Tourism Demand Spillovers between Australia and

569778

research-article2015

JTRXXX10.1177/0047287515569778Journal of Travel ResearchBalli and Kan

Empirical Research Articles

Tourism Demand Spillovers between Australia and New Zealand: Evidence from the Partner Countries

Journal of Travel Research 1–9 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0047287515569778 jtr.sagepub.com

Faruk Balli1,2 and Wai Hong Kan Tsui3

Abstract International tourism is susceptible to fluctuations and shocks. The spillovers of international inbound tourism between Australia and New Zealand have been one of the key issues for both governments and tourism authorities to address. This paper used a bivariate GARCH model to investigate the spillovers of international tourist arrivals between Australia and New Zealand from seven countries (Canada, China, Germany, Japan, Korea, United Kingdom, and United States). The monthly international tourist arrivals between 2000 and 2012 were used for the empirical analysis. The findings suggested a significant spillover of Chinese and Japanese tourists from New Zealand to Australia, whereas New Zealand’s tourism demand from China and Japan was not significantly affected by that of Australia. However, New Zealand’s inbound tourism from Canada, Germany, and United States was significantly affected by tourism demand from those countries to Australia. Furthermore, symmetric spillovers between Australia and New Zealand (in both directions) existed for UK tourists. Keywords Australia, bivariate GARCH, New Zealand, spillovers, tourism demand

Introduction International tourism demand is often measured in terms of the number of tourist arrivals from an origin country to a foreign destination (Lim and McAleer 1999). According to the United Nations World Tourism Organization (UNWTO), international tourist arrivals worldwide grew from 438 million in 1990 to 534 million in 1995 and to 684 million in 2000, and are expected to reach more than 1 billion in 2012 (UNWTO 2012). Having such unprecedented growth in global tourism over the last two decades, the Australian and New Zealand tourism sectors have also grown markedly: tourist arrivals to Australia and New Zealand grew more than 124% and 143%, respectively, between 2000 and 2012. In addition, international inbound tourism is one of the main contributors towards to the economic development of a country or city (Dwyer et al. 2000; Dwyer and Forsyth 1993; Oh 2005). It is also considered as a major source of foreign exchange, tourism-related employment, and other tourismrelated activities for Australia and New Zealand. As of 2012, international tourism expenditures in Australia and New Zealand were approximately AU$26,962 million and NZ$9,565 million, respectively; the direct tourism value added towards gross domestic product (GDP) was 3.8% and 3.7%, respectively (Australian Bureau of Statistics 2000– 2012; Statistics New Zealand 2000–2012). As a result, a large volume of literature was found on international tourism demand for Australia and New Zealand in recent years (e.g.,

Chan, Lim, and McAleer 2005; Kulendran 1996; Lim and McAleer 1999, 2005, 2008; Seetaram 2012). The time series of international tourism demand, just like that of other macroeconomic indicators, does not always have a smooth pattern; hence, the data show frequent fluctuations. Policy makers are cautious about the fluctuations (volatility) of international tourism demand, as they are with other macroeconomic indications. Accordingly, the reasons behind the fluctuations in tourism demand have begun to be studied (e.g. Kulendran and Shan 2002; Lim and McAleer 1999, 2001, 2008; Song et al. 2003; Song and Wit 2006; Tsui et al. 2014). Similar to other countries or cities, Australia and New Zealand also experienced the volatile inbound tourist flows from their key tourism source markets. Several factors caused this volatility, such as the 9/11 terrorist attacks, the severe acute respiratory syndrome (SARS) outbreak, higher aviation fuel prices, and the Christchurch Earthquake (e.g. Becken 2008; Becken and Lennox 2012; Dwyer et al. 2006a, 2006b; Hall 2010; Narayan 2008; Prideaux and Witt 2000; 1

Massey University, Auckland, New Zealand. Department of International Trade and Marketing, University of Gediz, Izmir Turkey. 3 School of Aviation, Massey University, Palmerston North, New Zealand. 2

Corresponding Author: Faruk Balli, School of Economics and Finance, Massey University, Auckland, New Zealand. Email: [email protected]

Downloaded from jtr.sagepub.com at Massey University Library on March 1, 2015

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Journal of Travel Research

Yeoman et al. 2012). In addition, seasonal and other calendar-related patterns can also be considered to be determinants of Australia’s and New Zealand’s international tourism demand (e.g. Lim and McAleer 2001, 2002, 2008; Narayan 2008). Apart from the factors mentioned above, the spillover effects of tourist flows (tourist spillovers) between two countries or cities have been introduced to the tourism literature in order to explain variations in tourism demand. It is worthwhile noting that spillover effects (or conditional variance) were initially applied in the finance literature. For example, the concept of spillovers in the autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models were adopted by Engle (1982) and Bollerslev (1986) to analyze the mean and variance of inflation in the United Kingdom and the United States, respectively. Also, this concept was subsequently applied by Engle, Ito, and Lin (1990) to investigate the causes of volatility clustering in exchange rates. Furthermore, Lin, Engle, and Ito (1994) also used the GARCH approach to investigate the return and volatility spillover effects of stock indices between the stock markets in New York and Tokyo. Moreover, Grossman and Helpman (1993) suggested that technological spillovers transferred from one country to another may foster the economic development of both nations. Following the studies in other fields of research, conceptually, the spillover effects have been applied to analyze tourist spillovers in the tourism literature. For example, a pioneer study of Gooroochurn and Hanley (2005) applied spillover effects to examine whether long-haul tourist numbers, income from tourism operations of the Republic of Ireland, may influence its neighbor (Northern Ireland) and vice versa. Li and Huang (2008) also used the Mundell–Fleming model to analyze the GDP spillover model in order to measure the intercity tourism spillovers between cities in the Pearl River Delta (China). Moreover, Lazzeretti and Capone (2009) analyzed the key factors behind the dynamics of the local Italian tourist system by considering the existence of spatial spillovers among the Italian cities. Li and Wang (2009) studied the regional spillovers of the tourism industry in China’s Yangtze River Delta. More recently, Li et al. (2011) improved the measurement model of intercity tourism spillovers of Li and Wang (2009) for modeling the tourism demand of cities in the same region. Importantly, the explanatory power of the improved model of Li et al. is much stronger than the original model suggested by Li and Wang. Our contribution to the literature emerged from a thorough investigation of Australia’s and New Zealand’s international tourism demand. After investigating the volatility of their international tourism demand, the findings suggested that their respective international tourism demands were not just influenced by the commonly known and the specific regional factors mentioned earlier, but that the tourism demand of a country or city may be affected by that of its neighbors, that is, the spillover effect of tourist flows.

Therefore, this paper modeled the volatility of international tourism demand for Australia and New Zealand from seven countries, namely, Canada, China, Germany, Japan, South Korea, the United Kingdom, and the United States, and used a simultaneous spillover model to measure the direction of tourist spillovers. These seven countries comprised more than 45.39% and 37.50% of the total tourist arrivals to Australia and New Zealand, respectively. In addition, two primary reasons make our paper meaningful. First, for the strategic planning and decision-making processes of policy makers (e.g., national tourism authorities, airport authorities) and tourism operators (tourism managers and airline management), it is important to investigate how volatility (fluctuations) affects tourist arrivals. A clear understanding of the level of volatility of tourist arrivals to Australia and New Zealand can help them effectively allocate resources and/or design appropriate recovery activities to deal with this issue. Secondly, we aim to extend the study of Chan, Lim, and McAleer (2005), which used three different GARCH models to investigate the relationship between Australia’s international tourism demand from four countries (Japan, New Zealand, the United Kingdom, and the United States) and volatility. Given the close geographical proximity between Australia and New Zealand (about three-hour flight times across the Tasman Sea) and a more liberalized regional aviation regime allowing frequent flight services, the problem of tourist spillovers may become apparent (Duval and Schiff 2011). Knowledge about tourist spillovers will assist Australia and New Zealand to compete with each other strategically through the provision of better tourism-related services and facilities, as well as attractive promotions. The format of this article is structured as follows. The next section provides an overview of the international inbound tourism of Australia and New Zealand, and presents the data set for analysis. The third section describes the empirical model used to investigate tourism demand spillovers between Australia and New Zealand. The fourth section presents the empirical findings of the study. The final section summarizes the key findings.

Overview of International Inbound Tourism and Descriptive Statistics Seven tourism partner countries (Canada, China, Germany, Japan, Korea, the United Kingdom, and the United States) were the top seven tourist markets to Australia and New Zealand, and also comprised more than 45.39% and 37.50% of the total international tourism demand of Australia and New Zealand, respectively, between 2000 and 2012 (Australian Bureau of Statistics 2000–2012; Statistics New Zealand 2000–2012).1 Figure 1 shows the market share of Australian and New Zealand tourism for each of these countries, and their average growth rates. For Australia, the seven countries contributed differently to Australia’s tourism growth during the study period. On average, the largest


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40%

18%

2.5%

35%

15%

2.0%

12%

1.5%

9%

1.0%

6%

0.5%

3%

0.0%

5%

0%

-0.5%

0%

Share of the market

21%

Canada

China

Germany

Japan

Korea

UK

USA

Average monthly tourist arrivals to Australia Average growth rate

Share of the market

45%

3.0% Average growth rate

3.5%

24%

30% 25% 20% 15% 10%

Australia Canada China Germany Japan

Korea

UK

USA

6.3% 5.6% 4.9% 4.2% 3.5% 2.8% 2.1% 1.4% 0.7% 0.0% -0.7%

Average growth rate

New Zealand

Australia

Average monthly tourist arrivals to New Zealand Average growth rate

Figure 1. Average monthly tourist arrivals to Australia and New Zealand (2000–2012). Sources: Australian Bureau of Statistics; Statistics New Zealand.

volumes of tourist arrivals originated from the United Kingdom (12.09%), followed by Japan (10.31%) and the United States (8.43%), whereas Canada (1.98%) had the smallest share of Australia’s total tourist arrivals.2 Regarding the average growth rate, China led the group with a 3.26% increase,3 followed by the United Kingdom (0.67%), Canada (0.60%), and the United States (0.39%). The growth in tourist arrivals originating from these major tourism markets was mainly attributable to the fall in the cost of air travel and Australian tourism services provided. However, Japanese tourists to Australia showed negative growth (–0.22%), which has become a major concern for Australia’s tourism sector, as Japanese tourists have been the top tourism spenders in the Asia-Pacific region historically and the fourth largest spender on international travel after the Americans, Germans, and British (Lim and McAleer 2005; Mak, Carlile, and Dai 2005; UNWTO 1999). Two key reasons that led to a decrease in Japanese tourists to Australia were the recent economic slowdown and the changing demographics (population aging) in Japan, which caused many Japanese outbound tourists to prefer to take their overseas trips to short-haul and cheaper Asian destinations as well as few younger Japanese travelers to visit Australia (Lim, Min, and McAleer 2008; Mak, Carlile, and Dai 2005). In terms of volatility, tourist arrivals from Japan and the United States are more stable relative to other countries in the group. For New Zealand, the United Kingdom led the group with approximately 11.0% of New Zealand’s total tourist arrivals, followed by the United States (8.53%), Japan (5.42%), and China (4.40%). Canada (1.87%) again had the smallest share of New Zealand’s total visitor arrivals. In particular, the number of Chinese visitors to New Zealand increased to 20,642 in December 2012, equalling an average of 5.43% growth per month over the years, which was the largest average growth rate among all of New Zealand’s key tourism source markets, indicating its increasingly important role in

supporting New Zealand’s tourism development in recent years. In addition, tourist arrivals from Korea and Japan fell significantly and showed negative growth: −0.26% for Korea and −0.17% for Japan. Apart from the prolonged economic recession in Japan, the increase in the strength in New Zealand dollars also caused the decline in Japanese visitation to New Zealand during the study period (Lim and McAleer 2005; Lim, Min, and McAleer 2008). Likewise, the volatility of tourist arrivals to New Zealand from the United States and Japan is relatively stable, and both countries were in the list of the top three countries in the group. Furthermore, there was an increase in tourist arrivals from China to both Australia and New Zealand, whereas tourist arrivals from Japan showed significant declines during recent years. Again, the tourist arrivals from North America (Canada and the United States) to Australia have been increasing lately, whereas those numbers are rather steady for New Zealand. Importantly, these trends also attracted us to investigate how they might affect international tourism demand of Australia and New Zealand. Table 1 shows the descriptive statistics for tourist arrivals (in logarithms) to Australia and New Zealand. Since we will be dealing with the data in logarithms, we have posted the results accordingly. The implications of the means and standard deviations (SDs) of tourism demand from the key tourism source markets have been discussed above. Looking at the skewness of the data, the bulk of the data is located on the right, suggesting that the distributions of the returns are negatively skewed and that there is a non-normal pattern in the data. For all of the seven countries, both Q(1) and Q(4) statistics were significant, providing evidence that the tourism demand time series are serially correlated at the fourth lags. The Q(1)† and Q(4)† statistics were statistically significant for all tourism demand indices, indicating evidence of strong second-moment dependencies (conditional heteroskedasticity) in the distribution of the sector equity indices. Overall,


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Table 1. Descriptive Statistics of Seven Key Tourism Source Markets for Australia and New Zealand (2000–2012). Countries

Mean

Tourist inflows to Australia from Canada 8.81 China 9.25 Germany 9.27 Japan 10.80 Korea 9.38 United Kingdom 10.59 United States 10.38 Tourist inflows to New Zealand from Canada 7.90 China 7.91 Germany 8.20 Japan 9.23 Korea 8.45 United Kingdom 9.51 United States 9.53

SD

Skew

Kurt

Q(1)

Q(4)

Q†(1)

Q†(4)

0.44 1.14 1.35 0.33 0.70 0.49 0.26

–0.15 –0.31 –0.68 –1.78 –1.41 –0.08 –0.41

2.35 1.99 2.82 3.39 4.34 2.42 2.54

0.81*** 0.82*** 0.65*** 0.82*** 0.71*** 0.79*** 0.67***

0.20*** 0.26*** 0.03*** 0.07*** 0.41*** 0.02*** 0.41***

0.61*** 0.64*** 0.55*** 0.64*** 0.63*** 0.59*** 0.47***

0.19*** 0.18*** 0.03*** –0.21*** 0.28*** 0.06*** 0.21***

0.54 1.36 0.68 0.41 0.87 0.65 0.34

0.06 –0.48 –0.24 –0.92 –1.33 0.07 –0.04

1.91 2.01 1.81 4.03 4.68 2.15 2.09

0.78*** 0.83*** 0.44*** 0.48*** 0.64*** 0.60*** 0.29***

0.19*** 0.49*** 0.14*** 0.17*** 0.28*** 0.18*** 0.08***

0.45*** 0.68*** 0.44*** 0.47*** 0.68*** 0.60*** 0.61***

–0.20*** 0.24*** –0.16*** 0.24*** 0.21*** 0.03*** 0.08***

Note: The table reports the summary statistics for the monthly international tourism demand for Australia and New Zealand from their major tourism source markets. The following statistics are reported: mean, standard deviation (SD), skewness (Skew), kurtosis (Kurt), autocorrelations of order 1 and 4 (Q(1) and Q(4)), and autocorrelations of the squared time series of orders 1 and 4 (Q†(1) and Q†(4)). The Ljung–Box (1978) test statistics are significant at *p < 0.10, **p < 0.05, and ***p < 0.01.

Table 2. Summary of the Unit Root Tests for Tourism Inflows to Australia and New Zealand (2000–2012). Countries

ADF

PP

Tourist inflows to Australia from Canada China Germany Japan Korea United Kingdom United States

0.03 0.21 0.01 0.02 0.05 0.00 0.04

0.00 0.01 0.00 0.00 0.01 0.00 0.00

Countries

ADF

Tourist inflows to New Zealand from Canada 0.05 China 0.12 Germany 0.03 Japan 0.07 Korea 0.04 United Kingdom 0.10 United States 0.07

PP 0.00 0.00 0.00 0.00 0.01 0.00 0.00

Note: ADF = Augmented Dickey–Fuller; PP = Phillips–Perron.

the Q(1), Q(4), Q(1)†, and Q(4)† statistics showed that ARCH properties existed in the time series, and the skewness results were also against the normality of the time series. Table 2 shows the results of the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests (in the logarithms of the time series of tourist arrivals). The PP test results suggested the rejection of the persistence of the unit root for all of the time series of tourist arrivals to Australia and New Zealand from their key tourism source markets during the study period, whereas the ADF results just rejected the unit root for most of the time series. Again, the results shown in Table 1 revealed the existence of both autocorrelation and heteroskedasticity. Therefore, the PP test would be a better choice over the ADF test to test the problem of unit roots within the time series of tourist arrivals in this study, as the PP test considers both serial correlation and heteroskedasticity, whereas the ADF test is modeled to test serial correlation.

Empirical Model In this study, we modeled the monthly international tourist arrivals (mean) and the volatility of tourist arrivals for Australia and New Zealand from their seven largest tourism markets (i.e., the United Kingdom, Germany, the United States, Canada, Japan, Korea, and China). In addition, we modeled and quantified the spillovers of the tourism demand of these countries from Australia to New Zealand or vice versa. The time series of tourist arrivals provided evidence of ARCH (see Table 1). Accordingly, for modeling tourism demand for Australia and New Zealand in this study, the multivariate GARCH model suggested by Chan, Lim, and McAleer (2005) was followed to model the volatility of tourism demand. We also incorporated the contemporary linkages between Australia and New Zealand. Here, it would be rather rational to expect that there will be international


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Country j: Canada, China, Germany, Japan, Korea, UK, US

Tourist arrivals from country j to New Zealand

Tourist arrivals from country j to Australia

Tourist spillovers from Australia to New Zealand Share of country j towards Australia’s total

Australia’s total inbound tourism demand

Share of country j 1 towards New Zealand’s total

Tourist spillovers from New Zealand to Australia

New Zealand’s total inbound tourism demand

Figure 2. Tourist spillovers between Australia and New Zealand (2000–2012).

tourist flows between Australia and New Zealand because of their isolated geographical locations and their close proximity. For example, tourists who originate from North America and visiting Australia might also travel to New Zealand during their journey or vice versa. There might be the spillover effect of tourist flows from Australia to New Zealand or from New Zealand to Australia, considering their respective international tourism demand. Figure 2 depicts the spillovers of tourist flows between Australia and New Zealand. Considering the autoregressive movement of the variables, we built the simple model with AR(p), as shown in Equation (1): Dij, t = α + ∑ np =1 Φ p D t − n + ε t (1) The dependent variable ( Dij, t ) is tourism demand for Australia or New Zealand (i) originating from the seven countries (j). We run the autoregressive model with an order of p. Φ p represents the matrix of coefficients with the lagged values (order p) of Dij, t . The error term (ε t ) is distributed normally with a mean of zero and a variance matrix, and is called H t (ε t / I t −1 ~ N(0, H t )). I t −1 is the matrix of past information that contains the p lagged values of Dij, t . Considering the spillover effects between Australia’s and New Zealand’s tourism demands, we employed the bivariate GARCH model that captures the volatility persistence of the time series of tourist arrivals as well as the spillover effects among different time series to each other. Employing the bivariate GARCH model, the Baba et al. (1990) parameterization was used (introduced by Engle and Kroner 1995). The bivariate GARCH model can be written as shown in Equation (2):

H t +1 = C'C + B'H t B + A' ε t ε t'A (2) where H t+1 corresponds to the conditional variance matrix, matrix C (c11 , c12 , c 22 ) contains the constants for H t +1 , and B (b11 , b12 , b 21 , b 22 ) is a 2 × 2 matrix that contains the square matrix of parameters and indicates the relationship between the contemporary conditional variances and the past values. Lastly, A is also 2 × 2 matrix containing the coefficients (a11 , a 22 ) of the square of the own error terms as well as the volatility spillover coefficients (a 21 , a12 ). Because we built two equations for Australia and New Zealand, we can expand the matrix form to the equation yields, which can be written as shown in Equations (3) and (4): 2 2 + b11 h11, t + h11, t +1 = c11 2 2 2b11b12 h12, t + b 221h 21, t + a11 ε1, t + (3)

2a11a12 ε1, t ε 2, t + a 221ε 22, t 2 2 h22, t +1 = c12 + c 222 + b12 h11, t + 2 2 2b12 b 22 h12, t + b 222 h 22, t + a12 ε1, t + (4)

2a12 a 22 ε1, t ε 2, t + a 222 ε 22, t In Equations (3) and (4), h11,t (h22, t ) is the lag value of the own conditional variance, ε12,t is the square of the error terms that make up the GARCH (1, 1). The spillover terms would be ε 22,t (ε12, t ), which contains the volatility spillover terms. The corresponding coefficients of the spillover terms measure the extent of the shocks to tourism demand for Australia originating from country j, being transmitted to New Zealand


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Table 3. Volatility Transmission from Australian Tourism Demand to New Zealand’s Tourism Demand. h22,t

Volatility Transmission Canadian tourist flows to Australia spilled over to New Zealand Chinese tourist flows to Australia spilled over to New Zealand German tourist flows to Australia spilled over to New Zealand Japanese tourist flows to Australia spilled over to New Zealand Korean tourist flows to Australia spilled over to New Zealand UK tourist flows to Australia spilled over to New Zealand U.S. tourist flows to Australia spilled over to New Zealand

0.24*** (0.02) 0.16** (0.07) 0.32*** (0.02) 0.60*** (0.01) 0.46*** (0.05) 0.16*** (0.02) 0.23*** (0.08)

ε 22,t 0.52*** (0.13) 0.26*** (0.11) 0.20*** (0.05) 0.12*** (0.02) 0.44*** (0.10) 0.38*** (0.06) 0.46*** (0.10)

ε1,2t 0.63*** (0.12) 0.05 (0.15) 0.09** (0.04) 0.14*** (0.04) 0.11 (0.12) 0.26*** (0.03) 0.16*** (0.03)

Note: The estimated equation is h22, t+1 = c122 + c222 + b122h11,t + 2 b12b22 h12,t + b222 h22, t + a122 ε1,2 t + 2a12 a22 ε1, tε2, t + a222 ε22, t . The coefficients of h22,t and ε1,2t represent the GARCH and ARCH effects, respectively, whereas the coefficient of ε1,2t corresponds to the volatility effect of tourism demand from country j to Australia spilling over to the tourism demand of country j to New Zealand. *p < 0.10, **p < 0.05, and ***p < 0.01.

or vice versa. Equation (3) represents the equation for the conditional variance for Australian tourism demand from country j, and Equation (4) represents the equation for New Zealand’s tourism demand. Accordingly, the coefficient of ε 22,t measures the extent of the volatility transmitted from tourism demand for New Zealand originating from country j. Similarly, the coefficient of ε12,t measures the extent of the volatility transmitted from tourism demand for Australia originating from country j to tourism demand for New Zealand originating from country j. Again, the dashed line is considered to be the corresponding spillover effect (see Figure 2). Given a sample of T observations of the tourism demand, vectors for the parameters of two variable systems are estimated by calculating the conditional log-likelihood functions for each period as shown in Equation (5): L ( θ ) = −Tln ( 2π ) −

1 2

T

∑(ln H

t

+ εt' H t−1εt ) (5)

t =1

Empirical Findings In this section, we provided empirical evidence of the volatility spillovers of tourism demand between Australia and New Zealand. Accordingly, a total of 14 bivariate GARCH models (i.e., seven models for Australia and seven models for New Zealand) were estimated; each model estimated tourism demand spillovers from Australia to New Zealand or vice versa. Tables 3 and 4 show the estimation results for Equations (3) and (4), respectively. Table 3 estimates the volatility spillover of tourism demand from Australia to New Zealand

relating to the seven major tourism source markets. In each row, the coefficients of h22,t and ε 22,t represent the GARCH and ARCH coefficients, respectively. For the sake of stationarity, the summation of these coefficients should be less than 1. We found that these coefficients were significant, and the summation of these coefficients was less than 1. The estimated coefficient of ε12,t corresponds to the spillover effect of tourist flows from Australia to New Zealand with respect to each of the key tourism source markets. In the first row, we estimated whether Canadian tourists who visited Australia might spill over to New Zealand, and the result suggested that it is statistically significant in the volatility of New Zealand’s tourism demand from Canada. The estimated coefficient is 0.63 (SD = 0.12), which indicated that there was a significant spillover effect of Canadian tourists from Australia to New Zealand. Similarly, three other countries also showed significant spillover coefficients: Germany (coefficient = 0.09, SD = 0.04), the United Kingdom (coefficient = 0.26, SD = 0.03), and the United States (coefficient = 0.16, SD = 0.03). These significant coefficients implied that among the tourists who originated from these four countries visiting Australia, a few also visited New Zealand (spilled over), consequently affecting the volatility of New Zealand’s tourism demand significantly. Table 4 shows the estimation results of the volatility of Australian tourism demand and tourist spillovers from New Zealand. Similar to Table 3, the h11,t and ε12,t coefficients correspond to the GARCH and ARCH coefficients; the statistically significant coefficients suggested the existence of the GARCH and ARCH effects. The summation of these coefficients was less than 1, indicating that they were stationary. The coefficient of ε 22,t corresponds to the spillover effect of New Zealand’s tourism demand to Australia associated


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Balli and Kan Table 4. Volatility Transmission from New Zealand’s Tourism Demand to Australian Tourism Demand. h11,t

Volatility transmission from Canadian tourist flows to New Zealand spilled over to Australia Chinese tourist flows to New Zealand spilled over to Australia German tourist flows to New Zealand spilled over to Australia Japanese tourist flows to New Zealand spilled over to Australia Korean tourist flows to New Zealand spilled over to Australia UK tourist flows to New Zealand spilled over to Australia U.S. tourist flows to New Zealand spilled over to Australia

0.29*** (0.01) 0.11** (0.06) 0.25*** (0.06) 0.28*** (0.05) 0.31*** (0.10) 0.52*** (0.04) 0.30*** (0.12)

ε1,2t 0.63*** (0.13) 0.35*** (0.03) 0.32*** (0.03) 0.38*** (0.06) 0.32*** (0.11) 0.32*** (0.11) 0.34*** (0.07)

ε22,t 0.06 (0.23) 0.17*** (0.05) 0.01 (0.13) 0.28*** (0.06) 0.06 (0.13) 0.08*** (0.02) 0.02 (0.09)

Note: The estimated equation is h11,t+1 = c112 + b112h11,t + 2 b11b12 h12,t + b212h21,t + a112ε1,2t + 2a11a12 ε1,t ε2,t + a212ε22,t . The coefficients of h11,t and ε1,t2 show the GARCH and ARCH effects, respectively, whereas the coefficient of ε22,t corresponds to the volatility effect of tourism demand from country j to New Zealand spilling over to the tourism demand of country j to Australia. *p < 0.10, **p < 0.05, and ***p < 0.01.

with these seven countries. The estimation results suggested that when tourists originated from China (coefficient = 0.17, SD = 0.05), Japan (coefficient = 0.28, SD = 0.06), and the United Kingdom (coefficient = 0.08, SD = 0.02), New Zealand has a significant spillover effect on the volatility of Australian tourism demand. This finding indicated that the volatility of tourism demand from these three countries to New Zealand would significantly affect the volatility of Australian tourism demand. Regarding the remaining major tourism source markets, we could not find any significant effects. In short, we found that Australian tourism demand spilled over to New Zealand when tourists originated from “Western” countries (i.e., Canada, the United Kingdom, the United States, and Germany). Additionally, New Zealand’s tourism demand spilled over to Australian tourism demand when tourists originated from “Far Eastern” countries (i.e., China and Japan). Interestingly, because of the strong historical connections of Australia and New Zealand with the United Kingdom, tourist spillovers were significant and become apparent in both directions when tourists originated from the United Kingdom.

Concluding Remarks This article modeled the bivariate GARCH model to investigate the spillovers of international tourist arrivals between Australia and New Zealand from the seven major tourism source markets for the period of 2000–2012, namely Canada, China, Germany, Japan, Korea, the United Kingdom, and the United States. The results suggested that volatility spillovers of “Far Eastern” tourists (i.e., Chinese and Japanese) from New Zealand to Australia occurred. Furthermore, international

inbound tourism demand for New Zealand from the “Western” countries (i.e., Canada, Germany, and the United States) was significantly affected by Australian tourism demand from those particular countries. Furthermore, the volatility spillover effect for United Kingdom tourists happened symmetrically between Australia’s and New Zealand’s tourism demands. Accordingly, modeling the volatility (or variations) of international tourism demand is imperative, particularly from the policy makers’ perspectives, in the sense that governments and tourism authorities or organizations should be certain about the volatility of tourism demand (e.g., due to tourist spillovers). With this in place, the main contribution of our study is that a thorough analysis of tourist spillovers and tourism demand fluctuations has implications for the future of tourism development in Australia and New Zealand; in practice, the findings of our article provided insights into the volatility of tourist arrivals to Australia and New Zealand from their major tourism source markets. Regarding the potential limitation of our paper, it should be noted that only tourist spillovers between Australia and New Zealand from their seven major tourism source markets were evaluated; however, the underlying reasons (or shocks) that caused tourist spillovers between Australia and New Zealand have not been explored, such as the short flying distance across the Tasman Sea and the more liberalized aviation regime in the region allowing more frequent flight services and good connectivity to stimulate tourism activities (i.e., the shocks). Also, the findings of our article implied that the impact of tourism marketing expenditure (one of the key variables in tourism demand modeling) need to be considered during the analysis of tourist spillovers between Australia and New Zealand from the seven key countries (e.g. Crouch 1994; Deskins and Seevers 2011; Dwyer et al.


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2014; Kulendran and Dwyer 2009; Kulendran and Sarath 2007; Li et al. 2005; Peng et al. 2014). Tourism Australia and New Zealand Tourism are the primary agencies responsible for advertising, promoting, and marketing Australia and New Zealand as the tourist destination. Marketing expenditure and promotional activities from both tourism authorities in Australia and New Zealand target their key tourist source markets and try to attract more international tourist visitation (particularly the high-spending Chinese and Japanese tourists), which will have a significant impact on their economic returns. As an extension of this study, it may be meaningful to include more data (when available) that may have a significant impact on tourist movements, such as airfare levels, the growth of low-cost carriers, airline alliance activities, and tourism marketing expenditure, as well as understanding their impact on tourist spillovers between Australia and New Zealand. 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) received no financial support for the research, authorship, and/or publication of this article.

Notes 1. Authors collected the data on tourist arrivals relating to the seven tourism partner countries from the websites of Australian Bureau of Statistics and Statistics New Zealand. 2. The number of Canada’s international arrivals to Australia and New Zealand reached 15,900 and 6,032 tourists in December 2012, respectively. In addition, its average monthly growth rate was the second largest growth for Australia (0.60%) and New Zealand (0.33%) during the analysis period. 3. China is forecasted to be the largest tourism source country market for Australia by 2017 (DITR, 2006).

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Author Biographies Faruk Balli is currently an Associate Professor of Economics at Massey University, New Zealand and a Professor of Economics at Gediz University, Turkey. Previously, he worked for Qatar Central Bank and University of Dubai. He has published numerous articles on topics related to risk sharing, financial markets and tourism. He obtained his PhD from University of Houston, USA. He can be reached at [email protected] Kan Wai Hong Tsui is a Senior Lecturer, teaching aviation operations, airline & airport strategies, and aviation safety management. The research covers different areas, and include airline and airport demand forecasting, airport productivity and efficiency, and future tourism activities and trends as its relationship with air transport industry.
