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Vol. 2, No. 1 International Journal of Economics and Finance
138
The Cointegration and Causality Tests for Tourism and Trade in Malaysia
Norsiah Kadir
Department of Economics, Faculty of Business and Management
Universiti Teknologi MARA Malaysia
Tel: 60-4-988-2836 E-mail: norsiahkadir@perlis.uitm.edu.my
Kamaruzaman Jusoff (Corresponding author)
Faculty of Forestry, Universiti Putra Malaysia, Serdang 43400, Selangor. Malaysia
Tel: 60-3-8946-7176 E-mail: kjusoff@yahoo.com
Abstract
This paper examines the relationship between tourism and trade that might have evolved in the development of
Malaysian economy by using cointegration and causality tests. All analyses have been conducted with quarterly data of
international tourism receipts, exports, imports and total trade of Malaysia, over the period of 1995:1 through 2006:4.
The results of the unit root tests indicate that the data are stationary in first-difference and not in level. The results of the
JJ co integration test however, show that all the series are not cointegrated in the long run, hence, long-run equilibrium
did not exist between all the series. Using Granger-causality tests the study found that there is one-way causal effect
(unidirectional causality) running from exports to international tourism receipts at 5% significance level. The causality
test also shows a one-way causal effect running from imports to international tourist receipts at 5% significance level
and total trade to international tourism receipts at 10% significance level. This leads to a conclusion that increase in
total trade, exports and imports will cause growth in the tourism sector, which means that most of tourist arrivals are
related to business tourism. Therefore, to increase and sustain in the growth of tourism sector, future economic policy
should focus more on tourism and trade related, in order to generate more foreign exchange earning to Malaysia.
Keywords: Tourism, Trade, Cointegration test, Causality test, Malaysia
1. Introduction
Tourism can be classified into several distinct categories. They would include holiday travel, visiting friends and
relatives (VFR), business travel, health treatment, shopping, conference, incentive travel, official mission, education,
sport and others travel (Malaysia Tourism Promotion Board, 2004). Despite uncertainty in the global economic
environment, Malaysian tourism industry continues to perform favorably as reflected in the growth of tourist arrivals
and tourist receipts. The share of tourism revenue in total earnings of the services account of the balance of payments
increased from 32.7% in 2000 to 43% in 2005. Taking into account the inflow of foreign tourists and outflow of local
residents traveling abroad, the net contribution by tourism improved from RM11.2 to RM18.1 billion for the same
period. The development in tourism also contributed positively to the expansion of activities in other subsectors,
particularly the hotel, travel and tour industry, retail and restaurants as well as transport.
During 2000-2005, total trade expanded at an average rate of 7.2% per annum from RM684.7 billion in 2000 to
RM967.8 billion in 2005. Gross exports grew at an average rate of 7.4% per annum to RM533.8 billion in 2005. With
this performance, Malaysia ranked 18th largest exporter, contributing 1.5% of world export. While gross imports
increased at an average rate of 6.9% per annum to RM434.0 billion in 2005 (Ninth Malaysia Plan, 2006-2010). The
pattern of trade and tourism data in Malaysia provides cursory evidence that there is a long-run relationship between the
two sectors (Figure 1). However, the significance of such a relationship can only be proven by undertaking appropriate
studies.
Is there a relationship between international trade and tourism? If so, how? This question can be taken to a more
disaggregated level of inquiry. Evidently, international business creates export sale and/or import purchase. Successful
business trips will further lead business travel flows and there will be a series of externality effects on both trade and
tourism. Increase business travel may also increase holiday and other travel when friends and relatives seeking
adventure and recreation accompany business travelers (Kulendran and Wilson, 2000). Earlier studies by Kulendran and
Wilson (2000) lend empirical support to the relationship between tourism and trade. They analyzed the direction of
causality between different travel and trade categories for Australia and its four major trading partners, using time series
data. The study provides empirical evidence in supporting the idea that there is a long-run relationship between
international trade and international travel. Using the same approach, Khan and Lin (2002) investigated the relationship
between international trade and tourism for Singapore and its major trading partners. Although the results tend to differ
International Journal of Economics and Finance February, 2010
139
across countries, the study provides strong support for a systematic relationship between business travel and total trade.
Furthermore, the causality between travel and trade could run both ways, whereby business arrivals influence trade or
vice versa. However, holiday or pleasure travel, is found to be somewhat unrelated to trade.
Other categories of tourism such as VFR, health treatment, shopping, conference, incentive travel, official mission,
education, sport and other travels can also influence trade because tourists may demand certain types of product that are
produced or otherwise in that country. If they demand products that are not produced in that country, therefore it has to
be imported, this may lead to an increase in import. On the other hand, if tourists purchase products that are produced
locally, this will increase income of that country. Higher trading volumes countries are likely to be more open
economies and more developed in tourism industry. Therefore, expansion in the tourism industry will also lead to an
expansion in the trade when demand for tourist commodities increase (Khan and Lin, 2002). According to the World
Tourism Organization (UNWTO) the number of international tourist arrivals recorded worldwide grew by 5.4% and
exceeded 800 million for the first time in 2005 (ever from 766 million in 2004). Although growth was more moderate, it
was still almost 1.5 percentage points above the long-term average annual growth rate of 4.1%. Tourist arrivals to
Malaysia reached its target of 16.4 million tourists by the end of December 2005 exceeding 15.7 million tourists in 2004.
From the 16.4 million tourist arrivals in 2005, Malaysia received RM32.0 billion in foreign exchange earnings,
representing an increase 0f 7.8% from 2004.
In terms of traveling pattern, holiday arrival, which represented 71.2% of total arrivals in 2005, had declined by 3.7% as
compared to 2004. While tourists who came for business, shopping, conference, official mission and sport has increased
by 2.4%, 0.1%, 0.4%, 0.3% and 0.9% respectively (see Table 1). This is in conjunction with aggressive promotions
made in new and non-traditional markets, particularly in West Asia as well as increasing number of international
conferences and exhibitions held in the country (Economic Report, 2005/2006). Average per capita expenditure also
registered an increase of 3.2% from RM1,888.20 to RM1,944.70. Tourists spent mostly on accommodation (33.5%),
followed by shopping (20.8%), food and beverage (19.9%), transportation (11.6%), and entertainment (4.0%). From the
distribution of items purchased in 2005, clothes/textiles/bag constituted more than 50% from overall shopping items.
The second most favorite items was handicraft/souvenir at 45.5%. Items such as liquor, DVD/VCD/CD/Cassette, toys,
cigarette/cigar, gold/jewellery, electrical/electronic appliances and others were consumed below 10% in 2005.
Besides, total trade consists of exports and imports. ASEAN accounted for 26.1% of Malaysia’s exports and 25.5% of
imports in 2005, while Japan 9.4% of exports and 14.5% of imports; Australia 3.4% of exports and 1.9% of imports; the
U.S 19.7% of exports and 12.9% of imports and the U.K 1.8% of exports and 1.5% of imports. Internationally, tourism
represents an important source of foreign exchange earnings and it has been suggested that the potential contribution to
the national balance of payments (Oppermann and Chon, 1997). Although tourism is a powerful tool for balance of
payments adjustments, its impact may vary according to countries. In the developed countries, the earnings from
international tourism could make a significant contribution to the balance of payments in general, and the invisible
account in particular. However, for many developing countries with a limited industrial sector and dependence on
international aid, tourism plays a significant role in securing foreign exchange, creating employment and attracting
overseas investment (Sharpley, 2002). As indicated by the UNWTO and International Monetary Fund (IMF) data,
international tourism is the top export earning sector in the world, exceeding both the automobile industry and the
chemicals industry, and together with international fare receipts represents about 8% of total export earnings on goods
and services worldwide. Tourism is also one of the top five export earning sectors for 83% of countries in the world,
and the main source of foreign currency for at least 38% of the countries. In the 1980s, international tourism receipts
grew faster than world trade (commercial service and merchandised exports).
Despite the fact that tourism is a major important sector for the world’s economy for many countries, it is one of the
largest single employers and exporting service sectors; however, to our knowledge there are very few attempts to
investigate the relationship between tourism and trade. In addition, literature on relationship between tourism and trade
in the developing countries is completely lacking. The present study seeks to investigate the relationship between
tourism and trade that might have evolved in the process of development of Malaysian economy by using cointegration
and causality tests. The results of the study seem to have important implications for Malaysia’s policy concerning
tourism and trade. If we can prove that, there is a long-run relationship between international trade and tourism, future
economic policy should focus more on tourism and trade related, in order to generate more foreign exchange earning to
Malaysia.
2. Methods and materials
All analyses were conducted with quarterly (time series) data of international tourism receipts, exports, imports and
total trade (on real terms) of Malaysia, over the period of 1995:1 through 2006:4. These series were obtained by
dividing the nominal series by the consumer price indices (2000:Q1=100). In this study, the tourism and trade
hypotheses to be tested are as follows, namely: (a) does international tourism receipts cause export. (b) does exports
cause international tourism receipts? (c) does an international tourism receipt cause imports? (d) does imports cause
Vol. 2, No. 1 International Journal of Economics and Finance
140
international tourism receipts? (e) does international tourism receipts cause total trade? (f) does total trade cause
international tourism receipts? Therefore, with this tourism and trade hypotheses, attempts were made to test and search
for evidence of existence of relationship.
Firstly, before estimating the co integration and VAR, it is required to examine the stationarity of the variables.
Stationarity means that the mean and variance of the series are constant through time and the auto covariance of the
series is not time varying (Enders, 2004). Therefore, the first step is to test the order of integration (I) of the variables.
Integration means that past shocks remaining undiluted affects the realizations of the series forever and a series has
theoretically infinite variance and a time-dependent mean. For the purpose of this study, we use tests proposed by
Dickey and Fuller (ADF, 1979, 1981), Phillips and Perron (PP, 1988) and Kwiatkowski, Phillips, Schmidt and Shin
(KPSS, 1992) in testing the properties of unit root for all variables used. If all of the series are non-stationary in levels,
it should be stationary in first difference with the same level of lags. For appropriate lag lengths, we use the Akaike
Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC).
The ADF test takes the following form:
p
ǻYt = Įo + įT+ȕYt-1 + ș
iǻYt-i + ȝt (1)
i=1
The ADF auxiliary regression tests for a unit root in Yt, namely the logarithm of total tourism receipts, total trade,
exports and imports. T denotes the deterministic time trend and ǻYt-i is the lagged first differences to accommodate a
serial correlation in the error, ȝt. While, Į,į,ȕ, and ș are the parameters to be estimated.
Meanwhile, the Phillips-Peron (PP) test is shown by the equation below.
ǻYt = ȝ + ȡYt-1 + İt (2)
The PP test is used because it will make a correction to the t-statistics of the coefficient from the AR (1) regression to
account for the serial correlation. The PP test is a test of the hypothesis ȡ=1 in the equation 2. But, unlike the ADF test,
there are no lagged difference terms. Instead, the equation is estimated by OLS and then the t-statistics of the ȡ
coefficient is corrected for serial correlation in İt.
In the first two methods, the unit root hypothesis corresponds to the null hypothesis. If we are unable to reject the
presence of a unit root, meaning that the series are integrated of order one. However, Kwiatkowski, Phillips, Schmidt
and Shin (1992) argued that not all series for which we cannot reject the unit root hypothesis are necessarily integrated
of order one. To circumvent the problem that unit root tests often have low power, they offer an alternative test, which
is KPSS test. In the KPSS test, stationarity is the null hypothesis and the existence of a unit root is the alternative. The
KPSS test is shown by the following equation
yt = x'tȕ + μ (3)
The LM statistics is given by:
T
LM = S2t/ıİ2 (4)
t=1 ˆ
where, ıİ2 is an estimator for the error variance. This latter estimator ıİ2 may involve corrections for autocorrelation
based on the Newey-West formula. In the KPSS test, if the null of stationarity cannot be rejected, the series might be
cointegrated. After identifying the order of intergration, we then use the Johansen (1988, 1991), and Johansen and
Juselius (1990) Full Information Maximum Likelihood (ML) technique to determine whether there is a long-run
relationships (cointegrating) between the various series. If there is a cointegration between two variables, there, exists a
long-run effect that prevents the two series from drifting away from each other and this will force the series to converge
into long-run equilibrium.
The study further explores the relationship between the series by using Granger-Causality test to test for the bivariate
equation. Granger (1988) points out that if two series are cointegrated, then there must be Granger-causation in at least
one direction. A variable Xt Granger causes Yt, if Ytcan be predicted with better accuracy by using past values of Xt
with other factors held constant.
The Granger causality test involves estimating the following model:
p q
Yt = ȝt + Į
iYt-i + ȕ
iXt-i + İt (5)
i=1 j=1
Where ȝtdenotes the deterministic component and İtis white noise. Meanwhile, the null hypothesis can be tested by
using F-test. When the p-value is significant, the null hypothesis of the F-statistic is rejected, which implies that the first
series Granger-causes the second series and vice versa.
International Journal of Economics and Finance February, 2010
141
In this study, we used quarterly data of international tourism receipts and total trade for Malaysia spanning from 1995:1
to 2006:4. The data used in this study are obtained from Malaysian Tourism Promotion Board (Planning and Research
Division), Annual report of Bank Negara Malaysia (the Malaysian Central Bank), the Statistical Yearbook (various
issues) published by the Malaysian Department of Statistic and the IMF International Financial Statistics Yearbook.
3. Results and discussion
In order to estimate the long-run relationship between the variables using the cointegration approach, first, we need to
examine the stationary properties of the time series data, to avoid spurious regression. Tables 1, 2, and 3 present the
stationarity test results of international tourism receipts, exports, imports and total trade in level and first differences,
using ADF, PP and KPSS tests. The results of the unit root tests indicate that we could not reject the null hypothesis of
the unit root at 1 per cent and 5 per cent critical value. However, the null hypothesis is rejected at 1 per cent critical
value, when we test on the first-difference. This suggests that the data are stationary in first-difference and not in level.
Having found that the variables are I(1), we proceed with the cointegration tests in testing for the cointegration between
the variables, which are non-stationary in level but stationary in first-difference. The Johansen and Juselius (JJ)
approach is employed to test whether there is a long- run relationship between the selected variables. The results of the
JJ co integration test however, shows that all the series are not cointegrated in the long-run. In a simple word, long-run
equilibrium did not exist between all the series.
In order to examine the causal relationships as well as directions of the series, then we run the Granger causality tests,
which can be implied by the non-cointegrating series. The results of Granger-causality tests as reported in Tables 4 and
5 indicate that there is one-way causal effect running from exports to international tourism receipts at 5% significance
level. The causality test also shows a one-way causal effect running from imports to international tourist receipts at 5%
significance level and total trade to international tourism receipts at 10% significance level.
4. Conclusion
This study attempts to investigate the relationship between tourism and trade that might have evolved in the
development of Malaysian economy by using cointegration and causality tests. The findings of the study indicate that
there is a one-way causal effect (unidirectional causality) between all the series. Subject to possible caveats of the study,
the following are some important policy implications for Malaysia in terms of tourism and trade that can be drawn from
the findings. It seems that increase in total trade, exports and imports will cause growth in the tourism sector, which
means that most of tourist arrivals are related to business tourism. Hence, future economic policy should focus more on
tourism and trade related, in order to generate more foreign exchange earning to Malaysia. Besides, in order to increase
and sustain in the growth of tourism sector, more attention should be given to the business tourism since this category of
tourism has shown higher growth rate (see Table 1). Furthermore, Malaysia Tourism Promotion Board should also
focus on MICE (Meetings, incentives, conferences and Exhibitions) market because this category of tourism also shown
a growth as well as relatively a higher value added market.
References
Dickey, D. A. & Fuller, W.A. (1979). Distribution of Estimators for Autoregressive Time Series with a Unit Root,
Journal of the American Statistical Association, 74(366), 427-431.
Dickey, D. A. & Fuller, W.A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,
Econometrica, 49(4), 1057-1072.
Enders, Walter. (2004). Applied Econometric Time Series. Second Edition, John Wiley & Sons, Inc., River Street,
Hoboken, New Jersey.
Granger, C.W.J. (1988). Some Recent developments in a Concepts of Causality, Journal of Econometrics, 39(1-2),
199-211.
Johansen, S. (1988). Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12(2-3),
231-254.
Johansen, S. (1988). Estimation and Hypothesis testing of Cointegration Vectors in Gaussian Vector Autoregressive
Models, Econometrica, 59(6), 1551-1580.
Johansen, S. & Juselius, K. (1990). Maximum Likelihood Estimation and Inference on Cointegration-with Application
to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52(2), 169-210.
Khan,H. & Lin, C.C. (2002). International Trade and Tourism: Evidence from Co integration and Causality Tests by
using Singapore Data. Annual Conference of Travel and Tourism Association (TTRA) 23-26 June, 2002, Arlington,
Virginia, USA.
Kulendran, N. and K.Wilson. (2000). Is there a relationship between international trade and international travel?
Applied Economics 32(8), 1001-1009.
Vol. 2, No. 1 International Journal of Economics and Finance
142
Kwiatkowski, D., Phillips, P., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of Stationarity against the
alternative of a unit root. Journal of Econometrics, 54 (1-3), 159-178.
MacKinnon, J.G. (1991). Critical Value for Cointegration Test. In R.F. Engle and C.W.J. granger (Eds) Long-run
Economic Relationship: Reading in Cointegration. Oxford: Oxford University Press.
Malaysia. (2006). Ninth Malaysia Plan 2006-2010. Kuala Lumpur: Economics Planning Unit, Prime Minister’s
Department.
Malaysia Tourism Promotion Board. (2004). Profile of Tourists by Selected Market 2004. Planning and Research
Division, Tourism Malaysia, Kuala Lumpur.
Oppermann, M. & Chon, K. (1997). Tourism in Developing Countries, London: International Thomson Business Press.
Phillips, P.C.B. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression, Biometrika, 75(2), 335-346.
Sharpley, R. (2002). Tourism: A Vehicle for Development? In R.Sharpley & D.J.Telfer (Eds), Tourism and
Development: Concepts and Issues, Clevedon, England: Channel View Publication.
Table 1. Malaysia: Categories of Tourism for 2004/2005
Category 2004 (%) 2005 (%) Growth
Holiday 74.9 71.2 -3.7
VFR 11.2 11.1 -0.1
Business 8.5 10.9 2.4
Shopping 1.2 1.3 0.1
Conference 0.8 1.2 0.4
Sport 0.2 1.1 0.9
Official Mission 0.4 0.7 0.3
Incentive Travel 0.5 0.4 -0.1
Health Treatment 1.7 0.3 -1.4
Education 0.3 0.2 -0.1
Source: Malaysia Profile of Tourists by Selected Markets 2005
Table 2. Unit Root Tests
Augmented Dickey Fuller (ADF) Test
Variable Constant Trend
Level First-Difference Conclusion Level First-Difference Conclusion
lnTOUR -1.2803 -8.3413** I(1) -2.9989 -8.2989** I(1)
lnEXP -1.0624 -6.3891** I(1) -2.9880 -6.3142** I(1)
lnIMP -1.1309 -6.1333** I(1) -2.4785 -5.9962** I(1)
lnTRADE -1.0546 -8.1603** I(1) -2.8937 -8.0772** I(1)
Phillip-Perron (PP) Test
Variable Constant Trend
Level First-Difference Conclusion Level First-Difference Conclusion
lnTOUR -1.0307 -10.5225** I(1) -3.1842 -11.4526** I(1)
lnEXP -1.0328 -7.1319** I(1) -3.1506 -7.0258** I(1)
lnIMP -1.1528 -6.2162** I(1) -2.5757 -6.1324** I(1)
lnTRADE -0.6781 -11.6934** I(1) -3.1664 -11.5027** I(1)
KPSS Test
Variable
Constant Trend
Level First-Difference Conclusion Level First-Difference Conclusion
lnTOUR 0.7723** 0.4674 I(1) 0.2381** 0.0304 I(1)
lnEXP 0.8778** 0.1356 I(1) 0.2371** 0.0464 I(1)
lnIMP 0.8659** 0.1293 I(1) 0.3792** 0.0563 I(1)
lnTRADE 0.8742** 0.1080 I(1) 0.2341** 0.0345 I(1)
Notes: 1) For ADF and PP tests, ** and * denote rejection of a unit root hypothesis based on Mackinnon (1991) critical values
at 1% and 5% respectively.
International Journal of Economics and Finance February, 2010
143
2) For KPSS tests, ** and * denote rejection of a unit root hypothesis based on Kwiatkowski et al. (1992) critical values at 1% and
5% respectively.
Table 3. Cointegration Tests based on the Johansen, and Johansen and Juselius (JJ) Approach
Ho Trace Statistic 5% CV Prob. Max-Eigen Statistic 5% CV Prob.
Series: TOUR, EXP
Ho: r = 0
Ho: r 1
12.440
0.463
15.495
3.841
0.127
0.496
12.221
0.463
14.265
3.841
0.103
0.496
Series: TOUR, IMP
Ho: r = 0
Ho: r 1
11.389
0.041
15.495
3.841
0.189
0.840
11.349
0.041
14.265
3.841
0.138
0.840
Series: TOUR, TRADE
Ho: r = 0
Ho: r 1
11.978
0.159
15.495
3.841
0.158
0.689
11.818
0.159
14.265
3.841
0.118
0.689
Notes: 1) r stands for number of cointegrating vectors
2) Column 1 lists the null hypothesis of zero, at least one cointegrating vector; column 2 lists the trace statistics; column 3 lists the
critical values for trace statistics at 5% significant level; columns 4 and 7 lists the probability value; column 5 lists the maximum
Eigen value statistics; column 6 list the critical value for maximum Eigen statistics at 5% significant level.
Table 4. Granger-Causality Test Results
Null Hypothesis F-Statistics P-value Conclusion (Hypothesis)
Exports does not Granger-cause Tourism 9.7203* 0.0032 Rejected
Tourism does not Granger-cause Export 0.2899 0.5930 Accepted
Import does not Granger-cause Tourism 9.3191* 0.0038 Rejected
Tourism does not Granger-cause Import 0.0013 0.9713 Accepted
Trade does not Granger-cause Tourism 3.1456** 0.083 Rejected
Tourism does not Granger-cause Trade 2.0654 0.1578 Accepted
Note: * and ** indicate statistical significance at 5% and 10% levels, respectively.
Table 5. Summary of Granger-Causality Test Results
Granger Causality Relationships Significance Level
Exports ĺ Tourism 5%
Imports ĺ Tourism 5%
Trade ĺ Tourism 10%
Figure 1. Malaysia: International Tourism Receipts and Trade Earnings
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
95 96 97 98 99 00 01 02 03 04 05 06
REC TD
(RM M illion)
Year
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