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DATA, METHODOLOGY AND RESULTS
The total tourist arrivals (TA) are used as the proxy of tourism expansion (Kim et al., 2006; Shan &
Wilson, 2001; Wang & Godbey, 1994). The choice of sample period for each country is in consideration of data
availability. Monthly data of industrial production (IP), exchange rate (EXCH) and export (EXP) for all four Asian
tourist destinations were obtained from the Taiwan Economic Journal database and the selected time period was
identical with that of TA
for each destination. The methodology used to examine the causal relationship among
tourism development, economic activity, exchange rates and exports follows three steps. We first performed
Augmented Dickey-Fuller (Dickey and Fuller, 1981) unit root test to examine the stationarity (the degree of
integration) of all variables in their natural logarithms, and then used a maximum likelihood approach developed by
Johansen and Juselius (1990) to conduct the Vector Autoregression (VAR)-based cointegration test. The direction of
the causal link among four factors was investigated based on Granger causality test in the next section. All monthly
time series data of IP, TA and EXP are seasonally adjusted. The terms
LIP
,
LTA
,
LEXCH
, and
LEXP
denote IP, TA,
EXCH and EXP in their natural logarithms respectively.
Results of the Augmented Dickey-Fuller unit root test show that the null hypothesis of one unit root cannot
be rejected for levels of
LIP
,
LTA
and
LEXP
, but is rejected for their first differences for the case of China.
Moreover, the null hypothesis is rejected for both level and first difference of
LEXCH
. That is,
LEXCH
are
I
(0), and
LIP
,
LTA
and
LEXP
are
I
(1) for their levels; however, all variables are
I
(0) for their first differences. For the case of
Singapore, we find that
LTA
is
I
(0) and
LIP
,
LEXCH
and
LEXP
are
I
(1) for their levels. All variables are
I
(0) for
their first differences. In Korea and Taiwan, all four factors are
I
(1) for their levels and
I
(0) for their first differences.
Based on the unit root test results, we then employ the Johansen cointegration techniques (Johansen, 1991; Johansen
& Juselius, 1990) to test whether there exists a long-run equilibrium (cointegrating) relationship among tourism
development, economic activity, exchange rates and exports in Korea and Taiwan. For cases of China and
Singapore,
LIP
,
LTA
,
LEXCH
, and
LEXP
are of different integration orders and thus cannot be cointegrated.
According to the Johansen cointegration test, the number of significant cointegrating vectors is determined using
two likelihood ratio test statistics, the trace statistic and the maximum eigenvalue statistic. Based on both trace and
maximum eigenvalue test statistics, we find that there are two cointegrating equations at the 1% level among four
variables in Korea. For the case of Taiwan, trace test indicates one cointegrating equation at the 1% level and
maximum eigenvalue test indicates two cointegrating equations at the 1% level among four factors. Thus,
cointegration tests confirm a long-run equilibrium relationship among tourism development, economic activity,
exchange rates and exports in Korea and Taiwan.
Because the four variables in China are of different integration orders and thus cannot be cointegrated, the
causality can be identified only through the first channel using the Wald test. Results of the Granger causality test
for the case of China reveal that the null hypothesis regarding no causality from export growth (
LEXP
Δ
) to
economic development (
LIP
Δ
) is rejected at the 1% significance level and the null hypothesis concerning no
causality from
LEXP
Δ
to tourism expansion (
LTA
Δ
) is also rejected at the 10% significance level. That is, there is
a one-way causality from
LEXP
Δ
to
LIP
Δ
and a one-way causality from
LEXP
Δ
to
.
LTA
Δ
In addition, we detect a
two-way causality between
LEXP
Δ
and
.
LEXCH
Δ
Both the tourism-led economic growth and the economy-led
tourism development are not found in China. Among three mega events, the SARS outbreak in 2001 was found to
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