Tourism, Openness and Grwoth Triangle in a
small Island: the Case of North Cyprus
Salih Turan KATIRCIOGLU
Department of Banking and Finance, Eastern Mediterranean University
P.O. Box 95, Gazimağusa, KKTCMersin 10, TURKEY
e-mail: salihk@emu.edu.tr
Summary. Although the relationship between trade and economic growth has found a wide
application area in the literature over the years, this can not be said about international
tourism and growth or trade and international tourism relationship. There are even very few
studies analyzing the relationhsip between international trade and international tourism in
the literature. Furthermore, to the best knowledge of the author of this present study, there
is no study in the existing literature searcing and analyzing the relationship between tradetourism-growth triangle till the moment. Thus, this study employs co-integration and
Granger causality tests in order to examine the relationship between international trade,
international tourism and economic growth triangle and the direction of causality among
themselves for the Turkish Cypriot economy, which has a closed and non-recognized state
since 1974. Results reveal that no co-integration exists between trade, tourism and growth
triangle based on the sample period, 1977-2004. The short run causality tests suggest that
undirectional causation running from foreign tourist arrivals to trade openness exists for the
Turkish Cypriot economy where the other pairs of variables do not indicate any causation
even in the short run.
Keywords: Trade, Tourism, Growth, Co-integration, Granger Causality, North
Cyprus
I. INTRODUCTION
Trade and tourism are two major sources of foreign exchanges for small countries as well as the larger ones. Especially, small countries have more trade dependency than the larger ones. According to Kuznets (1966), as the country gets
smaller dependency on foreign trade would increase. On the other hand, although
many economists agree that small countries have similar advantages and disadvantages when compared to larger ones, there are differences in the origin of these
disadvantages. Among common disadvantages are foreign trade dependency, vulnerability, high population growth rate, limited labor force, low labor efficiency,
diseconomies of scale, low GDP (Gross Domestic Product), high dependency on
imports of intermediate and consumption goods, and production of only a few basic goods/services. The small size of a country, in terms of area and population,
1164
may be economically advantageous. The smallness of a state in terms of area and
population may in fact be a source of comparative advantage rather than being a
constraint on economic growth and development. Specifically, smallness may be
more than compensated by certain unique characteristics possessed by small
states. Export-oriented services tend to represent such uniqueness and, therefore, a
basis for a potential comparative advantage (Mehmet and Tahiroglu, 2002).
International tourism and trade mean greater integration into the world economy which also brings benefits to the economies such as employment creation,
foreign exchange earnings, government revenues, and income and employment
multipliers (See also Clancy, 1999). There have been numerous studies analyzing
the effects of international tourism and trade sectors on especially developing
economies. However, the linkages between international tourism, international
trade and economic growth did not find a wide application area even for small and
developing countries.
This study empirically investigates a possible co integration and causal link between international tourism, trade openness and economic growth in a small island, North Cyprus, which is a typical small island that has an unrecognized state
in the Mediterranean Sea. It has a population of over 218,066 inhabitants, 3,355
km2 land area, 8,095 US $ per capita income (per capita Gross National Product),
1.72 billion US$ GDP (SPO, 2004), limited natural resources and limited workforce efficiency. It possesses the typical characteristics of a small island economy.
The ratio of net tourism revenues, exports and imports to GDP was 17.0%, 4.0%
and 50.0% respectively in 2004. This proves that North Cyprus suffers from persistent trade deficits due to the smallness. Total tourist arrivals to North Cyprus
were around 599,012 of which 73.0% were from Turkey in 2004 (SPO, 2004).
There are important implications and motivations for this study: First, majority
of empirical studies on tourism forecasting were built on tourism demand functions. As Shan and Wilson (2001) mention several areas remain incomplete in this
sort of studies and hence deserve further studies. For example, first, the role of international trade as one of the determinants of tourism demand is not well recognized in these studies. Second, the econometric techniques used in the previous
studies of international tourism are generally poor lacking new developments in
econometrics such as co integration and Granger causality concepts (Shan and
Wilson, 2001; Lim, 1997; Song et al., 1997; Witt and Witt, 1995). On the other
hand, this study is unique in the sense that it searches the link between trade, international tourism and economic growth where other empirical studies in the literature considered the link between trade and international tourism or international
tourism and economic growth or trade and economic growth for particular countries. Another implication of this study is that Cyprus problem has been in the
agenda of world countries for more than 40 years. Now, South Cyprus became a
member of European Union (EU) where North Cyprus did not. Thus, this situation
will continue to deserve attention from world countries and the results of this
study are expected to give important messages to policy makers.
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The following section defines methodology and data used in the study. Section
III gives and discusses empirical results of the study. Lastly, section IV concludes
and discusses the policy implications of the study.
2. DATA AND METHODOLOGY
Data used in this paper are annual figures covering the period 1977 – 2004 and
variables of the study are real gross domestic product (GDP) at 1977 constant
Turkish Lira prices, trade openness (exports plus imports as divided by GDP),
and international tourists visiting and accommodating in the tourist establishments
of North Cyprus. Data were gathered from State Planning Organization of North
Cyprus.
The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) 1 Unit Root
Tests are employed to test the integration level and the possible co-integration
among the variables (Dickey and Fuller, 1981; Phillips and Perron, 1988). The PP
procedures, which compute a residual variance that is robust to auto-correlation
are applied to test for unit roots as an alternative to ADF unit root test. Unless the
researcher knows the actual data generating process, there is a question concerning
whether it is most appropriate to include constant term and trend factor in the unit
root process (Enders, 1995). It might seem reasonable to test the existence of a
unit root in the series using the most general of the models. That is,
p
Δy t = a 0 + γy t −1 + a 2 t +
∑β
j Δy t −i −1 + ∈t
(1)
i =2
where y is the series; t = time (trend factor); a = constant term (drift); εt = Gaussian white noise and p = the lag order. The number of lags “p” in the dependent
variable was chosen by the Akaike Information Criteria (AIC) to ensure that the
errors are white noise. One problem with the presence of the additional estimated
parameters is that it reduces degrees of freedom and the power of the test.
After the order of integration is determined, co-integration between the variables should be tested to identify any long run relationship if they are stationary at
the same level. Johansen trace test is used for the co-integration test in this paper.
Cheung and Lai (1993) mention that the trace test is more robust than the maximum eigen value test for co-integration.
When series are not co-integrated, but stationary at the same level (for example,
I(1)), then, the Granger causality which is mainly known as the VAR (Vector
Autoregressive) model is appropriate for testing the direction of causality between
the variables as provided below where both X and Y variables are first differenced:
p
ln ΔYt = a +
∑
i =1
q
α i ln ΔYt -i +
∑β
j
ln ΔX t - j + μ t
(2)
j =1
1PP approach allows for the presence of unknown forms of autocorrelation with a structural break in the time series and conditional heteroscedasticity in the error term.
1166
r
ln ΔX t = b +
∑
γ i ln ΔX t -i +
i =1
s
∑δ
j
ln ΔYt - j + v t
(3)
j =1
where μt and vt are serially uncorrelated white-noise residuals; and p, q, r, and s
are lag lengths for each variable in each equation. A statistically significant F statistic would again be enough to have causation from X to Y in equation (2) and
from Y to X in equation (3).
3. RESULTS
Table I gives ADF and PP unit root test results for the variables of the study.
Both ADF and PP tests reveal that all of the variables of the study are not
stationary at their levels but stationary at their first differences. Therefore, all of
the variables are said to be integrated at I(1).
Since the variables are integrated at the same order, that is I(1), they are now
due to co-integration test. Table I gives co-integration test results for the variables:
Table I. ADF and PP Tests for Unit Root
Statistics (Levels)
τT (ADF)
τμ (ADF)
τ (ADF)
τT (PP)
τμ (PP)
τ (PP)
ln y
-2.71
0.19
3.06
-2.40
-0.29
4.52
Lag
(1)
(2)
(2)
(1)
(3)
(3)
ln Open
lag
Ln Tour
lag
-2.32
-1.28
-0.65
-2.32
-1.44
-0.70
(0)
(0)
(0)
(0)
(2)
(2)
-2.20
-0.08
2.11
-2.31
-0.08
2.11
(0)
(0)
(0)
(1)
(0)
(0)
Statistics
(First Differences)
∆ln y
Lag
∆ln Open
lag
∆lnTour
lag
τT (ADF)
τμ (ADF)
τ (ADF)
τT (PP)
τμ (PP)
τ (PP)
-4.19**
-4.23*
-2.83*
-4.05**
-4.08*
-2.79*
(0)
(0)
(0)
(4)
(4)
(1)
-4.05**
-4.13*
-4.22*
-4.05**
-4.12*
-4.22*
(0)
(0)
(0)
(1)
(1)
(1)
-4.10**
-4.14*
-3.63*
-4.07**
-4.10*
-3.63*
(0)
(0)
(0)
(2)
(2)
(0)
Note:
y represents real gross domestic product; Open is the openness ratio; Tour is total tourist arrivals to North Cyprus; All of the series are at their natural logarithms.
τT represents the most general model with a drift and trend; τμ is the model with
a drift and without trend; τ is the most restricted model without a drift and trend.
Numbers in brackets are lag lengths used in ADF test (as determined by AIC
set to maximum 3) to remove serial correlation in the residuals. When using PP
1167
test, numbers in brackets represent Newey-West bandwith (as determined by Bartlett-Kernel).
*
and **denote rejection of the null hypothesis at the 1% and 5% levels respectively.
Tests for unit roots have been carried out in E-VIEWS 4.1.
Johansen test results show that no co-integration exists between each pair of the
variables according to trace test results. However, there is still room for investigating short run causality links between our variables. For instance, when we
transform the logarithm of each variable into stationary form by first differencing,
the resulting transformed variables become now the growth rates. Thus, Granger
causality test can be employed for these variables within the VAR framework.
Table II. Co-integration Tests based on the Johansen (1988) and Johansen
and Juselius (1990) Approach
Trace
5%
1%
Variables
Critical
Critical
Statistic
Value
Value
(1) y, Open and Tour (VAR lag = 2)
H0: r = 0
H0: r ≤ 1
H0: r ≤ 2
22.08
7.90
0.72
29.68
15.41
3.76
35.65
20.04
6.65
11.57
0.37
15.41
3.76
20.04
6.65
8.61
0.03
15.41
3.76
20.04
6.65
8.94
0.29
15.41
3.76
20.04
6.65
(2) y and Open (VAR lag = 2)
H0: r = 0
H0: r ≤ 1
(3) y and Tour (VAR lag = 6)
H0: r = 0
H0: r ≤ 1
(4) Open and Tour (VAR lag = 2)
H0: r = 0
H0: r ≤ 1
Notes: 1. r denotes the number of co-integrating vectors.
2. Akaike Information Criterion (AIC) and Schwartz Criteria (SC) were
used to select the
Number of lags required in the co-integration test. Both gave the same
level of lag order.
Since there are methods for the lag length selection in the recent literature such
as AIC (Akaike Information), SIC (Schwartz Information Criterion) and Hsiao’s
(1979) sequential procedure (which combines Granger’s definition of causality
and Akaike’s minimum final prediction error (FPE) criterion) to find optimum lag
1168
levels for the VAR models, the lags from1 to 3 are preferred for testing the direction of causality between the variables.. Pindyck and Rubinheld (1991) point out
that it would be best to run the test for a few different lag structures and make sure
that the results were not sensitive to the choice of lag length.
Table III. Granger Causality Tests
Null Hypothesis
F–
Statistic
F–
Statistic
Result
F–
Statistic
Lag
1
2
3
0.64
0.20
0.20
1.41
0.33
1.14
y...….open
0.48
0.04
0.39
0.65
0.62
0.20
y.....Tour
5.38**
0.05
2.62***
0.20
1.72
0.34
Tour ⇒ Open
(1) y and Open
Open does not Granger cause y
y does not Granger cause Open
(2) y and Tour
\Tour does not Granger cause y
y does not Granger cause Tour
(3) Open and Tour
Tour does not Granger cause Open
Open does not Granger cause Tour
Note:
**
and *** denote significance at 5% and 10% levels respectively.
Table III reports Granger causality test results based on the VAR models. As
can be seen from the table, the only causation was obtained between trade openness and foreign tourist arrivals to North Cyprus, which is unidirectional causation
from tourist arrivals to trade openness, according to the results of this study. The
other pairs of the variables did not give any direction of causality for the Turkish
Cypriot case.
4. CONCLUSION
This study empirically investigated the relationship between international
tourist arrivals, trade openness and economic growth in the Turkish Cypriot
economy. Results suggest no co-integration and thus no long run equilibrium
relationship between these variables for the Turkish Cypriot case. But, the short
run Granger causality tests suggest only one causation, which is unidirectional
causation running from foreign tourist arrivals to trade openness. The other pair of
variables do not indicate any causation for the Turkish Cypriot economy even in
the short run context. Lastly, results of this study show that since the Turkish
Cypriot economy heavily depends on imports from abroad and foreign tourist
1169
arrivals give causation to trade openness, international tourism sector plays a
significant role in promoting imports for raw materials and investment goods, and
thus, investments in the tourism industry.
REFERENCES
Arize, A.C. (2002), Imports and Exports in 50 Countries: Tests of Co integration and Structural Breaks, International Review of Economics and Finance, 11, 101-115.
Bahmani-Oskooee, M. And Alse, J. (1993), Export Growth and Economic Growth: An Application of Co integration and Error Correction Modeling, The Journal of Developing
Areas, July, 535-542.
Balaguer, J. and Jorda, M. C. (2002), Tourism as a Long-Run Economic Growth Factor: the
Spanish Case, Applied Economics, 34, 877-884.
Bowe, M., Briguglio, L. And Dean, J.W. (Eds) (1998), Banking and Finance in Islands and
Small States, Pinter, London and Washington, DC.
Campbell, J.Y. and Perron, P. (1991), Pitfalls and Opportunities: What Macroeconomists
Should Know About Unit Roots, Technical Working Paper, 100, NBER Working Paper
Series, April.
Chang, T. et al. (2000), Exports, Imports and Income in Taiwan: An Examination of the
Export Led Growth Hypothesis, International Economic Journal, Summer, 151-160.
Cheung, Y. and Lai, K. (1993a), Finite-sample Sizes of Johansen’s Likelihood Ratio Tests
for Co integration, Oxford Bulletin of Economics and Statistics, August, 313-328.
Chow, P.C.Y. (1987), Causality between Export Growth and Industrial Development: Empirical Evidence from the NICs, Journal of Development Economics, February, 55-63.
Clancy, M. J. (1999), Tourism and Development: Evidence from Mexico, Annals of Tourism Research, 26 (1), 1-20.
Deme, M. (2002), An Examination of the Trade-Led Growth Hypothesis in Nigeria: A
Cointegration, Causality, and Impulse Response Analysis, The Journal of Developing
Areas, 36 (1), 1-15.
Dickey, D. and Fuller, W.A. (1981), Likelihood Ratio Statistics for Autoregressive Time
Series with a Unit Root, Econometrica, 49, 1057-72.
Dixit, A. and Norman, V. (1980), Theory of International Trade, Cambridge, Cambridge
University Press.
Doldado, J., Jenkinson, T. And Sosvilla-Rivero, S. (1990), Cointegration and Unit Roots,
Journal of Economic Surveys, 4, 249-73.
Doyle, E. (2001), Export-Output Causality and The Role of Exports in Irish Growth: 19501997, International Economic Journal, 15 (2), 147-161.
Enders, W. (1995), Applied Econometric Time Series, John Wiley & Sons, Inc., U.S.A..
Fountas, S. and Wu, J.L. (1999), Are the U.S. Current Account Deficits Really Sustainable?, International Economic Journal, 13 (Autumn), 51-58.
Giles, J.A. and Williams, C.L. (1999), Export-Led Growth: A Survey of the Empirical Literature and Some Noncausality Results, Econometrics Working Paper, EWP9901, Department of Economics, University of Victoria, Canada.
Gould, D.M. and Ruffin, R.J. (1996), Trade Deficits: Causes and Consequences, Federal
Reserve Bank of Dallas Economic Review, 10-20 (fourth quarter).
Granger, C.W.J. (1969), Investigating Causal Relations by Econometric Models and CrossSpectral Methods, Econometrica, 36, 424-438.
Granger, C.W.J. (1986), Developments in the Study of Co integrated Variables, Oxford
Bulletin of Economics and Statistics, 48 (August), 213-228.
Hamilton, J.D. (1994a), Time Series Analysis, Princeton University Press.
1170
Howard, M. (2002), Causality between Exports, Imports and Income in Trinidad and Tobago, International Economic Journal, 16 (4), 97-106.
Husted, S. (1992), The Emerging U.S. Current Account Deficit in the 1980s: A Cointegration Analysis, Review of Economics and Statistics, 74 (February), 159-166.
Hazari, B. R. and Sgro, P. (1995), Tourism and Growth in a Dynamic Model of Trade,
Journal of International Trade & Economic Development, 4 (2), 243-252.
Hsiao, C. (1979), Causality in Econometrics, Journal of Economic Dynamics and Control,
November, 321-346.
Jung, W.S. and Marshall, P.J. (1985), Exports, Growth and Causality in Developing Countries, Journal of Development Economics, February, 1-12.
Krugman, P. (1979), A Model of Balance of Payments Crises, Journal of Money, Credit
and Banking, 11 (3), 311-25.
Kuznets, S. (1966), Economic Growth of Small Nations, the Modern Economic Growth,
Yale University Press, London.
Lancaster, K. (1980), Intra-Industry Trade under Perfect Monopolistic Competition, Journal of International Economics, 10, 151-175.
Lim, C. (1997), An Econometric Classification and Review of International Tourism Demand Models, Tourism Economics, 3 (1), 69-81.
Mehmet, O. and Tahiroglu, M. (2002), Growth and Equity in Microstates: Does Size Matter
in Development?, International Journal of Social Economics, 29 (½), 152-162.
Phillips, P.C.B. and Perron, P. (1988), Testing for a Unit Root in Time Series Regression,
Biometrica, 75, 335-346.
Pindyck, R.S. and Rubinfeld, D.L. (1991), Econometric Models and Economic Forecasts,
New York, McGraw-Hill.
Proudman, J., Redding, S. and Bianchi, M. (1998), Openness and Growth, Proceedings of
the Bank of England Academic Conference on the Relationship Between Openness
and Growth in the United Kingdom, September 15th, 1997, Bank of England, Martins
Printing Group, U.K..
Rivera-Batiz, L. And Romer, P.M. (1991), Economic Integration and Endogeneous
Growth, Quarterly Journal of Economics, May, 531-556.
Shan, J. and Wilson, K. (2001), Causality between Trade and Tourism: Empirical Evidence
from China, Applied Economics Letters, 8, 279-283.
Sims, C. A. (1972), Money, Income, and Causality, American Economic Review, 62, 540552.
Smadi, S. (2001), Expatriate Adjustment and Organizational Citizenship Behaviour: A
Study in an Institute of Higher Education, Unpublished Ph.D Thesis in Organizational
Behaviour, Marmara University, Istanbul, Turkey.
Song, H., Romilly, P. and Liu, X. (1997), An Empirical Study of Outbound Tourism
Demand in the UK, Paper Presented at the 17th Annual Symposium on Forecasting,
Barbados, June 19-21.
SPO (State Planning Organization) (2003), Economic and Social Indicators, Prime
Ministry, Nicosia, North Cyprus.
Toda, H.Y. and Phillips, P.C.B. (1993), Vector Autoregression and Causality, Econometrica, 61, 1367-1393.
Witt, S. F. and Witt, C. A. (1992), Forecasting Tourism Demand: A Review of Empirical
Research, International Journal of Forecasting, 11, 447-75.
Yeşilada, B. and Bıçak, H. (1995), The European Court Of Justice Decision On Trade With
Northern Cyprus: Implications For The Cyprus Conflict, Presented to the Conference
of the 91th Annual Meeting and Exhibition of the American Political Science Association, Chicago, U.S.A..