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RESEARCH PAPER 23
Political stability and tourism sector development in
Mediterranean countries: a panel cointegration and
causality analysis
Yilmaz Bayar
1
* and Behçet Yener 2
Received: 08/03/2018 Accepted: 25/08/2018
1
Usak University, 64100, Turkey, E-mail: yilmaz.bayar@usak.edu.tr
2 Usak University, 64100, Turkey, E-mail: behcetyener@gmail.com
* Corresponding author
Abstract
Tourism sector has become an important source of domestic income for a great number of countries
across the world with the contribution of diminishing and removing the barriers over the international
mobility of people and considerable developments and cost reductions in transport sector. As a
consequence, many countries have designed and implemented various policies to draw tourists and
in turn raise their tourism receipts. In this regard, this study investigates the impact of political
stability on the development of tourism sector in selected countries from Mediterranean region over
the period 2002-2015 with Westerlund (2007) error-correction-based panel cointegration test and
Dumitrescu and Hurlin (2012) panel causality test. The findings of the study revealed that political
stability had a positive impact on the development of tourism sector in the long run. Furthermore, a
two-way causality between tourism sector development and political stability was discovered.
© 2019 Varna University of Management. All rights reserved
Keywords: political stability, trade openness, tourism sector development, panel data analysis
Citation: Bayar, Y., B. Yener (2019) Political stability and tourism sector development in
Mediterranean countries: a panel cointegration and causality analysis. European Journal of Tourism
Research 21, pp. 23-32
Introduction
Tourism sector has become an important
source of revenue and an item of international
trade together with the globalization and the
significant developments and cost reductions in
transportation. The number of international
tourists have reached 1,235 million in 2016
from 25 million in 1950 globally and the
countries hosting the international tourists
earned worldwide have increased US$ 1,220
billion in 2016 from US$ 2 billion in 1950.
Furthermore, international tourists spent about
US$ 216 billion for the services of international
passenger transportation in 2016. In
conclusion, global tourism industry constitutes
about 7% of the global exports of goods and
services (UNWTO, 2017).
Political stability and tourism sector development in Mediterranean countries: a panel cointegration and causality analysis.
24
Table 1. Political stability index of Mediterranean countries
Country
2002
2015
Albania
-0.39
0.36
Algeria
-1.69
-1.05
Bosnia and Herzegovina
-0.25
-0.45
Croatia
0.52
0.58
Cyprus
0.09
0.54
Egypt
-0.46
-1.34
France
0.85
0.27
Greece
0.79
-0.23
Israel
-1.51
-1.12
Italy
0.76
0.34
Lebanon
-0.45
-1.72
Malta
1.52
1.04
Morocco
-0.35
-0.34
Slovenia
1.20
0.92
Spain
0.39
0.29
Tunisia
0.07
-0.87
Turkey
-0.87
-1.28
West Bank and Gaza
-1.76
-2.13
Source: World Bank (2017c)
Tourism sector includes many economic
implications for the national economies. In this
regard, tourism can improve the balance of
payments, make a contribution to economic
growth through increasing the public and
private investments in the sector and demand
for local goods and services, decrease
unemployment by creating jobs and also raise
the tax revenues (Pablo-Romero and Molina,
2013). In as much as, the share of travel &
tourism sector in global GDP was 10.4% and
created 313 million jobs in 2017 and this
corresponded to the 9.9% of total employment
(World Travel & Tourism Council, 2018).
However, many social, economic and political
factors are determinative for tourism attraction.
In this paper, we will focus on the effect of
political stability and trade openness on the
development of tourism sector. Tourism sector
is seriously influenced by political instability and
violence and malfunctioning resulting from
political instability, because the tourists spend
money and time to have comfort, fun, and
peacefulness. Furthermore, security and risk of
incurring losses due to political instability are
the crucial factors in decision-making process
about destination selection for any tourism
types (Saha and Yap, 2014:510). Therefore,
political instability is expected to affect the
development of tourism sector in theoretical
terms.
Mediterranean region including exceptional
historical, cultural, and natural places for
various tourism types, one of the leading
tourism centre with about one third of tourism
revenues and half of the number of
international tourists in the world (Šimundić and
Kuliš, 2016), consists of the countries with
different economic development levels, political
stability, and openness. The Mediterranean
countries experienced significant improvements
and deteriorations at their political stability
during the 2002-2015 period as seen in Table
1. Especially Albania, Algeria, and Cyprus
experienced significant improvements at their
political stability, while Egypt, Greece,
Lebanon, Tunisia, Turkey, and West Bank and
Gaza experienced significant deteriorations at
their political stability.
We research the interplay among political
stability, trade openness, and tourism revenues
for the sample of Mediterranean countries
considering their share in global tourism sector
and considerable changes in their political
Bayar, Y., B. Yener (2019) / European Journal of Tourism Research 21, pp. 23-32
25
stability as seen in Table 1 during the study
period of 2002-2015. In this context, the paper
will be one of the early studies investigating the
interaction among tourism development,
political stability, and trade openness. The
relevant empirical literature was summarized in
the next section and Section 3 gives
information about dataset and econometric
method. Then Section 4 presents the findings
of empirical analysis and the paper is
concluded with Conclusion section.
Literature Review
The relevant literature generally has focused
on the interaction between tourism-growth
considering the quickly growing global tourism
sector and most of the studies have revealed
that tourism development makes a significant
positive contribution to the economic growth
(e.g., see Fayissa et al., 2008; Pablo-Romero
and Molina, 2013; Antonakakis et al. 2015; Du
et al., 2016; Phiri, 2016; Selimi et al., 2017;
Shahzad et al. 2017). However, a limited
number of scholars have explored the impact of
political stability and trade openness on tourism
revenues and the studies revealed that political
instability affected the tourism development
negatively (e.g., see Neumayer, 2004; Basu
and Marg, 2012; Ingram et al., 2012; Saha and
Yap, 2014; Mushtaq and Zaman, 2014). In
other respects, the limited relevant empirical
studies examining the interplay between trade
openness and tourism development reached
mixed findings for different countries (e.g. see,
Shan and Wilson, 2001; Habibi et al., 2009;
Surugiu and Surugiu, 2011; Santana-Gallego et
al., 2016).
Neumayer (2004) researched the effects of
various indicators related to the political
motivated violence on the development of
tourism sector with panel regression analysis
and discovered that various forms of political
violence affected the development of tourism
sector negatively. On the other side, Enders
and Sandler (1991) analysed the impact of
terrorism on the development of tourism sector
in Spain during 1970-1988 period and revealed
that terrorism affected tourism sector
development negatively. Llorca-Vivero (2008)
also researched the effects of terrorism on the
tourism development in the world (134
countries) over the period 2001-2003 and
revealed that terrorism affected the tourism
development negatively. Furthermore, Arana
and Leon (2008) reached similar findings with
Enders and Sandler (1991) and Llorca-Vivero
(2008).
Basu and Marg (2012) researched the impact
of political instability consisting of terrorism on
the development of tourism sector in Egypt,
Jordan and Lebanon from Middle East region
during different periods between 1997 and
2011 for each country and discovered that
political instability affected tourism receipts
negatively. On the other hand, Ingram et al.
(2012) investigated the effect of political
instability on the development of tourism sector
in Thailand with using questionnaire and semi-
structured interviews and discovered that
political instability affected the tourism
negatively, but the impact was found to be
relatively higher in the respondent who have
not been in Thailand before.
Saha and Yap (2014) also examined the effect
of political instability and terrorism on the
development of tourism sector in terms of
revenue and tourist arrivals in 139 countries
during the 1999–2009 with regression analysis
and discovered that political instability affected
the development of tourism sector negatively.
On the other side, Mushtaq and Zaman (2014)
researched the long-run interplay among
political instability, terrorism, and tourism sector
development in 4 SAARC (South Asian
Association for Regional Cooperation) states
including Bangladesh, India, Pakistan, and Sri
Lanka over the period 1995-2012 employing
panel cointegration analysis and discovered
that political instability affected tourism receipts
negatively. Ivanov et al. (2017) researched the
effect of political stability on the development of
tourism sector in Ukraine by the data collected
from the questionnaires with hotel and travel
agency managers. Their analysis revealed that
political instability negatively affected the
tourism development in Ukraine.
Shan and Wilson (2001) analysed the causality
between international trade and tourism for
China with Toda and Yamamoto (1995) test
and revealed a bilateral causality between two
variables. On the other side, Habibi et al.
(2009) explored the major determinants of the
Political stability and tourism sector development in Mediterranean countries: a panel cointegration and causality analysis.
26
Table 2. Dataset summary
Variables
Explanation
Data Source
TREV
International tourism, receipts (% of GDP)
World Bank (2017a and 2017b)
PS
Political stability and absence of
violence/terrorism
World Bank (2017c)
TO
Export and import total (% of GDP)
World Bank (2017d)
Table 3. Correlation matrix of the dataset
DTREV
DPS
DPS
0.114130
DTO
0.127906
0.192581
tourism sector development in Malaysia over
1995–2005 period with dynamic regression
analysis and discovered that trade openness
had no significant effects on tourism. Surugiu
and Surugiu (2011) also analysed the causal
interaction between tourism and trade
openness in Romania during 1990-2009 period
with Granger causality test and revealed a
unilateral causality from trade openness to the
development tourism sector. Lastly, Santana-
Gallego et al. (2016) researched the interplay
between trade and tourism sector development
in 195 countries in 2012 with employing gravity
model and found that tourism increased the
trade.
Data and Econometric Methodology
We explored the short and long run interplay
among political stability, trade openness, and
tourism development in 18 states from
Mediterranean region over the period 2002-
2015 with Westerlund (2007) panel
cointegration test and Dumitrescu and Hurlin
(2012) panel causality test.
Data
International tourism receipts as % of GDP
(TREV) representing tourism development
were used as the explained variable in the
study. On the other side, political stability and
absence of violence (PS) variable was
employed as a proxy for political stability. The
value of the PS index indicates the perceptions
of the possibility of political instability and/or
politically motivated violence, including
terrorism and varies between -2.5 and 2.5
(higher values mean higher levels of political
stability). PS index was calculated from major
information sources obtained from Economist
Intelligence Unit Riskwire & Democracy Index,
World Economic Forum Global
Competitiveness Report, Cingranelli Richards
Human Rights Database and Political Terror
Scale, iJET Country Security Risk Ratings,
Institutional Profiles Database, Political Risk
Services International Country Risk Guide, and
Global Insight Business Conditions and Risk
Indicators (see details Kaufmann et al. (2010)
and World Bank (2017c)). Also export and
import total as % of GDP representing trade
openness (TO) was used as a control variable
and all the variables were extracted from the
database of World Bank (2017a, 2017b, 2017c,
and 2017d).
The existence of data determined study sample
and period. The sample consisted of 18 states
from Mediterranean region including Albania,
Algeria, Bosnia-Herzegovina, Croatia, Cyprus,
Egypt, France, Greece, Israel, Italy, Lebanon,
Malta, Morocco, Slovenia, Spain, Tunisia,
Turkey, and West Bank and Gaza except
Gibraltar, Libya, Monaco, Montenegro, and
Syria. Stata 14.0, Gauss 11.0, and WinRATS
Pro. 8.0 statistical programs were utilized for
the conduct of empirical analysis. The
correlation matrix of the dataset was displayed
in Table 3 and the correlation matrix revealed a
positive correlation between both tourism
revenues and political stability and trade
openness and tourism revenues.
Econometric methodology
Westerlund (2007) error-correction–based
panel cointegration test and Dumitrescu and
Hurlin (2012) causality test were used to
analyse the interaction among tourism
revenues, political stability and trade openness.
Bayar, Y., B. Yener (2019) / European Journal of Tourism Research 21, pp. 23-32
27
Westerlund (2007) error-correction–based
panel cointegration test considers both cross-
sectional dependence and heterogeneity and
requires that all the series are I(1). The
cointegration test includes four cointegration
tests based error correction model and in turn
calculates four test statistics (two are panel
statistics and the others are group statistics).
Westerlund (2007) cointegration examines the
null hypothesis by investigating that the error
correction term equals to zero or not in a
conditional error correction model. The null
hypothesis asserting that there is no
cointegrating relationship is rejected in case the
null hypothesis about non-existence of error
correction is denied. The simulation results
verified that the results of Westerlund (2007)
cointegration test are more robust relative to
the tests based on residuals such as Pedroni
(1999) and (2004) for small samples. At the
first stage of the test, the model is estimated
with dynamic ordinary least squares as
following:
Then error-correction term and its standard
error are calculated for overall panel and the
panel cointegrating statistics are calculated as
follows:
Dumitrescu and Hurlin (2012) panel causality
test rests on vector autoregression and
disregards the cross-sectional dependence, but
regards heterogeneity. However, the causality
test is able to yield robust results even in case
of cross-sectional dependence. Dumitrescu
and Hurlin (2012) suggests that Zhnc test
statistic with asymptotic distribution should be
taken notice in case of TN and Ztild test
statistic with semi-asymptotic distribution
should be taken in consideration in case of
NT. The causality test statisticcs are figured
as follows:
Empirical Analysis
In the part of empirical analysis, first Pesaran
(2004) and Pesaran et al. (2008) cross-
sectional dependence tests and delta tilde and
adjusted delta tilde homogeneity tests of
Pesaran and Yamagata (2008) are conducted
to see the availability of cross-sectional
dependence among the series. Then,
Westerlund (2007) panel cointegration test is
used to investigate the cointegrating
relationship among the variables. Lastly,
Dumitrescu and Hurlin (2012) causality test is
used to analyse the causal interaction among
tourism revenues, political stability and trade
openness.
Results of cross-sectional dependence and
homogeneity tests
First, cross-sectional dependence among the
cross-section units was analysed with
test of Pesaran (2004) and test of
Pesaran et al. (2008), since the cross-section
size of the dataset (N=18) was higher than the
time dimension of the dataset (T=14). The test
results were shown in Table 3 and the null
hypothesis (there is cross-sectional
independence among the cross-section units)
was denied at 5% level of significance and we
revealed the availability of cross-sectional
among the series. Then, we analysed the
homogeneity of the cointegrating coefficients
with adjusted delta tilde test of Pesaran and
Yamaga (2008) and the results were shown in
Table 4. The null hypothesis (cointegrating
coefficients are homogeneous) was denied at
5% level of significance and we concluded that
the cointegrating coefficients differ among the
cross-section units.
Results of Pesaran (2007) CIPS unit root test
The integration levels of the series are
important for selection of cointegration test and
implementation of causality test. So, the
integration levels of the variables were
Political stability and tourism sector development in Mediterranean countries: a panel cointegration and causality analysis.
28
Table 4. Cross-sectional dependence and homogeneity test results
Cross-sectional dependency tests
Variables
Test Statistic (P value)
(P value)
TREV
9.562 (0.009)
28.905 (0.001)
PS
12.075 (0.015)
14.561 (0.004)
TO
7.349 (0.026)
17.228 (0.000)
Homogeneity tests
Test
Test Statistic
P value
18.321
0.003
20.724
0.016
Table 5. Panel CIPS unit root test results
Variables
Constant
Constant+Trend
TREV
-1.165
-1.189
D(TREV)
-7.552*
-9.569*
PS
-1.096
-1.135
D(PS)
-9.814*
-10.227*
TO
-0.842
-0.905
D(TO)
-9.773*
-9.804*
Note: Maximum lag length was taken as 2 and optimal lag length was determined considering Schwarz information
criterion
* Null hypothesis was rejected at 5% significance level.
Table 6. Westerlund (2007) panel cointegration test results
Test Statistic
Value
Bootstrap P Value
-9.562
0.016
8.671
0.002
-9.839
0.015
-8.224
0.004
analysed with CIPS (cross-sectionally
augmented Im-Pesaran-Shin (IPS) (2003)) unit
root test Pesaran (2007) considering the
existence of cross-sectional dependence
among the series and the test results were
shown in Table 5. The results demonstrated
that all the variables were I(1).
Westerlund (2007) error-correction–based
panel cointegration test results
The cointgerating relationship among tourism
revenues, political stability and trade openness
was analysed with Westerlund (2007) panel
cointegration test and the test results were
shown in Table 6. The group test statistics
should be considered due to the existence of
cross-sectional dependence among the
variables. Also the cointegration statistics
should be checked with bootstrap critical
values of Chang (2004) in the case of cross-
sectional dependence (Westerlund, 2007). So
the null hypothesis (there are no cointegrating
relationship among the variables) was denied
and we inferred that there was a long run
relationship among the series.
Estimation of cointegrating coefficients
The long-run coefficients were estimated by
FMOLS estimator regarding only heterogeneity
and DSUR estimator considering both cross-
sectional dependence and heterogeneity and
the estimations were shown in Table 7. The
findings of both FMOLS and DSUR estimators
indicated that political stability and trade
openness affected the development of tourism
sector positively. The estimations by FMOLS
revealed that 1% increase in PS and TO
respectively raise the tourism revenues by 12%
and 9.3%. On the other side, the estimations by
DSUR indicated that 1% increase in PS and
TO respectively raise the tourism revenues by
12.6 % and 10.4%. So both political stability
and trade openness were identified to be
Bayar, Y., B. Yener (2019) / European Journal of Tourism Research 21, pp. 23-32
29
Table 7. Cointegrating coefficients estimation
Country
PS
TO
FMOLS
DSUR
FMOLS
DSUR
Albania
0.129*
0.115*
0.094*
0.104*
Algeria
0.105*
0.091*
0.042*
0.078*
Bosnia-Herzegovina
0.128*
0.125*
0.061*
0.092*
Croatia
0.101*
0.112*
0.110*
0.128*
Cyprus
0.052*
0.074*
0.074*
0.091*
Egypt
0.083*
0.095*
0.036*
0.105*
France
0.254*
0.197*
0.142*
0.174*
Greece
0.120*
0.138*
0.119*
0.139*
Israel
0.103*
0.120*
0.143*
0.125*
Italy
0.162*
0.171*
0.179*
0.166*
Lebanon
0.094*
0.105*
0.077*
0.062*
Malta
0.117*
0.136*
0.112*
0.127*
Morocco
0.146*
0.155*
0.074*
0.094*
Slovenia
0.103*
0.123*
0.080*
0.042*
Spain
0.118*
0.127*
0.162*
0.131*
Tunisia
0.132*
0.142*
0.082*
0.090*
Turkey
0.135*
0.149*
0.063*
0.073*
West Bank and Gaza
0.092*
0.106*
0.041*
0.052*
Panel
0.120*
0.126*
0.093*
0.104*
*significant at 5% level of significance
Table 8. Dumitrescu and Hurlin (2012) panel causality test results
Null Hypothesis
Test
Test Statistics
Prob.
TREV↛ PS
Whnc
3.997
0.001
Zhnc
3.263
0.000
Ztild
2.564
0.017
PS↛TREV
Whnc
6.421
0.000
Zhnc
5.086
0.008
Ztild
3.724
0.002
TREV↛TO
Whnc
6.451
0.000
Zhnc
5.012
0.012
Ztild
4.997
0.000
TO↛TREV
Whnc
1.262
0.126
Zhnc
1.077
0.108
Ztild
1.009
0.137
significant determinants for tourist attraction in
the long run.
The problems of heteroscedasticity and
autocorrelation were eliminated by Newey-
West method.
The globalized world and the improvements in
telecommunication enable us to be transiently
informed about every development in the world.
So, social, economic and political
developments have become more important for
the tourists in selection of destination for
various touristic trips. In this regard, security
concern is one of the leading priorities for
selection of tourist destination and one step
ahead of economic conditions. There is a
mutual interaction among political stability,
political violence, social unrest, and terrorism
which pose a serious threat to the development
of tourism sector. The results of the study
verified these theoretical considerations for the
sample of Mediterranean region. Our findings
are also consistent with the relevant literature
(especially the findings of Basu and Marg
(2012) and Saha and Yap (2014)).
Political stability and tourism sector development in Mediterranean countries: a panel cointegration and causality analysis.
30
Dumitrescu and Hurlin (2012) panel causality
test
The causal interplay among tourism
development, political stability, and trade
openness was analysed with Dumitrescu and
Hurlin (2012) causality test and the test results
were shown in Table 8. The test results elicited
a bidirectional causal interplay between tourism
development and political stability and a one-
way causality from tourism development to
trade openness. So there was a mutual
interaction between political stability and
tourism development. In other words, political
stability feeds the development of tourism
sector and in turn tourism sector development
feeds the political stability. On the other side,
tourism development has significant impact on
trade openness by contributing to the
transnational flows of goods and services.
Moreover, the relevant literature showed that
the causal interplay between trade and tourism
(from trade to tourism or from tourism to trade)
varied as in Surugiu and Surugiu (2011) and
Gallego et al. (2016). But in both cases, we can
say the existence of mutual interaction between
trade and tourism sector development.
Conclusion
Tourism sector has been expanded
significantly in the world together with the
globalization process and significant
improvements and cost reductions especially in
the transport sector. The significant expansion
in tourism sector has potential to affect the
economies positively through encouraging the
public and private investments, improving the
balance of payments, and creating jobs.
However, many social, economic, political and
cultural factors will be determinative in tourist
attraction. In this paper, we focused on an
untouched area, the interplay between political
stability and tourism sector development, in the
relevant literature and therefore aimed making
a contribution to the relevant literature. So we
explored the interplay among political stability,
trade openness, and tourism development in
18 states from Mediterranean region over the
period 2002-2015 with Westerlund (2007) error
correction based panel cointegration test and
Dumitrescu and Hurlin (2012) panel causality
test. However, data availability constrained the
study sample and period.
The findings of the study revealed that political
stability and trade openness positively affected
the development of tourism sector in the long
run. Moreover, the panel causality test revealed
a bilateral causal interplay between tourism
development and political stability and a
unilateral causal interplay from tourism
revenues to trade openness. So there was a
mutual interaction between political stability
and tourism sector development for the sample
of Mediterranean countries. However, a
different interaction between political stability
and tourism development can vary for different
groups of countries, because most countries
from Mediterranean region such as Albania,
Algeria, Lebanon, and Tunisia have not
reached the threshold level in the areas of
economic and institutional development and
legal infrastructure. On the other side, tourism
development has significant impact on trade
openness by contributing to the transnational
flows of goods and services.
The findings of the paper showed that political
uncertainty and political motivated violence and
security problems significantly affect the
development of tourism sector. In this context,
establishment of an institutional and legal
framework promoting the political stability and
diminishing security concerns will also
contribute to the development of tourism
sector. Future studies can be focused the role
of institutions and rule of law in the interplay
between political stability and tourism sector
development for different groups of countries.
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