Energy supply, public debt, and economic growth: Causality analysis for a panel of OECD European countries

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Energy supply, public debt, and economic growth:
Causality analysis for a panel of OECD European countries
Mohamed AWADA1, Moustapha BADRAN2, Imtynan KHALIFEH3, Jules SADEFO KAMDEM4
Montpellier Research in Economics MRE, Faculty of Economics, University of Montpellier,
France.
Abstract
This paper addresses the causal relationship between economic growth, public debt, and energy
supply, using annual panel data analysis over the period 1970-2021 of 21 OECD European
countries divided into two groups: GIIPS and Non-GIIPS countries, through a tri- variate
causality model. The findings confirm a strong positive relationship between the energy supply
and economic growth, but also a negative relationship between the public debt and economic
growth on the long-term using a vector error correction model. Granger causality’s outcomes
show evidence that the panel causality relationships for the Non-GIIPS countries follow the
same path and directions for the full sample, emphasizing that the economic policies for these
countries rely on the energy supply which is a fundamental factor for economic growth and
living standards.
Keywords: Economic Growth, Public Debt, Energy Supply, OECD, Causality, Panel Data.
JEL Classification : C23, C30, F43, H63, Q41.
1 E-mail address: mohamedawada1@hotmail.com
2 E-mail address: bdrn.m@hotmail.com
3 E-mail address: imtynan.kh@hotmail.com
4 Corresponding author : jules.sadefo-kamdem@umontpellier.fr

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1. Introduction
Economists always focus on the importance of growth as they considered it as a core of
government discipline and living standards. Economic growth is strongly linked to production,
energy, debt, the natural environment, and human health. Therefore, countries must be aware
of the essential role of economic growth and its impact on economic development and global
sustainability. The challenge will be to integrate these ideas into mainstream policy processes
and consider how they can be effectively implemented to support Europe's sustainability goals.
Nowadays, the Covid-19 crisis and the Russo-Ukrainian conflict highlight the importance of
energy supply as an eco-political factor due to its role as a dynamo for the economic
development, and its implications on the worldwide economy controlling public debt and
inflation rates. The relationship between energy and economic development captures the
researcher’s attention and academic interest because of its crucial policy ramifications during
the last decade. There have been several studies undertaken to determine the causal linkages
between energy and economic growth applying various methods such as time-series analysis
and panel approaches especially after the seminal work of J. Kraft and A. Kraft in 1978
considered as starting point in this field. Energy plays a critical role in an economy, both on the
demand and supply sides. On the demand side, energy is one of the commodities that a
consumer chooses to purchase to maximize his or her utility. On the supply side, energy is an
important input to production, capital, human labor, and materials. In addition, energy supply
considers a vital factor for the economic and social development of countries, where the
countries that produce the energy has a high probability to increase economic growth and living
standards. While limiting energy supply can lead to negative affect on the development of the
country and its economic growth (Halldorsson and Svanberg, 2013). An energy shortage and
inadequate energy supply are essential factors to adversely impact energy security, economic,
and social welfare, and increase the costs of production and transportation. The other topic that
has been vigorously discussed in the literature resulting from the relationship between public
debt expansion and economic growth has grabbed the attention of many analysts during recent
years stimulated by the sharp increase in public debt after the financial crisis, especially in the
Euro area. Indeed, in response to the financial crisis, the government has employed fiscal
policies to raise aggregate demand. Economists approve that the increase in public debt in the
short run due to fiscal policies leads to boost economic growth. In contrast, in long term there
are two views, some consider a negative relationship between them, while others deny the
relation between economic growth and public debt in the long term.
The explanation of the economic growth either by debt or by energy appears to be more
complex than an intuitive interpretation of policymakers. Debt is a double-edged sword. On the
one side, it promotes consumption and accelerates capital accumulation, thus contributing to
economic growth. On the other side, by increasing debt servicing costs, it exposes countries to
financial risks. A strong expansion of the debt can be associated with a significant economic
contraction that can last for years (Fenske et al. 2021). Whatever the economic situation
(expansion or recession), we have noted that for some European OECD countries,

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the pace of growth is not the same as for others due to their high indebtedness ratios and lack
of energy resources (Gomez et al., 2015). Whereas, the pace of GDP growth is conditioned by
energy supply for some countries or by their debt policies for others. Given the financial and
economic downturn, the group of European OECD members especially the ones with weak
economies - the so-called GIIPS countries (Greece, Ireland, Italy, Portugal, and Spain) -
represent a valuable case for testing the dynamics and relationships of persistently high rates of
public deficit and external imbalances. It is therefore important to understand the link not only
between debt and economic growth but also between energy and economic growth to derive
useful policy indications, necessary to understand whether a reduction in debt or an increase in
energy supply is sufficient to resolve the imbalances (Algieri 2014). Therefore, our added value
lies in taking, on one hand, into account the energy supply for a better three- dimensional
explanation between energy, debt, and economic growth. On the other hand, by splitting the
OECD European countries into two sub-samples: GIIPS and Non-GIIPS counties. It would be
appropriate to highlight our variables of interest (energy supply, public debt, and economic
growth) which will be developed in the following parts of our article.
First of all, priority must be given to an efficient and sustainable energy supply. Sustainability
is no longer limited to the preservation of the environment. According to the OECD, primary
energy supply is defined as energy production plus energy imports, minus energy exports,
minus international bunkers, then plus or minus stock changes. The International Energy
Agency (IEA) energy balance methodology is based on the calorific content of the energy
commodities and a common unit of account: tone of oil equivalent (toe). Toe is defined as 107
kilocalories (41.868 gigajoules). This quantity of energy is, within a few percent, equal to the
net heat content of one tone of crude oil. The difference between the “net” and the “gross”
calorific value for each fuel is the latent heat of vaporization of the water produced during the
combustion of the fuel. For coal and oil, the net calorific value is about 5% less than the gross,
for most forms of natural and manufactured gas the difference is 9-10%, while for electricity
the concept of calorific has no meaning. The IEA calculates balances using the physical
energy content method to find the primary energy equivalent. This indicator is measured in
million tons of oil equivalent (toe) and toe per 1 000 USD. Secondly, Public debt refers to the
total financial commitments made in the form of loans by the State, public authorities, and the
organizations that depend directly on them. It is constantly changing as a function of the rate of
repayment of loans by the State and public administrations and the new loans they take out to
finance their deficits, which occur when expenditure, made possible by borrowing, exceeds
revenue, leading to an increase in the debt. Finally, a country's growth is measured by the
increase in its gross domestic product over a given period: month, quarter, half-year, or year.
It is calculated in "constant euros", i.e. excluding price increases. It is manifested by a
significant and lasting increase in the supply of goods and services. This positive fluctuation
is evaluated by the annual variation of the gross domestic product (GDP) indicator, evaluated
in constant currency to take account of inflation. This is an indispensable, but not always
sufficient, modality for development. Therefore, gaining a greater understanding of the
interactions between these variables is immensely useful for policy formulation.

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This paper proceeds as follows. The next section reviews the literature to establish existing
knowledge of the relationships between energy, debt, and economic growth. The methodology
section includes details of the model and hypotheses, then the results are analyzed and
discussed. Finally, future research directions are suggested.
2. Brief literature review
2.1. Energy – economic growth
In considering the relationship between energy consumption and economic growth, two
opposing views have emerged. One view is that energy consumption limits growth. It also
argued that the potential impact of energy consumption on growth varies with the structure of
the economy and the economic growth cycle of the country. Through the development of the
economy, its production structure should move toward services, which are not energy- intensive
activities, as argued by a group of authors such as Denison 1985, Cheng 1995, Asafu 2000,
and Solow 2016. The other view implies that energy can be the source and driver of economic
growth. The increase of energy consumption is one of the effects of economic growth.
Moreover, energy is a key source of economic growth because multiple consumption and
production activities involve energy as a basic production factor (Mouhtadi and Sadefo, 2018).
From a physical perspective, energy consumption drives economic productivity and industrial
growth and is essential to the development of any modern economy (Sacko, 2004).
Broadly speaking, the connections between energy and growth, developed by Jumbe 2004, Shiu
2004, Altinay 2005, Chen 2007, Mozmuder 2007, Squalli 2007, Apergis et al. 2010, Oztruk
2010, have been grouped into four categories, each of which has important implications for
energy policy:
1. The growth hypothesis: this hypothesis asserts that there is a unidirectional relationship
between energy consumption and economic growth. It argues that energy consumption
plays a dominant role in economic growth, both as a direct factor in the production
process and indirectly as a complement to labor and capital. Energy is considered here
as a complementary factor of production to the usual factors such as capital and labor.
Under these conditions, the implementation of energy policy influences the level of
production, according to Yu 1985, Tsani 2010, Belke 2011, and Destek 2016.
2. The conservation hypothesis: growth generates an increase in energy consumption. This
assumes that a restrictive energy policy can be implemented in an economy without
negative effects on growth. If there is a unidirectional Granger causality from growth to
energy consumption, this hypothesis is confirmed. Indeed, Paul et al. 2004, Hatemi et
al. 2005, and Gelo 2009 assert that energy conservation policies can be implemented
with little or no negative effects on economic growth.

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3. The neutrality hypothesis: implies that there is no causal relationship between energy
consumption and economic growth. These two variables are not correlated. In other
words, any increase or decrease in energy consumption does not affect economic
growth. This means that neither an energy-saving policy nor an energy-intensive policy
influences the level of wealth creation in an economy, as explained by Jobert and
Karanfil 2007.
4. The feedback hypothesis: states that there is a two-way causality between energy
consumption and economic growth. This means that energy and economic policies will
be implemented jointly. In this case, the energy consumption policy should be
developed to avoid a negative impact of energy consumption on economic growth
(studies applied to one or groups of countries: Greece, G7, OECD countries), according
to Hondroyiannis 2004, Lee 2008, Mutascu 2016, Dos et al. 2017.
Thus, empirical studies related to hypothesis mentioned above, show a unidirectional causality
that exists from total energy consumption to economic growth (applied to a group of African
countries) concluded by Akinlo 2008 and Adebola 2011. A unidirectional causality in the other
direction was concluded by Kraft and Kraft 1978 applied to the United States for a period from
1947 to 1974 and Abaidoo 2011 studied using quarterly data over 39 years. The study done by
Behname et al. 2012 leads to a bidirectional relationship between the two variables. No
relationship between energy and growth is concluded by Carminel 2015 explaining that the
decoupling between energy and growth is limited by concerns about the supply of raw materials.
As an example, energy-related technologies needed to extract certain materials are subject to
geopolitical constraints. The raw material supply problem in turn limits the deployment of the
equipment needed to improve energy intensity. Erol and Yu 1987, on a group of countries,
tried to find the causal link between energy consumption and GDP. Using the Granger and Sims
causality test, they concluded that there was one-way causality between energy consumption
and GDP for West Germany; two-way causality in Italy; and a lack of causality between the
two variables for France, the United Kingdom, and Canada. Masih and Masih 1996 applied
their work to six Asian countries (India, Indonesia, Malaysia, Pakistan, the Philippines, and
Singapore) using Johansen's methodology, vector error correction model, and variance
decomposition. The discrepancies in the results are summarized as cointegration between
energy consumption and GDP in India, Indonesia, and Pakistan, and non-cointegration between
the two variables in Malaysia, Singapore, Pakistan, and the Philippines; energy consumption
causing GDP (more energy consumption, more growth) in India; GDP causing energy
consumption in Indonesia; and two-way causality in Pakistan. Chontanawat et al. 2008 assessed
the relationship between the two variables on a panel of over 100 countries, including 30 OECD
and 78 non-OECD countries, to detect the relationship between energy and growth. Their
findings reveal that energy consumption drives GDP (more energy consumption, more growth)
in 21 OECD countries. For non-OECD countries, this relationship is found in 36 of the 78
countries, or 46%. In summary, a more common causality between energy consumption and
GDP is noted for advanced OECD countries. From this point of view, a measure focused on
limiting energy consumption would have a greater negative impact on GDP in OECD
countries than in non-OECD countries.

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Ozturk and Acaravci 2010 sought to test the relationship between energy and growth in 4
Eastern European countries such as Albania, Bulgaria, Hungary, and Romania over the period
(1980 - 2006) using the Engle and Granger model. Their findings lead to a lack of causality for
Albania, Bulgaria, and Romania. However, a presence of bidirectional causality is observed in
Hungary.
2.2. Public debt – economic growth
The second strand has related to the relationship between public debt and economic growth.
The empirical literature on this topic not only presents ambiguous results but focuses mainly on
the possible impact of high debt levels on economic growth, ignoring the possibility of reverse
causality from growth to debt with rare exceptions such as Ferreira 2009 and Puente 2015.
However, Bell et al. 2015 find that there is some theoretical evidence that public debt is likely
to accumulate when growth is low. In this regard, since low growth means more limited
government revenues, governments may be forced to increase their debt levels to maintain the
welfare state, stimulate demand in the short run, and increase growth in the long run, according
to Feldstein 2014. Theoretically, neoclassical and endogenous growth models, such as those of
Modigliani et al. 1961, Diamond 1965, Saint-Paul 1992, and Aizenman 2007, suggest that high
levels of public debt would undeniably reduce the rate of economic growth. Other channels to
support the negative effect of public debt on long-run growth include the debt overhang
hypothesis (Krugman 1988, Roubini et al. 1989), the liquidity constraint hypothesis (Moss et al.
2003), the crowding-out hypothesis (Hansen 2004), and the uncertainty hypothesis (Codogno
et al. 2003, Cochrane 2011). Another channel through which high debt can negatively impact
growth is through long-term interest rates (Elmendorf et al. 1999, Tanzi et al. 2000). Finally,
some of the effects associated with financial liberalization, ranging from increased risk-taking
by banks to the accumulation of large external debt, can make a country vulnerable to economic
shocks that often have severe recessionary consequences (Eichengreen et al. 2003, Nyambuu
et al. 2015). Given the theoretical predictions highlighted above, it is somewhat surprising that
Reinhart and Rogoff 2010's conclusion that countries' debt must reach a threshold of 90% of
GDP, beyond which the rate of GDP growth declines significantly, has generated such
controversy. Others have made a strong criticism by noting that over the period 1946-2009,
countries with a debt to GDP ratio above 90 percent had average annual real GDP growth of
2.2 percent, not -0.1 percent.
Additional empirical studies have shown the relationship between debt and growth. Checherita
et al. 2012 analyzed the average impact of public debt on GDP per capita growth in 12 euro
area countries over about 40 years, starting in 1970. They conclude that there is a non-linear
effect of debt on growth with a turning point beyond which the ratio of public debt to GDP
harms growth in the long run at around 90/100% of GDP. Panizza et al. 2014 studied the causal
effect of government debt on economic growth in a sample of OECD countries. The results
are consistent with the existing literature, showing a negative correlation between the two
variables. Mencinger et al. 2014 empirically analyzed the relationship between the ratio of
government debt to GDP and GDP growth on a panel dataset of 25 EU countries. To

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take into account the impact of the level of the debt to GDP ratio on the real GDP growth rate,
they used panel estimation on a generalized economic growth model augmented with a debt
variable, while also considering some methodological issues such as heterogeneity and
endogeneity problems. The results of all models indicate a statistically significant non-linear
impact of government debt ratios on annual growth rates of GDP per capita. Their research
has contributed to a better understanding of the problem of high public debt and its effect on
economic activity in the EU.
The debt-growth and energy-growth relationships have been discussed in the literature, which
leads us to look at the decoupling of growth and energy on the one hand, and the problem of
public debt in OECD European countries on the other. These two facts raise a fundamental
question: What can be said about the links between energy, debt, and GDP? The question is
crucial today because we are exposed to many challenges. The current fall in the global growth
rate and its consequences are the subjects of much debate. Is it due to pressure on energy
resources? The high level of public debt, is it related to the link between energy and economic
growth? All of these issues bring interest in determining the potential econometric short and
long-term relationships between energy, debt, and growth.
3. Model and methodology
3.1 Model
Based on our brief literature review, and to achieve the study’s objective, we form a long-run
relationship between economic growth, energy supply, and public debt in a linear logarithmic
form as follows:
 =  +  +  +  (1)
Where i = 1,…, N (number of the chosen countries) denotes the country; t = 1,…,T denotes the
time period from 1970 to 2021;  is assumed to be a serially uncorrelated error term; 0
represents the constant in our model; EGR represents the economic growth which is our
dependent variable; ESU and PDE represent the energy supply and the public debt respectively
which are our independent variables. The expected signs of energy supply and
public debt are positive and negative respectively, meaning that energy supply and public debt
have respectively a positive and negative impact on economic growth in the long term.

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3.2 Cointegration methodology
To test the existence of a long-run relationship among the variables and to capture the short-
run dynamics of the variables using the vector error-correction model (VECM), our
methodological strategy is composed of two essential steps.
The first step is to verify the order of integration for all variables because the several
cointegration tests are valid only if all the study’s variables are integrated in the same order,
meaning that they have the same order of integration. We test the stationary using four different
types of unit root tests divided into common and individual unit root: Levin, Lin and Chu (LLC);
Im, Pesaran and Shin (IPS); a Fisher-type test using Augmented Dickey-Fuller (ADF) and
Phillips-Perron (PP). In 1997, Zapata and Rambaldi reported that if the variables are not
integrated in the same order (meaning that some variables are I(0) and the others are integrated
of order one I(1)), the application of Toda and Yamamoto (TY) is required to be on the safe side
and to overcome this problem, whereas the TY procedure doesn’t impose any prerequisites and
knowledge on cointegration.
The second step requires testing the panel cointegration relationship. When all series and
variables are integrated into the same, we can move on to test the panel cointegration between
the variables based on the three fundamental tests: Pedroni; Kao, and Johansen Fisher. The
Pedroni and Kao tests are based on the Engle-Granger two-step (residual-based) cointegration
test. Overall, Pedroni provides seven statistics to test the null hypothesis of no cointegration in
the heterogeneous panels. These seven tests can be categorized as either within-dimension
(panel tests) or between-dimension (group tests). These tests are all based on the residuals from
Eq.(1) and are variants of the ADF and PP tests. The within-dimension tests pool the
autoregressive coefficients across different members of the panel. On the other hand, the
between-dimension tests are less restrictive than the within-dimension tests in the sense that
they allow for the heterogeneity of the parameters across countries. Kao's test follows the
same basic approach as Pedroni's tests but specifies cross-section-specific intercepts and
homogeneous coefficients in the first step. In addition, the Fisher test is a combined Johansen
and Juselius test. If cointegration exists between the variables, the ordinary least squares (OLS)
method is applied to ensure that the estimation of equation (1) doesn’t lead to a spurious
regression result. In addition, the parameters estimated by the OLS method are super-consistent.
3.3 Granger causality
Then, we look forward to examining the existence and the direction of the causality between
the variables in a panel context. The existence of cointegration implies that there are long-run
equilibrium relationships between the variables and thus Granger causality between them in at
least one direction. The vector error correction model (VECM) is used to correct the disequi-
librium in the cointegrating relationship, captured by “ECT”, as well as to test for long and
short-run causality between the cointegrated variables. The VEC model by definition is a re-
stricted VAR that has cointegration restrictions built into the specification so it is designed for
use with nonstationary series that are known to be cointegrated. The VEC specification re-
stricts the long-run behavior of the endogenous variables to converge to their cointegrating

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relationships while allowing a wide range of short-run dynamics. The cointegration term is
known as the error correction term since the deviation from long-run equilibrium is corrected
gradually through a series of partial short-run adjustments. The panel-based VECM is defined
as follows:


 = 

 + 󰇯  
  
  󰇰

 󰇯

󰇰 + 

  + 

 (2)
Where i = 1,…, N (number of the chosen countries) denotes the country; t = 1,…,T denotes the
time period from 1970 to 2021;  is assumed to be a serially uncorrelated error term; ECT is
the lagged error-correction term derived from the long-run cointegration relationship. We
follow Abdalla and Murinde’s process to determine the optimal lag length in each equation for
a linear system which is selected by maximizing the value of the R-squared (R2)
and minimizing the AIC criteria. In terms of causality, we test four main hypotheses that sum
up all the cases between the three chosen variables as follows:
H1: Unidirectional causality relation from variable1 to variable2 (Meaning that the first variable
does Granger causes the second one).
H2: Unidirectional causality relation from variable2 to variable1 (Meaning that the second
variable does Granger causes the first one).
H3: Bidirectional causality relation between variable1 and variable2 (Meaning that both
variables do Granger cause each other’s).
H4: No causality relation between both variable1 and variable2 (Meaning that the first variable
does not Granger causes the second variable).
4. Empirical results
4.1. Data analysis
In this paper, we have conducted a statistical and econometrical analysis of twenty-one OECD
European countries for which the most energetic and macroeconomic annual data are available
from 1970 to 2021. The list includes Greece, Ireland, Italy, Portugal, Spain, Germany, Belgium,
Finland, France, Netherlands, Luxembourg, Austria, Denmark, Poland, Hungary, Sweden,
Switzerland, Norway, England, Iceland, and Turkey. The objective was to select the largest and
the longest balanced panel. The full sample is divided into two sub- samples: GIIPS and Non-
GIIPS. Thus, we construct three different models (balance panel) for the analysis. The data come
from OECD Economic Outlook No 110-December 2021 (primary energy supply, general
government gross financial liabilities, and gross domestic product volume market prices). To
complete the series, we have also used OECD Economic Outlook No 73-June 2003 for all the
countries selected to have the largest observations possible (data calibration between two data
sources).

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Figure 1. Average for GDP per year
Figures 1-3 show the changing trends for each series of the OECD European countries (Full
sample; GIIPS and Non-GIIPS countries). Figure 1 shows that the full sample and Non-GIIPS
countries have shown the same significant increase in economic growth all over the period
(1970-2021). Whereas the series for the other sample (GIIPS countries) show an almost
monotonic increase over the entire time span. In term of economic growth, we can notice that
the full sample is well presented by the Non-GIIPS countries.
Figure 2. Average for energy supply per year
Figure 2 represents the evolution of energy supply from 1970 to 2021 for the three samples.
Overall, we observe that the series have almost the same trend. Two events to report. The first
was marked by the response of energy supply following the financial crisis of 2008, whose
Economic growth
4,5E+12
4E+12
3,5E+12
3E+12
2,5E+12
2E+12
1,5E+12
1E+12
5E+11
0
Full sample
GIIPS countries
NON GIIPS countries
Energy supply
100000000
90000000
80000000
70000000
60000000
50000000
40000000
30000000
20000000
10000000
0
Full sample
GIIPS countries
NON GIIPS countries
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

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effect was more pronounced for the GIIPS (countries with low growth and high debt as already
discussed in the literature). The second, in regard to the crisis of covid-19 which is in favor of
the GIIPS countries as the energy supply has been improved more in comparison to the other
two samples (Full sample and Non-GIIPS countries).
Figure 3. Average for public debt per year
Public debt’s trends are presented in figure 3. The graph shows that all three series have the
same upward trend reflected in an excessive increase of the debt mainly in response to the three
essential events: financial crisis (2008); sovereign debt crisis (2011) and covid-19 crisis (2019).
4.2. Descriptive statistics and correlation matrix
Table 1. Descriptive statistics
Full sample
Variables
Mean
Median
Min
Max
Std. Dev.
No. of Obs.
EGR
27.16812
27.24722
23.10801
31.36414
1.491998
1,092
ESU
16.9409
17.30243
10.83985
19.71831
2.138101
1,092
PDE
25.91719
26.1166
19.08927
28.91897
1.647482
1,092
GIIPS countries
Mean
Median
Min
Max
Std. Dev.
No. of Obs.
EGR
26.40955
26.0062
24.1153
28.21585
1.114771
260
ESU
17.3481
17.01438
15.56645
19.08631
1.090601
260
PDE
25.44128
25.53252
19.08927
28.81175
1.938355
260
Non-GIIPS countries
Mean
Median
Min
Max
Std. Dev.
No. of Obs.
EGR
27.40517
27.52745
23.10801
31.36414
1.516219
832
ESU
16.81364
17.34393
10.83958
19.71831
2.358597
832
PDE
26.06592
26.24476
10.85372
28.91897
1.516368
832
Public debt
1,6E+12
1,4E+12
1,2E+12
1E+12
8E+11
6E+11
4E+11
2E+11
0
Full sample
GIIPS countries
NON GIIPS countries
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

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We present the summary of descriptive statistics of the study’s variables as shown in table 1.
The mean of economic growth in the full sample is equal to 27.16 and ranges from 23.10 to
31.36 which are the same min and max values for the Non-GIIPS countries. We have recognized
that the energy supply and public debt have a difference in the mean between the GIIPS
countries and the Non-GIIPS countries. The results ensure the energy supply and public debt
vary between the GIIPS countries and the Non-GIIPS countries indicating the need to split the
full sample into two sub-samples to capture the difference.
Table 2. Correlation matrix
Economic growth
Energy supply
Public debt
EGR
1.0000
ESU
-0.0564*
(0.0626)
1.0000
PDE
0.5888***
(0.0000)
0.2355***
(0.0000)
1.0000
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
We present the Pearson correlation coefficients among the study’s variables. The correlation
between economic growth and energy supply is negative and significant at 1% while there is a
positive and significant correlation between economic growth and public debt. The result
supports the hypothesis according to there is a positive relationship between economic growth
and public debt and a negative relationship between economic growth and energy supply.
Regarding the correlation between the energy supply and public debt, the results indicate a
strong positive relationship between these two variables.
4.3. Panel unit root and panel cointegration tests
Table 3. Panel unit root tests
Variables Common unit root Individual unit root
LL
C
IP
S
ADF
PP
Level
1st diff.
Level
1st diff.
Level
1st diff.
Level
1st diff.
EGR
23.223
(1.0000)
-12.760***
(0.0000)
1.064
(0.8564)
-22.459***
(0.0000)
0.4149
(1.0000)
-12.449***
(0.0000)
0.0641
(1.0000)
-14.413***
(0.0000)
ESU
6.043
(1.0000)
-26.251***
(0.0000)
-2.856***
(0.0021)
-26.108***
(0.0000)
7.931
(1.0000)
-26.474***
(0.0000)
8.943
(1.0000)
-27.510***
(0.0000)
PDE
15.046
(1.0000)
-11.476***
(0.0000)
0.326
(0.6278)
-19.636***
(0.0000)
1.927
(1.0000)
-13.188***
(0.0000)
1.238
(1.0000)
-16.007***
(0.0000)
Note: *, **, and *** indicate the rejection of the null hypothesis at 10%, 5%, and 1% levels of
significance, respectively. The lag lengths are selected using AIC criteria.

13
Table 4. Panel cointegration tests
Pedroni test
Alternative hypothesis: common AR coefs. (within-dimension)
Panel v-stat
0.224690 (0.4111)
-0.773904 (0.7805)
Panel rho-stat
-6.756258*** (0.0000)
-6.738820*** (0.0000)
Panel PP-stat
-8.899870*** (0.0000)
-9.365574*** (0.0000)
Panel ADF-stat
-8.517234*** (0.0000)
-9.151758*** (0.0000)
Alternative hypothesis: individual AR coefs. (between-dimension)
Group rho-stat
-6.120249*** (0.0000)
Group PP-stat
-12.62997*** (0.0000)
Group ADF-stat
-11.65244*** (0.0000)
Kao test
t-Statistic
ADF
-8.623625*** (0.0000)
Fisher test
Trace test
Maximum eigenvalue
Null hypothesis
r = 0
301.8*** (0.0000)
215.8*** (0.0000)
r ≤ 1
161.0*** (0.0000)
138.0*** (0.0000)
r ≤ 2
77.83*** (0.0006)
77.83*** (0.0000)
Note: *, **, and *** indicate the rejection of the null hypothesis at 10%, 5%, and 1% level of signifi-
cance, respectively. r denotes the number of cointegration equations.
The time-series properties of the variables in Eq. (1) are checked through four types of panel
unit root tests: LLC, IPS, ADF, and PP tests. The panel unit root test is based on the null hy-
pothesis to prove the existence of the unit root, while the alternative hypothesis indicates the
absence of the unit root.
H0: Data are not stationary (Unit root exists) H1:
Data are stationary (Unit root does not exist)
We apply the common unit root test by using the LLC and the individual unit root by applying
IPS, ADF, and PP. The results indicate that all the series in Eq (1) appear to contain a panel unit
root in their level while stationary in their first difference, showing that they are integrat- ed at
order one I (1).
According to the finding from the panel unit root test, the cointegration between economic
growth, energy supply, and public debt is investigated. For that, we apply the panel cointegra-
tion test between economic growth and its determinates by using the Pedroni test, Kao test, and
Fisher test.
H0: there is no cointegration
H1: there is cointegration
Based on the Pedroni test, there is 9 out of 11 is significant at 1%, and this is an indicator of
panel cointegration. After applying the Koa test, the results indicate the existence of panel
cointegration where it is significant at 1%. Additionally, the Johansen Fisher test reveals the
existence of two cointegration vectors at 1%. We can summarize that there is strong statistical
evidence of panel cointegration among our variables (economic growth EGR, energy supply
Test statistics
Statistics (p-value)
Weighted statistic (p-value)

14
ESU, and public debt PDE). The existence of cointegration among the variables attempts to
exclude the possibility of having a bias in the equation.
4.4. VEC-Model and panel causality tests
Table 5. VEC model – Short and long-run equations for the full sample
Dep. Var.
Source of causation (independent variables)
R2
Short-run equation
F-statistics
ECT
ΔEGR(-1)
ΔEGR(-2)
ΔESU(-1)
ΔESU(-2)
ΔPDE(-1)
ΔPDE(-2)
ΔEGR
-0.53208***
-0.13937***
-0.13352***
-2.83E-06
-1.84E-05
-5.91E-06
-3.34E-06
0.3522
ΔESU
129.9695
29.9088
-10.8347
-0.3188***
-0.0117
0.0572**
-0.0015
0.1048
ΔPDE
-288.7375***
129.1592
168.0498
-0.0280
-0.0230
-0.1463***
-0.1714***
0.0471
Long-run equation
t-statistics
ΔEGR(-1)
ΔESU(-1)
ΔPDE(-1)
ECT
1.0000
6.77E-06***
-6.88E-06***
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
The time series of our model must be stationary in a long-run analysis to avoid dummy re- sults.
For that, before examining the panel cointegration, all variables (economic growth, en- ergy
supply, and public debt) must be integrated into order one.
The VECM model for the dependent variable EGR is as the following:
() = 0.53208  0.13937 (1)  0.00000283  (1)  0.0000184 (2) 
0.00000591 (1)  0.00000334 (2)
(1) = 0.00000677  (1)  0.00000688  󰇛󰇜
The result in the equation shows in the short run there is no impact of energy supply and pub-
lic debt on economic growth in the first equation. However, in the third model where the pub-
lic debt is the dependent variable, the coefficient of adjustment is negative and significant at
1%. Contrary in the long run both energy supply and public debt are significant at 1%. The
impact of energy supply is positive and significant by 0.00000677 on economic growth while
there is a negative and significant impact of public debt on economic growth by 0.00000688.
In the long run, the results indicate for every 1% increase in the energy supply will increase the
economic growth by 0.000677%. Results indicate also for every 1% increase in the public debt
will decrease the economic growth by 0.000688%.
(3)

15
Table 6. Panel Granger causality test for the full sample
Null hypothesis
No. of Obs.
F-statistic
P-value
ESU does not Granger cause EGR
1050
9.99874***
5.E-05
EGR does not Granger cause ESU
11.4395***
1.E-05
PDE does not Granger cause EGR
1050
1.79728
0.1663
EGR does not Granger cause PDE
13.9026***
1.E-06
PDE does not Granger cause ESU
1050
10.1356***
4.E-05
ESU does not Granger cause PDE
5.68112***
0.0035
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
The existence of a panel long-run cointegration relationship among economic growth, energy
supply, and public debt suggests that there is Granger causality in at least one direction. The
balanced panel Granger causality results are presented in table 6. We have divided our work
into three samples to have a deep close analysis between the GIIPS countries and Non-GIIPS
countries. After applying the panel Granger causality test for the full sample, the results state
the unidirectional causality from public debt to economic growth, while there is bidirectional
causality from energy supply to economic growth, economic growth to energy supply,
economic growth to public debt, public debt to energy supply, and energy supply to public debt.
Table 7. VEC model – Short and long-run equations for GIIPS countries
Dep. Var.
Source of causation (independent variables)
R2
Short-run equation
F-statistics
ECT
ΔEGR(-1)
ΔEGR(-2)
ΔESU(-1)
ΔESU(-2)
ΔPDE(-1)
ΔPDE(-2)
ΔEGR
0.005409***
0.365950***
0.156006*
-0.066823
0.003312
-0.007864
0.020506
0.1614
ΔESU
0.012910**
-0.358241**
0.610836***
-0.394976*
-0.026784
-0.007611
0.009621
0.0816
ΔPDE
0.053573***
-0.333889
-0.186522
-0.105665
-0.186808
0.139021**
0.081984
0.0819
Long-run equation
t-statistics
ΔEGR(-1)
ΔESU(-1)
ΔPDE(-1)
ECT
1.0000
-0.386387***
-0.707003***
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
Table 8. Panel Granger causality test for GIIPS countries
Null hypothesis
No. of Obs.
F-statistic
P-value
ESU does not Granger cause EGR
250
2.23562
0.1091
EGR does not Granger cause ESU
1.30216
0.2738
PDE does not Granger cause EGR
250
0.23344
0.7920
EGR does not Granger cause PDE
12.5350***
7.E-0.6
PDE does not Granger cause ESU
250
1.04650
0.3527
ESU does not Granger cause PDE
11.2017***
2.E-05
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.

16
To permit the use of the Granger causality test for the GIIPS countries, we have applied the
VEC model. The results indicate there is a negative and significant impact of 1% of energy
supply on economic growth, where every 1% increase in energy supply will decrease the
economic growth in the long run by 38.63%. For the public debt, the results demonstrate there
is a negative and significant impact of 1% of public debt on economic growth in the long run,
where every 1% increase in public debt will decrease the economic growth by 70.70%. The
panel Granger causality test for GIIPS countries demonstrates there is unidirectional causality
from energy supply to economic growth, from economic growth to energy supply, from public
debt to economic growth, and from public debt to energy supply. While there is bidirectional
causality from economic growth to public debt, and energy supply to public debt.
Table 9. VEC model – Short and long-run equations for NON-GIIPS countries
Dep. Var.
Source of causation (independent variables)
R2
Short-run equation
F-statistics
ECT
ΔEGR(-1)
ΔEGR(-2)
ΔESU(-1)
ΔESU(-2)
ΔPDE(-1)
ΔPDE(-2)
ΔEGR
-0.001581***
0.257541
0.144308
0.033658
-0.064739**
-0.005236
0.005667
0.1425
ΔESU
0.002676***
-0.316063**
0.251094***
0.019722**
0.039701
0.003066
0.015695
0.0433
ΔPDE
0.017706
-0.247384***
0.159324***
-0.086160
0.059519
0.104183
0.031107
0.0565
Long-run equation
t-statistics
ΔEGR(-1)
ΔESU(-1)
ΔPDE(-1)
ECT
1.0000
0.640416***
-1.100575***
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
Table 10. Panel Granger causality test for NON-GIIPS countries
Null hypothesis
No. of Obs.
F-statistic
P-value
ESU does not Granger cause EGR
800
9.09218***
0.0001
EGR does not Granger cause ESU
5.91700***
0.0028
PDE does not Granger cause EGR
800
0.81032
0.4451
EGR does not Granger cause PDE
8.48876***
0.0002
PDE does not Granger cause ESU
800
5.45424***
0.0044
ESU does not Granger cause PDE
2.96195*
0.0523
Note: *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively.
The same procedure follows to test the Granger causality test for the Non-GIIPS countries.
After applying the VEC model for the Non-GIIPS countries, the results indicate a long-run
relationship. The impact of energy supply has a positive and significant impact of 1% on the
economic growth, where the increase of 1% of energy supply will increase the economic growth
by 64.04%. While the impact of public debt on economic growth is negative and significant at
1%, where any increase of 1% of public debt will decrease the economic growth by 11.005%.
The panel Granger causality test for Non-GIIPS countries demonstrates there is just
unidirectional causality from public debt to economic growth, while there is bidirectional

17
causality from energy supply to economic growth, from economic growth to energy supply,
from economic growth to public debt, from public debt to energy supply, and energy supply to
public debt.
Figure 4. Panel causality relations
We recognize that the panel causality relations for the Non-GIIPS countries follow the same
path as the full sample, caused by the fact that the Non-GIIPS countries represent ¾ of the full
sample. Based on the strong causality results, evidence shows that economic growth does a
Granger cause of public debt. While public debt does not granger cause economic growth. We
can conclude that economic growth can attract the public debt in the full sample, GIIPS and
Non-GIIPS countries. Moreover, the causality runs from energy supply to public debt and
public debt to energy supply in the full sample and the Non-GIIPS countries. While in the GIIPS
countries, the direction is not reverse, where the causality is unidirectional. Nevertheless, the
results indicate that the development of energy is a key factor for domestic economic growth
which is an essential factor for the energy supply. Both economic growth and energy supply
depend on each other due to a bidirectional causality relationship. Our results are in line with
the findings of Checherita et al. (2012). They find that the public debt has a negative on
economic growth in the long term. The findings are also somewhat consistent with the findings
of Panizza et al. (2014) who reported evidence of a negative correlation between the
government debt and economic growth. In contrast, in 2009, Ferreira, using OECD annual data
for 20 countries from 1988 to 2001, finds evidence of a clear bidirectional Granger causality
relationship between economic growth and public debt.
EGR
ESU
PDE
Full
Sample
GIIPS
countries
Non GIIPS
countries

18
5. Conclusion
We study the impact of energy supply and public debt on economic growth taking the European
countries as a case study during 1970-2021 by applying a panel approach. To achieve our added
value, we divide our samples into GIIPS countries where they are the weak economies (Greece,
Ireland, Italy, Portugal, and Spain) and Non-GIIPS countries. In addition, scientific articles
studied and focused on energy consumption and its impact on economic growth and they did
not give attention to the energy supply, which elevates the value of our study. The main
findings, for GIIPS countries: are the non-evidence of a causal relationship between energy
supply and economic growth. These two variables aren’t correlated. In other words, any
increase or decrease in energy supply does not affect economic growth. This means that neither
an energy-saving policy nor an energy-intensive policy influences the level of wealth creation
in an economy. Regarding the two other samples (full and Non-GIIPS), there is a two-way
causality between energy supply and economic growth. This means that energy and economic
policies will be implemented jointly. In this case, the energy supply policy should be developed
as an essential factor to attain high levels of economic growth in the long term avoiding its
negative impact in the short term. In addition, there is a negative relationship between the public
debt and economic growth in full, GIIPS, and Non-GIIPS countries. Concerning the causality,
there is unidirectional causality from economic growth to public debt. Whereas the European
countries should consider the government liabilities as a main issue by the fact that the public
debt in the long-term will deactivate the economic wheel and disrupt economic growth.
Governments in European countries have to perceive the importance of public debt and energy
supply as part of their public policy, and they should be aware that the public debt is a double-
edged sword. The Government strategies direction should rely more on energy supply
especially renewable energy sources and other economic factors that can stimulate economic
growth as a confrontation of public debt in long run. Also, in terms of energy, it would be useful
for future papers to include in our model the primary energy consumption that represents the
demand to know its effect on the causal relationships between the variables.
Acknowledgments
The authors would like to thank anonymous reviewers for their valuable suggestions and helpful
comments which have greatly enhanced the quality of this paper.

19
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