Cultural diffusion and international inbound tourism: Evidence from China

Abstract

Cultural diffusion is an important noneconomic determinant of tourism demand but has received less focus in the literature. This study seeks to address this gap by focusing on the impact of the Confucius Institute, an important institution of Chinese cultural diffusion, on inbound tourism to China. It is shown that the Confucius Institute positively contributes to the Chinese inbound tourism flows, even when the endogeneity of Confucius Institutes is considered. Moreover, the impact of the Confucius Institute on China’s inbound tourism has a lagged effect and regional heterogeneity. In addition, cultural distance is a mediating variable of the Confucius Institute on tourism demand. As cultural distance increases, the impact of the Confucius Institute on China’s international tourism flow first rises and then decreases.

Keywords

Confucius Institute cultural diffusion cultural distance GMM international inbound tourism

Introduction

Determinants of tourism demand have always been an area of prolific research in tourism economics. Statistically, between 1960 and 2002, 420 papers on the issue were published (Li et al., 2005), and the number of studies published in the years between 2001 and 2008 showed rapid growth (Song and Li, 2008). Both economic and noneconomic factors are discussed when analyzing varied types of tourism demand. On the one hand, travel price and tourist income are considered the two main determinants, theoretically and empirically (Crouch, 2016; Song and Li, 2008). These two economic variables have also received considerable attention in modeling tourism demand (Koo et al., 2017; Martins et al., 2017; Morley et al., 2014; Tang and Tan, 2015; Yang et al., 2014; Yazdi and Khanalizadeh, 2017). On the other hand, noneconomic factors such as climate change (Damm et al., 2016; Martín, 2005), air quality (Wang et al., 2018), politics (Seddighi and Theocharous, 2002), meetings (Lim and Zhu, 2018), and religious factors (Fourie and Santanagallego, 2013) have gradually caught the attention of scholars.

Culture is one of the key noneconomic factors determining tourism demand. From the perspective of psychology, culture is predicted to have a significant impact on tourists’ decisions (Jackson, 2001; Jackson et al., 2000). The cultural determinants most commonly discussed refer to cultural attraction (Richards G, 2000; Richards GW, 2009) and cultural distance (Yang et al., 2018; Yang and Wong, 2012). Cultural attraction is closely related to existing cultural heritage, whereas cultural diffusion refers to the promotion of existing cultural heritage. Cultural distance indicates the cultural difference between destinations, while cultural diffusion refers to overcoming that cultural gap. According to the diffusion theory of sociology (Palloni, 2001; Wejnert, 2002), cultural diffusion can be defined as the mixing or blending of different ideas, beliefs, and social activities between different groups.

In the early stages, culture is often considered to be diffused during the process of immigration and colonization (Kulikoff, 1986; Reichman and Hasson, 1984). Studies have shown that immigrants have a significant impact on tourism demand (Dwyer et al., 2014). With the rapid development of technology, a deep mixture of different cultures occurs through more diversified channels like social media, economics and trade, international sports games, and international organizations (Hurn, 2016; Kaufman and Patterson, 2005). However, among all these patterns, the existing literature focuses mainly on the impact of diffusion via popular culture, such as television programs, movies, and music, on tourism (Connell, 2012; Kim et al, 2009). Some research has shown that the diffusion of Korean pop culture has a significant impact on tourists’ perception of and willingness to travel to Korea (Kim and Kang, 2007; Kim et al., 2008). Whang et al. (2016) have also found that the diffusion of Korean pop culture enhances the destination’s image and stimulates tourists’ willingness to travel. Hudson et al. (2011) analyzed the impact of cultural diffusion on tourists from a more micro perspective. They analyzed the influence of the film “The Motorcycle Diaries” on tourists and found that the film changed the viewers’ perceptions of South America, resulting in a large percentage of respondents expressing a desire to visit the countries in that region.

To sum up, although the impact of cultural diffusion on tourism has been studied to some extent, the linkage between the two and the potential mechanism behind this impact have not been fully discussed. More examples and deeper discussions need to be provided. Education is another important aspect of cultural diffusion. The Confucius Institute, supported by the Chinese central government, is a typical means of cultural diffusion through education. To our best knowledge, this is the first article focusing on the impact of the establishment of Confucius Institutes on Chinese inbound tourism from the procedural perspective of cultural diffusion.

We identified the mechanisms of how Confucius Institutes foster Chinese inbound tourism and employed a dynamic demand model to estimate the effect of cultural diffusion using the Confucius Institute’s data from 2005 to 2015 for the 59 markets in China. Meanwhile, the impact of cultural distance on the effectiveness of Confucius Institutes is developed, and the Confucius Institute’s heterogeneity effects are estimated. It is shown that Confucius Institutes are able to robustly increase the Chinese international tourism flows even when the endogeneity of the Confucius Institute is considered. Meanwhile, the impact of the Confucius Institute on China’s inbound tourism has a lagged effect and regional heterogeneity. In addition, an inverted U-shaped relationship between the Confucius Institute’s effectiveness and cultural distance is created when the mediating effect of cultural distance is introduced.

This article makes contributions in three aspects. First, cultural diffusion opens up a new window for the study of cultural tourism and has meaningful policy implications. Second, a new index of cultural diffusion called the Confucius Institute is put forward, and the difference between cultural diffusion and cultural distance is summarized. Third, the lagged effect, the regional heterogeneity, and the mediating effect of cultural diffusion are demonstrated empirically in the robustness checks.

The rest of the article is organized as follows: The second section introduces the Confucius Institute and proposes the hypothesis. In the third section, a dynamic tourism demand model for international arrivals is developed. The fourth section reports and discusses the results. The fifth section concludes the article.

Effects of Confucius Institutes on Chinese inbound tourism

The Confucius Institute

Cultural diffusion plays a vital role in enhancing the subtle power of a nation and shaping a positive national image. Since its reform and opening up, the Chinese government has adhered to the basic policy of cultural opening. The Confucius Institute has been an important way for Chinese culture to spread abroad at the beginning of the 21st century. The Confucius Institute is owned by the Hanban and is hosted by a foreign partner organization. Like Germany’s Goethe-Institute, France’s Alliance Française, and the United Kingdom’s British Council, the Confucius Institute is a nonprofit educational organization. It aims to enhance other countries’ understanding of the Chinese language and culture. We have two reasons to set the Confucius Institute as the specific index of cultural diffusion. First, the Confucius Institute is the core carrier of Chinese cultural diffusion. The construction of Confucius Institutes worldwide is emphasized by both the central and local Chinese governments. Official governmental leaders are welcomed to attend the ceremonies and events of the Confucius Institute. Second, Confucius Institutes are widely distributed. The first Confucius Institute was established in South Korea in November of 2004. By 2016, 512 Confucius Institutes had been established in 140 countries. Figure 1 presents the number of Confucius Institutes and the host countries in which they are present. It is evident that the number of Confucius Institutes and the host countries has grown rapidly from 2006 to 2016. Figure 2 presents the number of registered students at Confucius Institutes. This number has also risen greatly over several years.

Figure 1.

The number of Confucius Institutes and host countries (2006–2016).

Figure 2.

The number of registered students (2006–2016).

Hypotheses

The following aspects that contribute to the impact of the Confucius Institute on Chinese inbound tourism flows are summarized as follows. First, Confucius Institutes can increase the number of business travelers. Confucius Institutes can promote the Chinese language and cultural familiarity, which helps to reduce the transaction and information cost of investment and can contribute to bilateral trade (Akhtaruzzaman et al., 2017). Second, Confucius Institutes should be helpful in increasing the number of foreign students in China by enhancing their interest in learning about China, which is shown in Figure 2. Third, Confucius Institutes could increase the number of tourists for sightseeing. Experiencing an exotic culture has become an important motivation to travel. For example, Shoemaker (1989) found that museums and historical sites are the key destinations for elderly tourists. Antolović (1997) also found that 70% of British tourists went to the United States to visit cultural heritage sites. As a result, we propose hypothesis 1:

H1: The Confucius Institute contributes to increasing Chinese inbound tourism demand.

Apart from the direct mechanism of Confucius Institutes, the indirect impact of cultural distance could also be explored. Cultural distance is a concept measuring the extent to which consumers’ origin cultures are different from or similar to the culture of the host (Ahn and Mckercher, 2015; Shenkar, 2012).

In some ways, cultural distance can help enhance the impact of the Confucius Institute on tourism demand. According to the travel career ladder theory, it is commonly accepted that novelty seeking is an important driver for travel (Crompton, 1979; Pearce and Lee, 2005). Tourism is always accompanied by the purpose of experiencing new things (Lee and Crompton, 1992; Yavas, 1990). A familiar environment may become an inhibitor to traveling. One study found that people were more likely to intend to visit a novel destination (Basala and Klenosky, 2001). Empirical evidence has shown that foreign tourists’ purpose in visiting Hong Kong is to experience its unique culture (Murphy et al., 2007). Ng et al. (2009) also found that novelty seeking was an important positive influence for tourists’ intention to visit New Zealand. Tourists from culturally distant places are more likely to be motivated by the culture of the destination (Mckercher and Cros, 2010). Hence, in the process of becoming familiar with Chinese culture through the Confucius Institute, people with different cultural backgrounds may become more willing to travel to China. Cultural distance enhances the impact of the Confucius Institute on tourism demand.

Alternatively, cultural distance suggests risk and uncertainty and may become an inhibitor of tourism demand. Although novelty encourages people’s travel, some degree of familiarity with the destination is necessary. Excessive unfamiliarity will increase tourists’ difficulty of communication, anxiety, and emotional discomfort (Chen et al., 2011; Reisinger and Turner, 2014; Ye et al., 2013). Some research has found that cultural distance will negatively affect tourists’ service satisfaction (Leung et al., 2013). Cultural unfamiliarity is not conducive to communication among tourists. The present study also provides more direct evidence for the relationship between cultural distance and tourism demand. Using rigorous econometric settings, Yang and Wong (2012) and Yang et al. (2016) found that tourism demand is negatively correlated with cultural distance. Cultural distance may also weaken the impact of the Confucius Institute on tourism demand.

As a result, cultural distance may be a double-edged sword for tourism demand, and findings suggest that cultural distance may have a nonlinear relationship with tourism demand. Based on the latest finding of the inverted U-curve (Bi and Lehto, 2018), the mediating effect of culture distance is further tested in this study.

H2: As cultural distance increases, the impact of the Confucius Institute on tourism demand first rises and then decreases.

Empirical strategy

Model

Tourism demand is determined by various variables, including its lagged item, which is called the dynamic effect in the literature (Dogru and Sirakaya-Turk, 2016; Gallego et al., 2018; Garín-Muñoz and Montero-Martín, 2007; Ghaderi et al., 2017; Seetaram, 2010). Thus, this article employs a dynamic panel to model tourism demand. The model is able not only to portray the dynamic effect of tourists’ behavior but also to avoid missing variables as much as possible by including the lag term of the explanatory variable. We specify the dynamic panel model as follows

ln {arr}_{i t} = λ ln {arr}_{i t - 1} + α_{1} ln {ci}_{i t} + α_{i} {cv}_{i t} + u_{i} + v_{t} + ε_{i t}

where ln is the logarithmic transformations, ${arr}_{i t}$ is the number of inbound tourists from the origin country i at time t, ${arr}_{i t - 1}$ is the 1-year lag term of inbound tourism demand, ${ci}_{i t}$ denotes the number of Confucius Institutes in the origin country i at time t, and ${cv}_{i t}$ is the vector of control variables. $u_{i}, v_{t}, and ε_{i t}$ represent the individual effect, time effect, and noise, respectively. Since the variables in the model are logarithmic, coefficients represent the short-term elasticity. The impact of cultural diffusion on inbound tourism demand in China can be judged by the significance of ${ci}_{i t}$ . In the long run, there is a stable relationship between the variables and inbound tourism demand. This relationship leads to $ln {arr}_{i t} = ln {arr}_{i t - 1}$ . Therefore, the long-term elasticity between these variables and tourism demand can be obtained by equation (2)

α_{i} * = \frac{α_{i}}{1 - λ}

With reference to the previous research, we control the following variables. The previous research mainly includes the characteristics of the origin country and the destination. For characteristics of the origin, we choose GDP per capita (pgdp) to capture the income level of the origin country and tourism price (rp) to reflect the destination’s cost of living. Measurement of the cost of living is controversial in the literature. Since it is impossible to obtain the real cost of living for tourists, early research used the real exchange rate (REX) (Song et al., 2000; Song et al., 2003). Some recent research has shown that the tourism price represented by the REX may have the effect of multicollinearity with income (Dwyer et al., 2000). The price competitiveness index (PCI), based on purchasing power parity theory, may be a better substitute (Seetaram et al., 2016). Therefore, we use the PCI to index the tourism price. The price level $p_{i t}$ of the country i at time t could be calculated using the unadjusted $GDP$ and adjusted ${GDP}^{a d}$ based on purchasing power parity.

p_{i t} = \frac{{GDP}_{i t}}{{GDP}_{i t}^{a d}} \times \frac{1}{{ex}_{i t}}

where ex represents the exchange rate between countries and the United States. If the price level of China in the period is assumed to be $p_{c t}$ , the tourism price ${rp}_{i t}$ can be expressed as

{rp}_{i t} = \frac{p_{c t}}{p_{i t}}

The higher the price level relative to the origin, the higher the travel price. The expected sign is negative. To capture the substitution effect of other countries’ inbound tourism markets for China, the substitute price (sp) is introduced. Thailand, Korea, and Singapore are generally chosen as the substitutes for China’s tourism market (Song et al., 2010). The substitute price can be expressed as follows, according to Dogru et al. (2017)

{sp}_{i t} = \frac{p_{t t} + p_{k t} + p_{s t}}{3 p_{i t}}

where $p_{t t}$ , $p_{k t}$ , and $p_{s t}$ are the price level of Thailand, South Korea, and Singapore, respectively. Because Thailand and Singapore are the integrated origin in our sample, only two other countries are used as alternative destinations. The expected sign is positive.

In addition to the economic factors, noneconomic factors of the country of origin such as age structure (as), education level (edu), internet development level (int), and population (pop) are considered (Ramos and Rodrigues, 2013). Age structure is measured by the ratio of population aged 65 and over to total population. Generally, the elderly population will reduce inbound tourism demand due to their health conditions and safety considerations. The expected sign is negative. Internet development level can increase tourism demand by reducing the asymmetry between the origin and destination. It is measured by the ratio of internet users to the total population. The expected sign is positive. Education level is measured by the ratio of the number of students in universities (The International Standard Classification of Education [ISCED] 5 and 6) to the total number after middle school for a period of 5 years. Well-educated people are considered to have stronger traveling motivation and ability. The expected sign is positive. The population of origin country is expected to have a positive impact on inbound tourism to China.

As for characteristics of the destination, in accordance with the existing literature (Ghaderi et al., 2017; Yang et al., 2018), we control for China’s trade openness (ctrade), security (ccrime), tourism attraction (cwhs), and tourism infrastructure (croom). Trade openness is measured by the ratio of the total international trade (export and import) to GDP. Trade can promote the number of business trips to a country and is expected to have a positive impact on China’s tourism flows. For China’s tourism security, Yang et al. (2018) use times of armed conflict as a measure. In our sample, there is almost no armed conflict. We use international crime rate as the alternative (Tang and Tan, 2015). The expected sign is negative. Tourism attraction is measured by the number of United Nations Educational, Scientific and Cultural Organization (UNESCO) world heritage sites in China (Su and Lin, 2014; Yang et al., 2018). Its coefficient is expected to be positive. Tourism infrastructure is measured by the number of accommodation rooms (Ghaderi et al., 2017).

To test H1, the model is set as follows

\begin{array}{l} ln {arr}_{i t} = λ ln {arr}_{i t - 1} + a_{1} ln {ci}_{i t} + a_{2} ln {pgdp}_{i t} + a_{3} ln {rp}_{i t} + a_{4} ln {sp}_{i t} + a_{5} ln {int}_{i t} \\ + a_{6} ln {as}_{i t} + a_{7} ln {edu}_{i t} + α_{8} ln {pop}_{i t} + α_{9} ln {ctrade}_{t} + α_{10} ln {cwhs}_{t} \\ + α_{11} ln {ccrime}_{t} + α_{12} ln {croom}_{t} + u_{i} + v_{t} + ε_{i t} \end{array}

To verify H2, the cultural distance is first measured. In the literature, there are many methods used to measure national cultural distance. The most widely cited measures are the World Values Survey (Inglehart and Baker, 2000; Thyne et al., 2006), ratings of perceived cultural distance (Liu et al., 2018; Meschi, 1997; Rao and Schmidt, 1998), linguistic distance (West and Graham, 2004), cultural clusters (Ronen and Shenkar, 1985), and frameworks proposed by Hofstede (1980) and Schwartz (1994). Ng et al. (2007a) and Yang et al. (2018) have summarized these measures and found that they are related but not congruent. The cultural distance index needs to be carefully chosen depending on the field. For example, Schwartz’s cultural values may play a more significant role in the research of international trade (Ng et al., 2007b). This article measures cultural distance using the theory proposed by Hofstede (1980). There are two reasons for this choice. First, the method is the most popular and widely used (Kirkman et al., 2006; Reisinger and Crotts, 2010; Soares et al., 2007). Second, many studies have proved that the cultural distance measured by this theory has a significant impact on tourists’ behavior (Ahn and Mckercher, 2015; Bi and Lehto, 2018). Hence, in the research of tourism demand, this measure is common and appropriate. Hofstede’s cultural distance is constituted by six dimensions, including power distance, uncertainty avoidance, individualism and collectivism, masculinity and femininity, long-term and short-term orientation, and self-indulgence and restraint. The KSI method is then employed to calculate the index between countries (Kogut and Singh, 1988)

{cd}_{i j} = \frac{\sum_{n = 1}^{6} {(I_{i n} - I_{j n})}^{2} / V_{n}}{6}

where ${cd}_{i j}$ is the cultural distance between country i and country j, $I_{n i}$ denotes the score of the nth cultural dimension of country I, $I_{n j}$ denotes the score of the nth cultural dimension of country j, and V_n is the variance of nth cultural dimension of all countries.

We can obtain equation (8) by adding the cross item of cultural diffusion and cultural distance (cd) to equation (6)

\begin{array}{l} ln {arr}_{i t} = η ln {arr}_{i t - 1} + β_{1} ln {ci}_{i t} + β_{2} (ln {ci}_{i t} \times ln {cd}_{i t}) + β_{3} (ln {ci}_{i t} \times {ln}^{2} {cd}_{i t}) \\ + β_{4} ln {pgdp}_{i t} + β_{5} ln {rp}_{i t} + β_{6} ln {sp}_{i t} + β_{7} ln {int}_{i t} + β_{8} ln {as}_{i t} + β_{9} ln {edu}_{i t} \\ + β_{10} ln {pop}_{i t} + β_{11} ln {cwhs}_{t} + β_{12} ln {ctrade}_{t} + β_{13} ln {ccrime}_{t} \\ + β_{14} ln {croom}_{t} + u_{i} + v_{t} + ε_{i t} \end{array}

where lncd, ${ln}^{2} cd$ represent the log form of the cultural distance and its square, respectively. In equation (8), if β₂ is significantly positive, while β₃ is significantly negative, H2 can be validated. Moreover, through the derivation, we can obtain the elasticity of cultural diffusion to inbound tourism demand $θ_{ci}$ .

θ_{ci} = \frac{darr / arr}{dci / ci} = \frac{d ln arr}{d ln ci} = β_{1} + β_{2} lncd + β_{3} {ln}^{2} cd

From equation (9), we can determine that the impact of cultural diffusion on inbound tourism will be affected by cultural distance. The optimal value of cultural distance (lncd*) and the maximum value of elasticity ( $θ_{ci}^{max}$ ) can be obtained

lncd * = \frac{β_{2}}{- 2 β_{3}}

θ_{ci}^{max} = β_{1} - \frac{{β_{2}}^{2}}{4 β_{3}}

Estimation technology

Since the model is a dynamic panel with a lagged explained variable, it does not satisfy the classical econometric hypothesis that the explanatory variables are not linked to the noise. If the fixed-effect estimation is chosen, the dynamic panel error will occur. Due to the characteristics of the data, with a wide cross-section and short time period, this article considers the difference generalized methods of moments (GMM) and the system GMM. It is generally believed that the system GMM is more efficient because it combines difference equations and horizontal equations. However, the system GMM must satisfy more strict assumptions, so the difference GMM is chosen for this study.¹ In addition, the one-step method is chosen because the standard error of the two-step method is lower, and the coefficient significance is biased.

In regard to the difference GMM, two tests must be conducted. The first is the overidentification test. Its purpose is to ensure that all the instruments are valid. The second is the autocorrelation test. This test requires that the first-order difference of the noise term be autocorrelated and that the autocorrelation of the two-order difference does not exist (Arellano and Bond, 1991).

Data

International inbound tourism to China plays an essential role in the world tourism market. According to the World Tourism Organization, China will become the world’s largest international tourism-receiving country by 2020. This article selected 59 origin countries of the Chinese inbound tourism market along the One Belt and One Road network, which is also the subject of an initiative proposed by Xi Jinping, the President of China.² The Chinese government has placed great expectations on inbound tourism along the One Belt and One Road and has suggested that the regions in the network take responsibility to promote regional tourism cooperation and attract business investment. This regional network is also the area with the highest concentration of Confucius Institutes. Because the first Confucius Institute was established in 2005 in the selected country, the time period of our sample is 2005–2015. In addition, Hofstede’s official website provides cultural dimensions for only 32 countries along the One Belt and One Road, so 32 countries are considered when testing hypothesis 2.

The number of inbound tourists, China’s trade openness, and the number of accommodation rooms are collected from the “China Foreign Economic Statistical Yearbook.” The number of world heritage sites in China is collected from the official UNESCO website.³ The number of Confucius Institutes is collected from the official website of the Confucius Institute.⁴ The data regarding cultural distance are collected from Hofstede’s official website.⁵ Other data are collected from the world development indicators of the World Bank⁶. The statistical description of all the variables is shown in Table 1.

Table 1.

Statistical description of variables.

Variable	Description	N	Mean	SD	Min	Max
arr	International arrivals	649	17.41	40.78	0.026	310.00
ci	Confucius Institute	649	1.78	3.67	0.00	21.00
cd	Cultural distance	352	2.29	1.10	0.68	5.46
rp	Relative price	649	1.15	0.40	0.38	2.12
sp	Substitute price	649	1.41	0.51	0.52	3.22
pgdp	GDP per capita	649	11,418.89	13,655.13	389.42	72,670.96
int	Internet	649	35.81	25.14	0.24	93.48
as	Age structure	649	6.88	4.97	0.70	21.40
edu	Education level	649	39.01	24.99	2.6	113.87
Pop	Population	649	5E+07	1.6E + 08	321,000	1.3E + 09
ctrade	Chinese trade openness	649	50.8773	8.76215	40	64.77
ccrime	Chinese international crime rate	649	1.01818	0.28885	0.6	1.6
cwhs	Chinese world heritage sites	649	41.7273	5.51607	32	50
croom	Number of accommodation rooms in China	649	2,500,018	543,325	1,900,000	3,372,000

GDP: gross domestic product; SD: standard deviation.

Empirical results

The impact of the Confucius Institute on inbound tourism demand

The results of the impact of the Confucius Institute on inbound tourism demand are shown in Table 2. Because the Confucius Institute and tourism demand may interact with each other, we will regard the Confucius Institute as an endogenous variable in regression. In the difference GMM regression, the lagged term of endogenous variables is always used as a valid instrumental variable in dealing with its endogeneity. As a result, the lagged term of the Confucius Institute is chosen as the instrumental variable to eliminate the endogeneity.

Table 2.

The impact of the Confucius Institute on Chinese inbound tourism.

	CI: exogenous			CI: endogenous
	(1)	(2)	(3)	(4)	(5)	(6)
l.lnarr	0.524 (0.040)***	0.337 (0.059)***	0.271 (0.018)***	0.525 (0.038)***	0.370 (0.053)***	0.304 (0.034)***
ln ci	0.125 (0.023)***	0.077 (0.024)***	0.058 (0.007)***	0.126 (0.022)***	0.094 (0.023)***	0.063 (0.013)***
ln pgdp		0.796 (0.118)***	0.664 (0.034)***		0.666 (0.110)***	0.651 (0.078)***
ln rp		−0.364 (0.102)***	−0.152 (0.054)***		−0.430 (0.097)***	−0.191 (0.046)***
ln sp		0.410 (0.097)***	0.171 (0.048)***		0.455 (0.094)***	0.198 (0.060)***
ln int		0.043 (0.027)	0.049 (0.008)***		0.043 (0.027)	0.045 (0.010)***
ln as		−0.501 (0.153)***	−0.583 (0.093)***		−0.414 (0.149)***	−0.425 (0.262)
ln edu		0.133 (0.052)**	0.143 (0.021)***		0.189 (0.049)***	0.201 (0.023)***
ln pop		0.668 (0.144)***	0.669 (0.096)***		0.609 (0.139)***	0.820 (0.199)***
ln ctrade			0.283 (0.026)***			0.278 (0.029)***
ln cwhs			1.335 (0.304)***			−0.686 (1.025)
ln ccrime			−0.130 (0.025)***			−0.102 (0.042)**
ln croom			0.038 (0.012)***			0.033 (0.015)**
_cons	4.806 (0.396)***	−10.799 (2.567)***	−15.440 (1.827)***	4.794 (0.376)***	−9.399 (2.505)***	−11.015 (5.132)**
AR (1)	0.000	0.000	0.001	0.000	0.000	0.000
AR (2)	0.068	0.322	0.316	0.687	0.367	0.232
Sargan (p)	0.075	0.101	0.158	0.995	0.998	1.000
N	649	649	649	649	649	649

Note: CI: Confucius Institute; robust standard errors are reported in parentheses; the Sargan value is used to check whether the instrument variables are over-identified; l.lnarr means the lag term of lnarr.

*p < 0.1; **p < 0.05; ***p < 0.01.

To improve robustness of the results, the model is estimated by gradually adding control variables. Models (1) to (3) and (4) to (6) are the results of adding different control variables. First, the Sargan value in all models is larger than 0.05, which means that instrument variables are valid at the 95% significance level. The problem of weak instrument variables does not exist. The autocorrelation test demonstrates that the noise has first-order autocorrelation and no second-order autocorrelation, which is in accordance with the hypothesis of the difference GMM. The estimation technology is appropriate.

Both the exogenous and endogenous cases of the Confucius Institute are considered; the Confucius Institute has a robust and significantly positive impact on China’s inbound tourism, and the coefficients in the endogenous case are slightly larger. In the baseline model (6) with an endogenous setting, the lagged explained variable is significant, which means that the dynamic effect of China’s inbound tourism empirically exists. Meanwhile, cultural diffusion, represented by the number of Confucius Institutes, has a significant positive impact on inbound tourism demand. The short-term elasticity of cultural diffusion to inbound tourism demand is 0.063. Based on equation (2), the long-term elasticity of the cultural diffusion is 0.091. Hypothesis 1 is confirmed.

As for the other controlled variables, results consistent with the previous literature are found. Income and tourism price are the most important factors influencing inbound tourism demand. In the long run, the elasticity of income is 0.94, which means that China’s inbound tourism is not a luxury. Tourism price negatively influences tourism demand, and the substitution price has a positive influence. Internet development, age structure, education, and population of origin countries are also significant determinants. Comparatively, age structure and population share greater impacts than internet development and education.

From the perspective of destination, China’s trade openness has a positive impact on inbound tourism. Bilateral trade can strengthen economic intercourse and enhance the familiarity among countries. The coefficient of international crime rate is negative. An unsafe tourism environment is not preferred by tourists. The improvement of tourism infrastructure has a positive impact, but the impact of world heritage sites on China’s inbound tourism demand is unstable, primarily due to the poor tourism attraction indicator (Ribaudo and Figini, 2016).

The lagged effect and country heterogeneity of the Confucius Institute

So far, we have confirmed the positive effect of the Confucius Institute on tourism demand in China. As a more in-depth analysis, we probe the lagged effect and country heterogeneity of the Confucius Institute. To analyze the differences of the Confucius Institute’s effect in the different regions, the continent rule is adopted. In our sample, the countries along the One Belt and One Road are mainly located in Asia, Africa, and Europe. Most of them are in Asia and Europe. According to the regression model (12), we can obtain the impact of the Confucius Institute on different regional clusters including Asia, Africa, and Europe.

\begin{array}{l} ln {arr}_{i t} = λ ln {arr}_{i t - 1} + a_{1} ln {ci}_{i t} + α_{2} lnci \times region + a_{3} ln {pgdp}_{i t} + a_{4} ln {rp}_{i t} + a_{5} ln {sp}_{i t} \\ + a_{6} ln {int}_{i t} + a_{7} ln {as}_{i t} + a_{8} ln {edu}_{i t} + α_{9} ln {pop}_{i t} + α_{10} ln {ctrade}_{t} \\ + α_{11} ln {cwhs}_{t} + α_{12} ln {ccrime}_{t} + α_{13} ln {croom}_{t} + u_{i} + v_{t} + ε_{i t} \end{array}

In equation (12), region is a dummy variable for Asia, Europe, and Africa. Taking Asia as an example, when the origin country is located in Asia, the variable is 1, otherwise it is 0. Models (1) to (3) in Table 3 present the lagged effect of Confucius Institutes on China’s inbound tourism. The impacts of one, two, and three lagged Confucius Institutes on tourism demand are provided. The results show that one and two lagged Confucius Institutes have positive impacts on inbound tourism demand, but the three lagged variable is insignificant. Models (4) to (6) present the regional heterogeneity of Confucius Institutes. The impact of Confucius Institutes in European countries is greater than in Asia. The coefficient in European countries is 0.122 (0.101 + 0.021) and is 0.024 (0.116 − 0.092) in Asia. This difference is likely because the Asian countries are closer to China and are thus more familiar with China’s culture. In addition, lnci × Africa is not significant, which means that there is no obvious difference between African countries and others. This result could be due to the small sample size of African countries.

Table 3.

The lagged effect and regional heterogeneity of Confucius Institutes.

	(1)	(2)	(3)	(4)	(5)	(6)
l. ln arr	0.278 (0.017)***	0.241 (0.013)***	0.085 (0.024)***	0.257 (0.037)***	0.246 (0.030)***	0.308 (0.034)***
l. ln ci	0.030 (0.009)***
l2. ln ci		0.021 (0.009)**
l3. ln ci			−0.013 (0.008)
ln ci × Asia				−0.092 (0.021)***
ln ci × Europe					0.101 (0.035)***
ln ci × Africa						−0.035 (0.062)
ln ci				0.116 (0.025)***	0.021 (0.013)*	0.065 (0.013)***
ln pgdp	0.644 (0.036)***	0.739 (0.050)***	0.848 (0.038)***	0.673 (0.079)***	0.612 (0.045)***	0.619 (0.073)***
ln rp	−0.078 (0.041)*	−0.172 (0.038)***	0.615 (0.086)***	−0.169 (0.035)***	−0.184 (0.043)***	−0.190 (0.057)***
ln sp	0.109 (0.035)***	0.305 (0.035)***	−0.559 (0.090)***	0.124 (0.059)**	0.125 (0.042)***	0.180 (0.080)**
ln int	0.048 (0.009)***	0.084 (0.012)***	0.107 (0.013)***	0.047 (0.012)***	0.047 (0.013)***	0.048 (0.016)***
ln as	−0.564 (0.092)***	−0.394 (0.128)***	−0.032 (0.165)	−0.375 (0.240)	−0.611 (0.208)***	−0.408 (0.422)
ln edu	0.097 (0.011)***	0.102 (0.016)***	0.137 (0.027)***	0.162 (0.025)***	0.132 (0.021)***	0.143 (0.021)***
ln pop	0.625 (0.091)***	0.390 (0.089)***	0.368 (0.112)***	1.123 (0.283)***	0.945 (0.199)***	0.748 (0.287)***
ln ccrime	−0.172 (0.028)***	−0.006 (0.054)	0.066 (0.061)	−0.100 (0.041)**	−0.193 (0.048)***	−0.136 (0.044)***
ln ctrade	0.293 (0.022)***	0.232 (0.021)***	0.334 (0.024)***	0.293 (0.035)***	0.314 (0.041)***	0.290 (0.033)***
ln cwhs	1.152 (0.375)***	1.803 (0.447)***	2.011 (0.494)***	−2.135 (1.553)	0.125 (0.892)	0.145 (1.367)
ln croom	0.029 (0.009)***	0.146 (0.017)***	0.110 (0.017)***	0.027 (0.013)**	0.008 (0.014)	0.020 (0.016)
_cons	−13.720 (1.476)***	−14.750 (1.921)***	−15.066 (2.666)***	−10.173 (4.963)**	−14.359 (5.004)***	−12.452 (7.543)*
AR (1)	0.002	0.002	0.007	0.002	0.001	0.002
AR (2)	0.249	0.112	0.587	0.443	0.414	0.341
Sargan (p)	0.142	0.123	0.115	0.999	0.975	0.999
N	649	649	649	649	649	649

Note: robust standard errors are reported in parentheses; l. ln ci, l2. ln ci and l3. ln ci represent one, two, and three lagged Confucius Institutes, respectively; lnci is an endogenous variable.

*p < 0.1; **p < 0.05; ***p < 0.01.

The impact of cultural distance on the Confucius Institute’s effectiveness

Using the KSI index, we measured the cultural distance between 32 countries and China. The results are shown in Table 4.

Table 4.

Cultural distance between sample countries and china.

Country	Cultural distance	Country	Cultural distance
Indonesia	0.678	Lebanon	2.291
Nepal	0.711	Thailand	2.362
Vietnam	0.744	Ukraine	2.538
Singapore	0.778	Estonia	2.554
Albania	0.973	Turkey	2.561
India	1.010	Sri Lanka	2.578
Slovakia	1.461	Jordan	2.791
UAE	1.518	Lithuania	2.976
Bulgaria	1.576	Iran	3.088
Croatia	1.615	Slovenia	3.097
Philippines	1.784	Greece	3.168
Pakistan	1.890	Poland	3.234
Czech	1.899	Egypt	3.695
Malaysia	1.934	Latvia	3.983
Romania	2.013	Hungary	4.093
Russia	2.119	Israel	5.456

As seen in Table 4, Indonesia has the smallest cultural distance from China, and Israel has the largest. In fact, the countries that belong to the Confucian culture cluster have a smaller cultural distance from China, which is consistent with intuitive judgment.

Table 5 shows the estimated results of the impact of cultural distance on the Confucius Institute’s effectiveness, which means the inbound tourism demand induced by the spread of Confucius Institutes. We guarantee the robustness of the results in the same way by gradually adding control variables. The Sargan values and autocorrelation test show that all models have passed the test under the 95% significance level. According to estimation results, the coefficient of ln ci × ln cd is positive, and the coefficient of ln ci × ln² cd is negative, which confirms hypothesis 2. In other words, as cultural distance increases, the impact of the Confucius Institute on tourism demand first rises and then decreases. This result means that cultural distance is indeed a double-edged sword for tourists and indicates that cultural novelty is preferred in the beginning but that cultural distance also has an optimal level.

Table 5.

The impact of cultural distance on the Confucius Institute’s effectiveness.

	(1)	(2)	(3)	(4)
l. ln arr	0.559 (0.028)***	0.532 (0.031)***	0.371 (0.040)***	0.329 (0.053)***
ln ci	0.116 (0.013)***	0.108 (0.022)***	0.063 (0.017)***	0.045 (0.013)***
ln ci × ln cd	0.088 (0.035)**	0.117 (0.037)***	0.111 (0.036)***	0.085 (0.037)**
ln ci × ln² cd	−0.127 (0.046)***	−0.135 (0.061)**	−0.097 (0.041)**	−0.077 (0.045)*
ln pgdp		0.651 (0.079)***	0.945 (0.182)***	0.260 (0.114)**
ln rp		−0.245 (0.038)***	−0.530 (0.039)***	−0.535 (0.074)***
ln sp		0.372 (0.031)***	0.655 (0.054)***	0.528 (0.086)***
ln int			0.083 (0.017)***	0.047 (0.018)***
ln as			−1.331 (0.406)***	−0.102 (0.439)
ln edu			0.181 (0.043)***	0.090 (0.033)***
ln pop			1.744 (0.384)***	0.859 (0.168)***
ln ccrime				−0.291 (0.065)***
ln ctrade				0.511 (0.054)***
ln cwhs				1.360 (0.200)***
ln croom				0.509 (0.076)***
_cons	4.759 (0.314)***	−0.809 (0.699)	−29.418 (7.080)***	−9.480 (2.905)***
AR (1)	0.000	0.000	0.000	0.005
AR (2)	0.023	0.193	0.782	0.727
Sargan (p)	1.000	0.204	0.716	0.448
N	352	352	352	352

Note: Robust standard errors are reported in parentheses; lnci is an endogenous variable.

*p < 0.1; **p < 0.05; ***p < 0.01.

Regarding model (4) as the reference, the optimal cultural distance is 1.73 ( $e^{0.085 / 2 \times 0.077}$ ) according to equation (6). In our sample, the countries that correspond approximately to this specific cultural distance are Bulgaria and the Philippines. Once the cultural distance between the origin country and China is higher than 1.73, the impact of the Confucius Institute on inbound tourism will diminish. Figure 3 shows the optimal level of cultural distance.

Figure 3.

The optimal cultural distance with nested Confucius Institutes.

Conclusions and discussion

Key findings

Cultural diffusion can potentially influence tourism flows, but it has been neglected in the existing literature. We aim to address the missing gap with a concrete example using the Confucius Institute. Both the direct and indirect mechanisms of the Confucius Institute are studied, and a dynamic model is introduced to explore its impact on the inbound tourism to China.

First, we found that the establishment of Confucius Institutes positively and significantly promoted China’s inbound tourism demand. The effect is still significant after dealing with the endogeneity of Confucius Institutes. This finding is a supplement to the other influencing factors of tourism demand. Although the economic factors still dominate China’s inbound tourism market, noneconomic factors, such as cultural diffusion, are becoming more significant and important.

Second, the lagged effect and regional heterogeneity of impact of the Confucius Institute on China’s inbound tourism demand empirically exist. The Confucius Institute has a lasting impact on tourists for 2 years. After 3 years, as people’s familiarity with the Confucius Institute grows, the effect of cultural diffusion on tourism demand will gradually disappear. Meanwhile, the impacts of Confucius Institutes on Asian countries and European countries are significantly different, and the Confucius Institute has a greater impact in Europe.

Third, we also found that the impact of the Confucius Institute on tourism demand was influenced by cultural distance. Our empirical results show that as the cultural distance between the country of origin and China increases, the impact of the Confucius Institute on international tourism flows first rises and then decreases. The effect of cultural distance on cultural diffusion’s effectiveness may be a double-edged sword. In our sample, the countries with the maximum elasticity of cultural diffusion are Bulgaria and the Philippines.

Policy implications

One the one hand, economic factors must be considered in improving tourism demand. As shown by the regression results, economic factors are still the most important determinants. Policymakers should make clear which countries should be the destinations and which should be the countries of origin. According to the long-term elasticity of income level, we should focus on strengthening tourism cooperation with developed countries. Because the per capita income of developed countries is relatively high, the demand for tourism is relatively strong. Additionally, considering the negative effect of tourism price, we should strengthen the construction of tourism infrastructure and improving tourism services. These are helpful in improving the cost efficiency of tourism. Finally, we should also establish the comparative advantages of tourism to China relative to comparable countries.

On the other hand, the government should pay close attention to the function of the noneconomic factors such as cultural diffusion. The Confucius Institute plays an important role in increasing inbound tourism demand, although the induced inbound tourism demand might be limited. The Chinese government should actively promote the establishment of Confucius Institutes in European countries. The quantity of cultural diffusion is the focus of the first step; for the second, the quality is much more important and is urgent.

Limitations and future research

First, the study mainly considers the impact of the Confucius Institute and more indexes of cultural diffusion remain to be explored. Second, only the direct and indirect impacts of the Confucius Institute are tested, while deeper impacts and other potential mechanisms of cultural diffusion are worth discussing. Third, because different travel motivations may have a moderating effect on the relationship between tourism demand and cultural diffusion (Liu et al., 2018), micro survey data are necessary. Last but not the least, all the findings and policy implications are limited to the selected sample of the countries along the “One Belt and One Road,” and whether these findings and implications are appropriate for the United States and other developed countries is left for further studies. For example, several universities have begun closing Confucius Institutes since 2014, and more universities have become involved in these closing events, especially in 2018. Combined with the United States–China trade war, whether the presence of no or fewer Confucius Institutes could lead to decreasing the tourism demand is another interesting research topic. If the counter argument is true, the positive and linear relationship between cultural diffusion and inbound tourism demand should be rectified or mediated by more sophisticated variables. Meanwhile, more sophisticated methods, such as difference-in-difference, are suggested to be adopted to robustly and accurately estimate the impacts of cultural diffusion.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was financially supported by National Social Science Foundation of China (14CJY058), Zhejiang Provincial Natural Science Foundation of China (LQ14G020017), National Natural Science Foundation of China (71603140), Humanities and Social Sciences Key Research Base of Zhejiang Province (Applied Economics) (2015GJHZ04).

Notes

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