599***
0.588***
0.595***
(0.045)
(0.090)
(0.097)
(0.070)
(0.045)
(0.045)
lnrooms_GDP
0.00610
-0.0311
-0.144
-0.0174
0.00744
0.00309
(0.058)
(0.103)
(0.148)
(0.095)
(0.058)
(0.058)
lntourism_GDP
0.0397
0.219**
-0.0801
0.0718*
0.0471
0.0393
(0.030)
(0.085)
(0.079)
(0.041)
(0.030)
(0.030)
lnstar(domestic)
-0.940**
-0.398
-0.430
-1.869**
-0.808**
-0.606
(0.373)
(0.697)
(0.647)
(0.719)
(0.372)
(0.435)
lnSOE(domestic)
0.160***
0.221**
0.0939
0.155*
0.164***
0.138***
(0.051)
(0.085)
(0.109)
(0.085)
(0.052)
(0.052)
lnFDI*star1_2 (foreign)
0.0161
(0.023)
lnFDI* star3(foreign)
0.0531***
(0.017)
lnFDI* star4(foreign)
0.00645
(0.020)
lnFDI* star5(foreign)
0.0354
(0.028)
lnFDI* star1_2(domestic)
-0.0344
(0.043)
lnFDI* star3(domestic)
0.128**
(0.053)
lnFDI* star4(domestic)
-0.193
(0.124)
lnFDI* star5(domestic)
0.691***
(0.260)
Constant
0.861*
1.043
0.428
1.538**
0.766*
0.545
(0.447)
(0.832)
(0.947)
(0.743)
(0.441)
(0.513)
Observations
349
131
96
122
349
349
R-square
0.865
0.856
0.905
0.887
0.871
0.869
AIC
-358.5
-121.3
-87.19
-127.1
-366.0
-362.5
BIC
-289.1
-69.56
-41.03
-76.66
-285.0
-281.5
Note: Estimated standard errors are presented in parentheses. *** indicates statistical significance at
the 0.01 level; ** indicates statistical significance at the 0.05 level; * indicates statistical significance at
the 0.10 level.
Table 4. Estimation results for the empirical productivity spillover model (II)
Variable
Model 7
Model 8
Model 9
Model 10
Model 11
Model 12
Model 13
Model 14
Model 15
Model 16
All
All
All
All
All
All
All
All
All
All
lnFDI
0.0397
0.0220
0.0314*
0.0362**
0.0461**
0.0244
0.0478***
0.0378**
(0.032)
(0.018)
(0.017)
(0.016)
(0.018)
(0.017)
(0.017)
(0.016)
lncapital(domestic)
0.601***
0.592***
0.593***
0.589***
0.599***
0.591***
0.600***
0.602***
0.571***
0.601***
(0.045)
(0.045)
(0.046)
(0.045)
(0.045)
(0.045)
(0.045)
(0.045)
(0.046)
(0.045)
lnrooms_GDP
0.00634
0.0123
0.00970
0.0122
0.0156
0.0193
-0.00945
0.00634
0.0119
0.00722
(0.059)
(0.058)
(0.058)
(0.058)
(0.059)
(0.058)
(0.059)
(0.058)
(0.059)
(0.058)
lntourism_GDP
0.0395
0.0346
0.0420
0.0248
0.0400
0.0389
0.0447
0.0395
0.0371
0.0395
(0.030)
(0.030)
(0.030)
(0.030)
(0.030)
(0.030)
(0.030)
(0.030)
(0.031)
(0.030)
lnstar(domestic)
-0.940**
-0.811**
-0.938**
-0.903**
-0.935**
-0.881**
-0.913**
-0.945**
-0.760**
-0.951**
(0.374)
(0.376)
(0.373)
(0.371)
(0.373)
(0.372)
(0.373)
(0.374)
(0.374)
(0.375)
lnSOE(domestic)
0.158**
0.132**
0.158***
0.148***
0.178***
0.126**
0.185***
0.153***
0.197**
0.160***
(0.080)
(0.052)
(0.051)
(0.051)
(0.055)
(0.053)
(0.054)
(0.054)
(0.082)
(0.051)
lnFDI*ownership1(do
mestic)
-0.00147
0.0480*
(0.050)
(0.029)
lnFDI*ownership2(do
mestic)
0.233**
0.117
(0.110)
(0.114)
lnFDI*ownership3(do
mestic)
0.318
0.178
(0.265)
(0.272)
lnFDI*ownership4(do
mestic)
1.677**
1.411*
(0.708)
(0.775)
lnFDI*ownership5(do
mestic)
-0.0535
-0.0500
(0.059)
(0.059)
lnFDI*ownership6(do
mestic)
0.426**
0.395*
(0.203)
(0.222)
lnFDI*ownership7(do
mestic)
-0.0611
-0.0720
(0.047)
(0.048)
lnFDI*ownership8(do
0.0257
0.0405
mestic)
(0.065)
(0.066)
lnFDI* HMT(foreign)
0.0397**
(0.016)
lnFDI* non-
HMT(foreign)
0.0359*
(0.019)
Constant
0.859*
0.753*
0.859*
0.859*
0.837*
0.767*
0.934**
0.859*
0.851*
0.863*
(0.454)
(0.447)
(0.446)
(0.443)
(0.448)
(0.446)
(0.450)
(0.447)
(0.452)
(0.447)
Observations
349
349
349
349
349
349
349
349
349
349
R-square
0.865
0.867
0.866
0.868
0.866
0.867
0.866
0.865
0.872
0.865
AIC
-356.5
-361.6
-358.1
-362.9
-357.4
-361.6
-358.4
-356.6
-361.9
-356.6
BIC
-283.2
-288.4
-284.9
-289.7
-284.2
-288.3
-285.2
-283.4
-265.5
-283.3
Note: Estimated standard errors are presented in parentheses. *** indicates statistical significance at the 0.01 level; ** indicates statistical
significance at the 0.05 level; * indicates statistical significance at the 0.10 level.
