i want summary

Structural Change and Economic Dynamics 55 (2020) 49–58
Contents lists available at ScienceDirect
Structural Change and Economic Dynamics
journal homepage: www.elsevier.com/locate/strueco
The role of non-oil exports, tourism and renewable energy to achieve
sustainable economic growth: What we learn from the experience of
Saudi Arabia
Rida Waheed, Suleman Sarwar∗, Ashwaq Dignah
Finance and Economics Department, College of Business, University of Jeddah, Jeddah, Saudi Arabia
a r t i c l e
i n f o
Article history:
Received 4 March 2020
Revised 18 May 2020
Accepted 28 June 2020
Available online 29 August 2020
JEL classification:
C32
E43
L83
Q42
Q43
Keywords:
Non-oil exports
Tourism
Renewable energy
ARDL
Saudi Arabia
a b s t r a c t
This study attempts to examine the theoretical and empirical impacts of non-petroleum exports and
tourism on the economic growth of Saudi Arabia. In doing so, the author’s use the quarterly data of
studied variables covering the period of 1980q1-2017q4 and used the ARDL bound test, Johansen cointegration and Gregory-Hansen cointegration methods. The empirical findings mention that the non-oil
exports and tourism have positive effects on the economic growth. The results suggest that enhancing the non-petroleum exports might be a good strategy for sustainable growth and as alternative for
petroleum products. Further, the empirics mention long run cointegration between tourism, renewable
energy, capital, and economic growth in Saudi Arabia. The detailed findings imply that capital formation
can be utilized to enhance the investments on renewable energy and tourism facilities, as the tourism
and renewable energy are very crucial for the economic growth of Saudi Arabia. As a concluding remark,
the study argues that Saudi Arabia should enhance the investments on tourism and renewable energy in
the objective of reducing oil-dependence and for sustainable economic growth.
1. Introduction
The economy of Saudi Arabia is predominantly dependent on
natural resources, petroleum, and oil-based products, which face
several externalities and challenges during the period of recession,
crisis, and oil price shocks in the international market along with
an absolute pressure on the economic growth of the country. The
risk of economic dependence on one source is acute and very high
when petroleum products are depleting because the price in the
international market is contingent on geopolitical and economic
factors beyond the control of the Organization of the Petroleum Exporting Countries (OPEC) or other producing countries. Saudi Arabia being the largest oil producing countries with a GDP of $684
billion and 16 percent of the world’s petroleum reserves, play a
critical role in the OPEC region for economic policies. According to
Forbes magazine reports, petroleum products account for 87 percent of budget revenue, 42 percent of total GDP, and 90 percent
∗
Corresponding author.
E-mail address: ch.sulemansarwar@gmail.com (S. Sarwar).
https://doi.org/10.1016/j.strueco.2020.06.005
0954-349X/© 2020 Elsevier B.V. All rights reserved.
© 2020 Elsevier B.V. All rights reserved.
of total exports (Barbuscia, 2019; International Trade Administration, 2019). According to the perception of IMF, Saudia Arabia is
witnessing a budget deficit of 7.8 percent in 2019, due to decrease
in oil prices, while the growth of non-oil exports is 2.6 percent.
These facts are very interesting and surprising, but somehow point
a weakness of income dependence on oil exports. These main motive of selecting Saudi Arabia is to examine its response after oil
price crisis of 2014; does Saudi government need to restructure its
economic dynamics?
In view of curret economic challenges that Saudi governent is
facing, we try to propose significnat measures to control the economic degradation process. Howevre, the present study contributes
to the existing literarture with multipoints: firstly, as per our studies, this paper is the pioneer to investigate the impact of nonpetroleum exports on the economy of Saudi Arabia, by taking the
concepts and idea of export led growth hypothesis. One plausible explanation for this is justified from the reason that the Saudi
economy is almost 80 percent oil-dependent, which also creates
environmental degradation issues and affect the overall economic
progress (Alshammari and Sarathy, 2017). Though, we propose that
Saudi government has to change the structure of its economic
50
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
grounds, such as to reduce its dependence on exports of oil products and promote the exports of non-oil products which makes it
able to absorb oil price shocks. Similarly, the extensive efforts for
the growth of non-petroleum exports might be helpful in achieving
the strategic economic goals.
The second contribution of given study relates to openess the
Saudi Arabia for tourism; Saudi Arabia have great potential to generate significnat portion of its GDP throug relegious and cultural
tourism. Currently, according to World Travel Market report, the
tourism sector of Saudi Arabia could contribute up to $70 billion
into the country’s total GDP, which accounts for 9.3 percent of the
country’s GDP (World Travel Market, 2019; Siddiqui, 2019). At still,
the Saudi government has to introduce tourism policies to increase
its share in GDP.
The tourism industry affect the economy by several channels;
(i) tourism sector create new jobs and enhance tax income, (ii)
tourism industry push the government and relevant authorities to
make investments in infrastructure, technology, and human capital. (iii) Further, the tourism sector improves the efficiency of local companies by generating competition and facilities the economy of scale narrative (Shahzad et al., 2017; Mitra, 2019). The
toursim sector is considered as an alternative way of exports and
the main source of foreign exchange revenues, which minimize
the balance of payments deficit. Due to this reason, the literature have proposed the tourism led growth hypothesis and it has
been examined for the case of developed, developing and emerging economies (Parrilla et al., 2007; Matarrita-Cascante, 2010;
Tang and Tan, 2013; Yang and Fik, 2014; Bassil et al., 2015;
Jones and Li, 2015; Fahimi et al., 2018). For case of Saudi Arabia,
current study is the pioneer one that highlights the importance of
tourism, especially for after 2014 oil price crisis period.
The third important strand of this article is to explore the heterogeneous impacts of renewable energy on the economic growth
of Saudi Arabia. Currently, Saudi Arabia is generating its all electricity by using crude oil and fossil fuels, which consumes most
of the oil resources domestically, as well as becoming the source
of greenhouse emissions. However, according to the “The Saudi
Vision 2030” the country will generate 20 percent power from
the use of renewable sources e.g. solar and wind (Al-Saleh, 2009;
Allhibi et al., 2019). According to the reports of the Saudi government, the country is striving to reduce energy consumption by 2
million barrels of oil equivalent per day till 2030. To achieve this
target, the country has initiated 12 projects of renewable energy in
2019, which can change the overall energy structure of the country
(Gamal, 2019). Recently, the research initiatives have been taken by
several projects with the government that aims to reduce crude oil
consumption, which helps to decrease the burden on Saudi economy, and to control carbon emissions. The Saudi Vision 2030, is
primarily putting attention on renewable energy (solar and wind
sources etc.) (Alshammari and Sarathy, 2017; International Atomic
Energy Agency, 2018), however, as per these facts, the current
study is motivated to investigate the role of renewable energy in
the Saudi economy and how it can be twisted for future economic
goals (Antonakakis et al., 2015).
The prime objective of this study is to examine the impacts of
non-oil exports on economic development of Saudi Arabia, which
helps to reduce its dependence on oil related exports. Secondly,
the current study explores the validity of tourism led growth hypothesis for the Saudi economy by taking the tourism industry as
a key policy variable, especially in recent years. The tourism sector accounts for 9% of Saudi Arabia’s total GDP in 2018. It is a
well-known fact that Saudi Arabia has huge portion of religious
and cultural tourism, while by taking proper measures and refining tourism policies might enhance the tourism contribution in total revenue. Further, the Saudi Arabia’s economy mainly relies on
the tourism and petroleum exports. The energy related exports of
Saudi Arabia are relatively high as compared to other exports. For
instance, total natural resource rents of Saudi Arabia were 54% in
2014, in which 51% is from oil and 3% accounts for natural gas
(Alshehry and Belloumi, 2015). Hence, the present study aims to
provide new solutions and implications to enhance the tourism
growth and non-petroleum exports of Saudi Arabia in objective to
achieve the sustainable economic growth. The investigation into
tourism led growth and non-oil exports is logical and in line with
the recent studies Yang et al., (2014), Shahzad et al., (2017), Ben Jebli et al. (2019). For the case of Saudi Arabia, tourism, non-oil
exports and renewable energy are very important. This is due to
the reason the Saudi Arabia’s economy is mainly dependent on
the exports of oil and petroleum products and tourism industry.
On the other side, the country consumes abundant resources of
non-renewables. Such a study can unveil new windows for economic stability. These policies can include the tourism development, use of renewable energy and non-oil exports. The non-oil
exports can also decline the oil monopoly. The domestic and international tourism is considered as important factor to induce economic growth and employment in developing and developed countries (Fareed et al., 2018). The tourism sector and exports together
contribute to the economic growth, while there is dearth of literature on such research issues, specifically in the context of GCC
countries or Saudi Arabia. The third objective of given study is to
examine the contribution of renewable energy in economic growth,
as it lessens the dependence on fossil fuels for energy generation. Lastly, we distribute the dataset in two periods; pre-reforms
and post-reforms. Pre-reform period is before 2014 oil price crisis, while post-reform periods accounts the economic restructuring
and reforms that have introduced after the economic crisis. The
purpose is to analyze the results of economic reforms, either the
plans are beneficial or there is a need of modifications to achieve
the 2030 objectives. On the basis of findings, the study provides
fruitful implications concerning non-oil exports, tourism, and renewable energy in order to achieve the goals of Saudi Vision 2030.
2. Review of literature
The beneficial effects of exports and tourism on economic
development has been long identified by several existing studied for developed, developing and emerging economies using a variety of econometric techniques (Marin, 1992; MedinaSmith, 2001; Tang and Tan, 2013; Bassil et al., 2015; De Vita and
Kyaw, 2016; Jetter, 2016; Faisal et al., 2017; Shafiullah et al., 2017;
Shahzad et al., 2017; Fahimi et al., 2018). The present study has
mainly two strands; non-oil exports-economic growth nexus and
tourism led growth investigation. Marin (1992) studied the casual links between exports, productivity and economic growth for
four developed countries (Germany, Japan, United Kingdom and
the United States) by using the cointegration analysis. The findings
concluded that productivity and exports enhance the economic developed of countries. Medina-Smith (2001) examined the validity of export-led growth hypothesis for the case of Costa Rica by
using the annual data from 1950 to 1997. After applying multiple cointegration techniques and time-series regressions, the paper concluded that the neoclassical theory of production and explore led growth are valid for developing countries, and related
economies policies can be reshaped for the long run and short-run
economic goals. Squalli (2007) mentioned that economic growth of
OPEC countries mainly relies on the electricity consumption, which
is primary source of trade and manufacturing.
Jetter (2016) researched the relationship between total exports and economic growth for international market form by using global data of 157 countries. In empirical analysis, the study
gathered the data from 20 0 0 to 2010, and applied a multivariate OLS regression technique as full panel and regional anal-
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
ysis. After an in-depth empirical investigation, the study concluded that higher average export concentration index (AEC)
leads towards better economic progress in international markets.
Shafiullah et al. (2017) conducted an empirical investigation into
export-led growth hypothesis by using the sectoral level data for
agriculture, mining and fuels, manufacturing and, others for the
case of Australia. The empirical results argue that the mining and
fuel sector exports of Australia act as crucial contributors for positive economic growth of the country.
Faisal et al. (2017) researched the dynamic linkages between exports, imports, and economic growth by studying the data from
1968 to 2014 for the case of Saudi Arabia. For empirical analysis, the article mainly employed Auto Regressive Distributed Lag
(ARDL) and Granger causality techniques. Based on the empirical findings, the study concluded that exports of Saudi Arabia positively contribute to the GDP of Saudi Arabia, leading towards the validity of export led growth hypothesis. Tang and
Tan (2013) researched the relationship between 12 tourism markets and the economic growth of Malaysia by using the recursive Granger causality technique. The paper concluded that the
tourism-led growth hypothesis is valid in Malaysia in 8 out of 12
studied markets. Bassil et al. (2015) studied the role of the tourism
sector for the economic growth of Lebnan. The authors argued
that there is positive uni-directional causality between tourism and
economic growth in the short run, while in the long run tourism
does not support economic performance due to frequent terrorist
attacks in the country. Vita and Kyaw (2016) revisited the debated
question about the relationship between tourism development and
economic growth by using global data of 129 countries over the
period of 1995–2011. The authors concluded that tourist arrivals
positively contribute for high-income and middle-income countries, while this effect is slightly less for the case of low-income
countries. Shahzad et al. (2017) studied the validity of tourism led
growth hypothesis for the top ten tourist destinations in the world
using the quarterly data from 1990Q1 to 2015Q4. The study concluded that tourism reforms and policies can attract capital and
induce to increase the economic performance of tourist destination economies. Fahimi et al., (2018) explored the heterogeneous
impacts of the human capital and tourism industry on the economic growth of 10 micro-state countries. The paper utilized the
annual data of countries spanning the period of 1995–2015 and
employed panel cointegration and panel Granger causality techniques to conduct an in-depth empirical investigation. In summary,
the study provides strong evidence in support of tourism-induced
growth and human capital development-induced growth hypothesis.
Narayan and Doytch (2017) studied the impacts of renewable
energy and non-renewable energy on the economic growth of lowincome, low middle-income and high-income countries. The authors gathered the data for 89 countries from 1971 to 2011 and
applied the fixed effects and GMM methods. The study found that
the use of renewable energy drives economic growth positively in
the case of low-income and low middle-income. Meanwhile, the
paper found a feedback hypothesis for the case of non-renewable
energy and economic growth.
For the case of Saudi Arabia, Alsumairi and Hong Tsui (2017) examined the effects of low cost carriers on tourism demand. The
study further reported understanding between air transport and
tourism development in Gulf region. The empirical finding s suggested that airline capacity, religious tourism and competition will
increase the tourist arrivals to Saudi Arabia, which further improves the air transport development and tourism development.
In a more comprehensive study, Ahmad et al. (2018) examined
the effects of tourism sector on environmental pollution for five
provinces of China spanning the period of 1991-2016. The empirical findings of fully modified ordinary least squares (FMOLS) ap-
51
proach and Gregory-Hansen test highlighted that tourism sector
negative affects the environment in Ningxia, Qinghai, Gansu, and
Shanxi provinces, while improve the environment quality of Xinjiang.
For the case of high-income countries, Khan et al. (2019) explored the nexus between financial development, tourism, renewable energy, and greenhouse gas emissions as regional analysis.
The empirical results of Augmented Mean Group estimator indicated that tourism, international trade and financial development and induce to affect renewable energy in high income countries. Ben Jebli et al. (2019) reported the casual linkages between
tourism, trade, economic growth, foreign direct investment (FDI),
and carbon dioxide emissions for the case of 22 Central and South
American nations. The empirical conclusions of Granger causality
analysis illustrated that there is bidirectional causal relationships
between renewable energy, tourism and FDI. Further, the study reported positive casual links from tourism to trade and FDI. For
the case of Bulgaria, Can and Korkmaz (2019) explore the role of
renewable energy consumption and renewable electricity output
for economic growth. The detailed empirics of Autoregressive Distributed Lag (ARDL) test mentions that renewable energy projects
positively influence the economic progress of Bulgaria.
Rehman et al. (2019) reported the economic impacts of technology and internet in tourism sector for Belt and Road Initiative (BRI)
countries. The research used the annual data covering the period
of 1990 to 2017 and utilized Autoregressive-Distributed Lag (ARDL)
technique for empirical examination. The empirical results concluded that tourism revenue is low across those BRI countries having technological inaccessibility and underdeveloped infrastructure.
The researcher’s recommended that suffered economies should upgrade the technologies to enhance sustainable economic growth.
In the same line, Shehzad et al. (2019) investigated the role of
communication and technology for China and explored the validity
of tourism-led growth hypothesis. The empirical findings of ARDL
methodology mentioned that tourist arrivals in China positively influence the economic growth.
Lee (2019) argued that the use of renewable energy sources
in European Union member countries induces to enhance economic growth and minimize carbon emissions. This article is motivated from the recent changes in energy and tourism-related policies of Saudi Arabia and, more specifically from “The Saudi Vision
2030”,thus aims to provide more relevant and conclusive implications to achieve the strategic economic goals.
3. Data and methodology
3.1. Data sources and model
Annual time series data on economic growth, capital formation, tourist arrivals and renewable energy, covering the period
of 1980q1-2017q4, was gathered from World Development Indicators (2019) a reliable and authentic database of World Bank. While,
the data for non-oil exports taken as good exports (current US
$) was drawn from the balance of payment of the International
Monetary Fund (IMF) database (IMF, 2019). The reason for selecting the non-oil exports is justified from the reason that the economy of Saudi Arabia mainly relies on the exports of petroleum
related products. While, the share of non-oil exports in the total economy is $4.5 billion and this share declined1 from 23% to
20% from 2018 to 2019. As a matter of fact, the Saudi economy
need enhance the share of non-petroleum exports as alternatives
to meet the economic and sustainability goals (IMF, 2019). Eco-
1
The facts and information is accessed from https://english.aawsat.com//home/
article/1771591/saudi-arabia%e2%80%99s-non-oil-exports-amount-45bn-april.
52
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
nomic growth is expressed in GDP current US dollars; capital formation is represented by gross fixed capital formation current US
dollars, the tourism factor as a number of tourist arrivals and renewable energy is taken as renewable energy as share of total energy consumption for Saudi Arabia. The prime reason for studying the role of renewable energy and tourism is justified from the
facts the economy of Saudi Arabia relies on tourism sector for revenues. For instance, religious and cultural tourism contributes to
$65.2 billion in total revenues of Saudi Arabia. Furthermore, to fulfill the energy needs, Saudi Arabia is still utilizing the fossil fuels and non-renewable sources. Although, in the recent years, the
country has made investments on renewable energy sources to reduce pollutant emissions. Following the recent literature, the data
of variables was transformed into quarterly frequency using the
quadratic match-sum method. More recently, also employed similar strategy for their studies on time series data (Lahiani, 2018;
Shahbaz et al., 2018: Sharif et al., 2020).
GDPt = f (non − Oilt , T ourt , RECt , Ca pt )
D(ln GDPt ) = ∂0 + β1 (ln GDPt−1 ) + β2 (ln non − Oilt−1 )
+ β3 (ln T ourt−1 ) + β4 (ln RECt−1 ) + β5 (lnCa pt−1 )
p

+
a1 j D(ln(GDPt−1 ))
+
+
j=1
q

j=1
q

a2 j D(ln(non − Oilt−1 ))
a4 j D(ln(RECt−1 )) +
j=1
q

j=1
+
q

a3 j D(ln(T ourt−1 ))
(iii)
j=1
a5 j D(Ca pt−1 ) + μ1t
In equation (iii), the variables are same as per our preferred
model, while D shows the first difference (or other lag) as the
ARDL method undertakes automatic lag of variables as per AIC criteria and μt shows the error term in model. The ARDL technique
outcomes mainly relies on the joint F- statistics, t-test and its diagnostics in case of strong cointegration.
3.3. Johansen cointegration analysis
(i)
ln GDPt = ρ0 + ρ1 ln non − Oilt + ρ2 ln T ourt + ρ3 ln RECt
+ ρ4 ln Ca pt + εit
this article can be expressed as follows;
(ii)
In equation (i) and (ii), GDPt shows the economic growth,
non − Oilt refers to non-oil exports, Tourt is number of tourist arrivals, while RECt denotes use of renewable energy sources and
Capt shows the capital. Whereas, in equation (i) the data for our
primary variables of interest (economic growth, non-oil exports,
tourism, renewable energy consumption and capital have transformed into natural logarithm to avoid any mathematical concerns
(Sarwar, 2019; Sarwar and Alsaggaf, 2019; Shahzad et al., 2020).
The equation (i) was first estimated by applying the ordinary least
square (OLS) method, while to check the short run and long run
empirics we further apply ARDL methodology.
3.2. Unit root testing and auto regressive distributed lag (ARDL)
Approach
We begin our empirical analysis by applying the unit root
tests on time series data; first, we apply the Augmented Dickey
Fuller test introduced by Dickey and Fuller (1979). It is important to mention here the traditional unit root tests does not
consider the structural breaks and shocks in the data. Due to
such reason, the researcher’s further employ the (Zivot and Andrews, 1992) and Clemente, and Reyes two-stage structural break
test (Clemente et al., 1998). The null hypothesis of these tests indicates that “there exists unit problem at one stage or two stage
breaks”. While, the alternative hypothesis assumes that there is no
unit problem in the data.
After confirming the cointegration properties in data, th findings direct us to apply the ordinary least square (OLS) and Auto
Regressive Distributed Lag (ARDL) techniques to know the in-depth
and robust relationship of variables. The ARDL method proposed
by Pesaran (2001) offers certain advantages over traditional time
series cointegration techniques. Firstly, the ARDL model not require the studied variables to be cointegrated at the same order
and ARDL method can be applied when the variables are stationary at level and first difference or first difference (Shahzad et al.,
2018). Secondly, the ARDL method is relatively more suitable in
case of small or finite. Finally, the ARDL technology can report
the unbiased estimates in the long run and short-run periods
(Waheed et al., 2018; Zafar et al., 2019). The ARDL model used in
After checking the ARDL findings, we further employ the Johansen multivariate cointegration introduced by Johansen (1991).
The unit root empirics mention that variables are stationary at
level and first difference, which direct us to apply the Johansen
multivariate cointegration. The Johansen cointegration is based on
error correction represented by the VAR model. The VAR model is
presented as;
GDPt = αt + φ Zt +
n−1

GDPt−i + π GDPt−n + εt
(iv)
i=1
Whereas, GDPt and GDPt−i shows the logarithm of GDP,  is
the first difference operator, α t shows the intercept value, Zt is the
trend term, n is the order of model, π is the matrix of other variables and ɛt shows the error term. The number of cointegrations
that exist between variables is determined by the rank of matrix
π , based on the trace value and maximum eigenvalue statistics.
Notably, if the trace statistics value is higher than the 5% critical value, it indicates the presence of cointegration between variables and rejects the null hypothesis. The null hypothesis indicates that there is no cointegration between variables. Recently,
Alshehry and Belloumi (2015) also employed the Johansen multivariate cointegration analysis for their study on the nexus between energy consumption, carbon dioxide emissions and economic growth for the case of Saudi Arabia. In addition, we further utilize the Gregory and Hansen cointegration introduced by
Gregory and Hansen (1996) as a robustness check of Johansen
cointegration.
4. Empirical analysis and discussion
4.1. Descriptive and unit root testing
Table 1 reports the descriptive statistics of all studied variables,
which mentions that there are no outliers in data. To analyze the
empirical results, we used the equation (i) as a preferred model in
this study.
Table 2 reports the empirical findings for Augmented Dickey
and Fuller (ADF) unit root test at the level and first difference. The
ADF unit root statistics mention that capital formation and tourist
arrivals are stationary at level. While, interestingly, our all studied variables are cointegrated at first difference which validates the
narrative to apply the ARDL methodology. In extension to the ADF
unit root test, we use the Zivot–Andrews Structural break, which
also reports the structural breaks in the data. Table 3 mentions the
empirical outcomes for Zivot–Andrews’s structural break unit root
test. While, the results state that tourism, renewable energy, and
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
53
Table 1
Descriptive statistics.
Variables
Obs
Mean
S.Dev
Min
Max
p1
p99
Skew
Kurt
GDPt
non − Oilt
Tourt
RECt
Capt
152
152
152
152
152
26.144
25.2
15.267
0.009
24.627
.71
.885
1.106
.01
.795
25.155
23.698
12.643
-.006
23.527
27.37
26.69
16.738
.044
26.014
25.17
23.70
12.64
-.004
23.53
27.36
26.68
16.73
.042
26.01
.489
.191
-.428
1.702
.509
1.819
1.802
2.126
5.683
1.747
Table 2
Augmented Dickey and Fuller unit root test.
Table 5
Empirical findings for OLS regression.
At level
At first Difference
Variables
ADF statistics
critical value
ADF statistics
critical value
GDPt
non − Oilt
Tourt
RECt
Capt
-3.640
-3.332∗
-4.158∗ ∗ ∗
-3.313
-4.267∗ ∗ ∗
-3.443
-3.143
-4.023
-3.443
-4.023
-4.756∗ ∗ ∗
-4.597∗ ∗
-9.851∗ ∗ ∗
-5.147∗ ∗ ∗
-5.223∗ ∗ ∗
-4.024
-3.433
-4.027
-4.029
-4.0261
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the 1 and 5% and
10% levels, respectively.
Table 3
Zivot–Andrews structural break test.
Variables
GDPt
non − Oilt
Tourt
RECt
Capt
At level
P-Value
t-statistic
∗∗∗
0.0000
0.0000
0.0810
0.0000
0.0000
–
9.3700
13.3300
1.7600
14.3100
23.4500
–
0.2654
0.1592∗ ∗ ∗
1.5223∗
0.4206∗ ∗ ∗
6.6547∗ ∗ ∗
0.9814
0.9809
.09803
1.4127
1943.7
At first Difference
t-Statistic
Time break
t-Statistic
Time break
-3.430
-4.426
-5.172∗ ∗
-5.289∗ ∗ ∗
-4.178∗
1986q2
2009q2
1988q3
1991q3
1997q4
-6.970∗ ∗ ∗
-7.009∗ ∗ ∗
-13.587∗ ∗ ∗
-7.291∗ ∗ ∗
-6.806∗ ∗ ∗
2005q2
1986q4
1986q1
1989q4
1989q3
Table 4
Clemente–Montanes–Reyes test (multiple breaks).
GDPt
non − Oilt
Tourt
RECt
Capt
non − Oilt
Tourt
RECt
Capt
Constant
R-Squared
Adjusted R-Squared
Root MSE
Sum squared reside
F-statistic
Coefficient
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the
1 and 5% and 10% levels.
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the 1 and
5% and 10% levels, respectively.
Variables
Variable
At level
At first Difference
t-Statistic
Time breaks
t-Statistic
Time break
9.754∗ ∗ ∗
4.859∗ ∗ ∗
10.351∗ ∗ ∗
-14.519∗ ∗ ∗
9.584∗ ∗ ∗
2009q1, 2012q1
1991q1, 2005q2
1994q3, 2006q2
1990q2, 1996q2
2009q1, 2012q1
3.358∗ ∗ ∗
5.292∗ ∗ ∗
-1.320∗ ∗ ∗
1.104∗ ∗ ∗
5.292
1997q3,2008q3
1997q3,2008q3
1984q4, 1987q4
1989q3, 1993q3
1987q3,1993q3
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the 1 and 5% and 10%
levels, respectively.
capital formation are stationary at level. While, remaining variables
show the stationarity at first difference. Table 4 illustrate the unit
root empirics of Clemente and Reyes test with multiple structural
breaks. The findings highlight that the variables economic growth,
non-oil exports, tourism and renewable energy are stationary at
level and first difference with multiple breaks. While, the variable
capital is stationary at level. The structural breaks in the studied
variables can also be observed from the Fig. 1(a,b,c,d). Overall, we
find that all the variables have mix order of integration, which is
in consistent to apply the long run cointegration analysis. Hence,
based on our unit root tests, we conclude that the variables have
mixed outcomes as level I(0) and first difference I(I), which direct
us to apply the ARDL bound test technology to draw detailed findings and new conclusions for sustainable economic growth .
4.2. Empirical findings and discussion
Table 5 depicts the OLS regression outcomes of our specified
model. In most of the variables, we note the p-value as less than
1 percent, which indicates strong significance. The estimated
coefficients of OLS regression illustrate that our primary variables
of interest namely; non-oil exports and tourism are positively contributing to enhance economic growth, which further supports the
economic theories. The empirical results are consistent with the
findings of Jetter (2016), Faisal et al. (2017), Shahzad et al. (2017),
Mitra (2019) for their study on developing and developed countries. Similarly, we note that renewable energy and capital formation have a significant and positive impact on economic growth at
10 percent and 1 percent level, respectively. This is indicative that
capital formation and renewable energy has positive effects on the
economic growth of Saudia Arabia. Considering the role of renewable energy, it is significant at ten percent with a magnitude of 1.5
percent. This result is very important and surprising, implying that
Saudi Arabia might need to enhance the investments on renewable energy projects, which can also attain to achieve the “Saudi
Vision 2030” (Al-Saleh, 2009; Allhibi et al., 2019). Meanwhile, the
F-test and diagnostics of OLS regression indicate the validity of
outcomes.
In our extension of empirical analysis, we apply the ARDL technique on our preferred specification. Table 6 reports the empirical outcomes of the ARDL method and also explains the diagnostics to check the validity of estimates. The estimated coefficients
of the two indicators, lagged GDP, and non-oil exports, are positive
and statistically significant at 1 percent level in short-run empirics,
implying that export-led growth hypothesis can be valid in Saudi
Arabia. On other side, tourism, renewable energy, and capital investments are unable to prove its significance in short period of
time.
Interestingly, in our long-run estimates of ARDL we observe that
capital formation, tourism, and non-oil exports are significant at 1
percent level, while renewable energy has a positive and significant response at 10 percent level. Overall, the empirical results
of ARDL are in line with our OLS findings and demonstrate interesting outcomes. First, we can conclude that the role of nonpetroleum exports is significantly positive, and it can be a good
policy for Saudi Arabia to reduce economic dependence on the
trade of oil and petroleum products. The empirical finding further supports the narrative of environmental protection and Saudi
Vision 2030, on which the non-oil exports can boost the economy and reduce greenhouse emissions by reducing oil consump-
54
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
Fig. 1. (a): Structural breaks in GDP. (b): Structural breaks in non-oil exports. (c): Structural breaks in Tourism. (d): Structural breaks in renewable energy. (e): Structural
breaks in capital.
tion and oil trading (Shahzad et al., 2018). This is in line with
the findings of Aljebrin (2017), Parvin Hosseini and Tang (2014),
Raheem (2016) who concluded a positive relationship between exports and economic growth. In the short-run empirics of ARDL
the error correction term (ECT) is significant at 1 percent, and its
value is between 0 and -1, implying that error correction procedure is monotonic, and it quickly leads the convergence toward
equilibrium path. The significance of ECT confirms that any deviation from the equilibrium point of real GDP over the current
period might be adjusted by 35% in the future. In addition, the
OLS and ARDL empirics highlight significant and positive effects on
economic growth. It means that capital formation might be more
effective in establishing the new industries with latest technologies (cleaner and renewable sources) in Saudi Arabia. The findings
of this study are in consistent with the Destek (2016) for the case
of newly industrialized economies.
Furthermore, the long-run association between tourism and
economic growth indicates a positive relationship, with one percent increase in tourist arrivals the real GDP can be improved by
on average of 0.10 percent, which also support the tourism led
growth hypothesis in Saudi Arabia. The result implies that the government and relevant authorities need urgent reforms in tourism
sector, especially, to attract religious tourism by providing better
facilities. The empirical finding is in accordance with the conclusions of Shahzad et al. (2017), Fahimi et al. (2018) for their study
on developed and emerging economies.
Concerning the role of renewable energy, ARDL long run empirics mention that renewable energy can improve the economic
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
55
Table 6
ARDL empirics.
Regressors
Short-run estimates
 GDPt−1
 non − Oilt
 Tourt
 RECt
 Capt
ECT
Long-run estimates
non − Oilt
Tourt
RECt
Capt
Constant
Diagnostics:
F-bound test
T-test statistic
CUSUM test
CUSUMSQ test
Jarque–Bera test
Coefficient
Standard error
t-statistic
P-value
0.5260∗ ∗ ∗
0.4088∗ ∗ ∗
-0.0022
-0.1787
0.0141
-0.0357∗ ∗ ∗
0.0859
0.0115
0.0026
0.2639
0.0131
0.0113
6.1200
35.7000
-0.8400
-0.6800
1.0700
-3.1700
0.0000
0.0000
0.4030
0.4990
0.2840
0.0020
0.3601∗ ∗ ∗
0.1051∗ ∗ ∗
3.7158∗
0.4475∗ ∗ ∗
0.1603∗ ∗
0.1023
0.0395
2.3322
0.1050
0.0682
0.0010
0.0090
0.0930
0.0000
0.0200
Validity of ARDL
Test for hypothesis
Stability
Stability
Normality
3.429
3.170
0.4020
3.5200
2.6600
1.5900
4.2600
2.3500
Decision
Model is valid
Cointegration exists
Model is stable
Model is stable
Residuals are normal distributed
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the 1 and 5% and 10% levels, respectively. ARDL is
applied as per AIC selection criteria. Diagnostics indicate validity of model.
performance, while the coefficient is significant at 10 percent, implying that there is more than urgent need for Saudi Arabia to
make investments on renewable energy projects. In short run analysis, renewable energy is insignificant while positive and significant in long run analysis which indicate that in short period of
time the transformation of energy mix from non-renewable energy to renewable energy increase the expenses, in term of importing and installing of required materials. Notably, the investigation into renewable energy-growth for Saudi Arabia is important due to two reasons; (i) the Saudi Arabia’s economy heavily
consume non-renewables and petroleum products for energy demand, which creates environmental externalities, (ii) and the renewable energy sources can act as alternative to non-renewables
for cleaner growth and sustainable economic growth. The positive
coefficients of renewable energy for economic growth attracts the
attention of researchers. The findings suggest that increase in renewable energy consumption might enhance the economic growth
in Saudi Arabia. Our study argues that with the rising amount of
capital flows in Saudi Arabia, the country should enhance the investments on technological change and renewable energy sources.
Such innovative policies can help to achieve the sustainable economic growth and cleaner production objectives of the country.
The findings and conclusions of this research are also in line
with the Saudi Vision 2030 and sustainable development goals of
countries.
However, in this scenario, the transition of energy mix is unable to contributes in economy prosperity. In long run, higher utilization of renewable energy, instead of oil, helps the Saudi economy to reduce domestic consumption of oil which further reduce
the burden on Saudi economy. For such reason, we conclude that
transformation of energy mix leads towards sustainable economy.
The long-run empirics denote that there is a strong relationship
between studied variables, which is also determined by t-test, Fstatistic, and lagged GDP values. Overall, the diagnostic tests mention the validity of ARDL outcomes and allow us to draw fruitful
implications. Overall, the empirical results of non-oil exports and
renewable energy can be generalized in context of oil exporting
countries (GCC, OPEC etc.). However, tourism might have different
implications depending on the level of economy.
4.3. Robustness check with cointegration methods
To examine the cointegration between non-oil exports, tourism,
renewable energy, capital and economic growth, we further employ the Gregory and Hansen and Johansen multivariate cointegration technique. In doing so, we choose the optimal lag length criteria based on the AIC and SC by the estimations of unconstrained
VAR model. The study assume that level data has trends and cointegrations have intercepts. In the Johansen multivariate cointegration, the cointegration rank is estimated by using the maximum
Table 7
Johansen cointegration test.
Hypothesized No
Eigen value
Trace statistic
5% critical value
P-value
None∗
At most 1∗
At most 2∗
At most 3∗
At most 4∗
.
0.30724
0.16457
0.10667
0.0009
99.0864
44.0267
17.055
0.1343∗
47.21
29.68
15.41
3.76
0.0000
0.0000
0.0000
0.970
Hypothesized No
Eigen value
Trace statistic
5% critical value
P-value
None∗
At most 1∗
At most 2∗
At most 3∗
At most 4∗
.
0.30724
0.16457
0.10667
0.0009
55.0597
26.9716
16.9207
0.1343
27.07
20.97
14.07
3.76
0.0010
0.0000
0.0000
0.7510
0.0930
Notes: Superscripts ∗ ∗ ∗ , ∗ ∗ , ∗ denote statistical significance at the 1 and 5% and 10%
levels, respectively. The higher trace value from the critical values indicates to reject
the null hypothesis of no cointegration.
56
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
Table 8
Findings for Gregory-Hansen cointegration.
Cointegration for Economic Growth
Gregory-Hansen Models
ADFStatistic
Zt Statistic
Za Statistic
Intercept shift
Intercept shift with trend
Intercept shift-regime and trend
-5.78∗ ∗ (1985q3)
-5.42 ∗ (2010q3)
-5.04∗ (2010q3)
-7.61∗ ∗ ∗ (2010q2)
-11.77∗ ∗ (2002q3)
-9.49∗ ∗ ∗ (2010q3)
-44.62∗ (2010q2)
-48.07∗ (2002q3)
-40.12∗ (2010q3)
Notes: ∗ ∗ ∗ ,∗ ∗ ,∗ represents the significance at 1%, 5% and 10% respectively. Parenthesis shows the structural break point year.
eigenvalue and trace test statistics. Table 7 outlines long run empirics of Johansen cointegration. Notably, the empirics of trace
statistics are 99 and 44, which are higher the 5% critical values and
rejects the null hypothesis of no cointegration. Overall, the null hypothesis is rejected at three ranks in first and second portion of table. The empirics of Johansen cointegration validates the empirical
findings of ARDL and indicates that there is strong cointegration
between tourism, non-oil exports, renewable energy and economic
growth for the case of Saudi Arabia. The empirical findings of this
study are in line with the conclusions of Nguyen et al. (2020).
In our empirical analysis, we further employed the Gregory Hansen Cointegration technique developed by Gregory and
Hansen (1996), which identifies the presence of cointegration
among studied variables. Table 8 outline the empirics for GregoryHansen Cointegration methodology as per our preferred specification. The cointegration method is applied as an intercept shift,
intercept shift with the trend, and intercept shift with trend and
regime. The findings indicate the presence of strong cointegration among studied variables, as theADFStatistic, Zt Statistic and Za
Statistic, are significant. The Gregory-Hansen Cointegration empirics validate the empirical outcomes of ARDL technique and indicate that export led growth and tourism led growth hypothesis are
strongly existing in the case of Saudi Arabia.
5. Conclusion and implications
In this empirical study, we mainly analyze the export-led
growth hypothesis and tourism led growth hypothesis by using
the quarterly data from 1980q1 to 2017q4 of Saudi Arabia. Within
this context, the paper uses non-petroleum exports as a proxy of
exports and tourist arrivals as an indicator of tourism. The existing literature of Saudi Arabia has primarily focused on tourism-led
growth on total exports and economic growth nexus, while Saudi
Arabia economy is highly dependent on oil. In comparison of existing studies, this article contributes to analyze the role of nonpetroleum exports for economic development and propose fruitful implications. To test the primary hypothesis of study, we used
unit root testing to confirm the stationarity of series, while in order to establish the reliability and valid empirical findings of time
series data the study utilized ordinary least square (OLS), autoregressive distributed lag (ARDL) bound test, Johansen multivariate
cointegration and Gregory Hansen Cointegration methods, as these
techniques are sensitive to linear data characteristics and time attribution.
The cointegration analysis suggests the presence of a long-term
relationship between economic growth, non-oil exports, tourism,
renewable energy, and capital formation. Furthermore, the significance of the error correction term indicates that there is strong
linkage between our primary variables of interest (tourism and
non-oil exports and renewable energy) towards economic growth
in the long term. In the empirical analysis, we also find the
short-term impacts of non-oil exports and lagged economic growth
which guide us that previous year’s economic growth, tradable
goods and non-oil exports has positive heterogeneous effects on
overall economic progress. The value of non-oil exports to eco-
nomic growth implies that 1 percent increase in non-oil exports
contribute to enhance the economic growth with 0.41%, which
guide us to draw new conclusions.
The empirical findings suggest that tourism, non-oil exports
and renewable energy have positive effects on the sustainable economic growth of Saudi Arabia. The conclusions of non-oil exports
and renewable energy are very innovative in context of Saudi Arabia, which allow us to draw new implications. Notably, our study
argues that capital formation in Saudi Arabia can be positively used
to enhance investments on renewable energy, tourism policies, establishing of new industries. It is important to mention here that
the increase in non-petroleum exports might reduce the oil dependency and create new windows for economic stability. Given the
current crisis of oil prices, such results are very encouraging and
helpful to in policy making.
Further, due to the importance of the tourism sector for Saudi
Arabia, we note that tourist arrivals positively contribute to the
economic growth, this finding is very interesting and in line with
existing literature. In this context, it is more than urgent need
for the country to make tourism reforms and new policies in
such a way that it facilitates the religious and cultural tourist arrivals. A caveat of the present study is that the study only focused on the role of renewable energy, tourism, and non-oil exports. However, the future research can conduct an in-depth and
more wide analysis by using the data on non-renewable energy
sources, domestic and international tourism, and industrial contribution. In this context, the future research can also focus on the
data of oil exporting and importing countries such as OPEC and
GCC etc.
5.1. Practical implications
Based on the detailed empirical analysis, the study proposes three fruitful implications. Firstly, the policy makers and
economists should divert their attention on trade regulations, trade
policies such as export diversification or import diversification. In
doing so, the economic dependence of Saudi government from
oil and petroleum products can be reduced. While, promoting
the industry of non-oil exports might also be helpful in achieving the sustainable development goals (SDG’s) and can be favorable for green environment. Secondly, it is more than urgent
need for Saudi Arabia to make tourism reforms and new policies in such a way that it facilitates the religious and cultural
tourist arrivals. In doing so, the government of Saudi Arabia may
give more attention to the economic and structural transformation. Last but not the least, the Saudi government should encourage the investments on renewable energy and advanced technology. The investments on renewable infrastructure can reduce the
fossil fuels dependence and protect environment. In addition, the
innovative policies on overall energy mix can help in economic
and structural transformation and might help to achieve sustainable economic growth. Overall, the empirical findings might allow the researchers, economists, and policymakers to draw new
conclusions and to devise structural policies for overall energy
structure.
R. Waheed, S. Sarwar and A. Dignah / Structural Change and Economic Dynamics 55 (2020) 49–58
Supplementary materials
Supplementary material associated with this article can be
found, in the online version, at doi:10.1016/j.strueco.2020.06.005.
CRediT authorship contribution statement
Rida Waheed: Formal analysis, Writing – original draft.
Suleman Sarwar: Conceptualization, Methodology, Formal analysis,
Writing – original draft, Writing – review & editing. Ashwaq Dignah: Writing – review & editing.
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