Transcript materials and methods

An Empirical Research Study on
Thailand Sports Tourism: In a Case of
SEA Games
By
Dr. Abdul Rahim Abdul Samad
Ms.Dayang-Siti-Manirah
Prof. Dr. Mohd Shahwhid Hj. Othman
Faculty of Economics and Management, Universiti Putra Malaysia (UPM), Serdang, Malaysia
abrahımabsamad@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
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:
 lntourist arrival t = β0 + β1lnGDP t 1 + β2lnexchange rate t 1 + β3dummy
p
p
i 0
p
i 0
+  4  lntourist arrival t  i +  5  lnexchange rate t i
+  6 dummy t i + εt
(4)
i 0
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RESULTS
TABLE 1
Bound Test Results for Long Run Relationship
Critical value of the F-statistic: intercept and no trend
90% level
95% level
T30
I(0)
I(1)
I(0)
I(1)
2.68
3.58
3.27
4.31
Types of Commodity
Calculated F-statistic
4.58**
Tourist arrival
99%level
I(0)
4.61
I(1)
5.99
Notes: ** Significant at 5 percent. Critical values are taken from Narayan (2005).
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RESULTS con’t
TABLE 2
Estimates for Long Run Elasticities
Dependent variable: Tourist arrival
Regressor
Coefficient
Intercept
16.6***
Tourist arrival (-1)
0.58***
Tourist arrival (-2)
1.53***
GDP
0.18
Exchange rate
0.03
Dummy
0.39***
Standard error
0.39
0.16
0.22
0.01
0.05
0.03
P-value
0.00
0.00
0.00
0.23
0.52
0.00
Notes: *** Significant at 1 percent, * Significant at 10 percent.
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RESULTS con’t
TABLE 3
Estimates for Short Run Elasticities
Dependent variable: Tourist arrival
Regressor
Coefficient
Intercept
-18.53***
GDP
0.02
Exchange rate
0.03
Dummy
0.11**
ECM(-1)
-0.12***
Standard error
0.22
0.01
0.05
0.04
0.24
P-value
0.00
0.14
0.43
0.01
0.00
Notes: *** Significant at 1 percent, * Significant at 10 percent.
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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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