An Empirical Study on Tourism and Economic Growth in Greece:
An Autoregressive Distributed Lag Boundary Test Approach
ATHANASIA MAVROMMATI1, THANASSIS KAZANAS2, ALEXANDRA PLIAKOURA1,
STAVROS KALOGIANNIDIS3, FOTIOS CHATZITHEODORIDIS4*
1Department of Food Science and Technology
University of Patras,
Agrinio,
GREECE
2School of Economics,
Aristotle University of Thessaloniki,
GREECE
3Department of Business Administration,
University of Western Macedonia,
GREECE
4Department of Management Science and Technology,
University of Western Macedonia,
GREECE
Abstract: - The objective of this research is to analyze the impact of the tourism industry on the economic
growth of Greece. The study employs empirical analysis and time series econometric techniques to evaluate the
Tourism-Driven Growth hypothesis. Information spanning from 1995 to 2022 about the growth of tourism
(TR), expenditure on tourism (TE), average expenditure on tourism per capita (PCTE), and economic growth
(GDP) was utilized. Initially, the authors examined the interconnections among these variables using the
Autoregressive Distributed Lag (ARDL) Bounds Test. After identifying a statistically significant cointegration
relationship, the study proceeded to estimate the long-term and short-term coefficients associated with these
variables. Based on the results, it appears that there is a long-term correlation between economic growth and
tourism, indicating that international tourism can have a positive impact on economic expansion.
Key-Words: - Tourism, Economic growth, Tourism per capita, ARDL bounds tests, Tourist revenues, Greece,
Economic development, Tourism expenditures.
Received: August 29, 2023. Revised: December 11, 2023. Accepted: January 9, 2024. Published: January 26, 2024.
1 Introduction
Tourism makes a significant contribution to the
economy of many nations, creating employment,
producing cash, and driving economic progress.
Tourism and economic development have been
widely researched, with a growing body of
literature focusing explicitly on the setting of
developing nations, [1]. Tourism development has
become a major corporate activity, revenue,
employment, and foreign currency source for many
nations. Many nations, particularly developing
countries, depend on the dynamic tourist industry
as the primary source of income creation, private
sector growth, and infrastructure, [2], [3], [4].
Recognizing tourism's growing significance,
governments, local authorities, and the private
sector in many nations, [5], [6], [7], as well as the
public universities, [8], have started to dedicate
resources to tourist development.
Tourism development has become a major
corporate activity, revenue, employment, and
foreign currency source for many nations. Many
nations, particularly developing countries, depend
on the dynamic tourist industry as the primary
source of income creation, private sector growth,
and infrastructure, [2], [3], [4].
Without question, tourism is one of the most
significant sectors of the Greek economy. Despite
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
588
Volume 21, 2024
being a huge, planned, and holiday-oriented tourist
destination, Greece features various types of
tourism. Many experts believe that tourism is a
development instrument with many economic
advantages that can be objectively quantified and
utilized for further development. Official data for
the next seven years show that an increase in tourist
traffic may produce $16.5 billion in income and
225 thousand new employments. Furthermore, for
every million extra visitors that visit our nation,
GDP rises by 1%.
Tourism growth is critical to Greece's
economic success since it is one of the country's
most important industries and has a large beneficial
influence on environmental activities. Greece's
tourist business has prospered in the last ten years
as a result of its attractiveness as a holiday
destination and improvements to its infrastructure.
During this time, inbound visitors, and travel
receipts more than doubled, helping to counter the
extended recessionary effect of Greece's post-debt
crisis in 2010. While arrivals and the number of
significant tourist sources have increased, daily
expenditure remains low by worldwide standards,
and demand is concentrated on heritage sites, [9],
[10]. Nonetheless, Greece's dependence on tourism
makes it susceptible to exogenous shocks like the
recent epidemic.
Tourism and economic development have been
extensively researched, especially in developing
nations. Tourism provides economic benefits such
as foreign currency acquisition, job creation,
infrastructure development promotion, and
economic growth stimulation, [11], [12], [13], [14],
[15], [16], [17], [18], [19]. Furthermore, tourism
often has a multiplier impact on the economy by
increasing investment in adjacent industries such as
transportation, hotel, and retail. Understanding the
link between tourism and economic development is
so crucial for governments and corporate leaders
trying to maximize the tourist industry's potential
advantages. Empirical research on the link between
tourism and economic development has produced
conflicting findings. Some studies identified a
positive and statistically significant association
between tourism and economic growth, while
others found little or no evidence, [18], [20], [21],
[22], [23], [24].
This research adds two new chapters to the
existing literature. For starters, it is the first
empirical study of the short- and long-term link in
Greece between economic growth, tourist revenues,
tourism expenditures, and tourism per capita
spending. Second, to investigate the long-term
connection, the research used the Autoregressive
Distributed Lag (ARDL) limits test of cointegration
and employs the ARDL framework to evaluate both
long-term and short-term dynamics. The
methodology employed in this study, including the
versatile ARDL approach, can be effectively
applied to diverse sectors of the national economy.
By identifying sector-specific variables and
adjusting the multivariate regression model
accordingly, one can investigate the impact of
factors like investment, government spending, or
exports on economic growth. The ARDL approach
facilitates the exploration of both short-term and
long-term relationships between variables,
providing comprehensive insights into the
dynamics of different economic sectors. This
approach enhances our understanding of the key
drivers of economic growth, aiding policymakers
and researchers in informed decision-making.
2 Literature Review
In recent decades, both rich and developing nations
have collaborated extensively on economic growth
and tourist income. With tourism on the rise in
many countries, policymakers are turning to the
causal relationship between economic growth and
tourist earnings, [14], [25], [26], [27].
The concept of tourist-led growth, grounded in
extensive research, postulates the enduring impact
of tourism on economic development. This theory
posits tourism as a potent growth engine, wielding
the capacity to contribute significantly to GDP
growth, the creation of employment opportunities,
and the influx of foreign currency revenues. In this
symbiotic relationship, economic growth
reciprocally influences tourism development,
fostering a positive feedback loop. This influence is
evident in the development of crucial elements like
transportation, information and communication
technology, and the establishment of essential
facilities and infrastructure such as e-money
systems, hotels, restaurants, and various
entertainment services and amenities. The
interconnected nature of economic growth and
tourism underscores the reciprocal benefits each
confers upon the other, creating a synergistic
dynamic that propels sustained development and
prosperity.
GDP is a commonly used measure of a
country's economic performance and is often used
as an indication of a country's overall degree of
development. Several indicators are often used to
assess the link between economic growth and
tourist development. These variables include
tourism income, [14], [28], [29], [30], [31], [32],
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
589
Volume 21, 2024
[33], [34], tourism expenditures and average
tourism expenditures per capita, tourism arrivals,
[19], [29], tourism sector employment, and foreign
direct investment. In research and policy analysis,
these variables are often used to quantify and
evaluate the link between economic growth and
tourist development. Researchers and policymakers
may better understand the influence of tourism on
economic growth and design effective policies to
promote sustainable tourism development by
investigating these factors and their
interrelationships, [35], [36], [37]. [38], also argue
for the sustainable recovery of tourism and
hospitality organizations, during and after the
recent pandemic, [39], [40].
Various time series and panel data analysis-
based research approaches have been used;
however experimental investigations have shown
conflicting or inconsistent findings in favor of the
tourism-driven economic development concept.
Several researchers have used Granger causality
tests and time-series data analysis to investigate the
link between tourism and economic growth. [22],
[41], [42], [43], [44], [45], are just a few of the
studies that give data to support the tourism-led
growth concept.
[46], [47], [48], on the other hand, support the
feedback hypothesis, although, [49], [50], [51], do
not give evidence of a link between the two
variables. Other empirical research, however, has
used panel or cross-sectional data analysis to study
the relationship between tourist development and
economic growth. [42], [48], [52], [53], [54], [55],
[56], [57], [58], [59], are some of these
investigations. These research conclusions,
however, are ambiguous, with inconsistent results
regarding the association between tourism and
economic growth.
Tourism has grown rapidly and has emerged as
a substantial and economically competitive
industry, [2], [7], [60], [61], [62], [63], [64]. Aside
from its direct consequences, tourism has had a
hugely beneficial indirect influence on economic
advancement by expanding market possibilities,
raising living standards, boosting government
revenue via income and taxes, and even extending
the production of products and services. Tourism is
now an important component of the economies of
both developed and developing countries, [40],
[65], [66], [67].
3 Data and Model Specifications
Due to a shortage of data availability in The World
repository's data repository, we used a time series
of 28 annual observations from 1995 to 2022. In
our model, this time series should represent both
short-term and long-term correlations between
tourist growth (TR), tourism expenditure (TE),
average per capita tourism expenditure (PCTE),
and economic growth (GDP). All data sets were
acquired from the World Development Indicators
and were measured in current USD.
This study employed a multivariate regression
model to explore the connection between
dependent and independent variables and can be
formulated as follows:
(1)
The above equation tries to explain the
variance in economic development as measured by
GDP based on several independent factors. These
variables include Average Per Capita Tourism
Expenditure (LPCTE), International Tourism
Expenditures (TE), and International Tourism
Receipts (TR).
According to the model, the following is one
method in which the independent variables affect
the dependent variable, which is GDP:
The LPCTE variable, which is an independent
variable, stands for the typical amount of money
that each visitor to Greece spends while they are
there. Because expenditures on tourism contribute
to the economy, it is reasonable to anticipate that an
increase in the average amount spent by tourists per
person would lead to a rise in GDP.
The expenditures of international outbound
tourists from Greece in other countries are
represented by the independent variable TE. This
includes payments made to foreign carriers for
international transportation. These expenditures
include those by residents traveling abroad. It is
anticipated that increased spending from foreign
tourism would result in either an increase or a
decrease in GDP. The amount of money spent on
tourism may have both a good and a negative effect
on economic expansion. Careful planning and
management are required to guarantee that tourism
will contribute to the expansion of the economy in
a sustainable manner.
The total revenue TR (independent variable)
shows the amount of money spent by foreign
tourists who traveled to the United States, which
includes the amount paid to domestic carriers for
overseas travel. These receipts include any other
kind of prepayment that was made for goods or
services that were re-received in the country of
destination. It is expected that higher international
tourism receipts will result in a higher GDP, as it
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
590
Volume 21, 2024
indicates the amount of money that Greece earns
from tourism.
The contributions of each independent variable
to the overall change in GDP are represented by the
coefficients a0, a1, a2, and a3, respectively. A
positive coefficient for LPCTE (a1), for instance,
would imply that a rise in average per capita
expenditures on tourism in Greece is connected
with an increase in GDP.
The presence of an error term (denoted as et in the
equation) indicates the proportion of GDP variation
that cannot be explained by the independent
variables. This difference may be due to factors not
considered during model development, such as
changes in macroeconomic conditions or external
factors affecting tourism.
The data indicates that tourism plays a crucial role
in fostering economic development in Greece, as
demonstrated by its substantial influence on the
country's GDP. Equation (2) represents the
logarithmic conversion of the initial multiple
regression model (1), featuring each variable in its
logarithmic representation. The long-run model can
be articulated as follows:
(2)
In regression analysis, logarithmic
transformations are employed to enhance the
modeling of relationships between variables.
Specifically, in Equation (2), the dependent
variable, GDP, and the independent variables,
LPCTE, TE, and TR, are all represented in
logarithmic form (LGDP, LPCTE, LTE, and LTR,
respectively). This logarithmic transformation
serves to linearize the relationship, simplifying the
application of linear regression techniques for the
analysis and interpretation of the data.
4 Methodology
4.1 ARDL Approach
[68], devised a method to explore long- and short-
term relationships in time series data using the
Autoregressive Distributed Lag (ARDL) model.
This involves estimating the ARDL model with lag
values for both dependent and independent
variables. Criteria like the Akaike Information
Criterion (AIC) guide the selection of lag values.
The ARDL model is then used to constrain lagged
variable coefficients, aiming to validate long-term
relationships and identify connections between
variables. [69], boundary test checks if the
coefficient of the lagging variable, crucial for long-
term associations, falls within predefined upper and
lower bounds. Acceptance of the alternative
hypothesis (indicating a long-run association) and
rejection of the null hypothesis (implying no long-
run association) depend on this evaluation. By
including lagged changes of the dependent and
independent variables in the model; the ARDL
method also enables the estimate of short-run
dynamics. This is accomplished via the use of lags.
This approach is less restrictive, and as a result, it
offers more flexibility. As an alternative to the
conventional integration tests, it is becoming more
popular. Passing the ARDL test does not need all of
the variables in the model to be of the I(0) or I(1)
type. To investigate the dynamic nature of the
connection that exists among economic growth,
tourist revenues, and tourist expenditures, this
methodology was used.
Because it is not necessary for the verification
of this approach that the investigated time series be
integrated of the same degree, the most visible
difference and at the same time the greatest benefit
of this method is that it is not needed that the
inspected time series be integrated of the same
degree, as long as they are of zero or first degree.
Also, ARDL is the most statistically significant
method compared to previous ones for determining
whether or not a long-run association exists in
small samples, [70]. This is because ARDL
considers the likelihood of the relationship existing.
Last but not least, this technique is superior to
others in that it makes use of an error correction
model (ECM) to manage the cointegration of the
variables in the short term without discarding
information about the long term.
In this part of the article, both long-run and
short-run models are discussed, respectively. The
following equation provides a picture of the long-
run model that may be specified according to its
parameters:
(3)
where Δ is the difference operator n, m, c and g
are the lag order, and ut is the error term. Similarly,
the demonstration of the short-run analysis of the
study variable is drawn according to the error
correction model (ECM) of the ARDL and is
specified as follows.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
591
Volume 21, 2024
(4)
The short-run analysis among study variables is
shown in equation (4) by the ECM model, and s, j,
f, and show the lags order.
5 Results
The descriptive statistics of 28 observations from
the time series variables are shown in Table 1. The
normal distribution of the series has been
confirmed by the Jarque-Bera test, indicating that
the variables have a mean of zero and a constant
variance.
The descriptive statistics suggest that the data
is normally distributed with relatively small ranges
and standard deviations, indicating that the data
points are close to the mean values. The negative
skewness values suggest that the data is slightly
skewed to the left, but the Jarque-Bera test
confirms that the data is normally distributed. The
mean LGDP is 26.05490, indicating the central
tendency. The distribution is slightly positively
skewed (0.014679), suggesting a longer right tail.
Kurtosis (2.015732) implies a moderately peaked
distribution. The mean LPCTE is 6.587469,
representing the average percentage of total
employment. Negative skewness (-0.917193)
indicates a longer left tail. Kurtosis (3.297029)
suggests a more peaked distribution. The mean LTE
is 21.82108, signifying the average total revenue.
Negative skewness (-0.881773) suggests a longer
left tail. Kurtosis (2.521170) indicates a moderately
peaked distribution. The mean LTR is 23.18179,
representing another measure of total revenue.
Negative skewness (-0.996425) implies a longer
left tail. Kurtosis (2.852121) suggests a moderately
peaked distribution. Tests the assumption of
normality; lower probabilities (P-Values) indicate
departures from normal distribution. This nuanced
analysis provides specific insights into each
variable's characteristics, aiding in a more targeted
understanding of the data's statistical properties.
Table 1. Descriptive statistical analysis
LGDP
LPCTE
LTR
Mean
26.05490
6.587469
23.18179
Median
26.06381
6.643432
23.37480
Maximum
26.59794
7.007601
23.85889
Minimum
25.59432
5.880533
22.04742
Std. Dev.
0.293199
0.296227
0.543723
Skewness
0.014679
-0.917193
-0.996425
Kurtosis
2.015732
3.297029
2.852121
Jarque-Bera
1.131252
4.028728
4.658877
Probability
0.568004
0.133405
0.097350
Sum
729.5372
184.4491
649.0900
Sum Sq. Dev.
2.321072
2.369267
7.982124
Observations
28
28
28
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
592
Volume 21, 2024
5.1 ADF Unit Root Test Result
The unit root tests help identify the order of
integration for the time series variables, providing
essential information for the subsequent application
of the ARDL bounds test in the analysis of
cointegration relationships. The time series does
not need to be rigorously integrated at I(0) or I(1)
to pass the ARDL bounds test, but it cannot be
stationary at orders bigger than I(1) otherwise the
results would be skewed. Therefore, before doing
the cointegration test, the unit root test must be
applied to all-time series. [71] and ADF-GLS tests
that take into consideration endogenous structural
breaks in the data are utilized in this research to
identify the order of integration. Table 2 displays
the outcomes of the unit root tests for the various
time series.
For four variables—LGDP, LPCTE, LTE, and
LTR—the results of the Augmented Dickey-Fuller
(ADF) and DF-GLS unit root tests are shown in
Table 2 The test is used to detect whether or not a
time series is stationary. In conclusion, LPCTE and
LTR have unit roots in levels but become stationary
after taking the first difference, while LGDP and
LTE are stationary in levels. The ARDL model may
be used since it exhibits a mixed order of
integration between variables (I(0) or I(1)).
The unit root tests were applied to the time
series variables, as presented in Table 2. Gross
Domestic Product (LGDP) and Employment in the
Labor Force (LTE) were found to be stationary at
the level, with significance probabilities of
0.0047** and 0.0084*, respectively. In contrast,
Percentage of Total Employment in the Labor
Force (LPCTE) and Total Revenue (LTR) were
initially non-stationary at the level but exhibited
stationarity after taking the first difference,
supported by t-Statistics of -5.039689 (probability
0.0004*) and -4.838175 (probability 0.0007*),
respectively. The mixed order of integration (I(0) or
I(1)) among the variables suggests the potential
application of the Autoregressive Distributed Lag
(ARDL) model for subsequent analysis. These
results provide valuable insights into the behavior
of the time series data, laying the foundation for
further econometric modeling and cointegration
analysis.
5.2 Cointegration Test
After identifying the order of integration, the
ARDL joint test technique was utilized in this work
to assess the long-run connection between GDP,
tourist expenditures, the average amount tourists
spend in Greece, and tourist receipts. To test for the
existence of a level connection between LGDP and
the explanatory variables, the F-Bounds and t-
Bounds tests are utilized. The F-Bounds test is used
to test the null hypothesis of no levels connection,
while the t-Bounds test is used to test the null
hypothesis of no cointegration. If the estimated F-
statistic is less than the lower limit, the null
hypothesis of no cointegration is accepted; if it is
more than the upper bound, the null hypothesis is
rejected. Table 3 summarizes the findings.
Table 2. Descriptive statistical analysis
Variables
ADF
ADF-GLS
t-Statistic
Probability
t-Statistic
Probability
LGDP
Level
-4.113718
0.0047**
-4.648998
0.0281**
1st difference
LPCTE
Level
-2.224772
0.2026
-3.902767
0.1924
1st difference
-5.039689
0.0004*
-5.396091
<0.01*
LTE
Level
-3.771505
0.0084*
-6.698295
<0.01*
1st difference
LTR
Level
-2.224269
0.2028
-6.995523
<0.01*
1st difference
-4.838175
0.0007*
*, * * represents 1, and 5% significance level
Table 3. Results of F-Bounds and T-Bounds testing
F-Bounds Test
Value
Significance
F-Bounds Test
Value
F-statistic
4.691272
10%
3.47
4.45
5%
4.01
5.07
2.5%
4.52
5.52
1%
5.17
6.36
t-Bounds Test
t-statistic
-3.858037
10%
-3.13
-3.84
5%
-3.41
-4.16
2.5%
-3.65
-4.42
1%
-3.96
-4.73
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
593
Volume 21, 2024
The F-bounds and t-bounds tests are used to
check for the existence of a long-run relationship
between the variables. Both tests reject the null
hypothesis of no long-run relationship with a
significance level of 10%, indicating that there is a
long-run relationship between LGDP and the
explanatory variables.
5.3 Long-Run Analysis Results
Long-term estimates are presented in Table 4. The
output provided comes from an estimated ARDL
(2,1,4,4) model according to the Akaike
information criterion. The levels equation shows
the long-run relationship between LGDP and the
explanatory variables (LPCTE, LTE, and LTR).
The coefficients indicate that LGDP is positively
related to LPCTE and LTR, and negatively related
to LTE. This implies that an increase in the amount
of money that tourists spend in Greece (LPCTE)
and the amount of money received by Greece from
international tourists (LTR) leads to an increase in
GDP, while an increase in tourism expenditure in
(LTE) leads to a decrease in GDP.
The error correction term (EC) depicts the
short-run process of adjusting to departures from
long-run equilibrium. The EC coefficient is
negative, suggesting that the adjustment is aimed at
achieving long-run equilibrium. The LPCTE
coefficient is 0.626089, which implies that a 1%
rise in average per capita tourist spending is
connected with a 0.626089% increase in economic
growth, and this relationship is statistically
significant. This implies that the more money
visitors spend in Greece, the larger the country's
economic effect. Holding other factors equal, LTE
has a negative coefficient of -0.5557, suggesting
that an increase of one percent in the total amount
of tourism expenditures from Greece is connected
with a -0.5557% decline in domestic economic
growth. LTE has a t-statistic of -3.579, showing
that it is statistically significant at the 1% level. The
LTR coefficient is 0.986709, which implies that a
1% rise in tourist growth is connected with a
0.986709% increase in real GDP growth rate and is
statistically significant at the 0.01 level. This
implies that the bigger the amount of money
received by Greece from overseas visitors, the
greater the influence on the country's economic
development. Finally, the estimated error term for
the regression equation is EC = LGDP -
(0.6261LPCTE -0.5557LTE + 0.9867*LTR). It
indicates the difference between the actual and
expected LGDP values based on the three
independent variables.
The results indicate that international tourism
expenditures have a negative impact on economic
growth. When Greek tourists spend money abroad,
it represents a leakage of revenue from the
domestic economy. If a significant amount of
money is spent outside of Greece, it may result in
less money circulating within the domestic
economy, leading to reduced local business
revenues, employment opportunities, and tax
revenues. On the other hand, tourism receipts and
average per capita tourism expenditure represent
the money spent by international tourists in a
destination country and positively the economic
growth. This can generate significant revenue for
the local economy, as tourists spend money on
accommodation, meals, transportation, shopping,
and other goods and services.
5.4 Short-Run Analysis Results
An error correction model (ECM) should be used to
determine the presence of a cointegration
connection between variables. The system's short-
term dynamics and its coefficients, describe the rate
at which the shocks to the system are adjusted to
achieve equilibrium. The resultant short-run
dynamic growth equation is shown in Table 5. The
model includes lagged variables of the dependent
and independent variables to account for potential
time lags. C (constant) has a statistically significant
positive coefficient of 18.72544.
Table 4. Log-run estimated Coefficients (Dependent variable: LGDP).
Variables
Coefficient
Std. Error
t-statistic
Prob.
LPCTE
0.626089
0.120190
5.209173
0.0008*
LTE
-0.555671
0.155256
-3.579052
0.0072*
LTR
0.986709
0.092728
10.64086
0.0000*
EC = LGDP - (0.6261*LPCTE -0.5557*LTE + 0.9867*LTR)
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
594
Volume 21, 2024
Table 5. Short-run dynamic relationship results of ARDL-ECM.
Variables
Coefficient
Std. Error
t-statistic
Prob.
C
18.72544
3.682113
5.085517
0.0009*
@TREND
-0.057635
0.011442
-5.037326
0.0010*
D(LGDP(-1))
0.598348
0.228491
2.618698
0.0307**
D(LPCTE)
0.285737
0.200400
1.425833
0.1917
D(LTE)
-0.032263
0.066839
-0.482697
0.6422
D(LTE(-1))
0.801516
0.204235
3.924470
0.0044*
D(LTE(-2))
0.545798
0.144970
3.764912
0.0055*
D(LTE(-3))
0.406224
0.134129
3.028610
0.0163**
D(LTR)
0.144129
0.070040
2.057812
0.0736***
D(LTR(-1))
-1.527239
0.320980
-4.758058
0.0014*
D(LTR(-2))
-1.111478
0.247770
-4.485924
0.0020*
D(LTR(-3))
-0.670794
0.194052
-3.456779
0.0086*
CointEq(-1)*
-1.597212
0.314439
-5.079567
0.0010*
R-squared
0.903663
Mean dependent var
0.017914
Adjusted R-squared
0.798568
S.D. dependent var
0.109569
S.E. of regression
0.049176
Akaike info criterion
-2.883653
Sum squared resid
0.026601
Schwarz criterion
-2.245541
Log-likelihood
47.60384
Hannan-Quinn criter.
-2.714362
F-statistic
8.598525
Durbin-Watson stat
2.960751
Prob(F-statistic)
0.000581
Note: * significant at 1%; **significant at 5%; ***significant at 10%.
This implies that there is a long-run
equilibrium link between LGDP and the model's
independent variables, which may be accounted for
by the error correction factor. @TREND (trend)
has a -0.057635 coefficient, which is statistically
significant. This shows that the LGDP is declining
over time. The positive coefficient of D(LPCTE) is
0.285737, although it is not statistically significant.
The negative coefficient of D(LTE) is -0.032263,
although it is not statistically significant at the 5%
level. This suggests that a variation in TE exerts a
detrimental impact on LGDP, although the
observed effect is not statistically significant. The
positive coefficients for D(LTE(-1)), D(LTE(-2),
and D(LTE(-3)) are 0.801516, 0.545798, and
0.406224, respectivelyAt the 1% and 5%
significance levels, all three variables exhibit
statistical significance. This indicates that
adjustments in TE from the preceding three periods
positively impact LGDP in the current period. The
statistically significant coefficient for D(LTR) is
0.144129 at the 10% level, suggesting that the
alteration in TR has a positive impact on LGDP,
albeit without statistical significance. The negative
coefficients of D(LTR(-1)), D(LTR(-2), and
D(LTR(-3)) are -1.527239, -1.111478, and -
0.670794, respectively. All three variables
demonstrate statistical significance at the 1% level.
Alterations in TR over the preceding three periods
adversely affect LGDP in the current period.
In general, the model suggests that adjustments
in TE over the preceding three periods positively
impact the current LGDP, while changes in TR
from the previous three periods have a negative
effect on the present LGDP. The findings of this
research show that tourism may play an important
role in encouraging economic growth, emphasizing
the need for policymakers to identify and capitalize
on this potential for long-term economic
development.
5.5 Diagnostic and Stability Tests
Table 6 displays the results of the diagnostic and
stability tests. The CUSUM and CUSUM Square
tests indicate that the long-run and short-run
parameters are stable, with all values falling within
critical boundaries at a significance level of 5%.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
595
Volume 21, 2024
Table 6. Short-run dynamic relationship results of ARDL-ECM.
Test Statistics (LM version)
Statistics (p values)
Serial correlation
1.035260 (0.3526)
Heteroscedasticity
6.847981 (0.9617)
CUSUM
stable
CUSUM Square
stable
Figure 1 and Figure 2 provide a graphical
representation of the CUSUM and CUSUM Square
tests, respectively.
-10.0
-7.5
-5.0
-2.5
0.0
2.5
5.0
7.5
10.0
2015 2016 2017 2018 2019 2020 2021 2022
CUSUM 5% Significance
Fig. 1: Plot of CUSUM Test
-0.4
0.0
0.4
0.8
1.2
1.6
09 10 11 12 13 14 15 16 17 18 19 20 21 22
CUSUM of Squares 5% Significance
Fig. 2: The plot of CUSUM Squares Test
6 Discussion and Conclusion
6.1 Conclusions
Economic growth is closely related to tourism
receipts, tourism expenditures, and average tourism
expenditures per capita, as indicated by significant
correlations. The data used in this study were found
to be significant at zero and first-order differences
after unit root tests were conducted. Cointegration
tests were then conducted, which revealed the
existence of short- and long-term relationships
between endogenous and exogenous variables. This
suggests that the variables are connected in the long
run. Overall, the results highlight the strong
relationship between economic growth and various
tourism-related factors. Furthermore, the results
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
596
Volume 21, 2024
suggest that there are both short-and long-term
relationships among the variables studied. This
indicates that changes in tourism receipts, tourism
expenditures, and average tourism expenditures per
capita may have a lasting impact on economic
growth.
Long-term study shows that international
tourist expenditure per capita and tourism earnings
boost economic development. According to the
findings of this research, the Greek government
may need to pay more attention to the tourism
industry, enhance it, and implement better
regulations to attract more international visitors.
Tourism generates significant revenue for the
national economy while also employing a large
number of people. More investment in tourism
resources is required to attract more local and
international visitors.
In light of recent challenges faced by the
national economy, this study assumes added
significance. The correlations identified between
economic growth and key tourism-related factors,
such as tourism receipts, expenditures, and per
capita spending, become even more pertinent in the
context of recent economic adversities. The unit
root tests, significant at zero and first-order
differences, attest to the resilience of these
relationships even amid contemporary economic
challenges.
Cointegration tests reveal enduring links
between endogenous and exogenous variables,
suggesting that despite recent economic turmoil,
the intertwined nature of these factors persists in
both the short and long term. This underscores the
adaptability of the tourism sector and its capacity to
influence economic growth consistently.
In addressing recent economic challenges, this
study underscores the pivotal role of international
tourist spending per capita and tourism earnings in
fostering economic development. The findings
underscore the necessity for decisive government
action, including the revitalization of the tourism
sector, strategic initiatives, and regulatory reviews
to attract more foreign visitors. Despite ongoing
domestic economic difficulties, the study
showcases the tourism sector's resilience,
advocating for targeted investments to enhance its
influence. Policymakers can utilize insights from
the study to address economic concerns and
position the tourism industry as a vital driver of
economic recovery, strategically positioning the
country for resurgence.
6.2 Theoretical Implications
The tourism-led growth hypothesis, which proposes
that tourism can significantly contribute to
economic growth, is supported by this research. As
a result, the study suggests investing resources into
tourism development to promote a country's long-
term growth and maximize subsequent multiplier
effects. This research contributes novel aspects to
the current literature. It is the empirical
examination of the association between economic
growth, tourist revenues, tourism expenditures, and
tourism per capita spending in Greece, in both the
short and long term. Furthermore, to analyze the
long-term relationship, the research employs the
Autoregressive Distributed Lag (ARDL) limits test
of cointegration and uses the ARDL framework to
assess both long- and short-term dynamics. To
promote a country's long-term growth and
maximize subsequent multiplier effects, the study
suggests investing resources into tourism
development.
6.3 Practical Implications
The tourist business is now the most rapidly
expanding sector of the Greek economy, with good
worldwide performance. Tourism makes an
essential contribution to regional and local
socioeconomic development. Tourism may
therefore operate as a driver of economic growth in
Greece, increasing earnings, decreasing
unemployment, and raising inhabitants' quality of
life. This research was conducted to explore and
assess the contribution of tourism to Greece's
economic growth.
The findings of the study corroborate the
short- and long-term relationship between tourist
development and the country's economic growth as
measured by GDP. To preserve or enhance the
country's GDP, focus should be directed not just on
preserving and increasing tourist earnings, but also
on increasing per capita spending. This necessitates
the urgent development of strategies to increase
both the quality and breadth of services provided,
as well as incentives or motivating mechanisms for
visitors to devote a greater portion of their spending
to local and other services in the nation. To enhance
a satisfactory level of service quality, personnel
involved in tourism must be educated and trained to
boost productivity and create a competitive edge.
Tourism may help policymakers create economic
development by creating regional employment
possibilities, enabling foreign exchange, and
supporting the transportation, food, and
accommodation industries, [6], [72], [73], [74].
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
597
Volume 21, 2024
Furthermore, authorities may use tourism to
reduce regional economic gaps, allowing revenue
to flow from rich to underdeveloped areas, [43],
[52], [62], [75].
The thriving tourist industry stands as the
fastest-growing sector in the Greek economy,
contributing significantly to regional and local
socioeconomic development. This research
emphasizes the enduring link between tourist
development and overall economic growth,
measured by GDP. To boost and sustain Greece's
GDP, policymakers must not only focus on
preserving tourist earnings but also on increasing
per capita spending. Urgent strategies are needed to
enhance service quality, encourage diverse
spending, and invest in personnel training. Tourism
catalyzes economic development, offering
opportunities for regional employment, foreign
exchange, and support for related industries, [76],
[77]. Additionally, it can address regional economic
gaps by redistributing revenue from affluent to
underdeveloped areas. These insights highlight
tourism's pivotal role in fostering inclusive and
balanced economic growth in Greece, [78], [79],
[80].
6.4 Limitations and Future Research
The analysis was limited to 28 years due to a lack
of sufficient data. In future studies, it would be
beneficial to reassess the influence of tourism on
economic growth over longer periods. Furthermore,
it's crucial to explore the nonlinear effects of
independent factors on economic development in
upcoming research.
Acknowledgement:
This research did not receive any specific grant
from funding agencies in the public, commercial,
or not-for-profit sectors. The authors thank the
editor and the anonymous reviewers for the
feedback and their insightful comments on the
original submission. All errors and omissions
remain the responsibility of the authors.
References:
[1] Blake, A.; Sinclair, M.T.; Soria, J.A.C.
Tourism Productivity. Evidence from the
United Kingdom. Ann. Tour. Res. 2006, 33,
1099–1120,
doi:10.1016/J.ANNALS.2006.06.001.
CrossRef
[2] Comerio, N.; Strozzi, F. Tourism and Its
Economic Impact: A Literature Review
Using Bibliometric Tools. Tour. Econ. 2019,
25, 109–131,
doi:10.1177/1354816618793762. CrossRef
[3] Nunkoo, R.; Seetanah, B.; Jaffur, Z.R.K.;
Moraghen, P.G.W.; Sannassee, R.V. Tourism
and Economic Growth: A Meta-Regression
Analysis. J. Travel Res. 2020, 59, 404–423,
doi:10.1177/0047287519844833. CrossRef
[4] Shahbaz, M.; Shabbir, M.S.; Butt, M.S.
Effect of Financial Development on
Agricultural Growth in Pakistan: New
Extensions from Bounds Test to Level
Relationships and Granger Causality Tests.
Int. J. Soc. Econ. 2013, 40, 707–728,
doi:10.1108/IJSE-01-2012-0002. CrossRef
[5] Bianchi, C. Exploring the Attractiveness of
Chile as a Vacation Destination for
International Tourists. Tour. Anal. 2018, 23,
351–364,
doi:10.3727/108354218X15305418666977.
CrossRef
[6] Joppe, M. Sustainable Community Tourism
Development Revisited. Tour. Manag. 1996,
17, 475–479, doi:10.1016/S0261-
5177(96)00065-9. CrossRef
[7] Karakitsiou, A.; Mavrommati, A. Machine
Learning Methods in Tourism Demand
Forecasting: Some Evidence from Greece.
MIBES Trans. 2017, 11, 92–105. CrossRef
[8] Boğan, E.; Çalışkan, C.; Karatepe, O.M.;
Rezapouraghdam, H. An Investigation of the
Selected Antecedents and Consequences of
Work Engagement among Hospitality and
Tourism Academicians. J. Hosp. Tour.
Insights 2023, doi:10.1108/JHTI-05-2022-
0190. CrossRef
[9] OECD OECD-Tourism-Trends-Policies
2020-Highlights-ENG.Pdf. 2020. CrossRef
[10] OECD Rebuilding Tourism for the Future:
COVID-19 Policy Responses and Recovery.
OECD Tour. Pap. 2020, 2020, 1–8. CrossRef
[11] Andriotis, K. Scale of Hospitality Firms and
Local Economic Development—Evidence
from Crete. Tour. Manag. 2002, 23, 333–341,
doi:10.1016/S0261-5177(01)00094-2.
CrossRef
[12] Avci, U.; Madanoglu, M.; Okumus, F.
Strategic Orientation and Performance of
Tourism Firms: Evidence from a Developing
Country. Tour. Manag. 2011, 32, 147–157,
doi:10.1016/J.TOURMAN.2010.01.017.
CrossRef
[13] Bassil, C.; Hamadeh, M.; Samara, N. The
Tourism Led Growth Hypothesis: The
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
598
Volume 21, 2024
Lebanese Case. Tour. Rev. 2015, 70, 43–55,
doi:10.1108/TR-05-2014-0022. CrossRef
[14] Brida, J.G.; Cortes-Jimenez, I.; Pulina, M.
Has the Tourism-Led Growth Hypothesis
Been Validated? A Literature Review. Curr.
Issues Tour. 2016, 19, 394–430,
doi:10.1080/13683500.2013.868414.
CrossRef
[15] Grasso, F.; Schilirò, D. Tourism, Economic
Growth and Sustainability in the
Mediterranean Region. Tour. Mediterr. Sea
2021, 129–142, doi:10.1108/978-1-80043-
900-920211011. CrossRef
[16] Lee, C.C.; Chang, C.P. Tourism Development
and Economic Growth: A Closer Look at
Panels. Tour. Manag. 2008, 29, 180–192,
doi:10.1016/J.TOURMAN.2007.02.013.
CrossRef
[17] Croes, R.R. A Paradigm Shift to a New
Strategy for Small Island Economies:
Embracing Demand Side Economics for
Value Enhancement and Long Term
Economic Stability. Tour. Manag. 2006, 27,
453–465,
doi:10.1016/j.tourman.2004.12.003.
CrossRef
[18] Mavrommati, A.; Karakitsiou, A.;
Kourgiantakis, M. Regional Efficiency
Evaluation by Input-Oriented Data
Envelopment Analysis of Hotel and
Restaurant Sector.; 2020; pp. 2041–2058.
CrossRef
[19] Mavrommati, A.; Pendaraki, K.;
Kontogeorgos, A.; Chatzitheodoridis, F. A
Panel Data Model of International Tourism
Demand for Greece. Deturope 2021, 13,
142–157, doi:10.32725/det.2021.024.
CrossRef
[20] Copeland, B.R. Tourism, Welfare and De-
Industrialization in a Small Open Economy.
Economica 1991, 58, 515,
doi:10.2307/2554696. CrossRef
[21] Hazari, B.R.; Nowak, J.J.; Sahli, M.;
Zdravevski, D. Tourism and Regional
Immiserization. Pacific Econ. Rev. 2003, 8,
269–278, doi:10.1111/J.1468-
0106.2003.00227.X. CrossRef
[22] Tang, C.F.; Abosedra, S. Does Tourism
Expansion Effectively Spur Economic
Growth in Morocco and Tunisia? Evidence
from Time Series and Panel Data. J. Policy
Res. Tour. Leis. Events 2016, 8, 127–145,
doi:10.1080/19407963.2015.1113980.
CrossRef
[23] Brida, J.G.; Risso, W.A.; Such-Devesa, M.J.;
Zapata-Aguirre, S. The Tourism-Led Growth
Hypothesis: Empirical Evidence from
Colombia. Tourismos 2009, 4, 13–27.
CrossRef
[24] Yu-Chi, S.; Lin, H.-P. Causality Relationship
between Tourism, Foreign Direct Investment
and Economic Growth in Taiwan. Asian J.
Econ. Model. 2018, 6, 287–293,
doi:10.18488/JOURNAL.8.2018.63.287.293.
CrossRef
[25] Sokhanvar, A.; Çiftçioğlu, S.; Javid, E.
Another Look at Tourism- Economic
Development Nexus. Tour. Manag. Perspect.
2018, 26, 97–106,
doi:10.1016/J.TMP.2018.03.002. CrossRef
[26] Zuo, B.; Huang, S. A Structural Change and
Productivity Perspective of Tourism’s
Contribution to Economic Growth: The Case
of Zhangjiajie in China. J. Travel Res. 2020,
59, 465–476,
doi:10.1177/0047287519841720. CrossRef
[27] Sumner, A. Is Foreign Direct Investment
Good for the Poor? A Review and Stocktake.
Dev. Pract. 2005, 15, 269–285,
doi:10.1080/09614520500076183. CrossRef
[28] 28. F. De; Agdal, M. V; Agdal, F.D.R.;
Institut, A.M. The Determinants of Tourism
Demand: The Case of Morocco. Int. Res. J.
Financ. Econ. 2012, 92, 163–175. CrossRef
[29] Can, M.; Gozgor, G. Revisiting the Tourism-
Growth Nexus: Evidence from a New Index
for the Market Diversification of Tourist
Arrivals. Curr. Issues Tour. 2018, 21, 1157–
1170, doi:10.1080/13683500.2016.1268103.
CrossRef
[30] Dogru, T.; Isik, C.; Sirakaya-Turk, E. The
Balance of Trade and Exchange Rates:
Theory and Contemporary Evidence from
Tourism. Tour. Manag. 2019, 74, 12–23,
doi:10.1016/j.tourman.2019.01.014.
CrossRef
[31] Isik, C.; Dogru, T.; Turk, E.S. A Nexus of
Linear and Non-Linear Relationships
between Tourism Demand, Renewable
Energy Consumption, and Economic
Growth: Theory and Evidence. Int. J. Tour.
Res. 2018, 20, 38–49, doi:10.1002/jtr.2151.
CrossRef
[32] Kyophilavong, P.; Gallup, J.L.; Charoenrat,
T.; Nozaki, K. Testing Tourism-Led Growth
Hypothesis in Laos? Tour. Rev. 2018, 73,
242–251, doi:10.1108/TR-03-2017-0034.
CrossRef
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
599
Volume 21, 2024
[33] Yılancı, V.; Kırca, M. Testing the
Relationship between Employment and
Tourism: A Fresh Evidence from the ARDL
Bounds Test with Sharp and Smooth Breaks.
J. Hosp. Tour. Insights 2023,
doi:10.1108/JHTI-10-2022-0509. CrossRef
[34] Işık, C.; Sirakaya-Turk, E.; Ongan, S. Testing
the Efficacy of the Economic Policy
Uncertainty Index on Tourism Demand in
USMCA: Theory and Evidence. Tour. Econ.
2020, 26, 1344–1357,
doi:10.1177/1354816619888346. CrossRef
[35] Momeni, K.; Janati, A.; Imani, A.;
Khodayari-Zarnaq, R. Barriers to the
Development of Medical Tourism in East
Azerbaijan Province, Iran: A Qualitative
Study. Tour. Manag. 2018, 69, 307–316,
doi:10.1016/J.TOURMAN.2018.05.007.
CrossRef
[36] Yousaf, S.; Xiucheng, F. Halal Culinary and
Tourism Marketing Strategies on
Government Websites: A Preliminary
Analysis. Tour. Manag. 2018, 68, 423–443,
doi:10.1016/J.TOURMAN.2018.04.006.
CrossRef
[37] Samimi, A.J.; Sadeghi, S.; Sadeghi, S. The
Relationship between Foreign Direct
Investment and Tourism Development:
Evidence from Developing Countries.
Institutions Econ. 2013, 5, 59–68. CrossRef
[38] Rezapouraghdam, H.; Karatepe, O.M.; Enea,
C. Sustainable Recovery for People and the
Planet through Spirituality-Induced
Connectedness in the Hospitality and
Tourism Industry. J. Hosp. Tour. Insights
2022, doi:10.1108/JHTI-03-2022-0103.
CrossRef
[39] Kalogiannidis, S.; Kontsas, S.; Konteos, G.;
Chatzitheodoridis, F. Meta Covid Era: Impact
of COVID-19 on the Global Economy and
Ecosystems and Opportunities for Circular
Economy Strategies. 2023, 203–220,
doi:10.1007/978-3-031-34059-8_10.
CrossRef
[40] Chatzitheodoridis, F.; Melfou, K.;
Kontogeorgos, A.; Kalogiannidis, S.
Exploring Key Aspects of an Integrated
Sustainable Urban Development Strategy in
Greece: The Case of Thessaloniki City. Smart
Cities 2023, Vol. 6, Pages 19-39 2022, 6, 19–
39, doi:10.3390/SMARTCITIES6010002.
CrossRef
[41] Akinboade, O.A.; Braimoh, L.A.
International Tourism and Economic
Development in South Africa: A Granger
Causality Test. Int. J. Tour. Res. 2010, 12,
149–163, doi:10.1002/JTR.743. CrossRef
[42] Brida, J.G.; Sanchez Carrera, E.J.; Risso,
W.A. Tourism’s Impact on Long-Run
Mexican Economic Growth. Econ. Bull.
2008, 3, doi:10.2139/ssrn.1076225. CrossRef
[43] Dredge, D. Policy Networks and the Local
Organisation of Tourism. Tour. Manag. 2006,
27, 269–280,
doi:10.1016/j.tourman.2004.10.003.
CrossRef
[44] Manzoor, F.; Wei, L.; Asif, M.; Ul Haq, M.Z.;
Ur Rehman, H. The Contribution of
Sustainable Tourism to Economic Growth
and Employment in Pakistan. Int. J. Environ.
Res. Public Health 2019, 16,
doi:10.3390/ijerph16193785. CrossRef
[45] 45. Tang, C.F.; Tan, E.C. Does
Tourism Effectively Stimulate Malaysia’s
Economic Growth? Tour. Manag. 2015, 46,
158–163,
doi:10.1016/j.tourman.2014.06.020.
CrossRef
[46] Dritsakis, N. Tourism as a Long-Run
Economic Growth Factor: An Empirical
Investigation for Greece Using Causality
Analysis. Tour. Econ. 2004, 10, 305–316,
doi:10.5367/0000000041895094. CrossRef
[47] Kim, H.J.; Chen, M.H.; Jang, S.C.S. Tourism
Expansion and Economic Development: The
Case of Taiwan. Tour. Manag. 2006, 27, 925–
933, doi:10.1016/J.TOURMAN.2005.05.011.
CrossRef
[48] Lee, C.C.; Chien, M.S. Structural Breaks,
Tourism Development, and Economic
Growth: Evidence from Taiwan. Math.
Comput. Simul. 2008, 77, 358–368,
doi:10.1016/j.matcom.2007.03.004. CrossRef
[49] Oh, C.O. The Contribution of Tourism
Development to Economic Growth in the
Korean Economy. Tour. Manag. 2005, 26,
39–44,
doi:10.1016/J.TOURMAN.2003.09.014.
CrossRef
[50] Ozturk, I.; Acaravci, A. On the Causality
between Tourism Growth and Economic
Growth: Empirical Evidence from Turkey.
Transylvanian Rev. Adm. Sci. 2009, 73–81.
CrossRef
[51] Payne, J.E.; Mervar, A. Research Note: The
Tourism-Growth Nexus in Croatia. Tour.
Econ. 2010, 16, 1089–1094,
doi:10.5367/TE.2010.0014. CrossRef
[52] 52. Castellani, V.; Sala, S. Sustainable
Performance Index for Tourism Policy
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
600
Volume 21, 2024
Development. Tour. Manag. 2010, 31, 871–
880, doi:10.1016/j.tourman.2009.10.001.
CrossRef
[53] Işik, C.; Kasımatı, E.; Ongan, S. Analyzing
the Causalities between Economic Growth,
Financial Development, International Trade,
Tourism Expenditure and/on the CO2
Emissions in Greece. Energy Sources, Part B
Econ. Plan. Policy 2017, 12, 665–673,
doi:10.1080/15567249.2016.1263251.
CrossRef
[54] Kim, H.; Richardson, S.L. Motion Picture
Impacts on Destination Images. Ann. Tour.
Res. 2003, 30, 216–237, doi:10.1016/S0160-
7383(02)00062-2. CrossRef
[55] Po, W.C.; Huang, B.N. Tourism
Development and Economic Growth-a
Nonlinear Approach. Phys. A Stat. Mech. its
Appl. 2008, 387, 5535–5542,
doi:10.1016/j.physa.2008.05.037. CrossRef
[56] Proença, S.; Soukiazis, E. Tourism as an
Economic Growth Factor: A Case Study for
Southern European Countries. Tour. Econ.
2008, 14, 791–806,
doi:10.5367/000000008786440175. CrossRef
[57] Sokhanvar, A.; Jenkins, G.P. Impact of
Foreign Direct Investment and International
Tourism on Long-Run Economic Growth of
Estonia. J. Econ. Stud. 2022, 49, 364–378,
doi:10.1108/JES-11-2020-0543. CrossRef
[58] Tavares, J.M.; Leitao, N.C. The Determinants
of International Tourism Demand for Brazil.
Tour. Econ. 2017, 23, 834–845,
doi:10.5367/te.2016.0540. CrossRef
[59] Chatzitheodoridis, F.; Michailidis, A.;
Theodosiou, G.; Loizou, E. Local
Cooperation: A Dynamic Force for
Endogenous Rural Development. Contrib. to
Econ. 2013, 121–132, doi:10.1007/978-3-
7908-2873-3_9. CrossRef
[60] Croes, R. Tourism Specialization and
Economic Output in Small Islands. Tour. Rev.
2013, 68, 34–48, doi:10.1108/TR-09-2013-
0050. CrossRef
[61] Surugiu, C.; Surugiu, M.R. Is the Tourism
Sector Supportive of Economic Growth?
Empirical Evidence on Romanian Tourism.
Tour. Econ. 2013, 19, 115–132,
doi:10.5367/TE.2013.0196. CrossRef
[62] Tugcu, C.T. Tourism and Economic Growth
Nexus Revisited: A Panel Causality Analysis
for the Case of the Mediterranean Region.
Tour. Manag. 2014, 42, 207–212,
doi:10.1016/j.tourman.2013.12.007.
CrossRef
[63] Yap, G.; Saha, S. Do Political Instability,
Terrorism, and Corruption Have Deterring
Effects on Tourism Development Even in the
Presence of Unesco Heritage? A Cross-
Country Panel Estimate. Tour. Anal. 2013,
18, 587–599,
doi:10.3727/108354213X13782245307911.
CrossRef
[64] Kalogiannidis, S.; Kontsas, S.; Nikolaou,
E.E.; Chatzitheodoridis, F. The Rationale of
Integrated Planning and Sustainable
Development Strategies in the Development
of Local Government Regional Units. 2023,
433–453, doi:10.1007/978-3-031-22749-
3_27. CrossRef
[65] Nikolaou, E.E.; Konteos, G.; Kalogiannidis,
S.; Syndoukas, D. Mega Sporting Events and
Their Socio-Economic Impact: Case Study of
the 2022 FIFA World Cup. J. Infrastructure,
Policy Dev. 2023, 7, 2158,
doi:10.24294/JIPD.V7I2.2158. CrossRef
[66] Kalogiannidis, S.; Kontsas, S.;
Chatzitheodoridis, F. Managerial Styles and
Employee Performance. An Empirical Study
from Bank Sector Employees in Greece.
WSEAS Trans. Environ. Dev. 2021, 17, 1234–
1244, doi:10.37394/232015.2021.17.113.
CrossRef
[67] Papadopoulou, C.I.; Loizou, E.; Melfou, K.;
Chatzitheodoridis, F. The Knowledge-Based
Agricultural Bioeconomy: A Bibliometric
Network Analysis. Energies 2021, Vol. 14,
Page 6823 2021, 14, 6823,
doi:10.3390/EN14206823. CrossRef
[68] Pesaran, M.H. An Autoregressive
Distributed-Lag Modelling Approach to
Cointegration Analysis; 1998; ISBN
0521633230. CrossRef
[69] Pesaran, M.H.; Shin, Y.; Smith, R.J. Bounds
Testing Approaches to the Analysis of Level
Relationships. J. Appl. Econom. 2001, 16,
289–326, doi:10.1002/jae.616. CrossRef
[70] Nkoro, E.; Uko, A.K. Autoregressive
Distributed Lag (ARDL) Cointegration
Technique: Application and Interpretation. J.
Stat. Economy. Methods 2016, 5, 63–91.
CrossRef
[71] Dickey, D.A.; Fuller, W.A. Likelihood ratio
statistics for autoregressive time series with a
unit root. Econometrica: Journal of the
Econometric Society, 1981, 49, 1057-1072.
CrossRef
[72] 72. ThiMinh, L.-P.; LeMinh, H.;
ThiPhi, P.-T. The Relationship Between
International Tourist Arrivals and Foreign
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
601
Volume 21, 2024
Direct Investment: A Granger Causality
Analysis. Acta Oeconomica Pragensia 2017,
25, 3–12, doi:10.18267/j.aop.586. CrossRef
[73] Selvanathan, S.; Selvanathan, E.A.;
Viswanathan, B. Causality between Foreign
Direct Investment and Tourism: Empirical
Evidence from India. Tour. Anal. 2012, 17,
91–98,
doi:10.3727/108354212X13330406124296.
CrossRef
[74] Holzner, M. Tourism and Economic
Development: The Beach Disease? Tour.
Manag. 2011, 32, 922–933,
doi:10.1016/j.tourman.2010.08.007.
CrossRef
[75] Del P. Pablo-Romero, M.; Molina, J.A.
Tourism and Economic Growth: A Review of
Empirical Literature. Tour. Manag. Perspect.
2013, 8, 28–41,
doi:10.1016/J.TMP.2013.05.006. CrossRef
[76] Brown, R. L., Durbin, J., & Evans, J. M.
Technics for testing the constancy of
regression relationships over time. Journal of
the Royal Statistical Society: Series B
(Methodological). 1975, 37, 149-163.
CrossRef
[77] Karakitsiou, A., Mavrommati, A., Migdalas,
A., & Tsiakali, K. Efficiency measurement,
and evaluation of the Greek hotel industry.
Journal of Travel and Tourism Research.
2007, 7, 96-110. CrossRef
[78] Roukos, C., Papanikolaou, C., Mountousis,
I., Kandrelis, S.,
Chatzitheodoridis, F. 2011. Soil property
variations about plant community and
altitudinal zone in the rangelands of Epirus,
Greece. Bulgarian Journal of Agricultural
Science, 17(6), pp. 806–815
[79] Loizou, E., Chatzitheodoridis, F.,
Michailidis, A., Tsakiri,
M., Theodossiou, G. 2015, Linkages of the
energy sector in the Greek economy: an
input-output approach. International Journal
of Energy Sector Management, 9(3), pp. 393–
411
[80] Chatzitheodoridis, F., Michailidis, A.,
Theodosiou, G., Loizou, E. 2013, Local
cooperation: A dynamic force for endogenous
rural development. Contributions to
Economics, pp. 121–132
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research received no external funding.
Data Availability Statement
Data is available upon request.
Conflicts of Interest:
Conflict of Interest
The authors declare no conflict of interest.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.e
n_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.49
Athanasia Mavrommati, Thanassis Kazanas,
Alexandra Pliakoura, Stavros Kalogiannidis,
Fotios Chatzitheodoridis
E-ISSN: 2224-2899
602
Volume 21, 2024