Learning From "Alien Monks?" The Productivity Spillovers of Foreign-Invested Hotels in China

Abstract

As suggested by the strategic management literature, foreign-invested firms with
superior technology and managerial skills are likely to generate productivity spillovers that may
benefit local firms. In this paper, we examine productivity spillovers in the context of the hotel
industry. Using panel data from star-rated hotels in China’s major cities from 2001 to 2012, we
model the labor productivity of domestic hotels as dependent on the degree of foreign hotel
presence in the city and on other control variables. Our results confirm the existence of
productivity spillovers in China’s hotel industry and suggest that the presence of foreign capital
is associated with higher labor productivity among domestic hotels. Moreover, the magnitude of
these spillovers increases along with the productivity gap between domestic and foreign-invested
hotels. Finally, we present several policy implications based on the econometric estimation
results.

Full text

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LEARNING FROM “ALIEN MONKS?”
THE PRODUCTIVITY SPILLOVERS OF FOREIGN-INVESTED HOTELS IN CHINA
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
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

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LEARNING FROM “ALIEN MONKS?”
THE PRODUCTIVITY SPILLOVERS OF FOREIGN-INVESTED HOTELS IN CHINA
Abstract: As suggested by the strategic management literature, foreign-invested firms with
superior technology and managerial skills are likely to generate productivity spillovers that may
benefit local firms. In this paper, we examine productivity spillovers in the context of the hotel
industry. Using panel data from star-rated hotels in China’s major cities from 2001 to 2012, we
model the labor productivity of domestic hotels as dependent on the degree of foreign hotel
presence in the city and on other control variables. Our results confirm the existence of
productivity spillovers in China’s hotel industry and suggest that the presence of foreign capital
is associated with higher labor productivity among domestic hotels. Moreover, the magnitude of
these spillovers increases along with the productivity gap between domestic and foreign-invested
hotels. Finally, we present several policy implications based on the econometric estimation
results.
Keywords: productivity spillover, productivity gap, foreign-invested hotels, ownership
INTRODUCTION
There is an old saying in China: “Alien monks deliver better sermons.” When Buddhism was
first introduced to China, monks from ancient India appeared to have a more insightful
understanding of Buddhist philosophy and spiritual practices. Today, this saying is frequently
used to highlight the preeminence of the foreign knowledge and technology that has been
imported since China adopted its open-door policy in 1978 and reestablished foreign trade and
investment. To accelerate hotel development and attract greater investment inflows, the Chinese
government implemented a diversification and decentralization policy for hotel investment
(Zhang, Pine, & Lam, 2005). According to the China Tourism Statistical Yearbook, the number
of foreign-invested star-rated hotels increased from 272 in 1992 to 467 in 2012; furthermore,
foreign capital accounted for 16.18% of total capital in the hotel industry in 2012. In fact, the

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hotel industry in China has been criticized for its low productivity due to an underutilization of
available resources (Tsai, 2009; Yu & Gu, 2005), especially for domestic hotels (Luo, Yang, &
Law, 2014). The inefficiency of Chinese hotels may be attributed to several factors, including
excessive indebtedness (Yu & Gu, 2005), short-sighted marketing strategies (Kong & Cheung,
2009), lack of management skill and expertise (Mak, 2008), and unfamiliarity with international
business standards (Pine & Qi, 2004). In the context of the hotel business, the “alien monks” (i.e.,
the foreign-invested hotels) are operated more efficiently than their domestic counterparts (Pine,
2002; Pine & Qi, 2004). This technological gap can be explained by foreign-invested hotels’
competitive advantages in terms of operating scale (Heung, Zhang, & Jiang, 2008), managerial
and marketing know-how (Hung, et al., 2013), operational skills and technologies (Hsu, Liu, &
Huang, 2012), and government support (Pine & Qi, 2004). Therefore, domestic hotels use
benchmarks based on the performance of their foreign-invested counterparts as they seek to
improve the productivity and efficiency of their operations in China. For instance, Luo, Yang,
and Law (2014) found that the overall efficiency of a Chinese city’s hotel industry is positively
associated with the percentage of foreign-invested hotels in that city. Additionally, Zhou, Ye,
Pearce, and Wu (2014) indicated that international hotels were rated more positively by guests
online because of their favorable experiences in these hotels.
In the strategic management literature, productivity spillovers from foreign-invested firms and
multi-national enterprises (MNEs) are economic externalities that may boost the productivity of
indigenous firms (Blomström & Kokko, 1998). Spillovers work through several channels,
including inter-sectoral linkages, labor movements, the demonstration effect, exports, and the
competition effect (Blomström & Kokko, 1998). Spillovers have been examined in a large

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number of empirical studies, and scholars have evaluated whether the productivity of domestic
firms increases when foreign firms operate in the local economy (Wooster & Diebel, 2010).
Most early empirical endeavors were based on data from the manufacturing sector, and several
later studies started using data from various service industries (Ben Hamida, 2011) such as the
financial industry (Kolstad & Villanger, 2008), the transportation industry (Kolstad & Villanger,
2008) and the retailing industry (Higón & Vasilakos, 2011). However, to the best of our
knowledge, researchers have not yet systematically examined productivity spillovers from
foreign-invested to domestic firms in the hotel industry.
To fill this research gap, we investigate the productivity spillovers of foreign-invested hotels
using a sample of hotels in 27 major Chinese cities from 2001 to 2012. The purpose of this study
is therefore to understand the impact and consequences of Foreign Direct Investment (FDI) on
the productivity of Chinese domestic hotels. Based on a panel data model, we examine the effect
of foreign-invested hotels’ capital percentage in each city on the overall productivity of that
city’s domestic hotels. We aim to make two major contributions to current knowledge about the
hospitality industry. First, our paper represents the first empirical effort toward examining the
productivity spillovers of foreign firms in the hospitality industry. Because the hotel industry has
several unique characteristics compared to other industries (Sheel, 1994), the spillover patterns
could be different. Second, in past studies of Chinese hotels, ownership has been identified as
one of the most critical issues (Mak, 2008). Although the superiority of foreign-invested hotels
has been long recognized (Pine & Phillips, 2005), no studies have investigated how the presence
of foreign-invested hotels may affect domestic hotels. Our study is the first to offer insights into
the interconnectedness of these two types of hotels based on productivity spillovers.

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The rest of this paper is organized as follows. In the next section, we review the relevant
literature on productivity spillovers and ownership issues in the Chinese hotel industry. Then, we
describe the empirical model and dataset and present the estimation results from the econometric
models. Finally, we present our conclusions and discuss the implications of our findings.
LITERATURE REVIEW
FDI Spillover Theories
Many countries strive to attract FDI and the concomitant capital and technologies (Javorcik,
2004). More importantly, FDI improves managerial knowledge and skills and increases the
efficiency and productivity of the local economy (Bwalya, 2006; Fernandes & Paunov, 2012).
Moreover, knowledge from foreign investors is transmitted to indigenous firms and boosts their
productivity by enhancing efficiency, introducing best practices, transferring technology, and
stimulating competition—“contagion” effects that are often referred to as productivity spillovers
(Blomström & Kokko, 1998). Spillovers (or externalities) are impacts on third parties not
directly involved in an economic transaction; that is, when a transaction between firm A and firm
B affects firm C (Pigou, 1920). In such cases, firms A and B neither bear all of the costs nor reap
all of the benefits from the transaction (Eden, 2009). Consequently, spillovers, whether positive
(social benefits exceed private costs) or negative (social costs exceed private costs), are
generated and diffused. FDI spillovers can be evaluated through the change in the productivity of
domestic firms as a consequence of the presence of foreign firms in the local economy. Foreign
firms may generate spillovers that affect domestic firms in the same industry (i.e., horizontal or

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intra-sectoral spillovers) as well as upstream and downstream domestic firms (i.e., vertical
forward and backward or inter-sectoral spillovers).
For a firm to engage in FDI, it must possess some advantages over potential domestic
competitors. These advantages may consist of technological superiority or intangible, profit-
yielding assets such as management skills and brand recognition (Fan, 2002). Productivity
spillovers within an industry can occur through at least three major channels or mechanisms
(Blomström & Kokko, 1998). The first channel is the mobility effect in which highly skilled
workers move from foreign firms to domestic firms through the labor market. These employees
take knowledge with them that may benefit the domestic firm. Mobility is particularly important
in the service industry, as training is more directly focused on developing human capital and
strengthening employees’ skills and knowledge (Blomström & Kokko, 2002). The second
channel is the demonstration/imitation effect. When foreign firms demonstrate advanced
technologies in the local market, domestic firms may imitate and adopt these technologies. This
type of spillover can also take the form of reverse engineering, whereby a local firm creates a
product/service based on the design of a foreign competitor’s good or service. This effect
becomes successful only if the local firm has the technical capabilities to produce a similar
product/service. The third channel is a competition effect. Because foreign firms are often
operated more efficiently, local rivals are motivated to improve their productivity by introducing
new technologies or managerial skills to remain competitive.

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However, the spillovers generated by foreign firms are not always beneficial and nurturing.
Foreign firms endeavor to prevent their superior technology from leaking to domestic direct
competitors by filing patents and offering competitive salaries to retain workers. Foreign firms
can also drain resources from local companies through the so-called market stealing effect
(Marcin, 2007). Furthermore, foreign and domestic firms may not compete in the same market
and may have little in common in terms of products or technologies (Kokko, 1994). In such cases,
domestic firms may not be able to improve their productivity due to insignificant or even
negative spillover effects (aka. crowding-out effects) from FDI. For example, Aitken and
Harrison (1999) claimed that the entry of foreign firms into a market can crowd out some of the
demand from domestic firms, resulting in productivity losses. Rodrik (1999) even remarked,
“today’s policy literature is filled with extravagant claims about positive spillovers from FDI but
the evidence is sobering” (p. 37).
The effect of spillovers from FDI also depends greatly on the nature and condition of domestic
firms as potential spillover recipients. Several factors such as technology gaps between foreign
and domestic firms; the particularities of a given sector, region, or country; and the domestic
firm’s characteristics (e.g., size, capital intensity, and absorptive capacity) have been found to
affect the direction and magnitude of spillovers (Wang & Blomström, 1992). In particular,
absorptive capacity has been identified as a key determinant (Bijsterbosch & Kolasa, 2010;
Blomström & Kokko, 1998; Marcin, 2007). Absorptive capacity is a firm’s ability to recognize
valuable new knowledge, integrate it, and utilize it productively (Ben Hamida, 2011). A firm’s
absorptive capacity depends on its existing level of technological competence as well as its
investments in the learning and infrastructure required to use foreign knowledge (Ben Hamida,

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2011). The absorptive capacity hypothesis argues that only firms with a high level of absorptive
capacity are likely to benefit from FDI spillovers, whereas others may not be able to take
advantage of the opportunities created by a foreign presence (Cantwell, 1989; Wang &
Blomström, 1992). More recently, the concept of absorptive capacity has been incorporated into
an “awareness-motivation-capability” (AMC) framework (Chen, Su, & Tsai, 2007), which
postulates that the effects of FDI spillovers are determined by domestic firms’ (i) awareness of
the potential impacts of FDI entry; (ii) motivation to change their strategies in response to
foreign firms’ market entry; and (iii) capability to absorb potential spillovers from FDI entry
(Meyer & Sinani, 2009).
Although the majority of extant studies reported some degree of productivity gains via different
spillover channels and linkages, the empirical evidence on the presence and valence (i.e.,
positive, negative or insignificant) of spillovers is still mixed, particularly for horizontal
spillovers (Abumustafa & Mostafa, 2009; Crespo & Fontoura, 2007; Görg & Greenaway, 2004;
Görg & Strobl, 2001). Moreover, in recent studies, researchers have tested the short run market-
stealing effect hypothesized by Aitken and Harrison (1999), confirming that spillovers can be
nonlinear over time (Buckley, Clegg, & Wang, 2007; Liu, 2008). The time pattern shows that a
negative spillover effect is generated first, driven by the possible adjustment of local firms to the
foreign entrants; this is followed by a positive spillover effect and in the end, an insignificant
spillover effect. This result indicates that divergent spillover impacts could coexist in different
environments and time frames. Therefore, productivity spillovers may vary by country, sector
and type of firm and depend also on the nature of FDI and the absorptive capacity of domestic
firms (Bijsterbosch & Kolasa, 2010; Wooster & Diebel, 2010).

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FDI Spillovers in the Service Industry
FDI has been growing rapidly over the past several decades in the service industry. The gap
between service and manufacturing FDI began to grow in the 1970s and has continued to widen
ever since (Doytch & Uctum, 2011). For instance, the service industry accounted for 60% of the
world’s FDI in 2002; that amount represented a four-fold increase over 1990 and surpassed
foreign investment in the manufacturing industry (Fernandes & Paunov, 2012; United Nations
Conference on Trade and Development (UNCTAD), 2004). Studies that compare and contrast
FDI spillover effects between service and manufacturing industries are scarce. While the purpose
of FDI in manufacturing is to exploit resources, markets or efficiency potential (Lesher &
Miroudot, 2008), FDI in services is likely motivated by market-seeking (Kolstad & Villanger,
2008). Compared to their counterparts in manufacturing, technologies in service firms comprise
soft skills such as organizational, management and financial knowledge and practices, which are
often embodied in people rather than in products or machinery (Grosse, 1996) and are typically
not patented or copyrighted. Firms resort to internalizing service technologies, commonly via
trade secrets. Technology transfers in services vis-à-vis manufacturing are much more embedded
in human capital than in machinery and equipment (Ben Hamida, 2011). While service FDI
spillovers occur via the same channels as manufacturing, spillovers via employee turnover are
likely to be high (Ben Hamida, 2011). Therefore, service firms are generally characterized by a
high level of absorptive capacity, which enables them to take advantage of productivity
spillovers.

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Although the extant literature is concentrated on the manufacturing industry, a number of studies
have also emerged that focus on productivity spillovers in the service industry. Some scholars
have searched for inter-sectoral effects (i.e., vertical spillovers). Lesher and Miroudot (2008)
used firm level data for 17 Organization for Economic Co-operation and Development (OECD)
countries from 1993 to 2006 to identify FDI spillover effects across countries, sectors and time.
Their results indicate that the productivity-enhancing effects of FDI are strongest in service
industries, particularly through backward linkages to other non-service industries. Others
reported similar findings that service FDI has positive effects on productivity in domestic
manufacturing firms (Arnold, Javorcik, Lipscomb, & Mattoo, 2010; Arnold, Javorcik, & Mattoo,
2011; Fernandes, 2009; Fernandes & Paunov, 2012; Javorcik & Li, 2008). Regarding intra-
sectoral effects or horizontal spillovers, most empirical results have shown positive spillovers in
the service industry (Ben Hamida, 2011; Doytch & Uctum, 2011; Hale & Long, 2006; Marcin,
2007); however, in one exception, Alfaro (2003) found insignificant or ambiguous spillover
effects. Gorodnichenko, Svejnar, and Terrell (2007) tested both vertical and horizontal spillover
effects in service industries in 17 countries. Both inter- and intra-industry effects were significant
for their samples. In the context of the hotel industry, Niewiadomski (2015) adopted the theories
from evolutionary economic geography and recognized four areas in which international hotels
influence the local economy in Central and Eastern Europe: direct investment and infrastructure
upgrading; employment creation; knowledge transfer; and forging local linkages. However, the
final area has not been analyzed in detail.

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Foreign-invested and Domestic Hotels in China
The hotel industry in China has been transformed by tremendous progress and development
since the open-door policy was enacted in 1978. The number of hotels and hotel rooms in China
grew from 137 and 15,539, respectively, in 1978, to 11,367 and 1,497,188 in 2012,
accompanying the expected growth of international arrivals and strong domestic tourist demand
(CNTA, 2013). Over the same time period, a variety of hotel ownership structures emerged due
to China’s remarkable economic growth and its membership in the World Trade Organization, a
decentralized hotel ownership structure, and the government’s encouragement of foreign-
invested hotel companies to enter the market (Pine, 2002). Hotels are classified under nine
different categories (CNTA, 2013): state-owned; collective; shareholding co-operative; limited
liability; limited liability shares; privately owned; others; Hong Kong-, Macau-, or Taiwan–
invested (HMT); and foreign-invested. The first seven categories of hotel are not involved with
foreign investment and are considered to be domestic firms. In this study, foreign-invested hotels
are defined to be hotel properties financed by investors from foreign countries or HMT regions.
In 2012, investors from HMT controlled approximately 2.11% of the market, and foreign
investors controlled approximately 2.00% (CNTA, 2013).
Foreign-invested hotel firms have several competitive advantages over China’s domestic hotel
operators. First, foreign-invested hotels are usually large and operated by multinational hotel
companies (Pine & Phillips, 2005). While the top 10 international hotel chains have entered
China’s market, Jinjiang International Hotel Management Corp, China’s largest domestic hotel
chain, was ranked No. 13 worldwide in 2009 (Okoroafo, 2009). Economies of scale make
foreign firms better-positioned in terms of access to capital, human talent, and supply chains

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(Heung, et al., 2008). Second, foreign-invested hotels have mature management practices and
follow conventional market principles and international business standards with a clear
separation of ownership and management (Mak, 2008; Pine & Qi, 2004). Their longer history
and experience in managing multinational hotel properties all over the globe enable them to
develop better revenue management skills (Pine & Phillips, 2005) and maintain high-level hotel
operations (Hung, et al., 2013). Employees of foreign-invested firms have expertise in many
aspects of hotel management, including strategic management, service quality, branding,
corporate culture, operating efficiency, and marketing (Gu, Ryan, Bin, & Wei, 2013; Hsu, et al.,
2012; Kong & Cheung, 2009; Luo, et al., 2014). Unlike foreign invested hotels, state-owned
hotels must consider assorted non-business factors in their daily operation and view political ties
as more important than business ties (Hsu, et al., 2012).
Third, foreign invested hotels have technical and technological innovation advantages, especially
in the areas of reservation/distribution systems, guest relationship management systems, and
logistics systems (Heung, et al., 2008; Pine & Qi, 2004). Fourth, rooted in the global production
network, foreign-invested hotels have established links to external networks worldwide, which
can provide access to extra-regional sources of innovation, investment and expertise
(Niewiadomski, 2015). Lastly, foreign-invested hotels enjoy more favorable policies and
treatment than their domestic counterparts in terms of taxation and tariffs, foreign exchange rates,
pricing, and human resource policies (Pine & Qi, 2004). Previous empirical studies have
indicated that foreign hotel firms tend to outperform China’s domestic counterparts in terms of
occupancy, profits, Revenue per Available Room (RevPAR), and efficiency (Gu, et al., 2013;
Okoroafo, 2009; Pine & Phillips, 2005; Yu & Gu, 2005).

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However, China’s domestic hotels do have some advantages. They have knowledge of the
internal workings of the Chinese political, regulatory, financial and social systems (Heung, et al.,
2008). They also have a natural affinity for local cultural norms and Chinese business practices
(Pine, 2002). In particular, they may have a cultural advantage in China’s business environment
due to guanxi, a practice based on relationships, interdependence and reciprocity. As part of
social capital in China, guanxi is developed when organizations and individuals share scarce
resources and exploit structural holes to gain competitive advantages over others (Adler & Kwon,
2002). Guanxi provides a significant advantage, because research has shown that social capital is
an important factor affecting firm performance (Adler & Kwon, 2002; Luo & Chen, 1997;
Mahajan & Benson, 2013).
Previous empirical studies on productivity spillovers in the service industry have revealed
positive effects (Ben Hamida, 2011; Doytch & Uctum, 2011; Hale & Long, 2006; Marcin, 2007).
For the hotel industry in particular, this spillover can be more pronounced for several reasons.
First, because innovation intensity in the hotel industry is low, domestic competitors can easily
imitate technologies at a low additional cost. Second, the tourism and hospitality industry is
characterized by a high labor turnover rate (Yang & Wong, 2012), and high labor mobility may
facilitate productivity spillovers. Our first hypothesis offers a direct test of the main proposition:
Hypothesis 1: The productivity of domestic hotels in a city is positively associated with the share
of foreign-invested hotels’ capital in the city.

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Apart from the horizontal (intra-sectoral) spillovers considered in Hypothesis 1, productivity
spillovers can be generated by other foreign-invested firms outside of the hotel industry.
Therefore, we propose the following hypothesis regarding the effect of overall FDI penetration.
Hypothesis 2: The productivity of domestic hotels in a city is positively associated with overall
FDI penetration in the city.
It has been suggested in the literature that the magnitude of productivity spillovers from foreign-
invested to domestic firms depends on the context in which they operate. First, a large
productivity gap between domestic and foreign-invested hotels indicates great potential for
productivity improvement for inefficient domestic hotels (Aitken & Harrison, 1999; Görg &
Strobl, 2001; Meyer & Sinani, 2009), which, in turn, is likely to motivate domestic hotels to
internalize the full value of spillovers in order to catch up with the foreign-invested leaders.
Second, in a market with saturated supply, competitive threats stimulate domestic hotels to
leverage productivity spillovers through imitation and innovation (Ben Hamida, 2011). Lastly,
spillover benefits may decrease over time. In a saturated market with surplus service providers
such as the Chinese hotel market, the competition between foreign-invested and domestic hotels
becomes increasingly intense over time, and as a result, the crowding-out effect erodes the
benefits associated with productivity spillovers (Aitken & Harrison, 1999; Meyer & Sinani,
2009). Based on the arguments above, we propose the following hypotheses regarding the
moderating factors of productivity spillovers:
Hypothesis 3: The effect of horizontal (intra-sectoral) productivity spillovers on domestic hotels
is stronger in cities with a larger productivity gap between foreign-invested and domestic hotels.

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Hypothesis 4: The effect of horizontal (intra-sectoral) productivity spillovers on domestic hotels
is stronger in cities with more saturated supply.
Hypothesis 5: The effect of horizontal (intra-sectoral) productivity spillovers on domestic hotels
declines over time.
MODEL AND DATA
Meyer and Sinani (2009),Wooster and Diebel (2010), and Iršová and Havránek (2013) discussed
the general econometric specification of empirical models designed to investigate productivity
spillovers. These spillovers are captured as the impact of the presence of foreign-invested firms
on domestic firms’ productivity (Buckley, Clegg, & Wang, 2002). Guided by these studies, we
model labor productivity of domestic firms as being dependent on the degree of presence of
foreign capital and other control variables. Productivity is considered to be a general umbrella
concept that includes efficiency, effectiveness, quality, predictability, and other performance
dimensions (Sigala, 2004). It is also a top priority for hoteliers (Brown & Dev, 1999; Sigala,
2004; Tsai, 2009). Although numerous empirical FDI spillover-related studies have appeared
since Caves (1974), there is no consensus on the actual measurement of productivity. Görg and
Strobl (2001) noted that most management studies used one of three measurements to represent
productivity: capital (value added), output (product) and employment (labor), depending on data
availability. In the hotel literature, RevPAR, occupancy, and data envelopment analysis (DEA)
score, along with labor efficiency, are the common measurements for productivity (Brown &
Dev, 1999; Sigala, 2004; Wang, Hung, & Shang, 2006). Because labor costs generally account
for the highest percentage of hotel operating expenses, the recommendation is to measure

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productivity in relation to labor (Tsai, 2009). Thus, labor productivity is used in this study to
measure productivity. The following econometric model is proposed to test Hypotheses 1–5 with
a panel dataset of hotels located in 27 major Chinese cities from 2001 to 2012:
 
1 2 3
ln / ln _ ln ln _
it it it it it t i it
it
Y L foreign percent FDI foreign percent z
       
           X
where i indicates the city (i = 1, 2, …, 27), and t indicates the year (t = 2001, 2002, …, 2012).
The dependent and independent variables are specified as follows:
Dependent variable:
 ln(Y / L)it: log of labor productivity of domestic hotels in city i at year t. Y represents the
total value added (revenue less outside purchases, in 10,000 RMB) of domestic hotels,
whereas L refers to the total number of average annual employees in all domestic hotels.
Y / L therefore measures value added per capita, i.e., labor productivity.
Independent variables of major interest:
 lnforeign_percentit: log of percentage of foreign-invested hotels’ capital in city i at year t.
The percentage of foreign-invested hotels’ capital is calculated as a ratio of total capital
of foreign-invested hotels relative to total capital of all hotels. According to the definition
used by the China National Tourism Administration (CNTA), foreign investment
includes investments from Hong Kong, Macau, and Taiwan. This variable measures
horizontal (intra-sectoral) productivity spillovers. Its coefficient, β1, reflects the
contribution of foreign presence to domestic hotels’ productivity. A positive and
significant estimated coefficient of β1 will lend support to Hypothesis 1.

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 lnFDIit: log of all foreign direct investment (in all sectors including the hotel sector)
relative to Gross Domestic Product (GDP) of city i at year t, a proxy to capture total FDI
penetration. This measure covers the proportion of total FDI inflows in the citywide
economy as a whole. According the China Statistics Yearbook, the FDI in the hotel and
restaurant industry accounted for only 0.80% of total FDI. A positive and significant
estimated coefficient of β2 will lend support to Hypothesis 2.
Control variables in X:
 ln(K / L)it: log of capital per capita of domestic hotels in city i at year t. K denotes the
total net value of fixed capital stock (in 10,000 RMB), whereas L refers to the total
number of average annual employees for all domestic hotels. Following the rationale
behind the general Cobb-Douglas production function, the coefficient of this capital
intensity measure is expected to be positive.
 lntourismit: log of inbound tourism receipts relative to the GDP of city i at year t,
measuring the level of tourism specialization. Hotels in cities that are more specialized in
tourism are more likely to receive sustained guest flows and achieve higher productivity
(Luo, et al., 2014). Therefore, its coefficient is expected to be positive.
 lnhotel_supplyit: log of total capital of all hotels relative to the GDP of city i at year t,
measuring the level of hotel supply. Note that the city level data on hotel beds and rooms
are not available, and a pair-wise correlation table between rooms, beds, and asset capital
across 31 provinces indicated that these three variables are highly correlated with each
other. Therefore, hotel capital/asset values are used to capture the level of hotel supply.
Because China’s hotel industry has been long recognized for excess supply (Yu & Gu,
2005), fierce competition associated with market saturation can lead to a lower

18
productivity (Assaf & Cvelbar, 2011). Therefore, its coefficient is expected to be
negative.
Moderating variables in z:
 gapit: difference in labor productivity between foreign-invested and domestic hotels in
city i at year t. A positive and significant estimated coefficient of its interaction with
lnforeign_percentit will lend support to Hypothesis 3.
 hotel_supplyit: total capital of all hotels relative to GDP of city i at year t. A positive and
significant estimated coefficient of its interaction with lnforeign_percentit will lend
support to Hypothesis 4.
 t: year of observation. A negative and significant estimated coefficient of its interaction
with lnforeign_percentit will lend support to Hypothesis 5.
In the empirical model,
t

captures the year-specific effect for year t, and
i

captures the time-
invariant city-specific effect of city i that influences labor productivity but has not been
incorporated into any explanatory variables. Therefore, the proposed two-way panel data model
can remedy the potential problem of omitted-variable bias to some extent (Wooldridge, 2002).
The error tem
it

is assumed to follow a normal distribution with a mean of 0 and a definite
variance.
The panel data model is expected to provide more reliable estimates when examining
productivity spillovers (Görg & Strobl, 2001). To estimate the proposed model, we utilized a
fixed effect (FE) panel model. Unlike the random effect (RE) model, which has more restrictions

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and assumes independence between
i

and other explanatory variables, the FE model allows
interdependence between them and is therefore more appropriate for our dataset. Although the
Hausman test has been used previously to help decide between FE and RE models, the test was
found to be neither necessary nor sufficient for this decision (Clark & Linzer, 2012).
Furthermore, to check the robustness of our estimation results, we estimated the same model
specification using the first-difference (FD) estimator.
Our hotel-related dataset was obtained from the China Tourism Statistical Yearbook
(Supplementary) from 2002 to 2013. This yearbook was edited by CNTA and is known to be one
of the most reliable sources of data for the Chinese tourism and hotel industry. The data contain
aggregate information on total value added, number of average annual employees, and total net
value of fixed capital stock for both foreign-invested and domestic hotels at the city level. The
yearbook only covers the 27 cities with the most developed hotel industry, and a large number of
these cities are located in the Yangtze River Delta area and the Pearl River Delta area, two
important economic centers in China. Other data, such as inbound tourism revenue, total GDP,
and total FDI were obtained from the China City Statistical Yearbook from 2002 to 2013, which
was edited by the National Bureau of Statistics of China.
In Table 1, we present descriptive statistics for the variables in the econometric model. All
bivariate correlation coefficients between independent variables are below 0.4, suggesting the
absence of a multi-collinearity problem. We also compare the labor productivity measure
between domestic and foreign-invested hotels in each city over the research period. The average

20
labor productivity is 86,304 RMB for domestic hotels and 128,119 RMB for foreign-invested
hotels at city level. A paired t test is utilized to test the statistical significance of this difference,
and the t value is estimated to be -12.666, which is statistically significant at the 0.001
significance level (for either a one- or two- tailed test). We used the Im-Pesaran-Shin test to test the
unit root (with the option subtracting cross-sectional means and trends), and as suggested by the
significant values of W-t-bar (Table 1), the results rejected the null hypothesis of a unit root in the
dependent and independent variables of our model (Im, Pesaran, & Shin, 2003).
(Please insert Table 1 about here)
Figure 1 maps the location of all 27 cities in the sample and shows the foreign capital percentage
invested in the hotel industry (foreign_percent) in 2001 and 2012 for each. A high percentage of
foreign hotel investment is found in Southeastern China due to strong connections with Hong
Kong, Macau, and Taiwan and concomitant investment from these regions, which is counted as
foreign investment. Furthermore, this percentage generally declines from 2001 to 2010, owing to
the emergence of several domestic hotel chains and growing domestic investment in the hotel
industry (Gu, Ryan, & Yu, 2012).
(Please insert Figure 1 about here)
ESTIMATION RESULTS
In Table 2, we present the estimation results of the proposed econometric model using fixed-
effect panel data estimation. After excluding 21 observations without foreign hotel investment in

21
specific years, the model fits the panel dataset with a total of 303 observations. We first added
lnforeign_percent and lnFDI into the model separately, without any interaction terms. The
estimated coefficient of lnforeign_percent is positive and statistically significant at the 0.10
significance level in Model 1, whereas the coefficient of lnFDI is estimated to be positive but
insignificant in Model 2. Model 3 incorporates both variables and provides similar estimated
coefficients. The estimated coefficients for other independent variables vary little across Models
1, 2 and 3. The robustness of the model specification can be explained by the exceptional
capability of panel data models to overcome multi-collinearity problems (Hsiao, 2003, p. 311).
(Please insert Table 2 about here)
The coefficient of lnforeign_percent is estimated to be 0.0478 in Model 3 and is statistically
significant at the 0.10 level. The result suggests that a 1 percent increase in the share of foreign-
invested hotels’ capital in a city will contribute to a 0.0478 percent increase in the productivity of
domestic hotels in that city. Therefore, this finding from the empirical results supports
Hypothesis 1 and confirms findings from previous studies regarding positive productivity
spillovers from foreign-invested firms to domestic firms in the service industry (Ben Hamida,
2011; Doytch & Uctum, 2011; Hale & Long, 2006; Marcin, 2007). The coefficient of lnFDI is
estimated to be 0.0282 in Model 3 and is not statistically significant. Hypothesis 2 is therefore
rejected, as this result indicates that overall FDI penetration in a city does not necessarily boost
the productivity of its domestic hotels. Several factors can explain the insignificant impact of
FDI penetration. First, the tourism and hospitality industry is weakly linked to other industries

22
(Pratt, 2011), and as a result, few productivity spillovers are generated through vertical spillovers
from other economic sectors to hotels. Second, due to its nature as a service industry specialized
in producing experiences, intellectual assets in the hotel industry differ from those in other
industries, such as manufacturing. As a result, the knowledge and skills accumulated in other
industries may not be applicable to hotels, and domestic hospitality businesses are less motivated
to capitalize on productivity spillovers created by overall FDI penetration in other sectors.
Regarding other independent variables, ln(K/L) and lntourism are estimated to be statistically
significant, suggesting that total capital intensity and tourism specialization boost the
productivity of domestic hotels. The negative and significant coefficient of lnhotel_supply
indicates that the overall surplus of hotel properties and the concomitant fierce competition lead
to significantly deteriorated performance among domestic hotels. Because all variables are log
transformed, the magnitudes of their estimated coefficients are comparable and can be
interpreted as elasticities. Among all independent variables in Model 3, the coefficient of ln(K/L),
0.623, is of the largest magnitude, pinpointing the dominant role that capital intensity plays in
determining hotels’ productivity. A 1 percent increase in capital intensity contributes to a 0.623
percent increase in productivity for domestic hotels.
To test Hypotheses 3, 4, and 5, different interaction terms are incorporated into the empirical
model. Only one interaction term is included at a time to ensure that the model remains
parsimonious and to avoid potential confusion in interpretation. Model 4 examines the
interaction term of lnforeign_percent and gap, which is estimated to be positive and statistically

23
significant at the 0.05 level. This result indicates that when productivity spillovers from foreign-
invested to domestic hotels are positive and significant, they are also stronger when the
productivity gap between foreign-invested and domestic hotels is larger. Our result corroborates
Hypothesis 3 and contradicts findings from manufacturing industry studies indicating that
productivity spillovers are more pronounced when the productivity gap is small (Chuang & Hsu,
2004; Kokko, 1994). This discrepancy may be due to the relatively low-tech nature of the hotel
industry (Hertog, Gallouj, & Segers, 2011), in which case a large productivity gap is unlikely to
be a barrier for technology transfer between domestic and foreign-invested hotels. Instead, an
intense productivity gap motivates domestic hotels to absorb advanced knowledge and skills
from their foreign-invested peers. Moreover, a large gap indicates the opportunity for domestic
hotels to learn from foreign-invested peers.
As shown in Table 2, Model 5 incorporates the interaction term of lnforeign_percent and
hotel_supply to test Hypothesis 4. The coefficient of the interaction term has the expected
positive sign but is estimated to be statistically insignificant. Thus, Hypothesis 4 is not supported
by our empirical model. One possible reason for this insignificant result may be the fact that
although intense competition motivates domestic hotels to absorb potential spillovers, these
hotels are not able to fully reap the benefits due the crowding-out effect associated with excess
supply (Aitken & Harrison, 1999). Hence, positive and negative effects are likely to cancel each
other out. Moreover, Model 6 in Table 2 includes the interaction term for lnforeign_percent and t
to test Hypothesis 5. The coefficient of the interaction term is estimated to be negative and
insignificant, providing little empirical evidence to support Hypothesis 5.

24
To check the robustness of our results, we re-estimated the proposed empirical model using an
alternate method: the FD estimator (Hsiao, 2003). After first-differencing, only 296 observations
fit the model. In Table 3, we present the estimation results using the FD estimator. Compared to
the corresponding FE estimates, FD estimates have similar signs and significances, but with
larger magnitudes. Similar to the conclusion drawn from the FE estimates, Hypotheses 1 and 3
are supported, whereas Hypotheses 2 and 4 are rejected. For Hypothesis 5, as shown in Model 12
of Table 3, the interaction term of lnforeign_percent and t is estimated to be negative and
statistically significant at the 0.10 level, suggesting that the positive effect of productivity
spillovers from foreign-invested hotels diminishes over time. Therefore, Hypothesis 5 is
supported by the FD estimates.
(Please insert Table 3 about here)
CONCLUSIONS
We are among the first to empirically investigate productivity spillovers from foreign-invested to
domestic hotels using industry-level panel data in China. We examined whether horizontal
(intra-sectoral) spillovers from the presence of foreign firms exist and the resultant effects of FDI
on China’s hotel industry from 2001 to 2012. In this research, we found evidence in support of
productivity spillovers from foreign-invested hotels to domestic hotels. Foreign investment in
China’s hotel industry is strongly associated with the higher labor productivity of domestic
hotels. This result is consistent with the horizontal productivity spillover hypothesis and many

25
previous empirical findings. Furthermore, we found that domestic hotels with large productivity
gaps are more affected by the externalities created by the presence of foreign-invested hotels.
The spillover effect is significantly and positively associated with the size of the productivity gap
between foreign-invested and domestic hotels. This result contradicts the commonly held idea in
the manufacturing industry that the most advanced firms in emerging economies can benefit
most from FDI. An interpretation that we find plausible is that hotel knowledge and technologies
are mainly transferred through worker mobility (Ben Hamida, 2011). The rapid surge in wages
and jobs in China over the past decade have resulted in significant employee turnover in the hotel
job market, which has in turn greatly facilitated spillover effects. The less technologically
advanced domestic hotels have an opportunity to catch up by learning about advanced
managerial, organizational, operational, and distributional techniques from foreign-invested
hotels and thereby improve their productivity as a result of knowledge spillovers spawned by
FDI. These findings suggest that China’s policy of opening up to FDI has indeed produced
quantitatively measurable benefits.
The results of the study indicated that the productivity of domestic hotels was positively
correlated with both capital intensity and tourism specialization but negatively related to the
inventory of local hotel supply at significant levels. These findings were in agreement with our
expected results, which were explained in the previous model and data section. In addition, we
analyzed the time trend of spillover effects and found scant evidence of diminishing effects of
FDI spillovers over time from 2001 to 2012. FDI spillovers remained an important channel
through which Chinese hotels improve productivity. We also examined how competition affects
the productivity of domestic hotels and the role it plays on the degree of spillover from foreign-
invested hotels. The result suggested an overall insignificant effect of competition from FDI on

26
the increase in labor productivity of Chinese hotels. Our findings echo Aitken and Harrison
(1999) proposition that the negative influence of crowding-out effects offsets the positive
influence of spillovers on the productivity of Chinese hotels due to competition. Lastly, we tested
aggregate effects of FDI on the productivity of domestic hotels. Little evidence was found to
suggest that overall FDI penetration enhances the productivity of domestic hotels within the
same city. A possible interpretation is that the hotel industry is less interconnected and shares
less common knowledge and technologies with other industries (Pratt, 2011).
Our findings have clear FDI policy implications because the government plays a crucial role in
guiding and managing China’s hotel industry. First, the government should continue to attract
more foreign-invested hotels due to their favorable capital intensity impact and positive
productivity spillovers. On the one hand, foreign-invested hotels bring new capital, an additional
financial resource Chinese hotels can use to acquire new technology and renovate old technology,
which would directly improve their productivity. On the other hand, the presence of foreign
investment will indirectly boost the productivity of Chinese domestic hotels through positive
productivity spillovers. Financial means such as favorable tax treatment and other
accommodating resources such as liberal and transparent policies and incentives to invest more
capital in existing hotels should be in place to encourage new foreign investment. Second,
specialized policy reforms aimed at steering additional FDI toward lower productivity domestic-
invested hotels are clearly warranted. The local government and tourism administrative
authorities should build an economic environment conducive to facilitating information and
knowledge sharing and the diffusion of technology between foreign-invested and domestic hotels
(Luo, et al., 2014), especially for those with poor productivity performance. Third, as the tourism

27
and hotel industries go hand in hand, another way to enhance the productivity of Chinese hotels
is to stimulate tourism activities in China, which was empirically confirmed in this study.
Tourism specialization in cites can attract more business and pleasure visitors, thereby achieving
higher hotel productivity. The government is therefore called to promote further increases in
tourism demand, including investments in tourist attractions and infrastructure, the effective
implementation of paid vacations, and strong commitments to hosting events. Lastly, being fully
aware of the benefits and limitations of FDI spillovers on domestic hotels as well as the basics of
demand and supply in economics, the government is advised to formulate a policy to control and
regulate total hotel supply, which appears to be one of the most crucial factors in our empirical
study affecting the productivity of domestic hotels.
Some limitations may temper the generalizability of our findings. First, we did not capture
vertical productivity spillovers from other backward- and forward-linked industries (Lin, Liu, &
Zhang, 2009) due to data unavailability. Second, we used the city level aggregate data in this
study because we were unable to obtain firm-level data. Firm-level data would be a better choice
for evaluating the effectiveness of FDI spillovers (Girma & Gong, 2008; Sinani & Meyer, 2004).
Third, we did not further categorize domestic and foreign-invested hotels into different
ownership types. As suggested by Buckley, Wang, and Clegg (2007), ownership types play an
important role in determining the scope and scale of productivity spillovers. Fourth, we did not
specifically investigate each channel contributing to the spillovers and compare the magnitudes
of spillovers from each channel. Lastly, the fixed effect model focuses more on the variation of
the dependent variable within a city and is likely to overlook the variation between cities as well
as dynamic relationships. We recommend the use of international samples in future studies to

28
examine productivity spillovers in the hotel industry with a specific focus on vertical spillovers
and disaggregated types of foreign ownership.
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34
Table 1
Descriptive statistics of variables
Variable
Obs
Mean
Std. Dev.
Min
Max
W-t-bar
ln(Y/L)
303
2.246
0.517
0.068
5.156
-5.750***
lnforeign_percent
303
-1.481
0.932
-7.055
-0.281
-7.748***
lnFDI
303
-3.173
0.882
-9.393
-1.604
-2.717***
ln(K/L)
303
3.233
0.402
1.890
5.790
-5.253***
lntourism
303
-4.299
0.894
-6.455
-2.281
-2.890***
lnhotel_supply
303
-3.790
0.748
-5.585
-1.610
-4.978***
Note: *** indicates significance at 0.01

35
Table 2
Estimation results of fixed effect models
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
lnforeign_percent
0.0445*
0.0478*
0.0554**
0.0122
18.58
(0.024)
(0.024)
(0.024)
(0.023)
(20.856)
lnFDI
0.0195
0.0282
0.0256
0.0289
0.0207
(0.026)
(0.027)
(0.027)
(0.026)
(0.024)
ln(K/L)
0.628***
0.609***
0.623***
0.547***
0.634***
0.641***
(0.102)
(0.117)
(0.102)
(0.105)
(0.097)
(0.104)
lntourism
0.281**
0.252**
0.274**
0.265**
0.281**
0.252**
(0.116)
(0.108)
(0.110)
(0.116)
(0.104)
(0.111)
lnhotel_supply
-0.384***
-0.370***
-0.375***
-0.339**
-0.292**
-0.389***
(0.124)
(0.125)
(0.121)
(0.125)
(0.140)
(0.125)
lnforeign_percent
*gap
0.00274**
(0.001)
lnforeign_percent
*hotel_supply
1.896
(1.231)
lnforeign_percent
*t
-0.00922
(0.010)
constant
-0.136
-0.134
-0.0296
0.297
0.291
-0.170
(0.694)
(0.743)
(0.706)
(0.698)
(0.750)
(0.731)
Hausman test
5.71
9.59
11.16
5.71
4.65
7.96
N
303
303
303
303
303
303
R-sq
0.655
0.651
0.656
0.664
0.660
0.658
AIC
61.39
64.75
62.42
56.83
60.33
62.77
BIC
117.1
120.5
121.8
120.0
123.5
125.9
Note: *** indicates significance at 0.01, ** indicates significance at 0.05, * indicates significance
at 0.1. Robust standard errors are presented in parentheses. Estimates of year dummies are
not presented for brevity.

36
Table 3
Estimation results of first-differenced model for robustness check
Model 7
Model8
Model 9
Model 10
Model 11
Model 12
lnforeign_percent
0.0752**
0.0752**
0.0504*
0.0974*
66.27*
(0.033)
(0.033)
(0.030)
(0.051)
(34.449)
lnFDI
0.00500
0.00367
0.0118
0.00389
0.000744
(0.030)
(0.027)
(0.029)
(0.027)
(0.025)
ln(K/L)
0.715***
0.701***
0.714***
0.562***
0.707***
0.791***
(0.121)
(0.124)
(0.123)
(0.118)
(0.123)
(0.120)
lntourism
0.319*
0.305*
0.318*
0.328**
0.304*
0.286*
(0.170)
(0.173)
(0.170)
(0.166)
(0.171)
(0.169)
lnhotel_supply
-0.631***
-0.636***
-0.630***
-0.539***
-0.679***
-0.658***
(0.197)
(0.209)
(0.203)
(0.195)
(0.253)
(0.189)
lnforeign_percent
*gap
0.00418***
(0.001)
lnforeign_percent
*hotel_supply
-1.124
(2.245)
lnforeign_percent
*t
-0.0330*
(0.017)
constant
0.0426
0.0508
0.0428
0.0843
0.0445
-0.0300
(0.115)
(0.116)
(0.115)
(0.112)
(0.114)
(0.106)
N
296
296
296
296
296
296
R-sq
0.258
0.245
0.258
0.282
0.260
0.282
AIC
288.5
293.7
290.5
283.0
291.7
282.8
BIC
347.5
352.8
353.2
349.4
358.1
349.2
(Note: *** indicates significance at 0.01, ** indicates significance at 0.05, * indicates significance
at 0.1. Robust standard errors are presented in parentheses. Estimates of year dummies are
not presented for brevity.)

37
Figure 1. Foreign capital percentage in the hotel industry in major Chinese cities.