The Implications of Covid-19 on the Imports of Oil from Saudi Arabia:
The Case of Highest-importer Asian Countries
ISAM ELLAYTHY*, YOUSIF OSMAN
Economics Department, School of Business,
King Faisal University,
KINGDOM OF SAUDI ARABIA
*Corresponding Author
Abstract: - There is a widespread consensus that COVID-19 pandemic is an unprecedented global crisis, as it
has triggered waves of economic recession worldwide. Since the onset of the pandemic and until recently, a
heightened theoretical debate about the dynamics and the economic implications of the pandemic is going on.
In the context of this newly emerged literature on the macroeconomics of pandemics, the differences in the
numbers of infection cases, along with the associated containment measures of the pandemic, are considered
key factors to interpret the extent and magnitude of the adverse economic impacts. The objective of this study
is to deliver a theoretical interpretation as well as empirical evidence about the implications of the global
recession triggered by the pandemic on international trade with special emphasis on the exports of oil
commodities from Saudi Arabia. To do so, an auto-regressive distributed lag (ARDL) econometric model was
applied to data about the monthly infection cases of some Asian Countries with the previous highest record of
oil imports from Saudi Arabia for the period from January 2019 to December 2022. These countries include
China, Japan, South Korea, and India. The findings of the study indicate the existence of an indirect negative
relationship between the number of corona infection cases in the selected countries and the quantities of oil
imports from Saudi Arabia. In the short-run, an increase of one unit in corona cases is associated with a
decrease of 0.08 in the quantity of oil imported from Saudi Arabia, while in the long-run an increase of one unit
in corona cases, is associated with a decrease of 0.39. In addition, the findings indicate that the recession
associated with the pandemic containment measures reflects a W-Shaped or double dip pattern.
Key-Words: - COVID-19 pandemic, infection cases, global recession, oil imports, Saudi Arabia, Asian
Countries
Received: March 8, 2023. Revised: June 28, 2023. Accepted: July 9, 2023. Published: July 14, 2023.
1 Introduction
There is a worldwide consensus that the COVID-19
pandemic is an unprecedented global crisis, as its
adverse implications have pervaded more than 200
countries and territories along with considering it
the most serious challenge faced by the world
economy in more than a century. The negative
economic implications of the pandemic were wide-
ranging. According to [1], these implications vary
by the stringency of the pandemic containment
measures (e.g., social distancing, lockdowns, and
related policies), its length of implementation, and
the degree of compliance.
It is necessary to mention that the adopted
containment measures to flatten the pandemic curve
have led to a slowdown in production and mobility
worldwide. In this context, the International
Monetary Fund (IMF) expected that the contraction
of the global economy would be of far greater
magnitude than that of the 2008-2009 Global
Financial Crisis. Whereas an early report from the
IMF forecasted that, the global economy would
contract by about 3% in 2020. However, this
forecast was revised in a subsequent study to 4.9%,
[1].
In the domain of international trade, the
COVID-19 pandemic caused remarkable changes in
the structure as well as the flow of this trade. A
study published by the OECD reported that the
changes in the structure of global trade in a single
year were of a similar magnitude to the changes
typically seen over 4-5 years, [2]. While the flow of
international trade in 2020 marked the largest
reductions in trade and output volumes since the
Second World War episode, [3].
Taking the oil commodity as an example of vital
good, the global contraction was highly
synchronized with a significant drop in demand for
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oil commodity during the year 2020. Consequently,
the oil-exporting countries underwent severe
economic and financial disruptions, particularly
countries that are net exporters of oil. The vast
majority of these countries experienced an
unprecedented blow in April 2020 when the global
oil market collapsed and the oil prices briefly went
negative in real terms for the first time in history,
[4]. What seems ever surprising is that the global
demand for oil commodities under such lucrative
prices was within its minimum limits.
However, the pattern of the pandemic-specific
adverse effects is more pronounced in countries like
Saudi Arabia. This is due to the fact that Saudi
Arabia is the largest exporter of oil in the world and
maintains the world's largest oil production capacity
at nearly 12 million barrels per day. Furthermore, oil
exports account for a large share of Saudi Arabia’s
economy. They accounted for nearly 70% of the
country’s total exports in terms of value in 2020,
and about 53% of the Saudi government’s revenues,
[5].
Given the fact that the outbreak and rapid
spread of COVID-19 were across Asian countries,
which receive the lion's share of Saudi Arabia's oil
exports (estimated in 2020 at 77%), Saudi Arabia
underwent a real economic dilemma. This dilemma
manifested itself in harsh fiscal and monetary
measures from 2020 onwards. This is mainly
because the major trade partners from Asia have
adopted syncretized anti-epidemic measures to
prevent its spread.
Therefore, this study aims to apply economic
theoretical hypotheses about the dynamics and
mechanisms of the pandemic in an attempt to
deliver empirical evidence about the impact of the
infection cases of the Covid-19 pandemic in these
countries on the flow of oil imports from Saudi
Arabia.
The rest of this study is structured as follows.
Section 2 deals with the statement of the study
problem in which the research question, research
objective, and research hypothesis are highlighted.
Section 3 focuses on a review of the literature
related to the COVID-19 pandemic, including its
dynamics and transmission mechanism into the
economics domain, the patterns of economic
recession and its recovery along with investigating
the nexus between COVID-19 and oil. Section 4 is
devoted to reviewing the empirical results of the
study including the methodology, the model, and
details about the findings of the study. While section
5 focuses on discussing the study findings and the
conclusion.
2 Statement of the Problem
Although the economic consequences of Covid-19
have been a massive focus and substantial argument
from the first generation of papers with the onset of
the pandemic, there is a knowledge gap regarding
the empirical evidence, particularly the negative
impacts on oil exports of Saudi Arabia. To bridge
this gap, our study will address the following
aspects:
2.1 Research Question
The study tried to find out answers to the following
questions:
How do the infection cases in the major trade
partner's countries in Asia contribute to the
global recession in general and in the domain of
international trade specifically?
To which extent, has this global recession
affected the imports of oil commodities from
Saudi Arabia?
2.2 Research Aim and Objective
The study aims to:
Show the nexus between the COVID-19 driven
recession and the imports of oil commodities
from Saudi Arabia.
Test empirically the validity of the emerging
economic hypotheses or approaches in the
context of the so-called "coronanomics" or
economics of corona pandemic.
Suggest research topics, which might contribute
to the theoretical and empirical debate related to
the pandemics and international economics
nexus.
2.3 Research Hypotheses
This research addresses two hypotheses:
Hypothesis 1 (H1):
There is a negative indirect relation between the
number of infection cases in the trade partner
countries in Asia and the quantities of oil imported
from Saudi Arabia during the times of Covid-19
spread.
Hypothesis 2 (H2):
On the country level, the recession associated with
the pandemic containment measures has
heterogeneous patterns (not one size fits all
countries). While on the global level, this recession
reflects a pattern (shock geometry) of W-Shaped or
double dip recession.
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3 Literature Review
3.1 Background
At the very early stage of the pandemic, rapidly
growing literature emerged to synthesize the
insights around likely global recession driven by the
pandemic. Although the magnitude of the pandemic
and its economic impacts remain uncertain and
difficult to predict. This is mainly due to the rare
scientific contributions in this area and the unknown
number of likely infections and death cases. The
main interest of this growing literature was to
answer the questions:
What is the appropriate theoretical framework for
interpreting the cross-border transmission
mechanisms of the pandemic in the domain of
economics (which is later denoted as
coronanomics)?
How deep are the devastating consequences of
the global recession induced by the pandemic
among the different international trade partners?
Would the recession arising from the pandemic
be V-shape, U-shape, W-shape, or L-shape (the
recession durability)?
During this early stage and due to the lack of
uncertainty about the pandemic behavior, the vast
majority of the contributions tend to be explorative
or prospective-oriented. In a later stage, when it
seems that the pandemic will last longer, most of the
contributions tend to be empirical and address
economic policy issues. For more details about the
literature and documents focusing on the economic
and social consequences of the COVID-19
pandemic, see the contribution of [1].
3.2 Pandemic Dynamics and Transmission
Mechanism into the Economics Domain
To understand the negative economic implications
of the COVID-19 pandemic, it is important to
understand the dynamics and economic mechanisms
through which the pandemic will adversely affect
the global economy so as stated by [1]. In this
context, [6], delivered a lecture (At the London
School of Business) under the title “The economics
of a pandemic: the case of Covid-19”. Both authors
took the initiative to submit a pioneer theoretical
interpretation for the likely global recession caused
by the outbreak of the pandemic, [6]. The authors
built their interesting analysis on the hypothesis of
the so-called “race between supply and demand”,
which underlines four inter-related economic shocks
induced by the outbreak of the pandemic as shown
in Table 1 and Fig. 1:
Table 1. Scenarios of the race between supply and demand
Cycle/event
Stimulus
Ultimate
consequence
First, Supply Shock
- Disruption in global supply chains.
- Quarantine and social distancing.
- Decreasing labor supply.
Aggregate supply
(AS) moves from AS0
to AS1
Second, Demand Shock
- Uncertainty about the progress of the disease.
- Uncertainty about economic policies.
- Non-permanent workers affected industries will
lose income.
- Households increase precautionary savings
- Firms are wary of investing until the situation
clears (while many firms lack the liquidity to do
so).
Aggregate demand
(AD) shifts from AD0
to AD1
Third, Supply Shock
- Feedback loop into supply arises from the side
of the firms lacking liquidity to fulfill
commitments.
- Some of the above firms are facing lower
demand and thus are forced to file for
bankruptcy.
Aggregate supply
(AS) moves from AS1
to AS2
Fourth, Demand Shock
- Another feedback loop into demand arises from
workers who lose jobs from closing businesses
and do not have an income anymore.
- Eventually, lower consumption from jobless
workers depresses aggregate demand.
Aggregate demand
(AD) shifts from AD1
to AD2
Source: Based on the analysis in Surico P. and Galeotti A., [6]
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Fig. 1: Hypothesis of Race between Supply and Demand
Source: Surico P. and Galeotti A., [6]
Also in the same context, Baldwin R. and di Mauro
(eds.) compiled an e-Book containing briefs of
different authors. In an attempt to answer the
question of how COVID-19 affects the economy,
[7], adopted the so-called triple hit hypothesis.
According to this hypothesis, the wide-ranging
containment measures adopted worldwide to flatten
the pandemic curve have the following
implications, [7]:
“1. Direct supply disruptions will hinder
production since the disease is focused on the
world’s manufacturing heartland (East Asia) and
spreading fast in the other industrial giants – the
US and Germany.
2. Supply-chain contagion will amplify the
direct supply shocks as manufacturing sectors in
less-affected nations find it harder and/or more
expensive to acquire the necessary imported
industrial inputs from the hard-hit nations ،and
subsequently from each other.
3. There will be demand disruptions due to: (1)
macroeconomic drops in aggregate demand (i.e.
recessions); and (2) wait-and-see purchase
delays by consumers and investment delays by
firms.”
[8], discussed in another contribution how trade
volumes collapsed at the same time in all nations
and for almost all products at a pace never seen
before the spread of the pandemic. According to
Richard Baldwin and Eiichi Tomiura, the interplay
between the supply shock and the demand shock
are likely responsible for slowing down global
trade flows significantly. This is apparent from two
dimensions:
“1. To the extent that COVID-19 is a supply
shock, exports will fall, and they will fall most
in the nations that are most severely hit.
2. To the extent that COVID-19 is a demand
shock ،imports will fall ،and they will fall most
in the trade partners of the nations that are most
severely hit.”
As a consequence, some argue that a large
negative shock for oil prices would happen
specifically in the oil-producing countries, in
particular in the Middle East, [9].
Another interesting theoretical attempt at the
economic implications of COVID-19 was delivered
by Barua S. who tried to launch a model about the
economics of COVID-19 or the so-called
“coronanomics”. In this attempt, she adopted a
comprehensive approach to identifying the likely
current and future economic implications of the
pandemic, [10].
This model suggests general time-dependent
(short-run and long-run) mapping of the likely
macroeconomic impacts – such as on production,
supply chain, trade, investments, prices, finances,
exchange rates, growth, and cross-border
cooperation (See Fig. 2). This model systematically
reviews the impacts observed so far in the context
of the mapping.
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Fig. 2: Mapping of economic impacts of COVID-19
Source: [10]
It is necessary to mention that, the three
reviewed economic models are unanimous about
the fact that the recent global recession was
triggered by a supply shock resulting from the
containment measures to flatten the pandemic
curve. Nevertheless, there is no consensus among
these models regarding the depth and the length of
interplay mechanisms between supply and demand.
On one side, the Surico and Galeotti model and
the model of Baldwin and di Mauro seem to great
extent similar. On the other hand, the model of
Barua seems to be inclusive and more appropriate
to deliver a solid theoretical interpretation of the
implications of the COVID-19 pandemic on the
Imports of Oil from Saudi Arabia. Furthermore, the
latter model gives the possibility to interpret the
likelihood of post-recession phase secondary
effects (i. e. the recovery phase), which is not the
case in the other two models.
3.3 Patterns of Economic Recession and Its
Recovery
Recession as a sub-phase within a business cycle
can take variant patterns and forms, which are
depicted using alphabetic notations. The alphabets
generally denote the graph of growth rate, which
resembles the shape of the letter. The fundamental
difference between the different kinds of recession
is the time taken for economic activity to
normalize. The time taken is often a factor of
multiple things such as the depth of the economic
crisis where the deeper the recession, the longer the
time to get back to normal. Table 2. as well as Fig.
3 illustrate the most commonly known alphabetical
analogies for the likelihood of a recession and its
recovery:
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Table 2. Scenarios of the race between supply and demand
Pattern
Characteristics
V-shaped
- The V-shaped recession or the so-called single dip recession is deep, a
swift plunge but short-lived usually followed by bounces back strongly.
- The recession of 1953 in the United States is an example of a V-shaped
recession that economists typically attribute to the Korean War and the
government’s monetary policy at the time.
U-shaped
- A U-shaped recession is longer than a V-shaped recession and has a
less-clearly defined through GDP
- Examples of U-shaped recoveries are the 1973-75 Nixon recession and
the 1990-91 recession following the S&L crisis in the US.
W-shaped
- In a W-shaped recession (also known as a double-dip recession), the
economy falls into recession, recovers with a short period of growth,
then falls back into recession before finally recovering, giving a "down
up down up" pattern resembling the letter W.
- The United States experienced a W-shaped recovery in the early 1980s.
From January to July 1980 the U.S., the economy experienced the initial
recession, and then entered recovery for almost a full year before
dropping into a second recession from 1981 to 1982.
L-shaped
- L-shaped recession is and by far the worst pattern of depression, which
occurs when an economy has a severe recession and does not return to
trend line growth for many years, if ever. The steep decline, followed by
a flat line makes the shape of an L.
- What is known as the lost decade in Japan is widely considered an
example of an L-shaped recovery. Leading up to the 1990s, Japan was
experiencing remarkable economic growth.
Source: Compiled from [11], [12], [13], [14].
Fig. 3: Scenarios for alphabetical analogies of recession and its recovery
The bigger scenario question about the pattern or
geometry of the shock, at the early stage of the
outbreak of the pandemic, was the subject of
speculation. However, when there was a high
degree of likelihood that the COVID-19 pandemic
would last longer, rare empirical contributions
emerged to address this issue. For instance, [15],
applied a qualitative assessment of the impact of
Covid-19 on the economy using the Mark-0 Agent-
Based Model. Depending on the amplitude and
duration of the shock, this model described
different kinds of recoveries (V-, U-, W-shaped), or
even the absence of full recovery (L-shape).
Furthermore, the findings of an empirical study
conducted by [16], point in the direction of gradual,
lengthy global recovery without identifying its
displayed pattern whether it is U-shaped or it is L-
shaped.
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3.4 COVID-19 and Oil Commodities Nexus:
Global Perspective
During the episode of the COVID-19 pandemic oil
as a strategic commodity became one of the hottest
issues and subject to continuous debate, particularly
regarding the oil flow and the oil prices plunge
during 2020.
In this context, a pioneer work by [17], tried,
specifically to answer the question about what has
been the source of the 2020 oil price collapse. In
this paper, a three-pronged approach was used: an
assessment of the drivers of the oil price decline at
that time using a structural vector autoregression
model (SVAR); an examination of previous oil
market disruptions since 1970; and an estimation of
the impact of previous oil price plunges on output
using a local projections model. The study came
out with the finding that the predominant oil price
plunge is demand-driven and mainly arising from
the pandemic outbreak and the restrictions needed
to stem its spread. These restrictions have triggered
a global recession and a steep drop in oil demand.
The dynamics of oil price volatility in the
context of covid-19 were also examined by the
study, [18]. This study also applied the Vector
Autoregressive (VAR) econometric model. The
study concluded that the pandemic has had a
negative impact on the global oil industry in two
manners:
"First, it led to a demand shock as it reduced
global demand for crude oil while increasing
uncertainty for most developed and emerging
economies. Second, it led to a supply shock as
COVID-19 triggered an oil trade war between
the major oil-producing nations (Saudi Arabia
and Russia). Both shocks appear to have led to
excessive oil price volatility".
[19], conducted a study about quantifying the
long-term impacts of COVID-19 and oil price
shocks in a Gulf oil economy (Kuwait as an
example). The study used an economy-wide model
in a CGE framework to enable assessing both direct
and second-best effects of economic shocks or
policies, making them the ideal structure for
evaluating policy options or large-scale shocks
such as COVID-19. The findings of the model
indicate that the combination of COVID-19 shocks,
its mitigation measures, and oil price declines
largely harms the economy’s GDP and causes a
fiscal deficit.
Likewise, [20], tried to investigate the impact
of the number of daily infection cases of corona on
crude oil prices in Saudi Arabia. Using the ARDL
model, the results of the study showed that the
COVID-19 daily reported cases of new infections
have a marginally negative impact on crude oil
prices in the long-run. Nevertheless, by amplifying
the financial markets' volatility, COVID-19 also
has an indirect effect on the recent dynamics of
crude oil prices. More about the relationship
between the COVID-19 pandemic and the oil prices
in Saudi Arabia is to be found in contributions of
[21], [22].
3.5 The Difference between the Current
Study and the Available Literature
The following aspects show the difference between
our study and the previous studies in the literature
review:
First: From the theoretical perspective, this
study tried to compile inclusively the new emerging
theoretical approaches related to pandemic
economics or the so-called “coronanomics” in an
attempt to deliver a realistic interpretation of the
global recession induced by the pandemic.
Second: From an empirical perspective, the
study laid special emphasis on two influential
players in the domain of international trade, which
include Saudi Arabia as the largest exporter of oil
in the world and maintaining the largest oil
production capacity in the world on one side. On
the other side, the group of importer countries
(China, Japan, South Korea, and India), are
considered as manufacturing heartland of Asia and
the world as well. To the best knowledge of the
authors, the interaction between the two players
(during the phase of the pandemic) has not yet been
a major concern to any of the previous studies.
Third: Since there is no single standard
COVID-19 recession in different parts of the world
(No ‘one size fits all’), but rather a variety of
recession experiences, often varying with the
reasons, timing, and complexity of the
circumstances of the contraction, this study might
come out with some new insights on the theoretical
as well as on the empirical levels.
4 Empirical Results
4.1 Introduction
For testing the hypotheses of this study, four
countries within the Asian sphere (China, Japan,
South Korea, and India) were selected as they
receive the lion's share of Saudi Arabia’s oil
exports. Out of this share 26%, 15%, 13%, and
11% went to China, Japan, South Korea, and India
respectively (Fig. 4).
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Secondary data about these countries was
collected which includes statistics about corona
infection cases and the quantities of oil imports
from Saudi Arabia and the real GDP. The main
source of this data was the IEA database
(https://www.iea.org/data-and-statistics/data-
tools/oil-stocks-of-iea-count).
Fig. 4: Saudi Crude Oil Exports by Destination
Sources: EIA, [5]
4.2 Model Description
Equation (1) clarifies the linkage between Saudi oil
exports Qxs and oil price P, Corona Cases in China
CCc, Corona Cases in India CCi, Corona Cases in
Japan CCj Corona Cases in South Korea CCk,
Gross Domestic Product (GDP) in China GDPc,
GDP in India GDPi, GDP in Japan GDPj GDP in
South Korea GDPk, as follows :
Table 3. Description of variables
Variable
Notation
Unit (b)
Saudi oil export
Barrels
Oil price
U.S. dollars per barrel
Corona Cases in China
infection
Corona Cases in India
infection
infection
infection
GDP in China
Constant 2021 US$
GDP in India
Constant 2021 US$
GDP
Constant 2021 US$
Constant 2021 US$
Table 3 presents the description of the variables.
Moreover, we are overseeing the Autoregressive
Distributed-Lag (ARDL) in this research. The
ARDL model is one of the most recent dynamic
approaches that takes the element of time into
account. With the aim of identifying the short and
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long-run rapport among the variables, as well as the
speed of the system's convergence to equilibrium,
we analyze long-run rapport between variables
based on time series data. This model consists of
two components:
(1) Autoregressive (AR) i.e., a model
depending on its lagged values; meaning it uses the
dependent variable as a Lagged independent
variable, (2) Distributed Lagged (DL); indicating
that the dependent variable is influenced by the
changes in the independent variables and their
lagged values.
Table 4. Variables Statistical analysis
variables
Mean
0.855
29.567
28.677
56.934
62.202
80.261
25.047
74.725
1.1082
4.603
Median
1.206
29.058
28.830
55.941
63.437
89.578
20.320
66.343
0.4961
7.151
Maximum
5.450
36.338
34.944
61.686
73.743
96.048
21.346
102.67
7.2321
7.524
Minimum
-5.148
21.284
22.758
53.732
50.491
56.622
19.292
47.742
-6.995
5.863
Std. Dev.
2.868
3.3863
3.2145
2.557
5.678
14.973
0.5792
11.012
2.6321
0.475
Skewness
-0.430
-0.1584
0.1508
0.539
-0.257
-0.381
-0.0608
-0.5558
-0.0524
1.041
Kurtosis
2.372
3.3497
2.4974
1.8724
2.992
1.372
1.7571
3.2636
3.5848
2.783
Jarque-Bera
3.969
0.7795
1.2025
8.5223
0.927
11.311
1.3981
3.7478
2.3140
9.463
Probability
0.137
0.677
0.5481
0.0141
0.628
0.0034
0.3744
0.0943
0.2186
0.0032
Sum
71.840
2483.6
2408.907
4782.48
5225.04
6741.962
894.62
2925.2
44.322
307.3
Sum Sq. Dev.
682.96
951.79
857.691
543.023
2676.30
18607.92
16.947
6128.2
349.98
11.43
Observations
48
48
48
48
48
48
48
48
48
48
Table 4 shows the descriptive statistics for the
variables, to the homogeneity of the values of
these variables, while the rest of the variables
by percentage, when we applied logs, the
distribution is better behaved.
The following equation shows the ARDL model for
our study, as follows:
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Where (d) refers to the second-difference operator;
l, m, n, o, p, q, r, s, t, and u indicate lags; (α1 to
α10) refer to long-run parameters; (β1 to β10) refer
to short-run parameters; (α0) refers to the intercept;
(µt) refers to the error term. The short-run effects
are estimated from the following:
(3)
µ_ represents the speed of adjustments towards
long-run equilibrium. If the system is moving out
of equilibrium in one direction, then it will pull
back to equilibrium, [23]. The steps followed are
illustrated in Fig. 5 below:
Fig. 5: Steps of Applied Study
4.3 Study Findings
We refer to unit root tests to ensure that the data fit
the ARDL approach. A unit root test is performed
to verify whether our time series data have a unit
root. If it has a unit root, then the data is said to be
not stationary. Several approaches, such as Philip
Peron and Augmented Dickey–Fuller, were used to
examine data stationarity. The test was performed
to prevent us from developing a spurious or false
regression. Table 5 shows the results of unit root
tests, all variables were found to meet the
stationary, which is required for the application of
standard ARDL. Therefore, hypothesis testing
could be carried out.
Data & Variable
Study ARDL Approach
Unit Root Test
Correlation Matrix
Bounds Test
Equation Estimation in The Short Run
(Error Correction Form)
Equation Estimation in The Long Run
(Long Run Form)
Diagnostics Tests
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Table 5. PP Test and ADF Test Results
PP Test
ADF Test
Variable
Level
1st difference
Level
1st difference
t-Statis.
Prob.
t-Statis.
Prob.
t-Statis.
Prob.
t-Statis.
Prob.
-0.2102
0.2344
-6.3804
0.0016**
-2.1300
0.2344
-6.3804
0.0000***
-0.1674
0.4258
-5.0449
0.0004***
-1.6965
0.4258
-5.0449
0.0002***
-0.3473
0.7121**
-9.9726
0.0000*
-3.5190
0.0121**
-9.9726
0.0000***
-0.0402
0.0986
-7.1287
0.0096***
-0.4083
0.8986
-7.1287
0.0000***
-0.2102
0.2544
-6.3804
0.0000***
-2.1300
0.2344
-6.3804
0.0000***
-0.1674
0.4258
-5.0449
0.0002***
-1.6965
0.4258
-5.0449
0.0002***
-0.0868
0.1780
-2.4931
0.0000*
-0.8797
0.003025
-2.4931
0.0000***
-0.0105
0.0246
-1.7821
0.0096***
-0.1020
0.22465
-1.7821
0.0000***
-0.0525
0.0636
-1.5951
0.0000***
-0.5325
0.0586
-1.5951
0.0000***
-0.0418
0.1064
-1.2612
0.0002***
-0.4241
0.10645
-1.2612
0.0002***
Notes: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table 6. Correlation Matrix Result - pair-wise correlations
1.000
0.554
1.000
0.685
0.636
1.000
0.671
0.594
0.183
1.000
0.583
0.234
0.196
0.236
1.000
0.562
0.698
0.539
0.274
0.451
1.000
0.671
0.570
0.773
0.007
0.008
0.009
1.000
0.717
0.447
0.031
0.795
0.007
0.004
0.556
1.000
0.645
0.573
0.013
0.005
0.696
0.002
0.359
0.544
1.000
0.665
0.448
0.066
0.001
0.009
0.658
0.406
0.471
0.482
1.000
Table 7. Bound Test Results
F-Statistic = 6.4265
Significant Level
Lower Bound
Upper Bound
1%
2.08
3.25
5%
1.97
3.07
10%
1.87
2.37
One of the most important problems facing
standard models and regression analysis is the
linear correlation (multicollinearity) between the
independent variables. It relates to the failure to
fulfill one of the assumptions of the OLS method
that in the absence of a strong linear regression
model there is a correlation linkage between the
independent variable and dependent, which makes
it tricky to disconnect their individual effects on the
dependent variable, [24].
However, according to [25], there are some
indicators to detect this problem. Among these
indicators, we can use the correlation matrix
between the independent variables and the
variance-inflating factor (VIF). A VIF greater than
10 and a pair-wise or zero-order correlation
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coefficient between two regressions excess of 0.8
reveal severe multicollinearity, [26].
From Table 6, the correlation matrix indicates
that there is no multicollinearity linkage between
the independent variables as long as the Pearson
correlation coefficient is less than (0.8).
A bound test was carried out to determine a co-
integration relationship, and the results are reported
in Table 7. The F-statistical value of 6.4265 is
significant at 1%, higher than the upper bound of
3.25 and the lower bound of 2.08. Therefore, this
indicates that there is a long-term cointegration
relationship. However, there is no cointegration
relationship if the F-statistic is lower than the upper
and lower bounds. However, the results remain
inconclusive if the F-statistic is between the upper
and the lower bounds.
4.3.1 Short-run Estimation Results
The analysis of short-run and long-run relationships
is based on the result in Table 8. As the table
indicates the short-run CCc, CCi, CCj, and CCk
significantly affect the P and Qxs in Saudi Arabia,
and the parameter (P and Qxs) is positive, which
means that there is a direct relationship between oil
prices and the quantity imported. Whenever corona
cases and GDP in selected countries increase by
one unit, the quantity of oil imported from Saudi
Arabia decreases by approximately (8%).
With regard to the results of the error
correction model (C), the table shows that the error
correction term (C) is highly significant at a
specified level of significance, 5%, with the
expected negative sign. That means the existence of
a short-run equilibrium relationship cointegration
among the model variables equals (3.6). This
indicates the deviations corrected by approximately
(3.6) within one year towards the short-run
equilibrium. Based on the R-squared estimates in
the model, the explanatory variables have an effect
of (0.8913) of corona cases on the oil imports from
Saudi Arabia.
4.3.2 Long-run Estimation Results
While the estimates of the long-run reveal that
there is a statistically significant and negative
economic relationship between both prices and the
quantities imported from Saudi Arabia and the
number of corona cases. That means a negative
relationship in the long-run. That is an increase of
one unit in corona cases, the prices and quantities
of the exported oil decreased by (0.852), and
(0.398) respectively.
The results of diagnostic tests reported in Table
9 are based on Jarque–Bera test, the Breusch–
Godfrey serial correlation, the heteroskedasticity,
and Ramsey’s stability test. All these results
showed insignificant F-statistics. Although these
results indicate that the model used has no
diagnostic problems, the variables of the study
(imports, oil price, corona cases, and GDP in the
selected countries) do not suffer from diagnostic
problems as Table 9 depict.
To ensure the stability of the model, we refer to
Cumulative Sum (CUSUM) as well as Cumulative
Sum of Squares (CUSUMSQ) graphs (see Fig. 6
below). Since the two diagrams in the figure, show
that all the plotted points were between the two red-
colored bounds that mean the used model was
stable.
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Table 8. Long-run and Short-run Coefficient Results
Variables
Coefficient
Probability
Variables
Coefficient
Probability
Long-run
Short-run
0.398 ***
0.020
0.015
0.010
0.852 ***
0.001
0.088
0.060
−0.637 **
0.044
−0.266
0.008
−0.314 **
0.007
−0.011 ***
0.003
−0.149
0.111
−0.015
0.010
−0.012
0.001
−0.266
0.000
0.579
0.009
0.2417
0.005
0.285
0.028
0.009
0.047
0.135
0.098
0.0136
0.072
0.0109
0.035
0.241
0.008
C
−58.4600
0.0049
ECT (-1)
−3.6502
0.0037
= 0.8913 Adjusted = 0.8701
Note: ***, **, and * are significant at 1%, 5% and 10%, respectively.
Table 9. Diagnostic Tests Results
F-Statistic = 6.4265
Statistical Tests
F-Statistics
Probability
Jarque-Bera
0.082
0.673
Breush Godfrey collecting series
0.175
0.795
Heteroskedastisity test
1.870
0.102
Reset Ramsey Stability
1.435
0.1687
Fig. 6: Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMQ)
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5 Conclusion
This study was conducted to investigate the
implications of the global recession driven by the
COVID-19 pandemic on the imports of oil
commodities from Saudi Arabia. The study is based
on the following hypotheses:
First Hypothesis:
There is a negative indirect relation between the
number of infection cases in the trade partner
countries in Asia and the quantities of oil imported
from Saudi Arabia during the times of Covid-19
spread.
To test the validity of this hypothesis, the study
used the econometric ARDL model where the
short-run and long-run coefficient results indicate
the existence of an indirect negative relationship
between the number of corona cases in the selected
countries from Asia (China, Japan, South Korea,
and India) and the quantity of oil imported from
Saudi Arabia. In numerical terms, an increase of
one unit in corona cases in short-run, associated
with a decrease of 8.8% in the quantity of oil
imported from Saudi Arabia, in the long-run an
increase of one unit in corona cases, is associated
with a decrease by 39.8% in the quantities of the
imported oil from Saudi Arabia. Therefore, these
results provide evidence of the validity of the first
hypothesis.
The interpretation of this indirect negative
relation is as follows:
Due to the increase in the number of corona
cases, China, Japan, South Korea, and India
must apply simultaneous containment measures
to flatten the pandemic curve.
These measures trigger a global recession in the
form of supply shocks in the above group of oil-
importing countries along with disruptions in the
global supply chain (the triple hit hypothesis by
Baldwin R. and di Mauro (2020).
As a consequence of the supply shocks in the
group of the oil importing countries, Saudi
Arabia faces demand shocks related to its
exports of oil along with a remarkable decrease
in the oil price (the hypothesis of race between
supply and demand by Surico and Galeotti
(2020).
In light of these circumstances, a crucial
question arises for how long Saudi Arabia remains
suffering from this dilemma. The answer to this
question is found in the second hypothesis.
Second Hypothesis:
On the country level, the recession associated with
the pandemic containment measures has
heterogeneous patterns (not one size fits all
countries). While on the global level, this recession
reflects a pattern (shock geometry) of W-Shaped or
double dip recession.
As earlier mentioned, the fundamental
difference between the variant patterns of recession
is the time taken for economic activity to normalize
in terms of the contraction or expansion in the
GDP. In this context, Fig. 7 depicts the changes in
GDP in the group of oil-importing countries from
Saudi Arabia. Based on this figure the following
was observed:
In panel (a), the GDP for China reflects the
classic (dip) V-shaped pattern of recession.
In panel (b), the GDP for India reflects a pattern
that is similar to the W-shaped recession.
In panel (c), the GDP for South Korea reflects a
pattern more or less similar to the U-shaped
recession.
In panel (d), the GDP for Japan reflects a pattern
more or less similar to the V-shaped recession
with a longer span of time.
There is no doubt, that this heterogeneity in the
depth and the length of the recession among the
selected group of the oil importing countries from
Saudi Arabia provides one of the types of evidence
about the validity of the second hypothesis about
the pandemic-induced recession (not one size fits
all countries).
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Fig. 7: GDP in the Selected Countries from 2019 to 2022
Fig. 8: GDP of the country group from 2019 to 2022
When it came to the recession on the global
level, the total GDP for oil importing countries
from Saudi Arabia has been used as a proxy for that
(see Fig. 8). Figure 8 reflects a soft W-shaped
pattern of recession between the periods from
January 2020 up to November 2020. This particular
pattern of the recession contributed to the evolution
of the COVID-19 pandemic into four spikes/waves
and India was at the heart of one of these waves.
This analysis gives evidence for the second
hypothesis.The findings of our study underscore the
importance of a better understanding of the
implication of the COVID-19 pandemic shocks on
the flow of the oil commodity as well as oil prices.
That means policy-makers and market stakeholders
should explicitly consider any adverse
consequences that might threaten the conditions of
global health and at the same time, trigger global
economic shocks to avoid any conflicts or
contradicted outcomes between the policies in the
health domain and policies and economics domain.
This is typical, of what many countries face during
the time of corona pandemic.
To summarize, the evidence from the preceding
findings of our empirical study suggests the
following lessons:
- No doubt, the experience of COVID-19 identified
the fragility of our existing global trading system
and its intricate supply chains.
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- Economic diversification offers a kind of
immunity against shocks akin to COVID-19.
- A high level of regional economic integration
suggests adaptability and resilience against the
likely global crisis including pandemic-driven
crisis.
- Our current economic orthodoxy should be
reassessed; in particular, those about our global
trading order e.g. the Ricardian "theory of
comparative advantage".
Against the backdrop of these lessons,
policymakers and scientific research circles are
seriously required to heed these lessons; otherwise,
the next pandemic will hit us with even greater
force.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research was funded by the Deanship of
Scientific Research at KFU: GRANT3, 103.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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