Dynamics of Australia’s tourism in a multimarket context

Annals of Tourism Research xxx (2015) xxx–xxx

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Research Note

Dynamics of Australia’s tourism in a multimarket context q Abbas Valadkhani ⇑,1, Barry O’Mahony 2 Swinburne University of Technology, Australia

Forecasting tourism demand is critical to many economies including Australia’s where tourism generates 514,000 jobs and contributes AU$35 billion to GDP (Amelung & Nicholls, 2014).While the structural determinants of tourism demand have been extensively examined (e.g. Lim & McAleer, 2001; Seetaram, 2012; Song, Dwyer, Li, & Cao, 2012), Narayan’s (2008) seminal work has underpinned numerous studies into market co-movements (Abbott, De Vita, & Altinay, 2012; Lorde & Moore, 2008; Solarin, 2014; Yilanci & Eris, 2012). Using a five-variable VAR model, this study employs Granger causality and impulse responses to examine the dynamic interplay between tourist arrivals from Australia’s five largest markets (New Zealand, Japan, the UK, the US, and China). Song and Witt (2006) employed a VAR model to examine tourism demand in a single country (Macau) concluding that the technique produces accurate medium to long-term forecasts. However, in a multiple-country context this approach is inappropriate because structural VAR (SVAR) models cannot accommodate more than five to six variables (Campolieti, Gefang, & Koop, 2014). Our study comprises five countries and even if four variables were included for each country, the resulting SVAR would contain 20 variables. We address this by applying an unrestricted VAR model to Australian Bureau of Statistics (ABS, 2014) monthly (seasonally adjusted) arrival data (from January 1991 to September 2014). Table 1 presents the summary statistics of the data. Based on the average number of arrivals during the sample period, Australia’s five largest markets are: New Zealand, Japan, UK, US and China. Our analysis (Fig. 1) shows that of these five markets, only New Zealand and China exhibit an upward long-run trend, while the US and UK are stagnating. As the second largest inbound market Japan has demonstrated a downward trend since early 2000. Table 1 shows the results of three unit root tests: the Augmented Dickey-Fuller (ADF, 1981), Kwiatkowski, Phillips, Schmidt and Shin (KPSS, 1992) and Phillips and Perron (PP, 1988), which were q We wish to thank Professor Andreas Papatheodorou and three anonymous referees, whose useful feedback considerably improved an earlier version of this article. The usual caveat applies. ⇑ Corresponding author. E-mail addresses: [email protected] (A. Valadkhani), [email protected] (B. O’Mahony). 1 Address: Faculty of Business and Law, Swinburne University of Technology, PO Box 218, Mail H23, Hawthorn, VIC 3122, Australia. Tel.: +61 3 9214 8791. 2 Address: Faculty of Business and Law, Swinburne University of Technology, PO Box 218, Mail H23, Hawthorn, VIC 3122, Australia. Tel.: +61 3 9214 5170.

http://dx.doi.org/10.1016/j.annals.2015.09.007 0160-7383/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Valadkhani, A., & O’Mahony, B. Dynamics of Australia’s tourism in a multimarket context. Annals of Tourism Research (2015), http://dx.doi.org/10.1016/j.annals.2015.09.007

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A. Valadkhani, B. O’Mahony / Annals of Tourism Research xxx (2015) xxx–xxx

Table 1 Monthly descriptive statistics and unit root test results (1991m1–2014m9). Country

Mean

Std. Dev.

New Zealand Japan UK USA China

72,203 49,737 46,025 33,865 20,911

21,997 14,178 13,350 7124 19,027

Note:

⁄⁄⁄

⁄⁄

Significant at the 1% level;

ADF test 2.471 3.774⁄⁄ 1.373 4.338⁄⁄⁄ 1.8

KPSS test 0.174⁄⁄ 0.386⁄⁄⁄ 0.440⁄⁄⁄ 0.302⁄⁄⁄ 0.384⁄⁄⁄

PP test

ZA test

9.251⁄⁄⁄ 3.464⁄⁄ 5.418⁄⁄⁄ 9.796⁄⁄⁄ 2.632

3.528⁄ 6.484⁄⁄⁄ 8.059⁄⁄⁄ 5.982⁄⁄⁄ 6.484⁄⁄⁄

Break date 2003m9 1994m9 1994m9 1997m12 1994m9

significant at the 5% level; ⁄significant at the 10% level.

100,000

New Zealand

120,000

80,000 New Zealand 100,000 60,000 80,000 40,000 60,000

UK 20,000

Japan US

40,000

20,000

0 1992

1994

1996

China

1998 US

2000

2002 UK

2004

2006

Japan

2008

2010

2012

2014

New Zealand (right axis)

Fig. 1. Monthly tourist arrivals in Australia (1991m1–2014m9).

conducted to make robust inferences about the time series properties of the data. The lowest value of the Schwarz criterion is utilised to determine the optimal lag length in the testing procedure. Unlike the KPSS test, the ADF test results indicate that Japan and the US are stationary. Based on the PP test results, four out of the five series are I(0). According to the Zivot and Andrews (ZA, 1992) test, after capturing one endogenously-determined structural break, all series become stationary. This is

Table 2 VAR lag order selection criteria. Lag

LR

FPE

1 2 3 4 5 6 7 8

2266.8 118.4 72 49.4 44.5 52.1⁄ 22.7 26

0.000 0.000 0.000 0.000 0.000 0.000⁄ 0.000 0.000

AIC 10.393 10.658 10.753 10.766 10.762 10.794⁄ 10.707 10.637

SC 10.001⁄ 9.938 9.706 9.392 9.061 8.766 8.352 7.955

HQ 10.236 10.369⁄ 10.333 10.214 10.08 9.98 9.762 9.561

Notes: ⁄indicates that the optimal lag length was chosen based on the AIC criterion. LR = sequential modified LR test statistic (each test at 5% level); FPE = final prediction error criterion; AIC = Akaike information criterion; SC = Schwarz information criterion; and HQ = Hannan-Quinn information criterion.

Please cite this article in press as: Valadkhani, A., & O’Mahony, B. Dynamics of Australia’s tourism in a multimarket context. Annals of Tourism Research (2015), http://dx.doi.org/10.1016/j.annals.2015.09.007

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A. Valadkhani, B. O’Mahony / Annals of Tourism Research xxx (2015) xxx–xxx Table 3 Pairwise Granger causality test results. Null hypothesis China does not Granger cause: US does not Granger cause: UK does not Granger cause: Japan does not Granger cause: New Zealand does not Granger cause:

China

US

UK

Japan

New Zealand

v2 (6)

–

p-value v2 (6) p-value v2 (6) p-value v2 (6) p-value v2 (6) p-value

3.86 0.70 3.12 0.79 27.48 0.00 16.17 0.01

6.66 0.35 –

9.34 0.16 6.55 0.36 –

19.29 0.00 2.66 0.85 18.56 0.01 –

12.58 0.05 4.65 0.59 1.70 0.94 2.3 0.89 –

5.78 0.45 11.95 0.06 26.96 0.00

10.94 0.09 7.85 0.25

1.84 0.93

Note: The use of a bold font indicates that the results are statistically significant at the 10% level or better. Responses to Generalized One S.D. Innovations (US)

Responses to Generalized One S.D. Innovations (China)

.08

.16 US China

.06

.12

.04 .08

.06 Japan

.02

.012

UK

.04

.04

NZ

.008 NZ

.00

.02

.00

.004 .000

.00

-.004 -.008

-.02 2

4

6

China (right axis)

8

10

US

12 UK

14

16

18

Japan

2

20

4

6

China

New Zealand

8

10

US (right axis)

12

14

UK

16 Japan

18

20

New Zealand

Responses to Generalized One S.D. Innovations (Japan)

Responses to Generalized One S.D. Innovations (UK)

.08

.08

UK

Japan .06

.06 .04

.04

.04

China

.03

.02

.015 Japan

.02

.02

.00

.010

US

US .01

.005

.00

.00

.000

-.01

-.005 2 China

4

6 US

8

10

12

UK (right axis)

14

16

18

Japan

2

20

4

6

China

New Zealand

8

US

10 UK

Responses to Generalized One S.D. Innovations (New Zealand)

12

14

Japan (right axis)

16

18

20

New Zealand

.06

NZ .04

.02 .015 China .010

.00

US

.005 .000 -.005 -.010 2 China

4

6 US

8

10 UK

12 Japan

14

16

18

20

New Zealand (right axis)

Fig. 2. Generalised impulse responses to one standard shock (up to 20 months)

Please cite this article in press as: Valadkhani, A., & O’Mahony, B. Dynamics of Australia’s tourism in a multimarket context. Annals of Tourism Research (2015), http://dx.doi.org/10.1016/j.annals.2015.09.007

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A. Valadkhani, B. O’Mahony / Annals of Tourism Research xxx (2015) xxx–xxx

consistent with Narayan’s (2008) study which used univariate and panel Lagrange multiplier tests and also found that visitor arrivals to Australia from 28 countries were stationary. The estimated break dates for Japan, the UK and China occur in 1994m9, coinciding with a change in Australia’s ‘‘entry permit” system (Valadkhani and O’Mahony, 2015). Since there is no evidence of unit root, multivariate cointegration techniques and error correction models cannot be utilised. The most important aspect of estimating an unrestricted VAR model is to determine the optimum lag length. Table 2 reveals that according to the AIC (Akaike information criterion), the optimal lag length is 6 months. With k = 6, the estimated VAR system will generate a total of 155 coefficients (31 coefficients per country). To more effectively understand the dynamic interplay between these five markets the Granger causality test results are shown in Table 3 and the individual generalised impulse responses (up to 20 months ahead) are presented in Fig. 2. Table 3 indicates that there is a bi-directional causality between China–Japan, China–New Zealand and UK–Japan. This is consistent with the impulse responses presented in Fig. 2, suggesting that the declining Japanese market is likely to be compensated by arrivals from China, the UK and the US. This reflects a rising Chinese economy and ongoing downturn in Japan. Fig. 2 and Table 3 also reveals that the Chinese and New Zealand markets are

11.6

11.6

11.2

11.2

10.8

Ln(US)

Ln(UK)

10.8

10.4

10.4

10.0

10.0

9.6

9.6

6

7

8

9

10

11

12

6

13

7

8

Ln(China)

10

11

12

13

11.6

12.0

11.2

11.6

10.8

11.2

11

12

13

Ln(NZ)

Ln(Japan)

9

Ln(China)

10.4

10.8

10.0

10.4

9.6

10.0

6

7

8

9

10

Ln(China)

11

12

13

6

7

8

9

10

Ln(China)

Fig. 3. Scatterplots of Chinese’s inbound arrival vs the other four major Australian markets.

Please cite this article in press as: Valadkhani, A., & O’Mahony, B. Dynamics of Australia’s tourism in a multimarket context. Annals of Tourism Research (2015), http://dx.doi.org/10.1016/j.annals.2015.09.007

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highly integrated to the extent that one-standard shock in one market can lead to spillover effects over time in the other markets. Indeed, the results in Table 3 and Fig. 2 support the view that Australian inbound tourism is becoming increasingly inter-dependent with a significant degree of pathdependency among the five largest markets. These systematic co-movements are consistent with the fact that over 50% of Chinese tourists access Australia by indirect routes which include Singapore, Hong Kong and New Zealand and accounts for the rise in visitors from New Zealand and China. To minimise the volatility of inbound tourism, it is important for Australia to diversify crosscountry tourism portfolios because the Chinese market is closely inter-related with the other four markets in Fig. 3. While these results are of value to Australian tourism planners, another important finding is that unrestricted VAR modelling is still a useful technique for countries with multiple tourism markets. References Abbott, A., De Vita, G., & Altinay, L. (2012). Revisiting the convergence hypothesis for tourism markets: Evidence from Turkey using the pairwise approach. Tourism Management, 33, 537–544. Australian Bureau of Statistics (ABS, 2014). Overseas arrivals and departures: short-term movement, visitor arrivals – selected countries of residence: Seasonally adjusted, Table 4, Cat. No. 3401.0, Canberra. Amelung, B., & Nicholls, S. (2014). Implications of climate change for tourism in Australia. Tourism Management, 41, 228–244. Campolieti, M., Gefang, D., & Koop, G. (2014). Time variation in the dynamics of worker flows: Evidence from North America and Europe. Journal of Applied Econometrics, 29(2), 265–290. Dickey, D., & Fuller, W. (1981). Likelihood ratio tests for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54, 159–178. Lim, C., & McAleer, M. (2001). Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia. Applied Economics, 33(12), 1599–1619. Lorde, T., & Moore, W. (2008). Co-movement in tourist arrivals in the Caribbean. Tourism Economics, 14(3), 631–643. Narayan, P. K. (2008). Examining the behaviour of visitor arrivals to Australia from 28 different countries. Transportation Research Part A: Policy and Practice, 42(5), 751–761. Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346. Seetaram, N. (2012). Immigration and international inbound tourism: Empirical evidence from Australia. Tourism Management, 33(6), 1535–1543. Solarin, S. (2014). Revisiting the convergence hypothesis of tourism markets: Evidence from South Africa. Journal of Applied Economic Research, 8(1), 77–92. Song, H., Dwyer, L., Li, G., & Cao, Z. (2012). Tourism economics research: A review and assessment. Annals of Tourism Research, 39 (3), 1653–1682. Song, H., & Witt, S. F. (2006). Forecasting tourist flows to Macau. Tourism Management, 27(2), 214–224. Valadkhani, A., & O’Mahony, B. (2015). Identifying structural changes and regime switching in growing and declining inbound tourism markets in Australia. Current Issues in Tourism, 1–24. http://dx.doi.org/10.1080/13683500.2015.1072504 (in press). Yilanci, Y., & Eris, Z. A. (2012). Are tourism markets of Turkey converging or not? A Fourier stationary analysis. Anatolia: An International Journal of Tourism and Hospitality Research, 23(2), 207–216. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251–270. Assigned 11 March 2015; Submitted 31 August 2015; Accepted 21 September 2015. Available online xxxx

Please cite this article in press as: Valadkhani, A., & O’Mahony, B. Dynamics of Australia’s tourism in a multimarket context. Annals of Tourism Research (2015), http://dx.doi.org/10.1016/j.annals.2015.09.007