An Empirical Research Study on Thailand Sports Tourism: In a Case of

SEA Games

ByDr. Abdul Rahim Abdul Samad

Ms.Dayang-Siti-Manirah Prof. Dr. Mohd Shahwhid Hj. Othman

Faculty of Economics and Management, Universiti Putra Malaysia (UPM), Serdang, Malaysiaabrahımabsamad@gmaıl.com

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INTRODUCTION

• Sport events have become an important means for the economic development of local community, region or country.

• Being the host country of any sport event.

• Sports tourism:- influence people to tourism.

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INTRODUCTION con’t

• South East Asia Games (SEA) is a sport game where there are participating by 11-countries.

• Malaysia, Singapore, Indonesia, Thailand, Laos, Vietnam, Brunei, Myanmar, Philippines, and Cambodia.

• In 2007, many tourists came to Thailand as Thailand was a host for SEA Games. 100% increase from the last 3 years.

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PREVIOUS STUDIES

• Robinson & Gammon, 2004 have suggested that sports tourism might usefully be understood by examining trip purposes.

• Jackson and Glyptis (1992) explicitly focusing on the impact of sport on tourism and vice-versa.

• Weed (2009) suggested that sport and tourism might be linked for mutual benefit.

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MATERIALS AND METHODS

• This study employed the GDP data of participating countries, Thailand exchange rate, dummy variable which represents the SEA Games event, and tourists arrival, gathered over the period of 1985 to 2010.

• Published data on these variables were made available by the Department of Statistics, Thailand and the International Financial Statistics (IFS) online service.

• Specifically, this study evaluates the long-run elasticity and short-run causality as well as examining the determinant of tourism demand model for Thailand particularly in the case of SEA Games.

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MATERIALS AND METHODS con’t

• The variables employed in this study are in natural logarithmic form. The dependent variable is tourist arrival. The development of ARDL function is as follow:

Tourist arrival = f(GDP, exchange rate, dummy) (1)

Tourist arrival = GDPβ1.exchange rateβ2.dummyβ3 (2)

• To illustrate the ARDL modeling approach, we then express Eq. (3) in log-linear form as follow:

lntourist arrival = β0 + β1lnGDP + β2lnexchange rate + β3dummy + εt (3)

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MATERIALS AND METHODS con’t

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lntourist arrival t = β0 + β1lnGDP 1t + β2lnexchange rate 1t + β3dummy

+

p

i 04 lntourist arrival it +

p

i 05 lnexchange rate it

+

p

i 06 dummy it + εt (4)

The ARDL approach involves estimating the error correction version of the ARDL model for variables under estimation (Pesaran et al. 2001). From Eq. (3), the ARDL model of interest then can be written as follow:

RESULTS

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TABLE 1 Bound Test Results for Long Run Relationship

Critical value of the F-statistic: intercept and no trend

90% level 95% level 99%level T30 I(0) I(1) I(0) I(1) I(0) I(1)

2.68 3.58 3.27 4.31 4.61 5.99 Types of Commodity Calculated F-statistic Tourist arrival 4.58** Notes: ** Significant at 5 percent. Critical values are taken from Narayan (2005).

RESULTS con’t

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TABLE 2 Estimates for Long Run Elasticities

Dependent variable: Tourist arrival Regressor Coefficient Standard error P-value Intercept 16.6*** 0.39 0.00 Tourist arrival (-1) 0.58*** 0.16 0.00 Tourist arrival (-2) 1.53*** 0.22 0.00 GDP 0.18 0.01 0.23 Exchange rate 0.03 0.05 0.52 Dummy 0.39*** 0.03 0.00 Notes: *** Significant at 1 percent, * Significant at 10 percent.

RESULTS con’t

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TABLE 3 Estimates for Short Run Elasticities

Dependent variable: Tourist arrival Regressor Coefficient Standard error P-value Intercept -18.53*** 0.22 0.00 GDP 0.02 0.01 0.14 Exchange rate 0.03 0.05 0.43 Dummy 0.11** 0.04 0.01 ECM(-1) -0.12*** 0.24 0.00 Notes: *** Significant at 1 percent, * Significant at 10 percent.

CONCLUSIONS

• The results of the ARDL bound testing confirmed the presence of cointegration in the model of tourist demand in Thailand.

• Over the long run, the previous year of tourist arrival played an important indicator to influence the number of tourist arrival in the future. In addition, the SEA Games event shows a significant determinant in boosting the tourism industry by the host country.

• Finally, the results revealed that GDP and exchange rate do not give any significant impact on the tourism demand model.

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