Do FDI Inflows and ICT Affect Economic Growth? An Evidence from Arab Countries

Abstract

This article aims to examine the dynamic relationships between foreign direct investment inflows, information and communication technologies, and economic growth in a sample of 15 Arab countries over the period 1995–2019 by employing a panel ARDL approach. The results of estimation of the panel ARDL model reveal that ICT and FDI have positive and significant effects on economic growth in the long run, and ICT indicators have also positive impact on FDI inflows in the long run in the selected sample of Arab countries. From an empirical point of view, this study may have an important contribution. Its findings could be very interesting for better future management of ICT in Arab countries. Therefore, the Arab countries should further improve information and communication technology as an important infrastructure for receiving more foreign direct investment inflows and for better economic growth.

Full text

Citation: Belloumi, M.; Touati, K. Do
FDI Inflows and ICT Affect Economic
Growth? An Evidence from Arab
Countries. Sustainability 2022,14,
6293. https://doi.org/10.3390/
su14106293
Academic Editor: Bruce Morley
Received: 26 March 2022
Accepted: 17 May 2022
Published: 21 May 2022
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sustainability
Article
Do FDI Inflows and ICT Affect Economic Growth? An Evidence
from Arab Countries
Mounir Belloumi * and Kamel Touati
Department of Business Administration, College of Administrative Sciences, Najran University,
Najran 1988, Saudi Arabia; katouati@nu.edu.sa
*Correspondence: mrbelloumi@nu.edu.sa
Abstract:
This article aims to examine the dynamic relationships between foreign direct investment
inflows, information and communication technologies, and economic growth in a sample of 15 Arab
countries over the period 1995–2019 by employing a panel ARDL approach. The results of estimation
of the panel ARDL model reveal that ICT and FDI have positive and significant effects on economic
growth in the long run, and ICT indicators have also positive impact on FDI inflows in the long run
in the selected sample of Arab countries. From an empirical point of view, this study may have an
important contribution. Its findings could be very interesting for better future management of ICT in
Arab countries. Therefore, the Arab countries should further improve information and communica-
tion technology as an important infrastructure for receiving more foreign direct investment inflows
and for better economic growth.
Keywords:
information and communication technology; foreign direct investment; economic growth;
panel ARDL model; Arab countries
1. Introduction
The relationship between information and communication technology and economic
growth is an issue that continues to receive great theoretical and empirical interest. By
investing in ICT, developing and emerging countries can leapfrog development stages to
catch up with developed countries. The explanation for the importance of investing in
ICT infrastructure lies in how it attracts foreign investment and drives economic growth.
The development of information and communication technology infrastructures and their
increasing strength have led to a fundamental change in the nature of global economic
relations, sources of competitive advantage, and opportunities for economic and social
development that represent the main pillars of sustainability [1].
Technologies such as the Internet, personal computers, and cordless phones have
created an interconnected global network of individuals, businesses, and governments. For
the developed world, a modern telecommunications infrastructure is not only necessary
for domestic economic growth but is also a prerequisite for participating in increasingly
competitive global markets and for attracting foreign investment that, in turn, contributes
to the dissemination of technology and wealth creation [2].
While there is ample evidence that new information technologies are transforming in
many ways how modern economies function, the effects on productivity and economic
growth have been much more difficult to detect. Although an increasing number of mi-
croeconomic studies have found a positive correlation between investment in information
technology and various measures of economic performance across firms in industrialized
countries, macroeconomic studies have been less flexible in finding any correlation, or
even a negative association, between IT investment and economy-wide productivity (For a
survey, see: [3].
Today, however, the role that ICTs play in economic growth, as one of the aspects
of sustainability, is well documented. It is emerging today as a necessary factor for the
Sustainability 2022,14, 6293. https://doi.org/10.3390/su14106293 https://www.mdpi.com/journal/sustainability

Sustainability 2022,14, 6293 2 of 21
development of the country’s productive capacity in all sectors of the economy, linking the
country to the global economy and ensuring competitiveness. Like developing countries
around the world, Arab countries seek to improve their investments in ICT and take
advantage of the expected increases in economic activity, and it is often implicitly assumed
that there is a positive relationship between the two [
4
]. ICT could affect various aspects of
economic activities such as the creation of jobs, increasing incomes, improving business
activities, providing accessible information and communication networks, improving
education services, innovation, and human capital development. All these activities lead to
social and economic sustainable development.
Based on the foregoing, the study will address the following research problem: How
does information and communication technology affect economic growth in the group of
Arab countries during the period 1995–2019?
Theoretically and realistically, the significance of this study lies in the fact that the
mutual positive influence between information and communication technology on the one
hand and economic growth and foreign investment on the other hand seems well known
and familiar to the reader in general and the economic reader in particular. However, sup-
plementing and strengthening the Arabic library with modern standard studies, especially
with regard to Panel Data, is considered a qualitative addition to the Arab library and
the economic researcher. From a practical point of view, this study can recommend to
the governments of Arab countries to keep pace with recent developments in information
and communication technology and pay attention to this element because of its role in the
development of foreign investments and economic growth [5].
The main objective of this research is to study the relationships between information
and communication technology, foreign direct investment, and economic growth for a
sample of 15 Arab countries.
To answer this research question, the study relies on testing the following hypotheses:
Main hypothesis:
The increase in information and communication technology has a positive impact on
economic growth in Arab countries.
Dependent hypotheses:
There is a disparity in the availability of information and communication technology
among the Arab countries under study.
-
Information and communication technology has a positive and moral impact on
foreign direct investment in the short and long terms in the Arab countries under study.
-
Information and communications technology and foreign direct investment have a
positive and moral impact on economic growth in the short and long terms in the
Arab countries under study.
-
Foreign direct investment and economic growth positively and morally affect informa-
tion and communication technology in the short and long term in the Arab countries
under study.
The study focuses on the dynamic relationships between ICTs, foreign direct invest-
ment, and economic growth for a sample of 15 Arab countries over the period of 1995–2019.
ICT could be a solution for the sustainable development in Arab countries mainly by its
role of diversification of economic activities. The economic diversification can be a solution
to sharp decline in oil prices.
The researcher will divide the research study into six themes. In the first theme, the
researcher will present the research problem in general, its significance, objectives, and
hypotheses. The second theme discusses the reality of information and communication
technology and its development in the Arab countries. The third theme presents the
theoretical framework and some previous studies that deal with the relationship between
information and communication technology, foreign direct investment, and economic
growth in developing and developed countries to a certain extent. The fourth theme
exposes us to the data and its sources, in addition to presenting the models that were
estimated, with a description of the study variables. The fifth theme includes analyzing,

Sustainability 2022,14, 6293 3 of 21
discussing, and comparing the results obtained with previous studies. Finally, the sixth
theme presents the most important conclusions in addition to some recommendations.
2. Information and Communication Technology in the Arab Countries
ICTs are the most important key to strengthening transport, trade, and financial in-
frastructures and encouraging innovations that create an inclusive digital economy. ICT
services, such as smart grids, integrated management systems, and intelligent transporta-
tion systems, are the main drivers of economic growth. These broadband networks facilitate
the movement of goods, services, information, people, and money across borders in a more
efficient manner. The countries of the Arab region have been making real progress in the
process of adopting information and communication technology over the past decade,
to the extent that the statistics show some of the rich countries as having ranked high in
the indicators of the use of information and communication technology similar to devel-
oped countries.
In 2009, the United Nations (International: ITU Telecommunication Union) launched
the Information and Communication Technology Development Index (IDI) to assess and
measure ICT developments in various countries. It is a composite index (a compilation of
the individual ICT indicators agreed upon internationally) that measures the accessibility
of the latter as well as its skills, making it a valuable tool for measuring the most important
indexes of the information society. It is also a standard tool that governments, operators,
development agencies, researchers, and others can use to measure the digital divide and
compare ICT performance within and between countries. The ICT Development Index is
based on 11 ICT indicators, grouped into three groups: access, use, and skills. The most
recent ICT Development Index was published in the [6].
As for the situation in the Arab countries, they are diverse in terms of the performance
of the ICT Development Index (IDI). In Table 1, we will discuss the values of the ICT
Development Index and the ranking of Arab countries for the years 2011 and 2017. The
table shows that the most significant improvements between 2011 and 2017 in the value of
the ICT Development Index were for Jordan (the index increased by 2.1 points), Bahrain (the
index increased by 1.81 points), Algeria (the index increased by 1.69 points) and Lebanon
(the index increased by 1.69 points), and Oman (the index increased by 1.68 points). The
table below shows that there are significant differences in the ICT Development Index
between Arab countries because of differences in economic levels. This partly explains
why there were small changes in the regional order of countries between 2011 and 2017.
In the group of countries with high levels of GNI per capita, the Gulf countries top the
rankings. Qatar moved from first place in 2011 to second place in 2017, Bahrain moved from
second place in 2011 to first place in 2017, while the United Arab Emirates, Saudi Arabia,
and Oman maintained the same third, fourth, and fifth positions, respectively. These five
top-ranked countries improved their ICT Development Index values by an average of
1.40 points between 2011 and 2017. This made it the first place in this Index in the Arab
region. These countries are followed by six middle-income countries: Lebanon, Jordan,
Tunisia, Algeria, Morocco, and Egypt. This group of countries improved their performance
by an average of 1.48 points during the same period, which indicates that middle-income
countries in the region, especially Lebanon and Jordan, are approaching the performance
of the countries of the Gulf Cooperation Council. At the end of the ranking, we find the
lowest-income countries in the region: Sudan, Syria, Mauritania, Djibouti, and Comoros.
They also improved in the average value of their ICT Development Index, but by only
0.25 points in the period 2011–2017. Sudan and Djibouti improved their values in the
ICT Development Index by 0.37 points and 0.27 points, respectively, while Comoros and
Syria achieved only weak growth in the value of the ICT Development Index during the
same period.
The most significant improvement rates in the ICT Development Index were achieved
throughout the Arab States region because of the improvement in the international Internet
bandwidth and subscriptions to fixed and mobile phones. Referring to the [
7
] for the

Sustainability 2022,14, 6293 4 of 21
Arab region as a whole, the trends of the overall level in the development of information
and communication technology in the Arab world presented in Figure 1indicate that the
proportion of individuals who use the Internet in the population as a measure of ICT
development has witnessed a sharp upward trend over the period 1993–2019. However, it
should be noted here also that the growth of information and communication technology
slowed down a little in 2015 and then started to rise again in 2016. The trends of the overall
level in the development of information and communication technology in the Arab world,
which are presented in Figure 2, indicate that the number of mobile phone subscriptions
(per 100 people) as a measure of the development of information and communication
technology witnessed an upward trend over the period 2013–2019 and then declined
slightly during the period 2014–2018, and rebounded in 2019. The trends of the overall
level in the development of information and communication technology in the Arab
world, which are shown in Figure 3, indicate that the number of fixed-telephone service
subscriptions (per 100 people) as a measure also of the development of information and
communication technology witnessed a sharp upward trend over the period 1993–2008 and
then declined during the period 2009–2014, before resuming its rise again starting in 2015.
Table 1. ICT Development Index in Arab countries in 2011 and 2017.
Country
ICT
Development
Index in 2017
Regional
Ranking in 2017
ICT Development
Index in 2017
Regional Ranking
in 2017
Change in ICT
Development
Index 2011–2017
Bahrain 7.6 1 5.79 2 1.81
Qatar 7.21 2 6.41 1 0.8
United Arab Emirates 7.21 3 5.68 3 1.53
Saudi Arabia 6.67 4 5.46 4 1.21
Oman 6.43 5 4.8 5 1.63
Lebanon 6.3 6 4.62 6 1.68
Jordan 6 7 3.9 7 2.1
Tunisia 4.82 8 3.57 10 1.25
Morocco 4.77 9 3.59 9 1.18
Algeria 4.67 10 2.98 12 1.69
Egypt 4.63 11 3.64 8 0.99
Syria 3.34 12 3.12 11 0.22
Sudan 2.55 13 2.18 13 0.37
Djibouti 1.98 14 1.71 14 0.27
Comoros 1.82 15 1.68 15 0.14
Source: ITU’s Information Society Measurement Reports 2017 and 2011.
Sustainability 2022, 14, x FOR PEER REVIEW 5 of 22
Figure 1. Trends in the proportion of people using the Internet in the Arab world during the period
1993–2019. Note: The variable proportion of individuals using the Internet is PIUI. Source: World
Bank data based on Eviews 11 output.
Figure 2. Trends in the number of mobile phone subscriptions per 100 people in the Arab world
during the period 1993–2019. Note: The variable number of mobile subscriptions per 100 people
stands for MCS. Source: World Bank data based on Eviews 11 output.
Figure 3. Trends in the number of fixed telephone subscriptions per 100 persons in the Arab world
during the period 1993–2019. Note: The number of subscriptions to the fixed telephone service per
100 people is designed by FTS. Source: World Bank data based on Eviews 11 outputs.
0
10
20
30
40
50
60
70
94 96 98 00 02 04 06 08 10 12 14 16 18
PIUI
0
20
40
60
80
100
120
94 96 98 00 02 04 06 08 10 12 14 16 18
MCS
4
5
6
7
8
9
10
11
94 96 98 00 02 04 06 08 10 12 14 16 18
FTS
Figure 1. Trends in the proportion of people using the Internet in the Arab world during the period
1993–2019. Note: The variable proportion of individuals using the Internet is PIUI. Source: World
Bank data based on Eviews 11 output.

Sustainability 2022,14, 6293 5 of 21
Sustainability 2022, 14, x FOR PEER REVIEW 5 of 22
Figure 1. Trends in the proportion of people using the Internet in the Arab world during the period
1993–2019. Note: The variable proportion of individuals using the Internet is PIUI. Source: World
Bank data based on Eviews 11 output.
Figure 2. Trends in the number of mobile phone subscriptions per 100 people in the Arab world
during the period 1993–2019. Note: The variable number of mobile subscriptions per 100 people
stands for MCS. Source: World Bank data based on Eviews 11 output.
Figure 3. Trends in the number of fixed telephone subscriptions per 100 persons in the Arab world
during the period 1993–2019. Note: The number of subscriptions to the fixed telephone service per
100 people is designed by FTS. Source: World Bank data based on Eviews 11 outputs.
0
10
20
30
40
50
60
70
94 96 98 00 02 04 06 08 10 12 14 16 18
PIUI
0
20
40
60
80
100
120
94 96 98 00 02 04 06 08 10 12 14 16 18
MCS
4
5
6
7
8
9
10
11
94 96 98 00 02 04 06 08 10 12 14 16 18
FTS
Figure 2.
Trends in the number of mobile phone subscriptions per 100 people in the Arab world
during the period 1993–2019. Note: The variable number of mobile subscriptions per 100 people
stands for MCS. Source: World Bank data based on Eviews 11 output.
Sustainability 2022, 14, x FOR PEER REVIEW 5 of 22
Figure 1. Trends in the proportion of people using the Internet in the Arab world during the period
1993–2019. Note: The variable proportion of individuals using the Internet is PIUI. Source: World
Bank data based on Eviews 11 output.
Figure 2. Trends in the number of mobile phone subscriptions per 100 people in the Arab world
during the period 1993–2019. Note: The variable number of mobile subscriptions per 100 people
stands for MCS. Source: World Bank data based on Eviews 11 output.
Figure 3. Trends in the number of fixed telephone subscriptions per 100 persons in the Arab world
during the period 1993–2019. Note: The number of subscriptions to the fixed telephone service per
100 people is designed by FTS. Source: World Bank data based on Eviews 11 outputs.
0
10
20
30
40
50
60
70
94 96 98 00 02 04 06 08 10 12 14 16 18
PIUI
0
20
40
60
80
100
120
94 96 98 00 02 04 06 08 10 12 14 16 18
MCS
4
5
6
7
8
9
10
11
94 96 98 00 02 04 06 08 10 12 14 16 18
FTS
Figure 3.
Trends in the number of fixed telephone subscriptions per 100 persons in the Arab world
during the period 1993–2019. Note: The number of subscriptions to the fixed telephone service per
100 people is designed by FTS. Source: World Bank data based on Eviews 11 outputs.
3. Literature Review
3.1. Theoretical Framework
The neoclassical theory is one of the most important theories concerning itself with the
subject of the factors causing economic growth. The Solow growth model [
8
] is considered
one of the most famous neo-classical models, and it is an entry point for most studies
related to economic growth. In this model, Solow introduced an independent variable into
the economic growth equation, which is the technological level, considered by him to be
an external factor. According to this model, GDP growth can occur due to several factors,
the most important of which is an improvement in the technological level. We can also
refer to the model of Romer [
9
], the pioneer of the theory of internal growth, in relation to
technical development and its impact on economic growth. Romer believes that human
capital must be allocated between research and innovation activities on the one hand,
and between production activities on the other. This is because increasing the proportion
of human capital devoted to research and innovation activities enables the economy to
achieve a high growth rate in the long run. According to Romer’s model, the output is
always determined from within the model as determined by the level of technological

Sustainability 2022,14, 6293 6 of 21
development, which in turn depends on the balance of human capital allocated to research
and development activities.
The economic growth theories in general consider that investment in information
and communication technology plays an important role in driving economic growth [
10
].
However, empirical studies have yielded mixed results, varying according to the research
methodology used and the country or sample of the study. Economic growth models
consider investment in ICT as an important factor of production such as employment,
human capital, and physical capital. ICT can affect economic growth on the one hand, as
well as production on the other, in addition to productivity, through three main channels.
First, ICT goods and services are part of the added value of the economy. Second, the use
of ICT capital as an input in the production of all goods and services leads to an increase in
production as well as productivity, and thus to an increase in economic growth. Finally,
ICTs can cause economic growth through their contributions to technological change. If the
growth of ICT production is based on the efficiency and productivity benefits of activities,
it will lead to an increase in productivity growth at the macroeconomic level [
10
,
11
]. Many
microeconomic studies have also proven a positive correlation between investment in
information technology and various measures of economic performance for companies in
developed countries [3].
In addition to the important role of information and communication technology in
improving the productivity of the economy, it can allow developing countries to conduct
commercial and economic activities with an efficiency similar to that achieved in developed
countries. The development of information and communication technology infrastructures,
and their increasing strength, have led to a fundamental change in the nature of global
relations, sources of competitive advantage, and opportunities for economic and social
development. Technologies such as the internet, personal computers, fixed telephones, and
mobile phones have created an interconnected global network of individuals, businesses,
and governments. For developed or developing countries, a modern telecommunications
infrastructure is not only necessary for domestic economic growth but is also a prerequisite
for participating in the increasingly competitive global markets and for attracting foreign
investment. Ref. [
12
] analyzed the ICT infrastructure effects on economic growth in the
context of theoretical approach. They reported that policy support combined with adequate
funding, stable government, macroeconomic determinants, and an innovation environment
is essential to ICT-induced prosperity. In addition, countries should promote e-commerce
and e-governance activities with adequate support for research and development, technical
support, knowledge dissemination on AI-based robots and chatbots, stand-alone computer
applications, technology transfer to industry and society, application towards digitalization
acceptance, and user-centric rewards.
Recently, there have been increasing numbers of scientific studies on the relationship
between information and communication technology on the one hand and foreign direct
investment on the other. This research views ICT as a “location” factor that attracts FDI
and a factor that influences other determinants of FDI. Many studies conclude that ICT
lowers transaction and production costs for foreign investors and improves their access to
information on alternative investment opportunities.
Information and Communication Technology (ICT) is the major new determinant of
foreign direct investment in a world that is rapidly moving towards an information-based
economy. Economies with ICT infrastructure are moving towards an information-based
economy. Among the main benefits of ICT, we can mention the reduction in transportation
costs, the improvement of marketing information, and the increase in the efficiency of
industrial production. A large number of studies show that telecommunications infrastruc-
ture is essential not only for domestic economic growth, but also for attracting foreign direct
investment and participating in increasingly competitive global markets. As for the insuffi-
cient availability of ICT services, it constitutes an obstacle to economic growth in the least
developed countries [
13
,
14
]. Advanced telecommunications services facilitate international
communications between parent and subsidiaries abroad in the current trend of global

Sustainability 2022,14, 6293 7 of 21
economic integration driven by cross-border investment by multinational corporations.
Technological developments, particularly in the field of information and communications
technology, have facilitated new avenues for conducting business on a global scale [15].
When considering the relationship between information and communication technol-
ogy and foreign direct investment, the researcher should find a connection to economic
theory by considering the following: (i) skills and productivity, i.e., the human capital
aspects of ICT; (ii) technology transfer; (iii) transaction cost implications; and (iv) the ICT
infrastructure aspect or effects on the flow of foreign direct investment. As mentioned
earlier, previous studies consider that ICTs lower international transaction costs [
16
–
19
].
In addition, human capital is important in terms of the ability to absorb both ICT and
technology transfer.
3.2. Empirical Framework
Many previous studies dealt with the role of information and communication tech-
nology in foreign direct investment and, accordingly, in economic growth. Among these
country-level studies, ref. [
20
] found a significant relationship between IT investment
and productivity growth in 12 countries in the Asia-Pacific region. Ref. [
21
] used a data
set of 36 countries for the period 1985–1993 and showed that investment in information
technology is positive for developed countries but unimportant for developing countries.
Refs. [
10
,
22
] conducted two 39- and 42-country studies during 1980–1995 and 1985–1999, re-
spectively. The results of the two studies confirmed the conclusion of [
23
] that information
and communication technology plays an important role in economic growth in developed
countries but does not play any definite role in developing countries. However, single
country studies (e.g., refs. [
24
–
28
]) showed that ICT contributed to the economic growth
in all these countries. These studies have been influential in strengthening the consensus
among many economists that ICTs enhance foreign direct investment and economic growth.
If there is a relationship between FDI and ICT, FDI may increase due to the ability of a
country’s ICT infrastructure to further support its flow. FDI may encourage an increase in
information and communication technology in intermediate inputs, particularly between
parent and affiliate companies. While developed countries have the ability to attract foreign
direct investment by relying on the development of the field of information and communi-
cation technology, in turn, such capabilities must be built in developing countries. In turn,
the inflow of foreign direct investment increases the volume of investment in information
and communications technology and enhances its ability to enhance economic growth. The
rapid expansion of global foreign direct investment resulted from several factors, including
technical progress in telecommunications services and the reorganization of major cur-
rencies. On the other hand, technical advances in telecommunications services facilitated
international communications by involving parent companies and their subsidiaries abroad,
whereas the reorganization of the major currencies provided companies with opportunities
to make profits by undertaking foreign direct investment [
29
]. In the same vein, ref. [
30
]
argued that the beneficial effect of FDI is only enhanced in an environment characterized
by an open trading and investment system and macroeconomic stability. The relationship
between investment and economic growth is evident, for example, in the case of the South-
east Asian tiger economies, where investment rates were the main driver of growth in
these countries.
As for the studies that dealt with developing countries, especially Arab countries,
we find, for example, the study of [
31
] which tested the direct and indirect impact of
information and communication technology on economic growth in North African and
Middle Eastern countries, based on data for the period 1992–2004, using the generalized
method of moments (GMM). The study concluded that there is a direct negative impact of
information and communication technology on economic growth. While [
32
] found quite
the opposite result when they examined the impact of financial development and ICT on
economic growth in North African and Middle Eastern countries, using dynamic panel
data models. The results of the study indicated the presence of a positive impact of the ICT

Sustainability 2022,14, 6293 8 of 21
index on economic growth. This means that the countries of this region need to strengthen
their ICT policies and further improve the use of new ICTs. In the same direction, a study
by [
4
] found a similar result, where the researchers investigated the impact of information
and communication technology on the economic growth of selected developing countries
in North Africa, the Middle East, and sub-Saharan Africa, using the generalized method
of moments for the combined data during the period 2007–2016. The results showed that
the various indicators of information and communication technology represent one of the
main drivers of economic growth in the selected sample countries. Ref. [
33
] examined the
effects of information and communication technology on economic growth in a group of
50 developing countries during the period between 2005 and 2015, using panel data models.
Through static analysis of panel data models, the researcher concluded that the impact of
information and communication technology changes from one country to another. This
study also found that the Internet index negatively affects the long-term economic growth
of the group of developing countries under study. Ref. [
34
] study the impact of information
and communication technology on economic growth in Palestine over the period from
2000 to 2018, using a multiple linear regression model. The researchers concluded that
information and communication technology have a positive and significant impact on
economic growth in Palestine.
The study by [
35
] dealt with the impact of information and communication technology
on the financial development index of the Gulf Cooperation Council (GCC) group from
2000 to 2016, using static panel data models for fixed effects and dynamic panel data models.
Their results showed that information and communication technology had a significant
and positive impact on the variables of financial development in the Arab Gulf region. The
study of [
36
] examined the impact of information and communication technology, foreign
direct investment, and general government expenditures on the economic growth of the
countries of the Middle East and North Africa during the period from 1998 to 2019, using
the generalized method of moments. Their results reported that the impact of information
and communication technology on economic growth is positive and important, but the
effect of general government spending on economic growth is negative.
Ref. [
37
] studied the impact of information and communication technology on inclu-
sive growth, using the time series data methodology for cross-sectional data, and using
regression models with fixed effects and regression models with random effects on a sam-
ple of developing countries and a sample of Arab countries during the period 2010–2018.
The study concluded that there is a positive and significant impact of access and use of
information and communication technology on comprehensive growth, whether in the
sample of developing countries or in the sample of Arab countries. The impact of informa-
tion and communication technology skills is negative and insignificant in the sample of
developing countries, and negative and significant in the sample of Arab countries. Hence,
the study emphasizes the importance of increasing investments in the infrastructure of the
information and communication technology sector in order to support access and to access
opportunities in all regions in the developing or Arab countries.
A recent study by [
38
] examined causal relationships between the internet and eco-
nomic factors (GDP, FDI, imports, and exports) in Asian countries between 1997 and 2017,
using the panel vector autoregressive model. The results of this study showed a two-way
causal relationship between FDI and internet use in South Asia, a one-way causal relation-
ship from internet use to FDI in East Asia, and a one-way causal relationship from FDI to
internet use in West Asia. The results also indicated a one-way causal relationship from
exports to internet use in East Asia, a one-way causal relationship from internet use to
exports in South Asia, and a one-way causal relationship from internet use to GDP in West
Asia. The researchers concluded from these findings that the use of the internet enhances
economic performance in Asia. Therefore, they recommend decision makers to improve
the use of the internet with a focus on economic growth, improving transaction efficiency,
and facilitating foreign direct investment. In addition, ref. [
39
] noted a causal relationship
between ICT and economic growth in 25 high- and middle-income Asian countries, using

Sustainability 2022,14, 6293 9 of 21
panel data over the period 2000–2018. Through this study, the researcher concluded that
high-income Asian countries have achieved positive and significant economic development
because of the high rate of internet penetration. In addition, middle-income countries are
beginning to benefit from the development of the ICT index in boosting economic growth.
More recently, ref. [
40
] studied the effect of ICT and FDI inflows on the per-capita
GDP in India using annual data during the period from 1991 to 2019 by estimating si-
multaneous equations models. Their results indicated that FDI inflows and information
and communication technology represented by mobile density and internet density have
positive and significant impact on the per-capita GDP. In the same line, ref. [
41
] analyzed
the dynamic effects of ICT (e.g., telephone subscriptions, mobile subscriptions, broadband
subscriptions, internet subscribers, and secure internet servers), FDI inflows, and trade
openness on economic growth for the case of BRICS countries over the period 2000–2018
using generalized method of moments. Their findings reported that ICT affects positively
economic growth for a few countries while FDI inflows have negative impact on economic
growth. Ref. [
42
] investigated the impact of ICT infrastructure on FDI inflows in the group
of eight countries of Bangladesh, Indonesia, Iran, Egypt, Nigeria, Malaysia, Pakistan, and
Turkey over the period 1997–2018 by using fixed effects models. Their findings showed
positive and significant impact of ICT on FDI inflows.
4. Research Data and Methodology
4.1. Data
The data needed to conduct the analysis for each Arab country were sourced from
the World Bank’s World Development Indicators database [
7
]. The data set under study
contains annual data for each Arab country for the following variables: GDP per capita
(expressed in constant 2010 USD), abbreviated as GDPC; foreign direct investment flows
(% of GDP), abbreviated as FDI; number of subscriptions in fixed telephone service (per
100 people), abbreviated as FTS; number of mobile phone subscriptions (per 100 people),
abbreviated as MCS; and individuals using the Internet (% of population), abbreviated
as PIUI. Information and Communication Technology can be defined as a broad term for
information technology, which refers to all communication technologies, including the
Internet, fixed telephones, mobile phones, computers, software, medium devices, video
conferencing, social networking, and other such Media applications and services [43]. On
the other hand, economic growth represents the rates of increase in GDP per capita from
year to year [
33
]. Following the tradition in the literature, we define FDI as the net inflows
of FDI expressed as a percentage of GDP.
Data were selected based on the availability of ICT variables. According to the
availability of information, we chose the following group of (15) Arab countries: Algeria,
Morocco, Tunisia, Saudi Arabia, the United Arab Emirates, Bahrain, Kuwait, Oman, Egypt,
Lebanon, Jordan, Iraq, Sudan, Yemen, and Mauritania. These countries were selected
according to the availability of data for all study variables during the period 1995–2019.
The following variables were used to represent ICT: FTS, MCS, and PIUI. The first two
variables measure access to information and communication technology, while the third
variable measures the extent of its use. The table in Appendix Asummarizes definitions
and data sources for all study variables.
The main advantage of panel data is that it has the particularity of taking into ac-
count temporal dynamics (adaptation time, expectations, etc.) in the interpretation of
the dependent variable due to overlapping observations between sectors, thus improving
the effectiveness of policies (decisions, actions, etc.). In addition, the use of panel data
reduces the problem of heteroscedasticity, which often occurs when cross-sectional data are
used [
44
]. In contrast, in the case of the stationary model, the immediate (or non-diffusive)
explanation gives only a portion of the variance in the dependent variable.
In the first model, the dependent variable is economic growth, and the independent
variables are foreign direct investment and information and communication technology. In
the second model, the dependent variable is foreign direct investment, and the independent

Sustainability 2022,14, 6293 10 of 21
variables are economic growth and information and communication technology. In the
third model, the dependent variable is one of the ICT variables, and the independent
variables are economic growth and foreign direct investment. The focus here is on the
study of the dynamic relationships between foreign direct investment, real GDP per capita,
and information and communication technology. To obtain homogeneous data, we will use
the natural logarithmic for GDP per capita, which is abbreviated as LGDPC. The natural
logarithm of real GDP per capita measures economic growth.
The table in Appendix Bshows the descriptive statistics of the Arab countries under
study for the period from 1995 to 2019. The average GDP per capita in a sample of 15 Arab
countries is $11,936.57, with the minimum for Yemen in 2018 amounting to $632.90, and
the highest for the United Arab Emirates in 1997 with a value of about $64,864.74. The
average value of FDI inflows as a percentage of GDP is 2.96%, with the minimum value for
Mauritania reaching
−
11.62% in 2019, and the maximum value for Bahrain at 33.56% in 1996.
The main variable of the study is information and communication technology represented
by the variables: number of mobile phone subscriptions (per 100 people), number of
fixed phone subscriptions (per 100 people), and the proportion of individuals who use
the Internet (% of the population). The average value of mobile cellular subscriptions in
Arab countries is about 65 to 100 people, ranging from 0 in Sudan, Mauritania, and Iraq in
1995 to about 212 for 100 people in the United Arab Emirates in 2016. The average value of
fixed-line subscriptions (per 100 people) is 10.58, with a minimum value of 0.25 for Sudan
in 1995 and a maximum value of 32.87 for the United Arab Emirates in 1999. The average
value of the percentage of individuals using the internet was 26.27% with a minimum
value of 0% for Yemen, Sudan, and Mauritania in 1995, and a maximum value of 99.7% for
Bahrain in 2019.
We also present in the table in Appendix Cthe correlation matrix, which contains
the correlation coefficients between the different research variables with an indication of
their levels of significance at 1%, 5%, or 10%. It turns out that there is a direct statistically
significant correlation at the 1% level of significance between the per capita GDP variable
and the three variables that represent information and communication technology, while
there is no correlation between the foreign direct investment variable and the information
and communication technology variables. In addition, the results indicated that there is a
direct and strong correlation between the three variables that represent information and
communication technology.
4.2. Methods
The research methodology used in this research paper is based on estimating a hetero-
geneous dynamic panel model using annual data on real GDP per capita, foreign direct
investment, and information and communication technology for a sample of Arab countries
during the period 1995–2019. This methodology is implemented in three stages. In the first
stage, unit root tests are applied to consider the stationarity of various variables. These
tests are [
45
,
46
] and Fisher (ADF) and (PP) [
47
] tests. In the (LLC) (2002) test, the null
hypothesis assumes a common root unit, whereas in the rest of the tests, the null hypothesis
assumes the unity of the individual root. In the second stage, we conduct the [
48
–
50
]
panel cointegration tests. Both tests are established on the null hypothesis of absence of
cointegration against the alternative of existence of cointegration. However, the Kao test
supposes a common cointegration vector across all countries in the panel whereas the
Pedroni test permits for panel-specific cointegrating vectors. In the third step, we estimate
panel ARDL models, using the (Pooled Mean Group) to reveal the effects of independent
variables on the dependent variable in the short and long term. This methodology can
enable us to identify and avoid spurious results, which may happen using a simple method
such as the OLS method. This technique, as successfully applied in studies conducted
by [29,51], proves its record-breaking toughness and ability to root out false relationships.
In this study, we will analyze the relationships between economic growth, foreign
direct investment, and ICT in the short and long term without considering control variables.

Sustainability 2022,14, 6293 11 of 21
This allows us to get the overall effect of each variable on the other. The models that link
the different variables take the following forms:
LGDPCi,t=β0+µi+β1FDIi,t+β2ICTi,t+εi,t(1)
FD Ii,t=β0+µi+β1LGDPCi,t+β2ICTi,t+εi,t(2)
ICTi,t=β0+µi+β1LGDPCi,t+β2FDIi,t+εi,t(3)
where:
GDPC: GDP per capita (at constant 2010 USD).
FDI:FDI inflows as a percentage of GDP.
ICT: Information and Communication Technology. It is represented here by three vari-
ables: the percentage of internet users, the number of mobile phone subscriptions per
100 people, and the number of fixed phone subscriptions per 100 people (PIUI, MCS, and
FTS, respectively).
The index (i= 1, . . . , N)irefers to the country i of our sample (N= 15).
The index (t= 1, . . . , T)trepresents the period or years (T= 25).
β0,β1and β2: parameters to be estimated.
µi:These are the country-specific fixed effects.
εit:This is the random error term.
It can be said that we have a group of countries with some different properties rep-
resented by special fixed effects. On the other hand, it should be borne in mind that the
economies of the Arab region have been characterized for decades by a similar development
model that relies mainly on rentier activities. According to what many economists point
out, all the economies of the Arab countries can be described as rentier economies, albeit
to varying degrees. These countries depend on different sources of revenue, distributed
mainly between the export of raw materials, especially oil, gas and phosphates, tourism, or
remittances of immigrants and expatriates [52,53].
Macroeconomic variables are usually not stationary at levels (I (0)) but are more likely
to be stationary at their first difference (I (1)). This means that the model is dynamic
and assumes that lag-dependent variables are include explanatory variables. Hence, we
will use the heterogeneous dynamic panel data model. In this case, the panel ARDL
model is most appropriate. Various other approaches to dynamic modeling can lead to
inconsistent estimates of the average value of the parameters when these are identical
between countries. Moreover, the panel ARDL model is relatively more efficient in the case
of a small sample size. Therefore, the panel ARDL models that we will estimate take the
following forms [1,54]:
LGDPCi,t=∝0+∑P
j=1∝jLGDPCi,t−j+∑q
j=0δjFD Ii,t−j+∑r
j=0γjICTi,t−j+µi+εit (4)
FD Ii,t=∝0+
P
∑
j=1
∝jFD Ii,t−j+
q
∑
j=0
δjLGDPCi,t−j+
r
∑
j=0
γjICTi,t−j+µi+εi,t(5)
ICTi,t=∝0+
P
∑
j=1
∝jITCi,t−j+
q
∑
j=0
δjLGDPCi,t−j+
r
∑
j=0
γjFD Ii,t−j+µi+εi,t(6)
By reformulating the given models by Equations (4)–(6), they are converted into error
correction models (re-parametrized ARDL (p,q,r)) as follows:
∆LGD PCit =α0+Φi(LGDPCi,t−1−ρiFDIi,t−υiICTi,t)+
p−1
∑
j=1
αij ∆LGD PCi,t−j+
q−1
∑
j=0
δij ∆FDIi,t−j+
r−1
∑
j=0
γij ∆ICTi,t−j+µi+εit (7)

Sustainability 2022,14, 6293 12 of 21
∆FD Iit =α0+Φi(FD Ii,t−1−ρiLGD PCi,t−υiICTi,t)+
p−1
∑
j=1
αij ∆FD Ii,t−j+
q−1
∑
j=0
δij ∆LGDPCi,t−j+
r−1
∑
j=0
γij ∆ICTi,t−j+µi+εit (8)
∆ICTit =α0+Φi(I CTi,t−1−ρiLGDPCi,t−υiFDIi,t)+
p−1
∑
j=1
αij ∆ICTi,t−j+
q−1
∑
j=0
δij ∆LGDPCi,t−j+
r−1
∑
j=0
γij ∆FD Ii,t−j+µi+εit (9)
where:
Φiis the group’s adaptive velocity coefficient (Φi<0).
ϑ
and
ρ
: represent the coefficients that measure the effect of the independent variables on
the dependent variable in the long run.
δij
and
γij
:They are the coefficients that represent the effect of the independent variables
on the dependent variable in the short run.
µi
:They are the fixed effects that represent the specifies of each country, and they do not
change in time.
εit:This is the random error term.
ECT: This is the error correction term.
ECT =[(LGDPCi,t−1−ρFD Ii,t−υICTi,t)]
in model (7)
ECT =[(F DIi,t−1−ρLGDPCi,t−υICTi,t)]
in model (8)
ECT =[(ICTi,t−1−ρLGDPCi,t−υFDIi,t)]
in model (9).
According to [
54
,
55
], the models presented in Equations (7)–(9) can always be esti-
mated using the mean-group method (Mean Group estimator: MG). This method relies on
estimating the coefficients for each country and then estimating an average for the group.
However, the researchers admit that if the long-run coefficients are not heterogeneous from
one group to another, it is more appropriate to use a more efficient estimation method, the
Pooled Mean Group: PMG. The method for estimating the PMG parameter allows it to
differ from one country to another in the short term, but it is homogeneous in the long term.
The use of both methods requires that the variables be stationary on the level or on their
first differences. Hence, the next section of empirical results will first present the results of
the root unit tests, and then, the results of the Pooled Mean Group method for the different
study models.
This research studies the causal relationships between information and communication
technology and foreign direct investment and between information and communication
technology and economic growth for a group of Arab countries using time series data and
error correction models. If the non-stationary time series do not coincide, a high degree
of correlation between the two variables does not imply a causal relationship between the
variables. The methodology used enables us to identify and avoid spurious outcomes,
which may happen using a simple method such as the OLS method. It should be noted
that the causality test requires extreme accuracy in estimation, as any deletion of previous
information related to the study may give rise to misleading results, and the selection of
the optimal lag periods has a very important role in estimating the model.
5. Result Analysis
In this study, we estimated the model equations and analyzed various results by using
the econometric program Eviews 11.

Sustainability 2022,14, 6293 13 of 21
5.1. Results of Unit Root Tests and Cointegration Tests
The first step in analyzing the results is to test the stationarity of the variables. Table 2
presents all the results of unit root tests for the different variables. In order to test the
presence of unit roots in our data, we use the first-generation tests; Refs. [
45
,
46
] and Fisher
type tests (ADF and PP).
Table 2. Results of unit root tests.
Variable
Statistics of Tests
Order
of Integ.
LLC Test IPS Test ADF-Fisher Test PP-Fisher Test
H0: Common Unit Root H0: Individual Unit Root
At Level At First
Level At Level At First
Level At Level At First
Level At Level At First
Level
LGDPC −0.497 −4.50 * 0.437 −5.59 * 30.92 84.68 * 12.82 108.6 * I (1)
FDI −3.56 * - −5.60 * - 88.51 * - 88.00 * - I (0)
PIUI 3.51 −3.43 * 9.30 −4.82 * 3.76 95.27 * 0.85 138.2 * I (1)
MCS 0.541 −3.92 * −0.872 −4.09 * 32.61 66.01 * 7.60 76.77 * I (1)
FTS −3.22 * −5.48 * −1.96 ** −7.21 * 50.83 * 113.15 * 32.50 216.5 * I (0) or I (1)
Note: *, ** significance levels at 1% and 5%, respectively. Automatic selection is made using Akaike information
criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.
In the LLC test, the null hypothesis assumes a common unit root. In the rest of the
tests, the null hypothesis assumes that the panel data model adheres to the individual
unit root. In Table 2, we present the results of the various first-generation unit root tests.
According to the results of the LLC, IPS, ADF-Fisher, and PP-Fisher tests, it is clear that all
the variables are either stationary in their levels or in their first differences (I (0) or I (1)). In
particular, the foreign direct investment variable is stationary at the level. The economic
growth, the percentage of internet users, and the number of mobile subscribers’ variables
are stationary at their first differences, while the variable number of fixed-line subscribers
is stationary at the level or at the first differences. Therefore, it is necessary to use the panel
ARDL model to estimate the various models in the short and long terms.
Provided that the majority of variables are either stationary in their levels or in their
first differences, the second step of the analysis is to test for the presence of cointegration
between each dependent variable and the regressors using the Pedroni and Kao cointegra-
tion tests in all panels. The results of both cointegration tests are shown in Table 3. Their
findings indicate the rejection of null hypothesis of absence of cointegration in all models
considered. Hence, we can conclude the presence of long-run relationships between the
various variables.
Table 3. Results of Cointegration Tests.
LGDPC FDI MCS PIUI FTS
Pedroni Residual Cointegration Test
Alternative Hypothesis: Common AR Coefs. (Within-Dimension)
Panel v-Statistic −4.41 * −3.22 * −1.79 *** −0.98 −1.92 ***
Panel rho-Statistic −1.02 −0.79 −0.36 −0.53 −0.17
Panel PP-Statistic −7.45 * −6.21 * −2.44 ** −1.85 *** −2.51 **
Panel ADF-Statistic −8.66 * −6.74 * −2.51 ** −1.94 *** −2.63 **

Sustainability 2022,14, 6293 14 of 21
Table 3. Cont.
LGDPC FDI MCS PIUI FTS
Pedroni Residual Cointegration Test
Alternative Hypothesis: Common AR Coefs. (Within-Dimension)
Pedroni Residual Cointegration Test
Alternative Hypothesis: Individual AR Coefs. (Between-Dimension)
Group rho-Statistic −1.02 −0.79 −0.36 −0.53 −0.17
Group PP-Statistic −7.71 * −6.35 * −2.29 ** −1.78 *** −2.55 **
Group ADF-Statistic −8.44 * −6.88 * −2.61 ** −1.84 *** −2.59 **
Kao Cointegration Test
Augmented Dickey
Fuller
−5.67 * −4.58 * −0.81 −1.95 *** −2.78 **
Notes: *, **, ***: significance levels at 1%, 5%, and 10%, respectively. Trend assumption: Without trend. Automatic
lag selection is based on AIC with a maximum lag of 5. Source: Authors’ own estimations based on Eviews
11 Output.
5.2. Analysis of The Results of Panel ARDL Model
The models presented in Equations (7)–(9) are estimated using the three panel esti-
mators of Pooled Mean Group, Mean Group, and Dynamic Fixed Effect (DFE) and then
we select the best one based on Hausman test. The null hypothesis of Hausman test states
that the difference between PMG and MG or PMG and DFE estimators is not significant.
If the null hypothesis is not rejected, the PMG estimator is more efficient. We use the
PMG estimation if the p-value is higher than 5% level. Otherwise, we use the MG or DFE
estimator. The results of Hausman test are reported in Table 4. It is indicated that p-value is
usually less than 5%, which implies that the PMG estimator is more efficient for all models.
Thus, we consider the results of PMG estimation.
Table 4. Results of Hausman test.
Dep. Var.
LGDPC FDI FTS MCS PIUI
PMG
vs. MG
PMG
vs. DFE
PMG
vs. MG
PMG
vs. DFE
PMG
vs. MG
PMG
vs. DFE
PMG
vs. MG
PMG
vs. DFE
PMG
vs. MG
PMG
vs. DFE
Chi-squared
statistic 1.60 1.73 1.14 1.65 7.23 8.56 1.62 0.00 1.17 0.00
p-value 0.90 0.82 0.95 0.84 0.19 0.14 0.89 1.00 0.96 1.00
Decision The null hypothesis of homogeneity cannot be rejected
Best model PMG estimation
Source: Authors’ own estimations.
Through the analysis of the results of the dynamic model, it is possible to know
the various influences and relationships between the variables of economic growth, the
foreign direct investment inflows, and the variables of information and communication
technology in the long and short terms. The short-term dynamic model details how to make
adjustments between the various time series to re-establish long-term equilibrium. As for
the relationship between variables in the long run, it is held by the error correction term
coefficient (ECT). The ECT is the rate of adaptation, that is, the speed at which the system
returns to equilibrium after a shock. When the ECT coefficient is negative and significant,
this supports a long-term relationship between the variables. In Table 5, we present
the results of the impact of the foreign direct investment inflows and information and
communication technology variables on economic growth in the long and short terms, and
the error correction term. In the long term, we find that the two variables, the percentage
of internet users and the number of fixed-line subscribers, have a positive and significant
effect at 1% on economic growth, while the variable number of mobile subscribers has a
positive but not significant effect. In the short term, we find that the three variables of
information and communication technology have a positive impact on economic growth

Sustainability 2022,14, 6293 15 of 21
in the Arab countries, but it is not significant, and thus there is no effect of information
and communication technology on economic growth in the group of Arab countries in
the short term. These results highlight the importance of information and communication
technology in the development of electronic commerce, electronic marketing, electronic
financial transactions, and digitization of electronic management in driving long-term
economic growth in the Arab countries, especially the Arab Gulf countries. As for the
foreign direct investment inflows, it has always had a positive and significant effect at 1%
on economic growth in the long term, while it has no significant effect in the short term. As
for the error correction term coefficient, we find its negative and significant sign at 1% as
expected, which indicates the existence of a correlation between the study variables in the
long run. The value of the error correction term coefficient ranged between 8.8% and 21.1%,
which is medium, thus indicating that there is no return to the equilibrium position quickly
in the long term in the variables of foreign direct investment flows and the variables of
information and communication technology towards economic growth.
Table 5. Estimation results of model (7) using PMG method.
Independent Variables Dependent Variable: D (LGDPC)
Use of the ICT Index: Proportion of Individuals Using the Internet
Long run coefficients
FDI 0.025 *
PIUI 0.003 *
Short run coefficients
ECT −0.155 *
D(FDI) −0.005
D(PIUI) 0.001
Use of the ICT Index: Number of Mobile Subscriptions per 100 Individuals
Long run coefficients
FDI 0.018 *
MCS 0.0002
Short run coefficients
ECT −0.211 *
D(FDI) −0.004
D(MCS) 0.001
Use of the ICT Index: Number of Fixed Telephone Subscriptions per 100 Individuals
Long run Coefficients
FDI 0.026 *
FTS 0.048 *
Short run coefficients
ECT −0.088 *
D (FDI) −0.004
D (FTS) 0.005
Note: *: significance level at 1%. Automatic selection is made using Akaike information criteria (AIC). Source:
Authors’ own estimations based on Eviews 11 Output.
Table 6presents the results of the impact of ICT variables and GDP per capita on
foreign direct investment flows in the long and short terms, and the error correction term
coefficient. Through the results, we note that information and communication technology
have a positive and moral impact on foreign direct investment flows in the long term.
While in the short term, there is one ICT variable (number of fixed-line subscribers) that
negatively affects FDI inflows. These results highlight the importance of information and
communication technology in attracting foreign investments to Arab countries in the long
term. The results also show the importance of economic growth in driving foreign direct
investment flows in the long term, especially since we note that the coefficient of the per
capita GDP variable is always positive and significant at 1% in the long term, while it is not

Sustainability 2022,14, 6293 16 of 21
significant in the short term. As for the value of the coefficient of the error correction term,
it was always negative and significant, and this confirms the existence of a relationship
between the variables in the long term. The value of the coefficient of the error correction
term ranged between 62.3% and 131%, which is considered high, which indicates a return to
the equilibrium position quickly in the long term in the variables of GDP per capita and the
variables of information and communication technology towards foreign direct investment.
Table 6. Estimation results of model (8) using PMG method.
Independent Variables Dependent Variable: D (FDI)
Use of the ICT Index: Proportion of Individuals Using the Internet
Long run coefficients
LGDPC 3.14 *
PIUI 0.059 *
Short run coefficients
ECT −1.31 *
D (LGDPC) 7.44
D (PIUI) 0.302
Use of the ICT Index: Number of Mobile Subscriptions per 100 Individuals
Long run coefficients
LGDPC 0.174 *
MCS 0.001 *
Short run coefficients
ECT −1.061 *
D (LGDPC) −2.30
D (MCS) 0.069
Use of the ICT Index: Number of Fixed Telephone Subscriptions per 100 Individuals
Long run coefficients
LGDPC 0.207 *
FTS 0.026 **
Short run coefficients
ECT −0.623 *
D (LGDPC) 29.28
D (FTS) −1.302 ***
Note: *, **, ***: significance levels at 1%, 5%, and 10%, respectively. Automatic selection is made using Akaike
information criteria (AIC). Source: Authors’ own estimations based on Eviews 11 Output.
Finally, Table 7reports the results of the impact of GDP per capita and foreign direct
investment inflows on ICT variables in the long and short terms and the coefficient of the
error correction term. These results highlight that the coefficient of GDP per capita and
foreign direct investment inflows are positive and significant in the long run only. This
indicates that the per capita GDP and FDI inflows have a positive impact on the long-term
development of ICT in the Arab countries. This result is consistent with the descriptive
analysis of data, where high income countries (particularly the Gulf countries) appear to
have a high ICT Development Index as well. Therefore, it can be said that foreign direct
investment and economic growth play an important role in the further development of
information and communication technology in the group of Arab countries. As for the
value of the coefficient of the error correction term, it ranges between 2% and 83%, which is
considered weak to medium, which indicates a lack of return to the equilibrium position
quickly in the long term in the variables of GDP per capita and foreign direct investment
towards information and communication technology. In general, these results support the
existence of a positive relationship between information and communication technology,
foreign direct investment inflows, and economic growth.

Sustainability 2022,14, 6293 17 of 21
Table 7. Estimation results of model (9) using PMG method.
Independent
Variables
Dependent Variables
D (MCS) D (PIUI) D (FTS)
Long run coefficients
LGDPC 57.04 * 6.45 * 0.11 *
FDI 5.83 * 19.42 * 1.92 *
Short run coefficients
ECT −0.09 * −0.02 −0.83 *
D(LGDPC) 11.80 −0.54 −2.93
D(FDI) −0.36 −1.07 −0.01
Note: *: significance level at 1%. Automatic selection is made using Akaike information criteria (AIC). Source:
Authors’ own estimations based on Eviews 11 Output.
It can be concluded that the findings of the study are the consensus presented by
economists that information and communication technology enhances foreign direct in-
vestment and economic growth, especially the Solow model of economic growth [
8
] which
considers the technological level as a positive external factor affecting economic growth,
as well as Romer’s model [
9
], which considers technological development as an internal
factor positively affecting economic growth. The results of the study are also consistent
with many of the results of previous studies related to developing countries, such as the
studies of [4,32,34–37].
6. Main Conclusions and Recommendations
The results of the panel ARDL model showed that information and communication
technology have a direct and significant relationship in the long run with the foreign
direct investment inflows and economic growth in the Arab countries group. This is
demonstrated by the results of empirical estimates, which showed that information and
communication technology and foreign direct investment inflows have a positive and
significant impact on economic growth in the long run. In addition, all the variables of
information and communication technology have a positive and significant impact on
the foreign direct investment inflows to those countries in the long term. In addition,
the variables of foreign direct investment inflows and economic growth positively and
significantly affect the variables of information and communication technology in the long
term only, while the results also proved that all ICT variables have no impact on foreign
direct investment inflows and economic growth in the short term. These results support
the existence of a direct relationship between information and communication technology,
foreign direct investment inflows, and long-term economic growth in the study sample.
This highlights that the positive effects of information and communication technology are
not on the short level, but are usually on the medium and long levels, especially in attracting
foreign investments, which in turn contribute to boosting economic growth [
10
]. These
results show that the telecommunications infrastructure (fixed phones, mobile phones, the
Internet, etc.) is important and essential to attract foreign direct investment and to drive
economic growth, especially in the long term for the Arab countries that represent the study
sample. Advanced telecommunications services facilitate international transactions and
communications between parent companies and their subsidiaries abroad and create new
ways to conduct business on a global scale [
15
]. These results are consistent with economic
growth theories that consider investment in information and communication technology as
one of the production factors that contribute to increasing its benefit.
From the practical point of view, this study can recommend to the governments of
Arab countries to keep pace with recent developments in information and communication
technology and pay attention to this element because of its role in the development of
foreign investments and economic growth [
5
]. Consequently, some Arab countries can
further improve the quality of information and communication technology as an important
infrastructure to receive more inflows of foreign direct investment and to witness better

Sustainability 2022,14, 6293 18 of 21
economic growth. Thus, we have come to an important conclusion, that the least developed
Arab countries must increase investment in information and communication technology
because of the positive effect it has on the flow of foreign direct investment and economic
growth, especially in the long term. We therefore recommend that these countries plan
to increase investment in ICT capital sectors such as the Internet, mobile and broadband
infrastructure, e-commerce practices, etc., as well as increase investment in ICT skills such
as education. Therefore, Arab countries, especially weak and middle-income ones, are
called upon to restructure strategies and policies related to investment in information and
communication technology.
This study has some shortcomings. For example, we used data for 15 Arab countries,
and therefore, we can find it difficult to generalize the results to all other Arab countries.
However, this is due to the lack of data related to the variables of importance to the Arab
countries that were not considered within the study sample.
In this research, we study the relationship between information and communication
technology, foreign direct investment, and economic growth for a group of Arab countries
only, which makes the results one-sided. We recommend that researchers in the future
conduct a comparative study between the group of high-income countries, middle-income
countries, and low-income countries. It is also possible that future studies will focus on the
role of ICT development in inclusive growth.
Author Contributions:
Conceptualization, M.B. and K.T.; methodology, M.B.; software, M.B.; vali-
dation, M.B. and K.T.; formal analysis, K.T.; investigation, K.T.; resources, K.T.; data curation, M.B.;
writing—original draft preparation, M.B.; writing—review and editing, M.B.; visualization, K.T.;
supervision, M.B.; project administration, M.B.; funding acquisition, M.B. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was funded by the Deanship of Scientific Research—Najran University—Kingdom
of Saudi Arabia under the grant number NU/-/SHERC/10/998.
Acknowledgments:
The authors are thankful to the Deanship of Scientific Research at Najran Uni-
versity for funding this work under the General Research Funding program grant code (NU/-
/SHERC/10/998).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Definitions and data sources for study variables.
Variable Symbol Variable Name Variable Definition
GDPC GDP per capita
GDP per capita at constant 2010 prices in US dollars. GDP is
the sum of the total value added of all resident producers in
the economy, plus any product taxes, minus any subsidies
not included in the value of the products.
FDI FDI inflows
The percentage of net inflows of foreign direct investment
from the gross domestic product. Foreign direct investment
refers to the flows of direct investment shares in the
economy. It is the sum of equity capital, revenue
reinvestment, and other capital. Direct investment is a
category of cross-border investment associated with a
resident of one economy who has control or significant
influence over the management of an enterprise that is
resident in another economy.

Sustainability 2022,14, 6293 19 of 21
Table A1. Cont.
Variable Symbol Variable Name Variable Definition
FTS The number of fixed-line subscriptions The number of fixed-line subscriptions per 100 people.
MCS
Number of mobile phone subscriptions
The number of mobile phone subscriptions per 100 people.
PIUI Percentage of Internet users The percentage of individuals who use the Internet out of
the total population.
Source: World Bank database [7].
Appendix B
Table A2. Descriptive statistics.
GDPC FDI PIUI MCS FTS
Mean 11,936.57 2.96 26.27 64.73 10.58
Median 4221.84 2.001 14.90 59.472 9.334
Maximum 64,864.74 33.56 99.70 212.63 32.87
Minimum 632.90 −11.62 0.000 0.000 0.256
Standard deviation
14,923.72 4.325 29.15 57.245 7.655
observations 375 375 375 375 375
Source: Authors’ own estimations based on Eviews 11 Output.
Appendix C
Table A3. Correlation matrix between the variables.
Correlation T Stat
p-Value LGDPC FDI PIUI MCS FTS
LGDPC
1
-
-
- - - -
FDI
−0.035
−0.693
0.488
1
-
-
- - -
PIUI
0.444 *
9.59
0.00
−0.030
−0.585
0.55
1
-
-
- -
MCS
0.427 *
9.14
0.00
0.022
0.428
0.66
0.857 *
32.21
0.00
1
-
-
-
FTS
0.847 *
30.84
0.00
0.091 ***
1.768
0.07
0.354 *
7.31
0.00
0.283 *
5.69
0.00
1
-
-
Note: * and ***: significance levels at 1% and 10%, respectively. Source: Authors’ own estimations based on
Eviews 11 Output.
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