Кwilinski Alex


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Lamia Jamel 
Virtual Economics, Vol. 3, No. 4, 2020 

 

2020 Volume 3 Number 4 (October) 
 

 
THE RELATION BETWEEN TOURISM AND ECONOMIC GROWTH: A CASE OF 

SAUDI ARABIA AS AN EMERGING TOURISM DESTINATION 
 

Lamia Jamel 
 

Abstract. This paper examines empirically the relation between tourism and economic growth 
in Saudi Arabia. The author tries to justify how tourism contributes to the economic growth of 
Saudi Arabia. There are applied descriptive statistics, unit root test, VAR model and Granger 
Causality test as an econometric methodology to examine the connection between tourism 
and economic growth in Saudi Arabia for the annual data in the period from 1990 to 2018. The 
main empirical results of the study find out that tourism affects positively the economic 
growth in Saudi Arabia. Also, there is found a positive nexus among tourism and economic 
growth. Furthermore, CO2 emissions and financial development impact positively the tourism 
sector, while trade openness predicts a negative effect on tourism. Additionally, CO2 
emissions, financial development, and trade openness have a positive impact on economic 
growth in Saudi Arabia. Finally, the Granger causality test provides evidence of bidirectional 
nexus between tourism and economic growth in Saudi Arabia. This paper contributes to the 
current research by explaining the causal nexus among tourism and economic growth in Saudi 
Arabia during the period from 1990 to 2018, applying a vector autoregressive model and 
Granger Causality. 
Keywords: tourism, economic growth, time series, vector autoregressive, granger causality 
JEL Classification: F43, Z32 

 

 

Author:  
 
Lamia Jamel 
Department of Finance and Economics, College of Business Administration, Taibah University, Medinah, 
Saudi Arabia.  
Email: lajamel@yahoo.fr 
https://orcid.org/0000-0002-3244-068X 
 
Citation: Jamel, L. (2020). The Relation between Tourism and Economic Growth: A Case of Saudi Arabia 
as an Emerging Tourism Destination. Virtual Economics, 3(4), 29-47. 
https://doi.org/10.34021/ve.2020.03.04(2) 

 
 
Received: July 15, 2020. Revised: September 25, 2020. Accepted: October 3, 2020.  
© Author(s) 2020. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

1. Introduction 
 
For several decades, international tourism has experienced dynamic growth and sustained 
expansion. Cross-border travel for recreation, leisure or business has become one of the most 
dynamic economic activities in the world. The international tourism is an important part of 
economic growth in developing countries, particularly for the least developed ones and other 
small, structurally weak and vulnerable countries which, without this sector, would have faced 
enormous challenges in a trading system. Now highly competitive and rapidly evolving, 
tourism is “like an iceberg: the visible part consists of travel, accommodation and tourist 
services, below the surface, there are multiple possibilities for households, micro and small 
businesses". Many countries are endowed with often unique resources of a historical, natural 
or cultural nature capable of being transformed into travel destinations. Tourism is thus a 
universal activity with a significant local impact. 
 
Tourism stimulates the local economy in destination countries thanks to the links existing in 
the value chain with other economic sectors, notably with agriculture. It also promotes 
entrepreneurship and the growth of local SMEs; it allows better diversification of the local 
economy while generating income linked to exports. Goods and services from this industry 
create new jobs, especially for young people and women, contributing to poverty reduction. 
In poor and remote rural areas, tourism can help generate income for the population of these 
areas while reducing urban migration flows. In some countries, tourism revenues have created 
funds to finance biodiversity conservation and natural resource protection programs behind 
this tourism. Finally, tourism can help to make a country's external image positive. 
 
Tourism is mainly an activity handled by the private sector. However, without the existence 
and application of coherent policies and actions coordinated by the government, in the 
tourism sector but also in other related sectors, tourism cannot be fully integrated into an 
objective of inclusive and sustainable growth and development. Worse, without political will, 
financial leakage (the loss of tourism revenue linked to the acquisition of goods and services 
outside the country) can significantly erode the economic benefits of tourism and weaken the 
country’s environmental, social, cultural and economic development. 
 
Tourism is a sector made up of different branches (transport, accommodation, catering, 
cultural and sporting activities, etc.) which also concern non-tourists. It occupies an increasing 
place in service activities, and it has considerable effects on the economies, societies, cultures 
of the countries and territories concerned. 
 
Saudi Arabia is an important cultural and historical destination both religiously and 
economically. The state-sponsored modern tourism concept promote multiple benefits of 
tourism in the economic, cultural, heritage, social and environmental aspects. The Saudi 
museums contain artifacts found in modern archaeological excavations of the Umayyad and 
Abbasid period, the medieval and late Islamic era, and finally the reunification period of Saudi 
Arabia. There are also more than 10,000 heritage and cultural sites in the country, including 
500 mentioned in the ancient Arabic poetry, and about 400 others mentioned in the biography 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

of the Prophet, but Mecca remains the most attractive city for visitors, followed by Medina. 
Along the coast of the Red Sea there are beaches that qualify to be an attractive place for 
tourists: approximately 1800 km from the west and 700 km on the coast of the Arabian Gulf 
from the east. Saudi Arabia plans to attract 100 million visitors a year in 2030, and to increase 
tourism contribution to the economy from five to 18 percent. 
 
In the literature, there are some current research papers investigating the correlation 
between tourism and economic growth in various countries across the world (Manzoor et al., 
2019; Mishra et al., 2011; Ridderstaat et al., 2014; Letunovska et al., 2020; Kharazishvili et al., 
2020). Nevertheless, none of them concentrate on evaluating the link between tourism and 
the economic growth in Saudi Arabia. So, our purpose is to resolve the following questions: 
What is the connection between tourism and economic growth in Saudi Arabia? How do these 
indicators associate in the short run and long run? 
 
To do so, the main task of this research is to investigate empirically the link between tourism 
and economic growth in Saudi Arabia. There are applied a vector autoregressive (VAR) model 
and Causality Granger test as an appropriate econometric methodology for the annual data 
for Saudi Arabia in the period from 1990 to 2018. The main empirical findings of the study 
show that tourism positively affects  the economic growth in Saudi Arabia. Also, it was found 
that GDP has a positive impact on tourism. Furthermore, CO2 emissions and financial 
development have a positive impact on the tourism sector, while trade openness predicts a 
negative effect on tourism. However,, CO2 emissions, financial development, and trade 
openness have a positive impact on economic growth in Saudi Arabia. Additionally, the main 
conclusions of the Granger causality test provide evidence of bidirectional link between 
tourism and economic growth. Furthermore, our results indicate the existence of 
unidirectional link between tourism and trade openness in Granger's sense. 
 
The rest of this study is organized as follows: section 2 presents a literature review for the link 
between tourism and economic growth. Section 3 explains the econometric methodology 
used in this paper. The data is analysed in section 4. The main empirical findings are presented 
in section 6. Finally, section 7 makes conclusions. 
 
2. Literature Review 
 
For the reason that tourism potentially contributes to economic growth, efforts have been 
made to assess the economic effects of various modes of tourism. The current investigations 
comprise papers in sports tourism (Daniels et al., 2004; Li & Jago, 2013), rural tourism 
(Fleischer & Tchetchik, 2005; Park et al., 2014), ethnic tourism (Theerapappisit, 2009), park 
tourism (Ma et al., 2009; Mayer, 2014; Saayman & Saayman, 2006), conference, convention, 
and exhibition tourism (Hanly, 2012), religious tourism (Saayman et al., 2014), festival tourism 
(Clarke & Hoaas, 2007; Saayman & Rossouw, 2011), casino gaming tourism (Benar & Jenkins, 
2008; Wan, 2012), and heritage tourism (Dredge, 2004). 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

It is generally believed that tourism has contributed positively to economic growth as exports 
have strongly triggered economic development. The worldwide trade theories are supportive 
of a positive nexus among export and economic growth (Thornton, 1997; Xu, 1996; Ahmed & 
Kwan, 1991; Jin, 1995). Several papers including that by Marin (1992) suggest that a 
unidirectional exports growth influences economic expansion in developed countries such as 
United States, Japan, United Kingdom and Germany. 
 
Furthermore, export support and economic growth have significantly strengthened each other 
in South America and Africa (Jin, 1995; Bahmani-Oskooee & Alse, 1993). Moreover, Kulendran 
& Wilson (2000) and Shan & Wilson (2001) show a great reciprocal nexus among international 
trade and international travel in Australia and China. 
 
Theoretical models that consider a causal connection among non-traded goods, such as 
tourism and economic growth, are recent phenomenon (Kim et al., 2006). In a recent study of 
economic growth some academics maintain that tourism has a long-run economic growth 
influence (Lanza et al., 2003; Balaguer & Cantavella-Jorda, 2002; Tang & Jang, 2009; 
Katircioglu, 2009; Oh, 2005; Ridderstaat et al., 2014), and several of them suggest that there 
is a reciprocal causality nexus among tourism development and economic growth (Kim et al., 
2006; Dritsakis, 2004; Durbarry, 2004; Kumar & Kumar, 2012; Lee & Chang, 2008). 
 
The tourism-led growth literature argues that tourism specialization stimulates economic 
development (Algieri, 2006; Perez-Dacal et al., 2014; Ridderstaat et al., 2014). The possibility 
of tourism growth is revealed through augmented terms of trade (ToT). The empirical 
investigations confirm these theoretical proposals because despite the pessimistic predictions 
of the endogenous growth theory, small islands were able to develop (Brau et al., 2007; Croes, 
2013; Holzner, 2011). 
 
A various number of papers have employed many methods to investigate the nexus between 
tourism and economic growth, such as the econometric models, the tourism satellite account 
(TSA), computable general equilibrium (CGE) models and Dynamic Stochastic General 
Equilibrium (DSGE) models. This section briefly reviews the related papers and summarizes 
the research objectives of this paper. In the econometric models, cointegration and the 
Granger causality are commonly employed to study the nexus between tourism and economic 
growth (Brida et al., 2016; Song et al., 2012). 
 
For instance, Chiu & Yeh (2017) apply cross-sectional data models to detect a correlation link 
between tourism and economic growth. Several papers such as Belloumi (2010), Croes & 
Vanegas (2008) and Shahzad et al. (2017) employ time series models. Other papers use panel 
data models (Bilen et al., 2017; Liu & Song, 2018; Salifou and Haq, 2017), which give a larger 
degree of freedom in the model estimation and are particularly useful when the time series 
are brief (Song et al., 2019; Wu et al., 2017).  
 
Po & Huang (2008) use a cross-sectional study of 88 nations during the period from 1995 to 
2005. They find a nonlinear correlation between the expansion of tourism and economic 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

growth, divided all the data collected into three regimes for their investigation. The empirical 
results of the threshold regression when (qi) is below 4.048% (regime 1.57 nations) or above 
4.73% (regime 2.23 nations) show a confident nexus between tourism and economic growth. 
 
Karimi (2018) examines the connection between leisure industry and economic growth in 
Malaysia. Karimi (2018) shows a positive impact of the leisure industry on the economic 
growth. He demonstrates a significant and considerable long-term impact on variables such 
as exchange rate, trade, and inflation, which were essential for the Malaysian economic 
growth. Karimi (2018) reports that tourism can stimulate economic growth, but for it to have 
an impact the policymaking requires successful promotion of inbound tourism. 
 
As Brida et al. (2016) found, although various empirical investigations employing Granger 
causality and cointegration tests support a Granger causality connection between tourism and 
economic growth, they can only study the sequence of occurrence between tourism and 
economic growth, rather than a real cause-effect connection (Song et al., 2012). 
 
3. Methods 
 
In this part there are presented the model and all variables used in this paper. Compared to 
simultaneous equations (a basis of macro-econometrics until the 1970s), the VAR model has 
the advantage of capturing the variation of the model's parameters (system of equations) over 
time, and thus makes it possible to better restore the system dynamics, which gives credibility 
to the economic policy (macroeconomic forecasts) which adjusts and adapts to variations or 
shocks (innovations) experienced by the socioeconomic environment. Then, there is used a 
Vector Autoregressive (VAR) to estimate the dynamic relationship between tourism and 
economic growth in Saudi Arabia during the period of study from 1990 to 2018. Let a vector 
process {𝑦𝑡}𝑡∈ℤ of dimension (k,1) admit a representation VAR(p): 
 

𝑦𝑡 = 𝑐0 +𝐴1𝑦𝑡−1+.. .+𝐴𝑝𝑦𝑡−1 +𝜈𝑡 (1) 

𝐴(𝐿)𝑦𝑡 = 𝑐0 +𝜈𝑡 (2) 

 
With, 𝐴0 = 𝐼𝑘 , 𝐴𝑝 ≠ 0𝑘 and 𝐴𝑖 = 𝐴0 if i = 0 as mentioned in Equation 7. 

 
Where, 𝑦𝑡 represents the dependent variable, 𝑦𝑡−𝑖 (i periods back) is called the i-th lag of y, 
𝐴𝑖 is a time-invariant (k × k)-matrix, 𝑐0 is a k-vector of constants, p indicates the order of the 
VAR model which is obtained by the VAR Lag Order Selection Criteria Test, t indicates the time 

period (t = 1, …, 29), 𝜈𝑡 is a k-vector of error terms 𝐿
𝑖 = 𝑦𝑡−𝑖 and indicates the endogenous 

variables. 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

The matrices 𝐴𝑖,∀𝑖 ∈ [1,𝑝] are of dimension (k,k). The innovation vector 𝜈𝑡 is 𝐼.𝐼.𝐷(0𝑘,Ω) 
and where Ω is a positive definite symmetric square order matrix (k). The innovation vector 
must satisfy the following requirements: 
 

𝐸(𝜈𝑡) = 0𝑘 (3) 

𝐸(𝜈𝑡𝜈′𝑡) = {
Ω,𝑗 = 0
0,𝑗 ≠ 0

0𝑘 (4) 

 
The model VAR(p) is following: 
 

𝑦1𝑡 = 𝑎1
0 +𝑎11

1 𝑦1,𝑡−1 +𝑎12
1 𝑦2,𝑡−1+.. .+𝑎1𝑘

1 𝑦𝑘,𝑡−1 

+𝑎11
2 𝑦1,𝑡−2 +𝑎12

2 𝑦2,𝑡−2+.. .+𝑎1𝑘
2 𝑦𝑘,𝑡−2 

⋮ 

+𝑎11
𝑝
𝑦1,𝑡−𝑝 +𝑎12

𝑝
𝑦2,𝑡−𝑝+.. .+𝑎1𝑘

𝑝
𝑦𝑘,𝑡−𝑝 +𝜈1𝑡 

(5) 

 
 

𝑦2𝑡 = 𝑎2
0 +𝑎21

1 𝑦1,𝑡−1 +𝑎22
1 𝑦2,𝑡−1+.. .+𝑎2𝑘

1 𝑦𝑘,𝑡−1 

+𝑎21
2 𝑦1,𝑡−2 +𝑎22

2 𝑦2,𝑡−2+.. .+𝑎2𝑘
2 𝑦𝑘,𝑡−2 

⋮ 

+𝑎21
𝑝
𝑦1,𝑡−𝑝 +𝑎22

𝑝
𝑦2,𝑡−𝑝+.. .+𝑎2𝑘

𝑝
𝑦𝑘,𝑡−𝑝 +𝜈2𝑡 

(6) 

 
Where, 𝑎𝑖𝑗

𝑠  indicates the coefficients of endogenous variables with s indicating shift, i 

differentiates the coefficients of variables between equations and j differentiates the 
coefficients among variables in the same equation. 
 
Again, the matrix form can be found while complying with the following scriptures: 
 

𝑦𝑡 =

(

 
 
 

𝑦1𝑡
𝑦2𝑡
⋮
𝑦𝑗𝑡
⋮
𝑦𝑘𝑡)

 
 
 
 𝑐0 =

(

 
 
 
 

𝑎1
0

𝑎2
0

⋮
𝑎𝑗
0

⋮
𝑎𝑘
0)

 
 
 
 

𝐴𝑖 =

(

 
 
 
 

𝑎11
𝑖 𝑎12

𝑖 ⋯ 𝑎1𝑘
𝑖

𝑎21
𝑖 𝑎22

𝑖 ⋯ 𝑎2𝑘
𝑖

⋮
𝑎𝑗1
𝑖

⋮
𝑎𝑘1
𝑖

⋮  ⋮  ⋮
𝑎𝑗2
𝑖 ⋯ 𝑎𝑗𝑘

𝑖

⋮
𝑎𝑘2
𝑖

⋮
⋯

⋮
𝑎𝑘𝑘
𝑖 )

 
 
 
 

𝑖∈[1,𝑝]

 

 

𝑦𝑡−𝑖 =

(

 
 
 

𝑦1,𝑡−𝑖
𝑦2,𝑡−𝑖
⋮

𝑦𝑗,𝑡−𝑖
⋮

𝑦𝑘,𝑡−𝑖)

 
 
 

𝑖∈[1,𝑝]

 𝑣𝑡 =

(

 
 
 

𝑣1𝑡
𝑣2𝑡
⋮
𝑣𝑗𝑡
⋮
𝑣𝑘𝑡)

 
 
 

 

(7) 

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Lamia Jamel 
Virtual Economics, Vol. 3, No. 4, 2020 

 

 
Thus, we arrive at joining the general matrix writing of a multivariate VAR (p) which is 
represented in its reduced form. 
 

𝑦𝑡 = 𝑐0 +∑𝐴𝑖𝑦𝑡−𝑖 +𝑣𝑡 =

𝑝

𝑖=1

𝑐0 +∑𝐴𝑖𝐿
𝑖𝑦𝑡 +𝑣𝑡

𝑝

𝑖=1

 (8) 

 
To estimate our model and to test the impact of tourism on economic growth in Saudi Arabia, 
we use tourism and economic growth as endogenous variables, and we use CO2 emissions, 
financial development, and trade openness as exogenous variables. The definition of all 
variables used is presented in Table 1. 
 
Table 1. Variables’ definitions 
 

Indicator 
Name 

Code Extended definition Source 

GDP growth 
(annual %) 

GDP 

Annual percentage growth rate of GDP in market 
prices based on constant local currency. 
Aggregates are based on constant 2010 U.S. 
dollars. 

World Bank National 
accounts data, and OECD 
National Accounts data files. 

International 
tourism, 
number 
of arrivals 

NTA 

International inbound tourists (overnight visitors) 
are the number of tourists who travel to a country 
other than their permanent residence, outside 
their usual environment, for a period not 
exceeding 12 months and whose main purpose in 
visiting is other than an activity remunerated from 
within the country visited. 

World Tourism Organization, 
Yearbook of Tourism 
Statistics, Compendium of 
Tourism Statistics and data 
files. 

Stocks traded, 
total value 
 (% of GDP) 

TO 
The value of shares traded is the total number of 
shares traded, both domestic and foreign, 
multiplied by their respective matching prices. 

World Federation of 
Exchanges database. 

Domestic 
credit to 
private sector 
(% of GDP) 

FD 

Domestic credit to private sector refers to financial 
resources provided to the private sector by 
financial corporations, such as through loans, 
purchases of nonequity securities, and trade 
credits and other accounts receivable, that 
establish a claim for repayment. For some 
countries these claims include credit to public 
enterprises. 

International Monetary Fund, 
International Financial 
Statistics and data files, and 
World Bank and OECD GDP 
estimates. 

CO2 emissions 
(metric tons 
per capita) 

CO2 

Carbon dioxide emissions are those stemming 
from burning fossil fuels and the manufacture of 
cement. They include carbon dioxide produced 
during consumption of solid, liquid, and gas fuels 
and gas flaring. 

Carbon Dioxide Information 
Analysis Centre, 
Environmental Sciences 
Division, Oak Ridge National 
Laboratory, Tennessee, 
United States. 

Source: developed by the author. 

 
 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

4. Data 
 
4.1. Descriptive Analysis 
 
This study aims to examine the causal link between tourism and economic growth in Saudi 
Arabia during the period of study from 1990 to 2018. There were used the annual data 
obtained from the World Bank databases. Table 2 summarizes the descriptive statistics of the 
indicators used in this study. This table demonstrates that in average the higher value is for 
the number of arrivals in international tourism (9,207,963) followed, respectively, by total 
value of stocks traded (65.91166), financial development (32.99158), CO2 emissions 
(16.15351) and GDP growth (3.645280). 
 
For the two statistics of skewness (asymmetry) and kurtosis (leptokurtic), it can be remarked 
that the two variables used in this study are characterized by non-normal distribution. The 
negative sign of the skewness coefficients indicates that the CO2 variable is skewed to the left 
and it is far from being symmetrical for all variables. However, the skewness coefficients 
indicate that the variables GDP, NTA, TO and FD are skewed to the right, and all variables are 
far from being symmetrical. Also, the Kurtosis coefficients confirm that the leptokurtic for all 
variables employed in this paper show the existence of high peak or fat-tailed volatilities. 
 
Based on the estimate coefficients of Jarque-Bera, there can be rejected the null hypothesis 
of normal distribution of the variables used in our study. Then, the high value of Jarque-Bera 
coefficients reflects that the series is not normally distributed at the level of 1%. Finally, and 

based on the three statistics  skewness, kurtosis and Jarque-Bera  it can be concluded that 
all variables employed in this paper are not normally distributed at the level of 1%. 
 
Table 2. Descriptive statistics 
 

 TO GDP CO2 FD NTA 

Mean 65.91166 3.645280 16.15351 32.99158 9207963. 

Median 31.71435 2.788402 16.69630 32.35426 8037000. 

Maximum 372.2599 15.19343 19.71427 58.11449 18260000 

Minimum 10.48277 -3.763285 10.49777 14.82210 3114422. 

Std. Dev. 91.42413 4.766838 2.842125 12.34316 5565735. 

Skewness 2.322804 0.849053 -0.533751 0.497256 0.336534 

Kurtosis 7.689675 3.404697 2.192900 2.391514 1.616944 

Jarque-Bera 52.65280 36.82207 21.64091 46.42498 28.58754 

Probability 0.000000* 0.000000* 0.000000* 0.000000* 0.000000* 

Observations 29 29 29 29 29 

Note: This table reports the descriptive statistics of all indicators used in this paper. There was employed the 
annual data for Saudi Arabia during the period from 1990 to 2018. Statistical implication at the threshold 
level of 1 percent is denoted by *. 

Source: developed by the author. 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

4.2. A Correlation Matrix 
 
This section analyses the correlation among all variables used. Moreover, Table 3 summarises 
the estimation coefficients of the Pearson correlation matrix among all indicators employed 
in this paper. The empirical results presented in this table show that all estimating coefficients 
are inferior to the tolerance limit of Pearson (0.7), which does not cause problems in 
estimating the four equations. Since there is no correlation problem in this case, the analysis 
can be continued based on a set of econometric tests and estimates. 
 
Table 3. A Correlation Matrix 
 

 TO GDP CO2 FD NTA 

TO 1 0.09719 0.25163 0.21857 0.18646 

GDP 0.09719 1 -0.01440 -0.24915 -0.02403 

CO2 0.25163 -0.01440 1 0.39781 0.27419 

FD 0.21857 -0.24915 0.39781 1 0.29766 

NTA 0.18646 -0.02403 0.27419 0.29766 1 

Note: This table recapitulates the estimated coefficients of correlation among all indicators. There are used 
the annual data for Saudi Arabia during the period of study from 1990 to 2018.  

Source: developed by the author. 

 
4.3. A Unit Root Test 
 
A study of the causal relationship between economic growth and tourism in Saudi Arabia first 
requires stationarity tests to determine the order of integration of each series. The results of 
the Augmented Dickey-Fuller test applied to the series are shown in Table 4 for the case of 
Saudi Arabia. 
 
Thus, the acceptance or rejection of the null hypothesis of the test is based on the value of 
probabilities and statistics relating to the indicated test. These probabilities are compared to 
a 10% threshold. If these probabilities are less than 10%, then the null hypothesis is rejected 
and if these probabilities are greater than 10%, then the null hypothesis is accepted. 
 
For the case of Saudi Arabia and according to Table 4, it can be observed that only the variable 
GDP is stationary in the level according to the Augmented Dickey-Fuller test but all the 
variables are stationary in first difference according to this test. Then, all the variables are 
stationary in first difference based on the unit root test. So, all the variables are integrated of 
order 1. The unit root test confirms the stability of the VAR model which will be used in our 
study. 
 
 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

Table 4. A Unit Root Test 
 

Variables 

Augmented Dickey-Fuller test statistic 

Level First Difference 

t-statistic p-value t-statistic p-value 

NTA -0.607050 0.8537 -5.091188 0.0004* 

TO -2.870534 0.0621 -7.317033 0.0000* 

GDP -4.366631 0.0019* -5.665164 0.0001* 

CO2 -1.897034 0.3288 -4.711808 0.0013* 

FD -0.602136 0.8548 -4.897344 0.0005* 

Note: This table recapitulates the results of a stationary test. In this test the calculate p-value is compared to 
10%. If the calculate p-value <10% therefore, the hypothesis H0 is refused and if the calculate p-value > 10% 
then the hypothesis H0 is accepted. With the hypothesis H0: all variables are non-stationary. Statistical 
meaning at 1% is presented by (*). 

Source: developed by the author. 

 
5. Results and Discussion 
 
5.1. VAR Estimation 
 
After having the stationary variables, there is built a VAR (Vector Auto Regressive) model. Such 
models allow, on the one hand, analysing the effects of one variable on the other through 
random shock simulations and, on the other hand, conducting an analysis in terms of causality. 
In the case of a VAR process, each of the variables is modelled according to its own delays and 
the delays of the other variables. 
 
To determine the optimal delay number for the VAR representation, there are estimated 
several models for an order ranging from 1 to h (h being the maximum delay admissible by 
economic theory or by the available data). 
 
The delay P which will minimize the criteria LR (sequential modified LR test statistic (each test 
at 5% level)), FPE (Final prediction error), AIC (Akaike information criterion), SC (Schwarz 
information criterion) and HQ (Hannan-Quinn information criterion) will be retained. 
According to the criteria used in Table 5, it can be noticed that the optimal number of delays 
is equal to 4 for the four models to be estimated. In this case, a VAR model of order 4 is 
estimated. 
 
 
 
 
 
 

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Table 5. A VAR Lag Order Selection Criteria Test 
 

Lag LogL LR FPE AIC SC HQ 

0 -201.0897 NA 127346.2 17.42414 17.81683 17.52832 

1 -181.9726 28.67564 36690.76 16.16438 16.75341 16.32065 

2 -177.0315 6.588147 35002.13 16.08596 16.87133 16.29432 

3 -172.7422 5.004114 36099.69 16.06185 17.04357 16.32230 

4 -162.0020 10.74029* 22508.18* 15.50016* 16.67822* 15.81270* 

5 -160.6854 1.097161 32360.55 15.72378 17.09818 16.08841 

Note: * indicates a lag order selected by the criterion, LR: sequential modified LR test statistic (each test at 5% 
level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: 
Hannan-Quinn information criterion. 

Source: developed by the author. 

 
The VAR model of order 4 is applied to study the dynamic relationship between economic 
growth and tourism for the case of Saudi Arabia during the period from 1990 to 2018. Table 6 
summarizes the estimation of the VAR model. This Table shows that the coefficients of 
determination for the two estimates are greater than 0.6, therefore, the two estimates are 
characterized by a good linear fit. 
 
For the estimation of the variable NTA which measures the number of arrivals of international 
tourism, it can be noticed that there are six significant variables, but with different signs. 
 
It is shown that the variable NTA on date (t-1) which measures tourism expressed by the 
number of international tourists arriving in Saudi Arabia has a positive impact on tourism on 
the date t at a threshold of 5%. So, if the number of international tourists arriving in Saudi 
Arabia on date (t-1) increases by five units, then, the number of international tourists arriving 
in Saudi Arabia on date t increases by 0.650799 units. 
 
For the second endogenous variable GDP, which measures the economic growth rate in Saudi 
Arabia, it is found out that the variable GDP at date (t-1) has a positive impact on tourism at 
date t at a threshold of 1%. Then, if economic growth in Saudi Arabia on date (t-1) increases 
by one, then the number of international tourists arriving on date t increases by 6.320594 
units. 
 
Also, it is found that the variable GDP at date (t-4) has a negative impact on tourism at date t 
at a threshold of 5%. So, if economic growth in Saudi Arabia on date (t-1) increases by five 
units, then the number of international tourists arriving on date t decreases by 5.135529 units. 
 
For the exogenous variables, it is remarked that CO2 emissions have a positive impact on 
tourism at a threshold of 1%. This means that if the CO2 variable increases by one unit then 
the number of international tourists arriving in Saudi Arabia increases by 24.122230 units. 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

The variable FD which measures the level of financial development in Saudi Arabia has a 
positive impact on tourism at a threshold of 10%. This means that if financial development 
increases by ten units then the number of international tourists arriving in Saudi Arabia 
increases by 0.063256 units. This result reflects the importance of credits granted to the 
private sectors for financing tourism projects. 
 
The TO variable which measures trade openness in Saudi Arabia has a negative impact on 
tourism at a threshold of 5%. This means that if the trade openness increases by five units 
then the number of international tourists arriving in Saudi Arabia decreases by 1.19E-056 
units. 
 
For the second endogenous variable GDP which measures the growth rate of GDP in Saudi 
Arabia, it is noticed that there are six significant variables. 
 
We find that the variable NTA on date (t-1) which measures tourism expressed by the number 
of international tourists arriving in Saudi Arabia has a positive impact on economic growth on 
date t at a threshold of 1%. Then, if the number of international tourists arriving in Saudi Arabia 
on date (t-1) increases by one, then economic growth in Saudi Arabia on date t increases by 
0.011089 units. 
 
Also, it is shown that tourism at date (t-2) measured has a positive impact on economic growth 
at date t at a threshold of 1%. So, if the number of international tourists arriving on date (t-2) 
increases by one, then economic growth in Saudi Arabia on date t increases by 0.010192 units. 
 
The variable GDP at date (t-4) has a negative impact on economic growth in Saudi Arabia at 
date t at a threshold of 1%. So, if economic growth in Saudi Arabia on date (t-1) increases by 
one unit, then economic growth in Saudi Arabia on date t decreases by 0.190905 units. 
 
For the exogenous variables, it was found out that CO2 emissions have a positive impact on 
economic growth in Saudi Arabia at a threshold of 1%. This means that if the CO2 variable 
increases by one unit then economic growth in Saudi Arabia increases by 0.709785 units. 
 
The variable FD which measures the level of financial development in Saudi Arabia has a 
positive impact on economic growth in Saudi Arabia at a threshold of 1%. This means that, if 
financial development increases by one unit then economic growth in Saudi Arabia increases 
by 0.306281 units. This result confirms the importance of credits granted to the private sectors 
for the financing of the economy. 
 
The variable TO which measures trade openness in Saudi Arabia has a positive impact on 
economic growth in Saudi Arabia at a threshold of 5%. This means that if the trade openness 
increases by five units then economic growth in Saudi Arabia increases by 5.06E-07 units. 
 
 
 

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www.virtual-economics.eu                                                                                ISSN 2657-4047 (online) 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

Table 6. VAR estimations 
 

Variables NTA GDP 

NTA(-1) 
0.650799 0.011089 

[2.14147]** [5.41437]* 

NTA(-2) 
0.058536 0.010192 

[0.13208] [3.26119]* 

NTA(-3) 
0.107259 -0.018737 

[0.29664] [-0.58849] 

NTA(-4) 
-0.310363 0.009102 

[-1.48686] [ 0.49521] 

GDP(-1) 
6.320594 -0.160076 

[ 1.88945]*** [-0.54345] 

GDP(-2) 
0.912583 -0.441427 

[0.28915] [-1.58840] 

GDP(-3) 
-1.668374 -0.127812 

[-0.65700] [-0.57161] 

GDP(-4) 
-5.135529 -0.190905 

[-2.43354]** [-3.02736]* 

C 
-231.7766 -0.791857 

[-2.36522]** [-0.09177] 

CO2 
24.12230 0.709785 

[ 2.90869]* [2.97199]* 

FD 
0.063256 0.306281 

[1.93659]*** [4.17052]* 

TO 
-1.19E-05 5.06E-07 

[-2.02362]** [1.97816]** 

R-squared 0.888411 0.832525 

Adj. R-squared 0.793989 0.747646 

Sum sq. resids 24536.92 190.2444 

S.E. equation 43.44485 3.825465 

F-statistic 9.408961 0.900774 

Log likelihood -121.5867 -60.84138 

Akaike AIC 10.68694 5.827311 

Schwarz SC 11.27200 6.412371 

Mean dependent 74.74685 2.915520 

S.D. dependent 95.71787 3.737464 

Note: This table recapitulates the estimated coefficients from the VAR model. To examine empirically this 
model, there are used the annual data for Saudi Arabia in the period from 1990 to 2018. Statistical 
significance at 1%, 5%, and 10% level are presented by (*), (**), and (***), respectively. 

Source: developed by the author. 

 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Percent NTA variance due to NTA

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Percent NTA variance due to GDP

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Percent GDP variance due to NTA

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Percent GDP variance due to GDP

Variance Decomposition

 

Figure 1. The Variance Decomposition Test 
Source: developed by the author. 
 

In addition, the variance decomposition test was used to confirm the results received by 
means of the VAR model. However, the variance decomposition separates the volatility in an 
endogenous variable into the component shocks to the VAR model. Thus, the variance 
decomposition provides information about the relative significance of each random 
innovation in affecting the variables in the VAR. Figure 1 presents the variance decomposition 
test. This figure shows the impact of tourism on economic growth in Saudi Arabia after a 
period of two years. This result provides the same findings to the VAR estimation. 
 

Also, there was employed the Impulse Response Function test to continue the justification of 
our empirical findings (Figure 2). Nevertheless, the impulse response functions trace the 
impacts of a shock of one endogenous variable on to the other variables in the VAR model. 
Based on this figure, it can be observed that tourism in Saudi Arabia has an important and 
positive impact on economic growth. 
 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

-2,000,000

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

1 2 3 4 5 6 7 8 9 10

Response of NTA to NTA

-2,000,000

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

1 2 3 4 5 6 7 8 9 10

Response of NTA to GDP

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Response of GDP to NTA

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10

Response of GDP to GDP

Response to Cholesky One S.D. Innovations

 
 
Figure 2. The Impulse Response Function Test  
Source: developed by the author. 

 
5.2. A Causality Test 
 
It is necessary to check whether tourism causes economic growth or whether economic 
growth causes tourism in Saudi Arabia. The causal relationship between tourism and economic 
growth in Saudi Arabia is determined by using the Granger Causality test. The acceptance or 
rejection of the null hypothesis of the Granger causality test is based on a 5% threshold. If the 
probability of the test is less than 5%, in this case we reject the null hypothesis and if this 
probability is more than 5% then we accept the null hypothesis of no causal link. 
 
Table 7 summarizes all results of the Granger causality test between tourism and economic 
growth for the case of Saudi Arabia during the period from 1990 to 2018. 
 
Table 7 shows that there is a two-way relationship between tourism and economic growth in 
the sense of Granger. This result confirms that tourism can cause economic growth in the 
Granger's sense. Thus, economic growth can cause tourism in the sense of the Granger’s 
causality. 
 
In addition, it has been noticed that there is a one-way relationship between tourism and trade 
openness. That is to say that only tourism in Saudi Arabia can cause trade openness in the 
Granger's sense. 

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Virtual Economics, Vol. 3, No. 4, 2020 

 

Table 7. A Causality Granger’s Test 
 

Null Hypothesis: Obs F-Statistic Prob. 

GDP does not Granger Cause NTA 27 18.6017 0.0000* 

NTA does not Granger Cause GDP 21.9395 0.0000* 

CO2 does not Granger Cause NTA 27 0.07314 0.9297 

NTA does not Granger Cause CO2 0.02863 0.9718 

FDI does not Granger Cause NTA 27 0.06853 0.9340 

NTA does not Granger Cause FDI 0.10641 0.8995 

TO does not Granger Cause NTA 27 0.39716 0.6770 

NTA does not Granger Cause TO 17.7532 0.0000* 

Note: The * is significant at 5 percent. 

Source: developed by the author. 

 

6. Conclusions 
 

This paper examines the causal nexus between tourism and economic growth in Saudi Arabia. 
For that goal, there are used a VAR model and a Granger Causality test as an econometric 
methodology for the annual data of Saudi Arabia in the period from 1990 to 2018. There are 
employed a descriptive analysis, a Person correlation test, a unit root test, a VAR model and a 
Granger Causality test as an empirical methodology to examine the impact of tourism on 
economic growth in Saudi Arabia. 
 
The main empirical results of our study find out that tourism affects positively economic 
growth in Saudi Arabia. It is concluded that GDP at (t-1) has a positive impact on tourism. The 
GDP per capita at (t-4) predicts a negative link with tourism in Saudi Arabia. Furthermore, CO2 
emissions and financial development impact positively the tourism sector in Saudi Arabia. 
However, trade openness is predicted to have a negative influence on tourism. 
 
There is also found a positive connection between tourism at (t-1) and at (t-2) and economic 
growth. The GDP per capita at (t-4) predicts a negative link with GDP at t in Saudi Arabia. Then, 
CO2 emissions, financial development, and trade openness impact positively the tourism 
sector in Saudi Arabia. Also, the main empirical conclusions of the causality test prove an 
evidence of a bidirectional connection between tourism and economic growth. Also, there is 
found a unidirectional link between tourism and trade openness in the Granger's sense. 
 
Finally, these are macroeconomic aggregates that describe the size and economic importance 
of tourism, such as Value-Added Tourism (VAT), financial development, trade openness and 
Tourism GDP consistent with the same aggregates established for the national economy. In 
this context, Saudi Arabia is obliged to establish a link between monetary data and other non-
monetary information on tourism. Thus, there is needed the measurement of the economic 
impacts of tourism in the same way as it is done for other productive activities within the 
framework of the National Accounts. 
 

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Lamia Jamel 
Virtual Economics, Vol. 3, No. 4, 2020 

 

Based on the empirical conclusions of this study, it is proposed, firstly, that the Saudi 
government must implement a modern policy and strategy of economic growth based on the 
tourism industry. Second, public infrastructure among Saudi cities and increasing their 
connectivity should be implemented. Lastly, more social protection strategies should be 
established, which would increase the nation’s sustainability under conditions of its regional 
challenges. 
 

6. Acknowledgements 
 
The author is grateful to the Editor in Chief and anonymous Reviewers for their supportive and 
important remarks and suggestions to improve the quality of this paper. 
 
 
 
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