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 article, 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 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

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 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), shortsighted 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 et al. (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) have 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 multinational enterprises are economic externalities that may boost the productivity of indigenous firms (Blomström & Kokko, 1998). Spillovers work through several channels, including intersectoral linkages, labor movements, the demonstration effect, exports, and the competition effect (Blomström & Kokko, 1998). Spillovers have been examined in a large 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 firms 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 article 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 with 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.

The rest of this article 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 data set 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 important, 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 the costs nor reap all 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 intrasectoral spillovers) as well as upstream and downstream domestic firms (i.e., vertical forward and backward or intersectoral 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.

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, the domestic firms may not be able to improve their productivity due to insignificant or even negative spillover effects (also known as 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 on its investments in the learning and infrastructure required to use foreign knowledge (Ben Hamida, 2011). The absorptive capacity hypothesis argues that only those 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” framework (Chen, Su, & Tsai, 2007), which postulates that the effects of FDI spillovers are determined by domestic firms’ (a) awareness of the potential impacts of FDI entry, (b) motivation to change their strategies in response to foreign firms’ market entry, and (c) 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).

FDI Spillovers in the Service Industry

FDI has been growing rapidly over the past several decades in the service industry. The gap between service FDI 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, 2004). Studies that compare and contrast FDI spillover effects between service and manufacturing industries are scarce. While the purpose of an 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 with 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.

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 intersectoral effects (i.e., vertical spillovers). Lesher and Miroudot (2008) used firm-level data for 17 Organisation 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 the service industries, particularly through backward linkages to other nonservice industries. Others too 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 intrasectoral 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 the service industries in 17 countries. Both inter- and intraindustry 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.

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, respectively, in 2012, accompanying the expected growth of international arrivals and strong domestic tourist demand (China National Tourism Administration [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 cooperative; 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 as 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 are 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 (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 nonbusiness 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 extraregional sources of innovation, investment and expertise (Niewiadomski, 2015). Last, 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).

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.

Apart from the horizontal (intrasectoral) spillovers considered in Hypothesis 1, productivity spillovers can be generated by other foreign-invested firms outside 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 a 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). Last, 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 (intrasectoral) productivity spillovers on domestic hotels is stronger in cities with a larger productivity gap between foreign-invested and domestic hotels.

Hypothesis 4: The effect of horizontal (intrasectoral) productivity spillovers on domestic hotels is stronger in cities with more saturated supply.

Hypothesis 5: The effect of horizontal (intrasectoral) 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 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 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 to 5 with a panel data set of hotels located in 27 major Chinese cities from 2001 to 2012:

\ln {(Y / L)}_{i t} = α + β_{1} \cdot \ln f o r e i g n_p e r c e n t_{i t} + β_{2} \cdot \ln F D I_{i t} + β_{3} \cdot \ln f o r e i g n_p e r c e n t_{i t} \cdot z_{i t} + X_{i t} γ + η_{t} + μ_{i} + ε_{i t}

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 in year t. Y represents the total value added (revenue less outside purchases is 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—that is, labor productivity.

Independent Variables of Major Interest

ln foreign_percent_it—log of percentage of foreign-invested hotels’ capital in city i in 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 CNTA, foreign investment includes investments from Hong Kong, Macau, and Taiwan. This variable measures horizontal (intrasectoral) productivity spillovers. Its coefficient, β₁, reflects the contribution of the foreign presence to domestic hotels’ productivity. A positive and significant estimated coefficient of β₁ will lend support to Hypothesis 1.

ln FDI_it—log of all FDI (in all sectors including the hotel sector) relative to the gross domestic product (GDP) of city i in 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 to the China Statistical Yearbook, the FDI in the hotel and restaurant industry accounted for only 0.80% of the 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 in 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.

ln tourism_it—log of inbound tourism receipts relative to the GDP of city i in 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.

ln hotel_supply_it—log of total capital of all hotels relative to the GDP of city i in year t—measuring the level of hotel supply. Note that the city-level data on hotel beds and rooms are not available, and a pairwise 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 productivity (Assaf & Cvelbar, 2011). Therefore, its coefficient is expected to be negative.

Moderating Variables in z

gap_it—difference in labor productivity between foreign-invested and domestic hotels in city i in year t. A positive and significant estimated coefficient of its interaction with ln foreign_percent_it will lend support to Hypothesis 3.

hotel_supply_it—total capital of all hotels relative to the GDP of city i in year t. A positive and significant estimated coefficient of its interaction with ln foreign_percent_it will lend support to Hypothesis 4.

t—year of observation. A negative and significant estimated coefficient of its interaction with ln foreign_percent_it will lend support to Hypothesis 5.

In the empirical model, $η_{t}$ captures the year-specific effect for year t and $μ_{i}$ captures the time-invariant city-specific effect of city i that influences labor productivity but that 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 term $ε_{i t}$ 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 and assumes independence between $μ_{i}$ and other explanatory variables, the FE model allows for interdependence between them and is therefore more appropriate for our data set. 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 data set 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 the bivariate correlation coefficients between the independent variables are below 0.4, suggesting the absence of a multicollinearity problem. We also compare the labor productivity measure between domestic and foreign-invested hotels in each city over the research period. The average labor productivity is 86,304 RMB for domestic hotels and 128,119 RMB for foreign-invested hotels at the 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 .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 of 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).

Table 1

Descriptive Statistics of Variables

Variable	Observations	Mean	Stdard Deviation	Minimum	Maximum	W-t-bar
ln(Y/L)	303	2.246	0.517	0.068	5.156	−5.750***
ln foreign_percent	303	−1.481	0.932	−7.055	−0.281	−7.748***
ln FDI	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***
ln tourism	303	−4.299	0.894	−6.455	−2.281	−2.890***
ln hotel_supply	303	−3.790	0.748	−5.585	−1.610	−4.978***

***

p < .01.

Figure 1 maps the location of all the 27 cities in the sample and shows the foreign capital percentage invested in the hotel industry (foreign_percent) in 2001 and 2012, respectively. 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).

Figure 1

Foreign capital percentage in the hotel industry in major Chinese cities

Estimation Results

In Table 2, we present the estimation results of the proposed econometric model using a FE panel data estimation. After excluding 21 observations without foreign-hotel investment in specific years, the model fits the panel data set with a total of 303 observations. We first added ln foreign_percent and ln FDI into the model separately, without any interaction terms. The estimated coefficient of lnforeign_percent is positive and statistically significant at the .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 the 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 multicollinearity problems (Hsiao, 2003).

Table 2

Estimation Results of Fixed Effect Models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
ln foreign_percent	0.0445*		0.0478*	0.0554**	0.0122	18.58
	(0.024)		(0.024)	(0.024)	(0.023)	(20.856)
ln FDI		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)
ln tourism	0.281**	0.252**	0.274**	0.265**	0.281**	0.252**
	(0.116)	(0.108)	(0.110)	(0.116)	(0.104)	(0.111)
ln hotel_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)
ln foreign_percent * gap				0.00274**
ln foreign_percent * gap				(0.001)
ln foreign_percent * hotel_supply					1.896
ln foreign_percent * hotel_supply					(1.231)
ln foreign_percent * t						−0.00922
ln foreign_percent * t						(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 ²	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: AIC = Akaike information criterion; BIC = Bayesian information criterion. Robust standard errors are presented in parentheses. Estimates of year dummies are not presented for brevity.

p < .1. **p < .05. ***p < .01.

The coefficient of ln foreign_percent is estimated to be 0.0478 in Model 3 and is statistically significant at the .10 level. The result suggests that a 1% increase in the share of foreign-invested hotels’ capital in a city will contribute to a 0.0478% 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 (Pratt, 2011), and as a result, few productivity spillovers are generated through vertical spillovers from other economic sectors to hotels. Second, because of 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 ln tourism 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 ln hotel_supply indicates that the overall surplus of hotel properties and the concomitant fierce competition lead to significantly deteriorated performance among domestic hotels. Because all the variables are log transformed, the magnitudes of their estimated coefficients are comparable and can be interpreted as elasticities. Among all the 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% increase in capital intensity contributes to a 0.623% 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 ln foreign_percent and gap, which is estimated to be positive and statistically significant at the .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 ln foreign_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 to 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 ln foreign_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.

To check the robustness of our results, we reestimated 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 with 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 ln foreign_percent and t is estimated to be negative and statistically significant at the .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.

Table 3

Estimation Results of the First-Difference Model for Robustness Check

	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
ln foreign_percent	0.0752**		0.0752**	0.0504*	0.0974*	66.27*
	(0.033)		(0.033)	(0.030)	(0.051)	(34.449)
ln FDI		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)
ln tourism	0.319*	0.305*	0.318*	0.328**	0.304*	0.286*
	(0.170)	(0.173)	(0.170)	(0.166)	(0.171)	(0.169)
ln hotel_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)
ln foreign_percent * gap				0.00418***
ln foreign_percent * gap				(0.001)
ln foreign_percent * hotel_supply					−1.124
ln foreign_percent * hotel_supply					(2.245)
ln foreign_percent * t						−0.0330*
ln foreign_percent * t						(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 ²	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: AIC = Akaike information criterion; BIC = Bayesian information criterion. Robust standard errors are presented in parentheses. Estimates of year dummies are not presented for brevity.

p < .1. **p < .05. ***p < .01.

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 (intrasectoral) 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 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 notion 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 the 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 the increase in labor productivity of Chinese hotels. Our findings echo Aitken and Harrison’s (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. Last, 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 one hand, foreign-invested hotels bring new capital, an additional financial resource that 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 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 on 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. Last, 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. Last, the FE 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 the dynamic relationships. We recommend the use of international samples in future studies to examine productivity spillovers in the hotel industry with a specific focus on vertical spillovers and disaggregated types of foreign ownership.

Footnotes

Yang Yang, PhD (e-mail: yangy@temple.edu), is an assistant professor in the Department of Tourism and Hospitality Management at Temple University, Philadelphia, Pennsylvania. Zhenxing Mao, PhD (e-mail: zmao@cpp.edu), is a professor in the Collins College of Hospitality Management, California State Polytechnic University, Pomona, California.

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