A Statistical Analysis of the Impact of Tourism on Economic Growth in
Albania
MIFTAR RAMOSACAJ, ELMIRA KUSHTA
Department of Mathematics,
University of Vlora,
ALBANIA
Abstract: - Tourism has become an important sector in Albania in recent years. After the pandemic period where
the tourism industry suffered a big blow, the year 2022 has turned out to be very successful in this sector. Tourism
this year is extending throughout the year, unlike previous years, which focused only on summer. In this paper, we
analyzed the number of tourists and economic growth in Albania for the years 2016-2022 with quarterly
frequencies (until the third quarter of 2022). The purpose of the paper is to analyze the relationship between the
two variables in the short and long term periods. AIC, BIC, HQC model selection criteria are used throughout the
analysis, the ADF test is used for series stationarity, the Granger test for causality and the Johansen test for co
integration
Keywords: number of tourists, GDP, relationship, long-run.
Received: November 15, 2022. Revised: March 17, 2023. Accepted: April 6, 2023. Published: April 24, 2023.
1 Introduction
The communist system in Albania collapsed in 1991,
opening the path to a long and painful transition from
a fully centralized and isolated economy toward a
liberalized market economy. In the last 30 years the
country underwent a comprehensive political,
economic, and social transformation. The country
undertook deep reforms to transform its economy,
including the tourism sector.
No tourism legacy: The country suffered total
isolation for almost half a century, from the end of
the Second World War up until 1991. Under
communism, Albania had almost no tourism sector
and, as such no legacy. The little tourism entailed a
handful of hotels that received a very limited number
of foreign tourists per year. The locals were not
allowed to travel abroad, and only a small percentage
of population had the right to go for vacations on the
so-called workers camps mostly on the beach. This
was pretty much the entire tourism sector up until
1991.
Despite the lack of legacy, the favorable
geographic position, a long, beautiful, and diverse
coastline coupled with a fascinating country
landscape from hills to alps and lakes and rivers, rich
history, culture, and archeology represented a strong
baseline to support the tourism sector as one of the
key pillars of the Albanian economy.
A series of new conditions that were created after
1990, constituted very good premises for the
promotion of tourism in Albania. However, the
development of tourism sector took a major hit in
1996-1997 with the collapse of pyramid schemes and
the civil war that followed, to restart the revival only
after 2000, and become a priority sector only in the
last decade.
However, tourism is now concentrated around
summer months mostly around sea & sun, and suffers
from high seasonality as well as low spending per
tourist. Albania needs to develop a mix of tourism
offerings to mitigate the seasonality. In addition,
Albania needs to slowly shift its model away from
high intensity tourism to a tourism that enables
higher spending per tourist.
Types of tourism offerings Albania can develop
successfully are at least coastal, mountainous &
adventurous, and historical & cultural. The beach, the
sea and the sun have been the main product that
occupy the largest weight of the total of yearly tourist
products. This type of tourism in Albania has a
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DOI: 10.37394/23207.2023.20.81
Miftar Ramosacaj, Elmira Kushta
E-ISSN: 2224-2899
879
Volume 20, 2023
pronounced seasonal character, and therefore, most
of the accommodation structures in the coastal area
face difficulties.
In seasonal operation: Meanwhile, for tourism and
marine infrastructure it currently is missing and this
segment (yachting, sailing, cruising) is still in its
infancy, but the potential to develop is very large and
very significant for the economy and elite tourism in
Albania.
Nature tourism - Albania has been appreciated by
many operators and international visitors for its
nature and its beautiful landscapes. Natural and rural
areas in Albania offer opportunities for the
development of rural tourism, mountain tourism,
ecotourism and outdoor activities (rafting, throwing
sport parachute, mountain biking, fishing, trekking,
mountain climbing, nature walking-hiking, horse
riding, study tours, etc.). Some of these activities are
the main motive of the visits from foreign visitors to
natural areas.
As for mountain tourism, there have been positive
developments, as a result of which they have
stabilized a significant number of tours organized in
Theth, Vermosh-Lepushë, Valbonë and Tropoja, in
the Albanian Alps, Dibër and the area of Bulqiza, the
mountainous area of Tirana, the mountainous area of
Elbasan and Librazhdi, the mountainous area of
Korça, the mountain of Tomori, Llogara and
Karaburun and the mountainous area of Gjirokastra
and Përmet. On the other hand, the category I tourism
in the protected environmental areas is also
increased.
The basis of the system of environmental protected
areas consists of 15 national parks, some managed
natural reserves and protected landscapes, which
contain the greatest natural values and biodiversity in
the country.
Thematic tourism - This category includes a number
of specific forms of tourism, such as agrotourism,
event and business tourism, cultural tourism
(heritage, history, faith, etc.), enogastronomic
tourism and health tourism (thermal, wellness and
medical) etc. Although not the main purpose of
visiting Albania, archeology, heritage and culture are
identified as Albania's strengths in various studies
conducted with visitors and the tourism industry in
trips from foreign markets. Main destinations visited
by organized cultural tours are: Shkodra, Lezha,
Kruja, Durrësi, Tirana, Fieri, Berati, Elbasani, Korça,
Përmeti, Gjirokastra, Saranda and Vlora (INSTAT,
2021).
As for business tourism, developments in this
direction are concentrated at a national level and the
lack of a consolidated cooperation network between
the actors, together with the lack of new centers for
the organization of conferences and congresses,
centers of business near the poles of economic
development (Rinas Airport), accommodation
structures well-known international brands in the
organization of conferences and congresses, have
limited the perspective of the development of tourism
in this sector at an international level.
Located in a favorable position in the regional market
of the Balkans and some markets of important
European, with technology and know-how developed
in some of the sectors medicine (dental care, plastic
surgery, cardiovascular surgery and neurosurgery,
fertility treatment), natural resources and favorable
climatic conditions for the development of several
directions of this segment (thalassotherapy, thermal
water therapy), competitive prices, Albania has had
positive developments in the health and wellness
tourism segment. It should be noted that in recent
years we have had a considerable number of entries
made for health purposes.
2 Literature Review
There are studies on the effects of tourism on
economic growth on Albania. The study conducted
by, [1], presents an econometric model which
confirms the dependence of GDP on tourism
revenues and the real effective exchange rate. The
study has proven that the development of tourism has
a positive impact on economic development.
The aim of the study, [2] is to observe how the
growth of the tourism sector affects the economy of
the country. She concludes that steady growth of
tourism must be carried out while being overseen by
the government and all actors while working on the
three main aspects: social, environmental, and
economic.
According to, [3], the development of tourism has a
Granger causal relationship with the increase of
employment and this in turn will lead to the
economic growth of the country. Furthermore,
Johansen co integration shows a stable relationship
even in the long run period.
According to [4] a gravity-type equation is built
based on an annual database of international tourist
arrivals in Albania from 22 countries of origin during
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Miftar Ramosacaj, Elmira Kushta
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the period 2001–2018. The gravity model was
evaluated through three evaluation techniques:
pooled OLS, fixed effects and random effects.
Empirical results showed that international tourist
arrivals in Albania are positively correlated with
GDP per capita in the destination and in the countries
of origin, total investments in infrastructure, political
stability and the absence of violence/terrorism, and
the existence of common borders. On the other hand,
the dependent variable is negatively related to the
distance between Albania and the countries of origin,
and to the dummy variable “climate of similarity”.
According to, [5], careful forecasting of tourist
arrivals is a key factor in arranging and administering
tourist activities. In their study the time series
“number of tourist arrivals” is modeled as a
logistic growth model and then it is modeled as
ARIMA (2,1,2). The results of the Life Cycle of the
Albanian Tourist Area showed that the middle of
the life cycle is the year 2010, the duration of the
growth time is 13.6 years, and the capacity is
4,886,858 tourists.
According to, [6], in forecasting, macroeconomic
variables such as GDP play, an important role for
policymakers in assessing the future state of the
economy. In their study, the time series of GDP with
quarterly frequencies and in its logarithmic form is
modeled as an ARIMA Model (1,0,1) and is used for
forecasting, even though it is not the model with the
best performance.
Schiopoiu, [7], investigates the potential of Albanian
tourists, using a quantitative analysis and a regression
model. The results demonstrate that the tourist is a
rational decision maker, and there are differences in
expectations and perceptions among respondents.
These differences are not significantly related to the
gender of the respondents, but related to the level of
education, the differences are important for
sensitivity, where respondents with a college degree
have a higher level of expectations than respondents
who don’t. The findings highlight the practical
implications for the need of capable human resources
as tourists are very sensitive to the level of
understanding of their specific needs by the industry.
There are also studies on the impact of the pandemic
on the tourism industry. [8], based on a survey
conducted between August and early September
2020 on the perceived impact of COVID-19 on the
tourism sector in Albania with representatives of
hotels and accommodation units in Albania. It
presents us with their views on the impact of
COVID-19 in the sector. This paper focuses on
creating several scenarios from the perception of the
impact of COVID-19.
Many studies have attempted to identify the causal
link between international trade (especially export
growth) and economic expansion, [Bahmani-
Oskooee and Alse (1993); Chow (1987); Jin (1995);
Marin (1992); Shan and Sun (1998)]. They have
assessed a strong correlation between international
trade and economic development that has a strong
bilateral causality between export growth and
economic growth; Moreover, tourism growth and
economic growth have a reciprocal causal
relationship, as export-driven economic growth
causes an increase in tourism revenues [9].
Regarding causality between tourism and economic
growth we have a series of studies as Balaguer and
Cantavella Jorda (2002, 2010) in Spain, Belloum
(2010) in Tunisia, Kreishan (2011) in Jordan for the
first direction and Brida,Sanchez– Carrera, and Risso
(2008) in Mexico, Oh (2005) in South Korea for
other direction, whereas Khalil, Kakar, and Waliullah
(2007) have found a bilateral direction in Pakistan.
3 Empirical Analysis
In this paper, the series of the number of tourists
(Nrt) and GDP in Albania for the years 2016-2022
with quarterly frequencies have been analyzed. The
data is provided by INSTAT (2022).
Throughout the econometric analysis, the model
selection criteria were used to select the best model.
Based on the balance of payments report of the Bank
of Albania, the statistical data in travel and tourism
during the first 6 months of 2022 (January–June)
show that there is an increase of 64.6% compared to
2021. Also, this year there is a significant increase of
32.4% compared to 2019.
The net income from travel/tourism in this first 6-
month period has shown a comparative increase with
the previous year. Net income has increased by
17.2% compared to 2021. The increase in net income
is higher even compared to 2019. Only for the year
2021, the contribution of the tourism sector to the
GDP of the country was 7.5%, while if the sectors
that indirectly contribute to tourism are also counted,
this contribution goes up to 17.5% of the GDP, [10].
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As for the year 2022, only for the period June-
August, there was an increase in the turnover of the
sector by 30% and an increase in employment by 4%
compared to the previous year, which according to
the Minister shows a positive development, an
increase in the number of businesses, the number of
nights of stays, the increase in capacities and the use
of touristic capacities, [11].
The table below shows the number of tourists
according to their country of origin.
Table 1. Arrivals of foreign citizens according to regions, 2014-2021
Description
2014
2015
2017
2018
2019
2020
2021
Africa
859
2,973
2,756
3,321
24,203
1,636
3,147
America
90,084
96,763
125,339
148,846
156,726
30,020
115,833
East Asia and
the Pacific
30,874
33,032
54,343
68,152
78,050
4,993
7,968
South Asia
1,274
1,636
2,344
3,084
3,550
801
20,998
Central /
Eastern Europe
163,006
151,457
276,563
362,083
393,368
92,326
363,483
Northern
Europe
137,308
125513
204,099
212,248
234,956
65,173
127,767
Southern
Europe
2821920
3169174
3810464
4301996
2335914
2335914
4331888
West Europe
237,760
246,811
316,264
357,411
417,163
95,211
293,054
Mediterranean
63,671
66,468
86,878
97,878
114,379
28,284
56,653
Source: [12].
The largest number of tourists in Albania continues
to be from southern Europe, this is due to the fact
that in addition to others, they come for holidays
from Kosovo and North Macedonia.
The year 2022 looks like a good year for Albanian
tourism, after the negative period of Covid-19.
According to the data of the Bank of Albania, for the
first 6 months only, the inflow of foreign tourists is
calculated at 1.13 billion euros, over 440 million
euros more than compared to the same period last
year.
The income from tourism turns out to be higher even
than the 6-month period of 2019, which is considered
the best year for the tourism sector, where throughout
the year tourists brought in over 2 billion Euros, [13].
The increase in spending by tourists during this
period was also influenced by the sports and cultural
events held in our country, especially the final of the
Conference League that brought many fans and
visitors to the country, giving a significant impact on
activities related to tourism.
3.1 Model Selection Criteria
In 1951, Kullback and Leibler developed a measure
to capture the information that is lost when
approximating reality; that is, the Kullback and
Leibler measure is a criterion for a good model that
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minimizes the loss of information, [14]. Two decades
later, Akaike derives a criterion, referred to as the
Akaike information criterion, [15]. The Bayesian
information criterion (BIC), proposed by Schwarz
and hence also referred to as the Schwarz
information criterion and Schwarz Bayesian
information criterion, is another model selection
criterion based on information theory but set within a
Bayesian context, [16]. Burnham & Anderson, [17],
say that Hannan-Quinn information criterion (HQC)
[18], "while often cited, seems to have seen little use
in practice". They also note that HQC, like BIC, but
unlike AIC, is not an estimator of Kullback–Leibler
divergence.
The AIC, BIC, and HQC are used as statistics of a
good fit, and we use them for the selection of the
most appropriate-best fit model from a sum of
estimated ones. The mathematical formula for these
statistics is shown in the equation:
AIC(M) = −2 log L(M) + 2 · p(M)
L(M) is the likelihood function of the parameters in
model M evaluated at the MLE (Maximum
Likelihood Estimators) and p(M) is the number of
estimated parameters in the candidate model.
Schwarz’s Bayesian Information Criterion:
BIC(M) = −2 log L(M) + p(M) · log n
Hannan-Quinn information criterion:
HQ(M)= −2 log L(M)+2 p(M) log(log n)
As a user of these information criteria as a model
selection guide, you select the model with the
smallest information criterion.
Table 2. The results of selection test
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-236.16
32.60
9.16
9.23
9.19
1
-101.27
254.22
0.21
4.13
5.18
4.58
2
-100.34
1.67
0.24
4.24
4.62
4.39
3
-98.50
3.19
0.26
4.33
4.85
4.53
4
-92.87
9.32
0.25
4.26
4.94
4.52
5
-84.03
1.16
0.23
4.20
4.90
4.39
6
-78.93
13.92*
0.205*
4.07*
4.350*
4.211*
7
-81.94
1.89
0.26
4.31
5.43
4.74
8
-83.26
4.05
0.28
4.34
5.62
4.83
Source: Author’s calculation
Based on the values of the model selection criteria (in
table 2), the most appropriate model for our variables
is the VAR, [6], model.
According to Gujarati (2003), a key concept
underlying stochastic processes that has received a
great deal of attention and study by time series
analysts is the stationarity of the stochastic process.
In the general sense, "A time series is said to be
stationary if the mean and variance are constant over
time. ADF (Augmented Dickey-Fuller 1979, 1981)
test is a statistical significance test which means the
test will give results in hypothesis tests with null and
alternative hypotheses. The null hypothesis is the
series in not stationary, and for testing we used a t-
test. As a result, we will have a p-value, from which
if it is less than the 5% significance level, we say that
the basic hypothesis falls down and the time series is
stationary.
The following table shows the results of the ADF test
for the two series included in the analysis, the
number of tourists and GDP in Albania for the period
under study.
Table 3. The result of ADF test
Variable
t-test
for
ADF
Prob
Result for
Hypothesis
Resul
t for
series
Number of
tourist
0.054
0.6891
No Reject
D(Number
of tourist)
-2.6062
0.0119
Reject
I(1)
GDP
1.350
0.9502
No Reject
D(DGP)
-8.3118
0.0000
Reject
I(1)
Source: Author's Calculation
Based on the values of p, we conclude that both
series are non-stationary, they are the first order
integrated, I, [2].
In the graphs below, two series are presented, the
number of tourists and GDP according to seasonality.
We can see that as far as GDP is concerned, it has
higher values in the second quarter, while the number
of tourists in the third quarter.
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0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Q1 Q2 Q3 Q4
Means by Season
NRT by Season
300,000
350,000
400,000
450,000
500,000
550,000
600,000
Q1 Q2 Q3 Q4
Means by Season
GDP by Season
Fig. 1: Seasonality of series
Source: Authors Calculation
3.2 Granger Causality
To show the causal relationship between the
variables, the Granger, [19] test is used. In to
understand the short-term interdependence between
variables is helped by the Granger test (Granger,
1969&1980, Sims, 1972 Geweke et al., Hoover 2008;
Korda 2007; Xu 2015).
A variable X is said to 'Granger cause' a variable Y,
if past values of X can predict the current value of Y.
The Granger test empirically detects relationships
that exist between variables in the short run by
relying on VAR models.
The Granger causality test is based on VAR models,
in the case of our two variables, using the model
selection criteria, the most suitable model was the
VAR, [6], model, which is presented in the following
system:
t
n
iiti
n
iitit
t
iiti
iitit
GDPNrtGDP
GDPNrtNrt
,2
12
11
,1
6
12
6
11
The hypothesis are:
1. Ho: . 
 (Nrt not causality
DGP)
2. Ho: 
 (DGP not causality Nrt)
Fisher statistics are used to test the hypothesis, if the
p-value is less than the 5% significance level, the
basic hypothesis is rejected.
The Granger test results for the number of tourist and
GDP are given in the following table:
Table 4. The result of Granger test
Null
Hypothesis:
F-Statistic
Prob.
Result
NRT does
not Granger
Cause GDP
21.5601
0.00000
Reject
GDP does
not Granger
Cause NRT
7.15632
0.0048
Reject
Source: Author's Calculation
The p-values are smaller than the 5% significance
level, therefore the hypothesis have been dropped.
In conclusion, we say that there is a two-way causal
relationship between the number of tourists and GDP
in Albania.
3.3 Cointegration
Cointegration indicates the existence of a long-run
relationship between variables, [20]. Even when the
variables do not, they are cointegrated in the long
run, they may still be correlated in the short run.
According to, [21], if we have more than two
variables in the model, then there is a possibility that
there is more than one co-integrating vector. By this
we mean that the variables in the model can form
several equilibrium relationships. In general, for a
number k of variables, we can have only up to (k-1)
co integrating vectors, [22]. To find how many co
integrating relationships exist between the k
variables, the use of Johansen's method is required.
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When it is known that the variables are I, [1], then
there is a possible cointegration among them. So
there may be a long-term relationship with time lags
between them.
To test for cointegration, we used the Johansen test
(1988, 1991), which is based on the VEC model.
Maximum eigenvalue and trace statistics are used to
test the hypothesis.
For the trace statistics, it tests the null hypothesis of r
cointegrating vectors against the alternative
hypothesis of n cointegrating vectors. The maximum
eigenvalue test, on the other hand, tests the null
hypothesis of r cointegrating vectors against the
alternative hypothesis of r +1 cointegrating vectors.
Neither of these test statistics follows a chi-square
distribution in general. Asymptotic critical values can
be found in Johansen and Juselius (1990) and are also
given by most econometric software packages. If the
p-value is less than the 5% significance level, the
basic hypothesis is rejected.
The results of the Johansen test are shown in the
table below
Table 5. The result of Johansen test
Hyp.
Trace
0.05
Result
No. of
CE(s)
Eigen
value
Stat
Critical
Value
Prob.**
None *
0.934
51.82
18.39
0
Reject
At
most 1
0.009
0.174
3.841466
0.675
Not
reject
Source: Author's Calculation
The p-values are smaller than the 5% significance
level, for the case when r=0, therefore this hypothesis
falls down. The p-value is greater than the 5%
significance level, for the case when r=1, therefore
this hypothesis remains. In conclusion, we say that
the number of tourists and GDP in Albania are
cointegrated.
4 Conclusion
Tourism is an important sector with an impact on
economic development in Albania. The development
of tourism has an impact on the increase of
employment, on the increase of investments in
accommodation structures, in infrastructure, and
directly on the increase of national production. This
sector was severed during the Codiv-19 pandemic
period, but with the easing of measures, it will
continue to grow, culminating in 2022 with a
significant increase and, moreover, with the addition
of the countries of origin from which tourists come,
as well as the extension of time than tourism.
Cultural, historical and mountain tourism are
becoming even more attractive. From the analysis of
two series of the number of tourists and GDP in
Albania, it turned out that they are non-stationary
series, from the ADF test it turned out that the series
are I, [1]. The Granger causality test concluded that
there is a two-way relationship between the number
of tourists and GDP. Johansen’s test discovered that
these variables have stable relationships even in long-
term periods. These conclusions show us that the
increase in the demand for tourism affects economic
growth, but also the economic growth affects the
increase in the demand for tourism since the
conditions offered are better and this will make the
tourists increase the values spent in the country.
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US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.81
Miftar Ramosacaj, Elmira Kushta
E-ISSN: 2224-2899
886
Volume 20, 2023